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.

https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0376892997000088

A review of methods for the assessment of prediction errors in conservation presence/absence models

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.

/pdf/species-distribution-models-ecological-explanation-and-1szux2yl31.pdf

Species Distribution Models: Ecological Explanation and Prediction Across Space and Time

The package “adehabitat” for the R software: A tool for the analysis of space and habitat use by animals

Beginning with Emlen (1966) and MacArthur and Pianka (1966) and extending through the last ten years, several authors have sought to predict the foraging behavior of animals by means of mathematical models. These models are very similar,in that they all assume that the fitness of a foraging animal is a function of the efficiency of foraging measured in terms of some "currency" (Schoener, 1971) -usually energy- and that natural selection has resulted in animals that forage so as to maximize this fitness. As a result of these similarities, the models have become known as "optimal foraging models"; and the theory that embodies them, "optimal foraging theory." The situations to which optimal foraging theory has been applied, with the exception of a few recent studies, can be divided into the following four categories: (1) choice by an animal of which food types to eat (i.e., optimal diet); (2) choice of which patch type to feed in (i.e., optimal patch choice); (3) optimal allocation of time to different patches; and (4) optimal patterns and speed of movements. In this review we discuss each of these categories separately, dealing with both the theoretical developments and the data that permit tests of the predictions. The review is selective in the sense that we emphasize studies that either develop testable predictions or that attempt to test predictions in a precise quantitative manner. We also discuss what we see to be some of the future developments in the area of optimal foraging theory and how this theory can be related to other areas of biology. Our general conclusion is that the simple models so far formulated are supported are supported reasonably well by available data and that we are optimistic about the value both now and in the future of optimal foraging theory. We argue, however, that these simple models will requre much modification, espicially to deal with situations that either cannot easily be put into one or another of the above four categories or entail currencies more complicated that just energy.

Citation classic - optimal foraging - a selective review of theory and tests

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.

/pdf/sample-selection-bias-and-presence-only-distribution-models-5al33pm4t5.pdf

Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data

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.

Resource Selection by Animals : Statistical design and analysis for field studies

Resource selection by animals

Introduction to resource selection studies. Examples of the use of resource selectory studies. Examples of the use of resource selection functions. Statistical modelling procedures. Studies with resources defined by several categories. Estimating a resource selection probability function from a census of resource units using logistic regression. Estimating a resource selection probability function from a census of resource units at several points in time using the proportional hazards model. Estimating a resource selection function from samples of resource units using proportional hazards and log-linear models. Estimating a resource selection function from two samples of resource units using logistic regression and discriminant function methods. General log-linear modelling. Analysis of the amount of use. The comparison of selection for different types of resource unit. References. Index.

Resource Selection by Animals: Statistical Design and Analysis for Field Studies

In this chapter we provide motivation for the study of resource selection, define terms, discuss study designs and sampling, and give an historical perspective on the statistical evaluation of resource selection.

Introduction to resource selection studies

In this chapter designs and data analysis procedures are reviewed for studies of resource selection where each resource unit is classified into one of several categories. These studies are the simplest that can be carried out, and are also the ones that are used most often. They are therefore worth considering in their own right, although they can be thought of as special cases of the more general types of study design that are discussed in later chapters.

Bryan F. J. Manly

Papers

Resource Selection by Animals : Statistical design and analysis for field studies

Resource selection by animals

Resource Selection by Animals: Statistical Design and Analysis for Field Studies

Introduction to resource selection studies

Studies with resources defined by several categories