Pattern-oriented Modelling Research Articles

A simulation-based framework to explore the importance of non-selection and selection processes in structuring ecological communities.

The purpose of this framework is to identify the relative importance of selection and dispersion processes in structuring ecological communities. Using a "pattern-oriented modelling" approach, it consists of five steps: (a) aggregate information from the empirical community and its environment, (b) simulate communities under different degrees of dispersal and selection, (c) select the best set of simulations into a composite model using the environmental boundary (EB) and niche breadth (NB) of each observed species, (d) validate the composite model by comparing expected and observed results from three additional community patterns and (e) classify each observed species along the selection/non-selection continuum. A free-living marine nematodes data set from a coastal bay was used as empirical example. A total of 20 parameterizations were applied varying selection and dispersion levels. In the absence of selection, species from high-dispersal parameter sets showed maximum EBs and NBs, whilst selection parameter sets generated species with narrower EB and NB values. EB and NB values declined with decreasing dispersal. The composite model encompassed 96% of the 194 nematode species and predicted all the three patterns evaluated without further calibration, i.e., they are independent: (1) abundance-rank distribution, the assemblage structures along both the (2) spatial and (3) environmental gradients. Non-selection and selection parameter sets accounted for 34% and 85% of the observed species, respectively. The main advantage of this approach is that empirical niche measurements are placed in the context of model-generated expectations, enabling a deeper understanding of community assembly processes and how they vary from species to species.

New insights on the behaviour of alternative types of individual-based tree models for natural forests

Agent/individual-based models (A/IBM) help to explain in a mechanistic way how spatial plant patterns evolve through time. In the past, seemingly different and independent types of A/IBMs were developed for modelling the dynamics of tree populations, e.g. growth interaction (GI) and shot noise (SN) models. In this paper, we present a new, advanced methodology of pattern-oriented modelling (POM) for the comparative, synoptic analysis of the behaviour of different types of A/IBMs by using recombinations of model components, validation and sensitivity analysis. We analysed model behaviour for spatio-temporal data from natural forests of interior Douglas fir (Pseudotsuga menziesii var glauca (Mirb.) Franco) and Scots pine (Pinus sylvestris L.) populations from Canada and the UK, respectively. Our detailed analysis clarified that both models, GI and SN along with their recombinations performed similarly and belong to the same group of A/IBMs. From the application of our new methodology we learnt that SN models were able to describe interactions more accurately than GI models and additionally produce interaction fields that can be used for other modelling purposes. On the other hand the GI model was more robust when using observed data that did not include sufficient information on tree interactions. Maximum-likelihood estimations were more reliable in spatial regression analysis than least-squares methods and should be preferred in spatial A/IBM parametrisation.

Temporal plasticity in habitat selection criteria explains patterns of animal dispersal.

Patterns of dispersal behavior are often driven by the composition and configuration of suitable habitat in a matrix of unsuitable habitat. Interactions between animal behavior and landscapes can therefore influence population dynamics, population and species distributions, population genetic structure, and the evolution of behavior. Spatially explicit individual-based models (IBMs) are ideal tools for exploring the effects of landscape structure on dispersal. We developed an empirically parameterized IBM in the modeling framework SEARCH to simulate dispersal of translocated American martens in Wisconsin. We tested the hypothesis that a time-limited disperser should be willing to settle in lower quality habitat over time. To evaluate model performance, we used a pattern-oriented modeling approach. Our best model matched all empirical dispersal patterns (e.g., dispersal distance) except time to settlement. This model incorporated a required search phase as well as the mechanism for declining habitat selectivity over time, which represents the first demonstration of this hypothesis for a vertebrate species. We suggest that temporal plasticity in habitat selectivity allows individuals to maximize fitness by making a tradeoff between habitat quality and risk of mortality. Our IBM is pragmatic in that it addresses a management need for a species of conservation concern. However, our model is also paradigmatic in that we explicitly tested a theory of dispersal behavior. Linking these 2 approaches to ecological modeling can further the utility of individual-based modeling and provide direction for future theoretical and empirical work on animal behavior.

Emergent relationships with respect to burned area in global satellite observations and fire-enabled vegetation models

Abstract. Recent climate changes have increased fire-prone weather conditions in many regions and have likely affected fire occurrence, which might impact ecosystem functioning, biogeochemical cycles, and society. Prediction of how fire impacts may change in the future is difficult because of the complexity of the controls on fire occurrence and burned area. Here we aim to assess how process-based fire-enabled dynamic global vegetation models (DGVMs) represent relationships between controlling factors and burned area. We developed a pattern-oriented model evaluation approach using the random forest (RF) algorithm to identify emergent relationships between climate, vegetation, and socio-economic predictor variables and burned area. We applied this approach to monthly burned area time series for the period from 2005 to 2011 from satellite observations and from DGVMs from the “Fire Modeling Intercomparison Project” (FireMIP) that were run using a common protocol and forcing data sets. The satellite-derived relationships indicate strong sensitivity to climate variables (e.g. maximum temperature, number of wet days), vegetation properties (e.g. vegetation type, previous-season plant productivity and leaf area, woody litter), and to socio-economic variables (e.g. human population density). DGVMs broadly reproduce the relationships with climate variables and, for some models, with population density. Interestingly, satellite-derived responses show a strong increase in burned area with an increase in previous-season leaf area index and plant productivity in most fire-prone ecosystems, which was largely underestimated by most DGVMs. Hence, our pattern-oriented model evaluation approach allowed us to diagnose that vegetation effects on fire are a main deficiency regarding fire-enabled dynamic global vegetation models' ability to accurately simulate the role of fire under global environmental change.

Integrated modelling of Atlantic mackerel distribution patterns and movements: A template for dynamic impact assessments

Modelling is important for impact assessments of anthropogenic pressures on wildlife. Models are particularly useful when dealing with complex dynamic systems (as pelagic ecosystems) where data are limited and if various ‘what if’ scenarios should be tested. The aim of this study was to produce and implement an integrated modelling approach, linking high resolution hydrodynamic models (HDM) of the marine environment with correlative species distribution models (SDM) and agent-based models (ABM), for describing the spatio-temporal distribution and movements of Atlantic mackerel (Scomber scombrus) in the Norwegian Sea. The SDM was fitted with scientific mackerel trawl data as response variables (collected in July and August 2006–2014) and temperature (from the HDM), water depth and time period as predictors of spatial distributions. The SDM was able to produce dynamic predictions of a similar order of magnitude as observed catch per unit effort (CPUE) as well as realistic large-scale distribution patterns, when tested on independent data (not included in the modelling). The ABM was calibrated, with normalized SDM predictions (habitat suitability as a proxy for food availability) and hydrodynamics as input and simulated on a single year (2013) for the period May–October, when the migratory mackerel is present in the study area. A pattern-oriented modelling (POM) approach was used to verify if the model reproduced multiple observed real-world patterns. The ABM produced similar patterns as observed regarding migration timing, growth and large scale geographic distribution. Fine scaled information on mackerel movement and behaviour is limited, which is also reflected in the results. More data and knowledge are therefore required to improve the patterns emerging from fine scaled processes. The potential of the model for assessing an impact of a single seismic survey (mimicking a real survey) was finally evaluated. The exercise allowed estimating the number of affected fish (within 50 km from the sound source) and potential changes in local migrations, with the specific assumed minimum sound pressure thresholds (resulting in a fleeing reaction by the mackerel) set to 165 dB re 1 μPa. The model framework was shown to be useful by allowing simulations of impact scenarios in a realistic and dynamic environment. The model can be further updated when data on fine scale movements of mackerel and most importantly when improved data on response behaviour to impacts of sound become available.

Where to go goose hunting? Using pattern-oriented modeling to better understand human decision processes

ABSTRACTTo predict hunting pressure at a regional level for the adaptive harvest management of a European goose population, we created a predictive model within an existing agent-based model framework. In this paper, we outline the inputs, outputs, and learning from developing this model, using pattern-oriented modeling (POM), to predict the regional distribution of goose hunting locations. Our results showed that social aspects (e.g., crowding, how far hunters are prepared to travel) may influence hunter decisions when choosing hunting locations. However, access to multiple hunting locations and knowledge of goose behavior and likely foraging areas were more important decision drivers. A crucial model outcome was the secondary prediction of the size of the potential pool of goose hunters. We believe that POM is a beneficial framework for those wishing to define, test, and ultimately develop better predictive models of human decision-making and subsequent behaviors and feedbacks.

Applying Pattern Oriented Sampling in current fieldwork practice to enable more effective model evaluation in fluvial landscape evolution research

AbstractField geologists and geomorphologists are increasingly looking to numerical modelling to understand landscape change over time, particularly in river catchments. The application of landscape evolution models (LEMs) started with abstract research questions in synthetic landscapes. Now, however, studies using LEMs on real‐world catchments are becoming increasingly common. This development has philosophical implications for model specification and evaluation using geological and geomorphological data, besides practical implications for fieldwork targets and strategy. The type of data produced to drive and constrain LEM simulations has very little in common with that used to calibrate and validate models operating over shorter timescales, making a new approach necessary. Here we argue that catchment fieldwork and LEM studies are best synchronized by complementing the Pattern Oriented Modelling (POM) approach of most fluvial LEMs with Pattern Oriented Sampling (POS) fieldwork approaches. POS can embrace a wide range of field data types, without overly increasing the burden of data collection. In our approach, both POM output and POS field data for a specific catchment are used to quantify key characteristics of a catchment. These are then compared to provide an evaluation of the performance of the model. Early identification of these key characteristics should be undertaken to drive focused POS data collection and POM model specification. Once models are evaluated using this POM/POS approach, conclusions drawn from LEM studies can be used with greater confidence to improve understanding of landscape change. © 2018 John Wiley & Sons, Ltd.

The role of subjective risk perceptions in shaping coastal development dynamics

Social and economic costs in coastal zones resulting from natural hazard events, such as hurricanes, are increasing. Household residential location decisions and adaptive behaviors (i.e., purchasing insurance) are influenced by perceived risk of storms events, and can have long-term consequences for development patterns and regional resilience. Perceived risks may be capitalized into housing prices in hazardous areas, but the attraction of coastal amenities may dampen market responses to risk information. Empirical studies provide contradictory conclusions about the effect of storm events on housing market dynamics, and thus the decision-making processes leading to post-storm residential location and insurance purchase choices remain unclear. Here, an economic agent-based model (ABM) of coupled housing and land markets (CHALMS), adapted to a coastal setting, or C-CHALMS, is used to investigate alternative decision-making models and associated behavioral mechanisms driving post-storm responses. A pattern-oriented modeling (POM) and abductive reasoning approach is used to compare the ability of alternative decision-making models to explain empirical patterns of housing and insurance market dynamics. By explicitly modeling individual decision-making, results demonstrate that post-storm location and insurance purchasing decisions vary greatly within a coastal landscape. Coastal amenities dampen the effects of storm events on housing price dynamics for properties immediately adjacent to the coast, while areas with the lowest risk of damages (and lowest coastal amenities) are most responsive to storm events. Further, psychological factors, such as the perceived salience of positive and negative consequences, explain dynamics of insurance policy uptake after storms better than rational economic decision-making alone.

Pattern-oriented modelling as a novel way to verify and validate functional-structural plant models: a demonstration with the annual growth module of avocado.

Functional-structural plant (FSP) models have been widely used to understand the complex interactions between plant architecture and underlying developmental mechanisms. However, to obtain evidence that a model captures these mechanisms correctly, a clear distinction must be made between model outputs used for calibration and thus verification, and outputs used for validation. In pattern-oriented modelling (POM), multiple verification patterns are used as filters for rejecting unrealistic model structures and parameter combinations, while a second, independent set of patterns is used for validation. To test the potential of POM for FSP modelling, a model of avocado (Persea americana 'Hass') was developed. The model of shoot growth is based on a conceptual model, the annual growth module (AGM), and simulates photosynthesis and adaptive carbon allocation at the organ level. The model was first calibrated using a set of observed patterns from a published article. Then, for validation, model predictions were compared with a different set of empirical patterns from various field studies that were not used for calibration. After calibration, our model simultaneously reproduced multiple observed architectural patterns. The model then successfully predicted, without further calibration, the validation patterns. The model supports the hypothesis that carbon allocation can be modelled as being dependent on current organ biomass and sink strength of each organ type, and also predicted the observed developmental timing of the leaf sink-source transition stage. These findings suggest that POM can help to improve the 'structural realism' of FSP models, i.e. the likelihood that a model reproduces observed patterns for the right reasons. Structural realism increases predictive power so that the response of an AGM to changing environmental conditions can be predicted. Accordingly, our FSP model provides a better but still parsimonious understanding of the mechanisms underlying known patterns of AGM growth.

Integrating spatio-temporal variation in resource availability and herbivore movements into rangeland management: RaMDry—An agent-based model on livestock feeding ecology in a dynamic, heterogeneous, semi-arid environment

Fast growth of the human population puts high pressure on pasture habitats due to increasingly high grazing intensities on shrinking grazing land. Adapted herbivore management is needed to maintain the long term productivity of the rangeland ecosystem, especially in dry climates where precipitation is highly erratic and where forage yield and quality of the pasture are highly dynamic. We used an agent-based approach to develop a spatially explicit model for a case study region in Madagascar. Our model “RaMDry” integrates the movements and feeding metabolism of domesticated ruminants in order to assess the potential of adaptive livestock production in a highly dynamic, heterogeneous, semi-arid rangeland system. It evaluates the additional metabolic energy costs due to pastoral herd movements in search of forage, incorporates seasonal dynamics in forage quality in terms of feed digestibility and relates forage availability and quality to climatic conditions. In the presented study, we focused on describing the processes simulated in RaMDry in detail, simplifying model conditions by implying free-ranging conditions. We verified the results of the model through global and local sensitivity analysis and pattern-oriented modeling and compared findings with observed patterns and existing data from literature. Our model provides a useful tool to assess strategies and effects of locally adapted herd and rangeland utilization for sustained food security and household economy of livestock keepers in the semi-arid tropics and sub-tropics. Subject for further analysis of the model is its capability to simulate different management strategies and climatic conditions as well as the development of the models’ capacity to predict accumulative effects of environmental degradation.

Rabbit Population Landscape-Scale Simulation to Investigate the Relevance of Using Rabbits in Regulatory Environmental Risk Assessment

This paper describes the development and testing of the ALMaSS rabbit model and its baseline, and subsequently its application to the question of lagomorph population vulnerability in environmental risk assessment (ERA). Development and testing following a pattern-oriented modelling protocol resulted in a model able to replicate local and landscape-level rabbit population patterns. We then tested how robust rabbit populations are to an (imaginary) extreme toxic stressor at a landscape level in a variety of landscapes, and to what extremes key uncertain model parameters must be pushed to cause extinctions. This was contrasted with the same (imaginary) toxic stressor applied to the already existing ALMaSS hare model. For EU risk assessment of plant protection products, these results clearly indicate that if the protection goal is population-level impacts, either in abundance and/or distribution, then the hare is a much more vulnerable species than the rabbit under all the conditions tested. Rabbits would only be more vulnerable than hares if the entire population were to be exposed simultaneously, when lower body mass would then be a critical factor. This did not occur even though the toxicant and exposure scenarios tested here were extreme and, in fragmented landscapes at scales used here, will not occur in reality from the use of plant protection products on crop fields. As well as specifically answering the question on rabbit versus hare vulnerability, this study generally illustrates the potential application of models for setting focal species for risk assessments.

A neutral model for the simulation of linear networks in territories

A landscape matrix is the support of biotic and abiotic flows, and in that sense, requires increased interest from ecological modellers. This matrix is partly composed of linear elements, such as roads and field borders, that delimit land uses and are the result of socio-economic drivers. The geometrical properties of these elements could affect flows of water bodies, fauna, and flora. A large amount of research on landscape matrix simulation has been conducted using neutral models, but the efforts have been limited to 1km2 and have been principally devoted to field borders. However, simulating largest territories in neutral models requires consideration of supplementary elements, such as road networks. Furthermore, the sinuosities of the linear elements in territories have rarely been considered per se. We proposed a hierarchical model based on successive imbrication of deformed networks, with the deformation being realized on the basis of a reverse Douglas–Peucker algorithm. We first isolated the hierarchical levels of the landscape and analyzed their relative deformations. Then we constructed the hierarchical model and we tested it on a real territory in the Mediterranean zone. Its structural realism was tested against other common neutral models using the pattern-oriented modelling approach. The hierarchical model was the only neutral model able to represent simultaneously the variabilities of three patterns having implications in ecological processes: polyline lengths, sinuosities and polygon areas. Possible improvements of the model to address non-stationary processes and its potential for implementing geoprospective scenarios are discussed.

Island Rule, quantitative genetics and brain-body size evolution in Homo floresiensis.

Colonization of islands often activate a complex chain of adaptive events that, over a relatively short evolutionary time, may drive strong shifts in body size, a pattern known as the Island Rule. It is arguably difficult to perform a direct analysis of the natural selection forces behind such a change in body size. Here, we used quantitative evolutionary genetic models, coupled with simulations and pattern-oriented modelling, to analyse the evolution of brain and body size in Homo floresiensis, a diminutive hominin species that appeared around 700 kya and survived up to relatively recent times (60-90 kya) on Flores Island, Indonesia. The hypothesis of neutral evolution was rejected in 97% of the simulations, and estimated selection gradients are within the range found in living natural populations. We showed that insularity may have triggered slightly different evolutionary trajectories for body and brain size, which means explaining the exceedingly small cranial volume of H. floresiensis requires additional selective forces acting on brain size alone. Our analyses also support previous conclusions that H. floresiensis may be most likely derived from an early Indonesian H. erectus, which is coherent with currently accepted biogeographical scenario for Homo expansion out of Africa.

Energetic behavioural-strategy prioritization of Clark’s nutcrackers in whitebark pine communities: An agent-based modeling approach

While much is known about the relationship between Clark’s nutcracker and whitebark pine, information on nutcracker energetic behavioural strategies − the driving factors behind nutcracker emigration − and the impact of nutcracker behaviour on whitebark pine communities remain uncertain. To investigate nutcracker energetic behaviour, we developed a spatially explicit agent-based model (ABM) to simulate the underlying behavioural mechanisms nutcrackers are most likely to employ during foraging in the South Cascades near Mt. Rainier, Washington. The ABM is comprised of cognitive nutcracker agents possessing memory and decision-making heuristics that act to optimize energy acquisition and loss. Environmental data layers for elevation and basal area of tree species were used to represent the landscape in terms of habitat and energy resources. We employed the evaludation approach for an organized sequence of model development and analysis, including: data evaluation, conceptual model evaluation, implementation, verification, model output verification (calibration consisting of comparison of parameters informed by nutcracker ecology to real-world empirical values; pattern-oriented modeling − POM), model analysis (sensitivity of model to changes in parameters and processes), and model output corroboration (use of POM to compare model output to real-world patterns from empirical investigations of nutcracker ecology, independent of calibration). Simulations were conducted on alternative nutcracker behavioural-energetic mechanism strategies by assigning different fitness-maximizing goals to agents. We found that an integrated energetic requirement (IER) mechanism, which includes both the short-term and long-term energetic needs of nutcracker agents to be the best-fit scenario. Our results affirm previous research that nutcrackers are responsive to changes in their energetic environment, and that they are capable of projecting energy budgets well into the future. The development of this ABM provides a basis for future research, such as a means to assess the driving conditions necessary for nutcrackers when choosing between a resident and emigrant strategy and as a planning tool to model nutcracker responses to potential landscape changes, which may facilitate long-term WBP conservation.

Calibrating with Multiple Criteria: A Demonstration of Dominance

Pattern oriented modelling (POM) is an approach to calibration or validation that assesses a model using multiple weak patterns. We extend the concept of POM, using dominance to objectively identify the best parameter candidates. The TELL ME agent-based model is used to demonstrate the approach. This model simulates personal decisions to adopt protective behaviour during an influenza epidemic. The model fit is assessed by the size and timing of maximum behaviour adoption, as well as the more usual criterion of minimising mean squared error between actual and estimated behaviour. The rigorous approach to calibration supported explicit trading off between these criteria, and ultimately demonstrated that there were significant flaws in the model structure.

Next-Generation Individual-Based Models Integrate Biodiversity and Ecosystems: Yes We Can, and Yes We Must

Ecosystem and community ecology have evolved along different pathways, with little overlap. However, to meet societal demands for predicting changes in ecosystem services, the functional and structural view dominating these two branches of ecology, respectively, must be integrated. Biodiversity–ecosystem function research has addressed this integration for two decades, but full integration that makes predictions relevant to practical problems is still lacking. We argue that full integration requires going, in both branches, deeper by taking into account individual organisms and the evolutionary and physico-chemical principles that drive their behavior. Individual-based models are a major tool for this integration. They have matured by using individual-level mechanism to replace the demographic thinking which dominates classical theoretical ecology. Existing individual-based ecosystem models already have proven useful both for theory and application. Still, next-generation individual-based models will increasingly use standardized and re-usable submodels to represent behaviors and mechanisms such as growth, uptake of nutrients, foraging, and home range behavior. The strategy of pattern-oriented modeling then helps make such ecosystem models structurally realistic by developing theory for individual behaviors just detailed enough to reproduce and explain patterns observed at the system level. Next-generation ecosystem scientists should include the individual-based approach in their toolkit and focus on addressing real systems because theory development and solving applied problems go hand-in-hand in individual-based ecology.

Modelling food web structure using an end-to-end approach in the coastal ecosystem of the Gulf of Gabes (Tunisia)

Given the ecological importance and high socio-economic value of the fishery of the Gulf of Gabes, an end-to-end model was applied to its continental shelf ecosystem to characterize the structure of the food web in the 2000s. This approach consisted in forcing a high trophic level model (OSMOSE) with an existing biogeochemical model (Eco3M-MED) representing the seasonal dynamics of the low trophic levels. The two models were linked through trophic interactions to represent the ecosystem dynamics from primary producers to top predators. In this study, we developed the multispecies, individual-based model OSMOSE in the Gulf of Gabes (OSMOSE-GoG). This model aims to capture the main processes that influence species life cycle and simulate the functioning of the ecosystem according to opportunistic predation process based on size selection and spatio-temporal co-occurrence between a predator and its prey. The spatial distribution of the eleven modelled species was derived from a Multi-Scale Species Distribution Modelling approach. We calibrated OSMOSE-GoG model with available data of biomass and fishing yield, using an optimization method based on evolutionary algorithms which is suitable for complex and stochastic models. Finally, OSMOSE-GoG was validated against independent data sets at different hierarchical levels: the individual (diet composition), population (mean size of commercial catch) and community levels (mean trophic level) following the Pattern-Oriented Modelling approach. The model outputs were overall consistent with the diet compositions and mean trophic levels derived from the ECOPATH model of the Gulf of Gabes (ECOPATH-GoG) and the observations of mean size of catches. The OSMOSE-GoG can be considered as a baseline model to investigate ecosystem responses to environmental changes and fishing management measures in the Gulf of Gabes.

Overcoming challenges of sparse telemetry data to estimate caribou movement

Spatially explicit individual-based models (SE-IBMs) can simulate species’ movement behaviors. Although such models allow many applications to ecology and conservation biology and are useful for management purposes, they are difficult to parameterize directly from the kinds of observational data that are generally available. Coupled with pattern-oriented modeling strategy, SE-IBMs can be parameterized and assess alternate hypotheses on movement behaviors by comparing simulated to observed patterns of movement. We illustrated this with the endangered Atlantic-Gaspésie caribou population while using sparse Very High Frequency (VHF) telemetry data. We formulated alternative movement hypotheses built around proximate movement mechanisms and coded them into an SE-IBM to explain and predict caribou movement. These mechanisms were: a random walk, a biased correlated random walk, a foray loop to reproduce caribou extra-range movement patterns, and caribou fidelity during mating season. We combined these to test single- and two-behavior movement models regarding landscape quality. The best fitted model successfully reproduced most of the movement patterns derived from the VHF locations. We found that caribou movement in low quality habitat was better reproduced by a foray loop behavior than by a biased correlated random walk or a random walk. Adding an attraction to the individuals’ mating area during the mating season also improved the model. We used the selected model to estimate and map potential landscape use by the Atlantic-Gaspésie caribou. We confirmed areas of high use seen in the VHF data and identified some potential areas where no caribou locations were recorded. We also found that large areas of moderate to high quality habitat were unused because they could not be reached by caribou. We conclude that sparse data sets, such as VHF collar locations, can be used to fit movement models whose parameters could not be estimated directly from the data. SE-IBMs coupled with pattern-oriented modeling can reveal new insights about landscape use beyond what can be defined with habitat selection models, and can identify habitat locations where management actions could be taken to facilitate species persistence or recovery of endangered populations.

The Role of Serotype Interactions and Seasonality in Dengue Model Selection and Control: Insights from a Pattern Matching Approach.

The epidemiology of dengue fever is characterized by highly seasonal, multi-annual fluctuations, and the irregular circulation of its four serotypes. It is believed that this behaviour arises from the interplay between environmental drivers and serotype interactions. The exact mechanism, however, is uncertain. Constraining mathematical models to patterns characteristic to dengue epidemiology offers a means for detecting such mechanisms. Here, we used a pattern-oriented modelling (POM) strategy to fit and assess a range of dengue models, driven by combinations of temporary cross protective-immunity, cross-enhancement, and seasonal forcing, on their ability to capture the main characteristics of dengue dynamics. We show that all proposed models reproduce the observed dengue patterns across some part of the parameter space. Which model best supports the dengue dynamics is determined by the level of seasonal forcing. Further, when tertiary and quaternary infections are allowed, the inclusion of temporary cross-immunity alone is strongly supported, but the addition of cross-enhancement markedly reduces the parameter range at which dengue dynamics are produced, irrespective of the strength of seasonal forcing. The implication of these structural uncertainties on predicted vulnerability to control is also discussed. With ever expanding spread of dengue, greater understanding of dengue dynamics and control efforts (e.g. a near-future vaccine introduction) has become critically important. This study highlights the capacity of multi-level pattern-matching modelling approaches to offer an analytic tool for deeper insights into dengue epidemiology and control.

A Generic Individual-Based Spatially Explicit Model as a Novel Tool for Investigating Insect-Plant Interactions: A Case Study of the Behavioural Ecology of Frugivorous Tephritidae.

Computational modelling of mechanisms underlying processes in the real world can be of great value in understanding complex biological behaviours. Uptake in general biology and ecology has been rapid. However, it often requires specific data sets that are overly costly in time and resources to collect. The aim of the current study was to test whether a generic behavioural ecology model constructed using published data could give realistic outputs for individual species. An individual-based model was developed using the Pattern-Oriented Modelling (POM) strategy and protocol, based on behavioural rules associated with insect movement choices. Frugivorous Tephritidae (fruit flies) were chosen because of economic significance in global agriculture and the multiple published data sets available for a range of species. The Queensland fruit fly (Qfly), Bactrocera tryoni, was identified as a suitable individual species for testing. Plant canopies with modified architecture were used to run predictive simulations. A field study was then conducted to validate our model predictions on how plant architecture affects fruit flies’ behaviours. Characteristics of plant architecture such as different shapes, e.g., closed-canopy and vase-shaped, affected fly movement patterns and time spent on host fruit. The number of visits to host fruit also differed between the edge and centre in closed-canopy plants. Compared to plant architecture, host fruit has less contribution to effects on flies’ movement patterns. The results from this model, combined with our field study and published empirical data suggest that placing fly traps in the upper canopy at the edge should work best. Such a modelling approach allows rapid testing of ideas about organismal interactions with environmental substrates in silico rather than in vivo, to generate new perspectives. Using published data provides a saving in time and resources. Adjustments for specific questions can be achieved by refinement of parameters based on targeted experiments.