Example computer code (R script) and associated data to run the Greater Glider simulation example in the manuscript.

Mean, minimum, maximum, and standard deviation for final populations in year 2086 using 10,000 runs are shown for each scenario. Four emission trajectories were used (RCP2.6, RCP4.5, RCP6.0, RCP8.5), where the RCP2.6 scenario assumes the least change in emissions from historic levels and the RCP8.5 scenario assumes the largest change in carbon emissions.

In 'Simulation to evaluate response of population models to annual trends in detectability', we provide data and R code necessary to create simulation scenarios and estimate trends with different population models (Monroe et al. 2019). Literature cited: Monroe, A. P., G. T. Wann, C. L. Aldridge, and P. S. Coates. 2019. The importance of simulation assumptions when evaluating detectability in population models. Ecosphere 10(7):e02791. 10.1002/ecs2.2791

MIIND is a software platform for easily and efficiently simulating the behaviour of interacting populations of point neurons governed by any 1D or 2D dynamical system. The simulator is entirely agnostic to the underlying neuron model of each population and provides an intuitive method for controlling the amount of noise which can significantly affect the overall behaviour. A network of populations can be set up quickly and easily using MIIND's XML-style simulation file format describing simulation parameters such as how populations interact, transmission delays, post-synaptic potentials, and what output to record. During simulation, a visual display of each population's state is provided for immediate feedback of the behaviour and population activity can be output to a file or passed to a Python script for further processing. The Python support also means that MIIND can be integrated into other software such as The Virtual Brain. MIIND's population density technique is a geometric and visual method for describing the activity of each neuron population which encourages a deep consideration of the dynamics of the neuron model and provides insight into how the behaviour of each population is affected by the behaviour of its neighbours in the network. For 1D neuron models, MIIND performs far better than direct simulation solutions for large populations. For 2D models, performance comparison is more nuanced but the population density approach still confers certain advantages over direct simulation. MIIND can be used to build neural systems that bridge the scales between an individual neuron model and a population network. This allows researchers to maintain a plausible path back from mesoscopic to microscopic scales while minimising the complexity of managing large numbers of interconnected neurons. In this paper, we introduce the MIIND system, its usage, and provide implementation details where appropriate.

Abstract

The dataset is a relational dataset of 10,003,891 individuals (2,501,755 households), representing the entire population of an imaginary middle-income country. The dataset contains two data files: one with variables at the household level, the other one with variables at the individual level. It includes variables that are typically collected in population censuses (demography, education, occupation, dwelling characteristics, fertility, mortality, and migration) and in household surveys (household expenditure, anthropometric data for children, assets ownership). The data only includes ordinary households (no community households). The dataset was created using REaLTabFormer, a model that leverages deep learning methods. The dataset was created for the purpose of training and simulation and is not intended to be representative of any specific country.

A sample dataset of 8000 households was created out of this full-population dataset, and is also distributed as open data.

Geographic coverage

The dataset is a synthetic dataset for an imaginary country. It was created to represent the full national population of this country, by province and district (equivalent to admin1 and admin2 levels) and by urban/rural areas of residence.

Analysis unit

household, Individual

Universe

The dataset is a fully-synthetic dataset representative of the resident population of ordinary households for an imaginary middle-income country.

Kind of data

cen

Mode of data collection

other

Research instrument

The dataset is a synthetic dataset. Although the variables it contains are variables typically collected from sample surveys or population censuses, no questionnaire is available for this dataset. A "fake" questionnaire was however created for the sample dataset extracted from this dataset, to be used as training material.

Cleaning operations

The synthetic data generation process included a set of "validators" (consistency checks, based on which synthetic observation were assessed and rejected/replaced when needed). Also, some post-processing was applied to the data to result in the distributed data files.

This dataset contains the following ".PDF", ".R", and ".RData" files: (1) A PDF file "Description of the SimuBP function.PDF"; (2) R scripts for Algorithm 1 (SimuBP), Algorithm 2, and Algorithm 3; (3) R scripts for Simulations S1a, S1b, S1c, S2a, S2b, S2c, and S3a; (4) An R script "pLD.R" used in Simulation S1c. (5) Results generated in Simulations S1a, S1b, S1c, S2a, S2b, and S3a.

The file contains the individual-based simulation code that is used in the paper "Population rescue through an increase of the selfing rate under pollen limitation: plasticity vs. evolution". This study built eco-evolutionary models and individual-based simulations to explore the demographic and genetic conditions in which higher self-fertilization by plasticity and/or evolution rescues populations, following deficits due to sudden onset of pollen limitation. The code is written in C++. Please first read the README.txt file for instructions and details.

Background/Question/Methods Roads can have major impacts on wildlife populations. They fragment the landscape, thus reducing the dispersal ability and gene flow of species, and they are also a major source of mortality due to road killings. Thus, roads are responsible for the reduction of population sizes, and can even lead to the extinction of a given population. Therefore, the assessment of the impact that roads have on wildlife, especially in densely human populated areas, is a major concern for conservation efforts. Here, based on Skellam's diffusion model, we develop an analytical framework and simulation tools to assess this impact. Our model treats space explicitly and consists of a periodic landscape where the basic patch has a rectangular shape. Its analytical solution is only possible in simple situations, such as when populations have exponential growth or when individuals necessarily die when crossing a road. In order to deal with more realistic problems we used numerical simulations based on a discretized version of the original model. Results/Conclusions We exemplify the application of our methods by studying, first, how minimum patch size and its geometrical shape relate to the survival of a population, and, second, how the size of nonviable patch relates to time to extinction of a population. Concerning patch size and shape, our model highlights the negative relationship between diffusion and persistence of populations, and how it is influenced by the layout of the roads. Specifically, we show that the distance between roads and the shape of the area are determining factors. For instance, populations are at higher risk of … Palabras clave: Population, Simulation

Link to Populus Software

In order to anticipate the impact of local public policies, a synthetic population reflecting the characteristics of the local population provides a valuable test bed. While synthetic population datasets are now available for several countries, there is no open-source synthetic population for Canada. We propose an open-source synthetic population of individuals and households at a fine geographical level for Canada for the years 2021, 2023 and 2030. Based on 2016 census data and population projections, the synthetic individuals have detailed socio-demographic attributes, including age, sex, income, education level, employment status and geographic locations, and are related into households. A comparison of the 2021 synthetic population with 2021 census data over various geographical areas validates the reliability of the synthetic dataset. Users can extract populations from the dataset for specific zones, to explore ‘what if’ scenarios on present and future populations. They can extend the dataset using local survey data to add new characteristics to individuals. Users can also run the code to generate populations for years up to 2042.

To capture the full social and economic benefits of AI, new technologies must be sensitive to the diverse needs of the whole population. This means understanding and reflecting the complexity of individual needs, the variety of perceptions, and the constraints that might guide interaction with AI. This challenge is no more relevant than in building AI systems for older populations, where the role, potential, and outstanding challenges are all highly significant.

The RAIM (Responsible Automation for Inclusive Mobility) project will address how on-demand, electric autonomous vehicles (EAVs) might be integrated within public transport systems in the UK and Canada to meet the complex needs of older populations, resulting in improved social, economic, and health outcomes. The research integrates a multidisciplinary methodology - integrating qualitative perspectives and quantitative data analysis into AI-generated population simulations and supply optimisation. Throughout the project, there is a firm commitment to interdisciplinary interaction and learning, with researchers being drawn from urban geography, ageing population health, transport planning and engineering, and artificial intelligence.

The RAIM project will produce a diverse set of outputs that are intended to promote change and discussion in transport policymaking and planning. As a primary goal, the project will simulate and evaluate the feasibility of an on-demand EAV system for older populations. This requires advances around the understanding and prediction of the complex interaction of physical and cognitive constraints, preferences, locations, lifestyles and mobility needs within older populations, which differs significantly from other portions of society. With these patterns of demand captured and modelled, new methods for meeting this demand through optimisation of on-demand EAVs will be required. The project will adopt a forward-looking, interdisciplinary approach to the application of AI within these research domains, including using Deep Learning to model human behaviour, Deep Reinforcement Learning to optimise the supply of EAVs, and generative modelling to estimate population distributions.

A second component of the research involves exploring the potential adoption of on-demand EAVs for ageing populations within two regions of interest. The two areas of interest - Manitoba, Canada, and the West Midlands, UK - are facing the combined challenge of increasing older populations with service issues and reducing patronage on existing services for older travellers. The RAIM project has established partnerships with key local partners, including local transport authorities - Winnipeg Transit in Canada, and Transport for West Midlands in the UK - in addition to local support groups and industry bodies. These partnerships will provide insights and guidance into the feasibility of new AV-based mobility interventions, and a direct route to influencing future transport policy. As part of this work, the project will propose new approaches for assessing the economic case for transport infrastructure investment, by addressing the wider benefits of improved mobility in older populations.

At the heart of the project is a commitment to enhancing collaboration between academic communities in the UK and Canada. RAIM puts in place opportunities for cross-national learning and collaboration between partner organisations, ensuring that the challenges faced in relation to ageing mobility and AI are shared. RAIM furthermore will support the development of a next generation of researchers, through interdisciplinary mentoring, training, and networking opportunities.

Characterizing how frequently, and at what life stages and spatial scales, dispersal occurs can be difficult, especially for species with cryptic juvenile periods and long reproductive life spans. Using a combination of mark–recapture information, microsatellite genetic data, and demographic simulations, we characterize natal and breeding dispersal patterns in the long-lived, slow-maturing, and endangered Blanding’s turtle (Emydoidea blandingii), focusing on nesting females. We captured and genotyped 310 individual Blanding’s turtles (including 220 nesting females) in a central Wisconsin population from 2010 to 2013, with additional information on movements among 3 focal nesting areas within this population available from carapace-marking conducted from 2001 to 2009. Mark–recapture analyses indicated that dispersal among the 3 focal nesting areas was infrequent (<0.03 annual probability). Dyads of females with inferred first-order relationships were more likely to be found within the...

Use of the computer program's simulation of a Sonoran Desert community ultimately strengthens students' comprehension of what is required for a natural ecosystem to sustain itself (remain in balance). This computer simulation program has great flexibility. Students can manipulate the population numbers of five Sonoran Desert species. A species natural history attachment provides vital information for students to familiarize themselves with each species' behaviors, niche and food resource needs. The program includes two producers, Saguaro cactus and Ironwood Tree. It also includes three consumers, but their interactions both toward the producers and each other differ. The community's ability to remain in balance and sustain all five species so that none die out rests on students' assessment skills enabling them to correctly identify these dependencies. Students learn by trial and error as they continue to fine tune the ecosystem for which they maintain stewardship.

This upload contains the necessary R codes and data to reproduce the FDR and Power results described in our correspondence to Li Y, Ge X, Peng F, Li W, Li JJ, Exaggerated false positives by popular differential expression methods when analyzing human population samples, Genome Biology 23, 79, 2022, DOI: 10.1186/s13059-022-02648-4.

Long-term management planning for conservation-reliant migratory songbirds is particularly challenging because habitat quality in different stages and geographic locations of the annual cycle can have direct and carry-over effects that influence the population dynamics. The Neotropical migratory songbird Kirtland's warbler Setophaga kirtlandii (Baird 1852) is listed as endangered under the U.S. Endangered Species Act and Near Threatened under the IUCN Red List. This conservation-reliant species is being considered for U.S. federal delisting because the species has surpassed the designated 1000 breeding pairs recovery threshold since 2001. To help inform the delisting decision and long-term management efforts, we developed a population simulation model for the Kirtland's warbler that incorporated both breeding and wintering grounds habitat dynamics, and projected population viability based on current environmental conditions and potential future management scenarios. Future management scenarios included the continuation of current management conditions, reduced productivity and carrying capacity due to the changes in habitat suitability from the creation of experimental jack pine Pinus banksiana (Lamb.) plantations, and reduced productivity from alteration of the brown-headed cowbird Molothrus ater (Boddaert 1783) removal programme. Linking wintering grounds precipitation to productivity improved the accuracy of the model for replicating past observed population dynamics. Our future simulations indicate that the Kirtland's warbler population is stable under two potential future management scenarios: (i) continuation of current management practices and (ii) spatially restricting cowbird removal to the core breeding area, assuming that cowbirds reduce productivity in the remaining patches by ≤41%. The additional future management scenarios we assessed resulted in population declines. Synthesis and applications. Our study indicates that the Kirtland's warbler population is stable under current management conditions and that the jack pine plantation and cowbird removal programmes continue to be necessary for the long-term persistence of the species. This study represents one of the first attempts to incorporate full annual cycle dynamics into a population viability analysis for a migratory bird, and our results indicate that incorporating wintering grounds dynamics improved the model performance.

The simulated data is generated using msprime for testing SMC++ under different sets of demographic models. We have generated ten different replicates with each demographic model. The README file within the folder contains the information about the demographic models.

Since the rediscovery of Mendelian genetics over a century ago, there has been much debate about the evolutionary importance of mutations with large phenotypic effects. While population genetic models predict that large-effect mutations will typically contribute to adaptation following an abrupt change in environment, the prediction applies to populations of stable size and overlooks effects of population size change on adaptation (e.g., population decline following habitat loss; growth during range expansion). We evaluate the phenotypic and fitness effects of mutations contributing to adaptation immediately following an abrupt environmental shift that alters both selection and population size dynamics. We show that large-effect mutations are likely to contribute to adaptation in populations declining to a new carrying capacity, somewhat smaller-effect mutations contribute to evolutionary rescue, and small-effect mutations predominate in growing populations. We also show that the relati..., This is R simulation code used in the paper., The code will run in R/Rstudio.

This dataset includes succeeding versions of the simulation code for African population and migration, from 1994 to 2016. Versions rely on a single algorithm for population replication, enslavement, and migration; they vary in author, application, and analysis.

Multi-locus genetic processes in subdivided populations can be complex and difficult to interpret using theoretical population genetics models. Genetic simulators offer a valid alternative to study multi-locus genetic processes in arbitrarily complex scenarios. However, the use of forward-in-time simulators in realistic scenarios involving high numbers of individuals distributed in multiple local populations is limited by computation time and memory requirements. These limitations increase with the number of simulated individuals. We developed a genetic simulator, MetaPopGen 2.0, to model multi-locus population genetic processes in subdivided populations of arbitrarily large size. It allows for spatial and temporal variation in demographic parameters, age structure, adult and propagule dispersal, variable mutation rates and selection on survival and fecundity. We developed MetaPopGen 2.0 in the R environment to facilitate its use by non-modeler ecologists and evolutionary biologists. We...

The feasibility of a potential bull trout (Salvelinus confluentus) reintroduction was simulated across 12 streams and river segments in the upper Lake Chelan basin using a population matrix model. The model considered habitat availability, life history expression, and assumptions regarding constraints on potential bull trout populations. Details of the simulation framework are described in Benjamin et al., 2024 (submitted publication). Scenarios for model simulations included life stage, number of individuals, and years following potential reintroduction. We considered, four life stages of bull trout, eggs, juveniles, subadults and adults. Each life stage has three options for the numbers of individuals reintroduced. For eggs, it was the addition of 5,000, 10,000 and 20,000 individuals; for juveniles, 200, 500, and 2000 individuals; and for subadults and adults, 30, 60 and 100 individuals. Simulated numbers of adult bull trout are provided over 5 time periods (5,10, 30, 50 and 70 years after reintroduction). Here, in the data tables, we provide results from a one-way sensitivity analyses and global sensitivity analyses as well as the potential effect of dispersal on simulated bull trout populations.

simulator_v1_5Wright-Fisher implementing population simulation (in python)simExample parameter fileExperimental ResultsMutator frequencies in experimental yeast populations