Individual-based Model Framework Research Articles

Integrating resource memory and cue-based territoriality to simulate movement dynamics: a process-explicit and pattern-oriented approach

Animal movement is central to ecology and of paramount importance to understand processes underlying the emergence of empirical space-use patterns. In this study, we investigated how integrating two well-established movement processes in the field of theoretical movement ecology, (i) memory-based resource use; and (ii) cue- or scent-based territoriality, affects emergent space-use patterns. We integrated the two mechanisms of space-use into a single individual-based modelling framework, and then quantified how varying degrees of each process affects emergent space-use patterns. Both mechanisms together led to the formation of exclusive, resource- and population density-dependent utilisation distributions, which were responsive to perturbations in the conspecific environment (i.e., removing individuals). Utilisation distribution patterns (area and overlap) were primarily influenced by memory and scent decay rate parameters, controlling an animal's inherent exploratory tendency and its desire to avoid conspecifics respectively. Model application to a population of feral cats demonstrated that general space-use measures (e.g., median UD area, resident and transient dynamics) could be approximated through simulation, though replication of finer-scale space use patterns (e.g., cumulative UD area) was poorly matched. Though application of mechanistic movement models remains both challenging and limited, the potential of such endeavours to ultimately predict population response cannot be overstated.

In Situ and Individual-Based Analysis of the Influence of Polystyrene Microplastics on Escherichia coli Conjugative Gene Transfer at the Single-Cell Level.

The impact of microplastic particles of micro- and nanometer sizes on microbial horizontal gene transfer (HGT) remains a controversial topic. Existing studies rely on traditional approaches, which analyze population behavior, leading to conflicting conclusions and a limited understanding. The present study addressed these limitations by employing a novel microfluidic chamber system for in situ visualization and precise quantification of the effects of different concentrations of polystyrene (PS) microbeads on microbial HGT at the single-cell level. The statistical analysis indicated no significant difference in the division times of both the donor and recipient bacteria across different PS microbead concentrations. However, as the concentration of PS microbeads increased from 0 to 2000 mg L-1, the average conjugation frequency of Escherichia coli decreased from 0.028 ± 0.015 to 0.004 ± 0.003. Our observations from the microfluidic experiments revealed that 500 nm PS microbeads created a barrier effect on bacterial conjugative transfer. The presence of microbeads resulted in reduced contact and interaction between the donor and recipient strains, thereby causing a decrease in the conjugation transfer frequency. These findings were validated by an individual-based modeling framework parameterized by the data from the individual-level microfluidic experiments. Overall, this study offers a fresh perspective and strategy for investigating the risks associated with the dissemination of antibiotic resistance genes related to microplastics.

Estimating the health burden of road traffic injuries in Malawi using an individual-based model

BackgroundRoad traffic injuries are a significant cause of death and disability globally. However, in some countries the exact health burden caused by road traffic injuries is unknown. In Malawi, there is no central reporting mechanism for road traffic injuries and so the exact extent of the health burden caused by road traffic injuries is hard to determine. A limited number of models predict the incidence of mortality due to road traffic injury in Malawi. These estimates vary greatly, owing to differences in assumptions, and so the health burden caused on the population by road traffic injuries remains unclear.MethodsWe use an individual-based model and combine an epidemiological model of road traffic injuries with a health seeking behaviour and health system model. We provide a detailed representation of road traffic injuries in Malawi, from the onset of the injury through to the final health outcome. We also investigate the effects of an assumption made by other models that multiple injuries do not contribute to health burden caused by road accidents.ResultsOur model estimates an overall average incidence of mortality between 23.5 and 29.8 per 100,000 person years due to road traffic injuries and an average of 180,000 to 225,000 disability-adjusted life years (DALYs) per year between 2010 and 2020 in an estimated average population size of 1,364,000 over the 10-year period. Our estimated incidence of mortality falls within the range of other estimates currently available for Malawi, whereas our estimated number of DALYs is greater than the only other estimate available for Malawi, the GBD estimate predicting and average of 126,200 DALYs per year over the same time period. Our estimates, which account for multiple injuries, predict a 22–58% increase in overall health burden compared to the model ran as a single injury model.ConclusionsRoad traffic injuries are difficult to model with conventional modelling methods, owing to the numerous types of injuries that occur. Using an individual-based model framework, we can provide a detailed representation of road traffic injuries. Our results indicate a higher health burden caused by road traffic injuries than previously estimated.

Modeling weather-driven long-distance dispersal of spruce budworm moths (Choristoneura fumiferana). Part 1: Model description

Long-term studies of insect populations in the North American boreal forest have shown the vital importance of long-distance dispersal to the maintenance and expansion of insect outbreaks. In this work, we extend several concepts established previously in an empirically-based dispersal flight model with recent work on the physiology and behavior of the adult eastern spruce budworm (SBW) moth, Choristoneura fumiferana (Clemens). An outbreak of defoliating SBW in Quebec, ongoing since the mid-2000s, already covers millions of hectares of forests in eastern Canada and threatens to spread into neighboring areas through annual summertime episodes of long-distance dispersal. Such flight events in favorable conditions frequently include billions of SBW moths dispersing in the warm atmospheric boundary layer, typically starting around sunset and often lasting through several hours of wind-driven transport over hundreds of kilometers. Successful SBW dispersal to possibly distant host forest areas depends acutely on the weather. Here we describe the components and results of SBW–pyATM, an open-source individual-based modeling framework developed in Python for the simulation of these weather-driven SBW dispersal events. Using seasonal SBW phenology results from BioSIM at known outbreak locations and high-resolution Weather Research and Forecasting (WRF) model output, we focus on modeling dispersal flights over two successive nights in July 2013 in southern Quebec. Our flight model closely reproduces the SBW spatial patterns and motions observed by weather surveillance radar over the St. Lawrence estuary. With SBW–pyATM we can estimate landing locations for both male and female SBW and the resulting spatial patterns of egg distribution, allowing us eventually to forecast future larval defoliation activity in new locations where immigration could help overcome local limitations on SBW populations. This information could then support forest management decisions where SBW outbreaks threaten valuable resources.

Introduction: the secret lives of microbial mobile genetic elements.

Introduction: the secret lives of microbial mobile genetic elements.

Accounting for Seasonal Growth in Per-Recruit Analyses: A Case Study of Four Commercial Fish in Coastal China Seas

Biological reference points (BRPs) derived from per-recruit analyses are commonly used in inferring stock status and serve as the target or threshold in fisheries management. However, the estimation of BRPs may be impacted by the variability in life history processes, and particularly, individual growth rates often display substantial seasonal oscillations but are seldomly considered in per-recruit analyses. Using four commercial fish species Lophius litulon, Saurida elongata, Hexagrammos otakii, and Larimichthys polyactis in coastal China Seas as examples, this study examined the effects of seasonal growth variability on per-recruit analyses and on the estimation of BRPs. We developed an individual-based modeling framework to simulate growth patterns with and without variations at the seasonal and the individual levels and adopted two common assessment methods, age-based analysis and length-frequency analysis, to estimate growth parameters regarding data availability in data-rich or data-poor fisheries, respectively. We found that ignoring seasonality could lead to substantial errors in the estimation of BRPs for the small-size species H. otakii and L. polyactis in our evaluation; when seasonal growth was considered, the estimation could be largely improved. Length-frequency analysis might yield considerably less reliable estimations than age-based method. The time of year when fast growth occurs determines positive or negative bias in estimation, and the amplitude of seasonal growth determines the degree of biases. In general, ignoring the seasonality of growth when there is can lead to underestimated growth parameter K and trigger biases that propagate in stock assessment and management, whereas incorporating seasonality falsely in assessment when there is no seasonal variation will have little influences on the estimation of BRPs. This study contributes to demonstrate the risk of ignoring seasonality in stock assessment and the approaches accounting for seasonal variability in fishery management.

Stochastic challenges to interrupting helminth transmission

Predicting the effect of different programmes designed to control both the morbidity induced by helminth infections and parasite transmission is greatly facilitated by the use of mathematical models of transmission and control impact. In such models, it is essential to account for the many sources of uncertainty – natural, or otherwise – to ensure robustness in prediction and to accurately depict variation around an expected outcome. In this paper, we investigate how well the standard deterministic models match the predictions made using individual-based stochastic simulations. We also explore how well concepts which derive from deterministic models, such as ‘breakpoints’ in transmission, apply in the stochastic world. Employing an individual-based stochastic model framework we also investigate how transmission and control are affected by the migration of infected people into a defined community. To give our study focus we consider the control of soil-transmitted helminths (STH) by mass drug administration (MDA), though our methodology is readily applicable to the other helminth species such as the schistosome parasites and the filarial worms. We show it is possible to theoretically define a ‘stochastic breakpoint’ where much noise surrounds the expected deterministic breakpoint. We also discuss the concept of the ‘interruption of transmission’ independent of the ‘breakpoint’ concept where analyses of model behaviour illustrate the current limitations of deterministic models to account for the ‘fade-out’ or transmission extinction behaviour in simulations. Our analysis of migration confirms a relationship between the critical infected human migration rate scale (i.e., order of magnitude) per unit of time and the death rate of infective stages that are released into the free-living environment. This relationship is shown to determine the likelihood that control activities aim at chemotherapeutic treatment of the human host will eliminate transmission. The development of a new stochastic simulation code for STH in the form of a publicly-available open-source python package which includes features to incorporate many population stratifications, different control interventions including mass drug administration (with defined frequency, coverage levels and compliance patterns) and inter-village human migration is also described.

Development and Validation of a Mixture Toxicity Implementation in the Dynamic Energy Budget-Individual-Based Model: Effects of Copper and Zinc on Daphnia magna Populations.

Mechanistic population models are gaining considerable interest in ecological risk assessment. The dynamic energy budget approach for toxicity (DEBtox) and the general unified threshold model for survival (GUTS) are well-established theoretical frameworks that describe sublethal and lethal effects of a chemical stressor, respectively. However, there have been limited applications of these models for mixtures of chemicals, especially to predict long-term effects on populations. We used DEBtox and GUTS in an individual-based model (IBM) framework to predict both single and combined effects of copper and zinc on Daphnia magna populations. The model was calibrated based on standard chronic toxicity test results with the single substances. A mixture toxicity implementation based on the general independent action model for mixtures was developed and validated with data from a population experiment with copper and zinc mixtures. Population-level effects of exposure to individual metals were accurately predicted by DEB-IBM. The DEB-IBM framework also allowed us to identify the potential mechanisms underlying these observations. Under independent action the DEB-IBM was able to predict the population dynamics observed in populations exposed to the single metals and their mixtures (R2 > 65% in all treatments). Our modeling shows that it is possible to extrapolate from single-substance effects at the individual level to mixture toxicity effects at the population level, without the need for mixture toxicity data at the individual level from standard mixture toxicity tests. The application of such modeling techniques can increase the ecological realism in risk assessment. Environ Toxicol Chem 2021;40:513-527. © 2020 SETAC.

Modelling the household-level impact of a maternal respiratory syncytial virus (RSV) vaccine in a high-income setting

BackgroundRespiratory syncytial virus (RSV) infects almost all children by the age of 2 years, with the risk of hospitalisation highest in the first 6 months of life. Development and licensure of a vaccine to prevent severe RSV illness in infants is a public health priority. A recent phase 3 clinical trial estimated the efficacy of maternal vaccination at 39% over the first 90 days of life. Households play a key role in RSV transmission; however, few estimates of population-level RSV vaccine impact account for household structure.MethodsWe simulated RSV transmission within a stochastic, individual-based model framework, using an existing demographic model, structured by age and household and parameterised with Australian data, as an exemplar of a high-income country. We modelled vaccination by immunising pregnant women and explicitly linked the immune status of each mother-infant pair. We quantified the impact on children for a range of vaccine properties and uptake levels.ResultsWe found that a maternal immunisation strategy would have the most substantial impact in infants younger than 3 months, reducing RSV infection incidence in this age group by 16.6% at 70% vaccination coverage. In children aged 3–6 months, RSV infection was reduced by 5.3%. Over the first 6 months of life, the incidence rate for infants born to unvaccinated mothers was 1.26 times that of infants born to vaccinated mothers. The impact in older age groups was more modest, with evidence of infections being delayed to the second year of life.ConclusionsOur findings show that while individual benefit from maternal RSV vaccination could be substantial, population-level reductions may be more modest. Vaccination impact was sensitive to the extent that vaccination prevented infection, highlighting the need for more vaccine trial data.

An individual-based modeling framework for infectious disease spreading in clustered complex networks

We introduce an individual-based model with dynamical equations for susceptible-infected-susceptible (SIS) epidemics on clustered networks. Linking the mean-field and quenched mean-field models, a general method for deriving a cluster approximation for three-node loops in complex networks is proposed. The underlying epidemic threshold condition is derived by using the quasi-static approximation. Our method thus extends the pair quenched mean-field (pQMF) approach for SIS disease spreading in unclustered networks to the scenario of epidemic outbreaks in clustered systems with abundant transitive relationships.We found that clustering can significantly alter the epidemic threshold, depending nontrivially on topological details of the underlying population structure. The validity of our method is verified through the existence of bounded solutions to the clustered pQMF model equations, and is further attested via stochastic simulations on homogeneous small-world artificial networks and growing scale-free synthetic networks with tunable clustering, as well as on real-world complex networked systems. Our method has vital implications for the future policy development and implementation of intervention measures in highly clustered networks, especially in the early stages of an epidemic in which clustering can decisively alter the growth of a contagious outbreak.

Optimizing the use of endangered species in multi-population collection, captive breeding and release programs

Evaluating the spectrum of risks and rewards of captive breeding and release is central to identifying responsible conservation actions for declining species. Trade-offs among source, captive, and target wild populations are expected when optimizing the use of the last few remaining individuals of a population. Yet few analyses are conducted to optimize the choice of source population from which animals are collected and released. Using linked scenarios, we evaluated the risks and rewards of collection, ex situ rearing, and release of endangered Greater Sage-grouse in Canada. We integrated demographic rates from captive populations with wild population abundance, demography, and habitat relationships in a spatially explicit individual-based model framework for multiple populations across national and international borders. We quantified the potential for released birds to improve wild abundance and reduce extinction risk in two target wild populations. To gain general insight, we compared risks and rewards among different source and target population sizes and trajectories. The risks caused by removing individuals from the wild depended on the number of animals removed, and source abundance and trajectory, and were partially obscured by stochasticity. Releases into small and rapidly declining populations provided the greatest near-term reductions in extinction risk, but improvements were short-term. Yet releases into larger and more stable populations resulted in longer lasting conservation benefits than in more vulnerable populations but required greater initial release effort. Systematic modeling approaches that evaluate a spectrum of trade-offs and quantify conservation risks and benefits can help direct the expectations and effort invested in captive breeding and release programs.

Behavioral Specialization During the Neolithic—An Evolutionary Model

The emergence of agriculture and complex societies during the Near-Eastern Neolithic opened a new era in human evolution. Food production seriously affected the ecological environment, and societies answered this with large-scale division of labour and specialisation. In this paper we study this transition with an individual based model framework. Our model captures the connections between the appearance of agriculture, social division of labour, and behavioural diversity. Our two main settings represent different habitats: In the pre-Neolithic habitat resources fluctuate in time and there is no large-scale food storage. In the Neolithic habitat active food production results in economic surplus. We consider a sexually reproducing social group, where individuals solve different tasks for survival. We assume that the task solving effectiveness has a genetic basis, but also improves with experience and learning. Since tasks can require more or less different skills, there is a trade-off between the genetic affinities for different tasks. Individuals are born with inherited task choice strategies that they can improve by imitating more successful peers. We have shown that for the Neolithic case both phenotypic (task choice strategy) and genetic specialisation is possible, if scarcer goods are more valuable. As the number of tasks increase, specialisation evolves only in a much larger group. Although phenotypic specialisation is often present, genetic specialisation requires strong assortativity both during imitation and mate choice. Our model shows that if economic surplus becomes available, behavioural specialisation and large-scale division of labour is likely to appear.

Direction-dependent interaction rules enrich pattern formation in an individual-based model of collective behavior.

Direction-dependent interaction rules are incorporated into a one-dimensional discrete-time stochastic individual-based model (IBM) of collective behavior to compare pattern formation with an existing partial differential equation (PDE) model. The IBM is formulated in terms of three social interaction forces: repulsion, alignment, and attraction, and includes information regarding conspecifics’ direction of travel. The IBM produces a variety of spatial patterns which qualitatively match patterns observed in a PDE model. The addition of direction-dependent interaction rules exemplifies how directional information transfer within a group of individuals can result in enriched pattern formation. Our individual-based modelling framework reveals the influence that direction-dependent interaction rules such as biological communication can have upon individual movement trajectories and how these trajectories combine to form group patterns.

Interplay between contact risk, conspecific density, and landscape connectivity: An individual-based modeling framework

In many host-pathogen systems, pathogen transmission requires close contact between infectious and susceptible hosts. The contact rates among individuals depend upon how they move in the landscape because functional connectivity can affect interactions between individuals. Yet few studies have explored the interplay between contact rates, conspecific density, and functional connectivity. Using a spatially explicit individual-based model, we investigated how empirical movement rules translate into spatial patterns of contact rates among disease hosts in complex landscapes. We developed dynamic models of functional connectivity by characterizing movement behaviors of radio-collared raccoons, which are the main hosts of the raccoon rabies virus. On this basis, we simulated space-use dynamics of raccoons in virtual landscapes varying in conspecific density, forest availability, and landscape connectivity. We then characterized spatial patterns in the density of per capita contact rates of simulated raccoons. Radio-collared raccoons were more likely to choose large habitat patches that were reached by traveling along least-cost paths, and had relatively long residence times in anthropogenic areas, especially during daytime. These movement rules that were applied to simulated raccoons in virtual landscapes revealed three key patterns in contact rates. First, few simulated raccoons were responsible for most contacts between individuals, a pattern that emerged even though all simulated raccoons obeyed the same movement rules. Second, per capita contact rates increased linearly with individual density under most conditions, which indicates that raccoon rabies transmission should be density-dependent rather than frequency-dependent. Third, functional connectivity created a broad range of patterns in the density of per capita contact rates from decreasing to increasing values, depending upon the availability of land cover types. Overall, the contacts between hosts tended to occur at highest densities in forests, anthropogenic areas, and agricultural corridors. We show that complex patterns of contact rates can emerge from simple empirical movement rules and, therefore, can be key drivers of disease spread dynamics. The strong spatial heterogeneity observed in contact rates implies that the effectiveness of particular control interventions can strongly vary depending upon host density, landscape composition, and functional connectivity. This study provides key functional relationships for tailoring interventions to changes in local conditions.

HexSim: a modeling environment for ecology and conservation.

Simulation models are increasingly used in both theoretical and applied studies to explore system responses to natural and anthropogenic forcing functions, develop defensible predictions of future conditions, challenge simplifying assumptions that facilitated past research, and to train students in scientific concepts and technology. Researcher's increased use of simulation models has created a demand for new platforms that balance performance, utility, and flexibility. We describe HexSim, a powerful new spatially-explicit, individual-based modeling framework that will have applications spanning diverse landscape settings, species, stressors, and disciplines (e.g. ecology, conservation, genetics, epidemiology). We begin with a model overview and follow-up with a discussion of key formative studies that influenced HexSim's development. We then describe specific model applications of relevance to readers of Landscape Ecology. Our goal is to introduce readers to this new modeling platform, and to provide examples characterizing its novelty and utility. With this publication, we conclude a >10 year development effort, and assert that our HexSim model is mature, robust, extremely well tested, and ready for adoption by the research community. The HexSim model, documentation, worked examples, and other materials can be freely obtained from the website www.hexsim.net.

Stochastic Individual-Based Modeling of Bacterial Growth and Division Using Flow Cytometry.

A realistic description of the variability in bacterial growth and division is critical to produce reliable predictions of safety risks along the food chain. Individual-based modeling of bacteria provides the theoretical framework to deal with this variability, but it requires information about the individual behavior of bacteria inside populations. In this work, we overcome this problem by estimating the individual behavior of bacteria from population statistics obtained with flow cytometry. For this objective, a stochastic individual-based modeling framework is defined based on standard assumptions during division and exponential growth. The unknown single-cell parameters required for running the individual-based modeling simulations, such as cell size growth rate, are estimated from the flow cytometry data. Instead of using directly the individual-based model, we make use of a modified Fokker-Plank equation. This only equation simulates the population statistics in function of the unknown single-cell parameters. We test the validity of the approach by modeling the growth and division of Pediococcus acidilactici within the exponential phase. Estimations reveal the statistics of cell growth and division using only data from flow cytometry at a given time. From the relationship between the mother and daughter volumes, we also predict that P. acidilactici divide into two successive parallel planes.

Prioritizing actions for the recovery of endangered species: Emergent insights from Greater Sage-grouse simulation modeling

Urgent conservation situations require immediate action informed by existing data and information. Model-based analyses are well suited to rapidly identifying and evaluating alternative actions but often lack explicit linkages between habitat conditions and population outcomes. We provide an example of how spatially explicit population modeling can uniquely inform conservation planning by integrating changes in habitat conditions with population responses. Using a case study of the critically endangered Greater Sage-grouse in Canada, we integrated habitat selection maps, demography and demographic risk maps, movement, and behavior into a predictive individual-based modeling framework. We used this framework to simulate population dynamics, evaluate demographic sensitivities, assess source-sink dynamics, and compared the population gains from restoring different types (strengths) of sinks. Sensitivity analysis results underscored the need for multiple, simultaneous population recovery actions to stabilize the population, including improving chick and adult survival. Strong source-sink dynamics were an emergent property of simulations, driven by the maladaptive selection of habitats with low chick survival and nest success. Simulated habitat restorations improving chick survival conditions in strong sinks were more effective at increasing abundance than actions targeting all sinks, or removing sinks. Spatially explicit population modeling can be an informative means of predicting and comparing potential population responses to habitat restoration and population recovery options. Individual-based modeling can uniquely evaluate habitat-population dynamics and can be particularly useful for critically endangered species, when too few animals or time remains to conduct field experiments.

Simulation of Escherichia coli Dynamics in Biofilms and Submerged Colonies with an Individual-Based Model Including Metabolic Network Information.

Clustered microbial communities are omnipresent in the food industry, e.g., as colonies of microbial pathogens in/on food media or as biofilms on food processing surfaces. These clustered communities are often characterized by metabolic differentiation among their constituting cells as a result of heterogeneous environmental conditions in the cellular surroundings. This paper focuses on the role of metabolic differentiation due to oxygen gradients in the development of Escherichia coli cell communities, whereby low local oxygen concentrations lead to cellular secretion of weak acid products. For this reason, a metabolic model has been developed for the facultative anaerobe E. coli covering the range of aerobic, microaerobic, and anaerobic environmental conditions. This metabolic model is expressed as a multiparametric programming problem, in which the influence of low extracellular pH values and the presence of undissociated acid cell products in the environment has been taken into account. Furthermore, the developed metabolic model is incorporated in MICRODIMS, an in-house developed individual-based modeling framework to simulate microbial colony and biofilm dynamics. Two case studies have been elaborated using the MICRODIMS simulator: (i) biofilm growth on a substratum surface and (ii) submerged colony growth in a semi-solid mixed food product. In the first case study, the acidification of the biofilm environment and the emergence of typical biofilm morphologies have been observed, such as the mushroom-shaped structure of mature biofilms and the formation of cellular chains at the exterior surface of the biofilm. The simulations show that these morphological phenomena are respectively dependent on the initial affinity of pioneer cells for the substratum surface and the cell detachment process at the outer surface of the biofilm. In the second case study, a no-growth zone emerges in the colony center due to a local decline of the environmental pH. As a result, cellular growth in the submerged colony is limited to the colony periphery, implying a linear increase of the colony radius over time. MICRODIMS has been successfully used to reproduce complex dynamics of clustered microbial communities.

Predicting the effects of polychlorinated biphenyls on cetacean populations through impacts on immunity and calf survival.

The potential impact of exposure to polychlorinated biphenyls (PCBs) on the health and survival of cetaceans continues to be an issue for conservation and management, yet few quantitative approaches for estimating population level effects have been developed. An individual based model (IBM) for assessing effects on both calf survival and immunity was developed and tested. Three case study species (bottlenose dolphin, humpback whale and killer whale) in four populations were taken as examples and the impact of varying levels of PCB uptake on achievable population growth was assessed. The unique aspect of the model is its ability to evaluate likely effects of immunosuppression in addition to calf survival, enabling consequences of PCB exposure on immune function on all age-classes to be explored. By incorporating quantitative tissue concentration-response functions from laboratory animal model species into an IBM framework, population trajectories were generated. Model outputs included estimated concentrations of PCBs in the blubber of females by age, which were then compared to published empirical data. Achievable population growth rates were more affected by the inclusion of effects of PCBs on immunity than on calf survival, but the magnitude depended on the virulence of any subsequent encounter with a pathogen and the proportion of the population exposed. Since the starting population parameters were from historic studies, which may already be impacted by PCBs, the results should be interpreted on a relative rather than an absolute basis. The framework will assist in providing quantitative risk assessments for populations of concern.

Intrinsic and extrinsic drivers of source–sink dynamics

Many factors affect the presence and exchange of individuals among subpopulations and influence not only the emergence, but the strength of ensuing source-sink dynamics within metapopulations. Yet their relative contributions remain largely unexplored. To help identify the characteristics of empirical systems that are likely to exhibit strong versus weak source-sink dynamics and inform their differential management, we compared the relative roles of influential factors in strengthening source-sink dynamics. In a series of controlled experiments within a spatially explicit individual-based model framework, we varied patch quality, patch size, the dispersion of high- and low-quality patches, population growth rates, dispersal distances, and environmental stochasticity in a factorial design. We then recorded source-sink dynamics that emerged from the simulated habitat and population factors. Long-term differences in births and deaths were quantified for sources and sinks in each system and used in a statistical model to rank the influences of key factors. Our results suggest that systems with species capable of rapid growth, occupying habitat patches with more disparate qualities, with interspersed higher- and lower-quality habitats, and that experience relatively stable environments (i.e., fewer negative perturbations) are more likely to exhibit strong source-sink dynamics. Strong source-sink dynamics emerged under diverse combinations of factors, suggesting that simple inferences of process from pattern will likely be inadequate to predict and assess the strength of source-sink dynamics. Our results also suggest that it may be more difficult to detect and accurately measure source-sink dynamics in slow-growing populations, highly variable environments, and where a subtle gradient of habitat quality exists.