Financial overview and grant giving statistics of Population Resource Center Inc

Datasets archived here consist of all data analyzed in Duan et al. 2015 from Journal of Applied Ecology. Specifically, these data were collected from annual sampling of emerald ash borer (Agrilus planipennis) immature stages and associated parasitoids on infested ash trees (Fraxinus) in Southern Michigan, where three introduced biological control agents had been released between 2007 - 2010. Detailed data collection procedures can be found in Duan et al. 2012, 2013, and 2015. Resources in this dataset:Resource Title: Duan J Data on EAB larval density-bird predation and unknown factor from Journal of Applied Ecology. File Name: Duan J Data on EAB larval density-bird predation and unknown factor from Journal of Applied Ecology.xlsxResource Description: This data set is used to calculate mean EAB density (per m2 of ash phloem area), bird predation rate and mortality rate caused by unknown factors and analyzed with JMP (10.2) scripts for mixed effect linear models in Duan et al. 2015 (Journal of Applied Ecology).Resource Title: DUAN J Data on Parasitism L1-L2 Excluded from Journal of Applied Ecology. File Name: DUAN J Data on Parasitism L1-L2 Excluded from Journal of Applied Ecology.xlsxResource Description: This data set is used to construct life tables and calculation of net population growth rate of emerald ash borer for each site. The net population growth rates were then analyzed with JMP (10.2) scripts for mixed effect linear models in Duan et al. 2015 (Journal of Applied Ecology).Resource Title: DUAN J Data on EAB Life Tables Calculation from Journal of Applied Ecology. File Name: DUAN J Data on EAB Life Tables Calculation from Journal of Applied Ecology.xlsxResource Description: This data set is used to calculate parasitism rate of EAB larvae for each tree and then analyzed with JMP (10.2) scripts for mixed effect linear models on in Duan et al. 2015 (Journal of Applied Ecology).Resource Title: READ ME for Emerald Ash Borer Biocontrol Study from Journal of Applied Ecology. File Name: READ_ME_for_Emerald_Ash_Borer_Biocontrol_Study_from_Journal_of_Applied_Ecology.docxResource Description: Additional information and definitions for the variables/content in the three Emerald Ash Borer Biocontrol Study tables: Data on EAB Life Tables Calculation Data on EAB larval density-bird predation and unknown factor Data on Parasitism L1-L2 Excluded from Journal of Applied Ecology Resource Title: Data Dictionary for Emerald Ash Borer Biocontrol Study from Journal of Applied Ecology. File Name: AshBorerAnd Parasitoids_DataDictionary.csvResource Description: CSV data dictionary for the variables/content in the three Emerald Ash Borer Biocontrol Study tables: Data on EAB Life Tables Calculation Data on EAB larval density-bird predation and unknown factor Data on Parasitism L1-L2 Excluded from Journal of Applied Ecology Fore more information see the related READ ME file.

Before the activity, students are divided into 3 groups that are assigned 3 different reading assignments (land use, atmosphere, or water quality). On the day of the activity, students work collaboratively with students from the same reading assignment group for 20 – 40 minutes to answer questions and address concepts from their particular assigned reading. Next, students are shuffled (jigsaw-style) into small teams of 3 students (one student from each reading group). Students educate each other with concepts from their respective reading groups and then work collaboratively on a shared project to select, define, and potentially solve an environmental challenge.

Gender, age, language, income, poverty status, indigenous identity, immigrant status, visible minority, education and labour force status of the population residing in resource-based communities (census subdivisions where a relatively high proportion of employment income comes from fishing, forestry, or agriculture), for 2021.

This Excel spreadsheet is the primary output from the "county controls" development process. It includes forecasts of overall population for counties in the MORPC 15-county region in five-year intervals in the forecast horizon, as well as forecasts of housing, jobs, workers, and other variables related to these. It is updated approximately every four years in conjunction with updates to the Metropolitan Transportation Plan land use model (1), however it is also made available to the public as a standalone resource via the MORPC Population Hub (2). Referring to the MORPC Data User Personas (3), it includes summary tables intended for use by Engaged Elaine, Decisive Delaney, and Hopeful Hadiya, and a machine-readable long-form table intended for use by Savvy Sonja, Specialist Samir, and Coding Corey. The forecasts are produced using a proprietary time series model using historical population data from the U.S. Census Population Estimates Program (4) and various sources of contemporary data. A detailed methods document has not been released as of March 2023, however more information is available upon request.(1) MORPC Metropolitan Transportation Plan Appendix A: Future Land Use, p. v, https://www.morpc.org/wordpress/wp-content/uploads/2020/10/A_LandUse.pdf(2) MORPC Population Resource Hub: County Forecasts, https://www.morpc.org/popforecast(3) MORPC Data User Personas, https://www.morpc.org/tool-resource/data-user-personas/(4) Population Estimates Program, U.S. Census, https://www.census.gov/programs-surveys/popest.html

Average and median population, population change and proportion of rural population, as well as selected resource income statistics of all communities (census subdivisions) and resource-based communities, for 2016 and 2021.

Supplemental information for "A World3 analysis of the sensitivity of population/resource dynamics to pandemic-scale variation in life expectancy"

The debate on the causes of conflict in human societies has deep roots. In particular, the extent of conflict in hunter-gatherer groups remains unclear. Some authors suggest that large-scale violence only arose with the spreading of agriculture and the building of complex societies. To shed light on this issue, we developed a model based on operatorial techniques simulating population-resource dynamics within a two-dimensional lattice, with humans and natural resources interacting in each cell of the lattice. The model outcomes under different conditions were compared with recently available demographic data for prehistoric South America. Only under conditions that include migration among cells and conflict was the model able to consistently reproduce the empirical data at a continental scale. We argue that the interplay between resource competition, migration, and conflict drove the population dynamics of South America after the colonization phase and before the introduction of agriculture. The relation between population and resources indeed emerged as a key factor leading to migration and conflict once the carrying capacity of the environment has been reached.

The Bureau of Health Professions Area Resource File is a county-based data file summarizing secondary data from a wide variety of sources into a single file to facilitate health analysis. The file contains over 6,000 data elements for all counties in the United States with the exception of Alaska, for which there is a state total, and certain independent cities that have been combined into their appropriate counties. The data elements include: (1) County descriptor codes (name, FIPS, HSA, PSRO, SMSA, SEA, BEA, city size, P/MSA, Census Contiguous County, shortage area designation, etc.), (2) Health professions data (number of professionals registered as M.D., D.O., DDS, R.N., L.P.N., veterinarian, pharmacist, optometrist, podiatrist, and dental hygienist), (3) Health facility data (hospital size, type, utilization, staffing and services, and nursing home data), (4) Population data (size, composition, employment, housing, morbidity, natality, mortality by cause, by sex and race, and by age, and crime data), (5) Health Professions Training data (training programs, enrollments, and graduates by type), (6) Expenditure data (hospital expenditures, Medicare enrollments and reimbursements, and Medicare prevailing charge data), (7) Economic data (total, per capita, and median income, income distribution, and AFDC recipients), and (8) Environment data (land area, large animal population, elevation, latitude and longitude of population centroid, water hardness index, and climate data).

This dataset provides population estimate trends from 1998 to the current year for each of California’s 58 counties, further disaggregated by Detailed Analysis Units (DAUs) - the smallest geographic units historically used by the California Department of Water Resources for water planning as part of the California Water Plan. DAUs are subdivisions of Planning Areas and often align with county boundaries, although a single DAU may span multiple counties. They have traditionally supported water demand estimates based on crop and land use types.

The population estimates were developed using U.S. Bureau Census 2000, 2010 and 2020 data. Throughout the estimation process, intermediate results were reviewed and adjusted as needed, with professional judgment applied to smooth trends where appropriate.

Since the California Water Plan is retiring DAUs as its planning and analysis framework, future updates to this dataset will transition away from DAU based geography. Instead, population estimates will be provided based on other geographic units, such as the 8-digit Hydrologic Units (HUC8) defined by the U.S. Geological Survey’s Watershed Boundary Dataset.

A dashboard is available for visualizing historical population trends by county and DAU.

Whereas the population is expected to decrease somewhat until 2100 in Asia, Europe, and South America, it is predicted to grow significantly in Africa. While there were 1.5 billion inhabitants on the continent at the beginning of 2024, the number of inhabitants is expected to reach 3.8 billion by 2100. In total, the global population is expected to reach nearly 10.4 billion by 2100. Worldwide population In the United States, the total population is expected to steadily increase over the next couple of years. In 2024, Asia held over half of the global population and is expected to have the highest number of people living in urban areas in 2050. Asia is home to the two most populous countries, India and China, both with a population of over one billion people. However, the small country of Monaco had the highest population density worldwide in 2021. Effects of overpopulation Alongside the growing worldwide population, there are negative effects of overpopulation. The increasing population puts a higher pressure on existing resources and contributes to pollution. As the population grows, the demand for food grows, which requires more water, which in turn takes away from the freshwater available. Concurrently, food needs to be transported through different mechanisms, which contributes to air pollution. Not every resource is renewable, meaning the world is using up limited resources that will eventually run out. Furthermore, more species will become extinct which harms the ecosystem and food chain. Overpopulation was considered to be one of the most important environmental issues worldwide in 2020.

Resource specialists persist on a narrow range of resources. Consequently, the abundance of key resources should drive vital rates, individual fitness and population viability. While Neotropical forests feature both high levels of biodiversity and numbers of specialist species, no studies have directly evaluated how the variation of key resources affects the fitness of a tropical specialist. Here, we quantified the effect of key tree species density and forest cover on the fitness of three-toed sloths (Bradypus variegatus), an arboreal folivore strongly associated with Cecropia trees, in Costa Rica using a multi-year demographic, genetic and space use dataset. We found that the density of Cecropia trees was strongly and positively related to both adult survival and reproductive output. A matrix model parameterized with Cecropia-demography relationships suggested positive growth of sloth populations, even at low densities of Cecropia (0.7 trees/ha). Our study shows the first direct link between the density of a key resource to demographic consequences of a tropical specialist, underscoring the sensitivity of tropical specialists to the loss of a single key resource, but also point to targeted conservation measures to increase that resource. Finally, our study reveals that previously disturbed and regenerating environments can support viable populations of tropical specialists.

This is a repository of global and regional human population data collected from: the databases of scenarios assessed by the Intergovernmental Panel on Climate Change (Sixth Assessment Report, Special Report on 1.5 C; Fifth Assessment Report), multi-national databases of population projections (World Bank, International Database, United Nation population projections), and other very long-term population projections (Resources for the Future).

More specifically, it contains:

• in other_pop_data folder files from World Bank, the International Database from the US Census, and from IHME

• in the SSP folder, the Shared Socioeconomic Pathways, as in the version 2.0 downloaded from IIASA and as in the version 3.0 downloaded from IIASA workspace

• in the UN folder, the demographic projections from UN

• IAMstat.xlsx, an overview file of the metadata accompanying the scenarios present in the IPCC databases

• RFF.csv, an overview file containing the population projections obtained by Resources For the Future

'- the remaining .csv files with names AR6#, AR5#, IAMC15# contain the IPCC scenarios assessed by the IPCC for preparing the IPCC assessment reports. They can be downloaded from AR5, SR 1.5, and AR6

This data in intended to be downloaded for use together with the package downloadable here.

The dataset was used as a supporting material for the paper "Underestimating demographic uncertainties in the synthesis process of the IPCC" accepted on npj Climate Action (DOI : 10.1038/s44168-024-00152-y).

Analysis of ‘District Resource Statement - SNAP Population’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/4ef4a33e-02ab-4923-a148-6f6cfb57d4dc on 26 January 2022.

--- Dataset description provided by original source is as follows ---

Population of individuals and households receiving SNAP Benefits. For a complete list of District Resource Statement datasets,please follow this link.

--- Original source retains full ownership of the source dataset ---

The world's population first reached one billion people in 1803, and reach eight billion in 2023, and will peak at almost 11 billion by the end of the century. Although it took thousands of years to reach one billion people, it did so at the beginning of a phenomenon known as the demographic transition; from this point onwards, population growth has skyrocketed, and since the 1960s the population has increased by one billion people every 12 to 15 years. The demographic transition sees a sharp drop in mortality due to factors such as vaccination, sanitation, and improved food supply; the population boom that follows is due to increased survival rates among children and higher life expectancy among the general population; and fertility then drops in response to this population growth. Regional differences The demographic transition is a global phenomenon, but it has taken place at different times across the world. The industrialized countries of Europe and North America were the first to go through this process, followed by some states in the Western Pacific. Latin America's population then began growing at the turn of the 20th century, but the most significant period of global population growth occurred as Asia progressed in the late-1900s. As of the early 21st century, almost two thirds of the world's population live in Asia, although this is set to change significantly in the coming decades. Future growth The growth of Africa's population, particularly in Sub-Saharan Africa, will have the largest impact on global demographics in this century. From 2000 to 2100, it is expected that Africa's population will have increased by a factor of almost five. It overtook Europe in size in the late 1990s, and overtook the Americas a decade later. In contrast to Africa, Europe's population is now in decline, as birth rates are consistently below death rates in many countries, especially in the south and east, resulting in natural population decline. Similarly, the population of the Americas and Asia are expected to go into decline in the second half of this century, and only Oceania's population will still be growing alongside Africa. By 2100, the world's population will have over three billion more than today, with the vast majority of this concentrated in Africa. Demographers predict that climate change is exacerbating many of the challenges that currently hinder progress in Africa, such as political and food instability; if Africa's transition is prolonged, then it may result in further population growth that would place a strain on the region's resources, however, curbing this growth earlier would alleviate some of the pressure created by climate change.

Current studies have demonstrated that innate immunity possesses memory characteristics. Although the molecular mechanisms underlying innate immune memory have been addressed by numerous studies, genetic variations in innate immune memory and the associated genes remain unclear. Here, we explored innate immune memory in 163 lines of Drosophila melanogaster from the Drosophila Synthetic Population Resource. In our assay system, prior training with low pathogenic bacteria (Micrococcus luteus) increased the survival rate of flies after subsequent challenge with highly pathogenic bacteria (Staphylococcus aureus). This positive training effect was observed in most lines, but some lines exhibited negative training effects. Survival rates under training and control conditions were poorly correlated, suggesting that distinct genetic factors regulate training effects and normal immune responses. Subsequent quantitative trait loci analysis suggested that four loci containing 80 genes may be involved in regulating innate immune memory. Among them, Adgf-A, which encodes an extracellular adenosine deaminase-related growth factor, was shown to be associated with training effects. Our study findings help to elucidate the genetic architecture of innate immune memory in Drosophila and may provide insight for new therapeutic treatments aimed at boosting immunity.

The severity of the toxic side effects of chemotherapy varies among patients, and much of this variation is likely genetically based. Here, we use the model system Drosophila melanogaster to genetically dissect the toxicity of methotrexate (MTX), a drug used primarily to treat childhood acute lymphoblastic leukemia and rheumatoid arthritis. We use the Drosophila Synthetic Population Resource, a panel of recombinant inbred lines derived from a multiparent advanced intercross, and quantify MTX toxicity as a reduction in female fecundity. We identify three quantitative trait loci (QTL) affecting MTX toxicity; two colocalize with the fly orthologs of human genes believed to mediate MTX toxicity and one is a novel MTX toxicity gene with a human ortholog. A fourth suggestive QTL spans a centromere. Local single-marker association scans of candidate gene exons fail to implicate amino acid variants as the causative single-nucleotide polymorphisms, and we therefore hypothesize the causative variation is regulatory. In addition, the effects at our mapped QTL do not conform to a simple biallelic pattern, suggesting multiple causative factors underlie the QTL mapping results. Consistent with this observation, no single single-nucleotide polymorphism located in or near a candidate gene can explain the QTL mapping signal. Overall, our results validate D. melanogaster as a model for uncovering the genetic basis of chemotoxicity and suggest the genetic basis of MTX toxicity is due to a handful of genes each harboring multiple segregating regulatory factors.

This table provides 2021 data on the estimated total population according to use of other health resources by resource type. The information is disaggregated territorially at the level of large regions of the Canary Islands.

Populations along geographical range limits are often exposed to unsuitable climate and low resource availability relative to core populations. As such, there has been a renewed focus on understanding the factors that determine range limits to better predict how species will respond to global change. Using recent theory on range limits and classical understanding of density dependence, we evaluated the influence of resource availability on the snowshoe hare Lepus americanus along its trailing range edge. We estimated variation in population density, habitat use, survival, and parasite loads to test the Great Escape Hypothesis (GEH), i.e. that density dependence determines, in part, a species’ persistence along trailing edges. We found that variability in resource availability affected density and population fluctuations and led to trade-offs in survival for snowshoe hare populations in the northeastern USA. Hares living in resource-limited environments had lower and less variable popula...

The severity of the toxic side effects of chemotherapy shows a great deal of interindividual variability, and much of this variation is likely genetically based. Simple DNA tests predictive of toxic side effects could revolutionize the way chemotherapy is carried out. Due to the challenges in identifying polymorphisms that affect toxicity in humans, we use Drosophila fecundity following oral exposure to carboplatin, gemcitabine and mitomycin C as a model system to identify naturally occurring DNA variants predictive of toxicity. We use the Drosophila Synthetic Population Resource (DSPR), a panel of recombinant inbred lines derived from a multiparent advanced intercross, to map quantitative trait loci affecting chemotoxicity. We identify two QTL each for carboplatin and gemcitabine toxicity and none for mitomycin. One QTL is associated with fly orthologs of a priori human carboplatin candidate genes ABCC2 and MSH2, and a second QTL is associated with fly orthologs of human gemcitabine candidate genes RRM2 and RRM2B. The third, a carboplatin QTL, is associated with a posteriori human orthologs from solute carrier family 7A, INPP4A&B, and NALCN. The fourth, a gemcitabine QTL that also affects methotrexate toxicity, is associated with human ortholog GPx4. Mapped QTL each explain a significant fraction of variation in toxicity, yet individual SNPs and transposable elements in the candidate gene regions fail to singly explain QTL peaks. Furthermore, estimates of founder haplotype effects are consistent with genes harboring several segregating functional alleles. We find little evidence for nonsynonymous SNPs explaining mapped QTL; thus it seems likely that standing variation in toxicity is due to regulatory alleles.