The impact of data precision on the effectiveness of alien plant control programmes: a case study from a protected area

Abstract

Successful long-term invasive alien plant control programmes rely on alien plant distribution and abundance data to assess, prioritise, implement and monitor the efficacy of the programme. Here we assess the impact of data accuracy using the alien plant programme in Table Mountain National Park, South Africa. A systematic plot-based survey method was carried out to assess the distribution of alien plants in the park at a fine scale (systematic sampling). Alien plant richness, total area invaded and the degree of spatial overlap in species’ presence were compared between the systematic sample and a protected area (PA) managers’ dataset (collated from collective observations by park visitors, rangers and managers) and Working for Water (WfW) project data (data collected for the planning and implementation of the alien plant clearing programme) using a range of confusion matrix-based statistics to assess similarity and error rates between the datasets. A total of 106 alien plant taxa were detected across the three datasets, 12 in PA manager’s data, 23 in WfW data and 101 in the systematic survey. Overall, there was substantive disagreement between the datasets on the distribution of alien plants. For example both management datasets estimated species’ hectare coverage at orders of magnitude greater than indicated by systematic sampling. The inaccuracy of manager data has direct negative implications for funding allocation, which currently appears to be in excess of what is required. We recommend that contrary to perception, fine-scale surveys are a cost-effective way to inform long-term monitoring programmes and improve programme effectiveness.