This dataset includes data on 25 transitions of a matrix demographic model of the invasive species Vincetoxicum nigrum (L.) Moench (black swallow-wort or black dog-strangling vine) and Vincetoxicum rossicum (Kleopow) Barb. (pale swallow-wort or dog-strangling vine) (Apocynaceae, subfamily Asclepiadoideae), two invasive perennial vines in the northeastern U.S.A. and southeastern Canada. The matrix model was developed for projecting population growth rates as a result of changes to lower-level vital rates from biological control although the model is generalizable to any control tactic. Transitions occurred among the five life stages of seeds, seedlings, vegetative juveniles (defined as being in at least their second season of growth), small flowering plants (having 1–2 stems), and large flowering plants (having 3 or more stems). Transition values were calculated using deterministic equations and data from 20 lower-level vital rates collected from 2009-2012 from two open field and two forest understory populations of V. rossicum (43°51’N, 76°17’W; 42°48'N, 76°40'W) and two open field populations of V. nigrum (41°46’N, 73°44’W; 41°18’N, 73°58’W) in New York State. Sites varied in plant densities, soil depth, and light levels (forest populations). Detailed descriptions of vital rate data collection may be found in: Milbrath et al. 2017. Northeastern Naturalist 24(1):37-53. Five replicate sets of transition data obtained from five separate spatial regions of a particular infestation were produced for each of the six populations. Note: Added new excel file of vital rate data on 12/7/2018. Resources in this dataset:Resource Title: Matrix model transition data for Vincetoxicum species. File Name: Matrix_model_transition_data.csvResource Description: This data set includes data on 25 transitions of a matrix demographic model of two invasive Vincetoxicum species from six field and forest populations in New York State.Resource Title: Variable definitions. File Name: Matrix_model_metadata.csvResource Description: Definitions of variables including equations for each transition and definitions of the lower-level vital rates in the equationsResource Title: Vital Rate definitions. File Name: Vital_Rate.csvResource Description: Vital Rate definitions of lower-level vital rates used in transition equations - to be substituted into the Data Dictionary for full definition of each transition equation.Resource Title: Data Dictionary. File Name: Matrix_Model_transition_data_DD.csvResource Description: See Vital Rate resource for definitions of lower-level vital rates used in transition equations where noted.Resource Title: Matrix model vital rate data for Vincetoxicum species. File Name: Matrix_model_vital rate_data.csvResource Description: This data set includes data on 20 lower-level vital rates used in the calculation of transitions of a matrix demographic model of two invasive Vincetoxicum species in New York State as well as definitions of the vital rates. (File added on 12/7/2018)Resource Software Recommended: Microsoft Excel,url: https://office.microsoft.com/excel/

These data were developed by the Research & Analytics Department at the Atlanta Regional Commission using data from the U.S. Census Bureau across all standard and custom geographies at statewide summary level where applicable.For a deep dive into the data model including every specific metric, see the ACS 2019-2023. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics. Find naming convention prefixes/suffixes, geography definitions and user notes below.Prefixes:NoneCountpPercentrRatemMedianaMean (average)tAggregate (total)chChange in absolute terms (value in t2 - value in t1)pchPercent change ((value in t2 - value in t1) / value in t1)chpChange in percent (percent in t2 - percent in t1)sSignificance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computedSuffixes:_e23Estimate from 2019-23 ACS_m23Margin of Error from 2019-23 ACS_e102006-10 ACS, re-estimated to 2020 geography_m10Margin of Error from 2006-10 ACS, re-estimated to 2020 geography_e10_23Change, 2010-23 (holding constant at 2020 geography)GeographiesAAA = Area Agency on Aging (12 geographic units formed from counties providing statewide coverage)ARC21 = Atlanta Regional Commission modeling area (21 counties merged to a single geographic unit)ARWDB7 = Atlanta Regional Workforce Development Board (7 counties merged to a single geographic unit)BeltLineStatistical (buffer)BeltLineStatisticalSub (subareas)Census Tract (statewide)CFGA23 = Community Foundation for Greater Atlanta (23 counties merged to a single geographic unit)City (statewide)City of Atlanta Council Districts (City of Atlanta)City of Atlanta Neighborhood Planning Unit (City of Atlanta)City of Atlanta Neighborhood Statistical Areas (City of Atlanta)County (statewide)CCDIST = County Commission Districts (statewide where applicable)CCSUPERDIST = County Commission Superdistricts (DeKalb)Georgia House (statewide)Georgia Senate (statewide)HSSA = High School Statistical Area (11 county region)MetroWater15 = Atlanta Metropolitan Water District (15 counties merged to a single geographic unit)Regional Commissions (statewide)State of Georgia (single geographic unit)Superdistrict (ARC region)US Congress (statewide)UWGA13 = United Way of Greater Atlanta (13 counties merged to a single geographic unit)ZIP Code Tabulation Areas (statewide)The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2019-2023). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2019-2023Open Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the data manifest: https://opendata.atlantaregional.com/documents/182e6fcf8201449086b95adf39471831/about

Abstract copyright UK Data Service and data collection copyright owner. The aims of the project were to examine and analyse demographic processes of fertility, nuptiality, marital fertility, mortality and migration during periods encompassing the demographic transition in England and Wales. In particular, the goal was to reveal underlying relationships between demographic processes in the context of changing socio-economic conditions. With this goal in mind, population, occupational, and education data were compilated, and demographic and statistical models were employed to estimate key measures and indicators of demographic change. The large majority of the data and estimates were compiled and made at the registration district level for the period 1851-1911. In addition decennial inter-county migration flows were estimated for the period 1851-1911. Main Topics: This digital resource is based on two sources of official data that were published regularly for the registration districts and counties of England and Wales between 1851 and 1911. The published volumes of seven censuses provided most of the information on age, sex and marital status distributions. Census data also provided information on occupational distributions. Vital registration provided the intercensal numbers of births, deaths and marriages. Some supplementary data were also used such as the series of English life tables. From these data demographic and statistical models were employed to estimate key measures and indicators of demographic change at the registration district level, such as population growth rates, population density, birth and death rates, net migration rates, Coale's indices of nuptiality, marital fertility and overall fertility, estimates of the timing of the onset of fertility transition, estimates of life expectancy and more. Most of the district level variables are available for the period 1841-1911, although the data is less complete and less reliable for the period 1841-1850. In addition, decennial inter-county migration flows were estimated from lifetime migration information for the period 1851-1911. Please note: this study does not include information on named individuals and would therefore not be useful for personal family history research. Convenience sample Compilation or synthesis of existing material

In 2025, India overtook China as the world's most populous country and now has almost 1.46 billion people. China now has the second-largest population in the world, still with just over 1.4 billion inhabitants, however, its population went into decline in 2023. Global population As of 2025, the world's population stands at almost 8.2 billion people and is expected to reach around 10.3 billion people in the 2080s, when it will then go into decline. Due to improved healthcare, sanitation, and general living conditions, the global population continues to increase; mortality rates (particularly among infants and children) are decreasing and the median age of the world population has steadily increased for decades. As for the average life expectancy in industrial and developing countries, the gap has narrowed significantly since the mid-20th century. Asia is the most populous continent on Earth; 11 of the 20 largest countries are located there. It leads the ranking of the global population by continent by far, reporting four times as many inhabitants as Africa. The Demographic Transition The population explosion over the past two centuries is part of a phenomenon known as the demographic transition. Simply put, this transition results from a drastic reduction in mortality, which then leads to a reduction in fertility, and increase in life expectancy; this interim period where death rates are low and birth rates are high is where this population explosion occurs, and population growth can remain high as the population ages. In today's most-developed countries, the transition generally began with industrialization in the 1800s, and growth has now stabilized as birth and mortality rates have re-balanced. Across less-developed countries, the stage of this transition varies; for example, China is at a later stage than India, which accounts for the change in which country is more populous - understanding the demographic transition can help understand the reason why China's population is now going into decline. The least-developed region is Sub-Saharan Africa, where fertility rates remain close to pre-industrial levels in some countries. As these countries transition, they will undergo significant rates of population growth.

Butterflies are important bioindicators that can be used to monitor the effects of climate change, particularly in montane environments. Changes in butterfly population size over time, reflective of indicator life stages, can signal changes that have occurred or are occurring in their environment indicating ecosystem health. From the perspective of understanding butterflies as bioindicators in these systems, it is essential to identify influential environmental variables at each life stage that have the greatest effect on population dynamics. Life stage hypothesis modeling was used to assess the effects of multiple temperature and precipitation metrics on the population growth rate of a Parnassius clodius butterfly population from 2009 to 2018. Extreme maximum temperatures during the larval-pupal life stages were identified to have a significant negative effect on population growth rate. We speculate that higher temperatures during the spring ephemeral host plant’s flowering, and P. clodius' larval stage, may lead to earlier plant senescence and lower P. clodius growth. Because Parnassius butterflies are well studied from a global perspective, results may aid in understanding the potential indicator life stages of other insect species in montane environments to climatic changes. Findings from this study demonstrate the value in assessing a butterfly species’ response to short-term weather variation or long-term climatic changes at each life stage in order to protect and conserve insects and their interactions with other organisms. Methods Butterfly Mark-Recapture Mark-recapture methods were used to study a population of P. clodius at Pilgrim Creek in Grand Teton National Park, Wyoming, USA across annual flight seasons between 2009 and 2018 during June and July. Surveys were not carried out in 2012 and 2013. Six 50m x 50m plots a minimum of 100m apart, were located using GPS units, flagged prior to the flight season of P. clodius, and surveyed each year. Survey plots were initially established in 2000 in an effort to balance increasing the area sampled, decreasing the number of recaptures, and maintaining independent sampling within a single meadow (Auckland et al. 2004). Mark-recapture surveys began a few days after the beginning of the flight season and continued until only one or two butterflies per plot were caught during a survey period. Plots were monitored daily if weather permitted throughout each flight season. Surveys were conducted when temperatures were above 21°C, wind was <16kmh-1, and clouds were not obscuring the sun. If weather prevented all six plots from being sampled in one day, the rotation was completed the following day. The order in which plots were surveyed rotated throughout the flight season to vary the time of day surveys were conducted for each plot. Two people surveyed each plot for 20 minutes between 10:00 and 17:00 hours. All P. clodius butterflies in a plot were netted by hand and held in glassine envelopes until the survey ended. The time of capture and activity of the butterfly when captured (i.e., nectaring, chasing, stationary) was noted on each envelope. Following each survey, captured butterflies were marked with a permanent marker on each hindwing with an identification number indicating the plot it was caught in and the butterfly’s individual number (Figure 1). The number corresponded to the consecutive butterfly count throughout a season. The following metrics were recorded for each captured butterfly: date, plot number, identification number, time of capture, sex, and whether the butterfly was a recapture. After data were collected for each butterfly and butterfly markings were added, all butterflies were released in the center of each plot by placing butterflies on surrounding plants, rather than being placed on the ground where they could be stung by ants. Weather Variable Selection A list of meaningful weather variables expected to affect populations of P. clodius butterflies was formulated based upon previous research that identified influential weather variables affecting the population dynamics of other Parnassius species (Matter et al. 2011, Roland and Matter 2013, 2016, Matter and Roland 2017) as well as our observations at Pilgrim Creek over time. The list of explanatory variables included: date of snowmelt, maximum snow water equivalent (cm), mean snow water equivalent (cm), mean temperature (C), mean maximum temperature (C), extreme maximum temperature (C), mean minimum temperature (C), extreme minimum temperature (C), accumulated rainfall (cm) from 2008 to 2018 (Table 1). All temperature data were sourced from the USDA Base Camp SNOTEL site located at 43.93333°N, -110.45000°W at 2151 m in elevation and 10.7 km from Pilgrim Creek meadow. All snow water equivalent and precipitation data were sourced from the USDA Snake River Station SNOTEL site located at 44.13333°N, -110.66667°W at 2109 m in elevation and 25.1 km from Pilgrim Creek meadow. Analysis The goal of these analyses was to identify which weather variables, known to affect other butterfly species, explain variation in P. clodius population growth through each life stage. Estimates of population change were calculated for the Pilgrim Creek, WY population of P. clodius for the duration of the study (i.e., 2009-2011, 2014-2018). Calculations of population change (Rt) were estimated as the ratio of the number of caught butterflies (Nt) for one flight season after accounting for effort, relative to the number of caught butterflies of the previous flight season after accounting for effort. Effort was measured as the number of two-person 20-minute plot surveys conducted during each flight season. Rt was calculated as log10((Nt+1/effortt+1)/(Nt/effortt)) (Roland and Matter 2016). This simplified approach to estimating population size used in previous research (Roland and Matter 2016) uses information regarding known observed butterflies in combination with standardized sampling effort allowing researchers to produce tangible values. Our estimate of population size was included in each model to evaluate density dependence as an explanatory variable on population growth. Population size was estimated by taking the log of the number of caught butterflies divided by the number of surveys conducted for each flight season (i.e., log10(Nt/effortt)). A life stage hypothesis modeling approach was used to evaluate the effect of each chosen weather variable during each life stage on the population growth (i.e., Rt) of P. clodius butterflies. The observed values for each weather variable were partitioned by life stage to evaluate each variable’s effect during each stage of the P. clodius lifecycle. Dates of each life stage were estimated from researcher observations (L. Crees, personal communication, October 2022) of P. clodius over time to capture the estimated beginning and end dates of each life stage. We categorized the egg stage as July 21 - April 30, the larva-pupa stage as May 1 - May 31, and the adult stage June 1 - July 20. Each weather variable was tabulated for the egg stage, larva-pupa stage, and the adult butterfly stage (Table 1). The larva and pupa life stages were combined due to the uncertainty of the date of when one stage ended and the other began. Correlation tests were conducted between all explanatory weather variables to prevent the inclusion of highly correlated variables in models, which could lead to multicollinearity and unreliable estimates. Uncorrelated precipitation and temperature explanatory variables and logNt (i.e., density dependence) were included in each generalized linear model. Then, generalized linear models using the glm function in R were fit to evaluate which explanatory weather variables were the most descriptive in explaining population change (Rt). Additionally, generalized additive models using the gam function in R were fit to evaluate quadratic relationships between population change (Rt) and explanatory weather variables. AIC comparison between models was conducted to determine the most informative explanatory variables. Following generalized linear models that included logNt and interactions between the most informative temperature and precipitation explanatory variables were used to evaluate potential interactive effects between weather variables on population change. Final AIC comparison was conducted to determine the best model to describe population change for each P. clodius life stage.

IntroductionDemographic changes as a result of evacuation in the acute phase of the 2011 Fukushima nuclear disaster are not well evaluated. We estimated post-disaster demographic transitions in Minamisoma City—located 14–38 km north of the nuclear plant—in the first month of the disaster; and identified demographic factors associated with the population remaining in the affected areas.Materials and methodsWe extracted data from the evacuation behavior survey administered to participants in the city between July 11, 2011 and April 30, 2013. Using mathematical models, we estimated the total population in the city after the disaster according to sex, age group, and administrative divisions of the city. To investigate factors associated with the population remaining in place after the disaster, a probit regression model was employed, taking into account sex, age, pre-disaster dwelling area, and household composition.ResultsThe overall population decline in Minamisoma City peaked 11 days after the disaster, when the population reached 7,107 people—11% of the pre-disaster level. The remaining population levels differed by area: 1.1% for mandatory evacuation zone, 12.5% for indoor sheltering zone, and 12.6% for other areas of the city. Based on multiple regression analyses, higher odds for remaining in place were observed among men (odds ratio 1.72 [95% confidence intervals 1.64–1.85]) than women; among people aged 40–64 years (1.40 [1.24–1.58]) than those aged 75 years or older; and among those living with the elderly, aged 70 years or older (1.18 [1.09–1.27]) or those living alone (1.71 [1.50–1.94]) than among those who were not.DiscussionDespite the evacuation order, some residents of mandatory evacuation zones remained in place, signaling the need for preparation to respond to their post-disaster needs. Indoor sheltering instructions may have accelerated voluntary evacuation, and this demonstrates the need for preventing potentially disorganized evacuation in future nuclear events.

dataDroso - census dataDemographic transitions of Drosophyllum lusitanicum populations recorded in annual censuses (from 2011 to 2015) in five populations. These data are used to quantify vital rates of above-ground individuals.dataDroso.csvdataDrosoSB - seed bankSeed fates (in a binary format) inferred from two experiments. These data are used to quantify the transitions related to the seed bank and associated parameter uncertainties.dataDrosoSB.csvBayModel - Bayesian vital rate GLMMsExecutes and saves the results of a Bayesian model quantifying all vital rates; illustrates basic diagnostics that can be run on the results of an MCMC run (i.e., the posterior parameter distribution) to check for model convergence and autocorrelation of the posterior samples.BayModel.RmcmcOUT - parameter samplesIn case the reader wishes to forego the step of fitting the Bayesian models, we provided a mcmcOUT.csv file with 1000 posterior parameter values for each of the parameters estimated with Bayesian m...

Insecticide Netting In this study, we focused on two types of long-lasting insecticide netting (LLIN) that have been found to be effective for managing various stored product insect pests. One is an LLIN consisting of a polyethylene netting (2 × 2 mm mesh, D-Terrence, Vestergaard, Inc., Lausanne, Switzerland) with 0.4% deltamethrin active ingredient (a.i.), while the second one is Carifend® net (40 deniers with mesh size 97 knots/cm2; BASF AG, Ludwigshafen, Germany) containing 0.34% α-cypermethrin (a.i.). Foundational Model We used a standard Lefkovitch matrix model to project population growth for Tribolium castaneum, with four life stages (e.g., egg, larva, pupa, and adult;(Lefkovitch,1965). In equation (1), the Leftkovitch matrix L matrix (4 × 4) represents the life-stage structure of T. castaneum which has an egg, larvae, pupae, and an adult, where only the adults contribute to the fecundity, F. By multiplying L with the population vector ni(t), where t is time step (e.g., generation) and i is a life stage, we obtain the resultant vector ni(t + 1), which reveals the distribution of individuals across different life stages in the subsequent time period. In equation (1), P1 represents the probability of staying in the egg stage and G1 is the probability of moving from the egg to the larval stage, P2 is the probability of staying in the larval stage, G2 is probability of moving from the larval stage to pupal stage, P3 is the probability of staying in the pupal stage, G3 is probability of moving from the pupal stage to adult, while P4 is the probability of staying in the adult stage (Figure 1). Model Parameterization and Scenarios We simulated population outcomes for up to 15 generations by using the life table data for T. castaneum using the R package popbio. Survivorship, fecundity, and transition information for each stage were derived from the literature (summarized in Table 1). The developmental duration of eggs, larvae, and pupae were 3.82 ± 0.005, 22.81 ± 0.67, and 6.24 ± 0.071 days (Kollros,1944). The average life duration of the adult used in this study was 221.16 days (Park et al., 1961). We used 94 offspring for fertility from the study Park et al.,(1965) and 99% rate of eclosion from pupae to adult. In order to explore the sensitivity of the base model to changes in mortality and fecundity, both of these parameters were systematically varied from near zero to their maximum value given in the base model (e.g., F = 94, P4 = 0.871). The parameters were varied alone or in combination and the resulting population growth was plotted. All plots were created using ggplot2 (Wickham, 2016) in R software (R Core Team, 2022). Three empirical scenarios from the literature were modeled containing estimates of fecundity reduction only, survivorship reduction only, or both fecundity and survivorship reduction when using LLIN (R.V. Wilkins et al., 2021; Gerken et al., 2021;Scheff et al., 2021, Scheff et al., 2023; Table 2). An individual projection matrix was constructed for each of the three scenarios and combinations of the reductions in fecundity, survivorship, or both. Population growth and proportion in each life stage was projected for 15 generations for each case, including the base model. Overall variation and oscillation were calculated to compare trends among proportion of life stages in each case. In order to compare differences in population sizes between cases for all generations and for generation 15 only, population sizes for each generation were bootstrapped 1000 times to provide iterative replication. The bootstrapped data were then compared one case to another using proc ttest in SAS (Version 9.4) for all generations and for generation 15 only. In addition, a sensitivity analysis was performed to determine which stage should be targeted to most greatly affect the population growth after exposure to the netting. Moreover, a mortality function based on empirical data with LLIN exposure collected in the laboratory on T. castaneum was implemented. The three scenarios are derived from: Gerken, A. R., J. F. Campbell, S. R. Abts, F. Arthur, W. R. Morrison, and D. S. Scheff. 2021. “Long-Lasting Insecticide-Treated Netting Affects Reproductive Output and Mating Behavior in Tribolium castaneum (Coleoptera: Tenebrionidae) and Trogoderma variabile (Coleoptera: Dermestidae).” Edited by Rizana Mahroof. Journal of Economic Entomology 114 (6): 2598–2609. https://doi.org/10.1093/jee/toab204. Scheff, D. S., A. R. Gerken, W. R. Morrison, J. F. Campbell, F. H. Arthur, and K. Y. Zhu. 2021. “Assessing Repellency, Movement, and Mortality of Three Species of Stored Product Insects after Exposure to Deltamethrin-Incorporated Long-Lasting Polyethylene Netting.” Journal of Pest Science 94 (3): 885–98. https://doi.org/10.1007/s10340-020-01326-3. Wilkins, R.V., J.F. Campbell, K.Y. Zhu, L.A. Starkus, T. McKay, and W.R. Morrison. 2021. “Long-Lasting Insecticide-Incorporated Netting and Interception Traps at Pilot-Scale Warehouses and Commercial Facilities Prevents Infestation by Stored Product Beetles.” Frontiers in Sustainable Food Systems 4: https://doi.org/10.3389/fsufs.2020.561820. Resources in this dataset:

Resource Title: Script for Modeling of LLIN effects on T. castaneum MS File Name: ranabhat_etal_modeling_MS_r_script_final_agdata_commons.R

Approximately 25% of mammals are currently threatened with extinction, a risk that is amplified under climate change. Species persistence under climate change is determined by the combined effects of climatic factors on multiple demographic rates (survival, development, reproduction), and hence, population dynamics. Thus, to quantify which species and regions on Earth are most vulnerable to climate-driven extinction, a global understanding of how different demographic rates respond to climate is urgently needed. Here, we perform a systematic review of literature on demographic responses to climate, focusing on terrestrial mammals, for which extensive demographic data are available. To assess the full spectrum of responses, we synthesize information from studies that quantitatively link climate to multiple demographic rates. We find only 106 such studies, corresponding to 87 mammal species. These 87 species constitute < 1% of all terrestrial mammals. Our synthesis reveals a strong mismatch between the locations of demographic studies and the regions and taxa currently recognized as most vulnerable to climate change. Surprisingly, for most mammals and regions sensitive to climate change, holistic demographic responses to climate remain unknown. At the same time, we reveal that filling this knowledge gap is critical as the effects of climate change will operate via complex demographic mechanisms: a vast majority of mammal populations display projected increases in some demographic rates but declines in others, often depending on the specific environmental context, complicating simple projections of population fates. Assessments of population viability under climate change are in critical need to gather data that account for multiple demographic responses, and coordinated actions to assess demography holistically should be prioritized for mammals and other taxa.

Methods For each mammal species i with available life-history information, we searched SCOPUS for studies (published before 2018) where the title, abstract, or keywords contained the following search terms:

Scientific species namei AND (demograph* OR population OR life-history OR "life history" OR model) AND (climat* OR precipitation OR rain* OR temperature OR weather) AND (surv* OR reprod* OR recruit* OR brood OR breed* OR mass OR weight OR size OR grow* OR offspring OR litter OR lambda OR birth OR mortality OR body OR hatch* OR fledg* OR productiv* OR age OR inherit* OR sex OR nest* OR fecund* OR progression OR pregnan* OR newborn OR longevity).

We used the R package taxize (Chamberlain and Szöcs 2013) to resolve discrepancies in scientific names or taxonomic identifiers and, where applicable, searched SCOPUS using all scientific names associated with a species in the Integrated Taxonomic Information System (ITIS; http://www.itis.gov).

We did not extract information on demographic-rate-climate relationships if:

A study reported on single age or stage-specific demographic rates (e.g., Albon et al. 2002; Rézoiki et al. 2016)
A study used an experimental design to link demographic rates to climate variation (e.g., Cain et al. 2008)
A study considered the effects of climate only indirectly or qualitatively. In most cases, this occurred when demographic rates differed between seasons (e.g., dry vs. wet season) but were not linked explicitly to climatic factors (e.g., varying precipitation amount between seasons) driving these differences (e.g., de Silva et al. 2013; Gaillard et al. 2013).

We included several studies of the same population as different studies assessed different climatic variables or demographic rates or spanned different years (e.g., for Rangifer tarandus platyrhynchus, Albon et al. 2017; Douhard et al. 2016).

We note that we can miss a potentially relevant study if our search terms were not mentioned in the title, abstract, or keywords. To our knowledge, this occurred only once, for Mastomys natalensis (we included the relevant study [Leirs et al. 1997] into our review after we were made aware that it assesses climate-demography relationships in the main text).

Lastly, we checked for potential database bias by running the search terms for a subset of nine species in Web of Science. The subset included three species with > three climate-demography studies published and available in SCOPUS (Rangifer tarandus, Cervus elaphus, Myocastor coypus); three species with only one climate-demography study obtained from SCOPUS (Oryx gazella, Macropus rufus, Rhabdomys pumilio); and another three species where SCOPUS did not return any published study (Calcochloris obtusirostris, Cynomops greenhalli, Suncus remyi). Species in the three subcategories were randomly chosen. Web of Science did not return additional studies for the three species where SCOPUS also failed to return a potentially suitable study. For the remaining six species, the total number of studies returned by Web of Science differed, but the same studies used for this review were returned, and we could not find any additional studies that adhered to our extraction criteria.

Description of key collected data

From all studies quantitatively assessing climate-demography relationships, we extracted the following information:

Geographic location - The center of the study area was always used. If coordinates were not provided in a study, we assigned coordinates based on the study descriptions of field sites and data collection.
Terrestrial biome - The study population was assigned to one of 14 terrestrial biomes (Olson et al. 2001) corresponding to the center of the study area. As this review is focused on general climatic patterns affecting demographic rates, specific microhabitat conditions described for any study population were not considered.
Climatic driver - Drivers linked to demographic rates were grouped as either local/regional precipitation & temperature values or derived indices (e.g., ENSO, NAO). The temporal extent (e.g., monthly, seasonal, annual, etc.) and aggregation type (e.g., minimum, maximum, mean, etc.) of drivers was also noted.
Demographic rate modeled - To facilitate comparisons, we grouped the demographic rates into either survival, reproductive success (i.e., whether or not reproduction occurre, reproductive output (i.e., number or rate of offspring production), growth (including stage transitions), or condition that determines development (i.e., mass or size). 
Stage or sex modeled - We retrieved information on responses of demographic rates to climate for each age class, stage, or sex modeled in a given study.
Driver effect - We grouped effects of drivers as positive (i.e., increased demographic rates), negative (i.e., reduced demographic rate), no effect, or context-dependent (e.g., positive effects at low population densities and now effect at high densities). We initially also considered nonlinear effects (e.g., positive effects at intermediate values and negative at extremes of a driver), but only 4 studies explicitly tested for nonlinear effects, by modelling squared or cubic climatic drivers in combination with driver interactions. We therefore considered nonlinear demographic effects as context dependent.  
Driver interactions - We noted any density dependence modeled and any non-climatic covariates included (as additive or interactive effects) in the demographic-rate models assessing climatic effects.
Future projections of climatic driver - In studies that indicated projections of drivers under climate change, we noted whether drivers were projected to increase, decrease, or show context-dependent trends. For studies that provided no information on climatic projections, we quantified projections as described in Detailed description of climate-change projections below (see also climate_change_analyses_mammal_review.R).

Major disturbance events can have large impacts on the demography and dynamics of animal populations. Hurricanes are one example of an extreme climatic event, predicted to increase in intensity due to climate change, and thus expected to be a considerable threat to population viability. However, little is understood about the underlying demographic mechanisms shaping population response following these extreme disturbances. Here, we analyze 45 years of the most comprehensive free-ranging nonhuman primate demographic dataset to determine the effects of major hurricanes on the variability and maintenance of long-term population fitness. For this, we use individual-level data to build matrix population models and perform perturbation analyses. Despite reductions in population growth rate mediated through reduced fertility, our study reveals a demographic buffering during hurricane years. As long as survival does not decrease, our study shows that hurricanes do not result in detrimental eff...

The American black bear (Ursus americanus) has one of the broadest geographic distributions of any mammalian carnivore in North America. Populations occur from high to low elevations and from mesic to arid environments, and their demographic traits have been documented in a wide variety of environments. However, the demography of American black bears in semiarid environments, which comprise a significant portion of the geographic range, is poorly documented. To fill this gap in understanding, we used data from a long-term mark-recapture study of black bears in the semiarid environment of eastern Utah, USA. Cub and yearling survival were low and adult survival was high relative to other populations. Adult life stages had the highest reproductive value, comprised the largest proportion of the population, and exhibited the highest elasticity contribution to the population growth rate (i.e., λ). Vital rates of black bears in this semiarid environment are skewed toward higher survival of adu..., Mark-Recapture study We estimated survival rates from long-term mark-recapture data gathered as part of a 27-year study on American black bears of the East Tavaputs Plateau. During the first 12 years of the study (June to August 1991-2003) female bears were captured and radio-collared, and all bears were tagged in the ear, except for cubs and yearlings. For the entire study (1992 – 2019), collared females were visited in their dens annually during their winter hibernation to count newborn cubs and surviving yearlings. Age of individual bears was determined by 2 methods: (1) direct observation of cubs or yearlings (i.e., year of birth was known) or (2) cementum annuli analysis of a cross-section of the root of an extracted premolar (Palochak, 2004; Willey, 1974). The data we used to derive survival and fecundity rates consisted of the ID_number, cohort (cub, yearling, subadult, prime-aged adult, and old adult), age in years, sex (female, male, unknown), number of cubs, number of yearling..., , # Demography of American black bears (Ursus americanus) in a semiarid environment

https://doi.org/10.5061/dryad.98sf7m0t8

Description of the data and file structure

Files and variables

File: Age-Specific_Survivorship.csv

Description:Â

This CSV file contains data collected from a mark-recapture study during 1991 - 2019. We calculated the age-specific average survival rate for each cohort. The average survival rate of each cohort was later used in the matrix transition model as matrix elements to retrieve important demographic information about this population of North American black bears (Ursus americanus) found in a semiarid environment.Â

Variables

• Cohort:Â Yearling = 1 year to 2 years;Â Subadult = 2 years to 4 years;Â Prime-aged Adult = 4 years to 14 years;Â Old Adult = 15 years and older.
• Sex:Â M = male; F = female; U = unknown
• Cubs and Yearlings:Â NV = not visited; number = number of cubs or yearlings presen...

Estimation of responses of organisms to their environment using experimental manipulations, and comparison of such responses across sets of species, is one of the primary tools in ecology research. The most common approach is to compare response of a single life stage of species to an environmental factor and use this information to draw conclusions about population dynamics of these species. Such approach ignores the fact that interspecific fitness differences measured at a single life stage are not directly comparable and cannot be extrapolated to lifetime fitness of individuals and thus species’ population dynamics. Comparison of one life stage only while omitting demographic information can strongly bias conclusions, both in experimental studies with a few species, and in large comparative studies. We illustrate the effect of this omission using both an exaggerated fictitious example, and biological data on congeneric species differing in their demography. We are showing, taking simple assumptions, that different demography can completely revert conclusions reached by a comparison based on an experiment focusing on a single life stage. We show that a "demographic correction", namely translating observed effects into differences in outcomes of demographic models, is a solution to this problem. It requires turning the detected effects from the experiment into changes of transition probabilities of projection matrix models. Although such solution is limited by the low number of species with demographic data available, we believe that existing data (and data likely to be collected in the near future) permit at least approximate handling of this problem.

In 2025, there are six countries, all in Sub-Saharan Africa, where the average woman of childbearing age can expect to have between 5-6 children throughout their lifetime. In fact, of the 20 countries in the world with the highest fertility rates, Afghanistan and Yemen are the only countries not found in Sub-Saharan Africa. High fertility rates in Africa With a fertility rate of almost six children per woman, Chad is the country with the highest fertility rate in the world. Population growth in Chad is among the highest in the world. Lack of healthcare access, as well as food instability, political instability, and climate change, are all exacerbating conditions that keep Chad's infant mortality rates high, which is generally the driver behind high fertility rates. This situation is common across much of the continent, and, although there has been considerable progress in recent decades, development in Sub-Saharan Africa is not moving as quickly as it did in other regions. Demographic transition While these countries have the highest fertility rates in the world, their rates are all on a generally downward trajectory due to a phenomenon known as the demographic transition. The third stage (of five) of this transition sees birth rates drop in response to decreased infant and child mortality, as families no longer feel the need to compensate for lost children. Eventually, fertility rates fall below replacement level (approximately 2.1 children per woman), which eventually leads to natural population decline once life expectancy plateaus. In some of the most developed countries today, low fertility rates are creating severe econoic and societal challenges as workforces are shrinking while aging populations are placin a greater burden on both public and personal resources.

Data set of a community based cross-sectional survey done to find the prevalence , its correlates and patterns in a population of a district in southern Kerala, IndiaBackground: Multi-morbidity is the coexistence of multiple chronic conditions in the same individual. With advancing epidemiological and demographic transitions, the burden of multi-morbidity is expected to increase India. The state of Kerala in India is also in an advanced phase of epidemiological transition. However, very limited data on prevalence of multi-morbidity are available in the Kerala population.

Methods: A cross sectional survey was conducted among 410 participants in the age group of 30-69 years. A multi-stage cluster sampling method was employed to identify the study participants. Every eligible participant in the household were interviewed to assess the household prevalence. A structured interview schedule was used to assess socio-demographic variables, behavioral risk factors and prevailing clinical conditions, PHQ-9 questionnaire for screening of depression and active measurement of blood sugar and blood pressure. Co-existence of two or more conditions out of 11 was used as multi-morbidity case definition. Bivariate analyses were done to understand the association between socio-demographic factors and multi-morbidity. Logistic regression analyses were performed to estimate the effect size of these variables on multi-morbidity.

Results: Overall, the prevalence of multi-morbidity was 45.4% (95% CI: 40.5-50.3%). Nearly a quarter of study participants (25.4%) reported only one chronic condition (21.3-29.9%). Further, 30.7% (26.3-35.5), 10.7% (7.9-14.2), 3.7% (2.1-6.0) and 0.2% reported two, three, four and five chronic conditions, respectively. Nearly seven out of ten households (72%, 95%CI: 65-78%) had at least one person in the household with multi-morbidity and one in five households (22%, 95%CI: 16.7-28.9%) had more than one person with multi-morbidity. With every year increase in age, the propensity for multi-morbidity increased by 10 percent (OR=1.1; 95% CI: 1.1-1.2). Males and participants with low levels of education were less likely to suffer from multi-morbidity while unemployed and who do recommended level of physical activity were significantly more likely to suffer from multi-morbidity. Diabetes and hypertension was the most frequent dyad.

Conclusion: One of two participants in the productive age group of 30-69 years report multi-morbidity. Further, seven of ten households have at least one person with multi-morbidity. Preventive and management guidelines for chronic non-communicable conditions should focus on multi-morbidity especially in the older age group. Health-care systems that function within the limits of vertical disease management and episodic care (e.g., maternal health, tuberculosis, malaria, cardiovascular disease, mental health etc.) require optimal re-organization and horizontal integration of care across disease domains in managing people with multiple chronic conditions.

Key words: Multi-morbidity, cross-sectional, household, active measurement, rural, India, pattern

File Name: Matrix_model_transition_data.csvResource Description: This data set includes data on 25 transitions of a matrix demographic model of two invasive Vincetoxicum species from six field and forest populations in New York State.Resource Title: Variable definitions. File Name: Matrix_model_metadata.csvResource Description: Definitions of variables including equations for each transition and definitions of the lower-level vital rates in the equationsResource Title: Vital Rate definitions. File Name: Vital_Rate.csvResource Description: Vital Rate definitions of lower-level vital rates used in transition equations - to be substituted into the Data Dictionary for full definition of each transition equation.Resource Title: Data Dictionary. File Name: Matrix_Model_transition_data_DD.csvResource Description: See Vital Rate resource for definitions of lower-level vital rates used in transition equations where noted.Resource Title: Matrix model vital rate data for Vincetoxicum species. File Name: Matrix_model_vital rate_data.csvResource Description: This data set includes data on 20 lower-level vital rates used in the calculation of transitions of a matrix demographic model of two invasive Vincetoxicum species in New York State as well as definitions of the vital rates. (File added on 12/7/2018)Resource Software Recommended: Microsoft Excel,url: https://office.microsoft.com/excel/

The total fertility rate of the world has dropped from around 5 children per woman in 1950, to 2.2 children per woman in 2025, which means that women today are having fewer than half the number of children that women did 75 years ago. Replacement level fertility This change has come as a result of the global demographic transition, and is influenced by factors such as the significant reduction in infant and child mortality, reduced number of child marriages, increased educational and vocational opportunities for women, and the increased efficacy and availability of contraception. While this change has become synonymous with societal progress, it does have wide-reaching demographic impact - if the global average falls below replacement level (roughly 2.1 children per woman), as is expected to happen in the 2050s, then this will lead to long-term population decline on a global scale. Regional variations When broken down by continent, Africa is the only region with a fertility rate above the global average, and, alongside Oceania, it is the only region with a fertility rate above replacement level. Until the 1980s, the average woman in Africa could expect to have 6-7 children over the course of their lifetime, and there are still several countries in Africa where women can still expect to have 5 or more children in 2025. Historically, Europe has had the lowest fertility rates in the world over the past century, falling below replacement level in 1975. Europe's population has grown through a combination of migration and increasing life expectancy, however even high immigration rates could not prevent its population from going into decline in 2021.

1.Natural and anthropogenic forest canopy disturbances significantly alter forest dynamics and lead to multi-dimensional shifts in the forest understorey. An understorey plant's ability to exploit alterations to the light environment caused by canopy disturbance leads to changes in population dynamics. The purpose of this work was to determine if population growth of a species adapted to low light increases in response to additional light inputs caused by canopy disturbance, or alternatively, declines due to long-term selection under low light conditions. 2.To address this question, we quantified the demographic response of an understorey herb to three contrasting forest canopy disturbances (ice storms, tent caterpillar defoliation and lightning strikes) that encompass a broad range of disturbance severity. We used a model shade-adapted understorey species, Panax quinquefolius, to parameterize stage-based matrix models. Asymptotic growth rates, stochastic growth rates and simulations of transient dynamics were used to quantify the population-level response to canopy disturbance. Life table response experiments were used to partition the underlying controls over differences in population growth rates. 3.Population growth rates at all three disturbed sites increased in the transition period immediately after the canopy disturbance relative to the transition period prior to disturbance. Stochastic population models revealed that growth rates increased significantly in simulations that included disturbance matrices relative to those simulations that excluded disturbance. Additionally, transient models indicated that population size (n) was larger for all three populations when the respective disturbance matrix was included in the model. 4.Synthesis Obligate shade species are most likely to be pre-adapted to take advantage of canopy gaps and light influx to a degree, and this pre-adaptation may be due to long-term selection under dynamic old growth forest canopies. We propose a model whereby population performance is represented by a parabolic curve where performance is maximized under intermediate levels of canopy disturbance. This study provides new evidence to aid our understanding of the population-level response of understorey herbs to disturbances whose frequency and intensity are predicted to increase as global climates continue to shift.

For highly social species, population dynamics depend on hierarchical demography that links local processes, group dynamics, and population growth. Here, we describe a stage-structured matrix model of hierarchical demography, which provides a framework for understanding social influences on population change. Our approach accounts for dispersal and affords insight into population dynamics at multiple scales. The method has close parallels to integral projection models, but focuses on a discrete characteristic (group size). Using detailed long-term records for meerkats (Suricata suricatta), we apply our model to explore patterns of local density dependence and implications of group size for group and population growth. Taking into account dispersers, the model predicts a per-capita growth rate for social groups that declines with group size. It predicts that larger social groups should produce a greater number of new breeding groups; thus dominant breeding females (responsible for...

Summary of model elements and data sources for NACM.

Each year Eurostat collects demographic data at regional level from EU, EFTA and Candidate countries as part of the Population Statistics data collection. POPSTAT is Eurostat’s main annual demographic data collection and aims to gather information on demography and migration at national and regional levels by various breakdowns (for the full overview see the Eurostat dedicated section). More specifically, POPSTAT collects data at regional levels on:

• population stocks;
• vital events (live births and deaths).

Each country must send the statistics for the reference year (T) to Eurostat by 31 December of the following calendar year (T+1). Eurostat then publishes the data in March of the calendar year after that (T+2).

Demographic data at regional level include statistics on the population at the end of the calendar year and on live births and deaths during that year, according to the official classification for statistics at regional level (NUTS - nomenclature of territorial units for statistics) in force in the year. These data are broken down by NUTS 2 and 3 levels for EU countries. For more information on the NUTS classification and its versions please refer to the Eurostat dedicated pages. For EFTA and Candidate countries the data are collected according to the agreed statistical regions that have been coded in a way that resembles NUTS.

The breakdown of demographic data collected at regional level varies depending on the NUTS/statistical region level. These breakdowns are summarised below, along with the link to the corresponding online table:

NUTS 2 level

• Population by sex, age and region of residence — demo_r_d2jan
• Population on 1 January by age group, sex and region of residence — demo_r_pjangroup
• Live births by mother's age, mother's year of birth and mother's region of residence — demo_r_fagec
• Deaths by sex, age, and region of residence — demo_r_magec

NUTS 3 level

• Population on 1 January by sex, age group and region of residence — demo_r_pjangrp3
• Population on 1 January by broad age group, sex and region of residence — demo_r_pjanaggr3
• Live births (total) by region of residence — demo_r_births
• Live births by five-year age group of the mothers and region of residence — demo_r_fagec3
• Deaths (total) by region of residence — demo_r_deaths
• Deaths by five-year age group, sex and region of residence — demo_r_magec3

This more detailed breakdown (by five-year age group) of the data collected at NUTS 3 level started with the reference year 2013 and is in accordance with the European laws on demographic statistics. In addition to the regional codes set out in the NUTS classification in force, these online tables include few additional codes that are meant to cover data on persons and events that cannot be allocated to any official NUTS region. These codes are denoted as CCX/CCXX/CCXXX (Not regionalised/Unknown level 1/2/3; CC stands for country code) and are available only for France, Hungary, North Macedonia and Albania, reflecting the raw data as transmitted to Eurostat.

For the reference years from 1990 to 2012 all countries sent to Eurostat all the data on a voluntary basis, therefore the completeness of the tables and the length of time series reflect the level of data received from the responsible National Statistical Institutes’ (NSIs) data provider. As a general remark, a lower data breakdown is available at NUTS 3 level as detailed:

• population data are broken down by sex and broad age groups (0-14, 15-64 and 65 or more). The data have this disaggregation since the reference year 2007 for all countries, and even longer for some — demo_r_pjanaggr3
• vital events (live births and deaths) data are available only as totals, without any further breakdown — demo_r_births and demo_r_deaths

Demographic indicators are calculated by Eurostat based on the above raw data using a common methodology for all countries and regions. The regional demographic indicators computed by NUTS level and the corresponding online tables are summarised below:

NUTS 2 level

• Population structure indicators by region of residence (shares of various population age groups, dependency ratios and median age) — demo_r_pjanind2
• Fertility indicators by region of residence — demo_r_find2
• Fertility rates by age and region of residence — demo_r_frate2
• Life table by age, sex and region of residence — demo_r_mlife
• Life expectancy by age, sex and region of residence — demo_r_mlifexp
• Infant mortality rates by region of residence — demo_r_minfind

NUTS 3 level

• Population change - Demographic balance and crude rates at regional level — demo_r_gind3
• Population density by region — demo_r_d3dens
• Population structure indicators by region of residence (shares of various population age groups, dependency ratios and median age) — demo_r_pjanind3
• Fertility indicators by region of residence (total fertility rate, mean age of woman at childbirth and median age of woman at childbirth) — demo_r_find3

Notes:

1) All the indicators are computed for all lower NUTS regions included in the tables (e.g. data included in a table at NUTS 3 level will include also the data for NUTS 2, 1 and country levels).

2) Demographic indicators computed by NUTS 2 and 3 levels are calculated using input data that have different age breakdown. Therefore, minor differences can be noted between the values corresponding to the same indicator of the same region classified as NUTS 2, 1 or country level.

3) Since the reference year 2015, Eurostat has stopped collecting data on area; therefore, the table 'Area by NUTS 3 region (demo_r_d3area)' includes data up to the year 2015 included.

4) Starting with the reference year 2016, the population density indicator is computed using the new data on area 'Area by NUTS 3 region (reg_area3).