Identifying mismatches between conservation area networks and vulnerable populations using spatial randomization
Cite this dataset
Nunes, Laura A.; Ribic, Christine A.; Zuckerberg, Benjamin (2022). Identifying mismatches between conservation area networks and vulnerable populations using spatial randomization [Dataset]. Dryad. https://doi.org/10.5061/dryad.cnp5hqc5p
Grassland birds are among the most globally threatened bird groups due to substantial degradation of native grassland habitats. However, the current network of grassland conservation areas may not be adequate for halting population declines and biodiversity loss. Here, we evaluate a network of grassland conservation areas within Wisconsin, U.S.A. that includes both large Focal Landscapes and smaller targeted conservation areas (e.g., Grassland Bird Conservation Areas or GBCAs) established within them. To date, this conservation network has lacked baseline information to assess whether the current placement of these conservation areas aligns with population hotspots of grassland-dependent taxa. To do so, we fitted data from thousands of avian point-count surveys collected by citizen scientists as part of Wisconsin’s Breeding Bird Atlas II with multinomial N-mixture models to estimate habitat-abundance relationships, develop spatially-explicit predictions of abundance and establish ecological baselines within priority conservation areas for a suite of obligate grassland songbirds. Next, we developed spatial randomization tests to evaluate the placement of this conservation network relative to randomly placed conservation networks. Overall, less than 20% of species statewide populations were found within the current grassland conservation network. Spatial tests demonstrated high representation of this bird assemblage within the entire conservation network, but with a bias towards birds associated with moderately tall grasses relative to those associated with short or tall grasses. We also found that GBCAs had higher representation at Focal Landscape rather than statewide scales. Here, we demonstrated how combining citizen science data with hierarchical modeling is a powerful tool for estimating ecological baselines and conducting large-scale evaluations of an existing conservation network for multiple grassland birds. Our flexible spatial randomization approach offers the potential to be applied to other protected area networks and serve as a complementary tool for conservation planning efforts globally.
Spatially-explicit density estimates at 1x1km for obligate grassland birds based on Wisconsin's Breeding Bird Atlas II, Wiscland 2 landcover and PRISM climate data, and multinomial N-mixture models.
The readme file contains an explanation of each of the variables in the dataset. Information on how the data was generated can be found in the associated manuscript referenced above.
U.S. Geological Survey, Award: G19AC00089