• 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.
    • Red squirrel midden model prediction GIS data

      Robold, Richard; Huettmann, Falk (11/30/2019)
      This dataset features the best-available compilation about Red Squirrels (Tamiasciurus hudsonicus, taxonomic serial number 180168 ) GIS model predictions in a study area in Fairbanks,Alaska. This dataset starts in 20016 and ends in 2017. The data are referenced in time and in space (GPS) and it consist of GIS layers for the UAF campus trails, including LIDAR; the geographic projection is UTM 6N in meters. The dara are compiled from sightings and records by the first author. This dataset represents opportunistic as well as complete sightings for a study area at UAF campus. The actual squirrel data are compiled into an MS Excel sheet and all other data layers are in ESRI format: raster or shapefile Tthe size of the overall data package is app. 21 MB.
    • 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.
    • 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
    • AN ASSESSMENT OF SAMPLING DETECTABILITY FOR GLOBAL BIODIVERSITY MONITORING: RESULTS FROM SAMPLING GRIDS IN DIFFERENT CLIMATIC REGIONS

      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.
    • 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.
    • Spatial and Temporal Prediction Models of Alaska’s 11 Species Mega-Predator Community: Towards a First State-wide Ecological Habitat, Impact, and Climate Assessment

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

      Henkelmann, Antje (2011-02-21)
      As 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.
    • Model-predicting the Effect of Freshwater Inflow on Saltwater Layers, Migration and Life History of Zooplankton in the Arctic Ocean: Towards Scenarios and Future Trends

      Schmid, Moritz (2012-04)
      The 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.
    • EWHALE metadata

      Huettmann, Falk (2012-10-19)
    • Meta-Analysis on the Effects of Global Economic Growth on Birds in the Nations of the Three Poles

      Infante, Cynthia Gabriela Reséndiz (2012-11)
      Economic 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.
    • Rapid multi-nation distribution assessment of a charismatic species of conservation concern using ensemble model predictions: The Red Panda in the Hindu-Kush Himalaya region

      Kandel, Kamal; Suwal, Madan Krishna; Regmi, Ganga Ram; Nijman, Vincent; Nekaris, K.A.I.; Lama, Sonam Tashi; Thapa, Arjun; Sharma, Hari Prasad; Subedi, Tulsi Ram; Huettmann, Falk (2013)
      Background The red panda (Ailurus fulgens) is a globally threatened species living in the multi-nation Hindu-Kush Himalaya (HKH) region. It has a declining population trend due to anthropogenic pressures. Additionally, human-driven climate change is expected to have substantial impacts on the fragmented populations and the fragile habitats throughout its range. However, quantitative and transparent information on the ecological niche (potential as well as realized) of this species and the distribution across the vast and complex eight nations of the HKH region is still lacking. Such baseline information is not only crucial for identifying new populations but also for restoring locally extinct populations and for understanding its bio-geographical evolution, as well as for prioritizing regions and efficient management actions. Our study presents the first quantitative large-scale prediction of the potential ecological niche of red panda for the entire HKH. Methodology/Principal Findings We compiled, and made publicly available the best known ‘presence only’ red panda dataset with ISO compliant metadata. This was done through the International Centre for Integrated Mountain Development (ICIMOD.org) data-platform to the Global Biodiversity Information Facility (GBIF.org). We used data mining and machine learning algorithms such as high-performance commercial Classification and Regression Trees (CART), Random Forest, TreeNet, and Multivariate Adaptive Regression Splines (MARS) implementations (Salford Systems Ltd). We averaged all these models for the first produced Ensemble Model for HKH as well as for this species. Conclusions/Significance Our predictive model allows finding major drivers of the red panda ecological distribution niche, as well as to assess and fine-tune earlier habitat area estimates for management. Our models can be used by the Red Panda Recovery Team, Red Panda Action Plan etc. because they are robust, transparent, publicly available, fit for use, and have a good accuracy, as judged by several metrics e.g. Receiver Operating Characteristics (ROC-AUC) curves, expert opinion and assessed by known absence locations
    • Nest and attribute data for 24 Hooded Cranes (Grus monacha) and control plots from field surveys in Northeast China 1993-2010

      Huettmann, Falk (Yu Guo, S. Jiao and colleagues, 2013-05-31)
      This 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).
    • Non-invasive nocturnal surveys of sea turtle nesting beaches at La Flor (Pacific) Nicaragua, and Pacuare Reserve and Tortuguero (Caribbean) Costa Rica, July 2013

      Huettmann, Falk (Maderas Rainforest Conservancy, 2013-07)
      Marine turtles on the nesting beaches of La Flor public beach (latitude11.14282, longitude 11.14282, geographic datum WGS84), Pacific Nicaragua, and Pacuare Reserve public beach (latitude 10.20123, longitude 83.25925) and Tortuguero (latitude 10.59583, longitude 83.52520) , Caribbean, Costa Rica were observed during late hours after sunset. Observations where noninvasive, geo-referenced and observers stayed three meters away from the sea turtles, according to the national requirements (no light, and some limited red light was used for field clarifications). Many surrounding attributes were taken into consideration and measured including date, time, species, location, observed cysts present in the facial region, visually estimated carapace length, other disturbances present on the beach, number of people/tourists and dogs present, plastic encountered, and if applicable, start and end time of specific activities of nesting (such as start of nest time, start of egg laying and start of Ridley dance.) These data are part of a citizen science project and from a sea turtle fieldclass with Maderas Rainforest Conservancy. This dataset consists of an MS Excel sheet and is less than 1MB in size. Some photoes were taken to present the beaches and procedures.
    • Micro-habitat description of sea turtle nests on three public nesting beaches in LaFlor (Pacific) southeast Nicaragua, and Pacuare Reserve and Tortuguero (Caribbean) Costa Rica during July 2013

      Huettmann, Falk (Maderas Rainforest Conservancy, 2013-07)
      Descriptions of the micro habitat of sea turtle nests were recorded on three public beaches: La Flor (latitude 11.14282, longitude 85.79418, Pacific) in southwest Nicaragua, and Pacuar Reserve (latitude10.20123 longitude 83.25925), and Tourtuguero (latitude 10.59583, longitude 83.52520) at the Caribbean coast of Costa Rica. Species covered are the Olive Ridley sea turtle (Lepidochelys olivacea, TSN 173840), 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). After a nest was identified during night and the turtle left successfully, next day the following micro-habitat attributes were collected with dates: distance from waterline, distance from treeline, slope of beach, nearest known nest, and tree height. The presence of egg shells, tree canopy cover, vegatation type, occurence of plastic and driftwood were also recorded, as well as a description of the sand. Geo-referencing of nests was done with a GPS using decimal latitude and longitude (geographic datum of WGS84). This dataset consists of an MS Excel sheet and is <1MB in size. Some photos are also provided for visual purposes.
    • 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.
    • 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 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,
    • Rapid Biodiversity Assessment GRID Sampling (citizen science) in the White Mountains, northern Alaska, USA, June 2013

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