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Planetary health is crucial to human well-being, ecosystem sustainability, and biodiversity preservation. In this context, camera traps are an effective remote sensing tool for monitoring biodiversity. Given the rising importance of understanding biodiversity patterns and trends, this study examines possible factors influencing camera-trap studies and provides bibliometric insights from 2377 publications indexed in the Web of Science (WoS). To explore the potential drivers of camera-trap research growth, we used a logistic model based on specific variables, including global gross domestic product, temperature growth, a planetary health measure the declining living planet index, and human population growth. The living planet index was identified as a statistically significant driver of camera-trap research growth (p-value

Abstract 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.

Wind power is a rapidly growing renewable energy sector that reduces greenhouse gas emissions and provides sustainable energy. However, environmental destruction in wind power plant areas, particularly in wind farms within forests, is an emerging issue. This study aimed to analyze the impact of wind-farm roads on terrestrial animals in forested areas. A camera trap survey was conducted to investigate the impact of road management on wildlife behavior. We installed 52 cameras along roads connecting wind turbines for three months (1st October to 30th December 2021) on the Yeongyang-gun wind farm in South Korea and evaluated animal occupancy and detection probabilities using an occupancy model. Factors related to terrain and vegetation were used to estimate the occupancy probability (station use). The detection analysis included the presence or absence of guardrails, wind turbines, shrublands, and retaining walls. Additional variables included camera type, number of camera-operating days, and survey time. During the survey period, seven terrestrial mammals (roe deer, wild boar, water deer, raccoon dogs, badgers, leopards, cats, and martens) were captured using cameras. Based on camera trap records, roe deer was the most dominant species, followed by wild boars, raccoon dogs, and water deer, with fewer badgers and martens. The presence of forests in the road area was a significant factor for most species in terms of use probability, and camera type was significant for detection probability. Detecting animals along roads shows that roads are passageways for wildlife, affecting animal behavior during vehicle movement and can cause habitat disconnection. Our results demonstrate that wind farms are indirectly linked to wildlife distribution and welfare. Effective management policies for mitigating wildlife disruption can support sustainable ecosystems and biodiversity. The results of this study can serve as a reference for supporting wildlife conservation, terrestrial ecosystems, and environmental impact assessments.

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.

Abstract Background Understanding and patterning the possible causal variables of global warming is attributed to the development of effective prevention and mitigation strategies for climate change. Therefore, we aimed to pattern and visualize the possible causal variables of global warming and measure the causality between them. Methods We patterned and visualized the time series (103 years, from 1918 to 2020) of global surface temperature (GTemp) data with the gross domestic product (GDP) per capita, human population (Pop), and carbon dioxide (CO2) emissions of South Korea using a self-organizing map (SOM) and examined the causable local feature of global warming using the Granger causality (GC) test. The time-series data were trained and mapped in 4 × 4 SOM grids, and causality networks between variables were examined using multivariate Granger test statistics. Results SOM patterned 103 years of data, and a dominant cluster contained continuous time-series data from 2007 to 2020. Similarly, the CO2 emissions of South Korea were obtained as a predictable unidirectional causal variable for GTemp from GC analysis. Based on data from the past 34 years, significant causality (p-value = 0.01) was observed with the integrated effect of Pop, GDP, and CO2 on GTemp. Conclusion This study patterned the time-series data using SOM and examined the causal relationship between variables using the GC test. The study framework could be used as a reference by future scholars, ecologists, and the United Nations Sustainable Development Goals.

Understanding the village characteristics linked to forest networks is essential for the scientific management of forest resources. Forests are complex socio-ecological systems. This study classifies the resources and characteristics of forest networks and neighboring villages using unsupervised learning algorithms: self-organizing maps (SOM) and geographic-self-organizing maps (Geo-SOMs). Considering ecological, economic, and sociocultural indicators, 18 covariates of 379 villages in two forest networks of the Baekdudaegan Mountain Range in South Korea were analyzed. The data visualizing map size was fixed based on changes in quantization and topographic errors of the same grid maps, and the number of clusters was determined by comparing K-means and hierarchical clustering techniques. An optimal map size of 17 × 12 grids and six clusters was used for further classification of the input data for both SOM and Geo-SOM analyses. The common characteristics of villages were identified using SOM classification, whereas geographically bounded characteristics were identified using Geo-SOM. The approach introduced in this study can be applied to socio-ecological classification and the design of sustainable forest management policies that link the remote sensing and geographic information systems.

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 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.

The firefly species Luciola unmunsana was first discovered on the Unmunsan Mountain in Cheongdo-gun, Gyeongsangbuk-do, South Korea and consequently named after the mountain. The population and habitats of this once-abundant species have recently decreased significantly due to light and environmental pollution caused by industrialization and urbanization. This study investigated the distribution and density of L. unmunsana around the ecological landscape conservation area of the Unmunsan Mountain. Additionally, we conducted molecular experiments on regional variations, genetic diversity and phylogenetic relationships among the various populations of L. unmunsana in South Korea. The genetic relationships among populations were also analyzed using mitochondrial DNA by collecting 15 male adults from each of the 10 regions across South Korea selected for analysis. Differences were observed between populations in the east, west and south of the Baekdudaegan Mountain Range. The firefly populations collected from the eastern region, which included Gyeongsang-do, showed a close genetic relationship with fireflies collected from the Unmunsan Mountain. Thus, the findings of this study can be used as baseline data for re-introducing L. unmunsana to the Unmunsan Mountain.

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.

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.

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 NH3, all measured water quality parameters were strongly related (R² ≥ 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. [ABSTRACT FROM AUTHOR]