The effect of divergent and parallel selection on the genomic landscape of divergence.

While the role of selection in divergence along the speciation continuum is theoretically well understood, defining specific signatures of selection in the genomic landscape of divergence is empirically challenging. Modelling approaches can provide insight into the potential role of selection on the emergence of a heterogenous genomic landscape of divergence. Here, we extend and apply an individual-based approach that simulates the phenotypic and genotypic distributions of two populations under a variety of selection regimes, genotype-phenotype maps, modes of migration, and genotype-environment interactions. We show that genomic islands of high differentiation and genomic valleys of similarity may respectively form under divergent and parallel selection between populations. For both types of between-population selection, negative and positive frequency-dependent selection within populations generated genomic islands of higher magnitude and genomic valleys of similarity, respectively. Divergence rates decreased under strong dominance with divergent selection, as well as in models including genotype-environment interactions under parallel selection. For both divergent and parallel selection models, divergence rate was higher under an intermittent migration regime between populations, in contrast to a constant level of migration across generations, despite an equal number of total migrants. We highlight that interpreting a particular evolutionary history from an observed genomic pattern must be done cautiously, as similar patterns may be obtained from different combinations of evolutionary processes. Modelling approaches such as ours provide an opportunity to narrow the potential routes that generate the genomic patterns of specific evolutionary histories.

Patch biogeography under intermittent barriers: macroevolutionary consequences of microevolutionary processes.

The processes that generate biodiversity start on a microevolutionary scale, where each individual's history can impact the species' history. This manuscript presents a theoretical study that examines the macroevolutionary patterns that emerge from the microevolutionary dynamics of populations inhabiting two patches. The model is neutral, meaning that neither survival nor reproduction depends on a fixed genotype, yet individuals must have minimal genetic similarity to reproduce. We used historical sea level oscillation over the past 800 thousand years to hypothesize periods when individuals could migrate from one patch to another. In our study, we keep track of each speciation and extinction event, build the complete and extant phylogenies, and characterize the macroevolutionary patterns regarding phylogeny balance, acceleration of speciation, and crown age. We also evaluate ecological patterns: richness, beta diversity, and species distribution symmetry. The balance of the complete phylogeny can be a sign of the speciation mode, contrasting speciation induced by migration and isolation (vicariance). The acceleration of the speciation process is also affected by the geographical barriers and the duration of the isolation period, with high isolation times leading to accelerated speciation. We report the correlation between ecological and macroevolutionary patterns and show it decreases with the time spent in isolation. We discuss, in light of our results, the challenge of integrating present-time community ecology with macroevolutionary patterns.

Evolution of individual variation in a competitive trait: a theoretical analysis.

When competitive traits are costly, negative frequency dependence can maintain genetic variance. Most theoretical studies examining this problem assume binary polymorphisms, yet most trait variation in wild populations is continuous. We propose that continuous trait variation can result from continuous variation in resource quality and that, specifically, the shape of the resource distribution determines trait maintenance. We used an individual-based model to test which conditions favour the stable maintenance of variation and which cause temporal fluctuations in trait values. This approach, inspired by contrasting outcomes of previous studies regarding variance and fluctuations in trait values, clearly showed a decisive role played by the shape of resource distributions. Under extreme conditions, e.g., the absence of resource variation or with very scarce resources for weak competitors, traits evolved to a single non-competitive or highly competitive strategy, respectively. Most other distributions led to strong temporal fluctuations on trait values or the maintenance of stable, standing variation. Our results thus explain the contradicting outcomes of previous theoretical studies and, at the same time, provide hypotheses to explain the maintenance of genetic variation and individual differences. We suggest ways to empirically test the proposed effects of resource variation on trait maintenance.

Quantifying the impact of habitat modifications on species behavior and mortality: A case study of tropical tuna.

Ecosystems and biodiversity across the world are being altered by human activities. Habitat modification and degradation are among the most important drivers of biodiversity loss. These modifications can have an impact on species behavior, which can, in turn, impact their mortality. While several studies have investigated the impacts of habitat degradation and fragmentation on terrestrial species, the extent to which habitat modifications affect the behavior and fitness of marine species is still largely unknown, particularly for pelagic species. Since the early 1990s, industrial purse seine vessels targeting tuna have started deploying artificial floating objects-Drifting Fish Aggregating Devices (DFADs)-in all oceans to increase tuna catchability. Since then, the massive deployment of DFADs has modified tuna surface habitat, by increasing the density of floating objects, with potential impacts on tuna associative behavior and mortality. In this study, we investigate these impacts for yellowfin tuna in the Indian Ocean. Using an individual-based model based on a correlated random walk and newly available data on DFAD densities, we quantify for the first time how the increase in floating object density, due to DFAD use, affects the percentage of time that yellowfin tuna spend associated, which, in turn, directly impacts their availability to fishers and fishing mortality. This modification of tuna associative behavior could also have indirect impacts on their fitness, by retaining tuna in areas detrimental to them or disrupting schooling behavior. Hence, there is an urgent need to further investigate DFAD impacts on tuna behavior, in particular, taking social behavior into account, and to continue regulation efforts on DFAD use and monitoring.

Individual energetics scale up to community coexistence: Movement, metabolism and biodiversity dynamics in fragmented landscapes.

Unravelling the intricate mechanisms that govern community coexistence remains a daunting challenge, particularly amidst ongoing environmental change. Individual physiology and metabolism are often studied to understand the response of individual animals to environmental change. However, this perspective is currently largely lacking in community ecology. We argue that the integration of individual metabolism into community theory can offer new insights into coexistence. We present the first individual-based metabolic community model for a terrestrial mammal community to simulate energy dynamics and home range behaviour in different environments. Using this model, we investigate how ecologically similar species coexist and maintain their energy balance under food competition. Only if individuals of different species are able to balance their incoming and outgoing energy over the long-term will they be able to coexist. After thoroughly testing and validating the model against real-world patterns such as of home range dynamics and field metabolic rates, we applied it as a case study to scenarios of habitat fragmentation - a widely discussed topic in biodiversity research. First, comparing single-species simulations with community simulations, we find that the effect of habitat fragmentation on populations is strongly context-dependent. While populations of species living alone in the landscape were mostly positively affected by fragmentation, the diversity of a community of species was highest under medium fragmentation scenarios. Under medium fragmentation, energy balance and reproductive investment were also most similar among species. We therefore suggest that similarity in energy balance among species promotes coexistence. We argue that energetics should be part of community ecology theory, as the relative energetic status and reproductive investment can reveal why and under what environmental conditions coexistence is likely to occur. As a result, landscapes can potentially be protected and designed to maximize coexistence. The metabolic community model presented here can be a promising tool to investigate other scenarios of environmental change or other species communities to further disentangle global change effects and preserve biodiversity.

A metabolic perspective on polyploid invasion and the emergence of life histories: Insights from a mechanistic model.

PREMISE: Whole-genome duplication (WGD, polyploidization) has been identified as a driver of genetic and phenotypic novelty, having pervasive consequences for the evolution of lineages. While polyploids are widespread, especially among plants, the long-term establishment of polyploids is exceedingly rare. Genome doubling commonly results in increased cell sizes and metabolic expenses, which may be sufficient to modulate polyploid establishment in environments where their diploid ancestors thrive. METHODS: We developed a mechanistic simulation model of photosynthetic individuals to test whether changes in size and metabolic efficiency allow autopolyploids to coexist with, or even invade, ancestral diploid populations. Central to the model is metabolic efficiency, which determines how energy obtained from size-dependent photosynthetic production is allocated to basal metabolism as opposed to somatic and reproductive growth. We expected neopolyploids to establish successfully if they have equal or higher metabolic efficiency as diploids or to adapt their life history to offset metabolic inefficiency. RESULTS: Polyploid invasion was observed across a wide range of metabolic efficiency differences between polyploids and diploids. Polyploids became established in diploid populations even when they had a lower metabolic efficiency, which was facilitated by recurrent formation. Competition for nutrients is a major driver of population dynamics in this model. Perenniality did not qualitatively affect the relative metabolic efficiency from which tetraploids tended to establish. CONCLUSIONS: Feedback between size-dependent metabolism and energy allocation generated size and age differences between plants with different ploidies. We demonstrated that even small changes in metabolic efficiency are sufficient for the establishment of polyploids.

Simulating the efficacy of wolf-dog hybridization management with individual-based modeling.

Introgressive hybridization between wolves and dogs is a conservation concern due to its potentially deleterious long-term evolutionary consequences. European legislation requires that wolf-dog hybridization be mitigated through effective management. We developed an individual-based model (IBM) to simulate the life cycle of gray wolves that incorporates aspects of wolf sociality that affect hybridization rates (e.g., the dissolution of packs after the death of one/both breeders) with the goal of informing decision-making on management of wolf-dog hybridization. We applied our model by projecting hybridization dynamics in a local wolf population under different mate choice and immigration scenarios and contrasted results of removal of admixed individuals with their sterilization and release. In several scenarios, lack of management led to complete admixture, whereas reactive management interventions effectively reduced admixture in wolf populations. Management effectiveness, however, strongly depended on mate choice and number and admixture level of individuals immigrating into the wolf population. The inclusion of anthropogenic mortality affecting parental and admixed individuals (e.g., poaching) increased the probability of pack dissolution and thus increased the probability of interbreeding with dogs or admixed individuals and boosted hybridization and introgression rates in all simulation scenarios. Recognizing the necessity of additional model refinements (appropriate parameterization, thorough sensitivity analyses, and robust model validation) to generate management recommendations applicable in real-world scenarios, we maintain confidence in our model's potential as a valuable conservation tool that can be applied to diverse situations and species facing similar threats.

Simulación de la eficiencia de la gestión de híbridos de perro y lobo con modelos basados en individuos Resumen La hibridación introgresiva entre perros y lobos es un tema de conservación por las posibles consecuencias evolutivas deletéreas a largo plazo. Las leyes europeas requieren que estos híbridos se mitiguen mediante una gestión efectiva. Desarrollamos un modelo basado en individuos (MBI) para simular el ciclo de vida del lobo gris que además incorpora los aspectos sociales de los lobos que afectan las tasas de hibridación (p. ej.: la disolución de las manadas después de la muerte de uno o ambos reproductores) con el objetivo de guiar las decisiones de gestión de estos híbridos. Aplicamos nuestro modelo con la proyección de las dinámicas de hibridación en una población local de lobos bajo diferentes selecciones de pareja y escenarios de inmigración y contrastamos los resultados de la extirpación de individuos mezclados con su esterilización y liberación. En varios escenarios, la falta de gestión llevó a una mezcla completa, mientras que las intervenciones de gestión reactiva redujeron de forma efectiva la mezcla en las poblaciones de lobos. Sin embargo, la eficiencia de la gestión dependió en su mayoría de la selección de pareja y el número y nivel de mezcla de los individuos inmigrantes a la población de lobos. La inclusión de la mortalidad antropogénica que afecta a los individuos parentales y mezclados (p. ej.: la cacería) incrementó la probabilidad de que se disolviera la manada y por lo tanto incrementara la probabilidad del entrecruzamiento con perros o individuos mezclados, además de que aumentó la hibridación y las tasas de introgresión en todos los escenarios de simulación. Reconocemos la necesidad de refinar el modelo (parametrización adecuada, análisis detallados de sensibilidad y validación del modelo robusto) para generar recomendaciones de gestión aplicables en escenarios reales y mantenemos la confianza en el potencial de nuestro modelo como una herramienta valiosa de conservación que podría aplicarse a diferentes situaciones y especies que enfrentan amenazas similares.

Impact of interventions to reduce nosocomial transmission of SARS-CoV-2 in English NHS Trusts: a computational modelling study.

BACKGROUND: Prior to September 2021, 55,000-90,000 hospital inpatients in England were identified as having a potentially nosocomial SARS-CoV-2 infection. This includes cases that were likely missed due to pauci- or asymptomatic infection. Further, high numbers of healthcare workers (HCWs) are thought to have been infected, and there is evidence that some of these cases may also have been nosocomially linked, with both HCW to HCW and patient to HCW transmission being reported. From the start of the SARS-CoV-2 pandemic interventions in hospitals such as testing patients on admission and universal mask wearing were introduced to stop spread within and between patient and HCW populations, the effectiveness of which are largely unknown. MATERIALS/METHODS: Using an individual-based model of within-hospital transmission, we estimated the contribution of individual interventions (together and in combination) to the effectiveness of the overall package of interventions implemented in English hospitals during the COVID-19 pandemic. A panel of experts in infection prevention and control informed intervention choice and helped ensure the model reflected implementation in practice. Model parameters and associated uncertainty were derived using national and local data, literature review and formal elicitation of expert opinion. We simulated scenarios to explore how many nosocomial infections might have been seen in patients and HCWs if interventions had not been implemented. We simulated the time period from March-2020 to July-2022 encompassing different strains and multiple doses of vaccination. RESULTS: Modelling results suggest that in a scenario without inpatient testing, infection prevention and control measures, and reductions in occupancy and visitors, the number of patients developing a nosocomial SARS-CoV-2 infection could have been twice as high over the course of the pandemic, and over 600,000 HCWs could have been infected in the first wave alone. Isolation of symptomatic HCWs and universal masking by HCWs were the most effective interventions for preventing infections in both patient and HCW populations. Model findings suggest that collectively the interventions introduced over the SARS-CoV-2 pandemic in England averted 400,000 (240,000 - 500,000) infections in inpatients and 410,000 (370,000 - 450,000) HCW infections. CONCLUSIONS: Interventions to reduce the spread of nosocomial infections have varying impact, but the package of interventions implemented in England significantly reduced nosocomial transmission to both patients and HCWs over the SARS-CoV-2 pandemic.

Impacts on Atlantic Killifish from Neurotoxicants: Genes, Behavior, and Population-Relevant Outcomes.

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.

Mating system induced lags in rates of range expansion for different simulated mating systems and dispersal strategies: a modelling study.

Mismatches between current and potential species distributions are commonplace due to lags in the response of populations to changing environmental conditions. The prevailing mating system may contribute to such lags where it leads to mating failure at the range edge, but how active dispersers might mitigate these lags using social information to inform dispersal strategies warrants greater exploration. We used an individual-based model to explore how different mating systems for species that actively search for habitat can impose a filter on the ability to colonise empty, fragmented landscapes, and explored how using social information during dispersal can mitigate the lags caused by more constrained mating systems. The mate-finding requirements implemented in two-sex models consistently led to slower range expansion compared to those that were not mate limited (i.e., female only models), even when mating was polygynous. A mate-search settlement strategy reduced the proportion of unmated females at the range edge but had little impact on rate of spread. In contrast, a negative density-dependent settlement strategy resulted in much faster spread, which could be explained by a greater number of long-distance dispersal events. Our findings suggest that even low rates of mating failure at the range edge can lead to considerable lags in range expansion, though dispersal strategies that favour colonising more distant, sparsely occupied habitat patches may effectively mitigate these lags.

Mathematical Modelling of Parasite Dynamics: A Stochastic Simulation-Based Approach and Parameter Estimation via Modified Sequential-Type Approximate Bayesian Computation.

The development of mathematical models for studying newly emerging and re-emerging infectious diseases has gained momentum due to global events. The gyrodactylid-fish system, like many host-parasite systems, serves as a valuable resource for ecological, evolutionary, and epidemiological investigations owing to its ease of experimental manipulation and long-term monitoring. Although this system has an existing individual-based model, it falls short in capturing information about species-specific microhabitat preferences and other biological details for different Gyrodactylus strains across diverse fish populations. This current study introduces a new individual-based stochastic simulation model that uses a hybrid τ -leaping algorithm to incorporate this essential data, enhancing our understanding of the complexity of the gyrodactylid-fish system. We compare the infection dynamics of three gyrodactylid strains across three host populations. A modified sequential-type approximate Bayesian computation (ABC) method, based on sequential Monte Carlo and sequential importance sampling, is developed. Additionally, we establish two penalised local-linear regression methods (based on L1 and L2 regularisations) for ABC post-processing analysis to fit our model using existing empirical data. With the support of experimental data and the fitted mathematical model, we address open biological questions for the first time and propose directions for future studies on the gyrodactylid-fish system. The adaptability of the mathematical model extends beyond the gyrodactylid-fish system to other host-parasite systems. Furthermore, the modified ABC methodologies provide efficient calibration for other multi-parameter models characterised by a large set of correlated or independent summary statistics.

Understanding the Interplay of CAR-NK Cells and Triple-Negative Breast Cancer: Insights from Computational Modeling.

Chimeric antigen receptor (CAR)-engineered natural killer (NK) cells have recently emerged as a promising and safe alternative to CAR-T cells for targeting solid tumors. In the case of triple-negative breast cancer (TNBC), traditional cancer treatments and common immunotherapies have shown limited effectiveness. However, CAR-NK cells have been successfully employed to target epidermal growth factor receptor (EGFR) on TNBC cells, thereby enhancing the efficacy of immunotherapy. The effectiveness of CAR-NK-based immunotherapy is influenced by various factors, including the vaccination dose, vaccination pattern, and tumor immunosuppressive factors in the microenvironment. To gain insights into the dynamics and effects of CAR-NK-based immunotherapy, we propose a computational model based on experimental data and immunological theories. This model integrates an individual-based model that describes the interplay between the tumor and the immune system, along with an ordinary differential equation model that captures the variation of inflammatory cytokines. Computational results obtained from the proposed model shed light on the conditions necessary for initiating an effective anti-tumor response. Furthermore, global sensitivity analysis highlights the issue of low persistence of CAR-NK cells in vivo, which poses a significant challenge for the successful clinical application of these cells. Leveraging the model, we identify the optimal vaccination time, vaccination dose, and time interval between injections for maximizing therapeutic outcomes.

Local intraspecific aggregation in phytoplankton model communities: spatial scales of occurrence and implications for coexistence.

The coexistence of multiple phytoplankton species despite their reliance on similar resources is often explained with mean-field models assuming mixed populations. In reality, observations of phytoplankton indicate spatial aggregation at all scales, including at the scale of a few individuals. Local spatial aggregation can hinder competitive exclusion since individuals then interact mostly with other individuals of their own species, rather than competitors from different species. To evaluate how microscale spatial aggregation might explain phytoplankton diversity maintenance, an individual-based, multispecies representation of cells in a hydrodynamic environment is required. We formulate a three-dimensional and multispecies individual-based model of phytoplankton population dynamics at the Kolmogorov scale. The model is studied through both simulations and the derivation of spatial moment equations, in connection with point process theory. The spatial moment equations show a good match between theory and simulations. We parameterized the model based on phytoplankters' ecological and physical characteristics, for both large and small phytoplankton. Defining a zone of potential interactions as the overlap between nutrient depletion volumes, we show that local species composition-within the range of possible interactions-depends on the size class of phytoplankton. In small phytoplankton, individuals remain in mostly monospecific clusters. Spatial structure therefore favours intra- over inter-specific interactions for small phytoplankton, contributing to coexistence. Large phytoplankton cell neighbourhoods appear more mixed. Although some small-scale self-organizing spatial structure remains and could influence coexistence mechanisms, other factors may need to be explored to explain diversity maintenance in large phytoplankton.

An individual-based dynamic model to assess interventions to mitigate opioid overdose risk.

BACKGROUND: Illicit opioid overdose continues to rise in North America and is a leading cause of death. Mathematical modeling is a valuable tool to investigate the epidemiology of this public health issue, as it can characterize key features of population outcomes and quantify the broader effect of structural and interventional changes on overdose mortality. The aim of this study is to quantify and predict the impact of key harm reduction strategies at differing levels of scale-up on fatal and nonfatal overdose among a population of people engaging in unregulated opioid use in Toronto. METHODS: An individual-based model for opioid overdose was built featuring demographic and behavioural variation among members of the population. Key individual attributes known to scale the risk of fatal and nonfatal overdose were identified and incorporated into a dynamic modeling framework, wherein every member of the simulated population encompasses a set of distinct characteristics that govern demographics, intervention usage, and overdose incidence. The model was parametrized to fatal and nonfatal overdose events reported in Toronto in 2019. The interventions considered were opioid agonist therapy (OAT), supervised consumption sites (SCS), take-home naloxone (THN), drug-checking, and reducing fentanyl in the drug supply. Harm reduction scenarios were explored relative to a baseline model to examine the impact of each intervention being scaled from 0% use to 100% use on overdose events. RESULTS: Model simulations resulted in 3690.6 nonfatal and 295.4 fatal overdoses, coinciding with 2019 data from Toronto. From this baseline, at full scale-up, 290 deaths were averted by THN, 248 from eliminating fentanyl from the drug supply, 124 from SCS use, 173 from OAT, and 100 by drug-checking services. Drug-checking and reducing fentanyl in the drug supply were the only harm reduction strategies that reduced the number of nonfatal overdoses. CONCLUSIONS: Within a multi-faceted harm reduction approach, scaling up take-home naloxone, and reducing fentanyl in the drug supply led to the largest reduction in opioid overdose fatality in Toronto. Detailed model simulation studies provide an additional tool to assess and inform public health policy on harm reduction.

Exploring the effects of two-segment loop tunnels on termite food transport efficiency: a simulation study.

This study explores the food transport efficiency (E) in a termite tunnel consisting of a main tunnel and a 2-segment loop tunnel through a model simulation. Simulated termites navigate between the main and loop tunnels through branching nodes (a, b, c, d) with associated probabilities (P1, P2, P3, P4). The loop tunnel locations (δ) are considered: near the nest (δ = 1), at the center of the main tunnel (δ = 2), and close to the food site (δ = 3). The results reveal that for δ = 1, paths such as a → d → b → c and c → d → b → a exhibited high E values. Conversely, for δ = 2, P3 and P4 demonstrate elevated E values ranging from 0.4 to 0.6. For δ = 3, paths like c → d or c → b display high E values, emphasizing the significance of in-loop separation tunnels (characterized by P3 and P4) in alleviating traffic congestion. Partial rank correlation validates that P1 and P2 minimally influence E, while P3 and P4 significantly negatively impact E, regardless of δ. However, for δ = 2, the influence of P3 and P4 is notably reduced due to the positional symmetry of the loop tunnel. In the discussion, we address model limitations and propose strategies to overcome them. Additionally, we outline potential experimental validations to ensure a comprehensive understanding of the dynamics governing termite food transport within tunnels.

More management, less damage? With increasing population size, economic costs of managing geese to minimize yield losses may outweigh benefits.

Conflicts between farmers and geese are intensifying; yet, it remains unclear how interactions between goose population size and management regimes affect yield loss and economic costs. We investigate the cost-effectiveness of accommodation and scaring areas in relation to barnacle goose (Branta leucopsis) population size. We use an existing individual-based model of barnacle geese foraging in nature, accommodation, and scaring areas in Friesland, the Netherlands, to study the most cost-effective management under varying population sizes (i.e., between 20 and 200% of the current size). Our study shows that population size non-linearly affects yield loss costs and total costs per goose. The most cost-effective management scenario for intermediate to large populations is to avoid scaring of geese. For small populations, intensive scaring resulted in minimized yield loss costs and total costs, but also substantially lower goose body mass. Our results strongly suggest that scaring becomes a less effective management measure as goose populations increase.

Antagonistic Effects of Assortative Mating on the Evolution of Phenotypic Plasticity along Environmental Gradients.

AbstractPrevious theory has shown that assortative mating for plastic traits can maintain genetic divergence across environmental gradients despite high gene flow. Yet these models did not examine how assortative mating affects the evolution of plasticity. We here describe patterns of genetic variation across elevation for plasticity in a trait under assortative mating, using multiple-year observations of budburst date in a common garden of sessile oaks. Despite high gene flow, we found significant spatial genetic divergence for the intercept, but not for the slope, of reaction norms to temperature. We then used individual-based simulations, where both the slope and the intercept of the reaction norm evolve, to examine how assortative mating affects the evolution of plasticity, varying the intensity and distance of gene flow. Our model predicts the evolution of either suboptimal plasticity (reaction norms with a slope shallower than optimal) or hyperplasticity (slopes steeper than optimal) in the presence of assortative mating when optimal plasticity would evolve under random mating. Furthermore, a cogradient pattern of genetic divergence for the intercept of the reaction norm (where plastic and genetic effects are in the same direction) always evolves in simulations with assortative mating, consistent with our observations in the studied oak populations.

Phenotypic variations induced emergence of orientation order and morphology in Bacillus subtilis biofilm growth.

During the Bacillus subtilis biofilm growth on the solid MSgg substrate, the biofilm exhibits highly ordered structures such as matrix-producing-cell chains and Van Gogh bundles due to bacterial orientation order. These structures make the biofilm have strong mobility and environmental adaptability, thus making bacteria easier to survive and thrive in biofilms comparing to planktonic bacteria. We tested the behaviors of different phenotypes as well as their impacts on bacterial clusters: motile cells arrange disorderly, the biofilm made up of motile cells tends to be circular and isotropic; matrix-producing cells form cellular chains that guide motile cells along the chain to form a locally nematic phase, the morphology of the biofilm made up of both motile cells and matrix-producing cells is rendered irregular. Combining the results of a coarse-grained and individual-based model, we can control the biofilm growth through regulating environmental friction, bacterial growth rate and adhesion between cells.

Climate change is predicted to cause population collapse in a cooperative breeder.

It has been suggested that animals may have evolved cooperative breeding strategies in response to extreme climatic conditions. Climate change, however, may push species beyond their ability to cope with extreme climates, and reduce the group sizes in cooperatively breeding species to a point where populations are no longer viable. Predicting the impact of future climates on these species is challenging as modelling the impact of climate change on their population dynamics requires information on both group- and individual-level responses to climatic conditions. Using a single-sex individual-based model incorporating demographic responses to ambient temperature in an endangered species, the African wild dog Lycaon pictus, we show that there is a threshold temperature above which populations of the species are predicted to collapse. For simulated populations with carrying capacities equivalent to the median size of real-world populations (nine packs), extinction risk increases once temperatures exceed those predicted in the best-case climate warming scenario (Representative Concentration Pathway [RCP] 2.6). The threshold is higher (between RCP 4.5 and RCP 6.0) for larger simulated populations (30 packs), but 84% of real-world populations number <30 packs. Simulated populations collapsed because, at high ambient temperatures, juvenile survival was so low that packs were no longer recruiting enough individuals to persist, leading them to die out. This work highlights the importance of social dynamics in determining impacts of climatic variables on social species, and the critical role that recruitment can play in driving population-level impacts of climate change. Population models parameterised on long-term data are essential for predicting future population viability under climate change.

Demographic resilience may sustain significant coral populations in a 2°C-warmer world.

Projections of coral reefs under climate change have important policy implications, but most analyses have focused on the intensification of climate-related physical stress rather than explicitly modelling how coral populations respond to stressors. Here, we analyse the future of the Great Barrier Reef (GBR) under multiple, spatially realistic drivers which allows less impacted sites to facilitate recovery. Under a Representative Concentration Pathway (RCP) 2.6 CMIP5 climate ensemble, where warming is capped at ~2°C, GBR mean coral cover declined mid-century but approached present-day levels towards 2100. This is considerably more optimistic than most analyses. However, under RCP4.5, mean coral cover declined by >80% by late-century, and reached near zero under RCP ≥6.0. While these models do not allow for adaptation, they significantly extend past studies by revealing demographic resilience of coral populations to low levels of additional warming, though more pessimistic outcomes might be expected under CMIP6. Substantive coral populations under RCP2.6 would facilitate long-term genetic adaptation, adding value to ambitious greenhouse emissions mitigation.