# Presentation: 224971

## Monitoring and Estimating Species Richness

- Paul F. Doherty, Jr.
- Fishery and Wildlife Biology Department
- Colorado State University
- Fort Collins, CO

## 2 sample locations

- How does species richness and composition vary among different landscape contexts?
- 505, S=12
- 642, S=10

## Community Focus in Ecology/Conservation

- Understanding/management of species groups and communities
- Common environmental constraints associated with
- Life History
- Neotropical Migrant Birds (North America)
- Characteristic Habitats
- Grassland, interior forest
- Regions/landscapes
- Endemics
- Keystone species
- Pure logistical constraints
- Cannot evaluate dynamics of individual species
- Rareness
- Sampling methods

## Common Measures of Species diversity

- Diversity/evenness measures
- Simpson’s Index
- Shannon Index
- Rely on abundance information
- Species richness (presence/absence)
- No abundance information
- Historically, applied to any data set without regard to sampling issues

## Failures of Index-based Measures

- Diversity measures
- Require
- Estimates of abundance
- Documentation of presence
- Greatly influenced by sampling
- Species richness
- Also influenced by incomplete information on presence
- Miss species during counting

## Estimating Species Richness

- Detection issues also apply to species richness estimation
- Miss species during counting
- Differs due to habitat, observers, weather, etc
- Counts (C) of number of species underestimates actual species richness!
- Recall:  problems in estimating change from indexes

## Procedures for Estimating Species Richness

- Capture-recapture models,
- Applied to counts from replicate times or points
- Species are analogous to individuals
- Sites are analogous to capture occasions
- Estimate N of species
- Well-developed estimation procedures
- Know that species vary in detection rates
- Individual heterogeneity: Models allow for heterogeneity in species detection rates (Mh)
- Other factors influencing p
- Computer programs

## 2 Advantages

- Applicability
- Can apply to count data that otherwise could only be used as indexes
- May be only credible use of some data
- Breeding Bird Survey
- (assumption that species pool is accessible)
- Flexibility
- Can estimate species richness and change over space and time

## Sample Situation/Design

- Have replicated counts
- Over space
- Over time
- Over space and time
- Robust Design and Closed/open Populations
- Estimate richness when closed
- Estimate change when open
- Connection: Common observed species

## Replicated counts (over time) at single site 

- T1   T2   T3   T4   T5

- Replicated counts (over time) at single site
- 	Multiple species lists

- Short interval:  Closed population
- 	-Estimate N

- Model issues:
- Mh(Different individual)
- Mbh (Same individual)
- Mth(methods vary)

## Replicated counts (over space) at single time 

- L5

- L1

- L2

- L3

- L4

- Replicated counts (over space) at single time
- 	Quadrat samples

- Short interval:  Closed population
- 	-Estimate N

- Model issues:
- Mh
- Mth(spatial variation in p)

## Summary of information used in SPECRICH

- Empirical Species Abundance Distribution

## Analysis of Data

- Capture history format
- Over replicates (time or space)
- Each species encountered or not encountered
-  e.g., 5 sites  :00101
- Program Capture
- Use heterogeneity models
- Can use reduced input:
-  f(I):  freq of species occurring I times
-  n(I): n of species at site (time) i
- Reduced format
- Program Specrich
- Burnham and Overton Mh

## DC Birdscape Results

- Analyzed using Program Specrich

## North American Breeding Bird Survey

- Started in 1966
- Roadside survey
- Conducted in June, 1 survey/route/year
- 24.5 mi roadside survey “routes” conducted by volunteer observer
- 50, 3-min point counts along route
- Sum of counts for each species over 50 stops form the index of abundance for the route

## Slide 16

## Application to BBS

- Count locations are sites
- Use groups of 10 sites
- Estimate richness at route level
- Analyze subsets of species (e.g., Forest birds)
- Provide some examples from mid-Atlantic Region
- Richness, and associations with habitat variables

## Slide 18

## Survey Route in MD, Data from 1966 and 1992Estimating Species Richness

- Data: Capture history condensed from 50 stops
- 10 stops = 1 capture occasion
-  	e.g., Eastern Bluebird: 01111
-  Use program CAPTURE
-  Results:
-  1966: Count: 64 spp; Estimate=75 (5.62)
-  1992: Count: 50 spp; Estimate=83 (11.61)
-  No difference in estimates
- (χ2=0.38, df= 1,P = 0.54)

## Slide 20

## Slide 21

## Programs for Species Richness

- Specrich
- Capture frequency only (reduced form input)
- Specrich2
- Summarized capture history input for Mh
- Observed species, observed frequencies
- Capture
- All Mh varieties (most general)
- Unsummarized capture histories

## Evaluation of Change in Species Richness Over Time or Space

- Many opportunities
- Change over time or space
- Turnover
- Probability that a species selected at random from the community in year j was not present in year i
- Extinction
- Probability that a species present in year i is not present in some later year j  (i < j)
- Temporal variation
- Robust design
- Estimate richness with “Closed”
- Estimate change when “Open”

## Time 1

- Time 2

- Robust design:  Spatial Subsampling in primary periods

- Robust Designs:

## Area A

- Area B

- BT1  BT2  BT3  BT4  BT5

- Spatial robust design:  temporal subsampling within primary periods

## Area A

- Area B

- Spatial robust design:  Spatial subsampling within units

## Area A

- Area B

- Spatial robust design:  Spatial subsampling with capture frequences

## Estimation

- Rate of change in species richness: ratio of estimated richness
-  Can condition on species observed at a time (or place), and estimate the number of those species that occur at some other place (or time)
-  Extinction probabilities
- Community turnover
- Number of colonizing species
-  Can estimate (bootstrapped) variances, do statistical tests

## Local Extinction Probability (φij)

- Probability that species present at i not present at j
- Estimation
-  Ri = number of species present in year i
-          = estimated number of these species still present in year j
- Estimate with Mh

## Local Turnover (φji)

- Probability that species selected at random at time *j* is a new species (not present at time *i*)
- Estimation
-  Using the extinction probability estimator with data placed in reverse time order
-  Rj = number of species present in year j
-         = estimated number of species observed in j that were also present in i

## Number of Colonizing Species

- The number of species not present at time *i* that colonize and are present at time *j*.
- Number of surviving species is subtracted from species richness at j.

## Slide 32

## Slide 33

## Slide 34

## Slide 35

## Evaluation of Richness and Variation in Richness Over Time, From BBS Data

- Estimate species richness for area-sensitive and non area-sensitive species for MD, NY, and PA
- Calculate CV of richness over period 1975-1996
- Based on true temporal variation, as we can estimate measurement error from the variance in richness
- Also estimate richness in 1974
- Correlate with forest patch size

## Slide 37

## Slide 38

## Slide 39

## Slide 40

## Issues

- Species pools need to have a “reasonable” number of species for these estimators to work (>10?)
- If a species has a detection probability of 0, it will not be part of the species pool
- Heterogeneity estimators

## Programs for Estimating Change

- Comdyn
- Reduced  Mh input format