@article{cobb_haas_kruskamp_dillon_swiecki_rizzo_frankel_meentemeyer_2020, title={The Magnitude of Regional-Scale Tree Mortality Caused by the Invasive PathogenPhytophthora ramorum}, volume={8}, ISSN={["2328-4277"]}, url={https://doi.org/10.1029/2020EF001500}, DOI={10.1029/2020EF001500}, abstractNote={Abstract}, number={7}, journal={EARTHS FUTURE}, author={Cobb, Richard C. and Haas, Sarah E. and Kruskamp, Nicholas and Dillon, Whalen W. and Swiecki, Tedmund J. and Rizzo, David M. and Frankel, Susan J. and Meentemeyer, Ross K.}, year={2020}, month={Jul} }
 @article{dillon_meentemeyer_2019, title={Direct and indirect effects of forest microclimate on pathogen spillover}, volume={100}, ISSN={["1939-9170"]}, DOI={10.1002/ecy.2686}, abstractNote={Abstract}, number={5}, journal={ECOLOGY}, author={Dillon, Whalen W. and Meentemeyer, Ross K.}, year={2019}, month={May} }
 @article{serra-diaz_franklin_dillon_syphard_davis_meentemeyer_2016, title={California forests show early indications of both range shifts and local persistence under climate change}, volume={25}, ISSN={["1466-8238"]}, DOI={10.1111/geb.12396}, abstractNote={Abstract}, number={2}, journal={GLOBAL ECOLOGY AND BIOGEOGRAPHY}, author={Serra-Diaz, Josep M. and Franklin, Janet and Dillon, Whalen W. and Syphard, Alexandra D. and Davis, Frank W. and Meentemeyer, Ross K.}, year={2016}, month={Feb}, pages={164–175} }
 @article{haas_cushman_dillon_rank_rizzo_meentemeyer_2016, title={Effects of individual, community, and landscape drivers on the dynamics of a wildland forest epidemic}, volume={97}, DOI={10.1890/15-0767}, abstractNote={The challenges posed by observing host–pathogen–environment interactions across large geographic extents and over meaningful time scales limit our ability to understand and manage wildland epidemics. We conducted a landscape-scale, longitudinal study designed to analyze the dynamics of sudden oak death (an emerging forest disease caused by Phytophthora ramorum) across hierarchical levels of ecological interactions, from individual hosts up to the community and across the broader landscape. From 2004 to 2011, we annually assessed disease status of 732 coast live oak, 271 black oak, and 122 canyon live oak trees in 202 plots across a 275-km2 landscape in central California. The number of infected oak stems steadily increased during the eight-year study period. A survival analysis modeling framework was used to examine which level of ecological heterogeneity best predicted infection risk of susceptible oak species, considering variability at the level of individuals (species identity, stem size), the community (host density, inoculum load, and species richness), and the landscape (seasonal climate variability, habitat connectivity, and topographic gradients). After accounting for unobserved risk shared among oaks in the same plot, survival models incorporating heterogeneity across all three levels better predicted oak infection than did models focusing on only one level. We show that larger oak trees (especially coast live oak) were more susceptible, and that interannual variability in inoculum production by the highly infectious reservoir host, California bay laurel, more strongly influenced disease risk than simply the density of this important host. Concurrently, warmer and wetter rainy-season conditions in consecutive years intensified infection risk, presumably by creating a longer period of inoculum build-up and increased probability of pathogen spillover from bay laurel to oaks. Despite the presence of many alternate host species, we found evidence of pathogen dilution, where less competent hosts in species-rich communities reduce pathogen transmission and overall risk of oak infection. These results identify key parameters driving the dynamics of emerging infectious disease in California woodlands, while demonstrating how multiple levels of ecological heterogeneity jointly determine epidemic trajectories in wildland settings.}, number={3}, journal={Ecology}, author={Haas, S. E. and Cushman, J. H. and Dillon, W. W. and Rank, N. E. and Rizzo, D. M. and Meentemeyer, Ross K.}, year={2016}, pages={649–660} }
 @article{tonini_dillon_money_meentemeyer_2016, title={Spatio-temporal reconstruction of missing forest microclimate measurements}, volume={218}, ISSN={["1873-2240"]}, DOI={10.1016/j.agrformet.2015.11.004}, abstractNote={Scientists and land managers are increasingly monitoring forest microclimate environments to better understand ecosystem processes, such as carbon sequestration and the population dynamics of species. Obtaining reliable time-series measurements of microclimate conditions is often hindered by missing and erroneous values. In this study, we compare spatio-temporal techniques, space–time kriging (probabilistic) and empirical orthogonal functions (deterministic), for reconstructing hourly time series of near-surface air temperature recorded by a dense network of 200 forest understory sensors across a heterogeneous 349 km2 region in northern California. The reconstructed data were also aggregated to daily mean, minimum, and maximum in order to understand the sensitivity of model predictions to temporal scale of measurement. Empirical orthogonal functions performed best at both the hourly and daily time scale. We analyzed several scenarios to understand the effects that spatial coverage and patterns of missing data may have on model accuracy: (a) random reduction of the sample size/density by 25%, 50%, and 75% (spatial coverage); and (b) random removal of either 50% of the data, or three consecutive months of observations at randomly chosen stations (random and seasonal temporal missingness, respectively). Here, space–time kriging was less sensitive to scenarios of spatial coverage, but more sensitive to temporal missingness, with less marked differences between the two approaches when data were aggregated on a daily time scale. This research contextualizes trade-offs between techniques and provides practical guidelines, with free source code, for filling data gaps depending on the spatial density and coverage of measurements.}, journal={AGRICULTURAL AND FOREST METEOROLOGY}, publisher={Elsevier BV}, author={Tonini, Francesco and Dillon, Whalen W. and Money, Eric S. and Meentemeyer, Ross K.}, year={2016}, month={Mar}, pages={1–10} }
 @article{chen_metz_rizzo_dillon_meentemeyer_2015, title={Object-based assessment of burn severity in diseased forests using high-spatial and high-spectral resolution MASTER airborne imagery}, volume={102}, ISSN={["1872-8235"]}, DOI={10.1016/j.isprsjprs.2015.01.004}, abstractNote={Forest ecosystems are subject to a variety of disturbances with increasing intensities and frequencies, which may permanently change the trajectories of forest recovery and disrupt the ecosystem services provided by trees. Fire and invasive species, especially exotic disease-causing pathogens and insects, are examples of disturbances that together could pose major threats to forest health. This study examines the impacts of fire and exotic disease (sudden oak death) on forests, with an emphasis on the assessment of post-fire burn severity in a forest where trees have experienced three stages of disease progression pre-fire: early-stage (trees retaining dried foliage and fine twigs), middle-stage (trees losing fine crown fuels), and late-stage (trees falling down). The research was conducted by applying Geographic Object-Based Image Analysis (GEOBIA) to MASTER airborne images that were acquired immediately following the fire for rapid assessment and contained both high-spatial (4 m) and high-spectral (50 bands) resolutions. Although GEOBIA has gradually become a standard tool for analyzing high-spatial resolution imagery, high-spectral resolution data (dozens to hundreds of bands) can dramatically reduce computation efficiency in the process of segmentation and object-based variable extraction, leading to complicated variable selection for succeeding modeling. Hence, we also assessed two widely used band reduction algorithms, PCA (principal component analysis) and MNF (minimum noise fraction), for the delineation of image objects and the subsequent performance of burn severity models using either PCA or MNF derived variables. To increase computation efficiency, only the top 5 PCA and MNF and top 10 PCA and MNF components were evaluated, which accounted for 10% and 20% of the total number of the original 50 spectral bands, respectively. Results show that if no band reduction was applied the models developed for the three stages of disease progression had relatively similar performance, where both spectral responses and texture contributed to burn assessments. However, the application of PCA and MNF introduced much greater variation among models across the three stages. For the early-stage disease progression, neither band reduction algorithms improved or retained the accuracy of burn severity modeling (except for the use of 10 MNF components). Compared to the no-band-reduction scenario, band reduction led to a greater level of overestimation of low-degree burns and underestimation of medium-degree burns, suggesting that the spectral variation removed by PCA and MNF was vital for distinguishing between the spectral reflectance from disease-induced dried crowns (still retaining high structural complexity) and fire ash. For the middle-stage, both algorithms improved the model R2 values by 2–37%, while the late-stage models had comparable or better performance to those using the original 50 spectral bands. This could be explained by the loss of tree crowns enabling better signal penetration, thus leading to reduced spectral variation from canopies. Hence, spectral bands containing a high degree of random noise were correctly removed by the band reduction algorithms. Compared to the middle-stage, the late-stage forest stands were covered by large piles of fallen trees and branches, resulting in higher variability of MASTER imagery. The ability of band reduction to improve the model performance for these late-stage forest stands was reduced, because the valuable spectral variation representing the actual late-stage forest status was partially removed by both algorithms as noise. Our results indicate that PCA and MNF are promising for balancing computation efficiency and the performance of burn severity models in forest stands subject to the middle and late stages of sudden oak death disease progression. Compared to PCA, MNF dramatically reduced image spectral variation, generating larger image objects with less complexity of object shapes. Whereas, PCA-based models delivered superior performance in most evaluated cases suggesting that some key spectral variability contributing to the accuracy of burn severity models in diseased forests may have been removed together with true spectral noise through MNF transformations.}, journal={ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING}, author={Chen, Gang and Metz, Margaret R. and Rizzo, David M. and Dillon, Whalen W. and Meentemeyer, Ross K.}, year={2015}, month={Apr}, pages={38–47} }
 @article{dillon_haas_rizzo_meentemeyer_2014, title={Perspectives of spatial scale in a wildland forest epidemic}, volume={138}, ISSN={["1573-8469"]}, DOI={10.1007/s10658-013-0376-3}, number={3}, journal={EUROPEAN JOURNAL OF PLANT PATHOLOGY}, author={Dillon, Whalen W. and Haas, Sarah E. and Rizzo, David M. and Meentemeyer, Ross K.}, year={2014}, month={Mar}, pages={449–465} }