Density and spatial distribution of <i>Parkia biglobosa</i> pattern in Benin under climate change

  • Fafunkè Titilayo Dotchamou Laboratory of Study and Research in Applied Statistics and Biometrics. Faculty of Agronomic Sciences. University of Abomey-Calavi.
  • Gilbert Atindogbe Laboratory of Study and Research in Applied Statistics and Biometrics. Faculty of Agronomic Sciences. University of Abomey-Calavi.
  • Akouegnigan Idelphonse Sode Laboratory of Applied Ecology. Faculty of Agronomic Sciences. University of Abomey-Calavi.
  • Houédougbé Noël Fonton Laboratory of Study and Research in Applied Statistics and Biometrics. Faculty of Agronomic Sciences. University of Abomey-Calavi.


Parkia biglobosa is an indigenous species which, traditionally contributes to the resilience of the agricultural production system in terms of food security, source of income, poverty reduction and ecosystem stability. Therefore, it is important to improve knowledge on its density, current and future spatial distribution. The main objective of this study is to evaluate the tree density, the climate change effects on the spatial distribution of the species in the future for better conservation. The modeling of the current and future geographical distribution of the species is based on the principle of Maximum Entropy (MaxEnt) on a total of 286 occurrence points from field work and Global Biodiversity Information Facility GBIF-Data Portal-( Two climatic models (HadGEM2_ES and Csiro_mk3_6_0) have been used under two scenarios RCP 2.6 and RCP 8.5 for the projection of the species distribution at the horizon 2050. The correlation analyses and Jackknife test have helped to identify seven variables which are less correlated (r < 0.80) with highest modeling participation. The soil, annual precipitation (BIO12) and temperature (diurnal average Deviation) are the variables which have mostly contributed to performance of the models. Currently, 53% of national territory, spread from north to south is very suitable to the cultivation of P. biglobosa. The scenarios have predicted at the horizon 2050, a loss of the habitats which are currently very suitable for the cultivation and conservation of P. biglobosa, to the benefit of moderate and weak habitats. 51% and 57% are the highest proportion of this lost which will be registered with HadGEM2_ES model under two scenarios. These results revealed that the suitable habitat of the species is threatened by climate change in Benin. In order to limit damage such as decreased productivity, extinction of species, some appropriate solutions must be found.

Author Biography

Fafunkè Titilayo Dotchamou, Laboratory of Study and Research in Applied Statistics and Biometrics. Faculty of Agronomic Sciences. University of Abomey-Calavi.
Department of Atlantique


Allouché O., Tsoar A., Kadmon R., 2006. Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). Journal of Applied Ecology 2006 43, 1223–1232.

Araújo M.B., Pearson R.G., Thuillers W., Erhard M., 2005. Validation of species-climate impact models under climate change. Global Change Biology 11, 1504-1513.

Araújo M.B., Luoto M., 2007. The importance of biotic Interactions for modelling species distributions under climate change. Global Ecological Biogeography 16, 743-753.

Ayihouénou B., 2013. Impact des changements climatiques sur la répartition géographique des aires favorables à la culture et à la conservation du Parkia biglobosa (Jack.) R. Br. ex. Don., au Bénin. Mémoire de Master en gestion des ressources naturelles.

Badeau V., Dupouey J.L., Cluzeau C., Drapier J., 2005. Aires potentielles de répartition des essences forestières d’ici 2100. Forêt-Entreprise, vol. 162, avril 2005, pp. 25-29.

Beaumont L.J., Hughes L., Poulsen M., 2005. Predicting species’ distributions: use of climatic parameters in BIOCLIM and its impact on predictions of species’ current and future distribution. Ecological Modelling, 186, 250–269.

Beaumont L.J., Pitman A.J., Poulsen M., Hughes L., 2007. Where will species go? Incorporating new advances in climate modelling into projections of species distributions. Global Change Biology 13, 1368-1385.

Becker M., 1977. Forêt française : pour une définition et une cartographie des stations. Bulletin technique de l’Office national des forêts, vol. 9, 1977, p. 19.

Berry P. M., Jones, A. P., Nicholls, R. J., VOS, C. C., 2007. Assessment of the vulnerability of terrestrial and coastal habitats and species in Europe to climate change, Annex 2 of Planning for biodiversity in a changing climate - BRANCH: project Final Report, Natural England, UK, 2007.

Boko M., Niang I., Nyong A., Vogel C., 2007. Africa. In: Parry M.L. et al., eds. Climate change 2007: impacts, adaptation and vulnerability. Contribution of working group II to the 4th assessment report of the Intergovernmental Panel on Climate Change. Cambridge, UK: Cambridge University Press, 433-467.

Bourou S., Bowe C., Diouf M., Van Damme P., 2012. Ecological and human impacts on stand density and distribution of tamarind (Tamarindus indicaL.) in Senegal. Afr. J. Ecol., 50, 253-265.

Brands S., Herrera S., Gutiérrez J.J. M., 2013. How well do CMIP5 Earth System Models simulate present climate conditions in Europe and Africa? Springer-Verlag Berlin Heidelberg 2013.

Busby J.W., Smith T.G., White K.L., Strange S.M., 2010. Locating climate insecurity: where are the most vulnerable places in Africa? Austin, TX, USA: University of Texas, The Robert Strauss Center for International Security and Law, Climate Change and African Political Stability (CCAPS) Programme.

Carpenter G., Gilison A.N., Winter J., 1993. DOMAIN : A flexible modelling procedure for mapping potential distributions of animals and plants. Biodiversity and Conservation 2, 667- 680.

Coudun C., Gégout J.C., Piedallu C., Rameau J.C., 2006. Soil nutritional factors improve models of plant species distribution: an illustration with Acer campestre (L.) in France. Journal of Biogeography, vol. 33, n° 10, 2006, pp. 1750-1763.

Elith J., Graham C.H., Anderson R.P., Dudı k.M., Ferrier S., Guisan A., Hijmans R.J., Huettmann F., Leathwick J.R., Lehmann A.L.J., Lohmann L.G., Loiselle B.A., Manion G., Moritz C., Nakamura M., Nakazawa Y., Overton J.M.C., Peterson A.T., Phillips S.J., Richardson K.S., Scachetti-Pereira R., Schapire R.E., Soberon J., Williams S., Zimmermann N.E., 2006. Novel methods improve prediction of species’ distributions from occurrence data. Ecography, 29, 129-151.

Elith J., Leathwick J.R., 2009. Species Distribution Models: Ecological Explanation and Prediction Across Spaceand Time. Annual Review of Ecology Evolution and Systematics, 40: 677-697.

Elith J., Kearney M., Philips S., 2011. The art of modeling range-shifting species. Methods Ecol. Evol., 1, 330-342.

Fandohan B., Gouwakinnou G.N., Fonton N.H., Sinsin B., Liu J., 2013. Impact des changements climatiques sur la répartition géographique des aires favorables à la culture et à la conservation des fruitiers sous-utilisés : cas du tamarinier au Bénin. Biotechnol. Agron. Soc. Environ. (inpress).

Faure P., Volkoff B., 1998. Some factors affecting regional differentiation of the soils in the Republic of Benin (West Africa). Catena, 32, 281-306.

Fitzpatrick M.C., Hargrove W.W., 2009. The projection of species distribution models and the problem of non-analog climate. Biodivers. Conserv., 18, 2255-2261.

Gbesso F.H.G., Tenté B.H.A., Gouwakinnou N.G., Sinsin B.A., 2013. Influence des changements climatiques sur la distribution géographique de Chrysophyllum albidum G. Don (Sapotaceae) au Benin. International Journal of Biological and Chemical Science 7(5): 2007-2018.

Gouwakinnou G.N., Assogbadjo A.E., Lykke A.M., Sinsin, B. 2011. Phenotypic variations in fruits and potential for selection in Sclerocarya birrea subsp. birrea. Scientia Horticulturae, 129: 777 – 783.

Guibert H, Allé U.C., Dimon R.O., Dédéhouanou H., Vissoh P. V., Vodouhé S. D., Tossou R. C., Agbossou E.K. 2010. Correspondance entre savoirs locaux et scientifiques: Perceptions des changements climatiques et adaptations au Bénin. ISDA 2010, Montpellier, 1-12.

Guisan A., Zimmermann N. E., 2000. Predictive habitat distribution models in ecology. Ecol. Model., 135, 147-186.

Hannah L., Midgley G.F., Millar D., 2002. Climate change integrated conservation strategies. Global Ecol. Biogeogr., 11, 485-495.

Hijmans R.J., Cameron S.E., Parra J.L., Jones P.G., Jarvis A., 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology, 25, 1965–1978.

IPCC, 2007. Climate change: synthesis report. New York, USA: Cambridge University Press.

Koura K., Ganglo C.J., Assogbadjo A.E., Agbangla C., 2011. Ethnic differences in use values and use patterns of Parkia biglobosa in Northern Benin. Journal of Ethnobiology and Ethnomedicine 2011, 7:42.

Leakey R.R.B., Pate K., Lombard C., 2005. Domestication potential of Marula (Sclerocarya birrea subsp. caffra) in South Africa and Namibia: Phenotypic variation in nut and kernel traits. Agrofor. Syst., 64: 37-49.

Loiselle B.A., Howell C.A., Graham C.H., Goerck J.M., Brooks T., Smith K.G., Williams P.H., 2003. Avoiding pitfalls of using species distribution models in conservation planning. Conservation Biology, 17, 1591–1600.

Lugina K.M., Groisma, P.Y., Vinnikov K.Y., Koknaeva V.V., Speranskaya N.A., 2006. Monthly surface air temperature time series area-averaged over the 30-degree latitudinal belts of the globe, 1881–2005. In Trends: a compendium of data on global change. Oak Ridge, TN: Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, U.S. Department of Energy. See

Mahapatra A.K., Albers H.J., Robinson E. J.Z., 2005. The impact of NTFP sales on rural household’s cash income in India’s dry deciduous forest. Environmental Management 35(3): 258-265.

Martínez I., Carreño F., Escudero A., Rubio A., 2006. Are threatened lichen species well-protected in Spain? Effectiveness of a protected areas network. Biol. Conserv., 133, 500-511.

Moss R. H., Edmonds J.A., Hibbard K.A., Manning M.R., Rose S.K., van Vuuren D.P., Carter T. R., Emori S., Kainuma M., Kram T., 2010. The next generation of scenarios for climate change research and assessment. Nature 2010, 463:747–756.

Ortega-Huerta M.A., Peterson A.T., 2004. Modelling spatial patterns of biodiversity for conservation prioritization in north-eastern Mexico.Diversity and Distributions, 10, 39-54.

Parviainen M., Luoto M., Ryttari T., Heikkinen R.K., 2008. Modelling the occurrence of threatened plant species in taiga landscapes: methodological and ecological perspectives. J. Biogeogr., 35, 1888-1905.

Peterson A.T., Sanchez-Cordero V., Beard C.B., Ramsey, J.M., 2002b. Ecological niche modelling and potential reservoirs for Chagas diseases, Mexico. Emerging Infectious Diseases 8, 662-629.

Pearson R.G., Dawson T.P., 2003. Predicting the impacts of climate change on the distribution of species: are bioclimatic envelope models useful? Global Ecol. Biogeogr., 12, 361-371.

Peterson A. T., Robins C. R., 2003. Using ecological-niche modelling to predict barred owl invasions with implications for spotted owl conservation. Conservation Biology, 17, 1161–1165.

Phillips S.J., Dudik M., Schapire R.E., 2004. A maximum entropy approach to species distribution modeling. In: Proc. of the 21st International Conference on Machine Learning, Banff, Canada.

Phillips S.J., Anderson R.P.,Schapire R.E., 2006. Maximum entropy modelling of species geographic distributions. Ecol. Model., 190, 231-259.

Sala E., Ballesteros E., Starr R.M. 2001. Rapid decline of Nassau groupers pawning aggregations in Belize: Fishery Management and Conservation Needs,26: 23-30.

Schwartz M.W., 2012. Using niche models with climate projections to inform conservation management decisions. Biol. Conserv., 155, 149-156.

Sinsin B., Owolabi L., 2001. Monographie nationale de la diversité biologique. Rapport de synthèse. Ministère de l'Environnement, de l'Habitat et de l'urbanisme (MEHU), Cotonou, Bénin, p. 41.

Soberon J., Peterson A.T., 2005. Interpretation of models of fundamental ecological niches and species’ distributional areas. Biodiversity Informatics 2, 1-10.

Stockman A.K., Beamer D.A., Bond B.J.E., 2006. An evaluation of a GARP model as an approach to predicting the spatial distribution of non-vagile invertebrate species. Diversity and Distrib. 12, 81-89.

Swets J.A., 1988. Measuring the accuracy of diagnostic systems. Science, 240, 1285-1293.

Teklehaimanot Z., 2004. Exploiting the potentiel of indigenous agroforestry trees: Parkia biglobosa and Vitellaria paradoxa in sub-Saharan Africa. Agroforestry Systems 61: 207-220.

Thuiller W., 2003. BIOMOD: Optimizing predictions of species distributions and projecting potential future shifts under global change. Global Change Biology, vol. 9, 2003, pp. 1353-1362.

Thuiller W., Richardson D.M., Pysek P., Midgley G.F., Hughes G.O., Rouget M., 2005. Niched-based modelling as a tool for predicting the global risk of alien plant invasion. Global Change Biology 11, 2234-2250.

How to Cite
Dotchamou, F. T., Atindogbe, G., Sode, A. I., & Fonton, H. N. (2016). Density and spatial distribution of <i>Parkia biglobosa</i&gt; pattern in Benin under climate change. Journal of Agriculture and Environment for International Development (JAEID), 110(1), 173-194.
Research Papers