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4. Shared predation: positive effects of predator distraction

Author

Teixeira Alves, Mickaël, Grognard, Frédéric, Calcagno, Vincent, and Mailleret, Ludovic

Abstract

Simple rules based on population equilibria can characterize indirect interactions in threespecies systems but fail to predict them when considering behavioral mechanisms. In this paper, we revisit the effects of shared predation, i.e. the situation in which two prey are consumed by a common predator. Such predation usually induces negative indirect interactions between prey, or apparent competition, through an increase of predator density and thus of predation pressure. Two mechanisms can however weaken apparent competition and lead to equivocal signs of indirect interactions. On the one hand, predator distraction, which stems from the difficulty to efficiently forage for different prey at the same moment in time and diminishes the number of prey captured per predator. On the other hand, predator negative density dependence limits predator growth. To get further insights into simple rules describing indirect interactions brought about by shared predation, we studied two classes of one-predator–two-prey models exhibiting these two mechanisms. We found robust simple rules derived from predator equilibria which state that at least one prey is favored by the presence of the other when the predators partition their foraging effort between them. These rules thus characterize a surprising wide range of indirect effects including apparent predation, apparent commensalism and apparent mutualism. They also highlight different situations in which larger predator populations do not entail smaller prey populations and in which neither prey species can be negatively affected by the other.

Published
2016

6. Natural enemy-mediated indirect interactions among prey species: potential for enhancing biocontrol services in agroecosystems

Author

Chailleux, Anaïs, Mohl, Emily K, Teixeira Alves, Mickaël, Messelink, Gerben J, Desneux, Nicolas, InVivo NSA, Institut Sophia Agrobiotech (ISA), Centre National de la Recherche Scientifique (CNRS)-Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Institut National de la Recherche Agronomique (INRA), Fonctionnement agroécologique et performances des systèmes de cultures horticoles (UPR HORTSYS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Department of Ecology, Evolution, and Behavior, University of Minnesota, University of Minnesota [Twin Cities] (UMN), University of Minnesota System-University of Minnesota System, Biological control of artificial ecosystems (BIOCORE), Institut National de la Recherche Agronomique (INRA)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'océanographie de Villefranche (LOV), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Observatoire océanologique de Villefranche-sur-mer (OOVM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Observatoire océanologique de Villefranche-sur-mer (OOVM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), Department of Mathematical Sciences [Bath], University of Bath [Bath], Wageningen University and Research [Wageningen] (WUR), Institut National de la Recherche Agronomique (INRA)-Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'océanographie de Villefranche (LOV), Observatoire océanologique de Villefranche-sur-mer (OOVM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Observatoire océanologique de Villefranche-sur-mer (OOVM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de la Recherche Agronomique (INRA), Institut Sophia Agrobiotech [Sophia Antipolis] (ISA), Institut National de la Recherche Agronomique (INRA)-Université Nice Sophia Antipolis (... - 2019) (UNS), Université Côte d'Azur (UCA)-Université Côte d'Azur (UCA)-Centre National de la Recherche Scientifique (CNRS), Fonctionnement agroécologique et performances des systèmes de cultures horticoles (Cirad-Persyst-UPR 103 HORTSYS), Département Performances des systèmes de production et de transformation tropicaux (Cirad-PERSYST), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), University of Minnesota [Twin Cities], Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire océanologique de Villefranche-sur-mer (OOVM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire océanologique de Villefranche-sur-mer (OOVM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), and Wageningen University and Research Centre [Wageningen] (WUR)

Subjects
generalist predators, Écologie, soybean aphid, GTB Gewasgez. Bodem en Water, conservation biological control, apparent competition, [SDV]Life Sciences [q-bio], generalist predator, orius-insidiosus hemiptera, alternative prey, quantitative food webs, Interactions biologiques, [SPI]Engineering Sciences [physics], [INFO.INFO-AU]Computer Science [cs]/Automatic Control Engineering, Agent de lutte biologique, functional biodiversity, [MATH]Mathematics [math], Compétition biologique, WUR GTB Gewasgezondheid, Ravageur des plantes, food and beverages, corn leaf aphid, improving biological-control, H10 - Ravageurs des plantes, Lutte biologique, macrolophus-caliginosus, pest management, Agroécosystème, ecosystem services, intraguild predation
Abstract

Understanding how arthropod pests and their natural enemies interact in complex agroecosystems is essential for pest management programmes. Theory predicts that prey sharing a predator, such as a biological control agent, can indirectly reduce each other's density at equilibrium (apparent competition). From this premise, we (i) discuss the complexity of indirect interactions among pests in agroecosystems and highlight the importance of natural enemy-mediated indirect interactions other than apparent competition, (ii) outline factors that affect the nature of enemy-mediated indirect interactions in the field and (iii) identify the way to manipulate enemy-mediated interactions for biological control. We argue that there is a need to increase the link between community ecology theory and biological control to develop better agroecological methods of crop protection via conservation biological control. In conclusion, we identify (i) interventions to be chosen depending on agroecosystem characteristics and (ii) several lines of research that will improve the potential for enemy-mediated indirect interactions to be applied to biological control. © 2014 Society of Chemical Industry

Published
2014

8. Methods for IPM : advances in the methodological workpackage of PURE

Author

Aubertot, Jean-Noel, Holst, N., Messean, Antoine, Hennen, W., Langrel, S., Angevin, Frederique, Bockstaller, Christian, Bout, Alexandre, Buurma, J., Daehmlow, D., Ernst, D., Fortino, Gabriele, Golla, B., Grechi, I., Grognard, Frédéric, Gutsche, V., Heijne, B., Horney, P., Pelzer, Elise, Ould Ahmed, M.M., Mailleret, Ludovic, Dubois Peyrard, Nathalie, Sabbadin, Regis, Strassemeyer, J., Teixeira Alves, Mickaël, Thiard, Jérôme, Trepos, Ronan, Zerourou, Arezki, Lescourret, Francoise, Agrosystèmes Cultivés et Herbagers (ARCHE), Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Supérieure Agronomique de Toulouse-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, (AU), Unité Impacts Ecologiques des Innovations en Production Végétale (ECO-INNOV), Institut National de la Recherche Agronomique (INRA), Research Institut for Agrobiology and Soil Fertility (DLO), JRC Institute for Prospective Technological Studies (IPTS), European Commission - Joint Research Centre [Seville] (JRC), Laboratoire Agronomie et Environnement - Antenne Colmar (LAE-Colmar ), Laboratoire Agronomie et Environnement (LAE), Institut National de la Recherche Agronomique (INRA)-Université de Lorraine (UL)-Institut National de la Recherche Agronomique (INRA)-Université de Lorraine (UL), Institut Sophia Agrobiotech [Sophia Antipolis] (ISA), Institut National de la Recherche Agronomique (INRA)-Université Nice Sophia Antipolis (... - 2019) (UNS), Université Côte d'Azur (UCA)-Université Côte d'Azur (UCA)-Centre National de la Recherche Scientifique (CNRS), Julius Kühn Institute (JKI), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Agronomie, AgroParisTech-Institut National de la Recherche Agronomique (INRA), Sciences pour l'Action et le Développement : Activités, Produits, Territoires (SADAPT), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Unité de Biométrie et Intelligence Artificielle (UBIA), Sol Agro et hydrosystème Spatialisation (SAS), Institut National de la Recherche Agronomique (INRA)-AGROCAMPUS OUEST, Unité de recherche Plantes et Systèmes de Culture Horticoles (PSH), UE, European Project: 265865, Institut National de la Recherche Agronomique (INRA)-École nationale supérieure agronomique de Toulouse [ENSAT]-Institut National Polytechnique (Toulouse) (Toulouse INP), Institut Sophia Agrobiotech (ISA), Centre National de la Recherche Scientifique (CNRS)-Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Institut National de la Recherche Agronomique (INRA), Julius Kühn-Institut - Federal Research Centre for Cultivated Plants (JKI), Fonctionnement agroécologique et performances des systèmes de cultures horticoles (UPR HORTSYS), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Institut National de la Recherche Agronomique (INRA)-École nationale supérieure agronomique de Toulouse (ENSAT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université de Toulouse (UT), Institut National de la Recherche Agronomique (INRA)-Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS), and Unité de Biométrie et Intelligence Artificielle (ancêtre de MIAT) (UBIA)

Subjects
dependence on pesticides, [SDV]Life Sciences [q-bio], PURE, IPM, cropping systems
Abstract

The overall objective of PURE is to provide practical IPM solutions to reduce dependence on pesticides in selected major farming systems in Europe. This paper summarises methodological advances with regards to the design and assessment of IPM solutions. The presented case studies include major crops (cropping systems based on wheat or maize), field vegetables, orchards, vineyard and Controlled Environment Agriculture systems. - Ecological modelling. A software package (Universal Simulator) for collaborative ecological modelling is now available:http://www.ecolmod.org/. - Modelling for ex-ante and ex post assessment of IPM solutions. A multi-criteria model (DEXIPM) for sustainability assessment of innovative crop protection strategies has been developed along with SYNOPS, a web-based model for scaling up ex-post pesticide risk assessments at the individual crop level to the farm and regional levels. In addition, a model for ex-ante evaluation of IPM solutions is currently under development specifically for orchards (PREMISE). - Multiple pest modelling. An interactive generic modelling platform to help design models that simulate yield losses caused by an injury profile in a given production situation (X-PEST) is currently under development. Moreover, theoretical mathematical modelling approaches are conducted to represent the interactions between generalist biological control agents and multiple pests. - Optimisation techniques. Reinforcement learning methods have been adapted and applied to IPM. Multiobjective optimisation algorithms for model-based design of IPM solutions are being developed. The Graph based Markov Decision Process framework is being used for the optimisation of sequential decisions under uncertainty in a spatial context. - Cost-benefit analysis and consumers' willingness to pay. Cost-benefit analyses are conducted for IPM solutions tested in the PURE project. An experimental approach is planned to characterise consumers' willingness to pay for agricultural goods produced under IPM solutions as a function of their level of information on the mode of production. It is important to state that the methodological breakthroughs produced in this work package will not only benefit to the PURE project, but also aim at contributing to the design of practical IPM solutions to reduce dependence on pesticides for a wider range of farming systems. This is made possible by ensuring as much as possible genericity in the developed approaches.

Published
2013

9. Des interactions indirectes entre les proies: Modélisation et influence du comportement du prédateur commun

Author

Teixeira Alves, Mickaël, Institut Sophia Agrobiotech (ISA), Centre National de la Recherche Scientifique (CNRS)-Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Institut National de la Recherche Agronomique (INRA), Université Nice Sophia Antipolis, Frédéric Grognard, and Ludovic Mailleret

Subjects
[SDV.SA]Life Sciences [q-bio]/Agricultural sciences, [SDV.MP]Life Sciences [q-bio]/Microbiology and Parasitology, these
Abstract

This thesis deals with multi-prey–predators modeling. It is particularly focused on the influence of the behavior of a predator on indirect interactions between its prey, i.e, the effect of the addition of a prey on the other prey. Classical theory predicts the occurrence of negative indirect effects between prey, or apparent competition, resulting from the interaction with a common predator. More recent results identify mechanisms that mitigate these negative effects. Our work revisits the assumptions about these mechanisms in systems composed of two prey and their common predator. After setting the ecological framework by recalling the main types of direct and indirect interactions, we introduce classical predator-prey models. The work then focuses on a family of models with predator negative density dependence coupled with different models of predator behavior when faced with different types of prey. We show that the interactions between these mechanisms can reverse apparent competition and counter-intuitively, increase prey density through their common predator. Our results are relevant to biological control programs, where a common practice aims at fostering biological control agents by providing them with alternative food or shelters. Our theory suggests that such practices may be counter-productive, pest control being disrupted by a predator distraction effect.; Cette thèse a pour objet la modélisation de systèmes multi-proies–prédateurs. Elle s’intéresse particulièrement à l’influence du comportement d’un prédateur sur les interactions indirectes entre ses proies, i.e. l’effet de l’ajout d’une proie sur la densité des autres. La théorie classique prédit l’occurrence d’effets indirects négatifs entre les proies, ou compétition apparente, résultant de l’interaction avec un prédateur commun ; des résultats plus récents identifient certains mécanismes à même d’atténuer ces effets négatifs. Nos travaux revisitent les hypothèses autour de ces mécanismes dans des systèmes composés de deux proies et de leur prédateur commun. Après avoir fixé le cadre écologique en rappelant les principaux types d’interactions directes et indirectes, nous introduisons les modèles proies-prédateurs classiques. Les travaux se concentrent ensuite sur une famille de modèles présentant de la densité-dépendance négative chez les prédateurs couplée à différentes modélisations du comportement des prédateurs lorsqu’ils sont confrontés à plusieurs types de proies. Nous montrons notamment que les interactions entre ces mécanismes peuvent inverser la compétition apparente et, contre-intuitivement, accroître la densité des proies par l’intermédiaire d’un prédateur commun. Nos résultats trouvent pour partie application en lutte biologique, où il est courant de chercher à favoriser les auxiliaires en aménageant leur environnement (apport de nourriture alternative, refuge, ...). Ils suggèrent que de telles pratiques peuvent se révéler contre-productives, le contrôle des ravageurs pouvant être affaibli du fait d’une distraction de leurs prédateurs.

Published
2013

10. Optimal foraging in Leslie-Gower predator-prey models with alternative food

Author

Teixeira-Alves, Mickaël, Mailleret, Ludovic, Grognard, Frédéric, Unité Recherches Intégrées en Horticulture (URIH), Institut National de la Recherche Agronomique (INRA), Modeling and control of renewable resources (COMORE), Laboratoire d'océanographie de Villefranche (LOV), Observatoire océanologique de Villefranche-sur-mer (OOVM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Observatoire océanologique de Villefranche-sur-mer (OOVM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire océanologique de Villefranche-sur-mer (OOVM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire océanologique de Villefranche-sur-mer (OOVM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), and Gouzé, Jean-Luc

Subjects
[SDV] Life Sciences [q-bio], [SPI]Engineering Sciences [physics], [MATH.MATH-GM]Mathematics [math]/General Mathematics [math.GM], [SPI] Engineering Sciences [physics], [SDV]Life Sciences [q-bio], [INFO.INFO-AU]Computer Science [cs]/Automatic Control Engineering, [MATH.MATH-GM] Mathematics [math]/General Mathematics [math.GM], [INFO.INFO-AU] Computer Science [cs]/Automatic Control Engineering
Abstract

International audience; In generalized Lotka-Volterra prey-predator models, many studies have been lead to analyze the influence of an alternative food source for the predator, both when this source has a fixed density [1] or is an alternative dynamic prey ([2] and subsequent works). Here, we will lead such a study with fixed density preys for two Leslie-Gower like models, where the prey population determines the carrying capacity of the predator population [3], where N and P are the dynamic prey and predator population densities and A is a constant alternative food source. The optimally foraging predator population will have to make a choice by taking some q 2 [0;1] between foraging the dynamic prey (q = 1), the other one (q = 0) or to adopt an intermediate strategy, depending on the respective prey densities. Its objective will be to maximize its per capita growth rate Pdot/P. Predators adopt pure strategies except when N = alpha_A A/alpha_N (both food sources produce the same carrying capacity), where a Filippov definition of the solutions can give rise to sliding modes. The behavior depends on whether the positive equilibrium with q = 1 is (i) stable or (ii) unstable or (iii) does not exist in models (17) and (18). Case (i) results in the maximization of the predator density at equilibrium. In case (ii), the alternative food source stabilizes the system which was unstable without it; it keeps the oscillations under control and results in a limit cycle that goes through a sliding mode. In case (iii) however, the other food source cannot prevent the populations to grow unbounded. In conclusion, the predator makes the most of the presence of an alternative food source by obtaining a maximal value for its equilibrium from it (case (i)) or gaining some stability (case (ii)). The effect on the dynamic prey N is not as straightforward; in particular, we show that no apparent competition principle [4] needs to hold.

Published
2010

11. Natural enemy-mediated indirect interactions among prey species: potential for enhancing biocontrol services in agroecosystems.

Author

Chailleux A, Mohl EK, Teixeira Alves M, Messelink GJ, and Desneux N

Subjects
Agriculture methods, Animals, Food Chain, Arthropods physiology, Ecosystem, Pest Control, Biological methods, Population Dynamics, Predatory Behavior physiology
Abstract

Understanding how arthropod pests and their natural enemies interact in complex agroecosystems is essential for pest management programmes. Theory predicts that prey sharing a predator, such as a biological control agent, can indirectly reduce each other's density at equilibrium (apparent competition). From this premise, we (i) discuss the complexity of indirect interactions among pests in agroecosystems and highlight the importance of natural enemy-mediated indirect interactions other than apparent competition, (ii) outline factors that affect the nature of enemy-mediated indirect interactions in the field and (iii) identify the way to manipulate enemy-mediated interactions for biological control. We argue that there is a need to increase the link between community ecology theory and biological control to develop better agroecological methods of crop protection via conservation biological control. In conclusion, we identify (i) interventions to be chosen depending on agroecosystem characteristics and (ii) several lines of research that will improve the potential for enemy-mediated indirect interactions to be applied to biological control., (© 2014 Society of Chemical Industry.)

Published
2014
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