Integrating large-scale survey data sets with climate and land use data to model species distribution dynamics Andrew M. Latimer and John A. Silander Department of Ecology & Evolutionary Biology University of Connecticut Biodiversity Over Space

“distribution and abundance of species”

Data-model integration –New large data sets –Issues: Diverse kinds and scales of data Spatial and temporal covariance structures –Tools: Bayesian hierarchical models

In this talk Static single-species distribution models –Areal-unit models –Environment and colonization –Land use –Abundance Biodiversity – joint distribution of species Point process models –Computational advances –Multispecies models Current work: making these dynamic

Species observations Presence/absence Abundance Diversity Abiotic environment Why absent? -Not suitable -Not available -Not colonized E(Abundance) ≈ P(Presence)? Land use data

Hierarchical single-species model P(colonized | suitable & available) P(suitable) P(available | suitable) Neighborhood & connectivity info Land use data (satellite imagery) Environmental data (weather stations, soils) P(present) Species sample data

Hierarchical single-species model P(colonized | suitable & available) P(suitable) P(available | suitable) P(·) = f(U i ) = 1-U i where U i ≡ prop. human-altered P(·) = dbin(n i, p i ) logit(p i ) = X T β + w i P(·) = dbin(n i, q i ) logit(q i ) = g(‘neighborhood’) Latimer et al. (2006) Ecological Applications

Where the species was observed: L(y i ) = Binomial(n i, q i ) * f(p i ) Where not observed: L(y i ) = (1-f(p i )) + f(p i )*(1-q i ) n i Probability present given suitable & available Suitability, adjusted by availability function Probability unsuitable and/or unavailable Probability suitable & available but not observed P(present) Likelihood

P. lacticolor P. aurea P. punctata P. lacticolor P. aurea White Proteas (Protea spp.)

Hierarchical model: P(suitable)

Hierarchical model: P(available)*P(suitable)

Hierarchical: P(colonized | suitable & available)

Inference Primarily environmental limit on presence Some constraint at “colonization” stage

Adding an abundance level P(present) P(abundance (k) | present)Ordinal abundance scores, environmental data Introduce latent (log-scale) abundance surface Z and cutpoints {c 1, c 2, …, c k }. Abundance score = 1 if z i ≤ c 1 2 if z i > c 1 and z i ≤ c 2 … k if z i > c k

Latent log-scale abundance surface (Z)

Inference Different factors drive abundance –Cool winter temperature vs warm & wet growth season Mechanism? –Germination vs growth Latimer et al. Oecologia (in review)

Multi-species models Potential richness in the absence of human landscape alterations: Adjusted (transformed) richness given human transformed landscapes: Gelfand et al. (2005) Bayesian Analysis

Modeled subregion (for a subset of 40 species) Computational issues… Please help!

Species Richness

Multi-species results Different land uses; differential impacts. Latimer et al. (2004) S.A. Journal of Science

Modeling with point data Predictive process approach Banerjee, Gelfand et al. “Curse of dimensionality”

Multiple spatial processes: Multi-species model Cel. orbiculatus Rosa multiflora Berberis thungbergii Euonymus alatus

Canopy Closure Berberis thunbergii Celastrus orbiculatus Rosa multiflora Euonymus alatus Regression coefficient value Density

Celastrus orbiculatus Rosa multiflora Berberis thungbergii Euonymus alatus present absent Sample data:

Summing up Opportunities: –Physiological responses, abundance structure –Land use change impacts –Integrating satellite data Limitations: –Colonization and other spatial factors –Computer power

Current work Making dynamic: climate change Data: population-level performance data over time –Field survey plants and populations over gradients –Satellite data for phenology and productivity Latimer & Wilson et al. (in prep.) Global Change Biol

Acknowledgments U.S. NSF Grant DEB SANBI (Esp. Tony Rebelo & Guy Midgley) Duke ISDS (Esp. Alan Gelfand & Huiyan Sang) UCONN EEB (Esp. Inés Ibáñes, Adam Wilson)