Impacts, Monitoring and Management of Forest Pests and Diseases

Forest pests have diverse negative impacts on forestry economy, ecosystem services, biodiversity, and sustainable ecosystem management. The first step towards effectively managing forest pests would be to monitor their occurrence and assess their impact on forest ecosystems. The monitoring results can provide basic information for effective management strategies. The data from monitoring programs can result in the development of new methods for monitoring, assessing impact, and developing management techniques. This special issue aims to share information to assist in the effective management of forest pests, by understanding the responses of forest pests to natural and anthropogenic changes, and discussing new studies on the monitoring, assessment, and management of forest pests. The fourteen papers included in this issue focus on monitoring, assessing, and managing forest pests, including one editorial providing an overall idea of the monitoring, assessment and management of forest pests, two articles reviewing long-term changes in forest pests and forests, four papers focusing on the monitoring of forest pests, three papers on the assessment of forest pests, and four papers on the management of forest pests. These papers provide a better understanding of the structures and processes in forest ecosystems and fundamental information for the effective management of forest pests

Projecting White Pine Blister Rust Hazard Ratings Under Climate Change in Southwestern Oregon

Sugar pine and western white pine are widely distributed, economically valuable, and ecologically important native tree species in North America. However, white pine blister rust (WPBR), caused by a non-native fungal pathogen, Cronartium ribicola J.C. Fisch. in Rabh., has substantially affected populations of these species. Cronartium is an obligate parasite, requiring two living hosts to complete its life cycle. White pines are the primary host, and Ribes species, Castilleja, and Pedicularis spp. are alternate hosts. Once Cronartium infects trees, it damages and kills branches, stems, or entire trees by causing branch and stem cankers. Cronartium requires cool temperatures and sufficient moisture to inoculate and spread among alternate and tree hosts. Cronartium infects white pines of all size, but it is more prevalent on younger and smaller trees. Treatments to manage WPBR have included Ribes eradication, chemical spraying of Ribes, and thinning and pruning of tree hosts. However, these treatments are impractical and costly, and are not expected to eliminate WPBR. Another approach is to breed and deploy rust-resistant trees, and has been an ongoing effort for over 50 years. Finally, another option for managing WPBR is to use rust hazard ratings (RHRs). RHRs are metrics reflecting current or potential rust infection levels of sites. Specific climatic conditions, site characteristics, and tree characteristics have been used to rate the rust hazard of sites in white pine regions. For my research, I used data from Koester et al. (2018), who studied rust hazard at 265 sites in southwestern Oregon. The sites contained naturally regenerated sugar pine, naturally regenerated western white pine, rust-resistant sugar pine, and rust-resistant western white pine. I studied four rust traits, environmental variables, and tree characteristics. The rust traits consisted of percentage of trees with a stem canker (CANK%), average number of cankers per tree (NUM_CANK), average height of the highest canker (HT_CANK), and rust hazard index (RI). The environmental variables consisted of climate variables, aspect, slope, and elevation. The tree characteristics consisted of tree age, tree height, and average height growth (HT/AGE). In addition to the Koester et al data, I used the ClimateNA software program to obtain historical and future climate variables for the sites. I developed rust and tree growth random forest regression models and used other analyses to identify (1) which rust traits were best for characterizing rust hazard, (2) which environmental variables were most closely associated with rust hazard, (3) how were rust traits affected by tree characteristics, (4) how was tree growth affected by environmental variables and rust traits, (5) how will rust traits change under future climates, and (6) how will tree growth change under future climates. Based on random forest RSQ values, I concluded that CANK% and NUM_CANK were the best rust traits for characterizing rust hazard. My criteria for a good rust hazard index were that it is (1) closely associated with tree damage and death, (2) easy to measure precisely and accurately, (3) not confounded by non-rust tree variables, and (4) easy to predict from environmental variables. CANK% and NUM_CANK were relatively easy to measure precisely and accurately compared to HT_CANK and RI. Also, CANK% and NUM_CANK were more strongly associated with environmental variables, and were less confounded with non-rust tree variables. Thus, I used CANK% and NUM_CANK for further analyses. Among the environmental variables, climate variables were the most important predictors of rust hazard (i.e., based on random forest variable importance statistics). Correlations indicated that rust hazard was greater at sites with milder temperatures, sufficient moisture, and longer growing seasons. Rust hazard was also higher at sites with northern, eastern, and northeasterly aspects. Rust hazard was positively associated with tree growth and negatively associated with tree age, but had little relationship to tree height. The possible explanation is that young susceptible trees were subjected to natural selection by WPBR. Thus, older stands probably have a higher proportion of rust resistant trees. I also observed that greater tree growth was associated with harsher climates and higher rust disease, but the reasons for this were unclear. Next, I used my random forest regression models to project rust hazard and tree growth under climate change. In total, I analyzed 13 climate scenarios consisting of 12 future scenarios and one based on the 1931-1990 climate. The future climate variables were based on four Shared Socio-economic Pathways (SSPs), SSP1-26, SSP2-45, SSP3-70, and SSP5-85 and three 30-year time periods centered on 2025, 2055, and 2085. I found projected increases in spring, summer, and winter temperatures, indicating longer growing seasons in the future. In addition, summer and autumn relative humidity were projected to decrease under all SSPs. According to the projections of rust hazard, WPBR will decline, but these changes will probably be small and uncertain. Declines in WPBR may occur because warmer and drier climates inhibit rust spore germination, infection, and dispersal. In contrast, I projected a small increase in the average growth rate of the pines, probably because the models projected a longer growing season with less rust disease. The rust hazard models I developed should be particularly valuable for evaluating current rust hazards based on known climate conditions. However, many caveats make the future projections uncertain. To improve rust hazard prediction models, I recommend accounting for (1) the distributions of alternate hosts, (2) the occurrence of specific wave years, and (3) additional moisture-associated environmental variables

Refining Time-Activity Classification of Human Subjects Using the Global Positioning System

BACKGROUND:Detailed spatial location information is important in accurately estimating personal exposure to air pollution. Global Position System (GPS) has been widely used in tracking personal paths and activities. Previous researchers have developed time-activity classification models based on GPS data, most of them were developed for specific regions. An adaptive model for time-location classification can be widely applied to air pollution studies that use GPS to track individual level time-activity patterns. METHODS:Time-activity data were collected for seven days using GPS loggers and accelerometers from thirteen adult participants from Southern California under free living conditions. We developed an automated model based on random forests to classify major time-activity patterns (i.e. indoor, outdoor-static, outdoor-walking, and in-vehicle travel). Sensitivity analysis was conducted to examine the contribution of the accelerometer data and the supplemental spatial data (i.e. roadway and tax parcel data) to the accuracy of time-activity classification. Our model was evaluated using both leave-one-fold-out and leave-one-subject-out methods. RESULTS:Maximum speeds in averaging time intervals of 7 and 5 minutes, and distance to primary highways with limited access were found to be the three most important variables in the classification model. Leave-one-fold-out cross-validation showed an overall accuracy of 99.71%. Sensitivities varied from 84.62% (outdoor walking) to 99.90% (indoor). Specificities varied from 96.33% (indoor) to 99.98% (outdoor static). The exclusion of accelerometer and ambient light sensor variables caused a slight loss in sensitivity for outdoor walking, but little loss in overall accuracy. However, leave-one-subject-out cross-validation showed considerable loss in sensitivity for outdoor static and outdoor walking conditions. CONCLUSIONS:The random forests classification model can achieve high accuracy for the four major time-activity categories. The model also performed well with just GPS, road and tax parcel data. However, caution is warranted when generalizing the model developed from a small number of subjects to other populations

Global occurrence of pine wilt disease: biological interactions and integrated management

Plant pathogens cause severe losses in a wide range of crops and forestry plant species worldwide, being a major obstacle toward achieving sustainable agriculture and forestry. In forests, pathogens can affect sustainable management by affecting economic trade and serious ecological losses can occur, such as the ability to store carbon, reduce flood risk or purify water (Boyd et al., 2013). Ranking in the top ten of the most damaging plant-parasitic nematodes worldwide, the migratory endoparasitic nematode Bursaphelenchus xylophilus (pinewood nematode, PWN) is the causal agent of Pine wilt disease (PWD) being responsible for the tremendous decline of conifers species in Eurasian conifer forests”

Green Infrastructure and Climate Change Adaptation

This open access book introduces the function, implementation and governance of green infrastructure in Japan and other countries where lands are geologically fragile and climatologically susceptible to climate change. It proposes green infrastructure as an adaptation strategy for climate change and biodiversity conservation. In the face of climate change, dams, levees and floodways built as disaster prevention facilities do not sufficiently function against extraordinary events such as mega-floods and tsunami disasters. To prevent those disasters and loss of biodiversity in various ecosystems, we should shift from conventional hard measures to more adaptive strategies using various functions that natural and semi-natural ecosystems provide. Green infrastructure is an interconnected network of waterways, wetlands, woodlands, wildlife habitats and other natural areas that support native species, maintain natural ecological processes, sustain air and water resources and contribute to the health and quality of life for communities and people. Green infrastructure has mainly been discussed from adaptation strategy perspectives in cities and urban areas. However, to protect cities, which are generally situated at downstream lower elevations, we explore the preservation and restoration of forests at headwater basins and wetlands along rivers from a catchment perspective. In addition, the quantitative examination of flood risk, biodiversity, and social-economic benefits described in this book brings new perspectives to the discussion. The aim of this book is to accelerate the transformative changes from gray-based adaptation strategies to green- or hybrid-based strategies to adapt to climate change. The book provides essential information on the structure, function, and maintenance of green infrastructure for scientists, university students, government officers, and practitioners