{"paper_id":"25b90a05-cec7-4fc2-b62d-2186a66c2c68","body_text":"Climate Change Impact Assessment and Species Distribution Model of a Critically Endangered Tree Using MaxEnt Modelling | 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 Research Article Climate Change Impact Assessment and Species Distribution Model of a Critically Endangered Tree Using MaxEnt Modelling Minhazul Ferdous, Sudipta Sen Gupta, Mohd Imran Hossain Chowdhury, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5884962/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Climate change is a key factor driving species extinction by altering their habitats and populations. We can already see its impact on ecosystems around the globe, especially at the species level. Using species distribution models helps us understand how climate change might shift where species live under different climate scenarios, which is crucial for protecting endangered plants and animals. This study focuses on predicting how climate change will affect the important tree species Anisoptera scaphula in Bangladesh's Chittagong division, using the Maximum Entropy (MaxEnt) model. Under the SSP2-4.5 (2021-2040) and SSP2-4.5 (2041-2060), as well as SSP5-8.5 (2021-2040) and SSP5-8.5 (2041-2060) scenario, our model predicts suitable habitats for this species in 2040 and 2060. The results show minimal changes in suitable habitats, suggesting that A. scaphula is quite resilient to climate change. These findings can guide policies for wildlife conservation and forest management, highlighting the species' importance to various animals. Climate change Habitat suitability MaxEnt Species distribution model Boilam Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Bangladesh is a treasure trove of biodiversity, particularly within its lush forests that are home to an incredible variety of tree species (Islam et al., 2018 ; Pavel et al., 2016 ). The Chittagong division, in particular, is known for its diverse forest trees. As global warming becomes an increasingly pressing issue, Bangladesh has launched numerous initiatives to combat its effects. Universities and research institutions across the country are actively studying how climate change impacts various species (Islam et al., 2018 ; Masum et al., 2022 ; Maszura et al., 2018 ; Pavel et al., 2016 ). Many forest trees are now endangered or critically endangered due to the changing climate and the microclimate shifts necessary for their seed germination. Located along the Tropic of Cancer, Bangladesh benefits from a broad spectrum of sunlight and receives about 3000 millimeters of rainfall each year (Hossain et al., 2014 ). Among the 500 or so timber-producing tree species is Anisoptera scaphula (Roxb.) Kurz, a towering and endangered tree that grows between 32–45 meters high with an average trunk diameter of 3.4 meters. It’s known for its clear trunk and prominent buttresses, making it quite remarkable (Hossain et al., 2014 ; Saha et al., 2022 ). A. scaphula is native to several Southeast Asian countries, including Bangladesh, Malaysia, Laos, Myanmar, and Thailand. This species is primarily deciduous or semi-deciduous, shedding its leaves seasonally (Islam et al., 2018 ; Maszura et al., 2018 , 2018 ). The Bangladesh Forest Department has identified several reasons for the decline of A. scaphula ’s habitat. These include habitat loss, the absence of mother trees needed for seed production, and poor microclimatic conditions on the forest floor. Additionally, the species' biological traits pose challenges for its survival. For example, A. scaphula produces seeds that cannot be stored for long, and its winged fruits, while aiding in seed dispersal, make natural germination difficult. Germination begins with a root emerging from the wing side and a shoot from the stalk end (Saha et al., 2022 , Arman et al. 2024 ). The distribution of A. scaphula is significantly influenced by bioclimatic factors like rainfall, light, temperature, and day length. Conservationists increasingly use species distribution models to predict the future distribution of specific species under different climate scenarios (Masum et al., 2022 ). These models are essential tools for understanding how climate change might alter the habitats of endangered species and for developing effective conservation strategies. Given the urgent need to address the impacts of climate change on biodiversity, it is essential to focus on species like A. scaphula that are particularly vulnerable yet crucial to their ecosystems. A. scaphula , (Hossain et al., 2014 ) with its unique ecological role and economic value, is a prime candidate for such studies (Miah et al., 2023 ). The species plays a critical role in maintaining the structure and function of tropical forests in Bangladesh and beyond (Alam et al., 2023 ). Its large size and extensive root system help stabilize the soil, preventing erosion, while its canopy provides habitat and food for various wildlife species. Despite its importance, A. scaphula faces numerous threats that are exacerbated by climate change (Islam et al., 2020 ). Deforestation for agriculture, illegal logging, and land conversion for infrastructure development are significant factors leading to habitat loss (Roy et al., 2015 ). The lack of mother trees for seed production is another critical issue (Imran et al., 2024), as it hampers natural regeneration processes (Rahman et al., 2022 ). Poor microclimatic conditions on the forest floor, influenced by changes in temperature and precipitation patterns, further challenge the species' survival (Mehta et al., 2021 ; Mitra et al., 2023 ; Saxena et al., 2013 ). The biological characteristics of A. scaphula also make its conservation more complex. The species produces recalcitrant seeds that are sensitive to drying and cannot be stored for long periods, complicating efforts to preserve its genetic material. Moreover, its winged fruits, while adapted for wind dispersal, often fail to settle in suitable germination sites, leading to low seedling recruitment rates (Das et al., 2020 ; Khan & Shankar, 2014 ; Mehta et al., 2021 ; Mitra et al., 2023 ; Saxena et al., 2013 ). In light of these challenges, it is crucial to use advanced tools like species distribution models to forecast the future distribution of A. scaphula and identify potential thread under various climate scenarios. Such models integrate bioclimatic variables, species occurrence data, and climate projections to predict changes in habitat suitability. By understanding these dynamics (Akter et al., 2020 ; Dwivedi & Chopra, 2014 ; Kuniyal et al., 2013 ), conservationists can develop targeted strategies to protect and restore habitats, ensuring the long-term survival of A. scaphula . The current and future scenarios for A. scaphula based on bioclimatic variables using the Maximum Entropy (MaxEnt) platform. MaxEnt is a powerful tool for modeling species distributions, especially for species with limited occurrence data. It estimates the probability distribution of a species presence based on environmental constraints and known occurrences. The study focuses on the Chittagong division of Bangladesh, an area that exemplifies the complex interplay of climatic factors influencing species distribution. By predicting suitable habitats for A. scaphula for the years 2040 and 2060 under the SSP2-4.5 (2021–2040) and SSP2-4.5 (2041–2060), as well as SSP5-8.5 (2021–2040) and SSP5-8.5 (2041–2060) climate scenario, this research provides critical insights into the species' resilience to climate change. The study aimed to (i) assess the vulnerability of A. scaphula to global climate change using species distribution modeling (SDM) and (ii) project the potential risk of extinction resulting from climate impacts. The predictive models generated provide actionable insights for managing A. scaphula , offering evidence-based recommendations for its conservation. These findings will support policy development and conservation strategies, identifying key areas for protection and management. By safeguarding A. scaphula , the study also aims to enhance the resilience of the broader ecosystem, ensuring that the diverse plant and animal communities reliant on this species can continue to thrive amidst changing climatic conditions. 2. Materials and Methods 2.1 Study Site The Chittagong division, which includes Chattogram, Bandarban, Rangamati, and Cox's Bazar, is a natural habitat for A. scaphula . This region features tropical evergreen and semi-evergreen forests, with Tectona grandis being a dominant species that provides habitat for large birds( Hossain et al., 2020 ), which have significant relationships with A. scaphula . The area is rich in biodiversity, and A. scaphula is considered a flagship species. The region experiences heavy to moderate rainfall, averaging 3000mm per year, and abundant sunlight, resulting in a wide range of temperatures, with an average of 26°C(Hossain et al., 2020 ;Rahman et al., 2018 ; Uddin et al., 2020 ). The study site is located at 22°22'0.001\"N, 91°47'60\"E, with an elevation of 29 meters above sea level. 2.2 Current Status of Boilam Trees in Bangladesh In Bangladesh, a total of 21 Boilam trees have been identified across the country. Out of these, eight are still immature, meaning they haven't yet started flowering or fruiting. The remaining trees are either mature or over mature. Specifically, five of these trees are classified as mature, while six have reached an over mature stage (Islam et al., 2019; Saha et al., 2022 ). Unfortunately, two of the over mature trees are suffering from significant health issues: one is affected by root rot, and the other has heart rot disease. The two trees with these diseases are located in Faliar Para under Ukhia Upazila and in the Hazarikhil Wildlife Sanctuary within the Chittagong North Forest Division. The tree in Faliar Para is dealing with root rot, while the overmature tree in Hazarikhil Wildlife Sanctuary is struggling with heart rot disease. The six over mature trees are spread across various locations: Hazarikhil Wildlife Sanctuary, Sukhbilas Beat, Faliar Para, Shilkhali, and the Ukhia TV Station(Hossain et al., 2014 ). These older trees are particularly valuable as potential mother trees for seed collection, thanks to their advanced age and the genetic material they can provide. From July 2019 to June 2020, observations were made to study the phonological characteristics of the Boilam trees, including leaf shedding, flowering, and fruiting. During this year-long observation period, flowering was noted in only two trees: one located on the IFESCU campus and the other at the Ukhia TV Station. Table 1 Bioclimatic and environmental variables considered for the MaxEnt model. Variable Description Unit Bio1 Annual mean temperature °C Bio2 Mean diurnal range °C Bio3 Isothermality ((Bio2/Bio7)∗100) – Bio4 Temperature seasonality (standard deviation∗100) CV Bio5 Maximum temperature of the warmest month °C Bio6 Minimum temperature of the coldest month °C Bio7 Temperature annual range (Bio5-Bio6) °C Bio8 Mean temperature of wettest quarter °C Bio9 Mean temperature of driest quarter °C Bio10 Mean temperature of warmest quarter °C Bio11 Mean temperature of coldest quarter °C Bio12 Annual precipitation mm Bio13 Precipitation of wettest period mm Bio14 Precipitation of driest period mm Bio15 Precipitation seasonality CV Bio16 Precipitation of wettest quarter mm Bio17 Precipitation of driest quarter mm Bio18 Precipitation of warmest quarter mm Bio19 Precipitation of coldest quarter mm 2.3 Occurrence data In the wilds of Chittagong, a team of passionate researchers embarked on a journey of discovery from January to February 2021. Armed with a trusty Garmin GPSMAP 62s device, they ventured into a variety of landscapes, from lush forests to the scientific grounds of the Bangladesh Forest Research Institute (BFRI), even exploring protected areas and rural homesteads. Led by their unwavering dedication (Hossain et al., 2014 ;Islam et al., 2020 ; Shome et al., 2022 ), they braved rugged terrain and unpredictable weather, determined to uncover the secrets of the critically endangered A. Scaphula . Despite the challenges they faced, the team's perseverance paid off as they meticulously documented 21 GPS occurrence data points for A. Scaphula . These precious data points are more than just coordinates; they represent invaluable insights into the species' distribution and habitat preferences in the Chittagong region. With each data point, they lay the foundation for future conservation efforts, ensuring the survival of A. Scaphula for generations to come. 2.4 Understanding Bioclimatic Variables and Climate Projections To analyze how climate change might affect the distribution of the tree species A. scaphula , we used bioclimatic variables from the WorldClim 2.1 database (Accessed from https://www.worldclim.org/ ). These variables, such as temperature and precipitation (IPCC 2014), were collected as high-resolution raster files and processed using ArcMap 10.8 software. Climate scenarios from the IPCC (Machwitz et al., 2008 ; Mandal et al., 2022 ), including RCP2.6 (low greenhouse gas concentration) and RCP8.5 (high concentration), were used to understand potential future conditions. Updated scenarios (Hijmans et al., 2005 ), called Shared Socioeconomic Pathways (SSPs), align with these concentration levels and project conditions up to 2100. The analysis also used temperature data from the Coupled Model Intercomparison Project Phase 6 (CMIP6) to study the period from 1986 onwards. For future climate projections (2021–2040 and 2041–2060), we used the SSP2-4.5 and SSP5-8.5 scenario. The Met Office Hadley Centre (MOHC) HadGEM3-GC31-LL model was employed for downscaling and calibrating data, with current conditions as a baseline (Ridley et al., 2018 ).To simplify the data, Principal Component Analysis (PCA) was performed on the bioclimatic variables. MaxEnt, a machine learning algorithm, was then used for the analysis and projection, valued for its ability to handle various data types (Janekovi & Novak, 2012 ). 2.5 Modeling Species Distribution The distribution model for A. scaphula was developed in two steps. First, researchers organized occurrence data into a CSV file (Fig. 2), detailing the species name, longitude, and latitude to map its locations accurately. Second, bioclimatic variables (BioClim 1–19) were prepared as raster files and processed using R version 4.4.0 for accurate representation. These datasets were combined in MaxEnt version 3.4.1, which calculates the probability of A. scaphula's presence based on environmental factors (Cord et al., 2009 ). MaxEnt's flexibility allowed researchers to produce a detailed probability distribution map of A. scaphula under various conditions (Thapa et al., 2018 ). The same method was applied to project future distributions for 2021–2040 and 2021–2060 under the SSP2-4.5 and SSP5-8.5 scenarios (Warton et al., 2013 ). This approach provided valuable insights into how climate change could impact the species, aiding in conservation planning and climate change mitigation strategies. 2.6 Validating the Model To ensure the model's accuracy, researchers used the area under the curve (AUC) of the Receiver Operating Characteristic (ROC). The dataset was split, with 75% used for training and 25% for testing. Model performance by Thuiller et al. ( 2006 ) was assessed based on AUC values: a) AUC < 0.8: Poor or null model, b) 0.8 ≤ AUC < 0.9: Fairly good model, c) 0.9 ≤ AUC < 0.95: Good model, d) AUC ≥ 0.95: Excellent model This evaluation method ensures reliable predictions, offering a strong foundation for understanding how A. scaphula's habitat might shift due to climate change (Phillips et al., 2006 ; Phillips and Dudík 2008 ). This information is crucial for developing adaptive conservation strategies to protect biodiversity in a changing climate. 3. Result and discussion 3.1 Present status The study examined the distribution and maturity status of Boilam trees ( A. scaphula ) across various locations, revealing a total of 21 trees. The Hazarikhil Wildlife Sanctuary and the Chittagong University Campus (IFESCU) had the highest concentrations, each hosting 5 trees. Other locations like Dulahazra Safari Park and Sukhbilash Beat, Khurushia Range each contained 2 trees, while Bangladesh Forest Research Institute, Faliar Para, Shilkhali Beat, Ukhia TV Station, Saplapur, Lama, and the National Botanical Garden each had 1 tree. During our survey, we found Boilam trees in various forest areas, including the Chittagong North and South Forest Divisions, Chittagong Hill Tracts North and South Forest Divisions, and Cox’s Bazar North and South Forest Divisions, as well as in homesteads (Hossain et al., 2014 ). The highest number of trees was observed in Hazarikhil Wildlife Sanctuary and the IFESCU area. However, we faced several challenges that limited our survey coverage. In some regions of the Chittagong and Cox’s Bazar forest divisions, we couldn't conduct surveys due to risks posed by elephants, difficult terrain (Pavel et al., 2016 ), and constraints in time and funding. Additionally, a lack of local knowledge about Boilam trees further complicated our efforts, as many local people were unaware of where to find this species. Despite these limitations, the data we collected provides valuable insights into the distribution of Boilam trees in the accessible areas (Hossain et al., 2014 ). This information is crucial for future conservation efforts and highlights the need for more comprehensive studies to fully understand the species' distribution.We also looked at the maturity state of these trees and found that 8 were immature, 5 were matured, 6 were over matured, and 2 were defected(Fig. 3). Understanding the distribution and maturity of Boilam trees is essential for their conservation and resource management. 3.2 Prediction Accuracy and Model Evaluation The prediction accuracy for the distribution models of A. scaphula was remarkably high, with AUC values reaching 0.981 (Tables 1 and 2 ; Figs. 4, 5 and 6 ). This high AUC value indicates that the MaxEnt-derived distributions closely match the actual probability distribution of the species. Both the training and testing AUC values were over 90%(Islam et al., 2020 ), with very low standard deviations, suggesting consistent and reliable model performance. Since the AUC value is well above 0.5, it confirms that the models performed significantly better than random predictions. Figures 4 and 5 show high AUC values for both training data (red line, AUC = 0.968) and test data (blue line, AUC = 0.973), with the lines nearly coinciding, demonstrating excellent model reliability(Islam et al., 2019). The Jackknife test of leave-one-out cross-validation was used to evaluate the importance of bioclimatic and environmental variables. This test analyzed each predictor variable individually, when omitted, and in combination with all variables. The results highlighted that variables like bioclim11, bioclim13, bioclim16, bioclim4, and bioclim7 were the most significant in predicting species distribution. Interestingly, bioclim6 slightly outperformed bioclim7 in test gain. On the other hand, bioclim1, bioclim14, and bioclim18 contributed minimally and were excluded from the final predictions(Maszura et al., 2018 ). This robust model performance underscores its effectiveness in predicting the distribution of A. scaphula , pinpointing the critical bioclimatic variables that influence its habitat. This information is essential for conservation planning and developing strategies to protect this species amid changing climatic conditions. The consistent AUC values between training and testing data further bolster confidence in the model's predictive capabilities, making it a reliable tool for future studies and conservation efforts. Table 2 Prediction accuracy of the species distribution modeling. Training AUC Test AUC Stander deviation SSP2-4.5(2021–2040) 0.968 0.973 0.0023 SSP2-4.5(2041–2060) 0.976 0.957 0.0028 SSP5-8.5(2021–2040) 0.981 0.951 0.0029 SSP5-8.5(2041–2060) 0.969 0.978 0.0027 3.3 Probable distribution of A. scaphula The spatial distribution of A. scaphula , in terms of predictive probability, was segmented into 14 categories to represent the ranges of habitat suitability (Figs. 5 and 6). The probability values range from 0 to 1, with 0 indicating the lowest habitat suitability and 1 indicating the highest. Figure 6 suggests that the spatial distribution of A. scaphula across the Chittagong division might face increasing climate stress(Hossen & Hossain, 2018 ). However, the study's findings indicate that the likely distribution of A. scaphula under the SSP2-4.5 (2021–2040) and SSP2-4.5 (2041–2060), as well as SSP5-8.5 (2021–2040) and SSP5-8.5 (2041–2060) scenarios, would see only an insignificant decline due to the negative impacts of global climate change. Given these projections, it is recommended to concentrate conservation efforts in the southeastern region of Bangladesh. This area should be prioritized for habitat protection and restoration initiatives(Rahman et al., 2022 ). Additionally, continuous monitoring and updating of the habitat suitability model are essential to account for ongoing environmental changes and human activities. Engaging local communities and stakeholders in conservation activities can greatly enhance the effectiveness of these efforts and ensure the long-term survival of A. scaphula in Bangladesh. These findings have significant policy implications and should be reflected in the development of conservation strategies(Islam et al., 2018 ; Warton et al., 2013 ). Factors such as physiological characteristics, precipitation regimes, and habitat destruction are likely to influence the future distribution of this species. Therefore, a proactive and adaptive approach to conservation is crucial to mitigate the adverse effects of climate change on A. scaphula's habitat. 4. Discussion This study found that Anisoptera scaphula exhibits a higher degree of climate resilience compared to other plant and animal species in Bangladesh (Alamgir et al., 2015 ; Sohel et al., 2016 ; Akhter et al., 2017 ; Deb et al., 2017 ). Previous research indicated that species like Mangifera sylvatica may face extinction by 2070 due to climate change (Akhter et al., 2017 ). Similar concerns have been raised for species such as Hoolock hoolock and Panthera tigris (Alamgir et al., 2015 ; Mukul et al., 2019 ). Additionally, the habitat for Dipterocarpus turbinatus and Hopea odorata is projected to decrease by 24% and 34%, respectively, in the South Asian region by 2070 (Deb et al., 2017 ). Dipterocarpus turbinatus in Bangladesh may see a dramatic reduction in suitable habitat under RCP2.6 and RCP8.5 scenarios by 2050 and 2070 (Islam et al., 2020 ). In China, Pinus armandii is expected to lose suitable habitat under similar scenarios (Ning et al., 2021 ), while in Burkina Faso, the habitat for Ximenia americana could decline by 15% and 25% under RCP4.5 and RCP8.5, respectively (Lompo et al., 2021 ). The comparative resilience of A. scaphula could be due to its extensive distribution and relatively stable conservation status, classified as of least concern both globally and in Bangladesh (IUCN 2020). Similar resilience has been observed in Madhuca longifolia in India (Yadav et al., 2021 ) and Ammopiptanthus mongolicus in northwest China, which may see an increase in suitable habitat under future climate scenarios (Du et al., 2021 ). Temperature emerged as the dominant factor influencing species distribution in this study, with temperature-related variables like Bio11, Bio6, Bio7, and Bio9 playing significant roles. This contrasts with findings from Thailand, where precipitation was identified as the primary factor affecting Dipterocarpus alatus distribution (Kamyo and Asanok 2020 ). In other studies, both precipitation and temperature have been highlighted as key factors influencing the spatial distribution of species in China (Du et al., 2021 ; Ning et al., 2021 ) and Burkina Faso, where annual precipitation and temperature of the coldest quarter and coldest month were critical predictors (Lompo et al., 2021 ). The study's limitations include a narrow geographical scope and a limited number of species geo-locations due to funding and time constraints. Despite these limitations, the research contributes to understanding climate-induced species redistributions in this region, which is highly vulnerable to climate change impacts. Future studies are recommended to expand geographical coverage and include more species geo-locations to provide a comprehensive understanding of these dynamics. 5. Conclusion This study predicted possible responses of the two ecologically important but not yet endangered tree species - A. scaphula - in Chittagong districts of Bangladesh using the species distribution modeling approach. Our habitat suitability modeling based on present distribution data, bioclimatic and environmental variables, and under the SSP2-4.5 and SSP5-8.5 scenarios emission pathways for 2040 and 2060 showed insignificant change in case of the availability of suitable habitats for the A. scaphula . This study suggested that the future climate change trend may adversely affect the local habitat quality for selected tree species. However, none of the species will face extinction risk. Policymakers may use the outcome of this study in formulating policies regarding local level biodiversity conservation and forest management of these important wild fruit species. As A. scaphula will survive even in the harshest of climate change situations, Bangladesh Forest Department can think about these two species on a priority basis in plantation activities to enhance wildlife resilience. Planting these two species in augmented natural regeneration activities in denuded and degraded government forests will enhance the habitat resilience of the forests for wildlife conservation. Declarations Declaration of Competing Interest The authors declare no potential conflicts of interest with respect to the research, authorship, and publication of this article. Author Contribution: Data Collection: Minhazul Ferdous, Rabeya Khatun; Investigation: Minhazul Ferdous, Mohd Imran Hossain Chowdhury; Data Analysis: Minhazul Ferdous, Sudipta Sen Gupta, Mohd Imran Hossain Chowdhury; Data Curation: Minhazul Ferdous, Mehedi Hasan Rakib; Visualization: Mehedi Hasan Rakib; Literature Review: Rabeya Khatun, Mohammad Mostafizur Rahman, Md. Yeamim Aftad; Methodology: Sudipta Sen Gupta, Mohd Imran Hossain Chowdhury, Md. Salauddin; Formal Analysis: Mohd Imran Hossain Chowdhury, Mehedi Hasan Rakib, Md. Salauddin; MaxEnt Modeling: Sudipta Sen Gupta, Rabeya Khatun; Original Draft Writing: Mohd Imran Hossain Chowdhury, Mohammad Mostafizur Rahman; Script Review and Editing: Minhazul Ferdous, Sudipta Sen Gupta, Rabeya Khatun, Mehedi Hasan Rakib, Md. Salauddin, Mohammad Mostafizur Rahman, Md. Yeamim Aftad; Review Report: Rabeya Khatun, Md. Yeamim Aftad; Project Administration, Conceptualization and Resources: Md. Salauddin, Tanvir Hossen. Acknowledgment The research work was supported by the authors. Therefore, We wish to express our sincere gratitude to the anonymous reviewers. References Arman, A. H., Khatun, R., & Masum, K. M. (2024). Transformation of jhum (shifting cultivation) to forest plantation: Effect on soil properties in the hill tracts of Bangladesh. Forestist , 74(3), 333-341. DOI: 10.5152/forestist.2024.23079 Alam, E., Hridoy, A. E. E., Tusher, S. M. S. H., Islam, A. R. M. T., & Islam, M. K. (2023). Climate change in Bangladesh: Temperature and rainfall climatology of Bangladesh for 1949-2013 and its implication on rice yield. PLoS ONE , 18 (10 October), 1–26. https://doi.org/10.1371/journal.pone.0292668 Akter, A., Biella, P., Batáry, P., & Klečka, J. (2020). Changing pollinator communities along a disturbance gradient in the Sundarbans mangrove forest : a case study on Acanthus ilicifolius and Avicennia officinalis . 1–22. Akhter, S. , McDonald, M.A. , van Breugel, P. , Sohel, S. , Kjær, E.D. , Mariott, R. , (2017). Habi- tat distribution modelling to identify areas of high conservation value under climate change for Mangifera sylvatica Roxb. of Bangladesh. Land Use Policy 60, 223–232 . Alamgir, M., Mukul, S., & Turton, S. (2015). Modelling spatial distribution of critically endangered Asian elephant and Hoolock gibbon in Bangladesh forest ecosystems under a changing climate. Applied Geography , 60 , 10–19. https://doi.org/10.1016/j.apgeog.2015.03.001 Bajaj, S., & Amali, G. (2019). Species Environmental Niche Distribution Modeling for Panthera Tigris Tigris ‘Royal Bengal Tiger’ Using Machine Learning (pp. 251–263). https://doi.org/10.1007/978-981-13-5953-8_22 Cord, A., Colditz, R. R., Schmidt, M., & Dech, S. (2009). Species distribution and forest type mapping in Mexico. International Geoscience and Remote Sensing Symposium (IGARSS) , 5 . https://doi.org/10.1109/IGARSS.2009.5417681 Das, G., Kim, D. Y., Fan, C., Gutiérrez-Grijalva, E. P., Heredia, J. B., Nissapatorn, V., Mitsuwan, W., Pereira, M. L., Nawaz, M., Siyadatpanah, A., Norouzi, R., Sawicka, B., Shin, H. S., & Patra, J. K. (2020). Plants of the Genus Terminalia: An Insight on Its Biological Potentials, Pre-Clinical and Clinical Studies. Frontiers in Pharmacology , 11 (October), 1–30. https://doi.org/10.3389/fphar.2020.561248 Deb, J.C. , Phinn, S. , Butt, N. , McAlpine, C.A. , (2017). The impact of climate change on the distribution of two threatened Dipterocarp trees. Ecol. Evol. 7 (7), 2238–2248 Du, Z., He, Y., Wang, H., Wang, C., Duan, Y., (2021). Potential geographical distribu- tion and habitat shift of the genus Ammopiptanthus in China under current and future climate change based on the MaxEnt model. J. Arid. Environ. 184, 104328. doi: 10.1016/j.jaridenv.2020.104328. Dwivedi, S., & Chopra, D. (2014). Revisiting terminalia arjuna-an ancient cardiovascular drug. Journal of Traditional and Complementary Medicine , 4 (4), 224–231. https://doi.org/10.4103/2225-4110.139103 Hossain, A., Ferdous, J., Rahman, M. A., Azad, A. K., & Shukor, N. A. A. (2014). Towards the propagation of a critically endangered tree species Anisoptera scaphula. Dendrobiology , 71 , 137–148. https://doi.org/10.12657/denbio.071.014 Hossain, M. K., Alim, A., Hossen, S., Hossain, A., & Rahman, A. (2020). Diversity and conservation status of tree species in Hazarikhil Wildlife Sanctuary (HWS) of Chittagong, Bangladesh. Geology, Ecology, and Landscapes , 4 (4), 298–305. https://doi.org/10.1080/24749508.2019.1694131 Hossen, S., & Hossain, M. (2018). Conservation status of tree species in Himchari National Park of Cox’s Bazar, Bangladesh. Journal of Biodiversity Conservation and Bioresource Management , 4 (2), 1–10. https://doi.org/10.3329/jbcbm.v4i2.39842 Hijmans, R.J., Cameron, S.E., Parra, J.L., Jones, P.G., Jarvis, A., (2005). Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978. doi: 10.1002/joc.1276 . Chowdhury,M.I.H., Rakib, M. H., Das, C., & Hossain, Z. (2024). Tr ee Species Ger mination : A Compr ehensive Meta-Analysis and its Implications for Pr e-Sowing Tr eatment in Bangladesh. Journal OfSoil, Plant and Environment. , 3 (1), 24–40. https://doi.org/10.56946/jspae.v3i1.397 Islam, K., Rahman, M. F., Islam, K. N., Nath, T. K., & Jashimuddin, M. (2020). Modeling spatiotemporal distribution of Dipterocarpus turbinatus Gaertn. F in Bangladesh under climate change scenarios. Journal of Sustainable Forestry , 39 (3), 221–241. https://doi.org/10.1080/10549811.2019.1632721 Islam, N., Jaman, M. F., Rahman, M. M., & Alam, M. M. (2018). Wildlife Diversity and Population Status of Kashimpur Union, Gazipur, Bangladesh. Journal of the Asiatic Society of Bangladesh, Science , 44 (2), 101–115. https://doi.org/10.3329/jasbs.v44i2.46553 IPCC (2014) Climate change (2014): synthesis report. Contribution of working groups I, II and III to the fifth assessment report of the intergovernmental panel on climate change [Core writing team, R.K. Pachauri and L.A. Meyer (eds.)] Janekovi, F., & Novak, T. (2012). PCA – A Powerful Method for Analyze Ecological Niches. Principal Component Analysis - Multidisciplinary Applications , February 2012 . https://doi.org/10.5772/38538 Kamyo, T. , Asanok, L. , (2020). Modeling habitat suitability of Dipterocarpus alatus (Dipte- rocarpaceae) using MaxEnt along the Chao Phraya River in Central Thailand. Forest Sci. Technol. 16 (1), 1–7 . Khan, M. L., & Shankar, U. (2014). Seed Mass , Germination and Seedling Growth in Artocarpus chama. International Journal of Ecology and Environmental Sciences · , 30(4) (December 2004), 369–376. Kuniyal, C. P., Purohit, V., Butola, J. S., & Sundriyal, R. C. (2013). South African Journal of Botany Seed size correlates seedling emergence in Terminalia bellerica. South African Journal of Botany , 87 , 92–94. https://doi.org/10.1016/j.sajb.2013.03.016 Lompo, O., Dimobe, K., Mbayngone, E., Savadogo, S., Sambaré, O., Thiombiano, A., Ouédraogo, A., (2021). Climate influence on the distribution of the yellow plum (Ximenia americana L.) in Burkina Faso. Trees, Forests People, 100072 doi: 10.1016/j.tfp.2021.100072 . Machwitz, M., Landmannc, T., Conrad, C., Cord, A., & Dech, S. (2008). Land cover analysis on sub-continental scale: Fao lccs standard with 250 meter modis satellite observations in West Africa. International Geoscience and Remote Sensing Symposium (IGARSS) , 5 (1), 49–52. https://doi.org/10.1109/IGARSS.2008.4780024 Mandal, A., Jaman, M., Alam, M., Rabbe, M., & Shome, A. (2022). Vertebrate wildlife diversity of Sreepur upazila, Magura, Bangladesh. Journal of Biodiversity Conservation and Bioresource Management , 7 (1), 51–62. https://doi.org/10.3329/jbcbm.v7i1.57123 Masum, S. M., Halim, A., Mandal, M. S. H., Asaduzzaman, M., & Adkins, S. (2022). Predicting Current and Future Potential Distributions of Parthenium hysterophorus in Bangladesh Using Maximum Entropy Ecological Niche Modelling. Agronomy , 12 (7). https://doi.org/10.3390/agronomy12071592 Maszura, C. M., Karim, S. M. R., Norhafizah, M. Z., Kayat, F., & Arifullah, M. (2018). Distribution, Density, and Abundance of Parthenium Weed (Parthenium hysterophorus L.) at Kuala Muda, Malaysia. International Journal of Agronomy , 2018 . https://doi.org/10.1155/2018/1046214 Mehta, B., Nagar, B., Patel, B., Chaklashiya, P., Shah, M., Verma, P., & Shah, M. B. (2021). A review on a Lesser Known Indian Mangrove: Avicennia officinalis L. (Family: Acanthaceae). International Journal of Green Pharmacy , 15 (1), 1–10. Miah, M. D., Hasnat, G. N. T., Nath, B., Saeem, M. G. U., & Rahman, M. M. (2023). Spatial and Temporal Changes in the Urban Green Spaces and Land Surface Temperature in the Chittagong City Corporation of Bangladesh Between 2000 and 2020. Forestist , 73 (2), 171–182. https://doi.org/10.5152/forestist.2022.22013 Mitra, S., Naskar, N., Lahiri, S., & Chaudhuri, P. (2023). A study on phytochemical profiling of Avicennia marina mangrove leaves collected from Indian Sundarbans. Sustainable Chemistry for the Environment , 4 (August), 100041. https://doi.org/10.1016/j.scenv.2023.100041 Mukul, S.A. , Alamgir, M. , Sohel, M.S.I. , Pert, P.L. , Herbohn, J. , Turton, S.M. , Khan, M.S.I. , Munim, S.A. , Reza, A.H.M.A. , Laurance, W.F. , 2019. Combined effects of climate change and sea-level rise project dramatic habitat loss of the globally endangered Bengal tiger in the Bangladesh Sundarbans. Sci. Total Environ. 663, 830–840 . Nazrul Islam, A. K. M., Haque, A. E., Maniruzzaman, Jamali, T., Haque, T., Alfasane, M. A., Nahar, N., Jahan, N., Sultana, S., & Senthil Kumar, T. (2019). Species Distribution in Different Ecological Zones and Conservation Strategy of Halophytes of Sundarbans Mangrove Forest of Bangladesh (pp. 479–495). https://doi.org/10.1007/978-3-030-04417-6_30 Ning, H., Ling, L., Sun, X., Kang, X., Chen, H., (2021). Predicting the future redistribu- tion of Chinese white pine Pinus armandii Franch. Under climate change scenar- ios in China using species distribution models. Glob. Ecol. Conserv. 25, e01420. doi: 10.1016/j.gecco.2020.e01420 . Pavel, M. A. Al, Mukul, S. A., Uddin, M. B., Harada, K., & Arfin Khan, M. A. S. (2016). Effects of stand characteristics on tree species richness in and around a conservation area of northeast Bangladesh. Journal of Mountain Science , 13 (6), 1085–1095. https://doi.org/10.1007/s11629-015-3501-2 Phillips, S.J. , Anderson, R.P. , Schapire, R.E. , (2006). Maximum entropy modeling of species geographic distributions. Ecol. Modell. 190 (3–4), 231–259 . Phillips, S.J. , Dudík, M. , (2008). Modeling of species distributions with Maxent: new exten- sions and a comprehensive evaluation. Ecography 31 (2), 161–175 . Rahman, M. M., Nneji, L. M., Hossain, M. M., Nishikawa, K., & Habib, K. A. (2022). Diversity and distribution of amphibians in central and northwest Bangladesh, with an updated checklist for the country. Journal of Asia-Pacific Biodiversity , 15 (2), 147–156. https://doi.org/10.1016/j.japb.2021.12.002 Rahman, M., Parvin, W., Sultana, N., & Tarek, S. (2018). Ex-situ conservation of threatened forest tree species for sustainable use of forest genetic resources in Bangladesh. Journal of Biodiversity Conservation and Bioresource Management , 4 (2), 89–98. https://doi.org/10.3329/jbcbm.v4i2.39855 Ridley, Jeff; Menary, Matthew; Kuhlbrodt, Till; Andrews, Martin; Andrews, Tim (2018). MOHC HadGEM3-GC31-LL model output prepared for CMIP6 CMIP. Version-2021.11.15 [1] .Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.419 Roy, M., Biswas, B., & Ghosh, S. (2015). Trend Analysis of Climate Change in Chittagong Station in Bangladesh. International Letters of Natural Sciences , 47 (1), 42–53. https://doi.org/10.18052/www.scipress.com/ilns.47.42 Saha, S., Saha, A., Saha, K., Sarker, K. K., & Chowdhury, Z. J. (2022). Review on Plant Biodiversity and Conservation in Bangladesh: Drawbacks and Prospects. American Journal of Agricultural Science, Engineering, and Technology , 6 (2), 95–102. https://doi.org/10.54536/ajaset.v6i2.597 Saxena, V., Mishra, G., Saxena, A., & Vishwakarma, K. K. (2013). A comparative study on quantitative estimation of tannins in Terminalia chebula, Terminalia belerica, Terminalia arjuna and Saraca indica using spectrophotometer. Asian Journal of Pharmaceutical and Clinical Research , 6 (SUPPL.3), 148–149. Shome, A. R., Alam, M. M., Rabbe, M. F., Rahman, M. M., & Jaman, M. F. (2022). Ecology and diversity of wildlife in Dhaka University Campus, Bangladesh. Dhaka University Journal of Biological Sciences , 429–442. https://doi.org/10.3329/dujbs.v30i3.59035 Sohel S.I., Akhter S., Ullah H., Haque E., Rana P. (2016) Predicting impacts of climate change on forest tree species of Bangladesh: evidence from threatened Dysoxylum binectariferum (Roxb.) Hook.f. ex Bedd. (Meliaceae). iForest 10:154–160. Spiers, J. A., Oatham, M. P., Rostant, L. V., & Farrell, A. D. (2018). Applying species distribution modelling to improving conservation based decisions: A gap analysis of trinidad and tobago’s endemic vascular plants. Biodiversity and Conservation , 27 (11), 2931–2949. https://doi.org/10.1007/s10531-018-1578-y Thapa, S. , Chitale, V. , Rijal, S.J. , Bisht, N. , Shrestha, B.B. , (2018). Understanding the dy- namics in distribution of invasive alien plant species under predicted climate change in Western Himalaya. PLoS ONE 13 (4), e0195752 . Uddin, M., Chowdhury, F. I., & Hossain, M. K. (2020). Assessment of tree species diversity, composition and structure of Medha Kachhapia National Park, Cox’s Bazar, Bangladesh. Asian Journal of Forestry , 4 (1). https://doi.org/10.13057/asianjfor/r040104 Warton, D. I., Renner, I. W., & Ramp, D. (2013). Model-based control of observer bias for the analysis of presence-only data in ecology. PLoS ONE , 8 (11). https://doi.org/10.1371/journal.pone.0079168 Thuiller, W. , Broennimann, O. , Hughes, G. , Alkemade, J.R.M. , Midgley, G.F. , Corsi, F. , (2006). Vulnerability of African mammals to anthropogenic climate change under con- servative land transformation assumptions. Glob. Change Biol. 12 (3) , 424–440 . Yadav, S., Bhattacharya, P., Areendran, G., Sahana, M., Raj, K., Sajjad, H., (2021). Predicting impact of climate change on geographical distribution of major NTFP species in the Central India Region. Model. Earth Syst. Environ. 1–20. https://link.springer.com/article/10.1007/s40808-020-01074-4 . 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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-5884962\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":405942101,\"identity\":\"9f4e875a-2389-4620-afce-107380055086\",\"order_by\":0,\"name\":\"Minhazul Ferdous\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Minhazul\",\"middleName\":\"\",\"lastName\":\"Ferdous\",\"suffix\":\"\"},{\"id\":405942102,\"identity\":\"e6ddfe07-c2a6-453c-b0ea-52e609fefb14\",\"order_by\":1,\"name\":\"Sudipta Sen Gupta\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Sudipta\",\"middleName\":\"Sen\",\"lastName\":\"Gupta\",\"suffix\":\"\"},{\"id\":405942103,\"identity\":\"89a2855b-a6c9-40d2-ac51-15ff0492997c\",\"order_by\":2,\"name\":\"Mohd Imran Hossain Chowdhury\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Mohd\",\"middleName\":\"Imran Hossain\",\"lastName\":\"Chowdhury\",\"suffix\":\"\"},{\"id\":405942104,\"identity\":\"805aebf2-c3f6-4b71-9916-09d31e8b6169\",\"order_by\":3,\"name\":\"Rabeya Khatun\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+klEQVRIiWNgGAWjYFACHhBhwcDAzMBw4AOQycZOnBYJkBbGgzNAWpiJ1gLUdBjMJqTFvP3swcc8NRLyuu3MDw7b/Nomzwe07sPHHNxaZM7kJRvzHJMw3HaYzeBwbt9twzZmBmbJmdtwa5FgyDGT5mGTYNx2mAGopec2I1ALGzMvPi38b8x/8/yTsN92mP3DYcue2/aEtUjkmDHztkkkbjvMY3CY4cftRCK0vEuWnNsnkQzUUnCwt+F2chszYzN+v/DnHvzw5puN7bbzxzd/+PHntu389uaDHz7i0QICTDwwFmMbmGzArx6k5Aec+Yeg4lEwCkbBKBiBAACrYk+QjTSuwAAAAABJRU5ErkJggg==\",\"orcid\":\"\",\"institution\":\"\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Rabeya\",\"middleName\":\"\",\"lastName\":\"Khatun\",\"suffix\":\"\"},{\"id\":405942105,\"identity\":\"73e11cb3-6d74-4aaf-97ad-201a16f86cb2\",\"order_by\":4,\"name\":\"Mehedi Hasan Rakib\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Mehedi\",\"middleName\":\"Hasan\",\"lastName\":\"Rakib\",\"suffix\":\"\"},{\"id\":405942106,\"identity\":\"edddb177-bc74-4fcf-bc64-db2565d8dc7c\",\"order_by\":5,\"name\":\"Md. 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Hazarikhil Wildlife Sanctuary, 2. Hazarikhil Wildlife Sanctuary, 3. Dulahazra Safari Park Roadside, 4. IFESCU Campus, 5. Sukhbilas Beat, 6. National Botanical Garden, 7. Faliar Para, 8. Lama, 9. Ukhia TV Station, 10. Shilkhali Beat, and 11. Homestead of Dulahazra Safari Park Area\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5884962/v1/88a93c2c651b771ed32ffa99.png\"},{\"id\":75062961,\"identity\":\"92d52f6c-7701-4c06-acb2-1eafab480282\",\"added_by\":\"auto\",\"created_at\":\"2025-01-30 04:54:17\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":5713,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eThis image is not available with this version.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5884962/v1/e5a800bcfe20cccf9bea87ea.png\"},{\"id\":75062964,\"identity\":\"f47a88cc-64a5-4a66-be8f-7fe9c446a918\",\"added_by\":\"auto\",\"created_at\":\"2025-01-30 04:54:17\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":487993,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eOmission, predicted area and sensitivity vs. 1 for \\u003cem\\u003eA. scaphula\\u003c/em\\u003e, SSP5-8.5(2021-2040) and SSP5-8.5(2041-2060) scenarios.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003ea:\\u003c/strong\\u003e Omission, predicted area and sensitivity vs. 1 for \\u003cem\\u003eA. scaphula\\u003c/em\\u003e,SSP2-4.5(2021-2040) and SSP2-4.5(2041-2060) scenarios.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5884962/v1/7afc1a6d3aa8abefbfed75d4.png\"},{\"id\":75062966,\"identity\":\"1093f474-b7df-4189-96a0-5293c3b26e12\",\"added_by\":\"auto\",\"created_at\":\"2025-01-30 04:54:18\",\"extension\":\"png\",\"order_by\":6,\"title\":\"Figure 6\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":147454,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003ePredicted future distribution of \\u003cem\\u003eA. scaphula\\u003c/em\\u003e in three selected Chittagong division under climate change scenario.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"6.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5884962/v1/a76c187a6020fa2b26d81f92.png\"},{\"id\":75064113,\"identity\":\"5514274d-2b4e-433f-9ade-8c17d8202530\",\"added_by\":\"auto\",\"created_at\":\"2025-01-30 05:10:19\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":2740579,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5884962/v1/9a972cc6-b5ea-4044-8854-842479095cc9.pdf\"}],\"financialInterests\":\"The authors declare no competing interests.\",\"formattedTitle\":\"\\u003cp\\u003e\\u003cstrong\\u003eClimate Change Impact Assessment and Species Distribution Model of a Critically Endangered Tree Using MaxEnt Modelling\\u003c/strong\\u003e\\u003c/p\\u003e\",\"fulltext\":[{\"header\":\"1. Introduction\",\"content\":\"\\u003cp\\u003eBangladesh is a treasure trove of biodiversity, particularly within its lush forests that are home to an incredible variety of tree species (Islam et al., \\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e; Pavel et al., \\u003cspan citationid=\\\"CR60\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e). The Chittagong division, in particular, is known for its diverse forest trees. As global warming becomes an increasingly pressing issue, Bangladesh has launched numerous initiatives to combat its effects. Universities and research institutions across the country are actively studying how climate change impacts various species (Islam et al., \\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e; Masum et al., \\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Maszura et al., \\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e; Pavel et al., \\u003cspan citationid=\\\"CR60\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e). Many forest trees are now endangered or critically endangered due to the changing climate and the microclimate shifts necessary for their seed germination. Located along the Tropic of Cancer, Bangladesh benefits from a broad spectrum of sunlight and receives about 3000 millimeters of rainfall each year (Hossain et al., \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e). Among the 500 or so timber-producing tree species is \\u003cem\\u003eAnisoptera scaphula\\u003c/em\\u003e (Roxb.) Kurz, a towering and endangered tree that grows between 32\\u0026ndash;45 meters high with an average trunk diameter of 3.4 meters. It\\u0026rsquo;s known for its clear trunk and prominent buttresses, making it quite remarkable (Hossain et al., \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e; Saha et al., \\u003cspan citationid=\\\"CR71\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003cem\\u003eA. scaphula\\u003c/em\\u003e is native to several Southeast Asian countries, including Bangladesh, Malaysia, Laos, Myanmar, and Thailand. This species is primarily deciduous or semi-deciduous, shedding its leaves seasonally (Islam et al., \\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e; Maszura et al., \\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e). The Bangladesh Forest Department has identified several reasons for the decline of \\u003cem\\u003eA. scaphula\\u003c/em\\u003e\\u0026rsquo;s habitat. These include habitat loss, the absence of mother trees needed for seed production, and poor microclimatic conditions on the forest floor. Additionally, the species' biological traits pose challenges for its survival. For example, \\u003cem\\u003eA. scaphula\\u003c/em\\u003e produces seeds that cannot be stored for long, and its winged fruits, while aiding in seed dispersal, make natural germination difficult. Germination begins with a root emerging from the wing side and a shoot from the stalk end (Saha et al., \\u003cspan citationid=\\\"CR71\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e, Arman et al. \\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). The distribution of \\u003cem\\u003eA. scaphula\\u003c/em\\u003e is significantly influenced by bioclimatic factors like rainfall, light, temperature, and day length. Conservationists increasingly use species distribution models to predict the future distribution of specific species under different climate scenarios (Masum et al., \\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). These models are essential tools for understanding how climate change might alter the habitats of endangered species and for developing effective conservation strategies.\\u003c/p\\u003e \\u003cp\\u003eGiven the urgent need to address the impacts of climate change on biodiversity, it is essential to focus on species like \\u003cem\\u003eA. scaphula\\u003c/em\\u003e that are particularly vulnerable yet crucial to their ecosystems. \\u003cem\\u003eA. scaphula\\u003c/em\\u003e, (Hossain et al., \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e) with its unique ecological role and economic value, is a prime candidate for such studies (Miah et al., \\u003cspan citationid=\\\"CR51\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). The species plays a critical role in maintaining the structure and function of tropical forests in Bangladesh and beyond (Alam et al., \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). Its large size and extensive root system help stabilize the soil, preventing erosion, while its canopy provides habitat and food for various wildlife species. Despite its importance, \\u003cem\\u003eA. scaphula\\u003c/em\\u003e faces numerous threats that are exacerbated by climate change (Islam et al., \\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e). Deforestation for agriculture, illegal logging, and land conversion for infrastructure development are significant factors leading to habitat loss (Roy et al., \\u003cspan citationid=\\\"CR69\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e). The lack of mother trees for seed production is another critical issue (Imran et al., 2024), as it hampers natural regeneration processes (Rahman et al., \\u003cspan citationid=\\\"CR64\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). Poor microclimatic conditions on the forest floor, influenced by changes in temperature and precipitation patterns, further challenge the species' survival (Mehta et al., \\u003cspan citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Mitra et al., \\u003cspan citationid=\\\"CR53\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e; Saxena et al., \\u003cspan citationid=\\\"CR73\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e). The biological characteristics of \\u003cem\\u003eA. scaphula\\u003c/em\\u003e also make its conservation more complex. The species produces recalcitrant seeds that are sensitive to drying and cannot be stored for long periods, complicating efforts to preserve its genetic material. Moreover, its winged fruits, while adapted for wind dispersal, often fail to settle in suitable germination sites, leading to low seedling recruitment rates (Das et al., \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e; Khan \\u0026amp; Shankar, \\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e; Mehta et al., \\u003cspan citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Mitra et al., \\u003cspan citationid=\\\"CR53\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e; Saxena et al., \\u003cspan citationid=\\\"CR73\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e). In light of these challenges, it is crucial to use advanced tools like species distribution models to forecast the future distribution of \\u003cem\\u003eA. scaphula\\u003c/em\\u003e and identify potential thread under various climate scenarios. Such models integrate bioclimatic variables, species occurrence data, and climate projections to predict changes in habitat suitability. By understanding these dynamics (Akter et al., \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e; Dwivedi \\u0026amp; Chopra, \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e; Kuniyal et al., \\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e), conservationists can develop targeted strategies to protect and restore habitats, ensuring the long-term survival of \\u003cem\\u003eA. scaphula\\u003c/em\\u003e. The current and future scenarios for \\u003cem\\u003eA. scaphula\\u003c/em\\u003e based on bioclimatic variables using the Maximum Entropy (MaxEnt) platform. MaxEnt is a powerful tool for modeling species distributions, especially for species with limited occurrence data. It estimates the probability distribution of a species presence based on environmental constraints and known occurrences. The study focuses on the Chittagong division of Bangladesh, an area that exemplifies the complex interplay of climatic factors influencing species distribution. By predicting suitable habitats for \\u003cem\\u003eA. scaphula\\u003c/em\\u003e for the years 2040 and 2060 under the SSP2-4.5 (2021\\u0026ndash;2040) and SSP2-4.5 (2041\\u0026ndash;2060), as well as SSP5-8.5 (2021\\u0026ndash;2040) and SSP5-8.5 (2041\\u0026ndash;2060) climate scenario, this research provides critical insights into the species' resilience to climate change.\\u003c/p\\u003e \\u003cp\\u003eThe study aimed to (i) assess the vulnerability of \\u003cem\\u003eA. scaphula\\u003c/em\\u003e to global climate change using species distribution modeling (SDM) and (ii) project the potential risk of extinction resulting from climate impacts. The predictive models generated provide actionable insights for managing \\u003cem\\u003eA. scaphula\\u003c/em\\u003e, offering evidence-based recommendations for its conservation. These findings will support policy development and conservation strategies, identifying key areas for protection and management. By safeguarding \\u003cem\\u003eA. scaphula\\u003c/em\\u003e, the study also aims to enhance the resilience of the broader ecosystem, ensuring that the diverse plant and animal communities reliant on this species can continue to thrive amidst changing climatic conditions.\\u003c/p\\u003e\"},{\"header\":\"2. Materials and Methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.1 Study Site\\u003c/h2\\u003e \\u003cp\\u003eThe Chittagong division, which includes Chattogram, Bandarban, Rangamati, and Cox's Bazar, is a natural habitat for \\u003cem\\u003eA. scaphula\\u003c/em\\u003e. This region features tropical evergreen and semi-evergreen forests, with \\u003cem\\u003eTectona grandis\\u003c/em\\u003e being a dominant species that provides habitat for large birds( Hossain et al., \\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e), which have significant relationships with \\u003cem\\u003eA. scaphula\\u003c/em\\u003e. The area is rich in biodiversity, and \\u003cem\\u003eA. scaphula\\u003c/em\\u003e is considered a flagship species. The region experiences heavy to moderate rainfall, averaging 3000mm per year, and abundant sunlight, resulting in a wide range of temperatures, with an average of 26\\u0026deg;C(Hossain et al., \\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e;Rahman et al., \\u003cspan citationid=\\\"CR66\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e; Uddin et al., \\u003cspan citationid=\\\"CR80\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e). The study site is located at 22\\u0026deg;22'0.001\\\"N, 91\\u0026deg;47'60\\\"E, with an elevation of 29 meters above sea level.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.2 Current Status of Boilam Trees in Bangladesh\\u003c/h2\\u003e \\u003cp\\u003eIn Bangladesh, a total of 21 Boilam trees have been identified across the country. Out of these, eight are still immature, meaning they haven't yet started flowering or fruiting. The remaining trees are either mature or over mature. Specifically, five of these trees are classified as mature, while six have reached an over mature stage (Islam et al., 2019; Saha et al., \\u003cspan citationid=\\\"CR71\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). Unfortunately, two of the over mature trees are suffering from significant health issues: one is affected by root rot, and the other has heart rot disease. The two trees with these diseases are located in Faliar Para under Ukhia Upazila and in the Hazarikhil Wildlife Sanctuary within the Chittagong North Forest Division. The tree in Faliar Para is dealing with root rot, while the overmature tree in Hazarikhil Wildlife Sanctuary is struggling with heart rot disease. The six over mature trees are spread across various locations: Hazarikhil Wildlife Sanctuary, Sukhbilas Beat, Faliar Para, Shilkhali, and the Ukhia TV Station(Hossain et al., \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e). These older trees are particularly valuable as potential mother trees for seed collection, thanks to their advanced age and the genetic material they can provide. From July 2019 to June 2020, observations were made to study the phonological characteristics of the Boilam trees, including leaf shedding, flowering, and fruiting. During this year-long observation period, flowering was noted in only two trees: one located on the IFESCU campus and the other at the Ukhia TV Station.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eBioclimatic and environmental variables considered for the MaxEnt model.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"3\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eVariable\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eDescription\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eUnit\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBio1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eAnnual mean temperature\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026deg;C\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBio2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMean diurnal range\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026deg;C\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBio3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eIsothermality ((Bio2/Bio7)\\u0026lowast;100)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026ndash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBio4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eTemperature seasonality (standard deviation\\u0026lowast;100)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eCV\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBio5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMaximum temperature of the warmest month\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026deg;C\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBio6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMinimum temperature of the coldest month\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026deg;C\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBio7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eTemperature annual range (Bio5-Bio6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026deg;C\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBio8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMean temperature of wettest quarter\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026deg;C\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBio9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMean temperature of driest quarter\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026deg;C\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBio10\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMean temperature of warmest quarter\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026deg;C\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBio11\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMean temperature of coldest quarter\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026deg;C\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBio12\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eAnnual precipitation\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003emm\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBio13\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePrecipitation of wettest period\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003emm\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBio14\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePrecipitation of driest period\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003emm\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBio15\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePrecipitation seasonality\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eCV\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBio16\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePrecipitation of wettest quarter\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003emm\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBio17\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePrecipitation of driest quarter\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003emm\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBio18\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePrecipitation of warmest quarter\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003emm\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBio19\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePrecipitation of coldest quarter\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003emm\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.3 Occurrence data\\u003c/h2\\u003e \\u003cp\\u003eIn the wilds of Chittagong, a team of passionate researchers embarked on a journey of discovery from January to February 2021. Armed with a trusty Garmin GPSMAP 62s device, they ventured into a variety of landscapes, from lush forests to the scientific grounds of the Bangladesh Forest Research Institute (BFRI), even exploring protected areas and rural homesteads. Led by their unwavering dedication (Hossain et al., \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e;Islam et al., \\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e; Shome et al., \\u003cspan citationid=\\\"CR74\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e), they braved rugged terrain and unpredictable weather, determined to uncover the secrets of the critically endangered \\u003cem\\u003eA. Scaphula\\u003c/em\\u003e. Despite the challenges they faced, the team's perseverance paid off as they meticulously documented 21 GPS occurrence data points for \\u003cem\\u003eA. Scaphula\\u003c/em\\u003e. These precious data points are more than just coordinates; they represent invaluable insights into the species' distribution and habitat preferences in the Chittagong region. With each data point, they lay the foundation for future conservation efforts, ensuring the survival of \\u003cem\\u003eA. Scaphula\\u003c/em\\u003e for generations to come.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec6\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.4 Understanding Bioclimatic Variables and Climate Projections\\u003c/h2\\u003e \\u003cp\\u003eTo analyze how climate change might affect the distribution of the tree species \\u003cem\\u003eA. scaphula\\u003c/em\\u003e, we used bioclimatic variables from the WorldClim 2.1 database (Accessed from \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://www.worldclim.org/\\u003c/span\\u003e\\u003cspan address=\\\"https://www.worldclim.org/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e). These variables, such as temperature and precipitation (IPCC 2014), were collected as high-resolution raster files and processed using ArcMap 10.8 software. Climate scenarios from the IPCC (Machwitz et al., \\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e2008\\u003c/span\\u003e; Mandal et al., \\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e), including RCP2.6 (low greenhouse gas concentration) and RCP8.5 (high concentration), were used to understand potential future conditions. Updated scenarios (Hijmans et al., \\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e2005\\u003c/span\\u003e), called Shared Socioeconomic Pathways (SSPs), align with these concentration levels and project conditions up to 2100. The analysis also used temperature data from the Coupled Model Intercomparison Project Phase 6 (CMIP6) to study the period from 1986 onwards. For future climate projections (2021\\u0026ndash;2040 and 2041\\u0026ndash;2060), we used the SSP2-4.5 and SSP5-8.5 scenario. The Met Office Hadley Centre (MOHC) HadGEM3-GC31-LL model was employed for downscaling and calibrating data, with current conditions as a baseline (Ridley et al., \\u003cspan citationid=\\\"CR68\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e).To simplify the data, Principal Component Analysis (PCA) was performed on the bioclimatic variables. MaxEnt, a machine learning algorithm, was then used for the analysis and projection, valued for its ability to handle various data types (Janekovi \\u0026amp; Novak, \\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec7\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.5 Modeling Species Distribution\\u003c/h2\\u003e \\u003cp\\u003eThe distribution model for \\u003cem\\u003eA. scaphula\\u003c/em\\u003e was developed in two steps. First, researchers organized occurrence data into a CSV file (Fig.\\u0026nbsp;2), detailing the species name, longitude, and latitude to map its locations accurately. Second, bioclimatic variables (BioClim 1\\u0026ndash;19) were prepared as raster files and processed using R version 4.4.0 for accurate representation. These datasets were combined in MaxEnt version 3.4.1, which calculates the probability of \\u003cem\\u003eA. scaphula's\\u003c/em\\u003e presence based on environmental factors (Cord et al., \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e2009\\u003c/span\\u003e). MaxEnt's flexibility allowed researchers to produce a detailed probability distribution map of \\u003cem\\u003eA. scaphula\\u003c/em\\u003e under various conditions (Thapa et al., \\u003cspan citationid=\\\"CR79\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e). The same method was applied to project future distributions for 2021\\u0026ndash;2040 and 2021\\u0026ndash;2060 under the SSP2-4.5 and SSP5-8.5 scenarios (Warton et al., \\u003cspan citationid=\\\"CR81\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e). This approach provided valuable insights into how climate change could impact the species, aiding in conservation planning and climate change mitigation strategies.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.6 Validating the Model\\u003c/h2\\u003e \\u003cp\\u003eTo ensure the model's accuracy, researchers used the area under the curve (AUC) of the Receiver Operating Characteristic (ROC). The dataset was split, with 75% used for training and 25% for testing. Model performance by Thuiller et al. (\\u003cspan citationid=\\\"CR83\\\" class=\\\"CitationRef\\\"\\u003e2006\\u003c/span\\u003e) was assessed based on AUC values: a) AUC\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.8: Poor or null model, b) 0.8\\u0026thinsp;\\u0026le;\\u0026thinsp;AUC\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.9: Fairly good model, c) 0.9\\u0026thinsp;\\u0026le;\\u0026thinsp;AUC\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.95: Good model, d) AUC\\u0026thinsp;\\u0026ge;\\u0026thinsp;0.95: Excellent model\\u003c/p\\u003e \\u003cp\\u003eThis evaluation method ensures reliable predictions, offering a strong foundation for understanding how \\u003cem\\u003eA. scaphula's\\u003c/em\\u003e habitat might shift due to climate change (Phillips et al., \\u003cspan citationid=\\\"CR62\\\" class=\\\"CitationRef\\\"\\u003e2006\\u003c/span\\u003e; Phillips and Dud\\u0026iacute;k \\u003cspan citationid=\\\"CR63\\\" class=\\\"CitationRef\\\"\\u003e2008\\u003c/span\\u003e). This information is crucial for developing adaptive conservation strategies to protect biodiversity in a changing climate.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"3. Result and discussion\",\"content\":\"\\u003cdiv id=\\\"Sec10\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.1 Present status\\u003c/h2\\u003e \\u003cp\\u003eThe study examined the distribution and maturity status of Boilam trees (\\u003cem\\u003eA. scaphula\\u003c/em\\u003e) across various locations, revealing a total of 21 trees. The Hazarikhil Wildlife Sanctuary and the Chittagong University Campus (IFESCU) had the highest concentrations, each hosting 5 trees. Other locations like Dulahazra Safari Park and Sukhbilash Beat, Khurushia Range each contained 2 trees, while Bangladesh Forest Research Institute, Faliar Para, Shilkhali Beat, Ukhia TV Station, Saplapur, Lama, and the National Botanical Garden each had 1 tree.\\u003c/p\\u003e \\u003cp\\u003eDuring our survey, we found Boilam trees in various forest areas, including the Chittagong North and South Forest Divisions, Chittagong Hill Tracts North and South Forest Divisions, and Cox\\u0026rsquo;s Bazar North and South Forest Divisions, as well as in homesteads (Hossain et al., \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e). The highest number of trees was observed in Hazarikhil Wildlife Sanctuary and the IFESCU area. However, we faced several challenges that limited our survey coverage. In some regions of the Chittagong and Cox\\u0026rsquo;s Bazar forest divisions, we couldn't conduct surveys due to risks posed by elephants, difficult terrain (Pavel et al., \\u003cspan citationid=\\\"CR60\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e), and constraints in time and funding. Additionally, a lack of local knowledge about Boilam trees further complicated our efforts, as many local people were unaware of where to find this species. Despite these limitations, the data we collected provides valuable insights into the distribution of Boilam trees in the accessible areas (Hossain et al., \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e). This information is crucial for future conservation efforts and highlights the need for more comprehensive studies to fully understand the species' distribution.We also looked at the maturity state of these trees and found that 8 were immature, 5 were matured, 6 were over matured, and 2 were defected(Fig.\\u0026nbsp;3). Understanding the distribution and maturity of Boilam trees is essential for their conservation and resource management.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.2 Prediction Accuracy and Model Evaluation\\u003c/h2\\u003e \\u003cp\\u003eThe prediction accuracy for the distribution models of \\u003cem\\u003eA. scaphula\\u003c/em\\u003e was remarkably high, with AUC values reaching 0.981 (Tables\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e and \\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e; \\u003cb\\u003eFigs.\\u0026nbsp;4, 5 and 6\\u003c/b\\u003e). This high AUC value indicates that the MaxEnt-derived distributions closely match the actual probability distribution of the species. Both the training and testing AUC values were over 90%(Islam et al., \\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e), with very low standard deviations, suggesting consistent and reliable model performance. Since the AUC value is well above 0.5, it confirms that the models performed significantly better than random predictions. Figures\\u0026nbsp;4 and 5 show high AUC values for both training data (red line, AUC\\u0026thinsp;=\\u0026thinsp;0.968) and test data (blue line, AUC\\u0026thinsp;=\\u0026thinsp;0.973), with the lines nearly coinciding, demonstrating excellent model reliability(Islam et al., 2019). The Jackknife test of leave-one-out cross-validation was used to evaluate the importance of bioclimatic and environmental variables. This test analyzed each predictor variable individually, when omitted, and in combination with all variables. The results highlighted that variables like bioclim11, bioclim13, bioclim16, bioclim4, and bioclim7 were the most significant in predicting species distribution. Interestingly, bioclim6 slightly outperformed bioclim7 in test gain. On the other hand, bioclim1, bioclim14, and bioclim18 contributed minimally and were excluded from the final predictions(Maszura et al., \\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eThis robust model performance underscores its effectiveness in predicting the distribution of \\u003cem\\u003eA. scaphula\\u003c/em\\u003e, pinpointing the critical bioclimatic variables that influence its habitat. This information is essential for conservation planning and developing strategies to protect this species amid changing climatic conditions. The consistent AUC values between training and testing data further bolster confidence in the model's predictive capabilities, making it a reliable tool for future studies and conservation efforts.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003ePrediction accuracy of the species distribution modeling.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"4\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eTraining AUC\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eTest AUC\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eStander deviation\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSSP2-4.5(2021\\u0026ndash;2040)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.968\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.973\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.0023\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSSP2-4.5(2041\\u0026ndash;2060)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.976\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.957\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.0028\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSSP5-8.5(2021\\u0026ndash;2040)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.981\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.951\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.0029\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSSP5-8.5(2041\\u0026ndash;2060)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.969\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.978\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.0027\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.3 Probable distribution of A. scaphula\\u003c/h2\\u003e \\u003cp\\u003eThe spatial distribution of \\u003cem\\u003eA. scaphula\\u003c/em\\u003e, in terms of predictive probability, was segmented into 14 categories to represent the ranges of habitat suitability (Figs.\\u0026nbsp;5 and 6). The probability values range from 0 to 1, with 0 indicating the lowest habitat suitability and 1 indicating the highest. Figure\\u0026nbsp;6 suggests that the spatial distribution of \\u003cem\\u003eA. scaphula\\u003c/em\\u003e across the Chittagong division might face increasing climate stress(Hossen \\u0026amp; Hossain, \\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e). However, the study's findings indicate that the likely distribution of \\u003cem\\u003eA. scaphula\\u003c/em\\u003e under the SSP2-4.5 (2021\\u0026ndash;2040) and SSP2-4.5 (2041\\u0026ndash;2060), as well as SSP5-8.5 (2021\\u0026ndash;2040) and SSP5-8.5 (2041\\u0026ndash;2060) scenarios, would see only an insignificant decline due to the negative impacts of global climate change. Given these projections, it is recommended to concentrate conservation efforts in the southeastern region of Bangladesh. This area should be prioritized for habitat protection and restoration initiatives(Rahman et al., \\u003cspan citationid=\\\"CR64\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). Additionally, continuous monitoring and updating of the habitat suitability model are essential to account for ongoing environmental changes and human activities. Engaging local communities and stakeholders in conservation activities can greatly enhance the effectiveness of these efforts and ensure the long-term survival of \\u003cem\\u003eA. scaphula\\u003c/em\\u003e in Bangladesh. These findings have significant policy implications and should be reflected in the development of conservation strategies(Islam et al., \\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e; Warton et al., \\u003cspan citationid=\\\"CR81\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e). Factors such as physiological characteristics, precipitation regimes, and habitat destruction are likely to influence the future distribution of this species. Therefore, a proactive and adaptive approach to conservation is crucial to mitigate the adverse effects of climate change on \\u003cem\\u003eA. scaphula's\\u003c/em\\u003e habitat.\\u003c/p\\u003e \"},{\"header\":\"4. Discussion\",\"content\":\"\\u003cp\\u003eThis study found that \\u003cem\\u003eAnisoptera scaphula\\u003c/em\\u003e exhibits a higher degree of climate resilience compared to other plant and animal species in Bangladesh (Alamgir et al., \\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e; Sohel et al., \\u003cspan citationid=\\\"CR76\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e; Akhter et al., \\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e; Deb et al., \\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e). Previous research indicated that species like \\u003cem\\u003eMangifera sylvatica\\u003c/em\\u003e may face extinction by 2070 due to climate change (Akhter et al., \\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e). Similar concerns have been raised for species such as \\u003cem\\u003eHoolock hoolock\\u003c/em\\u003e and \\u003cem\\u003ePanthera tigris\\u003c/em\\u003e (Alamgir et al., \\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e; Mukul et al., \\u003cspan citationid=\\\"CR55\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e). Additionally, the habitat for \\u003cem\\u003eDipterocarpus turbinatus\\u003c/em\\u003e and \\u003cem\\u003eHopea odorata\\u003c/em\\u003e is projected to decrease by 24% and 34%, respectively, in the South Asian region by 2070 (Deb et al., \\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e). \\u003cem\\u003eDipterocarpus turbinatus\\u003c/em\\u003e in Bangladesh may see a dramatic reduction in suitable habitat under RCP2.6 and RCP8.5 scenarios by 2050 and 2070 (Islam et al., \\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e). In China, \\u003cem\\u003ePinus armandii\\u003c/em\\u003e is expected to lose suitable habitat under similar scenarios (Ning et al., \\u003cspan citationid=\\\"CR58\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e), while in Burkina Faso, the habitat for \\u003cem\\u003eXimenia americana\\u003c/em\\u003e could decline by 15% and 25% under RCP4.5 and RCP8.5, respectively (Lompo et al., \\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). The comparative resilience of \\u003cem\\u003eA. scaphula\\u003c/em\\u003e could be due to its extensive distribution and relatively stable conservation status, classified as of least concern both globally and in Bangladesh (IUCN 2020). Similar resilience has been observed in \\u003cem\\u003eMadhuca longifolia\\u003c/em\\u003e in India (Yadav et al., \\u003cspan citationid=\\\"CR84\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e) and \\u003cem\\u003eAmmopiptanthus mongolicus\\u003c/em\\u003e in northwest China, which may see an increase in suitable habitat under future climate scenarios (Du et al., \\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eTemperature emerged as the dominant factor influencing species distribution in this study, with temperature-related variables like Bio11, Bio6, Bio7, and Bio9 playing significant roles. This contrasts with findings from Thailand, where precipitation was identified as the primary factor affecting \\u003cem\\u003eDipterocarpus alatus\\u003c/em\\u003e distribution (Kamyo and Asanok \\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e). In other studies, both precipitation and temperature have been highlighted as key factors influencing the spatial distribution of species in China (Du et al., \\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Ning et al., \\u003cspan citationid=\\\"CR58\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e) and Burkina Faso, where annual precipitation and temperature of the coldest quarter and coldest month were critical predictors (Lompo et al., \\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eThe study's limitations include a narrow geographical scope and a limited number of species geo-locations due to funding and time constraints. Despite these limitations, the research contributes to understanding climate-induced species redistributions in this region, which is highly vulnerable to climate change impacts. Future studies are recommended to expand geographical coverage and include more species geo-locations to provide a comprehensive understanding of these dynamics.\\u003c/p\\u003e\"},{\"header\":\"5. Conclusion\",\"content\":\"\\u003cp\\u003eThis study predicted possible responses of the two ecologically important but not yet endangered tree species - \\u003cem\\u003eA. scaphula\\u003c/em\\u003e - in Chittagong districts of Bangladesh using the species distribution modeling approach. Our habitat suitability modeling based on present distribution data, bioclimatic and environmental variables, and under the SSP2-4.5 and SSP5-8.5 scenarios emission pathways for 2040 and 2060 showed insignificant change in case of the availability of suitable habitats for the \\u003cem\\u003eA. scaphula\\u003c/em\\u003e. This study suggested that the future climate change trend may adversely affect the local habitat quality for selected tree species. However, none of the species will face extinction risk. Policymakers may use the outcome of this study in formulating policies regarding local level biodiversity conservation and forest management of these important wild fruit species. As \\u003cem\\u003eA. scaphula\\u003c/em\\u003e will survive even in the harshest of climate change situations, Bangladesh Forest Department can think about these two species on a priority basis in plantation activities to enhance wildlife resilience. Planting these two species in augmented natural regeneration activities in denuded and degraded government forests will enhance the habitat resilience of the forests for wildlife conservation.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003ch2\\u003eDeclaration of Competing Interest\\u003c/h2\\u003e \\u003cp\\u003eThe authors declare no potential conflicts of interest with respect to the research, authorship, and publication of this article.\\u003c/p\\u003e \\u003ch2\\u003eAuthor Contribution:\\u003c/h2\\u003e \\u003cp\\u003eData Collection: Minhazul Ferdous, Rabeya Khatun; Investigation: Minhazul Ferdous, Mohd Imran Hossain Chowdhury; Data Analysis: Minhazul Ferdous, Sudipta Sen Gupta, Mohd Imran Hossain Chowdhury; Data Curation: Minhazul Ferdous, Mehedi Hasan Rakib; Visualization: Mehedi Hasan Rakib; Literature Review: Rabeya Khatun, Mohammad Mostafizur Rahman, Md. Yeamim Aftad; Methodology: Sudipta Sen Gupta, Mohd Imran Hossain Chowdhury, Md. Salauddin; Formal Analysis: Mohd Imran Hossain Chowdhury, Mehedi Hasan Rakib, Md. Salauddin; MaxEnt Modeling: Sudipta Sen Gupta, Rabeya Khatun; Original Draft Writing: Mohd Imran Hossain Chowdhury, Mohammad Mostafizur Rahman; Script Review and Editing: Minhazul Ferdous, Sudipta Sen Gupta, Rabeya Khatun, Mehedi Hasan Rakib, Md. Salauddin, Mohammad Mostafizur Rahman, Md. Yeamim Aftad; Review Report: Rabeya Khatun, Md. Yeamim Aftad; Project Administration, Conceptualization and Resources: Md. Salauddin, Tanvir Hossen.\\u003c/p\\u003e\\u003ch2\\u003eAcknowledgment\\u003c/h2\\u003e \\u003cp\\u003eThe research work was supported by the authors. Therefore, We wish to express our sincere gratitude to the anonymous reviewers.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n \\u003cli\\u003eArman, A. H., Khatun, R., \\u0026amp; Masum, K. M. (2024). Transformation of jhum (shifting cultivation) to forest plantation: Effect on soil properties in the hill tracts of Bangladesh. \\u003cem\\u003eForestist\\u003c/em\\u003e, 74(3), 333-341. DOI:\\u0026nbsp;10.5152/forestist.2024.23079\\u003c/li\\u003e\\n \\u003cli\\u003eAlam, E., Hridoy, A. E. E., Tusher, S. M. S. H., Islam, A. R. M. T., \\u0026amp; Islam, M. K. (2023). Climate change in Bangladesh: Temperature and rainfall climatology of Bangladesh for 1949-2013 and its implication on rice yield. \\u003cem\\u003ePLoS ONE\\u003c/em\\u003e, \\u003cem\\u003e18\\u003c/em\\u003e(10 October), 1\\u0026ndash;26. https://doi.org/10.1371/journal.pone.0292668\\u003c/li\\u003e\\n \\u003cli\\u003eAkter, A., Biella, P., Bat\\u0026aacute;ry, P., \\u0026amp; Klečka, J. (2020). \\u003cem\\u003eChanging pollinator communities along a disturbance gradient in the Sundarbans mangrove forest : a case study on Acanthus ilicifolius and Avicennia officinalis\\u003c/em\\u003e. 1\\u0026ndash;22.\\u003c/li\\u003e\\n \\u003cli\\u003eAkhter, S. , McDonald, M.A. , van Breugel, P. , Sohel, S. , Kj\\u0026aelig;r, E.D. , Mariott, R. , (2017). Habi- tat distribution modelling to identify areas of high conservation value under climate change for Mangifera sylvatica Roxb. of Bangladesh. Land Use Policy 60, 223\\u0026ndash;232 .\\u003c/li\\u003e\\n \\u003cli\\u003eAlamgir, M., Mukul, S., \\u0026amp; Turton, S. (2015). Modelling spatial distribution of critically endangered Asian elephant and Hoolock gibbon in Bangladesh forest ecosystems under a changing climate. \\u003cem\\u003eApplied Geography\\u003c/em\\u003e, \\u003cem\\u003e60\\u003c/em\\u003e, 10\\u0026ndash;19. https://doi.org/10.1016/j.apgeog.2015.03.001\\u003c/li\\u003e\\n \\u003cli\\u003eBajaj, S., \\u0026amp; Amali, G. (2019). \\u003cem\\u003eSpecies Environmental Niche Distribution Modeling for Panthera Tigris Tigris \\u0026lsquo;Royal Bengal Tiger\\u0026rsquo; Using Machine Learning\\u003c/em\\u003e (pp. 251\\u0026ndash;263). https://doi.org/10.1007/978-981-13-5953-8_22\\u003c/li\\u003e\\n \\u003cli\\u003eCord, A., Colditz, R. R., Schmidt, M., \\u0026amp; Dech, S. (2009). Species distribution and forest type mapping in Mexico. \\u003cem\\u003eInternational Geoscience and Remote Sensing Symposium (IGARSS)\\u003c/em\\u003e, \\u003cem\\u003e5\\u003c/em\\u003e. https://doi.org/10.1109/IGARSS.2009.5417681\\u003c/li\\u003e\\n \\u003cli\\u003eDas, G., Kim, D. Y., Fan, C., Guti\\u0026eacute;rrez-Grijalva, E. P., Heredia, J. B., Nissapatorn, V., Mitsuwan, W., Pereira, M. L., Nawaz, M., Siyadatpanah, A., Norouzi, R., Sawicka, B., Shin, H. S., \\u0026amp; Patra, J. K. (2020). Plants of the Genus Terminalia: An Insight on Its Biological Potentials, Pre-Clinical and Clinical Studies. \\u003cem\\u003eFrontiers in Pharmacology\\u003c/em\\u003e, \\u003cem\\u003e11\\u003c/em\\u003e(October), 1\\u0026ndash;30. https://doi.org/10.3389/fphar.2020.561248\\u003c/li\\u003e\\n \\u003cli\\u003eDeb, J.C. , Phinn, S. , Butt, N. , McAlpine, C.A. , (2017). The impact of climate change on the distribution of two threatened Dipterocarp trees. Ecol. Evol. 7 (7), 2238\\u0026ndash;2248\\u003c/li\\u003e\\n \\u003cli\\u003eDu, Z., He, Y., Wang, H., Wang, C., Duan, Y., (2021). Potential geographical distribu- tion and habitat shift of the genus Ammopiptanthus in China under current and future climate change based on the MaxEnt model. J. Arid. Environ. 184, 104328. doi: 10.1016/j.jaridenv.2020.104328.\\u003c/li\\u003e\\n \\u003cli\\u003eDwivedi, S., \\u0026amp; Chopra, D. (2014). Revisiting terminalia arjuna-an ancient cardiovascular drug. \\u003cem\\u003eJournal of Traditional and Complementary Medicine\\u003c/em\\u003e, \\u003cem\\u003e4\\u003c/em\\u003e(4), 224\\u0026ndash;231. https://doi.org/10.4103/2225-4110.139103\\u003c/li\\u003e\\n \\u003cli\\u003eHossain, A., Ferdous, J., Rahman, M. A., Azad, A. K., \\u0026amp; Shukor, N. A. A. (2014). Towards the propagation of a critically endangered tree species Anisoptera scaphula. \\u003cem\\u003eDendrobiology\\u003c/em\\u003e, \\u003cem\\u003e71\\u003c/em\\u003e, 137\\u0026ndash;148. https://doi.org/10.12657/denbio.071.014\\u003c/li\\u003e\\n \\u003cli\\u003eHossain, M. K., Alim, A., Hossen, S., Hossain, A., \\u0026amp; Rahman, A. (2020). Diversity and conservation status of tree species in Hazarikhil Wildlife Sanctuary (HWS) of Chittagong, Bangladesh. \\u003cem\\u003eGeology, Ecology, and Landscapes\\u003c/em\\u003e, \\u003cem\\u003e4\\u003c/em\\u003e(4), 298\\u0026ndash;305. https://doi.org/10.1080/24749508.2019.1694131\\u003c/li\\u003e\\n \\u003cli\\u003eHossen, S., \\u0026amp; Hossain, M. (2018). Conservation status of tree species in Himchari National Park of Cox\\u0026rsquo;s Bazar, Bangladesh. \\u003cem\\u003eJournal of Biodiversity Conservation and Bioresource Management\\u003c/em\\u003e, \\u003cem\\u003e4\\u003c/em\\u003e(2), 1\\u0026ndash;10. https://doi.org/10.3329/jbcbm.v4i2.39842\\u003c/li\\u003e\\n \\u003cli\\u003eHijmans, R.J., Cameron, S.E., Parra, J.L., Jones, P.G., Jarvis, A., (2005). Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965\\u0026ndash;1978. doi: 10.1002/joc.1276 .\\u003c/li\\u003e\\n \\u003cli\\u003eChowdhury,M.I.H., Rakib, M. H., Das, C., \\u0026amp; Hossain, Z. (2024). Tr ee Species Ger mination : A Compr ehensive Meta-Analysis and its Implications for Pr e-Sowing Tr eatment in Bangladesh. \\u003cem\\u003eJournal OfSoil, Plant and Environment.\\u003c/em\\u003e, \\u003cem\\u003e3\\u003c/em\\u003e(1), 24\\u0026ndash;40. https://doi.org/10.56946/jspae.v3i1.397\\u003c/li\\u003e\\n \\u003cli\\u003eIslam, K., Rahman, M. F., Islam, K. N., Nath, T. K., \\u0026amp; Jashimuddin, M. (2020). Modeling spatiotemporal distribution of Dipterocarpus turbinatus Gaertn. F in Bangladesh under climate change scenarios. \\u003cem\\u003eJournal of Sustainable Forestry\\u003c/em\\u003e, \\u003cem\\u003e39\\u003c/em\\u003e(3), 221\\u0026ndash;241. https://doi.org/10.1080/10549811.2019.1632721\\u003c/li\\u003e\\n \\u003cli\\u003eIslam, N., Jaman, M. F., Rahman, M. M., \\u0026amp; Alam, M. M. (2018). Wildlife Diversity and Population Status of Kashimpur Union, Gazipur, Bangladesh. \\u003cem\\u003eJournal of the Asiatic Society of Bangladesh, Science\\u003c/em\\u003e, \\u003cem\\u003e44\\u003c/em\\u003e(2), 101\\u0026ndash;115. https://doi.org/10.3329/jasbs.v44i2.46553\\u003c/li\\u003e\\n \\u003cli\\u003eIPCC (2014) Climate change (2014): synthesis report. Contribution of working groups I, II and III to the fifth assessment report of the intergovernmental panel on climate change [Core writing team, R.K. Pachauri and L.A. Meyer (eds.)]\\u003c/li\\u003e\\n \\u003cli\\u003eJanekovi, F., \\u0026amp; Novak, T. (2012). PCA \\u0026ndash; A Powerful Method for Analyze Ecological Niches. \\u003cem\\u003ePrincipal Component Analysis - Multidisciplinary Applications\\u003c/em\\u003e, \\u003cem\\u003eFebruary 2012\\u003c/em\\u003e. https://doi.org/10.5772/38538\\u003c/li\\u003e\\n \\u003cli\\u003eKamyo, T. , Asanok, L. , (2020). Modeling habitat suitability of Dipterocarpus alatus (Dipte- rocarpaceae) using MaxEnt along the Chao Phraya River in Central Thailand. Forest Sci. Technol. 16 (1), 1\\u0026ndash;7 .\\u003c/li\\u003e\\n \\u003cli\\u003eKhan, M. L., \\u0026amp; Shankar, U. (2014). Seed Mass , Germination and Seedling Growth in Artocarpus chama. \\u003cem\\u003eInternational Journal of Ecology and Environmental Sciences \\u0026middot;\\u003c/em\\u003e, \\u003cem\\u003e30(4)\\u003c/em\\u003e(December 2004), 369\\u0026ndash;376.\\u003c/li\\u003e\\n \\u003cli\\u003eKuniyal, C. P., Purohit, V., Butola, J. S., \\u0026amp; Sundriyal, R. C. (2013). South African Journal of Botany Seed size correlates seedling emergence in Terminalia bellerica. \\u003cem\\u003eSouth African Journal of Botany\\u003c/em\\u003e, \\u003cem\\u003e87\\u003c/em\\u003e, 92\\u0026ndash;94. https://doi.org/10.1016/j.sajb.2013.03.016\\u003c/li\\u003e\\n \\u003cli\\u003eLompo, O., Dimobe, K., Mbayngone, E., Savadogo, S., Sambar\\u0026eacute;, O., Thiombiano, A., Ou\\u0026eacute;draogo, A., (2021). Climate influence on the distribution of the yellow plum (Ximenia americana L.) in Burkina Faso. Trees, Forests People, 100072 doi: 10.1016/j.tfp.2021.100072 .\\u003c/li\\u003e\\n \\u003cli\\u003eMachwitz, M., Landmannc, T., Conrad, C., Cord, A., \\u0026amp; Dech, S. (2008). Land cover analysis on sub-continental scale: Fao lccs standard with 250 meter modis satellite observations in West Africa. \\u003cem\\u003eInternational Geoscience and Remote Sensing Symposium (IGARSS)\\u003c/em\\u003e, \\u003cem\\u003e5\\u003c/em\\u003e(1), 49\\u0026ndash;52. https://doi.org/10.1109/IGARSS.2008.4780024\\u003c/li\\u003e\\n \\u003cli\\u003eMandal, A., Jaman, M., Alam, M., Rabbe, M., \\u0026amp; Shome, A. (2022). Vertebrate wildlife diversity of Sreepur upazila, Magura, Bangladesh. \\u003cem\\u003eJournal of Biodiversity Conservation and Bioresource Management\\u003c/em\\u003e, \\u003cem\\u003e7\\u003c/em\\u003e(1), 51\\u0026ndash;62. https://doi.org/10.3329/jbcbm.v7i1.57123\\u003c/li\\u003e\\n \\u003cli\\u003eMasum, S. M., Halim, A., Mandal, M. S. H., Asaduzzaman, M., \\u0026amp; Adkins, S. (2022). Predicting Current and Future Potential Distributions of Parthenium hysterophorus in Bangladesh Using Maximum Entropy Ecological Niche Modelling. \\u003cem\\u003eAgronomy\\u003c/em\\u003e, \\u003cem\\u003e12\\u003c/em\\u003e(7). https://doi.org/10.3390/agronomy12071592\\u003c/li\\u003e\\n \\u003cli\\u003eMaszura, C. M., Karim, S. M. R., Norhafizah, M. Z., Kayat, F., \\u0026amp; Arifullah, M. (2018). Distribution, Density, and Abundance of Parthenium Weed (Parthenium hysterophorus L.) at Kuala Muda, Malaysia. \\u003cem\\u003eInternational Journal of Agronomy\\u003c/em\\u003e, \\u003cem\\u003e2018\\u003c/em\\u003e. https://doi.org/10.1155/2018/1046214\\u003c/li\\u003e\\n \\u003cli\\u003eMehta, B., Nagar, B., Patel, B., Chaklashiya, P., Shah, M., Verma, P., \\u0026amp; Shah, M. B. (2021). A review on a Lesser Known Indian Mangrove: Avicennia officinalis L. (Family: Acanthaceae). \\u003cem\\u003eInternational Journal of Green Pharmacy\\u003c/em\\u003e, \\u003cem\\u003e15\\u003c/em\\u003e(1), 1\\u0026ndash;10.\\u003c/li\\u003e\\n \\u003cli\\u003eMiah, M. D., Hasnat, G. N. T., Nath, B., Saeem, M. G. U., \\u0026amp; Rahman, M. M. (2023). Spatial and Temporal Changes in the Urban Green Spaces and Land Surface Temperature in the Chittagong City Corporation of Bangladesh Between 2000 and 2020. \\u003cem\\u003eForestist\\u003c/em\\u003e, \\u003cem\\u003e73\\u003c/em\\u003e(2), 171\\u0026ndash;182. https://doi.org/10.5152/forestist.2022.22013\\u003c/li\\u003e\\n \\u003cli\\u003eMitra, S., Naskar, N., Lahiri, S., \\u0026amp; Chaudhuri, P. (2023). A study on phytochemical profiling of Avicennia marina mangrove leaves collected from Indian Sundarbans. \\u003cem\\u003eSustainable Chemistry for the Environment\\u003c/em\\u003e, \\u003cem\\u003e4\\u003c/em\\u003e(August), 100041. https://doi.org/10.1016/j.scenv.2023.100041\\u003c/li\\u003e\\n \\u003cli\\u003eMukul, S.A. , Alamgir, M. , Sohel, M.S.I. , Pert, P.L. , Herbohn, J. , Turton, S.M. , Khan, M.S.I. , Munim, S.A. , Reza, A.H.M.A. , Laurance, W.F. , 2019. Combined effects of climate change and sea-level rise project dramatic habitat loss of the globally endangered Bengal tiger in the Bangladesh Sundarbans. Sci. Total Environ. 663, 830\\u0026ndash;840 .\\u003c/li\\u003e\\n \\u003cli\\u003eNazrul Islam, A. K. M., Haque, A. E., Maniruzzaman, Jamali, T., Haque, T., Alfasane, M. A., Nahar, N., Jahan, N., Sultana, S., \\u0026amp; Senthil Kumar, T. (2019). \\u003cem\\u003eSpecies Distribution in Different Ecological Zones and Conservation Strategy of Halophytes of Sundarbans Mangrove Forest of Bangladesh\\u003c/em\\u003e (pp. 479\\u0026ndash;495). https://doi.org/10.1007/978-3-030-04417-6_30\\u003c/li\\u003e\\n \\u003cli\\u003eNing, H., Ling, L., Sun, X., Kang, X., Chen, H., (2021). Predicting the future redistribu- tion of Chinese white pine Pinus armandii Franch. Under climate change scenar- ios in China using species distribution models. Glob. Ecol. Conserv. 25, e01420. doi: 10.1016/j.gecco.2020.e01420 .\\u003c/li\\u003e\\n \\u003cli\\u003ePavel, M. A. Al, Mukul, S. A., Uddin, M. B., Harada, K., \\u0026amp; Arfin Khan, M. A. S. (2016). Effects of stand characteristics on tree species richness in and around a conservation area of northeast Bangladesh. \\u003cem\\u003eJournal of Mountain Science\\u003c/em\\u003e, \\u003cem\\u003e13\\u003c/em\\u003e(6), 1085\\u0026ndash;1095. https://doi.org/10.1007/s11629-015-3501-2\\u003c/li\\u003e\\n \\u003cli\\u003ePhillips, S.J. , Anderson, R.P. , Schapire, R.E. , (2006). Maximum entropy modeling of species geographic distributions. Ecol. Modell. 190 (3\\u0026ndash;4), 231\\u0026ndash;259 .\\u0026nbsp;\\u003c/li\\u003e\\n \\u003cli\\u003ePhillips, S.J. , Dud\\u0026iacute;k, M. , (2008). Modeling of species distributions with Maxent: new exten- sions and a comprehensive evaluation. Ecography 31 (2), 161\\u0026ndash;175 .\\u003c/li\\u003e\\n \\u003cli\\u003eRahman, M. M., Nneji, L. M., Hossain, M. M., Nishikawa, K., \\u0026amp; Habib, K. A. (2022). Diversity and distribution of amphibians in central and northwest Bangladesh, with an updated checklist for the country. \\u003cem\\u003eJournal of Asia-Pacific Biodiversity\\u003c/em\\u003e, \\u003cem\\u003e15\\u003c/em\\u003e(2), 147\\u0026ndash;156. https://doi.org/10.1016/j.japb.2021.12.002\\u003c/li\\u003e\\n \\u003cli\\u003eRahman, M., Parvin, W., Sultana, N., \\u0026amp; Tarek, S. (2018). Ex-situ conservation of threatened forest tree species for sustainable use of forest genetic resources in Bangladesh. \\u003cem\\u003eJournal of Biodiversity Conservation and Bioresource Management\\u003c/em\\u003e, \\u003cem\\u003e4\\u003c/em\\u003e(2), 89\\u0026ndash;98. https://doi.org/10.3329/jbcbm.v4i2.39855\\u003c/li\\u003e\\n \\u003cli\\u003eRidley, Jeff; Menary, Matthew; Kuhlbrodt, Till; Andrews, Martin; Andrews, Tim (2018). MOHC HadGEM3-GC31-LL model output prepared for CMIP6 CMIP. Version-2021.11.15\\u003csup\\u003e[1]\\u003c/sup\\u003e.Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.419\\u003c/li\\u003e\\n \\u003cli\\u003eRoy, M., Biswas, B., \\u0026amp; Ghosh, S. (2015). Trend Analysis of Climate Change in Chittagong Station in Bangladesh. \\u003cem\\u003eInternational Letters of Natural Sciences\\u003c/em\\u003e, \\u003cem\\u003e47\\u003c/em\\u003e(1), 42\\u0026ndash;53. https://doi.org/10.18052/www.scipress.com/ilns.47.42\\u003c/li\\u003e\\n \\u003cli\\u003eSaha, S., Saha, A., Saha, K., Sarker, K. K., \\u0026amp; Chowdhury, Z. J. (2022). Review on Plant Biodiversity and Conservation in Bangladesh: Drawbacks and Prospects. \\u003cem\\u003eAmerican Journal of Agricultural Science, Engineering, and Technology\\u003c/em\\u003e, \\u003cem\\u003e6\\u003c/em\\u003e(2), 95\\u0026ndash;102. https://doi.org/10.54536/ajaset.v6i2.597\\u003c/li\\u003e\\n \\u003cli\\u003eSaxena, V., Mishra, G., Saxena, A., \\u0026amp; Vishwakarma, K. K. (2013). A comparative study on quantitative estimation of tannins in Terminalia chebula, Terminalia belerica, Terminalia arjuna and Saraca indica using spectrophotometer. \\u003cem\\u003eAsian Journal of Pharmaceutical and Clinical Research\\u003c/em\\u003e, \\u003cem\\u003e6\\u003c/em\\u003e(SUPPL.3), 148\\u0026ndash;149.\\u003c/li\\u003e\\n \\u003cli\\u003eShome, A. R., Alam, M. M., Rabbe, M. F., Rahman, M. M., \\u0026amp; Jaman, M. F. (2022). Ecology and diversity of wildlife in Dhaka University Campus, Bangladesh. \\u003cem\\u003eDhaka University Journal of Biological Sciences\\u003c/em\\u003e, 429\\u0026ndash;442. https://doi.org/10.3329/dujbs.v30i3.59035\\u003c/li\\u003e\\n \\u003cli\\u003eSohel S.I., Akhter S., Ullah H., Haque E., Rana P. (2016) Predicting impacts of climate change on forest tree species of Bangladesh: evidence from threatened Dysoxylum binectariferum (Roxb.) Hook.f. ex Bedd. (Meliaceae). iForest 10:154\\u0026ndash;160.\\u003c/li\\u003e\\n \\u003cli\\u003eSpiers, J. A., Oatham, M. P., Rostant, L. V., \\u0026amp; Farrell, A. D. (2018). Applying species distribution modelling to improving conservation based decisions: A gap analysis of trinidad and tobago\\u0026rsquo;s endemic vascular plants. \\u003cem\\u003eBiodiversity and Conservation\\u003c/em\\u003e, \\u003cem\\u003e27\\u003c/em\\u003e(11), 2931\\u0026ndash;2949. https://doi.org/10.1007/s10531-018-1578-y\\u003c/li\\u003e\\n \\u003cli\\u003eThapa, S. , Chitale, V. , Rijal, S.J. , Bisht, N. , Shrestha, B.B. , (2018). Understanding the dy- namics in distribution of invasive alien plant species under predicted climate change in Western Himalaya. PLoS ONE 13 (4), e0195752 .\\u003c/li\\u003e\\n \\u003cli\\u003eUddin, M., Chowdhury, F. I., \\u0026amp; Hossain, M. K. (2020). Assessment of tree species diversity, composition and structure of Medha Kachhapia National Park, Cox\\u0026rsquo;s Bazar, Bangladesh. \\u003cem\\u003eAsian Journal of Forestry\\u003c/em\\u003e, \\u003cem\\u003e4\\u003c/em\\u003e(1). https://doi.org/10.13057/asianjfor/r040104\\u003c/li\\u003e\\n \\u003cli\\u003eWarton, D. I., Renner, I. W., \\u0026amp; Ramp, D. (2013). Model-based control of observer bias for the analysis of presence-only data in ecology. \\u003cem\\u003ePLoS ONE\\u003c/em\\u003e, \\u003cem\\u003e8\\u003c/em\\u003e(11). https://doi.org/10.1371/journal.pone.0079168\\u003c/li\\u003e\\n \\u003cli\\u003eThuiller, W. , Broennimann, O. , Hughes, G. , Alkemade, J.R.M. , Midgley, G.F. , Corsi, F. , (2006). Vulnerability of African mammals to anthropogenic climate change under con- servative land transformation assumptions. Glob. Change Biol. 12 (3) , 424\\u0026ndash;440 .\\u003c/li\\u003e\\n \\u003cli\\u003eYadav, S., Bhattacharya, P., Areendran, G., Sahana, M., Raj, K., Sajjad, H., (2021). Predicting impact of climate change on geographical distribution of major NTFP species in the Central India Region. Model. Earth Syst. Environ. 1\\u0026ndash;20. https://link.springer.com/article/10.1007/s40808-020-01074-4 .\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":true,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Climate change, Habitat suitability, MaxEnt, Species distribution model, Boilam\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-5884962/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-5884962/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eClimate change is a key factor driving species extinction by altering their habitats and populations. We can already see its impact on ecosystems around the globe, especially at the species level. Using species distribution models helps us understand how climate change might shift where species live under different climate scenarios, which is crucial for protecting endangered plants and animals. This study focuses on predicting how climate change will affect the important tree species \\u003cem\\u003eAnisoptera scaphula\\u003c/em\\u003e in Bangladesh's Chittagong division, using the Maximum Entropy (MaxEnt) model. Under the SSP2-4.5 (2021-2040) and SSP2-4.5 (2041-2060), as well as SSP5-8.5 (2021-2040) and SSP5-8.5 (2041-2060) scenario, our model predicts suitable habitats for this species in 2040 and 2060. The results show minimal changes in suitable habitats, suggesting that \\u003cem\\u003eA. scaphula\\u003c/em\\u003eis quite resilient to climate change. These findings can guide policies for wildlife conservation and forest management, highlighting the species' importance to various animals.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Climate Change Impact Assessment and Species Distribution Model of a Critically Endangered Tree Using MaxEnt Modelling\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-01-30 04:54:13\",\"doi\":\"10.21203/rs.3.rs-5884962/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"ccbabf28-816d-4df7-92ff-0eda644f773a\",\"owner\":[],\"postedDate\":\"January 30th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2025-02-19T12:38:09+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-01-30 04:54:13\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-5884962\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-5884962\",\"identity\":\"rs-5884962\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}