Constructing ecological indices for urban environments using species distribution models

• Published in: Urban ecosystems 2022-12, Vol.25 (6), p.1745-1764
• Main Authors: SIMONS, ARIEL LEVI, CALDWELL, STEVIE, FU, MICHELLE, GALLEGOS, JOSE, GATHERU, MICHAEL, RICCARDELLI, LAURA, TRUONG, NHI, VIERA, VALERIA
• Format: Article
• Language: English
• Subjects: Anthropogenic factors; Aves; Biodiversity; Biomedical and Life Sciences; Ecology; Environmental factors; Environmental gradient; Environmental Management; Geographical distribution; Human influences; Indigenous species; Land cover; Life Sciences; Magnoliopsida; Nature Conservation; Remote sensing; Residential density; Species richness; Urban areas; Urban Ecology; Urban environments; Urban studies
• ISSN: 1083-8155; 1573-1642
• DOI: 10.1007/s11252-022-01265-0

In an increasingly urbanized world, there is a need to study urban areas as their own class of ecosystems as well as assess the impacts of anthropogenic impacts on biodiversity. However, collecting a sufficient number of species observations to estimate patterns of biodiversity in a city can be costly. Here we investigated the use of community science-based data on species occurrences, combined with species distribution models (SDMs), built using MaxEnt and remotely-sensed measures of the environment, to predict the distribution of a number of species across the urban environment of Los Angeles. By selecting species with the most accurate SDMs, and then summarizing these by class, we were able to produce two species richness models (SRMs) to predict biodiversity patterns for species in the class Aves and Magnoliopsida and how they respond to a variety of natural and anthropogenic environmental gradients. We found that species considered native to Los Angeles tend to have significantly more accurate SDMs than their non-native counterparts. For all species considered in this study we found environmental variables describing anthropogenic activities, such as housing density and alterations to land cover, tend to be more influential than natural factors, such as terrain and proximity to freshwater, in shaping SDMs. Using a random forest model we found our SRMs could account for approximately 54% and 62% of the predicted variation in species richness for species in the classes Aves and Magnoliopsida respectively. Using community science-based species occurrences, SRMs can be used to model patterns of urban biodiversity and assess the roles of environmental factors in shaping them.