• Benefits of using marginal opportunistic wildlife behavior data: Constraints and applications across taxa – a dominance hierarchy example relevant for wildlife management

      Jochum, Kim (2008-03-20)
      This study is a new approach on collecting, handling and examining wildlife behavior data across mammal species in order to provide new and unique conclusions from efficient data collection schemes. Sophisticated dominance hierarchy patterns and the ability of individual recognition are well described in many large mammals such as monkeys and cetaceans through the effort of detailed long-term studies. Their implications are well known as important topics regarding management strategies, especially for endangered species. However worldwide, for other large mammals, e.g. bears, detailed long-term wildlife behavior studies are virtually not available. This is due to the inaccessibility and inefficient observation abilities for many animal species in the wild, especially long-term studies. Up to now, it is believed that long-term studies are necessary to describe the existence of social structures like dominance hierarchies and individual perception abilities reliably and to present results in a sophisticated ‘significant’ manner. To accomplish more detailed behavior investigations on species where we lack such long-term data, here a new approach to this discipline ‘behavior modeling’ is presented, concentrating on the use of marginal opportunistic samples. This statistical approach has never been conducted to behavior analysis so far. Marginal behavior data for six species were investigated and c
    • Birding Data for Costa Rica

      Huettmann, Falk (2009)
      These data describe 703 species with geo-referencing information (latitude longitude) for 42 locations in Costa Rica. They are taken from the species lists presented in B. Lawson (2009; A bird-finding guide to Costa Rica. Comstock Publishing Associates and Cornell University Press. ISBN 978-0-8014-7584-9). This database is based on extensive fieldwork by B. Lawson and as described in his book. Here, these extensive species list data got geo-referenced via Google Maps. The resulting database described here consist of 4,829 rows and 8 columns (Page No,Site No,Site Name, latitude, longitude, SpeciesNo,SpeciestoExpect,Source) and is 969KB in size. The following locations were sampled: Arenal Volcano National Park, Bosque de Paz Biological Reserve and Lodge, Bosque del Rio Tigre, Braulio Carrillo National Park, Cabo Blanco Absolute Nature Reserve, Cano Negro National Refuge, Carara National Park, Cerro de la Muerte, Diria National Park, El Copal Biological Reserve, El Rodeo (University for Peace), Esquinas Rainforest Lodge, Irazu Volcano National Park, Kekoldi Hawk Watch, Km 70 (route 2), La Ensenada Wildlife Refuge, La Paz Waterfal Gardens, La Selva Biological Station, Laguna del Lagarto Lodge, Lankester Gardens, Las Alturas, Las Cruces Biological Station, Las Heliconias Lodge, Manuel Antonio National Park, Marenco Beach and Rainforest Lodge, Monteverde Cloud Forest Reserve, Oro Verde Biological Reserve, Palo Verede National Park, Poas Volcano National Park, Rancho Naturalista, Rara Avis Rainforest Lodge, Rincon de la Vieja National Park, Rio Negro, San Gerardo de Dota, Santa Rosa National Park, Selva Bananito Lodge, Talari Mountain Lodge, Tapanti National Park, The Coastline, The University of Costa Rica, Tortuguero National Park, Virgen del Socorro.
    • Camera Trapping Grid Data in Nepal Hindu Kush-Himalaya for regions of Humla 2015 , Manang 2014-2015 and Manang 2016-2017

      Ganga, Regmi; Lama, Rinzin Phunjok; Ghale, Tashi Rapte; Lama, Tenzing; Puri , Ganesh; Huettmann, Falk (2020-04)
      This multi-year, multi-site and multi-species dataset describes Bushnell Camera Trap data from three locations in remote Nepal: Humla 2015, Manang 2014-2015 and Manang 2016-2017. The data from the Hindu Kush-Himalaya region comes from a geo-referenced survey grid and are stored in three MS Excel sheets, also combined and available in CSV consisting of 175658 records and 15 columns with a file size of 19MB. Species covered are Blue Sheep/Bharal (Pseudois nayaur Taxonomic Serial Number TSN 180596 ), Snow Leopard (Panthera unica, Uncia uncia TSN 183811), Beech Marten (Martes foina TSN 621941), Wolf (Canis lupus TSN 180596), birds (Aves Golden Eagle etc.),Pika (Ochotona), Red Fox (Vulpes vulpes TSN 180604), Mountain Weasel (Mustela altaica TSN 621947), Pallas�s Cat (Otocolobus manul, Felis manul TSN 183791), and Golden Jackal (Canis aureus TSN 183817)
    • Climate (temperature & humidity) data logger data from EasyLogUSB in interior Alaska

      Huettmann, Falk (2017)
      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.
    • Compiled occurrence data of migratory Hooded Cranes in Southeast Asia

      Cai, T.; Guo, Y.; Huettmann, F.; Lee, K. (Beijing Forestry University, Beijing China, 2015-01-01)
      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.
    • Compiled Rabies and Trichinosis (presence only) outbreak data for Alaska

      Waltuch, Rebekah (2014-09-01)
      These are two data sets that were compiled during a UAF student research project, Landscape Ecology class 469/669 (eLearning). They represent a value-added data set and can easily be mapped in a Geographic Information System (GIS) etc. For rabies in Alaska, 237 confirmed cases were found of which 158 had complete information (year, coordinates and vector). The rabies cases in this database are from 1914 til 2013; vectors include Dog, Wolf, Red Fox, Coyote, Arctic Fox, Cat, Caribou, Little Brown Bat, Keen's Myotic Bat and Wolverine. The Alaskan trichinosis data cover 1976-2012 and with various details. Species covered are: Walrus Black Bear, Brown Bear, Bear (unspecified) and Polar Bear. These are student project data compiled from various accessible sources (e.g. the State of Alaska Epidiology website <http://www.epi.hss.state.ak.us> and references cited in the Methods), and they are incomplete. However, they can be used for predictive modeling and similar studies and investigations.
    • 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).