NPSMANDATORY for Data Store: NPS Alpha Unit Code (ACAD)MANDATORY: NPS Unit Type (National Park, National Monument, etc)MANDATORY IF APPLICABLE for Data StoreFalk Huettmann 1, Dirk Nemitz 2, 2007. 1. EWHALE lab, Biology and Wildlife Dept. Institute of Arctic Biology, University of Alaska Fairbanks. 2. Centre for Nature Conservation, University of Goettingen, Germany and Isaac Centre for Nature Conservation, Lincoln University, New Zealand. Rapid Biodiversity Assessment GRID Sampling Barrow, Northern Alaska, June/July 2008 NA NA This data set contains Rapid Biodiversity Sampling data from a GRID. It includes raw count data for 419 bird observations and 480 insect observations from 30 sampling plots in Barrow, Northern Alaska (app. 156.57717 longitude and 71.24467 latitude). All bird observations carry a radial distance from observer information and were collected according to DISTANCE Sampling point transects. Respectively, a DISTANCE trapping web protocol, allowing for detectability correction in abundance estimates, was applied for ground-living insects. Each plot was visited three times according to the PRESENCE software to obtain occupancy. These data have been data-mined by the author (Nemitz 2008) using RandomForest, Distance Sampling and PRESENCE. All species names are verified according to current ITIS taxonomy. A more detailed biological analysis is coming forward, and will be published elsewhere. Comparable Biodiversity GRID data so far is available for 5 other regions (Nicaragua, Central Alaska, Russian Far East, Papua New-Guinea, Costa Rica). Data from the other study sites are also available online. For details please contact authors.These GRID data were collected in order to develop low cost rapid biodiversity assessment methods corrected for detectability. Data from this site are compatible with 5 other locations at 2007 and 2008, where the same data were collected following an identical protocol. Findings from this study would allow to learn more about the state of biodiversity and multiple-species monitoring.The sampling sites are geo-referenced. DISTANCE Sampling abundance estimates, PRESENCE occupancy estimates and Random Forests predictive modeling were done for a master thesis (Nemitz 2008) and are underway for publication; other analysis findings of this study are currently in progress and will be reported elsewhere. All DISTANCE sampling and PRESENCE project files are available from the authors. For details, please contact authors.2008063020080702Current for 2008 None planned for this dataset 2008 Barrow, Alaska 156,57717156,5654671,2446771,24034National Park Service Theme Category Thesaurus Biodiversity Monitoring GRID Sampling DISTANCE Sampling PRESENCE Occupancy Random Forests Predictive Modeling Multiple-Species Survey Biodiversity Branta bernicla Calidris alpina Somateria mollissima Larus hyperboreus Calcarius lapponicus Limnodromus scolopaceus Clangula hyemalis Gavia immer Gavia pacifica Stercorarius parasiticus Phalaropus fulicarius Phalaropus lobatus Calidris pusilla Plectrophenax nivalis Somateria fischeri Calidris mauri ISO 19115 Topic CategoryBiodiversity Monitoring GRID Sampling DISTANCE Sampling PRESENCE Occupancy Random Forests Predictive Modeling Multiple-Species Survey Biodiversity Branta bernicla Calidris alpina Somateria mollissima Larus hyperboreus Calcarius lapponicus Limnodromus scolopaceus Clangula hyemalis Gavia immer Gavia pacifica Stercorarius parasiticus Phalaropus fulicarius Phalaropus lobatus Calidris pusilla Plectrophenax nivalis Somateria fischeri Calidris mauri Biodiversity Monitoring GRID Sampling DISTANCE Sampling PRESENCE Occupancy Random Forests Predictive Modeling Multiple-Species Survey Biodiversity Branta bernicla Calidris alpina Somateria mollissima Larus hyperboreus Calcarius lapponicus Limnodromus scolopaceus Clangula hyemalis Gavia immer Gavia pacifica Stercorarius parasiticus Phalaropus fulicarius Phalaropus lobatus Calidris pusilla Plectrophenax nivalis Somateria fischeri Calidris mauri National Park System Unit Name ThesaurusBarrow Alaska Arctic National Park System Unit Code ThesaurusBarrow Alaska Arctic Barrow Alaska Arctic Integrated Taxonomic Information System (ITIS) (http://www.itis.gov) KingdomAnimaliaPhylumArthropodaSubphylumChelicerataClassArachnidaarachnidsaracnídeoaraignéesOrderAraneaespidersaranhaSubclassAcariSuperorderAcariformesSubphylumHexapodaClassEntognathaOrderCollembolasnow fliesspringtailscolêmbolocollembolespuces des neigesClassInsectainsectshexapodainsetoinsectesSubclassPterygotaInfraclassNeopteraOrderColeopterabeetlescoléoptèresbesouroSuborderPolyphagaInfraorderCucujiformiaSuperfamilyCucujoideaFamilyCoccinellidaeladybird beetlescoccinellesOrderDipteramoscamosquitognatsmosquitoestrue fliesSuborderNematoceraInfraorderCulicomorphaFamilyCulicidaemosquitoesmaringouinsmoustiquesInfraorderTipulomorphaFamilyTipulidaecrane fliestipulesOrderHemipteratrue bugshemipteransSuborderHeteropteraInfraorderNepomorphaSuperfamilyNotonectoideaFamilyNotonectidaebackswimmersOrderLepidopterabutterfliesmothspapillonspapillons de nuitBorboletaMariposaPhylumChordataSubphylumVertebrataClassAvesBirdsoiseauxOrderAnseriformesDucksGeeseScreamersSwansWaterfowlcanardscygnesoiessauvagineFamilyAnatidaeDucksGeeseSwanscanardscygnesoiesSubfamilyAnatinaeGenusClangulaOldsquawsSpeciesClangula hyemalisLong-tailed DuckPato cola largaOldsquawharelde kakawiGenusSomateriaGreater EidersTypical EidersSpeciesSomateria fischeriSpectacled EiderSpeciesSomateria mollissimaCommon Eidereider à duvetSubfamilyAnserinaeGenusBrantaBrent GeeseSpeciesBranta berniclaBrant GooseBrent GooseGanso de collarBrantbernache cravantGenusCygnusSwansOrderCiconiiformesHeronsIbisesStorksaiglesaigles pêcheursalbatrosalcidéscigognescormoranséperviersfauconsflamantsgoélandsgrèbeshéronshuartshuîtriersibispélicanspétrelspingouinspluvierstotipalméstubinaresAuksPenguinsFamilyGaviidaeLoonsDiversGenusGaviaLoonsSpeciesGavia immerColimbo mayorCommon Loonplongeon huardGreat Northern LoonSpeciesGavia pacificaPacific Loonplongeon du PacifiqueColimbo pacíficoFamilyLaridaeGullsTernsbecs-en-ciseauxgoélandsguillemotslabbesmouettessternesAuksGuillemotsMurresPuffinsSubfamilyLarinaeGenusLarusGullsIvory GullsKittiwakesRoss' GullsSabine's GullsSpeciesLarus hyperboreusGaviota blancaGlaucous Gullgoéland bourgmestreFamilyScolopacidaeSandpipersbécasseauxGenusCalidrisStintsSpeciesCalidris alpinaPlayero dorso rojoDunlinbécasseau variableSpeciesCalidris mauriPlayero occidentalWestern Sandpiperbécasseau d'AlaskaSpeciesCalidris melanotosPlayero pectoralPectoral Sandpiperbécasseau à poitrine cendréeSpeciesCalidris pusillaPlayero semipalmeadoSemipalmated Sandpiperbécasseau semipalméGenusLimnodromusDowitchersSpeciesLimnodromus scolopaceusCosturero pico largoLong-billed Dowitcherbécassin à long becGenusPhalaropusPhalaropesSpeciesPhalaropus fulicariusphalarope à bec largeRed PhalaropeFalaropo pico gruesoSpeciesPhalaropus lobatusRed-necked PhalaropeFalaropo cuello rojophalarope à bec étroitFamilyStercorariidaeGenusStercorariusJaegersSpeciesStercorarius parasiticusSalteador parásitoParasitic Jaegerlabbe parasiteSpeciesStercorarius pomarinusSalteador pomarinoPomarine Jaegerlabbe pomarinPomarine SkuaOrderPasseriformesPerching BirdspassereauxFamilyEmberizidaeEmberizid FinchesAmerican SparrowsTowheesBuntingsNew World SparrowsGenusCalcariusLongspursSpeciesCalcarius lapponicusEscribano árticoLapland Longspurbruant laponGenusPlectrophenaxSnow BuntingsSpeciesPlectrophenax nivalisSnow Buntingbruant des neigesClassMammaliamammifèresmamíferomammalsSubclassTheriaInfraclassEutheriaOrderRodentiaesquilopreáratoroedorrongeursrodentsSuborderMyomorphaFamilyMuridaecampagnolsratssourismiceratsvolesSubfamilyArvicolinaeGenusLemmusbrown lemmingsThe authors and EWHALE/UAF remain the owners of this dataset. However, this data can be distributed or utilized by interested parties. The authors and EWHALE/UAF remain the owners of this dataset. This data can be distributed or utilized by interested parties. However, it is important to interprete the data and findings in the context of the overall study and the methods outlined. Please refer to “Citation” for directions on how to cite when using the data. Falk Huettmann EWHALE lab- Biology and Wildlife Dept., Institute of Arctic Biology, University of Alaska Fairbanks 419 IRVING I Fairbanks Alaska 99775-7000 USA 001 907 474 7882 fffh@uaf.edu Dirk Nemitz Unpublished materialAn assessment of sampling detectability for global bioidversity monitoring: results from sampling GRIDs in different climatic regions, Master thesis 5 Dec 2008 (unpublished) Consistent methods were used, see protocol Dataset is complete for 2008 Field & Lab DISTANCE Sampling PRESENCE / Occupancy BIODIVERSITY GRID For efficiency reasons a systematic sampling approach was chosen. First of all an equally spaced GRID was implemented: 25 points were arranged in five rows and five columns in order to cover a consistent area but also to have a known spatial neighbor relationship among all plots. The distance between plots was 100 m, resulting in a total GRID size of 500 m x 500 m. While the final GRID system ideally covers the globe systematically without intentional placement, for these initial studies the GRIDs were placed in a way that roughly half to two thirds of the plots fell inside a forested area, the remaining plots at the forest edge or inside the cultural landscape. This survey setup enables other studies on the same data set to make realistic and representative statements about fragmentation effects. The only exception is GRID in Barrow in northern Alaska, where naturally only one habitat type, arctic tundra, occurs. Additionally, five points were randomly placed within the GRID to be able to model the influence of random patterns on the results and their spatial relations (Figure 8). The coordinates of each plot were obtained from a regular hand-held GPS receiver and re-visited by using the “Go to” function. All plots as well as the path between them were marked with decomposing flagging tape to make recognition in the field easier. A simple schematic map was drawn by hand for each field work participant to ensure that plots are found when the GPS does not receive signals, as was often the case in dense forest settings. BUDGET CONSTRAINTS The biodiversity GRID is meant as a method for cost-efficient rapid biodiversity assessment that allows for an analysis of spatial relations as well. All methods involved have to work in relatively short time, with low costs and little demand of technological equipment. There is no objection to include more sophisticated methods in add-on protocols, but they are discouraged for the main protocol to keep the inhibition threshold for decision makers low. Trained taxonomists were not available, as they rarely are for many ecosystems. All notes regarding the observed species were made as precisely as possible, although most of the observers were not trained especially in tropical ornithology or entomology. Data collection followed the motto the more detail the better, but it was not intended to refuse data because of lacking taxonomic details. If the observer did not readily know the correct scientific name of a specimen, a common name or, in lack of knowledge of a common name, a short description was noted. This original field note is referred to as the “narrative name” of an observation respectively of a species, whenever possible to correct name according to current ITIS taxonomy was tracked down and added. Such process is common when dealing with large numbers of species and in largely unexplored environments, where huge fractions of the biodiversity remains still unknown, or where appropriate taxonomic guide books are missing. This resulted in good abundance and occupancy estimates, but in less detailed taxonomic data. Such is the characteristic in rapid biodiversity assessments on shoestring budgets, which allow for a first impression and provide detailed information for deeper investigation if desired. This type of rapid assessment additionally serves as a pilot study for further assessments. In the present study the focus lies on spatial global coverage, instead of local detail. ANIMAL SPECIES DATA COLLECTION In the ideal case, the protocol should result not only in information about the presence or absence of species, but also in an estimate of population size. The DISTANCE sampling approach uses the concept of a detection function based on distance of the observed object from the observer to estimate population density. It plays a central role in this study and is used in a number of ways. At each of the 30 plots (25 systematic and 5 random), five minute point transect DISTANCE sampling counts for birds were conducted within 360 degrees. A short settle-in period of one minute was granted prior to counting to allow for the snapshot character of DISTANCE sampling, especially meeting the assumption that presence of the observer does not introduce bias by causing responsive movements of animals. Following common practice the point counts took place only in the morning between 5:30 and 10 am. Birds are known to show higher activity at this time, which generally increases detectability and maximizes inventory accuracy. Each bird seen or heard was noted, including an estimate of the radial distance from the observer. Double counts were avoided by the observer’s attention and the relatively short counting period. Observers decided to make two adjustments: - in study area on Sakhalin Island, Russia seabird observations were excluded from plot A1; - in study area in Barrow, Alaska the survey time was reduced from five to four minutes. The second method of DISTANCE sampling used was a trapping web. 17 pitfall traps with a diameter of 9 cm each were arranged in a DISTANCE sampling trapping web design to estimate ground-living insects. This sampling method is very labor-intensive and could not be implemented at all 30 plots given the short time period available. Thus, four of the plots were systematically selected to capture the general patterns of species and abundances within the GRID: B2, D2, B4 and D4 (underlined in Figure 8) to gather at least some information about ground-living insects. Trapping webs were usually checked every 24 hours; and records were taken every 48 hours. In between check dates the cups were emptied without recording to avoid correlation in time between trapping events, and obtain spatially independent results. Because of the low number of traps and more available work force it was decided to add a third circle of traps at 3 m from the centre in study areas in Russia, Papua New Guinea and Barrow, Alaska. This increased the total number of pitfall traps in these areas to 25. The third application of DISTANCE sampling was an add-on sampling protocol using DISTANCE sampling line transects, conducted at each of the 30 plots. Transects with a length of 10 m and traversing the plot at its centre were surveyed to estimate numbers of butterflies, amphibians and reptiles. DISTANCE sampling point counts for birds and trapping webs for ground living insects were repeated three times. These repetitive visits further allow for an analysis with the software PRESENCE, which gives an estimate of general occurrence of a species in the area in a point-based sense. PRESENCE generates a detection function based on multiple visits under the assumption that the population is closed, meaning that no animals leave or enter the area of interest between several visits. Repetitions were not realized for the add-on protocol for DISTANCE sampling line transects. VEGETATION & ENVIRONMENT Additionally, basic data about the plot environment was collected. If at all possible, the GPS coordinates were noted. A plot picture and a canopy picture were taken with a digital camera to give a general impression of the area and also allow for an analysis of light conditions in other studies on the same data set, e.g. remote sensing investigations. All pictures are available from the authors. A short description of the ecosystem was noted as well (for example: pasture, forest interior, forest edge). Height and diameter at breast height were recorded for all trees within 5 m of plot centre. Estimates were noted regarding canopy cover percentage, understory cover percentage, shrub cover percentage (at 1.35 m height), bare soil percentage, duff coverage percentage, leaf browsing percentage, and number of flowers visible. The thickness of epiphytes, hemi-epiphytes, mosses and lichen was noted in categories (none, low, medium, high). Presence/absence of identified plant species or plant families was noted, as well as remarkable animal tracks (e.g. land crab holes, large mammal tracks, etc). Those are referred to as “Covariates 1 to 32” in all six study areas, but the actual meaning is different in each. Detailed lists and the full protocol are available from the authors. The covariates can have one of four effects: 1. affecting habitat quality (presence/ absence of a species) 2. affecting detectability (detection/ non-detection of a species that is present) 3. affecting both of the above 4. affecting none of the above. Buckland et al 2001Introduction to DISTANCE sampling MacKenzie et a. 2005Occupancy estimates and modeling Breiman 2001Statistical modelling: the two cultures Huettmann & Nemitz Biodiversity GRID Sampling Protocol 00ObservationsCharacteristics of animal observations and trappingsNemitz and HuettmannObservation_IDNoUnique observation identifierNemitz and HuettmannRegion_LabelUnique GRID identifierNemitz and HuettmannRegion_AreaObserved are in square metersNemitz and HuettmannObservation_YearYear of surveyNemitz and HuettmannObservation_TypeType of survey
Bi = DISTANCE sampling point transect, mainly for birds
DT = DISTANCE sampling line transect, mainly for butterflies, reptiles and amphibians
TW = DISTANCE sampling trapping webs, mainly for ground-living insectsNemitz and HuettmannTI_ConnectionDatabase field to connect tables, no value for userNemitz and HuettmannVi_ConnectionDatabase field to connect tables, no value for userNemitz and HuettmannSL_ConnectionDatabase field to connect fields, no value for userNemitz and HuettmannPoint Transect_LabelUnique identifier for each plot within a GRID
5 lines from A to E
5 rows from 1 to 5Nemitz and HuettmannPoint Transect_Visit EffortSurvey effort of each visit.
For Observation_Type = Bi: survey time in minutes
For Observation_Type = DT: length of transect in meters
For Observation_Type = TW: trapping time in minutesNemitz and HuettmannPoint Transect_VisitNumeration of visits at each plotNemitz and HuettmannObservation_Radial DistanceRadial distance from observer to observed animal for DISTANCE samplingNemitz and HuettmannObservation_Cluster sizeNumber of animals observed per observationNemitz and HuettmannObservation_NarrativeCommon name, taxon or description of animal as known by observerNemitz and HuettmannObservation_IdentType of identification (aural/visual)Nemitz and HuettmannObservation_StatusDead or aliveNemitz and HuettmannObservation_CupLabelUnique identifier for each single trap of a trapping webNemitz and HuettmannObservation_CommentAdditional comments as neededNemitz and HuettmannPlotsLocal plot attributes, especially vegetationNemitz and HuettmannTI_ConnectionDatabase field to connect tables, no value for userNemitz and HuettmannTIRegion_LabelAs Region_LabelNemitz and HuettmannTIPoint Transect_LabelAs Point Transect_LabelNemitz and HuettmannPoint Transect_GPSNorthGPS northingNemitz and HuettmannPoint Transect_GPSeastGPS eastingNemitz and HuettmannPoint Transect_Plot typeType of plot (systematically or randomly selected)Nemitz and HuettmannPoint Transect_HabitatQualitative statement of observer about habitat, habitat type (e.g. pasture or forest)Nemitz and HuettmannPoint Transect_EpiphytesCatDensity of epiphytes in four categories (none, low, medium, high)Nemitz and HuettmannPoint Transect_MossLichenCatDensity of moss and lichen in 4 categories (none, low, medium, high)Nemitz and HuettmannPoint Transect_MossPercPercentage of ground covered by mossNemitz and HuettmannPoint Transect_LichenPercPercentage of ground covered by lichenNemitz and HuettmannPoint Transect_BareSoilPercPercentage of ground covered by bare soil (no vegetation)Nemitz and HuettmannPoint Transect_DuffCoverPercPercentage of ground covered by duffNemitz and HuettmannPoint Transect_ShrubsPerc135cmPercentage of ground covered by shrubs at 1.35m heightNemitz and HuettmannPoint Transect_CanopyPercPercentage of ground covered by canopyNemitz and HuettmannPoint Transect_UnderstoryCoverPercPercentage of ground covered by understory/ 2nd canopyNemitz and HuettmannPoint Transect_LeafBrowsingPercPercentage of leaf biomass showing signs of browsingNemitz and HuettmannPoint Transect_FlowersNoNumber of flowers visible from plotNemitz and HuettmannPoint Transect_Leafs.Nemitz and HuettmannPoint Transect_DistNextLakeDistance to next lake in metersNemitz and HuettmannPoint Transect_DiamNextLakeDiameter of nearest lake in metersNemitz and HuettmannPoint Transect_MetersForestEdgeDistance to forest edgeNemitz and HuettmannPoint Transect_MetersForestTrailDistance to nearest forest trailNemitz and HuettmannPoint Transect_CanopyTreesNoNumber of trees at plot reaching into the canopyNemitz and HuettmannPoint Transect_HighestTreeMHeight of highest tree at plot in metersNemitz and HuettmannPoint Transect_HighestDBHcmDiameter at breast height of biggest tree at plot in centimetersNemitz and HuettmannPoint Transect_OpenWaterPercPercentage of ground covered by open waterNemitz and HuettmannPoint Transect_GrassPercPercentage of ground covered by grassNemitz and HuettmannPoint Transect_EpiphytesLowBinary field:
low density = 1
any other = 0Nemitz and HuettmannPoint Transect_EpiphytesMedBinary field:
medium density = 1
any other = 0Nemitz and HuettmannPoint Transect_EpiphytesHiBinary field:
high density = 1
any other = 0Nemitz and HuettmannPoint Transect_MossLichenLowBinary field:
low density = 1
any other = 0Nemitz and HuettmannPoint Transect_MossLichenMedBinary field:
medium density = 1
any other = 0Nemitz and HuettmannPoint Transect_MossLichenHiBinary field:
high density = 1
any other = 0Nemitz and HuettmannPoint Transect_Covariate01Presence/ absence of a specific landscape feature, e.g. holes of a specific animal, feces or a specific plant speciesNemitz and HuettmannPoint Transect_Covariate02Presence/ absence of a specific landscape feature, e.g. holes of a specific animal, feces or a specific plant speciesNemitz and HuettmannPoint Transect_Covariate03Presence/ absence of a specific landscape feature, e.g. holes of a specific animal, feces or a specific plant speciesNemitz and HuettmannPoint Transect_Covariate04Presence/ absence of a specific landscape feature, e.g. holes of a specific animal, feces or a specific plant speciesNemitz and HuettmannPoint Transect_Covariate05Presence/ absence of a specific landscape feature, e.g. holes of a specific animal, feces or a specific plant speciesNemitz and HuettmannPoint Transect_Covariate06Presence/ absence of a specific landscape feature, e.g. holes of a specific animal, feces or a specific plant speciesNemitz and HuettmannPoint Transect_Covariate07Presence/ absence of a specific landscape feature, e.g. holes of a specific animal, feces or a specific plant speciesNemitz and HuettmannPoint Transect_Covariate08Presence/ absence of a specific landscape feature, e.g. holes of a specific animal, feces or a specific plant speciesNemitz and HuettmannPoint Transect_Covariate09Presence/ absence of a specific landscape feature, e.g. holes of a specific animal, feces or a specific plant speciesNemitz and HuettmannPoint Transect_Covariate10Presence/ absence of a specific landscape feature, e.g. holes of a specific animal, feces or a specific plant speciesNemitz and HuettmannPoint Transect_Covariate11Presence/ absence of a specific landscape feature, e.g. holes of a specific animal, feces or a specific plant speciesNemitz and HuettmannPoint Transect_Covariate12Presence/ absence of a specific landscape feature, e.g. holes of a specific animal, feces or a specific plant speciesNemitz and HuettmannPoint Transect_Covariate13Presence/ absence of a specific landscape feature, e.g. holes of a specific animal, feces or a specific plant speciesNemitz and HuettmannPoint Transect_Covariate14Presence/ absence of a specific landscape feature, e.g. holes of a specific animal, feces or a specific plant speciesNemitz and HuettmannPoint Transect_Covariate15Presence/ absence of a specific landscape feature, e.g. holes of a specific animal, feces or a specific plant speciesNemitz and HuettmannPoint Transect_Covariate16Presence/ absence of a specific landscape feature, e.g. holes of a specific animal, feces or a specific plant speciesNemitz and HuettmannPoint Transect_Covariate17Presence/ absence of a specific landscape feature, e.g. holes of a specific animal, feces or a specific plant speciesNemitz and HuettmannPoint Transect_Covariate18Presence/ absence of a specific landscape feature, e.g. holes of a specific animal, feces or a specific plant speciesNemitz and HuettmannPoint Transect_Covariate19Presence/ absence of a specific landscape feature, e.g. holes of a specific animal, feces or a specific plant speciesNemitz and HuettmannPoint Transect_Covariate20Presence/ absence of a specific landscape feature, e.g. holes of a specific animal, feces or a specific plant speciesNemitz and HuettmannPoint Transect_Covariate21Presence/ absence of a specific landscape feature, e.g. holes of a specific animal, feces or a specific plant speciesNemitz and HuettmannPoint Transect_Covariate22Presence/ absence of a specific landscape feature, e.g. holes of a specific animal, feces or a specific plant speciesNemitz and HuettmannPoint Transect_Covariate23Presence/ absence of a specific landscape feature, e.g. holes of a specific animal, feces or a specific plant speciesNemitz and HuettmannPoint Transect_Covariate24Presence/ absence of a specific landscape feature, e.g. holes of a specific animal, feces or a specific plant speciesNemitz and HuettmannPoint Transect_Covariate25Presence/ absence of a specific landscape feature, e.g. holes of a specific animal, feces or a specific plant speciesNemitz and HuettmannPoint Transect_Covariate26Presence/ absence of a specific landscape feature, e.g. holes of a specific animal, feces or a specific plant speciesNemitz and HuettmannPoint Transect_Covariate27Presence/ absence of a specific landscape feature, e.g. holes of a specific animal, feces or a specific plant speciesNemitz and HuettmannPoint Transect_Covariate28Presence/ absence of a specific landscape feature, e.g. holes of a specific animal, feces or a specific plant speciesNemitz and HuettmannPoint Transect_Covariate29Presence/ absence of a specific landscape feature, e.g. holes of a specific animal, feces or a specific plant speciesNemitz and HuettmannPoint Transect_Covariate30Presence/ absence of a specific landscape feature, e.g. holes of a specific animal, feces or a specific plant speciesNemitz and HuettmannPoint Transect_Covariate31Presence/ absence of a specific landscape feature, e.g. holes of a specific animal, feces or a specific plant speciesNemitz and HuettmannSpecies_ListList of species encounteredNemitz and HuettmannSLRegion_LabelAs Region_LabelNemitz and HuettmannSLObservation_TypeAs Observation_TypeNemitz and HuettmannSLObservation_NoTotal number of observations of this particular species/ narrativeNemitz and HuettmannSLObservation_NarrativeAs Observation_NarrativeNemitz and HuettmannObservation_BiolKingdomTaxonomy: kingdomNemitz and HuettmannObservation_BiolPhylumTaxonomy: PhylumNemitz and HuettmannObservation_BiolSubphylumTaxonomy: subphylumNemitz and HuettmannObservation_BiolClassTaxonomy: classNemitz and HuettmannObservation_BiolOrderTaxonomy: orderNemitz and HuettmannObservation_BiolFamilyTaxonomy: familyNemitz and HuettmannObservation_BiolGenusTaxonomy: genusNemitz and HuettmannObservation_BiolSpeciesTaxonomy: species (only 2nd part of species name)Nemitz and HuettmannSL_ConnectionDatabase field to connect fields, no value for userNemitz and HuettmannVisitsSpecifications of visitNemitz and Huettmann
ViRegion_LabelAs Region_LabelNemitz and HuettmannVi_TypeAs Observation_TypeNemitz and HuettmannVi_Point Transect_LabelAs Point Transect_LabelNemitz and HuettmannVi_Point Transect_VisitAs Point Transect_VisitNemitz and HuettmannObservation_ObserverName of the person conducting the surveys of this visitNemitz and HuettmannVi_Observation_YearAs Observation_YearNemitz and HuettmannObservation_MonthMonth in which the survey took placeNemitz and HuettmannObservation_DayDay on which the observation took placeNemitz and HuettmannObservation_FullDateFull date on which the observation took place in format yyyymmddNemitz and HuettmannObservation_DawnDawn time on the day of observation (calculated)Nemitz and HuettmannObservation_StartTimeStart time of the surveyNemitz and HuettmannObservation_MinSinceDawnMinutes between calculated dawn time and start of surveyNemitz and HuettmannTI_ConnectionDatabase field to connect tables, no value for userNemitz and HuettmannVi_ConnectionDatabase field to connect tables, no value for userNemitz and HuettmannFalk Huettmann EWHALE lab- Biology and Wildlife Dept., Institute of Arctic Biology, University of Alaska Fairbanks 419 IRVING I Fairbanks Alaska 99775-7000 USA 001 907 474 7882 fffh@uaf.edu The authors and the hosting institutions shall not be held liable for improper or incorrect use of the data described and/or contained herein. These data and related graphics (i.e. GIF or JPG format files) are not legal documents and are not intended to be used as such. The information contained in these data is dynamic and may change over time. The data are not better than the original sources from which they were derived. It is the responsibility of the data user to use the data appropriately and consistent within the limitations of geospatial data in general and these data in particular. The related graphics are intended to aid the data user in acquiring relevant data; it is not appropriate to use the related graphics as data. The authors give no warranty, expressed or implied, as to the accuracy, reliability, or completeness of these data. It is strongly recommended that these data are directly acquired from an NPS server and not indirectly through other sources which may have changed the data in some way. Although these data have been processed successfully on computer systems at the University of Alaska, no warranty expressed or implied is made regarding the utility of the data on other systems for general or scientific purposes, nor shall the act of distribution constitute any such warranty. This disclaimer applies both to individual use of the data and aggregate use with other data.None20081008 Falk Huettmann EWHALE lab- Biology and Wildlife Dept., Institute of Arctic Biology, University of Alaska Fairbanks 419 IRVING I Fairbanks Alaska 99775-7000 USA 001 907 474 7882 fffh@uaf.edu; dirk@naturecon.de FGDC Biological Data Profile of the Content Standard for Digital Geospatial Metadata FGDC-STD-001-1998http://nrdata.nps.gov/profiles/NPS_Profile.xmlNPS NR and GIS Metadata Profile