Predicting mire distribution using species distribution models (SDMs) and GISc : a case study of the Prince Edward Islands (PEIs)

Abstract

Peatlands are wetlands with peat-producing plants that account for one-third of wetlands worldwide and provide a variety of ecological functions and ecosystem services such as carbon storage, biomass production, biodiversity conservation, and climate regulation. As important ecological systems that are vulnerable to climate change, it is critical to assess what drives mire distribution in order to predict the potential impact of climate change on their distribution. As a Special Nature Reserve, the Prince Edward Islands (PEIs) are important conservation areas for South Africa. They support extensive peatlands, which are actively accumulating peat, and are, therefore, referred to as mires. The Islands have experienced severe reductions in precipitation and significant warming in the last decades; anecdotal evidence suggests that these have affected the occurrence and extent of mires on the PEIs. Factors that drive mire occurrence are unclear and must be identified in order to improve the ability to monitor them over time and this can be achieved using Species distribution models (SDMs). SDMs are a significant tool in studies on species distribution, the ecological consequences of climate change, and efforts to protect specific species or biodiversity as a whole. Predictive models have been used effectively to map and detect wetlands at both the local and regional levels. The aim of this study was to use species distribution modelling to understand the drivers and predict the island-wide distribution of mires on the PEIs.

A total of 1415 mire presence-absence points from a vegetation field survey conducted on Marion Island from 2018 to 2020 were used. As there is no single best SDM algorithm, and it is difficult to accurately identify which environmental variables drive the distribution of mires on the PEIs, multiple regression- based and machine learning SDMs based on six different combinations of environmental factors were investigated. The environmental variables combinations included climate variables, topographic, geology and soils and satellite imagery variables, a combination thereof and wetland classification proxy variables from three wetland classification systems (Ramsar, Hydrogeomorphic (HGM), International Union for Conservation of Nature (IUCN) Global Ecosystem Typology 2.0 wetland classification systems).

Random Forest model performed the best, only performing fairly in terms of the AUC (0.74) and TSS (0.42) metrics but managing a 99% correct classification rate (CCR) of all the mire presence-absence MM Sadiki: MSc Dissertation Page | iiobservations when trained and tested on Marion Island. The distribution of mires was largely influenced by surface wetness and slope. Low annual mean temperature, low temperature and precipitation seasonality, and increasing distance from coast (up to 7.2 km inland) also influenced the distribution of mires on the PEIs, though less strongly than surface wetness and slope. According to the model predictions, mires occupy 8.7 km2 (of ~290 km2; ~ 3%) of Marion Island and 2.63 km2 (of ~ 45 km2; ~6%) of Prince Edward Island respectively.

The predictive performance and reliability of the models can be improved by making enhancements to the datasets of environmental variables in terms of resolution. This is especially true for the spatial, temporal, and spectral resolutions of satellite imagery used to model environmental variables, the spatial resolution of the WorldClim climate data (which is currently based on data from only one meteorological station on Marion Island), and the spatial resolution and accuracy of the geology dataset. The inclusion of other environmental variables may also improve the predictive ability of the models in this study.