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

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

Variation in migration behaviors used by Arctic Terns (Sterna paradisaea) breeding across a wide latitudinal gradient

Arctic Terns (Sterna paradisaea) share a few routes to undertake the longest annual migrations of any organism. To understand how the wide spatial range of their breeding colonies may affect their migration strategies (e.g., departure date), we tracked 53 terns from five North American colonies distributed across 30° of latitude and 90° of longitude. While birds from all colonies arrived in Antarctic waters at a similar time, terns nesting in the Arctic colonies migrated back north more slowly and arrived to their breeding grounds later than those nesting in the colony farther south. Arrival dates in Antarctic waters coincided with the start of favorable foraging conditions (i.e., increased ocean productivity), and similarly arrival dates at breeding colonies coincided with the start of local favorable breeding conditions (i.e., disappearance of snow and ice). Larger birds followed a more direct southbound migration route than smaller birds. On both southbound and northbound migrations, daily distances traveled declined as time spent in contact with the ocean increased, suggesting a trade-off between resting/foraging and traveling. There was more unexplained variation in behavior among individuals than among colonies, and one individual had a distinctive stop around Brazil. Terns nesting in the Arctic have a narrow time window for breeding that will likely increase with continuing declines in sea ice and snow. Departing Arctic Terns likely have few clues about the environmental conditions they will encounter on arrival, and their response to environmental changes at both poles may be assisted by large individual variation in migration strategy

Scale-insensitive estimation of speed and distance traveled from animal tracking data

Speed and distance traveled provide quantifiable links between behavior and energetics, and are among the metrics most routinely estimated from animal tracking data. Researchers typically sum over the straight-line displacements (SLDs) between sampled locations to quantify distance traveled, while speed is estimated by dividing these displacements by time. Problematically, this approach is highly sensitive to the measurement scale, with biases subject to the sampling frequency, the tortuosity of the animal’s movement, and the amount of measurement error. Compounding the issue of scale-sensitivity, SLD estimates do not come equipped with confidence intervals to quantify their uncertainty.https://doi.org/10.1186/s40462-019-0177-

Accuracy of ARGOS Locations of Pinnipeds at-Sea Estimated Using Fastloc GPS

Background: ARGOS satellite telemetry is one of the most widely used methods to track the movements of free-ranging marine and terrestrial animals and is fundamental to studies of foraging ecology, migratory behavior and habitat-use. ARGOS location estimates do not include complete error estimations, and for many marine organisms, the most commonly acquired locations (Location Class 0, A, B, or Z) are provided with no declared error estimate.Methodology/Principal Findings: We compared the accuracy of ARGOS locations to those obtained using Fastloc GPS from the same electronic tags on five species of pinnipeds: 9 California sea lions (Zalophus californianus), 4 Galapagos sea lions (Zalophus wollebaeki), 6 Cape fur seals (Arctocephalus pusillus pusillus), 3 Australian fur seals (A. p. doriferus) and 5 northern elephant seals (Mirounga angustirostris). These species encompass a range of marine habitats (highly pelagic vs coastal), diving behaviors (mean dive durations 2&ndash;21 min) and range of latitudes (equator to temperate). A total of 7,318 ARGOS positions and 27,046 GPS positions were collected. Of these, 1,105 ARGOS positions were obtained within five minutes of a GPS position and were used for comparison. The 68th percentile ARGOS location errors as measured in this study were LC-30.49 km, LC-2 1.01 km, LC-1 1.20 km, LC-0 4.18 km, LC-A 6.19 km, LC-B 10.28 km. Conclusions/Significance: The ARGOS errors measured here are greater than those provided by ARGOS, but within the range of other studies. The error was non-normally distributed with each LC highly right-skewed. Locations of species that make short duration dives and spend extended periods on the surface (sea lions and fur seals) had less error than species like elephant seals that spend more time underwater and have shorter surface intervals. Supplemental data (S1) are provided allowing the creation of density distributions that can be used in a variety of filtering algorithms to improve the quality of ARGOS tracking data.<br /

The political biogeography of migratory marine predators

During their migrations, marine predators experience varying levels of protection and face many threats as they travel through multiple countries' jurisdictions and across ocean basins. Some populations are declining rapidly. Contributing to such declines is a failure of some international agreements to ensure effective cooperation by the stakeholders responsible for managing species throughout their ranges, including in the high seas, a global commons. Here we use biologging data from marine predators to provide quantitative measures with great potential to inform local, national and international management efforts in the Pacific Ocean. We synthesized a large tracking data set to show how the movements and migratory phenology of 1,648 individuals representing 14 species-from leatherback turtles to white sharks-relate to the geopolitical boundaries of the Pacific Ocean throughout species' annual cycles. Cumulatively, these species visited 86% of Pacific Ocean countries and some spent three-quarters of their annual cycles in the high seas. With our results, we offer answers to questions posed when designing international strategies for managing migratory species

Translating Marine Animal Tracking Data into Conservation Policy and Management

There have been efforts around the globe to track individuals of many marine species and assess their movements and distribution with the putative goal of supporting their conservation and management. Determining whether, and how, tracking data have been successfully applied to address real-world conservation issues is however difficult. Here, we compile a broad range of case studies from diverse marine taxa to show how tracking data have helped inform conservation policy and management, including reductions in fisheries bycatch and vessel strikes, and the design and administration of marine protected areas and important habitats. Using these examples, we highlight pathways through which the past and future investment in collecting animal tracking data might be better used to achieve tangible conservation benefits

Diurnal timing of nonmigratory movement by birds: the importance of foraging spatial scales

Timing of activity can reveal an organism's efforts to optimize foraging either by minimizing energy loss through passive movement or by maximizing energetic gain through foraging. Here, we assess whether signals of either of these strategies are detectable in the timing of activity of daily, local movements by birds. We compare the similarities of timing of movement activity among species using six temporal variables: start of activity relative to sunrise, end of activity relative to sunset, relative speed at midday, number of movement bouts, bout duration and proportion of active daytime hours. We test for the influence of flight mode and foraging habitat on the timing of movement activity across avian guilds. We used 64 570 days of GPS movement data collected between 2002 and 2019 for local (non‐migratory) movements of 991 birds from 49 species, representing 14 orders. Dissimilarity among daily activity patterns was best explained by flight mode. Terrestrial soaring birds began activity later and stopped activity earlier than pelagic soaring or flapping birds. Broad‐scale foraging habitat explained less of the clustering patterns because of divergent timing of active periods of pelagic surface and diving foragers. Among pelagic birds, surface foragers were active throughout all 24 hrs of the day while diving foragers matched their active hours more closely to daylight hours. Pelagic surface foragers also had the greatest daily foraging distances, which was consistent with their daytime activity patterns. This study demonstrates that flight mode and foraging habitat influence temporal patterns of daily movement activity of birds.We thank the Nature Conservancy, the Bailey Wildlife Foundation, the Bluestone Foundation, the Ocean View Foundation, Biodiversity Research Institute, the Maine Outdoor Heritage Fund, the Davis Conservation Foundation and The U.S. Department of Energy (DE‐EE0005362), and the Darwin Initiative (19-026), EDP S.A. ‘Fundação para a Biodiversidade’ and the Portuguese Foundation for Science and Technology (FCT) (DL57/2019/CP 1440/CT 0021), Enterprise St Helena (ESH), Friends of National Zoo Conservation Research Grant Program and Conservation Nation, ConocoPhillips Global Signature Program, Maryland Department of Natural Resources, Cellular Tracking Technologies and Hawk Mountain Sanctuary for providing funding and in-kind support for the GPS data used in our analyses

Network analysis of sea turtle movements and connectivity: A tool for conservation prioritization

Aim: Understanding the spatial ecology of animal movements is a critical element in conserving long-lived, highly mobile marine species. Analyzing networks developed from movements of six sea turtle species reveals marine connectivity and can help prioritize conservation efforts. Location: Global. Methods: We collated telemetry data from 1235 individuals and reviewed the literature to determine our dataset's representativeness. We used the telemetry data to develop spatial networks at different scales to examine areas, connections, and their geographic arrangement. We used graph theory metrics to compare networks across regions and species and to identify the role of important areas and connections. Results: Relevant literature and citations for data used in this study had very little overlap. Network analysis showed that sampling effort influenced network structure, and the arrangement of areas and connections for most networks was complex. However, important areas and connections identified by graph theory metrics can be different than areas of high data density. For the global network, marine regions in the Mediterranean had high closeness, while links with high betweenness among marine regions in the South Atlantic were critical for maintaining connectivity. Comparisons among species-specific networks showed that functional connectivity was related to movement ecology, resulting in networks composed of different areas and links. Main conclusions: Network analysis identified the structure and functional connectivity of the sea turtles in our sample at multiple scales. These network characteristics could help guide the coordination of management strategies for wide-ranging animals throughout their geographic extent. Most networks had complex structures that can contribute to greater robustness but may be more difficult to manage changes when compared to simpler forms. Area-based conservation measures would benefit sea turtle populations when directed toward areas with high closeness dominating network function. Promoting seascape connectivity of links with high betweenness would decrease network vulnerability.Fil: Kot, Connie Y.. University of Duke; Estados UnidosFil: Åkesson, Susanne. Lund University; SueciaFil: Alfaro Shigueto, Joanna. Universidad Cientifica del Sur; Perú. University of Exeter; Reino Unido. Pro Delphinus; PerúFil: Amorocho Llanos, Diego Fernando. Research Center for Environmental Management and Development; ColombiaFil: Antonopoulou, Marina. Emirates Wildlife Society-world Wide Fund For Nature; Emiratos Arabes UnidosFil: Balazs, George H.. Noaa Fisheries Service; Estados UnidosFil: Baverstock, Warren R.. The Aquarium and Dubai Turtle Rehabilitation Project; Emiratos Arabes UnidosFil: Blumenthal, Janice M.. Cayman Islands Government; Islas CaimánFil: Broderick, Annette C.. University of Exeter; Reino UnidoFil: Bruno, Ignacio. Instituto Nacional de Investigaciones y Desarrollo Pesquero; ArgentinaFil: Canbolat, Ali Fuat. Hacettepe Üniversitesi; Turquía. Ecological Research Society; TurquíaFil: Casale, Paolo. Università degli Studi di Pisa; ItaliaFil: Cejudo, Daniel. Universidad de Las Palmas de Gran Canaria; EspañaFil: Coyne, Michael S.. Seaturtle.org; Estados UnidosFil: Curtice, Corrie. University of Duke; Estados UnidosFil: DeLand, Sarah. University of Duke; Estados UnidosFil: DiMatteo, Andrew. CheloniData; Estados UnidosFil: Dodge, Kara. New England Aquarium; Estados UnidosFil: Dunn, Daniel C.. University of Queensland; Australia. The University of Queensland; Australia. University of Duke; Estados UnidosFil: Esteban, Nicole. Swansea University; Reino UnidoFil: Formia, Angela. Wildlife Conservation Society; Estados UnidosFil: Fuentes, Mariana M. P. B.. Florida State University; Estados UnidosFil: Fujioka, Ei. University of Duke; Estados UnidosFil: Garnier, Julie. The Zoological Society of London; Reino UnidoFil: Godfrey, Matthew H.. North Carolina Wildlife Resources Commission; Estados UnidosFil: Godley, Brendan J.. University of Exeter; Reino UnidoFil: González Carman, Victoria. Instituto National de Investigación y Desarrollo Pesquero; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Harrison, Autumn Lynn. Smithsonian Institution; Estados UnidosFil: Hart, Catherine E.. Grupo Tortuguero de las Californias A.C; México. Investigacion, Capacitacion y Soluciones Ambientales y Sociales A.C; MéxicoFil: Hawkes, Lucy A.. University of Exeter; Reino UnidoFil: Hays, Graeme C.. Deakin University; AustraliaFil: Hill, Nicholas. The Zoological Society of London; Reino UnidoFil: Hochscheid, Sandra. Stazione Zoologica Anton Dohrn; ItaliaFil: Kaska, Yakup. Dekamer—Sea Turtle Rescue Center; Turquía. Pamukkale Üniversitesi; TurquíaFil: Levy, Yaniv. University Of Haifa; Israel. Israel Nature And Parks Authority; IsraelFil: Ley Quiñónez, César P.. Instituto Politécnico Nacional; MéxicoFil: Lockhart, Gwen G.. Virginia Aquarium Marine Science Foundation; Estados Unidos. Naval Facilities Engineering Command; Estados UnidosFil: López-Mendilaharsu, Milagros. Projeto TAMAR; BrasilFil: Luschi, Paolo. Università degli Studi di Pisa; ItaliaFil: Mangel, Jeffrey C.. University of Exeter; Reino Unido. Pro Delphinus; PerúFil: Margaritoulis, Dimitris. Archelon; GreciaFil: Maxwell, Sara M.. University of Washington; Estados UnidosFil: McClellan, Catherine M.. University of Duke; Estados UnidosFil: Metcalfe, Kristian. University of Exeter; Reino UnidoFil: Mingozzi, Antonio. Università Della Calabria; ItaliaFil: Moncada, Felix G.. Centro de Investigaciones Pesqueras; CubaFil: Nichols, Wallace J.. California Academy Of Sciences; Estados Unidos. Center For The Blue Economy And International Environmental Policy Program; Estados UnidosFil: Parker, Denise M.. Noaa Fisheries Service; Estados UnidosFil: Patel, Samir H.. Coonamessett Farm Foundation; Estados Unidos. Drexel University; Estados UnidosFil: Pilcher, Nicolas J.. Marine Research Foundation; MalasiaFil: Poulin, Sarah. University of Duke; Estados UnidosFil: Read, Andrew J.. Duke University Marine Laboratory; Estados UnidosFil: Rees, ALan F.. University of Exeter; Reino Unido. Archelon; GreciaFil: Robinson, David P.. The Aquarium and Dubai Turtle Rehabilitation Project; Emiratos Arabes UnidosFil: Robinson, Nathan J.. Fundación Oceanogràfic; EspañaFil: Sandoval-Lugo, Alejandra G.. Instituto Politécnico Nacional; MéxicoFil: Schofield, Gail. Queen Mary University of London; Reino UnidoFil: Seminoff, Jeffrey A.. Noaa National Marine Fisheries Service Southwest Regional Office; Estados UnidosFil: Seney, Erin E.. University Of Central Florida; Estados UnidosFil: Snape, Robin T. E.. University of Exeter; Reino UnidoFil: Sözbilen, Dogan. Dekamer—sea Turtle Rescue Center; Turquía. Pamukkale University; TurquíaFil: Tomás, Jesús. Institut Cavanilles de Biodiversitat I Biologia Evolutiva; EspañaFil: Varo Cruz, Nuria. Universidad de Las Palmas de Gran Canaria; España. Ads Biodiversidad; España. Instituto Canario de Ciencias Marinas; EspañaFil: Wallace, Bryan P.. University of Duke; Estados Unidos. Ecolibrium, Inc.; Estados UnidosFil: Wildermann, Natalie E.. Texas A&M University; Estados UnidosFil: Witt, Matthew J.. University of Exeter; Reino UnidoFil: Zavala Norzagaray, Alan A.. Instituto politecnico nacional; MéxicoFil: Halpin, Patrick N.. University of Duke; Estados Unido

Effects of body size on estimation of mammalian area requirements.

Accurately quantifying species' area requirements is a prerequisite for effective area-based conservation. This typically involves collecting tracking data on species of interest and then conducting home range analyses. Problematically, autocorrelation in tracking data can result in space needs being severely underestimated. Based on the previous work, we hypothesized the magnitude of underestimation varies with body mass, a relationship that could have serious conservation implications. To evaluate this hypothesis for terrestrial mammals, we estimated home-range areas with global positioning system (GPS) locations from 757 individuals across 61 globally distributed mammalian species with body masses ranging from 0.4 to 4000 kg. We then applied blockcross validation to quantify bias in empirical home range estimates. Area requirements of mammals 1, meaning the scaling of the relationship changedsubstantially at the upper end of the mass spectrum

A synthesis of marine predator migrations, distribution, species overlap, and use of Pacific Ocean Exclusive Economic Zones

Many marine predator populations are commercially important and are threatened by human activities. As a result, many of these populations are heavily depleted, declining, or are recovering from past depletion. Recovery and management of threatened and exploited marine predators are complicated by life histories that 1) span international waters, 2) are dynamic in space and time, and 3) are hidden from direct observation. My goal with this dissertation was to attain a synthetic understanding of the implications of marine predator migratory life histories on the spatio-temporal dynamics of distribution, species overlap, and residency in Exclusive Economic Zones of countries. I analyzed an electronic tracking dataset provided by the Tagging of Pacific Predators program that contained location data for pinnipeds, seabirds, sharks, tuna, turtles, and whales. This dataset included 257,133 daily locations recorded from 1,679 individuals representing 18 species of pelagic predators electronically tracked in the Pacific Ocean during an eight-year period. Many marine predators are broadly recognized as exceptional migrants but there has been little integration of traditional migratory theory with the study of their movements. In chapter one, I examined whether theoretical nonlinear models of migration developed for ungulates and based upon a fundamental statistic of random walk theory (net squared displacement) provide a useful framework for quantifying and predicting marine predator migratory behavior. I found that migration models fit species as ecologically dissimilar as moose and Pacific bluefin tuna suggesting that a unified approach to quantifying migration across taxa and biomes may be possible. The potential utility of marine protected areas (MPAs) for pelagic conservation is debated, especially for wide-ranging species with large, dynamic area requirements. In chapter two I used kernel density analysis to determine the spatial and temporal extents of the distributions and core habitats of marine predators and quantified patterns of species overlap that could help guide management strategies. I found that spatial management measures may not need to be prohibitively large to include major core habitats of wide-ranging species---at least in reference to the size distribution of large extant MPAs. However, to account for seasonal variability in distribution, spatial measures may need to be dynamic, numerous, and/or embedded within strategic multi-scale zoning strategies. Seals, sharks, tuna, and turtles had high probabilities of overlap with black-footed albatross and sooty shearwaters. Spatial conservation efforts targeted at seabirds could help focus ecosystem management in this vast pelagic realm. Integrated international efforts are required to effectively manage threatened and exploited populations of wide-ranging species. In chapter three I used generalized additive mixed-effects models to investigate non-linear daily trends in the probability of occurrence in Exclusive Economic Zones (EEZs) and in the high seas, and to account for the effects of tagging location, tagging date, track duration, and autocorrelated time-series data. Ninety-four percent of Pacific Ocean EEZs were visited. Land-breeding populations were estimated to spend 14-33% of their annual cycles within the waters of their breeding EEZs, and 53 to 76% of the year in the high seas. In contrast, most fish and shark populations were estimated to spend less than a quarter of their annual cycle in international waters. My results describe the suite of countries with shared management responsibility throughout the year for each species, and detail when this responsibility commences and concludes