Applying MaxEnt to Predict the Future Distribution of Human-Black Bear Conflicts in Toyama and Akita Prefectures, Japan

Applying MaxEnt to Predict the Future Distribution of Human-Black Bear Conflicts in Toyama and Akita Prefectures, Japan | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Applying MaxEnt to Predict the Future Distribution of Human-Black Bear Conflicts in Toyama and Akita Prefectures, Japan Didier Delgorge, Shoki Shimada, Wataru Takeuchi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8212652/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 11 You are reading this latest preprint version Abstract Human-bear conflict (HBC) is an increasingly urgent issue in Japan, causing severe injuries and fatalities. Previous studies link conflict patterns to climate change and mast fluctuations. More recently, Japan’s demographic trend has emerged as a key driver, yet its influence remains poorly understood, making future conflicts hard to predict as depopulation and ageing intensify. This study uses the MaxEnt species distribution model to map bear witness probabilities and integrates demographic projections to predict future HBC patterns in Akita and Toyama Prefectures. MaxEnt results indicate a high probability of bear sightings reaching Akita’s urban core, while in Toyama, human and bear habitats remain more clearly separated. In Toyama, high-risk clusters are scattered across towns, agricultural areas, and forest edges, with risk expected to rise gradually over the next 25 years, particularly around the urban fringe. In contrast, Akita’s urban core forms a concentrated hotspot, with risk projected to increase sharply in the same area. Current management approaches are becoming increasingly ineffective as hunter numbers have declined sharply. By identifying emerging hotspots and long-term trends, this study offers insights to improve resource allocation, strengthen local management strategies, and better support human-bear coexistence. Earth and environmental sciences/Climate sciences Biological sciences/Ecology Earth and environmental sciences/Ecology Earth and environmental sciences/Environmental social sciences Earth and environmental sciences/Natural hazards Human-wildlife conflict Asian black bear Species distribution modelling Maxent model Risk assessment Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction The primary mammals responsible for human-wildlife conflict in Japan are wild boar, sika deer, Japanese macaques and Asiatic black bears (Honda, 2009 ). Although these large mammals disappeared from many regions during the Meiji and early Shōwa periods (late 1800s to early 1900s) due to hunting and other human activities, their populations have since recovered and are now reoccupying much of their historical ranges (Saito et al., 2016 ). Among them, wild boar and sika deer are particularly notorious for causing extensive agricultural damage, to the extent that some farmers have been forced to abandon their fields after repeated crop losses (Ueda et al., 2018a ). While these species primarily inflict economic damage, Asiatic black bears pose a more serious threat due to their potential to cause injuries and fatalities. Japan is home to two bear species: the Asiatic black bear (Ursus thibetanus japonicus) and the Ussuri brown bear (Ursus arctos lasiotus) . The black bears inhabit northern and central Japan, on the islands of Honshu and Shikoku (Ikeya, 2023 ). Although smaller than brown bears - adults typically measuring 120–145 cm in length, 50–60 cm in height and weighing 70–120 kg (Takahata, 2014 ) - black bears remain highly dangerous as their sharp claws are capable of inflicting deep lacerations. In recent years, bear related incidents have risen sharply. In fiscal year 2023, the Ministry of the Environment of Japan recorded 192 black bear attacks between April and November, including four fatalities. Akita Prefecture accounted for the largest share of these incidents, representing one-third of all reported cases. Furthermore, fiscal year 2025 has already surpassed the 2023 record, with more than ten fatalities resulting from black bear attacks as of November 3, 2025 (Ministry of the Environment, 2025 ). In response to the rising number of attacks, the government has escalated bear control measures. On April 16, 2024, the Ministry of the Environment added both bear species to the "Designated Wildlife Species for Control" list, marking the first expansion of the list since 2014. Originally established to manage rapidly growing populations of sika deer and wild boar, the list provides central government subsidies for culling designated species. More recently, Japan’s Ground Self Defence Force have been deployed to assist in trapping bears (Yeung, 2025 ). However, these measures have sparked controversy among the public, raising questions about whether these approaches represent appropriate long-term management strategies. Their slower reproductive rate compared to species like boars and deer, as well as their role as seed dispersers in maintaining forest ecosystems, highlights the potential negative consequences of indiscriminate hunting (Koike et al., 2011 ). Additionally, studies have shown that population control measures intended to mitigate human-wildlife conflict can inadvertently worsen the issue by increasing the prevalence of conflict-causing or nuisance wildlife ((Izumiya, 2010 ); (Ueda et al., 2018b ); (Honda & Kawauchi, 2011 )). Moreover, lethal control methods are becoming increasingly unfeasible. Since 1970, the number of hunters in Japan has declined sharply, with the elderly now dominating the hunter demographic. In 2014, there were only 194,000 hunters, down from 518,000 in 1975 (Lovelock et al., 2022 ) Therefore, further research is essential to improve our understanding of bear distribution which can ultimately inform the development of management strategies that mitigate conflicts while ensuring bear conservation. In forested and mountainous areas, people can avoid conflict by staying alert to their surroundings and taking precautions, such as using bells on backpacks or carrying a spray. Alternatively, they can avoid visiting these areas during periods of high bear activity altogether. However, these strategies are far less effective when bears enter human settlements, a phenomenon that is becoming increasingly common. Residents are typically unprepared for bears appearing unexpectedly in daily life, such as in supermarkets (The Japan Times, 2025 ). This raises a key question: why are bears appearing closer to human spaces? Several factors contribute to this trend. Primarily, bears rely on hard mast as a critical food source before hibernation. During years of mast shortages, bears are forced to leave their natural habitats in search of food, often resorting to raiding agricultural crops (Malcolm et al., 2014 ). For instance, a positive correlation has been observed between the number of nuisance bears killed and beechnut crop failures in 5 out of 7 zones in the Tohoku region of Japan (Oka et al., 2004 ). This phenomenon often explains spikes in conflict during specific years. Additionally, climate change plays a role in increasing conflict frequency; Warmer winters result in delayed hibernation and shorter hibernation periods, leading to more frequent bear encounters (Evans et al., 2016 ). Recently, population decline and aging have been increasingly recognised as significant drivers of rising HBCs. This is particularly relevant in Japan since the population is projected to decrease by 24% by 2050, with approximately 20% of agricultural settlements expected to become completely depopulated (Tsunoda & Enari, 2020 ). Understanding how wildlife distribution and conflict respond to such demographic shifts is therefore essential for developing effective, long-term management strategies. However, research linking these two factors remains limited. Previous studies, such as the analysis by (Hoshizaki et al., 2016 ), have shown that bear sighting frequency in Akita Prefecture is positively correlated with local population decline. Nevertheless, there is still a critical gap in spatial analyses that explore how depopulation and ageing affect conflict risk across different areas. The primary objective of this study is to map the distribution of HBC in light of Japan’s ongoing population decline and aging. Specifically, it aims to analyse how demographic changes impact future conflict risk, identifying high-risk areas and emerging spatial trends. By examining how bear distribution and conflict are expected to evolve as human populations shrink, this research seeks to offer valuable insights that can inform the development of effective local management strategies. 2. Methodology This study is divided into two parts: The first part utilises the MaxEnt species distribution model to map the probability of bear sightings, while the second part combines this output with 1km grid demographic estimates to predict future HBC risk. 2.1 Study area Toyama and Akita Prefectures, located on Honshu’s Sea of Japan coast, experience a humid subtropical climate with hot summers and snowy winters. Average monthly temperatures in Toyama City range from 3°C to 26.9°C, with 2,374 mm of precipitation and 253 cm of snow annually. Similarly, in Akita City, temperatures range from 0.4°C to 25.0°C, with 1,742 mm of precipitation and 273 cm of snow annually (Japan Meteorological Agency, 2025). In Toyama, urban areas are concentrated along the coastal plain, while in Akita they are more evenly distributed, forming moderate population clusters across the prefecture. Both prefectures share similar terrain features, including coastal plains, river valleys, and mountains that rise to around 3,000 meters in Toyama, due to the Northern Alps located in the east, and up to 1,770 meters in Akita (Yamazaki et al., 2017a ). Both prefectures are predominantly covered by deciduous and coniferous forests, which provide food and shelter for black bears. Toyama spans 4,248 km² with a population of 1,007,000 as of October 2023, while Akita is larger, covering 11,638 km², but has a smaller population of 914,000. What sets Akita apart is its demographic challenges: it has the highest rate of population decline and the largest proportion of elderly residents among Japan's 47 prefectures − 39.0% of Akita's residents are aged 65 and older, compared to Toyama's 33.1% (Statistics Bureau of Japan, 2024 ). Akita was chosen for this study not only because it has the highest proportion of bear attacks in Japan but also because of its notable demographic trends. Toyama serves as a useful comparison due to similar climate conditions and the availability of bear sighting data. 2.2 Bear witness data Bear occurrence data were obtained from official government sources. For Toyama, data were acquired from the prefectural government's website, which compiles reports from municipal authorities (Toyama Prefectural Government, n.d.). For Akita, data were obtained through direct contact with the prefecture's Department of Living Environment, which compiles reports from police stations, the Nature Conservation Division, and citizen submissions via an online reporting system. Duplicate reports in the Akita dataset, resulting from multiple submissions of the same sighting by the same individual, were identified and removed using Excel. For this study, we collected records from fiscal years 2018 to 2023, totalling 2,733 bear witness locations for Toyama and 8,038 for Akita (see Fig. 1 for bear witness points). 2.3 Predictor variables Selecting the appropriate predictor variables is a critical step for accurately modelling species distribution (Guisan & Zimmermann, 2000 ). In this study, 15 variables (9 environmental and 6 anthropogenic) known to influence black bear distribution were selected based on a literature review (see Table 1 ). The variables were processed for analysis, with five "distances to land cover" variables calculated using Euclidean distance from the ESA WorldCover v200 dataset (Zanaga et al., 2022 ). Multicollinearity among predictor variables can compromise model accuracy by producing overconfident predictions. To mitigate this, it is preferable to retain only essential variables, rather than using all available ones(Winship & Western, 2016 ); Too few variables may result in inaccurate predictions, while too many can introduce unnecessary complexity (Beaumont et al., 2005 ). In this study, multicollinearity was assessed using the Variance Inflation Factor (VIF) and a correlation matrix in Python 3 on a Google Compute Engine backend. Pairs of variables with correlation coefficients exceeding 0.6 were addressed to reduce redundancy. The remaining seven variables - NDVI, snow cover, slope, distance to forest, distance to water, distance to orchards, and distance to roads - were selected for their direct relevance to bear encounters. In particular, the distance to orchards variable was included because bears often raid these areas during the autumn fruiting season, especially in poor harvest years when natural food sources are scarce. With VIF values below 10, these variables indicated acceptable multicollinearity and were deemed ready for modelling (Akinwande et al., 2015 ). Table 1 List of predictor variables used for assessing potential bear witness distribution. Variables included in the final model, after selection through multicollinearity testing, are highlighted in bold. Category Variables Description Unit Source Environmental NDVI \(\:NDVI=\frac{NIR-Red}{NIR+Red}\) / LANDSAT 8 Level 2, Collection 2, Tier 1 Snow cover NDSI (Normalised Difference Snow Index) % MODIS, MOD10A1 (Hall & Riggs, 2015 ) Temperature Land surface temperature Kelvin MODIS, MOD21A1D (Hulley & Hook, 2021 ) Precipitation Daily atmospheric precipitation mm/pentad UCSB/CHG CHIRPS (Funk et al., 2015 ) Elevation DEM (Digital Elevation Model) meters MERIT DEM (Yamazaki et al., 2017b ) Slope Slope of the terrain degrees (derived from MERIT DEM) Distance to forest Euclidian distance to the nearest forest meters ESA WorldCover v200 (Zanaga et al., 2022 ) Distance to grassland Euclidian distance to the nearest grassland meters ESA WorldCover v200 Distance to water Euclidian distance to the nearest water body meters ESA WorldCover v200 Anthropogenic Distance to build up Euclidian distance to the nearest built-up area meters ESA WorldCover v200 Distance to cropland Euclidian distance to the nearest cropland meters ESA WorldCover v200 Distance to orchards Euclidian distance to the nearest orchard meters (Biodiversity Center of Japan, n.d.) Distance to road Euclidian distance to the nearest road meters GRIP4 (Meijer et al., 2018 ) Nighttime lights Average radiance nW/cm 2 /sr NOAA VIRS (Elvidge et al., 2017 ) Population count Total population of both sexes in 2015 population/km 2 国土数値情報 (MLIT, 2018) 2.4 Probability mapping using MaxEnt model The Maximum Entropy (MaxEnt) model was used in this study to predict the potential distribution of Asiatic black bear sightings. MaxEnt was chosen for this study as it outperforms other species distribution modelling techniques in both accuracy and output quality, particularly when working with presence-only data (Wilting et al., 2010 ); (West et al., 2016 ). This machine learning approach integrates bear witness records with selected variables to generate a probability distribution, where each raster cell is assigned a value between 0 and 1 to represent the likelihood of bear sightings. It works by identifying the most uniform and least biased probability distribution possible, while ensuring that the expected values of the variables align with the observed conditions at the bear sighting locations. The entropy of the distribution p is given by the following formula (Phillips et al., 2006 ): $$\:H\left(p\right)=\:-\sum\:_{x}p\left(x\right)\:logp\left(x\right)$$ The model was implemented on the Google Earth Engine platform using the ee.classifier.amnhMaxent function since it provides accurate results considerably faster than traditional MaxEnt software (Campos et al., 2023 ). The presence data was split into training (80%) and testing (20%) sets. Background points were randomly generated within the study area in numbers equal to the witness points and replicated ten times to enhance precision (replications beyond ten did not improve performance). The model employed automatic feature selection allowing the algorithm to automatically select the most suitable feature classes to use, based on number of training samples. The regularization multiplier was kept at the default value of 1.0. The model's performance was evaluated using the Area Under the Receiver Operating Characteristic Curve (AUC), which measures the ability to discriminate between presence and absence sites (Hanley & McNeil, 1982 ). AUC provides a single accuracy measure, independent of threshold, with values ranging from 0.5, representing random chance, to 1, indicating perfect discrimination (Fielding & Bell, 1997 ); (Elith et al., 2006 ). The training AUC evaluates the model's fit to the training data, while the testing AUC assesses its ability to generalise to unseen data (Jin Huang & Ling, 2005 ). Similar AUC values between training and testing indicate good generalization, suggesting the model is not overfitting or underfitting. 2.5 Risk assessment of future human-black bear conflicts The second part of the study assessed future conflict risk by combining the bear sighting probability output from the model with 1km grid demographic estimates obtained from the National Land Information Division (MLIT, 2018). This analysis aimed to identify future HBC hotspots and determine where risk is expected to rise over the next 25 years. In this study, HBC risk was calculated following Crichton’s framework, which defines risk as the product of hazard, exposure, and vulnerability (risk = h*e*v) (Crichton, 1999 ). In this context, hazard was represented by the probability map generated by the MaxEnt model. Due to limitations in future environmental data, dynamic variables such as NDVI and snow cover were not projected forward; As a result, the same bear sighting probability distribution was applied across all future years, under the assumption that habitat conditions would remain relatively constant. Exposure was defined as the total population for the year under study, and vulnerability was calculated as the proportion of the population aged 65 and over in that year. Demographic data for each year were processed into raster format to match the spatial resolution of the MaxEnt outputs and allow for pixel-wise calculations. Risk was calculated at five-year intervals from 2025 to 2050. To detect long-term spatial trends, a linear regression was applied to the time series of risk values at each pixel. The resulting risk trend map highlights areas where conflict risk is projected to increase, providing valuable insights for future management strategies. 3. Results 3.1 Bear witness probability maps The probability maps for Toyama and Akita Prefectures (Fig. 2 ) illustrate clear differences in the spatial distribution of bear sighting probabilities, particularly in urban areas. In both prefectures, high-probability areas are generally concentrated along forest edges, as well as near rivers and roads, while the lowest probabilities are found within dense forest interiors, mountainous regions, and urban centres. However, noticeable contrasts emerge when comparing the two prefectural capitals, Toyama City and Akita City (as shown in Fig. 2 ). In Toyama City, high-probability areas are concentrated along the forest edge, with values dropping sharply toward both the urban area and forest interior. The average probability of bear sighting in Toyama City is 0.25 (to 2 s.f.), reflecting a relatively low likelihood of encounters. Conversely, Akita City exhibits a much more diffuse pattern, with an average probability of 0.38 (to 2 s.f.), indicating a higher likelihood of bear sightings. High-probability areas are not only present along the forest edge but also extend well into the urban core. In fact, portions of the city centre display moderate to high probabilities of bear presence. The response curves (Fig. 3), showing how bear sighting probability responds to the predictor variables, further support the observation that bear presence extends further into urban areas in Akita. The NDVI response curve shows that in Toyama, bear sighting probabilities remain near zero in areas with low NDVI, increasing only above 0.1, indicating that sightings are mostly restricted to vegetated areas. In contrast, Akita shows higher bear sighting probabilities even in areas with low NDVI, with noticeable peaks around − 0.3 and 0. For example, when the NDVI is − 0.3, the probability of a bear sighting is 0 in Toyama but over 0.2 in Akita, highlighting a greater bear presence within built-up environments in Akita. A similar pattern is observed with distance to forest. While bear sighting probabilities in Toyama decline sharply and stabilize near zero beyond 2000 m from forests, Akita maintains higher probabilities up to 6000 m, indicating that bears in Akita are sometimes observed further from their natural habitats. For distance to orchards, both regions show high probabilities near orchards, but Akita exhibits higher overall probabilities and a steeper decline with distance. Among all distance-based variables, orchards yields the highest sighting probabilities at zero distance, emphasising their role in attracting bears, especially in Akita. This suggests that special attention should be given to orchard management in Akita to reduce the risk of bear encounters. 3.2 Accuracy assessment In Toyama, the test AUC was 0.77 and the training AUC was 0.75, indicating good accuracy and effectiveness in predicting bear sighting probabilities. Akita showed slightly lower performance with both values at 0.72, still reflecting reasonable accuracy. The model fitting performance varied depending on the spatial scale used. Toyama City achieved a test AUC of 0.90 and a training AUC of 0.91, while Akita City had a test AUC of 0.80 and a training AUC of 0.81. The small difference between the test and training AUC values (± 0.02) suggests that the model generalizes well. This implies that it successfully learned from the training data and can be effectively applied to new, unseen data. 3.3 Future human-bear risk assessment The HBC risk map for 2025 (Fig. 4) reveals distinct spatial patterns between the two prefectures, particularly in the urban centres of Toyama and Akita prefectures. In Toyama, risk is broadly distributed across multiple towns and cities, with no single dominant hotspot. Instead, it is dispersed into numerous moderate-risk clusters, primarily along forest edges and at the boundaries between agricultural and urban areas. The urban core itself remains at relatively low risk, as illustrated by Toyama Station, which falls within a low-risk zone. In contrast, Akita Prefecture exhibits a more concentrated risk pattern, with only a handful of isolated moderate risk clusters scattered outside the capital city. In Akita City, risk is highly concentrated, with the highest-risk areas located within the urban core rather than along the forest boundary. This is illustrated by Akita Station, which falls directly within the area of highest predicted risk. The predicted risk trend map (Fig. 5) illustrates the spatial distribution of areas where HBC risk is expected to increase between 2025 and 2050. In Toyama Prefecture, risk is projected to increase in several areas, with the highest increase occurring in the southern half of a 5 km radius around Toyama Station. Overall, approximately 13% of Toyama Prefecture is projected to experience an increase in risk, with the maximum predicted risk increase reaching 130%. This suggests a moderate but widespread growth in HBCs across the region. In contrast, Akita Prefecture exhibits a more concentrated pattern of increasing risk, predominantly within central Akita City, with the largest increases occurring 2 km east of Akita Station and 3 km to its west and northwest. Although the area experiencing increasing risk in Akita is much smaller - covering just under 5% of the prefecture - the severity of risk growth is considerably higher than in Toyama; the maximum projected increase reaches 350%, highlighting a substantial escalation of conflict within a confined urban area. These contrasting patterns suggest that while Toyama is expected to face a gradual, widespread increase in HBC risk, Akita is likely to experience a sharp risk growth concentrated within its urban core. 4. Discussions 4.1 Bear witness probability distribution Both the bear sighting probability maps and response curves strongly indicate that bears are more likely to appear near urban areas in Akita than in Toyama. This is reflected in the higher average bear sighting probability in Akita City (0.38) compared to Toyama City (0.25), as well as in the response curves, which show elevated probabilities in areas with negative NDVI, typical of urban environments, in Akita. These findings are consistent with reports of bear incidents in central Akita City. For instance, on Nov 30, 2024, a supermarket employee was injured by a bear inside a store located in a densely populated residential area (Kumabe, 2024 ). One likely explanation for the increased proximity of bears to urban areas in Akita is the deterioration of the satoyama (里山) landscape. Historically, satoyamas, made up of secondary forests, rice paddies, grasslands, and small villages (Kobori & Primack, 2003 ), functioned as semi-managed buffer zones between human settlements and surrounding forests (K. Yamazaki et al., 2009). However, rapid depopulation and the aging of rural communities, especially in Akita, have sharply reduced satoyama activities, resulting in widespread land abandonment. With younger generations moving to cities, fewer people remain to maintain farmland or manage forest edges. Abandoned and overgrown land create corridors that enable wildlife to approach human settlements in search of food, often without being noticed. Hence, as formerly managed lands grow wild and revert to secondary forests, bears are able to expand their range into residential areas, increasing the frequency of human-bear conflicts. In Toyama City, the satoyama landscape remains relatively intact. As shown in the LULC maps (Fig. 2 ), a broad transitional area composed of agricultural land, grasslands, and rural settlements separates the urban core from the forest. This buffer reduces human-bear interactions by maintaining a large separation between human settlements and bear habitats. In contrast, Akita City lacks such a distinct buffer, with forests often directly adjacent to densely populated neighbourhoods. Therefore, the deterioration of this transitional zone likely contributes to the more diffuse probability distribution observed in Akita, where high bear sighting probabilities extend into the urban core. 4.2 Risk hotspots and future trends The advantage of this study lies in its ability to map the spatial distribution of HBC in light of projected population trends. The 2025 risk map (Fig. 4) shows that conflict risk in Toyama is moderately distributed across multiple towns and satoyama areas, whereas in Akita it is highly concentrated in the core of Akita City. Over the next 25 years (2025–2050), the risk trend map (Fig. 5) indicates a moderate but widespread increase in Toyama, while Akita is projected to experience a severe, concentrated rise, with risk increasing by up to 350% around Akita Station. This pattern is illustrated in Fig. 6 , which shows that over half of Toyama’s municipalities will experience a risk increase in at least 10% of their municipal area, while only 2 of Akita's 25 municipalities will see a similar increase. These patterns demonstrate how human-bear risk distribution differs significantly from bear witness probability due to the influence of demographic trends, with Akita's severe rural depopulation particularly apparent. 4.3 Management implications and Future Landscape Change The findings of this study offer valuable insights for local authorities, especially in resource-limited areas by identifying high-risk zones and forecasting areas where conflict is likely to intensify. What is novel about this study is that risk was calculated by considering not only bear sighting probabilities but also projected demographic changes, offering insights that could help promote human-bear coexistence. Specifically, in Akita Prefecture, where risk is highly concentrated in the urban core of Akita City, targeted measures are urgently needed. For instance, the study projects a roughly 350% increase in risk over the next 25 years in the area 2 km east of Akita Station. Therefore, measures such as wildlife barriers or detection systems along the Akita Expressway, particularly between the northern and southern interchanges, could help reduce the likelihood of conflict in this high-risk zone. It is important to note that while this study provides valuable insights into how demographic trends may shape the future distribution of HBC, it does not account for the dynamic effects of land abandonment. As farmland and rural areas are increasingly left unmanaged, forests and overgrown land expand, gradually shifting bear habitats over time. Unharvested crops, unattended fruits in orchards, and natural regrowth in these areas can attract bears (Krofel et al., 2020 ), meaning that future risk maps may look different from current ones as bear habitats evolve with the landscape. Therefore, the ongoing erosion of satoyama landscapes may heighten the risk of conflict beyond what this study predicts. Moreover, studies have shown that as wildlife adapt to urban environments, more assertive bears with bolder personalities tend to emerge (Martínez-Abraín et al., 2020 ), creating a snowball effect that further intensifies the risk of human-wildlife conflict over time. This phenomenon appears to be already occurring in some prefectures, particularly Akita, and is likely to intensify as other prefectures experience similar demographic trends. Therefore, the spatial patterns of conflict observed in this study are not static but will continue to evolve as land abandonment reshapes the landscape. 5. Conclusion Estimating the future distribution of HBCs is essential for developing effective, long-term management strategies that can reduce conflicts and support human-bear coexistence. While various factors influence the changing patterns of these conflicts - such as fluctuations in forest mast - this study specifically examined the impact of population decline and aging on future conflict risk. Using a MaxEnt model, we analysed the spatial distribution of bear sighting probabilities and assessed how demographic changes may reshape conflict patterns over time. The findings reveal clear regional differences, with bear sightings in Akita Prefecture more likely to occur within and around urban areas, while Toyama Prefecture maintains a more distinct separation between bear habitats and human settlements. The 2025 conflict risk map shows that risk in Toyama is broadly distributed in clusters across towns, agricultural areas, and forest edges, whereas in Akita it is highly concentrated in the urban core of Akita City. Over the next 25 years, risk is projected to rise sharply in central Akita, while Toyama is expected to see a moderate but widespread increase, particularly around the outskirts of its urban areas. These results highlight the need for targeted management strategies that prioritise urban centres where conflict risks are projected to be most severe. This study's risk assessment does not account for dynamic factors like hikers or individuals engaging in activities in forests and mountains, as it relies on census data that assumes no risk in areas with zero population. Future research should integrate detailed road and trail maps to better assess conflict risks for hikers and farmers who often use trails not represented in large-scale road datasets. In Japan, further research is needed to examine how risk distribution will be influenced by population decline, ageing and land abandonment. Additionally, future studies could adapt this research framework to assess conflicts involving other species such as wild boars or deer that frequently damage crops. By replacing demographic factors with variables related to agricultural products, similar risk assessments could be conducted to forecast and manage broader human-wildlife conflict scenarios. Declarations Acknowledgements I would like to thank the Nature Conservation Divisions of Akita and Toyama Prefectures for kindly providing the bear witness data used in this study. CRediT authorship contribution statement D.D.: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Resources, Software, Validation, Visualization, Writing - original draft, Writing - review and editing; S.S.: Data curation, Formal analysis, Investigation, Methodology, Visualization, Writing - review and editing; W.T.: Conceptualization, Investigation, Project administration, Resources, Supervision, Writing - review and editing Funding This study received no funding. Competing Interests The authors declare no competing interests. Data Availability The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. References 1km mesh estimated future population data. (2018). National Land Information Division, National Spatial Planning and Regional Policy Bureau, MLIT of Japan . Akinwande, M. O., Dikko, H. G., & Samson, A. (2015). Variance Inflation Factor: As a Condition for the Inclusion of Suppressor Variable(s) in Regression Analysis. Open Journal of Statistics , 05 (07), 754–767. https://doi.org/10.4236/ojs.2015.57075 Asian black bear witness records map “Kumap”. (n.d.). Toyama Prefectural Government, Department of Living Environment and Culture, Nature Conservation Division . Retrieved 15 October 2025, from https://www.pref.toyama.jp/1709/kurashi/kankyoushizen/shizen/yaseiseibutsu/kumap.html Beaumont, L. J., Hughes, L., & Poulsen, M. (2005). Predicting species distributions: use of climatic parameters in BIOCLIM and its impact on predictions of species’ current and future distributions. Ecological Modelling , 186 (2), 251–270. https://doi.org/10.1016/j.ecolmodel.2005.01.030 Biodiversity Center of Japan. (n.d.). The third national vegetation survey data . Retrieved 15 August 2025, from http://gis.biodic.go.jp/webgis/sc-025.html?kind=vg67 Campos, J. C., Garcia, N., Alírio, J., Arenas-Castro, S., Teodoro, A. C., & Sillero, N. (2023). Ecological Niche Models using MaxEnt in Google Earth Engine: Evaluation, guidelines and recommendations. Ecological Informatics , 76 , 102147. https://doi.org/10.1016/j.ecoinf.2023.102147 Crichton, D. (1999). The risk triangle. Natural Disaster Management , 102 (3), 102–103. Elith, J., H. Graham*, C., P. Anderson, R., Dudík, M., Ferrier, S., Guisan, A., J. Hijmans, R., Huettmann, F., R. Leathwick, J., Lehmann, A., Li, J., G. Lohmann, L., A. Loiselle, B., Manion, G., Moritz, C., Nakamura, M., Nakazawa, Y., McC. M. Overton, J., Townsend Peterson, A., … E. Zimmermann, N. (2006). Novel methods improve prediction of species’ distributions from occurrence data. Ecography , 29 (2), 129–151. https://doi.org/10.1111/j.2006.0906-7590.04596.x Elvidge, C. D., Baugh, K., Zhizhin, M., Hsu, F. C., & Ghosh, T. (2017). VIIRS night-time lights. International Journal of Remote Sensing , 38 (21), 5860–5879. https://doi.org/10.1080/01431161.2017.1342050 Evans, A. L., Singh, N. J., Friebe, A., Arnemo, J. M., Laske, T. G., Fröbert, O., Swenson, J. E., & Blanc, S. (2016). Drivers of hibernation in the brown bear. Frontiers in Zoology , 13 (1), 7. https://doi.org/10.1186/s12983-016-0140-6 Fielding, A. H., & Bell, J. F. (1997). A review of methods for the assessment of prediction errors in conservation presence/absence models. Environmental Conservation , 24 (1), 38–49. https://doi.org/10.1017/S0376892997000088 Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S., Husak, G., Rowland, J., Harrison, L., Hoell, A., & Michaelsen, J. (2015). The climate hazards infrared precipitation with stations - A new environmental record for monitoring extremes. Scientific Data , 2 . https://doi.org/10.1038/sdata.2015.66 Guisan, A., & Zimmermann, N. E. (2000). Predictive habitat distribution models in ecology. Ecological Modelling , 135 (2–3), 147–186. https://doi.org/10.1016/S0304-3800(00)00354-9 Hall, D., & Riggs, G. (2015). MODIS/Terra Snow Cover Daily L3 Global 500m SIN Grid. NASA National Snow and Ice Data Center Distributed Active Archive Center . Hanley, J. A., & McNeil, B. J. (1982). The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology , 143 (1), 29–36. https://doi.org/10.1148/radiology.143.1.7063747 Hirayama, S., Tadono, T., Mizukami, Y., Ohki, M., Imamura, K., Hirade, N., Ohgushi, F., Dotsu, M., Yamanokuchi, T., & Nasahara, K. N. (2022). Generation of the High-Resolution Land-Use and Land-Cover Map in Japan Version 21.11. IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium , 4339–4342. https://doi.org/10.1109/IGARSS46834.2022.9884315 Honda, T. (2009). Environmental Factors Affecting the Distribution of the Wild Boar, Sika Deer, Asiatic Black Bear and Japanese Macaque in Central Japan, with Implications for Human-Wildlife Conflict. Mammal Study , 34 (2), 107–116. https://doi.org/10.3106/041.034.0206 Honda, T., & Kawauchi, N. (2011). Methods for Constructing a Wild Boar Relative-Density Map to Resolve Human-Wild Boar Conflicts. Mammal Study , 36 (2), 79–85. https://doi.org/10.3106/041.036.0206 Hoshizaki, K., Maebashi, N., Wakamiya, S., Aomori, S., Narita, S., Kimura, K., Kuronuma, T., Takebayashi, Y., Ota, K., & Kai, S. (2016). Twenty-one-year trends (1994–2014) in the sighting records of Asiatic black bears (Ursus thibetanus) by citizens in Akita Prefecture, northern Japan. 秋田県立大学ウェブジャーナル B , 3 , 226–231. Hulley, G., & Hook, S. (2021). MODIS/Terra Land Surface Temperature/3-Band Emissivity Daily L3 Global 1km SIN Grid Day V061 [Data set]. NASA EOSDIS Land Processes Distributed Active Archive Center . Ikeya, K. (2023). Changing Mountain Landscapes in Japan: Wild Bear–Human Interactions in the Short Term. In Global Ecology in Historical Perspective (pp. 75–87). Springer Nature Singapore. https://doi.org/10.1007/978-981-19-6557-9_4 Izumiya, S. (2010). Effect of administrative extermination on the Japanese Macaque population. Bulletin Shinshu University Alpine Field Center , 51–56. Japan Meteorological Agency. (2025). Climate averages (1991-2020) . https://www.data.jma.go.jp/obd/stats/etrn/select/prefecture00.php?prec_no=32&block_no=47582&year=&month=&day=&view= Jin Huang, & Ling, C. X. (2005). Using AUC and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering , 17 (3), 299–310. https://doi.org/10.1109/TKDE.2005.50 Kobori, H., & Primack, R. B. (2003). Participatory Conservation Approaches for Satoyama , the Traditional Forest and Agricultural Landscape of Japan. AMBIO: A Journal of the Human Environment , 32 (4), 307–311. https://doi.org/10.1579/0044-7447-32.4.307 Koike, S., Masaki, T., Nemoto, Y., Kozakai, C., Yamazaki, K., Kasai, S., Nakajima, A., & Kaji, K. (2011). Estimate of the seed shadow created by the Asiatic black bear Ursus thibetanus and its characteristics as a seed disperser in Japanese cool‐temperate forest. Oikos , 120 (2), 280–290. https://doi.org/10.1111/j.1600-0706.2010.18626.x Krofel, M., Elfström, M., Ambarlı, H., Bombieri, G., González-Bernardo, E., Jerina, K., Laguna, A., Penteriani, V., Phillips, J. P., Selva, N., Wilson, S. M., Zarzo-Arias, A., Groff, C., Huber, D., Karamanlidis, A. A., Mertzanis, Y., Revilla, E., & Bautista, C. (2020). Human–Bear Conflicts at the Beginning of the Twenty-First Century: Patterns, Determinants, and Mitigation Measures. In Bears of the World (pp. 213–226). Cambridge University Press. https://doi.org/10.1017/9781108692571.016 Kumabe, Y. (2024, December 2). Bear captured in Akita grocery store after 2-day standoff. The Asahi Shinbun . Lovelock, B., Yanata, K., Seto, Y., & Yamaguchi, M. (2022). Societal Factors Influencing Hunting Participation Decline in Japan: An Exploratory Study of Two Prefectures. Society & Natural Resources , 35 (2), 149–166. https://doi.org/10.1080/08941920.2021.2006843 Malcolm, K. D., McShea, W. J., Garshelis, D. L., Luo, S.-J., Van Deelen, T. R., Liu, F., Li, S., Miao, L., Wang, D., & Brown, J. L. (2014). Increased stress in Asiatic black bears relates to food limitation, crop raiding, and foraging beyond nature reserve boundaries in China. Global Ecology and Conservation , 2 , 267–276. https://doi.org/10.1016/j.gecco.2014.09.010 Martínez-Abraín, A., Jiménez, J., Jiménez, I., Ferrer, X., Llaneza, L., Ferrer, M., Palomero, G., Ballesteros, F., Galán, P., & Oro, D. (2020). Ecological consequences of human depopulation of rural areas on wildlife: A unifying perspective. Biological Conservation , 252 , 108860. https://doi.org/10.1016/j.biocon.2020.108860 Meijer, J. R., Huijbregts, M. A. J., Schotten, K. C. G. J., & Schipper, A. M. (2018). Global patterns of current and future road infrastructure. Environmental Research Letters , 13 (6), 064006. https://doi.org/10.1088/1748-9326/aabd42 Ministry of the Environment. (2025). Human casualties caused by bears (preliminary figures) . https://www.env.go.jp/nature/choju/effort/effort12/injury-qe.pdf Oka, T., Miura, S., Masaki, T., Suzuki, W., Osumi, K., & Ssaitoh, S. (2004). Relationship between changes in beechnut production and Asiatic black bears in northern Japan. Journal of Wildlife Management , 68 (4), 979–986. https://doi.org/10.2193/0022-541x(2004)068[0979:rbcibp]2.0.co;2 Phillips, S. J., Anderson, R. P., & Schapire, R. E. (2006). Maximum entropy modeling of species geographic distributions. Ecological Modelling , 190 (3–4), 231–259. https://doi.org/10.1016/j.ecolmodel.2005.03.026 Saito, M. U., Momose, H., Inoue, S., Kurashima, O., & Matsuda, H. (2016). Range-expanding wildlife: modelling the distribution of large mammals in Japan, with management implications. International Journal of Geographical Information Science , 30 (1), 20–35. https://doi.org/10.1080/13658816.2014.952301 Statistics Bureau of Japan. (2024, April 12). Population estimates as of October 1, 2023 . Ministry of Internal Affairs and Communications. https://www.stat.go.jp/data/jinsui/2023np/index.html Takahata, C. (2014). Habitat Selection by Asiatic Black Bears Inhabiting on the Periphery of Human-Dominated Lands . The Japan Times. (2025, August 8). Bear injures two in Japan supermarket; man killed in separate attack . https://www.japantimes.co.jp/news/2025/10/08/japan/bear-attack-in-supermarket/ Tsunoda, H., & Enari, H. (2020). A strategy for wildlife management in depopulating rural areas of Japan. Conservation Biology , 34 (4), 819–828. https://doi.org/10.1111/cobi.13470 Ueda, Y., Kiyono, M., Nagano, T., Mochizuki, S., & Murakami, T. (2018a). Damage Control Strategies Affecting Crop-Raiding Japanese Macaque Behaviors in a Farming Community. Human Ecology , 46 (2), 259–268. https://doi.org/10.1007/s10745-018-9994-x Ueda, Y., Kiyono, M., Nagano, T., Mochizuki, S., & Murakami, T. (2018b). Damage Control Strategies Affecting Crop-Raiding Japanese Macaque Behaviors in a Farming Community. Human Ecology , 46 (2), 259–268. https://doi.org/10.1007/s10745-018-9994-x West, A. M., Kumar, S., Brown, C. S., Stohlgren, T. J., & Bromberg, J. (2016). Field validation of an invasive species Maxent model. Ecological Informatics , 36 , 126–134. https://doi.org/10.1016/j.ecoinf.2016.11.001 Wilting, A., Cord, A., Hearn, A. J., Hesse, D., Mohamed, A., Traeholdt, C., Cheyne, S. M., Sunarto, S., Jayasilan, M.-A., Ross, J., Shapiro, A. C., Sebastian, A., Dech, S., Breitenmoser, C., Sanderson, J., Duckworth, J. W., & Hofer, H. (2010). Modelling the Species Distribution of Flat-Headed Cats (Prionailurus planiceps), an Endangered South-East Asian Small Felid. PLoS ONE , 5 (3), e9612. https://doi.org/10.1371/journal.pone.0009612 Winship, C., & Western, B. (2016). Multicollinearity and Model Misspecification. Sociological Science , 3 , 627–649. https://doi.org/10.15195/v3.a27 Yamazaki, D., Ikeshima, D., Tawatari, R., Yamaguchi, T., O’Loughlin, F., Neal, J. C., Sampson, C. C., Kanae, S., & Bates, P. D. (2017a). A high‐accuracy map of global terrain elevations. Geophysical Research Letters , 44 (11), 5844–5853. https://doi.org/10.1002/2017GL072874 Yamazaki, D., Ikeshima, D., Tawatari, R., Yamaguchi, T., O’Loughlin, F., Neal, J. C., Sampson, C. C., Kanae, S., & Bates, P. D. (2017b). A high‐accuracy map of global terrain elevations. Geophysical Research Letters , 44 (11), 5844–5853. https://doi.org/10.1002/2017GL072874 Yeung, J. & O. J. (2025). Facing a spike in deadly bear attacks, Japan turns to the military and drones that bark. CNN . https://edition.cnn.com/2025/11/06/asia/japan-bear-attacks-military-sdf-intl-hnk Zanaga, D., Van De Kerchove, R., Daems, D., De Keersmaecker, W., Brockmann, C., Kirches, G., Wevers, J., Cartus, O., Santoro, M., Fritz, S., Lesiv, M., Herold, M., Tsendbazar, N. E., Xu, P., Ramoino, F., & Arino, O. (2022). ESA WorldCover 10 m 2021 v200. Zenodo . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 22 Jan, 2026 Reviews received at journal 20 Jan, 2026 Reviewers agreed at journal 18 Jan, 2026 Reviews received at journal 06 Jan, 2026 Reviewers agreed at journal 22 Dec, 2025 Reviewers agreed at journal 12 Dec, 2025 Reviewers invited by journal 04 Dec, 2025 Editor invited by journal 01 Dec, 2025 Editor assigned by journal 27 Nov, 2025 Submission checks completed at journal 27 Nov, 2025 First submitted to journal 26 Nov, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8212652","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":555237241,"identity":"fe7fee82-f618-4558-93d8-4346913254fa","order_by":0,"name":"Didier Delgorge","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABU0lEQVRIie2RMUvDQBTH/0fg6nD01gtI+xUSAl0q6VdJOWgXB0EQh6IBIVN0rtTNT1BcHU4O4lLbD9ClXTo5pCDSQappUzSt6S6YHwfv3j1+3Ht3QEHBX0TDUoCBcpoeZUpsE4mfr9A0bX0XxF5FwUJG0TnKDuVno6/mj/UK5ffR/O185Pqlq2lMAlxwNrAn6LgwelvXmJqePHVnbYeKmewdDsbSZ5EjEkWY16FjIZIgdyqrWJolS+lmIAaOYQZjCXFMQYJPYY1YTYAqkK73S/lQ+jJVlkOJ6utKwUZZ5itQ2qM8dMjcVy4E2ygvYS3p8JeyniVUbTsQVBpJ5x5lLUd4Q5i3YXRqNW8k25mlPNIP8ULVq5xrTRYdt8FLehrHZ+Ccyf4kfncr9vaL/SA8GMlPNINVknZyYK02zO7mG+BJ0wugkTkqTdahKvYoBQUFBf+EL+TXdNr4VxsXAAAAAElFTkSuQmCC","orcid":"","institution":"The University of Tokyo","correspondingAuthor":true,"prefix":"","firstName":"Didier","middleName":"","lastName":"Delgorge","suffix":""},{"id":555237242,"identity":"e1ec4410-1c3a-4eb6-95f5-42669f8230d0","order_by":1,"name":"Shoki Shimada","email":"","orcid":"","institution":"The University of Tokyo","correspondingAuthor":false,"prefix":"","firstName":"Shoki","middleName":"","lastName":"Shimada","suffix":""},{"id":555237245,"identity":"b1357bb7-a0c2-4fbf-8546-7490632c90bc","order_by":2,"name":"Wataru Takeuchi","email":"","orcid":"","institution":"The University of Tokyo","correspondingAuthor":false,"prefix":"","firstName":"Wataru","middleName":"","lastName":"Takeuchi","suffix":""}],"badges":[],"createdAt":"2025-11-26 11:53:51","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8212652/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8212652/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":97714680,"identity":"5459caf1-c7d1-483f-a914-f6adb9ee1883","added_by":"auto","created_at":"2025-12-08 14:27:01","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":8442278,"visible":true,"origin":"","legend":"","description":"","filename":"JournalPaper.docx","url":"https://assets-eu.researchsquare.com/files/rs-8212652/v1/f612b10e04bca8686a6a9ea1.docx"},{"id":97714675,"identity":"3d7ff203-4572-465e-9d47-cac37ff46fb9","added_by":"auto","created_at":"2025-12-08 14:27:01","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":3848668,"visible":true,"origin":"","legend":"","description":"","filename":"FIG1.tif","url":"https://assets-eu.researchsquare.com/files/rs-8212652/v1/7c7d11e0a43fcd4dd9d19b09.tif"},{"id":97895906,"identity":"72207feb-b0a9-4163-b89d-6dbf1460419b","added_by":"auto","created_at":"2025-12-10 15:35:21","extension":"tif","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":8862476,"visible":true,"origin":"","legend":"","description":"","filename":"FIG2.tif","url":"https://assets-eu.researchsquare.com/files/rs-8212652/v1/469b605e97fb04fe642bd1d3.tif"},{"id":97895478,"identity":"fa01b33e-6f86-4623-bfd0-d6b332f6c31d","added_by":"auto","created_at":"2025-12-10 15:34:18","extension":"jpg","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":942890,"visible":true,"origin":"","legend":"","description":"","filename":"FIG3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8212652/v1/e95b271d9699cecbb2da5369.jpg"},{"id":97895396,"identity":"adbcdf5e-c130-45a9-9de4-0bd0b3db5b18","added_by":"auto","created_at":"2025-12-10 15:34:07","extension":"tif","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":29614380,"visible":true,"origin":"","legend":"","description":"","filename":"FIG4.tif","url":"https://assets-eu.researchsquare.com/files/rs-8212652/v1/eda4df9d3af687ce65e955f6.tif"},{"id":97895547,"identity":"cd8305b0-4025-4db3-a796-831fd18bc379","added_by":"auto","created_at":"2025-12-10 15:34:26","extension":"tif","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":15043382,"visible":true,"origin":"","legend":"","description":"","filename":"FIG5.tif","url":"https://assets-eu.researchsquare.com/files/rs-8212652/v1/64567cd438502ccf9d3f5ee1.tif"},{"id":97895035,"identity":"a276d039-2fa4-401d-9f96-6118203aa912","added_by":"auto","created_at":"2025-12-10 15:33:25","extension":"jpg","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":436354,"visible":true,"origin":"","legend":"","description":"","filename":"FIG6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8212652/v1/b1bfc5f396da313265d82a3b.jpg"},{"id":97714677,"identity":"a3c326a5-674d-45b4-bcc4-7b823596056f","added_by":"auto","created_at":"2025-12-08 14:27:01","extension":"json","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":5303,"visible":true,"origin":"","legend":"","description":"","filename":"335c08301827450d949decbe9fd8a9e1.json","url":"https://assets-eu.researchsquare.com/files/rs-8212652/v1/cb85a189f1fc3ffb70508a1a.json"},{"id":97714676,"identity":"21fc047f-fdc7-4e3f-bc84-ce8a117e4e5b","added_by":"auto","created_at":"2025-12-08 14:27:01","extension":"xml","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":115968,"visible":true,"origin":"","legend":"","description":"","filename":"335c08301827450d949decbe9fd8a9e11enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8212652/v1/99e5dbccacddce021141b65b.xml"},{"id":97714684,"identity":"73d60c42-e0cb-4dd1-9008-e12161b8ed63","added_by":"auto","created_at":"2025-12-08 14:27:01","extension":"tif","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":3848668,"visible":true,"origin":"","legend":"","description":"","filename":"FIG1.tif","url":"https://assets-eu.researchsquare.com/files/rs-8212652/v1/ff11fc7db1f7adeb1559b536.tif"},{"id":97714695,"identity":"9dc26251-035b-4675-b9f3-ecb0cc76e046","added_by":"auto","created_at":"2025-12-08 14:27:01","extension":"tif","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":8862476,"visible":true,"origin":"","legend":"","description":"","filename":"FIG2.tif","url":"https://assets-eu.researchsquare.com/files/rs-8212652/v1/975ba19fcd402c9e86fd6eff.tif"},{"id":97895551,"identity":"b208ad8b-28cf-4dab-8a2c-0877629c6d86","added_by":"auto","created_at":"2025-12-10 15:34:26","extension":"jpg","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":942890,"visible":true,"origin":"","legend":"","description":"","filename":"FIG3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8212652/v1/616f5cc6104d785b3184695d.jpg"},{"id":97895983,"identity":"24b101e1-fb48-4883-959c-62fbe43c52c1","added_by":"auto","created_at":"2025-12-10 15:35:30","extension":"tif","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":29614380,"visible":true,"origin":"","legend":"","description":"","filename":"FIG4.tif","url":"https://assets-eu.researchsquare.com/files/rs-8212652/v1/287aa584c21ee2af18290933.tif"},{"id":97895133,"identity":"79047e3a-3912-41ad-9b25-bccca7dab2bd","added_by":"auto","created_at":"2025-12-10 15:33:38","extension":"tif","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":15043382,"visible":true,"origin":"","legend":"","description":"","filename":"FIG5.tif","url":"https://assets-eu.researchsquare.com/files/rs-8212652/v1/72377a9cfd23f8804c54a0fd.tif"},{"id":97714687,"identity":"dea5f456-4999-4b2f-bd9a-49d4b80601cd","added_by":"auto","created_at":"2025-12-08 14:27:01","extension":"jpg","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":436354,"visible":true,"origin":"","legend":"","description":"","filename":"FIG6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8212652/v1/3760fb961a2566af4c121ceb.jpg"},{"id":97714682,"identity":"d3baf21f-c29d-42a2-b888-5ebaf9bee8ad","added_by":"auto","created_at":"2025-12-08 14:27:01","extension":"jpeg","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":3848668,"visible":true,"origin":"","legend":"","description":"","filename":"FIG1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8212652/v1/8c993979251e498022def28d.jpeg"},{"id":97895499,"identity":"d21a9e06-9b8e-4781-a31d-d3ee15fd4d48","added_by":"auto","created_at":"2025-12-10 15:34:21","extension":"jpeg","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":4284990,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8212652/v1/4dc111cd995457a2f058b2dd.jpeg"},{"id":97893845,"identity":"17899181-023c-4758-aed2-99647626c7ba","added_by":"auto","created_at":"2025-12-10 15:31:21","extension":"jpeg","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":206646,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8212652/v1/01352524ef20ab0996460428.jpeg"},{"id":97714692,"identity":"1f792585-4f6a-4d36-b6eb-19e11238af46","added_by":"auto","created_at":"2025-12-08 14:27:01","extension":"jpeg","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":4828726,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8212652/v1/e8f1b10432a7b8d9f5f64606.jpeg"},{"id":97714691,"identity":"49cee990-1ab3-4b9d-8f5f-29d1cc919d8e","added_by":"auto","created_at":"2025-12-08 14:27:01","extension":"jpeg","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":4650318,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8212652/v1/9d87dc92662600f9b4ab182f.jpeg"},{"id":97714707,"identity":"9d8bf6a8-acbd-4e33-9cf7-0aa335ba7aaa","added_by":"auto","created_at":"2025-12-08 14:27:02","extension":"jpeg","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":451833,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8212652/v1/d4050d726e029042c53e41d5.jpeg"},{"id":97714696,"identity":"c8084edc-297e-4ff1-bcd6-1e78ea4f3647","added_by":"auto","created_at":"2025-12-08 14:27:01","extension":"png","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":787765,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFIG1.png","url":"https://assets-eu.researchsquare.com/files/rs-8212652/v1/14c01cd93170bce9d1f902d0.png"},{"id":97714689,"identity":"40b46cc1-4033-443c-8304-047690243986","added_by":"auto","created_at":"2025-12-08 14:27:01","extension":"png","order_by":22,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":308852,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFIG2.png","url":"https://assets-eu.researchsquare.com/files/rs-8212652/v1/2939dbd4d2516008b34f4ad6.png"},{"id":97714688,"identity":"f49b35fd-b65d-4da1-98df-c85fb8e24f91","added_by":"auto","created_at":"2025-12-08 14:27:01","extension":"png","order_by":23,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":285131,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFIG3.png","url":"https://assets-eu.researchsquare.com/files/rs-8212652/v1/9e7e3de15ba2d4c2372513c0.png"},{"id":97895105,"identity":"3814eb10-d12b-4361-827d-39f6e5939f60","added_by":"auto","created_at":"2025-12-10 15:33:35","extension":"png","order_by":24,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":2599233,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFIG4.png","url":"https://assets-eu.researchsquare.com/files/rs-8212652/v1/41b17d63fd1b3ed9c271e365.png"},{"id":97895926,"identity":"051f72d1-53c9-4b58-a629-d035e31dfd5d","added_by":"auto","created_at":"2025-12-10 15:35:23","extension":"png","order_by":25,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1421985,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFIG5.png","url":"https://assets-eu.researchsquare.com/files/rs-8212652/v1/c905c669f31a3d24a09c065e.png"},{"id":97895489,"identity":"fd00058b-c2df-4969-92a1-145b9e19d7d6","added_by":"auto","created_at":"2025-12-10 15:34:19","extension":"png","order_by":26,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":111825,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFIG6.png","url":"https://assets-eu.researchsquare.com/files/rs-8212652/v1/f6c04afbdacb24909bd8c6fb.png"},{"id":97895137,"identity":"1d49a61d-8d93-42f1-b8f7-4595e83cfd9d","added_by":"auto","created_at":"2025-12-10 15:33:38","extension":"png","order_by":27,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":787765,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8212652/v1/8a4b5a30badad2605c672069.png"},{"id":97714703,"identity":"74cd021f-7c0a-493b-843e-98570b79c7d5","added_by":"auto","created_at":"2025-12-08 14:27:01","extension":"png","order_by":28,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":130637,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8212652/v1/9eeb2196abccb5f6e980bf39.png"},{"id":97714694,"identity":"fe9d368e-61cd-4c25-b5f4-884745105421","added_by":"auto","created_at":"2025-12-08 14:27:01","extension":"png","order_by":29,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":41324,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8212652/v1/e11b606fdc474660c7942188.png"},{"id":97714699,"identity":"0747bc3a-5a57-4cd8-9411-3c953c1580e6","added_by":"auto","created_at":"2025-12-08 14:27:01","extension":"png","order_by":30,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":267978,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8212652/v1/925d816d9bfc952fae78faad.png"},{"id":97714704,"identity":"fe4c1aa0-92be-4ced-b87e-93a54b045c76","added_by":"auto","created_at":"2025-12-08 14:27:02","extension":"png","order_by":31,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":156820,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8212652/v1/f8c69d00b2dbc630d670e875.png"},{"id":97895670,"identity":"eafe4403-bc74-40d4-a7d8-be4dfed4d995","added_by":"auto","created_at":"2025-12-10 15:34:40","extension":"png","order_by":32,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":52407,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8212652/v1/d229360beb81577905edff1f.png"},{"id":97714709,"identity":"19779e52-9a9b-4d05-8bf9-3d9b23c4a8bd","added_by":"auto","created_at":"2025-12-08 14:27:02","extension":"xml","order_by":33,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":115279,"visible":true,"origin":"","legend":"","description":"","filename":"335c08301827450d949decbe9fd8a9e11structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8212652/v1/5394d0aad93e00aa27b3777c.xml"},{"id":97895535,"identity":"3d787bdc-55ae-490d-92dd-1a8eeb50d51a","added_by":"auto","created_at":"2025-12-10 15:34:24","extension":"html","order_by":34,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":124770,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8212652/v1/071fbcc2b42cbf3d79314a80.html"},{"id":97714670,"identity":"891a0f9c-93b3-4297-aa22-0856c7b4958f","added_by":"auto","created_at":"2025-12-08 14:27:01","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":5674045,"visible":true,"origin":"","legend":"\u003cp\u003eAsiatic black bear witness points (marked with orange dots) overlaid on LULC maps (obtained from (Hirayama et al., 2022)) of a) Toyama Prefecture and b) Akita Prefecture. In Toyama, urban areas are concentrated along the coastal plain, whereas in Akita they are more evenly distributed across the prefecture. Both regions are dominated by deciduous and coniferous forests that provide habitat for Asiatic black bears.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8212652/v1/403845d8717602800e68eddc.png"},{"id":97714669,"identity":"74c3b9c5-d55f-4974-b4fa-00efb93da555","added_by":"auto","created_at":"2025-12-08 14:27:01","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":614713,"visible":true,"origin":"","legend":"\u003cp\u003eBear witness probability maps calculated using the MaxEnt model for (a) Toyama Prefecture and (b) Akita Prefecture. In Toyama, high-probability areas are concentrated along forest boundaries, whereas in Akita, they extend into the urban core, indicating greater human–bear spatial overlap.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8212652/v1/b6ce691acde5eaea5f43124d.png"},{"id":97714672,"identity":"64a08a9c-5429-48e6-abaa-1e050b89cf15","added_by":"auto","created_at":"2025-12-08 14:27:01","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":344422,"visible":true,"origin":"","legend":"\u003cp\u003eResponse curves showing the relationship between variables and bear sighting probability in Toyama (red) and Akita (blue): (a) NDVI, (b) snow cover, (c) slope, (d) distance to forest, (e) distance to water, (f) distance to road, and (g) distance to orchards.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8212652/v1/1cf55362261f673f80ad56eb.png"},{"id":97894815,"identity":"5740f829-3a0e-43f7-8db7-b230e212edda","added_by":"auto","created_at":"2025-12-10 15:33:03","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":824251,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted risk maps showing the distribution of human-bear conflict risk in 2025 for (a) Toyama Prefecture and (b) Akita Prefecture, with outline of DIDs overlaid. In Toyama, risk is broadly distributed in clusters along forest edges, agricultural areas, and occasionally near the urban fringe, but does not reach the city centre; in contrast, Akita shows highly concentrated risk in the urban core, with Akita Station marked by a star within the highest-risk area.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8212652/v1/58f356246103b52bd4f97056.png"},{"id":97714673,"identity":"ea7fff1b-3076-43f0-9abc-8a59a9bf79ac","added_by":"auto","created_at":"2025-12-08 14:27:01","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":600898,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted risk trend map showing areas of increasing human-bear conflict in (a) Toyama Prefecture and (b) Akita Prefecture over the next 25 years (2025–2050). While HBC risk in Toyama is projected to rise gradually and broadly along urban fringes and agricultural areas, Akita is expected to experience a sharp increase concentrated in its urban core, especially around Akita Station.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8212652/v1/671efdc3c68cc72cf434a064.png"},{"id":97714674,"identity":"404ba736-12bf-4be9-9899-9075f568934a","added_by":"auto","created_at":"2025-12-08 14:27:01","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":394577,"visible":true,"origin":"","legend":"\u003cp\u003eThe percentage of municipality area affected by an increasing risk trend for a) Toyama Prefecture and b) Akita prefecture from 2025 to 2050. Toyama shows a broader distribution of increasing risk across municipalities, whereas in Akita the affected area is more limited.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8212652/v1/5999202c35962ff7586d2304.png"},{"id":98420807,"identity":"c8dc784c-386e-40d1-993b-b96ef6f46ce1","added_by":"auto","created_at":"2025-12-17 16:10:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9232493,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8212652/v1/efbd271f-b136-4637-84aa-a0e10002bc14.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Applying MaxEnt to Predict the Future Distribution of Human-Black Bear Conflicts in Toyama and Akita Prefectures, Japan","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe primary mammals responsible for human-wildlife conflict in Japan are wild boar, sika deer, Japanese macaques and Asiatic black bears (Honda, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Although these large mammals disappeared from many regions during the Meiji and early Shōwa periods (late 1800s to early 1900s) due to hunting and other human activities, their populations have since recovered and are now reoccupying much of their historical ranges (Saito et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Among them, wild boar and sika deer are particularly notorious for causing extensive agricultural damage, to the extent that some farmers have been forced to abandon their fields after repeated crop losses (Ueda et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018a\u003c/span\u003e). While these species primarily inflict economic damage, Asiatic black bears pose a more serious threat due to their potential to cause injuries and fatalities.\u003c/p\u003e\u003cp\u003eJapan is home to two bear species: the Asiatic black bear \u003cem\u003e(Ursus thibetanus japonicus)\u003c/em\u003e and the Ussuri brown bear \u003cem\u003e(Ursus arctos lasiotus)\u003c/em\u003e. The black bears inhabit northern and central Japan, on the islands of Honshu and Shikoku (Ikeya, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Although smaller than brown bears - adults typically measuring 120\u0026ndash;145 cm in length, 50\u0026ndash;60 cm in height and weighing 70\u0026ndash;120 kg (Takahata, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) - black bears remain highly dangerous as their sharp claws are capable of inflicting deep lacerations. In recent years, bear related incidents have risen sharply. In fiscal year 2023, the Ministry of the Environment of Japan recorded 192 black bear attacks between April and November, including four fatalities. Akita Prefecture accounted for the largest share of these incidents, representing one-third of all reported cases. Furthermore, fiscal year 2025 has already surpassed the 2023 record, with more than ten fatalities resulting from black bear attacks as of November 3, 2025 (Ministry of the Environment, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn response to the rising number of attacks, the government has escalated bear control measures. On April 16, 2024, the Ministry of the Environment added both bear species to the \"Designated Wildlife Species for Control\" list, marking the first expansion of the list since 2014. Originally established to manage rapidly growing populations of sika deer and wild boar, the list provides central government subsidies for culling designated species. More recently, Japan\u0026rsquo;s Ground Self Defence Force have been deployed to assist in trapping bears (Yeung, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). However, these measures have sparked controversy among the public, raising questions about whether these approaches represent appropriate long-term management strategies.\u003c/p\u003e\u003cp\u003eTheir slower reproductive rate compared to species like boars and deer, as well as their role as seed dispersers in maintaining forest ecosystems, highlights the potential negative consequences of indiscriminate hunting (Koike et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Additionally, studies have shown that population control measures intended to mitigate human-wildlife conflict can inadvertently worsen the issue by increasing the prevalence of conflict-causing or nuisance wildlife ((Izumiya, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2010\u003c/span\u003e); (Ueda et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2018b\u003c/span\u003e); (Honda \u0026amp; Kawauchi, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2011\u003c/span\u003e)).\u003c/p\u003e\u003cp\u003eMoreover, lethal control methods are becoming increasingly unfeasible. Since 1970, the number of hunters in Japan has declined sharply, with the elderly now dominating the hunter demographic. In 2014, there were only 194,000 hunters, down from 518,000 in 1975 (Lovelock et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) Therefore, further research is essential to improve our understanding of bear distribution which can ultimately inform the development of management strategies that mitigate conflicts while ensuring bear conservation.\u003c/p\u003e\u003cp\u003eIn forested and mountainous areas, people can avoid conflict by staying alert to their surroundings and taking precautions, such as using bells on backpacks or carrying a spray. Alternatively, they can avoid visiting these areas during periods of high bear activity altogether. However, these strategies are far less effective when bears enter human settlements, a phenomenon that is becoming increasingly common. Residents are typically unprepared for bears appearing unexpectedly in daily life, such as in supermarkets (The Japan Times, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This raises a key question: why are bears appearing closer to human spaces?\u003c/p\u003e\u003cp\u003eSeveral factors contribute to this trend. Primarily, bears rely on hard mast as a critical food source before hibernation. During years of mast shortages, bears are forced to leave their natural habitats in search of food, often resorting to raiding agricultural crops (Malcolm et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). For instance, a positive correlation has been observed between the number of nuisance bears killed and beechnut crop failures in 5 out of 7 zones in the Tohoku region of Japan (Oka et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). This phenomenon often explains spikes in conflict during specific years. Additionally, climate change plays a role in increasing conflict frequency; Warmer winters result in delayed hibernation and shorter hibernation periods, leading to more frequent bear encounters (Evans et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eRecently, population decline and aging have been increasingly recognised as significant drivers of rising HBCs. This is particularly relevant in Japan since the population is projected to decrease by 24% by 2050, with approximately 20% of agricultural settlements expected to become completely depopulated (Tsunoda \u0026amp; Enari, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Understanding how wildlife distribution and conflict respond to such demographic shifts is therefore essential for developing effective, long-term management strategies. However, research linking these two factors remains limited. Previous studies, such as the analysis by (Hoshizaki et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), have shown that bear sighting frequency in Akita Prefecture is positively correlated with local population decline. Nevertheless, there is still a critical gap in spatial analyses that explore how depopulation and ageing affect conflict risk across different areas.\u003c/p\u003e\u003cp\u003eThe primary objective of this study is to map the distribution of HBC in light of Japan\u0026rsquo;s ongoing population decline and aging. Specifically, it aims to analyse how demographic changes impact future conflict risk, identifying high-risk areas and emerging spatial trends. By examining how bear distribution and conflict are expected to evolve as human populations shrink, this research seeks to offer valuable insights that can inform the development of effective local management strategies.\u003c/p\u003e"},{"header":"2. Methodology","content":"\u003cp\u003eThis study is divided into two parts: The first part utilises the MaxEnt species distribution model to map the probability of bear sightings, while the second part combines this output with 1km grid demographic estimates to predict future HBC risk.\u003c/p\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 Study area\u003c/h2\u003e\n \u003cp\u003eToyama and Akita Prefectures, located on Honshu\u0026rsquo;s Sea of Japan coast, experience a humid subtropical climate with hot summers and snowy winters. Average monthly temperatures in Toyama City range from 3\u0026deg;C to 26.9\u0026deg;C, with 2,374 mm of precipitation and 253 cm of snow annually. Similarly, in Akita City, temperatures range from 0.4\u0026deg;C to 25.0\u0026deg;C, with 1,742 mm of precipitation and 273 cm of snow annually (Japan Meteorological Agency, 2025). In Toyama, urban areas are concentrated along the coastal plain, while in Akita they are more evenly distributed, forming moderate population clusters across the prefecture. Both prefectures share similar terrain features, including coastal plains, river valleys, and mountains that rise to around 3,000 meters in Toyama, due to the Northern Alps located in the east, and up to 1,770 meters in Akita (Yamazaki et al., \u003cspan class=\"CitationRef\"\u003e2017a\u003c/span\u003e). Both prefectures are predominantly covered by deciduous and coniferous forests, which provide food and shelter for black bears.\u003c/p\u003e\n \u003cp\u003eToyama spans 4,248 km\u0026sup2; with a population of 1,007,000 as of October 2023, while Akita is larger, covering 11,638 km\u0026sup2;, but has a smaller population of 914,000. What sets Akita apart is its demographic challenges: it has the highest rate of population decline and the largest proportion of elderly residents among Japan\u0026apos;s 47 prefectures \u0026minus;\u0026thinsp;39.0% of Akita\u0026apos;s residents are aged 65 and older, compared to Toyama\u0026apos;s 33.1% (Statistics Bureau of Japan, \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). Akita was chosen for this study not only because it has the highest proportion of bear attacks in Japan but also because of its notable demographic trends. Toyama serves as a useful comparison due to similar climate conditions and the availability of bear sighting data.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 Bear witness data\u003c/h2\u003e\n \u003cp\u003eBear occurrence data were obtained from official government sources. For Toyama, data were acquired from the prefectural government\u0026apos;s website, which compiles reports from municipal authorities (Toyama Prefectural Government, n.d.). For Akita, data were obtained through direct contact with the prefecture\u0026apos;s Department of Living Environment, which compiles reports from police stations, the Nature Conservation Division, and citizen submissions via an online reporting system. Duplicate reports in the Akita dataset, resulting from multiple submissions of the same sighting by the same individual, were identified and removed using Excel. For this study, we collected records from fiscal years 2018 to 2023, totalling 2,733 bear witness locations for Toyama and 8,038 for Akita (see Fig.\u0026nbsp;1 for bear witness points).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3 Predictor variables\u003c/h2\u003e\n \u003cp\u003eSelecting the appropriate predictor variables is a critical step for accurately modelling species distribution (Guisan \u0026amp; Zimmermann, \u003cspan class=\"CitationRef\"\u003e2000\u003c/span\u003e). In this study, 15 variables (9 environmental and 6 anthropogenic) known to influence black bear distribution were selected based on a literature review (see Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The variables were processed for analysis, with five \u0026quot;distances to land cover\u0026quot; variables calculated using Euclidean distance from the ESA WorldCover v200 dataset (Zanaga et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eMulticollinearity among predictor variables can compromise model accuracy by producing overconfident predictions. To mitigate this, it is preferable to retain only essential variables, rather than using all available ones(Winship \u0026amp; Western, \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e); Too few variables may result in inaccurate predictions, while too many can introduce unnecessary complexity (Beaumont et al., \u003cspan class=\"CitationRef\"\u003e2005\u003c/span\u003e). In this study, multicollinearity was assessed using the Variance Inflation Factor (VIF) and a correlation matrix in Python 3 on a Google Compute Engine backend. Pairs of variables with correlation coefficients exceeding 0.6 were addressed to reduce redundancy. The remaining seven variables - NDVI, snow cover, slope, distance to forest, distance to water, distance to orchards, and distance to roads - were selected for their direct relevance to bear encounters. In particular, the distance to orchards variable was included because bears often raid these areas during the autumn fruiting season, especially in poor harvest years when natural food sources are scarce. With VIF values below 10, these variables indicated acceptable multicollinearity and were deemed ready for modelling (Akinwande et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eList of predictor variables used for assessing potential bear witness distribution. Variables included in the final model, after selection through multicollinearity testing, are highlighted in bold.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCategory\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUnit\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSource\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"9\"\u003e\n \u003cp\u003eEnvironmental\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNDVI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:NDVI=\\frac{NIR-Red}{NIR+Red}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLANDSAT 8 Level 2, Collection 2, Tier 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSnow cover\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNDSI (Normalised Difference Snow Index)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMODIS, MOD10A1\u003c/p\u003e\n \u003cp\u003e(Hall \u0026amp; Riggs, \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTemperature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLand surface temperature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKelvin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMODIS, MOD21A1D (Hulley \u0026amp; Hook, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrecipitation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDaily atmospheric precipitation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emm/pentad\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUCSB/CHG CHIRPS\u003c/p\u003e\n \u003cp\u003e(Funk et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eElevation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDEM (Digital Elevation Model)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emeters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMERIT DEM\u003c/p\u003e\n \u003cp\u003e(Yamazaki et al., \u003cspan class=\"CitationRef\"\u003e2017b\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSlope\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSlope of the terrain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003edegrees\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(derived from\u003c/p\u003e\n \u003cp\u003eMERIT DEM)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDistance to forest\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEuclidian distance to the nearest forest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emeters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eESA WorldCover v200 (Zanaga et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDistance to grassland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEuclidian distance to the nearest grassland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emeters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eESA WorldCover v200\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDistance to water\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEuclidian distance to the nearest water body\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emeters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eESA WorldCover v200\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003eAnthropogenic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDistance to build up\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEuclidian distance to the nearest built-up area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emeters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eESA WorldCover v200\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDistance to cropland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEuclidian distance to the nearest cropland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emeters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eESA WorldCover v200\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDistance to orchards\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEuclidian distance to the nearest orchard\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emeters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Biodiversity Center of Japan, n.d.)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDistance to road\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEuclidian distance to the nearest road\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emeters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGRIP4\u003c/p\u003e\n \u003cp\u003e(Meijer et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNighttime lights\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAverage radiance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enW/cm\u003csup\u003e2\u003c/sup\u003e/sr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNOAA VIRS\u003c/p\u003e\n \u003cp\u003e(Elvidge et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePopulation count\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal population of both sexes in 2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epopulation/km\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e国土数値情報\u003c/p\u003e\n \u003cp\u003e(MLIT, 2018)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4 Probability mapping using MaxEnt model\u003c/h2\u003e\n \u003cp\u003eThe Maximum Entropy (MaxEnt) model was used in this study to predict the potential distribution of Asiatic black bear sightings. MaxEnt was chosen for this study as it outperforms other species distribution modelling techniques in both accuracy and output quality, particularly when working with presence-only data (Wilting et al., \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e); (West et al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e). This machine learning approach integrates bear witness records with selected variables to generate a probability distribution, where each raster cell is assigned a value between 0 and 1 to represent the likelihood of bear sightings. It works by identifying the most uniform and least biased probability distribution possible, while ensuring that the expected values of the variables align with the observed conditions at the bear sighting locations. The entropy of the distribution p is given by the following formula (Phillips et al., \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e):\u003c/p\u003e\n \u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e$$\\:H\\left(p\\right)=\\:-\\sum\\:_{x}p\\left(x\\right)\\:logp\\left(x\\right)$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eThe model was implemented on the Google Earth Engine platform using the \u003cem\u003eee.classifier.amnhMaxent\u003c/em\u003e function since it provides accurate results considerably faster than traditional MaxEnt software (Campos et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). The presence data was split into training (80%) and testing (20%) sets. Background points were randomly generated within the study area in numbers equal to the witness points and replicated ten times to enhance precision (replications beyond ten did not improve performance). The model employed automatic feature selection allowing the algorithm to automatically select the most suitable feature classes to use, based on number of training samples. The regularization multiplier was kept at the default value of 1.0. The model\u0026apos;s performance was evaluated using the Area Under the Receiver Operating Characteristic Curve (AUC), which measures the ability to discriminate between presence and absence sites (Hanley \u0026amp; McNeil, \u003cspan class=\"CitationRef\"\u003e1982\u003c/span\u003e). AUC provides a single accuracy measure, independent of threshold, with values ranging from 0.5, representing random chance, to 1, indicating perfect discrimination (Fielding \u0026amp; Bell, \u003cspan class=\"CitationRef\"\u003e1997\u003c/span\u003e); (Elith et al., \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e). The training AUC evaluates the model\u0026apos;s fit to the training data, while the testing AUC assesses its ability to generalise to unseen data (Jin Huang \u0026amp; Ling, \u003cspan class=\"CitationRef\"\u003e2005\u003c/span\u003e). Similar AUC values between training and testing indicate good generalization, suggesting the model is not overfitting or underfitting.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e2.5 Risk assessment of future human-black bear conflicts\u003c/h2\u003e\n \u003cp\u003eThe second part of the study assessed future conflict risk by combining the bear sighting probability output from the model with 1km grid demographic estimates obtained from the National Land Information Division (MLIT, 2018). This analysis aimed to identify future HBC hotspots and determine where risk is expected to rise over the next 25 years.\u003c/p\u003e\n \u003cp\u003eIn this study, HBC risk was calculated following Crichton\u0026rsquo;s framework, which defines risk as the product of hazard, exposure, and vulnerability (risk\u0026thinsp;=\u0026thinsp;h*e*v) (Crichton, \u003cspan class=\"CitationRef\"\u003e1999\u003c/span\u003e). In this context, hazard was represented by the probability map generated by the MaxEnt model. Due to limitations in future environmental data, dynamic variables such as NDVI and snow cover were not projected forward; As a result, the same bear sighting probability distribution was applied across all future years, under the assumption that habitat conditions would remain relatively constant. Exposure was defined as the total population for the year under study, and vulnerability was calculated as the proportion of the population aged 65 and over in that year. Demographic data for each year were processed into raster format to match the spatial resolution of the MaxEnt outputs and allow for pixel-wise calculations.\u003c/p\u003e\n \u003cp\u003eRisk was calculated at five-year intervals from 2025 to 2050. To detect long-term spatial trends, a linear regression was applied to the time series of risk values at each pixel. The resulting risk trend map highlights areas where conflict risk is projected to increase, providing valuable insights for future management strategies.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Bear witness probability maps\u003c/h2\u003e\n \u003cp\u003eThe probability maps for Toyama and Akita Prefectures (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e) illustrate clear differences in the spatial distribution of bear sighting probabilities, particularly in urban areas. In both prefectures, high-probability areas are generally concentrated along forest edges, as well as near rivers and roads, while the lowest probabilities are found within dense forest interiors, mountainous regions, and urban centres. However, noticeable contrasts emerge when comparing the two prefectural capitals, Toyama City and Akita City (as shown in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eIn Toyama City, high-probability areas are concentrated along the forest edge, with values dropping sharply toward both the urban area and forest interior. The average probability of bear sighting in Toyama City is 0.25 (to 2 s.f.), reflecting a relatively low likelihood of encounters. Conversely, Akita City exhibits a much more diffuse pattern, with an average probability of 0.38 (to 2 s.f.), indicating a higher likelihood of bear sightings. High-probability areas are not only present along the forest edge but also extend well into the urban core. In fact, portions of the city centre display moderate to high probabilities of bear presence.\u003c/p\u003e\n \u003cp\u003eThe response curves (Fig. 3), showing how bear sighting probability responds to the predictor variables, further support the observation that bear presence extends further into urban areas in Akita. The NDVI response curve shows that in Toyama, bear sighting probabilities remain near zero in areas with low NDVI, increasing only above 0.1, indicating that sightings are mostly restricted to vegetated areas. In contrast, Akita shows higher bear sighting probabilities even in areas with low NDVI, with noticeable peaks around \u0026minus;\u0026thinsp;0.3 and 0. For example, when the NDVI is \u0026minus;\u0026thinsp;0.3, the probability of a bear sighting is 0 in Toyama but over 0.2 in Akita, highlighting a greater bear presence within built-up environments in Akita. A similar pattern is observed with distance to forest. While bear sighting probabilities in Toyama decline sharply and stabilize near zero beyond 2000 m from forests, Akita maintains higher probabilities up to 6000 m, indicating that bears in Akita are sometimes observed further from their natural habitats. For distance to orchards, both regions show high probabilities near orchards, but Akita exhibits higher overall probabilities and a steeper decline with distance. Among all distance-based variables, orchards yields the highest sighting probabilities at zero distance, emphasising their role in attracting bears, especially in Akita. This suggests that special attention should be given to orchard management in Akita to reduce the risk of bear encounters.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Accuracy assessment\u003c/h2\u003e\n \u003cp\u003eIn Toyama, the test AUC was 0.77 and the training AUC was 0.75, indicating good accuracy and effectiveness in predicting bear sighting probabilities. Akita showed slightly lower performance with both values at 0.72, still reflecting reasonable accuracy. The model fitting performance varied depending on the spatial scale used. Toyama City achieved a test AUC of 0.90 and a training AUC of 0.91, while Akita City had a test AUC of 0.80 and a training AUC of 0.81. The small difference between the test and training AUC values (\u0026plusmn;\u0026thinsp;0.02) suggests that the model generalizes well. This implies that it successfully learned from the training data and can be effectively applied to new, unseen data.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Future human-bear risk assessment\u003c/h2\u003e\n \u003cp\u003eThe HBC risk map for 2025 (Fig.\u0026nbsp;4) reveals distinct spatial patterns between the two prefectures, particularly in the urban centres of Toyama and Akita prefectures. In Toyama, risk is broadly distributed across multiple towns and cities, with no single dominant hotspot. Instead, it is dispersed into numerous moderate-risk clusters, primarily along forest edges and at the boundaries between agricultural and urban areas. The urban core itself remains at relatively low risk, as illustrated by Toyama Station, which falls within a low-risk zone.\u003c/p\u003e\n \u003cp\u003eIn contrast, Akita Prefecture exhibits a more concentrated risk pattern, with only a handful of isolated moderate risk clusters scattered outside the capital city. In Akita City, risk is highly concentrated, with the highest-risk areas located within the urban core rather than along the forest boundary. This is illustrated by Akita Station, which falls directly within the area of highest predicted risk.\u003c/p\u003e\n \u003cp\u003eThe predicted risk trend map (Fig.\u0026nbsp;5) illustrates the spatial distribution of areas where HBC risk is expected to increase between 2025 and 2050. In Toyama Prefecture, risk is projected to increase in several areas, with the highest increase occurring in the southern half of a 5 km radius around Toyama Station. Overall, approximately 13% of Toyama Prefecture is projected to experience an increase in risk, with the maximum predicted risk increase reaching 130%. This suggests a moderate but widespread growth in HBCs across the region.\u003c/p\u003e\n \u003cp\u003eIn contrast, Akita Prefecture exhibits a more concentrated pattern of increasing risk, predominantly within central Akita City, with the largest increases occurring 2 km east of Akita Station and 3 km to its west and northwest. Although the area experiencing increasing risk in Akita is much smaller - covering just under 5% of the prefecture - the severity of risk growth is considerably higher than in Toyama; the maximum projected increase reaches 350%, highlighting a substantial escalation of conflict within a confined urban area. These contrasting patterns suggest that while Toyama is expected to face a gradual, widespread increase in HBC risk, Akita is likely to experience a sharp risk growth concentrated within its urban core.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussions","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Bear witness probability distribution\u003c/h2\u003e\u003cp\u003eBoth the bear sighting probability maps and response curves strongly indicate that bears are more likely to appear near urban areas in Akita than in Toyama. This is reflected in the higher average bear sighting probability in Akita City (0.38) compared to Toyama City (0.25), as well as in the response curves, which show elevated probabilities in areas with negative NDVI, typical of urban environments, in Akita. These findings are consistent with reports of bear incidents in central Akita City. For instance, on Nov 30, 2024, a supermarket employee was injured by a bear inside a store located in a densely populated residential area (Kumabe, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOne likely explanation for the increased proximity of bears to urban areas in Akita is the deterioration of the satoyama (里山) landscape. Historically, satoyamas, made up of secondary forests, rice paddies, grasslands, and small villages (Kobori \u0026amp; Primack, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), functioned as semi-managed buffer zones between human settlements and surrounding forests (K. Yamazaki et al., 2009). However, rapid depopulation and the aging of rural communities, especially in Akita, have sharply reduced satoyama activities, resulting in widespread land abandonment. With younger generations moving to cities, fewer people remain to maintain farmland or manage forest edges. Abandoned and overgrown land create corridors that enable wildlife to approach human settlements in search of food, often without being noticed. Hence, as formerly managed lands grow wild and revert to secondary forests, bears are able to expand their range into residential areas, increasing the frequency of human-bear conflicts.\u003c/p\u003e\u003cp\u003eIn Toyama City, the satoyama landscape remains relatively intact. As shown in the LULC maps (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e), a broad transitional area composed of agricultural land, grasslands, and rural settlements separates the urban core from the forest. This buffer reduces human-bear interactions by maintaining a large separation between human settlements and bear habitats. In contrast, Akita City lacks such a distinct buffer, with forests often directly adjacent to densely populated neighbourhoods. Therefore, the deterioration of this transitional zone likely contributes to the more diffuse probability distribution observed in Akita, where high bear sighting probabilities extend into the urban core.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Risk hotspots and future trends\u003c/h2\u003e\u003cp\u003eThe advantage of this study lies in its ability to map the spatial distribution of HBC in light of projected population trends. The 2025 risk map (Fig.\u0026nbsp;4) shows that conflict risk in Toyama is moderately distributed across multiple towns and satoyama areas, whereas in Akita it is highly concentrated in the core of Akita City. Over the next 25 years (2025\u0026ndash;2050), the risk trend map (Fig.\u0026nbsp;5) indicates a moderate but widespread increase in Toyama, while Akita is projected to experience a severe, concentrated rise, with risk increasing by up to 350% around Akita Station. This pattern is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e6\u003c/span\u003e, which shows that over half of Toyama\u0026rsquo;s municipalities will experience a risk increase in at least 10% of their municipal area, while only 2 of Akita's 25 municipalities will see a similar increase. These patterns demonstrate how human-bear risk distribution differs significantly from bear witness probability due to the influence of demographic trends, with Akita's severe rural depopulation particularly apparent.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Management implications and Future Landscape Change\u003c/h2\u003e\u003cp\u003eThe findings of this study offer valuable insights for local authorities, especially in resource-limited areas by identifying high-risk zones and forecasting areas where conflict is likely to intensify. What is novel about this study is that risk was calculated by considering not only bear sighting probabilities but also projected demographic changes, offering insights that could help promote human-bear coexistence. Specifically, in Akita Prefecture, where risk is highly concentrated in the urban core of Akita City, targeted measures are urgently needed. For instance, the study projects a roughly 350% increase in risk over the next 25 years in the area 2 km east of Akita Station. Therefore, measures such as wildlife barriers or detection systems along the Akita Expressway, particularly between the northern and southern interchanges, could help reduce the likelihood of conflict in this high-risk zone.\u003c/p\u003e\u003cp\u003eIt is important to note that while this study provides valuable insights into how demographic trends may shape the future distribution of HBC, it does not account for the dynamic effects of land abandonment. As farmland and rural areas are increasingly left unmanaged, forests and overgrown land expand, gradually shifting bear habitats over time. Unharvested crops, unattended fruits in orchards, and natural regrowth in these areas can attract bears (Krofel et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), meaning that future risk maps may look different from current ones as bear habitats evolve with the landscape. Therefore, the ongoing erosion of satoyama landscapes may heighten the risk of conflict beyond what this study predicts.\u003c/p\u003e\u003cp\u003eMoreover, studies have shown that as wildlife adapt to urban environments, more assertive bears with bolder personalities tend to emerge (Mart\u0026iacute;nez-Abra\u0026iacute;n et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), creating a snowball effect that further intensifies the risk of human-wildlife conflict over time. This phenomenon appears to be already occurring in some prefectures, particularly Akita, and is likely to intensify as other prefectures experience similar demographic trends. Therefore, the spatial patterns of conflict observed in this study are not static but will continue to evolve as land abandonment reshapes the landscape.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eEstimating the future distribution of HBCs is essential for developing effective, long-term management strategies that can reduce conflicts and support human-bear coexistence. While various factors influence the changing patterns of these conflicts - such as fluctuations in forest mast - this study specifically examined the impact of population decline and aging on future conflict risk. Using a MaxEnt model, we analysed the spatial distribution of bear sighting probabilities and assessed how demographic changes may reshape conflict patterns over time.\u003c/p\u003e\u003cp\u003eThe findings reveal clear regional differences, with bear sightings in Akita Prefecture more likely to occur within and around urban areas, while Toyama Prefecture maintains a more distinct separation between bear habitats and human settlements. The 2025 conflict risk map shows that risk in Toyama is broadly distributed in clusters across towns, agricultural areas, and forest edges, whereas in Akita it is highly concentrated in the urban core of Akita City. Over the next 25 years, risk is projected to rise sharply in central Akita, while Toyama is expected to see a moderate but widespread increase, particularly around the outskirts of its urban areas. These results highlight the need for targeted management strategies that prioritise urban centres where conflict risks are projected to be most severe.\u003c/p\u003e\u003cp\u003eThis study's risk assessment does not account for dynamic factors like hikers or individuals engaging in activities in forests and mountains, as it relies on census data that assumes no risk in areas with zero population. Future research should integrate detailed road and trail maps to better assess conflict risks for hikers and farmers who often use trails not represented in large-scale road datasets. In Japan, further research is needed to examine how risk distribution will be influenced by population decline, ageing and land abandonment. Additionally, future studies could adapt this research framework to assess conflicts involving other species such as wild boars or deer that frequently damage crops. By replacing demographic factors with variables related to agricultural products, similar risk assessments could be conducted to forecast and manage broader human-wildlife conflict scenarios.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eI would like to thank the Nature Conservation Divisions of Akita and Toyama Prefectures for kindly providing the bear witness data used in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCRediT authorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eD.D.:\u003c/strong\u003e Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Resources, Software, Validation, Visualization, Writing - original draft, Writing - review and editing; \u003cstrong\u003eS.S.:\u003c/strong\u003e Data curation, Formal analysis, Investigation, Methodology, Visualization, Writing - review and editing; \u003cstrong\u003eW.T.:\u003c/strong\u003e Conceptualization, Investigation, Project administration, Resources, Supervision, Writing - review and editing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study received no funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003e1km mesh estimated future population data. (2018). \u003cem\u003eNational Land Information Division, National Spatial Planning and Regional Policy Bureau, MLIT of Japan\u003c/em\u003e.\u003c/li\u003e\n \u003cli\u003eAkinwande, M. O., Dikko, H. G., \u0026amp; Samson, A. (2015). Variance Inflation Factor: As a Condition for the Inclusion of Suppressor Variable(s) in Regression Analysis. \u003cem\u003eOpen Journal of Statistics\u003c/em\u003e, \u003cem\u003e05\u003c/em\u003e(07), 754\u0026ndash;767. https://doi.org/10.4236/ojs.2015.57075\u003c/li\u003e\n \u003cli\u003eAsian black bear witness records map \u0026ldquo;Kumap\u0026rdquo;. (n.d.). \u003cem\u003eToyama Prefectural Government, Department of Living Environment and Culture, Nature Conservation Division\u003c/em\u003e. Retrieved 15 October 2025, from https://www.pref.toyama.jp/1709/kurashi/kankyoushizen/shizen/yaseiseibutsu/kumap.html\u003c/li\u003e\n \u003cli\u003eBeaumont, L. J., Hughes, L., \u0026amp; Poulsen, M. (2005). Predicting species distributions: use of climatic parameters in BIOCLIM and its impact on predictions of species\u0026rsquo; current and future distributions. \u003cem\u003eEcological Modelling\u003c/em\u003e, \u003cem\u003e186\u003c/em\u003e(2), 251\u0026ndash;270. https://doi.org/10.1016/j.ecolmodel.2005.01.030\u003c/li\u003e\n \u003cli\u003eBiodiversity Center of Japan. (n.d.). \u003cem\u003eThe third national vegetation survey data\u003c/em\u003e. Retrieved 15 August 2025, from http://gis.biodic.go.jp/webgis/sc-025.html?kind=vg67\u003c/li\u003e\n \u003cli\u003eCampos, J. C., Garcia, N., Al\u0026iacute;rio, J., Arenas-Castro, S., Teodoro, A. C., \u0026amp; Sillero, N. (2023). Ecological Niche Models using MaxEnt in Google Earth Engine: Evaluation, guidelines and recommendations. \u003cem\u003eEcological Informatics\u003c/em\u003e, \u003cem\u003e76\u003c/em\u003e, 102147. https://doi.org/10.1016/j.ecoinf.2023.102147\u003c/li\u003e\n \u003cli\u003eCrichton, D. (1999). The risk triangle. \u003cem\u003eNatural Disaster Management\u003c/em\u003e, \u003cem\u003e102\u003c/em\u003e(3), 102\u0026ndash;103.\u003c/li\u003e\n \u003cli\u003eElith, J., H. Graham*, C., P. Anderson, R., Dud\u0026iacute;k, M., Ferrier, S., Guisan, A., J. Hijmans, R., Huettmann, F., R. Leathwick, J., Lehmann, A., Li, J., G. Lohmann, L., A. Loiselle, B., Manion, G., Moritz, C., Nakamura, M., Nakazawa, Y., McC. M. Overton, J., Townsend Peterson, A., \u0026hellip; E. Zimmermann, N. (2006). Novel methods improve prediction of species\u0026rsquo; distributions from occurrence data. \u003cem\u003eEcography\u003c/em\u003e, \u003cem\u003e29\u003c/em\u003e(2), 129\u0026ndash;151. https://doi.org/10.1111/j.2006.0906-7590.04596.x\u003c/li\u003e\n \u003cli\u003eElvidge, C. D., Baugh, K., Zhizhin, M., Hsu, F. C., \u0026amp; Ghosh, T. (2017). VIIRS night-time lights. \u003cem\u003eInternational Journal of Remote Sensing\u003c/em\u003e, \u003cem\u003e38\u003c/em\u003e(21), 5860\u0026ndash;5879. https://doi.org/10.1080/01431161.2017.1342050\u003c/li\u003e\n \u003cli\u003eEvans, A. L., Singh, N. J., Friebe, A., Arnemo, J. M., Laske, T. G., Fr\u0026ouml;bert, O., Swenson, J. E., \u0026amp; Blanc, S. (2016). Drivers of hibernation in the brown bear. \u003cem\u003eFrontiers in Zoology\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(1), 7. https://doi.org/10.1186/s12983-016-0140-6\u003c/li\u003e\n \u003cli\u003eFielding, A. H., \u0026amp; Bell, J. F. (1997). A review of methods for the assessment of prediction errors in conservation presence/absence models. \u003cem\u003eEnvironmental Conservation\u003c/em\u003e, \u003cem\u003e24\u003c/em\u003e(1), 38\u0026ndash;49. https://doi.org/10.1017/S0376892997000088\u003c/li\u003e\n \u003cli\u003eFunk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S., Husak, G., Rowland, J., Harrison, L., Hoell, A., \u0026amp; Michaelsen, J. (2015). The climate hazards infrared precipitation with stations - A new environmental record for monitoring extremes. \u003cem\u003eScientific Data\u003c/em\u003e, \u003cem\u003e2\u003c/em\u003e. https://doi.org/10.1038/sdata.2015.66\u003c/li\u003e\n \u003cli\u003eGuisan, A., \u0026amp; Zimmermann, N. E. (2000). Predictive habitat distribution models in ecology. \u003cem\u003eEcological Modelling\u003c/em\u003e, \u003cem\u003e135\u003c/em\u003e(2\u0026ndash;3), 147\u0026ndash;186. https://doi.org/10.1016/S0304-3800(00)00354-9\u003c/li\u003e\n \u003cli\u003eHall, D., \u0026amp; Riggs, G. (2015). MODIS/Terra Snow Cover Daily L3 Global 500m SIN Grid. \u003cem\u003eNASA National Snow and Ice Data Center Distributed Active Archive Center\u003c/em\u003e.\u003c/li\u003e\n \u003cli\u003eHanley, J. A., \u0026amp; McNeil, B. J. (1982). The meaning and use of the area under a receiver operating characteristic (ROC) curve. \u003cem\u003eRadiology\u003c/em\u003e, \u003cem\u003e143\u003c/em\u003e(1), 29\u0026ndash;36. https://doi.org/10.1148/radiology.143.1.7063747\u003c/li\u003e\n \u003cli\u003eHirayama, S., Tadono, T., Mizukami, Y., Ohki, M., Imamura, K., Hirade, N., Ohgushi, F., Dotsu, M., Yamanokuchi, T., \u0026amp; Nasahara, K. N. (2022). Generation of the High-Resolution Land-Use and Land-Cover Map in Japan Version 21.11. \u003cem\u003eIGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium\u003c/em\u003e, 4339\u0026ndash;4342. https://doi.org/10.1109/IGARSS46834.2022.9884315\u003c/li\u003e\n \u003cli\u003eHonda, T. (2009). Environmental Factors Affecting the Distribution of the Wild Boar, Sika Deer, Asiatic Black Bear and Japanese Macaque in Central Japan, with Implications for Human-Wildlife Conflict. \u003cem\u003eMammal Study\u003c/em\u003e, \u003cem\u003e34\u003c/em\u003e(2), 107\u0026ndash;116. https://doi.org/10.3106/041.034.0206\u003c/li\u003e\n \u003cli\u003eHonda, T., \u0026amp; Kawauchi, N. (2011). Methods for Constructing a Wild Boar Relative-Density Map to Resolve Human-Wild Boar Conflicts. \u003cem\u003eMammal Study\u003c/em\u003e, \u003cem\u003e36\u003c/em\u003e(2), 79\u0026ndash;85. https://doi.org/10.3106/041.036.0206\u003c/li\u003e\n \u003cli\u003eHoshizaki, K., Maebashi, N., Wakamiya, S., Aomori, S., Narita, S., Kimura, K., Kuronuma, T., Takebayashi, Y., Ota, K., \u0026amp; Kai, S. (2016). Twenty-one-year trends (1994\u0026ndash;2014) in the sighting records of Asiatic black bears (Ursus thibetanus) by citizens in Akita Prefecture, northern Japan.\u0026nbsp;\u003cem\u003e秋田県立大学ウェブジャーナル\u003c/em\u003e\u003cem\u003e\u0026nbsp;B\u003c/em\u003e, \u003cem\u003e3\u003c/em\u003e, 226\u0026ndash;231.\u003c/li\u003e\n \u003cli\u003eHulley, G., \u0026amp; Hook, S. (2021). MODIS/Terra Land Surface Temperature/3-Band Emissivity Daily L3 Global 1km SIN Grid Day V061 [Data set]. \u003cem\u003eNASA EOSDIS Land Processes Distributed Active Archive Center\u003c/em\u003e.\u003c/li\u003e\n \u003cli\u003eIkeya, K. (2023). Changing Mountain Landscapes in Japan: Wild Bear\u0026ndash;Human Interactions in the Short Term. In \u003cem\u003eGlobal Ecology in Historical Perspective\u003c/em\u003e (pp. 75\u0026ndash;87). Springer Nature Singapore. https://doi.org/10.1007/978-981-19-6557-9_4\u003c/li\u003e\n \u003cli\u003eIzumiya, S. (2010). Effect of administrative extermination on the Japanese Macaque population. \u003cem\u003eBulletin Shinshu University Alpine Field Center\u003c/em\u003e, 51\u0026ndash;56.\u003c/li\u003e\n \u003cli\u003eJapan Meteorological Agency. (2025). \u003cem\u003eClimate averages (1991-2020)\u003c/em\u003e. https://www.data.jma.go.jp/obd/stats/etrn/select/prefecture00.php?prec_no=32\u0026amp;block_no=47582\u0026amp;year=\u0026amp;month=\u0026amp;day=\u0026amp;view=\u003c/li\u003e\n \u003cli\u003eJin Huang, \u0026amp; Ling, C. X. (2005). Using AUC and accuracy in evaluating learning algorithms. \u003cem\u003eIEEE Transactions on Knowledge and Data Engineering\u003c/em\u003e, \u003cem\u003e17\u003c/em\u003e(3), 299\u0026ndash;310. https://doi.org/10.1109/TKDE.2005.50\u003c/li\u003e\n \u003cli\u003eKobori, H., \u0026amp; Primack, R. B. (2003). Participatory Conservation Approaches for \u003cem\u003eSatoyama\u003c/em\u003e , the Traditional Forest and Agricultural Landscape of Japan. \u003cem\u003eAMBIO: A Journal of the Human Environment\u003c/em\u003e, \u003cem\u003e32\u003c/em\u003e(4), 307\u0026ndash;311. https://doi.org/10.1579/0044-7447-32.4.307\u003c/li\u003e\n \u003cli\u003eKoike, S., Masaki, T., Nemoto, Y., Kozakai, C., Yamazaki, K., Kasai, S., Nakajima, A., \u0026amp; Kaji, K. (2011). Estimate of the seed shadow created by the Asiatic black bear \u003cem\u003eUrsus thibetanus\u003c/em\u003e and its characteristics as a seed disperser in Japanese cool‐temperate forest. \u003cem\u003eOikos\u003c/em\u003e, \u003cem\u003e120\u003c/em\u003e(2), 280\u0026ndash;290. https://doi.org/10.1111/j.1600-0706.2010.18626.x\u003c/li\u003e\n \u003cli\u003eKrofel, M., Elfstr\u0026ouml;m, M., Ambarlı, H., Bombieri, G., Gonz\u0026aacute;lez-Bernardo, E., Jerina, K., Laguna, A., Penteriani, V., Phillips, J. P., Selva, N., Wilson, S. M., Zarzo-Arias, A., Groff, C., Huber, D., Karamanlidis, A. A., Mertzanis, Y., Revilla, E., \u0026amp; Bautista, C. (2020). Human\u0026ndash;Bear Conflicts at the Beginning of the Twenty-First Century: Patterns, Determinants, and Mitigation Measures. In \u003cem\u003eBears of the World\u003c/em\u003e (pp. 213\u0026ndash;226). Cambridge University Press. https://doi.org/10.1017/9781108692571.016\u003c/li\u003e\n \u003cli\u003eKumabe, Y. (2024, December 2). Bear captured in Akita grocery store after 2-day standoff. \u003cem\u003eThe Asahi Shinbun\u003c/em\u003e.\u003c/li\u003e\n \u003cli\u003eLovelock, B., Yanata, K., Seto, Y., \u0026amp; Yamaguchi, M. (2022). Societal Factors Influencing Hunting Participation Decline in Japan: An Exploratory Study of Two Prefectures. \u003cem\u003eSociety \u0026amp; Natural Resources\u003c/em\u003e, \u003cem\u003e35\u003c/em\u003e(2), 149\u0026ndash;166. https://doi.org/10.1080/08941920.2021.2006843\u003c/li\u003e\n \u003cli\u003eMalcolm, K. D., McShea, W. J., Garshelis, D. L., Luo, S.-J., Van Deelen, T. R., Liu, F., Li, S., Miao, L., Wang, D., \u0026amp; Brown, J. L. (2014). Increased stress in Asiatic black bears relates to food limitation, crop raiding, and foraging beyond nature reserve boundaries in China. \u003cem\u003eGlobal Ecology and Conservation\u003c/em\u003e, \u003cem\u003e2\u003c/em\u003e, 267\u0026ndash;276. https://doi.org/10.1016/j.gecco.2014.09.010\u003c/li\u003e\n \u003cli\u003eMart\u0026iacute;nez-Abra\u0026iacute;n, A., Jim\u0026eacute;nez, J., Jim\u0026eacute;nez, I., Ferrer, X., Llaneza, L., Ferrer, M., Palomero, G., Ballesteros, F., Gal\u0026aacute;n, P., \u0026amp; Oro, D. (2020). Ecological consequences of human depopulation of rural areas on wildlife: A unifying perspective. \u003cem\u003eBiological Conservation\u003c/em\u003e, \u003cem\u003e252\u003c/em\u003e, 108860. https://doi.org/10.1016/j.biocon.2020.108860\u003c/li\u003e\n \u003cli\u003eMeijer, J. R., Huijbregts, M. A. J., Schotten, K. C. G. J., \u0026amp; Schipper, A. M. (2018). Global patterns of current and future road infrastructure. \u003cem\u003eEnvironmental Research Letters\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(6), 064006. https://doi.org/10.1088/1748-9326/aabd42\u003c/li\u003e\n \u003cli\u003eMinistry of the Environment. (2025). \u003cem\u003eHuman casualties caused by bears (preliminary figures)\u003c/em\u003e. https://www.env.go.jp/nature/choju/effort/effort12/injury-qe.pdf\u003c/li\u003e\n \u003cli\u003eOka, T., Miura, S., Masaki, T., Suzuki, W., Osumi, K., \u0026amp; Ssaitoh, S. (2004). Relationship between changes in beechnut production and Asiatic black bears in northern Japan. \u003cem\u003eJournal of Wildlife Management\u003c/em\u003e, \u003cem\u003e68\u003c/em\u003e(4), 979\u0026ndash;986. https://doi.org/10.2193/0022-541x(2004)068[0979:rbcibp]2.0.co;2\u003c/li\u003e\n \u003cli\u003ePhillips, S. J., Anderson, R. P., \u0026amp; Schapire, R. E. (2006). Maximum entropy modeling of species geographic distributions. \u003cem\u003eEcological Modelling\u003c/em\u003e, \u003cem\u003e190\u003c/em\u003e(3\u0026ndash;4), 231\u0026ndash;259. https://doi.org/10.1016/j.ecolmodel.2005.03.026\u003c/li\u003e\n \u003cli\u003eSaito, M. U., Momose, H., Inoue, S., Kurashima, O., \u0026amp; Matsuda, H. (2016). Range-expanding wildlife: modelling the distribution of large mammals in Japan, with management implications. \u003cem\u003eInternational Journal of Geographical Information Science\u003c/em\u003e, \u003cem\u003e30\u003c/em\u003e(1), 20\u0026ndash;35. https://doi.org/10.1080/13658816.2014.952301\u003c/li\u003e\n \u003cli\u003eStatistics Bureau of Japan. (2024, April 12). \u003cem\u003ePopulation estimates as of October 1, 2023\u003c/em\u003e. Ministry of Internal Affairs and Communications. https://www.stat.go.jp/data/jinsui/2023np/index.html\u003c/li\u003e\n \u003cli\u003eTakahata, C. (2014). \u003cem\u003eHabitat Selection by Asiatic Black Bears Inhabiting on the Periphery of Human-Dominated Lands\u003c/em\u003e.\u003c/li\u003e\n \u003cli\u003eThe Japan Times. (2025, August 8). \u003cem\u003eBear injures two in Japan supermarket; man killed in separate attack\u003c/em\u003e. https://www.japantimes.co.jp/news/2025/10/08/japan/bear-attack-in-supermarket/\u003c/li\u003e\n \u003cli\u003eTsunoda, H., \u0026amp; Enari, H. (2020). A strategy for wildlife management in depopulating rural areas of Japan. \u003cem\u003eConservation Biology\u003c/em\u003e, \u003cem\u003e34\u003c/em\u003e(4), 819\u0026ndash;828. https://doi.org/10.1111/cobi.13470\u003c/li\u003e\n \u003cli\u003eUeda, Y., Kiyono, M., Nagano, T., Mochizuki, S., \u0026amp; Murakami, T. (2018a). Damage Control Strategies Affecting Crop-Raiding Japanese Macaque Behaviors in a Farming Community. \u003cem\u003eHuman Ecology\u003c/em\u003e, \u003cem\u003e46\u003c/em\u003e(2), 259\u0026ndash;268. https://doi.org/10.1007/s10745-018-9994-x\u003c/li\u003e\n \u003cli\u003eUeda, Y., Kiyono, M., Nagano, T., Mochizuki, S., \u0026amp; Murakami, T. (2018b). Damage Control Strategies Affecting Crop-Raiding Japanese Macaque Behaviors in a Farming Community. \u003cem\u003eHuman Ecology\u003c/em\u003e, \u003cem\u003e46\u003c/em\u003e(2), 259\u0026ndash;268. https://doi.org/10.1007/s10745-018-9994-x\u003c/li\u003e\n \u003cli\u003eWest, A. M., Kumar, S., Brown, C. S., Stohlgren, T. J., \u0026amp; Bromberg, J. (2016). Field validation of an invasive species Maxent model. \u003cem\u003eEcological Informatics\u003c/em\u003e, \u003cem\u003e36\u003c/em\u003e, 126\u0026ndash;134. https://doi.org/10.1016/j.ecoinf.2016.11.001\u003c/li\u003e\n \u003cli\u003eWilting, A., Cord, A., Hearn, A. J., Hesse, D., Mohamed, A., Traeholdt, C., Cheyne, S. M., Sunarto, S., Jayasilan, M.-A., Ross, J., Shapiro, A. C., Sebastian, A., Dech, S., Breitenmoser, C., Sanderson, J., Duckworth, J. W., \u0026amp; Hofer, H. (2010). Modelling the Species Distribution of Flat-Headed Cats (Prionailurus planiceps), an Endangered South-East Asian Small Felid. \u003cem\u003ePLoS ONE\u003c/em\u003e, \u003cem\u003e5\u003c/em\u003e(3), e9612. https://doi.org/10.1371/journal.pone.0009612\u003c/li\u003e\n \u003cli\u003eWinship, C., \u0026amp; Western, B. (2016). Multicollinearity and Model Misspecification. \u003cem\u003eSociological Science\u003c/em\u003e, \u003cem\u003e3\u003c/em\u003e, 627\u0026ndash;649. https://doi.org/10.15195/v3.a27\u003c/li\u003e\n \u003cli\u003eYamazaki, D., Ikeshima, D., Tawatari, R., Yamaguchi, T., O\u0026rsquo;Loughlin, F., Neal, J. C., Sampson, C. C., Kanae, S., \u0026amp; Bates, P. D. (2017a). A high‐accuracy map of global terrain elevations. \u003cem\u003eGeophysical Research Letters\u003c/em\u003e, \u003cem\u003e44\u003c/em\u003e(11), 5844\u0026ndash;5853. https://doi.org/10.1002/2017GL072874\u003c/li\u003e\n \u003cli\u003eYamazaki, D., Ikeshima, D., Tawatari, R., Yamaguchi, T., O\u0026rsquo;Loughlin, F., Neal, J. C., Sampson, C. C., Kanae, S., \u0026amp; Bates, P. D. (2017b). A high‐accuracy map of global terrain elevations. \u003cem\u003eGeophysical Research Letters\u003c/em\u003e, \u003cem\u003e44\u003c/em\u003e(11), 5844\u0026ndash;5853. https://doi.org/10.1002/2017GL072874\u003c/li\u003e\n \u003cli\u003eYeung, J. \u0026amp; O. J. (2025). Facing a spike in deadly bear attacks, Japan turns to the military and drones that bark. \u003cem\u003eCNN\u003c/em\u003e. https://edition.cnn.com/2025/11/06/asia/japan-bear-attacks-military-sdf-intl-hnk\u003c/li\u003e\n \u003cli\u003eZanaga, D., Van De Kerchove, R., Daems, D., De Keersmaecker, W., Brockmann, C., Kirches, G., Wevers, J., Cartus, O., Santoro, M., Fritz, S., Lesiv, M., Herold, M., Tsendbazar, N. E., Xu, P., Ramoino, F., \u0026amp; Arino, O. (2022). ESA WorldCover 10 m 2021 v200. \u003cem\u003eZenodo\u003c/em\u003e.\u0026nbsp;\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Human-wildlife conflict, Asian black bear, Species distribution modelling, Maxent model, Risk assessment","lastPublishedDoi":"10.21203/rs.3.rs-8212652/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8212652/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHuman-bear conflict (HBC) is an increasingly urgent issue in Japan, causing severe injuries and fatalities. Previous studies link conflict patterns to climate change and mast fluctuations. More recently, Japan\u0026rsquo;s demographic trend has emerged as a key driver, yet its influence remains poorly understood, making future conflicts hard to predict as depopulation and ageing intensify. This study uses the MaxEnt species distribution model to map bear witness probabilities and integrates demographic projections to predict future HBC patterns in Akita and Toyama Prefectures. MaxEnt results indicate a high probability of bear sightings reaching Akita\u0026rsquo;s urban core, while in Toyama, human and bear habitats remain more clearly separated. In Toyama, high-risk clusters are scattered across towns, agricultural areas, and forest edges, with risk expected to rise gradually over the next 25 years, particularly around the urban fringe. In contrast, Akita\u0026rsquo;s urban core forms a concentrated hotspot, with risk projected to increase sharply in the same area. Current management approaches are becoming increasingly ineffective as hunter numbers have declined sharply. By identifying emerging hotspots and long-term trends, this study offers insights to improve resource allocation, strengthen local management strategies, and better support human-bear coexistence.\u003c/p\u003e","manuscriptTitle":"Applying MaxEnt to Predict the Future Distribution of Human-Black Bear Conflicts in Toyama and Akita Prefectures, Japan","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-08 14:26:56","doi":"10.21203/rs.3.rs-8212652/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-22T09:54:11+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-20T06:27:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"30988212401085092507386827591401311752","date":"2026-01-19T03:04:05+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-06T06:33:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"108598115488795705382459584054507269262","date":"2025-12-22T07:37:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"299703065155138437956077916303268653372","date":"2025-12-13T01:55:18+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-04T12:04:08+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-01T17:30:51+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-28T00:59:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-28T00:58:19+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-11-26T11:46:25+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"822d6fc6-087e-4466-9329-e73ac02df22f","owner":[],"postedDate":"December 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[{"id":59092356,"name":"Earth and environmental sciences/Climate sciences"},{"id":59092357,"name":"Biological sciences/Ecology"},{"id":59092358,"name":"Earth and environmental sciences/Ecology"},{"id":59092359,"name":"Earth and environmental sciences/Environmental social sciences"},{"id":59092360,"name":"Earth and environmental sciences/Natural hazards"}],"tags":[],"updatedAt":"2026-01-22T10:08:50+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-08 14:26:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8212652","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8212652","identity":"rs-8212652","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}