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James D. Nichols - One of the best experts on this subject based on the ideXlab platform.
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modeling false positive detections in Species Occurrence data under different study designs
Ecology, 2015Co-Authors: David A. W. Miller, Thierry Chambert, James D. NicholsAbstract:The Occurrence of false positive detections in presence–absence data, even when they occur infrequently, can lead to severe bias when estimating Species occupancy patterns. Building upon previous efforts to account for this source of observational error, we established a general framework to model false positives in occupancy studies and extend existing modeling approaches to encompass a broader range of sampling designs. Specifically, we identified three common sampling designs that are likely to cover most scenarios encountered by researchers. The different designs all included ambiguous detections, as well as some known-truth data, but their modeling differed in the level of the model hierarchy at which the known-truth information was incorporated (site level or observation level). For each model, we provide the likelihood, as well as R and BUGS code needed for implementation. We also establish a clear terminology and provide guidance to help choosing the most appropriate design and modeling approach.
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likelihood analysis of Species Occurrence probability from presence only data for modelling Species distributions
Methods in Ecology and Evolution, 2012Co-Authors: Andrew J Royle, Richard B. Chandler, Charles B. Yackulic, James D. NicholsAbstract:Summary 1. Understanding the factors affecting Species Occurrence is a pre-eminent focus of applied ecological research. However, direct information about Species Occurrence is lacking for many Species. Instead, researchers sometimes have to rely on so-called presence-only data (i.e. when no direct information about absences is available), which often results from opportunistic, unstructured sampling. maxent is a widely used software program designed to model and map Species distribution using presence-only data. 2. We provide a critical review of maxent as applied to Species distribution modelling and discuss how it can lead to inferential errors. A chief concern is that maxent produces a number of poorly defined indices that are not directly related to the actual parameter of interest – the probability of Occurrence (ψ). This focus on an index was motivated by the belief that it is not possible to estimate ψ from presence-only data; however, we demonstrate that ψ is identifiable using conventional likelihood methods under the assumptions of random sampling and constant probability of Species detection. 3. The model is implemented in a convenient r package which we use to apply the model to simulated data and data from the North American Breeding Bird Survey. We demonstrate that maxent produces extreme under-predictions when compared to estimates produced by logistic regression which uses the full (presence/absence) data set. We note that maxent predictions are extremely sensitive to specification of the background prevalence, which is not objectively estimated using the maxent method. 4. As with maxent, formal model-based inference requires a random sample of presence locations. Many presence-only data sets, such as those based on museum records and herbarium collections, may not satisfy this assumption. However, when sampling is random, we believe that inference should be based on formal methods that facilitate inference about interpretable ecological quantities instead of vaguely defined indices.
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Likelihood analysis of Species Occurrence probability from presence‐only data for modelling Species distributions
Methods in Ecology and Evolution, 2012Co-Authors: J. Andrew Royle, Richard B. Chandler, Charles B. Yackulic, James D. NicholsAbstract:Summary 1. Understanding the factors affecting Species Occurrence is a pre-eminent focus of applied ecological research. However, direct information about Species Occurrence is lacking for many Species. Instead, researchers sometimes have to rely on so-called presence-only data (i.e. when no direct information about absences is available), which often results from opportunistic, unstructured sampling. maxent is a widely used software program designed to model and map Species distribution using presence-only data. 2. We provide a critical review of maxent as applied to Species distribution modelling and discuss how it can lead to inferential errors. A chief concern is that maxent produces a number of poorly defined indices that are not directly related to the actual parameter of interest – the probability of Occurrence (ψ). This focus on an index was motivated by the belief that it is not possible to estimate ψ from presence-only data; however, we demonstrate that ψ is identifiable using conventional likelihood methods under the assumptions of random sampling and constant probability of Species detection. 3. The model is implemented in a convenient r package which we use to apply the model to simulated data and data from the North American Breeding Bird Survey. We demonstrate that maxent produces extreme under-predictions when compared to estimates produced by logistic regression which uses the full (presence/absence) data set. We note that maxent predictions are extremely sensitive to specification of the background prevalence, which is not objectively estimated using the maxent method. 4. As with maxent, formal model-based inference requires a random sample of presence locations. Many presence-only data sets, such as those based on museum records and herbarium collections, may not satisfy this assumption. However, when sampling is random, we believe that inference should be based on formal methods that facilitate inference about interpretable ecological quantities instead of vaguely defined indices.
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An integrated model of habitat and Species Occurrence dynamics
Methods in Ecology and Evolution, 2011Co-Authors: Darryl I. Mackenzie, Larissa L. Bailey, James E. Hines, James D. NicholsAbstract:Summary 1. Relationships between animal populations and their habitats are well known and commonly acknowledged to be important by animal ecologists, conservation biologists and wildlife managers. Such relationships are most commonly viewed as static, such that habitat at time t is viewed as a determinant of animals present at that same time, t, or sometimes as a determinant of animal population or Occurrence dynamics (e.g. between t and t+1). 2. Here, we motivate interest in simultaneous dynamics of both habitat and occupancy state (e.g. Species presence or absence) and develop models to estimate parameters that describe the dynamics of such systems. 3. The models permit inference about transition probabilities for both habitat and focal Species occupancy, such that habitat transitions may influence focal Species transitions and vice versa. 4. Example analyses using data from salamanders in the eastern United States are presented for (i) the special case in which habitat is characterized as either suitable or unsuitable and (ii) the more general case in which different habitat states are expected to influence occupancy dynamics in a less extreme manner (occupancy is possible in the various habitat states). 5. We believe that the integrated inference methods presented here will be useful for a variety of ecological and conservation investigations and attain special relevance in the face of habitat dynamics driven by such factors as active management, land use changes and climate change.
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Patterns and determinants of mammal Species Occurrence in India
Journal of Applied Ecology, 2009Co-Authors: James D. Nichols, Krithi K. Karanth, James E. Hines, Norman L. ChristensenAbstract:1. Many Indian mammals face range contraction and extinction, but assessments of their population status are hindered by the lack of reliable distribution data and range maps. 2. We estimated the current geographical ranges of 20 Species of large mammals by applying occupancy models to data from country-wide expert. We modelled Species in relation to ecological and social covariates (protected areas, landscape characteristics and human influences) based on a priori hypotheses about plausible determinants of mammalian distribution patterns. 3. We demonstrated that failure to incorporate detection probability in distribution survey methods underestimated habitat occupancy for all Species. 4. Protected areas were important for the distribution of 16 Species. However, for many Species much of their current range remains unprotected. The availability of evergreen forests was important for the Occurrence of 14 Species, temperate forests for six Species, deciduous forests for 15 Species and higher altitude habitats for two Species. Low human population density was critical for the Occurrence of five Species, while culturally based tolerance was important for the Occurrence of nine other Species. 5. Rhino Rhinoceros unicornis, gaur Bos gaurus and elephant Elephas maximus showed the most restricted ranges among herbivores, and sun bear Helarctos malayanus, brown bear Ursus arctos and tiger Panthera tigris were most restricted among carnivores. While cultural tolerance has helped the survival of some mammals, legal protection has been critically associated with Occurrence of most Species. 6. Synthesis and applications. Extent of range is an important determinant of Species conservation status. Understanding the relationship of Species Occurrence with ecological and socio-cultural covariates is important for identification and management of key conservation areas. The combination of occupancy models with field data from country-wide experts enables reliable estimation of Species range and habitat associations for conservation at regional scales.
Graeme Newell - One of the best experts on this subject based on the ideXlab platform.
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On the selection of thresholds for predicting Species Occurrence with presence-only data.
Ecology and evolution, 2015Co-Authors: Canran Liu, Graeme Newell, Matt WhiteAbstract:Presence-only data present challenges for selecting thresholds to transform Species distribution modeling results into binary outputs. In this article, we compare two recently published threshold selection methods (maxSSS and maxF pb) and examine the effectiveness of the threshold-based prevalence estimation approach. Six virtual Species with varying prevalence were simulated within a real landscape in southeastern Australia. Presence-only models were built with DOMAIN, generalized linear model, Maxent, and Random Forest. Thresholds were selected with two methods maxSSS and maxF pb with four presence-only datasets with different ratios of the number of known presences to the number of random points (KP-RP ratio). Sensitivity, specificity, true skill statistic, and F measure were used to evaluate the performance of the results. Species prevalence was estimated as the ratio of the number of predicted presences to the total number of points in the evaluation dataset. Thresholds selected with maxF pb varied as the KP-RP ratio of the threshold selection datasets changed. Datasets with the KP-RP ratio around 1 generally produced better results than scores distant from 1. Results produced by We conclude that maxFpb had specificity too low for very common Species using Random Forest and Maxent models. In contrast, maxSSS produced consistent results whichever dataset was used. The estimation of prevalence was almost always biased, and the bias was very large for DOMAIN and Random Forest predictions. We conclude that maxF pb is affected by the KP-RP ratio of the threshold selection datasets, but maxSSS is almost unaffected by this ratio. Unbiased estimations of prevalence are difficult to be determined using the threshold-based approach.
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Selecting thresholds for the prediction of Species Occurrence with presence‐only data
Journal of Biogeography, 2013Co-Authors: Canran Liu, Matt White, Graeme NewellAbstract:Aim Species distribution models have been widely used to tackle ecological, evolutionary and conservation problems. Most Species distribution modelling techniques produce continuous suitability predictions, but many real applications (e.g. reserve design, Species invasion and climate change impact assessment) and model evaluations require binary outputs, and thresholds are needed for these transformations. Although there are many threshold selection methods for presence/absence data, it is unclear whether these are suitable for presence-only data. In this paper, we investigate mathematically and empirically which of the existing threshold selection methods can be used confidently with presence-only data. Location We used real spatially explicit environmental data derived from the western part of the state of Victoria, south-eastern Australia, and simulated Species distributions within this area. Methods Thirteen existing threshold selection methods were investigated mathematically to see whether the same threshold can be produced using either presence/absence data or presence-only data. We further adopted a simulation approach, created many virtual Species with differing prevalences in a real landscape in south-eastern Australia, generated data sets with different proportions of pseudo-absences, built eight types of models with four modelling techniques, and investigated the behaviours of four threshold selection methods in these situations. Results Three threshold selection methods were not affected by pseudo-absences, including max SSS (which is based on maximizing the sum of sensitivity and specificity), the prevalence of model training data and the mean predicted value of a set of random points. Max SSS produced higher sensitivity in most cases and higher true skill statistic and kappa in many cases than the other methods. The other methods produced different thresholds from presence-only data to those determined from presence/absence data. Main conclusions Max SSS is a promising method for threshold selection when only presence data are available.
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Selecting thresholds for the prediction of Species Occurrence with presence-only data
Journal of Biogeography, 2013Co-Authors: Canran Liu, Matt White, Graeme NewellAbstract:Aim Species distribution models have been widely used to tackle ecological, evolutionary and conservation problems. Most Species distribution modelling techniques produce continuous suitability predictions, but many real applications (e.g. reserve design, Species invasion and climate change impact assessment) and model evaluations require binary outputs, and thresholds are needed for these transformations. Although there are many threshold selection methods for presence/absence data, it is unclear whether these are suitable for presence-only data. In this paper, we investigate mathematically and empirically which of the existing threshold selection methods can be used confidently with presence-only data. Location We used real spatially explicit environmental data derived from the western part of the state of Victoria, south-eastern Australia, and simulated Species distributions within this area. Methods Thirteen existing threshold selection methods were investigated mathematically to see whether the same threshold can be produced using either presence/absence data or presence-only data. We further adopted a simulation approach, created many virtual Species with differing prevalences in a real landscape in south-eastern Australia, generated data sets with different proportions of pseudo-absences, built eight types of models with four modelling techniques, and investigated the behaviours of four threshold selection methods in these situations. Results Three threshold selection methods were not affected by pseudo-absences, including max SSS (which is based on maximizing the sum of sensitivity and specificity), the prevalence of model training data and the mean predicted value of a set of random points. Max SSS produced higher sensitivity in most cases and higher true skill statistic and kappa in many cases than the other methods. The other methods produced different thresholds from presence-only data to those determined from presence/absence data. Main conclusions Max SSS is a promising method for threshold selection when only presence data are available.
Daniel Taylor Rodriguez - One of the best experts on this subject based on the ideXlab platform.
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a gibbs sampler for bayesian analysis of site occupancy data
Methods in Ecology and Evolution, 2012Co-Authors: Robert M Dorazio, Daniel Taylor RodriguezAbstract:Summary 1. A Bayesian analysis of site-occupancy data containing covariates of Species Occurrence and Species detection probabilities is usually completed using Markov chain Monte Carlo methods in conjunction with software programs that can implement those methods for any statistical model, not just site-occupancy models. Although these software programs are quite flexible, considerable experience is often required to specify a model and to initialize the Markov chain so that summaries of the posterior distribution can be estimated efficiently and accurately. 2. As an alternative to these programs, we develop a Gibbs sampler for Bayesian analysis of site-occupancy data that include covariates of Species Occurrence and Species detection probabilities. This Gibbs sampler is based on a class of site-occupancy models in which probabilities of Species Occurrence and detection are specified as probit-regression functions of site- and survey-specific covariate measurements. 3. To illustrate the Gibbs sampler, we analyse site-occupancy data of the blue hawker, Aeshna cyanea (Odonata, Aeshnidae), a common dragonfly Species in Switzerland. Our analysis includes a comparison of results based on Bayesian and classical (non-Bayesian) methods of inference. We also provide code (based on the R software program) for conducting Bayesian and classical analyses of site-occupancy data.
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A Gibbs sampler for Bayesian analysis of site‐occupancy data
Methods in Ecology and Evolution, 2012Co-Authors: Robert M Dorazio, Daniel Taylor RodriguezAbstract:Summary 1. A Bayesian analysis of site-occupancy data containing covariates of Species Occurrence and Species detection probabilities is usually completed using Markov chain Monte Carlo methods in conjunction with software programs that can implement those methods for any statistical model, not just site-occupancy models. Although these software programs are quite flexible, considerable experience is often required to specify a model and to initialize the Markov chain so that summaries of the posterior distribution can be estimated efficiently and accurately. 2. As an alternative to these programs, we develop a Gibbs sampler for Bayesian analysis of site-occupancy data that include covariates of Species Occurrence and Species detection probabilities. This Gibbs sampler is based on a class of site-occupancy models in which probabilities of Species Occurrence and detection are specified as probit-regression functions of site- and survey-specific covariate measurements. 3. To illustrate the Gibbs sampler, we analyse site-occupancy data of the blue hawker, Aeshna cyanea (Odonata, Aeshnidae), a common dragonfly Species in Switzerland. Our analysis includes a comparison of results based on Bayesian and classical (non-Bayesian) methods of inference. We also provide code (based on the R software program) for conducting Bayesian and classical analyses of site-occupancy data.
Canran Liu - One of the best experts on this subject based on the ideXlab platform.
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On the selection of thresholds for predicting Species Occurrence with presence-only data.
Ecology and evolution, 2015Co-Authors: Canran Liu, Graeme Newell, Matt WhiteAbstract:Presence-only data present challenges for selecting thresholds to transform Species distribution modeling results into binary outputs. In this article, we compare two recently published threshold selection methods (maxSSS and maxF pb) and examine the effectiveness of the threshold-based prevalence estimation approach. Six virtual Species with varying prevalence were simulated within a real landscape in southeastern Australia. Presence-only models were built with DOMAIN, generalized linear model, Maxent, and Random Forest. Thresholds were selected with two methods maxSSS and maxF pb with four presence-only datasets with different ratios of the number of known presences to the number of random points (KP-RP ratio). Sensitivity, specificity, true skill statistic, and F measure were used to evaluate the performance of the results. Species prevalence was estimated as the ratio of the number of predicted presences to the total number of points in the evaluation dataset. Thresholds selected with maxF pb varied as the KP-RP ratio of the threshold selection datasets changed. Datasets with the KP-RP ratio around 1 generally produced better results than scores distant from 1. Results produced by We conclude that maxFpb had specificity too low for very common Species using Random Forest and Maxent models. In contrast, maxSSS produced consistent results whichever dataset was used. The estimation of prevalence was almost always biased, and the bias was very large for DOMAIN and Random Forest predictions. We conclude that maxF pb is affected by the KP-RP ratio of the threshold selection datasets, but maxSSS is almost unaffected by this ratio. Unbiased estimations of prevalence are difficult to be determined using the threshold-based approach.
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Selecting thresholds for the prediction of Species Occurrence with presence‐only data
Journal of Biogeography, 2013Co-Authors: Canran Liu, Matt White, Graeme NewellAbstract:Aim Species distribution models have been widely used to tackle ecological, evolutionary and conservation problems. Most Species distribution modelling techniques produce continuous suitability predictions, but many real applications (e.g. reserve design, Species invasion and climate change impact assessment) and model evaluations require binary outputs, and thresholds are needed for these transformations. Although there are many threshold selection methods for presence/absence data, it is unclear whether these are suitable for presence-only data. In this paper, we investigate mathematically and empirically which of the existing threshold selection methods can be used confidently with presence-only data. Location We used real spatially explicit environmental data derived from the western part of the state of Victoria, south-eastern Australia, and simulated Species distributions within this area. Methods Thirteen existing threshold selection methods were investigated mathematically to see whether the same threshold can be produced using either presence/absence data or presence-only data. We further adopted a simulation approach, created many virtual Species with differing prevalences in a real landscape in south-eastern Australia, generated data sets with different proportions of pseudo-absences, built eight types of models with four modelling techniques, and investigated the behaviours of four threshold selection methods in these situations. Results Three threshold selection methods were not affected by pseudo-absences, including max SSS (which is based on maximizing the sum of sensitivity and specificity), the prevalence of model training data and the mean predicted value of a set of random points. Max SSS produced higher sensitivity in most cases and higher true skill statistic and kappa in many cases than the other methods. The other methods produced different thresholds from presence-only data to those determined from presence/absence data. Main conclusions Max SSS is a promising method for threshold selection when only presence data are available.
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Selecting thresholds for the prediction of Species Occurrence with presence-only data
Journal of Biogeography, 2013Co-Authors: Canran Liu, Matt White, Graeme NewellAbstract:Aim Species distribution models have been widely used to tackle ecological, evolutionary and conservation problems. Most Species distribution modelling techniques produce continuous suitability predictions, but many real applications (e.g. reserve design, Species invasion and climate change impact assessment) and model evaluations require binary outputs, and thresholds are needed for these transformations. Although there are many threshold selection methods for presence/absence data, it is unclear whether these are suitable for presence-only data. In this paper, we investigate mathematically and empirically which of the existing threshold selection methods can be used confidently with presence-only data. Location We used real spatially explicit environmental data derived from the western part of the state of Victoria, south-eastern Australia, and simulated Species distributions within this area. Methods Thirteen existing threshold selection methods were investigated mathematically to see whether the same threshold can be produced using either presence/absence data or presence-only data. We further adopted a simulation approach, created many virtual Species with differing prevalences in a real landscape in south-eastern Australia, generated data sets with different proportions of pseudo-absences, built eight types of models with four modelling techniques, and investigated the behaviours of four threshold selection methods in these situations. Results Three threshold selection methods were not affected by pseudo-absences, including max SSS (which is based on maximizing the sum of sensitivity and specificity), the prevalence of model training data and the mean predicted value of a set of random points. Max SSS produced higher sensitivity in most cases and higher true skill statistic and kappa in many cases than the other methods. The other methods produced different thresholds from presence-only data to those determined from presence/absence data. Main conclusions Max SSS is a promising method for threshold selection when only presence data are available.
Bette A Loiselle - One of the best experts on this subject based on the ideXlab platform.
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the influence of spatial errors in Species Occurrence data used in distribution models
Journal of Applied Ecology, 2008Co-Authors: Catherine H Graham, Jane Elith, Robert J Hijmans, Antoine Guisan, Townsend A Peterson, Bette A LoiselleAbstract:Summary 1. Species distribution modelling is used increasingly in both applied and theoretical research to predict how Species are distributed and to understand attributes of Species’ environmental requirements. In Species distribution modelling, various statistical methods are used that combine Species Occurrence data with environmental spatial data layers to predict the suitability of any site for that Species. While the number of data sharing initiatives involving Species’ Occurrences in the scientific community has increased dramatically over the past few years, various data quality and methodological concerns related to using these data for Species distribution modelling have not been addressed adequately. 2. We evaluated how uncertainty in georeferences and associated locational error in Occurrences influence Species distribution modelling using two treatments: (1) a control treatment where models were calibrated with original, accurate data and (2) an error treatment where data were first degraded spatially to simulate locational error. To incorporate error into the coordinates, we moved each coordinate with a random number drawn from the normal distribution with a mean of zero and a standard deviation of 5 km. We evaluated the influence of error on the performance of 10 commonly used distributional modelling techniques applied to 40 Species in four distinct geographical regions. 3. Locational error in Occurrences reduced model performance in three of these regions; relatively accurate predictions of Species distributions were possible for most Species, even with degraded Occurrences. Two Species distribution modelling techniques, boosted regression trees and maximum entropy, were the best performing models in the face of locational errors. The results obtained with boosted regression trees were only slightly degraded by errors in location, and the results obtained with the maximum entropy approach were not affected by such errors. 4. Synthesis and applications . To use the vast array of Occurrence data that exists currently for research and management relating to the geographical ranges of Species, modellers need to know the influence of locational error on model quality and whether some modelling techniques are particularly robust to error. We show that certain modelling techniques are particularly robust to a moderate level of locational error and that useful predictions of Species distributions can be made even when Occurrence data include some error. Journal of Applied Ecology (2007)
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The influence of spatial errors in Species Occurrence data used in distribution models: Spatial error in Occurrence data for predictive modelling
Journal of Applied Ecology, 2007Co-Authors: Catherine H Graham, A. Townsend Peterson, Jane Elith, Robert J Hijmans, Antoine Guisan, Bette A LoiselleAbstract:Summary 1. Species distribution modelling is used increasingly in both applied and theoretical research to predict how Species are distributed and to understand attributes of Species’ environmental requirements. In Species distribution modelling, various statistical methods are used that combine Species Occurrence data with environmental spatial data layers to predict the suitability of any site for that Species. While the number of data sharing initiatives involving Species’ Occurrences in the scientific community has increased dramatically over the past few years, various data quality and methodological concerns related to using these data for Species distribution modelling have not been addressed adequately. 2. We evaluated how uncertainty in georeferences and associated locational error in Occurrences influence Species distribution modelling using two treatments: (1) a control treatment where models were calibrated with original, accurate data and (2) an error treatment where data were first degraded spatially to simulate locational error. To incorporate error into the coordinates, we moved each coordinate with a random number drawn from the normal distribution with a mean of zero and a standard deviation of 5 km. We evaluated the influence of error on the performance of 10 commonly used distributional modelling techniques applied to 40 Species in four distinct geographical regions. 3. Locational error in Occurrences reduced model performance in three of these regions; relatively accurate predictions of Species distributions were possible for most Species, even with degraded Occurrences. Two Species distribution modelling techniques, boosted regression trees and maximum entropy, were the best performing models in the face of locational errors. The results obtained with boosted regression trees were only slightly degraded by errors in location, and the results obtained with the maximum entropy approach were not affected by such errors. 4. Synthesis and applications . To use the vast array of Occurrence data that exists currently for research and management relating to the geographical ranges of Species, modellers need to know the influence of locational error on model quality and whether some modelling techniques are particularly robust to error. We show that certain modelling techniques are particularly robust to a moderate level of locational error and that useful predictions of Species distributions can be made even when Occurrence data include some error. Journal of Applied Ecology (2007)
