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Abstract Human‐carnivore conflicts pose significant challenges in the management and conservation of carnivores across the globe. Abundant research has led to generalizable insights into the causes of such conflicts. For example, conflicts predictably occur when carnivores have access to human food resources, particularly when their natural foods are scarce. However, similar insights into the effectiveness of interventions aimed at coexistence remains comparatively scarce. We hypothesized that this disparity might be reflected in a bias toward research focused on causes of conflict rather than interventions to address it. To test our hypothesis, we evaluated the content of studies on human–carnivore conflicts and coexistence in Canada and the United States from 2010 to 2021. We found that studies disproportionately focused on causes of conflict, with that discrepancy increasing through our study period. We also found a disproportionate focus on black bears and wolves and western jurisdictions, and a disproportionate use of observational (vs. experimental) approaches. Studies on conflict interventions were primarily directed at the carnivores themselves (e.g., lethal approaches) versus human elements (e.g., attractant management, policies), despite evidence that the latter are more effective. We expect that a shift in focus toward solutions‐oriented research, integrating insights across geographies, taxa, social contexts, and disciplines, would facilitate effective interventions and foster coexistence, improving outcomes for people and carnivores alike.

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, sampling and preference—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 free Plain Language Summary for this article on the Journal blog.