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491 results for “population modelling”
Training data for "Apply modeling on population or community data to see effect of year, habitat or site on species abundance"
<p>Datasets for the "Apply modeling on population or community data to see effect of year, habitat or site on species abundance" Galaxy for ecology tutorial</p>
Code for Individual Based Model to assess impact of KHV release on carp population
<p><b>1: </b>Common carp (<i>Cyprinus carpio</i>) is one of the top global invasive vertebrates and can cause significant ecological damage. The Australian Government's National Carp Control Program (NCCP) proposes to release Koi Herpesvirus (KHV) to eradicate feral carp in one of the largest ecological interventions ever attempted. Ecological and human health risks have been highlighted regarding the release of a highly pathogenic viral biocontrol for an aquatic species. The efficacy of KHV has also been questioned, and it has not been demonstrated to produce lasting population reductions.</p> <p> </p> <p>2. We developed an individual-based model (IBM) to examine the ecological and evolutionary response of a carp population after KHV release. This simulated the interaction between fish life history, viral epidemiology, host genetic resistance and population demography to critically evaluate the impact of KHV release under optimal conditions and a "best case scenario" for disease transmission.</p> <p> </p> <p>3. KHV will rarely result in prolonged reductions or population extinctions. Crucially, realistic scenarios result in a rapidly rebounding population of resistant individuals. Additional measures aimed to reduce carp population recovery rate (e.g. with genetic engineering) require rapid efficacy to significantly reduce carp numbers alongside KHV.<br> </p> <p>4. Fish fecundity has an overwhelming influence on viral efficacy as a biocontrol agent when combined with genetic resistance within a population. A high probability of population extinction is only met when carp fecundity is reduced to 1% of biological observations.<br> </p> <p><span>5.<i> Synthesis and applications. </i></span>We use an individual-based model to evaluate the efficacy of Koi Herpesvirus biocontrol in Common <a>Ca</a>rp, and find that high host fecundity combined with genetic resistance results in rapid population rebound after initial large fish-kills. Biocontrol approaches relying on natural selection lose efficacy over successive generations as resistance genes increases in frequency. Given the intense logistical effort and risks to ecosystems and human health associated with large fish kills after viral release, we suggest that sustained manual removal, alongside ecological restoration to favour recovery of native species, provides a risk-free approach to reducing populations.<br> </p>
Data from: Why we should care about movements: Using spatially explicit integrated population models to assess habitat source-sink dynamics
<p>1. Assessing the source-sink status of populations and habitats is of major importance for understanding population dynamics and for the management of natural populations. Sources produce a net surplus of individuals (per capita contribution to the metapopulation >1) and will be the main contributors for self-sustaining populations, whereas sinks produce a deficit (contribution < 1). However, making these types of assessments is generally hindered by the problem of separating mortality from permanent emigration, especially when survival probabilities as well as moved distances are habitat-specific.<br> 2. To address this long-standing issue, we propose a spatial multi-event Integrated Population Model (IPM) that incorporates habitat-specific dispersal distances of individuals. Using information about local movements, this IPM adjusts survival estimates for emigration outside the study area.<br> 3. Analyzing 24 years of data on a farmland passerine (the northern wheatear Oenanthe oenanthe) we assessed habitat-specific contributions, and hence the source-sink status and temporal variation of two key breeding habitats, while accounting for habitat- and sex-specific local dispersal distances of juveniles and adults. We then examined the sensitivity of the source-sink analysis by comparing results with and without accounting for these local movements.<br> 4. Estimates of first-year survival, and consequently habitat-specific contributions, were higher when local movement data were included. The consequences from including movement data were sex specific, with contribution shifting from sink to likely source in one habitat for males, and previously noted habitat differences for females disappearing.<br> 5. Assessing the source-sink status of habitats is extremely challenging. We show that our spatial IPM accounting for local movements can reduce biases in estimates of the contribution by different habitats, and thus reduce the overestimation of the occurrence of sink habitats. This approach allows combining all available data on demographic rates and movements, which will allow better assessment of source-sink dynamics and better informed conservation interventions.</p>
Raw in vitro screening data and R scripts for: A Bayesian method for population-wide cardiotoxicity hazard and risk characterization using an in vitro human model
<p>Human induced pluripotent stem cell (iPSC)-derived cardiomyocytes are an established model for testing potential chemical hazards. Inter-individual variability in toxicodynamic sensitivity has also been demonstrated <i>in vitro</i>; however, quantitative characterization of the population-wide variability has not been fully explored. We sought to develop a method to address this gap by combining a population-based iPSC-derived cardiomyocyte model with Bayesian concentration-response modeling. A total of 136 compounds, including 44 pharmaceuticals and 82 environmental chemicals, were tested in iPSC-derived cardiomyocytes from 43 non-diseased humans. Hierarchical Bayesian population concentration-response modeling was conducted for five phenotypes reflecting cardiomyocyte function or viability. Toxicodynamic variability was quantified through the derivation of chemical- and phenotype-specific variability factors (TDVF). Toxicokinetic modeling was used for probabilistic <i>in vitro</i>-to-<i>in vivo </i>extrapolation in order to derive population-wide margins of safety (MOS) for pharmaceuticals and margins of exposure (MOE) for environmental chemicals. Pharmaceuticals were found to be active across all phenotypes. Over half of tested environmental chemicals showed activity in at least one phenotype, most commonly positive chronotropy. TDVF estimates for the functional phenotypes were greater than those for cell viability, usually exceeding the generally-assumed default of ~3. Population variability-based MOS for pharmaceuticals were correctly predicted to be relatively narrow, between 10-100; however, MOE for environmental chemicals, based on population exposure estimates, generally exceeded 1000, suggesting they pose little risk at general population exposures even to sensitive sub populations. This study represents a first of its kind human <i>in vitro</i> model that can be used to characterize toxicodynamic population variability in cardiotoxic risk.</p>
Data from: False exclusion: a case to embed predator performance in classical population models
We argue that predator-prey dynamics, a cornerstone of ecology, can be driven by insufficiently-explored aspects of predator performance that are inherently prey-dependent: i.e., these have been falsely excluded. Classical -Lotka Volterra based- models tend to only consider prey-dependent ingestion rate. We highlight three other prey-dependent responses and provide empirically-derived functions to describe them. These functions introduce neglected nonlinearities and threshold behaviours into dynamic models leading to unexpected outcomes: specifically, as prey abundance increases predators: 1) become less efficient at using prey; 2) initially allocate resources towards survival and then allocate resources towards reproduction; and 3) are less likely to die. Based on experiments using model-zooplankton, we explore consequences of including these functions in the classical structure and show they alter qualitative and quantitative dynamics of an empirically-informed, generic predator-prey model. Through bifurcation analysis, our revised structure predicts: 1) predator extinctions, where the classical structure allows persistence; 2) predator survival, where the classical structure drives predators towards extinction; and 3) greater stability through smaller amplitude of cycles, relative to the classical structure. Then, by exploring parameter space, we show how these responses alter predictions of predator-prey stability and competition between predators. Based on our results, we suggest that classical assumptions about predator responses to prey abundance should be re-evaluated.
Data from: A structured population model suggests that long life and post-reproductive lifespan promote the evolution of cooperation
Social organization correlates with longevity across animal taxa. This correlation has been explained by selection for longevity by social evolution. The reverse causality is also conceivable but has not been sufficiently considered. We constructed a simple, spatially structured population model of asexually reproducing individuals to study the effect of temporal life history structuring on the evolution of cooperation. Individuals employed fixed strategies of cooperation or defection towards all neighbours in a basic Prisoner׳s Dilemma paradigm. Individuals aged and transitioned through different life history stages asynchronously without migration. An individual׳s death triggered a reproductive event by one immediate neighbour. The specific neighbour was chosen probabilistically according to the cumulative payoff from all local interactions. Varying the duration of pre-reproductive, reproductive, and post-reproductive life history stages, long-term simulations allowed a systematic evaluation of the influence of the duration of these specific life history stages. Our results revealed complex interactions among the effects of the three basic life history stages and the benefit to defect. Overall, a long post-reproductive stage promoted the evolution of cooperation, while a prolonged pre-reproductive stage has a negative effect. In general, the total length of life also increased the probability of the evolution of cooperation. Thus, our specific model suggests that the timing of life history transitions and total duration of life history stages may affect the evolution of cooperative behaviour. We conclude that the causation of the empirically observed association of life expectancy and sociality may be more complex than previously realized.
Data from: Multi-perspective predictive modeling for acute kidney injury in general hospital populations using electronic medical records
Objective: Acute kidney injury (AKI) in hospitalized patients puts them at much higher risk for developing future health problems such as chronic kidney disease, stroke, and heart disease. Accurate AKI prediction would allow timely prevention and intervention. However, current AKI prediction researches pay less attention to model building strategies that meet complex clinical application scenario. This study aims to build and evaluate AKI prediction models from multiple perspectives that reflect different clinical applications. Material and Methods: A retrospective cohort of 76,957 encounters and relevant clinical variables were extracted from a tertiary care, academic hospital electronic medical record (EMR) system between November 2007 and December 2016. Five machine learning methods were used to build prediction models. Prediction tasks from four clinical perspectives with different modeling and evaluation strategies were designed to build and evaluate the models. Results: Experimental analysis of the AKI prediction models built from four different clinical perspectives suggest a realistic prediction performance in cross-validated AUC ranging from 0.720 to 0.764. Discussion: Results show that models built at admission is effective for predicting AKI events in the next day; models built using data with a fixed lead time to AKI onset is still effective in the dynamic clinical application scenario in which each patient's lead time to AKI onset is different. Conclusion: To our best knowledge, this is the first systematic study to explore multiple clinical perspectives in building predictive models for AKI in the general inpatient population to reflect real performance in clinical application.
Data from: QuLinePlus: extending plant breeding strategy and genetic model simulation to cross-pollinated populations – case studies in forage breeding
Plant breeders are supported by a range of tools that assist them to make decisions about the conduct or design of plant breeding programs. Simulations are a strategic tool that enable the breeder to integrate the multiple components of a breeding program into a number of proposed scenarios that are compared by a range of statistics measuring the efficiency of the proposed systems. A simulation study for the trait growth score compared two major strategies for breeding forage species, among half-sib family selection and among and within half-sib family selection. These scenarios highlighted new features of the QuLine program, now called QuLinePlus, incorporated to enable the software platform to be used to simulate breeding programs for cross pollinated species. Each strategy was compared across three levels of HS family mean heritability (0.1, 0.5 and 0.9), across three sizes of the initial parental population (10, 50, and 100), and across three genetic effects models (fully additive model, a mixture of additive, partial and over dominance model, and a mixture of partial dominance and over dominance model). Among and within half-sib selection performed better than among half-sib selection for all scenarios. The new tools introduced into QuLinePlus should serve to accurately compare among methods and provide direction on how to achieve specific goals in the improvement of plant breeding programs for cross breeding species.
Data from: Modeling and quantifying frequency-dependent fitness in microbial populations with cross-feeding interactions
Coexistence of two or more populations by frequency-dependent selection is common in nature, and it often arises even in well-mixed experiments with microbes. If ecology is to be incorporated into models of population genetics, then it is important to represent accurately the functional form of frequency-dependent interactions. However, measuring this functional form is problematic for traditional fitness assays, which assume a constant fitness difference between competitors over the course of an assay. Here, we present a theoretical framework for measuring the functional form of frequency-dependent fitness by accounting for changes in abundance and relative fitness during a competition assay. Using two examples of ecological coexistence that arose in a long-term evolution experiment with Escherichia coli, we illustrate accurate quantification of the functional form of frequency-dependent relative fitness. Using a Monod-type model of growth dynamics, we show that two ecotypes in a typical cross-feeding interaction—such as when one bacterial population uses a byproduct generated by another—yields relative fitness that is linear with relative frequency.
Data from: Auxotrophy and intra-population complementary in the 'interactome' of a cultivated freshwater model community
Microorganisms are usually studied either in highly complex natural communities or in isolation as monoclonal model populations that we manage to grow in the laboratory. Here, we uncover the biology of some of the most common and yet-uncultured bacteria in freshwater environments using a mixed culture from Lake Grosse Fuchskuhle. From a single shotgun metagenome of a freshwater mixed culture of low complexity, we recovered four high-quality metagenome-assembled genomes (MAGs) for metabolic reconstruction. This analysis revealed the metabolic interconnectedness and niche partitioning of these naturally dominant bacteria. In particular, vitamin- and amino acid biosynthetic pathways were distributed unequally with a member of Crenarchaeota most likely being the sole producer of vitamin B12 in the mixed culture. Using coverage-based partitioning of the genes recovered from a single MAG intrapopulation metabolic complementarity was revealed pointing to 'social' interactions for the common good of populations dominating freshwater plankton. As such, our MAGs highlight the power of mixed cultures to extract naturally occurring 'interactomes' and to overcome our inability to isolate and grow the microbes dominating in nature.
Data from: Measuring population differentiation using GST or D? A simulation study with microsatellite DNA markers under a finite island model and nonequilibrium conditions
Genetic differentiation of populations is a key question in population genetic investigations. Wright's FST (and its relatives such as GST) has been a standard measure of differentiation. However, the deficiencies of these indexes and their significance have been increasing realized in recent years, leading to some new measures being proposed, such as Jost's (2008) D. This has also stimulated some considerable debate which, in certain sense, makes empirical biologists even more confused, for example, on statistics which should be used for estimating population differentiation. Here we report a simulation study with neutral microsatellite DNA loci under a finite island model to compare the performance of GST and D, under non-equilibrium conditions, in particular. Our results suggest that there exist fundamental differences between the two statistics and neither GST nor D operate satisfactorily in all situations for quantifying differentiation. D is very sensitive to mutation models but GST noticeably less so ...
Data from: Modeling the growth and decline of pathogen effective population size provides insight into epidemic dynamics and drivers of antimicrobial resistance
Non-parametric population genetic modeling provides a simple and flexible approach for studying demographic history and epidemic dynamics using pathogen sequence data. Existing Bayesian approaches are premised on stochastic processes with stationary increments which may provide an unrealistic prior for epidemic histories which feature extended period of exponential growth or decline. We show that non-parametric models defined in terms of the growth rate of the effective population size can provide a more realistic prior for epidemic history. We propose a non-parametric autoregressive model on the growth rate as a prior for effective population size, which corresponds to the dynamics expected under many epidemic situations. We demonstrate the use of this model within a Bayesian phylodynamic inference framework. Our method correctly reconstructs trends of epidemic growth and decline from pathogen genealogies even when genealogical data is sparse and conventional skyline estimators erroneously predict stable population size. We also propose a regression approach for relating growth rates of pathogen effective population size and time-varying variables that may impact the replicative fitness of a pathogen. The model is applied to real data from rabies virus and Staphylococcus aureus epidemics. We find a close correspondence between the estimated growth rates of a lineage of methicillin-resistant S. aureus and population-level prescription rates of beta-lactam antibiotics. The new models are implemented in an open source R package called skygrowth which is available at https://github.com/mrc-ide/skygrowth.
Estimating correlations among demographic parameters in population models
<p>Estimating correlations among demographic parameters is critical to understanding population dynamics and life-history evolution, where correlations among parameters can inform our understanding of life-history trade-offs, result in effective applied conservation actions, and shed light on evolutionary ecology. The most common approaches rely on the multivariate normal distribution, and its conjugate inverse Wishart prior distribtion. However, the inverse Wishart prior for the covariance matrix of multivariate normal distributions has a strong influence on posterior distributions. As an alternative to the inverse Wishart distribution, we individually parameterize the covariance matrix of a multivariate normal distribution to accurately estimate variances (σ<sup>2</sup>) of, and process correlations (ρ) between, demographic parameters. We evaluate this approach using simulated capture-mark-recapture data. We then use this method to examine process correlations between adult and juvenile survival of black brent marked on the Yukon-Kuskokwim River Delta, Alaska (1988-2014). Our parameterization consistently outperformed the conjugate inverse Wishart prior for simulated data, where the means of posterior distributions estimated using an inverse Wishart prior were substantially different from the values used to simulate the data. Brent adult and juvenile annual apparent survival rates were strongly positively correlated (ρ = 0.563, 95% CRI 0.181 − 0.823), suggesting that habitat conditions have significant effects on both adult and juvenile survival. We provide robust simulation tools, and our methods can readily be expanded for use in other capture-recapture or capture-recovery frameworks. Further, our work reveals limits on the utility of these approaches when study duration or sample sizes are small.</p>
Data from: Integrated population modelling reveals a perceived source to be a cryptic sink
Demographic links among fragmented populations are commonly studied as source-sink dynamics, whereby source populations exhibit net recruitment and net emigration, while sinks suffer net mortality but enjoy net immigration. It is commonly assumed that large, persistent aggregations of individuals must be sources, but this ignores the possibility that they are sinks instead, buoyed demographically by immigration. We tested this assumption using Bayesian integrated population modelling of Greenland white-fronted geese (Anser albifrons flavirostris) at their largest wintering site (Wexford, Ireland), combining capture–mark–recapture, census and recruitment data collected from 1982 to 2010. Management for this subspecies occurs largely on wintering areas; thus, study of source-sink dynamics of discrete regular wintering units provides unprecedented insights into population regulation and enables identification of likely processes influencing population dynamics at Wexford and among 70 other Greenland white-fronted goose wintering subpopulations. Using results from integrated population modelling, we parameterized an age-structured population projection matrix to determine the contribution of movement rates (emigration and immigration), recruitment and mortality to the dynamics of the Wexford subpopulation. Survival estimates for juvenile and adult birds at Wexford and adult birds elsewhere fluctuated over the 29-year study period, but were not identifiably different. However, per capita recruitment rates at Wexford in later years (post-1995) were identifiably lower than in earlier years (pre-1995). The observed persistence of the Wexford subpopulation was only possible with high rates of immigration, which exceeded emigration in each year. Thus, despite its apparent stability, Wexford has functioned as a sink over the entire study period. These results demonstrate that even large subpopulations can potentially be sinks, and that movement dynamics (e.g. immigration) among winters can dramatically obscure key processes driving subpopulation size. Further, novel population models which integrate capture–mark–recapture, census and recruitment data are essential to correctly ascribing source-sink status and accurately informing development of site-safeguard networks.
Data from: Integrated population models poorly estimate the demographic contribution of immigration
<p class="BodyA">Estimating the contribution of demographic parameters to changes in population growth is essential for understanding why populations fluctuate. Integrated Population Models (IPMs) offer a possibility to estimate contributions of additional demographic parameters, for which no data have been explicitly collected: typically immigration. Such parametersare often subsequently highlighted as important drivers of population growth. Yet, accuracy in estimating their temporal variation, and consequently their contribution to changes in population growth rate, has not been investigated.</p> <p class="BodyA">To quantify the magnitude and cause of potential biases when estimating the contribution of immigration using IPMs, we simulated data (using Northern Wheatear <i>Oenanthe oenanthe </i>population estimates) from controlled scenarios to examine potential biases and how they depend on IPM parameterization, formulation of priors, the level of temporal variation in immigration, and sample size. We also used empirical data on populations with known rates of immigration: Soay Sheep <i>Ovis aries</i> and Mauritius kestrel <i>Falco punctatus</i> with zero immigration and<i> </i>grey wolf <i>Canis lupus</i> in Scandinavia with<i> </i>near-zero immigration.</p> <p class="Default">IPMs strongly overestimated the contribution of immigration to changes in population growth in scenarios when immigration was simulated with zero temporal variation (proportion of variance attributed to immigration = 63% for the more constrained formulation and real sample size) and in the wild populations, where the true number of immigrants was zero or near-zero (Kestrel 19.1-98.2%, Sheep 4.2-36.1%, Wolf 84.0-99.2%). Although the estimation of immigration in the simulation study became more accurate with increasing temporal variation and sample size, it was often not possible to distinguish between an accurate estimation from data with high temporal variation versus an overestimation from data with low temporal variation. Unrealistically large sample sizes may be required to estimate the contribution of immigration well.</p> <p class="Body">To minimise the risk of overestimating the contribution of immigration (or any additional parameter) in IPMs, we recommend to: (i) look for evidence of variation in immigration before investigating its contribution to population growth, (ii) simulate and model data for comparison to the real data, and (iii) use explicit data on immigration when possible.</p>
Data from: The response of migratory populations to phenological change: a Migratory Flow Network modelling approach
1. Declines in migratory species have been linked to anthropogenic climate change through phenological mismatch, which arises due to asynchronies between the timing of life-history events (such as migration) and the phenology of available resources. Long-distance migratory species may be particularly vulnerable to phenological change in their breeding ranges, since the timing of migration departure is based on environmental cues at distant non-breeding sites. 2. Migrants may, however, be able to adjust migration speed en route to the breeding grounds and thus ability of migrants to update their timing of migration may depend critically on stopover frequency during migration; however, understanding how migratory strategy influences population dynamics is hindered by a lack of predictive models explicitly linking habitat quality to demography and movement patterns throughout the migratory cycle. 3. Here, we present a novel modelling framework, the Migratory Flow Network (MFN), in which the seasonally varying attractiveness of breeding, winter, and stopover regions drives the direction and timing of migration based on a simple general flux law. 4. We use the MFN to investigate how populations respond to shifts in breeding site phenology based on their frequency of stopover and ability to detect and adapt to these changes. 5. With perfect knowledge of advancing phenology, 'jump' migrants (low frequency stopover) require more adaptation for populations to recover than 'hop' and 'skip' (high or medium frequency stopover) migrants. If adaptation depends on proximity, hop and skip migrants' populations can recover but jump migrants cannot adjust and decline severely. 6. These results highlight the importance of understanding migratory strategies and maintaining high-quality stopover habitat to buffer migratory populations from climate-induced mismatch. 7. We discuss how MFNs could be applied to diverse migratory taxa, and highlight the potential of MFNs as a tool for exploring how migrants respond to other environmental changes such as habitat loss.
Data from: Understanding past population dynamics: Bayesian coalescent-based modeling with covariates
Effective population size characterizes the genetic variability in a population and is a parameter of paramount importance in population genetics and evolutionary biology. Kingman's coalescent process enables inference of past population dynamics directly from molecular sequence data, and researchers have developed a number of flexible coalescent-based models for Bayesian nonparametric estimation of the effective population size as a function of time. Major goals of demographic reconstruction include identifying driving factors of effective population size, and understanding the association between the effective population size and such factors. Building upon Bayesian nonparametric coalescent-based approaches, we introduce a flexible framework that incorporates time-varying covariates that exploit Gaussian Markov random fields to achieve temporal smoothing of effective population size trajectories. To approximate the posterior distribution, we adapt efficient Markov chain Monte Carlo algorithms designed for highly structured Gaussian models. Incorporating covariates into the demographic inference framework enables the modeling of associations between the effective population size and covariates while accounting for uncertainty in population histories. Furthermore, it can lead to more precise estimates of population dynamics. We apply our model to four examples. We reconstruct the demographic history of raccoon rabies in North America and find a significant association with the spatiotemporal spread of the outbreak. Next, we examine the effective population size trajectory of the DENV-4 virus in Puerto Rico along with viral isolate count data and find similar cyclic patterns. We compare the population history of the HIV-1 CRF02_AG clade in Cameroon with HIV incidence and prevalence data and find that the effective population size is more reflective of incidence rate. Finally, we explore the hypothesis that the population dynamics of musk ox during the Late Quaternary period were related to climate change.
Data from: Modeling effects of nonbreeders on population growth estimates
Adult individuals that do not breed in a given year occur in a wide range of natural populations. However, such nonbreeders are often ignored in theoretical and empirical population studies, limiting our knowledge of how nonbreeders affect realized and estimated population dynamics and potentially impeding projection of deterministic and stochastic population growth rates. We present and analyse a general modelling framework for systems where breeders and nonbreeders differ in key demographic rates, incorporating different forms of nonbreeding, different life histories and frequency-dependent effects of nonbreeders on demographic rates of breeders. Comparisons of estimates of deterministic population growth rate, λ, and demographic variance, math formula, from models with and without distinct nonbreeder classes show that models that do not explicitly incorporate nonbreeders give upwardly biased estimates of math formula, particularly when the equilibrium ratio of nonbreeders to breeders, math formula, is high. Estimates of λ from empirical observations of breeders only are substantially inflated when individuals frequently re-enter the breeding population after periods of nonbreeding. Sensitivity analyses of diverse parameterizations of our model framework, with and without negative frequency-dependent effects of nonbreeders on breeder demographic rates, show how changes in demographic rates of breeders vs. nonbreeders differentially affect λ. In particular, λ is most sensitive to nonbreeder parameters in long-lived species, when math formula, and when individuals are unlikely to breed at several consecutive time steps. Our results demonstrate that failing to account for nonbreeders in population studies can obscure low population growth rates that should cause management concern. Quantifying the size and demography of the nonbreeding section of populations and modelling appropriate demographic structuring is therefore essential to evaluate nonbreeders' influence on deterministic and stochastic population dynamics.
Data from: We happy few: using structured population models to identify the decisive events in the lives of exceptional individuals
In any population, some individuals make it big: they are among the few that produce many offspring, grow to large size, and so on. What distinguishes the lives of these happy few? We present three approaches for identifying what factors distinguish those "lucky" individuals who come to dominate reproduction in a population without fixed differences between individuals (genotype, site quality, etc.): comparing life-history trajectories for lucky and unlucky individuals and calculating the elasticity of the probability of becoming lucky to perturbations in demographic rates at a given size or a given age. As examples we consider published size-structured integral projection models for the tropical tree Dacrydium elatum and the semiarid shrub Artemisia ordosica and an age-size-structured matrix model for the tropical tree Cedrela ordosica. We find that good fortune (e.g., rapid growth) when small and young matters much more than good fortune when older and larger. Becoming lucky is primarily a matter of surviving while others die. For species with more variable growth (such as Cedrela and Ordosica), it is also a matter of growing fast. We focus on reproductive skew, but our methods are broadly applicable and can be used to investigate how individuals come to be exceptional in any aspect.
Supplementary material 1 from: Topping CJ, Duan X (2024) Managing large and complex population operations with agent-based models: The ALMaSS Population_Manager. Food and Ecological Systems Modelling Journal 5: e117593. https://doi.org/10.3897/fmj.5.117593
The code documentation for the ALMaSS Population_Manager class
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.