This dataset and map service provides information on the U.S. Housing and Urban Development's (HUD) low to moderate income areas. The term Low to Moderate Income, often referred to as low-mod, has a specific programmatic context within the Community Development Block Grant (CDBG) program. Over a 1, 2, or 3-year period, as selected by the grantee, not less than 70 percent of CDBG funds must be used for activities that benefit low- and moderate-income persons. HUD uses special tabulations of Census data to determine areas where at least 51% of households have incomes at or below 80% of the area median income (AMI). This dataset and map service contains the following layer.

This layer shows census tracts that meet the following definitions: Census tracts with median household incomes at or below 80 percent of the statewide median income or with median household incomes at or below the threshold designated as low income by the Department of Housing and Community Development’s list of state income limits adopted under Healthy and Safety Code section 50093 and/or Census tracts receiving the highest 25 percent of overall scores in CalEnviroScreen 4.0 or Census tracts lacking overall scores in CalEnviroScreen 4.0 due to data gaps, but receiving the highest 5 percent of CalEnviroScreen 4.0 cumulative population burden scores or Census tracts identified in the 2017 DAC designation as disadvantaged, regardless of their scores in CalEnviroScreen 4.0 or Lands under the control of federally recognized Tribes.

Listing of tax credits awarded by NYS Homes & Community Renewal’s Office of Finance and Development. Details include project identifier, developer name, project location, and project types.

California State Income Limits reflect updated median income and household income levels for acutely low-, extremely low-, very low-, low- and moderate-income households for California’s 58 counties (required by Health and Safety Code Section 50093). These income limits apply to State and local affordable housing programs statutorily linked to HUD income limits and differ from income limits applicable to other specific federal, State, or local programs.

This is a map to assist Department of Housing & Community Development staff determine if properties qualify for ARPA and repair funds.Targeted Rehab Boundaries Boundaries for the West Dallas Targeted Rehab Program (Census Tracts 106.01, 160.02, 105, 205, 101.01, 101.02, 43) and Tenth Street Rehab Program (Historic Tenth Street). Home repair programs available in these areas: Housing & Neighborhood Revitalization Targeted Rehabilitation Program (TRP) (dallascityhall.com) Unserved Areas Dallas Water Utilities (DWU) 's Unserved Areas Report identified geographical areas that need water and/or wastewater services throughout the City. DWU is in the process of building out service in these areas. (2020 update) Home repair programs available in these areas: Housing & Neighborhood Revitalization ARPA Septic Tank (dallascityhall.com) QCTs This service contains a list of census tracts that qualify for the American Rescue Plan Act (ARPA).  The list was provided to EGIS by BMS.  The data used to produce this service can be found at Qualified Census Tracts and Difficult Development Areas | HUD USER. Low-Income Housing Tax Credit Qualified Census Tracts must have 50 percent of households with incomes below 60 percent of the Area Median Gross Income (AMGI) or have a poverty rate of 25 percent or more. Difficult Development Areas (DDA) are areas with high land, construction and utility costs relative to the area median income and are based on Fair Market Rents, income limits, the 2010 census counts, and 5-year American Community Survey (ACS) data. Maps of Qualified Census Tracts and Difficult Development Areas are available at: 2022 and 2023 Small DDAs and QCTs | HUD USER. Qualified Census Tracts - Generate QCT Tables for Individual Areas (Also Includes DDA Information) This data was created by the Department of Housing and Urban Development in 2023.  This data is updated on a yearly basis.  Updated ARPA boundaries ARPA Home Repair Program boundaries for qualified neighborhoods. Home repair programs available in these areas: American Rescue Plan Act Neighborhood Revitalization Program (dallascityhall.com) (Limited availability, applications accepted based on funding available) Housing Opportunity Fund TIF DistrictsThis is the Housing Opportunity Fund TIF District map for Housing & Community Development and Economic Development in the City of Dallas. The three TIF districts in this map are areas within the City of Dallas with select TIF funds for homeowner stabilization programs that may include Home Improvement and Preservation Programs (HIPP) and the Dallas Homebuyer Assistance Program (DHAP). The three Housing Opportunity Fund TIF districts are: the Oak Cliff Housing TIF, the Fort Worth Avenue Housing TIF, and the Deep Ellum Housing TIF. Housing & Community Development is starting to implement these areas in 2025.

There are four files (two data and two data dictionaries), all in comma-delimited text format and a README.md file.

Human and machine readable replication dataset for "Housing Policies and Energy Efficiency Spillovers in Low- and Moderate-Income Communities" Omar I. Asensio, Olga Churkina, Becky Rafter, Kira E. O'Hare

This featured collection was created for use in EnviroAtlas: click here to open in the EnviroAtlas Interactive Map.

This featured collection is comprised of layers that cover broad topics relevant to assessing areas at-risk for environmental contamination, identifying vulnerable populations, and understanding important community characteristics. These national data, coming from both EnviroAtlas and external sources, have been curated based on Brownfields Program grant guidance. This collection provides a resource to assist brownfield grant applicants and awardees in presenting their stories and plans for redeveloping their local brownfields. Grant applicants should refer to the current year's guidance for grant funding.

In addition to national data, EnviroAtlas also provides very fine-scale data for selected communities, the option to view your own local data, and built-in tools that can help communities tell their stories: Learn more about EnviroAtlas resources for Brownfields. Use the EnviroAtlas Help to learn how to use available features, including adding your own data and using the Compare My Area tool, which generates reports with demographic variables and various health risks, allowing for comparing your area of interest to the surrounding county and state.

Here are some suggestions for how you might use the data in this collection:

Overlay demographic data on top of the Estimated Floodplains layer to determine what populations may be vulnerable to flooding. Add Dasymetric Population to more finely see where people live in the area of interest. Use the National Land Cover Database to identify land cover like developed areas with high impervious surface, which exacerbates urban heat and water runoff issues. Sites Reporting to EPA include the Brownfields Properties with EPA grants, Superfund sites, and more, which, if located in areas that flood, could present additional challenges for spreading contaminated materials. Data layers that present information about low-wage jobs, business vacancy, and residential populations with a low quality of life score, may indicate economically depressed areas and disadvantaged communities. A lack of farmers markets may indicate a lack of fresh food in the community that exacerbates existing health and economic burdens.

Data Layers in this Collection Data layers are grouped into four categories that relate back to grant guidance. View data individually or combine data from different categories. [SP] Sensitive and Disproportionately Impacted PopulationsThese data can help support your story by demonstrating community need. The presence of sensitive populations that are disproportionately impacted or overburdened is important when presenting your community's narrative.[SP] Percent low income workers (workplace location, Census block group) 2017[SP] Percent low income workers (home location, Census block group) 2017 [H] Adverse Health Conditions Connected to community need, these layers provide specific health-related data that can be used in your application and may be particularly useful if these health issues are a concern in your community. Also, the Compare My Area tool allows you to compare some of these health layers in your census tract to your county and state levels to present potential disparity near the brownfield.

[H] CDC Asthma Prevalence (Census tract, non-EnviroAtlas) 2017, 2018

[H] Respiratory risk (hazard index) due to cumulative air toxics (Census tract) NATA 2014 [H] Non-cancer neurological risk (hazard index) due to cumulative air toxics (Census tract) NATA 2014 [H] Cancer risk per million due to cumulative air toxics (Census tract) NATA 2014

[TC] Description of Target Area: Threats of Contamination These data can be used as part of a description of potential or known contamination that may exist in the area of interest.

[TC] EPA Underground Storage Tanks (non-EnviroAtlas) 2018 - 2021

[TC] Permitted Water Dischargers (Major; NPDES) Updated monthly [TC] Permitted Water Dischargers (NPDES) Updated monthly [TC] Air Quality System (AIRS AQS) Updated quarterly [TC] Integrated Compliance Information System-Air (ICIS-Air) Updated monthly [TC] Integrated Compliance Information System - Air Major (ICIS-Air Major) Updated monthly [TC] Toxic Release Inventory (TRI) Updated annually [TC] Superfund Sites (SEMS) Updated monthly [TC] Superfund Sites (NPL) Updated monthly [TC] Hazardous Waste Sites (RCRA; Inactive) Updated monthly [TC] Hazardous Waste Sites (RCRA; Active) Updated monthly [TC] Brownfields Properties (ACRES) Updated monthly [TC] Impaired waters 2015-2016

[CC] Description of Target Area: Community Characteristics These data provide useful characteristics about your community. This may take many forms. The dasymetric population layer will allow you to present where people live in relation to the brownfield and can be paired with floodplain data, land cover, or economic data to better demonstrate community need for a brownfield grant.

[CC] Housing density (units per acre, Census block group) 2014-2018 [CC] Percent Housing Units Built Before 1950 (Census block group) 2014-2018 [CC] Qualified Opportunity Zones (Census tract) 2018 [CC] Residential address vacancy rate for 2014 (Census tract) 2014 [CC] Business address vacancy rate for 2014 (Census tract) 2014 [CC] Percentage of households below the quality of life threshold income (Census block group) 2008-2012 [CC] Number of farmers markets (Census block group) 2016 [CC] FEMA Federally Designated Floodplains (non-EnviroAtlas) 2020 [CC] Estimated Floodplains 2016 [CC] National Land Cover Database (2019) 2019 [CC] Population density (Dasymetric allocation) 2010 [CC] State, County, Census tract, and Census block group boundaries 2010

Directions:

This featured collection is launched in Cleveland, OH. Navigate to any location by moving around in the map or enter your location of interest in the address search bar.

Turn layers on or off using the Layer List on the right of the interactive map. View layers in the legend by selecting the star icon at the top of the Layer List.

Use built-in analysis tools such as Compare my Area for additional information about your community. These tools are accessed from widgets at the top left side of the map.

To add fine-scale community data for any one of the 30 EnviroAtlas communities, use the community selection widget (located in the upper left corner of the map) to select a community and calculate the legend based on the values for that community only. Combined Communities will calculate the legend based on the values for all EnviroAtlas communities. Community data is denoted with a 'C' icon in the EnviroAtlas Data tab.

The Climate Leadership and Community Protection Act (CLCPA) directs the Climate Justice Working Group (CJWG) to establish criteria for defining disadvantaged communities. This dataset identifies areas throughout the State that meet the final disadvantaged community definition as voted on by the Climate Justice Working Group.

The New York State Energy Research and Development Authority (NYSERDA) offers objective information and analysis, innovative programs, technical expertise, and support to help New Yorkers increase energy efficiency, save money, use renewable energy, accelerate economic growth, and reduce reliance on fossil fuels. To learn more about NYSERDA’s programs, visit nyserda.ny.gov or follow us on X, Facebook, YouTube, or Instagram.

This layer is an archive of Version 1.0 of the CEJST data as a fully functional GIS layer. See an archive of the latest version of the CEJST tool using Version 2.0 of the data released in December 2024 here.This layer assesses and identifies communities that are disadvantaged according to updated Justice40 Initiative criteria. Census tracts in the U.S. and its territories that meet the Version 1.0 criteria are shaded in semi-transparent blue colors to work with a variety of basemaps. See this web map for use in your dashboards, story maps, and apps.Details of the assessment are provided in the popup for every census tract in the United States and its territories American Samoa, Guam, the Northern Mariana Islands, Puerto Rico, and the U.S. Virgin Islands. This map uses 2010 census tracts from Version 1.0 of the source data downloaded November 22, 2022.If you have been using a previous version of the Justice40 data, please know that this Version 1.0 differs in many ways. See the updated Justice40 Initiative criteria for current specifics. Use this layer to help plan for grant applications, to perform spatial analysis, and to create informative dashboards and web applications. See this blog post for more information.From the source:This data "highlights disadvantaged census tracts across all 50 states, the District of Columbia, and the U.S. territories. Communities are considered disadvantaged:If they are in census tracts that meet the thresholds for at least one of the tool’s categories of burden, orIf they are on land within the boundaries of Federally Recognized TribesCategories of BurdensThe tool uses datasets as indicators of burdens. The burdens are organized into categories. A community is highlighted as disadvantaged on the CEJST map if it is in a census tract that is (1) at or above the threshold for one or more environmental, climate, or other burdens, and (2) at or above the threshold for an associated socioeconomic burden.In addition, a census tract that is completely surrounded by disadvantaged communities and is at or above the 50% percentile for low income is also considered disadvantaged.Census tracts are small units of geography. Census tract boundaries for statistical areas are determined by the U.S. Census Bureau once every ten years. The tool utilizes the census tract boundaries from 2010. This was chosen because many of the data sources in the tool currently use the 2010 census boundaries."PurposeThe goal of the Justice40 Initiative is to provide 40 percent of the overall benefits of certain Federal investments in [eight] key areas to disadvantaged communities. These [eight] key areas are: climate change, clean energy and energy efficiency, clean transit, affordable and sustainable housing, training and workforce development, the remediation and reduction of legacy pollution, [health burdens] and the development of critical clean water infrastructure." Source: Climate and Economic Justice Screening tool"Sec. 219. Policy. To secure an equitable economic future, the United States must ensure that environmental and economic justice are key considerations in how we govern. That means investing and building a clean energy economy that creates well‑paying union jobs, turning disadvantaged communities — historically marginalized and overburdened — into healthy, thriving communities, and undertaking robust actions to mitigate climate change while preparing for the impacts of climate change across rural, urban, and Tribal areas. Agencies shall make achieving environmental justice part of their missions by developing programs, policies, and activities to address the disproportionately high and adverse human health, environmental, climate-related and other cumulative impacts on disadvantaged communities, as well as the accompanying economic challenges of such impacts. It is therefore the policy of my Administration to secure environmental justice and spur economic opportunity for disadvantaged communities that have been historically marginalized and overburdened by pollution and underinvestment in housing, transportation, water and wastewater infrastructure, and health care." Source: Executive Order on Tackling the Climate Crisis at Home and AbroadUse of this Data"The pilot identifies 21 priority programs to immediately begin enhancing benefits for disadvantaged communities. These priority programs will provide a blueprint for other agencies to help inform their work to implement the Justice40 Initiative across government." Source: The Path to Achieving Justice 40The layer has some transparency applied to allow it to work sufficiently well on top of many basemaps. For optimum map display where streets and labels are clearly shown on top of this layer, try one of the Human Geography basemaps and set transparency to 0%, as is done in this example web map.Browse the DataView the Data tab in the top right of this page to browse the data in a table and view the metadata available for each field, including field name, field alias, and a field description explaining what the field represents.Symbology updated 2/19/2023 to show additional tracts whose overlap with tribal lands is greater than 0% but less than 1%, to be designated as "Partially Disadvantaged" alongside tracts whose overlap with tribal lands is 1% or more.

Metro created the EFC designation to help us identify where transportation needs are greatest. Since its creation in 2019, the EFC Map considers the concentration of three characteristics:Low-income households;Black, Indigenous, and other People of Color (BIPOC) residents; andHouseholds with no access to a car.In 2022, Metro updated the EFC Map by creating the Metro Equity Need Index (MENI). The MENI is an analysis that allows for a more nuanced understanding of equity needs across the county. It includes five tiers of equity need (Very High Need, High Need, Moderate Need, Low Need, and Very Low Need). Within this index, only the top two tiers ("High Need" and "Very High Need") are designated as EFCs. The MENI and 2022 EFC Map are used by Metro for new analyses and prioritization as of July 1, 2022.The Office of Equity and Race (OER) provided updates to the Executive Management Committee of the Metro Board of Directors in May (File # 2022-0275) and August (File #2022-0489) of 2022. This feature layer displays only the census tracts designated as EFCs in the MENI. For the feature layer corresponding to the MENI, with all census tracts in the county, please click on this link or search for "Metro Equity Need Index (MENI) 2022."Please note that Metro issued the following data corrections/updates:In August 2023 to account for a miscalculation in the total number of low-income householdsIn November 2023 to include city/community associations for census tractsIn February 2024 to include percentile scores that were used to identify Equity Need TiersChanges to field names are included in the Supporting Documentation Data Dictionary.For more information regarding Equity Focus Communities (EFCs), see the EFC FAQ Documentation.Please note this feature layer displays the census tracts designated as EFCs in 2022. To view the census tracts designated as EFCs as of 2025, please click on this link or search for "Metro 2025 EFC Map" in Metro's AGOL.Questions? Contact OER at EquityandRace@metro.net.

Data was collected over the course of five trips throughout Orange County, California, between November 2021 and February 2022, yielding 420 photographs. Areas of focus were determined by utilizing the 2019 Family Financial Stability Index (FFSI; Parsons et al.), a multivariate metric developed for Orange County United Way to measure the financial stability of families with children under 18. Each trip, researchers navigated to financial services providers in neighborhoods of low family financial stability. In addition to photographing these providers, researchers drove block-by-block through the area and documented traditional and fringe financial advertisements found on telephone poles, billboards, bus shelters, and the like. Photographs were only taken in public spaces of material in plain view.

Background: After a COVID-19 diagnosis, vulnerable populations face considerable logistical and financial challenges to isolate and quarantine. We developed and evaluated a novel, community-based approach (‘Test-to-Care’ Model) designed to address these barriers for socioeconomically vulnerable Latinx individuals with newly diagnosed COVID-19 and their households.

Methods: This three-week demonstration project was nested within an epidemiologic surveillance study in a primarily Latinx neighborhood in the Mission district of San Francisco, California. The Test-to-Care model was developed with input from community members and public health leaders. Key components included: (1) provision of COVID-19-related education and information about available community resources, (2) home deliveries of material goods to facilitate safe isolation and quarantine (groceries, personal protective equipment and cleaning supplies), and (3) longitudinal clinical and social support. Newly SARS-CoV-2 PCR-po...

Method of Data Collection

Survey method of data collection was conducted by the World Bank in the collection of data from 3976 household in the rural communities in Kebbi, Nigeria. 4 variables were extracted from the original dataset to form a new dataset (Kebbi.csv).Below are the description of the extracted variables.

Method of Data Analysis

Different analytical models were applied to analyze the variables using R Programming. The frequencies and the mean of each variable were determined respectively by histogram and boxplot. The null hypothesis (H0)(H0) was tested using One-Way Analysis of Variance (ANOVA) and will be rejected if (p<0.05)(p<0.05).

The Home Owners' Loan Corporation (HOLC) was a U.S. government-sponsored program initiated in the 1930s to evaluate mortgage lending risk. The program resulted in hand-drawn 'security risk' maps intended to grade sections of cities where investment should be focused (greenlined areas) or limited (redlined zones). The security maps have since been widely criticized as being inherently racist and have been associated with high levels of segregation and lower levels of green amenities in cities across the country. Our goal was to explore the potential legacy effects of the HOLC grading practice on birds, their habitat, and the people who may experience them throughout a metropolis where the security risk maps were widely applied, Greater Los Angeles, California (L.A.). We used ground-collected, remotely sensed, and census data and descriptive and predictive modeling approaches to address our goal. Patterns of bird habitat and avian communities strongly aligned with the luxury-effect phenomenon, where green amenities were more robust, and bird communities were more diverse and abundant in the wealthiest parts of L.A. Our analysis also revealed potential legacy effects from the HOLC grading practice. Associations between bird habitat features and avian communities in redlined and greenlined zones were generally stronger than in areas of L.A. that did not experience the HOLC grading, in part because redlined zones, which included some of the poorest locations of L.A., had the highest levels of dense urban conditions, e.g., impervious surface cover. In contrast, greenlined zones, which included some of the city's wealthiest areas, had the highest levels of green amenities, e.g., tree canopy cover. The White population of L.A., which constitutes the highest percentage of a racial or ethnic group in greenlined areas, was aligned with a considerably greater abundance of birds affiliated with natural habitat features (e.g., trees and shrubs). Conversely, the Hispanic or Latino population, which is dominant in redlined zones, was positively related to a significantly greater abundance of synanthropic birds, which are species associated with dense urban conditions. Our results suggest that historical redlining and contemporary patterns of income inequality are associated with distinct avifaunal communities and their habitat, which potentially influence the human experience of these components of biodiversity throughout L.A. Redlined zones and low-income residential areas that were not graded by the HOLC can particularly benefit from deliberate urban greening and habitat enhancement projects, which would likely carry over to benefit birds and humans.

Few studies have considered the role of immigration in the rise of gentrification in the late twentieth century. Analysis of U.S. Census and American Community Survey data over 24 years and field surveys of gentrification in low-income neighborhoods across 23 U.S. cities reveal that most gentrifying neighborhoods were “ global” in the 1970s or became so over time. An early presence of Asians was positively associated with gentrification; and an early presence of Hispanics was positively associated with gentrification in neighborhoods with substantial shares of blacks and negatively associated with gentrification in cities with high Hispanic growth, where ethnic enclaves were more likely to form. Low-income, predominantly black neighborhoods and neighborhoods that became Asian and Hispanic destinations remained ungentrified despite the growth of gentrification during the late twentieth century. The findings suggest that the rise of immigration after 1965 brought pioneers to many low-income central-city neighborhoods, spurring gentrification in some neighborhoods and forming ethnic enclaves in others.

The DC Housing Authority provides quality affordable housing to extremely low- through moderate-income households, fosters sustainable communities, and cultivates opportunities for residents to improve their lives. The following is a subset of the District Government Land (Owned, Operated, and or managed) dataset that include buildings with a "public housing" use type.

This dataset is from a study focuses on the Minneapolis Park and Recreation Board’s planning process for a new park at the Upper Harbor Terminal (UHT) site - a defunct barge-to-rail terminal on the Mississippi River being redeveloped with a mix of housing, commercial uses, and park space (see "UHT_location_map in Other Entities). While the redevelopment affords an opportunity to remediate pollution and provide new greenspace, a high portion of nearby residents are low-income and housing-cost burdened, raising concerns among community members about gentrification. As a counter to green gentrification, the concept of “Just Green Enough” (JGE) became a common theme around the UHT development process. JGE aims to center community-centered greening efforts with broader community development goals in mind (i.e. living-wage jobs and affordable housing). Amidst growing community pressure, the Minneapolis Park and Recreation Board (MPRB) appointed a Community Advisory Committee (CAC). The CAC was tasked with meeting monthly for in-depth deliberations (facilitated by planning staff and external consultants) with the goal of making final park design and programming recommendations, which planning staff would present to the MPRB Board of Commissioners. The Upper Harbor Terminal CAC consisted of 16 members, mostly residents of North Minneapolis and Northeast, and many with a background in nonprofit, environmental, or community organizing work. Meetings began in July 2019 and lasted nearly two years until in May 2021. This dataset includes the qualitative codebooks for the CAC meetings, accompanied by the meeting minutes and other documents related to the UHT planning process. The transcripts of semi-structured interviews with stakeholders in the UHT planning process were also analyzed and coded, but specific quotations are omitted from this dataset to protect the privacy the participants.

Cities can have profound impacts on ecosystems, yet our understanding of these impacts is currently limited. First, the effects of socioeconomic dimensions of human society are often overlooked. Second, correlative analyses are common, limiting our causal understanding of mechanisms. Third, most research has focused on terrestrial systems, ignoring aquatic systems that also provide important ecosystem services. Here we compare the effects of human population density and low-income prevalence on the macroinvertebrate communities and ecosystem processes within water-filled artificial tree holes. We hypothesized that these human demographic variables would affect tree holes in different ways via changes in temperature, water nutrients, and the local tree hole environment. We recruited community scientists across Greater Vancouver (Canada) to provide host trees and tend 50 tree holes over 14 weeks of colonization. We quantified tree hole ecosystems in terms of aquatic invertebrates, litter decomposition, and chlorophyll-a. We compiled potential explanatory variables from field measurements, satellite images, or census databases. Using structural equation models, we showed that invertebrate abundance was affected by low-income prevalence but not human population density. This was driven by cosmopolitan species of Ceratopogonidae (Diptera) with known associations to anthropogenic containers. Invertebrate diversity and abundance were also affected by environmental factors, such as temperature, elevation, water nutrients, litter quantity, and exposure. By contrast, invertebrate biomass, chlorophyll-a, and litter decomposition were not affected by any measured variables. In summary, this study shows that some urban ecosystems can be largely unaffected by human population density. Our study also demonstrates the potential of using artificial tree holes as a standardized, replicated habitat for studying urbanization. Finally, by combining community science and urban ecology, we were able to involve our local community in this pandemic research pivot. This abstract is quoted from the original article "Insects in the city: Determinants of a contained aquatic microecosystem across an urbanized landscape" in Ecology (2023) by DS Srivastava et al. Methods These methods are quoted in abbreviated form from the original article [please also see README.md file for details on each script and data file, including description of every variable]: We installed 73 artificial tree holes (hereafter tree holes) throughout Greater Vancouver, specifically the cities of Vancouver, Abbottsford, Burnaby, Chilliwack, Delta, Maple Ridge, New Westminster, North Vancouver, Port Moody, Richmond, Surrey, and West Vancouver. We constructed artificial tree holes from black plastic buckets (950 ml, height: 12.2cm, diameter:11.5cm). Near the rim, we drilled 1-cm holes for water overflow and covered these with 1mm mesh to prevent loss of insects and litter (Figure 2a). We attached each tree hole to a deciduous tree with a cable tie, about 1.3 m above ground, before adding leaf litter and bottled spring water. The leaf litter consisted of dried (60°C for two days) and pre-weighed Acer macrophyllum (Sapindaceae) leaves collected in November 2020, both loose (2.50 g) and in a 0.5 mm mesh leaf bag (0.200 g). We filled each tree hole with ~750 ml spring water (Western FamilyTM). Community scientists were instructed to monitor water level in the tree holes during the experiment, topping up tree holes when they became half-empty with extra bottles of water (same brand) that we provided. We also added an iButtonTM temperature logger (Maxim Integrated, San Jose, CA, USA; models DS1921G, DS1921Z, and DS1922L) wrapped in ParafilmTM (Beemis Company, Neenah, WI, USA) and programmed it to collect data every hour for 85 days. We added a small stick to assist ovipositing insects to perch or pupating insects to emerge. We installed all tree holes 21–28 March 2021. We visited all tree holes 17–30 May 2021, to collect data on water chemistry (pH, chlorophyll-a concentration), light availability (canopy cover), potential oviposition cues (host tree diameter, nearby standing water), and potential source populations (distance to water bodies). We measured water pH directly using a calibrated OaktonⓇ pH 450 pH meter. To estimate chlorophyll-a concentration, we extracted 25 mL of water, filtered it through a glass microfiber (0.7 μm) filter, and froze the filters. In the lab, we extracted chlorophyll-a on filters with 90%-acetone. We used a Trilogy Laboratory Fluorometer (Turner Designs, San Jose, CA, USA) to determine chlorophyll-a concentration following Wasmund et al. (2006). To measure canopy cover, we took a photograph directly up by placing a smartphone flat on the tree hole and then used ImageJTM to differentiate open sky from any obstructing cover. We searched within 30m of tree holes for sources of persistent standing water, such as buckets, birdbaths, and tires holding >400 mL water. We retrieved tree holes 1–10 July 2021, in the same order as installation, standardizing the experimental duration to 14 weeks (± 4 days). Once in the lab, we measured water pH as before, turbidity with a portable Turner AquaflorⓇ fluorometer, and froze a 5mL volume of water for later nutrient analysis. To analyse nutrients NO2-, NO3-, NH4+, and PO4-3, we loaded water samples onto 96-well plates with standards corresponding to the nutrient of interest. We then added the relevant reagents to all wells and compared the absorbance of the samples to standards using a SpectraMax M2e spectrophotometer (Molecular Devices, San Jose, CA, USA). We averaged two measurements per sample. As NO2-, NO3-, and NH4+ represent three steps of the dissolved inorganic nitrogen (DIN) cycle, we summed their concentrations in a single measure of DIN. We retrieved the remaining leaf fragments in litter bags with tweezers, washing biofilm from them before drying (two days at 60°C) and determining their combined mass. Decomposition was quantified as the percent dry mass lost. We also collected all loose debris in tree holes manually and by filtering through a pre-weighted Fisherbrand™ Fluted Qualitative Circled Filter Paper before drying and determining dry mass. We recorded the total volume of water present in each tree hole. Finally, we searched tree hole contents for macroscopic (>1mm) invertebrates in small aliquots in white trays. We sorted invertebrates into morphospecies, preserved them in 70% ethanol, and later identified them to family or genus level with identification keys. We used allometric equations to estimate dry body mass of invertebrates from body length (mm), either at the individual (species > 10 mm) or species level (hellometry R package, P. Rogy). We preserved a few voucher specimens in 95% ethanol for DNA barcoding to unambiguously assign species identities. For DNA extraction, we used QIAGEN® DNeasy Blood & Tissue Kit. We amplified the barcoding region of the mitochondrial Cytochrome Oxidase I (COI) gene with the universal primers LCO 1490 and HCO 2198 (Folmer et al. 1994). PCR products were sequenced by Psomagen, Inc. The chromatograms were assembled with Geneious Prime® v. 2022.2.2, and the resulting sequences were compared with GenBank and BOLD databases.