Field notes related to on-water acoustic and diver surveys for reef fish spawning aggregations in the FL Keys, 2009-2014

This dataset represents the locations of licenced and permitted pits and quarries regulated by the Ministry of Natural Resources and Forestry under the Aggregate Resources Act, R.S.O. 1990.

Aggregate site data has been divided into active and inactive sites. Active sites may be further subdivided into partial surrenders. In partial surrenders, defined areas of a site are inactive while the rest of the site remains active.

The data includes:

• site location and size
• licensee name
• approval type (licence or permit)
• operation type (pit or quarry)
• maximum annual tonnage limit
• the MNRF district responsible for the site

Use our interactive pits and quarries map to find active sites.

This data does not include aggregate sites regulated by the Ministry of Transportation.

General information on the variations between the sites.

This repository contains data for the paper: Oudman et al. 2018. Resource landscapes explain contrasting patterns of aggregation and site fidelity by red knots at two wintering sites. Movement Ecology 6(14) 1-12. https://doi.org/10.1186/s40462-018-0142-4.

Please cite the original publication when using this data.

Points depict aggregate sources that have been tested by the WSDOT State Materials Laboratory and are created from data in the Aggregate Sources Approval database (ASA). The Aggregate Sources Approval database identifies aggregate sources that have been tested by WSDOT, and have been assigned a county letter code with a sequential number for that county. It should be noted that there are sources that have been tested, but not classified with a county code, and are therefore not included in the database. Also, there are sources in the database that are not currently approved for use as materials on construction projects.

This pie chart displays sites per category using the aggregation count in the United States. The data is about sites.

This pie chart displays sites per site using the aggregation count in China. The data is about sites.

Comprehensive dataset containing 100 verified Aggregate Industries locations in United States with complete contact information, ratings, reviews, and location data.

This pie chart displays sites per country using the aggregation count in Georgia. The data is about sites.

This pie chart displays sites per site using the aggregation count in Mexico. The data is about sites.

This dataset provides details on the location of MTO aggregate pits. Aggregate pits provide the material necessary to build roadways in the province. Official LIO title: Aggregate Sites MTO

Status

Completed: Production of the data has been completed

Maintenance and Update Frequency

Annually: Data is updated every year

Contact

Deb McIlwrath, Northwestern Region, Debbie.McIlwrath@ontario.ca

The dataset on aggregate extractions in the European seas was created in 2014 by AZTI for the European Marine Observation and Data Network (EMODnet). It is the result of the aggregation and harmonization of datasets provided by several sources from all across the EU. It is available for viewing and download on EMODnet web portal (Human Activities, https://emodnet.ec.europa.eu/en/human-activities). The dataset contains points representing aggregate extraction sites, by year (although some data are indicated by a period of years), in the following countries: Belgium, Denmark, Finland, France, Germany, Ireland, Italy, Lithuania, Poland, Portugal, Spain, Sweden, The Netherlands and United Kingdom. Where available, each point has the following attributes: Id (Identifier), Position Info (e.g.: Estimated, Original, Polygon centroid of dredging area, Estimated polygon centroid of dredging area), Country, Sea basin, Sea, Name of the extraction area, Area of activity (km2), Year (the year when the extraction took place; when a time period is available, the first year of the period is indicated), Permitted Amount (m3) (permitted amount of material to be extracted, in m3), Permitted Amount (t) (permitted amount of material to be extracted, in tonnes), Requested Amount (m3) (requested amount of material to be extracted, in m3), Requested Amount (t) (requested amount of material to be extracted, in tonnes), Extracted Amount (m3) (extracted amount of material, in m3), Extracted Amount (t) (extracted amount of material, in tonnes), Extraction Type (Marine sediment extraction), Purpose (e.g.: Commercial, Others, N/A), End Use (e.g.: Beach nourishment, Construction, Reclamation fill, N/A), Material type (e.g.: sand, gravel, maerl), Notes, Link to Web Sources. In 2018, a feature on areas for aggregate extractions was included. It contains polygons representing areas of seabed licensed for exploration or extraction of aggregates, in the following countries: Belgium, Denmark, Estonia, Finland, France, Germany, Italy, Lithuania, Poland, Portugal, Russia, Spain, Sweden, The Netherlands and United Kingdom. Where available, each polygon has the following attributes: Id (Identifier), Area code, Area name, Country, Sea basin, Sea, Starting year (the year when the license starts), End year (the year when the license ends), Site Type (exploration area, extraction area, extraction area (in use)), License status (Active, not active, expired, unknown), Material type (e.g.: sand, gravel, maerl), Notes, Distance to coast (in metres), Link to Web Sources. In the 2023 update, extraction data until 2022 and new areas have been included.

At Echo, our dedication to data curation is unmatched; we focus on providing our clients with an in-depth picture of a physical location based on activity in and around a point of interest over time. Our dataset empowers you to explore the “what” by allowing you to dig deeper into customer movement behaviors, eliminate gaps in your trade area and discover untapped potential. Leverage Echo's Activity datasets to identify new growth opportunities and gain a competitive advantage.

This sample of our Area Activity data provides you insights into the estimated total unique visitors and visits in an area. This helps you understand frequentation dynamics over time, identify emerging trends in people movements and measure the impact of external factors on how people move across a city.

Additional Information: - Understand the actual movement patterns of consumers without using PII data, gaining a 360-degree consumer view. Complement your online behavior knowledge with actual offline actions, and better attribute intent based on real-world behaviors. - Echo collects, cleans and updates its footfall on a daily basis. Normalization of the data occurs on a monthly basis. - We provide data aggregation on a weekly, monthly and quarterly basis. - Information about our country offering and data schema can be found here:

1) Data Schema: https://docs.echo-analytics.com/activity/data-schema
2) Country Availability: https://docs.echo-analytics.com/activity/country-coverage
3) Methodology: https://docs.echo-analytics.com/activity/methodology

Echo's commitment to customer service is evident in our exceptional data quality and dedicated team, providing 360° support throughout your location intelligence journey. We handle the complex tasks to deliver analysis-ready datasets to you.

Business Needs: 1. Site Selection: Leverage footfall data to identify the best location to open a new store. By analyzing areas with high footfall you can select sites that are likely to attract more customers. 2. Urban Planning Development: City planners can use footfall data to optimize the layout and infrastructure of urban areas, guide the development of commercial areas by indicating where pedestrian traffic is heaviest, and aid in traffic management and safety measures. 3. Real Estate Investment: Leverage footfall data to identify lucrative investment opportunities and optimize property management by analyzing pedestrian traffic patterns.

A. SUMMARY This dataset includes aggregate data on the type, status, population served, and individuals placed at each alternative housing site under contract with HSA. B. HOW THE DATASET IS CREATED Site Type, Status, and Population The HSA DOC leadership inform the data tracker owner when the legal status, site type, or intended population to serve changes. Daily Census and Units Available The site monitors at each site inform the data tracker owner at the HSA DOC at least once daily with the updates to the daily census. C. UPDATE PROCESS Updated several times daily, whenever new information is shared with the data tracker owner. The data tracker owner inputs the data directly into the underlying SharePoint spreadsheet. D. HOW TO USE THIS DATASET Use the data for aggregate data on the site type, status, and daily census of individuals placed in the sites. Do not use this spreadsheet for individual-level information. There is no personally identifying or medical information in this dataset.

Human disturbances can prompt natural antipredator behaviours in animals, affecting how energy is traded-off between immediate survival and reproduction. In our study of male squaretail groupers (Plectropomus areolatus) in India's Lakshadweep archipelago, we investigated the impact of fishing pressure on anti-predatory responses and reproductive behaviours by comparing a fished and unfished spawning aggregation site and tracking responses over time at the fished site. Using observational sampling and predator exposure experiments, we analysed fear responses (flight initiation distance, return time), as well as time spent in vigilance, courtship, and territorial defence. Unpaired males at fished sites were twice as likely to flee from simulated predators and took longer to return to mating territories. Contrastingly, males in the presence of females at both sites took greater risks during courtship, fleeing later than unpaired males, but paired males at the unfished site returned earlier. Our findings suggest that high fishing pressure reduces reproductive opportunities by increasing vigilance and compromising territorial defence, potentially affecting mate selection cues. Altered behavioural trade-offs may mitigate short-term capture risk but endanger long-term population survival through altered reproductive investment. Human extractive practices targeting reproductive aggregations can have disruptive effects beyond direct removal, influencing behaviours crucial for population survival. Methods Refer to paper and Supplementary Materials (ESM) Methods 2.1.Field sites Data were collected from a ‘fished’ squaretail grouper spawning aggregation site (Bitra) during the new moon phase in January and from an ‘unfished’ site (Site 2) in February 2023 and 2024, in the Lakshadweep archipelago (details in electronic supplementary material 1, site characteristics). Historical data wasere also used from the fished site from January 2013 and 2014 (i.e., when it was in an ‘’unfished’ state, [20]). Sampling, site and aggregation characteristics are summariszed in table 1. Two prominent modes of active in-water reef fishing were observed at the fished site: hook-and-line and spearfishing, both involveing fishers free-diving in the water (RK, personal observation, details in ESM, electronic supplementary material 1). Anti-predator behavioural responses have been demonstrated in response to both fishing scenarios [16,17,21]. 2.2.Squaretail grouper spawning aggregations Squaretail groupers aggregate at both study sites for 5–6 days around the new moon from December to March [20]. Male groupers arrive 2–3 days earlier, establishing temporary territories (5–15 m2) that are vigorously defended from neighbours. Females typically join a day before the new moon and stay for 2–3 days after. The mating system is described as a ‘'lek’' [20]. Given the males’' stationary behaviour and distinct body markings facilitating easy identification, our study focussed on analysing male reproductive behaviours. 2.3.Population surveys The total aggregation area, including the ‘'core’' lekking zone, was marked during snorkel surveys (refer to [20] and electronic supplementary material 1, for methods). Groupers were surveyed within this core area by two observers (RK, IMK) using underwater visual sampling on SCUBA along permanent 50 mx× 10 m belt transects. The number of transects varied based on the core area size (n = 5 fished site, n = 7 unfished site). Abundance estimates per transect were used to calculate average site-level population density (grouper.500 m−2). Observers also recorded fish lengths (5 -cm bins) and sex in situin situ [20]. Habitat characteristics such as structural complexity and percentage of live coral cover were assessed along three 50 -metre transects at the sites (details in electronic supplementary material 2). 2.4.Behavioural observations Males were ‘paired’ with a female, if females were observed within male territories during focal follows and if displaying ‘'mate-guarding’' behaviours, such as maintaining close contact with females or hovering nearby (electronic supplementary material 2: table S1). (i) Reactive response: Reactive responses were measured at the fished and unfished sites during surveys done in 2023–2024. To determine the reactive fear response in territorial male groupers, we used ‘Flight Initiation Distance (FID)’, i.e., the distance at which the focal animal moves to avoid an approaching human observer [22]. FID is commonly used as an indicator to test for fish wariness in response to fishing [16]. We simulated predation risk by horizontally approaching, opportunistically selected territorial males (n = 72) on SCUBA at a steady pace from a standardiszed distance of five5 metres [23]. The approaching observer dropped a marker when the male began to move away and the linear distance between the grouper position (prior to movement), and the marker was measured by a second observer with a tape measure. Time away from territory is known to positively influence territory intrusion in spawning territorial damselfish [24]. Therefore, we retreated five5 metres from the male territory, immediately after the simulated disturbance to record how quickly an individual returns to its territory. In a pilot assessment of 20 males, over 70% of males returned within a minute, suggesting that immediate responses were critical for territory holders. We therefore used 120 secondsec as a cut-off time in our extended dataset. (ii) Proactive response: Focal video sampling was used to estimate timed-activity budgets, comparing behaviour among the fished and unfished sites in 2023–2024, and within the fished site in 2013–2014 &and 2023–2024. Observations were conducted on 108 territorial males over three days at each site, encompassing a day before, during, and after the new moon. Male behaviours were categoriszed into three states: ‘'courtship’' (male-–female interactions), ‘'territorial aggression’' (male-–male interactions), and ‘'vigilance’' (electronic supplementary material 2: table S1). The proportion of time spent in each activity was calculated by dividing the total time in different states (seconds) by the total video length (seconds). To determine whether fishing rather than site-level population and environmental factors drove time activity budgets, we collected information on focal male body size (cm) and the number of males and females in an area of 25 m2 (i.e., competitor and mate density) from the centre of focal male territories. Habitat condition (live coral cover) and habitat structure (structural complexity) were ranked on a scale of 0–5 for each territory (electronic supplementary material 2,: table S2), owing to their influence on anti-predator behaviours like shelter-seeking [25,26] and predator avoidance [27]. Context data were not available for the historical dataset (2013-–2014), which was excluded from the analysis of drivers. Statistical analysis (i) Reactive response: We used bootstrap resampling (R = 1000) to generate 95% confidence intervals to compare mean Flight Initiation Distance (FID) and territory return times between paired and unpaired males at fished and unfished sites. Non-overlapping confidence intervals indicated significant differences [28]. Proactive response: We also employed bootstrap resampling to compare activity budgets between paired and unpaired males at fished and unfished sites and within the fished site (2013–2014 pre-fishing and 2023–2024 fishing scenario). Ternary plots illustrated trade-offs between courtship, aggression, and vigilance. (ii) Drivers of activity budgets: Bayesian Zero-and-One Inflated Dirichlet (zoid) regressions [29] were used to model the influence of fishing status, habitat condition, male size, and social context on time activity budgets. The analysis involved 70 individuals surveyed in 2023–2024. All analysis was conducted in R (electronic supplementary material 3,: Table 4table S4).

We are no longer updating this data. It is best suited for historical research and analysis.

This spatial dataset represents the locations of aggregate pits used to build roads for forestry purposes that may also provide recreational access to forests in Ontario.

Additional Documentation

  Aggregate category 14 site - Data Description (PDF)
  Aggregate category 14 site - Documentation (Word)

Status

Obsolete: data is no longer relevant

Maintenance and Update Frequency

Not planned: there are no plans to update the data

Contact

Ryan Lenethen, Integration Branch, ryan.lenethen@ontario.ca

This dataset is subject to licensing and approvals. Approval may be requested by contacting ryan.lenethen@ontario.ca. To request access to restricted use data, email the dataset contact or Information Access Analyst at stephanie.whyley@ontario.ca.

The data referenced here is licensed Electronic Intellectual Property of the Ontario Ministry of Natural Resources and Forestry and is provided for professional, non-commercial use only.

The purpose of this project was to conduct an evaluation of the impact on crime of the closing, renovation, and subsequent reopening of selected public housing developments under the United States Department of Housing and Urban Development's (HUD) Housing Opportunities for People Everywhere (HOPE VI) initiative. The study examined crime displacement and potential diffusion of benefits in and around five public housing developments that, since 2000, had been redeveloped using funds from HUD's HOPE VI initiative and other sources. In Milwaukee, Wisconsin, three sites were selected for inclusion in the study. However, due to substantial overlap between the various target sites and displacement zones, the research team ultimately decided to aggregate the three sites into a single target area. A comparison area was then chosen based on recommendations from the Housing Authority of the City of Milwaukee (HACM). In Washington, DC, two HOPE VI sites were selected for inclusion in the study. Based on recommendations from the District of Columbia Housing Authority (DCHA), the research team selected a comparison site for each of the two target areas. Displacement areas were then drawn as concentric rings ("buffers") around the target areas in both Milwaukee, Wisconsin and Washington, DC. Address-level incident data were collected for the city of Milwaukee from the Milwaukee Police Department for the period January 2002 through February 2010. Incident data included all "Group A" offenses as classified under National Incident Based Reporting System (NIBRS). The research team classified the offenses into personal and property offenses. The offenses were aggregated into monthly counts, yielding 98 months of data (Part 1: Milwaukee, Wisconsin Data). Address-level data were also collected for Washington, DC from the Metropolitan Police Department for the time period January 2000 through September 2009. Incident data included all Part I offenses as classified under the Uniform Crime Report (UCR) system. The data were classified by researchers into personal and property offenses and aggregated by month, yielding 117 months of data (Part 2: Washington, DC Data). Part 1 contains 15 variables, while Part 2 contains a total of 27 variables. Both datasets include variables on the number of personal offenses reported per month, the number of property offenses reported per month, and the total number of incidents reported per month for each target site, buffer zone area (1000 feet or 2000 feet), and comparison site. Month and year indicators are also included in each dataset.

Data from the paper:

Dalagnol, R. et al. Large-scale variations in the dynamics of Amazon forest canopy gaps from airborne lidar data and opportunities for tree mortality estimates. Sci Rep 11, 1388 (2021). https://doi.org/10.1038/s41598-020-80809-w

Link: https://www.nature.com/articles/s41598-020-80809-w

This repository contains:

1) Data frame with data from static and dynamic gaps used in Figure 2 (Dalagnol_2020_Data_Multitemporal_gaps.csv). Each row is the aggregated measurement at 5-km resolution. The site component referes to the five site studied with multitemporal data. Site order from 1 to 5 is DUC, TAP, FN1, BON and TAL.

2) Data frame with data from static gaps and environmental factors used in Table 1, Figure 3, 4, 5 (Dalagnol_2020_Data_Singledate_gaps_Modeling.csv). Each row is the aggregated measurement of one site observed by airborne lidar data.

3) Raster file at 5-km resolution with dynamic gap fraction estimates presented in Figure 5 (dynamic_gap_fraction_amazon.tif).

If you need anything else, please contact the corresponding author: Ricardo Dalagnol (ricds@hotmail.com).

Bed bugs live in an ecologically and spatially restricted indoor habitat comprised of overlapping aggregation and host odors, and they traverse relatively short distances between blood-hosts and aggregation sites. Although many studies demonstrated aggregation or host odor preference respectively, the modulation of their preferences between these divergent odors is poorly understood. Given the recurrent transitions of bed bugs between replete and hungry states, we evaluated the effects of six odorscapes containing aggregation and host skin odors on bed bug preferences. Hunger state modulated odor preference for aggregation and foraging in all tested odorscapes. Aggregation odor attracted both fed and unfed bed bugs. Host skin odor attracted unfed bed bugs but repelled recently fed bed bugs and the addition of carbon dioxide to host odor enhanced the behavioral responses. These findings suggest that orientation to aggregation sites in fed bed bugs is driven by two distinct odor processin..., 2 MATERIALS AND METHODS 2.1 Insects The tested laboratory strain (Harold Harlan strain) was collected in 1973 in Fort Dix NJ, and maintained on a human host until December 2008. Then, in our laboratory, it was fed on defibrinated rabbit blood until July 2021 and on human blood thereafter. It was maintained at 35–45% relative humidity, 25 °C, on a 12:12 (L:D) h cycle and fed weekly on heparinized human blood (supplied by the American Red Cross under IRB #00000288 and protocol #2018-026). We used an artificial feeding system, which has been previously described. The feeding system was housed in a North Carolina State University-approved BSL-2 facility (Biological Use Authorization #2020-09-836). Between feeding sessions, the glass feeders were sanitized with 7.5% sodium hypochlorite and 95% ethanol, and air-dried. Since we conducted 24 hr-observations in an open arena, only adult males (age unknown) ..., , # Data from: Bed bug preferences for host odor or aggregation odor are differentially modulated by physiological state in various odorscapes

Dataset DOI: 10.5061/dryad.nzs7h4529

Description of the data and file structure

Files and variables

File: DRYAD_Bed_bug_preferences_for_host_odor_or_aggregation_odor_are_differentially_modulated_by_physiological_state_in_various_odorscapes.xlsx

Description:Â Data sats of the results of two-choice bioassays

Variables

The file contains the seven sheets:

• Read me, description of dataset contains Name of Sheets, Column, Entry, Row, and Explanation.
• Experiment1, Tested insect post feeding (day), Number of Replication, Recorded site, The number of insects observed in recorded site was shown in 0, 1hr, 3hr, 6hr, 12hr, and 24hr of observation time point.
• Experiment2, Tested insect post feeding (day), Number of Replication, Recorded site, The number of insects observed in recorded site was shown i...,

This pie chart displays sites per site using the aggregation count in France. The data is about sites.