Addressing Challenges in the Application of Animal Movement Ecology to Aquatic Conservation and Management

Abstract

The dynamic nature of most environments forces many animals to move to meet their fundamental needs. This is especially true in aquatic environments where shifts in spatial ecology (which are a result of movements) are among the first adaptive responses of animals to changes in ecosystems. Changes in the movement and distribution of individuals will in turn alter population dynamics and ecosystem structure. Thus, understanding the drivers and impacts of variation in animal movements over time is critical to conservation and spatial planning. Here, we identify key challenges that impede aquatic animal movement science from informing management and conservation, and propose strategies for overcoming them. Challenges include: (1) Insufficient communication between terrestrial and aquatic movement scientists that could be increased through cross-pollination of analytical tools and development of new tools and outputs; (2) Incomplete coverage in many studies of animal space use (e.g., entire life span not considered); (3) Insufficient data archiving and availability; (4) Barriers to incorporating movement data into decision-making processes; and (5) Limited understanding of the value of movement data for management and conservation. We argue that the field of movement ecology is at present an under-tapped resource for aquatic decision-makers, but is poised to play a critical role in future management approaches and policy development.

Revealing migration and reproductive habitat of invasive fish under an active population suppression program

“…Although multi‐year, broad‐scale telemetry programs have benefits in adaptively managed fisheries (Ogburn et al, ), these programs can present major logistical and analytical challenges because the configuration of telemetry arrays may need to be modified to meet shifting project objectives (Buckley, ) or support may become limited (e.g., equipment, financial, in‐kind, and technical, Crossin et al, ). Tagging hundreds of fish may not effectively show the expected behavioral patterns of the population, which, in our study, could partly explain why migratory trajectories were not apparent at several confirmed spawning sites.…”

“…The spatially correlated random effect indicated several "hot spots" near confirmed spawning areas and several unconfirmed sites in the South and Southeast arms. We believe that latent processes (Zuur et al, 2017), possibly resulting from site-specific habitat characteristics (Binder et al, 2018;Marsden et al, 2016), further influence spatial (Ogburn et al, 2017), these programs can present major logistical and analytical challenges because the configuration of telemetry arrays may need to be modified to meet shifting project objectives (Buckley, 2008) or support may become limited (e.g., equipment, financial, in-kind, and technical, Crossin et al, 2017). Tagging hundreds of fish may not effectively show the expected behavioral patterns of the population, which, in our study, could partly explain why migratory trajectories were not apparent at several confirmed spawning sites.…”

Assessment of vessel disturbance to gray whales to inform sustainable ecotourism

“…However, it can be argued that time of season and prey availability are unlikely to influence behavior patterns between the 2 sites because of shared habitat characteristics, and because whales maintained similar activity budgets at each site. Animal behavior classification can be subjective and scale‐dependent (Ogburn et al ), yet the application of RST (Torres et al ) here allowed us to assess each track at an appropriate scale and assign behavior states objectively.…”

Scaling marine fish movement behavior from individuals to populations

“…The issue of sample size has been extensively discussed, especially when considering how movement studies can inform marine conservation and spatial management (Hebblewhite & Haydon, 2010;McGowan et al, 2017;Nguyen et al, 2017;Ogburn et al, 2017). Tags can be expensive (McGowan et al, 2017), are liable to occasional failure or loss, and often result in individual pathways that are data-poor or have a low number of observations.…”