2022
DOI: 10.1093/biosci/biac095
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Place-Based Bias in Environmental Scholarship Derived from Social–Ecological Landscapes of Fear

Abstract: Historical perspectives (e.g., moments of social, political, and economic significance) are increasingly relevant for developing insights into landscape change and ecosystem degradation. However, the question of how to incorporate historical events into ecological inquiry is still under development, owing to the evolving paradigm of transdisciplinary thinking between natural science and the humanities. In the present article, we call for the inclusion of negative human histories (e.g., evictions of communities… Show more

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Cited by 19 publications
(15 citation statements)
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References 132 publications
(130 reference statements)
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“…Moreover, the history of the United States as a land of immigrants founded on stolen land and built by stolen enslaved peoples has left lasting legacies on land, people and society (Cushing et al, 2015;Gadsden et al, 2023;Morello-Frosch et al, 2011;Norgaard & Reed, 2017;Pulido, 2016;Schell, Dyson, et al, 2020;Swope et al, 2022). Consequently, the United States illustrates how socialecological variables interact to produce biases in contributory data.…”
Section: Applying the Fr Ame Work : A C A S E S Tudy In The United S ...mentioning
confidence: 99%
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“…Moreover, the history of the United States as a land of immigrants founded on stolen land and built by stolen enslaved peoples has left lasting legacies on land, people and society (Cushing et al, 2015;Gadsden et al, 2023;Morello-Frosch et al, 2011;Norgaard & Reed, 2017;Pulido, 2016;Schell, Dyson, et al, 2020;Swope et al, 2022). Consequently, the United States illustrates how socialecological variables interact to produce biases in contributory data.…”
Section: Applying the Fr Ame Work : A C A S E S Tudy In The United S ...mentioning
confidence: 99%
“…At the landscape scale, place‐based bias due to negative human histories (e.g. war, segregation and displacement) may also influence where data are spatially reported (Gadsden et al., 2023), leading to race‐based spatial biases in the United States (Blake et al., 2020; Ellis‐Soto et al., 2023; Estien et al., 2023; Mahmoudi et al., 2022). In parallel, the policy of particular contributory projects or databases, for instance the Global Biodiversity Information Facility (GBIF), may lead to some species‐rich countries to be undersampled compared with species‐poor areas (Beck et al., 2013; Yesson et al., 2007).…”
Section: A Framework For Understanding How Bias In Contributory Data ...mentioning
confidence: 99%
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