Analysis of forest cover change and its driving factors in Senan district, Amhara Region, Ethiopia.

Forests are pivotal in upholding and stabilizing ecosystem functions and services globally. Assessing changes in forest cover serves as a crucial indicator to comprehend the scope, scale, and dynamics of land use and land cover alterations on regional and global scales. This study evaluates the forest cover changes between 2005 and 2021, pinpointing the key drivers of forest land changes within the Senan district in Ethiopia's Amhara region. The analysis incorporated Landsat satellite images from 2005, 2011, and 2021, supplemented by field surveys using questionnaire data. Results reveal a shift: forest cover declined from 13.6% (2005) to 11.2% (2011) but rose to 15.4% by 2021, averaging a 12.9% annual change. Several crucial factors were identified as contributors to this forest cover change. These include expanding agricultural land, population growth, urbanization, and using wood as a fuel source. Poverty, exacerbated by population growth, climate change impacts, and a scarcity of food resources, directly linked to a shortage of farmlands, emerged as significant drivers of forest cover change. In light of these findings, an in-depth analysis of land use and land cover dynamics should be conducted, particularly at the expense of forest lands. Moreover, implementing sustainable management practices by developing strategies for intensive agriculture and fostering environmentally friendly non-farm income-generating activities is essential. This study provides reference material to policymakers and land-use planners setting sustainable development goals, advocating for balanced economic growth and environmental conservation to foster a harmonious relationship between humans and forests.

Distribution of sika deer (Cervus nippon) and the bioclimatic impact on their habitats in South Korea.

Invasive species and climate change are primary factors influencing biodiversity, and examining the behavior of invasive species is essential for effective conservation management. Here, we report the global distribution of the sika deer (Cervus nippon) based on locations reported in published literature (Google Scholar), the Global Biodiversity Information Facility (GBIF) database, and the International Union for Conservation of Nature report. We used the maximum entropy (Maxent) model to examine the impact of climate change on sika deer habitats in South Korea based on GBIF occurrence data and WorldClim bioclimatic variables. Habitat suitability analysis was performed using the Maxent model under Representative Concentration Pathways (RCPs) 4.5 and 8.5 (for predicted climatic conditions in both 2050 and 2070) to project the effects of different climate change scenarios on South Korean sika deer habitats. We identified that the sika deer is distributed in 39 countries worldwide. Due to climate change effects, South Korean sika deer habitats will decline by approximately 24.98% and 20.63% (under RCP 4.5) and by 50.51% and 57.35% (under RCP 8.5) by 2050 and 2070, respectively. Our findings shed light on sika deer ecology and provide reference data for future conservation management strategies and policy design.

Habitat utilization distribution of sika deer (Cervus nippon).

Habitat-specific and movement-related behavioral studies are essential for the development of sustainable biodiversity management practices. Although the number of studies on sika deer is increasing, habitat utilization distribution (UD)-related studies remain limited. In this study, we investigated the habitat UD behavior of sika deer (Cervus nippon) using a literature survey and an experimental study on Suncheon Bonghwasan Mountain, South Korea. We reviewed home range-related literature on sika deer published between 1982 and 2019 in order to assess their estimation methods, study region, and research background. We observed that the number of studies on sika deer has increased. The minimum convex polygon (MCP) has been utilized the most to estimate habitat UD, followed by the kernel density (KD), the Brownian bridge model, and a combination of these methods. The average home ranges (95 % utilization distribution) of sika deer from the literature survey were 236.99 ha and 1183.96 ha using the minimum convex polygon and kernel density approaches, respectively. The five female deer in our experimental study on Suncheon Bonghwasan Mountain had a mean home range of 66.831 ± 15.241 ha using the MCP approach and 78.324 ± 20.82 ha using the KD approach. The UD behavior of sika deer explored in this research is expected to benefit future scholars and policymakers when formulating deer management and wildlife conservation strategies.

Spatial proliferation of African swine fever virus in South Korea.

The African swine fever virus (ASFV) was first detected in South Korea on a pig farm in September 2019. Despite active preventive measures to control the spread of ASFV, outbreaks on pig farms and in wild boar have been increasing. In this study, we investigated the spatial contamination area using the minimum convex polygon (MCP) approach, and growth rate using a logistic diffusion model. On the basis of the ASFV outbreak locations recorded from September 17th, 2019, to May 20th, 2022, the MCP area for the second week was 618.41 km2 and expanded to 37959.67 km2 in the final week. The maximum asymptote of the logistic function was considered as the land area of South Korea, and we estimated logistic growth rates of 0.022 km2 per week and 0.094 km2 per month. Administrative bodies should implement preventive and quarantine measures for infectious diseases. The results of this study will be a reference for epidemiologists, ecologists, and policy makers and contribute to the establishment of appropriate quarantine measures for disease control and management.

Climate Change Influences the Spread of African Swine Fever Virus.

Climate change is an inevitable and urgent issue in the current world. African swine fever virus (ASFV) is a re-emerging viral animal disease. This study investigates the quantitative association between climate change and the potential spread of ASFV to a global extent. ASFV in wild boar outbreak locations recorded from 1 January 2019 to 29 July 2022 were sampled and investigated using the ecological distribution tool, the Maxent model, with WorldClim bioclimatic data as the predictor variables. The future impacts of climate change on ASFV distribution based on the model were scoped with Representative Concentration Pathways (RCP 2.6, 4.5, 6.0, and 8.5) scenarios of Coupled Model Intercomparison Project 5 (CMIP5) bioclimatic data for 2050 and 2070. The results show that precipitation of the driest month (Bio14) was the highest contributor, and annual mean temperature (Bio1) was obtained as the highest permutation importance variable on the spread of ASFV. Based on the analyzed scenarios, we found that the future climate is favourable for ASFV disease; only quantitative ratios are different and directly associated with climate change. The current study could be a reference material for wildlife health management, climate change issues, and World Health Organization sustainability goal 13: climate action.

Spatial Movement Patterns and Local Co-Occurrence of Nutria Individuals in Association with Habitats Using Geo-Self-Organizing Map (Geo-SOM).

Nutrias (Myocastor coypus) were imported to South Korea for farming in 1985; individuals escaped captivity and established wild populations in natural ecosystems in the late 1990s. Numerous studies have focused on their monitoring and management; however, information on the continuous movement of individuals is not available. In this study, telemetry data from field conditions were used to identify the nearest-neighbor distances of individuals in association with environmental factors, including plant type, land cover, and biological parameters. The minimum nearest-neighbor distances for the different sexes were, overall, according to the minimum distances for the same sex. Local co-occurrences of individuals, either of the same or different sex, were seasonal. Tall grasslands, followed by herbaceous vegetation, were associated with the co-occurrence of different sexes. Conversely, floating-leaved hydrophytes, followed by xeric herbaceous vegetation, were correlated with the co-occurrence of the same sex. Local female-male co-occurrences were negatively associated with male-male co-occurrences but not with female-female co-occurrences, suggesting male dominance in group formations. Movement and co-occurrence information extracted using Geo-self-organizing maps furthers our understanding of population dispersal and helps formulate management strategies for nutria populations.

Detection of superior genotype of fatty acid synthase in Korean native cattle by an environment-adjusted statistical model.

OBJECTIVE: This study examines the genetic factors influencing the phenotypes (four economic traits:oleic acid [C18:1], monounsaturated fatty acids, carcass weight, and marbling score) of Hanwoo. METHODS: To enhance the accuracy of the genetic analysis, the study proposes a new statistical model that excludes environmental factors. A statistically adjusted, analysis of covariance model of environmental and genetic factors was developed, and estimated environmental effects (covariate effects of age and effects of calving farms) were excluded from the model. RESULTS: The accuracy was compared before and after adjustment. The accuracy of the best single nucleotide polymorphism (SNP) in C18:1 increased from 60.16% to 74.26%, and that of the two-factor interaction increased from 58.69% to 87.19%. Also, superior SNPs and SNP interactions were identified using the multifactor dimensionality reduction method in Table 1 to 4. Finally, high- and low-risk genotypes were compared based on their mean scores for each trait. CONCLUSION: The proposed method significantly improved the analysis accuracy and identified superior gene-gene interactions and genotypes for each of the four economic traits of Hanwoo.

Long-term agroecosystem research in the central Mississippi river basin: hyperspectral remote sensing of reservoir water quality.

In situ methods for estimating water quality parameters would facilitate efforts in spatial and temporal monitoring, and optical reflectance sensing has shown potential in this regard, particularly for chlorophyll, suspended sediment, and turbidity. The objective of this research was to develop and evaluate relationships between hyperspectral remote sensing and lake water quality parameters-chlorophyll, turbidity, and N and P species. Proximal hyperspectral water reflectance data were obtained on seven sampling dates for multiple arms of Mark Twain Lake, a large man-made reservoir in northeastern Missouri. Aerial hyperspectral data were also obtained on two dates. Water samples were collected and analyzed in the laboratory for chlorophyll, nutrients, and turbidity. Previously reported reflectance indices and full-spectrum (i.e., partial least squares regression) methods were used to develop relationships between spectral and water quality data. With the exception of dissolved NH, all measured water quality parameters were strongly related ( ≥ 0.7) to proximal reflectance across all measurement dates. Aerial hyperspectral sensing was somewhat less accurate than proximal sensing for the two measurement dates where both were obtained. Although full-spectrum calibrations were more accurate for chlorophyll and turbidity than results from previously reported models, those previous models performed better for an independent test set. Because extrapolation of estimation models to dates other than those used to calibrate the model greatly increased estimation error for some parameters, collection of calibration samples at each sensing date would be required for the most accurate remote sensing estimates of water quality.