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.

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/).

For years, we have relied on population surveys to keep track of regional public health statistics, including the prevalence of non-communicable diseases. Because of the cost and limitations of such surveys, we often do not have the up-to-date data on health outcomes of a region. In this paper, we examined the feasibility of inferring regional health outcomes from socio-demographic data that are widely available and timely updated through national censuses and community surveys. Using data for 50 American states (excluding Washington DC) from 2007 to 2012, we constructed a machine-learning model to predict the prevalence of six non-communicable disease (NCD) outcomes (four NCDs and two major clinical risk factors), based on population socio-demographic characteristics from the American Community Survey. We found that regional prevalence estimates for non-communicable diseases can be reasonably predicted. The predictions were highly correlated with the observed data, in both the states included in the derivation model (median correlation 0.88) and those excluded from the development for use as a completely separated validation sample (median correlation 0.85), demonstrating that the model had sufficient external validity to make good predictions, based on demographics alone, for areas not included in the model development. This highlights both the utility of this sophisticated approach to model development, and the vital importance of simple socio-demographic characteristics as both indicators and determinants of chronic disease.

This repository incorporates data and scripts associated with the paper "Biases in demographic modelling affect our understanding of recent divergence" published in Molecular Biology and Evolution (doi: 10.1093/molbev/msab047 ).

Included in the repository are the following compressed folders:

Alignment_and_SNP_Calling.zip. Contains bioinformatic pipelines to align raw data (available in GenBank from Bioproject PRJNA699405) and generate BAM and VCF files necessary for further analyses. Several VCF files are also included.

SFS.zip. Contains scripts to generate the 2d-SFS for demographic analyses of empirical data. Includes also input SNP files for moments.

pi_dxy.zip. This compressed folder contains a bash script to generate 1d-SFS and 2d-SFS in windows across the genome, and R scripts to derive summary statistics from those ((\pi), (\theta), (d_{xy}), Tajima's (D)) as well as the results (SFS and summary statistics in windows)

PCANGSD.zip. Contains arguments to generate the input file (in beagle format) for PCangsd, results from PCangsd (covariance matrix and selection test statistics) and a R script to plot a PCA.

Stairway_Plots.zip. Contains blueprint files to run StairwayPlots and results.

Simulations.zip. Contains commands to run all simulations, dadi and moments models for inference, results and some R scripts to reproduce plots.

Moments_models.zip. Contains the one-population and two-population models for moments that we designed for this study.

Moments_results.zip. Contains results from demographic models optimized for the empirical data, as well as scripts and results from Likelihood Ratio Tests and estimation of uncertainties using the Godambe Information Matrix.

Please note that most of these scripts and models are also available from GitHub (https://github.com/Nopaoli/Demographic-Modelling) .

This is a hybrid gridded dataset of demographic data for the world, given as 5-year population bands at a 0.5 degree grid resolution. This dataset combines the NASA SEDAC Gridded Population of the World version 4 (GPWv4) with the ISIMIP Histsoc gridded population data and the United Nations World Population Program (WPP) demographic modelling data. Demographic fractions are given for the time period covered by the UN WPP model (1950-2050) while demographic totals are given for the time period covered by the combination of GPWv4 and Histsoc (1950-2020) Method - demographic fractions Demographic breakdown of country population by grid cell is calculated by combining the GPWv4 demographic data given for 2010 with the yearly country breakdowns from the UN WPP. This combines the spatial distribution of demographics from GPWv4 with the temporal trends from the UN WPP. This makes it possible to calculate exposure trends from 1980 to the present day. To combine the UN WPP demographics with the GPWv4 demographics, we calculate for each country the proportional change in fraction of demographic in each age band relative to 2010 as: (\delta_{year,\ country,age}^{\text{wpp}} = f_{year,\ country,age}^{\text{wpp}}/f_{2010,country,age}^{\text{wpp}}) Where: - (\delta_{year,\ country,age}^{\text{wpp}}) is the ratio of change in demographic for a given age and and country from the UN WPP dataset. - (f_{year,\ country,age}^{\text{wpp}}) is the fraction of population in the UN WPP dataset for a given age band, country, and year. - (f_{2010,country,age}^{\text{wpp}}) is the fraction of population in the UN WPP dataset for a given age band, country for the year 2020. The gridded demographic fraction is then calculated relative to the 2010 demographic data given by GPWv4. For each subset of cells corresponding to a given country c, the fraction of population in a given age band is calculated as: (f_{year,c,age}^{\text{gpw}} = \delta_{year,\ country,age}^{\text{wpp}}*f_{2010,c,\text{age}}^{\text{gpw}}) Where: - (f_{year,c,age}^{\text{gpw}}) is the fraction of the population in a given age band for given year, for the grid cell c. - (f_{2010,c,age}^{\text{gpw}}) is the fraction of the population in a given age band for 2010, for the grid cell c. The matching between grid cells and country codes is performed using the GPWv4 gridded country code lookup data and country name lookup table. The final dataset is assembled by combining the cells from all countries into a single gridded time series. This time series covers the whole period from 1950-2050, corresponding to the data available in the UN WPP model. Method - demographic totals Total population data from 1950 to 1999 is drawn from ISIMIP Histsoc, while data from 2000-2020 is drawn from GPWv4. These two gridded time series are simply joined at the cut-over date to give a single dataset covering 1950-2020. The total population per age band per cell is calculated by multiplying the population fractions by the population totals per grid cell. Note that as the total population data only covers until 2020, the time span covered by the demographic population totals data is 1950-2020 (not 1950-2050). Disclaimer This dataset is a hybrid of different datasets with independent methodologies. No guarantees are made about the spatial or temporal consistency across dataset boundaries. The dataset may contain outlier points (e.g single cells with demographic fractions >1). This dataset is produced on a 'best effort' basis and has been found to be broadly consistent with other approaches, but may contain inconsistencies which not been identified. {"references": ["UN. (2019). World Population Prospects 2019: Data Booklet. Retrieved from https://population.un.org/wpp/Publications/Files/WPP2019_DataBooklet.pdf", "NASA SEDAC, & CIESIN. (2016). Gridded Population of the World, Version 4 (GPWv4): Population Count. New York, New York, USA: Columbia University. Retrieved from http://dx.doi.org/10.7927/H4X63JVC", "ISIMIP. (2018). ISIMIP Project Design and Simulation Protocol. Retrieved from https://www.isimip.org/gettingstarted/input-data-bias-correction/details/31/"]}

Estimating the demographic parameters of contemporary populations is essential to the success of elasmobranch conservation programmes, and to understanding their recent evolutionary history. For benthic elasmobranchs such as skates, traditional fisheries-independent approaches are often unsuitable as the data may be subject to various sources of bias, whilst low recapture rates can render mark-recapture programmes ineffectual. Close-kin mark-recapture (CKMR), a novel demographic modelling approach based on the genetic identification of close relatives within a sample, represents a promising alternative approach as it does not require physical recaptures. We evaluated the suitability of CKMR as a demographic modelling tool for the critically endangered blue skate (Dipturus batis) in the Celtic Sea using samples collected during fisheries-dependent trammel-net surveys that ran from 2011 to 2017. We identified three full-sibling and 16 half-sibling pairs among 662 skates, which were genotyped across 6,291 genome-wide single nucleotide polymorphisms (SNPs), 15 of which were cross-cohort half-sibling pairs that were included in a CKMR model. Despite limitations owing to a lack of validated life-history trait parameters for the species, we produced the first estimates of adult breeding abundance, population growth rate, and annual adult survival rate for D. batis in the Celtic Sea. The results were compared to estimates of genetic diversity, effective population size (N_e), and catch per unit effort (CPUE) estimates from the trammel-net survey. Although each method was characterised by wide uncertainty bounds, together they suggested a stable population size across the time-series. Recommendations for the implementation of CKMR as a conservation tool for data-limited elasmobranchs are discussed. In addition, the spatio-temporal distribution of the 19 sibling pairs revealed a pattern of site-fidelity in D. batis, and supported field observations suggesting an area of critical habitat that could qualify for protection might occur near the Isles of Scilly.

Aim: As the most biodiverse region of the world, the drivers of genetic diversity in Amazonia are still poorly understood. It has been debated that species on distinct ecosystems will show unique biogeographic histories tied to their habitats which in turn help understand landscape climatic history in Amazonia. We studied bird species associated with patchy Amazonian white-sand ecosystems (WSE) to evaluate the occurrence of shared patterns and its relationship to species habitat preferences and Amazonian environmental and landscape history. Location: Northern South America; Amazonia. Taxon: Passerine birds. Methods: We sequenced Ultra-conserved Elements (UCEs) from 177 samples of seven bird species associated with WSE that have overlapping ranges. We used the SNP matrices and sequence data to estimate genetic structure patterns and migration surfaces using ‘conStruct’ and eems, perform model-selection to obtain the most probable demographic histories on ‘PipeMaster’, and implement analy...

An interactive Story Map Series℠ explaining the links between the Demographic Transition Model and population pyramids (population structure) for almost all the countries in the world. It provides an excellent way to make spatial links with the demographic data. For example, each country is mapped using an interactive symbol representing its stage on the DTM. On clicking the symbol for any country, a pop-up provides a statement about its stage on the DTM and its 2018 population pyramid, provided by PopulationPyramid.net.The tabs in the Story Map Series℠ take the reader or presenter through an introduction and explanation of the DTM, followed by detail about particular places / countries currently at each stage including an example of anomalies which are less consistent with the model.The story map will be useful for a wide range of students and teachers of geography, demography and development at secondary and tertiary level.Credits and further study*Story Map template by Esri*Demographic Transition video by GeographyAllTheWay*Population structure diagrams from PopulationPyramid.net by Martin de Wulf based in Brussels, Belgium.*DTM diagram and population pyramid icons from Cool Geography *Population Education / PopEdBlog*BBC Bitesize Population growth and change*Thanks also to Ed Morgan of the ONS for very helpful feedback and further information.NB The DTM stages for each country are estimated and may be altered in due course.

Genetic (nuclear microsatellites) and population data from "Demographic history and spatial genetic structure in a remnant population of the subtropical tree Anadenanthera colubrina var. cebil (Griseb.) Altschul (Fabaceae)", published in Annals of Forest Sciences.

We developed a model for estimating demographic rates and population abundance based on multiple data sets revealing information about population age- and sex structure. Such models have previously been described in the literature as change-in-ratio models, but we extend the applicability of the models by i) using time series data allowing the full temporal dynamics to be modelled, by ii) casting the model in an explicit hierarchical modelling framework, and by iii) estimating parameters based on Bayesian inference. Based on sensitivity analyses we conclude that the approach developed here is able to obtain estimates of demographic rate with high precision whenever unbiased data of population structure are available. Our simulations revealed that this was true also when data on population abundance are not available or not included in the modelling framework. Nevertheless, when data on population structure are biased due to different observability of different age- and sex categories this will affect estimates of all demographic rates. Estimates of population size is particularly sensitive to such biases, whereas demographic rates can be relatively precisely estimated even with biased observation data as long as the bias is not severe. We then use the models to estimate demographic rates and population abundance for two Norwegian reindeer (Rangifer tarandus) populations where age-sex data were available for all harvested animals, and where population structure surveys were carried out in early summer (after calving) and late fall (after hunting season), and population size is counted in winter. We found that demographic rates were similar regardless whether we include population count data in the modelling, but that the estimated population size is affected by this decision. This suggest that monitoring programs that focus on population age- and sex structure will benefit from collecting additional data that allow estimation of observability for different age- and sex classes. In addition, our sensitivity analysis suggests that focusing monitoring towards changes in demographic rates might be more feasible than monitoring abundance in many situations where data on population age- and sex structure can be collected.

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.

The effects of genetic introgression on species boundaries and how they affect species’ integrity and persistence over evolutionary time have received increased attention. The increasing availability of genomic data has revealed contrasting patterns of gene flow across genomic regions, which impose challenges to inferences of evolutionary relationships and of patterns of genetic admixture across lineages. By characterizing patterns of variation across thousands of genomic loci in a widespread complex of true toads (Rhinella), we assess the true extent of genetic introgression across species thought to hybridize to extreme degrees based on natural history observations and multi-locus analyses. Comprehensive geographic sampling of five large-ranged Neotropical taxa revealed multiple distinct evolutionary lineages that span large geographic areas and, at times, distinct biomes. The inferred major clades and genetic clusters largely correspond to currently recognized taxa; however, we also ...

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.

Secondary contact of recently diverged species may have several outcomes, ranging from rampant hybridization to reinforced reproductive isolation. In plants, selfing tolerance and disjunct reproductive phenology may lead to reproductive isolation at contact zones. However, they can evolve under both allopatric or parapatric frameworks and originate from adaptive and/or neutral forces. Inferring the historical demography of diverging taxa is thus a crucial step to identify those factors that may lead to putative reproductive isolation. We explored various competing hypotheses to account for the rapid divergence of a fir species complex (Abies flinckii - A. religiosa) distributed in ‘sky-islands’ across central Mexico (i.e., along the Trans-Mexican Volcanic Belt; TMVB). Despite co-occurring in two independent sympatric regions (west and center), these taxa rarely interbreed because of disjunct reproductive phenologies. We genotyped 1,147 SNPs, generated by GBS, across 23 populations, and compared multiple demographic scenarios based on the geological history of the TMVB. The best-fitting model revealed one of the most rapid and complete speciation cases for a conifer species-pair, dating back to ~1.2 Ma. Coupled with the lack of support for stepwise colonization, our coalescent inferences point to an early cessation of interspecific gene flow under parapatric speciation; ancestral gene flow during divergence was asymmetrical (mostly from western firs into A. religiosa) and exclusive to the most ancient (i.e., central) contact zone. Factors promoting rapid reproductive isolation should be explored in other slowly-evolving species complexes as they may account for the large tropical and subtropical diversity. Methods Please, see Materials and Methods.

Inferring the processes underlying spatial patterns of genomic variation is fundamental to understand how organisms interact with landscape heterogeneity and to identify the factors determining species distributional shifts. Here, we employ genomic data (ddRADSeq) to test biologically-informed models representing historical and contemporary demographic scenarios of population connectivity for the Iberian cross-backed grasshopper Dociostaurus hispanicus, a species with a narrow distribution that currently forms highly fragmented populations. All models incorporated biological aspects of the focal taxon that could hypothetically impact its geographical patterns of genomic variation, including (a) spatial configuration of impassable barriers to dispersal defined by topographic landscapes not occupied by the species, (b) distributional shifts resulted from the interaction between the species bioclimatic envelope and Pleistocene glacial cycles, and (c) contemporary distribution of suitable h...

Samples.xlsx: Description of individual and population codes used in the different analyses and genomic datasets.

p7r08m5minMAF001.str: Input file used to perform genetic clustering (STRUCTURE) and principal component (PCA) analyses.

p7r08m5minMAF001.arp: Input file used to perform ARLEQUIN analyses.

Occurrence.csv: Occurrence data used for environmental niche modelling (ENM).

FASTSIMCOAL2.zip: This ZIP folder contains input files for the four models of population divergence run in FASTSIMCOAL2.

STAIRWAYPLOT.zip: This ZIP folder contains input files for demographic reconstructions in STAIRWAY PLOT.

SPLATCHE2.zip: This ZIP folder contains input files for the three spatially-explicit demographic models run using SPLATCHE2.

This repository contains scripts, input files, and some example output files for the Residential Population Generator, an R-based tool to generate synthetic human residental populations to use in making estimates of near-field chemical exposures. This tool is most readily adapted for using in the workflow for CHEM, the Combined Human Exposure Model, avaialable in two other GitHub repositories in the HumanExposure project, including ProductUseScheduler and source2dose. CHEM is currently best suited to estimating exposure to product use. Outputs from RPGen are translated into ProductUseScheduler, which with subsequent outputs used in source2dose.

This is a repository of global and regional human population data collected from: the databases of scenarios assessed by the Intergovernmental Panel on Climate Change (Sixth Assessment Report, Special Report on 1.5 C; Fifth Assessment Report), multi-national databases of population projections (World Bank, International Database, United Nation population projections), and other very long-term population projections (Resources for the Future).

More specifically, it contains:

- in `other_pop_data` folder files from World Bank, the International Database from the US Census, and from IHME

- in the `SSP` folder, the Shared Socioeconomic Pathways, as in the version 2.0 downloaded from IIASA and as in the version 3.0 downloaded from IIASA workspace

- in the `UN` folder, the demographic projections from UN

- `IAMstat.xlsx`, an overview file of the metadata accompanying the scenarios present in the IPCC databases

- `RFF.csv`, an overview file containing the population projections obtained by Resources For the Future

'- the remaining `.csv` files with names `AR6#`, `AR5#`, `IAMC15#` contain the IPCC scenarios assessed by the IPCC for preparing the IPCC assessment reports. They can be downloaded from AR5, SR 1.5, and AR6

This data in intended to be downloaded for use together with the package downloadable here.

The dataset was used as a supporting material for the paper "Underestimating demographic uncertainties in the synthesis process of the IPCC" accepted on npj Climate Action (DOI : 10.1038/s44168-024-00152-y).

AIC calculated using -2(log) +2k, (where k is the number of free parameters in the model). Models in bold as in table 5. Based on AIC score the model (ABADE) provides the best fit to our data. This model had equal population sizes between the Negro and ancestral populations and unequal gene flow between Negro and Chubut systems (Table 5). Estimates of the time of divergence (years before present [to the nearest thousand]) between river systems were obtained by dividing t by the geometric mean of the mutation rate across loci ( = 4.008839×10−5: see methods for further details).

Wildlife managers translocate greater sage-grouse (Centrocercus urophasianus; sage-grouse) to augment small populations, but translocated sage-grouse often fail to reproduce post-release, sometimes hampering conservation objectives. We performed two distinct sage-grouse translocation projects in California and North Dakota from 2017-2020 and employed two translocation methods at both sites: an established method of translocating females prior to the nesting season (i.e., a pre-nesting translocation), and a novel method wherein females were translocated with chicks after successfully hatching a nest in the source population (i.e., a brood translocation). Using an integrated population model (IPM), we estimated recruitment by females translocated with each method. We also estimated the finite rate of change in abundance in recipient and source populations that underwent brood and pre-nesting translocations to evaluate each method using a cost-benefit metric.