• 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.
    • Rapid Assessment Biodiversity Grid Data for ‘Lueneburger Heide’ (Lueneburg Heathland), Schneverdingen, Germany, August 2015

      Huettmann, Falk (2015-08)
      This data set consists of a citizen-science type rapid biodiversity assessment at landscape of ‘Lueneburger Heide’ (Lueneburg Heathland) near Schneverdingen, Germany.The data include 25 plots regularly 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: 53.03996 til 53.10003 Northern latitude, and 9.80769 til 9.81478 Western longitude. The grid was visited three times between August 8th til 12th in 2015 allowing for occupancy and distance sampling abundance estimates for instance. Point transects were carried out for bird sightings. Also, mammalian evidence, the vegetation (flowers and trees) and some insects (primarily ants) 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 human impacts (biofuel mais and tree plantations, sheep farming “Heidschnucken’, tourism, local train), as well as songbirds. Despite its esthetic and highly managed heath landscape appeal, the grid area is essentially a highly frequented surban nature recreation reserve but avian raptors, common cranes, rare amphibians (moor frog, roe deer, boar and re-introduced wolf can be found! Incidence of hunting occurs (e.g. high stands). This dataset consists of an MS Excel sheet and is less than 1MB in size.
    • Rapid Biodiversity Assessment GRID Sampling (citizen science) in the White Mountains, northern Alaska, USA, June 2013

      Huettmann, Falk (2013-07-03)
      This data set consists of a Rapid Biodiversity Sampling from a non-intrusive citizen science GRID (5*5 geo-referenced sampling + 5 random plots = 30 plots overall). It includes raw count data for 305 bird observations of 14 identified bird species, and plants from the 25+5 survey sampling plots (3m radius), as well as insects from four trapping webs (3m radius, 25 traps each, 3 repeats; mostly spiders were detected). The survey was done between 25th of June and 27th of June in 2013 at the White Mountains (north to the NorthForkBirch and Twelvemile Creeks - near the Steese Hgw 6) in Alaska USA (decimal degrees latitude 65.39590, longitude -145.72890, geographic datum of WGS84) . Plots are located near the public road in the rolling hills, subartic tundra on some slope, and some are located. All plot locations were photographed (sky, ground and habitat shots) and for a visual assessment. All bird detections (visual and oral) carry a radial distance 360 degrees from the observer, and were collected according to DISTANCE Sampling point transect protocols. Respectively, a DISTANCE Sampling trapping web protocol, with a 3m radius and allowing for detectability correction in abundance estimates, was applied for ground-living insects. Generally, these data do not cover high detailed taxonomic information (and usually just follow basic but accurate descriptions). Each plot was visited three times according to the PRESENCE software requirements to obtain occupancy estimates. All of these data can be data-mined using for instance freely available RandomForest, Distance Sampling and PRESENCE software packages. Comparable Biodiversity GRID data so far is available for over 11 other regions (e.g. Nicaragua, Central Alaska, Costa Rica, Papua New-Guinea, Northern & Interior Alaska, Northeastern China and Russian Far East). Data from the other study sites are also available online. For more details please contact authors. The fllowing species were identified: dwarf birch (Betula nana Taxonomic Serial Number TSN by ITIS.org 19479), Black crowberry (Empetrum nigrum 23743), Cottongrass (Eriophorum 40079), marsh Labrador tea (Rhododendron tomentosum 894434), Alaska blue berry (Vaccinium ovalifolium 23607), Bird vetch (Vicia cracca 26335), Ant (Formica 154211), Bohemian Waxwing (Bombycilla garrulus 178529), Fox Sparrow (Carduelis flammea 179230), Gray Jay (Perisoreus canadensis 179667), Swainson`s Thrush (Catharus ustulatus 179788), Raven (Corvus corax 179725), Yellow-rumped Warbler (Dendroica coronata 178891), Savannah Sparow (Passerculus sandwichensis 179314), Fox Sparrow (Passerella iliaca 179464), Grouse (Phasianidae 175861), Woodpecker (Picidae 178148), American Robin (Turdus migratorius 179759), Wilson Warbler (Wilsonia pusilla 178973), White-crowned Sparrow (Zonotrichia leucophrys 179455), Common Redpoll (Carduelis flammea 179230).
    • Rapid multi-nation distribution assessment of a charismatic species of conservation concern using ensemble model predictions: The Red Panda in the Hindu-Kush Himalaya region

      Kandel, Kamal; Suwal, Madan Krishna; Regmi, Ganga Ram; Nijman, Vincent; Nekaris, K.A.I.; Lama, Sonam Tashi; Thapa, Arjun; Sharma, Hari Prasad; Subedi, Tulsi Ram; Huettmann, Falk (2013)
      Background The red panda (Ailurus fulgens) is a globally threatened species living in the multi-nation Hindu-Kush Himalaya (HKH) region. It has a declining population trend due to anthropogenic pressures. Additionally, human-driven climate change is expected to have substantial impacts on the fragmented populations and the fragile habitats throughout its range. However, quantitative and transparent information on the ecological niche (potential as well as realized) of this species and the distribution across the vast and complex eight nations of the HKH region is still lacking. Such baseline information is not only crucial for identifying new populations but also for restoring locally extinct populations and for understanding its bio-geographical evolution, as well as for prioritizing regions and efficient management actions. Our study presents the first quantitative large-scale prediction of the potential ecological niche of red panda for the entire HKH. Methodology/Principal Findings We compiled, and made publicly available the best known ‘presence only’ red panda dataset with ISO compliant metadata. This was done through the International Centre for Integrated Mountain Development (ICIMOD.org) data-platform to the Global Biodiversity Information Facility (GBIF.org). We used data mining and machine learning algorithms such as high-performance commercial Classification and Regression Trees (CART), Random Forest, TreeNet, and Multivariate Adaptive Regression Splines (MARS) implementations (Salford Systems Ltd). We averaged all these models for the first produced Ensemble Model for HKH as well as for this species. Conclusions/Significance Our predictive model allows finding major drivers of the red panda ecological distribution niche, as well as to assess and fine-tune earlier habitat area estimates for management. Our models can be used by the Red Panda Recovery Team, Red Panda Action Plan etc. because they are robust, transparent, publicly available, fit for use, and have a good accuracy, as judged by several metrics e.g. Receiver Operating Characteristics (ROC-AUC) curves, expert opinion and assessed by known absence locations
    • Red squirrel midden model prediction GIS data

      Robold, Richard; Huettmann, Falk (11/30/2019)
      This dataset features the best-available compilation about Red Squirrels (Tamiasciurus hudsonicus, taxonomic serial number 180168 ) GIS model predictions in a study area in Fairbanks,Alaska. This dataset starts in 20016 and ends in 2017. The data are referenced in time and in space (GPS) and it consist of GIS layers for the UAF campus trails, including LIDAR; the geographic projection is UTM 6N in meters. The dara are compiled from sightings and records by the first author. This dataset represents opportunistic as well as complete sightings for a study area at UAF campus. The actual squirrel data are compiled into an MS Excel sheet and all other data layers are in ESRI format: raster or shapefile Tthe size of the overall data package is app. 21 MB.
    • Spatial and Temporal Prediction Models of Alaska’s 11 Species Mega-Predator Community: Towards a First State-wide Ecological Habitat, Impact, and Climate Assessment

      Semmler, Malte (2010-04-04)
      In this study, eleven mega predators, coyote (Canis latrans), wolf (Canis lupus), fox (Vulpes vulpes), arctic fox (Vulpes lagopus), black bear (Ursus americanus), brown bear (Ursus arctos), polar bear (Ursus maritimus), wolverine (Gulo gulo), marten (Martes americana), lynx (Lynx canadensis) and golden eagle (Aquila chrysaetos) were selected to represent an Ecosystem Unit entitled “Mega Predator”. The most influential factors affecting this Ecosystem Unit were determined using a machine learning algorithm (TreeNet) and a Geographic Information System (GIS). Public available range layers were corrected for errors and detectability using occupancy model, and several ‘robust’ hotspots of the predator community were identified. Anthropogenic variables, such as proximity to railways, together with regionalized IPCC climate variables (precipitation and temperature), Alaska SNAP data and spatial variables (e.g. distance to coast) proved to be the main predictors. A second predictive TreeNet model based on climate data forecasting the next 100 years was also performed to assess the resilience of these predators. The results indicate that the Ecosystem Unit “Mega Predator” shall undergo extreme changes in the next decades, commencing in 30 years or less. The TreeNet model points to a complete shattering of the current mega predator community food chain within the next century as a direct consequence of climate change alone. Owing to the fact that IPCC models are underestimates and other factors co-occur, the findings displayed herewith are consequently underestimates. The results of the first TreeNet model and the second predictive model were used to find the optimal potential protected areas for the predator community. This prioritization search was performed with the program MARXAN. Results of the MARXAN Model indicate that the main importance of protected areas for predators lies in the Brooks Range of Northern Alaska. This study could serve as a first (digital) platform and a first step to provide a basis for landscape planners and conservationists to react properly to the upcoming impact of climate and other changes on entire ecosystems.
    • Vulture and other ornithological survey data in Annapurna and Manaslu Conservation regions

      Karmacharya, Dikpal Krishna; Gyawali, Seejan; Virani, Munir; Huettmann, Falk (2013-10-01)
      This dataset presents geo-referenced summaries of vulture and raptor sightings (from field work as well as from published sources 1977-2013), as well as general avian species lists and their detected abundances, for the Annapurna (ACA) and Manaslu Conservation Areas (MCA). In addition, compiled 'presence only' data of vultures are provided for Nepal and Northern India as well. The bounding box (decimal degrees) of the data coverage is 77.4966 til 87.0667 latitude and 26.3666 til 28.58333 longitude, and Altitude covers 100m til 7969m. The data consist of MS Excel and include 6 worksheets (all bird list, number of bird sighting and counting, descending number of individuals, vulture survey in ACA, raptors in Manaslu, and compiled presence only sightings of vultures for Nepal and Northern India). While this data set is reatively small, it includes a large and complex set of information for a vast and globally relevant region. The following raptor species are primarily covered in this data set: Upland Buzzard (Buteo hemilasius, TSN 175385), Himalayan Griffon (Gyps fulvus, 175487), Golden Eagler (Aquila chrysaetos, 175407), Himalayan Vulture (Gyps himalayensis,175488), Bearded Vulture (Gypaetus barbatus,175483), Egyptian Vulture (Neophron percnopterus, 175481) and White-Rumped Vulture (Gyps bengalensis, 175485). Smaller predators like Common Kestrel (Falco tinnunculus, 175620), Peregrine Falcon (Falco peregrinus,175604), Shikra (Accipiter badius, 55890), Bonelli's Eagle (Aquila fasciata, 175565), Black Kite (Milvus migrans,175469), Mountain Hawk Eagle (Spizaetus nipalensis, 175580), Spotted Owlet (Athene brama, 555472), Western Osprey (Pandion haliaetus, 175590) and Crested Serpent Eagle (Spilornis cheela, 175506) were also reported, In addition, overall app. 763 sighting locations of 143 species of birds are also featured in the data (english names as well as scientific names). Naturalists, bird watchers, modelers as well as investigators of raptors and other birds in the Nepal and Northern Indian regions will find great value in this data set.