Acoustic telemetry and network analysis reveal the space use of multiple reef predators and enhance marine protected area design

Marine protected areas (MPAs) are commonly employed to protect ecosystems from threats like overfishing. Ideally, MPA design should incorporate movement data from multiple target species to ensure sufficient habitat is protected. We used long-term acoustic telemetry and network analysis to determine the fine-scale space use of five shark and one turtle species at a remote atoll in the Seychelles, Indian Ocean, and evaluate the efficacy of a proposed MPA. Results revealed strong, species-specific habitat use in both sharks and turtles, with corresponding variation in MPA use. Defining the MPA's boundary from the edge of the reef flat at low tide instead of the beach at high tide (the current best in Seychelles) significantly increased the MPA's coverage of predator movements by an average of 34%. Informed by these results, the larger MPA was adopted by the Seychelles government, demonstrating how telemetry data can improve shark spatial conservation by affecting policy directly.

Climate-Driven Range Shifts Within Benthic Habitats Across a Marine Biogeographic Transition Zone.

Anthropogenic climate change is causing unprecedented rapid responses in marine communities, with species across many different taxonomic groups showing faster shifts in biogeographic ranges than in any other ecosystem. Spatial and temporal trends for many marine species are difficult to quantify, however, due to the lack of long-term datasets across complete geographical distributions and the occurrence of small-scale variability from both natural and anthropogenic drivers. Understanding these changes requires a multidisciplinary approach to bring together patterns identified within long-term datasets and the processes driving those patterns using biologically relevant mechanistic information to accurately attribute cause and effect. This must include likely future biological responses, and detection of the underlying mechanisms in order to scale up from the organismal level to determine how communities and ecosystems are likely to respond across a range of future climate change scenarios. Using this multidisciplinary approach will improve the use of robust science to inform the development of fit-for-purpose policy to effectively manage marine environments in this rapidly changing world.

Climate-Driven Range Shifts Within Benthic Habitats Across a Marine Biogeographic Transition Zone.

Anthropogenic climate change is causing unprecedented rapid responses in marine communities, with species across many different taxonomic groups showing faster shifts in biogeographic ranges than in any other ecosystem. Spatial and temporal trends for many marine species are difficult to quantify, however, due to the lack of long-term datasets across complete geographical distributions and the occurrence of small-scale variability from both natural and anthropogenic drivers. Understanding these changes requires a multidisciplinary approach to bring together patterns identified within long-term datasets and the processes driving those patterns using biologically relevant mechanistic information to accurately attribute cause and effect. This must include likely future biological responses, and detection of the underlying mechanisms in order to scale up from the organismal level to determine how communities and ecosystems are likely to respond across a range of future climate change scenarios. Using this multidisciplinary approach will improve the use of robust science to inform the development of fit-for-purpose policy to effectively manage marine environments in this rapidly changing world.

Rationalizing spatial exploration patterns of wild animals and humans through a temporal discounting framework

Understanding the exploration patterns of foragers in the wild provides fundamental insight into animal behavior. Recent experimental evidence has demonstrated that path lengths (distances between consecutive turns) taken by foragers are well fitted by a power law distribution. Numerous theoretical contributions have posited that “Lévy random walks”—which can produce power law path length distributions—are optimal for memoryless agents searching a sparse reward landscape. It is unclear, however, whether such a strategy is efficient for cognitively complex agents, from wild animals to humans. Here, we developed a model to explain the emergence of apparent power law path length distributions in animals that can learn about their environments. In our model, the agent’s goal during search is to build an internal model of the distribution of rewards in space that takes into account the cost of time to reach distant locations (i.e., temporally discounting rewards). For an agent with such a goal, we find that an optimal model of exploration in fact produces hyperbolic path lengths, which are well approximated by power laws. We then provide support for our model by showing that humans in a laboratory spatial exploration task search space systematically and modify their search patterns under a cost of time. In addition, we find that path length distributions in a large dataset obtained from free-ranging marine vertebrates are well described by our hyperbolic model. Thus, we provide a general theoretical framework for understanding spatial exploration patterns of cognitively complex foragers.

Rationalizing spatial exploration patterns of wild animals and humans through a temporal discounting framework

Understanding the exploration patterns of foragers in the wild provides fundamental insight into animal behavior. Recent experimental evidence has demonstrated that path lengths (distances between consecutive turns) taken by foragers are well fitted by a power law distribution. Numerous theoretical contributions have posited that “Lévy random walks”—which can produce power law path length distributions—are optimal for memoryless agents searching a sparse reward landscape. It is unclear, however, whether such a strategy is efficient for cognitively complex agents, from wild animals to humans. Here, we developed a model to explain the emergence of apparent power law path length distributions in animals that can learn about their environments. In our model, the agent’s goal during search is to build an internal model of the distribution of rewards in space that takes into account the cost of time to reach distant locations (i.e., temporally discounting rewards). For an agent with such a goal, we find that an optimal model of exploration in fact produces hyperbolic path lengths, which are well approximated by power laws. We then provide support for our model by showing that humans in a laboratory spatial exploration task search space systematically and modify their search patterns under a cost of time. In addition, we find that path length distributions in a large dataset obtained from free-ranging marine vertebrates are well described by our hyperbolic model. Thus, we provide a general theoretical framework for understanding spatial exploration patterns of cognitively complex foragers.