Dataset Information

Relative Selection Strength: Quantifying effect size in habitat- and step-selection inference.

ABSTRACT: Habitat-selection analysis lacks an appropriate measure of the ecological significance of the statistical estimates-a practical interpretation of the magnitude of the selection coefficients. There is a need for a standard approach that allows relating the strength of selection to a change in habitat conditions across space, a quantification of the estimated effect size that can be compared both within and across studies. We offer a solution, based on the epidemiological risk ratio, which we term the relative selection strength (RSS). For a "used-available" design with an exponential selection function, the RSS provides an appropriate interpretation of the magnitude of the estimated selection coefficients, conditional on all other covariates being fixed. This is similar to the interpretation of the regression coefficients in any multivariable regression analysis. Although technically correct, the conditional interpretation may be inappropriate when attempting to predict habitat use across a given landscape. Hence, we also provide a simple graphical tool that communicates both the conditional and average effect of the change in one covariate. The average-effect plot answers the question: What is the average change in the space use probability as we change the covariate of interest, while averaging over possible values of other covariates? We illustrate an application of the average-effect plot for the average effect of distance to road on space use for elk (Cervus elaphus) during the hunting season. We provide a list of potentially useful RSS expressions and discuss the utility of the RSS in the context of common ecological applications.

SUBMITTER: Avgar T

PROVIDER: S-EPMC5528224 | biostudies-other | 2017 Jul

REPOSITORIES: biostudies-other

ACCESS DATA

Similar Datasets

Project description:While adult white sharks (Carcharodon carcharias) are apex predators with a circumglobal distribution, juvenile white sharks (JWS) feed primarily on bottom dwelling fishes and tend to be coastally associated. Despite the assumedly easier access to juveniles compared to large, migratory adults, limited information is available on the movements, environments, and distributions of individuals during this life stage. To quantify movement and understand their distribution in the southern California Bight, JWS were captured and fitted with dorsal fin-mounted satellite transmitters (SPOT tags; n = 18). Nine individuals crossed the U.S. border into Baja California, Mexico. Individuals used shallow habitats (134.96 ± 191.1 m) close to shore (7.16 ± 5.65 km). A generalized linear model with a binomial distribution was used to predict the presence of individuals based on several environmental predictors from these areas. Juveniles were found to select shallow habitats (< 1000 m deep) close to land (< 30 km of the shoreline) in waters ranging from 14 to 24°C. Southern California was found to be suitable eight months of the year, while coastal habitats in Baja California were suitable year-round. The model predicted seasonal movement with sharks moving from southern California to Baja California during winter. Additionally, habitat distribution changed inter-annually with sharks having a more northerly distribution during years with a higher Pacific Decadal Oscillation index, suggesting sharks may forego their annual fall migrations to Baja California, Mexico, during El Niño years. Model predictions aligned with fishery-dependent catch data, with a greater number of sharks being captured during periods and/or areas of increased habitat suitability. Thus, habitat models could be useful for predicting the presence of JWS in other areas, and can be used as a tool for potentially reducing fishery interactions during seasons and locations where there is increased susceptibility of incidental catch.

Project description:Conditional logistic regression (CLR) is widely used to analyze habitat selection and movement of animals when resource availability changes over space and time. Observations used for these analyses are typically autocorrelated, which biases model-based variance estimation of CLR parameters. This bias can be corrected using generalized estimating equations (GEE), an approach that requires partitioning the data into independent clusters. Here we establish the link between clustering rules in GEE and their effectiveness to remove statistical biases in variance estimation of CLR parameters. The current lack of guidelines is such that broad variation in clustering rules can be found among studies (e.g., 14-450 clusters) with unknown consequences on the robustness of statistical inference. We simulated datasets reflecting conditions typical of field studies. Longitudinal data were generated based on several parameters of habitat selection with varying strength of autocorrelation and some individuals having more observations than others. We then evaluated how changing the number of clusters impacted the effectiveness of variance estimators. Simulations revealed that 30 clusters were sufficient to get unbiased and relatively precise estimates of variance of parameter estimates. The use of destructive sampling to increase the number of independent clusters was successful at removing statistical bias, but only when observations were temporally autocorrelated and the strength of inter-individual heterogeneity was weak. GEE also provided robust estimates of variance for different magnitudes of unbalanced datasets. Our simulations demonstrate that GEE should be estimated by assigning each individual to a cluster when at least 30 animals are followed, or by using destructive sampling for studies with fewer individuals having intermediate level of behavioural plasticity in selection and temporally autocorrelated observations. The simulations provide valuable information to build reliable habitat selection and movement models that allow for robustness of statistical inference without removing excessive amounts of ecological information.

Project description:Habitat selection is an active behavioral process that may vary across spatial and temporal scales. Animals choose an area of primary utilization (i.e., home range) then make decisions focused on resource needs within patches. Dominance may affect the spatial distribution of conspecifics and concomitant habitat selection. Size-dependent social dominance hierarchies have been documented in captive alligators, but evidence is lacking from wild populations. We studied habitat selection for adult male American alligators (Alligator mississippiensis; n = 17) on the Pearl River in central Mississippi, USA, to test whether habitat selection was scale-dependent and individual resource selectivity was a function of conspecific body size. We used K-select analysis to quantify selection at the home range scale and patches within the home range to determine selection congruency and important habitat variables. In addition, we used linear models to determine if body size was related to selection patterns and strengths. Our results indicated habitat selection of adult male alligators was a scale-dependent process. Alligators demonstrated greater overall selection for habitat variables at the patch level and less at the home range level, suggesting resources may not be limited when selecting a home range for animals in our study area. Further, diurnal habitat selection patterns may depend on thermoregulatory needs. There was no relationship between resource selection or home range size and body size, suggesting size-dependent dominance hierarchies may not have influenced alligator resource selection or space use in our sample. Though apparent habitat suitability and low alligator density did not manifest in an observed dominance hierarchy, we hypothesize that a change in either could increase intraspecific interactions, facilitating a dominance hierarchy. Due to the broad and diverse ecological roles of alligators, understanding the factors that influence their social dominance and space use can provide great insight into their functional role in the ecosystem.

Dataset Information

Relative Selection Strength: Quantifying effect size in habitat- and step-selection inference.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets