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Statistical models of habitat preference and species distribution (e.g., Resource Selection Functions and Maximum Entropy approaches) perform a quantitative comparison of the use of space with the availability of all habitats in an animal's environment. However, not all of space is accessible all of the time to all individuals, so availability is in fact determined by limitations in animal perception and mobility. Therefore, measuring habitat availability at biologically relevant scales is essential for understanding preference, but herein lies a trade-off: Models fitted at large spatial scales, will tend to average across the responses of different individuals that happen to be in regions with contrasting habitat compositions. We suggest that such models may fail to capture local extremes (hotspots and coldspots) in animal usage and call this potential problem, homogenization. In contrast, models fitted at smaller scales will vary stochastically depending on the particular habitat composition of their narrow spatial neighborhood, and hence fail to describe responses when predicting for different sampling instances. This is the now well-documented issue of non-transferability of habitat models. We illustrate this tradeoff, using a range of simulated experiments, incorporating variations in environmental gradients, richness and fragmentation. We propose diagnostics for detecting the two issues of homogenization and non-transferability and show that these scale-related symptoms are likely to be more pronounced in highly fragmented or steeply graded landscapes. Further, we address these problems by treating the neighborhood of each cell in the landscape grid as an individual sampling instance (with its own neighborhood), hence allowing coefficients to respond to the local expectations of environmental variables according to a Generalized Functional Response ( GFR). Under simulation this approach is consistently better at estimating robust (i.e., transferable) habitat models at smaller scales, and less susceptible to homogenization at larger scales. At the same time, it represents the first application of a GFR to continuous space (rather than multiple, spatially distinct datasets), allowing the predictive advantages of this extension of species distribution models to become available to data from large-scale but single-site field studies. [ABSTRACT FROM AUTHOR]

Researchers employing resource selection functions (RSFs) and other related methods aim to detect correlates of space-use and mitigate against detrimental environmental change. However, an empirical model fit to data from one place or time is unlikely to capture species responses under different conditions because organisms respond nonlinearly to changes in habitat availability. This phenomenon, known as a functional response in resource selection, has been debated extensively in the RSF literature but continues to be ignored by practitioners for lack of a practical treatment. We therefore extend the RSF approach to enable it to estimate generalized functional responses (GFRs) from spatial data. GFRs employ data from several sampling instances characterized by diverse profiles of habitat availability. By modeling the regression coefficients of the underlying RSF as functions of availability, GFRs can account for environmental change and thus predict population distributions in new environments. We formulate the approach as a mixed-effects model so that it is estimable by readily available statistical software. We illustrate its application using (1) simulation and (2) wolf home-range telemetry. Our results indicate that GFRs can offer considerable improvements in estimation speed and predictive ability over existing mixed-effects approaches. [ABSTRACT FROM AUTHOR]

Ecological theory predicts that generalist predators should damp or suppress long-term periodic fluctuations (cycles) in their prey populations and depress their average densities. However, the magnitude of these impacts is likely to vary depending on the availability of alternative prey species and the nature of ecological mechanisms driving the prey cycles. These multispecies effects can be modeled explicitly if parameterized functions relating prey consumption to prey abundance, and realistic population dynamical models for the prey, are available. These requirements are met by the interaction between the Hen Harrier (Circus cyaneus) and three of its prey species in the United Kingdom, the Meadow Pipit (Anthus pratensis), the field vole (Microtus agrestis), and the Red Grouse (Lagopus lagopus scoticus). We used this system to investigate how the availability of alternative prey and the way in which prey dynamics are modeled might affect the behavior of simple trophic networks. We generated cycles in one of the prey species (Red Grouse) in three different ways: through (1) the interaction between grouse density and macroparasites, (2) the interaction between grouse density and male grouse aggressiveness, and (3) a generic, delayed density-dependent mechanism. Our results confirm that generalist predation can damp or suppress grouse cycles, but only when the densities of alternative prey are low. They also demonstrate that diametrically opposite indirect effects between pairs of prey species can occur together in simple systems. In this ease, pipits and grouse are apparent competitors, whereas voles and grouse are apparent facilitators. Finally, we found that the quantitative impacts of the predator on prey density differed among the three models of prey dynamics, and these differences were robust to uncertainty in parameter estimation and environmental stochasticity. [ABSTRACT FROM AUTHOR]

Recent field experiments tested the hypothesis that variations in the aggressiveness of territorial cocks drive Red Grouse (Lagopus lagopus scoticus) population cycles. The implications of these results were previously explored with parametrically flexible models that made specific assumptions about the functional form of direct density dependence and the form and timing of delayed density dependence. Although these models were characterized by apparently different stability conditions, they pointed at the same conclusion: that the occurrence of population cycles under this hypothesis relies on the strength of the interaction between density and aggressiveness around the system's equilibrium. To investigate if this important result is valid more generally, we develop a minimally specified model by lifting most of the assumptions on direct and delayed density dependence. Stability analysis of this functionally flexible model confirms that unstable dynamics are indeed more likely if small perturbations from equilibrium density have a strong impact on aggressiveness, and it unifies the stability conditions previously derived for the more specific models under a single, general condition. Further, we derive global, necessary, and sufficient conditions for instability and express them in terms of proportional changes in aggressiveness and density. For the first time since the inception of the hypothesis, the necessary condition quantifies the minimum strength of the intrinsic mechanism that would be required to cause unstable dynamics. We predict that unstable population dynamics are possible if proportional perturbations from equilibrium density are at least matched by proportional changes in aggressiveness. Existing field data indicate that the necessary condition for intrinsic cycles is satisfied in Red Grouse populations. In contrast, the sufficient condition is considerably more strict, implying that intrinsic instability is not an inevitable feature of the system. We conclude that the model is consistent with the demographic patterns of cyclic population fluctuations in Red Grouse and other birds of the grouse family. [ABSTRACT FROM AUTHOR]

Abundance estimation methods that combine several types of data are becoming increasingly common because they yield more accurate and precise parameter estimates and predictions than are possible from a single data source. These beneficial effects result from increasing sample size (through data pooling) and complementarity between different data types. Here, we test whether integrating mark–recapture data with passive acoustic detections into a joint likelihood improves estimates of population size in a multi‐guild community. We compared the integrated model to a mark–recapture‐only model using simulated data first and then using a data set of mist‐net captures and acoustic recordings from an Afrotropical agroforest bird community. The integrated model with simulated data improved accuracy and precision of estimated population size and detection parameters. When applied to field data, the integrated model was able to produce, for each bird guild, ecologically plausible estimates of population size and detection parameters, with more precision compared with the mark–recapture model. Overall, our results show that adding acoustic data to mark–recapture analyses improves estimates of population size. With the increasing availability of acoustic recording devices, this data collection technique could readily be added to routine field protocols, leading to a cost‐efficient improvement of traditional mark–recapture population estimation. [ABSTRACT FROM AUTHOR]

Recent technological advances have permitted the collection of detailed animal location and ancillary biotelemetry data that facilitate inference about animal movement and associated behaviors. However, these rich sources of individual information, location, and biotelemetry data, are typically analyzed independently, with population-level inferences remaining largely post hoc. We describe a hierarchical modeling approach, which is able to integrate location and ancillary biotelernetry (e.g., physiological or accelerometer) data from many individuals. We can thus obtain robust estimates of (1) population-level movement parameters and (2) activity budgets for a set of behaviors among which animals transition as they respond to changes in their internal and external environment. Measurement error and missing data are easily accommodated using a state-space formulation of the proposed hierarchical model. Using Bayesian analysis methods, we demonstrate our modeling approach with location and dive activity data from 17 harbor seals (Phoca vilulina) in the United Kingdom. Based jointly on movement and diving activity, we identified three distinct movement behavior states: resting, foraging, and transit, and estimated population-level activity budgets to these three states. Because harbor seals are known to dive for both foraging and transit (but not usually for resting), we compared these results to a similar population- level analysis utilizing only location data. We found that a large proportion of time steps were mischaracterized when behavior states were inferred from horizontal trajectory alone, with 33% of time steps exhibiting a majority of dive activity assigned to the resting state. Only 1% of these time steps were assigned to resting when inferred from both trajectory and dive activity data using our integrated modeling approach. There is mounting evidence of the potential perils of inferring animal behavior based on trajectory alone, but there fortunately now exist many flexible analytical techniques for extracting more out of the increasing wealth of information afforded by recent advances in biologging technology. [ABSTRACT FROM AUTHOR]

We discuss hidden Markov-type models for fitting a variety of multistate random walks to wildlife movement data. Discrete-time hidden Markov models (HMMs) achieve considerable computational gains by focusing on observations that are regularly spaced in time, and for which the measurement error is negligible. These conditions are often met, in particular for data related to terrestrial animals, so that a likelihood-based HMM approach is feasible. We describe a number of extensions of HMMs for animal movement modeling, including more flexible state transition models and individual random effects (fitted in a non-Bayesian framework). In particular we consider so-called hidden semi-Markov models, which may substantially improve the goodness of fit and provide important insights into the behavioral state switching dynamics. To showcase the expediency of these methods, we consider an application of a hierarchical hidden semi-Markov model to multiple bison movement paths. [ABSTRACT FROM AUTHOR]

The contribution of interspecific competition to structuring population and community dynamics remains controversial and poorly tested. Interspecific competition has long been thought to influence the structure of migrant–resident bird communities in winter, yet experimental evidence remains elusive. The arrival of billions of songbirds into Neotropical habitats, where they co‐exist with residents, provides a unique opportunity to assess interspecific competition and its consequences. Working in 15 ha of Jamaican black mangrove forest, we used removal experiments to test whether dominant resident Yellow Warblers compete interspecifically with subordinate wintering American Redstarts; we also used observational evidence (interspecific territorial overlap) to understand whether this coexistence influences physical condition, spring departure dates or annual return rates. Consistent with interspecific competition, after experimental removal of the resident, yearling male Redstarts (but not females or adult males) immediately moved into vacated Yellow Warbler territories, increasing their overlap with the space by 7.3%. Yearling Redstarts also appeared to adjust their territorial space use by actively avoiding Yellow Warblers; for example, Redstarts departing the wintering grounds as yearlings and returning the following winter shifted such that their territories overlapped 32% less with those of Yellow Warblers. Adult Redstarts showed no such territorial flexibility. Adult male Redstarts also showed evidence supporting the consequences of coexistence: territorial overlap with Yellow Warblers was negatively correlated with body condition and annual return rates. Adult male Redstarts with <25% territorial overlap with Yellow Warblers were more than three times as likely to return between seasons than those with 100% overlap. We propose that the territorial inflexibility of adult male Redstarts produces these consequences, which may be due to their years‐long investment in that particular territory. More generally, the temporary nature of migrant–resident interspecific competition is likely what allows coexistence during winter, the most resource‐poor time of year. Interspecific competition and the consequences of coexistence are likely age‐ and sex‐specific and the product of intraspecific dominance hierarchy in Redstarts. Our observations suggest that interspecific coexistence has measurable consequences, and our experiments support the long‐held, but previously untested belief that resident birds compete interspecifically with wintering migrants. [ABSTRACT FROM AUTHOR]

The two dominant approaches for the analysis of species–habitat associations in animals have been shown to reach divergent conclusions. Models fitted from the viewpoint of an individual (step selection functions), once scaled up, do not agree with models fitted from a population viewpoint (resource selection functions [RSFs]). We explain this fundamental incompatibility, and propose a solution by introducing to the animal movement field a novel use for the well‐known family of Markov chain Monte Carlo (MCMC) algorithms. By design, the step selection rules of MCMC lead to a steady‐state distribution that coincides with a given underlying function: the target distribution. We therefore propose an analogy between the movements of an animal and the movements of an MCMC sampler, to guarantee convergence of the step selection rules to the parameters underlying the population's utilization distribution. We introduce a rejection‐free MCMC algorithm, the local Gibbs sampler, that better resembles real animal movement, and discuss the wide range of biological assumptions that it can accommodate. We illustrate our method with simulations on a known utilization distribution, and show theoretically and empirically that locations simulated from the local Gibbs sampler give rise to the correct RSF. Using simulated data, we demonstrate how this framework can be used to estimate resource selection and movement parameters. [ABSTRACT FROM AUTHOR]