Two prominent limitations of species distribution models (SDMs) are spatial biases in existing occurrence data and a lack of spatially explicit predictor variables to fully capture habitat characteristics of species. Can existing and emerging remote sensing technologies meet these challenges and improve future SDMs? We believe so. Novel products derived from multispectral and hyperspectral sensors, as well as future Light Detection and Ranging (LiDAR) and RADAR missions, may play a key role in improving model performance. In this perspective piece, we demonstrate how modern sensors onboard satellites, planes and unmanned aerial vehicles are revolutionizing the way we can detect and monitor both plant and animal species in terrestrial and aquatic ecosystems as well as allowing the emergence of novel predictor variables appropriate for species distribution modeling. We hope this interdisciplinary perspective will motivate ecologists, remote sensing experts and modelers to work together for developing a more refined SDM framework in the near future.

BackgroundSpecies Distribution Models (SDMs) aim on the characterization of a species' ecological niche and project it into geographic space. The result is a map of the species' potential distribution, which is, for instance, helpful to predict the capability of alien invasive species. With regard to alien invasive species, recently several authors observed a mismatch between potential distributions of native and invasive ranges derived from SDMs and, as an explanation, ecological niche shift during biological invasion has been suggested. We studied the physiologically well known Slider turtle from North America which today is widely distributed over the globe and address the issue of ecological niche shift versus choice of ecological predictors used for model building, i.e., by deriving SDMs using multiple sets of climatic predictor.Principal FindingsIn one SDM, predictors were used aiming to mirror the physiological limits of the Slider turtle. It was compared to numerous other models based on various sets of ecological predictors or predictors aiming at comprehensiveness. The SDM focusing on the study species' physiological limits depicts the target species' worldwide potential distribution better than any of the other approaches.ConclusionThese results suggest that a natural history-driven understanding is crucial in developing statistical models of ecological niches (as SDMs) while “comprehensive” or “standard” sets of ecological predictors may be of limited use.

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Species are being lost at increasing rates due to anthropogenic effects, leading to the recognition that we are witnessing the onset of a sixth mass extinction. Emerging infectious disease has been shown to increase species loss and any attempts to reduce extinction rates need to squarely confront this challenge. Here, we develop a procedure for identifying amphibian species that are most at risk from the effects of chytridiomycosis by OPEN ACCESSDiversity 2009, 1 53 combining spatial analyses of key host life-history variables with the pathogen's predicted distribution. We apply our rule set to the known global diversity of amphibians in order to prioritize species that are most at risk of loss from disease emergence. This risk assessment shows where limited conservation funds are best deployed in order to prevent further loss of species by enabling ex situ amphibian salvage operations and focusing any potential disease mitigation projects.

Aims Classification of vegetation is an essential tool to describe, understand, predict and manage biodiversity. Given the multiplicity of approaches to classify vegetation, it is important to develop international consensus around a set of general guidelines and purpose‐specific standard protocols. Before these goals can be achieved, however, it is necessary to identify and understand the different choices that are made during the process of classifying vegetation. This paper presents a framework to facilitate comparisons between broad‐scale plot‐based classification approaches. Results Our framework is based on the distinction of four structural elements (plot record, vegetation type, consistent classification section and classification system) and two procedural elements (classification protocol and classification approach). For each element we describe essential properties that can be used for comparisons. We also review alternative choices regarding critical decisions of classification approaches; with a special focus on the procedures used to define vegetation types from plot records. We illustrate our comparative framework by applying it to different broad‐scale classification approaches. Conclusions Our framework will be useful for understanding and comparing plot‐based vegetation classification approaches, as well as for integrating classification systems and their sections.