Abstract
Remnant areas hosting natural vegetation in agricultural landscapes can impact the disease epidemiology and evolutionary dynamics of crop pathogens. However, the potential consequences for crop diseases of the composition, the spatial configuration and the persistence time of the agro-ecological interface – the area where crops and remnant vegetation are in contact – have been poorly studied. Here, we develop a demographic–genetic simulation model to study how the spatial and temporal distribution of remnant wild vegetation patches embedded in an agricultural landscape can drive the emergence of a crop pathogen and its subsequent specialization on the crop host. We found that landscape structures that promoted larger pathogen populations on the wild host facilitated the emergence of a crop pathogen, but such landscape structures also reduced the potential for the pathogen population to adapt to the crop. In addition, the evolutionary trajectory of the pathogen population was determined by interactions between the factors describing the landscape structure and those describing the pathogen life histories. Our study contributes to a better understanding of how the shift of land-use patterns in agricultural landscapes might influence crop diseases to provide predictive tools to evaluate management practices.
Highlights
Integrating ecosystem processes occurring at large spatial scales into the design of agricultural landscapes with the aim of improving productivity while decreasing the negative impact of agricultural practices on the environment is increasingly recognized as key to addressing global food security concerns (Bianchi et al 2006; Tscharntke et al 2012; Bommarco et al 2013)
Pathogen evolution resulted in coexistence of a crop specialist and a wild host specialist when the agricultural season was long enough and the trade-off not too strong and to unsuccessful emergence of a crop pathogen otherwise
Modelling the spatial structure of the AE interface explicitly enabled us to assess its effects on the emergence of a new crop pathogen and the potential for evolution towards increased aggressiveness (Fig. 2)
Summary
Integrating ecosystem processes occurring at large spatial scales into the design of agricultural landscapes with the aim of improving productivity while decreasing the negative impact of agricultural practices on the environment is increasingly recognized as key to addressing global food security concerns (Bianchi et al 2006; Tscharntke et al 2012; Bommarco et al 2013). Statistical models The effects of the input factors (cropping season duration, proportion and aggregation level of wild patches, trade-off shape and pathogen mean dispersal distance) on the descriptors of the pathogen evolutionary trajectory (E, TE, and Rc0;loc) were assessed by fitting generalized linear models (GLM) with various link functions using the R software (R Core Team 2014) (Table 2). LOC, two-level factor of wild patches spatial aggregation (random or clustered); PROP, scaled wild host proportion; BETA, scaled parameter of the trade-off function (b); CROP, scaled cropping season duration; DISP, scaled pathogen mean dispersal distance; :, interactions
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