Extending coverage and thematic resolution of compositional land cover maps in a hierarchical Bayesian framework.

Ecological models are constrained by the availability of high-quality data at biologically appropriate resolutions and extents. Modeling a species' affinity or aversion with a particular land cover class requires data detailing that class across the full study area. Data sets with detailed legends (i.e., high thematic resolution) and/or high accuracy often sacrifice geographic extent, while large-area data sets often compromise on the number of classes and local accuracy. Consequently, ecologists must often restrict their study extent to match that of the more precise data set, or ignore potentially key land cover associations to study a larger area. We introduce a hierarchical Bayesian model to capitalize on the thematic resolution and accuracy of a regional land cover data set, and on the geographic breadth of a large area land cover data set. For the full extent (i.e., beyond the regional data set), the model predicts systematic discrepancies of the large-area data set with the regional data set, and divides an aggregated class into two more specific classes detailed by the regional data set. We illustrate the application of our model for mapping eastern white pine (Pinus strobus) forests, an important timber species that also provides habitat for an invasive shrub in the northeastern United States. We use the National Land Cover Database (NLCD), which covers the full study area but includes only generalized forest classes, and the NH GRANIT land cover data set, which maps White Pine Forest and has high accuracy, but only exists within New Hampshire. We evaluate the model at coarse (20 km2 ) and fine (2 km2 ) resolutions, with and without spatial random effects. The hierarchical model produced improved maps of compositional land cover for the full extent, reducing inaccuracy relative to NLCD while partitioning a White Pine Forest class out of the Evergreen Forest class. Accuracy was higher with spatial random effects and at the coarse resolution. All models improved upon simply partitioning Evergreen Forest in NLCD based on the predicted distribution of white pine. This flexible statistical method helps ecologists leverage localized mapping efforts to expand models of species distributions, population dynamics, and management strategies beyond the political boundaries that frequently delineate land cover data sets.

Defoliation severity is positively related to soil solution nitrogen availability and negatively related to soil nitrogen concentrations following a multi-year invasive insect irruption.

Understanding connections between ecosystem nitrogen (N) cycling and invasive insect defoliation could facilitate the prediction of disturbance impacts across a range of spatial scales. In this study we investigated relationships between ecosystem N cycling and tree defoliation during a recent 2015-18 irruption of invasive gypsy moth caterpillars (Lymantria dispar), which can cause tree stress and sometimes mortality following multiple years of defoliation. Nitrogen is a critical nutrient that limits the growth of caterpillars and plants in temperate forests. In this study, we assessed the associations among N concentrations, soil solution N availability and defoliation intensity by L. dispar at the scale of individual trees and forest plots. We measured leaf and soil N concentrations and soil solution inorganic N availability among individual red oak trees (Quercus rubra) in Amherst, MA and across a network of forest plots in Central Massachusetts. We combined these field data with estimated defoliation severity derived from Landsat imagery to assess relationships between plot-scale defoliation and ecosystem N cycling. We found that trees in soil with lower N concentrations experienced more herbivory than trees in soil with higher N concentrations. Additionally, forest plots with lower N soil were correlated with more severe L. dispar defoliation, which matched the tree-level relationship. The amount of inorganic N in soil solution was strongly positively correlated with defoliation intensity and the number of sequential years of defoliation. These results suggested that higher ecosystem N pools might promote the resistance of oak trees to L. dispar defoliation and that defoliation severity across multiple years is associated with a linear increase in soil solution inorganic N.

Relating Aerial Deposition of Entomophaga maimaiga Conidia (Zoopagomycota: Entomophthorales) to Mortality of Gypsy Moth (Lepidoptera: Erebidae) Larvae and Nearby Defoliation.

We collected data on mortality of late-instar gypsy moth, Lymantria dispar (L.), from outbreak populations over 4 wk in June 2017 at 10 sites in the New England region of the United States, along with estimated rainfall at these sites. Deposition of airborne conidia of the fungal pathogen, Entomophaga maimaiga Humber, Shimazu & R.S. Soper, was measured at these same sites as well as at seven other locations in New England. We also quantified the geographical distribution of gypsy moth-caused defoliation in New England in 2017 and 2018 from Landsat imagery. Weekly mortality of gypsy moth larvae caused by E. maimaiga correlated with local deposition of conidia from the previous week, but not with rainfall. Mortality from this pathogen reached a peak during the last 2 wk of gypsy moth larval development and always exceeded that caused by LdNPV, the viral pathogen of gypsy moth that has long been associated with gypsy moth outbreaks, especially prior to 1989. Cotesia melanoscela (Ratzeburg) was by far the most abundant parasitoid recovered and caused an average of 12.6% cumulative parasitism, but varied widely among sites. Deposition of E. maimaiga conidia was highly correlated with percent land area defoliated by gypsy moths within distances of 1 and 2 km but was not significantly correlated with defoliation at distances greater than 2 km. This is the first study to relate deposition of airborne conidia of E. maimaiga to mortality of gypsy moths from that agent.