1. Monitoring and Estimating Species Richness Paul F. Doherty, Jr. Fishery and Wildlife Biology Department Colorado State University Fort Collins, CO

2. 2 sample locations How does species richness and composition vary among different landscape contexts? 505, S=12 642, S=10

3. 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

4. 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

5. 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

6. 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

7. 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 (M h ) –Other factors influencing p –Computer programs

8. 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

9. 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

10. T1 T2 T3 T4 T5 Replicated counts (over time) at single site Multiple species lists Short interval: Closed population -Estimate N Model issues: M h (Different individual) M bh (Same individual) M th (methods vary)

11. L5 L1 L2 L3 L4 Replicated counts (over space) at single time Quadrat samples Short interval: Closed population -Estimate N Model issues: M h M th (spatial variation in p)

12. 1 2 3 4... # Individuals # Species Summary of information used in SPECRICH Empirical Species Abundance Distribution

13. 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 M h

14. DC Birdscape Results Analyzed using Program Specrich

15. 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

17. 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

19. 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 –(χ 2 =0.38, df= 1,P = 0.54)

22. Programs for Species Richness Specrich –Capture frequency only (reduced form input) Specrich2 –Summarized capture history input for M h –Observed species, observed frequencies Capture –All M h varieties (most general) –Unsummarized capture histories

23. 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”

24. T1 L5 T1 L1 T1 L2 T1 L3 T1 L4 T2 L2 T2 L1 T2 L5 T2 L4 T2 L3 Time 1Time 2 Robust design: Spatial Subsampling in primary periods Robust Designs:

25. A T1 A T2 A T3 A T4 A T5 Area AArea B B T1 B T2 B T3 B T4 B T5 Spatial robust design: temporal subsampling within primary periods

26. A L5 A L4 A L3 A L2 A L1 B L5 B L1 B L2 B L3 B L4 Area AArea B Spatial robust design: Spatial subsampling within units

27. 1 2 3 4... # Individuals # Species Area AArea B 1 2 3 4... # Individuals # Species Spatial robust design: Spatial subsampling with capture frequences

28. 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

29. Local Extinction Probability (φ ij ) Probability that species present at i not present at j Estimation – R i = number of species present in year i = estimated number of these species still present in year j Estimate with M h

30. Local Turnover (φ ji ) 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 R j = number of species present in year j = estimated number of species observed in j that were also present in i

31. 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.

36. 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

41. 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

42. Programs for Estimating Change Comdyn –Reduced M h input format