ANT 154B Course notes- Lecture _16

ANT 154B Course notes- Lecture _16 - ANT 154BN Course notes...

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Unformatted text preview: ANT 154BN Course notes Lecture #16: Diversity: patterns and processes 1 March 2011 Key terms and concepts are indicated in blue Outline 1. Explaining patterns of diversity neutrality vs. niches island biogeography prey switching Neutrality vs. niches • these are not the only two models proposed to explain patterns of species diversity, species composition, and relative abundance , but they serve to frame the range of variation among the various models, and are a reasonable, simplified comparison for our purposes • this is an extremely hot area in community ecology at the moment Neutrality vs. niches Niche models: Non-random • division of resources •!effects of various types of ecological variation and interactions 183 1. A comparison of the neutral and niche models, listing the key assumptions, Table cf. population genetics (genetic drift, selection) inputs a Forum nd outputs of each. This is not meant to be an exhaustive list, and merely shows Tuesday, March 2, 2010 5 Key assumptions and data inputs Chase 2005 Neutral models: • size of the community • rates of species dispersal •!rates of speciation and Random extinction some of he key inputs Key assumptions andtdata inputs and outputs of each Neutral Key Assumption Inputs No differences among species Metacommunity size Speciation rate Dispersal rate Niche Trade-offs prevalent Spatial heterogeneity Intrinsic Extrinsic Temporal heterogeneity Intrinsic Extrinsic Spatial configuration Interactions in the food web Rank–Abundance Diversity Area Several types of heterogeneity Outputs Rank–Abundance Diversity Area Speciation their relative compet colonize patches. Other species (enem Predators can facilita there is a trade-off in resources and avoid p 1996). Similarly, pred t h e re i s f re q u e n c y d e prey species as they 1970; Connell 1971). facilitate any number predator limits its prey much less studied, trad sts (e.g. mycorrhizal f for resources may als coexistence (see, e.g. P Interactions among 1996). Similarly, predators t h e re i s f re q u e n c y d e p e n d e Inputs Metacommunity size Spatial heterogeneity prey species as they becom Speciation rate Intrinsic 183 1. A comparison Table models, Dispersal r neutr ANT 154B Lecture #16 course notes of the ate al and niche Extrinsiclisting the key assumptions,f 1970; Connell ti971). Fina page 2 o 9 their rela 1ve compe inputs a Temporal hetero list, and colonize patches Forum nd outputs of each. This is not meant to be an exhaustivegeneity merely shows facilitate any number .of p some of the key inputs and outputs of each Intrinsic Ot mi s i e i re ( snem predator liherts ptscpes y epec Key outputs Extrinsic Pr ss s tors d, tr fde-offs much leedatudiecan aacilita Spatial configuration Neutral Niche sts (ethereyis a trade-off in .g. m corrhizal fungi, Interactions in the food web for rresouces smay alsoidrp esour rce and avo c e Key Assumption Rank–Abundance No differences Trade-offs prev Outputs Rank–Abundance alent coexistence Similarly, alme among 1996). (see, e.g. P pred Diversityspecies Diversity Area Area Interere is framong cty de th actions equen he f Inputs Metacommunity size Spatial heterogeneity Speciation Several types of heterogeneity d i s c u s s e d species( a n dthey prey above as oth Speciation rate Intrinsic Dispersal Food web structure another. This creates a rich Dispersal rate Extrinsic 1970; Connell 1971). Compositional turnover Compositional turnogeneity tionsfacilitate any numbe and outcomes and the Temporal hetero ver Space Space Intrinsic coexpredattorllimiltls ias prre ist, bo h oca y t nd e EnExtrinsic vironment (see, e.g. Shurin & Allen Response to environmental change much less studied, trad Spatial configuration 2002, 2003). Diversity changes among species Key outputs sts (e.g. mycorrhizal f for resources may al Outputs Rank–Abundance coexistence : , .g. ’ (see e Diversity Area Above Interiactions among and n Table 1, I em Speciation d i s c al heory is i ( in the neutru s ste d ab ove n ats Dispersal n i c hanother. Thisu c h m o e t h e o r y i s m creates Compositional turnover Compositional turnover m o dtionsfae n m a ke s i m iaa e l s o t nd outcomes l Space Space Chase 2005 coatterns. otnelwcalty Neutrality vs. niches for primates some pexist, b O h o ay lo Environment Tuesday, March 2, 2010 7 s odels c S pr rin & tiple (mee, e.g.anhuedict thA Response to environmental change Niche models: • primate species clearly occupy distinct niches Occam’s razor, or parsimo so long as there is spatial variaersity n the local ratios of 2002, 2003). Div tion i changes Composition changes Rank–Abundance Invasive species Effects of and on diversity Diversity Effects of and on composition Area Ecosystem al types of heterogeneity Sever function Effects of diversity e Food web structur Effects of composition Interactions in the food web multiple models predict th Composition changes accept the one with the sim ance on different resource-rasive species 1982). Many Inv atios ( Tilman is : Indeed, this ’exactly what species can also coexist on fEffectsoofrces when they vary ew res u and on diversity Neutral models:• rates of speciation, n how they and ze their envipoorlyncharacterized dingprimatesavour his neutral theo Efonm of ; so on composition to f Above and in Table 1 i dispersal, utili extinction rfects e tandme respon for Ecosystem and others respondmore parneutral theory is to localized resoclearly have ained) function urces (fine-gr effects • random, historical contingencies the ameters) niche-th Effects of diversity if one were to consider only ing to more dispersed resources (coarse-grained) (Ritchie n i c h e t h e o r y i s mu c Effects of composition • supported by some biogeographic patterns pecies vary in their performthose two resources and s abundance curves (e.g. Vo & Olff 1999). m o d e l s o f t e n m a ke because neutral and niche • very few empirical tests published Intrinsic spatial variation. When organisms trade-off some patterns. One w predictions on other patter in their ability to consume resources to low levels and tiple models can pred studies with multiple empi to find new patches, a spatial mosaic of patch quality Occam’s neutral r ni cso lbe craatted whicspaltial variacies to cthe local rone of better tests of razor, ovspa an ong e s here is h a lows spe tion in oexist on atios multiple ailable pred rthose c e ( e.resourcesrdnd tspecies0vary io mheirtpve ly m- Data are avmodelsfrom e s o u r two g . R i c h a as e a l . 2 0 0 ) . C n t p e t i i erfor accept the For examp ance ospecies can aesource-ratios (sTilman 1982).by any allow such with th n different r lso coexist on a ingle resource M similar Indeed, t da is exactlr species can also ated st on f w resources The aggreintrinsically genercoexispatialeaggregation. whisn they vary a very detailedhis taset of ty to fa Island (BCI), P n on c t n y bi i av the ral i v ndi meua sho r a bias giatihow ahebeutelhze iouir enf ironvidnt;ls omew esponding Coloradovour his neutra t wards izen r n o r ces (fin i- cs ( ned) aa d other res4 o heory’s p ameters) e ic too localbeidg eseaurconspecefigraie.g. Zhnng et al.s200p) nd- tral tmore parredicted znro or a to moref dispersed desourcespcobase-grained) (Ritchie to show ne significantly bet if o a were to conside ing result o differing r ispersal (roar bilities and segrank–abundance curves (e. regation 1999). if dispersal is localized (Murrell & Law 2003). abundance relationsh & Olff Space as asresourceariationspace itself is a resource, f tionship (but see Etienne & because neutral and Intrinsic patial v . When . When organisms trade-of pattern availab e we ‘regional arade-offs’consume resources coelow ilfevhey and only predictions on lo,ther m t bility to can allow species to to xist t els in their t ra d endff eh e i raab i l i t y ao patialemosaic e sf u rc e s iquality acceptudies eutral tultiple s t the nwith m heory to fi -o n t w p tches, t s comp te for r o o patch n a pcachbancreatled iwe ichptllowtcspec(iKntotel & ist ase one based theories that cneutra at n e d co on z h em a y pas hes es ei coexCh on better tests of an also shape indiscernible from 2004). For example, Tilman (1994) showed theoretData are available re s o u rc e ( e. g . R i c h a rd s e t a l . 2 0 0 0 ) . C o m p e t i t ive ly © 2005 British (Chave et al. 2002; Mouqu ically that a large number of species can coexist in a Ecological Society, allow such tests. For e similar species can also coexist on a single resource by et al. 2003). However, data region so long as there are always open patches created Functional Ecology, a very detailed datase intrinsically generated spatial aggregation. This aggreneotropical areas) on specie by death or disturbances, and the species trade-off in 19, 182–186 gation can be behavioural if individuals show a bias towards being near conspecifics (e.g. Zhang et al. 2004) or a result of differing dispersal probabilities and segregation if dispersal is localized (Murrell & Law 2003). Colorado Island (BC tral theory’s predicte to show a significant rank–abundance rela ANT 154B Lecture #16 course notes page 3 of 9 Primate community assembly rules: hypotheses Dispersal limitation (neutral) Ecological partitioning (niche) Community similarity Community similarity Geographic distance Community similarity Community similarity Geographic distance Page 31 of 37 Ecological distance Submitted to Proceedings of the Royal Society B Ecological distance Regional species pools T • data on 124 sites able 2. Number per taxa Africa S. America Madagascar Borneo Diurnal species 35 28 13 11 Nocturnal species 9 3 16 2 Variables Genera 17 13 14 8 Variables Community similarity ssimilarity imilarity Community Community (a + b – j) Africa: Pan troglodytes & Perodicticus potto at 70% (16/23) of sites South America: CJebus apella atindex (42/45) of sites = Jaccard 93% of similarity Geographic Madagascar: Eulemur fulvus at 86% (24/28) of sites distance a = # species at site a b = # species at of b Borneo: Hylobates muelleri at 79%site(22/28) sites j = # species occurring at both sites Common taxa J= j r Fo Re Geographic distance distance Ecological Variables vi Ecological distance similarity Community Vegdist function, Vegan community ecology package, R A composite of 14 ecological variables Net Primary Productivity ew Geographic distance Elevation On Climatic and weather variables Soil variables Ecological distance ly Mahlanobis distances, R 2.8.1 ANT 154B Lecture #16 course notes page 4 of 9 Community similarity Community similarity residuals Geographic distance Ecological distance residuals Ecological distance Geographic distance e.g., assessing the effects of ecological distance while controlling for geographic distance r = 0.07, p = 0.03 r = 0.61 Africa r = 0.33 Diurnal primate community similarity residuals (Jaccard index) South America r = 0.28 r = 0.25 Madagascar r = 0.27 Borneo Pearson method ,10,000 permutations solid lines, p-values ! 0.001 no line: p > 0.5 Ecological distance residuals Geographic distance Beaudrot & Marshall residuals 2011 J. Anim. Ecol. Dispersal limitation for primates? Potential costs of colonization Geographic barriers to dispersal ANT 154B Lecture #16 course notes page 5 of 9 Island biogeography essentially a neutral model, explains species assemblages on islands, or in forest fragments Island biogeography: effects of island size Amphibians & reptiles Freshwater birds geography: distance from mainland ANT 154B Lecture #16 course notes Island biogeography: distance from mainland page 6 of 9 Species richness distance from New Guinea (km) Equilibrium model of island biogeography Diamond 1972 Island biogeography: additional considerations patterns will differ between arbitrary islands and oceanic islands Species richness patterns species time species time ANT 154B Lecture #16 course notes Island biogeography: key implications page 7 of 9 Habitat fragmentation Island biogeography and conservation 633 Primates in tropical forest fragments Table 2. Results of Pearson and Spearman correlation tests on log10 number of species by log10 forest fragment area. Results shown for total data (rT) and after removal of outliers (rT − O). n in parentheses shows number of outlier fragments. Spearman correlations (rS) are with outliers omitted.; rS −1, Spearman coefficients when site per continent with most fragments is omitted, in order to reduce spatial autocorrelation. Total n less than Table 1, because sites with only one species detected to be in a region are omitted Region Globe Africa Asia Madagascar South America n= 136 (3) 41 (1) 26 18 (1) 51 (3) rT = 0·30 0·11 0·56 0·88 0·25 P< 0·001 NS 0·01 0·0001 0·1 rT − O = 0·40 0·22 0·56 0·95 0·36 P< 0·0001 NS 0·01 0·0001 0·02 rS = 0·32 0·20 0·58 0·92 0·34 P< 0·0001 NS 0·01 0·0001 0·05 r S −1 = ( n ) 0·35 (79) 0·52 (20) 0·12 (7) 0·83 (9) 0·36 (42) P< 0·01 0·05 NS 0·01 0·05 Table 3. Results ofturnover and Spearman’s correlation analyses of Extinctions of available species by log10 arcsine √proportion Species linear regression forest fragment area, with available total number taken to be that reported for the adjacent main forest block. Outliers removed before analysis. n in parentheses in first column show number of outlier fragments. rS, Spearman correlation coefficient; rS −1, Spearman coefficients when site per continent with most fragments is omitted in order to reduce spatial autocorrelation. Results shown only if n ≥ 5 Newmark 1995 Region Globe Africa Asia Madagascar South America n= 58 (4) 21 (2) 1 18 (1) 23 r2 = 0·39 0·06 – 0·42 0·20 F= 38·2 0·05 – 12·5 11·1 P< 0·0001 NS – 0·01 0·01 Slope 0·15 −0·01 – 0·14 0·20 Intercept 0·81 0·58 – 0·70 0·95 rS = 0·62 −0·06 – 0·67 0·58 P< 0·0001 NS – 0·01 0·01 r S −1 = ( n ) 0·60 (20) – – 0·70 (9) 0·50 (16) P< 0·001 – – 0·05 0·1 Fig. 1. Spearman correlation coefficients (corr. coeffs) of nu m b e r o f s p e c i e s by f rag m e n t a re a p e r s i t e i n t h e fo u r continents. Circles, values for individual sites; bar, median. (Outliers not omitted, because sample size per site so small.) Binomial test, assuming p = q = 0·5 n, P < 0·02. *Individual sites with significant coefficients. among the continents. The fragments at the single s i t e w i t h a n e g at i v e c o r r e l at i o n ( i n B r a z i l ) w e r e a l l extremely similar in size (0·09 – 0·1 km2), and the small- Fig. 2 Proportional species richness (% of number in nearby main forest block) by fragment area for the globe and the four continents (double log10). Globe, P < 0·0001; Africa, NS; Asia, n = 1; Madagascar, P = 0·01; South America, P < 0·01. S t at i s t i c a l d e t a i l s i n Tabl e 3 . S t at i s t i c s a re fo r a rc s i n e √proportional richness (outliers removed). Log10 percentage richness shown in graph, because log values are more ANT 154B Lecture #16 course notes The matrix matters page 8 of 9 Species sensitivity to: Prugh et al. 2008 Prey switching would be classified as a niche model, in that focuses on ecological interactions Prey switching: two prey species prey switching amount of prey type in diet random foraging 0: 100 50: 50 100: 0 relative abundance of two prey types ANT 154B Lecture #16 course notes page 9 of 9 Take home messages 1. Neutral and niche models, which invoke distinct mechanisms, have both been applied to explain patterns of diversity. Niche models are probably more applicable to primate communities, but few formal tests of neutral models for primates have been published to date. 2. Island biogeography theory predicts that the equilibrium number of species on an islands will be positively correlated with island area, and negatively correlated with distance from the mainland. 3. Prey switching may help to maintain species diversity in ecological communities, but is unlikely to be an important factor determining species diversity in most primate communities. Question to ponder The theory of island biogeography has been very influential on the field of conservation biology, particularly in the context of the design of protected areas. Based on your understanding of island biogeography, in each of the following six pairs of potential reserve designs select the one that would more effectively conserve biodiversity. For each pair, explain briefly why you made your choice, referencing island biogeography theory and other relevant theoretical relationships that you have learned about. Bonus: Are there conflicting factors that make selection difficult in some cases? If so, which, and why? Reserve design choices or or (assume equal total area) or (assume equal total area) (buffer zone) or or or (assume equal total area) 53 Tuesday, March 2, 2010 ...
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This note was uploaded on 04/05/2011 for the course ANT 154bn taught by Professor Debello during the Winter '10 term at UC Davis.

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