Rapid Biodiversity Assessment GRID Sampling on Ometepe Island, Nicaragua, June/July 2007

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Metadata:

Contact_Information:
Contact_Address:
Address: MANDATORY IF APPLICABLE for Data Store
Identification_Information:
Citation:
Citation_Information:
Originator:
Dirk Nemitz 1, Falk Huettmann 2, 2007. 1. Centre for Nature Conservation, University of Goettingen, Germany and Isaac Centre for Nature Conservation, Lincoln University, New Zealand. 2. EWHALE lab, Biology and Wildlife Dept. Institute of Arctic Biology, University of Alaska Fairbanks.
Title:
Rapid Biodiversity Assessment GRID Sampling on Ometepe Island, Nicaragua, June/July 2007
Other_Citation_Details: NA
Online_Linkage: NA
Description:
Abstract:
This data set contains Rapid Biodiversity Sampling data from a GRID. It includes raw count data for 361 bird observations and 480 insect observations from 30 sampling plots on Ometepe Island, Nicaragua (app. 85.32143 longitude and 11.25388 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. 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 (Costa Rica, Central Alaska, Russian Far East, Papua New-Guinea, Northern Alaska). Data from the other study sites are also available online. For details please contact authors.
Purpose:
This data set contains Rapid Biodiversity Sampling data from a GRID. It includes raw count data for 646 bird observations and 210 insect observations from 30 sampling plots in La Suerta, NorthEast Costa Rica (app. 83.46919 longitude and 10.26805 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. 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, Northern Alaska). Data from the other study sites are also available online. For details please contact authors.
Supplemental_Information:
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.
Time_Period_of_Content:
Time_Period_Information:
Range_of_Dates/Times:
Beginning_Date: 20070626
Ending_Date: 20070702
Currentness_Reference: Current for 2007
Status:
Progress: Complete
Maintenance_and_Update_Frequency: None planned for this dataset 2007
Spatial_Domain:
Bounding_Coordinates:
West_Bounding_Coordinate: 85,32143
East_Bounding_Coordinate: 85,31858
North_Bounding_Coordinate: 11,25388
South_Bounding_Coordinate: 11,25120
Keywords:
Theme:
Theme_Keyword_Thesaurus: National Park Service Theme Category Thesaurus
Theme_Keyword: Biodiversity Monitoring
Theme_Keyword: GRID Sampling
Theme_Keyword: DISTANCE Sampling
Theme_Keyword: PRESENCE
Theme_Keyword: Occupancy
Theme_Keyword: Random Forests
Theme_Keyword: Predictive Modeling
Theme_Keyword: Multiple-Species Survey
Theme_Keyword: Biodiversity
Theme_Keyword: Thryothorus pleurostictus
Theme_Keyword: Trogon melanocephalus
Theme_Keyword: Myiarchus tyrannulus
Theme_Keyword: Bubulcus ibis
Theme_Keyword: Buteo nitidus
Theme_Keyword: Pitangus sulphuratus
Theme_Keyword: Crotophaga sulcirostris
Theme_Keyword: Melanerpes hoffmannii
Theme_Keyword: Fregata magnificens
Theme_Keyword: Tityra semifasciata
Theme_Keyword: Nyctidromus albicollis
Theme_Keyword: Patagioenas flavirostris
Theme_Keyword: Cathartes aura
Theme_Keyword: Chaetura vauxi
Theme_Keyword: Calocitta formosa
Theme_Keyword: Amazona auropalliata
Theme:
Theme_Keyword_Thesaurus: ISO 19115 Topic Category
Theme_Keyword: Biodiversity Monitoring
Theme_Keyword: GRID Sampling
Theme_Keyword: DISTANCE Sampling
Theme_Keyword: PRESENCE
Theme_Keyword: Occupancy
Theme_Keyword: Random Forests
Theme_Keyword: Predictive Modeling
Theme_Keyword: Multiple-Species Survey
Theme_Keyword: Biodiversity
Theme_Keyword: Thryothorus pleurostictus
Theme_Keyword: Trogon melanocephalus
Theme_Keyword: Myiarchus tyrannulus
Theme_Keyword: Bubulcus ibis
Theme_Keyword: Buteo nitidus
Theme_Keyword: Pitangus sulphuratus
Theme_Keyword: Crotophaga sulcirostris
Theme_Keyword: Melanerpes hoffmannii
Theme_Keyword: Fregata magnificens
Theme_Keyword: Tityra semifasciata
Theme_Keyword: Nyctidromus albicollis
Theme_Keyword: Patagioenas flavirostris
Theme_Keyword: Cathartes aura
Theme_Keyword: Chaetura vauxi
Theme_Keyword: Calocitta formosa
Theme_Keyword: Amazona auropalliata
Theme:
Theme_Keyword: Biodiversity Monitoring
Theme_Keyword: GRID Sampling
Theme_Keyword: DISTANCE Sampling
Theme_Keyword: PRESENCE
Theme_Keyword: Occupancy
Theme_Keyword: Random Forests
Theme_Keyword: Predictive Modeling
Theme_Keyword: Multiple-Species Survey
Theme_Keyword: Biodiversity
Theme_Keyword: Thryothorus pleurostictus
Theme_Keyword: Trogon melanocephalus
Theme_Keyword: Myiarchus tyrannulus
Theme_Keyword: Bubulcus ibis
Theme_Keyword: Buteo nitidus
Theme_Keyword: Pitangus sulphuratus
Theme_Keyword: Crotophaga sulcirostris
Theme_Keyword: Melanerpes hoffmannii
Theme_Keyword: Fregata magnificens
Theme_Keyword: Tityra semifasciata
Theme_Keyword: Nyctidromus albicollis
Theme_Keyword: Patagioenas flavirostris
Theme_Keyword: Cathartes aura
Theme_Keyword: Chaetura vauxi
Theme_Keyword: Calocitta formosa
Theme_Keyword: Amazona auropalliata
Place:
Place_Keyword_Thesaurus: National Park System Unit Name Thesaurus
Place_Keyword: Nicaragua
Place_Keyword: Central America
Place_Keyword: Ometepe Island
Place:
Place_Keyword_Thesaurus: National Park System Unit Code Thesaurus
Place_Keyword: Nicaragua
Place_Keyword: Central America
Place_Keyword: Ometepe Island
Place:
Place_Keyword: Nicaragua
Place_Keyword: Central America
Place_Keyword: Ometepe Island
Taxonomy:
Keywords/Taxon:
Taxonomic_Keywords: multiple species
Taxonomic_Classification:
Taxon_Rank_Name: Kingdom
Taxon_Rank_Value: Animalia
Taxonomic_Classification:
Taxon_Rank_Name: Phylum
Taxon_Rank_Value: Annelida
Taxonomic_Classification:
Taxon_Rank_Name: Class
Taxon_Rank_Value: Clitellata
Taxonomic_Classification:
Taxon_Rank_Name: Subclass
Taxon_Rank_Value: Oligochaeta
Taxonomic_Classification:
Taxon_Rank_Name: Order
Taxon_Rank_Value: Haplotaxida
Taxonomic_Classification:
Taxon_Rank_Name: Phylum
Taxon_Rank_Value: Arthropoda
Taxonomic_Classification:
Taxon_Rank_Name: Subphylum
Taxon_Rank_Value: Chelicerata
Taxonomic_Classification:
Taxon_Rank_Name: Class
Taxon_Rank_Value: Arachnida
Applicable_Common_Name: arachnids
Applicable_Common_Name: aracnídeo
Applicable_Common_Name: araignées
Taxonomic_Classification:
Taxon_Rank_Name: Order
Taxon_Rank_Value: Araneae
Applicable_Common_Name: spiders
Applicable_Common_Name: aranha
Taxonomic_Classification:
Taxon_Rank_Name: Order
Taxon_Rank_Value: Scorpiones
Applicable_Common_Name: scorpions
Applicable_Common_Name: escorpião
Taxonomic_Classification:
Taxon_Rank_Name: Subclass
Taxon_Rank_Value: Acari
Taxonomic_Classification:
Taxon_Rank_Name: Superorder
Taxon_Rank_Value: Acariformes
Taxonomic_Classification:
Taxon_Rank_Name: Subphylum
Taxon_Rank_Value: Crustacea
Taxonomic_Classification:
Taxon_Rank_Name: Class
Taxon_Rank_Value: Malacostraca
Taxonomic_Classification:
Taxon_Rank_Name: Subclass
Taxon_Rank_Value: Eumalacostraca
Taxonomic_Classification:
Taxon_Rank_Name: Superorder
Taxon_Rank_Value: Peracarida
Taxonomic_Classification:
Taxon_Rank_Name: Order
Taxon_Rank_Value: Isopoda
Applicable_Common_Name: isopods
Applicable_Common_Name: pillbugs
Applicable_Common_Name: sowbugs
Applicable_Common_Name: cloportes
Applicable_Common_Name: isopodes
Taxonomic_Classification:
Taxon_Rank_Name: Subphylum
Taxon_Rank_Value: Hexapoda
Taxonomic_Classification:
Taxon_Rank_Name: Class
Taxon_Rank_Value: Entognatha
Taxonomic_Classification:
Taxon_Rank_Name: Order
Taxon_Rank_Value: Collembola
Applicable_Common_Name: snow flies
Applicable_Common_Name: springtails
Applicable_Common_Name: colêmbolo
Applicable_Common_Name: collemboles
Applicable_Common_Name: puces des neiges
Taxonomic_Classification:
Taxon_Rank_Name: Class
Taxon_Rank_Value: Insecta
Applicable_Common_Name: insects
Applicable_Common_Name: hexapoda
Applicable_Common_Name: inseto
Applicable_Common_Name: insectes
Taxonomic_Classification:
Taxon_Rank_Name: Subclass
Taxon_Rank_Value: Pterygota
Taxonomic_Classification:
Taxon_Rank_Name: Infraclass
Taxon_Rank_Value: Neoptera
Taxonomic_Classification:
Taxon_Rank_Name: Order
Taxon_Rank_Value: Coleoptera
Applicable_Common_Name: beetles
Applicable_Common_Name: coléoptères
Applicable_Common_Name: besouro
Taxonomic_Classification:
Taxon_Rank_Name: Order
Taxon_Rank_Value: Hemiptera
Applicable_Common_Name: true bugs
Applicable_Common_Name: hemipterans
Taxonomic_Classification:
Taxon_Rank_Name: Order
Taxon_Rank_Value: Hymenoptera
Applicable_Common_Name: abelha
Applicable_Common_Name: formiga
Applicable_Common_Name: vespa
Applicable_Common_Name: ants
Applicable_Common_Name: bees
Applicable_Common_Name: wasps
Taxonomic_Classification:
Taxon_Rank_Name: Suborder
Taxon_Rank_Value: Apocrita
Taxonomic_Classification:
Taxon_Rank_Name: Infraorder
Taxon_Rank_Value: Aculeata
Taxonomic_Classification:
Taxon_Rank_Name: Superfamily
Taxon_Rank_Value: Vespoidea
Taxonomic_Classification:
Taxon_Rank_Name: Family
Taxon_Rank_Value: Formicidae
Applicable_Common_Name: ants
Applicable_Common_Name: fourmis
Taxonomic_Classification:
Taxon_Rank_Name: Order
Taxon_Rank_Value: Lepidoptera
Applicable_Common_Name: butterflies
Applicable_Common_Name: moths
Applicable_Common_Name: papillons
Applicable_Common_Name: papillons de nuit
Applicable_Common_Name: Borboleta
Applicable_Common_Name: Mariposa
Taxonomic_Classification:
Taxon_Rank_Name: Superfamily
Taxon_Rank_Value: Papilionoidea
Taxonomic_Classification:
Taxon_Rank_Name: Family
Taxon_Rank_Value: Papilionidae
Applicable_Common_Name: swallowtail butterflies
Applicable_Common_Name: swallowtails
Taxonomic_Classification:
Taxon_Rank_Name: Subfamily
Taxon_Rank_Value: Papilioninae
Taxonomic_Classification:
Taxon_Rank_Name: Tribe
Taxon_Rank_Value: Papilionini
Taxonomic_Classification:
Taxon_Rank_Name: Genus
Taxon_Rank_Value: Papilio
Applicable_Common_Name: swallowtails
Taxonomic_Classification:
Taxon_Rank_Name: Order
Taxon_Rank_Value: Orthoptera
Applicable_Common_Name: grasshoppers
Applicable_Common_Name: locusts
Applicable_Common_Name: criquet-locustes
Applicable_Common_Name: locustes
Applicable_Common_Name: sauterelles
Applicable_Common_Name: gafanhoto
Applicable_Common_Name: grilo
Applicable_Common_Name: crickets
Applicable_Common_Name: katydids
Taxonomic_Classification:
Taxon_Rank_Name: Subphylum
Taxon_Rank_Value: Myriapoda
Taxonomic_Classification:
Taxon_Rank_Name: Class
Taxon_Rank_Value: Chilopoda
Applicable_Common_Name: centipedes
Applicable_Common_Name: centopéia
Applicable_Common_Name: quilópode
Taxonomic_Classification:
Taxon_Rank_Name: Phylum
Taxon_Rank_Value: Chordata
Taxonomic_Classification:
Taxon_Rank_Name: Subphylum
Taxon_Rank_Value: Vertebrata
Taxonomic_Classification:
Taxon_Rank_Name: Class
Taxon_Rank_Value: Amphibia
Applicable_Common_Name: amphibiens
Applicable_Common_Name: anfíbio
Applicable_Common_Name: Amphibians
Taxonomic_Classification:
Taxon_Rank_Name: Subclass
Taxon_Rank_Value: Lissamphibia
Taxonomic_Classification:
Taxon_Rank_Name: Superorder
Taxon_Rank_Value: Salientia
Taxonomic_Classification:
Taxon_Rank_Name: Order
Taxon_Rank_Value: Anura
Applicable_Common_Name: Frogs
Applicable_Common_Name: perereca
Applicable_Common_Name: r
Applicable_Common_Name: sapo
Applicable_Common_Name: crapauds
Applicable_Common_Name: grenouilles
Applicable_Common_Name: Toads
Taxonomic_Classification:
Taxon_Rank_Name: Family
Taxon_Rank_Value: Bufonidae
Applicable_Common_Name: Bufonids
Applicable_Common_Name: bufonidés
Applicable_Common_Name: crapauds
Applicable_Common_Name: Toads
Taxonomic_Classification:
Taxon_Rank_Name: Genus
Taxon_Rank_Value: Bufo
Applicable_Common_Name: Toads
Taxonomic_Classification:
Taxon_Rank_Name: Class
Taxon_Rank_Value: Aves
Applicable_Common_Name: Birds
Applicable_Common_Name: oiseaux
Taxonomic_Classification:
Taxon_Rank_Name: Order
Taxon_Rank_Value: Apodiformes
Applicable_Common_Name: Swifts
Applicable_Common_Name: Hummingbirds
Taxonomic_Classification:
Taxon_Rank_Name: Family
Taxon_Rank_Value: Apodidae
Applicable_Common_Name: Swifts
Taxonomic_Classification:
Taxon_Rank_Name: Subfamily
Taxon_Rank_Value: Apodinae
Taxonomic_Classification:
Taxon_Rank_Name: Genus
Taxon_Rank_Value: Chaetura
Applicable_Common_Name: Stiff-tailed Swifts
Taxonomic_Classification:
Taxon_Rank_Name: Species
Taxon_Rank_Value: Chaetura vauxi
Applicable_Common_Name: Vaux's Swift
Applicable_Common_Name: Vencejo de Vaux
Taxonomic_Classification:
Taxon_Rank_Name: Family
Taxon_Rank_Value: Trochilidae
Applicable_Common_Name: Hummingbirds
Applicable_Common_Name: oiseaux-mouches
Taxonomic_Classification:
Taxon_Rank_Name: Order
Taxon_Rank_Value: Ciconiiformes
Applicable_Common_Name: Herons
Applicable_Common_Name: Ibises
Applicable_Common_Name: Storks
Applicable_Common_Name: aigles
Applicable_Common_Name: aigles pêcheurs
Applicable_Common_Name: albatros
Applicable_Common_Name: alcidés
Applicable_Common_Name: cigognes
Applicable_Common_Name: cormorans
Applicable_Common_Name: perviers
Applicable_Common_Name: faucons
Applicable_Common_Name: flamants
Applicable_Common_Name: goélands
Applicable_Common_Name: grèbes
Applicable_Common_Name: hérons
Applicable_Common_Name: huarts
Applicable_Common_Name: huîtriers
Applicable_Common_Name: ibis
Applicable_Common_Name: pélicans
Applicable_Common_Name: pétrels
Applicable_Common_Name: pingouins
Applicable_Common_Name: pluviers
Applicable_Common_Name: totipalmés
Applicable_Common_Name: tubinares
Applicable_Common_Name: Auks
Applicable_Common_Name: Penguins
Taxonomic_Classification:
Taxon_Rank_Name: Family
Taxon_Rank_Value: Accipitridae
Applicable_Common_Name: Eagles
Applicable_Common_Name: Hawks
Applicable_Common_Name: Kites
Applicable_Common_Name: aigles
Applicable_Common_Name: buses
Applicable_Common_Name: perviers
Taxonomic_Classification:
Taxon_Rank_Name: Subfamily
Taxon_Rank_Value: Accipitrinae
Taxonomic_Classification:
Taxon_Rank_Name: Genus
Taxon_Rank_Value: Buteo
Applicable_Common_Name: Buteonine Hawks
Taxonomic_Classification:
Taxon_Rank_Name: Species
Taxon_Rank_Value: Buteo nitidus
Applicable_Common_Name: Gray Hawk
Applicable_Common_Name: Aguililla gris
Taxonomic_Classification:
Taxon_Rank_Name: Family
Taxon_Rank_Value: Ardeidae
Applicable_Common_Name: Bitterns
Applicable_Common_Name: Egrets
Applicable_Common_Name: Herons
Taxonomic_Classification:
Taxon_Rank_Name: Genus
Taxon_Rank_Value: Bubulcus
Applicable_Common_Name: Cattle Egrets
Taxonomic_Classification:
Taxon_Rank_Name: Species
Taxon_Rank_Value: Bubulcus ibis
Applicable_Common_Name: Garza ganadera
Applicable_Common_Name: Cattle Egret
Applicable_Common_Name: héron garde-boeufs
Taxonomic_Classification:
Taxon_Rank_Name: Family
Taxon_Rank_Value: Ciconiidae
Applicable_Common_Name: American Vultures
Taxonomic_Classification:
Taxon_Rank_Name: Genus
Taxon_Rank_Value: Cathartes
Applicable_Common_Name: Turkey Vultures
Taxonomic_Classification:
Taxon_Rank_Name: Species
Taxon_Rank_Value: Cathartes aura
Applicable_Common_Name: Zopilote aura
Applicable_Common_Name: Turkey Vulture
Applicable_Common_Name: urubu à tête rouge
Taxonomic_Classification:
Taxon_Rank_Name: Family
Taxon_Rank_Value: Fregatidae
Applicable_Common_Name: Frigatebirds
Taxonomic_Classification:
Taxon_Rank_Name: Genus
Taxon_Rank_Value: Fregata
Applicable_Common_Name: Frigatebirds
Taxonomic_Classification:
Taxon_Rank_Name: Species
Taxon_Rank_Value: Fregata magnificens
Applicable_Common_Name: Fragata magnífica
Applicable_Common_Name: Magnificent Frigatebird
Applicable_Common_Name: frégate superbe
Taxonomic_Classification:
Taxon_Rank_Name: Order
Taxon_Rank_Value: Columbiformes
Applicable_Common_Name: Doves
Applicable_Common_Name: colombes
Applicable_Common_Name: pigeons
Applicable_Common_Name: Pigeons
Taxonomic_Classification:
Taxon_Rank_Name: Family
Taxon_Rank_Value: Columbidae
Applicable_Common_Name: Doves
Applicable_Common_Name: colombes
Applicable_Common_Name: pigeons
Taxonomic_Classification:
Taxon_Rank_Name: Subfamily
Taxon_Rank_Value: Columbinae
Taxonomic_Classification:
Taxon_Rank_Name: Genus
Taxon_Rank_Value: Patagioenas
Taxonomic_Classification:
Taxon_Rank_Name: Species
Taxon_Rank_Value: Patagioenas flavirostris
Applicable_Common_Name: Red-billed Pigeon
Taxonomic_Classification:
Taxon_Rank_Name: Order
Taxon_Rank_Value: Cuculiformes
Applicable_Common_Name: Cuckoos
Applicable_Common_Name: coucoua
Taxonomic_Classification:
Taxon_Rank_Name: Family
Taxon_Rank_Value: Cuculidae
Applicable_Common_Name: Anis
Applicable_Common_Name: Cuckoos
Applicable_Common_Name: Roadrunners
Taxonomic_Classification:
Taxon_Rank_Name: Subfamily
Taxon_Rank_Value: Crotophaginae
Taxonomic_Classification:
Taxon_Rank_Name: Genus
Taxon_Rank_Value: Crotophaga
Applicable_Common_Name: Anis
Taxonomic_Classification:
Taxon_Rank_Name: Species
Taxon_Rank_Value: Crotophaga sulcirostris
Applicable_Common_Name: Groove-billed Ani
Applicable_Common_Name: Garrapatero pijuy
Taxonomic_Classification:
Taxon_Rank_Name: Order
Taxon_Rank_Value: Passeriformes
Applicable_Common_Name: Perching Birds
Applicable_Common_Name: passereaux
Taxonomic_Classification:
Taxon_Rank_Name: Family
Taxon_Rank_Value: Corvidae
Applicable_Common_Name: Crows
Applicable_Common_Name: Jays
Applicable_Common_Name: Magpies
Applicable_Common_Name: corneilles
Applicable_Common_Name: geais
Applicable_Common_Name: pies
Taxonomic_Classification:
Taxon_Rank_Name: Genus
Taxon_Rank_Value: Calocitta
Taxonomic_Classification:
Taxon_Rank_Name: Species
Taxon_Rank_Value: Calocitta formosa
Applicable_Common_Name: Urraca-hermosa cara blanca
Applicable_Common_Name: White-throated Magpie-Jay
Taxonomic_Classification:
Taxon_Rank_Name: Family
Taxon_Rank_Value: Cotingidae
Taxonomic_Classification:
Taxon_Rank_Name: Subfamily
Taxon_Rank_Value: Tityrinae
Taxonomic_Classification:
Taxon_Rank_Name: Genus
Taxon_Rank_Value: Tityra
Applicable_Common_Name: Tityras
Taxonomic_Classification:
Taxon_Rank_Name: Species
Taxon_Rank_Value: Tityra semifasciata
Applicable_Common_Name: Masked Tityra
Applicable_Common_Name: Titira enmascarada
Taxonomic_Classification:
Taxon_Rank_Name: Family
Taxon_Rank_Value: Hirundinidae
Applicable_Common_Name: Swallows
Taxonomic_Classification:
Taxon_Rank_Name: Family
Taxon_Rank_Value: Thraupidae
Applicable_Common_Name: Tanagers
Taxonomic_Classification:
Taxon_Rank_Name: Family
Taxon_Rank_Value: Troglodytidae
Applicable_Common_Name: Wrens
Taxonomic_Classification:
Taxon_Rank_Name: Genus
Taxon_Rank_Value: Thryothorus
Applicable_Common_Name: Carolina Wrens
Taxonomic_Classification:
Taxon_Rank_Name: Species
Taxon_Rank_Value: Thryothorus pleurostictus
Applicable_Common_Name: Chivirín barrado
Applicable_Common_Name: Banded Wren
Taxonomic_Classification:
Taxon_Rank_Name: Family
Taxon_Rank_Value: Tyrannidae
Applicable_Common_Name: Tyrant Flycatchers
Applicable_Common_Name: tyrans gobe-mouches
Taxonomic_Classification:
Taxon_Rank_Name: Genus
Taxon_Rank_Value: Myiarchus
Applicable_Common_Name: Crested Flycatchers
Taxonomic_Classification:
Taxon_Rank_Name: Species
Taxon_Rank_Value: Myiarchus tyrannulus
Applicable_Common_Name: Brown-crested Flycatcher
Applicable_Common_Name: Papamoscas tirano
Taxonomic_Classification:
Taxon_Rank_Name: Genus
Taxon_Rank_Value: Pitangus
Applicable_Common_Name: Kiskadees
Taxonomic_Classification:
Taxon_Rank_Name: Species
Taxon_Rank_Value: Pitangus sulphuratus
Applicable_Common_Name: Great Kiskadee
Applicable_Common_Name: Luis bienteveo
Taxonomic_Classification:
Taxon_Rank_Name: Order
Taxon_Rank_Value: Piciformes
Applicable_Common_Name: Woodpeckers
Applicable_Common_Name: pic-bois
Taxonomic_Classification:
Taxon_Rank_Name: Family
Taxon_Rank_Value: Picidae
Applicable_Common_Name: Woodpeckers
Applicable_Common_Name: Wrynecks
Applicable_Common_Name: fourmiliers
Applicable_Common_Name: pic-bois
Taxonomic_Classification:
Taxon_Rank_Name: Subfamily
Taxon_Rank_Value: Picinae
Taxonomic_Classification:
Taxon_Rank_Name: Genus
Taxon_Rank_Value: Melanerpes
Applicable_Common_Name: Melanerpine Woodpeckers
Taxonomic_Classification:
Taxon_Rank_Name: Species
Taxon_Rank_Value: Melanerpes hoffmannii
Applicable_Common_Name: Hoffmann's Woodpecker
Taxonomic_Classification:
Taxon_Rank_Name: Order
Taxon_Rank_Value: Psittaciformes
Applicable_Common_Name: Parrots
Applicable_Common_Name: perroquets
Taxonomic_Classification:
Taxon_Rank_Name: Family
Taxon_Rank_Value: Psittacidae
Applicable_Common_Name: Cockatoos
Applicable_Common_Name: Lories
Applicable_Common_Name: Macaws
Applicable_Common_Name: Parrots
Applicable_Common_Name: aras
Applicable_Common_Name: cacatoès
Applicable_Common_Name: perroquets
Taxonomic_Classification:
Taxon_Rank_Name: Subfamily
Taxon_Rank_Value: Psittacinae
Taxonomic_Classification:
Taxon_Rank_Name: Genus
Taxon_Rank_Value: Amazona
Applicable_Common_Name: Amazon Parrots
Taxonomic_Classification:
Taxon_Rank_Name: Species
Taxon_Rank_Value: Amazona auropalliata
Applicable_Common_Name: Yellow-naped Amazon
Applicable_Common_Name: Yellow-naped Parrot
Applicable_Common_Name: Loro nuca amarilla
Taxonomic_Classification:
Taxon_Rank_Name: Genus
Taxon_Rank_Value: Aratinga
Applicable_Common_Name: Parakeets
Taxonomic_Classification:
Taxon_Rank_Name: Order
Taxon_Rank_Value: Strigiformes
Applicable_Common_Name: Owls
Applicable_Common_Name: chouettes
Applicable_Common_Name: engoulevents
Applicable_Common_Name: hiboux
Applicable_Common_Name: Goatsuckers
Taxonomic_Classification:
Taxon_Rank_Name: Family
Taxon_Rank_Value: Caprimulgidae
Applicable_Common_Name: Nightjars
Taxonomic_Classification:
Taxon_Rank_Name: Subfamily
Taxon_Rank_Value: Caprimulginae
Taxonomic_Classification:
Taxon_Rank_Name: Genus
Taxon_Rank_Value: Nyctidromus
Applicable_Common_Name: Pauraques
Taxonomic_Classification:
Taxon_Rank_Name: Species
Taxon_Rank_Value: Nyctidromus albicollis
Applicable_Common_Name: Common Pauraque
Applicable_Common_Name: Chotacabras pauraque
Applicable_Common_Name: Pauraque
Taxonomic_Classification:
Taxon_Rank_Name: Order
Taxon_Rank_Value: Trogoniformes
Applicable_Common_Name: Trogons
Taxonomic_Classification:
Taxon_Rank_Name: Family
Taxon_Rank_Value: Trogonidae
Applicable_Common_Name: Trogons
Taxonomic_Classification:
Taxon_Rank_Name: Genus
Taxon_Rank_Value: Trogon
Applicable_Common_Name: Trogons
Taxonomic_Classification:
Taxon_Rank_Name: Species
Taxon_Rank_Value: Trogon melanocephalus
Applicable_Common_Name: Black-headed Trogon
Applicable_Common_Name: Trogón cabeza negra
Taxonomic_Classification:
Taxon_Rank_Name: Phylum
Taxon_Rank_Value: Mollusca
Taxonomic_Classification:
Taxon_Rank_Name: Class
Taxon_Rank_Value: Gastropoda
Applicable_Common_Name: gastropods
Applicable_Common_Name: slugs
Applicable_Common_Name: snails
Applicable_Common_Name: escargots
Applicable_Common_Name: gastéropodes
Applicable_Common_Name: limaces
Applicable_Common_Name: caracol
Applicable_Common_Name: caramujo
Applicable_Common_Name: lesma
Access_Constraints:
The sampling sites are geo-referenced. DISTANCE Sampling abundance estimates, PRESENCE occupancy estimates and Random Forests predictive modeling were done for a master thesis 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.
Use_Constraints:
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.
Point_of_Contact:
Contact_Information:
Contact_Person_Primary:
Contact_Person: Falk Huettmann and Dirk Nemitz
Contact_Organization:
EWHALE lab- Biology and Wildlife Dept., Institute of Arctic Biology, University of Alaska Fairbanks
Contact_Address:
Address: 419 IRVING I
City: Fairbanks
State_or_Province: Alaska
Postal_Code: 99775-7000
Country: USA
Contact_Voice_Telephone: 001 907 474 7882
Contact_Electronic_Mail_Address: fffh@uaf.edu; dirk@naturecon.de
Cross_Reference:
Citation_Information:
Originator: Dirk Nemitz
Publication_Date: Unpublished material
Title:
An assessment of sampling detectability for global bioidversity monitoring: results from sampling GRIDs in different climatic regions, Master thesis 5 Dec 2008 (unpublished)
Data_Quality_Information:
Logical_Consistency_Report: Consistent methods were used, see protocol
Completeness_Report: Dataset is complete for 2007
Lineage:
Methodology:
Methodology_Type: Field & Lab
Methodology_Identifier:
Methodology_Keyword: DISTANCE Sampling
Methodology_Keyword: PRESENCE / Occupancy
Methodology_Description:
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. 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 observers 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.
Methodology_Citation:
Citation_Information:
Originator: Buckland et al
Publication_Date: 2001
Title: Introduction to DISTANCE sampling
Methodology_Citation:
Citation_Information:
Originator: MacKenzie et a.
Publication_Date: 2005
Title: Occupancy estimates and modeling
Methodology_Citation:
Citation_Information:
Originator: Breiman
Publication_Date: 2001
Title: Statistical modelling: the two cultures
Methodology_Citation:
Citation_Information:
Originator: Huettmann & Nemitz
Title: Biodiversity GRID Sampling Protocol
Distribution_Information:
Distributor:
Contact_Information:
Contact_Person_Primary:
Contact_Person: Falk Huettmann & Dirk Nemitz
Contact_Organization:
EWHALE lab- Biology and Wildlife Dept., Institute of Arctic Biology, University of Alaska Fairbanks
Contact_Address:
Address: 419 IRVING I
City: Fairbanks
State_or_Province: Alaska
Postal_Code: 99775-7000
Country: USA
Contact_Voice_Telephone: 001 907 474 7882
Contact_Electronic_Mail_Address: fffh@uaf.edu; dirk@naturecon.de
Distribution_Liability:
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.
Standard_Order_Process:
Fees: None
Metadata_Reference_Information:
Metadata_Date: 20090217
Metadata_Contact:
Contact_Information:
Contact_Person_Primary:
Contact_Person: Falk Huettmann, Dirk Nemitz
Contact_Organization:
EWHALE lab- Biology and Wildlife Dept., Institute of Arctic Biology, University of Alaska Fairbanks
Contact_Address:
Address: 419 IRVING I
City: Fairbanks
State_or_Province: Alaska
Postal_Code: 99775-7000
Country: USA
Contact_Voice_Telephone: 001 907 474 7882
Contact_Electronic_Mail_Address: fffh@uaf.edu; dirk@naturecon.de
Metadata_Standard_Name:
FGDC Biological Data Profile of the Content Standard for Digital Geospatial Metadata
Metadata_Standard_Version: FGDC-STD-001-1998
Metadata_Extensions:
Online_Linkage: <http://nrdata.nps.gov/profiles/NPS_Profile.xml>
Profile_Name: NPS NR and GIS Metadata Profile
Generated by mp version 2.8.25 on Sun Feb 22 16:09:52 2009