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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]

Abstract Assessing the potential impacts of proposed offshore wind farm developments on seabird populations requires estimation of nocturnal flight activity of seabirds for input into collision risk models. One of the seabirds considered most at risk from collision with offshore wind turbines is the northern gannet Morus bassanus. The recommended correction for gannet nocturnal flight activity is currently a highly precautionary value. Here we use data from tracking studies to derive evidence-based correction factors for nocturnal flight activity of adult gannets during the breeding and nonbreeding seasons, and of immature gannets during the summer prospecting phase. Flight and diving activity of gannets was minimal during the night, astronomical and nautical twilight, for adults during the breeding season and nonbreeding season, and for immatures. Some flight activity occurred during the short period of civil twilight, but on average at about half the level seen during the day. Based on evidence from numerous tracking studies, we recommend that precautionary values of the nocturnal (sunset to sunrise) flight activity factor for estimating collision risk should be 8% of daytime flight activity during the breeding season and 3% of daytime flight activity during the nonbreeding season. Use of these evidence-based correction factors will improve the accuracy, and reduce the uncertainty of collision risk models, providing a more reliable assessment of the impacts of offshore wind farms on gannets. [ABSTRACT FROM AUTHOR]

Predictive species distribution models (SDMs) are becoming increasingly important in ecology, in the light of rapid environmental change. However, the predictions of most current SDMs are specific to the habitat composition of the environments in which they were fitted. This may limit SDM predictive power because species may respond differently to a given habitat depending on the availability of all habitats in their environment, a phenomenon known as a functional response in resource selection. The Generalised Functional Response (GFR) framework captures this dependence by formulating the SDM coefficients as functions of habitat availability. The original GFR implementation used global polynomial functions of habitat availability to describe the functional responses. In this study, we develop several refinements of this approach and compare their predictive performance using two simulated and two real datasets. We first use local radial basis functions (RBF), a more flexible approach than global polynomials, to represent the habitat selection coefficients, and balance bias with precision via regularization to prevent overfitting. Second, we use the RBF-GFR and GFR models in combination with the classification and regression tree CART, which has more flexibility and better predictive powers for non-linear modelling. As further extensions, we use random forests (RFs) and extreme gradient boosting (XGBoost), ensemble approaches that consistently lead to variance reduction in generalization error. We find that the different methods are ranked consistently across the datasets for out-of-data prediction. The traditional stationary approach to SDMs and the GFR model consistently perform at the bottom of the ranking (simple SDMs underfit, and polynomial GFRs overfit the data). The best methods in our list provide non-negligible improvements in predictive performance, in some cases taking the out-of-sample R2 from 0.3 up to 0.7 across datasets. At times of rapid environmental change and spatial non-stationarity ignoring the effects of functional responses on SDMs, results in two different types of prediction bias (under-prediction or mis-positioning of distribution hotspots). However, not all functional response models perform equally well. The more volatile polynomial GFR models can generate biases through over-prediction. Our results indicate that there are consistently robust GFR approaches that achieve impressive gains in transferability across very different datasets. [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 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]

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]

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]

Detailed observation of the movement of individual animals offers the potential to understand spatial population processes as the ultimate consequence of individual behaviour, physiological constraints and fine-scale environmental influences. However, movement data from individuals are intrinsically stochastic and often subject to severe observation error. Linking such complex data to dynamical models of movement is a major challenge for animal ecology. Here, we review a statistical approach, state–space modelling, which involves changing how we analyse movement data and draw inferences about the behaviours that shape it. The statistical robustness and predictive ability of state–space models make them the most promising avenue towards a new type of movement ecology that fuses insights from the study of animal behaviour, biogeography and spatial population dynamics. [Copyright &y& Elsevier]