Pollutants and the health of green sea turtles resident to an urbanized estuary in San Diego, CA

Rapid expansion of coastal anthropogenic development means that critical foraging and developmental habitats often occur near highly polluted and urbanized environments. Although coastal contamination is widespread, the impact this has on long-lived vertebrates like the green turtle (Chelonia mydas) is unclear because traditional experimental methods cannot be applied. We coupled minimally invasive sampling techniques with health assessments to quantify contaminant patterns in a population of green turtles resident to San Diego Bay, CA, a highly urbanized and contaminated estuary. Several chemicals were correlated with turtle size, suggesting possible differences in physiological processes or habitat utilization between life stages. With the exception of mercury, higher concentrations of carapace metals as well as 4,4′-dichlorodiphenyldichloroethylene (DDE) and γ chlordane in blood plasma relative to other sea turtle studies raises important questions about the chemical risks to turtles resident to San Diego Bay. Mercury concentrations exceeded immune function no-effects thresholds and increased carapace metal loads were correlated with higher levels of multiple health markers. These results indicate immunological and physiological effects studies are needed in this population. Our results give insight into the potential conservation risk contaminants pose to sea turtles inhabiting this contaminated coastal habitat, and highlight the need to better manage and mitigate contaminant exposure in San Diego Bay.     

Modeling spatial patterns in fisheries bycatch: improving bycatch maps to aid fisheries management.

Fisheries bycatch, or incidental take, of large vertebrates such as sea turtles, seabirds, and marine mammals, is a pressing conservation and fisheries management issue. Identifying spatial patterns of bycatch is an important element in managing and mitigating bycatch occurrences. Because bycatch of these taxa involves rare events and fishing effort is highly variable in space and time, maps of raw bycatch rates (the ratio of bycatch to fishing effort) can be misleading. Here we show how mapping bycatch can be enhanced through the use of Bayesian hierarchical spatial models. We compare model-based estimates of bycatch rates to raw rates. The model-based estimates were more precise and fit the data well. Using these results, we demonstrate the utility of this approach for providing information to managers on bycatch probabilities and cross-taxa bycatch comparisons. To illustrate this approach, we present an analysis of bycatch data from the U.S. gill net fishery for groundfish in the northwest Atlantic. The goals of this analysis are to produce more reliable estimates of bycatch rates, assess similarity of spatial patterns between taxa, and identify areas of elevated risk of bycatch.     

A landscape-scale framework to identify refugia from multiple stressors

From a conservation perspective, quantifying potential refugial capacity has been predominantly focused on climate refugia, which is critical for maintaining the persistence of species and ecosystems. However, protection from other stressors, such as human-induced changes in fire and hydrology, that cause habitat loss, degradation, and fragmentation is also necessary to ensure that conservation efforts focused on climate are not undermined by other threats. Thus, conceptual and methodological advances for quantifying potential refugia from multiple anthropogenic stressors are important to support conservation efforts. We devised a new conceptual approach, the domains of refugia, for assessing refugial capacity that identifies areas where exposure to multiple stressors is low. In our framework, patterns of environmental variability (e.g., increased frequency of warm summers), thresholds of resilience, and extent and intensity of stressors are used to identify areas of potential refugia from a suite of ongoing anthropogenic stressors (e.g., changes in fire regime). To demonstrate its utility, we applied the framework to a Southern California landscape. Sites with high refugial capacity (super-refugia sites) had on average 30% fewer extremely warm summers, 20% fewer fire events, 10% less exposure to altered river channels and riparian areas, and 50% fewer recreational trails than the surrounding landscape. Our results suggest that super-refugia sites (∼8200 km²) for some natural communities are underrepresented in the existing protected area network, a finding that can inform efforts to expand protected areas. Our case study highlights how considering exposure to multiple stressors can inform planning and practice to conserve biodiversity in a changing world.     

Using community‐level metrics to monitor the effects of marine protected areas on biodiversity

Marine protected areas (MPAs) are used to protect species, communities, and their associated habitats, among other goals. Measuring MPA efficacy can be challenging, however, particularly when considering responses at the community level. We gathered 36 abundance and 14 biomass data sets on fish assemblages and used meta‐analysis to evaluate the ability of 22 distinct community diversity metrics to detect differences in community structure between MPAs and nearby control sites. We also considered the effects of 6 covariates—MPA size and age, MPA size and age interaction, latitude, total species richness, and level of protection—on each metric. Some common metrics, such as species richness and Shannon diversity, did not differ consistently between MPA and control sites, whereas other metrics, such as total abundance and biomass, were consistently different across studies. Metric responses derived from the biomass data sets were more consistent than those based on the abundance data sets, suggesting that community‐level biomass differs more predictably than abundance between MPA and control sites. Covariate analyses indicated that level of protection, latitude, MPA size, and the interaction between MPA size and age affect metric performance. These results highlight a handful of metrics, several of which are little known, that could be used to meet the increasing demand for community‐level indicators of MPA effectiveness.