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Addressing ongoing biodiversity loss requires collaboration between conservation scientists and practitioners. However, such collaboration has proved challenging. Despite the potential importance of tracking animal movements for conservation, reviews of the tracking literature have identified a gap between the academic discipline of movement ecology and its application to biodiversity conservation. Through structured conversations with movement ecologists and conservation practitioners, we aimed to understand whether the identified gap is also perceived in practice, and if so, what factors hamper collaboration and how these factors can be remediated. We found that both groups are motivated and willing to collaborate. However, because their motivations differ, there is potential for misunderstandings and miscommunications. In addition, external factors such as funder requirements, academic metrics, and journal scopes may limit the applicability of scientific results in a conservation setting. Potential solutions we identified included improved communication and better presentation of results, acknowledging each other's motivations and desired outputs, and adjustment of funder priorities. Addressing gaps between science and implementation can enhance collaboration and support conservation action to address the global biodiversity crisis more effectively.

Habitat fragmentation is a principal threat to biodiversity and artificial river barriers are a leading cause of the global decline in freshwater biota. Whilst the impact of barriers on diadromous fish is well established, impacts on river-resident fish communities remain unclear, especially for low-head barriers. We examined the movement of five contrasting freshwater fish (topmouth gudgeon, European minnow, stone loach, bullhead, and brown trout) in an experimental cascade mesocosm with seven pools separated by small vertical barriers. Passage rates differed significantly among species and increased with body size and sustained swimming speed (Usus), ranging from an average of 0.2 passes/hr in topmouth gudgeon to 3.4 passes/hr in brown trout. A random-walk simulation indicated that barriers can result in net downstream movement and shifts in community composition. Passage rates in brown trout were leptokurtic: i.e. most individuals were relatively sedentary while a small proportion showed frequent movements. Upstream passage rates of brown trout increased with body length and boldness, while fish with lower aerobic scope tended to move downstream. Passage rates showed significant individual repeatability in brown trout, independent of body size, indicating the potential for in-stream barriers to exert selective effects on fish populations. Our results show that barrier effects can be more complex than simply blocking fish passage, and that river-resident fish can be impacted even by very small barriers. We show that fish passage depends on a wide range of morphological, physiological, and behavioural drivers, and that barriers can exert selective effects on these traits and cause shifts in community composition. Policy implications. Barrier mitigation measures need to embrace inter- and intraspecific variation in fish passage to avoid inadvertent artificial selection on fish communities. Given the high abundance of low-head structures in river systems worldwide, a

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.5. Taking advantage of the biologging revolution will require a large improvement in the theoretical and mathematical foundations of movement ecology, to include the rich set of high‐frequency multivariate data, which greatly expand the fundamentally limited and coarse data that could be collected using location‐only technology such as GPS. Equally important will be the establishment of multidisciplinary collaborations to catalyse the opportunities offered by current and future biologging technology. If this is achieved, clear potential exists for developing a vastly improved mechanistic understanding of animal movements and their roles in ecological processes and for building realistic predictive models.

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.5. Taking advantage of the biologging revolution will require a large improvement in the theoretical and mathematical foundations of movement ecology, to include the rich set of high‐frequency multivariate data, which greatly expand the fundamentally limited and coarse data that could be collected using location‐only technology such as GPS. Equally important will be the establishment of multidisciplinary collaborations to catalyse the opportunities offered by current and future biologging technology. If this is achieved, clear potential exists for developing a vastly improved mechanistic understanding of animal movements and their roles in ecological processes and for building realistic predictive models.

An understanding of the direct links between animals and their environment can offer insights into the drivers and constraints to animal movement. Constraints on movement interact in complex ways with the physiology of the animal (metabolism) and physical environment (food and weather), but can be modelled using physical principles of energy and mass exchange. Here, we describe a general, spatially explicit individual‐based movement model that couples a nutritional energy and mass budget model (dynamic energy budget theory) with a biophysical model of heat exchange. This provides a highly integrated method for constraining an ectothermic animal's movement in response to how food and microclimates vary in space and time. The model uses r to drive a NetLogo individual‐based model together with microclimate and energy‐ and mass‐budget modelling functions from the r package “NicheMapR”. It explicitly incorporates physiological and morphological traits, behavioural thermoregulation, movement strategies and movement costs. From this, the model generates activity budgets of foraging and shade‐seeking, home range behaviour, spatial movement patterns and life history consequences under user‐defined configurations of food and microclimates. To illustrate the model, we run simulations of the Australian sleepy lizard Tiliqua rugosa under different movement strategies (optimising or satisficing) in two contrasting habitats of varying food and shade (sparse and dense). We then compare the results with real, fine‐scale movement data of a wild population throughout the breeding season. Our results show that (1) the extremes of movement behaviour observed in sleepy lizards are consistent with feeding requirements (passive movement) and thermal constraints (active movement), (2) the model realistically captures majority of the distribution of observed home range size, (3) both satisficing and optimising movement strategies appear to exist in the wild population, but home ran