Understanding animal movement is essential to elucidate how animals interact, survive, and thrive in a changing world. Recent technological advances in data collection and management have transformed our understanding of animal “movement ecology” (the integrated study of organismal movement), creating a big-data discipline that benefits from rapid, cost-effective generation of large amounts of data on movements of animals in the wild. These high-throughput wildlife tracking systems now allow more thorough investigation of variation among individuals and species across space and time, the nature of biological interactions, and behavioral responses to the environment. Movement ecology is rapidly expanding scientific frontiers through large interdisciplinary and collaborative frameworks, providing improved opportunities for conservation and insights into the movements of wild animals, and their causes and consequences.
1. The paradigm-changing opportunities of biologging sensors for ecological research, especially movement ecology, are vast, but the crucial questions of how best to match the most appropriate sensors and sensor combinations to specific biological questions and how to analyse complex biologging data, are mostly ignored.2. Here, we fill this gap by reviewing how to optimize the use of biologging techniques to answer questions in movement ecology and synthesize this into an Integrated Biologging Framework (IBF).3. We highlight that multisensor approaches are a new frontier in biologging, while identifying current limitations and avenues for future development in sensor technology.4. We focus on the importance of efficient data exploration, and more advanced multidimensional visualization methods, combined with appropriate archiving and sharing approaches, to tackle the big data issues presented by biologging. We also discuss the challenges and opportunities in matching the peculiarities of specific sensor data to the statistical models used, highlighting at the same time the large advances which will be required in the latter to properly analyse biologging data.
The evolutionary function and maintenance of variation in animal personality is still under debate. Variation in the size of metabolic organs has recently been suggested to cause and maintain variation in personality. Here, we examine two main underlying notions: (i) that organ sizes vary consistently between individuals and cause consistent behavioural patterns, and (ii) that a more exploratory personality is associated with reduced survival. Exploratory behaviour of captive red knots (Calidris canutus, a migrant shorebird) was negatively rather than positively correlated with digestive organ (gizzard) mass, as well as with body mass. In an experiment, we reciprocally reduced and increased individual gizzard masses and found that exploration scores were unaffected. Whether or not these birds were resighted locally over the 19 months after release was negatively correlated with their exploration scores. Moreover, a long-term mark–recapture effort on free-living red knots with known gizzard masses at capture confirmed that local resighting probability (an inverse measure of exploratory behaviour) was correlated with gizzard mass without detrimental effects on survival. We conclude that personality drives physiological adjustments, rather than the other way around, and suggest that physiological adjustments mitigate the survival costs of exploratory behaviour. Our results show that we need to reconsider hypotheses explaining personality variation based on organ sizes and differential survival.
Summary1. Sound conservation and management advice usually requires spatial data on animal and plant abundances. The expense of programmes to determine species distributions and estimates of population sizes often limits sample size. To maximise effectiveness at minimal costs, optimisations of such monitoring efforts are critical. A monitoring programme can have multiple objectives with demands on the optimal sampling design that are often in conflict. Here, we develop an optimal sampling design for monitoring programmes with conflicting objectives, building on an existing intertidal benthic monitoring programme in the Dutch Wadden Sea and simulation models bounded in their parameter spaces by these data. 2. We distinguish three possible objectives: (1) estimation of temporal changes and spatial differences in abundance and (2) mapping, that is, prediction of abundances at unsampled locations. Mapping abundances requires model-based analyses using autocorrelation models. Such analyses are as good as the model fits the data; therefore, the final objective was (3) accurately estimating model autocorrelation parameters. To compare sampling designs, we used the following criteria: (1) minimum detectable difference in mean between two time periods or two areas, (2) mean prediction error and (3) estimation bias of autocorrelation parameters. 3. Using Monte Carlo simulations, we compared five sampling designs with respect to these criteria (i.e. simple random, grid, two types of transects, and grid with random replacements) at four levels of naturally occurring spatial autocorrelation. 4. The ideal sampling design for objectives (1) and (2) was grid sampling and for objective (3) random sampling. The sampling design that catered best for all three objectives combined was grid sampling with a number of random samples placed on gridlines. 5. Grid sampling with a number of random samples is considered an accurate and powerful tool with the highest effectiveness. This sampling design is widely applicable and allows for accurate estimates of population sizes, monitoring of population trends, comparisons of populations ⁄ trends between years or areas, modelling autocorrelation, mapping species distributions and a mechanistic understanding of species distribution processes.
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