About this dataset

Context

The dataset tabulates the Canadian population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Canadian across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Canadian was 2,210, a 1.07% decrease year-by-year from 2022. Previously, in 2022, Canadian population was 2,234, a decline of 3.04% compared to a population of 2,304 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Canadian decreased by 13. In this period, the peak population was 3,038 in the year 2015. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Canadian is shown in this column.
• Year on Year Change: This column displays the change in Canadian population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Canadian Population by Year. You can refer the same here

Of the G7 countries, Canada, the United Kingdom, and the United States were forecast to have a constant population ******** until 2050. In Japan, Germany, and Italy, the population is forecast to constantly ******* due to aging populations and falling fertility rates. In France, the population was first expected to decline by 2048.

About this dataset

Context

The dataset tabulates the Little Canada population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Little Canada across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Little Canada was 10,297, a 0.38% increase year-by-year from 2022. Previously, in 2022, Little Canada population was 10,258, a decline of 2.42% compared to a population of 10,512 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Little Canada increased by 470. In this period, the peak population was 10,780 in the year 2020. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Little Canada is shown in this column.
• Year on Year Change: This column displays the change in Little Canada population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Little Canada Population by Year. You can refer the same here

Both landscape structure and population size fluctuations influence population genetics. While independent effects of these factors on genetic patterns and processes are well studied, a key challenge is to understand their interaction, as populations are simultaneously exposed to habitat fragmentation and climatic changes that increase variability in population size. In a population network of an alpine butterfly, abundance declined 60–100% in 2003 because of low over-winter survival. Across the network, mean microsatellite genetic diversity did not change. However, patch connectivity and local severity of the collapse interacted to determine allelic richness change within populations, indicating that patch connectivity can mediate genetic response to a demographic collapse. The collapse strongly affected spatial genetic structure, leading to a breakdown of isolation-by-distance and loss of landscape genetic pattern. Our study reveals important interactions between landscape structure and temporal demographic variability on the genetic diversity and genetic differentiation of populations. Projected future changes to both landscape and climate may lead to loss of genetic variability from the studied populations, and selection acting on adaptive variation will likely occur within the context of an increasing influence of genetic drift. DryadData_MatricesData matrices containing: 1. pairwise Fst scores for all 13 populations. 2. geographic distances between each population according to whether the distance occurs over forest or meadow habitat, the sum of which is the total distance. 3. movement indices for individuals before the demographic crash and after.PreCollapseGENEPOP_UpdateGenotypic data for 7 microsatelite loci from individuals collected prior to the demographic collapse.PostCollapseGENEPOP_UpdateGenotypic data from 7 microsatelite loci from individuals collected after the demographic collapse.

It is presumed that the first humans migrated from Siberia to North America approximately twelve thousand years ago, where they then moved southwards to warmer lands. It was not until many centuries later that humans returned to the north and began to settle regions that are now part of Canada. Despite a few short-lived Viking settlements on Newfoundland around the turn of the first millennium CE, the Italian explorer Giovanni Caboto (John Cabot), became the first European to explore the coast of North America in the late 1400s. The French and British crowns both made claims to areas of Canada throughout the sixteenth century, but real colonization and settlement did not begin until the early seventeenth century. Over the next 150 years, France and Britain competed to take control of the booming fur and fishing trade, and to expand their overseas empires. In the Seven Year's War, Britain eventually defeated the French colonists in North America, through superior numbers and a stronger agriculture resources in the southern colonies, and the outcome of the war saw France cede practically all of it's colonies in North America to the British.

Increased migration and declining native populations

The early 1800s saw a large influx of migrants into Canada, with the Irish Potato Famine bringing the first wave of mass-migration to the country, with further migration coming from Scandinavia and Northern Europe. It is estimated that the region received just shy of one million migrants from the British Isles alone, between 1815 and 1850, which helped the population grow to 2.5 million in the mid-1800s and 5.5 million in 1900. It is also estimated that infectious diseases killed around 25 to 33 percent of all Europeans who migrated to Canada before 1891, and around a third of the Canadian population is estimated to have emigrated southwards to the United States in the 1871-1896 period. From the time of European colonization until the mid-nineteenth century, the native population of Canada dropped from roughly 500,000 (some estimates put it as high as two million) to just over 100,000; this was due to a mixture of disease, starvation and warfare, instigated by European migration to the region. The native population was generally segregated and oppressed until the second half of the 1900s; Native Canadians were given the vote in 1960, and, despite their complicated and difficult history, the Canadian government has made significant progress in trying to include indigenous cultures in the country's national identity in recent years. As of 2020, Indigenous Canadians make up more than five percent of the total Canadian population, and a higher birth rate means that this share of the population is expected to grow in the coming decades.

Independence and modern Canada

Canadian independence was finally acknowledged in 1931 by the Statute of Westminster, putting it on equal terms with the United Kingdom within the Commonwealth; virtually granting independence and sovereignty until the Canada Act of 1982 formalized it. Over the past century, Canada has had a relatively stable political system and economy (although it was hit particularly badly by the Wall Street Crash of 1929). Canada entered the First World War with Britain, and as an independent Allied Power in the Second World War; Canadian forces played pivotal roles in a number of campaigns, notably Canada's Hundred Days in WWI, and the country lost more than 100,000 men across both conflicts. The economy boomed in the aftermath of the Second World War, and a stream of socially democratic programs such as universal health care and the Canadian pension plan were introduced, which contributed to a rise in the standard of living. The post war period also saw various territories deciding to join Canada, with Newfoundland joining in 1949, and Nunavut in 1999. Today Canada is among the most highly ranked in countries in terms of civil liberties, quality of life and economic growth. It promotes and welcomes immigrants from all over the world and, as a result, it has one of the most ethnically diverse and multicultural populations of any country in the world. As of 2020, Canada's population stands at around 38 million people, and continues to grow due to high migration levels and life expectancy, and a steady birth rate.

About this dataset

Context

The dataset tabulates the Canadian population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Canadian across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2022, the population of Canadian was 2,230, a 2.96% decrease year-by-year from 2021. Previously, in 2021, Canadian population was 2,298, a decline of 0.86% compared to a population of 2,318 in 2020. Over the last 20 plus years, between 2000 and 2022, population of Canadian increased by 7. In this period, the peak population was 3,038 in the year 2015. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2022

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2022)
• Population: The population for the specific year for the Canadian is shown in this column.
• Year on Year Change: This column displays the change in Canadian population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Canadian Population by Year. You can refer the same here

Data on eligibility for instruction in the minority official language and collapsed criteria of eligibility accounting for parents’ citizenship for the population of children in private households in Canada, provinces and territories, census divisions and census subdivisions.

This document includes census data on the declining goose population at Wheeler National Wildlife Refuge.

Protected areas are important in species conservation, but high rates of human-caused mortality outside their borders and increasing popularity for recreation can negatively affect wildlife populations. We quantified wolverine (Gulo gulo) population trends from 2011 to 2020 in >14 000 km2 protected and non-protected habitat in southwestern Canada. We conducted wolverine and multi-species surveys using non-invasive DNA and remote camera-based methods. We developed Bayesian integrated models combining spatial capture-recapture data of marked and unmarked individuals with occupancy data. Wolverine density and occupancy declined by 39 percent, with an annual population growth rate of 0.925. Density within protected areas was 3 times higher than outside and declined between 2011 (3.6 wolverines/1000 km2) and 2020 (2.1 wolverines/1000 km2). Wolverine density and detection probability increased with snow cover and decreased near development. Detection probability also decreased with human recreational activity. The annual harvest rate of 13% was above the maximum sustainable rate. We conclude that humans negatively affected the population through direct mortality, sub-lethal effects and habitat impacts. Our study exemplifies the need to monitor population trends for species at risk – within and between protected areas - as steep declines can occur unnoticed if key conservation concerns are not identified and addressed.

Walleye (Sander vitreus) populations in Alberta, Canada, collapsed by the mid-1990s and were a case study in the paper Canada’s Recreational Fisheries: The Invisible Collapse? Here we fit age-structured population dynamics models to data from a landscape-scale monitoring program to assess walleye population status and reconstruct recruitment dynamics following the invisible collapse. Assessments indicated that populations featured low Fmsy values of approximately 0.2–0.3 under conservative assumptions for the stock–recruitment relationship but that many populations were lightly exploited during 2000–2018. Recruitment reconstructions showed that recovery from collapse in 33/55 lakes was driven in part by large positive recruitment anomalies that occurred during 1998–2002. Additionally, 15/55 lakes demonstrated cyclic recruitment dynamics. The documented recruitment anomalies and cyclic fluctuations could be due to environmental effect(s) or cannibalism, and experimentation is likely necessary to resolve this uncertainty. These findings contribute new information on the recovery dynamics of walleye following the invisible collapse and demonstrate the effectiveness of coupling traditional fisheries science models with broad-scale monitoring data to improve understanding of population dynamics and sustainability across landscapes.

The muskrat (Ondatra zibethicus) is an iconic species in Canada, valued for both its fur and its integral role in wetland ecosystems, and widely regarded for its perseverance. However, the resilience of this semi-aquatic mammal seems to be in question now as increasing evidence points to widespread population declines. Recent analyses of harvest data across North America suggest a reduction in their numbers, but this has not been widely corroborated by population surveys. In this study we replicated historic muskrat house count surveys at two large Great Lakes coastal wetlands and present confirmation that declines in muskrat harvest correspond to actual declines in muskrat abundance. At the Point Pelee National Park marsh and the Matchedash Bay-Gray Marsh wetland we found that mean muskrat house counts declined by 93% and 91% respectively between historic surveys 40-50 years ago and contemporary surveys over the past seven years. The factors responsible for these dramatic declines remain unclear but there may be a relationship with changes in the habitat quality of these wetlands that have occurred over the same time frame. Not only is the loss of muskrats an issue for the resulting loss of the wetland ecosystem services they provide, but it may be an indication of broader marsh ecosystem degradation. As such, a scarcity of muskrats should be considered a red flag for the state of biodiversity in our wetlands. Continued surveys and ongoing research are needed to shed more light on the current status of muskrat populations and their marsh habitats across their native range.

Methods This dataset was collected by conducting annual field surveys for muskrat houses in two large wetlands in Ontario, Canada over two different time periods, each comprising several years of surveys: a historic survey period between 1957-1986 and a contemporary survey period between 2014-2019. Surveys were conducted at the Point Pelee National Park marsh and the Matchedash Bay-Gray Marsh wetland.

Historic data was compiled by this dataset's authors by obtaining historic survey reports and extracting muskrat house count data provided in these reports. Contemporary data was collected by this dataset's authors by revisiting historic survey sites and replicating the historic survey methods, as described in the historic survey reports and in the manuscript published from this dataset.

The data presented here are numbers of new or active muskrat houses found in each study area during each survey year. The data have not been processed; only grouped into different categories, as indicated in the data tables.

About this dataset

Context

The dataset tabulates the Canadian population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Canadian across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2022, the population of Canadian was 145, a 0.68% decrease year-by-year from 2021. Previously, in 2021, Canadian population was 146, a decline of 0.68% compared to a population of 147 in 2020. Over the last 20 plus years, between 2000 and 2022, population of Canadian decreased by 93. In this period, the peak population was 247 in the year 2009. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2022

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2022)
• Population: The population for the specific year for the Canadian is shown in this column.
• Year on Year Change: This column displays the change in Canadian population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Canadian Population by Year. You can refer the same here

In Canada, the crude birth rate in 1860 was forty live births per thousand people, meaning that four percent of the population had been born in that year. From this point until the turn of the century, the crude birth rate decreases gradually, to just over thirty births per thousand. Over the next twenty years, this number hovers just below thirty, and thereafter it decreases much more rapidly than before, to 20.7 in 1940, before Canada's baby boom in the 1940s, 50s and 60s, where the birth rate increased to over 27. From the end of the baby boom until the late 1970s the population decreases rapidly again, before the rate of decline then slows. Since 1975, the crude birth rate of Canada will have dropped from 15.6, to it's lowest point in 2020, where it is expected to be just 10.5 births per thousand people.

Identifying genetic conservation units (CUs) in threatened species is critical for the preservation of adaptive capacity and evolutionary potential in the face of climate change. However, delineating CUs in highly mobile species remains a challenge due to high rates of gene flow and genetic signatures of isolation by distance. Even when CUs are delineated in highly mobile species, the CUs often lack key biological information about what populations have the most conservation need to guide management decisions. Here we implement a framework for rigorous CU identification in the Canada Warbler (Cardellina canadensis), a highly mobile migratory bird species of conservation concern, and then integrate demographic modeling and genomic offset within a CU framework to guide conservation decisions. We find that whole-genome structure in this highly mobile species is primarily driven by putative adaptive variation. Identification of CUs across the breeding range revealed that Canada Warblers fall into two Evolutionarily Significant Units (ESU), and three putative Adaptive Units (AUs) in the South, East, and Northwest. Quantification of genomic offset within each AU reveals significant spatial variation in climate vulnerability, with the Northwestern AU being identified as the most vulnerable to future climate change based on genomic offset predictions. Alternatively, quantification of past population trends within each AU revealed the steepest population declines have occurred within the Eastern AU. Overall, we illustrate that genomics-informed CUs provide a strong foundation for identifying current and potential future regional threats that can be used to manage highly mobile species in a rapidly changing world. Methods Resequencing sample collection and DNA extraction We collected samples from an additional 181 breeding adult Canada Warblers from across the breeding range in North America in collaboration with multiple university researchers, private environmental companies, and state and federal agencies (Supplemental Figure 1). For DNA extraction, we collected blood from 134 individuals (~80 µl), via brachial venipuncture, preserved it in Queen’s lysis buffer, and stored it at room temperature. Blood (50-80 µl) was extracted using Qiagen DNeasy Blood and Tissue Kits (QIAGEN) and eluted into 100 µl of provided AE buffer. For the remaining 47 individuals, we collected tail feathers by pulling 2 tail feathers from each bird and storing feathers at -20C. We cut the calamus of one feather from the shaft and extracted the calamus using the modified Qiagen DNeasy Blood and Tissue protocol (Schweizer & DeSaix, 2023). After DNA extraction, we quantified samples using Qubit dsDNA assay. DNA resequencing We prepared the breeding samples for low coverage whole genome sequencing using a modified Nextera prep (Schweizer & DeSaix, 2023) with normalized DNA input. We sequenced samples in two libraries, 110 individuals on an Illumina HiSeq 4000 using paired end 150bp reads and 71 individuals on an Illumina NovaSeq 6000 using paired end 150bp reads. The 71 individuals on the NovaSeq were sequenced across multiple lanes to get to the targeted sequencing depth of 2-3X coverage per sample and included replicates of 32 samples with lower than 1.5X coverage from the HiSeq 4000 run. Bioinformatic processing We used Conda v4.13.0 (Anaconda Documentation, 2020) environments to manage bioinformatic packages on the RMACC Summit supercomputer managed jointly by Colorado State University and University of Colorado, Boulder. To process raw fastqs from the 181 individuals that underwent low coverage whole genome sequencing, we used Trim Galore v0.6.7 (Krueger, 2012), a wrapper for cutadapt v1.18 (Martin, 2011), and FastQC v0.11.9 (Andrews, 2010) to trim any remaining Illumina adaptors in the fastqs. Next, based on recommendations for low coverage data generated with NovaSeq platforms (Lou & Therkildsen, 2022) we performed a sliding window cut of the 3-prime end of the reads to remove low quality tails, defined as 4 bases in a row with mean QUAL scores less than 20, using fastp v0.22.0 (Chen et al., 2018). We checked fastqs for quality using FastQC and MultiQC v1.0.dev0 (Ewels et al., 2016) before and after trimming reads. After processing raw fastqs, we aligned samples to the Canada Warbler reference genome using Burrows-Wheeler Alignment software (bwa mem, bwa v0.7.17) (Li & Durbin, 2009). Then we added read group information using Picard v2.26.11 AddorReplaceReadGroups (Picard Toolkit, 2014/2019)and marked duplicate reads using samtools v1.11 markdup (Danecek et al., 2011) before merging individuals with multiple bams. After merging bams, we checked sample coverage using bedtools v2.30.0 genomecov (Quinlan & Hall, 2010), and samples with less than 1X coverage were removed, leaving 169 individuals. We used the processed bams to call variants using GATK v4.2.5.0 HaplotypeCaller (McKenna et al., 2010) and BCFtools v1.15.1 mpileup (Danecek et al., 2021). Then, we stringently filtered the variant sets using BCFtools, allowing only biallelic sites, a minor allele frequency of greater than 5%, QUAL score of greater than 30 and less than 10% missing across the 169 individuals. We intersected the filtered variant sets from bedtools and GATK to create a high-quality variant set to use for base quality score recalibration. Using the intersected variants, we recalibrated the sample bams using GATK BaseRecalibrator and ApplyBQSR. With the recalibrated bams, we used HaplotypeCaller to call a recalibrated set of variants. Then we filtered the recalibrated variant set allowing only biallelic sites, a minor allele frequency of less than 5%, QUAL score of greater than 30 and less than 20% missing data across the 169 individuals. Using the recalibrated, filtered variant set we performed an exploratory analysis using R (R Core Team, 2022) and the package srsStuff (Anderson, 2020) to produce single-read sampling principal components analysis (PCA) of whole genome structure. We used single-read sampling because differences in the average coverage across samples can be mistaken for population structure on PCA in low coverage data (Lou & Therkildsen, 2022). Single read sampling equalizes coverage for all samples. Despite equalizing coverage, we found significant platform effects, where samples sequenced on different platforms have inherent bias that can be mistaken for population structure (for example of platform effects on low coverage data, see Lou & Therkildsen, 2022). We removed platform-associated variants from the dataset and proceeded with the analysis once samples no longer clustered in platform groups by PCA (for full methods to remove platform effects, see Supplemental Methods). Adaptive loci identification To select environmental variables, we used gradient forest (Ellis et al., 2012), an extension of random forest (Liaw et al., 2002), and 23 environmental variables potentially important to Canada Warbler breeding ecology based on previous research (Supplemental Table 3, Ferrari et al., 2018; Reitsma et al., 2020). Environmental data were extracted from each of 16 sampling locations, excluding two sampling sites with fewer than 4 individuals. To identify putatively adaptive loci, we used two approaches, redundancy analysis (RDA) and Latent Factor Mixed Models (LFMM). Once loci were identified using both RDA and LFMM, the union of loci discovered by both methods was used as our set of candidate adaptive loci which was filtered from the original vcf.

About this dataset

Context

The dataset tabulates the New Canada town population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of New Canada town across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of New Canada town was 316, a 0.64% increase year-by-year from 2022. Previously, in 2022, New Canada town population was 314, an increase of 0.64% compared to a population of 312 in 2021. Over the last 20 plus years, between 2000 and 2023, population of New Canada town increased by 15. In this period, the peak population was 320 in the year 2010. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the New Canada town is shown in this column.
• Year on Year Change: This column displays the change in New Canada town population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for New Canada town Population by Year. You can refer the same here

Bumble bees (Bombus) are vitally important pollinators of wild plants and agricultural crops worldwide. Fragmentary observations, however, have suggested population declines in several North American species. Despite rising concern over these observations in the United States, highlighted in a recent National Academy of Sciences report, a national assessment of the geographic scope and possible causal factors of bumble bee decline is lacking. Here, we report results of a 3-y interdisciplinary study of changing distributions, population genetic structure, and levels of pathogen infection in bumble bee populations across the United States. We compare current and historical distributions of eight species, compiling a database of >73,000 museum records for comparison with data from intensive nationwide surveys of >16,000 specimens. We show that the relative abundances of four species have declined by up to 96% and that their surveyed geographic ranges have contracted by 23–87%, some within the last 20 y. We also show that declining populations have significantly higher infection levels of the microsporidian pathogen Nosema bombi and lower genetic diversity compared with co-occurring populations of the stable (nondeclining) species. Higher pathogen prevalence and reduced genetic diversity are, thus, realistic predictors of these alarming patterns of decline in North America, although cause and effect remain uncertain. Bumble bees (Bombus) are integral wild pollinators within native plant communities throughout temperate ecosystems, and recent domestication has boosted their economic importance in crop pollination to a level surpassed only by the honey bee. Their robust size, long tongues, and buzz-pollination behavior (high-frequency buzzing to release pollen from flowers) significantly increase the efficiency of pollen transfer in multibillion dollar crops such as tomatoes and berries. Disturbing reports of bumble bee population declines in Europe have recently spilled over into North America, fueling environmental and economic concerns of global decline. However, the evidence for large-scale range reductions across North America is lacking. Many reports of decline are unpublished, and the few published studies are limited to independent local surveys in northern California/southern Oregon, Ontario, Canada, and Illinois. Furthermore, causal factors leading to the alleged decline of bumble bee populations in North America remain speculative. One compelling but untested hypothesis for the cause of decline in the United States entails the spread of a putatively introduced pathogen, Nosema bombi, which is an obligate intracellular microsporidian parasite found commonly in bumble bees throughout Europe but largely unstudied in North America. Pathogenic effects of N. bombi may vary depending on the host species and reproductive caste and include reductions in colony growth and individual life span and fitness. Population genetic factors could also play a role in Bombus population decline. For instance, small effective population sizes and reduced gene flow among fragmented habitats can result in losses of genetic diversity with negative consequences, and the detrimental impacts of these genetic factors can be especially intensified in bees. Population genetic studies of Bombus are rare worldwide. A single study in the United States identified lower genetic diversity and elevated genetic differentiation (FST) among Illinois populations of the putatively declining B. pensylvanicus relative to those of a codistributed stable species. Similar patterns have been observed in comparative studies of some European species, but most investigations have been geographically restricted and based on limited sampling within and among populations. Although the investigations to date have provided important information on the increasing rarity of some bumble bee species in local populations, the different survey protocols and limited geographic scope of these studies cannot fully capture the general patterns necessary to evaluate the underlying processes or overall gravity of declines. Furthermore, valid tests of the N. bombi hypothesis and its risk to populations across North America call for data on its geographic distribution and infection prevalence among species. Likewise, testing the general importance of population genetic factors in bumble bee decline requires genetic comparisons derived from sampling of multiple stable and declining populations on a large geographic scale. From such range-wide comparisons, we provide incontrovertible evidence that multiple Bombus species have experienced sharp population declines at the national level. We also show that declining populations are associated with both high N. bombi infection levels and low genetic diversity. This data was used in the paper "Patterns of widespread decline in North American bumble bees" published in the Proceedings of the National Academy of United States of America. For more information about this dataset contact: Sydney A. Cameron: scameron@life.illinois.edu James Strange: James.Strange@ars.usda.gov Resources in this dataset:Resource Title: Data from: Patterns of Widespread Decline in North American Bumble Bees (Data Dictionary). File Name: meta.xmlResource Description: This is an XML data dictionary for Data from: Patterns of Widespread Decline in North American Bumble Bees.Resource Title: Patterns of Widespread Decline in North American Bumble Bees (DWC Archive). File Name: occurrence.csvResource Description: File modified to remove fields with no recorded values.Resource Title: Patterns of Widespread Decline in North American Bumble Bees (DWC Archive). File Name: dwca-usda-ars-patternsofwidespreaddecline-bumblebees-v1.1.zipResource Description: Data from: Patterns of Widespread Decline in North American Bumble Bees -- this is a Darwin Core Archive file. The Darwin Core Archive is a zip file that contains three documents.

The occurrence data is stored in the occurrence.txt file. The metadata that describes the columns of this document is called meta.xml. This document is also the data dictionary for this dataset. The metadata that describes the dataset, including author and contact information for this dataset is called eml.xml.

Find the data files at https://bison.usgs.gov/ipt/resource?r=usda-ars-patternsofwidespreaddecline-bumblebees

Estimating nestling body condition.

About this dataset

Context

The dataset tabulates the New Canada town population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of New Canada town across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2022, the population of New Canada town was 318, a 0.63% increase year-by-year from 2021. Previously, in 2021, New Canada town population was 316, an increase of 1.28% compared to a population of 312 in 2020. Over the last 20 plus years, between 2000 and 2022, population of New Canada town increased by 17. In this period, the peak population was 320 in the year 2010. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2022

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2022)
• Population: The population for the specific year for the New Canada town is shown in this column.
• Year on Year Change: This column displays the change in New Canada town population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for New Canada town Population by Year. You can refer the same here

This data package includes data files and an R script to reproduce results reported in the paper "Demographic declines and responses of breeding bird populations to human footprint in the Athabasca Oil Sands Region, Alberta, Canada". Analyses include hierarchical multispecies models applied to data from 31 bird species at 38 Monitoring Avian Productivity and Survivorship (MAPS) stations to assess 10-year (2011–2020) demographic trends and responses to energy sector disturbance (human footprint proportion) in the Athabasca oil sands region of Alberta, Canada. Adult captures, productivity, and residency probability all declined over the study period, and adult apparent survival probability also tended to decline. Trends in adult captures, productivity, and survival were all more negative at stations with larger increases in disturbance over the study period. Species associated with early seral stages were more commonly captured at more disturbed stations, while species typical of mature forests were more commonly captured at less disturbed stations. Productivity was positively correlated with disturbance within 5 km of stations after controlling for disturbance within 1 km of stations. Adult apparent survival showed relatively little response to disturbance; stresses experienced beyond the breeding grounds likely play a larger role in influencing survival. Residency probability was negatively related to disturbance within 1-km scale of stations and could reflect processes affecting the ability of birds to establish or maintain territories in disturbed landscapes. Methods Avian capture and recapture data were collected for 31 species at 38 bird-banding stations in the Athabasca oil sands region of Alberta, Canada following protocols of the Monitoring Avian Productivity and Survivorship (MAPS) program (see https://birdpop.org/docs/misc/MAPSManual22.pdf for detail). Capture data for models indexing the abundance of adult birds (numbers of adult birds captured) and productivity (probability of a captured bird being a juvenile bird) were summarized by banding station and year for the years 2011–2020. Individual stations were operated from 2 to 10 years. Capture-recapture data for adults were summarized in an individual × year matrix with station identifiers and indicator variable denoting whether a bird was captured at least twice > 6 d apart in the year it was banded; capture-recapture data were only included for 35 stations operated in > 4 years. Also included are human footprint proportion data (https://www.abmi.ca/home/data-analytics/da-top/da-product-overview/Human-Footprint-Products/HF-inventory.html) summarized at 1-km (2018) and 5-km radius (2010 and 2018) buffers around banding station centers (proportions for individual disturbance classes also available for the 2018 data) and tree cover data from the Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Continuous Fields data product (MOD44B; DiMiceli et al. 2015) at 0.25-km pixel resolution for each station and averaged across captures for each bird species.

New census data on age and sex show that as of May 15, 2001, the median age of Canada's population reached an all-time high of 37.6 years, an increase of 2.3 years from 35.3 in 1996. This was the biggest census-to-census increase in a century. Median age is the point where exactly one-half of the population is older, and the other half is younger. The nation's median age has been rising steadily since the end of the baby boom in 1966, when it was only 25.4 years. Nova Scotia and Quebec were the nation's oldest provinces, each with a median age of 38.8 years. Alberta was the youngest with a median age of 35.0. The group to increase at the fastest pace was that aged 80 and over. From 1991 to 2001, their numbers soared 41.2% to 932,000. The number of people aged 80 or over is expected to increase an additional 43% from 2001 to 2011, during which time it will surpass an estimated 1.3 million. At the same time, Canada has undergone a substantial decline in the number of children aged four and under. In 2001, the census counted 1.7 million children in this age group, down 11.0% from 1991, the result mostly of Canada's declining fertility rate. By 2011, this group may decline to an estimated 1.6 million.