Biodiversity prediction in the Atlantic rainforest
This work is co-funded by FAPESP (BIOTA, 2013/50297-0), NSF (DEB 1343578) and NASA, through the Dimensions of Biodiversity Program.
CO-PIs on the grant:
Ana Carnaval, City College of CUNY
Mike Hikerson, City College of CUNY
Kyle McDonald, City College of CUNY
Fabian Michelangeli, NYBG
Wayt Thomas, NYBG
Cristina Miyaki, USP
Francisco Cruz, USP
We apply a hypothesis-testing framework to predict spatial patterns of biodiversity in the megadiverse and threatened Atlantic Forest (AF) of Brazil. To this end, we have gathered five US-based and three Brazilian PIs to generate and integrate:
1. novel remote sensing-based datasets on land cover and climate, combined with meteorological data,
2. locality data, phylogenetic, and genomic-scale analyses from 30+ families of plants, vertebrates and invertebrates,
3. information about functional traits (physiology) and biotic interactions, and
4. paleoenvironmental information from geological archives, including records of fossil pollen and speleothem isotopes (a proxy for precipitation changes, based on deposits in caves).
To describe the spatial patterns of diversity in the AF, we are synthesizing the distribution of taxonomic diversity by integrating data from producers, consumers, parasites, and bacterial symbionts. We are expanding on phylogenetic analyses and summarize broad patterns of endemism and turnover, at the species and lineage levels. To advance diversity prediction, we are integrating data on the ecological mechanisms acting on the AF flora and fauna (the functional dimension of diversity) with dynamic climatic models that describe variability of precipitation and temperature during the last six glacial- interglacial cycles. These models are being informed by paleoclimatological studies, including our pollen fossil and speleothem records.
Through validated Approximate Bayesian Computation methods, we are also using genetic, UCE, and RAD-Seq genomic diversity data from our multiple target taxa to statistically test the fit of the aggregate population histories to the inferred time-calibrated landscape shifts and demographic processes. These models will permit the description of dissimilarity of communities as a function of geographical and environmental turnover in space and time.