Abstract :
Woody vegetation is crucial in maintaining ecological balance, supporting biodiversity, and contributing to carbon storage. However, these ecosystems face increasing threats from deforestation, climate change, and human activities. Despite the current challenges, diagnostics and preliminary information for guiding regreening interventions to restore ecosystems are notably lacking. This study employed Species Distribution Models (SDMs) to predict the spatial distribution and suitability of four woody tree covers (Mangroves, Close Woodlands, Open Woodlands, and Plantations). In each woody cover, a hundred occurrence points were used. The study used machine learning approaches such as Random Forest (RF), MaxEnt, and Generalized Linear Models (GLM) to analyse the relationships between woody cover occurrence data and environmental predictors, including climate, soil properties, anthropogenic factors, and natural disturbances. Results indicate that Salinity is the most significant driver affecting all vegetation types, particularly mangroves. Rainfall strongly influences Close Woodlands and Plantations, while fire disturbances shape Open Woodlands. Predicted suitability maps reveal potential habitat suitability, indicating areas of high restoration potential and underscoring the need for targeted conservation and restoration strategies. Comparison between current coverage and the predicted suitability revealed the smallest gap in Mangroves to cover the optimum suitable area (3.47%) while substantial areas still exist for Close woodlands, Open Woodlands and Plantations with 5,49, 6,03 and 6,41, respectively. Findings from this study provide essential insights for sustainable land management, regreening policy initiatives, and woody ecosystem restoration planning in West Africa’s woody coastal areas. By integrating Geographic Information System (GIS) and ecological modelling, this research enhances decision-making for biodiversity conservation and climate resilience.
Keywords :
Ecological Restoration, Ramsar Site, Saloum Delta, Species Distribution Models, Woody VegetationReferences :
- Aide, T. M., Grau, H. R., Graesser, J., Andrade-Nuñez, M. J., Aráoz, E., Barros, A. P., Campos-Cerqueira, M., Chacon-Moreno, E., Cuesta, F., Espinoza, R., Peralvo, M., Polk, M. H., Rueda, X., Sanchez, A., Young, K. R., Zarbá, L., & Zimmerer, K. S. (2019). Woody vegetation dynamics in the tropical and subtropical Andes from 2001 to 2014: Satellite image interpretation and expert validation. Global Change Biology, 25(6), 2112–2126. https://doi.org/10.1111/gcb.14618
- Amissah, L., Mohren, G. M. J., Bongers, F., Hawthorne, W. D., & Poorter, L. (2014). Rainfall and Temperature affect tree species distribution in Ghana. Journal of Tropical Ecology, 30(5), 435–446. https://doi.org/10.1017/S026646741400025X
- Aoki, I. (2012). Ecological Communities. Entropy Principle for the Development of Complex Biotic Systems, 63–71. https://doi.org/10.1016/B978-0-12-391493-4.00006-8
- Barik, J., Mukhopadhyay, A., Ghosh, T., Mukhopadhyay, S. K., Chowdhury, S. M., & Hazra, S. (2018). Mangrove species distribution and water salinity: an indicator species approach to Sundarban. Journal of Coastal Conservation, 22(2), 361–368. https://doi.org/10.1007/S11852-017-0584-7
- Bracken, J. T., Davis, A. Y., O’Donnell, K. M., Barichivich, W. J., Walls, S. C., & Jezkova, T. (2022). Maximising species distribution model performance when using historical occurrences and variables of varying persistency. Ecosphere, 13(3), e3951. https://doi.org/10.1002/ECS2.3951
- Cheng, Z., Aakala, T., & Larjavaara, M. (2023). Elevation, aspect, and slope influence woody vegetation structure and composition but not species richness in a human-influenced landscape in northwestern Yunnan, China. Frontiers in Forests and Global Change, 6(June), 1–12. https://doi.org/10.3389/ffgc.2023.1187724
- Chollet Ramampiandra, E., Scheidegger, A., Wydler, J., & Schuwirth, N. (2023). A comparison of machine learning and statistical species distribution models: Quantifying overfitting supports model interpretation. Ecological Modelling, 481, 110353. https://doi.org/10.1016/J.ECOLMODEL.2023.110353
- Dahan, K. S., Kasei, R. A., & Husseini, R. (2023). Contribution of remote sensing to wildfire trend and dynamic analysis in two of Ghana’s ecological zones: Guinea-savanna and Forest-savanna mosaic. Fire Ecology, 19(1). https://doi.org/10.1186/s42408-023-00198-z
- Descombes, P., Chauvier, Y., Brun, P., Righetti, D., Wüest, R. O., Karger, D. N., Zurell, D., & Zimmermann, N. E. (2022). Strategies for sampling pseudo-absences for species distribution models in complex mountainous terrain. https://doi.org/10.1101/2022.03.24.485693
- Descroix, L., San, Y., Thior, M., Manga, S., Ba, B. D., Mingou, J., Mendy, V., Coly, S., Di, A., Badiane, A., Senghor, M., Diedhiou, A., Sow, D., Bouaita, Y., Soumar, S., Diop, A., Faty, B., & Sow, B. A. (2020). Inverse Estuaries in West Africa : Evidence of the.
- Dia, M. I. (2012). Vulnerability Assessment of Central Coast Senegal (Saloum) and The Gambia Marine Coast and Estuary to Climate Change Induced Effects. Coastal Resources Center and WWF-WAMPO, April, 1–40.
- Díaz-Pacheco, J., Van Delden, H., & Hewitt, R. (2018). The Importance of Scale in Land Use Models: Experiments in Data Conversion, Data Resampling, Resolution and Neighborhood Extent. Lecture Notes in Geoinformation and Cartography, 163–186. https://doi.org/10.1007/978-3-319-60801-3_9
- Dimobe, K., Ouédraogo, A., Soma, S., Goetze, D., Porembski, S., & Thiombiano, A. (2015). Identification of driving factors of land degradation and deforestation in the Wildlife Reserve of Bontioli (Burkina Faso, West Africa). Global Ecology and Conservation, 4(July), 559–571. https://doi.org/10.1016/j.gecco.2015.10.006
- Dittmann, S., Mosley, L., Stangoulis, J., Nguyen, V. L., Beaumont, K., Dang, T., Guan, H., Gutierrez-Jurado, K., Lam-Gordillo, O., & McGrath, A. (2022). Effects of Extreme Salinity Stress on a Temperate Mangrove Ecosystem. Frontiers in Forests and Global Change, 5(May), 1–18. https://doi.org/10.3389/ffgc.2022.859283
- Doherty, T. S., Geary, W. L., Jolly, C. J., Macdonald, K. J., Miritis, V., Watchorn, D. J., Cherry, M. J., Conner, L. M., González, T. M., Legge, S. M., Ritchie, E. G., Stawski, C., & Dickman, C. R. (2022). Fire as a driver and mediator of predator–prey interactions. Biological Reviews, 97(4), 1539–1558. https://doi.org/10.1111/brv.12853
- Dubos, N., Préau, C., Lenormand, M., Papuga, G., Monsarrat, S., Denelle, P., Louarn, M. Le, Heremans, S., May, R., Roche, P., & Luque, S. (2022). Assessing the effect of sample bias correction in species distribution models. Ecological Indicators, 145, 109487. https://doi.org/10.1016/J.ECOLIND.2022.109487
- S. Diop. (1998). Contribution à l’élaboration du plan de gestion intégrée de la Réserve de la Biosphère du Delta de Saloum – Recherche Google [UCAD-UNESCO-MAB]. https://www.google.com/search?q=Diop+E.+S.%2C+1998.+Contribution+à+l’élaboration+du+plan+de+gestion+intégrée+de+la+Réserve+de+la+Biosphère+du+Delta+de+Saloum&rlz=1C1EJFC_enCV886CV886&sxsrf=AOaemvJWihD57WeIPvQB1n9hHLm6ONnf6Q%3A1630375122442&ei=0owtYYiwGqq5gwfTzoEw&oq=Diop+E.+S.%2C+1998.+Contribution+à+l’élaboration+du+plan+de+gestion+intégrée+de+la+Réserve+de+la+Biosphère+du+Delta+de+Saloum&gs_lcp=Cgdnd3Mtd2l6EANKBAhBGABQ0vAJWNLwCWC58wloAHAAeACAAQCIAQCSAQCYAQCgAQHAAQE&sclient=gws-wiz&ved=0ahUKEwiI4ZfQlNryAhWq3OAKHVNnAAYQ4dUDCA8&uact=5
- Emmanuel, O., & Williams, A. (2017). Effects of Deforestation on Land Degradation in Gbonyin LGA of EKITI. July, 30–32. https://www.researchgate.net/publication/318921682_Effects_of_Deforestation_on_Land_Degradation
- FARM RADIO.FM. (2022). The cashew tree: A defense against bush fires – Farm Radio Scripts.
- Fournier, A., Barbet-Massin, M., Rome, Q., & Courchamp, F. (2017). Predicting species distribution combining multi-scale drivers. Global Ecology and Conservation, 12, 215–226. https://doi.org/10.1016/j.gecco.2017.11.002
- Franklin, J. (2023). Species distribution modelling supports the study of past, present and future biogeographies. Journal of Biogeography, 50(9), 1533–1545. https://doi.org/10.1111/jbi.14617
- Gomez, J. J., & Cassini, M. H. (2015). Environmental predictors of habitat suitability and biogeographical range of Franciscana dolphins (Pontoporia blainvillei). Global Ecology and Conservation, 3, 90–99. https://doi.org/10.1016/J.GECCO.2014.11.007
- Gonzalez, P., Tucker, C. J., & Sy, H. (2012). Tree density and species decline in the African Sahel attributable to climate. Journal of Arid Environments, 78, 55–64. https://doi.org/10.1016/j.jaridenv.2011.11.001
- Grieco, E., Vangi, E., Chiti, T., & Collalti, A. (2024). Impacts of deforestation and land use/land cover change on carbon stock dynamics in Jomoro District, Ghana. Journal of Environmental Management, 367, 121993. https://doi.org/10.1016/J.JENVMAN.2024.121993
- KOMBATE, B., Atakpama, W., Egbelou, H., Yandja, M., Dourma, M., Batawila, K., Akpagana, K., Dourma, M., Batawila, K., & Akpagana, K. (2023). Structure and Modeling of the Forest Carbone of the Classified Forest of Missahohoé in Togo. African Journal on Land Policy and Geospatial Sciences, 6(1), 2657–2664. https://doi.org/10.48346/IMIST.PRSM/ajlp-gs.v6i1.35320
- Matyukira, C., & Mhangara, P. (2024). Advances in vegetation mapping through remote sensing and machine learning techniques: a scientometric review. European Journal of Remote Sensing, 57(1). https://doi.org/10.1080/22797254.2024.2422330
- Mbow, C., Nielsen, T. T., & Rasmussen, K. (2000). Savanna Fires in East-Central Senegal : Distribution Patterns , Resource Management and Perceptions Author ( s ): C . Mbow , T . T . Nielsen and K . Rasmussen Published by : Springer Stable URL : http://www.jstor.org/stable/4603372 Savanna Fires in East-C. 28(4), 561–583.
- Mussa, M., Ebro, A., & Nigatu, L. (2016). Impact of woody plants species on soil physico-chemical properties along grazing gradients in rangelands of eastern Ethiopia. Tropical and Subtropical Agroecosystems, 19(3), 343–355. https://doi.org/10.56369/TSAES.2254
- Ntshotsho, P., Esler, K. J., & Reyers, B. (2015). Identifying Challenges to Building an Evidence Base for Restoration Practice. Sustainability 2015, Vol. 7, Pages 15871-15881, 7(12), 15871–15881. https://doi.org/10.3390/SU71215788
- Panwar, P., Shukla, G., Bhat, J. A., & Chakravarty, S. (2022). Land Degradation Neutrality: Achieving SDG 15 by Forest Management. Land Degradation Neutrality: Achieving SDG 15 by Forest Management, January 2023, 1–452. https://doi.org/10.1007/978-981-19-5478-8
- Richards, S. J., Warneke, J. E., Marsh, A. W., & Aljibury, F. K. (2024). Physical properties of soil mixes. Soil Science, 98(2), 129–132. https://doi.org/10.1097/00010694-196408000-00009
- Rueda-M, N., Salgado-Roa, F. C., Gantiva-Q, C. H., Pardo-Díaz, C., & Salazar, C. (2021). Environmental Drivers of Diversification and Hybridization in Neotropical Butterflies. Frontiers in Ecology and Evolution, 9. https://doi.org/10.3389/FEVO.2021.750703
- Salvini, G., Ligtenberg, A., van Paassen, A., Bregt, A. K., Avitabile, V., & Herold, M. (2016). REDD+ and climate smart agriculture in landscapes: A case study in Vietnam using companion modelling. Journal of Environmental Management, 172, 58–70. https://doi.org/10.1016/j.jenvman.2015.11.060
- Sambou, S. (2015). Land Use-Land Cover Change and Drivers of Deforestation in the Patako Protected Area (Center-West of Senegal). American Journal of Environmental Protection, 4(6), 306. https://doi.org/10.11648/j.ajep.20150406.17
- Seghieri, J., Vescovo, A., Padel, K., Soubie, R., Arjounin, M., Boulain, N., de Rosnay, P., Galle, S., Gosset, M., Mouctar, A. H., Peugeot, C., & Timouk, F. (2009). Relationships between climate, soil moisture and phenology of the woody cover in two sites located along the West African latitudinal gradient. Journal of Hydrology, 375(1–2), 78–89. https://doi.org/10.1016/J.JHYDROL.2009.01.023
- Sharma, R. C. (2022). Countrywide Mapping of Plant Ecological Communities with 101 Legends including Land Cover Types for the First Time at 10 m Resolution through Convolutional Learning of Satellite Images. Applied Sciences (Switzerland), 12(14). https://doi.org/10.3390/app12147125
- Sinare, H., & Gordon, L. J. (2015). Ecosystem services from woody vegetation on agricultural lands in Sudano-Sahelian West Africa. Agriculture, Ecosystems and Environment, 200, 186–199. https://doi.org/10.1016/j.agee.2014.11.009
- Singh, R., Behera, M. D., Das, P., Rizvi, J., Dhyani, S. K., & Biradar, C. M. (2022). Agroforestry Suitability for Planning Site-Specific Interventions Using Machine Learning Approaches. Sustainability (Switzerland), 14(9). https://doi.org/10.3390/su14095189
- Spracklen, D. V., Baker, J. C. A., Garcia-Carreras, L., & Marsham, J. H. (2018). The effects of tropical vegetation on Rainfall. Annual Review of Environment and Resources, 43(December), 193–218. https://doi.org/10.1146/annurev-environ-102017-030136
- Srivastava, V., Lafond, V., & Griess, V. C. (2019). Species distribution models (SDM): Applications, benefits and challenges in invasive species management. CAB Reviews: Perspectives in Agriculture, Veterinary Science, Nutrition and Natural Resources, 14(December). https://doi.org/10.1079/PAVSNNR201914020
- Swaine, M. D. (1992). Characteristics of dry forest in West Africa and the influence of fire. Journal of Vegetation Science, 3(3), 365–374. https://doi.org/10.2307/3235762
- Thiam, S., Villamor, G. B., Faye, L. C., Sène, J. H. B., Diwediga, B., & Kyei-Baffour, N. (2021). Monitoring land use and soil salinity changes in coastal landscape: a case study from Senegal. Environmental Monitoring and Assessment, 193(5).
https://doi.org/10.1007/s10661-021-08958-7
- Tian, W., & Zhao, Y. (2015). An Introduction to Cloud Computing. Optimised Cloud Resource Management and Scheduling, 1–15. https://doi.org/10.1016/B978-0-12-801476-9.00001-X
- Tong, R., Davies, A. J., Yesson, C., Yu, J., Luo, Y., Zhang, L., & Burgos, J. M. (2023). Environmental drivers and the distribution of cold-water corals in the global ocean. Frontiers in Marine Science, 10(October). https://doi.org/10.3389/fmars.2023.1217851
- Tsamardinos, I., Greasidou, E., & Borboudakis, G. (2018). Bootstrapping the out-of-sample predictions for efficient and accurate cross-validation. Machine Learning, 107(12), 1895–1922. https://doi.org/10.1007/S10994-018-5714-4/FIGURES/7
- van Straaten, O., Doamba, S. W. M. F., Corre, M. D., & Veldkamp, E. (2019). Impacts of burning on soil trace gas fluxes in two wooded savanna sites in Burkina Faso. Journal of Arid Environments, 165(July 2018), 132–140. https://doi.org/10.1016/j.jaridenv.2019.02.013
- Veenendaal, E. M., Torello-Raventos, M., Miranda, H. S., Sato, N. M., Oliveras, I., van Langevelde, F., Asner, G. P., & Lloyd, J. (2018). On the relationship between fire regime and vegetation structure in the tropics. New Phytologist, 218(1), 153–166. https://doi.org/10.1111/nph.14940
- Wallis, C. I. B., Tiede, Y. C., Beck, E., Böhning-Gaese, K., Brandl, R., Donoso, D. A., Espinosa, C. I., Fries, A., Homeier, J., Inclan, D., Leuschner, C., Maraun, M., Mikolajewski, K., Neuschulz, E. L., Scheu, S., Schleuning, M., Suárez, J. P., Tinoco, B. A., Farwig, N., & Bendix, J. (2021). Biodiversity and ecosystem functions depend on environmental conditions and resources rather than the geodiversity of a tropical biodiversity hotspot. Scientific Reports, 11(1), 1–15. https://doi.org/10.1038/s41598-021-03488-1
- Xue, Y., Zhang, B., He, C., & Shao, R. (2019). Detecting vegetation variations and main drivers over the agropastoral ecotone of northern China through the ensemble empirical mode decomposition method. Remote Sensing, 11(16). https://doi.org/10.3390/rs11161860
- Yao, N. A. (2010). Mapping Bushfire Distribution and Burn Severity in West Africa Using Remote Sensing Observations. 137.
- Zhang, J., & Li, S. (2017). A Review of Machine Learning Based Species’ Distribution Modelling. Proceedings – 2017 International Conference on Industrial Informatics – Computing Technology, Intelligent Technology, Industrial Information Integration, ICIICII 2017, 2017–December, 199–206. https://doi.org/10.1109/ICIICII.2017.76
- Zhang, W., Brandt, M., Wang, Q., Prishchepov, A. V., Tucker, C. J., Li, Y., Lyu, H., & Fensholt, R. (2019). From woody cover to woody canopies: How Sentinel-1 and Sentinel-2 data advance the mapping of woody plants in savannas. Remote Sensing of Environment, 234(May), 111465. https://doi.org/10.1016/j.rse.2019.111465
- Zhao, Z., Xiao, N., Shen, M., & Li, J. (2022). Comparison between optimised MaxEnt and random forest modeling in predicting potential distribution: A case study with Quasipaa boulengeri in China. Science of The Total Environment, 842, 156867. https://doi.org/10.1016/J.SCITOTENV.2022.156867

