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

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., ,

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 demographic_model_1 includes population bottleneck followed by constant population size and then population expansion. demographic_model_2 simulates a population with constant population size throughout history. demographic_model_3 simulates population expansion post-population formulation. The README file within the folder contains the information about the demographic models.

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.

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.

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

Raw Data for Figure 4. Monte Carlo simulations of the colonization-competition dynamics.Colonization of free habitat started from single individuals of each species. The number of Monte Carlo simulations equals to 200. Parameter P1 reflects competitivness of the Species 1 and parameter P2 reflects competitivness of the Species 2.(a, b) Competitive exclusion of the recessive Species 2 was observed in all cases. Species differences in competitivness equal to 100% (a) and 32% (b).(c-d) Stable coexistence of competing species was observed in all cases. Species differences in competitivness equal to 31% (c) and 0% (d).

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.

These data were compiled to determine whether transient population dynamics substantially alter population growth rates of sagebrush after disturbance, impede resilience and restoration, and in turn drive ecosystem transformation. Data were collected from 2014-2016 on sagebrush population height distributions at 531 sites across the Great Basin that had burned and were subsequently reseeded by the BLM. These data include field data on sagebrush density in 6 size classes and site attributes (seeding year, sampling year, random site designation, elevation, seeding rate). Also included are modeled spring soil moisture data at each site from the year of seeding to sampling. This data release includes associated software code allows the inference of demographic rates (survival, reproduction, and individual growth) of sagebrush using Hamiltonian Monte Carlo approaches in Stan (https://mc-stan.org/).

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.

Social networks are tied to population dynamics; interactions are driven by population density and demographic structure, while social relationships can be key determinants of survival and reproductive success. However, difficulties integrating models used in demography and network analysis have limited research at this interface. We introduce the R package genNetDem for simulating integrated network-demographic datasets. It can be used to create longitudinal social networks and/or capture-recapture datasets with known properties. It incorporates the ability to generate populations and their social networks, generate grouping events using these networks, simulate social network effects on individual survival, and flexibly sample these longitudinal datasets of social associations. By generating co-capture data with known statistical relationships it provides functionality for methodological research. We demonstrate its use with case studies testing how imputation and sampling design influence the success of adding network traits to conventional Cormack-Jolly-Seber (CJS) models. We show that incorporating social network effects in CJS models generates qualitatively accurate results, but with downward-biased parameter estimates when network position influences survival. Biases are greater when fewer interactions are sampled or fewer individuals are observed in each interaction. While our results indicate the potential of incorporating social effects within demographic models, they show that imputing missing network measures alone is insufficient to accurately estimate social effects on survival, pointing to the importance of incorporating network imputation approaches. genNetDem provides a flexible tool to aid these methodological advancements and help researchers test other sampling considerations in social network studies. Methods The dataset and code stored here is for Case Studies 1 and 2 in the paper. Datsets were generated using simulations in R. Here we provide 1) the R code used for the simulations; 2) the simulation outputs (as .RDS files); and 3) the R code to analyse simulation outputs and generate the tables and figures in the paper.

Raw Data for Figure 3. Monte Carlo simulations of two species competition. Colonization of free habitat started from single individuals of each species. The number of Monte Carlo simulations equals to 200. Parameter P1 reflects competitivness of the Species 1 and parameter P2 reflects competitivness of the Species 2.(A, B) Competitive exclusion of the recessive Species 2 was observed in all cases. Species differences in competitivness equal to 100% (A) and 32% (B). (C-D) Stable coexistence of competing species was observed in all cases. Species differences in competitivness equal to 31% (C) and 0% (D).

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.

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...

Data Dictionary and RAMAS CodeDescription of the spatial layers used, and code for parameterizing the Spatial Data subprogram in program RAMAS GIS using the spatial files provided and equations/values given in the manuscript and Supplementary Information document.DataDictionaryAndRAMASCode.txtHistorical ProjectionsHistoricalProjections -> HabitatSuitability

hs1979-hs2013 are spatial layers that distinguish 32 ha cells in Michigan, USA as nonsuitable (0), low suitability (1), moderate suitability (2), and high suitability (3) habitat for Kirtland's warbler annually between 1979 and 2013. The layers can be used as input files for the Spatial Data subprogram in program RAMAS GIS.

HistoricalProjections -> Precipitation

p1979-p2013 are spatial layers that contain total March precipitation values for the Nassau NOAA station (Bahamas) annually between 1979 and 2013 in cells that contain suitable habitat for Kirtland's warbler. The layers can be used as input files for the Spatial Dat...

Obtaining robust estimates of population abundance is a central challenge hindering the conservation and management of many threatened and exploited species. Close-kin mark-recapture (CKMR) is a genetics-based approach that has strong potential to improve monitoring of data-limited species by enabling estimates of abundance, survival, and other parameters for populations that are challenging to assess. However, CKMR models have received limited sensitivity testing under realistic population dynamics and sampling scenarios, impeding application of the method in population monitoring programs and stock assessments. Here, we use individual-based simulation to examine how unmodeled population dynamics and aging uncertainty affect the accuracy and precision of CKMR parameter estimates under different sampling strategies. We then present adapted models that correct the biases that arise from model misspecification. Our results demonstrate that a simple base-case CKMR model produces robust estimates of population abundance with stable populations that breed annually; however, if a population trend or non-annual breeding dynamics are present, or if year-specific estimates of abundance are desired, a more complex CKMR model must be constructed. In addition, we show that CKMR can generate reliable abundance estimates for adults from a variety of sampling strategies, including juvenile-focused sampling where adults are never directly observed (and aging error is minimal). Finally, we apply a CKMR model that has been adapted for population growth and intermittent breeding to two decades of genetic data from juvenile lemon sharks (Negaprion brevirostris) in Bimini, Bahamas, to demonstrate how application of CKMR to samples drawn solely from juveniles can contribute to monitoring efforts for highly mobile populations. Overall, this study expands our understanding of the biological factors and sampling decisions that cause bias in CKMR models, identifies key areas for future inquiry, and provides recommendations that can aid biologists in planning and implementing an effective CKMR study, particularly for long-lived data-limited species. Methods The simulation code was written for this manuscript and the results were generated by running the code on the Massachusetts Green High Performance Computing Cluster. Kinship data for lemon sharks comes from: Feldheim, K. A., S. H. Gruber, J. D. DiBattista, E. A. Babcock, S. T. Kessel, A. P. Hendry, E. K. Pikitch, M. V. Ashley, and D. D. Chapman. 2014. Two decades of genetic profiling yields first evidence of natal philopatry and long-term fidelity to parturition sites in sharks. Molecular Ecology 23:110–117 and can be accessed at: https://doi.org/10.5061/dryad.1q9r8.

Simulations_Hartfield2012_JEBREADME.TXT FOR SIMULATION FILES: These files are the source code for the one-dimensional and two-dimensional population simulations used in the Hartfield 2012 paper "A framework for estimating the fixation time of an advantageous allele in stepping-stone models". Simulations are written in C and need to be compiled prior to execution. See blurb at start of each program for description and how to execute. Please also note that simulations use routines that are part of the GNU Scientific Library (GSL). Since GSL is distributed under the GNU General Public License (http://www.gnu.org/copyleft/gpl.html), you must download it separately from these files. Comments should be sent to Matthew Hartfield (m.hartfield@sms.ed.ac.uk).

Mate-choice copying is a type of social learning in which females can change their mate preference after observing the choice of others. This behaviour can potentially affect population evolution and ecology, namely through increased dispersal and reduced local adaptation. Here, we simulated the effects of mate-choice copying in populations expanding across an environmental gradient to understand whether it can accelerate or retard the expansion process. Two mate-choice copying strategies were used: when females target a single individual, and when females target similar individuals. We also simulated cases where the male trait singled out by females with mate-choice maps perfectly onto their genotype or is influenced by genotype-by-environment interactions. These rules have different effects on the results. When a trait is determined by genotype alone, populations where copier females target all similar males expand faster, and the number of potential copiers increases. However, when p..., , , # Mate-choice copying accelerates species range expansion

https://doi.org/10.5061/dryad.bzkh189jh

This database contains the code used to run the individual-based simulations used to study the effects of mate-choice copying in a population expanding through an environmental gradient. These simulations show that different preference rules and mate-choice copying strategies can affect the speed of population range expansion.

Description of the data and file structure

The simulation code is contained in 5 different files:

• main.c: This is the main file for the simulation. It contains the fixed parameters for the simulation (described in the comments of the code itself), the link to all the other files, and the main code to run each generation loop and the simulation itself.
• input.h: This is the file with the parameters that were changed during the different simulations. The file also contains a description of each parame...