Alaska is widely recognized as a global center for shorebirds. Ninety percent of the migratory species in the Western Hemisphere have breeding populations in Alaska. The North Pacific hosts one-third of the world's shorebird fauna. Coastal habitats are critical during some phase of their annual cycle, particularly during the nonbreeding period. Many coastal areas are being altered at an alarming rate. Effective conservation requires an understanding of population dynamics and habitat requirements. This is being done in cooperation with numerous local, national, and international shorebird interests. Objectives of this work is to obtain critical life history information, design and test methodologies for regional, national, and international programs, implement priority research and management activities identified in the Alaska Shorebird Conservation Plan and, where applicable, the United States and Canada Shorebird Conservation Plan, and provide management agencies and conservation administrators with information upon which to base informed decisions concerning the welfare of shorebirds and their habitats.

This data set contains observations of dead or alive harbor porpoises made by the public, mostly around the Swedish coast. A few observations are from Norwegian, Danish, Finish and German waters. Each observation of harbor porpoise is verified at the Swedish Museum of Natural History before it is approved and published on the web. The verification consists of controlling the accuracy of number of animals sighted, if the coordinates are correct and if pictures are attached that they really show a porpoise and not another species. If any of these three seem unlikely, the reporter is contacted and asked more detailed questions. The report is approved or denied depending on the answers given. Pictures and movies that can’t be uploaded to the database due to size problems are saved at the museum server and marked with the identification number given by the database. By the end of the year the data is submitted to HELCOM who then summarize all the member state’s data from the Baltic proper to the Kattegat basin. The porpoise is one of the smallest tooth whales in the world and the only whale species that breeds in Swedish waters. They are to be found in temperate water in the northern hemisphere where they live in small groups of 1-3 individuals. The females give birth to a calf in the summer months which then suckles for about 10 months before it is left on its own and she has a new calf. The porpoises around Sweden are divided in to three groups that don’t mix very often. The North Sea population is found on the west coast in Skagerrak down to the Falkenberg area. The Belt Sea population is to be found a bit north of Falkenberg down to Blekinge archipelago in the Baltic. The Baltic proper population is the smallest population and consists only of a few hundred animals and is considered as an endangered sub species. They are most commonly found from the Blekinge archipelago up to Åland Sea with a hot spot area south of Gotland at Hoburg’s bank and the Mid-Sea bank. The Porpoise Observation Database was started in 2005 at the request of the Swedish Environmental Protection Agency to get a better understanding of where to find porpoises with the idea to use the public to expand the “survey area”. The first year 26 sightings were reported, where 4 was from the Baltic Sea. The museum is particularly interested in sightings from the Baltic Sea due to the low numbers of animals and lack of data and knowledge about this group. In the beginning only live sightings were reported but later also found dead animals were added. Some of the animals that are reported dead are collected. Depending on where it is found and its state of decay, the animal can be subsampled in the field. A piece of blubber and some teeth are then send in by mail and stored in the Environmental Specimen Bank at the Swedish Museum of Natural History in Stockholm. If the whole animal is collected an autopsy is performed at the National Veterinary Institute in Uppsala to try and determine cause of death. Organs, teeth and parasites are sampled and saved at the Environmental Specimen Bank as well. Information about the animal i.e. location, founding date, sex, age, length, weight, blubber thickness as well as type of organ and the amount that is sampled is then added to the Specimen Bank database. If there is an interest in getting samples or data from the Specimen Bank, one have to send in an application to the Department of Environmental research and monitoring and state the purpose of the study and the amount of samples needed.

This data collection consists of behavioural task data for measures of attention and interpretation bias, specifically: emotional Stroop, attention probe (both measuring attention bias) and similarity ratings task and scrambled sentence task (both measuring interpretation bias). Data on the following 6 participant groups are included in the dataset: native UK (n=36), native HK (n=39), UK migrants to HK (short term = 31, long term = 28) and HK migrants to UK (short term = 37, long term = 31). Also included are personal characteristics and questionnaire measures.

The way in which we process information in the world around us has a significant effect on our health and well being. For example, some people are more prone than others to notice potential dangers, to remember bad things from the past and assume the worst, when the meaning of an event or comment is uncertain. These tendencies are called negative cognitive biases and can lead to low mood and poor quality of life. They also make people vulnerable to mental illnesses. In contrast, those with positive cognitive biases tend to function well and remain healthy. To date most of this work has been conducted on white, western populations and we do not know whether similar cognitive biases exist in Eastern cultures. This project will examine cognitive biases in Eastern (Hong Kong nationals ) and Western (UK nationals) people to see whether there are any differences between the two. It will also examine what happens to cognitive biases when someone migrates to a different culture. This will tell us whether influences from the society and culture around us have any effect on our cognitive biases. Finally the project will consider how much our own cognitive biases are inherited from our parents. Together these results will tell us whether the known good and bad effects of cognitive biases apply to non Western cultural groups as well, and how much cognitive biases are decided by our genes or our environment.

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.

Coccidioides whole population genome SNP analysis reveals local population structure and patterns of dispersal in the Western Hemisphere.

Original provider: The Center for Conservation Biology

Dataset credits: Data provider Center for Conservation Biology Originating data center Satellite Tracking and Analysis Tool (STAT) Project partner Funding, staff, and additional resources for this project were provided by the following partners: The Nature Conservancy (Virginia and Georgia Chapters), Georgia Department of Natural Resources, US Fish and Wildlife Service, Canadian Wildlife Service, Manomet Center for Conservation Studies, and the Center for Conservation Biology.

Abstract: The whimbrel is a large, holarctic, highly migratory shorebird. The North American race includes two disjunct breeding populations both of which winter primarily in Central and South America. The western population breeds in Alaska and the Northwest Territories of Canada. The eastern population breeds south and west of Hudson Bay in Manitoba and Ontario. It has generally been believed that the western population follows a Pacific Coast migration route between breeding and wintering areas and that the Hudson Bay population follows an Atlantic Coast route. Both populations are of high conservation concern due to dramatic declines in recent decades.

For more than a decade, scientists have believed that the seaside of the lower Delmarva Peninsula in Virginia played a significant role in the life cycle of the whimbrel. During spring migration in the mid-1990s, Bryan Watts from the Center for Conservation Biology at the College of William and Mary and Barry Truitt of The Nature Conservancy documented the densest concentration of whimbrels ever recorded in the western hemisphere within the barrier island lagoon system of the lower Delmarva Peninsula. Since that time, it has been believed that the Eastern Shore of Virginia represents a critical, coastal staging area where birds feed on the staggering numbers of fiddler crabs that inhabit the lagoon system and build up energy reserves before making their last overland flight to the breeding grounds. However, it has always been assumed that the birds staging along the lower Delmarva were exclusively from the Hudson Bay population. The flight documented in spring 2008 (see Winnie's map) has forced a change in thinking regarding the origin of birds using this stopover site.

Beginning in 2008, the Center for Conservation Biology collaborated with The Nature Conservancy to investigate the stopover ecology of whimbrels along the Delmarva Peninsula. The study includes aerial surveys to estimate seasonal numbers, traditional transmitters to examine stopover periods, and satellite transmitters to document migration pathways and breeding destinations for birds leaving the site. The seaside of the Delmarva Peninsula has been recognized as a globally important bird area, a hemispheric shorebird reserve, and a UNESCO biosphere reserve. The discovery that whimbrels use the site as a terminal staging area before embarking on a transcontinental flight suggests that the site is uniquely suited to provide the tremendous amount of energy required to prepare birds for such a flight.

Continued research planned by CCB and TNC in Virginia will investigate whimbrel stopover ecology and the broader strategic importance of this site to whimbrel populations.

In 2010, Georgia Department of Natural Resources began tracking Whimbrel from another important migration stopover on the east coast of North America. Georgia's barrier island and salt-marsh complex provide excellent stopover habitat for refueling on their migration from their wintering grounds in South America to the breeding grounds in the Arctic.

Names for the Vriginia Whimbrels are landmarks near where the Whimbrel congregate on the Eastern Shore of Virginia (Hope Creek, Box Tree, Fowling Point, Elkins Marsh, Hope Creek, Indian Creek, town of Machipongo, Webb Island, Ramshorn Channel, Mill Creek, and Kitt Creek).

MIROC6-AGCM simulation dataset used in the paper “Northern Hemisphere winter atmospheric teleconnections are intensified by extratropical ocean-atmosphere coupling” submitted to Communications Earth & Environment.

Data Format

All data are 4-byte binary files accompanied by GrADS control file (http://cola.gmu.edu/grads/) to access the data.

Variables

The dataset uploaded includes a 50-member ensemble of winter-mean (December–January–February) fields for 1980–2020 (a year refers to that including January of each DJF season) over the Northern Hemisphere (0°–360°, 20°–90°N).

Variables include wind velocities (u, v), pressure velocity (omg), temperature (T), geopotential height (z), eddy momentum (UU, VV, UV) and heat (UT, VT) fluxes by transient eddies, surface sea level pressure (slp), 10m wind velocity (u10, v10), 10m wind speed (uvabs), 2m air temperature (T2), surface pressure (Ps), sensible and latent heat fluxes (sens, evap).

The diabatic heating rate consists of only ten ensemble members from 1980-2014. The dtphy is the sum of the diabatic heating terms from different physical processes in the model, which include vertical diffusion (dtvdf), cumulus heating (dtcum), large-scale condensation heating (dtlsc), shallow convection heating (dtscn), cloud physics heating (dtcph), and radiative heating by long wave (dtradl) and short wave (dtrads).

The datasets are issued from the combination of records of the ESA EO4PAC and Permafrost_cci and HORIZON 2020 Nunataryuk projects. The EO4PAC project aimed to develop a new generation of geospatial products for the observation of permafrost and associated changes from space with a special focus on the coastal Arctic. Four components were considered in the creation of the datasets:

(1) Landsat-7/8 for the detection of coastline changes over the 2000-2020 period (Tanguy et al., 2024).

(2) Sentinel-1/2 for the detection and mapping of coastal infrastructures (Bartsch et al. 2024), updating Wang et al. (2021).

(3) Permafrost_cci timeseries for retrieval of trends of ground temperature and active layer thickness for the 2000-2020 period (Obu et al. 2021a,b), evaluated based on Martin et al (2023) and CALM et al. (2024).

(4) Sea level rise by 2100 (Garner et al. 2022).

The respective output provides a consistent mapping of settlements along arctic and permafrost-dominated coasts (2), and associated coastline and permafrost conditions changes during the last 20 years (1, 3). Combined together, an assessment of Arctic infrastructures at risk due to permafrost change (GT, ALT) and coastline erosion was possible, the latter with projections for the years 2030, 2050 and 2100.

References

Bartsch, Annett, Pointner, Georg, & Nitze, Ingmar. (2023). Sentinel-1/2 derived Arctic Coastal Human Impact dataset (SACHI) (Version 2) [Data set]. Zenodo. https://zenodo.org/records/10160636.

CALM, GTN-P, Wieczorek, M., Heim, B., Streletskiy, D., Bartsch, A., 2024, GTN-P CALM: 34 years of Active Layer Thickness (ALT) across latitudinal and elevational gradients in the Northern Hemisphere [dataset]. PANGAEA, https://doi.pangaea.de/10.1594/PANGAEA.972777

Garner, G. G., Hermans, T., Kopp, R. E., Slangen, A. B. A., Edwards, T. L., Levermann, A., et al. (2022). IPCC AR6 sea level projections [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.6382554

Martin, Julia; Boike, Julia; Chadburn, Sarah; Zwieback, Simon; Anselm, Norbert; Goldau, Maybrit; Hammar, Jennika; Abramova, Ekatarina N; Lisovski, Simeon; Coulombe, Stéphanie; Dakin, Brampton; Wilcox, Evan James; Giamberini, Mariasilvia; Rader, Fieke; Suominen, Otso; Rudd, Daniel Alexander; Mastepanov, Mikhail; Young, Amanda (2023): T-MOSAiC 2021 myThaw data set [dataset]. PANGAEA, https://doi.org/10.1594/PANGAEA.956039, In: Boike, Julia; Hammar, Jennika; Goldau, Maybrit; Miesner, Frederieke; Anselm, Norbert (2024): Circumarctic seasonal measurements of permafrost parameters (thaw depth, snow depth, vegetation and tree height, water level and soil properties) [dataset publication series]. PANGAEA, https://doi.org/10.1594/PANGAEA.971787

Obu, J., Westermann, S., Barboux, C., Bartsch, A., Delaloye, R., Grosse, G., Heim, B., Hugelius, G., Irrgang, A., Kääb, A. M., Kroisleitner, C., Matthes, H., Nitze, I., Pellet, C., Seifert, F. M., Strozzi, T., Wegmüller, U., Wieczorek, M., and Wiesmann, A.: ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost active layer thickness for the Northern Hemisphere, v3.0, CEDA, 2021. https://doi.org/10.5285/29C4AF5986BA4B9C8A3CFC33CA8D7C85

Obu, J., Westermann, S., Barboux, C., Bartsch, A., Delaloye, R., Grosse, G., Heim, B., Hugelius, G., Irrgang, A., Kääb, A. M., Kroisleitner, C., Matthes, H., Nitze, I., Pellet, C., Seifert, F. M., Strozzi, T., Wegmüller, U., Wieczorek, M., and Wiesmann, A.: ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost active layer thickness for the Northern Hemisphere, v3.0, CEDA, 2021. https://doi.org/10.5285/29C4AF5986BA4B9C8A3CFC33CA8D7C85

Tanguy, R., Bartsch, A., Nitze, I., Irrgang, A., Petzold, P., Widhalm, B., von Baeckmann, C., Boike, J., Martin, J., Efimova, A., Vieira, G., Whalen, D., Heim, B., Wieszorek, M., Grosse, G.: Pan‐Arctic Assessment of Coastal Settlements and Infrastructure Vulnerable to Coastal Erosion, Sea‐Level Rise, and Permafrost Thaw, Earth’s Future, 10.1029/2024EF005013.

Wang, S., Ramage, J., Bartsch, A., & Efimova, A. (2021). Population in the Arctic Circumpolar Permafrost Region at settlement level (Version 2) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4529610

This dataset provides values for GDP reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

2018-08-10 - these data have been superseded by a new metadata record and dataset - see the provided URL for more details.

This record describes a compilation of trophic data from across the Southern Ocean. Data have been drawn from published literature, existing trophic data collections, AADC data sets, and unpublished collections. The database comprises two principal tables. The first table relates to direct sampling methods of dietary assessment, including gut, scat, and bolus content analyses, stomach flushing, and observed feeding. The second table is a compilation of stable isotope values. Each record in these two tables includes details such as the location and date of sampling, predator size and mass, prey size and mass, and estimates of dietary importance. Names have been validated against the World Register of Marine Species (http://www.marinespecies.org/).

The schemas of these tables are described below, and a list of the sources used to populate the tables is provided with the data.

A range of manual and automated checks were used to ensure that the entered data were as accurate as possible. These included visual checking of transcribed values, checking of row or column sums against known totals, and checking for values outside of allowed ranges. Suspicious entries were re-checked against original source. Apparent errors that could not be resolved were marked as such in the QUALITY_FLAG column, with the reason in the NOTES column.

Notes on names 'Sp.' indicates unidentified members of a genus (e.g. 'Pachyptila sp.'). For unidentified taxa at other taxonomic levels, the taxonomic name has been used (e.g. Amphipoda, Myctophidae, Decapoda). Uncertain species identifications (e.g. 'Notothenia rossii?' or 'Gymnoscopelus cf. piabilis') were assigned the genus name (e.g. 'Notothenia sp.'). Original names were retained in a separate column to allow future cross-checking. WoRMS identifiers (APHIA_ID numbers) were recorded with each matched taxon.

Grouped prey data in the diet sample table need to be handled with a bit of care. Papers commonly report prey statistics aggregated over groups of prey - e.g. one might give the diet composition by individual cephalopod prey species, and then an overall record for all cephalopod prey. The prey_is_aggregate column identifies such records. This allows us to differentiate grouped data like this from unidentified prey items from a certain prey group - for example, an unidentifiable cephalopod record would be entered as Cephalopoda (the scientific name), with 0 in the prey_is_aggregate column. A record that groups together a number of cephalopod records, possibly including some unidentifiable cephalopods, would also be entered as Cephalopoda, but with a 1 in the prey_is_aggregate column. See the notes on prey_is_aggregate, below.

Schema: Diet sample table

• LINK_ID: The unique identifier of this record
• SOURCE_ID: The reference number of the source of this data record. The list of references is provided with the database and also kept at: http://data.aad.gov.au/aadc/trophic/?tab=3
• LOCATION: The name of the location at which the data was collected.
• WEST: The westernmost longitude of the sampling region, in decimal degrees (negative values indicate western hemisphere longitudes)
• EAST: The easternmost longitude of the sampling region, in decimal degrees (negative values indicate western hemisphere longitudes)
• SOUTH: The southernmost latitude of the sampling region, in decimal degrees (negative values indicate southern hemisphere latitudes)
• NORTH: The northernmost latitude of the sampling region, in decimal degrees (negative values indicate southern hemisphere latitudes)
• OBSERVATION_DATE_START: The start of the sampling period (UTC)
• OBSERVATION_DATE_END: The end of the sampling period (UTC). If sampling was carried out over multiple seasons (e.g. during January of 2002 and January of 2003), these dates will indicate the first and last dates (as if the sampling was carried out from 1-Jan-2002 to 31-Jan-2003)
• ALTITUDE_MIN: The minimum altitude of the sampling region, in metres (if applicable)
• ALTITUDE_MAX: The maximum altitude of the sampling region, in metres (if applicable)
• DEPTH_MIN: The shallowest depth of the sampling, in metres (if applicable)
• DEPTH_MAX: The deepest depth of the sampling, in metres (if applicable)
• PREDATOR_NAME_ORIGINAL: The name of the predator, as it appeared in the original source
• PREDATOR_NAME: The scientific name of the predator (corrected, if necessary).
• PREDATOR_COMMON_NAME: The common name of the predator (from the WoRMS taxonomic register)
• PREDATOR_APHIA_ID: The numeric identifier of the predator in the WoRMS taxonomic register
• PREDATOR_LIFE_STAGE: Life stage of the predator. e.g. 'adult', 'chick', 'larva'. Values 'C1'-'C3' refer to calyptopis larval stages of euphausiids. 'F1'-'F6' refer to furcilia larval stages of euphausiids. 'N1'-'N6' refer to nauplius stages of crustaceans. 'Copepodite 1'-'Copepodite 6' refer to developmental stages of copepodites
• PREDATOR_BREEDING_STAGE: Stage of the breeding season of the predator, if applicable. e.g. 'brooding', 'chick rearing', 'nonbreeding', 'posthatching'
• PREDATOR_SEX: Sex of the predator. 'male', 'female', 'both', or 'unknown'
• PREDATOR_SAMPLE_COUNT: The number of predators for which data are given. If (say) 50 predators were caught but only 20 analysed, this column will contain 20.
• PREDATOR_TOTAL_COUNT: The total number of predators sampled. If (say) 50 predators were caught but only 20 analysed, this column will contain 50.
• PREDATOR_SAMPLE_COUNT: The identifier of this predator sample. PREDATOR_SAMPLE_ID values are unique within a source (i.e. - SOURCE_ID, PREDATOR_SAMPLE_ID pairs are globally unique). Rows with the same SOURCE_ID and PREDATOR_SAMPLE_ID values relate to the same predator individual or population, and so can be combined (e.g. for prey diversity analyses). Subsamples are indicated by a decimal number S.nnn, where S is the parent PREDATOR_SAMPLE_ID, and nnn (001-999) is the subsample number. Studies will often report detailed prey information for a large sample, and also report prey information for various subsamples of that sample (e.g. broken down by predator sex, or sampling season).
• PREDATOR_SIZE_MIN: The minimum size of the predators in the sample
• PREDATOR_SIZE_MAX: The maximum size of the predators in the sample
• PREDATOR_SIZE_MEAN: The mean size of the predators in the sample
• PREDATOR_SIZE_SD: The standard deviation of the size of the predators in the sample
• PREDATOR_SIZE_UNITS: The units of size. Current values 'mm', 'cm', 'm'
• PREDATOR_SIZE_NOTES: Notes on the predator size information, including a definition of what the size value represents (e.g. 'total length', 'standard length')
• PREDATOR_MASS_MIN: The minimum mass of the predators in the sample
• PREDATOR_MASS_MAX: The maximum mass of the predators in the sample
• PREDATOR_MASS_MEAN: The mean mass of the predators in the sample
• PREDATOR_MASS_SD: The standard deviation of the mass of the predators in the sample
• PREDATOR_MASS_UNITS: The units of mass (e.g. 'g' or 'kg')
• PREDATOR_MASS_NOTES: Notes on the predator mass information, including a definition of what the mass value represents (blank implies total body weight). Current values 'g', 'kg', 't'
• PREY_NAME_ORIGINAL: The name of the prey item, as it appeared in the original source.
• PREY_NAME: The scientific name of the prey item (corrected, if necessary).
• PREY_COMMON_NAME: The common name of the prey item (from the WoRMS taxonomic register)
• PREY_APHIA_ID: The numeric identifier of the prey in the WoRMS taxonomic register
• PREY_IS_AGGREGATE: 'Y' indicates that this row is an aggregation of other rows in this data source. For example, a study might give a number of individual squid species records, and then an overall squid record that encompasses the individual records. Use the PREY_IS_AGGREGATE information to avoid double-counting during analyses. If there no entry in this column, it means that this information is not included anywhere else in the database and can be used freely when aggregating over taxonomic groups, for example
• PREY_LIFE_STAGE: Life stage of the prey. e.g. 'adult', 'chick', 'larva'
• PREY_SAMPLE_COUNT: The number of prey individuals from which size and mass measurements were made (note: NOT the total number of individuals of this prey type, unless all individuals in the sample were measured)
• PREY_SIZE_MIN: The minimum size of the prey in the sample
• PREY_SIZE_MAX: The maximum size of the prey in the sample
• PREY_SIZE_MEAN: The mean size of the prey in the sample
• PREY_SIZE_SD: The standard deviation of the size of the prey in the sample
• PREY_SIZE_UNITS: The units of size. Current values 'mm', 'cm', 'm'
• PREY_SIZE_NOTES: Notes on the prey size information, including a definition of what the size value represents (e.g. 'total length', 'standard length')
• PREY_MASS_MIN: The minimum mass of the prey in the sample
• PREY_MASS_MAX: The maximum mass of the prey in the sample
• PREY_MASS_MEAN: The mean mass of the prey in the sample
• PREY_MASS_SD: The standard deviation of the mass of the prey in the sample
• PREY_MASS_UNITS: The units of mass. Current values 'mg', 'g', 'kg'
• PREY_MASS_NOTES: Notes on the prey mass information, including a definition of what the mass value represents (blank implies total body weight)
• FRACTION_DIET_BY_WEIGHT: The fraction (by weight) of the predator diet that this prey type made up (e.g. if Euphausia superba contributed 50% of the total mass of prey items, this value would be 0.5). Many papers represent very small dietary contributions as 'trace' or sometimes 'less than 0.1%'. These have been entered as -999
• FRACTION_DIET_BY_PREY_ITEMS: The fraction (by number) of prey items that this prey type made up (e.g. if 1000 Euphausia superba were found out of a total of 2000 prey items, this value would be 0.5). Note: many

The sugarcane borer moth, Diatraea saccharalis, is one of the most important pests of sugarcane and maize crops in the Western Hemisphere. The pest is widespread throughout South and Central America, the Caribbean region and the southern United States. One of the most intriguing features of D. saccharalis population dynamics is the high rate of range expansion reported in recent years. To shed light on the history of colonization of D. saccharalis, we investigated the genetic structure and diversity in American populations using single nucleotide polymorphism (SNPs) markers throughout the genome and sequences of the mitochondrial gene cytochrome oxidase (COI). Our primary goal was to propose possible dispersal routes from the putative center of origin that can explain the spatial pattern of genetic diversity. Our findings showed a clear correspondence between genetic structure and the geographical distributions of this pest insect on the American continents. The clustering analyses indicated three distinct groups: one composed of Brazilian populations, a second group composed of populations from El Salvador, Mexico, Texas and Louisiana and a third group composed of the Florida population. The predicted time of divergence predates the agriculture expansion period, but the pattern of distribution of haplotype diversity suggests that human-mediated movement was most likely the factor responsible for the widespread distribution in the Americas. The study of the early history of D. saccharalis promotes a better understanding of range expansion, the history of invasion, and demographic patterns of pest populations in the Americas.

The blue whale is an endangered and globally distributed species of baleen whale with multiple described subspecies assignments, including the morphologically and molecularly distinct pygmy blue whale, among others. North Atlantic and North Pacific populations, however, are currently regarded as a single subspecies despite being separated by continental land masses and differences in their acoustic communication. To determine the degree of isolation among the Northern Hemisphere populations, fourteen North Pacific and six Western Australian blue whale nuclear and mitochondrial genomes were sequenced and analyzed combinedly with eleven publicly available North Atlantic blue whale genomes. This allowed to contrast the genetic differentiation and genetic exchange among Northern Hemisphere populations to the Western Australian pygmy blue whale subspecies. Population genomic analyses revealed distinctly differentiated clusters and limited exchange among all three populations, indicating a hi..., We sequenced the genomes from 20 blue whale specimens gathered from the North Pacific blue whale and West-Australian pygmy blue whale populations and analyze the data together with 12 publicly available genomes of other blue whales, including those of the North-Atlantic blue whales (Jossey et al., 2024). This sampling is further complemented by three genomes of the closely related sei whale (Balaenoptera borealis), of which one is sequenced in this study. All Illumina paired-end libraries were prepared by Novogene, Cambridge, United Kingdom using the NEBNEXT DNA LIBRARY PREP kit with a read length of 150 base pairs (bp) and an insert size of 350 bp. Illumina sequencing was performed on a NovaSeq 6000 platform targeting ~20x coverage per individual. A comprehensive pipeline used to process the data and perform many of the here presented downstream analyses can be found on GitHub: mag-wolf/RESEQ-to-Popanalyses/. Short read data were trimmed for quality and adapter sequences using FASTP V0..., Usage Notes: All variance sets are contained in zipped vcf files and might be viewed and altered with BCFTOOLS (Danecek et al., 2021). While the genome wide set only contains SNPs, the haploid mitochondrial and sex-chromosomal data contain also conserved sites!, # Supporting Data for: Ocean-wide conservation genomics of blue whales suggest new Northern Hemisphere subspecies

[Access this dataset on Dryad: https://doi.org/10.5061/dryad.47d7wm3jz]

We sequenced the genomes from 20 blue whale specimens gathered from the North Pacific blue whale and West-Australian pygmy blue whale populations and analyze the data together with 12 publicly available genomes of other blue whales, including those of the North-Atlantic blue whales (Jossey et al., 2024). The data was used for SNP calling of genomic, mitogenomic, mt_Marker region, and y-chromosomal data. The SNPs were subsequently used for population genetic analyses regarding gene flow, genetic divergence, phylogenetic reconstruction and genetic viability.

Description of the data and file structure

In this data repository, we provide filtered, high-quality SNPs called from our genomic resequencing and subsequent mapping to the blue whale reference genome...

The Mesoamerican Barrier Reef System (MBRS), extending from Isla Contoy on the north of the Yucatan Peninsula to the Bay Islands of Honduras, includes the second longest barrier reef in the world. It is approximately 1,000 km long and spans over four countries and two trans-boundary areas: Chetumal Bay, between Belize and Mexico; and the Gulf of Honduras, between Belize, Guatemala and Honduras. The MBRS is unique in the Western Hemisphere due to its length, composition of reef types, and diverse assemblage of corals and related species. The MBRS contributes to the stabilization and protection of coastal landscapes, maintenance of coastal water quality, and serves as breeding and feeding grounds for marine mammals, reptiles, fish and invertebrates, many of which are of commercial importance. The MBRS is also of immense socioeconomic significance providing employment and a source of income to an estimated one million people living in adjacent coastal areas.

The living tree sloths Choloepus and Bradypus are the only remaining members of Folivora, a major xenarthran radiation that occupied a wide range of habitats in many parts of the western hemisphere during the Cenozoic, including both continents and the West Indies. Ancient DNA evidence has played only a minor role in folivoran systematics, as most sloths lived in places not conducive to genomic preservation. Here we utilize collagen sequence information, both separately and in combination with published mitochondrial DNA evidence, to assess the relationships of tree sloths and their extinct relatives. Results from phylogenetic analysis of these datasets differ substantially from morphology-based concepts: Choloepus groups with Mylodontidae, not Megalonychidae; Bradypus and Megalonyx pair together as megatherioids, while monophyletic Antillean sloths may be sister to all other folivorans. Divergence estimates are consistent with fossil evidence for mid-Cenozoic presence of sloths in the West Indies and an early Miocene radiation in South America.

Expanding the Red Knot resight program to include other important staging areas along the Atlantic Coast is a stated priority of the USFWS Red Knot Spotlight Species Action Plan (2010) and the Red Knot Conservation Plan (2010). Our objectives in expanding the program into the Georgia Coast during spring migration are to: 1) estimate the population of Red Knots using the Georgia Coast as a spring stopover, 2) estimate spring stopover duration along the Georgia Coast, 3) determine the primary stopover locations and provide this information to local land managers, 4) contribute to the range-wide demographic studies and studies in migratory connectivity of the Red Knot in the Western Hemisphere, and 5) contribute data to the current listing process initiated by the US Fish and Wildlife Service.

This product shows the snow cover duration for a hydrological year. Its beginning differs from the calendar year, since some of the precipitation that falls in late autumn and winter falls as snow and only drains away when the snow melts in the following spring or summer. The meteorological seasons are used for subdivision and the hydrological year begins in autumn and ends in summer. The snow cover duration is made available for three time periods: the snow cover duration for the entire hydrological year (SCD), the early snow cover duration (SCDE), which extends from autumn to midwinter (), and the late snow cover duration (SCDL), which in turn extends over the period from mid-winter to the end of summer. For the northern hemisphere SCD lasts from September 1st to August 31st, for the southern hemisphere it lasts from March 1st to February 28th/29th. The SCDE lasts from September 1st to January 14th in the northern hemisphere and from March 1st to July 14th in the southern hemisphere. The SCDL lasts from January 15th to August 31st in the northern hemisphere and from July 15th to February 28th/29th in the southern hemisphere. The “Global SnowPack” is derived from daily, operational MODIS snow cover product for each day since February 2000. Data gaps due to polar night and cloud cover are filled in several processing steps, which provides a unique global data set characterized by its high accuracy, spatial resolution of 500 meters and continuous future expansion. It consists of the two main elements daily snow cover extent (SCE) and seasonal snow cover duration (SCD; full and for early and late season). Both parameters have been designated by the WMO as essential climate variables, the accurate determination of which is important in order to be able to record the effects of climate change. Changes in the largest part of the cryosphere in terms of area have drastic effects on people and the environment. For more information please also refer to: Dietz, A.J., Kuenzer, C., Conrad, C., 2013. Snow-cover variability in central Asia between 2000 and 2011 derived from improved MODIS daily snow-cover products. International Journal of Remote Sensing 34, 3879–3902. https://doi.org/10.1080/01431161.2013.767480 Dietz, A.J., Kuenzer, C., Dech, S., 2015. Global SnowPack: a new set of snow cover parameters for studying status and dynamics of the planetary snow cover extent. Remote Sensing Letters 6, 844–853. https://doi.org/10.1080/2150704X.2015.1084551 Dietz, A.J., Wohner, C., Kuenzer, C., 2012. European Snow Cover Characteristics between 2000 and 2011 Derived from Improved MODIS Daily Snow Cover Products. Remote Sensing 4. https://doi.org/10.3390/rs4082432 Dietz, J.A., Conrad, C., Kuenzer, C., Gesell, G., Dech, S., 2014. Identifying Changing Snow Cover Characteristics in Central Asia between 1986 and 2014 from Remote Sensing Data. Remote Sensing 6. https://doi.org/10.3390/rs61212752 Rößler, S., Witt, M.S., Ikonen, J., Brown, I.A., Dietz, A.J., 2021. Remote Sensing of Snow Cover Variability and Its Influence on the Runoff of Sápmi’s Rivers. Geosciences 11, 130. https://doi.org/10.3390/geosciences11030130

LANDFIRE's (LF) 2016 Remap (Remap) Existing Vegetation Type (EVT) represents the current distribution of the terrestrial ecological systems classification developed by NatureServe for the western hemisphere. In the context, a terrestrial ecological system is defined as a group of plant community types that tend to co-occur within landscapes with similar ecological processes, substrates, and/or environmental gradients. EVT also includes ruderal or semi-natural vegetation types within the U.S. National Vegetation Classification [(NVC) http://usnvc.org/]. See the EVT product page (https://www.landfire.gov/evt.php) for more information about ecological systems and NVC. EVT is mapped using decision tree models, field data, Landsat imagery, elevation, and biophysical gradient data. Decision tree models are developed separately for tree, shrub, and herbaceous lifeforms which are then used to produce a lifeform specific EVT product. These models are generated for each Environmental Protection Agency (EPA) Level III Ecoregion (https://www.epa.gov/eco-research/ecoregions). Riparian, alpine, sparse and other site-specific EVTs are constrained by predetermined masks. Urban and developed areas are derived from the National Land Cover Database (NLCD), whereas agricultural lands originate from the Cropland Data Layer (CDL) and Common Land Unit (CLU) database. Developed ruderal classes are identified by combining wildland-urban-interface (WUI) data with population density information from the US Census Bureau. Annual Disturbance products are included to describe areas that have experienced landscape change within the previous 10-year period. EVT is then reconciled through QA/QC measures to ensure lifeform is synchronized with both Existing Vegetation Cover (EVC) and Height (EVH) products.

LANDFIRE's (LF) 2016 Remap (Remap) Existing Vegetation Type (EVT) represents the current distribution of the terrestrial ecological systems classification developed by NatureServe for the western hemisphere. In the context, a terrestrial ecological system is defined as a group of plant community types that tend to co-occur within landscapes with similar ecological processes, substrates, and/or environmental gradients. EVT also includes ruderal or semi-natural vegetation types within the U.S. National Vegetation Classification [(NVC) http://usnvc.org/]. See the EVT product page (https://www.landfire.gov/evt.php) for more information about ecological systems and NVC. EVT is mapped using decision tree models, field data, Landsat imagery, elevation, and biophysical gradient data. Decision tree models are developed separately for tree, shrub, and herbaceous lifeforms which are then used to produce a lifeform specific EVT product. These models are generated for each Environmental Protection Agency (EPA) Level III Ecoregion (https://www.epa.gov/eco-research/ecoregions). Riparian, alpine, sparse and other site-specific EVTs are constrained by predetermined masks. Urban and developed areas are derived from the National Land Cover Database (NLCD), whereas agricultural lands originate from the Cropland Data Layer (CDL) and Common Land Unit (CLU) database. Developed ruderal classes are identified by combining wildland-urban-interface (WUI) data with population density information from the US Census Bureau. Annual Disturbance products are included to describe areas that have experienced landscape change within the previous 10-year period. EVT is then reconciled through QA/QC measures to ensure lifeform is synchronized with both Existing Vegetation Cover (EVC) and Height (EVH) products.

This product shows the mean snow cover duration (SCDmean), which is updated each year and consists of the arithmetic mean for the entire time series since the hydrological year 2001. The hydrological year begins in the meteorological autumn (October 1 of the previous year in the northern hemisphere or March 1 of the reference year in the southern hemisphere) and ends with the meteorological summer (northern hemisphere: August 31 of the reference year; southern hemisphere: February 28/29 of the following year). Analogous to the annual products for snow cover duration, the entire year as well as the early season (until mid-winter) and the late season (from mid-winter) are taken into account here. The “Global SnowPack” is derived from daily, operational MODIS snow cover product for each day since February 2000. Data gaps due to polar night and cloud cover are filled in several processing steps, which provides a unique global data set characterized by its high accuracy, spatial resolution of 500 meters and continuous future expansion. It consists of the two main elements daily snow cover extent (SCE) and seasonal snow cover duration (SCD; full and for early and late season). Both parameters have been designated by the WMO as essential climate variables, the accurate determination of which is important in order to be able to record the effects of climate change. Changes in the largest part of the cryosphere in terms of area have drastic effects on people and the environment. For more information please also refer to:

Dietz, A.J., Kuenzer, C., Conrad, C., 2013. Snow-cover variability in central Asia between 2000 and 2011 derived from improved MODIS daily snow-cover products. International Journal of Remote Sensing 34, 3879–3902. https://doi.org/10.1080/01431161.2013.767480 Dietz, A.J., Kuenzer, C., Dech, S., 2015. Global SnowPack: a new set of snow cover parameters for studying status and dynamics of the planetary snow cover extent. Remote Sensing Letters 6, 844–853. https://doi.org/10.1080/2150704X.2015.1084551 Dietz, A.J., Wohner, C., Kuenzer, C., 2012. European Snow Cover Characteristics between 2000 and 2011 Derived from Improved MODIS Daily Snow Cover Products. Remote Sensing 4. https://doi.org/10.3390/rs4082432 Dietz, J.A., Conrad, C., Kuenzer, C., Gesell, G., Dech, S., 2014. Identifying Changing Snow Cover Characteristics in Central Asia between 1986 and 2014 from Remote Sensing Data. Remote Sensing 6. https://doi.org/10.3390/rs61212752 Rößler, S., Witt, M.S., Ikonen, J., Brown, I.A., Dietz, A.J., 2021. Remote Sensing of Snow Cover Variability and Its Influence on the Runoff of Sápmi’s Rivers. Geosciences 11, 130. https://doi.org/10.3390/geosciences11030130

ACCLIP_Model_WB57_Data contains modeled meteorological, chemical, and aerosol data along the flight tracks of the WB-57 aircraft during the Asian Summer Monsoon Chemical & Climate Impact Project (ACCLIP). Data collection for this product is complete.

ACCLIP is an international, multi-organizational suborbital campaign that aims to study aerosols and chemical transport that is associated with the Asian Summer Monsoon (ASM) in the Western Pacific region from 15 July 2022 to 31 August 2022. The ASM is the largest meteorological pattern in the Northern Hemisphere (NH) during the summer and is associated with persistent convection and large anticyclonic flow patterns in the upper troposphere and lower stratosphere (UTLS). This leads to significant enhancements in the UTLS of trace species that originate from pollution or biomass burning. Convection connected to the ASM occurs over South, Southeast, and East Asia, a region with complex and rapidly changing emissions due to its high population density and economic growth. Pollution that reaches the UTLS from this region can have significant effects on the climate and chemistry of the atmosphere, making it important to have an accurate representation and understanding of ASM transport, chemical, and microphysical processes for chemistry-climate models to characterize these interactions and for predicting future impacts on climate.

The ACCLIP campaign is conducted by the National Aeronautics and Space Administration (NASA) and the National Center for Atmospheric Research (NCAR) with the primary goal of investigating the impacts of Asian gas and aerosol emissions on global chemistry and climate. The NASA WB-57 and NCAR G-V aircraft are outfitted with state-of-the-art sensors to accomplish this. ACCLIP seeks to address four scientific objectives related to its main goal. The first is to investigate the transport pathways of ASM uplifted air from inside of the anticyclone to the global UTLS. Another objective is to sample the chemical content of air processed in the ASM in order to quantify the role of the ASM in transporting chemically active species and short-lived climate forcing agents to the UTLS to determine their impact on stratospheric ozone chemistry and global climate. Third, information is obtained on aerosol size, mass, and chemical composition that is necessary for determining the radiative effects of the ASM to constrain models of aerosol formation and for contrasting the organic-rich ASM UTLS aerosol population with that of the background aerosols. Last, ACCLIP seeks to measure the water vapor distribution associated with the monsoon dynamical structure to evaluate transport across the tropopause and determine the role of the ASM in water vapor transport in the stratosphere.