RSS 1.0Huettmann, Falk
Recent Submissions
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Distribution and density of the American Red Squirrel (Tamiasciurus hudsonicus ) in the interior Alaskan old-growth forest for 2019The 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).
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Climate (temperature & humidity) data logger data from EasyLogUSB in interior AlaskaThese 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.
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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, FairbanksThis 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.
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Rapid Assessment Biodiversity Grid Data for ‘Lueneburger Heide’ (Lueneburg Heathland), Schneverdingen, Germany, August 2015This 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.
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Rapid Assessment Biodiversity Grid Data for Snow Leopard and Pallas Cat habitats in Manang, Annapurna Conservation Area, Nepal, early June 2015This 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.
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Rapid Assessment Biodiversity Grid Data for a farming field in Lumbini, Nepal, June 2015This 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.
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Presence points and behavioral data of Sarus Crane in Lumbini-Nepal September and October 2014This 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.
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Opportunistic Survey Data of Common Gull (Larus canus) and other detections in an Urban Environment, downtown Fairbanks, Interior Alaska during mid-May 2015This 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.
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Marine surveys (at sea and 16 ports) of Atlantic seabirds, passerines and marine mammals during Semester at Sea cruise fall 2014This 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.
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Model-predicting the Effect of Freshwater Inflow on Saltwater Layers, Migration and Life History of Zooplankton in the Arctic Ocean: Towards Scenarios and Future TrendsThe 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.
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Meta-Analysis on the Effects of Global Economic Growth on Birds in the Nations of the Three PolesEconomic 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.
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Predictive modeling of Alaskan brown bears (Ursus arctos): assessing future climate impacts with open access online softwareAs 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.
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Compiled occurrence data of migratory Hooded Cranes in Southeast AsiaThis 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.
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Nest and attribute data for 24 Hooded Cranes (Grus monacha) and control plots from field surveys in Northeast China 1993-2010This 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).
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Opportunistic Survey Data of Common Gull (Larus canus) and other detections in an Urban Environment, downtown Fairbanks, Interior Alaska during mid-May 2014This 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.
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Compiled Rabies and Trichinosis (presence only) outbreak data for AlaskaThese 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.
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Spatial and Temporal Prediction Models of Alaska’s 11 Species Mega-Predator Community: Towards a First State-wide Ecological Habitat, Impact, and Climate AssessmentIn 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.
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DETERMINATION OF VALUABLE AREAS FOR MIGRATORY SONGBIRDS ALONG THE EAST-ASIAN AUSTRALASIAN FLYWAY (EEAF), AND AN APPROACH FOR STRATEGIC CONSERVATION PLANNINGHaving 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.
