Harnessing iNaturalist to quantify hotspots of urban biodiversity: the Los Angeles case study - Beninde, Joscha; Delaney, Tatum W.; Gonzalez, Germar; Shaffer, H. Bradley (Frontiers in Ecology and Evolution, 2023)
A major goal for conservation planning is the prioritized protection and management of areas that harbor maximal biodiversity. However, such spatial prioritization often suffers from limited data availability, resulting in decisions driven by a handful of iconic or endangered species, with uncertain benefits for co-occurring taxa. We argue that multi-species habitat preferences based on field observations should guide conservation planning to optimize the long-term persistence of as many species as possible. Using habitat suitability modelling techniques and data from the community-science platform iNaturalist, we provide a strategy to develop spatially explicit models of habitat suitability that enable better informed, place-based conservation prioritization. Our case study in Greater Los Angeles used Maxent and Random Forests to generate suitability models for 1,200 terrestrial species with at least 25 occurrence records, drawn from plants (45.5%), arthropods (27.45%), vertebrates (22.2%), fungi (3.2%), molluscs (1.3%), and other taxonomic groups (<0.3%). This modelling strategy further compared spatial thinning and taxonomic bias file corrections to account for the biases inherent to the iNaturalist dataset, modeling species jointly and separately in wildland and urban sub-regions and validated model performance using null models and a “test” dataset of species and occurrences that were not used to train models. Mean models of habitat suitability of all species combined were similar across model settings, but the mean Random Forest model received the highest median AUCROC and AUCPRG scores in model evaluation. Taxonomic groups showed relatively modest differences in their response to the urbanization gradient, while native and non-native species showed contrasting patterns in the most urban and the most wildland habitats and both peaked in mean habitat suitability near the urban-wildland interface. Our modelling framework is based entirely on open-source software and our code is provided for further use. Given the increasing availability of urban biodiversity data via platforms such as iNaturalist, this modelling framework can easily be applied to other regions. Quantifying habitat suitability for a large, representative subset of the locally occurring pool of species in this way provides a clear, data-driven basis for further ecological research and conservation decision-making, maximizing the impact of current and future conservation efforts.