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/

In a time of global change, having an understanding of the nature of biotic and abiotic factors that drive a species’ range may be the sharpest tool in the arsenal of conservation and management of threatened species. However, such information is lacking for most tropical and epiphytic species due to the complexity of life history, the roles of stochastic events, and the diversity of habitat across the span of a distribution. In this study, we conducted repeated censuses across the core and peripheral range of Trichocentrum undulatum, a threatened orchid that is found throughout the island of Cuba (species core range) and southern Florida (the northern peripheral range). We used demographic matrix modeling as well as stochastic simulations to investigate the impacts of herbivory, hurricanes, and logging (in Cuba) on projected population growth rates (? and ?s) among sites. Methods Field methods Censuses took place between 2013 and 2021. The longest census period was that of the Peripheral population with a total of nine years (2013–2021). All four populations in Cuba used in demographic modeling that were censused more than once: Core 1 site (2016–2019, four years), Core 2 site (2018–2019, two years), Core 3 (2016 and 2018 two years), and Core 4 (2018–2019, two years) (Appendix S1: Table S1). In November 2017, Hurricane Irma hit parts of Cuba and southern Florida, impacting the Peripheral population. The Core 5 population (censused on 2016 and 2018) was small (N=17) with low survival on the second census due to logging. Three additional populations in Cuba were visited only once, Core 6, Core 7, and Core 8 (Table 1). Sites with one census or with a small sample size (Core 5) were not included in the life history and matrix model analyses of this paper due to the lack of population transition information, but they were included in the analysis on the correlation between herbivory and fruit rate, as well as the use of mortality observations from logging for modeling. All Cuban sites were located between Western and Central Cuba, spanning four provinces: Mayabeque (Core 1), Pinar del Rio (Core 2 and Core 6), Matanzas (Core 3 and Core 5), and Sancti Spiritus (Core 4, Core 7, Core 8). At each population of T. undulatum presented in this study, individuals were studied within ~1-km strips where T. undulatum occurrence was deemed representative of the site, mostly occurring along informal forest trails. Once an individual of T. undulatum was located, all trees within a 5-m radius were searched for additional individuals. Since tagging was not permitted, we used a combination of information to track individual plants for the repeated censuses. These include the host species, height of the orchid, DBH of the host tree, and hand-drawn maps. Individual plants were also marked by GPS at the Everglades Peripheral site. If a host tree was found bearing more than one T. undulatum, then we systematically recorded the orchids in order from the lowest to highest as well as used the previous years’ observations in future censuses for individualized notes and size records. We recorded plant size and reproductive variables during each census including: the number of leaves, length of the longest leaf (cm), number of inflorescence stalks, number of flowers, and the number of mature fruits. We also noted any presence of herbivory, such as signs of being bored by M. miamensis, and whether an inflorescence was partially or completely affected by the fly, and whether there was other herbivory, such as D. boisduvalii on leaves. We used logistic regression analysis to examine the effects of year (at the Peripheral site) and sites (all sites) on the presence or absence of inflorescence herbivory at all the sites. Cross tabulation and chi-square analysis were done to examine the associations between whether a plant was able to fruit and the presence of floral herbivory by M. miamensis. The herbivory was scored as either complete or partial. During the orchid population scouting expeditions, we came across a small population in the Matanzas province (Core 5, within 10 km of the Core 3 site) and recorded the demographic information. Although the sampled population was small (N = 17), we were able to observe logging impacts at the site and recorded logging-associated mortality on the subsequent return to the site. Matrix modeling Definition of size-stage classes To assess the life stage transitions and population structures for each plant for each population’s census period we first defined the stage classes for the species. The categorization for each plant’s stage class depended on both its size and reproductive capabilities, a method deemed appropriate for plants (Lefkovitch 1965, Cochran and Ellner 1992). A size index score was calculated for each plant by taking the total number of observed leaves and adding the length of the longest leaf, an indication of accumulated biomass (Borrero et al. 2016). The smallest plant size that attempted to produce an inflorescence is considered the minimum size for an adult plant. Plants were classified by stage based on their size index and flowering capacity as the following: (1) seedlings (or new recruits), i.e., new and small plants with a size index score of less than 6, (2) juveniles, i.e., plants with a size index score of less than 15 with no observed history of flowering, (3) adults, plants with size index scores of 15 or greater. Adult plants of this size or larger are capable of flowering but may not produce an inflorescence in a given year. The orchid’s population matrix models were constructed based on these stages. In general, orchid seedlings are notoriously difficult to observe and easily overlooked in the field due to the small size of protocorms. A newly found juvenile on a subsequent site visit (not the first year) may therefore be considered having previously been a seedling in the preceding year. In this study, we use the discovered “seedlings” as indicatory of recruitment for the populations. Adult plants are able to shrink or transition into the smaller juvenile stage class, but a juvenile cannot shrink to the seedling stage. Matrix elements and population vital rates calculations Annual transition probabilities for every stage class were calculated. A total of 16 site- and year-specific matrices were constructed. When seedling or juvenile sample sizes were < 9, the transitions were estimated using the nearest year or site matrix elements as a proxy. Due to the length of the study and variety of vegetation types with a generally large population size at each site, transition substitutions were made with the average stage transition from all years at the site as priors. If the sample size of the averaged stage was still too small, the averaged transition from a different population located at the same vegetation type was used. We avoided using transition values from populations found in different vegetation types to conserve potential environmental differences. A total of 20% (27/135) of the matrix elements were estimated in this fashion, the majority being seedling stage transitions (19/27) and noted in the Appendices alongside population size (Appendix S1: Table S1). The fertility element transitions from reproductive adults to seedlings were calculated as the number of seedlings produced (and that survived to the census) per adult plant. Deterministic modeling analysis We used integral projection models (IPM) to project the long-term population growth rates for each time period and population. The finite population growth rate (?), stochastic long-term growth rate (?s), and the elasticity were projected for each matrices using R Popbio Package 2.4.4 (Stubben and Milligan 2007, Caswell 2001). The elasticity matrices were summarized by placing each element into one of three categories: fecundity (transition from reproductive adults to seedling stage), growth (all transitions to new and more advanced stage, excluding the fecundity), and stasis (plants that transitioned into the same or a less advanced stage on subsequent census) (Liu et al. 2005). Life table response experiments (LTREs) were conducted to identify the stage transitions that had the greatest effects on observed differences in population growth between select sites and years (i.e., pre-post hurricane impact and site comparisons of same vegetation type). Due to the frequent disturbances that epiphytes in general experience as well as our species’ distribution in hurricane-prone areas, we ran transient dynamic models that assume that the populations censused were not at stable stage distributions (Stott et al. 2011). We calculated three indices for short-term transient dynamics to capture the variation during a 15-year transition period: reactivity, maximum amplification, and amplified inertia. Reactivity measures a population’s growth in a single measured timestep relative to the stable-stage growth, during the simulated transition period. Maximum amplification and amplified inertia are the maximum of future population density and the maximum long-term population density, respectively, relative to a stable-stage population that began at the same initial density (Stott et al. 2011). For these analyses, we used a mean matrix for Core 1, Core 2 Core 3, and Core 4 sites and the population structure of their last census. For the Peripheral site, we averaged the last three matrices post-hurricane disturbance and used the most-recent population structure. We standardized the indices across sites with the assumption of initial population density equal to 1 (Stott et al. 2011). Analysis was done using R Popdemo version 1.3-0 (Stott et al. 2012b). Stochastic simulation We created matrices to simulate the effects of episodic recruitment, hurricane impacts, herbivory, and logging (Appendix S1: Table S2). The Peripheral population is the longest-running site with nine years of censuses (eight

Abstract The demographic dynamics of the city of Porto Alegre (Southern Brazil) was characterized, in the last decade, by a reduction in fertility rates, low population growth and an increasing number of elderly people, according to data from the IBGE Census (2010). This indicates, therefore, a demographic transition phase. This study aims to relate the demographic transition to the production of the urban space of Porto Alegre in the intercensal period from 2000 to 2010. Urban space production, specifically property development, is analyzed here through the identification of the relationship between urban growth/population growth and the city’s spatial configuration trends.

This study explores the relationship between grandparental co-residence and net fertility – measured as the number of children under five – in Mexico across three key phases of its demographic transition: 1930 (pre-transitional), 1970 (population growth), and 2015 (fertility decline). Using census microdata and Poisson and multinomial regression models, we assess how intergenerational household structures interact with family socioeconomic status and cultural context to influence fertility outcomes. A central innovation is the use of a reconstructed 10% sample of the 1930 census, complemented by an imputation strategy to infer kinship ties not recorded in the original data. This enabled one of the earliest large-scale analyses of family co-residence and reproduction in historical Mexico. Findings reveal that the effects of grandparental co-residence vary by context. In 1930, cohabitation with grandmothers – especially in rural indigenous households – was associated with lower fertility, while cohabitation with grandfathers in non-indigenous rural areas corresponded to higher fertility. In 1970, amid pronatalist policies and economic growth, these effects weakened overall but persisted modestly in rural contexts. By 2015, co-residence – particularly with both grandparents – was associated with higher fertility and lower variability in fertility (CV), suggesting a stabilizing role in reproductive behavior. In contrast, households without grandparents exhibited lower fertility and greater heterogeneity, appearing to lead the shift toward reduced fertility. These findings illustrate how extended family structures both reflect and shape reproductive adaptation across shifting demographic contexts. By integrating evolutionary concepts such as cooperative breeding and social learning biases, the study offers insight into how kin networks can either support or constrain fertility depending on historical, socioeconomic, and cultural conditions. In doing so, it also contributes methodologically by addressing the complexity of nested and interactive effects – an essential step for understanding fertility dynamics in culturally diverse populations undergoing demographic transformation.

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...

There are approximately 8.16 billion people living in the world today, a figure that shows a dramatic increase since the beginning of the Common Era. Since the 1970s, the global population has also more than doubled in size. It is estimated that the world's population will reach and surpass 10 billion people by 2060 and plateau at around 10.3 billion in the 2080s, before it then begins to fall. Asia When it comes to number of inhabitants per continent, Asia is the most populous continent in the world by a significant margin, with roughly 60 percent of the world's population living there. Similar to other global regions, a quarter of inhabitants in Asia are under 15 years of age. The most populous nations in the world are India and China respectively; each inhabit more than three times the amount of people than the third-ranked United States. 10 of the 20 most populous countries in the world are found in Asia. Africa Interestingly, the top 20 countries with highest population growth rate are mainly countries in Africa. This is due to the present stage of Sub-Saharan Africa's demographic transition, where mortality rates are falling significantly, although fertility rates are yet to drop and match this. As much of Asia is nearing the end of its demographic transition, population growth is predicted to be much slower in this century than in the previous; in contrast, Africa's population is expected to reach almost four billion by the year 2100. Unlike demographic transitions in other continents, Africa's population development is being influenced by climate change on a scale unseen by most other global regions. Rising temperatures are exacerbating challenges such as poor sanitation, lack of infrastructure, and political instability, which have historically hindered societal progress. It remains to be seen how Africa and the world at large adapts to this crisis as it continues to cause drought, desertification, natural disasters, and climate migration across the region.

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...

Species distribution models (SDMs) are widely used to infer species-environment relationships, predict spatial distributions, and characterise species’ environmental niches. While the importance of space and spatial scales is widely acknowledged in SDM applications, temporal components of the niche are rarely addressed. We discuss how phenology and demographic stages affect model inference in plant SDMs. Ignoring conspicuousness and timing of phenological stages may bias niche estimates through increased observer bias, while ignoring stand age may bias niche estimates through temporal mismatches with environmental variables, especially during times of rapid global warming. We present different methods to consider phenology and demographic stages in plant SDMs, including the selection of causal, spatiotemporally explicit predictors, and the calibration of stage-specific SDMs. Based on a case study with citizen science data, we illustrate how spatiotemporal SDMs provide deeper insights on..., We conducted a keyword-based search in the Web of Science to quantify how often temporal components related to phenology and demographic stages are explicitly considered in plant SDMs. A full list of keywords is provided in the Supporting Information Table S1. We used a nested set of keywords to identify all studies that mentioned SDMs (or common synonyms), were focused on plants, and were listing relevant keywords related to phenology or to demographic stages, respectively. The search was carried out on 5-Oct-2023 and was restricted to English-language journal articles in the period 1945-2022 (no studies using SDMs were published before that start year). Overall, we found more than 40,000 articles mentioning SDM and over 10,000 articles in our refined search for plant SDMs, with a strong increase in the number of articles over time. Among these, phenology (or related search terms) was mentioned in 970 articles and demographic stages (or related terms) in 1188 articles, each averaging c..., , # The niche through time: considering phenology and demographic stages in plant distribution models

https://doi.org/10.5061/dryad.sn02v6xct

Description of the data and file structure

Columns from WoS (Web of Science) search – these are identical in both excel sheets

These columns are the standard columns provided as WoS search output. If the entries contain "n/a", then no information was provided by WoS because those items are not applicable. For example, a journal article does not have any entries for book authors.

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.

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.

Environmental change continually perturbs populations from a stable state, leading to transient dynamics that can last multiple generations. Several long-term studies have reported changes in trait distributions along with demographic response to environmental change. Here we conducted an experimental study on soil mites and investigated the interaction between demography and an individual trait over a period of nonstationary dynamics. By following individual fates and body sizes at each life-history stage, we investigated how body size and population density influenced demographic rates. By comparing the ability of two alternative approaches, a matrix projection model and an integral projection model, we investigated whether consideration of trait-based demography enhances our ability to predict transient dynamics. By utilizing a prospective perturbation analysis, we addressed which stage-specific demographic or trait-transition rate had the greatest influence on population dynamics. Both body size and population density had important effects on most rates; however, these effects differed substantially among life-history stages. Considering the observed trait-demography relationships resulted in better predictions of a population’s response to perturbations, which highlights the role of phenotypic plasticity in transient dynamics. Although the perturbation analyses provided comparable predictions of stage-specific elasticities between the matrix and integral projection models, the order of importance of the life-history stages differed between the two analyses. In conclusion, we demonstrate how a trait-based demographic approach provides further insight into transient population dynamics.

Abstract

The Thai Demographic and Health Survey (TDHS) was a nationally representative sample survey conducted from March through June 1988 to collect data on fertility, family planning, and child and maternal health. A total of 9,045 households and 6,775 ever-married women aged 15 to 49 were interviewed. Thai Demographic and Health Survey (TDHS) is carried out by the Institute of Population Studies (IPS) of Chulalongkorn University with the financial support from USAID through the Institute for Resource Development (IRD) at Westinghouse. The Institute of Population Studies was responsible for the overall implementation of the survey including sample design, preparation of field work, data collection and processing, and analysis of data. IPS has made available its personnel and office facilities to the project throughout the project duration. It serves as the headquarters for the survey.

The Thai Demographic and Health Survey (TDHS) was undertaken for the main purpose of providing data concerning fertility, family planning and maternal and child health to program managers and policy makers to facilitate their evaluation and planning of programs, and to population and health researchers to assist in their efforts to document and analyze the demographic and health situation. It is intended to provide information both on topics for which comparable data is not available from previous nationally representative surveys as well as to update trends with respect to a number of indicators available from previous surveys, in particular the Longitudinal Study of Social Economic and Demographic Change in 1969-73, the Survey of Fertility in Thailand in 1975, the National Survey of Family Planning Practices, Fertility and Mortality in 1979, and the three Contraceptive Prevalence Surveys in 1978/79, 1981 and 1984.

Geographic coverage

National

Analysis unit

• Household
• Women age 15-49

Universe

The population covered by the 1987 THADHS is defined as the universe of all women Ever-married women in the reproductive ages (i.e., women 15-49). This covered women in private households on the basis of a de facto coverage definition. Visitors and usual residents who were in the household the night before the first visit or before any subsequent visit during the few days the interviewing team was in the area were eligible. Excluded were the small number of married women aged under 15 and women not present in private households.

Kind of data

Sample survey data

Sampling procedure

SAMPLE SIZE AND ALLOCATION

The objective of the survey was to provide reliable estimates for major domains of the country. This consisted of two overlapping sets of reporting domains: (a) Five regions of the country namely Bangkok, north, northeast, central region (excluding Bangkok), and south; (b) Bangkok versus all provincial urban and all rural areas of the country. These requirements could be met by defining six non-overlapping sampling domains (Bangkok, provincial urban, and rural areas of each of the remaining 4 regions), and allocating approximately equal sample sizes to them. On the basis of past experience, available budget and overall reporting requirement, the target sample size was fixed at 7,000 interviews of ever-married women aged 15-49, expected to be found in around 9,000 households. Table A.I shows the actual number of households as well as eligible women selected and interviewed, by sampling domain (see Table i.I for reporting domains).

THE FRAME AND SAMPLE SELECTION

The frame for selecting the sample for urban areas, was provided by the National Statistical Office of Thailand and by the Ministry of the Interior for rural areas. It consisted of information on population size of various levels of administrative and census units, down to blocks in urban areas and villages in rural areas. The frame also included adequate maps and descriptions to identify these units. The extent to which the data were up-to-date as well as the quality of the data varied somewhat in different parts of the frame. Basically, the multi-stage stratified sampling design involved the following procedure. A specified number of sample areas were selected systematically from geographically/administratively ordered lists with probabilities proportional to the best available measure of size (PPS). Within selected areas (blocks or villages) new lists of households were prepared and systematic samples of households were selected. In principle, the sampling interval for the selection of households from lists was determined so as to yield a self weighting sample of households within each domain. However, in the absence of good measures of population size for all areas, these sampling intervals often required adjustments in the interest of controlling the size of the resulting sample. Variations in selection probabilities introduced due to such adjustment, where required, were compensated for by appropriate weighting of sample cases at the tabulation stage.

SAMPLE OUTCOME

The final sample of households was selected from lists prepared in the sample areas. The time interval between household listing and enumeration was generally very short, except to some extent in Bangkok where the listing itself took more time. In principle, the units of listing were the same as the ultimate units of sampling, namely households. However in a small proportion of cases, the former differed from the latter in several respects, identified at the stage of final enumeration: a) Some units listed actually contained more than one household each b) Some units were "blanks", that is, were demolished or not found to contain any eligible households at the time of enumeration. c) Some units were doubtful cases in as much as the household was reported as "not found" by the interviewer, but may in fact have existed.

Mode of data collection

Face-to-face

Research instrument

The DHS core questionnaires (Household, Eligible Women Respondent, and Community) were translated into Thai. A number of modifications were made largely to adapt them for use with an ever- married woman sample and to add a number of questions in areas that are of special interest to the Thai investigators but which were not covered in the standard core. Examples of such modifications included adding marital status and educational attainment to the household schedule, elaboration on questions in the individual questionnaire on educational attainment to take account of changes in the educational system during recent years, elaboration on questions on postnuptial residence, and adaptation of the questionnaire to take into account that only ever-married women are being interviewed rather than all women. More generally, attention was given to the wording of questions in Thai to ensure that the intent of the original English-language version was preserved.

a) Household questionnaire

The household questionnaire was used to list every member of the household who usually lives in the household and as well as visitors who slept in the household the night before the interviewer's visit. Information contained in the household questionnaire are age, sex, marital status, and education for each member (the last two items were asked only to members aged 13 and over). The head of the household or the spouse of the head of the household was the preferred respondent for the household questionnaire. However, if neither was available for interview, any adult member of the household was accepted as the respondent. Information from the household questionnaire was used to identify eligible women for the individual interview. To be eligible, a respondent had to be an ever-married woman aged 15-49 years old who had slept in the household 'the previous night'.

Prior evidence has indicated that when asked about current age, Thais are as likely to report age at next birthday as age at last birthday (the usual demographic definition of age). Since the birth date of each household number was not asked in the household questionnaire, it was not possible to calculate age at last birthday from the birthdate. Therefore a special procedure was followed to ensure that eligible women just under the higher boundary for eligible ages (i.e. 49 years old) were not mistakenly excluded from the eligible woman sample because of an overstated age. Ever-married women whose reported age was between 50-52 years old and who slept in the household the night before birthdate of the woman, it was discovered that these women (or any others being interviewed) were not actually within the eligible age range of 15-49, the interview was terminated and the case disqualified. This attempt recovered 69 eligible women who otherwise would have been missed because their reported age was over 50 years old or over.

b) Individual questionnaire

The questionnaire administered to eligible women was based on the DHS Model A Questionnaire for high contraceptive prevalence countries. The individual questionnaire has 8 sections: - Respondent's background - Reproduction - Contraception - Health and breastfeeding - Marriage - Fertility preference - Husband's background and woman's work - Heights and weights of children and mothers

The questionnaire was modified to suit the Thai context. As noted above, several questions were added to the standard DHS core questionnaire not only to meet the interest of IPS researchers hut also because of their relevance to the current demographic situation in Thailand. The supplemental questions are marked with an asterisk in the individual questionnaire. Questions concerning the following items were added in the individual questionnaire: - Did the respondent ever

Many of the processes that govern the viability of animal populations vary spatially, yet population viability analyses (PVAs) that account explicitly for spatial variation are rare. We develop a PVA model that incorporates autocorrelation into the analysis of local demographic information to produce spatially explicit estimates of demography and viability at relatively fine spatial scales across a large spatial extent. We use a hierarchical, spatial autoregressive model for capture-recapture data from multiple locations to obtain spatially explicit estimates of adult survival (Φad), juvenile survival (Φjuv), and juvenile-to-adult transition rates (ψ), and a spatial autoregressive model for recruitment data from multiple locations to obtain spatially explicit estimates of recruitment (R). We combine local estimates of demographic rates in stage-structured population models to estimate the rate of population change (λ), then use estimates of λ (and its uncertainty) to forecast changes in local abundance and produce spatially explicit estimates of viability (probability of extirpation, Pex). We apply the model to demographic data for the Sonoran desert tortoise (Gopherus morafkai) collected across its geographic range in Arizona. There was modest spatial variation in λ (0.94–1.03), which reflected spatial variation in Φad (0.85–0.95), Φjuv (0.70–0.89), and ψ (0.07–0.13). Recruitment data were too sparse for spatially explicit estimates, therefore we used a range-wide estimate (R = 0.32 one-year old females per female per year). Spatial patterns in demographic rates were complex, but Φad, Φjuv, and λ tended to be lower and ψ higher in the northwestern portion of the range. Spatial patterns in Pex varied with local abundance. For local abundances > 500, Pex was near zero (Pex approached one in the northwestern portion of the range and remained low elsewhere. When local abundances were Pex > 0.25). This approach to PVA offers the potential to reveal spatial patterns in demography and viability that can inform conservation and management at multiple spatial scales, provide insight into scale-related investigations in population ecology, and improve basic ecological knowledge of landscape-level phenomena.

Conserving species requires knowledge of demographic rates (survival, recruitment) that govern population dynamics to allow the allocation of limited resources to the most vulnerable stages of target species' life cycles. Additionally, quantifying drivers of demographic change facilitates the enactment of specific remediation strategies. However, knowledge gaps persist in how similar environmental changes lead to contrasting population dynamics through demographic rates. For sympatric hummingbird species, the population of urban-associated partial-migrant Anna's hummigbird (Calypte anna) has increased, yet the populations of Neotropical migrants including rufous, calliope, and black-chinned hummingbirds have decreased. Here, we developed an integrated population model to jointly analyze 25 years of mark-recapture data and population survey data for these four species. We examined the contributions of demographic rates on population growth and evaluated the effects of anthropogenic stres..., This R data file contains a named list for each species in our study. It has been processed to remove covariates and data that are not public domain but are available for download at the links provided (indicated with * in the readme file). Each species list contains mark-recapture records (y), the known-state records (z), number of years spanned by the analysis (n.years), numbers banded individuals (n.ind), banding station membership (sta), number of banding stations (n.sta), year of first encounter for each individual (first), year of last possible encounter of each individual if it were to be alive (last), first and last years of mark recapture data (first_yr / last_yr), sex (1 = male, 2 = female) and age (1 = juvenile, 2 = adult) membership for each individual, the observed residency information for each individual in each year (r), the partially observed residency state information for each individual (u), the standardized human population density and crop data in the 3 kilometers ..., Data can be opened in R and analyzed using Nimble., ## Hummingbird IPM data file

This data file contains a list of lists named for each of the four species in our analysis: Anna's (ANHU; Calypte anna), black-chinned (BCHU; Archilochus alexandri), calliope (CAHU; Selasphorus calliope), and rufous hummingbirds (RUHU; Selasphorus rufus). Each of these lists contains the required mark-recapture inputs for integrated population modelling in R/Nimble. Raw covariates of human population density, land cover classification, as well as Breeding Bird Survey data can be accessed as described under Sharing/Access information. To load the file in R from the current working directory:

load("./IPM.shared.Rdata")

Description of the data and file structure

Within each named list, there are data for mark-recapture records (NA = station not active, 0 = not captured, 1 = captured; y), the known state, either alive (1) or unknown (NA) , of each individual in each year (z), number of years spanned by the analysis (n.years), nu...

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. clo..., 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 obs..., , # Life stage hypothesis modeling determines insect vulnerability during developmental life stages to climate extremes

https://doi.org/10.5061/dryad.w0vt4b92t

Description of the data and file structure

Files and variables

File: p.clodius_environmental.variables.binned.by.lifestage_2009-2021_SR.BC_Jan2023_ALL_metric.csv

Description:Â Mark-Recapture-Release data for Parnassius clodius butterflies in Pilgrim Creek, Wyoming, U.S.A. from 2009-2011 and 2014-2018

Variables

• Year: calendar year
• effort.num.surveys: number of surveys conducted per year
• caught: total number of butterflies caught per year
• caught.effort: total number of butterflies caught per year divided by the total number of surveys conducted per year
• caught.recap: total number of butterflies caught, including recaptures, per year
• est.popsize: estimated population size using Rmark
• logNt.caught: log transformation of the total number of butter...

The demographic dataset of Cayo Santiago rhesus macaques was shared by the Caribbean Primate Research Center, University of Puerto Rico.

Wages earned by men are often used as an indicator of the material standard of living (MSoL). However, this indicator relies on several assumptions when used for comparisons across time and space. Considering these assumptions will improve estimates of the MSoL from wages. One necessary assumption is that households in the compared populations relied on the primary income of the male head of household to a comparable degree. I demonstrate that the degree of reliance on the male income was closely associated with the complexity of households within the population. Nuclear households—typical of English-speaking countries—were more reliant on the male income than more complex households found elsewhere. Consequently, estimates based on male wages are less accurate for populations with complex households, likely underestimating their MSoL. While the complexity of households in historical populations is seldom known, it can be predicted using demographic and economic indicators. I conclude that populations at similar stages of industrialization and the demographic transition are the most comparable when using male wages to estimate their MSoL. Further, I use a reductive model to show that a household’s MSoL is determined by three factors: time spent on productive work, the market wage for men, and the female/male wage ratio. My analysis shows that including the female/male wage ratio does not change the ranking of the MSoL based on male wages. Nonetheless, I argue that there are compelling reasons to expect the wage ratio to be a useful addition when comparing the MSoL of historical populations.(Abstract of the associated article.)

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.

Large-scale fluctuations in abundance are a common feature of small mammal populations and have been the subject of extensive research. These demographic fluctuations are often associated with concurrent changes in the average body mass of individuals, sometimes referred to as the 'Chitty effect'. Despite the long-standing recognition of this phenomenon, an empirical investigation of the underlying coupled dynamics of body mass and population growth has been lacking. Using long-term life-history data combined with a trait-based demographic approach, we examined the relationship between body mass and demography in a small mammal population that exhibits non-cyclic, large-scale fluctuations in abundance. We used data from the male segment of a 25-year study of the monogamous prairie vole, Microtus ochrogaster, in Illinois, USA. Specifically, we investigated how trait–demography relationships and trait distributions changed between different phases of population fluctuations, and the consequences of these changes for both trait and population dynamics. We observed phase-specific changes in male adult body mass distribution in this population of prairie voles. Our analyses revealed that these changes were driven by variation in ontogenetic growth, rather than selection acting on the trait. The resulting changes in body mass influenced most life-history processes, and these effects varied among phases of population fluctuation. However, these changes did not propagate to affect the population growth rate due to the small effect of body mass on vital rates, compared to the overall differences in vital rates between phases. The increase phase of the fluctuations was initiated by enhanced survival, particularly of juveniles and fecundity, whereas the decline phase was driven by an overall reduction in fecundity, survival and maturation rates. Our study provides empirical support, as well as a potential mechanism, underlying the observed trait changes accompanying population fluctuations. Body size dynamics and population fluctuations resulted from different life-history processes. Therefore, we conclude that body size dynamics in our population do not drive the observed population dynamics. This more in-depth understanding of different components of small mammal population fluctuations will help us to better identify the mechanistic drivers of this interesting phenomenon.

The world's population first reached one billion people in 1805, and reached eight billion in 2022, and will peak at almost 10.2 billion by the end of the century. Although it took thousands of years to reach one billion people, it did so at the beginning of a phenomenon known as the demographic transition; from this point onwards, population growth has skyrocketed, and since the 1960s the population has increased by one billion people every 12 to 15 years. The demographic transition sees a sharp drop in mortality due to factors such as vaccination, sanitation, and improved food supply; the population boom that follows is due to increased survival rates among children and higher life expectancy among the general population; and fertility then drops in response to this population growth. Regional differences The demographic transition is a global phenomenon, but it has taken place at different times across the world. The industrialized countries of Europe and North America were the first to go through this process, followed by some states in the Western Pacific. Latin America's population then began growing at the turn of the 20th century, but the most significant period of global population growth occurred as Asia progressed in the late-1900s. As of the early 21st century, almost two-thirds of the world's population lives in Asia, although this is set to change significantly in the coming decades. Future growth The growth of Africa's population, particularly in Sub-Saharan Africa, will have the largest impact on global demographics in this century. From 2000 to 2100, it is expected that Africa's population will have increased by a factor of almost five. It overtook Europe in size in the late 1990s, and overtook the Americas a few years later. In contrast to Africa, Europe's population is now in decline, as birth rates are consistently below death rates in many countries, especially in the south and east, resulting in natural population decline. Similarly, the population of the Americas and Asia are expected to go into decline in the second half of this century, and only Oceania's population will still be growing alongside Africa. By 2100, the world's population will have over three billion more than today, with the vast majority of this concentrated in Africa. Demographers predict that climate change is exacerbating many of the challenges that currently hinder progress in Africa, such as political and food instability; if Africa's transition is prolonged, then it may result in further population growth that would place a strain on the region's resources, however, curbing this growth earlier would alleviate some of the pressure created by climate change.