[Show abstract][Hide abstract] ABSTRACT: Where conservation resources are limited and conservation targets are diverse, robust yet flexible priority-setting frameworks are vital. Priority-setting is especially important for geographically widespread species with distinct populations subject to multiple threats that operate on different spatial and temporal scales. Marine turtles are widely distributed and exhibit intra-specific variations in population sizes and trends, as well as reproduction and morphology. However, current global extinction risk assessment frameworks do not assess conservation status of spatially and biologically distinct marine turtle Regional Management Units (RMUs), and thus do not capture variations in population trends, impacts of threats, or necessary conservation actions across individual populations. To address this issue, we developed a new assessment framework that allowed us to evaluate, compare and organize marine turtle RMUs according to status and threats criteria. Because conservation priorities can vary widely (i.e. from avoiding imminent extinction to maintaining long-term monitoring efforts) we developed a "conservation priorities portfolio" system using categories of paired risk and threats scores for all RMUs (n = 58). We performed these assessments and rankings globally, by species, by ocean basin, and by recognized geopolitical bodies to identify patterns in risk, threats, and data gaps at different scales. This process resulted in characterization of risk and threats to all marine turtle RMUs, including identification of the world's 11 most endangered marine turtle RMUs based on highest risk and threats scores. This system also highlighted important gaps in available information that is crucial for accurate conservation assessments. Overall, this priority-setting framework can provide guidance for research and conservation priorities at multiple relevant scales, and should serve as a model for conservation status assessments and priority-setting for widespread, long-lived taxa.
PLoS ONE 09/2011; 6(9):e24510. DOI:10.1371/journal.pone.0024510 · 3.23 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Globally distributed, highly migratory marine megafauna present serious challenges to designing effective conserva-tion strategies that target specific habitats and threats to population persistence. Marine turtles exhibit several characteristics that make multiple population levels, life-history traits, and stages potentially appropriate targets for conservation (Wallace et al., 2010), including distinct feeding and breeding areas for adults, geographically separated ontogenetic habitats, and complex population structures (Bowen & Karl, 2007). Different threats that operate on various spatial scales can differentially affect the same marine turtle population, warranting distinct conservation actions. Although the International Union for the Conservation of Nature (IUCN) Red List TM provides conservation status assess-ments for marine turtle species at a global level, these listings belie regional variations in population sizes and trends (Wallace et al., 2010). This discrepancy has led the IUCN/ SSC Marine Turtle Specialist Group (MTSG) to advocate for regional assessments at several scales below the species level that have been defined as biologically discrete popula-tion units (Seminoff & Shanker, 2008). In a new paper, Bass, Anderson & De Silva (2011) applied the Key Biodiversity Area (KBA) approach to marine turtle nesting sites in Melanesia. As KBAs originally were devel-oped for terrestrial species, highly migratory, widespread marine species, such as marine turtles, are questionable candidates for the KBA approach because their mere presence can trigger KBA status, despite the relative lack of importance of that area to the species' survival (Edgar et al. 2008). Perhaps unsurprisingly, then, Bass et al.'s (2011) initial application of generic KBA thresholds based on presence of IUCN Red List of Threatened Species Critically Endangered and Endangered species generated extremely high numbers of marine turtle KBAs within the region, a result that was far too inclusive for meaningful conserva-tion. However, the authors then adjusted the thresholds to be more biologically relevant for marine turtle nesting distributions, and generated a subsequent list of KBAs that prioritized for established nesting sites with consistently higher numbers of nesting females. This underscores that the most significant issue for the KBA approach to taxa like marine turtles is the popula-tion-level context of the analysis. Bass et al.'s (2011) identification of KBAs within Management Units (MUs), or separate breeding populations defined primarily by genetic distinctiveness (Moritz, 1994) that are considered functionally independent (i.e. exhibit distinct demographic processes) was of critical importance. MUs are recognized as logical targets for conservation efforts, so identifying KBAs within MU boundaries makes the process more applicable to informing existing conservation strategies in the broader Melanesia region. The lessons learned for migratory marine species KBA identification are the need to interpret criteria in the relevant population context, and to calibrate thresholds appropriately to generate mean-ingful recommendations. Along the lines of delineation of marine turtle population segments, Wallace et al. (2010) recently introduced a multi-scaled, nested envelope framework for organizing marine turtle populations below the level of species but above the level of individual nesting populations, termed Regional Management Units (RMUs). RMUs integrate information from nesting sites, genetics, tag returns, satellite telemetry and other data to identify geographically defined and biologically discrete population segments for all marine turtle species. As such, these RMUs spatially integrate sufficient information to account for complexities in marine
[Show abstract][Hide abstract] ABSTRACT: Resolving threats to widely distributed marine megafauna requires definition of the geographic distributions of both the threats as well as the population unit(s) of interest. In turn, because individual threats can operate on varying spatial scales, their impacts can affect different segments of a population of the same species. Therefore, integration of multiple tools and techniques--including site-based monitoring, genetic analyses, mark-recapture studies and telemetry--can facilitate robust definitions of population segments at multiple biological and spatial scales to address different management and research challenges.
To address these issues for marine turtles, we collated all available studies on marine turtle biogeography, including nesting sites, population abundances and trends, population genetics, and satellite telemetry. We georeferenced this information to generate separate layers for nesting sites, genetic stocks, and core distributions of population segments of all marine turtle species. We then spatially integrated this information from fine- to coarse-spatial scales to develop nested envelope models, or Regional Management Units (RMUs), for marine turtles globally.
The RMU framework is a solution to the challenge of how to organize marine turtles into units of protection above the level of nesting populations, but below the level of species, within regional entities that might be on independent evolutionary trajectories. Among many potential applications, RMUs provide a framework for identifying data gaps, assessing high diversity areas for multiple species and genetic stocks, and evaluating conservation status of marine turtles. Furthermore, RMUs allow for identification of geographic barriers to gene flow, and can provide valuable guidance to marine spatial planning initiatives that integrate spatial distributions of protected species and human activities. In addition, the RMU framework--including maps and supporting metadata--will be an iterative, user-driven tool made publicly available in an online application for comments, improvements, download and analysis.
PLoS ONE 12/2010; 5(12):e15465. DOI:10.1371/journal.pone.0015465 · 3.23 Impact Factor