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Resource Selection by Animals : Statistical design and analysis for field studies
01 Oct 2011-
TL;DR: This paper presents a meta-modelling procedure for estimating a resource selection probability function from a census of resource units using logistic regression and discriminant function methods and its applications in resource selection and resource selectory studies.
Abstract: Preface. List of Symbols. 1. Introduction to Resource Selection Studies. 2. Statistical Modelling Procedures. 3. Examples of the Use of Resource Selection Functions. 4. Studies with Resources Defined by Several Categories. 5. Resource Selection Functions from Logistic Regression. 6. Resource Selection over Several Time Periods. 7. Log-Linear Modelling. 8. Discrete Choice Models with Changing Availability. 9. Applications Using Geographic Information Systems. 10. Discriminant Function Analysis. 11. Analysis of the Amount of Use. 12. Some Other Types of Analysis. 13. Risk Assessment and Population Size Estimation. 14. Computing. References. Name Index. Subject Index.
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TL;DR: Thirteen recommendations are made to enable the objective selection of an error assessment technique for ecological presence/absence models and a new approach to estimating prediction error, which is based on the spatial characteristics of the errors, is proposed.
Abstract: Predicting the distribution of endangered species from habitat data is frequently perceived to be a useful technique. Models that predict the presence or absence of a species are normally judged by the number of prediction errors. These may be of two types: false positives and false negatives. Many of the prediction errors can be traced to ecological processes such as unsaturated habitat and species interactions. Consequently, if prediction errors are not placed in an ecological context the results of the model may be misleading. The simplest, and most widely used, measure of prediction accuracy is the number of correctly classified cases. There are other measures of prediction success that may be more appropriate. Strategies for assessing the causes and costs of these errors are discussed. A range of techniques for measuring error in presence/absence models, including some that are seldom used by ecologists (e.g. ROC plots and cost matrices), are described. A new approach to estimating prediction error, which is based on the spatial characteristics of the errors, is proposed. Thirteen recommendations are made to enable the objective selection of an error assessment technique for ecological presence/absence models.
6,044 citations
Cites methods from "Resource Selection by Animals : Sta..."
...Some of these methods are reviewed by Manly et al. (1992) and Morrison et al. (1992)....
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TL;DR: Species distribution models (SDMs) as mentioned in this paper are numerical tools that combine observations of species occurrence or abundance with environmental estimates, and are used to gain ecological and evolutionary insights and to predict distributions across landscapes, sometimes requiring extrapolation in space and time.
Abstract: Species distribution models (SDMs) are numerical tools that combine observations of species occurrence or abundance with environmental estimates. They are used to gain ecological and evolutionary insights and to predict distributions across landscapes, sometimes requiring extrapolation in space and time. SDMs are now widely used across terrestrial, freshwater, and marine realms. Differences in methods between disciplines reflect both differences in species mobility and in “established use.” Model realism and robustness is influenced by selection of relevant predictors and modeling method, consideration of scale, how the interplay between environmental and geographic factors is handled, and the extent of extrapolation. Current linkages between SDM practice and ecological theory are often weak, hindering progress. Remaining challenges include: improvement of methods for modeling presence-only data and for model selection and evaluation; accounting for biotic interactions; and assessing model uncertainty.
5,076 citations
TL;DR: The “adehabitat” package for the R software is presented, which offers basic GIS functions, methods to analyze radio-tracking data and habitat selection by wildlife, and interfaces with other R packages.
3,252 citations
TL;DR: It is argued that increased awareness of the implications of spatial bias in surveys, and possible modeling remedies, will substantially improve predictions of species distributions and as large an effect on predictive performance as the choice of modeling method.
Abstract: Most methods for modeling species distributions from occurrence records require additional data representing the range of environmental conditions in the modeled region. These data, called background or pseudo-absence data, are usually drawn at random from the entire region, whereas occurrence collection is often spatially biased toward easily accessed areas. Since the spatial bias generally results in environmental bias, the difference between occurrence collection and background sampling may lead to inaccurate models. To correct the estimation, we propose choosing background data with the same bias as occurrence data. We investigate theoretical and practical implications of this approach. Accurate information about spatial bias is usually lacking, so explicit biased sampling of background sites may not be possible. However, it is likely that an entire target group of species observed by similar methods will share similar bias. We therefore explore the use of all occurrences within a target group as biased background data. We compare model performance using target-group background and randomly sampled background on a comprehensive collection of data for 226 species from diverse regions of the world. We find that target-group background improves average performance for all the modeling methods we consider, with the choice of background data having as large an effect on predictive performance as the choice of modeling method. The performance improvement due to target-group background is greatest when there is strong bias in the target-group presence records. Our approach applies to regression-based modeling methods that have been adapted for use with occurrence data, such as generalized linear or additive models and boosted regression trees, and to Maxent, a probability density estimation method. We argue that increased awareness of the implications of spatial bias in surveys, and possible modeling remedies, will substantially improve predictions of species distributions.
2,307 citations
Cites methods from "Resource Selection by Animals : Sta..."
...A logistic model fitted to PUA(s ¼ 1 j x) can thus be used to infer parameters of an exponential model for P(y ¼ 1 j x) (Boyce et al. 2002, Manly et al. 2002)....
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TL;DR: A form of k -fold cross validation for evaluating prediction success is proposed for presence/available RSF models, which involves calculating the correlation between RSF ranks and area-adjusted frequencies for a withheld sub-sample of data.
2,107 citations