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87 results for “leaf growth”
Leaf litter decomposition in old-growth and selectively logged forest
<p><strong>Description: </strong></p> <p>In a multifactorial experiment we investigated the consequences of selective logging for decomposition and nutrient cycling in Southeast Asia by testing the effects of chemical composition of leaf litter and site factors on leaf litter mass loss. Litterbags were used to estimate decomposition over a period of 24 weeks, litterbags were collected 2, 4, 6, 8, 13, 24 weeks after the start of the experiment.</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/124"><strong>Biodiversity and land-use impacts on tropical ecosystem function (BALI): Quantifying biogeochemistry across forest disturbance gradients in Sabah</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(383))</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=3247639">here</a></p> <p><strong>Files: </strong>This consists of 1 file: Both_litter_decomposition_experiment.xlsx</p> <p><strong>Both_litter_decomposition_experiment.xlsx</strong></p> <p>This file contains dataset metadata and 3 data tables:</p> <ol> <li> <p><strong>chemical_composition_start</strong> (described in worksheet chemical_composition_start)</p> <p>Description: Chemical properties from the two leaf litter types before the experiment</p> <p>Number of fields: 19</p> <p>Number of data rows: 10</p> <p>Fields:</p> <ul> <li><strong>replicate</strong>: Replicate number (Field type: Replicate)</li> <li><strong>litter_type</strong>: Litter type (Field type: ID)</li> <li><strong>P_mg.g</strong>: Phosporus concentration in mg per g dry weight of leaf litter (Field type: Numeric)</li> <li><strong>N_perc</strong>: Nitrogen concentration in % of leaf litter, analysed in University of Aberdeen (Field type: Numeric)</li> <li><strong>C_perc</strong>: Carbon concentration in % of leaf litter, analysed in University of Aberdeen (Field type: Numeric)</li> <li><strong>C.N</strong>: Carbon nitrogen ratio of leaf litter, analysed in University of Aberdeen (Field type: Numeric)</li> <li><strong>P_perc</strong>: Phosporus concentration in % of leaf litter, analysed in University of Aberdeen (Field type: Numeric)</li> <li><strong>C.P</strong>: Carbon phosporus ratio of leaf litter, analysed in University of Aberdeen (Field type: Numeric)</li> <li><strong>N.P</strong>: Nitrogen phosporus ratio of leaf litter, analysed in University of Aberdeen (Field type: Numeric)</li> <li><strong>Ca_mg.g</strong>: Calcium concentration in mg per g dry weight of leaf litter (Field type: Numeric)</li> <li><strong>Mg_mg.g</strong>: Magnesium concentration in mg per g dry weight of leaf litter (Field type: Numeric)</li> <li><strong>Al_mg.g</strong>: Aluminium concentration in mg per g dry weight of leaf litter (Field type: Numeric)</li> <li><strong>K_mg.g</strong>: Potassium concentration in mg per g dry weight of leaf litter (Field type: Numeric)</li> <li><strong>soluble_cell_content</strong>: Soluble cell content in percent (Field type: Numeric)</li> <li><strong>nonsoluble_cell_content</strong>: Non-soluble cell content in percent (Field type: Numeric)</li> <li><strong>hemicellulose_bound_proteins</strong>: Hemicellulose and bound proteins content in percent (Field type: Numeric)</li> <li><strong>cellulose_lignin_recalcitrants</strong>: Soluble cell content in percent (Field type: Numeric)</li> <li><strong>cellulose</strong>: Cellulose content in percent (Field type: Numeric)</li> <li><strong>lignin_recalcitrants</strong>: Lignin and recalcitrants content in percent (Field type: Numeric)</li> </ul> </li> <li> <p><strong>chemical_composition_end</strong> (described in worksheet chemical_composition_end)</p> <p>Description: Chemical properties from individual leaf litter bags at the end of the experiment</p> <p>Number of fields: 18</p> <p>Number of data rows: 64</p> <p>Fields:</p> <ul> <li><strong>code</strong>: Identifyer for each leaf litter bag, coding for location-plotname-subplot pair-leaf litter type-mesh size-replicate (Field type: ID)</li> <li><strong>location</strong>: Location of experimental plots (M: Maliau; S: SAFE) (Field type: ID)</li> <li><strong>location_name</strong>: Plot name (Field type: Location)</li> <li><strong>plot</strong>: Running number of experimental plots (Field type: ID)</li> <li><strong>pair</strong>: Each plot contains two experimental units (making up a pair) (Field type: ID)</li> <li><strong>replicate</strong>: Each pair contained two replicates of the same treatment (litter type x mesh size) (Field type: replicate)</li> <li><strong>litter_type</strong>: Litter type (Field type: Categorical)</li> <li><strong>mesh</strong>: Mesh size of the litter bags (Field type: Categorical)</li> <li><strong>P_mg.g</strong>: Phosporus concentration in mg per g dry weight of dry leaf litter (Field type: Numeric)</li> <li><strong>K_mg.g</strong>: Potassium concentration in mg per g dry weight of dry leaf litter (Field type: Numeric)</li> <li><strong>Ca_mg.g</strong>: Calcium concentration in mg per g dry weight of dry leaf litter (Field type: Numeric)</li> <li><strong>Mg_mg.g</strong>: Magnesium concentration in mg per g dry weight of dry leaf litter (Field type: Numeric)</li> <li><strong>Al_mg.g</strong>: Aluminium concentration in mg per g dry weight of dry leaf litter (Field type: Numeric)</li> <li><strong>N_perc</strong>: Nitrogen concentration in % of dry leaf litter (Field type: Numeric)</li> <li><strong>C_perc</strong>: Carbon concentration in % of dry leaf litter (Field type: Numeric)</li> <li><strong>C.N</strong>: Carbon nitrogen ratio of dry leaf litter (Field type: Numeric)</li> <li><strong>cellulose</strong>: Cellulose concentration in % of dry leaf litter (Field type: Numeric)</li> <li><strong>lignin_recalcitrants</strong>: Lignin and recalcitrants concentration in % of dry leaf litter (Field type: Numeric)</li> </ul> </li> <li> <p><strong>litterbags_massloss</strong> (described in worksheet litterbags_massloss)</p> <p>Description: Mass loss of litter in litterbags over the experimental period of 24 weeks</p> <p>Number of fields: 17</p> <p>Number of data rows: 128</p> <p>Fields:</p> <ul> <li><strong>code</strong>: Identifyer for each leaf litter bag, coding for location-plotname-subplot pair-leaf litter type-mesh size-replicate (Field type: ID)</li> <li><strong>location</strong>: Location of experimental plots (Field type: ID)</li> <li><strong>plotname</strong>: Plot name (Field type: Location)</li> <li><strong>plot</strong>: Running number of experimental plots (Field type: ID)</li> <li><strong>pair</strong>: Each plot contains two experimental units (making up a pair) (Field type: ID)</li> <li><strong>replicate</strong>: Each pair contained two replicates of the same treatment (litter type x mesh size) (Field type: Replicate)</li> <li><strong>litter_type</strong>: Litter type (Field type: Categorical)</li> <li><strong>mesh</strong>: Mesh size of the litter bags (Field type: Categorical)</li> <li><strong>weight_t0</strong>: Initial weight at the beginning of the experiment, around 10 g per litter bag (Field type: Numeric)</li> <li><strong>weight_t1</strong>: Weight at time step 1 after 2 weeks (Field type: Numeric)</li> <li><strong>weight_t2</strong>: Weight at time step 1 after 4 weeks (Field type: Numeric)</li> <li><strong>weight_t3</strong>: Weight at time step 1 after 6 weeks (Field type: Numeric)</li> <li><strong>weight_t4</strong>: Weight at time step 1 after 8 weeks (Field type: Numeric)</li> <li><strong>weight_t5</strong>: Weight at time step 1 after 13 weeks (Field type: Numeric)</li> <li><strong>weight_t6</strong>: Weight at time step 1 after 24 weeks (Field type: Numeric)</li> <li><strong>t6_corrected</strong>: Weight at time step 1 after 24 weeks, corrected for contaminating material, mostly ingrown plant roots and fungal hyphae. This is the data to use. (Field type: Numeric)</li> <li><strong>mass_loss_%</strong>: Mass loss at the end of the experiment compared to the start of the experiment, in percent (Field type: Numeric)</li> </ul> </li> </ol> <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>
Large seeds provide an intrinsic growth advantage that depends on leaf traits and root allocation
<p>Seed mass and growth rate are important dimensions of plant ecological diversity, but their relationship remains unresolved. Negative relationships between relative growth rate (RGR) and seed mass are well established. However, RGR is size-dependent, so small-seeded species might achieve fast growth simply because they are initially small.</p> <p>Using a dataset of unprecedented size, sampling 382 grass species, we investigated seed mass and growth rate using both RGR and SGR (RGR at a specific size), accounting for diversity in phylogeny, ecology (e.g. life history, photosynthetic pathway) and environment (mean annual temperature and precipitation).</p> <p>RGR and SGR showed contrasting relationships with seed mass, such that large-seeded species had lower RGR but higher SGR than small-seeded species. However, the relationship between SGR and seed mass depended on leaf dry matter content (LDMC), and was only positive in high-LDMC species. When compared at a common size, the fast growth of large-seeded and low-LDMC species was associated with greater biomass allocation to roots in the hot, high-light environment used for our experiment. Photosynthetic pathway and life history contributed to variation in SGR, with C4 annuals having higher SGRs than C3 perennials regardless of seed size.</p> <p>Large seeds therefore afford an intrinsic growth advantage in species with resource-conserving leaf traits, and may provide a competitive edge in resource-poor environments. This work advances understanding of how seed mass and growth rate coevolve with other ecological factors.</p>
Hydraulic prediction of drought-induced plant dieback and top-kill depends on leaf habit and growth form
<p>Hydraulic failure caused by severe drought contributes to aboveground dieback and whole-plant death. The extent to which dieback or whole-plant death can be predicted by plant hydraulic traits has rarely been tested among species with different leaf habits and/or growth forms. We investigated 19 hydraulic traits in 40 woody species in a tropical savanna and their potential correlations with drought response during an extreme drought event during the El Niño–Southern Oscillation in 2015. Plant hydraulic trait variation was partitioned substantially by leaf habit but not growth form along a trade-off axis between traits that support drought tolerance versus avoidance. Semi-deciduous species and shrubs had the highest branch dieback and top-kill (complete aboveground death) among the leaf habits or growth forms. Dieback and top-kill were well explained by combining hydraulic traits with leaf habit and growth form, suggesting integrating life history traits with hydraulic traits will yield better predictions.</p>
Hydraulic prediction of drought-induced plant dieback and top-kill depends on leaf habit and growth form
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Data from: Genetic variation in growth and leaf traits associated with local adaptation to climate in yellow birch (Betula alleghaniensis Britton)
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Data from: Detrital nutrient content and leaf species differentially affect growth and nutritional regulation of detritivores
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Data from: Variation in seed traits, leaf phenology and growth performance among sessile oak provenances from Baden-Württemberg and Alsace
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Data from: Scaling of leaf area with biomass in trees reconsidered: Constant metabolically active sapwood volume per unit leaf area with height growth
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Global leaf sulphur stoichiometry and the relationships with nitrogen and phosphorus: phylogeny, growth form and environmental controls
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Large seeds provide an intrinsic growth advantage that depends on leaf traits and root allocation
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Data from: Drought and heat-induced mortality of conifer trees is explained by leaf and growth legacies
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Wood density and leaf size jointly predict woody plant growth rates across (but not within) species along a steep precipitation gradient
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Effects of leaf litter traits on terrestrial isopod and millipede consumption, assimilation and growth
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C4 photosynthesis and the economic spectra of leaf and root traits independently influence growth rates in grasses
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Are leaf, stem and hydraulic traits good predictors of individual tree growth? (FUN2FUN project)
<ol> <li>A major foundation of trait-based ecology is that traits have an impact on individual performance. However, trait-growth relationships have not been extensively tested in trees, especially outside tropical ecosystems. In addition, measuring traits directly related to physiological processes ('hard traits') remains difficult and the differences between inter- and intraspecific relationships are seldom explored.</li> <li> <span>Here, we use individual-level data on a set of hydraulic, leaf and stem traits to explore which traits are the best predictors of basal area increment (BAI) and growth efficiency (BAI per unit of tree leaf area, GE) among and within species for six dominant tree species along a water availability gradient under Mediterranean climate (Catalonia, NE Spain). Measured traits include: </span>leaf mass per area (LMA), leaf nitrogen concentration (N), leaf C isotopic composition (d<sup>13</sup>C), stem wood density (WD), branch-level estimates of the Huber value (Hv), the sapwood-specific hydraulic conductivity (K<sub>S</sub>), the leaf-specific hydraulic conductivity (K<sub>L</sub>) and resistance to xylem embolism (P<sub>50</sub>), and the leaf water potential at turgor loss (P<sub>tlp</sub>).</li> <li>Traits were better predictors of GE than BAI and significant relationships were largely driven by differences among species means. Contrary to our initial hypotheses, high values of both growth metrics<span> were associated with 'conservative' leaf and hydraulic traits. In particular, BAI was negatively associated with wood density and hydraulic efficiency per unit leaf area (</span>K<sub>L</sub><span>), while GE increased with LMA, allocation to sapwood relative to leaves (Hv) and resistance to xylem embolism </span>(P<sub>50</sub>)<span>. </span>Climate effects on BAI and GE were indirectly mediated by changes in traits, stand structure and tree size. Overall, these results suggest that maintaining functionality over extended periods of time may be more important that maximum gas exchange or hydraulic capacity to achieve high radial growth under Mediterranean climates.</li> <li>Our study reveals that the relationships between 'functional' traits and tree performance along environmental gradients are complex and do not necessarily conform to simple hypotheses based on our understanding of organ-level processes. Trait integration along common axes of variation together with a revaluation of the variables that better reflect whole-tree performance can greatly improve our understanding of trait-growth relationships.</li> </ol>
FIGURE 22.—A. Palhinhaea cernua—(Ecuador: Holm-Nielsen et al. 4494, AAU) A. Growth habit. A1. Branchlet with strobili. A2. Close-up of Sporophyll.—B. Palhinhaea riofrioi—(Ecuador: Harling & Andersson 12811, AAU). B-B1. Growth habit. B2. Close-up of branchlet leaf. B3. Close-up of Sporophyll. Drawing by B. Johnsen in The Lycopodiaceae of Panamá
FIGURE 22.—A. Palhinhaea cernua—(Ecuador: Holm-Nielsen et al. 4494, AAU) A. Growth habit. A1. Branchlet with strobili. A2. Close-up of Sporophyll.—B. Palhinhaea riofrioi—(Ecuador: Harling & Andersson 12811, AAU). B-B1. Growth habit. B2. Close-up of branchlet leaf. B3. Close-up of Sporophyll. Drawing by B. Johnsen
FIGURE 2.—A. Lycopodium clavatum ssp. clavatum—(Ecuador: Harling 4091, S).A. Growth habit. A1. Close-up of branchlet leaf. A2. Close-up of sporophyll.—B. Lycopodium clavatum ssp. contiguum—(Ecuador: Cazalet & Pennington 5409, US). Growth habit. B1. Close-up of sporophylls.—C. Diphasiastrum thyoides—(Ecuador: Holm-Nielsen et al. 4818, AAU). C. Branchlet upper side left, and underside right.—D. Diphasium jussiaei—(Ecuador: Holm-Nielsen et al. 4670, AAU) D. Branchlet upper side left and underside right. Drawing by B. Johnsen in The Lycopodiaceae of Panamá
FIGURE 2.—A. Lycopodium clavatum ssp. clavatum—(Ecuador: Harling 4091, S).A. Growth habit. A1. Close-up of branchlet leaf. A2. Close-up of sporophyll.—B. Lycopodium clavatum ssp. contiguum—(Ecuador: Cazalet & Pennington 5409, US). Growth habit. B1. Close-up of sporophylls.—C. Diphasiastrum thyoides—(Ecuador: Holm-Nielsen et al. 4818, AAU). C. Branchlet upper side left, and underside right.—D. Diphasium jussiaei—(Ecuador: Holm-Nielsen et al. 4670, AAU) D. Branchlet upper side left and underside right. Drawing by B. Johnsen
FIGURE.A. Phlegmariurus homocarpus—(Ecuador: Prieto P-224, AAU). A. Growth habit. A1. Close-up of sporangiate division.— B. Phlegmariurus linifolius var. linifolius:—(Ecuador: Holm-Nielsen et al. 2734, AAU). B. Growth habit. B1. Close-up of leaf from basal division. B2. Sporangiate middle division. B3. Close-up of terminal sporangiate division.—C. Phlegmariurus subulatus:—(Ecuador: Camp E-5189, AAU). C. Growth habit. C1. Close-up of basal expanded division. C2. Close-up of terminal sporangiate division.—D. Phlegmariurus funiformis—(Ecuador, Asplund 19290, S). D. Growth habit. D1. Close-up of sporangiate division. Drawings by B.Johnsen in The Lycopodiaceae of Panamá
FIGURE.A. Phlegmariurus homocarpus—(Ecuador: Prieto P-224, AAU). A. Growth habit. A1. Close-up of sporangiate division.— B. Phlegmariurus linifolius var. linifolius:—(Ecuador: Holm-Nielsen et al. 2734, AAU). B. Growth habit. B1. Close-up of leaf from basal division. B2. Sporangiate middle division. B3. Close-up of terminal sporangiate division.—C. Phlegmariurus subulatus:—(Ecuador: Camp E-5189, AAU). C. Growth habit. C1. Close-up of basal expanded division. C2. Close-up of terminal sporangiate division.—D. Phlegmariurus funiformis—(Ecuador, Asplund 19290, S). D. Growth habit. D1. Close-up of sporangiate division. Drawings by B.Johnsen
Tree_seedling_survival_growth_leaf_traits_in_disturbed_conditions
<p class="MsoNormal"><span>1.<span> </span></span><span>Restoration of tropical montane cloud forest landscapes is urgently required. Assisting the regeneration of endangered and shade tolerant tree species is essential for both the recovery of this vulnerable group and of ecological processes. However, there is limited species-specific information regarding tree performance under different disturbance conditions with which to implement effective interventions. </span></p> <p class="MsoNormal"><span>2.<span> </span></span><span>We assessed the performance of shade tolerant tree seedlings in restoration plantings under different disturbance settings and determined whether leaf mass area (LMA) and leaf dry mass content (LDMC) – functional traits typically associated with resource capture or stress tolerance – could serve as predictors of survival and growth among species. Since conservative leaf morphological traits can maximize survival, we expected species with higher LMA and LDMC to present higher survival. For a set of eight native cloud forest species, a total of 2202 seedlings were planted in four pastures, five secondary forests and three forests subjected to traditional selective logging, in tropical montane cloud forest landscapes in Eastern Mexico.</span></p> <p class="MsoNormal"><span>3.<span> </span></span><span>Seedling survival was high after three years: 62% in pastures, 80% in secondary forests and 88% in logged forests. Growth rates were lowest in pastures, followed by secondary forests and highest in logged forests. LMA was a strong predictor of seedling survival in all of the environments; tree species with higher LMA presented greater survival. LDMC was related to seedling survival in the three environments, although to a lesser extent than LMA. In the pastures, higher LMA and LDMC were linked to lower growth.</span></p> <p class="MsoNormal"><a name="_heading=h.30j0zll"></a><span>4.<span> </span></span><span>Synthesis and applications. This study support the potential of shade tolerant tree species in restoration efforts to assist the recovery of this important functional group and to accelerate succession across altered environments. Our results support the notion that conservative leaf functional traits are linked to a higher probability of survival, not only in the shaded understorey, but also under high solar radiation in transformed habitats. Leaf Mass Area (LMA) in particular is a reliable predictor of seedling survival for shade tolerant species. Species selection based on LMA could thus improve restoration initiative outcomes: tree species with high LMA present higher survival probability and can be introduced into pastures, secondary forests and selectively logged forests.</span></p>
Effect of Water Level and Submergence Time on Leaf Growth, Stoichiometry and Homeostasis of Carex brevicuspis
<p>Water level and submergence time are important factors influencing plant growth and plant C:N:P characteristics in freshwater wetlands. However, the effect of their interaction on plant stoichiometry and homeostasis is far from clear. Here, we performed a controlled experiment using four water levels (0, 10, 20, and 40 cm, relative to the soil surface) and under three submergence times (30, 60, and 90 days) with the aim of investigating the changes in plant growth and stoichiometric characteristics, as well as the strength of N, P, and N:P homeostasis in <em>C. brevicuspis</em> in the Dongting Lake, China. Results showed that biomass, density, height, and total carbon (TC) decreased significantly with increasing water level; however, the latter total nitrogen (TN) and total phosphorus (TP) increased significantly. The highest TN and TP were at 40 cm water level, while the highest C:N, C:P, and N:P ratios were at 0 cm water level. Furthermore, significant stoichiometric homeostasis of <em>C. brevicuspis</em> was found for N (<em>H</em><sub>N</sub>) and P (<em>H</em><sub>P</sub>) except for N:P (<em>H</em><sub>N:P</sub>). <em>H</em><sub>N</sub> and <em>H</em><sub>P </sub>decreased with increasing submergence time, and<em> H</em><sub>N</sub> was consistently greater than <em>H</em><sub>P</sub>, indicating that <em>C. brevicuspis</em> had a weak ability to maintain its internal P balance under long-term submergence. Therefore, the impact of water level and submergence time on plant growth and stoichiometric characteristics in this study, are applicable to the conservation and management of the wetlands dominated with <em>C. brevicuspis</em>.</p>
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