Abstract

Agricultural intensification has contributed to the loss of biodiversity and of the ecosystem services that it supports, such as natural pest control. Decision support tools are needed to understand and predict where natural pest control can be enhanced and pesticide applications decreased. While many studies have assessed the impact of field and landscape-scale management in a range of crops, few attempts have been made to synthesize this knowledge in a single model. In this study, we developed an expert-based moving window model of natural pest control potential. This model builds on the knowledge of 52 experts across Europe regarding the importance of herbaceous areas, forest interiors, and edges, and field scale agricultural management practices (i.e. farming system, field size and crop diversity) for the abundance of generalist predators (e.g. carabids, spiders), specialist predators (e.g. coccinelids) and parasitoid natural enemies. We assessed the model's performance by comparing its predictions to field data on natural enemy abundance from 117 sites in Sweden. The natural pest control potential scores predicted by the model explained 11% of the variation in carabid field abundances. However, the model's performance was less satisfactory for spiders and parasitoids. We provide guidance for improving this indicator, particularly by incorporating more ecological processes, such as accounting for the functional diversity of spiders and the density-dependent effects of parasitoid-host interactions. In addition, the model could be further refined by accounting for non -linear relations and potential threshold effects and interactions among field and landscape-scale management practices. In its current state, the developed indicator can be used to identify areas where further ecological intensification practices can be promoted to enhance natural pest control potential, especially for carabids.

Keywords

Ecosystem services; Biological pest control; Natural enemies; Ecosystem service mapping; Expert-based modelling; Landscape management

Published in

Ecological Indicators
2024, Volume: 159, article number: 111684
Publisher: ELSEVIER

SLU Authors

• Riggi, Laura

  • Department of Ecology, Swedish University of Agricultural Sciences
    
  • Wageningen University and Research

• Aguilera Nuñez, Guillermo

  • Department of Ecology, Swedish University of Agricultural Sciences
    

• Chopin, Pierre

  • Department of Crop Production Ecology, Swedish University of Agricultural Sciences
    
  • Vrije Universiteit Amsterdam

UKÄ Subject classification

Environmental Sciences related to Agriculture and Land-use
Ecology


Publication identifier

DOI: https://doi.org/10.1016/j.ecolind.2024.111684

Permanent link to this page (URI)

https://res.slu.se/id/publ/129239