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a global database for metacommunity ecology, integrating species, traits, environment and space alienor Jeliazkov et al. #the use of functional information in the form of species traits plays an important role in explaining biodiversity patterns and responses to environmental changes. although relationships between species composition, their traits, and the environment have been extensively studied on a case-by-case basis, results are variable, and it remains unclear how generalizable these relationships are across ecosystems, taxa and spatial scales. to address this gap, we collated 80 datasets from trait-based studies into a global database for metaCommunity Ecology: Species, Traits, Environment and Space; "CEStES". Each dataset includes four matrices: species community abundances or presences/absences across multiple sites, species trait information, environmental variables and spatial coordinates of the sampling sites. the CEStES database is a live database: it will be maintained and expanded in the future as new datasets become available. By its harmonized structure, and the diversity of ecosystem types, taxonomic groups, and spatial scales it covers, the CEStES database provides an important opportunity for synthetic trait-based research in community ecology. Background & SummaryA major challenge in ecology is to understand the processes underlying community assembly and biodiversity patterns across space 1,2 . Over the three last decades, trait-based research, by taking up this challenge, has drawn increasing interest 3 , in particular with the aim of predicting biodiversity response to environment. In community ecology, it has been equated to the 'Holy Grail' that would allow ecologists to approach the potential processes underlying metacommunity patterns 4-7 . In macroecology, it is common to study biodiversity variation through its taxonomic and functional facets along gradients of environmental drivers 8-10 . In biodiversity-ecosystem functioning research, trait-based diversity measures complement taxonomic ones to predict ecosystem functions 11 offering early-warning signs of ecosystem perturbation 12 .The topic of Trait-Environment Relationships (TER) has been extensively studied across the globe and across the tree of life. However, each study deals with a specific system, taxonomic group, and geographic region and uses different methods to assess the relationship between species traits and the environment. As a consequence, we do not know how generalizable apparent relationships are, nor how they vary across ecosystems, realms, and taxonomic groups. In addition, while there is an emerging synthesis about the role of traits for terrestrial plant communities 13,14 , we know much less about other groups and ecosystem types.To address these gaps, we introduce the CESTES database -a global database for metaCommunity Ecology: Species, Traits, Environment and Space. This database assembles 80 datasets from studies that analysed empirical multivariate trait-environment relationships between 1996 (the first...

Abstract:The monitoring and prediction of biodiversity and environmental changes is constrained by the availability of accurate and spatially contiguous climatic variables at fine temporal and spatial grains. In this study, we evaluate best practices for generating gridded, one-kilometer resolution, daily maximum air temperature surfaces in a regional context, the state of Oregon, USA. Covariates used in the interpolation include remote sensing derived elevation, aspect, canopy height, percent forest cover and MODIS Land Surface Temperature (LST). Because of missing values, we aggregated daily LST values as long term (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010) monthly climatologies to leverage its spatial detail in the interpolation. We predicted temperature with three methods-Universal Kriging, Geographically Weighted 8640Regression (GWR) and Generalized Additive Models (GAM)-and assessed predictions using meteorological stations over 365 days in 2010. We find that GAM is least sensitive to overtraining (overfitting) and results in lowest errors in term of distance to closest training stations. Mean elevation, LST, and distance to ocean are flagged most frequently as significant covariates among all daily predictions. Results indicate that GAM with latitude, longitude and elevation is the top model but that LST has potential in providing additional fine-grained spatial structure related to land cover effects. The study also highlights the need for more rigorous methods and data to evaluate the spatial structure and fine grained accuracy of predicted surfaces.