Conflicts and solutions related to marine turtle conservation initiatives in the Caribbean basin: identifying new challenges

Conflicts among and between local, national, regional and international stakeholders involved in marine turtle conservation are increasing. Often, they arise because of different socio-economic backgrounds of the people or groups involved. Here, we identified and assessed the conservation-based conflicts occurring in 24 of the 39 Caribbean countries, including their frequency, level of severity, number of stakeholders' groups involved, the degree to which they hinder conservation goals, and potential solutions. Using a cross-sectional social survey, we evaluated the presence and details of conservation conflicts provided by 72 respondents. The respondents included conservation-based project leaders, researchers, people involved in policy-based decision-making, conservation volunteers (community-based conservation groups), and species experts with experience working on marine turtle conservation programs in the Caribbean. The respondents identified 136 conflicts, and we grouped them into 16 different categories. The most commonly mentioned causes of conflicts were: 1) the ‘lack of enforcement by local authorities to support conservation-based legislation or programs’ (18%); 2) ‘legal consumption of turtles by one sector of community clashing the conservation aspirations of other sectors of community (14%); and 3) ’variable enforcement of legislation to limit/prohibit use across range states of the species (10%). From our data it is also apparent that illicit activities in the region are also likely to impact the future success of conservation or monitoring based projects and programs. Overall, an exhaustive review was carried out, and the potential solutions were gathered. Due to the level of severity (physical violence) that some conflicts have reached, achieving solutions will be challenging without mediation, mutual cooperation around shared values, and adaptive management arrangements. Achieving this will require combinations of bottom up and top down collaborative governance approaches

Evaluating the use of marine protected areas by endangered species: A habitat selection approach

1. Optimizing the design of marine protected area (MPA) networks for the conservation of migratory marine species and their habitats involves a suite of important considerations, such as appropriate scale requirements and the distribution of anthropogenic impacts. Often, a fundamental component of the conservation planning process is delineating areas of high use or high biodiversity within a region of interest. 2. However, basing conservation strategies off merely the number of individuals in an ecosystem is outdated and potentially subject to arbitrary thresholds. To be effective at protecting marine megafauna, MPAs would ideally encompass habitats used by focal species. Through satellite-tracking studies, evidence of whether species actually use protected areas is emerging. 3. Here, we present a multispecies perspective on habitat selection within existing MPAs throughout the Floridian ecoregion, which encompasses coastal Florida and the Gulf of Mexico. Using an 11-year satellite-tracking dataset on 235 marine turtles, we used integrated step selection analysis to quantify the effects of sea turtle behavioural state (identified by a switching state-space model), protected area status, chlorophyll and bathymetry on habitat selection. 4. Our results show that sea turtles do select for existing protected areas, specifically multi-use zones, while controlling for the effects of depth and primary productivity. However, our analysis revealed that turtles showed no selection for the no-take zones within MPAs, during either transiting or foraging. 5. These findings contribute to the existing literature base of MPA use for highly mobile, imperilled species and could inform management of existing MPAs or changes to zoning, specifically multi-use to no-take. Our use of a robust spatial modelling framework to evaluate habitat selection relative to MPAs could be incorporated into conservation planning to build MPA networks designed to accommodate migratory species

Towards a multisensor station for automated biodiversity monitoring

Rapid changes of the biosphere observed in recent years are caused by both small and large scale drivers, like shifts in temperature, transformations in land-use, or changes in the energy budget of systems. While the latter processes are easily quantifiable, documentation of the loss of biodiversity and community structure is more difficult. Changes in organismal abundance and diversity are barely documented. Censuses of species are usually fragmentary and inferred by often spatially, temporally and ecologically unsatisfactory simple species lists for individual study sites. Thus, detrimental global processes and their drivers often remain unrevealed. A major impediment to monitoring species diversity is the lack of human taxonomic expertise that is implicitly required for large-scale and fine-grained assessments. Another is the large amount of personnel and associated costs needed to cover large scales, or the inaccessibility of remote but nonetheless affected areas. To overcome these limitations we propose a network of Automated Multisensor stations for Monitoring of species Diversity (AMMODs) to pave the way for a new generation of biodiversity assessment centers. This network combines cutting-edge technologies with biodiversity informatics and expert systems that conserve expert knowledge. Each AMMOD station combines autonomous samplers for insects, pollen and spores, audio recorders for vocalizing animals, sensors for volatile organic compounds emitted by plants (pVOCs) and camera traps for mammals and small invertebrates. AMMODs are largely self-containing and have the ability to pre-process data (e.g. for noise filtering) prior to transmission to receiver stations for storage, integration and analyses. Installation on sites that are difficult to access require a sophisticated and challenging system design with optimum balance between power requirements, bandwidth for data transmission, required service, and operation under all environmental conditions for years. An important prerequisite for automated species identification are databases of DNA barcodes, animal sounds, for pVOCs, and images used as training data for automated species identification. AMMOD stations thus become a key component to advance the field of biodiversity monitoring for research and policy by delivering biodiversity data at an unprecedented spatial and temporal resolution. (C) 2022 Published by Elsevier GmbH on behalf of Gesellschaft fur Okologie

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

This is the final version. Available on open access from Wiley via the DOI in this recordData availability statement: The data that support the findings of this study are available in the Supplementary Material of this article and Zenodo (https://doi.org/10.5281/zenodo.5898578). Details for all animals included in this study are provided in Appendices S1 and S2. Data used to create the spatial networks are listed in the Appendices S3 and S4. The geospatial files for all networks are available on the Migratory Connectivity in the Ocean Project website (https://mico.eco) and Dryad (https://doi.org/10.5061/dryad.j3tx95xg9). Additional data that support the findings of this study are available from the corresponding author upon reasonable request.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.International Climate Initiative (IKI)German Federal Ministry for the Environment, Nature Conservation and Nuclear Safety (BMU

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

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

Trade of marine turtles along the Southwestern Coast of the Gulf of Venezuela

Abstract Background Marine turtles play an important role in the culture and economy of numerous coastal communities around the world. However, the legal framework that regulates the consumptive use of these reptiles varies among countries.For example, the consumption of these reptiles has been regarded as common in several rural areas of Venezuela, especially in the eastern coast of the Guajira Peninsula. Methods To assess the scale and cultural component of this use, we interviewed 35 residents from the southwestern coast of the Gulf of Venezuela (Venezuelan part of the Guajira Peninsula), using a combination of in-depth and semi-structured interviews. We carried out a field and detailed market-based observations on the Guajira Peninsula to detect the sale and use of marine turtle products. We focused on three main categories of use; the type of product, routes of trade, and the price of products. Results All of the marine turtle species reported from the Gulf of Venezuela were used, and the prices of products varied among their type, species of origin, and the distance from the capture area to a marketplace. We obtained evidence connecting Wayuú Indigenous people's traditions and beliefs with marine turtle use, and also they are used as traditional products such as medicine, and as an economic resource to sustain their communities. Conclusion It is probable that trade of marine turtle products is placing pressure on populations in the Gulf of Venezuela. We recommend the implementation of an inter-institutional conservation-portfolio be developed for the Peninsula to evaluate actions related to this concern

Optimising sample sizes for animal distribution analysis using tracking data

Knowledge of the spatial distribution of populations is fundamental to management plans for any species. When tracking data are used to describe distributions, it is sometimes assumed that the reported locations of individuals delineate the spatial extent of areas used by the target population. Here we examine existing approaches to validate this assumption, highlight caveats, and propose a new method for a more informative assessment of the number of tracked animals (i.e. sample size) necessary to identify distribution patterns. We show how this assessment can be achieved by considering the heterogeneous use of habitats by a target species using the probabilistic property of a utilisation distribution. Our methods are compiled in the r package SDLfilter. We illustrate and compare the protocols underlying existing and new methods using conceptual models and demonstrate an application of our approach using a large satellite tracking dataset of flatback turtles Natator depressus tagged with accurate Fastloc‐GPS tags (n = 69). Our approach has applicability for the post hoc validation of sample sizes required for the robust estimation of distribution patterns across a wide range of taxa, populations and life‐history stages of animals