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by Copyright Jeffrey Greer Baguley 2004 The Dissertation Committee for Jeffrey Greer Baguley certifies that this is the approved version of the following dissertation: MEIOFAUNA COMMUNITY STRUCTURE AND FUNCTION IN THE NORTHERN GULF OF MEXICO DEEP SEA Committee: _______________________________ Paul A. Montagna, Supervisor _______________________________ Gilbert T. Rowe _______________________________ Kenneth H. Dunton _______________________________ Jay A. Brandes _______________________________ Edward J. Buskey MEIOFAUNA COMMUNITY STRUCTURE AND FUNCTION IN THE NORTHERN GULF OF MEXICO DEEP SEA by Jeffrey Greer Baguley, B.A. Dissertation Presented to the Faculty of the Graduate School of The University of Texas at Austin in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy The University of Texas at Austin December 2004 DEDICATION For opening my eyes to an unknown world of microscopic life, this work is dedicated to Dr. Richard Farris; and for their love and support, this work is dedicated to my family: Thomas G. Baguley, Janis M. Baguley, Timothy A. Baguley, Rosa Lee Baguley, Irma Mayernick, and especially my fianc Brittany M. Hartzell. we must not recoil with childish aversion from the examination of the humbler animals. In every realm of nature there is something marvelous. -Aristotle ...they will feast on the abundance of the seas, on the treasures hidden in the sand." -Deuteronomy 33:19 ACKNOWLEDGMENTS This manuscript would not have been possible without the effort of many mentors, colleagues, and friends. Paul Montagna, thank you for the opportunity to become part of a unique scientific community, for your guidance, and your unique ability to make sense all types of data. For there guidance and insight I thank my committee members: Ken Dunton, Jay Brandes, Ed Buskey, and especially Gil Rowe, director of the Deep Gulf of Mexico Benthos research project. A special thanks is extended to Dr. Wonchoel Lee (Hanyang University, S. Korea) for many months of harpacticoid species identification. Many people contributed time and energy to the processing of over 700 deep-sea box cores; from Texas A&M University I thank Lindsey Loughry, Karen Sell, Sophie DeBeukelaer, Melanie Beazley, and Andy Hebert; from The University of Washington I thank Shelly Carpenter; from Woods Hole Oceanographic Institution I thank Joan Bernhard, and from La Universidad Nacional Aut noma de M xico I thank Elva Escobar and her students. Bathymetry for all Gulf of Mexico maps was provided by Dr. William Bryant (Texas A&M University). Completion of this research would not have been possible without the countless hours of sample processing and by Larry Hyde, Kristi Jones, and Chris Kalke. For their insight and input, I thank Rick Kalke, Marc Russell, Sally Morehead, and Terry Palmer. Finally, for their love, support, and prayers I thank my parents Thomas and Janis Baguley, grandmothers Irma Mayernick and Rosa Lee Baguley, and fianc Brittany Hartzell. v This research was funded in part by the U.S. Department of Interior, Minerals Management Service, contract No. 1435-01-99-CT-30991 via a subcontract from the Texas Engineering Experiment Station. The research is part of the Deepwater Program: Northern Gulf of Mexico Continental Slope Habitats and Benthic Ecology (DGoMB, Deep Gulf of Mexico Benthos, G.T. Rowe PI). Other partial support was provided by the University of Texas Marine Science Institute via a Lund Graduate Fellowship. vi MEIOFAUNA COMMUNITY STRUCTURE AND FUNCTION IN THE NORTHERN GULF OF MEXICO DEEP SEA Publication No.____________ Jeffrey Greer Baguley, Ph.D. The University of Texas at Austin, 2004 Supervisor: Paul A. Montagna Meiofauna, a highly diverse group of small metazoans, are ubiquitous in deep-sea soft sediments and exhibit high abundance and biomass compared to larger-sized invertebrates (e.g., macrofauna). The northern Gulf of Mexico deep sea is characterized by topographical contrasts, with the flat topography of the Florida slope followed by the precipitous depth increase of the Florida escarpment; the complex Texas/Louisiana slope with numerous basins and knolls; and numerous canyon features such as the Mississippi Trough and DeSoto Canyon. Meiofauna community structure (abundance, biomass, and diversity) and function (respiration and feeding rates) were analyzed along with environmental variables in a hypothesis-based univariate and multivariate design, to more fully understand the distribution meiofauna, regional species pools, processes vii structuring communities, and how they respond to topographic, geochemical and physical forcing in the northern Gulf of Mexico. Meiofauna abundance is significantly related to water depth, but also exhibits significant longitudinal differences resulting from proximity to Mississippi River outflow. Multivariate comparisons of meiofauna abundance and diversity with environmental variables reveals a strong Mississippi River influence. River outflow alters local sediment characteristics, and interacts with loop current eddies and dynamic slope topography to increase POM flux in the northeastern region, thus creating areas of enhanced meiofauna abundance, biomass, and respiration, but lower functional harpacticoid copepod diversity. However, most stations have unique harpacticoid species compositions, suggesting high regional (2700 species) and global (105 - 106 species) diversity by extrapolation. Although highest harpacticoid diversity, in terms of expected number of species (rarefraction), is found at approximately 1200 meters, average taxonomic and average phylogenetic diversity continue to increase with depth, indicating greater morphological or functional diversity. High within versus between station variability suggests an interaction between small and region-scale processes maintaining high diversity. Allometric estimates indicate that meiofauna require 7% of their biomass per day to meet their metabolic energy budget, and account for 10-25% of whole sediment community respiration, indicating their importance in global biogeochemical cycles. viii TABLE OF CONTENTS LIST OF TABLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi LIST OF FIGURES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xv Chapter 1: Meiofauna abundance in relation to environmental variables in the Northern Gulf of Mexico deep sea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 Chapter 2: Spatial and Bathymetric Trends in Harpacticoida (Copepoda) Community Structure in the Northern Gulf Of Mexico Deep Sea . . . . . . . . . . . . . . . . . . 62 Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 Chapter 3: Meiofauna biomass and weight-dependant respiration in the Northern Gulf of Mexico deep sea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 ix Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138 Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180 Vita . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201 x LIST OF TABLES Table 1.1: Summary of meiofauna community structure experimental design: null hypotheses, design criteria, and stations included in analysis. . . . . . . . . . . . 35 Table 1.2: Effect of core tubes size (inner diameter) on meiofauna counts and average density. Based on five replicates taken at station W-2. Abundance is detransformed from natural log (ln), so taxa averages do not sum to the total average. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 Table 1.3: DGoMB station locations, depth, and average meiofaunal abundance (five replicate cores) for pooled taxonomic groups. . . . . . . . . . . . . . . . . . . . . . . . . 37 Table 1.4: Average abundance (AA), percent contribution (Contrib.%), and cumulative percent contribution (T%) of meiofauna major taxa per core (5.5 cm i.d.). Data summarized for all 51 stations (five replicates per station). . . . . . . . . . . . . . 39 Table 1.5: ANOVA results, tests for differences in meiofauna abundance. Dependent variable = log10(N+1). ANOVA abbreviations: DF = degrees of freedom, SS = sum of squares, MS = mean square, F = F-test value, P = Pr > F. Factor abbreviations: long. = longitude, basin = basin vs. non-basin stations, dfs = distance from shore, escarp. = escarpment vs. non-escarpment transects. . . 40 Table 1.6: Eigenvalues of the Correlation Matrix for the environmental PCA, proportion of variance explained by each principal component, and cumulative variance. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 xi Table 1.7: Variable loads for the rotated (Varimax) factor pattern of the environmental PCA. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 Table 2.1: Results of SIMPER analysis (Primer 5.0) indicating family percent contributions to total harpacticoid abundance. AA = Average abundance, Contrib.% = percent contribution of family, T% = cumulative percent contribution of families. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96 Table 2.2: Total species (S) and total individuals (N) per five pooled replicates cores (= 118.8 cm2). Species diversity indices: expected species per 30 individuals [ES(30)], Shannon-Wiener diversity (H ), average taxonomic diversity ()), and average phylogenetic diversity (M+) at each of the 43 stations where harpacticoid copepods were identified to species. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 Table 2.3: ANOVA results of test for differences in Harpacticoida diversity. Dependent variable is average phylogenetic diversity (M+). DFS = distance from shore; DFFS = distance from first station. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 Table 3.1: Biomass contribution of the major taxonomic groups to total meiofaunal biomass (mg C m-2) at each DGoMB station. NEMA = Nematoda, HARP = Harpacticoida, NAUP = Harpacticoida nauplii, POLY = Polychaeta, OSTR = Ostracoda, CYCL = Cyclopoida, TANA = Tanaidacea, ISOP = Isopoda, KINO = Kinorhyncha. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140 Table 3.2: Allometric estimations of the mass-dependent meiofauna respiration rate (R, in d-1 units) and meiofauna community respiration (CO2, mg C m-2 d-1) and total xii organic carbon demand (OrgC, mg C m-2 d-1). Mass-dependent respiration was calculated using an allometric rate law (sensu Mahaut et al. 1995) which is dependent upon the ratio (W) of biomass (B, mg C m-2 d-1) to abundance (A, N m-2). Respiration (CO2, mg C m-2 d-1) is the product of the mass-dependent rate (R) and the total biomass (B), and total carbon demand (OrgC) was calculated under the assumption that respiration equals 80% of the total metabolic budget (see discussion). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142 Table 3.3: Mean meiofaunal (MB) and bacterial (BB) biomass for pooled replicates and pooled taxonomic groups at the four experimental stations. Bacterial biomass courtesy of Jody Deming, University of Washington (unpublished DGoMB data). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144 Table 3.4: ANOVA results of the test for differences in grazing rate between treatments (experimental vs. control) and stations, separated by taxonomic group. Significant treatment by station interactions were observed for all taxa (a = 0.05). Polychaetes were the only taxa to show consistent grazing, and overall grazing rates for all taxa were low (refer to Table 3.5 & Fig. 3.9 below). . . . . . . . 145 Table 3.5: Measured meiofaunal grazing on bacteria is only 9.8 to 0.0001% of their theoretical required consumption. Measured meiofauna grazing rate (GR, d-1 units), bacterial biomsss (BB, mg C m-2 d-1), measured grazed bacterial carbon (GC = GRxBB, mg C m-2 d-1), allometric carbon requirement (OrgC, mg C m-2 xiii d-1), and the ratio of measured grazing to the allometric requirement (GC/OrgC), expressed as a percent. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146 Table 3.6: Comparison of whole community respiration (CR, mg C m-2 d-1) to meiofauna allometric respiration estimates (MR, mg C m-2 d-1). Meiofauna account for 1025% of whole community respiration. Note: whole community respiration (mg C m-2 d-1), converted from sediment community oxygen consumption (SCOC) as measured by the Benthic Lander (Gil Rowe, unpublished DGoMB data). SCOC (mmol O2 m-2 d-1) was converted to carbon using a respiratory quotient of 0.85 and stoichiometric conversion factor of 12 mg C/mmol O2. . . . . . . . . . . . . 147 xiv LIST OF FIGURES Figure 1.1: DGoMB station locations in the northern Gulf of Mexico deep sea. Note transect and topographic feature descriptions. . . . . . . . . . . . . . . . . . . . . . . . . 43 Figure 1.2: Vertical distribution of meiofauna taxa from sediment cores collected at station W2 for Shakedown Cruise. Cores were sectioned in 1-cm increments, and fauna were enumerated from each 1-cm section. Plotted data is the average of five replicates (error bars = standard deviation) . . . . . . . . . . . . . . . . . . . . . . 44 Figure 1.3: Log (x+1) transformed meiofauna abundance (N m-2) versus water depth (m) for all stations sampled during DGoMB project. . . . . . . . . . . . . . . . . . . . . . . 45 Figure 1.4: Spatial analysis of meiofauna abundance (N m-2) at all DGoMB stations. Buffer size equals relative meiofauna abundance. The highlighted contour equals 2000 meters. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 Figure 1.5: Number of major meiofauna taxa per core as a function of water depth. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 Figure 1.6: Expected number of taxa per 1000 individuals [ES(1000)] as a function of water depth, and quadratic regression. Dashed lines = 95% confidence intervals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 Figure 1.7: Expected number of taxa per 20 individuals [ES(20)] for non-dominant meiofauna taxa (excluding nematodes, harpacticoid copepods, harpacticoid nauplii, and unknowns). A quadratic regression was fit to the data (dashed lines = 95% confidence intervals). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 xv Figure 1.8: Meiofauna abundance (N m-2) as a function of depth for transects included in the test for differences over depth and longitude (H01 & H02). . . . . . . . . . 50 Figure 1.9: Comparison of meiofauna abundance (N m-2) on two parallel transects to determine abundance differences related to canyon (MT transect) versus noncanyon (C transect) areas (H04) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 Figure 1.10: Comparison of miofauna abundance and a function of water depth along two transects to determine abundance differences related to the Florida Escarpment (S39-S44) versus a reference transect (W1-W6). . . . . . . . . . . . 52 Figure 1.11 SeaWIFS chl-a (mg/L) biweekly average (November 1999 through April 2000). Chl-a concentration was adjusted for remineralization with depth (see Berger et al. 1988). A) Log-Log relationship of adjusted chl-a with depth, and B) Meiofauna abundance versus adjusted chl-a. . . . . . . . . . . . . . . . . . . . . . . 53 Figure 1.12: Meiofauna abundance (N -2) as a function of sediment particulate organic carbon (POC). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 Figure 1.13: Principal components analysis of environmental variables, A) variable loading scores for PC1 versus PC2, B) variable loading scores for PC2 versus PC3. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 Figure 1.14: Meiofauna abundance (N m-2) regressed against enviornmental PC1 (A), designated sediment properties, and environmental PC2 (B), POM Flux. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 xvi Figure 1.15: MDS ordination of DGoMB stations, based on Bray-Curtis similarity (4th root transformation) of major taxa abundance. . . . . . . . . . . . . . . . . . . . . . . . 57 Figure 1.16: MDS ordination of DGoMB stations, based on Bray-Curtis similarity (4th root transformation) of major taxa abundance. Symbols indicate depth zones of 1000 meter increments: = 200-1000 meters, = 1000-2000 meters, = 2000-3000 meters, and = >3000 meters. Circled areas approximate stations above and below 2000 m. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 Figure 1.17: MDS ordination of DGoMB stations, based on Bray-Curtis similarity (4th root transformation) of major taxa abundance. Bubble size equals relative nematode abundance at each station. The MDS plot strongly represents decreasing abundance with depth. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 Figure 1.18: MDS ordination of DGoMB stations, based on Bray-Curtis similarity (4th root transformation) of major taxa abundance. Bubble size equals relative abundance of Tardigrada at each station. Tardigrades were one of the major taxonomic groups that did not follow the general pattern of decreased abundance with depth. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 Figure 1.19: MDS ordination of DGoMB stations, based on Bray-Curtis similarity (4th root transformation) of major taxa abundance. Bubble size equals relative particulate organic carbon (POC) concentration. . . . . . . . . . . . . . . . . . . . . . 61 Figure 2.1: Harpacticoid copepods were identified to species at a total of 43 stations in the northern Gulf of Mexico deep sea. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100 xvii Figure 2.2: Harpacticoid copepod abundance (N) and species richness (S), adjusted to the number per 10 cm2, as a function of depth. Abundance and richness are highly correlated (r = 0.91) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 Figure 2.3: Shannon-Wiener diversity index (H') as a function of depth for pooled replicate core samples of harpacticoid copepods. . . . . . . . . . . . . . . . . . . . . 102 Figure 2.4: Expected number of harpacticoid species per 30 individuals [ES(30)], for pooled replicate core samples of harpacticoid copepods. . . . . . . . . . . . . . . 103 Figure 2.5: Average taxonomic diversity ()) for pooled replicate core samples of harpacticoid. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104 Figure 2.6: Average phylogenetic diversity (M+) for pooled replicate core samples of harpacticoid copepods. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 Figure 2.7: The ratio of harpacticoid copepod species (S), genera (G), and families (F) per total individuals (N) in each depth zone. Zones are significantly different (P<0.01), with pairwise comparisons indicating differences among shallowest and deepest zones only . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106 Figure 2.8: Average phylogenetic diversity (F+) as a function of depth for transects included in the test for depth and longitude differences (H01 and H02). . . . . 107 Figure 2.9: Average phylogenetic diversity (F+) as a function of depth for transects included in the test for diversity differences between canyon (MT) and noncanyon (C) areas (H04). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108 xviii Figure 2.10: Average phylogenetic diversity (F+) as a function of depth for transects included in the test for escarpment (S transect) effects on diversity (H05). . 109 Figure 2.11: Regression of average phylogenetic diversity (F+) as a function of A) environmentalPC1 and B) PC2. F+ is not significantly related to sediment properties (PC1), but is significantly related to POM flux (PC2). . . . . . . . 110 Figure 2.12: MDS orientation of stations based on harpacticoid species abundance. Symbols indicate depth zone: = 200-1000 meters, = 1000-2000 meters, = 2000-3000 meters, > 3000 meters. One-way analysis of similarity (ANOSIM) indicates significant depth differences (P<0.01). . . . . . . . . . . . . . . . . . . . . 111 Figure 2.13: MDS orientation of stations based on harpacticoid species abundance. Symbols indicate longitudinal zone: = 94-96N W, = 91-93N W, = 88-90N W, = 85-87N W. One-way ANOSIM indicates significant longitudinal differences (P<0.01). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112 Figure 2.14: Cluster analysis of Harpacticoid community composition, created using Bray-Curtis similarity, and group average linking. Zonation determined on basis of >20% similarity. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 Figure 2.15: Harpacticoid copepod species zonation in the northern Gulf of Mexico deep sea. Zones were chosen on the basis of >20% similarity using the Bray-Curtis similarity index. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114 Figure 2.16: Species accumulation curves used to estimate regional Harpacticoida species abundance in the Gulf of Mexico (extrapolation). . . . . . . . . . . . . . 115 xix Figure 2.17: Cluster analysis illustrating within versus between station differences in harpacticoid community structure for all replicates at stations NB3 and NB4. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116 Figure 3.1: DGoMB station locations in the northern Gulf of Mexico deep-sea where meiofauna community biomass (sensu Baguley et al. 2004) and allometric respiration (sensu Mahaut et al. 1995) were estimated. . . . . . . . . . . . . . . . . 148 Figure 3.2: Process station locations for 2001 cruise. MT1 = 482 m; S42 = 763 m; S36 = 1826 m; MT6 = 2643 m . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149 Figure 3.3: Meiofauna biomass (mg wet wt/m2) versus water depth at all DGoMB station. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150 Figure 3.4: Average nematode wet weight (mg) per individual versus depth . . . . 151 Figure 3.5: Average harpacticoid wet weight (mg) per individual versus depth . . 152 Figure 3.6: Meiofauna grazing rates by taxonomic group for the four experimental stations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153 Figure 3.7: A) Meiofauna mass-dependent respiration rate (d-1) and B) meiofauna community respiration (mg C m-2 d-1) at each of the 51 DGoMB stations in the northern Gulf of Mexico deep-sea. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154 Figure 3.8: Spatial comparison of relative meiofauna biomass (mg wet wt. m-2), where bubbles size is proportional to biomass. . . . . . . . . . . . . . . . . . . . . . . . . . . . 155 Figure 3.9: Spatial interpolation of meiofauna biomass (mg wet mass m-2) in the northern Gulf of Mexico deep sea. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 156 xx Figure 3.10: Spatial interpolation of the meiofaunal organic carbon requirement (mg C m-2 d-1), assuming respiration equals 80% of total metabolism. . . . . . . . . . 157 Figure 3.11: Conceptual model of complex meiofaunal trophic interactions with microfauna (bacteria and protists) and two different detrital pools (phytodetritus, and recycled detritus). Not shown are predatory meiofauna (prey upon other meiofauna) or meiofaunal deposit feeders that ingest whole sediment particles and obtain carbon from one or more of the above standing stocks. Carbon is lost via respiration transfer to higher trophic levels via predation (cloud symbols). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158 xxi CHAPTER 1: MEIOFAUNA ABUNDANCE IN RELATION TO ENVIRONMENTAL VARIABLES IN THE NORTHERN GULF OF MEXICO DEEP SEA ABSTRACT Meiofauna are ubiquitous in deep-sea soft sediments and exhibit high abundance compared to larger-sized invertebrates (e.g., macrofauna). The northern Gulf of Mexico (NGOM) deep sea is characterized by topographical contrasts, with the flat topography of the Florida slope followed by the precipitous depth increase of the Florida escarpment; the complex Texas/Louisiana slope with numerous basins and knolls; and numerous canyon features such as the Mississippi Trough and DeSoto Canyon. In order to more fully understand the distribution of meiofauna and how they respond to topographic, geochemical and physical forcing in the northern Gulf of Mexico, meiofauna abundance and environmental variables were analyzed in a hypothesis-based univariate and multivariate design. Meiofauna abundance is significantly related to water depth, but also exhibits significant longitudinal differences resulting from proximity to Mississippi River outflow. Canyon features in proximity of Mississippi River outflow were found to greatly enhance meiofauna abundance. The Florida Escarpment interacts with Mississippi River inflow and the Loop Current to enhance meiofauna abundance at stations lying directly above and below the escarpment. Multivariate comparisons of meiofauna abundance with environmental variables reveals a strong Mississippi River influence. River outflow alters local sediment characteristics, and interacts with loop 1 current eddies and dynamic slope topography to increase POM flux in the northeastern region, thus creating areas of higher than normal meiofauna abundance. INTRODUCTION The term meiobenthos was first used by Mare (1942) to describe benthic organisms of intermediate size, however studies of meiofauna-sized benthic invertebrates began with the discovery of the Kinorhyncha in 1851 (Dujardin 1851). Meiofauna are metazoan and protistan fauna that are smaller than macrofauna but generally larger than the microbenthos (e.g., bacteria, microalgae, and many protozoans). Since Mare s seminal work, meiofauna research has become a specialized sub-discipline within the general field of benthic ecology. Several studies have inferred that marine meiobenthos are a biologically and ecologically separate group of animals (Schwinghamer 1981, Warwick 1984, Warwick et al. 1986, Giere 1993), a community concept that was described by Remane (1933) with respect to meiofaunal adaptations to the interstitial (between sand grains) environment. Meiofauna, although often overlooked in largescale benthic studies, are ubiquitous in marine and freshwater sediments, an environment spanning approximately three quarters of the globe (Hick and Coull 1983, Giere 1993, Soltwedel 2000). Meiofauna also exhibit systematic diversity unparalleled by any other group of organisms, and have species diversity comparable to the Insecta (May 1980). Giere (1993) lists 24 of the 34 recognized phyla of the Kingdom Animalia as having meiofaunal representatives, and at least three taxa within the Kingdom Protista as having free-living meiofaunal representatives. Meiofauna are an important component of all 2 marine soft-sediment communities, play a key role in nutrient remineralization and transfer of carbon to higher trophic levels (Coull & Bell 1979 and references therein). Yet, despite the incredible abundance, biomass, and diversity of meiofauna, this group remains generally understudied, compared to their larger relatives. The first quantitative study of deep-sea meiofauna ecology was by Wigley & McIntyre (1964). Subsequent studies have been conducted in all major ocean basins; including the Atlantic, Pacific, North Sea, Mediterranean, Red Sea, Gulf of Mexico, and Weddell Sea (see review by Soltwedel 2000). Recent deep-sea investigations have focused on bathymetric gradients of abundance (Tietjen 1971; Coull et al. 1977; Shirayama 1984a), relationships of community structure with food availability (Thiel 1978; Pfannkuche 1993; Danovaro et al. 1995; Gooday 1996; Relexans et al. 1996; Soltwedel 1997; Fabiano and Danovaro 1999) and relationships with environmental factors (Shirayama 1984a; Alongi and Pichon 1988; Vanhove et al. 1995; Soltwedel et al. 1996). Most meiofaunal studies have focused on limited geographic areas, not allowing region- or basin-scale patterns to emerge. Therefore, deep-sea investigations of meiofauna abundance in relation to environmental factors have been limited primarily to variation on the sample scale, i.e., correlations between abundance and physical or geochemical variables (Shirayama 1984a). Of studies in 48 regions, reviewed by Soltwedel (2000), between 2 and 21 stations were sampled, with 31 of 48 study locations having less than 10 stations sampled. Meiofauna abundance has been reported from only 15 deep-sea stations (350-2800 meters) in the Northern Gulf of Mexico (Pequegnat et 3 al. 1990), and from 16 stations (200-540 meters) in the Southern Gulf of Mexico (Escobar et al. 1997). Understanding organism distributions and how they respond to topographic, geochemical and physical oceanographic features has not been fully elucidated for deepsea communities (Etter and Mullineaux 2001). The northern Gulf of Mexico continental slope is physically and geologically complex, with numerous basins, knolls, and canyons. The anticyclonic loop current is a permanent feature in the Gulf of Mexico and produces anticyclonic/cyclonic gyre pairs that can be both short (days) or long-lived (weeks to months) mesoscale features (Biggs and M ller-Karger 1994). Meiofauna ecology in the Gulf of Mexico deep sea has only been sparsely investigated, and focused primarily on bathymetric abundance gradients (Pequegnat et al. 1990) or regional trends along the same isobath (Escobar et al. 1997). In order to more fully understand meiofaunal community structure in relation to the complex physical setting of the Northern Gulf of Mexico continental slope, meiofauna abundance was compared from the Florida continental slope to the Texas continental slope. The study was hypothesis-based in order to select stations covering nearly the entire northern region, taking into account the diverse physical setting. The sampling design was formulated based on the following six null hypotheses: H01) there is no difference in meiofaunal abundance with depth, H02) there is no difference in meiofaunal abundance with longitude, H03) there is no difference in meiofaunal abundance in versus out of submarine basins, H04) there is no difference in meiofaunal 4 abundance in versus out of submarine canyons, H05) there is no difference in meiofaunal abundance with respect to escarpments, and H06) there is no difference in meiofaunal abundance with respect to overlying water column primary production. The depth hypothesis (H01) follows one of the fundamental observations of deepsea ecology, as depth increases abundance decreases (Soltwedel 2000) reflecting the decrease in particulate organic matter (POM) flux with depth (Turley et al. 1995). The longitude hypothesis (H02) was specifically designed to test for effects of the Mississippi River in shaping faunal compositions. Mississippi River discharge is a major source of new nutrients and organic matter into the northern Gulf of Mexico, with a mean daily discharge of nearly 1 billion m3 (http://water.usgs.gov). The a priori hypothesis was that a longitudinal gradient of abundance exists, which is maximized near Mississippi River inflow. The basin hypothesis (H03) was designed to test for faunal differences in basins and adjacent non-basin stations on the Texas/Louisiana slope. Basins, along with canyons (H04) may have a concentrating effect on the rain of POM and therefore enhance meiofauna abundance. Escarpments (H05) may interact with deep water currents and internal waves to create flows that influence food supply (Etter and Mullineaux 2001). The a priori hypothesis was increased abundance directly below the escarpment, as a result of increased sedimentation of particulate organic matter (POM) resulting from the interaction with physical oceanographic processes (Etter & Mullineaux 2001). The escarpment transect was compared to a reference transect in the Western Gulf (W) that experiences a gradual and relatively constant depth increase. 5 Increased overlying chlorophyll(a) biomass (H06) due to interactions among Mississippi River outflow, the Loop Current, and mesoscale anticyclonic/cyclonic gyre pairs likely affect meiofauna abundance. Univariate and multivariate statistical methods was used to integrate regional differences in relation to the physical environment with environmental variables in order to more fully understand the processes controlling meiofauna abundance in the Northern Gulf of Mexico deep sea. METHODS Field Methods Station Locations A total of 51 stations in the northern Gulf of Mexico were sampled for meiofauna community structure (Fig. 1.1) as part of the Deepwater Program: Northern Gulf of Mexico Continental Slope Habitats and Benthic Ecology program (henceforth referred to as DGoMB). A total of seven transects were investigated from 200 to 3000 meters. In the northwest (RW) region, seven stations were sampled, including one station in the Alaminos Canyon (AC1). An additional western (W) transect was included, which was a historical transect from a previous study (Pequegnat et al. 1990). In the west-central region (WC) two historical stations from Pequegnat et al. (1990) study were included, but stations in this region were mainly designed to test for faunal differences between basin (B) and non-basin (NB) locations. The central transect (C) was also sampled by Pequegnat et al. (1990), but included to test for differences from the adjacent Mississippi Trough (MT) transect. In the northeast region 10 stations, from two transects, were sampled perpendicular to the Florida slope and 6 escarpment (S). Additional stations not included in the original experimental design, but added to the sampling scheme, were a high productivity station (HIPRO) in the northeastern region, a station from the Green Knoll Furrow (GKF) region, a station on Bush Hill (BH), and five stations on the Sigsbee abyssal plain as part of the Joint Studies of the Sigsbee Deep (JSSD) collaboration with La Universidad Nacional Aut noma de M xico. Sample Collection Survey samples were collected on a 60-day cruise aboard the R/V Gyre (Texas A&M University) during the months of May and June 2000. One core sample was taken from each boxcore sample, and stored for meiofaunal community analysis. Five total replicate cores were taken at each community structure station. Meiofauna were collected by a 5.5 cm inner diameter (i.d.) core tube that was mounted inside the boxcorer. A mounted corer within a box will ensure that meiofauna are collected from an undisturbed surface. Insertion of a core tube after the sample has already been sloshed around the deck of the ship is known to create artifacts including loss of organisms. Surface disturbance can occur when the boxcore is placed on the ship s deck. Taking the sample from an inner subcore reduces edge effects (Eckman and Thistle 1988). The bow waves of sampling devices in deep water can have an impact on estimates of surface dwelling meiofauna (Hulings and Gray 1971). Bow wave effects were reduced by heavily weighting the boxcore and slowing penetrating sediments. There are two critical issues for sampling deep sea meiofauna: core size and sampling depth. To resolve these two issues, a study was performed during a shakedown 7 cruise aboard the R/V Gyre (Texas A&M University), 16-18 February 2000, to determine the most appropriate core size and vertical sampling depth for meiofauna in the current study area. To compare sizes, four cores ranging from 2.2 cm to 6.7 cm inner diameter (i.d.) were used to collect the top 1 cm of sediment, and five replicates were taken. Differences in meiofauna abundance with core size was compared using one-way analysis of variance (ANOVA). To exam the vertical distribution of meiofauna, a 5.5 cm core tube was used. Samples were taken at 1 cm intervals down to 20 cm, and five replicates were taken. All samples were taken at station W2 in water depths of approximately 661 meters. A third, although less critical issue was sampling gear type. Two types of boxcores, the GOMEX (Gulf of Mexico) boxcore (Boland and Rowe 1991) and USNEL(US Naval Electronic Laboratory) boxcore (Hessler and Jumars 1974) are commonly used. To exam for differences between two different boxcores, a 2.2 cm core tube was mounted within each and samples were taken to a depth of 1 cm. The sampling characteristics of the GOMEX boxcore and USNEL boxcore were compared using a paired t-test. Preservation After core sections were collected, meiofauna were narcotized in 7% MgCl2 (isotonic to seawater). Narcotizing meiofauna is necessary to minimize body shape distortion during the preservation process, allowing for more accurate biomass estimates by the semi-automated microphotographic approach (Baguley et al. 2004) (see chapter three of this dissertation). Samples were then preserved in an equal volume of 10% buffered formalin (yielding a final concentration of 5% formalin) (Hulings and 8 Gray 1971). The buffered formalin was made up with seawater that was filtered through a 0.042 mm mesh to exclude plankton. Rose bengal was added to the preservative to easily distinguish meiofauna during the sorting process. Samples were then stored and returned to The University of Texas Marine Science Institute (Port Aransas, TX) for analysis. Laboratory Methods Abundance By convention, the definition of meiofauna is those animals that pass through a 500 micron mesh sieve but are retained on a 63 micron mesh sieve (Hulings and Gray 1971; Coull and Bell 1979; Giere 1993). Because deep-sea organisms are small, most meiofaunal ecologists use 42 micron mesh sieves to retain meiofauna (eight of nine papers reviewed in Thistle et al. 1991). To conform with other studies of deep-sea meiofauna, a 45 micron mesh sieve was used to retain meiofauna. Meiofauna were extracted from sediment using the Ludox centrifugation technique (deJonge and Bouwman 1977). Recent quality control studies have shown that the technique extracts 95-99% of organisms over all sediment grain sizes (Burgess 2001). Samples were then sorted and counted to a major metazoan taxonomic category. Meiofaunal communities are composed of two groups. Temporary meiofauna are those juveniles of the macrofauna that will eventually grow into larger organisms. Permanent meiofauna are those groups where adults are less than 300 micrometers in length, e.g., Nematoda, Copepoda, Gastrotricha, Turbellaria, Acari, Gnathostomulida, Kinoryncha, Tardigrada, Ostracoda, and some Nemertinea, Oligochaeta, and Polychaeta. The two 9 standard meiofauna texts (Higgins and Thiel 1988; Giere 1993) were used in the identification of major taxonomic groups. Environmental Variables A full suite of sedimentary environmental variables were analytically measured from 5 replicate cores (6.7 cm i.d.), from separate boxcores, at most DGoMB stations. All chemical, geochemical, and geological analyses were performed by collaborators at Texas A&M University including: Drs. Luis Cifuentes, Bobby J. Presley, William Bryant, Terry Wade, John Morse, Doug Biggs, and their respective associates. Sediment grain size was determined using the standard Folk settling method (Folk 1974). Total organic and inorganic carbon were determined by standard LECO combustion techniques or by Carlo Erba elemental analyzer. Hydrocarbon contaminants (mainly PAH s) were measured using NOAA status and trends methods (Denoux et al. 1998; Qian et al. 1998) using gas chromatography-mass spectrometry (GC-MS). Trace metal analyses included atomic absorption spectroscopy (AAS), instrumental neutron activation analysis (INAA), and/or inductively coupled plasma-mass spectroscopy (ICP-MS) (e.g., Taylor and Presley 1998). Geochemical variables were measured using a number of methods and/or instruments: O2, H2S, Fe, and Mn were measured with microelectrodes (Brendel and Luther 1995; Luther et al. 1998); total CO2 (DIC) was measured via gas chromatography; sulfate was measured via ion chromatography; pH was measured with electrodes; nutrients (nitrate, nitrite, ammonia, urea, phosphate, and silicate) were measured using standard autoanalyzer techniques; dissolved organic carbon was measured using a high temperature combustion DOC 10 analyzer; organic carbon and nitrogen were measured using a Carlo Erba elemental analyzer. Surface seawater chlorophyll(a) (chl-a) was estimated from Sea viewing Wide-Field Sensor (SeaWiFS) satellite imagery. The complete environmental variable data set is not presented here, but will be publically accessible from the Geochemical & Environmental Research Group (http://www.gerg.tamu.edu/) upon completion of the DGoMB final report (Rowe et al., in prep). Statistical Analysis Hypothesis Testing Six main hypotheses were investigated for differences in meiofauna abundance. For statistical power, five replicate samples were taken per station, from five separate box cores. Five transects, RW, W, C, MT and S, were included in the depth/longitude (H01/H02) analysis ranging from the Texas slope in the West to the Florida Escarpment in the east (Fig. 1.1, Table 1.1). Five stations were included per transect, over five depth zones, consistent between transects. Differences in meiofaunal abundance at different depths and longitudes were tested using at two-way completely random analysis of variance (ANOVA) that is described by the following model: Yijk = + j + k + jk + (jk) where Yijk is the measurement for each individual replicate, is the overall sample mean, j is the main effect for transects and j = 1-5, k is the main effect for depths and k = 1-5, jk is the interaction term, and (jk) is the random error for each replicate measurement and i = 1-5. The test for differences between basin and non-basin (H03) locations 11 included three basin (B) and three non-basin (NB) stations (Fig. 1.1, Table 1.1). The sampling design blocked basin and non-basin stations (B1 with NB2, B2 with NB3, and B3 with NB4), to control for differing distances from shore. The experiment is a twoway completely random analysis of variance (ANOVA) that is described by the following model: Yijk = + j + k + jk + (jk) where Yijk is the measurement for each individual replicate, is the overall sample mean, j is the main effect for treatments and j = 1-3, k is the main effect for distance from shore and k = 1-3, jk is the interaction term, and (jk) is the random error for each replicate measurement and i = 1-5. The test for differences in meiofauna abundance between canyon and non-canyon (H04) locations included stations from the Mississippi Trough (MT) and adjacent central (C) transect (Fig. 1.1, Table 1.1). Five MT stations were paired with five C stations at five common depth zones, thus removing the effect of depth. Canyon differences were tested using at two-way completely random analysis of variance (ANOVA) that is described by the following model: Yijk = + j + k + jk + (jk) where Yijk is the measurement for each individual replicate, is the overall sample mean, j is the main effect for canyon and j = 1-2, k is the main effect for depths and k = 1-5, jk is the interaction term, and (jk) is the random error for each replicate measurement and i = 1-5. The effect of escarpments (H05) on meiofaunal abundance was tested by 12 comparing an escarpment transect to a non-escarpment transect (Fig. 1.1, Table 1.1). Six stations per transect were paired at approximately equal distance from shore and distances between stations. The experiment was tested using at two-way completely random analysis of variance (ANOVA) that is described by the following model: Yijk = + j + k + jk + (jk) where Yijk is the measurement for each individual replicate, is the overall sample mean, j is the main effect for transects and j = 1-2, k is the main effect for distance from first station, k = 1-6, jk is the interaction term, and (jk) is the random error for each replicate measurement and i = 1-5. Differences in meiofaunal abundance due to overlying water column primary production (H06) was tested by comparing surface water primary production estimates to meiofauna abundance at 43 stations. Mean biweekly chlorophyll(a) (chl-a) data (SeaWIFS satellite imagery), for the two months prior to community structure sampling (March-April 2000), was plotted against meiofauna abundance, and included in a nonlinear regression and multivariate comparison (see below) of biotic and abiotic variables. Averaging for two months prior to sampling was done to remove small-scale temporal variation Multivariate Analysis Abiotic variables are often correlated, so it is necessary to create reduced data sets that remove this autocorrelation. Then, these reduced data sets can be correlated with patters of biotic responses (e.g., abundance). Principal components analysis was used to reduce the environmental variables. Principal components analysis is a procedure to reduce a large set of intercorrelated variables into 13 a smaller set of orthogonal (completely uncorrelated) variables. Each new variable (principal component) accounts for a percentage of the total variance in the original data set. The new variables are extracted in decreasing order of variance, such that the first few principal components (PC) explain most of the variation in the data set. The contribution of each environmental variable to the new PC is called a load. Typically, the new PC loads can be interpreted to indicate structure in the data set. Each observation contributing to the PC is called a score. Thus, the main advantage of PCA is the generation of station scores, which are interpretable, and can subsequently be used in other analyses (i.e. correlation or regression with abundance). Environmental variables included in the environmental PCA included chl-a in the overlying water column as measured from SeaWIFS satellite images. Chl-a was adjusted for remineralization with increasing water depth by application of the exponential model proposed by Betzer et al. (1984), and updated by Berger et al. (1988). The amount of surface chl-a reaching the sea floor is described by the equation: J(z) = 0.409PP1.41/Z0.628 where J(z) is the flux of chl-a transported downwards through some depth Z, and PP is the overlying water column chl-a concentration. The remaining variables were all from sediments and included grain size (sand, silt, and clay content), total polycyclic aromatic hydrocarbons (PAH) excluding perylene, the trace metals calcium (Ca), chromium (Cr), tin (Sn), and strontium (Sr), total organic nitrogen (OrgN), particulate organic carbon (POC), dissolved organic carbon (DOC), ammonium (NH4), urea, and nitrate (NO3). 14 Prior to analysis all data were transformed to validate assumptions of parametric tests, and to weight then contribution of high or low measurements. The angular transformation (x = arcsin sqrt[y]) was used for the sediment grain size data, and a natural logarithm transformation (x = loge[y+1]) was used on all other data. One common problem with environmental data is that many variables measuring the same effects can skew the result. Thus, pre-analysis was performed to determine if certain classes of variables could be dropped from the analysis. Only the total PAHs was used because it served as a proxy for all organic contaminants. A total of 29 metals were measured and had to be reduced for the final analysis using an initial PCA of metals only. The first metals principal component (PC1) accounted for 70.1% of the total variance in the metals data set, and was the only PC with an eigenvalue greater than one. Thus, four metals, the two with highest positive and negative loadings, were chosen for the final PCA analysis. These four metals (listed above) served as a proxy for the general trace metal pattern seen at all stations. Non-parametric procedures included multidimensional scaling (MDS) and analysis of similarity (ANOSIM) of meiofauna abundance data, and application of the BIOENV procedure (an analysis that gives maximum Spearman rank correlations between the major taxa abundance matrix and a subset of the most important environmental variables). MDS is often used in lieu of PCA to analyze multivariate abundance data, because this data may not conform to the assumptions of the general linear model (Clarke and Warwick 2001). MDS is a non-parametric method that is 15 based on similarities or dissimilarities between each observation (sample). The most commonly used similarity index is Bray-Curtis (Clarke and Warwick 2001), which serves to maximize the distances between observations in multidimensional space. Thus, the distances between stations in the MDS plot is proportional their similarities. Analysis of similarity (ANOSIM) is conceptually similar to multivariate analysis of variance (MANOVA), but ANOSIM is not based on the general linear model. ANOSIM can be performed to test for a statistical difference between stations, based on different factors, and was used to test for depth differences in meiofauna major taxa abundance on a Gulf-wide scale. The BIOENV procedure calculates Spearman rank correlations between meiofauna abundance and environmental variables. Thus, it is possible to determine the subset of environmental variables with the highest correlations with meiofauna major taxa abundance. Statistical Software ANOVA and PCA procedures were accomplished using SAS statistical software (SAS Institute Inc. 1991). Non-parametric MDS and BIOENV procedures, as well as rarefraction indices (ES) were conducted with Primer 5.0 (PrimerE, 2000). GIS Spatial Analysis Geographic information systems (GIS)-based analyses were performed (ArcView 9.0, ESRI) to further examine spatial trends in the data set. The relative abundance at each station was compared by generating bubble values, where bubble size (the size of circle at each station location) is relative to total meiofauna abundance at each station. Observing spatial trends in meiofauna abundance on a Gulf- 16 wide scale allows for comparison with physical oceanographic processes (Loop Current, Loop Current eddies, the effect of River inflow, etc.), or other mechanisms that may interacting to influence the benthic community. RESULTS Sampling Issues Core size comparison More organisms were found in progressively larger cores (Table 1.2). But, there were no statistically significant differences for abundances of total meiofauna (P = 0.6324), nematodes (P = 0.7800), harpacticoids (P = 0.3385) and other taxa (P = 0.8238) among different core sizes. Thus, even though total abundance (adjusted for unit area) in the smallest core was about half that found in the three larger cores, it was not statistically different. Because each core yielded the same abundance estimate, total counts per core were used to choose the appropriate core size. For statistical purposes, it is imperative to obtain > 30 organisms per taxa per sample. Therefore, the 5.5 cm core was chosen for the benthic survey. Sediment sampling depth Nearly all meiofauna were found in surface sediments and no meiofauna were found below 13 cm sediment depth, so just the top 13 cm are plotted (Fig. 1.2). Most organisms were found in the top 3 cm. A total of 87% of total meiofauna were in the top 3 cm, and 97% of the harpacticoid copepods. In addition, 77% of the harpacticoid copepods were found in the top 1 cm. Because the distribution is so skewed to the surface, it was decided to sample the top 3 cm only during the benthic 17 community survey cruise. Because harpacticoid copepods were so restricted to the top 1 cm, the core was split into 2 sections: 0 - 1 cm and 1- 3 cm. Box core comparison There were no statistically significant differences for abundances of total meiofauna (P = 0.2281), nematodes (P = 0.0632), harpacticoids (P = 0.9999) and other taxa (P = 0.1988) among different box core types. Because of convenience, the GOMEX boxcore (Boland and Rowe 1991) was used throughout the study. General Results A total of 586 samples from 51 stations in the study yielded 1.71x105 individuals from 21 meiofauna taxa. Samples were collected from a depth range of 200-3700 meters in the northern Gulf of Mexico. Mean abundance (extrapolated to number of individuals per m2, Nm-2) per station was 2.63 105 Nm-2, with standard deviation of 2.01x105 (calculated from Table 1.3). Maximum and minimum meiofauna abundances were found at stations MT1 and JSSD3 with values of 9.46x105 and 0.60x105 Nm-2, respectively (Table 1.3). A strong linear relationship exists between log abundance (R2 = 0.658, P<0.0001) water depth (Fig. 1.3). Spatial variability in meiofauna abundance (bubble size representing relative abundance) indicates highest values in the shallow northeastern stations (Figs. 1.4) . Relatively lower abundance was observed in the western transects, but the general trend of decreasing abundance was consistent along western transects (RW & W), and in the west-central area (WC5, WC12, B1-B3, NB2-NB5). Exceptionally high abundance was found at stations MT1, MT3, S35, S36, S42 and C7, 18 all located in the northeast region at depths ranging from approximately 450 - 1900 m. Variation from the general pattern of decreasing abundance with depth was observed at these northeastern stations. The meiofauna community was composed of individuals from 21 taxonomic groups (Table 1.4). Nematoda and Harpacticoida (including nauplii) were the two dominant groups accounting for 65.3 and 25.4% of individuals, respectively. Unknown fauna were the next most abundant, comprising 6.6% of individuals. The unknown group likely included representatives from various soft-bodied taxa including (but not limited to) various taxa within the Turbellaria and representatives of the Protista (e.g., Ciliophora). Soft-bodied taxa, such as these, often become unrecognizable during bulk fixation with buffered formaldehyde. The remaining 3.7% of the meiofauna community was composed of representatives from various taxa, including: Polychaeta, Kinorhyncha, Ostracoda, Cyclopoida, Tardigrada, Tanaidacea, Nemertinea, Acari, Isopoda, Bivalvia, Gastrotricha, Anthozoa, Priapulida, Gastropoda, Aplacophora, Rotifera, Sipuncula, and Loricifera (Table 1.4). The complete major taxa data set is not presented here, but will be publically accessible from the Geochemical & Environmental Research Group (http://www.gerg.tamu.edu/) upon completion of the DGoMB final report (Rowe et al., in prep). The number of major taxa decreased with increasing water depth (Fig. 1.5). The expected number of major taxa per 1000 individuals [ES(1000)], follows a quadratic pattern, where major taxa diversity is maximized at stations just over 1000 meters, and 19 then decreases with increasing depth (Fig. 1.6). This pattern is also observed when only non-dominant taxa are considered (Fig. 1.7). The ES(20) for non-dominant taxa (excluding nematodes, harpacticoids, nauplii, and polychaetes) follows a similar quadratic pattern, but major taxa diversity is maximized at stations around 1800 meters, decreasing thereafter. Univariate Analysis Hypothesis Testing In the test for differences over depth and longitude (H01 & H02), significant main effects for longitude and depth were observed (P<0.0001, Table 1.5), as well as a significant longitude by depth interaction term (P<0.0001, Table 1.5). The two western transects (RW and W) had a gradual (very linear) decrease in abundance with depth (Fig. 1.8). With increasing proximity to the Mississippi River, (transects C and MT) abundance increases greatly at stations between 300 and 1000 meters (Fig 1.8). The Florida slope transect (S) has highest abundance at station S36 (ca. 2000 meters), which is located in the DeSoto Canyon. Transects become more similar with increasing depth, with abundance being very similar at all stations > 2500 meters water depth (Fig. 1.8). In the test for differences between basin and adjacent non-basin stations (H03), no significant difference was observed between main effects (P = 0.5421, and P = 0.7773, Table 1.5), and no significant interaction was observed (P = 0.6980, Table 1.5). 20 In the test for differences between canyon and adjacent non-canyon stations (H04), significant treatment (P = 0.0059) and depth zone (P<0.0001) effects were observed (Table 1.5), but a significant interaction term was also observed (P = 0.0058, Table 1.5). Abundance is elevated at the head of the canyon (MT1 & MT3) compared to stations of similar depth in the adjacent transect (C1 & C7) (Fig. 1.9). However, the two transects become increasingly similar with depth, and show no differences at stations greater than 1500 meters (Fig. 1.9). In the test for differences in meiofauna abundance due to the presence of an escarpment (H05), no significant escarpment effect was observed in comparison to the reference transect (P = 0.791). However, a significant main effect was observed for distance from shore (P<0.0001, Table 1.5), and a significant interaction term was observed (P = 0.002, Table 1.5). Abundance was dramatically lower below the Florida Escarpment than above (Fig. 1.10). Station S41 had nearly twice the abundance of stations S40 and S39, which are of similar depth, but further offshore. Comparison to the reference transect in the western gulf (stations W1-W6) illustrates deviation of the escarpment transect from an expected decrease in abundance along a relatively constant slope, with elevated abundance just above and below the escarpment (Fig. 1.10). The amount of overlying water column chl-a biomass that reaches the sea floor decreases in a log-log relationship with depth (Fig. 1.11A). Meiofauna abundance increases with increasing overlying water column chl-a biomass (Fig. 1.11B). Although considerable variability exists in the linear regression (R2 = 0.413), the relationship is 21 significant (P<0.0001). Accordingly, meiofauna had a moderate, and significant relationship with sediment POC concentration (Fig. 1.12, R2 = 0.331). Although null hypothesis six (H06) was not tested with ANOVA as above, detailed analysis of the meiofauna community with respect to food availability and sediment environmental variables was accomplish with multivariate procedures (see below). Multivariate Analysis Principal Components Analysis In the PCA, the first three principal components accounted for 61.5 percent of the total variance in the data set (Table 1.6). However, four PCs out of 15 had eigenvalues greater than one, which means the first four were significant. The sign of variable loads (negative or positive) indicates gradients in concentrations. Variables that load negatively will have highest concentrations for negative PC loads with decreasing concentrations moving in the positive direction, and vice versa. PC1 accounted for 33.5% of the total variance, had high positive loadings by clay, total PAH s, tin, chromium, and high negative loadings by sand, strontium and calcium (Table 1.7, Fig. 1.13A). PC1 is interpreted as the sediment properties, with high silt, clay, organic (PAH) and metal (Cr and Sn) contaminants near the Mississippi River, and higher sand and natural background metals (Ca and Sr) with increasing distance from the Mississippi River. PC2 accounted for 16.7% of the total variance and highly positive loadings by chl-a and POC, weak positive loadings by OrgN and NH4+, and weak negative loadings by NO3- and urea (Table 1.7, Fig 1.13A). PC2 is interpreted as particulate organic 22 matter (POM) flux. PC3 accounted for 11.3% of the total variance and had highly positive loadings by DOC, and highly negative loadings by urea (Table 1.7, Fig. 1.13B). PC4 accounted for 10.2 % of the total variance and had moderate positive loadings by silt, NH4+, NO3-, and PAH (Table 1.7). However, PC3 and PC4 did not have obvious interpretations. PCs 1-4 were regressed against abundance to determine if they were significantly related to the biotic community. PC1 had a weak, but significant, positive relationship with meiofauna abundance, but accounted for only 22% of the variance in the biotic data set (Fig. 1.14A, R2 = 0.215). PC2 had a moderate, and significant, relationship with meiofauna abundance (Fig. 1.14B, R2 = 0.303). PC3 and PC4 did not have significant relationships with abundance. Multidimensional Scaling MDS ordination of meiofauna major taxa abundance (Fig. 1.15) condenses multivariate station similarities into a two-dimensional plot, where distances between stations are proportional to their similarities (Bray-Curtis similarity). MDS analysis of major taxa abundance data shows a strong trend with depth (Fig. 1.16). Depth zones of 1000-meter depth increments group together with only moderate overlap. Two groups can be defined, one representing stations less than 2000 m, a second group greater than 2000 meters (Fig. 1.16). These two groups are statistically different by ANOSIM (P<0.01). The depth trend is a reflection of decreasing abundance of the dominant taxanomic groups, for example Nematoda (Fig. 1.17), where the bubble value is proportional to nematode abundance. Variation in the MDS vertical dimension (i.e. not reflecting depth) is due to minor taxonomic groups that do not follow the general 23 pattern of decreasing abundance with increasing depth, for example the Tardigrada (Fig. 1.18). POC also influences the MDS pattern, which can be observed by overlaying bubble values proportionate to variable concentration (Fig. 1.19). POC concentration also follows the MDS depth trend, and reflects decreasing food supply, and therefore abundance, with increasing water depth. BIOENV Procedure The final abiotic-biotic matching analysis was performed using the BIOENV procedure (Primer-E Ltd). This process is conceptually similar to regressing environmental PC s against meiofauna abundance, but is more informative in that Spearman rank correlation values are generated for multiple pairs of abiotic variables. The BIOENV procedure was performed on the major taxa similarity matrix (Bray-Curtis, with 4th root transformation), allowing up to 5 variables in the output. Highest Spearman rank correlation (0.474) was found with 5 variables: chl-a, POC, OrgN, Cu and P. Second highest correlation (0.467) was found with 4 variables: chl-a, POC, OrgN, and Cu. DISCUSSION Meiofauna are ubiquitous in all marine ecosystems and especially prominent in soft-sediment communities (Coull and Bell 1979; Hicks and Coull 1983; Giere 1993), including the deep sea (Soltwedel 2000). However, most ecological studies of deep sea community structure have been focused on macro- or megafaunal-sized organisms (Etter and Mullineaux 2001). But, ecological literature since 1971 has shown that meiofauna are different from macrofauna and have different roles in marine ecosystems (for reviews 24 see: Coull and Bell 1979, Coull and Palmer 1984, Giere 1993). Even where meiofauna share ecological properties with macrofauna the processes operate on much smaller spatial and shorter temporal scales for the meiofauna (Bell 1980). Regardless of size, the distributions of organisms and how they respond to topographic, geochemical, any physical forcing features is largely unknown for deep-sea environments (Etter and Mullineaux 2001). Therefore, the purpose of the current study was to integrate the physical complexity of the northern Gulf of Mexico continental slope with environmental variables, in a hypothesis-based study, in order to more fully understand the processes controlling meiofauna abundance. Meiofauna abundance is significantly correlated with water depth (Figs. 1.3, Table 1.5), a trend that has been observed worldwide (Soltwedel 2000, and references therein). Depth related trends are attributed to a decreasing supply of organic matter with increasing depth and distance from land (Thiel 1978; Pfannkuche 1993; Danovaro et al. 1995; Gooday 1996; Relexans et al. 1996; Soltwedel 1997; Fabiano and Danovaro 1999; Shimanaga and Shirayama 2000; Gooday 2002). This general pattern is observed in the Northern Gulf of Mexico (Figs. 1.3 & 1.4), but some variability exists that may be attributed to physical and geological complexity of the continental slope and interactions with overlying water column processes. A significant longitude by depth interaction (P<0.0001, Table 1.5), indicates that meiofauna abundance changes differently with depth depending on proximity to Mississippi River outflow (Fig. 1.8). Maximum abundance values were observed in the 25 Mississippi Trough (Fig. 1.4). Mississippi River outflow brings nutrients that drive overlying primary production, but also carries terrigenous organic matter, further fueling benthic secondary production (Meybeck 1993). Highest meiofauna abundance values within the Mississippi Trough also correspond with a significant canyon effect (P = 0.006), compared to the adjacent C transect (Fig. 1.9). Although not included in the statistical analysis for canyon effects, station S36, which lies in the DeSoto Canyon, also has unusually high abundance; further evidence that canyon features support higher meiofaunal standing stocks (Figs. 1.4). On the contrary, basin features do not support higher meiofauna abundance compared with adjacent non-basin areas (P = 0.542). This is not surprising because the basin/non-basin regions of the Texas/Louisiana slope lie west of Mississippi River influence, which is deflected to the east by the Loop Current and Coriolis forces. The effect of the Florida Escarpment on meiofauna abundance, in comparison to a reference transect, had a highly significant interaction with distance from shore. The significant interaction indicates that meiofauna abundance responds differently to precipitous depth increases, compared to gradual depth increases. Spatial analysis using GIS (Fig. 1.4), and comparison to the reference transect in the Western Gulf (Fig. 1.10), both indicate abundance hot spots directly above (S42) and below (S41) the escarpment, confirming the a priori hypothesis. Deflection of the Loop Current to the East by Coriolis forces results in current impingement on the escarpment, which likely results in advection of nutrients and organic material from Mississippi River inflow and 26 additionally could create upwelling or downwelling zones along the escarpment, depending on the depth of the current. Upwelling would bring new nutrients to the surface and enhance surface primary production; conversely, downwelling could facilitate advected surface primary and secondary production to the benthos. Meiofauna abundance at station S42 is two fold greater than stations S43 and S44 (Fig. 1.10). Meiofauna abundance is greatly enhanced in the vicinity of the escarpment compared to the relatively constant Texas/Louisiana slope (Fig. 1.10). Although a direct comparison of photosynthetic pigments within the benthic boundary layer water column was not possible due to a lack of CTD data, meiofauna abundance was compared to surface water chl-a biomass estimates by SeaWIFS satellite imagery, adjusted for remineralization with depth (Berger et al. 1988) (Fig. 1.11B). Meiofauna abundance had a significant relationship with adjusted chl-a biomass (Fig. 1.11B). It is not surprising that highest chl-a biomass is observed in the vicinity of the Mississippi Trough, corresponding to highest meiofauna abundance. Estimates of chl-a biomass by satellite imagery should be interpreted with caution. SeaWIFS accuracy is quite high and acceptable for blue water environments with little or no river plume influence (Hu et al. 2003). On the contrary, stations with high river plume influence tend to be overestimated due to the presence particulate inorganic and organic matter in terrestrial runoff. All DGoMB stations were in blue water, but occasional interactions with Loop Current eddies in the Mississippi Trough region, which results in offshore 27 advection of turbid shelf water (Hu et al. 2003), could have resulted in slight overestimation of chl-a biomass in this region. Benthic-pelagic coupling has been well studied in recent investigations (reviewed by Gooday 2002). Abundance and body size of benthic metazoan and protistan fauna have both been correlated with overlying chloroplastic pigment equivalents. However, seasonal responses to food pulses have not been well demonstrated for metazoan meiofauna in deep-sea environments (Pfannkuche 1992, 1993). Soltwedel et al. (1996) observed seasonal changes in nematode body length and volume. Shimanaga and Shirayama (2000) found that meiofauna abundance fluctuated seasonally, but they were not able to confirm statistical differences between seasons. Bacteria and Foraminifera show much more pronounced responses to pulses of phytodetritus. Lochte (1992) found that bacteria standing stocks were capable of doubling from spring to summer months following the spring bloom. Likewise, benthic Foraminifera production is highly responsive to food pulses (Gooday et al. 1992). Slower responses by metazoan meiofauna suggest slower population turnover times and life cycles on the order of one year (Soltwedel et al. 1996). Previous deep-sea investigation has found that abundance is regulated by numerous spatial and temporal factors, including: depth (Soltwedel 2000), current regimes (Thiel 1975; Eckman and Thistle 1991), seasonal variations in primary production (Thiel et al. 1987), and bottom-up (regulated by primary production) (Rieper 1978) or top-down (regulated by predation) (Marinelli and Coull 1987) trophic 28 interactions. Several previous studies have compared biological communities with environmental variables in a multivariate design (Shirayama 1984a; Gray et al. 1990; Warwick and Clarke 1991; Montagna and Harper 1996; de Skowronski and Corbisier 2002; and others). Multivariate analysis generally involves a parametric procedure to analyze continuous data (i.e., environmental variables), sensu Montagna and Harper (1996), and non-parametric procedures to detect differences in community structure (sensu Warwick and Clarke 1991). PCA of environmental variables indicates that stations differ with respect to geochemistry, trace metal concentration, grain size, and organic contaminants (Figs. 1.14A & B), depending on their proximity to Mississippi River outflow. Regression of environmental PC1 (sediment properties) against meiofauna abundance (Fig. 1.14A) indicated that this component accounted for 22% of the variance in the meiofauna standing stock. Differences in sediment grain size and heterogeneity have been previously shown to influence meiofauna abundance (Gerlach 1977; Coull et al. 1982); with a trend toward increasing meiofauna abundance in silt dominated sediments, as observed here for stations with positive PC1 scores (Fig. 1.14A). Sediment porosity greatly affects vertical meiofauna distribution, with deeper dwelling organisms in sandy or calcareous ooze environments compared to clay-dominated sediments (Shirayama 1984b). Although a few northern Gulf of Mexico stations highly sandy sediments (S43, S44, W2, W3, and MT5), most stations had moderate sand and clay, and low silt, except stations near Mississippi River outflow, which had high silt (Bryant, DGoMB data) and 29 highest meiofauna abundance. Given the nature of NGOM sediments, it is not surprising that most metazoan meiofauna reside in above the 3-cm sediment depth horizon (Fig. 1.2). Meiofauna and other benthic organisms are concentrated into surface sediments (Thiel 1972; Coull et al. 1977; Dinet & Vivier 1977; Vivier 1978; Shirayama 1984b), which has been attributed to food and oxygen availabilities (Ansari et al. 1980; Shirayama 1984b) resulting from differing sediment regimes. Meiofauna abundance was more strongly related to environmental PC2 (POM flux), which accounted for 30% of the variance in meiofaunal abundance (Fig. 1.14B). Meiofauna respond to organic matter input (Thiel 1978; Pfannkuche 1993; Danovaro et al. 1995; Gooday 1996; Relexans et al. 1996; Soltwedel 1997; Fabiano and Danovaro 1999; Vanaverbeke et al. 2004). Recent investigations over the North Sea continental shelf have observed significant temporal changes in nematode abundance and diversity (both species and functional diversity) with spring bloom phytodetrital deposition (Vanaverbeke et al. 2004). Although the current study did not have a temporal component, non-parametric MDS analysis (Figs. 1.15-1.19) revealed differences in major taxa community composition. The MDS station ordination (Fig. 1.16) reflects decreasing abundance of dominant taxa, e.g., Nematoda (Fig. 1.17) and Harpacticoida (not shown) with depth. However, minor taxonomic groups are less affected by the depth gradient with greatest numbers of individuals at mid-bathyal to lower bathyal, e.g., Tardigrada (Fig. 1.18). The expected number of major taxa per 1000 individuals (ES[1000]) for all taxa (Fig. 1.6), 30 and expected number of major taxa per 20 individuals (ES[20]) for non-dominant taxa (Fig. 1.7), follow the previously observed parabolic relationship with maximum diversity in the mid-bathyal and decreasing diversity moving into abyssal environments (Paterson and Lambshead 1995; Etter and Mullineaux 2001, and references therein; Lambshead et al. 2002; and others). Maximum diversity of non-dominant taxa is found nearly 1000 meters deeper (Fig. 1.7), compared to all taxonomic groups combined (Fig. 1.6), suggesting a reduction in dominance by nematodes and harpacticoids with depth. Comparison of major meiofaunal abundance with environmental variables via the BIOENV procedure closely matched the results generated from regressing abundance versus environmental principal components. The greatest Spearman rank correlation (0.474) corresponded with five variables chl-a, POC, OrgN, Cu, and P. Overlaying POC concentration on the MDS abundance ordination reveals a similar pattern of decreasing POC with increasing depth. Shirayama (1984a) used stepwise regression analysis to discriminate important from unimportant environmental factors, and found that two variables, organic carbon and calcium carbonate, were able to account for 64% of the variance in the dataset (i.e., r = 0.80). Alongi and Pichon (1988) used simple correlation analysis and found significant relationships between metazoan meiobenthos and bacterial abundance, ciliate abundance, chlorophyll, and phaeopigments. Spatial analysis of meiofaunal abundance (Fig. 1.4) across the entire NGOM reveals strong differences between northwestern and northeastern stations, which was confirmed by a significant depth by longitude interaction (Table 1.5, Fig. 1.8). The 31 northwestern GOM is also characterized by very regular patterns of seasonal primary production, with winter chl-a maxima (December - February), and summer chl-a minima (M ller-Karger et al. 1991). In comparison, the northeastern GOM has high biweekly variations in surface chl-a biomass, even throughout summer months (Hu et al. 2003; Belabbassi et al., in revision). Differences in surface water chl-a biomass between northwestern and northeastern regions of the GOM are attributed an interaction between two factors; 1) the presence of the Loop Current, which enters the GOM through the Yucatan Straights and turns anticyclonically exiting the GOM through the Florida Straights (Schmitz 2004), and 2) Mississippi River outflow in the northeastern GOM, which averages 1 billion m3 d-1 (http://water.usgs.gov). Loop Current eddies impinge onto the continental slope and shelf in the northeastern GOM resulting in lateral transport of low salinity/high chl-a waters from the shelf over the slope (Qian et al. 2003; Belabbassi et al., in revision). Offshore transport of shelf waters over the slope can influence underlying benthic communities by stimulating greater overlying water column primary production, by lateral input terrigenous organic matter, or lateral input of organic matter produced over the shelf. Loop current eddies were regularly observed in the northeastern GOM in the months prior to and during community structure sampling (Rowe et al., in prep). Canyon and escarpment features in the northeastern GOM also interact to enhance meiofaunal abundance (Table 1.5, Figs. 1.9 and 1.10, respectively). Large topographical features likely interact to create flows that alter food supply (Gage and 32 Tyler 1991; Etter and Mullineaux 2001), and therefore increases abundance (Thistle et al. 1985, 1991). High shallow and mid-depth abundance in the Mississippi Trough and DeSoto Canyon, respectively, suggest canyon features have a concentrating effect on POM flux. Loop Current, or Loop Current eddy impingement on the Florida Escarpment (Schmitz 2004) creates a high energy hydrodynamic environment as observed by shipboard ADCP (Acoustic Doppler Current Profiler) current profiles during sampling cruises (Rowe et al., in prep). Therefore, it is not surprising that meiofauna abundance was greatly enhanced directly above and below the Florida Escarpment (Figs. 1.4 & 1.10). CONCLUSION Meiofauna abundance in the northern Gulf of Mexico deep sea is regulated by interactions between sediment characteristics and POM flux, which are related to Mississippi River outflow, physical oceanographic circulation processes, and the complex topographic nature of the continental slope. Meiofauna abundance is significantly related to water depth, but also exhibits significant longitudinal differences resulting from proximity to Mississippi River outflow. Canyon features in proximity of Mississippi River outflow were found to greatly enhance meiofauna abundance. The Florida Escarpment interacts with Mississippi River outflow and the Loop Current to enhance meiofauna abundance at stations lying directly above and below the escarpment. Multivariate comparisons of meiofauna abundance with environmental variables reveals a strong Mississippi River influence. River outflow alters local sediment characteristics, 33 and interacts with loop current eddies and dynamic slope topography to increase POM flux in the northeastern region, thus creating meiofauna abundance hot spots. In contrast, northwestern Gulf of Mexico stations exhibited a typical bathymetric pattern of deceasing abundance with depth, and basin features west of Mississippi River influence did not support enhanced abundance. Therefore, the meiofauna community in the northern Gulf of Mexico deep sea is regulated by complex spatial interactions between Mississippi River outflow, physical oceanographic processes, and the physical complexity of the continental slope, which regulate the supply of particulate organic matter to the sea floor. 34 Table 1.1: Summary of meiofauna community structure experimental design: null hypotheses, design criteria, and stations included in analysis. Null Hypotheses Design Criteria Stations Included No. Stations H01 & H02: Depth & longitude Five replicate transects spanning the entire northern GOM contental slope, over five depths Three basin stations, three non-basin stations, over three distances from shore Two replicate transects over five depths Two replicate transects over six distances from shore MT1, 3-6 RW1-2, 4-6, AC1 C1, 4, 7, 12, 14 W1, 3-6 S35-37, 39, 44 B1-3 NB2-4 25 H03: Basin/non-basin 6 H04: Canyon/noncanyon MT1, 3-6 C1, 4, 7, 12, 14 10 H05: Escarpment/nonescarpment S39-44 W1-6 12 35 Table 1.2: Effect of core tubes size (inner diameter) on meiofauna counts and average density. Based on five replicates taken at station W-2. Abundance is detransformed from natural log (ln), so taxa averages do not sum to the total average. Counts (n/core) Abundance (n/10 cm2) Core Size (cm i.d.) Taxa Nematodes Harpacticoids Nauplii Others Total 2.2 3.1 5.5 6.7 2.2 3.1 5.5 6.7 20.2 5.4 7.2 12.2 45.0 64.8 30.8 38.6 14.0 148.2 161.0 74.2 72.2 27.8 335.2 219.0 99.4 85.6 45.8 449.8 31.3 2.3 0.2 3.6 54.0 59.4 26.1 29.2 14.5 135.7 50.5 20.8 22.9 8.5 111.3 54.3 24.4 19.0 10.6 112.2 36 Table 1.3: DGoMB station locations, depth, and average meiofaunal abundance (five replicate cores) for pooled taxonomic groups. Station AC1 B1 B2 B3 BH C1 C12 C14 C4 C7 GKF HIPRO JSSD1 JSSD2 JSSD3 JSSD4 JSSD5 MT1 MT2 MT3 MT4 MT5 MT6 NB2 NB3 NB4 NB5 RW1 RW2 RW3 RW4 RW5 RW6 S35 S36 Latitude 26.393567 27.202542 26.550012 26.164445 27.780000 28.059838 26.379730 26.938238 27.453150 27.730437 27.000000 28.550000 25.000000 23.500000 24.750000 24.250000 25.500000 28.541110 28.447925 28.221510 27.827605 27.332838 27.001648 27.134833 26.558033 26.246750 26.245400 27.500142 27.254027 27.008356 26.751420 26.507527 25.997303 29.335152 28.918513 Longitude -94.573082 -91.405218 -92.215082 -91.735100 -91.500000 -90.249917 -89.240298 -89.572505 -89.763083 -89.982033 -90.250000 -88.580000 -92.000000 -92.000000 -90.750000 -85.500000 -88.250000 -89.825018 -89.671945 -89.494045 -89.166145 -88.656065 -87.999130 -92.000068 -91.822550 -92.392287 -91.209908 -96.002847 -95.746807 -95.492362 -95.250175 -94.996722 -94.495578 -87.046363 -87.672150 Depth (m) 2440 2253 2635 2600 545 336 2924 2495 1463 1066 2460 1565 3545 3725 3635 3400 3350 482 677 990 1401 2267 2743 1530 1875 2020 2065 212 950 1340 1575 1620 3000 668 1826 Abundance (N m-2) 129974 157417 139907 155817 407852 369129 138792 146578 273585 542119 84348 343118 87547 87295 60441 63451 135698 945657 535216 885995 246058 128964 155312 168276 165245 148409 117263 411809 219457 248752 232842 170633 144453 501629 799963 37 Station S37 S38 S39 S40 S41 S42 S43 S44 W1 W2 W3 W4 W5 W6 WC12 WC5 Latitude 28.553627 28.279947 27.483675 27.839477 28.013642 28.251003 28.502943 28.749993 27.577165 27.413927 27.172397 26.730823 26.267772 26.002845 27.323242 27.775912 Longitude -87.766848 -87.327592 -86.999815 -86.751415 -86.573348 -86.419270 -86.076790 -85.747703 -93.551005 -93.340328 -93.323293 -93.319727 -93.332723 -93.320277 -91.555810 -91.765678 Depth (m) 2387 2627 3000 2972 2974 763 362 212 420 625 875 1460 2750 3150 1175 348 Abundance (N m-2) 291179 157164 83170 99501 181408 492537 276279 318516 387228 263315 262642 187806 104552 124166 218447 412061 38 Table 1.4: Average abundance (AA), percent contribution (Contrib.%), and cumulative percent contribution (T%) of meiofauna major taxa per core (5.5 cm i.d.). Data summarized for all 51 stations (five replicates per station). Taxa Nematoda Nauplii Harpacticoida Unknown Polychaeta Kinorhyncha Ostracoda Cyclopoida Tardigrada Tanaidacea Nemertinea Acari Isopoda Bivalvia Gastrotricha Anthozoa Priapulida Gastropoda Aplacophora Rotifera Sipuncula Loricifera AA 415.0 74.8 74.0 37.3 12.5 2.7 2.7 2.1 1.0 0.8 0.6 0.3 0.3 0.2 0.1 0.1 0.1 0.0 0.0 0.0 0.1 0.0 Contrib.% 65.3 13.1 12.3 6.6 1.5 0.3 0.3 0.2 0.1 0.1 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 T% 65.3 78.4 90.7 97.3 98.8 99.1 99.5 99.6 99.8 99.9 99.9 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 39 Table 1.5: ANOVA results, tests for differences in meiofauna abundance. Dependent variable = log10(N+1). ANOVA abbreviations: DF = degrees of freedom, SS = sum of squares, MS = mean square, F = F-test value, P = Pr > F. Factor abbreviations: long. = longitude, basin = basin vs. non-basin stations, dfs = distance from shore, escarp. = escarpment vs. non-escarpment transects. Source DF SS MS F P H01 & H02 Depth/Longitude Long. Depth Long.*Depth Error H03 Basins Basin DFS Basin*DFS Error H04 Canyons Canyon Depth Canyon*Depth Crror H05 Escarpment Escarp. DFS Escarp.*DFS Error 4 4 16 100 1.222 5.867 3.104 2.531 0.305 1.467 0.194 0.025 12.07 57.94 7.67 <.0001 <.0001 <.0001 1 2 2 56 0.012 0.016 0.023 1.708 0.012 0.008 0.011 0.031 0.38 0.25 0.37 0.5421 0.7773 0.6980 1 4 4 36 0.167 3.931 0.338 0.702 0.167 0.982 0.085 0.019 8.58 50.41 4.34 0.0059 <.0001 0.0058 1 5 5 46 0.001 2.998 0.279 0.546 0.001 0.600 0.056 0.012 0.07 50.51 4.70 0.7906 <.0001 0.0015 40 Table 1.6: Eigenvalues of the Correlation Matrix for the environmental PCA, proportion of variance explained by each principal component, and cumulative variance. Eigenvalue 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 5.019 2.515 1.697 1.535 0.929 0.851 0.635 0.591 0.530 0.312 0.201 0.135 0.030 0.014 0.005 Difference 2.504 0.818 0.162 0.606 0.077 0.217 0.044 0.060 0.218 0.111 0.066 0.105 0.017 0.008 0.000 Proportion 0.335 0.168 0.113 0.102 0.062 0.057 0.042 0.039 0.035 0.021 0.013 0.009 0.002 0.001 1.000 Cumulative 0.335 0.502 0.615 0.718 0.780 0.837 0.879 0.918 0.954 0.974 0.988 0.997 0.999 1.000 1.000 41 Table 1.7: Variable loads for the rotated (Varimax) factor pattern of the environmental PCA. Factor1 Chla Sand Silt Clay NH4 POC UREA NO3 DOC OrgN TPAHWP Ca Sr Cr Sn 0.11096 -0.76437 0.43118 0.65541 -0.26511 -0.04478 0.01605 -0.19939 -0.09904 -0.00531 0.55797 -0.87212 -0.85733 0.86572 0.83863 Factor2 0.90356 0.21025 -0.03573 -0.28278 0.35890 0.81893 -0.14464 -0.49356 -0.03622 0.27374 0.14311 -0.30214 -0.25022 -0.12582 -0.00196 Factor3 -0.00047 0.20489 0.05246 -0.27347 0.18627 0.06908 -0.80871 -0.21657 0.87375 -0.10288 0.14306 0.09420 0.14018 -0.06502 0.14965 Factor4 0.08619 -0.19623 0.65701 -0.09372 0.66324 0.05581 -0.09793 0.53803 -0.00081 0.11126 0.55440 -0.23840 -0.20549 -0.16531 -0.17825 Factor5 0.03993 -0.48221 0.25268 0.52084 0.12841 0.08796 0.09566 -0.07637 -0.06273 0.88406 -0.19339 0.02223 0.05741 0.09411 -0.01304 42 43 Figure 1.1: DGoMB station locations in the northern Gulf of Mexico deep sea. Note transect and topographic feature descriptions. 0 Section Depth (cm) 2 4 6 8 10 12 14 0 0 100 200 300 400 Total M eiofauna (n/section) Section Depth (cm) 2 4 6 8 10 12 14 0 0 50 100 150 200 Nematoda (n/section) Section Depth (cm) 2 4 6 8 10 12 14 0 20 40 60 80 100 Harpacticoida (n/section) Figure 1.2: Vertical distribution of meiofauna taxa from sediment cores collected at station W2 for Shakedown Cruise. Cores were sectioned in 1-cm increments, and fauna were enumerated from each 1-cm section. Plotted data is the average of five replicates (error bars = standard deviation). 44 6.2 6.0 5.8 5.6 5.4 5.2 5.0 4.8 4.6 0 1000 2000 3000 4000 R2 = 0.658 P<0.0001 Log meiofauna abundance (N m ) -2 Water depth (m) Figure 1.3: Log (x+1) transformed meiofauna abundance (N m-2) versus water depth (m) for all stations sampled during DGoMB project. 45 46 Figure 1.4: Spatial analysis of meiofauna abundance (N m-2) at all DGoMB stations. Buffer size equals relative meiofauna abundance. The highlighted contour equals 2000 meters. 18 Meiofauna major taxa diversity (taxa/core) R2 = 0.416 P < 0.0001 16 14 12 10 8 6 0 1000 2000 3000 4000 Water depth (m) Figure 1.5: Number of major meiofauna taxa per core as a function of water depth. 47 14 S40 R2 = 0.213 P = 0.0032 13 12 11 WC5 W1 RW2 HIPRO MT4 RW3 RW4 C7 WC12 C4RW5 NB2 MT3 W4 MT5 NB4 B1 NB5 W2S42 MT2 BH S35 S38 MT6 S39 S41 S43 S44 RW1 C1 ES(1000) C14B2 AC1 S37 C12 RW6 W6 JSSD5 10 9 MT1 W3 S36 NB3 B3 W5 JSSD3 JSSD2 JSSD4 8 7 6 0 1000 2000 GKF JSSD1 3000 4000 Water depth (m) Figure 1.6: Expected number of taxa per 1000 individuals [ES(1000)] as a function of water depth, and quadratic regression. Dashed lines = 95% confidence intervals. 48 8 R2 = 0.156 P<0.0001 C14 S38 MT6 S41 W2 RW5 S39 S40 C12 W1 C4 B1 MT5AC1 RW3 MT4 S37 NB5 RW4 RW6 S42 B2 NB3 WC12 RW1 BH JSSD5 S35 NB2 S36 S43 C7 W6 W4 W3 HIPRO MT2 MT3 JSSD3 JSSD2 W5 C1 S44 B3 WC5 NB4 MT1 RW2 7 ES(20) Non-dominant taxa 6 5 4 GKF JSSD4 3 JSSD1 2 0 1000 2000 3000 4000 Water depth (m) Figure 1.7: Expected number of taxa per 20 individuals [ES(20)] for non-dominant meiofauna taxa (excluding nematodes, harpacticoid copepods, harpacticoid nauplii, and unknowns). A quadratic regression was fit to the data (dashed lines = 95% confidence intervals). 49 1e+6 MT1 MT3 Meiofauna abundance (N m ) -2 8e+5 S36 6e+5 C7 S35 RW1 S44 W3 C4 MT4 RW4 W4 C14 S38MT6 C12 RW6 MT5 AC1 W6 W5 S37 4e+5 W1 C1 2e+5 RW2 0 0 500 1000 1500 2000 2500 3000 3500 Water depth (m) Figure 1.8: Meiofauna abundance (N m-2) as a function of depth for transects included in the test for differences over depth and longitude (H01 & H02). 50 1e+6 MT1 MT3 Meiofauna abundance (N m ) -2 8e+5 6e+5 C7 4e+5 C1 C4 MT4 2e+5 MT6 C12 MT5 C14 0 0 500 1000 1500 2000 2500 3000 3500 Water depth (m) Figure 1.9: Comparison of meiofauna abundance (N m-2) on two parallel transects to determine abundance differences related to canyon (MT transect) versus non-canyon (C transect) areas (H04). 51 6e+5 Meiofauna abundance (N m ) 5e+5 -2 S42 4e+5 W1 S44 S43 W2 W3 W4 3e+5 2e+5 S41 W6 W5 S40 S39 1e+5 0 0 1000 2000 3000 Depth (m) Figure 1.10: Comparison of miofauna abundance and a function of water depth along two transects to determine abundance differences related to the Florida Escarpment (S39S44) versus a reference transect (W1-W6). 52 100 A. Water depth (m) 1000 10000 0.01 1e+6 0.001 0.0001 R 2 = 0.413 P<0.0001 B. Meiofauna abundance (N m ) -2 8e+5 6e+5 4e+5 2e+5 0 0.000 0.001 0.002 0.003 0.004 0.005 SeaWIFS Adjusted Chl-a ( g/L) (Biweekly Avg. Nov 1999 - April 2000) Figure 1.11 SeaWIFS chl-a ( g/L) biweekly average (November 1999 through April 2000). Chl-a concentration was adjusted for remineralization with depth (see Berger et al. 1988). A) Log-Log relationship of adjusted chl-a with depth, and B) Meiofauna abundance versus adjusted chl-a. 53 1e+6 R = 0.331 P<0.0001 2 Meiofauna abundance (N m ) -2 8e+5 6e+5 4e+5 2e+5 0 2 4 6 8 Sediment POC ( M) Figure 1.12: Meiofauna abundance (N -2) as a function of sediment particulate organic carbon (POC). 54 1 A. Clay Cr Sn TPAHWP Silt PC1 Chla 0 NO3 UREA DOC OrgN POC NH4 Sand CaSr -1 -1 1 0 DOC 1 B. PC3 Sr Ca 0 NO3 Sand NH4 Sn TPAHWP Silt Cr OrgN POC Chla Clay UREA -1 -1 0 1 PC2 Figure 1.13: Principal components analysis of environmental variables, A) variable loading scores for PC1 versus PC2, B) variable loading scores for PC2 versus PC3. 55 1e+6 A. R2 = 0.215 P = 0.003 S36 MT1 MT3 Meiofauna Abundance (N m ) -2 8e+5 6e+5 MT2 S42S35 4e+5 S44 W3 S43 W2 WC5 RW1 W1 C12 C7 RW3 RW4 MT4 RW2 WC12 RW5 NB2 NB3 S41B3 C4 RW6 B1 AC1 MT5 W6 C14 B2 W5 NB5 S40 S37 C1 2e+5 0 -3 -2 -1 0 1 2 3 PC1 1e+6 B. MT3 MT1 R2 = 0.303 P<0.001 Meiofauna Abundance (N m ) -2 8e+5 S36 6e+5 MT2 S42 S35 WC5 RW1 W1 C1 S44 4e+5 C12 2e+5 S37 C7 W3 W2 S43 RW3 MT4 RW4 RW2 WC12 S41 RW5 NB3NB2 C4 RW6 B1 B2 B3 C14 AC1 MT5 W6 W5 NB5 S40 0 -2 -1 0 1 2 3 4 PC2 Figure 1.14: Meiofauna abundance (N m-2) regressed against enviornmental PC1 (A), designated sediment properties, and environmental PC2 (B), POM Flux. 56 Stress: 0.11 HIPRO MT2 MT1 S44 BH S42 W1 S36 S35 RW1 C7 WC5 RW2 MT4 WC12 C1 W3 RW4 W4 W2 S43 RW3 NB2 S38 B3 NB3 JSSD5 C14 NB5 B1 NB4 B2 MT5 RW6 MT6 W6 RW5 AC1 C12 W5 JSSD1 JSSD4 S40 S39 MT3 S37 S41 C4 GKF JSSD2 JSSD3 Figure 1.15: MDS ordination of DGoMB stations, based on Bray-Curtis similarity (4th root transformation) of major taxa abundance. 57 Stress: 0.11 HIPRO MT2 MT1 S44 WC5 BH S42 W1 S36 S35 RW1 C7 RW2 MT4 WC12 C1 W3 RW4 W4 W2 S43 RW3 NB2 S38 B3 NB3 JSSD5 C14 NB5 B1 NB4 B2 MT5 RW6 MT6 W6 RW5 AC1 C12 W5 JSSD1 JSSD4 S40 S39 MT3 S37 S41 C4 GKF JSSD2 JSSD3 Figure 1.16: MDS ordination of DGoMB stations, based on Bray-Curtis similarity (4th root transformation) of major taxa abundance. Symbols indicate depth zones of 1000 meter increments: = 200-1000 meters, = 1000-2000 meters, = 2000-3000 meters, and = >3000 meters. Circled areas approximate stations above and below 2000 m. 58 Stress: 0.11 HIPRO MT2 MT1 S44 BH S42 W1 S36 S35 RW1 C7 WC5 RW2 MT4 WC12 C1 W3 RW4 W4 W2 S43 RW3 NB2 S38 B3 NB3 JSSD5 C14 NB5 B1 NB4 B2 MT5 RW6 MT6 W6 RW5 AC1 C12 W5 JSSD1 JSSD4 S40 S39 MT3 S37 S41 C4 GKF JSSD2 JSSD3 Figure 1.17: MDS ordination of DGoMB stations, based on Bray-Curtis similarity (4th root transformation) of major taxa abundance. Bubble size equals relative nematode abundance at each station. The MDS plot strongly represents decreasing abundance with depth. 59 Stress: 0.11 HIPRO MT2 MT1 S44 BH S42 W1 S36 S35 RW1 C7 WC5 RW2 MT4 WC12 C1 W3 RW4 W4 W2 S43 RW3 NB2 S38 B3 NB3 JSSD5 C14 NB5 B1 NB4 B2 MT5 RW6 MT6 W6 RW5 AC1 C12 W5 JSSD1 JSSD4 S40 S39 MT3 S37 S41 C4 GKF JSSD2 JSSD3 Figure 1.18: MDS ordination of DGoMB stations, based on Bray-Curtis similarity (4th root transformation) of major taxa abundance. Bubble size equals relative abundance of Tardigrada at each station. Tardigrades were one of the major taxonomic groups that did not follow the general pattern of decreased abundance with depth. 60 Stress: 0.13 MT1 C4 MT2 S44 S36 S35 S42 W1 WC5 C1 RW1 W3 W2 S43 RW3 S37 RW2 WC12 MT4 NB2 B3 NB5 B2 MT3 W4 B1 MT5 NB3 RW6 C14 C7 RW4 RW5 S41 W6 W5 AC1 C12 S40 Figure 1.19: MDS ordination of DGoMB stations, based on Bray-Curtis similarity (4th root transformation) of major taxa abundance. Bubble size equals relative particulate organic carbon (POC) concentration. 61 CHAPTER 2: SPATIAL AND BATHYMETRIC TRENDS IN HARPACTICOIDA (COPEPODA) COMMUNITY STRUCTURE IN THE NORTHERN GULF OF MEXICO DEEP SEA ABSTRACT The deep sea has been a focus of intense research because of its vast size and importance in global biogeochemical cycles, and because it has been shown to have a highly diverse fauna. Meiofauna are ubiquitous in marine soft-sediment communities, are often dominant in deep-sea sediments, and have incredible phylogenetic diversity. Harpacticoida (Copepoda) are the second most abundant taxon within the meiofauna and an important component of deep-sea meiofaunal communities. The northern Gulf of Mexico is a dynamic environment with complex continental shelf topography and longitudinal gradients of water column primary production due to Mississippi River outflow. Harpacticoid copepod community structure was analyzed at 43 stations in the northern Gulf of Mexico deep sea to test regional and bathymetric patters of diversity. Harpacticoid copepod diversity is significantly related to depth and longitude. Most stations have unique species compositions, suggesting high regional (2700 species) and global (105 - 106 species) diversity by extrapolation. Although highest diversity, in terms of expected number of species (rarefraction), is found at approximately 1200 meters, average taxonomic and average phylogenetic diversity continue to increase with depth, indicating greater morphological or functional diversity. Multivariate analysis reveals 62 significant inverse relationships between diversity and POM flux, which are confirmed by a significant region-scale depth and longitude differences. However, within versus between station variability suggests an interaction between small and region-scale processes maintaining high diversity. INTRODUCTION Harpacticoida, an order within the subclass Copepoda, is comprised of individuals ranging in size from 0.2 to 0.5 mm (Hicks and Coull 1983). This primarily meiobenthic order contains 54 families (Integrated Taxonomic Information System, http://www.itis.usda.gov), approximately 600 genera, and more than 4500 described species (Giere 1993). Harpacticoid copepods inhabit multiple habitat types, including: all marine environments, most freshwater environments, and some terrestrial habitats where sufficient availability of water allows for existence (Hicks and Coull 1983, Dahms and Qian 2004). Harpacticoid copepods are ubiquitous in marine soft-sediment habitats, and generally the second most abundant meiobenthic taxon after the numerically dominant Nematoda (Coull and Bell 1979; Hick and Coull 1983; Higgins and Thiel 1988; Giere 1993). Harpacticoid copepod ubiquity extends into deep-sea environments where they have been shown to have morphological adaptations (Montagna 1982), have proportionally increasing abundance compared to macrobenthos (Thistle 2001), and exhibit high diversity (Coull 1972; Thistle 1978). Deep-sea species diversity has been a topic of much interest and debate since Hessler and Sanders (1967) presented evidence that these communities are often more 63 diverse than those in similar shallow water environments. Several hypotheses have been presented during the past 35 years of research attempting to explain why an oligotrophic environment, in which virtually all the fauna rely on precious few labile components of surface derived detritus for nutrition (Sanders and Hessler 1969; Hessler and Jumars 1974; Gage and Tyler 1991), and that is apparently less structurally complex than a typical high diversity environment, can support such a rich fauna. Etter and Mullineaux (2001) summarized some recent hypotheses attempting to answer this question: 1) local spatial heterogeneity (MacArther 1972; Tilman 1982), 2) nonequilibrium dynamics (Caswell 1978; Armstrong and McGehee 1980), 3) interactions among three or more trophic levels (Janzen 1970), and 4) recruitment limitation (Tilman 1994; Hurtt and Pacala 1995). However, all of these hypotheses seem to be explainable by the balance between competitive exclusion and frequency of disturbance, which results in patchiness on biologically influencial scales, i.e. millimeterto-meter (Grassle and Sanders 1973; Grassle 1989; Lambshead 1993) leading to microhabitat specialization (Jumars 1975, 1976; Thistle 1983; 1998; Thistle and Eckman 1990). Meiobenthic community structure is regulated on small spatial scales (mm to cm) where patch dynamics are a function of biogenic structures (Thistle 1983; Thistle and Eckman 1990), and conversely on larger scales (m to km) where benthic currents (Hicks 1988; Thistle 1998) and shifts in sediment grain size (Gray 1968, 1974) regulate community structure. Although much work has been accomplished describing meiofauna community structure on small spatial scales, few studies have addressed large 64 region-scale patterns. More specifically, the knowledge of regional species pools, processes structuring communities on various scales, and the distributions of organisms and how they respond to topographic, geochemical, and physical oceanographic forcing is largely unknown for deep-sea environments (Etter and Mullineaux 2001). The present study focuses on harpacticoid copepod community structure in the northern Gulf of Mexico (NGOM) deep sea. The sampling design was formulated based on the following six null hypotheses: H01) there is no difference in harpacticoid diversity with depth, H02) there is no difference in harpacticoid diversity with longitude, H03) there is no difference in harpacticoid diversity in versus out of submarine basins, H04) there is no difference in harpacticoid diversity in versus out of submarine canyons, H05) there is no difference in harpacticoid diversity with respect to escarpments, and H06) there is no difference in harpacticoid diversity with respect to overlying water column primary production. The depth hypothesis (H01) follows one of the most dramatic paradigm shifts in marine ecology, that the deep sea is actually more diverse than shallow water environments (Hessler and Sanders 1967). However, the majority of deep-sea diversity studies, and hypotheses of mechanisms maintaining deep-sea diversity, have come from studies of macro- or megafaunal-sized organisms (Etter and Mullineaux 2001). However, meiofauna live on much smaller spatial and temporal scales (Bell 1980; Schwinghamer 1981), and mechanisms maintaining meiofauna diversity may be different than those for the larger-sized fauna. As discussed thoroughly in chapter one of this dissertation, interactions between 65 Mississippi River outflow, physical oceanographic processes, and sea floor topography create areas of enhanced meiofauna abundance in the northeastern Gulf of Mexico due to increased POM flux. The effect of these interactions on diversity must be explored. The longitude hypothesis (H02) was specifically designed to test for effects of the Mississippi River in shaping harpacticoid diversity. Mississippi River discharge is a major source of new nutrients and organic matter into the northern Gulf of Mexico (Meybeck 1993), with a mean daily discharge of nearly 1 billion m3 (http://water.usgs.gov). The a priori hypothesis was that a longitudinal gradient of diversity exists due to organic enrichment from Mississippi River outflow. Bathymetric and latitudinal patterns in deep-sea diversity have been attributed in part to gradients in POM flux (Rex 1981, Rex et al. 1993). Increased productivity is thought to increase the number of species that can coexist (Wright 1983), but very high levels of POM flux to the deep-sea floor may lower diversity due to increased dominance by opportunistic species (Levin and Gage 1988). The basin hypothesis (H03) was designed to test for diversity differences in basins and adjacent non-basin stations on the Texas/Louisiana slope. Although basins were shown not to enhance meiofauna abundance (chapter one), they may isolate populations, therefore creating zones of distinct harpacticoid communities. Canyons (H04) do have a concentrating effect on POM flux (chapter one), and may enhance or depress diversity as discussed above. Increased abundance directly above and below the Florida Escarpment (H05) (chapter one), as well as the precipitous depth change, may significantly alter harpacticoid bathymetric diversity patterns. 66 Therefore processes affecting overlying primary production (H06) and interactions among physical oceanographic process, sediment geologic and geochemical properties, and sea floor topography, likely affect harpacticoid species diversity in the northern Gulf of Mexico. Univariate and multivariate statistical methods were used to integrate these regional differences in relation to the physical environment with environmental variables in order to more fully understand the processes controlling harpacticoid copepod diversity. METHODS Field & Laboratory Methods Field and laboratory methods for the collection and enumeration of harpacticoid copepods are described in chapter one of this dissertation. The reader is also referred to chapter one for descriptions of laboratory methods associated with environmental geochemical and geological variables. Briefly, survey samples were collected on a 60day cruise aboard the R/V Gyre (Texas A&M University) during the months of May and June 2000. Meiofauna were collected by a 5.5 cm inner diameter (i.d.) core tube that was mounted inside the GOMEX boxcorer (Boland and Rowe 1991). One core sample was taken from each boxcore sample, and stored for meiofaunal community analysis. Five total replicate cores were taken at each station. In the laboratory, meiofauna were extracted from sediment using the Ludox centrifugation technique (deJonge and Bouwman 1977), and harpacticoid copepods were separated from the bulk meiofauna sample by manual picking. Animals were identified to species by Dr. Wonchoel Lee 67 (Hanyang University, Korea). Species were differentiated according to the two standard taxonomic dichotomous keys for marine Harpacticoida (Wells 1976; Huys et al. 1996), selected reference texts (Huys and Boxshall 1991, among others), and numerous recent descriptions of new species from peer reviewed journals. Lucid descriptions of harpacticoid species differentiation can be found in Huys and Boxshall (1991) and Huys et al. (1996). Experimental Design, and Statistical Analyses The experimental design included a total of 43 stations, from seven transects, along the northern continental slope and abyssal plain of the northern Gulf of Mexico deep sea (Fig. 2.1). Although a total of 51 stations were sampled for bulk meiofauna abundance and major taxonomic community structure, only 43 were selected for harpacticoid copepod identification, due to funding limitations. In the northwest (RW) region, seven stations were sampled, including one station in the Alaminos canyon (AC1). An additional western (W) transect was included, which was a historical transect from a previous study (Pequegnat et al. 1990). In the west-central region (WC) two historical stations a previous study were included (Pequegnat et al. 1990), but stations in this region were mainly designed to test for faunal differences between basin (B) and non-basin (NB) locations. The central transect (C) was also sampled by Pequegnat et al. (1990), and was included here to test for differences from the adjacent Mississippi Trough (MT) transect. In the northeast region 10 stations, from two transects, were sampled perpendicular to the Florida slope and escarpment (S). 68 Hypothesis Testing Six main hypotheses were investigated for differences in harpacticoid copepod diversity. Five transects, RW, W, C, MT and S, were included in the test for depth longitude differences (H01 & H02 ), ranging from the Texas slope in the West to the Florida Escarpment in the east (Fig. 2.1, Table 1.1). Five stations were included per transect, over five depth zones, consistent between transects. Diversity differences at different depths and longitudes were tested using at two-way completely random analysis of variance (ANOVA) that is described by the following model: Yijk = + j + k + jk + i(jk) where Yijk is the measurement for each individual replicate, m is the overall sample mean, aj is the main effect for transects and j = 1-5, bk is the main effect for depths and k = 1-5, abjk is the interaction term, and ei(jk) is the random error for each replicate measurement and i = 1-5. The basin hypothesis (H03) three basins and three adjacent non-basin stations on the Texas/Louisiana slope (Fig. 2.1, Table 1.1). The experimental design blocked basin and non-basin stations (B1 with NB2, B2 with NB3, and B3 with NB4), to control for differing distances from shore. The experiment is a two-way completely random analysis of variance (ANOVA) that is described by the following model: Yijk = + j + k + jk + i(jk) where Yijk is the measurement for each individual replicate, m is the overall sample mean, aj is the main effect for treatments and j = 1-3, bk is the main effect for distance from shore and k = 1-3, abjk is the interaction term, and ei(jk) is the random error for each 69 replicate measurement and i = 1-5. The canyon hypothesis (H04) was formulated to test for diversity differences between stations located in the Mississippi Trough (MT) compared to adjacent stations in the central transect (C) (Fig. 2.1, Table 1.1). Five MT stations were paired with five C stations at five common depth zones, thus removing the effect of depth. Canyon differences were tested using at two-way completely random analysis of variance (ANOVA) that is described by the following model: Yijk = + j + k + jk + i(jk) where Yijk is the measurement for each individual replicate, m is the overall sample mean, aj is the main effect for canyon and j = 1-2, bk is the main effect for depths and k = 1-5, abjk is the interaction term, and ei(jk) is the random error for each replicate measurement and i = 1-5. The escarpment hypothesis (H05) compared the escarpment transect to a northwestern Gulf trasect (W) that experiences a gradual and relatively constant depth increase (Fig. 1, Table 1). Six stations per transect were paired at equal distance from shore to remove this effect. The experiment was tested using at two-way completely random analysis of variance (ANOVA) that is described by the following model: Yijk = + j + k + jk + i(jk) where Yijk is the measurement for each individual replicate, m is the overall sample mean, aj is the main effect for transects and j = 1-2, bk is the main effect for distance from shore and k = 1-6, abjk is the interaction term, and ei(jk) is the random error for each 70 replicate measurement and i = 1-5. Multivariate analysis Detailed methodology of the environmental principal components analysis and multidimensional scaling of biotic data can be found in chapter one of this dissertation. Briefly, matching species data with environmental variables requires multivariate analyses, and included parametric and non-parametric procedures. Parametric multivariate analysis (principal components analysis, PCA) is performed on environmental variables, creating new variables (principal components), that are uncorrelated. The new variables are extracted in decreasing order of variance, such that the first few principal components (PC) explain most of the variation in the data set. The contribution of each environmental variable to the new PC is called a load. Typically, the new PC loads can be interpreted to indicate structure in the data set. Each observation contributing to the PC is called a score. Thus, the main advantage of PCA is the generation of station scores, which are interpretable, and can subsequently be used in other analyses (i.e. correlation or regression with abundance). Non-parametric procedures (multidimensional scaling, MDS; and the BIOENV procedure) were used to compare station similarity based on species composition, and further for further comparison with environmental variables. The 43 survey stations were also subjected to cluster analysis, based on BrayCurtis Similarity, with 4th root transformation (Primer-E v.5). Stations were grouped into zones where station similarity was greater than or equal to 20% of the species 71 composition. Stations with less than 20% similarity to any other station were identified as unique. GIS-based analyses were performed (ArcView 8.0, ESRI) to analyze harpacticoid community zonation over the entire northern GOM. Stations were plotted and labeled with the appropriate zone to identify region-scale similarities in species composition. ANOVA and PCA procedures were accomplished using SAS statistical software (SAS Institute Inc. 1991). Non-parametric MDS, cluster analysis, BIOENV procedures, were conducted with Primer 5.0 (Primer-E, 2000). Diversity Estimates Harpacticoid diversity was calculated using several common ecological indices. The Shannon index is the average uncertainty per species in and infinite community made up of species with known proportional abundances (Shannon and Weaver 1949). The Shannon index is calculated by the following expression: ni ni H = ln n i =1 n S where ni is the proportion of individuals belonging to the ith of S species in the sample and n is the total number of individuals in the sample. Rarefraction was used to estimate the expected number of species, thus, accounting for differences in abundance due to bathymetric gradients. Hurlbert (1971) 72 describes the model as calculating the proportion of potential inter-individual encounters in a given sample. The model is described by the equation: N N 1 n E ( Sn) = l N i =1 n S E(Sn) describes the expected number of species found in a sample of n individuals drawn from a population of N total individuals distributed among S species. New diversity indices have been developed based on phylogenetic structure within the sample (Clarke and Warwick 2001), and therefore give a measure of relative functional diversity. Average taxonomic diversity is defined as (Warwick and Clarke 1995): = [ ] / [ N ( N 1) / 2] i< j where the double summation is over all pairs of species i and j, and N is the total number of individuals in the sample. Simply put, average taxonomic diversity can be thought of as the average taxonomic distance apart of every pair of individuals in the sample, or the expected path length between any two individuals chosen at random (Clarke and Warwick 2001). Similarly, average phylogenetic diversity: 73 M+ = PD/S is the cumulative branch length of a sample s phylogenetic tree (PD), divided by the number of species (S) in the sample (Clarke and Warwick 2001). Average phylogenetic diversity can be thought of as the total evolutionary history, genetic turnover, or morphological richness represented within the sample (Clarke and Warwick 2001). All diversity indices were calculated in Primer 5.0 (Primer-E Ltd.) Regional and Global Biodiversity Estimates Regional and global species richness is by extrapolation. This method uses a single survey, from a single region of the world, to plot species found versus sample number (Lambshead and Boucher 2003). When the species number reaches a maximal accumulation rate it is possible to estimate the rate of encounter of new species with distance traveled. This rate can be expanded to the area of the geographic region sampled, allowing for further extrapolation to global scales. Extrapolation estimates generally require large data sets, preferably from the deep-sea, which accounts for the majority of marine surface area and probably the majority of benthic marine species. Harpacticoid copepod species abundance was analyzed for DGoMB stations along with a previous study on the Texas continental shelf (Montagna and Harper 1996) in order to estimate the entire northern Gulf of Mexico species pool. Species accumulations curves were constructed using Colwell s EstimateS 6.1 program, with fifty randomized runs (http://viceroy.eeb.uconn.edu/EstimateS). A sigmoidal growth model [y = (ab + cxd)/(b + xd)] was fitted to each data set in order to extrapolate regional diversity (Hyams Curve 74 Expert 1.3, http://www.ebicom.net/~dhyams/cvxpt.htm) (see also Lambshead, in press). RESULTS General Results Harpacticoid copepods were collected from 423 samples at 43 locations in the northern Gulf of Mexico deep sea. In total 12,480 individuals were collected, of which 7667 were in the copepodite stage, 1159 were damaged adults (unidentifiable), and 3654 were adult specimens suitable for identification. Of 3654 individuals, 696 species were identified from 22 families and 175 genera (see Appendix for complete species list, grouped by family). Nine families accounted for approximately 93% of all harpacticoida (Table 2.1), and two, Tisbidae and Ectinosomatidae, accounted for 46%. Only 182 of these species (27%) have been formally described in the literature. Average abundance over all stations (from five pooled replicate cores = 118.8 cm2) was 172 94, with maximum and minimum values of 412 and 54, found at stations WC5 and MT6, respectively (Table 2.2). The average number of species was 52 19 (from pooled replicate cores) with maximum and minimum values of 104 and 23, found at stations WC5 and MT6, respectively (Table 2.2). Total abundance (N) and species richness (S) decrease significantly (R2 = 0.466 and 0.495, respectively) with increasing water depth (Fig. 2.2A,B). The number of species is highly correlated to the number of individuals encountered (r = 0.91). However, rate of decrease with water depth is approximately five times greater for abundance than species richness (slope = -0.068 and -0.014, respectively). 75 Shannon-Wiener diversity (H') decreases in a strong relationship with depth (R2 = 0.501, P<0.0001, Fig. 2.3). H' had a mean of 3.73 and standard deviation of 0.32 (calculated from Table 2.2). Maximum values of H' were found at stations S35, WC5, MT2 and MT1, while minimum values were found at stations NB2, NB4, B1, B2 and MT6 (Table 2.2). The expected number of species per 30 harpacticoid individuals [ES(30)] shows a moderate non-linear, unimodal relationship with depth (R2 = 0.312, P = 0.0006, Fig. 2.4). Mean ES(30) was 22.24 with standard deviation of 2.08 (calculated from Table 2.2). Maximum values of ES(30) are found at stations WC5 and W1, while minimum values are found at stations C12, C14, and W6. Although the relationship is moderately significant, ES(30) is highly variable at both shallow and deep stations (Fig. 2.4). Average taxonomic diversity ()), the average taxonomic distance apart of any two individuals chosen at random within a sample, increases with increasing water depth (R2 = 0.185, P = 0.004, Fig. 2.5). Determination of average taxonomic diversity was based on a 4-level taxonomic scheme, from species to order. ) had a mean value of 98.76 and standard deviation of 4.24 (calculated from Table 2.2). Highest average taxonomic diversity was found at station MT6 (111.88), with lowest values at station MT1 (91.44). Although the relationship is moderately significant, variance in ) increases at deep stations (Fig. 2.5). Another index of taxonomic relatedness is average phylogenetic diversity (M+), which is the cumulative branch length of the phylogenetic tree within each sample (Clarke and Warwick 2001). As with average taxonomic 76 diversity (above), average phylogenetic diversity was calculated with a 4-level taxonomic scheme, from species to order. Average phylogenetic diversity shows a strong, and highly signficant, increasing trend with depth (R2 = 0.500, P<0.0001, Fig. 2.6). The mean value of average phylogenetic diversity was 62.33 with standard deviation of 5.73 (calculated from Table 2.2). Maximum average phylogenetic diversity of 79.92 was found at station MT6, while the minimum was 54.62 at station C7 (Table 2.2). Both ) and M+ suggest proportionally more higher order taxa (genera and families) per individual with increasing depth (see discussion). Therefore, the ratios of species (S), genera (G), and families (F) to individuals was compared over 1000-meter depth increments (Fig. 2.6). The ratios of S, G, and F to N, increase with depth (Fig. 2.6). Depth zones were significantly different (P<0.01) by two-way analysis of variance. Pairwise comparisons using Tukey s HSD test indicated significant differences only among the shallowest and deepest zones (i.e. 200-999 meters and >3000 meters). Univariate Analysis Hypothesis Testing In the test for differences between depth and longitude (H01 and H02), a highly significant depth main effect was observed (P<0.0001, Table 2.3, Fig.2.8), but a weak significant interaction was also observed (P = 0.0203, Table 2.3, Fig. 2.8). Overall, harpacticoid + responded similarly over most of the transects with a small peak in + at mid depths (approx.1500 m) followed by relatively constant diversity until a second peak at depths greater than 3000 meters. The eastern stations (S transect) has a decrease in + at shallow depths (S44 to S35), but then a constant 77 increase in + with increasing depth. In the test for differences in + between basin and non-basin stations (H03) no significant differences were observed for main effects, and no interaction was observed (Table 2.3) . Similarly, in the test for canyon effects (H04) no significant difference in + was observed between canyon and non-canyon transects, but a significant depth effect was observed (P<0.0001, Table 2.3, Fig. 2.9). No significant canyon by depth interaction was observed between (Table 2.3). In the test for escarpment effects on +, a weak transect main effect was observed (P = 0.035, Table 2.3) but strongly significant interaction between transect and distance from first station was observed (P = 0.0003, Table 2.3, Fig. 2.10). There is a peak in diversity at the two deep stations below the Florida Escarpment (S40 and S41), and then a decrease in diversity moving away from the escarpment (S39). The western (W) transect had a peak in + at approximately 1500 meters (W4), and a second peak at the deepest station (W6). Multivariate Analysis PCA The reader is referred to the environmental variable PCA in chapter one of this dissertation for complete details on variable loads and PC interpretation. Briefly, PC1 was interpreted as sediment properties. Highly positive station scores (>1) on PC1 characterized stations near Mississippi River outflow with high silt, Cr, Sn and total PAH. Station scores between 1 and -1 represented the general offshore environment with moderate silt, clay, sand, Ca, and Sr. Highly negative station scores on PC1 (<-1) 78 represented stations with a high sand fraction relative to silt and clay. PC2 was interpreted as POM flux. Higly positive station scores on PC2 (>1) were those near Mississippi River outflow in the northeastern GOM. Stations scores <1 and moving in the negative direction were deeper and further away from Mississippi River outflow. PC1 through PC4 were regressed against average phylogenetic diversity (M+) to determine the percentage of variance within the diversity data set that is accounted for by environmental variables. PC1 had a non-significant relationship with M+ (Fig. 2.11A), but reveals the overall homogeneity in sediment properties in the northern GOM; with the exception of a few stations near Mississippi River outflow with high silt (MT13, C1), and a few stations in the west (W3, W4) and east (S43, S44) with high sand. Conversely, PC2 (POM flux) was strongly related to average phylogenetic diversity ( +), with highest values of diversity corresponding to lowest values of POM flux (Fig. 2.11B, R2 = 0.316, P = 0.0002). Multidimensional Scaling MDS was used to analyze harpacticoid species abundance from a multivariate perspective (Fig. 2.12 & 2.13). Depth zones of 1000 meter increments show clear separation at the 1000 meter threshold, with stations below 1000 meters showing considerable overlap. Stress, a measure of the ability of the analysis to display multidimensional differences in a two dimensional space, is high for the harpacticoid MDS ordination (Stress = 0.26). Any stress value greater than 0.2 is considered high, and thus the ordination is not appropriately displaying multidimensional station differences (Clarke and Warwick 2001). However, zones are significantly 79 different in a one-way analysis of similarity (ANOSIM) (P<0.01). Similarly, stations were grouped using longitudinal zones as a factor in the MDS plot (Fig. 2.13). Stations group together by longitudinal zone with little overlap, and zones are significantly different by one-way ANOSIM (P<0.01). Harpacticoid species abundance and environmental data were compared using the BIOENV analysis (Primer-E v.5). No strong correlations were observed between harpacticoid community structure and the environmental variables, but a weak correlation (0.228) was found between the biotic data and five environmental variables (%sand, %clay, %silt, ChlA, and OrgN). Spatial and Bathymetric Species Zonation The concept of geographic or bathymetric zonation was analyzed using cluster and subsequent GIS analysis (Figs. 2.14 & 2.15). Cluster analysis was performed on the harpacticoid data set implementing Bray-Curtis similarity and group average linking (Primer-E v.5.) (Fig. 2.14). Minimum Bray-Curtis similarity, i.e. the similarity of all 43 survey stations, was 8.4%. Groups, or zones, were chosen on the basis of >20% similarity. A total of 17 zones were determined, with 1-7 stations per zone (Fig. 2.14). Stations that did not group with at least one other station at the 20% level were designated as unique zones. Highest zone similarity was found for stations MT1, MT2 and MT3 (36.8%), and highest similarity between any two stations was 48.4%, for stations MT2 and MT3. All other zones, that included at least two stations, had similarity values between 20 and 36%. Harpacticoid species composition differs both longitudinally and bathymetrically (Fig. 2.15). Only three zones are found both east and west of 90.5N W longitude (groups 80 9, 11, and 13). All remaining groups are isolated to either the west or east of 90.5N W longitude. Bathymetrically, groups 4, 10, and 13 are found at shallow stations, groups 1, 12, and 6 are found at mid depths, groups 2, 3, 7, 8, 14, 15, 16, and 17 are found at deep stations and groups 5, 9, and 11 are found a virtually all depths. Seven stations are characterized as zone 5 in the northwestern GOM, but these stations are all less than 40% similar. In total, 77 species are found in zone 5, with 5 species accounting for 34% of the total richness (Halectinosoma aff. gothiceps, Neozosime bisetosa, Neozosime trisetosa, Bradya aff. congenera, Ameira aff. parvula). Four of the above species, Neozosime bisetosa, Neozosime trisetosa, Halectinosoma aff. gothiceps, and Bradya aff. congenera, are cosmopolitan over all stations, accounting for 25 % of all individual; and together with Tachidiopsis aff. bozici, Halectinosoma aff.herdmani, Paraleptopsyllus sp. Zosime aff. mediterranea, and Zosime aff. incrassata, account for 40% of the total abundance at all 43 stations. Regional and Global Biodiversity Estimates Two regional data sets were analyzed in order to extrapolate to regional and global scales. Species accumulation curves (Fig. 2.16) were constructed for each data set. The convex nature of the curves suggests that an asymptote exists, given an unlimited sample pool (Lambshead and Boucher 2003). The sigmoidal growth model parameters for the two data sets were as follows: NGOM, a = -7.5, b = 499.4, c = 2241.4, and d = 0.75; NWGOM, a = -2.28, b = 166.08, c = 457.06, and d = 0.57. The models for both NGOM and NWGOM fitted the data very closely, R2 = 0.99996 and 0.99980, respectively. The model interpolation 81 indicates that asymptotes exist at 2241 and 457 species for deep-sea (NGOM) and shallow (NWGOM) regions of the Gulf of Mexico respectively. Species accumulation curves become linear as they approach the asymptotes (estimates of regional diversity), and thus suggest the rate of encounter of new species is relatively constant with increasing geographic distance or area sampled. Summing the shelf and deep-sea estimates yields the regional biodiversity of the entire Gulf of Mexico at approximately 2700 species, assuming there is no overlap between shallow shelf and deep-sea species. The approximate area of the Gulf of Mexico is 1.5 106 km2, which is about 0.4 % of the world s oceans. Assuming the rate of increase of new species with area remains constant then extrapolation suggests a global species richness of 6.5 105 for the Harpacticoida. DISCUSSION Harpacticoid copepods are ubiquitous in deep-sea environments where they have been shown to have particular adaptations (Montagna 1982), are relatively successful compared to macrobenthos (Thistle 2001), and exhibit high diversity (Coull 1972; Thistle 1978). Although much work has been accomplished describing harpacticoid community structure on small spatial scales, few studies have addressed large regionscale patterns. More specifically, the knowledge of regional species pools, processes structuring communities on various scales, and the distributions of organisms and how they respond to topographic, geochemical, and physical oceanographic forcing is largely unknown for deep-sea environments (Etter and Mullineaux 2001). Therefore the purpose of the current study was to investigate harpacticoid species diversity with 82 respect to the complex interactions between Mississippi River outflow, physical oceanographic processes, and sea floor topography in the northern Gulf of Mexico deep sea. Harpacticoida abundance (N) and species richness (S) decrease in strong linear relationships with depth (Fig. 2.2A,B), as observed in previous deep-sea investigations (Tietjen 1971; Coull et al. 1977; Shirayama 1984b; Soltwedel 2000, and reference therein). However, the rate of decrease is approximately five times greater for abundance than richness (comparison of slopes, N/S = -0.068/-0.014). Gray et al. (1997) reviewed several studies and found the number of macrofauna species per individual was comparable in coastal and deep-sea environments; with the exception of a deep-sea data set by Etter and Grassle (1992), which showed much higher S:N ratios in the deep sea. The overall ratio of species to individuals (696/3680) reveals a new species being encountered in one out of every five individuals. Spatial and bathymetric trends in harpacticoid diversity exist in the northern Gulf of Mexico deep sea, as confirmed by the significant depth (H01) by longitude (H02) interaction (P = 0.0203, Table 2.2). Bathymetric patterns of average phylogenetic diversity (M+) indicate that communities are structured differently with increasing depth and distance from the Mississippi River (Fig. 2.8). However, this difference is mainly due to low mid-depth diversity along the S transect, compared to the other four transects; as well as a deep diversity maximum for station MT6 compared with other stations at approximately 3000 meters depth. Average phylogenetic diversity is a measure of higher 83 taxonomic, or morphological richness (Clarke and Warwick 2001). Therefore, harpacticoid copepod communities have increased morphological complexity with increasing water depth. Average phylogenetic diversity was not significantly different between basin and adjacent non-basin stations (H03), suggesting communities located in the west-central Gulf have similar morphological complexity. Although they reside in contrasting topographic environments, basin and non-basin stations have comparable sediment structure (William Behrens, personal communication) and receive comparable POM flux (see chapter one of this dissertation). Average phylogenetic diversity is not significantly different between canyon and non-canyon stations (H04) (Table 2.3, Fig. 2.9), however, a significant depth effect does suggest that morphological complexity increases similarly with depth along the MT and C transects (Fig. 2.9). Average phylogenetic diversity changed differently with depth between escarpment (H05) and non-escarpment transects (P = 0.0003). Strong hydrodynamic regimes can significantly alter abundance and diversity of meiofauna (Thistle et al. 1985, 1991, 1999). Stations along the Florida Escarpment transect S39-S44, especially S40-S42, experience strong current regimes due to impingement by the loop current (see chapter one discussion), and also have a high sand fraction within the sediment structure, compared to other DGoMB stations. In a study of the Fieberling Guyot, a physically reworked site, Thistle et al. (1999) found lower abundance of surface-dwelling harpacticoids, a higher proportion of interstitial harpacticoids, and a higher harpacticoid to nematode ratio. Sediments at the Fieberling 84 Guyot are dominated by sand (greater than 90% by mass), whereas sediments along the Florida Escarpment transect are comprised of 20% to 50% sand mass. However, sediments at the majority of station in the northern GOM are comprised of 2 to 20% sand mass. The dynamic conditions associated with loop current interaction with the Florida escarpment, rapid increase in water column depth, and high sand fraction are likely responsible for the increase in harpacticoid diversity at stations S41 and S40. Diversity can be estimated by either species dependent or species independent indices. Species independent indices (Shannon-Weiner, Hill s N1, etc.) are useful for generally describing diversity; however, they do not represent the structure of the community. For example two communities may have the same number and relative abundance of species, but have completely different sets of species present. In this situation the two communities would have the same diversity value according to Shannon s (or Hill s) index when in reality they look very different. Ecological diversity indices (e.g. H' and J') are not commonly used to analyze community structure over bathymetric gradients because of strong dependence on sample size. For example, harpacticoid (H') diversity decreases strongly, and significantly, with depth (Fig. 2.3) in a linear relationship that strongly reflects decreasing harpacticoid abundance. Because the number of species encountered is strongly related to the number of individuals in a sample (r = 0.91), diversity must be analyzed by indices that are independent of sample size. The most common abundance-independent index used in deep-sea studies is 85 Hurlbert Rarefraction (Hurlbert 1971), which is the expected number of species encountered per number of individuals (ESn). The expected number of harpacticoid species per 30 individuals follows the typical parabolic relationship (Paterson and Lambshead 1995; Etter and Mullineaux 2001; Lambshead et al. 2002; and others) with a maximum diversity found at approximately 1200 meters water depth and decreasing diversity moving into deeper water (Fig. 2.6, R2 = 0.312, P = 0.0006). Coull (1972) used rarefaction curves to compare continental shelf and deep-sea harpacticoid species in the north Atlantic and found maximum diversity at 3000 meters, with decreasing diversity thereafter. Macrofaunal diversity in the north Atlantic has been shown to peak at 1250 meters using the Hurlbert rarefraction method (Maciolek-Blake et al. 1985; Maciolek et al. 1987). Similarly, polychaete diversity in the northeast Atlantic peaks between 1000 and 2000 meters (Paterson and Lambshead 1995). Alternatively, species dependent measures of biodiversity take into consideration the taxonomic composition calculate diversity based on multi-level phylogenetic trees (Warwick and Clarke 1995; Clarke and Warwick 1999; Clarke and Warwick 2001); with the only disadvantage being the semi-arbitrary nature of taxonomy. Perhaps the best approach to comparing community structure over large spatial scales is by a measure of taxonomic distances (Warwick and Clarke 1995), such as average taxonomic diversity ()) and average phylogenetic diversity (M+) (Clarke and Warwick 2001). Average taxonomic diversity is sample size independent, and reflects the average branch length between any two individuals chosen at random from, provided they are not from the 86 same species (Clarke and Warwick 2001). Average phylogenetic diversity (the total branch length of the phylogenetic tree) is not sample size independent, but corrects for sample size differences by dividing by the number of species in the sample. Harpacticoid average taxonomic and average phylogenetic diversity both increase linearly increasing water depth (Figs. 2.5 & 2.6), although average taxonomic diversity shows considerably more variance at stations at depths of approximately 3000 m. Taken together, these indices suggest an increase in harpacticoid diversity with increasing depth and proportionally more genera and families per individual with increasing water depth. This hypothesis was tested by comparing ratios of species, genera, and families per individual, pooling stations into 1000-meter depth zones, in a 2-way analysis of variance. ANOVA indicated significant differences, with pairwise tests (Tukey s) indicating differences between shallowest (200-999 m) and deepest (>3000 m) zones (Fig. 2.9). Thus, dominance by particular species, genera or families seems to decrease with increasing water depth in the northern Gulf of Mexico, which is partially reflected in the pattern of decreasing H' (a dominance index) with increasing depth (Fig. 2.5). Phylogenetic-based diversity indices are useful for understanding higher taxonomic, or morphological diversity, but do not differentiate on the basis of actual species composition. Stations could conceivably have identical ) values, but have entirely different species present. Therefore, to compare many stations on a regional scale it is necessary to employ multivariate procedures to analyze similarities between stations, based on their species composition (Warwick and Clarke 1991; Montagna and 87 Harper 1996). Cluster analysis of stations based on species similarity revealed 17 distinct zones (Figs. 2.10 & 2.11). However, relatively small similarity between stations (20-40%) could justify assigning every station to a unique zone, especially since every fifth individual encountered is a new species. However, the 20% benchmark was sufficient to differentiate between the northeastern and northwestern Gulf of Mexico, with only three similar zones found on both sides of the 90.5N W longitude boundary (Zones 9, 11 & 13; Fig. 2.11). Common zones over bathymetric and longitudinal gradients are explained by the existence of a few cosmopolitan species, and a higher percent similarity benchmark would have likely eliminated common zones over large longitudinal and bathymetric distances. Hence, most stations are very different with respect to harpacticoid species composition in the northern Gulf of Mexico. On the contrary, recent analysis of nematode diversity from the north-central equatorial Pacific revealed 71% of all species being found at four stations spanning more than 3000 km of abyssal plain (Brown 1998, Lambshead et al. 2003, Lambshead and Boucher 2003), suggesting an increase in cosmopolitan species at abyssal depths. This is consistent with previously observed bathymetric diversity patterns where diversity is maximized in the bathyal and decreases in the abyssal (Maciolek-Blake et al. 1985; Maciolek et al. 1987; Paterson and Lambshead 1995; Etter and Mullineaux 2001; Lambshead et al. 2002). Species extinction associated with habitat loss and fragmentation on a global scale has led to increased interest in regional and global scale biodiversity studies in both the terrestrial and marine environments (Wilson 1985, 1988; May 1988) as well as 88 associated studies attempting to understand the connection between natural biodiversity and ecosystem function (Emmerson and Raffaelli 2000; Rothman 2001; Loreau et al. 2001; Raffaelli et al. 2003). Regional and global diversity of deep-sea soft sediments has received increasing attention since the discovery that this environment is generally more species rich than coastal systems (Hessler and Sanders 1967). On global scales, macrobenthos diversity has been estimated to be at least 5x105 10x107 species (Grassle and Maciolek 1992; May 1992, Poore and Wilson 1993), with meiofauna diversity equaling or exceeding that estimate by one to two orders of magnitude (potentially 109 species) (Lambshead, in press). It is estimated that harpacticoid regional species richness within the northern Gulf of Mexico is approximately 2700 species. Zonation results suggest very little overlap between stations in the northern GOM, therefore a regional diversity estimate of 2700 species is not unreasonable. Furthermore, estimates of global species richness, by extrapolation from regional species accumulation (sensu Lambshead and Boucher 2003) of Harpacticoida suggest between 105 and 106 harpacticoid species. Although estimates are dramatically higher than the number of described species (4500, for marine and freshwater species), they are in line with global estimates of other highly diverse taxa, e.g., Nematoda (May 1988, Lambshead and Boucher 2003). Given what we know of harpacticoid ecology (Hicks and Coull 1983), and their ubiquity and abundance, the potential for speciation is high in this taxa. Harpacticoida, and other meiofauna, have shorter generation times than macrofauna or megafauna, have 89 slower movement, have non-planktonic larvae, and with smaller body sizes live on ecologically smaller scales (Hicks and Coull 1983; Higgins and Thiel 1988; Giere 1993; Thistle 2003). Without planktonic larval stages, harpacticoid (meiofauna in general) dispersal is dependent upon suspension and transport by current flow, turbidity currents, or some other transport mechanism. The deep-sea is known to have dynamic current regimes, which have been shown to alter meiofauna abundance and diversity at locations such as the HEBBLE site (High Energy Benthic Boundary Layer Experiment) (Thistle et al. 1985, 1991; Aller 1997), the Rockall Trough (Gage 1977; Patterson and Lambshead 1995), and the Setubal Canyon (Gage 1977; Gage et al. 1995). The rate of dispersal of meiofaunal organisms on a global scale is unknown. Limited evidence suggests harpacticoid patchiness on 100meter, meter, and centimeter scales (Thistle 1978) is consistent with Jumar s (1975) grain matching hypothesis (microhabitat specialization), and such patchiness should result in species dispersions on these scales (Thistle 1978). Furthermore, genetic evidence suggests the existence of cryptic species within previously assumed cosmopolitan populations (Schizas et al. 1999; Rocha-Olivares et al. 2001), and smallscale (100 m) dispersion patterns of genetic haplotypes within populations (Street and Montagna 1996). Therefore, if rates of speciation in the deep sea exceed rates of dispersal, then high regional and global species richness will be purely a function of the deep sea s vast area (sensu Abele and Walters 1979). Parametric and non-parametric multivariate analyses yield contrasting results, 90 with respect to environmental variables that affect diversity, but together give a complete picture. Comparing environmental principal components (PC1 & PC2) to a single diversity index (M+) reveals no relationship with sediment characteristics (PC1), but an inverse relationship between POM flux (PC2) and functional diversity (Figs. 2.11A&B). The availability of food resources has been used to explain both bathymetric (Rex 1981; Levin et al. 1994) and geographic (Levin et al. 1991; Rex et al. 1993; Lambshead et al. 2000; Lambshead et al. 2002) diversity patterns. Because deep-sea communities are reliant upon sinking POM derived from surface water production, it is logical that food resources play a significant role in the number and types of species present in a community. The general consensus is that increased POM flux results in increased abundance, a shift towards dominance, and therefore a decrease in diversity. Short term laboratory experiments of organic enrichment (Grassle and Morse-Porteous 1987; Snelgrove et al. 1996) confirm what has been observed in several field experiments (Rex 1983; Schaff et al. 1992; Rex et al. 1993; Levin et al. 1994; Levin and Gage 1998), that diversity is maximized at moderate levels of organic enrichment. Large scale bathymetric and latitudinal diversity patterns are generally unimodal, with highest diversity at intermediate levels of production (depth, latitude, etc.) (Rex 1981; Levin and Gage 1998), which is confirmed here (Fig. 2.6). The mechanism for decrease in diversity at high levels of production appears to result from competitive exclusion due to increased dominance (Levin and Gage 1998), but could also result from chemical stress associated with increased biological oxygen demand (Levin et al. 2000). 91 Microelectrode profiles of stations MT1 and MT3 in this study (John Morse, DGoMB data) indicate oxygen penetration depths of 2 mm, compared to 8 cm at deeper more oligotrophic stations, further evidence supporting decreased functional diversity in areas of high organic enrichment. On the other side of the unimodal diversity/production curve is a decrease in diversity at lower bathyal and abyssal depths. This has been hypothesized to occur as a result of a chronic allee affect, or a point at which populations are no longer reproductively viable due to death rates exceeding birth rates (M.A. Rex, personal communication). Multidimensional scaling plots confirm significant differences in harpacticoid community structure with depth and longitude in the northern GOM deep sea (Figs. 2.12 & 2.13). Comparison of harpacticoid species abundance and environmental data with the BIOENV gives only weak Spearman Rank Correlations with grain size and chl-a biomass. PC1 (sediment properties) was not correlated with average taxonomic diversity in the parametric analysis, but is important with respect to the actual species list as reflected by stations similarities in the MDS analysis. But, the amount of variance explained by the BIOENV procedure is low. Many studies have shown that softsediment community structure is related to sediment characteristics (Sanders 1958; Rhoads 1974; Gray 1981), but as emphasized by Etter and Mullineaux (2001), the relationships between diversity and sediment properties remain numerous and often controversial. In the current study, most stations were characterized by high silt and clay fractions, with little sand. Only 10 stations (MT5, MT6, S38-S44, and W1) had greater 92 than 30% sand, and were all located in the northeastern GOM, except station W1. Therefore, grain size may partially explain longitudinal variability in harpacticoid diversity within the northern GOM. Several hypotheses have been formulated to explain the mechanisms regulating high deep-sea diversity, but the fall under two broad categories: 1) small-scale patch dynamics and 2) large-scale regional processes. In general, diversity is highly related to bathymetric and regional variation in POM flux, as confirmed here by a significant relationship with PC2 (Fig. 2.11B). However, a wealth of literature has focused on small-scale processes creating spatial sediment heterogeneity (Jumars 1975, 1976; Gage 1977), particularly biogenic structures (Thistle 1979; 1983; Thistle and Eckman 1988, 1990; Eckman and Thistle 1991; Thistle et al. 1993), which can exist for large periods of time and provide distinct microhabitats. Small scale heterogeneity in sediment grain size has been hypothesized to provide diverse food resources, because the majority of deep-sea species are deposit feeders (Sanders 1958; Rhoads 1974; Gray 1981). However, perhaps the most convincing and unifying argument comes from patch dynamics theory; small-scale disturbances creating a network of patches in multiple stages of succession (Grassle and Sanders 1973; Caswell 1978; Connell 1978; Sousa 1979; Paine and Levin 1981; reviewed by Etter and Mullineaux 2001). Highly variable life history characteristics in the deep sea (Gage and Tyler 1991) may therefore enable multiple inferior species to colonize patches, and slower dynamics in deep water may reduce the rate at which these inferior species are excluded (Caswell and Cohen 1991; 93 Caswell and Etter 1999). Although the current study was not specifically designed to test small-scale hypotheses, it is possible to assess within versus between-station variability. For example, cluster analysis of all replicates at stations NB3 and NB4 (Fig. 2.18) reveals as much within-station similarity as between-station similarity. Replicates three and five at station NB4 are approximately 40% similar, but are, at most, 10% similar to other NB4 replicates. At station NB3, replicates one and two are approximately 20% similar. However, replicate two from station NB3 is 30% similar to replicate two at station NB4; likewise replicate two at NB3 and replicate two at NB4 are 30% similar. This pattern of within-station variability equaling or exceeding between-station variability allows for at least three conclusions to be made. First, the harpacticoid species pool in the northern GOM is large, and this study only captured a small percentage of the total. This reinforces our regional diversity estimate of at least 2700 species. Second, there are a few cosmopolitan species that are observed Gulf-wide, producing small between-station similarities. Third, processes maintaining harpacticoid diversity in the northern GOM rely on both small-scale and large-scale mechanisms. 94 CONCLUSION The northern Gulf of Mexico deep sea is a dynamic environment, with the complex morphology of the Texas/Louisiana continental slope, dramatic canyon features such as the Mississippi Trough, and the precipitous Florida escarpment. Inflow from the Mississippi River interacts with the Loop Current to enhance POM flux, which strongly influences diversity. The harpacticoid community shows remarkable diversity with approximately one in five individuals belonging to a different species. With the exception of a few cosmopolitan species, most stations have different species compositions, which suggests high regional (2700 species) and global (105 - 106 species) diversity by extrapolation. Although highest diversity, with respect to the expected number of species (rarefraction), is found at approximately 1200 meters, average taxonomic and average phylogenetic diversity continue to increase with depth. Multivariate analysis reveals significant inverse relationships between diversity and production, which are confirmed by a significant region-scale depth and longitude differences. However, within versus between station variability suggests an interaction between small and region-scale processes maintaining diversity. Low rates of dispersion, coupled with high local diversity, result in high regional and global diversity, thus, speciation in the deep-sea likely exceeds dispersion. Therefore, global species richness of the Harpacticoida is likely a function of the vast area of deep-sea soft sediments, in agreement with Abele and Walter s (1979) hypothesis. Table 2.1: Results of SIMPER analysis (Primer 5.0) indicating family percent 95 contributions to total harpacticoid abundance. AA = Average abundance, Contrib.% = percent contribution of family, T% = cumulative percent contribution of families. Family Tisbidae Ectinosomatidae Diosaccidae Ameiriidae Argestidae Paranannopidae Canthocamptidae Paramesochriidae Cletodidae Neobradyidae Thalestridae Normanellidae Cerviniidae Danielssenidae Huntemannidae Unid. family Ancorabolidae Laophontidae Canuellidae Darcythompsonidae Longipedidae Euterpinidae AA 40.19 24.12 19.74 15.71 11.00 9.15 12.38 6.73 6.62 2.73 2.34 2.41 2.55 3.55 1.70 1.79 1.21 0.42 0.28 0.19 0.16 0.05 Contrib.% 32.98 13.27 9.84 8.24 8.08 6.50 6.03 4.15 3.42 1.39 1.09 1.09 1.05 0.93 0.93 0.61 0.32 0.03 0.03 0.01 0.00 0.00 T% 32.98 46.24 56.09 64.33 72.41 78.91 84.95 89.10 92.52 93.91 95.00 96.08 97.13 98.06 98.99 99.60 99.93 99.96 99.99 100.0 100.0 100.0 96 Table 2.2: Total species (S) and total individuals (N) per five pooled replicates cores (= 118.8 cm2). Species diversity indices: expected species per 30 individuals [ES(30)], Shannon-Wiener diversity (H ), average taxonomic diversity ()), and average phylogenetic diversity (M+) at each of the 43 stations where harpacticoid copepods were identified to species. Station AC1 B1 B2 B3 C1 C12 C14 C4 C7 MT1 MT2 MT3 MT4 MT5 MT6 NB2 NB3 NB4 NB5 RW1 RW2 RW3 RW4 RW5 RW6 S35 S36 S37 S38 S39 S40 S41 Depth 2440 2253 2635 2600 336 2924 2495 1463 1066 482 677 990 1401 2267 2743 1530 1875 2020 2065 212 950 1340 1575 1620 3000 668 1826 2387 2627 3000 2972 2974 S 51 27 32 37 51 34 33 56 74 69 67 81 53 44 23 40 50 39 36 66 57 61 57 43 49 89 74 70 44 38 30 31 N 114 74 90 108 195 125 118 148 212 322 338 418 144 110 54 112 120 108 94 240 178 174 148 112 130 388 306 198 108 96 78 84 ES(30) 24.91 19.56 20.27 21.00 19.70 17.34 17.37 23.98 24.64 22.68 21.45 21.68 23.80 23.35 18.92 22.14 24.02 21.76 21.90 23.54 23.41 23.47 24.28 22.56 22.99 23.02 23.14 24.49 23.36 21.85 19.94 20.69 H' 3.90 3.21 3.33 3.46 3.73 3.31 3.31 3.91 4.14 3.95 3.82 3.92 3.87 3.71 3.04 3.59 3.83 3.54 3.50 3.99 3.89 3.92 3.94 3.64 3.74 4.10 4.02 4.09 3.71 3.50 3.24 3.35 ) 102.14 105.25 102.51 96.59 96.85 95.37 93.49 101.77 96.27 91.44 92.72 92.99 101.76 102.31 111.88 99.71 101.57 100.51 101.88 95.83 97.50 95.49 101.27 101.49 96.16 93.75 95.58 99.48 100.96 102.23 105.63 104.42 + 59.79 70.97 62.74 64.92 58.63 70.14 66.12 63.23 54.62 57.51 53.06 55.02 63.93 62.27 79.92 64.78 63.81 63.91 65.86 57.38 59.27 57.34 65.85 66.40 61.75 54.72 56.98 60.17 63.09 66.89 71.18 72.10 97 Station S42 S43 S44 W1 W2 W3 W4 W5 W6 WC12 WC5 Depth 763 362 212 420 625 875 1460 2750 3150 1175 348 S 58 57 41 94 65 65 52 35 33 52 104 N 178 178 133 306 214 212 146 86 108 180 412 ES(30) 23.33 23.17 20.14 25.46 23.12 23.55 23.79 22.10 17.39 21.75 25.26 H' 3.89 3.87 3.64 4.37 3.93 3.98 3.86 3.49 3.02 3.71 4.42 ) 98.33 95.31 99.64 96.45 97.02 96.13 100.75 101.27 90.01 98.55 96.28 + 57.55 56.08 64.76 56.50 56.36 57.16 66.58 64.93 68.15 62.62 55.24 98 Table 2.3: ANOVA results of test for differences in Harpacticoida diversity. Dependent variable is average phylogenetic diversity (M+). DFS = distance from shore; DFFS = distance from first station. Source H01&02 Transect Depth T*Depth Error H03 Treatment DFS T*DFS Error H04 Transect Depth T*Depth Error H05 Transect DFFS T*DFFS Error 1 5 5 47 241.31 56.15 1450.89 2396.81 241.31 11.23 290.18 50.99 4.73 0.22 5.69 0.0347 0.9521 0.0003 1 4 4 37 2.67 1870.96 302.41 2141.06 2.67 467.74 75.60 57.87 0.05 8.08 1.31 0.8312 <.0001 0.2855 1 2 2 23 16.45 109.87 57.33 2045.74 16.45 54.93 28.67 88.95 0.18 0.62 0.32 0.6711 0.5479 0.7277 4 4 16 96 110.02 2198.28 1749.36 5243.96 27.51 549.57 109.34 54.62 0.50 10.06 2.00 0.7332 <.0001 0.0203 DF SS MS F Value Pr > F 99 100 Figure 2.1: Harpacticoid copepods were identified to species at a total of 43 stations in the northern Gulf of Mexico deep sea. 40 35 30 25 A. R = 0.466 P<0.0001 2 N 10cm -2 20 15 10 5 0 0 10 1000 2000 3000 R = 0.495 P<0.0001 2 B. 8 S 10cm-2 6 4 2 0 0 1000 2000 3000 Water depth (m) Figure 2.2: Harpacticoid copepod abundance (N) and species richness (S), adjusted to the number per 10 cm2, as a function of depth. Abundance and richness are highly correlated (r = 0.91). 101 4.6 4.4 WC5 W1 R2 = 0.501 P<0.0001 Shannon-Wiener Diversity (H') 4.2 S35 C7 S36 S37 4.0 3.8 3.6 3.4 RW1 W3 MT1W2 MT3 RW3 RW4 C4 AC1 S42 RW2 S43 MT4 W4 NB3 MT2 C1 MT5 WC12 S38 RW5 S44 NB2 NB4 NB5 B3 W5 C14 B2 RW6 S39 S41 C12 S40 3.2 3.0 2.8 0 1000 2000 B1 MT6 W6 3000 Water depth (m) Figure 2.3: Shannon-Wiener diversity index (H') as a function of depth for pooled replicate core samples of harpacticoid copepods. 102 26 W1 WC5 C7 RW4 C4 MT4 W4 RW3 RW5 NB2 AC1 S37 NB3 S36 MT5 S38 R2 = 0.312 P = 0.0006 24 W3 RW1 S42 RW2 S43 W2 S35 MT1 RW6 NB5 NB4 B3 W5 S39 22 ES(30) MT2 MT3WC12 S41 S40 20 S44 C1 B2 B1 MT6 18 C14 C12 W6 16 0 1000 2000 3000 Water depth (m) Figure 2.4: Expected number of harpacticoid species per 30 individuals [ES(30)], for pooled replicate core samples of harpacticoid copepods. 103 115 MT6 R2 = 0.185 P = 0.004 Average taxonomic diversity (delta) 110 105 B1 MT5 AC1 B2 C4 RW5 NB3 NB5 MT4RW4 S38W5 W4 NB4 NB2 S37 B3 S36 C14 S40 S41 S39 100 S44 S42 W2 C1 W1 WC5 RW1 S43 S35 MT2 MT1 95 WC12 RW2 W3 C7 RW3 MT3 RW6 C12 90 W6 85 0 1000 2000 3000 Water depth (m) Figure 2.5: Average taxonomic diversity ()) for pooled replicate core samples of harpacticoid. 104 85 Average phylogenetic diversity (delta*) R2 = 0.500 P<0.0001 MT6 80 75 B1 S41 S40 C12 W6 C14 B3 W5 MT5 S37 AC1 RW3 S36 S38 B2 S39 70 W4 RW5 RW4 NB5 NB2 MT4 NB3NB4 C4 65 S44 WC12 RW6 60 55 RW2 C1 S42 MT1 RW1 W3 W1 W2 S43 WC5 MT3 S35 C7 MT2 50 0 1000 2000 3000 Water depth (m) Figure 2.6: Average phylogenetic diversity (M+) for pooled replicate core samples of harpacticoid copepods. 105 0.7 F/N G/N S/N 0.6 Harpacticoida S, G, or F (N ) -1 0.5 0.4 0.3 0.2 0.1 0.0 200-999 1000-1999 2000-2999 >3000 Depth zone (m) Figure 2.7: The ratio of harpacticoid copepod species (S), genera (G), and families (F) per total individuals (N) in each depth zone. Zones are significantly different (P<0.01), with pairwise comparisons indicating differences among shallowest and deepest zones only. 106 85 Average phylogenetic diversity (phi+) 80 MT6 75 70 W4 RW4 S44 MT4 C4 RW2 W3 S35 MT3 C7 S36 MT5 S37 AC1 C14 W5 C12 W6 S39 65 RW6 60 C1 RW1 MT1 W1 55 50 0 500 1000 1500 2000 2500 3000 3500 Water depth (m) Figure 2.8: Average phylogenetic diversity ( +) as a function of depth for transects included in the test for depth and longitude differences (H01 and H02). 107 85 Average phylogenetic diversity (phi+) 80 MT6 75 70 C14 MT4 C4 MT5 C12 65 60 C1 MT1 55 MT3 C7 50 0 500 1000 1500 2000 2500 3000 3500 Water depth (m) Figure 2.9: Average phylogenetic diversity ( +) as a function of depth for transects included in the test for diversity differences between canyon (MT) and non-canyon (C) areas (H04). 108 74 Average phylogenetic diversity (phi+) 72 70 68 66 S44 W4 W5 S41 S40 W6 S39 64 62 60 58 56 54 0 500 1000 1500 2000 2500 3000 3500 W1 W2 S43 S42 W3 Water depth (m) Figure 2.10: Average phylogenetic diversity ( +) as a function of depth for transects included in the test for escarpment (S transect) effects on diversity (H05). 109 75 A. Average phylogenetic diversity (phi ) S 41 S 40 + R = 0.039 P = 0.228 B1 2 70 C 12 W6 W4 RW 5 N BR W 4 C 14 5 B3 NB 2W 5 M T4 NB3 C4 B2 W C 12 M T5 RW 6 S 37 RW 2 W3 S 43 AC1 C1 M T1 M T3 MT2 65 S 44 60 55 S 42 R W 1 R S 36 W 3 W2 W1 W C5 S 35 C7 50 -3 -2 -1 0 1 2 3 PC 1 75 B. Average phylogenetic diversity (phi ) + S 41 R 2 = 0.316 P = 0.0002 S 401 B C 12 70 W6 65 W4 C 14 R W 5N B 54 RW W B3 5 NB2 MN B 3 T4 C4 B2 W C 12 RW 6 AC1 RW 3 S 37 RW 2 S 44 MT5 60 C1 M T1 55 MT3 S 42 3 R W 1 W36 S W 2 SW 1 43 W C5 S 35 C7 M T2 50 -2 -1 0 1 2 3 4 PC 2 Figure 2.11: Regression of average phylogenetic diversity ( +) as a function of A) environmentalPC1 and B) PC2. + is not significantly related to sediment properties (PC1), but is significantly related to POM flux (PC2). 110 Stress: 0.26 S36 MT1 S35 S44 S43 MT3 MT2 S37 W3 NB5 WC5 WC12 W2 RW3 W4 S39 RW5 C14 S38 AC1 NB4 B3 B2 B1 W6 NB2 NB3 W5 MT6 RW4 MT5 C12 RW1 C7 C1 RW6 RW2 C4 S40 S42 MT4 S41 W1 Figure 2.12: MDS orientation of stations based on harpacticoid species abundance. Symbols indicate depth zone: = 200-1000 meters, = 1000-2000 meters, = 20003000 meters, > 3000 meters. One-way analysis of similarity (ANOSIM) indicates significant depth differences (P<0.01). 111 Stress: 0.26 S36 MT1 S35 S44 S43 MT3 MT2 S37 W3 NB5 WC5 WC12 W2 RW3 W4 S39 RW5 C14 S38 AC1 NB4 B3 B2 B1 W6 NB2 NB3 W5 MT6 RW4 MT5 C12 RW1 C7 C1 RW6 RW2 C4 S40 S42 MT4 S41 W1 Figure 2.13: MDS orientation of stations based on harpacticoid species abundance. Symbols indicate longitudinal zone: = 94-96N W, = 91-93N W, = 88-90N W, = 85-87N W. One-way ANOSIM indicates significant longitudinal differences (P<0.01). 112 Bray-Curtis Similarity (%) Figure 2.14: Cluster analysis of Harpacticoid community composition, created using Bray-Curtis similarity, and group average linking. Zonation determined on basis of >20% similarity. 113 114 Figure 2.15: Harpacticoid copepod species zonation in the northern Gulf of Mexico deep sea. Zones were chosen on the basis of >20% similarity using the Bray-Curtis similarity index. Figure 2.16: Species accumulation curves used to estimate regional Harpacticoida species abundance in the Gulf of Mexico (extrapolation). 115 Figure 2.17: Cluster analysis illustrating within versus between station differences in harpacticoid community structure for all replicates at stations NB3 and NB4. 116 CHAPTER 3: MEIOFAUNA BIOMASS AND WEIGHT-DEPENDANT RESPIRATION IN THE NORTHERN GULF OF MEXICO DEEP SEA ABSTRACT Meiofauna exhibit high biomass in deep-sea soft sediments, compared to larger invertebrates (e.g. macro- and megafauna), and play an important role in the global carbon cycle. However, deep-sea meiofauna community function (grazing, respiration, etc.) has only been sparsely investigated. In the present study, meiofauna biomass was calculated at 51 stations using a newly developed, semi-automated, digital microphotographic method; grazing rates on bacteria were measured at four stations using radiotracer techniques in controlled temperature and pressure shipboard experiments; and meiofauna mass-dependent respiration was estimated at 51 stations using an allometric power law. Strong relationships exist between biomass and meiofauna community respiration with depth. Highest biomass and respiration occurred in the proximity of high particulate organic matter flux; where surface currents interact with Mississippi River inflow complex slope topography. Allometric estimates indicate that meiofauna require 7% of their biomass per day to meet their metabolic energy budget, and are therefore not food limited with respect to sediment bacterial biomass. Meiofauna account for 10-25% of whole sediment community respiration indicating their importance in global biogeochemical cycles. 117 INTRODUCTION Meiofauna are ubiquitous in marine soft-sediment communities (Coull and Bell 1979; Hicks and Coull 1983; Soltwedel 2000), and are an important link in transferring carbon primary and secondary production to higher trophic levels (Montagna 1984, 1995). Meiofauna ubiquity extends into deep-sea environments, with greater (Pequegnat et al. 1990), or proportionally greater (Rowe et al. 1991), biomass compared to megaand macrofauna-sized invertebrates. However, meiofauna trophic interactions as well as community respiration in the deep-sea are poorly understood. Rates of meiofauna grazing (Montagna 1984, 1993, 1995) as well as accurate biomass measurements (Baguley et al. 2004) are necessary to validate deep-sea trophic structure models and gain understanding of meiofauna community function. Prior to the 1980 s little was known of meiofauna trophic interactions with the microflora (i.e. bacteria and microphytobenthos); specifically, whole community grazing rates. Although, it had been discovered that meiofauna (Nematoda and Harpacticoida) are largely selective feeders (Marcotte 1977; Hicks and Coull 1983; Jensen 1987). Food particle selectivity, or grazing, was first measured by Montagna (1984) using radiolabeled tracer experiments originally designed for planktonic food web studies (Daro 1978), which subsequently spawned extensive investigation in shallow-water estuarine and coastal environments (Carman and Thistle 1985; Decho 1988; Decho and Fleeger 1988; Montagna and Bauer 1988; Carman 1990; Blanchard 1991; Montagna and Yoon 1991; Montagna et al. 1995; Pace and Carman 1996; Carman et al. 1997; Buffan-Dubau 118 and Carman 2000; Pinckney et al. 2003). Stable isotope chemistry has also been applied to shallow water benthic studies to identify natural food sources (Couch 1989; Riera et al. 1996; Peterson 1999) and trace food movement through two or more trophic levels (Peterson 1999; Middelburg et al. 2000; Herman et al. 2000; Moens et al. 2002; Carman and Fry 2002). An understanding of deep-sea metabolic rates, in the context of wholecommunity respiration, evolved parallel to meiofauna grazing studies, beginning in the 1970 s, and continuing to the present (Smith and Teal 1972; Smith et al. 1979; Smith 1987, 1992; Rowe et al. 1994; Smith et al. 2001, 2002). However, contribution of the meiofaunal component to whole-community respiration has only been sparingly studied (Shirayama 1992; Mahaut et al. 1995). Deep-sea floor carbon budgets (Smith et al. 1992; Rowe et al. 1994; Anderson et al. 1994), and trophic structure models (Rowe 1996), have been constructed to explain soft-sediment community function. However, meiofauna grazing on bacteria, phytodetritus, or any other food source, has never been empirically measured. Therefore the meiofauna contribution to deep-sea food webs, carbon cycling, and whole community respiration is largely unknown. Given the vast size of the deep-sea, the ubiquity of meiofauna in this environment, and the dominance of meiofauna biomass, it is imperative that meiofauna trophic interactions be understood. Meiofauna are an important, but often ignored, component of deep-sea soft sediments, and although meiofauna biomass often exceeds macrofaunal biomass (Pequegnat et al. 1990), their contribution to whole-community metabolism is largely 119 unknown. Additionally, trophic interactions in the deep sea, and dynamics that structure deep-sea communities have not been elucidated (Etter and Mullineaux 2001). To gain an understanding of deep-sea meiofauna community function, a carbon budget is needed in which both standing stocks and fluxes are quantified. The purpose of this study was to determine meiofaunal biomass, meiofauna contribution to whole-community respiration, and to quantify meiofaunal-bacterial trophic linkages in the northern Gulf of Mexico deep sea. METHODS Biomass and mass-dependent respiration were estimated at 51 stations (Fig. 3.1), and shipboard grazing experiments were conducted at four experimental stations (Fig. 3.2). The stations were sampled as part of a larger, comprehensive study of deep Gulf of Mexico benthos (DGoMB). For complete field and laboratory methods associated with sample collection and processing see chapter one of this dissertation. Biomass Meiofauna biomass was calculated using a newly-developed digital microphotographic approach (Baguley et al. 2004) for all samples at all DGoMB stations (Fig. 3.1). Typically, meiobenthic samples are dominated by two taxa: Harpacticoida and Nematoda. Nematodes generally dominate the sample contributing 70 to 95% of total individuals while harpacticoids constitute a lesser proportion ranging from 5 to 20%. Other taxa usually comprise a minor proportion of individuals ranging from 5 to 20%. For this reason, meiofaunal biomass is frequently based on harpacticoid and nematode values alone (Montagna 2002). All harpacticoids and a subsample of 30 nematodes were 120 digitally photographed using a compound microscope. Area and width measurements were calculated using Sigma Scan Pro 4.0, analytical graphics software (Baguley et al. 2004). Nematode biovolume (V, in nL units) was estimated sensu Baguley et al. (2004) by assuming nematode body shape is approximately cylindrical: V (nL) = Br2L/106 where, L equals the total length of the nematode (area/width), and r equals the radius (mid- body width/2). Nematode biovolume estimates by the above equation have are not significantly different from direct measurement with analytical balance or elemental analyzer (Baguley et al., 2004). Harpacticoid biovolume calculation relied on area and width measurements along with two conversion factors (Cbf = body form and Co = orientation). Body volume was estimated from a formula used by Feller and Warwick (1988) and Warwick and Price (1979) to measure harpacticoid biovolume: V (nl) = [A (mm) x W (mm)](Cbf x Co)/109 Harpacticoid body volume estimates relied on eight body type-specific conversion factors (Cbf) derived from volumetric displacement of plasticene scale models (McIntyre and Warwick 1984, Warwick and Gee 1984). Application of these factors required matching SigmaScan images of individual harpacticoids to line-drawings of different body forms (cylindrical, semi- cylindrical compressed, semi-cylindrical, semi-cylindrical depressed, fusiform, pyriform, pyriform depressed, and scutelliform) and their 121 corresponding conversion factors (McIntyre and Warwick 1984, Warwick and Gee 1984). Photographic images that did not approximate one of these eight body forms because of variable axial orientation and rotation were assigned a default value (Cbf = 440). The default value was derived from average conversion factors of five of the most commonly encountered body forms (semi-cylindrical, semi-cylindrical depressed, fusiform, pyriform, pyriform depressed). Cbf values for all minor taxa were taken from Feller and Warwick (1979). One additional conversion factor was required to account for the average loss of image area resulting from variable body orientations. The longitudinal axis of most animals was generally parallel (~ 0 ) to the photographic plane displaying a ventral, dorsal or lateral aspect, as desired. However, individuals were frequently oriented at an angle to the photographic plane (1 - 90 ), yielding underestimates of biovolume. A correction factor (Co = 1.5) was determined by comparing the average biovolume of 90 harpacticoids photographed in the standard mode (avg. biovolume = 1.17 1.09 nL) to the biovolume of the same individuals after being manipulated into a flat, noncompressed, dorsal orientation (avg. biovolume = 1.75 2.19 nL). Nematode and harpacticoid wet mass was calculated from biovolume using a specific gravity of 1.13, and wet mass was converted to dry mass assuming a ratio of 25% (Weiser 1960; Feller and Warwick 1988). Previous studies have used a carbon to dry mass ratio of 40% (Feller and Warwick 1988; Warwick and Price 1979; Danovaro et al. 1995; and others), which was estimated for chaetognaths by Steele (1974). 122 Baguley et al. (2004) found carbon to dry mass ratios of 51.4% for nematodes and 45.8% for harpacticoids by direct measurement. These empirically measured values were used to convert dry mass to carbon mass. For the less abundant taxa, a sub-sample of at least 10 individuals (taken from random samples) was digitally photographed under a compound microscope, and biomass was calculated as described above for the harpacticoid copepods. However, taxon-speciefic conversion factors were applied for different body forms (Cbf), as proposed by Feller and Warwick (1988). For these taxa, a uniform conversion factor of 48% was used to convert dry mass to carbon mass, but specific gravity (1.13), and dry to wet mass (0.25) were assumed to be uniform (Feller and Warwick 1988). Bacterial biomass was estimated by Jody Deming (unpublished DGoMB data) using epifluorescence microscopy for enumeration of Acridine Orange and DAPI-stained cells (Deming et al. 1997; Schmitt et al. 1998), and a conversion factor of 10 fg C cell-1 was used to determine estimate (Alongi 1990; Relaxans et al. 1996). For more detailed information regarding bacterial abundance and biomass methodology, see the above references and Rowe et al. (in prep). The complete bacterial abundance and biomass data set can be accessed, with permission, from the DGoMB database at http://www.gerg.tamu.edu. Grazing Experiments Meiofauna grazing experiments were carried out using radiolabeled tritiated thymidine (3HTdr) to measure feeding rates on bacteria (Montagna 1984, 1993). Grazing experiments were conducted at four experimental stations (Fig. 123 3.2) during June 2001. The experimental design for all grazing studies included 6 replicates per station, with 3 replicates designated as experimental and 3 as killed controls. Core tubes (5.5 cm i.d.) were mounted inside the GOMEX boxcore (Boland and Rowe 1991) and removed immediately upon return to the ship s deck. The top centimeter of each core was extruded (in a shaded area of the ship s deck), rinsed into pre-sterilized 50 ml polypropylene centrifuge tubes with ice-cold 0.2 micron-filtered bottom water (obtained from CTD cast). Samples were placed immediately on ice, and transported to the refrigerated van where all experiments took place. The refrigerated van was kept at in situ temperature (4-7 C), as determined from a CTD cast prior to sampling. To test for meiofauna grazing on bacteria, 5 Ci of 3HTdr was added by pipetting 1 ml of 3HTdr stock solution (5 Ci/ml) into each centrifuge tube. Control samples were immediately fixed with 10 ml of 10% buffered formaldehyde solution. Any remaining headspace in tubes was eliminated by adding 0.2 micron-filtered bottom water, and tubes were then sealed with two layers of Parafilm (making sure no air bubbles remained in tubes). Both experimental and control samples were placed in stainless steel vessels (Deming 1997, 2001) and pressurized to in situ conditions. Incubations were run for 24 hours, after which time experimental samples were fixed with 50 ml of 10% buffered formaldehyde solution. An equal volume of 10% buffered formaldehyde solution was added to control tubes to maintain volume proportions. 124 Estimation of bacterial label uptake was accomplished by taking 1.0 ml aliquots from both experimental and control tubes and rinsing over 0.2 m Millipore filters. Aliquots were rinsed three times with de-ionized (DI) water to ensure removal of free label. Filters were placed into 20 ml glass scintillation vials and stored under refrigeration and returned to The University of Texas Marine Science Institute (UTMSI), Port Aransas, Texas, USA for analysis. The remaining sample was pre-sieved shipboard over a 45 m Nitex mesh and rinsed thoroughly with DI water until the entire silt and clay fractions were removed. An equal volume of 10% buffered formaldehyde and DI water were added to samples making the final formaldehyde concentration 5%. Samples were frozen, to prevent isotope leakage from animals (Moens et al. 1999), and returned to UTMSI for sorting and analysis. Laboratory analysis of grazing samples includes extraction of meiofauna from sediments and counting of animal and bacterial radiolabel uptake (disintegrations per minute, DPM). The 1.0 ml aliquot sub-sample is used to measure bacterial uptake of 3 HTdr. The subsample was dispersed and suspended in 5 ml of distilled water and 15 ml ScintiVerse BOA (Fisher) scintillation cocktail. Meiofauna were separated from sediments by isopycnic centrifugation with Ludox-AM (DuPont). Animals were then picked and sorted into four groups using a dissecting microscope: Nematoda, Harpacticoida, Polychaeta, and other taxa. Animals were placed in 7 ml glass scintillation vials with 1 ml of DI water. The meiofauna are then dried at 60 C for 24 hours and then solubilized in 200 l Hemo-De (Fisher) tissue solubilizer for 24 h. 125 Samples were counted by scintillation spectrophotometry in 5 ml of ScintiVerse BOA (Fisher) scintillation cocktail. Liquid scintillation analysis was carried out using a Beckman LS5801 liquid scintillation spectrophotometer (Beckman Instruments Inc., Fullerton, CA, USA). Quenching was corrected for by using external standards. Meiofauna grazing rates are calculated by the following model (Montagna 1984, 1993): G = 2F/t F = M/B Where G is the grazing rate expressed in units of d-1, F is the fraction of label uptake in meiofauna (M), relative to bacteria (B), at time, t (days). M and B are both in units of disintegrations per minute (DPM). Allometric Respiration Estimates Meiofauna mass-dependent respiration rate (R, in units of d-1) was estimated by an allometric law which is described by the following power function: R = aWb where W is the mean weight of the organism ( Biomass/ Abundance), a = 7.4 10-3, and b = -0.24. The constants, a and b, were determined by Mahaut et al. (1995) by regressing published respiration values for metazoan invertebrates and fish in both deepsea and shallow water environments. In their deep-sea regression, the correlation coefficient was -0.94, and the 95% confidence interval of b was -0.263 to -0.228. Thus, the mass-dependent respiration rate (R) is a rate constant relating to the average 126 individual biomass. Therefore, total community respiration, in terms of CO2 mass (mg) released per meter squared per day, can be estimated by multiplying the total biomass ( Bi , mg C m-2) by the mass-dependent rate constant (Ri, d-1 units): CO2 = ( Bi X Ri) The total metabolic organic carbon (OrgC) requirement was estimated by assuming respiration is only 80% of consumption. Consumption (C) is the sum of respiration (R), secondary production (P), and egestion (E), following the equation reviewed by Valiela (1995): C=R+P+E GIS Analysis of Regional Biomass and Respiration GIS-based analyses were performed (ArcView 9.0, ESRI) to further examine spatial trends in the data set. The relative biomass at each station was compared by generating bubble values, where bubble size is relative to the standing stock. Biomass was interpolated to raster using the inverse distance weighted model, with variable search radius, and cell size of 2.8 km2. The interpolated raster was fixed to the extent of Northern Gulf of Mexico bathymetry (courtesy of Bill Bryant, TAMU), ranging from 200 to 3600 meters. The total meiofauna standing stock within the model study area (kg Carbon 1 st. dev.) was calculated by multiplying the average cell value (32 kg C km-2) by the total area (6.6 105 km2), both generated by the model. Meiofauna respiration in the sampling region was also interpolated using the inverse distance weighted model. Total meiofauna respiration within the model study area (kg O2 1 st. dev.) was calculated by multiplying the 127 average cell value (2.3 kg C km-2) by the total area (6.6 105 km2), both generated by the model. RESULTS Meiofauna Biomass Meiofauna biomass is dominated by the two dominant taxa, Nematoda and Harpacticoida (Table 3.1). Mean biomass per station was 273 mg wet weight m-2 (43.3 mg C m-2). Maximum and minimum biomass values of 157.1 and 3.5 mg C m-2 were found at stations S42 and JSSD3, respectively (Table 3.2). A strong linear relationship exists between log meiofauna biomass (R2 = 0.726, P<0.0001) and water depth (Fig. 3.3). A general trend of decreasing biomass per individual with increasing water depth was observed for Nematoda (R2 = 0.125, P = 0.0202, Fig. 3.4), while Harpacticoida had a general increasing trend of biomass per individual with increasing water depth (R2 = 0.167, P = 0.0066, Fig. 3.5). Spatial trends in meiofauna biomass (Fig. 3.6) closely parallel those observed with abundance (Chapter one, Fig. 1.4). However, highest biomass was observed at staion S42 (Table 3.2, Fig. 3.6) High biomass at station S42 reflects proportionally larger nematode individuals at this location (Fig. 3.4). At the four experimental stations (MT3, MT6, S36, S42) meiofauna biomass decreased in a general linear relationship with depth (Table 3.3, Fig. 3.6). Nematodes and harpacticoids account for 95-98% of meiofaunal biomass at the four experimental stations (calculated from Table 3.1). Allometric Respiration Estimates The mean mass-dependent respiration rate of meiofauna was estimated using the average individual biomass at each DGoMB 128 station (Table 3.2, Fig. 3.7A). Mean respiration (d-1) increases as function of depth, in a weak, but significant linear relationship (Fig. 3.7A, R2 = 0.256, P<0.0001). Mean respiration (d-1) increases with decreasing average biomass (Table 3.2). Meiofauna community respiration (mg C m-2 d-1) decreases in a strong linear relationship with depth (Fig. 3.7B, R2 = 0.598, P<0.0001). Variance in community respiration is higher in shallower water (200-1500 meters; Fig. 3.10). Community respiration ranges from a low of 0.3 mg C m-2 d-1 at stations JSSD2-4 to a high of 6.3 mg C m-2 d-1 at S36 and S42. Grazing Experiments Grazing rates by all four meiofauna taxa exhibited significant treatment by station interactions in two-way block analysis of variance (Table 3.4). Polychaetes and others accounted for the majority of label uptake (Fig. 3.8), with harpacticoid grazing only measured at one station (S36), and nematode grazing was measurable at one station, but was sufficiently small that it was not comparable in magnitude to the other taxonomic groups (Fig. 3.8). The average grazing rate for pooled taxonomic groups at each of the experimental stations ranges from a high of 4.6x10-4 d-1 at station S36 to a low of 1.1x10-9 at station MT6. This rate (d-1) reflects a total carbon flux from bacteria to meiofauna ranging from 1.0x10-6 to 7.7x10-1 mg C m-2 d-1, when multiplied by bacterial biomass (mg C m-2), and is between 0.0001 and 9.8% of the theoretical carbon requirement (Table 3.5). Regional Biomass and Community Carbon Requirement Estimates The general relationship of decreasing meiofauna biomass with water depth is seen over the entire northern GOM deep-sea. Variance from this pattern is observed primarily in the 129 northeastern GOM (Fig. 3.6), where highest biomass is often observed at mid-depth stations (S36, S42, MT3). Interpolated biomass estimates over the entire sampling region, and to the extent of the available bathymetry, illustrates the general pattern of decreasing biomass with depth, and also highlights biomass hot spots (Fig. 3.9). Average biomass per 2.8 km2 cell, as calculated by the model, was 2.7x105 1.6x105 :g wet mass m-2. A total of 83,844 cells (each 2.8 km2) were created by the model, equaling a total area of 6.57x105 km2. Total meiofaunal biomass within the region was found to be 2.1x107 1.2x107 kg Carbon (wet mass converted to carbon using conversion factors of 0.25 dry/wet mass, and 0.48 carbon/dry mass, Baguley et al. 2004). Geographic variation in community organic carbon requirement (assuming respiration = 80% of total metabolic requirement) is observed mainly at water depths less than 1500 meters (Fig. 3.10). Stations MT1 and MT3 in the Mississippi Trough, station S36 located in the De Soto Canyon, and station S42 directly above the Florida Escarpment, have the highest community organic carbon requirement. These four stations are located in the northeastern Gulf of Mexico and also have highest biomass. Stations in the western Gulf of Mexico have comparably lower community respiration and lower biomass (Fig. 3.13 and 3.14). The theoretical organic carbon requirement over the northern GOM deep sea study area was estimated to be 1.5x106 ( 8.4x105) mg C km-2 d-1. The ratio of total estimated organic carbon requirement to total estimated biomass is 0.07 (1.5x106 kg C d-1/2.1x107 kg C) in units of d-1; which equals 7% of the community biomass. 130 DISCUSSION Meiofauna are an important, but often ignored, organisms living within deep-sea soft sediments, and although meiofauna biomass often exceeds macrofaunal biomass (Pequegnat et al. 1990), their contribution to whole-community metabolism is largely unknown. Additionally, trophic interactions in the deep sea, and community dynamics that structure deep-sea communities have not been elucidated (Etter and Mullineaux 2001). To gain an understanding of deep-sea meiofauna community function, a carbon budget is needed in which both standing stocks and fluxes are quantified. The purpose of this study was to determine meiofaunal biomass, meiofauna contribution to wholecommunity respiration, and to quantify meiofaunal-bacterial trophic linkages in the northern Gulf of Mexico deep sea. Meiofauna biomass was strongly related to water depth (Fig. 3.3), and highest values occur in the northeastern GOM (Fig. 3.6). As previously discussed for the meiofauna abundance pattern (chapter one), interactions with Mississippi River outflow, the Loop Current, and complex slope topography in the northeast GOM worked to enhance biomass. Highest biomass above the Florida Escarpment at station S42 was due primarily to greater average body size by the nematodes (Fig. 3.4). Meiofauna biomass was found to be roughly equivalent to benthic foraminiferal biomass, but exceeded foram biomass at 5 of 10 stations sampled by Bernhard et al. (submitted manuscript). However, meiofauna biomass was one to two orders of magnitude lower than bacterial biomass (Deming, unpublished DGoMB data). Interpolation of point data over the entire 131 sampling region (approx. 2/3 of the GOM deep-sea) gave a conservative estimate of the total meiofaunal standing stock of 2.1x107 kg C (Fig. 3.13). The model could not accurately estimate biomass of the Florida and Campeche escarpments due to a lack of sampling over most of this area, thus regional biomass estimates are conservative. However, if converted to units of energy 2.1x107 kg C could provide roughly enough energy to power one million houses for a month. Meiofauna respiration over the sampling area (Fig. 3.14) is responsible for processing approximately 1.5x106 kg C d-1, or 7% of the total biomass. Observed meiofauna grazing rates on heterotrophic aerobic bacteria (Table 3.5) were extremely low, and likely due to the lack of measured grazing by nematodes and harpacticoids (Fig. 3.7), which account for 95-98% of meiofaunal biomass at the four experimental stations (Table 3.1). Overall, grazing rates on bacterial carbon ranged from 7.7x10-1 to 1.0x10-6 mg C m-2 d-1 (Table 3.5), corresponding to removal of 1.1x10-7 to 5.0x10-2 % of the bacterial standing stock, which equals 1.0x10-4 to 9.8 % of the meiofauna metabolic requirement (Table 3.5) per day. In shallow water systems meiofauna consume approximately 1% of microfaunal standing stocks on a daily basis (Montagna 1984, 1995). Grazing rates measured here were at least one an a half orders of magnitude less than rates observed in shallow water. Additionally, the huge variability in the measurements suggest both poor precision and accuracy. The radiotracer grazing method (Montagna 1993) is limited and has large sources of variability (Montagna et al. 1995), which are exacerbated when attempting 132 shipboard incubations for deep-sea samples. The single-label approach limits the types of prey items that can be labeled (aerobic heterotrophic bacteria), although 3HThymidine appears to be the most robust choice for labeling bacteria due to incorporation via DNA synthesis (Montagna 1993). However, variability in natural communities can affect the types of food eaten and rates of meiofauna grazing (Montagna et al. 1995). Species specific (Carman and Thistle 1985) and ontogenetic feeding preferences exist (Decho and Fleeger 1988), but error associated with label uptake by epicuticular bacteria can be a major source of experimental error (Carman 1990). However, the most likely sources of error within the current study arise from depressurization and subsequent re-pressurization for incubation, as well as thermal shock during core extrusion and pre-incubation processing. Although meiofauna (mainly nematodes and harpacticoids) appeared to survive after core extrusion (Baguley, personal observation), animals may not have survived rapid re-pressurization for incubation at in situ conditions, or may have been sufficiently stressed that natural grazing processes were disrupted. Additionally, label loss occurs unless samples are analyzed immediately after sampling (Moens et al. 1999). Even with ideal sample processing (i.e., preservation using ice cold formalin, immediate freezing, and analyzing samples less than 2 hours after thawing), average label loss of 50% still occurs (Moens et al. 1999). However, even if this adjustment was made, it does not account for the order of magnitude, or greater, discrepancy in measured grazing rates of this study. Although measured grazing rates were unreasonable, it was possible to estimate 133 a whole-community metabolic budget using allometry. Allometric respiration measurements require only abundance and biomass to determine mean respiration rate (Mahaut et al. 1995). Whole-community respiration (Smith 1978a; Reimers and Smith 1986; Smith 1992; Smith et al. 1997; Drazen et al 1998; Smith et al. 2001; Smith et al. 2002), and respiration of various macro- and megafuanal organisms (Smith and Hessler 1974; Smith 1978b; Smith and Laver 1981; Smith 1983; Childress et al. 1990), have been documented in previous deep-sea investigations but the contribution of meiofauna has only been sparsely investigated (Shirayama 1992; Mahaut et al. 1995). A general allometric equation for the calculation of weight-dependent respiration of deep-sea organisms (Mahaut et al. 1995) was used to calculate meiofaunal respiration at all DGoMB stations (Table 3.2). However, the total metabolic budget for all consumers includes secondary production and egestion (Valiela 1995, and references therein), yet these processes are often ignored in the estimation of deep-sea whole-community metabolism. Respiration generally accounts for 40-80% of total consumption, with 030% of consumed energy being allocated to secondary production (Valiela 1995, and references therein). Therefore, as a conservative estimate, respiration was assumed to account for 80% of the total metabolic budget (Table 3.2). The mean meiofaunal respiration rate increases with increasing water depth, reflecting overall decrease in animal size with depth (Fig. 3.7A), although harpacticoids show an opposing trend (Fig. 3.5) (Baguley et al. 2004). Meiofauna community respiration decreases with depth (Fig. 3.7B) reflecting a decrease in overall biomass. 134 Geographic variation in community respiration, and therefore the overall metabolic budget, is greatest at depths less than 2000 meters (Figs. 3.8B, 3.11). Highest respiration is found at stations near Mississippi River outflow, and where there is an interaction between the Loop Current and canyon (MT1-3, S36) and escarpment (S42) features (see chapter one). A comparison of experimentally measured consumption versus allometrically-derived requirements (Table 3.5), indicates poor agreement by measured grazing, confirming poor results from the grazing study (Table 3.5). Alternatively, it is possible that there is a lack of trophic linkage between bacteria and meiofauna, and that deep-sea meiofauna depend heavily on surfaced-derived detritus. Compared to total bacterial biomass, meiofauna only require approximately 0.1 to 0.7% of the bacterial standing stock to meet their theoretical metabolic requirements (Table 3.5). But, grazing rates were, at most, 10% of the theoretical metabolic requirement (Table 3.5). If bacteria are a primary food source for meiofauna in deep-sea sediments, then the standing stock does not appear to be food limited. Although multiple feeding types exist within the meiofauna (Hicks and Coull 1983; Jensen 1987), most metazoan meiofauna selectively feed on microalgae, bacteria, and protists (Montagna 1995). In deep-sea sediments, microalgal cells are in the form of partially or highly remineralized phytodetritus, derived from surface primary production. It is likely that deep-sea meiofauna are largely dependent upon phytodetrital flux from surface waters, and are therefore food-limited with respect to overlying water column primary production, as 135 suggested by modeling studies (Rowe 1996), bathymetric gradients of abundance and biomass (Soltwedel 2000), and strong dependence on POM flux as outlined in chapter one of this dissertation. Shallow-water meiofauna have been shown to increase feeding rates in response to increased microphytobenthos stocks (Montagna et al. 1995). Deep-water communities likely exhibit similar functional responses (Taghon and Green 1990) to seasonal phytodetrital pulses, as suggested by trophic structure models (Rowe 1996). Deep-sea communities do respond to seasonally varying fluxes of organic matter (see Gooday 2002 for a thorough review). Meiofauna may be slow to respond to fresh phytodetritus inputs compared to single-celled Foraminifera or bacteria (Gooday et al. 1996; Gooday 2002), with lagged increases in standing stocks due to slower rates of somatic growth and high energetic costs of gamete production (Graf 1992; Eckelbarger 1994; Gooday et al. 1996). However, a recent shallow water (20 m) investigation on the North Sea continental shelf demonstrated temporal changes in abundance, biomass, and diversity of nematodes, in response to the spring bloom and flux of fresh phytodetrital cells to the sea floor (Vanaverbeke et al. 2004). In comparison to total benthic community respiration (CO2, converted from sediment community oxygen consumption, G. Rowe, unpublished DGoMB data), meiofauna are responsible for approximately 10-25 % of the total benthic community CO2 flux (Table 3.6). Total global oxygen utilization in the deep sea has been estimated to be 1.2x1014 mol O2 yr-1 (Jahnke et al. 1996), which equals approximately 1.22x1015 136 g C yr-1 (converted to units of carbon using a respiratory quotient of 0.85, and stoichometric conversion of 12g C per mole O2). If meiofauna are responsible for 10 % of this flux (conservative) then they are responsible for processing 1.2x1014 g C yr-1, and are therefore a globally significant component of the carbon cycle. Many questions remain unanswered, particularly with respect to deep-sea trophic interactions. A conceptual model of meiofaunal community trophic interactions in the deep-sea (adapted from Rowe 1996) reveals just a small portion of the complexity within this system (Fig. 3.11). Although meiofaunal standing stocks can now be more easily and accurately estimated (Baguley et al. 2004), the relative importance of bacterial, protist, phytodetrital, or recycled detrital carbon in sustaining meiofaunal metabolic demands remains enigmatic. However, the total theoretical consumption (metabolic requirement as a function of respiration) can be estimated based on accurate estimates of meiofaunal biomass. Although specific trophic interactions were not elucidated here, changes in community structure have been demonstrated with temporal changes in quality and quantity of food supply to the benthos (Vanaverbeke et al. 2004). Areas of high POM flux (e.g., the Mississippi Trough region) likely have higher proportions of selective deposit feeding (pick specific bacterial or detrital cells) and epistrate feeding (suck the juice out of cells) nematodes, as observed during spring bloom conditions by Vanaverbeke et al. (2004). Similar feeding types (Marcotte 1977) would be expected among the harpacticoid copepods (point feeders = selective epistrate feeders; line feeders = selective deposit feeders). Increased dominance by two of the four general feeding 137 types (see Marcotte 1977; Jensen 1987) in areas of high POM flux would be consistent with low observed average phylogenetic diversity (functional diversity) at these stations (chapter two of this dissertation). Conversely, high average phylogenetic diversity observed at deeper stations suggests more functional diversity, and therefore more complicated trophic interactions with increasing depth. Future investigation of deep-sea meiofauna community function (respiration, trophic interactions, etc.) should seek to elucidate specific meiofaunal-microbial trophic interactions, or quantify the relative importance of microbial versus phytodetrital or recycled detrital food sources (Fig. 3.11). Analysis of natural carbon and nitrogen stable isotopes may be useful in uncovering these interactions. Recent developments have allowed for increased sensitivity in stable isotope analysis of meiofaunal samples (Carman and Fry 2002). Grazing studies may also prove useful, specifically stable isotope enrichments (Carman and Fry 2002). Radioisotope studies (Montagna 1993) may still have utility in uncovering bacterial-meiofaunal trophic interactions, or uptake of pre-labeled (14C - HCO3-) phytodetrital carbon, but I must stress that grazing studies should be done in situ using a remotely operated vehicle (ROV) or deep sea research vessel (DSRV). Understanding temporal dynamics associated with seasonal POM flux, trophic interactions, etc., is essential and must be further investigated (Smith et al. 2002). CONCLUSION A carbon budget was created for 51 stations in the northern Gulf of Mexico deep sea by determination of standing stocks and estimates of total metabolic requirement, as 138 a function of respired carbon. Empirical measurements of meiofauna grazing rates on bacteria were unsuccessful. Biomass, respiration, and grazing all decreased with increasing water depth. Regional variation in biomass and respiration indicated highest values in the northeastern Gulf of Mexico, where the Loop Current interacts with Mississippi River outflow and topo-geographic features such as the Mississippi Trough, DeSoto Canyon, and Florida Escarpment. Estimates of total meiofaunal biomass and respiration in the northern Gulf of Mexico indicate that meiofauna require 7% of their own biomass on a daily basis (which equals, at most, 0.7% of the bacterial standing stock) to meet their metabolic needs. Meiofauna account for 10-25 % of total sediment community respiration and are therefore a significant component of the global carbon cycle. Lack of food limitation with respect to bacterial carbon, and may suggest preferential reliance upon other carbon sources, such as surface-derived phytodetritus. 139 Table 3.1: Biomass contribution of the major taxonomic groups to total meiofaunal biomass (mg C m-2) at each DGoMB station. NEMA = Nematoda, HARP = Harpacticoida, NAUP = Harpacticoida nauplii, POLY = Polychaeta, OSTR = Ostracoda, CYCL = Cyclopoida, TANA = Tanaidacea, ISOP = Isopoda, KINO = Kinorhyncha. STA AC1 B1 B2 B3 BH C1 C12 C14 C4 C7 GKF HIPRO JSSD1 JSSD2 JSSD3 JSSD4 JSSD5 MT1 MT2 MT3 MT4 MT5 MT6 NB2 NB3 NB4 NB5 RW1 RW2 RW3 RW4 RW5 RW6 NEMA 17.52 23.97 12.50 11.88 23.31 89.37 12.83 14.30 60.26 69.86 3.20 9.24 1.76 1.90 1.51 1.28 3.51 110.02 45.12 87.39 33.69 24.92 9.99 15.55 17.31 10.23 10.63 66.47 23.33 36.20 37.36 24.12 16.95 HARP 6.83 8.60 3.52 5.41 20.80 8.04 7.27 7.45 9.12 10.97 2.87 8.88 2.32 1.57 1.54 1.87 2.71 6.44 8.22 15.57 9.46 4.38 1.37 6.24 8.12 6.51 9.68 9.47 7.96 7.45 7.74 8.61 10.46 NAUP 0.18 0.36 0.34 0.55 0.98 0.64 0.54 0.32 0.79 0.68 0.13 0.44 0.30 0.26 0.13 0.22 0.25 0.56 0.82 0.76 0.51 0.33 0.18 0.43 0.91 0.46 0.35 1.06 0.56 0.53 0.53 0.35 0.28 POLY 0.20 0.22 0.25 0.28 1.42 1.27 0.17 0.17 0.36 0.81 0.10 1.71 0.13 0.14 0.13 0.13 0.13 1.32 3.07 1.43 0.75 0.42 0.17 0.36 0.42 0.21 0.15 1.04 0.53 0.59 0.61 0.29 0.25 OSTR 0.08 0.31 0.12 0.04 0.48 0.24 0.10 0.12 0.44 0.33 0.00 0.25 0.00 0.02 0.06 0.07 0.15 0.74 0.33 0.75 0.21 0.23 0.03 0.17 0.04 0.04 0.15 0.50 0.30 0.38 0.02 0.12 0.06 CYCL 0.09 0.05 0.00 0.00 0.16 0.24 0.04 0.02 0.07 0.09 0.00 0.37 0.00 0.00 0.00 0.00 0.00 0.40 0.94 0.34 0.16 0.09 0.04 0.02 0.00 0.02 0.02 0.39 0.24 0.10 0.19 0.05 0.02 TANA 0.30 0.10 0.20 0.22 0.50 0.38 0.13 0.38 0.30 0.45 0.00 0.30 0.00 0.00 0.00 0.00 0.20 0.29 1.70 0.60 0.60 0.50 0.05 0.20 0.20 0.30 0.30 0.10 0.56 0.45 0.70 0.10 0.20 ISOP 0.00 0.07 0.07 0.00 0.40 0.00 0.00 0.00 0.13 0.00 0.00 0.07 0.00 0.13 0.07 0.00 0.00 0.05 0.20 0.20 0.07 0.00 0.03 0.35 0.00 0.07 0.07 0.20 0.07 0.00 0.00 0.13 0.00 KINO 0.01 0.02 0.00 0.01 0.02 0.04 0.01 0.01 0.01 0.02 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.02 0.05 0.04 0.01 0.01 0.00 0.01 0.01 0.00 0.01 0.03 0.02 0.01 0.03 0.01 0.01 140 STA S35 S36 S37 S38 S39 S40 S41 S42 S43 S44 W1 W2 W3 W4 W5 W6 WC12 WC5 NEMA 49.83 98.41 20.51 9.70 6.37 9.82 23.79 141.54 36.72 29.38 46.80 41.97 58.87 16.81 10.23 15.95 61.48 81.55 HARP 14.81 11.48 10.73 6.46 3.10 5.95 5.33 12.92 6.98 7.48 17.31 10.77 10.97 8.73 4.50 5.94 14.56 13.25 NAUP 1.34 0.89 0.71 0.48 0.21 0.31 0.27 0.68 0.37 0.69 1.16 0.62 0.95 0.56 0.19 0.27 0.57 1.04 POLY 1.59 0.83 0.53 0.38 0.25 0.35 0.21 0.72 0.95 1.83 1.19 0.62 0.55 0.55 0.09 0.28 0.53 1.00 OSTR 0.67 0.43 0.33 0.15 0.04 0.15 0.10 0.17 0.19 0.38 0.33 0.13 0.33 0.15 0.04 0.02 0.37 0.15 CYCL 0.58 0.09 0.16 0.11 0.05 0.19 0.04 0.22 0.30 0.60 0.37 0.32 0.35 0.18 0.00 0.05 0.02 0.41 TANA 1.00 0.70 0.40 0.20 0.20 0.30 0.22 0.30 0.50 0.50 0.50 1.00 0.50 0.40 0.30 0.10 0.80 0.80 ISOP 0.47 0.20 0.20 0.00 0.07 0.07 0.04 0.13 0.07 0.00 0.07 0.07 0.07 0.00 0.00 0.07 0.07 0.20 KINO 0.05 0.02 0.01 0.01 0.00 0.00 0.00 0.02 0.03 0.02 0.04 0.01 0.02 0.00 0.01 0.01 0.00 0.03 141 Table 3.2: Allometric estimations of the mass-dependent meiofauna respiration rate (R, in d-1 units) and meiofauna community respiration (CO2, mg C m-2 d-1) and total organic carbon demand (OrgC, mg C m-2 d-1). Mass-dependent respiration was calculated using an allometric rate law (sensu Mahaut et al. 1995) which is dependent upon the ratio (W) of biomass (B, mg C m-2 d-1) to abundance (A, N m-2). Respiration (CO2, mg C m-2 d-1) is the product of the mass-dependent rate (R) and the total biomass (B), and total carbon demand (OrgC) was calculated under the assumption that respiration equals 80% of the total metabolic budget (see discussion). Sta AC1 B1 B2 B3 BH C1 C12 C14 C4 C7 GKF HIPRO JSSD1 JSSD2 JSSD3 JSSD4 JSSD5 MT1 MT2 MT3 MT4 MT5 MT6 NB2 NB3 NB4 NB5 RW1 RW2 Lat. 26.3936 27.2025 26.5500 26.1644 27.7800 28.0598 26.3797 26.9382 27.4532 27.7304 27.0000 28.5500 25.0000 23.5000 24.7500 24.2500 25.5000 28.5411 28.4479 28.2215 27.8276 27.3328 27.0016 27.1348 26.5580 26.2468 26.2454 27.5001 27.2540 Long. -94.5731 -91.4052 -92.2151 -91.7351 -91.5000 -90.2499 -89.2403 -89.5725 -89.7631 -89.9820 -90.2500 -88.5800 -92.0000 -92.0000 -90.7500 -85.5000 -88.2500 -89.8250 -89.6719 -89.4940 -89.1661 -88.6561 -87.9991 -92.0001 -91.8226 -92.3923 -91.2099 -96.0028 -95.7468 Depth 2440 2253 2635 2600 545 336 2924 2495 1463 1066 2460 1565 3545 3725 3635 3400 3350 482 677 990 1401 2267 2743 1530 1875 2020 2065 212 950 B 25.2 33.8 17.1 18.5 48.4 100.4 21.2 22.8 71.6 83.3 6.4 21.8 4.5 4.1 3.5 3.6 7.0 119.9 60.9 107.4 45.5 31.0 12.0 23.4 27.0 17.9 21.4 79.6 33.7 A 129974 157417 139907 155817 407852 369129 138792 146578 273585 542119 84348 343118 87547 87295 60441 63451 135698 945657 535216 885995 246058 128964 155312 168276 165245 148409 117263 411809 219457 W 1.9E-04 2.1E-04 1.2E-04 1.2E-04 1.2E-04 2.7E-04 1.5E-04 1.6E-04 2.6E-04 1.5E-04 7.6E-05 6.3E-05 1.3E-05 4.6E-05 4.0E-05 5.9E-05 1.1E-04 8.8E-04 6.4E-05 2.0E-04 5.1E-05 1.3E-04 9.3E-05 1.5E-04 1.6E-04 1.1E-04 1.4E-04 6.8E-04 8.2E-05 R 0.06 0.06 0.06 0.06 0.06 0.05 0.06 0.06 0.05 0.06 0.07 0.08 0.11 0.08 0.08 0.08 0.07 0.04 0.08 0.06 0.08 0.06 0.07 0.06 0.06 0.07 0.06 0.04 0.07 CO2 1.5 1.9 1.1 1.2 3.1 5.3 1.3 1.4 3.8 5.1 0.5 1.6 0.5 0.3 0.3 0.3 0.5 4.8 4.6 6.1 3.6 2.0 0.8 1.4 1.6 1.2 1.3 3.4 2.4 OrgC 1.8 2.4 1.4 1.5 3.9 6.7 1.6 1.7 4.8 6.3 0.6 2.0 0.6 0.4 0.4 0.3 0.6 6.0 5.7 7.7 4.5 2.5 1.0 1.8 2.0 1.5 1.7 4.2 3.0 142 Sta RW3 RW4 RW5 RW6 S35 S36 S37 S38 S39 S40 S41 S42 S43 S44 W1 W2 W3 W4 W5 W6 WC12 WC5 Lat. 27.0084 26.7514 26.5075 25.9973 29.3352 28.9185 28.5536 28.2799 27.4837 27.8395 28.0136 28.2510 28.5029 28.7500 27.5772 27.4139 27.1724 26.7308 26.2678 26.0028 27.3232 27.7759 Long. -95.4924 -95.2502 -94.9967 -94.4956 -87.0464 -87.6722 -87.7668 -87.3276 -86.9998 -86.7514 -86.5733 -86.4193 -86.0768 -85.7477 -93.5510 -93.3403 -93.3233 -93.3197 -93.3327 -93.3203 -91.5558 -91.7657 Depth 1340 1575 1620 3000 668 1826 2387 2627 3000 2972 2974 763 362 212 420 625 875 1460 2750 3150 1175 348 B 45.9 47.4 33.8 28.3 70.5 113.2 33.6 17.5 10.5 17.3 30.2 157.1 46.4 41.1 68.7 55.7 72.7 27.5 15.4 22.7 78.5 98.9 A 248752 232842 170633 144453 501629 799963 291179 157164 83170 99501 181408 492537 276279 318516 387228 263315 262642 187806 104552 124166 218447 412061 W 2.1E-04 1.9E-04 1.5E-04 1.7E-04 4.9E-04 2.3E-04 4.2E-05 6.0E-05 6.7E-05 2.1E-04 3.0E-04 8.7E-04 9.4E-05 1.5E-04 2.2E-04 1.4E-04 2.8E-04 1.0E-04 8.2E-05 2.2E-04 6.3E-04 4.5E-04 R 0.06 0.06 0.06 0.06 0.05 0.06 0.08 0.08 0.07 0.06 0.05 0.04 0.07 0.06 0.06 0.06 0.05 0.07 0.07 0.06 0.04 0.05 CO2 2.6 2.7 2.1 1.7 3.3 6.3 2.8 1.3 0.8 1.0 1.6 6.3 3.2 2.5 3.9 3.4 3.8 1.8 1.1 1.3 3.4 4.6 OrgC 3.2 3.4 2.6 2.1 4.1 7.9 3.5 1.7 1.0 1.2 2.0 7.9 4.0 3.2 4.8 4.3 4.8 2.3 1.4 1.6 4.3 5.8 143 Table 3.3: Mean meiofaunal (MB) and bacterial (BB) biomass for pooled replicates and pooled taxonomic groups at the four experimental stations. Bacterial biomass courtesy of Jody Deming, University of Washington (unpublished DGoMB data). Station Depth (m) 768 985 1838 2737 Latitude (N) 28.2565 28.2170 28.9118 26.9956 Longitude (W) 86.4284 89.5106 87.6773 88.0090 MB (mg C m-2) 157 107 113 12 BB (mg C m-2) 1180 2320 1680 920 S42 MT3 S36 MT6 144 Table 3.4: ANOVA results of the test for differences in grazing rate between treatments (experimental vs. control) and stations, separated by taxonomic group. Significant treatment by station interactions were observed for all taxa ( = 0.05). Polychaetes were the only taxa to show consistent grazing, and overall grazing rates for all taxa were low (refer to Table 3.5 & Fig. 3.9 below). Taxa Nematoda Source Treatment Station TRT *STA DF 1 3 3 SS 16.48 49.43 49.43 MS 16.48 16.48 16.48 F value 3.43 3.43 3.43 P 0.0825 0.0425 0.0425 Harpacticoida Treatment Station TRT *STA 1 3 3 119.50 134.30 134.30 119.50 44.77 44.77 25.86 9.69 9.69 0.0001 0.0007 0.0007 Polychaeta Treatment Station TRT *STA 1 3 3 511.71 161.28 161.28 511.71 53.76 53.76 35.16 3.69 3.69 <0.0001 0.0341 0.0341 Others Treatment Station TRT *STA 1 3 3 120.47 233.87 175.91 120.47 77.96 58.64 10.68 6.91 5.20 0.0048 0.0034 0.0107 145 Table 3.5: Measured meiofaunal grazing on bacteria is only 9.8 to 0.0001% of their theoretical required consumption. Measured meiofauna grazing rate (GR, d-1 units), bacterial biomsss (BB, mg C m-2 d-1), measured grazed bacterial carbon (GC = GRxBB, mg C m-2 d-1), allometric carbon requirement (OrgC, mg C m-2 d-1), and the ratio of measured grazing to the allometric requirement (GC/OrgC), expressed as a percent. STA S42 MT3 S36 MT6 GR 7.8E-05 1.1E-06 4.6E-04 1.1E-09 BB 1180 2320 1680 920 GC 9.2E-02 2.6E-03 7.7E-01 1.0E-06 OrgC 7.9 7.7 7.9 1.0 GC/OrgC (%) 1.2 0.03 9.8 0.0001 146 Table 3.6: Comparison of whole community respiration (CR, mg C m-2 d-1) to meiofauna allometric respiration estimates (MR, mg C m-2 d-1). Meiofauna account for 10-25% of whole community respiration. Note: whole community respiration (mg C m-2 d-1), converted from sediment community oxygen consumption (SCOC) as measured by the Benthic Lander (Gil Rowe, unpublished DGoMB data). SCOC (mmol O2 m-2 d-1) was converted to carbon using a respiratory quotient of 0.85 and stoichiometric conversion factor of 12 mg C/mmol O2. Station S42 S36 MT1 MT3 C7 JSSD1 JSSD4 Depth 763 1826 482 990 1066 3545 3400 CR 41 26 40 28 49 4 2.6 MR 6.3 6.3 4.8 6.1 5.1 0.5 0.3 %MR 15.4 24.2 12.0 21.8 10.4 12.5 11.5 147 148 Figure 3.1: DGoMB station locations in the northern Gulf of Mexico deep-sea where meiofauna community biomass (sensu Baguley et al. 2004) and allometric respiration (sensu Mahaut et al. 1995) were estimated. Figure 3.2: Process station locations for 2001 cruise. MT1 = 482 m; S42 = 763 m; S36 = 1826 m; MT6 = 2643 m. 149 6.2 Log meiofauna biomass (ug Wet mass/m ) S42 6.0 5.8 5.6 5.4 5.2 5.0 4.8 MT1 S36 MT3 C1 WC5 C7 RW1 WC12 C4 W1 S35 W3 MT2 W2 S43 BH RW3 RW4 MT4 S44 RW5 RW2 W4 NB3 NB2 HIPRO R2 = 0.700 P<0.0001 2 B1S37 MT5 S41 RW6 AC1 C14 NB5 C12 W6 B3 NB4 S38 S40 B2 W5 MT6 S39 JSSD5 JSSD1 JSSD2 JSSD4 JSSD3 GKF 4.6 4.4 4.2 0 1000 2000 3000 4000 Water depth (m) Figure 3.3: Meiofauna biomass ( g wet wt/m2) versus water depth at all DGoMB station. 150 1.4 Nematoda S42 1.2 WC12 W3 R2 = 0.125 P = 0.0202 Avg. wet weight ind (ug) -1 1.0 C1 WC5 C4 MT5 W2 RW1 0.8 0.6 W1 S43 C7 RW2 S35 MT2 MT3 0.4 S44 MT1 RW4 B1 S41 RW3 RW5 W6 MT4 AC1 S36 S40 NB3 RW6 C12 NB5 C14 W5 S39 W4 NB2 B2 B3 NB4 S37 MT6 S38 0.2 0 1000 2000 3000 Water depth (m) Figure 3.4: Average nematode wet weight ( g) per individual versus depth. 151 6 Harpacticoida 5 R2 = 0.167 P = 0.0066 RW6 WC12 B1 NB5 Avg. wet weight ind (ug) -1 4 S42 RW4 RW5 NB3 W4 RW3 S36 C4 MT4 NB2 NB4 C14 AC1 MT5 S38W5 S40 S41 C12 W6 3 W1 W2 S43 C1 S44 RW1 WC5 C7 RW2 W3 S35 MT2 MT3 S39 S37 B3 B2 MT6 2 MT1 1 0 1000 2000 3000 Water depth (m) Figure 3.5: Average harpacticoid wet weight ( g) per individual versus depth. 152 1e-3 MT3 MT6 S36 S42 Avg. meiofauna grazing rate (d ) -1 1e-4 1e-5 1e-6 1e-7 Harp Nematoda Others Poly Figure 3.6: Meiofauna grazing rates by taxonomic group for the four experimental stations. 153 Meiofauna mass-dependent respiration rate (d-1) 0.12 R2 = 0.256 P<0.0001 0.10 0.08 0.06 0.04 0.02 0 1000 2000 3000 4000 Water depth (m) Meiofauna community respiration (mg C m d ) -1 7 6 5 4 3 2 1 0 -2 R2 = 0.598 P<0.0001 0 1000 2000 3000 4000 Water depth (m) Figu re 3.7: A) Meiofauna mass-dependent respiration rate (d-1) and B) meiofauna community respiration (mg C m-2 d-1) at each of the 51 DGoMB stations in the northern Gulf of Mexico deep-sea. 154 155 Figure 3.8: Spatial comparison of relative meiofauna biomass ( g wet wt. m-2), where bubbles size is proportional to biomass. 156 Figure 3.9: Spatial interpolation of meiofauna biomass (mg wet mass m-2) in the northern Gulf of Mexico deep sea. 157 Figure 3.10: Spatial interpolation of the meiofaunal organic carbon requirement (mg C m-2 d-1), assuming respiration equals 80% of total metabolism. Recycled Detritus Protista ProtistGrazing Scavenging Respiration Meiofauna BacteriaGrazing Bacteria Predation Epistrate feeding and grazing Phytodetritus Figure 3.11: Conceptual model of complex meiofaunal trophic interactions with microfauna (bacteria and protists) and two different detrital pools (phytodetritus, and recycled detritus). Not shown are predatory meiofauna (prey upon other meiofauna) or meiofaunal deposit feeders that ingest whole sediment particles and obtain carbon from one or more of the above standing stocks. Carbon is lost via respiration transfer to higher trophic levels via predation (cloud symbols). 158 APPENDIX (HARPACTICOIDA SPECIES LIST) The species list is separated by family (in bold text), and includes the total number of individuals (N) identified per species (S). Species designated as sp., aff., or not are not described in the scientific literature. The abbreviations aff. and not denote that the individual most closely resembles, but is not, that species. For example Cervinia aff. bradyi most closely resembles the described species Cervinia bradyi, but is actually a new species within the genus Cervinia. S Longipedidae Longipedia sp. Canuellidae Ellucana sp. Canuella sp. Ceratonus crownius Intersunaristes sp. Cerviniidae Cervinia aff. bradyi Cervinia aff. synarthra Cerviniopsis sp. Cerviniopsis breviseta Cerviniopsis inermis Cerviniopsis aff.breviseta Cerviniopsis aff.langi Cerviniopsis aff.longicaudata N 3 1 3 1 1 5 1 5 1 1 1 1 7 159 S Cerviniopsis aff.stylicaudata Cerviniella sp.1 Cerviniella sp.2 Cerviniella aff. hamata Cerviniella aff.langarderei Cerviniella aff.langi Cerviniella aff.talpa Hemicervinia sp. Pontostratiotes sp. Pontostratiotes aff. denticalatus Pontostratiotes aff. horida Pontostrariotes aff. pori Pontostratiotes texanus Neopontostratiotes typica Tonpostratiotes sp. Ectinosomatidae Ectinosoma aff. compressum Ectinosoma aff. califormicum Ectinosoma aff. litorale Ectinosoma aff melaniceps Ectinosoma aff. normani Ectinosoma aff. reductum Ectinosoma aff.tenerum Ectinosoma aff. tenuipes Ectinosoma sp. Halectinosoma aff. anglifrons Halectinosoma aff.arenical Halectinosoma aff.armiferum Halectinosoma aff. barroisis Halectinosoma aff. curticorne Halectinosoma aff. chrystalli Halectinosoma aff.distinctum Halectinosoma aff.elongatum Halectinosoma aff. finmarchicum Halectinosoma aff. gothiceps Halectinosoma aff.herdmani Halectinosoma longisegmenta N 2 2 1 2 1 6 3 2 2 1 3 2 2 1 1 4 1 1 6 2 1 1 2 1 43 2 17 11 8 13 5 4 4 86 52 1 160 S Halectinosoma aff.littorale Halectinosoma aff.mixtum Halectinosoma aff. neglectum Halectinosoma aff propinquum Halectinosoma aff propinquum II Halectinosoma aff.proximum Halectinosoma aff.sarsi Halectinosoma aff.tenerum Halectinosoma aff.conccinum Halcetinosoma hyalinus Halectinosoma longiseta Halectinosoma oligosegmenta Halectinosoma oligoseta Halectinosoma quatra Halectinosoma mexicana Halectinosoma tenuis Halectinosoma sp. Halectinosomella texana Ectinosomella brevicauda Ectinosomella sp. Pseudoectinosoma sp. Bradya aff. congenera Bradya longicauda Bradya longispina Bradya aff.macrochaeta Bradya magnus Bradya oligochaeta Bradya aff.proxima Bradya aff.scotti Bradya aff.dilatata Bradya aff.pymaea Bradya aff. simulans Bradya aff.typica Bradya aff.furcata Bradya curvatus Pseudobradya sp. Pseudobradya aff. acuta Pseudobradya aff.ambigua N 1 1 5 18 3 1 2 23 2 1 2 1 3 1 1 14 2 1 1 1 19 48 2 1 6 6 4 9 3 9 1 3 1 6 3 1 3 1 161 S Pseudobradya aff.parvula Pseudobradya aff.maxima Pseudobradya aff.pygmaea Pseudobradya aff. pelogones Pseudobradya aff.robusta Sigmatidium sp.1 Sigmatidium sp.2 Sigmatidium aff.difficile Sigmatidium texana Halophytophilus sp. Halophytophilus aff.fusiformis Neobradyidae Neobradya sp. Marsteinia sp.1 Marsteinia sp.2 Marsteinia aff.similis Marsteinia texana Marsteinia aff typica Marsteinia oligosegmenta Marsteinia oligoseta Marsteina longicauda Marsteinia mexicana Marsteinia texana Antarcticobradya sp. Paraneobradya sp. Texasneobradya typica Mexiconeobradya triangulata Euterpinidae Euterpina aff.acutifrons Darcythompsonidae Paradarcythompsonia texana Kristensenia texana Kristensenia triangulata Leptocaris aff.minutus N 2 1 2 7 10 5 4 3 2 1 4 1 12 1 22 2 4 1 1 2 1 2 3 2 1 3 1 1 1 1 1 162 S Tisbidae Tisbe aff. compacta Tisbe aff. graciloides Tisbe aff.finmarchica Tisbe sp. Tisbintra elongata Zosime abberanta Zosime depressa Zosime aff.atlantica Zosime aff. bathyalis Zosime aff.bathybia Zosime aff.bergensis Zosime aff.erythraea Zosime aff.gisleni Zosime aff. mediterranea Zosime not.mediterranea Zosime aff major Zosime aff.major(large form) Zosime aff.paramajor Zosime aff. typica Zosime aff. incrassata Zosime not incrassata Zosime aff incrassta II Zosime longicauda Zosime aff. valida Zosime mexicana Zosime texana Pseudozosime sp.1 Pseudozosime sp.2 Pseudozosime sp.3 Pseudozosime texana Pseudozosime trisetosa Tachidiella aff.minuta Tachidiella aff. kimi Tachidiella aff.parva Tachidiella oligoseta Tachidiella texana Plesiotachidiella quatra N 1 1 2 1 1 1 1 10 8 39 27 10 35 64 3 24 2 20 27 57 3 2 4 1 13 1 5 2 2 1 7 9 2 1 2 1 2 163 S Idyanthe sp.1 Idyanthe sp.2 Idyanthe aff. dilatata Idyanthe aff.pusilla Idyanthe aff.tenella Idyella sp. Idyella aff. major Idyella aff.pallidula Idyella aff.exigua Idyellopsis sp. Idyellopsis trisetosa Idyellopsis minuta Idyellopsis aff.typica Idyellopsis aberrans Idyellopsis texana Neozosime bisetosa Neozosime bisegmenta Neozosime longicauda Neozosime trisetosa Neozosime sp. Peresime brevifurca Peresime aff.reducta Peresime aff.abyssalis Peresime trisetosa Parazosime longicauda Parazosime sp. Tachidiopsis aff.typica Tachidiopsis aff. bozici Tachidiopsis not.bozici Tachidiopsis aff.cyclopoides Tachidiopsis aff.laubieri Tachidiopsis aff.parasimilis Tachidiopsis aff.similis Tachidiopsis reducta Tachidiopsis texanus Danielssenidae Daniellsenia mexicana N 6 1 1 2 4 4 4 5 2 13 2 2 11 1 51 95 2 18 56 13 15 19 1 3 8 5 5 51 1 14 23 13 4 1 1 7 164 S Daniellsenia aff. minuta Daniellsenia aff.reducta Daniellsenia aff.quadriseta Daniellsenia aff.spinipes Daniellsenia aff.typica Jonesiella sp. Jonesiella aff. fusiformis Pseudodanielssenia noendopoda Paradanielssenia aff.biclavata Paradanielssenia aff.kathleenae Paradanielssenia aff.kunzi Telopsammis texana Telopsammis sp. Cylindromexicana texana Psammis aff.longipes Micropsammis texanus Mucrosenia texana Texasdanielssenia typica Neoparanannopus longicauda Thalestridae Dactylopodopsis aff.dilatata Dactylopodella aff. clypeata Dactylopodella aff. vervoorti Dactylopodella sp. Pseudotachidius sp.1 Pseudotachidius sp.2 Pseudotachidius sp.3 Pseudotachidius aff.abysallis Pseudotachidius aff.bipartitus Pseudotachidius aff.similis Pseudotachidius aff.coronatus Pseudotachidius aff. vikigus Pseudotachidius bisegmentus Pseudotachidius texana Dactylopodia aff. signata Idomene aff. forficata N 1 8 21 1 1 5 2 1 2 8 2 2 5 1 2 1 3 2 1 1 1 1 1 3 1 1 14 1 13 6 3 1 1 1 1 165 S Diosaccidae Amonardia aff.normani Robertsonia aff. tenuis Amphiascus aff.congenera Amphiascus aff.gracilis Amphiascus aff.hirtus Amphiascus aff.minutus Amphiascus aff.varians Amphiascus aff.propinquus Amphiascus aff.tenuiremis Amphiascus sp. Amphiascoides aff.atopus Amphiascoides aff.breviarticulatus Amphiascoides aff. subdebilis Amphiascoides aff.proxima Amphiascoides aff.lancisetiger Amphiascoides aff.nanoides Amphiascoides aff. neglecta Amphiascoides aff. petkovskii Paramphiacella sp. Paramphiascella aff.robinsoni Paramphiascella aff.vararensis Paramphiascella aff.intermedia Paramphiascella aff.hispida Paramphiascella aff.pacifica Pseudoparamesochra texana Robertsonia sp. Pseudodiosaccus sp. Stenhelia sp.1 Stenhelia sp.2 Stenhelia sp.3 Stenhelia sp. Stenhelia unisegmenta Stenhelia aff.aemula Stenhelia aff.arenicola Stenhelia aff.bisetosa Stenhelia aff. confluens Stenhelia aff.gibba N 1 1 1 1 1 1 1 1 1 1 17 1 42 4 2 5 1 4 3 7 3 1 3 4 1 2 1 4 3 2 1 2 3 4 1 11 1 166 S Stenhelia aff.peniculata Delavalia aff.incerta Delavalia aff. reflexa Delavalia not.reflexa Delavalia aff. hanstromi Delavalia aff.latifes Delavalia aff longicaudata Delavalia aff.longifurca Delavalia aff.incerta Delavalia aff.indica Delavalia aff.latisetosa Delavalia bisegmenta Neodelavalia texana Neostenhelia texana Texastenhelia sp. Haloschizopera aff.abyssi Haloschizopera aff.aegyptica Haloschizopera aff. latisetifera Haloschizopera aff.lima Haloschizopera aff. mathoi Haloschizopera aff. junodi Haloschizopera aff.phyllura Haloschizopera aff. pygmaea Haloschizopera aff. marmarae Haloschizopera aff. tenuipes Haloschizopera aff.ruthorum Schizopera aff.akatovae Schizopera aff.haitiana Schizopera aff.inopinata Schizopera aff.jugurtha Schizopera aff.negelecta Schizopera aff.spinulosa Schizopera aff.triacantha Schizopera sp. Pseudodiosaccus sp.1 Pseudodiosaccopsis aff.brunneus Robertgurneya aff.brevipes Robertgurneya aff.ecaudata N 5 1 7 2 13 2 6 12 1 1 1 2 1 1 2 9 1 1 5 19 11 6 1 6 4 3 2 1 1 1 3 3 2 1 1 3 8 2 167 S Robertgurneya aff. falklandiensis Robertgurneya aff. Ilievecensis Robertgurneya aff.rostrata Robertgurneya aff.similes similis Robertgurneta aff.simulans Robertgurneya aff.spinulosa Robertgurneya sp. Bulbamphiascus aff. imus Bulbamphiascus aff. minutus Bulbamphiascus aff. inermis Parampiascopsis aff.gieshrechti Paramphiascopsis aff.longirostris Paramphiascopsis aff. pallidus Paramphiascopsis aff.soyeri Paramphiascopsis aff.triaticulatus Paramphiascopsis aff wihonu Typhlamphiascus brevicaudatus Typhlamphiascus aff.confusus Typhlamphiascus aff.gracilis Typhlamphiascus aff.gracilicaudatus Typhlamphiascus aff. lamellifer Rhyncholagena sp. Ameiriidae Interleptomesochra sp. Leptomesochra sp. Leptomesochra oligoseta Leptomesochra pygmaea Leptomesochra aff. tenuicornis Parapseudoleptomesochra aff.botosaneanni Nitoca aff. affinis Nitocra aff. bdellurae Nitocra aff. hibernica Nitocra aff. mediterranea Nitocra aff pusilla Nitocra aff.reducta N 3 2 7 2 6 18 2 31 7 5 9 7 6 1 3 1 1 3 1 1 6 1 1 2 1 5 1 2 2 2 2 1 3 1 168 S Nitocra aff.typica Nitocrella sp1(A sp3) Nitocrella aff.chappuisi Nitocrella aff.delayi Nitocrella aff. incerta Nitocrella aff. intermedia Nitocrella aff.negreai Nitocrella aff.omega Nitocrella aff.reducta Nitocrella aff. subterranea Nitocrella not.subterranea Nitocrella aff. tonsa Nitocrella sp. Ameira aff.longipes Ameira aff. parvula Ameira aff.scotti Ameira aff. speciosa Ameira aff.tenella Ameira aff.tenuicornis Pseudameira sp.1 Pseudameira sp.2 Pseudameira sp.3 Pseudameira aff. gracilis Pseudameira aff. crassicornis Pseudameira aff. furcata Pseudameira aff.minutissima Pseudameira longispina Proameira aff. arenicola Proameira aff. dubia Proameira aff. phaedra Proameira aff.simplex Proameira longicauda Parameiropsis longicauda Parameiropsis longirostris Parameiropsis sp. Parameiropsis aff.magnus Parameiropsis aff.peruanus Parameiropsis texana N 3 2 6 1 7 3 1 1 1 2 1 1 1 6 52 1 22 8 5 2 3 1 3 17 16 9 1 5 3 2 5 1 8 1 5 1 7 1 169 S Ameiropsis aff. abbreviata Ameiropsis aff.brevicornis Ameiropsis aff.longicornis Ameiropsis aff. minor Ameiropsis aff.nobilis Ameiropsis aff.robinsoni Ameiropsis sp. Sarsameira sp.1 Sarsameira sp.2 Sarsameira sp.3 Sarsameira aff.elongata Sarsameira aff.longiremis Sarsameira aff major Sarsameira aff.giraulti Sarsameira aff.parva Sarsameira aff. propinqua Sarsameira aff.sarsi Sicameira sp. Stenocopia aff.antarctica Anoplasoma multisegmenta Anoplasoma longicauda Malacopsyllus sp. Malacopsyllus multispinatus Malacopsyllus elongatus Paramesochriidae Paramesochra aff.acutata Paramesochra aff. brevifurca Paramesochra aff. coelebs Paramesochra aff.constricta Paramesochra aff. holsatica Paramesochra aff.similis Paramesochra aff.unaspina Paramesochra aff.dubia Paramesochra sp.1 Paramesochra sp.2 Paramesochra sp.3 Paramesochra aff.helgolandica N 20 3 1 12 1 4 1 2 1 2 1 2 14 4 9 6 3 1 1 1 1 4 1 1 6 3 6 1 2 2 6 2 1 2 1 10 170 S Paramesochra aff.pterocaudata Leptopsyllus aff. typicus Leptopsyllus aff.abyssalis Leptopsyllus aff.dubatyi Leptopsyllus aff. harveyi Leptopsyllus aff.platyspinosus Leptopsyllus aff.reductus Leptosyllus sp. Leptopsyllus texana Paraleptopsyllus sp. Scotopsyllus aff herdmani Kliopsyllus sp.1 Kliopsyllus sp2 (P.sp1) Kliopsyllus aff.californicus Kliopsyllus aff.laurenticus Kliopsyllus sp longicauda Kliopsyllus aff.longisetosus Kliopsyllus aff.spiniger Kliopsyllus aff.minutus Rosopsyllus texanus Remanea texana Texaspsyllus typicus Canthocamptidae Cletocamptus sp. Mesochra sp. Mesochra aff. lilljeborgi Mesochra aff.nana Mesochra aff pallaresi Mesochra aff. pygmaea Bathycamptus aff.eckmani Bathycamptus texanus Bathycamptus aff. minutus Bathycamptus sp. Neobathycamptus texana Oligobathycamptus texana Parabathycamptus sp. Cylindromesochra texana 1 N 2 8 1 1 3 2 1 1 1 44 3 3 7 12 1 2 1 4 1 1 1 1 4 2 3 1 1 2 17 9 2 10 1 1 2 4 171 S Cylindromesochra texana 2 Texascamptus taxanus Boreolimella brevifurca Boreolimella longispina Boreolimella oligochaeta Boreolimella rostrata Boreolimella texana Boreolimella aff.trisetosa Leimia aff. vaga Leimia texana Leimia longicauda Leimia mexicana Bathycamptonia longifurca Mesopsyllus sp. Mesopsyllus aff.atargatis Mesopsyllus areolatus Mesopsyllus neos Mesopsyllus gracilis Mesopsyllus longicauda Mesopsyllus longifurca Mesopsyllus texanus Heteropsyllus aff. confluens Heteropsyllus aff.cuticaudatus Heteropsyllus aff. exigus Heteropsyllus aff.nannus Heteropsyllus aff.nunni Heteropsyllus aff.rostratus Heteropsyllus aff.major Heteropsyllus aff. meridionalis Heteropsyllus longisegmenta Heteropsylllus oligosetus Heteropsyllus texanus Heteropsyllus sp. Bushia texana Parepactophanes sp. Pusillargillus aff.nixe Stenocaris sp. Stenocaropsis sp. N 1 6 3 2 2 1 25 1 2 7 4 2 1 2 3 2 1 3 4 1 6 3 5 15 2 1 29 14 38 1 2 1 2 1 1 1 1 1 172 S Cylindropsyllus areolatus Cylindrotexanella mexicana Arenopontia texana Cletodidae Acrenhydrosoma hamus Cletodes sp. Cletodes aff.contiginiensis Cletodes aff.dorae Cletodes aff.latirostris Cletodes aff.longifurca Cletodes aff.longicaudatus Cletodes aff.longifurcatus Cletodes aff.limicola Cletodes aff. macrura Cletodes aff.pusillus Cletodes aff. reyssi Cletodes aff.yotabis Cletodes bifidas Cletodes texana Poria sp. Echinocletodes aff. armatus Echinocletodes longicauda Odiliacletodes texanus Stylicletodes aff.longicaudatus Stylicletodes aff.oligochaeta Stylicletodes aff.reductus Enhydrosoma aff.buchholtzi Enhydrosoma aff.lacunae Schizacran texana Huntemannidae Nannopus sp. Pseudonannopus texanus Pseudonannopus secundus Pseudonannopus similis Metahuntemannia sp. Metahuntemannia aff. crassa N 4 1 1 2 7 3 1 3 17 2 21 1 7 15 23 2 1 1 3 2 1 2 16 4 2 1 1 2 3 1 1 0 4 1 173 S Metahuntemannia aff.iberica Metahuntemannia aff. magniceps Metahuntemannia noendopoda Metahuntemannia aff.pseudomagniceps Metahuntemannia aff.spinifes Metahuntemannia texana Metahuntemannia mexicana Talpina aff. bifida Talpina aff. fodens Talpina aff.talpa Pseudocletodes sp. Pseudocletodes longicauda Paranannopidae Pseudomesochra aff.abberans Pseudomeshchra aff.abyssalis Pseudomesochra aff.beckeri Pseudomesochra aff. brucei Pseudomesochra aff brucei II Pseudomesochra aff.crispata Pseudomesochra aff.divaricata Pseudomesochra aff.latifurca Pseudomesochra aff.longifurcata Pseudomesochra aff. media Pseudomesochra aff.meridionensis Pseudomesochra aff.minor Pseudomesochra aff.scheibeli Pseudomesochra aff. similis Pseudomesochra aff. tatianae Pseudomesochra aff. tamara Pseudomesochra exopodata Pseudomesochra texana Pseudomesochra sp.1 Pseudomesochra sp.2 Pseudomesochra sp.3 Pseudomesochra sp.4 Pseudomesochra sp.5 Pseudomesocra sp. N 8 4 3 2 2 1 1 1 1 1 1 1 4 6 7 17 2 21 2 9 6 5 6 6 6 30 8 1 1 2 3 2 1 1 1 1 174 S Paranannopus sp. Paranannopus aff.sarsi Paranannopus oligosetus Paranannopus aff.longithorax Paranannopus aff. minutus Paranannopus aff. reductus Paranannopus aff.singulosetosus Paranannopus aff.trisetosus Paranannopus texanus Paranannopus aff.triarticulatus Paranannopus aff.truncatus Paranannopus aff. deticulatus Archisenia sp. Cylindronannopus aff.elongatus Cylindronannopus aff. primus Cylindronannopus texanus Cylindronannopus bisetosus Cylindronannopus sp. Micropsammis sp. Mucrosenia aff. kendalli Argestidae Fultonia sp.1 Fultonia sp.2 Fultonia aff.bouisi carollicola Fultonia aff.gascognensis Fultonia aff.hirsuta Fultonia aff.sarsi Fultonia elongata Fultonia texana Fultonia trisetosa Argestes sp. Argestes aff.mollis Argestes aff.reducta Argestes oligoseta Argestes texana Parargestes brevifurca Paragestes unisetosa N 1 3 1 4 1 1 1 1 3 2 1 1 1 8 1 12 1 1 2 1 14 1 4 6 11 15 1 1 3 1 11 1 1 2 1 1 175 S Paragestes aff tenuis Argestigens aff.abyssalis Argestigens aff.uniremis Argestigens aff.glacialis Argestigens reducta Argestinella texana Argestigens sp. Argestia pseudocervinia Rostrina texana Limnocletodes sp. Limnocletodes aff.behningi Limnocletodes aff. mucratus Mesocletodes sp. Mesocletodes aff.abyssicola Mesocletodes aff. ameliae Mesocletodes aff.brevifurca Mesocletodes aff. farauni Mesocletodes aff.fladensis Mesocletodes aff.foroerensis Mesocletodes aff. inermis Mesocletodes aff irrasus Mesocletodes not.irrasus Mesocletodes aff. commixtus Mesocletodes aff.katharinae Mesocletodes aff.kunzi Mesocletodes aff.makarovi Mesocletodes aff.parirrasus Mesocletodes texana Mesocletodes aff.thielli Eurycletodes aff.aculeatus Eurycletodes aff.echinatus Eurycletodes aff.monardi Eurycletodes aff. serratus Eurycletodes aff.rectangulatus Eurycletodes aff.verisimilis Eurycletodes aff. goburnovi Eurycletodes sp.1 Eurycletodes sp.2 N 13 1 7 7 3 1 1 1 2 2 1 1 3 2 1 1 1 4 3 2 17 2 1 1 4 1 1 1 2 1 1 1 2 6 3 2 2 1 176 S Eurycletodes (O.) aff. major Eurycletodes (O.) aff. similes Leptocletodes sp.1 Leptocletodes sp.2 Leptocletodes brevicaudatus Leptocletodes aff.debilis Leptocletodes gigantes Leptocletodes longicauda Hemimesochra sp.1 Hemimesochra sp.2 Hemimesochra sp.3 Hemimesochra aff.clavularis Hemimesochra gracilis Hemimesochra micronica Hemimesochra longicauda Hemimesochra longisementa Neohemimesochra texana Nannopodella texana Monocletodes sp. Oligocletodes areolatus Bathycletopsyllus longicauda Laophontidae Troglophonte texana Troglophonte longicada Texaslaoohonte sp. Platychelipus sp. Pontopolites sp. Paleolaophontodes sp. Normanellidae Sagamiella levisa Sagamiella longipedesta Sagamiella brevicauda Sagamiella sp. Texanella brevicauda Texanella longisegmenta Texanella oligoseta N 3 1 1 2 5 4 1 3 6 1 1 5 2 3 5 1 1 4 1 1 1 4 1 1 1 1 1 16 19 3 1 4 1 5 177 S Normanella aff.reducta Normanella sp. Ancorabolidae Ancorabolus aff.mirabilis Ancorabolus mexicana Ancorabolus texanus Ancorabolus lateralspinus Neoancorabolus texana Echinopsyllus sp. Echinopsyllus gladiatus Arthropsyllus sp.1 Arthropsyllus sp.2 Arthropsyllus serratus Laophontodes aff.wilsoni Ceratonotus sp. Unid. family Taekwoenvia mexicana Aesthetascia longicauda Noendopoda texana Noendopoda texana2 Cylindrotexanella texana Neohemimesochra antenata Doolia typica Unidentified family Unidentified Ancorabolidae Unidentified Ameiriidae Unidentified Argestidae Unidentified Canthocamptidae Unidnetified Cerviniidae Unidentified Cylindropsyllinae Unidentified Danielsseniidae Unidentified Diosaccidae Unidentified Ectinosomatidae Unidentified Laophontidae Unidentified Neobradyidae N 1 1 1 2 2 1 1 6 5 3 1 1 1 2 27 1 6 1 1 3 1 19 3 42 7 8 1 2 4 26 26 1 1 178 S Unidentified Paramesochridae Unidentified Tisbidae Unidentified harpacticoid(damaged) Canuellidae copepodites Ameiriidae copepodites Cerviniidae copepodites Cylindropsyllidae copepodites Paranannopidae cepepodites Paramesochridae copepodites Canthocamptidae copepodites Danielssenidae copepodites Diosaccidae copepodites Huntemannidae copepodites Argestidae copepodites Ectinosomatidae copepodites Tisbidae copepodites Cletodidae copepodites Thalestridae copepodites Normanellidae copepodites Neobradyidae copepodites Ancorbolidae copepodites Unidentified copepodites Total harpacticoida N 6 7 1159 5 29 26 1 37 2 16 7 77 6 54 66 85 10 15 5 6 3 7217 12480 179 REFERENCES Abele LG, Walters K (1979) Marine benthic diversity - critique and alternative explanation. 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Oikos 41: 496-506 200 VITA Jeffrey Greer Baguley was born in Medford, Oregon on December 30, 1977, the son of Thomas G. Baguley and Janis M. Baguley. Jeff attended Galena High School in Reno, Nevada, where he graduated with honors in June 1996. He began university study at Linfield College in September 1996, and received his Bachelor of Arts with Honors in Biology in May 2000. Jeff was a four year intercollegiate letterman on the Linfield College baseball team, with all conference recognition in 1999 and 2001; he also played semi-professional baseball for the Reno Diamonds/Reno Astros from 1997 to 2001. In August 2000, he matriculated into the Graduate School of The University of Texas at Austin, Department of Marine Science. While in graduate school, Jeff has participated in numerous national and international conferences, including six oral presentations. Permanent Address: 123 Granby Crossing, Cayce, SC 29033 This dissertation was typed by the author. 201

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MATH107A46024536.doc
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Path: MD University College >> ASIA >> 2092 Fall, 2009
bonningew86532.pdf
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CMIS102AA42086692.doc
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