Modeling current and future distributions of key fodder grasses under climate change in Senegal using biomod2

Modeling current and future distributions of key fodder grasses under climate change in Senegal using biomod2 | 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 Modeling current and future distributions of key fodder grasses under climate change in Senegal using biomod2 Idrissa Sawadogo, Jean Kouao Koffi, Sié Sylvestre Da, Jean Baptiste Dembélé, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7781208/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 30 Apr, 2026 Read the published version in Discover Sustainability → Version 1 posted 14 You are reading this latest preprint version Abstract Climate change and increasing human pressure are among the primary factors shaping the distribution of ecological niches for forage species in rangelands. This study investigates the impact of climate change on both current and future geographic distributions of Zornia glochidiata and Andropogon pseudapricus in Senegal, under two climate models (HadGEM3-GC31-LL and MIROC6) and two greenhouse gas emission scenarios (SSP245 and SSP585), with projections for 2070 and 2100. The analysis covers the entire Senegalese territory, with particular attention to rangelands and protected areas. Key environmental variables influencing the distribution of these two fodder species were identified, and future habitat changes were forecasted using climatic projections. We employed six modeling techniques: artificial neural networks (ANN), generalized boosted models (GBM), generalized linear models (GLM), maximum entropy (MaxEnt), random forest (RF), and surface range envelope (SRE). Among the bioclimatic predictors, precipitation of the wettest quarter (Bio16), annual precipitation (Bio12), isothermality (Bio3), and elevation were the most influential. Random forest showed the highest predictive accuracy, with AUC values exceeding 0.95 for both species. Currently, Z. glochidiata and A. pseudapricus occupy 54.4% and 36.17% of Senegal’s surface area, respectively. However, all future scenarios forecast a substantial loss of suitable habitat for both species. These projected reductions underline their high vulnerability to changing climatic conditions. The results provide crucial insights for the conservation and management of forage resources in Sahelian ecosystems, supporting strategies for ecological resilience and socio-economic sustainability. Fodder species Climate change Conservation Species distribution modeling Rangelands Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction In Sub-Saharan Africa, many farmers communities depend heavily on rangelands for their ecosystem services [ 1 ]. However, the sustainability and functioning of these ecosystems are increasingly threatened by climate change and human pressures [ 2 ]. Current projections indicate that greenhouse gas emissions could cause a rise in global average temperatures by 1.4 to 5.8°C during the 21st century [ 3 , 4 ].This temperature rise could have significant impacts on the key ecosystem’s services of rangelands, particularly net primary productivity and biomass quality [ 5 ]. It is important to note that the effects of climate change on rangelands are being exacerbated by overgrazing and unsustainable land use practices [ 6 ]. Natural pastures are the main source of fodder for livestock, especially in the rainy season [ 7 ]. During the dry season, the availability of herbaceous fodder becomes scarce, pushing livestock to rely mainly on woody rangeland species [ 8 ]. These woody rangeland species are also the main sources of energy for most rural households [ 9 ]. Thus, grazing land and the harvesting of woody and non-woody forest products are the most significant sources of food and feed in pastoral and agro-pastoral ecosystems [ 10 ]. However, the continual expansion of farmlands has led to a reduction in natural grazing land, thereby decreasing pasture productivity [ 11 ]. These land use changes, together with recurrent climate variability and the spread of invasive species, have contributed to a significant decline in livestock productivity [ 12 ]. This decline has, in turn, disrupted the socio-economic stability of local communities [ 12 ]. In Sahelian countries, ensuring regular supply of fodder for animals throughout the year is essential for achieving resilient livestock production [ 13 ]. Seasonal fodder availability remains a key constraint for livestock farmers in Sahelian region [ 14 ]. This challenge could be expected worsen due to increased livestock numbers, environmental and land use changes [ 7 , 15 ]. Climate change is projected to have a negative effect on fodder supply chains, particularly in vulnerable regions where people’s livelihoods are already limited [ 16 , 10 ] Predicting the geographic distribution of fodder species is crucial for promoting rangeland species conservation [ 17 ]. Identifying suitable and unsuitable habitats for fodder species is also essential to prevent biodiversity loss and support climate change adaptation strategies in rangeland areas [ 18 ]. Species Distribution Models (SDMs) are widely used to estimate potential species ranges and to the effects of climate change on various plant species groups [ 19 , 9 ]. As noted by Coulibaly et al. (2023) [ 9 ] anticipating shifts in plant distribution due to climate change is essential for effectively guiding sustainable management policies and thus maintaining ecosystem services. SDMs also allow for the projection of shifts in suitable habitats over time and help identify species that may persist or disappear under changing environmental conditions [ 20 ]. Forage species with high pastoral value play an important role in animal nutrition and ecological balance [ 21 ]. Among the key forage species in the Ferlo rangelands of northern Senegal are Andropogon pseudapricus and Zornia glochidiata , both of which are widely consumed by livestock [ 22 ]. Given their importance, further research is needed to assess the potential impacts of climate change on their geographic distribution and habitat suitability to support their conservation. In West Africa, climate change and human pressure have been shown to negatively affect species diversity and dynamics [ 23 , 24 ]. SDMs have also been successfully used to identify suitable habitats for the conservation of multipurpose species such as Vitellaria paradoxa CF Gaertn. [ 25 ], Garcinia kola [ 17 ], Cordyla pinnata (A.Rich.) Milne-Redh. [ 26 ] and Parkia biglobosa (Jacq.) R.Br. ex G.Don [ 27 ]. Despite these growing publications on Senegal rangelands, there is a lack of SDMs applications targeting herbaceous fodder species with high pastoral value. This gap limits the ability to design effective conservation and management strategies for these important resources. Through this study, we aim to bridge this knowledge gap by modeling the geographic distribution of two herbaceous fodder species with high pastoral value in West Africa: Andropogon pseudapricus Stapf (a grass) and Zornia glochidiata Rchb. Ex DC. (a legume), both prominent in the Ferlo rangelands. The aims of this study were to assess the geographic distribution of Andropogon pseudapricus and Zornia glochidiata in response to current and future climatic conditions. Specially, the research addresses the following questions: (i) which bioclimatic variables control the geographic distribution of each species? (ii) what is the current spatial extent of suitable habitats for the conservation of these species? (iii) how are the suitable habitats of Andropogon pseudapricus and Zornia glochidiata expected to change over time ? (iv) and which factors affect the variations in species distribution models? 2. Methodology 2.1. Study areas The current research study focused on the protected area, the rangelands, fallow lands and farmlands in the Senegal. Senegal is located in West Africa, between latitudes 12°20′ -16°20′ N and longitudes 11°20′ -17°30′ W (Fig. 1 ) [ 16 ]. The country has a unimodal rainfall pattern characterized by a short rainy season from June to September and a long dry season from October to May [ 22 ]. It is divided into three main climatic zones: Sahelian, North Sudanian, and South Sudanian [ 26 ]. In the Sahelian region, annual precipitation varies between 100 and 500 mm, while mean annual temperatures range from 23.3°C to 29.6°C [ 28 ]. In Senegal, the Wolof represent the largest ethnic group, followed by the Fulani [ 29 ]. Other ethnic groups are beginning to take their place in the composition of this population [ 30 ]. 2.2. Study species Andropogon pseudapricus is a tufted annual or perennial grass, with ascending, branched stems that can reach up to 150 cm in height [ 31 ]. The plant provides good fodder when young and is grazed even when in flower [ 32 ]. This specie is also appreciated as hay during the dry season by the herding communities in Senegal. A. pseudapricus is an important component of savannah vegetation in areas with an average annual rainfall of 500 to 700 mm [ 31 ]. It grows on shallow, sandy or gravelly lateritic soils [ 32 ]. Zornia glochidiata is a leguminous herb from the Fabaceae family, widely distributed throughout the Sahelian zone of West Africa [ 33 ]. It is recognized as an important forage species in the region [ 34 ]. It is an annual herb, with erect stems and grows up to 45 cm tall [ 35 ]. Its leaves are compound and broad leaves, typically bifoliate [ 34 ]. The stipules are lanceolate in shape and can reach up to 15 mm in length [ 36 ]. It is common in sandy areas with a mean annual rainfall of 300 to 600 mm or more during the rainy season and a dry season for up to 8 months [ 35 ]. 2.3. Data collection 2.3.1. Species occurrence data Presence data of the two species were recorded using a Global Positioning System (GPS) during fieldwork conducted throughout the distribution range of both species in Senegal (Fig. 2 ). The field data were supplemented by additional records gathered from the Global Biodiversity Information Facility website (GBIF, www.gbif.org/ ). To improve relevance and accuracy of the data, GBIF records were filtered to include only occurrences from 1990 to 2025, excluding older records of individuals that may not reflect. A total dataset of 2327 occurrence records were obtained for Z. glochidiata , of which 1523 records (65.45%) were collected from field surveys and 804 records (34.55%) from the GBIF database. Similarly, a total dataset of 1306 occurrence records were obtained for A. pseudapricus , of which 930 records (71.25%) were collected from field surveys and 376 records (28.75%) from the GBIF database. Following rigorous data quality control procedures, duplicate records were removed using the function ‘ spatially rarefy occurrence data’ in SDMtoolbox [ 19 ]. To reduce sampling bias, spatial thinning was performed using the ENMTools package in R [ 37 ]. Duplicate records within the same 1km 2 grid cell were removed to avoid over-representation [ 38 , 39 ]. 2.3.2. Environmental data In this study environmental variables included bioclimatic, elevation and human footprint index data. A total of 19 bioclimatic variables covering current (1970–2000) and future (2070 and 2100) climate conditions, along with elevation data, were downloaded from WorldClim website ( https://www.worldclim.org/ ; accessed on January 09, 2025) [ 40 ]. In addition, the human footprint index was downloaded from Wildlife Conservation Society ( https://wcshumanfootprint.org/data-access ). Thus, elevation and human footprint index data were resampled to match the resolution of the bioclimatic variables, which is 30 arc seconds (approx. 1 km 2 ) [ 41 ]. To predict the future distribution of species, two Shared Socioeconomic Pathways (SSPs) scenarios from the updated CMIP6 were selected for analysis, the SSP 245(moderate) and SSP 585 (strong) for two periods 2070 and 2100. These scenarios were chosen because they represent more pessimistic and more optimistic greenhouse gas emission scenarios respectively [ 42 ]. Climate projection were based on two global climate models (GCMs): HadGEM3-GC31-LL and MIROC6, widely used in CMIP6 experiments [ 43 ]. In addition, it should be noted that these two climate models are widely used in the prediction of ecological niches in West Africa [ 44 , 9 , 11 , 45 ]. 2.3.3. Variable selection for fodder species modeling To select relevant environmental predictors for ecological niche modeling, we conducted a variance inflation factors (VIF) analysis on 19 initial bioclimatic variables and the Human Footprint Index to assess multicollinearity [ 46 ]. We progressively eliminated variables with a Pearson correlation coefficient equal or higher to |0.7| (r \(\:\ge\:\) |0.7|) [ 25 ]. Then, 6 least correlated environmental variables for Andropogon pseudapricus and 7 variables for Zornia glochidiata and elevation were identified to run the model. Finally, a Jackknife test was conducted to identify the variables with the greatest contribution to the model [ 47 ]. 2.3.4. Evaluation of model and potential distribution of forage species The prediction of current and future suitable areas for forage species was carried out using R 4.3.2 software based on 6 machine learning algorithms via Biomod2 [ 48 ]. Biomod2 function was used to randomly generate pseudo-absence points with the same number as the recorded species occurrence data [ 48 , 47 ]. Then, six algorithms best suited to species prediction were applied: artificial neural networks (ANN), generalized boosted models (GBM), generalized linear models (GLM), random forest (RF), surface range envelope (SRE) and maximum entropy (MaxEnt) [ 49 , 47 , 11 ]. These algorithms combine species occurrence data with current bioclimatic variables to generate a map of potential species distribution in the study area and future habitat suitability based on climate projections [ 9 ]. For each species, we used 25% of species occurrence records for model testing and 75% for model calibration [ 51 ]. The predictive capacity of the model was evaluated according to four measures, including the Area Under the Curve (AUC) which is threshold-independent, the threshold-dependent True Skill Statistic (TSS), the Correlation Statistic (COR) and the KAPPA [ 52 ]. An AUC value equal to 0.5 indicates poor model performance, and when its value is close to 1 (AUC \(\:>\) 0.75) it indicates a very good performance of the model [ 53 ]. The TSS value ranges from − 1 to + 1, and is calculated using the following formula [ 54 ]: $$\:TSS=\:\frac{ad-bc}{(a+c)(b+d)}=Sensitivity+Specificity-1$$ 1 Suitable habitat distributions for fodder species, both current and projected under future climate scenarios, were mapped based on logistic occurrence probabilities (p). Three suitable areas classes were considered: Low suitability (p \(\:\le\:\) 0.4), medium suitability (0.4 \(\:\) 0.6) [ 55 ]. Moreover, the centroid shift of both species from the present to future SSP scenarios, across both time frames, were performed using R (version 4.3.2), primarily with the packages sf, ggplot2, ggspatial , and patchwork [ 73 ]. Maps of the species suitable areas were finally produced using ArcMap 10.8 for current and future climatic conditions under the two scenarios at the two horizons. 3. Results 3.1. Key environmental variables influencing the species distribution The main bioclimatic variables influencing the potential distribution of the two fodder species in Senegal are shown in (Table 1 ). The main variables influencing Z. glochidiata were elevation (21%), min temperature of coldest month (15.6%), isothermality (13.5%), precipitation of coldest quarter (12.6%) and precipitation of wettest month (10.6%). Bio 16 (precipitation of wettest quarter) is the least influential bioclimatic variable for Z. glochidiata . The bioclimatic variables significantly affecting the A. pseudapricus species were Bio16 (precipitation of wettest quarter) with a contribution of 34.8%, Bio12 (annual precipitation) with a contribution of 26.6%. Bio17 (precipitation of driest quarter) is the least influential variable, with a contribution of 5.3%. Although climatic variables were predominant, the internal Jackknife test revealed that human footprint index variables also play a significant role, albeit to a lesser extent. Specifically, human footprint index contributed 18.3% to the potential distribution of Z. glochidiata and 8.5% for A. pseudapricus . These results highlight the complex interaction between climatic and human factors in determining the habitat suitability for the two fodder species. Table 1 Variables contribution used in modelling of the two fodder species. Species Code Variables name Contribution (%) Zornia glochidiata Bio3 Isothermality (BIO2/BIO7) (×100) 13.5 Bio6 Min temperature of coldest month 15.6 Bio13 Precipitation of wettest month 10.6 Bio16 Precipitation of wettest quarter 8.4 Bio19 Precipitation of coldest quarter 12.6 Elev Elevation 21 HFP Human Footprint Index 18.3 Andropogon pseudapricus Bio9 Mean temperature of driest quarter 12.7 Bio12 Annual precipitation 26.6 Bio16 Precipitation of wettest quarter 34.8 Bio17 Precipitation of driest quarter 5.3 Elev Elevation 12.1 HFP Human Foot print Index 8.5 3.2. Model accuracy and performance (Table 2 ) shows the performance of the six algorithms used to predict the distribution of the two fodder species based on various statistical metrics such as AUC, TSS, COR and KAPPA. All six algorithms demonstrated good predictive performance (AUC \(\:>\) 0.8), except for the ANN, which had an AUC value of 0.75 for Zornia glochidiata . Indeed, Zornia glochidiata achieved an AUC of 0.96 and TSS 0.90 with RF, while Andropogon pseudapricus has an AUC of 0.98 and a TSS of 0.71with the same algorithm (Table 2 ). ANN presented lower values than other algorithms, as seen in the case of Z. glochidata with an AUC of 0.75 and TSS of 0.63. For each species the performance of the models was also evaluated by the correlation statistic (COR) value and the KAPPA (Table 2 ). Table 2 Performance of the six algorithms (RF, MaxEnt, GLM, GBM, SRE, ANN) Species Algorithm AUC COR TSS KAPPA Zornia glochidiata RF 0.98 0.95 0.91 0.89 MaxEnt 0.95 0.87 0.86 0.84 GLM 0.83 0.75 0.85 0.69 GBM 0.91 0.71 0.68 0.67 SRE 0.88 0.63 0.7 0.85 ANN 0.75 0.67 0.63 0.78 Andropogon pseudapricus RF 0.98 0.97 0.9 0.86 MaxEnt 0.93 0.8 0.88 0.82 GLM 0.89 0.71 0.82 0.66 GBM 0.92 0.7 0.67 0.65 SRE 0.8 0.72 0.61 0.7 ANN 0.59 0.57 0.5 0.53 3.3. Current and future distribution of two fodder species 3.3.1. Zornia glochidiata The current high suitable habitats for the species represent 22.9% (43,843.77 km 2 ) of Senegal's total surface area (196,712 km 2 ). Less suitable habitats cover 32.11% (63,161.36 km 2 ), while 45.16% (88,834.02 km 2 ) of the country's land area is unsuitable (Appendix A; Fig. 3 ). These habitats are mainly located in the Sudano-Sahelian zone and the Sudanian zones of the country (Fig. 4 ). Both climate models (HadGEM3-GC31-LL and MIROC6) predicted a significant overall decline in the species' range under the moderate (SSP 245) and high-emission (SSP 585) scenarios across all time horizons. Under the HadGEM3-GC31-LL model's moderate scenario (SSP245), habitat loss is projected to reach 48.99% (22,364.17 km 2 ) by 2061–2080 and 47.96% (22,815.99 km 2 ) by 2081–2100. In the worst-case scenario (SSP585) for the same model, projected losses range from 48.58% (22,543.28 km 2) by 2061–2080 to 50.18% (21844.18 km 2 ) by 2081–2100.The MIROC6 model under SSP585 also forecasted drastic changes in the species' spatial distribution, with only 9.65% and 8.9% of Senegal's area remaining suitable by 2070 and 2100, respectively. 3.3.2. Andropogon pseudapricus The predicted high suitable areas for A. pseudapricus currently account for 20.14% (19,942.38 km 2 ) of Senegal's total area (Appendix A, Fig. 5 ), while less suitable areas cover 36.03% (51,197.06 km 2 ). About 43.83% (12,5578.23 km 2 ) of the total area of Senegal is unsuitable for A. pseudapricus conservation under current climatic conditions. The species’ habitat range highly and moderately suitable to the species were found in the northern part of the Sahelian zone of Senegal (Fig. 6 ). The total potential distribution area of A. pseudapricus was projected to decrease substantially over time under the two future emission scenarios. Specifically, the areas accounted for 88.11% and 88.37% of the total area of Senegal in the years 2061–2080 under moderate and strong scenarios, respectively with the HadGEM3-GC31-LL model. However, under SSP 585, the MIROC6 model predicts a crucial decrease of 5448.75 km 2 (89.36%) in 2061–2080, followed by a small decrease of 14.59% (43,729.66 km 2 ) in 2081–2100 (Fig. 5 ). These trends highlight the species’ vulnerability to future climate conditions. 3.3.3. Trajectory of the two species according to current and future climate conditions Centroid shift analysis under future climate scenarios revealed contrasting patterns between the two species (Fig. 7 ). For A. pseudapricus , the centroid consistently moved northeastward across all scenarios, with a particularly pronounced shift under SSP585-2100, indicating a likely migration of suitable habitat toward higher latitudes and eastern regions. In contrast, Z. glochidiata exhibited a southwestward shift, especially under MIROC6 projections, suggesting a relocation of favorable conditions toward lower latitudes and western areas. These divergent directions of centroid movement highlight species-specific responses to climate change and underscore the need for tailored management strategies. 4. Discussion 4.1. Variable importance for the habitat suitability of the two fodder species in Senegal Temperature and precipitation are the primary bioclimatic variables influencing plant growth and reproduction [ 12 ]. Based on the relative importance of the environmental predictors, the distribution of Z. glochidiata is primarily shaped by elevation, minimum temperature of the coldest month, isothermality, and precipitation during the coldest quarter. In contrast, A. pseudapricus is most affected by precipitation in the wettest quarter, total annual rainfall, and mean temperature of the driest quarter. Comparable patterns have been highlighted in earlier research conducted across West Africa [ 11 , 18 , 56 ], reinforcing the role of temperature and rainfall in determining the ecological niches of tropical species [ 58 ]. According to O’donnell and Ignizio [ 60 ], species can adapt to environments characterized by lower temperature variability, which enhances their persistence across different climatic zones. Comparable observations were made for Afzelia africana [ 19 ] and Carapa procera [ 61 ] in Burkina Faso, as well as for other multipurpose species in Benin [ 18 ]. The present findings are consistent with previous research on the spatial distribution of fodder species in West Africa [ 73 ]. Numerous studies have emphasized that precipitation and temperature are the dominant climatic factors influencing the distribution patterns of woody vegetation throughout the region [ 61 , 62 , 63 , 64 ]. In this study, human activities accounted for 18.3% and 8.5% of the factors influencing the distribution of Z. glochidiata and A. pseudapricus , respectively. These results indicate that anthropogenic pressures such as land-use change, infrastructure development, and population expansion negatively affect the growth, habitat suitability, and reproductive capacity of both species [ 12 ]. The growing intensity of human use of rangelands has been identified as a major driver of the widespread degradation of fodder vegetation in recent years [ 12 ]. 4.2. Model performance The present study employed the Biomod2 package to model both current and projected suitable habitats for the two fodder species, using six algorithms: ANN, GBM, GLM, MaxEnt, RF, and SRE. Among these approaches, the Random Forest (RF) algorithm consistently produced the most reliable predictions for both species, as reflected by its superior AUC and TSS scores. This finding aligns with earlier works by Huang et al. [ 48 ]; Coulibaly et al.[ 9 ], and Sambou et al. [ 26 ], which also identified RF as the most effective method for species distribution modeling. Similarly, recent studies by Dogbo et al. [ 18 ] and Gbadamassi et al. [ 57 ] reported that Random Forest was the most robust algorithm for predicting the spatial distribution of twelve multipurpose species in Benin and Prosopis africana in Togo. However, the integration of multiple algorithms in species distribution modeling remains a topic of scientific debate [ 18 ]. Araújo and New (2007)[ 66 ], emphasized that combining different modeling techniques can improve predictive robustness and minimize algorithm specific bias. Several studies have further demonstrated that ensemble models, which aggregate multiple algorithms, generally outperform single-model approaches in predicting species distributions and evaluating the potential effects of climate change [ 67 ]. 4.3. Current distribution of the two species The predicted current suitable habitats for Z. glochidiata in Senegal cover more than half of the national territory (54.84%), whereas those of A. pseudapricus occupy approximately 36.17% of the country’s surface area. These favorable habitats extend mainly across the Sudanian and Sudano-Sahelian ecological zones. This pattern aligns with the known distribution range of Z. glochidiata in West Africa [ 34 ]. In contrast, model projections for A. pseudapricus indicate a broader presence throughout Senegal, with a stronger concentration in the northern Sahelian region. Furthermore, the species appears to be absent from certain protected areas as well as from some unprotected rangelands. These results indicate that protected areas do not always provide effective conservation sites for species such as A. pseudapricus . Some regions currently classified as unsuitable may become more favorable for the species in the future, potentially offering better conservation opportunities than areas already considered suitable. Additionally, the extent of suitable habitat for Z. glochidiata identified in this study exceeds that reported for other socio-economically important plant species in West Africa. For example, Balima et al. [ 19 ] estimated that approximately 25.54% of Burkina Faso’s territory is suitable for Afzelia africana conservation, while only about 2.37% of Mali’s territory was deemed highly suitable for Bombax costatum Pellegr. & Vuill. [ 9 ]. The substantial areas identified as unsuitable for Z. glochidiata and A. pseudapricus highlight the urgent need to prioritize their conservation in Senegal. 4.4. Future distribution of the two species Climate projections for 2070 suggest that A. pseudapricus and Z. glochidiata are likely to lose between 53.24% and 89.36% of their highly suitable habitats under the SSP2-4.5 and SSP5-8.5 scenarios, respectively. These results are consistent with observed distribution patterns for Bombax costatum Pellegr. & Vuill. in Mali, reported by Coulibaly et al. [ 9 ], and for Combretum glutinosum Perr. ex-DC. in Burkina Faso, documented by Dembélé et al. [ 45 ]. Compared to other multipurpose species in West Africa, the projected future ranges for these two fodder species are relatively extensive. For instance, Djibo Moussa et al. [ 65 ] estimated that 37.89% of Niger’s territory could be suitable for Balanites senegalensis conservation, while approximately 32.91% and 26.44% of the territories of Senegal and Benin were identified as suitable for the conservation of Cordyla pinnata [ 11 ] and Parkia biglobosa (Jacq.) R.Br. ex G. Don [ 18 ], respectively. Our results indicate that suitable habitats for both Z. glochidiata and A. pseudapricus are projected to contract primarily within the Sudano-Sahelian region of West Africa. This pattern is consistent with previous studies predicting future range reductions for Pterocarpus erinaceus [ 68 ] and Adansonia digitata under changing climatic conditions in the region [ 9 ]. The observed decline can be attributed to rising temperatures, reduced precipitation, and the increasing frequency of extreme climatic events, all of which compromise the ecological viability of these species in areas that were previously suitable. These findings also align with climate models forecasting a general decrease in rainfall across West Africa [ 69 ]. By 2100, Z. glochidiata is expected to lose approximately 69.25% (19,419.32 km²) of its less suitable habitats, while A. pseudapricus is projected to lose about 56.81% under the MIROC6 / SSP2-4.5 scenario. This difference may reflect the higher anthropogenic pressures on Z. glochidiata , as well as species-specific ecological and environmental sensitivities. This decline may be further exacerbated by the expansion of croplands and urban areas. Our results are consistent with those of Trisurat et al. [ 70 ] and Gbadamassi et al. [ 57 ], who reported that fodder species often occupy only a fraction of their potential ecological niche due to human activities, bushfires, and interspecific competition. Similarly, Saliou et al. [ 62 ] predicted a marked reduction in the habitats of three Andropogon species in Benin, highlighting the urgent need for targeted conservation actions. The projected shift of suitable habitats towards the Sudanian zone under future climate scenarios can be attributed to significant increases in temperature, particularly in the semi-arid regions of West Africa, which are likely to reduce areas suitable for plant biodiversity [ 71 , 19 , 72 , 44 ]. Climatic changes are altering habitats, while human pressures such as agricultural expansion and infrastructure development continue to impact fodder species Gbadamassi et al. [ 57 ]. These combined challenges highlight the need for integrated management strategies that address both environmental and anthropogenic factors to ensure long-term conservation 4.5. Implications for management and conservation Based on the results of this study, current management efforts should focus on monitoring areas identified as highly and moderately suitable habitats, with the aim of implementing timely and targeted conservation measures. In anticipation of future climate change, strategies should prioritize regions projected to remain suitable or become suitable for Z. glochidiata and A. pseudapricus . Concurrently, reducing human-induced pressures is essential to support the growth, regeneration, and expansion of these species within their potential distribution zones. This study utilized Biomod2 simulations integrating six modeling algorithms to predict the geographic distribution of Z. glochidiata and A. pseudapricus across Senegalese rangelands under both current and future climate scenarios. Continuous monitoring and early warning systems for shifts in plant distribution can provide a scientific foundation for proactive conservation planning, enabling interventions at both local and regional scales. These insights have substantial theoretical and practical implications for guiding adaptive rangeland management and biodiversity conservation throughout the Sahelian region. 5. Conclusion This study modeled the ecological niches of two forage species of high pastoral value in Senegal by combining six different algorithms. Our results identified precipitation during the wettest quarter, annual precipitation, and minimum temperature of the coldest month as the main environmental factors influencing their geographic distribution. Currently, favorable habitats for Zornia glochidiata cover approximately 54.4% of Senegal’s total land area, including 22.29% classified as highly suitable. Similarly, potential habitats for Andropogon pseudapricus occupy about 43.17% of the country, but only 3.10% of these areas are highly suitable. Under future climate scenarios, these suitable habitats are projected to decline significantly. The models consistently predict a substantial reduction in the ecological niches of both species by 2070 and 2100, regardless of the climate scenario considered. Moreover, the spatial extent of suitable habitats varies according to the choice of climate model, scenario, and time horizon. The study also reveals that many highly suitable habitats lie outside existing protected areas, indicating that current conservation zones may be insufficient for the long-term preservation of these forage species. To prevent the degradation of important forage resources in Sahelian rangelands, targeted strategies are essential. This includes implementing forestry policies aimed at endangered forage species and strengthening the protection of key habitats within and beyond protected areas. Finally, to better safeguard forage biodiversity, ecological niche modeling should be expanded to include other forage species of high pastoral value in Sahelian rangelands. Incorporating additional environmental variables and utilizing a broader range of climate models could further improve the accuracy and effectiveness of future distribution predictions. Declarations Author contribution statement Idrissa Sawadogo: Writing original draft, Visualization, Software, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation and Conceptualization. Jean Kouao Koffi: Visualization, Resources, Methodology, Data curation, Conceptualization and draft editing. Sié Sylvestre Da : Writing original draft, Visualization, Software, Methodology, Data curation, Conceptualization and draft editing. Jean Baptiste Dembélé : Visualization, Resources, Methodology, Data curation, Conceptualization and draft editing. Innocent Charles Emmanuel Traoré : Writing original draft, Visualization, Software, Methodology, Data curation, Conceptualization and draft editing. Faustine Akossoua Kouassi: Writing original draft, Visualization, Software, Methodology, Data curation, Conceptualization and draft editing. Philippe Bayen: Writing original draft, Visualization, Software, Methodology, Data curation, Conceptualization and draft editing. Omonlola Nadine Worou: Writing original draft, Visualization, Software, Methodology, Data curation, Conceptualization and draft editing. Declaration of competing 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. Funding This research was funded by the Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) project of Senegal. Acknowledgments We express our gratitude to the Ferlo population for their cooperation and to the people who helped collect this data. Data availability The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. Consent for publication Not applicable Ethics approval and consent to participate Not applicable References Brychkova G, Kekae K, McKeown PC, Hanson J, Jones CS, Thornton P, et al. Climate change and land-use change impacts on future availability of forage grass species for Ethiopian dairy systems. Sci Rep 2022;12. https://doi.org/10.1038/s41598-022-23461-w. 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17:02:53","extension":"html","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":216283,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7781208/v1/343ecd0385f6fa85f66648e7.html"},{"id":98451057,"identity":"7d25ab84-10ff-48f3-9a54-90cba3e9ddc2","added_by":"auto","created_at":"2025-12-17 17:31:23","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":66255,"visible":true,"origin":"","legend":"\u003cp\u003eLocation of the study areas\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7781208/v1/4c80821bf0789da2453112f6.png"},{"id":98451148,"identity":"1abd3281-5caa-4904-837e-35fee168a1e3","added_by":"auto","created_at":"2025-12-17 17:31:52","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":51202,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution map of two key herbaceous forage with high pastoral value\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7781208/v1/74692902677b3b2aa3c996c1.png"},{"id":98450999,"identity":"e4ff03b2-fb44-4d0d-bca8-cde3bede8aa3","added_by":"auto","created_at":"2025-12-17 17:31:11","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":27104,"visible":true,"origin":"","legend":"\u003cp\u003eChange in the range of suitable habitat areas for \u003cem\u003eZornia glochidiata\u003c/em\u003e under current and future climate scenarios based on SSP245; SSP585; HadGEM3-GC31-LL and MIROC6\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7781208/v1/52af3cf5200df6c605f16280.png"},{"id":98451115,"identity":"25af997a-dadc-463f-b736-7293bfeb778e","added_by":"auto","created_at":"2025-12-17 17:31:39","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":110653,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution predictions under current and future climate conditions (the upper bigger map is the current distribution and the small maps are the future distributions)\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7781208/v1/a6133ee92007e64ba4b5f338.png"},{"id":98622932,"identity":"18276717-a697-4df1-84d9-428bb89430ad","added_by":"auto","created_at":"2025-12-19 17:03:37","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":28001,"visible":true,"origin":"","legend":"\u003cp\u003eChange in the range of suitable habitat areas for \u003cem\u003eAndropogon pseudapricus \u003c/em\u003eunder current and future climate scenarios based on SSP245; SSP585; HadGEM3-GC31-LL and MIROC6\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7781208/v1/30842a4b72076f597ca5935b.png"},{"id":98451068,"identity":"a5c4df37-7157-440f-89f3-1cbae0a9bb71","added_by":"auto","created_at":"2025-12-17 17:31:26","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":100031,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution predictions under current and future climate conditions (the upper bigger map is the current distribution and the small maps are the future distributions)\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7781208/v1/da758f7874a029130f657ee7.png"},{"id":98450988,"identity":"795b454b-e16f-4652-8222-6c399d096c05","added_by":"auto","created_at":"2025-12-17 17:31:09","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":67940,"visible":true,"origin":"","legend":"\u003cp\u003eMaps illustrating the directional shifts of the centroids for \u003cem\u003eA. pseudapricus\u003c/em\u003eand \u003cem\u003eZ. glochidiata\u003c/em\u003e from the current distribution to future SSP scenarios\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7781208/v1/3a7508a8d919a883dd42034e.png"},{"id":108437673,"identity":"f63e1574-9370-43d4-84fd-7d4a36ead8f5","added_by":"auto","created_at":"2026-05-04 16:01:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":829409,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7781208/v1/6ecefbbe-7286-46e4-9d63-7af7c03244c0.pdf"},{"id":98450959,"identity":"c5475595-60a8-4fe3-aff7-8a7d8104e983","added_by":"auto","created_at":"2025-12-17 17:31:05","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":18372,"visible":true,"origin":"","legend":"","description":"","filename":"AppendixA.docx","url":"https://assets-eu.researchsquare.com/files/rs-7781208/v1/410ac076bc48f81e5b898078.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Modeling current and future distributions of key fodder grasses under climate change in Senegal using biomod2","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIn Sub-Saharan Africa, many farmers communities depend heavily on rangelands for their ecosystem services [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. However, the sustainability and functioning of these ecosystems are increasingly threatened by climate change and human pressures [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Current projections indicate that greenhouse gas emissions could cause a rise in global average temperatures by 1.4 to 5.8\u0026deg;C during the 21st century [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].This temperature rise could have significant impacts on the key ecosystem\u0026rsquo;s services of rangelands, particularly net primary productivity and biomass quality [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. It is important to note that the effects of climate change on rangelands are being exacerbated by overgrazing and unsustainable land use practices [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNatural pastures are the main source of fodder for livestock, especially in the rainy season [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. During the dry season, the availability of herbaceous fodder becomes scarce, pushing livestock to rely mainly on woody rangeland species [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. These woody rangeland species are also the main sources of energy for most rural households [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Thus, grazing land and the harvesting of woody and non-woody forest products are the most significant sources of food and feed in pastoral and agro-pastoral ecosystems [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, the continual expansion of farmlands has led to a reduction in natural grazing land, thereby decreasing pasture productivity [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. These land use changes, together with recurrent climate variability and the spread of invasive species, have contributed to a significant decline in livestock productivity [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. This decline has, in turn, disrupted the socio-economic stability of local communities [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn Sahelian countries, ensuring regular supply of fodder for animals throughout the year is essential for achieving resilient livestock production [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Seasonal fodder availability remains a key constraint for livestock farmers in Sahelian region [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. This challenge could be expected worsen due to increased livestock numbers, environmental and land use changes [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Climate change is projected to have a negative effect on fodder supply chains, particularly in vulnerable regions where people\u0026rsquo;s livelihoods are already limited [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/p\u003e \u003cp\u003ePredicting the geographic distribution of fodder species is crucial for promoting rangeland species conservation [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Identifying suitable and unsuitable habitats for fodder species is also essential to prevent biodiversity loss and support climate change adaptation strategies in rangeland areas [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Species Distribution Models (SDMs) are widely used to estimate potential species ranges and to the effects of climate change on various plant species groups [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. As noted by Coulibaly et al. (2023) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] anticipating shifts in plant distribution due to climate change is essential for effectively guiding sustainable management policies and thus maintaining ecosystem services. SDMs also allow for the projection of shifts in suitable habitats over time and help identify species that may persist or disappear under changing environmental conditions [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Forage species with high pastoral value play an important role in animal nutrition and ecological balance [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Among the key forage species in the Ferlo rangelands of northern Senegal are \u003cem\u003eAndropogon pseudapricus\u003c/em\u003e and \u003cem\u003eZornia glochidiata\u003c/em\u003e, both of which are widely consumed by livestock [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Given their importance, further research is needed to assess the potential impacts of climate change on their geographic distribution and habitat suitability to support their conservation. In West Africa, climate change and human pressure have been shown to negatively affect species diversity and dynamics [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. SDMs have also been successfully used to identify suitable habitats for the conservation of multipurpose species such as \u003cem\u003eVitellaria paradoxa\u003c/em\u003e CF Gaertn. [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], \u003cem\u003eGarcinia kola\u003c/em\u003e [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], \u003cem\u003eCordyla pinnata\u003c/em\u003e (A.Rich.) Milne-Redh. [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] and \u003cem\u003eParkia biglobosa\u003c/em\u003e (Jacq.) R.Br. ex G.Don [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Despite these growing publications on Senegal rangelands, there is a lack of SDMs applications targeting herbaceous fodder species with high pastoral value. This gap limits the ability to design effective conservation and management strategies for these important resources. Through this study, we aim to bridge this knowledge gap by modeling the geographic distribution of two herbaceous fodder species with high pastoral value in West Africa: \u003cem\u003eAndropogon pseudapricus\u003c/em\u003e Stapf (a grass) and \u003cem\u003eZornia glochidiata\u003c/em\u003e Rchb. Ex DC. (a legume), both prominent in the Ferlo rangelands.\u003c/p\u003e \u003cp\u003eThe aims of this study were to assess the geographic distribution of \u003cem\u003eAndropogon pseudapricus\u003c/em\u003e and \u003cem\u003eZornia glochidiata\u003c/em\u003e in response to current and future climatic conditions. Specially, the research addresses the following questions: (i) which bioclimatic variables control the geographic distribution of each species? (ii) what is the current spatial extent of suitable habitats for the conservation of these species? (iii) how are the suitable habitats of \u003cem\u003eAndropogon pseudapricus\u003c/em\u003e and \u003cem\u003eZornia glochidiata\u003c/em\u003e expected to change over time\u003cem\u003e?\u003c/em\u003e (iv) and which factors affect the variations in species distribution models?\u003c/p\u003e"},{"header":"2. Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1. Study areas\u003c/h2\u003e\n \u003cp\u003eThe current research study focused on the protected area, the rangelands, fallow lands and farmlands in the Senegal. Senegal is located in West Africa, between latitudes 12\u0026deg;20\u0026prime; -16\u0026deg;20\u0026prime; N and longitudes 11\u0026deg;20\u0026prime; -17\u0026deg;30\u0026prime; W (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e) [\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e]. The country has a unimodal rainfall pattern characterized by a short rainy season from June to September and a long dry season from October to May [\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e]. It is divided into three main climatic zones: Sahelian, North Sudanian, and South Sudanian [\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e]. In the Sahelian region, annual precipitation varies between 100 and 500 mm, while mean annual temperatures range from 23.3\u0026deg;C to 29.6\u0026deg;C [\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e]. In Senegal, the Wolof represent the largest ethnic group, followed by the Fulani [\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e]. Other ethnic groups are beginning to take their place in the composition of this population [\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2. Study species\u003c/h2\u003e\n \u003cp\u003e\u003cem\u003eAndropogon pseudapricus\u003c/em\u003e is a tufted annual or perennial grass, with ascending, branched stems that can reach up to 150 cm in height [\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e]. The plant provides good fodder when young and is grazed even when in flower [\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e]. This specie is also appreciated as hay during the dry season by the herding communities in Senegal. \u003cem\u003eA. pseudapricus\u003c/em\u003e is an important component of savannah vegetation in areas with an average annual rainfall of 500 to 700 mm [\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e]. It grows on shallow, sandy or gravelly lateritic soils [\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eZornia glochidiata\u003c/em\u003e is a leguminous herb from the Fabaceae family, widely distributed throughout the Sahelian zone of West Africa [\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e]. It is recognized as an important forage species in the region [\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e]. It is an annual herb, with erect stems and grows up to 45 cm tall [\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e]. Its leaves are compound and broad leaves, typically bifoliate [\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e]. The stipules are lanceolate in shape and can reach up to 15 mm in length [\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e]. It is common in sandy areas with a mean annual rainfall of 300 to 600 mm or more during the rainy season and a dry season for up to 8 months [\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3. Data collection\u003c/h2\u003e\n \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\n \u003ch2\u003e2.3.1. Species occurrence data\u003c/h2\u003e\n \u003cp\u003ePresence data of the two species were recorded using a Global Positioning System (GPS) during fieldwork conducted throughout the distribution range of both species in Senegal (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The field data were supplemented by additional records gathered from the Global Biodiversity Information Facility website (GBIF, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.gbif.org/\u003c/span\u003e\u003c/span\u003e). To improve relevance and accuracy of the data, GBIF records were filtered to include only occurrences from 1990 to 2025, excluding older records of individuals that may not reflect. A total dataset of 2327 occurrence records were obtained for \u003cem\u003eZ. glochidiata\u003c/em\u003e, of which 1523 records (65.45%) were collected from field surveys and 804 records (34.55%) from the GBIF database. Similarly, a total dataset of 1306 occurrence records were obtained for \u003cem\u003eA. pseudapricus\u003c/em\u003e, of which 930 records (71.25%) were collected from field surveys and 376 records (28.75%) from the GBIF database. Following rigorous data quality control procedures, duplicate records were removed using the function \u0026lsquo;\u003cem\u003espatially rarefy occurrence data\u0026rsquo;\u003c/em\u003e in SDMtoolbox [\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e]. To reduce sampling bias, spatial thinning was performed using the ENMTools package in R [\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e]. Duplicate records within the same 1km\u003csup\u003e2\u003c/sup\u003e grid cell were removed to avoid over-representation [\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\n \u003ch2\u003e2.3.2. Environmental data\u003c/h2\u003e\n \u003cp\u003eIn this study environmental variables included bioclimatic, elevation and human footprint index data. A total of 19 bioclimatic variables covering current (1970\u0026ndash;2000) and future (2070 and 2100) climate conditions, along with elevation data, were downloaded from WorldClim website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.worldclim.org/\u003c/span\u003e\u003c/span\u003e; accessed on January 09, 2025) [\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e]. In addition, the human footprint index was downloaded from Wildlife Conservation Society (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://wcshumanfootprint.org/data-access\u003c/span\u003e\u003c/span\u003e). Thus, elevation and human footprint index data were resampled to match the resolution of the bioclimatic variables, which is 30 arc seconds (approx. 1 km\u003csup\u003e2\u003c/sup\u003e) [\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e]. To predict the future distribution of species, two Shared Socioeconomic Pathways (SSPs) scenarios from the updated CMIP6 were selected for analysis, the SSP 245(moderate) and SSP 585 (strong) for two periods 2070 and 2100. These scenarios were chosen because they represent more pessimistic and more optimistic greenhouse gas emission scenarios respectively [\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e]. Climate projection were based on two global climate models (GCMs): HadGEM3-GC31-LL and MIROC6, widely used in CMIP6 experiments [\u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e]. In addition, it should be noted that these two climate models are widely used in the prediction of ecological niches in West Africa [\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\n \u003ch2\u003e2.3.3. Variable selection for fodder species modeling\u003c/h2\u003e\n \u003cp\u003eTo select relevant environmental predictors for ecological niche modeling, we conducted a variance inflation factors (VIF) analysis on 19 initial bioclimatic variables and the Human Footprint Index to assess multicollinearity [\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e]. We progressively eliminated variables with a Pearson correlation coefficient equal or higher to |0.7| (r\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\ge\\:\\)\u003c/span\u003e\u003c/span\u003e|0.7|) [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e]. Then, 6 least correlated environmental variables for \u003cem\u003eAndropogon pseudapricus\u003c/em\u003e and 7 variables for \u003cem\u003eZornia glochidiata\u003c/em\u003e and elevation were identified to run the model. Finally, a Jackknife test was conducted to identify the variables with the greatest contribution to the model [\u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\n \u003ch2\u003e2.3.4. Evaluation of model and potential distribution of forage species\u003c/h2\u003e\n \u003cp\u003eThe prediction of current and future suitable areas for forage species was carried out using R 4.3.2 software based on 6 machine learning algorithms via Biomod2 [\u003cspan class=\"CitationRef\"\u003e48\u003c/span\u003e]. Biomod2 function was used to randomly generate pseudo-absence points with the same number as the recorded species occurrence data [\u003cspan class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e]. Then, six algorithms best suited to species prediction were applied: artificial neural networks (ANN), generalized boosted models (GBM), generalized linear models (GLM), random forest (RF), surface range envelope (SRE) and maximum entropy (MaxEnt) [\u003cspan class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e]. These algorithms combine species occurrence data with current bioclimatic variables to generate a map of potential species distribution in the study area and future habitat suitability based on climate projections [\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e]. For each species, we used 25% of species occurrence records for model testing and 75% for model calibration [\u003cspan class=\"CitationRef\"\u003e51\u003c/span\u003e]. The predictive capacity of the model was evaluated according to four measures, including the Area Under the Curve (AUC) which is threshold-independent, the threshold-dependent True Skill Statistic (TSS), the Correlation Statistic (COR) and the KAPPA [\u003cspan class=\"CitationRef\"\u003e52\u003c/span\u003e]. An AUC value equal to 0.5 indicates poor model performance, and when its value is close to 1 (AUC\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\u0026gt;\\)\u003c/span\u003e\u003c/span\u003e 0.75) it indicates a very good performance of the model [\u003cspan class=\"CitationRef\"\u003e53\u003c/span\u003e]. The TSS value ranges from \u0026minus;\u0026thinsp;1 to +\u0026thinsp;1, and is calculated using the following formula [\u003cspan class=\"CitationRef\"\u003e54\u003c/span\u003e]:\u003c/p\u003e\n \u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e$$\\:TSS=\\:\\frac{ad-bc}{(a+c)(b+d)}=Sensitivity+Specificity-1$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eSuitable habitat distributions for fodder species, both current and projected under future climate scenarios, were mapped based on logistic occurrence probabilities (p). Three suitable areas classes were considered: Low suitability (p \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\le\\:\\)\u003c/span\u003e\u003c/span\u003e0.4), medium suitability (0.4\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\u0026lt;\\)\u003c/span\u003e\u003c/span\u003e p \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\le\\:\\)\u003c/span\u003e\u003c/span\u003e 0.6) and high suitability (p\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\u0026gt;\\)\u003c/span\u003e\u003c/span\u003e 0.6) [\u003cspan class=\"CitationRef\"\u003e55\u003c/span\u003e]. Moreover, the centroid shift of both species from the present to future SSP scenarios, across both time frames, were performed using R (version 4.3.2), primarily with the packages \u003cem\u003esf, ggplot2, ggspatial\u003c/em\u003e, and \u003cem\u003epatchwork\u003c/em\u003e [\u003cspan class=\"CitationRef\"\u003e73\u003c/span\u003e]. Maps of the species suitable areas were finally produced using ArcMap 10.8 for current and future climatic conditions under the two scenarios at the two horizons.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Key environmental variables influencing the species distribution\u003c/h2\u003e \u003cp\u003eThe main bioclimatic variables influencing the potential distribution of the two fodder species in Senegal are shown in (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The main variables influencing \u003cem\u003eZ. glochidiata\u003c/em\u003e were elevation (21%), min temperature of coldest month (15.6%), isothermality (13.5%), precipitation of coldest quarter (12.6%) and precipitation of wettest month (10.6%). Bio 16 (precipitation of wettest quarter) is the least influential bioclimatic variable for \u003cem\u003eZ. glochidiata\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eThe bioclimatic variables significantly affecting the \u003cem\u003eA. pseudapricus\u003c/em\u003e species were Bio16 (precipitation of wettest quarter) with a contribution of 34.8%, Bio12 (annual precipitation) with a contribution of 26.6%. Bio17 (precipitation of driest quarter) is the least influential variable, with a contribution of 5.3%.\u003c/p\u003e \u003cp\u003eAlthough climatic variables were predominant, the internal Jackknife test revealed that human footprint index variables also play a significant role, albeit to a lesser extent. Specifically, human footprint index contributed 18.3% to the potential distribution of \u003cem\u003eZ. glochidiata\u003c/em\u003e and 8.5% for \u003cem\u003eA. pseudapricus\u003c/em\u003e. These results highlight the complex interaction between climatic and human factors in determining the habitat suitability for the two fodder species.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eVariables contribution used in modelling of the two fodder species.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCode\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVariables name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eContribution (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eZornia glochidiata\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBio3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIsothermality (BIO2/BIO7) (\u0026times;100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBio6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMin temperature of coldest month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBio13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrecipitation of wettest month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBio16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrecipitation of wettest quarter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBio19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrecipitation of coldest quarter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eElev\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eElevation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHFP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHuman Footprint Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAndropogon pseudapricus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBio9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean temperature of driest quarter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBio12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAnnual precipitation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBio16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrecipitation of wettest quarter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBio17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrecipitation of driest quarter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eElev\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eElevation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHFP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHuman Foot print Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Model accuracy and performance\u003c/h2\u003e \u003cp\u003e(Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) shows the performance of the six algorithms used to predict the distribution of the two fodder species based on various statistical metrics such as AUC, TSS, COR and KAPPA. All six algorithms demonstrated good predictive performance (AUC \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\u0026gt;\\)\u003c/span\u003e\u003c/span\u003e0.8), except for the ANN, which had an AUC value of 0.75 for \u003cem\u003eZornia glochidiata\u003c/em\u003e. Indeed, \u003cem\u003eZornia glochidiata\u003c/em\u003e achieved an AUC of 0.96 and TSS 0.90 with RF, while \u003cem\u003eAndropogon pseudapricus\u003c/em\u003e has an AUC of 0.98 and a TSS of 0.71with the same algorithm (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). ANN presented lower values than other algorithms, as seen in the case of \u003cem\u003eZ. glochidata\u003c/em\u003e with an AUC of 0.75 and TSS of 0.63. For each species the performance of the models was also evaluated by the correlation statistic (COR) value and the KAPPA (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePerformance of the six algorithms (RF, MaxEnt, GLM, GBM, SRE, ANN)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlgorithm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTSS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eKAPPA\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eZornia glochidiata\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMaxEnt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGLM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSRE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eANN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAndropogon pseudapricus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMaxEnt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGLM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSRE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eANN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Current and future distribution of two fodder species\u003c/h2\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1. \u003cem\u003eZornia glochidiata\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eThe current high suitable habitats for the species represent 22.9% (43,843.77 km\u003csup\u003e2\u003c/sup\u003e) of Senegal's total surface area (196,712 km\u003csup\u003e2\u003c/sup\u003e). Less suitable habitats cover 32.11% (63,161.36 km\u003csup\u003e2\u003c/sup\u003e), while 45.16% (88,834.02 km\u003csup\u003e2\u003c/sup\u003e) of the country's land area is unsuitable (Appendix A; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). These habitats are mainly located in the Sudano-Sahelian zone and the Sudanian zones of the country (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Both climate models (HadGEM3-GC31-LL and MIROC6) predicted a significant overall decline in the species' range under the moderate (SSP 245) and high-emission (SSP 585) scenarios across all time horizons. Under the HadGEM3-GC31-LL model's moderate scenario (SSP245), habitat loss is projected to reach 48.99% (22,364.17 km\u003csup\u003e2\u003c/sup\u003e) by 2061\u0026ndash;2080 and 47.96% (22,815.99 km\u003csup\u003e2\u003c/sup\u003e) by 2081\u0026ndash;2100. In the worst-case scenario (SSP585) for the same model, projected losses range from 48.58% (22,543.28 km\u003csup\u003e2)\u003c/sup\u003e by 2061\u0026ndash;2080 to 50.18% (21844.18 km\u003csup\u003e2\u003c/sup\u003e) by 2081\u0026ndash;2100.The MIROC6 model under SSP585 also forecasted drastic changes in the species' spatial distribution, with only 9.65% and 8.9% of Senegal's area remaining suitable by 2070 and 2100, respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2. \u003cem\u003eAndropogon pseudapricus\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eThe predicted high suitable areas for \u003cem\u003eA. pseudapricus\u003c/em\u003e currently account for 20.14% (19,942.38 km\u003csup\u003e2\u003c/sup\u003e) of Senegal's total area (Appendix A, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), while less suitable areas cover 36.03% (51,197.06 km\u003csup\u003e2\u003c/sup\u003e). About 43.83% (12,5578.23 km\u003csup\u003e2\u003c/sup\u003e) of the total area of Senegal is unsuitable for \u003cem\u003eA. pseudapricus\u003c/em\u003e conservation under current climatic conditions. The species\u0026rsquo; habitat range highly and moderately suitable to the species were found in the northern part of the Sahelian zone of Senegal (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The total potential distribution area of \u003cem\u003eA. pseudapricus\u003c/em\u003e was projected to decrease substantially over time under the two future emission scenarios. Specifically, the areas accounted for 88.11% and 88.37% of the total area of Senegal in the years 2061\u0026ndash;2080 under moderate and strong scenarios, respectively with the HadGEM3-GC31-LL model. However, under SSP 585, the MIROC6 model predicts a crucial decrease of 5448.75 km\u003csup\u003e2\u003c/sup\u003e (89.36%) in 2061\u0026ndash;2080, followed by a small decrease of 14.59% (43,729.66 km\u003csup\u003e2\u003c/sup\u003e) in 2081\u0026ndash;2100 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). These trends highlight the species\u0026rsquo; vulnerability to future climate conditions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e3.3.3. Trajectory of the two species according to current and future climate conditions\u003c/h2\u003e \u003cp\u003eCentroid shift analysis under future climate scenarios revealed contrasting patterns between the two species (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). For \u003cem\u003eA. pseudapricus\u003c/em\u003e, the centroid consistently moved northeastward across all scenarios, with a particularly pronounced shift under SSP585-2100, indicating a likely migration of suitable habitat toward higher latitudes and eastern regions. In contrast, \u003cem\u003eZ. glochidiata\u003c/em\u003e exhibited a southwestward shift, especially under MIROC6 projections, suggesting a relocation of favorable conditions toward lower latitudes and western areas. These divergent directions of centroid movement highlight species-specific responses to climate change and underscore the need for tailored management strategies.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Variable importance for the habitat suitability of the two fodder species in Senegal\u003c/h2\u003e \u003cp\u003eTemperature and precipitation are the primary bioclimatic variables influencing plant growth and reproduction [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Based on the relative importance of the environmental predictors, the distribution of \u003cem\u003eZ. glochidiata\u003c/em\u003e is primarily shaped by elevation, minimum temperature of the coldest month, isothermality, and precipitation during the coldest quarter. In contrast, \u003cem\u003eA. pseudapricus\u003c/em\u003e is most affected by precipitation in the wettest quarter, total annual rainfall, and mean temperature of the driest quarter. Comparable patterns have been highlighted in earlier research conducted across West Africa [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e], reinforcing the role of temperature and rainfall in determining the ecological niches of tropical species [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. According to O\u0026rsquo;donnell and Ignizio [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e], species can adapt to environments characterized by lower temperature variability, which enhances their persistence across different climatic zones. Comparable observations were made for \u003cem\u003eAfzelia africana\u003c/em\u003e [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] and \u003cem\u003eCarapa procera\u003c/em\u003e [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e] in Burkina Faso, as well as for other multipurpose species in Benin [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The present findings are consistent with previous research on the spatial distribution of fodder species in West Africa [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]. Numerous studies have emphasized that precipitation and temperature are the dominant climatic factors influencing the distribution patterns of woody vegetation throughout the region [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, human activities accounted for 18.3% and 8.5% of the factors influencing the distribution of \u003cem\u003eZ. glochidiata\u003c/em\u003e and \u003cem\u003eA. pseudapricus\u003c/em\u003e, respectively. These results indicate that anthropogenic pressures such as land-use change, infrastructure development, and population expansion negatively affect the growth, habitat suitability, and reproductive capacity of both species [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The growing intensity of human use of rangelands has been identified as a major driver of the widespread degradation of fodder vegetation in recent years [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Model performance\u003c/h2\u003e \u003cp\u003eThe present study employed the Biomod2 package to model both current and projected suitable habitats for the two fodder species, using six algorithms: ANN, GBM, GLM, MaxEnt, RF, and SRE. Among these approaches, the Random Forest (RF) algorithm consistently produced the most reliable predictions for both species, as reflected by its superior AUC and TSS scores. This finding aligns with earlier works by Huang et al. [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]; Coulibaly et al.[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], and Sambou et al. [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], which also identified RF as the most effective method for species distribution modeling. Similarly, recent studies by Dogbo et al. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] and Gbadamassi et al. [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e] reported that Random Forest was the most robust algorithm for predicting the spatial distribution of twelve multipurpose species in Benin and \u003cem\u003eProsopis africana\u003c/em\u003e in Togo. However, the integration of multiple algorithms in species distribution modeling remains a topic of scientific debate [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Ara\u0026uacute;jo and New (2007)[\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e], emphasized that combining different modeling techniques can improve predictive robustness and minimize algorithm specific bias. Several studies have further demonstrated that ensemble models, which aggregate multiple algorithms, generally outperform single-model approaches in predicting species distributions and evaluating the potential effects of climate change [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Current distribution of the two species\u003c/h2\u003e \u003cp\u003eThe predicted current suitable habitats for \u003cem\u003eZ. glochidiata\u003c/em\u003e in Senegal cover more than half of the national territory (54.84%), whereas those of \u003cem\u003eA. pseudapricus\u003c/em\u003e occupy approximately 36.17% of the country\u0026rsquo;s surface area. These favorable habitats extend mainly across the Sudanian and Sudano-Sahelian ecological zones. This pattern aligns with the known distribution range of \u003cem\u003eZ. glochidiata\u003c/em\u003e in West Africa [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. In contrast, model projections for \u003cem\u003eA. pseudapricus\u003c/em\u003e indicate a broader presence throughout Senegal, with a stronger concentration in the northern Sahelian region. Furthermore, the species appears to be absent from certain protected areas as well as from some unprotected rangelands.\u003c/p\u003e \u003cp\u003eThese results indicate that protected areas do not always provide effective conservation sites for species such as \u003cem\u003eA. pseudapricus\u003c/em\u003e. Some regions currently classified as unsuitable may become more favorable for the species in the future, potentially offering better conservation opportunities than areas already considered suitable. Additionally, the extent of suitable habitat for \u003cem\u003eZ. glochidiata\u003c/em\u003e identified in this study exceeds that reported for other socio-economically important plant species in West Africa. For example, Balima et al. [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] estimated that approximately 25.54% of Burkina Faso\u0026rsquo;s territory is suitable for \u003cem\u003eAfzelia africana\u003c/em\u003e conservation, while only about 2.37% of Mali\u0026rsquo;s territory was deemed highly suitable for \u003cem\u003eBombax costatum\u003c/em\u003e Pellegr. \u0026amp; Vuill. [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The substantial areas identified as unsuitable for \u003cem\u003eZ. glochidiata\u003c/em\u003e and \u003cem\u003eA. pseudapricus\u003c/em\u003e highlight the urgent need to prioritize their conservation in Senegal.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.4. Future distribution of the two species\u003c/h2\u003e \u003cp\u003eClimate projections for 2070 suggest that \u003cem\u003eA. pseudapricus\u003c/em\u003e and \u003cem\u003eZ. glochidiata\u003c/em\u003e are likely to lose between 53.24% and 89.36% of their highly suitable habitats under the SSP2-4.5 and SSP5-8.5 scenarios, respectively. These results are consistent with observed distribution patterns for \u003cem\u003eBombax costatum\u003c/em\u003e Pellegr. \u0026amp; Vuill. in Mali, reported by Coulibaly et al. [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], and for Combretum glutinosum Perr. ex-DC. in Burkina Faso, documented by Demb\u0026eacute;l\u0026eacute; et al. [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Compared to other multipurpose species in West Africa, the projected future ranges for these two fodder species are relatively extensive. For instance, Djibo Moussa et al. [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e] estimated that 37.89% of Niger\u0026rsquo;s territory could be suitable for \u003cem\u003eBalanites senegalensis\u003c/em\u003e conservation, while approximately 32.91% and 26.44% of the territories of Senegal and Benin were identified as suitable for the conservation of \u003cem\u003eCordyla pinnata\u003c/em\u003e [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] and \u003cem\u003eParkia biglobosa\u003c/em\u003e (Jacq.) R.Br. ex G. Don [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], respectively.\u003c/p\u003e \u003cp\u003eOur results indicate that suitable habitats for both \u003cem\u003eZ. glochidiata\u003c/em\u003e and \u003cem\u003eA. pseudapricus\u003c/em\u003e are projected to contract primarily within the Sudano-Sahelian region of West Africa. This pattern is consistent with previous studies predicting future range reductions for \u003cem\u003ePterocarpus erinaceus\u003c/em\u003e [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e] and \u003cem\u003eAdansonia digitata\u003c/em\u003e under changing climatic conditions in the region [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The observed decline can be attributed to rising temperatures, reduced precipitation, and the increasing frequency of extreme climatic events, all of which compromise the ecological viability of these species in areas that were previously suitable. These findings also align with climate models forecasting a general decrease in rainfall across West Africa [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. By 2100, \u003cem\u003eZ. glochidiata\u003c/em\u003e is expected to lose approximately 69.25% (19,419.32 km\u0026sup2;) of its less suitable habitats, while \u003cem\u003eA. pseudapricus\u003c/em\u003e is projected to lose about 56.81% under the MIROC6 / SSP2-4.5 scenario. This difference may reflect the higher anthropogenic pressures on \u003cem\u003eZ. glochidiata\u003c/em\u003e, as well as species-specific ecological and environmental sensitivities. This decline may be further exacerbated by the expansion of croplands and urban areas. Our results are consistent with those of Trisurat et al. [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e] and Gbadamassi et al. [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e], who reported that fodder species often occupy only a fraction of their potential ecological niche due to human activities, bushfires, and interspecific competition. Similarly, Saliou et al. [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e] predicted a marked reduction in the habitats of three Andropogon species in Benin, highlighting the urgent need for targeted conservation actions. The projected shift of suitable habitats towards the Sudanian zone under future climate scenarios can be attributed to significant increases in temperature, particularly in the semi-arid regions of West Africa, which are likely to reduce areas suitable for plant biodiversity [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Climatic changes are altering habitats, while human pressures such as agricultural expansion and infrastructure development continue to impact fodder species Gbadamassi et al. [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. These combined challenges highlight the need for integrated management strategies that address both environmental and anthropogenic factors to ensure long-term conservation\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e4.5. Implications for management and conservation\u003c/h2\u003e \u003cp\u003eBased on the results of this study, current management efforts should focus on monitoring areas identified as highly and moderately suitable habitats, with the aim of implementing timely and targeted conservation measures. In anticipation of future climate change, strategies should prioritize regions projected to remain suitable or become suitable for \u003cem\u003eZ. glochidiata\u003c/em\u003e and \u003cem\u003eA. pseudapricus\u003c/em\u003e. Concurrently, reducing human-induced pressures is essential to support the growth, regeneration, and expansion of these species within their potential distribution zones.\u003c/p\u003e \u003cp\u003eThis study utilized Biomod2 simulations integrating six modeling algorithms to predict the geographic distribution of \u003cem\u003eZ. glochidiata\u003c/em\u003e and \u003cem\u003eA. pseudapricus\u003c/em\u003e across Senegalese rangelands under both current and future climate scenarios. Continuous monitoring and early warning systems for shifts in plant distribution can provide a scientific foundation for proactive conservation planning, enabling interventions at both local and regional scales. These insights have substantial theoretical and practical implications for guiding adaptive rangeland management and biodiversity conservation throughout the Sahelian region.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study modeled the ecological niches of two forage species of high pastoral value in Senegal by combining six different algorithms. Our results identified precipitation during the wettest quarter, annual precipitation, and minimum temperature of the coldest month as the main environmental factors influencing their geographic distribution. Currently, favorable habitats for \u003cem\u003eZornia glochidiata\u003c/em\u003e cover approximately 54.4% of Senegal\u0026rsquo;s total land area, including 22.29% classified as highly suitable. Similarly, potential habitats for \u003cem\u003eAndropogon pseudapricus\u003c/em\u003e occupy about 43.17% of the country, but only 3.10% of these areas are highly suitable. Under future climate scenarios, these suitable habitats are projected to decline significantly. The models consistently predict a substantial reduction in the ecological niches of both species by 2070 and 2100, regardless of the climate scenario considered. Moreover, the spatial extent of suitable habitats varies according to the choice of climate model, scenario, and time horizon.\u003c/p\u003e\n\u003cp\u003eThe study also reveals that many highly suitable habitats lie outside existing protected areas, indicating that current conservation zones may be insufficient for the long-term preservation of these forage species. To prevent the degradation of important forage resources in Sahelian rangelands, targeted strategies are essential. This includes implementing forestry policies aimed at endangered forage species and strengthening the protection of key habitats within and beyond protected areas.\u003c/p\u003e\n\u003cp\u003eFinally, to better safeguard forage biodiversity, ecological niche modeling should be expanded to include other forage species of high pastoral value in Sahelian rangelands. Incorporating additional environmental variables and utilizing a broader range of climate models could further improve the accuracy and effectiveness of future distribution predictions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contribution statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIdrissa Sawadogo:\u0026nbsp;\u003c/strong\u003eWriting original draft, Visualization, Software, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation and Conceptualization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJean Kouao Koffi:\u0026nbsp;\u003c/strong\u003eVisualization, Resources, Methodology, Data curation, Conceptualization and draft editing.\u0026nbsp;\u003cstrong\u003eSi\u0026eacute; Sylvestre\u003c/strong\u003e \u003cstrong\u003eDa\u003c/strong\u003e: Writing original draft, Visualization, Software, Methodology, Data curation, Conceptualization and draft editing.\u0026nbsp;\u003cstrong\u003eJean Baptiste Demb\u0026eacute;l\u0026eacute;\u003c/strong\u003e: Visualization, Resources, Methodology, Data curation, Conceptualization and draft editing.\u0026nbsp;\u003cstrong\u003eInnocent Charles Emmanuel Traor\u0026eacute;\u003c/strong\u003e: Writing original draft, Visualization, Software, Methodology, Data curation, Conceptualization and draft editing.\u0026nbsp;\u003cstrong\u003eFaustine Akossoua\u003c/strong\u003e \u003cstrong\u003eKouassi:\u0026nbsp;\u003c/strong\u003eWriting original draft, Visualization, Software, Methodology, Data curation, Conceptualization and draft editing.\u0026nbsp;\u003cstrong\u003ePhilippe\u003c/strong\u003e \u003cstrong\u003eBayen:\u0026nbsp;\u003c/strong\u003eWriting original draft, Visualization, Software, Methodology, Data curation, Conceptualization and draft editing.\u0026nbsp;\u003cstrong\u003eOmonlola Nadine\u003c/strong\u003e \u003cstrong\u003eWorou:\u0026nbsp;\u003c/strong\u003eWriting original draft, Visualization, Software, Methodology, Data curation, Conceptualization and draft editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest\u0026nbsp;\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\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by the\u0026nbsp;Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) project of Senegal.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe express our gratitude to the Ferlo population for their cooperation and to the people who helped collect this data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBrychkova G, Kekae K, McKeown PC, Hanson J, Jones CS, Thornton P, et al. 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Present and future climate change in the semi-arid region of West Africa: A crucial input for practical adaptation in agriculture. Atmospheric Science Letters 2012;13:108\u0026ndash;12. https://doi.org/10.1002/asl.368. \u003c/li\u003e\n\u003cli\u003eYao Z, Xin Y, Yang L, Zhao L, Ali A. Precipitation and temperature regulate species diversity, plant coverage and aboveground biomass through opposing mechanisms in large-scale grasslands. Front Plant Sci 2022;13. https://doi.org/10.3389/fpls.2022.999636.\u003c/li\u003e\n\u003cli\u003eSawadogo I, Bayen P, Balima LH, Sanou CL, Kouassi FA, Worou ON. Climate-driven shifts in the geographic distribution of fodder trees in West Africaʼs Grazing landscapes. Trees, Forests and People 2025;22. https://doi.org/10.1016/j.tfp.2025.100995. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"disu","sideBox":"Learn more about [Discover Sustainability](https://www.springer.com/43621)","snPcode":"","submissionUrl":"","title":"Discover Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Fodder species, Climate change, Conservation, Species distribution modeling, Rangelands ","lastPublishedDoi":"10.21203/rs.3.rs-7781208/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7781208/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eClimate change and increasing human pressure are among the primary factors shaping the distribution of ecological niches for forage species in rangelands. This study investigates the impact of climate change on both current and future geographic distributions of \u003cem\u003eZornia\u003c/em\u003e \u003cem\u003eglochidiata\u003c/em\u003e and \u003cem\u003eAndropogon pseudapricus\u003c/em\u003e in Senegal, under two climate models (HadGEM3-GC31-LL and MIROC6) and two greenhouse gas emission scenarios (SSP245 and SSP585), with projections for 2070 and 2100. The analysis covers the entire Senegalese territory, with particular attention to rangelands and protected areas. Key environmental variables influencing the distribution of these two fodder species were identified, and future habitat changes were forecasted using climatic projections.\u003c/p\u003e\n\u003cp\u003eWe employed six modeling techniques: artificial neural networks (ANN), generalized boosted models (GBM), generalized linear models (GLM), maximum entropy (MaxEnt), random forest (RF), and surface range envelope (SRE). Among the bioclimatic predictors, precipitation of the wettest quarter (Bio16), annual precipitation (Bio12), isothermality (Bio3), and elevation were the most influential. Random forest showed the highest predictive accuracy, with AUC values exceeding 0.95 for both species.\u003c/p\u003e\n\u003cp\u003eCurrently, \u003cem\u003eZ. glochidiata\u003c/em\u003e and \u003cem\u003eA. pseudapricus\u003c/em\u003e occupy 54.4% and 36.17% of Senegal’s surface area, respectively. However, all future scenarios forecast a substantial loss of suitable habitat for both species. These projected reductions underline their high vulnerability to changing climatic conditions. The results provide crucial insights for the conservation and management of forage resources in Sahelian ecosystems, supporting strategies for ecological resilience and socio-economic sustainability.\u003c/p\u003e","manuscriptTitle":"Modeling current and future distributions of key fodder grasses under climate change in Senegal using biomod2","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-17 17:09:46","doi":"10.21203/rs.3.rs-7781208/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-21T14:01:04+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-17T17:31:49+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-10T07:07:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"243868796924317720178511988412691300216","date":"2026-01-08T08:29:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"313350095282491807053449257656156351631","date":"2026-01-04T06:45:15+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-24T12:06:29+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-22T13:42:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"227788827226623741849837829116392361270","date":"2025-12-14T14:31:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"169749716227751870800178146371816977990","date":"2025-12-13T08:34:09+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-12T12:01:28+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-11-25T07:01:21+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-24T11:19:07+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-24T23:50:24+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Sustainability","date":"2025-10-24T23:46:42+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"disu","sideBox":"Learn more about [Discover Sustainability](https://www.springer.com/43621)","snPcode":"","submissionUrl":"","title":"Discover Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cb630082-a17a-487a-8582-d706ba5c1447","owner":[],"postedDate":"December 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T16:00:37+00:00","versionOfRecord":{"articleIdentity":"rs-7781208","link":"https://doi.org/10.1007/s43621-026-03250-5","journal":{"identity":"discover-sustainability","isVorOnly":false,"title":"Discover Sustainability"},"publishedOn":"2026-04-30 15:57:00","publishedOnDateReadable":"April 30th, 2026"},"versionCreatedAt":"2025-12-17 17:09:46","video":"","vorDoi":"10.1007/s43621-026-03250-5","vorDoiUrl":"https://doi.org/10.1007/s43621-026-03250-5","workflowStages":[]},"version":"v1","identity":"rs-7781208","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7781208","identity":"rs-7781208","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}