Predicting Disparity between ASF-Managed Areas and Wild Boar Habitats: A Case of South Korea

African swine fever (ASF) is a highly contagious viral disease affecting both domestic and wild boars. Since its first outbreak in South Korea in 2019, substantial efforts have been made to prevent ASF transmission by reducing the wild boar population and eliminating infected carcasses; however, the persistence of ASF transmission has posed challenges to these efforts. To improve ASF management strategies, the limitations of current management strategies must be identified by considering disparities between wild boar habitats and ASF-managed areas with environmental and anthropogenic characteristics of wild boars and their management strategies. Here, ensemble species distribution models were used to estimate wild boar habitats and potential ASF-managed areas, with elevation, distance to urban areas, and Normalized Difference Vegetation Index as important variables. Binary maps of wild boar habitats and potential ASF-managed areas were generated using the maxSSS as the threshold criterion. Disparity areas of ASF management were identified by overlying regions evaluated as wild boar habitats with those not classified as ASF-managed areas. Dense forests near urban regions like Chungcheongbuk-do, Gyeongsangbuk-do, and Gyeongsangnam-do were evaluated as disparity areas having high risk of ASF transmission. These findings hold significant potential for refining ASF management strategies and establishing proactive control measures

Simulating Hunting Effects on the Wild Boar Population and African Swine Fever Expansion Using Agent-Based Modeling

African swine fever (ASF) is a viral hemorrhagic fever fatal to animals of the Suidae family. It has spread from Africa to Europe and Asia, causing significant damage to wildlife and domesticated pig production. Since the first confirmed case in South Korea in September 2019, the number of infected wild boars has continued to increase, despite quarantine fences and hunting operations. Hence, new strategies are needed for the effective control of ASF. We developed an agent-based model (ABM) to estimate the ASF expansion area and the efficacy of infection control strategies. In addition, we simulated the agents’ (wild boars) behavior and daily movement range based on their ecological and behavioral characteristics, by applying annual hunting scenarios from past three years (2019.09–2022.08). The results of the simulation based on the annual changes in the number of infected agents and the ASF expansion area showed that the higher the hunting intensity, the smaller the expansion area (24,987 km2 at 0% vs. 3533 km2 at 70%); a hunting intensity exceeding 70% minimally affected the expansion area. A complete removal of agents during the simulation period was shown to be possible. In conclusion, an annual hunting intensity of 70% should be maintained to effectively control ASF

Population Dynamics of American Bullfrog (Lithobates catesbeianus) and Implications for Control

Lithobates catesbeianus (American bullfrog), known to be one of the notorious invasive species, was introduced to South Korea and has proliferated in the Korean natural environment for the past 25 years. The ecological impact caused by the species is well known, and several management decisions have been implemented to cull its population. However, the effectiveness of past control decisions is largely unknown. We built a population dynamics model for L. catesbeianus in the Onseok reservoir, South Korea, using STELLA architect software. The population model was based on the demographics and ecological process of the species developing through several life stages, with respective parameters for survivorship and carrying capacity. Control scenarios with varying intensities were simulated to evaluate their effectiveness. The limitations of isolated control methods and the importance of integrated management are shown in our results. The population of the American bullfrog in the reservoir was reduced to a manageable level under intensive control of the tadpole stage, using three sets of double fyke nets and 80% direct removal of juvenile and adult stages. According to our results, integrated, intensive, and continuous control is essential for managing the invasive American bullfrog population. Finally, our modeling approach can assist in determining the control intensity to improve the efficiency of measures against L. catesbeianus

A Modeling Approach for Quantifying Human-Beneficial Terpene Emission in the Forest: A Pilot Study Applying to a Recreational Forest in South Korea

(1) Background: Recent economic developments in South Korea have shifted people’s interest in forests from provisioning to cultural services such as forest healing. Although policymakers have attempted to designate more forests for healing purposes, there are few established standards for carrying out such designations based on the quantified estimation. (2) Methods: We suggest a modeling approach to estimate and analyze the emission rate of human-beneficial terpenes. For this purpose, we adopted and modified the Model of Emissions of Gases and Aerosols from Nature (MEGAN), a commonly used biogenic volatile organic compounds (BVOCs) estimation model which was suitable for estimating the study site’s terpene emissions. We estimated the terpene emission rate for the whole year and analyzed the diurnal and seasonal patterns. (3) Results: The results from our model correspond well with other studies upon comparing temporal patterns and ranges of values. According to our study, the emission rate of terpenes varies significantly temporally and spatially. The model effectively predicted spatiotemporal patterns of terpene emission in the study site. (4) Conclusions: The modeling approach in our study is suitable for quantifying human-beneficial terpene emission and helping policymakers and forest managers plan the efficient therapeutic use of forests

A bayesian network analysis of reforestation decisions by rural mountain communities in Vietnam

Reforestation is a primary factor in protecting upland forests providing economic sustenance for livelihood and keeping watersheds intact. In this study, we evaluated the importance of several direct and indirect drivers that can influence people’s decision for reforestation. Acquiring data from Cao Phong district of Vietnam, we utilized Bayesian Network (BN) to analyze how household characteristics, socio-economic status, biophysical environment, institutional support, and farm characteristics influenced reforestation decisions of local people. BN allowed us to identify 1) the main drivers that affect landholders ‘planted forest area, 2) how the key drivers affect among themselves, and 3) what causes constraints in tree planting. We surveyed 100 households for potential drivers, identified significant drivers by using bivariate analysis and stepwise linear regression, and created a BN to predict scenarios with different household’s perception regarding the planted forest area. The results revealed five direct drivers (attitude of household to tree planting, sources of investment capital for planting practice, land area, distance from household to market, experience of participating in forestry program) and seven indirect drivers (information about forestry program, incentives supported for tree planters, land tenure, accessibility to plantation forest, rotation length of planting trees, forest area, household income) that significantly influenced farmers’ reforestation decisions. Constraints in planting trees were due to the difficulties in protecting property from mortality and unreliable profit. Our results can assist design efficient forestry programs in Vietnam and in other comparable areas

Development of a Prediction Model of Fuel Moisture Changes in a Deciduous Forest of Yeongdong Region in Korea

Understanding how fuel moisture changes after a rainfall is important to predict forest fire risk, and knowing such change in advance can greatly assist in fire risk monitoring. To better understand the fuel moisture dynamics after a rainfall, we investigated how fuel moisture level changes across four different fuel layers (fall leaves, humus, top soil layer (<5 cm depth), and lower soil layer (5–10 cm depth)) under three different stand density levels (high, medium and low) after a significant rainfall event (>5 mm) during spring season. We measured variables including effective humidity, solar irradiation, wind speed, and days after rainfall. These variables were incorporated into developing a fuel moisture prediction regression model. Variables were measured daily for 6 days after a rainfall, for a total of 4 rainfall events in the spring of 2008 for model development, and one event in the spring of 2009 for model validation. Results show that in a low density stand, fuel moisture of the fallen leaves layer reached a dangerously dry level of 17% only after 3 days since rainfall, while at the medium and high density stands, fuel moisture level remained at 19–20% after 6 days since rainfall. Fuel moisture at the humus level was highest among all fuel layers, and remained at greater than 57% even after 6 days since rainfall. Top and lower soil layers both showed small to no changes in fuel moisture content throughout the sampling period. The prediction regression model showed a reasonable performance (R^2=0.56–0.90, p–value <0.001) and validated well against an independent set of measurements

Distribution, demography and dispersal model of spatial spread of invasive plant populations with limited data

Invasive weeds are a major cause of biodiversity loss and economic damage world-wide. There is often a limited understanding of the biology of emerging invasive species, but delay in action may result in escalating costs of control, reduced economic returns from management actions and decreased feasibility of management. Therefore, spread models that inform and facilitate on-ground control of invasions are needed. We developed a spatially explicit, individual-based spread model that can be applied to both data-poor and data-rich situations to model future spread and inform effective management of the invasion. The model is developed using a minimum of two mapped distributions for the target species at different times, together with habitat suitability variables and basic population data. We present a novel method for internally calibrating the reproduction and dispersal distance parameters. We use a sensitivity analysis to identify variables that should be prioritized in future research to increase robustness of model predictions. We apply the model to two case studies, gamba grass and para grass, to provide management advice on emerging weed priorities in northern Australia. For both species, we find that the current extent of invasion in our study regions is expected to double in the next 10years in the absence of management actions. The predicted future distribution identifies priority areas for eradication, control and containment to reduce the predicted increase in infestation. The model was built for managers and policymakers in northern Australia working on species where expert knowledge and environmental data are often lacking, but is flexible and can be easily adapted for other situations, for example where good data are available. The model provides predicted probability of occurrence over a user-specified, typically short-term, time horizon. This output can be used to direct surveillance and management actions to areas that have the highest likelihood of rapid invasion and spread. Directing efforts to these areas provides the greatest likelihood of management success and maximizes the return on investment in management response