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

Demographic links among fragmented populations are commonly studied as source-sink dynamics, whereby source populations exhibit net recruitment and net emigration, while sinks suffer net mortality but enjoy net immigration. It is commonly assumed that large, persistent aggregations of individuals must be sources, but this ignores the possibility that they are sinks instead, buoyed demographically by immigration. We tested this assumption using Bayesian integrated population modelling of Greenland white-fronted geese (Anser albifrons flavirostris) at their largest wintering site (Wexford, Ireland), combining capture–mark–recapture, census and recruitment data collected from 1982 to 2010. Management for this subspecies occurs largely on wintering areas; thus, study of source-sink dynamics of discrete regular wintering units provides unprecedented insights into population regulation and enables identification of likely processes influencing population dynamics at Wexford and among 70 other Greenland white-fronted goose wintering subpopulations. Using results from integrated population modelling, we parameterized an age-structured population projection matrix to determine the contribution of movement rates (emigration and immigration), recruitment and mortality to the dynamics of the Wexford subpopulation. Survival estimates for juvenile and adult birds at Wexford and adult birds elsewhere fluctuated over the 29-year study period, but were not identifiably different. However, per capita recruitment rates at Wexford in later years (post-1995) were identifiably lower than in earlier years (pre-1995). The observed persistence of the Wexford subpopulation was only possible with high rates of immigration, which exceeded emigration in each year. Thus, despite its apparent stability, Wexford has functioned as a sink over the entire study period. These results demonstrate that even large subpopulations can potentially be sinks, and that movement dynamics (e.g. immigration) among winters can dramatically obscure key processes driving subpopulation size. Further, novel population models which integrate capture–mark–recapture, census and recruitment data are essential to correctly ascribing source-sink status and accurately informing development of site-safeguard networks.

The net job and business growth indicator measures the annual change in both the number of firms and the number of employees between 1978 and 2022. The data is categorized by the size of the firm: those with 1-19 employees, those with between 20 and 499 employees, and those with more than 500 employees.

This data contributes to the big picture of economic conditions in Champaign County. More firms and larger employment numbers are generally positive economic indicators, but any strictly economic indicator should be considered in the context of other factors.

The number of firms and number of employees show very different trends.

Historically, there have been significantly more firms with 1-19 employees than firms in the larger two size categories. The number of firms with 1-19 employees has also been relatively consistent until 2021: there were 95 fewer such firms in 2021 than 1978, and the largest year-to-year change in that 43-year period of analysis was a -3.2% decrease between 1979 and 1980. However, there were 437 fewer such firms in 2022 than 1978. There was a decrease in these firms of 12.5% from 2021 to 2022, the only double-digit year-to-year change and the largest year-to-year change over 44 years.

The larger two size categories have shown an increasing trend over the period of analysis. There were 43 more firms with 20-499 employees in 2022 than 1978, a total increase of 9%. The number of firms with more than 500 employees almost doubled, increasing by 206 firms from 212 in 1978 to 418 in 2022, a total increase of 97.2%.

The trends of employment also vary based on firm size. Firms with 1-19 employees have consistently, and unsurprisingly, accounted for less of the total employment than the larger two categories. Employment in firms with 1-19 employees has also remained relatively consistent over the period of analysis. Employment in firms with more than 500 employees saw an overall trend of growth, interrupted by brief and intermittent decreases, between 1978 and 2022. Employment in the middle category (firms with between 20 and 499 employees) was also greater in 2022 than in 1978.

This data is from the U.S. Census Bureau’s Business Dynamics Statistics Data Tables. This data is at the geographic scale of the Champaign-Urbana Metropolitan Statistical Area (MSA), which is comprised of Champaign and Piatt Counties, or a larger area than the cities or Champaign County.

Source: U.S. Census Bureau; 2022 Business Dynamics Statistics Data Tables; "BDSFSIZE - Business Dynamics Statistics: Firm Size: 1978-2022"; retrieved 21 October 2024.

Sampled households will meet one or more of the following criteria at the time of the first survey: 1) at least one household member conducted rice field fishing at least once in the past 12 months; 2) household members consumed fish at least once in the past 4 months; 3) at least one household member has earned income by trading fish (from fish harvest to market) in the past 4 months, or is currently employed in the fish trade; 4) at least one household member has been involved in other value chain activities in the past 4 months. The monitoring will be conducted in a manner to set baseline values for zone of influence catch and employment in the fishery value chain, with which subsequent monitoring results will be compared to assess change over the life of the project. The baseline values will include kg/year for fish production (including a trend over multiple years, if possible, or otherwise the previous year value for kg/year), household fish consumption, and threshold price of fish and other aquatic animals purchased by food insecure households. The Catch, consumption, and employment survey and procedures are based on the recommendations made and applied for the USAID RFFII project (see Hortle, 2012). Sampling of fishing households will be conducted four times each year, once each during flood recession, dry season, flood rising, and wet season. A minimum of 20 households are sampled per CFR site. The survey is designed to be longitudinal by revisiting the same households during each survey occasion. If a household has migrated or is unavailable during the survey period, another household will be surveyed in its place for that occasion only. Questions will be asked on the amount of household catch (including fish, other aquatic animals, and aquatic plants) by habitat and species, market price of the catch by species, use of the catch (consumption, sale, processing, livestock feed, other uses, and loss due to cleaning, discards, and/or spoilage), the number of hours household members have spent working in the fish value chain (including to manage, harvest, process, and market fish, for income or subsistence purposes), and the ID Poor status of the household. Responses will be recorded using Kobo toolbox, a mobile data collection platform. The recorded data will be used to calculate average household quantity of fish catch from rice field ecosystems in the CFR zone of influence, fish consumption, and fishery related incomes and employment. The proportion of surveyed households that fished, the average household fish catch per site from the four sampling occasions in a year, along with the estimated area of the CFR zone of influence, will be used to calculate annual rice field fishery productivity. Fishery incomes will be calculated from the average value of household catch. For employment, fishery labour hours will be reported as FTE. Numbers of fish powder producers and hours spent producing fish powder will be cross-checked with records from nutrition training groups and household visioning exercises. National population census statistics on household size, proportions of men, women, and youth and adults, will be used to convert the household averages to estimated population-level values for the indicators.

A dataset to advance the study of life-cycle interactions of biomedical and socioeconomic factors in the aging process. The EI project has assembled a variety of large datasets covering the life histories of approximately 39,616 white male volunteers (drawn from a random sample of 331 companies) who served in the Union Army (UA), and of about 6,000 African-American veterans from 51 randomly selected United States Colored Troops companies (USCT). Their military records were linked to pension and medical records that detailed the soldiers������?? health status and socioeconomic and family characteristics. Each soldier was searched for in the US decennial census for the years in which they were most likely to be found alive (1850, 1860, 1880, 1900, 1910). In addition, a sample consisting of 70,000 men examined for service in the Union Army between September 1864 and April 1865 has been assembled and linked only to census records. These records will be useful for life-cycle comparisons of those accepted and rejected for service. Military Data: The military service and wartime medical histories of the UA and USCT men were collected from the Union Army and United States Colored Troops military service records, carded medical records, and other wartime documents. Pension Data: Wherever possible, the UA and USCT samples have been linked to pension records, including surgeon''''s certificates. About 70% of men in the Union Army sample have a pension. These records provide the bulk of the socioeconomic and demographic information on these men from the late 1800s through the early 1900s, including family structure and employment information. In addition, the surgeon''''s certificates provide rich medical histories, with an average of 5 examinations per linked recruit for the UA, and about 2.5 exams per USCT recruit. Census Data: Both early and late-age familial and socioeconomic information is collected from the manuscript schedules of the federal censuses of 1850, 1860, 1870 (incomplete), 1880, 1900, and 1910. Data Availability: All of the datasets (Military Union Army; linked Census; Surgeon''''s Certificates; Examination Records, and supporting ecological and environmental variables) are publicly available from ICPSR. In addition, copies on CD-ROM may be obtained from the CPE, which also maintains an interactive Internet Data Archive and Documentation Library, which can be accessed on the Project Website. * Dates of Study: 1850-1910 * Study Features: Longitudinal, Minority Oversamples * Sample Size: ** Union Army: 35,747 ** Colored Troops: 6,187 ** Examination Sample: 70,800 ICPSR Link: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06836

Employment Service (ES) is one component of the suite of services known as Employment Ontario (EO). ES provides Ontarians with access to all the employment services and supports they need in one location, so they can find and keep a job, apply for training, and plan a career that’s right for them. The goal of the ES program is to help Ontarians find sustainable employment.

Employment Service is delivered by third-party service providers at service delivery sites (SDS) across Ontario on behalf of the Ministry of Labour, Training and Skills Development (MLTSD). The services provided by ES are tailored to meet the individual needs of each client and can be provided one-on-one or in a group setting.

Employment Service has two broad categories: unassisted and assisted services.Unassisted services, or the Resource and Information (RI) service component, provides individuals with information on local training and employment opportunities, community service supports, and resources to support independent or “unassisted” job search. These services can be delivered through structured orientation or information sessions (on or off site), e-learning sessions, or one-to-one sessions up to two days in duration. The RI component also helps employers to attract and recruit employees and skilled labour by posting positions and offering opportunities to participate in job fairs and other community events.

This service component is available to all Ontarians as there are no eligibility or access requirements.

Assisted services are offered to individuals who display the need for more intensive, structured, and/or one-on-one employment supports, and includes the following components: job search assistance (including individualized assistance in career goal setting, skills assessment, and interview preparation) job matching, placement and incentives (which match client skills and interests with employment opportunities, and include placement into employment, on-the-job training opportunities, and incentives to employers to hire ES clients), and job training/retention (which supports longer-term attachment to or advancement in the labour market or completion of training)

Each assisted services client has a service plan, which is developed with the assistance of the service provider. This service plan lists all of the ES components that the client accesses, and the service provider monitors, evaluates, and adjusts this plan over the duration of the service plan. When an assisted services client completes the ES components of his/her service plan, the service provider closes the service plan (i.e. exit). As closed service plans cannot be reopened, if the client subsequently returns to access the assisted services of Employment Service (either at the same service delivery site or a different service delivery site), a new service plan is created.

To be eligible for assisted services, clients must be unemployed (defined as working less than an average of twenty hours a week) and not participating in full-time education or training. Clients are also assessed on a number of suitability indicators covering economic, social and other barriers to employment, and service providers are to prioritize serving those clients with multiple suitability indicators.Definitions for fields in this layer are available in the abbreviated Technical Dictionary.

In 1998, formal demographic censusing of wild ginseng (Panax quinquefolius L.) populations was initiated in West Virginia. By 2004, thirty populations had been added to the census effort, spanning seven states (IN-2, KY-6, MD-1, NY-2, PA-2, VA-5, WV-12) and a wide variety of land use histories and eastern deciduous forest communities. The censusing effort continued without interruption at all populations until June, 2016. Annually, each population was visited twice. The first visit generally occurred between late May and the end of June. The second visit generally occurred in the first three weeks of August. The purpose of the spring census was to assess the population status at the time of year when the largest number of individuals were visible aboveground (post-germination, prior to substantial losses due to browsing and other causes). Detailed measures of plant size were made, with an emphasis on total leaf area calculation. In addition, a variety of plant condition notations were made, with the ultimate goal of determining mortality and recruitment in the population, as well as individual size transitions. The primary purpose of the second census each year was to assess seed production on each plant. In addition, further notations of plant condition were made to assess changes over the growing season. To maintain methodological consistency with field personnel turnover, the lead author participated in fieldwork throughout the study, visiting each population at least once every two years. In addition, after being trained themselves, graduate students trained undergraduate conservation interns to assure consistent methods were used each year. The data are suitable for demographic modeling, and the unique spatial and temporal extent allow the exploration of important questions about variability in population growth and viability of ginseng, America’s premiere wild harvested medicinal plant.

Publications derived from this dataset:

Peer reviewed publications:

McGraw, J. B., S. M. Sanders, and M. E. Van der Voort. 2003. Distribution and Abundance of Hydrastis canadensis L. (Ranunculaceae) and Panax quinquefolius L. (Araliaceae) in the Central Appalachian Region. Journal of the Torrey Botanical Club 130(2): 62-69.

Furedi, M. A. and J. B. McGraw. 2004. White-tailed deer: Dispersers or predators of American ginseng seeds? American Midland Naturalist 152:268-276.

McGraw, J. B. and M. A. Furedi. 2005. Deer browsing and population viability of a forest understory plant. Science 307: 920-922.

McGraw, J. B., M. A. Furedi, K. Maiers, C. Carroll, G. Kauffman, A. Lubbers, J. Wolf, R. Anderson, R. Anderson, B. Wilcox, D. Drees, M. E. Van der Voort, M. Albrecht, A. Nault, H. MacCulloch, and A. Gibbs. 2005. Berry ripening and harvest season in wild American ginseng. Northeastern Naturalist 12(2): 141-152.

Van der Voort, M. E. and J. B. McGraw. 2006. Effects of harvester behavior on population growth rate affects sustainability of ginseng trade. Biological Conservation 130: 505-516.

Mooney, E. H. and J. B. McGraw. 2007. Unintentional effects of harvest on selection in wild American ginseng. Conservation Genetics 8: 57-67.

Wixted, K. and J. B. McGraw. 2009. A Panax-centric view of invasive species. Biological Invasions 11(4): 883-893.

Mooney, E. H. and J. B. McGraw. 2009. Relationship between age, size and reproduction in populations of American ginseng, Panax quinquefolius (Araliaceae), across a range of harvest pressures. Ecoscience 16(1): 84-94.

McGraw, J. B., S. Souther, and A. E. Lubbers. 2010. Rates of harvest and compliance with regulations in natural populations of American ginseng (Panax quinquefolius L.). Natural Areas Journal 30: 202-210.

Souther, S. and J. B. McGraw. 2011. Vulnerability of wild American ginseng to an extreme early spring temperature fluctuation. Population Ecology 53(1):119-129.

Souther, S. and J. B. McGraw. 2011. Local adaptation to temperature and its implications for species conservation in a changing climate. Conservation Biology 25(5): 922-931.

McGraw, J. B., A. E. Lubbers, M. E. Van der Voort, E. H. Mooney, M. A. Furedi, S. Souther, J. B. Turner, J. Chandler. 2013. Ecology and conservation of ginseng (Panax quinquefolius) in a changing world. Annals of the New York Academy of Sciences 1286: 62-91. {ISSN 0077-8923. DOI: 10.1111/nyas.12032. (Invited Review)}

Wagner, A. and J. B. McGraw. 2013. Sunfleck effects on physiology, growth, and local demography of American ginseng (Panax quinquefolius L.). Forest Ecology and Management 291:220-227.

Souther, S. and J. B. McGraw. 2014. Synergistic effects of climate change and harvest on extinction risk of American ginseng. Ecological Applications 24(6): 1463-1477.

*Data taken from the audit commission website: http://www.areaprofiles.audit-commission.gov.uk. Accessed 9 October 2009.

Abstract copyright UK Data Service and data collection copyright owner.The Annual Population Survey (APS) is a major survey series, which aims to provide data that can produce reliable estimates at the local authority level. Key topics covered in the survey include education, employment, health and ethnicity. The APS comprises key variables from the Labour Force Survey (LFS), all its associated LFS boosts and the APS boost. The APS aims to provide enhanced annual data for England, covering a target sample of at least 510 economically active persons for each Unitary Authority (UA)/Local Authority District (LAD) and at least 450 in each Greater London Borough. In combination with local LFS boost samples, the survey provides estimates for a range of indicators down to Local Education Authority (LEA) level across the United Kingdom.For further detailed information about methodology, users should consult the Labour Force Survey User Guide, included with the APS documentation. For variable and value labelling and coding frames that are not included either in the data or in the current APS documentation, users are advised to consult the latest versions of the LFS User Guides, which are available from the ONS Labour Force Survey - User Guidance webpages.Occupation data for 2021 and 2022The ONS has identified an issue with the collection of some occupational data in 2021 and 2022 data files in a number of their surveys. While they estimate any impacts will be small overall, this will affect the accuracy of the breakdowns of some detailed (four-digit Standard Occupational Classification (SOC)) occupations, and data derived from them. None of ONS' headline statistics, other than those directly sourced from occupational data, are affected and you can continue to rely on their accuracy. The affected datasets have now been updated. Further information can be found in the ONS article published on 11 July 2023: Revision of miscoded occupational data in the ONS Labour Force Survey, UK: January 2021 to September 2022APS Well-Being DatasetsFrom 2012-2015, the ONS published separate APS datasets aimed at providing initial estimates of subjective well-being, based on the Integrated Household Survey. In 2015 these were discontinued. A separate set of well-being variables and a corresponding weighting variable have been added to the April-March APS person datasets from A11M12 onwards. Further information on the transition can be found in the Personal well-being in the UK: 2015 to 2016 article on the ONS website.APS disability variablesOver time, there have been some updates to disability variables in the APS. An article explaining the quality assurance investigations on these variables that have been conducted so far is available on the ONS Methodology webpage. End User Licence and Secure Access APS dataUsers should note that there are two versions of each APS dataset. One is available under the standard End User Licence (EUL) agreement, and the other is a Secure Access version. The EUL version includes Government Office Region geography, banded age, 3-digit SOC and industry sector for main, second and last job. The Secure Access version contains more detailed variables relating to: age: single year of age, year and month of birth, age completed full-time education and age obtained highest qualification, age of oldest dependent child and age of youngest dependent child family unit and household: including a number of variables concerning the number of dependent children in the family according to their ages, relationship to head of household and relationship to head of family nationality and country of origin geography: including county, unitary/local authority, place of work, Nomenclature of Territorial Units for Statistics 2 (NUTS2) and NUTS3 regions, and whether lives and works in same local authority district health: including main health problem, and current and past health problems education and apprenticeship: including numbers and subjects of various qualifications and variables concerning apprenticeships industry: including industry, industry class and industry group for main, second and last job, and industry made redundant from occupation: including 4-digit Standard Occupational Classification (SOC) for main, second and last job and job made redundant from system variables: including week number when interview took place and number of households at address The Secure Access data have more restrictive access conditions than those made available under the standard EUL. Prospective users will need to gain ONS Accredited Researcher status, complete an extra application form and demonstrate to the data owners exactly why they need access to the additional variables. Users are strongly advised to first obtain the standard EUL version of the data to see if they are sufficient for their research requirements. The Special Licence version of the APS April 2004 - March 2005 is held at the UK Data Archive under SN 5257. For the sixth edition (April 2015) an updated version of the data was deposited, weighted to 2014 population figures (based on Census 2011). An updated APS user guide is also available. Main Topics:Topics covered include: household composition and relationships, housing tenure, nationality, ethnicity and residential history, employment and training (including government schemes), workplace and location, job hunting, educational background and qualifications. Many of the variables included in the survey are the same as those in the LFS. Multi-stage stratified random sample Face-to-face interview Telephone interview 2004 2005 ACADEMIC ACHIEVEMENT ADVANCED LEVEL EXAM... ADVANCED SUPPLEMENT... AGE APPLICATION FOR EMP... APPOINTMENT TO JOB ATTITUDES BONUS PAYMENTS BUSINESS AND TECHNO... BUSINESSES CARDIOVASCULAR DISE... CARE OF DEPENDANTS CERTIFICATE OF SECO... CERTIFICATE OF SIXT... CHILDREN CHRONIC ILLNESS CITY AND GUILDS OF ... COHABITATION CONDITIONS OF EMPLO... DEBILITATIVE ILLNESS DEGREES DEPRESSION DIABETES DIGESTIVE SYSTEM DI... DISABILITIES Demography population ECONOMIC ACTIVITY EDUCATIONAL BACKGROUND EDUCATIONAL CERTIFI... EDUCATIONAL COURSES EMPLOYEES EMPLOYER SPONSORED ... EMPLOYMENT EMPLOYMENT HISTORY EMPLOYMENT PROGRAMMES ENDOCRINE DISORDERS EPILEPSY ETHNIC GROUPS FAMILIES FAMILY MEMBERS FIELDS OF STUDY FULL TIME EMPLOYMENT FURNISHED ACCOMMODA... GENDER GENERAL CERTIFICATE... GENERAL NATIONAL VO... GENERAL SCOTTISH VO... HEADS OF HOUSEHOLD HEALTH HEARING IMPAIRMENTS HIGHER EDUCATION HIGHER NATIONAL CER... HOME BUYING HOME OWNERSHIP HOURS OF WORK HOUSEHOLDS HOUSING HOUSING TENURE INCOME INDUSTRIES JOB CHANGING JOB HUNTING JOB SEEKER S ALLOWANCE LANDLORDS LEARNING DISABILITIES Labour and employment MANAGERS MARITAL STATUS MENTAL DISORDERS MUSCULOSKELETAL DIS... NATIONAL IDENTITY NATIONAL VOCATIONAL... NATIONALITY NERVOUS SYSTEM DISE... OCCUPATIONAL QUALIF... OCCUPATIONS ORDINARY LEVEL EXAM... ORDINARY NATIONAL C... OVERTIME PART TIME COURSES PART TIME EMPLOYMENT PLACE OF RESIDENCE PRIVATE SECTOR PUBLIC SECTOR QUALIFICATIONS RECRUITMENT REDUNDANCY REDUNDANCY PAY RELIGIOUS AFFILIATION RENTED ACCOMMODATION RESIDENTIAL MOBILITY RESPIRATORY TRACT D... ROYAL SOCIETY OF AR... SCOTTISH CERTIFICAT... SCOTTISH VOCATIONAL... SCOTTISH VOCATIONAL... SELF EMPLOYED SICK LEAVE SKIN DISEASES SOCIAL HOUSING SOCIAL SECURITY BEN... SOCIO ECONOMIC STATUS SPEECH IMPAIRMENTS STATE RETIREMENT PE... STUDENTS SUBSIDIARY EMPLOYMENT SUPERVISORS SUPERVISORY STATUS TAX RELIEF TEMPORARY EMPLOYMENT TERMINATION OF SERVICE TIED HOUSING TRAINING TRAINING COURSES UNEMPLOYED UNEMPLOYMENT UNFURNISHED ACCOMMO... VISION IMPAIRMENTS VOCATIONAL EDUCATIO... WAGES WELSH LANGUAGE WORKING CONDITIONS WORKPLACE vital statistics an...

Landscape changes can alter pollinator movement and foraging patterns which can in turn influence demographic processes of plant populations. In the Cascade Mountains of the Pacific Northwest, USA, forests are encroaching on alpine meadows that harbor diverse plant and pollinator communities. Whether encroachment and isolation of sub-meadows will influence pollinator foraging behaviors is unknown. To help assess those behaviors, subcutaneous Passive Integrated Transponders were implanted into 163 Rufous Hummingbirds (Selasphorus rufus), common avian pollinators in western North America and four arrays of five hummingbird feeders were established equipped with Radio Frequency Identification data loggers to passively relocate individuals at points throughout the landscape. The feeder arrays were established on four peaks along Frizzel Ridge in the H. J. Andrews Experimental Forest (Lookout Mountain, M1, M2, and Carpenter Mountain). A center feeder was established in a large, central alpine meadow and four satellite feeders c.a. 250m from the center. The satellite feeders were positioned such that at least one was in the open and connected to the center feeder by open habitat, one was in the open but separated from the center by coniferous forest canopy, and one was placed under coniferous forest canopy. Feeders were maintained for 1.5-12 weeks per year from 2014-2017.

Abstract copyright UK Data Service and data collection copyright owner.The Annual Population Survey (APS) is a major survey series, which aims to provide data that can produce reliable estimates at the local authority level. Key topics covered in the survey include education, employment, health and ethnicity. The APS comprises key variables from the Labour Force Survey (LFS), all its associated LFS boosts and the APS boost. The APS aims to provide enhanced annual data for England, covering a target sample of at least 510 economically active persons for each Unitary Authority (UA)/Local Authority District (LAD) and at least 450 in each Greater London Borough. In combination with local LFS boost samples, the survey provides estimates for a range of indicators down to Local Education Authority (LEA) level across the United Kingdom.For further detailed information about methodology, users should consult the Labour Force Survey User Guide, included with the APS documentation. For variable and value labelling and coding frames that are not included either in the data or in the current APS documentation, users are advised to consult the latest versions of the LFS User Guides, which are available from the ONS Labour Force Survey - User Guidance webpages.Occupation data for 2021 and 2022The ONS has identified an issue with the collection of some occupational data in 2021 and 2022 data files in a number of their surveys. While they estimate any impacts will be small overall, this will affect the accuracy of the breakdowns of some detailed (four-digit Standard Occupational Classification (SOC)) occupations, and data derived from them. None of ONS' headline statistics, other than those directly sourced from occupational data, are affected and you can continue to rely on their accuracy. The affected datasets have now been updated. Further information can be found in the ONS article published on 11 July 2023: Revision of miscoded occupational data in the ONS Labour Force Survey, UK: January 2021 to September 2022APS Well-Being DatasetsFrom 2012-2015, the ONS published separate APS datasets aimed at providing initial estimates of subjective well-being, based on the Integrated Household Survey. In 2015 these were discontinued. A separate set of well-being variables and a corresponding weighting variable have been added to the April-March APS person datasets from A11M12 onwards. Further information on the transition can be found in the Personal well-being in the UK: 2015 to 2016 article on the ONS website.APS disability variablesOver time, there have been some updates to disability variables in the APS. An article explaining the quality assurance investigations on these variables that have been conducted so far is available on the ONS Methodology webpage. End User Licence and Secure Access APS dataUsers should note that there are two versions of each APS dataset. One is available under the standard End User Licence (EUL) agreement, and the other is a Secure Access version. The EUL version includes Government Office Region geography, banded age, 3-digit SOC and industry sector for main, second and last job. The Secure Access version contains more detailed variables relating to: age: single year of age, year and month of birth, age completed full-time education and age obtained highest qualification, age of oldest dependent child and age of youngest dependent child family unit and household: including a number of variables concerning the number of dependent children in the family according to their ages, relationship to head of household and relationship to head of family nationality and country of origin geography: including county, unitary/local authority, place of work, Nomenclature of Territorial Units for Statistics 2 (NUTS2) and NUTS3 regions, and whether lives and works in same local authority district health: including main health problem, and current and past health problems education and apprenticeship: including numbers and subjects of various qualifications and variables concerning apprenticeships industry: including industry, industry class and industry group for main, second and last job, and industry made redundant from occupation: including 4-digit Standard Occupational Classification (SOC) for main, second and last job and job made redundant from system variables: including week number when interview took place and number of households at address The Secure Access data have more restrictive access conditions than those made available under the standard EUL. Prospective users will need to gain ONS Accredited Researcher status, complete an extra application form and demonstrate to the data owners exactly why they need access to the additional variables. Users are strongly advised to first obtain the standard EUL version of the data to see if they are sufficient for their research requirements. The Special Licence version of the APS January - December, 2007 is held at the UK Data Archive under SN 5990. For the fourth edition (May 2015) an updated version of the data was deposited, weighted to 2014 population figures (based on Census 2011). The new weighting variable is PWTA14. An updated APS user guide is also available. Main Topics:Topics covered include: household composition and relationships, housing tenure, nationality, ethnicity and residential history, employment and training (including government schemes), workplace and location, job hunting, educational background and qualifications. Many of the variables included in the survey are the same as those in the LFS. Multi-stage stratified random sample Face-to-face interview Telephone interview 2007 ACADEMIC ACHIEVEMENT ADVANCED LEVEL EXAM... ADVANCED SUPPLEMENT... AGE APPLICATION FOR EMP... APPOINTMENT TO JOB ATTITUDES BONUS PAYMENTS BUSINESS AND TECHNO... BUSINESSES CARDIOVASCULAR DISE... CARE OF DEPENDANTS CERTIFICATE OF SECO... CERTIFICATE OF SIXT... CHILDREN CHRONIC ILLNESS CITY AND GUILDS OF ... COHABITATION CONDITIONS OF EMPLO... DEBILITATIVE ILLNESS DEGREES DEPRESSION DIABETES DIGESTIVE SYSTEM DI... DISABILITIES Demography population ECONOMIC ACTIVITY EDUCATIONAL BACKGROUND EDUCATIONAL CERTIFI... EDUCATIONAL COURSES EMPLOYEES EMPLOYER SPONSORED ... EMPLOYMENT EMPLOYMENT HISTORY EMPLOYMENT PROGRAMMES ENDOCRINE DISORDERS EPILEPSY ETHNIC GROUPS FAMILIES FAMILY MEMBERS FIELDS OF STUDY FULL TIME EMPLOYMENT FURNISHED ACCOMMODA... GENDER GENERAL CERTIFICATE... GENERAL NATIONAL VO... GENERAL SCOTTISH VO... HEADS OF HOUSEHOLD HEALTH HEARING IMPAIRMENTS HIGHER EDUCATION HIGHER NATIONAL CER... HOME BUYING HOME OWNERSHIP HOURS OF WORK HOUSEHOLDS HOUSING HOUSING TENURE INCOME INDUSTRIES JOB CHANGING JOB HUNTING JOB SEEKER S ALLOWANCE LANDLORDS LEARNING DISABILITIES Labour and employment MANAGERS MARITAL STATUS MENTAL DISORDERS MUSCULOSKELETAL DIS... NATIONAL IDENTITY NATIONAL VOCATIONAL... NATIONALITY NERVOUS SYSTEM DISE... OCCUPATIONAL QUALIF... OCCUPATIONS ORDINARY LEVEL EXAM... ORDINARY NATIONAL C... OVERTIME PART TIME COURSES PART TIME EMPLOYMENT PLACE OF RESIDENCE PRIVATE SECTOR PUBLIC SECTOR QUALIFICATIONS RECRUITMENT REDUNDANCY REDUNDANCY PAY RELIGIOUS AFFILIATION RENTED ACCOMMODATION RESIDENTIAL MOBILITY RESPIRATORY TRACT D... ROYAL SOCIETY OF AR... SCOTTISH CERTIFICAT... SCOTTISH VOCATIONAL... SCOTTISH VOCATIONAL... SELF EMPLOYED SICK LEAVE SKIN DISEASES SOCIAL HOUSING SOCIAL SECURITY BEN... SOCIO ECONOMIC STATUS SPEECH IMPAIRMENTS STATE RETIREMENT PE... STUDENTS SUBSIDIARY EMPLOYMENT SUPERVISORS SUPERVISORY STATUS TAX RELIEF TEMPORARY EMPLOYMENT TERMINATION OF SERVICE TIED HOUSING TRAINING TRAINING COURSES UNEMPLOYED UNEMPLOYMENT UNFURNISHED ACCOMMO... VISION IMPAIRMENTS VOCATIONAL EDUCATIO... WAGES WELSH LANGUAGE WORKING CONDITIONS WORKPLACE vital statistics an...

The census is undertaken by the Office for National Statistics every 10 years and gives us a picture of all the people and households in England and Wales. The most recent census took place in March of 2021.The census asks every household questions about the people who live there and the type of home they live in. In doing so, it helps to build a detailed snapshot of society. Information from the census helps the government and local authorities to plan and fund local services, such as education, doctors' surgeries and roads.Key census statistics for Leicester are published on the open data platform to make information accessible to local services, voluntary and community groups, and residents. There is also a dashboard published showcasing various datasets from the census allowing users to view data for Leicester and compare this with national statistics.Further information about the census and full datasets can be found on the ONS website - https://www.ons.gov.uk/census/aboutcensus/censusproductsOccupationThis dataset provides Census 2021 estimates that classify usual residents aged 16 years and over in employment the week before the census in England and Wales by occupation. The estimates are as at Census Day, 21 March 2021.Definition: Classifies what people aged 16 years and over do as their main job. Their job title or details of activities they do in their job and any supervisory or management responsibilities form this classification. This information is used to code responses to an occupation using the Standard Occupational Classification (SOC) 2020.It classifies people who were in employment between 15 March and 21 March 2021, by the SOC code that represents their current occupation.This dataset includes details for Leicester city and England overall.

Abstract copyright UK Data Service and data collection copyright owner.The Annual Population Survey (APS) is a major survey series, which aims to provide data that can produce reliable estimates at the local authority level. Key topics covered in the survey include education, employment, health and ethnicity. The APS comprises key variables from the Labour Force Survey (LFS), all its associated LFS boosts and the APS boost. The APS aims to provide enhanced annual data for England, covering a target sample of at least 510 economically active persons for each Unitary Authority (UA)/Local Authority District (LAD) and at least 450 in each Greater London Borough. In combination with local LFS boost samples, the survey provides estimates for a range of indicators down to Local Education Authority (LEA) level across the United Kingdom.For further detailed information about methodology, users should consult the Labour Force Survey User Guide, included with the APS documentation. For variable and value labelling and coding frames that are not included either in the data or in the current APS documentation, users are advised to consult the latest versions of the LFS User Guides, which are available from the ONS Labour Force Survey - User Guidance webpages.Occupation data for 2021 and 2022The ONS has identified an issue with the collection of some occupational data in 2021 and 2022 data files in a number of their surveys. While they estimate any impacts will be small overall, this will affect the accuracy of the breakdowns of some detailed (four-digit Standard Occupational Classification (SOC)) occupations, and data derived from them. None of ONS' headline statistics, other than those directly sourced from occupational data, are affected and you can continue to rely on their accuracy. The affected datasets have now been updated. Further information can be found in the ONS article published on 11 July 2023: Revision of miscoded occupational data in the ONS Labour Force Survey, UK: January 2021 to September 2022APS Well-Being DatasetsFrom 2012-2015, the ONS published separate APS datasets aimed at providing initial estimates of subjective well-being, based on the Integrated Household Survey. In 2015 these were discontinued. A separate set of well-being variables and a corresponding weighting variable have been added to the April-March APS person datasets from A11M12 onwards. Further information on the transition can be found in the Personal well-being in the UK: 2015 to 2016 article on the ONS website.APS disability variablesOver time, there have been some updates to disability variables in the APS. An article explaining the quality assurance investigations on these variables that have been conducted so far is available on the ONS Methodology webpage. End User Licence and Secure Access APS dataUsers should note that there are two versions of each APS dataset. One is available under the standard End User Licence (EUL) agreement, and the other is a Secure Access version. The EUL version includes Government Office Region geography, banded age, 3-digit SOC and industry sector for main, second and last job. The Secure Access version contains more detailed variables relating to: age: single year of age, year and month of birth, age completed full-time education and age obtained highest qualification, age of oldest dependent child and age of youngest dependent child family unit and household: including a number of variables concerning the number of dependent children in the family according to their ages, relationship to head of household and relationship to head of family nationality and country of origin geography: including county, unitary/local authority, place of work, Nomenclature of Territorial Units for Statistics 2 (NUTS2) and NUTS3 regions, and whether lives and works in same local authority district health: including main health problem, and current and past health problems education and apprenticeship: including numbers and subjects of various qualifications and variables concerning apprenticeships industry: including industry, industry class and industry group for main, second and last job, and industry made redundant from occupation: including 4-digit Standard Occupational Classification (SOC) for main, second and last job and job made redundant from system variables: including week number when interview took place and number of households at address The Secure Access data have more restrictive access conditions than those made available under the standard EUL. Prospective users will need to gain ONS Accredited Researcher status, complete an extra application form and demonstrate to the data owners exactly why they need access to the additional variables. Users are strongly advised to first obtain the standard EUL version of the data to see if they are sufficient for their research requirements. The Special Licence version of the APS July 2007 - June 2008 is held at the UK Data Archive under SN 6094. For the fourth edition (April 2015) an updated version of the data was deposited, weighted to 2014 population figures (based on Census 2011). The new weighting variable is PWTA14. An updated APS user guide is also available. Main Topics:Topics covered include: household composition and relationships, housing tenure, nationality, ethnicity and residential history, employment and training (including government schemes), workplace and location, job hunting, educational background and qualifications. Many of the variables included in the survey are the same as those in the LFS. Multi-stage stratified random sample Face-to-face interview Telephone interview 2007 2008 ACADEMIC ACHIEVEMENT ADVANCED LEVEL EXAM... ADVANCED SUPPLEMENT... AGE APPLICATION FOR EMP... APPOINTMENT TO JOB ATTITUDES BONUS PAYMENTS BUSINESS AND TECHNO... CARDIOVASCULAR DISE... CARE OF DEPENDANTS CERTIFICATE OF SECO... CERTIFICATE OF SIXT... CHILDREN CHRONIC ILLNESS CITY AND GUILDS OF ... COHABITATION CONDITIONS OF EMPLO... DEBILITATIVE ILLNESS DEGREES DEPRESSION DIABETES DIGESTIVE SYSTEM DI... DISABILITIES Demography population ECONOMIC ACTIVITY EDUCATIONAL BACKGROUND EDUCATIONAL CERTIFI... EDUCATIONAL COURSES EMPLOYEES EMPLOYER SPONSORED ... EMPLOYMENT EMPLOYMENT HISTORY EMPLOYMENT PROGRAMMES ENDOCRINE DISORDERS EPILEPSY ETHNIC GROUPS FAMILIES FAMILY MEMBERS FIELDS OF STUDY FULL TIME EMPLOYMENT FURNISHED ACCOMMODA... GENDER GENERAL CERTIFICATE... GENERAL NATIONAL VO... GENERAL SCOTTISH VO... HEADS OF HOUSEHOLD HEALTH HEARING IMPAIRMENTS HIGHER EDUCATION HIGHER NATIONAL CER... HOME BUYING HOME OWNERSHIP HOURS OF WORK HOUSEHOLDS HOUSING HOUSING TENURE INCOME INDUSTRIES JOB CHANGING JOB HUNTING JOB SEEKER S ALLOWANCE LANDLORDS LEARNING DISABILITIES Labour and employment MANAGERS MARITAL STATUS MENTAL DISORDERS MUSCULOSKELETAL DIS... NATIONAL IDENTITY NATIONAL VOCATIONAL... NATIONALITY NERVOUS SYSTEM DISE... OCCUPATIONAL QUALIF... OCCUPATIONS ORDINARY LEVEL EXAM... ORDINARY NATIONAL C... OVERTIME PART TIME COURSES PART TIME EMPLOYMENT PLACE OF RESIDENCE PRIVATE SECTOR PUBLIC SECTOR QUALIFICATIONS RECRUITMENT REDUNDANCY REDUNDANCY PAY RELIGIOUS AFFILIATION RENTED ACCOMMODATION RESIDENTIAL MOBILITY RESPIRATORY TRACT D... ROYAL SOCIETY OF AR... SCOTTISH CERTIFICAT... SCOTTISH VOCATIONAL... SCOTTISH VOCATIONAL... SELF EMPLOYED SICK LEAVE SKIN DISEASES SOCIAL HOUSING SOCIAL SECURITY BEN... SOCIO ECONOMIC STATUS SPEECH IMPAIRMENTS STATE RETIREMENT PE... STUDENTS SUBSIDIARY EMPLOYMENT SUPERVISORS SUPERVISORY STATUS TAX RELIEF TEMPORARY EMPLOYMENT TERMINATION OF SERVICE TIED HOUSING TRAINING TRAINING COURSES UNEMPLOYED UNEMPLOYMENT UNFURNISHED ACCOMMO... VISION IMPAIRMENTS VOCATIONAL EDUCATIO... WAGES WELSH LANGUAGE WORKING CONDITIONS WORKPLACE vital statistics an...

Long-term monitoring of waterbirds in the Coorong and Lower Lakes in South Australia is undertaken by the University of Adelaide and forms part of the annual waterbird census in the Lower Lakes, Coorong and the Murray Mouth (LLCMM) region. Waterbird monitoring in the Coorong commenced in 2000, and it expanded in 2009 to include the Lower Lakes. The LLCMM region is a Ramsar-listed wetland of international importance for migratory waterbirds. It is also one of the icon sites under The Living Murray program. The condition of the LLCMM region, and waterbird recruitment and populations, have been identified as targets against which to assess progress towards achieving the objectives of the Murray-Darling Basin Plan. The waterbird census data and findings form part of the ecological information used for this assessment. The 2016-17 monitoring program was funded by the Murray-Darling Basin Authority (MDBA). Between 2000 and 2016, the MDBA, South Australia’s Department of Environment, Water and Natural Resources (DEWNR), Nature Foundation South Australia, Earthwatch Australia and the University of Adelaide funded the monitoring program in different years. The MDBA has made the waterbird databases and related resources publicly available on data.gov.au as part of its commitment to the Australian Government policy on public data and information. The terms and conditions for using the data and related resources from this website can be found at https://www.data.gov.au/about.