Population responses to past and future environmental variability across the LTER network

Benedicte Bachelot, Plant Biology, Ecology, and Evolution, Oklahoma State University, Stillwater, OK, Aldo Compagnoni, Helmholtz Centre for Environmental Research–UFZ, Leipzig, Germany, Pedro Brandão Dias Ferreira Pinto, Biosciences, Rice University, Houston, TX, Marion Donald, Department of Biosciences, Rice University, Houston, TX, Joshua Fowler, Daniel Gorczynski and Thomas Miller, Department of BioSciences, Rice University, Houston, TX, Carsten G.B. Grupstra, Zoey T. Neale and Linyi Zhang, BioSciences, Rice University, Houston, TX, Jennifer Rudgers, Department of Biology, University of New Mexico, Albuquerque, NM, Kai Zhu, University of California, Santa Cruz

Population responses to past and future environmental variability across the LTER network
Background/Question/Methods
A key goal of Ecology is to predict species abundance and distribution across environmental and climatic spaces. This ecological goal is becoming more challenging as climate change is altering mean and variability in temperature and precipitation and species populations respond to both climatic mean and variance. During the last century, changes in temperature variability has been associated with an increase in species extinction, hasting the need to understand population responses to climate change. Because climate can have non-linear effects on demography, increase in climate variability can result in a decrease or an increase in species growth rate. This wide range of species responses to climate emphasizes the need for general principles to help ecologists predict the impact of climate change on species distribution and abundance. In this study, we used data from long-term experimental research sites across North America to ask two questions: 1) How do species growth rates change with past and future climate? and 2) Can species functional traits help us predict the effects of climate? To address these questions, we used long-term studies to calculate population growth rates using a Bayesian framework. Then, we used climateNA to extract past and future climate for each study location. Using, generalized additive models, we investigated how past and future climates were correlated with population growth rates. We used the full posterior distributions of population growth rates in order to propagate uncertainty.
Results/Conclusions
Across all the species, we found strong evidence for both neutral and negative effects of climate variability. We found little evidence for positive effects of climate variability. The functional trait analyses are on-going, and we expect species with different slow growth and long-life expectancy to be less sensitive to climate variability than species with short-life expectancy. Overall, these results highlight two important points. First, future climate change will more often be associated with negative effects of population growth rates than positive. Second, our work emphasizes the importance of long-term studies and the need for more of them and longer time series.