How practitioners integrate decision triggers with existing metrics in conservation monitoring.

Decision triggers are defined thresholds in the status of monitored variables that indicate when to undertake management, and avoid undesirable ecosystem change. Decision triggers are frequently recommended to conservation practitioners as a tool to facilitate evidence-based management practices, but there has been limited attention paid to how practitioners are integrating decision triggers into existing monitoring programs. We sought to understand whether conservation practitioners' use of decision triggers was influenced by the type of variables in their monitoring programs. We investigated this question using a practitioner-focused workshop involving a structured discussion and review of eight monitoring programs. Among our case studies, direct measures of biodiversity (e.g. native species) were more commonly monitored, but less likely to be linked to decision triggers (10% with triggers) than measures being used as surrogates (54% with triggers) for program objectives. This was because decision triggers were associated with management of threatening processes, which were often monitored as a surrogate for a biodiversity asset of interest. By contrast, direct measures of biodiversity were more commonly associated with informal decision processes that led to activities such as management reviews or external consultation. Workshop participants were in favor of including more formalized decision triggers in their programs, but were limited by incomplete ecological knowledge, lack of appropriately skilled staff, funding constraints, and/or uncertainty regarding intervention effectiveness. We recommend that practitioners consider including decision triggers for discussion activities (such as external consultation) in their programs as more than just early warning points for future interventions, particularly for direct measures. Decision triggers for discussions should be recognized as a critical feature of monitoring programs where information and operational limitations inhibit the use of decision triggers for interventions.

Cost-Effective Large-Scale Occupancy-Abundance Monitoring of Invasive Brushtail Possums (Trichosurus Vulpecula) on New Zealand's Public Conservation Land.

There is interest in large-scale and unbiased monitoring of biodiversity status and trend, but there are few published examples of such monitoring being implemented. The New Zealand Department of Conservation is implementing a monitoring program that involves sampling selected biota at the vertices of an 8-km grid superimposed over the 8.6 million hectares of public conservation land that it manages. The introduced brushtail possum (Trichosurus Vulpecula) is a major threat to some biota and is one taxon that they wish to monitor and report on. A pilot study revealed that the traditional method of monitoring possums using leg-hold traps set for two nights, termed the Trap Catch Index, was a constraint on the cost and logistical feasibility of the monitoring program. A phased implementation of the monitoring program was therefore conducted to collect data for evaluating the trade-off between possum occupancy-abundance estimates and the costs of sampling for one night rather than two nights. Reducing trapping effort from two nights to one night along four trap-lines reduced the estimated costs of monitoring by 5.8% due to savings in labour, food and allowances; it had a negligible effect on estimated national possum occupancy but resulted in slightly higher and less precise estimates of relative possum abundance. Monitoring possums for one night rather than two nights would provide an annual saving of NZ$72,400, with 271 fewer field days required for sampling. Possums occupied 60% (95% credible interval; 53-68) of sampling locations on New Zealand's public conservation land, with a mean relative abundance (Trap Catch Index) of 2.7% (2.0-3.5). Possum occupancy and abundance were higher in forest than in non-forest habitats. Our case study illustrates the need to evaluate relationships between sampling design, cost, and occupancy-abundance estimates when designing and implementing large-scale occupancy-abundance monitoring programs.