Rural populations maintain complex relationships with forest ecosystems through the exploitation of non-timber forest products (NTFPs), which play an essential role in contributing significantly to health care, energy, food, monetary income, and various other aspects of human well-being (
Gbesso, Tente, Gouwakinnou, & Sinsin, 2013;
Doyon & Roy-Malo, 2020
). In this context, African species, particularly Senegalia senegal, hold an essential position in the rural development of arid zones, providing high-quality fodder for livestock and producing gums and tannins, which generate monetary income for local populations (
Guinko, 1991
;
Benam, Ganota, & Froumsia, 2023
).
Furthermore, these species play a key role in reforestation and land rehabilitation programs due to their resilience to water stress and ability to improve soil fertility (
Klein, Roose, & Peltier, 2023
;
Ly, 2024
). In Niger, agricultural land degradation has had a major impact on food security. Considering its multiple benefits, including carbon sequestration and combating the effects of climate change, Senegalia senegal is a promising species for reforestation and desertification control initiatives. The Kyoto Protocol’s mechanisms have highlighted the carbon market’s potential in fighting poverty and desertification through reforestation and agroforestry projects in dry regions (
Tsegay, 2023
).
However, this species faces extreme climatic events. Increasing variability in climate factors, such as precipitation and temperature, is likely to impact biodiversity and the geographical distribution of suitable species habitats (
Traoré et al., 2004
). Studies indicate that by 2085, 25% - 42% of plant species could be at risk of extinction due to the loss of 81% - 97% of suitable habitats (
Ezéchiel, Joel, Abdon, & Roger, 2022
). Other projections suggest a heightened extinction risk for 20% - 30% of plant and animal species if global warming exceeds 1.5˚C to 2.5˚C in Africa (
Busby, Smith, White, & Strange, 2012
).
Given such threats, the woody forage vegetation of natural rangelands is highly vulnerable, undergoing significant pressures (
Zakari et al., 2017
). Considering the socioeconomic and ecological value of Senegalia senegal in Sahelian regions, understanding the state of its habitats is crucial for sustainable resource management.
In an environmental scenario marked by climate change and rising anthropogenic pressures, maps depicting current and projected future distributions can elucidate spatial dynamics and endow us with significant insights for responsible and sustainable management of the species’ populations in Niger. This study aims to evaluate the impact of climate change on the status of the natural populations of this species. The aim is to identify potential habitats favorable for the species’ distribution and to model its current and future distribution.
2. Materials and Methods
The methodology for this study is underpinned by a detailed exploration of the study environment and a systematic approach to methodological choices.
2.1. Distribution of the Species
This study was performed nationwide, specifically targeting gum-producing sites distributed across the eight regions of the country. To ensure comprehensive coverage, individuals from the natural populations of the species were included (
Figure 1
). The Senegalia senegal gum sites are primarily found in the Sahelian and Sahelo-Sudanian zones, where average annual precipitation ranges from 300 to 400 mm and 400 to 600 mm, respectively. These sites are located on degraded lands, including bare plateaus or crusted barren soils unsuitable for agriculture, with clay or sandy-clay soils. Vegetation and fauna are nearly non-existent in these ecosystems.
<xref ref-type="bibr" rid="scirp.137295-"></xref>Figure 1. Map of gum sites in Niger.
2.2. Senegalia senegal Occurrence Data
Senegalia senegal occurrence data include the geographic coordinates of the presence points of the gum tree sites and individuals of the species’ natural stands, obtained from GPS (Global Positioning System) surveys as well as downloads from the GBIF (Global Biodiversity Information Facility,
https://www.gbif.org
) website. In total, 361 presence points were collected to constitute the species database. The GBIF website is an online platform dedicated to the biodiversity information system.
2.3. Bioclimatic and Environmental Data
Current and future climate data for the S. senegal distribution area were downloaded from the WorldClim website (
http://www.worldclim.org
). These data include nineteen bioclimatic variables (
Fandohan et al., 2013
) (
Table 1
), collected at a resolution of 2.5 arc-minutes on the ground (
Gbesso et al., 2013
;
Hijmans et al., 2006
;
Laouali, 2016
). For future modeling, three of the most recommended models were used (
Solomon, Manning, Marquis, & Qin, 2007
): CCCMA (Canadian Centre for Climate Modelling and Analysis), HadCM3 (Hadley Centre for Climate Prediction and Research model version 3), and CSIRO (Commonwealth Scientific and Industrial Research Organisation). These models were applied taking into account the A2 emissions scenario, which is considered the most likely for Africa by 2050 (
Williams, Jackson, & Kutzbach, 2007
). This scenario describes a very heterogeneous world, with a fast-growing population but a low level of technology and development. The correlation coefficients determined between the 19 variables enabled the identification of highly correlated variables. Two variables are considered highly correlated if the Pearson correlation coefficient exceeds 0.7.
<xref ref-type="bibr" rid="scirp.137295-"></xref>Table 1. Bioclimatic variables used to model the distribution of S. senegal.
Codes
Environmental variables
BIO 1
Average annual temperature
BIO 2
Average daily temperature variation [monthly average (max temperature-min temperature)]
BIO 3
Ratio of daily thermal amplitude to annual thermal amplitude
BIO 4
Temperature seasonality (standard deviation*100)
BIO 5
Maximum temperature of the hottest month
BIO 6
Maximum temperature of the coldest month
BIO 7
Annual temperature variation
BIO 8
Average temperature of the wettest quarter
BIO 9
Average temperature of the driest quarter
BIO 10
Average temperature of the warmest quarter
BIO 11
Average temperature of the coldest quarter
BIO 12
Annual precipitation
BIO 13
Precipitation in wettest month
BIO 14
Precipitation in the driest month
BIO 15
Seasonality of precipitation (coefficient of variation)
BIO 16
Precipitation in the wettest quarter
BIO 17
Precipitation in the driest quarter
BIO 18
Precipitation in the warmest quarter
BIO 19
Precipitation in the coldest quarter
Source:
http://www.wordclim.org
.
2.4. Data Processing and Analysis
What are the technical foundations for data processing and analysis? Although this question may seem complex at first, it will be effectively addressed through examining the concepts of validating the MaxEnt model, modeling the current and future distribution of S. senegal, as well as mapping its present and future habitats.
To model the current and future distribution of S. senegal, the MaxEnt program was used (
Phillips et al., 2006
). It is one of the most appropriate modeling methods as it allows for the clear distinction between favorable and unfavorable habitats for a species from a bioclimatic perspective (
Abdou et al., 2016
;
Gbesso et al., 2013
;
Moukrim et al., 2018
). Its effectiveness is linked to the fact that it uses a statistical approach called maximum entropy to make predictions from incomplete data. MaxEnt estimates the spatial distribution of the species based on the maximum entropy of each environmental variable submitted (
Phillips et al., 2006
).
For the modeling of the distribution area, the bioclimatic variables were subjected to a correlation test to select those with weak correlations (r < 0.85), aiming to avoid bias in future predictions (
Elith & Leathwick, 2009
). Thus, five bioclimatic variables were retained due to their weak correlation (
Laouali, 2016
). A Jackknife test was performed on these variables to identify those contributing the most to the modeling (
Table 2
, infra). To evaluate the model, 25% of the species observation points were used to test the model, while 75% were used to calibrate it. The accuracy of the model was measured by the AUC (Area Under the Curve) statistic, in accordance with
Phillips et al. (2006)
. A model is considered of good quality if the AUC value exceeds 0.90 (
Swets, 1988
). Cross-validation was repeated three times to improve model performance.
<xref ref-type="bibr" rid="scirp.137295-"></xref>Table 2. Bioclimatic variables retained for current and future distribution.
Codes
Environmental variables
BIO 3
Ratio of daily thermal amplitude to annual thermal amplitude
BIO 9
Average temperature of the driest quarter
BIO 10
Average temperature of the warmest quarter
BIO 12
Annual precipitation
BIO 18
Precipitation in the warmest quarter
As part of the mapping of the current and future distribution areas of S. senegal in Niger, the results obtained from MaxEnt modeling (raster image) were integrated into ArcGIS 10.1 software. This integration allowed us to classify habitats according to the logistic probabilities of occurrence: very favorable, moderately favorable, not very favorable, and not favorable. This tool was also used to assess the extent of each habitat type. Subsequently, the proportions of these areas in relation to the total surface area of Niger were determined and their trends were estimated.
3. Results
The results of the research conducted will be analyzed by focusing on the following areas: the contribution of the variables, the validation of the model, and the current and future distribution of S. senegal habitats.
3.1. Contribution of Variables and Validation of the Model
Figure 2
below shows the AUC (Area Under the Curve) curve, with values of 0.909 and 0.878, respectively. These results indicate that the distribution model predicted by the MaxEnt algorithm is of excellent quality, with a margin of error of 0.5%.
Figure 2. AUC value.
Figure 3
illustrates the Jackknife test, while
Table 3
presents the contribution of the different variables to the model. From this analysis, BIO 12 (annual precipitation) emerges as the environmental predictor with the greatest gain, contributing 54.8%. When used alone in the model, this variable provides the most valuable information. It is followed by BIO3 (ratio of daily thermal amplitude to annual thermal amplitude) and BIO9 (mean temperature of the driest quarter), which contribute 31.2% and 5.7%, respectively.
<xref ref-type="bibr" rid="scirp.137295-"></xref>Table 3. Contribution of variables to the model.
Codes
Bioclimatic variables
Contribution (%)
BIO 3
Ratio of daily thermal amplitude to annual thermal amplitude
31.2
BIO 9
Average temperature of the driest quarter
5
BIO 10
Average temperature of the warmest quarter
5.7
BIO 12
Annual precipitation
54.8
BIO 18
Precipitation in the warmest quarter
3.2
Figure 3. Jackknife test.
3.2. Current and Future Habitat Distribution of S. senegal
The modeling results reveal that the habitats currently highly favorable for the conservation of S. senegal are located in the Sahelo-Sudanian and Sudanian zones, covering an area of 86,259.80 km2, or 6.81% of the entire territory of Niger. Moderately favorable and unfavorable habitats represent 18.83% and 18.19% of the territory, respectively. The rest of the territory, or 56.17%, is occupied by unfavorable habitats.
Regarding future projections to 2050, all models predict an increase in the area of highly suitable habitats for S. senegal. According to the CCMA, CSIRO, and HadCM3 models, these highly suitable habitats are projected to increase by 35.41%, 163.84%, and 249.65%, respectively. In contrast, for moderately suitable and unsuitable habitats, a slight decrease is projected by 2050 (
Figure 4
). Overall, their areas are projected to decrease by 106.26% and 78.59%, respectively (
Table 4
).
Figure 4. Spatialization of habitats favorable to the conservation of S. senegal in Niger.
Figure 4. Spatialization of habitats favorable to the conservation of S. senegal in Niger.
Figure 4. Spatialization of habitats favorable to the conservation of S. senegal in Niger.
Figure 4. Spatialization of habitats favorable to the conservation of S. senegal in Niger.
<xref ref-type="bibr" rid="scirp.137295-"></xref>Table 4. Variation in the areas of habitats favorable to the conservation of S. senegal.
Scenarios
Habitats
Very favorable
Moderately favorable
Unfavorable
Areas (km2)
Tendency (%)
Areas (km2)
Tendency (%)
Areas (km2)
Tendency (%)
Here
86259.79851
-
238,538.43
-
230513.99
-
CCCMA
116800.2649
+35.41
187,500.98
−21.40
184222.982
−20.08
CSIRO
227590.0454
+163.84
166 131.63
−30.35
168568.951
−26.87
HadCM3
301604.4565
+249.65
108,508.81
−54.51
157569.005
−31.64
4. Discussion
The current and future distribution of Senegalia senegal was modeled and mapped using bioclimatic variables. Among these, the BIO 12 variable (annual precipitation) plays the most significant role in predicting areas favorable to the conservation of the species in Niger, followed by the BIO3 variable (ratio of daily thermal amplitude to annual thermal amplitude). These results reveal that annual precipitation and temperatures significantly influence the distribution of the species, which aligns with the conclusions of
Maydell (1983)
, stating that Senegalia senegal can tolerate annual precipitations of between 100 and 800 mm. Similarly, the work of
Zerbo et al. (2011)
underscores that temperature and precipitation are determining factors in its distribution.
The deployment of predictive niche models demonstrates their value in delineating, with high probability, areas where species are likely to persist, a crucial step in defining priority areas for reintroduction (
Gouwakinnou, 2011
).
In this study, all models project an increase in the area of highly favorable habitats for the future distribution of S. senegal, accompanied by a decrease in the areas of moderately and unfavorable habitats in Niger. These variations are consistent with studies by
Fandohan et al. (2013)
,
Gbesso et al. (2013)
,
Pittock (2009)
, and
Zakari et al. (2017)
, who used similar scenarios to predict the distribution of potential plant niches, related to the type of model and scenario used. However,
Tosso (2013)
obtained different results regarding the current distribution of S. senegal using MaxEnt and logistic models, reporting an absence of the species in the Guinean-Congolese and Guinean-Congolese-Sudanese transition phytogeographic territories in Benin (1950-2000).
The results of
Tosso (2013)
also show that S. senegal has a high probability of occurrence in dry tropical areas with non-desert vegetation formations, where only 5.15% of the projected distribution area constituted an area of absence and 68.21% represented highly favorable habitat.
For future projections, the
Tosso (2013)
logistic model indicates a loss of more than 45% of suitable habitats by 2060, while the MaxEnt model envisages a reduction of only 1.2%. According to IUCN criterion A3, the MaxEnt model does not predict that S. senegal will be classified as a species threatened by climate change, unlike the logistic model which considers it vulnerable. Accordingly, climate change can affect the distribution of Senegalia senegal; however, measures must be taken to ensure its sustainable exploitation.
5. Conclusion
The results of this study demonstrate that modeling remains a valuable tool in ecology to predict ecological niches for both plant and animal species. Faced with the threats of environmental changes on species, this tool allows anticipation of variations in distribution areas and can offer avenues for their sustainable conservation. The development of the current and future map of S. senegal can provide decision-makers and land managers with a decisive tool, promoting reasoned and sustainable management of the species. Thus, areas identified as highly favorable but currently unoccupied could be considered for the development and domestication of the species. In habitats where conditions are moderately favorable or unfavorable, measures must be taken to ensure the sustainable exploitation of the species. The present study addresses the effect of climate change on the distribution of Senegalia senegal in an area where anthropogenic pressure is high due to the significant population increase in the region. The combined effect of human activities and climate change on the distribution of the species still needs to be assessed.
Acknowledgements
We extend our sincere gratitude to all the staff of the Garba Mounkaila Laboratory at Abdou Moumouni University, whose efforts enabled us to process and analyze the data we collected and downloaded from the Internet. We also warmly thank the residents of Lido (Commune of Guéchémé) for their hospitality during our stay. Our heartfelt thanks go as well to all the members of the management committees of the Senegalia senegal plantations (Biocarb Initiative) in Niger.
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