Hibernation site requirements of bats in man-made hibernacula in a spatial context.

Bat hibernacula selection depends on various spatial and nonspatial variables that differ widely between sites. However, previous studies have focused mainly on nonspatial variables. This research investigated factors that determined the abundance and species richness of hibernating bats in hibernation objects of the New Dutch Waterline, The Netherlands, and determined the relevant scales over which spatial factors operate using regression techniques and ecological-niche factor analyses. The effects of 32 predictor variables on several response variables, i.e., the total bat abundance, species richness, and abundance and presence of bat species, were investigated. Predictor variables were classified as internal variables (e.g., building size, climatic conditions, and human access) or external variables (e.g., ground and vegetation cover and land cover type) that were measured at different spatial scales to study the influence of the spatial context. The internal building variables (mainly the size of hibernacula and the number of hiding possibilities) affected the hibernating bat abundance and species richness. Climatic variables, such as changes in temperature and humidity, were less important. The hibernation site suitability was also influenced by spatial variables at a variety of scales, thereby indicating the importance of scale-dependent species-environment relationships. The absence of human use and public access enhanced hibernation site suitability, but the internal size-related variables had the greatest positive effect on hibernation site suitability. These results demonstrate the importance of considering the different spatial scales of the surrounding landscape to better understand habitat selection, and they offer directives to managers to optimize objects for hibernating bats and to improve management and bat conservation. The analyses have wider applications to other wildlife-habitat studies.

Spatial autocorrelation and the scaling of species-environment relationships.

Issues of residual spatial autocorrelation (RSA) and spatial scale are critical to the study of species-environment relationships, because RSA invalidates many statistical procedures, while the scale of analysis affects the quantification of these relationships. Although these issues independently are widely covered in the literature, only sparse attention is given to their integration. This paper focuses on the interplay between RSA and the spatial scaling of species-environment relationships. Using a hypothetical species in an artificial landscape, we show that a mismatch between the scale of analysis and the scale of a species' response to its environment leads to a decrease in the portion of variation explained by environmental predictors. Moreover, it results in RSA and biased regression coefficients. This bias stems from error-predictor dependencies due to the scale mismatch, the magnitude of which depends on the interaction between the scale of landscape heterogeneity and the scale of a species' response to this heterogeneity. We show that explicitly considering scale effects on RSA can reveal the characteristic scale of a species' response to its environment. This is important, because the estimation of species-environment relationships using spatial regression methods proves to be erroneous in case of a scale mismatch, leading to spurious conclusions when scaling issues are not explicitly considered. The findings presented here highlight the importance of examining the appropriateness of the spatial scales used in analyses, since scale mismatches affect the rigor of statistical analyses and thereby the ability to understand the processes underlying spatial patterning in ecological phenomena.

Inferring an animal's environment through biologging: quantifying the environmental influence on animal movement.

BACKGROUND: Animals respond to environmental variation by changing their movement in a multifaceted way. Recent advancements in biologging increasingly allow for detailed measurements of the multifaceted nature of movement, from descriptors of animal movement trajectories (e.g., using GPS) to descriptors of body part movements (e.g., using tri-axial accelerometers). Because this multivariate richness of movement data complicates inference on the environmental influence on animal movement, studies generally use simplified movement descriptors in statistical analyses. However, doing so limits the inference on the environmental influence on movement, as this requires that the multivariate richness of movement data can be fully considered in an analysis. METHODS: We propose a data-driven analytic framework, based on existing methods, to quantify the environmental influence on animal movement that can accommodate the multifaceted nature of animal movement. Instead of fitting a simplified movement descriptor to a suite of environmental variables, our proposed framework centres on predicting an environmental variable from the full set of multivariate movement data. The measure of fit of this prediction is taken to be the metric that quantifies how much of the environmental variation relates to the multivariate variation in animal movement. We demonstrate the usefulness of this framework through a case study about the influence of grass availability and time since milking on cow movements using machine learning algorithms. RESULTS: We show that on a one-hour timescale 37% of the variation in grass availability and 33% of time since milking influenced cow movements. Grass availability mostly influenced the cows' neck movement during grazing, while time since milking mostly influenced the movement through the landscape and the shared variation of accelerometer and GPS data (e.g., activity patterns). Furthermore, this framework proved to be insensitive to spurious correlations between environmental variables in quantifying the influence on animal movement. CONCLUSIONS: Not only is our proposed framework well-suited to study the environmental influence on animal movement; we argue that it can also be applied in any field that uses multivariate biologging data, e.g., animal physiology, to study the relationships between animals and their environment. SUPPLEMENTARY INFORMATION: Supplementary information accompanies this paper at 10.1186/s40462-020-00228-4.