Generalist species, by definition, exhibit variation in niche attributes that promote survival in changing environments. Increasingly, phenotypes previously associated with a species, particularly those with wide or expanding ranges, are dissolving and compelling greater emphasis on population‐level characteristics. In the present study, we assessed spatial variation in diet characteristics, gut microbiome and associations between these two ecological traits across populations of coyotes Canis latrans. We highlight the influence of the carnivore community in shaping these relationships, as the coyote varied from being an apex predator to a subordinate, mesopredator across sampled populations. We implemented a scat survey across three distinct coyote populations in Michigan, USA. We used carbon (δ13C) and nitrogen (δ15N) isotopic values to reflect consumption patterns and trophic level, respectively. Corresponding samples were also paired with 16S rRNA sequencing to describe the microbial community and correlate with isotopic values. Although consumption patterns were comparable, we found spatial variation in trophic level among coyote populations. Specifically, δ15N was highest where coyotes were the apex predator and lowest where coyotes co‐occurred with grey wolves Canis lupus. The gut microbial community exhibited marked spatial variation across populations with the lowest operational taxonomic units diversity found where coyotes occurred at their lowest trophic level. Bacteriodes and Fusobacterium dominated the microbiome and were positively correlated across all populations. We found no correlation between δ13C and microbial community attributes. However, positive associations between δ15N and specific microbial genera increased as coyotes ascended trophic levels. Coyotes provide a model for exploring implications of niche plasticity because they are a highly adaptable, wide‐ranging omnivore. As coyotes continue to vary in trophic position and expand their geographic range, we might expect increased divergence within their microbial community, changes in physiology and alterations in behaviour.

Range maps are critical for understanding and conserving biodiversity, but current range maps often omit important context, negating the dynamism and variation of populations, environmental conditions, and ecological attributes to functionally oversimplify biogeography theory. Moreover, the gross underrepresentation of spatial heterogeneity throughout a species distribution limits the utility of range maps in decision making and for community engagement, weakening applications to disciplines outside the natural sciences. As climate change and other anthropogenic factors outpace our understanding of their impacts, robust and informative range maps for species will be critical in anticipating how environmental changes affect coupled ecological, evolutionary, and social processes. Here, we highlight the expansion of “flat” range maps by adding “texture”, which can represent a myriad of conditions that are spatially explicit across a species range. Using examples of variations (in human pressures, presence of competitor species, and extent of Indigenous lands) as texture, we demonstrate how range maps can address broader questions and promote enhanced capacity for interdisciplinary research.

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 and environmental injustices) as important factors that drive landscape change and shape research questions relevant to environmental conservation. We outline the detrimental effects of conservationists not addressing negative human histories by likening this social phenomenon to the ecological concept of landscapes of fear, which describes how not acknowledging these histories produces a landscape that constrains where and how research is conducted by scientists. Finally, we provide three positive recommendations for scholars or practitioners to address the manifestation of historic place-based bias in ecological research. What we call the social–ecological landscapes of fear provides a conceptual framework for more inclusive practices in ecology to increase the success of environmental and conservation goals.

Camera trap studies have become a popular medium to assess many ecological phenomena including population dynamics, patterns of biodiversity, and monitoring of endangered species. In conjunction with the benefit to scientists, camera traps present an unprecedented opportunity to involve the public in scientific research via image classifications. However, this engagement strategy comes with a myriad of complications. Volunteers vary in their familiarity with wildlife, thus, the accuracy of user‐derived classifications may be biased by the commonness or popularity of species and user‐experience. From an extensive multi‐site camera trap study across Michigan, U.S.A, we compiled and classified images through a public science platform called Michigan ZoomIN. We aggregated responses from 15 independent users per image using multiple consensus methods to assess accuracy by comparing to species identification completed by wildlife experts. We also evaluated how different factors including consensus algorithms, study area, wildlife species, user support, and camera type influenced the accuracy of user‐derived classifications. Overall accuracy of user‐derived classification was 97%; although, several canid (e.g., Canis lupus, Vulpes vulpes) and mustelid (e.g., Neovison vison) species were repeatedly difficult to identify by users and had lower accuracy. When validating user‐derived classification, we found that study area, consensus method, and user support best explained accuracy. To overcome hesitancy associated with data collected by untrained participants, we demonstrated their value by showing that the accuracy from volunteers was comparable to experts when classifying North American mammals. Our hierarchical workflow that integrated multiple consensus methods led to more image classifications without extensive training and even when the expertise of the volunteer was unknown. Ultimately, adopting such an approach can harness broader participation, expedite future camera trap data synthesis, and improve allocation of resources by scholars to enhance performance of public participants and increase accuracy of user‐derived data. © 2021 The Wildlife Society.

Camera trap studies have become a popular medium to assess many ecological phenomena including population dynamics, patterns of biodiversity, and monitoring of endangered species. In conjunction with the benefit to scientists, camera traps present an unprecedented opportunity to involve the public in scientific research via image classifications. However, this engagement strategy comes with a myriad of complications. Volunteers vary in their familiarity with wildlife, and thus, the accuracy of user-derived classifications may be biased by the commonness or popularity of species and user-experience. From an extensive multisite camera trap study across Michigan U.S.A, images were compiled and identified through a public science platform called Michigan ZoomIN. We aggregated responses from 15 independent users per image using multiple consensus methods to assess accuracy by comparing to species identification completed by wildlife experts. We also evaluated how different factors including consensus algorithms, study area, wildlife species, user support, and camera type influenced the accuracy of user-derived classifications. Overall accuracy of user-derived classification was 97%; although, several canids (e.g., Canis lupus, Vulpes vulpes) and mustelid (e.g., Neovison vison) species were repeatedly difficult to identify by users and had lower accuracy. When validating user-derived classification, we found that study area, consensus method, and user support best explained accuracy. To continue to overcome stigma associated with data from untrained participants, we demonstrated both the contributions and limitations of their capacity. Ultimately, our work elucidated new insights that will harness broader participation, expedite future camera trap data synthesis, and improve allocation of resources by scholars to enhance performance of public participants and increase accuracy of user-derived data.