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Why sampling ratio matters: Logistic regression and studies of habitat use.

ABSTRACT: Logistic regression (LR) models are among the most frequently used statistical tools in ecology. With LR one can infer if a species' habitat use is related to environmental factors and estimate the probability of species occurrence based on the values of these factors. However, studies often use inadequate sampling with regards to the arbitrarily chosen ratio between occupied and unoccupied (or available) locations, and this has a profound effect on the inference and predictive power of LR models. To demonstrate the effect of various sampling strategies/efforts on the quality of LR models, we used a unique census dataset containing all the used roosting cavities of the tree-dwelling bat Nyctalus leisleri and all cavities where the species was absent. We compared models constructed from randomly selected data subsets with varying ratios of occupied and unoccupied cavities (1:1, 1:5, 1:10) with a full dataset model (ratio 1:31). These comparisons revealed that the power of LR models was low when the sampling did not reflect the population ratio of occupied and unoccupied cavities. The use of weights improved the subsampled models. Thus, this study warns against inadequate data sampling and highly encourages a randomized sampling procedure to estimate the true ratio of occupied:unoccupied locations, which can then be used to optimize a manageable sampling effort and apply weights to improve the LR model. Such an approach may provide robust and reliable models suitable for both inference and prediction.

SUBMITTER: Nad'o L

PROVIDER: S-EPMC6056037 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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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.

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Why sampling ratio matters: Logistic regression and studies of habitat use.

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