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Abstract Conserving species' ability to traverse the landscape is vital for maintaining biodiversity in the face of global change. Connectivity conservation requires identifying important pathways for species' movements and aligning societal support for conservation of those pathways. Contemporary connectivity analyses emphasize the impacts of topography, vegetation and human footprint on species' movements; but largely ignore the role that attitudes, economics and institutions play in practitioners' ability to conserve those movements.We introduce implementation resistance as an analogue of biophysical resistance that combines social, economic and institutional factors that promote or impede connectivity conservation. We demonstrate the utility of integrating implementation resistance as a means of choosing between competing connectivity conservation strategies using wolves in Colorado (USA) as a case study.Our analysis of five potential corridor locations based on biophysical costs revealed substantial differences in the social costs associated with implementing each corridor despite relatively minimal differences in the biophysical costs.Our comparison of hypothetical interventions to reduce implementation resistance illustrates that interventions that reduce conflicts between land use and wolves may substantially reduce overall resistance, those reductions are not as well aligned with connectivity priorities as those resulting from changes in land management agency policy.Our results highlight the need to design conservation interventions that fit both the social and ecological landscape and provide a framework for developing robust, interdisciplinary methods that facilitate implementable connectivity conservation. Read the freePlain Language Summaryfor this article on the Journal blog.
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A framework for contextualizing social‐ecological biases in contributory science data
Abstract Contributory science—including citizen and community science—allows scientists to leverage participant‐generated data while providing an opportunity for engaging with local community members. Data yielded by participant‐generated biodiversity platforms allow professional scientists to answer ecological and evolutionary questions across both geographic and temporal scales, which is incredibly valuable for conservation efforts.The data reported to contributory biodiversity platforms, such as eBird and iNaturalist, can be driven by social and ecological variables, leading to biased data. Though empirical work has highlighted the biases in contributory data, little work has articulated how biases arise in contributory data and the societal consequences of these biases.We present a conceptual framework illustrating how social and ecological variables create bias in contributory science data. In this framework, we present four filters—participation,detectability,samplingandpreference—that ultimately shape the type and location of contributory biodiversity data. We leverage this framework to examine data from the largest contributory science platforms—eBird and iNaturalist—in St. Louis, Missouri, the United States, and discuss the potential consequences of biased data.Lastly, we conclude by providing several recommendations for researchers and institutions to move towards a more inclusive field. With these recommendations, we provide opportunities to ameliorate biases in contributory data and an opportunity to practice equitable biodiversity conservation. Read the freePlain Language Summaryfor this article on the Journal blog.
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- Award ID(s):
- 2223973
- PAR ID:
- 10545583
- Publisher / Repository:
- Wiley
- Date Published:
- Journal Name:
- People and Nature
- Volume:
- 6
- Issue:
- 2
- ISSN:
- 2575-8314
- Page Range / eLocation ID:
- 377 to 390
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1002/pan3.10592
