. Novel methods improve prediction of species' distributions from occurrence data. Á/ Ecography 29: 129 Á/151.Prediction of species' distributions is central to diverse applications in ecology, evolution and conservation science. There is increasing electronic access to vast sets of occurrence records in museums and herbaria, yet little effective guidance on how best to use this information in the context of numerous approaches for modelling distributions. To meet this need, we compared 16 modelling methods over 226 species from 6 regions of the world, creating the most comprehensive set of model comparisons to date. We used presence-only data to fit models, and independent presence-absence data to evaluate the predictions. Along with well-established modelling methods such as generalised additive models and GARP and BIOCLIM, we explored methods that either have been developed recently or have rarely been applied to modelling species' distributions. These include machine-learning methods and community models, both of which have features that may make them particularly well suited to noisy or sparse information, as is typical of species' occurrence data. Presence-only data were effective for modelling species' distributions for many species and regions. The novel methods consistently outperformed more established methods. The results of our analysis are promising for the use of data from museums and herbaria, especially as methods suited to the noise inherent in such data improve. J. Elith

Analyses of fine-scale and macrogeographic genetic structure in plant populations provide an initial indication of how gene flow, natural selection, and genetic drift may collectively influence the distribution of genetic variation. The objective of our study is to evaluate the spatial dispersion of alleles within and among subpopulations of a tropical shrub, Psychotria officinalis (Rubiaceae), in a lowland wet forest in Costa Rica. This insect-pollinated, self-incompatible understory plant is dispersed primarily by birds, some species of which drop the seeds immediately while others transport seeds away from the parent plant. Thus, pollination should promote gene flow while at least one type of seed dispersal agent might restrict gene flow. Sampling from five subpopulations in undisturbed wet forest at Estaci6n Biologfca La Selva, Costa Rica, we used electrophoretically detected isozyme markers to examine the spatial scale of genetic structure. Our goals are: 1) describe genetic diversity of each of the five subpopulations of Psychotria officinalis sampled within a contiguous wet tropical forest; 2) evaluate fine-scale genetic structure of adults of P. officinalis within a single 2.25-ha mapped plot; and 3) estimate genetic structure of P. officinalis using data from five subpopulations located up to 2 km apart. Using estimates of coancestry, statistical analyses reveal significant positive genetic correlations between individuals on a scale of 5 m but no significant genetic relatedness beyond that interplant distance within the studied subpopulation. Multilocus estimates of genetic differentiation among subpopulations were low, but significant (F" = 0.095). Significant F" estimates were largely attributable to a single locus (Lap-Z), Thus, multilocus estimates of F" may be influenced by microgeographic selection. If true, then the observed levels of IBD may be overestimates.

JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org.Analyses of fine-scale and macrogeographic genetic structure in plant populations provide an initial indication of how gene flow, natural selection, and genetic drift may collectively influence the distribution of genetic variation. The objective of our study is to evaluate the spatial dispersion of alleles within and among subpopulations of a tropical shrub, Psychotria officinalis (Rubiaceae), in a lowland wet forest in Costa Rica. This insect-pollinated, self-incompatible understory plant is dispersed primarily by birds, some species of which drop the seeds immediately while others transport seeds away from the parent plant. Thus, pollination should promote gene flow while at least one type of seed dispersal agent might restrict gene flow. Sampling from five subpopulations in undisturbed wet forest at Estaci6n Biologfca La Selva, Costa Rica, we used electrophoretically detected isozyme markers to examine the spatial scale of genetic structure. Our goals are: 1) describe genetic diversity of each of the five subpopulations of Psychotria officinalis sampled within a contiguous wet tropical forest; 2) evaluate fine-scale genetic structure of adults of P. officinalis within a single 2.25-ha mapped plot; and 3) estimate genetic structure of P. officinalis using data from five subpopulations located up to 2 km apart. Using estimates of coancestry, statistical analyses reveal significant positive genetic correlations between individuals on a scale of 5 m but no significant genetic relatedness beyond that interplant distance within the studied subpopulation. Multilocus estimates of genetic differentiation among subpopulations were low, but significant (Fs, = 0.095). Significant Fst estimates were largely attributable to a single locus (Lap-2). Thus, multilocus estimates of Fs, may be influenced by microgeographic selection. If true, then the observed levels of IBD may be overestimates.

We studied temporal fluctuations in fruit production by plants and in populations of understory fruit—eating birds at three elevations (50, 500, and 1000 m) in Costa Rican wet forests over a 12—16 mo period to investigate effects of resource variation on bird movements and community structure. We used mist nets to monitor changes in frugivore abundance, migration patterns, and breeding and molting cycles. We sampled understory fruits of each forest concurrent with studies of frugivores. Both frugivores and fruit exhibited considerable seasonal variation in abundance. Peak frugivore capture rates occurred during peak periods of ripe fruit abundance. Altitudinal migrants left lower montane (1000 m) forest during periods of fruit scarcity and wee present in lowland (50 m) and foothill (500 m) forest when ripe fruit was abundant. Migrants, both altitudinal and temperate, accumulated fat before migration, and perhaps (for altitudinal migrants) in anticipation of breeding. Some residents also put on fat before breeding. Breeding was seasonal at all forests and occurred when ripe fruit abundance was low. Results of this study indicate that birds may track changes in resource abundance. Thus, variation in resource abundance influences dynamics of bird communities, both in terms of species composition and abundance. Further, results illustrate the importance of viewing communities from different scales; dynamics at a local scale (e.g., one elevation) can be influenced by changes in conditions (e.g., fruit abundance) elsewhere. That some species regularly moved along elevational gradients implies that preservation of many species and of the biotic integrity of entire systems may require conservation of large, connected blocks of suitable habitat.

Museum records have great potential to provide valuable insights into the vulnerability, historic distribution, and conservation of species, especially when coupled with species-distribution models used to predict species' ranges. Yet, the increasing dependence on species-distribution models in identifying conservation priorities calls for a more critical evaluation of model robustness. We used 11 bird species of conservation concern in Brazil's highly fragmented Atlantic Forest and data on environmental conditions in the region to predict species distributions. These predictions were repeated for five different model types for each of the 11 bird species. We then combined these species distributions for each model separately and applied a reserveselection algorithm to identify priority sites. We compared the potential outcomes from the reserve selection among the models. Although similarity in identification of conservation reserve networks occurred among models, models differed markedly in geographic scope and flexibility of reserve networks. It is essential for planners to evaluate the conservation implications of false-positive and false-negative errors for their specific management scenario before beginning the modeling process. Reserve networks selected by models that minimized false-positive errors provided a better match with priority areas identified by specialists. Thus, we urge caution in the use of models that overestimate species' occurrences because they may misdirect conservation action. Our approach further demonstrates the great potential value of museum records to biodiversity studies and the utility of species-distribution models to conservation decision-making. Our results also demonstrate, however, that these models must be applied critically and cautiously.Resumen: Los registros de museos tienen un gran valor potencial al proporcionar entendimiento sobre la vulnerabilidad, distribución histórica y conservación de especies, especialmente cuando se combinan con modelos de distribución de especies utilizados para predecir los rangos de distribución de las especies. No obstante, la mayor dependencia sobre los modelos de distribución de especies para la identificación de prioridades de conservación requiere una evaluación crítica de la robustez del modelo. Utilizamos 11 especies de aves de interés para la conservación en el muy fragmentado Bosque Atlántico en Brasil así como datos de condiciones ambientales en la región para predecir la distribución de las especies. Estas predicciones fueron repetidas para cinco tipos diferentes de modelos para cada una de las 11 especies de aves. Luego combinamos estas distribuciones de especies para cada modelo por separado y aplicamos un algoritmo de selección de reservas para identificar sitios prioritarios. Comparamos los resultados potenciales de la selección de reservas ‡ 1592Using Models to Inform Conservation Planning Loiselle et al.entre modelos. Aunque hubo similitud entre los modelos en la identificación de redes de reservas, los modelos difirieron ma...

Summary 1.Species distribution modelling is used increasingly in both applied and theoretical research to predict how species are distributed and to understand attributes of species' environmental requirements. In species distribution modelling, various statistical methods are used that combine species occurrence data with environmental spatial data layers to predict the suitability of any site for that species. While the number of data sharing initiatives involving species' occurrences in the scientific community has increased dramatically over the past few years, various data quality and methodological concerns related to using these data for species distribution modelling have not been addressed adequately. 2. We evaluated how uncertainty in georeferences and associated locational error in occurrences influence species distribution modelling using two treatments: (1) a control treatment where models were calibrated with original, accurate data and (2) an error treatment where data were first degraded spatially to simulate locational error. To incorporate error into the coordinates, we moved each coordinate with a random number drawn from the normal distribution with a mean of zero and a standard deviation of 5 km. We evaluated the influence of error on the performance of 10 commonly used distributional modelling techniques applied to 40 species in four distinct geographical regions. 3. Locational error in occurrences reduced model performance in three of these regions; relatively accurate predictions of species distributions were possible for most species, even with degraded occurrences. Two species distribution modelling techniques, boosted regression trees and maximum entropy, were the best performing models in the face of locational errors. The results obtained with boosted regression trees were only slightly degraded by errors in location, and the results obtained with the maximum entropy approach were not affected by such errors. 4. Synthesis and applications . To use the vast array of occurrence data that exists currently for research and management relating to the geographical ranges of species, modellers need to know the influence of locational error on model quality and whether some modelling techniques are particularly robust to error. We show that certain modelling techniques are particularly robust to a moderate level of locational error and that useful predictions of species distributions can be made even when occurrence data include some error.

Whole-genome sequencing projects are increasingly populating the tree of life and characterizing biodiversity1–4. Sparse taxon sampling has previously been proposed to confound phylogenetic inference5, and captures only a fraction of the genomic diversity. Here we report a substantial step towards the dense representation of avian phylogenetic and molecular diversity, by analysing 363 genomes from 92.4% of bird families—including 267 newly sequenced genomes produced for phase II of the Bird 10,000 Genomes (B10K) Project. We use this comparative genome dataset in combination with a pipeline that leverages a reference-free whole-genome alignment to identify orthologous regions in greater numbers than has previously been possible and to recognize genomic novelties in particular bird lineages. The densely sampled alignment provides a single-base-pair map of selection, has more than doubled the fraction of bases that are confidently predicted to be under conservation and reveals extensive patterns of weak selection in predominantly non-coding DNA. Our results demonstrate that increasing the diversity of genomes used in comparative studies can reveal more shared and lineage-specific variation, and improve the investigation of genomic characteristics. We anticipate that this genomic resource will offer new perspectives on evolutionary processes in cross-species comparative analyses and assist in efforts to conserve species.

Aim Species distribution models and geographical information system (GIS) technologies are becoming increasingly important tools in conservation planning and decision-making. Often the rich data bases of museums and herbaria serve as the primary data for predicting species distributions. Yet key assumptions about the primary data often are untested, and violation of such assumptions may have consequences for model predictions. For example, users of primary data assume that sampling has been random with respect to geography and environmental gradients. Here we evaluate the assumption that plant voucher specimens adequately sample the climatic gradient and test whether violation of this assumption influences model predictions.Location Bolivia and Ecuador.Methods Using 323,711 georeferenced herbarium collections and nine climatic variables, we predicted the distribution of 76 plant species using maximum entropy models (MAXENT) with training points that sampled the climate environments randomly and training points that reflected the climate bias in the herbarium collections. To estimate the distribution of species, MAXENT finds the distribution of maximum entropy (i.e. closest to uniform) subject to the constraint that the expected value for each environmental variable under the estimated distribution matches its empirical average. The experimental design included species that differed in geographical range and elevation; all species were modelled with 20 and 100 training points. We examined the influence of the number of training points and climate bias in training points, elevation and range size on model performance using analysis of variance models. ResultsWe found that significant parts of the climatic gradient were poorly represented in herbarium collections for both countries. For the most part, existing climatic bias in collections did not greatly affect distribution predictions when compared with an unbiased data set. Although the effects of climate bias on prediction accuracy were found to be greater where geographical ranges were characterized by high spatial variation in the degree of climate bias (i.e. ranges where the bias of the various climates sampled by collections deviated considerably from the mean bias), the greatest influence on model performance was the number of presence points used to train the model. Main conclusionsThese results demonstrate that predictions of species distributions can be quite good despite existing climatic biases in primary data found in natural history collections, if a sufficiently large number of training points is available. Because of consistent overprediction of models, these results also confirm the importance of validating models with independent data or expert opinion. Failure to include independent model validation, especially in