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 | |
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 | |
Analytical regression | Prediction of the timing of migration into crops from primary host (holocyclic populations only) | Denmark/Scandinavia | Within-field | Initial Distribution | |
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 | |
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 | |
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 | |
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 | |
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] |