Validation of Individual-Based Markov-Like Stochastic Process Model of Insect Behavior and a “Virtual Farm” Concept for Enhancement of Site-Specific IPM

Abstract

The paper reports application of a Markov-like stochastic process agent-based model and a "virtual farm" concept for enhancement of site-specific Integrated Pest Management. Conceptually, the model represents a "bottom-up ethological" approach and emulates behavior of the "primary IPM actors"-large cohorts of individual insects-within seasonally changing mosaics of spatiotemporally complex faming landscape, under the challenge of the local IPM actions. Algorithms of the proprietary PESTonFARM model were adjusted to reflect behavior and ecology of R. cerasi. Model parametrization was based on compiled published information about R. cerasi and the results of auxiliary on-farm experiments. The experiments were conducted on sweet cherry farms located in Austria, Germany, and Belgium. For each farm, a customized model-module was prepared, reflecting its spatiotemporal features. Historical data about pest monitoring, IPM treatments and fruit infestation were used to specify the model assumptions and calibrate it further. Finally, for each of the farms, virtual IPM experiments were simulated and the model-generated results were compared with the results of the real experiments conducted on the same farms. Implications of the findings for broader applicability of the model and the "virtual farm" approach-were discussed.

Individual-Based Modeling Approach to Assessment of the Impacts of Landscape Complexity and Climate on Dispersion, Detectability and Fate of Incipient Medfly Populations

“…In fairly uniform environments, generic population models, mostly based on “random diffusion” paradigms (Rudd and Gandour, 1985 ; Kot et al, 2004 ; Liebhold and Tobin, 2008 ; Roques et al, 2008 ), largely explain the growth and spread of the initial populations, and estimate the relation between propagule pressure and probability of establishment (Memmott et al, 2005 ; Colautti et al, 2006 ; Drake and Jerde, 2009 ). But at the ultra-low densities and fine spatial scales of the species-typical daily exploration ranges, translocations of the individual insects are determined by the proximate configurations of environmental attributes and transient availability of resources, and thus are far from random (Lux, 2014 ; Manoukis et al, 2014 ; Lux et al, 2016 , 2017 ). It is broadly recognized that during the initial cryptic “latency phase,” such small cohorts may linger for some time undetected (Sakai et al, 2001 ).…”

“…Confronted with the paucity of experimental options, we propose the individual-focused “ethological” approach (Lux, 2014 )-stochastic simulation of lifetime events and behaviors of “virtual” individual members of the incipient cohorts, operating under hypothetical agro-ecological scenarios of varying complexity and climates. Such in silico emulation of the pest-landscape system offers unique possibility to capture the wealth of information about behavioral particularities of the target insect species, and reflect the impacts of the local conditions with their spatiotemporal dynamics at insect-relevant scales (An et al, 2009 ; Lux et al, 2016 ). Importantly, for very small populations scattered at ultra-low densities, such an approach permits more realistic reflection of the individual stochastic uncertainty and non-random behavioral mechanisms of the choices made in locally heterogeneous environment.…”

“…Importantly, for very small populations scattered at ultra-low densities, such an approach permits more realistic reflection of the individual stochastic uncertainty and non-random behavioral mechanisms of the choices made in locally heterogeneous environment. Once parametrised and validated, the model permits “virtual” emulation of an unlimited number of scenarios—quantification of the net effects of even minor modifications to the topography and/or climate of the studied system (Lux et al, 2016 ). Such agent-based models, often in combination with cellular automata, can serve as “virtual environmental laboratories” for emulation of complex systems, and are currently used to study ecological and evolutionary processes (DeAngelis and Mooij, 2005 ; Jovani and Grimm, 2008 ; DeAngelis and Grimm, 2014 ), for development of environmental decision support tools (Parker et al, 2002 ; Parker, 2005 ; Grimm et al, 2014 ; Reed et al, 2016 ), modeling effects of land use and climate change (Louca et al, 2015 ; Hyandye and Martz, 2017 ), or site-specific integrated pest management (IPM) (Lux et al, 2016 ).…”

“…The objective of the study presented here was to demonstrate the potential of such an approach for quantification of the latency phase of “cryptic” pest existence, its detectability and establishment chances, in relation to the species-specific pest traits, the initial propagule size, the local landscape topography and climate. For this purpose, an individual-based Markov-like stochastic process model (PESTonFARM, Lux, 2014 ; Lux et al, 2016 ) was used, with its algorithms and parameters adjusted to reflect the biology and behavior of Ceratits capitata , a well-researched species of enormous economic importance, used here as a case-example. The potential of the presented approach and the model for site-specific assessments of the local pest establishment risks and detection chances, or for optimisation of the pest detection schemes according to the local site topography-was also discussed.…”

Marking Drosophila suzukii (Diptera: Drosophilidae) with Fluorescent Dusts

“…In the search for Integrated Pest Management (IPM) tools, an efficient marking methodology can be an important instrument. MRR, MR, and MC studies can be used for assessing (seasonal) dispersal [31,70,73,[79][80][81][82] or estimating population sizes [83,84] and can assist in the development of preventative strategies, better targeted control measures or prediction models [85]. MR and MRR studies can also be useful for determining trapping parameters in the development of monitoring [30,[86][87][88] and mass trapping strategies [89].…”