Design and Statistical Issues Related to Habitat Analysis

There are many different design and statistical issues that a researcher should consider when developing the data collection protocol or when interpreting results from a habitat analysis. One of the first considerations is simply the area to include in the study. This depends on the behavior (particularly mobility) of the focal species and logistical constraints. The amount of area also relates to the number of survey locations (plots, transects, or other) and their spatial placement. Survey data often include many instances of a species absent from a spatial sampling unit. These could be true absences or might represent very low species detection probability. There are different statistical techniques for estimating detection probability as well as analyzing data with a substantial proportion of zero-abundance values. The spatial dispersion of the species within the overall study area or region is never random. Even apart from the effect of habitat, individuals are often aggregated due to various environmental factors or species traits. This can affect count data collected from survey plots. Related to spatial dispersion, the overall background density of the species within the study area can introduce particular challenges in identifying meaningful habitat associations. Statistical issues such as normality, multicollinearity, spatial and temporal autocorrelation may be relatively common and need to be addressed prior to an analysis. None of these design and statistical issues presents insurmountable challenges to a habitat analysis.

Other Techniques Related to Habitat Analysis

Habitat analysis is strictly defined as a statistical examination to identify the environmental variables that a species associates with, wherein association is taken as some form of correspondence between a species response variable (e.g., presence–absence or abundance) and the environmental variables. There are other statistical techniques and empirical goals that extend this basic framework. These techniques often rely on a habitat analysis having been conducted as an initial step. Resource selection functions quantify an individual’s and a species’ use of a resource based upon the properties of the resource. Resource is broadly defined and can include particular types of habitat. Selectivity and preference indices are used to assess an individual’s preference and active choice of different resource types. Compositional data analysis is a statistical method for examining the composition of an individual’s territory or home range with regard to different habitat types that may be represented. Habitat suitability modeling and species distribution modeling are closely related techniques designed to map the spatial distribution of a species’ habitat and sometimes the species itself based upon its habitat requirements and other factors.

Analysis of the Habitat Associations of a Hypothetical Beetle Species

A dataset for a hypothetical ground-dwelling beetle species is used to illustrate five methods of habitat analysis: (1) comparison of group means, (2) multiple linear regression, (3) multiple logistic regression, (4) classification and regression trees, and (5) principal components analysis. The dataset consists of abundance (counts of individuals) recorded in each of 100 small survey plots located throughout forested study sites. The following environmental predictor variables were measured in each plot: percentage canopy cover, depth of leaf litter, volume of woody debris, ratio of oak to non-oak trees, and soil type. Techniques for assessing normality of each variable and multicollinearity among variables are discussed and recommended prior to conducting the habitat analysis. Assumptions, strengths, and weaknesses of each method are discussed.

Issues Based on Species Life History and Behavior

Species vary tremendously in their life histories and behavior. The particular life history traits and behavior of the focal species must be considered when designing a study to examine habitat associations. For some species, individuals use different areas (of the landscape or territory) for breeding and foraging. As such, the important characteristics for the foraging and breeding habitats may be different. The dramatically different life stages of some organisms (e.g., amphibians and some insects) often correspond to equally dramatic differences in habitat use between juveniles and adults. For some species, habitat use differs among seasons. Species that are highly mobile and have individuals that move around substantially on a daily or weekly basis are particularly challenging for a habitat analysis. For these species, the most efficient and appropriate study design may be one that tracks individuals (through radio-telemetry or GPS) and analyzes the environmental or habitat characteristics at locations where the individual has stopped, rather than trying to survey for the species in pre-established and insufficiently small survey plots. In addition, individual movement and the issues mentioned above may necessitate that environmental variables are measured and analyzed at multiple spatial scales.

Goals of Characterizing a Species Habitat

There are several reasons for conducting a habitat analysis and identifying the environmental (habitat) characteristics that a species associates with. (1) Knowledge of a species’ habitat requirements is crucial in restoring and managing habitat for the species. (2) Carrying capacity informs us about the potential (or lack thereof) for future population growth based on resource availability. Knowledge of a species’ habitat requirements allows us to interpret the importance of carrying capacity in a habitat-specific way. (3) The study of species interactions and the potential for species coexistence is supported by having knowledge of the habitat of each species under investigation. (4) Habitat preference and selection as eco-evolutionary processes continue to be widely studied by ecologists—interpretation of the results of such studies is best done with knowledge of the species–habitat associations. Such knowledge can also be useful in the design of preference and selection studies. (5) Knowledge of species–habitat associations can also be of great use in selecting the environmental variables to use in species distribution models. All five of these goals point to the great utility of conducting a habitat analysis as a supporting investigation or as a way to obtain knowledge to put to a practical purpose.

Post-analysis Procedures

There are several additional statistical procedures that can be conducted after a habitat analysis. The statistical model produced by a habitat analysis can be assessed for fit to the data. Model fit describes how well the predictor variables explain the variance in the response variable, typically species presence–absence or abundance. When more than one statistical model has been produced by the habitat analysis, these can be compared by a formal procedure called model comparison. This usually involves identifying the model with the lowest Akaike information criterion (AIC) value. If the statistical model is considered a predictive tool then its predictive accuracy needs to be assessed. There are many metrics for assessing the predictive performance of a model and quantifying rates of correct and incorrect classification; the latter are error rates. Many of these metrics are based on the numbers of true positive, true negative, false positive, and false negative observations in an independent dataset. “True” and “false” refer to whether species presence–absence was correctly predicted or not. Predictive performance can also be assessed by constructing a receiver operating characteristic (ROC) curve and calculating area under the curve (AUC) values. High AUC values approaching 1 indicate good predictive performance, whereas a value near 0.5 indicates a poor model that predicts species presence–absence no better than a random guess.

Conclusion

Because habitat is so crucial to the survival and reproduction of individual organisms and persistence of populations, it has long been studied by wildlife ecologists. However, the modern concept of habitat originated with ecologists before the field and practice of wildlife ecology arose. The fields of ecology and wildlife ecology have developed along separate historical paths, but, given that research in each field continues to involve the study of species–habitat relationships, there is common ground for practitioners and students in both fields to better engage with one another. Such collaboration could involve a shared recognition that habitat largely determines a species spatial distribution in nature. Through a behavioral process of dispersal, settlement, and establishment, an individual organism finds appropriate habitat by searching and responding to environmental cues. These cues may primarily be characteristics of the habitat such as vegetation structure. Characterization or statistical analysis of habitat is an obvious and important component of studying the habitat requirements of a species. It is recommended that multiple logistic regression will often be the most appropriate method for characterizing habitat. Of most importance, a habitat analysis should recognize that the habitat of a species involves an integrated set of environmental variables that synergistically influence the survival and reproduction of the individual and existence of the species. The study of habitat can help us learn more about the autecology of the focal species, its role in ecological communities, and proper strategies for its preservation.

Types of Data and the General Framework of Analysis

For most habitat analyses, researchers typically collect and examine environmental data from the landscape scale (a few square kilometers to hundreds of square kilometers) all the way down to the scale of a microhabitat (tens of square meters). At the larger spatial extents, the data may be GIS-based such as spatially referenced land cover data. At smaller spatial scales, the data may be collected (variables measured) in the field at the study sites. Data for a habitat analysis are often based on randomly located and spatially delineated sampling or survey plots. The environmental data compose a set of a few to tens of predictor variables that are used in statistical tests for a relationship with the response variable that is typically species presence–absence, abundance (counts of individuals), or activity level. Depending on the spatial scale of analysis, predictor variables could represent different environmental variables such as vegetation structure, soil properties, and other characteristics of the substrate. Climate and weather variables are environmental, but they are not considered to be characteristics of the habitat. The formal habitat analysis consists of testing for a statistical relationship between the response variable and one or more environmental predictor variables so as to identify those variables that truly are habitat characteristics. A study of the habitat of the brown-throated sloth in Costa Rica is used to further explain the type of data used in characterizing the habitat of a species.

The Primacy of Habitat

Habitat may have a primary role in determining the distribution and abundance of species, yet ecologists have historically overlooked its importance. Models of habitat selection are briefly reviewed. A new conceptual and analytical model is presented that explains how dispersing organisms find and settle at a given location based upon habitat structural features providing cues for settlement. The model is based on a sequential process of dispersal, settlement, and establishment that can be described by probabilities. The spatial settlement pattern of juvenile individuals determines adult distribution and abundance. Evidence is provided that structural features of the habitat may be more effective cues than are food supply, conspecific density, or the absence of an antagonistic species. This is the habitat-cue hypothesis of species distribution and abundance. The hypothesis is intended to stimulate greater investigation into the role of physical structure and environmental cueing in habitat selection by all types of organism. The hypothesis also predicts that a species distribution in nature is determined by habitat more than any other factor.

Ecology and Wildlife Ecology as Distinct Academic Disciplines

As academic disciplines ecology and wildlife ecology both recognize the importance of habitat to the daily survival of individuals and long-term persistence of populations. Although the explicit and direct study of habitat originally emerged in ecology, wildlife ecologists historically have been more involved in its study and in the analysis of species–habitat relationships. This is partly due to wildlife ecologists being interested in habitat management for particular species and applying a resource-based concept of habitat to better understand population growth rates, particularly for harvested or hunted species. In the 1930s onward for several decades, Aldo Leopold played a prominent role in establishing wildlife ecology (and management) as its own academic field and practice. Leopold was keenly aware of population dynamics although he seemed to not directly link his empirical observations of population fluctuations to any of the emerging mathematical population growth models of the day. This may have also indirectly allowed the early growth of wildlife ecology to proceed without any of its own emergent theory. Despite historical, paradigmatic, practical, and subject matter differences between the two disciplines, both are becoming more similar to one another as interdisciplinary collaboration and communication continue to increase.