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Species Occurrence Data (species + occurrence_data)
Selected AbstractsModelling species distributions without using species distributions: the cane toad in Australia under current and future climatesECOGRAPHY, Issue 4 2008Michael Kearney Accurate predictions of the potential distribution of range-shifting species are required for effective management of invasive species, and for assessments of the impact of climate change on native species. Range-shifting species pose a challenge for traditional correlative approaches to range prediction, often requiring the extrapolation of complex statistical associations into novel environmental space. Here we take an alternative approach that does not use species occurrence data, but instead captures the fundamental niche of a species by mechanistically linking key organismal traits with spatial data using biophysical models. We demonstrate this approach with a major invasive species, the cane toad Bufo marinus in Australia, assessing the direct climatic constraints on its ability to move, survive, and reproduce. We show that the current range can be explained by thermal constraints on the locomotor potential of the adult stage together with limitations on the availability of water for the larval stage. Our analysis provides a framework for biologically grounded predictions of the potential for cane toads to expand their range under current and future climate scenarios. More generally, by quantifying spatial variation in physiological constraints on an organism, trait-based approaches can be used to investigate the range-limits of any species. Assessments of spatial variation in the physiological constraints on an organism may also provide a mechanistic basis for forecasting the rate of range expansion and for understanding a species' potential to evolve at range-edges. Mechanistic approaches thus have broad application to process-based ecological and evolutionary models of range-shift. [source] The value of georeferenced collection records for predicting patterns of mosquito species richness and endemism in the NeotropicsECOLOGICAL ENTOMOLOGY, Issue 1 2008DESMOND H. FOLEY Abstract 1.,Determining large-scale distribution patterns for mosquitoes could advance knowledge of global mosquito biogeography and inform decisions about where mosquito inventory needs are greatest. 2.,Over 43 000 georeferenced records are presented of identified and vouchered mosquitoes from collections undertaken between 1899 and 1982, from 1853 locations in 42 countries throughout the Neotropics. Of 492 species in the data set, 23% were only recorded from one location, and Anopheles albimanus Wiedemann is the most common species. 3.,A linear log,log species,area relationship was found for mosquito species number and country area. Chile had the lowest relative density of species and Trinidad-Tobago the highest, followed by Panama and French Guiana. 4.,The potential distribution of species was predicted using an Ecological Niche Modelling (ENM) approach. Anopheles species had the largest predicted species ranges, whereas species of Deinocerites and Wyeomyia had the smallest. 5.,Species richness was estimated for 1° grids and by summing predicted presence of species from ENM. These methods both showed areas of high species richness in French Guiana, Panama, Trinidad-Tobago, and Colombia. Potential hotspots in endemicity included unsampled areas in Panama, French Guiana, Colombia, Belize, Venezuela, and Brazil. 6.,Argentina, The Bahamas, Bermuda, Bolivia, Cuba, and Peru were the most under-represented countries in the database compared with known country species occurrence data. Analysis of species accumulation curves suggested patchiness in the distribution of data points, which may affect estimates of species richness. 7.,The data set is a first step towards the development of a global-scale repository of georeferenced mosquito collection records. [source] ENVIRONMENTAL NICHE EQUIVALENCY VERSUS CONSERVATISM: QUANTITATIVE APPROACHES TO NICHE EVOLUTIONEVOLUTION, Issue 11 2008Dan L. Warren Environmental niche models, which are generated by combining species occurrence data with environmental GIS data layers, are increasingly used to answer fundamental questions about niche evolution, speciation, and the accumulation of ecological diversity within clades. The question of whether environmental niches are conserved over evolutionary time scales has attracted considerable attention, but often produced conflicting conclusions. This conflict, however, may result from differences in how niche similarity is measured and the specific null hypothesis being tested. We develop new methods for quantifying niche overlap that rely on a traditional ecological measure and a metric from mathematical statistics. We reexamine a classic study of niche conservatism between sister species in several groups of Mexican animals, and, for the first time, address alternative definitions of "niche conservatism" within a single framework using consistent methods. As expected, we find that environmental niches of sister species are more similar than expected under three distinct null hypotheses, but that they are rarely identical. We demonstrate how our measures can be used in phylogenetic comparative analyses by reexamining niche divergence in an adaptive radiation of Cuban anoles. Our results show that environmental niche overlap is closely tied to geographic overlap, but not to phylogenetic distances, suggesting that niche conservatism has not constrained local communities in this group to consist of closely related species. We suggest various randomization tests that may prove useful in other areas of ecology and evolutionary biology. [source] The influence of spatial errors in species occurrence data used in distribution modelsJOURNAL OF APPLIED ECOLOGY, Issue 1 2008Catherine H Graham Summary 1Species 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. 2We 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. 3Locational 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. 4Synthesis 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. [source] Biogeography of European land mammals shows environmentally distinct and spatially coherent clustersJOURNAL OF BIOGEOGRAPHY, Issue 6 2007H. Heikinheimo Abstract Aim, To produce a spatial clustering of Europe on the basis of species occurrence data for the land mammal fauna. Location, Europe defined by the following boundaries: 11°W, 32°E, 71°N, 35°N. Methods, Presence/absence records of mammal species collected by the Societas Europaea Mammalogica with a resolution of 50 × 50 km were used in the analysis. After pre-processing, the data provide information on 124 species in 2183 grid cells. The data were clustered using the k -means and probabilistic expectation maximization (EM) clustering algorithms. The resulting geographical pattern of clusters was compared against climate variables and against an environmental stratification of Europe based on climate, geomorphology and soil characteristics (EnS). Results, The mammalian presence/absence data divide naturally into clusters, which are highly connected spatially and most strongly determined by the small mammals with the highest grid cell incidence. The clusters reflect major physiographic and environmental features and differ significantly in the values of basic climate variables. The geographical pattern is a fair match for the EnS stratification and is robust between non-overlapping subsets of the data, such as trophic groups. Main conclusions, The pattern of clusters is regarded as reflecting the spatial expression of biologically distinct, metacommunity-like entities influenced by deterministic forces ultimately related to the physical environment. Small mammals give the most spatially coherent clusters of any subgroup, while large mammals show stronger relationships to climate variables. The spatial pattern is mainly due to small mammals with high grid cell incidence and is robust to noise from other subsets. The results support the use of spatially resolved environmental reconstructions based on fossil mammal data, especially when based on species with the highest incidence. [source] Spatial congruence of ecological transition at the regional scale in South AfricaJOURNAL OF BIOGEOGRAPHY, Issue 5 2004Berndt J. Van Rensburg Abstract Aim, To determine whether patterns of avian species turnover reflect either biome or climate transitions at a regional scale, and whether anthropogenic landscape transformation affects those patterns. Location South Africa and Lesotho. Methods, Biome and land transformation data were used to identify sets of transition areas, and avian species occurrence data were used to measure species turnover rates (, -diversity). Spatial congruence between areas of biome transition, areas of high vegetation heterogeneity, high climatic heterogeneity, and high , -diversity was assessed using random draw techniques. Spatial overlap in anthropogenically transformed areas, areas of high climatic heterogeneity and high , -diversity areas was also assessed. Results, Biome transition areas had greater vegetation heterogeneity, climatic heterogeneity, and , -diversity than expected by chance. For the land transformation transition areas, this was only true for land transformation heterogeneity values and for one of the , -diversity measures. Avian presence/absence data clearly separated the biome types but not the land transformation types. Main conclusions, Biome edges have elevated climatic and vegetation heterogeneity. More importantly, elevated , -diversity in the avifauna is clearly reflected in the heterogeneous biome transition areas. Thus, there is spatial congruence in biome transition areas (identified on vegetation and climatic grounds) and avian turnover patterns. However, there is no congruence between avian turnover and land transformation transition areas. This suggests that biogeographical patterns can be recovered using modern data despite landscape transformation. [source] Novel methods improve prediction of species' distributions from occurrence dataECOGRAPHY, Issue 2 2006Jane Elith 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. [source] | ||||||||||