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1.
Aim To determine the relationship between the distribution of climate, climatic heterogeneity and pteridophyte species richness gradients in Australia, using an approach that does not assume potential relationships are spatially invariant and allows for scale effects (extent of analysis) to be explicitly examined. Location Australia, extending from 10° S to 43° S and 112° E to 153° E. Method Species richness within 50 × 50 km grid cells was determined using point distribution data. Climatic surfaces representing the distribution and availability of water and energy at 1 km and 5 km cell resolutions were obtained. Climate at the 50 km resolution of analysis was represented by their mean and standard deviation in that area. Relationships were assessed using geographically weighted linear regression at a range of spatial bandwidths to investigate scale effects. Results The parameters and the predictive strength of all models varied across space at all extents of analysis. Overall, climatic variables representing water availability were more highly correlated to pteridophyte richness gradients in Australia than those representing energy. Their variance in cells further increased the strength of the relationships in topographically heterogeneous regions. Relationships with water were strong across all extents of analysis, particularly in the tropical and subtropical parts of the continent. Water availability explained less of the variation in richness at higher latitudes. Main conclusions This study brings into question the ability of aspatial and single‐extent models, searching for a unified explanation of macro‐scaled patterns in gradients of diversity, to adequately represent reality. It showed that, across Australia, there is a positive relationship between pteridophyte species richness and water availability but the strength and nature of the relationship varies spatially with scale in a highly complex manner. The spatial variance, or actual complexity, in these relationships could not have been demonstrated had a traditional aspatial global regression approach been used. Regional scale variation in relationships may be at least as important as more general relationships for a true understanding of the distribution of broad‐scale diversity.  相似文献   

2.
Aim One of the limitations to using species’ distribution atlases in conservation planning is their coarse resolution relative to the needs of local planners. In this study, a simple approach to downscale original species atlas distributions to a finer resolution is outlined. If such a procedure yielded accurate downscaled predictions, then it could be an aid to using available distribution atlases in real‐world local conservation decisions. Location Europe. Methods An iterative procedure based on generalized additive modelling is used to downscale original European 50 × 50 km distributions of 2189 plant and terrestrial vertebrate species to c. 10 × 10 km grid resolution. Models are trained on 70% of the original data and evaluated on the remaining 30%, using the receiver operating characteristic (ROC) procedure. Fitted models are then interpolated to a finer resolution. A British dataset comprising distributions of 81 passerine‐bird species in a 10 × 10 km grid is used as a test bed to assess the accuracy of the downscaled predictions. European‐wide, downscaled predictions are further evaluated in terms of their ability to reproduce: (1) spatial patterns of coincidence in species richness scores among different groups; and (2) spatial patterns of coincidence in richness, rarity and complementarity hotspots. Results There was a generally good agreement between downscaled and observed fine‐resolution distributions for passerine species in Britain (median Jaccard similarity = 70%; lower quartile = 36%; upper quartile = 88%). In contrast, the correlation between downscaled and observed passerine species richness was relatively low (rho = 0.31) indicating a pattern of error propagation through the process of overlaying downscaled distributions for many species. It was also found that measures of model accuracy in fitting original data (ROC) were a poor predictor of models’ ability to interpolate distributions at fine resolutions (rho = ?0.10). Although European hotspots were not fully coincident between observed and modelled coarse‐resolution data, or between modelled coarse resolution and modelled downscaled data, there was evidence that downscaled distributions were able to maintain original cross‐taxon coincidence of species‐richness scores, at least for terrestrial vertebrate groups. Downscaled distributions were also able to uncover important environmental gradients otherwise blurred by coarse‐resolution data. Main conclusions Despite uncertainties, downscaling procedures may prove useful to identify reserves that are more meaningfully related to local patterns of environmental variation. Potential errors arising from the presence of false positives may be reduced if downscaled‐distribution records projected to occur outside the range of original coarse‐resolution data are excluded. However, the usefulness of this procedure may be limited to data‐rich regions. If downscaling procedures are applied to data‐poor regions, then there is a need to undertake further research to understand the structure of error in models. In particular, it would be important to investigate which species are poorly modelled, where and why. Without such an assessment it is difficult to support unsupervised use of downscaled data in most real‐world situations.  相似文献   

3.
Aim To predict French Scarabaeidae dung beetle species richness distribution, and to determine the possible underlying causal factors. Location The entire French territory has been studied by dividing it into 301 grid cells of 0.72 × 0.36 degrees. Method Species richness distribution was predicted using generalized linear models to relate the number of species with spatial, topographic and climate variables in grid squares previously identified as well sampled (n = 66). The predictive function includes the curvilinear relationship between variables, interaction terms and the significant third‐degree polynomial terms of latitude and longitude. The final model was validated by a jack‐knife procedure. The underlying causal factors were investigated by partial regression analysis, decomposing the variation in species richness among spatial, topographic and climate type variables. Results The final model accounts for 86.2% of total deviance, with a mean jack‐knife predictive error of 17.7%. The species richness map obtained highlights the Mediterranean as the region richest in species, and the less well‐explored south‐western region as also being species‐rich. The largest fraction of variability (38%) in the number of species is accounted for by the combined effect of the three groups of explanatory variables. The spatially structured climate component explains 21% of variation, while the pure climate and pure spatial components explain 14% and 11%, respectively. The effect of topography was negligible. Conclusions Delimiting the adequately inventoried areas and elaborating forecasting models using simple environmental variables can rapidly produce an estimate of the species richness distribution. Scarabaeidae species richness distribution seems to be mainly influenced by temperature. Minimum mean temperature is the most influential variable on a local scale, while maximum and mean temperature are the most important spatially structured variables. We suggest that species richness variation is mainly conditioned by the failure of many species to go beyond determined temperature range limits.  相似文献   

4.
Aim The role of biotic interactions in influencing species distributions at macro‐scales remains poorly understood. Here we test whether predictions of distributions for four boreal owl species at two macro‐scales (10 × 10 km and 40 × 40 km grid resolutions) are improved by incorporating interactions with woodpeckers into climate envelope models. Location Finland, northern Europe. Methods Distribution data for four owl and six woodpecker species, along with data for six land cover and three climatic variables, were collated from 2861 10 × 10 km grid cells. Generalized additive models were calibrated using a 50% random sample of the species data from western Finland, and by repeating this procedure 20 times for each of the four owl species. Models were fitted using three sets of explanatory variables: (1) climate only; (2) climate and land cover; and (3) climate, land cover and two woodpecker interaction variables. Models were evaluated using three approaches: (1) examination of explained deviance; (2) four‐fold cross‐validation using the model calibration data; and (3) comparison of predicted and observed values for independent grid cells in eastern Finland. The model accuracy for approaches (2) and (3) was measured using the area under the curve of a receiver operating characteristic plot. Results At 10‐km resolution, inclusion of the distribution of woodpeckers as a predictor variable significantly improved the explanatory power, cross‐validation statistics and the predictive accuracy of the models. Inclusion of land cover led to similar improvements at 10‐km resolution, although these improvements were less apparent at 40‐km resolution for both land cover and biotic interactions. Main conclusions Predictions of species distributions at macro‐scales may be significantly improved by incorporating biotic interactions and land cover variables into models. Our results are important for models used to predict the impacts of climate change, and emphasize the need for comprehensive evaluation of the reliability of species–climate impact models.  相似文献   

5.
The role of land cover in bioclimatic models depends on spatial resolution   总被引:2,自引:0,他引:2  
Aim We explored the importance of climate and land cover in bird species distribution models on multiple spatial scales. In particular, we tested whether the integration of land cover data improves the performance of pure bioclimatic models. Location Finland, northern Europe. Methods The data of the bird atlas survey carried out in 1986–89 using a 10 × 10 km uniform grid system in Finland were employed in the analyses. Land cover and climatic variables were compiled using the same grid system. The dependent and explanatory variables were resampled to 20‐km, 40‐km and 80‐km resolutions. Generalized additive models (GAM) were constructed for each of the 88 land bird species studied in order to estimate the probability of occurrence as a function of (1) climate and (2) climate and land cover variables. Model accuracy was measured by a cross‐validation approach using the area under the curve (AUC) of a receiver operating characteristic (ROC) plot. Results In general, the accuracies of the 88 bird–climate models were good at all studied resolutions. However, the inclusion of land cover increased the performance of 79 and 78 of the 88 bioclimatic models at 10‐km and 20‐km resolutions, respectively. There was no significant improvement at the 40‐km resolution. In contrast to the finer resolutions, the inclusion of land cover variables decreased the modelling accuracy at 80km resolution. Main conclusions Our results suggest that the determinants of bird species distributions are hierarchically structured: climatic variables are large‐scale determinants, followed by land cover at finer resolutions. The majority of the land bird species in Finland are rather clearly correlated with climate, and bioclimate envelope models can provide useful tools for identifying the relationships between these species and the environment at resolutions ranging from 10 km to 80 km. However, the notable contribution of land cover to the accuracy of bioclimatic models at 10–20‐km resolutions indicates that the integration of climate and land cover information can improve our understanding and model predictions of biogeographical patterns under global change.  相似文献   

6.
We studied species richness patterns of obligate subterranean (troglobiotic) beetles in the Dinaric karst of the western Balkans, using five grid sizes with cells of 80 × 80, 40 × 40, 20 × 20, 10 × 10, and 5 × 5 km. The same two hotspots could be recognized at all scales, although details differed. Differences in sampling intensity were not sufficient to explain these patterns. Correlations between number of species and number of sampled localities increased with increasing cell size. Additional species are expected to be found in the region, as indicated by jackknife 1, jackknife 2, Chao2, bootstrap, and incidence‐based coverage (ICE) species richness estimators. All estimates increased with increasing cell size, except Chao2, with the lowest prediction at the intermediate 20 × 20 km cell size. Jackknife 2 and ICE gave highest estimates and jackknife 1 and bootstrap the lowest. Jackknife 1 and bootstrap estimates changed least with cell size, while the number of single cell species increased. In the highly endemic subterranean fauna with many rare species, bootstrap may be most appropriate to consider. Positive autocorrelation of species numbers was highest at 20 × 20 km scale, so we used this cell size for further analyses. At this scale we added 137 localities with less positional accuracy to 1572 previously considered, and increased 254 troglobiotic species considered to 276. Previously discovered hotspots and their positions did not change, except for a new species‐rich cell which appeared in the south‐eastern region. There are two centres of troglobiotic species richness in the Dinaric karst. The one in the north‐west exhibited high species richness of Trechinae (Carabidae), while in the south‐east, the Leptodirinae (Cholevidae) were much more diverse. These centres of species richness should serve as the starting point for establishing a conservation network of important subterranean areas in Dinaric karst.  相似文献   

7.
Although land use change is a key driver of biodiversity change, related variables such as habitat area and habitat heterogeneity are seldom considered in modeling approaches at larger extents. To address this knowledge gap we tested the contribution of land use related variables to models describing richness patterns of amphibians, reptiles and passerines in the Iberian Peninsula. We analyzed the relationship between species richness and habitat heterogeneity at two spatial resolutions (i.e., 10 km × 10 km and 50 km × 50 km). Using both ordinary least square and simultaneous autoregressive models, we assessed the relative importance of land use variables, climate variables and topographic variables. We also compare the species–area relationship with a multi-habitat model, the countryside species–area relationship, to assess the role of the area of different types of habitats on species diversity across scales. The association between habitat heterogeneity and species richness varied with the taxa and spatial resolution. A positive relationship was detected for all taxa at a grain size of 10 km × 10 km, but only passerines responded at a grain size of 50 km × 50 km. Species richness patterns were well described by abiotic predictors, but habitat predictors also explained a considerable portion of the variation. Moreover, species richness patterns were better described by a multi-habitat species-area model, incorporating land use variables, than by the classic power model, which only includes area as the single explanatory variable. Our results suggest that the role of land use in shaping species richness patterns goes beyond the local scale and persists at larger spatial scales. These findings call for the need of integrating land use variables in models designed to assess species richness response to large scale environmental changes.  相似文献   

8.
Predation is a key determinant of prey community structure, but few studies have measured the effect of multiple predators on a highly diverse prey community. In this study, we asked whether the abundance, species richness, and species composition of a species‐rich assemblage of termites in an Amazonian rain forest is more strongly associated with the density of predatory ants or with measures of vegetation, and soil texture and chemistry. We sampled termite assemblages with standardized hand‐collecting in 30 transects arranged in a 5 km × 6 km grid in a terra firme Amazonian rain forest. For each transect, we also measured vegetation structure, soil texture, and soil phosphorus, and estimated the density of predatory ants from baits, pitfall traps, and Winkler samples. Seventy‐nine termite species were recorded, and the total density of predatory ants was the strongest single predictor of local termite abundance (r = ?0.66) and termite species richness (r = ?0.44). In contrast, termite abundance and species richness were not strongly correlated with edaphic conditions (¦r¦ < 0.01), or with the density of non‐predatory ants (rabund = ?0.27; rs = ?0.06). Termite species composition was correlated with soil phosphorus content (r = 0.79), clay content (r = ?0.75), and tree density (r = ?0.42). Assemblage patterns were consistent with the hypothesis that ants collectively behaved as generalist predators, reducing total termite abundance, and species richness. There was no evidence that ants behaved as keystone predators, or that any single termite species benefited from the reduction in the abundance of potential competitors.  相似文献   

9.
Aims We present an analysis of grid‐based species‐richness data for European plants, mammals, birds, amphibians and reptiles, designed to test the proposition of Hawkins et al. (2003a ) that the single best factor describing richness variation switches from the water regime to the energy regime in the mid‐latitudes and that the ‘breakpoint’ is related to the physiological character of the taxa. We go on to develop subregional models showing the extent to which regional model fits vary as a function of the extent of the study system, and compare the relative performance of ‘water’, ‘energy’ and ‘water–energy’ models of richness for southern, northern and pan‐European models. Location Western Europe. Methods We use atlas data comprising species range data for 187 species of mammals, 445 species of breeding birds, 58 amphibians, 91 reptiles and 2362 plant species, inserted into a c. 50 × 50 km grid cell system. We used 11 modelled climate variables, averaged for the period 1961–90. Statistical analyses were carried out using generalized additive models (GAMs), with splines simplified to a maximum of four degrees of freedom, and we tested for spatial autocorrelation using Moran's I values obtained at 10 different distance intervals. We selected favoured models on the grounds of deviance explained combined with a simple parsimony criterion, such that we selected either: (1) the best two‐variable energy, water or water–energy model, or (2) a four‐variable water–energy model, where the latter improved on the best two‐variable model by a minimum of 5% deviance explained. Results Threshold energy values, at which richness shows a transition from an increasing to a decreasing function of annual solar radiation, were identified for all taxa apart from reptiles. We found conditional support for the switch from dominance of water variables (southern models) to energy variables (northern models). Our favoured models switched between ‘water’ and ‘energy’ for mammals, and between ‘energy’ and ‘water–energy’ for birds, depending on whether we used data of pan‐European extent, southern or northern subsets. Deviance explained in our favoured models varied from 15% (birds, southern Europe) to 72% (amphibians, northern Europe), i.e. ranging from very poor to good fits with the data. Comparison with previous work indicates that our models are generally consistent with (if sometimes weaker than) previous findings. Main conclusions Our models are incomplete representations of factors influencing macro‐scale richness patterns across Europe, taking no explicit account of, for example, topographic variation, human influences or long‐term climatic variation. However, with the exception of birds, for which only the northern model attains over one‐third deviance explained, the models show that climate can account for meaningful proportions of the deviance. We find general support for considering water and energy regimes together in modelling species richness, and for the proposition that water is more limiting in southern Europe and energy in the north. Our analyses demonstrate the sensitivity of model outcomes to the geographical location and extent of the study system, illustrating that simple curve‐fitting exercises like these, particularly if based on regions with the complex history and geography characteristic of Europe, are unlikely to provide the basis for global, predictive models. However, such exercises may be of value in detecting which aspects of water and energy regimes may be of most importance in refining independently generated global models for regional application.  相似文献   

10.
Aim The aims of this work were (1) to study how well land‐cover and climatic data are capable of explaining distribution patterns of ten bird species breeding and/or feeding primarily on marshes and other wetlands and (2) to compare the differences between red‐listed and common marshland species in explanatory variables, and to study the predictability of their distribution patterns. Location Finland, northern Europe. Methods The data of the bird atlas survey carried out in 1986–89 using a 10 × 10 km uniform grid system in Finland were used in the analyses. Land‐cover data based on CORINE (Coordination of Information on the Environment) classification and climatic variables were compiled using the same 10 × 10 km grid. Generalized additive models (GAM) with a stepwise selection procedure were used to select relevant explanatory variables and to examine the complexity of the response shapes of the different species to each variable. The original data set was randomly divided into model training (70%) and model evaluation (30%) sets. The final models of common and red‐listed bird species richness were validated by fitting them to the model evaluation set, and the correlation between observed and predicted species richness was calculated. We assessed the discrimination ability of the binary models (single species) with the area under the curve (AUC) of a receiver operating characteristic (ROC) plot and the Kappa coefficient. Results Cover of marshland, shoreline length and mean temperature in April–June were significantly (P < 0.01) related to the common marshland species richness. Cover and clumping of marshland and mean temperature and precipitation in April–June were selected in the model of red‐listed marshland species richness. The level of discrimination in our single species models varied in ROC from fair to excellent (AUC values 0.70–0.95). Cover of marshland was included in all GAM models built for the target species, but clumping of marshland, shoreline length and cover of mires also appeared as important predictors in single species models. Seven species had statistically significant relationships with climatic variables in the multivariate GAMs. Cover of marshland was highest in squares in which the red‐listed bittern Botaurus stellaris, marsh harrier Circus aeruginosus and great reed warbler Acrocephalus arundinaceus and the water rail Rallus aquaticus were observed. Main conclusions Cover of marshland was the only variable which was included in all the models, reinforcing the close connection between the studied species and marshlands. Broad‐scale clumping of marshlands was important for the red‐listed species, probably due to the much lower population sizes of red‐listed species than those of common species. Land‐cover data produced in CORINE seems to be well suited for modelling the distribution patterns of marshland birds. Although climatic variables also strongly affect the studied marshland birds, habitat availability plays a crucial role in their occurrence. The distribution patterns of marshland birds at the scale of 10 × 10 km reflect the interplay between habitat availability and direct climatic variables.  相似文献   

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