Leveraging Open-Source Geographic Databases to Enhance the Representation of Landscape Heterogeneity in Ecological Models

Abstract

Wildlife abundance and movement are strongly impacted by landscape heterogeneity, especially in cities which are among the world's most heterogeneous landscapes. Nonetheless, current global land cover maps, which are used as a basis for large-scale spatial ecological modeling, represent urban areas as a single, homogeneous, class. This often requires urban ecologists to rely on geographic resources from local governments, which are not comparable between cities and are not available in underserved countries, limiting the spatial scale at which urban conservation issues can be tackled. The recent expansion of community-based geographic databases, for example, OpenStreetMap (OSM), represents an opportunity for ecologists to generate large-scale maps geared toward their specific research needs. However, computational differences in language and format, and the high diversity of information within, limit the access to these data. We provide a framework, using R, to extract geographic features from the OSM database, classify, and integrate them into global land cover maps. The framework includes an exhaustive list of OSM features describing urban and peri-urban landscapes and is validated by quantifying the completeness of the OSM features characterized, and the accuracy of its final output in 34 cities in North America. We portray its application as the basis for generating landscape variables for ecological analysis by using the OSM-enhanced map to generate an urbanization index, and subsequently analyze the spatial occupancy of six mammals throughout Chicago, Illinois, USA. The OSM features characterized had high completeness values for impervious land cover classes (50%-100%). The final output, the OSM-enhance map, provided an 89% accurate representation of the landscape at 30m resolution. The OSM-derived urbanization index outperformed other global spatial data layers in the spatial occupancy analysis and concurred with previously seen local response trends, whereby lagomorphs and squirrels responded positively to urbanization, while skunks, raccoons, opossums, and deer responded negatively. This study provides a roadmap for ecologists to leverage the fine resolution of open-source geographic databases and apply it to spatial modeling by generating research-specific landscape variables. As our occupancy results show, using context-specific maps can improve modeling outputs and reduce uncertainty, especially when trying to understand anthropogenic impacts on wildlife populations.

Keywords: R programming; animal movement; confusion matrix; human disturbance; landscape configuration; mammal occupancy analysis; spatial ecology; urban wildlife ecology.

FIGURE 1

Visual representation of the framework for enhancing the resolution of developed areas in global landcover maps using OpenStreetMap (OSM) attributes. The representation follows the steps described in the methods section, along with representative R code for each step. In the R code representation, commented sections on theworkflow's script refer only to the layers containing linear features. Full code and functions are provided in the “OSM_for_Ecology” Github repository provided in the data availability statement.

FIGURE 2

Visual comparison of the global land cover used in this study (Commission for Environmental Cooperation (CEC) LULC map, 30 m resolution, left), and the final output of our workflow, the OSM‐enhanced land cover map (30 m resolution, right) at the same location, in Chicago, Illinois, USA. Vegetation areas are represented in green shades, barren soil in brown, built environment or land use areas are represented in red shades, different road types are represented in yellow shades, and building footprints are represented in black.

FIGURE 3

Completeness of the OSM features characterized in this framework, with the increasing proportion of the surface area occupied by developed land cover (i.e., land use and infrastructure) for 34 cities (dots, dark blue to light blue following increasing total completeness). (a) Overall completeness, including all 27 land cover classes, (b) completeness of green cover classes excluding water, and (c) completeness of impervious classes including land use (e.g., industrial, commercial, residential) and urban infrastructure (e.g. roads, buildings).

FIGURE 4

Left: Rasterized building footprints in Chicago, IL, USA, represented by the OSM building layer (OSM; cyan), remote sensing products, that is, Global Human Settlement Layer—Built‐up Surface (GHL‐S; yellow) and Global Human Settlement Layer—Morphological Settlement Zone (GHL‐MSZ; purple), and building footprints provided by the local institutions (City of Chicago, aerial photography, black). Right: Correlation between the total building surface area estimated from locally provided building footprints (y‐axis) and that estimated from the OSM‐enhanced LULC map (OSM; cyan) and the Global Human Settlement remote‐sensing Layers (GHL‐S; yellow and GHL‐MSZ; purple) (x‐axis).

FIGURE 5

Occupancy response (0–1; y‐axis) of six mammal species cottontail rabbits (Sylvilagus spp.), eastern gray squirrels (Sciurus carolinensis), raccoons (Procyon lotor), striped skunks (Mephitis mephitis), Virginia opossums (Didelphis virginiana), and white‐tailed deer (Odocoileus virginianus) in the Chicago Area (USA), using the urbanization index and the human influence index as environmental variables (x‐axis).