Search and navigation in dynamic environments - from individual behaviors to population distributions

A hidden Markov model to identify and adjust for selection bias: an example involving mixed migration strategies

An important assumption in observational studies is that sampled individuals are representative of some larger study population. Yet, this assumption is often unrealistic. Notable examples include online public-opinion polls, publication biases associated with statistically significant results, and in ecology, telemetry studies with significant habitat-induced probabilities of missed locations. This problem can be overcome by modeling selection probabilities simultaneously with other predictor–response relationships or by weighting observations by inverse selection probabilities. We illustrate the problem and a solution when modeling mixed migration strategies of northern white-tailed deer (Odocoileus virginianus). Captures occur on winter yards where deer migrate in response to changing environmental conditions. Yet, not all deer migrate in all years, and captures during mild years are more likely to target deer that migrate every year (i.e., obligate migrators). Characterizing deer as conditional or obligate migrators is also challenging unless deer are observed for many years and under a variety of winter conditions. We developed a hidden Markov model where the probability of capture depends on each individual's migration strategy (conditional versus obligate migrator), a partially latent variable that depends on winter severity in the year of capture. In a 15-year study, involving 168 white-tailed deer, the estimated probability of migrating for conditional migrators increased nonlinearly with an index of winter severity. We estimated a higher proportion of obligates in the study cohort than in the population, except during a span of 3 years surrounding back-to-back severe winters. These results support the hypothesis that selection biases occur as a result of capturing deer on winter yards, with the magnitude of bias depending on the severity of winter weather. Hidden Markov models offer an attractive framework for addressing selection biases due to their ability to incorporate latent variables and model direct and indirect links between state variables and capture probabilities.

On the adaptive benefits of mammal migration

Migration is well developed among mammals, but there has been little attempt to date to review common ecological constraints that may guide the evolution of migration among mammals, nor to consider its prevalence across different taxa. Here we review several alternate hypotheses for the evolution of migration in mammals based on improvements in energetic gain and mate-finding contrasted with reduction in energetic costs or the risk of predation and parasitism. While there are well-documented examples of each across the order Mammalia, the available evidence to date most strongly supports the energy gain and predation risk hypotheses in the terrestrial realm, whereas a combined strategy of reducing energetic costs in one season but improving energetic gain in another season seems to characterize aquatic mammal species, as well as bats. We further discuss behavioral and physiological specialization and provide a taxonomic cross section of mammalian migration.

Residence times and boundary-following behavior in animals

Many animals in heterogeneous environments bias their trajectories displaying a preference for the vicinity of boundaries. Here we propose a criterion, relying on recent invariance properties of residence times for microreversible Boltzmann's walks, that permits detecting and quantifying boundary-following behaviors. On this basis we introduce a boundary-following model that is a nonmicroreversible Boltzmann's walk and that can represent all kinds of boundary-following distributions. This allows us to perform a theoretical analysis of field-resolved boundary following in animals. Two consequences are pointed out and are illustrated: A systematic procedure can now be used for extraction of individual properties from experimental field measurements, and boundary-curvature influence can be recovered as an emerging property without the need for individuals perceiving the curvature via complex physiological mechanisms. The presented results apply to any memoryless correlated random walk, such as the run-and-tumble models that are widely used in cell motility studies.

Homing of invasive Burmese pythons in South Florida: evidence for map and compass senses in snakes

Navigational ability is a critical component of an animal's spatial ecology and may influence the invasive potential of species. Burmese pythons (Python molurus bivittatus) are apex predators invasive to South Florida. We tracked the movements of 12 adult Burmese pythons in Everglades National Park, six of which were translocated 21-36 km from their capture locations. Translocated snakes oriented movement homeward relative to the capture location, and five of six snakes returned to within 5 km of the original capture location. Translocated snakes moved straighter and faster than control snakes and displayed movement path structure indicative of oriented movement. This study provides evidence that Burmese pythons have navigational map and compass senses and has implications for predictions of spatial spread and impacts as well as our understanding of reptile cognitive abilities.

From fine-scale foraging to home ranges: a semivariance approach to identifying movement modes across spatiotemporal scales

Understanding animal movement is a key challenge in ecology and conservation biology. Relocation data often represent a complex mixture of different movement behaviors, and reliably decomposing this mix into its component parts is an unresolved problem in movement ecology. Traditional approaches, such as composite random walk models, require that the timescales characterizing the movement are all similar to the usually arbitrary data-sampling rate. Movement behaviors such as long-distance searching and fine-scale foraging, however, are often intermixed but operate on vastly different spatial and temporal scales. An approach that integrates the full sweep of movement behaviors across scales is currently lacking. Here we show how the semivariance function (SVF) of a stochastic movement process can both identify multiple movement modes and solve the sampling rate problem. We express a broad range of continuous-space, continuous-time stochastic movement models in terms of their SVFs, connect them to relocation data via variogram regression, and compare them using standard model selection techniques. We illustrate our approach using Mongolian gazelle relocation data and show that gazelle movement is characterized by ballistic foraging movements on a 6-h timescale, fast diffusive searching with a 10-week timescale, and asymptotic diffusion over longer timescales.

Integrating population- and individual-level information in a movement model of Yellowstone bison

Throughout the world, fragmentation of landscapes by human activities has constrained the opportunity for large herbivores to migrate. Conflict between people and wildlife results when migrating animals transmit disease to livestock, damage property, and threaten human safety. Mitigating this conflict requires understanding the forces that shape migration patterns. Bison Bos bison migrating from Yellowstone National Park into the state of Montana during winter and spring concern ranchers on lands surrounding the park because bison can transmit brucellosis (Brucella abortus) to cattle. Migrations have been constrained, with bison being lethally removed or moved back into the park. We developed a state-space model to support decisions on bison management aimed at mitigating conflict with landowners outside the park. The model integrated recent GPS observations with 22 years (1990-2012) of aerial counts to forecast monthly distributions and identify factors driving migration. Wintering areas were located along decreasing elevation gradients, and bison accumulated in wintering areas prior to moving to areas progressively lower in elevation. Bison movements were affected by time since the onset of snowpack, snowpack magnitude, standing crop, and herd size. Migration pathways were increasingly used over time, suggesting that experience or learning influenced movements. To support adaptive management of Yellowstone bison, we forecast future movements to evaluate alternatives. Our approach of developing models capable of making explicit probabilistic forecasts of large herbivore movements and seasonal distributions is applicable to managing the migratory movements of large herbivores worldwide. These forecasts allow managers to develop and refine strategies in advance, and promote sound decision-making that reduces conflict as migratory animals come into contact with people.

Success, failure, and spreading speeds for invasions on spatial gradients

We study a model that describes the spatial spread of a species along a habitat gradient on which the species' growth increases. Mathematical analysis is provided to determine the spreading dynamics of the model. We demonstrate that the species may succeed or fail in local invasion depending on the species' growth function and dispersal kernel. We delineate the conditions under which a spreading species may be stopped by poor quality habitat, and demonstrate how a species can escape a region of poor quality habitat by climbing a resource gradient to good quality habitat where it spreads at a constant spreading speed. We show that dispersal may take the species from a good quality region to a poor quality region where the species becomes extinct. We also provide formulas for spreading speeds for the model that are determined by the dispersal kernel and linearized growth rates in both directions.

Using diel movement behavior to infer foraging strategies related to ecological and social factors in elephants

BACKGROUND

Adaptive movement behaviors allow individuals to respond to fluctuations in resource quality and distribution in order to maintain fitness. Classically, studies of the interaction between ecological conditions and movement behavior have focused on such metrics as travel distance, velocity, home range size or patch occupancy time as the salient metrics of behavior. Driven by the emergence of very regular high frequency data, more recently the importance of interpreting the autocorrelation structure of movement as a behavioral metric has become apparent. Studying movement of a free ranging African savannah elephant population, we evaluated how two movement metrics, diel displacement (DD) and movement predictability (MP - the degree of autocorrelated movement activity at diel time scales), changed in response to variation in resource availability as measured by the Normalized Difference Vegetation Index. We were able to capitalize on long term (multi-year) yet high resolution (hourly) global positioning system tracking datasets, the sample size of which allows robust analysis of complex models. We use optimal foraging theory predictions as a framework to interpret our results, in particular contrasting the behaviors across changes in social rank and resource availability to infer which movement behaviors at diel time scales may be optimal in this highly social species.

RESULTS

Both DD and MP increased with increasing forage availability, irrespective of rank, reflecting increased energy expenditure and movement predictability during time periods of overall high resource availability. However, significant interactions between forage availability and social rank indicated a stronger response in DD, and a weaker response in MP, with increasing social status.

CONCLUSIONS

Relative to high ranking individuals, low ranking individuals expended more energy and exhibited less behavioral movement autocorrelation during lower forage availability conditions, likely reflecting sub-optimal movement behavior. Beyond situations of contest competition, rank status appears to influence the extent to which individuals can modify their movement strategies across periods with differing forage availability. Large-scale spatiotemporal resource complexity not only impacts fine scale movement and optimal foraging strategies directly, but likely impacts rates of inter- and intra-specific interactions and competition resulting in socially based movement responses to ecological dynamics.

A space oddity: geographic and specific modulation of migration in Eudyptes penguins

Post-breeding migration in land-based marine animals is thought to offset seasonal deterioration in foraging or other important environmental conditions at the breeding site. However the inter-breeding distribution of such animals may reflect not only their optimal habitat, but more subtle influences on an individual’s migration path, including such factors as the intrinsic influence of each locality’s paleoenvironment, thereby influencing animals’ wintering distribution. In this study we investigated the influence of the regional marine environment on the migration patterns of a poorly known, but important seabird group. We studied the inter-breeding migration patterns in three species of Eudyptes penguins (E. chrysolophus, E. filholi and E. moseleyi), the main marine prey consumers amongst the World’s seabirds. Using ultra-miniaturized logging devices (light-based geolocators) and satellite tags, we tracked 87 migrating individuals originating from 4 sites in the southern Indian Ocean (Marion, Crozet, Kerguelen and Amsterdam Islands) and modelled their wintering habitat using the MADIFA niche modelling technique. For each site, sympatric species followed a similar compass bearing during migration with consistent species-specific latitudinal shifts. Within each species, individuals breeding on different islands showed contrasting migration patterns but similar winter habitat preferences driven by sea-surface temperatures. Our results show that inter-breeding migration patterns in sibling penguin species depend primarily on the site of origin and secondly on the species. Such site-specific migration bearings, together with similar wintering habitat used by parapatrics, support the hypothesis that migration behaviour is affected by the intrinsic characteristics of each site. The paleo-oceanographic conditions (primarily, sea-surface temperatures) when the populations first colonized each of these sites may have been an important determinant of subsequent migration patterns. Based on previous chronological schemes of taxonomic radiation and geographical expansion of the genus Eudyptes, we propose a simple scenario to depict the chronological onset of contrasting migration patterns within this penguin group.

Fragmentation of the habitat of wild ungulates by anthropogenic barriers in Mongolia

Habitat loss and habitat fragmentation caused by anthropogenic activities are the main factors that constrain long-distance movement of ungulates. Mongolian gazelles (Procapra gutturosa) and Asiatic wild asses (Equus hemionus) in Mongolia are facing habitat fragmentation and loss. To better understand how their movements respond to potential anthropogenic and natural barriers, we tracked 24 Mongolian gazelles and 12 wild asses near the Ulaanbaatar-Beijing Railroad and the fenced international border between Mongolia and China between 2002 and 2012. None of the tracked gazelles crossed the railroad, even though gazelles were captured on both sides of the tracks at the start of the study. Similarly, we did not observe cross-border movements between Mongolia and China for either species, even though some animals used areas adjacent to the border. The both species used close areas to the anthropogenic barriers more frequently during winter than summer. These results suggest strong impacts by the artificial barriers. The construction of new railroads and roads to permit mining and other resource development therefore creates the threat of further habitat fragmentation, because the planned routes will divide the remaining non-fragmented habitats of the ungulates into smaller pieces. To conserve long-distance movement of the ungulates in this area, it will be necessary to remove or mitigate the barrier effects of the existing and planned roads and railroads and to adopt a landscape-level approach to allow access by ungulates to wide ranges throughout their distribution.

Understanding scales of movement: animals ride waves and ripples of environmental change

Animal movements are the primary behavioural adaptation to spatiotemporal heterogeneity in resource availability. Depending on their spatiotemporal scale, movements have been categorized into distinct functional groups (e.g. foraging movements, dispersal, migration), and have been studied using different methodologies. We suggest striving towards the development of a coherent framework based on the ultimate function of all movement types, which is to increase individual fitness through an optimal exploitation of resources varying in space and time. We developed a novel approach to simultaneously study movements at different spatiotemporal scales based on the following proposed theory: the length and frequency of animal movements are determined by the interaction between temporal autocorrelation in resource availability and spatial autocorrelation in changes in resource availability. We hypothesized that for each time interval the spatiotemporal scales of moose Alces alces movements correspond to the spatiotemporal scales of variation in the gains derived from resource exploitation when taking into account the costs of movements (represented by their proxies, forage availability NDVI and snow depth respectively). The scales of change in NDVI and snow were quantified using wave theory, and were related to the scale of moose movement using linear mixed models. In support of the proposed theory we found that frequent, smaller scale movements were triggered by fast, small-scale ripples of changes, whereas infrequent, larger scale movements matched slow, large-scale waves of change in resource availability. Similarly, moose inhabiting ranges characterized by larger scale waves of change in the onset of spring migrated longer distances. We showed that the scales of movements are driven by the scales of changes in the net profitability of trophic resources. Our approach can be extended to include drivers of movements other than trophic resources (e.g. population density, density of related individuals, predation risk) and may facilitate the assessment of the impact of environmental changes on community dynamics and conservation.

Inferring spatial memory and spatiotemporal scaling from GPS data: comparing red deer Cervus elaphus movements with simulation models

1. Increased inference regarding underlying behavioural mechanisms of animal movement can be achieved by comparing GPS data with statistical mechanical movement models such as random walk and Lévy walk with known underlying behaviour and statistical properties. 2. GPS data are typically collected with ≥ 1 h intervals not exactly tracking every mechanistic step along the movement path, so a statistical mechanical model approach rather than a mechanistic approach is appropriate. However, comparisons require a coherent framework involving both scaling and memory aspects of the underlying process. Thus, simulation models have recently been extended to include memory-guided returns to previously visited patches, that is, site fidelity. 3. We define four main classes of movement, differing in incorporation of memory and scaling (based on respective intervals of the statistical fractal dimension D and presence/absence of site fidelity). Using three statistical protocols to estimate D and site fidelity, we compare these main movement classes with patterns observed in GPS data from 52 females of red deer (Cervus elaphus). 4. The results show best compliance with a scale-free and memory-enhanced kind of space use; that is, a power law distribution of step lengths, a fractal distribution of the spatial scatter of fixes and site fidelity. 5. Our study thus demonstrates how inference regarding memory effects and a hierarchical pattern of space use can be derived from analysis of GPS data.

New Insights into Pelagic Migrations: Implications for Ecology and Conservation

Highly pelagic large marine vertebrates have evolved the capability of moving across large expanses of the marine environment; some species routinely move across entire ocean basins. Our understanding of these movements has been enhanced by new technologies that now allow us to follow their movements over great distances and long time periods in great detail. This technology provides not only detailed information on the movements of a wide variety of marine species, but also detailed characteristics of the habitats they use and clues to their navigation abilities. Advances in electronic tracking technologies have been coupled with rapid development of statistical and analytical techniques. With these developments, conservation of highly migratory species has been aided by providing new information on where uncommon or endangered species go, what behaviors they perform and why, which habitats are critical, and where they range, as well as, in many cases, better estimates of their population size and the interconnectedness of subpopulations. Together these tools are providing critical insights into the ecology of highly pelagic marine vertebrates that are key for their conservation and management.

Migration or residency? The evolution of movement behavior and information usage in seasonal environments

Migration, the seasonal movement of individuals among different locations, is a behavior found throughout the animal kingdom. Although migration is widely studied at taxonomically restricted levels, cross-taxonomic syntheses of migration are less common. As a result, we lack answers to broad questions such as what ultimate factors generally drive animal migration. Here we present such a synthesis by using a spatially explicit, individual-based model in which we evolve behavior rules via simulations under a wide range of ecological conditions to answer two questions. First, under what types of ecological conditions can an individual maximize its fitness by migrating (vs. being a resident)? Second, what types of information do individuals use to guide their movement? We show that migration is selected for when resource distributions are dominated more by seasonality than by local patchiness, and residency (nonmigratory behavior) is selected for when the reverse is true. When selected for, migration evolves as both a movement behavior and an information usage strategy. We also find that different types of migration can evolve, depending on the ecological conditions and availability of information. Finally, we present empirical support for our main results, drawn from migration patterns exhibited by a variety of taxonomic groups.

From migration to nomadism: movement variability in a northern ungulate across its latitudinal range

Understanding the causes and consequences of animal movements is of fundamental biological interest because any alteration in movement can have direct and indirect effects on ecosystem structure and function. It is also crucial for assisting spatial wildlife management under variable environmental change scenarios. Recent research has highlighted the need of quantifying individual variability in movement behavior and how it is generated by interactions between individual requirements and environmental conditions, to understand the emergence of population-level patterns. Using a multi-annual movement data set of 213 individual moose (Alces alces) across a latitudinal gradient (from 56 degrees to 67 degrees N) that spans over 1100 km of varying environmental conditions, we analyze the differences in individual and population-level movements. We tested the effect of climate, risk, and human presence in the landscape on moose movements. The variation in these factors explained the existence of multiple movements (migration, nomadism, dispersal, sedentary) among individuals and seven populations. Population differences were primarily related to latitudinal variation in snow depth and road density. Individuals showed both fixed and flexible behaviors across years, and were less likely to migrate with age in interaction with snow and roads. For the predominant movement strategy, migration, the distance, timing, and duration at all latitudes varied between years. Males traveled longer distances and began migrating later in spring than females. Our study provides strong quantitative evidence for the dynamics of animal movements in response to changes in environmental conditions along with varying risk from human influence across the landscape. For moose, given its wide distributional range, changes in the distribution and migratory behavior are expected under future warming scenarios.

Tracking climate impacts on the migratory monarch butterfly

Understanding the impacts of climate on migratory species is complicated by the fact that these species travel through several climates that may be changing in diverse ways throughout their complete migratory cycle. Most studies are not designed to tease out the direct and indirect effects of climate at various stages along the migration route. We assess the impacts of spring and summer climate conditions on breeding monarch butterflies, a species that completes its annual migration cycle over several generations. No single, broad-scale climate metric can explain summer breeding phenology or the substantial year-to-year fluctuations observed in population abundances. As such, we built a Poisson regression model to help explain annual arrival times and abundances in the Midwestern United States. We incorporated the climate conditions experienced both during a spring migration/breeding phase in Texas as well as during subsequent arrival and breeding during the main recruitment period in Ohio. Using data from a state-wide butterfly monitoring network in Ohio, our results suggest that climate acts in conflicting ways during the spring and summer seasons. High spring precipitation in Texas is associated with the largest annual population growth in Ohio and the earliest arrival to the summer breeding ground, as are intermediate spring temperatures in Texas. On the other hand, the timing of monarch arrivals to the summer breeding grounds is not affected by climate conditions within Ohio. Once in Ohio for summer breeding, precipitation has minimal impacts on overall abundances, whereas warmer summer temperatures are generally associated with the highest expected abundances, yet this effect is mitigated by the average seasonal temperature of each location in that the warmest sites receive no benefit of above average summer temperatures. Our results highlight the complex relationship between climate and performance for a migrating species and suggest that attempts to understand how monarchs will be affected by future climate conditions will be challenging.

Inferring animal population distributions from individual tracking data: theoretical insights and potential pitfalls

Animal population-level phenomena are often inferred from large tracking data sets obtained from only a few individuals. Two key challenges are to understand how these two scales are related, and to identify the factors that influence the extent to which small samples consisting of a few individuals can predict spatial patterns at the population scale. We used a simple spatially explicit theoretical model to explore some of the factors that affect inferences made at the population level from individual tracking data. We adopted a 'mixtures of correlated random walks' approach to simulate two discrete movement modes with different step lengths and turning angles in a hypothetical ungulate population with contrasting population sizes and sampling intensities. Movement state was assumed to be influenced by habitat type (patch or matrix) and social cues. We explored the predictive power of a tracked population subsample by regressing the space-use map generated by a few randomly chosen individuals against the map generated by the entire population (the 'true' map) for different scenarios (e.g. random and clumped habitat distributions) and parameter values. We show that the predictive power of the tracking sample varies nonlinearly and often counter-intuitively with factors such as habitat preference, the spatial context of the landscape and the importance of social interactions. We suggest that movement models coupled with individual tracking data can be used with Monte Carlo simulations to improve tracking studies by better understanding the links between detailed individual movement data and population distributions.

The Metatron: an experimental system to study dispersal and metaecosystems for terrestrial organisms

Dispersal of organisms generates gene flow between populations. Identifying factors that influence dispersal will help predict how species will cope with rapid environmental change. We developed an innovative infrastructure, the Metatron, composed of 48 interconnected patches, designed for the study of terrestrial organism movement as a model for dispersal. Corridors between patches can be flexibly open or closed. Temperature, humidity and illuminance can be independently controlled within each patch. The modularity and adaptability of the Metatron provide the opportunity for robust experimental design for the study of 'meta-systems'. We describe a pilot experiment on populations of the butterfly Pieris brassicae and the lizard Zootoca vivipara in the Metatron. Both species survived and showed both disperser and resident phenotypes. The Metatron offers the opportunity to test theoretical models in spatial ecology.

Human movement is both diffusive and directed

Understanding the influence of the built environment on human movement requires quantifying spatial structure in a general sense. Because of the difficulty of this task, studies of movement dynamics often ignore spatial heterogeneity and treat movement through journey lengths or distances alone. This study analyses public bicycle data from central London to reveal that, although journey distances, directions, and frequencies of occurrence are spatially variable, their relative spatial patterns remain largely constant, suggesting the influence of a fixed spatial template. A method is presented to describe this underlying space in terms of the relative orientation of movements toward, away from, and around locations of geographical or cultural significance. This produces two fields: one of convergence and one of divergence, which are able to accurately reconstruct the observed spatial variations in movement. These two fields also reveal categorical distinctions between shorter journeys merely serving diffusion away from significant locations, and longer journeys intentionally serving transport between spatially distinct centres of collective importance. Collective patterns of human movement are thus revealed to arise from a combination of both diffusive and directed movement, with aggregate statistics such as mean travel distances primarily determined by relative numbers of these two kinds of journeys.

Inferring ecological and behavioral drivers of African elephant movement using a linear filtering approach

Understanding the environmental factors influencing animal movements is fundamental to theoretical and applied research in the field of movement ecology. Studies relating fine-scale movement paths to spatiotemporally structured landscape data, such as vegetation productivity or human activity, are particularly lacking despite the obvious importance of such information to understanding drivers of animal movement. In part, this may be because few approaches provide the sophistication to characterize the complexity of movement behavior and relate it to diverse, varying environmental stimuli. We overcame this hurdle by applying, for the first time to an ecological question, a finite impulse-response signal-filtering approach to identify human and natural environmental drivers of movements of 13 free-ranging African elephants (Loxodonta africana) from distinct social groups collected over seven years. A minimum mean-square error (MMSE) estimation criterion allowed comparison of the predictive power of landscape and ecological model inputs. We showed that a filter combining vegetation dynamics, human and physical landscape features, and previous movement outperformed simpler filter structures, indicating the importance of both dynamic and static landscape features, as well as habit, on movement decisions taken by elephants. Elephant responses to vegetation productivity indices were not uniform in time or space, indicating that elephant foraging strategies are more complex than simply gravitation toward areas of high productivity. Predictions were most frequently inaccurate outside protected area boundaries near human settlements, suggesting that human activity disrupts typical elephant movement behavior. Successful management strategies at the human-elephant interface, therefore, are likely to be context specific and dynamic. Signal processing provides a promising approach for elucidating environmental factors that drive animal movements over large time and spatial scales.

Local orientation and the evolution of foraging: changes in decision making can eliminate evolutionary trade-offs

Information processing is a major aspect of the evolution of animal behavior. In foraging, responsiveness to local feeding opportunities can generate patterns of behavior which reflect or "recognize patterns" in the environment beyond the perception of individuals. Theory on the evolution of behavior generally neglects such opportunity-based adaptation. Using a spatial individual-based model we study the role of opportunity-based adaptation in the evolution of foraging, and how it depends on local decision making. We compare two model variants which differ in the individual decision making that can evolve (restricted and extended model), and study the evolution of simple foraging behavior in environments where food is distributed either uniformly or in patches. We find that opportunity-based adaptation and the pattern recognition it generates, plays an important role in foraging success, particularly in patchy environments where one of the main challenges is "staying in patches". In the restricted model this is achieved by genetic adaptation of move and search behavior, in light of a trade-off on within- and between-patch behavior. In the extended model this trade-off does not arise because decision making capabilities allow for differentiated behavioral patterns. As a consequence, it becomes possible for properties of movement to be specialized for detection of patches with more food, a larger scale information processing not present in the restricted model. Our results show that changes in decision making abilities can alter what kinds of pattern recognition are possible, eliminate an evolutionary trade-off and change the adaptive landscape.

Breaking functional connectivity into components: a novel approach using an individual-based model, and first outcomes

Landscape connectivity is a key factor determining the viability of populations in fragmented landscapes. Predicting 'functional connectivity', namely whether a patch or a landscape functions as connected from the perspective of a focal species, poses various challenges. First, empirical data on the movement behaviour of species is often scarce. Second, animal-landscape interactions are bound to yield complex patterns. Lastly, functional connectivity involves various components that are rarely assessed separately. We introduce the spatially explicit, individual-based model FunCon as means to distinguish between components of functional connectivity and to assess how each of them affects the sensitivity of species and communities to landscape structures. We then present the results of exploratory simulations over six landscapes of different fragmentation levels and across a range of hypothetical bird species that differ in their response to habitat edges. i) Our results demonstrate that estimations of functional connectivity depend not only on the response of species to edges (avoidance versus penetration into the matrix), the movement mode investigated (home range movements versus dispersal), and the way in which the matrix is being crossed (random walk versus gap crossing), but also on the choice of connectivity measure (in this case, the model output examined). ii) We further show a strong effect of the mortality scenario applied, indicating that movement decisions that do not fully match the mortality risks are likely to reduce connectivity and enhance sensitivity to fragmentation. iii) Despite these complexities, some consistent patterns emerged. For instance, the ranking order of landscapes in terms of functional connectivity was mostly consistent across the entire range of hypothetical species, indicating that simple landscape indices can potentially serve as valuable surrogates for functional connectivity. Yet such simplifications must be carefully evaluated in terms of the components of functional connectivity they actually predict.

Memory matters: influence from a cognitive map on animal space use

A vertebrate individual's cognitive map provides a capacity for site fidelity and long-distance returns to favorable patches. Fractal-geometrical analysis of individual space use based on collection of telemetry fixes makes it possible to verify the influence of a cognitive map on the spatial scatter of habitat use and also to what extent space use has been of a scale-specific versus a scale-free kind. This approach rests on a statistical mechanical level of system abstraction, where micro-scale details of behavioral interactions are coarse-grained to macro-scale observables like the fractal dimension of space use. In this manner, the magnitude of the fractal dimension becomes a proxy variable for distinguishing between main classes of habitat exploration and site fidelity, like memory-less (Markovian) Brownian motion and Levy walk and memory-enhanced space use like Multi-scaled Random Walk (MRW). In this paper previous analyses are extended by exploring MRW simulations under three scenarios: (1) central place foraging, (2) behavioral adaptation to resource depletion (avoidance of latest visited locations) and (3) transition from MRW towards Levy walk by narrowing memory capacity to a trailing time window. A generalized statistical-mechanical theory with the power to model cognitive map influence on individual space use will be important for statistical analyses of animal habitat preferences and the mechanics behind site fidelity and home ranges.

Consumer-resource dynamics: quantity, quality, and allocation

BACKGROUND

The dominant paradigm for modeling the complexities of interacting populations and food webs is a system of coupled ordinary differential equations in which the state of each species, population, or functional trophic group is represented by an aggregated numbers-density or biomass-density variable. Here, using the metaphysiological approach to model consumer-resource interactions, we formulate a two-state paradigm that represents each population or group in a food web in terms of both its quantity and quality.

METHODOLOGY AND PRINCIPAL FINDINGS

The formulation includes an allocation function controlling the relative proportion of extracted resources to increasing quantity versus elevating quality. Since lower quality individuals senescence more rapidly than higher quality individuals, an optimal allocation proportion exists and we derive an expression for how this proportion depends on population parameters that determine the senescence rate, the per-capita mortality rate, and the effects of these rates on the dynamics of the quality variable. We demonstrate that oscillations do not arise in our model from quantity-quality interactions alone, but require consumer-resource interactions across trophic levels that can be stabilized through judicious resource allocation strategies. Analysis and simulations provide compelling arguments for the necessity of populations to evolve quality-related dynamics in the form of maternal effects, storage or other appropriate structures. They also indicate that resource allocation switching between investments in abundance versus quality provide a powerful mechanism for promoting the stability of consumer-resource interactions in seasonally forcing environments.

CONCLUSIONS/SIGNIFICANCE

Our simulations show that physiological inefficiencies associated with this switching can be favored by selection due to the diminished exposure of inefficient consumers to strong oscillations associated with the well-known paradox of enrichment. Also our results demonstrate how allocation switching can explain observed growth patterns in experimental microbial cultures and discuss how our formulation can address questions that cannot be answered using the quantity-only paradigms that currently predominate.