Individual-based Simulation Model Research Articles

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\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ au $$\\end{document}-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.

Exploring Appropriate Strategies for Vaccination against Classical Swine Fever under a Dynamic Change in Antibody Titer in Sows after Starting Vaccination in a Japanese Farm Setting

After 26 years of absence in Japan, a classical swine fever (CSF) outbreak occurred at a domestic pig farm in 2018. Vaccination against the CSF virus with a live attenuated vaccine at pig farms was restarted in October 2019, which was 13 years after the 2006 ban on vaccination. An individual-based simulation model for CSF antibody dynamics was developed to determine an effective CSF vaccination strategy for pig populations. In creating a simulated pig herd, the optimal vaccination age of piglets and the effect of vaccinating piglets twice were evaluated. Additionally, the herd immunity was monitored every 6 months for 4 years after the start of vaccination, and the effects of intensive sow replacement policies were assessed. The simulation results indicated that the vaccination age should be delayed relative to the age used before the 2006 ban on vaccination and shifted earlier, from 8 weeks to 6 weeks, as time elapses. The simulations indicated a tradeoff in protection between the weaning period (i.e., maternally derived antibodies) and the fattening period (i.e., by vaccine-induced antibodies). Mixing sows with high and low antibody titers, particularly sows that received the first vaccination and those born after the start of vaccination, resulted in a high variation in antibody titer among pigs on the farm. This study also clarified the positive effect of intensive sow replacement strategies on shortening the period in which sows show diverse titers. Differences in sow replacement rates among farms and/or the time lag in starting vaccination in different prefectures result in heterogeneity in herd immunity in Japan; thus, herd immunity status should be examined at every farm using this simulation model.

Directional selection coupled with kin selection favors the establishment of senescence.

Conventional wisdom in evolutionary theory considers aging as a non-selected byproduct of natural selection. Based on this, conviction aging was regarded as an inevitable phenomenon. It was also thought that in the wild organisms tend to die from diseases, predation and other accidents before they could reach the time when senescence takes its course. Evidence has accumulated, however, that aging is not inevitable and there are organisms that show negative aging even. Furthermore, old age does play a role in the deaths of many different organisms in the wild also. The hypothesis of programmed aging posits that a limited lifespan can evolve as an adaptation (i.e., positively selected for) in its own right, partly because it can enhance evolvability by eliminating "outdated" genotypes. A major shortcoming of this idea is that non-aging sexual individuals that fail to pay the demographic cost of aging would be able to steal good genes by recombination from aging ones. Here, we show by a spatially explicit, individual-based simulation model that aging can positively be selected for if a sufficient degree of kin selection complements directional selection. Under such conditions, senescence enhances evolvability because the rate of aging and the rate of recombination play complementary roles. The selected aging rate is highest at zero recombination (clonal reproduction). In our model, increasing extrinsic mortality favors evolved aging by making up free space, thereby decreasing competition and increasing drift, even when selection is stabilizing and the level of aging is set by mutation-selection balance. Importantly, higher extrinsic mortality is not a substitute for evolved aging under directional selection either. Reduction of relatedness decreases the evolved level of aging; chance relatedness favors non-aging genotypes. The applicability of our results depends on empirical values of directional and kin selection in the wild. We found that aging can positively be selected for in a spatially explicit population model when sufficiently strong directional and kin selection prevail, even if reproduction is sexual. The view that there is a conceptual link between giving up clonal reproduction and evolving an aging genotype is supported by computational results.

Modelling the impact of urban form on daily mobility energy consumption using archetypal cities

Urban form is regularly identified as a potential key factor for reducing road traffic and the resulting energy consumption, but the nature of the link between mobility and urban form has been subject to considerable debate. This paper employs quantitative research methods to investigate the influence of urban form on daily mobility energy consumption. First, we used archetypal cities, representative of French large-sized cities, which allow us to isolate the impact of urban form and to have more generalised results. Second, we simulated daily mobility in those archetypal cities by using an individual-based daily mobility simulation model, Mobisim-Soft and then calculated the resulting energy consumptions. Our results confirm that the periurban city is the most energy consuming compare to the axialized and the polycentric cities, but it appears that the global structure of an agglomeration is a second-order factor compared to the inner arrangement of its neighbourhoods.

Simulating biogeomorphological coevolutionary feedback among pocket gophers, grassland vegetation, and the associated bioengineered landforms

Landscape evolution is often driven by the bioengineering activities of multiple species interacting with one another, which modify the Earth’s surface topography and processes. In this study, we used a spatially explicit, individual-based simulation model to investigate potential biogeomorphological coevolutionary feedback relationships. Specifically, we examined how pocket gophers, grassland vegetation, and associated bioengineered soil landscapes may co-define and co-adjust with each other. The model used in this study was parameterized to represent a hypothetical grassland ecosystem typical of those found in California, USA. We found that, as pocket gopher preference for annual plants increased, (i) the number of mounds constructed by these animals increased, (ii) the length of the burrow systems increased, (iii) the complexity of the burrow systems increased, and (iv) the area supporting perennial plants decreased. Collectively, these results would lead to greater food availability (i.e., annual plants) and a more favorable habitat for foraging, breeding, raising offspring, and evading predators. Therefore, the emerging habitat conditions engineered by pocket gophers are expected to eventually enhance their fitness when they increasingly burrow under areas dominated by annual vegetation. As traits associated with preference for annual plants are favored and passed on to future generations, a reinforcing feedback loop between preference for annuals and gopher fitness is anticipated to develop and persist over evolutionary timescales. Our model provides a comprehensive overview by integrating three types of selection pressures: environmental conditions, species interactions, and habitat conditions modified by multiple coevolving engineer species. We anticipate that this study will motivate geoecologists, evolutionary biologists, and geomorphologists to expand their focus beyond their traditional domains within the biological and physical sciences, encouraging exploration of the integrative discipline of biogeomorphology.

Development and validation of an individual-based state-transition model for the prediction of frailty and frailty-related events.

Frailty is a biological syndrome that is associated with increased risks of morbidity and mortality. To assess the value of interventions to prevent or manage frailty, all important impacts on costs and outcomes should be estimated. The aim of this study is to describe the development and validation of an individual-based state transition model that predicts the incidence and progression of frailty and frailty-related events over the remaining lifetime of older Australians. An individual-based state transition simulation model comprising integrated sub models that represent the occurrence of seven events (mortality, hip fracture, falls, admission to hospital, delirium, physical disability, and transitioning to residential care) was developed. The initial parameterisation used data from the Survey of Health, Ageing, and Retirement in Europe (SHARE). The model was then calibrated for an Australian population using data from the Household, Income and Labour Dynamics in Australia (HILDA) Survey. The simulation model established internal validity with respect to predicting outcomes at 24 months for the SHARE population. Calibration was required to predict longer terms outcomes at 48 months in the SHARE and HILDA data. Using probabilistic calibration methods, over 1,000 sampled sets of input parameter met the convergence criteria across six external calibration targets. The developed model provides a tool for predicting frailty and frailty-related events in a representative community dwelling Australian population aged over 65 years and provides the basis for economic evaluation of frailty-focussed interventions. Calibration to outcomes observed over an extended time horizon would improve model validity.

Individual-based model simulations indicate a non-linear catch equation of drifting Fish Aggregating Device-associated tuna

Abstract Catch per unit of fishing effort (CPUE) is often used as an indicator of tuna abundance, where it is assumed that the two are proportional to each other. Tuna catch is therefore typically simplified in tuna population dynamics models and depends linearly on their abundance. In this paper, we use an individual-based model of tuna and their interactions with drifting Fish Aggregating Devices (dFADs) to identify which behavioural, ocean flow, and fishing strategy scenarios lead to an emergent, non-linear dependency between catch, and both tuna and dFAD density at the ∼1○ grid scale. We apply a series of catch response equations to evaluate their ability to model associated catch rate, using tuna and dFAD density as terms. Our results indicate that, regardless of ocean flow, behavioural, or fisher strategy scenario, simulated catch is best modelled with a non-linear dependence on both tuna and dFAD abundance. We discuss how estimators of CPUE at the population scale are potentially biased when assuming a linear catch response.

Modelling COVID-19 epidemic with confirmed cases-driven contact tracing quarantine

The pandemic of novel coronavirus disease 2019 (COVID-19) has been a severe threat to public health. The policy of close contract tracing quarantine is an effective strategy in controlling the COVID-19 epidemic outbreak. In this paper, we developed a mathematical model of the COVID-19 epidemic with confirmed case-driven contact tracing quarantine, and applied the model to evaluate the effectiveness of the policy of contact tracing and quarantine. The model is established based on the combination of the compartmental model and individual-based model simulations, which results in a closed-form delay differential equation model. The proposed model includes a novel form of quarantine functions to represent the number of quarantine individuals following the confirmed cases every day and provides analytic expressions to study the effects of changing the quarantine rate. The proposed model can be applied to epidemic dynamics during the period of community spread and when the policy of confirmed cases-driven contact tracing quarantine is efficient. We applied the model to study the effectiveness of contact tracing and quarantine. The proposed delay differential equation model can describe the average epidemic dynamics of the stochastic-individual-based model, however, it is not enough to describe the diverse response due to the stochastic effect. Based on model simulations, we found that the policy of contact tracing and quarantine can obviously reduce the epidemic size, however, may not be enough to achieve zero-infectious in a short time, a combination of close contact quarantine and social contact restriction is required to achieve zero-infectious. Moreover, the effect of reducing epidemic size is insensitive to the period of quarantine, there are no significant changes in the epidemic dynamics when the quarantine days vary from 7 to 21 days.

Prioritizing Quality Measures in Acute Stroke Care : A Cost-Effectiveness Analysis.

The American Heart Association and American Stroke Association (AHA/ASA) endorsed 15 process measures for acute ischemic stroke (AIS) to improve the quality of care. Identifying the highest-value measures could reduce the administrative burden of quality measure adoption while retaining much of the value of quality improvement. To prioritize AHA/ASA-endorsed quality measures for AIS on the basis of health impact and cost-effectiveness. Individual-based stroke simulation model. Published literature. U.S. patients with incident AIS. Lifetime. Health care sector. Current versus complete (100%) implementation at the population level of quality measures endorsed by the AHA/ASA with sufficient clinical evidence (10 of 15). Life-years, quality-adjusted life-years (QALYs), incremental cost-effectiveness ratios, and incremental net health benefits. Discounted life-years gained from complete implementation would range from 472 (tobacco use counseling) to 34 688 (early carotid imaging) for an annual AIS patient cohort. All AIS quality measures were cost-saving or highly cost-effective by AHA standards (<$50 000 per QALY for high-value care). Early carotid imaging and intravenous tissue plasminogen activator contributed the largest fraction of the total potential value of quality improvement (measured as incremental net health benefit), accounting for 72% of the total value. The top 5 quality measures accounted for 92% of the total potential value. A web-based user interface allows for context-specific sensitivity and scenario analyses. Correlations between quality measures were not incorporated. Substantial variation exists in the potential net benefit of quality improvement across AIS quality measures. Benefits were highly concentrated among 5 of 10 measures assessed. Our results can help providers and payers set priorities for quality improvement efforts and value-based payments in AIS care. National Institute of Neurological Disorders and Stroke.

Niche Specificity, Polygeny, and Pleiotropy in Herbivorous Insects.

AbstractWhat causes host use specificity in herbivorous insects? Population genetic models predict specialization when habitat preference can evolve and there is antagonistic pleiotropy at a performance-affecting locus. But empirically for herbivorous insects, host use performance is governed by many genetic loci, and antagonistic pleiotropy seems to be rare. Here, we use individual-based quantitative genetic simulation models to investigate the role of pleiotropy in the evolution of sympatric host use specialization when performance and preference are quantitative traits. We look first at pleiotropies affecting only host use performance. We find that when the host environment changes slowly, the evolution of host use specialization requires levels of antagonistic pleiotropy much higher than what has been observed in nature. On the other hand, with rapid environmental change or pronounced asymmetries in productivity across host species, the evolution of host use specialization readily occurs without pleiotropy. When pleiotropies affect preference as well as performance, even with slow environmental change and host species of equal productivity, we observe fluctuations in host use breadth, with mean specificity increasing with the pervasiveness of antagonistic pleiotropy. Thus, our simulations show that pleiotropy is not necessary for specialization, although it can be sufficient, provided it is extensive or multifarious.

Tough places and safe spaces: Can refuges save salmon from a warming climate?

The importance of thermal refuges in a rapidly warming world is particularly evident for migratory species, where individuals encounter a wide range of conditions throughout their lives. In this study, we used a spatially explicit, individual-based simulation model to evaluate the buffering potential of cold-water thermal refuges for anadromous salmon and trout (Oncorhynchus spp.) migrating upstream through a warm river corridor that can expose individuals to physiologically stressful temperatures. We considered upstream migration in relation to migratory phenotypes that were defined in terms of migration timing, spawn timing, swim speed, and use of cold-water thermal refuges. Individuals with different migratory phenotypes migrated upstream through riverine corridors with variable availability of cold-water thermal refuges and mainstem temperatures. Use of cold-water refuges (CWRs) decreased accumulated sublethal exposures to physiologically stressful temperatures when measured in degree-days above 20, 21, and 22°C. The availability of CWRs was an order of magnitude more effective in lowering accumulated sublethal exposures under current and future mainstem temperatures for summer steelhead than fall Chinook Salmon. We considered two emergent model outcomes, survival and percent of available energy used, in relation to thermal heterogeneity and migratory phenotype. Mean percent energy loss attributed to future warmer mainstem temperatures was at least two times larger than the difference in energy used in simulations without CWRs for steelhead and salmon. We also found that loss of CWRs reduced the diversity of energy-conserving migratory phenotypes when we examined the variability in entry timing and travel time outside of CWRs in relation to energy loss. Energy-conserving phenotypic space contracted by 7%-23% when CWRs were unavailable under the current thermal regime. Our simulations suggest that, while CWRs do not entirely mitigate for stressful thermal exposures in mainstem rivers, these features are important for maintaining a diversity of migration phenotypes. Our study suggests that the maintenance of diverse portfolios of migratory phenotypes and cool- and cold-water refuges might be added to the suite of policies and management actions presently being deployed to improve the likelihood of Pacific salmonid persistence into a future characterized by climate change.

Nonparametric upscaling of bark beetle infestations and management from plot to landscape level by combining individual-based with Markov chain models

Linked to climate change, drivers such as increased temperatures and decreased water availability affect forest health in complex ways by simultaneously weakening tree vitality and promoting insect pest activity. One major beneficiary of climate-induced changes is the European spruce bark beetle (Ips typographus). To improve the mechanistic understanding of climate change impacts on long-term beetle infestation risks, individual-based simulation models (IBM) such as the bark beetle dispersion model IPS-SPREADS have been proven as effective tools. However, the computational costs of IBMs limit their spatial scale of application. While these tools are best suitable to simulate bark beetle dynamics on the plot level, upscaling the process to larger areas is challenging. The larger spatial scale is, nevertheless, often required to support the selection of adequate management intervention. Here, we introduce a novel two-step approach to address this challenge: (1) we use the IPS-SPREADS model to simulate the bark beetle dispersal at a local scale by dividing the research area into 250 × 250 m grid cells; and (2) we then apply a metamodel framework to upscale the results to the landscape level. The metamodel is based on Markov chains derived from the infestation probabilities of IPS-SPREADS results and extended by considering neighbor interaction and spruce dieback of each focal cell. We validated the metamodel by comparing its predictions with infestations observed in 2017 and 2018 in the Saxon Switzerland national park, Germany, and tested sanitation felling as a measure to prevent potential further outbreaks in the region. Validation showed an improvement in predictions by introducing the model extension of beetle spreading from one cell to another. The metamodel forecasts indicated an increase in the risk of infestation for adjacent forest areas. In case of a beetle mass outbreak, sanitation felling intensities of 80 percent and above seem to mitigate further outbreak progression.

A two-phase approach to re-calibrating expensive computer simulation for sex-specific colorectal neoplasia development modeling

BackgroundMedical evidence from more recent observational studies may significantly alter our understanding of disease incidence and progression, and would require recalibration of existing computational and predictive disease models. However, it is often challenging to perform recalibration when there are a large number of model parameters to be estimated. Moreover, comparing the fitting performances of candidate parameter designs can be difficult due to significant variation in simulated outcomes under limited computational budget and long runtime, even for one simulation replication.MethodsWe developed a two-phase recalibration procedure. As a proof-of-the-concept study, we verified the procedure in the context of sex-specific colorectal neoplasia development. We considered two individual-based state-transition stochastic simulation models, estimating model parameters that govern colorectal adenoma occurrence and its growth through three preclinical states: non-advanced precancerous polyp, advanced precancerous polyp, and cancerous polyp. For the calibration, we used a weighted-sum-squared error between three prevalence values reported in the literature and the corresponding simulation outcomes. In phase 1 of the calibration procedure, we first extracted the baseline parameter design from relevant studies on the same model. We then performed sampling-based searches within a proper range around the baseline design to identify the initial set of good candidate designs. In phase 2, we performed local search (e.g., the Nelder-Mead algorithm), starting from the candidate designs identified at the end of phase 1. Further, we investigated the efficiency of exploring dimensions of the parameter space sequentially based on our prior knowledge of the system dynamics.ResultsThe efficiency of our two-phase re-calibration procedure was first investigated with CMOST, a relatively inexpensive computational model. It was then further verified with the V/NCS model, which is much more expensive. Overall, our two-phase procedure showed a better goodness-of-fit than the straightforward employment of the Nelder-Mead algorithm, when only a limited number of simulation replications were allowed. In addition, in phase 2, performing local search along parameter space dimensions sequentially was more efficient than performing the search over all dimensions concurrently.ConclusionThe proposed two-phase re-calibration procedure is efficient at estimating parameters of computationally expensive stochastic dynamic disease models.

Are there plenty of fish in the sea? How life history traits affect the eco-evolutionary consequences of population oscillations

Understanding fish population oscillations is important for both fundamental population biology and for fisheries science. Much research has focused on the causes of population oscillations, but the eco-evolutionary consequences of population oscillations are unclear. Here, we used an empirically parametrised individual-based simulation model to explore the consequences of oscillations with different amplitudes and wavelengths. We show that oscillations with a wavelength shorter than the maximum lifespan of the fish produce marked differences in the evolutionary trajectories of asymptotic length. Wavelengths longer than the maximum lifespan of the fish, in turn, mainly manifest as ecological effects seen as the population biomass oscillation. The evolutionary and ecological differences increase with increasing amplitude, however, the two-year wavelength causes opposing results from all the other scenarios. This is likely facilitated by the relatively stable number of fish in the population as a poor year is always counteracted by the previous good year and vice versa. Our results highlight the evolutionary signatures and following ecological consequences that natural population oscillations can cause.

Mechanistic support for increased primary production around artificial reefs.

Understanding factors controlling primary production is fundamental for the protection, management, and restoration of ecosystems. Tropical seagrass ecosystems are among the most productive ecosystems worldwide, yielding tremendous services for society. Yet they are also among the most impaired from anthropogenic stressors, prompting calls for ecosystem-based restoration approaches. Artificial reefs (ARs) are commonly applied in coastal marine ecosystems to rebuild failing fisheries and have recently gained attention for their potential to promote carbon sequestration. Nutrient hotspots formed via excretion from aggregating fishes have been empirically shown to enhance local primary production around ARs in seagrass systems. Yet, if and how increased local production affects primary production at ecosystem scale remains unclear, and empirical tests are challenging. We used a spatially explicit individual-based simulation model that combined a data-rich single-nutrient primary production model for seagrass and bioenergetics models for fish to test how aggregating fish on ARs affect seagrass primary production at patch and ecosystem scales. Specifically, we tested how the aggregation of fish alters (i) ecosystem seagrass primary production at varying fish densities and levels of ambient nutrient availability and (ii) the spatial distribution of seagrass primary production. Comparing model ecosystems with equivalent nutrient levels, we found that when fish aggregate around ARs, ecosystem-scale primary production is enhanced synergistically. This synergistic increase in production was caused by nonlinear dynamics associated with nutrient uptake and biomass allocation that enhances aboveground primary production more than belowground production. Seagrass production increased near the AR and decreased in areas away from the AR, despite marginal reductions in seagrass biomass at the ecosystem level. Our simulation's findings that ARs can increase ecosystem production provide novel support for ARs in seagrass ecosystems as an effective means to promote (i) fishery restoration (increased primary production can increase energy input to the food web) and (ii) carbon sequestration, via higher rates of primary production. Although our model represents a simplified, closed seagrass system without complex trophic interactions, it nonetheless provides an important first step in quantifying ecosystem-level implications of ARs as a tool for ecological restoration.

Evolution of anticipatory effects mediated by epigenetic changes

Anticipatory effects mediated by epigenetic changes occur when parents modify the phenotype of their offspring by making epigenetic changes in their gametes, guided by information from an environmental cue. To investigate when do anticipatory effects mediated by epigenetic changes evolve in a fluctuating environment, I use an individual-based simulation model with explicit genetic architecture. The model allows for the population to respond to environmental changes by evolving plasticity, bet hedging, or by tracking the environment with genetic adaptation, in addition to the evolution of anticipatory effects. The results show that anticipatory effects evolve when the environmental cue provides reliable information about the environment and the environment changes at intermediate rates, provided that fitness costs of anticipatory effects are rather low. Moreover, evolution of anticipatory effects is quite robust to different genetic architectures when reliability of the environmental cue is high. Anticipatory effects always give smaller fitness benefits than within-generation plasticity, suggesting a possible reason for generally small observed anticipatory effects in empirical studies.

Cost-effectiveness of Vitamin A supplementation among children in three sub-Saharan African countries: An individual-based simulation model using estimates from Global Burden of Disease 2019.

Vitamin A Supplementation (VAS) is a cost-effective intervention to decrease mortality associated with measles and diarrheal diseases among children aged 6-59 months in low-income countries. Recently, experts have suggested that other interventions like large-scale food fortification and increasing the coverage of measles vaccination might provide greater impact than VAS. In this study, we conducted a cost-effectiveness analysis of a VAS scale-up in three sub-Saharan African countries. We developed an individual-based microsimulation using the Vivarium simulation framework to estimate the cost and effect of scaling up VAS from 2019 to 2023 in Nigeria, Kenya, and Burkina Faso, three countries with different levels of baseline coverage. We calibrated the model with disease and risk factor estimates from the Global Burden of Disease 2019 (GBD 2019). We obtained baseline coverage, intervention effects, and costs from a systematic review. After the model was validated against GBD inputs, we modeled an alternative scenario where we scaled-up VAS coverage from 2019 to a level that halved the exposure to lack of VAS in 2023. Based on the simulation outputs for DALYs averted and intervention cost, we determined estimates for the incremental cost-effectiveness ratio (ICER) in USD/DALY. Our estimates for ICER are as follows: $860/DALY [95% UI; 320, 3530] in Nigeria, $550/DALY [240, 2230] in Kenya, and $220/DALY [80, 2470] in Burkina Faso. Examining the data for DALYs averted for the three countries over the time span, we found that the scale-up led to 21 [5, 56] DALYs averted per 100,000 person-years in Nigeria, 21 [5, 47] DALYs averted per 100,000 person-years in Kenya, and 14 [0, 37] DALYs averted per 100,000 person-years in Burkina Faso. VAS may no longer be as cost-effective in low-income regions as it has been previously. Updated estimates in GBD 2019 for the effect of Vitamin A Deficiency on causes of death are an additional driver of this lower estimate of cost-effectiveness.

Problems seeded in the past: lagged effects of historical land-use changes can cause an extinction debt in long-lived species due to movement limitation

ContextLand-use change is one of the main threats to biodiversity on the global scale. Legacy effects of historical land-use changes may affect population dynamics of long-lived species, but they are difficult to evaluate through observational studies alone. We present here an interdisciplinary modelling approach as an alternative to address this problem in landscape ecology.ObjectivesAssess effects of agricultural abandonment and anthropisation on the population dynamics of long-lived species. Specifically, we evaluated: (a) how changes in movement patterns caused by land-use change might impact population dynamics; (b) time-lag responses of demographic variables in relation to land-use changes.MethodsWe applied an individual-based and spatial-explicit simulation model of the spur-tighed tortoise (Testudo graeca), an endangered species, to sequences of real-world landscape changes representing agricultural abandonment and anthropisation at the local scale. We analysed different demographic variables and compared an “impact scenario” (i.e., historical land-use changes) with a “control scenario” (no land-use changes).ResultsWhile agricultural abandonment did not lead to relevant changes in demographic variables, anthropisation negatively affected the reproductive rate, population density and the extinction probability with time-lag responses of 20, 30 and 130 years, respectively, and caused an extinction debt of 22%.ConclusionsWe provide an understanding of how changes in animal movement driven by land-use changes can translate into lagged impacts on demography and, ultimately, on population viability. Implementation of proactive mitigation management are needed to promote landscape connectivity, especially for long-lived species for which first signatures of an extinction debt may arise only after decades.

The Dimensions and Units of the Population Interaction Coefficients

We discuss the interpretation and dimensions of the population dynamic parameters that are commonly used to quantify the strength of intraspecific and interspecific interactions. The concept of “interaction strength” is not unequivocal. Its theoretical formalization relies on the generalized Lotka–Volterra model. However, four different ways of parameterizing the model have been proposed in the literature, leading to four different definitions of the term “interaction strength.” In particular, the dimensions of these four definitions are not identical, some of these incorporating explicitly the dimension used to measure the population size. Using an individual-based simulation model as an illustration, we show that, in the latter case, the interaction strength depends implicitly on the habitat size. As a consequence, it is of crucial importance to quantify the population as a density rather than an absolute population abundance. We insist that the dimension of the interaction strength coefficient should not be overlooked and any quantitative estimation must be given with explicit units.

Complex eco-evolutionary dynamics induced by the coevolution of predator\u2013prey movement strategies

The coevolution of predators and prey has been the subject of much empirical and theoretical research that produced intriguing insights into the interplay of ecology and evolution. To allow for mathematical analysis, models of predator–prey coevolution are often coarse-grained, focussing on population-level processes and largely neglecting individual-level behaviour. As selection is acting on individual-level properties, we here present a more mechanistic approach: an individual-based simulation model for the coevolution of predators and prey on a fine-grained resource landscape, where features relevant for ecology (like changes in local densities) and evolution (like differences in survival and reproduction) emerge naturally from interactions between individuals. Our focus is on predator–prey movement behaviour, and we present a new method for implementing evolving movement strategies in an efficient and intuitively appealing manner. Throughout their lifetime, predators and prey make repeated movement decisions on the basis of their movement strategies. Over the generations, the movement strategies evolve, as individuals that successfully survive and reproduce leave their strategy to more descendants. We show that the movement strategies in our model evolve rapidly, thereby inducing characteristic spatial patterns like spiral waves and static spots. Transitions between these patterns occur frequently, induced by antagonistic coevolution rather than by external events. Regularly, evolution leads to the emergence and stable coexistence of qualitatively different movement strategies within the same population. Although the strategy space of our model is continuous, we often observe the evolution of discrete movement types. We argue that rapid evolution, coexistent movement types, and phase shifts between different ecological regimes are not a peculiarity of our model but a result of more realistic assumptions on eco-evolutionary feedbacks and the number of evolutionary degrees of freedom.