• Data (Appendix) for Book Chapter 22: Rapid Assessment of Urban Birds and GIS models of Kathmandu and Pokhara, Nepal with Regmi and Huettmann 2020 Hindu Kush Himalaya: Watersheds Downhill, Springer

      Hansen, Lindsay; Huettmann, Falk (4/2/2020)
      This compiled dataset consists of a field data from rapid assessment of common birds found in urban areas of Kathmandu and Pokhara, Nepal, Hindu Kush-Himalaya (HKH) region.The dataset consists of 31 bird and animal species from a detection survey of 2 transects and photos in MS Excel sheets. It is overlaid with Open Street GIS map predictors for the study areas, and model predicted with GIS. We used the following 6 layers:waterways, natural places, shop polygons, land use, roads and highways and computed proximities for each in GIS. Methods and details are specified in the book chapter by Huettmann in Regmi and Huettmann 2020. This is the first and best compiled field and GIS data for the study area and is to set a start of such views and investigations towards a better and more fair access to data, as part of a better and more democratic decision-making process. Here an example is presented using avian species and GIS habitat layers.
    • Data (Appendix) for Book Chapter 25: Museum Data holdings and Libraries in Nepal and Hindu Kush Himalaya region with Regmi and Huettmann 2020 Hindu Kush Himalaya: Watersheds Downhill, Springer

      Huettmann, Falk (4/2/2020)
      This compiled dataset consists of a value-added analysed GBIF data set in the wider Hindu Kush-Himalaya (HKH) region. The original data source is from individual national contributors found in GBIF. Data are used here for research purposes for the wider HKH region watersheds and to show institutional spread and distribution. Some major outside museums internationally are mentioned too. The dataset consists of MS Excel sheets Methods and details are specified in the book chapter by Huettmann in Regmi and Huettmann 2020. This is the first and best compiled data for the study area and is to set a start of such views and investigations towards a better and more fair access to data, as part of a better and more democratic decision-making process.
    • Data (Appendix) for Book Chapter 28: Sarus Crane GIS Model with Regmi and Huettmann 2020 Hindu Kush-Himalaya: Watersheds Downhill, Springer

      Karmacharya, D. K.; Huettmann, F.; Mi, C; Han, X; Duwal, R; Yadav, SK; Guo, Y (4/2/2020)
      This dataset consist of an appendix of GIS model predictions of Sarus Cranes (GRus antigone Taxonomic Serial Number TSN: 176181) in Nepal. Details are specified in the book chapter by Karmacharya et al in G.R.Regmi and F. Huettmann 2020. This is the first model for this species and shows conservation management implications for the Terai landscape between Nepal and India.
    • Data (Appendix) for Book Chapter 33: Persistent Langur (Semnopithecus) decline in Nepal with Regmi and Huettmann 2020 Hindu Kush Himalaya: Watersheds Downhill, Springer

      Ale, Purna Bahadur; Regmi, Ganga Ram; Huettmann, Falk (4/2/2020)
      This dataset consists of an appendix of a GIS map of langur sp information in Nepal. The datasets are locations, presences and absences from a value-added GBIF.org query, transect data by the authors and literature data Details are specified in the book chapter by Ale et al in Regmi and Huettmann 2020. This is the first and best compiled data for this species in Nepal and shows national declines with large conservation management implications.
    • Data (Appendix) for Book Chapter 37: 'Road, Railroad and Airport data for the Hindu Kush Himalaya region' with Regmi and Huettmann 2020 Hindu Kush Himalaya: Watersheds Downhill, Springer

      Huettmann, Falk (4/2/2020)
      This compiled dataset consists of an appendix of value-added merged GIS maps for roads, railroads and airports in the wider Hindu Kush-Himalaya (HKH) region. The original data source is from individual national DIVA-GIS files and used here for research purposes for the wider HKH region watersheds. Nations included are: Nepal, India, China, Buthan, Kazachstan, Tajikistan, Kyrgystan, Uzbekistan, Turkmenistan, Afghanistan, Iran, Laos, Myanmar, Thailand, Vietnam, Pakistan, Bangladesh and Cambodia. The dataset consists of 21zip archives of these nations also covering railways and airports. Methods and details are specified in the book chapter by Huettmann in Regmi and Huettmann 2020. This is the first and best compiled data for the study area.
    • Data (Appendix) for Book Chapter 43: Citizen Science Experience in Lumbini/Nepali for Sarus Cranes and Lesser Adjudants (Storks) with Regmi and Huettmann 2020 Hindu Kush Himalaya: Watersheds Downhill, Springer

      Karmacharya, D.K.; Duwal, R.; Yadav, S.K. (4/2/2020)
      This dataset consist of an appendix of citizen science data for the Sarus Crane and Adjudant storks in Lumbini and Jagdishpur Reservoir, Nepal. It's a plain MS Excel sheet.
    • Data Submission Package for Manuscript 'Model-predicting Matschie's Tree Kangaroo in Papua New Guinea'

      Falk Huettmann et al. (30-Jul-20)
      These are the GIS data used for modeling Matschie's Tree Kangaroo (Huon Tree Kangaroo) in Papua New Guinea PNG; for details please see metadata. THe manuscript is currently in revision phase.
    • DETERMINATION OF VALUABLE AREAS FOR MIGRATORY SONGBIRDS ALONG THE EAST-ASIAN AUSTRALASIAN FLYWAY (EEAF), AND AN APPROACH FOR STRATEGIC CONSERVATION PLANNING

      Beiring, Maria (2013-07)
      Having valuable high-quality stopover sites available for migratory birds is one of the key factors for the success of migration. However, beside the conservation of breeding and wintering grounds, the actual protection of valuable stopover sites has often been somewhat neglected. Overall 93 of 315 passerine species along the East-Asian Australasian Flyway (EEAF) are declining. That’s the highest overall number of threatened passerines on any known flyway. Additionally, the high human density in South-East Asia and the ongoing degradation of natural resources further poses a serious problem and threat to migratory songbirds and necessitates urgent action. This study aims to identify valuable areas for migratory songbirds along the vast EAAF (China, Japan, Korea, Far Eastern Russia and Alaska) and to develop a first approach for Strategic Conservation Planning. The main methodological framework encompasses predictive modeling (TreeNet, stochastic gradient boosting) and the Strategic Conservation Planning Tool ‘Marxan’. Overall, six models were created by using mistnet data (fall migration) of five selected index species (Arctic Warbler, Yellow Wagtail, Bluethroat, Siberian Rubythroat & Black- faced Bunting) as well as a by developing a ‘Species Richness Index’ (songbirds) and choosing widely used predictive environmental layers. In northern Russia and Alaska, most contiguous areas with a high index of occurrence are concentrated on the coastline of the Pacific Rim with smaller patterns in the interior and differences between their extents. In central-east Asia contiguous areas were found along the coastline stretching deeper inland than for the other regions. For the ‘Species Richness Index’, valuable areas were mostly predicted for the areas along the border of China and Russia, and comprise large parts of the Manchurian forest (deciduous). In general, it’s notable that the characteristics of the predicted hotspots seem to be linked to the habitat preferences of the selected songbirds during the breeding season. At the same time the generally extensive contiguous areas with a high index of occurrence indicate a higher variability in habitat use during fall migration than during the breeding season, too. Moreover the results indicate broad-front migration and putting the concept of a few and narrow migration hotspots in doubt. Nevertheless, the areas with a high index of occurrence have to be seen in view of the actual availability of high-quality staging sites as well. In the framework of Strategic Conservation Planning, five reserve solution scenarios with different focuses (Species Richness, boreal index Species, subboreal index species & all species with consideration of vulnerable areas) were created by using a simulated annealing algorithm implemented in Marxan. In general, only a low percentage (10 - 31 %) of the current protection network covers the reserves for the selected index species generated by Marxan. All reserve solutions should be seen as a first approach and public baseline for future conservation planning processes whereby there is a need of further refinement and assessment throughout a stakeholder’s involvement. Nevertheless, because this is the first Top-down approach for the given study area, the results are important to conservation planners for incorporating areas of high conservation value for migratory songbirds.
    • Distribution and density of the American Red Squirrel (Tamiasciurus hudsonicus ) in the interior Alaskan old-growth forest for 2019

      Huettmann, Falk; Steiner, Moriz (2019-07-31)
      The aim of this project -carried out in July 2019 - was to determine the distribution and density of the American Red Squirrel (Tamiasciurus hudsonicus; taxonomic serial number 180166) in the old-growth forest of interior Alaska; region of Fairbanks. Also the distribution and density of squirrel middens (construction built by the squirrel, which is used for nutrition storing for the winter. Middens also provide as a nest for the squirrel's which can be used as protection from predators. We carried out opportunistic surveys along trails and within forest stands using GPS and notebook. Google Maps were used for navigation and planning help. This work can be used for subsequent model predictions with GIS software and other modelling software programs to obtain the detection rates and the distribution and density of middens in the whole study area (Tanana State Forest).
    • Distribution of White Spruce in Alaska. An Open Access prediction surface from climatic and bioclimatic parameters using ESRI GRID formats.

      Huettmann, Falk (Bettina Ohse, Falk Huettmann, Steffi Ickert-Bond, 2008)
      This open access data set contains a spatially gridded distribution of White Spruce in Alaska (ESRI GRID format), predicted from climatic and bioclimatic parameters (temperature, precipitation, elevation, and aspect). A species distribution model, developed by Bettina Ohse, was used to determine the ecological niche of the species based on the environmental variables. The model was developed within TreeNet, a classification and regression tree software. The ecological niche was then projected into geographical space, resulting in a predictive map of the species distribution in Alaska (4km resolution, tested accuracy of c. 95 %). We used ArcGIS 9.2. Data sources were freely available for the global public, and so were all tools used (prediction algorithms and specific GIS tools). We promote these data and this concept as a role model how to model plant distributions in wilderness areas and for overcoming data gaps in species distributions world-wide. We encourage the use and update of these data for further updating of this concept and its findings.
    • EWHALE metadata

      Huettmann, Falk (2012-10-19)
    • Geo-referenced and documented red squirrel (Tamiasciurus hudsonicus) midden sites from 2016 and 2017 in a highly used forest area behind the University of Alaska, Fairbanks

      Huettmann, Falk; Robold, Richard; Adams, R. (EWHALE Lab, University of Alaska Fairbanks, 2017)
      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.
    • GRID habitat plot survey data for the nesting sea turtles beach La Flor beach, Pacific, southwestern Nicaragua July of 2013

      Huettmann, Falk (Maderas Rainforest Conservancy, 2013-07-29)
      This GRID habitat plot survey was done at a globally relevant sea turtle nesting beach: La Flor (latitude 11.14282, longitude 85.79418, geographic datum WGS84). This sand beach is located at the Pacific Ocean in southwestern Nicaragua, approx. 20 km far from San Juan Del Sur and approx. 30 km far from the Costa Rican border. We did our grid-based habitat survey on the 11th of July in 2013. The GRID points are geo-referenced by latitude and longitude (decimal degrees, WGS84, +- 10 meters acuracy) and were visited only once (no 3 repeats were done because it consists of sand and private/reserve property) and no species information is provided (sand beach). From 25 regular GRID points 13 were inaccessable because of reserve land holdings or dense bush forests. We took three photos (sky, ground vertical view) for every plot, more details can be seen there. This grid can be used for change detection, shoreline location, develeopment questions, and beach erosion questions over time for turtle nest habitat, for instance. Known sea turtles for this region are predominately Olive`s Ridley sea turtle (Lepidochelys olivacea, TSN 173840), but also Hawksbill (Eretmochelys imbricata TSN 208666) and Leatherback sea turtle (Dermochelys coriacea, TSN 173843).
    • GRID survey habitat data for Playa el Coco beach (nesting sea turtles) - Pacific, southwestern Nicaragua July of 2013

      Huettmann, Falk (Maderas Rainforest Conservancy, 2013-07-29)
      This GRID survey was done at the beach of Playa de Coco - near a globally relevant sea turtle nesting beach (La Flor). Playa el Coco (latitude 11.15382, longitude 85.80051, geographic datum WGS84) is situated at the Pacific Ocean in southwestern Nicaragua, approx. 20 km far from San Juan Del Sur and approx. 30 km far from the Costa Rican border, just adjacent to La Flor. We did our grid-based habitat survey on 8th and 9th of July in 2013. The GRID points are geo-referenced by latitude and longitude (decimal degrees, WGS84, +- 10 meters acuracy) and were visited only once (no 3 repeats because it consists of sand and private property) and no species information (mostly empty sand beach). From 25 regular GRID points 9 were inaccessable because of private land holdings or very bushy forests. We took photos for every point (horizontal, vertical, sky), more details can be seen there. This grid can be used for change detection, shoreline location, develeopment, and beach erosion questions over time for turtle nest habitat, for instance. Known sea turtles for this region are predominately Olive`s Ridley turtle (Lepidochelys olivacea, TSN 17384), other species could potentially occur too. On the beach, we also detected Black-throated Magpie-Jay Calocitta colliei 558992, Balck Vulture Coragyps atratus 175272, Groove-billed Ani Crotophaga sulcirostris 177839, Great Kiskadee Pitangus sulphuratus 178301, Magnificent Frigate Bird Fregata magnificens 174763, Black Oystercatcher Haematopus bachman 176475, Sooty Tern Onychoprion fuscatus 824105, Brown Pelican Pelecanus occidentalis 174685, Neotropical Cormorant Phalacrocorax brasilianus 554375 and Banded Wren Thryothorus pleurostictus 563460). This dataset is an MS Excel format and less than 1MB in size.
    • GRID-based habitat plot data for the public nesting sea turtle beach of Pacuare, Caribean Sea, Costa Rica July of 2013

      Huettmann, Falk (Maderas Rainforest Conservancy, 2013-07-29)
      This GRID habitat survey was done at a globally relevant public sea turtle nesting beach: Pacuare (Playa Vigilada). It is situated at the Caribean Sea in Costa Rica ((latitude10.20123 longitude 83.25925; geographic datum WGS84). We did our grid-based habitat survey on 18th of July in 2013. The individual GRID points are geo-referenced by latitude and longitude (decimal degrees, +- 10 meters acuracy) and were visited only once (no 3 repeats because it consists of sand and private property) and no species information was taken (sand beach habitat). From 25 regular GRID points 15 were inaccessable because the nearby jungle and reserve. We took photos for every point (horizontally, ground and sky), more details can be seen there. This grid can for instance be used for change detection, shoreline location, develeopment, and beach erosion questions over time for turtle nest habitat. Known and observed sea turtles to occur for this region are 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). This dataset is in an MS Excel format and is less than 1MB in size.
    • 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,