An efficient method of evaluating multiple concurrent management actions on invasive populations

Abstract

Evaluating the efficacy of management actions to control invasive species is crucial for maintaining funding and to provide feedback for the continual improvement of management efforts. However, it is often difficult to assess the efficacy of control methods due to limited resources for monitoring. Managers may view effort on monitoring as effort taken away from performing management actions. We developed a method to estimate invasive species abundance, evaluate management effectiveness, and evaluate population growth over time from a combination of removal activities (e.g., trapping, ground shooting) using only data collected during removal efforts (method of removal, date, location, number of animals removed, and effort). This dynamic approach allows for abundance estimation at discrete time points and the estimation of population growth between removal periods. To test this approach, we simulated over 1 million conditions, including varying the length of the study, the size of the area examined, the number of removal events, the capture rates, and the area impacted by removal efforts. Our estimates were unbiased (within 10% of truth) 81% of the time and were correlated with truth 91% of the time. This method performs well overall and, in particular, at monitoring trends in abundances over time. We applied this method to removal data from Mingo National Wildlife Refuge in Missouri from December 2015 to September 2019, where the management objective is elimination. Populations of feral swine on Mingo NWR have fluctuated over time but showed marked declines in the last 3–6 months of the time series corresponding to increased removal pressure. Our approach allows for the estimation of population growth across time (from both births and immigration) and therefore, provides a target removal rate (above that of the population growth) to ensure the population will decline. In Mingo NWR, the target monthly removal rate is 18% to cause a population decline. Our method provides advancement over traditional removal modeling approaches because it can be applied to evaluate management programs that use a broad range of removal techniques concurrently and whose management effort and spatial coverage vary across time.

Incorporating metapopulation dynamics to inform invasive species management: Evaluating bighead and silver carp control strategies in the Illinois River

“…Although sensitivity analysis indicated that our results (i.e. proportional declines in abundance) were largely insensitive to the steepness parameter in this relationship , can be managed by suppressing source populations (Baker, 2017;Perry et al, 2017), reducing dispersal (Lurgi et al, 2016), and combining multiple strategies can lead to more effective control (Davis et al, 2021;Day et al, 2018). In the long term, using metapopulation modelling to inform conservation strategies will increase effectiveness of management programs (Lustig et al, 2019).…”

Adaptive risk‐based targeted surveillance for foreign animal diseases at the wildlife‐livestock interface

“…Bayesian catch-effort model implemented on the scale of management units (Davis et al, 2021) and scaled up to the county-level using spatial statistics and environmental covariates (Miller et known to serve as a potential forage resource for wild pigs (Mayer et al, 2021). While no empirical studies are available to substantiate this proposed risk pathway, it may be an important route of introduction.…”

“…These data represent the known nationwide county‐level distribution of wild pigs over the past 38 years and have been used to forecast the spread of wild pigs (Snow et al., 2017 ), estimate occurrence (McClure et al., 2015 ), estimate effects of management on spatial spread (Pepin et al., 2019 ), determine wild pig risks posed to agriculture (Miller et al., 2017 ) and predict corresponding policy activity (Miller et al., 2018 ). These occurrence data were used with management removal data using a Bayesian catch‐effort model implemented on the scale of management units (Davis et al., 2021 ) and scaled up to the county‐level using spatial statistics and environmental covariates (Miller et al., Unpublished data). The catch effort model generates predictions of wild pig density for each county at a monthly scale while accounting for differing removal methods, habitat, climate and other factors affecting either population growth or probability of capture.…”

Optimizing management of invasions in an uncertain world using dynamic spatial models

“…Thus, it is important to establish the value of monitoring to the management objectives, which can be accomplished using uncertainty analysis and evaluating the optimization criteria under different levels of uncertainty. A complementary approach is to develop monitoring techniques that impose little burden on managers (e.g., Davis, Leland, et al, 2018; Davis et al, 2022; Moore et al, 2017). As an example, controlling gray sallow invasion into alpine bogs in Australia demonstrated the importance of investing time in developing less burdensome monitoring techniques—in this case moving from paper‐based data recording to automated GPS tracking—to decrease the costs of monitoring and improve the quality of the monitoring data for model evaluation and analysis (Moore et al, 2017).…”