{"data":[{"id":"10.5063/f1b27ssq","type":"dois","attributes":{"doi":"10.5063/f1b27ssq","identifiers":[],"creators":[{"name":"V. Joseph, Shimat","nameType":"Personal","givenName":"Shimat","familyName":"V. 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Joseph, Shimat","nameType":"Personal","givenName":"Shimat","familyName":"V. Joseph","affiliation":["University of Georgia"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-3686-0024","nameIdentifierScheme":"ORCID"}]}],"titles":[{"lang":"en","title":"Flatheaded borers in Georgia Specialty crops"}],"publisher":"KNB Data Repository","container":{},"publicationYear":2026,"subjects":[{"lang":"en","subject":"Flatheaded apply borer"}],"contributors":[],"dates":[{"date":"2026","dateType":"Issued"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[],"relatedItems":[],"sizes":[],"formats":["text/xml"],"version":null,"rightsList":[],"descriptions":[{"lang":"en","description":"The dataset about flatheaded borers (Coleoptera: Buprestidae) studies in Georgia. 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Data was collected in plots embedded in grids were established by researchers from the Brazilian Biodiversity Research Program (PPBio), which is a long-term ecological program that aims to compare the distribution of multiple taxa using standardized protocols. Butterfly surveys were conducted via passive sampling with Van Someren-Rydon baited traps.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://knb.ecoinformatics.org/metacatui/view/doi:10.5063/F1KK999R","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-05-21T00:48:42Z","registered":"2026-05-21T00:48:43Z","published":null,"updated":"2026-05-21T00:48:43Z"},"relationships":{"client":{"data":{"id":"cdl.ucsb","type":"clients"}}}},{"id":"10.5063/f1qc0211","type":"dois","attributes":{"doi":"10.5063/f1qc0211","identifiers":[],"creators":[{"name":"O'Hara, Casey","nameType":"Personal","givenName":"Casey","familyName":"O'Hara","affiliation":["National Center for Ecological Analysis and Synthesis"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-2968-7005","nameIdentifierScheme":"ORCID"}]}],"titles":[{"lang":"en","title":"Species traits and vulnerability scores: global marine fauna"}],"publisher":"KNB Data Repository","container":{},"publicationYear":2026,"subjects":[{"lang":"en","subject":"marine fauna"},{"lang":"en","subject":"climate change"},{"lang":"en","subject":"anthropogenic stressors"},{"lang":"en","subject":"anthropogenic pressures"},{"lang":"en","subject":"cumulative human impact"}],"contributors":[],"dates":[{"date":"2026","dateType":"Issued"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[],"relatedItems":[],"sizes":[],"formats":["text/xml"],"version":null,"rightsList":[],"descriptions":[{"lang":"en","description":"Updated datasets from Butt et al. (2022). A trait-based framework for assessing the vulnerability of marine species to human impacts. Ecosphere, 13(2), e3919. https://doi.org/10.1002/ecs2.3919.These data are generated from scripts in https://github.com/mapping-marine-spp-vuln/spp_vuln_framework, updated since the original publication. These files contain trait information for 47 different traits (with categorical values) across 54,718 marine faunal species. These traits were then used to calculate vulnerability scores for species vulnerability to 23 different anthropogenic stressors.Species/trait data are contained in normalized relational tables: * spp_traits_junction.csv (trait probability for species-trait pairs; keys for species, trait values, gapfill levels)* spp_traits_taxa_lookup.csv (WoRMS Aphia ID, sci name, taxon)* spp_traits_trait_val_lookup.csv (trait/value pairs with key)* spp_traits_gapfill_levels.csv (taxonomic ranks at which trait values were matched or gapfilled - see Butt et al. 2022 for details).Species/vulnearbility data are contained in normalized relational tables:* spp_vuln_junction.csv (vulnerability scores and components for species-stressor pairs; keys for species and stressors)* spp_vuln_taxa_lookup.csv (WoRMS Aphia ID, scientific name, taxon)* spp_vuln_stressor_lookup.csv (stressor/ID pairs)Each .zip also contains a readme with R code to show how to join tables easily.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://knb.ecoinformatics.org/metacatui/view/doi:10.5063/F1QC0211","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-05-15T16:49:22Z","registered":"2026-05-15T16:49:23Z","published":null,"updated":"2026-05-15T16:49:23Z"},"relationships":{"client":{"data":{"id":"cdl.ucsb","type":"clients"}}}},{"id":"10.5063/f1v40sqh","type":"dois","attributes":{"doi":"10.5063/f1v40sqh","identifiers":[],"creators":[{"name":"Fong, Caitlin","nameType":"Personal","givenName":"Caitlin","familyName":"Fong","affiliation":["NCEAS - UC Santa Barbara"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-7241-3862","nameIdentifierScheme":"ORCID"}]},{"name":"Broderick, Carlo","nameType":"Personal","givenName":"Carlo","familyName":"Broderick","affiliation":["NCEAS - UC Santa Barbara"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0009-0008-7775-6304","nameIdentifierScheme":"ORCID"}]},{"name":"Farnisa, Mona","nameType":"Personal","givenName":"Mona","familyName":"Farnisa","affiliation":["NCEAS - UC Santa Barbara"],"nameIdentifiers":[]},{"name":"Egg, Erika","nameType":"Personal","givenName":"Erika","familyName":"Egg","affiliation":["NCEAS - UC Santa Barbara"],"nameIdentifiers":[]},{"name":"King, Rachel","nameType":"Personal","givenName":"Rachel","familyName":"King","affiliation":["NCEAS - UC Santa Barbara"],"nameIdentifiers":[]},{"name":"DeCesaro, Joe","nameType":"Personal","givenName":"Joe","familyName":"DeCesaro","affiliation":["NCEAS - UC Santa Barbara"],"nameIdentifiers":[]},{"name":"Halpern, Benjamin","nameType":"Personal","givenName":"Benjamin","familyName":"Halpern","affiliation":["NCEAS - UC Santa Barbara"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-8844-2302","nameIdentifierScheme":"ORCID"}]}],"titles":[{"lang":"en","title":"Western Wildfire Resilience Index (WRI)"}],"publisher":"KNB Data Repository","container":{},"publicationYear":2026,"subjects":[{"lang":"en","subject":"wildfire resilience"},{"lang":"en","subject":"wildfire index"},{"lang":"en","subject":"western United States"},{"lang":"en","subject":"Canada"},{"lang":"en","subject":"socio-ecological systems"},{"lang":"en","subject":"composite index"},{"lang":"en","subject":"geospatial raster"},{"lang":"en","subject":"COG"},{"lang":"en","subject":"wildfire preparedness."}],"contributors":[],"dates":[{"date":"2026","dateType":"Issued"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[],"relatedItems":[],"sizes":[],"formats":["text/xml"],"version":null,"rightsList":[],"descriptions":[{"lang":"en","description":"The Western Wildfire Resilience Index (WRI) is the first open-access, holistic index to quantify wildfire resilience across the North American West at fine spatial resolution. It integrates eight socio-ecological domains (Infrastructure, Communities, Livelihoods, Sense of Place, Species, Natural Habitats, Water, and Air Quality) into a single composite score ranging from 0 (low resilience) to 100 (high resilience). The Index is designed to inform wildfire preparedness, response, and recovery policy across communities and landscapes.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://knb.ecoinformatics.org/view/doi:10.5063/F1V40SQH","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-05-05T03:01:32Z","registered":"2026-05-05T03:01:33Z","published":null,"updated":"2026-05-05T03:01:33Z"},"relationships":{"client":{"data":{"id":"cdl.ucsb","type":"clients"}}}},{"id":"10.5063/f1zs2v05","type":"dois","attributes":{"doi":"10.5063/f1zs2v05","identifiers":[],"creators":[{"name":"Gephart, Jessica","nameType":"Personal","givenName":"Jessica","familyName":"Gephart","affiliation":["University of Washington"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-6836-9291","nameIdentifierScheme":"ORCID"}]},{"name":"Marks, Althea","nameType":"Personal","givenName":"Althea","familyName":"Marks","affiliation":["University of Washington"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-9370-9128","nameIdentifierScheme":"ORCID"}]}],"titles":[{"lang":"en","title":"Aquatic Resource Trade in Species (ARTIS) Database v1.2 FAO 1996-2020"}],"publisher":"KNB Data Repository","container":{},"publicationYear":2026,"subjects":[{"lang":"en","subject":"Seafood"},{"lang":"en","subject":"Trade"},{"lang":"en","subject":"Aquatic food"},{"lang":"en","subject":"Globalization"},{"lang":"en","subject":"Supply Chain"},{"lang":"en","subject":"Traceability"},{"lang":"en","subject":"Fisheries"},{"lang":"en","subject":"Aquaculture"}],"contributors":[],"dates":[{"date":"2026","dateType":"Issued"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[],"relatedItems":[],"sizes":[],"formats":["text/xml"],"version":null,"rightsList":[],"descriptions":[{"lang":"en","description":"Food systems have become increasingly globalized, with over a quarter of all food now traded internationally. \nSeafood is among the most highly traded foods and it is becoming increasingly globalized, with trade doubling in recent decades. \nAt the same time, seafood is now widely recognized as a critical source of nutrition. \nThus, social and environmental threats to local seafood production, including environmental extremes, price impacts of market integration, networked risks, and increased availability of processed foods, must be evaluated in the context of global trade. \nHowever, our collective understanding of the environmental and human outcomes of food system globalization is limited by a fundamental gap between production and trade data. \nWe bridge this gap in the Aquatic Resource Trade in Species (ARTIS) database by providing the first global estimates of aquatic species trade flows.\nARTIS includes bilateral trade flows disaggregated by taxa, production source, and geographic source in the \"trade\" files and estimated apparent consumption that uses the disaggregated trade data along with production to estimate apparent consumption by taxa, production source, and geographic source in the \"consumption\" files.\nThe ARTIS v1.2 FAO database covers global aquatic species trade from 1996 to 2020 across six Harmonized System (HS) versions, providing consumption and trade estimates for 2774 taxa across 194 countries, alongside six reference tables.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://knb.ecoinformatics.org/view/doi:10.5063/F1ZS2V05","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-05-04T16:16:28Z","registered":"2026-05-04T16:16:29Z","published":null,"updated":"2026-05-04T16:16:29Z"},"relationships":{"client":{"data":{"id":"cdl.ucsb","type":"clients"}}}},{"id":"10.5063/f13j3bf0","type":"dois","attributes":{"doi":"10.5063/f13j3bf0","identifiers":[],"creators":[{"name":"J Johnson, Carter","nameType":"Personal","givenName":"Carter","familyName":"J Johnson","affiliation":["University of Alaska Fairbanks"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-6983-6426","nameIdentifierScheme":"ORCID"}]},{"name":"Eckert, Ginny","nameType":"Personal","givenName":"Ginny","familyName":"Eckert","affiliation":["University of Alaska Fairbanks"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-7538-7081","nameIdentifierScheme":"ORCID"}]}],"titles":[{"lang":"en","title":"Invertebrate communities and sea otter foraging in Southeast Alaska (2022-2023)"}],"publisher":"KNB Data Repository","container":{},"publicationYear":2026,"subjects":[{"lang":"en","subject":"predation"},{"lang":"en","subject":"fisheries"}],"contributors":[],"dates":[{"date":"2026","dateType":"Issued"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[],"relatedItems":[],"sizes":[],"formats":["text/xml"],"version":null,"rightsList":[],"descriptions":[{"lang":"en","description":"Invertebrate community data from pot sampling efforts in northern Southeast Alaska during summers of 2022 and 2023. Shore-based sea otter foraging data from summers of 2022 and 2023. Data collected to test for the effects of sea otter predation on crab species' depth distribution, size, and relative abundance.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://knb.ecoinformatics.org/view/doi:10.5063/F13J3BF0","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-04-30T22:52:38Z","registered":"2026-04-30T22:52:39Z","published":null,"updated":"2026-04-30T22:52:39Z"},"relationships":{"client":{"data":{"id":"cdl.ucsb","type":"clients"}}}},{"id":"10.5063/f179435q","type":"dois","attributes":{"doi":"10.5063/f179435q","identifiers":[],"creators":[{"name":"Gallardo Alvarez, Manuel Ignacio","nameType":"Personal","givenName":"Manuel Ignacio","familyName":"Gallardo Alvarez","affiliation":["UJAT"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-6038-194X","nameIdentifierScheme":"ORCID"}]},{"name":"Lopez Luna, Marco Antonio","nameType":"Personal","givenName":"Marco Antonio","familyName":"Lopez Luna","affiliation":["UJAT"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-2259-544X","nameIdentifierScheme":"ORCID"}]},{"name":"Recino Reyes, Elsi Beatriz","nameType":"Personal","givenName":"Elsi Beatriz","familyName":"Recino Reyes","affiliation":["UJAT"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-4689-7891","nameIdentifierScheme":"ORCID"}]}],"titles":[{"lang":"en","title":"Morphometric data of Basiliscus vittatus in a suburban population of Villahermosa, Tabasco (labels in Spanish)"}],"publisher":"KNB Data Repository","container":{},"publicationYear":2026,"subjects":[{"lang":"en","subject":"Morphometrics"},{"lang":"en","subject":"Basiliscus vittatus"},{"lang":"en","subject":"Suburban population"}],"contributors":[],"dates":[{"date":"2026","dateType":"Issued"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[],"relatedItems":[],"sizes":[],"formats":["text/xml"],"version":null,"rightsList":[],"descriptions":[{"lang":"en","description":"We present a morphometric dataset of Basiliscus vittatus obtained from individuals captured in a suburban population of Villahermosa, Tabasco, in southeastern Mexico. This lizard species is common in disturbed and peri-urban environments, but little information is available on its body parameters in suburban contexts. The dataset includes standard measurements (snout-cloaca length, tail length, head length, head width, hind limb length, weight, among others) taken during capture events. All variable labels are in Spanish. This dataset is useful for urban ecology studies, functional morphology (including its ability to run on water), and assessments of population health in anthropized environments.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://knb.ecoinformatics.org/view/doi:10.5063/F179435Q","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-04-30T03:52:45Z","registered":"2026-04-30T03:52:46Z","published":null,"updated":"2026-04-30T03:52:46Z"},"relationships":{"client":{"data":{"id":"cdl.ucsb","type":"clients"}}}},{"id":"10.5063/f1c24tx3","type":"dois","attributes":{"doi":"10.5063/f1c24tx3","identifiers":[],"creators":[{"name":"Gallardo Alvarez, Manuel Ignacio","nameType":"Personal","givenName":"Manuel Ignacio","familyName":"Gallardo Alvarez","affiliation":["UJAT"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-6038-194X","nameIdentifierScheme":"ORCID"}]},{"name":"Lopez Luna, Marco Antonio","nameType":"Personal","givenName":"Marco Antonio","familyName":"Lopez Luna","affiliation":["UJAT"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-2259-544X","nameIdentifierScheme":"ORCID"}]}],"titles":[{"lang":"en","title":"Morphometric data of Crocodylus moreletii captured in an urban lagoon in southeastern Mexico (labels in Spanish)"}],"publisher":"KNB Data Repository","container":{},"publicationYear":2012,"subjects":[{"lang":"en","subject":"Morfometría"},{"lang":"en","subject":"Crocodylus moreletii"},{"lang":"en","subject":"Urban lagoon"},{"lang":"en","subject":"Southeastern Mexico"}],"contributors":[],"dates":[{"date":"2012","dateType":"Issued"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[],"relatedItems":[],"sizes":[],"formats":["text/xml"],"version":null,"rightsList":[],"descriptions":[{"lang":"en","description":"We present a morphometric dataset of Crocodylus moreletii obtained from individuals captured in an urban lagoon in southeastern Mexico. This species, listed as subject to special protection in Mexico, has managed to persist in urbanized environments. The dataset includes standard body measurements (total length, snout-cloaca length, head length, head width, weight, among others) taken during capture and handling events. All variable labels are in Spanish. This dataset is useful for urban ecology studies, human-crocodile conflict, and assessments of health status and population structure of the species in anthropized environments.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://knb.ecoinformatics.org/view/doi:10.5063/F1C24TX3","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-04-30T03:37:05Z","registered":"2026-04-30T03:37:06Z","published":null,"updated":"2026-04-30T03:37:06Z"},"relationships":{"client":{"data":{"id":"cdl.ucsb","type":"clients"}}}},{"id":"10.5063/f1gt5kpz","type":"dois","attributes":{"doi":"10.5063/f1gt5kpz","identifiers":[],"creators":[{"name":"Gallardo, Manuel","nameType":"Personal","givenName":"Manuel","familyName":"Gallardo","affiliation":["UJAT"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-6038-194X","nameIdentifierScheme":"ORCID"}]},{"name":"Lesher Gordillo, Julia María","nameType":"Personal","givenName":"Julia María","familyName":"Lesher Gordillo","affiliation":["UJAT"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-6973-2304","nameIdentifierScheme":"ORCID"}]},{"name":"Zenteno Ruiz, Claudia Elena","nameType":"Personal","givenName":"Claudia Elena","familyName":"Zenteno Ruiz","affiliation":["UJAT"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-0662-7501","nameIdentifierScheme":"ORCID"}]},{"name":"Recino Reyes, Elsi Beatriz","nameType":"Personal","givenName":"Elsi Beatriz","familyName":"Recino Reyes","affiliation":["UJAT"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-4689-7891","nameIdentifierScheme":"ORCID"}]},{"name":"Montejo Méndez, Heidi Beatriz","nameType":"Personal","givenName":"Heidi Beatriz","familyName":"Montejo Méndez","affiliation":["UJAT"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-6633-0916","nameIdentifierScheme":"ORCID"}]}],"titles":[{"lang":"en","title":"Morphometrics of Dermatemys mawii breeders – data labeled in Spanish"}],"publisher":"KNB Data Repository","container":{},"publicationYear":2019,"subjects":[{"lang":"en","subject":"Morphometrics"},{"lang":"en","subject":"Founder breeders"},{"lang":"en","subject":"Captive breeding"}],"contributors":[],"dates":[{"date":"2019","dateType":"Issued"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[],"relatedItems":[],"sizes":[],"formats":["text/xml"],"version":null,"rightsList":[],"descriptions":[{"lang":"en","description":"We present a morphometric dataset of reproductive individuals of the freshwater turtle Dermatemys mawii, a threatened species. 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This was part of the NSTP program and CANPOLIN.Focal speciesSilene acaulis","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://knb.ecoinformatics.org/view/doi:10.5063/F14J0CMT","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-04-25T00:03:05Z","registered":"2026-04-25T00:03:06Z","published":null,"updated":"2026-04-25T00:03:06Z"},"relationships":{"client":{"data":{"id":"cdl.ucsb","type":"clients"}}}},{"id":"10.5063/f18914c0","type":"dois","attributes":{"doi":"10.5063/f18914c0","identifiers":[],"creators":[{"name":"Yamamura, Kohji","nameType":"Personal","givenName":"Kohji","familyName":"Yamamura","affiliation":["National Agriculture and Food Research Organization"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-4056-3049","nameIdentifierScheme":"ORCID"}]}],"titles":[{"lang":"en","title":"Data for identifying the causal relationship on global warming from 1959-2024"}],"publisher":"KNB Data Repository","container":{},"publicationYear":2026,"subjects":[],"contributors":[],"dates":[{"date":"2026","dateType":"Issued"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[],"relatedItems":[],"sizes":[],"formats":["text/xml"],"version":null,"rightsList":[],"descriptions":[{"lang":"en","description":"The causal relationship between atmospheric CO₂ concentration and global warming has, to date, been recognized primarily on the basis of computer-simulated results, lacking robust statistical foundations. To address this limitation, we should reanalyze the time-series data on CO₂ concentration and global surface temperature. Additionally, other correlated time-series data, such as urban surface temperature and global population, should also be examined.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://knb.ecoinformatics.org/view/doi:10.5063/F18914C0","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-04-16T13:32:38Z","registered":"2026-04-16T13:32:38Z","published":null,"updated":"2026-04-16T13:32:38Z"},"relationships":{"client":{"data":{"id":"cdl.ucsb","type":"clients"}}}},{"id":"10.5063/f1d21w4s","type":"dois","attributes":{"doi":"10.5063/f1d21w4s","identifiers":[],"creators":[{"name":"Domke, Lia K.","nameType":"Personal","givenName":"Lia K.","familyName":"Domke","affiliation":[],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-1155-989X","nameIdentifierScheme":"ORCID"}]},{"name":"Whitney, Jessica M.","nameType":"Personal","givenName":"Jessica M.","familyName":"Whitney","affiliation":[],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0009-0008-4152-1212","nameIdentifierScheme":"ORCID"}]},{"name":"Johnson, Carter J.","nameType":"Personal","givenName":"Carter J.","familyName":"Johnson","affiliation":[],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-6983-6426","nameIdentifierScheme":"ORCID"}]},{"name":"Eckert, Ginny L.","nameType":"Personal","givenName":"Ginny L.","familyName":"Eckert","affiliation":[],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-7538-7081","nameIdentifierScheme":"ORCID"}]}],"titles":[{"lang":"en","title":"Seascape complexity and habitat heterogeneity influence Alaskan eelgrass fish assemblages"}],"publisher":"KNB Data Repository","container":{},"publicationYear":2025,"subjects":[{"lang":"en","subject":"seascape ecology"},{"lang":"en","subject":"nursery habitats"},{"lang":"en","subject":"macroalgae"},{"lang":"en","subject":"unoccupied aerial vehicles"},{"lang":"en","subject":"habitat mapping"},{"lang":"en","subject":"coastal mosaics"},{"lang":"en","subject":"drones"}],"contributors":[],"dates":[{"date":"2025","dateType":"Issued"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[],"relatedItems":[],"sizes":[],"formats":["text/xml"],"version":null,"rightsList":[],"descriptions":[{"lang":"en","description":"Enclosed data and code are an archive for accepted manuscript at MEPS entitled: \"Seascape complexity and habitat heterogeneity influence Alaskan eelgrass fish assemblages\". We include five scripts, numbered by the order in which they should be run. The first two are scripts that calculate seascape composition and heterogeneity from drone imagery (1) and a cleaning script for all variables involved in analyses (2). The last three are analyses scripts. This includes abundance-based multivariate analysis (3), univariate analysis (pulls data from mv analysis) (4), and biomass-based multivariate analysis (5). These scripts generate all included analyses in manuscript and corresponding figures from analyses.Nearshore marine ecosystems along heterogeneous coasts are composed of dynamic and complex habitats that provide important services including shelter and prey for juvenile, migratory, and residential species. Habitats often intermix, though we regularly study them in isolation without considering surrounding influences. We examined how fish assemblages differ in Southeast Alaska eelgrass meadows when the surrounding seascape includes or does not include adjacent kelp habitat to test the role of spatial arrangement and composition of nearshore coastlines on associated fish assemblages. We characterized habitats using aerial imagery and in-situ sampling while quantifying eelgrass fish assemblages using beach seines. We found increased fish species richness and diversity in eelgrass when kelps are adjacent. The fish assemblage included greater prevalence of lingcod (Ophiodon elongatus), whitespotted greenling (Hexagrammous stelleri), sculpins (Cottoidea), Pacific sand lance (Ammodytes personatus), and pink salmon (Oncorhynchus gorbuscha). Some fish species, however, were more abundant within eelgrass habitat without adjacent habitats, including Pacific staghorn sculpin (Leptocottus armatus), shiner perch (Cymatogaster aggregata), and threespine stickleback (Gasterosteus aculeatus). While both threespine sticklebacks and shiner perch were caught in both types of eelgrass habitats, juveniles of these species were more abundant in eelgrass without other adjacent habitat, highlighting the role of eelgrass as nursery habitat. Seascape complexity, in which nearshore habitats are contextualized, is important for evaluating the role of these habitats for nearshore habitat conservation and fisheries management.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://knb.ecoinformatics.org/view/doi:10.5063/F1D21W4S","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-04-13T05:05:41Z","registered":"2026-04-13T05:05:42Z","published":null,"updated":"2026-04-13T05:05:42Z"},"relationships":{"client":{"data":{"id":"cdl.ucsb","type":"clients"}}}},{"id":"10.5063/f1ht2mtb","type":"dois","attributes":{"doi":"10.5063/f1ht2mtb","identifiers":[],"creators":[{"name":"Donadi, Serena","nameType":"Personal","givenName":"Serena","familyName":"Donadi","affiliation":["Swedish University Of Agricultural Sciences"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-8920-9630","nameIdentifierScheme":"ORCID"}]}],"titles":[{"lang":"en","title":"sea trout recruit density in Swedish rivers, catches from fisheries, predators density and habitat variables collected (2000 - 2022)"}],"publisher":"KNB Data Repository","container":{},"publicationYear":2027,"subjects":[{"lang":"en","subject":"Aquatic sciences"},{"lang":"en","subject":"Fisheries"}],"contributors":[],"dates":[{"date":"2027","dateType":"Issued"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[],"relatedItems":[],"sizes":[],"formats":["text/xml"],"version":null,"rightsList":[],"descriptions":[{"lang":"en","description":"This is a dataset containing data collected in rivers and at sea, which were spatially harmonized to explore corss-system effects of stressors on sea trout recruit density in Swedish rivers. Specifically, the data were used to assess the influence of inland stressors (recreational fishing and river barriers) and marine stressors (commercial and recreational fishing, and predator densities of grey seals Halichoerus grypus and great cormorants Phalacrocorax carbo) on sea trout recruits in rivers, while accounting for habitat quality variables.Column headers and explanation:ID: unique identifier for observations, numericYear: year of data collectionHflodomr: unique identifier for different catchments, numericSD: name of ICES subdivisionSD_coarse: name of ICES subdivision or group of subdivisionsvatten_areal_catch_km2: total water area in the catchment (squared km)Length_Swedish_coastline_km_SD: length of the coastline of the subdivision (km)Length_Swedish_coastline_km_SD_coarse: length of the coastline of the subdivision or group of subdivisions (km)sum_fish_RECR_RIVER: catches of sea trout from recreational fishing in rivers (number of fish retained)n_fishing_days_in_thousands_tot: number of fishing days of recreational fishing at sea (thousand days)Kg_RECR_SEA_SDcoarse: catches of sea trout from recreational fishing at sea (kg of fish retained)sum_NUMB_COMM_SEA_forSERS: catches of sea trout from commercial fishing at sea (number of fish retained)seals_per_km: number of seal per km of coastlinecormorants_per_km: number of cormorant breeding pairs per km of coastlineRiver_ID: unique identifier for different rivers (obtained by merging the catchment code and the river name)VDRAGNAM: name of the riveravg_river_Trout0P: average river sea trout recruit density (number per 100 squared meters)avg_river_MEDELDJU: average river depth (m)avg_river_LUTNING_PR: average river bed slopeavg_river_MEDTEMPAR: average annual air temperature from the meteorological stations closest to each sampling location (degree Celsius)avg_river_BREDD: average river width (m)avg_dam: average number of dams without fishways downstream of sampling sites in a riveravg_dam_w_and_without_fishway: average number of dams with or without fishways downstream of sampling sites in a riverratio_dam0: ratio between the number of sites with no dam without fishways downstream and the total number of sites in a riverratio_dam_w_and_without_fishway0: ratio between the number of sites with no dam with or without fishways downstream and the total number of sites in a riversum_Releases: hatchery sea trout released in a river (number fish per year)","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://knb.ecoinformatics.org/view/doi:10.5063/F1HT2MTB","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-04-01T08:44:50Z","registered":"2026-04-01T08:44:51Z","published":null,"updated":"2026-04-01T08:44:51Z"},"relationships":{"client":{"data":{"id":"cdl.ucsb","type":"clients"}}}},{"id":"10.5063/f1nk3chm","type":"dois","attributes":{"doi":"10.5063/f1nk3chm","identifiers":[],"creators":[{"name":"Morelock, Seth","nameType":"Personal","givenName":"Seth","familyName":"Morelock","affiliation":["Center for Wildlife Sustainability Research"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0009-0003-1657-1895","nameIdentifierScheme":"ORCID"}]},{"name":"Stephens, Ryan","nameType":"Personal","givenName":"Ryan","familyName":"Stephens","affiliation":["East Tennessee State University - Department of Biological Sciences"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-8524-9873","nameIdentifierScheme":"ORCID"}]},{"name":"Carter, Richard","nameType":"Personal","givenName":"Richard","familyName":"Carter","affiliation":["East Tennessee State University - Department of Biological Sciences"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-5874-7367","nameIdentifierScheme":"ORCID"}]}],"titles":[{"lang":"en","title":"Data for manuscript: Cattle regenerative grazing alters bat activity in southern Appalachia"}],"publisher":"KNB Data Repository","container":{},"publicationYear":2026,"subjects":[{"lang":"en","subject":"Animal Management Systems"},{"lang":"en","subject":"Bat"},{"lang":"en","subject":"Landscape Managment"}],"contributors":[],"dates":[{"date":"2026","dateType":"Issued"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[],"relatedItems":[],"sizes":[],"formats":["text/xml"],"version":null,"rightsList":[],"descriptions":[{"lang":"en","description":"This dataset is for the manuscript: Cattle rangeland grazing alters bat activity in southern Appalachia. This dataset includes files used to model bat activity response to cattle rotational grazing, weather variables (wind speed, precipitation, and temperature), moon luminosity, and forest cover (%). This dataset also includes data on insect abundance responses to cattle rotational grazing.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://knb.ecoinformatics.org/view/doi:10.5063/F1NK3CHM","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-03-27T22:25:59Z","registered":"2026-03-27T22:26:01Z","published":null,"updated":"2026-03-27T22:26:01Z"},"relationships":{"client":{"data":{"id":"cdl.ucsb","type":"clients"}}}},{"id":"10.5063/f1s75dv2","type":"dois","attributes":{"doi":"10.5063/f1s75dv2","identifiers":[],"creators":[{"name":"Frazier, Melanie","nameType":"Personal","givenName":"Melanie","familyName":"Frazier","affiliation":["National Center for Ecological Analysis and Synthesis"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-7181-195X","nameIdentifierScheme":"ORCID"}]}],"titles":[{"lang":"en","title":"Recent pace of change in human impact on the world's ocean: Commercial fishing - demersal non-destructive low-bycatch"}],"publisher":"KNB Data Repository","container":{},"publicationYear":2026,"subjects":[],"contributors":[],"dates":[{"date":"2026","dateType":"Issued"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[],"relatedItems":[],"sizes":[],"formats":["text/xml"],"version":null,"rightsList":[],"descriptions":[{"lang":"en","description":"These are the stressor and impact data for commercial demersal nondestructive high bycatch fishing for the publication: 'Recent pace of change in human impact on the world's ocean' (Halpern et al. 2019). \nThis stressor describes tonnes of demersal fisheries catch using nondestructive, but high bycatch, gear types. Catch is standardized by Net Primary Productivity. We describe the stressor intensity of demersal nondestructive high bycatch fishing and the corresponding impact on 21 marine ecosystems over 11 years (2003-2013). To determine annual change in impact, we applied a linear regression model to each raster cell. Code used in analyzing these data is available as a zipped GitHub repository, impact_acceleration-1.0.zip, in this dataset's parent (CITATION HERE).\n\nThese data include the following:\n \n \n \n Raw stressor intensity rasters for 2003 to 2013. These rasters are created using scripts located in the 'stressors' folder of the 'impact_acceleration' Github repository.\n \n \n Rescaled stressor intensity rasters for 2003 to 2013. Raw stressor intensities are rescaled to values between 0 and 1, using either known or estimated ecosystem thresholds or upper quantile values from the distribution of global stressor intensity values across years. These rasters are created using scripts located in the 'stressors' folder of the 'impact_acceleration' Github repository.\n \n \n Impact rasters for 2003 to 2013 which describe the impact of the stressor based on the vulnerability of 21 marine ecosystems to the stressor. The impact of the stressor is estimated by multiplying the stressor's intensity by the corresponding ecosystem vulnerability where the ecosystem occurs. The average impact of each stressor, across all ecosystems, is estimated by summing the stressor-by-ecosystem vulnerability combinations and dividing by the number of ecosystems within each cell. These rasters are created using scripts located in the 'impacts' folder of the 'impact_acceleration' Github repository.\n \n \n A trend raster describing the average annual change in impact from 2003 to 2013. This raster is created using scripts located in the 'trend' folder of the 'impact_acceleration' Github repository. \n \n \n \n Files describing the cumulative impacts and additional stressors are provided in other KNB datasets.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://knb.ecoinformatics.org/view/doi:10.5063/F1S75DV2","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-03-27T17:12:05Z","registered":"2026-03-27T17:12:06Z","published":null,"updated":"2026-03-27T17:12:06Z"},"relationships":{"client":{"data":{"id":"cdl.ucsb","type":"clients"}}}},{"id":"10.5063/f1x065jb","type":"dois","attributes":{"doi":"10.5063/f1x065jb","identifiers":[],"creators":[{"name":"Frazier, Melanie","nameType":"Personal","givenName":"Melanie","familyName":"Frazier","affiliation":["National Center for Ecological Analysis and Synthesis"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-7181-195X","nameIdentifierScheme":"ORCID"}]}],"titles":[{"lang":"en","title":"Recent pace of change in human impact on the world's ocean: Commercial fishing - demersal non-desctructive high-bycatch"}],"publisher":"KNB Data Repository","container":{},"publicationYear":2026,"subjects":[],"contributors":[],"dates":[{"date":"2026","dateType":"Issued"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[],"relatedItems":[],"sizes":[],"formats":["text/xml"],"version":null,"rightsList":[],"descriptions":[{"lang":"en","description":"These are the stressor and impact data for commercial demersal nondestructive low bycatch fishing for the publication: 'Recent pace of change in human impact on the world's ocean' (Halpern et al. 2019). \nThis stressor describes tonnes of demersal fisheries catch using nondestructive and low bycatch gear types. Catch is standardized by Net Primary Productivity. We describe the stressor intensity of demersal nondestructive low bycatch fishing and the corresponding impact on 21 marine ecosystems over 11 years (2003-2013). To determine annual change in impact, we applied a linear regression model to each raster cell. Code used in analyzing these data is available as a zipped GitHub repository, impact_acceleration-1.0.zip, in this dataset's parent (CITATION HERE).\n\nThese data include the following:\n \n \n \n Raw stressor intensity rasters for 2003 to 2013. These rasters are created using scripts located in the 'stressors' folder of the 'impact_acceleration' Github repository.\n \n \n Rescaled stressor intensity rasters for 2003 to 2013. Raw stressor intensities are rescaled to values between 0 and 1, using either known or estimated ecosystem thresholds or upper quantile values from the distribution of global stressor intensity values across years. These rasters are created using scripts located in the 'stressors' folder of the 'impact_acceleration' Github repository.\n \n \n Impact rasters for 2003 to 2013 which describe the impact of the stressor based on the vulnerability of 21 marine ecosystems to the stressor. The impact of the stressor is estimated by multiplying the stressor's intensity by the corresponding ecosystem vulnerability where the ecosystem occurs. The average impact of each stressor, across all ecosystems, is estimated by summing the stressor-by-ecosystem vulnerability combinations and dividing by the number of ecosystems within each cell. These rasters are created using scripts located in the 'impacts' folder of the 'impact_acceleration' Github repository.\n \n \n A trend raster describing the average annual change in impact from 2003 to 2013. This raster is created using scripts located in the 'trend' folder of the 'impact_acceleration' Github repository. \n \n \n \n Files describing the cumulative impacts and additional stressors are provided in other KNB datasets.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://knb.ecoinformatics.org/view/doi:10.5063/F1X065JB","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-03-27T17:10:10Z","registered":"2026-03-27T17:10:11Z","published":null,"updated":"2026-03-27T17:10:11Z"},"relationships":{"client":{"data":{"id":"cdl.ucsb","type":"clients"}}}},{"id":"10.5063/f11r6p1b","type":"dois","attributes":{"doi":"10.5063/f11r6p1b","identifiers":[],"creators":[{"name":"Vourlitis, George","nameType":"Personal","givenName":"George","familyName":"Vourlitis","affiliation":["CSUSM"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-4304-3951","nameIdentifierScheme":"ORCID"}]},{"name":"Pratt, R. Brandon","nameType":"Personal","givenName":"R. Brandon","familyName":"Pratt","affiliation":["California State University Bakersfield"],"nameIdentifiers":[]},{"name":"Meyer, Wallace","nameType":"Personal","givenName":"Wallace","familyName":"Meyer","affiliation":["Pomona College"],"nameIdentifiers":[]}],"titles":[{"lang":"en","title":"Carbon and nitrogen stable isotope variation in semi-arid woody plant species growing in managed and natural settings across Southern California"}],"publisher":"KNB Data Repository","container":{},"publicationYear":2026,"subjects":[{"lang":"en","subject":"delta 13C natural abundance"},{"lang":"en","subject":"delta 15N natural abundance"},{"lang":"en","subject":"water use efficiency"},{"lang":"en","subject":"urban landscapes"}],"contributors":[],"dates":[{"date":"2026","dateType":"Issued"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[],"relatedItems":[],"sizes":[],"formats":["text/xml"],"version":null,"rightsList":[],"descriptions":[{"lang":"en","description":"Data include leaf nitrogen (N) and carbon (C) concentrations (%) and delta 13C and 15N natural abundances for native Mediterranean-type plant species growing in adjacent urban and natural areas at six different universities throughout Southern California.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://knb.ecoinformatics.org/view/doi:10.5063/F11R6P1B","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-03-24T22:07:41Z","registered":"2026-03-24T22:07:42Z","published":null,"updated":"2026-03-24T22:07:42Z"},"relationships":{"client":{"data":{"id":"cdl.ucsb","type":"clients"}}}},{"id":"10.5063/f15h7ds3","type":"dois","attributes":{"doi":"10.5063/f15h7ds3","identifiers":[],"creators":[{"name":"Uiterwaal, Stella F.","nameType":"Personal","givenName":"Stella F.","familyName":"Uiterwaal","affiliation":["Smithsonian Conservation Biology Institute"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-2745-5817","nameIdentifierScheme":"ORCID"}]},{"name":"La Sorte, Frank A.","nameType":"Personal","givenName":"Frank A.","familyName":"La Sorte","affiliation":["Yale University"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-8521-2501","nameIdentifierScheme":"ORCID"}]},{"name":"Coblentz, Kyle E.","nameType":"Personal","givenName":"Kyle E.","familyName":"Coblentz","affiliation":["Colby College"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-0069-8491","nameIdentifierScheme":"ORCID"}]},{"name":"DeLong, John P.","nameType":"Personal","givenName":"John P.","familyName":"DeLong","affiliation":["University of Nebraska - Lincoln"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-0558-8213","nameIdentifierScheme":"ORCID"}]}],"titles":[{"lang":"en","title":"OS-Prey (Omnibus study of prey) V3"}],"publisher":"KNB Data Repository","container":{},"publicationYear":2026,"subjects":[{"lang":"en","subject":"raptor"},{"lang":"en","subject":"diet"},{"lang":"en","subject":"predation"},{"lang":"en","subject":"bird of prey"},{"lang":"en","subject":"hawk"},{"lang":"en","subject":"falcon"},{"lang":"en","subject":"owl"},{"lang":"en","subject":"foraging"},{"lang":"en","subject":"predator"},{"lang":"en","subject":"prey"}],"contributors":[],"dates":[{"date":"2026","dateType":"Issued"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[],"relatedItems":[],"sizes":[],"formats":["text/xml"],"version":null,"rightsList":[],"descriptions":[{"lang":"en","description":"The diet composition of a predator is a direct reflection of its role in a food web, resulting from interactions with prey species. Raptors (including hawks, eagles, owls, and falcons) are ubiquitous predators with diverse diets. We present a database of over 3500 raptor diets, representing 173 raptor species from across the world. The database comprises published records of raptor diets from pellets, prey remains, direct or photographic observations, prey DNA, and raptor gut contents. For each diet, we present the amounts and proportions of consumed prey as well as metadata such as location, habitat, and season.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://knb.ecoinformatics.org/view/doi:10.5063/F15H7DS3","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-03-24T13:01:16Z","registered":"2026-03-24T13:01:17Z","published":null,"updated":"2026-03-24T13:01:17Z"},"relationships":{"client":{"data":{"id":"cdl.ucsb","type":"clients"}}}},{"id":"10.5063/f19885j8","type":"dois","attributes":{"doi":"10.5063/f19885j8","identifiers":[],"creators":[{"name":"McCune, Kelsey","nameType":"Personal","givenName":"Kelsey","familyName":"McCune","affiliation":[],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-0951-0827","nameIdentifierScheme":"ORCID"}]}],"titles":[{"lang":"en","title":"Flexibility training does not increase behavioral diversity of Florida scrub-jays"}],"publisher":"KNB Data Repository","container":{},"publicationYear":2026,"subjects":[],"contributors":[],"dates":[{"date":"2026","dateType":"Issued"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[],"relatedItems":[],"sizes":[],"formats":["text/xml"],"version":null,"rightsList":[],"descriptions":[{"lang":"en","description":"Human modifications of environments are expanding, causing global changes that other species must adjust to or suffer from. Behavioral flexibility (hereafter 'flexibility') could be key to coping with rapid change. Behavioral research can contribute to conservation by determining which behaviors can predict the ability to adjust to human modified environments and whether these can be manipulated. When research that manipulates behavior in a conservation context occurs, it primarily trains a specific behavior to address one potential threat improve individual success in the wild. However, training a domain general cognitive ability, such as flexibility, has the potential to change a whole suite of behaviors, which could have a larger impact on influencing success in adjusting to human modified environments. This project asks whether flexibility can be increased by experimentally increasing environmental heterogeneity and whether such an increase can help species succeed in human modified environments. We explore whether it is possible to take insights from highly divergent species and apply them to address critical conservation challenges. This pushes the limits in terms of understanding how conserved these abilities may be and to what extent they can be shaped by the environment. We aim to 1) conduct flexibility interventions in flexible species that are successful in human modified environments (California scrub-jays or blue jays) to understand how flexibility relates to success; and 2) implement these interventions in a vulnerable species (Florida scrub-jays) to determine whether flexibility as a generalizable cognitive ability can be trained and whether such training improves success in human-modified environments. This research will significantly advance our understanding of the causes and consequences of flexibility, linking behavior to environmental change, cognition, and success in human modified environments through a comparative framework.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://knb.ecoinformatics.org/view/doi:10.5063/F19885J8","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-03-17T21:54:16Z","registered":"2026-03-17T21:54:17Z","published":null,"updated":"2026-03-17T21:54:17Z"},"relationships":{"client":{"data":{"id":"cdl.ucsb","type":"clients"}}}},{"id":"10.5063/f1f18x75","type":"dois","attributes":{"doi":"10.5063/f1f18x75","identifiers":[],"creators":[{"name":"McCune, Kelsey","nameType":"Personal","givenName":"Kelsey","familyName":"McCune","affiliation":["Auburn University"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-0951-0827","nameIdentifierScheme":"ORCID"}]}],"titles":[{"lang":"en","title":"Who are the smart birds?"}],"publisher":"KNB Data Repository","container":{},"publicationYear":2026,"subjects":[{"lang":"en","subject":"Animal cognition"},{"lang":"en","subject":"corvids"},{"lang":"en","subject":"parrots"},{"lang":"en","subject":"learning"},{"lang":"en","subject":"bias"}],"contributors":[],"dates":[{"date":"2026","dateType":"Issued"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[],"relatedItems":[],"sizes":[],"formats":["text/xml"],"version":null,"rightsList":[],"descriptions":[{"lang":"en","description":"To understand the evolution and function of cognition, it is important to know the breadth of species demonstrating a variety of cognitive abilities. Statements in scientific and popular literature commonly assert that avian species in the corvid and parrot families are the most intelligent. However, it is unclear whether there is sufficient data from a diversity of taxa to support this claim. I reviewed research from the last 25 years to evaluate birds that have been tested for any cognitive ability. Current research encompasses less than 30% of families and 2% of species. The most studied family was Corvidae, followed by Columbidae, Paridae, Phasianidae and Psittacidae. Species from these top five families comprised 64% of studies, illustrating a bias towards these families for avian cognition research. There was also a bias in the focal species for testing different categories of cognitive ability. Traditional lab species from Columbidae and Phasianidae were primarily tested for fundamental cognition, while Corvidae and Psittacidae were more frequently tested on social cognition. This small and biased sample of focal species and cognitive abilities precludes conclusions about the cognitive superiority of any species or the characteristics favoring the evolution of certain cognitive abilities.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://knb.ecoinformatics.org/view/doi:10.5063/F1F18X75","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-03-16T20:47:22Z","registered":"2026-03-16T20:47:23Z","published":null,"updated":"2026-03-16T20:47:23Z"},"relationships":{"client":{"data":{"id":"cdl.ucsb","type":"clients"}}}}],"meta":{"total":8515,"totalPages":341,"page":1},"links":{"self":"https://api.datacite.org/dois?prefix=10.5063","next":"https://api.datacite.org/dois?page%5Bnumber%5D=2\u0026page%5Bsize%5D=25\u0026prefix=10.5063"}}