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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]

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]

The decline in malaria across Africa has been largely attributed to vector control using long-lasting insecticidal nets (LLINs). However, this intervention has prompted widespread insecticide resistance (IR) and been associated with changes in mosquito behaviour that reduce their contact with LLINs. The relative importance and rate at which IR and behavioural adaptations emerge are poorly understood. We conducted surveillance of mosquito behaviour and IR at 12 sites in Burkina Faso to assess the magnitude and temporal dynamics of insecticide, biting and resting behaviours in vectors in the 2-year period following mass LLIN distribution. Insecticide resistance was present in all vector populations and increased rapidly over the study period. In contrast, no longitudinal shifts in LLIN-avoidance behaviours (earlier or outdoor biting and resting) were detected. There was a moderate but statistically significant shift in vector species composition from Anopheles coluzzii to Anopheles gambiae which coincided with a reduction in the proportion of bites preventable by LLINs; possibly driven by between-species variation in behaviour. These findings indicate that adaptations based on insecticide resistance arise and intensify more rapidly than behavioural shifts within mosquito vectors. However, longitudinal shifts in mosquito vector species composition were evident within 2 years following a mass LLIN distribution. This ecological shift was characterized by a significant increase in the exophagic species (An. gambiae) and coincided with a predicted decline in the degree of protection expected from LLINs. Although human exposure fell through the study period due to reducing vector densities and infection rates, such ecological shifts in vector species along with insecticide resistance were likely to have eroded the efficacy of LLINs. While both adaptations impact malaria control, the rapid increase of the former indicates this strategy develops more quickly in response to selection from LLINS. However, interventions targeting both resistance strategies will be needed. [ABSTRACT FROM AUTHOR]

Central place foragers often segregate in space, even without signs of direct agonistic interactions. Using parsimonious individual-based simulations, we show that for species with spatial cognitive abilities, individual-level memory of resource availability can be sufficient to cause spatial segregation in the foraging ranges of colonial animals. The shapes of the foraging distributions are governed by commuting costs, the emerging distribution of depleted resources, and the fidelity of foragers to their colonies. When colony fidelity is weak and foragers can easily switch to colonies located closer to favorable foraging grounds, this leads to space partitioning with equidistant borders between neighboring colonies. In contrast, when colony fidelity is strong—for example, because larger colonies provide safety in numbers or individuals are unable to leave—it can create a regional imbalance between resource requirements and resource availability. This leads to nontrivial space-use patterns that propagate through the landscape. Interestingly, while better spatial memory creates more defined boundaries between neighboring colonies, it can lower the average intake rate of the population, suggesting a potential trade-off between an individual's attempt for increased intake and population growth rates. [ABSTRACT FROM AUTHOR]

Predicting how populations may respond to climate change and anthropogenic pressures requires detailed knowledge of demographic traits, such as survival and reproduction. However, the availability of these data varies greatly across space and taxa. Therefore, it is common practice to conduct population assessments by filling in missing values from surrogate species or other populations of the same species. Using these independent surrogate values concurrently with observed data neglects the life‐history trade‐offs that connect the different aspects of a population's demography. Consequently, this approach introduces biases that could ultimately lead to erroneous management decisions.We use a Bayesian hierarchical framework to combine fragmented multi‐population data with established life‐history theory and reconstruct population‐specific demographic data across a substantial part of a species breeding range. We apply our analysis to a long‐lived colonial species, the black‐legged kittiwake Rissa tridactyla, that is classified as globally Vulnerable and is highly threatened by increasing anthropogenic pressures, including offshore renewable energy development. We then use a projection analysis to examine how the reconstructed demographic parameters may improve population assessments, compared to models that combine observed data with independent surrogate values.Demographic parameters reconstructed using a hierarchical framework can be utilised in a range of population modelling approaches. They can also be used as reference estimates to assess whether independent surrogate values are likely to over or underestimate missing demographic parameters. We show that surrogate values from independent sources are often used to fill in missing parameters that have large potential demographic impact, and that resulting biases are driven in unpredictable directions thus precluding assessments from being consistently precautionary.Synthesis and applications. Our study dramatically increases the spatial coverage of population‐specific demographic data for black‐legged kittiwakes. The reconstructed demographic parameters presented can also be used immediately to reduce uncertainty in the consenting process for offshore wind development in the United Kingdom and Ireland. More broadly, we show that the reconstruction approach used here provides a new avenue for improving evidence‐based management and policy action for animal and plant populations with fragmented and error prone demographic data. [ABSTRACT FROM AUTHOR]

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

Habitat selection models are used in ecology to link the spatial distribution of animals to environmental covariates and identify preferred habitats. The most widely used models of this type, resource selection functions, aim to capture the steady‐state distribution of space use of the animal, but they assume independence between the observed locations of an animal. This is unrealistic when location data display temporal autocorrelation. The alternative approach of step selection functions embed habitat selection in a model of animal movement, to account for the autocorrelation. However, inferences from step selection functions depend on the underlying movement model, and they do not readily predict steady‐state space use. We suggest an analogy between parameter updates and target distributions in Markov chain Monte Carlo (MCMC) algorithms, and step selection and steady‐state distributions in movement ecology, leading to a step selection model with an explicit steady‐state distribution. In this framework, we explain how maximum likelihood estimation can be used for simultaneous inference about movement and habitat selection. We describe the local Gibbs sampler, a novel rejection‐free MCMC scheme, use it as the basis of a flexible class of animal movement models, and derive its likelihood function for several important special cases. In a simulation study, we verify that maximum likelihood estimation can recover all model parameters. We illustrate the application of the method with data from a zebra. [ABSTRACT FROM AUTHOR]

Background: Distance sampling methods are widely used in ecology to estimate and map the abundance of animal and plant populations from spatial survey data. The key underlying concept in distance sampling is the detection function, the probability of detecting the occurrence of an event as a function of its distance from the observer, as well as other covariates that may influence detection. In epidemiology, the burden and distribution of infectious disease is often inferred from cases that are reported at clinics and hospitals. In areas with few public health facilities and low accessibility, the probability of detecting a case is also a function of the distance between an infected person and the "observer" (e.g. a health centre). While the problem of distance-related under-reporting is acknowledged in public health; there are few quantitative methods for assessing and correcting for this bias when mapping disease incidence. Here, we develop a modified version of distance sampling for prediction of infectious disease incidence by relaxing some of the framework's fundamental assumptions. We illustrate the utility of this approach using as our example malaria distribution in rural Burkina Faso, where there is a large population at risk but relatively low accessibility of health facilities. Results: The modified distance-sampling framework was used to predict the probability of reporting malaria infection at 8 rural clinics, based on road-travel distances from villages. The rate at which reporting probability dropped with distance varied between clinics, depending on road and clinic positions. The probability of case detection was estimated as 0.3–1 in the immediate vicinity of the clinic, dropping to 0.1–0.6 at a travel distance of 10 km, and effectively zero at distances > 30–40 km. Conclusions: To enhance the method's strategic impact, we provide an interactive mapping tool (as a self-contained R Shiny app) that can be used by non-specialists to interrogate model outputs and visualize how the overall probability of under-reporting and the catchment area of each clinic is influenced by changing the number and spatial allocation of health centres. [ABSTRACT FROM AUTHOR]

The balance between economic growth and wildlife conservation is a priority for many governments. Enhancing realism in assessment of population‐level impacts of anthropogenic mortality can help achieve this balance. Population Viability Analysis (PVA) is commonly applied to investigate population vulnerability, but outcomes of PVA are sensitive to formulations of density‐dependence, environmental stochasticity and life history. Current practice in marine assessments is to use precautionary models that assume no compensation from density‐dependence or rescue‐effects via "re‐seeding" from other colonies. However, if we could empirically quantify regulatory population processes, the responses of populations to additional anthropogenic mortality may be assessed with more realism in PVA.Using Bayesian state‐space models fitted to population time series from three sympatric seabird populations, selected for varied life histories, we inferred the extent to which their dynamics are driven by environmental stochasticity and density‐dependence.Based on these inferences, we conducted an exhaustive PVA across credible parameterizations for intrinsic and extrinsic population regulation, simulated as a closed and re‐seeded system. Scenarios of anthropogenic mortality, along a sliding scale of precaution, were applied both proportionally and as a fixed quota using Potential Biological Removal (PBR).Baseline results from fitting revealed clear environmental regulation in two of our three species. Crucially, we found that for our empirically derived, realistic model parameterizations there are risks of decline to real populations even under very precautionary mortality scenarios. We find that PBR is dubious in application as a sustainable tool for population assessment when we account for regulation. Closed versus re‐seeded models showed a large divergence in outcomes, with sharper declines in closed simulations. Fixed‐quota mortality typically induced greater population declines comparative to proportional mortality, subject to regulation and re‐seeding.Synthesis and applications. Practitioners using arbitrary formulations of population regulation risk over‐precaution (economic constraint) or under‐precaution (endangering populations). The demands of increased economic development and preservation of wildlife require that methodologies apply techniques that confer reality and rigour to assessment. The current practice of employing models lacking density‐dependence and empirical environmental information imposes limitations in the efficacy of estimating impacts. Here, we provide a method to quantify the conditions that predominantly regulate a population and exacerbate the risk of decline from anthropogenic mortality. It is in the interests of both developers and conservationists to apply methods in population impact assessments that capture realism in the processes driving population dynamics. Practitioners using arbitrary formulations of population regulation risk over‐precaution (economic constraint) or under‐precaution (endangering populations). The demands of increased economic development and preservation of wildlife require that methodologies apply techniques that confer reality and rigour to assessment. The current practice of employing models lacking density‐dependence and empirical environmental information imposes limitations in the efficacy of estimating impacts. Here, we provide a method to quantify the conditions that predominantly regulate a population and exacerbate the risk of decline from anthropogenic mortality. It is in the interests of both developers and conservationists to apply methods in population impact assessments that capture realism in the processes driving population dynamics. Editor's Choice [ABSTRACT FROM AUTHOR]

High pathogenicity avian influenza virus (HPAIV) caused the worst seabird mass-mortalities in Europe across 2021–2022. The northern gannet (Morus bassanus) was one of the most affected species, with tens of thousands of casualties in the northeast Atlantic between April–September 2022. Disease outbreaks can modify the movement ecology of animals by diminishing spatial consistency, thereby increasing the potential for disease transmission. To detect potential changes in movement behaviour, we GPS-tracked breeding adults following the initial HPAIV outbreak, at three of the largest northern gannet breeding colonies where major mortality of adults and chicks occurred (Bass Rock, Scotland, UK; Grassholm, Wales, UK; Rouzic, Brittany, France). We also gathered background epidemiological information and northern gannet colony dynamics during the outbreak. Our data indicate that HPAIV killed at least 50 % of northern gannets, and suggest the presence of HPAIV H5N1 antibodies in juveniles. GPS-tracked adult northern gannets remained faithful to their breeding sites despite the HPAIV outbreak and did not prospect other breeding colonies. They performed regular foraging trips at sea, similar to their behaviour before the outbreak. Comparison with GPS-tracking data gathered in 2019, i.e. before the HPAIV outbreak, suggested lower foraging effort in birds which survived HPAIV in 2022, potentially as a consequence of reduced intra- and interspecific food competition. Breeding colony fidelity of surviving adult northern gannets following HPAIV mass-mortalities indicates limited capacity for viral spread during our study. This may contrast with the behaviour of adults during the initial disease outbreak, and with that of younger individuals. [ABSTRACT FROM AUTHOR]

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

The impact of control measures on mosquito vector fitness and demography is usually estimated from bioassays or indirect variables in the field. Whilst indicative, neither approach is sufficient to quantify the potentially complex response of mosquito populations to combined interventions. Here, large replicated mesocosms were used to measure the population-level response of the malaria vector Anopheles arabiensis to long-lasting insecticidal nets (LLINs) when used in isolation, or combined with insecticidal eave louvers (EL), or treatment of cattle with the endectocide Ivermectin (IM). State-space models (SSM) were fit to these experimental data, revealing that LLIN introduction reduced adult mosquito survival by 91% but allowed population persistence. ELs provided no additional benefit, but IM reduced mosquito fecundity by 59% and nearly eliminated all populations when combined with LLINs. This highlights the value of IM for integrated vector control, and mesocosm population experiments combined with SSM for identifying optimal combinations for vector population elimination. [ABSTRACT FROM AUTHOR]

Aim: Mangrove forests are among the most threatened and rapidly vanishing, but poorly understood ecosystems. We aim to uncover the variables driving mangrove biodiversity and produce baseline biodiversity maps for the Sundarbans world heritage site—the Earth's largest contiguous mangrove ecosystem. Location: The Bangladesh Sundarbans, South Asia. Methods: We collected species abundance, environmental and disturbance data from 110 permanent sample plots (PSPs) covering the entire Bangladesh Sundarbans (6,017 km2). We applied generalized additive models to determine the key variables shaping the spatial distributions of mangrove diversity and community composition. Biodiversity maps were constructed using covariate‐driven habitat models, and their predictive performances were compared with covariate‐free (i.e., direct interpolation) approaches to see whether the inclusion of habitat variables bolster spatial predictions of biodiversity or whether we can rely on direct interpolation approaches when environmental data are not available. Results: Historical forest exploitation, disease, siltation and soil alkalinity were the key stressors causing loss of alpha and gamma diversity in mangrove communities. Both alpha and gamma diversity increased along the downstream‐to‐upstream and riverbank‐to‐forest interior gradients. Mangrove communities subjected to intensive past tree harvesting, disease outbreaks and siltation were more homogeneous in species composition (beta diversity). In contrast, heterogeneity in species composition increased along decreasing salinity and downstream‐to‐upstream gradients. We find that the surviving biodiversity hotspots (comprising many globally endangered tree species) are located outside the established protected area network and hence open to human exploitation. We therefore suggest bringing them immediately under protected area management. Main conclusions: We provide the first habitat‐based modelling and mapping of alpha, beta and gamma diversity in threatened mangrove communities. In general, habitat‐based models showed better predictive ability than the covariate‐free approach. Nevertheless, the small margin of differences between the approaches demonstrates the utility of direct interpolation approaches when environmental data are unavailable. [ABSTRACT FROM AUTHOR]

Measuring the demographic parameters of exploited populations is central to predicting their vulnerability and extinction risk. However, current rates of population decline and species loss greatly outpace our ability to empirically monitor all populations that are potentially threatened.The scale of this problem cannot be addressed through additional data collection alone, and therefore it is a common practice to conduct population assessments based on surrogate data collected from similar species. However, this approach introduces biases and imprecisions that are difficult to quantify. Recent developments in hierarchical modelling have enabled missing values to be reconstructed based on the correlations between available life‐history data, linking similar species based on phylogeny and environmental conditions.However, these methods cannot resolve life‐history variability among populations or species that are closely placed spatially or taxonomically. Here, theoretically motivated constraints that align with life‐history theory offer a new avenue for addressing this problem. We describe a Bayesian hierarchical approach that combines fragmented, multispecies and multi‐population data with established life‐history theory, in order to objectively determine similarity between populations based on trait correlations (life‐history trade‐offs) obtained from model fitting.We reconstruct 59 unobserved life‐history parameters for 23 populations of tuna that sustain some of the world's most valuable fisheries. Testing by cross‐validation across different scenarios indicated that life‐histories were accurately reconstructed when information was available for other populations of the same species. The reconstruction of several traits was also accurate for species represented by a single population, although credible intervals increased dramatically.Synthesis and applications. The described Bayesian hierarchical method provides access to life‐history traits that are difficult to measure directly and reconstructs missing life‐history information useful for assessing populations and species that are directly or indirectly affected by human exploitation of natural resources. The method is particularly useful for examining populations that are spatially or taxonomically similar, and the reconstructed life‐history strategies described for the principal market tunas have immediate application to the world‐wide management of these fisheries. The described Bayesian hierarchical method provides access to life‐history traits that are difficult to measure directly and reconstructs missing life‐history information useful for assessing populations and species that are directly or indirectly affected by human exploitation of natural resources. The method is particularly useful for examining populations that are spatially or taxonomically similar, and the reconstructed life‐history strategies described for the principal market tunas have immediate application to the world‐wide management of these fisheries. [ABSTRACT FROM AUTHOR]

How can we track population trends when monitoring data are sparse? Population declines can go undetected, despite ongoing threats. For example, only one of every 200 harvested species are monitored. This gap leads to uncertainty about the seriousness of declines and hampers effective conservation. Collecting more data is important, but we can also make better use of existing information. Prior knowledge of physiology, life history, and community ecology can be used to inform population models. Additionally, in multispecies models, information can be shared among taxa based on phylogenetic, spatial, or temporal proximity. By exploiting generalities across species that share evolutionary or ecological characteristics within Bayesian hierarchical models, we can fill crucial gaps in the assessment of species’ status with unparalleled quantitative rigor. [ABSTRACT FROM AUTHOR]

‘Species distribution modeling’ was recently ranked as one of the top five ‘research fronts’ in ecology and the environmental sciences by ISI's Essential Science Indicators, reflecting the importance of predicting how species distributions will respond to anthropogenic change. Unfortunately, species distribution models (SDMs) often perform poorly when applied to novel environments. Compounding on this problem is the shortage of methods for evaluating SDMs (hence, we may be getting our predictions wrong and not even know it). Traditional methods for validating SDMs quantify a model's ability to classify locations as used or unused. Instead, we propose to focus on how well SDMs can predict the characteristics of used locations. This subtle shift in viewpoint leads to a more natural and informative evaluation and validation of models across the entire spectrum of SDMs. Through a series of examples, we show how simple graphical methods can help with three fundamental challenges of habitat modeling: identifying missing covariates, non‐linearity, and multicollinearity. Identifying habitat characteristics that are not well‐predicted by the model can provide insights into variables affecting the distribution of species, suggest appropriate model modifications, and ultimately improve the reliability and generality of conservation and management recommendations. [ABSTRACT FROM AUTHOR]

As part of global efforts to reduce dependence on carbon-based energy sources there has been a rapid increase in the installation of renewable energy devices. The installation and operation of these devices can result in conflicts with wildlife. In the marine environment, mammals may avoid wind farms that are under construction or operating. Such avoidance may lead to more time spent travelling or displacement from key habitats. A paucity of data on at-sea movements of marine mammals around wind farms limits our understanding of the nature of their potential impacts., Here, we present the results of a telemetry study on harbour seals Phoca vitulina in The Wash, south-east England, an area where wind farms are being constructed using impact pile driving. We investigated whether seals avoid wind farms during operation, construction in its entirety, or during piling activity. The study was carried out using historical telemetry data collected prior to any wind farm development and telemetry data collected in 2012 during the construction of one wind farm and the operation of another., Within an operational wind farm, there was a close-to-significant increase in seal usage compared to prior to wind farm development. However, the wind farm was at the edge of a large area of increased usage, so the presence of the wind farm was unlikely to be the cause., There was no significant displacement during construction as a whole. However, during piling, seal usage (abundance) was significantly reduced up to 25 km from the piling activity; within 25 km of the centre of the wind farm, there was a 19 to 83% (95% confidence intervals) decrease in usage compared to during breaks in piling, equating to a mean estimated displacement of 440 individuals. This amounts to significant displacement starting from predicted received levels of between 166 and 178 dB re 1 μPa(p-p). Displacement was limited to piling activity; within 2 h of cessation of pile driving, seals were distributed as per the non-piling scenario., Synthesis and applications. Our spatial and temporal quantification of avoidance of wind farms by harbour seals is critical to reduce uncertainty and increase robustness in environmental impact assessments of future developments. Specifically, the results will allow policymakers to produce industry guidance on the likelihood of displacement of seals in response to pile driving; the relationship between sound levels and avoidance rates; and the duration of any avoidance, thus allowing far more accurate environmental assessments to be carried out during the consenting process. Further, our results can be used to inform mitigation strategies in terms of both the sound levels likely to cause displacement and what temporal patterns of piling would minimize the magnitude of the energetic impacts of displacement. [ABSTRACT FROM AUTHOR]

1. Like many wildlife populations across Africa, recent analyses indicate that African lions are declining rapidly outside of small fenced areas. Community conservancies – privately protected areas that engage community members in conservation – may potentially maintain wildlife populations in unfenced pastoralist regions, but their effectiveness in conserving large carnivores has been largely unknown until now. 2. We identify drivers of lion survival in community conservancies within the Masai Mara ecosystem, Kenya, applying mark–recapture analyses to continuous sight–resight surveys. We use the number of livestock and human settlements as proxies for potential human–lion conflict whilst controlling for environmental variables and lion socioecology. 3. Average lion densities within the Mara conservancies between 2008 and 2013 (11·87 lions 100 km-2) were 2·6 times higher than those previously reported in 2003. Survival rates varied amongst prides and were highest for lions utilizing central regions of conservancies. 4. The number of livestock settlements (bomas [corrals] and manyattas) that were not members of a conservancy, and that fell within a pride home-range, had a large negative effect on female survival and was the most influential external predictor in models. These nonconservancy settlements accounted for 37·2% of total observed variation in survival, whereas conservancy settlements, which benefit financially from wildlife through their membership, had no effect on lion survival. 5. Internal drivers of survival agreed with known ecology and social behaviour including age plus a negative effect of male takeovers on cub survival <6 months. Vegetation cover, prey availability and the density of grazing livestock within a pride’s range did not explain patterns in lion survival. 6. Synthesis and applications. We show that lion densities have increased substantially within the Mara conservancies over the last decade and suggest that the creation of community conservancies has benefitted their survival. This suggests that lions can survive outside of fenced areas within pastoral regions if communities gain benefits from wildlife. We highlight the importance of expanding existing conservancies beyond their current geographical and political scope and forming buffer zones if wildlife ranges outside them. We suggest that changing attitudes to predators should be a key goal of community-based conservancies. Further work is recommended to identify what specific aspects of conservancy membership promote lion survival. [ABSTRACT FROM AUTHOR]

1. Impediments to animal movement are ubiquitous and vary widely in both scale and permeability. It is essential to understand how impediments alter ecological dynamics via their influence on animal behavioural strategies governing space use and, for anthropogenic features such as roads and fences, how to mitigate these effects to effectively manage species and landscapes. 2. Here, we focused primarily on barriers to movement, which we define as features that cannot be circumnavigated but may be crossed. Responses to barriers will be influenced by the movement capabilities of the animal, its proximity to the barriers, and habitat preference. We developed a mechanistic modelling framework for simultaneously quantifying the permeability and proximity effects of barriers on habitat preference and movement. 3. We used simulations based on our model to demonstrate how parameters on movement, habitat preference and barrier permeability can be estimated statistically. We then applied the model to a case study of road effects on wild mountain reindeer summer movements. 4. This framework provided unbiased and precise parameter estimates across a range of strengths of preferences and barrier permeabilities. The quality of permeability estimates, however, was correlated with the number of times the barrier is crossed and the number of locations in proximity to barriers. In the case study we found that reindeer avoided areas near roads and that roads are semi-permeable barriers to movement. There was strong avoidance of roads extending up to c. 1 km for four of five animals, and having to cross roads reduced the probability of movement by 68.6% (range 3.5-99.5%). 5. Human infrastructure has embedded within it the idea of networks: nodes connected by linear features such as roads, rail tracks, pipelines, fences and cables, many of which divide the landscape and limit animal movement. The unintended but potentially profound consequences of infrastructure on animals remain poorly understood. The rigorous framework for simultaneously quantifying movement, habitat preference and barrier permeability developed here begins to address this knowledge gap. [ABSTRACT FROM AUTHOR]

We develop a theory of generalist predation showing how alternative prey species are affected by changes in both mean abundance and variability (coefficient of variation) of their predator’s primary prey. The theory is motivated by the indirect effects of cyclic rodent populations on ground-breeding birds, and developed through progressive analytic simplifications of an empirically-based model. It applies nonetheless to many other systems where primary prey have fast life-histories and can become superabundant, thus facilitating impact on alternative prey species and generating highly asymmetric interactions. Our results suggest that predator effects on alternative prey should generally decrease with mean primary prey abundance, and increase with primary prey variability (low to high CV)—unless predators have strong aggregative responses, in which case these results can be reversed. Approximations of models including predator dynamics (general numerical response with possible delays) confirm these results but further suggest that negative temporal correlation between predator and primary prey is harmful to alternative prey. Finally, we find that measurements of predator numerical responses are crucial to predict–even qualitatively–the response of ecosystems to changes in the dynamics of outbreaking prey species. [ABSTRACT FROM AUTHOR]