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Williams BK, Brown ED. Four conservation challenges and a synthesis. Ecol Evol 2023; 13:e10052. [PMID: 37153016 PMCID: PMC10154884 DOI: 10.1002/ece3.10052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 04/12/2023] [Accepted: 04/17/2023] [Indexed: 05/09/2023] Open
Abstract
Conservation and management of biological systems involves decision-making over time, with a generic goal of sustaining systems and their capacity to function in the future. We address four persistent and difficult conservation challenges: (1) prediction of future consequences of management, (2) uncertainty about the system's structure, (3) inability to observe ecological systems fully, and (4) nonstationary system dynamics. We describe these challenges in terms of dynamic systems subject to different sources of uncertainty, and we present a basic Markovian framework that can encompass approaches to all four challenges. Finding optimal conservation strategies for each challenge requires issue-specific structural features, including adaptations of state transition models, uncertainty metrics, valuation of accumulated returns, and solution methods. Strategy valuation exhibits not only some remarkable similarities among approaches but also some important operational differences. Technical linkages among the models highlight synergies in solution approaches, as well as possibilities for combining them in particular conservation problems. As methodology and computing software advance, such an integrated conservation framework offers the potential to improve conservation outcomes with strategies to allocate management resources efficiently and avoid negative consequences.
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Affiliation(s)
| | - Eleanor D. Brown
- Science and Decisions CenterU.S. Geological SurveyRestonVirginiaUSA
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Moore JF, Martin J, Waddle H, Campbell Grant EH, Fleming J, Bohnett E, Akre TSB, Brown DJ, Jones MT, Meck JR, Oxenrider K, Tur A, Willey LL, Johnson F. Evaluating the effect of expert elicitation techniques on population status assessment in the face of large uncertainty. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2022; 306:114453. [PMID: 35033890 DOI: 10.1016/j.jenvman.2022.114453] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Revised: 12/08/2021] [Accepted: 01/03/2022] [Indexed: 06/14/2023]
Abstract
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.
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Affiliation(s)
- Jennifer F Moore
- Moore Ecological Analysis and Management, LLC, Gainesville, FL, USA.
| | - Julien Martin
- U.S. Geological Survey, Wetland and Aquatic Research Center, Gainesville, FL, USA
| | - Hardin Waddle
- U.S. Geological Survey, Wetland and Aquatic Research Center, Gainesville, FL, USA
| | - Evan H Campbell Grant
- U.S. Geological Survey, Eastern Ecological Research Center (formerly the Patuxent Wildlife Research Center), S.O. Conte Anadromous Fish Research Lab, 1 Migratory Way, Turners Falls, MA, 01376, USA
| | - Jill Fleming
- U.S. Geological Survey, Eastern Ecological Research Center (formerly the Patuxent Wildlife Research Center), S.O. Conte Anadromous Fish Research Lab, 1 Migratory Way, Turners Falls, MA, 01376, USA
| | - Eve Bohnett
- University of Florida, Department of Landscape Architecture, Gainesville, FL, USA
| | - Thomas S B Akre
- Conservation Ecology Center, Smithsonian Conservation Biology Institute, 1500 Remount Rd, Front Royal, VA, 22630, USA
| | - Donald J Brown
- School of Natural Resources, West Virginia University, Morgantown, WV, 26506, USA; Northern Research Station, U.S.D.A. Forest Service, Parsons, WV, 26287, USA
| | - Michael T Jones
- Natural Heritage and Endangered Species Program, Massachusetts Division of Fisheries and Wildlife, 1 Rabbit Hill Road, Westborough, MA, 01581, USA
| | - Jessica R Meck
- Conservation Ecology Center, Smithsonian Conservation Biology Institute, 1500 Remount Road, Front Royal, VA, 22630, USA
| | - Kevin Oxenrider
- West Virginia Division of Natural Resources, 1 Depot St, Romney, WV, 26757, USA
| | - Anthony Tur
- U.S. Fish and Wildlife Service, 300 Westgate Center, Hadley, MA, 01035, USA
| | - Lisabeth L Willey
- Antioch University New England, Dept. of Environmental Studies, 40 Avon St, Keene, NH, 03431, USA
| | - Fred Johnson
- University of Florida, Dept of Wildlife Ecology and Conservation, Gainesville, FL, USA
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