Assessing shipping noise as a potential driver of harbour seal (Phoca vitulina) habitat selection

Machine learning algorithms are often used to model and predict animal habitat selection—the relationships between animal occurrences and habitat characteristics. For broadly distributed species, habitat selection often varies among populations and regions; thus, it would seem preferable to fit region- or population-specific models of habitat selection for more accurate inference and prediction, rather than fitting large-scale models using pooled data. However, where the aim is to make range-wide predictions, including areas for which there are no existing data or models of habitat selection, how can regional models best be combined? We propose that ensemble approaches commonly used to combine different algorithms for a single region can be reframed, treating regional habitat selection models as the candidate models. By doing so, we can incorporate regional variation when fitting predictive models of animal habitat selection across large ranges. We test this approach using satellite telemetry data from 168 humpback whales across five geographic regions in the Southern Ocean. Using random forests, we fitted a large-scale model relating humpback whale locations, versus background locations, to 10 environmental covariates, and made a circumpolar prediction of humpback whale habitat selection. We also fitted five regional models, the predictions of which we used as input features for four ensemble approaches: an unweighted ensemble, an ensemble weighted by environmental similarity in each cell, stacked generalization, and a hybrid approach wherein the environmental covariates and regional predictions were used as input features in a new model. We tested the predictive performance of these approaches on an independent validation dataset of humpback whale sightings and whaling catches. These multiregional ensemble approaches resulted in models with higher predictive performance than the circumpolar naive model. These approaches can be used to incorporate regional variation in animal habitat selection when fitting range-wide predictive models using machine learning algorithms. This can yield more accurate predictions across regions or populations of animals that may show variation in habitat selection.

Seasonal variation in coastal marine habitat use by the European shag: Insights from fine scale habitat selection modeling and diet

Recent advances in optical remote sensing (RS) technology in combination with lightweight Global Positioning System (GPS) tracking devices now make analyzing the multi-scale habitat selection (HS) of small mammals < 2 kg possible. However, there have been relatively few multi-scale HS studies integrating fine-scale RS data with data-rich, GPS-derived movement data from small mammals. This is critical because small mammals commonly select habitat features across multiple scales. To address this gap, we investigated the HS of a small mammal, fox squirrels (Sciurus niger), which are known to cover relatively large areas and select fine-scale environmental features. We specifically asked the following questions: (1) Do next-generation RS variables improve HS models at single spatial scales? (2) Do multi-scale HS models improve upon those at single spatial scales? Using data from 45 individuals, we constructed HS models at three spatial scales: 4 ha (210 m × 210 m), 0.09 ha (30 m × 30 m), and 0.01 ha (10 m × 10 m) using traditional and next-generation RS data. The 4-ha model, using traditional and next-generation RS data, produced the best single-scale model, explaining 58% of the variations in HS. However, the multi-scale model provided the most informative model, explaining 68% of the variations in HS. Our models provide evidence for the value of next-generation RS data when quantifying HS and additional support for the idea of studying HS at multiple spatial scales.

Understanding how dispersal patterns are influenced by landscape heterogeneity is critical for modeling species connectivity. Resource selection function (RSF) models are increasingly used in landscape genetics approaches. However, because the ecological factors that drive habitat selection may be different from those influencing dispersal and gene flow, it is important to consider explicit assumptions and spatial scales of measurement. We calculated pairwise genetic distance among 301 Dall's sheep (Ovis dalli dalli) in southcentral Alaska using an intensive noninvasive sampling effort and 15 microsatellite loci. We used multiple regression of distance matrices to assess the correlation of pairwise genetic distance and landscape resistance derived from an RSF, and combinations of landscape features hypothesized to influence dispersal. Dall's sheep gene flow was positively correlated with steep slopes, moderate peak normalized difference vegetation indices (NDVI), and open land cover. Whereas RSF covariates were significant in predicting genetic distance, the RSF model itself was not significantly correlated with Dall's sheep gene flow, suggesting that certain habitat features important during summer (rugged terrain, mid‐range elevation) were not influential to effective dispersal. This work underscores that consideration of both habitat selection and landscape genetics models may be useful in developing management strategies to both meet the immediate survival of a species and allow for long‐term genetic connectivity.

Habitat selection is an important behavioural process widely studied for its population-level effects. Models of habitat selection are, however, often fit without a mechanistic consideration. Here, we investigated whether patterns in habitat selection result from instinct or learning for a population of grizzly bears (Ursus arctos) in Alberta, Canada. We found that habitat selection and relatedness were positively correlated in female bears during the fall season, with a trend in the spring, but not during any season for males. This suggests that habitat selection is a learned behaviour because males do not participate in parental care: a genetically predetermined behaviour (instinct) would have resulted in habitat selection and relatedness correlations for both sexes. Geographic distance and home range overlap among animals did not alter correlations indicating that dispersal and spatial autocorrelation had little effect on the observed trends. These results suggest that habitat selection in grizzly bears are partly learned from their mothers, which could have implications for the translocation of wildlife to novel environments.

We review the general theory regarding habitat selection in fishes and integrate this theory with recent data to evaluate habitat selection by marsh fishes. Models of habitat selection in fishes have evolved rapidly. The earliest models predicted habitat selection based on simple variables like temperature or salinity. Optimal foraging models project habitat selection of individuals based on food availability. Environmental factors and food can be integrated using bioenergetic models to predict the distribution of individuals based on bioenergetic optimization. Habitat selection can be modified by the presence of other individuals including competitors (theory based on ideal free distributions) and predators (theory based on trade-offs of growth vs. predation risk). The most current models use game theory to project the dynamics of habitat selection for both prey species and their mobile predators. In a review of marsh ecosystems, we found that little information is currently available with which to evaluate potential mechanisms underlying patterns of habitat use in these systems. Though marshes are widely considered important for foraging and predator refuge, this function has rarely been measured or critically evaluated. In contrast, measurement has focused on abiotic factors, resulting in a mismatch between factors cited as important and those actually measured. Existing theory on habitat selection in fishes combined with the strong empirical base that has been developed on patterns of habitat use in marsh fishes provides a unique opportunity to begin testing relevant mechanisms underlying distributional patterns. A mechanistic approach is necessary to understand the functional value of habitat to organisms as well as provide a basis for the implementation and evaluation of habitat restoration and management initiatives.

Habitat selection models frequently use data collected from a small geographic area over a short window of time to extrapolate patterns of relative abundance into unobserved areas or periods of time. However, such models often poorly predict the distribution of animal space‐use intensity beyond the place and time of data collection, presumably because space‐use behaviors vary between individuals and environmental contexts. Similarly, ecological inference based on habitat selection models could be muddied or biased due to unaccounted individual and context dependencies. Here, we present a modeling workflow designed to allow transparent variance‐decomposition of habitat‐selection patterns, and consequently improved inferential and predictive capacities. Using global positioning system (GPS) data collected from 238 individual pronghorn, Antilocapra americana , across three years in Utah, USA, we combine individual‐year‐season‐specific exponential habitat‐selection models with weighted mixed‐effects regressions to both draw inference about the drivers of habitat selection and predict space‐use in areas/times where/when pronghorn were not monitored. We found a tremendous amount of variation in both the magnitude and direction of habitat selection behavior across seasons, but also across individuals, geographic regions, and years. We were able to attribute portions of this variation to season, movement strategy, sex, and regional variability in resources, conditions, and risks. We were also able to partition residual variation into inter‐ and intra‐individual components. We then used the results to predict population‐level, spatially and temporally dynamic, habitat‐selection coefficients across Utah, resulting in a temporally dynamic map of pronghorn distribution at a 30 × 30 m resolution but an extent of 220 000 km 2 . We believe our transferable workflow can provide managers and researchers alike a way to turn limitations of traditional habitat selection models – variability in habitat selection – into a tool to understand and predict species‐habitat associations across space and time.

Resource selection function (RSF) models are commonly used to quantify species/habitat associations and predict species occurrence on the landscape. However, these models are sensitive to changes in resource availability and can result in a functional response to resource abundance, where preferences change as a function of availability. For generalist species, which utilize a wide range of habitats and resources, quantifying habitat selection is particularly challenging. Spatial and temporal changes in resource abundance can result in changes in selection preference affecting the robustness of habitat selection models. We examined selection preference across a wide range of ecological conditions for a generalist mega‐herbivore, the African savanna elephant Loxodonta africana , to quantify general patterns in selection and to illustrate the importance of functional responses in elephant habitat selection. We found a functional response in habitat selection across both space and time for tree cover, with tree cover being unimportant to habitat selection in the mesic, eastern populations during the wet season. A temporal functional response for water was also evident, with greater variability in selection during the wet season. Selection for low slopes, high tree cover, and far distance from people was consistent across populations; however, variability in selection coefficients changed as a function of the abundance of a given resource within the home range. This variability of selection coefficients could be used to improve confidence estimations for inferences drawn from habitat selection models. Quantifying functional responses in habitat selection is one way to better predict how wildlife will respond to an ever‐changing environment, and they provide promising insights into the habitat selection of generalist species.

Despite being widely used, habitat selection models are rarely reliable and informative when applied across different ecosystems or over time. One possible explanation is that habitat selection is context‐dependent due to variation in consumer density and/or resource availability. The goal of this paper is to provide a general theoretical perspective on the contributory mechanisms of consumer and resource density‐dependent habitat selection, as well as on our capacity to account for their effects.Towards this goal we revisit the ideal free distribution (IFD), where consumers are assumed to be omniscient, equally competitive and freely moving, and are hence expected to instantaneously distribute themselves across a heterogeneous landscape such that fitness is equalised across the population. Although these assumptions are clearly unrealistic to some degree, the simplicity of the structure in IFD provides a useful theoretical vantage point to help clarify our understanding of more complex spatial processes. Of equal importance, IFD assumptions are compatible with the assumptions underlying common habitat selection models.Here we show how a fitness‐maximising space use model, based on IFD, gives rise to resource and consumer density‐dependent shifts in consumer distribution, providing a mechanistic explanation for the context‐dependent outcomes often reported in habitat selection analysis. Our model suggests that adaptive shifts in consumer distribution patterns would be expected to lead to nonlinear and often non‐monotonic patterns of habitat selection.These results indicate that even under the simplest of assumptions about adaptive organismal behaviour, habitat selection strength should critically depend on system‐wide characteristics. Clarifying the impact of adaptive behavioural responses may be pivotal in making meaningful ecological inferences about observed patterns of habitat selection and allow reliable transferability of habitat selection predictions across time and space.

AimHabitat selection is a behavioural mechanism by which animals attempt to maximize their inclusive fitness while balancing competing demands, such as finding food and rearing offspring while avoiding predation, in a heterogeneous and changing environment. Different habitat characteristics may be associated with each of these demands, implying that habitat selection varies depending on the behavioural motivations of the animal. Here, we investigate behaviour‐specific habitat selection inAfrican elephants and discuss its implications for distribution modelling and conservation.LocationNorthern Botswana, Africa, case study.MethodsWe useBayesian state‐space models to characterize location time series data of elephants into two behavioural states (encamped and exploratory). We then develop habitat selection models for each behavioural state and contrast them to models based on data pooled among behaviours.ResultsSpatial predictions of habitat use were often markedly different among the models. Behaviour‐specific and pooled habitat selection models differed in model structure, the magnitude of model coefficients and the form of the selection curve (linear or quadratic). Selection was typically strongest in the behaviour‐specific models, although this varied according to behavioural state and habitat covariate.Main conclusionsIgnoring behavioural states often had important consequences for quantifying habitat selection. Quantifying selection irrespective of behaviour (among all behaviours) can obscure important species–habitat relationships, thereby risking weak or incorrect inferences. Behaviour‐specific habitat selection provides greater insight into the process of habitat selection and can improve predictive habitat selection estimates. As some behaviours are more relevant to specific conservation objectives than others, focusing on behaviour‐specific selection could improve how habitats are prioritized for conservation or management.

Evaluating relocation extent versus covariate resolution in habitat selection models across spatiotemporal scales

ABSTRACTEffective conservation of critically endangered species should be guided by empirical evidence on how they interact with the environment at multiple scales. Yet, such information is lacking for many endangered species such as African White‐backed Vultures (AWbV) Gyps africanus. Habitat selection modelling is a promising tool for inferring habitat selection strategies by species to guide conservation planning. This study investigated how habitat selection patterns for AWbV differ in respect of intrinsic individual traits and seasonality. To achieve this goal, six AWbV were captured and attached with solar‐powered Global Positioning Systems (GPS) tracking devices. GPS data were then integrated with biologically relevant environmental predictors. Two modelling frameworks, namely binary logistic regression and Ecological Niche Factor Analysis, were fitted to develop habitat selection models at three scales based on pooled, individual and seasonal data. Results indicate that the six AWbV reflect specialist tendencies, with a narrow ecological niche. Further, results reveal a significant positive relationship between predicted presence of the six AWbV and the human footprint index while a consistent negative relationship with mammalian density index was also uncovered. Complex but significant relationships were also uncovered between AWbV and other variables such as the Normalised Difference Vegetation Index, mean daily temperature and thermal uplift. Results from the study suggest that AWbV response strategies to environmental heterogeneity are individual and season‐specific. This therefore calls for researchers to disaggregate movement ecology data to multiple scales as this may improve the utility of habitat selection modelling to inform biodiversity conservation planning.

Traffic noise is an associated effect of roads, potentially impacting wildlife. In the case of birds, it may alter spatial distribution, behavioural responses and physiological status, frequently masking the acoustic signals of conspecifics and predators. We analyse how road traffic noise affects habitat selection of Little Bustard males during the breeding season, when they produce brief territorial snort calls. The study site is in a typical agrarian area in central Spain, markedly affected by traffic noise. A noise map was built using specific environmental noise modelling software. The habitat in the territories of 26 individually-recognisable males (62% of the male population in the study year) was characterised in relation to noise levels, agrarian substrate composition and distance to nearest males. Habitat selection models were performed using MaxEnt, and an averaged model of the first 20 significant ones was generated. The noise map revealed high noise pollution levels for the whole study area (range: 50.13–62.35 dB). Distance to the nearest male was the most important variable in habitat selection models, so that as distance increased suitability decreased, while the effect of traffic noise was nearly negligible. This lack of traffic noise effect on the habitat selection of Little Bustard males might be explained by the low overlap between their snort call frequency and that of traffic noise, but it also suggests a poor capacity by this bird to cope with recent, anthropogenic disturbance. In this respect, noisy but otherwise suitable habitats could be functioning as ecological traps for this rapidly declining species. — Martinez-Marivela, I., Morales, M.B., Iglesias-Merchan, C., Delgado, M.P., Tarjuelo, R. & Traba, J. (2018). Traffic noise pollution does not influence habitat selection in the endangered Little Bustard. Ardeola, 65: 261–270.

Evolution in fine-grained environments. II. Habitat selection as a homeostatic mechanism

Habitats have substantial influence on the distribution and abundance of animals. Animals' selective movement yields their habitat use. Animals generally are more abundant in habitats that are selected most strongly. Models of habitat selection can be used to distribute animals on the landscape or their distribution can be modelled based on data of habitat use, occupancy, intensity of use or counts of animals. When the population is at carrying capacity or in an ideal-free distribution, habitat selection and related metrics of habitat use can be used to estimate abundance. If the population is not at equilibrium, models have the flexibility to incorporate density into models of habitat selection; but abundance might be influenced by factors influencing fitness that are not directly related to habitat thereby compromising the use of habitat-based models for predicting population size. Scale and domain of the sampling frame, both in time and space, are crucial considerations limiting application of these models. Ultimately, identifying reliable models for predicting abundance from habitat data requires an understanding of the mechanisms underlying population regulation and limitation.