Testing a generalized leaf mass estimation method for diverse tree species and climates of the continental United States

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dc.contributor.authorDettmann, Garret T.en
dc.contributor.authorMacFarlane, David W.en
dc.contributor.authorRadtke, Philip J.en
dc.contributor.authorWeiskittel, Aaron R.en
dc.contributor.authorAffleck, David L. R.en
dc.contributor.authorPoudel, Krishna P.en
dc.contributor.authorWestfall, Jamesen
dc.coverage.countryUnited Statesen
dc.date.accessioned2023-05-25T19:07:39Zen
dc.date.available2023-05-25T19:07:39Zen
dc.date.issued2022-10en
dc.description.abstractEstimating tree leaf biomass can be challenging in applications where predictions for multiple tree species is required. This is especially evident where there is limited or no data available for some of the species of interest. Here we use an extensive national database of observations (61 species, 3628 trees) and formulate models of varying complexity, ranging from a simple model with diameter at breast height (DBH) as the only predictor to more complex models with up to 8 predictors (DBH, leaf longevity, live crown ratio, wood specific gravity, shade tolerance, mean annual temperature, and mean annual precipitation), to estimate tree leaf biomass for any species across the continental United States. The most complex with all eight predictors was the best and explained 74%-86% of the variation in leaf mass. Consideration was given to the difficulty of measuring all of these predictor variables for model application, but many are easily obtained or already widely collected. Because most of the model variables are independent of species and key species-level variables are available from published values, our results show that leaf biomass can be estimated for new species not included in the data used to fit the model. The latter assertion was evaluated using a novel "leave-one-species-out" cross-validation approach, which showed that our chosen model performs similarly for species used to calibrate the model, as well as those not used to develop it. The models exhibited a strong bias toward overestimation for a relatively small subset of the trees. Despite these limitations, the models presented here can provide leaf biomass estimates for multiple species over large spatial scales and can be applied to new species or species with limited leaf biomass data available.en
dc.description.adminPublic domain – authored by a U.S. government employeeen
dc.description.notesUSDA Forest Service Forest Inventory and Analysis Program; National Institute of Food and Agricultureen
dc.description.sponsorshipUSDA Forest Service Forest Inventory and Analysis Program; National Institute of Food and Agricultureen
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1002/eap.2646en
dc.identifier.eissn1939-5582en
dc.identifier.issn1051-0761en
dc.identifier.issue7en
dc.identifier.pmid35524985en
dc.identifier.urihttp://hdl.handle.net/10919/115194en
dc.identifier.volume32en
dc.language.isoenen
dc.publisherWileyen
dc.rightsPublic Domain (U.S.)en
dc.rights.urihttp://creativecommons.org/publicdomain/mark/1.0/en
dc.subjectallometryen
dc.subjectbiomassen
dc.subjectfoliage massen
dc.subjectnational forest inventoryen
dc.subjectspecies functional traitsen
dc.titleTesting a generalized leaf mass estimation method for diverse tree species and climates of the continental United Statesen
dc.title.serialEcological Applicationsen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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