• Macro description of public beach attributes that may effect turtle nesting in Playa de Coco, La Flor (Pacific, southwest Nicaragua) and Pacuare and Tortuguero, (Caribbean, Costa Rica), July 2013

      Huettmann, Falk (Maderas Rainforest Conservancy, 2013-07-29)
      We observed several macro beach features of four public sea turtle nesting beaches for Playa Coco (latitude11.15382, longitude 85.80051; geographic datum WGS84) and La Flor (latitude 11.14282, longitude 85.79418) in southwest Nicaragua (Pacific), and Pacuare Reserve (latitude10.20123, longitude, 83.25925) and Tortugero (latitude 10.59583, longitude 83.52520) in Costa Rica (Caribbean). Recorded features included the intensity of tourism, number of different predator species of sea turtle hatchlings, number of tourists, and line transects for density of plastic, wood, metal, and crab burrows. Light and sound disturbances at night were also recorded as well as man-made objects left overnight on the shore of the beaches. This data set is part of a sea turtle class with Maderas Rainforest Conservancy and provides a basic and non-invasive description and a snap shot in time and space for public nesting beaches of relevance for Olive Ridley (Lepidochelys olivacea, TSN 173840), Green (Chelonia mydas TSN 173833), Hawksbill (Eretmochelys imbricata TSN ) and Leatherback (Dermochelys coriacea TSN 173843 ) sea turtles. Some photos were taken for vizualisation purposes.
    • Marine boat surveys for the olive ridley sea turtle (Lepidochelys olivacea) off of the coast of La Flor Playa de Coco, Nicaragua, 9th and 13th July 2013

      Huettmann, Falk (2013-07)
      Two marine monitoring boat surveys were conducted off the coasts of La Flor and Playa de Coco, southwest Nicaragua. Single and mating pairs of Olive Ridley sea turtles (Lepidochelys olivacea, TSN 1738400), were observed and activity and geo-referencing recorded with a GPS (decial latitude and longitude, geographic datum WGS84). Single turtles were seen basking and breathing at the surface of the water and subsequently would dive into the water. Mating pairs were also observed. Males held onto the females via a hook on the front flippers, scars were observed but no tumors were detected. Some photos were taken for visualisation.
    • Marine surveys (at sea and 16 ports) of Atlantic seabirds, passerines and marine mammals during Semester at Sea cruise fall 2014

      Huettmann, Falk (2014-12-08)
      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.
    • Marine turtle nesting track survey on Playa de Coco, Nicaragua, and Playa Vigilada (Pacuare) and Tortuguero, Costa Rica, July 2013

      Huettmann, Falk (Maderas Rainforest Conservancy, 2013-07)
      Marine turtle tracks were observed on the beaches of Playa de Coco (Pacific, southwest Nicaragua; latitude 11.15382 , longitude 85.80051; WGS 84 ), Playa Vigilada (Pacuare; latitude 10.20123, longitude 83.25925) and Tortuguero (Caribbean, Costa Rica; latitude 10.59583, longitude 83.52520) during the early morning hours. Number of sea turtle tracks were counted along a transect. This was done by walking along the shore every morning to look for specific tracks that matched the tracks of sea turtle species. Maximum number of tracks observed was 8, while the minimum number obsereved was 0. Tracks had distinct features with flipper tracks visible on the edges of the tracts with a single line down the middle were the tail would drag. Tracks were seen to go straight onto the shore while some came up the shore and curved slightly before going back to the ocean. Tracks were observed near resturants and hotels. We observed Olive Ridely sea turtle (Lepidochelys olivacea, TSN 173840) in Playa de Coco, hatchling Leatherback sea turtles (Dermochelys coriacea, TSN 173843), and Green sea turtles (Chelonya mydas, TSN173833) in Playa Vigilada and Tortuguero,
    • Meta-Analysis on the Effects of Global Economic Growth on Birds in the Nations of the Three Poles

      Infante, Cynthia Gabriela Reséndiz (2012-11)
      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.
    • Metadata of six NCEAS data sets in Elith et al. 2020

      Elith, J.; Graham, C.H.; Valavi, R.; Abegg, M.; Bruce, C.; Ford, A.; Guisan, A.; Hijmans, R.J.; Huettmann, F.; Lohmann, L.G.; et al. (7/29/2020)
      The publication by Elith et al. 2020 publishes data from Elith et al. 2006 and consists of six data set; metadata descriptions provided here. Data are found in OSF and as an R package; details provided in Elith et al. 2020 and/or with authors.
    • Micro-habitat description of sea turtle nests on three public nesting beaches in LaFlor (Pacific) southeast Nicaragua, and Pacuare Reserve and Tortuguero (Caribbean) Costa Rica during July 2013

      Huettmann, Falk (Maderas Rainforest Conservancy, 2013-07)
      Descriptions of the micro habitat of sea turtle nests were recorded on three public beaches: La Flor (latitude 11.14282, longitude 85.79418, Pacific) in southwest Nicaragua, and Pacuar Reserve (latitude10.20123 longitude 83.25925), and Tourtuguero (latitude 10.59583, longitude 83.52520) at the Caribbean coast of Costa Rica. Species covered are the Olive Ridley sea turtle (Lepidochelys olivacea, TSN 173840), Leatherback sea turtle (Dermochelys coriacea TSN 173843), and presumably Hawksbill sea turtle (Eretmochelys imbricata TSN 208666) and the Green sea turtle (Chelonia mydas TSN 173833). After a nest was identified during night and the turtle left successfully, next day the following micro-habitat attributes were collected with dates: distance from waterline, distance from treeline, slope of beach, nearest known nest, and tree height. The presence of egg shells, tree canopy cover, vegatation type, occurence of plastic and driftwood were also recorded, as well as a description of the sand. Geo-referencing of nests was done with a GPS using decimal latitude and longitude (geographic datum of WGS84). This dataset consists of an MS Excel sheet and is <1MB in size. Some photos are also provided for visual purposes.
    • Model-predicting the Effect of Freshwater Inflow on Saltwater Layers, Migration and Life History of Zooplankton in the Arctic Ocean: Towards Scenarios and Future Trends

      Schmid, Moritz (2012-04)
      The Arctic Ocean is warming up and an increasing freshwater inflow is triggering major changes in ocean layers. This model study aims at creating a baseline, and analyzing the effect of freshwater content changes, subsequent freshwater sealing as well as related parameters in the Arctic Ocean on migration and life history of zooplankton such as copepods and euphausiids. Copepods and euphausiids make for a major part of the zooplankton biomass in the Arctic Ocean, and are an important part of the food chain. Analyses are carried out using an ecosystem-based, spatial modeling approach with machine learning algorithms (Salford Systems TreeNet®, Random Forests® and R implementations). The underlying data consists of over 100 predictors including a globally unique data set of physical oceanography. Raw data that was used in this project is available as metadata from the Core Science Metadata Clearinghouse (former National Biological Information Infrastructure) and available at http://mercury.ornl.gov/clearinghouse/ and on servers from the University of Alaska Fairbanks. The Canadian Earth System Model 2 (CanESM2) was utilized to model the effect of changing climate on zooplankton for the next 100 years and for a low emission (RCP26) and a high emission scenario (RCP85). The results consist of spatially explicit (where every point in the layer is geo referenced) and predicted layers for Geographic Information Systems (GIS) that show predicted plankton presence/random absence as well as the relative index of depth and life stage distribution where the zooplankton is most likely to occur. The models show a clear trend towards an increasing relative index of depth where zooplankton is most likely to be found for the year 2100. Moreover, a trend towards a diminishing ecological niche for adult life stages of zooplankton was observed. These changes add stress to the life of zooplankton, especially regarding the diel vertical migration of mostly adult life stages. If zooplankton has to migrate a longer way, this will most likely increase energy expenditure and predation risk which ultimately decreases fitness. When accounting for other man-made impacts on the ocean such as ocean acidification and increasing shipping in the Arctic and taking the big picture into account, the outlook and conditions for zooplankton in 2100 are negative.
    • Nest and attribute data for 24 Hooded Cranes (Grus monacha) and control plots from field surveys in Northeast China 1993-2010

      Huettmann, Falk (Yu Guo, S. Jiao and colleagues, 2013-05-31)
      This dataset conists of 24 nests and their environmental attributes for the Hooded Crane (Grus monarcha, taxonomic serial number TSN 176186, Avibase ID38F36091DBC85095) in China. It is a legacy dataset, presents the best available information, and covers 9 years of survey work (time window 1993-2010). The Hooded Crane is a vulnerable (VU) species according to the IUCN Red list. The estimated world population of this species is just 10,160 individuals, of which more than 8,000 winter in Izumi, Japan. The Hooded Crane breeds in landscapes of Eastern Russia and Northeastern China. It generally nests in forest swamps, mostly within the permafrost zone. This data set can be used for nest preference studies and is available as an Open Office, ASCII or MS Excel format data sheet. The following environmental attributes have been collected for nests as well as for 81 control plots: elevation (in Meters), aspect (in Degrees), slope position (in degrees), distance to the nearest tree (Meters), distance to the nearest road (meters), distance to the nearest human settlement (Meters), distance to the nearest feeding site (Meters), distance to the nearest skidding road (Meters), water surface area around the nest (Square Meters), average water depth around the nest (Centimeter), canopy coverage (Percent), shrub coverage (Percent), grass coverage (Percent). number of trees (Count).
    • Non-invasive nocturnal surveys of sea turtle nesting beaches at La Flor (Pacific) Nicaragua, and Pacuare Reserve and Tortuguero (Caribbean) Costa Rica, July 2013

      Huettmann, Falk (Maderas Rainforest Conservancy, 2013-07)
      Marine turtles on the nesting beaches of La Flor public beach (latitude11.14282, longitude 11.14282, geographic datum WGS84), Pacific Nicaragua, and Pacuare Reserve public beach (latitude 10.20123, longitude 83.25925) and Tortuguero (latitude 10.59583, longitude 83.52520) , Caribbean, Costa Rica were observed during late hours after sunset. Observations where noninvasive, geo-referenced and observers stayed three meters away from the sea turtles, according to the national requirements (no light, and some limited red light was used for field clarifications). Many surrounding attributes were taken into consideration and measured including date, time, species, location, observed cysts present in the facial region, visually estimated carapace length, other disturbances present on the beach, number of people/tourists and dogs present, plastic encountered, and if applicable, start and end time of specific activities of nesting (such as start of nest time, start of egg laying and start of Ridley dance.) These data are part of a citizen science project and from a sea turtle fieldclass with Maderas Rainforest Conservancy. This dataset consists of an MS Excel sheet and is less than 1MB in size. Some photoes were taken to present the beaches and procedures.
    • Opportunistic Survey Data of Common Gull (Larus canus) and other detections in an Urban Environment, downtown Fairbanks, Interior Alaska during mid-May 2014

      Huettmann, Falk; Spangler, Mark (2014-05-17)
      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.
    • Opportunistic Survey Data of Common Gull (Larus canus) and other detections in an Urban Environment, downtown Fairbanks, Interior Alaska during mid-May 2015

      Huettmann, Falk (2015-05-12)
      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 50 geo-referenced point surveys and detections in a rapid assessment fashion. Some opportunistic data were also taken when cruising. Gull data included presence/absenc. Data were geo-referenced with a GPS in decimal degrees (latitude and longitude, geographic datum of WGS84) collected in early breeding season, 12th of May in 2015 on a Tuesday (regular shopping and business times in the U.S.) between 7 AM and 7 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). Other species were also recorded but received less attention. 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 and (gravel) lakes 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, Pigeon, White-crowned Sparrow, Swallow sp, Shorebirds sp, Mallard, Lesser Yellowleg, American Robin, Common Raven, Grassland birds, Northern Shoveler, Grebe sp., and Dark-eyed Junco. Noteworthy are the very high raven and gull numbers at the city garbage dump. 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 156 rows and is less than 1MB in size.
    • Opportunistic Survey Data of Mew Gull (Larus canus) and other detections in an Urban Environment, downtown Fairbanks, Interior Alaska in mid-June 2013

      Huettmann, Falk (EWHALE lab, University of Alaska-Fairbanks (UAF), 2013-07-31)
      This dataset presents a 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 80 point surveys and detections. Data were geo-referenced with a GPS in decimal degrees (latitude and longitude, geographic datum of WGS84) collected 22th of June in 2013 on a Saturday (regular shopping and business times in the U.S.) between 9.30 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. Stripmall parking lots near rivers represent a typical situation of where Mew Gulls are found in an urban habitat during breeding season. We reported basic gull behaviour also. Some other bird species were recorded as well, e.g. Golden Eagle (Aquila chrysaetos 175407), Northern Raven (Corvus corax 179725), Sandhill Crane (Grus canadensis 176177), Bald Eagle (Haliaeetus leucocephalus 175420), Gull sp. (Larus 176803), American Robin (Turdus migratorius 179759). 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. This survey is unique and contributes to baseline information relevant for gulls, ravens, urban subsidized predators (e.g. eagles) 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 are provided for visual information. The dataset is provided in MS Excel and less than 1MB in size.
    • Predictive modeling of Alaskan brown bears (Ursus arctos): assessing future climate impacts with open access online software

      Henkelmann, Antje (2011-02-21)
      As vital representative indicators of the state of the ecosystem, Alaskan brown bear (Ursus arctos) populations have been studied extensively. However, an updated statewide density estimate is still absent, as are models predicting future occurrence and abundance. This kind of information is crucial to ensure population viability by adapting conservation planning to future needs. In this study, a predictive model for brown bear densities in Alaska was developed based on brown bear estimates derived on the best publicly available data (Miller et al. 1997). Salford’s TreeNet data mining software was applied to determine the impact of different environmental variables on bear density and for the first state-wide GIS prediction map for Alaska. The results emphasize the importance of ecoregions, climatic factors in December, human influence and food availability such as salmon. In order to assess the influence of changing climate conditions on brown bear populations, two different IPCC scenarios (A1B and A2) were applied to establish different predictive climate models. The results of these projections indicate a large expansion of brown bear densities within the next 100 years. High density habitat would thus expand from southern coastal areas towards central Alaska. Based on the modeling results, optimum potential protected areas were determined by means of the program Marxan. According to the outcome, the protection of brown bear populations and bear habitat should accordingly focus on areas along the southern coast of Alaska. The study provides a first digital GIS modeling infrastructure for bear densities in Alaska. Through the pro-active temporal and spatial identification of important brown bear habitats and connectivity zones ahead of time, measures ranging from conservation to the planning of transport facilities could be more effectively focused on minimizing and mitigating impacts to these critical areas before real-world problems occur, as well as in an Adaptive Sustainability Management framework.
    • Presence points and behavioral data of Sarus Crane in Lumbini-Nepal September and October 2014

      Huettmann, Falk; Karmacharya, Dikpal Krishna; Duwal, Rabita (EWHALE Lab, University of Alaska Fairbanks, 2015-06-27)
      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.
    • Rabies data for Canada and Alaska/US for GIS model predictions

      Hueffer, K.; Huettmann, F. (2020-04)
      This value-added data set is part of a publication by Huettmann and Hueffer (in prep) and includes the GIS layers for rabies and predictions of Canada, assessed with Alaska locations ((taken from Huettmann et al. 2015). This project compiled the best publically available rabies data for Canada, and models them for the Northern part of the the North American continent (Alaska and Arctic Canada). The environmental data sets are in a common GIS format (ESRI and ASCII grids) and are taken from public Open Access sources. The rabies data sets are point data, as rabies was reported by the Canadian Food Inspection Agency, and processed in the lab. The dataset is 5GB in size and consists of 20 files.
    • Rapid Assessment Biodiversity Grid Data for a farming field in Lumbini, Nepal, June 2015

      Huettmann, Falk; Karmacharya, Dikpal; Spangler, Mark (EWHALE Lab, University of Alaska Fairbanks, 2015-06-27)
      This dataset consists of a rapid biodiversity assessment at a farming site near a village in Lumbini, 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: 27.481920 to 27.486120 Northern latitude, and 83.261799 to 83.266640 Western longitude. The grid was visited three times from June 7th, 8th and 9th in 2015 allowing for occupancy and distance sampling abundance estimates. Point transects were carried out for bird sightings; the insects 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 occurrences of crows, black kites, myrnas and larks, as well the detection of sarus cranes in this landscape that is dominated by domesticated zebu, water buffalo and goats. The grid area was heavily used by humans, e.g. for walking and sanitary purposes. A high incidence of microplastics was found. At the time of survey a heat wave over up to 47 degree Celsius was measured. This dataset consists of an MS Excel sheet and is less than 1MB in size.
    • Rapid Assessment Biodiversity Grid Data for Broad Pass 'divide' in Denali Preserve, Alaska, U.S., July 2016

      Huettmann, Falk; Andrew, Phillip (EWHALE lab, Inst of Arctic Biology, Biology & Wildlife Dept., University of Alaska Fairbanks (UAF), 2016-07)
      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.
    • Rapid Assessment Biodiversity Grid Data for Snow Leopard and Pallas Cat habitats in Manang, Annapurna Conservation Area, Nepal, early June 2015

      Huettmann, Falk; Lama, Rinzin Phunjok; Ghale, Tashi R. (EWHALE Lab, University of Alaska Fairbanks, 2015-06-27)
      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.