Strengthening Ecosystem Sustainability and Climate Resilience Through Integrative Nature-Based Solutions in Bontioli Natural Reserve, West African Drylands

Abstract

Natural reserves in the West African drylands play a critical role in sustaining livelihoods and preserving ecological integrity. However, these ecosystems face growing threats from climate variability and anthropogenic pressure. This study assesses the potential of Nature-based Solutions (NbSs) to enhance climate resilience and mitigate human-induced degradation in Bontioli Natural Reserve (BNR), one of the region’s key biodiversity hotspots. We employed an integrated approach combining ecological assessments, climate and anthropogenic pressures analysis, and participatory governance framework. Generalized additive modeling (GAM) is applied to assess the long-term vegetation response to climate stressors. A conceptual framework that integrates climate resilience with socio-ecological systems is developed for synergies conservation. Our findings indicate a consistent vegetation decline at a rate of 0.051 ± 0.043/year, driven by rising temperatures, and declining rainfall, which is exacerbated by anthropogenic land use pressure since 2000. Human population growth is strongly correlated with cropland expansion (R² = 0.903) and vegetation loss (R² = 0.793). As a result, 53.85% of species populations are declining, with 30.77% classified as endangered or vulnerable. Based on the scientific evidence, NbSs have emerged as cost-effective and sustainable strategies to restore ecological function and strengthen communities-based conservation. The proposed NbS framework offers a holistic pathway for safeguarding long-term ecosystem resilience in dryland reserves, directly contributing to Sustainable Development Goals (SDGs) 13 and 15.

1. Introduction

Protected areas are essential for biodiversity conservation, ecosystem service provision, and climate resilience [1]. They serve as refuges for species, maintain ecological connectivity, and buffer vulnerable communities, especially in regions such as the West African drylands, against the impacts of climate change [2,3,4]. Given that over 80% of Sustainable Development Goals (SDGs) achievement is linked to climate actions, emphasizing the vital role of natural reserves as carbon sinks and sources of ecosystem services is critically important [5,6,7]. Despite their significant contribution to ecosystem services, natural reserves in West Africa’s drylands face mounting pressure from land use change (LUC), often leading to deforestation and overgrazing, which undermines the regional ecological integrity [8,9,10]. Climate change further compounds these threats through increased temperatures, reduced precipitation, prolonged droughts, and other extreme climate events [11]. As the dryland ecosystems of West Africa cover more than 70% of the landscape, natural reserves have a pivotal role in environmental challenge mitigation [12].

The Bontioli Natural Reserve (BNR) in Burkina Faso exemplifies these challenges. Previous studies investigating the reserve’s degradation highlighted a significant anthropogenic pressure that leads to wooded savanna, tree, and shrub savanna vegetation decline, while cropland is increasingly expanding [8]. These transformations, driven by human activities, threaten biodiversity, reduce carbon sequestration potential, and impair the reserve’s ecological stability. It severely contributes to land degradation due to deforestation, wood cutting, and agricultural land expansion [9]. A recent investigation reveals that climate variability also threatens the reserve, which highly contributes to vegetation decline [13]. Thus, anthropogenic pressure and climate change constitute the main drivers of the reserve’s degradation. They compromise its ecosystem services delivery to the nearby communities, reducing carbon sequestration potential, and affecting its ecological stability. This reveals that the reserve lacks effective management practices in the face of anthropogenic and climate change effects. In this context, actions like Nature-Based Solution (NbS) implementations are required to safeguard the reserve’s integrity.

Addressing these pressures on natural reserves requires integrative management strategies. NbSs, defined as actions to protect, sustainably manage, or restore natural ecosystems, offer a promising response in this context. The NbS approach enhances ecological functions, supports local livelihoods, and improves resilience to socio-environmental changes [14,15]. Thus, in dryland ecosystems like BNR, NbSs can help reverse land degradation, stabilize vegetation cover, and enable sustainable resource governance without leaving anyone behind [16,17]. Therefore, this study aims to evaluate the effectiveness of NbSs in addressing the combined impacts of climate change and anthropogenic pressures on BNR. It analyzes vegetation dynamics as a scientific basis to guide the integration of NbSs into sustainable reserve management. The specific objectives are (i) to analyze vegetation dynamics and the associated socio-environmental pressures; (ii) to assess the ecological status of the reserve and identify key threats; (iii) to establish an evidence-based foundation for integrating NbSs into strategies for resilient and sustainable reserve management. The results can provide valuable guidance for policymakers, park managers, and the engagement of stakeholders in conserving dryland landscapes under growing pressures from both climate change and anthropogenic pressures.

2. Literature Review and Conceptual Framework

Ecosystem sustainability in the face of climate shifts and anthropogenic pressure has tremendous social and economic value. It can maintain essential ecological functions and preserve biodiversity over time [18,19]. The goal is to ensure that the ecosystem continues to support life for plants, animals, and people in the present and future. Thus, effective biodiversity conservation and ecosystem functioning are key components of this ecosystem sustainability. NbSs enable the capacity of nature to regenerate following disturbances from droughts, wildfires, and deforestation. It stabilizes ecosystem services such as clean air, food, raw materials, carbon storage, water purification, and pollination [20]. Since the degradation of natural reserves is often associated with anthropogenic pressures and climate change stressors, it is important to involve local communities in sustainable management, which is vital for delivering the required ecological services [18,21]. In the context of BNR degradation, an NbS can prevent the collapse of the reserve in this West African dryland [8,13,22,23].

Moreover, an effective NbS approach is needed to support a natural reserve’s recovery and restoration [24]. Thus, NbS actions can maintain the provision of ecosystem services and leverage natural processes and systems [21]. The implemented actions can include reforestation, agroforestry, ecological corridors, and community-based natural resource management [25,26]. This was broadcast by the World Bank group in the 2000s as a pivotal solution to handle environmental disaster and emphasize the standing of conservation biology [14,27]. The International Union for the Conservation of Nature (IUCN) further positioned this to strengthen nature conservation, which suggested the principles in its implementation as fundamental actions. This includes harnessing public and private capital, cost-effectiveness, communication among stakeholders, and the replicability of solutions adopted by any party [28]. This makes NbSs serve as a climate resilience option.

In protected areas, NbSs offer integrated strategies for conserving biodiversity while promoting climate resilience and local development goals, as mentioned by the IUCN [15,29]. When effectively implemented, NbSs can actively address the challenges of the sustainable management of the reserve and can contribute to the restoration of degraded ecosystems [30]. It provides a promising approach to reducing anthropogenic pressures on the ecosystem. Moreover, NbSs represent one of the strategies with demonstrated potential to enhance the ecological resilience of dryland ecosystems [31]. As the West African drylands are naturally exposed to ecological risks from climatic extremes and anthropogenic pressure, NbSs are increasingly recognized for their dual benefits, serving both conservation targets and climate adaptation and mitigation needs [32,33]. Yet, their application frequently encounters obstacles such as policy fragmentation, poor institutional capacity, and a lack of community participation [34,35]. Thus, an effective NbS in natural reserve preservation, like BNR, must integrate functional, structural, and process-oriented actions tailored to the socio-ecological context.

The integration of NbSs into conservation planning can therefore help improve resilience through the restoration of degraded landscapes, the sequestration of more carbon, and the protection of critical habitats. The measures can also create buffer zones that reduce exposure to climate stressors, thereby boosting the adaptive capacity of both ecosystems and the communities that depend on them [36]. This is highly important in the context of the West African drylands, which face increased water stress, soil erosion, and vegetation loss [13,37,38].

This study applies a socio-ecological systems (SES) approach for assessing the capacity of NbSs to enhance climate resilience and reduce anthropogenic pressure in the BNR. The method consists of three interlinked dimensions, such as the functional dimension, which consists of evaluating the intended effects of NbS. This consists of reducing forest degradation, augmenting carbon sinks, and promoting local climate regulation. The second aspect is the structural dimension that consists of analyzing institution arrangements, stakeholders’ networks, and governing structures enabling or constraining NbS deployment. The framework also includes a process dimension, which focuses on community engagement, co-management processes, and learning that shape NbSs in the long run. This multifunctional perspective approach can enable the holistic analysis of the interaction between ecological contexts, governance processes, and human pressures in shaping the BNR conservation processes (Figure 1).

3. Materials and Methods

3.1. Study Area

The Bontioli Natural Reserve (BNR), which covers 46,764 ha, is situated in Burkina Faso’s Southwest Region (Figure 2). The reserve is divided into 13,797 ha of total reserve that belongs to IUCN Category I, and 32,967 ha of partial reserve belonging to IUCN Category IV. Thus, the total reserve is a strictly protected area with limited human access, whereas the partial reserve area is for species management only. Like Burkina Faso’s other protected areas, the reserve was established during the colonial era in 1957. It serves as a transitional zone between the Sudanese savannas and the Guinean forests. This position makes the reserve an important humid zone within the West African drylands. It is located entirely within the South Sudanese phytogeographical zone, which has a measured annual average rainfall of 900 to 1000 mm. The vegetation is dominated by trees and wooded savanna [9]. The duration of the dry season is almost 6 months and runs from November to April, with monthly normal temperatures ranging from 26 to 32 °C, with an average monthly temperature of 27 °C. The area falls at 262 m altitude above sea level, characterized by the Bougouriba River, which dominates the topography as the only watershed where gallery forests occur alongside the hydrological network [8]. The main dominant vegetation species are as follows: Daniellia oliveri (Rolfe) Hutch. & Dalziel), Isoberlinia doka ((Craib & Stapf) Stapf), Pterocarpus erinaceus (Poir), Terminalia laxiflora (Guill. & Perr.), Terminalia macroptera (Guill. & Perr.), Terminalia avicennioides (Guill. & Perr), Anogeissus leiocarpa ((DC.) Guill. & Perr), Burkea Africana (Hook), Mitragyna inermis ((Willd.) Kuntze), Afzelia Africana (Sm), Lannea microcarpa (Engl. & K. Krause), Lannea acida (A. Rich), Parkia biglobosa ((Jacq.) G. Don), and Vitellaria paradoxa (C.F. Gaertn) [9,39].

3.2. Satellite and Climate Datasets

Multitemporal satellite and climate data are used to assess vegetation dynamics. Landsat imagery (TM, ETM+, and OLI-TIRS), which provides long-term observations at a spatial resolution of 30 m, was used as it is well suited for temporal vegetation analysis. The images, acquired from the United States Geological Survey (USGS), were processed and analyzed using the Google Earth Engine (GEE) platform [40]. Specifically, following continuity protocols, we used the red and near-infrared bands (Bands 3 and Band 4 for Landsat 4, 5, and 7, and Bands 4 and 5 for Landsat 8) [41]. A three-decade time window (1993–2023) was selected to capture long-term vegetation trends, consistent with the recommendations of the Intergovernmental Science Policy Platform on Biodiversity and Ecosystem Services [42].

To assess the climatic influences on vegetation, we used climatologies at high resolution for the Earth’s land surface areas (CHELSA) version 2.1 climate datasets (30 arc s, ~1 km), including downscaled CMIP6 projections [43]. These datasets integrate topographic and atmospheric variables, including elevation, wind exposure, and boundary layer height, thereby improving the accuracy of temperature and precipitation estimates in semi-arid ecosystems.

Future climate projections were obtained using Shared Socioeconomic Pathways (SSP1-2.6 and SSP5-8.5) from the CMIP6 dataset [44]. These scenarios represent low-emission (mitigation-focused) and high-emission (business-as-usual) trajectories. Projected climatologies were used to evaluate the potential impacts of future climate on vegetation and to inform the spatial prioritization of NbS intervention [45]. To assess anthropogenic pressure, we used high-resolution (1 km²) gridded population datasets from WorldPop [46,47].

Table 1. Overview of satellite and climate data used in this study.

Data Type	Data Item	Spatial/Temporal Resolution	Image’s Variables	Dates	Sources
Satellite data	Landsat ETM	30 m/16 days	Bands 3, 4	January 1993 to December 2000	https://www.usgs.gov/landsat-missions/landsat-collection-2-level-2-science-products (accessed on 18 February 2025)
	Landsat ETM+	30 m/16 days	Bands 3, 4	January 2001 to December 2013
	Landsat OLI-TIRS	30 m/16 days	Bands 4, 5	January 2014 to December 2023
	SRTM	30 m	1	Since 2000	https://glovis.usgs.gov/app (accessed on 2 February 2025)
Climate variables	Monthly Rainfall	30 days/900 m	Rainfall	January 1993 to December 2023	https://chelsa-climate.org/downloads/ (accessed on 27 December 2024)
	Monthly Temperature	30 days/900 m	Temperature	January 1993 to December 2023
Population data	Population Density	~1 km2	Population density	2000	https://hub.worldpop.org/geodata/summary?id=44536 (accessed on 27 January 2024)

describes the main data used in this study.

3.3. Satellite Data Processing

To ensure accuracy and consistency in vegetation assessment, all satellite images were preprocessed to minimize atmospheric noise, including clouds and haze. Cloud and shadow masking were applied using the Landsat Quality Assessment (QA) bands, along with standard cloud removal algorithms available on the Google Earth Engine (GEE) platform [48]. This procedure is to ensure consistent surface reflectance for the calculation of vegetation indices such as the Normalized Difference Vegetation Index (NDVI) and Fractional Vegetation Cover. All these processes are performed on the Google Earth Engine platform for efficiency and scalability in handling multitemporal imagery. Thus, the NDVI is quantified from the processed images, using near-infrared and the red bands in GEE from 1993 to 2023 [49] using the following equation:

$${\mathrm{N}\mathrm{D}\mathrm{V}\mathrm{I}}_{\mathrm{i}}\mathbf{=}{\displaystyle \frac{{\mathbf{N}\mathbf{I}\mathbf{R}}_{\mathbf{i}}-{\mathbf{R}\mathbf{E}\mathbf{D}}_{\mathbf{i}}}{{\mathbf{N}\mathbf{I}\mathbf{R}}_{\mathbf{i}}+{\mathbf{R}\mathbf{E}\mathbf{D}}_{\mathbf{i}}}}$$

(1)

where NDVI is the Normalized Difference Vegetation Index, NIR is the near-infrared band, and RED, i is the time scale (months and years)

Furthermore, to access the vegetation cover dynamics of the reserve, the FVC is calculated using the remote sensing method based on NDVI in the GEE platform. This assessment is vital as it provides patterns of green vegetation cover for a given area. Thus, the remote sensing method accurately estimates FVC for a small area or region with a high spatial resolution [50,51]. The following equation is applied.

$${\mathrm{F}\mathrm{V}\mathrm{C}}_{\mathrm{i}}={\displaystyle \frac{{\mathrm{N}\mathrm{D}\mathrm{V}\mathrm{I}}_{\mathrm{i}}-{\mathrm{N}\mathrm{D}\mathrm{V}\mathrm{I}\mathsf{\rho }}_{\mathrm{i}}}{{\mathrm{N}\mathrm{D}\mathrm{V}\mathrm{I}}_{\mathsf{\beta }\mathrm{i}}-{\mathrm{N}\mathrm{D}\mathrm{V}\mathrm{I}\mathsf{\rho }}_{\mathrm{i}}}}$$

(2)

where FVC is the Fractional Vegetation Cover, ${\mathrm{N}\mathrm{D}\mathrm{V}\mathrm{I}}_{\mathrm{i}}$ is the standard weighted NDVI adapted to the study area, ${\mathrm{N}\mathrm{D}\mathrm{V}\mathrm{I}\mathsf{\rho }}_{\mathrm{i}}$ is the minimum or the bare soil NDVI of the area, ${\mathrm{N}\mathrm{D}\mathrm{V}\mathrm{I}}_{\mathsf{\beta }\mathrm{i}}$ is the NDVI of the vegetation cover, and i is the time scale (month and year).

These vegetation indices, such as NDVI and FVC, are suitable and cost-effective variables that provide information about vegetation health and plant productivity. It is reliable and useful to monitor and track the climate change and anthropogenic pressure (land use effects) on large and long-term vegetation in any geographical area [52]. The FVC index, which considers the vegetation cover rate, is useful for monitoring vegetation recovery or restoration interventions in vulnerable ecosystems such as drylands [50]. Both variables (NDVI and FVC) are used in this study to monitor vegetation dynamics and track climate and anthropogenic pressure impacts on the reserve.

3.4. Vegetation Dynamics and Associated Socio-Environmental Change Pressures

To analyze the change in vegetation cover in relation to environmental change and anthropogenic pressures, the study area is divided into grid cells with a resolution of 2.5 km, and these grid cells as the analysis units. This allowed the inclusion of the population density within the BNR boundary and land use change (LUC) analysis, which focuses on cropland expansion. Thus, 8 LULC categories are applied following the nomenclature of African vegetation types defined in the Yangambi Agreement [53] (Table S1). We used a total of 736 ground samples to perform LULC for the years 2000, 2010, and 2022 by applying the supervised machine learning Random Forest method (RF) validated with Kappa indices of 0.96, 0.92, and 0.89, for the years 2000, 2020, and 2022, respectively. The investigation of the human impact in relation to the change in vegetation cover within the reserve is performed every ten years between 2000 and 2022. This is achieved by calculating the difference in the proportion of vegetation cover across the study area from one period to the next, i.e.,

∆VegI_i,t = VegI_i,t − VegI_i,t+1
for each i ∈ {LUC class} and t ∈ {2000, 2022}

(3)

where VegI_i,t denotes the proportion of LULC at time t and LULCi, t₊₁ denotes the proportion of LULC type in the next time period.

We focused our analysis on the changes in the extent of vegetation degradation that are likely influenced by human activities and climate stressors, such as cropland and temperature. For this analysis, two land use classes are considered, such as cropland and dense vegetation (woodland). We applied a regression analysis to model the relationship between the change in population and the extent of each vegetation cover, as shown in the equation as follows:

APi,t = β0,i + β1.i Popt
for each i ∈ {cropland, woodland}

(4)

where APi,t denotes the extent of anthropogenic land use i at time t and Popt is the total population at time t. Parameters β0, i, and β1.i are to be estimated from the Ordinary Least Squares (OLS) model for each land use i or climate variables.

To assess the vegetation response to climate stressors such as temperature and rainfall dynamics, we applied a generalized additive model (GAM) [54] in R software, version 4.1.2 [55]. GAM is a flexible extension of generalized linear models that allows non-linear relationships between the response variable and predictors via smooth functions [56]. This approach is suitable for ecological data where the form of the relationship is not a priori known. The following equation is used:

$$\mathrm{g}\left\{\mathrm{E}\left(\mathrm{Y}\right)\right\}=\mathsf{\alpha }+{\mathrm{f}}_1\left({\mathrm{x}}_1\right)+{\mathrm{f}}_2\left({\mathrm{x}}_2\right)+\dots +{\mathrm{f}}_{\mathrm{p}}\left({\mathrm{x}}_{\mathrm{p}}\right)$$

(5)

where Y is the response variable, ${\mathrm{f}}_{\mathrm{i}}$ are smooth functions of covariates ${\mathrm{x}}_{\mathrm{i}},$ and g is a link function (e.g., logit, log, identity) relating the mean of the response ($\mathrm{E}\left(\mathrm{Y}\right)$) to the additive predictors.

To understand the vegetation alteration dynamics, we computed the vegetation transformation rate using the following formula:

$${\mathsf{\Delta }}_{\mathrm{\%}}={\displaystyle \frac{{\mathrm{V}\mathrm{i}}_{\mathrm{t}2}-{\mathrm{V}\mathrm{i}}_{\mathrm{t}1}}{{\mathrm{V}\mathrm{i}}_{\mathrm{t}1}}}\mathrm{\ast }100$$

(6)

where ${\mathsf{\Delta }}_{\mathrm{\%}}$ is the percentage of change within years, $\mathrm{V}\mathrm{i}$ is the vegetation indices, t1 is the initial year, and $\mathrm{t}2$ is the following year.

3.5. Ecological Status and Key Specific Threats

To support BNR’s long-term resilience, we assessed the ecological status in alignment with the IUCN Global Standard for NbSs [28]. Thus, the ecological integrity is assessed to determine its conservation status against climate impact and anthropogenic pressures. This is performed by using the dominant tree species retrieved from the previous studies [9] and validated with a field survey conducted in June 2025. It includes species within the reserve, such as Daniellia oliveri (Rolfe) Hutch. & Dalziel), Isoberlinia doka ((Craib & Stapf) Stapf), Pterocarpus erinaceus (Poir), Terminalia laxiflora (Guill. & Perr.), Terminalia macroptera (Guill. & Perr.), Terminalia avicennioides (Guill. & Perr), Anogeissus leiocarpa ((DC.) Guill. & Perr), Burkea Africana (Hook), Mitragyna inermis ((Willd.) Kuntze), Afzelia Africana (Sm), Lannea microcarpa (Engl. & K. Krause), Lannea acida (A. Rich), Parkia biglobosa ((Jacq.) G. Don), and Vitellaria paradoxa (C.F. Gaertn) [9,39]. Those species’ status is retrieved from the IUCN Red List platform [57].

Furthermore, for integrated climate resilience, the future climatology of 30-year periods of CMIP6 (2011–2040 and 2041–2070) under Shared Socioeconomic Pathways (SSP1–2.6 and SSP5-8.5) is analyzed and mapped over the area. The projected climatology under the two scenarios is designated to identify areas where climate change stressors will likely affect vegetation health. This information supports long-term climate-resilient NbS planning by highlighting zones with high ecological feasibility under future conditions. The two SSP scenarios represent two cases of mitigating efforts (SSP1.2.6), and a scenario without any efforts in reducing carbon emissions (SSP585).

3.6. Nature-Based Solution Framework for NBR Sustainability

The suggested Nature-based Solutions (NbSs) implementation for BNR effective management integrates ecological, social, and governance dimensions (Figure 1). It builds on the IUCN Global Standard for evidence-based and adaptation management [30], which integrates scientific evidence with local context to design NbS interventions for the preservation of natural reserves [31]. Thus, the NbS approach for BNR management emphasizes ecosystem health by prioritizing biodiversity conservation and vegetation monitoring through indices such as NDVI and FVC. This links biophysical conditions with social indicators for effective ecosystem restoration [24]. Climate resilience is strengthened by incorporating climate projections from CMIP6 [43] to identify vulnerable zones and prioritize restoration in areas with high ecological feasibility under future conditions. The anthropogenic pressure through LULC is mapped to address co-management practices and livelihood-sensitive strategies (

Table 2. Nature-based solution assessment data and tools.

Aspect	Data/Tool	Purpose
Climate Exposure	Future climate projections (e.g., WorldClim CMIP6, CHELSA),	Identify areas most affected by temperature or rainfall shifts
Ecological Sensitivity	Species status (IUCN), land cover trends.	Find areas with fragile biodiversity
Anthropogenic Pressure	Land use changes, fire history, proximity to roads/settlements	Map human impact zones (LULC map)

). This multifunctional framework provides an understanding of how ecological contexts, governance, and human pressures interact to shape reserve conservation outcomes, thereby enabling effective NbS implementation. This creates an adaptive learning loop to strengthen reserve governance.

The prioritization of native species for NbSs is guided by their ecological resilience, restoration potential, and biodiversity value (ecological status). A field survey identifies them to ensure alignment with the NbS principle of protecting and enhancing ecosystem services [31]. The species status analysis with key specific threats is implemented to support prioritizing these native species for NbS strategies in the BNR management. Furthermore, to ensure fair outcomes and enhance the sustainability of the BNR under increasing anthropogenic and climate pressures, we include continuous monitoring by identifying risks for each NbS target. This enables adaptive learning loops of the NbS framework that build on climate exposure, ecological sensitivity, and anthropogenic pressure to foster resilience. (Table 2).

4. Results

4.1. Vegetation Dynamics and Its Responses to Socio-Environmental Pressure

The vegetation dynamically varies and decreases temporally and spatially. It varied from 0.2 to 0.727 in 1993, and from 0.2 to 0.664 in 2023 for the maximum vegetation index. The temporal dynamics of the Fractional Vegetation Cover (FVC) and the Normalized Difference Vegetation Index (NDVI) show a consistent decline over three decades (Figure 3). Thus, the vegetation in the BNR is undergoing long-term degradation with a decline value of 0.11, as shown in the trend, or 0.36% from 1993 to 2023. The monthly variation in both varies over time in each year, with peaks and troughs reflecting vegetation development cycles due to seasonal influences such as wet and dry seasons.

The seasonal variation in vegetation cover shows low greenness from January to April and high greenness from May to October, corresponding to the rainy season. This tendency reveals a clear downward slope of the vegetation cover decline over the past 30 years due to many factors. The continuous decrease in FVC suggests a loss in vegetation density or coverage due to the dual effects of climate change and anthropogenic pressure. This indicates a seasonal pattern of rising temperatures and declining precipitation, which significantly affects vegetation growth and regeneration potential. Consequently, this suggests a decline in vegetation greenness and health, reflecting reduced photosynthetic activity and weakening productivity.

Plant development is characterized by a continuous spatial decline in dense vegetation, as indicated by a consistent reduction in vegetation cover and a significant positive correlation between NDVI and FVC (Figure S1). The year 1993 has the highest NDVI (0.7), while the year 2023 reveals a low NDVI (0.6), a loss of 0.1 value in vegetation greenness. The FVC also demonstrates the same trend, with a decrease in denser vegetation at a rate of 0.051 ± 0.043 per year. The denser or healthier vegetation was present in 1993, likely indicating good growing conditions, while the situation in 2023 shows degraded vegetation that becomes perceptible, reflecting the most stressed vegetation condition (Figure 4A). The deterioration is further exacerbated by the anthropogenic LULC that is clearly observed between 2000 and 2022 with a significant increase in cropland from 8.5% in 2000 to 34.8% in 2022. In the meantime, wooded savanna decreased from 33.3% in 2000 to 12.5% in 2022 (Figure S2). Thus, the LULC dynamic affects wooded and tree savanna areas that declined significantly by 20.8% and 6.8%, respectively, while water bodies also shrank. The socio-environmental pressure, particularly driven by LULC, has accelerated the degradation of the reserve, particularly in tree savanna and wooded savanna ecosystems. The cropland expansion has been evident across both periods, with the most pronounced increase occurring between 2010 and 2022 (+18%). This trend is accompanied by a positive change in bare soil cover, further signaling intensifying land degradation (Figure 4B). Thus, the anthropogenic pressure is evidenced through agricultural expansion at the expense of vegetation decline. Overgrazing or deforestation intensity is shown by the positive change in bare soil.

Agricultural expansion is the key driver of human pressure, resulting in significant vegetation loss and declining ecosystem services. Furthermore, it has a detrimental effect on the reserve integrity. This trend in landscape transformation from vegetation cover to cropland shows strong ecosystem degradation, biodiversity loss, and increased carbon emissions risk. A deeper investigation of the reserve’s responses to climate and anthropogenic pressures’ impacts can deliver more insight into the drivers of the reserve’s continuous degradation.

The vegetation response to the dual effect of anthropogenic pressure and climate change stressors revealed a high positive correlation. Over the 30 years, it indicates a substantial NDVI decline with an R² of 0.954, and deviance of 0.935 explained by GAM. The vegetation response to rainfall peaks at 250–300 mm, while it is observed around 28 °C for temperature (Figure 5B,D). This suggests an optimal temperature threshold, beyond which vegetation condition declined. These responses are due to climate variability with some extreme events, such as prolonged drought and heatwaves, as the NDVI has a peak response to temperature and rainfall. The vegetation response to anthropogenic pressure is evidenced by a strong positive correlation between human population density and the proportion of dense vegetation (R² = 0.66; Figure 5A). Cropland expansion also shows a very strong correlation with population density (R² = 0.90; Figure 5C).

This vegetation response to both anthropogenic and climate pressure evidences the challenges of the effective sustainable management of the reserve. In this context, it is important to assess the reserve ecological status, identifying climate and anthropogenic specific threats to enhance the reserve management and its sustainability.

4.2. Ecological Status with Associated Specific Climate and Anthropogenic Threats

The anthropogenic threats that affect vegetation species are logging and wood harvesting and livestock farming and ranching, while habitat shifting and alteration and drought are the dominant climate threats. Some species are resilient to drier conditions and droughts in the face of climate variability. Thus, the low regeneration shows the ecological vulnerabilities to climate change impacts. Numerous plant species, such as Afzelia africana, Burkea africana, Parkia biglobosa, and Pterocarpus erinaceus, face multiple threats from climatic and anthropogenic pressures. For example, Afzelia africana is threatened by logging and drought, while Terminalia species (e.g., T. avicennioides and T. laxiflora) face both human-induced and climate-driven threats (Figure 6).

Both climate and human threats have made many species vulnerable (VU) or endangered (EN). Among them are species such as “Afzelia Africana” and “Afzelia Africana”, while “Pterocarpus erinaceus” and “Pterocarpus erinaceus” are endangered (EN). Overall, 30.77% of species are either EN or VU, and 53.85% of species populations are declining (Table S2).

In terms of threat frequency, anthropogenic threats are more frequent than those caused by climate change (Figure S3). This relative frequency indicates that logging and wood harvesting are the most dominant, while livestock and farming are moderate. This points out the role of LULC effects that lead to deforestation. The ecosystem response to climate change impacts specifies that few species show climate adaptability, while some species are resilient to drier conditions. Compared with anthropogenic threats, climate threats are less frequently mentioned. It is therefore important to explore future climate scenarios to identify possible impacts for integrated long-term sustainable reserve management.

The investigation on climate scenarios with Shared Socioeconomic Pathways (SSPs 1.2.6 and 5.8.5) indicates that the BNR will experience a warming trend. The analysis evidences a cooler area for the period of 2011–2040, particularly in the northwest region of the reserve, while the warmer tones (orange/yellow) are dominant in 2041–2070, especially in SSP5-8.5, showing a homogenization of warming across the reserve (Figure 7B). It further reveals a climatically significant absolute temperature change of ~1 °C, which is particularly important in this dryland ecosystem. This illustrates that temperatures are projected to rise under both scenarios (SSP1-2.6 and SSP5-8.5). Since warming is much more severe under the SSP5-8.5 scenario, this implies greater climate stress on ecosystems. The high spatial temperature variation indicates that the warming will be more intense in SSP5-8.5 of 2041–2070, signifying an increased climate risk under business-as-usual conditions (Figure 7B). Furthermore, climate change is likely to increase rainfall, with the extent of this increase depending heavily on the emissions scenario. Under SSP5-8.5, for example, rainfall could increase dramatically (Figure 7A).

4.3. NbS Options to Enhance the Reserve Resilience and Sustainability

Under the combined effects of climate variability and anthropogenic pressure, the findings indicate that the most relevant NbS approaches are the protection, restoration, and sustainable management of BNR. Thus, NbS implementation should prioritize drought-tolerant tree species for long-term biodiversity conservation and effective restoration. It also highlighted that degraded areas should be prioritized for NbSs implementation, with the use of climate-resilient tree species to address climate-related threats. Among the dominant plant species found in the reserve, many of them are drought-resilient species (Anogeissus leiocarpa, Diospyros mespiliformis, Isoberlinia doka, Burkea Africana, Vitellaria paradoxa, Afzelia Africana, Lannea acida, Pterocarpus erinaceus, and Combretum nigricans). These species are key pillars for sustainable protected area management and climate adaptation strategies. They tolerate extreme climate conditions and are the most suitable for natural regeneration and landscape regreening. The findings show that they also contribute to carbon sequestration, underscoring their primary role in climate adaptation. Their drought tolerance, soil improvement capacity, and nitrogen fixation reveal a diverse ecosystem service portfolio that enhances both climate resilience and local community well-being (Figure 8).

Fzelia africana and Pterocarpus erinaceus provide ecosystem services across both categories, indicating their role as multipurpose keystone species. The strongest climate-resilient species (Anogeissus leiocarpa and Burkea africana) exhibit a high drought tolerance, carbon sequestration capacity, and soil protection benefits. Other tree species like Diospyros mespiliformis, Isoberlinia doka, Lannea acida, and Combretum nigricans offer a more balanced mix of ecosystem services that support both climate resilience and biodiversity-based livelihoods. Vitellaria paradoxa proves to be the most prominent species for the local community economy and livelihood, as it provides soil structure improvement, biodiversity conservation, as well as food (e.g., shea butter).

Furthermore, the findings show that under the dual impacts of climate change and anthropogenic pressure, the NbS framework establishes a link between overarching principles and field-level interventions. These interventions are aligned with the Sustainable Development Goals (SDGs), ensuring both ecological sustainability and community benefits. It also provides contextualized risk information that supports policymakers in anticipating anthropogenic pressures and environmental challenges. This integrative NbS, which engages communities in long-term conservation, is a suitable response to anthropogenic pressures and promotes BNR sustainability. It also enhances landscape connectivity and strengthens community-based conservation. Capacity-building and sensitization actions, including conservation agreements coupled with secured land tenure, address anthropogenic pressure, thereby reinforcing NbS implementation in BNR.

The results further show that alternative livelihood measures, including honey production, eco-tourism, participatory land use planning, the recognition of customary rights, and land tenure security, enhance local benefits. These measures, in turn, strengthen community participation in enforcing strict reserve protection. Thus, ensuring that the local population benefits is the key enabler to enhance their participation in sustainable management. Thus, the goal of NbS implementation based on ecosystem health is an umbrella action in achieving other goals (

Table 3. Integrative NbS option implementation approach for the reserve’s sustainability.

Goal	Target	Instrument	Outcome	Risk	SDG
Ecosystem Health	Regreen degraded areas	Native species planting, drought-tolerant species, regenerative grazing	NDVI and FVC increase % (vegetation cover)	Accelerated desertification (% NDVI and FVC decrease)	SDG 13 (Climate), SDG 15 (Life on Land)
Equity Inclusion	Boost livelihoods	Ecotourism, Community grants, value chains for dryland crops, women-led cooperatives	Income levels, % local employment	Displacement, increased inequality	SDG 1 (Poverty), SDG 5 (Gender), SDG 8 (Decent Work)
Governance	Decentralize and sensitize NbS policies	Local land-use committees, land tenure, and participatory monitoring of NbS implementation	% of co-managed zones	Land conflicts, lack of trust	SDG 16 (Institutions), SDG 17 (Partnerships)
Climate Adaptation	Drought and erosion resilience	Regreen Gallery forest, Soil restoration, early warning systems	Water retention index, Soil erosion index	Increased climate risk and Soil erosion	SDG 6 (Water), SDG 13 (Climate Action)

). Focusing on native plant species planting will regenerate degraded areas and enable the reserve’s recovery. The goal measurement outcomes are the proportion of NDVI and FVC from the initial NbSs implementation period.

The findings identify governance as the key enabler of NbS for reserve ecosystem health. It acts as an overarching framework that supports ecosystem health, climate resilience, equity, and inclusion by enabling policies that particularly benefit vulnerable communities. Governance further guides the design of buffer zones and reinforces species management. This interlinks NbS goals, where ecosystem health serves as the physical foundation and equity and inclusion provide the social guarantee for BNR sustainability. This, therefore, creates an interdependency of NbS goals (Figure 9).

Governance acts as a key umbrella for NbS implementation in BNR, as it secures land tenure and fosters stakeholder participation, thereby supporting effective natural reserve restoration. It ensures equity and inclusion with policy mechanisms that guarantee marginalized groups’ benefits. Moreover, coordinated early warning systems, policy adaptation, and funding mechanisms also rely on governance. Restored ecosystems provide jobs, food, resources, as well as climate adaptation and vegetation cover enhancement. Therefore, any NbS approach in ecosystem management should integrate people’s benefits to build the long-term resilience of the reserve.

5. Discussion

5.1. NbSs for the Ecosystem Sustainability and Climate Resilience Nexus in BNR

The main objective of this study was to identify context-appropriate NbSs based on scientific findings for BNR ecosystems’ resilience. The focus was on strengthening ecosystem sustainability and climate adaptation potential while reducing anthropogenic pressures. The findings underscore the evidence-based nature of integrative NbS implementation for BNR’s effective management. The results identified areas where interventions can be focused with native vegetation restoration, soil retention techniques, and community-based land governance. These are highly important in participatory ecological and socio-economic resilience perspectives. The main area of the reserve restoration remains the partially protected area (IUCN Category IV) for species management. Thus, the southeastern and northeastern part of the reserve need an urgent intervention for vegetation restoration. These parts are most affected by the dual effects of anthropogenic and climate change. This aligns with the IUCN Global Standard of NbS implementation for biodiversity and ecosystem integrity [28]. The NbS application should directly respond to the evidence-based assessment of the current state of ecosystem degradation and loss, with clearly identified and measurable biodiversity conservation outcomes [28,58]. Beyond the recognized areas for NbS intervention in BNR, this study clearly identified most climate and anthropogenic threats that have to be taken into account in the long-term restoration. These threats, such as wood harvesting and logging, livestock, farming and grazing, habitat shifting and alteration, drought, and fire, can be effectively reduced through integrative NbSs. Weiskopf et al. [19] found that climate change is disrupting ecosystems at the individual and species levels. Extreme events such as drought amplifies vegetation stress, with ecosystem services being altered [19]. Thus, this finding suggests that ecology management should select effective NbSs for not just the current conditions, but also to anticipate future species stresses. Moreover, in assessing human pressure on nature conservation, Luo et al. [59] found that over 75% of Earth’s land is impacted by anthropogenic pressures that are mainly threatening biodiversity and ecosystem function in protected areas [59]. In the context of the prevalence of the dual anthropogenic and climate threat on BNR, which leads to species populations’ decline, an integrative NbS intervention is crucial for the reserve’s sustainability.

Local community participation is key to the success of effective NbS implementation. In West Africa, where most research on NbSs focuses on coastal areas and forests, it indicates limited community engagement in NbS implementation due to knowledge gaps in understanding the principles that guide NbS actions [60]. These challenges are due to context-specific evidence based on NbS implementations. Thus, capacity building of the local community is important for NbS success.

5.2. Opportunities and Constraints of NbS Implementation in Dryland Socio-Ecological Systems

The main constraints that jeopardize NbS implementation are the institutional arrangements that involve taking into account local community needs. For instance, in the implementation of REDD+, the government has initiated a restoration and management project with the local community in BNR. But due to a lack of financial resources and capacity building, these initiatives have failed to reduce anthropogenic pressures to date. Some of the initiatives also lack clarity on stakeholders’ roles and take into consideration indigenous knowledge. Thus, in light of these constraints, governance in NbS options’ goal of reserve effective resilience is most suited and recommended. This goal stands as an umbrella for other goals, as it plays a key role in the successful design, implementation, and sustainability of NbSs. It also clarifies land tenure and access rights by enabling inclusiveness and equitable decision-making with a clear financial framework without leaving marginalized groups behind.

Since the reserve will experience climate change effects in the future under both SSPs (SSP126 and SSP585), governance plays a key role in the reserve’s sustainability. The most prominent example of forest restoration governance is Ethiopia. In this country, many forests are managed in a participatory approach with the local population to ensure resource management or a green legacy initiative [61]. This was a successful approach that can be implemented in a dryland ecosystem. In the context of rapidly changing LULC dynamics, it recommends that the stricter reimplementation of land use regulations be inspired by Costa Rica’s Payment for Ecosystem Services program [62], which has effectively reduced deforestation through financially incentivizing landowners to maintain forest cover. It can also promote sustainable agricultural practices such as agroforestry and controlled grazing, similar to those used in Namibia’s Community-based Natural Resource Management [63]. These actions can enhance biodiversity and reduce the environmental impact of the BNR. Thus, initiatives like the Great Green Wall in the Sahel, which combat desertification through large-scale reforestation [64], are also an opportunity to reinforce NbS implementation in dryland forest restoration.

5.3. Limitations and Future Research Direction

NbSs are still a theoretical concept and lack empirical implementation evidence, particularly for dryland ecosystems. Many studies related to NbSs remain pilot-based or theoretical and are mostly focused on coastal or forest areas in West Africa. The main limitation is inherent to long-term empirical data from dryland protected areas, showing how NbSs actually improve ecosystem functions and resilience to socio-environmental challenges. Moreover, many outcomes from NbS implementation are site-specific and context-dependent because the NbS that works in one dryland protected area may not be applicable elsewhere due to socio-ecological heterogeneity. There are monitoring challenges with NbS implementation due to no universal set of indicators to measure the success of NbSs across ecological, climatic, and socio-economic dimensions. This also includes the limited integration of indigenous knowledge and governance and institutional barriers. Moreover, compared with anthropogenic threats, climate threats are less frequently mentioned; this is possibly due to data gaps or insufficient assessment.

There is a need for context-specific NbS modeling for dryland ecosystems. This research should focus on designing, modeling, and testing NbSs tailored to arid and semi-arid ecosystems, where water stress and soil degradation are key concerns. This could also integrate a longitudinal study to assess the long-term impact of NbS implementation on vegetation health, climate mitigation, and community resilience over time. This could also use vegetation indices alongside remote sensing or GIS tools to assess the hydrological functions of the reserve’s landscape by delineating the watershed and hydrologic response unit (HRU) mapping that this study did not include.

6. Conclusions

In the context of climate change and anthropogenic pressures on the BNR, effective NbSs for ecosystem health are urgently needed. Governance is the root enabler for the reserve’s NbS implementation. It provides a physical foundation and promotes equity and inclusion, which are social guarantees for the long-term sustainability of the reserve. Thus, from 1993 to 2023, the reserve experienced a significant vegetation decline due to the dual impact of climate variability and anthropogenic pressure. Wooded and tree savanna areas declined by 20.8% and 6.8%, respectively, while cropland dramatically increased from 8.5% to 34.8% over time. The reserve’s water bodies also shrank, highlighting one of the visible effects of land degradation. These threats have made many species vulnerable or endangered. Among the species, “Afzelia Africana” and “Afzelia Africana” are vulnerable, while “Pterocarpus Erinaceus” and “Pterocarpus Erinaceus” are endangered. Overall, 30.77% of species are either endangered or vulnerable, and 53.85% of species populations are declining. Future climate projections indicate that the reserve will experience significant warming under the SSP1-2.6 and SSP5-8.5 scenarios, accompanied by increased temperatures and sporadic heavy rainfall. These projections underscore the urgent need for the sustainable management of BNR, in which NbSs play a critical role in preserving ecological integrity. Therefore, any NbS approach should consider people’s needs when building the BNR’s long-term resilience. Thus, future research should focus on evaluating the effectiveness of specific NbS strategies through participatory monitoring frameworks. It also has to integrate socio-economic feedback into ecosystem restoration models. This would enhance evidence-based policymaking and support adaptive management tailored to both ecological and community priorities.