Glenn Manion

TL;DR: This work compared 16 modelling methods over 226 species from 6 regions of the world, creating the most comprehensive set of model comparisons to date and found that presence-only data were effective for modelling species' distributions for many species and regions.

TL;DR: Generalized dissimilarity modeling (GDM) as discussed by the authors is a statistical technique for analyzing and predicting spatial patterns of turnover in community composition (beta diversity) across large regions, which is an extension of matrix regression, designed specifically to accommodate two types of nonlinearity commonly encountered in large-scaled ecological data sets: (1) the curvilinear relationship between increasing ecological distance, and observed compositional dissimilarities, between sites; and (2) the variation in the rate of compositional turnover at different positions along environmental gradients.

TL;DR: A new approach to describing and mapping the global distribution of terrestrial biodiversity that focuses on estimating spatial pattern in emergent properties of biodiversity (richness and compositional turnover) rather than distributions of individual species, making it well suited to lesser-known, yet highly diverse, biological groups.

TL;DR: In this paper, the authors use generalized dissimilarity modeling (GDM) as a basis for estimating forest biodiversity remaining at different points in time, and predict that 9.1% of species in Madagascar have been committed to extinction from deforestation between 1950 and 2000.

TL;DR: The spatial links tool (SLT) as mentioned in this paper maps link value across a region and combines connectivity measures from metapopulation ecology with the least cost path algorithm from graph theory, and can be applied to continuously variable landscape data.