Abstract

SMIT, Izak P.J.; RIDDELL, Edward S.; CULLUM, Carola  and  PETERSEN, Robin. Kruger National Park research supersites: Establishing long-term research sites for cross-disciplinary, multiscaled learning. Koedoe [online]. 2013, vol.55, n.1, pp.01-07. ISSN 2071-0771.

Researchers interested in studying the effects of fire or herbivory in the Kruger National Park (KNP) often focus their research activities on the experimental burn plots or herbivore exclosure camps, respectively. These are manipulated sites that apply treatments, for example annual fires or total exclusion of fire and herbivores. However, many projects aim to study or monitor patterns and processes emerging under non-manipulated conditions, typically at sites with contrasting geologies and rainfall. Yet, these sites are usually selected in a haphazard and uncoordinated manner for different projects and, as a consequence, it is often not possible to integrate datasets and knowledge. An alternative to the ever-increasing number of unrelated sites scattered across the park are the 'KNP research supersites' which have been earmarked to geographically focus future research effort, acting as data-rich, long-term sites for monitoring and research. In this paper, we introduced the four recently established KNP research supersites, which cover the rainfall gradient and geological contrast of the KNP, presenting their rationale, selection criteria and location, along with existing datasets that describe their herbaceous biomass, woody cover, phenology, fire history, levels of herbivory. Additional site-specific datasets, which are already available, or which are in preparation, were outlined together with details for assessing these open-source datasets online. Conservation Implications: The KNP research supersites will become increasingly used for research, monitoring and remote-sensing calibration and ground-truthing purposes. Scientists are encouraged to gain from, and contribute towards, these sites, which will facilitate longterm data collection, data-sharing and co-learning and, ultimately, lead to a more integrated, multiscaled and multitemporal understanding of savannahs.

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