Woody species diversity as a function of land use types and environmental factors in agroforestry landscapes of Senegal

Woody species diversity as a function of land use types and environmental factors in agroforestry landscapes of Senegal | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Woody species diversity as a function of land use types and environmental factors in agroforestry landscapes of Senegal Diara SYLLA, Louise LEROUX, Babacar NDAO, Adama LO, Cheikh MBOW, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6261997/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study examines the diversity and distribution of woody species in relation to land use and environmental factors across three agroforestry landscapes in the Senegalese Sahel (Ouarkhokh, Niakhar, and Koussanar). A total of 91 species from 27 families were recorded across 400 one-hectare plots, established using a stratified weighted sampling technique. The highest species richness was observed in the Sahelo-Sudanian (60 species, 21 families) and Sudano-Sahelian zones (56 species, 16 families), while the Sahelian zone (Ouarkhokh) had 31 species from 13 families. Dominant families included Fabaceae, Combretaceae, Anacardiaceae, Apocynaceae, Malvaceae, Rubiaceae , and Rhamnaceae . Land use mapping, based on supervised classification, identified natural vegetation, cultivated lands, plantations, water bodies, bare soils, and artificial surfaces. Canonical Correspondence Analysis (CCA) revealed three species groups: Fabaceae and Malvaceae , dominant in cultivated areas and plantations, influenced by high temperature and evapotranspiration; Combretaceae , prevalent in natural vegetation zones, associated with higher elevation and temperature; and species adapted to high rainfall, biomass, and clay content. The study highlights that temperature, rainfall, evapotranspiration, and topography play key roles in shaping woody species distribution, with land use significantly influencing their spatial patterns. Wood diversity land use environmental factors agroforestry landscape Senegal Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Over the past fifty years, the Sahelian landscape has undergone significant socio-environmental transformations, marked by severe droughts, a significant decrease in annual precipitation, and an increase in human activities, resulting in a considerable impact on local ecosystems (Trémolières, 2010 ; Emeterio, 2013; Brandt and al., 2014; Alexandre & Mering, 2019 ; Tsegai and al., 2022). These changes have led to soil impoverishment (Descroix and Diedhiou, 2012 ; Brandt and al., 2017; Brandt and al., 2019) and adjustments in the structure and composition of plant species, adapting to more arid climatic conditions. Concurrently, natural habitats, crucial for biodiversity, are increasingly exploited by humans for agricultural, forestry, and livestock activities (Doumbia and al., 2020). Agroforestry systems, which combine trees, crops, and pastures on the same piece of land and the same time (Anthony, 1983 ; Nair, 1993), are traditional agricultural systems well-established in the semi-arid regions of West Africa (Khemiri, 2017 ; Sanogo and al., 2019). These systems, recognized for their sustainability (Sanogo and al., 2019), offer diversification of production for subsistence and income while minimizing ecological risks associated with climate variations in the region (Mbow and al., 2014; Sinare and Gordon, 2015 ; Reed and al., 2017; Kuyah and al., 2019). Their importance for livelihoods, especially for vulnerable populations, as well as their role as reservoirs of genetic biodiversity (Jose, 2012; Gonçalves and al., 2021), is increasingly recognized, encouraging their preservation and improved management. In Senegal, these agroforestry systems are integrated part of agricultural landscapes and are present in various forms adapted to ecological, agronomic, and biological factors (Diatta and al., 2016; Ba and al., 2018; Ndao and al., 2021). The tree constitutes an essential element there (Lawesson, 1990 ; Tappan and al., 2004) and provides numerous socio-ecosystem services to the populations (Ganaba and Guinko, 1995 ; Godron and Sinare, 2015 ; Goffner and al., 2019). It serves as a source of forage for animals (Ngom and al., 2014; Diatta and al., 2016) soil stabilization, protection, and fertilization (Tounkara and al., 2022), and improve the household food security through the provision of a wide range of ecosystem services (Leroux and al., 2022). It is also a primary source of energy and provides timber, lumber, and traditional pharmacopeia products for rural populations (Laouali and al., 2014). However, differences in tree diversity are noted from one agroforestry system to another or within the same system (Massaoudou, 2015 ; Ndao and al., 2021a). This difference may be due to the influence of environmental factors (Ndong and al., 2015). Although previous research has addressed the issue of floristic diversity of woody vegetation in agroforestry systems in Senegal, few studies have analyzed it along a bioclimatic gradient, highlighting the influence of environmental factors, with most focusing on specific aspects in given agroforestry landscapes (Coly and al., 2020; Sane and al., 2021; Badji and al., 2023; Gomez and al., 2023; Ndao and al., 2022; Ndao and al., 2021). Yet, understanding the composition and distribution of woody species in these different agroforestry landscapes is essential to identify tree species that perform best under specific site conditions and the main tree-land use-site combinations in each system. In this context, this study proposes to analyze the diversity of woody species in three agroforestry landscapes in Senegal along a bioclimatic gradient, to determine the influence of land cover and land use classes on woody composition, and to analyze these relationships with various agro-environmental variables. 2. Methodology 2.1. Study area The study was carried out at three sites covering three phytogeographical zones following a spatial gradient: the Sahelian zone (Ouarkhokh site), the Sahelo-Sudanian zone (Niakhar site) and the Sudano-Sahelian zone (Koussanar site) as presented in (Fig. 1 ). This subdivision is similar to that of Trochain ( 1940 ) and Faye ( 2010 ) where the Sahelian domain comprises the Sahelian, Sahelo-Sudanian and Sudano-Sahelian sub-domains. Each site consists of a 30 x 30 km square zone. Senegal's climate is characterized by a long dry season and a shorter rainy season (three to four months) with a north-south rainfall gradient. Based on the TAMSAT (Tropical Applications of Meteorology using SATellite data and ground-based observations) (Pearson and al., 2020) averaged between 2011 and 2020, the sahelian zone is located between the 300 and 400 mm rainfall isohyets, with an average of 447.6 mm/year. The Sahelo-Sudanian zone is located between the 400 and 600 mm isohyets, with an average rainfall of 540.16 mm/year, while the Sudano-Sahelian zone is at the lower end of the 600 mm and 1200 mm isohyets, with an average rainfall of 680.4 mm/year. The relief consists of alternating sand dunes oriented from southwest to northeast, 10 to 30 m high and 0.5 to 5 km wide. The dunes are separated by longitudinal plains of 1 to 5 km wide (Le Houérou, 1989 ) in the Sahelian zone. In the Sahelo-Sudanian zone, these plains are crossed by the Sine and Saloum valleys and marigots, which disappear in the dry season (l'Homme, 1978). The relief of the Sudano-Sahelian zone is flat, with a few slight depressions formed by pools and streams (Thiam, 2006 ). From north to south of the Sahel, the main soil types on the plateaus are arenosols (strictly Sahelian), lixisols (Sahelo-Sudanian) and acrisols (Sudano-Sahelian). The vegetation forms a mosaic of shrub savannah and shrub savannah with scattered trees in the sahelian zone (Bourlière and al., 1972), bushy in the Sahelo-Sudanian zone (Diouf, 2019 ) and valley vegetation in the Sudano-Sahelian zone. The population of the Sahelian region is made up of Peulhs, Wolofs and Sérères, with an increasing density from north to south (Fall, 2014 ; Codiat, 2020 ). The presence of trees and livestock farming and agriculture activities coexist, with varying dominance depending on the sub-zone. In the northern Sahel, the predominant agroforestry systems are sylvopastoral systems (Fall, 2017a ), while in other regions they are characterized by small-scale agrosylvopastoral farming, with small parcel sizes (around 0.6 hectares) and significant intra-parcel heterogeneity (Mbaye and al., 2003; Grillot, 2018 ). Logging is mostly noted in the Sudano-Sahelian zone and concerns energy wood (charcoal and firewood), timber, service wood and wood products (Sarr, 2015 ). 2.2. Methodological approach The methodological approach used in this work is presented in Fig. 2 . First, a land use map was created using Sentinel data (spectral bands and spectral index), and field data on land use and land cover (LULC). Second, tree data were collected using a stratified sampling protocol based on an object-based segmentation (OBIA) and corresponding hierarchical classification. Finally, the influence of environmental factors on trees and land use was studied through a Canonical Correspondence Analysis (CCA). 2.2.1. Data collection and pre-processing Woody vegetation data collection Sampling plan A weighted stratified sampling (Ndao and al., 2021) was implemented on the three sites. The different strata were identified in the following stages. The data used consist of a time series of Normalized Difference Vegetation Index (NDVI, Rouse and al., 1974), calculated from Sentinel-2 (S2) level 1C, 10-m resolution images from the COPERNICUS/S2 collection, obtained via the Google Earth Engine (GEE) platform (Gorelick and al., 2017). These data have been ortho-rectified and radio-corrected, providing reflectance values at the top of the atmosphere. There were acquired from January 1st to December 31st, 2021, together with 8 agro-environmental variables composed of bioclimatic variables, vegetation variables and Soil type (see IS1). First, an object-based segmentation of the Sentinel-2 NDVI time series was carried out over the study area, resulting in the delimitation of 1,280 homogeneous units of five hectares for all sites studied. The 5-hectare threshold was chosen to achieve an optimal balance between measurement precision and the homogeneity of the analysis units, while considering the specific characteristics of the Sahelian environment. The mean values and standard deviation of 9 relevant agri-environmental variables were extracted for each unit. Next, a Pearson correlation matrix was applied to eliminate highly correlated agri-environmental variables, thus ensuring the robustness of the analyses. Next these units were then classified using a Hierarchical Clustering on Principal Components (HCPC) (Kassambara, 2017 ) integrated with a Mixed Data Factor Analysis (MDFA) (Kassambara, 2017 ) to group plots with similar landscape characteristics. The value of the v-test (see IS2) (Escofier & Pages, 2008 ) was used to establish the relationship between the sites and the agri-environmental variables, thus indicating the significant association between these variables. All these operations were carried out using ArcGIS 10.8 (ESRI, 2020) and R version 4.3.1 (R Development Core Team 2023) softwares. Finally, on the basis of the results obtained, seven landscape classes were identified (Table I). A weighting was applied according to the surface area of each landscape class identified, in order to distribute 400 one-hectare plots evenly. Details of this weighting are shown in the table below. Table 1 Description of the landscape types identified (class) and distribution of woody inventory units per site within these classes Class Site Number of Plots Description 1 Total 125 Includes the Ouarkhokh site and the western part of Niakhar, is characterized by values significantly below the general average for variables such as temperature, evapotranspiration, and precipitation. This suggests a relatively dry and cool climate in these areas. Koussanar - Ouarkhokh 100 Niakhar 25 2 Total 28 Located in the north and west of Niakhar, shows values slightly below the average for the studied variables, with moderate evapotranspiration and precipitation. This indicates a slightly drier climate than the average. Koussanar - Ouarkhokh 28 Niakhar - 3 Total 102 Found in the eastern part of Niakhar. It is distinguished by precipitation and evapotranspiration levels below the average, suggesting a drier climate in this region. Koussanar - Ouarkhokh - Niakhar 102 4 Total 12 Extends to the northeast of Ouarkhokh as well as the southeast and southern parts of Niakhar. It presents evapotranspiration and precipitation values below the average, but with more moderate temperatures, reflecting a relatively dry climate with temperate temperatures. Koussanar - Ouarkhokh 4 Niakhar 8 5 Total 26 Located in the central part of the Koussanar site, displays values above the average for all variables, indicating a more humid and warm climate in this area. Koussanar 26 Ouarkhokh - Niakhar - 6 Total 42 Located in the northwest and forms a band in the southeast koussanar région, is characterized by high evapotranspiration and precipitation values, along with relatively warm temperatures. This suggests a warm and humid climate. Koussanar 42 Ouarkhokh - Niakhar - 7 Total 65 Extends into the southwestern part of the Koussanar region, with high precipitation, evapotranspiration, and temperature values. This reflects a particularly warm and humid climate in this class. Ouarkhokh - Niakhar - Collection of woody data In each plot, an exhaustive inventory of all woody individuals was carried out. For each individual tree, the name of the species and its geographical coordinates were recorded. The nomenclature adopted was that of Angiosperm Phylogeny Group III (APG, 2009 ). Summary characteristics of altitude, slope and soil type were also recorded for each plot. Land Use and Land Cover (LULC) mapping The land cover and land use data were obtained by supervised pixel-based classification using a Random Forest (Breiman, 2001 ) machine learning classifier with 300 trees. For each study site, the training data were collected in the field in October and November 2021 over the entire study area and correspond to the polygons of the various land use and land cover units (LULC). These correspond mainly to cultivated vegetation, natural vegetation, habitat, water, plantation and bare land polygons. 80% of these data was used to train the classification algorithm and 20% to validate the resulting classification. A time window of S2 images from 1 July to 31 December 2021 and a cloud percentage of less than 5% were selected. This period corresponds to the crop growing season in Senegal. This selection of a series of images made it possible to group together a sufficient number of tiles to cover the entire study area, and to more easily discriminate the spectral signatures of the different units (Mainguy-Seers, 2019 ). At the same time, six spectral indices were used in this study namely NDVI (Normalize Difference Vegetation Index), NDWI (Normalized Difference Water Index), GNDVI (Green Normalized Difference Vegetation Index), NDRE (Normalized Difference Red Edge Index), UI (Urban Index), BSI (Bare Soil Index). They were calculated using the formulae presented in IS 3. They were used to distinguish more clearly between the different landscape units. The overall accuracy (OA), the Kappa coefficient and the confusion matrix were calculated for each site to assess the accuracy of the classifications (McHugh, 2012 ). Ancillary data :agro-environmental variables In order to understand the influence of agro-environmental variables (see Table II) on the richness and abundance of woody species in agroforestry landscapes, nine agro-environmental variables were used: 1.Annual temperature downloaded from the WorldClim database ; 2. Elevation from the US Geological Survey SRTM digital elevation model ; 3.Actual Evapotranspiration and Interception (AETI) ; 4.Quantity of Nitrogen 5.Average rainfall over a 2011–2021 period extracted from Tamsat 6.Soil Organic Carbon from Isric 7. Tree Cover from the Brandt et al (2020) database, 8. Total Biomass Production (TBP) 2016–2021 from the WAPOR database, 9. The lay content from the ISRIC database For each variable, the mean and standard deviation value centroid of the 400 plots was quantified. Table 2 Agro-environmental variables used to analyse the influence of agro-environmental variables on the richness and abundance of woody species in agroforestry landscapes. Agro-environmental variables Abbreviations Resolution (m) Date Database Link Temperature T 250 2016–2021 World clim https://www.worldclim.org Digital elevation model DEM 250 2017 USGS https://earthexplorer.usgs.gov Nitrogen NTO 250 2017 ISRIC Soil https://data.isric.org/geonetwork/srv/fre/catalog.search#/metadata/c824bc78-2c6a-42b5-bfc8-9555a1c7b11c Rainfall Rf 4000 2016–2021 Tamsat https://www.tamsat.org.uk/data Actual EvapoTranspiration and Interception ETIa 250 2016–2021 Water Productivity Open-access portal (WaPOR 2.1) https://wapor.apps.fao.org/catalog/WAPOR_2/1/L1_AETI_A Soil Organic Carbon SOC 250 2017 ISRIC Soil https://data.isric.org/geonetwork/srv/fre/catalog.search#/metadata/9a66a37e-8a4e-463b-b83a-fd49049c323a Tree cover TC 100 2021 Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC) https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1832 Total Biomasse Production TBP 100 2016–2021 Water Productivity Open-access portal (WaPOR 2.1) https://wapor.apps.fao.org/catalog/WAPOR_2/1/L1_TBP_A Clay Cl 250 2017 ISRIC Soil https://data.isric.org/geonetwork/srv/fre/catalog.search#/metadata/d559a21a-4ef1-43ee-94da-798ac267747e 2.2.2. Analysis and processing of woody inventory data The woody data collected were organised in a woody database in shapefile format. The Angiosperm Phylogeny Group (APG) III classification (APG, 2009 ) was used to update species names and their respective families. Spelling (species, genus and family names) and authorship were checked at https://www.tela-botanica.org/ . Floristic richness and species abundance (SA) were calculated for each inventory plot and for each land occupation and use unit (natural vegetation, cropland, and plantation). The species abundance (SA) is given by the ratio of the number of individuals of the species (Ni) to the total number of individuals of all species combined (Nt): $$\:\varvec{S}\varvec{A}=\frac{\varvec{N}\varvec{i}}{\varvec{N}\varvec{t}}\varvec{x}100$$ 1 Where: Ni Number of individuals of the species. Nt Total number of individuals of all species combined. A comparative analysis of floristic richness and species abundance between land-use categories was carried out using the ANOVA statistical test. In addition Tuckey pairwise comparisons by Tuckey HSD function (Zhang and al., 2016) were performed between the different LULCs with floristic richness on the one hand and abundance on the other. These differences were illustrated by a whisker box plot. To study the environmental factors affecting woody diversity in the study area, an analysis of Pearson's correlation (Pearson, 1904 ) was used to determine correlations between environmental factors (see IS4). If the correlation between two variables was too strong (r) > 0.7, an ANOVA (Escofier and Pages, 2008 ) was used to select the variable that best explained the (most significant) variation in the data. A Principal Component Analysis was used to confirm that each variable selected made a specific contribution to our dataset. Finally, the relationship between tree species distribution and environmental factors was assessed using a Canonical Correspondence Analysis (CCA) (McCune, 2002 ). Richness and environmental factors were analysed in the CCA ordination. Clustering was carried out on the basis of the three occupancy units identified in the study area: cropland, vegetation and plantation. Statistical calculations were performed using R software version 4.3.1 (R Development Core Team 2023). 3. Result 3.1. Land use in agroforestry systems The results of the LULC mapping are presented in Fig. 3 . The classifications were obtained with an overall accuracy (OA) of 87%, 87% and 84% respectively in Ouarkhokh, Niakhar and Koussanar sites (Fig. 3 ). Natural vegetation, cropland, ponds and artificial areas were identified at all three sites, whereas plantations were only present at the Ouarkhokh site (Fig. 3 a). Analysis of these occupation units shows that cultivated vegetation and built-up areas are dominant in the Sahelo-Sudanian zone (Niakhar - Fig. 3 b), followed by the Sudano-Sahelian zone (Koussanar -Fig. 3 c), with less than 1,000 ha in the Sahelian zone (Fig. 3 a). On the other hand, natural vegetation is more important in the Sahelian and Sudano-Sahelian regions respectively Fig. 3 a and 3 b. Water and bare land, although present over small areas, are dominant in the Sahelo-Sudanian zone (Fig. 3 b). For all the land use classes identified, the average surface area varies significantly between sites (p-value < 0.01, see IS5 s). The accuracy of the land use classes varies from site to site, with good classification of crops (90% in Ouarkhokh, 96% in Niakhar, 98% in Koussanar), built up (70% in Ouarkhokh, 95% in Niakhar, 80% in Koussanar) and vegetation (80% in Ouarkhokh, 75% in Niakhar). Water bodies, on the other hand, are only moderately accurate (60% in Ouarkhokh, 66% in Niakhar, 83% in Koussanar), as is vegetation in Koussanar (59%) (see IS6 s). 3.2. Floristic composition of species in Senegalese agroforestry landscapes 91 species belonging to 27 families were recorded on the three sites. They are divided into 31 species in 13 families in the Sahelian zone (Ouarkhokh), 60 species in 21 families in the Sahelo-Sudanian zone and 56 species in 16 families in the Sudano-Sahelian zone. The dominant families are Fabaceae , Combretaceae , Moraceae , Malvaceae , Anacardiaceae , Rubiaceae , Rhammaceae and Apocynaceae (Fig. 4 ). The most represented families (Fig. 5 ) in the three land use categories are Fabaceae, Combretaceae, Moraceae, Anacardiaceae and Malvaceae . The Fabaceae family is the most represented, with 23 species in cultivated land, 16 in natural vegetation and 3 in plantations. The species found in the plantations belong either to the Combretaceae or the Fabaceae . 3.3. Variation of the floristic composition by LULC category Floristic richness (ANOVA, df = 2, F = 7.3, P ≤ 0.00073) and abundance (ANOVA, df = 2, F = 37.85, P ≤ 2.54e-16) varied significantly among the three land-use categories (natural vegetation, cropland, plantation; Fig. 6 ). Figure 6 a shows that floristic richness is highest in croplands, with a median of around 6 species and a wide distribution reaching up to approximately 15 species, followed by natural vegetation (with a median of around 4 species), and lowest in plantations (with a median of around 3 species). Tukey's pairwise comparisons between LULC types for floristic richness showed a statistically significant difference between cropland and natural vegetation (a and b)). However, the difference between the cropland and plantation (a, ab) and plantation and natural vegetation (b, ab) pairs did not show statistically significant differences (Fig. 6 a). Figure 6 b shows that species abundance is lowest in croplands (with a median below 50), higher in natural vegetation (with a median of about 100), and comparable in plantations (with a median close to 100). Pairwise comparisons of species abundance between land-use types showed a statistically significant difference for the natural vegetation-cropland (b, a) and plantation-cropland (a, b) pairs, but not between plantation and natural vegetation (a, a), as illustrated by the boxplots (Fig. 6 b). 3.4. Relationship between environmental factors between LULC According to the ANOVA, the following agro-environmental variables (table III): Temperature_mean, Clay_mean, SOC_mean, AETI_mean, Rainfall_mean, DEM_mean, Biomass_mean, SOC_stdev, AETI_stdev, Clay_stdev, DEM_stdev, and TBP_stdev show significant differences between natural vegetation, cropland, and plantations (p < 0.05). However, the variables NTO_mean, T_stdev, NTO_stdev, TC_stdev, RF_stdev, and TC_mean do not present significant differences. Tukey's HSD test revealed that the values for Clay, AETI_mean, SOC_stdev, AETI_stdev, and DEM_stdev do not significantly differ between the plantation-cultivated land and plantation-natural vegetation groups. Additionally, the differences in values for SOC_mean, DEM_mean, and TBP_mean between the plantation and natural vegetation groups are also not significant. Table 3 Analysis of Variance and Tukey Post-Hoc Comparisons of Environmental Variables between LULC Variable Pr_F Tukey_ Naturalvegetation-Cropland Tukey_Plantation_Cropland Tukey_Plantation_Natural-vegetation T_mean 8.4e-63*** 0 *** 0 *** 0.0021 ** Cl_mean 2.2e-06*** 0 *** 0.1776 N.S. 0.9309 N.S. NTO_mean 0.7 N.S. 0.8482 N.S. 0.8322 N.S. 0.9091 N.S. SOC_mean 2.3e-24*** 0 *** 1e-04 *** 0.46 N.S. AETI_mean 0.0003*** 8e-04 *** 0.1251 N.S. 0.6413 N.S. TC_mean 0.8 N.S. 0.8703 N.S. 0.9447 N.S. 0.8914 N.S. Rf_mean 1.2e-30*** 0 *** 0 *** 0.002 ** DEM_mean 3.4e-14*** 0 *** 3e-04 *** 0.1813 N.S. TBP_mean 8.6e-13*** 0 *** 6e-04 *** 0.1964 N.S. SOC_stdev 0.03* 0.0276 * 0.93 N.S. 0.5284 N.S. T_stdev 0.2 N.S. 0.8596 N.S. 0.2511 N.S. 0.332 N.S. NTO_stdev 0.71072492192407 N.S. 0.9268 N.S. 0.7618 N.S. 0.7054 N.S. TC_stdev 0.215449688962166 N.S. 0.2032 N.S. 0.846 N.S. 0.9977 N.S. AETI_stdev 6.84293718723184e-05 1e-04 *** 0.1614 N.S. 0.7998 N.S. Cl_stdev 0.0306268382970616*** 0.1745 N.S. 0.0902 N.S. 0.2593 N.S. DEM_stdev 0.00457104085323054** 0.0031 ** 1 N.S. 0.6236 N.S. TBP_stdev 0.00192349069236531** 0.002 ** 0.3845 N.S. 0.9351 N.S. Rf_stdev 0.300911641904056 N.S. 0.322 N.S. 0.7602 N.S. 0.9538 N.S. N.S.= not significant at * p < 0.05, ** p < 0.01, *** p < 0.001 A correlation matrix of the variables (table IV) with significantly different values revealed strong correlations between AETI_mean and Clay_mean (r = 0.88), between Rf_mean and Cl_mean (r = 0.83), between TBP_mean and Cl_mean (r = 0.83), between Rf_mean and SOC_mean (r = 0.89), between Rf_mean and AETI_mean (r = 0.85), between TBP_mean and AETI_mean (r = 0.80), and between TBP_mean and Rf_mean (r = 0.87). The Principal Component Analysis (PCA) showed that these variables primarily contribute to the same component. The variables AETI_mean, Rf_mean, and Cl_mean were selected due to their strong contribution to the principal components, thus optimizing the representation of the variance explained by the PCA (Fig. 7). The correlations between environmental factors and the axes of the Canonical Correspondence Analysis (CCA), as shown in the Table 3 , indicate that for axis 1, the strongest correlations are observed with the variables DEM_mean (r=-0.94) and T_mean (r=-0.83), indicating a significant relationship with this axis. In contrast, AETI_mean has the weakest positive correlation (0.18) with the axis. Conversely, Rf_mean displays a moderate negative correlation with axis 1 (r = 0.44), while SOC_stdev, TBP_stdev, and AETI_stdev exhibit weaker negative correlations (r = 0.24, r = 0.26, r = 0.17, respectively). Cl_mean shows the weakest correlation with axis 1 (r = 0.02). For axis 2, the correlations are generally low. The most notable relationship is with the variable AETI_mean (r = 0.43). T_mean, Cl_mean, Rf_mean, and DEM_mean exhibit weak correlations, with values of r=-0.37, r = 0.26, r = 0.27, r = 0.20, respectively, while AETI_mean and SOC_stdev show correlations close to zero (r = 0.09 and r=-0.04), suggesting a limited relationship with this axis. Regarding axis 3, Cl_mean presents the strongest negative correlation (r=-0.88), followed by AETI_mean and Rf_mean, which also display significant negative correlations (r=-0.77 and r=-0.75, respectively). T_mean and SOC_stdev show weak positive correlations (r = 0.38 and r = 0.32). Finally, AETI_stdev, DEM_mean, and TBP_stdev exhibit weak correlations with axis 3, indicating very weak inverse relationships with this dimension. Table 4 Correlation coefficients between the six environmental factors and the first three PCA axis Environmental variables Abbreviations Correlation Axis 1 Axis 2 Axis 3 Temperature T_mean 0.83489761 -0.37203975 0.38629901 Clay Cl_mean -0.02828842 0.26082880 -0.88599100 Actual Evapotranspiration and Interception_mean AETI_mean 0.18855787 0.43464435 -0.77727222 Rainfall Rf_mean -0.44018875 0.27784031 -0.75438712 Digital Elevation Model DEM_mean 0.94000441 0.20505002 -0.16387080 Soil organic Carbonic SOC_stdev -0.24206387 -0.04281015 0.32508962 Actual Evapotranspiration and Interception__stdev AETI_stdev -0.31760666 0.09858385 0.21879462 Above Ground Biomasse_stdev TBP_stdev -0.26869675 0.14812635 0.08599833 The canonical correspondence analysis (CCA) (Fig. 7), based on the different types of land use and land cover (LULC), highlights the relationships between the dominant plant families and the influence of environmental variables, making it possible to identify the following characteristics: Group 1 This group is composed of species belonging to the Fabaceae and Malvaceae families. It is characterized by high values of T_mean and lower values of AETI_mean, DEM_mean, and Rf_mean. The dominant LULC type is cropland, although natural vegetation and plantations are also present. Group 2 Group 2 includes species from the Combretaceae family, associated with high values of DEM_mean and T_mean, and low values of AETI_mean. The dominant LULC type is natural vegetation. Group 3 This group is characterized by species from the Anacardiaceae and Moraceae families, along with other less abundant families, located near cultivated lands. The associated environmental conditions show high values of Rf_mean, TBP, AETI_stdev, SOC_stdev, and Cl_mean (Fig. 7 IS). 4. Discussion 4.1. Composition and variation in floristic richness A total of 91 species belonging to 27 families were recorded at all the sites studied. Floristic richness was higher in the Sahelo-Sudanian zone (60 species, 21 families) and lower in the North Sudanian (56 species, 16 families) and Sahelian (31 species, 13 families) zones. This diversity reflects the agro-environmental conditions specific to each zone. The Sahelian zone, in northern Senegal, is characterised by very low rainfall and high temperatures. Rainfall is scarce and irregular, creating a semi-arid environment. Vegetation adapted to these conditions includes drought-resistant species such as Acacia and Balanites (Dendoncker and al., 2020). Soils are often shallow and poor in Nitrogen, which limits plant diversity (Kebe and al., 2020). The Sahelo-Sudanian and Sudano-Sahelian zones, located in the central and southern parts of the country, receive slightly higher rainfall than the Sahelian zone. This extra rainfall encourages greater plant diversity, with a greater variety of trees, bushes and grasses (Cisse, 2016 ). Soils can be more fertile in these areas, allowing more lush vegetation to develop (Fall, 2017a ). In addition to climatic variations, differences in soil composition and altitude can also influence the distribution of plant species (Ouattara and al., 2023). For example, the sandy, shallow soils predominant in the Sahelian zone may favour the growth of species adapted to these conditions, while soils richer in organic matter in the Sudano-Sahelian zones may support greater plant diversity (Ouoba and al., 2023). In addition, altitude can influence local temperatures and climatic conditions, which also affect the presence and abundance of plant species. The result obtained in the Sahelo-Sudanian zone for floristic richness is lower than that obtained by (Ndao and al., 2021) for the same site, i.e. 63 species divided into 21 families. The result on the number of species observed in the Sahelian zone in the present study is higher than that obtained by (Ndiaye and al., 2013) on a site with the same agroecological characteristics (15 species divided into 10 families). In the Sudano-Sahelian zone, the floristic richness obtained is higher than that observed by (M. A. Mbow and al., 2008), whose study identified 48 species in 26 families in the same zone. The differences in floristic richness observed between the previous studies and this one are due to the sampling effort. The surveys were carried out at the level of the landscape units sampled. Overall, these results corroborate those found by (Zounon and al., 2019) in the same agro-ecological zones of Burkina Faso. He inventoried 35 species in the Sahelo-Sudanian zone, 24 species in the northern Sudanian zone and 21 species in the Sahelian zone. The dominant families are Fabaceae, Combretaceae, Moraceae, Malvaceae, Anacardiaceae and Rubiaceae . These families account for over 65% of the plant community at all sites. The dominance of these families suggests a significant influence of these groups on the structure and composition of agroforestry ecosystems in Senegal. This result corroborates those of (Cissé and al., 2018) in Burkina Faso, Thiam and al., 2023) in Fatick (Sénégal). These families are well adapted to local environmental conditions and play an important role in providing ecosystem services such as nitrogen fixation, climate regulation and food and shelter for local wildlife (Gueye and Ndoye, 2003; Ngom and al., 2014; Diatta and al., 2016). Floristic richness is higher in cultivated land than in plantations and natural vegetation. Indeed, farmland can feature greater species diversity, because agroforestry systems often involve the cultivation of several plant species, including food crops, cash crops, fruit trees, and sometimes native woody species (Ndiaye and al., 2017). A high ratio of useful species (agroforestry and food species) was found in fields compared to forests/biosphere reserves, fallow land, and marsh by Sambou and al., 2017. Farmers also select species that can provide them with a wide use and suitable for their crop association to keep in the field (Coly and al., 2020). Studies such as those carried out by (Ndao and al., 2021) have shown that these traditional agroforestry systems can support a high diversity of plant species. In contrast, species abundance is higher in natural vegetation than in plantations. This is due to the natural presence of vegetation in areas where cultivation is little practiced, where there are several types of plant formations ranging from forests, to wooded savannahs, arborescent to shrubby and shrubby (Sylla and al., 2019; Ndao and al., 2022; Ndao and al., 2021). Plantations, on the other hand, are more abundant in individuals and have reduced diversity because the species present like Acacia senegal are exploited for commercial purposes (Diallo and al., 2023). 4.2. Relationships between environmental factors and species distribution in land-use types The results of the canonical correspondence analysis (CCA) revealed that species composition and distribution are influenced by topographic, edaphic, and anthropogenic factors. The specific associations between plant families and environmental factors in each land use type highlight the importance of local conditions for species distribution. This study shows that the Actual Evapotranspiration Index (AETI), temperature, and Digital Elevation Model (DEM) are the most determining factors, followed by rainfall, the amount of available biomass, and, to a lesser extent, clay content. These results confirm the conclusions of Dendoncker and Vincke ( 2020 ) and Sagna and al., (2024) in the Ferlo, who also emphasized the influence of topography on the development of woody vegetation. Group 1 is associated with high values of temperature, DEM, and AETI. The dominant families are Fabaceae and Malvaceae . The species of these two families vary across the Sahelian, Sahelo-Sudanian, and Sudanian-Sahelian zones. Their presence is influenced by climatic conditions, soil types, and site-specific land use practices. specific to each site. In addition to being present in plantations in Sahelian (Ouarkhokh), these species are mainly found in cultivated areas as well as in the natural vegetation of the three studied sites. This suggests that these plant families have a wide geographic distribution and are capable of thriving in relatively warm and semi-arid environments. This observation corroborates the assertion by Savadogo and al., (2016) that Fabaceae include species that resist drought and high temperatures. Indeed, they encompass species with varied phytogeographical affinities (Thiombiano and al., 2012) according to the rainfall gradient, topography, AETI, and human activities. In Ouarkhokh, the dominant species are from the Acacia genus (Diouf, 2011 ; Ndiaye and al., 2013; Niang and al., 2014). Other species belonging to the Fabaceae family, such as Dalbergia melanoxylon, Bauhinia rufescens, Prosopis glandulosa, Tamarindus indica, Faidherbia albida, Piliostigma reticulatum , as well as Malvaceae like Adansonia digitata , are also present (Ndong and al., 2015). These species are primarily found in savanna areas and mainly in lowlands where vegetation diversity and density are high (Niang and al., 2014; Sagna and al., 2024). This site is characterized by high temperatures (29°C to 32°C), an altitude ranging from 2.5 to 62.5 meters, and low AETI values, ranging from 3 mm to 1,000 mm. These plants are well adapted to the arid conditions and sandy soils of the region. In soudanian-sahelian (Niakhar), where temperatures are similar to those in Ouarkhokh and rainfall is higher, Fabaceae and Malvaceae are key components of agrosylvopastoral systems, contributing to agricultural productivity, environmental sustainability, and resilience to climate challenges in Senegal’s peanut basin. Indeed, the woody diversity is dominated by the species Faidherbia albida (Ndao and al., 2021), which is preserved in cultivated areas due to its numerous ecosystem services. Faidherbia albida parks, important in central Senegal (Dugué and al., 2024), play a crucial role in the peanut basin, particularly in Niakhar, by improving soil fertility through nitrogen fixation and providing quality fodder for livestock during the lean season. In sudano sahelian (Koussanar), which benefits from a tropical-Sahelian climate with slightly lower temperatures (28°C to 30°C) and more varied altitudes, reaching up to 60 meters, the diversity of species belonging to the Fabaceae family and the variety of vegetation are greater. Sarr and al. (2024) inventoried 11 Fabaceae species in Kougnheul, located in the same agro-ecological zone as Koussanar. The AETI values at this site range from 3 mm to 1,500 mm, with areas of high evapotranspiration favoring denser vegetation. Species belonging to the Fabaceae, Malvaceae, Anacardiaceae, Loganiaceae , and Moraceae families particularly thrive in forest galleries and wooded savannas, where they play a key role in nitrogen fixation and biomass production. Cordyla pinnata, Acacia raddiana , and Parkia biglobosa are among the species found, especially in agroforestry areas. They contribute to soil structure improvement and erosion prevention. This distribution of Fabaceae corresponds to that described in Laweson, ( 1995 ) and Spichiger, ( 2010 ). Group 2 is characterized by the dominance of species belonging to the Combretaceae family. This family is associated with a landscape marked by high DEM_mean and T_mean values but low AETI_mean. This suggests that these species are adapted to higher altitudes and warmer temperatures while tolerating relatively low evapotranspiration. The dominant land use type (LULC) in this group is natural vegetation. In Ouarkhokh, species of the Combretaceae family, such as Guiera senegalensis, Combretum glutinosum , and Combretum micranthum , are found as isolated individuals in lowlands (Sagna and al., 2024). They have deep root systems that allow them to draw water from deeper layers, making them well adapted to the poor, sandy soils of the Ferlo, which offer few nutrients and retain little water. Their resilience to the significant climate variability of the Ferlo, characterized by prolonged droughts and irregular rainfall, allows them to survive and maintain their presence in these arid landscapes. In Niakhar, where rainfall conditions are better, Combretaceae are located in lowlands, forming vegetation clusters. They are also found in cultivated lands due to their ability to fertilize soils or provide food resources. The structure of Combretaceae populations in Niakhar, different from that in Ouarkhokh, suggests that they can serve as indicators of the bioclimatic transition between these two sites, marking a gradual adaptation to varying environmental conditions, as shown in Fall, (2017). In Koussanar, the wettest site, Combretaceae are more diverse and found in significant populations within natural vegetation. They are often subject to cutting and exploitation for their wood. Sarr and al., (2024) showed that the Sudanian-Sahelian zone is characterized by a Combretaceae savanna. Group 3 is influenced by several major environmental factors: high mean precipitation (Rf_mean), high terrestrial biomass (TBP), as well as high variations in AETI (AETI_stdev) and soil organic carbon (SOC_stdev), and high clay content (Clay_mean). This group is characterized by the presence of species belonging to the Anacardiaceae and Moraceae families, as well as other less abundant woody families. The high mean precipitation values (Rf_mean) indicate that these species are found in areas with relatively significant rainfall, typical of regions like Niakhar and Koussanar. This group is located further south in Senegal, where water is relatively abundant, as suggested by the high values of AETI_mean and Rainfall_mean, and where vegetation is still present. The high DEM value shows that there is a diversity of topographic gradients; indeed, the area is composed of tabular plateaus cut by the hydrographic network, leading to depressions (Ndiaye, 2000). These conditions favor the growth of trees and shrubs from the Anacardiaceae and Moraceae families. The presence of these species is also explained by the wide variety of vegetation formations, ranging from shrub savanna to dense forest galleries, suggesting that these areas have a high capacity to support dense and productive vegetation. The high levels of organic carbon and clay content can be explained by their integration into cultivated areas in Koussanar and Niakhar. Their presence in cultivated lands suggests that they play a crucial role in agroforestry, providing important ecological services such as improving soil fertility and protecting against erosion. 5. Conclusion This study highlighted the diversity of woody species in three agroforestry landscapes distributed along a bioclimatic gradient in Senegal. It determined the influence of land cover and land use classes on the composition of these woody species, and analysed these relationships with various agro-environmental variables. 91 species belonging to 27 families were recorded on the three sites. Species richness was higher in the Sahelo-Sudanian zone and lower in the Sahelian zone. In terms of their presence within occupation classes, floristic richness is greater in cultivated areas than in areas of natural vegetation, due to a selection of species by farmers with high ecosystem services. On the other hand, the abundance of individuals is greater in plantations than in natural vegetation. The dominant families are Fabaceae, Combretaceae, Apocynaceae, Anacardiaceae, Malvaceae, Moraceae, Rhamnaceae and Rubiaceae . Canonical component analysis (CCA) of the distribution of these families as a function of agro-environmental variables showed that: Fabaceae and Malvaceae are correlated with high temperatures and high reference evapotranspiration; these families are found in cultivated areas, natural vegetation and plantations. A second group, dominated by the Combretaceae , is associated with high temperatures but low evapotranspiration, as well as reduced values of mean altitude (DEM) and rainfall. Finally, a third group is characterised by high values of DEM and mean temperature. The study suggests that species diversity and distribution are largely influenced by environmental variables. This research is important for informing policy makers about how diversity varies as a function of environmental variables and land-use units for subsequent planning and intervention. It assessed the effect of environmental variables and LULCs on woody species diversity, but further studies should investigate the effect of anthropogenic factors on species diversity and look at the regeneration of influencing factors along the agro-ecological gradient. Declarations Author Contribution D. S. contributed to the conceptualization, methodology, formal analysis, and drafting of the original manuscript. L. L. participated in the methodology, formal analysis, software development, and revision. A. A. D. was involved in the methodology, data curation, and software development. A. 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Mbow C, Van Noordwijk M, Luedeling E, Neufeldt H, Minang PA, Kowero G (2014) Agroforestry solutions to address food security and climate change challenges in Africa. Current Opinion in Environmental Sustainability 6 61–67. https://doi.org/https://doi.org/10.1016/j.cosust.2013.10.014 Mbow MA, Faye EH, Kaire M, Akpo LE, Diouf M (2008) Diversité d’une végétation ligneuse soudanienne dans les systèmes d’utilisation des terres du Sud-Ouest du Bassin arachidier (Sénégal) Journal Des Sciences et Technologies 7 (1) 21–34. McCune BGJ (2002) Analysis of ecological communities. Mcfeeters SK (1996) The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. International Journal of Remote Sensing 17 (7) 1425–1432. https://doi.org/10.1080/01431169608948714 McHugh ML (2012) Lessons in biostatistics interrater reliability : the kappa statistic. Biochemica Medica 22 (3) 276–282. https://hrcak.srce.hr/89395 Naguib NS, Daliman S (2022) Analysis of NDVI and NDRE Indices Using Satellite Images for Crop Identification at Kelantan. IOP Conference Series: Earth and Environmental Science 1102 (1) 12054. https://doi.org/10.1088/1755-1315/1102/1/012054 Ndao B, Leroux L, Gaetano R, Diouf AA, Soti V, Bégué A, Mbow C, Sambou B (2021) Landscape heterogeneity analysis using geospatial techniques and a priori knowledge in Sahelian agroforestry systems of Senegal. Ecological Indicators 125 107481. https://doi.org/https://doi.org/10.1016/j.ecolind.2021.107481 Ndao B, Leroux L, Hema A, Diouf AA, Bégué A, Sambou B (2022) Tree species diversity analysis using species distribution models: A Faidherbia albida parkland case study in Senegal. Ecological Indicators 144 (September) 109443. https://doi.org/10.1016/j.ecolind.2022.109443 Ndiaye I, Camara B, Ngom D (2017) Diversité spécifique et usages ethnobotaniques des ligneux suivant un gradient pluviométrique Nord-Sud dans le bassin arachidier sénégalais. Journal of Applied Biosciences September 2018 11123–11137. https://doi.org/10.4314/jab.v113i1.2 Ndiaye O, Diallo A, Sagna MB, Guisse A (2013) Diversité floristique des peuplements ligneux du Ferlo Sénégal. VertigO 13 (3) 19. Ngom D, Charahabil MM Sarr O, Bakhoum A, Akpo LE (2014) Perceptions communautaires sur les services écosystémiques d’approvisionnement fournis par le peuplement ligneux de la Réserve de Biosphère du Ferlo (Sénégal) VertigO Volume 14 Numéro 2 . https://doi.org/10.4000/vertigo.15188 Niang K, Ndiaye O, Diallo A, Guisse A (2014) Flore et structure de la végétation ligneuse le long de la Grande Muraille Verte au Ferlo nord Sénégal. September . https://doi.org/10.4314/jab.v79i0.15 Ouango M, Savadogo K, Ouattara S, P Issa, Ouedraogo S, Sawadogo-Kabore JB (2016) Structure composition spécifique et diversité des ligneux dans deux zones contrastées en zone Sahélienne du Burkina Faso. VertigO - La Revue Électronique En Sciences de l’environnement 16 (Numéro 1) https://doi.org/10.4000/vertigo.17282 Ouattara DN, Tro HH, Soro D, Bakayoko A (2023) Altitudinal gradient of the diversity and structure of plants on the highlands of the “Bowé de Kiendi” in the Gontougo Region (North-East Côte d’Ivoire) International Journal of Biological and Chemical Sciences 17 (2) 417–429. https://doi.org/10.4314/ijbcs.v17i2.11 Ouoba HY, Bastide B, Kabore SA, Seghieri J, Boussim IJ (2023) Population structure of Vitellaria paradoxa C.F. Gaertn (shea tree) parklands in Burkina Faso. Biotechnology Agronomy Society and Environment 27 (Special issue) https://doi.org/10.25518/1780-4507.20329 Pearson K (1904) “On the theory of contingency and its relation to association and normal correlation.” (Drapers’ C) PKR N (1993) An introduction to agroforestry (Kluwer Aca) Reed J, van Vianen J, Foli S, Clendenning J, Yang K, MacDonald M, Petrokofsky G, Padoch C, Sunderland T (2017) Trees for life: The ecosystem service contribution of trees to food production and livelihoods in the tropics. Forest Policy and Economics 84 62–71. https://doi.org/https://doi.org/10.1016/j.forpol.2017.01.012 Rikimaru A, Roy PS, Miyatake S (2002) Tropical Forest Cover Density Mapping. Société Internationale d’écologie Tropicale 43 39–47. Rouse JW, Haas RH, Schell JA, Deering D W (1974) Monitoring vegetation systems in the Great Plains with ERTS. in Third earth Resources Technology Satellite-1 National Symposium Volume I: Technical Presentations: Vol. Vol I (In: Freden pp. 309–3017) Seghieri J (2012) Agroforestry for conserving and enhancing biodiversity. Agroforestry Systems 85 1–8. doi.org/10.1007/s10457-012-9517-5 Sagna MB, Thiam AN, Niang K, Sarr O, Diallo A, Diatta S, Ngom D, Guissé A (2024) Influence of Topography on the Distribution and Structure of Woody Plants in the Senegalese Sahel (Sandy Ferlo) American Journal of Plant Sciences 15 (01) 14–28. https://doi.org/10.4236/ajps.2024.151002 Sambou A, Sambou B, Ræbild A (2017) Farmers’ contributions to the conservation of tree diversity in the Groundnut Basin Senegal. Journal of Forestry Research 28 (5) 1083–1096. https://doi.org/10.1007/s11676-017-0374-y San Emeterio J, Alexandre F (2013) Changements socio-environnementaux et dynamiques des paysages ruraux le long du gradient bioclimatique nord-sud dans le sud-ouest du Niger (régions de Tillabery et de Dosso) VertigO 13 (3) Sane B, Coly I, Badji A, Diatta TC, Goudiaby AOK, Ngom D (2021) Characteristics of the Flora and Woody Vegetation of Agroforestry Parks in the District of Kataba 1 (Bignona Lower Casamance) Open Journal of Ecology 11 (11) 741–757. https://doi.org/10.4236/oje.2021.1111046 Sarr MD (2015) Contribution à l’étude d’ensablement de la vallée de Koussanar un ancien affluent de Sandougou . Université Cheikh Anta DIOP. Sarr O, Sagna MB, Bakhoum A, Diatta S, Guissé A (2024) Diversity and Structure of the Woody Stand in a Sudano-Sahelian Transition Zone in Senegal. Open Journal of Ecology 14 (01) 1–16. https://doi.org/10.4236/oje.2024.141001 Sinare H, Gordon LJ (2015) Ecosystem services from woody vegetation on agricultural lands in Sudano-Sahelian West Africa. Agriculture Ecosystems & Environment 200 186–199. https://doi.org/https://doi.org/10.1016/j.agee.2014.11.009 Cisse S (2016) Etude de la variabilité intra saisonnière des précipitations au Sahel : impacts sur la végétation ( cas du Ferlo au Sénégal ) To cite this version : HAL Id : tel-01407442 . Université Pierre et Marie Curie - Paris VI; Université Cheikh Anta Diop (Dakar) Spichiger R (2010) Végétations sèches des ceintures sahéliennes et soudaniennes du Sénégal à Djibouti ( A. D. et R. Duponnois (ed.); IRD Éditio) https://doi.org/https://doi.org/10.4000/books.irdeditions.2159. Sylla D, Ba T, Guisse A (2019) Mapping of changes in plant coverage in Ferlo protected areas (North Senegal): case of the “Biosphere Reserve.” Physio-Geo 13 (January 2019) 115–132. https://doi.org/10.4000/physio-geo.8178 Tappan GG, Sall M, Wood EC, Cushing M (2004) Ecoregions and land cover trends in Senegal. Journal of Arid Environments 59 (3) 427–462. https://doi.org/https://doi.org/10.1016/j.jaridenv.2004.03.018 Thiam AN, Ndiaye O, Diallo A (2015) Caractérisation de la végétation ligneuse sahélienne du Sénégal : cas du Ferlo Characterization of the Sahelian woody vegetation of Senegal : case of Ferlo. Int. J. Biol. Chem. Sci. 9(6) (June 2016) 2582–2594. https://doi.org/10.4314/ijbcs.v9i6.6 Thiam D, Mbaye MS, Diouf J, Diouf N, Faye M, Mohamed SA, Noba K (2023) Flore des zones humides de Diofior et périphérie (Fatick Sénégal) International Journal of Biological and Chemical Sciences 17 (5) 1992–2007. https://doi.org/10.4314/ijbcs.v17i5.18 Thiam E (2006) Activités rurales et patrimoine ligneux: implication des populations enjeux et perspectives de gestion dans la communauté rurale de Koussanar (département de Tambacounda au Sénégal) Université Gaston Berger de Saint-Louis Sénégal. Thiombiano A, Schmidt M, Dressler S, Ouedraogo A, Hahn KZG (2012) Catalogue des plantes vasculaires du Burkina Faso ( Boissiera) Trémolières M (2010) Sécurités et variables environnementales : débat et analyse des liens au Sahel . Trochain J (1940) Contribution à l’étude de la végétation du Sénégal Mémoires de l’Institut Français d’Afrique Noire (Larose) Tsegai D, Medel M, Augenstein P, Huang Z (2022) La Secheresse En Chiffres 2022 . Zhang C, Li X, Chen L, Xie G, Liu C, Pei S (2016) Effects of Topographical and Edaphic Factors on Tree Community Structure and Diversity of Subtropical Mountain Forests in the Lower Lancang River Basin. Forests 7 (10) https://doi.org/10.3390/f7100222 Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6261997","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":444466777,"identity":"9c755dd6-bce5-4469-bd63-7c7b822297fe","order_by":0,"name":"Diara SYLLA","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1UlEQVRIiWNgGAWjYDCCw1Caj52B8QFpWtiYGZgNiNNyAKGFTYIoHXzHmZ99+MFQl9jGzPys4scfGwb52Q34tUgeZjOe2cNwGKiFzexmb1sag8GdA/i1GBxmMGbgYTgA1MJgdpux4TCDgUQCIS3snxn/gB3G/q2Y4c9/BvkZBLXwGDPzMDADtfCYAUMA6KgbBLRIHuYpZpYxOGwM1FIs2duWzGNASAvf+eObGd9U1Mn2s7dv/PDjj50cQYdBncfg2ABl8hCjHgzsiVY5CkbBKBgFIw8AAGUROzvJvRYYAAAAAElFTkSuQmCC","orcid":"","institution":"Centre de Suivi Écologique","correspondingAuthor":true,"prefix":"","firstName":"Diara","middleName":"","lastName":"SYLLA","suffix":""},{"id":444466778,"identity":"42b96d5d-2cb7-48a7-9734-fb3e4e3f6782","order_by":1,"name":"Louise LEROUX","email":"","orcid":"","institution":"CIRAD, UPR AIDA","correspondingAuthor":false,"prefix":"","firstName":"Louise","middleName":"","lastName":"LEROUX","suffix":""},{"id":444466779,"identity":"f61936e9-a49d-4204-97b7-595e4b9543df","order_by":2,"name":"Babacar NDAO","email":"","orcid":"","institution":"CIRAD, UMR TETIS","correspondingAuthor":false,"prefix":"","firstName":"Babacar","middleName":"","lastName":"NDAO","suffix":""},{"id":444466780,"identity":"86356fa9-3979-44cc-9e63-b54f8735afb5","order_by":3,"name":"Adama LO","email":"","orcid":"","institution":"Centre de Suivi Écologique","correspondingAuthor":false,"prefix":"","firstName":"Adama","middleName":"","lastName":"LO","suffix":""},{"id":444466781,"identity":"e6a4a03e-93bc-4ac2-af65-03cfcef10307","order_by":4,"name":"Cheikh MBOW","email":"","orcid":"","institution":"Centre de Suivi Écologique","correspondingAuthor":false,"prefix":"","firstName":"Cheikh","middleName":"","lastName":"MBOW","suffix":""},{"id":444466782,"identity":"021533d7-0159-4dc3-bc5b-9e3d898c052b","order_by":5,"name":"Abdoul Aziz DIOUF","email":"","orcid":"","institution":"Centre de Suivi Écologique","correspondingAuthor":false,"prefix":"","firstName":"Abdoul","middleName":"Aziz","lastName":"DIOUF","suffix":""}],"badges":[],"createdAt":"2025-03-19 13:23:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6261997/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6261997/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81143093,"identity":"f130eb3b-2913-41b9-ab00-89105d391678","added_by":"auto","created_at":"2025-04-22 17:12:47","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":256034,"visible":true,"origin":"","legend":"\u003cp\u003eLocation of the study area. The three red squares represent the three study sites. The isohyets for the 2011-2020 period are represented with a 100 mm/year step\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6261997/v1/2e15c4f3d09a1f30fddf9a6e.png"},{"id":81143938,"identity":"2e35ce99-6383-4ce9-998f-a0ef8e0416c4","added_by":"auto","created_at":"2025-04-22 17:28:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":179637,"visible":true,"origin":"","legend":"\u003cp\u003eMethodological approach used to study the diversity of woody species in relation to land use and environmental factors in the agroforestry landscapes of Senegal.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6261997/v1/aebd26e0efbdb4b0f2b92b96.png"},{"id":81143369,"identity":"5cc286be-fa1b-4258-a07e-ecff1ec90f77","added_by":"auto","created_at":"2025-04-22 17:20:47","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":419649,"visible":true,"origin":"","legend":"\u003cp\u003eLand use and Land cover maps in 2021 for a): Ouarkhokh site, b) Niakhar site and c) Koussanar site. d) LULC area by site. The LULC maps were derived from Sentinel-2 images and a pixel-based classification based on the Random Forest algorithm\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6261997/v1/17cea23cc99e781281f43c58.png"},{"id":81143100,"identity":"0dda3321-9e29-4777-85bc-1ca86936f26e","added_by":"auto","created_at":"2025-04-22 17:12:47","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":119405,"visible":true,"origin":"","legend":"\u003cp\u003eMost represented families in agroforestry landscapes\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6261997/v1/ab545384f475b7f5b152a238.png"},{"id":81143098,"identity":"0a5d4815-817a-47f6-829e-ada05f2b5279","added_by":"auto","created_at":"2025-04-22 17:12:47","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":101296,"visible":true,"origin":"","legend":"\u003cp\u003eMost represented families in land use classes\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6261997/v1/015ba6dd9a0e28630dd4d27d.png"},{"id":81143103,"identity":"7df2891f-174a-4bd9-b070-42adda3ea96f","added_by":"auto","created_at":"2025-04-22 17:12:47","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":80793,"visible":true,"origin":"","legend":"\u003cp\u003eBoxplot for comparison of species (a) richness and (b) abundance values as a function of the vegetated land use classes,across the three study sites the letters after values indicate the significant difference among the stands\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-6261997/v1/52cb9e36776fb01e49555d98.png"},{"id":81143104,"identity":"3a545f53-d758-4d1b-ba58-f5008ddbdd07","added_by":"auto","created_at":"2025-04-22 17:12:47","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":178641,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig.8\u003c/strong\u003e CCA ordination diagram representing the distribution of the 400 one ha plots and tree species and environmental factors\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-6261997/v1/8983ba2cbac7a91f4ef10ee4.png"},{"id":91354536,"identity":"85f37b44-9510-4a41-9e00-7c150d75ece4","added_by":"auto","created_at":"2025-09-15 15:17:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2455840,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6261997/v1/e5afc949-ed4b-4690-9eed-5da4f6cd1827.pdf"},{"id":81143365,"identity":"68a16601-aab5-4c63-a6f8-b095b160a59b","added_by":"auto","created_at":"2025-04-22 17:20:47","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":71755,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-6261997/v1/c14c35710ddf7a31f11f57d0.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Woody species diversity as a function of land use types and environmental factors in agroforestry landscapes of Senegal","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eOver the past fifty years, the Sahelian landscape has undergone significant socio-environmental transformations, marked by severe droughts, a significant decrease in annual precipitation, and an increase in human activities, resulting in a considerable impact on local ecosystems (Tr\u0026eacute;moli\u0026egrave;res, \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Emeterio, 2013; Brandt and al., 2014; Alexandre \u0026amp; Mering, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Tsegai and al., 2022). These changes have led to soil impoverishment (Descroix and Diedhiou, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Brandt and al., 2017; Brandt and al., 2019) and adjustments in the structure and composition of plant species, adapting to more arid climatic conditions. Concurrently, natural habitats, crucial for biodiversity, are increasingly exploited by humans for agricultural, forestry, and livestock activities (Doumbia and al., 2020).\u003c/p\u003e \u003cp\u003eAgroforestry systems, which combine trees, crops, and pastures on the same piece of land and the same time (Anthony, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1983\u003c/span\u003e; Nair, 1993), are traditional agricultural systems well-established in the semi-arid regions of West Africa (Khemiri, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Sanogo and al., 2019). These systems, recognized for their sustainability (Sanogo and al., 2019), offer diversification of production for subsistence and income while minimizing ecological risks associated with climate variations in the region (Mbow and al., 2014; Sinare and Gordon, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Reed and al., 2017; Kuyah and al., 2019). Their importance for livelihoods, especially for vulnerable populations, as well as their role as reservoirs of genetic biodiversity (Jose, 2012; Gon\u0026ccedil;alves and al., 2021), is increasingly recognized, encouraging their preservation and improved management.\u003c/p\u003e \u003cp\u003eIn Senegal, these agroforestry systems are integrated part of agricultural landscapes and are present in various forms adapted to ecological, agronomic, and biological factors (Diatta and al., 2016; Ba and al., 2018; Ndao and al., 2021). The tree constitutes an essential element there (Lawesson, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Tappan and al., 2004) and provides numerous socio-ecosystem services to the populations (Ganaba and Guinko, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Godron and Sinare, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Goffner and al., 2019). It serves as a source of forage for animals (Ngom and al., 2014; Diatta and al., 2016) soil stabilization, protection, and fertilization (Tounkara and al., 2022), and improve the household food security through the provision of a wide range of ecosystem services (Leroux and al., 2022). It is also a primary source of energy and provides timber, lumber, and traditional pharmacopeia products for rural populations (Laouali and al., 2014).\u003c/p\u003e \u003cp\u003eHowever, differences in tree diversity are noted from one agroforestry system to another or within the same system (Massaoudou, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Ndao and al., 2021a). This difference may be due to the influence of environmental factors (Ndong and al., 2015). Although previous research has addressed the issue of floristic diversity of woody vegetation in agroforestry systems in Senegal, few studies have analyzed it along a bioclimatic gradient, highlighting the influence of environmental factors, with most focusing on specific aspects in given agroforestry landscapes (Coly and al., 2020; Sane and al., 2021; Badji and al., 2023; Gomez and al., 2023; Ndao and al., 2022; Ndao and al., 2021). Yet, understanding the composition and distribution of woody species in these different agroforestry landscapes is essential to identify tree species that perform best under specific site conditions and the main tree-land use-site combinations in each system.\u003c/p\u003e \u003cp\u003eIn this context, this study proposes to analyze the diversity of woody species in three agroforestry landscapes in Senegal along a bioclimatic gradient, to determine the influence of land cover and land use classes on woody composition, and to analyze these relationships with various agro-environmental variables.\u003c/p\u003e"},{"header":"2. Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study area\u003c/h2\u003e \u003cp\u003eThe study was carried out at three sites covering three phytogeographical zones following a spatial gradient: the Sahelian zone (Ouarkhokh site), the Sahelo-Sudanian zone (Niakhar site) and the Sudano-Sahelian zone (Koussanar site) as presented in (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This subdivision is similar to that of Trochain (\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e1940\u003c/span\u003e) and Faye (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) where the Sahelian domain comprises the Sahelian, Sahelo-Sudanian and Sudano-Sahelian sub-domains. Each site consists of a 30 x 30 km square zone.\u003c/p\u003e \u003cp\u003eSenegal's climate is characterized by a long dry season and a shorter rainy season (three to four months) with a north-south rainfall gradient. Based on the TAMSAT (Tropical Applications of Meteorology using SATellite data and ground-based observations) (Pearson and al., 2020) averaged between 2011 and 2020, the sahelian zone is located between the 300 and 400 mm rainfall isohyets, with an average of 447.6 mm/year. The Sahelo-Sudanian zone is located between the 400 and 600 mm isohyets, with an average rainfall of 540.16 mm/year, while the Sudano-Sahelian zone is at the lower end of the 600 mm and 1200 mm isohyets, with an average rainfall of 680.4 mm/year.\u003c/p\u003e \u003cp\u003eThe relief consists of alternating sand dunes oriented from southwest to northeast, 10 to 30 m high and 0.5 to 5 km wide. The dunes are separated by longitudinal plains of 1 to 5 km wide (Le Hou\u0026eacute;rou, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e1989\u003c/span\u003e) in the Sahelian zone. In the Sahelo-Sudanian zone, these plains are crossed by the \u003cem\u003eSine and Saloum\u003c/em\u003e valleys and marigots, which disappear in the dry season (l'Homme, 1978). The relief of the Sudano-Sahelian zone is flat, with a few slight depressions formed by pools and streams (Thiam, \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). From north to south of the Sahel, the main soil types on the plateaus are arenosols (strictly Sahelian), lixisols (Sahelo-Sudanian) and acrisols (Sudano-Sahelian). The vegetation forms a mosaic of shrub savannah and shrub savannah with scattered trees in the sahelian zone (Bourli\u0026egrave;re and al., 1972), bushy in the Sahelo-Sudanian zone (Diouf, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and valley vegetation in the Sudano-Sahelian zone.\u003c/p\u003e \u003cp\u003eThe population of the Sahelian region is made up of Peulhs, Wolofs and S\u0026eacute;r\u0026egrave;res, with an increasing density from north to south (Fall, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Codiat, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The presence of trees and livestock farming and agriculture activities coexist, with varying dominance depending on the sub-zone. In the northern Sahel, the predominant agroforestry systems are sylvopastoral systems (Fall, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2017a\u003c/span\u003e), while in other regions they are characterized by small-scale agrosylvopastoral farming, with small parcel sizes (around 0.6 hectares) and significant intra-parcel heterogeneity (Mbaye and al., 2003; Grillot, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLogging is mostly noted in the Sudano-Sahelian zone and concerns energy wood (charcoal and firewood), timber, service wood and wood products (Sarr, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Methodological approach\u003c/h2\u003e \u003cp\u003eThe methodological approach used in this work is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. First, a land use map was created using Sentinel data (spectral bands and spectral index), and field data on land use and land cover (LULC). Second, tree data were collected using a stratified sampling protocol based on an object-based segmentation (OBIA) and corresponding hierarchical classification. Finally, the influence of environmental factors on trees and land use was studied through a Canonical Correspondence Analysis (CCA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1. Data collection and pre-processing\u003c/h2\u003e \u003cp\u003e \u003cem\u003eWoody vegetation data collection\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eSampling plan\u003c/em\u003e \u003c/p\u003e \u003cp\u003eA weighted stratified sampling (Ndao and al., 2021) was implemented on the three sites. The different strata were identified in the following stages. The data used consist of a time series of Normalized Difference Vegetation Index (NDVI, Rouse and al., 1974), calculated from Sentinel-2 (S2) level 1C, 10-m resolution images from the COPERNICUS/S2 collection, obtained via the Google Earth Engine (GEE) platform (Gorelick and al., 2017). These data have been ortho-rectified and radio-corrected, providing reflectance values at the top of the atmosphere. There were acquired from January 1st to December 31st, 2021, together with 8 agro-environmental variables composed of bioclimatic variables, vegetation variables and Soil type (see IS1).\u003c/p\u003e \u003cp\u003eFirst, an object-based segmentation of the Sentinel-2 NDVI time series was carried out over the study area, resulting in the delimitation of 1,280 homogeneous units of five hectares for all sites studied. The 5-hectare threshold was chosen to achieve an optimal balance between measurement precision and the homogeneity of the analysis units, while considering the specific characteristics of the Sahelian environment. The mean values and standard deviation of 9 relevant agri-environmental variables were extracted for each unit. Next, a Pearson correlation matrix was applied to eliminate highly correlated agri-environmental variables, thus ensuring the robustness of the analyses.\u003c/p\u003e \u003cp\u003eNext these units were then classified using a Hierarchical Clustering on Principal Components (HCPC) (Kassambara, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) integrated with a Mixed Data Factor Analysis (MDFA) (Kassambara, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) to group plots with similar landscape characteristics.\u003c/p\u003e \u003cp\u003eThe value of the v-test (see IS2) (Escofier \u0026amp; Pages, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) was used to establish the relationship between the sites and the agri-environmental variables, thus indicating the significant association between these variables. All these operations were carried out using ArcGIS 10.8 (ESRI, 2020) and R version 4.3.1 (R Development Core Team 2023) softwares.\u003c/p\u003e \u003cp\u003eFinally, on the basis of the results obtained, seven landscape classes were identified (Table I). A weighting was applied according to the surface area of each landscape class identified, in order to distribute 400 one-hectare plots evenly. Details of this weighting are shown in the table below.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescription of the landscape types identified (class) and distribution of woody inventory units per site within these classes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSite\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of Plots\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e125\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eIncludes the Ouarkhokh site and the western part of Niakhar, is characterized by values significantly below the general average for variables such as temperature, evapotranspiration, and precipitation. This suggests a relatively dry and cool climate in these areas.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKoussanar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOuarkhokh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNiakhar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e28\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eLocated in the north and west of Niakhar, shows values slightly below the average for the studied variables, with moderate evapotranspiration and precipitation. This indicates a slightly drier climate than the average.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKoussanar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOuarkhokh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNiakhar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e102\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eFound in the eastern part of Niakhar. It is distinguished by precipitation and evapotranspiration levels below the average, suggesting a drier climate in this region.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKoussanar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOuarkhokh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNiakhar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eExtends to the northeast of Ouarkhokh as well as the southeast and southern parts of Niakhar. It presents evapotranspiration and precipitation values below the average, but with more moderate temperatures, reflecting a relatively dry climate with temperate temperatures.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKoussanar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOuarkhokh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNiakhar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e26\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eLocated in the central part of the Koussanar site, displays values above the average for all variables, indicating a more humid and warm climate in this area.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKoussanar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOuarkhokh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNiakhar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e42\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eLocated in the northwest and forms a band in the southeast koussanar r\u0026eacute;gion, is characterized by high evapotranspiration and precipitation values, along with relatively warm temperatures. This suggests a warm and humid climate.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKoussanar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOuarkhokh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNiakhar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e65\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eExtends into the southwestern part of the Koussanar region, with high precipitation, evapotranspiration, and temperature values. This reflects a particularly warm and humid climate in this class.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOuarkhokh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNiakhar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eCollection of woody data\u003c/em\u003e \u003c/p\u003e \u003cp\u003eIn each plot, an exhaustive inventory of all woody individuals was carried out. For each individual tree, the name of the species and its geographical coordinates were recorded. The nomenclature adopted was that of Angiosperm Phylogeny Group III (APG, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Summary characteristics of altitude, slope and soil type were also recorded for each plot.\u003c/p\u003e \u003cp\u003e \u003cem\u003eLand Use and Land Cover (LULC) mapping\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe land cover and land use data were obtained by supervised pixel-based classification using a Random Forest (Breiman, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) machine learning classifier with 300 trees.\u003c/p\u003e \u003cp\u003eFor each study site, the training data were collected in the field in October and November 2021 over the entire study area and correspond to the polygons of the various land use and land cover units (LULC). These correspond mainly to cultivated vegetation, natural vegetation, habitat, water, plantation and bare land polygons. 80% of these data was used to train the classification algorithm and 20% to validate the resulting classification.\u003c/p\u003e \u003cp\u003eA time window of S2 images from 1 July to 31 December 2021 and a cloud percentage of less than 5% were selected. This period corresponds to the crop growing season in Senegal. This selection of a series of images made it possible to group together a sufficient number of tiles to cover the entire study area, and to more easily discriminate the spectral signatures of the different units (Mainguy-Seers, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAt the same time, six spectral indices were used in this study namely NDVI (Normalize Difference Vegetation Index), NDWI (Normalized Difference Water Index), GNDVI (Green Normalized Difference Vegetation Index), NDRE (Normalized Difference Red Edge Index), UI (Urban Index), BSI (Bare Soil Index). They were calculated using the formulae presented in IS 3. They were used to distinguish more clearly between the different landscape units.\u003c/p\u003e \u003cp\u003eThe overall accuracy (OA), the Kappa coefficient and the confusion matrix were calculated for each site to assess the accuracy of the classifications (McHugh, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003eAncillary data :agro-environmental variables\u003c/em\u003e \u003c/p\u003e \u003cp\u003eIn order to understand the influence of agro-environmental variables (see Table II) on the richness and abundance of woody species in agroforestry landscapes, nine agro-environmental variables were used:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e1.Annual temperature downloaded from the WorldClim database ;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e2. Elevation from the US Geological Survey SRTM digital elevation model ;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e3.Actual Evapotranspiration and Interception (AETI) ;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e4.Quantity of Nitrogen\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e5.Average rainfall over a 2011\u0026ndash;2021 period extracted from Tamsat\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e6.Soil Organic Carbon from Isric\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e7. Tree Cover from the Brandt et al (2020) database,\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e8. Total Biomass Production (TBP) 2016\u0026ndash;2021 from the WAPOR database,\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e9. The lay content from the ISRIC database\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eFor each variable, the mean and standard deviation value centroid of the 400 plots was quantified.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAgro-environmental variables used to analyse the influence of agro-environmental variables on the richness and abundance of woody species in agroforestry landscapes.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgro-environmental variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbbreviations\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eResolution (m)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDatabase\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLink\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemperature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2016\u0026ndash;2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWorld clim\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.worldclim.org\u003c/span\u003e\u003cspan address=\"https://www.worldclim.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital elevation model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDEM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUSGS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://earthexplorer.usgs.gov\u003c/span\u003e\u003cspan address=\"https://earthexplorer.usgs.gov\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNitrogen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNTO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eISRIC Soil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://data.isric.org/geonetwork/srv/fre/catalog.search#/metadata/c824bc78-2c6a-42b5-bfc8-9555a1c7b11c\u003c/span\u003e\u003cspan address=\"https://data.isric.org/geonetwork/srv/fre/catalog.search#/metadata/c824bc78-2c6a-42b5-bfc8-9555a1c7b11c\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRainfall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRf\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2016\u0026ndash;2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTamsat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.tamsat.org.uk/data\u003c/span\u003e\u003cspan address=\"https://www.tamsat.org.uk/data\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActual EvapoTranspiration and Interception\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eETIa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2016\u0026ndash;2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWater Productivity Open-access portal (WaPOR 2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://wapor.apps.fao.org/catalog/WAPOR_2/1/L1_AETI_A\u003c/span\u003e\u003cspan address=\"https://wapor.apps.fao.org/catalog/WAPOR_2/1/L1_AETI_A\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoil Organic Carbon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSOC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eISRIC Soil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://data.isric.org/geonetwork/srv/fre/catalog.search#/metadata/9a66a37e-8a4e-463b-b83a-fd49049c323a\u003c/span\u003e\u003cspan address=\"https://data.isric.org/geonetwork/srv/fre/catalog.search#/metadata/9a66a37e-8a4e-463b-b83a-fd49049c323a\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTree cover\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1832\u003c/span\u003e\u003cspan address=\"https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1832\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Biomasse Production\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2016\u0026ndash;2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWater Productivity Open-access portal (WaPOR 2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://wapor.apps.fao.org/catalog/WAPOR_2/1/L1_TBP_A\u003c/span\u003e\u003cspan address=\"https://wapor.apps.fao.org/catalog/WAPOR_2/1/L1_TBP_A\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eISRIC Soil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://data.isric.org/geonetwork/srv/fre/catalog.search#/metadata/d559a21a-4ef1-43ee-94da-798ac267747e\u003c/span\u003e\u003cspan address=\"https://data.isric.org/geonetwork/srv/fre/catalog.search#/metadata/d559a21a-4ef1-43ee-94da-798ac267747e\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2. Analysis and processing of woody inventory data\u003c/h2\u003e \u003cp\u003eThe woody data collected were organised in a woody database in shapefile format. The Angiosperm Phylogeny Group (APG) III classification (APG, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) was used to update species names and their respective families. Spelling (species, genus and family names) and authorship were checked at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.tela-botanica.org/\u003c/span\u003e\u003cspan address=\"https://www.tela-botanica.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Floristic richness and species abundance (SA) were calculated for each inventory plot and for each land occupation and use unit (natural vegetation, cropland, and plantation). The species abundance (SA) is given by the ratio of the number of individuals of the species (Ni) to the total number of individuals of all species combined (Nt):\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:\\varvec{S}\\varvec{A}=\\frac{\\varvec{N}\\varvec{i}}{\\varvec{N}\\varvec{t}}\\varvec{x}100$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNi\u003c/strong\u003e \u003cp\u003eNumber of individuals of the species.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNt\u003c/strong\u003e \u003cp\u003eTotal number of individuals of all species combined.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eA comparative analysis of floristic richness and species abundance between land-use categories was carried out using the ANOVA statistical test. In addition Tuckey pairwise comparisons by Tuckey HSD function (Zhang and al., 2016) were performed between the different LULCs with floristic richness on the one hand and abundance on the other. These differences were illustrated by a whisker box plot.\u003c/p\u003e \u003cp\u003eTo study the environmental factors affecting woody diversity in the study area, an analysis of Pearson's correlation (Pearson, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e1904\u003c/span\u003e) was used to determine correlations between environmental factors (see IS4). If the correlation between two variables was too strong (r)\u0026thinsp;\u0026gt;\u0026thinsp;0.7, an ANOVA (Escofier and Pages, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) was used to select the variable that best explained the (most significant) variation in the data. A Principal Component Analysis was used to confirm that each variable selected made a specific contribution to our dataset.\u003c/p\u003e \u003cp\u003eFinally, the relationship between tree species distribution and environmental factors was assessed using a Canonical Correspondence Analysis (CCA) (McCune, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Richness and environmental factors were analysed in the CCA ordination. Clustering was carried out on the basis of the three occupancy units identified in the study area: cropland, vegetation and plantation. Statistical calculations were performed using R software version 4.3.1 (R Development Core Team 2023).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Result","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Land use in agroforestry systems\u003c/h2\u003e \u003cp\u003eThe results of the LULC mapping are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The classifications were obtained with an overall accuracy (OA) of 87%, 87% and 84% respectively in Ouarkhokh, Niakhar and Koussanar sites (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Natural vegetation, cropland, ponds and artificial areas were identified at all three sites, whereas plantations were only present at the Ouarkhokh site (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). Analysis of these occupation units shows that cultivated vegetation and built-up areas are dominant in the Sahelo-Sudanian zone (Niakhar - Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb), followed by the Sudano-Sahelian zone (Koussanar -Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec), with less than 1,000 ha in the Sahelian zone (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). On the other hand, natural vegetation is more important in the Sahelian and Sudano-Sahelian regions respectively Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb. Water and bare land, although present over small areas, are dominant in the Sahelo-Sudanian zone (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). For all the land use classes identified, the average surface area varies significantly between sites (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01, see IS5 s). The accuracy of the land use classes varies from site to site, with good classification of crops (90% in Ouarkhokh, 96% in Niakhar, 98% in Koussanar), built up (70% in Ouarkhokh, 95% in Niakhar, 80% in Koussanar) and vegetation (80% in Ouarkhokh, 75% in Niakhar). Water bodies, on the other hand, are only moderately accurate (60% in Ouarkhokh, 66% in Niakhar, 83% in Koussanar), as is vegetation in Koussanar (59%) (see IS6 s).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Floristic composition of species in Senegalese agroforestry landscapes\u003c/h2\u003e \u003cp\u003e91 species belonging to 27 families were recorded on the three sites. They are divided into 31 species in 13 families in the Sahelian zone (Ouarkhokh), 60 species in 21 families in the Sahelo-Sudanian zone and 56 species in 16 families in the Sudano-Sahelian zone. The dominant families are \u003cem\u003eFabaceae\u003c/em\u003e, \u003cem\u003eCombretaceae\u003c/em\u003e, \u003cem\u003eMoraceae\u003c/em\u003e, \u003cem\u003eMalvaceae\u003c/em\u003e, \u003cem\u003eAnacardiaceae\u003c/em\u003e, \u003cem\u003eRubiaceae\u003c/em\u003e, \u003cem\u003eRhammaceae\u003c/em\u003e and \u003cem\u003eApocynaceae\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe most represented families (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) in the three land use categories are \u003cem\u003eFabaceae, Combretaceae, Moraceae, Anacardiaceae\u003c/em\u003e and \u003cem\u003eMalvaceae\u003c/em\u003e. The \u003cem\u003eFabaceae\u003c/em\u003e family is the most represented, with 23 species in cultivated land, 16 in natural vegetation and 3 in plantations. The species found in the plantations belong either to the \u003cem\u003eCombretaceae\u003c/em\u003e or the \u003cem\u003eFabaceae\u003c/em\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Variation of the floristic composition by LULC category\u003c/h2\u003e \u003cp\u003eFloristic richness (ANOVA, df\u0026thinsp;=\u0026thinsp;2, F\u0026thinsp;=\u0026thinsp;7.3, P\u0026thinsp;\u0026le;\u0026thinsp;0.00073) and abundance (ANOVA, df\u0026thinsp;=\u0026thinsp;2, F\u0026thinsp;=\u0026thinsp;37.85, P\u0026thinsp;\u0026le;\u0026thinsp;2.54e-16) varied significantly among the three land-use categories (natural vegetation, cropland, plantation; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea shows that floristic richness is highest in croplands, with a median of around 6 species and a wide distribution reaching up to approximately 15 species, followed by natural vegetation (with a median of around 4 species), and lowest in plantations (with a median of around 3 species). Tukey's pairwise comparisons between LULC types for floristic richness showed a statistically significant difference between cropland and natural vegetation (a and b)). However, the difference between the cropland and plantation (a, ab) and plantation and natural vegetation (b, ab) pairs did not show statistically significant differences (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea).\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb shows that species abundance is lowest in croplands (with a median below 50), higher in natural vegetation (with a median of about 100), and comparable in plantations (with a median close to 100). Pairwise comparisons of species abundance between land-use types showed a statistically significant difference for the natural vegetation-cropland (b, a) and plantation-cropland (a, b) pairs, but not between plantation and natural vegetation (a, a), as illustrated by the boxplots (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Relationship between environmental factors between LULC\u003c/h2\u003e \u003cp\u003eAccording to the ANOVA, the following agro-environmental variables (table III): Temperature_mean, Clay_mean, SOC_mean, AETI_mean, Rainfall_mean, DEM_mean, Biomass_mean, SOC_stdev, AETI_stdev, Clay_stdev, DEM_stdev, and TBP_stdev show significant differences between natural vegetation, cropland, and plantations (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). However, the variables NTO_mean, T_stdev, NTO_stdev, TC_stdev, RF_stdev, and TC_mean do not present significant differences. Tukey's HSD test revealed that the values for Clay, AETI_mean, SOC_stdev, AETI_stdev, and DEM_stdev do not significantly differ between the plantation-cultivated land and plantation-natural vegetation groups. Additionally, the differences in values for SOC_mean, DEM_mean, and TBP_mean between the plantation and natural vegetation groups are also not significant.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnalysis of Variance and Tukey Post-Hoc Comparisons of Environmental Variables between LULC\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePr_F\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTukey_\u003c/p\u003e \u003cp\u003eNaturalvegetation-Cropland\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTukey_Plantation_Cropland\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTukey_Plantation_Natural-vegetation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT_mean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.4e-63***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0021 **\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCl_mean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.2e-06***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1776 N.S.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.9309 N.S.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNTO_mean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.7 N.S.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8482 N.S.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8322 N.S.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.9091 N.S.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSOC_mean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.3e-24***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1e-04 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.46 N.S.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAETI_mean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0003***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8e-04 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1251 N.S.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6413 N.S.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC_mean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.8 N.S.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8703 N.S.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9447 N.S.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.8914 N.S.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRf_mean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.2e-30***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002 **\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDEM_mean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.4e-14***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3e-04 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1813 N.S.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTBP_mean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.6e-13***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6e-04 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1964 N.S.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSOC_stdev\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.03*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0276 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.93 N.S.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.5284 N.S.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT_stdev\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2 N.S.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8596 N.S.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2511 N.S.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.332 N.S.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNTO_stdev\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.71072492192407 N.S.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9268 N.S.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.7618 N.S.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.7054 N.S.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC_stdev\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.215449688962166 N.S.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2032 N.S.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.846 N.S.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.9977 N.S.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAETI_stdev\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.84293718723184e-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1e-04 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1614 N.S.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.7998 N.S.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCl_stdev\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0306268382970616***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1745 N.S.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0902 N.S.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2593 N.S.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDEM_stdev\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00457104085323054**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0031 **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 N.S.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6236 N.S.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTBP_stdev\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00192349069236531**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.002 **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3845 N.S.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.9351 N.S.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRf_stdev\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.300911641904056 N.S.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.322 N.S.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.7602 N.S.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.9538 N.S.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eN.S.= not significant at *\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eA correlation matrix of the variables (table IV) with significantly different values revealed strong correlations between AETI_mean and Clay_mean (r\u0026thinsp;=\u0026thinsp;0.88), between Rf_mean and Cl_mean (r\u0026thinsp;=\u0026thinsp;0.83), between TBP_mean and Cl_mean (r\u0026thinsp;=\u0026thinsp;0.83), between Rf_mean and SOC_mean (r\u0026thinsp;=\u0026thinsp;0.89), between Rf_mean and AETI_mean (r\u0026thinsp;=\u0026thinsp;0.85), between TBP_mean and AETI_mean (r\u0026thinsp;=\u0026thinsp;0.80), and between TBP_mean and Rf_mean (r\u0026thinsp;=\u0026thinsp;0.87). The Principal Component Analysis (PCA) showed that these variables primarily contribute to the same component. The variables AETI_mean, Rf_mean, and Cl_mean were selected due to their strong contribution to the principal components, thus optimizing the representation of the variance explained by the PCA (Fig.\u0026nbsp;7).\u003c/p\u003e \u003cp\u003eThe correlations between environmental factors and the axes of the Canonical Correspondence Analysis (CCA), as shown in the Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, indicate that for axis 1, the strongest correlations are observed with the variables DEM_mean (r=-0.94) and T_mean (r=-0.83), indicating a significant relationship with this axis. In contrast, AETI_mean has the weakest positive correlation (0.18) with the axis. Conversely, Rf_mean displays a moderate negative correlation with axis 1 (r\u0026thinsp;=\u0026thinsp;0.44), while SOC_stdev, TBP_stdev, and AETI_stdev exhibit weaker negative correlations (r\u0026thinsp;=\u0026thinsp;0.24, r\u0026thinsp;=\u0026thinsp;0.26, r\u0026thinsp;=\u0026thinsp;0.17, respectively). Cl_mean shows the weakest correlation with axis 1 (r\u0026thinsp;=\u0026thinsp;0.02).\u003c/p\u003e \u003cp\u003eFor axis 2, the correlations are generally low. The most notable relationship is with the variable AETI_mean (r\u0026thinsp;=\u0026thinsp;0.43). T_mean, Cl_mean, Rf_mean, and DEM_mean exhibit weak correlations, with values of r=-0.37, r\u0026thinsp;=\u0026thinsp;0.26, r\u0026thinsp;=\u0026thinsp;0.27, r\u0026thinsp;=\u0026thinsp;0.20, respectively, while AETI_mean and SOC_stdev show correlations close to zero (r\u0026thinsp;=\u0026thinsp;0.09 and r=-0.04), suggesting a limited relationship with this axis.\u003c/p\u003e \u003cp\u003eRegarding axis 3, Cl_mean presents the strongest negative correlation (r=-0.88), followed by AETI_mean and Rf_mean, which also display significant negative correlations (r=-0.77 and r=-0.75, respectively). T_mean and SOC_stdev show weak positive correlations (r\u0026thinsp;=\u0026thinsp;0.38 and r\u0026thinsp;=\u0026thinsp;0.32). Finally, AETI_stdev, DEM_mean, and TBP_stdev exhibit weak correlations with axis 3, indicating very weak inverse relationships with this dimension.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation coefficients between the six environmental factors and the first three PCA axis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnvironmental variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbbreviations\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eCorrelation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAxis 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAxis 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAxis 3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemperature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT_mean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.83489761\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.37203975\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.38629901\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCl_mean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.02828842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.26082880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.88599100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActual Evapotranspiration and Interception_mean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAETI_mean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.18855787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.43464435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.77727222\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRainfall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRf_mean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.44018875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.27784031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.75438712\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital Elevation Model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDEM_mean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.94000441\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.20505002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.16387080\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoil organic Carbonic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSOC_stdev\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.24206387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.04281015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.32508962\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActual Evapotranspiration and Interception__stdev\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAETI_stdev\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.31760666\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.09858385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.21879462\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbove Ground Biomasse_stdev\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTBP_stdev\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.26869675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.14812635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.08599833\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eThe canonical correspondence analysis (CCA) (Fig.\u0026nbsp;7), based on the different types of land use and land cover (LULC), highlights the relationships between the dominant plant families and the influence of environmental variables, making it possible to identify the following characteristics:\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eGroup 1\u003c/strong\u003e \u003cp\u003eThis group is composed of species belonging to the \u003cem\u003eFabaceae\u003c/em\u003e and \u003cem\u003eMalvaceae\u003c/em\u003e families. It is characterized by high values of T_mean and lower values of AETI_mean, DEM_mean, and Rf_mean. The dominant LULC type is cropland, although natural vegetation and plantations are also present.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eGroup 2\u003c/strong\u003e \u003cp\u003eGroup 2 includes species from the \u003cem\u003eCombretaceae\u003c/em\u003e family, associated with high values of DEM_mean and T_mean, and low values of AETI_mean. The dominant LULC type is natural vegetation.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eGroup 3\u003c/strong\u003e \u003cp\u003eThis group is characterized by species from the \u003cem\u003eAnacardiaceae\u003c/em\u003e and \u003cem\u003eMoraceae\u003c/em\u003e families, along with other less abundant families, located near cultivated lands. The associated environmental conditions show high values of Rf_mean, TBP, AETI_stdev, SOC_stdev, and Cl_mean (Fig.\u0026nbsp;7 IS).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Composition and variation in floristic richness\u003c/h2\u003e \u003cp\u003eA total of 91 species belonging to 27 families were recorded at all the sites studied. Floristic richness was higher in the Sahelo-Sudanian zone (60 species, 21 families) and lower in the North Sudanian (56 species, 16 families) and Sahelian (31 species, 13 families) zones. This diversity reflects the agro-environmental conditions specific to each zone. The Sahelian zone, in northern Senegal, is characterised by very low rainfall and high temperatures. Rainfall is scarce and irregular, creating a semi-arid environment. Vegetation adapted to these conditions includes drought-resistant species such as \u003cem\u003eAcacia\u003c/em\u003e and \u003cem\u003eBalanites\u003c/em\u003e (Dendoncker and al., 2020). Soils are often shallow and poor in Nitrogen, which limits plant diversity (Kebe and al., 2020). The Sahelo-Sudanian and Sudano-Sahelian zones, located in the central and southern parts of the country, receive slightly higher rainfall than the Sahelian zone. This extra rainfall encourages greater plant diversity, with a greater variety of trees, bushes and grasses (Cisse, \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Soils can be more fertile in these areas, allowing more lush vegetation to develop (Fall, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2017a\u003c/span\u003e). In addition to climatic variations, differences in soil composition and altitude can also influence the distribution of plant species (Ouattara and al., 2023). For example, the sandy, shallow soils predominant in the Sahelian zone may favour the growth of species adapted to these conditions, while soils richer in organic matter in the Sudano-Sahelian zones may support greater plant diversity (Ouoba and al., 2023). In addition, altitude can influence local temperatures and climatic conditions, which also affect the presence and abundance of plant species. The result obtained in the Sahelo-Sudanian zone for floristic richness is lower than that obtained by (Ndao and al., 2021) for the same site, i.e. 63 species divided into 21 families. The result on the number of species observed in the Sahelian zone in the present study is higher than that obtained by (Ndiaye and al., 2013) on a site with the same agroecological characteristics (15 species divided into 10 families). In the Sudano-Sahelian zone, the floristic richness obtained is higher than that observed by (M. A. Mbow and al., 2008), whose study identified 48 species in 26 families in the same zone. The differences in floristic richness observed between the previous studies and this one are due to the sampling effort. The surveys were carried out at the level of the landscape units sampled. Overall, these results corroborate those found by (Zounon and al., 2019) in the same agro-ecological zones of Burkina Faso. He inventoried 35 species in the Sahelo-Sudanian zone, 24 species in the northern Sudanian zone and 21 species in the Sahelian zone.\u003c/p\u003e \u003cp\u003eThe dominant families are \u003cem\u003eFabaceae, Combretaceae, Moraceae, Malvaceae, Anacardiaceae\u003c/em\u003e and \u003cem\u003eRubiaceae\u003c/em\u003e. These families account for over 65% of the plant community at all sites. The dominance of these families suggests a significant influence of these groups on the structure and composition of agroforestry ecosystems in Senegal. This result corroborates those of (Ciss\u0026eacute; and al., 2018) in Burkina Faso, Thiam and al., 2023) in Fatick (S\u0026eacute;n\u0026eacute;gal). These families are well adapted to local environmental conditions and play an important role in providing ecosystem services such as nitrogen fixation, climate regulation and food and shelter for local wildlife (Gueye and Ndoye, 2003; Ngom and al., 2014; Diatta and al., 2016).\u003c/p\u003e \u003cp\u003eFloristic richness is higher in cultivated land than in plantations and natural vegetation. Indeed, farmland can feature greater species diversity, because agroforestry systems often involve the cultivation of several plant species, including food crops, cash crops, fruit trees, and sometimes native woody species (Ndiaye and al., 2017). A high ratio of useful species (agroforestry and food species) was found in fields compared to forests/biosphere reserves, fallow land, and marsh by Sambou and al., 2017. Farmers also select species that can provide them with a wide use and suitable for their crop association to keep in the field (Coly and al., 2020). Studies such as those carried out by (Ndao and al., 2021) have shown that these traditional agroforestry systems can support a high diversity of plant species.\u003c/p\u003e \u003cp\u003eIn contrast, species abundance is higher in natural vegetation than in plantations. This is due to the natural presence of vegetation in areas where cultivation is little practiced, where there are several types of plant formations ranging from forests, to wooded savannahs, arborescent to shrubby and shrubby (Sylla and al., 2019; Ndao and al., 2022; Ndao and al., 2021). Plantations, on the other hand, are more abundant in individuals and have reduced diversity because the species present like \u003cem\u003eAcacia senegal\u003c/em\u003e are exploited for commercial purposes (Diallo and al., 2023).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Relationships between environmental factors and species distribution in land-use types\u003c/h2\u003e \u003cp\u003eThe results of the canonical correspondence analysis (CCA) revealed that species composition and distribution are influenced by topographic, edaphic, and anthropogenic factors. The specific associations between plant families and environmental factors in each land use type highlight the importance of local conditions for species distribution. This study shows that the Actual Evapotranspiration Index (AETI), temperature, and Digital Elevation Model (DEM) are the most determining factors, followed by rainfall, the amount of available biomass, and, to a lesser extent, clay content. These results confirm the conclusions of Dendoncker and Vincke (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and Sagna and al., (2024) in the Ferlo, who also emphasized the influence of topography on the development of woody vegetation.\u003c/p\u003e \u003cp\u003eGroup 1 is associated with high values of temperature, DEM, and AETI. The dominant families are \u003cem\u003eFabaceae\u003c/em\u003e and \u003cem\u003eMalvaceae\u003c/em\u003e. The species of these two families vary across the Sahelian, Sahelo-Sudanian, and Sudanian-Sahelian zones. Their presence is influenced by climatic conditions, soil types, and site-specific land use practices. specific to each site. In addition to being present in plantations in Sahelian (Ouarkhokh), these species are mainly found in cultivated areas as well as in the natural vegetation of the three studied sites. This suggests that these plant families have a wide geographic distribution and are capable of thriving in relatively warm and semi-arid environments. This observation corroborates the assertion by Savadogo and al., (2016) that \u003cem\u003eFabaceae\u003c/em\u003e include species that resist drought and high temperatures. Indeed, they encompass species with varied phytogeographical affinities (Thiombiano and al., 2012) according to the rainfall gradient, topography, AETI, and human activities. In Ouarkhokh, the dominant species are from the \u003cem\u003eAcacia\u003c/em\u003e genus (Diouf, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Ndiaye and al., 2013; Niang and al., 2014). Other species belonging to the \u003cem\u003eFabaceae\u003c/em\u003e family, such as \u003cem\u003eDalbergia melanoxylon, Bauhinia rufescens, Prosopis glandulosa, Tamarindus indica, Faidherbia albida, Piliostigma reticulatum\u003c/em\u003e, as well as \u003cem\u003eMalvaceae\u003c/em\u003e like \u003cem\u003eAdansonia digitata\u003c/em\u003e, are also present (Ndong and al., 2015). These species are primarily found in savanna areas and mainly in lowlands where vegetation diversity and density are high (Niang and al., 2014; Sagna and al., 2024). This site is characterized by high temperatures (29\u0026deg;C to 32\u0026deg;C), an altitude ranging from 2.5 to 62.5 meters, and low AETI values, ranging from 3 mm to 1,000 mm. These plants are well adapted to the arid conditions and sandy soils of the region. In soudanian-sahelian (Niakhar), where temperatures are similar to those in Ouarkhokh and rainfall is higher, \u003cem\u003eFabaceae\u003c/em\u003e and \u003cem\u003eMalvaceae\u003c/em\u003e are key components of agrosylvopastoral systems, contributing to agricultural productivity, environmental sustainability, and resilience to climate challenges in Senegal\u0026rsquo;s peanut basin. Indeed, the woody diversity is dominated by the species \u003cem\u003eFaidherbia albida\u003c/em\u003e (Ndao and al., 2021), which is preserved in cultivated areas due to its numerous ecosystem services. \u003cem\u003eFaidherbia albida\u003c/em\u003e parks, important in central Senegal (Dugu\u0026eacute; and al., 2024), play a crucial role in the peanut basin, particularly in Niakhar, by improving soil fertility through nitrogen fixation and providing quality fodder for livestock during the lean season. In sudano sahelian (Koussanar), which benefits from a tropical-Sahelian climate with slightly lower temperatures (28\u0026deg;C to 30\u0026deg;C) and more varied altitudes, reaching up to 60 meters, the diversity of species belonging to the \u003cem\u003eFabaceae\u003c/em\u003e family and the variety of vegetation are greater. Sarr and al. (2024) inventoried 11 \u003cem\u003eFabaceae\u003c/em\u003e species in Kougnheul, located in the same agro-ecological zone as Koussanar. The AETI values at this site range from 3 mm to 1,500 mm, with areas of high evapotranspiration favoring denser vegetation. Species belonging to the \u003cem\u003eFabaceae, Malvaceae, Anacardiaceae, Loganiaceae\u003c/em\u003e, and \u003cem\u003eMoraceae\u003c/em\u003e families particularly thrive in forest galleries and wooded savannas, where they play a key role in nitrogen fixation and biomass production. \u003cem\u003eCordyla pinnata, Acacia raddiana\u003c/em\u003e, and \u003cem\u003eParkia biglobosa\u003c/em\u003e are among the species found, especially in agroforestry areas. They contribute to soil structure improvement and erosion prevention. This distribution of \u003cem\u003eFabaceae\u003c/em\u003e corresponds to that described in Laweson, (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e1995\u003c/span\u003e) and Spichiger, (\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGroup 2 is characterized by the dominance of species belonging to the \u003cem\u003eCombretaceae\u003c/em\u003e family. This family is associated with a landscape marked by high DEM_mean and T_mean values but low AETI_mean. This suggests that these species are adapted to higher altitudes and warmer temperatures while tolerating relatively low evapotranspiration. The dominant land use type (LULC) in this group is natural vegetation. In Ouarkhokh, species of the \u003cem\u003eCombretaceae\u003c/em\u003e family, such as \u003cem\u003eGuiera senegalensis, Combretum glutinosum\u003c/em\u003e, and \u003cem\u003eCombretum micranthum\u003c/em\u003e, are found as isolated individuals in lowlands (Sagna and al., 2024). They have deep root systems that allow them to draw water from deeper layers, making them well adapted to the poor, sandy soils of the Ferlo, which offer few nutrients and retain little water. Their resilience to the significant climate variability of the Ferlo, characterized by prolonged droughts and irregular rainfall, allows them to survive and maintain their presence in these arid landscapes. In Niakhar, where rainfall conditions are better, \u003cem\u003eCombretaceae\u003c/em\u003e are located in lowlands, forming vegetation clusters. They are also found in cultivated lands due to their ability to fertilize soils or provide food resources. The structure of \u003cem\u003eCombretaceae\u003c/em\u003e populations in Niakhar, different from that in Ouarkhokh, suggests that they can serve as indicators of the bioclimatic transition between these two sites, marking a gradual adaptation to varying environmental conditions, as shown in Fall, (2017). In Koussanar, the wettest site, \u003cem\u003eCombretaceae\u003c/em\u003e are more diverse and found in significant populations within natural vegetation. They are often subject to cutting and exploitation for their wood. Sarr and al., (2024) showed that the Sudanian-Sahelian zone is characterized by a \u003cem\u003eCombretaceae\u003c/em\u003e savanna.\u003c/p\u003e \u003cp\u003eGroup 3 is influenced by several major environmental factors: high mean precipitation (Rf_mean), high terrestrial biomass (TBP), as well as high variations in AETI (AETI_stdev) and soil organic carbon (SOC_stdev), and high clay content (Clay_mean). This group is characterized by the presence of species belonging to the \u003cem\u003eAnacardiaceae\u003c/em\u003e and \u003cem\u003eMoraceae\u003c/em\u003e families, as well as other less abundant woody families. The high mean precipitation values (Rf_mean) indicate that these species are found in areas with relatively significant rainfall, typical of regions like Niakhar and Koussanar. This group is located further south in Senegal, where water is relatively abundant, as suggested by the high values of AETI_mean and Rainfall_mean, and where vegetation is still present. The high DEM value shows that there is a diversity of topographic gradients; indeed, the area is composed of tabular plateaus cut by the hydrographic network, leading to depressions (Ndiaye, 2000). These conditions favor the growth of trees and shrubs from the \u003cem\u003eAnacardiaceae\u003c/em\u003e and \u003cem\u003eMoraceae\u003c/em\u003e families. The presence of these species is also explained by the wide variety of vegetation formations, ranging from shrub savanna to dense forest galleries, suggesting that these areas have a high capacity to support dense and productive vegetation. The high levels of organic carbon and clay content can be explained by their integration into cultivated areas in Koussanar and Niakhar. Their presence in cultivated lands suggests that they play a crucial role in agroforestry, providing important ecological services such as improving soil fertility and protecting against erosion.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study highlighted the diversity of woody species in three agroforestry landscapes distributed along a bioclimatic gradient in Senegal. It determined the influence of land cover and land use classes on the composition of these woody species, and analysed these relationships with various agro-environmental variables. 91 species belonging to 27 families were recorded on the three sites. Species richness was higher in the Sahelo-Sudanian zone and lower in the Sahelian zone. In terms of their presence within occupation classes, floristic richness is greater in cultivated areas than in areas of natural vegetation, due to a selection of species by farmers with high ecosystem services. On the other hand, the abundance of individuals is greater in plantations than in natural vegetation. The dominant families are \u003cem\u003eFabaceae, Combretaceae, Apocynaceae, Anacardiaceae, Malvaceae, Moraceae, Rhamnaceae\u003c/em\u003e and \u003cem\u003eRubiaceae\u003c/em\u003e. Canonical component analysis (CCA) of the distribution of these families as a function of agro-environmental variables showed that: \u003cem\u003eFabaceae\u003c/em\u003e and \u003cem\u003eMalvaceae\u003c/em\u003e are correlated with high temperatures and high reference evapotranspiration; these families are found in cultivated areas, natural vegetation and plantations. A second group, dominated by the \u003cem\u003eCombretaceae\u003c/em\u003e, is associated with high temperatures but low evapotranspiration, as well as reduced values of mean altitude (DEM) and rainfall. Finally, a third group is characterised by high values of DEM and mean temperature. The study suggests that species diversity and distribution are largely influenced by environmental variables. This research is important for informing policy makers about how diversity varies as a function of environmental variables and land-use units for subsequent planning and intervention. It assessed the effect of environmental variables and LULCs on woody species diversity, but further studies should investigate the effect of anthropogenic factors on species diversity and look at the regeneration of influencing factors along the agro-ecological gradient.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eD. S. contributed to the conceptualization, methodology, formal analysis, and drafting of the original manuscript. L. L. participated in the methodology, formal analysis, software development, and revision. A. A. D. was involved in the methodology, data curation, and software development. A. L. was responsible for the revision and editing of the manuscript. C. M. supervised the study and contributed to the writing and revision of the manuscript. All authors reviewed the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdama T, Saer S, Malick N, \u0026amp; Senghor Y (2022) \u003cem\u003eRenouvellement de la fertilit\u0026eacute; des sols dans les syst\u0026egrave;mes de culture \u0026agrave; base de mil du Bassin Arachidier du S\u0026eacute;n\u0026eacute;gal : Evolution et voies d \u0026rsquo; am\u0026eacute;lioration\u003c/em\u003e. 139\u0026ndash;161.\u003c/li\u003e\n\u003cli\u003eAlexandre F, Mering C (2019) Perception et repr\u0026eacute;sentation des changements socio-environnementaux dans les soci\u0026eacute;t\u0026eacute;s rurales en Afrique de l\u0026rsquo;Ouest sah\u0026eacute;lienne et soudanienne. \u003cem\u003eL\u0026rsquo;Espace g\u0026eacute;ographique\u003c/em\u003e, \u003cem\u003e48\u003c/em\u003e(2), 97\u0026ndash;102. https://doi.org/10.3917/eg.482.0097\u003c/li\u003e\n\u003cli\u003eAnthony Y (1983) An environmental data base for agroforestry. \u003cem\u003eInternational Council for Research in Agroforestry,\u003c/em\u003e \u003cem\u003eNo. 5\u003c/em\u003e, 69.\u003c/li\u003e\n\u003cli\u003eAPG (2009) An update of the Angiosperm Phylogeny Group classification for the orders and families of flowering plants: APG III. \u003cem\u003eBotanical Journal of the Linnean Society\u003c/em\u003e, \u003cem\u003e161\u003c/em\u003e(2), 105\u0026ndash;121. https://doi.org/10.1111/j.1095-8339.2009.00996.x\u003c/li\u003e\n\u003cli\u003eBa M, Bourgoin J, Thiaw I, Soti V (2018) Impact des modes de gestion des parcs arbor\u0026eacute;s sur la dynamique des paysages agricoles, un cas d\u0026rsquo;\u0026eacute;tude au S\u0026eacute;n\u0026eacute;gal. \u003cem\u003eVertigO\u003c/em\u003e, \u003cem\u003eVolume 18 \u003c/em\u003e(num\u0026eacute;ro 2), 25. https://doi.org/10.4000/vertigo.20397\u003c/li\u003e\n\u003cli\u003eBadji N, Coly I, Sane O (2023) Diversit\u0026eacute; des syst\u0026egrave;mes agroforestiers associ\u0026eacute;s aux parcelles potag\u0026egrave;res dans la commune de Thionck-Essyl (D\u0026eacute;partement de Bignona, S\u0026eacute;n\u0026eacute;gal). \u003cem\u003eJournal Am\u0026eacute;ricain Des Sciences V\u0026eacute;g\u0026eacute;tales\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e, 162-176.\u003c/li\u003e\n\u003cli\u003eBlaschke T (2010) Object based image analysis for remote sensing. \u003cem\u003eISPRS Journal of Photogrammetry and Remote Sensing\u003c/em\u003e, \u003cem\u003e65\u003c/em\u003e(1), 2\u0026ndash;16. https://doi.org/10.1016/j.isprsjprs.2009.06.004\u003c/li\u003e\n\u003cli\u003eBourli\u0026egrave;re F, Morel G, Bille JC, Poupon H, Lepage M, Morel MY, Poulet AR (1972) Recherches Ecologiques Sur Une Savane Sah\u0026eacute;lienne Du Ferlo Septentrional, Senegal. \u003cem\u003eLa Terre Et La Vie\u003c/em\u003e, \u003cem\u003e26\u003c/em\u003e, 325\u0026ndash;472.\u003c/li\u003e\n\u003cli\u003eBrandt M, Hiernaux P, Rasmussen K, Tucker CJ, Wigneron JP, Diouf AA, Herrmann SM, Zhang W, Kergoat L, Mbow C, Abel C, Auda Y, Fensholt R (2019) Changes in rainfall distribution promote woody foliage production in the Sahel. \u003cem\u003eCommunications Biology\u003c/em\u003e, \u003cem\u003e2\u003c/em\u003e(1), 1\u0026ndash;10. https://doi.org/10.1038/s42003-019-0383-9\u003c/li\u003e\n\u003cli\u003eBrandt M, Tappan G, Diouf A A, Beye G, Mbow C, Fensholt R (2017) Woody Vegetation Die off and Regeneration in Response to Rainfall Variability in the West African Sahel. \u003cem\u003eRemote Sensing\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(1). https://doi.org/10.3390/rs9010039\u003c/li\u003e\n\u003cli\u003eBrandt M, Verger A, Diouf AA, Baret F, Samimi C (2014) Local vegetation trends in the sahel of mali and senegal using long time series FAPAR satellite products and field measurement (1982-2010). \u003cem\u003eRemote Sensing\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(3), 2408\u0026ndash;2434. https://doi.org/10.3390/rs6032408\u003c/li\u003e\n\u003cli\u003eBreiman LEO (2001) Random Forests. \u003cem\u003eMachine Learning\u003c/em\u003e, \u003cem\u003e45\u003c/em\u003e, 5\u0026ndash;32.\u003c/li\u003e\n\u003cli\u003eChristian SF, Tougiani A, Moussa M, Rabiou H, kiari A, Author C (2019) Diversit\u0026eacute; Et Structure Des Peuplements Ligneux Issus De La R\u0026eacute;g\u0026eacute;n\u0026eacute;ration Naturelle Assist\u0026eacute;e (RNA) Suivant Un Gradient Agro-Ecologique Au Centre Sud Du Niger. \u003cem\u003eIssue 1 Ser. 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Objectifs m\u0026eacute;thodes et interpr\u0026eacute;tation\u003c/em\u003e (Dunod) \u003c/li\u003e\n\u003cli\u003eESRI 2020 (2020) \u003cem\u003ePart II Working with spatial data\u003c/em\u003e. 5\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eFall A (2017a) Du Ferlo au Bassin arachidier (S\u0026eacute;n\u0026eacute;gal) : analyse de la composition floristique de la v\u0026eacute;g\u0026eacute;tation envisag\u0026eacute;e comme ressource et comme indicatrice des changements socio-environnementaux. \u003cem\u003ePhysio-G\u0026eacute;o\u003c/em\u003e \u003cem\u003eVolume 11\u003c/em\u003e 65\u0026ndash;91. doi.org/10.4000/physio-geo.5265\u003c/li\u003e\n\u003cli\u003eFall A (2017b) Du Ferlo au Bassin arachidier (S\u0026eacute;n\u0026eacute;gal) : analyse de la composition floristique de la v\u0026eacute;g\u0026eacute;tation envisag\u0026eacute;e comme ressource et comme indicatrice des changements socio-environnementaux. \u003cem\u003ePhysio-G\u0026eacute;o\u003c/em\u003e \u003cem\u003eVolume 11\u003c/em\u003e 65\u0026ndash;91. https://doi.org/10.4000/physio-geo.5265\u003c/li\u003e\n\u003cli\u003eFall A (2014) \u003cem\u003eFerlo s\u0026eacute;n\u0026eacute;galais : approche g\u0026eacute;ographique de la vuln\u0026eacute;rabilit\u0026eacute; des anthroposyst\u0026egrave;mes sah\u0026eacute;liens\u003c/em\u003e. UNIVERSITE PARIS 13 SORBONNE PARIS CITE.\u003c/li\u003e\n\u003cli\u003eFaye E (2010) \u003cem\u003eDiagnostic partiel de la flore et de la v\u0026eacute;g\u0026eacute;tation des Niayes et du Bassin arachidier au S\u0026eacute;n\u0026eacute;gal : application de m\u0026eacute;thodes floristique phytosociologique ethnobotanique et cartographique\u003c/em\u003e [Universit\u0026eacute; Libre de Bruxelles universit\u0026eacute; d\u0026rsquo;europe]. http://theses.ulb.ac.be/ETD-db/collection/available/ULBetd-09072010-200715/\u003c/li\u003e\n\u003cli\u003eFunk C, Peterson P, Landsfeld M, Pedreros D, Verdin J, Shukla S, Husak G, Rowland J, Harrison L, Hoell A, Michaelsen J (2015) The climate hazards infrared precipitation with stations\u0026mdash;a new environmental record for monitoring extremes. \u003cem\u003eScientific Data\u003c/em\u003e \u003cem\u003e2\u003c/em\u003e(1) 150066. https://doi.org/10.1038/sdata.2015.66\u003c/li\u003e\n\u003cli\u003eGanaba S, Guinko S (1995) Morphologie et r\u0026ocirc;le des structures racinaires dans la mortalit\u0026eacute; de Pterocarpus lucens Lepr. dans la r\u0026eacute;gion sah\u0026eacute;lienne de la mare d\u0026rsquo;Oursi (Burkina Faso) \u003cem\u003e\u0026Eacute;tudes Sur La Flore et La V\u0026eacute;g\u0026eacute;tation Du Burkina Faso et Des Pays Avoisinants\u003c/em\u003e \u003cem\u003eVol. \u003c/em\u003e\u003cem\u003eII\u003c/em\u003e 15\u0026ndash;24.\u003c/li\u003e\n\u003cli\u003eGitelson AA, Kaufman YJ, Merzlyak MN (1996) Use of a green channel in remote sensing of global vegetation from EOS- MODIS. \u003cem\u003eRemote Sensing of Environment\u003c/em\u003e \u003cem\u003e58\u003c/em\u003e(3) 289\u0026ndash;298. https://doi.org/10.1016/S0034-4257(96)00072-7\u003c/li\u003e\n\u003cli\u003eGomez FA, Coly I, Goudiaby AOK, Ndao M (2023) Characterization of Woody Vegetation in Different Land Uses in the Commune of Coubalan (Bignona Department Senegal) \u003cem\u003eAmerican Journal of Plant Sciences\u003c/em\u003e \u003cem\u003e14\u003c/em\u003e(11) 1343\u0026ndash;1359. https://doi.org/10.4236/ajps.2023.1411091\u003c/li\u003e\n\u003cli\u003eGon\u0026ccedil;alves B, Morais MC, Pereira S, Mosquera-Losada MR, Santos M (2021) Tree\u0026ndash;Crop Ecological and Physiological Interactions Within Climate Change Contexts: A Mini-Review. \u003cem\u003eFrontiers in Ecology and Evolution\u003c/em\u003e \u003cem\u003e9\u003c/em\u003e. https://doi.org/10.3389/fevo.2021.661978\u003c/li\u003e\n\u003cli\u003eGrillot M (2018) \u003cem\u003eMod\u0026eacute;lisation multi-agents et pluri-niveaux de la r\u0026eacute;organisation du cycle de l\u0026rsquo;azote dans des syst\u0026egrave;mes agro-sylvo-pastoraux en transition : le cas du bassin Arachidier au S\u0026eacute;n\u0026eacute;gal\u003c/em\u003e. 167.\u003c/li\u003e\n\u003cli\u003eGueye M (2003) Le potentiel fixateur d\u0026rsquo;azote d\u0026rsquo;Acacia raddiana compar\u0026eacute; \u0026agrave; celui d\u0026rsquo;Acacia senegal Acacia seyal et Faidherbia albida. 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L\u0026rsquo;agroforesterie : une bonne pratique agricole Elabor\u0026eacute;e par Hichem KHEMIRI Table des mati\u0026egrave;res\u003c/em\u003e (pp. 1\u0026ndash;45) \u003c/li\u003e\n\u003cli\u003eKuyah S, Whitney CW, Jonsson M, Sileshi GW, \u0026Ouml;born I, Muthuri CW, Luedeling E (2019) Agroforestry delivers a win-win solution for ecosystem services in sub-Saharan Africa. A meta-analysis. \u003cem\u003eAgronomy for Sustainable Development\u003c/em\u003e \u003cem\u003e39\u003c/em\u003e(5) 47. https://doi.org/10.1007/s13593-019-0589-8\u003c/li\u003e\n\u003cli\u003eL\u0026rsquo;Homme F (1978) \u003cem\u003eS\u0026eacute;n\u0026eacute;gal Sine Saloum Gambie Casamance : hydrographie p\u0026ecirc;che crevetti\u0026egrave;re\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eLaouali A Guimbo IDAN, Larwanou M, Ma\u0026acirc;rouhi M, Mahamane A (2014) Utilisation de Prosopis africana ( G . et Perr .) 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des ligneux dans deux zones contrast\u0026eacute;es en zone Sah\u0026eacute;lienne du Burkina Faso. \u003cem\u003eVertigO - La Revue \u0026Eacute;lectronique En Sciences de l\u0026rsquo;environnement\u003c/em\u003e \u003cem\u003e16\u003c/em\u003e(Num\u0026eacute;ro 1) https://doi.org/10.4000/vertigo.17282\u003c/li\u003e\n\u003cli\u003eOuattara DN, Tro HH, Soro D, Bakayoko A (2023) Altitudinal gradient of the diversity and structure of plants on the highlands of the \u0026ldquo;Bow\u0026eacute; de Kiendi\u0026rdquo; in the Gontougo Region (North-East C\u0026ocirc;te d\u0026rsquo;Ivoire) \u003cem\u003eInternational Journal of Biological and Chemical Sciences\u003c/em\u003e \u003cem\u003e17\u003c/em\u003e(2) 417\u0026ndash;429. https://doi.org/10.4314/ijbcs.v17i2.11\u003c/li\u003e\n\u003cli\u003eOuoba HY, Bastide B, Kabore SA, Seghieri J, Boussim IJ (2023) Population structure of Vitellaria paradoxa C.F. Gaertn (shea tree) parklands in Burkina Faso. \u003cem\u003eBiotechnology Agronomy Society and Environment\u003c/em\u003e \u003cem\u003e27\u003c/em\u003e(Special issue) https://doi.org/10.25518/1780-4507.20329\u003c/li\u003e\n\u003cli\u003ePearson K (1904) \u003cem\u003e\u0026ldquo;On the theory of contingency and its relation to association and normal correlation.\u0026rdquo;\u003c/em\u003e (Drapers\u0026rsquo; C) \u003c/li\u003e\n\u003cli\u003ePKR N (1993) \u003cem\u003eAn introduction to agroforestry\u003c/em\u003e (Kluwer Aca) \u003c/li\u003e\n\u003cli\u003eReed J, van Vianen J, Foli S, Clendenning J, Yang K, MacDonald M, Petrokofsky G, Padoch C, Sunderland T (2017) Trees for life: The ecosystem service contribution of trees to food production and livelihoods in the tropics. \u003cem\u003eForest Policy and Economics\u003c/em\u003e \u003cem\u003e84\u003c/em\u003e 62\u0026ndash;71. https://doi.org/https://doi.org/10.1016/j.forpol.2017.01.012\u003c/li\u003e\n\u003cli\u003eRikimaru A, Roy PS, Miyatake S (2002) Tropical Forest Cover Density Mapping. \u003cem\u003eSoci\u0026eacute;t\u0026eacute; Internationale d\u0026rsquo;\u0026eacute;cologie Tropicale\u003c/em\u003e \u003cem\u003e43\u003c/em\u003e 39\u0026ndash;47.\u003c/li\u003e\n\u003cli\u003eRouse JW, Haas RH, Schell JA, Deering D W (1974) Monitoring vegetation systems in the Great Plains with ERTS. in \u003cem\u003eThird earth Resources Technology Satellite-1 National Symposium Volume I: Technical Presentations: Vol. Vol I\u003c/em\u003e (In: Freden pp. 309\u0026ndash;3017) \u003c/li\u003e\n\u003cli\u003eSeghieri J (2012) Agroforestry for conserving and enhancing biodiversity. \u003cem\u003eAgroforestry Systems\u003c/em\u003e \u003cem\u003e85\u003c/em\u003e 1\u0026ndash;8. doi.org/10.1007/s10457-012-9517-5\u003c/li\u003e\n\u003cli\u003eSagna MB, Thiam AN, Niang K, Sarr O, Diallo A, Diatta S, Ngom D, Guiss\u0026eacute; A (2024) Influence of Topography on the Distribution and Structure of Woody Plants in the Senegalese Sahel (Sandy Ferlo) \u003cem\u003eAmerican Journal of Plant Sciences\u003c/em\u003e \u003cem\u003e15\u003c/em\u003e(01) 14\u0026ndash;28. https://doi.org/10.4236/ajps.2024.151002\u003c/li\u003e\n\u003cli\u003eSambou A, Sambou B, R\u0026aelig;bild A (2017) Farmers\u0026rsquo; contributions to the conservation of tree diversity in the Groundnut Basin Senegal. \u003cem\u003eJournal of Forestry Research\u003c/em\u003e \u003cem\u003e28\u003c/em\u003e(5) 1083\u0026ndash;1096. https://doi.org/10.1007/s11676-017-0374-y\u003c/li\u003e\n\u003cli\u003eSan Emeterio J, Alexandre F (2013) Changements socio-environnementaux et dynamiques des paysages ruraux le long du gradient bioclimatique nord-sud dans le sud-ouest du Niger (r\u0026eacute;gions de Tillabery et de Dosso) \u003cem\u003eVertigO\u003c/em\u003e \u003cem\u003e13\u003c/em\u003e(3) \u003c/li\u003e\n\u003cli\u003eSane B, Coly I, Badji A, Diatta TC, Goudiaby AOK, Ngom D (2021) Characteristics of the Flora and Woody Vegetation of Agroforestry Parks in the District of Kataba 1 (Bignona Lower Casamance) \u003cem\u003eOpen Journal of Ecology\u003c/em\u003e \u003cem\u003e11\u003c/em\u003e(11) 741\u0026ndash;757. https://doi.org/10.4236/oje.2021.1111046\u003c/li\u003e\n\u003cli\u003eSarr MD (2015) \u003cem\u003eContribution \u0026agrave; l\u0026rsquo;\u0026eacute;tude d\u0026rsquo;ensablement de la vall\u0026eacute;e de Koussanar un ancien affluent de Sandougou\u003c/em\u003e. Universit\u0026eacute; Cheikh Anta DIOP.\u003c/li\u003e\n\u003cli\u003eSarr O, Sagna MB, Bakhoum A, Diatta S, Guiss\u0026eacute; A (2024) Diversity and Structure of the Woody Stand in a Sudano-Sahelian Transition Zone in Senegal. \u003cem\u003eOpen Journal of Ecology\u003c/em\u003e \u003cem\u003e14\u003c/em\u003e(01) 1\u0026ndash;16. https://doi.org/10.4236/oje.2024.141001\u003c/li\u003e\n\u003cli\u003eSinare H, Gordon LJ (2015) Ecosystem services from woody vegetation on agricultural lands in Sudano-Sahelian West Africa. \u003cem\u003eAgriculture Ecosystems \u0026amp; Environment\u003c/em\u003e \u003cem\u003e200\u003c/em\u003e 186\u0026ndash;199. https://doi.org/https://doi.org/10.1016/j.agee.2014.11.009\u003c/li\u003e\n\u003cli\u003eCisse S (2016) \u003cem\u003eEtude de la variabilit\u0026eacute; intra saisonni\u0026egrave;re des pr\u0026eacute;cipitations au Sahel : impacts sur la v\u0026eacute;g\u0026eacute;tation ( cas du Ferlo au S\u0026eacute;n\u0026eacute;gal ) To cite this version : HAL Id : tel-01407442\u003c/em\u003e. Universit\u0026eacute; Pierre et Marie Curie - Paris VI; Universit\u0026eacute; Cheikh Anta Diop (Dakar) \u003c/li\u003e\n\u003cli\u003eSpichiger R (2010) \u003cem\u003eV\u0026eacute;g\u0026eacute;tations s\u0026egrave;ches des ceintures sah\u0026eacute;liennes et soudaniennes du S\u0026eacute;n\u0026eacute;gal \u0026agrave; Djibouti (\u003c/em\u003eA. D. et R. Duponnois (ed.); IRD \u0026Eacute;ditio) https://doi.org/https://doi.org/10.4000/books.irdeditions.2159.\u003c/li\u003e\n\u003cli\u003eSylla D, Ba T, Guisse A (2019) Mapping of changes in plant coverage in Ferlo protected areas (North Senegal): case of the \u0026ldquo;Biosphere Reserve.\u0026rdquo; \u003cem\u003ePhysio-Geo\u003c/em\u003e \u003cem\u003e13\u003c/em\u003e(January 2019) 115\u0026ndash;132. https://doi.org/10.4000/physio-geo.8178\u003c/li\u003e\n\u003cli\u003eTappan GG, Sall M, Wood EC, Cushing M (2004) Ecoregions and land cover trends in Senegal. \u003cem\u003eJournal of Arid Environments\u003c/em\u003e \u003cem\u003e59\u003c/em\u003e(3) 427\u0026ndash;462. https://doi.org/https://doi.org/10.1016/j.jaridenv.2004.03.018\u003c/li\u003e\n\u003cli\u003eThiam AN, Ndiaye O, Diallo A (2015) Caract\u0026eacute;risation de la v\u0026eacute;g\u0026eacute;tation ligneuse sah\u0026eacute;lienne du S\u0026eacute;n\u0026eacute;gal : cas du Ferlo Characterization of the Sahelian woody vegetation of Senegal : case of Ferlo. \u003cem\u003eInt. J. Biol. Chem. 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A total of 91 species from 27 families were recorded across 400 one-hectare plots, established using a stratified weighted sampling technique. The highest species richness was observed in the Sahelo-Sudanian (60 species, 21 families) and Sudano-Sahelian zones (56 species, 16 families), while the Sahelian zone (Ouarkhokh) had 31 species from 13 families. Dominant families included \u003cem\u003eFabaceae, Combretaceae, Anacardiaceae, Apocynaceae, Malvaceae, Rubiaceae\u003c/em\u003e, and \u003cem\u003eRhamnaceae\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eLand use mapping, based on supervised classification, identified natural vegetation, cultivated lands, plantations, water bodies, bare soils, and artificial surfaces. Canonical Correspondence Analysis (CCA) revealed three species groups: \u003cem\u003eFabaceae\u003c/em\u003e and \u003cem\u003eMalvaceae\u003c/em\u003e, dominant in cultivated areas and plantations, influenced by high temperature and evapotranspiration; \u003cem\u003eCombretaceae\u003c/em\u003e, prevalent in natural vegetation zones, associated with higher elevation and temperature; and species adapted to high rainfall, biomass, and clay content. The study highlights that temperature, rainfall, evapotranspiration, and topography play key roles in shaping woody species distribution, with land use significantly influencing their spatial patterns.\u003c/p\u003e","manuscriptTitle":"Woody species diversity as a function of land use types and environmental factors in agroforestry landscapes of Senegal","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-22 17:12:42","doi":"10.21203/rs.3.rs-6261997/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ba622725-1c59-4f90-9943-6cb01bd2f1ee","owner":[],"postedDate":"April 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-15T15:08:55+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-22 17:12:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6261997","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6261997","identity":"rs-6261997","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}