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Use of openly available occurrence data to generate biodiversity maps within the South African EEZ
Authors:D Yemane  SP Kirkman  T Samaai
Institution:1. Department of Environment, Forestry and Fisheries (DEFF), Cape Town, South Africa;2. Marine Research Institute, University of Cape Town, Cape Town, South Africadawityemane@gmail.comORCID Iconhttps://orcid.org/0000-0001-7293-2534;4. Institute for Coastal and Marine Research, Nelson Mandela University, Port Elizabeth, South AfricaORCID Iconhttps://orcid.org/0000-0001-5428-7375;5. Biodiversity and Conservation Department, Faculty of Natural Sciences, University of the Western Cape, Cape Town, South Africa;6. Department of Biological Sciences, University of Cape Town, Cape Town, South Africa;7. Marine Biology Department, Iziko Museums, Cape Town, South AfricaORCID Iconhttps://orcid.org/0000-0001-7269-293X
Abstract:Biodiversity maps are an important component of ecosystem-based management and conservation. In the past, biodiversity maps were largely generated using patchy occurrence data from a range of data sources. Currently, substantial species occurrence data are readily available for large parts of the world and are accessible programmatically. Considering both the observed and expected changes in species distributions, and hence biodiversity, in response to present and future climate change, it is important to utilise readily available species occurrence databases to generate temporal and spatial biodiversity maps. In this study biodiversity maps were generated for three generic functional groups within the exclusive economic zone of South Africa: zooplankton, fishes and benthos. This was achieved by stacking individual species-distribution maps. Freely available occurrence data from the Ocean Biogeographic Information System (OBIS) were accessed for this purpose. Ensemble species-distribution modelling, employing five widely used statistical methods, was used to generate species-distribution maps for each functional group. The resulting spatial patterns of biodiversity for the three functional groups were largely in agreement with known patterns. The results of this study highlight the value of open-source occurrence and environmental data to generate biodiversity maps that can potentially be used in future spatial prioritisation or planning for management of marine areas and in environmental-change studies.
Keywords:conservation planning  environmental data  mapping  marine areas  model stacking  open-source data  spatial pattern  species distribution model
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