Rapid prototyping for quantifying belief weights of competing hypotheses about emergent diseases.

Emerging diseases can have devastating consequences for wildlife and require a rapid response. A critical first step towards developing appropriate management is identifying the etiology of the disease, which can be difficult to determine, particularly early in emergence. Gathering and synthesizing existing information about potential disease causes, by leveraging expert knowledge or relevant existing studies, provides a principled approach to quickly inform decision-making and management efforts. Additionally, updating the current state of knowledge as more information becomes available over time can reduce scientific uncertainty and lead to substantial improvement in the decision-making process and the application of management actions that incorporate and adapt to newly acquired scientific understanding. Here we present a rapid prototyping method for quantifying belief weights for competing hypotheses about the etiology of disease using a combination of formal expert elicitation and Bayesian hierarchical modeling. We illustrate the application of this approach for investigating the etiology of stony coral tissue loss disease (SCTLD) and discuss the opportunities and challenges of this approach for addressing emergent diseases. Lastly, we detail how our work may apply to other pressing management or conservation problems that require quick responses. We found the rapid prototyping methods to be an efficient and rapid means to narrow down the number of potential hypotheses, synthesize current understanding, and help prioritize future studies and experiments. This approach is rapid by providing a snapshot assessment of the current state of knowledge. It can also be updated periodically (e.g., annually) to assess changes in belief weights over time as scientific understanding increases. Synthesis and applications: The rapid prototyping approaches demonstrated here can be used to combine knowledge from multiple experts and/or studies to help with fast decision-making needed for urgent conservation issues including emerging diseases and other management problems that require rapid responses. These approaches can also be used to adjust belief weights over time as studies and expert knowledge accumulate and can be a helpful tool for adapting management decisions.

Evaluating the effect of expert elicitation techniques on population status assessment in the face of large uncertainty.

Population projection models are important tools for conservation and management. They are often used for population status assessments, for threat analyses, and to predict the consequences of conservation actions. Although conservation decisions should be informed by science, critical decisions are often made with very little information to support decision-making. Conversely, postponing decisions until better information is available may reduce the benefit of a conservation decision. When empirical data are limited or lacking, expert elicitation can be used to supplement existing data and inform model parameter estimates. The use of rigorous techniques for expert elicitation that account for uncertainty can improve the quality of the expert elicited values and therefore the accuracy of the projection models. One recurring challenge for summarizing expert elicited values is how to aggregate them. Here, we illustrate a process for population status assessment using a combination of expert elicitation and data from the ecological literature. We discuss the importance of considering various aggregation techniques, and illustrate this process using matrix population models for the wood turtle (Glyptemys insculpta) to assist U.S. Fish and Wildlife Service decision-makers with their Species Status Assessment. We compare estimates of population growth using data from the ecological literature and four alternative aggregation techniques for the expert-elicited values. The estimate of population growth rate based on estimates from the literature (λmean = 0.952, 95% CI: 0.87-1.01) could not be used to unequivocally reject the hypotheses of a rapidly declining population nor the hypothesis of a stable, or even slightly growing population, whereas our results for the expert-elicited estimates supported the hypothesis that the wood turtle population will decline over time. Our results showed that the aggregation techniques used had an impact on model estimates, suggesting that the choice of techniques should be carefully considered. We discuss the benefits and limitations associated with each method and their relevance to the population status assessment. We note a difference in the temporal scope or inference between the literature-based estimates that provided insights about historical changes, whereas the expert-based estimates were forward looking. Therefore, conducting an expert-elicitation in addition to using parameter estimates from the literature improved our understanding of our species of interest.

What is the effect of poaching activity on wildlife species?

Poaching is a pervasive threat to wildlife, yet quantifying the direct effect of poaching on wildlife is rarely possible because both wildlife and threat data are infrequently collected concurrently. In this study, we used poaching data collected through the Management Information System (MIST) and wildlife camera trap data collected by the Tropical Ecology Assessment and Monitoring (TEAM) network from 2014 to 2017 in Volcanoes National Park, Rwanda. We implemented co-occurrence multi-season occupancy models that accounted for imperfect detection to investigate the effect of poaching on initial occupancy, colonization, and extinction of five mammal species. Specifically, we focused on two species of conservation concern (mountain gorilla [Gorilla beringei beringei] and golden monkey [Cercopithecus mitis kandti]), and three species targeted by poachers (black-fronted duiker [Cephalophus nigrifrons], bushbuck [Tragelaphus scriptus], and African buffalo [Syncerus caffer]). We found that the probability of local extinction was highest in sites with poaching activity for golden monkey and bushbuck. In addition, the probability of initial occupancy for golden monkey was highest in sites without poaching activity. We only found weak evidence of effects of poaching on parameters governing the occupancy dynamics of the other species. All species showed evidence of poaching presence affecting the probability of detection of the wildlife species. This is the first study to our knowledge to combine direct threat observations from ranger-based monitoring data with camera trap wildlife observations to quantify the effect of poaching on wildlife. Given the widespread collection of ranger-based monitoring and camera trap data, our approach is broadly applicable to numerous protected areas and has the potential to significantly improve conservation management. Specifically, the relationship between poaching activity and wildlife population dynamics can be combined with information on the relationship between ranger patrols and poaching activity to develop models useful for making wise decisions about ranger patrol deployment.

Bootstrap methods can help evaluate monitoring program performance to inform restoration as part of an adaptive management program.

The objective of many fish and wildlife restoration programs is to utilize management actions to change the state of a system. Because restoration programs are often expensive, iteratively assessing whether the restoration is having the desired outcome is a critical aspect of learning how to inform ongoing and sampling designs to evaluate proposed restoration programs. We provide an example of how we are using data resampling as part of an adaptive restoration process to test the effectiveness of a restoration action and associated monitoring program to restore the degraded Lone Cabbage oyster reef in Suwannee Sound, Florida in the northeast Gulf of Mexico. We use a resampling framework through simulations to inform the progress of the restoration efforts by examining the direction and magnitude of the differences in live oyster counts between restored and unrestored (wild) reefs over time. In addition, we evaluated the effort (number of sites sampled) needed to determine the effect of restoration to understand how many surveys should be conducted in subsequent sampling seasons. These efforts allow us to provide timely insight into the effectiveness of both our monitoring efforts and restoration strategy which is of critical importance not only to the restoration of Lone Cabbage Reef but to larger restoration efforts within the Gulf of Mexico as part of the consolidated Deepwater Horizon settlements and funded restoration efforts.

Optimal allocation of law enforcement patrol effort to mitigate poaching activities.

Poaching is a global problem causing the decline of species worldwide. Optimizing the efficiency of ranger patrols to deter poaching activity at the lowest possible cost is crucial for protecting species with limited resources. We applied decision analysis and spatial optimization algorithms to allocate efforts of ranger patrols throughout a national park. Our objective was to mitigate poaching activity at or below management risk targets for the lowest monetary cost. We examined this trade-off by constructing a Pareto efficiency frontier using integer linear programming. We used data from a ranger-based monitoring program in Nyungwe National Park, Rwanda. Our measure of poaching risk is based on dynamic occupancy models that account for imperfect detection of poaching activities. We found that in order to achieve a 5% reduction in poaching risk, 622 ranger patrol events (each corresponding to patrolling 1-km2 sites) were needed within a year at a cost of US$49,760. In order to attain a 60% reduction in poaching risk, 15,560 patrol events were needed at a cost of US$1,244,800. We evaluated the trade-off between patrol cost and poaching risk based on our model by constructing a Pareto efficiency frontier and park managers found the solution for a 50% risk reduction to be a practical trade-off based on funding constraints (comparable to recent years) and the diminishing returns between risk mitigation and cost. This expected reduction in risk required 8,558 patrol events per year at a cost of US$684,640. Our results suggest that optimal solutions could increase efficiency compared to the actual effort allocations from 2006 to 2016 in Nyungwe National Park (e.g., risk reductions of ~30% under recent budgets compared to ~50% reduction in risk under the optimal strategy). The modeling framework in this study took into account imperfect detection of poaching risk as well as the directional and conditional nature of ranger patrol events given the spatial adjacency relationships of neighboring sites and access points. Our analyses can help to improve the efficiency of ranger patrols, and the modeling framework can be broadly applied to other spatial conservation planning problems with conditional, multilevel, site selection.

Shifting through the forest: home range, movement patterns, and diet of the eastern chimpanzee (Pan troglodytes schweinfurthii) in Nyungwe National Park, Rwanda.

Populations of the endangered eastern chimpanzee (Pan troglodytes schweinfurthii) are declining throughout their range. Although Nyungwe National Park (NNP) harbors the largest remaining eastern chimpanzee population in Rwanda, we know little about their space use and dietary patterns. We studied home range, movement, and diet of two communities of chimpanzees in NNP using daily tracking data (6:00 am to 6:00 pm) collected from 2000 to 2015. One community, Mayebe, resided in the forest center, and the other community, Cyamudongo, inhabited a forest fragment located about 10 km from the main forest. Home range estimated with the 95% kernel density estimation (KDE) method was 21 km2 for the Mayebe community and 4 km2 for the Cyamudongo community. Chimpanzee home range sizes were smaller during the dry versus wet season and varied monthly throughout the year. The Mayebe community had an average hourly step length of 75 ± SE 5 m with a daily movement range of 987 ± SE 71 m, while the Cyamudongo community had a shorter hourly step length of 52 ± SE 3 m with a daily movement range of 651 ± SE 71 m. Both chimpanzee communities fed primarily on Ficus spp. Other important dietary items included fruits of Symphonia globulifera, Syzygium guineense, and Chrysophyllum gorungosanum for the Mayebe community and Trilepisium madagascariense for the Cyamudongo community. Food choice varied monthly and seasonally for each chimpanzee community. Our study provides the first estimates of home range size and movement parameters for chimpanzees in Rwanda and documents their food habits and seasonal variations therein. We also identified the 50% core home range for each chimpanzee community and suggest this area as the focus of management actions. These results could help park management reduce threats to chimpanzees and other sympatric species by improving the efficiency of ranger patrols.