Geology topics

Lucas P. Griffin

Publications and source records attributed to Lucas P. Griffin.

3 recordsLinked to original sources

USGS2026

A conceptual framework and methods for studying the connectivity of fishes

Connectivity is a multifaceted concept that has important implications for the management and conservation of marine and freshwater fishes. We developed a conceptual framework that encompasses multiple, interrelated categories of connectedness, including landscape (e.g., structural, functional) connectivity and ecological (e.g., trophic, genetic, demographic) connectivity, that together shape the flow of organisms, energy and information across ecosystems. We also synthesised six key methods that can be used to study connectivity of fishes: (1) telemetry, including satellite, acoustic, radio and passive integrated transponders (PIT), (2) mark-recapture, (3) environmental tracers, including stable isotopes and otolith-microchemistry, (4) genetics, (5) community structure analysis and (6) emerging technologies and tools (e.g., remote sensing and artificial intelligence). For each method, we describe the categories of connectivity it can assess and provide real-world examples where they have been effectively used. We also identify limitations of each method. This article highlights the diverse and evolving toolbox of methods used to assess fish connectivity, underscoring the need for continued collaboration, innovation and integration of new approaches to refine our understanding and address remaining challenges in this critical area of aquatic ecology and fisheries management.

Fish and Fisheries↗

USGS2021

A novel framework to predict relative habitat selection in aquatic systems: Applying machine learning and resource selection functions to acoustic telemetry data from multiple shark species

Resource selection functions (RSFs) have been widely applied to animal tracking data to examine relative habitat selection and to help guide management and conservation strategies. While readily used in terrestrial ecology, RSFs have yet to be extensively used within marine systems. As acoustic telemetry continues to be a pervasive approach within marine environments, incorporation of RSFs can provide new insights to help prioritize habitat protection and restoration to meet conservation goals. To overcome statistical hurdles and achieve high prediction accuracy, machine learning algorithms could be paired with RSFs to predict relative habitat selection for a species within and even outside the monitoring range of acoustic receiver arrays, making this a valuable tool for marine ecologists and resource managers. Here, we apply RSFs using machine learning to an acoustic telemetry dataset of four shark species to explore and predict species-specific habitat selection within a marine protected area. In addition, we also apply this RSF-machine learning approach to investigate predator-prey relationships by comparing and averaging tiger shark relative selection values with the relative selection values derived for eight potential prey-species. We provide methodological considerations along with a framework and flexible approach to apply RSFs with machine learning algorithms to acoustic telemetry data and suggest marine ecologists and resource managers consider adopting such tools to help guide both conservation and management strategies.

Buck Island Reef National Monument, St Croix↗

USGS2021

Regional-scale variability in the movement ecology of marine fishes revealed by an integrative acoustic tracking network

Marine fish movement plays a critical role in ecosystem functioning and is increasingly studied with acoustic telemetry. Traditionally, this research has focused on single species and small spatial scales. However, integrated tracking networks, such as the Integrated Tracking of Aquatic Animals in the Gulf of Mexico (iTAG) network, are building the capacity to monitor multiple species over larger spatial scales. We conducted a synthesis of passive acoustic monitoring data for 29 species (889 transmitters), ranging from large top predators to small consumers, monitored along the west coast of Florida, USA, over 3 yr (2016-2018). Space use was highly variable, with some groups using all monitored areas and others using only the area where they were tagged. The most extensive space use was found for Atlantic tarpon Megalops atlanticus and bull sharks Carcharhinus leucas . Individual detection patterns clustered into 4 groups, ranging from occasionally detected long-distance movers to frequently detected juvenile or adult residents. Synchronized, alongshore, long-distance movements were found for Atlantic tarpon, cobia Rachycentron canadum , and several elasmobranch species. These movements were predominantly northbound in spring and southbound in fall. Detections of top predators were highest in summer, except for nearshore Tampa Bay where the most detections occurred in fall, coinciding with large red drum Sciaenops ocellatus spawning aggregations. We discuss the future of collaborative telemetry research, including current limitations and potential solutions to maximize its impact for understanding movement ecology, conducting ecosystem monitoring, and supporting fisheries management.

Marine Ecology Progress Series↗