Diversity and Distributions

A wide range of modelling algorithms is used by ecologists, conservation practitioners, and others to predict species ranges from point locality data. Unfortunately, the amount of data available is limited for many taxa and regions, making it essential to quantify the sensitivity of these algorithms to sample size. This is the first study to address this need by rigorously evaluating a broad suite of algorithms with independent presence–absence data from multiple species and regions. We evaluated predictions from 12 algorithms for 46 species (from six different regions of the world) at three sample sizes (100, 30, and 10 records). We used data from natural history collections to run the models, and evaluated the quality of model predictions with area under the receiver operating characteristic curve (AUC). With decreasing sample size, model accuracy decreased and variability increased across species and between models. Novel modelling methods that incorporate both interactions between predictor variables and complex response shapes (i.e. GBM, MARS‐INT, BRUTO) performed better than most methods at large sample sizes but not at the smallest sample sizes. Other algorithms were much less sensitive to sample size, including an algorithm based on maximum entropy (MAXENT) that had among the best predictive power across all sample sizes. Relative to other algorithms, a distance metric algorithm (DOMAIN) and a genetic algorithm (OM‐GARP) had intermediate performance at the largest sample size and among the best performance at the lowest sample size. No algorithm predicted consistently well with small sample size (n < 30) and this should encourage highly conservative use of predictions based on small sample size and restrict their use to exploratory modelling.

There is general agreement among scientists that biodiversity is under assault on a global basis and that species are being lost at a greatly enhanced rate. This article examines the role played by biogeographical science in the emergence of conservation guidance and makes the case for the recognition of Conservation Biogeography as a key subfield of conservation biology delimited as:the application of biogeographical principles, theories, and analyses, being those concerned with the distributional dynamics of taxa individually and collectively, to problems concerning the conservation of biodiversity.Conservation biogeography thus encompasses both a substantial body of theory and analysis, and some of the most prominent planning frameworks used in conservation. Considerable advances in conservation guidelines have been made over the last few decades by applying biogeographical methods and principles. Herein we provide a critical review focussed on the sensitivity to assumptions inherent in the applications we examine. In particular, we focus on four inter‐related factors: (i) scale dependency (both spatial and temporal); (ii) inadequacies in taxonomic and distributional data (the so‐called Linnean and Wallacean shortfalls); (iii) effects of model structure and parameterisation; and (iv) inadequacies of theory. These generic problems are illustrated by reference to studies ranging from the application of historical biogeography, through island biogeography, and complementarity analyses to bioclimatic envelope modelling. There is a great deal of uncertainty inherent in predictive analyses in conservation biogeography and this area in particular presents considerable challenges.

 Protected area planning frameworks and their resulting map outputs are amongst the most powerful and influential applications within conservation biogeography, and at the global scale are characterised by the production, by a small number of prominent NGOs, of bespoke schemes, which serve both to mobilise funds and channel efforts in a highly targeted fashion. We provide a simple typology of protected area planning frameworks, with particular reference to the global scale, and provide a brief critique of some of their strengths and weaknesses. Finally, we discuss the importance, especially at regional scales, of developing more responsive analyses and models that integrate pattern (the compositionalist approach) and processes (the functionalist approach) such as range collapse and climate change, again noting the sensitivity of outcomes to starting assumptions. We make the case for the greater engagement of the biogeographical community in a programme of evaluation and refinement of all such schemes to test their robustness and their sensitivity to alternative conservation priorities and goals.

Aim  Spatial modelling techniques are increasingly used in species distribution modelling. However, the implemented techniques differ in their modelling performance, and some consensus methods are needed to reduce the uncertainty of predictions. In this study, we tested the predictive accuracies of five consensus methods, namely Weighted Average (WA), Mean(All), Median(All), Median(PCA), and Best, for 28 threatened plant species.

Location  North‐eastern Finland, Europe.

Methods  The spatial distributions of the plant species were forecasted using eight state‐of‐the‐art single‐modelling techniques providing an ensemble of predictions. The probability values of occurrence were then combined using five consensus algorithms. The predictive accuracies of the single‐model and consensus methods were assessed by computing the area under the curve (AUC) of the receiver‐operating characteristic plot.

Results  The mean AUC values varied between 0.697 (classification tree analysis) and 0.813 (random forest) for the single‐models, and from 0.757 to 0.850 for the consensus methods. WA and Mean(All) consensus methods provided significantly more robust predictions than all the single‐models and the other consensus methods.

Main conclusions  Consensus methods based on average function algorithms may increase significantly the accuracy of species distribution forecasts, and thus they show considerable promise for different conservation biological and biogeographical applications.

Abstract. Reports of malformed amphibians and global amphibian declines have led to public concern, particularly because amphibians are thought to be indicator species of overall environmental health. The topic also draws scientific attention because there is no obvious, simple answer to the question of what is causing amphibian declines? Complex interactions of several anthropogenic factors are probably at work, and understanding amphibian declines may thus serve as a model for understanding species declines in general. While we have fewer answers than we would like, there are six leading hypotheses that we sort into two classes. For class I hypotheses, alien species, over‐exploitation and land use change, we have a good understanding of the ecological mechanisms underlying declines; these causes have affected amphibian populations negatively for more than a century. However, the question remains as to whether the magnitude of these negative effects increased in the 1980s, as scientists began to notice a global decline of amphibians. Further, remedies for these problems are not simple. For class II hypotheses, global change (including UV radiation and global climate change), contaminants and emerging infectious diseases we have a poor, but improving understanding of how each might cause declines. Class II factors involve complex and subtle mechanistic underpinnings, with probable interactions among multiple ecological and evolutionary variables. They may also interact with class I hypotheses. Suspected mechanisms associated with class II hypotheses are relatively recent, dating from at least the middle of the 20th century. Did these causes act independently or in concert with pre‐existing negative forces of class I hypotheses to increase the rate of amphibian declines to a level that drew global attention? We need more studies that connect the suspected mechanisms underlying both classes of hypotheses with quantitative changes in amphibian population sizes and species numbers. An important step forward in this task is clarifying the hypotheses and conditions under which the various causes operate alone or together.

Generalized dissimilarity modelling (GDM) is a statistical technique for analysing and predicting spatial patterns of turnover in community composition (beta diversity) across large regions. The approach 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 dissimilarity, between sites; and (2) the variation in the rate of compositional turnover at different positions along environmental gradients. GDM can be further adapted to accommodate special types of biological and environmental data including, for example, information on phylogenetic relationships between species and information on barriers to dispersal between geographical locations. The approach can be applied to a wide range of assessment activities including visualization of spatial patterns in community composition, constrained environmental classification, distributional modelling of species or community types, survey gap analysis, conservation assessment, and climate‐change impact assessment.

Abstract.A series of recent papers have implicated pathogens and parasites in amphibian population declines. Here, we review evidence on the link between infectious disease and amphibian population declines. We conclude that available data provide the clearest link for the fungal disease amphibian chytridiomycosis, although other pathogens are also implicated. We suggest additional experimental and observational data that need to be collected to provide further support that these other pathogens are associated with declines. We suggest that, in common with many emerging infectious diseases (EIDs) of humans, domestic animals and other wildlife species, emergence of chytridiomycosis may be driven by anthropogenic introduction (pathogen pollution). Finally, we review a number of recent advances in the host–parasite ecology of chytridiomycosis that help explain its emergence and impact.

Over the last two decades, numerous studies have shown that alien predators contributed to amphibian population declines. Both experimental studies and correlative field surveys implicated alien species of fish, bullfrogs and crayfish as major contributors to amphibian population decline, and in some instances local extinction. Additional studies have demonstrated that alien predators also caused long‐term changes in aquatic communities. Recent studies have examined the feasibility of removing alien predators, and provide some evidence that amphibian populations can recover. Applying information gained from past studies to the recovery of amphibian populations will be the challenge of future studies. International, national and local policies that regulate alien predators should be based largely on the body of scientific evidence already in the literature. Scientists need to be more involved with policy‐makers to most effectively change laws that regulate alien predators.