Abstract
AbstractWith the regulation of pesticides in European agricultural landscapes, it is important to understand how pest populations respond to climate and landscape variables in the absence of pesticides at different spatial–temporal scales. While models have described individual biological processes, few have simulated complete life cycles at such scales. We developed a spatially explicit simulation model of the dynamics of the bird cherry–oat aphid (Rhopalosiphum padi) in a pesticide‐free simulated landscape using data from an agricultural landscape located in southwest France. Using GLMMs, we ran two statistical methods, one at the crop level, focusing on aphid densities within each crop individually (wheat and its regrowth, corn, and sorghum), and another at the landscape level where aphid densities were not differentiated by crops. For each season, we analyzed how temperature, immigration, and habitat availability impacted on aphid densities. Predictors of aphid densities varied between crops and between seasons, and models for each individual crop resulted in better predictions of aphid densities than landscape‐level models. Aphid immigration and temperature were important predictors of aphid densities across models but varied in the directionality of their effects. Moreover, landscape composition was a significant predictor in only four of the nine seasonal crop models. This highlights the complexity of pest–landscape interactions and the necessity of considering fine spatial–temporal scales to identify key factors that influence aphid densities, essential for developing future regulation methods. We used our model to explore the potential effects of two agronomic scenarios on aphid densities: (1) replacement of corn with sorghum, where increases in available sorghum led to the dilution of aphid populations in sorghum in spring and their concentration in summer, and (2) abandonment of pastures for wheat fields, which had no significant effect on aphid densities at the landscape scale. By simulating potential future agronomic practices, we can identify the risks of such changes and inform policy and decision‐makers to better anticipate pest dynamics in the absence of pesticides. This approach can be applied to other systems where agronomic and land cover data are available, and to other pest species for which biological processes are described in the literature.
Highlights
Agriculture is highly impacted by the presence of pests (Deutsch et al 2018)
We explored two agronomic scenarios, based on changes in agricultural practices currently observed in our study region, to determine whether R. padi populations are likely to increase in the future with agricultural landscape changes
Simulation planning We explored six different versions of the simulation model (Table 1): (1) a reference model integrating habitat quality and a 30% potential daily mortality rate due to predation pressure, (2) a null model where phenological stages of all crops had the same effect of aphid dynamics, (3) a high predation model where predation pressure was increased (50% potential daily mortality rate), (4) a low predation model where predation pressure was decreased (10% potential daily mortality rate), (5) a pasture scenario where livestock was abandoned for wheat, and (6) a sorghum scenario where corn was replaced entirely with sorghum
Summary
Agriculture is highly impacted by the presence of pests (Deutsch et al 2018). Aphids, in particular, affect a wide variety of crops and can be found worldwide (Van Emden and Harrington 2017). Other than favorable climatic conditions, aphids require different habitats to fulfill their biological cycles, which can be unevenly distributed through space and time (Schellhorn et al 2015). The composition and configuration of habitats within landscapes (Chaplin-Kramer et al 2011) should be considered when studying aphid population dynamics. Recent meta-analyses have, highlighted that natural pest regulation exhibits inconsistent responses to surrounding landscape structure (Rusch et al 2016, Karp et al 2018). One hypothesis explaining this result is that agricultural landscapes are subject to frequent changes due to their strong anthropogenic nature, which leads to highly variable spatial–temporal dynamics (Urruty et al 2016). Farm management shapes spatial–temporal variability of land cover through factors such as crop rotation (Wibberley 1996), crop variety (Asrat et al 2010), or individual crops managed differently due to the experience and ideological beliefs of farmers (McGuire et al 2015)
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