Pattern-oriented Modelling Research Articles

An individual-based model for direct and indirect transmission of chronic wasting disease in free-ranging white-tailed deer

Chronic wasting disease (CWD) is an infectious prion disease that infects members of the Cervidae family (i.e., deer) resulting in widespread ecological, economic, and recreational ramifications. We introduce a spatially explicit individual-based model (IBM) that integrates individual deer movement and behavior with population and disease dynamics to forecast CWD in populations of free-ranging white-tailed deer (Odocoileus virginianus). We use a Susceptible-Exposed-Infectious-Dead (S-E-I-D) epidemiological framework to explore spatiotemporal dynamics of CWD within an agriculturally dominated area in Michigan, USA. The IBM results closely mimicked documented short- and long-term dynamics of white-tailed deer populations and CWD in the Midwestern, USA. We applied pattern-oriented modeling using annual apparent CWD prevalence rates reported by Midwestern state wildlife agencies to validate the disease model. The introduction of a single infected deer to the modeled landscape (93 km2) led to an outbreak of CWD in 100 out of 350 model simulations (29 %); prevalence never exceeded 1.47 % for repetitions where the outbreak ended. For the 100 simulations where disease persisted, the deer population declined by 87 % by year 50 following initial introduction of CWD. Mean (±SD) prevalence after 5, 10, 25, and 50 years was 1.1 % (±1.0 %), 3.4 % (±3.3 %), 46.5 % (±18.8 %), and 51.8 % (±18.1 %), respectively, which highly correlated (r = 0.99) with annual CWD prevalence reported in Wisconsin white-tailed deer populations for years 1–21 post initial detection. Combined with a global sensitivity analysis, the IBM indicated that prevalence of CWD at year 20 was most sensitive to harvest rate of yearling and adult female deer and least sensitive to prion shedding rate, prion half-life, and deer group numbers, indicating that deer population parameters were more influential than disease parameters on CWD dynamics. Our IBM serves as a tool to explore and better understand indirect and direct transmission of CWD within free-ranging cervid populations. Users of this model can adjust parameter values to explore how interactions among individual deer and between deer and their environment affect CWD dynamics. This IBM also serves as a framework for applying and assessing spatially and temporally explicit management scenarios.

A Pipeline for Calibrating Agent-Based Models of Microbial Populations: From Image Collection to Model Parameterization

We present a framework for calibration of agent-based models of bacilliform (rod-shaped) bacterial populations based on time-lapse microscopy at single-cell resolution. Our approach draws on pattern-oriented modelling for feature selection, followed by sensitivity analysis and Bayesian inference to arrive at a model calibration that aligns with experimental observations. We illustrate this pipeline by calibrating a model of microcolony formation against observations of monolayer E. coli population growth.

Cost hierarchies and the pattern of product cost cross-subsidization: Extending a computational model of costing system design.

Cost information is critical to ease managers' decisions in daily business, but its provision is informationally demanding and error prone. Effective design choices for costing systems that can reduce errors are the subject of a growing body of research. The computational model by Anand, Balakrishnan, and Labro (2019) collates previous research in a unifying framework, turning it into a potential standard for future studies. This paper uses this framework and aims to investigate the mechanism behind the well-documented empirical pattern of product cost cross-subsidization in a large-scale simulation experiment. According to this pattern, volume-based costing systems bias the costs of high-volume products upward and of low-volume products downward. Although this pattern has important implications for firms and is discussed extensively in the literature, it has not yet been investigated with computational models. As the first objective of this paper, we replicate the original model by following a pattern-oriented model replication approach. The second objective is to study the mechanism behind the pattern of product cost cross-subsidization. We are unable to reproduce it systematically with the original model. However, the pattern emerges when we extend the model to include a simple cost hierarchy with distinct resource consumption types and volume-based cost drivers. This allows us to specify the likely mechanism behind it. Building on these results, we further extend the model with empirical and theory-based ABC cost hierarchies and assess their effect on product cost cross-subsidization. Our results suggest that production environments underpin more diverse cost hierarchies in practice than previously implemented in the model. Overall, we argue that our extension provides relevant insights into the pattern of product cost cross-subsidization, while our replication and extension strengthen the models' credibility and usability for future research.

A simulation model of functional habitat connectivity demonstrates the importance of species establishment in older forests

Fragmented natural habitats, such as old-growth forests, have been often considered island-like systems. However, whereas the isolation of real islands is mostly a function of their spatial arrangement, the biodiversity of old-growth forest patches is also affected by how well the surrounding suboptimal habitats, such as younger forests, can support the functional connectivity between the old-forest fragments. The latter topic remains largely unclear due to the lack of mechanistic knowledge about the processes behind species’ affinity for old forest. Although in animal ecology, functional connectivity has often been investigated with mechanistic agent-based models, these have rarely been applied for passively dispersing organism groups. We used a pattern-oriented modeling approach by combining a spatially explicit agent-based model and an empirical biodiversity dataset of Estonian old-growth nemoral forest sites, to study how forests of different ages between the old-forest fragments are exploited by different passively or slowly dispersing functional groups. We found that the establishment of ground layer vegetation, epiphytes, ectomycorrhizal and pathogenic fungi is significantly restricted in the younger forests, whereas no clear difference between forest-age classes emerged for survival. Our results thus suggest that establishment, not survival, is the main process behind the species’ affinity for old forest, concurring with studies that have demonstrated the importance of suitable microhabitat availability in older forests. We conclude that young secondary forests do not support the connectivity – and consequently, biodiversity – of many old-forest specific groups.

Modelling fine scale route choice of upstream migrating fish as they approach an instream structure

This study used pattern-oriented modelling (POM) to investigate the space use and behavioural response of upstream migrating European river lamprey (Lampetra fluviatilis) to the two-dimensional hydrodynamic conditions created by an instream structure (triangular profile gauging weir). Passive Integrated Transponder (PIT) and acoustic telemetry were used to map the spatial-temporal distribution patterns of lamprey as they migrated upstream. Acoustic Doppler velocimetry and computer modelling were used to quantify the hydrodynamic environment. In adherence with the POM methodology, multiple movement models, incorporating increasingly complex environmental feedback mechanisms and behavioural rules were created and systematically assessed to identify which factors might reproduce the observed patterns. The best model was a spatially explicit Eulerian-Lagrangian Individual Based Model (IBM) that included two simple behaviours: 1) tortuous non-directed swimming when in low flow velocity (< 0.1 m s−1) and 2) persistent directed (against the flow) swimming in moderate to high flow velocity (≥ 0.1 m s−1). The POM indicated that flow heterogeneity was an important influence of lamprey space use and that simple behavioural rules (i.e. two separate movement behaviours in response to flow velocity) were sufficient to reproduce the main movement pattern observed: avoidance of flow recirculating regions near the banks. The combination of field telemetry, hydrodynamic modelling and POM provided a useful framework for systematically identifying the key factors (hydrodynamic and behavioural) that governed the space use of the target species and would likely work well for investigating similar relationships in other aquatic species.

Spatial Validation of Agent-Based Models

This paper adapts an existing techno–social agent-based model (ABM) in order to develop a new framework for spatially validating ABMs. The ABM simulates citizen opposition to locally unwanted land uses, using historical data from an energy infrastructure siting process in Southern California. Spatial theory, as well as the model’s design, suggest that adequate validation requires multiple tests rather than relying solely on a single test-statistic. A pattern-oriented modeling approach was employed that first mapped real and simulated citizen comments across the US Census tract. The suite of spatial tests included Global Moran’s I, complemented with bivariate correlations, as well as the local indicators of spatial association (LISA) test. The global tests showed the model explained up to 65% of the variation in the historical data for US Census tract-level citizen comments on a locally unwanted land use. These global tests were also found helpful to inform the model’s calibration for the current application. The LISA results were even stronger, showing that the model predicted citizen comment clustering correctly in five of six Census tracts. It slightly over predicted comments further away from the land use. The LISA results and pattern-oriented modeling validation techniques identified theoretical factors to improve the modeling specification in future applications. The combined suite of validation techniques helped improve confidence in the model’s predictions.

Range and extinction dynamics of the steppe bison in Siberia: A pattern‐oriented modelling approach

AbstractAimTo determine the ecological processes and drivers of range collapse, population decline and eventual extinction of the steppe bison in Eurasia.LocationSiberia.Time periodPleistocene and Holocene.Major taxa studiedSteppe bison (Bison priscus).MethodsWe configured 110,000 spatially explicit population models (SEPMs) of climate–human–steppe bison interactions in Siberia, which we ran at generational time steps from 50,000 years before present. We used pattern‐oriented modelling (POM) and fossil‐based inferences of distribution and demographic change of steppe bison to identify which SEPMs adequately simulated important interactions between ecological processes and biological threats. These “best models” were then used to disentangle the mechanisms that were integral in the population decline and later extinction of the steppe bison in its last stronghold in Eurasia.ResultsOur continuous reconstructions of the range and extinction dynamics of steppe bison were able to reconcile inferences of spatio‐temporal occurrence and the timing and location of extinction in Siberia based on hundreds of radiocarbon‐dated steppe bison fossils. We showed that simulating the ecological pathway to extinction for steppe bison in Siberia in the early Holocene required very specific ecological niche constraints, demographic processes and a constrained synergy of climate and human hunting dynamics during the Pleistocene–Holocene transition.Main conclusionsEcological processes and drivers that caused ancient population declines of species can be reconstructed at high spatio‐temporal resolutions using SEPMs and POM. Using this approach, we found that climatic change and hunting by humans are likely to have interacted with key ecological processes to cause the extinction of the steppe bison in its last refuge in Eurasia.

Climate Normalized Spatial Patterns of Evapotranspiration Enhance the Calibration of a Hydrological Model

Spatial pattern-oriented evaluations of distributed hydrological models have contributed towards an improved realism of hydrological simulations. This advancement has been supported by the broad range of readily available satellite-based datasets of key hydrological variables, such as evapotranspiration (ET). At larger scale, spatial patterns of ET are often driven by underlying climate gradients, and with this study, we argue that gradient dominated patterns may hamper the potential of spatial pattern-oriented evaluation frameworks. We hypothesize that the climate control of spatial patterns of ET overshadows the effect model parameters have on the simulated patterns. To address this, we propose a climate normalization strategy. This is demonstrated for the Senegal River basin as a modeling case study, where the dominant north-south precipitation gradient is the main driver of the observed hydrological variability. We apply the mesoscale Hydrological Model (mHM) to model the hydrological cycle of the Senegal River basin. Two multi-objective calibration experiments investigate the effect of climate normalization. Both calibrations utilize observed discharge (Q) in combination with remote sensing ET data, where one is based on the original ET pattern and the other utilizes the normalized ET pattern. As objective functions we applied the Kling-Gupta-Efficiency (KGE) for Q and the Spatial Efficiency (SPAEF) for ET. We identify parameter sets that balance the tradeoffs between the two independent observations and find that the calibration using the normalized ET pattern does not compromise the spatial pattern performance of the original pattern. However, vice versa, this is not necessarily the case, since the calibration using the original ET pattern showed a poorer performance for the normalized pattern, i.e., a 30% decrease in SPAEF. Both calibrations reached comparable performance of Q, i.e., KGE around 0.7. With this study, we identified a general shortcoming of spatial pattern-oriented model evaluations using ET in basins dominated by a climate gradient, but we argue that this also applies to other variables such as, soil moisture or land surface temperature.

Pattern-oriented calibration and validation of urban growth models: Case studies of Dublin, Milan and Warsaw

Urban growth models are established to simulate complex dynamic processes of urban development, such as urban sprawl. According to the pattern-oriented modelling (POM) paradigm, recently gaining weight in ecology as a strategy for modelling complex systems, patterns at multiple scales should be considered to reflect the underlying processes of a complex system. Yet, calibration and validation of urban growth models is typically performed with a goal function of locational (cell-by-cell) agreement only, thus not in line with POM. We therefore examined POM as an approach to calibrate and validate (constrained) cellular automata for the European cities Warsaw, Milan, and Dublin. For Milan and Warsaw, the model structures identified with POM outperformed reference solutions calibrated on a single pattern with improvements up to 25% and 30%, respectively. For Dublin, no good model structure was found, but POM did help to recognize this problem, while locational agreement only failed to do so. Furthermore, the model structures identified with POM were more diverse, i.e. including more driving factors. In these diverse structures, the importance of the neighborhood effect relative to the infrastructure and land use effects reflected the polycentricity of the city as well as its type of sprawl: from monocentric edge expansion in Dublin to in-between ribbon sprawl in Warsaw to polycentric infill development in Milan. We conclude that POM improves the robustness of urban growth model calibration and validation, and obtains more dependable information about the processes driving urban sprawl that may serve the design of instruments to limit it.

Modelling the long-term dynamics of tropical forests: From leaf traits to whole-tree growth patterns

Tropical forests are the most diverse terrestrial ecosystems and home to numerous tree species competing for resources in space and time. Functional traits influence the ecophysiological performance of tree species, yet the relationship between traits and emergent long-term growth patterns is poorly understood. Here, we present a novel 3D forest stand model in which growth patterns of individual trees and forest stands are emergent properties of leaf traits. Individual trees are simulated as 3D functional-structural tree models (FSTMs), considering branches up to the second order and leaf dynamics at a resolution of one cubic meter. Each species is characterized by a set of leaf traits that corresponds to a specific position on the leaf economics spectrum and determines light-driven carbon assimilation, respiration and mortality rates. Applying principles of the pipe model theory, these leaf scale processes are coupled with within-tree carbon allocation, i.e., 3D tree growth emerges from low-level processes. By integrating these FSTMs into a dynamic forest stand model, we go beyond modern stand models to integrate structurally detailed internal physiological processes with interspecific competition and interactions with the environment in diverse tree communities. For model calibration and validation, we simultaneously compared a large number of emergent patterns at both the tree and forest levels in a pattern-oriented modelling framework. At the tree level, the specific leaf area and correlated leaf traits determined the maximum height and age of trees, as well as their size-dependent growth rate and shade tolerance. Trait variations along the leaf economics spectrum led to a continuous transition from fast-growing, short-lived and shade-intolerant to slow-growing, long-lived and shade-tolerant trees. These emerging patterns resemble well-known functional tree types, indicating a fundamental impact of leaf traits on long-term growth patterns. At the forest level, a large number of patterns taken from lowland Neotropical forests were reproduced, indicating that our forest model simulates structurally realistic forests over long time spans. Our ecophysiological approach improves the understanding of how leaf level processes scale up to the tree and the stand level, and facilitates the development of next-generation forest models for species-rich forests in which tree performance emerges directly from functional traits.

Consideration of Complexity in the Management of Construction and Demolition Waste Flow in French Regions: An Agent-Based Computational Economics Approach

For each region of France, there is currently a program to implement a plan for regional prevention and management of construction and demolition waste (CDW) used in the buildings and public works (e.g., roads) sector, also called the BTP (from the French Bâtiment et Travaux Publics) sector. To implement such a plan, its complexity must be considered; i.e., account (a) for how different scales are endogenously connected and (b) for decision-making rules at each scale being introduced. However, this complexity has rarely been taken into account in the literature. Using the PACA region as a case-study, this paper presents the first results of modelling that determines a hypotheses for the geographic distribution of the road renovation rate in each municipality (microscale) and Department (mesoscale) in a region of France. Such a renovation requires recycled aggregates (gravel) and asphalt supplies simultaneously. To consider this endogenous connection between scales, the model at the micro-scale must also be calibrated so the simulated values emerging at a higher-scale approach a supply–demand balance. We also discuss the transposition of the model to another French region (Ile-de-France). The method we used is the Agent-based Computational Economics (ACE) modelling approach. In addition, the coherent interplay between scales is determined by an approach called pattern-oriented modelling (POM). Our research revealed, at a thematic level, that for a circular economy to develop, the network of facilities in the territory is very important, and effective commercialization of secondary resources is major in the areas that group together recycling platforms and nearby asphalt plants. At a methodological level, our research revealed that in any multi-level modelling exercise, POM can be seen as an essential approach to accompany the ACE approach, particularly for a macroeconomic (here macro = regional) looping of a model designed at a microscale. However, convincing the BTP sector to integrate ACE/POM as a full part of a methodological support for regional prevention and management of CDW remains a challenge

Eco-epidemiological scaling of Leptospirosis: Vulnerability mapping and early warning forecasts

Infectious disease epidemics are plaguing the world and a lot of research is focused on the development of models to reproduce disease dynamics for eco-environmental and biological investigation, and disease management. Leptospirosis is an example of a neglected zoonosis strongly mediated by ecohydrological dynamics with emerging endemic and epidemic patterns worldwide in both animal and human populations.By accounting for large heterogeneities of affected areas we show how exponential endemics and scale-free epidemics are largely predictable and linked to common socio-environmental features via scaling laws with different exponents that inform about vulnerability factors. This led to the development of a novel pattern-oriented integrated model that can be used as an early-warning signal (EWS) tool for endemic-epidemic regime classification, risk determinant attribution, and near real-time forecast of outbreaks. Forecasts are grounded on expected outbreak recurrence time dependent on exceedance probabilities and statistical EWS that sense outbreak onset. A stochastic spatially-explicit model is shown to comprehensively predict outbreak dynamics (early sensing, timing, magnitude, decay, and eco-environmental determinants) and derive a spreading factor characterizing endemics and epidemics, where average over maximum rainfall is the critical factor characterizing disease transitions. Dynamically, case cross-correlation considering neighboring communities senses 2-weeks in advance outbreaks. Eco-environmental scaling relationships highlight how predicted host suitability and topographic index can be used as epidemiological footprints to effectively distinguish and control Leptospirosis regimes and areas dependent on hydro-climatological dynamics as the main trigger. The spatio-temporal scale-invariance of epidemics – underpinning persistent criticality and neutrality or independence among areas – is emphasized by the high accuracy in reproducing sequence and magnitude of cases via reliable surveillance.Further investigations of robustness and universality of eco-environmental determinants are required; nonetheless a comprehensive and computationally simple EWS method for the full characterization of Leptospirosis is provided. The tool is extendable to other climate-sensitive zoonoses to define vulnerability factors and predict outbreaks useful for optimal disease risk prevention and control.

From theory to practice in pattern-oriented modelling: identifying and using empirical patterns in predictive models.

To robustly predict the effects of disturbance and ecosystem changes on species, it is necessary to produce structurally realistic models with high predictive power and flexibility. To ensure that these models reflect the natural conditions necessary for reliable prediction, models must be informed and tested using relevant empirical observations. Pattern-oriented modelling (POM) offers a systematic framework for employing empirical patterns throughout the modelling process and has been coupled with complex systems modelling, such as in agent-based models (ABMs). However, while the production of ABMs has been rising rapidly, the explicit use of POM has not increased. Challenges with identifying patterns and an absence of specific guidelines on how to implement empirical observations may limit the accessibility of POM and lead to the production of models which lack a systematic consideration of reality. This review serves to provide guidance on how to identify and apply patterns following a POM approach in ABMs (POM-ABMs), specifically addressing: where in the ecological hierarchy can we find patterns; what kinds of patterns are useful; how should simulations and observations be compared; and when in the modelling cycle are patterns used? The guidance and examples provided herein are intended to encourage the application of POM and inspire efficient identification and implementation of patterns for both new and experienced modellers alike. Additionally, by generalising patterns found especially useful for POM-ABM development, these guidelines provide practical help for the identification of data gaps and guide the collection of observations useful for the development and verification of predictive models. Improving the accessibility and explicitness of POM could facilitate the production of robust and structurally realistic models in the ecological community, contributing to the advancement of predictive ecology at large.

SenSCOPE: Modeling mixed canopies combining green and brown senesced leaves. Evaluation in a Mediterranean Grassland

The coupling of radiative transfer, energy balance, and photosynthesis models has brought new opportunities to characterize vegetation functional properties from space. However, these models do not accurately represent processes in ecosystems characterized by mixtures of green vegetation and senescent plant material (SPM), in particular grasslands. These inaccuracies limit the retrieval of vegetation biophysical and functional properties. Green and senesced plants feature contrasting spectral properties and carry out different functions that current coupled models do not represent separately. Besides, senescent pigments' absorption features change as SPM decomposes, and neither is this process well parameterized in radiative transfer models. This manuscript aims at overcoming these limitations. On the one hand, we have developed senSCOPE, a version of the Soil-Canopy Observation of Photosynthesis and Energy fluxes (SCOPE) that separately represents light interaction and physiology of green and senesced leaves. On the other, we have characterized new specific absorption coefficients of senescent pigments (Ks) from optical measurements of SPM from a Mediterranean grassland. Sensitivity analyses revealed that compared to SCOPE, senSCOPE 1) predicts variables that respond more linearly to the faction of green leaf area; and 2) keeps high levels of absorbed photosynthetically active radiation in the green leaves, which leads to significant differences in leaf photosynthesis, non-photochemical quenching, and transpiration. Moreover, we compared SCOPE vs. senSCOPE's capability to provide estimates of functional and biophysical parameters of vegetation. We assimilated different combinations of reflectance factors (R), chlorophyll sun-induced fluorescence radiance in the O2-A band (F760), gross primary production (GPP), and thermal radiance (Lt) measured in a Mediterranean grassland. Besides, we compared the role of three different sets of Ks coefficients in the inversion of senSCOPE, two estimated from SPM. The performance of the inversions was assessed using field data and a pattern-oriented model evaluation approach. Unlike SCOPE, senSCOPE provided unbiased estimates of chlorophyll content (Cab) during the dry season. The use of SPM-specific Ks improved the representation of R in the near-infrared wavelengths; and, consequently, the estimation of leaf area index (LAI). Compared with field LAI, the coefficient of determination R2 increased from ~0.4 to ~0.6, depending on the inversion constraints. Compared with SCOPE, the new model and coefficients together reduced the root mean squared error between observed and modeled R (~40%), F760 (~30%), and GPP (~5%). Both models failed to represent Lt; in this case, senSCOPE featured larger uncertainties. The modeling approach we propose improves the simulation and retrieval of vegetation properties and function in grasslands. Further work is needed to test the applicability of senSCOPE in different ecosystems, improve the simulation of the thermal spectral domain, and better characterize the optical parameters of SPM. To do so, new databases of SPM optical and biophysical properties should be produced.

From mycelia to mastodons – A general approach for simulating biotic disturbances in forest ecosystems

Forest disturbance regimes are changing around the globe. Of particular concern are biotic disturbance agents, as they respond strongly to climate warming and invade new ecosystems as alien pests and pathogens. To date, biotic disturbances are either ignored in simulations of vegetation dynamics or only a small number of common agents are considered explicitly. Here we present BITE, a general, process-based approach to simulate biotic forest disturbance agents from fungi to large mammals. BITE considers the processes of agent introduction, dispersal, colonization, population dynamics, and vegetation impact explicitly. Here we parameterize the model for six widely different biotic disturbance agents (Heterobasidion annosum, Hymenoscyphus fraxineus, Lymanthia dispar, Anoplophora glabripennis, Capreolus capreolus, Mammut americanum) and evaluate it using pattern-oriented modeling. BITE enables the inclusion of both established and novel biotic disturbance agents in vegetation models, and is a step towards the comprehensive simulation of forest disturbance regimes in a changing world.

PeatFire: an agent-based model to simulate fire ignition and spreading in a tropical peatland ecosystem

The increased frequency and spread of tropical peat fires over the last two decades have attracted global attention because they cause significant environmental and health impacts at local to global scales. To understand the relative importance of key factors controlling tropical peatland burning events, we developed PeatFire, an agent-based model simulating the interaction between human-induced ignitions, fire and peat characteristics. The model describes (1) above- and belowground fires, which spread independently but interact with each other; (2) above- and belowground biomass; and (3) the watertable determining peat dryness and susceptibility to fire. We applied PeatFire to a region in South Sumatra that has experienced profound natural rainforest loss due to peat fires. Sensitivity analysis of the model suggests that fire sizes depend mostly on watertable depth, peat-dry-index and number of dry days before ignition. Using pattern-oriented modelling, these factors were parameterised so that the model output matches spatiotemporal fire patterns observed in the study region in 2015. Our results emphasise the risk of a sudden shift from moderate fire occurrence to complete burning and highlight the importance of local context to peatland regulation, which should consider both biophysical and socioeconomic factors and strategies for peatland fire management.

Combining the strengths of agent-based modelling and network statistics to understand animal movement and interactions with resources: example from within-patch foraging decisions of bumblebees

Understanding interactions between individual animals and their resources is fundamental to ecology. Agent-Based Models (ABMs) offer an opportunity to study how individuals move given the spatial distribution and characteristics of their resources. When contrasted with empirical individual-resource network data, ABMs can be a powerful method to detect the processes behind observed movement patterns, as they allow for a complete and quantitative analysis of the agent-to-environment relationships. Here we use the small-scale, within-patch movement of bumblebees (Bombus pascuorum) as a case study to demonstrate how ABMs can be combined with network statistics to provide a deeper understanding of the mechanisms behind the interactions between individuals and their resources.We build an ABM that explicitly simulates the influence of distance to the nearest flowering plant (allowing minimal energy expenditure and maximum time spent foraging), plant height and number of flower heads (as a proxy of food availability) on local foraging decisions of bumblebees. The relative importance of these three elements is determined using pattern-oriented modelling (POM), where we confront the network statistics (number of visited plants, number of interactions, nestedness and modularity) of a real B. pascuorum individual-resource network with the emergent patterns of our ABM. We also explore the model results using spatial analysis.The model is able to reproduce the observed network statistics. Despite the complex behaviour of bumblebees, our results show a surprisingly precise match between the structure of the simulated and empirical networks after adjusting a single model parameter controlling the importance of distance to the next plant visited.Our study illustrates the potential of combining field data, ABMs and individual-resource networks for evaluating small-scale, within-patch movement decisions to better understand animal movements in natural habitats. We discuss the benefits of our approach when compared to more classical statistical methods, and its ability to test various scenarios in a new or altered environment.

Capturing ecological processes in dynamic forest models: why there is no silver bullet to cope with complexity

AbstractDynamic forest models are a key tool to better understand, assess, and project decadal‐ to centennial‐scale forest dynamics. Despite their success, many questions regarding appropriate model formulations remain unresolved, and few models have found widespread application, for example, across a whole continent. We aimed to scrutinize the representation of ecological processes in dynamic forest models so as to rigorously test core assumptions underlying forest dynamics and the consistency of their interplay, taking the ForClim model as a case study. We developed a set of alternative representations for the main ecological processes, that is, tree establishment, growth, and mortality, and light extinction through the canopy, based on diverse sources of empirical data. We applied a pattern‐oriented modeling (POM) approach to test all combinations of the standard and alternative formulations (&gt;500 model versions) against a comprehensive set of patterns for diverse model applications across a wide range of site conditions. We found that adapting one process in isolation can improve model performance for one specific application. However, the best model versions typically included more than one alternative formulation. Importantly, the best version for an individual application was generally not the best across multiple applications, emphasizing the varying influences of ecological processes. We conclude that the behavior and performance of complex models should not be analyzed for a few specific applications only. Rather, multiple applications, system states, and dynamics of interest should be scrutinized across a wide range of site conditions. This allows for avoiding overfitting and detecting and eliminating structural shortcomings and parameterization problems. We thus propose to make use of the ever‐increasing data availability and the POM framework to challenge the core processes of dynamic models in a holistic manner. For model applications, we propose that a set of alternative formulations (ensemble simulations) should be used to quantify the impacts of structural uncertainty, rather than to rely on the projections from one single model version.

Parameter estimation for functional–structural plant models when data are scarce: using multiple patterns for rejecting unsuitable parameter sets

Functional-structural plant (FSP) models provide insights into the complex interactions between plant architecture and underlying developmental mechanisms. However, parameter estimation of FSP models remains challenging. We therefore used pattern-oriented modelling (POM) to test whether parameterization of FSP models can be made more efficient, systematic and powerful. With POM, a set of weak patterns is used to determine uncertain parameter values, instead of measuring them in experiments or observations, which often is infeasible. We used an existing FSP model of avocado (Persea americana 'Hass') and tested whether POM parameterization would converge to an existing manual parameterization. The model was run for 10 000 parameter sets and model outputs were compared with verification patterns. Each verification pattern served as a filter for rejecting unrealistic parameter sets. The model was then validated by running it with the surviving parameter sets that passed all filters and then comparing their pooled model outputs with additional validation patterns that were not used for parameterization. POM calibration led to 22 surviving parameter sets. Within these sets, most individual parameters varied over a large range. One of the resulting sets was similar to the manually parameterized set. Using the entire suite of surviving parameter sets, the model successfully predicted all validation patterns. However, two of the surviving parameter sets could not make the model predict all validation patterns. Our findings suggest strong interactions among model parameters and their corresponding processes, respectively. Using all surviving parameter sets takes these interactions into account fully, thereby improving model performance regarding validation and model output uncertainty. We conclude that POM calibration allows FSP models to be developed in a timely manner without having to rely on field or laboratory experiments, or on cumbersome manual parameterization. POM also increases the predictive power of FSP models.

Multiple-constraint inversion of SCOPE. Evaluating the potential of GPP and SIF for the retrieval of plant functional traits

The most recent efforts to provide remote sensing (RS) estimates of plant function rely on the combination of Radiative Transfer Models (RTM) and Soil-Vegetation-Atmosphere Transfer (SVAT) models, such as the Soil-Canopy Observation Photosynthesis and Energy fluxes (SCOPE) model. In this work we used ground spectroradiometric and chamber-based CO2 flux measurements in a nutrient manipulated Mediterranean grassland in order to: 1) develop a multiple-constraint inversion approach of SCOPE able to retrieve vegetation biochemical, structural as well as key functional traits, such as chlorophyll concentration (Cab), leaf area index (LAI), maximum carboxylation rate (Vcmax) and the Ball-Berry sensitivity parameter (m); and 2) compare the potential of the of gross primary production (GPP) and sun-induced fluorescence (SIF), together with up-welling Thermal Infrared (TIR) radiance and optical reflectance factors (RF), to estimate such parameters. The performance of the proposed inversion method as well as of the different sets of constraints was assessed with contemporary measurements of water and heat fluxes and leaf nitrogen content, using pattern-oriented model evaluation.The multiple-constraint inversion approach proposed together with the combination of optical RF and diel GPP and TIR data provided reliable estimates of parameters, and improved predicted water and heat fluxes. The addition of SIF to this scheme slightly improved the estimation of m. Parameter estimates were coherent with the variability imposed by the fertilization and the seasonality of the grassland. Results revealed that fertilization had an impact on Vcmax, while no significant differences were found for m. The combination of RF, SIF and diel TIR data weakly constrained functional traits. Approaches not including GPP failed to estimate LAI; however GPP overestimated Cab in the dry period. These problems might be related to the presence of high fractions of senescent leaves in the grassland. The proposed inversion approach together with pattern-oriented model evaluation open new perspectives for the retrieval of plant functional traits relevant for land surface models, and can be utilized at various research sites where hyperspectral remote sensing imagery and eddy covariance flux measurements are simultaneously taken.