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Table 1 Cereal aphid models simulating aphid movement or timing of arrival in crops

From: Cereal aphid movement: general principles and simulation modelling

Model characteristics Aim Country Scale Phase(s) of the transport process included Reference
Turbulent advection simulation/Lagrangian stochastic To investigate aerial density profiles in relation to simplified aphid behaviours UK Long distance migration Transport in Atmosphere [64]
Atmospheric trajectory model of dispersal To estimate migration pathways Finland Long distance migration Transport in Atmosphere [95]
Trajectory Modelling aphid migration from source to sink Illinois, USA Long distance migration Transport in Atmosphere [13, 31, 106]
Trajectory coupled to cohort-based population dynamics Mechanistic simulation of aphid population dynamics at source and factors leading to take-off, coupled to wind a trajectory simulation model to estimate potential long distance movement risk from irrigated pastures to crops. South-western Australia Long distance migration Source, Transport in Atmosphere, Initial Distribution [126]
Large-scale: Diffusion–advection-reaction equations To simulate the landing rate of Sitobion avenae in crop fields across landscapes. Explores landing behaviours and responses to landscape (e.g. wavelengths). France Landscape (multi-scale) Initial Distribution [123]
Small-scale: cellular automata incorporating behavioural rules.
Hierarchical Bayesian Driven by field observations to gain knowledge on processes such as insect landing and mortality Germany Within-field Initial Distribution [55]. See also [125, 128, 129]
Analytical regression Prediction of the timing of migration into crops from primary host (holocyclic populations only) Denmark/Scandinavia Within-field Initial Distribution [130, 131]
Analytical regression Prediction of the timing of migration into crops from primary host (holocyclic populations only) – requires suction trap data Sweden Within-field Initial Distribution [132]
Analytical regression Prediction of the timing of migration into autumn crops – requires suction trap data Wales Within-field Initial Distribution [92]
Analytical regression Prediction of the timing of migration into autumn crops – requires suction trap data UK Within-field Initial Distribution [133]
Analytical regression Prediction of the timing of migration into spring crops – requires suction trap data UK Within-field Initial Distribution [134, 135]
Individual-based Stochastic wind-driven dispersal model to examine difference in dispersal and population dynamics depending on pesticide regime UK Small landscape Local Movement [76]
Cohort-based population dynamics model (STELLA) Population dynamics model that simulates immigration from a ‘background’ source population. Spatial variation in immigration at the regional scale driven by differences in soil moisture levels. South-western Australia Within-field Initial Distribution (from local source) [136]
Analytical mathematical model Estimation of the percentage of plants infected with BYDV, given the number of aphids per plant. Distinction between alate migrant transmission and apterous transmission. UK Within-field Initial Distribution, Local Movement [137]
Cohort-based Aphid population dynamics, local dispersal and virus sub-models. UK Within-field/small landscape Local Movement [138]. See also [69, 139]
Cellular Automata Rate of spread of BYDV from an origin cell, based on probabilities of infection transferring to the next cell (combined with field observations). UK Within-field Local Movement [140]
Individual-based Simplified model of aphid population dynamics and virus transmission from plant to plant. Focus on computing methods rather than ecology. UK Within-field/small scale Local Movement [141, 142]
Analytical probabilistic model and Markov chain model of disease transmission. Individual-based aphid movement through field. Examines aspatially the implications of vector preference for diseased or healthy hosts on the spread of BYDV. A Markov chain model and a stochastic individual-based model examine disease transmission and the effects of spatial patchiness. USA Non-spatial (analytical) and spatial within-field (Markov chain). Local Movement [143] see also [144]
Artificial Neural Networks and multiple regression Aphid autumn flight timing/numbers. No BYDV. New Zealand Autumn flight Source [145]
Analytical linear and probit models Soybean aphid early season colonisation of fields from overwintering hosts. Canada Spring flight. Within-field. Source, Local Movement [146]