Quantifying impacts of white-tailed deer (Odocoileus virginianus Zimmerman) browse using forest inventory and socio-environmental datasets
Level 4 - case-series / case-control
Level 4 by design analogy (cross-sectional ecological predictive modeling using regional inventory and public datasets; non-clinical CEBM).
OpenAlex W2888403142 · doi:10.1371/journal.pone.0201334
What was done
Researchers evaluated the predictive capacity of socio-environmental datasets to estimate deer browse impacts at the county level across three US states (Michigan, Minnesota, and Wisconsin). They combined tree seedling and sapling regeneration data from the USDA Forest Service Forest Inventory and Analysis (FIA) program with county-level socio-environmental data—including Lyme disease case counts, deer-vehicle collisions, and deer density estimates—and trained random forest machine learning models to classify deer browse pressure.
What was found
Random forest models correctly predicted deer browse impact categories 70% to 90% of the time across the study area. County-level deer-vehicle collisions were highly important predictors across all three individual states. Lyme disease case reports were also highly ranked predictors for the three states combined and for Minnesota and Wisconsin individually.
Why it matters
Directly measuring deer populations and their silvicultural impacts across large geographic scales is challenging and resource-intensive. This approach demonstrates that publicly available socio-environmental proxy data can supplement forest inventories to map deer browsing pressure and inform forest regeneration planning.
Limits
The abstract does not report the total number of FIA inventory plots or counties evaluated, nor does it provide detailed model performance metrics (such as confusion matrices, sensitivity, or confidence intervals) beyond the aggregate 70–90% range. Findings are based on ecological correlation in three Great Lakes states and may not generalize to ecosystems with different hunting regulations, traffic patterns, or Lyme disease vectors.
Cited by
- supports High populations of deer cause significant ecological damage in Midwestern woodlands.