Tree species diversity analysis using species distribution models: A Faidherbia albida parkland case study in Senegal

Ndao, Babacar; Leroux, Louise; Hema, Aboubacar; Diouf, Abdoul Aziz; Bégué, Agnès; Sambou, Bienvenu

doi:10.1016/j.ecolind.2022.109443

Cited by 5 publications

(5 citation statements)

References 96 publications

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“…Furthermore, Varela et al (2022) concluded that RF was the best model to estimate the climatic niche of endemic tree species in Cabo Verde. According to the values of those parameters, we obtained better model fits than those previously obtained when modelling F. albida in Senegal (Ndao et al, 2022).…”

Section: Discussionmentioning

confidence: 43%

“…Faidherbia albida is a monospecific taxon (POWO -Plants of the World Online, 2021) and is a potentially good model to test possible environmental correlations among different geographic areas, using climatic modelling and analyses of land use and land cover (LULC) changes. The distribution of this tree has been successfully modeled using MAXENT, in central Senegal, although with a relatively low AUC (0.64), where a relationship between tree diversity and human activities was found (Ndao et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

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Present and future distribution of Faidherbia albida in Cabo Verde as revealed by climatic modelling and LULC analysis

2023

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Climate change poses one of the most significant challenges to conserve biodiversity, especially in tropical dry islands, as is the case of Cabo Verde (northeast Atlantic Ocean). This archipelago has a low percentage of forest cover and hosts only seven native tree species, among them, Faidherbia albida (Delile) A.Chev. (Fabaceae). Therefore, protective afforestation is extremely important in Cabo Verde, one of the most vulnerable West African countries to climate change. With this work, we aimed to estimate the current distribution and potential shifts in suitable areas for F. albida under climate change, using species distribution models (i.e., random forest, generalized linear and additive models), covering its distribution range in Cabo Verde and mainland Africa. The best model was then projected for the studied area, at two different slice times, using Representative Concentration Pathway (RCP) 4.5 and 8.5 scenarios. Based on current bioclimatic variables, we estimated that almost two thirds of Cabo Verde’s territory is highly suitable for F. albida, which contrasts with its current occurrence. By overlaying the present habitat suitability with land use and land cover data, we concluded that habitat availability and suitability could be constrained by that factor. On average, the predicted suitable habitat for future distributions gradually decreases by 2080 under both scenarios compared with the current, with a smaller effect of RCP4.5 than of RCP8.5. Local authorities can benefit from this research and develop actions to promote sustainable reforestation in Cabo Verde, which should include native tree species that are best adapted to the local climate and could thus contribute to mitigate the effects of climate change.

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Section: Discussionmentioning

confidence: 43%

Section: Introductionmentioning

confidence: 99%

Present and future distribution of Faidherbia albida in Cabo Verde as revealed by climatic modelling and LULC analysis

2023

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“…In a typical cropping season, pearl millet is sown around May-June, before the first rain and harvested from October to November. The study area is also characterized by a treebased cropping system dominated by F. albida, representing 42% of the trees among the 60 tree species inventoried in the study area [22]. F. albida is a leguminous nitrogen-fixing species with phreatophytic behavior and a reverse phenology [64].…”

Section: A Study Areamentioning

confidence: 99%

“…In addition, F. albida parklands have been shown to increase crop productivity (e.g., [16]- [18]) through an improvement in nutrient concentrations [16], soil carbon [19], water infiltration [20], and a modification of the microclimate below the crown [21]. However, F. albida parklands are also highly diversified in terms of species composition and structure [22], which introduces a very large within-field heterogeneity in terms of crop productivity, with a decay-gradient effect with distance from the trunk [18]. For instance, using a geostatistical approach and multispectral indices from unmanned aerial vehicle (UAV) imagery, [23] identified that the distance of influence of F. albida trees on millet productivity was 17 m, while in more recent studies, [15] showed that the provision of multiple ecosystem services by F. albida trees is optimal within a range of 10 m from the trunk.…”

Section: Introductionmentioning

confidence: 99%

Combining UAV and Sentinel-2 Imagery for Estimating Millet FCover in a Heterogeneous Agricultural Landscape of Senegal

Diack,

Diene,

Louise

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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In recent decades, remote sensing has been shown to be useful for crop cover monitoring over smallholder agricultural landscapes such as agroforestry parklands. However, the fraction of green vegetation cover (FCover) has received little attention. Indeed, the collection of FCover ground data representative of the within-field heterogeneity is time-consuming. Thus, this study aims to bridge this gap by proposing an original methodological framework combining FCover data derived from unmanned aerial vehicle (UAV) and Sentinel-2 (S2) images for estimating millet FCover at the landscape scale in an agroforestry parkland of the groundnut basin of Senegal during the 2021 and 2022 cropping seasons. UAV-based FCover was computed over a 3 m x 3 m grid using a thresholding approach for 6 dates over the cropping seasons and then used as ground observation for the upscaling of millet FCover at the landscape scale with S2 data. Various spectral vegetation indices (VIs) and textural features (TFs) were derived from S2, and several modeling approaches based on machine learning algorithms were benchmarked. Our results showed that the modeling approach using the full time series in combination with a random forest algorithm was able to explain 73% (RMSE = 12.13%) of the UAV-FCover variability after validation in 2021 and 2022. Additionally, UAV images are suitable for consistent monitoring of millet FCover over heterogeneous agricultural landscapes by training S2 satellite images. To further check its robustness, this approach should be tested for different crops and practices across a variety of agricultural landscapes in Sub-Saharan Africa.

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“…Since the sampled pixels are often a mixture of the tree canopy and the immediate surroundings of the tree, the Faidherbia albida mapping is thus conducted at a canopy level with a tree canopy minimum mapping unit of one pixel. Field samples were collected in two different semi-arid regions of Senegal (Niakhar and Nioro; Figure 1) in 2018 and 2019 using an optimized sampling strategy based on a landscape heterogeneity classification [59,60]. At each sampling site, an exhaustive inventory of all trees was conducted in a 1-hectare plot.…”

Section: Study Area and Sample Datamentioning

confidence: 99%

Mapping the Abundance of Multipurpose Agroforestry Faidherbia albida Trees in Senegal

Brandt

Tong

et al. 2022

Remote Sensing

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Multi-purpose Faidherbia albida trees represent a vital component of agroforestry parklands in West Africa as they provide resources (fodder for livestock, fruits and firewood) and support water lifting and nutrient recycling for cropping. Faidherbia albida trees are characterized by their inverse phenology, growing leaf flowers and pods during the dry season, thereby providing fodder and shedding leaves during the wet season, which minimizes competition with pastures and crops for resources. Multi-spectral and multi-temporal satellite systems and novel computational methods open new doors for classifying single trees and identifying species. This study used a Multi-Layer Perception feedforward artificial neural network to classify pixels covered by Faidherbia albida canopies from Sentinel-2 time series in Senegal, West Africa. To better discriminate the Faidherbia albida signal from the background, monthly images from vegetation indices were used to form relevant variables for the model. We found that NDI54/NDVI from the period covering onset of leaf senescence (February) until end of senescence (leaf-off in June) to be the most important, resulting in a high precision and recall rate of 0.91 and 0.85. We compared our result with a potential Faidherbia albida occurrence map derived by empirical modelling of the species ecology, which deviates notably from the actual species occurrence mapped by this study. We have shown that even small differences in dry season leaf phenology can be used to distinguish tree species. The Faidherbia albida distribution maps, as provided here, will be key in managing farmlands in drylands, helping to optimize economic and ecological services from both tree and crop products.

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Tree species diversity analysis using species distribution models: A Faidherbia albida parkland case study in Senegal

Cited by 5 publications

References 96 publications

Present and future distribution of Faidherbia albida in Cabo Verde as revealed by climatic modelling and LULC analysis

Present and future distribution of Faidherbia albida in Cabo Verde as revealed by climatic modelling and LULC analysis

Combining UAV and Sentinel-2 Imagery for Estimating Millet FCover in a Heterogeneous Agricultural Landscape of Senegal

Mapping the Abundance of Multipurpose Agroforestry Faidherbia albida Trees in Senegal

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