The potential distributions, and estimated spatial requirements and population sizes, of the medium to large-sized mammals in the planning domain of the Greater Addo Elephant National Park project

摘要

The Greater Addo Elephant National Park project (GAENP) involves the establishment of a mega biodiversity reserve in the Eastern Cape, South Africa. Conservation planning in the GAENP planning domain requires systematic information on the potential distributions and estimated spatial requirements, and population sizes of the medium to largesized mammals. The potential distribution of each species is based on a combination of literature survey, a review of their ecological requirements, and consultation with conservation scientists and managers. Spatial requirements were estimated within 21 Mammal Habitat Classes derived from 43 Land Classes delineated by expert-based vegetation and river mapping procedures. These estimates were derived from spreadsheet models based on forage availability estimates and the metabolic requirements of the respective mammal species, and that incorporate modifications of the agriculture-based Large Stock Unit approach. The potential population size of each species was calculated by multiplying its density estimate with the area of suitable habitat. Population sizes were calculated for pristine, or near pristine, habitats alone, and then for these habitats together with potentially restorable habitats for two park planning domain scenarios. These data will enable (a) the measurement of the effectiveness of the GAENP in achieving predetermined demographic, genetic and evolutionary targets for mammals that can potentially occur in selected park sizes and configurations, (b) decisions regarding acquisition of additional land to achieve these targets to be informed, (c) the identification of species for which targets can only be met through metapopulation management,(d) park managers to be guided regarding the re-introduction of appropriate species, and (e) the application of realistic stocking rates. Where possible, the model predictions were tested by comparison with empirical data, which in general corroborated the predictions. All estimates should be considered as testable hypotheses.