Examining the relationship between Lyme Disease and Peridomestic Forest Fragmentation using High-Resolution Open-Source Land Cover Data

Abstract:

Over the last 20 years, Lyme disease has grown to become the most common vector-borne disease in the United States, affecting an estimated 329,000 per year. Originally confined to New England, it has since spread across much of the east coast and has become endemic in Virginia. Since 2010 the state has averaged 1200 cases per year, with 200 annually in the New River Health District (NRHD), the location of our study.

Efforts to geographically model Lyme disease primarily focus on landscape and climatic variables. The disease depends highly on the survival of the tick vector, and white-footed mouse, the primary reservoir. Both depend on the existence of forest-herbaceous edge-habitats, as well as warm summer temperatures, mild winter lows, and summer wetness. While many studies have also investigated the effect of forest fragmentation on Lyme, none have made use of high-resolution land cover data to do so at the peridomestic level.

To fill this knowledge gap, we made use of the Virginia Geographic Information Network’s open 1-meter land cover dataset and identified forest-herbaceous edge-habitats for the NRHD. We then calculated the density of these edge-habitats at 100, 200 and 300-meter radii. We also calculated the density sub-hectare forest patches at the same distance thresholds. We also calculated mean summer temperatures, total summer rainfall, and number of consecutive days below freezing of the prior winters. Adding to these data, elevation, terrain shape index, slope, and aspect, and including lags on each of our climatic variables, we then created environmental niche models of Lyme in the NRHD using both Boosted Regression Trees and Maximum Entropy modeling.

We found that Lyme is strongly associated with higher density of forest-herbaceous edges within 100-meters from the home. Forest patch density was also significant at both 100-meter and 300-meter levels. This supports the notion that the fine-scale peridomestic environment is significant to Lyme outcomes, and must be considered even if one were to account for variations in climate and terrain. This work also demonstrates the advantage that the next generation of open-source high-resolution land cover datasets have over traditional products when concerning fine-scale phenomena.