1 Introduction

In any region, tree species have huge potentials to provide options for rural livelihoods and biodiversity conservation [1, 2]. Trees are contributing a great role in poverty mitigation serving as subsistence “safety nets” or low income “gap fillers” for any rural community of the region [1]. They supply edible products such as bark, flowers, fruits, leaves, nuts, roots, seeds and tubers that contribute to food security and diet sustainability [3] and promote the wellbeing of hundreds of millions of people in the tropics [4]. Anogeissus leïocarpa (DC.) Guill & Perr. (Combretacea) is a widespread species distributed throughout sub-Saharan Africa. Anogeissus leïocarpa (DC.) Guill & Perr. occupies different habitats, varying from natural habitat to farmland, indicating wide patterns of allelic variation [5]. It is found in Niger but its best growth is in the south-west part of the country.

The species has important economic and cultural value in West Africa as a source of wood, dye and medicine [6]. In light of all these ecosystem services, Anogeissus leïocarpa (DC.) Guill. & Perr. contributes to alleviating rural poverty and improve their livelihoods, especially during the food shortage period which remains a challenge in the Sahel due to erratic rainfall. Such rainfall pattern may partly explain the decrease of the tree density of a certain number of species. The delivery of these ecosystem services is highly dependent on the environment, especially the climatic conditions.

Species distribution is projected to be increasingly impacted by the climate as well as to be a limiting variable in the location of the species [7]. There is a high risk of extinction of species due to the unexpected impact of climate change. Understanding how local species are distributed is a critical prerequisite for effective conservation strategies. Ecological niche modelling (ENM) has been considered a useful tool to identify the habitats, for predicting the presence of species population as well as for predicting the effects of climate change on different taxonomic groups [8].

A way to assess species distribution patterns is to combine occurrence records from published literature and international databases. Then we incorporated them into species distribution models (SDMs). These models provide information on which areas have greater suitable habitats along a predicted distribution for a species and will be essential to maintain a civilized society. In this study, available data from species occurrence databases, personal occurrence data and environmental layers were used to: (1) determine the current distribution of Anogeissus leïocarpa (DC.) Guill. & Perr. across Niger; (2) identify where suitable habitats might exist for Anogeissus leïocarpa (DC.) Guill. & Perr.; (3) identify the areas with most potential for it conservation.

2 Material and methods

2.1 Study area

Niger is located between 30° to 72° N and 12° W to 68° E and covers an area of 1,267,000 km2. Niger is a relatively low rainfall (100 mm/yr–1100 mm/yr), amidst arid to semi-arid land. Only about 10–18% is covered by native vegetation with an understory, primarily Acacia senegal, A. raddiana, A. nilotica, Anogeissus leïocarpa (DC.) Guill. & Perr., Bauhinia rufescens, Balanites aegyptiaca in a matrix of agroforestry systems and pasture land. Elevation across sites ranged from 170 to 1144 m.

2.2 Study species

This species has been chosen because at present, little is known about Anogeissus leïocarpa (DC.) Guill. & Perr. distribution or habitat requirements. Anogeissus leïocarpa (DC.) Guill. & Perr. has been found in a wide range of elevations throughout the south-west Niger. Populations of the Sahel developed an agrosilvipastoral systems in which shrubs and trees play an important role in animal and crop production. It is a species with a wide ecological amplitude, tolerant of water conditions and withstands both temporary floods and prolonged droughts. Yet, many studies focused on the biology of the species, leaving socioeconomic issues largely unexplored. This paper points up the importance of some of the socioeconomic and cultural functions of Anogeissus leïocarpa (DC.) Guill. & Perr. in West Africa and argues that future research should concentrate more on socioeconomic study.

2.3 Species occurrence data

Species occurrence records were used to build the species distribution models and carry out the habitat suitability map. Data were obtained from reports, through global online occurrence databases such as Global Biological Information Facility (GBIF: https://www.gbif.org/fr/), inventories carried out within the study area and the rest by ourselves by using a hand held GPS (Garmin Etrex 20X) with an accuracy of ± 5 m. However, there is some bias in the species occurrence records, due to geographic sampling bias and because of the methods used to record species habitat distributions; these points are removed from the database during the analysis. Also, all occurrence records (1013 points) for each individuals were plotted on a map and visually inspected for any remaining outliers.

2.4 Environmental data

Environmental data include the 19 bioclimatic variables, the elevation and the soil data that were respectively downloaded on Worldclim version 2.1 (www.worldclim.org) for climatic and elevation data) and the World Soil Database. All the data were downloaded at a spatial resolution of approximately 1 × 1 km2, meaning at 30′ resolution.

Predictions from two models of the Coupled Model Intercomparison Project phase 5 (CMIP5) were used: MIROC 5 and CCSM4 for projections of future climate conditions. Figure 1 showed the projections were run for the horizon 2070 under two of the four scenarios developed by the Intergovernmental Panel on Climate Change (IPCC) in its Fifth Assessment Report (AR5): RCP 4.5 (an optimistic scenario where emissions peak around 2040) and RCP 8.5 (a pessimistic scenario, which reflects high levels of energy demand and greenhouse gas emissions, resulting in 8.5 W/m).

Fig. 1
figure 1

Main environmental variables contributing to the model

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2.5 Data analysis and model validation

In order to select the most fitting predictors and to eliminate multicollinearity that show more contribution power to the model, Variance Inflation Factors (VIFs) of 22 environmental variables were tested. VIF is based on the square of the multiple correlation coefficients resulting from regressing a predictor variable against all other predictor variables. Consequently, variables with VIF \(>\) 5 were excluded [9] and nine (9) variables were subsequently kept to establish the distribution model of Anogeissus leïocarpa (DC.) Guill. & Perr. (Table 1).

Table 1 Environmental variables used for modelling the potential distribution of Anogeissus leïocarpa (DC.) Guill. & Perr.
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We implemented an ensemble modeling in R 3.6.2 through the SDM package using by weighting five algorithms). The algorithms used were: GLM, RF, BRT, MARS and MAXENT. The training and test data points were 70% and 30%, respectively [10]. The relative importance of each environmental predictor for the model was assessed using the variable importance. As all the algorithms give and AUC ≥ 0.9, we make the weighted ensemble model with the AUC. We executed 10 replicates using bootstrap to measure the variability in the model and then averaged the results. The mean threshold (0.10) was used to transform the probability maps into binary maps.

3 Results

3.1 Selected environmental variables and model validation

Within the environmental, soil and elevation variables, bio 2 (Mean Diurnal Range (Mean of monthly (max temp − min temp))), bio 18 (Precipitation of Warmest Quarter) and soil data had the highest importance to the modelling process of habitat suitability for Anogeissus leïocarpa (DC.) Guill. & Perr. while bio 17 showed less importance (Fig. 2).

Fig. 2
figure 2

Histogram of the ensemble variable importance of all variables used to fit the SDMs for Anogeissus leïocarpa (DC.) Guill. & Perr. species

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3.2 Model evaluation

In this paper, the best model was chosen based on the highest AUC value (Fig. 3). It can vary from 0.5 to 1.0 where 0.5 is interpreted as random predictions and value above 0.5 indicate a performance better than random. The mean AUC values of the five (5) single-models (mars, rf, brt, maxent, glm) ranged from 0.92 to 0.97 for the consensus methods. The highest AUC values indicated the successful modelling of the distribution of Anogeissus leïocarpa (DC.) Guill. & Perr. Then the bioclimatic variables used to interpret the distribution of the species were very good and show a high level of accuracy [11].

Fig. 3
figure 3

area under the curve (AUC) with respect to the eight modelling techniques and the five consensus methods. Rating of the model accuracy: excellent = AUC > 0.9

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3.3 Suitable habitat distribution and conservation status under current and future climate models

The predicted distribution of Anogeissus leïocarpa (DC.) Guill. & Perr. under current bioclimatic settings is displayed in Fig. 4. The results indicate 10.795% of the area of this sites is ‘highly potential Anogeissus leïocarpa (DC.) Guill. & Perr. species, while 89.204% of the area is unsuitable for this habitat (Figs. 4 and 5). The lack of occurrence records in certain areas, such as in north, might be due to the presence of marginal land. The current distribution of Anogeissus leïocarpa (DC.) Guill. & Perr. is in equilibrium with the environment, meaning all suitable habitat is occupied by the species. The high potential distribution is detected in the south-west of Niger.

Fig. 4
figure 4

Occurrence points and predicted distribution of Anogeissus leïocarpa (DC.) Guill. & Perr. over all of Niger

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Fig. 5
figure 5

The suitable areas for A. leocarpa in the present and the two future climate models

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Projected climate change is expected to induce vegetation disequilibrium with climate at trailing edges of species ranges [12]. While unavoidable, this assumption should be acknowledged when interpreting SDM results. At best, the study results indicate that the whole Niger may experience a shift of Anogeissus leïocarpa (DC.) Guill. & Perr. climatic suitability under future climatic conditions. However projections of potential future distributions also need to be interpreted with caution. The approach is based on the assumption that climate is the major driving factor of species distribution [13].

The study results showed that the addition of soil and elevation as a predictor variable improves the quality of the distribution of Anogeissus leïocarpa (DC.) Guill. & Perr. This indicates that soil and elevation are more relevant to the spatial distribution for high-elevation species than low-elevation species and also the percentage contribution can be informative. Although the models predict more or less favorable conditions in Niger, based on the currently available species distribution data. Projections to future climate scenarios of RCP 4.5 and RCP 8.5 with two different periods of the 2070s consistently predicted a net decrease in areas with unsuitable habitat for Anogeissus leïocarpa (DC.) Guill. & Perr. (+ 309 to + 233 for different time periods), although there was a variation among climate models (range =  + 18 to + 49% for different time periods), which is related to differences in the severity of changes in climate predicted by different models. Midcentury (2050–2070) suitable habitat maps show projected changes in suitable habitat under each of five future climate scenarios relative to baseline suitable habitat. In all maps, suitable habitat that has already been converted to agriculture land use. Therefore, the capacity of Anogeissus leïocarpa (DC.) Guill. & Perr. to recolonize new climatically suitable areas is doubtful, due to anthropogenic activities. This demonstrate the climate to be an important factor limiting conservation effectiveness and the negative effects of a changing climate is making this species especially at risk.

4 Discussion

Anogeissus leïocarpa (DC.) Guill. & Perr. presented a geographic distribution in South west of Niger, covering only some districts. Thus, it often grows gregariously on fertile soil in moist situations [14] that restricts its natural geographical distribution, since it requires fertile soils, such as those found in savannah [15]. Consistent with the literature, the study results showed that rainfall was the variable most highly associated with the current distribution of Anogeissus leïocarpa (DC.) Guill. & Perr. The highest concentration of Anogeissus leïocarpa (DC.) Guill. & Perr. was in South west Niger, regions that also received rainfall in the country. Although rainfall is not the only forcing parameter [6], also stated that biodiversity at high latitudes seems to be highly sensitive to global warming. It is important to highlight that one of the most vulnerable areas to the changing climate are arid ecosystems. The mechanisms behind this are unclear, but it could be that warmer and drier conditions in this area will reduce the suitability of soils for many species. This findings offer support to the ongoing debate of suitability–abundance models and can be used to support spatial conservation prioritization schemes.

However, it is not yet known whether areas outside the Niger provide suitable habitats for Anogeissus leïocarpa (DC.) Guill. & Perr. species. Furthermore, the species is projected to have severe range losses that, combined with an inability to follow suitable habitat and climate space, mean they could benefit from conservation tool. Then species distributions reflect the cumulative impacts of both climatic and habitat variables [16], and the nature of species requirements appears to create individualistic responses to environmental change. This study can be useful for selecting species like Anogeissus leïocarpa (DC.) Guill. & Perr. for plantation in the future flora conservation programs because the species can have a positive impact on species diversity and density [14]. It also improves soil physical conditions water holding capacity and organic matter [17]. This pattern supports the earlier findings that elevation and soil can interactively influence the assessment of species distributions [18]. Collectively, these results suggest that while soil is an important variable, it may affect a model when its effect is not constrained by other geographic variables [18], such as restoration. The predicted changes for species at high altitudes and fertile soil demonstrate that, in future, there might be a stronger correlation between extinction risk and species occurring at high elevation.

To be fully effective, A. leïocarpa recovery planning should be undertaken by the non-governmental organisms and the government; and also it is good practice to involve, or at least consult, all interested and knowledgeable parties in the preparation of conservation and recovery strategies and action plans, especially the local population. Somehow the local knowledge may be important and when available should be taken into account. Unfortunately, many recovery programs are prepared and implemented by people without the necessary knowledge or expertise.

5 Conclusion

The modelling showed that Anogeissus leïocarpa (DC.) Guill. & Perr. will lose suitable habitat on the south western side and will gain the western side because this zone is one of the most heavily populated and rainfed areas in Niger, and the populated areas are moving westward. The predicted decrease in Anogeissus leïocarpa (DC.) Guill. & Perr. distribution suggests that land managers should monitor its population closely, and evaluate whether it meets criteria for a protected legal status. Anogeissus leïocarpa (DC.) Guill. & Perr. will be negatively affected by climate change. Species distribution models appear to be extremely useful for conservation planning under a changing climate, particularly when only sparse data are available. In light of the study findings we could say that the local populations of Anogeissus leïocarpa are at high risk of extirpation from their natural habitats and need more attention. Further research on morphological and genetic diversity of the species must be undertaken in order to select the appropriate characters which will match with the forecasted climatic conditions. Moreover, experiments are highly needed to validate the current models for a successful introduction of the species in the traditional agroforestry systems in Niger. Based on the results of this study, immediate action should be initiated to conserve these valued species (Anogeissus leïocarpa (DC.) Guill. & Perr.). For instance, with regards to the importance of the species in the feeding system of animals, farmers should be advised to collect the fruits, to domesticate it in order to have new individuals. In short, future research between different services acting in the area of soil restoration, Anogeissus leïocarpa plantation should be undertaken for environmental protection.