Search

Predicting the spatial occurrence of wildlife is a major challenge for ecology and management. In Latin America, limited knowledge of the number and locations of vampire bat roosts precludes informed allocation of measures intended to prevent rabies spillover to humans and livestock. We inferred the spatial distribution of vampire bat roosts while accounting for observation effort and environmental effects by fitting a log Gaussian Cox process model to the locations of 563 roosts in three regions of Peru. Our model explained 45% of the variance in the observed roost distribution and identified environmental drivers of roost establishment. When correcting for uneven observation effort, our model estimated a total of 2340 roosts, indicating that undetected roosts (76%) exceed known roosts (24%) by threefold. Predicted hotspots of undetected roosts in rabies-free areas revealed high-risk areas for future viral incursions. Using the predicted roost distribution to inform a spatial model of rabies spillover to livestock identified areas with disproportionate underreporting and indicated a higher rabies burden than previously recognized. We provide a transferrable approach to infer the distribution of a mostly unobserved bat reservoir that can inform strategies to prevent the re-emergence of an important zoonosis. [ABSTRACT FROM AUTHOR]

Quantifying current and future overlap between human activities and wildlife is a core and growing aim of ecological study, spurring ever more spatial data collection and diversification of observation techniques (surveys, telemetry, citizen science, etc.).To meet this aim, data collected via multiple platforms, across different geographical and temporal regions, may need to be integrated, yet many ecologists remain unclear about the relationships between data types and therefore how they can be combined.In seabird research, these applied questions can be particularly pressing because many human activities (e.g. tidal and wind renewables, fishing, shipping, etc.) are concentrated in coastal waters, where many seabirds also aggregate, especially while breeding. In addition, seabird coloniality and density dependence present unique analytical challenges.We review the relevant literature on data integration and illustrate it with example models and data (in an accompanying R‐library and supplementary vignette), to derive methodological and quantitative guidelines for best practice in conducting joint inference for multi‐platform data. We use systematic survey data to motivate the key arguments, but also overview developments in integration with other data (e.g. telemetry tracking, citizen science, mark–recapture).We make recommendations on (a) the use of response and explanatory data, (b) the treatment of survey design and observation errors, (c) exploiting dependencies across space and time, (d) accounting for biological phenomena, such as commuting costs from the colony (i.e. accessibility) and density dependence, and (e) the choice of statistical framework.Synthesis and application. Integrated analysis of multi‐platform data turns many of the seabird‐specific challenges into opportunities for inferring habitat associations and predicting future distributions. Our review proposes practical recommendations for data collection and analysis that will allow seabird conservation to derive maximal benefits from these opportunities. [ABSTRACT FROM AUTHOR]

Organisms need access to particular habitats for their survival and reproduction. However, even if all necessary habitats are available within the broader environment, they may not all be easily reachable from the position of a single individual. Many species distribution models consider populations in environmental (or niche) space, hence overlooking this fundamental aspect of geographical accessibility. Here, we develop a formal way of thinking about habitat availability in environmental spaces by describing how limitations in accessibility can cause animals to experience a more limited or simply different mixture of habitats than those more broadly available. We develop an analytical framework for characterizing constrained habitat availability based on the statistical properties of movement and environmental autocorrelation. Using simulation experiments, we show that our general statistical representation of constrained availability is a good approximation of habitat availability for particular realizations of landscape-organism interactions. We present two applications of our approach, one to the statistical analysis of habitat preference (using step-selection functions to analyze harbor seal telemetry data) and a second that derives theoretical insights about population viability from knowledge of the underlying environment. Analytical expressions for habitat availability, such as those we develop here, can yield gains in analytical speed, biological realism, and conceptual generality by allowing us to formulate models that are habitat sensitive without needing to be spatially explicit. [ABSTRACT FROM AUTHOR]

Infectious diseases are often transmitted through local interactions. Yet, both surveillance and control measures are implemented within administrative units. Capturing local transmission processes and spatial coupling between regions from aggregate level data is therefore a technical challenge that can shed light on both theoretical questions and practical decisions. Fox rabies has been eliminated from much of Europe through oral rabies vaccination (ORV) programmes. The European Union (EU) co-finances ORV to maintain rabies freedom in EU member and border states via a cordon sanitaire. Models to capture local transmission dynamics and spatial coupling have immediate application to the planning of these ORV campaigns and to other parts of the world considering oral vaccination. We fitted a hierarchical Bayesian state-space model to data on three decades of fox rabies cases and ORV campaigns from Eastern Germany. Specifically, we find that (i) combining regional spatial coupling and heterogeneous local transmission allows us to capture regional rabies dynamics; (ii) incursions from other regions account for less than 1% of cases, but allow for re-emergence of disease; (iii) herd immunity achieved through bi-annual vaccination campaigns is short-lived due to population turnover. Together, these findings highlight the need for regular and sustained vaccination efforts and our modelling approach can be used to provide strategic guidance for ORV delivery. Moreover, we show that biological understanding can be gained from inference from partially observed data on wildlife disease. [ABSTRACT FROM AUTHOR]

The impacts of environmental predictability on the ecology and evolution of animal movement have been the subject of vigorous speculation for several decades. Recently, the swell of new biologging technologies has further stimulated their investigation. This advancing research frontier, however, still lacks conceptual unification and has so far focused little on converse effects. Populations of moving animals have ubiquitous effects on processes such as nutrient cycling and seed dispersal and may therefore shape patterns of environmental predictability. Here, we synthesise the main strands of the literature on the feedbacks between environmental predictability and animal movement and discuss how they may react to anthropogenic disruption, leading to unexpected threats for wildlife and the environment. Environmental predictability acts as a selective pressure on animal cognition and behaviour. Together, animals' cognition, movement abilities, and environmental predictability interactively determine the emergence of movement patterns. Conversely, animal movement can impact environmental predictability. This could create ecoevolutionary feedback loops, which are still very little studied. Human activities can impact environmental predictability and therefore animal movement and wildlife populations' viability. The study of the environmental predictability–animal movement interface has recently benefited from the improvement of tracking and remote sensing technologies but is lacking unification. Here we propose a unified view of this critical interface. [ABSTRACT FROM AUTHOR]

Understanding howthe spatial deployment of interventions affects elimination time horizons and potential for disease re-emergence has broad application to control programmes targeting human, animal and plant pathogens. We previously developed an epidemiological model that captures the main features of rabies spread and the impacts of vaccination based on detailed records of fox rabies in eastern Germany during the implementation of an oral rabies vaccination (ORV) programme. Here, we use simulations from this fitted model to determine the best vaccination strategy, in terms of spatial placement and timing ofORVefforts, for three epidemiological scenarios representative of current situations in Europe. We found that consecutive and comprehensive twice-yearly vaccinations across all regions rapidly controlled and eliminated rabies and that the autumn campaigns had the greater impact on increasing the probability of elimination. This appears to result fromthe need to maintain sufficient herd immunity in the face of large birth pulses, as autumn vaccinations reach susceptible juveniles and therefore a larger proportion of the population than spring vaccinations. Incomplete vaccination compromised time to elimination requiring the same or more vaccination effort to meet similar timelines. Our results have important practical implications that could inform policies for rabies containment and elimination in Europe and elsewhere. [ABSTRACT FROM AUTHOR]

The need to understand the impacts of land management for conservation, agriculture and disease prevention are driving demand for new predictive ecology approaches that can reliably forecast future changes in population size. Currently, although the link between habitat composition and animal population dynamics is undisputed, its function has not been quantified in a way that enables accurate prediction of population change in nature. Here, using 12 house sparrow colonies as a proof-of-concept, we apply recent theoretical advances to predict population growth or decline from detailed data on habitat composition and habitat selection. We show, for the first time, that statistical population models using derived covariates constructed from parametric descriptions of habitat composition and habitat selection can explain an impressive 92% of observed population variation. More importantly, they provide excellent predictive power under cross-validation, anticipating 81% of variability in population change. These models may be embedded in readily available generalized linear modelling frameworks, allowing their rapid application to field systems. Furthermore, we use optimization on our sample of sparrow colonies to demonstrate how such models, linking populations to their habitats, permit the design of practical and environmentally sound habitat manipulations for managing populations. [ABSTRACT FROM AUTHOR]

Animal aggregations occur in almost all taxa and can be strongly influential for consumer-resource dynamics and population health. Their adaptive value and underlying mechanisms are thus fundamental questions. Many animals use information about resource locations inadvertently broadcasted by other individuals through visual, acoustic, or olfactory cues. Such simple, involuntary information transfer is commonly employed in groups of social animals. However, it remains unknown whether public information use could have been the initial cause of social aggregations. Here, using agent-based modeling, in the absence of inclusive fitness benefits or direct conspecific attraction, we show that the use of ephemeral public information about resource locations can cause memory-based foragers to spontaneously and permanently aggregate into communal home ranges that take the form of movement circuits (also called traplines) along which individuals travel asynchronously. Even though experienced individuals only rely on their personal memory to inform their movement decisions, we find that the use of public information during the learning phase is very beneficial in the long term because the communal circuits are more efficient than those established by individuals that do not use public information. Our results reveal how simple, inadvertent information transfer between naïve, selfish foragers can cause the emergence of long-term aggregations, which are a prerequisite for the evolution of more complex social behaviors. They also suggest that individuals may not necessarily need to witness the entire sequences of actions performed by others to converge to the same behavioral routines. [ABSTRACT FROM AUTHOR]

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]

Although classical ecological theory (e.g., on ideal free consumers) recognizes the potential effect of population density on the spatial distribution of animals, empirical species distribution models assume that species-habitat relationships remain unchanged across a range of population sizes. Conversely, even though ecologicalmodels and experiments have demonstrated the importance of spatial heterogeneity for the rate of population change, we still have no practical method for making the connection between the makeup of real environments, the expected distribution and fitness of their occupants, and the long-termimplications of fitness for population growth. Here, we synthesize several conceptual advances into a mathematical framework using a measure of fitness to link habitat availability/selection to (density-dependent) population growth in mobile animal species. A key feature of this approach is that it distinguishes between apparent habitat suitability and the true, underlying contribution of a habitat to fitness, allowing the statistical coefficients of both to be estimated frommultiple observation instances of the species in different environments and stages of numerical growth. Hence, it leverages data from both historical population time series and snapshots of species distribution to predict population performance under environmental change. We propose this framework as a foundation for building more realistic connections between a population's use of space and its subsequent dynamics (and hence a contribution to the ongoing efforts to estimate a species' critical habitat and fundamental niche). We therefore detail its associated definitions and simplifying assumptions, because they point to the framework's future extensions. We show how the model can be fit to data on species distributions and population dynamics, using standard statistical methods, and we illustrate its application with an individual-based simulation. When contrasted with nonspatial population models, our approach is better at fitting and predicting population growth rates and carrying capacities. Our approach can be generalized to include a diverse range of biological considerations. We discuss these possible extensions and applications to real data. [ABSTRACT FROM AUTHOR]

Understanding the demographic response of free-living animal populations to different drivers is the first step towards reliable prediction of population trends., Penguins have exhibited dramatic declines in population size, and many studies have linked this to bottom-up processes altering the abundance of prey species. The effects of individual traits have been considered to a lesser extent, and top-down regulation through predation has been largely overlooked due to the difficulties in empirically measuring this at sea where it usually occurs., For 10 years (2003-2012), macaroni penguins ( Eudyptes chrysolophus) were marked with subcutaneous electronic transponder tags and re-encountered using an automated gateway system fitted at the entrance to the colony. We used multistate mark-recapture modelling to identify the different drivers influencing survival rates and a sensitivity analysis to assess their relative importance across different life stages., Survival rates were low and variable during the fledging year (mean = 0·33), increasing to much higher levels from age 1 onwards (mean = 0·89). We show that survival of macaroni penguins is driven by a combination of individual quality, top-down predation pressure and bottom-up environmental forces. The relative importance of these covariates was age specific. During the fledging year, survival rates were most sensitive to top-down predation pressure, followed by individual fledging mass, and finally bottom-up environmental effects. In contrast, birds older than 1 year showed a similar response to bottom-up environmental effects and top-down predation pressure., We infer from our results that macaroni penguins will most likely be negatively impacted by an increase in the local population size of giant petrels. Furthermore, this population is, at least in the short term, likely to be positively influenced by local warming. More broadly, our results highlight the importance of considering multiple causal effects across different life stages when examining the survival rates of seabirds. [ABSTRACT FROM AUTHOR]

Declines in large vertebrate populations are widespread but difficult to detect from monitoring data and hard to understand due to a multiplicity of plausible biological explanations. In parts of Scotland, harbour seals ( Phoca vitulina) have been in decline for 10 years. To evaluate the contributions of different proximate causes (survival, fecundity, observation artefacts) to this decline, we collated behavioural, demographic and population data from one intensively studied population in part of the Moray Firth (north-east Scotland). To these, we fit a state-space model comprising age-structured dynamics and a detailed account of observation errors. After accounting for culling (estimated by our model as 14 % of total mortality), the main driver of the historical population decline was a decreasing trend in survival of young individuals combined with (previously unrecognised) low levels of pupping success. In more recent years, the model provides evidence for considerable increases in breeding success and consistently high levels of adult survival. However, breeding success remains the most volatile demographic component of the population. Forecasts from the model indicate a slow population recovery, providing cautious support for recent management measures. Such investigations of the proximate causes of population change (survival, fecundity and observation errors) provide valuable short-term support for the management of population declines, helping to focus future data collection on those ultimate causal mechanisms that are not excluded by the demographic evidence. The contribution of specific ultimate drivers (e.g. shooting mortality or competitors) can also be quantified by including them as covariates to survival or fecundity. [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]

Recent advances in animal tracking and telemetry technology have allowed the collection of location data at an ever-increasing rate and accuracy, and these advances have been accompanied by the development of new methods of data analysis for portraying space use, home ranges and utilization distributions. New statistical approaches include data-intensive techniques such as kriging and nonlinear generalized regression models for habitat use. In addition, mechanistic home-range models, derived from models of animal movement behaviour, promise to offer new insights into how home ranges emerge as the result of specific patterns of movements by individuals in response to their environment. Traditional methods such as kernel density estimators are likely to remain popular because of their ease of use. Large datasets make it possible to apply these methods over relatively short periods of time such as weeks or months, and these estimates may be analysed using mixed effects models, offering another approach to studying temporal variation in space-use patterns. Although new technologies open new avenues in ecological research, our knowledge of why animals use space in the ways we observe will only advance by researchers using these new technologies and asking new and innovative questions about the empirical patterns they observe. [ABSTRACT FROM AUTHOR]

With the advent of new technologies, animal locations are being collected at ever finer spatiotemporal scales. We review analytical methods for dealing with correlated data in the context of resource selection, including post hoc variance inflation techniques, 'two-stage' approaches based on models fit to each individual, generalized estimating equations and hierarchical mixed-effects models. These methods are applicable to a wide range of correlated data problems, but can be difficult to apply and remain especially challenging for use-availability sampling designs because the correlation structure for combinations of used and available points are not likely to follow common parametric forms. We also review emerging approaches to studying habitat selection that use finescale temporal data to arrive at biologically based definitions of available habitat, while naturally accounting for autocorrelation by modelling animal movement between telemetry locations. Sophisticated analyses that explicitly model correlation rather than consider it a nuisance, like mixed effects and state-space models, offer potentially novel insights into the process of resource selection, but additional work is needed to make them more generally applicable to large datasets based on the use-availability designs. Until then, variance inflation techniques and two-stage approaches should offer pragmatic and flexible approaches to modelling correlated data. [ABSTRACT FROM AUTHOR]

There are two postulated causes for the observed periodic fluctuations (cycles) in red grouse (Lagopus lagopus scoticus). The first involves interaction with the parasitic nematode Trichostrongylus tenuis. The second invokes delayed regulation through the effect of male aggressiveness on territoriality. Empirical evidence exists to support both hypotheses, and each hypothesis has been modeled deterministically. However, little effort has gone into looking at the combined effects of the two mechanisms or formally fitting the corresponding models to field data. Here we present a model for red grouse dynamics that includes both parasites and territoriality. To explore the single and combined hypotheses, we specify three versions of this model and fit them to data using Bayesian state-space modeling, a method that allows statistical inference to be performed on mechanistic models such as ours. Output from the three models is then examined to determine their goodness of fit and the biological plausibility of the parameter values required by each to fit the population data. While all three models are capable of emulating the observed cyclic dynamics, only the model including both aggression and parasites does so under consistently realistic parameter values, providing theoretical support for the idea that both mechanisms shape red grouse cycles. [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]

1. Many far-ranging bird and mammal species aggregate in colonies to breed, and most individuals remain faithful to one colony. Here, we use modelling to explore the consequences of this site fidelity for the metapopulation dynamics of such species. 2. We develop a spatially explicit model of the annual transfer process between colonies. We apply it to different spatial arrangements of 20 identical colonies and thus demonstrate that connectivity alone can, in the short term, give rise to heterogeneity in colony size. 3. We place the annual transfer model within a state-structured population model and examine the consequences of local and global density dependence for long-term dynamics. For each scenario, we investigate the implications of the strength of site fidelity, the cost of migration and the population's intrinsic growth rate. 4. Our results suggest that, under local density dependence, site fidelity slows down the colonization process and can temporarily trap the entire population in a subset of the available potential colonies. When site fidelity is strong, the metapopulation follows a step-like trajectory. Population growth occurs only rarely because individuals must overcome their site-fidelity to found new colonies. Even though this effect is temporary, it renders the entire metapopulation vulnerable to rare catastrophic collapses. 5. Under global density dependence, site fidelity imposes competition between colonies for the limiting resource. Stochastic events lead to the dominance of certain colonies and the temporary extinction of others. If site fidelity is strong, it can permanently prevent the metapopulation from occupying all available potential colonies. 6. We conclude that, irrespective of the mechanism of population regulation, colonially breeding species that show strong site fidelity are likely to occupy only a portion of the breeding habitat available to them. Journal of Animal Ecology (2005) doi: 10.1111/j.1365-2656.2005.00970.x [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]

In the UK, resolving conflicts between the conservation of grey seals, the management of fish stocks and marine exploitation requires knowledge of the seals' use of space. We present a map of grey seal usage around the British Isles based on satellite telemetry data from adult animals and haul-out survey data. Our approach combined modelling and interpolation. To model the seals' association with particular coastal sites (the haul-outs), we divided the population into subpopulations associated with 24 haul-out groups. Haul-out-specific maps of accessibility were used to supervise usage estimation from satellite telemetry. The mean and variance of seal numbers at each haul-out group were obtained from haul-out counts. The aggregate map of usage for the entire population was produced by adding together the haul-out-specific usage maps, weighted by mean number of animals using that haul-out. Seal usage was primarily concentrated (i) off the northern coasts of the British Isles, (ii) closer to the coast than might be expected purely on the basis of accessibility from the haul-outs and (iii) in a limited number of marine hot-spots. Although our results currently represent the best estimate of how grey seals use the marine environment around Britain, they are neither definitive nor equally precise for all haul-outs. Further data collection should focus in the south-west of the British isles and aerial counts should be repeated for all haul-outs. This work provides environmental managers with current estimates of grey seal usage and describes a methodology for maximizing data efficiency. Our results could guide government departments in licensing marine exploitation by the oil industry, in estimating grey seal predation pressure on vulnerable or economically important prey and in delineating marine special areas of conservation(SAC).Our finding that grey seal usage is characterized by a limited number of hot-spots means that the species is particularly suited to localized conservation efforts. [ABSTRACT FROM AUTHOR]

Part II. Examines a simple model of interaction between population density and aggressiveness in Lagopus lagopus scoticus. Changes in aggressiveness as a function of population density; Kinship hypothesis for red grouse cycles; Population dynamics.

The analysis of telemetry data obtained from tagged animals often requires that a smooth surface of spatial usage is fitted to the observations. Well-established statistical techniques for doing this, such as kernel smoothing (KS), are based on asymptotic arguments that guarantee the convergence of their estimates to the truth with increasing sample size. Often, in addition to telemetry data, ecologists have access to a wealth of information relating to the animals’ distribution and movement. This additional information is potentially useful for the estimation of spatial usage but currently remains unused by existing methods. In this paper, I outline and begin the validation of model-supervised kernel smoothing (MSKS), a modification of KS that uses such information to supervise surface-fitting to telemetry data. MSKS initially requires an ad-hoc synthesis of all the available information, excluding telemetry, into an auxiliary usage surface (the model). This is then combined with the kernel-smoothed telemetry data into a hybrid surface that is the weighted average of the two. Automatic selection of the smoothing coefficient and the weight associated with the model is done by means of likelihood cross-validation. I examine the performance of MSKS first, by extensive, numerical exploration on simulated data in one-dimensional space and second, on two-dimensional data obtained from an individual-based simulation of a central-place forager. The results for different models and sample sizes indicate that MSKS has three important properties. Firstly, it generally outperforms KS by an extent that depends on the quality of the auxiliary model. Secondly, when the auxiliary model is not informative, MSKS automatically reverts to a similar output as KS. Finally, in practical terms, MSKS is easy to implement and adds little to the computational requirements already made by KS methods. I illustrate the application of MSKS and further validate its performance on... [ABSTRACT FROM AUTHOR]

Heterogeneous usage of space by individual animals or animal populations is partly due to their preference for particular resources that are, themselves, heterogeneously distributed. When all points in the environment are equally accessible, a direct relationship between usage and preference can be assumed. However, when accessibility is restricted, spatial variations in usage can no longer be attributed entirely to preference. In such cases, it is necessary to control for the effects of accessibility on observed usage before conclusions about preference can be drawn. In this paper, I develop a modelling framework that treats the use of space by animals as a joint function of preference and accessibility. I specify a null version of the framework that assumes no preference and propose that its output can be used to control for the effect of accessibility on the observed, spatial distribution of usage. I briefly discuss how the framework can subsequently be used to provide insights about the animals’ preference for different resources and types of movement, and to predict usage in areas where no usage data exist. I explore the properties of the methodology using data from a population of simulated animals and present the first results of its application to a sub-set of the British population of grey seals (Halichoerus grypus). [Copyright &y& Elsevier]

The kin facilitation hypothesis attributes red grouse population cycles to the formation of spatial assemblages comprising related territory holders, and to a reduction in aggressiveness within such family clusters. This well-documented process is hypothesised to increase annual recruitment into the territorial population and the resulting increase in density leads to increased aggressiveness within the population. After some years, the cumulative increase in density and aggressiveness is assumed to lead to the breakdown of the family clusters, inflation of territorial requirements and consequent population decline. The territorial dynamics of a single family cluster constitute an important and little investigated part of the hypothesis. We develop a simple deterministic model to examine the effects of crowding and family size on the formation of a single family cluster. Analysis of two versions of the model, one containing no territory sharing between neighbouring relatives and one containing a territory-sharing response function developed elsewhere, indicates that a continuous increase in crowding has a discontinuous effect on the ability of the cluster to form. In the case of the model containing territory sharing this change is irreversible as, due to the occurrence of multiple equilibria, solutions are sensitive to initial conditions. We discuss the implications of this result for the kin facilitation hypothesis. [ABSTRACT FROM AUTHOR]

In this article author discusses the research "State-space modeling reveals proximate causes of harbour seal population declines" by J. Matthiopoulos and others published within a 2014 issue. The topics discussed include demographic impact of shooting on harbour seal populations, drivers affecting different harbour seal population and criticism of the auhtor's model for seal population.

Zoonotic disease dynamics in wildlife hosts are rarely quantified at macroecological scales due to the lack of systematic surveys. Non-human primates (NHPs) host Plasmodium knowlesi, a zoonotic malaria of public health concern and the main barrier to malaria elimination in Southeast Asia. Understanding of regional P. knowlesi infection dynamics in wildlife is limited. Here, we systematically assemble reports of NHP P. knowlesi and investigate geographic determinants of prevalence in reservoir species. Meta-analysis of 6322 NHPs from 148 sites reveals that prevalence is heterogeneous across Southeast Asia, with low overall prevalence and high estimates for Malaysian Borneo. We find that regions exhibiting higher prevalence in NHPs overlap with human infection hotspots. In wildlife and humans, parasite transmission is linked to land conversion and fragmentation. By assembling remote sensing data and fitting statistical models to prevalence at multiple spatial scales, we identify novel relationships between P. knowlesi in NHPs and forest fragmentation. This suggests that higher prevalence may be contingent on habitat complexity, which would begin to explain observed geographic variation in parasite burden. These findings address critical gaps in understanding regional P. knowlesi epidemiology and indicate that prevalence in simian reservoirs may be a key spatial driver of human spillover risk. [ABSTRACT FROM AUTHOR]

In ecology, the observation process (how we collect data) can be as complex as the biological process we are investigating. Failure to account for complex observation processes leads to uncertainty, biased inference and poor predictions, resulting in misleading research results. Often, field scientists are best placed to describe observation problems that occur but are excluded from discussions about how to tackle these problems statistically. Statisticians are often unaware of the nuances of observation processes leading to the problems being ignored, or tackled on a case-by-case basis. We propose a typology of observation problems and inferential solutions, hence facilitating the linkages between field protocols and statistical treatments. Advances in statistics mean that it is now possible to tackle increasingly sophisticated observation processes. The intricacies and ambitious scale of modern data collection techniques mean that this is now essential. Methodological research to make inference about the biological process while accounting for the observation process has expanded dramatically, but solutions are often presented in field-specific terms, limiting our ability to identify commonalities between methods. We suggest a typology of observation processes that could improve translation between fields and aid methodological synthesis. We propose the LIES framework (defining observation processes in terms of issues of Latency, Identifiability, Effort and Scale) and illustrate its use with both simple examples and more complex case studies. [ABSTRACT FROM AUTHOR]

Disease outbreaks can drastically disturb the environment of surviving animals, but the behavioural, ecological, and epidemiological consequences of disease-driven disturbance are poorly understood. Here, we show that an outbreak of High Pathogenicity Avian Influenza Virus (HPAIV) coincided with unprecedented short-term behavioural changes in Northern gannets (Morus bassanus). Breeding gannets show characteristically strong fidelity to their nest sites and foraging areas (2015–2019; n = 120), but during the 2022 HPAIV outbreak, GPS-tagged gannets instigated long-distance movements beyond well-documented previous ranges and the first ever recorded visits of GPS-tagged adults to other gannet breeding colonies. Our findings suggest that the HPAIV outbreak triggered changes in space use patterns of exposed individuals that amplified the epidemiological connectivity among colonies and may generate super-spreader events that accelerate disease transmission across the metapopulation. Such self-propagating transmission from and towards high density animal aggregations may explain the unexpectedly rapid pan-European spread of HPAIV in the gannet. [ABSTRACT FROM AUTHOR]

Understanding how growth and reproduction will adapt to changing environmental conditions is a fundamental question in evolutionary ecology, but predicting the responses of specific taxa is challenging. Analyses of the physiological effects of climate change upon life history evolution rarely consider alternative hypothesized mechanisms, such as size‐dependent foraging and the risk of predation, simultaneously shaping optimal growth patterns. To test for interactions between these mechanisms, we embedded a state‐dependent energetic model in an ecosystem size‐spectrum to ask whether prey availability (foraging) and risk of predation experienced by individual fish can explain observed diversity in life histories of fishes. We found that asymptotic growth emerged from size‐based foraging and reproductive and mortality patterns in the context of ecosystem food web interactions. While more productive ecosystems led to larger body sizes, the effects of temperature on metabolic costs had only small effects on size. To validate our model, we ran it for abiotic scenarios corresponding to the ecological lifestyles of three tuna species, considering environments that included seasonal variation in temperature. We successfully predicted realistic patterns of growth, reproduction, and mortality of all three tuna species. We found that individuals grew larger when environmental conditions varied seasonally, and spawning was restricted to part of the year (corresponding to their migration from temperate to tropical waters). Growing larger was advantageous because foraging and spawning opportunities were seasonally constrained. This mechanism could explain the evolution of gigantism in temperate tunas. Our approach addresses variation in food availability and individual risk as well as metabolic processes and offers a promising approach to understand fish life‐history responses to changing ocean conditions. [ABSTRACT FROM AUTHOR]

Agricultural intensification is leading to conversion of cocoa agroforestry towards monocultures across the tropics. In the context of cocoa agriculture, arthropods provide a range of ecosystem services and dis‐services. Arthropod pests (e.g., mirids and mealybugs) can cause major damage to crops, whilst pollinators and natural enemies (e.g., predatory insects and parasitoids) have the potential to enhance agricultural yields. Understanding how intensification of cocoa farming affects different arthropod groups is therefore important in maximising the abundance of beneficial arthropod taxa and reducing pest burdens. However, little is known about the influences of agricultural intensification on tropical arthropod communities, especially in Africa, where ~70% of the world's cocoa is produced.Most research on arthropod communities considers data from different sampling methods separately, as proxies of abundance; whilst these proxies can be informative, estimating true abundance enables direct comparison between arthropod taxa, and therefore the study of community dynamics. Here, we develop a Bayesian hierarchical model that integrates data from three common arthropod survey techniques to estimate population size of arthropod orders and to investigate how arthropod community composition responds to farm shade cover (an indicator of management intensity).Our results show that eight of 11 arthropod taxa responded to farm shade cover; importantly, brown capsids (the primary pest of cocoa in Africa), Coleoptera pests and Hemiptera pests decreased with increasing farm shade cover, whilst Araneae (natural enemies) and Diptera (potential pollinators) were more abundant in shady farms.Synthesis and applications. To achieve lower pest burdens and higher abundances of potential pollinators and natural enemies, African cocoa farms should maintain a dense canopy of shade trees. The current shift towards high‐intensity cocoa farming in Africa could result in long‐term losses due to pest infestations and loss of arthropod‐mediated ecosystem services. [ABSTRACT FROM AUTHOR]

Forecasting productivity and stress across an ecosystem is complicated by the multiple interactions between competing species, the unknown levels of intra‐ and interspecific trait plasticity, and the dependencies between those traits within individuals. Integrating these features into a trait‐based quantitative framework requires a conceptual and quantitative synthesis of how multiple species and their functional traits interact and respond to changing environments, a challenge known in community ecology as the "fourth‐corner problem." We propose such a novel synthesis, implemented as an integrated Bayesian hierarchical model. This allows us to (1) simultaneously model trait–trait and trait–environment relationships by explicitly accounting for both intra‐ and interspecific trait variabilities in a single analysis using all available data types, (2) quantify the strength of the trait–environment relationships, (3) identify trade‐offs between multiple traits in multiple species, and (4) faithfully propagate our modeling uncertainties when making species‐specific and community‐wide trait predictions, reducing false confidence in our spatial prediction results. We apply this integrated approach to the world's largest mangrove forest, the Sundarbans, a sentinel ecosystem impacted simultaneously by both climate change and multiple types of human exploitation. The Sundarbans presents extensive variability in environmental variables, such as salinity and siltation, driven by changing seawater levels from the south and freshwater damming from the north. We find that tree species growing under stress have a typical functional response to the environmental drivers with inter‐specific variability around this average, and the amount of variability is further contingent upon the nature and magnitude of the environmental drivers. Our model captures the retreat in traits related to resource acquisition and a plastic enhancement of traits related to resource conservation, both clear indications of stress. We predict that, if historical increases in salinity and siltation are maintained, one‐third of whole‐ecosystem productivity will be lost by 2050. Our integrated modeling approach bridges community and ecosystem ecology through simultaneously modeling trait–environment correlations and trait–trait trade‐offs at organismal, community, and ecosystem levels; provides a generalizable foundation for powerful modeling of trait‐environment linkages under changing environments to predict their consequences on ecosystem functioning and services; and is readily applicable across the Earth's ecosystems. [ABSTRACT FROM AUTHOR]

Small petrels are the most abundant seabirds in the Southern Ocean. However, because they breed in burrows on remote and often densely vegetated islands, their colony sizes and conservation status remain poorly known. To estimate the abundance of these species on Bird Island in the Falkland archipelago, we systematically surveyed their breeding burrow density and occupancy across this near-pristine tussac (Poa flabellata)-covered island. By modelling burrow density as functions of topography and Sentinel 2 satellite-derived Normalised Difference Vegetation Index data, we inferred habitat associations and predicted burrow abundance of the commonest species—Thin-billed Prions (Pachyptila belcheri) and Wilson's Storm-petrels (Oceanites oceanicus). We estimate that there are 631,000 Thin-billed Prion burrows on the island (95% CI 496,000–904,000 burrows). Assuming that burrow occupancy lies between 12 and 97%, this equates to around 76,000–612,000 breeding pairs, making Bird Island the second or third largest P. belcheri colony in the world, holding approximately 3–27% of the species' breeding population. We estimate that 8200–9800 (95% CI 5,200–18,300 pairs) pairs of Wilson's Storm-petrels also breed on the island. Notably, the latter burrowed predominantly under and within tussac pedestals, whereas they are usually assumed to breed in rock cavities. Thin-billed Prions are declining in the Kerguelen archipelago, but their population trends in the Falklands are unknown. Given the wide confidence intervals around our own and other population estimates for these cryptic species, we recommend that their populations should be monitored regularly, at multiple sites. [ABSTRACT FROM AUTHOR]

Background: The malaria vector Anopheles funestus is increasingly recognized as a dominant vector of residual transmission in many African settings. Efforts to better understand its biology and control are significantly impeded by the difficulties of colonizing it under laboratory conditions. To identify key bottlenecks in colonization, this study compared the development and fitness characteristics of wild An. funestus from Tanzania (FUTAZ) and their F1 offspring during colonization attempts. The demography and reproductive success of wild FUTAZ offspring were compared to that of individuals from one of the only An. funestus strains that has been successfully colonized (FUMOZ, from Mozambique) under similar laboratory conditions. Methods: Wild An. funestus (FUTAZ) were collected from three Tanzanian villages and maintained inside an insectary at 70–85% RH, 25–27 °C and 12 h:12 h photoperiod. Eggs from these females were used to establish three replicate F1 laboratory generations. Larval development, survival, fecundity, mating success, percentage pupation and wing length were measured in the F1 -FUTAZ offspring and compared with wild FUTAZ and FUMOZ mosquitoes. Results: Wild FUTAZ laid fewer eggs (64.1; 95% CI [63.2, 65.0]) than FUMOZ females (76.1; 95% CI [73.3, 79.1]). Survival of F1-FUTAZ larvae under laboratory conditions was low, with an egg-to-pupae conversion rate of only 5.9% compared to 27.4% in FUMOZ. The median lifespan of F1-FUTAZ females (32 days) and males (33 days) was lower than FUMOZ (52 and 49 for females and males respectively). The proportion of female F1-FUTAZ inseminated under laboratory conditions (9%) was considerably lower than either FUMOZ (72%) or wild-caught FUTAZ females (92%). This resulted in nearly zero viable F2-FUTAZ eggs produced. Wild FUTAZ wings appear to be larger compared to the lab reared F1-FUTAZ and FUMOZ. Conclusions: This study indicates that poor larval survival, mating success, low fecundity and shorter survival under laboratory conditions all contribute to difficulties in colonizing of An. funestus. Future studies should focus on enhancing these aspects of An. funestus fitness in the laboratory, with the biggest barrier likely to be poor mating. [ABSTRACT FROM AUTHOR]

Ideally, the data used for robust spatial prediction of disease distribution should be both high-resolution and spatially expansive. However, such in-depth and geographically broad data are rarely available in practice. Instead, researchers usually acquire either detailed epidemiological data with high resolution at a small number of active sampling sites, or more broad-ranging but less precise data from passive case surveillance. We propose a novel inferential framework, capable of simultaneously drawing insights from both passive and active data types. We developed a Bayesian latent point process approach, combining active data collection in a limited set of points, where in-depth covariates are measured, with passive case detection, where error-prone, large-scale disease data are accompanied only by coarse or remotely-sensed covariate layers. Using the example of malaria, we tested our method's efficiency under several hypothetical scenarios of reported incidence in different combinations of imperfect detection and spatial complexity of the environmental variables. We provide a simple solution to a widespread problem in spatial epidemiology, combining latent process modelling and spatially autoregressive modelling. By using active sampling and passive case detection in a complementary way, we achieved the best-of-both-worlds, in effect, a formal calibration of spatially extensive, error-prone data by localised, high-quality data. [ABSTRACT FROM AUTHOR]

Density‐dependent feedback is recognized as important regulatory mechanisms of population size. Considering the spatial scales over which such feedback operates has advanced our theoretical understanding of metapopulation dynamics. Yet, metapopulation models are rarely fit to time‐series data and tend to omit details of the natural history and behavior of long‐lived, highly mobile species such as colonial mammals and birds. Seabird metapopulations consist of breeding colonies that are connected across large spatial scales, within a heterogeneous marine environment that is increasingly affected by anthropogenic disturbance. Currently, we know little about the strength and spatial scale of density‐dependent regulation and connectivity between colonies. Thus, many important seabird conservation and management decisions rely on outdated assumptions of closed populations that lack density‐dependent regulation. We investigated metapopulation dynamics and connectivity in an exemplar seabird species, the Northern gannet (Morus bassanus), using more than a century of census data of breeding colonies distributed across the Northeast Atlantic. We developed and fitted these data to a novel hierarchical Bayesian state‐space model, to compare increasingly complex scenarios of metapopulation regulation through lagged, local, regional, and global density dependence, as well as different mechanisms for immigration. Models with conspecific attraction fit the data better than the equipartitioning of immigrants. Considering local and regional density dependence jointly improved model fit slightly, but importantly, future colony size projections based on different mechanistic regulatory scenarios varied widely: a model with local and regional dynamics estimated a lower metapopulation capacity (645,655 Apparently Occupied Site [AOS]) and consequently higher present saturation (63%) than a model with local density dependence (1,367,352 AOS, 34%). Our findings suggest that metapopulation regulation in the gannet is more complex than traditionally assumed, and highlight the importance of using models that consider colony connectivity and regional dynamics for conservation management applications guided by precautionary principles. Our study advances our understanding of metapopulation dynamics in long‐lived colonial species and our approach provides a template for the development of metapopulation models for colonially living birds and mammals. [ABSTRACT FROM AUTHOR]

Malaria transmission has been substantially reduced across Africa through the distribution of long-lasting insecticidal nets (LLINs). However, the emergence of insecticide resistance within mosquito vectors risks jeopardizing the future efficacy of this control strategy. The severity of this threat is uncertain because the consequences of resistance for mosquito fitness are poorly understood: while resistant mosquitoes are no longer immediately killed upon contact with LLINs, their transmission potential may be curtailed because of longer-term fitness costs that persist beyond the first 24 h after exposure. Here, we used a Bayesian state-space model to quantify the immediate (within 24 h of exposure) and delayed (>24 h after exposure) impact of insecticides on daily survival and malaria transmission potential of moderately and highly resistant laboratory populations of the major African malaria vector Anopheles gambiae. Contact with LLINs reduced the immediate survival of moderately and highly resistant An. gambiae strains by 60-100% and 3-61%, respectively, and delayed mortality impacts occurring beyond the first 24 h after exposure further reduced their overall life spans by nearly one-half. In total, insecticide exposure was predicted to reduce the lifetime malaria transmission potential of insecticide-resistant vectors by two-thirds, with delayed effects accounting for at least one-half of this reduction. The existence of substantial, previously unreported, delayed mortality effects within highly resistant malaria vectors following exposure to insecticides does not diminish the threat of growing resistance, but posits an explanation for the apparent paradox of continued LLIN effectiveness in the presence of high insecticide resistance. [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]

Background: Improved methods for sampling outdoor-biting mosquitoes are urgently needed to improve surveillance of vector-borne diseases. Such tools could potentially replace the human landing catch (HLC), which, despite being the most direct option for measuring human exposures, raises significant ethical and logistical concerns. Several alternatives are under development, but detailed evaluation still requires common frameworks for calibration relative to HLC. The aim of this study was to develop and validate a statistical framework for predicting human-biting rates from different exposure-free alternatives. Methods: We obtained mosquito abundance data (Anopheles arabiensis, Anopheles funestus and Culex spp.) from a year-long Tanzanian study comparing six outdoor traps [Suna Trap (SUN), BG Sentinel (BGS), M-Trap (MTR), M-Trap + CDC (MTRC), Ifakara Tent Trap-C (ITT-C) and Mosquito Magnet-X Trap (MMX)] and HLC. Generalised linear models were developed within a Bayesian framework to investigate associations between the traps and HLC, taking intra- and inter-specific density dependence into account. The best model was used to create a calibration tool for predicting HLC-equivalents. Results: For An. arabiensis, SUN catches had the strongest correlation with HLC (R2 = 19.4), followed by BGS (R2 = 17.2) and MTRC (R2 = 13.1) catches. The least correlated catch was MMX (R2 = 2.5). For An. funestus, BGS had the strongest correlation with the HLC (R2 = 53.4), followed by MTRC (R2 = 37.4) and MTR (R2 = 37.4). For Culex mosquitoes, the traps most highly correlated with the HLC were MTR (R2 = 45.4) and MTRC (R2 = 44.2). Density dependence, both between and within species, influenced the performance of only BGS traps. An interactive Shiny App calibration tool was developed for this and similar applications. Conclusion: We successfully developed a calibration tool to assess the performance of different traps for assessing outdoor-biting risk, and established a valuable framework for estimating human exposures based on the trap catches. The performance of candidate traps varied between mosquito taxa; thus, there was no single optimum. Although all the traps tested underestimated the HLC-derived exposures, it was possible to mathematically define their representativeness of the true biting risk, with or without density dependence. The results of this study emphasise the need to aim for a consistent and representative sampling approach, as opposed to simply seeking traps that catch the most mosquitoes. [ABSTRACT FROM AUTHOR]

Investigation of activity budgets in relation to seasonal, intrinsic (age, sex) and extrinsic (time of day, spatial) covariates enables an understanding of how such covariates shape behavioural strategies. However, conducting such investigations in the wild is challenging, because of the required large sample size of individuals across the annual cycle, and difficulties in categorising behavioural states and analysing the resulting individual-referenced and serially correlated data. In this study, from telemetry tags deployed on 63 grey seals Halichoerus grypus and 126 harbour seals Phoca vitulina we used behavioural data, and movement data within a Bayesian state-space model (SSM), to define population-level activity budgets around Britain. Using generalised estimating equations (GEEs) we then examined how time spent in four states (resting on land (hauled out), resting at sea, foraging and travelling) was influenced by seasonal, intrinsic and extrinsic covariates. We present and discuss the following key findings. 1) We found no evidence that regional variation in foraging effort was linked to regional population trajectories in harbour seals. 2) Grey seals demonstrated sex-specific seasonal differences in their activity budgets, independent from those related to reproductive costs. 3) In these sympatric species there was evidence of temporal separation in time hauled out, but not in time foraging. 4) In both species, time spent resting at sea was separated into inshore (associated with tidal haul out availability) and offshore areas. Time spent resting at sea and on land was interchangeable to some extent, suggesting a degree of overlap in their functionality. This may result in a relaxation of the constraints associated with a central place foraging strategy. More generally, we demonstrate how a large dataset, incorporating differing tag parameters, can be analysed to define activity budgets and subsequently address important ecological questions. [ABSTRACT FROM AUTHOR]

ABSTRACT In France, the first marine national park was set up in 2007 in the Iroise Sea, western Brittany, encompassing several Special Areas of Conservation (SAC). The park is resident to 1 of only 3 gray seal colonies in France. The at-sea habitat use of gray seals in the marine park is poorly understood and direct interactions with human activities are expected. We investigated habitat selection of gray seals within the marine park to provide spatial planning recommendations to local conservation management. We obtained data from 19 seals tracked between 2010 and 2013. We used generalized linear models to investigate their habitat selection using distance to haulout, distance to shore, bathymetry, tidal current, and sediment type as explanatory variables. The tracked individuals spent 67% of their time within the boundaries of the marine park. We investigated return trips to focus on habitat selection within the Iroise Sea. The deviance explained by the final model was 76%. Distance to haulout explained 47% of the deviance (seals selected short distances), bathymetry explained 40% (with bimodal selection for waters shallower than 50 m or deeper than 150 m), distance to shore explained 6% (selected distances less than 40 km), current explained 5% (selected weak currents), and sediment explained 2% (selected rocky areas). Habitat selection was concentrated in shallow waters located in the main archipelago of the marine park. The map of selected habitat will help identify potential conflicts of space use between seals and human activities such as fishing and leisure activities. This study has an important ecological and management value. It is the first of its kind in a marine park in France, and one of the few studies of gray seals at the periphery of their range. © 2015 The Wildlife Society. [ABSTRACT FROM AUTHOR]

The ability of individual animals to balance their energy budgets throughout the annual cycle is important for their survival, reproduction and population dynamics. However, the annual cycles of many wild, mobile animals are difficult to observe and our understanding of how individuals balance their energy budgets throughout the year therefore remains poor.We developed a hierarchical Bayesian state‐space model to investigate how key components of animal energy budgets (namely individual energy gain and storage) varied in space and time. Our model used biologger‐derived estimates of time‐activity budgets, locations and energy expenditure to infer year‐round time series of energy income and reserves. The model accounted for seasonality in environmental drivers such as sea surface temperature and daylength, allowing us to identify times and locations of high energy gain.Our study system was a population of common guillemots Uria aalge breeding at a western North Sea colony. These seabirds manage their energy budgets by adjusting their behaviour and accumulating fat reserves. However, typically during severe weather conditions, birds can experience an energy deficit over a sustained period, leading to starvation and large‐scale mortality events.We show that guillemot energy gain varied in both time and space. Estimates of guillemot body mass varied throughout the annual cycle and birds periodically experienced losses in mass. Mass losses were likely to have either been adaptive, or due to energetic bottlenecks, the latter leading to increased susceptibility to mortality. Guillemots tended to be lighter towards the edge of their spatial distribution.We describe a framework that combines biologging data, time‐activity budget analysis and Bayesian state‐space modelling to identify times and locations of high energetic reward or potential energetic bottlenecks in a wild animal population. Our approach can be extended to address ecological and conservation‐driven questions that were previously unanswerable due to logistical complexities in collecting data on wild, mobile animals across full annual cycles. Read the free Plain Language Summary for this article on the Journal blog. [ABSTRACT FROM AUTHOR]

Background: It is often assumed that the population dynamics of the malaria vector Anopheles funestus, its role in malaria transmission and the way it responds to interventions are similar to the more elaborately characterized Anopheles gambiae. However, An. funestus has several unique ecological features that could generate distinct transmission dynamics and responsiveness to interventions. The objectives of this work were to develop a model which will: (1) reconstruct the population dynamics, survival, and fecundity of wild An. funestus populations in southern Tanzania, (2) quantify impacts of density dependence on the dynamics, and (3) assess seasonal fluctuations in An. funestus demography. Through quantifying the population dynamics of An. funestus, this model will enable analysis of how their stability and response to interventions may differ from that of An. gambiae sensu lato. Methods: A Bayesian State Space Model (SSM) based on mosquito life history was fit to time series data on the abundance of female An. funestus sensu stricto collected over 2 years in southern Tanzania. Prior values of fitness and demography were incorporated from empirical data on larval development, adult survival and fecundity from laboratory-reared first generation progeny of wild caught An. funestus. The model was structured to allow larval and adult fitness traits to vary seasonally in response to environmental covariates (i.e. temperature and rainfall), and for density dependency in larvae. The effects of density dependence and seasonality were measured through counterfactual examination of model fit with or without these covariates. Results: The model accurately reconstructed the seasonal population dynamics of An. funestus and generated biologically-plausible values of their survival larval, development and fecundity in the wild. This model suggests that An. funestus survival and fecundity annual pattern was highly variable across the year, but did not show consistent seasonal trends either rainfall or temperature. While the model fit was somewhat improved by inclusion of density dependence, this was a relatively minor effect and suggests that this process is not as important for An. funestus as it is for An. gambiae populations. Conclusion: The model's ability to accurately reconstruct the dynamics and demography of An. funestus could potentially be useful in simulating the response of these populations to vector control techniques deployed separately or in combination. The observed and simulated dynamics also suggests that An. funestus could be playing a role in year-round malaria transmission, with any apparent seasonality attributed to other vector species. [ABSTRACT FROM AUTHOR]

The software routines for data sampling and processing that are implemented on-board telemetry devices (tags) called Conductivity-Temperature-Depth Satellite Relay Data Loggers (CTD-SRDLs) enable the simultaneous collection of biological and in-situ environmental data by animal-platforms over periods of weeks to months, despite severe energy and bandwidth limitations imposed by their relatively small size. This extended operational lifetime is made possible by the use of software protocols on-board the tags that manage sensors, data collection, storage, compression and transmission to ensure that the most useful data are sent at appropriate resolution while minimizing redundancy. While tag software is tailored to the particular species under study and the questions being addressed with a given field deployment, the philosophy behind Sea Mammal Research Unit Instrumentation Group (SMRU-IG) software protocols is to adopt a general set of principles to achieve the best results within the energy and bandwidth constraints. Here, we discuss these and review the general protocol that is used to simultaneously collect information on geographical movements, diving behaviour and in-situ oceanographic information from marine mammals. [ABSTRACT FROM AUTHOR]

Attempts to control natural systems through management have often met with success but have also led to unexpected and often undesirable outcomes. Unfortunately, the ultimate result of such management programmes may not be apparent until long after the control efforts have begun. This is particularly true for forest-defoliating species that exhibit long-period cycles such as the invasive gypsy moth Lymantria dispar, which causes widespread damage in some years but is rare in other years., We studied the effects of two commonly employed biocontrol agents on gypsy moth dynamics using a series of field-tested and empirically parameterized mathematical models, which allowed us to examine various potential control strategies and assess long-term effects., In a non-spatial model, addition of either a manufactured version of the same baculovirus involved in natural epizootics, or a general bioinsecticide Bacillus thuringiensis var. kurstaki ( Btk), which directly kills a fraction of the population, decreases the amplitude between boom and bust portions of the cycle., However, ill-planned biocontrol applications can result in increased gypsy moth densities over the long term. Thus, control efforts may maintain pest populations at unexpectedly high numbers, which could result in constant forest defoliation., In a spatial two-patch model, where one patch is sprayed and the other is left untreated, there is also considerable danger that migration between patches may drive the unsprayed population to levels that could result in constant forest defoliation., Synthesis and applications: Perturbations to host-pathogen systems may have unexpected results, driving and maintaining populations at multiple levels including those far from desired management goals. It is often assumed that any control strategy that decreases pest populations in the short term is beneficial, but our results show that undesirable outcomes may often occur. The mechanisms we describe apply to many systems that undergo population cycles or outbreaks regulated by density-dependent processes, and in which disease or pesticide application is used for pest control. We suggest that successful management strategies should closely monitor population responses immediately following the control application to ensure that pest populations are not being maintained at artificially high levels compared with historic data. [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]

While the mechanistic links between animal movement and population dynamics are ecologically obvious, it is much less clear when knowledge of animal movement is a prerequisite for understanding and predicting population dynamics. GPS and other technologies enable detailed tracking of animal location concurrently with acquisition of landscape data and information on individual physiology. These tools can be used to refine our understanding of the mechanistic links between behaviour and individual condition through 'spatially informed' movement models where time allocation to different behaviours affects individual survival and reproduction. For some species, socially informed models that address the movements and average fitness of differently sized groups and how they are affected by fission-fusion processes at relevant temporal scales are required. Furthermore, as most animals revisit some places and avoid others based on their previous experiences, we foresee the incorporation of long-term memory and intention in movement models. The way animals move has important consequences for the degree of mixing that we expect to find both within a population and between individuals of different species. The mixing rate dictates the level of detail required by models to capture the influence of heterogeneity and the dynamics of intra- and interspecific interaction. [ABSTRACT FROM AUTHOR]

Background: Predators can have profound impacts on the dynamics of their prey that depend on how predator consumption is affected by prey density (the predator's functional response). Consumption by a generalist predator is expected to depend on the densities of all its major prey species (its multispecies functional response, or MSFR), but most studies of generalists have focussed on their functional response to only one prey species. Methodology and principal findings: Using Bayesian methods, we fit an MSFR to field data from an avian predator (the hen harrier Circus cyaneus) feeding on three different prey species. We use a simple graphical approach to show that ignoring the effects of alternative prey can give a misleading impression of the predator's effect on the prey of interest. For example, in our system, a ''predator pit'' for one prey species only occurs when the availability of other prey species is low. Conclusions and significance: The Bayesian approach is effective in fitting the MSFR model to field data. It allows flexibility in modelling over-dispersion, incorporates additional biological information into the parameter priors, and generates estimates of uncertainty in the model's predictions. These features of robustness and data efficiency make our approach ideal for the study of long-lived predators, for which data may be sparse and management/conservation priorities pressing. [ABSTRACT FROM AUTHOR]

By extracting energy from the highly dynamic wind and wave fields that typify pelagic habitats, albatrosses are able to proceed almost exclusively by gliding flight. Although energetic costs of gliding are low, enabling breeding albatrosses to forage hundreds to thousands of kilometers from their colonies, these and time costs vary with relative wind direction. This causes albatrosses in some areas to route provisioning trips to avoid headwind flight, potentially limiting habitat accessibility during the breeding season. In addition, because female albatrosses have lower wing loadings than males, it has been argued that they are better adapted to flight in light winds, leading to sexual segregation of foraging areas. We used satellite telemetry and immersion logger data to quantify the effects of relative wind speed, sex, breeding stage, and trip stage on the ground speeds (Vg) of four species of Southern Ocean albatrosses breeding at South Georgia. Vg was linearly related to the wind speed component in the direction of flight (Vwf), its effect being greatest on Wandering Albatrosses Diomedea exulans, followed by Black-browed Albatrosses Thalassarche melanophrys, Light-mantled Sooty Albatrosses Phoebatria palpebrata, and Gray-headed Albatrosses T. chrysostoma. Ground speeds at Vwf = 0 were similar to airspeeds predicted by aerodynamic theory and were higher in males than in females. However, we found no evidence that this led to sexual segregation, as males and females experienced comparable wind speeds during foraging trips. Black-browed, Gray-headed, and Light-mantled Sooty Albatrosses did not engage in direct, uninterrupted bouts of flight on moonless nights, but Wandering Albatrosses attained comparable Vg night and day, regardless of lunar phase. Relative flight direction was more important in determining Vg than absolute wind speed. When birds were less constrained in the middle stage of foraging trips, all species flew predominantly across the wind. However, in some instances, commuting birds encountered headwinds during outward trips and tail winds on their return, with the result that Vg was 1.0-3.4 m/s faster during return trips. This, we hypothesize, could result from constraints imposed by the location of prey resources relative to the colony at South Georgia or could represent an energy optimization strategy. [ABSTRACT FROM AUTHOR]