NPSMANDATORY for Data Store: NPS Alpha Unit Code (ACAD)MANDATORY for Data Store: NPS Unit Type (National Park, National Monument, etc)MANDATORY IF APPLICABLE for Data StoreFalk Huettmann 1, Aleksey Antonov 2, 2009. 1. EWHALE lab, Biology and Wildlife Dept. Institute of Arctic Biology, University of Alaska Fairbanks. University, USA.UnknownRapid Biodiversity Assessment based on GRID Sampling in the Shainjang Wetland Reserve, Fuyan city, Amur river, Northeast China, July 2009 NA NA This data set contains Rapid Biodiversity Sampling data from a GRID. It includes raw count data for 541 bird observations of 38 identified bird species, plants from 30 sampling plots (5*5 100m grid plus 5 random ones lcoated within the grid), and insects from one trapping web, done at the Shainjang Wetland Reserve (RAMSAR Site, and World Heritage Site) at the Amur River, 30km near Fuyan city, Northeastern China (app. 134.57387 longitude and 48.13049 latitude). The site located near the Russian Border. Plots are located in the wetland, with many of them being located on grassland slightly covered by water, and some being located on an access road. All plot locations were photographed (sky and habitat shot). All bird detections (visual and oral) carry a radial distance 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 (only at one plot location due to water coverage elsewhere). Each plot was visited three times according to the PRESENCE software to obtain occupancy. In addition, two cross-profile line transects, app. 200m long each, were carried out in the study area also using DISTANCE SAMPLING. The strongest data set are the birds (species identification and densities), insect identifications are coarse but very good for densities; plant species identifications are coarse, too, but photos exist. They can be used for habitat types and cover percentages, e.g. in Remote Sensing studies nd for groundtruthing. These data can be data-mined using for instance RandomForest, Distance Sampling and PRESENCE. A more detailed biological analysis is coming forward, and will be published elsewhere. Raw data are available as Excel sheets from the authors. Comparable Biodiversity GRID data so far is available for over 5 other regions (e.g. Nicaragua, Central Alaska, Costa Rica, Papua New-Guinea, Northern Alaska). Data from the other study sites are also planned to be available online, and metadata are found online (see with NBII National Biological Information Infrastructure webpage). For more 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, 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 for such types of GRIDs were done for a master thesis (Nemitz 2008).For details, please contact authors.2007080620070824Current for 2009 None planned for this dataset Shainjang Wetland Reserve, Fuyan city, Amur river near Russian Border, Northeast China143.69052143.6952650.6023450.59892NoneBiodiversity MonitoringGRID SamplingDISTANCE SamplingPRESENCEOccupancyData MiningPredictive ModelingMultiple-Species SurveyBiodiversityChinaAmur RiverRAMSAR siteWetlandWorld Heritage SiteNational Park Service Theme Category ThesaurusBiodiversity MonitoringGRID SamplingDISTANCE SamplingPRESENCEOccupancyData MiningPredictive ModelingMultiple-Species SurveyBiodiversityChinaAmur RiverRAMSAR siteWetlandWorld Heritage SiteISO 19115 Topic CategoryBiodiversity MonitoringGRID SamplingDISTANCE SamplingPRESENCEOccupancyData MiningPredictive ModelingMultiple-Species SurveyBiodiversityChinaAmur RiverRAMSAR siteWetlandWorld Heritage SiteNoneChinaAmur RiverRussian BorderShainjang Wetland ReserveFuyan cityNortheastern ChinaNational Park System Unit Name ThesaurusChinaAmur RiverRussian BorderShainjang Wetland ReserveFuyan cityNortheastern ChinaNational Park System Unit Code ThesaurusChinaAmur RiverRussian BorderShainjang Wetland ReserveFuyan cityNortheastern ChinaNonecollectionmultiple speciessingle speciesinvertebratesplantsvegetationvertebratesAcrocephalus bistrigicepsAlcedo atthisAnas creccaAnas falcataAnas platyrhynchos Anas spAnas streperaArdea cinereaCircus melanoleucosCircus spilonotusCorvus coroneCuculus canorusDomestic DuckEgretta albaEmberiza fucataEmberiza schoeniclus Emberiza spFalco amurensisFalco subbuteoFulica atraGallinula chloropusHirundo dauricaHirundo rusticaHirundo spIxobrychus eurythmos Lanius cristatusLarus ridibundusLocustella certhiolaMotacilla albaPandion haliaetus Passer montanusPasserinesPhalacrocorax carboPhasianus colchicusPica picaRallus spSaxicola torquataSterna hirundoStreptopelia orientalisTachybaptus ruficollisTringa nebulariaVanellus vanellusCarabidaeITIS (nonmatches are listed)UnknownFalk Huettmann and Aleksey AntonovEWHALE labmailing and physical419 Irving IFairbanksAlaska99775USA907 474 7882fffh@uaf.eduBirds were identified visually (or orally), and confirmed by binocular. The strongest data set are the birds (species identification and densities), insect identifications are coarse but good to be used for densities; plant species identifications are coarse, too, but photos exist. They can be used for habitat types and cover percentages, e.g. in Remote Sensing studies nd for groundtruthing.complete for birds, incomplete for insects and plants (photos were taken)KingdomAnimaliaPhylumArthropodaSubphylumHexapodaClassInsectaSubclassPterygotaInfraclassNeopteraOrderColeopteraSuborderAdephagaFamilyCarabidaecarabesPhylumChordataSubphylumVertebrataClassAvesOrderAnseriformesFamilyAnatidaeSubfamilyAnatinaeGenusAnasDabbling DucksSpeciesAnas creccasarcelle d'hiverSpeciesAnas falcataFalcated TealSpeciesAnas platyrhynchoscanard colvertSpeciesAnas streperacanard chipeauOrderCiconiiformesFamilyAccipitridaeSubfamilyAccipitrinaeGenusCircusSpeciesCircus melanoleucosPied HarrierSpeciesCircus spilonotusEastern Marsh HarrierSubfamilyPandioninaeGenusPandionSpeciesPandion haliaetusbalbuzard pêcheurFamilyArdeidaeGenusArdeaSpeciesArdea albaGreat EgretSpeciesArdea cinereaGrey HeronGenusIxobrychusSpeciesIxobrychus eurhythmusVon Schrenck's BitternFamilyCharadriidaeGenusVanellusSpeciesVanellus vanellusvanneau huppéFamilyFalconidaeGenusFalcoSpeciesFalco amurensisAmur FalconSpeciesFalco subbuteoEurasian HobbyFamilyLaridaeSubfamilyLarinaeGenusLarusSpeciesLarus ridibundusmouette rieuseSubfamilySterninaeGenusSternaSpeciesSterna hirundosterne pierregarinFamilyPhalacrocoracidaeGenusPhalacrocoraxSpeciesPhalacrocorax carbogrand cormoranFamilyPodicipedidaeGenusTachybaptusSpeciesTachybaptus ruficollisLittle GrebeFamilyScolopacidaeGenusTringaSpeciesTringa nebulariaCommon GreenshankOrderColumbiformesFamilyColumbidaeSubfamilyColumbinaeGenusStreptopeliaSpeciesStreptopelia orientalisOriental Turtle DoveOrderCoraciiformesFamilyAlcedinidaeSubfamilyAlcedininaeGenusAlcedoSpeciesAlcedo atthisCommon KingfisherOrderCuculiformesFamilyCuculidaeSubfamilyCuculinaeGenusCuculusSpeciesCuculus canorusCommon CuckooOrderGalliformesFamilyPhasianidaeSubfamilyPhasianinaeGenusPhasianusSpeciesPhasianus colchicusFaisán de collarOrderGruiformesFamilyRallidaeGenusFulicaSpeciesFulica atraEurasian CootGenusGallinulaSpeciesGallinula chloropusgallinule poule-d'eauGenusRallusGreater RailsOrderPasseriformesFamilyCorvidaeGenusCorvusSpeciesCorvus coroneCarrion CrowGenusPicaSpeciesPica picaEurasian MagpieFamilyEmberizidaeGenusEmberizaEurasian BuntingsSpeciesEmberiza fucataChestnut-eared BuntingSpeciesEmberiza schoeniclusReed BuntingFamilyHirundinidaeGenusHirundoBarn SwallowsSpeciesHirundo dauricaSpeciesHirundo rusticahirondelle rustiqueFamilyLaniidaeGenusLaniusSpeciesLanius cristatusBrown ShrikeFamilyMotacillidaeGenusMotacillaSpeciesMotacilla albaWhite WagtailFamilyMuscicapidaeGenusSaxicolaSpeciesSaxicola torquatusFamilyPasseridaeGenusPasserSpeciesPasser montanusEurasian Tree SparrowFamilySylviidaeGenusAcrocephalusSpeciesAcrocephalus bistrigicepsBlack-browed Reed WarblerGenusLocustellaSpeciesLocustella certhiolaPallas's Grasshopper WarblerThe 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 HuettmannEWHALE lab- Biology and Wildlife Dept., Institute of Arctic Biology, University of Alaska Fairbanks419 IRVING IFairbanksAlaska99775-7000USA001 907 474 7882fffh@uaf.eduDirk Nemitz 2008An assessment of sampling detectability for global bioidversity monitoring: results from sampling GRIDs in different climatic regions, Master thesis 5 Dec 2008 (unpublished) 1documentGoettingenUniversity of Goettingen, MINC projectThis work was co-supervised between University of Goettingen and University of Alaska-FairbanksFalk Huettmann and Aleksey AntonovnaUnclassifiednaExcel sheet and notebookConsistent methods were used, see GRID protocol in Nemitz (2008)Dataset is complete for July 2009Field & LabNoneDISTANCE SamplingPRESENCE / OccupancyBIODIVERSITY 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. 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 UnknownBiodiversity GRID Sampling Protocol No process steps have been described for this data setUnknownData were collected according to the GRID protocols, and as outlined in Nemitz (2008). However, for time and organizational reasons, only ONE (not four as usually done; but with the usual 3 repeats) trapping web was done.Location namesPoint0.0010.001Decimal degreesWorld Geodetic System of 1984World Geodetic System of 19846378137298.25722210088Local surfaceVegetationVegetation for each plotFalk Huettmann, EWHALE labExcel sheet columnsThe attributes followed the standard Excel sheets of previous GRID studies; see D. Nemitz thesis 2008 (also cited in cross reference section)
Falk Huettmann and Dirk NemitzBirdsBird detection information for each plotFalk Huettmann, EWHALE labExcel sheet columnsThe attributes followed the standard Excel sheets of previous GRID studies; see D. Nemitz thesis 2008 (also cited in cross reference section)
Falk Huettmann and Dirk NemitzTrapping WebsDistance Sampling Trapping Web for insectsFalk Huettmann, EWHALE labExcel sheet columnsThe attributes followed the standard Excel sheets of previous GRID studies; see D. Nemitz thesis 2008 (also cited in cross reference section)
Falk Huettmann and Dirk NemitzDistanceTransectCrossProfileDistance Sampling Cross Profile of the study areaFalk Huettmann, EWHALE labExcel sheet columnsThe attributes followed the standard Excel sheets of previous GRID studies; see D. Nemitz thesis 2008 (also cited in cross reference section)
Falk Huettmann and Dirk NemitzExplanationShort details of the data and Excel sheetFalk Huettmann, EWHALE labFalk Huettmann PhD, Associate ProfessorEWHALE lab- Biology and Wildlife Dept., Institute of Arctic Biology, University of Alaska Fairbanks419 IRVING IFairbanksAlaska99775-7000USA001 907 474 7882fffh@uaf.eduThe 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.None20081008Falk Huettmann PhD, Associate ProfessorEWHALE lab- Biology and Wildlife Dept., Institute of Arctic Biology, University of Alaska Fairbanks419 IRVING IFairbanksAlaska99775-7000USA001 907 474 7882fffh@uaf.eduFGDC Biological Data Profile of the Content Standard for Digital Geospatial MetadataFGDC-STD-001-1998http://nrdata.nps.gov/profiles/NPS_Profile.xmlNPS NR and GIS Metadata ProfileNoneNoneNAUnclassifiedNA