Browsing by Subject "leaf traits"
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Item Content of leaf pigments of tree and grassland species collected at the Cedar Creek Ecosystem Science Reserve in 2015 and 2016(2020-09-01) Schweiger, Anna K; Fredericksen, Brett; Lapadat, Cathleen; Cavender-Bares, Jeannine; cavender@umn.edu; Cavender-Bares, JeannineThis data set contains the content of chlorophyll a, chlorophyll b, β-carotene, lutein, neoxanthin, violaxanthin, antheraxanthin and zeaxanthin pigments from tree and grassland species sampled at the Cedar Creek Ecosystem Science Reserve in East Bethel, MN. Mass- and area-based pigment contents were determined using high-performance liquid chromatography (HPLC). Data were collected as part of the Dimensions of Biodiversity project “Linking remotely sensed optical diversity to genetic, phylogenetic and functional diversity to predict ecosystem processes”. Samples were collected in or near the old fields chronosequence, the oak savanna, and the Forest and Biodiversity Experiment (FAB 1) plots. We used this data together with leaf-level spectral measurements to build partial least squares regression (PLSR) models for predicting leaf traits from spectra.Item Correlations among leaf traits provide a significant constraint on the estimate of global gross primary production(2012) Wang, Y P; Lu, X J; Wright, I J; Dai, Y J; Rayner, P J; Reich, Peter BCurrent estimates of gross primary productivity (GPP) of the terrestrial biosphere vary widely, from 100 to 175 Gt C year−1. Ecosystem GPP cannot be measured directly, and is commonly estimated using models. Among the many parameters in those models, three leaf parameters have strong influences on the modelled GPP: leaf mass per area, leaf lifespan and leaf nitrogen concentration. The first two parameters affect the modelled canopy leaf area and the last two determine the maximal leaf photosynthetic rate. Ecological studies have firmly established that these three parameters are significantly correlated at regional to global scales, but this knowledge is yet to be used in predicting global GPP. We hypothesize that incorporating multi-trait covariance can reduce uncertainties of model predictions in a way likely to provide improved realism. Using the Australian community land surface model (CABLE), we find that correlations among these three parameters reduce the variance among GPP estimates by CABLE by over 20% for shrub, C4 grassland and tundra, and by between 5% and 20% for most other PFTs, as compared with the simulated GPP without considering the correlations. Globally the correlations do not alter the mean but reduce the variance of modeled GPP by CABLE by 28% and result in fewer extremely high or extremely low (and unlikely) global GPP predictions. Therefore correlations among the three leaf parameters, and possibly other parameters, can be used as a significant constraint on the estimates of model parameters or predictions by those models.Item Leaf carbon and nitrogen content of tree and grassland species collected at the Cedar Creek Ecosystem Science Reserve in 2015 and 2016(2020-09-01) Schweiger, Anna K; Lapadat, Cathleen; Cavender-Bares, Jeannine; cavender@umn.edu; Cavender-Bares, JeannineThis data set contains carbon and nitrogen content from combustion–reduction elemental analysis (TruSpec CN Analyzer, LECO) from tree and grassland species sampled at the Cedar Creek Ecosystem Science Reserve in East Bethel, MN. Data were collected as part of the Dimensions of Biodiversity project “Linking remotely sensed optical diversity to genetic, phylogenetic and functional diversity to predict ecosystem processes”. Samples were collected in or near the old fields chronosequence, the oak savanna, and the Forest and Biodiversity Experiment (FAB 1) plots. We used this data together with leaf-level spectral measurements to build partial least squares regression (PLSR) models for predicting leaf traits from spectra.Item Leaf carbon fraction data from tree and grassland species collected at the Cedar Creek Ecosystem Science Reserve in 2015 and 2016(2020-08-12) Schweiger, Anna K; Lapadat, Cathleen; Kothari, Shan; Cavender-Bares, Jeannine; cavender@umn.edu; Cavender-Bares, JeannineThis data set contains results from carbon fraction analysis (Fiber Analyzer 200, ANKOM Technology), including non-structural carbohydrates, hemicellulose, cellulose, lignin, neutral detergent fiber, and acid detergent fiber contents in percent (%) from tree and grassland species sampled at the Cedar Creek Ecosystem Science Reserve in East Bethel, MN. The data was collected as part of the Dimensions of Biodiversity project “Linking remotely sensed optical diversity to genetic, phylogenetic and functional diversity to predict ecosystem processes”. Samples were collected in or near the old fields chronosequence, the oak savanna, and the Forest and Biodiversity Experiment (FAB 1) plots. We used this data together with leaf-level spectral measurements to build partial least squares regression (PLSR) models for predicting leaf traits from spectra.