Projecting the performance of conservation interventions

Law, Elizabeth A.; Ferraro, Paul J.; Arcese, Peter; Bryan, Brett A.; Davis, Katrina; Gordon, Ascelin; Holden, Matthew; Iacona, Gwenllian D.; Marcos-Martínez, Raymundo; McAlpine, Clive; Rhodes, Jonathan R.; Sze, Jocelyne S.; Wilson, Kerrie A.

doi:10.1016/j.biocon.2017.08.029

Cited by 39 publications

(37 citation statements)

References 66 publications

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“…Given our reliance on observational data, more insight into causal processes could be gained by more widely applying novel statistical methods that seek to strengthen a causality inference from observational data (Law et al, ). Causal inference approaches force researchers to think more deeply about the direct and indirect relationships of variables in their study systems (Ferraro, Sanchirico, & Smith, ).…”

Section: Rigorous Science In Applied Ecologymentioning

confidence: 99%

“…Causal inference approaches force researchers to think more deeply about the direct and indirect relationships of variables in their study systems (Ferraro, Sanchirico, & Smith, ). These approaches include controlling for confounding factors by matching (to control observable confounders) and use of panel data and synthetic controls to control for unobservable confounders, as well as instrumental variables to eliminate unobservable confounders (reviewed by Law et al, ). Time‐series observational data are particularly useful because they are unidirectional—cause must precede effect (Dornelas et al, ) and approaches such as convergent cross mapping are designed to test for causal effects (Sugihara et al, ).…”

Section: Rigorous Science In Applied Ecologymentioning

confidence: 99%

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Exploratory and confirmatory research in the open science era

Nilsen

Bowler

Linnell

2020

Journal of Applied Ecology

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Applied ecological research is increasingly inspired by the open science movement. However, new challenges about how we define our science when biodiversity data are being shared and re‐used are not solved. Among these challenges is the risk associated with blurring the distinction between research that mainly seeks to explore patterns with no a‐priori articulated hypotheses (exploratory research), and research that explicitly tests a‐priori formulated hypotheses (confirmatory research). A rapid screening of a random selection of the peer‐reviewed literature suggests that neither experimental protocols nor hypothesis‐testing sensu stricto are common in applied ecological research. In addition, most experiments are carried out on small spatial scales, which contrast with current global policy needs and research trends towards addressing large spatial and temporal scales. This latter trend makes it unfeasible for policy to rely mainly on insights gained from experimental research. To solve fundamental local, regional and global societal challenges, we need both exploratory and confirmatory research. However, the fundamental roles that confirmatory research testing a‐priori hypothesis play in establishing causal relationships need to be revaluated in applied ecological research. A clearer distinction between exploratory and confirmatory research is currently needed, and could be facilitated by allocating journal sections to different types of research; by embracing new tools offered by the open science era, such as pre‐registration of hypothesis; by establishing new systems where post‐hoc hypotheses emerging through exploration can also be registered for later testing; and by more broad adoption of causal inference methods that foster more structured testing of hypotheses about causal mechanisms from observational biodiversity data. Synthesis and applications. To gain the full benefits of the open science era, researchers, funding bodies and journal editors should explicitly consider approaches and incentives that encourage openness about methods and approaches, as well as value the plurality of scientific approaches needed to address questions in applied ecology and conservation science.

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Section: Rigorous Science In Applied Ecologymentioning

confidence: 99%

Section: Rigorous Science In Applied Ecologymentioning

confidence: 99%

Exploratory and confirmatory research in the open science era

Nilsen

Bowler

Linnell

2020

Journal of Applied Ecology

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“…Provide fundamental insights into the dynamics of both target species and ecological systems (Conroy & Peterson, 2013;Evans et al, 2013;Salafsky, Margoluis, & Redford, 2016;Saunders, Cuthbert, & Zipkin, 2018); 2. Offer a transparent, systematic, and repeatable way to assess, contrast and project the potential efficacy of conservation management solutions (Holden & Ellner, 2016;Law et al, 2017;McCarthy et al, 2004). Offer a transparent, systematic, and repeatable way to assess, contrast and project the potential efficacy of conservation management solutions (Holden & Ellner, 2016;Law et al, 2017;McCarthy et al, 2004).…”

Section: Quantitative Models In Conservation Managementmentioning

confidence: 99%

“…Modeling tools are being used by specialists but also by non-modelers and conservation managers with little quantitative training (Barraquand et al, 2014;Conroy & Peterson, 2013;Dietze et al, 2018;Schmolke, Thorbek, DeAngelis, & Grimm, 2010;Touchon & McCoy, 2016;Yackulic et al, 2013). As a result, quantitative models are rapidly becoming entrenched in the toolbox of conservation practice, policy, and management (Akçakaya et al, 2016;Conroy & Peterson, 2013;Getz et al, 2018;Guisan et al, 2013;Law et al, 2017;Nicholson et al, 2018;Schmolke et al, 2010). In fact, quantitative models are fundamental components of some formal conservation decision-making frameworks (Conroy & Peterson, 2013;Schwartz et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…We suggest using our review alongside those previously published by Schmolke et al (2010), Dormann et al (2012), Addison et al (2013), and Law et al (2017), whose perspectives and recommendations provide complementary and additional details on specific methods, for example, on species distribution models (Dormann et al, 2012). In the context of environmental decision-making, Schmolke et al (2010) and Addison et al (2013) conducted reviews of quantitative modeling and listed their own recommendations.…”

Section: Introductionmentioning

confidence: 99%

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A concise guide to developing and using quantitative models in conservation management

García‐Díaz

Prowse

Anderson

et al. 2019

Conservation Science and Practice

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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. K E Y W O R D Sapplied conservation, ecological models, prediction, projection, simulation model, statistical model, uncertainty GLOSSARY Conservation management: activities conducted with the primary aim of conserving species and systems to achieve maintenance or restoration of biodiversity features. Correlative or correlational model: a model representing noncausative associations between two or more variables.Differential equation: Mathematical equation that relates a function with one or more of its derivatives (i.e., the instantaneous rate of change of a function). For example, an expression for the rate of change (in space or time) of a population. Estimation: using data to approximate, with some degree of uncertainty, the parameter values of a model. Gene drive: a synthetic, self-replicating genetic element that can propagate one or more focal genes through a population.

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Characterizing tourism benefits associated with top‐predator conservation in coastal British Columbia

Martone

Naidoo

Coyle

et al. 2020

Aquatic Conservation

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Facing public concern over costs related to top‐predator reintroductions and conservation, ecosystem services such as ecotourism are often used to evoke benefits that outweigh or offset those costs. Quantifying these benefits using rigorous scientific methods can provide confidence to policymakers and other stakeholders that predators can in fact deliver positive outcomes to people living alongside them. The evaluation of these benefits is often anecdotal or qualitative, however, and empirical quantifications are rare. In coastal marine ecosystems, sea otter reintroduction is seen as a conservation success to some but a bane to others. The contribution of sea otters (Enyhdra lutris) to tourism revenue is touted as a crucial ecosystem service benefit to offset the loss of shellfish harvesting and associated revenue, but remains unquantified, weakening the favourable reception of conservation action. The potential economic benefits of sea otters associated with tourism and the extent to which benefits are realized were evaluated based on: (i) choice‐experiment surveys of tourists; and (ii) interviews with tourism operators in British Columbia. Sea otters were a strong factor in people's choices regarding wildlife viewing, and sea otters could have large benefits for local economies. Alongside socio‐economic characteristics, tourism experience influences tourists’ preferences. Tourism operators did not perceive sea otters as strongly influencing tourist choice, highlighting the gap that can occur between the perception and the reality of tourist preferences, leading to missed opportunities for the alignment of economic development with conservation actions.

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Projecting the performance of conservation interventions

Abstract: Successful decision-making for environmental management requires evidence of the performance and efficacy of proposed conservation interventions. Projecting the future impacts of prospective conservation

Cited by 39 publications

References 66 publications

Exploratory and confirmatory research in the open science era

Exploratory and confirmatory research in the open science era

A concise guide to developing and using quantitative models in conservation management

Characterizing tourism benefits associated with top‐predator conservation in coastal British Columbia

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