Using community photography to investigate phenology: A case study of coat molt in the mountain goat () with missing data.

Participatory approaches, such as community photography, can engage the public in questions of societal and scientific interest while helping advance understanding of ecological patterns and processes. We combined data extracted from community-sourced, spatially explicit photographs with research findings from 2018 fieldwork in the Yukon, Canada, to evaluate winter coat molt patterns and phenology in mountain goats (), a cold-adapted, alpine mammal. Leveraging the community science portals iNaturalist and CitSci, in less than a year we amassed a database of almost seven hundred unique photographs spanning some 4,500 km between latitudes 37.

Studying citizen science through adaptive management and learning feedbacks as mechanisms for improving conservation.

Citizen science has generated a growing interest among scientists and community groups, and citizen science programs have been created specifically for conservation. We examined collaborative science, a highly interactive form of citizen science, which we developed within a theoretically informed framework. In this essay, we focused on 2 aspects of our framework: social learning and adaptive management.

CitSci.org: A New Model for Managing, Documenting, and Sharing Citizen Science Data.

Citizen science projects have the potential to advance science by increasing the volume and variety of data, as well as innovation. Yet this potential has not been fully realized, in part because citizen science data are typically not widely shared and reused. To address this and related challenges, we built CitSci.

The hyper-envelope modeling interface (HEMI): a novel approach illustrated through predicting tamarisk (Tamarix spp.) habitat in the Western USA.

Habitat suitability maps are commonly created by modeling a species’ environmental niche from occurrences and environmental characteristics. Here, we introduce the hyper-envelope modeling interface (HEMI), providing a new method for creating habitat suitability models using Bezier surfaces to model a species niche in environmental space. HEMI allows modeled surfaces to be visualized and edited in environmental space based on expert knowledge and does not require absence points for model development.

Regional data refine local predictions: modeling the distribution of plant species abundance on a portion of the central plains.

Species distribution models are frequently used to predict species occurrences in novel conditions, yet few studies have examined the consequences of extrapolating locally collected data to regional landscapes. Similarly, the process of using regional data to inform local prediction for species distribution models has not been adequately evaluated. Using boosted regression trees, we examined errors associated with extrapolating models developed with locally collected abundance data to regional-scale spatial extents and associated with using regional data for predictions at a local extent for a native and non-native plant species across the northeastern central plains of Colorado.