Context

I have taken this dataset from the NYC Open Data Website: https://data.cityofnewyork.us

I wanted to use the cleaned version of this dataset and I thought people might like to use this version. The original dataset was last updated on 10th September 2018.

Description: All dog owners residing in NYC are required by law to license their dogs. The data is sourced from the DOHMH Dog Licensing System (https://a816-healthpsi.nyc.gov/DogLicense), where owners can apply for and renew dog licenses. Each record represents a unique dog license that was active during the year, but not necessarily a unique record per dog, since a license that is renewed during the year results in a separate record of an active license period. Each record stands as a unique license period for the dog over the course of the yearlong time frame.

Content

The original dataset contained 122K rows and 15 columns. After cleaning the data, the count has reduced to 121862 rows.

Acknowledgements

Thank you to the city of new york for collecting and providing this data! As well as the NYC Department of Health who acquired this data from owners who registered their dogs for the dog license.

Inspiration

I'll let you guys get creative and explore the dataset.

Dataset Card for Cats Vs. Dogs

  Dataset Summary

A large set of images of cats and dogs. There are 1738 corrupted images that are dropped. This dataset is part of a now-closed Kaggle competition and represents a subset of the so-called Asirra dataset. From the competition page:

The Asirra data set Web services are often protected with a challenge that's supposed to be easy for people to solve, but difficult for computers. Such a challenge is often called a CAPTCHA… See the full description on the dataset page: https://huggingface.co/datasets/microsoft/cats_vs_dogs.

This study was undertaken to obtain information on the characteristics of gun ownership, gun-carrying practices, and weapons-related incidents in the United States -- specifically, gun use and other weapons used in self-defense against humans and animals. Data were gathered using a national random-digit-dial telephone survey. The respondents were comprised of 1,905 randomly-selected adults aged 18 and older living in the 50 United States. All interviews were completed between May 28 and July 2, 1996. The sample was designed to be a representative sample of households, not of individuals, so researchers did not interview more than one adult from each household. To start the interview, six qualifying questions were asked, dealing with (1) gun ownership, (2) gun-carrying practices, (3) gun display against the respondent, (4) gun use in self-defense against animals, (5) gun use in self-defense against people, and (6) other weapons used in self-defense. A "yes" response to a qualifying question led to a series of additional questions on the same topic as the qualifying question. Part 1, Survey Data, contains the coded data obtained during the interviews, and Part 2, Open-Ended-Verbatim Responses, consists of the answers to open-ended questions provided by the respondents. Information collected for Part 1 covers how many firearms were owned by household members, types of firearms owned (handguns, revolvers, pistols, fully automatic weapons, and assault weapons), whether the respondent personally owned a gun, reasons for owning a gun, type of gun carried, whether the gun was ever kept loaded, kept concealed, used for personal protection, or used for work, and whether the respondent had a permit to carry the gun. Additional questions focused on incidents in which a gun was displayed in a hostile manner against the respondent, including the number of times such an incident took place, the location of the event in which the gun was displayed against the respondent, whether the police were contacted, whether the individual displaying the gun was known to the respondent, whether the incident was a burglary, robbery, or other planned assault, and the number of shots fired during the incident. Variables concerning gun use by the respondent in self-defense against an animal include the number of times the respondent used a gun in this manner and whether the respondent was hunting at the time of the incident. Other variables in Part 1 deal with gun use in self-defense against people, such as the location of the event, if the other individual knew the respondent had a gun, the type of gun used, any injuries to the respondent or to the individual that required medical attention or hospitalization, whether the incident was reported to the police, whether there were any arrests, whether other weapons were used in self-defense, the type of other weapon used, location of the incident in which the other weapon was used, and whether the respondent was working as a police officer or security guard or was in the military at the time of the event. Demographic variables in Part 1 include the gender, race, age, household income, and type of community (city, suburb, or rural) in which the respondent lived. Open-ended questions asked during the interview comprise the variables in Part 2. Responses include descriptions of where the respondent was when he or she displayed a gun (in self-defense or otherwise), specific reasons why the respondent displayed a gun, how the other individual reacted when the respondent displayed the gun, how the individual knew the respondent had a gun, whether the police were contacted for specific self-defense events, and if not, why not.

USDA 2016 Data on Animal Testing in the United States

Broken down by State / Territory

Required Reporting by Testing Facilities in the US

Each USDA-registered research facility and Federal research facility is required by the Animal Welfare Act (AWA) to submit an Annual Report (APHIS Form 7023) that documents its use of animals for research, testing, teaching, experimentation, and/or surgery. USDA-APHIS Animal Care receives copy of each research facility’s annual report on or before December 1. Animal Care reviews the data to ensure the calculated totals are consistent with the number of reported animals in each pain/distress category. Reports with inconsistent data are returned to the research facility for correction. The completeness and accuracy of the non-Federal research facility annual reports might be validated during USDA animal welfare compliance inspections. However, research facilities sometimes include additional data on their annual reports that is not required under the Animal Welfare Act, such as data about rats of the genus rattus, mice of the genus mus, and birds bred for use in research, animals used in excluded field studies, animals used in clinical trials in the context of a veterinary client relationship, and reptiles, fish, or other animals that are not covered by the AWA.

Summary

The data in the summary reports represent the number of animals USDA-registered research facilities and Federal research facilities reported as being used or held for research, testing, teaching, experimentation, and/or surgery. The summary reports are based on the Annual Report column designations:

• Column B (animals held by a facility but not used in any research that year).

• Column C (animals used in research; no pain involved; no pain drugs administered).

• Column D (animals used in research; pain involved; pain drugs administered).

• Column E (animals used in research; pain involved; no pain drugs administered).

• ALL_PAINTYPES_2016 = (total number of animals used in research; Column C + Column D + Column E).

States include 50 states plus Puerto Rico (PR) and District of Columbia (DC).

Acknowledgements

USDA Animal and Plant Health Inspection Service More years found here: https://www.aphis.usda.gov/aphis/ourfocus/animalwelfare/sa_obtain_research_facility_annual_report/ct_research_facility_annual_summary_reports

Inspiration

The Beagle Freedom Project (Photo taken from there website)

The Humane Society

Peta

Bruna, Chewy, Cat Stevens, Remy, Owen, Neumann and Timmy (dogs and one cat).

The human footprint is rapidly expanding, and wildlife habitat is continuously being converted to human residential properties. Surviving wildlife that reside in developing areas are displaced to nearby undeveloped areas. However, some animals can co-exist with humans and acquire the necessary resources (food, water, shelter) within the human environment. This may be particularly true when development is low intensity, as in residential suburban yards. Yards are individually managed “greenspaces” that can provide a range of food (e.g., bird feeders, compost, gardens), water (bird baths and garden ponds), and shelter resources (e.g., brush-piles, outbuildings) and are surrounded by varying landscape cover. To evaluate which residential landscape and yard features influence the richness and diversity of mammalian herbivores and mesopredators; we deployed wildlife game cameras in 46 residential yards in summer 2021 and 96 yards in summer 2022. We found that mesopredator diversity had a negative relationship with fences and was positively influenced by the number of bird feeders present in a yard. Mesopredator richness increased with the amount of forest within 400m of the camera. Herbivore diversity and richness were positively correlated to the area of forest within 400m surrounding yard and by garden area within yards, respectively. Our results suggest that while landscape does play a role in the presence of wildlife in a residential area, homeowners also have agency over the richness and diversity of mammals occurring in their yards based on the features they create or maintain on their properties. Methods 2.1 Study Sites Our study took place from 4 April to 4 August 2021 and 2022 within an 80.5 km radius of downtown Fayetteville, Arkansas USA. Northwest Arkansas is a rapidly developing area with a current population of approximately 349,000 people. Fayetteville is located in the Ozark Highlands ecoregion and the landscape is primarily forested by mixed hardwood trees with open areas used for cattle pastures and some scattered agriculture. Our study took place in residential yards ranging from downtown Fayetteville to yards situated in more rural areas. We solicited volunteers from the Arkansas Master Naturalist Program and the University of Arkansas Department of Biological Sciences who allowed us to place cameras in their yards. We attempted to choose yards that represented the continuum of urban to rural settings and provided a range of yard features to which wildlife was likely to respond to. 2.2 Camera Setup To document the presence of wildlife in residential yards, we deployed motion-triggered wildlife cameras (Browning StrikeForce or Spypoint ForceDark) in numerous residential yards (46 yards in 2021 and 96 yards in 2022). We placed cameras approximately 0.95 m above the ground on either a tripod or a tree and at least 5 m from houses and at most 100 m from houses. When possible, we positioned cameras near features such as compost piles, water sources (natural or man-made), and fence lines to maximize detections of wildlife. We coordinated with homeowners to choose locations that would not interfere with yard maintenance or compromise homeowner privacy. We placed cameras in both front and back yards, although most cameras were placed in backyards. Backyards often had more features predicted to be of interest to wildlife (Belaire et al., 2015) and cameras placed in backyards were less likely to have false triggers associated with vehicular traffic or be vulnerable to theft. When necessary, we removed small amounts of vegetation that may have blocked the view of the camera or triggered the camera although this was always done on such a small scale as not alter the environment but just to clear the view of the camera. We set cameras to trigger with motion and take bursts of 3 photographs per trigger with a 5 s reset time. We did not use any bait or lures. We checked and downloaded cameras every 2 weeks to check batteries and download data. We moved cameras around the same yard upwards of 3 times within the season to ensure we captured the full range of wildlife present in each yard. At each yard, we recorded eight variables associated with food, water, or shelter features in the yard area surrounding the camera, these variables were recorded in both front and backyard (Table 1). First, we recorded the area of maintained gardens occurring in each yard. Next, we recorded the volume of potential den sites available in each yard. Potential denning sites included the total available area under sheds and outbuildings as well as decking that was less than 0.3 meters off the ground and provided opportunities for wildlife to burrow beneath and be sheltered. Similarly, we also measured the volume of all brush and firewood piles present in each yard that could be used by smaller wildlife species for shelter or foraging. We counted the number of bird feeders in each yard that were regularly maintained during the study period. We also counted the number of water sources available including bird baths and garden ponds (any human subsidized water source on the ground usually within a lined basin or container). We distinguished between these types of water sources in analyses because bird baths were likely not available to all wildlife because of their height. We also categorized the presence and type of natural water source present in each yard including vernal streams, permanent streams or ponds, rivers, or lakes. We also recorded the presence of agricultural animals (such as chickens or ducks) and pets (type and indoor/outdoor) present in each yard (although we ultimately excluded the presence of pets from analyses – see below). We documented whether the part of the yard where each camera was deployed was surrounded by a fence and if so, we categorized the fence type based upon its permeability to wildlife. We categorized fences into one of four categories ranging from those that posed little barrier to wildlife movement to those that were impassable to most species. For example, fences in our first category presented relatively little resistance to wildlife movement (i.e., barbed wire). A second category of fence consisted of fences made of semi-spaced wood slats or beams that offered enough room for most animals to squeeze through but that may have prevented passage of the largest bodied of the species. Fences that were about at least 1 m in height, but were closed off on the bottom (i.e., privacy or chain-link), meaning that few wildlife would be able to pass through without climbing or jumping over were placed in a third category. Finally, the fourth category of fences were those that were 1.8 m or greater in height and were made from a solid material that would prevent all wildlife except capable climbers from entering. 2.3 Landscape Variables We used a GIS (ArcGIS Pro 10.2; ESRI, Inc. Redlands Inc) to plot the location of all cameras and to quantify the composition of the surrounding landscape. We first created 400m buffers around each camera, to encompass the average home range area of most wildlife species likely to occur in suburban yards (e.g., Trent and Rongtad, 1974; Hoffman and Gotschang, 1977; Atkins and Stott, 1998). Within each buffer, we calculated the amount of forest cover, developed open land (e.g., cemeteries, parks, and grass lawns), agriculture, and development using the 2016 National Land Cover Database (Dewitz 2019). We also quantified the maximum housing unit density (HUD) around each camera using the SILVIS Housing Data Layer (Hammer et al., 2004). Finally, we calculated the straight-line distance from each camera to the nearest downtown city center (Fayetteville, Rogers, Bentonville, or Eureka Springs). Distance to downtown is an additional index of urbanization and human activity that has been correlated with animal behavior in this area (DeGregorio et al. 2021). Table 1 Description of all variables predicted to affect diversity and richness of mammals in residential yards within 80km of downtown Fayetteville, Arkansas USA during the April- August of 2021 and 2022.

Landscape Variables

Variable Statistics

Range

Average ( 1 SD)

Forest Cover

Area of forest cover within 400m buffer

0-0.45

0.18 0.13

Open Land

Area of open land, (parks, cemeteries, and lawns) within 400m buffer

0.003-0.31

0.09 0.06

Agricultural Land

Area of land used for agricultural purposes within 400m buffer

0-0.43

0.08 0.11

Developed Land

Area of developed land within 400m buffer

0-0.47

0.13

Housing Unit Density (HUD)

Maximum Housing Unit Density within 400m buffer of camera (houses/ )

1-5095

657

Yard Variables

Volume of Denning Sites

Volume under sheds/outbuildings and under decks less than 1m off the ground ( )

0-700

27.3

Volume of Brush/Firewood Piles

Total volume of denning sites including brush and firewood piles ( )

0-335.94

42.99 69.16

Water Source

Number of human-maintained water sources

0-7

1

• Bird Bath

Water source that is raised off the ground, so much so that animals that cannot climb or jump cannot access it

0-7

• Garden Pond

Water source on or embedded within the ground

0-3

0 1

Bird Feeder

Number of bird feeders present in yard

0-19

4

Garden

Area of total maintained gardens ( )

0-525

46.13 85.13

Compost Pile

Area of compost pile

0-12

0.64

Fence Type

If a camera was within a fence, it was given a score between 1-4, 1 being the most permeable fence and 4 being the most impassable to terrestrial mammals. 0: not in a fence 1: Barbed wire 2: Open slat fence 3: 1.2 m Chain-link or Privacy 4: 1.8 m chain-link or Privacy

NA

NA

Poultry Presence

Presence or absence of poultry being kept in yard

NA

NA

Water

Score of presence or absence of natural water source. 0: No natural water source 1: Vernal

Original provider: USGS Alaska Science Center & U.S. Fish and Wildlife Service

Dataset credits: North Pacific Pelagic Seabird Database

Abstract: This database was prepared by Jenny Wetzel and John Piatt at the United States Geological Survey (USGS) Alaska Science Center for Greg Balogh, United States Fish and Wildlife Service (USFWS) Endangered Species Office, Anchorage, as part of a collaborative USGS/FWS project to compile data on seabirds at sea. The North Pacific Pelagic Seabird Database (NPPSD) is a work in progress (contact Gary Drew for information on the NPPSD). Updates to this database can be found on the NPPSD web site. For more information/updated versions of this database, please contact the primary contacts (John Piatt, Greg Balogh, or Gary Drew).

Purpose: This dataset includes short-tailed albatross sightings from different sources that were gathered by many different people over a long period of time. We started with a database compiled by the USFWS, verified records where we could and double-checked computer records against all hard copy reports and publications, cleaned up many mistakes in the data (those which were apparent and fixable), eliminated duplicate records that had crept into the database over time, and added additional records gleaned from new sources.

A frequent source of confusion was determining whether longitude records were in the Eastern or Western hemisphere. When this was not explicitly stated, we made decisions based on available evidence or logic (e.g., STAL do not fly inland). We cannot vouch for the accuracy of most sightings reported in this database, and if you have any doubts about individual records, you should seek out the source of the data, or simply delete it. In addition, there may be duplicate records and typographical errors still present. We noted 'questionable record' where previous investigators raised questions about quality of the observation, or we had some concerns. For all these reasons, you should use some discretion when using these data for analysis and/or interpreting results. If you find an error, please notify one of the people indicated below in the contacts section.

Users of this database should seek permission from the USFWS (Greg Balogh) before reporting or publishing any results of analyses conducted on this database. Two manuscripts describing the distribution of STAL in relation to the environment (1) and other albatrosses (2) are in preparation by USGS and FWS.

In this version of the database, we have excluded confidential information on fishing vessel names, observers, and associated comments, and we deleted all notes about corrections. This database is available at the NPPSD web site, and can be distributed freely. The confidential dataset can only be obtained from the FWS (Greg Balogh).

If you use this database, we would appreciate that you cite NPPSD (2005).

Supplemental information: Before this dataset was incorporated into the OBIS-SEAMAP system, several fields and records were discarded.

We removed those sightings without complete latitude/longitude information or without complete date/time information. We also discarded those fields relating to vessel name, observer name, and comments from all the remaining records. Ancillary sighting information, including fisheries association and bird age class, are available from NSPPD. Please use the individual record numbers to retrieve additional information from the original NPPSD records.

Northern elephant seals (Mirounga angustirostris) have been integral to the development and progress of biologging technology and movement data analysis. Adult female elephant seals at Año Nuevo State Park and other colonies along the west coast of North America were tracked annually from 2004 to 2020 for a total of 653 instrument deployments and 561 recoveries. These high-resolution diving and location data have been compiled, curated, and processed. This repository has netCDF files containing the raw tracking and diving data. The raw data are available in a second repository (https://doi.org/10.7291/D10D61). Methods These data were collected from biotelemetry devices attached to adult female northern elephant seals (Mirounga angustirostris) from 2004 to 2020. The instruments collected locations (Argos and/or GPS) and continuously recorded depth throughout the animals' trips. Data were processed in MATLAB and R using custom code, the IKNOS package for dive data processing, and the aniMotum package for track processing. The details of data collection and processing are documented in the data descriptor paper associated with this dataset. In addition, all code used to process the data are available on GitHub and Zenodo. The data presented here are freely available for use under the CC0 (Creative Commons Zero), with attribution highly encouraged given to the data descriptor (DOI: 10.1038/s41597-024-04084-4) and this Dryad repository. We encourage users to reach out to the data owner for richer insight into the dataset. Subsets of this dataset have been made available through other projects and data portals and we caution users that these are not independent northern elephant seal datasets. This includes the AniBOS/MEOP data portal (https://www.meop.net/database/meop-databases/), the Animal Tracking Network (ATN) (https://portal.atn.ioos.us/), Movebank (https://www.movebank.org/cms/movebank-main), and MegaMove (https://megamove.org/data-portal/). Additional data about the instrumented animals, such as morphometrics, demographics, and other biologging data (e.g., acceleration, jaw motion, temperature), are available for many of these animals but are beyond the scope of this dataset. For more information, contact the data owner. Sampling Biases Generally, we have been careful to select healthy animals for sedation and instrumentation. For animals deployed at Año Nuevo (most of the tracks), typically individuals with known site fidelity to the colony were selected and if age was known it was usually restricted to 4- to 12-year-olds. Furthermore, the data reported here span two decades of work. During this time, different studies prompted additional non-random population sampling. Examples include focusing on one age for a year, repeat tracking the same individuals two trips in a row, and intentionally selecting previously tracked females who had used a coastal foraging strategy. Many individuals in the dataset have been tracked multiple times. We strongly encourage researchers to evaluate the metadata provided carefully and contact the data owner with inquiries. Code Availability All the code written for data processing and NetCDF data import code for MATLAB, R, and Python are available at GitHub (https://github.com/rholser/NES_TrackDive_DataProcessing) and Zenodo (https://doi.org/10.5281/zenodo.12511548). Extensive documentation of functions and scripts is also provided there. In addition, the authors have provided code in Python, R, and MATLAB for basic access to the netCDF files (GitHub link). They should serve as a model to enable users unfamiliar with the format to access the data.

The National Rabies Management Program conducts ORV operations in many US states. State summaries, maps, and statistics for oral rabies vaccine distribution can be accessed through this database. Rabies is caused by a virus that infects the central nervous system in mammals. It is almost always transmitted through the bite of a rabid animal. The majority of rabies cases in the United States occur in wildlife including raccoons, skunks, foxes and bats. Rabies is invariably fatal, however, effective vaccines are available to protect people, pets and livestock. The National Rabies Management Program was established in recognition of the changing scope of rabies. The goal of the program is to prevent the further spread of wildlife rabies and eventually eliminate terrestrial rabies in the United States through an integrated program that involves the use of oral rabies vaccination targeting wild animals. Since, 1995, Wildlife Services (WS) has been working cooperatively with local, State, and Federal governments, universities and other partners to address this public health problem by distributing oral rabies vaccination (ORV) baits in targeted areas. This cooperative program targets the raccoon variant, canine variant in coyotes and a unique variant of gray fox rabies Resources in this dataset:Resource Title: ORV Information by State. File Name: Web Page, url: https://www.aphis.usda.gov/aphis/ourfocus/wildlifedamage/programs/nrmp/orv-information-by-state Links with resources including shapefiles, maps, and reports.

NYC Reported Dog Bites.

Section 11.03 of NYC Health Code requires all animals bites to be reported within 24 hours of the event.

Information reported assists the Health Department to determine if the biting dog is healthy ten days after the person was bitten in order to avoid having the person bitten receive unnecessary rabies shots. Data is collected from reports received online, mail, fax or by phone to 311 or NYC DOHMH Animal Bite Unit. Each record represents a single dog bite incident. Information on breed, age, gender and Spayed or Neutered status have not been verified by DOHMH and is listed only as reported to DOHMH. A blank space in the dataset means no data was available.

Original provider: HDR Environmental, Operations and Construction, Inc.

Dataset credits: The U.S. Navy Marine Species Monitoring Program

Abstract: A combination of visual line-transect survey, photo- identification (photo-ID), and automated acoustic monitoring methods was used to gather important baseline information on the occurrence, distribution, and density of marine mammals near Naval Station Norfolk (NSN) and adjacent areas. The study area was designed to cover areas where United States Navy activity is substantial, including Chesapeake Bay waters near NSN and Joint Expeditionary Base Little Creek-Fort Story, as well as a Mine Exercise (MINEX) Area (W-50) in the Atlantic Ocean off the coast of Virginia Beach, Virginia. Sixty-one line- transect surveys were completed in two zones (INSHORE and MINEX) between August 2012 and August 2015, with 6,550 kilometers (km) and 349.6 hours completed on-effort. The majority of sightings were of bottlenose dolphins (Tursiops truncatus), although humpback whales (Megaptera novaeangliae), harbor porpoises (Phocoena phocoena), and short-beaked common dolphins (Delphinus delphis) were also sighted in the study area on occasion. In addition, loggerhead sea turtles (Caretta caretta), leatherback sea turtles (Dermochelys coriacea), and a Kemp’s ridley sea turtle (Lepidochelys kempii) were sighted during surveys. Conventional line-transect analysis of bottlenose dolphin sightings showed both spatial and seasonal variation in density and abundance, with greatest density in the INSHORE zone during fall months. Densities in the INSHORE zone were calculated as 3.88 individuals per square kilometer (km2) (abundance[N]=1,203) in fall, 0.63 individuals per km2 (N=195) in winter, 1.00 individuals per km2 (N=311) in spring, and 3.55 individuals per km2 (N=1,101) in summer. Densities in the MINEX zone were calculated as 2.14 individuals per km2 (N=1,277) in fall, 0.06 individuals per km2 (N=37) in winter, 1.53 individuals per km2 (N=913) in spring, and 1.39 individuals per km2 (N=829) in summer. Twenty-seven photo- ID surveys were completed, and a photo-ID catalog was created using photos taken during both dedicated photo-ID and line-transect surveys through May 2014; it contains 878 identified individuals to date. Subsequent photos will continue to be added and analyzed. One hundred ten individuals were re-sighted; however, most re-sightings were less than 4 months and 30 km apart. Additional survey effort and further analysis will be required before any clear movement patterns can be determined. C-POD acoustic data-loggers were initially deployed at four sites throughout the study area to cover areas of high United States Navy activity. Bottlenose dolphins were detected in each deployment location during all deployments from August 2012 to December 2015. Though deployments did not provide consistent coverage in all seasons for all sites due to loss of gear, results from two deployment sites nearest to NSN showed a greater level of occurrence during fall months, and a diel pattern of occurrence with increased detections during nighttime hours for three deployment sites.

Purpose: The HDR Marine Species Monitoring (MSM) Team was tasked to initiate a monitoring project in coastal waters around NSN, JEB-LC, JEB-FS, and the Virginia Beach waterfront, including the VACAPES MINEX W-50 training area. The main objective is to provide quantitative data and information on the seasonal occurrence, distribution, and density of marine mammals. Effort was dedicated to working with local researchers and employing proven marine mammal monitoring and research techniques to accomplish the following:

1. Conduct monthly systematic line-transect surveys to determine distribution of marine mammals in the vicinity of NSN, JEB-LC, JEB-FS, and the MINEX W-50 area.

2. Conduct monthly photo-identification (photo-ID) surveys during summer months to determine the site fidelity and distributional patterns of marine mammals utilizing the areas listed above.

3. Supplement visual surveys by deploying and retrieving four C-POD acoustic recording devices to monitor for dolphin echolocation clicks in specific locations.

Supplemental information: [2019-08-27] New data were appended and some columns with empty values were removed. The dataset name is changed by dropping the time period part.

This dataset includes sightings from photo-id surveys. No images and information on individual animals are provided.

This dataset includes a subset of the data collection for the Norfolk-VABeach Vessel surveys. Other data of the collection are available in the following datasets: "http://seamap.env.duke.edu/dataset/1071">Norfolk/VA Beach Inshore Vessel Surveys Nov 2012- Nov 2013 Norfolk/VA Beach MINEX Vessel Surveys

All the US Navy-funded survey datasets are found in the OBIS-SEAMAP US Navy page.

Overview

Detecting the sounds produced by animals is the foundation of bioacoustics research. This task must often be performed using noisy recordings that include many overlapping sounds from multiple individuals. Identifying each individual acoustic unit is necessary for a diversity of tasks, including species recognition and population estimation, which are critical to research on topics such as ecology and conservation.

This dataset consists of eight real component datasets for evaluating bioacoustic sound event detection performance, as well as six synthetic component datasets. Each dataset consists of several audio recordings. Annotations consist of the start- and stop-times of each event of interest, as well a class label.

Component datasets

This dataset consists of eight real component datasets which are used to evaluate bioacoustic sound event detection performance. Seven of these datasets are derived from data that appeared in previous publications. For the license and original citation for each component dataset, please see the license file for each dataset. If you are using a component dataset, please cite our paper (see below) in addition to the original work. Dataset characteristics are summarized below:

This dataset also consists of six synthetic component datasets Overlapping Zebra Finch Synthetic (OZF Synthetic) x, where x can be any of [0.0, 0.2, 0.4, 0.6, 0.8, 1.0]. The value of x is the ratio of the number of overlapping call pairs, to the number of calls. Each of these synthetic component datasets has the same characteristics. These were designed to mirror those of the real OZF dataset:

Dataset details

Each dataset consists of three info files: train_info.csv, val_info.csv, and test_info.csv, which define splits into train, validation, and test sets. Each info file has the following columns, where each row corresponds to one audio file:

- fn: Each audio file has a unique filename associated with it.
- audio_fp: Relative filepath to audio file.
- selection_table_filepath: Relative filepath to annotations, which are in the form of a Raven selection table

Selection tables are tab-separated `.txt` files. Each row corresponds to one annotated audio event (typically, a vocalization). Each selection table has (at least) the following columns:

- Begin Time (s): The time the event starts in the audio file
- End Time (s): The time the event ends in the audio file
- Annotation: A label (e.g. species) associated with the audio event

Some selection tables have no rows. This indicates that no events of interest occur in the corresponding recording.

Associated papers and code

If you use this data please cite the associated paper, which is currently available at https://arxiv.org/abs/2503.02389.

The code associated with the paper can be found at https://github.com/earthspecies/voxaboxen.

If you use any of the datasets besides OZF and OZF synthetic, please also cite the original work (found in the license file for each dataset).

This data publication contains urban tree growth data collected over a period of 14 years (1998-2012) in 17 cities from 13 states across the United States: Arizona, California, Colorado, Florida, Hawaii, Idaho, Indiana, Minnesota, New Mexico, New York, North Carolina, Oregon, and South Carolina.

Measurements were taken on over 14,000 urban street and park trees. Key information collected for each tree species includes bole and crown size, location, and age. Based on these measurements, 365 sets of allometric equations were developed for tree species from around the U.S. Each “set” consists of eight equations for each of the approximately 20 most abundant species in each of 16 climate regions. Tree age is used to predict a species diameter at breast height (dbh), and dbh is used to predict tree height, crown diameter, crown height, and leaf area. Dbh is also used to predict age. For applications with remote sensing, average crown diameter is used to predict dbh. There are 171 distinct species represented within this database. Some species grow in more than one region. The Urban Tree Database (UTD) contains foliar biomass data (raw data and summarized results from the foliar sampling for each species and region) that are fundamental to calculating leaf area, as well as tree biomass equations (compiled from literature) for carbon storage estimates. An expanded list of dry weight biomass density factors for common urban species is made available to assist users in using volumetric equations.Information on urban tree growth underpins models used to calculate effects of trees on the environment and human well-being. Maximum tree size and other growth data are used by urban forest managers, landscape architects and planners to select trees most suitable to the amount of growing space, thereby reducing costly future conflicts between trees and infrastructure. Growth data are used to develop correlations between growth and influencing factors such as site conditions and stewardship practices. Despite the importance of tree growth data to the science and practice of urban forestry, our knowledge is scant. Over a period of 14 years scientists with the U.S. Forest Service recorded data from a consistent set of measurements on over 14,000 trees in 17 U.S. cities.These data were originally published on 03/02/2016. The metadata was updated on 10/06/2016 to include reference to a new publication. Minor metadata updates were made on 12/15/2016. On 01/07/2020 this data publication was updated to correct a few species' names and systematic errors in the data that were found. A complete list of these changes is included (\Supplements\Errata_Jan2020_RDS-2016-0005.pdf). In addition, we have included a list of changes for the General Technical Report associated with these data (\Supplements\Errata_Jan2020_PNW-GTR-253.pdf).

The National Survey of Fishing, Hunting, and Wildlife-Associated Recreation (FHWAR) is a series conducted by the Census Bureau for the United States Department of the Interior Fish and Wildlife Service. This collection contains information regarding fishing, hunting, and other wildlife-associated activities for 1996. The survey is conducted every 5 years and includes 3 waves. Wave 1 is household-based and consists of a screener with the possibility of detailed interviews asking about a person's hunting, fishing or wildlife-watching activities and the likelihood that they will hunt, fish or watch wildlife. Wave 2 and Wave 3 are person-based, detailed interviews in which respondents were selected for the sample based on data collected from the screener in the first wave. The Sportsman and Wildlife-Watching surveys for wave 2 and 3 gathered specific information about respondents' recreational participation, including species hunted, fished, and watched; the state in which these activities occurred; number of trips taken; days of participation; and expenditures for food, lodging, transportation, and equipment. The questions asked throughout the 3 waves have been organized by topic into 3 datasets. The three datasets, (1) Screening, (2) Sportsman (Fishing and Hunting), and (3) Wildlife Watching, may contain responses from people surveyed during multiple waves. Demographic variables include sex, age, race, marital status and parental relations, education level, household income, state of residence, and type of residential area (e.g., urban or rural).

In summer 1989, BP Exploration (Alaska) Inc. (BPX) and LGL Alaska Research Associates, Inc. (LGL) initiated a study of wildlife use of disturbed habitats in arctic Alaska. This study mapped selected disturbed sites and made observations of bird and mammal use of these sites over a 2-month period. The ultimate goal of this study over the next few years is to assess the impacts of gravel fill and man-made impoundments on the wildlife community and to collect information useful for rehabilitating habitats affected by these kinds of disturbance. Gravel fill is widely used in oilfield development and past work has substantiated that impounding of water upslope from the fill is common. But responses of wildlife to these changes is poorly documented. Qualitative observations made to date suggest that some animals avoid areas covered by gravel but that others might be attracted by the fill material. Likewise, there may be contrasting responses among wildlife species to impoundment areas; the few quantitative studies made suggest that response may also vary seasonally. To fill gaps in the existing data, we observed wildlife use of abandoned drilling pads and impoundments; and of tundra, river alluvium, and pond sites undisturbed by man for comparison. These sites were situated mostly in and near the Prudhoe Bay and Kuparuk oilfields on the Arctic Coastal Plain. The observations documented bird and mammal use by 3-min periods during 2-hr observation intervals from mid-June to mid-August. Observations were made on gravel pad, river alluvium, and undisturbed tundra sites that were roughly similar in size; and on impoundments and ponds of similar average sizes. The analyses calculated level of use of sites as average number of species per 2-hr interval and average number of individuals per 3-min period, and type of use as percent of total time animals were engaged in specific activities.

A great deal of collaboration is currently occurring among individuals, companies, organizations, and agencies in the region. However, there are many places on the landscape where key threats and stressors to habitats, such as land conversion and climate impacts, require focused efforts and discussion to make efficient use of limited resources to move the conservation needle while maintaining working lands. This dataset is a combination of coastal forest threats and conservation value.Threats: can be seen here: http://eemsonline.org/?model=sHuQLRfIhFh9oE2czQD95yyHskXkvjlSData Used: Housing Density: Theobald, D. 2005. Landscape patterns of exurban growth in the USA from 1980 to 2020. Ecology and Society 10(1): 32. [online] URL: http://www.ecologyandsociety.org/vol10/iss1/art32/). National Park Service. 2010. NPScape housing measure – Phase 1 metrics processing SOP: Current housing density, historic housing density, and projected housing density metrics. National Park Service, Natural Resource Program Center. Fort Collins, Colorado. Natural Resource Report. NPS/NRPC/IMD/NRR—2010/251. Published Report-2165448. Terrestrial Resilience Stratified by Land Facet and Ecoregion (Pacific Northwest): Buttrick, S., K. Popper, M. Schindel, B. McRae, B. Unnasch, A. Jones, and J. Platt. 2015. Conserving Nature's Stage: Identifying Resilient Terrestrial Landscapes in the Pacific Northwest. The Nature Conservancy, Portland, Oregon. 104 pp. Available online at: http://nature.ly/resilienceNW March 3, 2015. Conversion Potential: Wilson, T.S., Sleeter, B.M., Sleeter, R. R., Soulard, C.E. 2014, Land use threats and protected areas: a scenario-based landscape level approach, Land, 3 (2): 362-389 "Conversion potential into developed, agriculture and forest harvest lands in the Pacific Northwest from 2000 to 2100. Values 1–7 represent the number of scenarios projecting land-use conversion over the modeled period." Data inputs in Model: Terrestrial Resilience Stratified by Land Facet and Ecoregion = Average Climate Change Resilience – in model used not tool to identify areas that are not as resilient to climate change. Conversion Potential = average Conversion Potential Housing Density = Average housing density increase. Used raster calculator to determine areas with an increase in housing density between 2100 and 2010. Weighted union for Increase in development Housing density increase (weight 1) and conversion potential (weight 0.5). Thresholds: Used first and third quartile values unless stated otherwise. If the first and third quartile were both zero then the third quartile value was taken from array with just HUCs with values.Conservation Value: can be seen here: http://eemsonline.org/?model=XsCKKBhKtQt5i8i020cwQfY0JFkHTCjBData Used: WGA CHAT: State Wildlife Agencies of the Western United States. West-wide Crucial Habitat Data Set. Western Association of Fish and Wildlife Agencies Crucial Habitat Assessment Tool: Mapping Fish and Wildlife Across the West. Western Association of Fish and Wildlife Agencies. Published 12/02/2013. Accessed November 2017. http://www.wafwachat.org Theobald Landscape Condition Index: Theobald et al 2013: metadata: https://www.sciencebase.gov/catalog/item/55538c61e4b0a92fa7e94d0e OmniScape current flow: McRae, B.H., K. Popper, A. Jones, M. Schindel, S. Buttrick, K. Hall, R.S. Unnasch, and J. Platt. 2016. Conserving Nature’s Stage: Mapping Omnidirectional Connectivity for Resilient Terrestrial Landscapes in the Pacific Northwest. The Nature Conservancy, Portland Oregon. 47 pp. Available online at: http://nature.org/resilienceNW June 30, 2016. Bird density data: American Bird Conservancy, Klamath Bird Observatory, PRBO Conservation Science Veloz, S., L. Salas, B. Altman, J. Alexander, D. Jongsomjit, N. Elliott, D. Moody, S. Michaile, M. Fitzgibbon and G. Ballard. 2013. Projected effects of climate change on the distribution and abundance of North Pacific birds and their habitats. Final report to the North Pacific Landscape Conservation Cooperative. Data inputs in Model: Percent area WGA CHAT intact_LS = Core Areas in model Average Theobald Landscape Condition Index = Landscape Condition Average of normalized Current Density of Townsend's Warbler, Olive-Sided Flycatcher, and Brown Creeper = Average Bird density Bird’s were picked to represent different forest types Late successional – Brown Creeper; Mid Successional – Townsend’s Warbler; Early successional – Olive-Sided Flycatcher. Minimum WGA CHAT SOC ter_soc and aq_soc = Species of Concern Percent Area Diffuse Current Flow = Diffuse Connectivity Thresholds: Used first and third quartile values unless stated otherwise. If the first and third quartile were both zero then the third quartile value was taken from array with just HUCs with values.

Photo-identification data on killer whales occupying the northern Gulf of Mexico have been collected in association with large vessel surveys since 1991. Photographs of killer whales are taken during encounters and unique dorsal fin marking can be used to identify individual animals. These images have been reviewed and individuals cataloged to evaluate residency and demographic patterns in killer whales in the northern Gulf of Mexico.

Understanding the effects of climate change on the phenological structure of plant communities will require measuring variation in sensitivity among thousands of co-occurring species across regions. Herbarium collections provide vast resources with which to do this, but may also exhibit biases as sources of phenological data. Despite general recognition of these caveats, validation of herbarium-based estimates of phenological sensitivity against estimates obtained using field observations remain rare and limited in scope. Here, we leveraged extensive datasets of herbarium specimens and of field observations from the USA National Phenology Network for 21 species in the United States and, for each species, compared herbarium- and field-based standardized estimates of peak flowering dates and of sensitivity of peak flowering time to geographic and interannual variation in mean spring minimum temperatures (TMIN). We found strong agreement between herbarium- and field-based estimates for standardized peak flowering time (r=0.91, p<0.001) and for the direction and magnitude of sensitivity to both geographic TMIN variation (r=0.88, p <0.001) and interannual TMIN variation (r=0.82, p<0.001). This agreement was robust to substantial differences between datasets in 1) the long-term TMIN conditions observed among collection and phenological monitoring sites and 2) the interannual TMIN conditions observed in the time periods encompassed by both datasets for most species. Our results show that herbarium-based sensitivity estimates are reliable among species spanning a wide diversity of life histories and biomes, demonstrating their utility in a broad range of ecological contexts, and underscoring the potential of herbarium collections to enable phenoclimatic analysis at taxonomic and spatiotemporal scales not yet captured by observational data.

Methods Phenological data The dataset of field observations consisted of all records of flowering onset and termination available in the USA National Phenology Network database (NPNdb), representing an initial 1,105,764 phenological observations. To ensure the quality of the observational data, we retained only observations for which we could determine that the dates of onset and termination of flowering had an arbitrary maximum error of 14 days. To do this, we filtered the data to include only records for which the date on which the first open flower on an individual was observed was preceded by an observation of the same individual without flowers no more than 14 days prior, and for which the date on which the last flower was recorded was followed by an observation of the same individual without flowers no more than 14 days later. After filtering, field observations in our data had an average maximum error of 6.4 days for the onset of flowering, and of 6.6 days for the termination of flowering. The herbarium dataset was constructed using an initial 894,392 digital herbarium specimen records archived by 72 herbaria across North America. We excluded from analysis all specimens not explicitly recorded as being in flower, or for which GPS coordinates or dates of collection were not available. We further filtered both datasets by only retaining species that were found in both datasets and that were represented by observations at a minimum of 15 unique sites in the NPN dataset. For each species, and to more closely match the geographic ranges covered by each dataset, we filtered the herbarium dataset to include only specimens within the range of latitudes and longitudes represented by the field observations in the NPN data. Finally, we retained only species represented by 70 or more herbarium specimens to ensure sufficient sample sizes for phenoclimatic modeling. This procedure identified a final set of 21 native species represented in 3,243 field observations across 1,406 unique site-year combinations, and a final sample of 5,405 herbarium specimens across 4,906 unique site-year combinations. For the herbarium dataset, sample sizes ranged from 69 unique sites and 74 specimens for Prosopis velutina, to 1,323 unique sites containing 1,368 specimens for Achillea millefolium. Sample sizes in the NPN dataset ranged from 15 unique sites with 74 observations for Impatiens capensis, 108 unique sites with 321 observations for Cornus florida. These 21 species represented 15 families and 17 genera, spanning a diverse range of life-history strategies and growth forms, including evergreen and deciduous shrubs and trees (e.g., Quercus agrifolia and Tilia americana, respectively), as well as herbaceous perennials (e.g., Achillea millefolium) and annuals (e.g., Impatiens capensis). Our focal species covered a wide variety of biomes and regions including Western deserts (e.g., Fouquieria splendens), Mediterranean shrublands and oak woodlands (e.g., Baccharis pilularis, Quercus agrifolia), and Eastern deciduous forests (e.g., Quercus rubra, Tilia Americana). To estimate flowering dates in the herbarium dataset, we employed the day of year of collection (henceforth ‘DOY’) of each specimen collected while in flower as a proxy. Herbarium specimens in flower could have been collected at any point between the onset and termination of their flowering period and botanists may preferentially collect individuals in their flowering peak for many species. Therefore, herbarium specimen collection dates are more likely to reflect peak flowering dates than flowering onset dates. To maximize the phenological equivalence of the field and herbarium datasets, we used the median date between onset and termination of flowering for each individual in each year in the NPN data as a proxy for peak flowering time. Due to the maximum error of 14 days for flowering onset and termination dates in the NPN dataset, median flowering dates also had a maximum error of 14 days, with an average maximum error among observations of 6.5 days. To account for the artificial DOY discontinuity between December 31st (DOY = 365 or 366 in a leap year) to January 1st (DOY = 1), we converted DOY in both datasets into a circular variable using an Azimuthal correction. Climate data Daily minimum temperatures mediate key developmental processes including the break of dormancy, floral induction, and anthesis. Therefore, we used minimum surface temperatures averaged over the three months leading up to (and including) the mean flowering month for each species (hereafter ‘TMIN’) as the climatic correlate of flowering time in this study; consequently, the specific months over which temperatures were averaged varied among species. Using TMIN calculated over different time periods instead (e.g., during spring for all species) did not qualitatively affect our results. Then, we partitioned variation among sites into spatial and temporal components, characterizing TMIN for each observation by the long-term mean TMIN at its site of collection (henceforth ‘TMIN normals’), and by the deviation between its TMIN in the year of collection (for the three-month window of interest) and its long-term mean TMIN (henceforth ‘TMIN anomalies’). For each site, we obtained a monthly time series of TMIN from January, 1901, and December, 2016, using ClimateNA v6.30, a software package that interpolates 4km2 resolution climate data from PRISM Climate Group from Oregon State University, (http://prism.oregonstate.edu) to generate elevation-adjusted climate estimates. To calculate TMIN normals, we averaged observed TMIN for the three months leading up to the mean flowering date of each species across all years between 1901 and 2016 for each site. TMIN anomalies relative to long-term conditions were calculated by subtracting TMIN normals from observed TMIN conditions in the year of collection. Therefore, positive and negative values of the anomalies respectively reflect warmer-than-average and colder-than-average conditions in a given year. Analysis We also provide R code to reproduce all results presented in the main text and the supplemental materials of our study. This code includes 1) all steps necessary to merge herbarium and field data into a single dataset ready for analysis, 2) the formulation and specification of the varying-intercepts and varying-slopes Bayesian model used to generate herbarium- vs. field-based estimates of phenology and its sensitivity to TMINsp, 3) the steps required to process the output of the Bayesian model and to obtain all metrics required for the analyses in the paper, and 4) the code used to generate each figure. Contributing Herbaria Data used in this study was contributed by the Yale Peabody Museum of Natural History, the George Safford Torrey Herbarium at the University of Connecticut, the Acadia University Herbarium, the Chrysler Herbarium at Rutgers University, the University of Montreal Herbarium, the Harvard University Herbarium, the Albion Hodgdon Herbarium at the University of New Hampshire, the Academy of Natural Sciences of Drexel University, the Jepson Herbarium at the University of California-Berkeley, the University of California-Berkeley Sagehen Creek Field Station Herbarium, the California Polytechnic State University Herbarium, the University of Santa Cruz Herbarium, the Black Hills State University Herbarium, the Luther College Herbarium, the Minot State University Herbarium, the Tarleton State University Herbarium, the South Dakota State University Herbarium, the Pittsburg State University Herbarium, the Montana State University-Billings Herbarium, the Sul Ross University Herbarium, the Fort Hays State University Herbarium, the Utah State University Herbarium, the Brigham Young University Herbarium, the Eastern Nevada Landscape Coalition Herbarium, the University of Nevada Herbarium, the Natural History Museum of Utah, the Western Illinois University Herbarium, the Eastern Illinois University Herbarium, the Northern Illinois University Herbarium, the Morton Arboretum Herbarium, the Chicago Botanic Garden

The Census of Agriculture provides a detailed picture every five years of U.S. farms and ranches and the people who operate them. Conducted by USDA's National Agricultural Statistics Service, the 2012 Census of Agriculture collected more than six million data items directly from farmers. The Ag Census Web Maps application makes this information available at the county level through a few clicks. The maps and accompanying data help users visualize, download, and analyze Census of Agriculture data in a geospatial context. Resources in this dataset:Resource Title: Ag Census Web Maps. File Name: Web Page, url: https://www.nass.usda.gov/Publications/AgCensus/2012/Online_Resources/Ag_Census_Web_Maps/Overview/index.php/ The interactive map application assembles maps and statistics from the 2012 Census of Agriculture in five broad categories:

Crops and Plants – Data on harvested acreage for major field crops, hay, and other forage crops, as well as acreage data for vegetables, fruits, tree nuts, and berries. Economics – Data on agriculture sales, farm income, government payments from conservation and farm programs, amounts received from loans, a broad range of production expenses, and value of buildings and equipment. Farms – Information on farm size, ownership, and Internet access, as well as data on total land in farms, land use, irrigation, fertilized cropland, and enrollment in crop insurance programs. Livestock and Animals – Statistics on cattle and calves, cows and heifers, milk cows, and other cattle, as well as hogs, sheep, goats, horses, and broilers. Operators – Statistics on hired farm labor, tenure, land rented or leased, primary occupation of farm operator, and demographic characteristics such as age, sex, race/ethnicity, and residence location.

The Ag Census Web Maps application allows you to:

Select a map to display from a the above five general categories and associated subcategories. Zoom and pan to a specific area; use the inset buttons to center the map on the continental United States; zoom to a specific state; and show the state mask to fade areas surrounding the state. Create and print maps showing the variation in a single data item across the United States (for example, average value of agricultural products sold per farm). Select a county and view and download the county’s data for a general category. Download the U.S. county-level dataset of mapped values for all categories in Microsoft ® Excel format.

Estimation of functional diversity in biological communities requires extensive and complete data on numerous functional traits of species or even individuals. When estimating functional diversity at large scales, this fact possesses an issue that may be hard to overcome: for many species, there might not be sufficient data on their functional traits. In such cases, even if there is missing information on functional trait value for one species in a community, this makes the trait impossible to use for the estimation of the functional diversity of a community. On the other hand, there are available datasets on the functional traits of all extant species within certain lineages across the world, but such datasets are often limited to very few functional traits, missing some dimensions of species' ecological niches. In this dataset, I compiled the available data from various sources that describe 23 functional traits of 703 bird species that occur in Canada, the United States, and Mexico. These functional traits include the following: diet type, diurnal and nocturnal feeding, diet items, feeding methods, feeding substrate, nest type, nest substrates, breeding system, chick development at hatching, nest aggregation, clutch size, first breeding age, number of clutches a year, breeding success, adult annual survival, mean biomass, maximum lifespan, hand-wing index, kleptoparasitism, nest parasitism, and the extent of dependency on other species for building a nest.

Southwest reGAP modeled distribution of white-tailed prairie dogs in the Colorado Plateau ecoregion, USAThe Southwest Regional Gap Analysis Project predicted habitat for 819 vertebrate species that reside, breed, or use habitat in the five-state region for a substantial portion of the their life history. The list of species to model was determined by identifying decision rules for taxon inclusion (These rules can be provided upon request). To create the most accurate models possible we are engaging taxa experts to provide a review of these habitat models.These models are based on the concept of Wildlife Habitat Relationships (WHRs). We have defined WHRs as a statement describing resources and conditions present in areas where a species persists and reproduces or otherwise occurs. Relationships can be modeled to predict habitat composition, and if the relationships are represented in a cartographic plane they can predict the presence of habitat spatially. For each species, these relationships were identified by reviewing the available literature and then generating a spatial representation of habitat within the species known range.An important factor for model implementation is understanding the objectives of the modeling effort and the assumptions of the models. The objective of the habitat models are to: 1) Provide maps that predict the distribution of terrestrial vertebrate species in the project area to support analysis of conservation status; and 2) Develop a database of geographic range, wildlife habitat relationships, and predicted distribution of each vertebrate species for the long-term utility of GAP and its cooperators (Csuti and Crist 2000). Along with these objectives are several assumptions associated with GAP vertebrate habitat models (Csuti and Crist 1998):1. Species are assumed to occur within a polygon representing potential habitat but are not predicted to occur at any particular point within that polygon.2. Species are assumed to be present within a polygon, but no assumptions are made about the abundance of the species in the polygon.3. Species are assumed to be present in a polygon at least once in the last 10 years but need not be present every year in the last decade.4. Species are assumed to be present during some portion of their life history, not necessarily during the entire year.There are many challenges to creating habitat maps. GAP uses expert review and a measure of agreement method in an effort to create the most accurate models possible. This document describes the expert review process within SWReGAP.We solicited habitat model review from knowledgeable individuals on the modeled terrestrial vertebrates across the five-state region.