Nemitz, Dirk (2008-12-05)
      This thesis provides important input for the development of a cost-effective global biodiversity assessment and monitoring system. The study is embedded in a larger project to evaluate possibilities of multiple-species surveys using biodiversity GRIDs. As a pilot study six GRIDs in diverse ecosystem settings are sampled. Sampling methods used for animal species are point transects for birds and trapping webs for arthropods; additionally a line transects add-on protocol is used at some study areas for amphibians, reptiles and butterflies. Within this framework the task is taken over to develop predictive models for sampled animal species with Random Forests. Additionally the data is analyzed to derive abundance estimates with multiple covariate DISTANCE sampling and occupancy estimates through the software PRESENCE. A total of 5,007 observations from six study areas from all over the world are analyzed in detail. Total sampling time is about 12 weeks. High quality non-random predictive models with a ROC value > 0.5 are gained with Random Forests analysis for 116 described animal narratives. Half of these observations origin from point transect sampling, the other half from trapping web catches. The line transects add-on protocol results in another 3 predictive models. Abundance and occupancy estimates are derived from the data for 46 animal narratives, 23 of those for point transect data, 22 for trapping web data, and 1 for line transect data. Predictive modeling with Random Forests proves to be a very powerful tool. DISTANCE sampling estimates from this study show large confidence interval ranges, but are extremely cost-efficient to gather initial information for multiple species rapidly. PRESENCE estimates are partly unsatisfying because of a large portion of animal narratives with perfect occupancy estimates (Psi = 1.0). It is assumed that this is an effect of small sampling size which will not be problematic for larger amounts of data. This has to be kept in mind when comparing DISTANCE and PRESENCE results. Correlation between DISTANCE and PRESENCE detection probability estimates is negative, while correlation between DISTANCE abundance estimates and PRESENCE occupancy estimates is positive for all but one study area. It is recommended to repeat the comparison when data from more plots is available. On one hand the results, the cost-effectiveness of the study, and possibilities opened by this kind of multiple-species multi-method sampling are promising, on the other hand funding for this visionary approach was not available.
    • 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.
    • 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.

      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,