Recovery based on plot experiments is a poor predictor of landscape‐level population impacts of agricultural pesticides

Abstract

Current European Union regulatory risk assessment allows application of pesticides provided that recovery of nontarget arthropods in-crop occurs within a year. Despite the long-established theory of source-sink dynamics, risk assessment ignores depletion of surrounding populations and typical field trials are restricted to plot-scale experiments. In the present study, the authors used agent-based modeling of 2 contrasting invertebrates, a spider and a beetle, to assess how the area of pesticide application and environmental half-life affect the assessment of recovery at the plot scale and impact the population at the landscape scale. Small-scale plot experiments were simulated for pesticides with different application rates and environmental half-lives. The same pesticides were then evaluated at the landscape scale (10 km × 10 km) assuming continuous year-on-year usage. The authors' results show that recovery time estimated from plot experiments is a poor indicator of long-term population impact at the landscape level and that the spatial scale of pesticide application strongly determines population-level impact. This raises serious doubts as to the utility of plot-recovery experiments in pesticide regulatory risk assessment for population-level protection. Predictions from the model are supported by empirical evidence from a series of studies carried out in the decade starting in 1988. The issues raised then can now be addressed using simulation. Prediction of impacts at landscape scales should be more widely used in assessing the risks posed by environmental stressors.

Towards a landscape scale management of pesticides: ERA using changes in modelled occupancy and abundance to assess long-term population impacts of pesticides

“…For example, in ERA focus is often placed on recovery of in-field populations, utilizing the spatial dynamics of mobile agricultural land species. However, this recovery is normally based on small plot experiments that do not take into account the landscape-scale impacts of source-sink dynamics (Topping and Lagisz 2012, Topping, Kjaer et al 2013, Focks, ter Horst et al 2014). …”

The pros and cons of ecological risk assessment based on data from different levels of biological organization

“…Consequently, most landscape studies are correlational, at best taking advantage of natural experiments. The exception would be mathematical modeling studies that attempt to assess the effects of a contaminant within a metapopulation (connected populations) or metacommunity (connected communities) context (Leibold et al 2004, Topping et al 2015, Topping et al 2016, Topping et al 2014). Nevertheless, landscape-level ERA is becoming more feasible as countries have begun national water quality monitoring programs (e.g.…”

“…However, once landscape models have been developed and validated, the cost could decrease (e.g. Topping et al 2015, Topping et al 2016, Topping et al 2014). …”

“…But given the ephemeral nature of some chemicals and the associated logistical challenges of covering large spatial scales, we do not propose that landscape field studies are an effective or efficient approach for screening large numbers of chemicals. In contrast, it might be possible to develop and validate landscape models across multiple chemicals and then use these validated models to predict effects on untested chemicals (see Topping et al 2015, Topping et al 2016, Topping et al 2014), but this remains to be seen. Because of these logistical challenges, landscape studies often only focus on one to a few species and thus often have measurement endpoints that far from the most defensible assessment endpoints.…”

How to use mechanistic effect models in environmental risk assessment of pesticides: Case studies and recommendations from the SETAC workshop MODELINK

“…Population models range from very simple generic models, such as exponential or logistic growth (Barnthouse ), over‐matrix models (Caswell ; Stark et al ; Ibrahim et al ), which differentiate age classes or developmental states, to individual‐based models (sometimes called agent‐based models) taking into account variability among individuals, their local interactions, and their adaptive behaviour (e.g., Van den Brink et al ; Preuss et al ). More complex individual‐based models also may take into account resource dynamics and spatial landscape patterns (e.g., Topping et al ; Wang and Grimm ; Topping et al ).…”