Introduction

Biodiversity loss has intensified in recent times due to both climatic and non-climatic factors such as anthropogenic activities1,2,3,4. Consequently, many species have evolved physiological adjustments (e.g., changes in reproductive rates) and adaptive capacities at global and local scales5,6,7,8. While species occupying broad ecological niches may maintain stable populations despite exposure to diverse environmental and human pressures, those with narrow distributions, particularly habitat specialists and endemic species, are far more vulnerable to population decline and extinction risk9,10,11. This raises important questions: Which climatic and non-climatic factors strongly predict species distributions? How reliable are projections of climatically suitable areas? And does the global IUCN conservation assessment accurately reflect local conservation status? However, limited knowledge of species’ actual distribution ranges and their interactions with environmental drivers complicates accurate assessments of conservation status, especially for taxa with high conservation priority, economic importance, or narrow distributional ranges. Addressing these knowledge gaps through integrative approaches, such as Species Distribution Modeling (SDMs) combined with ground-truthing field assessments, is essential for strengthening species conservation and management within their native ranges.

SDM is a statistical approach used to predict suitable habitats for species by relating their distribution records to environmental and/or anthropogenic predictor variables12,13,14,15,16. Among the many available SDM algorithms (see Pecchi et al.17, Maximum Entropy (MaxEnt) is widely used due to its flexibility in handling diverse types of input data18,19,20. MaxEnt can incorporate presence-only occurrence records and categorical predictors (e.g., soil types) to project potential distribution shifts under various climatic scenarios and across different geographical scales21,22. Its efficiency in accurately predicting suitable habitats for species with narrow distributions or limited occurrence records has also been well documented e.g., Sphenocentrum jollyanum Pierre.23, Thuja plicata Donn ex D.Don24, Tectona grandis L.25, Garcinia kola Heckel26, Commiphora wightii (Arn.) Bhandari27, and Irvingia gabonensis (Aubry-Lecomte ex O’Rorke) Baill.28, and Triplochiton scleroxylon K.Schum29.

Lophira alata Banks ex C.F. Gaertn. (Family Ochnaceae) is an economically important yet highly threatened tropical species in Nigeria (Fig. 1), currently facing severe pressures from overexploitation and habitat loss. The species is habitat-specific, and its seeds lose viability rapidly; therefore, protection from predation and timely sowing during sunny periods are recommended to optimize germination success (https://forestcenter.iita.org/wp-content/uploads/2017/12/Lophira-alata.pdf). Unfortunately, the lack of comprehensive documentation of its occurrences in Nigeria has hindered efforts to evaluate current and future conservation strategies amid increasing human activities and climate change. Consequently, a thorough assessment and modeling of the spatiotemporal dynamics of suitable habitats is necessary to clarify its distributional range under projected climate shifts and persistent anthropogenic pressures. This approach aligns with previous studies on local and regional species in Nigeria that have applied similar modeling frameworks30,31,32,33,34. To date, no study has examined how climate change and human activities affect the current and future distribution of this species within its Nigerian range.

Fig. 1
Fig. 1
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Map of Nigeria showing the delimitation of 15 ecoregions and elevation ranges, following Olson et al.35. Base map created in ArcGIS Desktop (ArcMap) 10.8 (ESRI, Redlands, CA, USA; http://www.esri.com).

In this study, we employed the MaxEnt algorithm to predict the potential habitat distribution of L. alata in Nigeria under both current and future climate scenarios. Our overarching goal was to evaluate the conservation status of L. alata in light of projected climate change and ongoing anthropogenic pressures across its Nigerian range. To achieve this, we developed three specific objectives: (1) to identify the environmental variables shaping the current and future habitats of L. alata in Nigeria, (2) to project its distribution under present and projected climate scenarios, and (3) to assess its local conservation status within Nigeria. The findings of this work aim to support the sustainable use and conservation of the species by providing a detailed conservation assessment grounded in a rigorous approach that integrates field surveys with species distribution modeling.

Materials and methods

Study species

The focal species, commonly known as the red ironwood tree (“Ekki” in Yoruba and “Okopia” in Igbo), is native to subtropical and tropical moist lowland rainforests in Africa. Nigeria, a country with diverse ecoregions and rich plant biodiversity, supports a notable portion of the distribution range of L. alata (Fig. 1). The species typically has a straight trunk with reddish-brown bark and may reach heights of up to 30 m and trunk diameters of about 50 centimeters, with mature individuals often exceeding this size (https://tropical.theferns.info/viewtropical.php?id=Lophira+alata). Its flowers are large, white, and fragrant, and bears membranous, winged fruits (https://eol2.eol.org/pages/5715979/articles). Currently listed as Vulnerable by the IUCN, L. alata is among the most heavily exploited and threatened tropical tree species. According to herbarium records and online databases, its distribution in Nigeria is largely confined to lowland rainforest zones, particularly within the Cross River National Park (CRNP), one of Africa’s oldest rainforest blocks. In Nigeria, its populations have declined significantly due to extensive local exploitation for construction, timber harvesting, and medicinal uses (https://www.iucnredlist.org/species/33056/9745747).

Species occurrence data

Occurrence records of L. alata used in this study were obtained from online archives, including the Global Biodiversity Information Facility36, the RAINBIO database37, and herbarium records. To supplement these datasets, we conducted extensive field surveys in an underrepresented region of Nigeria, specifically CRNP in southern Nigeria. Details of the field survey methods and results are provided in Appendix File S1 and Appendix Fig. S1, respectively. A total of 1,260 distribution records from all sources were merged, cleaned, and plotted over the study-area shapefile following established procedures38, which enabled the removal of erroneous and duplicate points. Records were then spatially thinned using the “spThin” package39 to retain unique occurrences. After thinning at a 1 km distance, 166 occurrence records remained, which fulfilled MaxEnt’s minimum input requirement (at least five coordinates) and improved model accuracy and reliability40. These final occurrence points were used for subsequent modeling analyses. All geographic distribution of these points, along with all other spatial layers, was mapped and visualized in ArcMap 10.841.

Environmental variables

Twenty-five environmental variables potentially influencing the current and future distribution of L. alata were retrieved from multiple sources (Appendix Table S1). Nineteen bioclimatic variables and one topographic variable (elevation) were downloaded from the WorldClim 2.1 database at a 2.5-arc-minute resolution42. Two additional topographic variables (slope and aspect) were derived from the digital elevation model in ArcGIS 10.8 (ESRI, Redlands, CA, USA) using the Spatial Analyst toolbox. Two soil variables (organic carbon stocks and total nitrogen) were obtained from the World Soil Information database43, and one anthropogenic variable (human footprint index, hfp) from Human Footprint database44. All variables were resampled to a uniform spatial resolution of 2.5 arc minutes, masked to the study-area shapefile, and converted to ASCII format as required by MaxEnt45. To eliminate multicollinearity that could bias model performance, we applied a variance inflation factor (VIF) filter using the vifstep function in the R package usdm (v. 2.1-7)46,47. The remaining non-collinear variables were retained for subsequent analyses (Appendix Table S2).

For future climate scenarios, we projected the potential distribution of L. alata using four Global Climate Models (GCMs): BCC-CSM2-MR, MIROC6, HadGEM3-GC31-LL, and EC-Earth3-Veg. These GCMs were selected based on their strong performance among CMIP6 climate models48. All four models were applied to both the mid-century (2050–2060) and end-of-century (2090–2100) periods, hereafter referred to as 2050 and 2090, respectively. Future projections were generated using ensemble averages across the four GCMs. To represent a broad range of possible climate outcomes, we employed two Shared Socio-economic Pathways (SSPs): SSP1-2.6, indicating minimal habitat alteration under low radiative forcing (1.6 W/m2 by 2100) with substantial reductions in CO₂ emissions, and SSP5-8.5, representing the most extreme habitat alteration under high radiative forcing (8.5 W/m2 by 2100) driven by continued fossil fuel use49.

Current and future habitat suitability modeling

Species distribution models for L. alata were generated using MaxEnt v.3.4.418,50. To reduce the risk of overfitting, key parameters were adjusted from the default settings51, and a cross-validation procedure with 10 replicate runs and 1000 maximum iterations was conducted to ensure robust predictions18. The output format was set to logistic, and a jackknife test was applied to assess the relative importance of environmental variables (Appendix Figs. S2–S4). Model performance was evaluated using the threshold-independent area under the curve (AUC) of the receiver operating characteristic, where values > 0.9 indicate excellent performance, > 0.8 good, > 0.7 moderate, and ≤ 0.5 poor50,52,53.

The current and future potential distribution maps produced were visualized in ArcGIS. ASCII outputs were converted to floating-point raster layers and reclassified into five habitat-suitability classes: (i) highly unsuitable (0–0.1), (ii) unsuitable (0.1–0.25), (iii) low suitable (0.25–0.45), (iv) medium suitable (0.45–0.65), and (v) highly suitable (0.65–1). Continuous suitability rasters for all scenarios were further converted into binary maps (suitable/unsuitable) using the maximum training sensitivity plus specificity threshold generated by MaxEnt, implemented through the SDM Toolbox in ArcGIS. Binary outputs were visualized in ArcGIS to illustrate suitable and unsuitable habitats. Finally, to quantify distributional changes under future scenarios, each binary future map (2050 and 2090) was subtracted from the current binary map in the SDM Toolbox, enabling calculation of areas of habitat loss, gain, and stability.

Threat assessment and gap analyses

We used a three-pronged approach to assess in situ threats to the extant population and evaluate the conservation status of L. alata in Nigeria. First, during ground-truthing, we documented ecological disturbances such as agricultural encroachment, debarking of mature trees, and logging activities. Second, we conducted a preliminary threat assessment according to the IUCN Red List criteria following the extent of occurrence (EOO) and the area of occupancy (AOO). The EOO represents the area within the smallest continuous boundary that encompasses all occurrence points, while the AOO represents the area within the EOO that is actually occupied, based on a defined grid system. For this study, AOO was estimated using the minimum number of 2 × 2 km grid cells required to contain all occurrence records. These calculations were performed using the Redlistr R package54, an analytical tool designed to support consistent Red List assessments for species and ecosystems. The conservation status of L. alata was then categorized using these metrics and the methodology of Khoury et al.55 (Appendix Table S3). By comparing the ratios of these metrics, we projected the local conservation status of L. alata in Nigeria. Lastly, we conducted a gap analysis56 to evaluate the effectiveness of existing Protected Areas (PAs) in safeguarding L. alata in Nigeria, a method widely applied in conservation assessments. If both current and future climatically suitable habitats fall within designated PAs, conservation gaps would be minimal or absent. For this analysis, we used the November 2023 Protected Areas dataset from the World Database of Protected Areas (WDPA) (IUCN, 2024), a widely recognized resource in conservation biology and biogeography. The dataset for Nigeria, which included 324 polygons, was clipped to our study region (Appendix Table S4). We excluded marine PAs, retained only terrestrial polygons, selected polygons with the “reserve” or “protected area” designation in the DESIG_ENG field, and included only those ranked in IUCN categories I to IV. Finally, we overlaid the potential current distribution model onto the protected-area shapefile and identified climatically suitable regions occurring within designated PAs using ArcGIS 10.8 (ESRI, Redlands, CA, USA) (Fig. 2).

Fig. 2
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Map of Nigeria showing vegetation classifications based on UNEP-WCMC and IUCN57, along with distribution records of L. alata. Occurrence points from our current field sampling (bottom right) were supplemented with records from GBIF, RAINBIO, and herbarium databases. Base map created in ArcGIS Desktop (ArcMap) 10.8 (ESRI, Redlands, CA, USA; http://www.esri.com).

Results

Model performance and important environmental variables

The model for L. alata performed substantially better than random (AUC = 0.5), yielding an AUC value of 0.93, which indicates high predictive accuracy and reliability (Appendix Figs. S2–S4). After removing highly collinear variables, ten environmental predictors were retained: five climatic variables (bio8, mean temperature of the wettest quarter; bio11, mean temperature of the coldest quarter; bio19, precipitation of the coldest quarter; bio18, precipitation of the warmest quarter; and bio13, precipitation of the wettest month), four non-climatic variables (ocs, organic carbon stocks; slop, slope; tn, total nitrogen; and aspt, aspect), and one anthropogenic variable (hfp, human footprint index) (Fig. 3).

Fig. 3
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Percentage contributions and permutation importance of the ten selected environmental variables predicting the distribution of L. alata. Abbreviations: bio8, mean temperature of the wettest quarter; bio11, mean temperature of the coldest quarter; bio19, precipitation of the coldest quarter; bio18, precipitation of the warmest quarter; bio13, precipitation of the wettest month; ocs, organic carbon stocks; slop, slope; tn, total nitrogen; aspt, aspect; hfp, human footprint index. See Supplementary Figures S1–S3 for additional details.

In the current distribution model, precipitation of the coldest quarter (bio19) had the highest percent contribution (59.4%), while precipitation of the warmest quarter (bio18) was the most influential variable in both future scenarios (≥ 62%). Based on importance, precipitation of the wettest month (bio13, 66.1%) and mean temperature of the coldest quarter (bio11, ~ 53%) were the most ecologically important predictors for the current and future distributions of L. alata. Overall, non-climatic variables contributed the least, although hfp showed consistent contribution across scenarios, and total nitrogen (≥ 2%) had the highest importance among non-climatic variables in future projections (Appendix Table S2).

Predicted current and future suitable habitats

The current potential habitat range for L. alata aligns closely with its known distribution (Fig. 4A). Highly suitable habitats were concentrated in southern Nigeria, covering an estimated 17,974 km2 (Table S5). These climatically suitable areas occur within anthropic landscapes, tropical lowland forests, and swamp forests zones of southern Nigeria (see Figs. 1 and 2 for administrative boundaries), and this pattern remains largely consistent across time (Figs. 4A–E). The southern states, e.g., Bayelsa, Delta, Rivers, Akwa Ibom, and Cross River, were identified as stable regions, suggesting that they represent potential climatic refugia for the species. Future projection maps similarly indicate that southern Nigeria will largely remain as the climatically suitable habitats, supporting our hypothesis that future suitability will likely remain confined to current distribution zones (Figs. 4B–E; Table 1), although with varying degrees of habitat loss. All four future scenarios predict reductions in suitable habitat relative to the present (Figs. 4B–C; D–E), with the greatest loss, approximately 53%, occurring under the worst-case SSP5-8.5 scenario for the year 2090 (Fig. 4; Table 1).

Fig. 4
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Predicted distribution ranges of L. alata under current and future climate scenarios in Nigeria using the MaxEnt model. (A) Current distribution; (B,C) projected distribution for 2050 and 2090 under SSP1-2.6; (D,E) projected distribution for 2050 and 2090 under SSP5-8.5. Base map created in ArcGIS Desktop (ArcMap) 10.8 (ESRI, Redlands, CA, USA; http://www.esri.com).

Table 1 Predicted area changes in the distribution of L. alata.
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Future distribution range changes of L. alata

The area changes between the current distribution and future projections for 2050 and 2090 revealed significant shifts in the range of L. alata. In all transitions from the current distribution to the four future scenarios, the area of habitat contraction exceeded the area of expansion (Fig. 5A–D; Table 1). Under SSP1-2.6 in 2050, the model projected a range expansion of 343 km2, a non-occupancy area of 859,127 km2, a stable area of 53,206 km2, and a range contraction of 12,155 km2. This pattern indicates that the species is expected to continue losing climatically suitable habitat by 2090. The contraction of suitable habitat is projected to be greater in 2090 than in 2050 under both SSP1-2.6 and SSP5-8.5. However, in both 2050 and 2090, the models projected gains of suitable habitat in parts of western Niger and Kwara States, and Taraba State (Fig. 5D).

Fig. 5
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Projected shift in climatic suitability for L. alata from Current to future under low SSP1-2.6 (A–B) and high SSP5-8.5 (C–D) emission pathways. Base map created in ArcGIS Desktop (ArcMap) 10.8 (ESRI, Redlands, CA, USA; www.esri.com).

Preliminary threat assessment and gap analyses

Results showed that L. alata is primarily distributed in southern Nigeria, although its actual area of occupancy and suitable habitat remain limited with the observed in situ ecological threats (Appendix File 1; Appendix Fig. S1). Comparison of the EOO and AOO values (Fig. 6A; Table 2; Appendix Table S4) indicated that the preliminary threat assessment classifies the species as Endangered within Nigeria, which contrasts with its global IUCN Red List classification of Vulnerable (Table 2). The complementary empirical gap analysis revealed that most of the predicted highly suitable habitats occur outside the existing protected areas in Nigeria; furthermore, the remnant stands are under threats (e.g., debarking, Fig. 6B), indicating a substantial conservation gap (Fig. 6; Appendix Table S4).

Fig. 6
Fig. 6
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Conservation gaps for L. alata in Nigeria. (A) Overlap between predicted current suitable habitats and existing protected areas; (B) an example of debarked merchantable stem height recorded during field surveys (photo credit, Emmanuel Chukwuma). Base map created in ArcGIS Desktop (ArcMap) 10.8 (ESRI, Redlands, CA, USA; http://www.esri.com).

Table 2 Preliminary threat assessment. Extent of occurrence (EOO) and Area of occupancy (AOO) values calculated using the redlistr R package, and the recommended IUCN classification.
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Discussion

Model performance and environmental variable contributions

Our evaluation confirms that the models are robust and reliable in predicting the niche range and distribution patterns of L. alata in Nigeria, which is consistent with numerous studies reporting the high predictive accuracy of MaxEnt58,59,60. As noted by De Marco and Nóbrega61, reducing multicollinearity and retaining only the most relevant predictors enhances model performance, and similar practices have been widely adopted across multiple spatial scales29,30,62. The strong model performance in this study further underscores the suitability of presence-only algorithms for species with limited or unevenly distributed occurrence data. This is particularly important for tropical tree species like L. alata, where field sampling is often constrained by accessibility, funding, and historical underrepresentation in biodiversity datasets.

In our study, precipitation-related variables emerged as the most influential predictors, exceeding the explanatory power of temperature and other abiotic factors, a pattern that aligns with previous findings from Nigeria63. Although climatic variables broadly influence species distributions, their relative importance varies across taxa and ecosystems26,29,30,64,65,66. Some terrestrial species respond more strongly to temperature and precipitation67,68, whereas others show contrasting patterns (e.g., Huang et al.69. For L. alata, the dominance of precipitation as a key predictor is ecologically consistent with its affinity for the humid rainforest ecosystems of southern Nigeria, where moisture availability plays a central role in plant function. While temperature contributes to photosynthesis, precipitation is essential for regulating transpiration and carbon sequestration18,70,71,72,73, suggesting that humid climates with moderately warm conditions are optimal for the species’ growth and persistence. In particular, precipitation of the coldest quarter (bio19) appears to be a critical factor, likely affecting water availability, phenology, soil moisture, and microclimatic stability during sensitive growth periods74.

Beyond climatic factors, L. alata appears less sensitive to topographic variation and more responsive to changes in soil nutrient dynamics and human disturbance. In tropical forests, nitrogen inputs arise from atmospheric deposition, biological nitrogen fixation, decomposition of organic matter, plant residues, animal excreta, mycorrhizal interactions, and various human activities75. Our field surveys documented extensive agricultural activity, including fertilizer use, and previous studies have reported additional nutrient inputs from wildlife fecal deposits in similar forest ecosystems (e.g., Bankole and Azeez76. Total soil nitrogen is vital for plant health and productivity because it supports vegetative growth, reproduction, soil fertility, and key biochemical processes77,78,79,80. Although elevated nitrogen levels may enhance habitat suitability, human activities that modify forest structure, soil properties, or disturbance regimes can simultaneously undermine the species’ long-term survival of the species. Combined with its sensitivity to hfp, this underscores the substantial influence of anthropogenic pressures on habitat quality for tropical forest species70,71,72,73,74,75,76,77,78,79,80,81,82.

In sum, our findings indicate that effective management and protection of L. alata in Nigeria must explicitly consider the species’ dependence on precipitation-driven environmental stability and the integrity of its rainforest ecosystem. Conservation actions should prioritize maintaining intact forest cover, limiting land-use conversion, and regulating agricultural expansion and logging activities that alter soil nutrient dynamics and microclimatic conditions. Given the combined pressures of climate variability and human disturbance, proactive and targeted interventions are essential to halt further habitat loss and ensure the long-term persistence of L. alata for future generations.

Habitat suitability and future projections

Under the current climatic scenario, the highly suitable habitats for L. alata are predicted to occur within anthropic landscapes, tropical lowland forests, and swamp forest vegetation zones in southern Nigeria (Fig. 4), which is consistent with existing occurrence records predominantly concentrated in the same region (Fig. 2). These vegetation zones represent typical habitats for many African timber species and support extensive biodiversity, and they have similarly been highlighted as suitable areas for other merchantable tropical trees in Nigeria26,29. However, our model estimates the current distribution range to be 60,945 km2 and further indicates that its climatically suitable habitat will shrink by 22% and 53% under the SSP5-8.5 scenario by 2050 and 2090, respectively. Although southern Nigeria is expected to remain the core region of suitability, these declines indicate that even the strongest refugial areas for the species will be affected by future climate conditions (Fig. 4; Table 2). The projected contractions reflect shifts in temperature and precipitation regimes that increasingly deviate from the humid, stable rainforest conditions required by the species, reinforcing concerns raised by Midgley et al.83 that combined impacts of climate change and land-use transformation will reduce suitable habitats more severely than previously anticipated. Collectively, these findings reveal a clear trajectory of habitat decline and distributional tightening for L. alata, emphasizing the urgent need to integrate climate-driven habitat changes into future conservation and management planning.

Species distribution across tropical ecosystems, particularly in Africa, and their responses to climate change have recently gained increasing attention29,30,84,85,86. In Nigeria, lowland forests receive more rainfall than most other ecosystems and are characterized by relatively stable temperatures and a distinct dry season87,88. Because precipitation emerged as a key driver of L. alata distribution, its restriction to the wetter southern region rather than the drier central and northern zones is ecologically consistent and supports our projections of stable suitable habitats in both current and future scenarios. These results highlight southern Nigeria as a critical region for conservation efforts89,90. Anticipated changes in habitat suitability reinforce the importance of adaptive planning that can respond to evolving climatic conditions and patterns of human land use. Based on these patterns, prioritizing stable suitable habitats in southern states such as Delta, Cross River, Bayelsa, Rivers, and Akwa Ibom is essential for ensuring the long-term persistence of the species. Many of these areas fall within Nigeria’s oil exploration zone, making it particularly important that protected areas in these landscapes be managed with strict conservation measures to limit habitat fragmentation and safeguard the remaining populations of L. alata.

Conservation and management of L. alata in Nigeria

Our study provides important insights into the current conservation status of L. alata in Nigeria. First, our results offer strong empirical evidence that the ecology of existing populations is locally threatened by in situ anthropogenic activities such as habitat fragmentation, overexploitation, and agricultural and medicinal harvesting, alongside broader environmental pressures in Nigeria. These findings are consistent with previous studies documenting the role of various abiotic and biotic factors in driving biodiversity decline in the country91. Second, our preliminary local threat assessment categorizes L. alata as Endangered in Nigeria, which contrasts the Vulnerable status on global IUCN Red List (Table 2), highlighting the heightened susceptibility of the species to localized threats and underscoring the need for immediate conservation attention. This further emphasizes the importance of conducting conservation assessments at local scales to accurately reflect a species’ extent and area of occupancy. Third, our findings show that existing protected areas in Nigeria do not adequately overlap with the highly suitable habitats of L. alata, which constitute the conservation priority zones for the species. Consequently, a considerable conservation gap exists, as these priority regions remain vulnerable to ongoing resource extraction and habitat degradation. Taken together, these results predict that the local conservation status of L. alata is more critical than previously recognized, necessitating urgent and targeted conservation strategies to prevent further habitat loss and potential local extinction. To address this gap, we recommend a data-driven approach to strengthen the conservation of this species including: (a) generating further scientific evidence by integrating in situ ecological data with SDM to identify micro-refugia, (b) engaging local communities in threat document and population monitoring, and (c) initiating reforestation efforts by planting L. alata seedlings in the climatically suitable habitats we have identified. This integrated approach is imperative to prevent further habitat loss and potential local extinction.

Conclusion

Although SDM has been critiqued for not fully capturing species dispersal processes, it remains a valuable tool for ecological restoration planning, estimating species richness, managing biological invasions, and monitoring biodiversity across broad geographic regions. In this study, despite a limited number of occurrence records, which can affect model precision, our projection aligns with field-based observations and identify precipitation dynamics and human footprint as the key predictors shaping the spatial distribution of climatically suitable habitats for L. alata in Nigeria. These findings underscore the need to mitigate anthropogenic pressures, improve landscape connectivity, and address habitat fragmentation while implementing regular monitoring of existing populations. The distribution maps generated here provided a valuable resource for conservation planning. Comprehensive survey within both current and projected suitable habitats to refine conservation actions. As the first ecological and conservation assessment of L. alata in Nigeria, our study provides critical baseline data; however, the distribution patterns presented do not reflect the full distribution range of the species. Therefore, future research should incorporate broader datasets to enhance the accuracy of regional-scale conservation planning for this important species.