Many invasive plant species are sparsely distributed across large areas Management of these species is often undertaken using a search and destroy approach where people search the landscape and treat (destroy) any individuals found However detection is imperfect and so these searches need to be undertaken on multiple occasions Given limited resources an explicit objective is to optimize efforts by targeting those areas for follow up visits that have the highest predicted abundance In order to simultaneously estimate both abundances and detection rates from data on search and destroy efforts it is necessary to have a good model of the detection process itself In a case study of invasive willow control across 120 km2 in alpine Australia intensively monitored sample plots were used to characterise how detection rates depend on perceived abundance for three groups of willow control contractors Bayesian models were used to fit an exponential detection function where the detection rate varied with plant size between contractors with the total number of willows treated and on features of the contractors movements It was found that detection rates decreased with increasing abundance but areas with high abundance were subject to greater search effort These models were combined with GPS tracking data representing 6 weeks of search and destroy missions to predict the remaining abundance of willows across the landscape and hence areas that are priorities for follow up control were identified
Moore, J.L., and E. Gurarie. “Accounting for Imperfect Detection When Evaluating the Effectiveness of Invasive Species Control”. New Zealand Plant Protection 67 (January 8, 2014): 320–320. Accessed February 25, 2021. https://journal.nzpps.org/index.php/nzpp/article/view/5782.