Effects of climate change on the distribution of the native Carob tree (Ceratonia siliqua L.) in Morocco

Effects of climate change on the distribution of the native Carob tree (Ceratonia siliqua L.) in Morocco | 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 Effects of climate change on the distribution of the native Carob tree (Ceratonia siliqua L.) in Morocco Jalal Kassout, Soufian Chakkour, Abdeltif El Ouahrani, Younes Hmimsa, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3910804/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Climate change is expected to alter many natural ecosystems around the world, by affecting plants growth and distribution. This is particularly emphasized for several Mediterranean plant species and communities. In this study, we investigate the suitable habitat and geographical distribution of a remarkable Mediterranean tree, Ceratonia siliqua L. (Leguminosae), in Morocco. We hypothesized a reduction in the carob tree suitable habitats under climate change scenarios. To this end, we applied the maximum entropy algorithm (Maxent) to generate current and future models using 303 occurrence points coupled with 19 bioclimatic variables. Two representative concentration pathways (RCP4.5 and RCP8.5) by 2050 and 2070 were considered as future input scenarios. The maximum entropy model provides satisfactory results, with a high value of the Area Under Curve equal to 0.987 (±0.001). Jackknife tests indicate that precipitation followed by temperature play a significant role in the biogeographical dynamics of the Moroccan carob tree. Thus, the obtained results confirm our hypothesis of a reduction of the suitable area under the projected climate change scenarios by 2050 and 2070. The approaches developed in this study is promising to predict the potential distribution of native Mediterranean species and can be an effective tool to support conservation and restoration programs. Ceratonia siliqua L. Morocco MaxEnt Species distribution modelling Climate change scenarios Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction The ongoing global warming is already altering plant species growth and geographical distribution (Doblas-Miranda et al. 2017; Vellend et al. 2017; Kassout et al. 2022a). In fact, with the current rapid rate of warming, global temperature is expected to reach +1.5°C between 2030 and 2050 (IPCC 2018). The complexity of climate change impacts on naturel ecosystems could lead to expansions, reductions or range shifts in the patterns of plant species geographical distribution (Lenoir et al. 2008), leading to significant effects on terrestrial energy, water fluxes and therefore CO 2 emissions (Forzieri et al. 2020). Furthermore, this warming is affecting biodiversity at multiple levels; from individuals and communities to the entire ecosystems (Franklin et al. 2017). As noticed in the Mediterranean region, natural ecosystems are most affected by global warming and extreme climatic events (Doblas-Miranda et al. 2017; Lionello and Scarascia 2018). Therefore, there is a tremendous interest in understanding geographical distribution of plant species under predicted climate change scenarios (Franklin et al. 2017), particularly to set well-adapted conservation and management programs (Kozak et al. 2008). To assess plant species vulnerability to climate change, the approach of species distribution models (SDM) is commonly and increasingly used to predict species geographical range through interpolating and extrapolating their distributions according to environmental factors (Guisan et al. 2017; Pecchi et al. 2019). Moreover, species distribution models provide a comprehensive basis for species conservation and naturel resources management (Sinclair et al. 2010; Qin et al. 2017). Currently, a considerable number of SDMs method were available and widely used such as, BIOCLIM (bioclimatic modeling), Domain (domain environmental envelope), GAM (generalized additive models), MARS (multivariate adaptive regression splines) and MaxEnt (Maximum entropy) (Pecchi et al. 2019). Among theme, MaxEnt algorithm (Phillips et al. 2006) provides reliable results of suitability when presence-only data are available and high flexibility to deal with both widely distributed and rare species occurrences (Elith et al. 2006; Moukrim et al. 2019; Kassout et al. 2022a). For instance, the maximum entropy model has been used to predict macroecological patterns (Harte 2011), species abundance distributions (White et al. 2012), trait-based community assembly (Shipley et al. 2011) and species ecological niche models at multiple scales (Elith et al. 2010; Guisan et al. 2017). Ceratonia siliqua L. (Leguminosae) is an evergreen, thermophilous and dioecious Mediterranean fruit tree (Batlle and Tous 1997; Baumel et al. 2018), with some rare hermaphrodite and monoecious cases (Batlle and Tous 1997). The carob ( C. siliqua ) is slow-growing and long-lived tree dotted with high resistance to drought with limited resistance to extreme cold (Batlle and Tous,1997), thus it shows important genetic diversity (Viruel et al. 2019) and phenotypic variability (Kassout et al. 2022b, 2023). The carob tree has been exploited around the Mediterranean region since antiquity as food and forage source (Zohary 2002), thus, it represents an important component of its semi-natural and traditional agroecosystems (Ramón-Laca and Mabberley 2004; Viruel et al. 2019). Nowadays, carob pods are highly used in the agri-food industry to produce syrups and powder (Papaefstathiou et al. 2018) and the gum extracted from the seed is extremely sought for pharmaceuticals and cosmetics products (Batlle and Tous 1997; Stavrou et al. 2018). Furthermore, the carob tree has shown great potential in reforestation programs and for soil restoration and rehabilitation purposes (Batlle and Tous 1997). Even though its relevant economic and ecological importance, the native status of C. siliqua still source of debate (Ramón-Laca and Mabberley 2004; Baumel et al. 2018). Previous archaeological and historical studies (Hillcoat et al. 1980; Zohary 2002) suggested an Eastern domestication center of the carob tree followed by human-driven dissemination to the West parts of the Mediterranean region. However, recent phylogeographic evidences suggest a strong west-east genetic structuring and the presence of multiple domestication centers from native populations throughout the Mediterranean basin (Viruel et al. 2019). Moreover, distribution models for past periods during the Last Glacial Maximum ( c . 22 ka) suggests probable presence of the carob tree in the Western Mediterranean before its domestication (Viruel et al. 2019). Though, floristic data shows a higher species richness of the Western Mediterranean plant communities associated with carob tree compared to the Eastern parts (Baumel et al. 2018). In Morocco, carob communities show significant differentiation between the North, characterized by a thermo-Mediterranean vegetation under sub-humid and semi-arid climate, and the South region communities characterized by a quasi-steppic vegetation under semi-arid climate (Baumel et al. 2018; Taleb and Fennane 2019; Kassout et al. 2022b, 2023). Despite their interest, the carob tree populations show a declining trend (Rankou et al. 2017), mainly due to the multiple threats from the rapid global warming and anthropogenic pressures. Therefore, it is crucial to understand possible responses of Ceratonia siliqua to climate change and predict the area of its potential distribution under different climate change scenarios. In this study, we used the MaxEnt SDM model to predict the potential distribution of Ceratonia siliqua in Morocco under climate change scenarios. Specifically, we used species distribution models (SDMs) to answer two main questions: (1) what are the key climatic factors that affect current C. siliqua distribution and suitable area in Morocco? and (2) how the future climate scenarios would affect their habitat suitability? Finally, our study will provide a theoretical basis for management and conservation decisions and possible plantation and reforestation efforts. 2. Materials and Methods 2.1 Study region The study region covers the whole Moroccan territory, situated in the western part of the Mediterranean Basin and the extreme north-west of Africa (Fig. 1). As a biodiversity hotspot (Médail and Diadema 2009), the study region presents a wide range of phytogeographic and phytoecological units including forests, matorrals and shrub-lands (Taleb and Fennane 2019), with an important variation in climatic conditions (Kassout et al. 2019, 2022b, 2023). 2.2 Occurrence data Occurrence data of Ceratonia siliqua were collected through field surveys recording each presence points with GPS coordinates. In addition, we extracted additional occurrence data from the Global Biodiversity Information Facility-GBIF (2020) and from previous published studies dealing with carob presence in Morocco (e.g., Baumel et al. 2018; Viruel et al. 2019; Kassout et al. 2022b, 2023). In this study, we considered only occurrence data within natural conditions. All occurrence data were merged into one single presence-only dataset. Finally, we got 303 occurrence points, a sufficient dataset to construct MaxEnt models of current and future potential distribution of C. siliqua suitable habitats. The spatial distribution of this occurrence data is shown in Fig. 1. 2.3 Bioclimatic variables and future scenarios We used 19 bioclimatic variables (Table 1) extracted from the Worldclim database version 2.0 (Fick and Hijmans 2017) with a spatial resolution of approximately 1 km² (30 arc seconds). These variables consisted of monthly averages of temperature and precipitation covering the period between 1970 and 2000. Considering their relevant influence on plant growth, productivity, and physiological processes (Moles et al. 2014; Li et al. 2016; Kassout et al. 2021, 2022b, 2023), the extracted bioclimatic variables are largely used in species distribution models (Elith et al. 2010; Guisan et al. 2017; Du et al. 2021; Kassout et al. 2022a). The future projections by 2050 (average for 2041 - 2060) and 2070 (average for 2061 - 2080) were used to predict future distribution of Ceratonia siliqua L. in Morocco. Two representative concentration pathways (RCPs) trajectories were considered RCP 4.5 and RCP 8.5 corresponding to an increase by 4.5- and 8.5-watts m -2 of radiative forcing and an increase of CO 2 levels by 650 ppm and 1350 ppm, respectively, by 2100 (IPPC 2018). We supposed a moderate and an extreme scenario to build the future projections (van Vuuren et al. 2011). From the Global Circulation Models (CGMs), and for all projections, we adopted the Community Climate System Model 4.0 (CCSM4) from the CMIP5 (Coupled Model Inter-comparison Project phase 5) model developed by the Intergovernmental Panel on Climate Change (IPCC). The CCSM4 has been reported to better predict precipitation and temperature variables (Gent et al. 2011), thus, gives efficient predictions of future plant species distribution (Al-Qaddi et al. 2017). To avoid collinearity issues, the bioclimatic variables were tested for multicollinearity using Pearson’s correlation test. The highly correlated variables (|r| > 0.9) were removed from predictor variables dataset before species distribution modeling (Dormann et al. 2013). Multicollinearity of variables can result in over-fitting of species distribution models and lead to miss interpretation of variables contribution (Elith et al. 2010). Pearson’s correlation coefficient was calculated with the open R software v 3.5.1 (R Core Team 2018). As a result, we used a subset of 12 bioclimatic variables to construct current and future models of C. siliqua in Morocco (see Table 1). Table 1 Bioclimatic variables used as environmental input in the modelling of Ceratonia siliqua potential distribution in Morocco and their percentage contribution. The highlighted variables were selected after multicollinearity test Bioclimatic Variable Description Units % contribution Permutation importance Bio1 Annual mean temperature °C 12.1 2 Bio2 Mean diurnal range (mean of monthly max. and min. temp.) °C 3.4 3.5 Bio3 Isothermality ((Bio2/Bio7) × 100) Index 3.9 1.9 Bio4 Temperature seasonality (standard deviation ×100) Index 14.7 36.5 Bio5 Maximum temperature of warmest month °C 3 1.5 Bio6 Minimum temperature of coldest month °C 2.9 15.4 Bio7 Temperature annual range (Bio5–Bio6) °C - - Bio8 Mean temperature of wettest quarter °C 3.8 3.2 Bio9 Mean temperature of driest quarter °C - - Bio10 Mean temperature of warmest quarter °C - - Bio11 Mean temperature of coldest quarter °C - - Bio12 Annual precipitation mm - - Bio13 Precipitation of wettest period mm - - Bio14 Precipitation of driest period mm 0.2 0.4 Bio15 Precipitation seasonality (Coefficient of variation) Index 5.3 6.3 Bio16 Precipitation of wettest quarter mm - - Bio17 Precipitation of driest quarter mm 2.4 2.6 Bio18 Precipitation of warmest quarter mm 2.7 1.6 Bio19 Precipitation of coldest quarter mm 45.7 25.2 2.4 Model structure and data processing MaxEnt 3.4.1 (Phillips et al. 2017) software was used to predict the potential geographical distribution of C. siliqua under current and four future scenarios (RCP4.5-2050s, RCP4.5-2070s, RCP8.5-2050s, RCP8.5-2070s). This modelling approach is based on the Maximum entropy algorithm allowing to estimate habitat suitability and ecological niche (Phillips et al., 2006) using to bioclimatic variables. All models were executed using 10 bootstrap runs with a 25 percent random test percentage, which allows to use 75% of presence data to construct and calibrate the models and 25% to test and evaluate the accuracy and the predictive ability of the models. This procedure is appropriate for estimating occurrence probability (Phillips et al. 2017). The average of all runs was used as final models. As settings, and for all models, we used an auto-feature option (linear, quadratic, product, threshold and hinge methods) and the loglog output format (ranging from 0 to 1), a maximum number of 5000 iterations with a convergence threshold of 10 –5 , a maximum number of background points as 10000, and a regularization parameter value of 1. The use of a loglog transform provides a stronger theoretical justification than the logistic transform, which replaces it by default (Phillips et al. 2017). The jackknife test was used to determine the climatic variables that influence significantly the potential distribution of C. siliqua (Li et al. 2016). To determine the accuracy of the models, we used the Area Under the Curve (AUC) of the Receiver Operating characteristics Curve (ROC) (Phillips et al. 2017). High AUC values indicates that the model can distinguish between presence and background points, and therefore indicates a better performance of the model (Elith et al. 2006). AUC values less than 0.5 indicates a model with random prediction, values between 0.5 and 0.7 are considered low accuracy, values between 0.7 and 0.9 are adequate and values higher than 0.9 indicates excellent accuracy of the predicted model (Elith et al. 2006; Yang et al. 2013; Kurpis et al. 2019). For display and further analysis, we first used binary maps for current and future distribution to calculate the suitability and unsuitability habitat using the maximum training sensitivity plus specificity threshold, which is more suitable for large distributed species (Liu et al. 2013). Secondly, and to highlight area of optimal occurrence of C. siliqua we divided the potential suitability probability to four classes (Yang et al. 2013; Qin et al. 2017) as follow: unsuitable habitat (0-0.2), barley suitable habitat (0.2-0.4), suitable habitat (0.4-0.6), optimal habitat (0.6-0.7) and highly optimal habitat (0.7-1). Finally, spatial analysis and cartographic processing of the final results were performed using QGIS, version 2.18.20, open sources GIS software (QGIS Development Team 2015). 3. Results 3.1. Model performance Table 2 resumes the area under the curve (AUC) average values of MaxEnt models performance for Ceratonia siliqua and suitable area under current and future RCPs scenarios by 2050 and 2070. These results showed that the average AUC values for training and testing was higher than 0.9 for both the current and the future scenarios, which indicates an excellent model performance (Phillips et al. 2006). The average AUC training values of the 10 bootstrap runs of current climatic conditions was 0.987 (± 0.001) with a maximum training sensitivity plus specificity threshold of 1.952. The future suitability distribution of C. siliqua predicted by the models showed high AUC values for both RCPs scenarios by 2050 and 2070 representing a good success of the models. These results showed that the bioclimatic variables included in this study could led to an excellent prediction of C. siliqua distribution in Morocco. Table 2 The area under the curve (AUC) average values of Maxent models performance for Ceratonia siliqua and suitable area under current and future RCPs scenarios by 2050 and 2070. AUC mean values of training and test data are given after partitioning subspecies presence-only data into training (70%) and test (30%) with standard deviation. Suitable area was determined using the maximum training sensitivity plus specificity threshold Period/Climate scenario AUC Suitable Area AUC mean † Training AUC mean † Test Area (Km²) Percentage (%) Current (1970 – 2000) 0.987 ± 0.001 0.978 ± 0.004 78 233 11.46 Projected 2050 - RCP4.5 0.969 ± 0.003 0.961 ± 0.007 70 185 10.28 2050 - RCP8.5 0.971 ± 0.003 0.957 ± 0.009 70 579 10.35 2070 - RCP4.5 0.970 ± 0.005 0.955 ± 0.008 68 746 10.07 2070 - RCP8.5 0.967 ± 0.004 0.962 ± 0.006 71 457 10.47 † Mean values of 10 bootstrap runs. 3.2. Contributions of bioclimatic variables Fig. 2 shows the Jackknife test results for the regularized training gain (Fig. 2a) and the AUC of C. siliqua potential suitable habitat (Fig. 2b). These results indicated that the precipitation of the coldest quarter (Bio19) has the most influential impact on the current distribution of C. siliqua and contributes with 45.7% to the final model. The temperature seasonality (Bio4) and the annual mean temperature (Bio1) were the second highest contributor to the model with, respectively, 14.7% and 12.1%. The precipitation seasonality (Bio15), the isothermality (Bio3), the mean temperature of the wettest quarter (Bio8), and the mean diurnal range (Bio2) contributed respectively with 5.3%, 3.9%, 3.8%, and 3.4%. The lowest contributors were the maximum temperature of warmest month (Bio5, 3%), the minimum temperature of coldest month (Bio6, 2.9%), the precipitation of the warmest quarter (Bio18, 2.7%), the precipitation of the driest quarter (Bio17, 2.4%) and the precipitation of the driest period (Bio14, 0.2%). The cumulative contributions of the precipitation-related variables were 56.5% and the temperature-related variables was 43.8% (Table 1). 3.3. Predicted habitat suitability for the Ceratonia siliqua in Morocco Fig. 3 represents the habitat suitability maps of C. siliqua in Morocco under current and future climate scenarios by 2050 and 2070. After these maps, the potential current suitable area of C. siliqua in Morocco is 78,233 km², which represents 11.46% of the total studied area. Thus, the optimal (0.6-0.7) and the highly optimal (0.7-1) distribution area were 4765 km² and 15,054 km², respectively (Table 3; Fig. 4). The majority of the optimal habitats (≥0.6) was located in the north-west and the south-west parts of the country (Fig. 4). Habitat suitability distribution for C. siliqua showed significant changes under global climate models (GCMs) of CCSM4 for both RCPs pathways (RCP4.5 and RCP8.5) by 2050 and 2070 (Table 2; Fig. 5). Overall, suitable area variation between current and future conditions showed progressive reduction along with increasing climate warming. Under RCP4.5 scenario, the potential distribution of C. siliqua would decrease by 1.18% and 1.39% respectively for 2050 and 2070. A similar pattern was observed under RCP8.5 with decreasing suitability by 1.11% and 0.99%, respectively for 2050 and 2070 (Table 2; Fig. 3). Regarding variable contributions, the precipitation of the coldest quarter (Bio19) represented the most bioclimatic variable contributing to the prediction models of C. siliqua habitat suitability distribution by 2050 and 2070 (Fig. 6). By focusing on optimal area (0.6-0.7), we observed a slight gain in area suitability by 2050 from 4765 km² to 5060 km² and 4781 km², respectively, under RCP4.5 and RCP8.5 (Table 3; Fig. 4). However, by 2070, only under RCP4.5, we observed a gain in suitability area from 4765 km² to 5090 km², while we observed a decrease in suitability under RCP8.5 from 4765 km² to 4571 km² (Table 3; Fig. 4). Compared to the current potential distribution, the future scenarios show a progressive reduction in optimal habitats (≥0.6) of C. siliqua in the north-west and the south-west parts, replaced by suitable (0.4-0.6) habitats, while a gaining of suitable area (≥0.6) in the middle-center region of the country was observed by 2050 under RCP8.5. Table 3 Habitat suitability area (Km²) of Ceratonia siliqua under different suitability classes. Percentage of suitable area are shown between brackets Period/Climate scenario Suitability classes area (km²) Unsuitable (0-0.2) Barley suitable (0.2-0.4) Suitable (0.4-0.6) Optimal (0.6-0.7) Highly optimal (0.7-1) Current (1970 – 2000) 60 4796 (88.63) 41 287 (6.05) 15 361 (2.25) 4 765 (0.70) 15 054 (2.21) Future 2050 - RCP 4.5 61 2778 (89.80) 32 414 (4.75) 18 154 (2.66) 5 060 (0.74) 12 852 (1.88) 2050 - RCP 8.5 61 2202 (89.71) 34 955 (5.12) 16 803 (2.46) 4 781 (0.70) 12 514 (1.83) 2070 - RCP 4.5 61 4103 (89.99) 31 847 (4.67) 17 445 (2.56) 4 571 (0.67) 13 291 (1.95) 2070 - RCP 8.5 61 1289 (89.58) 33 864 (4.96) 17 219 (2.52) 5 090 (0.75) 13 796 (2.02) 4. Discussion Modelling the impacts of climate change on potential habitat suitability of plant species at global and local scales is critical to understand their ecological and evolutionary future pathways (Kozak et al. 2008; Dawson et al. 2011). In this context, species distribution models (SDMs) are relevant tools to forecast natural habitat expansions or contractions (Guissan and Thuiller 2005; Guissan et al. 2017). They are required for setting adequate monitoring and restoration programs of natural ecosystems (Pecchi et al. 2019), particularly in the light of the rapid rate of biodiversity loss (Dawson et al. 2011). Looking at the vulnerability of Mediterranean natural ecosystems under the current global changes (Blondel et al. 2010), it is crucial to understand the future dynamics of native plant species and its genetic resources. Here, we highlighted the potential distribution of the multipurpose species Ceratonia siliqua in Morocco under four different climate change scenarios by 2050 and 2070, which presents a contribution to set both conservation and reforestation programs of this remarkable Mediterranean plant species. Our results show that the potential geographical suitable habitat of the carob tree in Morocco can be accurately predicted by bioclimatic factors. The average AUC values of 10 bootstrap runs were greater than 0.9, for both current and future scenarios, indicating the high accuracy and reliability of the final models (Elith et al. 2006; Merow et al. 2013). Thus, our results agree with other studies showing that the maximum entropy algorithm can provide high accurate prediction of plant species potential habitat (Yang et al. 2013; Qin et al. 2017; Moukrim et al. 2019; Kurpis et al. 2019; Du et al. 2021; Kassout et al. 2022a). Moreover, our findings highlight the importance of climatic factors in controlling the carob tree suitable habitats in Morocco, as previously demonstrated for the establishment of several Mediterranean plant species (Médail and Diadema 2009). The potential distribution model of C. siliqua show that the precipitation of the coldest quarter (Bio9) has the largest impact on defining its current suitable habitat. Thus, temperature seasonality (Bio3) and the annual mean temperature (Bio1) has substantial contribution on carob tree potential distribution. These results consolidate the findings suggesting that precipitations and temperature are the most common factors shaping the distribution of plant communities and diversity (Moles et al. 2014) with direct implication in the ecological and evolutionary trajectory of plant species (Donoghue 2008; Frank 2011). However, our results show that the precipitation-related variables have greater influence on the geographical distribution of C. siliqua compared to temperature-related variables. The low contribution of temperature variables might be explained by the thermophilous nature of C. siliqua and its distribution under various climatic conditions form sub-humid to semi-arid climates (Baumel et al. 2018; Kassout et al. 2022b). Hence, the weak sensitivity of suitable habitat models to temperatures could be interpreted as an adaptation form of the carob tree. The high contribution of precipitation-related variables can be viewed as the effect of aridity and its direct relationship with water availability, which is the limiting factor for physiological responses and adaptation process for most plant species (Doblas-Miranda et al. 2017; Kassout et al. 2019, 2021, 2023). Thus, these variables largely design the structure of Mediterranean plant communities (Doblas-Miranda et al. 2017). In addition, seasonal variation of precipitation regime within the Mediterranean region induced stressful conditions, which limiting the plant growth, development and survival (Vellend et al. 2017). The correlations between precipitations-related variables and plant physiological responses were also reported for the thermophilous and Mediterranean wild native olive tree ( Olea europaea subsp europaea var. sylvestris ) in Morocco (Kassout et al., 2019, 2021). This observed pattern might be regarded as the results of the biogeographic history of Mediterranean flora (Blondel et al. 2010). Predicted climate change scenarios (RCP4.5 and RCP8.5) by 2050 and 2070 show a declining rate of carob suitable habitat in Morocco. For instance, suitable area will decrease from 78,233 km² (11.46%) to 70,185 km² (10.28%) and 68,746 km² (10.07%), respectively, under RCP4.5 by 2050 and 2070 (Fig. 5). In fact, precipitation-related variables largely defined the future potential habitat of C. siliqua in Morocco (Fig. 6). These results are consistent with some findings stated that suitable habitat of plant species might decrease under climate change scenarios (Moukrim et al. 2019; Kassout et al. 2022a). Hence, the loss of carob tree suitable habitat might be greatly influenced by the projected increasing temperature and decreasing precipitations in the Mediterranean region (Driouech et al. 2010; Lionello and Scarascia 2018). It is indisputable that climate change is expected to reduce plant species capacity to withstand new challenging conditions, such as extreme aridity (Dawson et al. 2011; Doblas-Miranda et al. 2017). However, when taking in consideration highly suitable habitat (Fig. 4), we observed a slight increasing in suitable conditions of carob tree. In fact, the future suitable climatic envelope of the species ranges from Northern to Southern regions of the country can explain this tendency. Meanwhile, the response of C. siliqua to climate change will depend on several factors such as anthropogenic activities, phenotypic plasticity, genetic diversity and competing ability (Phillips et al. 2006; Parmesan and Hanley 2015). In the case of C. siliqua , a species with long-term longevity and limited dispersal ability, gain of suitable habitat under climate change is significantly limited. Using MaxEnt algorithm, we show that climate change might impose challenging conditions for this multipurpose plant species. However, looking into biological and ecological process underlying spatial distribution of plant species (Shipley et al. 2011), such as genetic diversity, dissemination rate, and inter-specific interactions, SDMs has its limitation to predict the exact relationship between species occurrence and environmental factors without considering biological and ecological variables when constructing the models (Bedia et al. 2013). These limitations can be explained by the lack of comprehensive database, integrating accurate climatic data at local scales with reasonable resolution, coupled with data on plants functional and ecological characteristics. 5. Conclusion In this study we used MaxEnt approach to provide a comprehensive basis for current and future potential habitat suitability of the carob tree in Morocco using two IPCC climatic scenarios (RCP4.5 and RCP8.5) by the years 2050 and 2070. Our results conclude that the geographic distribution of the carob might experience a considerable decline regarding climate change scenarios with noticeable risk of disappearance in some areas, in particular those projected to be drier as the case of the south-eastern parts of Morocco. Therefore, the loss of natural vegetation of C. siliqua communities will greatly affect the ecological functions and services of natural ecosystems and, therefore, affecting valuable genetic resources. Our results contribute to understand the biogeography and the history of Mediterranean native tree species, and the potential dynamics of natural vegetation communities facing the global change. The prediction method used in this study can be applied into several tree species distribution to predict future trends at the ecosystem and population levels under climate change scenarios. Furthermore, it is wise, in light of the above, to encourage and promote C. siliqua for reforestation programs. Declarations Author contributions Jalal kassout Conceptualization, Methodology, Data curation, Formal analysis, Software, Investigation, Writing - original draft, Writing - review & editing, Visualization. Soufian Chakkour Investigation, Writing - original draft. Abdeltif El Ouahrani Investigation, Writing - original draft. Younes Hmimsa Investigation, Writing - original draft. S alama El Fatehi: Investigation, Writing - original draft. Yanzheng Yang Writing - original draft, Writing - review & editing. Rachid Hadria: Visualization. David Ariza-Mateos Project administration; Resources. Guillermo Palacios-Rodríguez: Project administration; Resources. Rafael Navarro-Cerrillo Funding acquisition, Resources, Writing - review & editing. Mohammed Ater Conceptualization, Funding acquisition, Resources, Writing - original draft, Writing - review & editing, Supervision. Acknowledgment The authors thank the members of the BioAgrodiversity Team for helpful discussions and comments. Likewise, the authors thank agents of the Water and Forests Department of Morocco for helpful information during fieldwork. This study was supported by the Agencia Andaluza de Cooperación Internacional para el Desarrollo (AACID) and the project « Amélioration de la productivité des cultures forestières d’intérêt socio-économique élevé dans les zones rurales du nord du Maroc, n° 2018004 ». Data Availability Statemen t The occurrence records used to calibrate and evaluate SDMs are available on request from the corresponding authors. Ethic approval No approval of research ethics committees was required to accomplish the goals of this study. Conflict of interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Informed consen Informed consent was obtained from all individual participants included in the study. Financial or non‑financial interests None. References Al-Qaddi N, Vessella F, Stephan J, Al-Eisawi D, Schirone B (2017) Current and future suitability areas of kermes oak ( Quercus coccifera L.) in the Levant under climate change. Regional Environmental Change, 17, 143–156. https://doi.org/10.1007/s10113-016-0987-2 Batlle I, Tous J (1997) Carob tree Ceratonia siliqua L. Promoting the conservation and use of underutilized and neglected crops. 17. Rome, Institute of Plant Genetics and Crop Plant Research, Gatersleben/International Plant Genetic Resources Institute. Baumel, A., Mirleau, P., Viruel, J., Bou Dagher Kharrat et al (2018) Assessment of plant species diversity associated with the carob tree ( Ceratonia siliqua , Fabaceae) at the Mediterranean scale. Plant Ecology and Evolution, 151(2), 185–193. Bedia J, Herrera S, Gutiérrez JM (2013) Dangers of using global bioclimatic datasets for ecological niche modeling. Limitations for future climate projections. Global and Planetary Change 107, 1–12. https://doi.org/10.1016/j.gloplacha.2013.04.005 Blondel J, Aronson J, Bodiou J-Y, Boeu G (2010) The Mediterranean region: biological diversity through time and space. Oxford: Oxford University Press. pp 376. Dawson TP, Jackson ST, House JI, Prentice IC, Mace GM (2011) Beyond predictions: biodiversity conservation in a changing climate. Science, 332(6025), 53–8. https://doi.org/10.1126/science.1200303 Doblas-Miranda E, Alonso R, Arnan X, Bermejo V et al (2017) A review of the combination among global change factors in forests, shrublands and pastures of the Mediterranean Region: beyond drought effects. Global and Planetary Change, 148, 42–54. https://doi.org/10.1016/j.gloplacha.2016.11.012 Donoghue MJ (2008) A phylogenetic perspective on the distribution of plant diversity. Proceeding of the National Academy of Sciences of the United Stets of America, 105, 11549–11555. https://doi.org/10.1073/pnas.0801962105 Dormann CF, Elith J, Bacher S, Buchmann C, Carl G, Carré G et al (2013) Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography, 36(1), 27–46. https://doi.org/10.1111/j.1600-0587.2012.07348.x Driouech F, Déqué M, Sanchez-Gomez E (2010) Weather regimes-Moroccan precipitation link in a regional climate change simulation. Global Planet Change, 72(1-2), 1–10. https://doi.org/10.1016/j.gloplacha.2010.03.004 Du Z, He Y, Wang H, Wang C, Duan Y (2021) Potential geographical distribution and habitat shift of the genus Ammopiptanthus in China under current and future climate change based on the MaxEnt model. Journal of Arid Environments, 184, 104328. Elith J, Graham G, Anderson R, Dudik M, Ferrier S et al (2006) Novel methods improve prediction of species’ distributions from occurrence data. Ecography, 29(2), 129–151. https://doi.org/10.1111/j.2006.0906-7590.04596.x Elith J, Kearney M, Phillips S (2010) The art of modelling range-shifting species. Methods in Ecology and Evolution, 1(4), 330-342. https://doi.org/10.1111/j.2041-210X.2010.00036.x Fick S, Hijmans R (2017) WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology, 37(12), 4302–4315. https://doi.org/10.1002/joc.5086 Forzieri G, Miralles DG, Ciais P, Alkama R et al (2020) Increased control of vegetation on global terrestrial energy fluxes. Nature Climate Change, 10, 356–362. https://doi.org/10.1038/s41558-020-0717-0 Frank SA (2011) Natural selection. II. Developmental variability and evolutionary rate. Journal of Evolutionary Biology, 24(11), 2310–2320. https://doi.org/10.1111/j.1420-9101.2011.02373.x Franklin J, Serra-Diaz JM, Syphard AD, Regan HM (2017) Big data for forecasting the impacts of global change on plant communities. Global Ecology and Biogeography, 26, 6–17. https://doi.org/10.1111/geb.12501 GBIF. Global Biodiversity Information Facility (2020) GBIF Occurrence Download, https://doi.org/10.15468/dl.vv4gfm (accessed 30 September 2020) Gent PR, Danabasoglu G, Donner LJ, Holland MM et al (2011) The Community Climate System Model Version 4. Journal of Climate, 24(19), 4973–4991. https://doi.org/10.1175/2011JCLI4083.1 Guisan A, Thuiller W, Zimmermann NE (2017) Habitat suitability and distribution models. With applications in R. Cambridge: Cambridge University Press. https://doi.org/10.1017/9781139028271 Harte J (2011) Maximum Entropy and Ecology: A Theory of Abundance, Distribution, and Energetics. London: Oxford University Press. https://doi.org/10.1093/acprof:oso/9780199593415.001.0001 Hillcoat D, Lewis G, Verdcourt B (1980) A new species of Ceratonia (Leguminosae-Caesalpinioideae) from Arabia and the Somali Republic. Kew Bulletin, 35, 261–271. https://doi.org/10.2307/4114570 IPCC (2018) Summary for Policymakers, in: Delmotte, V., P. Zhai, H.-O. Pörtner, D. Roberts, J. et al., (Eds.), Global Warming of 1.5°C. An IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty World Meteorological Organization, Geneva, Switzerlan, pp. 32. Kassout J, Terral J-F, Hodgson J, Ater M (2019) Trait-based plant ecology a flawed tool in climate studies? The leaf traits of wild olive that pattern with climate are not those routinely measured. PLoS ONE, 14(7):e0219908. https://doi.org/10.1371/journal.pone.0219908 Kassout J, Ater M, Ivorra S, Barbara H, Limier B, Ros J, Girard V, Paradis L, Terral J-F (2021) Resisting Aridification: Adaptation of Sap Conduction Performance in Moroccan Wild Olive Subspecies Distributed Over an Aridity Gradient. Frontiers in Plant Science, 12:663721. https://doi.org/10.3389/fpls.2021.663721. Kassout J, Hmimsa Y, El Fatehi S, El Ouahrani A, Kadaoui K et al. (2022b) Image analysis of Moroccan carob seeds ( Ceratonia siliqua L.) revealed substantial intraspecific variations depending on climate and geographic origin. Ecological Processes, 11(1), 1-14. Kassout J, Terral J-F, El Ouhrani A, Houssni M, Ivorra S et al (2022a) Species Distribution Based-Modelling Under Climate Change: The Case of Two Native Wild Olea europaea Subspecies in Morocco, O. e. subsp. europaea var. sylvestris and O. e. subsp. maroccana . In: Climate Change in the Mediterranean and Middle Eastern Region. Climate Change Management ed. Leal Filho W., Manolas E. Springer, Cham. Kassout J, Hmimsa Y, Fatehi SE, Kadaoui K, Houssni M, Chakkour S et al (2023) Aridity Gradients Shape Intraspecific Variability of Morphological Traits in Native Ceratonia siliqua L. of Morocco. Plants, 12(19), 3447. Kozak KH, Graham CH, Wiens JJ (2008) Integrating GIS-based environmental data into evolutionary biology. Trends in Ecology and Evolution, 23(3), 141–148. https://doi.org/10.1016/j.tree.2008.02.001 Kurpis J, Serrato-Cruz MA, Arroyo TPF (2019) Modeling the effects of climate change on the distribution of Tagetes lucida Cav. (Asteraceae). Global Ecology and Conservation, 20, e00747. https://doi.org/10.1016/j.gecco.2019.e00747 Lenoir J, Gégout JC, Marquet PA, De RP, Brisse H (2008) A significant upward shift in plant species optimum elevation during the 20th century. Science, 320(5884), 1768–1771. https://doi.org/10.1126/science.1156831 Li GQ, Du S, Wen ZM (2016) Mapping the climatic suitable habitat of oriental arborvitae ( Platycladus orientalis ) for introduction and cultivation at a global scale. Scientific Reports, 6, 30009. https://doi.org/10.1038/srep30009 Lionello P, Scarascia L (2018) The relation between climate change in the Mediterranean region and global warming. Regional Environmental Change, 18(5), 1481–1493. https://doi.org/10.1007/s10113-018-1290-1 Liu C, White M, Newell G (2013) Selecting thresholds for the prediction of species occurrence with presence-only data. Journal of Biogeography, 40, 778–789. Médail F, Diadema K (2009) Glacial refugia influence plant diversity patterns in the Mediterranean Basin. Journal of Biogeography, 36(7), 1333–1345. https://doi.org/10.1111/j.1365-2699.2008.02051.x Moles AT, Perkins SE, Laffan SW, et al (2014) Which is a better predictor of plant traits: temperature or precipitation?. Journal of Vegetation Science, 25(5), 1167–1180. https://doi.org/10.1111/jvs.12190 Moukrim S, Lahssini S, Rhazi M et al (2019) Climate change impacts on potential distribution of multipurpose agro-forestry species: Argania spinosa (L.) Skeels as case study. Agroforestry Systems, 93, 1209–1219. https://doi.org/10.1007/s10457-018-0232-8 Papaefstathiou E, Agapiou A, Giannopoulos S, Kokkinofta R (2018) Nutritional characterization of carobs and traditional carob products. Food Science and Nutrition, 6(8), 2151–2161. https://doi.org/10.1002/fsn3.776 Parmesan C, Hanley ME (2015) Plants and climate change: complexities and surprises. Annals of Botany, 116(6), 849–864. https://doi.org/10.1093/aob/mcv169 Pecchi M, Marchi M, Burton V, Giannetti F, Moriondo M et al (2019) Species distribution modelling to support forest management. A literature review. Ecological Modeling, 411(1), 1–12. https://doi.org/10.1016/j.ecolmodel.2019.108817 Phillips SJ, Anderson RP, Schapire RE (2006) Maximum entropy modeling of species geographic distributions. Ecological Modelling, 190(3-4), 231–259. https://doi.org/10.1016/j.ecolmodel.2005.03.026 Phillips SJ, Anderson RP, Dudík M, Schapire RE, Blair ME (2017) Opening the black box: an open‐source release of Maxent. Ecography, 40(7), 887–893. https://doi.org/10.1111/ecog.03049 QGIS Development Team (2012) QGIS Geographic Information System. Open Source Geospatial Foundation Project. http://qgis.org Qin AL, Liu B, Guo QS, Bussmann RW, Ma F et al (2017) Maxent modeling for predicting impacts of climate change on the potential distribution of Thuja sutchuenensis Franch. an extremely endangered conifer from southwestern China. Global Ecology and Conservation, 10, 139e146. https://doi.org/10.1016/j.gecco.2017.02.004 R Core Team (2018) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Available online at https://www.R-project.org/ Ramón-Laca L, Mabberley DJ (2004) The ecological status of the carob-tree ( Ceratonia siliqua , Leguminosae) in the Mediterranean. Botanical Journal of the Linnean Society, 144, 431–436. https://doi.org/10.1111/j.1095-8339.2003.00254.x Rankou H, M’Sou S, Chadburn H, Rivers M, Ouhammou A, Martin G (2017) Ceratonia siliqua . The IUCN Red List of Threatened Species. https://doi.org/10.2305/IUCN.UK.2017-3.RLTS.T202951A112823254.en Schröder B (2008) Challenges of species distribution modeling belowground. Journal of Plant Nutrition and Soil Science, 171(3), 325–337. https://doi.org/10.1002/jpln.200700027 Shipley B, Laughlin DC, Sonnier G, Otfinowski R (2011) A strong test of a maximum entropy model of trait- based community assembly. Ecology, 92, 507–517. https://doi.org/10.1890/10-0394.1 Sinclair S, White M, Newell G (2010) How useful are species distribution models for managing biodiversity under future climates. Ecology and Society, 15(1):8. http://www.ecologyandsociety.org/vol15/iss1/art8/ Stavrou IJ, Christou A, Kapnissi-Christodoulou CP (2018) Polyphenols in carobs: A review on their composition, antioxidant capacity and cytotoxic effects, and health impact. Food Chemistry, 269, 355–374. https://doi.org/10.1016/j.foodchem.2018.06.152 Taleb MS, Fennane M (2019) Vascular Plant Communities of Morocco, Phytosociology, Ecology and Geography. Geobotany Studies: Basics, Methods and Case Studies. Springer, 161pp. van Vuuren DP, Edmonds J, Kainuma MLT, Riahi K, Thomson A, Matsui et al (2011) Representative concentration pathways: An overview. Climatic Change, 109, 5. https://doi.org/10.1007/s10584-011-0148-z Vellend M, Baeten L, Becker-Scarpitta A, Boucher-Lalonde V et al (2017) Plant biodiversity change across scales during the Anthropocene. Annual Review of Plant Biology, 68, 563–586. https://doi.org/10.1146/annurev-arplant-042916-040949 Viruel J, Le Galliot N, Pironon S, Nieto Feliner G, Suc J et al (2019) A strong east–west Mediterranean divergence supports a new phylogeographic history of the carob tree ( Ceratonia siliqua , Leguminosae) and multiple domestications from native populations. Journal of Biogeography, 47, 460–471. https://doi.org/10.1111/jbi.13726 White EP, Katherine M, Thibault M, Xiao X (2012) Characterizing species abundance distributions across taxa and ecosystems using a simple maximum entropy model. Ecology, 93, 1772–1778. https://doi.org/10.1890/11-2177.1 Yang XQ, Kushwaha SPS, Saran S, Xu J, Roy PS (2013) Maxent modeling for predicting the potential distribution of medicinal plant, Justicia adhatoda L. in Lesser Himalayan foothills. Ecological Engineering, 51, 83–87. https://doi.org/10.1016/j.ecoleng.2012.12.004 Zohary D (2002) Domestication of the carob ( Ceratonia siliqua L.). Israel Journal of Plant Science, 50, 141–215. https://doi.org/10.1560/BW6B‐4M9P‐U2UA‐C6NN. 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3910804","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":270060697,"identity":"898a734f-117f-436f-8e8a-1f74c60ac1ce","order_by":0,"name":"Jalal Kassout","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8klEQVRIiWNgGAWjYBACxgbmBoYHDAlQboUEMVoYG4AaoFoOnCFCC0gTQsvBNiI0MLcfbPyQwJAmL99/+Jj0x3kWeebtB1g3/MBnR09is0QCQ47hhhtpaRIHt0kUy5xJYLvZg9dZiQ1ALRWMGyR4zEBaEmcwJLDd4MGnpf9h8w+gFvv5/WeAWuYAtfA/YLv5B5+WGYltIIclNhzIAWppAGqRSGC7jdeWGQ/bLBIM0pKBfkm2OHMMpOVh220ZPFoM+5MP3/hQkWw7v//wwRsVNXVAhyUfu/kGn5YGEGmAanMDHg0MDPJ4ZUfBKBgFo2AUgAAAHwlTaYJ5eA0AAAAASUVORK5CYII=","orcid":"","institution":"National Institute of Agricultural Research","correspondingAuthor":true,"prefix":"","firstName":"Jalal","middleName":"","lastName":"Kassout","suffix":""},{"id":270060698,"identity":"f8c29b67-132b-4b8e-86c9-58977bc8e7cb","order_by":1,"name":"Soufian Chakkour","email":"","orcid":"","institution":"University of Abdelmalek Essaadi","correspondingAuthor":false,"prefix":"","firstName":"Soufian","middleName":"","lastName":"Chakkour","suffix":""},{"id":270060699,"identity":"8ea281f5-2443-4ffc-b079-8fa07e17063a","order_by":2,"name":"Abdeltif El Ouahrani","email":"","orcid":"","institution":"University of Abdelmalek Essaadi","correspondingAuthor":false,"prefix":"","firstName":"Abdeltif","middleName":"El","lastName":"Ouahrani","suffix":""},{"id":270060700,"identity":"fee3f9e1-9003-4aef-844a-93d8a15f21f1","order_by":3,"name":"Younes Hmimsa","email":"","orcid":"","institution":"University of Abdelmalek Essaadi","correspondingAuthor":false,"prefix":"","firstName":"Younes","middleName":"","lastName":"Hmimsa","suffix":""},{"id":270060701,"identity":"beb044c4-3653-4c62-931e-3400ee26d82e","order_by":4,"name":"Salama El Fatehi","email":"","orcid":"","institution":"University of Abdelmalek Essaadi","correspondingAuthor":false,"prefix":"","firstName":"Salama","middleName":"El","lastName":"Fatehi","suffix":""},{"id":270060702,"identity":"fec47c58-e77d-40a5-b3e3-de99c1e52495","order_by":5,"name":"Yanzheng Yang","email":"","orcid":"","institution":"Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Yanzheng","middleName":"","lastName":"Yang","suffix":""},{"id":270060703,"identity":"4cf495ea-5721-4ea3-812e-2b4cd6c1ff67","order_by":6,"name":"Rachid Hadria","email":"","orcid":"","institution":"National Institute of Agricultural Research","correspondingAuthor":false,"prefix":"","firstName":"Rachid","middleName":"","lastName":"Hadria","suffix":""},{"id":270060704,"identity":"53eda1fa-18df-4342-9a2d-6a185c0532ea","order_by":7,"name":"David Ariza-Mateos","email":"","orcid":"","institution":"University of Córdoba","correspondingAuthor":false,"prefix":"","firstName":"David","middleName":"","lastName":"Ariza-Mateos","suffix":""},{"id":270060705,"identity":"3e5b411e-ec91-4d5e-95f5-b815f8deaec8","order_by":8,"name":"Guillermo Palacios-Rodríguez","email":"","orcid":"","institution":"University of Córdoba","correspondingAuthor":false,"prefix":"","firstName":"Guillermo","middleName":"","lastName":"Palacios-Rodríguez","suffix":""},{"id":270060706,"identity":"d5040c29-4cef-4de1-9b5b-aa2dd4c70054","order_by":9,"name":"Rafael Navarro-Cerrillo","email":"","orcid":"","institution":"University of Córdoba","correspondingAuthor":false,"prefix":"","firstName":"Rafael","middleName":"","lastName":"Navarro-Cerrillo","suffix":""},{"id":270060707,"identity":"081349fa-4ad9-43da-b184-0217d809d02e","order_by":10,"name":"Mohammed Ater","email":"","orcid":"","institution":"University of Abdelmalek Essaadi","correspondingAuthor":false,"prefix":"","firstName":"Mohammed","middleName":"","lastName":"Ater","suffix":""}],"badges":[],"createdAt":"2024-01-30 14:02:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3910804/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3910804/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50498511,"identity":"38f397b5-f5bc-405b-90bd-46a04f36b3c8","added_by":"auto","created_at":"2024-02-01 12:51:18","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":50397,"visible":true,"origin":"","legend":"\u003cp\u003eStudy area and occurrence records (red points) of \u003cem\u003eCeratonia siliqua\u003c/em\u003e L. in Morocco\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3910804/v1/36241387422b7d4798f1e7a7.jpg"},{"id":50498082,"identity":"7672c859-4b02-44e8-8dcc-59eca6085b67","added_by":"auto","created_at":"2024-02-01 12:43:18","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":76872,"visible":true,"origin":"","legend":"\u003cp\u003eThe Jackknife test plot for the regularized training gain (a) and the AUC of \u003cem\u003eC. siliqua\u003c/em\u003e (b)\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3910804/v1/983f7e9a1fa4e7d5d75dd751.jpg"},{"id":50498512,"identity":"29e18e8a-9c4a-40e7-83ec-de2409a6a31f","added_by":"auto","created_at":"2024-02-01 12:51:18","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":48515,"visible":true,"origin":"","legend":"\u003cp\u003eHabitat suitability of \u003cem\u003eC. siliqua\u003c/em\u003e in Morocco under current and future climate scenarios by 2050 and 2070\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3910804/v1/9aa579e178a68dbde5cd7da6.jpg"},{"id":50498084,"identity":"00a001ec-8105-4170-b606-c0ff0397a679","added_by":"auto","created_at":"2024-02-01 12:43:18","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":46521,"visible":true,"origin":"","legend":"\u003cp\u003eClasses of habitat suitability of \u003cem\u003eC. siliqua\u003c/em\u003e in Morocco under current and future climate scenarios by 2050 and 2070\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3910804/v1/2726fdd27a10f0ddb75e6966.jpg"},{"id":50498085,"identity":"8063444c-9404-4c26-ae70-29bd1e5ad6ac","added_by":"auto","created_at":"2024-02-01 12:43:18","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":16155,"visible":true,"origin":"","legend":"\u003cp\u003eSuitable area in Km² of \u003cem\u003eC. siliqua\u003c/em\u003e for both RCP scenarios by 2050 and 2070\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3910804/v1/c8dc8876a936e77fd600529c.jpg"},{"id":50498086,"identity":"faeb8a49-e974-4a4e-b40d-033839d1ca72","added_by":"auto","created_at":"2024-02-01 12:43:18","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":52243,"visible":true,"origin":"","legend":"\u003cp\u003eBioclimatic variables contributions for the current and future climate scenarios by 2050 and 2070. Abbreviation of climate variables are given in Table 1\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3910804/v1/442b18eb7ef74eeb859ae193.jpg"},{"id":50534451,"identity":"0b85705b-4da5-41c9-9807-0941bf416934","added_by":"auto","created_at":"2024-02-02 03:54:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":589378,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3910804/v1/9326b5c9-bbe5-4b88-8def-d6bf2a523a12.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Effects of climate change on the distribution of the native Carob tree (Ceratonia siliqua L.) in Morocco","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe ongoing global warming is already altering plant species growth and geographical distribution (Doblas-Miranda et al. 2017; Vellend et al. 2017; Kassout et al. 2022a). In fact, with the current rapid rate of warming, global temperature is expected to reach +1.5\u0026deg;C between 2030 and 2050 (IPCC 2018). The complexity of climate change impacts on naturel ecosystems could lead to expansions, reductions or range shifts in the patterns of plant species geographical distribution (Lenoir et al. 2008), leading to significant effects on terrestrial energy, water fluxes and therefore CO\u003csub\u003e2\u003c/sub\u003e emissions (Forzieri et al. 2020). Furthermore, this warming is affecting biodiversity at multiple levels; from individuals and communities to the entire ecosystems (Franklin et al. 2017). As noticed in the Mediterranean region, natural ecosystems are most affected by global warming and extreme climatic events (Doblas-Miranda et al. 2017;\u0026nbsp;Lionello and Scarascia 2018). Therefore, there is a tremendous interest in understanding geographical distribution of plant species under predicted climate change scenarios (Franklin et al. 2017), particularly to set well-adapted conservation and management programs (Kozak et al. 2008).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo assess plant species vulnerability to climate change, the approach of species distribution models (SDM) is commonly and increasingly used to predict species geographical range through interpolating and extrapolating their distributions according to environmental factors (Guisan et al. 2017; Pecchi et al. 2019). Moreover, species distribution models provide a comprehensive basis for species conservation and naturel resources management (Sinclair et al. 2010; Qin et al. 2017). Currently, a considerable number of SDMs method were available and widely used such as, BIOCLIM (bioclimatic modeling), Domain (domain environmental envelope), GAM (generalized additive models), MARS (multivariate adaptive regression splines) and MaxEnt (Maximum entropy) (Pecchi et al. 2019). Among theme, MaxEnt algorithm (Phillips et al. 2006) provides reliable results of suitability when presence-only data are available and high flexibility to deal with both widely distributed and rare species occurrences (Elith et al. 2006; Moukrim et al. 2019; Kassout et al. 2022a). For instance, the maximum entropy model has been used to predict macroecological patterns (Harte 2011), species abundance distributions (White et al. 2012), trait-based community assembly (Shipley et al. 2011) and species ecological niche models at multiple scales (Elith et al. 2010; Guisan et al. 2017).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCeratonia siliqua\u003c/em\u003e L. (Leguminosae) is an evergreen, thermophilous and dioecious Mediterranean fruit tree (Batlle and Tous 1997; Baumel et al. 2018), with some rare hermaphrodite and monoecious cases (Batlle and Tous 1997). The carob (\u003cem\u003eC. siliqua\u003c/em\u003e) is slow-growing and long-lived tree dotted with high resistance to drought with limited resistance to extreme cold (Batlle and Tous,1997), thus it shows important genetic diversity (Viruel et al. 2019) and phenotypic variability (Kassout et al. 2022b, 2023). The carob tree has been exploited around the Mediterranean region since antiquity as food and forage source (Zohary 2002), thus, it represents an important component of its semi-natural and traditional agroecosystems (Ram\u0026oacute;n-Laca and Mabberley 2004; Viruel et al. 2019). Nowadays, carob pods are highly used in the agri-food industry to produce syrups and powder (Papaefstathiou et al. 2018) and the gum extracted from the seed is extremely sought for pharmaceuticals and cosmetics products (Batlle and Tous 1997; Stavrou et al. 2018). Furthermore, the carob tree has shown great potential in reforestation programs and for soil restoration and rehabilitation purposes (Batlle and Tous 1997). Even though its relevant economic and ecological importance, the native status of \u003cem\u003eC. siliqua\u003c/em\u003e still source of debate (Ram\u0026oacute;n-Laca and Mabberley 2004; Baumel et al. 2018). Previous archaeological and historical studies (Hillcoat et al. 1980; Zohary 2002) suggested an Eastern domestication center of the carob tree followed by human-driven dissemination to the West parts of the Mediterranean region. However, recent phylogeographic evidences suggest a strong west-east genetic structuring and the presence of multiple domestication centers from native populations throughout the Mediterranean basin (Viruel et al. 2019). Moreover, distribution models for past periods during the Last Glacial Maximum (\u003cem\u003ec\u003c/em\u003e. 22 ka) suggests probable presence of the carob tree in the Western Mediterranean before its domestication (Viruel et al. 2019). Though, floristic data shows a higher species richness of the Western Mediterranean plant communities associated with carob tree compared to the Eastern parts (Baumel et al. 2018).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn Morocco, carob communities show significant differentiation between the North, characterized by a thermo-Mediterranean vegetation under sub-humid and semi-arid climate, and the South region communities characterized by a quasi-steppic vegetation under semi-arid climate (Baumel et al. 2018; Taleb and Fennane 2019; Kassout et al. 2022b, 2023). Despite their interest, the carob tree populations show a declining trend (Rankou et al. 2017), mainly due to the multiple threats from the rapid global warming and anthropogenic pressures. Therefore, it is crucial to understand possible responses of \u003cem\u003eCeratonia siliqua\u003c/em\u003e to climate change and predict the area of its potential distribution under different climate change scenarios. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn \u0026nbsp;this \u0026nbsp;study, we used the MaxEnt SDM model to predict the potential distribution of \u003cem\u003eCeratonia siliqua\u003c/em\u003e in Morocco under climate change scenarios. Specifically, we used species distribution models (SDMs) to answer two main questions: (1) what are the key climatic factors that affect current \u003cem\u003eC. siliqua\u003c/em\u003e distribution and suitable area in Morocco? and (2) how the future climate scenarios would affect their habitat suitability? Finally, our study will provide a theoretical basis for management and conservation decisions and possible plantation and reforestation efforts.\u0026nbsp;\u003c/p\u003e"},{"header":"2.\tMaterials and Methods","content":"\u003ch1\u003e2.1 Study region\u0026nbsp;\u003c/h1\u003e\n\u003cp\u003eThe study region covers the whole Moroccan territory, situated in the western part of the Mediterranean Basin and the extreme north-west of Africa (Fig. 1). As a biodiversity hotspot (M\u0026eacute;dail and Diadema 2009), the study region presents a wide range of phytogeographic and phytoecological units including forests, matorrals and shrub-lands (Taleb and Fennane 2019), with an important variation in climatic conditions (Kassout et al. 2019, 2022b, 2023).\u003c/p\u003e\n\u003ch1\u003e2.2 Occurrence data\u003c/h1\u003e\n\u003cp\u003eOccurrence data of \u003cem\u003eCeratonia siliqua\u003c/em\u003e were collected through field surveys recording each presence points with GPS coordinates. In addition, we extracted additional occurrence data from the Global Biodiversity Information Facility-GBIF (2020) and from previous published studies dealing with carob presence in Morocco (e.g., Baumel et al. 2018; Viruel et al. 2019; Kassout et al. 2022b, 2023). In this study, we considered only occurrence data within natural conditions. All occurrence data were merged into one single presence-only dataset. Finally, we got 303 occurrence points, a sufficient dataset to construct MaxEnt models of current and future potential distribution of \u003cem\u003eC. siliqua\u003c/em\u003e suitable habitats. The spatial distribution of this occurrence data is shown in Fig. 1.\u0026nbsp;\u003c/p\u003e\n\u003ch1\u003e2.3 Bioclimatic variables and future scenarios\u003c/h1\u003e\n\u003cp\u003eWe used 19 bioclimatic variables (Table 1) extracted from the Worldclim database version 2.0 (Fick and Hijmans 2017) with a spatial resolution of approximately 1 km\u0026sup2; (30 arc seconds). These variables consisted of monthly averages of temperature and precipitation covering the period between 1970 and 2000. Considering their relevant influence on plant growth, productivity, and physiological processes (Moles et al. 2014; Li et al. 2016; Kassout et al. 2021, 2022b, 2023), the extracted bioclimatic variables are largely used in species distribution models (Elith et al. 2010; Guisan et al. 2017; Du et al. 2021; Kassout et al. 2022a). The future projections by 2050 (average for 2041 - 2060) and 2070 (average for 2061 - 2080) were used to predict future distribution of \u003cem\u003eCeratonia siliqua\u003c/em\u003e L. in Morocco.\u003c/p\u003e\n\u003cp\u003eTwo representative concentration pathways (RCPs) trajectories were considered RCP 4.5 and RCP 8.5 corresponding to an increase by 4.5- and 8.5-watts m\u003csup\u003e-2\u003c/sup\u003e of radiative forcing and an increase of CO\u003csub\u003e2\u003c/sub\u003e levels by 650 ppm and 1350 ppm, respectively, by 2100 (IPPC 2018). We supposed a moderate and an extreme scenario to build the future projections (van Vuuren et al. 2011). From the Global Circulation Models (CGMs), and for all projections, we adopted the Community Climate System Model 4.0 (CCSM4) from the CMIP5 (Coupled Model Inter-comparison Project phase 5) model developed by the Intergovernmental Panel on Climate Change (IPCC). The CCSM4 has been reported to better predict precipitation and temperature variables (Gent et al. 2011), thus, gives efficient predictions of future plant species distribution (Al-Qaddi et al. 2017).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo avoid collinearity issues, the bioclimatic variables were tested for multicollinearity using Pearson\u0026rsquo;s correlation test. The highly correlated variables (|r| \u0026gt; 0.9) were removed from predictor variables dataset before species distribution modeling (Dormann et al. 2013). Multicollinearity of variables can result in over-fitting of species distribution models and lead to miss interpretation of variables contribution (Elith et al. 2010). Pearson\u0026rsquo;s correlation coefficient was calculated with the open R software v 3.5.1 (R Core Team 2018). As a result, we used a subset of 12 bioclimatic variables to construct current and future models of \u003cem\u003eC. siliqua\u003c/em\u003e in Morocco (see Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e Bioclimatic variables used as environmental input in the modelling of \u003cem\u003eCeratonia siliqua\u0026nbsp;\u003c/em\u003epotential distribution in Morocco and their percentage contribution. The highlighted variables were selected after multicollinearity test\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"643\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.774494556765163%\" valign=\"top\"\u003e\n \u003cp\u003eBioclimatic\u003c/p\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.01244167962675%\" valign=\"top\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.26438569206843%\" valign=\"top\"\u003e\n \u003cp\u003eUnits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.485225505443236%\" valign=\"top\"\u003e\n \u003cp\u003e% contribution\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.463452566096423%\" valign=\"top\"\u003e\n \u003cp\u003ePermutation importance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.774494556765163%\" valign=\"top\"\u003e\n \u003cp\u003eBio1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.01244167962675%\" valign=\"top\"\u003e\n \u003cp\u003eAnnual mean temperature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.26438569206843%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.485225505443236%\" valign=\"top\"\u003e\n \u003cp\u003e12.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.463452566096423%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.774494556765163%\" valign=\"top\"\u003e\n \u003cp\u003eBio2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.01244167962675%\" valign=\"top\"\u003e\n \u003cp\u003eMean diurnal range (mean of monthly\u003c/p\u003e\n \u003cp\u003emax. and min. temp.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.26438569206843%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.485225505443236%\" valign=\"top\"\u003e\n \u003cp\u003e3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.463452566096423%\" valign=\"top\"\u003e\n \u003cp\u003e3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.774494556765163%\" valign=\"top\"\u003e\n \u003cp\u003eBio3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.01244167962675%\" valign=\"top\"\u003e\n \u003cp\u003eIsothermality ((Bio2/Bio7) \u0026times; 100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.26438569206843%\" valign=\"top\"\u003e\n \u003cp\u003eIndex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.485225505443236%\" valign=\"top\"\u003e\n \u003cp\u003e3.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.463452566096423%\" valign=\"top\"\u003e\n \u003cp\u003e1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.774494556765163%\" valign=\"top\"\u003e\n \u003cp\u003eBio4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.01244167962675%\" valign=\"top\"\u003e\n \u003cp\u003eTemperature seasonality (standard\u003c/p\u003e\n \u003cp\u003edeviation \u0026times;100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.26438569206843%\" valign=\"top\"\u003e\n \u003cp\u003eIndex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.485225505443236%\" valign=\"top\"\u003e\n \u003cp\u003e14.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.463452566096423%\" valign=\"top\"\u003e\n \u003cp\u003e36.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.774494556765163%\" valign=\"top\"\u003e\n \u003cp\u003eBio5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.01244167962675%\" valign=\"top\"\u003e\n \u003cp\u003eMaximum temperature of warmest\u003c/p\u003e\n \u003cp\u003emonth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.26438569206843%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.485225505443236%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.463452566096423%\" valign=\"top\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.774494556765163%\" valign=\"top\"\u003e\n \u003cp\u003eBio6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.01244167962675%\" valign=\"top\"\u003e\n \u003cp\u003eMinimum temperature of coldest\u003c/p\u003e\n \u003cp\u003emonth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.26438569206843%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.485225505443236%\" valign=\"top\"\u003e\n \u003cp\u003e2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.463452566096423%\" valign=\"top\"\u003e\n \u003cp\u003e15.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.774494556765163%\" valign=\"top\"\u003e\n \u003cp\u003eBio7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.01244167962675%\" valign=\"top\"\u003e\n \u003cp\u003eTemperature annual range (Bio5\u0026ndash;Bio6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.26438569206843%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.485225505443236%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.463452566096423%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.774494556765163%\" valign=\"top\"\u003e\n \u003cp\u003eBio8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.01244167962675%\" valign=\"top\"\u003e\n \u003cp\u003eMean temperature of wettest quarter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.26438569206843%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.485225505443236%\" valign=\"top\"\u003e\n \u003cp\u003e3.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.463452566096423%\" valign=\"top\"\u003e\n \u003cp\u003e3.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.774494556765163%\" valign=\"top\"\u003e\n \u003cp\u003eBio9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.01244167962675%\" valign=\"top\"\u003e\n \u003cp\u003eMean temperature of driest quarter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.26438569206843%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.485225505443236%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.463452566096423%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.774494556765163%\" valign=\"top\"\u003e\n \u003cp\u003eBio10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.01244167962675%\" valign=\"top\"\u003e\n \u003cp\u003eMean temperature of warmest quarter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.26438569206843%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.485225505443236%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.463452566096423%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.774494556765163%\" valign=\"top\"\u003e\n \u003cp\u003eBio11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.01244167962675%\" valign=\"top\"\u003e\n \u003cp\u003eMean temperature of coldest quarter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.26438569206843%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.485225505443236%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.463452566096423%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.774494556765163%\" valign=\"top\"\u003e\n \u003cp\u003eBio12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.01244167962675%\" valign=\"top\"\u003e\n \u003cp\u003eAnnual precipitation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.26438569206843%\" valign=\"top\"\u003e\n \u003cp\u003emm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.485225505443236%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.463452566096423%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.774494556765163%\" valign=\"top\"\u003e\n \u003cp\u003eBio13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.01244167962675%\" valign=\"top\"\u003e\n \u003cp\u003ePrecipitation of wettest period\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.26438569206843%\" valign=\"top\"\u003e\n \u003cp\u003emm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.485225505443236%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.463452566096423%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.774494556765163%\" valign=\"top\"\u003e\n \u003cp\u003eBio14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.01244167962675%\" valign=\"top\"\u003e\n \u003cp\u003ePrecipitation of driest period\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.26438569206843%\" valign=\"top\"\u003e\n \u003cp\u003emm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.485225505443236%\" valign=\"top\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.463452566096423%\" valign=\"top\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.774494556765163%\" valign=\"top\"\u003e\n \u003cp\u003eBio15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.01244167962675%\" valign=\"top\"\u003e\n \u003cp\u003ePrecipitation seasonality (Coefficient of variation)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.26438569206843%\" valign=\"top\"\u003e\n \u003cp\u003eIndex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.485225505443236%\" valign=\"top\"\u003e\n \u003cp\u003e5.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.463452566096423%\" valign=\"top\"\u003e\n \u003cp\u003e6.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.774494556765163%\" valign=\"top\"\u003e\n \u003cp\u003eBio16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.01244167962675%\" valign=\"top\"\u003e\n \u003cp\u003ePrecipitation of wettest quarter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.26438569206843%\" valign=\"top\"\u003e\n \u003cp\u003emm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.485225505443236%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.463452566096423%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.774494556765163%\" valign=\"top\"\u003e\n \u003cp\u003eBio17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.01244167962675%\" valign=\"top\"\u003e\n \u003cp\u003ePrecipitation of driest quarter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.26438569206843%\" valign=\"top\"\u003e\n \u003cp\u003emm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.485225505443236%\" valign=\"top\"\u003e\n \u003cp\u003e2.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.463452566096423%\" valign=\"top\"\u003e\n \u003cp\u003e2.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.774494556765163%\" valign=\"top\"\u003e\n \u003cp\u003eBio18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.01244167962675%\" valign=\"top\"\u003e\n \u003cp\u003ePrecipitation of warmest quarter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.26438569206843%\" valign=\"top\"\u003e\n \u003cp\u003emm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.485225505443236%\" valign=\"top\"\u003e\n \u003cp\u003e2.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.463452566096423%\" valign=\"top\"\u003e\n \u003cp\u003e1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.774494556765163%\" valign=\"top\"\u003e\n \u003cp\u003eBio19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.01244167962675%\" valign=\"top\"\u003e\n \u003cp\u003ePrecipitation of coldest quarter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.26438569206843%\" valign=\"top\"\u003e\n \u003cp\u003emm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.485225505443236%\" valign=\"top\"\u003e\n \u003cp\u003e45.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.463452566096423%\" valign=\"top\"\u003e\n \u003cp\u003e25.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch1\u003e2.4 Model structure and data processing \u0026nbsp;\u003c/h1\u003e\n\u003cp\u003eMaxEnt 3.4.1 (Phillips et al. 2017) software was used to predict the potential geographical distribution of \u003cem\u003eC. siliqua\u0026nbsp;\u003c/em\u003eunder current and four future scenarios (RCP4.5-2050s, RCP4.5-2070s, RCP8.5-2050s, RCP8.5-2070s). This modelling approach is based on the Maximum entropy algorithm allowing to estimate habitat suitability and ecological niche (Phillips et al., 2006) using to bioclimatic variables. All models were executed using 10 bootstrap runs with a 25 percent random test percentage, which allows to use 75% of presence data to construct and calibrate the models and 25% to test and evaluate the accuracy and the predictive ability of the models. This procedure is appropriate for estimating occurrence probability (Phillips et al. 2017). The average of all runs was used as final models. As settings, and for all models, we used an auto-feature option (linear, quadratic, product, threshold and hinge methods) and the loglog output format (ranging from 0 to 1), a maximum number of 5000 iterations with a convergence threshold of 10\u003csup\u003e\u0026ndash;5\u003c/sup\u003e, a maximum number of background points as 10000, and a regularization parameter value of 1. The use of a loglog transform provides a stronger theoretical justification than the logistic transform, which replaces it by default (Phillips et al. 2017). The jackknife test was used to determine the climatic variables that influence significantly the potential distribution of \u003cem\u003eC. siliqua\u0026nbsp;\u003c/em\u003e(Li et al. 2016). To determine the accuracy of the models, we used the Area Under the Curve (AUC) of the Receiver Operating characteristics Curve (ROC) (Phillips et al. 2017). High AUC values indicates that the model can distinguish between presence and background points, and therefore indicates a better performance of the model (Elith et al. 2006). AUC values less than 0.5 indicates a model with random prediction, values between 0.5 and 0.7 are considered low accuracy, values between 0.7 and 0.9 are adequate and values higher than 0.9 indicates excellent accuracy of the predicted model (Elith et al. 2006; Yang et al. 2013; Kurpis et al. 2019).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor display and further analysis, we first used binary maps for current and future distribution to calculate the suitability and unsuitability habitat using the maximum training sensitivity plus specificity threshold, which is more suitable for large distributed species (Liu et al. 2013). Secondly, and to highlight area of optimal occurrence of \u003cem\u003eC. siliqua\u0026nbsp;\u003c/em\u003ewe divided the potential suitability probability to four classes (Yang et al. 2013; Qin et al. 2017) as follow: unsuitable habitat (0-0.2), barley suitable habitat (0.2-0.4), suitable habitat (0.4-0.6), optimal habitat (0.6-0.7) and highly optimal habitat (0.7-1). Finally, spatial analysis and cartographic processing of the final results were performed using QGIS, version 2.18.20, open sources GIS software (QGIS Development Team 2015).\u003c/p\u003e"},{"header":"3.\tResults ","content":"\u003ch1\u003e3.1. Model performance\u0026nbsp;\u003c/h1\u003e\n\u003cp\u003eTable 2 resumes the area under the curve (AUC) average values of MaxEnt models performance for \u003cem\u003eCeratonia siliqua\u0026nbsp;\u003c/em\u003eand suitable area under current and future RCPs scenarios by 2050 and 2070. These results showed that the average AUC values for training and testing was higher than 0.9 for both the current and the future scenarios, which indicates an excellent model performance (Phillips et al. 2006). The average AUC training values of the 10 bootstrap runs of current climatic conditions was 0.987 (\u0026plusmn; 0.001) with a maximum training sensitivity plus specificity threshold of 1.952. The future suitability distribution of \u003cem\u003eC. siliqua\u003c/em\u003e predicted by the models showed high AUC values for both RCPs scenarios by 2050 and 2070 representing a good success of the models. These results showed that the bioclimatic variables included in this study could led to an excellent prediction of \u003cem\u003eC. siliqua\u003c/em\u003e distribution in Morocco.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u0026nbsp;\u003c/strong\u003eThe area under the curve (AUC) average values of Maxent models performance for \u003cem\u003eCeratonia siliqua\u0026nbsp;\u003c/em\u003eand suitable area under current and future RCPs scenarios by 2050 and 2070. AUC mean values of training and test data are given after partitioning subspecies presence-only data into training (70%) and test (30%) with standard deviation. Suitable area was determined using the maximum training sensitivity plus specificity threshold\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.77564102564103%\" colspan=\"2\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003ePeriod/Climate scenario\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.97435897435897%\" colspan=\"2\" valign=\"top\"\u003e\n \u003col\u003e\n \u003cli\u003eAUC\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.25%\" colspan=\"2\" valign=\"top\"\u003e\n \u003col\u003e\n \u003cli\u003eSuitable Area\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.781326781326783%\" valign=\"top\"\u003e\n \u003cp\u003eAUC\u003csub\u003emean\u003c/sub\u003e\u0026dagger;\u0026nbsp; Training\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.307125307125308%\" valign=\"top\"\u003e\n \u003cp\u003eAUC\u003csub\u003emean\u003c/sub\u003e\u0026dagger;\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eTest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.307125307125308%\" valign=\"top\"\u003e\n \u003cp\u003eArea (Km\u0026sup2;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.604422604422606%\" valign=\"top\"\u003e\n \u003cp\u003ePercentage (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.77564102564103%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eCurrent (1970 \u0026ndash; 2000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.46794871794872%\" valign=\"top\"\u003e\n \u003cp\u003e0.987\u0026nbsp;\u0026plusmn; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.506410256410255%\" valign=\"top\"\u003e\n \u003cp\u003e0.978\u0026nbsp;\u0026plusmn; 0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.506410256410255%\" valign=\"top\"\u003e\n \u003cp\u003e78 233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.743589743589743%\" valign=\"top\"\u003e\n \u003cp\u003e11.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.32%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eProjected\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.56%\" valign=\"top\"\u003e\n \u003cp\u003e2050 - RCP4.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.44%\" valign=\"top\"\u003e\n \u003cp\u003e0.969\u0026nbsp;\u0026plusmn; 0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.48%\" valign=\"top\"\u003e\n \u003cp\u003e0.961\u0026nbsp;\u0026plusmn; 0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.48%\" valign=\"top\"\u003e\n \u003cp\u003e70 185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.72%\" valign=\"top\"\u003e\n \u003cp\u003e10.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.179732313575524%\" valign=\"top\"\u003e\n \u003cp\u003e2050 - RCP8.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.84130019120459%\" valign=\"top\"\u003e\n \u003cp\u003e0.971\u0026nbsp;\u0026plusmn; 0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.694072657743785%\" valign=\"top\"\u003e\n \u003cp\u003e0.957\u0026nbsp;\u0026plusmn; 0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.694072657743785%\" valign=\"top\"\u003e\n \u003cp\u003e70 579\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.590822179732314%\" valign=\"top\"\u003e\n \u003cp\u003e10.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.179732313575524%\" valign=\"top\"\u003e\n \u003cp\u003e2070 - RCP4.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.84130019120459%\" valign=\"top\"\u003e\n \u003cp\u003e0.970\u0026nbsp;\u0026plusmn; 0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.694072657743785%\" valign=\"top\"\u003e\n \u003cp\u003e0.955\u0026nbsp;\u0026plusmn;\u0026nbsp;0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.694072657743785%\" valign=\"top\"\u003e\n \u003cp\u003e68 746\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.590822179732314%\" valign=\"top\"\u003e\n \u003cp\u003e10.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.179732313575524%\" valign=\"top\"\u003e\n \u003cp\u003e2070 - RCP8.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.84130019120459%\" valign=\"top\"\u003e\n \u003cp\u003e0.967\u0026nbsp;\u0026plusmn; 0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.694072657743785%\" valign=\"top\"\u003e\n \u003cp\u003e0.962\u0026nbsp;\u0026plusmn;\u0026nbsp;0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.694072657743785%\" valign=\"top\"\u003e\n \u003cp\u003e71 457\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.590822179732314%\" valign=\"top\"\u003e\n \u003cp\u003e10.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026dagger;\u003csup\u003e\u0026nbsp;\u003c/sup\u003eMean values of 10 bootstrap runs.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003ch1\u003e3.2. Contributions of bioclimatic variables\u003c/h1\u003e\n\u003cp\u003eFig. 2 shows the Jackknife test results for the regularized training gain (Fig. 2a) and the AUC of \u003cem\u003eC. siliqua\u0026nbsp;\u003c/em\u003epotential suitable habitat (Fig. 2b). These results indicated that the precipitation of the coldest quarter (Bio19) has the most influential impact on the current distribution of \u003cem\u003eC. siliqua\u003c/em\u003e and contributes with 45.7% to the final model. The temperature seasonality (Bio4) and the annual mean temperature (Bio1) were the second highest contributor to the model with, respectively, 14.7% and 12.1%. The precipitation seasonality (Bio15), the isothermality (Bio3), the mean temperature of the wettest quarter (Bio8), and the mean diurnal range (Bio2) contributed respectively with 5.3%, 3.9%, 3.8%, and 3.4%. The lowest contributors were the maximum temperature of warmest month (Bio5, 3%), the minimum temperature of coldest month (Bio6, 2.9%), the precipitation of the warmest quarter (Bio18, 2.7%), the precipitation of the driest quarter (Bio17, 2.4%) and the precipitation of the driest period (Bio14, 0.2%). The cumulative contributions of the precipitation-related variables were 56.5% and the temperature-related variables was 43.8% (Table 1).\u0026nbsp;\u003c/p\u003e\n\u003ch1\u003e3.3. Predicted habitat suitability for the \u003cem\u003eCeratonia siliqua\u003c/em\u003e in Morocco\u003c/h1\u003e\n\u003cp\u003eFig. 3 represents the habitat suitability maps of \u003cem\u003eC. siliqua\u003c/em\u003e in Morocco under current and future climate scenarios by 2050 and 2070. After these maps, the potential current suitable area of \u003cem\u003eC. siliqua\u003c/em\u003e in Morocco is 78,233 km\u0026sup2;, which represents 11.46% of the total studied area. Thus, the optimal (0.6-0.7) and the highly optimal (0.7-1) distribution area were 4765 km\u0026sup2; and 15,054 km\u0026sup2;, respectively (Table 3; Fig. 4). The majority of the optimal habitats (\u0026ge;0.6) was located in the north-west and the south-west parts of the country (Fig. 4).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHabitat suitability distribution for \u003cem\u003eC. siliqua\u003c/em\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eshowed significant changes under global climate models (GCMs) of CCSM4 for both RCPs pathways (RCP4.5 and RCP8.5) by 2050 and 2070 (Table 2; Fig. 5). Overall, suitable area variation between current and future conditions showed progressive reduction along with increasing climate warming. Under RCP4.5 scenario, the potential distribution of \u003cem\u003eC. siliqua\u003c/em\u003e would decrease by 1.18% and 1.39% respectively for 2050 and 2070. A similar pattern was observed under RCP8.5 with decreasing suitability by 1.11% and 0.99%, respectively for 2050 and 2070 (Table 2; Fig. 3).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRegarding variable contributions, the precipitation of the coldest quarter (Bio19) represented the most bioclimatic variable contributing to the prediction models of \u003cem\u003eC. siliqua\u0026nbsp;\u003c/em\u003ehabitat suitability distribution by 2050 and 2070 (Fig. 6). By focusing on optimal area (0.6-0.7), we observed a slight gain in area suitability by 2050 from 4765 km\u0026sup2; to 5060 km\u0026sup2; and 4781 km\u0026sup2;, respectively, under RCP4.5 and RCP8.5 (Table 3; Fig. 4). However, by 2070, only under RCP4.5, we observed a gain in suitability area from 4765 km\u0026sup2; to 5090 km\u0026sup2;, while we observed a decrease in suitability under RCP8.5 from 4765 km\u0026sup2; to 4571 km\u0026sup2; (Table 3; Fig. 4). Compared to the current potential distribution, the future scenarios show a progressive reduction in optimal habitats (\u0026ge;0.6) of \u003cem\u003eC. siliqua\u003c/em\u003e in the north-west and the south-west parts, replaced by suitable (0.4-0.6) habitats, while a gaining of suitable area (\u0026ge;0.6) in the middle-center region of the country was observed by 2050 under RCP8.5.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e Habitat suitability area (Km\u0026sup2;) of \u003cem\u003eCeratonia siliqua\u0026nbsp;\u003c/em\u003eunder different suitability classes. Percentage of suitable area are shown between brackets\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"652\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.368098159509202%\" colspan=\"2\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003ePeriod/Climate scenario\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"69.6319018404908%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003eSuitability classes area (km\u0026sup2;)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.0989010989011%\" valign=\"top\"\u003e\n \u003cp\u003eUnsuitable\u003c/p\u003e\n \u003cp\u003e(0-0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.65934065934066%\" valign=\"top\"\u003e\n \u003cp\u003eBarley suitable\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.2-0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.65934065934066%\" valign=\"top\"\u003e\n \u003cp\u003eSuitable\u003c/p\u003e\n \u003cp\u003e(0.4-0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.65934065934066%\" valign=\"top\"\u003e\n \u003cp\u003eOptimal\u003c/p\u003e\n \u003cp\u003e(0.6-0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.923076923076923%\" valign=\"top\"\u003e\n \u003cp\u003eHighly optimal\u003c/p\u003e\n \u003cp\u003e(0.7-1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.321592649310873%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eCurrent (1970 \u0026ndash; 2000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.701378254211333%\" valign=\"top\"\u003e\n \u003cp\u003e60 4796 (88.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.39509954058193%\" valign=\"top\"\u003e\n \u003cp\u003e41 287\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(6.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.39509954058193%\" valign=\"top\"\u003e\n \u003cp\u003e15 361\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(2.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.39509954058193%\" valign=\"top\"\u003e\n \u003cp\u003e4 765\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.791730474732006%\" valign=\"top\"\u003e\n \u003cp\u003e15 054\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(2.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.251148545176111%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eFuture\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.07044410413476%\" valign=\"top\"\u003e\n \u003cp\u003e2050 - RCP 4.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.701378254211333%\" valign=\"top\"\u003e\n \u003cp\u003e61 2778 (89.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.39509954058193%\" valign=\"top\"\u003e\n \u003cp\u003e32 414\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(4.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.39509954058193%\" valign=\"top\"\u003e\n \u003cp\u003e18 154\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(2.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.39509954058193%\" valign=\"top\"\u003e\n \u003cp\u003e5 060\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.791730474732006%\" valign=\"top\"\u003e\n \u003cp\u003e12 852\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(1.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.593368237347295%\" valign=\"top\"\u003e\n \u003cp\u003e2050 - RCP 8.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.75392670157068%\" valign=\"top\"\u003e\n \u003cp\u003e61 2202 (89.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.404886561954626%\" valign=\"top\"\u003e\n \u003cp\u003e34 955\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(5.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.404886561954626%\" valign=\"top\"\u003e\n \u003cp\u003e16 803\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(2.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.404886561954626%\" valign=\"top\"\u003e\n \u003cp\u003e4 781\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(0.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.43804537521815%\" valign=\"top\"\u003e\n \u003cp\u003e12 514\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(1.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.593368237347295%\" valign=\"top\"\u003e\n \u003cp\u003e2070 - RCP 4.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.75392670157068%\" valign=\"top\"\u003e\n \u003cp\u003e61 4103 (89.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.404886561954626%\" valign=\"top\"\u003e\n \u003cp\u003e31 847\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(4.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.404886561954626%\" valign=\"top\"\u003e\n \u003cp\u003e17 445\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(2.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.404886561954626%\" valign=\"top\"\u003e\n \u003cp\u003e4 571\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(0.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.43804537521815%\" valign=\"top\"\u003e\n \u003cp\u003e13 291\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(1.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.593368237347295%\" valign=\"top\"\u003e\n \u003cp\u003e2070 - RCP 8.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.75392670157068%\" valign=\"top\"\u003e\n \u003cp\u003e61 1289 (89.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.404886561954626%\" valign=\"top\"\u003e\n \u003cp\u003e33 864\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(4.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.404886561954626%\" valign=\"top\"\u003e\n \u003cp\u003e17 219\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(2.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.404886561954626%\" valign=\"top\"\u003e\n \u003cp\u003e5 090\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.43804537521815%\" valign=\"top\"\u003e\n \u003cp\u003e13 796\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(2.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eModelling the impacts of climate change on potential habitat suitability of plant species at global and local scales is critical to understand their ecological and evolutionary future pathways (Kozak et al. 2008; Dawson et al. 2011). In this context, species distribution models (SDMs) are relevant tools to forecast natural habitat expansions or contractions (Guissan and Thuiller 2005; Guissan et al. 2017). They are required for setting adequate monitoring and restoration programs of natural ecosystems (Pecchi et al. 2019), particularly in the light of the rapid rate of biodiversity loss (Dawson et al. 2011). Looking at the vulnerability of Mediterranean natural ecosystems under the current global changes (Blondel et al. 2010), it is crucial to understand the future dynamics of native plant species and its genetic resources. Here, we highlighted the potential distribution of the multipurpose species \u003cem\u003eCeratonia siliqua\u003c/em\u003e in Morocco under four different climate change scenarios by 2050 and 2070, which presents a contribution to set both conservation and reforestation programs of this remarkable Mediterranean plant species.\u003c/p\u003e\n\u003cp\u003eOur results show that the potential geographical suitable habitat of the carob tree in Morocco can be accurately predicted by bioclimatic factors. The average AUC values of 10 bootstrap runs were greater than 0.9, for both current and future scenarios, indicating the high accuracy and reliability of the final models (Elith et al. 2006; Merow et al. 2013). Thus, our results agree with other studies showing that the maximum entropy algorithm can provide high accurate prediction of plant species potential habitat (Yang et al. 2013; Qin et al. 2017; Moukrim et al. 2019; Kurpis et al. 2019; Du et al. 2021; Kassout et al. 2022a). Moreover, our findings highlight the importance of climatic factors in controlling the carob tree suitable habitats in Morocco, as previously demonstrated for the establishment of several Mediterranean plant species (M\u0026eacute;dail and Diadema 2009). The potential distribution model of \u003cem\u003eC. siliqua\u003c/em\u003e show that the precipitation of the coldest quarter (Bio9) has the largest impact on defining its current suitable habitat. Thus, temperature seasonality (Bio3) and the annual mean temperature (Bio1) has substantial contribution on carob tree potential distribution. These results consolidate the findings suggesting that precipitations and temperature are the most common factors shaping the distribution of plant communities and diversity (Moles et al. 2014) with direct implication in the ecological and evolutionary trajectory of plant species (Donoghue 2008; Frank 2011). However, our results show that the precipitation-related variables have greater influence on the geographical distribution of \u003cem\u003eC. siliqua\u003c/em\u003e compared to temperature-related variables. The low contribution of temperature variables might be explained by the thermophilous nature of \u003cem\u003eC. siliqua\u003c/em\u003e and its distribution under various climatic conditions form sub-humid to semi-arid climates (Baumel et al. 2018; Kassout et al. 2022b). Hence, the weak sensitivity of suitable habitat models to temperatures could be interpreted as an adaptation form of the carob tree. The high contribution of precipitation-related variables can be viewed as the effect of aridity and its direct relationship with water availability, which is the limiting factor for physiological responses and adaptation process for most plant species (Doblas-Miranda et al. 2017; Kassout et al. 2019, 2021, 2023). Thus, these variables largely design the structure of Mediterranean plant communities (Doblas-Miranda et al. 2017). In addition, seasonal variation of precipitation regime within the Mediterranean region induced stressful conditions, which limiting the plant growth, development and survival (Vellend et al. 2017). The correlations between precipitations-related variables and plant physiological responses were also reported for the thermophilous and Mediterranean wild native olive tree (\u003cem\u003eOlea europaea\u003c/em\u003e subsp \u003cem\u003eeuropaea\u003c/em\u003e var. \u003cem\u003esylvestris\u003c/em\u003e) in Morocco (Kassout et al., 2019, 2021). This observed pattern might be regarded as the results of the biogeographic history of Mediterranean flora (Blondel et al. 2010).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePredicted climate change scenarios (RCP4.5 and RCP8.5) by 2050 and 2070 show a declining rate of carob suitable habitat in Morocco. For instance, suitable area will decrease from 78,233 km\u0026sup2; (11.46%) to 70,185 km\u0026sup2; (10.28%) and 68,746 km\u0026sup2; (10.07%), respectively, under RCP4.5 by 2050 and 2070 (Fig. 5). In fact, precipitation-related variables largely defined the future potential habitat of \u003cem\u003eC. siliqua\u003c/em\u003e in Morocco (Fig. 6). These results are consistent with some findings stated that suitable habitat of plant species might decrease under climate change scenarios (Moukrim et al. 2019; Kassout et al. 2022a). Hence, the loss of carob tree suitable habitat might be greatly influenced by the projected increasing temperature and decreasing precipitations in the Mediterranean region (Driouech et al. 2010; Lionello and Scarascia 2018). It is indisputable that climate change is expected to reduce plant species capacity to withstand new challenging conditions, such as extreme aridity (Dawson et al. 2011; Doblas-Miranda et al. 2017). However, when taking in consideration highly suitable habitat (Fig. 4), we observed a slight increasing in suitable conditions of carob tree. In fact, the future suitable climatic envelope of the species ranges from Northern to Southern regions of the country can explain this tendency. Meanwhile, the response of \u003cem\u003eC. siliqua\u003c/em\u003e to climate change will depend on several factors such as anthropogenic activities, phenotypic plasticity, genetic diversity and competing ability (Phillips et al. 2006; Parmesan and Hanley 2015).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the case of \u003cem\u003eC. siliqua\u003c/em\u003e, a species with long-term longevity and limited dispersal ability, gain of suitable habitat under climate change is significantly limited. Using MaxEnt algorithm, we show that climate change might impose challenging conditions for this multipurpose plant species. However, looking into biological and ecological process underlying spatial distribution of plant species (Shipley et al. 2011), such as genetic diversity, dissemination rate, and inter-specific interactions, SDMs has its limitation to predict the exact relationship between species occurrence and environmental factors without considering biological and ecological variables when constructing the models (Bedia et al. 2013). These limitations can be explained by the lack of comprehensive database, integrating accurate climatic data at local scales with reasonable resolution, coupled with data on plants functional and ecological characteristics. \u0026nbsp;\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn this study we used MaxEnt approach to provide a comprehensive basis for current and future potential habitat suitability of the carob tree in Morocco using two IPCC climatic scenarios (RCP4.5 and RCP8.5) by the years 2050 and 2070. Our results conclude that the geographic distribution of the carob might experience a considerable decline regarding climate change scenarios with noticeable risk of disappearance in some areas, in particular those projected to be drier as the case of the south-eastern parts of Morocco. Therefore, the loss of natural vegetation of\u0026nbsp;\u003cem\u003eC. siliqua\u003c/em\u003e communities will greatly affect the ecological functions and services of natural ecosystems and, therefore, affecting valuable genetic resources.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur results contribute to understand the biogeography and the history of Mediterranean native tree species, and the potential dynamics of natural vegetation communities facing the global change. The prediction method used in this study can be applied into several tree species distribution to predict future trends at the ecosystem and population levels under climate change scenarios. Furthermore, it is wise, in light of the above, to encourage and promote\u003cem\u003e\u0026nbsp;C. siliqua\u003c/em\u003e for reforestation programs.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJalal kassout\u003c/strong\u003e Conceptualization, Methodology, Data curation, Formal analysis, Software, Investigation, Writing - original draft, Writing - review \u0026amp; editing, Visualization. \u003cstrong\u003eSoufian Chakkour\u003c/strong\u003e Investigation, Writing - original draft. \u003cstrong\u003eAbdeltif El Ouahrani\u0026nbsp;\u003c/strong\u003eInvestigation, Writing - original draft. \u003cstrong\u003eYounes Hmimsa\u0026nbsp;\u003c/strong\u003eInvestigation, Writing - original draft. S\u003cstrong\u003ealama El Fatehi:\u003c/strong\u003e Investigation, Writing - original draft. \u003cstrong\u003eYanzheng Yang\u003c/strong\u003e Writing - original draft, Writing - review \u0026amp; editing. \u003cstrong\u003eRachid Hadria:\u0026nbsp;\u003c/strong\u003eVisualization. \u003cstrong\u003eDavid Ariza-Mateos\u0026nbsp;\u003c/strong\u003eProject administration; Resources. \u003cstrong\u003eGuillermo Palacios-Rodr\u0026iacute;guez:\u0026nbsp;\u003c/strong\u003eProject administration; Resources. \u003cstrong\u003eRafael Navarro-Cerrillo\u0026nbsp;\u003c/strong\u003eFunding acquisition, Resources, Writing - review \u0026amp; editing. \u003cstrong\u003eMohammed Ater\u0026nbsp;\u003c/strong\u003eConceptualization, Funding acquisition, Resources, Writing - original draft, Writing - review \u0026amp; editing, Supervision.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank the members of the BioAgrodiversity Team for helpful discussions and comments. Likewise, the authors thank agents of the Water and Forests Department of Morocco for helpful information during fieldwork. This study was supported by the Agencia Andaluza de Cooperaci\u0026oacute;n Internacional para el Desarrollo (AACID) and the project \u0026laquo; Am\u0026eacute;lioration de la productivit\u0026eacute; des cultures foresti\u0026egrave;res d\u0026rsquo;int\u0026eacute;r\u0026ecirc;t socio-\u0026eacute;conomique \u0026eacute;lev\u0026eacute; dans les zones rurales du nord du Maroc, n\u0026deg; 2018004 \u0026raquo;.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statemen\u003c/strong\u003et\u003c/p\u003e\n\u003cp\u003eThe occurrence records used to calibrate and evaluate SDMs are available on request from the corresponding authors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthic approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo approval of research ethics committees was required to accomplish the goals of this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consen\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFinancial or non‑financial interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAl-Qaddi N, Vessella F, Stephan J, Al-Eisawi D, Schirone B (2017) Current and future suitability areas of kermes oak (\u003cem\u003eQuercus coccifera\u003c/em\u003e L.) in the Levant under climate change. Regional Environmental Change, 17, 143\u0026ndash;156. https://doi.org/10.1007/s10113-016-0987-2\u003c/li\u003e\n\u003cli\u003eBatlle I, Tous J (1997) Carob tree \u003cem\u003eCeratonia siliqua\u003c/em\u003e L. Promoting the conservation and use of underutilized and neglected crops. 17. Rome, Institute of Plant Genetics and Crop Plant Research, Gatersleben/International Plant Genetic Resources Institute.\u003c/li\u003e\n\u003cli\u003eBaumel, A., Mirleau, P., Viruel, J., Bou Dagher Kharrat et al (2018) Assessment of plant species diversity associated with the carob tree (\u003cem\u003eCeratonia siliqua\u003c/em\u003e, Fabaceae) at the Mediterranean scale. Plant Ecology and Evolution, 151(2), 185\u0026ndash;193. \u003c/li\u003e\n\u003cli\u003eBedia J, Herrera S, Guti\u0026eacute;rrez JM (2013) Dangers of using global bioclimatic datasets for ecological niche modeling. Limitations for future climate projections. Global and Planetary Change 107, 1\u0026ndash;12. https://doi.org/10.1016/j.gloplacha.2013.04.005\u003c/li\u003e\n\u003cli\u003eBlondel J, Aronson J, Bodiou J-Y, Boeu G (2010) The Mediterranean region: biological diversity through time and space. Oxford: Oxford University Press. pp 376.\u003c/li\u003e\n\u003cli\u003eDawson TP, Jackson ST, House JI, Prentice IC, Mace GM (2011) Beyond predictions: biodiversity conservation in a changing climate. Science, 332(6025), 53\u0026ndash;8. https://doi.org/10.1126/science.1200303\u003c/li\u003e\n\u003cli\u003eDoblas-Miranda E, Alonso R, Arnan X, Bermejo V et al (2017) A review of the combination among global change factors in forests, shrublands and pastures of the Mediterranean Region: beyond drought effects. Global and Planetary Change, 148, 42\u0026ndash;54. https://doi.org/10.1016/j.gloplacha.2016.11.012\u003c/li\u003e\n\u003cli\u003eDonoghue MJ (2008) A phylogenetic perspective on the distribution of plant diversity. Proceeding of the National Academy of Sciences of the United Stets of America, 105, 11549\u0026ndash;11555. https://doi.org/10.1073/pnas.0801962105\u003c/li\u003e\n\u003cli\u003eDormann CF, Elith J, Bacher S, Buchmann C, Carl G, Carr\u0026eacute; G et al (2013) Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography, 36(1), 27\u0026ndash;46. https://doi.org/10.1111/j.1600-0587.2012.07348.x\u003c/li\u003e\n\u003cli\u003eDriouech F, D\u0026eacute;qu\u0026eacute; M, Sanchez-Gomez E (2010) Weather regimes-Moroccan precipitation link in a regional climate change simulation. Global Planet Change, 72(1-2), 1\u0026ndash;10. https://doi.org/10.1016/j.gloplacha.2010.03.004\u003c/li\u003e\n\u003cli\u003eDu Z, He Y, Wang H, Wang C, Duan Y (2021) Potential geographical distribution and habitat shift of the genus Ammopiptanthus in China under current and future climate change based on the MaxEnt model. Journal of Arid Environments, 184, 104328. \u003c/li\u003e\n\u003cli\u003eElith J, Graham G, Anderson R, Dudik M, Ferrier S et al (2006) Novel methods improve prediction of species\u0026rsquo; distributions from occurrence data. Ecography, 29(2), 129\u0026ndash;151. https://doi.org/10.1111/j.2006.0906-7590.04596.x\u003c/li\u003e\n\u003cli\u003eElith J, Kearney M, Phillips S (2010) The art of modelling range-shifting species. Methods in Ecology and Evolution, 1(4), 330-342. https://doi.org/10.1111/j.2041-210X.2010.00036.x\u003c/li\u003e\n\u003cli\u003eFick S, Hijmans R (2017) WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology, 37(12), 4302\u0026ndash;4315. https://doi.org/10.1002/joc.5086\u003c/li\u003e\n\u003cli\u003eForzieri G, Miralles DG, Ciais P, Alkama R et al (2020) Increased control of vegetation on global terrestrial energy fluxes. Nature Climate Change, 10, 356\u0026ndash;362. https://doi.org/10.1038/s41558-020-0717-0\u003c/li\u003e\n\u003cli\u003eFrank SA (2011) Natural selection. II. Developmental variability and evolutionary rate. Journal of Evolutionary Biology, 24(11), 2310\u0026ndash;2320. https://doi.org/10.1111/j.1420-9101.2011.02373.x\u003c/li\u003e\n\u003cli\u003eFranklin J, Serra-Diaz JM, Syphard AD, Regan HM (2017) Big data for forecasting the impacts of global change on plant communities. Global Ecology and Biogeography, 26, 6\u0026ndash;17. https://doi.org/10.1111/geb.12501\u003c/li\u003e\n\u003cli\u003eGBIF. Global Biodiversity Information Facility (2020) GBIF Occurrence Download, https://doi.org/10.15468/dl.vv4gfm (accessed 30 September 2020)\u003c/li\u003e\n\u003cli\u003eGent PR, Danabasoglu G, Donner LJ, Holland MM et al (2011) The Community Climate System Model Version 4. Journal of Climate, 24(19), 4973\u0026ndash;4991. https://doi.org/10.1175/2011JCLI4083.1\u003c/li\u003e\n\u003cli\u003eGuisan A, Thuiller W, Zimmermann NE (2017) Habitat suitability and distribution models. With applications in R. Cambridge: Cambridge University Press. https://doi.org/10.1017/9781139028271\u003c/li\u003e\n\u003cli\u003eHarte J (2011) Maximum Entropy and Ecology: A Theory of Abundance, Distribution, and Energetics. London: Oxford University Press. https://doi.org/10.1093/acprof:oso/9780199593415.001.0001\u003c/li\u003e\n\u003cli\u003eHillcoat D, Lewis G, Verdcourt B (1980) A new species of Ceratonia (Leguminosae-Caesalpinioideae) from Arabia and the Somali Republic. Kew Bulletin, 35, 261\u0026ndash;271. https://doi.org/10.2307/4114570\u003c/li\u003e\n\u003cli\u003eIPCC (2018) Summary for Policymakers, in: Delmotte, V., P. Zhai, H.-O. P\u0026ouml;rtner, D. Roberts, J. et al., (Eds.), Global Warming of 1.5\u0026deg;C. An IPCC Special Report on the impacts of global warming of 1.5\u0026deg;C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty World Meteorological Organization, Geneva, Switzerlan, pp. 32. \u003c/li\u003e\n\u003cli\u003eKassout J, Terral J-F, Hodgson J, Ater M (2019) Trait-based plant ecology a flawed tool in climate studies? The leaf traits of wild olive that pattern with climate are not those routinely measured. PLoS ONE, 14(7):e0219908. https://doi.org/10.1371/journal.pone.0219908\u003c/li\u003e\n\u003cli\u003eKassout J, Ater M, Ivorra S, Barbara H, Limier B, Ros J, Girard V, Paradis L, Terral J-F (2021) Resisting Aridification: Adaptation of Sap Conduction Performance in Moroccan Wild Olive Subspecies Distributed Over an Aridity Gradient. Frontiers in Plant Science, 12:663721. https://doi.org/10.3389/fpls.2021.663721. \u003c/li\u003e\n\u003cli\u003eKassout J, Hmimsa Y, El Fatehi S, El Ouahrani A, Kadaoui K et al. (2022b) Image analysis of Moroccan carob seeds (\u003cem\u003eCeratonia siliqua\u003c/em\u003e L.) revealed substantial intraspecific variations depending on climate and geographic origin. Ecological Processes, 11(1), 1-14.\u003c/li\u003e\n\u003cli\u003eKassout J, Terral J-F, El Ouhrani A, Houssni M, Ivorra S et al (2022a) Species Distribution Based-Modelling Under Climate Change: The Case of Two Native Wild \u003cem\u003eOlea europaea\u003c/em\u003e Subspecies in Morocco, \u003cem\u003eO. e.\u003c/em\u003e subsp. \u003cem\u003eeuropaea \u003c/em\u003evar. \u003cem\u003esylvestris\u003c/em\u003e and \u003cem\u003eO. e.\u003c/em\u003e subsp. \u003cem\u003emaroccana\u003c/em\u003e. In: Climate Change in the Mediterranean and Middle Eastern Region. Climate Change Management ed. Leal Filho W., Manolas E. Springer, Cham.\u003c/li\u003e\n\u003cli\u003eKassout J, Hmimsa Y, Fatehi SE, Kadaoui K, Houssni M, Chakkour S et al (2023) Aridity Gradients Shape Intraspecific Variability of Morphological Traits in Native \u003cem\u003eCeratonia siliqua\u003c/em\u003e L. of Morocco. Plants, 12(19), 3447.\u003c/li\u003e\n\u003cli\u003eKozak KH, Graham CH, Wiens JJ (2008) Integrating GIS-based environmental data into evolutionary biology. Trends in Ecology and Evolution, 23(3), 141\u0026ndash;148. https://doi.org/10.1016/j.tree.2008.02.001\u003c/li\u003e\n\u003cli\u003eKurpis J, Serrato-Cruz MA, Arroyo TPF (2019) Modeling the effects of climate change on the distribution of \u003cem\u003eTagetes lucida\u003c/em\u003e Cav. (Asteraceae). Global Ecology and Conservation, 20, e00747. https://doi.org/10.1016/j.gecco.2019.e00747\u003c/li\u003e\n\u003cli\u003eLenoir J, G\u0026eacute;gout JC, Marquet PA, De RP, Brisse H (2008) A significant upward shift in plant species optimum elevation during the 20th century. Science, 320(5884), 1768\u0026ndash;1771. https://doi.org/10.1126/science.1156831\u003c/li\u003e\n\u003cli\u003eLi GQ, Du S, Wen ZM (2016) Mapping the climatic suitable habitat of oriental arborvitae (\u003cem\u003ePlatycladus orientalis\u003c/em\u003e) for introduction and cultivation at a global scale. Scientific Reports, 6, 30009. https://doi.org/10.1038/srep30009\u003c/li\u003e\n\u003cli\u003eLionello P, Scarascia L (2018) The relation between climate change in the Mediterranean region and global warming. Regional Environmental Change, 18(5), 1481\u0026ndash;1493. https://doi.org/10.1007/s10113-018-1290-1\u003c/li\u003e\n\u003cli\u003eLiu C, White M, Newell G (2013) Selecting thresholds for the prediction of species occurrence with presence-only data. Journal of Biogeography, 40, 778\u0026ndash;789. \u003c/li\u003e\n\u003cli\u003eM\u0026eacute;dail F, Diadema K (2009) Glacial refugia influence plant diversity patterns in the Mediterranean Basin. Journal of Biogeography, 36(7), 1333\u0026ndash;1345. https://doi.org/10.1111/j.1365-2699.2008.02051.x\u003c/li\u003e\n\u003cli\u003eMoles AT, Perkins SE, Laffan SW, et al (2014) Which is a better predictor of plant traits: temperature or precipitation?. Journal of Vegetation Science, 25(5), 1167\u0026ndash;1180. https://doi.org/10.1111/jvs.12190\u003c/li\u003e\n\u003cli\u003eMoukrim S, Lahssini S, Rhazi M et al (2019) Climate change impacts on potential distribution of multipurpose agro-forestry species: \u003cem\u003eArgania spinosa\u003c/em\u003e (L.) Skeels as case study. Agroforestry Systems, 93, 1209\u0026ndash;1219. https://doi.org/10.1007/s10457-018-0232-8\u003c/li\u003e\n\u003cli\u003ePapaefstathiou E, Agapiou A, Giannopoulos S, Kokkinofta R (2018) Nutritional characterization of carobs and traditional carob products. Food Science and Nutrition, 6(8), 2151\u0026ndash;2161. https://doi.org/10.1002/fsn3.776\u003c/li\u003e\n\u003cli\u003eParmesan C, Hanley ME (2015) Plants and climate change: complexities and surprises. Annals of Botany, 116(6), 849\u0026ndash;864. https://doi.org/10.1093/aob/mcv169\u003c/li\u003e\n\u003cli\u003ePecchi M, Marchi M, Burton V, Giannetti F, Moriondo M et al (2019) Species distribution modelling to support forest management. A literature review. Ecological Modeling, 411(1), 1\u0026ndash;12. https://doi.org/10.1016/j.ecolmodel.2019.108817\u003c/li\u003e\n\u003cli\u003ePhillips SJ, Anderson RP, Schapire RE (2006) Maximum entropy modeling of species geographic distributions. Ecological Modelling, 190(3-4), 231\u0026ndash;259. https://doi.org/10.1016/j.ecolmodel.2005.03.026\u003c/li\u003e\n\u003cli\u003ePhillips SJ, Anderson RP, Dud\u0026iacute;k M, Schapire RE, Blair ME (2017) Opening the black box: an open‐source release of Maxent. Ecography, 40(7), 887\u0026ndash;893. https://doi.org/10.1111/ecog.03049\u003c/li\u003e\n\u003cli\u003eQGIS Development Team (2012) QGIS Geographic Information System. Open Source Geospatial Foundation Project. http://qgis.org \u003c/li\u003e\n\u003cli\u003eQin AL, Liu B, Guo QS, Bussmann RW, Ma F et al (2017) Maxent modeling for predicting impacts of climate change on the potential distribution of \u003cem\u003eThuja sutchuenensis\u003c/em\u003e Franch. an extremely endangered conifer from southwestern China. Global Ecology and Conservation, 10, 139e146. https://doi.org/10.1016/j.gecco.2017.02.004\u003c/li\u003e\n\u003cli\u003eR Core Team (2018) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Available online at https://www.R-project.org/ \u003c/li\u003e\n\u003cli\u003eRam\u0026oacute;n-Laca L, Mabberley DJ (2004) The ecological status of the carob-tree (\u003cem\u003eCeratonia siliqua\u003c/em\u003e, Leguminosae) in the Mediterranean. Botanical Journal of the Linnean Society, 144, 431\u0026ndash;436. https://doi.org/10.1111/j.1095-8339.2003.00254.x\u003c/li\u003e\n\u003cli\u003eRankou H, M\u0026rsquo;Sou S, Chadburn H, Rivers M, Ouhammou A, Martin G (2017) \u003cem\u003eCeratonia siliqua\u003c/em\u003e. The IUCN Red List of Threatened Species. https://doi.org/10.2305/IUCN.UK.2017-3.RLTS.T202951A112823254.en \u003c/li\u003e\n\u003cli\u003eSchr\u0026ouml;der B (2008) Challenges of species distribution modeling belowground. Journal of Plant Nutrition and Soil Science, 171(3), 325\u0026ndash;337. https://doi.org/10.1002/jpln.200700027\u003c/li\u003e\n\u003cli\u003eShipley B, Laughlin DC, Sonnier G, Otfinowski R (2011) A strong test of a maximum entropy model of trait- based community assembly. Ecology, 92, 507\u0026ndash;517. https://doi.org/10.1890/10-0394.1\u003c/li\u003e\n\u003cli\u003eSinclair S, White M, Newell G (2010) How useful are species distribution models for managing biodiversity under future climates. Ecology and Society, 15(1):8. http://www.ecologyandsociety.org/vol15/iss1/art8/\u003c/li\u003e\n\u003cli\u003eStavrou IJ, Christou A, Kapnissi-Christodoulou CP (2018) Polyphenols in carobs: A review on their composition, antioxidant capacity and cytotoxic effects, and health impact. Food Chemistry, 269, 355\u0026ndash;374. https://doi.org/10.1016/j.foodchem.2018.06.152\u003c/li\u003e\n\u003cli\u003eTaleb MS, Fennane M (2019) Vascular Plant Communities of Morocco, Phytosociology, Ecology and Geography. Geobotany Studies: Basics, Methods and Case Studies. Springer, 161pp.\u003c/li\u003e\n\u003cli\u003evan Vuuren DP, Edmonds J, Kainuma MLT, Riahi K, Thomson A, Matsui et al (2011) Representative concentration pathways: An overview. Climatic Change, 109, 5. https://doi.org/10.1007/s10584-011-0148-z\u003c/li\u003e\n\u003cli\u003eVellend M, Baeten L, Becker-Scarpitta A, Boucher-Lalonde V et al (2017) Plant biodiversity change across scales during the Anthropocene. Annual Review of Plant Biology, 68, 563\u0026ndash;586. https://doi.org/10.1146/annurev-arplant-042916-040949\u003c/li\u003e\n\u003cli\u003eViruel J, Le Galliot N, Pironon S, Nieto Feliner G, Suc J et al (2019) A strong east\u0026ndash;west Mediterranean divergence supports a new phylogeographic history of the carob tree (\u003cem\u003eCeratonia siliqua\u003c/em\u003e, Leguminosae) and multiple domestications from native populations. Journal of Biogeography, 47, 460\u0026ndash;471. https://doi.org/10.1111/jbi.13726\u003c/li\u003e\n\u003cli\u003eWhite EP, Katherine M, Thibault M, Xiao X (2012) Characterizing species abundance distributions across taxa and ecosystems using a simple maximum entropy model. Ecology, 93, 1772\u0026ndash;1778. https://doi.org/10.1890/11-2177.1\u003c/li\u003e\n\u003cli\u003eYang XQ, Kushwaha SPS, Saran S, Xu J, Roy PS (2013) Maxent modeling for predicting the potential distribution of medicinal plant, \u003cem\u003eJusticia adhatoda\u003c/em\u003e L. in Lesser Himalayan foothills. Ecological Engineering, 51, 83\u0026ndash;87. https://doi.org/10.1016/j.ecoleng.2012.12.004\u003c/li\u003e\n\u003cli\u003eZohary D (2002) Domestication of the carob (\u003cem\u003eCeratonia siliqua\u003c/em\u003e L.). Israel Journal of Plant Science, 50, 141\u0026ndash;215. https://doi.org/10.1560/BW6B‐4M9P‐U2UA‐C6NN.\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":"Ceratonia siliqua L., Morocco, MaxEnt, Species distribution modelling, Climate change scenarios","lastPublishedDoi":"10.21203/rs.3.rs-3910804/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3910804/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eClimate change is expected to alter many natural ecosystems around the world, by affecting plants growth and distribution. This is particularly emphasized for several Mediterranean plant species and communities. In this study, we investigate the suitable habitat and geographical distribution of a remarkable Mediterranean tree, \u003cem\u003eCeratonia siliqua\u003c/em\u003e L. (Leguminosae), in Morocco. We hypothesized a reduction in the carob tree suitable habitats under climate change scenarios. To this end, we applied the maximum entropy algorithm (Maxent) to generate current and future models using 303 occurrence points coupled with 19 bioclimatic variables. Two representative concentration pathways (RCP4.5 and RCP8.5) by 2050 and 2070 were considered as future input scenarios. The maximum entropy model provides satisfactory results, with a high value of the Area Under Curve equal to 0.987 (±0.001). Jackknife tests indicate that precipitation followed by temperature play a significant role in the biogeographical dynamics of the Moroccan carob tree. Thus, the obtained results confirm our hypothesis of a reduction of the suitable area under the projected climate change scenarios by 2050 and 2070. The approaches developed in this study is promising to predict the potential distribution of native Mediterranean species and can be an effective tool to support conservation and restoration programs.\u003c/p\u003e","manuscriptTitle":"Effects of climate change on the distribution of the native Carob tree (Ceratonia siliqua L.) in Morocco","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-01 12:43:13","doi":"10.21203/rs.3.rs-3910804/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":"8dbc0659-0035-4eb4-a0cd-0796234c0bf2","owner":[],"postedDate":"February 1st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-02-02T03:46:40+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-01 12:43:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3910804","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3910804","identity":"rs-3910804","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}