Monitoring and estimating species richness
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Monitoring and Estimating Species Richness. Paul F. Doherty, Jr. Fishery and Wildlife Biology Department Colorado State University Fort Collins, CO. Background. Species diversity or richness may be an important objective of monitoring/management programs Especially with careful definitions

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Monitoring and Estimating Species Richness

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Monitoring and estimating species richness

Monitoring and Estimating Species Richness

Paul F. Doherty, Jr.

Fishery and Wildlife Biology Department

Colorado State University

Fort Collins, CO


Background

Background

  • Species diversity or richness may be an important objective of monitoring/management programs

    • Especially with careful definitions

      • e.g. maximizing the number neotropical migrants, bats, frogs


Background1

Background

  • In estimating species diversity the most common measures are:

  • Measure of evenness (relative abundances)

    • Simpson’s Index

    • Shannon Index (a.k.a. Shannon-Weiner, Shannon-Weaver)

  • Species richness (presence/absence)

    • No need to tell individuals apart

  • Often-made assumptions

    • detection rates = 1

    • all species sampled with the same probability.

  • In estimating species diversity the most common measures are:

  • Measure of evenness (relative abundances)

    • Simpson’s Index

    • Shannon Index (a.k.a. Shannon-Weiner, Shannon-Weaver)

  • Species richness (presence/absence)

    • No need to tell individuals apart

  • Often-made assumptions

    • detection rates = 1

    • all species sampled with the same probability.


Challenges

Challenges

  • Different species will have different detectabilities

    • Certainly some species will be missed

      • e.g. Compare a thrush with a gnatcatcher song

      • Abundance will vary for each species

  • To use Shannon or Simpson’s Indices

    • must estimate abundance of each species

    • Not clear how to interpret these indices (notions of evenness and dominance)

  • Focus on specie richness

    • Easier to tell species apart than individuals

    • In past studies species richness and these indices are often highly correlated.


  • Direct connection in estimating richness to estimating abundance

    Direct connection in estimating richness to estimating abundance

    • Instead of estimating abundance (# of individuals), estimate species richness (# of species)

    • Instead of estimating survival, estimate extinction, etc.


    Time scale

    Time scale

    • Local or global

      • Local probably most relevant in our case


    Estimation

    Estimation

    • Importance of detection probability (C = count, p = probabilty of detection, S = richness)

    • Relative change – issue does not go away


    Estimation1

    Estimation

    • Can use ‘heterogeneity’ estimators such as Burnham and Overton’s (1978,1979) Jackknife.

      • Makes sense to use estimator such as this.

      • Desirable to have equal sampling effort (e.g. quadrats, time spent listening).

      • Can be used with ‘capture history’ or X matrix

      • N = number of species detected, t = number of quadrats sampled, f = number of species found on only one quadrat

      • For stats-minded folks – this is probably one area where growth will occur


    Sampling

    Sampling

    • Sampling over time or space

    • Capture history for each species

      • 010110

      • Can generate frequencies (# of species seen exactly once, twice, etc)

      • Can estimate richness using widely available software (Capture, SpecRich, Comdyn).

    T1

    T2

    T3

    T4

    T5

    T6

    L2

    L1

    L3

    L4

    L6

    L5


    Community dynamics

    closed

    closed

    closed

    open

    open

    Community dynamics

    • Change in species richness

    • Extinction

    • Turnover

    • Colonization

    • Use Pollock’s Robust Design

    X1X2X3 Y1Y2Y3 Z1Z2Z3


    Community dynamics1

    Community dynamics

    • Change in species richness over time

    • Not changes in composition, just numbers

    • ‘Stability’ (temporal variance)


    Local extinction

    Local Extinction

    • Probability that a species present in sampling period i is not present in a later period j.


    Local species turnover

    Local Species Turnover

    • Probability that a species selected at random from the community at time j is a “new” species as compared to the community at time i.

      • 1 if every species is “new”

      • 0 if every species is “old”


    Number of colonizing species

    Number of colonizing species

    • The number of species not present at time i that colonize and are present at time j.


    Spatial analogs

    Spatial Analogs

    • Comparing two sites

    • Relative species richness

    • Species co-occurance and omission

      • Comparing reference and altered sites

      • (Forest Plan)


    Example

    Example

    • Comdyn, SpecRich

    • http://www.mbr-pwrc.usgs.gov/software.html#comdyn


    Example1

    Example

    • Compare a BBS route (WI in 1970 and 1990)


    Summary

    Summary

    • Community richness and dynamics can be estimated nicely and easily

    • May be useful in monitoring programs

      • Especially where monitoring concerns are ‘broad’.

      • Data are often easier/cheaper to collect than those associated with abundance


    Monitoring and estimating species richness

    Example:Sexual Selection affects local extinction and turnover in bird communitiesDoherty P.F., Sorci, G., Royle, A., Nichols, J.D., and T. Boulinier


    Background2

    Background

    • Sexual selection is thought to drive the evolution of secondary sexual traits such as bright colors and plumage dimorphism


    Benefit of these traits

    Benefit of these traits

    • Increased reproductive success (Andersson 1994)


    Costs of such traits

    Costs of such traits

    • Increased predation and sensitivity to environmental and demographic stochasticity (Haskell 1996, Legendre et al. 1999)

      • Prediction: Extinction rates should be higher for species with intense natural selection (Tanaka 1996)


    Support for prediction

    Support for prediction

    • Dichromatic bird species have been shown to have higher mortality rates than monochromatic (Promislow et al. 1994)

    • Dichromatic bird species introduced onto islands went extinct at a higher rate than monochromatic species (McLain et al. 1995, Sorci et al. 1998)


    Methodological issues

    Methodological Issues

    • Detection error

      • Dimorphic and monomorphic species may not be detected with equal probability

      • Differ with habitat

      • Temporal differences

    • Variation in space

      • Local populations close together may be synchronous

      • Spatial autocorrelation shown to be important is island study of species richness (Selmi and Boulinier 2001)


    Objective of our study

    Objective of our study

    • To test the prediction that dichromatic species have higher local extinction rates

    • Improvements

      • Also examined turnover rates

      • Tested on continental scale.

      • Incorporated detection probabilities

      • Incorporated spatial autocorrelation


    Data set

    Data set

    • US Breeding Bird Survey data (1975-96)

    • Restricted dataset to daytime species from 6 orders and grouped them as either having dichromatic (n = 153) or monochromatic (n=185) plumage.


    Definition

    Definition

    • Extinction - proportion of species becoming locally extinct (on a BBS survey route) between two successive years among species present the first year.

    • Turnover – proportion of new species among species present the second year as compared to the first


    Detection error

    Detection Error

    • We used COMDYN (1999) to estimate species richness, extinction rate, and turnover rate for dimorphic and monomorphic species

    • This program and associated metrics was developed to better estimate these quantities as well as detection probabilities

    • Based on ‘closed’ capture models with heterogeneity (Mh) and robust design (Boulinier et al. 1998, Nichols et al. 1998)


    Spatial modeling

    Spatial modeling

    • We used a Bayesian approach

    • Computed the difference between dichromatic and monchromatic extinction rates


    Spatial modeling1

    Spatial modeling

    • T-test would assume yi independent normally distributed

    • However, yi‘s probably not independent due to spatial proximity

    • We could resort to a subjective stratification scheme with associated restrictive forms of spatial dependence

    • Or we could introduce spatial correlation into the problem explicitly by adding an error term which we assume is spatially correlated


    Spatial modeling2

    Normally distributed

    random variable

    Correlated spatial

    random variables

    Spatial modeling

    • Many ways to parameterize the spatial correlation among the αi’s

    • Could insert parameters from a variogram (parametric approach)

    • We used a convolution approach suggested by Higdon (1998)


    Results

    Results

    • Dichromatic species had a 23% higher local extinction rate per survey than monochromatic species (0.079 ± 0.001 vs. 0.064 ± 0.001)

    • Widespread result across continent indicating that sexual selection influences the communities of birds in many differing habitats and places.

    • We also found that a model incorporating a spatial component fit better than one without (ΔAIC = 117), and that we should qualify above statement


    Monitoring and estimating species richness

    Mean difference in extinction rate

    Lower 95% CI


    Results1

    Results

    • If extinction rates are higher in dichromatic species, then some might expect the number of dichromatic species to decrease over time.

    • We found the number of species in both groups increased over the time period (4.8% and 3.4% respectively).

    • We examined turnover rates too


    Monitoring and estimating species richness

    Mean difference in

    turnover rate

    Lower 95% CI


    Conclusions

    Conclusions

    • Higher local extinction and turnover rates for dichromatic vs. monochromatic species

    • Spatial structure in these rates suggest metacommunities (Wilson 1992)

    • Regionally connected communities

      • Regional or local factors may have dramatic effects over a large area.


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