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Species Distribution Data (species + distribution_data)
Selected AbstractsUsing multi-scale species distribution data to infer drivers of biological invasion in riparian wetlandsDIVERSITY AND DISTRIBUTIONS, Issue 1 2010Jane A. Catford Abstract Aim, Biological invasion is a major conservation problem that is of interest to ecological science. Understanding mechanisms of invasion is a high priority, heightened by the management imperative of acting quickly after species introduction. While information about invading species' ecology is often unavailable, species distribution data can be collected near the onset of invasion. By examining distribution patterns of exotic and native plant species at multiple spatial scales, we aim to identify the scale (of those studied) that accounts for most variability in exotic species abundance, and infer likely drivers of invasion. Location, River Murray wetlands, south-eastern Australia. Methods, A nested, crossed survey design was used to determine the extent of variation in wetland plant abundance, grazing intensity and water depth at four spatial scales (reaches, wetland clumps, wetlands, wetland sections), and among three Depth-strata. We examined responses of exotic and native species groups (grouped into terrestrial and amphibious taxa), native weeds and 10 individual species using hierarchical ANOVA. Results, As a group dominated by terrestrial taxa, exotic species cover varied at reach-, wetland- and section-scales. This likely reflects differences in abiotic characteristics and propagule pressure at these scales. Groups based on native species did not vary at any scale examined. Cover of 10 species mostly varied among and within wetlands (patterns unrelated to species' origin or functional group), but species' responses differed, despite individual plants being similar in size. While flora mostly varied among wetlands, exotic cover varied most among reaches (26%), which was attributed to hydrological modification and human activities. Main conclusions, Multi-scale surveys can rapidly identify factors likely to affect species' distributions and can indicate where future research should be directed. By highlighting disproportionate variation in exotic cover among reaches, this study suggests that flow regulation and human-mediated dispersal facilitate exotic plant invasion in River Murray wetlands. [source] ModEco: an integrated software package for ecological niche modelingECOGRAPHY, Issue 4 2010Qinghua Guo ModEco is a software package for ecological niche modeling. It integrates a range of niche modeling methods within a geographical information system. ModEco provides a user friendly platform that enables users to explore, analyze, and model species distribution data with relative ease. ModEco has several unique features: 1) it deals with different types of ecological observation data, such as presence and absence data, presence-only data, and abundance data; 2) it provides a range of models when dealing with presence-only data, such as presence-only models, pseudo-absence models, background vs presence data models, and ensemble models; and 3) it includes relatively comprehensive tools for data visualization, feature selection, and accuracy assessment. [source] Methods to account for spatial autocorrelation in the analysis of species distributional data: a reviewECOGRAPHY, Issue 5 2007Carsten F. Dormann Species distributional or trait data based on range map (extent-of-occurrence) or atlas survey data often display spatial autocorrelation, i.e. locations close to each other exhibit more similar values than those further apart. If this pattern remains present in the residuals of a statistical model based on such data, one of the key assumptions of standard statistical analyses, that residuals are independent and identically distributed (i.i.d), is violated. The violation of the assumption of i.i.d. residuals may bias parameter estimates and can increase type I error rates (falsely rejecting the null hypothesis of no effect). While this is increasingly recognised by researchers analysing species distribution data, there is, to our knowledge, no comprehensive overview of the many available spatial statistical methods to take spatial autocorrelation into account in tests of statistical significance. Here, we describe six different statistical approaches to infer correlates of species' distributions, for both presence/absence (binary response) and species abundance data (poisson or normally distributed response), while accounting for spatial autocorrelation in model residuals: autocovariate regression; spatial eigenvector mapping; generalised least squares; (conditional and simultaneous) autoregressive models and generalised estimating equations. A comprehensive comparison of the relative merits of these methods is beyond the scope of this paper. To demonstrate each method's implementation, however, we undertook preliminary tests based on simulated data. These preliminary tests verified that most of the spatial modeling techniques we examined showed good type I error control and precise parameter estimates, at least when confronted with simplistic simulated data containing spatial autocorrelation in the errors. However, we found that for presence/absence data the results and conclusions were very variable between the different methods. This is likely due to the low information content of binary maps. Also, in contrast with previous studies, we found that autocovariate methods consistently underestimated the effects of environmental controls of species distributions. Given their widespread use, in particular for the modelling of species presence/absence data (e.g. climate envelope models), we argue that this warrants further study and caution in their use. To aid other ecologists in making use of the methods described, code to implement them in freely available software is provided in an electronic appendix. [source] Is biofuel policy harming biodiversity in Europe?GCB BIOENERGY, Issue 1 2009JEANNETTE EGGERS Abstract We assessed the potential impacts of land-use changes resulting from a change in the current biofuel policy on biodiversity in Europe. We evaluated the possible impact of both arable and woody biofuel crops on changes in distribution of 313 species pertaining to different taxonomic groups. Using species-specific information on habitat suitability as well as land use simulations for three different biofuel policy options, we downscaled available species distribution data from the original resolution of 50 to 1 km. The downscaled maps were then applied to analyse potential changes in habitat size and species composition at different spatial levels. Our results indicate that more species might suffer from habitat losses rather than benefit from a doubled biofuel target, while abolishing the biofuel target would mainly have positive effects. However, the possible impacts vary spatially and depend on the biofuel crop choice, with woody crops being less detrimental than arable crops. Our results give an indication for policy and decision makers of what might happen to biodiversity under a changed biofuel policy in the European Union. The presented approach is considered to be innovative as to date no comparable policy impact assessment has been applied to such a large set of key species at the European scale. [source] Predicting habitat distribution and frequency from plant species co-occurrence dataJOURNAL OF BIOGEOGRAPHY, Issue 6 2007Christine Römermann Abstract Aim, Species frequency data have been widely used in nature conservation to aid management decisions. To determine species frequencies, information on habitat occurrence is important: a species with a low frequency is not necessarily rare if it occupies all suitable habitats. Often, information on habitat distribution is available for small geographic areas only. We aim to predict grid-based habitat occurrence from grid-based plant species distribution data in a meso-scale analysis. Location, The study was carried out over two spatial extents: Germany and Bavaria. Methods, Two simple models were set up to examine the number of characteristic plant species needed per grid cell to predict the occurrence of four selected habitats (species data from FlorKart, http://www.floraweb.de). Both models were calibrated in Bavaria using available information on habitat distribution, validated for other federal states, and applied to Germany. First, a spatially explicit regression model (generalized linear model (GLM) with assumed binomial error distribution of response variable) was obtained. Second, a spatially independent optimization model was derived that estimated species numbers without using spatial information on habitat distribution. Finally, an additional uncalibrated model was derived that calculated the frequencies of 24 habitats. It was validated using NATURA2000 habitat maps. Results, Using the Bavarian models it was possible to predict habitat distribution and frequency from the co-occurrence of habitat-specific species per grid cell. As the model validations for other German federal states were successful, the models were applied to all of Germany, and habitat distribution and frequencies could be retrieved for the national scale on the basis of habitat-specific species co-occurrences per grid cell. Using the third, uncalibrated model, which includes species distribution data only, it was possible to predict the frequencies of 24 habitats based on the co-occurrence of 24% of formation-specific species per grid cell. Predicted habitat frequencies deduced from this third model were strongly related to frequencies of NATURA2000 habitat maps. Main conclusions, It was concluded that it is possible to deduce habitat distributions and frequencies from the co-occurrence of habitat-specific species. For areas partly covered by habitat mappings, calibrated models can be developed and extrapolated to larger areas. If information on habitat distribution is completely lacking, uncalibrated models can still be applied, providing coarse information on habitat frequencies. Predicted habitat distributions and frequencies can be used as a tool in nature conservation, for example as correction factors for species frequencies, as long as the species of interest is not included in the model set-up. [source] | ||||||||||