This graph shows the population density in the federal state of Minnesota from 1960 to 2018. In 2018, the population density of Minnesota stood at **** residents per square mile of land area.

This resource is a member of a series. The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity, and were defined by local participants as part of the 2020 Census Participant Statistical Areas Program. The Census Bureau delineated the census tracts in situations where no local participant existed or where all the potential participants declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of census data and comparison back to previous decennial censuses. Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. When first delineated, census tracts were designed to be homogeneous with respect to population characteristics, economic status, and living conditions. The spatial size of census tracts varies widely depending on the density of settlement. Physical changes in street patterns caused by highway construction, new development, and so forth, may require boundary revisions. In addition, census tracts occasionally are split due to population growth, or combined as a result of substantial population decline. Census tract boundaries generally follow visible and identifiable features. They may follow legal boundaries such as minor civil division (MCD) or incorporated place boundaries in some States and situations to allow for census tract-to-governmental unit relationships where the governmental boundaries tend to remain unchanged between censuses. State and county boundaries always are census tract boundaries in the standard census geographic hierarchy. In a few rare instances, a census tract may consist of noncontiguous areas. These noncontiguous areas may occur where the census tracts are coextensive with all or parts of legal entities that are themselves noncontiguous. For the 2010 Census, the census tract code range of 9400 through 9499 was enforced for census tracts that include a majority American Indian population according to Census 2000 data and/or their area was primarily covered by federally recognized American Indian reservations and/or off-reservation trust lands; the code range 9800 through 9899 was enforced for those census tracts that contained little or no population and represented a relatively large special land use area such as a National Park, military installation, or a business/industrial park; and the code range 9900 through 9998 was enforced for those census tracts that contained only water area, no land area.

Mongolia MN: Population Density: Inhabitants per sq km data was reported at 2.170 Person in 2022. This records an increase from the previous number of 2.140 Person for 2021. Mongolia MN: Population Density: Inhabitants per sq km data is updated yearly, averaging 1.660 Person from Dec 1990 (Median) to 2022, with 33 observations. The data reached an all-time high of 2.170 Person in 2022 and a record low of 1.400 Person in 1990. Mongolia MN: Population Density: Inhabitants per sq km data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Mongolia – Table MN.OECD.GGI: Social: Demography: Non OECD Member: Annual.

Business Analyst Layer: Color-coded map '2023 Population Density'

Geospatial Analysis of Population Demographics and Traffic Density in MinneapolisIntroductionThis interactive web map provides a geospatial analysis of population distribution and traffic density for the city of Minneapolis, Minnesota. By integrating demographic data at the census tract level with real-time traffic information, the application serves as a critical tool for urban planning, transportation management, and sociological research.Data Visualization and SymbologyThe map employs distinct color schemes to represent the core datasets, allowing for intuitive visual analysis: Traffic Density: The city's road network is symbolized using a color gradient to indicate traffic volume. Segments rendered in deep red represent a high traffic density index, signifying areas of significant vehicular congestion. This transitions to a light yellow for segments experiencing lower traffic flow. Population Density: The demographic landscape is visualized using a green color ramp applied to census tract polygons. Dark green shades correspond to areas with a high population concentration, whereas lighter green shades denote regions with a lower population density. Analytical Utility and ApplicationsThe juxtaposition of these datasets reveals spatial correlations between residential density and transportation bottlenecks. This allows for data-driven inquiry into key urban challenges. The patterns visualized can help city planners and transportation authorities identify specific corridors where infrastructure investment could be most effective. Strategic improvements in these areas have the potential to optimize traffic flow, reduce commuter travel times, and decrease vehicle fuel consumption and emissions, thereby enhancing the overall sustainability and livability of Minneapolis.Interactive Features and Data ExplorationUsers are encouraged to engage with the map's interactive features for a deeper understanding of the data: Layers and Legend: Utilize the "Layers" and "Legend" tools to deconstruct the map's composition and understand the specific values associated with the color symbology. Pop-up Information: Click on individual census tracts or road segments to activate pop-up windows. These provide detailed attribute information, such as total population counts, demographic breakdowns, household income statistics, and spatial relationship metrics like nearest neighbor analysis. This application is built upon a foundational demographic data layer for Minneapolis and is enhanced by the integration of a dynamic traffic layer from the ArcGIS Living Atlas of the World.

Author: S Wicklund, educator, Minnesota Alliance for Geographic EducationGrade/Audience: high schoolResource type: lessonSubject topic(s): population, mapsRegion: worldStandards: Minnesota Social Studies Standards

Standard 1. People use geographic representations and geospatial technologies to acquire, process and report information within a spatial context.

Standard 3. Places have physical characteristics (such as climate, topography and vegetation) and human characteristics (such as culture, population, political and economic systems).

Standard 5. The characteristics, distribution and migration of human populations on the earth’s surface influence human systems (cultural, economic and political systems).Objectives: Students will be able to:

1. Use maps of population distribution to examine the history of world population growth.
2. Construct a dot map to show current world population distribution.
3. Describe the difference between arithmetic and physiological densities.
4. Craft a response to a prompt to evaluate the Negative Population Growth perspective. Summary: Students will use maps of population distribution to examine the history of world population growth. They will also examine current world population distribution. Students will role-play the difference between arithmetic and physiologic densities using Egypt as an example. They will then craft a response to a prompt where they evaluate the Negative Population Growth perspective.

This datalayer displays the Urbanized Areas (UAs) for the state based on a January 1, 1990 ground condition. Note that the Census Bureau made significant changes in Urban/Rural designations for the Census 2000 data layers. Some of these delineations and definitions are explained below. 1990 Urban/Rural The U.S. Census Bureau defined urban for the 1990 census as consisting of all territory and population in urbanized areas (UAs) and in the urban portion of places with 2,500 or more people located outside of the UAs. The 1990 urban and rural classification applied to the 50 states, the District of Columbia, and Puerto Rico. 1990 Urbanized Areas A 1990 urbanized area (UA) consisted of at least one central place and the adjacent densely settled surrounding territory that together had a minimum population of 50,000 people. The densely settled surrounding territory generally consisted of an area with continuous residential development and a general overall population density of at least 1,000 people per square mile. 1990 Extended Cities For the 1990 census, the U.S. Census Bureau distinguished the urban and rural population within incorporated places whose boundaries contained large, sparsely populated, or even unpopulated area. Under the 1990 criteria, an extended city had to contain either 25 percent of the total land area or at least 25 square miles with an overall population density lower than 100 people per square mile. Such pieces of territory had to cover at least 5 square miles. This low-density area was classified as rural and the other, more densely settled portion of the incorporated place was classified as urban. Unlike previous censuses where the U.S. Census Bureau defined extended cities only within UAs, for the 1990 census the U.S. Census Bureau applied the extended city criteria to qualifying incorporated places located outside UAs. 1990 Urbanized Area Codes Each 1990 UA was assigned a 4-digit numeric census code in alphabetical sequence on a nationwide basis based on the metropolitan area codes. Note that in Record Type C, the 1990 UA 4-digit numeric census code and Census 2000 UA 5-digit numeric census code share a 5-character field. Because of this, the 1990 4-digit UA code, in Record Type C only, appears with a trailing blank. For Census 2000 the U.S. Census Bureau classifies as urban all territory, population, and housing units located within urbanized areas (UAs) and urban clusters (UCs). It delineates UA and UC boundaries to encompass densely settled territory, which generally consists of: - A cluster of one or more block groups or census blocks each of which has a population density of at least 1,000 people per square mile at the time - Surrounding block groups and census blocks each of which has a population density of at least 500 people per square mile at the time, and - Less densely settled blocks that form enclaves or indentations, or are used to connect discontiguous areas with qualifying densities. Rural consists of all territory, population, and housing units located outside of UAs and UCs. For Census 2000 this urban and rural classification applies to the 50 states, the District of Columbia, Puerto Rico, American Samoa, Guam, the Northern Mariana Islands, and the Virgin Islands of the United States. Urbanized Areas (UAs) An urbanized area consists of densely settled territory that contains 50,000 or more people. The U.S. Census Bureau delineates UAs to provide a better separation of urban and rural territory, population, and housing in the vicinity of large places. For Census 2000, the UA criteria were extensively revised and the delineations were performed using a zero-based approach. Because of more stringent density requirements, some territory that was classified as urbanized for the 1990 census has been reclassified as rural. (Area that was part of a 1990 UA has not been automatically grandfathered into the 2000 UA.) In addition, some areas that were identified as UAs for the 1990 census have been reclassified as urban clusters. Urban Clusters (UCs) An urban cluster consists of densely settled territory that has at least 2,500 people but fewer than 50,000 people. The U.S. Census Bureau introduced the UC for Census 2000 to provide a more consistent and accurate measure of the population concentration in and around places. UCs are defined using the same criteria that are used to define UAs. UCs replace the provision in the 1990 and previous censuses that defined as urban only those places with 2,500 or more people located outside of urbanized areas. Urban Area Title and Code The title of each UA and UC may contain up to three incorporated place names, and will include the two-letter U.S. Postal Service abbreviation for each state into which the UA or UC extends. However, if the UA or UC does not contain an incorporated place, the urban area title will include the single name of a census designated place (CDP), minor civil division, or populated place recognized by the U.S. Geological Survey's Geographic Names Information System. Each UC and UA is assigned a 5-digit numeric code, based on a national alphabetical sequence of all urban area names. For the 1990 census, the U.S. Census Bureau assigned as four-digit UA code based on the metropolitan area codes. Urban Area Central Places A central place functions as the dominant center of an urban area. The U.S. Census Bureau identifies one or more central places for each UA or UC that contains a place. Any incorporated place or census designated place (CDP) that is in the title of the urban area is a central place of that UA or UC. In addition, any other incorporated place or CDP that has an urban population of 50,000 or an urban population of at least 2,500 people and is at least 2/3 the size of the largest place within the urban area also is a central place. Extended Places As a result of the UA and UC delineations, an incorporated place or census designated place (CDP) may be partially within and partially outside of a UA or UC. Any place that is split by a UA or UC is referred to as an extended place.

Abstract

IPUMS-International is an effort to inventory, preserve, harmonize, and disseminate census microdata from around the world. The project has collected the world's largest archive of publicly available census samples. The data are coded and documented consistently across countries and over time to facillitate comparative research. IPUMS-International makes these data available to qualified researchers free of charge through a web dissemination system.

The IPUMS project is a collaboration of the Minnesota Population Center, National Statistical Offices, and international data archives. Major funding is provided by the U.S. National Science Foundation and the Demographic and Behavioral Sciences Branch of the National Institute of Child Health and Human Development. Additional support is provided by the University of Minnesota Office of the Vice President for Research, the Minnesota Population Center, and Sun Microsystems.

Geographic coverage

National coverage

Analysis unit

Household

UNITS IDENTIFIED: - Dwellings: No - Vacant units: No - Households: Yes - Individuals: Yes - Group quarters: No

UNIT DESCRIPTIONS: - Dwellings: Not available - Households: An individual or group of people who inhabit part or all of the physical or census building and usually live together and eat together from one kitchen. One kitchen means that the daily needs are managed and provided by one budget. - Group quarters: Not applicable for public use sample

Universe

Permanent residents. Special census blocks and institutions are not included.

Kind of data

Census/enumeration data [cen]

Sampling procedure

MICRODATA SOURCE: Statistics Indonesia

SAMPLE DESIGN: Multistage sample of census blocks using urban/rural status and population density of the province.

SAMPLE UNIT: Census block

SAMPLE FRACTION: 0.37%

SAMPLE SIZE (person records): 605,858

Mode of data collection

Face-to-face [f2f]

Research instrument

One questionnaire with dwelling information and social and demographic characteristics of individuals.

The 2015 cartographic boundary shapefiles are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files. The records in this file allow users to map the parts of Urban Areas that overlap a particular county. After each decennial census, the Census Bureau delineates urban areas that represent densely developed territory, encompassing residential, commercial, and other nonresidential urban land uses. In general, this territory consists of areas of high population density and urban land use resulting in a representation of the "urban footprint." There are two types of urban areas: urbanized areas (UAs) that contain 50,000 or more people and urban clusters (UCs) that contain at least 2,500 people, but fewer than 50,000 people (except in the U.S. Virgin Islands and Guam which each contain urban clusters with populations greater than 50,000). Each urban area is identified by a 5-character numeric census code that may contain leading zeroes. The primary legal divisions of most states are termed counties. In Louisiana, these divisions are known as parishes. In Alaska, which has no counties, the equivalent entities are the organized boroughs, city and boroughs, municipalities, and for the unorganized area, census areas. The latter are delineated cooperatively for statistical purposes by the State of Alaska and the Census Bureau. In four states (Maryland, Missouri, Nevada, and Virginia), there are one or more incorporated places that are independent of any county organization and thus constitute primary divisions of their states. These incorporated places are known as independent cities and are treated as equivalent entities for purposes of data presentation. The District of Columbia and Guam have no primary divisions, and each area is considered an equivalent entity for purposes of data presentation. The Census Bureau treats the following entities as equivalents of counties for purposes of data presentation: Municipios in Puerto Rico, Districts and Islands in American Samoa, Municipalities in the Commonwealth of the Northern Mariana Islands, and Islands in the U.S. Virgin Islands. The entire area of the United States, Puerto Rico, and the Island Areas is covered by counties or equivalent entities. The boundaries for counties and equivalent entities are as of January 1, 2010.

Household data are collected as of March.

As stated in the Census's "Source and Accuracy of Estimates for Income, Poverty, and Health Insurance Coverage in the United States: 2011" (http://www.census.gov/hhes/www/p60_243sa.pdf):

Estimation of Median Incomes. The Census Bureau has changed the methodology for computing median income over time. The Census Bureau has computed medians using either Pareto interpolation or linear interpolation. Currently, we are using linear interpolation to estimate all medians. Pareto interpolation assumes a decreasing density of population within an income interval, whereas linear interpolation assumes a constant density of population within an income interval. The Census Bureau calculated estimates of median income and associated standard errors for 1979 through 1987 using Pareto interpolation if the estimate was larger than $20,000 for people or $40,000 for families and households. This is because the width of the income interval containing the estimate is greater than $2,500.

We calculated estimates of median income and associated standard errors for 1976, 1977, and 1978 using Pareto interpolation if the estimate was larger than $12,000 for people or $18,000 for families and households. This is because the width of the income interval containing the estimate is greater than $1,000. All other estimates of median income and associated standard errors for 1976 through 2011 (2012 ASEC) and almost all of the estimates of median income and associated standard errors for 1975 and earlier were calculated using linear interpolation.

Thus, use caution when comparing median incomes above $12,000 for people or $18,000 for families and households for different years. Median incomes below those levels are more comparable from year to year since they have always been calculated using linear interpolation. For an indication of the comparability of medians calculated using Pareto interpolation with medians calculated using linear interpolation, see Series P-60, Number 114, Money Income in 1976 of Families and Persons in the United States (www2.census.gov/prod2/popscan/p60-114.pdf).

Author: M Leba, educator, Minnesota Alliance for Geographic EducationGrade/Audience: grade 4, grade 8Resource type: activitySubject topic(s): populationRegion: worldStandards: Minnesota Social Studies Standards

Standard 3. Places have physical characteristics (such as climate, topography vegetation).Objectives: Students will be able to:

1. Identify how population is distributed throughout the world
2. Explain the difference between population distribution and population densitySummary: Students will explore global population and the difference between population distribution and population density through an interactive activity.

Like many ecological processes, natural disturbances exhibit scale-dependent dynamics that are largely a function of the magnitude, frequency, and scale at which they are assessed. Ecosystem engineers create patch-scale disturbances that affect ecological processes, yet we know little about how these effects scale across space or vary through time. Here, we investigate how patch disturbances by beavers (Castor canadensis), ecosystem engineers renowned for their pond-creation behavior, affect ecological processes across space and time. We evaluated how beaver population recovery influenced surface water dynamics in relation to population density over 70 years across multiple spatial scales (pond, watershed, and regional) in northern Minnesota. Surface water area was positively related to population density at the watershed scale; however, despite variation in beaver densities (and therefore surface water area) at the watershed scale, regional-scale surface water area was stable through t...

Author: J Trygestad, educator, Minnesota Alliance for Geographic EducationGrade/Audience: grade 8Resource type: lessonSubject topic(s): population, migrationRegion: australia oceaniaStandards: Minnesota Social Studies Standards

Standard 5. The characteristics, distribution and migration of human populations on the earth’s surface influence human systems (cultural, economic and political systems).Objectives: Students will be able to:

1. Compare Australia’s population and culture with the U.S.’s population and culture.
2. Describe the population, population distribution, and population density of Australia.
3. Describe why people moved to Australia by naming push and pull factors.
4. Identify when and where immigrants settled in Australia.
5. Identify how Aborigines and immigrants were treated and the influence they have on Australia.Summary: This lesson is a series of activities and extensions detailing Australia’s population and culture characteristics. Also included are activities to learn about immigration and Aborigines. An Australia skit is a summative activity on Australian culture and history.

In 2008, the Quality Deer Management Association (QDMA) developed a map of white-tailed deer density with information obtained from state wildlife agencies. The map contains information from 2001 to 2005, with noticeable changes since the development of the first deer density map made by QDMA in 2001. The University of Minnesota, Forest Ecosystem Health Lab and the US Department of Agriculture, Forest Service-Northern Research Station have digitized the deer density map to provide information on the status and trends of forest health across the eastern United States. The QDMA spatial map depicting deer density (deer per square mile) was digitized across the eastern United States. Estimates of deer density were: White = rare, absent, or urban area with unknown population, Green = less than 15 deer per square mile, Yellow = 15 to 30 deer per square mile, Orange = 30 to 40 deer per square mile, or Red = greater than 45 deer per square mile. These categories represent coarse deer density levels as identified in the QDMA report in 2009 and should not be used to represent current or future deer densities across the study region. Sponsorship: Quality Deer Management Association; US Department of Agriculture, Forest Service-Northern Research Station; Minnesota Agricultural Experiment Station. Resources in this dataset:Resource Title: Link to DRUM catalog record. File Name: Web Page, url: https://conservancy.umn.edu/handle/11299/178246

Abstract

IPUMS-International is an effort to inventory, preserve, harmonize, and disseminate census microdata from around the world. The project has collected the world's largest archive of publicly available census samples. The data are coded and documented consistently across countries and over time to facillitate comparative research. IPUMS-International makes these data available to qualified researchers free of charge through a web dissemination system.

The IPUMS project is a collaboration of the Minnesota Population Center, National Statistical Offices, and international data archives. Major funding is provided by the U.S. National Science Foundation and the Demographic and Behavioral Sciences Branch of the National Institute of Child Health and Human Development. Additional support is provided by the University of Minnesota Office of the Vice President for Research, the Minnesota Population Center, and Sun Microsystems.

Geographic coverage

National coverage

Analysis unit

Dwelling

UNITS IDENTIFIED: - Dwellings: No - Vacant units: No - Households: Yes - Individuals: Yes - Group quarters: Yes

UNIT DESCRIPTIONS: - Dwellings: This is a building persons use solely for lodging purposes. Buildings which are not used for lodging such factories or warehouses as are not dwellings. - Households: The group of people related or not that usually live together in one or more dwellings, eat together, and together take care of their daily fundamental needs. Usually a household is made up of a man, woman, and their children. Other relatives, guests and servants are counted as part of the household if they spent the night preceding the census in the household. As an exception a household can be made up of a single person. - Group quarters: The group of people living together in camps, boarding schools, hospitals, prisons, and other collective households such as these.

Kind of data

Census/enumeration data [cen]

Sampling procedure

MICRODATA SOURCE: National Bureau of Statistics

SAMPLE DESIGN: Sample drawn by NBS from long form questionnaire. Weights provide expansion factors. Approximately 15% of rural enumeration areas within each district received the long form questionnaire; urban areas were sampled at a higher density. IPUMS drew a systematic two-thirds subsample to reduce the original dataset from 15 to 10%.

SAMPLE UNIT: Dwelling

SAMPLE FRACTION: 10%

SAMPLE SIZE (person records): 3,732,735

Mode of data collection

Face-to-face [f2f]

Research instrument

A short questionnaire administered to 75% of enumeration areas and a long questionnaire adminstered to 25% of enumeration areas.

Author: J Trygestad, educator, Minnesota Alliance for Geographic EducationGrade/Audience: grade 8Resource type: lessonSubject topic(s): population, physical geography, migrationRegion: north americaStandards: Minnesota Social Studies Standards

Standard 5. The characteristics, distribution and migration of human populations on the earth’s surface influence human systems (cultural, economic and political systems). Objectives: Students will be able to:

1. Describe Canada’s population distribution, density and growth.
2. Describe how Canada’s physical geography affects the location and size of its population centers.
3. Describe the patterns of Canada’s recent immigration and migration.
4. Describe the diversity of Canada’s population as a culture mosaic.Summary: Students describe the distribution, density and growth of Canada’s population based on patterns of current and historic settlement and physical features. Students also describe Canada’s culture mosaic addressing current immigration, migration, and culture patterns.

This layer was created by merging the approved AUBs for the Minnesota and Wisconsin portions of the urban areas. Minnesota approved our recommendations with line work aligned to the local parcel files. WisDOT modified our recommendations to align with the State's roadway network instead of parcel lines.The TIGER/Line Files are shapefiles and related database files (.dbf) that are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line File is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. After each decennial census, the Census Bureau delineates urban areas that represent densely developed territory, encompassing residential, commercial, and other nonresidential urban land uses. In general, this territory consists of areas of high population density and urban land use resulting in a representation of the "urban footprint." There are two types of urban areas: urbanized areas (UAs) that contain 50,000 or more people and urban clusters (UCs) that contain at least 2,500 people, but fewer than 50,000 people (except in the U.S. Virgin Islands and Guam which each contain urban clusters with populations greater than 50,000). Each urban area is identified by a 5-character numeric census code that may contain leading zeroes.

When planning abundance surveys, the impact of search effort on the quality of the density estimates is rarely considered. We constructed a time-budget modeling framework for abundance surveys using principles from optimal foraging theory. We link search effort to the number of sample units surveyed, searcher detection probability, the number of detections made, and the precision of the estimated resource density. This framework allowed us to determine how a surveyor should behave to produce optimal density estimates. Using data collected from quadrat and removal surveys of zebra mussels (Dreissena polymorpha) in central Minnesota, we applied this framework to evaluate potential improvements. By tuning searcher behavior, we find that density estimates from removal surveys of zebra mussels could be improved by up to 60% in some cases, without changing the overall survey effort. Our framework also predicts a critical population density where the best survey method switches from removal su..., Data on search times for removal and quadrat surveys was collected by divers in three Minnesota lakes. Density estimates, also provided here, are discussed in detail in the Digital Repository of the University of Minnesota at https://doi.org/10.13020/655p-j357. Functions for formatting the dataset for analysis are also included., , # Data from: A framework for modeling the impacts of searcher behavior on the efficiency of abundance surveys

Contains data and R files to reproduce analyses from "A common framework for modeling animal search: Linking foraging ecology to survey design through trade-offs between search effort and detection"

Description of the Data and file structure

R files to reproduce analyses in the main manuscript:

CV_calculation.R: To reproduce the zebra mussel analyses, run this code. This file calculates optimal search times by minimizing the coefficient of variation in the estimated density of a removal survey. Reproduces Fig 3. Calls DensityEstimates.R.

CV_sensitivity.R - contains code to produce the sensitivity analysis of the zebra mussel surveys. Reproduces Fig 4. Calls DensityEstimates.R.

DensityEstimates.R: This calls the necessary functions to estimate density and model the time budget data. Also produces density estimates from the empirical survey data. More information on t...

Results of analysis of deviance for a generalized linear model with binomial response.

description: Line-transects were used to survey American bitterns (Botaurus lentiginosus) and least bitterns (Ixobrychus exilis) on Agassiz National Wildlife Refufe (NWR), Marshall County, Minnesota, May-July 1990. Agassiz Pool (12.7 mi ) composed 35% of the transect area but accounted for 62% of the American bittern and 98% of the least bittern sightings. American bitterns selected burned emergent vegetation in May, and were sighted most frequently in pools that were reflooded after 1-3 years in drawdown. Density estimates for American and least bitterns were 68.0 95% CI 12.4 and 119.2 95% CI 33.2 birds/mi2 emergent marsh respectively. June population estimates were 384 95% CI 72 American bitterns and 310 95% CI 86 least bitterns.; abstract: Line-transects were used to survey American bitterns (Botaurus lentiginosus) and least bitterns (Ixobrychus exilis) on Agassiz National Wildlife Refufe (NWR), Marshall County, Minnesota, May-July 1990. Agassiz Pool (12.7 mi ) composed 35% of the transect area but accounted for 62% of the American bittern and 98% of the least bittern sightings. American bitterns selected burned emergent vegetation in May, and were sighted most frequently in pools that were reflooded after 1-3 years in drawdown. Density estimates for American and least bitterns were 68.0 95% CI 12.4 and 119.2 95% CI 33.2 birds/mi2 emergent marsh respectively. June population estimates were 384 95% CI 72 American bitterns and 310 95% CI 86 least bitterns.