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7 results for “community-weighted traits”
Community-weighted mean traits in old-growth and selectively logged forest
<p><strong>Description: </strong></p> <p>Community-weighted mean traits from tree species that make up more than 80% basal area in plots in selectively logged forest at SAFE and in old-growth forest in Danum Valley and Maliau Basin. Sampled during the BALI project traits campaign</p> <p><strong>Project: </strong>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/55"><strong>Biodiversity and land-use impacts on tropical ecosystem function (BALI): Quantifying functional trait distributions across the disturbance gradient</strong></a></p> <p><strong>Funding: </strong>These data were collected as part of research funded by:</p> <ul> <li>NERC (Standard grant, NE/K016253/1)</li> </ul> <p>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.</p> <p> </p> <p><strong>Permits: </strong>These data were collected under permit from the following authorities:</p> <ul> <li>Sabah Biodiversity Centre (Research licence JKM/MBS.1000-2.2(385))</li> </ul> <p> </p> <p><strong>XML metadata: </strong>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3247602">here</a></p> <p><strong>Files: </strong>This dataset consists of 3 files: Both_CWM_traits.xlsx, CSP_protocol_Chlorophyll_and_Carotenoids.pdf, CSP_protocol_Phenols_Tannins_Analysis.pdf</p> <p><strong>Both_CWM_traits.xlsx</strong></p> <p>This file contains dataset metadata and 1 data tables:</p> <ol> <li> <p><strong>CMW_traits</strong> (described in worksheet CMW_traits)</p> <p>Description: Community-weighted mean traits of tree in plots in SAFE , Danum Valley and Maliau Basin sampled during the BALI project traits campaign</p> <p>Number of fields: 36</p> <p>Number of data rows: 8</p> <p>Fields:</p> <ul> <li><strong>location</strong>: Location (Field type: Categorical)</li> <li><strong>forest_type</strong>: Forest type (Field type: Categorical)</li> <li><strong>forestplots_name</strong>: Plot name coherent with forestplots database (Field type: ID)</li> <li><strong>plot_name_trait_campaign</strong>: Plot name used during the BALI trait campaign (Field type: ID)</li> <li><strong>CWM_total_K_mg.g_log</strong>: CWM foliar potassium concentration in mg per g dry weight, log transformed data (Field type: Numeric)</li> <li><strong>CWM_total_Ca_mg.g_log</strong>: CWM foliar calcium concentration in mg per g dry weight, log transformed data (Field type: Numeric)</li> <li><strong>CWM_total_Mg_mg.g_log</strong>: CWM foliar magnesium concentration in mg per g dry weight, log transformed data (Field type: Numeric)</li> <li><strong>CWM_total_P_mg.g_log</strong>: CWM foliar phosporus concentration in mg per g dry weight, log transformed data (Field type: Numeric)</li> <li><strong>CWM_N_perc</strong>: CWM foliar nitrogen concentration (Field type: Numeric)</li> <li><strong>CWM_15N_per_mil</strong>: CWM foliar 15N isotope concentration (Field type: Numeric)</li> <li><strong>CWM_C_perc</strong>: CWM foliar carbon concentration (Field type: Numeric)</li> <li><strong>CWM_13C_per_mil</strong>: CWM foliar 13C isotope concentration, expressed relative to Vienna Pee Dee Belemnite (VPDB) as δ13C in units of per mil [‰] (Field type: Numeric)</li> <li><strong>CWM_DR</strong>: CWM dark respiration measured on leaf attached to a branch that is cut under water and remains in water (Field type: Numeric)</li> <li><strong>CWM_Asat</strong>: CWM light-saturated net photosynthesis measured on leaf attached to a branch that is cut under water and remains in water. (Field type: Numeric)</li> <li><strong>CWM_Amax</strong>: CWM maximum photosynthetic capacity measured on leaf attached to a branch that is cut under water and remains in water. (Field type: Numeric)</li> <li><strong>CWM_leaf_thickness_mm_log</strong>: CWM thickness of leaf, log transformed data (Field type: Numeric)</li> <li><strong>CWM_dry_weight_mg_log</strong>: CWM leaf oven-dried weight, log transformed data (Field type: Numeric)</li> <li><strong>CWM_LA_mm2_log</strong>: CWM leaf area (LA) calculated from fresh leaves collected from branches, scanned immediately, log transformed data (Field type: Numeric)</li> <li><strong>CWM_SLA_mm2_mg</strong>: CWM specific leaf area (SLA) determined as the one-sided area of a fresh leaf, divided by its oven-dry mass. (Field type: Numeric)</li> <li><strong>CWM_LDMC_mg.g</strong>: CWM leaf dry-matter content (LDMC) is the oven-dry mass (mg) of a leaf, divided by its water-saturated fresh mass (g) mg g–1 (Field type: Numeric)</li> <li><strong>CWM_chla_mg.g</strong>: CWM foliar chlorophyll a content (Field type: Numeric)</li> <li><strong>CWM_chlb_mg.g</strong>: CWM foliar chlorophyll b content (Field type: Numeric)</li> <li><strong>CWM_carot_mg.g</strong>: CWM foliar carotenoids content (Field type: Numeric)</li> <li><strong>CWM_Fp_N_mm_log</strong>: CWM force to punch leaf, dividing the observed force (N) required to puncture the leaf lamina by the circumference of the instrument's rod, log transformed data (Field type: Numeric)</li> <li><strong>CWM_specific_Fp_log</strong>: CWM specific force to punch (Fp divided by lamina thickness), log transformed data (Field type: Numeric)</li> <li><strong>CWM_WD_B</strong>: CWM branch wood density from branch segment with bark (Field type: Numeric)</li> <li><strong>CWM_hemicellulose_perc</strong>: CWM foliar hemicellulose concentration (Field type: Numeric)</li> <li><strong>CWM_cellulose_perc</strong>: CWM foliar cellulose concentration (Field type: Numeric)</li> <li><strong>CWM_lignin_recalcitrants_perc</strong>: CWM foliar lignin and recalcitrants concentration (Field type: Numeric)</li> <li><strong>CWM_total_tannin_mg.g</strong>: CWM foliar tannin concentration (Field type: Numeric)</li> <li><strong>CWM_total_phenol_mg.g</strong>: CWM total foliar phenol concentration (Field type: Numeric)</li> <li><strong>CWM_chla_mg.mm2</strong>: CWM foliar chlorophyll a content expressed on leaf area basis (Field type: Numeric)</li> <li><strong>CWM_chlb_mg.mm2</strong>: CWM foliar chlorophyll b content expressed on leaf area basis (Field type: Numeric)</li> <li><strong>CWM_carot_mg.mm2</strong>: CWM foliar carotenoids content expressed on leaf area basis (Field type: Numeric)</li> <li><strong>CWM_N_mg.mm2</strong>: CWM foliar nitrogen concentration expressed on leaf area basis (Field type: Numeric)</li> <li><strong>CWM_total_P_mg.mm2.l</strong>: CWM foliar phosporus concentration expressed on leaf area basis, log transformed data (Field type: Numeric)</li> </ul> </li> </ol> <p><strong>CSP_protocol_Chlorophyll_and_Carotenoids.pdf</strong></p> <p>Description: Methodology of chlorophyll and carotenoids analysis, Carnegie Spectranomics protocol: https://drive.google.com/file/d/0B58dyv8L3FpMdGw0QWtiZElHQzQ/view</p> <p><strong>CSP_protocol_Phenols_Tannins_Analysis.pdf</strong></p> <p>Description: Methodology of phenols and tannins analysis, Carnegie Spectranomics protocol: https://drive.google.com/file/d/0B58dyv8L3FpMcTBHblQwRHdyRE0/view</p> <p><strong>Date range: </strong>2014-05-01 to 2018-09-01</p> <p><strong>Latitudinal extent: </strong>4.5000 to 5.0700</p> <p><strong>Longitudinal extent: </strong>116.7500 to 117.8200</p>
Changes in community-weighted trait mean, functional diversity, precipitation, temperature and surface area along an elevational gradient in Tenerife, Canary Islands
<p>This dataset comprises community-weighted trait means and functional diversity of leaf traits, precipitation, temperature and surface area of the elevational belt recorded in roadside (disturbed) and interior (less disturbed) plots, along an elevational gradient of 2,300 m in Tenerife, Canary Islands. The leaf traits measured were specific leaf area (SLA), nitrogen, carbon, phosphorous, nitrogen to carbon ratio, leaf dry matter content (LDMC), sodium, potassium and magnesium. The environmental variables measured are total precipitation of the growing season, mean temperature of the growing season and surface area of the elevation belt. This dataset has been used for the analysis presented in Ratier Backes et al. (in press). Mechanisms behind elevational plant species richness patterns revealed by a trait-based approach. <em>Journal of Vegetation Science</em>.</p>
Data from: Functional traits and community composition: a comparison among community-weighted means, weighted correlations, and multilevel models
1. Of the several approaches that are used to analyze functional trait-environment relationships, the most popular is community-weighted mean regressions (CWMr) in which species trait values are averaged at the site level and then regressed against environmental variables. Other approaches include model-based methods and weighted correlations of different metrics of trait-environment associations, the best known of which is the fourth-corner correlation method. 2. We investigated these three general statistical approaches for trait-environment associations: CWMr, five weighted correlation metrics (Peres-Neto et al. 2017), and two multilevel models (MLM) using four different methods for computing p-values. We first compared the methods applied to a plant community dataset. To determine the validity of the statistical conclusions, we then performed a simulation study. 3. CWMr gave highly significant associations for both traits, while the other methods gave a mix of support. CWMr had inflated type I errors for some simulation scenarios, implying that the significant results for the data could be spurious. The weighted correlation methods had generally good type I error control but had low power. One of the multilevel models, that from Jamil et al. (2013), had both good type I error control and high power when an appropriate method was used to obtain p-values. In particular, if there was no correlation among species in their abundances among sites, a parametric bootstrap likelihood ratio test (LRT) gave the best power. When there was correlation among species in their abundances, a conditional parametric LRT had correct type I errors but had lower power. 4. There is no overall best method for identifying trait-environment associations. For the simple task of testing, one-by-one, associations between single environmental variables and single traits, the weighted correlations with permutation tests all had good type I error control, and their ease of implementation is an advantage. For the more complex task of multivariate analyses and model fitting, and when high statistical power is needed, we recommend MLM2 (Jamil et al. 2013); however, care must be taken to ensure against inflated type I errors. Because CWMr exhibited highly inflated type I error rates, it should always be avoided. 2. We investigated these three general statistical approaches for trait-environment associations: CWMr, five weighted correlation metrics (Peres-Neto et al. 2017), and two multilevel models (MLM) using five different methods for computing p-values. We first compared the methods applied to a plant community dataset. To determine the validity of the statistical conclusions, we then performed a simulation study. 3. CWMr gave highly significant associations for both traits, while the other methods gave a mix of support. CWMr had inflated type I errors for some simulation scenarios. The weighted correlation methods had generally good type I error control but had low power. One of the multilevel models, that from Jamil et al. (2013), had both good type I error control and high power when an appropriate method was used to obtain p-values. In particular, if there was no correlation among species in their abundances among sites, a parametric bootstrap likelihood ratio test (LRT) gave the best power. When there was correlation among species in their abundances, a conditional parametric LRT had correct type I errors but suffered from low power. 4. There is no overall best method for identifying trait-environment associations. For the simple task of testing, one-by-one, associations between single environmental variables and single traits, the weighted correlations with permutation tests all had good type I error control, and their ease of implementation is an advantage. For the more complex task of multivariate analyses and model fitting, and when high statistical power is needed, we recommend MLM2 (Jamil et al. 2013); however, care must be taken to ensure against inflated type I errors. Because CWMr exhibited highly inflated type I error rates, it should be avoided.
Changes in community-weighted trait mean, functional diversity, soil chemical properties and temperature along an elevational gradient in Tenerife, Canary Islands
<p>This dataset comprises community-weighted trait means and functional diversity of leaf traits, chemical soil properties and temperature recorded in roadside (disturbed) and interior (less disturbed) plots, along an elevational gradient of 2,300 m in Tenerife, Canary Islands. The leaf traits measured were specific leaf area (SLA), nitrogen, nitrogen to phosphorus ratio, leaf dry matter content (LDMC) and carbon to phosphorus ratio. The soil chemical properties measured were pH, nitrogen, nitrogen to phosphorus ratio, carbon to phosphorus ratio, calcium, potassium, magnesium and cation exchange capacity. Also the scores of the three first axes derived from a PCA analysis including the soil chemical properties are included. The temperature variables consist of bioclimatic variables Bio10 (mean temperature of the warmest quarter) and Bio11 (mean temperature of the coldest quarter). This dataset has been used for the analysis presented in Ratier Backes et al. (2021).</p>
Changes in community-weighted trait mean, functional diversity, soil chemical properties and temperature along an elevational gradient in Tenerife, Canary Islands
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Data from: Functional traits and community composition: a comparison among community-weighted means, weighted correlations, and multilevel models
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Data from: Do community-weighted mean functional traits reflect optimal strategies?
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