Antipoaching standards in onshore hydrocarbon concessions drawn from a Central African case study

Unsustainable hunting outside protected areas is threatening tropical biodiversity worldwide and requires conservationists to engage increasingly in antipoaching activities. Following the example of ecocertified logging companies, we argue that other extractive industries managing large concessions should engage in antipoaching activities as part of their environmental management plans. Onshore hydrocarbon concessions should also adopt antipoaching protocols as a standard because they represent a biodiversity threat comparable to logging. We examined the spatiotemporal patterns of small‐ and large‐mammal poaching in an onshore oil concession in Gabon, Central Africa, with a Bayesian occupancy model based on signs of poaching collected from 2010 to 2015 on antipoaching patrols. Patrol locations were initially determined based on local intelligence and past patrol successes (adaptive management) and subsequently with a systematic sampling of the concession. We generated maps of poaching probability in the concession and determined the temporal trends of this threat over 5 years. The spatiotemporal patterns of large‐ and small‐mammal poaching differed throughout the concession, and likely these groups will need different management strategies. By elucidating the relationship between site‐specific sampling effort and detection probability, the Bayesian method allowed us to set goals for future antipoaching patrols. Our results indicate that a combination of systematic sampling and adaptive management data is necessary to infer spatiotemporal patterns with the statistical method we used. On the basis of our case study, we recommend hydrocarbon companies interested in implementing efficient antipoaching activities in their onshore concessions to lay the foundation of long‐needed industry standards by: adequately measuring antipoaching effort; mixing adaptive management and balanced sampling; setting goals for antipoaching effort; pairing patrols with large‐mammal monitoring; supporting antipoaching patrols across the landscape; restricting access to their concessions; performing random searches for bushmeat and mammal products at points of entry; controlling urban and agricultural expansion; supporting bushmeat alternatives; and supporting land‐use planning.     

Estimating freshwater turtle mortality rates and population declines following hook ingestion

Freshwater turtle populations are susceptible to declines following small increases in the mortality of adults, making it essential to identify and understand potential threats. Freshwater turtles ingest fish hooks associated with recreational angling, and this is likely a problem because hook ingestion is a source of additive mortality for sea turtles. We used a Bayesian‐modeling framework, observed rates of hook ingestion by freshwater turtles, and mortality of sea turtles from hook ingestion to examine the probability that a freshwater turtle in a given population ingests a hook and subsequently dies from it. We used the results of these analyses and previously published life‐history data to simulate the effects of hook ingestion on population growth for 3 species of freshwater turtle. In our simulation, the probability that an individual turtle ingests a hook and dies as a result was 1.2–11%. Our simulation results suggest that this rate of mortality from hook ingestion is sufficient to cause population declines. We believe we have identified fish‐hook ingestion as a serious yet generally overlooked threat to the viability of freshwater turtle populations.     

Prospects for stakeholder coordination by protected‐area managers in Europe

Growing resource demands by humans, invasive species, natural hazards, and a changing climate have created broad‐scale impacts and the need for broader‐extent conservation activities that span ownerships and even political borders. Implementing regional‐scale conservation brings great challenges, and learning how to overcome these challenges is essential for maintaining biodiversity (i.e., richness and evenness of biological communities) and ecosystem functions and services across scales and borders in the face of system change. We administered an online survey to examine factors potentially driving perspectives of protected‐area (PA) managers regarding coordination with neighboring PAs and other stakeholders (i.e., stakeholder coordination) for conserving biodiversity and ecosystem services during the next decade within diverse regions across Europe. Although >70% (n = 58) of responding PA managers indicated that climate change and invasive species are relevant for their PAs, they gave <50% probability that these threats could be mitigated through stakeholder coordination. They thought there was a >60% probability (n = 85) that stakeholder coordination would take place with the aim to improve conservation outcomes. Consistent with the foundation on which many European PAs were established, managers viewed maintaining or enhancing biodiversity as the most important (>70%; n = 61) expected benefit. Other benefits included maintaining or enhancing human resources and environmental education (range of Bayesian credibility intervals [CIs] 57–93%). They thought the main barriers to stakeholder coordination were the lack of human and economic resources (CI 59–67% chance of hindering; n = 64) followed by communication and interstakeholder differences in political structures and laws (CI 51–64% probability of hindering). European policies and strategies that address these hindering factors could be particularly effective means of enabling implementation of green infrastructure networks in which PAs are the nodes.     

Inferring the nature of anthropogenic threats from long‐term abundance records

Diagnosing the processes that threaten species persistence is critical for recovery planning and risk forecasting. Dominant threats are typically inferred by experts on the basis of a patchwork of informal methods. Transparent, quantitative diagnostic tools would contribute much‐needed consistency, objectivity, and rigor to the process of diagnosing anthropogenic threats. Long‐term census records, available for an increasingly large and diverse set of taxa, may exhibit characteristic signatures of specific threatening processes and thereby provide information for threat diagnosis. We developed a flexible Bayesian framework for diagnosing threats on the basis of long‐term census records and diverse ancillary sources of information. We tested this framework with simulated data from artificial populations subjected to varying degrees of exploitation and habitat loss and several real‐world abundance time series for which threatening processes are relatively well understood: bluefin tuna (Thunnus maccoyii) and Atlantic cod (Gadus morhua) (exploitation) and Red Grouse (Lagopus lagopus scotica) and Eurasian Skylark (Alauda arvensis) (habitat loss). Our method correctly identified the process driving population decline for over 90% of time series simulated under moderate to severe threat scenarios. Successful identification of threats approached 100% for severe exploitation and habitat loss scenarios. Our method identified threats less successfully when threatening processes were weak and when populations were simultaneously affected by multiple threats. Our method selected the presumed true threat model for all real‐world case studies, although results were somewhat ambiguous in the case of the Eurasian Skylark. In the latter case, incorporation of an ancillary source of information (records of land‐use change) increased the weight assigned to the presumed true model from 70% to 92%, illustrating the value of the proposed framework in bringing diverse sources of information into a common rigorous framework. Ultimately, our framework may greatly assist conservation organizations in documenting threatening processes and planning species recovery. Inferencia la Naturaleza de las Amenazas Antropogénicas para los Registros de Abundancia a Largo Plazo     

Effects of Multiple Levels of Social Organization on Survival and Abundance

Abstract: Identifying how social organization shapes individual behavior, survival, and fecundity of animals that live in groups can inform conservation efforts and improve forecasts of population abundance, even when the mechanism responsible for group‐level differences is unknown. We constructed a hierarchical Bayesian model to quantify the relative variability in survival rates among different levels of social organization (matrilines and pods) of an endangered population of killer whales (Orcinus orca). Individual killer whales often participate in group activities such as prey sharing and cooperative hunting. The estimated age‐specific survival probabilities and survivorship curves differed considerably among pods and to a lesser extent among matrilines (within pods). Across all pods, males had lower life expectancy than females. Differences in survival between pods may be caused by a combination of factors that vary across the population's range, including reduced prey availability, contaminants in prey, and human activity. Our modeling approach could be applied to demographic rates for other species and for parameters other than survival, including reproduction, prey selection, movement, and detection probabilities.     

Bayesian Decision‐Network Modeling of Multiple Stakeholders for Reef Ecosystem Restoration in the Coral Triangle

Proposals for marine conservation measures have proliferated in the last 2 decades due to increased reports of fishery declines and interest in conservation. Fishers and fisheries managers have often disagreed strongly when discussing controls on fisheries. In such situations, ecosystem‐based models and fisheries‐stock assessment models can help resolve disagreements by highlighting the trade‐offs that would be made under alternative management scenarios. We extended the analytical framework for modeling such trade‐offs by including additional stakeholders whose livelihoods and the value they place on conservation depend on the condition of the marine ecosystem. To do so, we used Bayesian decision‐network models (BDNs) in a case study of an Indonesian coral reef fishery. Our model included interests of the fishers and fishery managers; individuals in the tourism industry; conservation interests of the state, nongovernmental organizations, and the local public; and uncertainties in ecosystem status, projections of fisheries revenues, tourism growth, and levels of interest in conservation. We calculated the total utility (i.e., value) of a range of restoration scenarios. Restricting net fisheries and live‐fish fisheries appeared to be the best compromise solutions under several combinations of settings of modeled variables. Results of our case study highlight the implications of alternate formulations for coral reef stakeholder utility functions and discount rates for the calculation of the net benefits of alternative fisheries management options. This case study may also serve as a useful example for other decision analyses with multiple stakeholders. Modelo de Red de Decisión Bayesiana de Múltiples Actores Interesados en la Restauración de Ecosistemas de Arrecife en el Triángulo de Coral     

A dynamic simulation model to support reduction in illegal trade within legal wildlife markets

Sustainable wildlife trade is critical for biodiversity conservation, livelihoods, and food security. Regulatory frameworks are needed to secure these diverse benefits of sustainable wildlife trade. However, regulations limiting trade can backfire, sparking illegal trade if demand is not met by legal trade alone. Assessing how regulations affect wildlife market participants’ incentives is key to controlling illegal trade. Although much research has assessed how incentives at both the harvester and consumer ends of markets are affected by regulations, little has been done to understand the incentives of traders (i.e., intermediaries). We built a dynamic simulation model to support reduction in illegal wildlife trade within legal markets by focusing on incentives traders face to trade legal or illegal products. We used an Approximate Bayesian Computation approach to infer illegal trading dynamics and parameters that might be unknown (e.g., price of illegal products). We showcased the utility of the approach with a small-scale fishery case study in Chile, where we disentangled within-year dynamics of legal and illegal trading and found that the majority (∼77%) of traded fish is illegal. We utilized the model to assess the effect of policy interventions to improve the fishery's sustainability and explore the trade-offs between ecological, economic, and social goals. Scenario simulations showed that even significant increases (over 200%) in parameters proxying for policy interventions enabled only moderate improvements in ecological and social sustainability of the fishery at substantial economic cost. These results expose how unbalanced trader incentives are toward trading illegal over legal products in this fishery. Our model provides a novel tool for promoting sustainable wildlife trade in data-limited settings, which explicitly considers traders as critical players in wildlife markets. Sustainable wildlife trade requires incentivizing legal over illegal wildlife trade and consideration of the social, ecological, and economic impacts of interventions.