Environmental map layers for Los Angeles environmental assessment project

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.