FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4380610267> ?p ?o ?g. }

• W4380610267 abstract "<strong class=journal-contentHeaderColor>Abstract.</strong> Trait-based approaches are of increasing concern in predicting vegetation changes and linking ecosystem structure to functions at large scales. However, a critical challenge for such approaches is acquiring spatially continuous plant functional trait distribution. Here, eight key plant functional traits were selected to represent two-dimensional spectrum of plant form and function, including leaf area (LA), leaf dry matter content (LDMC), leaf N concentration (LNC), leaf P concentration (LPC), plant height, seed mass (SM), specific leaf area (SLA) and wood density (WD). A total of 52477 trait measurements of 4291 seed plant species were collected from 1541 sampling sites in China and were used to generate a spatial plant functional trait dataset (1 km), together with environmental variables and vegetation indices based on two machine learning models (random forest and boosted regression trees). The two models showed a good accuracy in estimating WD, LPC and SLA, with average R² values ranging from 0.45 to 0.66. In contrast, both the two models had a weak performance in estimating SM and LDMC, with average R² values below 0.25. Meanwhile, LA, SM and plant height showed considerable differences between two models in some regions. To obtain the optimal estimates, a weighted average algorithm was further applied to merge the predictions of the two models to derive the final spatial plant functional trait dataset. The optimal estimates showed that climatic effects were more important than those of edaphic factors in predicting the spatial distribution of plant functional traits. Estimates of plant functional traits in northeast China and the Qinghai-Tibet Plateau had relatively high uncertainties due to sparse samplings, implying a need of more observations in these regions in future. Our trait dataset could provide critical support for trait-based vegetation models and allows exploration into the relationships between vegetation characteristics and ecosystem functions at large scales. The eight plant functional traits datasets for China with 1 km spatial resolution are now available at <a href=https://figshare.com/s/c527c12d310cb8156ed2 target=_blank rel=noopener>https://figshare.com/s/c527c12d310cb8156ed2</a> (An et al., 2023)." @default.
• W4380610267 created "2023-06-15" @default.
• W4380610267 date "2023-06-14" @default.
• W4380610267 modified "2023-10-03" @default.
• W4380610267 title "Comment on essd-2023-121" @default.
• W4380610267 doi "https://doi.org/10.5194/essd-2023-121-rc2" @default.
• W4380610267 hasPublicationYear "2023" @default.
• W4380610267 type Work @default.
• W4380610267 citedByCount "0" @default.
• W4380610267 crossrefType "peer-review" @default.
• W4380610267 hasBestOaLocation W43806102671 @default.
• W4380610267 hasConcept C105795698 @default.
• W4380610267 hasConcept C106934330 @default.
• W4380610267 hasConcept C110872660 @default.
• W4380610267 hasConcept C159750122 @default.
• W4380610267 hasConcept C183688256 @default.
• W4380610267 hasConcept C18903297 @default.
• W4380610267 hasConcept C197129107 @default.
• W4380610267 hasConcept C199360897 @default.
• W4380610267 hasConcept C23123220 @default.
• W4380610267 hasConcept C2776615292 @default.
• W4380610267 hasConcept C2780946806 @default.
• W4380610267 hasConcept C33923547 @default.
• W4380610267 hasConcept C41008148 @default.
• W4380610267 hasConcept C59822182 @default.
• W4380610267 hasConcept C71908930 @default.
• W4380610267 hasConcept C86803240 @default.
• W4380610267 hasConceptScore W4380610267C105795698 @default.
• W4380610267 hasConceptScore W4380610267C106934330 @default.
• W4380610267 hasConceptScore W4380610267C110872660 @default.
• W4380610267 hasConceptScore W4380610267C159750122 @default.
• W4380610267 hasConceptScore W4380610267C183688256 @default.
• W4380610267 hasConceptScore W4380610267C18903297 @default.
• W4380610267 hasConceptScore W4380610267C197129107 @default.
• W4380610267 hasConceptScore W4380610267C199360897 @default.
• W4380610267 hasConceptScore W4380610267C23123220 @default.
• W4380610267 hasConceptScore W4380610267C2776615292 @default.
• W4380610267 hasConceptScore W4380610267C2780946806 @default.
• W4380610267 hasConceptScore W4380610267C33923547 @default.
• W4380610267 hasConceptScore W4380610267C41008148 @default.
• W4380610267 hasConceptScore W4380610267C59822182 @default.
• W4380610267 hasConceptScore W4380610267C71908930 @default.
• W4380610267 hasConceptScore W4380610267C86803240 @default.
• W4380610267 hasLocation W43806102671 @default.
• W4380610267 hasOpenAccess W4380610267 @default.
• W4380610267 hasPrimaryLocation W43806102671 @default.
• W4380610267 hasRelatedWork W1853276468 @default.
• W4380610267 hasRelatedWork W1999326491 @default.
• W4380610267 hasRelatedWork W2523225074 @default.
• W4380610267 hasRelatedWork W2612475021 @default.
• W4380610267 hasRelatedWork W2613635154 @default.
• W4380610267 hasRelatedWork W2884680164 @default.
• W4380610267 hasRelatedWork W3109515336 @default.
• W4380610267 hasRelatedWork W4213215063 @default.
• W4380610267 hasRelatedWork W4296699777 @default.
• W4380610267 hasRelatedWork W4366988303 @default.
• W4380610267 isParatext "false" @default.
• W4380610267 isRetracted "false" @default.
• W4380610267 workType "peer-review" @default.