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Johnson FA, Eraud C, Francesiaz C, Zimmerman GS, Koneff MD. Using the R package popharvest to assess the sustainability of offtake in birds. Ecol Evol 2024; 14:e11059. [PMID: 38571795 PMCID: PMC10985383 DOI: 10.1002/ece3.11059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 01/14/2024] [Accepted: 02/05/2024] [Indexed: 04/05/2024] Open
Abstract
The R package popharvest was designed to help assess the sustainability of offtake in birds when only limited demographic information is available. In this article, we describe some basics of harvest theory and then discuss several considerations when using the different approaches in popharvest to assess whether observed harvests are unsustainable. Throughout, we emphasize the importance of distinguishing between the scientific and policy aspects of managing offtake. The principal product of popharvest is a sustainable harvest index (SHI), which can indicate whether the harvest is unsustainable but not the converse. SHI is estimated based on a simple, scalar model of logistic population growth, whose parameters may be estimated using limited knowledge of demography. Uncertainty in demography leads to a distribution of SHI values and it is the purview of the decision-maker to determine what amounts to an acceptable risk when failing to reject the null hypothesis of sustainability. The attitude toward risk, in turn, will likely depend on the decision-maker's objective(s) in managing offtake. The management objective as specified in popharvest is a social construct, informed by biology, but ultimately it is an expression of social values that usually vary among stakeholders. We therefore suggest that any standardization of criteria for management objectives in popharvest will necessarily be subjective and, thus, hard to defend in diverse decision-making situations. Because of its ease of use, diverse functionalities, and a minimal requirement of demographic information, we expect the use of popharvest to become widespread. Nonetheless, we suggest that while popharvest provides a useful platform for rapid assessments of sustainability, it cannot substitute for sufficient expertise and experience in harvest theory and management.
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Affiliation(s)
| | - Cyril Eraud
- Office Français de la Biodiversité, Direction de la Recherche et de l'Appui Scientifique, Service Conservation et Gestion des Espèces à EnjeuxVilliers‐en‐BoisFrance
| | - Charlotte Francesiaz
- Office Français de la Biodiversité, Direction de la Recherche et de l'Appui Scientifique, Service Conservation et Gestion des Espèces ExploitéesJuvignacFrance
| | - Guthrie S. Zimmerman
- Division of Migratory Bird ManagementU.S. Fish and Wildlife ServiceSacramentoCaliforniaUSA
| | - Mark D. Koneff
- Division of Migratory Bird ManagementU.S. Fish and Wildlife ServiceOronoMaineUSA
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Keating LM, Randall L, Stanton R, McCormack C, Lucid M, Seaborn T, Converse SJ, Canessa S, Moehrenschlager A. Using Decision Analysis to Determine the Feasibility of a Conservation Translocation. DECISION ANALYSIS 2023. [DOI: 10.1287/deca.2023.0472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2023]
Abstract
Conservation translocations, intentional movements of species to protect against extinction, have become widespread in recent decades and are projected to increase further as biodiversity loss continues worldwide. The literature abounds with analyses to inform translocations and assess whether they are successful, but the fundamental question of whether they should be initiated at all is rarely addressed formally. We used decision analysis to assess northern leopard frog reintroduction in northern Idaho, with success defined as a population that persists for at least 50 years. The Idaho Department of Fish and Game was the decision maker (i.e., the agency that will use this assessment to inform their decisions). Stakeholders from government, indigenous groups, academia, land management agencies, and conservation organizations also participated. We built an age-structured population model to predict how management alternatives would affect probability of success. In the model, we explicitly represented epistemic uncertainty around a success criterion (probability of persistence) characterized by aleatory uncertainty. For the leading alternative, the mean probability of persistence was 40%. The distribution of the modelling results was bimodal, with most parameter combinations resulting in either very low (<5%) or relatively high (>95%) probabilities of success. Along with other considerations, including cost, the Idaho Department of Fish and Game will use this assessment to inform a decision regarding reintroduction of northern leopard frogs. Conservation translocations may benefit greatly from more widespread use of decision analysis to counter the complexity and uncertainty inherent in these decisions. History: This paper has been accepted for the Decision Analysis Special Issue on Further Environmental Sustainability. Funding: This work was supported by the Wilder Institute/Calgary Zoo, the U.S. Fish and Wildlife Service [Grant F18AS00095], the NSF Idaho EPSCoR Program and the National Science Foundation [Grant OIA-1757324], and the Hunt Family Foundation. Supplemental Material: The online appendix is available at https://doi.org/10.1287/deca.2023.0472 .
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Fischer JH, Wittmer HU, Kenup CF, Parker KA, Cole R, Debski I, Taylor GA, Ewen JG, Armstrong DP. Predicting harvest impact and establishment success when translocating highly mobile and endangered species. J Appl Ecol 2022. [DOI: 10.1111/1365-2664.14219] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Affiliation(s)
- JH Fischer
- School of Biological Sciences Victoria University of Wellington New Zealand
- Aquatic Unit Department of Conservation Wellington
| | - HU Wittmer
- School of Biological Sciences Victoria University of Wellington New Zealand
| | - CF Kenup
- Wildlife Ecology Group Massey University Palmerston North New Zealand
| | | | - R Cole
- Murihiku District Office Department of Conservation Invercargill
| | - I Debski
- Aquatic Unit Department of Conservation Wellington
| | - GA Taylor
- Aquatic Unit Department of Conservation Wellington
| | - JG Ewen
- Institute of Zoology Zoological Society of London London UK
| | - DP Armstrong
- Wildlife Ecology Group Massey University Palmerston North New Zealand
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4
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Plein M, O'Brien KR, Holden MH, Adams MP, Baker CM, Bean NG, Sisson SA, Bode M, Mengersen KL, McDonald‐Madden E. Modeling total predation to avoid perverse outcomes from cat control in a data-poor island ecosystem. CONSERVATION BIOLOGY : THE JOURNAL OF THE SOCIETY FOR CONSERVATION BIOLOGY 2022; 36:e13916. [PMID: 35352431 PMCID: PMC9804458 DOI: 10.1111/cobi.13916] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Revised: 12/22/2021] [Accepted: 03/21/2022] [Indexed: 06/14/2023]
Abstract
Data hungry, complex ecosystem models are often used to predict the consequences of threatened species management, including perverse outcomes. Unfortunately, this approach is impractical in many systems, which have insufficient data to parameterize ecosystem interactions or reliably calibrate or validate such models. Here we demonstrate a different approach, using a minimum realistic model to guide decisions in data- and resource-scarce systems. We illustrate our approach with a case-study in an invaded ecosystem from Christmas Island, Australia, where there are concerns that cat eradication to protect native species, including the red-tailed tropicbird, could release meso-predation by invasive rats. We use biophysical constraints (metabolic demand) and observable parameters (e.g. prey preferences) to assess the combined cat and rat abundances which would threaten the tropicbird population. We find that the population of tropicbirds cannot be sustained if predated by 1607 rats (95% credible interval (CI) [103, 5910]) in the absence of cats, or 21 cats (95% CI [2, 82]) in the absence of rats. For every cat removed from the island, the bird's net population growth rate improves, provided that the rats do not increase by more than 77 individuals (95% CI [30, 174]). Thus, in this context, one cat is equivalent to 30-174 rats. Our methods are especially useful for on-the-ground predator control in the absence of knowledge of predator-predator interactions, to assess whether 1) the current abundance of predators threatens the prey population of interest, 2) managing one predator species alone is sufficient to protect the prey species given potential release of another predator, and 3) control of multiple predator species is needed to meet the conservation goal. Our approach demonstrates how to use limited information for maximum value in data-poor systems, by shifting the focus from predicting future trajectories, to identifying conditions which threaten the conservation goal. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Michaela Plein
- School of Earth and Environmental ScienceUniversity of QueenslandSt LuciaQueenslandAustralia
- Centre for Biodiversity and Conservation ScienceUniversity of QueenslandSt LuciaQueenslandAustralia
- Administration de la nature et des forêtsDiekirchLuxembourg
| | - Katherine R. O'Brien
- School of Chemical EngineeringUniversity of QueenslandSt LuciaQueenslandAustralia
| | - Matthew H. Holden
- Centre for Biodiversity and Conservation ScienceUniversity of QueenslandSt LuciaQueenslandAustralia
- School of Biological SciencesUniversity of QueenslandBrisbaneQueenslandAustralia
- School of Mathematics and PhysicsUniversity of QueenslandBrisbaneQueenslandAustralia
| | - Matthew P. Adams
- School of Earth and Environmental ScienceUniversity of QueenslandSt LuciaQueenslandAustralia
- Centre for Biodiversity and Conservation ScienceUniversity of QueenslandSt LuciaQueenslandAustralia
- School of Chemical EngineeringUniversity of QueenslandSt LuciaQueenslandAustralia
- School of Mathematical SciencesQueensland University of TechnologyBrisbaneQueenslandAustralia
- ARC Centre of Excellence for Mathematical and Statistical FrontiersQueensland University of, TechnologyBrisbaneQueenslandAustralia
| | - Christopher M. Baker
- School of Mathematics and StatisticsThe University of MelbourneParkvilleVictoriaAustralia
- Melbourne Centre for Data ScienceThe University of MelbourneParkvilleVictoriaAustralia
- Centre of Excellence for Biosecurity Risk AnalysisThe University of MelbourneMelbourneVictoriaAustralia
| | - Nigel G. Bean
- School of Mathematical SciencesUniversity of AdelaideAdelaideSouth AustraliaAustralia
- Australian Research Council Centre of Excellence for Mathematical and Statistical FrontiersUniversity of AdelaideAdelaideSouth AustraliaAustralia
| | - Scott A. Sisson
- School of Mathematics and StatisticsUniversity of New South WalesSydneyNew South WalesAustralia
- UNSW Data Science HubUniversity of New SouthWales, SydneyNew South WalesAustralia
| | - Michael Bode
- School of Mathematical SciencesQueensland University of TechnologyBrisbaneQueenslandAustralia
| | - Kerrie L. Mengersen
- School of Mathematical SciencesQueensland University of TechnologyBrisbaneQueenslandAustralia
- ARC Centre of Excellence for Mathematical and Statistical FrontiersQueensland University of, TechnologyBrisbaneQueenslandAustralia
| | - Eve McDonald‐Madden
- School of Earth and Environmental ScienceUniversity of QueenslandSt LuciaQueenslandAustralia
- Centre for Biodiversity and Conservation ScienceUniversity of QueenslandSt LuciaQueenslandAustralia
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5
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Bozzuto C, Canessa S, Koella JC. Exploring artificial habitat fragmentation to control invasion by infectious wildlife diseases. Theor Popul Biol 2021; 141:14-23. [PMID: 34139201 DOI: 10.1016/j.tpb.2021.06.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Revised: 05/26/2021] [Accepted: 06/01/2021] [Indexed: 10/21/2022]
Abstract
One way to reduce the impacts of invading wildlife diseases is setting up fences that would reduce the spread of pathogens by limiting connectivity, similarly to exclusion fences that are commonly used to conserve threatened species against invasive predators. One of the problems with fences is that, while they may have the short-term benefit of impeding the spread of disease, this benefit may be offset by potential long-term ecological costs of fragmentation by fencing. However, managers facing situations where a pathogen has been detected near the habitat of a (highly) vulnerable species may be willing to explore such a trade-off. To aid such exploration quantitatively, we present a series of models trading off the benefits of fragmentation (potential reduction of disease impacts on susceptible individuals) against its costs (both financial and ecological, i.e. reduced viability in the patches created by fragmentation), and exploring the effects of fragmentation on non-target species richness. For all model variants we derive the optimal number of artificial patches. We show that pre-emptive disease fences may have benefits when the risk of disease exceeds the impacts of fragmentation, when fence failure rates are lower than a specific threshold, and when sufficient resources are available to implement optimal solutions. A useful step to initiate planning is to obtain information about the expected number of initial infection events and on the host's extinction threshold with respect to the focal habitat and management duration. Our approach can assist managers to identify whether the trade-offs support the decision to fence and how intensive fragmentation should be.
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Affiliation(s)
- Claudio Bozzuto
- Wildlife Analysis GmbH, Oetlisbergstrasse 38, 8053 Zurich, Switzerland.
| | - Stefano Canessa
- Wildlife Health Ghent, Department of Pathology, Bacteriology and Avian Diseases, Faculty of Veterinary Medicine, Ghent University, Salisburylaan 133, 9820 Merelbeke, Belgium
| | - Jacob C Koella
- Laboratory of ecology and epidemiology of parasites, Institute of Biology, University of Neuchâtel, Neuchâtel, Switzerland
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Armstrong DP, Parlato EH, Egli B, Dimond WJ, Kwikkel R, Berggren Å, McCready M, Parker KA, Ewen JG. Using long-term data for a reintroduced population to empirically estimate future consequences of inbreeding. CONSERVATION BIOLOGY : THE JOURNAL OF THE SOCIETY FOR CONSERVATION BIOLOGY 2021; 35:859-869. [PMID: 32997349 DOI: 10.1111/cobi.13646] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2020] [Revised: 09/08/2020] [Accepted: 09/23/2020] [Indexed: 06/11/2023]
Abstract
Inbreeding depression is an important long-term threat to reintroduced populations. However, the strength of inbreeding depression is difficult to estimate in wild populations because pedigree data are inevitably incomplete and because good data are needed on survival and reproduction. Predicting future population consequences is especially difficult because this also requires projecting future inbreeding levels and their impacts on long-term population dynamics, which are subject to many uncertainties. We illustrate how such projections can be derived through Bayesian state-space modeling methods based on a 26-year data set for North Island Robins (Petroica longipes) reintroduced to Tiritiri Matangi Island in 1992. We used pedigree data to model increases in the average inbreeding level (F) over time based on kinship of possible breeding pairs and to estimate empirically Ne /N (effective/census population size). We used multiple imputation to model the unknown components of inbreeding coefficients, which allowed us to estimate effects of inbreeding on survival for all 1458 birds in the data set while modeling density dependence and environmental stochasticity. This modeling indicated that inbreeding reduced juvenile survival (1.83 lethal equivalents [SE 0.81]) and may have reduced subsequent adult survival (0.44 lethal equivalents [0.81]) but had no apparent effect on numbers of fledglings produced. Average inbreeding level increased to 0.10 (SE 0.001) as the population grew from 33 (0.3) to 160 (6) individuals over the 25 years, giving a Ne/N ratio of 0.56 (0.01). Based on a model that also incorporated habitat regeneration, the population was projected to reach a maximum of 331-1144 birds (median 726) in 2130, then to begin a slow decline. Without inbreeding, the population would be expected stabilize at 887-1465 birds (median 1131). Such analysis, therefore, makes it possible to empirically derive the information needed for rational decisions about inbreeding management while accounting for multiple sources of uncertainty.
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Affiliation(s)
- Doug P Armstrong
- Wildlife Ecology Group, Massey University, Private Bag 11222, Palmerston North, New Zealand
| | - Elizabeth H Parlato
- Wildlife Ecology Group, Massey University, Private Bag 11222, Palmerston North, New Zealand
| | - Barbara Egli
- Wildlife Ecology Group, Massey University, Private Bag 11222, Palmerston North, New Zealand
| | - Wendy J Dimond
- Wildlife Ecology Group, Massey University, Private Bag 11222, Palmerston North, New Zealand
- Current address: ANU Medical School, ANU College of Health and Medicine, The Australian National University, Parkville, ACT, 2601, Australia
| | - Renske Kwikkel
- Van Hall Instituut, Agora 1, 8934 CJ, Leeuwarden, Netherlands
| | - Åsa Berggren
- Department of Ecology, Swedish University of Agricultural Sciences, Box 7070, Uppsala, 750 07, Sweden
| | | | - Kevin A Parker
- Parker Conservation, PO Box 130, Warkworth, Auckland, New Zealand
| | - John G Ewen
- Institute of Zoology, Zoological Society of London, Regent's Park, London, U.K
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7
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Ferrière C, Zuël N, Ewen JG, Jones CG, Tatayah V, Canessa S. Assessing the risks of changing ongoing management of endangered species. Anim Conserv 2020. [DOI: 10.1111/acv.12602] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- C. Ferrière
- Mauritian Wildlife Foundation Vacoas Mauritius
- Ebony Forest Reserve Chamarel Chamarel Mauritius
| | - N. Zuël
- Mauritian Wildlife Foundation Vacoas Mauritius
- Ebony Forest Reserve Chamarel Chamarel Mauritius
| | - J. G. Ewen
- Institute of Zoology Zoological Society of London London UK
| | - C. G. Jones
- Mauritian Wildlife Foundation Vacoas Mauritius
- Durrell Wildlife Conservation Trust St Helier UK
| | - V. Tatayah
- Mauritian Wildlife Foundation Vacoas Mauritius
| | - S. Canessa
- Institute of Zoology Zoological Society of London London UK
- Wildlife Health Ghent Department of Pathology, Bacteriology and Avian Diseases Faculty of Veterinary Medicine Ghent University Merelbeke Belgium
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8
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Ringma J, Barnes MD, Bode M. Australian birds could benefit from predator exclusion fencing. CONSERVATION SCIENCE AND PRACTICE 2020. [DOI: 10.1111/csp2.168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022] Open
Affiliation(s)
- Jeremy Ringma
- School of Global, Urban and Social StudiesRMIT University Melbourne Victoria Australia
| | - Megan D. Barnes
- School of Biological SciencesThe University of Queensland Brisbane Queensland Australia
| | - Michael Bode
- Science and Engineering Faculty, Mathematical Sciences, Applied and Computational MathematicsQueensland University of Technology Brisbane Queensland Australia
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Martin DM, Johnson FA. Incorporating uncertainty and risk into decision making to reduce nitrogen inputs to impaired waters. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2019; 249:109380. [PMID: 31434050 PMCID: PMC7901712 DOI: 10.1016/j.jenvman.2019.109380] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/11/2019] [Revised: 08/05/2019] [Accepted: 08/07/2019] [Indexed: 06/10/2023]
Abstract
This article aims to understand decision making under uncertainty and risk, with a case study on Cape Cod, Massachusetts. Decision makers need to consider imperfect information on the cost and effectiveness of advanced nitrogen-removing on-site wastewater treatment systems as options to mitigate water quality degradation. Research included modeling nitrogen load reduction to impaired coastal waters from seven treatment system technologies and eliciting expert knowledge on their costs. Predictions of nitrogen load removal and cost for each technology incorporated variation in effectiveness and uncertainty in household water use, costs, and expert confidence in costs. The predictions were evaluated using the Pareto efficiency concept to reveal tradeoffs between cost and effectiveness. The stochastic dominance index was used to identify preferred technologies for risk-averse decision making, assuming no further learning is possible. Lastly, the predictions were combined into a cost-effectiveness metric to estimate the expected payoff of implementing the best treatment system in the face of uncertainty and the expected payoff of learning which treatment systems are most cost-effective over time. The expected value of perfect information was calculated as the difference between the expected payoffs. Three technologies revealed Pareto efficient tradeoffs between cost and effectiveness, whereas one technology was the preferred risk-averse option in the absence of future learning. There was a high expected value of perfect information, which could motivate adaptive management on Cape Cod. This research demonstrated decision analysis methods to guide future research and decision making toward meeting water quality objectives and reducing uncertainty.
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Affiliation(s)
- David M Martin
- National Health and Environmental Effects Research Laboratory, U.S. Environmental Protection Agency, Narragansett, RI, USA; Maryland/DC Chapter, The Nature Conservancy, Bethesda, MD, USA.
| | - Fred A Johnson
- Wetland and Aquatic Research Center, U.S. Geological Survey, Gainesville, FL, USA
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10
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García-Díaz P, Prowse TAA, Anderson DP, Lurgi M, Binny RN, Cassey P. A concise guide to developing and using quantitative models in conservation management. CONSERVATION SCIENCE AND PRACTICE 2019; 1:e11. [PMID: 31915752 PMCID: PMC6949132 DOI: 10.1002/csp2.11] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Quantitative models are powerful tools for informing conservation
management and decision-making. As applied modeling is increasingly used to
address conservation problems, guidelines are required to clarify the scope of
modeling applications and to facilitate the impact and acceptance of models by
practitioners. We identify three key roles for quantitative models in
conservation management: (a) to assess the extent of a conservation problem; (b)
to provide insights into the dynamics of complex social and ecological systems;
and, (c) to evaluate the efficacy of proposed conservation interventions. We
describe 10 recommendations to facilitate the acceptance of quantitative models
in conservation management, providing a basis for good practice to guide their
development and evaluation in conservation applications. We structure these
recommendations within four established phases of model construction, enabling
their integration within existing workflows: (a) design (two recommendations);
(b) specification (two); (c) evaluation (one); and (d) inference (five).
Quantitative modeling can support effective conservation management provided
that both managers and modelers understand and agree on the place for models in
conservation. Our concise review and recommendations will assist conservation
managers and modelers to collaborate in the development of quantitative models
that are fit-for-purpose, and to trust and use these models appropriately while
understanding key drivers of uncertainty.
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Affiliation(s)
| | - Thomas A A Prowse
- School of Mathematical Sciences, The University of Adelaide, North Terrace, South Australia, Australia
| | | | - Miguel Lurgi
- Centre for Biodiversity Theory and Modelling, Theoretical and Experimental Ecology Station, CNRS-Paul Sabatier University, Moulis, France
| | - Rachelle N Binny
- Manaaki Whenua - Landcare Research, Lincoln, New Zealand.,Te Pūnaha Matatini, Centre of Research Excellence for Complex Systems and Networks, Auckland, New Zealand
| | - Phillip Cassey
- School of Biological Sciences, The University of Adelaide, North Terrace, South Australia, Australia
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11
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García‐Díaz P, Prowse TA, Anderson DP, Lurgi M, Binny RN, Cassey P. A concise guide to developing and using quantitative models in conservation management. CONSERVATION SCIENCE AND PRACTICE 2019. [DOI: 10.1111/csp2.11] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022] Open
Affiliation(s)
| | - Thomas A.A. Prowse
- School of Mathematical SciencesThe University of Adelaide North Terrace South Australia Australia
| | | | - Miguel Lurgi
- Centre for Biodiversity Theory and Modelling, Theoretical and Experimental Ecology StationCNRS‐Paul Sabatier University Moulis France
| | - Rachelle N. Binny
- Manaaki Whenua ‐ Landcare Research Lincoln New Zealand
- Te Pūnaha MatatiniCentre of Research Excellence for Complex Systems and Networks Auckland New Zealand
| | - Phillip Cassey
- School of Biological SciencesThe University of Adelaide North Terrace South Australia Australia
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12
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Yemshanov D, Koch FH, Lu B, Fournier R, Cook G, Turgeon JJ. A new hypervolume approach for assessing environmental risks. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2017; 193:188-200. [PMID: 28226258 DOI: 10.1016/j.jenvman.2017.02.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/21/2016] [Revised: 02/06/2017] [Accepted: 02/11/2017] [Indexed: 06/06/2023]
Abstract
Assessing risks of uncertain but potentially damaging events, such as environmental disturbances, disease outbreaks and pest invasions, is a key analytical step that informs subsequent decisions about how to respond to these events. We present a continuous risk measure that can be used to assess and prioritize environmental risks from uncertain data in a geographical domain. The metric is influenced by both the expected magnitude of risk and its uncertainty. We demonstrate the approach by assessing risks of human-mediated spread of Asian longhorned beetle (ALB, Anoplophora glabripennis) in Greater Toronto (Ontario, Canada). Information about the human-mediated spread of ALB through this urban environment to individual geographical locations is uncertain, so each location was characterized by a set of probabilistic rates of spread, derived in this case using a network model. We represented the sets of spread rates for the locations by their cumulative distribution functions (CDFs) and then, using the first-order stochastic dominance rule, found ordered non-dominant subsets of these CDFs, which we then used to define different classes of risk across the geographical domain, from high to low. Because each non-dominant subset was estimated with respect to all elements of the distribution, the uncertainty in the underlying data was factored into the delineation of the risk classes; essentially, fewer non-dominant subsets can be defined in portions of the full set where information is sparse. We then depicted each non-dominant subset as a point cloud, where points represented the CDF values of each subset element at specific sampling intervals. For each subset, we then defined a hypervolume bounded by the outermost convex frontier of that point cloud. This resulted in a collection of hypervolumes for every non-dominant subset that together serve as a continuous measure of risk, which may be more practically useful than averaging metrics or ordinal rank measures. Overall, the approach offers a rigorous depiction of risk in a geographical domain when the underlying estimates of risk for individual locations are represented by sets or distributions of uncertain estimates. Our hypervolume-based approach can be used to compare assessments made with different datasets and assumptions.
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Affiliation(s)
- Denys Yemshanov
- Natural Resources Canada, Canadian Forest Service, Great Lakes Forestry Centre, 1219 Queen Street East, Sault Ste. Marie, ON, P6A 2E5, Canada.
| | - Frank H Koch
- USDA Forest Service, Southern Research Station, Eastern Forest Environmental Threat Assessment Center, 3041 East Cornwallis Road, Research Triangle Park, NC, 27709, USA
| | - Bo Lu
- Natural Resources Canada, Canadian Forest Service, Great Lakes Forestry Centre, Sault Ste. Marie, ON, Canada
| | - Ronald Fournier
- Natural Resources Canada, Canadian Forest Service, Great Lakes Forestry Centre, Sault Ste. Marie, ON, Canada
| | - Gericke Cook
- USDA Animal and Plant Health Inspection Service, Centre for Plant Health Science and Technology, Plant Protection and Quarantine, Fort Collins, CO, USA
| | - Jean J Turgeon
- Natural Resources Canada, Canadian Forest Service, Great Lakes Forestry Centre, Sault Ste. Marie, ON, Canada
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