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.

A priority-setting scheme for the management of invasive non-native species in protected areas

“…In this study, formal sampling techniques were applied for data collection on nonnative species. Although systematic sampling will produce the best possible data set (Cheney et al 2018), this is, however, not the only approach for data collection. With training and experience, protected area staff can conduct field surveys to register the occurrence of non-native species with a GPS, while noting down invasion stage and frequency, as well as inferring invasion risk.…”

“…Information from other references is useful to corroborate these assumptions, as mentioned before. Cheney et al (2018) demonstrated how mistaken general perceptions can be, as there was substantive disagreement between datasets produced by managers and by systematic sampling, implying that field data has to be collected locally. The application of our scheme entails full cover of protected areas, or of entire sections in the case of former prioritization of sites for control, very large areas, or very limited financial resources.…”

“…Species invasiveness, habitat invasibility and impact also differ between ecosystems, so the risk of invasion varies. At the same time, the opportunities to effectively eradicate or control invasive species are limited due to constraints on time, financial and other resources, lost opportunity costs and conflicting priorities (Robertson et al 2003;Cheney et al 2018). Therefore, priority-setting is key to help natural area managers invest efforts on biological invasions that offer the best chances of producing large scale positive results at the lowest cost possible (Gallardo and Aldridge 2013).…”

“…Later schemes, such as Forsyth et al (2012) prioritized species first, then combined spatial data with stakeholder input to identify priority catchments for control in South Africa. Cheney et al (2018) showed that systematic surveys are the best option to generate reliable data for priority-setting schemes, although the cost may be high. Available schemes such as these were however considered too complex for application by managers or field practitioners, too costly, or too time-consuming.…”

The Extent and Effectiveness of Alien Plant Control Projects in South Africa

“…These assumptions include that effective and professional clearing would continue into the future; that co-financing would be available; that due compensation for the services rendered and the value-added products produced would be realised; that the extent of the invasions was accurately known; and that the costs had been accurately estimated. Most, or even all, of the above assumptions will not hold, however, because alien plant invasions are rarely accurately mapped (see, for example, Cheney et al 2018); the costs of control are routinely under-estimated by a factor of 3-7 times; there are low levels of efficiency associated with control work; and including value-added products could lead to unintended consequences. Consequently, there can only be a low level of confidence in these predictions of the return on investment from control projects.…”