Estimated number of persons by quarter of a year and by year, Canada, provinces and territories.

This table contains 2288 series, with data for years 1971 - 2016 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (176 items: Canada; Maritime provinces (major drainage area); Saint John and Southern Bay of Fundy, New Brunswick (sub-drainage area); Gulf of St. Lawrence and Northern Bay of Fundy, New Brunswick (sub-drainage area); ...); Population characteristics (13 items: Total population; Population in population centres; Population in rural areas; Total population density; ...).

Contained within the 3rd Edition (1957) of the Atlas of Canada is a plate that shows two condensed maps, and three sets of graphs to show population change for the period 1851 to 1951. The top map shows the percent changes in population in eastern Canada for the period 1851 to 1901 (Newfoundland data is for 1857 to 1901). The bottom map shows the percent changes in population for Canada for the period 1901 to 1951 (Northwest Territories data is for 1911 to 1951). The first set of graphs show birth, death and natural increase rates per 1000 population for the period 1931 to 1951 for Canada and the provinces. The second set shows the changes in density of population for the period 1851 to 1951 for Prince Edward Island, Nova Scotia, New Brunswick, Ontario, Quebec, the Western Provinces and Canada. The third graph shows the percent increase in Canada's total population by decade for the period 1851 to 1951.

This table contains 13 series, with data for years 1926 - 1960 (not all combinations necessarily have data for all years), and was last released on 2000-02-18. This table contains data described by the following dimensions (Not all combinations are available): Geography (13 items: Canada; Newfoundland and Labrador; Prince Edward Island; Nova Scotia ...).

SummaryThe repository includes the data and R script for performing an analysis of among- and within-individual differences in the timing of first nesting attempts of the year in natal and pre-breeding environmental conditions (see reference). The data come from a long-term study of the demography of Savannah sparrows (Passerculus sandwichensis) breeding on Kent Island, New Brunswick, Canada (44.58°N, 66.76°W). Climate data were taken from an Environment and Climate Change Canada weather station at the airport in Saint John, NB (45.32°N, 65.89°W; https://www.climate.weather.gc.ca)Datasets(1) SAVS_all_nests_samp.csv: contains summary information for all nest attempts observed for all females included in the analysis (i.e., including both first-of-year and subsequent lay dates).(2) SAVS_first_nest_per_year_samp.csv: contains detailed information on the first nesting attempt by each female Savannah sparrow monitored in the population over the course of the study (1987-2019, excluding the years 2005-2007; see Methods: Study site and field sampling in reference).(3) mean_daily_temperature.csv: contains mean daily temperature records from the ECCC weather station at Saint John, NB (see above). These mean daily temperatures were used in a climate sensitivity analysis to determine the optimum pre-breeding window on Kent Island.(4) SAVS_annual_summary.csv: contains annual summaries of average lay dates, breeding density, reproductive output, etc.Variables- female.id = factor; unique aluminum band number (USGS or Canadian Wildlife Service) assigned to each female- rain.categorical = binary (0 = low rainfall; 1 = high rainfall); groups females into low (81-171 mm) and high (172-378 mm) natal rainfall groups, based on the natal environmental conditions observed in each year (see Methods: Statistical analysis in reference)- year = integer (1987-2019); study year. The population on Savannah sparrows on Kent Island has been monitored since 1987 (excluding three years, 2005-2007)- nest.id = factor; an alpha-numeric code assigned to each nest; unique within years (the combination of year and nest.id would create a unique identifier for each nest)- fledglings = integer; number of offspring fledged from a nest- total.fledglings = integer; the total number of fledglings reared by a given female over the course of her lifetime- nest.attempts = integer; the total number of nest attempts per female (the number of nests over which the total number of fledglings is divided; includes both successful and unsuccessful clutches)hatch.yday = integer; day of the year on which the first egg hatched in a given nestlay.ydate = integer; day of the year on which the first egg was laid in a given nestlay.caldate = date (dd/mm/yyyy); calendar date on which the first egg in a given nest was laidnestling.year = integer; the year in which the female/mother of a given nest was born- nestling.density = integer; the density of adult breeders in the year in which a given female (associated with a particular nest) was born- total.nestling.rain = numeric; cumulative rainfall (in mm) experienced by a female during the nestling period in her natal year of life (01 June to 31 July; see Methods: Temperature and precipitation data in reference)- years.experience = integer; number of previous breeding years per female in a particular year- density.total = integer; total number of adult breeders in the study site in a particular year- MCfden = numeric; mean-centred female density- MCbfden = numeric; mean-centred between-female density- MCwfden = numeric; mean-centred within-female density- mean.t.window = numeric; mean temperature during the identified pre-breeding window (03 May to 26 May; see Methods: Climate sensitivity analysis in reference)- MCtemp = numeric; mean-centred temperature during the optimal pre-breeding window- MCbtemp = numeric; mean-centred between-female temperature during the optimal pre-breeding window- MCwtemp = numeric; mean-centred within-female temperature during the optimal pre-breeding window- female.age = integer; age (in years) of a given female in a given year- MCage = numeric; mean-centred female age- MCbage = numeric; mean-centred between-female age- MCwage = numeric; mean-centred within-female age- mean_temp_c = numeric; mean daily temperature in °C- meanLD = numeric; mean lay date (in days of the year) across all first nest attempts in a given year- sdLD = numeric; standard deviation in lay date (in days of the year) across all first nest attempts in a given year- seLD = numeric; standard error n lay date (in days of the year) across all first nest attempts in a given year- meanTEMP = numeric; mean temperature (in °C) during the breeding period in a given year- records = integer; number of first nest attempts from each year included in the analysis- total.nestling.precip = numeric; total rainfall (in mm) during the nestling period (01 June to 31 July) in a given year- total.breeding.precip = numeric; total rainfall (in mm) during the breeding period (15 April to 31 July) in a given year- density.total = integer; total density of adult breeders on the study site in a given year- total.fledglings = integer; total number of offspring fledged by all breeders in the study site on a given year- cohort.fecundity = numeric; average number of offspring per breeder in a given yearCodecode for Burant et al. - SAVS lay date plasticity analysis.RThe R script provided includes all the code required to import the data and perform the statistical analyses presented in the manuscript. These include:- t-tests investigating the effects of natal conditions (rain.categorical) on female age, nest attempts, and reproductive success- linear models of changes in temperature, precipitation, reproductive success, and population density over time, and lay dates in response to female age, density, etc.- a climate sensing analysis to identify the optimal pre-breeding window on Kent Island- mixed effects models investigating how lay dates respond to changes in within- and between-female age, density, and temperaturesee readme.rtf for a list of datasets and variables.

Contained within the 3rd Edition (1957) of the Atlas of Canada is a plate that shows two condensed maps, and three sets of graphs to show population change for the period 1851 to 1951. The top map shows the percent changes in population in eastern Canada for the period 1851 to 1901 (Newfoundland data is for 1857 to 1901). The bottom map shows the percent changes in population for Canada for the period 1901 to 1951 (Northwest Territories data is for 1911 to 1951). The first set of graphs show birth, death and natural increase rates per 1000 population for the period 1931 to 1951 for Canada and the provinces. The second set shows the changes in density of population for the period 1851 to 1951 for Prince Edward Island, Nova Scotia, New Brunswick, Ontario, Quebec, the Western Provinces and Canada. The third graph shows the percent increase in Canada's total population by decade for the period 1851 to 1951.

This statistic shows the number of PET-CT units per million population in Canada in 2019/2020, by province. PET-CT stands for positron emission tomography–computed tomography. In that year, the province of New Brunswick had *** PET-CT units per every million of its population.

We conducted a 14-year intensive study of spruce budworm (Choristoneura fumiferana (Clem.)) survivorship at three study plots in largely balsam fir (Abies balsamea (L.) Mill.) stands in New Brunswick, Canada, to elucidate certain key mechanisms underlying spruce budworm outbreak cycles. The study covered a peak-to-declining phase (from 1981 and 1994) of the budworm outbreak cycle that had started in the early 1960s. Frequent sampling was carried out in each plot-year to construct a practically continuous survivorship curve, and the annual variation in population density was estimated. We found a high level of correlation between the studied phase of the outbreak cycle and annual variations in the survivorship over the postdiapause period, suggesting that postdiapause survivorship was the chief determinant of the cycle. We found the annual changes in population density in the present study to be closely similar in pattern to those from the provincial budworm surveys conducted in much larger areas. This implies that the mechanism underlying the population process found in the few study plots in largely balsam fir stands also applies to the process in much larger areas of diverse stand types. The main source of postdiapause mortality is found to be natural enemies. The impacts of parasitoids and disease are evaluated by rearing budworm samples in the laboratory. Hymenopteran and dipteran parasitoids are by far the major sources of mortality, and microsporidians are the most prevalent pathogen. Occurrences of other entomopathogenic fungi and viruses were insignificant throughout the study. Seasonal changes in laboratory survivorship are compared with the corresponding field survivorship to estimate the effect of predation. No major mortality factor is found to singly play a predominant role in determining the outbreak cycle. Conversely, some minor factors are shown to have played significant roles. Thus, the importance of recognizing the action of natural enemies as a complex is emphasized for understanding the budworm outbreak cycle. Finally, centered around the roles played by the chronological succession of natural enemies in the present study, the results of budworm research in New Brunswick since the mid-1940s are synthesized to outline basic mechanisms underlying the outbreak processes as a guide for further studies.

Counts of SBW larvae collected in the fall as L2 or in the following spring as L4, from 75-cm branches or 45-cm branch tips (respectively) of balsam fir in the Ottawa River valley from 1998-2001, North Shore 2008-2010, and New Brunswick 2017-2022. Defoliation measured at the end of the feeding period after the L4 sample. Data linked to scientific paper: Régnière, J., Johns, R., Edwards, S., Owens, E., Dupont, A., 2023. Overwintering spruce budworm population density as predictor of following-year larval density and defoliation on balsam fir. For. Ecol. Manag. (submitted).

Counts of SBW larvae collected in the fall as L2 or in the following spring as L4, from 75-cm branches or 45-cm branch tips (respectively) of balsam fir in the Ottawa River valley from 1998-2001, North Shore 2008-2010, and New Brunswick 2017-2022. Defoliation measured at the end of the feeding period after the L4 sample. Data linked to scientific paper: Régnière, J., Johns, R., Edwards, S., Owens, E., Dupont, A., 2023. Overwintering spruce budworm population density as predictor of following-year larval density and defoliation on balsam fir. For. Ecol. Manag. (submitted).

Population outbreaks are characterized by irruptive changes in population density and connectivity resulting in rapid demographic and spatial expansion, often at the landscape scale. Outbreaks are common across multiple taxa, many of which inhabit northern ecosystems. Outbreaks of Lepidopteran defoliators in forest ecosystems are a particularly compelling example of this phenomenon, given the massive spatial scales over which these outbreaks can occur, their frequency, and socioeconomic impacts. Theeastern spruce budworm(SBW) is a native outbreaking Lepidopteran defoliator of North American boreal forests. Cyclic outbreaks of the SBW influence ecosystem functioning and resilience, as well as forest productivity, timber supply, and other socioeconomic values related to management and mitigation. Despite these significant impacts, the ecological and biological drivers and outcomes of these outbreaks remain poorly understood. Here, we present an extensive genotypic dataset for 1998 geo-referenced SBW individuals collected between the years of 2012 and 2017, during the rising and peak phases of an outbreak that began approximately in 2006. Our sampling covers an unprecedented scope in extent and number of individuals collected between 2012 and 2017 from Quebec, and in 2015 from New Brunswick (Canada) and from Maine (USA), from multiple SBW life stages, including early and late instar larvae (L2 - L6), pupae, and adult moths. Genomic DNA extraction was followed by library preparation and high-throughput sequencing using Genotyping-by-Sequencing (GBS). Samples were genotyped for single nucleotide polymorphisms (SNPs) and aligned to the bw6 version of the SBW genome. This dataset represents one of the most extensive genotypic datasets to date for a boreal insect, and is unique as it includes multiple years during a developing (ongoing, at time of sampling) outbreak. Sampling effort covered areas close to the epicenter of the outbreak (Quebec/Canada) and adjacent areas affected by the outbreak progress. This dataset also provides genome-wide characterization of SBW populations from Quebec, serving as a standard for identification of future samples regarding their locality of origin, structure and connectivity. These data represent a valuable novel resource for further study of the spatial and temporal dynamics of SBW, and how spatial genetic diversity and gene flow is affected by population outbreaks. These data provide a temporal snapshot of SBW genetic diversity, which can serve as baseline for future studies regarding outbreaks, and the impact of human-induced environmental changes on complex population dynamics. This genotype dataset comprises a unique representation of genomic-level composition and variation observed in subsequent generations of an irruptive, cyclic outbreaking species, and is of utmost importance for exploring and describing how accelerated demographic variation impacts the development of spatial genetic structure across heterogeneous landscapes. We believe this dataset is essential to management and conservation biology initiatives not only for SBW and boreal forests, but also providing a starting point for broader evolutionary and ecological studies of complex population dynamics. Furthermore, the knowledge, data collection, curation framework we present here can be used to inform similar spatial temporal baseline studies of other outbreaking (e.g., mountain pine beetle, red-backed voles) and invasive species (e.g., spongy moth, emerald ash borer).

The map shows the location of the six hydrogeological regions in Canada and the location of observation wells. The terrain composition is also shown on the map, which includes crystalline rocks, mixed crystalline rocks, folded sedimentary rocks and flat lying sedimentary rocks. The southern limit of continuous permafrost zone and the limit of the discontinuous permafrost zone appear on the map. Canada has been divided into six hydrogeological regions on the basis of similarities of geology, climate, and topography. These six hydrogeological regions are (1) the Appalachians, covering the area of New Brunswick, Prince Edward Island, Nova Scotia, Newfoundland, and the Gaspé and Eastern Townships of Quebec; (2) the St. Lawrence Lowlands, covering Anticosti Island, the extreme southern area of Quebec, and the southern part of Ontario; (3) the Canadian Shield, lying north of the St. Lawrence Lowlands and extending northward to a line joining the north end of Lake Winnipeg to Anticosti Island; (4) the Interior Plains, lying approximately south of the southern limit of discontinuous permafrost and consisting largely of the southern prairie regions of the provinces of Manitoba, Saskatchewan, and Alberta; (5) the Cordilleran Region, the mountainous part of western Canada within British Columbia; and (6) the Northern Region, approximately covering the area north of the southern limit of discontinuous permafrost. To monitor the groundwater flow systems and fluctuations in these hydrogeological regions a series of groundwater observation wells and piezometers have been established in various parts of Canada, as is shown on the map. The groundwater observation well map indicates the extent of provincial observation well and piezometer networks in Canada. Because of scale limitations, the symbols on the map may indicate more than one well. These wells and piezometers have been established in the southern part of Canada to monitor groundwater fluctuations and may also be used to monitor groundwater quality. Since this region of Canada has the largest population density, groundwater is of more immediate interest here. In the areas of discontinuous and continuous permafrost little has been done at present to monitor groundwater conditions, although this is changing as mineral exploration looks north for new reserves.