# Presentation: 074097

## 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
- e.g. maximizing the number neotropical migrants, bats, frogs

## 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

- 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

- Instead of estimating abundance (# of individuals), estimate species richness (# of species)
- Instead of estimating survival, estimate extinction, etc.

## Time scale

- Local or global
- Local probably most relevant in our case

## Estimation

- Importance of detection probability (C = count, p = probabilty of detection, S = richness)
- Relative change – issue does not go away

## 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 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

- L1

- L2

- L3

- L4

- L5

- L6

## Community dynamics

- Change in species richness
- Extinction
- Turnover
- Colonization
- Use Pollock’s Robust Design

- X1X2X3                   Y1Y2Y3                  Z1Z2Z3

## Community dynamics

- Change in species richness over time
- Not changes in composition, just numbers
- ‘Stability’ (temporal variance)

## Local Extinction

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

## 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

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

## Spatial Analogs

- Comparing two sites
- Relative species richness
- Species co-occurance and omission
- Comparing reference and altered sites
- (Forest Plan)

## Example

- Comdyn, SpecRich
- http://www.mbr-pwrc.usgs.gov/software.html#comdyn

## Example

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

## 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

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

## Background

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

## Benefit of these traits

- Increased reproductive success (Andersson 1994)

## 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

- 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

- 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

- 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

- 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

- 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

- 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

- We used a Bayesian approach
- Computed the difference between dichromatic and monchromatic extinction rates

## Spatial modeling 

- T-test would assume *y**i* independent normally distributed
- However, *y**i*‘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 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

- 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

## Slide 33

## Mean difference in extinction rate

- Lower 95% CI

## 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

## Mean difference in 

- Mean difference in
- turnover rate

- Lower 95% CI

## Slide 37

## 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.