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Species Distribution Models (species + distribution_models)
Selected AbstractsCorrelative and mechanistic models of species distribution provide congruent forecasts under climate changeCONSERVATION LETTERS, Issue 3 2010Michael R. Kearney Abstract Good forecasts of climate change impacts on extinction risks are critical for effective conservation management responses. Species distribution models (SDMs) are central to extinction risk analyses. The reliability of predictions of SDMs has been questioned because models often lack a mechanistic underpinning and rely on assumptions that are untenable under climate change. We show how integrating predictions from fundamentally different modeling strategies produces robust forecasts of climate change impacts on habitat and population parameters. We illustrate the principle by applying mechanistic (Niche Mapper) and correlative (Maxent, Bioclim) SDMs to predict current and future distributions and fertility of an Australian gliding possum. The two approaches make congruent, accurate predictions of current distribution and similar, dire predictions about the impact of a warming scenario, supporting previous correlative-only predictions for similar species. We argue that convergent lines of independent evidence provide a robust basis for predicting and managing extinctions risks under climate change. [source] The effect of sample size and species characteristics on performance of different species distribution modeling methodsECOGRAPHY, Issue 5 2006Pilar A. Hernandez Species distribution models should provide conservation practioners with estimates of the spatial distributions of species requiring attention. These species are often rare and have limited known occurrences, posing challenges for creating accurate species distribution models. We tested four modeling methods (Bioclim, Domain, GARP, and Maxent) across 18 species with different levels of ecological specialization using six different sample size treatments and three different evaluation measures. Our assessment revealed that Maxent was the most capable of the four modeling methods in producing useful results with sample sizes as small as 5, 10 and 25 occurrences. The other methods compensated reasonably well (Domain and GARP) to poorly (Bioclim) when presented with datasets of small sample sizes. We show that multiple evaluation measures are necessary to determine accuracy of models produced with presence-only data. Further, we found that accuracy of models is greater for species with small geographic ranges and limited environmental tolerance, ecological characteristics of many rare species. Our results indicate that reasonable models can be made for some rare species, a result that should encourage conservationists to add distribution modeling to their toolbox. [source] Why is the choice of future climate scenarios for species distribution modelling important?ECOLOGY LETTERS, Issue 11 2008Linda J. Beaumont Abstract Species distribution models (SDMs) are common tools for assessing the potential impact of climate change on species ranges. Uncertainty in SDM output occurs due to differences among alternate models, species characteristics and scenarios of future climate. While considerable effort is being devoted to identifying and quantifying the first two sources of variation, a greater understanding of climate scenarios and how they affect SDM output is also needed. Climate models are complex tools: variability occurs among alternate simulations, and no single ,best' model exists. The selection of climate scenarios for impacts assessments should not be undertaken arbitrarily - strengths and weakness of different climate models should be considered. In this paper, we provide bioclimatic modellers with an overview of emissions scenarios and climate models, discuss uncertainty surrounding projections of future climate and suggest steps that can be taken to reduce and communicate climate scenario-related uncertainty in assessments of future species responses to climate change. [source] Adaptation, migration or extirpation: climate change outcomes for tree populationsEVOLUTIONARY APPLICATIONS (ELECTRONIC), Issue 1 2008Sally N. Aitken Abstract Species distribution models predict a wholesale redistribution of trees in the next century, yet migratory responses necessary to spatially track climates far exceed maximum post-glacial rates. The extent to which populations will adapt will depend upon phenotypic variation, strength of selection, fecundity, interspecific competition, and biotic interactions. Populations of temperate and boreal trees show moderate to strong clines in phenology and growth along temperature gradients, indicating substantial local adaptation. Traits involved in local adaptation appear to be the product of small effects of many genes, and the resulting genotypic redundancy combined with high fecundity may facilitate rapid local adaptation despite high gene flow. Gene flow with preadapted alleles from warmer climates may promote adaptation and migration at the leading edge, while populations at the rear will likely face extirpation. Widespread species with large populations and high fecundity are likely to persist and adapt, but will likely suffer adaptational lag for a few generations. As all tree species will be suffering lags, interspecific competition may weaken, facilitating persistence under suboptimal conditions. Species with small populations, fragmented ranges, low fecundity, or suffering declines due to introduced insects or diseases should be candidates for facilitated migration. [source] Projecting climate change impacts on species distributions in megadiverse South African Cape and Southwest Australian Floristic Regions: Opportunities and challengesAUSTRAL ECOLOGY, Issue 4 2010COLIN J. YATES Abstract Increasing evidence shows that anthropogenic climate change is affecting biodiversity. Reducing or stabilizing greenhouse gas emissions may slow global warming, but past emissions will continue to contribute to further unavoidable warming for more than a century. With obvious signs of difficulties in achieving effective mitigation worldwide in the short term at least, sound scientific predictions of future impacts on biodiversity will be required to guide conservation planning and adaptation. This is especially true in Mediterranean type ecosystems that are projected to be among the most significantly affected by anthropogenic climate change, and show the highest levels of confidence in rainfall projections. Multiple methods are available for projecting the consequences of climate change on the main unit of interest , the species , with each method having strengths and weaknesses. Species distribution models (SDMs) are increasingly applied for forecasting climate change impacts on species geographic ranges. Aggregation of models for different species allows inferences of impacts on biodiversity, though excluding the effects of species interactions. The modelling approach is based on several further assumptions and projections and should be treated cautiously. In the absence of comparable approaches that address large numbers of species, SDMs remain valuable in estimating the vulnerability of species. In this review we discuss the application of SDMs in predicting the impacts of climate change on biodiversity with special reference to the species-rich South West Australian Floristic Region and South African Cape Floristic Region. We discuss the advantages and challenges in applying SDMs in biodiverse regions with high levels of endemicity, and how a similar biogeographical history in both regions may assist us in understanding their vulnerability to climate change. We suggest how the process of predicting the impacts of climate change on biodiversity with SDMs can be improved and emphasize the role of field monitoring and experiments in validating the predictions of SDMs. [source] Can distribution models help refine inventory-based estimates of conservation priority?DIVERSITY AND DISTRIBUTIONS, Issue 4 2010A case study in the Eastern Arc forests of Tanzania, Kenya Abstract Aim, Data shortages mean that conservation priorities can be highly sensitive to historical patterns of exploration. Here, we investigate the potential of regionally focussed species distribution models to elucidate fine-scale patterns of richness, rarity and endemism. Location, Eastern Arc Mountains, Tanzania and Kenya. Methods, Generalized additive models and land cover data are used to estimate the distributions of 452 forest plant taxa (trees, lianas, shrubs and herbs). Presence records from a newly compiled database are regressed against environmental variables in a stepwise multimodel. Estimates of occurrence in forest patches are collated across target groups and analysed alongside inventory-based estimates of conservation priority. Results, Predicted richness is higher than observed richness, with the biggest disparities in regions that have had the least research. North Pare and Nguu in particular are predicted to be more important than the inventory data suggest. Environmental conditions in parts of Nguru could support as many range-restricted and endemic taxa as Uluguru, although realized niches are subject to unknown colonization histories. Concentrations of rare plants are especially high in the Usambaras, a pattern mediated in models by moisture indices, whilst overall richness is better explained by temperature gradients. Tree data dominate the botanical inventory; we find that priorities based on other growth forms might favour the mountains in a different order. Main conclusions, Distribution models can provide conservation planning with high-resolution estimates of richness in well-researched areas, and predictive estimates of conservation importance elsewhere. Spatial and taxonomic biases in the data are essential considerations, as is the spatial scale used for models. We caution that predictive estimates are most uncertain for the species of highest conservation concern, and advocate using models and targeted field assessments iteratively to refine our understanding of which areas should be prioritised for conservation. [source] Past and present potential distribution of the Iberian Abies species: a phytogeographic approach using fossil pollen data and species distribution modelsDIVERSITY AND DISTRIBUTIONS, Issue 2 2010Francisca Alba-Sánchez Abstract Aim, Quaternary palaeopalynological records collected throughout the Iberian Peninsula and species distribution models (SDMs) were integrated to gain a better understanding of the historical biogeography of the Iberian Abies species (i.e. Abies pinsapo and Abies alba). We hypothesize that SDMs and Abies palaeorecords are closely correlated, assuming a certain stasis in climatic and topographic ecological niche dimensions. In addition, the modelling results were used to assign the fossil records to A. alba or A. pinsapo, to identify environmental variables affecting their distribution, and to evaluate the ecological segregation between the two taxa. Location, The Iberian Peninsula. Methods, For the estimation of past Abies distributions, a hindcasting process was used. Abies pinsapo and A. alba were modelled individually, first calibrating the model for their current distributions in relation to the present climate, and then projecting it into the past,the last glacial maximum (LGM) and the Middle Holocene periods,in relation to palaeoclimate simulations. The resulting models were compared with Iberian-wide fossil pollen records to detect areas of overlap. Results, The overlap observed between past Abies refugia,inferred from fossil pollen records,and the SDMs helped to construct the Quaternary distribution of the Iberian Abies species. SDMs yielded two well-differentiated potential distributions: A. pinsapo throughout the Baetic mountain Range and A. alba along the Pyrenees and Cantabrian Range. These results propose that the two taxa remained isolated throughout the Quaternary, indicating a significant geographical and ecological segregation. In addition, no significant differences were detected comparing the three projections (present-day, Mid-Holocene and LGM), suggesting a relative climate stasis in the refuge areas during the Quaternary. Main conclusions, Our results confirm that SDM projections can provide a useful complement to palaeoecological studies, offering a less subjective and spatially explicit hypothesis concerning past geographic patterns of Iberian Abies species. The integration of ecological-niche characteristics from known occurrences of Abies species in conjunction with palaeoecological studies could constitute a suitable tool to define appropriate areas in which to focus proactive conservation strategies. [source] Effects of herbivore species richness on the niche dynamics and distribution of blue sheep in the Trans-HimalayaDIVERSITY AND DISTRIBUTIONS, Issue 6 2009Tsewang Namgail Abstract Aim, To understand the community structure of mountain ungulates by exploring their niche dynamics in response to sympatric species richness. Location, Ladakh and Spiti Regions of the Western Indian Trans-Himalaya. Methods, We used the blue sheep Pseudois nayaur, a relatively widely distributed mountain ungulate, as a model species to address the issue. We selected three discrete valleys in three protected areas with similar environmental features but varying wild ungulate species richness, and studied blue sheep's diet and habitat utilization in them. Habitat variables such as slope angle, distance to cliff and elevation at blue sheep locations were recorded to determine the habitat width of the species. Faecal pellets were collected and microhistological faecal analysis was carried out to determine the diet width of blue sheep in the three areas with different ungulate species richness. Blue sheep's niche width in terms of habitat and diet was determined using the Shannon's Index. Results, The habitat width of blue sheep had a negative relationship with the number of sympatric species. However, contrary to our expectation, there was a hump-shaped relationship between blue sheep's diet width and the sympatric species richness, with the diet width being narrower in areas of allopatry as well as in areas with high herbivore species richness, and the greatest in areas with moderate species richness. Main conclusions, We suspect that the narrow diet width in allopatry is out of choice, whereas it is out of necessity in areas with high herbivore species richness because of resource partitioning that enables coexistence. We suggest that interactions with sympatric species lead to niche adjustment of mountain ungulates, implying that competition may play a role in structuring Trans-Himalayan mountain ungulate assemblages. Given these results, we underscore the importance of including biotic interactions in species distribution models, which have often been neglected. [source] Effects of species and habitat positional errors on the performance and interpretation of species distribution modelsDIVERSITY AND DISTRIBUTIONS, Issue 4 2009Patrick E. Osborne Abstract Aim, A key assumption in species distribution modelling is that both species and environmental data layers contain no positional errors, yet this will rarely be true. This study assesses the effect of introduced positional errors on the performance and interpretation of species distribution models. Location, Baixo Alentejo region of Portugal. Methods, Data on steppe bird occurrence were collected using a random stratified sampling design on a 1-km2 pixel grid. Environmental data were sourced from satellite imagery and digital maps. Error was deliberately introduced into the species data as shifts in a random direction of 0,1, 2,3, 4,5 and 0,5 pixels. Whole habitat layers were shifted by 1 pixel to cause mis-registration, and the cumulative effect of one to three shifted layers investigated. Distribution models were built for three species using three algorithms with three replicates. Test models were compared with controls without errors. Results, Positional errors in the species data led to a drop in model performance (larger errors having larger effects , typically up to 10% drop in area under the curve on average), although not enough for models to be rejected. Model interpretation was more severely affected with inconsistencies in the contributing variables. Errors in the habitat layers had similar although lesser effects. Main conclusions, Models with species positional errors are hard to detect, often statistically good, ecologically plausible and useful for prediction, but interpreting them is dangerous. Mis-registered habitat layers produce smaller effects probably because shifting entire layers does not break down the correlation structure to the same extent as random shifts in individual species observations. Spatial autocorrelation in the habitat layers may protect against species positional errors to some extent but the relationship is complex and requires further work. The key recommendation must be that positional errors should be minimised through careful field design and data processing. [source] From introduction to the establishment of alien species: bioclimatic differences between presence and reproduction localities in the slider turtleDIVERSITY AND DISTRIBUTIONS, Issue 1 2009Gentile Francesco Ficetola ABSTRACT Aim, Understanding the factors determining the transition from introduction of aliens to the establishment of invasive populations is a critical issue of the study of biological invasions, and has key implications for management. Differences in fitness among areas of introduction can define the zones where aliens become invasive. The American slider turtle Trachemys scripta has been introduced worldwide, and has negative effects on freshwater communities, but only a subset of introduced populations breed successfully. We used species distribution models to assess the factors influencing the slider distribution in Italy, by analysing bioclimatic features that can cause the transition from presence of feral adults to breeding populations. We also evaluated whether climate change might increase the future suitability for reproduction. Location,, Central and Northern Italy. Methods,, The distribution of slider turtle was obtained from the literature, unpublished reports and field surveys. We used Maxent to build bioclimatic models. Results,, Reproductive populations are associated to a clear bioclimatic envelope with warmer climate, more solar radiation and higher precipitations than populations where reproduction is not observed. Several Mediterranean areas currently have climatic features suitable for sliders. Scenarios of climate change predict the expansion of these areas. In the near future (2020), the proportion of populations in areas suitable for reproduction will dramatically increase. Main conclusion,, Our study shows that bioclimatic differences can determine the areas where aliens become invaders. Management should be focused to these source areas. However, climate change can increase fitness in the future, and therefore the interactions between climate change and fitness can boost the invasiveness of this alien species. [source] Differences in spatial predictions among species distribution modeling methods vary with species traits and environmental predictorsECOGRAPHY, Issue 6 2009Alexandra D. Syphard Prediction maps produced by species distribution models (SDMs) influence decision-making in resource management or designation of land in conservation planning. Many studies have compared the prediction accuracy of different SDM modeling methods, but few have quantified the similarity among prediction maps. There has also been little systematic exploration of how the relative importance of different predictor variables varies among model types and affects map similarity. Our objective was to expand the evaluation of SDM performance for 45 plant species in southern California to better understand how map predictions vary among model types, and to explain what factors may affect spatial correspondence, including the selection and relative importance of different environmental variables. Four types of models were tested. Correlation among maps was highest between generalized linear models (GLMs) and generalized additive models (GAMs) and lowest between classification trees and GAMs or GLMs. Correlation between Random Forests (RFs) and GAMs was the same as between RFs and classification trees. Spatial correspondence among maps was influenced the most by model prediction accuracy (AUC) and species prevalence; map correspondence was highest when accuracy was high and prevalence was intermediate (average prevalence for all species was 0.124). Species functional type and the selection of climate variables also influenced map correspondence. For most (but not all) species, climate variables were more important than terrain or soil in predicting their distributions. Environmental variable selection varied according to modeling method, but the largest differences were between RFs and GLMs or GAMs. Although prediction accuracy was equal for GLMs, GAMs, and RFs, the differences in spatial predictions suggest that it may be important to evaluate the results of more than one model to estimate the range of spatial uncertainty before making planning decisions based on map outputs. This may be particularly important if models have low accuracy or if species prevalence is not intermediate. [source] Botanical richness and endemicity patterns of Borneo derived from species distribution modelsECOGRAPHY, Issue 1 2009Niels Raes This study provides a Borneo-wide, quantitative assessment of botanical richness and endemicity at a high spatial resolution, and based on actual collection data. To overcome the bias in collection effort, and to be able to predict the presence and absence of species, even for areas where no collections have been made, we constructed species distribution models (SDMs) for all species taxonomically revised in Flora Malesiana. Species richness and endemicity maps were based on 1439 significant SDMs. Mapping of the residuals of the richness-endemicity relationship identified areas with higher levels of endemicity than can be expected on the basis of species richness, the endemicity hotspots. We were able to identify one previously unknown region of high diversity, the high mountain peaks of East Kalimantan; and two additional endemicity hotspots, the Müller Mountains and the Sangkulirang peninsula. The areas of high diversity and endemicity were characterized by a relatively small range in annual temperature, but with seasonality in temperatures within that range. Furthermore, these areas were least affected by El Niño Southern Oscillation drought events. The endemicity hotspots were found in areas, which were ecologically distinct in altitude, edaphic conditions, annual precipitation, or a combination of these factors. These results can be used to guide conservation efforts of the highly threatened forests of Borneo. [source] Effects of species' ecology on the accuracy of distribution modelsECOGRAPHY, Issue 1 2007Jana M. McPherson In the face of accelerating biodiversity loss and limited data, species distribution models , which statistically capture and predict species' occurrences based on environmental correlates , are increasingly used to inform conservation strategies. Additionally, distribution models and their fit provide insights on the broad-scale environmental niche of species. To investigate whether the performance of such models varies with species' ecological characteristics, we examined distribution models for 1329 bird species in southern and eastern Africa. The models were constructed at two spatial resolutions with both logistic and autologistic regression. Satellite-derived environmental indices served as predictors, and model accuracy was assessed with three metrics: sensitivity, specificity and the area under the curve (AUC) of receiver operating characteristics plots. We then determined the relationship between each measure of accuracy and ten ecological species characteristics using generalised linear models. Among the ecological traits tested, species' range size, migratory status, affinity for wetlands and endemism proved most influential on the performance of distribution models. The number of habitat types frequented (habitat tolerance), trophic rank, body mass, preferred habitat structure and association with sub-resolution habitats also showed some effect. In contrast, conservation status made no significant impact. These findings did not differ from one spatial resolution to the next. Our analyses thus provide conservation scientists and resource managers with a rule of thumb that helps distinguish, on the basis of ecological traits, between species whose occurrence is reliably or less reliably predicted by distribution models. Reasonably accurate distribution models should, however, be attainable for most species, because the influence ecological traits bore on model performance was only limited. These results suggest that none of the ecological traits tested provides an obvious correlate for environmental niche breadth or intra-specific niche differentiation. [source] The effect of sample size and species characteristics on performance of different species distribution modeling methodsECOGRAPHY, Issue 5 2006Pilar A. Hernandez Species distribution models should provide conservation practioners with estimates of the spatial distributions of species requiring attention. These species are often rare and have limited known occurrences, posing challenges for creating accurate species distribution models. We tested four modeling methods (Bioclim, Domain, GARP, and Maxent) across 18 species with different levels of ecological specialization using six different sample size treatments and three different evaluation measures. Our assessment revealed that Maxent was the most capable of the four modeling methods in producing useful results with sample sizes as small as 5, 10 and 25 occurrences. The other methods compensated reasonably well (Domain and GARP) to poorly (Bioclim) when presented with datasets of small sample sizes. We show that multiple evaluation measures are necessary to determine accuracy of models produced with presence-only data. Further, we found that accuracy of models is greater for species with small geographic ranges and limited environmental tolerance, ecological characteristics of many rare species. Our results indicate that reasonable models can be made for some rare species, a result that should encourage conservationists to add distribution modeling to their toolbox. [source] Predicting species distribution: offering more than simple habitat modelsECOLOGY LETTERS, Issue 9 2005Antoine Guisan Abstract In the last two decades, interest in species distribution models (SDMs) of plants and animals has grown dramatically. Recent advances in SDMs allow us to potentially forecast anthropogenic effects on patterns of biodiversity at different spatial scales. However, some limitations still preclude the use of SDMs in many theoretical and practical applications. Here, we provide an overview of recent advances in this field, discuss the ecological principles and assumptions underpinning SDMs, and highlight critical limitations and decisions inherent in the construction and evaluation of SDMs. Particular emphasis is given to the use of SDMs for the assessment of climate change impacts and conservation management issues. We suggest new avenues for incorporating species migration, population dynamics, biotic interactions and community ecology into SDMs at multiple spatial scales. Addressing all these issues requires a better integration of SDMs with ecological theory. [source] Using species distribution models to identify suitable areas for biofuel feedstock productionGCB BIOENERGY, Issue 2 2010JASON M. EVANS Abstract The 2007 Energy Independence and Security Act mandates a five-fold increase in US biofuel production by 2022. Given this ambitious policy target, there is a need for spatially explicit estimates of landscape suitability for growing biofuel feedstocks. We developed a suitability modeling approach for two major US biofuel crops, corn (Zea mays) and switchgrass (Panicum virgatum), based upon the use of two presence-only species distribution models (SDMs): maximum entropy (Maxent) and support vector machines (SVM). SDMs are commonly used for modeling animal and plant distributions in natural environments, but have rarely been used to develop landscape models for cultivated crops. AUC, Kappa, and correlation measures derived from test data indicate that SVM slightly outperformed Maxent in modeling US corn production, although both models produced significantly accurate results. When compared with results from a mechanistic switchgrass model recently developed by Oak Ridge National Laboratory (ORNL), SVM results showed higher correlation than Maxent results with models fit using county-scale point inputs of switchgrass production derived from expert opinion estimates. However, Maxent results for an alternative switchgrass model developed with point inputs from research trial sites showed higher correlation to the ORNL model than the corresponding results obtained from SVM. Further analysis indicates that both modeling approaches were effective in predicting county-scale increases in corn production from 2006 to 2007, a time period in which US corn production increased by 24%. We conclude that presence-only methods are a powerful first-cut tool for estimating relative land suitability across geographic regions in which candidate biofuel feedstocks can be grown, and may also provide important insight into potential land-use change patterns likely to be associated with increased biofuel demand. [source] Developing an approach to defining the potential distributions of invasive plant species: a case study of Hakea species in South AfricaGLOBAL ECOLOGY, Issue 5 2008David C. Le Maitre ABSTRACT Aim, Models of the potential distributions of invading species have to deal with a number of issues. The key one is the high likelihood that the absence of an invading species in an area is a false absence because it may not have invaded that area yet, or that it may not have been detected. This paper develops an approach for screening pseudo-absences in a way that is logical and defensible. Innovation, The step-wise approach involves: (1) screening environmental variables to identify those most likely to indicate conditions where the species cannot invade; (2) identifying and selecting the most likely limiting variables; (3) using these to define the limits of its invasion potential; and (4) selecting points outside these limits as true absence records for input into species distribution models. This approach was adopted and used for the study of three prominent Hakea species in South Africa. Models with and without the false absence records were compared. Two rainfall variables and the mean minimum temperature of the coldest month were the strongest predictors of potential distributions. Models which excluded false absences predicted that more of the potential distribution would have a high invasion potential than those which included them. Main conclusions, The approach of applying a priori knowledge can be useful in refining the potential distribution of a species by excluding pseudo-absence records which are likely to be due to the species not having invaded an area yet or being undetected. The differences between the potential distributions predicted by the different models convey more information than making a single prediction, albeit a consensus model. The robustness of this approach depends strongly on an adequate knowledge of the ecology, invasion history and current distribution of that species. [source] The biogeography of prediction error: why does the introduced range of the fire ant over-predict its native range?GLOBAL ECOLOGY, Issue 1 2007Matthew C. Fitzpatrick ABSTRACT Aim, The use of species distribution models (SDMs) to predict biological invasions is a rapidly developing area of ecology. However, most studies investigating SDMs typically ignore prediction errors and instead focus on regions where native distributions correctly predict invaded ranges. We investigated the ecological significance of prediction errors using reciprocal comparisons between the predicted invaded and native range of the red imported fire ant (Solenopsis invicta) (hereafter called the fire ant). We questioned whether fire ants occupy similar environments in their native and introduced range, how the environments that fire ants occupy in their introduced range changed through time relative to their native range, and where fire ant propagules are likely to have originated. Location, We developed models for South America and the conterminous United States (US) of America. Methods, We developed models using the Genetic Algorithm for Rule-set Prediction (GARP) and 12 environmental layers. Occurrence data from the native range in South America were used to predict the introduced range in the US and vice versa. Further, time-series data recording the invasion of fire ants in the US were used to predict the native range. Results, Native range occurrences under-predicted the invasive potential of fire ants, whereas occurrence data from the US over-predicted the southern boundary of the native range. Secondly, introduced fire ants initially established in environments similar to those in their native range, but subsequently invaded harsher environments. Time-series data suggest that fire ant propagules originated near the southern limit of their native range. Conclusions, Our findings suggest that fire ants from a peripheral native population established in an environment similar to their native environment, and then ultimately expanded into environments in which they are not found in their native range. We argue that reciprocal comparisons between predicted native and invaded ranges will facilitate a better understanding of the biogeography of invasive and native species and of the role of SDMs in predicting future distributions. [source] Hierarchical spatial models for predicting pygmy rabbit distribution and relative abundanceJOURNAL OF APPLIED ECOLOGY, Issue 2 2010Tammy L. Wilson Summary 1.,Conservationists routinely use species distribution models to plan conservation, restoration and development actions, while ecologists use them to infer process from pattern. These models tend to work well for common or easily observable species, but are of limited utility for rare and cryptic species. This may be because honest accounting of known observation bias and spatial autocorrelation are rarely included, thereby limiting statistical inference of resulting distribution maps. 2.,We specified and implemented a spatially explicit Bayesian hierarchical model for a cryptic mammal species (pygmy rabbit Brachylagus idahoensis). Our approach used two levels of indirect sign that are naturally hierarchical (burrows and faecal pellets) to build a model that allows for inference on regression coefficients as well as spatially explicit model parameters. We also produced maps of rabbit distribution (occupied burrows) and relative abundance (number of burrows expected to be occupied by pygmy rabbits). The model demonstrated statistically rigorous spatial prediction by including spatial autocorrelation and measurement uncertainty. 3.,We demonstrated flexibility of our modelling framework by depicting probabilistic distribution predictions using different assumptions of pygmy rabbit habitat requirements. 4.,Spatial representations of the variance of posterior predictive distributions were obtained to evaluate heterogeneity in model fit across the spatial domain. Leave-one-out cross-validation was conducted to evaluate the overall model fit. 5.,Synthesis and applications. Our method draws on the strengths of previous work, thereby bridging and extending two active areas of ecological research: species distribution models and multi-state occupancy modelling. Our framework can be extended to encompass both larger extents and other species for which direct estimation of abundance is difficult. [source] Using habitat distribution models to evaluate large-scale landscape priorities for spatially dynamic speciesJOURNAL OF APPLIED ECOLOGY, Issue 1 2008Regan Early Summary 1Large-scale conservation planning requires the identification of priority areas in which species have a high likelihood of long-term persistence. This typically requires high spatial resolution data on species and their habitat. Such data are rarely available at a large geographical scale, so distribution modelling is often required to identify the locations of priority areas. However, distribution modelling may be difficult when a species is either not recorded, or not present, at many of the locations that are actually suitable for it. This is an inherent problem for species that exhibit metapopulation dynamics. 2Rather than basing species distribution models on species locations, we investigated the consequences of predicting the distribution of suitable habitat, and thus inferring species presence/absence. We used habitat surveys to define a vegetation category which is suitable for a threatened species that has spatially dynamic populations (the butterfly Euphydryas aurinia), and used this as the response variable in distribution models. Thus, we developed a practical strategy to obtain high resolution (1 ha) large scale conservation solutions for E. aurinia in Wales, UK. 3Habitat-based distribution models had high discriminatory power. They could generalize over a large spatial extent and on average predicted 86% of the current distribution of E. aurinia in Wales. Models based on species locations had lower discriminatory power and were poorer at generalizing throughout Wales. 4Surfaces depicting the connectivity of each grid cell were calculated for the predicted distribution of E. aurinia habitat. Connectivity surfaces provided a distance-weighted measure of the concentration of habitat in the surrounding landscape, and helped identify areas where the persistence of E. aurinia populations is expected to be highest. These identified successfully known areas of high conservation priority for E. aurinia. These connectivity surfaces allow conservation planning to take into account long-term spatial population dynamics, which would be impossible without being able to predict the species' distribution over a large spatial extent. 5Synthesis and applications. Where species location data are unsuitable for building high resolution predictive habitat distribution models, habitat data of sufficient quality can be easier to collect. We show that they can perform as well as or better than species data as a response variable. When coupled with a technique to translate distribution model predictions into landscape priority (such as connectivity calculations), we believe this approach will be a powerful tool for large-scale conservation planning. [source] Climate-based models of spatial patterns of species richness in Egypt's butterfly and mammal faunaJOURNAL OF BIOGEOGRAPHY, Issue 11 2009Tim Newbold Abstract Aim, Identifying areas of high species richness is an important goal of conservation biogeography. In this study we compared alternative methods for generating climate-based estimates of spatial patterns of butterfly and mammal species richness. Location, Egypt. Methods, Data on the occurrence of butterflies and mammals in Egypt were taken from an electronic database compiled from museum records and the literature. Using Maxent, species distribution models were built with these data and with variables describing climate and habitat. Species richness predictions were made by summing distribution models for individual species and by modelling observed species richness directly using the same environmental variables. Results, Estimates of species richness from both methods correlated positively with each other and with observed species richness. Protected areas had higher species richness (both predicted and actual) than unprotected areas. Main conclusions, Our results suggest that climate-based models of species richness could provide a rapid method for selecting potential areas for protection and thus have important implications for biodiversity conservation. [source] Are niche-based species distribution models transferable in space?JOURNAL OF BIOGEOGRAPHY, Issue 10 2006Christophe F. Randin Abstract Aim, To assess the geographical transferability of niche-based species distribution models fitted with two modelling techniques. Location, Two distinct geographical study areas in Switzerland and Austria, in the subalpine and alpine belts. Methods, Generalized linear and generalized additive models (GLM and GAM) with a binomial probability distribution and a logit link were fitted for 54 plant species, based on topoclimatic predictor variables. These models were then evaluated quantitatively and used for spatially explicit predictions within (internal evaluation and prediction) and between (external evaluation and prediction) the two regions. Comparisons of evaluations and spatial predictions between regions and models were conducted in order to test if species and methods meet the criteria of full transferability. By full transferability, we mean that: (1) the internal evaluation of models fitted in region A and B must be similar; (2) a model fitted in region A must at least retain a comparable external evaluation when projected into region B, and vice-versa; and (3) internal and external spatial predictions have to match within both regions. Results, The measures of model fit are, on average, 24% higher for GAMs than for GLMs in both regions. However, the differences between internal and external evaluations (AUC coefficient) are also higher for GAMs than for GLMs (a difference of 30% for models fitted in Switzerland and 54% for models fitted in Austria). Transferability, as measured with the AUC evaluation, fails for 68% of the species in Switzerland and 55% in Austria for GLMs (respectively for 67% and 53% of the species for GAMs). For both GAMs and GLMs, the agreement between internal and external predictions is rather weak on average (Kulczynski's coefficient in the range 0.3,0.4), but varies widely among individual species. The dominant pattern is an asymmetrical transferability between the two study regions (a mean decrease of 20% for the AUC coefficient when the models are transferred from Switzerland and 13% when they are transferred from Austria). Main conclusions, The large inter-specific variability observed among the 54 study species underlines the need to consider more than a few species to test properly the transferability of species distribution models. The pronounced asymmetry in transferability between the two study regions may be due to peculiarities of these regions, such as differences in the ranges of environmental predictors or the varied impact of land-use history, or to species-specific reasons like differential phenotypic plasticity, existence of ecotypes or varied dependence on biotic interactions that are not properly incorporated into niche-based models. The lower variation between internal and external evaluation of GLMs compared to GAMs further suggests that overfitting may reduce transferability. Overall, a limited geographical transferability calls for caution when projecting niche-based models for assessing the fate of species in future environments. [source] Can mechanism inform species' distribution models?ECOLOGY LETTERS, Issue 8 2010Lauren B. Buckley Ecology Letters (2010) 13: 1041,1054 Abstract Two major approaches address the need to predict species distributions in response to environmental changes. Correlative models estimate parameters phenomenologically by relating current distributions to environmental conditions. By contrast, mechanistic models incorporate explicit relationships between environmental conditions and organismal performance, estimated independently of current distributions. Mechanistic approaches include models that translate environmental conditions into biologically relevant metrics (e.g. potential duration of activity), models that capture environmental sensitivities of survivorship and fecundity, and models that use energetics to link environmental conditions and demography. We compared how two correlative and three mechanistic models predicted the ranges of two species: a skipper butterfly (Atalopedes campestris) and a fence lizard (Sceloporus undulatus). Correlative and mechanistic models performed similarly in predicting current distributions, but mechanistic models predicted larger range shifts in response to climate change. Although mechanistic models theoretically should provide more accurate distribution predictions, there is much potential for improving their flexibility and performance. [source] Making better biogeographical predictions of species' distributionsJOURNAL OF APPLIED ECOLOGY, Issue 3 2006ANTOINE GUISAN Summary 1Biogeographical models of species' distributions are essential tools for assessing impacts of changing environmental conditions on natural communities and ecosystems. Practitioners need more reliable predictions to integrate into conservation planning (e.g. reserve design and management). 2Most models still largely ignore or inappropriately take into account important features of species' distributions, such as spatial autocorrelation, dispersal and migration, biotic and environmental interactions. Whether distributions of natural communities or ecosystems are better modelled by assembling individual species' predictions in a bottom-up approach or modelled as collective entities is another important issue. An international workshop was organized to address these issues. 3We discuss more specifically six issues in a methodological framework for generalized regression: (i) links with ecological theory; (ii) optimal use of existing data and artificially generated data; (iii) incorporating spatial context; (iv) integrating ecological and environmental interactions; (v) assessing prediction errors and uncertainties; and (vi) predicting distributions of communities or collective properties of biodiversity. 4Synthesis and applications. Better predictions of the effects of impacts on biological communities and ecosystems can emerge only from more robust species' distribution models and better documentation of the uncertainty associated with these models. An improved understanding of causes of species' distributions, especially at their range limits, as well as of ecological assembly rules and ecosystem functioning, is necessary if further progress is to be made. A better collaborative effort between theoretical and functional ecologists, ecological modellers and statisticians is required to reach these goals. [source] | |||||||||||||||||||||||||