ANT 154BN-12 Abundance _ rarity

ANT 154BN-12 Abundance _ rarity - ANT 154B: Lecture #12...

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Unformatted text preview: ANT 154B: Lecture #12 Abundance and rarity 15 February 2011 Exam results 4 Mean: 77.5 / 100 SD: 3 13.98 Frequency 0 1 2 50 60 70 Midterm score 80 90 100 Exam results: rough grade breakdown Grade A+ A AB+ B BC+ C CD+ D DF Cutoff 97 93 90 87 83 80 77 73 70 67 63 60 <60 Cumulative % 6% 18% 26% 30% 42% 44% 55% 63% 67% 71% 77% 89% 100% 70.0 Grading Discussion sections Two lab write-ups Writing assignment Midterm exam Final exam 35.0 0 2 2004 2006 10% 20% 20% 25% 25% No extra credit! N Abundance and rarity t 1. Population ecology 101 2. Population dynamics 101 3. Measuring primate abundance 4. What determines primate abundance? N Abundance and rarity t >1. Population ecology 101 2. Population dynamics 101 3. Measuring primate abundance 4. What determines primate abundance? Population: a group of interbreeding individuals Principle goals of population ecology Individual characteristics (e.g., age, sex, body size, behavior) Population characteristics (e.g., density, abundance, age distribution, sex ratio, spatial distribution) Individual processes (e.g., growth, development, feeding, reproduction, death) Population processes (e.g., population growth rates, changes in age distribution, mortality, extinction) Population growth models N = population size Nt +: births, immigration -: deaths, emigration Nt+1 Population growth models N = population size Nt+1 = Nt + b + i - d - e b = births d = deaths i = immigration e = emigration Nt+1 (assume closed population) = Nt + b +X - d -X i e Nt+1 = Nt + b - d dN dt dN dt = (b-d)N = rN r = population growth rate (aka, intrinsic rate of increase) Population growth when resources unlimited population size time rmax = maximum rate of population growth Population growth when resources unlimited high rmax population size moderate rmax low rmax time ( = CV m e a n m o n t h l y temps). 4) CV o f ppt. within the y e a r ( = CV m e a n m o n t h l y precipitation). CV = coefficient o f v a r i a t i o n MNTH.CV.ppt Primate relative rmax varies iables m i g h t c o m r,, a n d these varit a n d latitude and f o r species within t y o f the variation a r i a t i o n o f species problems t h a t m a y 982) analysis a t the a r i a t i o n in relative e m e a n values, b u t bfamily d a t a were All analyses were d f o r smaller tax, i.e. species within 0.08 E 0.04 t- o.oo -0.04 -0.08 1 2 3 4 5 Habitat group Fig. 1. H a b i t a t type a n d mean relative rm f o r 72 primate species, where relative r m = o b s e r v e d log10 rm-expected loglo r,,. Habitat grous: / = s p e c i e s restricted t o o r preferring p r i m a r y forest; 2 = forest species n o t restricted t o p r i m a r y forest; 3 = species restricted t o o r preferring s e c o n d a r y a n d edge forest; 4 = w o o d l a n d species; 5 = species f o u n d in highly seasonal habitats fferent habitats ithin each habb e t w e e n t h e rm d a t a reveal t h a t f o r t h e " p r i m a r y f o r e s t " species o n l y one, the ruffed lemur (Varecia variegatus), has a positive r e l a t i v e rm, h a v i n g t h e h i g h e s t r e l a t i v e v a l u e o f a l l o f t h e Ross 1992 But resources are limited population size K (= carrying capacity) time population growth rates slow down as N -> K population size K sigmoidal curve of logistic population growth time dN dt = rN ( K-N K ) ( K-N K ) N is small (well below K) ~ 1 as N -> K ~ 0 Life history strategies population size K “K” species “r” species time R vs. K. r -strategists offspring smaller, weaker/ vulnerable mature & reproduce rapidly inhabit unstable environments short lifespan K -strategists offspring larger, stronger/more protected mature & reproduce slowly inhabit stable environments long lifespan Most primates are K strategists most primate populations most of the time population size K time N Abundance and rarity t 1. Population ecology 101 >2. Population dynamics 101 3. Measuring primate abundance 4. What determines primate abundance? Classic population dynamics studies: predator-prey interactions Population dynamics 101 N time K strategists experience less extreme population fluctuations boom boom bust N K fairly stable N time K strategists time r strategists Population limitation vs. regulation N time Population dynamics 101: regulation N K regulating forces // time Regulatory forces have density-dependent effects around K Population dynamics 101: regulation dN dt = rN ( K-N K ) Regulatory forces are mechanisms that: N<K N>K dN dt dN dt populations grow populations decline Factors influencing populations Density dependent factors (vary based on “N”) • competition • predation • infectious disease Population dynamics 101: limitation N K limiting forces // time Effects of limiting forces ~ density-independent Factors influencing populations Density dependent factors (vary based on “N”) • competition • predation • infectious disease Density independent factors (do not vary based on “N”) • weather • climate • soil nutrient availability • geographic features (e.g., available land mass) Factors influencing populations Density dependent factors Regulate populations Density independent factors Limit populations dN dt K N Abundance and rarity t 1. Population ecology 101 2. Population dynamics 101 >3. Measuring primate abundance 4. What determines primate abundance? (a) Line transect methods 0 Number of observations Number of observations 0 Distance from transect (b) (b) Distance from transect 0 Number of observations 0 Number of observations Distance from transect (c) Distance from transect (c) Number of observations Number of observations 0 Marshall, Lovett & White 2008 Distance from transect 0 Line transect methods: direct observations Center of group (accurate estimate) Edge of group (overestimate) Marshall, Lovett & White 2008 Line transect methods: direct observations Whitesides et al. 1998 Line transect methods: indirect observations e.g., nest transects Require estimates of: • proportion of nest builders • nest production * nest decay rate van Schaik et al. 1995 Limitations of traditional nest transects Costly Time consuming Limited coverage Survey coverage: Sumatra Husson et al. 2009 Survey coverage: Borneo Husson et al. 2009 Limitations of traditional nest transects Costly Time consuming Limited coverage Variable, uncertain parameters Limited accuracy and precision Nest decay rates are highly variable variable among sites range 73 – 602 days Mathewson et al 2008 Ecol. Appl. Nest decay rates are highly variable and within sites: Estimated decay time (days) 1600 200 Mathewson et al 2008 Ecol. Appl. Inaccurate nest decomposition rates can have large effects on population estimates Mathewson et al. 2008 At least 6 months needed to establish reliable decay time estimate Mathewson et al. 2008 Need substantial survey effort to obtain a reliable density estimate Matrix method Marked recount method Boyko & Marshall 2010 transects. In the conver-utan population density ameter, inversely proporchaik et al., 1995; Buckland Unfortunately, nest decay eral spatial and temporal decay rates takes a minimum of 6 months (Mathewson et al., 2008). Frequently employed shortcuts are based on the assumption that nest decay rates in a particular location are stable over time or that it is appropriate to measure nest decay at one location and extrapolate the estimate obtained across a much larger (and often highly heterogeneous) area. However, neither of these assumptions appears to be true, and succumbing hropology andRyan H. Group in*, Andrew J. Marshallto the temptation to use such shortcuts is Graduate Boyko SA. Department of Anthropology aunwise and potentially damaging Davis,conservation efforts. f America nd Graduate Group in Ecology, University of California to Davis, California, United States o At best, these practices will result in imprecise orang-utan Tropical Forest Initiative, The servation Services Program, Jalan density estimates with wide confidence intervals, hamperAbstract an, East Kalimantan, Indonesia. ing our ability to identify or monitor priority populations. Background: Conservationists frequently use nest count surveys to estimate great ape population densities, yet the At worst, ignoring uncertainty to nest accuracy and precision of the resulting estimates are difficultin assess. decay rates may sted 13 November 2008. result in inaccurate estimates that are worse than useless, Methodology/Principal Findings: We used mathematical simulations to model nest building behavior in an orangutan Using Simulation Models to Evaluate Ape Nest Survey Techniques population to compare the quality of the population size estimates produced by two of the2009 Oryx used nest count Marshall & Meijaard commonly methods, the ‘marked recount method’ and the ‘matrix method.’ We found that when observers missed even small proportions of nests in the 416–418 doi:10.1017/S0030605309001513 rinted in the United Kingdom ª 2009 Fauna & Flora International, Oryx, 43(3),first survey, the marked recount methodPproduced large overestimates of the population size. Regardless of observer reliability, the matrix method produced substantial overestimates of the population size when surveying effort was low. With high observer reliability, both methods required surveying approximately 0.26% of the study area (0.26 km2 out of 100 km2 in this simulation) to achieve an accurate estimate of population size; at or above this sampling effort both methods produced estimates within 33% of the true population size 50% of the time. Both methods showed diminishing returns at survey efforts above 0.26% of the study area. The use of published nest decay estimates derived from other sites resulted in widely varying population size estimates that spanned nearly an entire order of magnitude. The marked recount method proved much better at detecting population declines, detecting 5% declines Boyko & Marshall 2010 PLoS One nearly 80% of the time even in the first year of decline. Conclusions/Significance: These results highlight the fact that neither nest surveying method produces highly reliable population size estimates with any reasonable surveying effort, though either method could be used to obtain a gross population size estimate in an area. Conservation managers should determine if the quality of these estimates are worth the money and effort required to produce them, and should generally limit surveying effort to 0.26% of the study area, unless specific management goals require more intensive sampling. Using site- and time- specific nest decay rates (or the marked recount method) are essential for accurate population size estimation. Marked recount survey methods with sufficient sampling effort hold promise for detecting population declines. Citation: Boyko RH, Marshall AJ (2010) Using Simulation Models to Evaluate Ape Nest Survey Techniques. PLoS ONE 5(5): e10754. doi:10.1371/ journal.pone.0010754 Editor: Andy Hector, University of Zurich, Switzerland Received October 29, 2009; Accepted March 15, 2010; Published May 21, 2010 Limitations of traditional nest transects Costly Time consuming Limited coverage Variable, uncertain parameters Limited accuracy and precision Ill suited to be rapid survey techniques When are traditional nest transects appropriate? In well-circumscribed areas with long-term monitoring of site-specific nest decay rates Potential alternative methods Helicopter surveys can cover large areas, but expensive, logistically demanding, and still require conversion factors Remote sensing can cover large areas, monitor some trends, relatively cheap, lots of local technical ability, but can’t assess effects of some threats (e.g., hunting), in some cases is post hoc Mark-recapture (genetic) intensive surveys needed, technically demanding, expensive Structured village surveys data can be messy, people can give poor information, misidentifying orangutans, but can be done quickly and cheaply, thereby covering large areas and can be used to gather additional data on threats Structured village surveys Structured village surveys N Abundance and rarity t 1. Population ecology 101 2. Population dynamics 101 3. Measuring primate abundance >4. What determines primate abundance? How does food limit population density? Opinions differ…. “fig or palm nuts were not good predictors of total primate biomass or that of any (Neotropical) primate guild…. Preferred plant foods were significantly related to total biomass…” (Stevenson 1999) Altmann et al. 1985; Balcomb et al. 2001, Djojosudharmo & van Schaik 1992 “Food may be sufficiently abundant for long periods of time when resource limitation, if present, may operate in very subtle ways. When on rare occasions resources decrease dramatically, monkeys do indeed fall out of trees dead from hunger… reducing population density…” (Cant 1980) Davies 1994; Foster 1982; Nakagawa et al. 1996; Tutin et al. 1997 “Both the total annual food abundance and the food availability during the bottleneck period are important determinants of (primate) density …” (Hanya 2004) Chapman et al. 1999; Clutton-Brock and Harvey, 1997; Decker 1994 ; Mather 1992 Habitat A versus Habitat B food time Density A A B > Density B Habitat A versus Habitat B food time Density A A B food A B time > Density B Density A > Density B Habitat A versus Habitat B food time Density A A B food A B time > Density B A B Density A > Density B food time Density A ? Density B Habitat A versus Habitat B food time Density A A B food A B time > Density B A B Density A > Density B A B food time Density A food time Density A ? Density B ? Density B Density variation across habitat types 14 Gibbons 12 Leaf monkeys 10 Denisty (indiv/km2) +/- SE 8 6 4 2 0 Peat Swamp Freshwater Swamp Alluvial Bench Lowland Sandstone Lowland Granite Upland Granite Montane Plot locations Fallback foods limit gibbon populations 1.4 Gibbon density log (indiv/km2) 1.2 1 0.8 0.6 0.4 0.2 0 0 0.25 0.5 0.75 1 Availability of figs log (fig stems per ha) R2 = 0.70, p = 0.01, n = 7 habitats Marshall & Leighton 2006 Gibbon biomass and fig density across SE Asia 2.4 2.2 Gibbon biomass log (kg/km2) 2 1.8 1.6 1.4 1.2 1 0 0.25 0.5 0.75 1 1.25 1.5 1.75 n= 11 sites, r2 = 0.82 p= 0.0001 Availability of figs log (fig stems per ha) Marshall et al. in prep. Preferred foods limit leaf monkey populations 1.2 Leaf monkey density log (indiv/km2) 1 0.8 0.6 0.4 0.2 0 0.5 0.75 1 1.25 1.5 R2 = 0.90, p = 0.001, n = 7 habitats Mean PREFERRED food patches/ha during mast and high fruit periods log (stems per ha) Marshall et al. 2009 Leaf monkey biomass vs. preferred foods across SE Asia 700 600 500 400 2 Leaf monkey biomass (kg/km ) n = 12 sites 300 200 100 0 0.0% R2 = 0.67 p = 0.001 2.0% 4.0% 6.0% 8.0% Percent of stems that are Legumes (rough index of preferred food availability) Marshall et al. in prep. Summary: I Gibbons Leaf monkeys Total food Fallback foods Marshall, Boyko, Feilen, Boyko, & Leighton 2009 Summary: II Gibbons Leaf monkeys Preferred food (stem density) Preferred food (phenological measure) Marshall, Boyko, Feilen, Boyko, & Leighton 2009 N Take home messages t 1. Population ecology links individual characteristics and processes to population characteristics and processes. 2. Density-dependent factors can regulate populations, density independent factors limit them. 3. Most primate populations are relatively stable over time, although they are subject to both density-dependent and densityindependent factors. 4. Each survey method has associated strengths, limitations, and assumptions. Selection of a survey technique depends on the goals of the survey. 5. Food may be a key limiting (but rarely regulating) factor for primates, but species are affected in different ways by different types of foods (e.g., gibbon vs. leaf monkey example). N Question to ponder t Imagine you are trying to manage a patch of forest to increase K (carrying capacity) for a particular primate species. Based on your understanding of primate feeding behavior, population ecology, and life history, explain the factors you would consider in deciding what set of plant species to plant in the forest patch. In your answer, be sure to discuss the particular phenological characteristics that your chosen plants would exhibit and how this might depend on the life history and population biology of the species that you are hoping to manage. ...
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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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