These data are daily climate data (temperature and humidity) collected on transects in interior Alaska throughout the year. Data were collected for the years 2015, 2016 and 2017 by ski, with dogs, by bike and by car. An attached EasyLogUSB data logger was used and usually 10 second interval records were collected during a 1hour data session, or more (daily profiles, some are stationary for 24hours). Temperature is collected as degrees Celsius and humidity as percent; data exist as a txt/ASCII format in columns. These data are referenced to time and locations, and they can be used as cross-profiles for landscape climate and ground-truthing of climate models using GIS and geo-referencing. Data include the �sampling of altitudinal profiles, landscape cover, river crossings and various topographies, including coastal-interior gradients. Data collection is still ongoing.

This dataset consists out of 29 presence points of red squirrel (Tamiasciurus hudsonicus, Taxonomic Serial No.: 180166) midden sites. Data was collected in a highly human-used forest area behind the University of Alaska, Fairbanks for summer 2016 (n= 29) and winter/spring 2017 (n=20).The data set consists of an ESRI shapefile for each year. Data was collected in two consecutive years (2016-2017). The first set of data points (summer 2016) was collected with a land cruising survey design and recorded with a GPS unit, based on an opportunistic course project work by R. Adams. The second data collection campaign took place in spring 2017 to check whether the squirrel midden sites from 2016 are still in use (data collected by R. Robold). The coordinate system is decimal degree (5 decimals) and with a geographic projection NAD_1983_Alaska_Albers. The excel sheet has five columns (site ID, a short description of the vegetation, latitude, longitude, and one if the middens are still used in 2017); the excel document (midden_data_with_control.xlsx) size is 10 KB (2017). The map is a JPEG-file (Midden_sites_2016-27_RR) with a size of 3MB and the shapefiles have an overall size of ca. 50 KB each. This data set is the basis for ongoing study on squirrels in the boreal forest and urban areas.

This data set consists of a citizen-science type rapid biodiversity assessment at landscape watershed divide in Denali Preserve, Alaska, U.S..The data include 25 plots spaced 100m apart from each other, and 5 additional random points. The geo-referencing was done with a GPS, a geographic datum of WGS84 was used with decimal degrees (5 decimals) of latitude and longitude. The bounding box of this data set is: 63.33421 til 63.33847 Northern latitude, and 149.10451 til 149.11398 Western longitude. The grid was visited three times from July 7th, 8th and 9th in 2016 allowing for occupancy and distance sampling abundance estimates for instance. Point transects were carried out for bird sightings; the vegetation (flowers and trees) and insects (trapping web of 100 cups) were briefly assessed. Three photos were taken for each grid plot, including one sky shot to capture the atmospheric light conditions, e.g. for Remote Sensing work. Noteworthy are the high occurrences of moose browse, as well as savannah and white-crowned sparrows. Despite its esthetic landscape appeal, the grid area was heavily affected by humans, e.g. a highway, local airport and railway and a camp ground are located at the grid. Incidence of hunting and snowmobiling was found. Basic weather data were also collected for 24h during the survey period. This dataset consists of an MS Excel sheet and is less than 1MB in size.

This dataset consists of a rapid biodiversity assessment at a Snow Leopard (Panthera uncia) and Pallas Cat (Otocolobus manul) habitat site near Manang village in the Annapurna Conservation Area (ACA), Nepal. The data include 25 plots spaced 100m apart from each other, and 5 additional random points. The geo-referencing was done with a GPS, a geographic datum of WGS84 was used with decinal degrees (5 decimals) of latitude and longitude. The bounding box of this data set is: 27.691960 to 28.287450 Northern latitude, and 84.004010 to 83.998410 Western longitude. The grid is located on a slope app. on 4200m above sea level and was visited three times on May 30th, May 31st and June 1st in 2015 allowing for occupancy and distance sampling abundance estimates. Point transects were carried out for bird sightings; animal tracks, insects (butterflies) and vegetation (flowers) were briefly assessed. Two photos were taken for each grid plot, and one sky shot to capture the atmospheric light conditions, e.g. for Remote Sensing work.Noteworthy are the high grazing pressures and strong occurrences of yaks, horses, and goats, as well the detection of bharal (blue sheep), Lammergeier, Himalaya Griffon, Golden Eagle, snow cock, cockoo, pipits and wagtails in this mountain high altitude landscape (all scientific names and details are provided in the taxonomic section of the metadata). Small white snails were found too. A snowleopard kill site (blue sheep and yak) was found, as well as tracks and a resting site on a nearby higher cliff site (where Pallas Cat was also observed earlier). This dataset consists of an MS Excel sheet and is less than 1MB in size.

This dataset presents geo-referenced summaries of Sarus Crane (Grus antigone) and their behavioral sightings from field work. The Lumbini region is the hot spot with more than 250 species of birds and included as an Important Bird Area (IBA) of Nepal. It comprises beautiful cultural and religious resources. Hence, it is under the World Heritage Site of UNESCO. The occurrence of Sarus Crane in this region has religious value related with Buddhism. The 'presence only' data of Sarus Crane are provided for low land Nepal covering most of the VDCs of Rupandehi and Kapilbastu districts. The bounding box (decimal degrees) of the data coverage is 27.30015 til 27.576548 latitude (North) and 83.36193 til 82.990292 longitude (East), and an altitude covering 72m til 111m above sea level. Eight human resources including two experts and six local observers compiled the field-based data. The data consist of an MS Excel which includes 19 rows and 74 columns with the the following column headings: Time, Day, Month and Year of sightings, Observers, Latitude, Longitude and Elevation (m) of the sighting points, Geo-referencing method, Geographic datum, Location, District, Country, Male, Female, ClusterTotal, Habitat, Behaviour and Remarks. While this data set is relatively small, it reflects a large and complex set of representative information for the entire lowland of Nepal. Altogether 201 Sarus Cranes individuals weer counted including 104 male and 97 female within three habitats and four behaviours of birds are also described in the data. Naturalists, bird watchers, modelers as well as investigators of cranes and other birds in the region of Nepal and Northern India will find good value in this data set.

This data set was collected during the Fall 2014 cruise with Semester at Sea (SAS). It covers 2 MS Excel files of marine species surveys (three worksheets: opportunistic 5 minutes, and Distance Sampling 10 minutes, Individual Sightings) as well as harbour species surveys (one file with one worksheet). The SAS cruise of fall 2014 took place in the Atlantic, southern Baltic, the Mediterrean and Caribbean Seas and has the following bounding box coordinates (decimal degrees of latitude and longitude WGS 1984): Highest latitude 59.88929 North, lowest latitude -22.28687 South, most western longitude -81.80984, most eastern longitude -40.91894. The following 16 ports were covered: Southampton (UK), St. Petersburg (Russia), Gdansk (Poland), Rostock (Germany), Antwerp (Belgium), Le Havre (France), Dublin (Ireland), Lisbon (Portugal), Cadiz (Spain), Casablanca (Morocco), Citivecchia (Italy), Barcelona (Spain), Rio De Janeiro (Brazil), Salvador (Brazil), Bridgetown (Barbados), Havanna (Cuba) and Fort Lauderdale (Florida US). The starting date was August 21st and the ending date was Dec 7th 2014. The cruise included app. 632 students and 380 staff and crew (including professors). This dataset includes data from opportunistic 5 minute surveys ('presence only'), as well as 10minute long Distance Sampling surveys (abundances) done 2-3 times per day. All data are geo-referenced. In addition, many additional individual sigthings by the shipboard community and local excursion (terrestrial) data are also included, photos were collected whenever possible. Additional data like state of the ocean, plastic pollution, vessels in view, and weather were also collected. This data set can serve as a basic quantitative large-scale snapshot for the Atlantic region and its ecosystem in fall 2014. The 5 minute-long opportunistic at-sea surveys featured app 102 species; more species were detected in the other surveys. Most of these species are seabirds, with a few mamrine mammals, squid, fish sightings etc. Noteworthy are the high ocurrences of passerines at sea! This dataset has maximum 29 columns, and 314 rows (5 minute Opportunistic Survey), 146 rows (10min abundance surveys) and 271 rows (Individual Opportunistic Sightings); it is <1MB in size. A few photos exist and can be obtained upon request.

Economic and population growth as well as global macroeconomic policies are contributing to increasing global greenhouse gas (GHG) emissions. Climate change effects are more pronounced in cold regions such as the ‘Three Poles’ (the Arctic, the Antarctic and the Hindu Kush-Himalaya regions) than anywhere else. The Three Poles are rich in natural resources but the extraction of resources is degrading ecosystems and processes, and affecting species. In the Three Poles, many species depend on ice and snow habitats, but these species are competing with human activities for space and resources on a finite globe. Local and global pressures cause bird species populations to decline. In this study I investigated how bird populations are affected by economic growth and subsequent effects on the environment in the Three Poles regions. Data mining based on machine learning algorithms was used to perform the analyses. TreeNet (based on regression trees), included in the software Salford Predictive Modeler Builder® v.6.6 was used to develop the models. An additional Random Forests analysis (classification trees) was used to analyze the datasets. Two response variables were chosen based on bird distribution maps provided by BirdLife and the IUCN RedList categories, including those that have risk of extinction or are in the Least Concern category but with declining populations. Data from the WDI Data Catalog of The World Bank (World Development Indicators) were used as predictors. The results include a total of 24 models, classified by pole and type of country according to their direct (primary countries) or indirect (secondary countries) link to the Three Poles regions. A combind model was also run (primary and secondary countries). Models were evaluated according to the response curves and gains charts. Models confirm that global demand for, and consumtion of, resources is affecting the Three Poles. Food production, rural population, CO2 emissions, Gross Domestic Product (GDP) and agricultural land were the top ranked predictors to explain the number of birds classified as threatened or with populations decreasing. This finding supports that the global demand for resources such as oil, gas and fish that were exploited from the Three Poles, added to global warming from anthropogenic causes are significantly affecting bird species populations. However, further research has to be carried out in order to obtain sound information on what the best management and governance is for a sustainable outlook for the Three Poles and beyond.

This dataset represents the best available science-based ocurrences (presence only) of Hooded Cranes during fall and spring migration along the flyway in Asia. This dataset consists of 115 geo-referenced sightings with the source/observer in a comma delimited file format. The geo-referencing was done in decimal latitude and longitude with six decimals. Each record carries a source information and is derived from 21 sources. The biggest data sections come from field obervations of the local authors as well as GBIF, satellite telemetry, and Higuchi (1994) and Chang (1999). This data set has four columns and 115 rows with a size of 30KB.

This dataset presents a Common Gull (Mew Gull, Larus canus, Taxonomic Serial Number TSN 176832) survey data set for urban areas and stripmall parking lots (app. 300mx600m), super markets, fast food restaurants, gravel pits, small ponds and the riverside (Chena) in Fairbanks, interior Alaska located app. 120 miles south of the arctic circle. We did geo-referenced 50 point surveys and detections in a rapid assessment fashion. Some opportunistic data were also taken when cruising. Gull data included detection width to obtain correction factors. Data were geo-referenced with a GPS in decimal degrees (latitude and longitude, geographic datum of WGS84) collected in early breeding season, 17th of May in 2014 on a Saturday (regular shopping and business times in the U.S.) between 8.15 AM and 5 PM by driving opportunistically on public strip mall parking lots, domestic areas and other locations of relevance for gull presences and absences in the survey area (app. 200m radius). These non-intrusive citizen science car-based surveys result into a virtually unbiased data providing a representative snap shot in space and time for the study area. Stripmall parking lots near rivers represent a typical situation of where Common Gulls are found in an urban habitat during breeding season. We reported basic gull behaviour also. Some other bird species were recorded as well but not in great detail, e.g. American Wigeon, Lesser Scaup, Pigeon, White Crowned Sparrow, Swallow sp, Shorebirds sp, Mallard, Lesser Yellowleg, American Robin, Scaups sp., Common Raven, Bunting sp., Grassland birds, Northern Shoveler, Grebe sp., Dark-eyed Junco, Herring Gull, Grouse sp. Noteworthy are the higher abundances of planes and helicopters. The gull abundance seems to be driven by food items on the parking lot, e.g. provided by an adjacent fast food restaurant and by wetland areas nearby (e.g. Chena river and gravel pits) where the gulls find good conditions for nearby nesting. Gulls seem to be quite colonial. This survey is unique and contributes to multi-year baseline information relevant for gulls, ravens, urban subsidized predators (e.g. raptors) in the interior of Alaska. They are a simple but powerful snapshots in time and space and when put into an overall ecological context, e.g. as done with Geographic Information System (GIS) analysis. Some photos exist for visual information. The dataset is provided in MS Excel, consists of 144 rows and is less than 1MB in size.