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Atlas Data (atlas + data)
Selected AbstractsTesting abundance-range size relationships in European carabid beetles (Coleoptera, Carabidae)ECOGRAPHY, Issue 5 2003D. Johan Kotze Four of the eight hypotheses proposed in the literature for explaining the relationship between abundance and range size (the sampling artifact, phylogenetic non-independence, range position and resource breadth hypotheses) were tested by using atlas data for carabid beetles (Coleoptera, Carabidae) from Belgium, Denmark and the Netherlands. A positive relationship between abundance and partial range size was found in all three countries, and the variation in abundance was lower for widespread species. Analysis of the data did not support three of the proposed hypotheses, but did support the resource breadth hypothesis (species having broader environmental tolerances and being able to use a wider range or resources will have higher local densities and be more widely distributed than more specialised species). Examination of species' characteristics revealed that widespread species are generally large bodied, generalists (species with wide niche breadths occurring in a variety of habitat types) and are little influenced by human-altered landscapes, while species with restricted distributions are smaller bodied, specialists (species with small niche breadths occurring in only one or two habitat types), and favour natural habitat. Landscape alteration may be an important factor influencing carabid abundance and range size in these three countries with a long history of human-induced environmental changes. [source] Type and spatial structure of distribution data and the perceived determinants of geographical gradients in ecology: the species richness of African birdsGLOBAL ECOLOGY, Issue 5 2007Jana M. McPherson ABSTRACT Aim, Studies exploring the determinants of geographical gradients in the occurrence of species or their traits obtain data by: (1) overlaying species range maps; (2) mapping survey-based species counts; or (3) superimposing models of individual species' distributions. These data types have different spatial characteristics. We investigated whether these differences influence conclusions regarding postulated determinants of species richness patterns. Location, Our study examined terrestrial bird diversity patterns in 13 nations of southern and eastern Africa, spanning temperate to tropical climates. Methods, Four species richness maps were compiled based on range maps, field-derived bird atlas data, logistic and autologistic distribution models. Ordinary and spatial regression models served to examine how well each of five hypotheses predicted patterns in each map. These hypotheses propose productivity, temperature, the heat,water balance, habitat heterogeneity and climatic stability as the predominant determinants of species richness. Results, The four richness maps portrayed broadly similar geographical patterns but, due to the nature of underlying data types, exhibited marked differences in spatial autocorrelation structure. These differences in spatial structure emerged as important in determining which hypothesis appeared most capable of explaining each map's patterns. This was true even when regressions accounted for spurious effects of spatial autocorrelation. Each richness map, therefore, identified a different hypothesis as the most likely cause of broad-scale gradients in species diversity. Main conclusions, Because the ,true' spatial structure of species richness patterns remains elusive, firm conclusions regarding their underlying environmental drivers remain difficult. More broadly, our findings suggest that care should be taken to interpret putative determinants of large-scale ecological gradients in light of the type and spatial characteristics of the underlying data. Indeed, closer scrutiny of these underlying data , here the distributions of individual species , and their environmental associations may offer important insights into the ultimate causes of observed broad-scale patterns. [source] Local environmental effects and spatial effects in macroecological studies using mapped abundance classes: the case of the rook Corvus frugilegus in ScotlandJOURNAL OF ANIMAL ECOLOGY, Issue 5 2006A. GIMONA Summary 1The study of the spatial pattern of species abundance is complicated by statistical problems, such as spatial autocorrelation of the abundance data, which lead to the confusion of environmental effects and dispersal. 2Atlas-derived data for the rook in Scotland are used as a case study to propose an approach for assessing the likely contribution of dispersal and local environmental effects, based on a Bayesian Conditional Autoregressive (CAR) approach. 3The availability of moist grasslands is a key factor explaining the spatial pattern of abundance. This is influenced by a combination of climatic and soil-related factors. A direct link to soil properties is for the first time reported for the wide-scale distribution of a bird species. In addition, for this species, dispersal seems to contribute significantly to the spatial pattern and produces a smoother than expected decline in abundance at the north-western edge of its distribution range. Areas where dispersal is most likely to be important are highlighted. 4The approach described can help ecologists make more efficient use of atlas data for the investigation of the structure of species abundance, and can highlight potential sink areas at the landscape and regional scale. 5Bayesian spatial models can deal with data autocorrelation in atlas-type data, while clearly communicating uncertainty through the estimation of the full posterior probability distribution of all parameters. [source] Nested assemblages of Orthoptera species in the Netherlands: the importance of habitat features and life-history traitsJOURNAL OF BIOGEOGRAPHY, Issue 11 2007M. A. Schouten Abstract Aim, Species communities often exhibit nestedness, the species found in species-poor sites representing subsets of richer ones. In the Netherlands, where intensification of land use has led to severe fragmentation of nature, we examined the degree of nestedness in the distribution of Orthoptera species. An assessment was made of how environmental conditions and species life-history traits are related to this pattern, and how variation in sampling intensity across sites may influence the observed degree of nestedness. Location, The analysis includes a total of 178 semi-natural sites in the Pleistocene sand region of the Netherlands. Methods, A matrix recording the presence or absence of all Orthoptera species in each site was compiled using atlas data. Additionally, separate matrices were constructed for the species of suborders Ensifera and Caelifera. The degree of nestedness was measured using the binmatnest calculator. binmatnest uses an algorithm to sort the matrices to maximal nestedness. We used Spearman's rank correlations to evaluate whether sites were sorted by area, isolation or habitat heterogeneity, and whether species were sorted by their dispersal ability, rate of development or degree of habitat specificity. Results, We found the Orthoptera assemblages to be significantly nested. The rank correlation between site order and sampling intensity was high. The degree of nestedness was lower, but remained significant when under- and over-sampled sites were excluded from the analysis. Site order was strongly correlated with both size of sample site and number of habitat types per site. Rank correlations showed that species were probably ordered by variation in habitat specificity, rather than by variation in dispersal capacity or rate of development of the species. Main conclusions, Variation in sampling intensity among sites had a strong impact on the observed degree of nestedness. Nestedness in habitats may underlie the observed nestedness within the Orthoptera assemblages. Habitat heterogeneity is closely related to site area, which suggests that several large sites should be preserved, rather than many small sites. Furthermore, the results corroborate a focus of nature conservation policy on sites where rare species occur, as long as the full spectrum of habitat conditions and underlying ecological processes is secured. [source] Multivariate analysis of a fine-scale breeding bird atlas using a geographical information system and partial canonical correspondence analysis: environmental and spatial effectsJOURNAL OF BIOGEOGRAPHY, Issue 11 2004Nicolas Titeux Abstract Aim, To assess the relative roles of environment and space in driving bird species distribution and to identify relevant drivers of bird assemblage composition, in the case of a fine-scale bird atlas data set. Location, The study was carried out in southern Belgium using grid cells of 1 × 1 km, based on the distribution maps of the Oiseaux nicheurs de Famenne: Atlas de Lesse et Lomme which contains abundance for 103 bird species. Methods, Species found in < 10% or > 90% of the atlas cells were omitted from the bird data set for the analysis. Each cell was characterized by 59 landscape metrics, quantifying its composition and spatial patterns, using a Geographical Information System. Partial canonical correspondence analysis was used to partition the variance of bird species matrix into independent components: (a) ,pure' environmental variation, (b) spatially-structured environmental variation, (c) ,pure' spatial variation and (d) unexplained, non-spatial variation. Results, The variance partitioning method shows that the selected landscape metrics explain 27.5% of the variation, whilst ,pure' spatial and spatially-structured environmental variables explain only a weak percentage of the variation in the bird species matrix (2.5% and 4%, respectively). Avian community composition is primarily related to the degree of urbanization and the amount and composition of forested and open areas. These variables explain more than half of the variation for three species and over one-third of the variation for 12 species. Main conclusions, The results seem to indicate that the majority of explained variation in species assemblages is attributable to local environmental factors. At such a fine spatial resolution, however, the method does not seem to be appropriated for detecting and extracting the spatial variation of assemblages. Consequently, the large amount of unexplained variation is probably because of missing spatial structures and ,noise' in species abundance data. Furthermore, it is possible that other relevant environmental factors, that were not taken into account in this study and which may operate at different spatial scales, can drive bird assemblage structure. As a large proportion of ecological variation can be shared by environment and space, the applied partitioning method was found to be useful when analysing multispecific atlas data, but it needs improvement to factor out all-scale spatial components of this variation (the source of ,false correlation') and to bring out the ,pure' environmental variation for ecological interpretation. [source] Conservation planning and viability: problems associated with identifying priority sites in Swaziland using species list dataAFRICAN JOURNAL OF ECOLOGY, Issue 3 2010Robert J. Smith Abstract Conservation planning assessments based on species atlas data are known to select planning units containing ecotones because these areas are relatively species-rich. However, this richness is often dependent on the presence of adjoining core habitat, so populations within these ecotones might not be viable. This suggests that atlas data may also fail to distinguish between planning units that are highly transformed by agriculture or urbanization with those from neighbouring untransformed units. These highly transformed units could also be identified as priority sites, based solely on the presence of species that require adjoining habitat patches to persist. This potential problem was investigated using bird and mammal atlas data from Swaziland and a landcover map and found that: (i) there was no correlation between planning unit species richness and proportion of natural landcover for both taxa; (ii) the priority areas that were identified for both birds and mammals were no less transformed than if the units had been chosen at random and (iii) an approach that aimed to meet conservation targets and minimize transformation levels failed to identify more viable priority areas. This third result probably arose because 4.8% of the bird species and 22% of the mammal species were recorded in only one planning unit, reducing the opportunity to choose between units when aiming to represent each species. Therefore, it is suggested that using species lists to design protected area networks at a fine spatial scale may not conserve species effectively unless population viability data are explicitly included in the analysis. Résumé On sait que les évaluations de planifications de la conservation qui se basent sur les données d'atlas des espèces choisissent des unités de planification qui contiennent des écotones parce que ces zones sont relativement riches en espèces. Cependant, cette richesse dépend souvent de la présence proche d'un habitat principal, de sorte que les populations de ces écotones pourraient en fait ne pas être viables. Cela signifie que les données des atlas pourraient aussi ne pas faire la distinction entre les unités de planification qui sont fortement modifiées par l'agriculture ou l'urbanization et celles, voisines, qui ne sont pas modifiées. Des unités profondément modifiées pourraient aussi être identifiées comme sites prioritaires, si l'on se base seulement sur la présence d'espèces qui ont besoin des îlots d'habitats voisins pour subsister. Ce problème potentiel fut étudié en utilisant les données d'atlas sur des oiseaux et des mammifères du Swaziland et une carte de la couverture du terrain, et on a découvert que (i) il n'y avait pas de corrélation entre la richesse en espèces des unités de planification et la proportion de couverture naturelle pour les deux taxons; (ii) les zones prioritaires qui avaient été identifiées pour les oiseaux et pour les mammifères n'étaient pas moins transformées que si les unités avaient été choisies au hasard et (iii) une approche qui visait à atteindre des cibles de conservation et à minimizer le taux de transformation n'avait pas réussi à identifier les zones prioritaires les plus viables. Ce troisième résultat vient peut-être du fait que 4.8% des espèces d'oiseaux et 22% des espèces de mammifères avaient été rapportés pour une seule unité de planification, ce qui a réduit la possibilité de choisir entre les unités lorsque l'on a cherchéà représenter chaque espèce. C'est pourquoi on attire l'attention sur le fait que l'utilization des listes d'espèces pour concevoir les réseaux d'AP à petite échelle spatiale risque de ne pas préserver efficacement les espèces à moins que les données sur la viabilité de leur population ne soient explicitement incluses dans l'analyzse. [source] Modelling species diversity through species level hierarchical modellingJOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES C (APPLIED STATISTICS), Issue 1 2005Alan E. Gelfand Summary., Understanding spatial patterns of species diversity and the distributions of individ-ual species is a consuming problem in biogeography and conservation. The Cape floristic region of South Africa is a global hot spot of diversity and endemism, and the Protea atlas project, with about 60 000 site records across the region, provides an extraordinarily rich data set to model patterns of biodiversity. Model development is focused spatially at the scale of 1, grid cells (about 37 000 cells total for the region). We report on results for 23 species of a flowering plant family known as Proteaceae (of about 330 in the Cape floristic region) for a defined subregion. Using a Bayesian framework, we developed a two-stage, spatially explicit, hierarchical logistic regression. Stage 1 models the potential probability of presence or absence for each species at each cell, given species attributes, grid cell (site level) environmental data with species level coefficients, and a spatial random effect. The second level of the hierarchy models the probability of observing each species in each cell given that it is present. Because the atlas data are not evenly distributed across the landscape, grid cells contain variable numbers of sampling localities. Thus this model takes the sampling intensity at each site into account by assuming that the total number of times that a particular species was observed within a site follows a binomial distribution. After assigning prior distributions to all quantities in the model, samples from the posterior distribution were obtained via Markov chain Monte Carlo methods. Results are mapped as the model-estimated probability of presence for each species across the domain. This provides an alternative to customary empirical ,range-of-occupancy' displays. Summing yields the predicted richness of species over the region. Summaries of the posterior for each environmental coefficient show which variables are most important in explaining the presence of species. Our initial results describe biogeographical patterns over the modelled region remarkably well. In particular, species local population size and mode of dispersal contribute significantly to predicting patterns, along with annual precipitation, the coefficient of variation in rainfall and elevation. [source] Modelling the occurrence of rainbow lorikeets (Trichoglossus haematodus) in MelbourneAUSTRAL ECOLOGY, Issue 2 2006PAVLINA SHUKUROGLOU Abstract Over the previous three decades, the rainbow lorikeet (Trichoglossus haematodus Family Psittacidae) has increased in urbanized areas of Australia. To help understand the nature of this increase, we investigated the influence of road density, tree cover and season on the occurrence of the rainbow lorikeet in the Melbourne region. Bayesian logistic regression was used to construct models to predict the occurrence of rainbow lorikeets, using Birds Australia atlas data at 207 2-ha sites. The results demonstrate a strong relationship between tree cover and urbanization and the distribution of the species. The best model incorporated quadratic terms for road density and tree cover, and interaction terms, as well as season as a categorical variable. Probability of occurrence of rainbow lorikeets was highest at medium tree cover (40% to 70% of the site covered) and medium road density (9% to 12% of the surrounding area covered by roads). There was a close correspondence between the predictions of the model and new observations from bird surveys conducted at randomly selected field sites. The increased abundance of the species in urban areas has occurred despite a paucity of hollows that would act as suitable nesting sites, suggesting that only a small proportion of the population is breeding in these areas. [source] | ||||||||||