Environmental and socio-economic impacts of the changes in distribution areas of Pinus pinea L. (stone pine) due to climate change in Türkiye

Environmental and socio-economic impacts of the changes in distribution areas of Pinus pinea L. (stone pine) due to climate change in Türkiye | 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 Environmental and socio-economic impacts of the changes in distribution areas of Pinus pinea L. (stone pine) due to climate change in Türkiye Merve Karayol, Ayhan Akyol This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4395237/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 In this study, present and future distributions of stone pine due to climate changes were modeled with MaxEnt. CNRM ESM2-1 climate model and bioclimatic variables obtained from the WorldClim database were used as climate models. As climate scenarios, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 climate change scenarios and 2041–2060 and 2081–2100 periods were used. Pearson Correlation analysis was performed to prevent high correlation in bioclimatic variables and the multicollinearity problem was eliminated by reducing 19 bioclimatic variables to 9 variables. The contribution of bioclimatic variables to the model was determined by the Jackknife test. To determine the spatial and locational differences between the present and future potential distributions estimated for the species, an analysis of change was conducted. According to the findings of the study, our model has a very high predictive power and the Jackknife test results, the bioclimatic variables BIO19, BIO6, and BIO4 contribute the most to the model. Our prediction model predicts that the distribution area of stone pine will decrease, shifting northward and towards higher altitudes. We believe that this will lead to increased risk of forest fires, loss of ecosystem services, and reduced income from stone pine. For these reasons, benefit from stone pine need to take into account the effects of climate change in their land use planning and give importance to climate change adaptation efforts. These maps, created with current and future predictions of potential habitat distribution, can be use in afforestation, ecological restoration, rural development, conservation, and all kinds of land use studies. Climate change Maxent stone pine species distribution model socio-economic impacts Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Introduction Climate change is one of the complex environmental problems with significant impacts on ecosystems, biodiversity, and human societies. Climate change occurs as a result of the interaction and combination of various factors and can result in serious environmental, economic, and social impacts (Türkeş, 2008 ; IPCC, 2014 ; WMO, 2015 ; NASA, 2023 ). Plant species are distributed in specific ecological niches in various regions by adapting to specific environmental conditions. The ecological niche of an organism is influenced by several factors, including its habitat, feeding habits, reproductive timing, preferred climatic conditions, and other environmental factors (Guisan & Thuiller, 2005 ; MEA, 2005 ; Blackburn et al., 2007 ; Lawler et al., 2009 ; Elith & Graham, 2009 ; Chikerema et al., 2017 ). However, climate change is significantly affecting the distribution and habitats of plant species, including stone pine ( Pinus pinea L.) (Polat et al., 2011 ). Changes in plant species distributions affect the structure and functionality of ecosystems (Tolunay, 2013 ; Uzun & Sarıkaya, 2021 ). Changes in the distribution of species due to climatic conditions along with other relevant factors directly affect the continuation of the species' extinction. While some plant species can migrate to more northern and southern regions by adapting to increasing temperatures and changing precipitation patterns, some species may lose their habitats and face the risk of extinction (Öztürk, 2002 ). Species Distribution Models (SDMs) are one of the tools used to predict the current and future distribution of species (Franklin, 2013 ; Booth, 2018 ). With the advancement of machine learning techniques, prediction models using different algorithms and quantum approaches have been developed (Guisan & Zimmermann, 2000 ; Brito et al., 2009 ; Elith & Leathwick, 2009 ; Özkan, 2016 ; Özdemir, 2018 ; Wei et al., 2018 ). One of the most widely used machine learning techniques in plant species modeling is MaxEnt, which uses the maximum entropy algorithm (Zeng et al., 2016 ; Koch et al., 2017 ; Xu et al., 2019 ). MaxEnt uses digital climate data and spatial data to create a probability distribution map showing the most suitable habitats for species. Thanks to the layers created using spatial data expressing the areas where species are distributed, the present and future potential distributions of species can be determined with various species distribution models according to different climate models and climate scenarios (Phillips et al., 2006 ; Hijmans & Graham, 2006 ; Ward, 2007 ; Sérgio et al., 2007 ; Phillips & Dudík, 2008 ; Williams et al., 2009 ; Wollan et al., 2008 ; Tittensor et al., 2009 ; Nagendra et al., 2013 ; Yuan et al., 2015 ; Yi et al., 2017 ; Arslan, 2019 ; Uzun & Sarıkaya, 2023 ; Eker et al., 2023 ; Wang et al., 2024 ; Jin et al., 2024 ). When creating an ecological distribution model, species observations, climate data, and other environmental data are combined using a computer-based geographic information system. Based on these data, a map representing the ecological niche of the species is created and predictions are made about the present and future distribution of the species (Booth et al., 2014 ). However, the construction and interpretation of these models are complex and model results should be carefully evaluated (Zhang & Zhao, 2021 ). Climate models used in forecasting are tools to understand how the climate has changed from the past to the present and how it may change in the future (Garcia et al., 2014 ; Ashraf et al., 2017 ). As different groups of models on Earth (MIROC6, CanESM5, CNRM-ESM2-1, etc.) include new physical processes, biogeochemical cycles and higher spatial resolution, climate models are constantly updated depending on the preparation of data sets (IPCC, 2014 ; Jones et al., 2016 ; Djalante, 2019 ; Örücü et al., 2021 ; Uzun & Sarıkaya, 2021 ). Changes in climate and anthropogenic pressures are affecting ecosystems more and more every day, which necessitates more effective and functional planning efforts for target species and related ecosystems (Falcucci et al., 2007 ; Loewe-Muñoz et al., 2021 ). Understanding the links between climate change and plant species distributions is crucial for combating climate change and taking appropriate measures for the conservation of biodiversity and the sustainability of ecosystems. How the distribution of stone pine will be affected due to climate change, the reflections of these changes on ecosystems, and the possible effects of future climate scenarios on plant species distributions are one of the main objectives of this study. Many previous studies on the subject were based on the scenarios in the 5th Assessment Report of the Intergovernmental Panel on Climate Change (IPCC) (Akyol & Örücü, 2019 ; Akyol et al., 2020 ). In this study, unlike previous studies, CMIP6 Shared Socioeconomic Pathways scenarios in the 6th Assessment Report of IPCC were used (IPCC, 2014 ; Balaji et al., 2017 ; Örücü et al., 2023 ). Materials and methods Species presence data and bioclimatic variables It is known that studies on commercially important species have increased considerably in recent years (Ticktin, 2004 ; Bonari et al., 2020 ; Karpukhin & Yussef, 2021 ; Yıldırım et al., 2022 ; Özdarici-Ok et al., 2022 ; Simões, et al., 2024 ). Stone pine, which is distributed in Mediterranean forest ecosystems, is an important species frequently preferred for ecological and economic purposes (Kılcı et al., 2000 ; Bravo-Oviedo & Montero, 2005 ; Batur, 2015 ; Güleç, 2015 ). Stone pine is ecologically well adapted to high temperatures and drought, and it also shows significant tolerance to water stress owing to its deep roots (Teobaldelli et al., 2004 ; Castillo et al., 2002 ). However, the degree of this tolerance may vary according to origin (Balekoglu et al., 2023 ). Since it is a very valuable product in Türkiye and international markets, it ensures more income to producers than other forest products (Şafak & Okan, 2004 ; Mutke et al., 2005 ; Korkmaz & Duman, 2019 ). For this reason, stone pine plantations are being intensively developed to support many ecosystem services such as nut production, recreational uses, and wood production (Moreno-Fernández et al., 2013 ; Üçler & Arpacı, 2017 ; Fernández et al., 2023 ). In this study, presence data for stone pine were obtained by taking coordinates from 13 points where the species is distributed and by reviewing the literature (Davis et al., 1988 ; GBIF, 2024 ). Sample coordinates were marked in the WGS 84 coordinate system using Google Satellite Hybrid maps in the QGIS 3.34 program (QGIS, 2023 ) (Fig. 1 ). Bioclimatic variables derived from 2.5 min spatial resolution data from the WorldClim (version 2.1) database (Hijmans et al., 2005 ; Moss et al., 2010 ; Hunt et al., 2007 ; Remya et al., 2015 ; Fick & Hijmans, 2017 ; Arslan et al., 2020 ; WorldClim, 2023 ) were used to estimate present and future distribution areas. CNRM ESM2-1 (Centre National de Recherches Météorologiques Earth System Model 2.1) was used as the climate model and CMIP6 (SSP1 2.6, SSP2 4.5, SSP3 7.0, SSP5 8.5) Shared Socioeconomic Pathways (SSP) scenarios from the 6th Assessment Report of the IPCC and the periods 2041–2060 and 2081–2100 were used as climate scenarios. SSPs are used in climate modeling and research to refer to climate scenarios defined based on specific socioeconomic developments and different greenhouse gas emission intensities soon (O'Neill et al., 2016 ; Eyring et al., 2016 ; Chen et al., 2020 ; Bulut & Aytaş, 2023 ; Carbon Brief, 2023 ). Modeling method and statistical analysis The MaxEnt algorithm was used in this study due to its ease of use and the fact that it produces successful models for estimating the distribution areas of species with limited distributions (Pearson et al., 2007 ; Wisz et al., 2008 ; Graham et al., 2008 ; Wilting et al., 2010 ; Clements et al., 2012 ; Kalkvik et al., 2012 ). MaxEnt is based on the principle of maximum entropy, which aims to generate a certain probability distribution that best represents the available information under a given set of conditions (Phillips et al., 2006 ; Elith & Leathwick, 2009 ; Örücü et al., 2021 ; Mathur & Mathur, 2023 ). This modeling method estimates the present and potential future distributions of the target species by taking into account its confirmed presence records (Li et al., 2020 ; Öztop, 2023 ). For these reasons, MaxEnt 3.4.1 software was used in the study. To solve the multicollinearity problem (Zhang & Liu, 2017 ), which reduces the predictive power of the model, the Pearson correlation test was applied to 19 bioclimatic variables used in the model in SPSS 25.0 package statistical program (Sillero, 2011 ; Cao et al., 2016 ). As a result of this test, one of the bioclimatic variables with a Pearson correlation coefficient (r) value of ± 0.8 and above was removed from the model and the multicollinearity problem was eliminated (Yang et al., 2013 , Cao et al., 2016 ; Akyol et al., 2023 ). For modeling, the output format was set as logistic in MaxEnt 3.4.1, and the number of randomly selected background points was chosen as 10.012. Since the number of samples was between 10 and 15, the program was run by selecting the Linear feature (Phillips & Dudík, 2008 ; Wang et al., 2007 ; Phillips et al., 2017 ; Şen et al., 2022 ). ROC (Receiving Operator Curve) analysis was performed to determine the performance of the model and the model was evaluated using the AUC (Area under the Receiving Operator Curve) value under the ROC curve. [AUC AUC ≥ 0.8] = good, and [AUC ≥ 0.9] = very good thresholds were used for the AUC value (Gassó et al., 2012 ; Hosmer Jr et al., 2013 ). As the AUC value approaches 1, the identification power of the model increases (Phillips et al., 2006 ; Wang et al., 2007 ; Phillips & Elith, 2010 ; Örücü, 2019 ; Uzun & Örücü, 2020 ). The contribution of bioclimatic variables to the model was determined by selecting the Jackknife test in the MaxEnt program (Pearson et al., 2007 , Shcheglovitova & Anderson, 2013 ; Qin et al., 2017 ). The model results were converted into distribution maps using the raster/vector transformation function with the QGIS 3.34 program. In the potential habitat maps created for the current and future situation, the suitability levels for the distribution areas were classified as [0.75-1] very high suitable, [0.50–0.75] high suitable, [0.25–0.50] moderate suitable, [0-0.25] low suitable, and [0] unsuitable areas. According to this classification, the distribution areas in estimated present and future scenarios were calculated in km 2 (Çoban et al., 2020 ; Örücü et al., 2023 ). Eventually, a change analysis was conducted to make a comparison between the distributions of the estimated present and future scenarios. For the change analysis, the suitability values were classified as [0 = 0], [0-0.25 = 1], [0.25–0.50 = 2], [0.50–0.75 = 3], [0.75-1 = 4] and the intersection function was applied to these data. According to the suitability values, areas with [0–0] were defined as unsuitable, areas in the same class were defined as stable, areas moving to a higher class were defined as gain, and areas moving to a lower class were defined as loss. These areas were calculated in km 2 and change maps were created, thus revealing the direction and magnitude of change. Results and discussion Evaluation of model performance and contribution of bioclimatic variables Using Pearson Correlation analysis, variables that weakened the predictive power of the model were eliminated from the model and 9 out of 19 bioclimatic variables, including BIO2, BIO4, BIO6, BIO7, BIO11, BIO12, BIO13, BIO16 and BIO19 variables, were used in the model. Thus, the predictive power of the model was increased and the multicollinearity problem was resolved. Accordingly, the performance of the model was assessed with the AUC value under the ROC curve and AUC = 0.944 was found. This result shows that the model is highly accurate and has high predictive power (Fig. 2 ) (Anderson et al., 2003 ; Phillips et al., 2006 ; Phillips & Dudík, 2008 ; Sharma et al., 2018 ). According to the Jackknife test gain table, the bioclimatic variable with the highest gain is BIO19 (precipitation of coldest quarter). This is followed by the bioclimatic variables BIO6 (min temperature of coldest month) and BIO4 (temperature seasonality (standard deviation x100)) (Fig. 3 ). In a previous study conducted by Akyol & Örücü ( 2019 ) to predict the future distribution of stone pine due to climate change, according to the results of the Jackknife test, the most effective bioclimatic variables in the distribution were determined as the lowest temperature of the coldest month (BIO6), the average annual precipitation (BIO12) and the precipitation amount of the coldest season (BIO19), respectively (Akyol & Örücü, 2019 ). Although these two studies use different climate models and scenarios, the Jackknife test results are approximately similar. In a study by Gonçalves et al. ( 2023 ), rainfall was reported to be one of the main factors limiting the growth of stone pine (Gonçalves et al., 2023 ). In our study, the most significant bioclimatic variable limiting the distribution of stone pine is the amount of precipitation in the coldest season. In this context, the results of the studies support each other. Present and future estimated potential distribution of stone pine Our model predicted the most suitable potential areas for the distribution of stone pine in Türkiye under current climatic conditions. When the present potential distribution map produced from the model results is compared with the natural distributions of stone pine, it is seen that they are highly similar and compatible (Fig. 4 ). Accordingly, the species is distributed along the Mediterranean, Aegean, and Black Sea coasts in Türkiye. The estimated potential distributions of stone pine for the periods 2041–2060 and 2081–2100 for the SSP1 2.6 scenario are given in Fig. 5 , for the SSP2 4.5 scenario in Fig. 6 , for the SSP3 7.0 scenario in Fig. 7 and the SSP5 8.5 scenario in Fig. 8 . When the maps given in the relevant figures are analyzed, it is seen that the future distribution of stone pine shows a decreasing trend in all scenarios and periods. When the distributions in different scenarios and periods are analyzed, it is noteworthy that the distribution areas are decreased compared to the current situation, and these decreases are especially concentrated on the Aegean and Mediterranean coasts. On the other hand, it can be said that the potential distributions of stone pine on the Black Sea coasts remain more stable. This situation observed in the distribution maps was also evaluated numerically. For this purpose, data for the scenarios defined as medium and strict scenarios were used. Accordingly, spatial distributions of stone pine for SSP5 8.5 and SSP2 4.5 scenarios and the periods 2041–2060, 2081–2100 are given in Table 1 . When Table 1 is examined, it is seen that the distribution area of stone pine, which is currently classified as "very high suitable", is 15,516.72 km 2 . The future distribution areas of the species are estimated to decrease to 791.57 km 2 in the SSP2 4.5-2041-2060 scenario and 254.84 km 2 in the SSP2 4.5-2081-2100 scenario. According to the SSP5 8.5 scenario, which is a more negative scenario compared to the SSP2 4.5 scenario, the areas classified as "very high suitable" decrease to 424,974 km 2 in the SSP5 8.5-2041-2060 scenario and there is no "very high suitable" distribution area left in the SSP5 8.5-2081-2100 scenario. In other words, the "very high suitable" distribution areas, which are approximately 15,516 km 2 in the current situation, are completely zero in the SSP5 8.5-2081-2100 scenario. In this case, it can be said that the distribution areas of stone pine in Türkiye will be negatively affected by climate changes, large-scale losses may occur in the distributions in the Aegean and Mediterranean, and the distribution areas will shift further north. Table 1 Habitat distributions of stone pine CNRM-ESM2-1 climate model and SSP2 4.5 - SSP5 8.5 scenarios 2041–2060 and 2081–2100 periods (km 2 ) Habitat Suitability Current SSP2 4.5 2041–2060 SSP2 4.5 2081–2100 SSP5 8.5 2041–2060 SSP5 8.5 2081–2100 "0" Unsuitable 492,932.52 492,759.40 523,086.40 508,906.69 671,433.50 "0-0.25" Low Suitable 111,129.50 18,515.02 193,205.60 186,595.92 101,060.49 "0.25–0.50" Moderate Suitable 84,788.30 80,680.63 55,973.99 71,286.24 7,897.07 "0.50–0.75" High Suitable 76,088.71 19,705.58 7,936.10 13,238.73 68.01 "0.75-1" Very High Suitable 15,516.72 791.57 254.84 424.97 - These findings are similar to the results of a study conducted by Akyol and Örücü ( 2019 ) on the distribution of stone pine in Türkiye using Representative Concentration Pathways (RCP) climate scenarios and HadGEM2-ES (Hadley Global Environment Model2-Earth System) climate model. Similarly, our findings coincide with the findings of another study by Akyol et al. ( 2020 ) on the distribution of stone pine in Europe and Türkiye, which showed that the distribution of stone pine on the Aegean and Mediterranean coasts are decreasing and the distribution areas will shift further north (Akyol et al., 2020 ). Climate change and increasing land use pressures cause habitat loss and fragmentation (Kumar, 2012 ; Khanum et al., 2013 ; Newbold et al., 2015 ). Habitat loss and fragmentation are some of the most important factors of biodiversity loss (Shengwu et al., 2016 ; Linshan et al., 2017 ). The reduction of stone pine distribution areas due to climate change will have many negative ecological and economic consequences. This situation indicates that serious income losses may occur for those who earn income from this species in the future and the benefits of the species as a food contribution may also decrease. However, some studies found that some species in the Mediterranean region are more resistant to drought and climatic changes (Dogan et al., 2024). For these reasons, research to determine which forest regions and which tree species are resilient to climate variability should be increased to adapt forest ecosystems and forest management to climate change. Change analysis of present and future estimated potential distribution areas of stone pine The analysis maps for the present and future estimated potential distribution areas change of stone pine are given in Fig. 9 for the SSP1 2.6 2041–2060 and 2081–2100 periods, Fig. 10 for the SSP2 4.5 scenario 2041–2060 and 2081–2100 periods, Fig. 11 for the SSP3 7.0 scenario 2041–2060 and 2081–2100 periods and Fig. 12 for the SSP5 8.5 scenario 2041–2060 and 2081–2100 periods. When the figures are analyzed and compared with each other, it is seen that the loss of distribution areas is concentrated on the Aegean, Mediterranean, and Marmara coasts of Türkiye. On the Black Sea coasts, the change is generally less, and it is seen that areal losses increase on the Black Sea coasts, especially in the harsh scenario. From the related maps, it can be seen that the gain-loss relationships of the species change according to altitude and latitude, and the geographical distribution of the species changes towards the north and higher altitudes. From the maps created according to the findings of the analysis of the present distribution areas of stone pine and the change in distribution areas according to future scenarios, it is seen that the species may experience habitat losses in the future. It can also be observed from the model outputs that the direction of change will be towards high altitudes and northern latitudes. Similar findings were found in a study by Varol et al. ( 2022 ). In the investigation, it is pointed out that there may be decreases in the distribution areas of Shimshir in the future due to climate changes and that the potential distribution areas may shift towards the north and higher altitudes compared to the current regions (Varol et al., 2022 ). Another study by Mirhashemi et al. ( 2023 ) for oak forests also predicted severe reductions in oak distributions due to climate change in the future (Mirhashemi et al., 2023 ). Similar findings are also available for Kermes oak (Babalik et al., 2021 ). In another study conducted for Tilia species, it was predicted that the distributions of Tilia species will change and decrease due to climate change (Canturk & Kulaç, 2021 ). In our research, the areal losses predicted in the distribution and change analysis maps were also calculated numerically. For the calculation, SSP5 8.5 and SSP2 4.5 scenarios and analysis data for the periods 2041–2060 and 2081–2100 were used (Table 2 ). Table 2 Spatial changes between current and predicted distributions of stone pine under SSP2 4.5 and SSP5 8.5 scenarios for 2041–2060 and 2081–2100 (km 2 ) Change SSP2 4.5 2041–2060 SSP2 4.5 2081–2100 SSP5 8.5 2041–2060 SSP5 8.5 2081–2100 Gain 1,361.64 1,426.13 1,107.52 16.01 Loss 182,437.46 210,664.40 195,049.14 259,451.20 Stable 103,721.53 75,446.44 91,364.18 28,072.95 Unsuitable 492,931.18 492,916.70 492,931.44 492,916.06 When Table 2 is analyzed, in the period 2041–2060 of the SSP2 4.5 scenario, an area of 1,361.64 km 2 is calculated as gain by moving to a higher class, while 182,437.46 km 2 is calculated as loss. In the SSP2 4.5 scenario 2081–2100 period, the areas considered as gains are calculated as 1.426,13 km 2 and the areas considered as losses are calculated as 210.664,40 km 2 . In the SSP5 8.5 scenario 2041–2060 period, an area of 1,107.52 km 2 is calculated as a gain by moving to a higher class, while an area of 195,049.14 km 2 is calculated as a loss by moving to a lower suitability class. In the SSP5 8.5 scenario for the period 2081–2100, the areas considered as gains are calculated as only 16.01 km 2 and the areas considered as losses are calculated as 259,451.20 km 2 . When the areas with no change from the medium scenario to the hard scenario are analyzed, it is noteworthy that the areas with no change decreased from approximately 103,721 km 2 to 28,072 km 2 . This situation shows that the habitat of the species has decreased considerably even in the current geographical distributions where the impacts of climate change are felt less. Therefore, it is obligatory to take some measures against the effects of climate change. According to the findings of the study conducted by Loewe-Muñoz et al. ( 2024 ), it was emphasized that fertilization, especially in semi-arid areas, affects the development of stone pine and may contribute to mitigating the effects of climate change (Loewe-Muñoz et al., 2024 ). In a study conducted by Bedair et al. ( 2024 ), it was stated that research and investment programs should be developed especially for genetic diversity and resilience to reduce species losses due to climate change influences (Bedair et al., 2024 ). In addition, integrating climate change impacts into studies to identify bioclimatic comfort zones, which are useful for issues such as tourism planning and investment decisions, can also make important contributions to the conservation of important species (Yeşil et al., 2021 ). Studies on the impacts of climate change generally indicate that species' adaptation abilities are decreasing while habitat losses and species losses are increasing (Rogers et al., 2017 ). Model findings indicate that stone pine will change its geographical distribution and suffer habitat loss in the future. However, beside to habitat loss, climate change negatively affects many variables of the ecosystem and causes biodiversity loss by changing the biology and ecology of different species (Garzón et al., 2019 , Nascimbene et al., 2020 ; Mechergui et al., 2021 ). According to a study by Wiens & Zelinka ( 2024 ), to understand the effects of climate change on biodiversity, a better understanding of the number, distribution and vulnerability of species on Earth is needed. It is also suggested that studies include information on how species are currently responding to recent climate change (Wiens & Zelinka, 2024 ). This situation necessitates more detailed and comprehensive studies on the interaction of climate change and species and land use changes by integrating the issue of climate change into planning, especially in ecosystems with high biodiversity such as forest ecosystems, protected areas, etc. For instance, Leptoglossus occidentalis (Heidemann, 1910), a seed pest that has not been recorded in Türkiye in the past, has recently been observed and recorded especially in stone pine areas in western Türkiye (Arslangündoğdu & Hizal, 2010 ). Although not directly associated with climate change, this situation reveals that climate changes do not only result in habitat loss but also affect many variables of the ecosystem (Özden et al., 2022 ). However, there are also studies indicating that some species are positively affected by climatic changes and may even become invasive species (Yang et al., 2015 , DeMarche et al., 2019 ). In addition, considering that some species are better adapted to ex situ than to their natural distribution, analyzes of species' responses to climate change should also evaluate their climatic adaptation to ex situ (Booth, 2017 ). Therefore, it is of great importance to use powerful algorithms and tools that can provide realistic results in climate prediction modeling. Conclusions Stone pine is a species with high economic value and is used for various purposes. The species is frequently preferred especially in rural development studies as an income-generating and food-contributing species. In the study, CMIP6 SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios, which have higher predictive power compared to RCP models, were used to model the potential distribution areas of stone pine. Therefore, it is predicted that the accuracy of the study results is higher and provides more information compared to previous studies (Eyring et al., 2016 ; Walentowski et al., 2017 ; Hausfather, 2019 ; Örücü et al., 2021 ). The research findings provide important information to formulate strategies to protect the stone pine species in the face of climate change and to sustain the economic benefits provided by the species. In conclusion, the geographical distribution of stone pine is influenced by many factors. However, climate change has emerged as one of the most critical factors in recent years. Model findings indicate that stone pine will change its geographical distribution and suffer habitat loss in the future. Habitat loss is predicted to be concentrated in the southern and western regions of Türkiye. One of the adaptation strategies that can be implemented for habitat loss is to protect areas where the threatened species has a high potential to be found and to prioritize these areas in afforestation efforts. However, changes in the species' distribution pose several challenges for forest ecosystem managers, planners, and conservationists. Increases in the number and severity of forest fires can be observed, especially in regions under the Mediterranean climate. Conventional forestry management practices may therefore need to be adapted to climate change impacts and changes in species distribution, and new strategies may need to be developed. The species has a significant contribution to economic activities for rural development such as pine nut and timber production and tourism in its current range. Changes in distribution and reduced yields may affect the supply of pine nuts, leading to price fluctuations and significant increases. In addition, pine nuts are used for landscaping purposes and attract tourists and nature lovers. Bergama-Kozak Plateau (Türkiye), which attracts attention especially with its pine nut production, has also attracted attention in terms of ecotourism in recent years. Changes in the distribution of the species and habitat losses may reduce the attractiveness of tourism and recreation and negatively affect local economies dependent on these activities. For these reasons, to ensure that the ecological and economic benefits provided by the species continue sustainably by reducing the effects of climate change, the changes in the distribution of the species should be continuously monitored and areas of critical importance for the survival and conservation of the species should be identified. Habitat restoration projects, sustainable land use practices, and various financing tools should be developed for the creation, management, and protection of these areas. Declarations Acknowledgements This work was supported by İzmir Kâtip Çelebi University Scientific Research Projects Coordination Office [Project number: 2023-TYL-FEBE-0011]. Funding İzmir Kâtip Çelebi University Scientific Research Projects Coordination Office [Project number: 2023-TYL-FEBE-0011]. Author contribution This study, conducted as part of Merve Karayol's master’s thesis, was led by Ayhan Akyol, who initially devised the research proposal and supervised the collection of datas. Merve Karayol performed the investigation, field works, data acquisition, and writing-original draft preparation. Ayhan Akyol performed the investigation, formal analysis, data interpretation, writing-reviewing and editing. Merve Karayol and Ayhan Akyol have reviewed and approved the final version. Data availability The data that support the findings of this study are available from the corresponding author upon reasonable request. Ethical approval This study does not involve human participants and animals subjects. Consent to participate Author and all co-authors agree to submit the present research work in the EMAS journal and disclose that it has not been published previously and that it is not under consideration for publication elsewhere. Consent to publication All authors agree to publish the present work in the EMAS journal. Competing interests The authors declare no competing interests. References Akyol, A., & Örücü, Ö.K. (2019). Investigation and evaluation of stone pine ( Pinus pinea L.) current and future potential distribution under climate change in Turkey. Cerne, 25 (4), 15–423. https://doi.org/10.1590/01047760201925042643 Akyol, A., Örücü, Ö.K., & Arslan, E.S. (2020). Habitat suitability mapping of stone pine ( Pinus pinea L.) under the effects of climate change. Biologia, 75 , 2175-2187. https://doi.org/10.2478/s11756-020-00594-9 Akyol, A., Örücü, Ö.K., Arslan, E.S., & Sarıkaya, A.G. (2023). 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Evaluating the ecological influence of hydraulic projects: A review of aquatic habitat suitability models. Renewable and Sustainable Energy Reviews, 68 (1), 748–762. https://doi.org/10.1016/j.rser.2016.09.138 Yıldırım, E., Nazar, M., Sefercik, U.G., & Kavzoglu, T. (2022). Stone pine ( Pinus pinea L.) detection from high-resolution UAV Imagery using deep learning model. IGARSS 2022- 2022 IEEE International Geoscience and Remote Sensing Symposium , 17-22 July 2022, Kuala Lumpur, Malaysia, 441–444. https://doi.org/10.1109/IGARSS46834.2022.9883964. Yuan, H.S., Wei, Y.L., & Wang, X.G. (2015). Maxent modeling for predicting the potential distribution of Sanghuang, an important group of medicinal fungi in China. Fungal Ecology 17 , 140–145. https://doi.org/10.1016/j.funeco.2015.06.001 Zeng, Y., Low, B.W., & Yeo, D.C.J. (2016). Novel methods to select environmental variables in MaxEnt: A case study using invasive crayfish. Ecological Modelling, 341 , 5–13. https://doi.org/10.1016/j.ecolmodel.2016.09.019 Zhang, H., & Zhao, H. (2021). Study on rare and endangered plants under climate: maxent modeling for identifying hot spots in northwest China. CERNE, 27 , e-102667. https://doi.org/10.1590/01047760202127012667 Zhang, T., & Liu, G. (2017). Study of methods to improve the temporal transfer ability of niche model. Journal of China Agricultural University, 22 (2), 98–105. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-4395237","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":313324028,"identity":"9170a769-9e2a-446d-98cb-3992d883b8f3","order_by":0,"name":"Merve Karayol","email":"","orcid":"","institution":"Izmir Kâtip Çelebi University","correspondingAuthor":false,"prefix":"","firstName":"Merve","middleName":"","lastName":"Karayol","suffix":""},{"id":313324029,"identity":"57c683fd-fd2b-43d5-996a-c39727bcb01f","order_by":1,"name":"Ayhan 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of stone pine (a) 2041-2060, (b) 2081-2100\u003c/p\u003e","description":"","filename":"Fig.5300dpi.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4395237/v1/2b81a569dfebb16c8b66cf05.jpg"},{"id":58272325,"identity":"860db5e1-4fc3-41bd-ab23-8898449892ec","added_by":"auto","created_at":"2024-06-13 08:53:57","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1990952,"visible":true,"origin":"","legend":"\u003cp\u003eCNRM-ESM2-1 climate model and SSP2 4.5 scenario projected distributions of stone pine (a) 2041-2060, (b) 2081-2100\u003c/p\u003e","description":"","filename":"Fig.6300dpi.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4395237/v1/505d054c591f96db7e2a9d0b.jpg"},{"id":58272330,"identity":"e5a11dde-be00-4d09-89fc-fc81a9914f9c","added_by":"auto","created_at":"2024-06-13 08:53:57","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1955651,"visible":true,"origin":"","legend":"\u003cp\u003eCNRM-ESM2-1 climate model and SSP3 7.0 scenario projected distributions of stone pine (a) 2041-2060, (b) 2081-2100\u003c/p\u003e","description":"","filename":"Fig.7300dpi.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4395237/v1/1123a218a3ee3b2180e327bb.jpg"},{"id":58272329,"identity":"d4d8a6eb-735e-4940-a2ea-44ee3cdbb222","added_by":"auto","created_at":"2024-06-13 08:53:57","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1882498,"visible":true,"origin":"","legend":"\u003cp\u003eCNRM-ESM2-1 climate model and SSP5 8.5 scenario projected distributions of stone pine (a) 2041-2060, (b) 2081-2100\u003c/p\u003e","description":"","filename":"Fig.8300dpi.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4395237/v1/7438e18d6f176af70beaeef2.jpg"},{"id":58272331,"identity":"f65dbaac-8753-40ad-a748-f24bae2d8f8c","added_by":"auto","created_at":"2024-06-13 08:53:57","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":2292117,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial changes between current and predicted distributions of stone pine under the SSP1 2.6 scenario for the periods 2041-2060 (a) and 2081-2100 (b) in Türkiye\u003c/p\u003e","description":"","filename":"Fig.9300dpi.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4395237/v1/88f22726d095dba664992810.jpg"},{"id":58272332,"identity":"da6b142a-9bbb-47d5-a52f-cd86a5954a42","added_by":"auto","created_at":"2024-06-13 08:53:58","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":2049998,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial changes between current and predicted distributions of stone pine under the SSP2 4.5 scenario for the periods 2041-2060 (a) and 2081-2100 (b) in Türkiye\u003c/p\u003e","description":"","filename":"Fig.10300dpi.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4395237/v1/40289c7365af5f73c70adfdb.jpg"},{"id":58272991,"identity":"72f355b0-c15f-415b-b379-cbc9cbbd948b","added_by":"auto","created_at":"2024-06-13 09:01:57","extension":"jpg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":2177732,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial changes between current and predicted distributions of stone pine under the SSP3 7.0 scenario for the periods 2041-2060 (a) and 2081-2100 (b) in Türkiye\u003c/p\u003e","description":"","filename":"Fig.11300dpi.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4395237/v1/9369aab05c34f33c3346dbe6.jpg"},{"id":58272992,"identity":"8c792348-d873-4cf7-af29-4b43cff5e96b","added_by":"auto","created_at":"2024-06-13 09:01:57","extension":"jpg","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":1954617,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial changes between current and predicted distributions of stone pine under the SSP5 8.5 scenario for the periods 2041-2060 (a) and 2081-2100 (b) in Türkiye\u003c/p\u003e","description":"","filename":"Fig.12300dpi.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4395237/v1/7ddf61cc2f1a34839b014200.jpg"},{"id":61026558,"identity":"aa7de988-77cf-4ba1-9c6e-b17333c1c497","added_by":"auto","created_at":"2024-07-24 17:49:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":19719681,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4395237/v1/8852b000-191e-4ddc-89f1-5f8d5e2dfecc.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Environmental and socio-economic impacts of the changes in distribution areas of Pinus pinea L. (stone pine) due to climate change in Türkiye","fulltext":[{"header":"Introduction","content":"\u003cp\u003eClimate change is one of the complex environmental problems with significant impacts on ecosystems, biodiversity, and human societies. Climate change occurs as a result of the interaction and combination of various factors and can result in serious environmental, economic, and social impacts (T\u0026uuml;rkeş, \u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; IPCC, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; WMO, \u003cspan citationid=\"CR129\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; NASA, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Plant species are distributed in specific ecological niches in various regions by adapting to specific environmental conditions. The ecological niche of an organism is influenced by several factors, including its habitat, feeding habits, reproductive timing, preferred climatic conditions, and other environmental factors (Guisan \u0026amp; Thuiller, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; MEA, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Blackburn et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Lawler et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Elith \u0026amp; Graham, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Chikerema et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). However, climate change is significantly affecting the distribution and habitats of plant species, including stone pine (\u003cem\u003ePinus pinea\u003c/em\u003e L.) (Polat et al., \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Changes in plant species distributions affect the structure and functionality of ecosystems (Tolunay, \u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Uzun \u0026amp; Sarıkaya, \u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Changes in the distribution of species due to climatic conditions along with other relevant factors directly affect the continuation of the species' extinction. While some plant species can migrate to more northern and southern regions by adapting to increasing temperatures and changing precipitation patterns, some species may lose their habitats and face the risk of extinction (\u0026Ouml;zt\u0026uuml;rk, \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSpecies Distribution Models (SDMs) are one of the tools used to predict the current and future distribution of species (Franklin, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Booth, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). With the advancement of machine learning techniques, prediction models using different algorithms and quantum approaches have been developed (Guisan \u0026amp; Zimmermann, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Brito et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Elith \u0026amp; Leathwick, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; \u0026Ouml;zkan, \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; \u0026Ouml;zdemir, \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Wei et al., \u003cspan citationid=\"CR124\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). One of the most widely used machine learning techniques in plant species modeling is MaxEnt, which uses the maximum entropy algorithm (Zeng et al., \u003cspan citationid=\"CR139\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Koch et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Xu et al., \u003cspan citationid=\"CR132\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). MaxEnt uses digital climate data and spatial data to create a probability distribution map showing the most suitable habitats for species. Thanks to the layers created using spatial data expressing the areas where species are distributed, the present and future potential distributions of species can be determined with various species distribution models according to different climate models and climate scenarios (Phillips et al., \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Hijmans \u0026amp; Graham, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Ward, \u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; S\u0026eacute;rgio et al., \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Phillips \u0026amp; Dud\u0026iacute;k, \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Williams et al., \u003cspan citationid=\"CR126\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Wollan et al., \u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Tittensor et al., \u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Nagendra et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Yuan et al., \u003cspan citationid=\"CR138\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Yi et al., \u003cspan citationid=\"CR136\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Arslan, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Uzun \u0026amp; Sarıkaya, \u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Eker et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR121\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Jin et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhen creating an ecological distribution model, species observations, climate data, and other environmental data are combined using a computer-based geographic information system. Based on these data, a map representing the ecological niche of the species is created and predictions are made about the present and future distribution of the species (Booth et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). However, the construction and interpretation of these models are complex and model results should be carefully evaluated (Zhang \u0026amp; Zhao, \u003cspan citationid=\"CR140\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Climate models used in forecasting are tools to understand how the climate has changed from the past to the present and how it may change in the future (Garcia et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Ashraf et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). As different groups of models on Earth (MIROC6, CanESM5, CNRM-ESM2-1, etc.) include new physical processes, biogeochemical cycles and higher spatial resolution, climate models are constantly updated depending on the preparation of data sets (IPCC, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Jones et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Djalante, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; \u0026Ouml;r\u0026uuml;c\u0026uuml; et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Uzun \u0026amp; Sarıkaya, \u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eChanges in climate and anthropogenic pressures are affecting ecosystems more and more every day, which necessitates more effective and functional planning efforts for target species and related ecosystems (Falcucci et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Loewe-Mu\u0026ntilde;oz et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Understanding the links between climate change and plant species distributions is crucial for combating climate change and taking appropriate measures for the conservation of biodiversity and the sustainability of ecosystems. How the distribution of stone pine will be affected due to climate change, the reflections of these changes on ecosystems, and the possible effects of future climate scenarios on plant species distributions are one of the main objectives of this study. Many previous studies on the subject were based on the scenarios in the 5th Assessment Report of the Intergovernmental Panel on Climate Change (IPCC) (Akyol \u0026amp; \u0026Ouml;r\u0026uuml;c\u0026uuml;, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Akyol et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In this study, unlike previous studies, CMIP6 Shared Socioeconomic Pathways scenarios in the 6th Assessment Report of IPCC were used (IPCC, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Balaji et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; \u0026Ouml;r\u0026uuml;c\u0026uuml; et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSpecies presence data and bioclimatic variables\u003c/h2\u003e \u003cp\u003eIt is known that studies on commercially important species have increased considerably in recent years (Ticktin, \u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Bonari et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Karpukhin \u0026amp; Yussef, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Yıldırım et al., \u003cspan citationid=\"CR137\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; \u0026Ouml;zdarici-Ok et al., \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Sim\u0026otilde;es, et al., \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Stone pine, which is distributed in Mediterranean forest ecosystems, is an important species frequently preferred for ecological and economic purposes (Kılcı et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Bravo-Oviedo \u0026amp; Montero, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Batur, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; G\u0026uuml;le\u0026ccedil;, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Stone pine is ecologically well adapted to high temperatures and drought, and it also shows significant tolerance to water stress owing to its deep roots (Teobaldelli et al., \u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Castillo et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). However, the degree of this tolerance may vary according to origin (Balekoglu et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Since it is a very valuable product in T\u0026uuml;rkiye and international markets, it ensures more income to producers than other forest products (Şafak \u0026amp; Okan, \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Mutke et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Korkmaz \u0026amp; Duman, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). For this reason, stone pine plantations are being intensively developed to support many ecosystem services such as nut production, recreational uses, and wood production (Moreno-Fern\u0026aacute;ndez et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; \u0026Uuml;\u0026ccedil;ler \u0026amp; Arpacı, \u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Fern\u0026aacute;ndez et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study, presence data for stone pine were obtained by taking coordinates from 13 points where the species is distributed and by reviewing the literature (Davis et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1988\u003c/span\u003e; GBIF, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Sample coordinates were marked in the WGS 84 coordinate system using Google Satellite Hybrid maps in the QGIS 3.34 program (QGIS, \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Bioclimatic variables derived from 2.5 min spatial resolution data from the WorldClim (version 2.1) database (Hijmans et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Moss et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Hunt et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Remya et al., \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Fick \u0026amp; Hijmans, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Arslan et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; WorldClim, \u003cspan citationid=\"CR131\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) were used to estimate present and future distribution areas.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCNRM ESM2-1 (Centre National de Recherches M\u0026eacute;t\u0026eacute;orologiques Earth System Model 2.1) was used as the climate model and CMIP6 (SSP1 2.6, SSP2 4.5, SSP3 7.0, SSP5 8.5) Shared Socioeconomic Pathways (SSP) scenarios from the 6th Assessment Report of the IPCC and the periods 2041\u0026ndash;2060 and 2081\u0026ndash;2100 were used as climate scenarios. SSPs are used in climate modeling and research to refer to climate scenarios defined based on specific socioeconomic developments and different greenhouse gas emission intensities soon (O'Neill et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Eyring et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Chen et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Bulut \u0026amp; Aytaş, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Carbon Brief, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eModeling method and statistical analysis\u003c/h2\u003e \u003cp\u003eThe MaxEnt algorithm was used in this study due to its ease of use and the fact that it produces successful models for estimating the distribution areas of species with limited distributions (Pearson et al., \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Wisz et al., \u003cspan citationid=\"CR128\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Graham et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Wilting et al., \u003cspan citationid=\"CR127\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Clements et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Kalkvik et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). MaxEnt is based on the principle of maximum entropy, which aims to generate a certain probability distribution that best represents the available information under a given set of conditions (Phillips et al., \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Elith \u0026amp; Leathwick, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; \u0026Ouml;r\u0026uuml;c\u0026uuml; et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Mathur \u0026amp; Mathur, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This modeling method estimates the present and potential future distributions of the target species by taking into account its confirmed presence records (Li et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; \u0026Ouml;ztop, \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). For these reasons, MaxEnt 3.4.1 software was used in the study.\u003c/p\u003e \u003cp\u003eTo solve the multicollinearity problem (Zhang \u0026amp; Liu, \u003cspan citationid=\"CR141\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), which reduces the predictive power of the model, the Pearson correlation test was applied to 19 bioclimatic variables used in the model in SPSS 25.0 package statistical program (Sillero, \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Cao et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). As a result of this test, one of the bioclimatic variables with a Pearson correlation coefficient (r) value of \u0026plusmn;\u0026thinsp;0.8 and above was removed from the model and the multicollinearity problem was eliminated (Yang et al., \u003cspan citationid=\"CR134\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, Cao et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Akyol et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor modeling, the output format was set as logistic in MaxEnt 3.4.1, and the number of randomly selected background points was chosen as 10.012. Since the number of samples was between 10 and 15, the program was run by selecting the Linear feature (Phillips \u0026amp; Dud\u0026iacute;k, \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Phillips et al., \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Şen et al., \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). ROC (Receiving Operator Curve) analysis was performed to determine the performance of the model and the model was evaluated using the AUC (Area under the Receiving Operator Curve) value under the ROC curve. [AUC\u0026thinsp;\u0026lt;\u0026thinsp;0.8]\u0026thinsp;=\u0026thinsp;poor, [0.9\u0026thinsp;\u0026gt;\u0026thinsp;AUC\u0026thinsp;\u0026ge;\u0026thinsp;0.8]\u0026thinsp;=\u0026thinsp;good, and [AUC\u0026thinsp;\u0026ge;\u0026thinsp;0.9]\u0026thinsp;=\u0026thinsp;very good thresholds were used for the AUC value (Gass\u0026oacute; et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Hosmer Jr et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). As the AUC value approaches 1, the identification power of the model increases (Phillips et al., \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Phillips \u0026amp; Elith, \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; \u0026Ouml;r\u0026uuml;c\u0026uuml;, \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Uzun \u0026amp; \u0026Ouml;r\u0026uuml;c\u0026uuml;, \u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The contribution of bioclimatic variables to the model was determined by selecting the Jackknife test in the MaxEnt program (Pearson et al., \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2007\u003c/span\u003e, Shcheglovitova \u0026amp; Anderson, \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Qin et al., \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The model results were converted into distribution maps using the raster/vector transformation function with the QGIS 3.34 program. In the potential habitat maps created for the current and future situation, the suitability levels for the distribution areas were classified as [0.75-1] very high suitable, [0.50\u0026ndash;0.75] high suitable, [0.25\u0026ndash;0.50] moderate suitable, [0-0.25] low suitable, and [0] unsuitable areas. According to this classification, the distribution areas in estimated present and future scenarios were calculated in km\u003csup\u003e2\u003c/sup\u003e (\u0026Ccedil;oban et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; \u0026Ouml;r\u0026uuml;c\u0026uuml; et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Eventually, a change analysis was conducted to make a comparison between the distributions of the estimated present and future scenarios. For the change analysis, the suitability values were classified as [0\u0026thinsp;=\u0026thinsp;0], [0-0.25\u0026thinsp;=\u0026thinsp;1], [0.25\u0026ndash;0.50\u0026thinsp;=\u0026thinsp;2], [0.50\u0026ndash;0.75\u0026thinsp;=\u0026thinsp;3], [0.75-1\u0026thinsp;=\u0026thinsp;4] and the intersection function was applied to these data. According to the suitability values, areas with [0\u0026ndash;0] were defined as unsuitable, areas in the same class were defined as stable, areas moving to a higher class were defined as gain, and areas moving to a lower class were defined as loss. These areas were calculated in km\u003csup\u003e2\u003c/sup\u003e and change maps were created, thus revealing the direction and magnitude of change.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results and discussion","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eEvaluation of model performance and contribution of bioclimatic variables\u003c/h2\u003e \u003cp\u003eUsing Pearson Correlation analysis, variables that weakened the predictive power of the model were eliminated from the model and 9 out of 19 bioclimatic variables, including BIO2, BIO4, BIO6, BIO7, BIO11, BIO12, BIO13, BIO16 and BIO19 variables, were used in the model. Thus, the predictive power of the model was increased and the multicollinearity problem was resolved. Accordingly, the performance of the model was assessed with the AUC value under the ROC curve and AUC\u0026thinsp;=\u0026thinsp;0.944 was found. This result shows that the model is highly accurate and has high predictive power (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) (Anderson et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Phillips et al., \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Phillips \u0026amp; Dud\u0026iacute;k, \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Sharma et al., \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAccording to the Jackknife test gain table, the bioclimatic variable with the highest gain is BIO19 (precipitation of coldest quarter). This is followed by the bioclimatic variables BIO6 (min temperature of coldest month) and BIO4 (temperature seasonality (standard deviation x100)) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn a previous study conducted by Akyol \u0026amp; \u0026Ouml;r\u0026uuml;c\u0026uuml; (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) to predict the future distribution of stone pine due to climate change, according to the results of the Jackknife test, the most effective bioclimatic variables in the distribution were determined as the lowest temperature of the coldest month (BIO6), the average annual precipitation (BIO12) and the precipitation amount of the coldest season (BIO19), respectively (Akyol \u0026amp; \u0026Ouml;r\u0026uuml;c\u0026uuml;, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Although these two studies use different climate models and scenarios, the Jackknife test results are approximately similar. In a study by Gon\u0026ccedil;alves et al. (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), rainfall was reported to be one of the main factors limiting the growth of stone pine (Gon\u0026ccedil;alves et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In our study, the most significant bioclimatic variable limiting the distribution of stone pine is the amount of precipitation in the coldest season. In this context, the results of the studies support each other.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003ePresent and future estimated potential distribution of stone pine\u003c/h2\u003e \u003cp\u003eOur model predicted the most suitable potential areas for the distribution of stone pine in T\u0026uuml;rkiye under current climatic conditions. When the present potential distribution map produced from the model results is compared with the natural distributions of stone pine, it is seen that they are highly similar and compatible (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Accordingly, the species is distributed along the Mediterranean, Aegean, and Black Sea coasts in T\u0026uuml;rkiye.\u003c/p\u003e\u003cp\u003eThe estimated potential distributions of stone pine for the periods 2041\u0026ndash;2060 and 2081\u0026ndash;2100 for the SSP1 2.6 scenario are given in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, for the SSP2 4.5 scenario in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, for the SSP3 7.0 scenario in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e and the SSP5 8.5 scenario in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e. When the maps given in the relevant figures are analyzed, it is seen that the future distribution of stone pine shows a decreasing trend in all scenarios and periods. When the distributions in different scenarios and periods are analyzed, it is noteworthy that the distribution areas are decreased compared to the current situation, and these decreases are especially concentrated on the Aegean and Mediterranean coasts. On the other hand, it can be said that the potential distributions of stone pine on the Black Sea coasts remain more stable. This situation observed in the distribution maps was also evaluated numerically. For this purpose, data for the scenarios defined as medium and strict scenarios were used. Accordingly, spatial distributions of stone pine for SSP5 8.5 and SSP2 4.5 scenarios and the periods 2041\u0026ndash;2060, 2081\u0026ndash;2100 are given in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eWhen Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e is examined, it is seen that the distribution area of stone pine, which is currently classified as \"very high suitable\", is 15,516.72 km\u003csup\u003e2\u003c/sup\u003e. The future distribution areas of the species are estimated to decrease to 791.57 km\u003csup\u003e2\u003c/sup\u003e in the SSP2 4.5-2041-2060 scenario and 254.84 km\u003csup\u003e2\u003c/sup\u003e in the SSP2 4.5-2081-2100 scenario. According to the SSP5 8.5 scenario, which is a more negative scenario compared to the SSP2 4.5 scenario, the areas classified as \"very high suitable\" decrease to 424,974 km\u003csup\u003e2\u003c/sup\u003e in the SSP5 8.5-2041-2060 scenario and there is no \"very high suitable\" distribution area left in the SSP5 8.5-2081-2100 scenario. In other words, the \"very high suitable\" distribution areas, which are approximately 15,516 km\u003csup\u003e2\u003c/sup\u003e in the current situation, are completely zero in the SSP5 8.5-2081-2100 scenario. In this case, it can be said that the distribution areas of stone pine in T\u0026uuml;rkiye will be negatively affected by climate changes, large-scale losses may occur in the distributions in the Aegean and Mediterranean, and the distribution areas will shift further north.\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\u003eHabitat distributions of stone pine CNRM-ESM2-1 climate model and SSP2 4.5 - SSP5 8.5 scenarios 2041\u0026ndash;2060 and 2081\u0026ndash;2100 periods (km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHabitat Suitability\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSSP2 4.5\u003c/p\u003e \u003cp\u003e2041\u0026ndash;2060\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSSP2 4.5\u003c/p\u003e \u003cp\u003e2081\u0026ndash;2100\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSSP5 8.5\u003c/p\u003e \u003cp\u003e2041\u0026ndash;2060\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSSP5 8.5\u003c/p\u003e \u003cp\u003e2081\u0026ndash;2100\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\"0\"\u003c/p\u003e \u003cp\u003eUnsuitable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e492,932.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e492,759.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e523,086.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e508,906.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e671,433.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\"0-0.25\"\u003c/p\u003e \u003cp\u003eLow Suitable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e111,129.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18,515.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e193,205.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e186,595.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e101,060.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\"0.25\u0026ndash;0.50\"\u003c/p\u003e \u003cp\u003eModerate Suitable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e84,788.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e80,680.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e55,973.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e71,286.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7,897.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\"0.50\u0026ndash;0.75\"\u003c/p\u003e \u003cp\u003eHigh Suitable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e76,088.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19,705.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7,936.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13,238.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e68.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\"0.75-1\"\u003c/p\u003e \u003cp\u003eVery High Suitable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15,516.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e791.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e254.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e424.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThese findings are similar to the results of a study conducted by Akyol and \u0026Ouml;r\u0026uuml;c\u0026uuml; (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) on the distribution of stone pine in T\u0026uuml;rkiye using Representative Concentration Pathways (RCP) climate scenarios and HadGEM2-ES (Hadley Global Environment Model2-Earth System) climate model. Similarly, our findings coincide with the findings of another study by Akyol et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) on the distribution of stone pine in Europe and T\u0026uuml;rkiye, which showed that the distribution of stone pine on the Aegean and Mediterranean coasts are decreasing and the distribution areas will shift further north (Akyol et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eClimate change and increasing land use pressures cause habitat loss and fragmentation (Kumar, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Khanum et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Newbold et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Habitat loss and fragmentation are some of the most important factors of biodiversity loss (Shengwu et al., \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Linshan et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The reduction of stone pine distribution areas due to climate change will have many negative ecological and economic consequences. This situation indicates that serious income losses may occur for those who earn income from this species in the future and the benefits of the species as a food contribution may also decrease. However, some studies found that some species in the Mediterranean region are more resistant to drought and climatic changes (Dogan et al., 2024). For these reasons, research to determine which forest regions and which tree species are resilient to climate variability should be increased to adapt forest ecosystems and forest management to climate change.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eChange analysis of present and future estimated potential distribution areas of stone pine\u003c/h2\u003e \u003cp\u003eThe analysis maps for the present and future estimated potential distribution areas change of stone pine are given in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e for the SSP1 2.6 2041\u0026ndash;2060 and 2081\u0026ndash;2100 periods, Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e for the SSP2 4.5 scenario 2041\u0026ndash;2060 and 2081\u0026ndash;2100 periods, Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e for the SSP3 7.0 scenario 2041\u0026ndash;2060 and 2081\u0026ndash;2100 periods and Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e for the SSP5 8.5 scenario 2041\u0026ndash;2060 and 2081\u0026ndash;2100 periods. When the figures are analyzed and compared with each other, it is seen that the loss of distribution areas is concentrated on the Aegean, Mediterranean, and Marmara coasts of T\u0026uuml;rkiye. On the Black Sea coasts, the change is generally less, and it is seen that areal losses increase on the Black Sea coasts, especially in the harsh scenario. From the related maps, it can be seen that the gain-loss relationships of the species change according to altitude and latitude, and the geographical distribution of the species changes towards the north and higher altitudes.\u003c/p\u003e \u003cp\u003eFrom the maps created according to the findings of the analysis of the present distribution areas of stone pine and the change in distribution areas according to future scenarios, it is seen that the species may experience habitat losses in the future. It can also be observed from the model outputs that the direction of change will be towards high altitudes and northern latitudes. Similar findings were found in a study by Varol et al. (\u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In the investigation, it is pointed out that there may be decreases in the distribution areas of Shimshir in the future due to climate changes and that the potential distribution areas may shift towards the north and higher altitudes compared to the current regions (Varol et al., \u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAnother study by Mirhashemi et al. (\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) for oak forests also predicted severe reductions in oak distributions due to climate change in the future (Mirhashemi et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Similar findings are also available for Kermes oak (Babalik et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In another study conducted for Tilia species, it was predicted that the distributions of Tilia species will change and decrease due to climate change (Canturk \u0026amp; Kula\u0026ccedil;, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn our research, the areal losses predicted in the distribution and change analysis maps were also calculated numerically. For the calculation, SSP5 8.5 and SSP2 4.5 scenarios and analysis data for the periods 2041\u0026ndash;2060 and 2081\u0026ndash;2100 were used (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\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\u003eSpatial changes between current and predicted distributions of stone pine under SSP2 4.5 and SSP5 8.5 scenarios for 2041\u0026ndash;2060 and 2081\u0026ndash;2100 (km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChange\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSSP2 4.5\u003c/p\u003e \u003cp\u003e2041\u0026ndash;2060\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSSP2 4.5\u003c/p\u003e \u003cp\u003e2081\u0026ndash;2100\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSSP5 8.5\u003c/p\u003e \u003cp\u003e2041\u0026ndash;2060\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSSP5 8.5\u003c/p\u003e \u003cp\u003e2081\u0026ndash;2100\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,361.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,426.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,107.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLoss\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e182,437.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e210,664.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e195,049.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e259,451.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e103,721.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e75,446.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e91,364.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e28,072.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnsuitable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e492,931.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e492,916.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e492,931.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e492,916.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWhen Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e is analyzed, in the period 2041\u0026ndash;2060 of the SSP2 4.5 scenario, an area of 1,361.64 km\u003csup\u003e2\u003c/sup\u003e is calculated as gain by moving to a higher class, while 182,437.46 km\u003csup\u003e2\u003c/sup\u003e is calculated as loss. In the SSP2 4.5 scenario 2081\u0026ndash;2100 period, the areas considered as gains are calculated as 1.426,13 km\u003csup\u003e2\u003c/sup\u003e and the areas considered as losses are calculated as 210.664,40 km\u003csup\u003e2\u003c/sup\u003e. In the SSP5 8.5 scenario 2041\u0026ndash;2060 period, an area of 1,107.52 km\u003csup\u003e2\u003c/sup\u003e is calculated as a gain by moving to a higher class, while an area of 195,049.14 km\u003csup\u003e2\u003c/sup\u003e is calculated as a loss by moving to a lower suitability class. In the SSP5 8.5 scenario for the period 2081\u0026ndash;2100, the areas considered as gains are calculated as only 16.01 km\u003csup\u003e2\u003c/sup\u003e and the areas considered as losses are calculated as 259,451.20 km\u003csup\u003e2\u003c/sup\u003e. When the areas with no change from the medium scenario to the hard scenario are analyzed, it is noteworthy that the areas with no change decreased from approximately 103,721 km\u003csup\u003e2\u003c/sup\u003e to 28,072 km\u003csup\u003e2\u003c/sup\u003e. This situation shows that the habitat of the species has decreased considerably even in the current geographical distributions where the impacts of climate change are felt less. Therefore, it is obligatory to take some measures against the effects of climate change. According to the findings of the study conducted by Loewe-Mu\u0026ntilde;oz et al. (\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), it was emphasized that fertilization, especially in semi-arid areas, affects the development of stone pine and may contribute to mitigating the effects of climate change (Loewe-Mu\u0026ntilde;oz et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In a study conducted by Bedair et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), it was stated that research and investment programs should be developed especially for genetic diversity and resilience to reduce species losses due to climate change influences (Bedair et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In addition, integrating climate change impacts into studies to identify bioclimatic comfort zones, which are useful for issues such as tourism planning and investment decisions, can also make important contributions to the conservation of important species (Yeşil et al., \u003cspan citationid=\"CR135\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Studies on the impacts of climate change generally indicate that species' adaptation abilities are decreasing while habitat losses and species losses are increasing (Rogers et al., \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eModel findings indicate that stone pine will change its geographical distribution and suffer habitat loss in the future. However, beside to habitat loss, climate change negatively affects many variables of the ecosystem and causes biodiversity loss by changing the biology and ecology of different species (Garz\u0026oacute;n et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, Nascimbene et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Mechergui et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). According to a study by Wiens \u0026amp; Zelinka (\u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), to understand the effects of climate change on biodiversity, a better understanding of the number, distribution and vulnerability of species on Earth is needed. It is also suggested that studies include information on how species are currently responding to recent climate change (Wiens \u0026amp; Zelinka, \u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This situation necessitates more detailed and comprehensive studies on the interaction of climate change and species and land use changes by integrating the issue of climate change into planning, especially in ecosystems with high biodiversity such as forest ecosystems, protected areas, etc. For instance, \u003cem\u003eLeptoglossus occidentalis\u003c/em\u003e (Heidemann, 1910), a seed pest that has not been recorded in T\u0026uuml;rkiye in the past, has recently been observed and recorded especially in stone pine areas in western T\u0026uuml;rkiye (Arslang\u0026uuml;ndoğdu \u0026amp; Hizal, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Although not directly associated with climate change, this situation reveals that climate changes do not only result in habitat loss but also affect many variables of the ecosystem (\u0026Ouml;zden et al., \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, there are also studies indicating that some species are positively affected by climatic changes and may even become invasive species (Yang et al., \u003cspan citationid=\"CR133\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, DeMarche et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In addition, considering that some species are better adapted to ex situ than to their natural distribution, analyzes of species' responses to climate change should also evaluate their climatic adaptation to ex situ (Booth, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Therefore, it is of great importance to use powerful algorithms and tools that can provide realistic results in climate prediction modeling.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eStone pine is a species with high economic value and is used for various purposes. The species is frequently preferred especially in rural development studies as an income-generating and food-contributing species. In the study, CMIP6 SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios, which have higher predictive power compared to RCP models, were used to model the potential distribution areas of stone pine. Therefore, it is predicted that the accuracy of the study results is higher and provides more information compared to previous studies (Eyring et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Walentowski et al., \u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Hausfather, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; \u0026Ouml;r\u0026uuml;c\u0026uuml; et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The research findings provide important information to formulate strategies to protect the stone pine species in the face of climate change and to sustain the economic benefits provided by the species.\u003c/p\u003e \u003cp\u003eIn conclusion, the geographical distribution of stone pine is influenced by many factors. However, climate change has emerged as one of the most critical factors in recent years. Model findings indicate that stone pine will change its geographical distribution and suffer habitat loss in the future. Habitat loss is predicted to be concentrated in the southern and western regions of T\u0026uuml;rkiye. One of the adaptation strategies that can be implemented for habitat loss is to protect areas where the threatened species has a high potential to be found and to prioritize these areas in afforestation efforts. However, changes in the species' distribution pose several challenges for forest ecosystem managers, planners, and conservationists. Increases in the number and severity of forest fires can be observed, especially in regions under the Mediterranean climate. Conventional forestry management practices may therefore need to be adapted to climate change impacts and changes in species distribution, and new strategies may need to be developed. The species has a significant contribution to economic activities for rural development such as pine nut and timber production and tourism in its current range. Changes in distribution and reduced yields may affect the supply of pine nuts, leading to price fluctuations and significant increases. In addition, pine nuts are used for landscaping purposes and attract tourists and nature lovers. Bergama-Kozak Plateau (T\u0026uuml;rkiye), which attracts attention especially with its pine nut production, has also attracted attention in terms of ecotourism in recent years. Changes in the distribution of the species and habitat losses may reduce the attractiveness of tourism and recreation and negatively affect local economies dependent on these activities. For these reasons, to ensure that the ecological and economic benefits provided by the species continue sustainably by reducing the effects of climate change, the changes in the distribution of the species should be continuously monitored and areas of critical importance for the survival and conservation of the species should be identified. Habitat restoration projects, sustainable land use practices, and various financing tools should be developed for the creation, management, and protection of these areas.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by İzmir K\u0026acirc;tip \u0026Ccedil;elebi University Scientific Research Projects Coordination Office [Project number: 2023-TYL-FEBE-0011].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003eİzmir K\u0026acirc;tip \u0026Ccedil;elebi University Scientific Research Projects Coordination Office [Project number: 2023-TYL-FEBE-0011].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution\u003c/strong\u003e This study, conducted as part of Merve Karayol\u0026apos;s master\u0026rsquo;s thesis, was led by Ayhan Akyol, who initially devised the research proposal and supervised the collection of datas. Merve Karayol performed the investigation, field works, data acquisition, and writing-original draft preparation. Ayhan Akyol performed the investigation, formal analysis, data interpretation, writing-reviewing and editing. Merve Karayol and Ayhan Akyol have reviewed and approved the final version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e The data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e This study does not involve human participants and animals subjects.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e Author and all co-authors agree to submit the present research work in the EMAS journal and disclose that it has not been published previously and that it is not under consideration for publication elsewhere.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publication\u003c/strong\u003e All authors agree to publish the present work in the EMAS journal.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e The authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAkyol, A., \u0026amp; \u0026Ouml;r\u0026uuml;c\u0026uuml;, \u0026Ouml;.K. 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Study of methods to improve the temporal transfer ability of niche model. \u003cem\u003eJournal of China Agricultural University, 22\u003c/em\u003e(2), 98\u0026ndash;105.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"[email protected]","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, Maxent, stone pine, species distribution model, socio-economic impacts","lastPublishedDoi":"10.21203/rs.3.rs-4395237/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4395237/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn this study, present and future distributions of stone pine due to climate changes were modeled with MaxEnt. CNRM ESM2-1 climate model and bioclimatic variables obtained from the WorldClim database were used as climate models. As climate scenarios, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 climate change scenarios and 2041\u0026ndash;2060 and 2081\u0026ndash;2100 periods were used. Pearson Correlation analysis was performed to prevent high correlation in bioclimatic variables and the multicollinearity problem was eliminated by reducing 19 bioclimatic variables to 9 variables. The contribution of bioclimatic variables to the model was determined by the Jackknife test. To determine the spatial and locational differences between the present and future potential distributions estimated for the species, an analysis of change was conducted. According to the findings of the study, our model has a very high predictive power and the Jackknife test results, the bioclimatic variables BIO19, BIO6, and BIO4 contribute the most to the model. Our prediction model predicts that the distribution area of stone pine will decrease, shifting northward and towards higher altitudes. We believe that this will lead to increased risk of forest fires, loss of ecosystem services, and reduced income from stone pine. For these reasons, benefit from stone pine need to take into account the effects of climate change in their land use planning and give importance to climate change adaptation efforts. These maps, created with current and future predictions of potential habitat distribution, can be use in afforestation, ecological restoration, rural development, conservation, and all kinds of land use studies.\u003c/p\u003e","manuscriptTitle":"Environmental and socio-economic impacts of the changes in distribution areas of Pinus pinea L. (stone pine) due to climate change in Türkiye","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-13 08:53:51","doi":"10.21203/rs.3.rs-4395237/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","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":"b139b5e6-87fb-4435-9f35-ba3b4bd73e5e","owner":[],"postedDate":"June 13th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-07-24T17:41:12+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-13 08:53:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4395237","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4395237","identity":"rs-4395237","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}