Pattern-oriented modelling: a 'multi-scope' for predictive systems ecology

Modern ecology recognizes that modelling systems across scales and at multiple levels-especially to link population and ecosystem dynamics to individual adaptive behaviour-is essential for making the science predictive. 'Pattern-oriented modelling' (POM) is a strategy for doing just this. POM is the multi-criteria design, selection and calibration of models of complex systems. POM starts with identifying a set of patterns observed at multiple scales and levels that characterize a system with respect to the particular problem being modelled; a model from which the patterns emerge should contain the right mechanisms to address the problem. These patterns are then used to (i) determine what scales, entities, variables and processes the model needs, (ii) test and select submodels to represent key low-level processes such as adaptive behaviour, and (iii) find useful parameter values during calibration. Patterns are already often used in these ways, but a mini-review of applications of POM confirms that making the selection and use of patterns more explicit and rigorous can facilitate the development of models with the right level of complexity to understand ecological systems and predict their response to novel conditions.     

Exploiting Human Resource Requirements to Infer Human Movement Patterns for Use in Modelling Disease Transmission Systems: An Example from Eastern Province,Zambia

In this research, an agent-based model (ABM) was developed to generate human movement routes between homes and water resources in a rural setting, given commonly available geospatial datasets on population distribution, land cover and landscape resources. ABMs are an object-oriented computational approach to modelling a system, focusing on the interactions of autonomous agents, and aiming to assess the impact of these agents and their interactions on the system as a whole. An A* pathfinding algorithm was implemented to produce walking routes, given data on the terrain in the area. A* is an extension of Dijkstra’s algorithm with an enhanced time performance through the use of heuristics. In this example, it was possible to impute daily activity movement patterns to the water resource for all villages in a 75 km long study transect across the Luangwa Valley, Zambia, and the simulated human movements were statistically similar to empirical observations on travel times to the water resource (Chi-squared, 95% confidence interval). This indicates that it is possible to produce realistic data regarding human movements without costly measurement as is commonly achieved, for example, through GPS, or retrospective or real-time diaries. The approach is transferable between different geographical locations, and the product can be useful in providing an insight into human movement patterns, and therefore has use in many human exposure-related applications, specifically epidemiological research in rural areas, where spatial heterogeneity in the disease landscape, and space-time proximity of individuals, can play a crucial role in disease spread.     

Stochastic flowering phenology in Dactylis glomerata populations described by Markov chain modelling

Understanding the relationship between flowering patterns and pollen dispersal is important in climate change modelling, pollen forecasting, forestry and agriculture. Enhanced understanding of this connection can be gained through detailed spatial and temporal flowering observations on a population level, combined with modelling simulating the dynamics. Species with large distribution ranges, long flowering seasons, high pollen production and naturally large populations can be used to illustrate these dynamics. Revealing and simulating species-specific demographic and stochastic elements in the flowering process will likely be important in determining when pollen release is likely to happen in flowering plants. Spatial and temporal dynamics of eight populations of Dactylis glomerata were collected over the course of two years to determine high-resolution demographic elements. Stochastic elements were accounted for using Markov chain approaches in order to evaluate tiller-specific contribution to overall population dynamics. Tiller-specific developmental dynamics were evaluated using three different RV matrix correlation coefficients. We found that the demographic patterns in population development were the same for all populations with key phenological events differing only by a few days over the course of the seasons. Many tillers transitioned very quickly from non-flowering to full flowering, a process that can be replicated with Markov chain modelling. Our novel approach demonstrates the identification and quantification of stochastic elements in the flowering process of D. glomerata, an element likely to be found in many flowering plants. The stochastic modelling approach can be used to develop detailed pollen release models for Dactylis, other grass species and probably other flowering plants.

     

Statistical ecology comes of age

The desire to predict the consequences of global environmental change has been the driver towards more realistic models embracing the variability and uncertainties inherent in ecology. Statistical ecology has gelled over the past decade as a discipline that moves away from describing patterns towards modelling the ecological processes that generate these patterns. Following the fourth International Statistical Ecology Conference (1–4 July 2014) in Montpellier, France, we analyse current trends in statistical ecology. Important advances in the analysis of individual movement, and in the modelling of population dynamics and species distributions, are made possible by the increasing use of hierarchical and hidden process models. Exciting research perspectives include the development of methods to interpret citizen science data and of efficient, flexible computational algorithms for model fitting. Statistical ecology has come of age: it now provides a general and mathematically rigorous framework linking ecological theory and empirical data.     

Predicting the impacts of an introduced species from its invasion history: an empirical approach applied to zebra mussel invasions

1. Quantitative models of impact are lacking for the vast majority of known invasive species, particularly in aquatic ecosystems. Consequently, managers lack predictive tools to help them prioritise invasion threats and decide where they can most effectively allocate limited resources. Predictive tools would also enhance the accuracy of water quality assessments, so that impacts caused by an invader are not erroneously attributed to other anthropogenic stressors. 2. The invasion history of a species is a valuable guide for predicting the consequences of its introduction into a new environment. Regression analysis of data from multiple invaded sites can generate empirical models of impact, as is shown here for the zebra mussel Dreissena polymorpha. Dreissena's impacts on benthic invertebrate abundance and diversity follow predictable patterns that are robust across a range of habitat types and geographic regions. Similar empirical models could be developed for other invaders with a documented invasion history. 3. Because an invader's impact is correlated with its abundance, a surrogate model may be generated (when impact data are unavailable) by relating the invader's abundance to environmental variables. Such a model could help anticipate which habitats will be most affected by invasion. Lack of precision should not be a deterrent to developing predictive models where none exist. Crude predictions can be refined as additional data become available. Empirical modelling is a highly informative and inexpensive, but underused, approach in the management of aquatic invasive species.     

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.     

A model for the prediction of harmful algae blooms in the Vietnamese upwelling area

Satellite pictures and in situ observations indicate strong phytoplankton blooms including harmful algae blooms (HABs) during southwest (SW) summer monsoon in the Vietnamese upwelling area. In this period, nutrients are provided by coastal upwelling and by the very high river runoff from the Mekong River. During SW monsoon, in general two circulation patterns exist which allow the prediction of advection and diffusion of HAB patches. A Lagrangian HAB model that is driven by a circulation model and applied to HABs in Vietnamese waters is presented. Advection which is the most complicated part in modelling transport of passive substances is validated with a Lagrangian sediment trap experiment. The model produces realistic results compared to in situ observations and satellite images and might be used for real time forecast in the future.     

Feeding ecology of pelagic fish larvae and juveniles in slope waters of the Gulf of Mexico

Stable isotope ratios of carbon (δ¹³C) and nitrogen (δ¹⁵N) were used to investigate feeding patterns of larval and early juvenile pelagic fishes in slope waters of the Gulf of Mexico. Contribution of organic matter supplied to fishes and trophic position within this pelagic food web was estimated in 2007 and 2008 by comparing dietary signatures of the two main producers in this ecosystem: phytoplankton [based on particulate organic matter (POM)] and Sargassum spp. Stable isotope ratios of POM and pelagic Sargassum spp. were significantly different from one another with δ¹³C values of POM depleted by 3–6‰ and δ¹⁵N values enriched by 2 relative to Sargassum spp. Stable isotope ratios were significantly different among the five pelagic fishes examined: blue marlin Makaira nigricans, dolphinfish Coryphaena hippurus, pompano dolphinfish Coryphaena equiselis, sailfish Istiophorus platypterus and swordfish Xiphias gladius. Mean δ¹³C values ranged almost 2 among fishes and were most depleted in I. platypterus. In addition, mean δ¹⁵N values ranged 4–5 with highest mean values found for both C. hippurus and C. equiselis and the lowest mean value for M. nigricans during both years. Increasing δ¹³C or δ¹⁵N with standard length suggested that shifts in trophic position and diet occurred during early life for several species examined. Results of a two‐source mixing model suggest approximately an equal contribution of organic matter by both sources (POM = 55%; pelagic Sargassum spp. = 45%) to the early life stages of pelagic fishes examined. Contribution of organic matter, however, varied among species, and sensitivity analyses indicated that organic source estimates changed from 2 to 13% for a δ¹³C fractionation change of ±0·25‰ or a δ¹⁵N fractionation change of ± 1·0‰ relative to original fractionation values.     

Forecasting the combined effects of anticipated climate change and agricultural conservation practices on fish recruitment dynamics in Lake Erie

1. Many aquatic ecosystems are experiencing multiple anthropogenic stressors that threaten their ability to support ecologically and economically important fish species. Two of the most ubiquitous stressors are climate change and non-point source nutrient pollution.
2. Agricultural conservation practices (ACPs, i.e. farming practices that reduce runoff, prevent erosion, and curb excessive nutrient loading) offer a potential means to mitigate the negative effects of non-point source pollution on fish populations. However, our understanding of how ACP implementation amidst a changing climate will affect fish production in large ecosystems that receive substantial upstream sediment and nutrient inputs remains incomplete.
3. Towards this end, we explored how anticipated climate change and the implementation of realistic ACPs might alter the recruitment dynamics of three fish populations (native walleye Sander vitreus and yellow perch Perca flavescens and invasive white perch Morone americana) in the highly productive, dynamic west basin of Lake Erie. We projected future (2020–2065) recruitment under different combinations of anticipated climate change (n = 2 levels) and ACP implementation (n = 4 levels) in the western Lake Erie catchment using predictive biological models driven by forecasted winter severity, spring warming rate, and Maumee River total phosphorus loads that were generated from linked climate, catchment-hydrology, and agricultural-practice-simulation models.
4. In general, our models projected reduced walleye and yellow perch recruitment whereas invasive white perch recruitment was projected to remain stable or increase relative to the recent past. Our modelling also suggests the potential for trade-offs, as ACP implementation was projected to reduce yellow perch recruitment with anticipated climate change.
5. Overall, our study presents a useful modelling framework to forecast fish recruitment in Lake Erie and elsewhere, as well as offering projections and new avenues of research that could help resource management agencies and policy-makers develop adaptive and resilient management strategies in the face of anticipated climate and land-management change.

     

A socio‐ecological model for predicting impacts of land‐use and climate change on regional plant diversity in the Austrian Alps

Climate and land‐use change jointly affect the future of biodiversity. Yet, biodiversity scenarios have so far concentrated on climatic effects because forecasts of land use are rarely available at appropriate spatial and thematic scales. Agent‐based models (ABMs) represent a potentially powerful but little explored tool for establishing thematically and spatially fine‐grained land‐use scenarios. Here, we use an ABM parameterized for 1,329 agents, mostly farmers, in a Central European model region, and simulate the changes to land‐use patterns resulting from their response to three scenarios of changing socio‐economic conditions and three scenarios of climate change until the mid of the century. Subsequently, we use species distribution models to, first, analyse relationships between the realized niches of 832 plant species and climatic gradients or land‐use types, respectively, and, second, to project consequent changes in potential regional ranges of these species as triggered by changes in both the altered land‐use patterns and the changing climate. We find that both drivers determine the realized niches of the studied plants, with land use having a stronger effect than any single climatic variable in the model. Nevertheless, the plants' future distributions appear much more responsive to climate than to land‐use changes because alternative future socio‐economic backgrounds have only modest impact on land‐use decisions in the model region. However, relative effects of climate and land‐use changes on biodiversity may differ drastically in other regions, especially where landscapes are still dominated by natural or semi‐natural habitat. We conclude that agent‐based modelling of land use is able to provide scenarios at scales relevant to individual species distribution and suggest that coupling ABMs with models of species' range change should be intensified to provide more realistic biodiversity forecasts.     

Geographic range determinants of two commercially important marine molluscs

Aim We modelled the spatial abundance patterns of two abalone species (Haliotis rubra Donovan 1808 and H. laevigata Leach 1814) inhabiting inshore rocky reefs to better understand the importance of current sea surface temperature (SST) (among other predictors) and, ultimately, the effect of future climate change, on marine molluscs. Location Southern Australia. Methods We used an ensemble species distribution modelling approach that combined likelihood‐based generalized linear models and boosted regression trees. For each modelling technique, a two‐step procedure was used to predict: (1) the current probability of presence, followed by (2) current abundance conditional on presence. The resulting models were validated using an independent, spatially explicit dataset of abalone abundance patterns in Victoria. Results For both species, the presence of reef was the main driver of abalone occurrence, while SST was the main driver of spatial abundance patterns. Predictive maps at c. 1‐km resolution showed maximal abundance on shallow coastal reefs characterized by mild winter SSTs for both species. Main conclusions Sea surface temperature was a major driver of abundance patterns for both abalone species, and the resulting ensemble models were used to build fine‐resolution predictive range maps (c. 1 km) that incorporate measures of habitat suitability and quality in support of resource management. By integrating this output with structured spatial population models, a more robust understanding of the potential impacts of threatening human processes such as climate change can be established.     

Unpacking the ‘black box’: Improving ecological interpretation of regression-based models

Aim

Many tree species distribution models use black-box machine learning techniques that often neglect interpretative aspects and instead focus mainly on maximizing predictive accuracy. In this study, we outline an interpretative modelling framework to gain better ecological insights while mapping abundance patterns of six North American species.

Location

Continental United States and Canada.

Methods

We develop an innovative procedure using regression trees by stabilizing variance, and mapping dominant rules which we term ‘optimized regression tree bagging for interpretation and mapping’ (ORTBIM). We apply this technique to understand ecological features influencing the abundance patterns of three eastern (Pinus strobus, Acer saccharum and Quercus montana), and three western (Picea engelmannii, Pinus ponderosa and Pseudotsuga menziesii) tree species in North America. For these species, we assess and map the dominant climate–terrain interactions that partly determine abundance patterns in the eastern and western regions. In the process, we examine the role of varying responses and scales and explore finer-scale species climate–terrain niches and non-linear relationships.

Results

Our study emphasizes the prominent role of elevation and heat–moisture variables in the west and the greater importance of seasonal precipitation and seasonal temperature in the east. The abundance patterns under future climate (SSP5-8.5) show climate–terrain habitats shifting northward and westward into Canada and Alaska for the eastern species, and predominantly north-westward for the western species.

Conclusion

Our interpretative modelling framework can be used to gain a more comprehensive understanding of the abundance patterns across the full species range, formulate better predictive models and facilitate improved management practices under climate change.     

Tracing the origin of disjunct distributions: a case of biogeographical convergence in Pyrgus butterflies

Aim To study the biogeographical factors responsible for the current disjunct distributions of two closely related species of butterflies (Pyrgus cinarae and Pyrgus sidae, Lepidoptera: Hesperioidea). Both species have small populations in the Iberian Peninsula that are isolated by more than 1000 km from their nearest conspecifics. Because these species possess similar ecological preferences and geographical distributions, they are excellent candidates for congruent biogeographical histories. Location The Palaearctic region, with a special focus on the Mediterranean peninsulas as glacial refugia. Methods We integrated phylogeography and population genetic analyses with ecological niche modelling. The mitochondrial gene cytochrome c oxidase subunit 1 (COI) and the non‐coding nuclear marker internal transcribed spacer 2 (ITS2) were analysed for 62 specimens of P. cinarae and for 80 of P. sidae to infer phylogeography and to date the origin of disjunct distributions. Current and ancestral [Last Glacial Maximum using MIROC (Model for Interdisciplinary Research on Climate) and CCSM (Community Climate System Model) circulation models] distribution models were calculated with Maxent . Using present climatic conditions, we delimited the ecological space for each species. Results The genetic structure and potential ancestral distribution of the two species were markedly different. While the Iberian population of P. cinarae had an old origin (c. 1 Ma), that of P. sidae was closely related to French and Italian lineages (which jointly diverged from eastern populations c. 0.27 Ma). Ecological niche modelling showed that minor differences in the ecological preferences of the two species seem to account for their drastically different distributional response to the last glacial to post‐glacial environmental conditions. Although the potential distribution of P. cinarae was largely unaffected by climate change, suitable habitat for P. sidae strongly shifted in both elevation and latitude. This result might explain the early origin of the disjunct distribution of P. cinarae, in contrast to the more recent disjunction of P. sidae. Main conclusions We show that convergent biogeographical patterns can be analysed with a combination of genetic and ecological niche modelling data. The results demonstrate that species with similar distributional patterns and ecology may still have different biogeographical histories, highlighting the importance of including the temporal dimension when studying biogeographical patterns.     

A discrete-time logistic model of frond dynamics for Mazzaella parksii (Rhodophyta, Gigartinales)

Mathematical modelling is useful in population ecology and resource management. Logistic models have traditionally been applied to unitary organisms, but it is unclear whether they could be used at the frond (ramet) level for clonal seaweeds. This study shows that frond dynamics for the clonal seaweed Mazzaella parksii (=M. cornucopiae) can be described by a discrete-time logistic model. The model is realistic in that it includes density-dependence, which was previously demonstrated experimentally for this species, and only necessitates data on frond density measured at discrete time intervals. This may constitute a useful tool for the management of clonal seaweeds of economic importance that occur in dense stands. This revised version was published online in August 2006 with corrections to the Cover Date.     

Animal-based measures on fattening heavy pigs at the slaughterhouse and the association with animal welfare at the farm level: a preliminary study

Monitoring animal welfare (AW) in pig farms requires both proper indicators and a feasible approach. Animal-based measures (ABMs) are well-established AW indicators. Furthermore, AW screening at the slaughterhouses could be useful for identifying problems on farm. The aim of this study was to evaluate ABMs at the slaughterhouse and, when possible, to compare these ABMs with those collected on the farm. The study was carried out in northern Italy in a commercial abattoir and in a sample of farms. Animal-based measures were recorded on pigs from 62 batches of 54 farms, during ante-mortem (n=10 085 pigs) and post-mortem (n=7952 pigs) inspections. Sixteen of 54 farms were selected to compare ABMs collected at the slaughterhouse with ABMs collected on the farm. Overall, 2295 pigs (mean pigs examined per farm 119±45) were inspected at the slaughterhouse (group S) and 420 pigs (mean pigs per farm 26±5) on the farm (group F). Non-animal-based measures were also collected at the 16 farms. Differences between groups S and F, at the animal level, were assessed by a two-tailed paired Wilcoxon–Mann–Whitney test. Differences at the site of observation level (farm and slaughterhouse) were assessed by Fisher’s exact test using a hierarchical log-linear modelling for contingency tables. The most frequent ABMs at the slaughterhouse were manure on the body (47.7%), followed by dermatitis (28.0%), white spot (25.4%) and bursitis (24.7%). Recording ABMs at the slaughterhouse and on the farm usually yielded similar results; however, there were some exceptions. In particular, significant differences were found for non-uniformity of size (P<0.05) and dermatitis (P<0.001), which were higher at the slaughterhouse than on the farm. Results of log-linear modelling underlined the effect of the farm of origin on the percentage of pigs with bursitis, manure on the body and ear injuries that were observed at the slaughterhouse. In group S, significant associations between manure on the body and insufficient presence of clean and dry areas in the corresponding farm were found (P<0.05). Although these results should be interpreted with care due to the limited sample of farms, the slaughterhouse could be a feasible site of observation of ABMs, which could then be integrated in monitoring of AW on farm. Considering the number of slaughtered batches per farm, it would be possible to repeat assessments several times throughout the year for each farm, which could help provide an index for the continuous monitoring of AW.     

Combining mathematics and empirical data to predict emergence of RNA viruses that differ in reservoir use

RNA viruses may be particularly capable of contributing to the increasing biomedical problem of infectious disease emergence. Empirical studies and epidemiological models are informative for the understanding of evolutionary processes that promote pathogen emergence, but rarely are these approaches combined in the same study. Here, we used an epidemiology model containing observations of pathogen productivity in reservoirs, as a means to predict which pathogens should be most prone to emerge in a primary host such as humans. We employed as a model system a collection of vesicular stomatitis virus populations that had previously diverged in host-use strategy: specialists, directly selected generalists and indirectly selected (fortuitous) generalists. Using data from experiments where these viral strategists were challenged to grow on unencountered novel hosts in vitro, logistic growth models determined that the directly selected generalist viruses tended to grow best on model reservoirs. Furthermore, when we used the growth data to estimate average reproductive rate across secondary reservoirs, we showed that the combined approach could be used to estimate relative success of the differing virus strategists when encountering a primary host. Our study suggests that synergistic approaches combining epidemiological modelling with empirical data from experimental evolution may be useful for developing efforts to predict which types of pathogens pose the greatest probability of emerging in the future.     

A mean field model for competition: from neutral ecology to the Red Queen

Individual species are distributed inhomogeneously over space and time, yet, within large communities of species, aggregated patterns of biodiversity seem to display nearly universal behaviour. Neutral models assume that an individual's demographic prospects are independent of its species identity. They have successfully predicted certain static, time‐independent patterns. But they have generally failed to predict temporal patterns, such as species ages or population dynamics. We construct a new, multispecies framework incorporating competitive differences between species, and assess the impact of this competition on static and dynamic patterns of biodiversity. We solve this model exactly for the special case of a Red Queen hypothesis, where fitter species are continually arising. The model predicts more realistic species ages than neutral models, without greatly changing predictions for static species abundance distributions. Our modelling approach may allow users to incorporate a broad range of ecological mechanisms.     

Assessing the effectiveness of foraging radius models for seabird distributions using biotelemetry and survey data

Relatively simple foraging radius models have the potential to generate predictive distributions for a large number of species rapidly, thus providing a cost-effective alternative to large-scale surveys or complex modelling approaches. Their effectiveness, however, remains largely untested. Here we compare foraging radius distribution models for all breeding seabirds in Ireland, to distributions of empirical data collected from tracking studies and aerial surveys. At the local/colony level, we compared foraging radius distributions to GPS tracking data from seabirds with short (Atlantic puffin Fratercula arctica, and razorbill Alca torda) and long (Manx shearwater Puffinus puffinus, and European storm-petrel Hydrobates pelagicus) foraging ranges. At the regional/national level, we compared foraging radius distributions to extensive aerial surveys conducted over a two-year period. Foraging radius distributions were significantly positively correlated with tracking data for all species except Manx shearwater. Correlations between foraging radius distributions and aerial survey data were also significant, but generally weaker than those for tracking data. Correlations between foraging radius distributions and aerial survey data were benchmarked against generalised additive models (GAMs) of the aerial survey data that included a range of environmental covariates. While GAM distributions had slightly higher correlations with aerial survey data, the results highlight that the foraging radius approach can be a useful and pragmatic approach for assessing breeding distributions for many seabird species. The approach is likely to have acceptable utility in complex, temporally variable ecosystems and when logistic and financial resources are limited.     

Post-Hoc Pattern-Oriented Testing and Tuning of an Existing Large Model: Lessons from the Field Vole

Pattern-oriented modeling (POM) is a general strategy for modeling complex systems. In POM, multiple patterns observed at different scales and hierarchical levels are used to optimize model structure, to test and select sub-models of key processes, and for calibration. So far, POM has been used for developing new models and for models of low to moderate complexity. It remains unclear, though, whether the basic idea of POM to utilize multiple patterns, could also be used to test and possibly develop existing and established models of high complexity. Here, we use POM to test, calibrate, and further develop an existing agent-based model of the field vole (Microtus agrestis), which was developed and tested within the ALMaSS framework. This framework is complex because it includes a high-resolution representation of the landscape and its dynamics, of the individual’s behavior, and of the interaction between landscape and individual behavior. Results of fitting to the range of patterns chosen were generally very good, but the procedure required to achieve this was long and complicated. To obtain good correspondence between model and the real world it was often necessary to model the real world environment closely. We therefore conclude that post-hoc POM is a useful and viable way to test a highly complex simulation model, but also warn against the dangers of over-fitting to real world patterns that lack details in their explanatory driving factors. To overcome some of these obstacles we suggest the adoption of open-science and open-source approaches to ecological simulation modeling.