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Predicting mire distribution using species distribution models: a case study of the sub-Antarctic Prince Edward Islands

Published online by Cambridge University Press:  23 December 2024

Maleho M. Sadiki Open the ORCID record for Maleho M. Sadiki [Opens in a new window] ,
Michelle Greve Open the ORCID record for Michelle Greve [Opens in a new window]  and
Christel D. Hansen Open the ORCID record for Christel D. Hansen [Opens in a new window]
Maleho M. Sadiki
Affiliation:
Department of Geography, Geoinformatics and Meteorology, University of Pretoria, Hatfield, Pretoria, South Africa
Michelle Greve
Affiliation:
Department of Plant and Soil Sciences, University of Pretoria, Hatfield, Pretoria, South Africa
Christel D. Hansen*
Affiliation:
Department of Geography, Geoinformatics and Meteorology, University of Pretoria, Hatfield, Pretoria, South Africa
*
Corresponding author: Christel D. Hansen; email: [email protected]
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Abstract

Peatlands, covering approximately one-third of global wetlands, provide various ecological functions but are highly vulnerable to climate change, with their changes in space and time requiring monitoring. The sub-Antarctic Prince Edward Islands (PEIs) are a key conservation area for South Africa, as well as for the preservation of terrestrial ecosystems in the region. Peatlands (mires) found here are threatened by climate change, yet their distribution factors are poorly understood. This study attempted to predict mire distribution on the PEIs using species distribution models (SDMs) employing multiple regression-based and machine-learning models. The random forest model performed best. Key influencing factors were the Normalized Difference Water Index and slope, with low annual mean temperature, with low annual mean temperature, precipitation seasonality and distance from the coast being less influential. Despite moderate predictive ability, the model could only identify general areas of mires, not specific ones. Therefore, this study showed limited support for the use of SDMs in predicting mire distributions on the sub-Antarctic PEIs. It is recommended to refine the criteria used to select environmental factors and enhance the geospatial resolution of the data to improve the predictive accuracy of the models.

Keywords

GISmachine learningpeatland distributionrandom forestsub-Antarctic
Type
Earth Sciences
Information
Antarctic Science , Volume 36 , Issue 6 , December 2024 , pp. 487 - 499
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Copyright
Copyright © The Author(s), 2024. Published by Cambridge University Press on behalf of Antarctic Science Ltd

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References

Allouche, 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, 43, 10.1111/j.1365-2664.2006.01214.x.CrossRefGoogle Scholar
Amler, E., Schmidt, M. & Menz, G. 2015. Definitions and mapping of East African wetlands: a review. Remote Sensing, 7, 10.3390/rs70505256.CrossRefGoogle Scholar
Austin, M. 2007. Species distribution models and ecological theory: a critical assessment and some possible new approaches. Ecological Modelling, 200, 10.1016/j.ecolmodel.2006.07.005.CrossRefGoogle Scholar
Beven, K.J. & Kirkby, M.J. 1979. A physically based, variable contributing area model of basin hydrology/Un modèle à base physique de zone d'appel variable de l'hydrologie du bassin versant. Hydrological Sciences Journal, 24, 10.1080/02626667909491834.Google Scholar
Boelhouwers, J.C., Meiklejohn, K.I., Holness, S.D. & Hedding, D.W. 2008. Geology, geomorphology and climate change. In Chown, S.L. & Froneman, P.W., eds, The Prince Edward Islands: land-sea interactions in a changing ecosystem. Stellenbosch: SUN MeDIA, 65–96.Google Scholar
Bourgeau-Chavez, L., Endres, S.L., Graham, J., Hribljan, J.A., Chimner, R., Lillieskov, E. & Battaglia, M. 2018. Mapping peatlands in boreal and tropical ecoregions. In Liang, S., ed., Comprehensive remote sensing (vol. 6). Amsterdam: Elsevier, 24–44.CrossRefGoogle Scholar
Breiman, L. 2001. Random forests. Machine Learning, 45, 5–32.CrossRefGoogle Scholar
Breiman, L., Friedman, J., Olshen, R. & Stone, C. 1984. Classification and regression trees. New York: Chapman and Hall/CRC Press, 368 pp.Google Scholar
Bricher, P.K., Lucieer, A., Shaw, J., Terauds, A., Bergstrom, D.M. 2013. Mapping sub-Antarctic cushion plants using random forests to combine very high resolution satellite imagery and terrain modelling. PLoS One, 8, 10.1371/journal.pone.0072093.CrossRefGoogle ScholarPubMed
Brown, J.L. 2014. SDM toolbox: a Python-based GIS toolkit for landscape genetic, biogeographic and species distribution model analyses. Methods in Ecology and Evolution, 5, 694–700.CrossRefGoogle Scholar
Brinson, M.M. 1993. A hydrogeomorphic classification for wetlands (Wetlands research program technical report WRP-DE-4. Washington, DC: US Army Corps of Engineers, 103 pp.Google Scholar
Chown, S.L. & Smith, V.R. 1993. Climate change and the short-term impact of feral house mice at the sub-Antarctic Prince Edward Islands. Oecologia, 96, 10.1007/BF00320508.CrossRefGoogle ScholarPubMed
Congedo, L. 2021. Semi-Automatic Classification Plugin: a Python tool for the download and processing of remote sensing images in QGIS. Journal of Open Source Software, 6, 3172.CrossRefGoogle Scholar
Dartnall, H.J. & Smith, V.R. 2012. Freshwater invertebrates of sub-Antarctic Marion Island. African Zoology, 47, 10.1080/15627020.2012.11407548.CrossRefGoogle Scholar
Elith, J., Leathwick, J.R. & Hastie, T. 2008. A working guide to boosted regression trees. Journal of Animal Ecology, 77, 10.1111/j.1365-2656.2008.01390.x.CrossRefGoogle ScholarPubMed
Essl, F., Dullinger, S., Moser, D., Rabitsch, W. & Kleinbauer, I. 2012. Vulnerability of mires under climate change: implications for nature conservation and climate change adaptation. Biodiversity and Conservation, 21, 10.1007/s10531-011-0206-x.CrossRefGoogle Scholar
Fick, S.E. & Hijmans, R.J. 2017. WorldClim 2: new 1 km spatial resolution climate surfaces for global land areas. International Journal of Climatology, 37, 10.1002/joc.5086.CrossRefGoogle Scholar
Fielding, A.H. & Bell, J.F. 1997. A review of methods for the assessment of prediction errors in conservation presence/absence models. Environmental Conservation, 24, 38–49.CrossRefGoogle Scholar
Finlayson, C. 2018. Ramsar Convention typology of wetlands. In Finlayson, C., Everard, M., Irvine, K., McInnes, R.J., Middleton, B.A., van Dam, A.A. & Davidson, N.C., eds, The wetland book I: structure and function, management and methods. Berlin: Springer, 1529–1532.CrossRefGoogle Scholar
Fitzgerald, N.B., Kirkpatrick, J.B., Dickson, C.R., Williams, L.K., Fergus, A.J. & Whinam, J. 2022. Determining the distributions of plant communities in subantarctic vegetation using species distribution models. Australian Journal of Botany, 70, 311–322.CrossRefGoogle Scholar
Food and Agriculture Organization of the United Nations. 2020. Peatland mapping and monitoring - recommendations and technical overview. Retrieved from https://doi.org/10.4060/ca8200en.CrossRefGoogle Scholar
Franklin, J. 2009. Mapping species distributions: spatial inference and prediction. Cambridge: Cambridge University Press, 340 pp.Google Scholar
Friedman, J.H. 1991. Multivariate adaptive regression splines. The Annals of Statistics, 19, 1–67.Google Scholar
González-Ferreras, A., Barquín, J. & Peñas, F. 2016. Integration of habitat models to predict fish distributions in several watersheds of northern Spain. Journal of Applied Ichthyology, 32, 10.1111/jai.13024.CrossRefGoogle Scholar
Gremmen, N.J.M. 1981. The vegetation of the subantarctic islands, Marion and Prince Edward. Thesis. Nijmegen: Radboud University, 149 pp.Google Scholar
Greve, M., von der Meden, C.E.O. & Janion-Scheepers, C. 2020. Biological invasions in South Africa's offshore sub-Antarctic territories. In Van Wilgen, B.W., Measey, J., Richardson, D.M., Wilson, J.R. & Zengeya, T.A., eds, Biological invasions in South Africa. New York: Springer Nature, 207–227.CrossRefGoogle Scholar
Greve, M., Mathakutha, R., Steyn, C. & Chown, S.L. 2017. Terrestrial invasions on sub-Antarctic Marion and Prince Edward Islands. Bothalia - African Biodiversity & Conservation, 47, 1–21.CrossRefGoogle Scholar
Grundling, P., Grundling, A. & Pretorius, L. 2017. South African peatlands: ecohydrological characteristics and socio-economic value: report to the Water Research Commission. Pretoria: Water Research Commission, 147 pp.Google Scholar
Guisan, A. & Zimmermann, N.E. 2000. Predictive habitat distribution models in ecology. Ecological Modelling, 135, 10.1016/S0304-3800(00)00354-9.CrossRefGoogle Scholar
Guisan, A., Edwards, T.C. Jr & Hastie, T. 2002. Generalized linear and generalized additive models in studies of species distributions: setting the scene. Ecological Modelling, 157, 10.1016/S0304-3800(02)00204-1.CrossRefGoogle Scholar
Harenda, K.M., Lamentowicz, M., Samson, M. & Chojnicki, B.H. 2018. The role of peatlands and their carbon storage function in the context of climate change. In Zielinski, T., Sagan, I. & Surosz, W., eds, Interdisciplinary approaches for sustainable development goals. Berlin: Springer, 169–187.CrossRefGoogle Scholar
Hedding, D.W. & Greve, M. 2018. Letters. Weather, 73, 10.1002/wea.3245.CrossRefGoogle Scholar
Hiestermann, J. & Rivers-Moore, N. 2015. Predictive modelling of wetland occurrence in KwaZulu-Natal, South Africa. South African Journal of Science, 111, 10.17159/SAJS.2015/20140179.CrossRefGoogle Scholar
Hunter, E.A., Raney, P.A., Gibbs, J.P. & Leopold, D.J. 2012. Improving wetland mitigation site identification through community distribution modeling and a patch-based ranking scheme. Wetlands, 32, 841–850.CrossRefGoogle Scholar
Intergovernmental Panel on Climate Change. 2021. Climate change 2021: the physical science basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press, 2391 pp.Google Scholar
Jiménez-Valverde, A. 2012. Insights into the area under the receiver operating characteristic curve (AUC) as a discrimination measure in species distribution modelling. Global Ecology and Biogeography, 21, 10.1111/j.1466-8238.2011.00683.x.CrossRefGoogle Scholar
Joosten, H. 2012. Status and prospects of global peatlands. Natur und Landschaft, 87, 50.Google Scholar
Joosten, H. & Clarke, D. 2002. Wise use of mires and peatlands. Greifswald: International Mire Conservation Group and International Peat Society, 304 pp.Google Scholar
Keith, D.A., Essl, F., Young, K. & Körner, C. 2020a. Polar tundra and deserts. In Keith, D.A., Ferrer-Paris, J.R., Nicholson, E. & Kingsford, R.T., eds, The IUCN Global Ecosystem Typology 2.0: descriptive profiles for biomes and ecosystem functional groups. Gland: IUCN, 70.CrossRefGoogle Scholar
Keith, D.A., Ferrer-Paris, J.R., Nicholson, E. & Kingsford, R.T. 2020b. IUCN Global Ecosystem Typology 2.0: descriptive profiles for biomes and ecosystem functional groups. Gland: IUCN, 170 pp.CrossRefGoogle Scholar
Könönen, O.H., Karjalainen, O., Aalto, J., Luoto, M. & Hjort, J. 2023. Environmental spaces for palsas and peat plateaus are disappearing at a circumpolar scale. The Cryosphere Discussions, 17, 10.5194/tc-17-3157-2023.Google Scholar
le Roux, P.C. 2008. Climate and climate change. In Chown, S. & Froneman, P.W., eds, The Prince Edward Islands: land-sea interactions in a changing ecosystem. Stellenbosch: SUN MeDIA, 39–64.Google Scholar
le Roux, P.C. & McGeoch, M.A. 2007. Changes in climate extremes, variability and signature on sub-Antarctic Marion Island. Climatic Change, 86, 10.1007/s10584-007-9259-y.Google Scholar
Leihy, R.I., Duffy, G.A., Nortje, E. & Chown, S.L. 2018. High resolution temperature data for ecological research and management on the Southern Ocean islands. Scientific Data, 5, 10.1038/sdata.2018.177.CrossRefGoogle ScholarPubMed
Leroy, B., Delsol, R., Hugueny, B., Meynard, C.N., Barhoumi, C., Barbet-Massin, M. & Bellard, C. 2018. Without quality presence-absence data, discrimination metrics such as TSS can be misleading measures of model performance. Journal of Biogeography, 45, 10.1111/jbi.13402.CrossRefGoogle Scholar
Lindsay, J.B. & Creed, I.F. 2005. Removal of artifact depressions from digital elevation models: towards a minimum impact approach. Hydrological Processes: An International Journal, 19, 10.1002/hyp.5835.CrossRefGoogle Scholar
Liu, C., Berry, P.M., Dawson, T.P. & Pearson, R.G. 2005. Selecting thresholds of occurrence in the prediction of species distributions. Ecography, 28, 385–393.CrossRefGoogle Scholar
Lobo, J.M., Jiménez-Valverde, A. & Real, R. 2008. AUC: a misleading measure of the performance of predictive distribution models. Global Ecology and Biogeography, 17, 10.1111/j.1466-8238.2007.00358.x.CrossRefGoogle Scholar
Lubbe, N.R. 2010. Soil characteristics and pedogenesis on sub-Antarctic Marion Island. Doctoral dissertation. Pretoria: University of Pretoria, 100 pp.Google Scholar
Mainali, K.P., Warren, D.L., Dhileepan, K., McConnachie, A., Strathie, L., Hassan, G., et al. 2015. Projecting future expansion of invasive species: comparing and improving methodologies for species distribution modeling. Global Change Biology, 21, 10.1111/gcb.13038.CrossRefGoogle ScholarPubMed
McCullagh, P. & Nelder, J. 1989. Generalized linear models II. Advanced School and Conference on Statistics and Applied Probability in Life Sciences.CrossRefGoogle Scholar
McPherson, J.M., Jetz, W. & Rogers, D.J. 2004. The effects of species’ range sizes on the accuracy of distribution models: ecological phenomenon or statistical artefact? Journal of Applied Ecology, 41, 10.1111/j.0021-8901.2004.00943.x.CrossRefGoogle Scholar
Millennium Ecosystem Assessment. 2005. Ecosystems and human well-being (vol. 5). Chicago, IL: Island Press, 137 pp.Google Scholar
Minasny, B., Berglund, Ö., Connolly, J., Hedley, C., de Vries, F., Gimona, A., et al. 2019. Digital mapping of peatlands - a critical review. Earth-Science Reviews, 196, 10.1016/j.earscirev.2019.05.014.CrossRefGoogle Scholar
Momberg, M., Hedding, D.W., Luoto, M. & le Roux, P.C. 2021. Exposing wind stress as a driver of fine-scale variation in plant communities. Journal of Ecology, 109, 10.1111/1365-2745.13625.CrossRefGoogle Scholar
Mucina, L. 2023. Biomes of the Southern Hemisphere. Cham: Springer Nature, 233 pp.CrossRefGoogle Scholar
Naimi, B. & Araújo, M.B. 2016. sdm: a reproducible and extensible R platform for species distribution modelling. Ecography, 39, 10.1111/ecog.01881.CrossRefGoogle Scholar
Ollis, D., Snaddon, C. & Job, N. 2013. Classification system for wetlands and other aquatic ecosystems in South Africa. Water SA, 41, 10.4314/wsa.v41i5.16.Google Scholar
Pakhomov, E.A. & Chown, S.L. 2003. The Prince Edward Islands: Southern Ocean oasis. Ocean Yearbook Online, 17, 348–379.CrossRefGoogle Scholar
Pendlebury, S. & Barnes-Keoghan, I.P. 2007. Climate and climate change in the sub-Antarctic. Papers and Proceedings of the Royal Society of Tasmania, 141, 10.26749/rstpp.141.1.67.Google Scholar
Peterson, A.T., Papeş, M. & Soberón, J. 2008. Rethinking receiver operating characteristic analysis applications in ecological niche modeling. Ecological Modelling, 213, 10.1016/j.ecolmodel.2007.11.008.CrossRefGoogle Scholar
R Core Team. 2024. R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing. Retrieved from https://www.R-project.org/.Google Scholar
Raeymaekers, G., Sundseth, K. & Gazenbeek, A. 2000. Conserving mires in the European Union. Luxembourg: Office for Official Publications of the European Communities, 90 pp.Google Scholar
Ramsar Convention on Wetlands. 2018. Global wetland outlook: state of the world's wetlands and their services to people. Gland: Ramsar Convention Secretariat. Retrieved from https://ssrn.com/abstract=3261606.Google Scholar
Rebelo, A.J., Scheunders, P., Esler, K.J. & Meire, P. 2017. Detecting, mapping and classifying wetland fragments at a landscape scale. Remote Sensing Applications: Society and Environment, 8, 10.1016/j.rsase.2017.09.005.Google Scholar
Rudolph, E., Hedding, D. & Nel, W. 2020. The surface geology of the Prince Edward Islands: refined spatial data and call for geoconservation. South African Journal of Geology, 124, 10.25131/sajg.124.0014.Google Scholar
Rydin, H., Jeglum, J.K. & Bennett, K.D. 2013. The biology of peatlands, 2nd edition. Oxford: Oxford University Press, 398 pp.CrossRefGoogle Scholar
Rydin, H., Sjörs, H. & Löfroth, M. 1999. Mires. Acta Phytogeographica Suecica, 84, 91–112.Google Scholar
Sadiki, M.M. 2019. Quantifying changes in surface water bodies in response to climate change on Marion Island. Unpublished honours project. Pretoria: University of Pretoria.Google Scholar
Selkirk, P. 2007. The nature and importance of the sub-Antarctic. Papers and Proceedings of the Royal Society of Tasmania, 141, 10.26749/rstpp.141.1.1.Google Scholar
Semeniuk, C. & Semeniuk, V. 1995. A geomorphic approach to global classification for inland wetlands. In Finlayson, C. & van der Valk, A., eds, Classification and inventory of the world's wetlands. Berlin: Springer, 103–124.CrossRefGoogle Scholar
Shambani, F., Kumar, L. & Ahmadii, M. 2018. Assessing accuracy methods of species distribution models: AUC, specificity, sensitivity and the true skill statistic. Global Journal of Human-Social Science: B Geography, Geo-Sciences, Environmental Science & Disaster Management, 18, 7–18.Google Scholar
Smith, V.R. 2002. Climate change in the sub-Antarctic: an illustration from Marion Island. Climatic Change, 52, 345–357.CrossRefGoogle Scholar
Smith, V.R. 2008. Terrestrial and freshwater primary production and nutrient cycling. In Chown, S.L. & Froneman, P.W., eds, The Prince Edward Islands: land-Sea Interactions in a changing ecosystem. Stellenbosch: SUN MeDIA, 181–214.Google Scholar
Smith, V.R. & Mucina, L. 2006. Vegetation of subantarctic Marion and Prince Edward islands. In Mucina, L. & Rutherford, M.C., eds, The vegetation of South Africa, Lesotho and Swaziland. Pretoria: South African National Biodiversity Institute, 699–723.Google Scholar
Smith, V.R. & Steenkamp, M. 1990. Climatic change and its ecological implications at a subantarctic island. Oecologia, 85, 10.1007/BF00317338.CrossRefGoogle Scholar
Smith, V.R., Steenkamp, M. & Gremmen, N.J.M. 2001. Terrestrial habitats on sub-Antarctic Marion Island: their vegetation, edaphic attributes, distribution and response to climate change. South African Journal of Botany, 67, 10.1016/s0254-6299(15)31195-9.CrossRefGoogle Scholar
Swets, J.A. 1988. Measuring the accuracy of diagnostic systems. Science, 240, 10.1126/science.3287615.CrossRefGoogle ScholarPubMed
Termansen, M., McClean, C.J. & Preston, C.D. 2006. The use of genetic algorithms and Bayesian classification to model species distributions. Ecological Modelling, 192, 10.1016/j.ecolmodel.2005.07.009.CrossRefGoogle Scholar
Wang, L. & Liu, H. 2006. An efficient method for identifying and filling surface depressions in digital elevation models for hydrologic analysis and modelling. International Journal of Geographical Information Science, 20, 10.1080/13658810500433453.CrossRefGoogle Scholar
Weiss, A. 2001. Topographic position and landforms analysis. Poster presentation, ESRI User Conference, San Diego, CA.Google Scholar
Wolf, J. & Fricker, A. 2013. Topographic-Wetness-Index: a revised version of the TWI written for arcpython. Retrieved from https://github.com/africker/Topographic-Wetness-Index.Google Scholar
Yeloff, D., Mauquoy, D., Barber, K., Way, S., van Geel, B. & Turney, C.S. 2007. Volcanic ash deposition and long-term vegetation change on subantarctic Marion Island. Arctic, Antarctic, and Alpine Research, 39, 500–511.CrossRefGoogle Scholar
Yu, Z., Beilman, D.W. & Jones, M.C. 2009. Sensitivity of northern peatland carbon dynamics to Holocene climate change. Carbon Cycling in Northern Peatlands, 184, 55–69.Google Scholar