 1 Species Richness 5.19 UF

2 Community-level Studies Many community-level studies collect occupancy-type data (species lists). Imperfect detection will also create biases in measures of species richness. Despite this, the majority of ecological studies reporting species richness are based on raw species counts.

Estimation of Species Richness: Three Approaches Single or a small number of sample units  (1) Capture-recapture approaches  (2) Occupancy approaches Larger number of units  (3) Occupancy approaches 3

Single Sample Unit: CR Approach Basic data: species lists at replicate samples Replication:  Temporal replicates (multiple visits to site over relatively short period of time)  Spatial replicates within the sample unit Use capture-recapture models for closed populations  Treat species as individuals 4

Single Sample Unit: CR Approach Strong expectation of heterogeneous detection probabilities among species Focus on CR models designed for heterogeneity 5

T1 T2 T3 T4 T5 Replicated counts (over time) at single site Multiple species lists Short interval: Closed population -Estimate N Models: M h (Different investigators,each T) M bh (Same investigators) M th (Survey methods vary over T) Single Sample Unit: CR Approach Occasion (T) Species12345 A B C

L5 L1 L2 L3 L4 Replicated counts (over space) at single time Quadrat samples Short interval: Closed population -Estimate N Models: M h M th (spatial variation in p) Single Sample Unit: CR Approach

# Individuals # Species Information: Empirical Species Abundance Distribution Single Sample Unit: CR Approach Model: Limiting form of M h jackknife estimator (Burnham & Overton 1979)

Analysis of Data Capture history format  Over replicates (time or space) Each species encountered or not encountered e.g., 5 sites:  Program CAPTURE (Rexstad and Burnham 1991) or MARK (e.g., White and Burnham 1999) Use heterogeneity models Can use reduced input (CAPTURE): f(i): frequency of species occurring i times n(i): n of species at site (time) i

Analysis of Data Programs SPECRICH & COMDYN (Hines et al. 1999)  Temporal or spatial replication Can use capture frequency data, f(i); number of species detected on i occasions Use Burnham and Overton (1979) jackknife estimator for model M h  No replication but collect extra information on number of individuals detected for each species (empirical species abundance distribution) Use limiting form (unlimited capture “occasions”) of jackknife estimator (Burnham and Overton 1979) 10

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

Application to BBS Count locations are sites/stops  Use groups of 10 sites/stops Estimate richness at route level Analyze groups of species (e.g., Forest birds)

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: Use program CAPTURE, Model M h 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)

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  Estimate species richness in 1974 and correlate with forest patch size (remote sensing data for area surrounding each BBS route)  Estimate temporal (process) CV of richness over period Focus on true temporal variation (estimate and subtract out measurement error)

22 Species Richness Estimation Using Occupancy Modeling: Single Unit Compose list of s species of interest in pool (or use data augmentation). Multiple surveys are conducted for the species.  Either temporal or spatial Species are detected/not detected. Resulting data are similar to the single- species, single-season situation with species on the list being analogous to ‘sample units’.

23 Single Unit: Occupancy Approach Species richness equates to number of the species on the list present at the unit. State-space formulation is particularly useful because the species list represents the entire population of interest.

24 Single Unit: Occupancy Approach Similar intent to application of mark- recapture methods to estimating species richness.  e.g., SPECRICH, COMDYN (Hines et al. 1999) An advantage is attributes of each species could be included as covariates. Multi-season models could be applied to investigate changes in the community or species richness over time.

25 How to Set Up Data for PRESENCE Each row (“site”) represents the detection/nondetection of a species in the repeated surveys. ‘Site-specific’ covariates represent covariates about the individual species.  e.g., resident, size, coloration

26 Example: Species Richness Number of species present at BBS route in Maryland, USA 50 stops along route, with species detected recorded at each stop. 10-stop summaries used; 5 survey units Data from June 1990

27 Example: Species Richness 85 bird species on master list (s) Established after the fact here Species categorised by migratory status Resident, short distance or neo-tropical 51 species detected at least once in survey

28 Example: Species Richness Analysis conducted in WinBUGS   ’s different for each migratory group (j)  Also include species (i) random effect (ε i )   2 chains of 51,000 iterations; 1 st 1,000 discarded as burn-in period

29 Example: Species Richness

30 Example: Species Richness Median = 32, (25, 40)Median = 65, (54, 78) Median = 22, (19, 30) Median = 10, (8, 13)

31 Large Number of Units: Occupancy Same data type as for single-species occupancy models, but collected for many species. Focus may be:  Estimation of species richness across sampled locations and larger areas.  Development of maps of species richness as functions of landscape or habitat covariates

32 Large Number of Units: Occupancy Data collected on M possible species at s sampling units. Fit single-species models to each species. Species richness at unit i can be estimated as: or:

33 Large Number of Units: Occupancy State-space approach especially useful as many relevant community-level summaries can be calculated directly from the predicted occurrence of the species. Also possible to construct species accumulation curves corrected for detection probability.

34 Large Number of Units: Occupancy Dorazio et al. (2006) also show that the total number of species in an area can be estimated in absence of species pool info.  add an arbitrarily-large number of ‘species’ to the data set with the ‘all-zeros’ detection history (data augmentation).  there are now S ‘species’ on the list.  Ω is the fraction of the list that may be real species.

35 Large Number of Units: Occupancy Detectability can not be species-specific without some functional relationship.  Can not estimate detection probability for species never detected in absence of species pool info.  Can estimate detection probability for species in known pool that are not detected if p follows covariate relationship.

36 How to Set Up Data for PRESENCE Data will consist of M x s rows. More difficult to replicate Dorazio et al. (2006) type analysis in PRESENCE.

Summary: 3 Approaches to Estimation of Species Richness: Single or a small number of sample units  (1) Capture-recapture approaches  (2) Occupancy approaches Larger number of units  (3) Occupancy approaches All 3 approaches should work well. Occupancy models with known species pools permit species-specific covariates 37

38 Summary Plenty of scope for applying single-species occupancy and capture-recapture models to address community level questions.  Species richness/biodiversity.  Changes in community structure through time.  Response of different species to similar environmental changes.