Individual-based Approach Research Articles

An SIS network model with flow driven infection rates

A flow-regulated infection rate is defined for a Susceptible-Infected-Susceptible (SIS) model revealing the network structural properties that influence the spread of infections. The infection rate is linked to the flow between compartments in the associated positive compartmental system, providing a self-regulatory effect on the spreading dynamics. This translates to infection carriers preferring to visit healthier sites over more infected ones. A flow-independent epidemic threshold is defined that sets the conditions on the graph’s structure and the aggregate infection rate for a disease to either spread or die out. An individual-based mean-field approach returns results comparable to models with constant infection rates. This approach lends itself well to model the spread of infectious diseases as well as threats in IT networks, pharmacokinetics and the spread of disruptions on infrastructure networks.

End-to-End Pedestrian Trajectory Forecasting with Transformer Network

Analysis of pedestrians’ motion is important to real-world applications in public scenes. Due to the complex temporal and spatial factors, trajectory prediction is a challenging task. With the development of attention mechanism recently, transformer network has been successfully applied in natural language processing, computer vision, and audio processing. We propose an end-to-end transformer network embedded with random deviation queries for pedestrian trajectory forecasting. The self-correcting scheme can enhance the robustness of the network. Moreover, we present a co-training strategy to improve the training effect. The whole scheme is trained collaboratively by the original loss and classification loss. Therefore, we also achieve more accurate prediction results. Experimental results on several datasets indicate the validity and robustness of the network. We achieve the best performance in individual forecasting and comparable results in social forecasting. Encouragingly, our approach achieves a new state of the art on the Hotel and Zara2 datasets compared with the social-based and individual-based approaches.

Effects of rodents' behaviours on leptospirosis spread: an individual-based modeling approach

Leptospirosis is a zoonotic disease common in tropical and subtropical regions. This infectious disease is endemic in Sarawak, Malaysia. In recent years, the threat of leptospirosis is on an increasing trend in Sarawak since 2010. The traditional compartmental models assume that the population is homogeneous and shares the same characteristics and behaviours. However, each individual in the population has different and unique behaviour in the real world. Thus, this paper aims to model the leptospirosis spread by adopting the individual-based modeling approach to address the heterogeneity that affects the transmission of the disease. Rodents' behaviours such as active period duration and movement range are incorporated into the model. From the sensitivity analyses, the results show that the movement range of the rodents has a significant impact on the spread of the disease compared to the active period duration. The comparison between simulation results and the actual prevalence data in Sarawak is performed to validate the model. Through regression analysis, the correlations of determination for three outbreaks in Sarawak for the year 2017 are more than 90%. In addition, the normal probability plots for three outbreaks indicate the points follow the line well and are normally distributed. This shows that the proposed individual-based model can predict leptospirosis transmission.

Individual-based and continuum models of phenotypically heterogeneous growing cell populations

<abstract> <p>Existing comparative studies between individual-based models of growing cell populations and their continuum counterparts have mainly been focused on homogeneous populations, in which all cells have the same phenotypic characteristics. However, significant intercellular phenotypic variability is commonly observed in cellular systems. In light of these considerations, we develop here an individual-based model for the growth of phenotypically heterogeneous cell populations. In this model, the phenotypic state of each cell is described by a structuring variable that captures intercellular variability in cell proliferation and migration rates. The model tracks the spatial evolutionary dynamics of single cells, which undergo pressure-dependent proliferation, heritable phenotypic changes and directional movement in response to pressure differentials. We formally show that the continuum limit of this model comprises a non-local partial differential equation for the cell population density function, which generalises earlier models of growing cell populations. We report on the results of numerical simulations of the individual-based model which illustrate how proliferation-migration tradeoffs shaping the evolutionary dynamics of single cells can lead to the formation, at the population level, of travelling waves whereby highly-mobile cells locally dominate at the invasive front, while more-proliferative cells are found at the rear. Moreover, we demonstrate that there is an excellent quantitative agreement between these results and the results of numerical simulations and formal travelling-wave analysis of the continuum model, when sufficiently large cell numbers are considered. We also provide numerical evidence of scenarios in which the predictions of the two models may differ due to demographic stochasticity, which cannot be captured by the continuum model. This indicates the importance of integrating individual-based and continuum approaches when modelling the growth of phenotypically heterogeneous cell populations.</p> </abstract>

Normative and Theoretical References of Post Penitentiary Probation in the Republic of Moldova

This article discusses the institution of post penitentiary probation from the perspective of penal-executional legislation of the Republic of Moldova and the relevant doctrine. Thus, it is mentioned that probation is considered to be an important stage in the context of the adjustment of national legislations to international standards aiming at setting up an intermediate area within the penalty system, re-evaluating the repressive concept to replace it with a curative model. In the conclusions, it is outlined that in order to solve the issue of resocialisation and full integration of the inmates/convicts in every single case, it is absolutely necessary to have a differential treatment, an individual-based approach by applying the most effective means and methods of work. However, the supervision will only have a minimal effect if not accompanied by the granting of social support. On the other hand, social support without any supervision has no effect either.

Deforestation is the turning point for the spreading of a weedy epiphyte: an IBM approach

The rapid spread of many weeds into intensely disturbed landscapes is boosted by clonal growth and self-fertilization strategies, which conversely increases the genetic structure of populations. Here, we use empirical and modeling approaches to evaluate the spreading dynamics of Tillandsia recurvata (L.) L. populations, a common epiphytic weed with self-reproduction and clonal growth widespread in dry forests and deforested landscapes in the American continent. We introduce the TRec model, an individual-based approach to simulate the spreading of T. recurvata over time and across landscapes subjected to abrupt changes in tree density with the parameters adjusted according to the empirical genetic data based on microsatellites genotypes. Simulations with this model showed that the strong spatial genetic structure observed from empirical data in T. recurvata can be explained by a rapid increase in abundance and gene flow followed by stabilization after ca. 25 years. TRec model’s results also indicate that deforestation is a turning point for the rapid increase in both individual abundance and gene flow among T. recurvata subpopulations occurring in formerly dense forests. Active reforestation can, in turn, reverse such a scenario, although with a milder intensity. The genetic-based study suggests that anthropogenic changes in landscapes may strongly affect the population dynamics of species with ‘weedy’ traits.

Location and Species Matters: Variable Influence of the Environment on the Gene Flow of Imperiled, Native and Invasive Cottontails.

The environment plays an important role in the movement of individuals and their associated genes among populations, which facilitates gene flow. Gene flow can help maintain the genetic diversity both within and between populations and counter the negative impact of genetic drift, which can decrease the fitness of individuals. Sympatric species can have different habitat preferences, and thus can exhibit different patterns of genetic variability and population structure. The specialist-generalist variation hypothesis (SGVH) predicts that specialists will have lower genetic diversity, lower effective population sizes (Ne), and less gene flow among populations. In this study, we used spatially explicit, individual-based comparative approaches to test SGVH predictions in two sympatric cottontail species and identify environmental variables that influence their gene flow. New England cottontail (Sylvilagus transitionalis) is the only native cottontail in the Northeast US, an early successional habitat specialist, and a species of conservation concern. Eastern cottontail (S. floridanus) is an invasive species in the Northeast US and a habitat generalist. We characterized each species’ genomic variation by developing double-digest Restriction-site Associated DNA sequence single nucleotide polymorphism markers, quantified their habitat with Geographic Information System environmental variables, and conducted our analyses at multiple scales. Surprisingly, both species had similar levels of genetic diversity and eastern cottontail’s Ne was only higher than New England cottontail in one of three subregions. At a regional level, the population clusters of New England cottontail were more distinct than eastern cottontail, but the subregional levels showed more geographic areas of restricted gene flow for eastern cottontail than New England cottontail. In general, the environmental variables had the predicted effect on each species’ gene flow. However, the most important environmental variable varied by subregion and species, which shows that location and species matter. Our results provide partial support for the SGVH and the identification of environmental variables that facilitate or impede gene flow can be used to help inform management decisions to conserve New England cottontail.

Undersampling correction methods to control γ-dependence for comparing β-diversity between regions.

Measures of β-diversity are known to be biased by differences in γ-diversity (i.e., γ-dependence), making it challenging to compare β-diversity across regions. Undersampling corrections have been designed to reduce effects of γ-dependence on β-diversity arising from the problem of incomplete sampling. However, no study has systematically tested the effectiveness of these corrections or examined how well they reflect β-diversity patterns across ecological gradients. Here, we conduct these tests by comparing two undersampling corrections with the widely used individual-based null model approach, using both simulated communities along an ecological gradient and empirical data across a wide range of γ-diversity and sample sizes. We found that undersampling corrections using diversity accumulation curves were more effective than the null-model approaches in removing γ-dependence. In particular, the corrected β-Shannon diversity index was least dependent on γ-diversity, and was the most reflective of the β-diversity pattern along a simulated ecological gradient. Moreover, a corrected Jaccard-Chao index applied to null model results removed γ-dependence more effectively than either the correction alone or the null model alone. Undersampling corrections are effective tools for removing γ-dependence bias, thus facilitating comparisons of β-diversity across regions.

The Effect of Sanitation Felling on the Spread of the European Spruce Bark Beetle—An Individual-Based Modeling Approach

Sanitation felling is considered as the main measure to protect managed forests from damage due to outbreaks of the European Spruce Bark Beetle. In this study, we investigate the effectiveness of sanitation felling on stopping the spread of a bark beetle population from an un-managed to a managed forest area. For this, we advance an individual-based dispersion model of Ips typographus by adding the influence of wind on the beetle dispersion and by importing GIS data to simulate real world forests. To validate the new model version and to find reasonable parameter values, we conduct simulation experiments to reproduce infestation patterns that occurred in 2015, 2016, and 2017 within the national park Saxon Switzerland, Germany. With the then calibrated model IPS-SPREADS (Infestation Pattern Simulation Supporting PREdisposition Assessment DetailS), we investigate the impact of different factors such as the distance between beetle source trees and the forest border on the amount of damage within the managed forest stand and test the effectiveness of different levels of sanitation felling and its point of action on reducing the amount of damaged trees. As expected, the results of the model calibration show that the direction of wind plays an important role for the occurring infestation patterns and that bark beetle energy reserve is reduced during mass outbreaks. The results of the second experiment show that the main drivers for the amount of damaged trees are the primary attractiveness and the distance to beetle source trees. Sanitation felling effectiveness is highest when performed near the beetle source trees, with considerably high felling intensities and if there is at least some distance to the managed forest. IPS-SPREADS can be used in future studies as a tool for testing further management measures (e.g., pheromone traps) or to assess the risk for bark beetle infestations of forest areas near to wind-felled or already infested trees.

Population structure and the influence of microenvironment and genetic similarity on individual growth at Alaskan white spruce treelines

Knowledge on the adaptation of trees to rapid environmental changes is essential to preserve forests and their ecosystem services under climate change. Treeline populations are particularly suitable for studying adaptation processes in trees, as environmental stress together with reduced gene flow can enhance local adaptation. We investigated white spruce (Picea glauca) populations in Alaska on one moisture-limited and two cold-limited treeline sites with a paired plot design of one forest and one treeline population each, resulting in six plots. Additionally, one forest plot in the middle of the distribution range complements the study design. We combined spatial, climatic and dendrochronological data with neutral genetic marker of 2203 trees to investigate population genetic structure and drivers of tree growth. We used several individual-based approaches including random slope mixed-effects models to test the influence of genetic similarity and microenvironment on growth performance. A high degree of genetic diversity was found within each of the seven plots associated with high rates of gene flow. We discovered a low genetic differentiation between the three sites which was better explained by geographic distances than by environmental differences, indicating genetic drift as the main driver of population differentiation. Our findings indicated that microenvironmental features had an overall larger influence on growth performances than genetic similarity among individuals. The effects of climate on growth differed between sites but were smaller than the effect of tree size. Overall, our results suggest that the high genetic diversity of white spruce may result in a wider range of phenotypes which enhances the efficiency of selection when the species is facing rapid climatic changes. In addition, the large intra-individual variability in growth responses may indicate the high phenotypic plasticity of white spruce which can buffer short-term environmental changes and, thus, allow enduring the present changing climate conditions.

Distribution patterns and seasonal variations in phytoplankton communities of the hypersaline Pulicat lagoon, India.

Phytoplankton structure and patterns are key components to forecast the net result of the gain and loss process that outline the resilience of the lagoon ecosystem. In order to understand the phytoplankton community structure and its relationship with the environmental variables in the shallow hypersaline Pulicat lagoon, east coast of India, observations were carried out during August 2018-January 2019 covering the three seasons: premonsoon (PrM), monsoon (M), and postmonsoon (PoM). The salinity of the lagoon varied with a minimum of 12.1 for the M and a maximum of 81.65 during the PoM. The clustering analysis performed on the phytoplankton abundance data separated the lagoon into three sectors: north sector (NS), central sector (CS), and south sector (SS). A total of 59 taxa/morphotypes from four taxonomic classes (Bacillariophyceae, Chlorophyceae, Cyanophyceae, and Dinophyceae) were recorded during the study period. The class Bacillariophyceae was dominant in the lagoon both spatially and temporally by 44.06% with Chaetoceros borealis as dominant species. Presence of characteristic species like Dunaliella sp. was observed in the higher salinity, whereas Pediastrum duplex and Scenedesmus sp. were dominant in the freshwater influx areas. The individual-based functional approach allowed grouping these taxa into 11 functional entities based on the derived 4 functional trait values (cell size, trophic regime, mobility, and coloniality). Formation of algal blooms of Protoperidinium sp. (3.3×105ind L-1) and Odentella sp. (2.8×105ind L-1) was observed in the SS during PoM as a result of reduced water exchange in the lagoon. During the same period, toxin-producing strains like Anabaena sp. and Nostoc sp. of Cyanophyceae were also recorded. Correlating the three sectors of the lake (NS, CS, and SS), it is observed that the physical, chemical, and biological properties of the lake varied continuously depending on the season and freshwater availability. Seasonal nutrient stoichiometry played a significant role in regulating the community structure and distribution pattern of phytoplankton communities of the lagoon.

A narrow window for geographic cline analysis using genomic data: Effects of age, drift, and migration on error rates.

The use of genomic and phenotypic data to scan for outliers is a mainstay for studies of hybridization and speciation. Geographic cline analysis of natural hybrid zones is widely used to identify putative signatures of selection by detecting deviations from baseline patterns of introgression. As with other outlier-based approaches, demographic histories can make neutral regions appear to be under selection and vice versa. In this study, we use a forward-time individual-based simulation approach to evaluate the robustness of geographic cline analysis under different evolutionary scenarios. We modelled multiple stepping-stone hybrid zones with distinct age, deme sizes, and migration rates, and evolving under different types of selection. We found that drift distorts cline shapes and increases false positive rates for signatures of selection. This effect increases with hybrid zone age, particularly if migration between demes is low. Drift can also distort the signature of deleterious effects of hybridization, with genetic incompatibilities and particularly underdominance prone to spurious typing as adaptive introgression. Our results suggest that geographic clines are most useful for outlier analysis in young hybrid zones with large populations of hybrid individuals. Current approaches may overestimate adaptive introgression and underestimate selection against maladaptive genotypes.

Impact of presymptomatic transmission on epidemic spreading in contact networks: A dynamic message-passing analysis

Infectious diseases that incorporate presymptomatic transmission are challenging to monitor, model, predict, and contain. We address this scenario by studying a variant of a stochastic susceptible-exposed-infected-recovered model on arbitrary network instances using an analytical framework based on the method of dynamic message passing. This framework provides a good estimate of the probabilistic evolution of the spread on both static and contact networks, offering a significantly improved accuracy with respect to individual-based mean-field approaches while requiring a much lower computational cost compared to numerical simulations. It facilitates the derivation of epidemic thresholds, which are phase boundaries separating parameter regimes where infections can be effectively contained from those where they cannot. These have clear implications on different containment strategies through topological (reducing contacts) and infection parameter changes (e.g., social distancing and wearing face masks), with relevance to the recent COVID-19 pandemic.

Higher Physical Activity Levels May Help Buffer the Negative Psychological Consequences of Coronavirus Disease 2019 Pandemic

This study explored the associations between physical activity (PA) anxiety levels, and the perception of satisfaction of basic psychological needs (BPN), during Coronavirus Disease 2019 (COVID-19) lockdown. Thus, 1,404 participants (977 women, 426 men, and one respondent preferred not to answer) ranging from 18 to 89 years old (36.4 ± 11.7 year-old) completed a questionnaire in the period between 1st and 15th April 2021. The survey included sociodemographic data and the following validated instruments: the International Physical Activity Questionnaire (IPAQ), the Basic Need General Satisfaction Scale and the State-Trait Anxiety Inventory. The Kruskal-Wallis test was performed to examine variation in anxiety levels and BPN satisfaction according to PA category (low, moderate, and high). Spearman’s Rho correlations coefficients were used to determine the association between anxiety levels and psychological needs. Individuals presenting a higher level of PA revealed lower levels of anxiety-state (H = 20.14; p < 0.01). Differences between elements from different levels of PA were found for the autonomy (H = 23.52; p < 0.001), competence (H = 18.89; p < 0.001), and relatedness (H = 24.42; p < 0.001) psychological needs, suggesting that those who feel their BPN as more satisfied have higher levels of PA. The study found statistically significant correlations between anxiety-state and the satisfaction of the needs for autonomy (p = 0.01; r = −0.46), competence (p = 0.01; r = −0.40), and relatedness (p = 0.01; r = −0.21). These findings support the importance that PA has in the anxiety levels during social isolation, emphasizing the importance of multidisciplinary teams in an individual-based approach.

The Role of Stochasticity in Noise-Induced Tipping Point Cascades: A Master Equation Approach.

Tipping points have been shown to be ubiquitous, both in models and empirically in a range of physical and biological systems. The question of how tipping points cascade through systems has been less explored and is an important one. A study of noise-induced tipping, in particular, could provide key insights into tipping cascades. Here, we consider a specific example of a simple model system that could have cascading tipping points. This model consists of two interacting populations with underlying Allee effects and stochastic dynamics, in separate patches connected by dispersal, which can generate bistability. From an ecological standpoint, we look for rescue effects whereby one population can prevent the collapse of a second population. As a way to investigate the stochastic dynamics, we use an individual-based modeling approach rooted in chemical reaction network theory. Then, using continuous-time Markov chains and the theory of first passage times, we essentially approximate, or emulate, the original high-dimensional model by a Markov chain with just three states, where each state corresponds to a combination of population thresholds. Analysis of this reduced model shows when the system is likely to recover, as well as when tipping cascades through the whole system.

A solution to the dilemma `limiting similarity vs. limiting dissimilarity' by a method of transparent artificial intelligence

Ecological hypotheses of ‘limiting similarity’ and ‘limiting dissimilarity’ formulate the alleged conditions for implementing the competitive exclusion. The limiting similarity hypothesis assumes that exceeding a threshold of interspecific similarity leads to competitive exclusion. The hypothesis of limiting dissimilarity states the opposite - a condition of implementing competitive exclusion is exceeding of a threshold of interspecific dissimilarity. These hypotheses clearly contradict each other and we tested them on the model of competition at different values of relative competitiveness of the species. The model of competition under study we implemented as automatic deductive inference by a method of a completely transparent and completely explainable artificial intelligence. In order to strictly verify the hypotheses, we have excluded different possible factors promoting competitive coexistence. We have achieved that by ensuring that all conditions for the studied complete competitors were equal, with the exception of competitiveness of the species. Using our individual-based cellular automata model we demonstrate that two trophically identical species aggressively competing for a single limiting resource, all other things being equal, may coexist in one limited habitat until the threshold of differences in competitiveness exceeds 31%. The obtained results reject the hypothesis of limiting similarity and confirm the hypothesis about the limiting dissimilarity. The results are of importance for evolution theory and for finding pathways to ecological sustainability. Our individual-based modelling approach, based on the transparent and explainable artificial intelligence, opens up great prospects for a variety of theoretical and applied fields.

Can a More Variable Species Win Interspecific Competition?

An individual-based approach is used to describe population dynamics. Two kinds of models have been constructed with different distributions illustrating individual variability. In both models, the growth rate of an individual and its final body weight at the end of the growth period, which determines the number of offspring, are functions of the amount of resources assimilated by an individual. In the model with a symmetric distribution, the half saturation constant in the Michaelis–Menten function describing the relationship between the growth of individuals and the amount of resources has a normal distribution. In the model with an asymmetric distribution, resources are not equally partitioned among individuals. The individual who acquired more resources in the past, will acquire more resources in the future. A single population comprising identical individuals has a very short extinction time. If individuals differ in the amount of food assimilated, this time significantly increases irrespectively of the type of model describing population dynamics. Individuals of two populations of competing species use common resources. For larger differences in individual variability, the more variable species will have a longer extinction time and will exclude less variable species. Both populations can also coexist when their variabilities are equal or even when they are slightly different, in the latter case under the condition of high variability of both species. These conclusions have a deterministic nature in the case of the model with the asymmetric distribution—repeated simulations give the same results. In the case of the model with the symmetric distribution, these conclusions are of a statistical nature—if we repeat the simulation many times, then the more variable species will have a longer extinction time more frequently, but some results will happen (although less often) when the less variable species has a longer extinction time. Additionally, in the model with the asymmetric distribution, the result of competition will depend on the way of the introduction of variability into the model. If the higher variability is due to an increase in the proportion of individuals with a low assimilation of resources, it can produce a longer extinction time of the less variable species.

A mesoscopic simulator to uncover heterogeneity and evolutionary dynamics in tumors.

Increasingly complex in silico modeling approaches offer a way to simultaneously access cancerous processes at different spatio-temporal scales. High-level models, such as those based on partial differential equations, are computationally affordable and allow large tumor sizes and long temporal windows to be studied, but miss the discrete nature of many key underlying cellular processes. Individual-based approaches provide a much more detailed description of tumors, but have difficulties when trying to handle full-sized real cancers. Thus, there exists a trade-off between the integration of macroscopic and microscopic information, now widely available, and the ability to attain clinical tumor sizes. In this paper we put forward a stochastic mesoscopic simulation framework that incorporates key cellular processes during tumor progression while keeping computational costs to a minimum. Our framework captures a physical scale that allows both the incorporation of microscopic information, tracking the spatio-temporal emergence of tumor heterogeneity and the underlying evolutionary dynamics, and the reconstruction of clinically sized tumors from high-resolution medical imaging data, with the additional benefit of low computational cost. We illustrate the functionality of our modeling approach for the case of glioblastoma, a paradigm of tumor heterogeneity that remains extremely challenging in the clinical setting.

Individual-fMRI-approaches reveal cerebellum and visual communities to be functionally connected in obsessive compulsive disorder

There is significant interest in understanding the pathophysiology of Obsessive–Compulsive Disorder (OCD) using resting-state fMRI (rsfMRI). Previous studies acknowledge abnormalities within and beyond the fronto-striato-limbic circuit in OCD that require further clarifications. However, limited information could be inferred from the conventional way of investigating the functional connectivity differences between OCD and healthy controls. Here, we identified altered brain organization in patients with OCD by applying individual-based approaches to maximize the identification of underlying network-based features specific to the OCD group. rsfMRI of 20 patients with OCD and 22 controls were preprocessed, and individual-fMRI-subspace was derived for each subject within each group. We evaluated group differences in functional connectivity using individual-fMRI-subspace and established its advantage over conventional-fMRI methodology. We applied prediction-based approaches to highlight the group differences by evaluating the differences in functional connections that predicted the clinical scores (namely, the Obsessive–Compulsive Inventory-Revised (OCI-R) and Hamilton Anxiety Rating Scale). Then, we explored the brain network organization of both groups by estimating the subject-specific communities within each group. Lastly, we evaluated associations between the inter-individual variation of nodes in the communities to clinical measures using linear regression. Functional connectivity analysis using individual-fMRI-subspace detected 83 connections that were different between OCD and control groups, compared to none found using conventional-fMRI methodology. Connectome-based prediction analysis did not show significant overlap between the two groups in the functional connections that predicted the clinical scores. This suggests that the functional architecture in patients with OCD may be different compared to controls. Seven communities were found in both groups. Interestingly, within the OCD group but not controls, we observed functional connectivity between cerebellar and visual regions, and lack of connectivity between striato-limbic and frontal areas. Inter-individual variations in the community-size of these two communities were also associated with the OCI-R score (p < .005). Due to our small sample size, we further validated our results by (i) accounting for head motion, (ii) applying global signal regression (GSR) in data processing, and (iii) using an alternate atlas for parcellation. While the main results were consistently observed with accounting for head motion and using another atlas, the key findings were not reproduced with GSR application. The study demonstrated the existence of disconnectedness in fronto-striato-limbic community and connectedness between cerebellar and visual areas in OCD patients, which was also related to the clinical symptomatology of OCD.

Modeling salmon lice effects on sea trout population dynamics using an individual-based approach

Salmon lice Lepeophtheirus salmonis infestation of sea trout Salmo trutta results in both additional marine mortality and behavioral changes which may contribute to sea trout population decline. For effective management of activities that increase exposure to salmon lice, such as salmon aquaculture, it is necessary to have a full understanding of how salmon lice may affect sea trout populations. An individual-based model (IBTRUTTA) was therefore developed to investigate the potential effects of salmon lice infestation on sea trout population abundance and dynamics based on data from the River Halselva and Altafjord system in northern Norway. This model allowed investigation of the effect of lice-induced mortality and also the compensatory salmonid behavioral mechanisms of premature return to freshwater, either persistent for overwintering or transitory after which sea trout could go back to sea. It was found that, in the absence of compensatory mechanisms, even low rates of lice infestation could lead to marked declines in sea trout abundance. Compensatory behavioral mechanisms had the potential to reduce these declines, but persistent premature return resulted in reduced body mass of returning adults. The shape of the stock-recruitment relationship was also shown to strongly affect how lice-induced mortality impacted the population.