Spatio-temporal ranging behaviour and its relevance to foraging strategies in wide-ranging wolverines

Conservation of carnivores in an increasingly changing environment is greatly helped by understanding the decision-making processes underlying habitat patch choice. Foraging theory may give us insight into spatio-temporal search patterns and consequent foraging decisions that carnivores make in heterogeneous and fluctuating environments. Constraints placed on central-place foragers in particular are likely to influence both foraging decisions and related spatio-temporal movement patterns. We used discrete choice models to investigate the spatio-temporal ranging behaviour of GPS collared female wolverines (Gulo gulo) with dependent cubs in south-central Norway. Activity patterns, home range use and selection for elevation were analyzed in relation to spatial and temporal covariates (daily and seasonal) and related to different foraging behaviours. In spring, wolverines showed restricted movement patterns around rendezvous sites at high elevations by day, whereas during the night animals were active at lower elevations. Over the summer, this daily pattern in intensity of use diminished and their overall selectiveness for elevation decreased as cubs grow more mobile and independent. At the onset of autumn, wolverines showed intensive use of the profitable forest-alpine tundra ecotone. We argue that reproducing wolverines deployed a foraging strategy attuned to altering their movement patterns throughout the summer to address a continuous, but diminishing, trade-off between providing both food and shelter for their offspring. Incorporating spatially and temporally explicit activity patterns and home range use in discrete choice resource selection models thus enhances the understanding of the motives behind wolverine resource utilization in space and time. Such knowledge may provide guidance to managers designing regional-scale zoning, in order to facilitate carnivore recovery and to minimize conflicts with human activities.     

Spatial memory, habitat auto-facilitation and the emergence of fractal home range patterns

Animals interact with their habitat in a manner which involves both negative and positive feedback mechanisms. We apply a specific modeling approach, “multi-scaled random walk”, for the scenario where a spatially explicit positive feedback process emerges from a combination of a spatial memory-dependent tendency to return to familiar patches and a consequently objective or subjective improvement of the quality of these patches (habitat auto-facilitation). In addition to the potential for local resource improvement from physically altering a patch, primarily known from the ecology of grazing ungulates, auto-facilitation from site fidelity may also embed more subtle subjective, individual-specific advantages from patch familiarity. Under the condition of resource superabundance, fitness gain from intra-home range patch fidelity creates a self-reinforcing use of the preferred patches on expense of a broader foraging in a priori equally favorable patches. Through this process, our simulations show that a spatially fractal dispersion of accumulated locations of the individual will emerge under the given model assumptions. Based on a conjecture that intra-home range patch fidelity depends on spatial memory we apply the multi-scaled random walk model to construct a spatially explicit habitat suitability parameter H_ij, which quantifies the dispersion of the generally most constraining resource from the individual's perspective. An intra-home range set of observed H-scores, H_obs, can then be estimated from a simple 2-scale calculation that is derived from the local dispersion of fixes. We show how the spatially explicit habitat utilization index H_obs not necessarily correlates positively with the local density fluctuations of fixes. The H-index solves some well-known problems from using the pattern of local densities of telemetry fixes - the classic utilization distribution - as a proxy variable for relative intra-home range habitat quality and resource selection. A pilot study on a set of telemetry fixes collected from a herd of free-ranging domestic sheep with overlapping summer home ranges illustrates how the H-index may be estimated and interpreted as a first-level approach towards a more extensive analysis of intra-home range habitat resource availability and patch preferences. Spatial memory in combination with site fidelity requires a modeling framework that explicitly describes the property of positive feedback mechanism under auto-facilitation in a spatio-temporally explicit manner.     

Estimated home ranges can misrepresent habitat relationships on patchy landscapes

Home ranges of animals are generally structured by the selective use of resource-bearing patches that comprise habitat. Based on this concept, home ranges of animals estimated from location data are commonly used to infer habitat relationships. Because home ranges estimated from animal locations are largely continuous in space, the resource-bearing patches selected by an animal from a fragmented distribution of patches would be difficult to discern; unselected patches included in the home range estimate would bias an understanding of important habitat relationships. To evaluate potential for this bias, we generated simulated home ranges based on optimal selection of resource-bearing patches across a series of simulated resource distributions that varied in the spatial continuity of resources. For simulated home ranges where selected patches were spatially disjunct, we included interstitial, unselected cells most likely to be traveled by an animal moving among selected patches. We compared characteristics of the simulated home ranges with and without interstitial patches to evaluate how insights derived from field estimates can differ from actual characteristics of home ranges, depending on patchiness of landscapes. Our results showed that contiguous home range estimates could lead to misleading insights on the quality, size, resource content, and efficiency of home ranges, proportional to the spatial discontinuity of resource-bearing patches. We conclude the potential bias of including unselected, largely irrelevant patches in the field estimates of home ranges of animals can be high, particularly for home range estimators that assume uniform use of space within home range boundaries. Thus, inferences about the habitat relationships that ultimately define an animal's home range can be misleading where animals occupy landscapes with patchily distributed resources.     

Modeling resource selection using polytomous logistic regression and kernel density estimates

Wildlife resource selection studies typically compare used to available resources; selection or avoidance occurs when use is disproportionately greater or less than availability. Comparing used to available resources is problematic because results are often greatly influenced by what is considered available to the animal. Moreover, placing relocation points within resource units is often difficult due to radiotelemetry and mapping errors. Given these problems, we suggest that an animal’s resource use be summarized at the scale of the home range (i.e., the spatial distribution of all point locations of an animal) rather than by individual points that are considered used or available. To account for differences in use-intensity throughout an animal’s home range, we model resource selection using kernel density estimates and polytomous logistic regression. We present a case study of elk (Cervus elaphus) resource selection in South Dakota to illustrate the procedure. There are several advantages of our proposed approach. First, resource availability goes undefined by the investigator, which is a difficult and often arbitrary decision. Instead, the technique compares the intensity of animal use throughout the home range. This technique also avoids problems with classifying locations rigidly as used or unused. Second, location coordinates do not need to be placed within mapped resource units, which is problematic given mapping and telemetry error. Finally, resource use is considered at an appropriate scale for management because most wildlife resource decisions are made at the level of the patch. Despite the advantages of this use-intensity procedure, future research should address spatial autocorrelation and develop spatial models for ordered categorical variables.     

Spatiotemporal dynamics of habitat use by koalas: the checkerboard model

Animal movement patterns and use of space depend upon food and nonfood resources, as well as conspecific and heterospecific interactions, but models of habitat use often neglect to examine multiple factors and rarely include marsupials. We studied habitat use in an Australian population of koalas (Phascolarctos cinereus) over a 6-year period in order to determine how koalas navigate their environment and partition limited patchy food and nonfood resources. Tree selection among koalas appears to be mediated by folar chemistry, but nonfood tree selection exerts a major impact on home range use due to thermoregulatory constraints. Koalas moved on a daily basis, during both day and night, but daytime resting site was not necessarily in the same location as nighttime feeding site. Koalas had substantial home range overlap in the near absence of resource sharing with less than 1% of trees located in areas of overlap used by multiple koalas. We suggest that koala spatiotemporal distribution and habitat use are probably based upon a community structure of individuals, with a checkerboard model best describing overlap in home range area but not in resource use. Nonfood refugia and social networks should be incorporated into models of animal range and habitat use.     

Incorporating the perceptual range of animals into connectivity models

The perceptual range of an animal towards different landscape elements affects its movements through heterogeneous landscapes. However, empirical knowledge and modeling tools are lacking to assess the consequences of variation in the perceptual range for movement patterns and connectivity. In this study we tested how changes in the assumed perception of different landscape elements affect the outcomes of a connectivity model. We used an existing individual-based, spatially explicit model for the dispersal of Eurasian lynx (Lynx lynx). We systematically altered the perceptual range in which animals recognize forest fragments, water bodies or cities, as well as the probability that they respond to these landscape elements. Overall, increasing the perceptual range of the animals enhanced connectivity substantially, both qualitatively and quantitatively. An enhanced range of attraction to forests had the strongest impact, doubling immigration success; an enhanced range of attraction to rivers had a slightly lower impact; and an enhanced range of avoidance of cities had the lowest impact. Correcting the enhancement in connectivity by the abundance of each of the landscape elements in question reversed the results, indicating the potential sensitivity of connectivity models to rare landscape elements (in our case barriers such as cities). Qualitatively, the enhanced perception resulted in strong changes in movement patterns and connectivity. Furthermore, model results were highly parameter-specific and patch-specific. These results emphasize the need for further empirical research on the perceptual capabilities of different animals in different landscapes and conditions. They further indicate the usefulness of spatially explicit individual-based simulation models for recognizing consistent patterns that emerge, despite uncertainty regarding animals’ movement behavior. Altogether, this study demonstrates the need to extend the concept of ‘perceptual ranges’ beyond patch detection processes, to encompass the wide range of elements that can direct animal movements during dispersal through heterogeneous landscapes.     

Modeled historical land use and land cover for the conterminous United States

The landscape of the conterminous United States has changed dramatically over the last 200 years, with agricultural land use, urban expansion, forestry, and other anthropogenic activities altering land cover across vast swaths of the country. While land use and land cover (LULC) models have been developed to model potential future LULC change, few efforts have focused on recreating historical landscapes. Researchers at the US Geological Survey have used a wide range of historical data sources and a spatially explicit modeling framework to model spatially explicit historical LULC change in the conterminous United States from 1992 back to 1938. Annual LULC maps were produced at 250-m resolution, with 14 LULC classes. Assessment of model results showed good agreement with trends and spatial patterns in historical data sources such as the Census of Agriculture and historical housing density data, although comparison with historical data is complicated by definitional and methodological differences. The completion of this dataset allows researchers to assess historical LULC impacts on a range of ecological processes.     

Bisexually bonded ranging in chimpanzees (Pan troglodytes verus)

Abstact While male mammals seek to maximize access to potential mates, females maximize feeding efficiency. Ranging patterns are therefore often sex specific. Sex-specific ranging patterns have also been reported for East African chimpanzees and a recent study on female ranging patterns concludes that social organization is best described by a male-bonded community model, where females occupy individual home ranges that are distributed within the boundaries of the male-defended range. In West African chimpanzees, however, such sex-specific ranging patterns have not been consistently observed and a bisexually bonded community model, where both sexes use the entire home range equally, has been suggested to best describe social organization. In this study we analyze 5 years of data on individual ranging patterns of chimpanzees in the Taï National Park (Côte dIvoire) to test specific predictions of the different models of chimpanzee social organization. We found that although males in Taï had slightly larger home ranges than females, all individual home ranges and core areas overlapped highly. Small individual home range size differences were entirely due to the use of peripheral areas and were correlated with female social dominance. These findings strongly support the bisexually bonded community model for Taï chimpanzees. Thus, we conclude that there are fundamental differences in the space-use patterns of East and West African chimpanzees and discuss possible factors leading to such differences.Communicated by D. Watts     

The comparative evidence relating to functional and neutral interpretations of biological communities

Neutral and functional theories provide rival interpretations of community patterns involving distribution, abundance, and diversity. One group of patterns describes the overall properties of species or sites, and derives principally from the frequency distribution of abundance among species. According to neutral theory, these patterns are determined by the number of individuals of novel type appearing each generation in the community, whereas functional theory relates them to the distribution of the extent of niches. A second group of patterns describes the spatial attributes of communities, and derives principally from the decay of similarity in species composition with distance. Neutral theory interprets these patterns as consequences of local dispersal alone, whereas the functional interpretation is that more distant sites are likely to be ecologically different. Neutral theory often provides good predictions of community patterns, yet is at variance with a wide range of experimental results involving the manipulation of environments or communities. One explanation for this discrepancy is that spatially explicit models where selection is generally weak, or where selection acts strongly on only a few species at each site, have essentially the same output as neutral models with respect to the distribution of abundance and the decay of similarity. Detecting a non-neutral signal in survey data requires careful spatial or phylogenetic analysis; we emphasize the potential utility of incorporating phylogenetic information in order to detect functional processes that lead to ecological variation among clades.     

Ontogenetic and sex-specific differences in density-dependent habitat selection of a marine fish population

The spatial dynamics of species are the result of complex interactions between density-independent and density-dependent sources of variability. Disentangling these two sources of variability has challenged ecologists working in both terrestrial and aquatic ecosystems. Using a novel spatially explicit statistical model, we tested for the presence of density-independent and density-dependent habitat selection in yellowfin sole (Limanda aspera) in the eastern Bering Sea. We found specificities in the density-dependent processes operating across ontogeny and particularly with gender. Density-dependent habitat expansion occurred primarily in females, and to a lesser degree in males. These patterns were especially evident in adult stages, while juvenile stages of both sexes exhibited a mix of different dynamics. Association of yellowfin sole with substrate type also varied by sex and to a lesser degree with size, with large females distributed over a wider range of substrates than males. Moreover, yellowfin sole expanded northward as cold subsurface waters retracted in summer, suggesting high sensitivity to arctic warming. Our findings illustrate how marginal habitats can play an important role in buffering density-dependent habitat expansion, with direct implications for resource management. Our spatially explicit modeling approach is effective in evaluating density-dependent spatial dynamics, and can easily be used to test similar hypotheses from a variety of aquatic and terrestrial ecosystems.     

Estimation of population density by spatially explicit capture-recapture analysis of data from area searches

The recent development of capture-recapture methods for estimating animal population density has focused on passive detection using devices such as traps or automatic cameras. Some species lend themselves more to active searching: a polygonal plot may be searched repeatedly and the locations of detected individuals recorded, or a plot may be searched just once and multiple cues (feces or other sign) identified as belonging to particular individuals. This report presents new likelihood-based spatially explicit capture-recapture (SECR) methods for such data. The methods are shown to be at least as robust in simulations as an equivalent Bayesian analysis, and to have negligible bias and near-nominal confidence interval coverage with parameter values from a lizard data set. It is recommended on the basis of simulation that plots for SECR should be at least as large as the home range of the target species. The R package "secr" may be used to fit the models. The likelihood-based implementation extends the spatially explicit analyses available for search data to include binary data (animal detected or not detected on each occasion) or count data (multiple detections per occasion) from multiple irregular polygons, with or without dependence among polygons. It is also shown how the method may be adapted for detections along a linear transect.     

The Application of Neutral Landscape Models in Conservation Biology

Neutral landscape models, derived from percolation theory in the field of landscape ecology, are grid-based maps in which complex habitat distributions are generated by random or fractal algorithms. This grid-based representation of landscape structure is compatible with the raster-based format of geographical information systems (GIS), which facilitates comparisons between theoretical and real landscapes. Neutral landscape models permit the identification of critical thresholds in connectivity, which can be used to predict when landscapes will become fragmented. The coupling of neutral landscape models with generalized population models, such as metapopulation theory, provides a null model for generating predictions about population dynamics in fragmented landscapes. Neutral landscape models can contribute to the following applications in conservation: (1) incorporation of complex spatial patterns in (meta)population models; (2) identification of species' perceptions of landscape structure; (3) determination of landscape connectivity; (4) evaluation of the consequences of habitat fragmentation for population subdivision; (5) identification of the domain of metapopulation dynamics; (6) prediction of the occurrence of extinction thresholds; ( 7) determination of the genetic consequences of habitat fragmentation; and (8) reserve design and ecosystem management. This generalized, spatially explicit framework bridges the gap between spatially implicit, patch-based models and spatially realistic GIS applications which are usually parameterized for a single species in a specific landscape. Development of a generalized, spatially explicit framework is essential in conservation biology because we will not be able to develop individual models for every species of management concern.     

Selecting the best home range model: an information-theoretic approach

Choosing an appropriate home range model is important for describing space use by animals and understanding the ecological processes affecting animal movement. Traditional approaches for choosing among home range models have not resulted in general, consistent, and unambiguous criteria that can be applied to individual data sets. We present a new application of information-theoretic model selection that overcomes many of the limitations of traditional approaches, as follows. (1) It alleviates the need to know the true home range to assess home range models, thus allowing performance to be evaluated with data on individual animals. (2) The best model can be chosen from a set of candidate models with the proper balance between fit and complexity. (3) If candidate home range models are based on underlying ecological processes, researchers can use the selected model not only to describe the home range, but also to infer the importance of various ecological processes affecting animal movements within the home range.     

Does scale matter in predicting species distributions? case study with the Marbled Murrelet.

Hierarchical selection orders (selection of microsite, patch, home range, population block, and geographic range) are ideal for dictating spatial grain and extent of animal habitat models, but the resultant conditional models are poor for creating predictive maps. I proposed a two-step approach for accurately mapping probability of animal use that incorporates a single-grain analysis of each selection order in the first step and creates a multi-grain model that combines key variables from each selection order in the second step. Such two-step multi-grain models are strongly recommended because they allow interpretation of the scale of selection for a variable. Using a large data set for the Marbled Murrelet (Brachyramphus marmoratus) as a case study and five selection orders, information theory criteria provided strong support that such models are superior to simpler one-step single-grain models for the murrelet. However, a single-grain model can produce high classification accuracy if it represents the most limiting scale. Notably, accuracy of the two-step multi-grain model was no better than a traditional one-step multi-grain model that ignores selection orders, indicating the advantage of two-step modeling is in elucidating scaling effects, not necessarily in improving accuracy of species distribution maps.     

Mechanistic analysis of interspecific competition using foraging trade-offs: implications for duck assemblages

The role of interspecific competition and resource partitioning in determining the composition of species assemblages is often controversial. In many cases data on species co-occurrence or resource use (prey or habitat) have been interpreted without a clear understanding of how, or even whether, phenotypic differences constrain performance to allow resource partitioning or how these constraints and the density of resources and competitors should shape resource selection by each species. Instead, predictions have been based on assumed constraints, possibly leading to conflicting results. One such controversy involves the role of bill morphology in mediating resource partitioning among dabbling ducks (Anas spp.). To determine whether incorrect assumptions may have contributed to this controversy, I constructed mechanistic models that predict filter-feeding performance for seven species of ducks directly from bill morphology and kinetics and compared these predictions to those of earlier studies that tested the bill morphology hypothesis. The models predicted that species should share a preference for their most profitable (primary) prey while partitioning their less profitable (secondary) prey by size. Consequently, ducks should forage in the same habitats and exhibit high overlap in prey size when competitor/resource ratios are either high or low. In contrast, earlier studies expected that resource partitioning should always be evident, which implicitly assumes that species partition their primary resources. The models also predicted that the ecological similarity of species in assemblages should increase as prey abundance and size variability declines, contrary to the expectations of an earlier study. A more consistent understanding of the mechanisms regulating assemblages of dabbling ducks, and other species, might emerge if patterns of resource use and species co-occurrence were predicted directly from a mechanistic understanding of how performance trade-offs affect resource selection in the context of varying resource and competitor densities.     

Scaling-up spatially-explicit ecological models using graphics processors

How the properties of ecosystems relate to spatial scale is a prominent topic in current ecosystem research. Despite this, spatially explicit models typically include only a limited range of spatial scales, mostly because of computing limitations. Here, we describe the use of graphics processors to efficiently solve spatially explicit ecological models at large spatial scale using the CUDA language extension. We explain this technique by implementing three classical models of spatial self-organization in ecology: a spiral-wave forming predator-prey model, a model of pattern formation in arid vegetation, and a model of disturbance in mussel beds on rocky shores. Using these models, we show that the solutions of models on large spatial grids can be obtained on graphics processors with up to two orders of magnitude reduction in simulation time relative to normal pc processors. This allows for efficient simulation of very large spatial grids, which is crucial for, for instance, the study of the effect of spatial heterogeneity on the formation of self-organized spatial patterns, thereby facilitating the comparison between theoretical results and empirical data. Finally, we show that large-scale spatial simulations are preferable over repetitions at smaller spatial scales in identifying the presence of scaling relations in spatially self-organized ecosystems. Hence, the study of scaling laws in ecology may benefit significantly from implementation of ecological models on graphics processors.     

Spectrum of selection: new approaches to detecting the scale-dependent response to habitat

Detecting habitat selection depends on the spatial scale of analysis, but multi-scale studies have been limited by the use of a few, spatially variable, hierarchical levels. We developed spatially explicit approaches to quantify selection along a continuum of scales using spatial (coarse-graining) and geostatistical (variogram) pattern analyses at multiple levels of habitat use (seasonal range, travel routes, feeding areas, and microsites). We illustrate these continuum-based approaches by applying them to winter habitat selection by woodland caribou (Rangifer tarandus caribou) using two key habitat components, Cladina lichens and snow depth. We quantified selection as the reduction in variance in used relative to available sites, thus avoiding reliance on correlations between organism and habitat, for which interpretation can be impeded by cross-scale correlations. By consistently selecting favorable habitat features, caribou experienced reduced variance in these features. The degree to which selection was accounted for by the travel route, feeding area, or microsite levels varied across the scale continuum. Caribou selected for Cladina within a 13-km scale domain and selected shallower snow at all scales. Caribou responded most strongly at the dominant scales of patchiness, implicating habitat heterogeneity as an underlying cause of multi-scale habitat selection. These novel approaches enable a spatial understanding of resource selection behavior.     

Dispersal in Spatially Explicit Population Models

Abstract: Ruckelshaus et al. (1997) outlined a simulation model of dispersal between patches in a fragmented landscape. They showed that dispersal success—the proportion of dispersers successfully locating a patch—was particularly sensitive to errors in dispersal mortality and concluded that this limits the utility of spatially explicit population models in conservation biology. I contend that, although they explored error propagation in a simple dispersal model, they did not explore how errors are propagated in spatially explicit population models, as no consideration of population processes was included. I developed a simple simulation model to investigate the effect of varying dispersal success on predictions of patch occupancy and population viability, the conventional outputs of spatially explicit population models. The model simulates births and deaths within habitat patches and dispersal as the transfer of individuals between them. Model predictions were sensitive to changes in dispersal success across a restricted range of within-patch growth rates, which depended on the dispersal initiation mechanism, patch carrying capacities, and number of generations simulated. Predictions of persistence and patch occupancy were generally more sensitive to changes in dispersal success (1) under presaturation rather than saturation dispersal; (2) at lower patch carrying capacities; and (3) over longer time periods. The framework I present provides a means of assessing, quantitatively, the regions of parameter space for which differences in dispersal success are likely to have a large effect on population model outputs. Investigating the effect of the representation of dispersal behavior within the demographic and landscape context provides a more useful assessment of whether our lack of knowledge is likely to cause unacceptable uncertainty in the predictions of spatially explicit population models.     

Incorporating animal behavior into seed dispersal models: implications for seed shadows

Seed dispersal fundamentally influences plant population and community dynamics but is difficult to quantify directly. Consequently, models are frequently used to describe the seed shadow (the seed deposition pattern of a plant population). For vertebrate-dispersed plants, animal behavior is known to influence seed shadows but is poorly integrated in seed dispersal models. Here, we illustrate a modeling approach that incorporates animal behavior and develop a stochastic, spatially explicit simulation model that predicts the seed shadow for a primate-dispersed tree species (Virola calophylla, Myristicaceae) at the forest stand scale. The model was parameterized from field-collected data on fruit production and seed dispersal, behaviors and movement patterns of the key disperser, the spider monkey (Ateles paniscus), densities of dispersed and non-dispersed seeds, and direct estimates of seed dispersal distances. Our model demonstrated that the spatial scale of dispersal for this V. calophylla population was large, as spider monkeys routinely dispersed seeds >100 m, a commonly used threshold for long-distance dispersal. The simulated seed shadow was heterogeneous, with high spatial variance in seed density resulting largely from behaviors and movement patterns of spider monkeys that aggregated seeds (dispersal at their sleeping sites) and that scattered seeds (dispersal during diurnal foraging and resting). The single-distribution dispersal kernels frequently used to model dispersal substantially underestimated this variance and poorly fit the simulated seed-dispersal curve, primarily because of its multimodality, and a mixture distribution always fit the simulated dispersal curve better. Both seed shadow heterogeneity and dispersal curve multimodality arose directly from these different dispersal processes generated by spider monkeys. Compared to models that did not account for disperser behavior, our modeling approach improved prediction of the seed shadow of this V. calophylla population. An important function of seed dispersal models is to use the seed shadows they predict to estimate components of plant demography, particularly seedling population dynamics and distributions. Our model demonstrated that improved seed shadow prediction for animal-dispersed plants can be accomplished by incorporating spatially explicit information on disperser behavior and movements, using scales large enough to capture routine long-distance dispersal, and using dispersal kernels, such as mixture distributions, that account for spatially aggregated dispersal.