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/

Abstract When fertility declines, it is not only the number of children that becomes smaller, but the number of siblings as well. To determine changes in the number of siblings over time in Brazil, this study uses a method that is designed to estimate, through mathematical models which use only fertility and mortality rates, the availability of surviving siblings in different cohorts. The results indicate that, at the beginning of the demographic transition, the mean number of the born alive siblings is established at high levels and suffers a sharp decline during the transition, mainly due to falling fertility. They also show that the mean number of the surviving siblings at older ages tends to be very similar for older and younger cohorts. However, the mean number of surviving siblings during the childhood of these cohorts tends to differ greatly. This is due to high mortality, especially infant mortality, on the one hand and, on the other, from fertility decline which reduces the number of live births in more recent cohorts while the reduction of mortality increases their chances of survival. The study's conclusion points out the following trends: the mean number of surviving siblings will tend to settle at lower levels in coming years and the mean number of surviving siblings tends to be increasingly closer to the mean number born alive. Despite current low levels of fertility, it would be incorrect to speak of the extinction of siblings and, consequently, of cousins, uncles, etc.

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

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

Annual Resident Population Estimates, Estimated Components of Resident Population Change, and Rates of the Components of Resident Population Change for States and Counties // Source: U.S. Census Bureau, Population Division // Note: Total population change includes a residual. This residual represents the change in population that cannot be attributed to any specific demographic component. See Population Estimates Terms and Definitions at http://www.census.gov/popest/about/terms.html. // Net international migration in the United States includes the international migration of both native and foreign-born populations. Specifically, it includes: (a) the net international migration of the foreign born, (b) the net migration between the United States and Puerto Rico, (c) the net migration of natives to and from the United States, and (d) the net movement of the Armed Forces population between the United States and overseas. // The estimates are based on the 2010 Census and reflect changes to the April 1, 2010 population due to the Count Question Resolution program and geographic program revisions. See Geographic Terms and Definitions at http://www.census.gov/popest/about/geo/terms.html for a list of the states that are included in each region and division. // For detailed information about the methods used to create the population estimates, see http://www.census.gov/popest/methodology/index.html. // Each year, the Census Bureaus Population Estimates Program (PEP) utilizes current data on births, deaths, and migration to calculate population change since the most recent decennial census, and produces a time series of estimates of population. The annual time series of estimates begins with the most recent decennial census data and extends to the vintage year. The vintage year (e.g., V2014) refers to the final year of the time series. The reference date for all estimates is July 1, unless otherwise specified. With each new issue of estimates, the Census Bureau revises estimates for years back to the last census. As each vintage of estimates includes all years since the most recent decennial census, the latest vintage of data available supersedes all previously produced estimates for those dates. The Population Estimates Program provides additional information including historical and intercensal estimates, evaluation estimates, demographic analysis, and research papers on its website: http://www.census.gov/popest/index.html.

In 2023, it is estimated that the BRICS countries have a combined population of 3.25 billion people, which is over 40 percent of the world population. The majority of these people live in either China or India, which have a population of more than 1.4 billion people each, while the other three countries have a combined population of just under 420 million. Comparisons Although the BRICS countries are considered the five foremost emerging economies, they are all at various stages of the demographic transition and have different levels of population development. For all of modern history, China has had the world's largest population, but rapidly dropping fertility and birth rates in recent decades mean that its population growth has slowed. In contrast, India's population growth remains much higher, and it is expected to overtake China in the next few years to become the world's most populous country. The fastest growing population in the BRICS bloc, however, is that of South Africa, which is at the earliest stage of demographic development. Russia, is the only BRICS country whose population is currently in decline, and it has been experiencing a consistent natural decline for most of the past three decades. Growing populations = growing opportunities Between 2000 and 2026, the populations of the BRICS countries is expected to grow by 625 million people, and the majority of this will be in India and China. As the economies of these two countries grow, so too do living standards and disposable income; this has resulted in the world's two most populous countries emerging as two of the most profitable markets in the world. China, sometimes called the "world's factory" has seen a rapid growth in its middle class, increased potential of its low-tier market, and its manufacturing sector is now transitioning to the production of more technologically advanced and high-end goods to meet its domestic demand.

Annual Population Estimates, Estimated Components of Resident Population Change, and Rates of the Components of Resident Population Change for the United States, States, and Puerto Rico // File: National and State Population Estimates // Source: U.S. Census Bureau, Population Division // Note: Total population change includes a residual. This residual represents the change in population that cannot be attributed to any specific demographic component. See Population Estimates Terms and Definitions at http://www.census.gov/popest/about/terms.html. // Net international migration (except for Puerto Rico) includes the international migration of both native and foreign-born populations. Specifically, it includes: (a) the net international migration of the foreign born, (b) the net migration between the United States and Puerto Rico, (c) the net migration of natives to and from the United States, and (d) the net movement of the Armed Forces population between the United States and overseas. Net international migration for Puerto Rico includes the migration of native and foreign-born populations between the United States and Puerto Rico. // The estimates are based on the 2010 Census and reflect changes to the April 1, 2010 population due to the Count Question Resolution program and geographic program revisions. See Geographic Terms and Definitions at http://www.census.gov/popest/about/geo/terms.html for a list of the states that are included in each region and division. All geographic boundaries for these population estimates are as of January 1, 2013. // For detailed information about the methods used to create the population estimates, see http://www.census.gov/popest/methodology/index.html. // Each year, the Census Bureau's Population Estimates Program (PEP) utilizes current data on births, deaths, and migration to calculate population change since the most recent decennial census, and produces a time series of estimates of population. The annual time series of estimates begins with the most recent decennial census data and extends to the vintage year. The vintage year (e.g., V2013) refers to the final year of the time series. The reference date for all estimates is July 1, unless otherwise specified. With each new issue of estimates, the Census Bureau revises estimates for years back to the last census. As each vintage of estimates includes all years since the most recent decennial census, the latest vintage of data available supersedes all previously produced estimates for those dates. The Population Estimates Program provides additional information including historical and intercensal estimates, evaluation estimates, demographic analysis, and research papers on its website: http://www.census.gov/popest/index.html.

This dataset was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau.For a deep dive into the data model including every specific metric, see the Infrastructure Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics.Naming conventions:Prefixes: None Countp Percentr Ratem Mediana Mean (average)t Aggregate (total)ch Change in absolute terms (value in t2 - value in t1)pch Percent change ((value in t2 - value in t1) / value in t1)chp Change in percent (percent in t2 - percent in t1)s Significance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computed Suffixes: _e19 Estimate from 2014-19 ACS_m19 Margin of Error from 2014-19 ACS_00_v19 Decennial 2000, re-estimated to 2019 geography_00_19 Change, 2000-19_e10_v19 2006-10 ACS, re-estimated to 2019 geography_m10_v19 Margin of Error from 2006-10 ACS, re-estimated to 2019 geography_e10_19 Change, 2010-19The 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 2015-2019). 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: 2015-2019Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the manifest: https://www.arcgis.com/sharing/rest/content/items/3d489c725bb24f52a987b302147c46ee/data

This dataset was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau.For a deep dive into the data model including every specific metric, see the Infrastructure Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics.Naming conventions:Prefixes: None Countp Percentr Ratem Mediana Mean (average)t Aggregate (total)ch Change in absolute terms (value in t2 - value in t1)pch Percent change ((value in t2 - value in t1) / value in t1)chp Change in percent (percent in t2 - percent in t1)s Significance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computed Suffixes: _e19 Estimate from 2014-19 ACS_m19 Margin of Error from 2014-19 ACS_00_v19 Decennial 2000, re-estimated to 2019 geography_00_19 Change, 2000-19_e10_v19 2006-10 ACS, re-estimated to 2019 geography_m10_v19 Margin of Error from 2006-10 ACS, re-estimated to 2019 geography_e10_19 Change, 2010-19The 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 2015-2019). 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: 2015-2019Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the manifest: https://www.arcgis.com/sharing/rest/content/items/3d489c725bb24f52a987b302147c46ee/data

The CSIRO Atmospheric Research Mark 2b climate model (Hirst et al., 1996, 1999) has recently been used for a number of more sophisticated climate change simulations. These start from 1880 to avoid the "cold start problem". This version of the CSIRO model includes the Gent-McWilliams mixing scheme in the ocean and shows greatly reduced climate drift relative to earlier versions (e.g. Dix and Hunt, 1998). The drift in global mean surface temperature in the new control run is about -0.02 degrees C/century. Note that the model uses flux correction. The model atmosphere has 9 levels in the vertical and horizontal resolution of spectral R21 (approximately 5.6 by 3.2 degrees). The ocean model has the same horizontal resolution with 21 levels. The equilibrium sensitivity to doubled CO2 of a mixed layer ocean version of the model is 4.3 degrees. This is at the high end of the range of model sensitivities (e.g. IPCC 1995, Table 6.3). In the basic greenhouse gas experiment the model combines the effect of all radiatively active trace gases into an "equivalent" CO2 concentration. Observed concentrations are used from 1880 to 1990 and the IS92a projections into the future. This gives close to a 1%/year compounding increase of equivalent CO2. Another model experiment includes the negative radiative forcing from atmospheric sulphate aerosol. The direct aerosol forcing is included via a perturbation of the surface albedo, similarly to the Hadley Centre experiments described by Mitchell et al (1995) and Mitchell and Johns (1997) . The sulphate concentrations are the same as used in the Hadley Centre experiments. However the chosen aerosol optical properties are different, giving a present day forcing due to anthropogenic sulphate of about -0.4 W/m^2. This can be compared to the 1880-1990 greenhouse gas forcing of about 2 W/m^2. The magnitude of the 20th century warming in the model including aerosol matches the observed reasonably well. However there are a number of forcings missing from the model, including solar variability, sulphate indirect effect and the effect of soot. The climate sensitivity of CSIRO-Mk2 is about 4.3 degrees C (Watterson et al.,1997). The central elements of the B1 future are a high level of environmental and social consciousness combined with a globally coherent approach to sustainable development. A strong welfare net prevents social exclusion on the basis of poverty. However, counter-currents may develop and in some places people may not conform to the main social and environmental intentions of the mainstream in this scenario family. Particular effort is devoted to increasing resource efficiency. Comprehensive incentive systems, combined with advances in international institutions, permit the rapid diffusion of cleaner technology. R and D to this end is also enhanced together with education and capacity building for clean and equitable development. Organizational measures are adopted to reduce material wastage, maximizing reuse and recycling. The combination of technical and organizational change yields high levels of material and energy saving as well as reductions in pollution. Labor productivity also improves as a byproduct of these efforts. Variants considered within the B1 family of scenarios include different rates of GDP growth and dematerialization (e.g., energy intensity declines). The demographic transition to low mortality and fertility occurs at the same rate as in A1 but for slightly different reasons, motivated partly by social and environmental concerns. Global population reaches nine billion by 2050 and declines to about seven billion by 2100. This is a world with high levels of economic activity and significant and deliberate progress toward international and national income equality. Global income per capita in 2050 averages US$13,000; somewhat lower than in A1. A higher proportion of this income is spent on services rather than on material goods, and on quality rather than quantity, because of less emphasis on material goods and also higher resource prices. The B1 storyline sees a relatively smooth transition to alternative energy systems as conventional oil resources decline. There is extensive use of conventional and unconventional gas as the cleanest fossil resource during the transition, but the major push is towards post fossil technologies driven in large part by environmental concerns. Given the high environmental consciousness and institutional effectiveness in the B1 storyline, environmental quality is high, as most potentially negative environmental aspects of rapid development are anticipated and dealt with effectively locally, nationally, and internationally. For example, transboundary air pollution (acid rain) is basically eliminated in the long-term. Land-use is carefully managed to counteract the impacts of activities potentially damaging to the environment. Cities are compact and designed for public and non-motorized transport, with suburban developments tightly controlled. Strong incentives for low-input, low-impact agriculture along with maintenance of large areas of wilderness contribute to high food prices with much lower levels of meat consumption than those in A1. These proactive local and regional environmental measures and policies also lead to relatively low GHG emissions even in the absence of explicit interventions directed at mitigating climate change.

The CSIRO Atmospheric Research Mark 2b climate model (Hirst et al., 1996, 1999) has recently been used for a number of more sophisticated climate change simulations. These start from 1880 to avoid the "cold start problem". This version of the CSIRO model includes the Gent-McWilliams mixing scheme in the ocean and shows greatly reduced climate drift relative to earlier versions (e.g. Dix and Hunt, 1998). The drift in global mean surface temperature in the new control run is about -0.02 degrees C/century. Note that the model uses flux correction. The model atmosphere has 9 levels in the vertical and horizontal resolution of spectral R21 (approximately 5.6 by 3.2 degrees). The ocean model has the same horizontal resolution with 21 levels. The equilibrium sensitivity to doubled CO2 of a mixed layer ocean version of the model is 4.3 degrees. This is at the high end of the range of model sensitivities (e.g. IPCC 1995, Table 6.3). In the basic greenhouse gas experiment the model combines the effect of all radiatively active trace gases into an "equivalent" CO2 concentration. Observed concentrations are used from 1880 to 1990 and the IS92a projections into the future. This gives close to a 1%/year compounding increase of equivalent CO2. Another model experiment includes the negative radiative forcing from atmospheric sulphate aerosol. The direct aerosol forcing is included via a perturbation of the surface albedo, similarly to the Hadley Centre experiments described by Mitchell et al (1995) and Mitchell and Johns (1997) . The sulphate concentrations are the same as used in the Hadley Centre experiments. However the chosen aerosol optical properties are different, giving a present day forcing due to anthropogenic sulphate of about -0.4 W/m^2. This can be compared to the 1880-1990 greenhouse gas forcing of about 2 W/m^2. The magnitude of the 20th century warming in the model including aerosol matches the observed reasonably well. However there are a number of forcings missing from the model, including solar variability, sulphate indirect effect and the effect of soot. The climate sensitivity of CSIRO-Mk2 is about 4.3 degrees C (Watterson et al.,1997). The central elements of the B1 future are a high level of environmental and social consciousness combined with a globally coherent approach to sustainable development. A strong welfare net prevents social exclusion on the basis of poverty. However, counter-currents may develop and in some places people may not conform to the main social and environmental intentions of the mainstream in this scenario family. Particular effort is devoted to increasing resource efficiency. Comprehensive incentive systems, combined with advances in international institutions, permit the rapid diffusion of cleaner technology. R and D to this end is also enhanced together with education and capacity building for clean and equitable development. Organizational measures are adopted to reduce material wastage, maximizing reuse and recycling. The combination of technical and organizational change yields high levels of material and energy saving as well as reductions in pollution. Labor productivity also improves as a byproduct of these efforts. Variants considered within the B1 family of scenarios include different rates of GDP growth and dematerialization (e.g., energy intensity declines). The demographic transition to low mortality and fertility occurs at the same rate as in A1 but for slightly different reasons, motivated partly by social and environmental concerns. Global population reaches nine billion by 2050 and declines to about seven billion by 2100. This is a world with high levels of economic activity and significant and deliberate progress toward international and national income equality. Global income per capita in 2050 averages US$13,000; somewhat lower than in A1. A higher proportion of this income is spent on services rather than on material goods, and on quality rather than quantity, because of less emphasis on material goods and also higher resource prices. The B1 storyline sees a relatively smooth transition to alternative energy systems as conventional oil resources decline. There is extensive use of conventional and unconventional gas as the cleanest fossil resource during the transition, but the major push is ... Visit https://dataone.org/datasets/doi%3A10.5063%2FAA%2Fdpennington.117.2 for complete metadata about this dataset.

These data identify the mean annual population growth rate and ratio change in abundance of common raven (Corvus corax; ravens) populations from 1966 through 2018.

The CSIRO Atmospheric Research Mark 2b climate model (Hirst et al., 1996, 1999) has recently been used for a number of more sophisticated climate change simulations. These start from 1880 to avoid the "cold start problem". This version of the CSIRO model includes the Gent-McWilliams mixing scheme in the ocean and shows greatly reduced climate drift relative to earlier versions (e.g. Dix and Hunt, 1998). The drift in global mean surface temperature in the new control run is about -0.02 degrees C/century. Note that the model uses flux correction. The model atmosphere has 9 levels in the vertical and horizontal resolution of spectral R21 (approximately 5.6 by 3.2 degrees). The ocean model has the same horizontal resolution with 21 levels. The equilibrium sensitivity to doubled CO2 of a mixed layer ocean version of the model is 4.3 degrees. This is at the high end of the range of model sensitivities (e.g. IPCC 1995, Table 6.3). In the basic greenhouse gas experiment the model combines the effect of all radiatively active trace gases into an "equivalent" CO2 concentration. Observed concentrations are used from 1880 to 1990 and the IS92a projections into the future. This gives close to a 1%/year compounding increase of equivalent CO2. Another model experiment includes the negative radiative forcing from atmospheric sulphate aerosol. The direct aerosol forcing is included via a perturbation of the surface albedo, similarly to the Hadley Centre experiments described by Mitchell et al (1995) and Mitchell and Johns (1997) . The sulphate concentrations are the same as used in the Hadley Centre experiments. However the chosen aerosol optical properties are different, giving a present day forcing due to anthropogenic sulphate of about -0.4 W/m^2. This can be compared to the 1880-1990 greenhouse gas forcing of about 2 W/m^2. The magnitude of the 20th century warming in the model including aerosol matches the observed reasonably well. However there are a number of forcings missing from the model, including solar variability, sulphate indirect effect and the effect of soot. The climate sensitivity of CSIRO-Mk2 is about 4.3 degrees C (Watterson et al.,1997). The central elements of the B1 future are a high level of environmental and social consciousness combined with a globally coherent approach to sustainable development. A strong welfare net prevents social exclusion on the basis of poverty. However, counter-currents may develop and in some places people may not conform to the main social and environmental intentions of the mainstream in this scenario family. Particular effort is devoted to increasing resource efficiency. Comprehensive incentive systems, combined with advances in international institutions, permit the rapid diffusion of cleaner technology. R and D to this end is also enhanced together with education and capacity building for clean and equitable development. Organizational measures are adopted to reduce material wastage, maximizing reuse and recycling. The combination of technical and organizational change yields high levels of material and energy saving as well as reductions in pollution. Labor productivity also improves as a byproduct of these efforts. Variants considered within the B1 family of scenarios include different rates of GDP growth and dematerialization (e.g., energy intensity declines). The demographic transition to low mortality and fertility occurs at the same rate as in A1 but for slightly different reasons, motivated partly by social and environmental concerns. Global population reaches nine billion by 2050 and declines to about seven billion by 2100. This is a world with high levels of economic activity and significant and deliberate progress toward international and national income equality. Global income per capita in 2050 averages US$13,000; somewhat lower than in A1. A higher proportion of this income is spent on services rather than on material goods, and on quality rather than quantity, because of less emphasis on material goods and also higher resource prices. The B1 storyline sees a relatively smooth transition to alternative energy systems as conventional oil resources decline. There is extensive use of conventional and unconventional gas as the cleanest fossil resource during the transition, but the major push is ... Visit https://dataone.org/datasets/doi%3A10.5063%2FAA%2Fdpennington.119.2 for complete metadata about this dataset.

Models capturing the full effects of weather conditions on animal populations are scarce. Here we decompose yearly temperature and rainfall into mean trends, yearly amplitude of change and residual variation, using daily records. We establish from multi-model inference procedures, based on 1125 life histories (from 1987 to 2008), that European badger (Meles meles) annual mortality and recruitment rates respond to changes in mean trends and to variability in proximate weather components. Variation in mean rainfall was by far the most influential predictor in our analysis. Juvenile survival and recruitment rates were highest at intermediate levels of mean rainfall, whereas low adult survival rates were associated with only the driest, and not the wettest, years. Both juvenile and adult survival rates also exhibited a range of tolerance for residual standard deviation around daily predicted temperature values, beyond which survival rates declined. Life-history parameters, annual routines and adaptive behavioural responses, which define the badgers’ climatic niche, thus appear to be predicated upon a bounded range of climatic conditions, which support optimal survival and recruitment dynamics. That variability in weather conditions is influential, in combination with mean climatic trends, on the vital rates of a generalist, wide ranging and K-selected medium-sized carnivore, has major implications for evolutionary ecology and conservation.

Climate change is projected to cause extensive plant range shifts, and in many cases such shifts already are underway. Most long-term studies of range shifts measure emergent changes in species distributions but not the underlying demographic patterns that shape them. To better understand species’ elevational range shifts and their underlying demographic processes, we use the powerful approach of rephotography, comparing historical (1978-82) and modern (2015-16) photographs taken along a 1000 m elevational gradient in theColorado Desert of Southern California. This approach allowed us to track demographic outcomes for 4263 individual plants of 11 long-lived, perennial species over the past ~36 years. All species showed an upward shift in mean elevation (average = 45 m), consistent with observed increasing temperature and severe drought in the region. We found that varying demographic processes underlaid these elevational shifts, with some species showing higher recruitment and some showing higher survival with increasing elevation. Species with faster life history rates (higher background recruitment and mortality rates) underwent larger elevational shifts. Our findings emphasize the importance of demography and life history in shaping range shift responses and future community composition, as well as the sensitivity of desert systems to climate change despite the typical ‘slow motion’ population dynamics of perennial desert plants. Methods We utilized photos originally taken by Dr. Wilbur Mayhew between 1977 and 1982 (Mayhew 1981), which we digitized from 35 mm slides stored at Philip L. Boyd Deep Canyon Desert Research Center (doi:10.21973/N3V66D). We relocated permanently marked sites where historical photos had been taken and rephotographed them using a Canon 5D Mark II camera and tripod in 2015 and 2016. We took one additional set of photos in April 2017 after the end of a multi-year drought, so that we could distinguish dormant from dead individuals of two drought-deciduous species (brittlebush, Encelia farinosa and white bursage, Ambrosia dumosa). We approximated the original view of the original photos as closely as possible in modern photos. For each photo view, we chose a single historical and modern photo for analysis based on resolution, contrast and coloration. The mean timespan between paired historical and modern photos was 36 years. We perfected the alignment between the paired historical and modern photos in Photoshop by making one photo semi-transparent, then rotating and resizing it while maintaining original aspect ratios. Data extraction We extracted data on 11 perennial species that appeared in 5+ sites. We extracted data from the photos in ArcGIS, arranging the paired photos as map layers. We created polygons to delimit a survey area close enough to the camera to identify species; these polygons serve as the “sites” in our subsequent analysis. In some cases, we collected data on larger-bodied or particularly conspicuous species, such as ocotillo (Fouquieria splendens) and creosote (Larrea tridentata), in a larger area including locations farther from the camera than for smaller, less conspicuous species. We recorded whether each plant underwent recruitment (absent historical, alive modern), mortality (alive historical, dead modern) or survival (alive both). We excluded plants that were dead in the historical period or with main stems outside the site polygon. In some cases we consulted other historical and modern photos of the same site to determine species identity or assess whether an individual was alive. We counted and measured clusters of agave (Agave deserti) and Mojave yucca (Yucca schidigera) as single individuals. Rarely, we may have misidentified pygmy cedar (Peucephyllum schottii) for creosote where these species co-occur on steep slopes, since they have similar morphology and are difficult to distinguish from a distance. We measured individual relative change in plant size by measuring the height (perpendicular to the ground) and width (the largest horizontal extent of the plant perpendicular to the camera, i.e. canopy width) of surviving plants in both time periods, using the ruler tool in ArcGIS and focusing on woody biomass. When dead agave rosettes were surrounded by live rosettes, we did not include the width that was dead if it was >20% the total width. We calculated the relative change in height of each plant as (H1–H0) / H0, where H indicates plant height and the subscripts 0 and 1 indicate the historical and modern period, respectively. We used an equivalent equation for relative change in width. For some species at some sites, we could not track the fate of individuals between the two time periods. This most often occurred for narrow-bodied and relatively short-lived species (e.g. teddy bear cholla, Cylindropuntia bigelovii) in photo pairs that were difficult to perfectly align, thereby making it difficult to tell whether plants either survived, or died and were replaced by recruits. It also occurred when a large plant died and a new plant “appeared” in a spot that was previously hidden, such that we were unable to determine whether the second plant was a recruit or a surviving plant. We therefore designated two site types for each species: “trackable” sites – those where we could track the fate of at least one third of individuals of a given species over time, and “count-only” sites – those where we could track fewer than one third of individuals, and instead only counted individuals. Count-only sites were retained for analyses of mean elevation shifts but not demographic rates. Geophysical data We used Google Earth Pro “ground level view” to draw polygons matching the extent of the site polygons outlined in the photos. To do so, we first “stood” at the camera’s locality and angle, then used corresponding features (e.g. washes, large creosote, hills) to find the exact site, and finally dropped pins to mark polygon vertices. We used these polygons to extract data on each site’s size, as well as its mean elevation, aspect, slope and annual solar radiation (“insolation”) using USGS NED Contiguous US 1/3 arc-second digital elevation model (2013) in ArcGIS. We took the cosine of aspect to create linear values ranging from -1 (South) to 1 (North). Additional details Additional details on how these data were collected and processed can be found in the Methods and Supplementary Materials of Skikne et al. 2024. Contrasting demographic processes underlie uphill shifts in a desert ecosystem.

Vital rate mean values for each population and transition.

Mean transition probabilities (and standard deviation of the estimate) for a 13-month period for population 2.

Low-and middle-income countries (LMICs) are experiencing a nutritional transition in which the burden of obesity tends to shift towards the lower-socioeconomic status (SES) group. We propose a system dynamics (SD) model for assessing the nutritional stage dynamics of the Colombian urban population by age and SES projected to 2030. This SD model captures the ageing population according to body mass index (BMI) categories and SES. In this model, the transference rates (TRs) between BMI categories by age and SES are estimated using a heuristic based on data obtained from national surveys. The simulation results show that the Colombian population, particularly those aged 20 to 39 years with a lower SES, is moving towards the overweight and obese categories. The TRs for overweight and obese categories in the lower SES group (the mean TR from not overweight to overweight = 0.0215 (per year) and mean TR from overweight to obese = 0.0098 (per year)) are increasing more rapidly than the those in the middle (the mean TR from not overweight to overweight = 0.0162 (per year) and mean TR from overweight to obese = 0.0065 (per year)) and higher SES groups (the mean TR from not overweight to overweight = 0.0166 and mean TR from overweight to obese = 0.0054 (per year)). Additionally, from 2005 to 2010, individuals aged 20 to 39 years had the highest TRs towards the overweight and obese categories (from 0.026 to 0.036 per year and from 0.0064 to 0.012 per year, respectively). The TRs also indicated that children aged 0 to 14 years are moving from the obese to overweight and from the overweight to not overweight categories. These TRs show that the Colombian population is experiencing an SES-related nutritional transition that is affecting the lower SES population. The proposed model could be implemented to assess the nutritional transitions experienced in other LMICs.

This dataset was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau.For a deep dive into the data model including every specific metric, see the Infrastructure Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics.Naming conventions:Prefixes: None Countp Percentr Ratem Mediana Mean (average)t Aggregate (total)ch Change in absolute terms (value in t2 - value in t1)pch Percent change ((value in t2 - value in t1) / value in t1)chp Change in percent (percent in t2 - percent in t1)s Significance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computed Suffixes: _e19 Estimate from 2014-19 ACS_m19 Margin of Error from 2014-19 ACS_00_v19 Decennial 2000, re-estimated to 2019 geography_00_19 Change, 2000-19_e10_v19 2006-10 ACS, re-estimated to 2019 geography_m10_v19 Margin of Error from 2006-10 ACS, re-estimated to 2019 geography_e10_19 Change, 2010-19The 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 2015-2019). 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: 2015-2019Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the manifest: https://www.arcgis.com/sharing/rest/content/items/3d489c725bb24f52a987b302147c46ee/data

This dataset was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau.For a deep dive into the data model including every specific metric, see the Infrastructure Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics.Naming conventions:Prefixes: None Countp Percentr Ratem Mediana Mean (average)t Aggregate (total)ch Change in absolute terms (value in t2 - value in t1)pch Percent change ((value in t2 - value in t1) / value in t1)chp Change in percent (percent in t2 - percent in t1)s Significance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computed Suffixes: _e19 Estimate from 2014-19 ACS_m19 Margin of Error from 2014-19 ACS_00_v19 Decennial 2000, re-estimated to 2019 geography_00_19 Change, 2000-19_e10_v19 2006-10 ACS, re-estimated to 2019 geography_m10_v19 Margin of Error from 2006-10 ACS, re-estimated to 2019 geography_e10_19 Change, 2010-19The 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 2015-2019). 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: 2015-2019Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the manifest: https://www.arcgis.com/sharing/rest/content/items/3d489c725bb24f52a987b302147c46ee/data