A tool for testing integrated pest management strategies on a tritrophic system involving pollen beetle, its parasitoid and oilseed rape at the landscape scale

The intensification of agriculture has led to a loss of biodiversity and subsequently to a decrease in ecosystem services, including regulation of pests by natural enemies. Biological regulation of pests is a complex process affected by both landscape configuration and agricultural practices. Although modeling tools are needed to design innovative integrated pest management strategies that consider tritrophic interactions at the landscape scale, landscape models that consider agricultural practices as levers to enhance biological regulation are lacking. To begin filling this gap, we developed a grid-based lattice model called Mosaic-Pest that simulates the spatio-temporal dynamics of Meligethes aeneus, a major pest of oilseed rape, and its parasitoid, Tersilochus heterocerus through a landscape that changes through time according to agricultural practices. The following agricultural practices were assumed to influence the tritrophic system and were included in the model: crop allocation in time and space, ploughing, and trap crop planting. To test the effect of agricultural practices on biological regulation across landscape configurations, we used a complete factorial design with the variables described below and ran long-term simulations using Mosaic-Pest. The model showed that crop rotation and the use of trap crop greatly affected pollen beetle densities and parasitism rates while ploughing had only a small effect. The use of Mosaic-Pest as a tool to select the combination of agricultural practices that best limit the pest population is discussed.

[1]  David A. Bohan,et al.  Responses of plants and invertebrate trophic groups to contrasting herbicide regimes in the Farm Scale Evaluations of genetically modified herbicide-tolerant crops. , 2003, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[2]  D. H. Slone.  Increasing accuracy of dispersal kernels in grid-based population models , 2011 .

[3]  Wayne M. Getz,et al.  Modelling the biological control of insect pests: a review of host-parasitoid models , 1996 .

[4]  I. Williams.  The Major Insect Pests of Oilseed Rape in Europe and Their Management: An Overview , 2010 .

[5]  U. Netlogo Wilensky,et al.  Center for Connected Learning and Computer-Based Modeling , 1999 .

[6]  S. Aviron,et al.  Temporal variability of connectivity in agricultural landscapes: do farming activities help? , 2003, Landscape Ecology.

[7]  Hervé Monod,et al.  Spatial sensitivity of maize gene-flow to landscape pattern: a simulation approach , 2008, Landscape Ecology.

[8]  J. Sarthou,et al.  Multi-scale effects of landscape complexity and crop management on pollen beetle parasitism rate , 2011, Landscape Ecology.

[9]  I. Williams,et al.  Exploitation of host plant preferences in pest management strategies for oilseed rape (Brassica napus) , 2006 .

[10]  M. Mugglestone,et al.  The spatio‐temporal distribution of adult Ceutorhynchus assimilis in a crop of winter oilseed rape in relation to the distribution of their larvae and that of the parasitoid Trichomalus perfectus , 2000 .

[11]  H. Possingham,et al.  Spatial variability in ecosystem services: simple rules for predator-mediated pest suppression. , 2010, Ecological applications : a publication of the Ecological Society of America.

[12]  Christophe Le Page,et al.  COSMOS, a spatially explicit model to simulate the epidemiology of Cosmopolites sordidus in banana fields , 2009 .

[13]  Claire Lavigne,et al.  The influence of landscape on insect pest dynamics: a case study in southeastern France , 2009, Landscape Ecology.

[14]  I. Williams,et al.  Crop Location by Oilseed Rape Pests and Host Location by Their Parasitoids , 2010 .

[15]  I. Williams,et al.  The role of pollen odour in the attraction of pollen beetles to oilseed rape flowers , 2002 .

[16]  H. Buckley,et al.  Floral diversity, parasitoids and hyperparasitoids - A laboratory approach , 2008 .

[17]  S. L. Lima,et al.  Behavioral tradeoffs when dispersing across a patchy landscape , 2005 .

[18]  Carsten Thies,et al.  Landscape structure and biological control in agroecosystems , 1999, Science.

[19]  Francis K C Hui,et al.  The arcsine is asinine: the analysis of proportions in ecology. , 2011, Ecology.

[20]  Carsten Thies,et al.  Crop–noncrop spillover: arable fields affect trophic interactions on wild plants in surrounding habitats , 2010, Oecologia.

[21]  Felix L. Wäckers,et al.  Management of field margins to maximize multiple ecological services , 2006 .

[22]  Andres Baeza,et al.  Effect of the landscape context on the density and persistence of a predator population in a protected area subject to environmental variability. , 2010 .

[23]  Carsten Thies,et al.  REVIEWS AND SYNTHESES Landscape perspectives on agricultural intensification and biodiversity - ecosystem service management , 2005 .

[24]  I. Williams,et al.  Behavioural and chemical ecology underlying the success of turnip rape (Brassica rapa) trap crops in protecting oilseed rape (Brassica napus) from the pollen beetle (Meligethes aeneus) , 2007, Arthropod-Plant Interactions.

[25]  D. Moser,et al.  Insect pests in winter oilseed rape affected by field and landscape characteristics , 2008 .

[26]  I. Williams.  Biocontrol-based integrated management of oilseed rape pests , 2010 .

[27]  L. M. Hansen.  Economic damage threshold model for pollen beetles (Meligethes aeneus F.) in spring oilseed rape (Brassica napus L.) crops , 2004 .

[28]  Moshi Arthur Charnell.  An individual-based model of a tritrophic ecology , 2008 .

[29]  R. Büchi.  Mortality of pollen beetle (Meligethes spp.) larvae due to predators and parasitoids in rape fields and the effect of conservation strips , 2002 .

[30]  D. Makowski,et al.  Chapter 3 Uncertainty and sensitivity analysis for crop models , 2006 .

[31]  H. Barclay,et al.  A dynamic population model for tsetse (Diptera: Glossinidae) area-wide integrated pest management , 2010, Population Ecology.

[32]  Gareth Hughes,et al.  Validating models of plant disease progress in space and time , 1997 .

[33]  B. Ekbom,et al.  Host plant affects pollen beetle (Meligethes aeneus) egg size , 2004 .

[34]  Marc Deconchat,et al.  Modelling the overwintering strategy of a beneficial insect in a heterogeneous landscape using a multi-agent system , 2007 .

[35]  Neil McRoberts,et al.  Validating mathematical models of plant-disease progress in space and time , 1997 .

[36]  J. Free,et al.  Compensation of oil-seed rape (Brassica napus L.) plants after damage to their buds and pods , 1979, The Journal of Agricultural Science.

[37]  M. Roberts,et al.  Application of a spatial meta-population model with stochastic parameters to the management of the invasive grass Nassella trichotoma in North Canterbury, New Zealand. , 2011 .

[38]  Adrien Rusch.  Analyse des déterminants des attaques de Meligethes aeneus (Coleoptera, Nitidulidae) et de sa régulation biologique à l'échelle d'un paysage agricole: contribution à l'amélioration de la protection intégrée du colza. , 2010 .

[39]  Bryan F. J. Manly,et al.  Assessing habitat selection when availability changes , 1996 .

[40]  P. Mather,et al.  Habitat Heterogeneity Influences Connectivity in a Spatially Structured Pest Population , 2006 .

[41]  N. Schellhorn,et al.  Foraging behaviour of predators in heterogeneous landscapes: the role of perceptual ability and diet breadth. , 2009 .

[42]  J. Sarthou,et al.  Local and landscape determinants of pollen beetle abundance in overwintering habitats , 2012 .

[43]  Françoise Lescourret,et al.  Factors and mechanisms explaining spatial heterogeneity: a review of methods for insect populations , 2011 .

[44]  P. Jourdheuil.  Influence de quelques facteurs écologiques sur les fluctuations de population d'une biocénose parasitaire , 1960 .

[45]  Nicolas Munier-Jolain,et al.  Weeds in agricultural landscapes. A review , 2011, Agronomy for Sustainable Development.

[46]  R. Senoussi,et al.  Should I Stay or Should I Go? A Habitat-Dependent Dispersal Kernel Improves Prediction of Movement , 2011, PloS one.