1. The climate on our planet is changing and the range distributions of organisms are shifting in response. In aquatic environments, species might not be able to redistribute poleward or into deeper water when temperatures rise because of barriers, reduced light availability, altered water chemistry, or any combination of these. How species respond to climate change may depend on physiological adaptability, but also on the population dynamics of the species.

2. Density dependence is a ubiquitous force that governs population dynamics and regulates population growth, yet its connections to the impacts of climate change remain little known, especially in marine studies. Reductions in density below an environmental carrying capacity may cause compensatory increases in demographic parameters and population growth rate, hence masking the impacts of climate change on populations. On the other hand, climate-driven deterioration of conditions may reduce environmental carrying capacities, making compensation less likely and populations more susceptible to the effects of stochastic processes.

3. Here we investigate the effects of climate change on Baltic blue mussels using a 17-year data set on population density. Using a Bayesian modelling framework, we investigate the impacts of climate change, assess the magnitude and effects of density dependence, and project the likelihood of population decline by the year 2030.

4. Our findings show negative impacts of warmer and less saline waters, both outcomes of climate change. We also show that density-dependence increases the likelihood of population decline by subjecting the population to the detrimental effects of stochastic processes (i.e., low densities where random bad years can cause local extinction, negating the possibility for random good years to offset bad years).

5. We highlight the importance of understanding, and accounting for both density dependence and climate variation when predicting the impact of climate change on keystone species, such as the Baltic blue mussel. 08-Oct-2020

When females mate with more than one male, sexual selection acts both before and after mating. The interaction between pre- and post-mating episodes of selection is expected to be context dependent, but few studies have investigated how total sexual selection changes under different ecological conditions. We examined how population density mediates the interaction between pre- and post-mating sexual selection by establishing replicate populations of the horned dung beetle Onthophagus taurus at low, medium, and high densities, and using microsatellite-based parentage analyses to measure male fitness. We found that mating success and fertilization success were positively correlated at all three densities, but the strength of the correlation decreased with increasing density. We also found a shift from negative to positive linear selection on testes mass as density increased, and opposing selection on weapons and testes at high densities. These patterns suggest that the importance of post-...

These data were compiled to help understand how climate change may impact dryland pinyon-juniper ecosystems in coming decades, and how resource management might be able to minimize those impacts. Objective(s) of our study were to model the demographic rates of PJ woodlands to estimate the areas that may decline in the future vs. those that will be stable. We quantified populations growth rates across broad geographic areas, and identified the relative roles of recruitment and mortality in driving potential future changes in population viability in 5 tree species that are major components of these dry forests. We used this demographic model to project pinyon-juniper population stability under future climate conditions, assess how robust these projected changes are, and to identify where on the landscape management strategies that decrease tree competition would effectively resist population decline. These data represent estimated recruitment, mortality and population growth across the distribution of five common pinyon-juniper species across the US Southwest. These data were collected by the US Forest service in their monitoring program, which is a systematic survey of forested regions across the entire US. Our data is from western US states, including AZ, CA, CO, ID, MT, NM, ND, NV, OR, SD, TX, UT, and was collected between 2000-2007, depending on state census collection times. These data were collected by the Forest Inventory and Analysis program of the USDA US Forest Service. Within each established plot, all adult trees greater than 12.7 cm (5 in.) diameter at breast height (DBH) are assigned unique tags and tracked within four, 7.32 m (24 ft.) radius subplots. All saplings <12.7 cm & > 2.54 cm (1 in.) DBH are assigned unique tags and tracked within four, 2.07 m (6.8 ft.) radius microplots within the larger adult plots. Finally, seedlings <2.54 cm DBH are counted within the same microplots as the saplings. Two censuses were conducted 10 years apart in each plot. These data can be used to inform how tree species have unique responses to changing climate conditions and how management actions, like tree density reduction, may effectively resist transformation away from pinyon-juniper woodland to other ecosystem types.

How plants cope with the increase of population density via root plasticity is not well documented. Abiotic environments and plant ontogeny may play an important role in determining plant response to density and thus contribute to understanding this issue. We aimed to investigate root plasticity in response to density under contrasting soil conditions at three stages of plant growth in an annual herbaceous species Abutilon theophrasti. We conducted a field experiment by subjecting plant individuals to low, medium and high densities (13.4, 36.0 and 121.0 plants m^-2, respectively) under fertile and infertile soil conditions, and a series of root traits were measured at three harvests when they had grown for 30, 50 and 70 d. Results revealed the complexity of root response to density, which may increase, decrease or canalize, depending on the strength of above- and below-ground interactions, which varied with soil conditions or growth stage. The intensity of above- and/or below-ground interactions increased with decreased soil resources, but first increased then decreased with growth stage. Facilitation is more likely to occur at low to moderate below-ground interaction, when above-ground interaction is negligible, and resources are abundant and at early stage of plant growth. Plants may prefer to adjust biomass allocation to maintain total mass stable initially, before suffering decreased total mass, in response to intraspecific interactions.

SummaryThe repository includes the data and R script for performing an analysis of among- and within-individual differences in the timing of first nesting attempts of the year in natal and pre-breeding environmental conditions (see reference). The data come from a long-term study of the demography of Savannah sparrows (Passerculus sandwichensis) breeding on Kent Island, New Brunswick, Canada (44.58°N, 66.76°W). Climate data were taken from an Environment and Climate Change Canada weather station at the airport in Saint John, NB (45.32°N, 65.89°W; https://www.climate.weather.gc.ca)Datasets(1) SAVS_all_nests_samp.csv: contains summary information for all nest attempts observed for all females included in the analysis (i.e., including both first-of-year and subsequent lay dates).(2) SAVS_first_nest_per_year_samp.csv: contains detailed information on the first nesting attempt by each female Savannah sparrow monitored in the population over the course of the study (1987-2019, excluding the years 2005-2007; see Methods: Study site and field sampling in reference).(3) mean_daily_temperature.csv: contains mean daily temperature records from the ECCC weather station at Saint John, NB (see above). These mean daily temperatures were used in a climate sensitivity analysis to determine the optimum pre-breeding window on Kent Island.(4) SAVS_annual_summary.csv: contains annual summaries of average lay dates, breeding density, reproductive output, etc.Variables- female.id = factor; unique aluminum band number (USGS or Canadian Wildlife Service) assigned to each female- rain.categorical = binary (0 = low rainfall; 1 = high rainfall); groups females into low (81-171 mm) and high (172-378 mm) natal rainfall groups, based on the natal environmental conditions observed in each year (see Methods: Statistical analysis in reference)- year = integer (1987-2019); study year. The population on Savannah sparrows on Kent Island has been monitored since 1987 (excluding three years, 2005-2007)- nest.id = factor; an alpha-numeric code assigned to each nest; unique within years (the combination of year and nest.id would create a unique identifier for each nest)- fledglings = integer; number of offspring fledged from a nest- total.fledglings = integer; the total number of fledglings reared by a given female over the course of her lifetime- nest.attempts = integer; the total number of nest attempts per female (the number of nests over which the total number of fledglings is divided; includes both successful and unsuccessful clutches)hatch.yday = integer; day of the year on which the first egg hatched in a given nestlay.ydate = integer; day of the year on which the first egg was laid in a given nestlay.caldate = date (dd/mm/yyyy); calendar date on which the first egg in a given nest was laidnestling.year = integer; the year in which the female/mother of a given nest was born- nestling.density = integer; the density of adult breeders in the year in which a given female (associated with a particular nest) was born- total.nestling.rain = numeric; cumulative rainfall (in mm) experienced by a female during the nestling period in her natal year of life (01 June to 31 July; see Methods: Temperature and precipitation data in reference)- years.experience = integer; number of previous breeding years per female in a particular year- density.total = integer; total number of adult breeders in the study site in a particular year- MCfden = numeric; mean-centred female density- MCbfden = numeric; mean-centred between-female density- MCwfden = numeric; mean-centred within-female density- mean.t.window = numeric; mean temperature during the identified pre-breeding window (03 May to 26 May; see Methods: Climate sensitivity analysis in reference)- MCtemp = numeric; mean-centred temperature during the optimal pre-breeding window- MCbtemp = numeric; mean-centred between-female temperature during the optimal pre-breeding window- MCwtemp = numeric; mean-centred within-female temperature during the optimal pre-breeding window- female.age = integer; age (in years) of a given female in a given year- MCage = numeric; mean-centred female age- MCbage = numeric; mean-centred between-female age- MCwage = numeric; mean-centred within-female age- mean_temp_c = numeric; mean daily temperature in °C- meanLD = numeric; mean lay date (in days of the year) across all first nest attempts in a given year- sdLD = numeric; standard deviation in lay date (in days of the year) across all first nest attempts in a given year- seLD = numeric; standard error n lay date (in days of the year) across all first nest attempts in a given year- meanTEMP = numeric; mean temperature (in °C) during the breeding period in a given year- records = integer; number of first nest attempts from each year included in the analysis- total.nestling.precip = numeric; total rainfall (in mm) during the nestling period (01 June to 31 July) in a given year- total.breeding.precip = numeric; total rainfall (in mm) during the breeding period (15 April to 31 July) in a given year- density.total = integer; total density of adult breeders on the study site in a given year- total.fledglings = integer; total number of offspring fledged by all breeders in the study site on a given year- cohort.fecundity = numeric; average number of offspring per breeder in a given yearCodecode for Burant et al. - SAVS lay date plasticity analysis.RThe R script provided includes all the code required to import the data and perform the statistical analyses presented in the manuscript. These include:- t-tests investigating the effects of natal conditions (rain.categorical) on female age, nest attempts, and reproductive success- linear models of changes in temperature, precipitation, reproductive success, and population density over time, and lay dates in response to female age, density, etc.- a climate sensing analysis to identify the optimal pre-breeding window on Kent Island- mixed effects models investigating how lay dates respond to changes in within- and between-female age, density, and temperaturesee readme.rtf for a list of datasets and variables.

The considerable threats of invasive rodents to island biodiversity are likely to be compounded by climate change. Forecasts for such interactions have been most pronounced for the Southern Ocean islands where ameliorating conditions are expected to decrease thermal and resource restrictions on rodents. Firm evidence for changing rodent populations in response to climate change, and demonstrations of associated impacts on the terrestrial environment, are nonetheless entirely absent for the region. Using data collected over three decades on sub-Antarctic Marion Island, we tested empirically whether mouse populations have changed through time and whether these changes can be associated significantly with changing abiotic conditions. Changes in invertebrate populations, which have previously been attributed to mouse predation, but with little explicit demographic analysis, were also examined to determine whether they can be associated with changing mouse populations. The total number of mice on the island at annual peak density increased by 530.0% between 1979-80 and 2008-11. This increase was due to an advanced breeding season, which was robustly related to the number of precipitation-free days during the non-breeding season. Mice directly reduced invertebrate densities, with biomass losses of up to two orders of magnitude in some habitats. Such invertebrate declines are expected to have significant consequences for ecosystem processes over the long term. Our results demonstrate that as climate change continues to create ameliorating conditions for invasive rodents on sub-Antarctic islands, the severity of their impacts will increase. They also emphasize the importance of rodent eradication for the restoration of invaded islands.

This submission contains all that is necessary to generate Figures 1-6 and Appendices A-D of the paper entitled "Shifting Precipitation Regimes Influence Optimal Germination Strategies and Population Dynamics in Bet-hedging Desert Annuals". A readme.txt file is incorporated and describes all csv and R-script files. It also includes a roadmap for running the scripts on one's own if one wants to re-generate their own data files. However, data files have been included so that one can instantly generate the figures., The data collection was supported by NSF: DEB-9107324, DEB-9419905 (LTREB), DEB-0212782 (LTREB), DEB-0717466 (LTREB), DEB-0817121 (LTREB), DEB-1256792 (LTREB) and DEB- 0844780. See the paper entitled "Shifting Precipitation Regimes Influence Optimal Germination Strategies and Population Dynamics in Bet-hedging Desert Annuals" for more details. See also Gremer, Jennifer R., and D. Lawrence Venable. "Bet hedging in desert winter annual plants: optimal germination strategies in a variable environment." Ecology letters 17.3 (2014): 380-387; Venable, D. Lawrence. "Bet hedging in a guild of desert annuals." Ecology 88.5 (2007): 1086-1090.; see also the website for the LTREB Data Sets for desert annual field data: http://www.eebweb.arizona.edu/faculty/venable/LTREB/LTREB%20data.htm., Any text editor will open the readme.txt file. All other scripts should be visible with any typical editor; scripts may be more readable in Rstudio. , # Data from: Shifting precipitation regimes influence optimal germination strategies and population dynamics in bet-hedging desert annuals

Climate change will affect both the mean and variability in environmental conditions and may have major, negative impacts on population densities in the future. For annual plants that already live in an extreme environment like the Sonoran Desert, keeping a fraction of their seeds dormant underground (for possibly years at a time) is critical to survive. Here, we consider how this form of bet-hedging (i.e., delayed germination) for ten Sonoran Desert annuals mediates responses to precipitation shifts. We use a demographic model parameterized with long-term field and precipitation data to explore how forecasted changes in precipitation impact annual plant species’ population densities. We then examine how instantaneous adaptation to optimal germination fractions in the shifted precipitation regimes bolsters population densities. Our results indicate...

Isolation caused by anthropogenic habitat fragmentation can destabilize populations. Populations relying on the inflow of immigrants can face reduced fitness due to inbreeding depression as fewer new individuals arrive. Empirical studies of the demographic consequences of isolation are critical to understanding how populations persist through changing conditions. We used a 34-year demographic and environmental dataset from a population of cooperatively-breeding Florida Scrub-Jays (Aphelocoma coerulescens) to create mechanistic models linking environmental and demographic factors to population growth rates. We found that the population has not declined despite both declining immigration and increasing inbreeding, owing to a coinciding response in breeder survival. We find evidence of density-dependent immigration, breeder survival, and fecundity, indicating that interactions between vital rates and local density play a role in buffering the population against change. Our study elucidates..., All work was approved by the Cornell University Institutional Animal Care and Use Committee (IACUC 2010-0015) and authorized by permits from the US Fish and Wildlife Service (TE824723-8), the US Geological Survey (banding permit 07732), and the Florida Fish and Wildlife Conservation Commission (LSSC-10-00205)., , # Density dependence maintains long-term stability despite increased isolation and inbreeding in the Florida Scrub-Jay

https://doi.org/10.5061/dryad.p2ngf1vz3

This dataset contains raw census data (FullLOI.txt), derived vital rates (vr_clean_F_4stageDemo.rdata, vr_clean_M_4stageDemo.rdata), ecological metrics (reqsoi_update.txt, acorns_update.txt, TerrYrBurnArea.txt, TerrMap.txt, TerrsToKeep.txt, densityCalcDemo.rdata, env_var_updateDemo.txt, envFac_annual.txt), pedigree information (pedInbr.txt, kinship_coef_Demo.rdata), and demographic models created using these data (vr_modelsDemo_revision_20240518.rdata, vr_modelsDemo.rdata, Demo_LTRE_results_20240518.rdata), including model validation results (vr_modelsDemo_validation_revisions_20240518.rdata).

Description of the data and file structure

FullLOI.txt

• USFWS: Individual ID
• Year: Year of census
• TerrYr: Te...

In the middle of 2023, about 60 percent of the global population was living in Asia.The total world population amounted to 8.1 billion people on the planet. In other words 4.7 billion people were living in Asia as of 2023. Global populationDue to medical advances, better living conditions and the increase of agricultural productivity, the world population increased rapidly over the past century, and is expected to continue to grow. After reaching eight billion in 2023, the global population is estimated to pass 10 billion by 2060. Africa expected to drive population increase Most of the future population increase is expected to happen in Africa. The countries with the highest population growth rate in 2024 were mostly African countries. While around 1.47 billion people live on the continent as of 2024, this is forecast to grow to 3.9 billion by 2100. This is underlined by the fact that most of the countries wit the highest population growth rate are found in Africa. The growing population, in combination with climate change, puts increasing pressure on the world's resources.

Total population in counties around the Appalachian Trail in 2019. The dashboard includes maps of population density and growth by county; and county specific values when selected. Data is based on American Community Survey of 2019 and change rates refers to increases since Decennial Census 2000. Chart illustrates graphically the evolution of the population in the last 50 years in the county.

Part of the Integrated Vulnerability Assessment in the Arab Region, this 1km pixel resolution raster dataset provides a representation of sensitivity to climate change impact for the population dimension indicator – Population Density - in the Middle East and North Africa Region. Vulnerability is a concept used to express the complex interaction of climate change effects and the susceptibility of a system to its impacts. The integrated vulnerability assessment methodology is based on an understanding of vulnerability as a function of a system’s climate change exposure, sensitivity and adaptive capacity to cope with climate change effects, consistent with the approach put forward by the Intergovernmental Panel on Climate Change (IPCC) in its Fourth Assessment Report (AR4). Within this conceptual framework, Sensitivity provides information about the status quo of the physical and natural environment that makes the affected systems particularly susceptible to climate change. Sensitivity indicators were clustered into three dimensions: population, natural, and manmade. Within the vulnerability assessment methodology it is assumed the indicators retain the same values for reference period and future periods. A class value of 1 was assigned to represent a favorable condition (low sensitivity) while a class value of 10 designates an unfavorable condition.

Urban growth and climate change together complicate planning efforts meant to adapt to increasingly scarce water supplies. Several studies have shown the impacts of urban planning and climate change separately, but little attention has been given to their combined impact on long-term urban water demand forecasting. Here we coupled land and climate change projections with empirically-derived coefficient estimates of urban water use (sum of public supply, industrial, and domestic use) to forecast water demand under scenarios of future population densities and climate warming. We simulated two scenarios of urban growth from 2012 to 2065 using the FUTure Urban-Regional Environment Simulation (FUTURES) framework. FUTURES is an open-source probabilistic land change model designed to address the regional-scale environmental and ecological impacts of urbanization. We simulated an urbanization scenario that continues the historic trend of growth referred to as “Status Quo” and a scenario that simulates patterns of clustered higher density development, referred to as “Urban Infill". We initialized land change projections in 2011 and run forward on an annual time step to 2065. We captured the uncertainty associated with future climate conditions by integrating three Global Climate Models (GCMs), representative of dry, wet, and median future conditions. GCMs follow a continuously increasing greenhouse gas emissions scenario (Representative Concentration Pathway; RCP 8.5). This data release includes: a) land change projections for both urbanization scenarios in a spatial resolution consistent with the National Land Cover Database; b) development-related water demand projections for scenarios of environmental change at the census tract spatial unit summarized by 2030 and 2065; and c) the spatial boundaries of census tracts presented as a shapefile.

Understanding how indirect effects of climate can interact with density-dependent processes has become increasingly important as variability changes resource availability for wildlife. Both climate and animal density can drive the abundance of vegetation and control the degree of competition for forage between ungulates. Further, climate-density relationships may be more pronounced for females in the population as they may need to compensate nutritionally for the energetic costs of raising young. Quantifying the effects of these relationships on individual animal performance is challenging because it requires long-term data that spans changing densities and climatic patterns to observe the mechanisms in play. Our objectives were to: 1) evaluate differences in fall (Nov–Dec) female elk body condition based on lactation status; 2) assess the relationships between seasonal bottom-up covariates, elk density, and changes in elk body fat; and 3) examine the timing of growing season conditions associated with variation in elk body fat. We used a 20-year dataset of female elk (Cervus canadensis) across varying population densities and seasonal bottom-up patterns to quantify changes in body fat in a semi-arid forested rangeland system in northeastern Oregon, USA. Body fat of lactating elk was negatively associated with severe drought at higher elk densities. Body fat of lactating elk was greater following wet summers with a later green-up date. Higher precipitation during the growing season significantly increased body fat for all groups of elk. These results collectively support the importance of the indirect, bottom-up effects on female elk nutrition. If summer drought continues to increase in duration and intensity in the Pacific Northwest, USA, we expect to see declines in elk body condition with potential impacts to population-level performance.

A population is a subgroup of individuals within the same species that are living and breeding within a geographic area. The number of individuals living within that specific location determines the population density, or the number of individuals divided by the size of the area.Population density can be used to describe the location, growth, and migration of many organisms. In the case of humans, population density is often discussed in relation to urbanization, immigration, and population demographics.Globally, statistics related to population density are tracked by the United Nations Statistics Division, and the United States Constitution requires population data to be collected every 10 years, an operation carried out by the U.S. Census Bureau. However, data on human population density at the country level, and even at regional levels, may not be very informative; society tends to form clusters that can be surrounded by sparsely inhabited areas. Therefore, the most useful data describes smaller, more discrete population centers.Dense population clusters generally coincide with geographical locations often referred to as city, or as an urban or metropolitan area; sparsely populated areas are often referred to as rural. These terms do not have globally agreed upon definitions, but they are useful in general discussions about population density and geographic location.Population density data can be important for many related studies, including studies of ecosystems and improvements to human health and infrastructure. For example, the World Health Organization, the U.S. Energy Information Administration, the U.S. Global Change Research Program, and the U.S. Departments of Energy and Agriculture all use population data from the U.S. Census or UN statistics to understand and better predict resource use and health trends.Key areas of study include the following:Ecology: how increasing population density in certain areas impacts biodiversity and use of natural resources.Epidemiology: how densely populated areas differ with respect to incidence, prevalence, and transmission of infectious disease.Infrastructure: how population density drives specific requirements for energy use and the transport of goods.This list is not inclusive—the way society structures its living spaces affects many other fields of study as well. Scientists have even studied how happiness correlates with population density. A substantial area of study, however, focuses on demographics of populations as they relate to density. Areas of demographic breakdown and study include, but are not limited to:age (including tracking of elderly population centers);sex (biological classification as male or female); andrace and ethnic group, or cultural characteristics (ethnic origin and language use).

Aim: Invasive plants may evolve a suite of distinctive traits during spread in the new range. Among these traits, dispersal ability is an important trait determining the invasion speed of exotic plants. There is evidence that higher dispersal ability is favored at the invasion front, where population density may be low. However, no study has explicitly tested how planting density in a common garden affects the dispersal ability of invasive plants. Location: Hainan island of China. Methods: In this study, using 27 populations of an invasive plant, Mikania micrantha, which is expanding its range on Hainan island of China, we examine how three dispersal-related traits (i.e., dispersal ability, fruit mass, and pappus radius) change with distance from invasion centre and field population density, and how planting density in a common garden affects dispersal traits. Results: Dispersal traits did not change with distance from the invasion centre and field population cover either in the natural environment or in the common garden. In the common garden, increasing planting density from one to five plants per pot increased fruit mass and decreased dispersal ability, indicating that the effect of density on dispersal traits could not be detected in the field. The relationship between dispersal ability in the natural environment and that in the common garden was positive but significant only under the five plants per pot treatment, possibly because dispersal traits in natural conditions were selected under high density growth conditions. Main conclusions: Our results indicate that increasing population density may increase fruit mass and reduce the dispersal ability of range-expanding invasive plants. We suggest that further studies exploring the patterns of dispersal traits in range-expanding invasive plants in a common garden should consider intraspecific competition.

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Urbanization results in pervasive habitat fragmentation and reduces standing genetic variation through bottlenecks and drift. Loss of genomewide variation may ultimately reduce the evolutionary potential of animal populations experiencing rapidly changing conditions. In this study, we examined genomewide variation among 23 white-footed mouse (Peromyscus leucopus) populations sampled along an urbanization gradient in the New York City metropolitan area. Genomewide variation was estimated as a proxy for evolutionary potential using more than 10 000 single nucleotide polymorphism (SNP) markers generated by ddRAD-Seq. We found that genomewide variation is inversely related to urbanization as measured by percent impervious surface cover, and to a lesser extent, human population density. We also report that urbanization results in enhanced genomewide differentiation between populations in cities. There was no pattern of isolation by distance among these populations, but an isolation by resist...

DATA:

GENERAL INFORMATION

1. Title of Dataset: Effects of local density dependence and temperature on the spatial synchrony of marine fish populations
2. Author Information A. Principal Investigator Contact Information Name: Jonatan F. Marquez
   Institution: 1Centre for Biodiversity Dynamics, Department of Biology, Norwegian University of Science and Technology, 7491 Trondheim, Norway Email: jonatan.fredricson@gmail.com
3. Data collected through the annual scientific bottom trawl surveys conducted from 1985 to 2016, between January and March, by the Norwegian Institute for Marine Research and the Polar Research Institute of Marine Fisheries and Oceanography (Fall et al., 2020). More details on the survey sampling design can be found in (Fall et al., 2020; Jakobsen et al., 1997).
4. Geographic location of data collection: Barents Sea, Norway

Resolutions_list.rda

R data file contai...

A mating system is an important life history for animals dealing with changing environments. Population density affects the plasticity of a mating system and subsequently the family structure of animals, but its impacts on mating systems and social structures are rarely investigated by using molecular markers in field conditions. In this study, using microsatellite genetic markers, we examined the changes in the social and genetic mating system and family structure of Brandt’s voles in the grassland of Inner Mongolia, China, under low-, medium-, and high-density enclosures (each enclosure 0.48-ha with 4 replicates.) We found, that with the increase in population density of the founder voles introduced into the enclosure in early spring, both sexes increased their number of genetic mating partners, while males increased their social partners, resulting in a more promiscuous mating system. The number of genetic fathers and mothers per family, the number of social offspring per founder mal..., The study site had pre-constructed twenty-four 0.48-ha enclosures (80 × 60 m) with galvanized iron sheets extending 1 m below the ground’s surface and 1.4 m above the surface to prevent escaping, intrusion and movement of burrowing rodents into, out of, and between enclosures (Li et al., 2016). A raptor†proof nylon netting (10 cm mesh size) covered the top of each enclosure to obstruct avian predators. The integrity of each enclosure’s construction was regularly checked and maintained. Twelve enclosures were randomly assigned to one of three treatments that differed in founder population size: Low Density (6 ♂:6 ♀), Medium Density (12 ♂:12 ♀) and High Density (18 ♂:18 ♀). Each treatment had four replicates. The density level was based on a previous test in which 13-15 pairs of male and female voles were released into each enclosure in April (Li et al., 2016). The highest population density of an enclosure was recorded in one of the high-density enclosures at 138 individuals by the end o..., Data can be opened using Microsoft Excel and other similar software such as LibreOffice Calc. , # Density-dependent changes of mating system and family structure in Brandt's voles (Lasiopodomys brandtii)

https://doi.org/10.5061/dryad.qv9s4mwhx

DATA & FILE OVERVIEW

Dataset.xlxs

This dataset consists of 6 sheets named:

• Population size
• Per capita reproductivity
• Mating system
• Parent structure
• Offspring structure
• Family link

Variables:

LD - low density treatment

MD - medium density treatment

HD - high density treatment

Code/Software

R software (v. 3.6.1)

AbstractThe adaptation of populations to changing conditions may be affected by interactions between individuals. For example, when cooperative interactions increase fecundity, they may allow populations to maintain high densities and thus keep track of moving environmental optima. Simultaneously, changes in population density alter the marginal benefits of cooperative investments, creating a feedback loop between population dynamics and the evolution of cooperation. Here we model how the evolution of cooperation interacts with adaptation to changing environments. We hypothesize that environmental change lowers population size and thus promotes the evolution of cooperation, and that this, in turn, helps the population keep up with the moving optimum. However, we find that the evolution of cooperation can have qualitatively different effects, depending on which fitness component is reduced by the costs of cooperation. If the costs decrease fecundity, cooperation indeed speeds adaptation by increasing population density; if, in contrast, the costs decrease viability, cooperation may instead slow adaptation by lowering the effective population size, leading to evolutionary suicide. Thus, cooperation can either promote or—counter-intuitively—hinder adaptation to a changing environment. Finally, we show that our model can also be generalized to other social interactions by discussing the evolution of competition during environmental change., Methods , Usage notesThis repository includes: A Mathematica notebook with all the calculations required to replicate the results presented in the paper and in its Supplemental Information. For users without Mathematica, we also include the corresponding .cdf notebook and .pdf file. A set of R scripts with the code the individual-based simulations, as well as the data generated by these scripts and used in the paper.