Spatial distribution and impacts of climate change on Milicia excelsa in Benin, West Africa

Abstract

African teak (Milicia excelsa (Welw.) C.C. Berg) is an endangered multi-use species. Understanding the impact of climate change on the distribution of this species may improve the ability to anticipate or recognize its decline or expansion and to take appropriate conservation measures if necessary. Ecological niche modeling was projected in geographical space to study the current and future distribution of M. excelsa in Bénin. MaxEnt was used to estimate the potential geographic distribution of the species under two Representative Concentration Pathways (RCP). Miroc 5 summaries and two RCP 4.5 and RCP 8.5 scenarios were used as predictor variables for projections of the geographic potential of this species. The performance of the model was assessed by the area under the curve (AUC), true skill statistics (TSS) and partial receiver operating characteristics (Partial ROC). From the results, M. excelsa was more a secondary species in the Guinean climatic zone and part of the Sudanian-Guinean and Sudanian climatic zone. The projections show a significant decrease in suitable habitats for the species from the two RCP scenarios. Only a part of the Guinean climatic zone remained suitable and few protected areas will conserve in situ M. excelsa. For the sustainable conservation of M. excelsa, it is essential to strengthen the protection of sacred forests located in the Guinean climatic zone.

Keywords

Ecological niche modeling / Climate change / Milicia excelsa / Benin / West Africa

References

[1] Adjahossou SGC, Gouwakinnou GN, Houehanou DT, Sode AI, Yaoitcha AS, Houinato MRB, Sinsin B. Effectiveness of protected areas for the conservation of favourable and priority habitats for valuable tree species in Benin. Bois et Forets des Tropiques, 2016, 328(2): 67-76.

[2] Adomou AC, Sinsin B, Akoégninou AA, van der Maesen J. Burgt X, Maesen J, Onana J-M. Plant species and ecosystems with high conservation priority in Benin. Systematics and conservation of african plants, 2010, Kew: Royal Botanic Gardens 429 444

[3] Allouche O, Tsoar A, Kadmon R. Assessing the accuracy of species distribution models: prevalence, kappa and true skill statistic (TSS). J Appl Ecol, 2006, 43: 1223-1232.

[4] Altamiranda-Saavedra M, Arboleda S, Parra JL, Peterson AT, Correa MM. Potential distribution of mosquito vector species in a primary malaria endemic region of Colombia. PLoS ONE, 2017 12 6 e0179093

[5] Anderson RP, Dudík M, Ferrier S, Guisan A, Hijmans RJ, Huettmann F, Leathwick JR, Lehmann A, Li J, Lohmann LG. Novel methods improve prediction of species distributions from occurrence data. Ecography, 2006, 29: 129-151.

[6] Ayihouenou EB, Fandohan AB, Sodé AI, Gouwakinnou NG et Djossa AB (2016) Biogéographie du néré (Parkia biglobosa (Jack.) R. Br. ex. Don.) sous les conditions environnementales actuelles et futures au Bénin. Bulletin de la Recherche Agronomique du Bénin (BRAB) (Agronomie, Société, Environnement & Sécurité Alimentaire), pp 1840−7099

[7] Barve N, Barve V, JimeÂnez-Valverde A, Lira-Noriega A, Maher SP, Peterson AT, Soberón J, Villalobos F. The crucial role of the accessible area in ecological niche modeling and species distribution modeling. Ecol Model, 2011, 222(11): 1810-1819.

[8] Breiner FT, Guisan A, Nobis MP, Bergamini A. Including environmental niche information to improve IUCN red list assessments. Divers Distrib, 2017, 23: 484-495.

[9] Busby JW, Smith TG, White KL, Strange SM (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

[10] Buse J, Griebeler EM. Incorporating classified dispersal assumptions in predictive distribution models—a case study with grasshoppers and bush-crickets. Ecol Model, 2011, 222: 2130-2141.

[11] Clarke LE, Edmonds JA, Jacoby HD, Pitcher H, Reilly JM, Richels R (2007) Scenarios of greenhouse gas emissions and atmospheric concentrations. Sub-report 2.1a of synthesis and assessment product 2.1. Climate change science program and the subcommittee on global change research, Washington DC

[12] Daïnou K, Doucet JL, Sinsin B, Mahy G. Identity and ecology of Central African timber tree species: the case of Milicia spp. (a review). Biotechnologie Agronomie Société Et Environnement, 2012, 16(2): 229-241.

[13] Darrah SE, Bland LM, Bachman SP, Clubbe CP, Trias-Blasi A. Using coarse-scale species distribution data to predict extinction risk in plants. Divers Distrib, 2017, 23: 435-447.

[14] Djotan AKG, Aoudji AKN, Yêhouénou Tessi DR, Kakpo SB, Gbètoho JA, Koura K, Ganglo JC. Vulnerability of Khaya senegalensis Desr & Juss to climate change and to the invasion of Hypsipyla robusta Moore in Benin (West Africa). Int J Biol Chem Sci, 2018, 12(1): 24-42.

[15] Dotchamou FT, Atindogbe G, Sode AI, Fonton HN (2016) Density and spatial pattern of Parkia biglobosa under climate change: the case of Benin. J Agric Environ Int Dev 110(1): 173–194. 10.12895/jaeid.20161.447.

[16] Doxa A, Albert CH, Leriche A, Saatkamp A. Prioritizing conservation areas for coastal plant diversity under increasing urbanization. J Environ Manag, 2017, 201: 425-434.

[17] Drayton B, Primack RB. Success rates for reintroductions of eight perennial plant species after. Soc Ecol Restor, 2012, 20: 299-303.

[18] Elith J, Graham CH, Anderson RP, Dudik M, Ferrier S, Guisan A, Hijmans RJ, Huettmann F, Leathwick JR, Lehmann A, Li J, Lohmann LG, Loiselle BA, Manion G, Moritz C, Nakamura M, Nakazawa Y, Overton JM, Peterson AT, Phillips SJ, Richardson K, Scachetti-Pereira R, Schapire RE, Soberon J, Williams S, Wisz MS, Zimmermann NE. Novel methods improve prediction of species’ distributions from occurrence data. Ecography, 2006, 29: 129-151.

[19] Elith J, Kearney M, Philips S. The art of modeling range-shifting species. Methods Ecol Evol, 2010, 1: 330-342.

[20] Fandohan AB, Moutouama JK, Biaou SSH, Gouwakinnou GN, Adomou CA. Le réseau d’aires protégées Bénin-Togo assure-t-il la conservation de Thunbergia atacorensis (Acanthaceae)?. CAMES, Science de la vie, de la terre et agronomie, 2010, 3: 25.

[21] Fourcade Y, Engler JO, Rödder D, Secondi J. Mapping species distributions with MAXENT using a geographically biased sample of presence data: A performance assessment of methods for correcting sampling bias. PLoS ONE, 2014, 9: e97122.

[22] Galdino TVdS, Kumar S, Oliveira LSS, Alfenas AC, Neven LG, Al-Sadi AM, Picanço MC. Mapping global potential risk of mango sudden decline disease caused by Ceratocystis fimbriata. PLoS ONE, 2016 11 7 e0159450

[23] Ganglo JC, Djotan GK, Gbètoho JA, Kakpo SB, Aoudji AKN, Koura K, Yêhouénou Tessi DR. Ecological niche modeling and strategies for the conservation of Dialium guineense Willd (Black velvet) in West Africa. Int J Biodivers Conserv, 2017, 9(12): 373-388.

[24] Ganglo JC, Kakpo SB. Completeness of digital accessible knowledge of the plants of Benin and priorities for future inventory and data discovery. Biodivers Inform, 2016, 11: 23-39.

[25] Gbètoho AJ, Aoudji AKN, Roxburgh L, Ganglo JC. Assessing the suitability of pioneer species for secondary forest restoration in Benin in the context of global climate change. Bois et forêts des tropiques, 2017, 332(2): 43-55.

[26] GBIF (2016) Occurrence data download. Denmark: global biodiversity information facility. https://doi.org/10.15468/dl.ujt6rx

[27] Godefroid S, Piazza C, Rossi G, Buord S, Stevens AD, Aguraiuja R, Cowell C, Weekley CW, Vogg G, Iriondo JM. How successful are plant species reintroductions?. Biol Cons, 2011, 144: 672-682.

[28] Guisan A, Zimmermann NE. Predictive habitat distribution models in ecology. Ecol Model, 2000, 135: 147-186.

[29] Idohou R, Assogbadjo AE, Glèlè Kakaï R, Peterson AT. Spatio-temporal dynamic of suitable areas for species conservation in West Africa: eight economically important wild palms under present and future climates. Agrofor Syst, 2016

[30] IPCC (2013) Climate change (2013) The physical science basis. IPCC working group I contribution to the IPCC fifth assessment report. Cambridge University Press, New York, New York, USA.

[31] Jarnevich CS, Stohlgren TJ, Kumar S, Morisette JT, Holcombe TR. Caveats for correlative species distribution modeling. Ecological Informatics, 2015, 29: 6-15.

[32] Kakpo SB, Yêhouénou Tessi DR, Gbètoho JA, Aoudji AKN, Ganglo JC. Répartition spatiale de Cola millenii K. Schum., Dialium guineense Wild. et Afzelia africana Smith ex Pers. dans les forêts secondaires du Sud Benin (Afrique de l’Ouest). Int J Biol Chem Sci, 2018, 12(1): 353-362.

[33] Moss RH, Edmonds JA, Hibbard KA, Manning MR, Rose SK, Van Vuuren DP, Carter TR, Emori S, Kainuma M, Kram T, Meehl GA, Mitchell JFB, Nakicenovic N, Riahi K, Smith SJ, Stouffer RJ, Thomson AM, Weyant JP, Wilbanks TJ. The next generation of scenarios for climate change research and assessment. Nature, 2010, 463: 747-756.

[34] Moutouama JK, Fandohan AB, Biaou SSH, Amahowe OI, Moutouama Ft, Natta Ak. Potential climate change favored expansion of a range limited species, Haematostaphis barteri Hook f. J Agric Environ Int Dev, 2014, 110: 397.

[35] Ouinsavi C, Sokpon N. Morphological variation and ecological structure of Iroko (Milicia excelsa Welw. C.C. Berg) populations across different Biogeographical Zones in Benin. International Journal of Forestry Research., 2010, 10: 10.

[36] Pacifici M, Foden WB, Visconti P, Watson JEM, Butchart SHM, Kovacs KM, Scheffers BR, Hole DG, Martin TG, Akcakaya HR, Corlett RT, Huntley B, Bickford D, Carr JA, Hoffmann AA, Midgley GF, Pearce-Kelly P, Pearson RG, Williams SE, Willis SG, Young B, Rondinini C. Assessing species vulnerability to climate change. Nature Climate Change, 2015, 5: 215-225.

[37] Pearson RG, Raxworthy CJ, Nakamura M, Peterson AT. Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in Madagascar. J Biogeogr, 2007, 34: 102-117.

[38] Peterson AT, Nyari AS. Ecological niche conservatism and pleistocene refugia in the thrush-like mourner, Schiffornis sp., in the neotropics. Evolution, 2008, 62: 173-183.

[39] Peterson AT, Papes M, Soberon J. Rethinking receiver operating characteristic analysis applications in ecological niche modeling. Ecol Model, 2008, 2131: 63-72.

[40] Peterson AT, Soberon J, Pearson RG, Anderson RP, Martínez-Meyer E, Nakamura M, Araújo MB. Ecological niches and geographic distributions, 2011, Princeton: Princeton University Press

[41] Phillips SJ, Anderson RP, Schapire RE. Maximum entropy modelling of species geographic distributions. Ecol Model, 2006, 190: 231-259.

[42] Pimm SL, Russell GJ, Gittleman JL, Brooks TM. The future of biodiversity., 1995, 269(5222): 347-350.

[43] Platts PJ, Omeny PA, Marchant R. AFRICLIM: high-resolution climate projections for ecological applications in Africa. Afr J Ecol, 2015, 53: 103-108.

[44] Questad EJ, Kellner JR, Kinney K, Cordell S, Asner GP, Thaxton J, Diep J, Uowolo A, Brooks S, Inman-Narahari N, Evans SA, Tucker B. Mapping habitat suitability for at-risk plant species and its implications for restoration and reintroduction. Ecological Applications A Publication of the Ecological Society of America, 2014, 24: 385-395.

[45] Radosavljevic A, Anderson RP. Making better Maxent models of species distributions: Complexity, overfitting and evaluation. J Biogeogr, 2014, 41(4): 629-643.

[46] Ramirez-Villegas J, Jarvis A. Downscaling global circulation model outputs: the delta method decision and policy analysis Working Paper No. 1. J Policy Anal Manag, 2010, 1: 1-18.

[47] Ricketts TH, Dinerstein E, Boucher T, Brooks TM, Butchart SHM, Hoffmann M, Lamoreux JF, Morrison J, Parr M, Pilgrim JD, Rodrigues ASL, Sechrest W, Wallace GE, Berlin K, Bielby J, Burgess ND, Church DR, Cox N, Knox D, Loucks C, Luck GW, Master LL, Moore R, Naidoo R, Ridgely R, Schatz GE, Shire G, Strand H, Wettengel W, Wikramanayake E. Pinpointing and preventing imminent extinctions. Proc Natl Acad Sci USA, 2005, 102: 18497-18501.

[48] Rovzar C, Gillespie TW, Kawelo K. Landscape to site variations in species distribution models for endangered plants. For Ecol Manag, 2016, 369: 20-28.

[49] Schwartz MW. Using niche models with climate projections to inform conservation management decisions. Biol Conserv, 2012, 155: 149-156.

[50] Sharma S, Jackson DA. Predicting smallmouth bass (Micropterus dolomieu) occurrence across North America under climate change: a comparison of statistical approaches. Can J Fish Aquat Sci, 2008, 65: 471-481.

[51] Thompson K. The soil seed banks of North West Europe: methodology, density and longevity, 1997, Cambridge: Cambridge University Press.

[52] Urban MC. Accelerating extinction risk from climate change. Science, 2015, 348: 571-573.

[53] van Vuuren DP, Carter TR. Climate and socio-economic scenarios for climate change research and assessment: reconciling the new with the old. Clim Change, 2014, 122: 415-429.

[54] van Vuuren PD, Edmonds J, Kainuma M, Riahi K, Thomson A, Hibbard K, Hurtt GC, Kram T, Krey V, Lamarque JF, Masui T, Meinshausen M, Nakicenovic N, Smith SJ, Rose SK. The representative concentration pathways: an overview. Climatic Change, 2011, 109: 5-31.

[55] Zuur AF, Ieno EN, Elphick CS. A protocol for data exploration to avoid common statistical problems. Methods Ecol Evol, 2010, 1: 3-14.