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The Northeast Arctic stock of haddock (Melanogrammus aeglefinus) is the most northerly stock of the species. It is one of the largest haddock stocks but not the most studied. Similar to the more extensively studied sympatric cod (Gadus morhua) and herring (Clupea harengus) stocks, it relies on Atlantic currents for egg and larvae transportation from spawning grounds to nursery areas in the Barents Sea. The exact locations of the spawning grounds have not been known and the maps that have been published differ substantially. Here, we present results from the first survey dedicated to exploring the spawning habitat of Northeast Arctic (NEA) haddock, and a particle drift and larvae growth model with physical forcing from a high‐resolution ocean model. Gadoid eggs, later identified as haddock by DNA analyses, as well as spawning individuals were sampled at the survey. Spawning haddock was found in Atlantic water in temperatures 3.6–6.2°C and depths of 240–560 m. Sampled eggs were mostly in early development stages, suggesting that they were largely spawned in the surveyed area. Using the drift model, we found that most of the modelled particles released as eggs at our surveyed area ended up in the nursery area in the Barents Sea. A crude estimate of the spawning stock biomass based on the survey suggests that the most updated spawning ground map of NEA haddock might be too restricted in its extent. Particle drift and growth with physical forcing for the years 2012–2022 indicated small interannual variation in aberrant drift, and that growth driven by ambient temperature could be important in explaining interannual variation in haddock recruitment. [ABSTRACT FROM AUTHOR]

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To control an outbreak of an infectious disease it is essential to understand the different routes of transmission and how they contribute to the overall spread of the pathogen. With this information, policy makers can choose the most efficient methods of detection and control during an outbreak. Here we assess the contributions of direct contact and environmental contamination to the transmission of foot-and-mouth disease virus (FMDV) in a cattle herd using an individual-based model that includes both routes. Model parameters are inferred using approximate Bayesian computation with sequential Monte Carlo sampling (ABC-SMC) applied to data from transmission experiments and the 2007 epidemic in Great Britain. This demonstrates that the parameters derived from transmission experiments are applicable to outbreaks in the field, at least for closely related strains. Under the assumptions made in the model we show that environmental transmission likely contributes a majority of infections within a herd during an outbreak, although there is a lot of variation between simulated outbreaks. The accumulation of environmental contamination not only causes infections within a farm, but also has the potential to spread between farms via fomites. We also demonstrate the importance and effectiveness of rapid detection of infected farms in reducing transmission between farms, whether via direct contact or the environment.

Landscape structure plays a key role in mediating a variety of ecological processes affecting biodiversity patterns; however, its precise effects and the mechanisms underpinning them remain unclear. While the effects of landscape structure have been extensively investigated both empirically and theoretically from a metapopulation perspective, the effects of spatial structure at the landscape scale remain poorly explored from a metacommunity perspective. Here, we attempt to address this gap using a spatially explicit, individual‐based metacommunity model to explore the effects of landscape compositional heterogeneity and per se spatial configuration on diversity at the landscape and patch levels via their influence on long‐term community assembly processes. Our model simulates communities composed of species of annual, asexual organisms living, reproducing, dispersing, and competing within grid‐based, fractal landscapes that vary in their magnitude of spatial environmental heterogeneity and in their degree of spatial environmental autocorrelation. Communities are additionally subject to temporal environmental fluctuations and external immigration, allowing for turnover in community composition. We found that compositional heterogeneity and spatial autocorrelation had differing effects on richness, diversity, and the landscape and patch scales. Landscape‐level diversity was driven by community dissimilarity at the patch level and increased with greater heterogeneity, while landscape richness was largely the result of the short‐term accumulation of immigrants and decreased with greater compositional heterogeneity. Both richness and diversity decreased in variance with greater compositional heterogeneity, indicating a reduction in community turnover over time. Patch‐level richness and diversity patterns appeared to be driven by overall landscape richness and local mass effects, resulting in maximum patch‐level richness and diversity at moderate levels of compositional heterogeneity and high spatial autocorrelation. [ABSTRACT FROM AUTHOR]

Abstract Landscape structure plays a key role in mediating a variety of ecological processes affecting biodiversity patterns; however, its precise effects and the mechanisms underpinning them remain unclear. While the effects of landscape structure have been extensively investigated both empirically and theoretically from a metapopulation perspective, the effects of spatial structure at the landscape scale remain poorly explored from a metacommunity perspective. Here, we attempt to address this gap using a spatially explicit, individual‐based metacommunity model to explore the effects of landscape compositional heterogeneity and per se spatial configuration on diversity at the landscape and patch levels via their influence on long‐term community assembly processes. Our model simulates communities composed of species of annual, asexual organisms living, reproducing, dispersing, and competing within grid‐based, fractal landscapes that vary in their magnitude of spatial environmental heterogeneity and in their degree of spatial environmental autocorrelation. Communities are additionally subject to temporal environmental fluctuations and external immigration, allowing for turnover in community composition. We found that compositional heterogeneity and spatial autocorrelation had differing effects on richness, diversity, and the landscape and patch scales. Landscape‐level diversity was driven by community dissimilarity at the patch level and increased with greater heterogeneity, while landscape richness was largely the result of the short‐term accumulation of immigrants and decreased with greater compositional heterogeneity. Both richness and diversity decreased in variance with greater compositional heterogeneity, indicating a reduction in community turnover over time. Patch‐level richness and diversity patterns appeared to be driven by overall landscape richness and local mass effects, resulting in maximum patch‐level richness and diversity at moderate levels of compositional heterogeneity and high spatial autocorrelation.

Testing for infection with SARS-CoV-2 is an important intervention in reducing onwards transmission of COVID-19, particularly when combined with the isolation and contact-tracing of positive cases. Many countries with the capacity to do so have made use of lab-processed Polymerase Chain Reaction (PCR) testing targeted at individuals with symptoms and the contacts of confirmed cases. Alternatively, Lateral Flow Tests (LFTs) are able to deliver a result quickly, without lab-processing and at a relatively low cost. Their adoption can support regular mass asymptomatic testing, allowing earlier detection of infection and isolation of infectious individuals. In this paper we extend and apply the agent-based epidemic modelling framework Covasim to explore the impact of regular asymptomatic testing on the peak and total number of infections in an emerging COVID-19 wave. We explore testing with LFTs at different frequency levels within a population with high levels of immunity and with background symptomatic PCR testing, case isolation and contact tracing for testing. The effectiveness of regular asymptomatic testing was compared with ‘lockdown’ interventions seeking to reduce the number of non-household contacts across the whole population through measures such as mandating working from home and restrictions on gatherings. Since regular asymptomatic testing requires only those with a positive result to reduce contact, while lockdown measures require the whole population to reduce contact, any policy decision that seeks to trade off harms from infection against other harms will not automatically favour one over the other. Our results demonstrate that, where such a trade off is being made, at moderate rates of early exponential growth regular asymptomatic testing has the potential to achieve significant infection control without the wider harms associated with additional lockdown measures.

An individual based model (IBM) of the female brown crab Cancer pagurus population exploited off South Devon, UK is described. Size dependent movement rules are ascribed to individuals based on previous observations of predominantly westward migration down the English Channel. Two additional versions of the movement rules explored whether the empirically derived rule was necessary to model the temporal and spatial distribution of crabs. Local crab movement was dependent on substrate type and water depth. Females prefer a soft substrate in which they can bury when temperatures are low or they have eggs to incubate. Crabs have size dependent depth preferences with larger crabs preferring greater depths. Two recruitment functions are used which relate the number of incoming crabs to the sea surface temperature five years earlier. Model outputs were tested against 10 years of logbook data from three crab fishers and against data from a year-long sampling programme on eight of the vessels exploiting the area. The model reproduces the long-term pattern which is mostly temperature driven. Spatial variation in catch is captured effectively by the model with more crabs being caught in the east of the area than the west and more caught offshore than inshore. The significance of the results is discussed in relation to the crab life cycle, management of the fishery and the potential effects of increasing temperatures. [ABSTRACT FROM AUTHOR]

• Our study introduces a coupled machine-learning-individual-based model (ML-IBM) for migration dynamic simulation. • Multiple hydraulic and inertia indicators were considered. • The stochastic nature of fish movement was considered during the modeling process. • Our model outperformed traditional methods in migration dynamic simulation. • Flow velocity and initial swimming speed play a crucial role in migration dynamic modeling. The extensive development of hydropower projects has notably changed the ecohydrological conditions of fish habitats, affecting fish behavior, including habitat usage and migration, to varying extents. Understanding fish migration dynamics is essential for quantitatively assessing the impact of ecological restoration measures on migratory fish. However, no model has yet demonstrated sufficient accuracy to be considered valuable in ecological restoration engineering. To address this issue, in this article, a coupled machine-learning-individual-based model (ML-IBM) consisting of random forest (RF) and Eulerian–Lagrangian–agent method (ELAM) is constructed for predicting fish migration, aiming to find effective fish passage solutions before implementation. In this study, the passage data of ya-fish (Schizothorax prenanti) in vertical slot fishways (VSFs) is compiled to train ML-IBM to simulate fish migration in fish passage facilities. In movement prediction, the accuracy of swimming behavior classification reaches 83.4 %, and the R² for swimming speed regression exceeds 0.77. Compared with other state-of-the-art migration dynamic models, the proposed ML-IBM achieves the lowest root mean square error (RMSE) of 7.35 and a mean absolute error (MAE) of 6.26 in migration simulation results. Further, RF is used to quantitatively calculate the importance of input features. The contributions of each feature are analyzed and discussed from a hydrodynamic perspective, with the importance ranked as follows: flow velocity (FV) > turbulent kinetic energy (TKE) > total hydraulic strain (THS). This approach enhances the interpretability of the model and provides further insights into the mechanism of fish migration. The results presented in this study have significant implications for informing decision-making in the management of living resources and guiding engineering design processes. [ABSTRACT FROM AUTHOR]

Molecular, cellular, and organismal alterations are important descriptors of toxic effects, but our ability to extrapolate and predict ecological risks is limited by the availability of studies that link measurable end points to adverse population relevant outcomes such as cohort survival and growth. In this study, we used laboratory gene expression and behavior data from two populations of Atlantic killifish Fundulus heteroclitus [one reference site (SCOKF) and one PCB-contaminated site (NBHKF)] to inform individual-based models simulating cohort growth and survival from embryonic exposures to environmentally relevant concentrations of neurotoxicants. Methylmercury exposed SCOKF exhibited brain gene expression changes in the si:ch211-186j3.6, si:dkey-21c1.4, scamp1, and klhl6 genes, which coincided with changes in feeding and swimming behaviors, but our models simulated no growth or survival effects of exposures. PCB126-exposed SCOKF had lower physical activity levels coinciding with a general upregulation in nucleic and cellular brain gene sets (BGS) and downregulation in signaling, nucleic, and cellular BGS. The NBHKF, known to be tolerant to PCBs, had altered swimming behaviors that coincided with 98% fewer altered BGS. Our models simulated PCB126 decreased growth in SCOKF and survival in SCOKF and NBHKF. Overall, our study provides a unique demonstration linking molecular and behavioral data to develop quantitative, testable predictions of ecological risk.

Microbial conflicts have a particularly aggressive nature. In addition to other chemical, mechanical, and biological weapons in their repertoire, bacteria have evolved bacteriocins, which are narrow-spectrum toxins that kill closely related strains. Bacterial cells are known to frequently use their arsenal while competing against each other for nutrients and space. This stands in contrast with the animal world, where conflicts over resources and mating opportunities are far less lethal, and get commonly resolved via ritualized fighting or “limited war” tactics. Prevalence of aggression in microbial communities is usually explained as due to their limited ability to resolve conflicts via signaling as well as their limited ability to pull out from conflicts due to the sessile nature of their life within biofilms. We use an approach that combines Evolutionary Game Theory (EGT) and Individual-based Modeling (IbM) to investigate the origins of aggression in microbial conflicts. In order to understand how the spatial mode of growth aects the cost of a fight, we compare the growth dynamics emerging from engaging in aggression in a well-mixed system to a spatially structured system. To this end, a mathematical model is constructed for the competition between two bacterial strains where each strain produces a diusible toxin to which the other strain is sensitive. It is observed that in the biofilm growth mode, starting from a mixed layer of two strains, mutual aggression gives rise to an exceedingly high level of spatial segregation, which in turn reduces the cost of aggression on both strains compared to when the same competition occurs in a well-mixed culture. Another observation is that the transition from a mixed layer to segregated growth is characterized by a switch in the overall growth dynamics. An increased “lag time” is observed in the overall population growth curve that is associated with the earlier stages of growth, when each strain is still experiencing the inhibiting eect of the toxin produced by its competitor. Afterwards, an exponential phase of growth kicks in once the competing strains start segregating from each other. The emerging “lag time” arises from the spiteful interactions between the two strains rather than acclimation of cells’ internal physiology. Our analysis highlights the territorial nature of microbial conflicts as the key driver to their elevated levels of aggression as it increases the benefit-to-cost ratio of participating in antagonistic interactions. [ABSTRACT FROM AUTHOR]

A stochastic individual based model, SCHISTOX, has been developed for the study of schistosome transmission dynamics and the impact of control by mass drug administration. More novel aspects that can be investigated include individual level adherence and access to treatment, multiple communities, human sex population dynamics, and implementation of a potential vaccine. Many of the model parameters have been estimated within previous studies and have been shown to vary between communities, such as the age-specific contact rates governing the age profiles of infection. However, uncertainty remains as there are wide ranges for certain parameter values and a few remain relatively unknown. We analyse the model dynamics by parameterizing it with published parameter values. We also discuss the development of SCHISTOX in the form of a publicly available open-source GitHub repository. The next key development stage involves validating the model by calibrating to epidemiological data.

Abstract Background The Northwest Atlantic (NWA) leatherback turtle (Dermochelys coriacea) subpopulation is one of the last healthy ones on Earth. Its conservation is thus of major importance for the conservation of the species itself. While adults are relatively well monitored, pelagic juveniles remain largely unobserved. In an attempt to reduce this knowledge gap, this paper presents the first detailed simulation of the open ocean dispersal of juveniles born on the main nesting beaches of French Guiana and Suriname (FGS). Methods Dispersal is simulated using STAMM, an Individual Based Model in which juveniles actively disperse under the combined effects of oceanic currents and habitat-driven movements. For comparison purposes, passive dispersal under the sole effect of oceanic currents is also simulated. Results Simulation results show that oceanic currents lead juveniles to cross the Atlantic at mid-latitudes. Unlike passive individuals, active juveniles undertake important north-south seasonal migrations while crossing the North Atlantic. They finally reach the European or North African coast and enter the Mediterranean Sea. Less than 4-year-old active turtles first arrive off Mauritania. Other productive areas on the eastern side of the Atlantic (the coast of Galicia and Portugal, the Gulf of Cadiz, the Bay of Biscay) and in the Mediterranean Sea are first reached by 6 to 9-year-old individuals. This active dispersal scheme, and its timing, appear to be consistent with all available stranding and bycatch data gathered on the Atlantic and Mediterranean coasts of Europe and North Africa. Simulation results also suggest that the timing of the dispersal and the quality of the habitats encountered by juveniles can, at least partly, explain why the NWA leatherback subpopulation is doing much better than the West Pacific one. Conclusion This paper provides the first detailed simulation of the spatial and temporal distribution of juvenile leatherback turtles dispersing from their FGS nesting beaches into the North Atlantic Ocean and Mediterranean Sea. Simulation results, corroborated by stranding and bycatch data, pinpoint several important developmental areas on the eastern side of the Atlantic Ocean and in the Mediterranean Sea. These results shall help focus observation and conservation efforts in these critical areas.

Typical examples of cultural phenomena all exhibit a degree of similarity across time and space at the level of the population. As such, a fundamental question for any science of culture is, what ensures this stability in the first place? Here we focus on the evolutionary and stabilising role of ‘convergent transformation’, in which one item causes the production of another item whose form tends to deviate from the original in a directed, non-random way. We present a series of stochastic models of cultural evolution investigating its effects. The results show that cultural stability can emerge and be maintained by virtue of convergent transformation alone, in the absence of any form of copying or selection process. We show how high-fidelity copying and convergent transformation need not be opposing forces, and can jointly contribute to cultural stability. We finally analyse how non-random transformation and high-fidelity copying can have different evolutionary signatures at population level, and hence how their distinct effects can be distinguished in empirical records. Collectively, these results supplement existing approaches to cultural evolution based on the Darwinian analogy, while also providing formal support for other frameworks – such as Cultural Attraction Theory – that entail its further loosening.

Spatial patchiness of plankton enhances fishery production and carbon export in the ocean. While diel vertical migration (DVM) has been identified as an important factor contributing to vertical patchiness, its effect on horizontal patchiness has never been investigated. We use a simple individual-based zooplankton model to examine the effect of DVM on the horizontal patchiness of four zooplankton groups with differing DVM patterns in a two-dimensional ocean circulation model. We find that zooplankton horizontal patchiness can be induced by two mechanisms: (1) in stratified waters, DVM can synchronize zooplankton vertical positions with the horizontal current velocities that drive them, resulting in horizontal patchiness; and (2) migrating zooplankton tend to aggregate in deep waters when they encounter sea bottom. Due to these mechanisms, zooplankton horizontal patchiness may be ubiquitous in the ocean, enhancing secondary production and fisheries. [ABSTRACT FROM AUTHOR]

The complex network theory constitutes a natural support for the study of a disease propagation. In this work, we present a study of an infectious disease spread with the use of this theory in combination with the Individual Based Model. More specifically, we use several complex network models widely known in the literature to verify their topological effects in the propagation of the disease. In general, complex networks with different properties result in curves of infected individuals with different behaviors, and thus, the growth of a given disease is highly sensitive to the network model used. The disease eradication is observed when the vaccination strategy of 10% of the population is used in combination with the random, small world or modular network models, which opens an important space for control actions that focus on changing the topology of a complex network as a form of reduction or even elimination of an infectious disease.

In the face of ongoing global climate and land use change, organisms have multiple possibilities to cope with the modification of their environment. The two main possibilities are to either adapt locally or disperse to a more suitable habitat. The evolution of both local adaptation and dispersal interacts and can be influenced by the spatial and temporal variation (of e.g. temperature or precipitation). In an individual based model (IBM), we explore evolution of phenotypes in landscapes with varying degree of spatial relative to global temporal variation in order to examine its influence on the evolution of dispersal, niche optimum and niche width. The relationship between temporal and spatial variation did neither influence the evolution of local adaptation in the niche optimum nor of niche widths. Dispersal probability is highly influenced by the spatio‐temporal relationship: with increasing spatial variation, dispersal probability decreases. Additionally, the shape of the distribution of the trait values over patch attributes switches from hump‐ to U‐shaped. At low spatial variance more individuals emigrate from average habitats, at high spatial variance more from extreme habitats. The comparatively high dispersal probability in extreme patches of landscapes with a high spatial variation can be explained by evolutionary succession of two kinds of adaptive response. Early in the simulations, extreme patches in landscapes with a high spatial variability act as sink habitats, where population persistence depends on highly dispersive individuals with a wide niche. With ongoing evolution, local adaptation of the remaining individuals takes over, but simultaneously a possible bet‐hedging strategy promotes higher dispersal probabilities in those habitats. Here, in generations that experience extreme shifts from the temporal mean of the patch attribute, the expected fitness becomes higher for dispersing individuals than for philopatric individuals. This means that under certain circumstances, both local adaptation and high dispersal probability can be selected for for coping with the projected environmental changes in the future. [ABSTRACT FROM AUTHOR]

To control an outbreak of an infectious disease it is essential to understand the different routes of transmission and how they contribute to the overall spread of the pathogen. With this information, policy makers can choose the most efficient methods of detection and control during an outbreak. Here we assess the contributions of direct contact and environmental contamination to the transmission of foot-and-mouth disease virus (FMDV) in a cattle herd using an individual-based model that includes both routes. Model parameters are inferred using approximate Bayesian computation with sequential Monte Carlo sampling (ABC-SMC) applied to data from transmission experiments and the 2007 epidemic in Great Britain. This demonstrates that the parameters derived from transmission experiments are applicable to outbreaks in the field, at least for closely related strains. Under the assumptions made in the model we show that environmental transmission likely contributes a majority of infections within a herd during an outbreak, although there is a lot of variation between simulated outbreaks. The accumulation of environmental contamination not only causes infections within a farm, but also has the potential to spread between farms via fomites. We also demonstrate the importance and effectiveness of rapid detection of infected farms in reducing transmission between farms, whether via direct contact or the environment. • ABC-SMC for parameter estimation is used to infer transmission routes of FMDV. • Environmental contamination plays a key role in transmission. • Between-herd generation time shorter for direct transmission than environmental. [ABSTRACT FROM AUTHOR]

Antarctic krill (Euphausia superba) is a key species in the marine ecosystem of the Southern Ocean, but is also the target of a commercial fishery, with an important fishing ground in the South Orkney Islands region. The potential for competition for krill between predators and the fishery requires risk management strategies for the fishery, underpinned by an understanding of the key physical and behavioural drivers of krill movement and retention in target areas. Here, we present the results of a regional modelling study, combining a high-resolution ocean-sea ice model and an individual-based model parameterised for krill, to elucidate the roles of oceanographic variability and krill behaviour on patterns of transport and retention on and around the South Orkney Plateau. Simulations suggest that oceanic transport from sources around the Antarctic Peninsula is restricted by the northward flowing Antarctic Slope Current. Around the South Orkney Plateau, anticyclonic flows associated with the Weddell Front and the shelf edge transport krill rapidly towards the main fishing grounds to the northwest of the plateau. Transport onto the shelf and subsequent retention are influenced by the strength and direction of regional winds; weaker on-shelf transport and shorter retention times are associated with stronger westerly and northerly winds. The incorporation of sea-ice associated behaviour, whereby krill are moved with sea ice when sea ice is present, significantly modifies the patterns from purely oceanic transport; it reduces the influence of strong regional oceanic flows and increases transport of krill to the South Orkneys region from the Antarctic Peninsula. The inclusion of diel vertical migration also modifies predicted patterns from oceanic transport, but to a lesser extent, and moderates the impact of including sea-ice associated behaviour. We highlight the importance of understanding the behaviour of krill, including age-dependent behavioural changes in response to sea ice conditions, for modelling and management of Antarctic krill populations. • Numerical modelling reveals drivers of krill transport and distribution variability. • Antarctic Slope Current restricts transport of krill from the Antarctic Peninsula. • Sea-ice associated behaviour significantly alters krill transport and retention. • Diel vertical migration impact on simulated krill transport and distribution is relatively weak. • Stronger westerly and northerly winds increase influx but reduce local retention. [ABSTRACT FROM AUTHOR]

• This study presents the first regional-scale estimates of the spatial distribution of post-hatchling of all sea turtle species nesting in the dominant WIO rookeries. • We used the novel sea turtle active movement model to simulate post-hatchling dispersal under the combined effects of ocean currents and habitat-driven movements. • Simulations results indicated that dispersal is mostly driven by ocean currents but differs among species and years, while active swimming appeared to have little influence (during the first year). • We identified three distinct dispersal corridors, that differ between species, except for loggerhead and leatherback, that share a similar one. • The results can also assist in developing more targeted management measures, like RMU designation or marine spatial planning, for the lost years. Identifying dispersal pathways and critical habitats is essential to evaluate risks and inform effective management strategies of migratory marine species during all life stages. This is especially true for sea turtles that are conservation-dependent and for which management needs usually precedes comprehensive data collection. The aim of this study was to model dispersal pathways (representative of individual behaviour) and compare potential dispersal corridors (representative of population-level behaviour) of hawksbill, loggerhead, leatherback, and green sea turtles from key rookeries in the Western Indian Ocean (WIO) with different dispersal strategies. We used the Sea Turtle Active Movement Model (STAMM) to simulate post-hatchling dispersal under the combined effects of ocean currents and habitat-driven movements. Simulation results confirmed the high connectivity between hatching sites and developmental areas in the WIO; dispersal is mostly driven by ocean currents but differs among species and years with habitat quality also differing among species. Active swimming appeared to have little influence on their dispersal patterns during the first year. We then analysed simulation results using a movement-based kernel density estimation to identify dispersal corridors for each species. There were three distinct dispersal corridors: among equatorial Indian Ocean Islands (hawksbills); along East Africa (green turtles); and around southern Africa (loggerheads and leatherbacks). These results provide a first estimation of the dispersal pathways used by neonate turtles, that are usually lacking in conservation assessments. The results can also assist to develop more targeted management measures like RMU designation or marine spatial planning for the lost years. [Display omitted] [ABSTRACT FROM AUTHOR]

Individual based Models (IbM) must transition from research tools to engineering tools. To make the transition we must aspire to develop large, three dimensional and physically and biologically credible models. Biological credibility can be promoted by grounding, as far as possible, the biology in thermodynamics. Thermodynamic principles are known to have predictive power in microbial ecology. However, this in turn requires a model that incorporates pH and chemical speciation. Physical credibility implies plausible mechanics and a connection with the wider environment. Here, we propose a step toward that ideal by presenting an individual based model connecting thermodynamics, pH and chemical speciation and environmental conditions to microbial growth for 5·105 individuals. We have showcased the model in two scenarios: a two functional group nitrification model and a three functional group anaerobic community. In the former, pH and connection to the environment had an important effect on the outcomes simulated. Whilst in the latter pH was less important but the spatial arrangements and community productivity (that is, methane production) were highly dependent on thermodynamic and reactor coupling. We conclude that if IbM are to attain their potential as tools to evaluate the emergent properties of engineered biological systems it will be necessary to combine the chemical, physical, mechanical and biological along the lines we have proposed. We have still fallen short of our ideals because we cannot (yet) calculate specific uptake rates and must develop the capacity for longer runs in larger models. However, we believe such advances are attainable. Ideally in a common, fast and modular platform. For future innovations in IbM will only be of use if they can be coupled with all the previous advances.

Coastal habitats (e.g., seagrass beds, shallow mud, and sand flats) strongly influence survival, growth, and reproduction of marine fish and invertebrate species. Many of these species have declined over the past decades, coincident with widespread degradation of coastal habitats, such that an urgent need exists to model the quantitative value of coastal habitats to their population dynamics. For exploited species, demand for habitat considerations will increase as fisheries management contends with habitat issues in stock assessments and management in general moves toward a more ecosystem-based approach. The modeling of habitat function has, to date, been done on a case-by-case basis involving diverse approaches and types of population models, which has made it difficult to generalize about methods for incorporating habitat into population models. In this review, we offer guiding concepts for how habitat effects can be incorporated in population models commonly used to simulate the population dynamics of fish and invertebrate species. Many marine species share a similar life-history strategy as long-lived adults with indeterminate growth, high fecundity, a planktonic larval form, and benthic juveniles and adults using coastal habitats. This suite of life-history traits unites the marine species across the case studies, such that the population models can be adapted for other marine species. We categorize population models based on whether they are static or dynamic representations of population status, and for dynamic, further into unstructured, age/size class structured, and individual-based. We then use examples, with an emphasis on exploited species, to illustrate how habitat has been incorporated, implicitly (correlative) and explicitly (mechanistically), into each of these categories. We describe the methods used and provide details on their implementation and utility to facilitate adaptation of the approaches for other species and systems. We anticipate that our review can serve as a stimulus for more widespread use of population models to quantify the value of coastal habitats, so that their importance can be accurately realized and to facilitate cross-species and cross-system comparisons. Quantitative evaluation of habitat effects in population dynamics will increasingly be needed for traditional stock assessments, ecosystem-based management, conservation of at-risk habitats, and recovery of overexploited stocks that rely on critical coastal habitats during their life cycle.

Resource use and conservation objectives can conflict where protected species and commercially fished species occur together. We tested the potential for a previously overfished deep-sea shark (Centrophorus zeehaani) with very low biological productivity to recover in a network of three reserves. An individual-based simulation model was developed and applied using reproduction and movement data. Without any reserves or reductions in fishing mortality the population will never recover and will fail in 27.9 ± 6.1 years. With three reserves, recovery from 8% of initial numbers to a target of 20% would take 63.1 ± 3.1 years with the network in place. Length of the female cycle, natural mortality, and density dependent female dispersal are uncertain, but could delay recovery time by an additional 16.5, 98.3, or 61.9 years, respectively. Effectiveness of the reserve network was particularly sensitive to location and patchiness of the population. Doubling the size of a reserve where C. zeehaani are abundant would reduce recovery time by 12.5 years; halving it would increase recovery time by 12.0 years. Future re-opening of orange roughy fishing in waters deeper than 750 m would delay recovery of C. zeehaani by 45.9 years. The methods developed here can be used to explore co-management options for target species and protected species in fisheries. [ABSTRACT FROM AUTHOR]

Individual based Models (IbM) must transition from research tools to engineering tools. To make the transition we must aspire to develop large, three dimensional and physically and biologically credible models. Biological credibility can be promoted by grounding, as far as possible, the biology in thermodynamics. Thermodynamic principles are known to have predictive power in microbial ecology. However, this in turn requires a model that incorporates pH and chemical speciation. Physical credibility implies plausible mechanics and a connection with the wider environment. Here, we propose a step toward that ideal by presenting an individual based model connecting thermodynamics, pH and chemical speciation and environmental conditions to microbial growth for 5·105 individuals. We have showcased the model in two scenarios: a two functional group nitrification model and a three functional group anaerobic community. In the former, pH and connection to the environment had an important effect on the outcomes simulated. Whilst in the latter pH was less important but the spatial arrangements and community productivity (that is, methane production) were highly dependent on thermodynamic and reactor coupling. We conclude that if IbM are to attain their potential as tools to evaluate the emergent properties of engineered biological systems it will be necessary to combine the chemical, physical, mechanical and biological along the lines we have proposed. We have still fallen short of our ideals because we cannot (yet) calculate specific uptake rates and must develop the capacity for longer runs in larger models. However, we believe such advances are attainable. Ideally in a common, fast and modular platform. For future innovations in IbM will only be of use if they can be coupled with all the previous advances. [ABSTRACT FROM AUTHOR]

Globally, large carnivores have been heavily affected by habitat loss, fragmentation and persecution, sometimes resulting in local extinctions. With increasing recognition of top-down trophic cascades and complex predator-prey dynamics, reintroductions are of growing interest for restoration of ecosystem functioning. Many reintroductions have however failed, in part due to poor planning and inability to model complex eco-evolutionary processes to give reliable predictions. Using the case study of Eurasian lynx (Lynx lynx), a large predator being considered for reintroduction to Scotland, we demonstrate how an individual-based model that integrates demography with three distinct phases of dispersal (emigration, transfer and settlement) can be used to explore the relative suitability of three geographically-distant potential reintroduction sites, multi-site reintroductions and two founding population sizes. For a single-site reintroduction of 10 lynx, our simulation results show a clear hierarchy of suitability across all metrics. Reintroduction in the Kintyre Peninsula (west coast) consistently performed best, with a probability of population persistence at year 100 of 83%, and the Scottish component of Kielder Forest (southern Scotland) worst, with only a 21% chance of population persistence to year 100. Simultaneous two-site reintroduction in the Kintyre Peninsula and in Aberdeenshire (near the east coast) of 32 lynx gave a 96% persistence at 100 years. Our model was highly sensitive to survival, particularly of adults, highlighting this parameter's importance for reintroduction success. The results strongly indicate the potential viability of Eurasian lynx reintroduction to Scotland given the current cover of suitable woodland habitat. More generally, our work demonstrates how emerging modelling approaches incorporating increased realism in representing species' demography, ecology and dispersal can have high value for quick, inexpensive assessment of likely reintroduction success and for selection between alternative strategies. • IBM's integrating stochastic movement and population dynamics provide greater realism in reintroduction modelling. • This modelling approach enables quick, effective assessments of reintroduction proposals and management scenarios. • Models of this nature could significantly improve the probability of reintroduction successes in large carnivores. • Scotland's existing habitat is suitable for Eurasian lynx reintroduction, but appropriate site selection is key to success. [ABSTRACT FROM AUTHOR]

Highlights • Preparatory disease modelling exercises often make following three key assumptions for livestock movement patterns. • Livestock movement remains consistent in future, random animals are sold, and animals' fate is independent of its past movement histories. • Using a national-scale livestock movement and production data, we showed how these assumptions are violated. • These violations may be critical for slow spreading diseases because probabilities of being sold and infected with disease can be interdependent. Abstract Livestock disease simulation models that incorporate animal movements often assume (1) that farmers' livestock trading practices remain consistent over time in future, (2) that animals sold to other farms are chosen randomly from a herd, and (3) that the animals' fate on the destination farm is not influenced by their past production and movement histories. The objective of this study was to assess the extent to which these assumptions are violated in the real world using records from a national database in New Zealand that captures both milk production and movement data for individual dairy cattle. All individual animal milk test records from 2006 through 2010 were extracted from the database and processed to generate different animal and herd level variables including cow demographics, previous movement history, milk volume, and milk composition (somatic cell counts (SCC), protein percentage, and fat percentage). Various statistical models were used to explore factors associated with farms' selling practice and characteristics of animals being sold. The results showed farms' livestock selling practices were highly influenced by both external factors such as market milk price and internal factors such as previous year's cow mortality and how long farms had been in business. Higher milk price increased both the number of cows being sold and the number of farms selling cows. Compared with cows that remained in the herd at the end of lactation, cows sold to other farms had lower fat and protein percentages, but similar milk volumes and SCCs. Cows that had been sold more often in the past were more likely to be sold after controlling for the effects of age. Overall, these findings highlight the potential need for disease simulation models to account for dynamics in selling practices and animal characteristics when determining which animals will be sold to other herds. [ABSTRACT FROM AUTHOR]

Context: Diadromous fish populations in the Pacific Northwest face challenges along their migratory routes from declining habitat quality, harvest, and barriers to longitudinal connectivity. These stressors complicate the prioritization of proposed management actions intended to improve conditions for migratory fishes including anadromous salmon and trout. Objectives: We describe a multi-scale hybrid mechanistic–probabilistic simulation model linking migration corridor conditions to fish fitness outcomes. We demonstrate the model's utility using a case study of salmon and steelhead adults in the Columbia River migration corridor exposed to spatially- and temporally-varying stressors. Methods: The migration corridor simulation model is based on a behavioral decision tree that governs individual interactions with the environment, and an energetic submodel that estimates the hourly costs of migration. Emergent properties of the migration corridor simulation model include passage time, energy use, and survival. Results: We observed that the simulated fish's initial energy density, the migration corridor temperatures they experienced, and their history of behavioral thermoregulation were the primary determinants of their fitness outcomes. Insights gained from use of the model might be exploited to identify management interventions that increase successful migration outcomes. Conclusions: This paper describes new methods that extend the suite of tools available to aquatic biologists and conservation practitioners. We have developed a 2-dimensional spatially-explicit behavioral and physiological model and illustrated how it can be used to simulate fish migration within a river system. Our model can be used to evaluate trade-offs between behavioral thermoregulation and fish fitness at population scales. [ABSTRACT FROM AUTHOR]

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 challenge with large-scale division of labor and specialization. In this paper we study this transition with an individual based model. Our model captures the connections between the appearance of agriculture, the social division of labor, and human behavioral diversity. Our two main settings are different habitats: in the pre-Neolithic habitat, resources fluctuated in time and there was no large-scale food storage. In the Neolithic habitat active food production resulted in economic surplus. We consider a sexually reproducing social group, in which individuals solve different tasks for survival. We assume that the task-solving effectiveness has a partial genetic basis but also improves with experience and learning. Since different tasks can require somewhat different skills, we assume trade-offs between genetic propensities for different tasks. Individuals are born with inherited task-choice strategies that they can improve by imitating more successful peers. We show that for the Neolithic case, both phenotypic specialization (task choice strategy) and the emergence of genetic polymorphism are possible, as long as scarcer goods are more valuable. As the number of tasks increases, specialization can evolve only in very large groups. Although phenotypic specialization often emerges in our model, the emergence of genetic polymorphism requires strong assortativity during both imitation and mate choice. In sum, our model shows that if an economic surplus becomes available, behavioral specialization and large-scale division of labor are likely to appear. Thus, our model can help understanding certain aspects of the Neolithic transition, and may have implications for the present genetic polymorphism, too.

The demographic structure of populations in both more developed and less developed countries is changing: increases in life expectancy and declining fertility have led to older populations and smaller households. The implications of these demographic changes for the spread and control of infectious diseases are not fully understood. Here we use an individual based model with realistic and dynamic age and household structure to demonstrate the marked effect that demographic change has on disease transmission at the population and household level. The decline in fertility is associated with a decrease in disease incidence and an increase in the age of first infection, even in the absence of vaccination or other control measures. Although large households become rarer as fertility decreases, we show that there is a proportionate increase in incidence of disease in these households as the accumulation of susceptible clusters increases the potential for explosive outbreaks. By modelling vaccination, we provide a direct comparison of the relative importance of demographic change and vaccination on incidence of disease. We highlight the increased risks associated with unvaccinated households in a low fertility setting if vaccine behaviour is correlated with household membership. We suggest that models that do not account for future demographic change, and especially its effect on household structure, may potentially overestimate the impact of vaccination.

Abstract: The spatial distribution of relatives has profound effects on kin interactions, inbreeding, and inclusive fitness. Yet, in the marine environment, the processes that generate patterns of kin structure remain understudied because larval dispersal on ocean currents was historically assumed to disrupt kin associations. Recent genetic evidence of co‐occurring siblings challenges this assumption and raises the intriguing question of how siblings are found together after a (potentially) disruptive larval phase. Here, we develop individual‐based models to explore how stochastic processes operating at the individual level affect expected kinship at equilibrium. Specifically, we predict how limited dispersal, sibling cohesion, and variability in reproductive success differentially affect patterns of kin structure. All three mechanisms increase mean kinship within populations, but their spatial effects are markedly different. We find that (1) when dispersal is limited, kinship declines monotonically as a function of the distance between individuals; (2) when siblings disperse cohesively, kinship increases within a site relative to between sites; and (3) when reproductive success varies, kinship increases equally at all distances. The differential effects of these processes therefore only become apparent when individuals are sampled at multiple spatial scales. Notably, our models suggest that aggregative larval behaviors, such as sibling cohesion, are not necessary to explain documented levels of relatedness within marine populations. Together, these findings establish a theoretical framework for disentangling the drivers of marine kin structure. [ABSTRACT FROM AUTHOR]

Fine roots are of major importance for belowground processes in mangrove ecosystems. Little is known about individual mangrove root systems, particularly the fine root component. We measured fine root biomass distribution of solitary standing Rhizophora mangle L. individuals with the dual aim of (a) deepening our understanding of the belowground ecology and allometric relations of this species; and (b) gaining further information about its climatic relevance. Twelve trees of variable height (45-240 cm) were measured on three reforested sites in south-east Florida, USA. Soil cores were collected from individual trees at transects by means of auger sampling. Fine roots were extracted, sorted, dried and weighed. Mean fine root biomass varied between 20.56-253.12 g/m2. Two separate mixed-effects models led to statistically sound predictions of spatial fine root biomass distribution. The first model was based on distance function and tree height (Model 1, R2=0.77, p value ≤ 0.001), and the second on prop root density (Model 2, R2=0.56, p value ≤ 0.001). Besides the aforementioned fixed effects, the results of both models indicated random, site-specific variation with regards to fine root biomass distribution. Nevertheless, we were able to explain individual fine root biomass distribution with reference to aboveground characteristics alone. These findings may help to improve the modelling of belowground plant interaction and carbon storage in mangroves, both of which are intrinsically linked to fine roots. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Collective cell spreading takes place in spatially continuous environments, yet it is often modelled using discrete lattice-based approaches. Here, we use data from a series of cell proliferation assays, with a prostate cancer cell line, to calibrate a spatially continuous individual based model (IBM) of collective cell migration and proliferation. The IBM explicitly accounts for crowding effects by modifying the rate of movement, direction of movement, and the rate of proliferation by accounting for pair-wise interactions. Taking a Bayesian approach we estimate the free parameters in the IBM using rejection sampling on three separate, independent experimental data sets. Since the posterior distributions for each experiment are similar, we perform simulations with parameters sampled from a new posterior distribution generated by combining the three data sets. To explore the predictive power of the calibrated IBM, we forecast the evolution of a fourth experimental data set. Overall, we show how to calibrate a lattice-free IBM to experimental data, and our work highlights the importance of interactions between individuals. Despite great care taken to distribute cells as uniformly as possible experimentally, we find evidence of significant spatial clustering over short distances, suggesting that standard mean-field models could be inappropriate. [ABSTRACT FROM AUTHOR]

Testing for infection with SARS-CoV-2 is an important intervention in reducing onwards transmission of COVID-19, particularly when combined with the isolation and contact-tracing of positive cases. Many countries with the capacity to do so have made use of lab-processed Polymerase Chain Reaction (PCR) testing targeted at individuals with symptoms and the contacts of confirmed cases. Alternatively, Lateral Flow Tests (LFTs) are able to deliver a result quickly, without lab-processing and at a relatively low cost. Their adoption can support regular mass asymptomatic testing, allowing earlier detection of infection and isolation of infectious individuals. In this paper we extend and apply the agent-based epidemic modelling framework Covasim to explore the impact of regular asymptomatic testing on the peak and total number of infections in an emerging COVID-19 wave. We explore testing with LFTs at different frequency levels within a population with high levels of immunity and with background symptomatic PCR testing, case isolation and contact tracing for testing. The effectiveness of regular asymptomatic testing was compared with 'lockdown' interventions seeking to reduce the number of non-household contacts across the whole population through measures such as mandating working from home and restrictions on gatherings. Since regular asymptomatic testing requires only those with a positive result to reduce contact, while lockdown measures require the whole population to reduce contact, any policy decision that seeks to trade off harms from infection against other harms will not automatically favour one over the other. Our results demonstrate that, where such a trade off is being made, at moderate rates of early exponential growth regular asymptomatic testing has the potential to achieve significant infection control without the wider harms associated with additional lockdown measures. • Asymptomatic testing can be as effective as a 30% reduction in the number of contacts. • High rates of testing and contact tracing can be as effective as preemptive isolation. • Replacing PCR tests with LFTs can lead to a smaller number of total infections. • This reduction is achieved with an increase in number of tests and time in isolation. [ABSTRACT FROM AUTHOR]

Indirect effects index (IEI) and cycling index (FCI) are two key measures of ecological network analysis that are known to be correlated. Systems with high IEI values are assumed to be more stable and resilient. While this makes sense from a theoretical point of view, there is lack of evidence on the contribution of indirect interactions to ecosystem stability and resilience. Investigation of this relationship between resilience and indirect effects leads to a better understanding of IEI, that it is actually a sum of three distinct components (IEI-cycle, IEI-mixed and IEI-pure), each of which is identified by their relation to cycling. IEI and FCI are highly correlated because both values increase with average path length path, which is the average number of compartments a unit flow material passes through before exiting the system. While IEI and FCI are highly correlated, the third component of IEI, IEI-pure, is totally uncorrelated with cycling.This is because the effects of cycling are excluded from IEI-pure, by definition. Interestingly, we find out that IEI-pure is the major contributor to ecosystem resilience. • Indirect effects and cycling are two key measures of ecological network analysis. • Indirect effects index (IEI) and Finn's cycling index (FCI) are highly correlated. • Indirect effects index is believed to be correlated with stability and resilience. • IEI is composed of three different parts, two of which correlate with FCI. • Part of IEI with the highest correlation to resilience is independent of cycling. [ABSTRACT FROM AUTHOR]

The lipid-rich calanoid copepod, Calanus finmarchicus, plays a critical role in the pelagic food web of the western North Atlantic and particularly in the Gulf of Maine ecosystem. Deep basins along the continental shelf harbour high abundance of diapausing C. finmarchicus during the summer and fall. In Wilkinson Basin in the western Gulf of Maine, C. finmarchicus has persisted in large concentrations despite recent significant warming that could potentially threaten the existence of the population in this region. Identifying the major source of diapausing individuals is critical to the understanding of mechanisms that allow population persistence. In this study, Lagrangian tracking experiments using an individual-based copepod life cycle model and simulation of environmental conditions during an exceptionally warm year (2012) suggest that coastal waters are the major upstream source for individuals entering dormancy in Wilkinson Basin over summertime, although pathways and distribution patterns vary with the release timing of particles. Both model results and observation data support the Coastal Amplification of Supply and Transport (CAST) hypothesis as an explanation for the persistence of C. finmarchicus population in the western Gulf of Maine. The mechanism involves the coastal amplification of supply (spring reproduction/summer growth in the food-rich coastal region) and transport to the receiving Wilkinson Basin that is capable of harbouring the diapausing stock. [ABSTRACT FROM AUTHOR]

Sea-level rise (SLR) threatens the nesting success of salt marsh breeding birds, Including Seaside Sparrows (Ammodramus maritimus), by Increasing the magnitude and frequency of extreme high tides that flood nests. However, the threat to nesting success from tidal flooding Is Intertwined with that of predation because the threats are connected through a trade-off along a nest height gradient. Therefore, to understand the risk to nesting success from SLR, It Is necessary to consider predation threats simultaneously. I used an Individual-based model of Seaside Sparrow nesting behavior, calibrated using empirical data on nest success rates and nest-site selection behaviors, to project the effects of SLR conditions on the relative Importance of predation and flooding threats In affecting nesting success, and to Investigate whether nest-site selection along a gradient of nest height can modulate the risk of SLR. Outputs from the model revealed that present-day levels of predation risk pose as great a risk to nesting success as tidal flooding under simulated SLR conditions with extreme flooding risks. Nest success rates could become very low under extreme SLR scenarios, especially when predation risk Is very high. The risks of failure from either threat are linked through nest-slte selection behaviors; In hlgh-predatlon-rlsk seasons, failure probability from flooding Is greater than It would be under lower predation risk, due to the predation avoidance behavior of nesting closer to the ground. Therefore, management actions to reduce the risk of excessive failures from predation could reduce the risk of failures from both threats--a potentially useful management strategy, given that controlling predation Is more tractable than controlling Increased flooding from SLR at a local level. [ABSTRACT FROM AUTHOR]

Minimal mathematical models able to explain complex patterns of animal behavior are essential parts of simulation systems describing large-scale spatiotemporal dynamics of trophic communities, particularly those with wide-ranging species, such as occur in pelagic environments. We present results obtained with three different modelling approaches: ( i ) an individual-based model of animal spatial behavior; ( ii ) a continuous taxis-diffusion-reaction system of partial-difference equations; ( iii ) a ‘hybrid’ approach combining the individual-based algorithm of organism movements with explicit description of decay and diffusion of the movement stimuli. Though the models are based on extremely simple rules, they all allow description of spatial movements of animals in a predator–prey system within a closed habitat, reproducing some typical patterns of the pursuit-evasion behavior observed in natural populations. In all three models, at each spatial position the animal movements are determined by local conditions only, so the pattern of collective behavior emerges due to self-organization. The movement velocities of animals are proportional to the density gradients of specific cues emitted by individuals of the antagonistic species (pheromones, exometabolites or mechanical waves of the media, e.g., sound). These cues play a role of taxis stimuli: prey attract predators, while predators repel prey. Depending on the nature and the properties of the movement stimulus we propose using either a simplified individual-based model, a continuous taxis pursuit-evasion system, or a little more detailed ‘hybrid’ approach that combines simulation of the individual movements with the continuous model describing diffusion and decay of the stimuli in an explicit way. These can be used to improve movement models for many species, including large marine predators. [ABSTRACT FROM AUTHOR]

Population size estimates are an integral part of any species conservation or management project. They are often used to evaluate the impact of management intervention and can be critical for making decisions for future management. Distance sampling and camera trapping of unmarked populations are commonly used for such a task as they can yield rapid and relatively inexpensive estimates of density. Yet, while accuracy is key for decision-making, the potential bias associated with densities estimated with each method have seldom been investigated and compared. We built a spatially-explicit individual based model to investigate the accuracy and precision of both monitoring techniques in estimating known densities. We used the wild boar population of the Forest of Dean, UK, as a case study because both methods have been employed in situ and offer the chance of using real life parameters in the model. Moreover, this is an introduced species in the UK that has the potential to impact natural and agricultural ecosystems. Therefore, improving the accuracy of density estimates is a priority for the species’ management. We found that both distance sampling and camera trapping produce biased density estimates for unmarked populations. Despite large uncertainties, distance sampling estimates were on average closer to known densities than those from camera trapping, and robust to group size. Camera trapping estimates were highly sensitive to group size but could be improved with better survey design. This is the first time that the amount of bias associated with each method is quantified. Our model could be used to correct estimated field-based densities from distance sampling and camera trapping of wild boar and other species with similar life-history traits. Our work serves to increase confidence in the results produced by these two commonly-used methods, ensuring they can in turn be relied upon by wildlife managers and conservationists. [ABSTRACT FROM AUTHOR]

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

European populations of the Large Blue Butterfly Maculinea arion have experienced severe declines in the last decades, especially in the northern part of the species range. This endangered lycaenid butterfly needs two resources for development: flower buds of specific plants (Thymus spp., Origanum vulgare), on which young caterpillars briefly feed, and red ants of the genus Myrmica, whose nests support caterpillars during a prolonged final instar. I present an analytically solvable deterministic model to estimate the vulnerability of populations of M. arion. Results obtained from the sensitivity analysis of this mathematical model (MM) are contrasted to the respective results that had been derived from a spatially explicit individual based model (IBM) for this butterfly. I demonstrate that details in landscape configuration which are neglected by the MM but are easily taken into consideration by the IBM result in a different degree of intraspecific competition of caterpillars on flower buds and within host ant nests. The resulting differences in mortalities of caterpillars lead to erroneous estimates of the extinction risk of a butterfly population living in habitat with low food plant coverage and low abundance in host ant nests. This observation favors the use of an individual based modeling approach over the deterministic approach at least for the management of this threatened butterfly.

In this paper, we study the macroscopic model of [P. Degond, F. Delebecque, and D. Peurichard, Math. Models Methods Appl. Sci., 26 (2016), pp. 269-318] for fiber elements having the ability to cross-link, or unlink each other, to align with each other at the cross-links, and subjected to an external potential. We first aim to gain insight into the properties of the solutions of the macroscopic model: we provide an existence result under structural conditions for the external potential and perform numerical simulations. The numerical study of the macroscopic model reveals interesting features such as the emergence of a buckling phenomenon. Physical properties of the macroscopic fiber network are then deduced. We finally propose a numerical comparison between the macroscopic model and the microscopic one, a starting point for the derivation of the macroscopic equations. The numerical simulations reveal a good agreement between both models, providing we adapt the regime of study according to the model parameters. [ABSTRACT FROM AUTHOR]

The predictability of fisher behaviour is an area of considerable uncertainty in fisheries management models. Fisher-derived data could underpin a better understanding, and more realistic predictions of fishing behaviour. Face to face interviews and a choice-based survey were conducted with scallop fishers to collect foraging parameters that could inform a model of fishing behaviour, and to better understand patch choice behaviour. Importantly, we validated survey data against vessel monitoring system and logbook data where possible, demonstrating a good level of accuracy. Environmental parameters central to patch choice were determined (e.g. wave height, distance to port), and three strategies of patch choice behaviour were identified, termed quantity maximiser, quality maximiser, and efficient fisher. Individuals' VMS and logbook data further confirmed and explained these behavioural patterns. This approach provided reliable, highly relevant data for the parameterisation of a fisheries behavioural model, which could lead to more robust and realistic predictive fisheries models. [ABSTRACT FROM AUTHOR]

In the dairy industry, Johne's disease (JD), caused by Mycobacterium avium subsp . paratuberculosis (MAP) is one of the major investigated diseases. To date, researchers have suggested some control strategies for JD, such as test-and-cull based herd management, isolated calf rearing management, and vaccinations. Due to the slow progressing nature of MAP, tests with low diagnostic test sensitivity and specificity, and economic limitations, implementing these strategies has not resulted in elimination of MAP from farms. To date, no study has integrated detailed dairy herd dynamics with different MAP transmission routes. We have developed an individual-based dairy herd model by incorporating basic herd dynamics in a closed herd environment where no new animals have been bought from outside. The model considered three age groups of animals: calves, heifers and adults. It includes sequential life events of a dairy animal and such key dynamic processes of the dairy herd as lactation cycle, calving, voluntary waiting period, insemination, pregnancy, dry-off period and calf and heifer rearing. After initially validating that the model reproduced typical herd dynamics, it was extended by incorporating MAP infection dynamics, where each individual adult animal belonged to one of four infection compartments: susceptible, latent, low shedding and high shedding. The model includes two disease transmission routes: horizontal transmission (i.e., fecal-oral) and vertical transmission (i.e., in utero infection). The results confirm that this model can simulate a realistic dairy herd and that inclusion of the above-mentioned dynamic processes provides useful information about individual infected animals to farmers. Access to the individual animal information offers more validity to assessment of appropriate control strategies for an endemically MAP infected herd. This model can serve as an accurate and novel tool not only to better understand MAP dynamics, but is also valuable as an individual based system of a typical dairy herd that can be applied to other research questions. [ABSTRACT FROM AUTHOR]

The dynamics of coevolution is a spatio-temporal process that cannot be understood by mean field approximations, where populations are considered well mixed and interactions are random. This intrinsic characteristic makes comprehensive empirical studies difficult and computer simulations can help to understand the interplay between the many components of the interactions. Here we created an individual-based model to study the coevolution of sexually reproducing populations of prey and predators that engage in an arms race. The phenotype interface of the interaction is a defensive trait for the prey and a counter-defensive one for the predator, both having costs that decrease reproduction chances. The simulations captured several features of natural systems, such as oscillations of the phenotypes levels and abundances. More importantly, the simulations show that local depletion of prey by predators with high levels of counter-defenses followed by recolonization by less defensive prey is a key mechanism that regulates the arms race and the spatio-temporal distribution of phenotypes, creating mismatches similar to those observed in natural systems. [ABSTRACT FROM AUTHOR]