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

In 2023, Washington, D.C. had the highest population density in the United States, with 11,130.69 people per square mile. As a whole, there were about 94.83 residents per square mile in the U.S., and Alaska was the state with the lowest population density, with 1.29 residents per square mile. The problem of population density Simply put, population density is the population of a country divided by the area of the country. While this can be an interesting measure of how many people live in a country and how large the country is, it does not account for the degree of urbanization, or the share of people who live in urban centers. For example, Russia is the largest country in the world and has a comparatively low population, so its population density is very low. However, much of the country is uninhabited, so cities in Russia are much more densely populated than the rest of the country. Urbanization in the United States While the United States is not very densely populated compared to other countries, its population density has increased significantly over the past few decades. The degree of urbanization has also increased, and well over half of the population lives in urban centers.

This data is used in the Materials Management Facilities Web App (Item Details). From the Michigan Association of Regions (MAR) website: "The Michigan Association of Regions is a state association of the fourteen (14) regional councils in Michigan. MAR consists of a policy board of local elected and appointed officials that meets periodically to discuss regional policy issues and programs, and adopts legislative positions. MAR also has an Executive Directors Committee that meets monthly. Member services consists of advocacy of regional programs, training and education, research, membership surveys, networking, as well as liaison to national associations, including the National Association of Regional Councils (NARC) and the National Association of Development Organizations (NADO).State Designated Planning and Development Regions are voluntary organizations comprised of local governments dedicated to serving the regional planning needs of multi-county areas in all parts of Michigan. They are a form of local government voluntarily created by their members, which are largely representative of local governments in the region; although membership also includes road authorities, nonprofit organizations and representatives of the business community in many regions.The land area of Michigan is divided into 14 planning & development regions with counties as the organizing unit. They range widely in size. Five have only three counties, while one has fourteen counties. The two smallest are only 1,711-13 square miles each in size, while the largest is 8,735 square miles in size. Population served varies from 57,510 persons to 4,833,493 based on Census estimates in 2000. Population density ranges from under 14 persons/square mile in Region 13 (Western U.P.), to over 1,043 persons/square mile in Region 1 (Southeast Michigan). The oldest of today’s regions, Tri-County Regional Planning Commission (Region 6 in Lansing, formed in 1956), and the three county Detroit Metropolitan Area Regional Planning Commission (formed in 1947and subsequently replaced by the Southeast Michigan Council of Governments in 1968 (SEMCOG, which covers seven counties in SE Michigan), originated out of a desire by local officials to coordinate transportation infrastructure planning and to serve as a forum for other regional issues."These boundaries are static and were digitized from boundaries shared on the Michigan Association of Regions (MAR) website in March 2023. They were digitized for inclusion on the Materials Management Division's facilities web map. For questions or comments, reach out to EGLE-Maps@Michigan.gov.

The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Kellogg Biological Station (KBS) contains human population density measurements in numberPerKilometerSquared units and were aggregated to a yearly timescale.

Population change by square mile is based on county-level data from the USGS, 2010-2015.

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).

Understanding patterns of genetic structure and adaptive variation in natural populations is crucial for informing conservation and management. Past genetic research using 11 microsatellite loci identified six genetic stocks of lake whitefish (Coregonus clupeaformis) within Lake Michigan, USA. However, ambiguity in genetic stock assignments suggested those neutral microsatellite markers did not provide adequate power for delineating lake whitefish stocks in this system, prompting calls for a genomics approach to investigate stock structure. Here, we generated a dense genomic dataset to characterize population structure and investigate patterns of neutral and adaptive genetic diversity among lake whitefish populations in Lake Michigan. Using Rapture sequencing, we genotyped 829 individuals collected from 17 baseline populations at 197,588 SNP markers after quality filtering. Although the overall pattern of genetic structure was similar to the previous microsatellite study, our genomic data provided several novel insights. Our results indicated a large genetic break between the northwestern and eastern sides of Lake Michigan, and we found a much greater level of population structure on the eastern side compared to the northwestern side. Collectively, we observed five genomic islands of adaptive divergence on five different chromosomes. Each island displayed a different pattern of population structure, suggesting that combinations of genotypes at these adaptive regions are facilitating local adaptation to spatially heterogenous selection pressures. Additionally, we identified a large linkage disequilibrium block of ~8.5 Mb on chromosome 20 that is suggestive of a putative inversion but with a low frequency of the minor haplotype. Our study provides a comprehensive assessment of population structure and adaptive variation that can help inform management of Lake Michigan's lake whitefish fishery and highlights the utility of incorporating adaptive loci into fisheries management.

Copy of https://www.kaggle.com/datasets/kisoibo/countries-databasesqlite

Updated the name of the table from 'countries of the world' to 'countries', for ease of writing queries.

Info about the dataset:

Content

Table Total Rows Total Columns countries of the world **0 ** ** 20** Country, Region, Population, Area (sq. mi.), Pop. Density (per sq. mi.), Coastline (coast/area ratio), Net migration, Infant mortality (per 1000 births), GDP ($ per capita), Literacy (%), Phones (per 1000), Arable (%), Crops (%), Other (%), Climate, Birthrate, Deathrate, Agriculture, Industry, Service

Acknowledgements

Acknowledgements Source: All these data sets are made up of data from the US government. Generally they are free to use if you use the data in the US. If you are outside of the US, you may need to contact the US Govt to ask. Data from the World Factbook is public domain. The website says "The World Factbook is in the public domain and may be used freely by anyone at anytime without seeking permission." https://www.cia.gov/library/publications/the-world-factbook/docs/faqs.html

Inspiration

When making visualisations related to countries, sometimes it is interesting to group them by attributes such as region, or weigh their importance by population, GDP or other variables.

Localized management of white-tailed deer (Odocoileus virginianus) involves the removal of matriarchal family units with the intent to create areas of reduced deer density. However, application of this approach has not always been successful, possibly because of female dispersal and high deer densities. We developed a spatially explicit, agent-based model to investigate the intensity of deer removal required to locally reduce deer density depending on the surrounding deer density, dispersal behavior, and size and shape of the area of localized reduction. Application of this model is illustrated using the example of abundant deer populations in Pennsylvania, USA. Most scenarios required at least 5 years before substantial deer density reductions occurred. Our model indicated that a localized reduction was successful for scenarios in which the surrounding deer density was lowest (30 deer/mi²), localized antlerless harvest rates were ≥ 30%, and the removal area was 5 mi² or larger. When the size of the removal area was < 5 mi², end population density was highly variable and, in some scenarios, exceeded the initial density. The shape of the area of localized reduction had less influence on the ability to reduce deer density than the size. There were no differences in mean deer density in the same size circle or square removal areas. Similarly, increasing the ratio of sides (length : width) in rectangular removal areas had little influence on the ability to locally reduce deer densities. Situations in which deer density was higher (40 or 50 deer/mi²) required antlerless removal rates to exceed 30% and took more than 5 years to considerably reduce density in the localized area regardless of its size. These results indicate that the size of the area of reduction, surrounding deer density, and antlerless harvest rate are the most influential factors in locally reducing deer density. Therefore, localized management likely can be an effective strategy for lower density herds, especially in larger removal areas. For high density herds, the success of this strategy would depend most on the ability of resource managers to achieve consistently high antlerless harvest rates.

These Demographic Data are U.S. Census American Community Survey Data, from the 2014 5-year set. Data Driven Detroit calculated densities (Per Sq Mile) by dividing the population by the ALAND10 field, which is the census land area field, in square meters.

The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Kellogg Biological Station (KBS) contains percent urban population measurements in percent units and were aggregated to a yearly timescale.

Our study on saplings was conducted in six forested sites in three southern Michigan counties: Ingham Co. (three sites), Gratiot Co. (two sites), and Shiawassee Co. (one site), with 10 to 60 km between sites.Data set one - on the fate and density of emerald ash borer larvae and associated parasitoids on ash saplings from both biocontrol-release and non-release control plots in southern Michigan during the three-year study (2013–2015). Data set one was used for calculations and associated analyses for of the parameters presented in Figure 1, 2, 3, and 4.Data set two - on ash tree abundance (per 100 m2) and healthy conditions (or crown classes) at the six study sites in southern Michigan observed in summer 2015. Data set two was used for estimation of tree density (Figure 5) and healthy condition (or crown classes).Resources in this dataset:Resource Title: Emerald ash borer biocontrol in ash saplings: the potential for early stage recovery of North American ash trees. File Name: Sapling Data 2013-2015 FINAL.xlsx Resource Description: Data set one - on fate and density of emerald ash borer larvae and/or pupae and associated mortality factors (parasitoids, predators, and undetermined diseases/plant resistance /competition)Resource Title: Emerald ash borer biocontrol in ash saplings: the potential for early stage recovery of North American ash trees. File Name: MI Ash Transect 2015 - All trees.xlsx Resource Description: Data on ash abundance and healthy conditions from transect surveyResource Title: Data Dictionary - EAB biocontrol in ash saplings. File Name: EAB_data_dictionary.csvResource Title: 2013-2014 data sorted. File Name: 2013-2014_data_sorted_EAB.csv Resource Description: Data set one - on fate and density of emerald ash borer larvae and/or pupae and associated mortality factors (parasitoids, predators, and undetermined diseases/plant resistance /competition)Resource Title: 2014-2015 data sorted. File Name: 2014-2015_data_sorted_EAB.csv Resource Description: Data set one - on fate and density of emerald ash borer larvae and/or pupae and associated mortality factors (parasitoids, predators, and undetermined diseases/plant resistance /competition)Resource Title: 2015-2016 data sorted. File Name: 2015-2016_data_sorted_EAB.csv Resource Description: Data set one - on fate and density of emerald ash borer larvae and/or pupae and associated mortality factors (parasitoids, predators, and undetermined diseases/plant resistance /competition)Resource Title: Combined: Emerald ash borer biocontrol in ash saplings: the potential for early stage recovery of North American ash trees. File Name: Emerald ash borer biocontrol in ash saplings the potential for early stage recovery of North American ash trees.csv Resource Description: Data set one - on fate and density of emerald ash borer larvae and/or pupae and associated mortality factors (parasitoids, predators, and undetermined diseases/plant resistance /competition) All 3 sets (2013-2016) combined into a CSV for visualization purposesResource Title: Emerald ash borer biocontrol in ash saplings: the potential for early stage recovery of North American ash trees. File Name: MI Ash Transect 2015 - All trees.csv Resource Description: Data on ash abundance and healthy conditions from transect survey (CSV version for data visualization)Resource Title: Estimates of the net population growth rate of emerald ash borer on saplings from life tables constructed from Dataset One. File Name: DUAN J Data on EAB Life Tables Calculation for Saplings 2013-2015.xlsx Resource Description: This life table of emerald ash borer on saplings was constructed from Dataset One and used to estimate the next population growth rate according to method described in Duan et al. (2014, 2017)Resource Title: Estimates of the net population growth rate of emerald ash borer on saplings from life tables constructed from Dataset One. File Name: EAB_Life_Tables_Calculation_for_Saplings_2013-2015.csv Resource Description: CSV version of the data - This life table of emerald ash borer on saplings was constructed from Dataset One and used to estimate the next population growth rate according to method described in Duan et al. (2014, 2017)

Current (2021) and projected numbers of Plug-in Electrical Vehicles (PEVs) at the census block group level for the Delaware Valley region. The projected PEV distribution is based on a scenario in which 5 percent of passenger vehicles in the Greater Philadelphia region (or about 200,000 vehicles) are PEVs.

Also includes data projecting workplace charging demand in number of charging events and kilowatt-hours of demand by census block group based on the aforementioned PEV projections for the following three scenarios:

- workplace charging is free (free charging),

- workplace charging is twice the cost of home charging (paid charging).

The following datasets were used to obtain these results using the EV Planning Toolkit:

- 2021 vehicle registration data provided by the Pennsylvania Department of Transportation

- 2021 vehicle registration data from the New Jersey Motor Vehicle Commission provided by the New Jersey Department of Environmental Protection

• 2015-2019 American Community Survey 5-year Estimates

-

• LEHD Origin-Destination Employment Statistics (LODES), version 7

- Block group-to-block group commuting distances from DVRPC's Travel Improvement Model 2.1, accessed December 2017.

• Block group-to-block group commuting distances calculated from Esri’s ArcGIS Network Analyst, used in 2017. Field Alias Description GEOID10 GEOID10 Census Block Group identifier Mun_Name Municipality Name The name of the municipality in which the Block Group lies GEOID_Muni GEOID of Municipality Municipality identifier SQMI_LAND Land area Square miles of land area POP Population Number of people HOUSUNIT Housing Units Number of housing units JOBS Jobs Number of jobs PASS_VEH Number of Passenger Vehicles Number of passenger vehicles per block group as of 2021 CurPEV Current Number of PEVs Number of PEVs per block group as of 2021 FutPEV Projected Number of PEVs Number of projected PEVs per block group at 5% regional penetration CuPEV_SM Current PEVs per square mile Number of PEVs per square mile in the block group as of 2021 FUPEV_SM Projected PEVs per square mile Number of projected PEVs per square mile per block group at 5% regional penetration CuPEVPop Current number of PEVs per 100 people Number of PEVs per 100 people per block group as of 2021 FuPEVPop Projected number of PEVs per 100 people Number of projected PEVs per 100 people per block group at 5% regional penetration CuPEV_HU Current number of PEVs per 100 housing units Number of PEVs per 100 housing units per block group as of 2021 FuPEV_HU Projected number of PEVs per 100 housing units Number of projected PEVs per 100 housing units per block group at 5% regional penetration PerCuPEV Current Percentage of Passenger Vehicles That Are PEVs Percentage of total passenger vehicles that are PEVs per block group as of 2021 PerFuPEV Projected Percentage of Passenger Vehicles That Are PEVs Percentage of total passenger vehicles that are projected to be PEVs per block group at 5% regional penetration FC_KD Free Charging - kWh of Demand Kilowatt-hours of workplace charging demand per day per block group when workplace charging is free at 5% regional PEV penetration FC_CE Free Charging - Number of Charging Events Number of workplace charging events per day per block group when workplace charging is free at 5% regional PEV penetration FC_KD_SM Free Charging - kWh of Demand per sq. mi. Kilowatt-hours of workplace charging demand per day per square mile per block group when workplace charging is free at 5% regional PEV penetration FC_CE_SM Free Charging - Charging Events per sq. mi. Number of workplace charging events per day per square mile per block group when workplace charging is free at 5% regional PEV penetration FC_KPE Free Charging - kWh per charging event Kilowatt-hours per workplace charging event per block group when workplace charging is free at 5% regional PEV penetration FC_KD_JB Free Charging - kWh of Demand per Job Kilowatt-hours of workplace charging demand per day per job per block group when workplace charging is free at 5% regional PEV penetration FC_CE_JB Free Charging - Charging Events per Job Number of workplace charging events per job per block group when workplace charging is free at 5% regional PEV penetration PC_KD Paid Charging - kWh of Demand Kilowatt-hours of workplace charging demand per day per block group when workplace charging is the same cost as home charging at 5% regional PEV penetration PC_CE Paid Charging - Number of Charging Events Number of workplace charging events per day per block group when workplace charging is the same cost as home charging at 5% regional PEV penetration PC_KD_SM Paid Charging - kWh of Demand per sq. mi. Kilowatt-hours of workplace charging demand per day per square mile per block group when workplace charging is the same cost as home charging at 5% regional PEV penetration PC_CE_SM Paid Charging - Charging Events per sq. mi. Number of workplace charging events per day per square mile per block group when workplace charging is the same cost as home charging at 5% regional PEV penetration PC_KPE Paid Charging - kWh per charging event Kilowatt-hours per workplace charging event per block group when workplace charging is the same cost as home charging at 5% regional PEV penetration PC_KD_JB Paid Charging - kWh of Demand per Job Kilowatt-hours of workplace charging demand per day per job per block group when workplace charging is the same cost as home charging at 5% regional PEV penetration PC_CE_JB Paid Charging - Charging Events per Job Number of workplace charging events per job per block group when workplace charging is the same cost as home charging at 5% regional PEV penetration If you have any questions regarding this analysis or datasets used in the analysis, please contact: Sean Greene, Manager, Air Quality Programs | sgreene@dvrpc.org | (215) 238-2860

description: This shapefile contains landscape factors representing human disturbances summarized to local and network catchments of river reaches for the state of Michigan. This dataset is the result of clipping the feature class 'NFHAP 2010 HCI Scores and Human Disturbance Data for the Conterminous United States linked to NHDPLUSV1.gdb' to the state boundary of Michigan. Landscape factors include land uses, population density, roads, dams, mines, and point-source pollution sites. The source datasets that were compiled and attributed to catchments were identified as being: (1) meaningful for assessing fish habitat; (2) consistent across the entire study area in the way that they were assembled; (3) representative of conditions in the past 10 years, and (4) of sufficient spatial resolution that they could be used to make valid comparisons among local catchment units. In this data set, these variables are linked to the catchments of the National Hydrography Dataset Plus Version 1 (NHDPlusV1) using the COMID identifier. They can also be linked to the reaches of the NHDPlusV1 using the COMID identifier. Catchment attributes are available for both local catchments (defined as the land area draining directly to a reach; attributes begin with "L_" prefix) and network catchments (defined by all upstream contributing catchments to the reach's outlet, including the reach's own local catchment; attributes begin with "N_" prefix). This shapefile also includes habitat condition scores created based on responsiveness of biological metrics to anthropogenic landscape disturbances throughout ecoregions. Separate scores were created by considering disturbances within local catchments, network catchments, and a cumulative score that accounted for the most limiting disturbance operating on a given biological metric in either local or network catchments. This assessment only scored reaches representing streams and rivers (see the process section for more details). Please use the following citation: Esselman, P., D.M. Infante, L. Wang, W. Taylor, W. Daniel, R. Tingley, J. Fenner, A. Cooper, D. Wieferich, D. Thornbrugh and J. Ross. (April 2011) National Fish Habitat Action Plan (NFHAP) 2010 HCI Scores and Human Disturbance Data (linked to NHDPLUSV1) for Michigan. National Fish Habitat Partnership Data System. http://dx.doi.org/doi:10.5066/F7GH9FZG; abstract: This shapefile contains landscape factors representing human disturbances summarized to local and network catchments of river reaches for the state of Michigan. This dataset is the result of clipping the feature class 'NFHAP 2010 HCI Scores and Human Disturbance Data for the Conterminous United States linked to NHDPLUSV1.gdb' to the state boundary of Michigan. Landscape factors include land uses, population density, roads, dams, mines, and point-source pollution sites. The source datasets that were compiled and attributed to catchments were identified as being: (1) meaningful for assessing fish habitat; (2) consistent across the entire study area in the way that they were assembled; (3) representative of conditions in the past 10 years, and (4) of sufficient spatial resolution that they could be used to make valid comparisons among local catchment units. In this data set, these variables are linked to the catchments of the National Hydrography Dataset Plus Version 1 (NHDPlusV1) using the COMID identifier. They can also be linked to the reaches of the NHDPlusV1 using the COMID identifier. Catchment attributes are available for both local catchments (defined as the land area draining directly to a reach; attributes begin with "L_" prefix) and network catchments (defined by all upstream contributing catchments to the reach's outlet, including the reach's own local catchment; attributes begin with "N_" prefix). This shapefile also includes habitat condition scores created based on responsiveness of biological metrics to anthropogenic landscape disturbances throughout ecoregions. Separate scores were created by considering disturbances within local catchments, network catchments, and a cumulative score that accounted for the most limiting disturbance operating on a given biological metric in either local or network catchments. This assessment only scored reaches representing streams and rivers (see the process section for more details). Please use the following citation: Esselman, P., D.M. Infante, L. Wang, W. Taylor, W. Daniel, R. Tingley, J. Fenner, A. Cooper, D. Wieferich, D. Thornbrugh and J. Ross. (April 2011) National Fish Habitat Action Plan (NFHAP) 2010 HCI Scores and Human Disturbance Data (linked to NHDPLUSV1) for Michigan. National Fish Habitat Partnership Data System. http://dx.doi.org/doi:10.5066/F7GH9FZG

Determining the degree to which predation affects prey abundance in natural communities constitutes a key goal of ecological research. Predators can affect prey through both consumptive effects (CEs) and nonconsumptive effects (NCEs), although the contributions of each mechanism to the density of prey populations remain largely hypothetical in most systems. Common statistical methods applied to time series data cannot elucidate the mechanisms responsible for hypothesized predator effects on prey density (e.g., differentiate CEs from NCEs), nor provide parameters for predictive models. State space models (SSMs) applied to time series data offer a way to meet these goals. Here, we employ SSMs to assess effects of an invasive predatory zooplankter, Bythotrephes longimanus, on an important prey species, Daphnia mendotae, in Lake Michigan. We fit mechanistic models in a SSM framework to seasonal time series (1994-2012) using a recently developed, maximum likelihood-based optimization method, iterated filtering, which can overcome challenges in ecological data (e.g. nonlinearities, measurement error, and irregular sampling intervals). Our results indicate that B. longimanus strongly influences D. mendotae dynamics, with mean annual peak densities of B. longimanus observed in Lake Michigan estimated to cause a 61% reduction in D. mendotae population growth rate and a 59% reduction in peak biomass density. Further, the mechanism underlying the B. longimanus effect is most consistent with an NCE via reduced birth rates. The SSM approach also provided estimates for key biological parameters (e.g., demographic rates) and the contribution of dynamic stochasticity and measurement error. Our study therefore highlights the utility of SSMs to enhance inference for species interactions from time series data. In particular, our findings provide evidence derived directly from survey data that the invasive zooplankter B. longimanus is affecting zooplankton demographics and offer parameter estimates needed to inform predictive models that explore the effect of B. longimanus under different scenarios such as climate change.

Place Name A Type of incorporation Population 2011 Area 2010 Density 2010 sq mi km2 sq mi km2Abbeville Abbeville County

Out of all 50 states, New York had the highest per-capita real gross domestic product (GDP) in 2023, at 90,730 U.S. dollars, followed closely by Massachusetts. Mississippi had the lowest per-capita real GDP, at 39,102 U.S. dollars. While not a state, the District of Columbia had a per capita GDP of more than 214,000 U.S. dollars. What is real GDP? A country’s real GDP is a measure that shows the value of the goods and services produced by an economy and is adjusted for inflation. The real GDP of a country helps economists to see the health of a country’s economy and its standard of living. Downturns in GDP growth can indicate financial difficulties, such as the financial crisis of 2008 and 2009, when the U.S. GDP decreased by 2.5 percent. The COVID-19 pandemic had a significant impact on U.S. GDP, shrinking the economy 2.8 percent. The U.S. economy rebounded in 2021, however, growing by nearly six percent. Why real GDP per capita matters Real GDP per capita takes the GDP of a country, state, or metropolitan area and divides it by the number of people in that area. Some argue that per-capita GDP is more important than the GDP of a country, as it is a good indicator of whether or not the country’s population is getting wealthier, thus increasing the standard of living in that area. The best measure of standard of living when comparing across countries is thought to be GDP per capita at purchasing power parity (PPP) which uses the prices of specific goods to compare the absolute purchasing power of a countries currency.

This layer displays several key factors relevant to the siting of large-scale solar power and combines them, demonstrating the “suitability” of regions within Michigan to this technology. The purpose of this tool is to aid proactive planning and zoning for local governments by highlighting the quantity and location of areas of potential development interest. The suitability models were created using the Suitability Modeler in ArcGIS Pro for both solar and wind energy. These models were based on the Geospatial Energy Mapper (GEM) but have been enhanced with higher-resolution data and modifications tailored to Michigan's landscape. The key factors involved are land use type, land slope, distance to substations, and population density. For land use type, each type was assigned a suitability score by the creators. These four factors are then weighted based on total relevance to energy development, also assigned by the creators, to ultimately produce a total suitability score. The creators determined land use type scores and weighting based on research, peer feedback, and general experience with energy siting. Notably, this map does not include solar potential/solar insolation due to relative consistency across the state, which led to the decreased distinction between other key factors, reducing the utility of this tool for local governments to plan and zone with. An additional layer of high voltage transmission lines is included for further relevant context, though this is not involved in the suitability score.

Model Parameters (Modified upon GEM models): Utility Scale PV (Solar)

Parameters

Weight

Slope

2

Land Cover

4

Population Density

1

Distance to substation

3

Data Sources and Model Weighting:

Slope (Land Fire slope)SUITABILITYRANGE/CLASS1000 - 1%902%903%304%105%16 - 10%0≥ 11% Land Cover (Multi-Resolution Land Characteristics (MRLC) Consortium – CONUS 2021)GEM - Solar REA - solarSUITABILITYRANGE/CLASS SUITABILITYRANGE/CLASS100Unclassified (0) 0Unclassified (0)1Open Water (11) 0Open Water (11)10Perennial Snow/Ice (12) 0Perennial Snow/Ice (12)75Developed, Open Space (21) 0Developed, Open Space (21)75Developed, Low Intensity (22) 0Developed, Low Intensity (22)75Developed, Medium Intensity (23) 0Developed, Medium Intensity (23)75Developed, High Intensity (24) 0Developed, High Intensity (24)100Barren Land (31) 50Barren Land (31)50Deciduous Forest (41) 0Deciduous Forest (41)50Evergreen Forest (42) 0Evergreen Forest (42)50Mixed Forest 0Mixed Forest90Shrub/Scrub (52) 90Herbaceous (71)90Hay/Pasture (81) 100Hay/Pasture (81)90Cultivated Crops (82) 100Cultivated Crops (82)40Woody Wetlands (90) 0Woody Wetlands (90) Distance to Substation (220 to 345kV) (ArcGIS Substations)SUITABILITYRANGE/CLASS1000 - 1 miles901 - 5 miles755 - 10 miles50Over 10 miles Population Density (GPW v4 Population Density)SUITABILITYRANGE/CLASS75101 - 15050151 - 20025201 - 3000301 and higher0No Data

For any questions, please contact Ian O’Leary at olearyi@michigan.gov, or reference the Renewable Energy Academy website to see how EGLE offers technical assistance for renewable energy.

The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Kellogg Biological Station (KBS) contains percent urban population measurements in percent units and were aggregated to a yearly timescale.

The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Kellogg Biological Station (KBS) contains human population density measurements in numberPerKilometerSquared units and were aggregated to a yearly timescale.