Measuring the ecological success of the Communal Areas Management Programme For Indigenous Resources (CAMPFIRE) in north-western Zimbabwe.
The Communal Areas Management Programme for Indigenous Resources (CAMPFIRE) is a flagship Community Based Natural Resources Management (CBNRM) developed in Zimbabwe. CBNRMs are growing in the unprotected lands surrounding many of the Africa’s protected areas. The inﬂuence of these initiatives on ecological processes is poorly understood. The goal of the study was to draw on ecological theory to provide a synthetic framework for understanding how CBNRMs around protected areas may alter ecological processes and biodiversity to provide a basis for identifying scientiﬁcally based management alternatives. We hypothesized that hunting produces a mosaic of sources and sinks that allows elephant and kudu to persist outside protected areas. Specifically the source sink model was tested empirically to determine how population dynamics in sinks differ significantly. Data for hunted ungulates species - Loxodonta africana africana (African elephant) and Tragelaphus strepsiceros (Greater kudu). The data recorded during road strip counts includes the species, number of individuals, age and sex composition, GPS location and distance category from park boundary. The study compared group size and age structure dynamics of elephants and kudus between two spatially heterogeneous sinks (a CAMPFIRE area not under protection versus protected safari hunting area).
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The study was conducted in Tsholotsho (TSH) District, situated in Matabeleland North Province of Zimbabwe and is adjacent to Hwange National Park (HNP) which is to the north-west of the District. Being located next to the massive HNP, TSH is home to mostly all the animal species found in Zimbabwe. The study area covered part of Matetsi Safari Area (MSA) which is also adjacent to the Hwange National Park. Matetsi Safari Area is a major hunting complex in Zimbabwe on the northwestern border of HNP. Matetsi (MSA) and Hwange National Park (HNP) are unfenced state-owned lands administered by Zimbabwe Parks and Wildlife Management Authority (ZPWMA). The target population was the elephant and kudu. Surveys on 46 survey routes were established throughout the sites during 2009 - 2015. Road count data for TSH which is a CAMPFIRE district was compared with MSA which is a protected area under the Zimbabwe National Parks Estates. Surveys were conducted in four distance categories, T1 (0 – 4km), T2 (4 – 8km), T3 (8 – 12km) and T4 (> 12km) from the source boundary (HNP). Surveys began between 0630 and 0800 hours and conducted them by driving along transects at a speed of 5 - 20 km/hour. A maximum of five and a minimum of two observers performed the surveys. Data logged for each direct sighting included species, (observer's) distance along a transect, estimated distance and angle of the observer to the first animal seen (for calculation of perpendicular distances), the estimated number of animals, and vegetation type. A total of 740 km of transects in the four zones inside and outside a protected area, were surveyed. Outside a protected area (TSH), transects in the T1 category, were observed 123 times. Sixty-six surveys were conducted in distance category T2, and categories T3 and T4 were surveyed on 27 and 38 different occasions, respectively. Group size and age structure distribution maps were generated for elephant and kudu for the two sink areas (TSH and MSA) to assess differences in patterns. Group sizes were considered as an index of elephant and kudu abundance. Ordinary kriging was used to develop prediction maps for elephant and kudu group size distribution. Kriging was conducted using Quantum GIS. The data collected were analysed using summary statistics (means, standard error of mean and variance). To test for a variation in group sizes across the four distance categories, we performed analyses of variance. The categories were considered independent. The hypothesis was tested using general linear models (SPSS), which allows for unbalanced replicates, to test for significant differences between distance categories. To each pair of a distance, the category was further analysed for statistical differences. The mean of the significantly different group sizes was separated using Student t tests and Least Significance Difference (LSD). To test differences in proportions of age classes’ chi-square tests were used.