COS 39-6
Do tropical forests with higher above ground carbon storage support more endothermic terrestrial species?

Tuesday, August 12, 2014: 3:20 PM
311/312, Sacramento Convention Center
Lydia Beaudrot, TEAM Network, Moore Center for Science and Oceans, Conservation International, Arlington, VA
Jorge Ahumada, TEAM Network, Moore Center for Science and Oceans, Conservation International

REDD+ advocates promote that maximizing conservation efforts for carbon storage in the form of above ground plant biomass will result in co-benefits for biodiversity conservation. While coarse-grained global analyses have supported the pattern that the greatest levels of terrestrial vertebrate biodiversity and carbon stocks are both found in the tropics, current debate about the potential for biodiversity co-benefits requires evaluation of these relationships using high-resolution data within the tropics. We use data from the world’s pre-eminent tropical camera trap network, the Tropical Ecology Assessment and Monitoring (TEAM) Network, to examine the relationship between aboveground carbon storage, plant diversity and terrestrial vertebrate diversity. Specifically, we assess whether forests that store more carbon contain more terrestrial vertebrate species. We estimate terrestrial endotherm species richness from camera trap data for each TEAM site using a Bayesian modeling approach that simultaneously accounts for species occurrence and detection. This method accounts for unseen species and thus includes rare and difficult to detect species. We used published data on aboveground C storage, stem density, plant diversity, precipitation and elevation from corresponding TEAM Network sites to predict modeled estimates of species richness. We evaluate standardized predictors of terrestrial vertebrate species richness using linear regression and AIC model comparison. 


Bayesian models of species richness consistently estimated more species than were detected at each site from camera trap sampling, which is due to the occurrence of species in tropical forests that are rare and difficult to sample. In addition, the species richness estimates were comparable to known values for the sites with the most extensive previous survey effort. These results suggest that Bayesian methods that account for detection provide an important tool for the estimation of species richness in tropical forests and provide a methodological advancement with useful outputs for the REDD+ debate. According to AIC model selection, the top model explaining terrestrial vertebrate species richness contained only the stem density of trees as a predictor variable. Stem density was the only significant predictor across all models. There was an inverse relationship between the stem density of trees and terrestrial vertebrate species richness (linear regression: R2=0.58, df=9, p=0.006, β=-4.37). Importantly, there was no relationship between above-ground carbon storage and the terrestrial vertebrate species richness at a site (linear regression: R2=0.001, df=9, p=0.89, β=-0.25). These results indicate that tropical forests with high carbon storage do not contain more endothermic terrestrial species and safeguards for biodiversity are necessary.