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310 results for “Tree growth”
Data from: Does biomass growth increase in the largest trees? Flaws, fallacies and alternative analyses
The long-standing view that biomass growth in trees typically follows a rise-and-fall unimodal pattern has been challenged by studies concluding that biomass growth increases with size even among the largest stems in both closed forests and in open competition-free environments. We highlight challenges and pitfalls that influence such interpretations. The ability to observe and calibrate biomass change in large stems requires adequate data regarding these specific stems. Data checking and control procedures can bias estimates of biomass growth and generate false increases with stem size. It is important to distinguish aggregate and individual-level trends: a failure to do so results in flawed interpretations. Our assessment of biomass growth in 706 tropical forest stems indicates that individual biomass growth patterns often plateau for extended periods, with no significant difference in the number of stems indicating positive and negative trends in all but one of the 14 species. Nonetheless, when comparing aggregate growth during the most recent five years, 13 out of our 14 species indicate that biomass growth increases with size even among the largest sizes. Thus, individual and aggregate patterns of biomass growth with size are distinct. Claims concerning general biomass growth patterns for large trees remain unconvincing. We suggest how future studies can improve our knowledge of growth patterns in and among large trees.
Improving intra- and inter-annual GPP predictions by using individual-tree inventories and leaf growth dynamics
<p>Carbon sequestration is a key ecosystem service provided by forests. Inventory data based on individual trees are considered to be the most accurate method for estimating forest productivity. However, estimations of forest photosynthesis itself from inventory data remains understudied, particularly when considering the growth and development of individual trees under the background of global change. Here, we used the leaf growth process with phenology and non-structural carbohydrates (NSC) storage to revise an individual-tree based carbon model, FORCCHN. This model couples leaf development and biomass to quantify gross primary productivity (GPP) in the forests, where growth is decoupled from photosynthesis in daily step. The model was initialized with inventory-based forest data rather than the more widely used satellite-based data. We tested the model against measured aboveground woody biomass, growth of leaf biomass, daily gross ecosystem exchange (GEE), and yearly GEE at five representative forest sites in the Northern Hemisphere. We also compared the results from the original model and the revised model at five forest sites. Including leaf growth dynamics and inventory-based initialization improved the predicted performance (r2) of GPP by an average of 33%. Synthesis and applications. Our results suggest that the appropriate vegetation data sources (i.e. inventory or satellite selection) and the effective predictions of the growth process should be considered when developing future carbon cycle models and forest carbon estimation options. Applying and improving such carbon models to evaluate carbon sequestration is an important part of forest carbon sink management.</p>
Tree seedling trait optimization and growth in response to local-scale soil and light variability
At local scales, it has been suggested that high levels of resources lead to increased tree growth via trait optimization (highly peaked trait distribution). However, this contrasts with (i) theories that suggest that trait optimization and high growth occur in the most common resource level and (ii) empirical evidence showing that high trait optimization can be also found at low resource levels. This raises the question of how are traits and growth optimized in highly diverse plant communities? Here, we propose a series of hypotheses about how traits and growth are expected to be maximized under different resource levels (low, the most common, and high) in tree seedling communities from a subtropical forest in Puerto Rico. We studied the variation in the distribution of biomass allocation and leaf traits and seedlings growth rate along four resource gradients: light availability (canopy openness) and soil K, Mg, and N contents. Our analyses consisted of comparing community trait means, trait kurtosis (a measurement of trait optimization), and relative growth rates at three resource levels (low, common, and high). Trait optimization varied across the three resource levels depending on the type of resource and trait, with leaf traits being optimized under high N and in the most common K and Mg conditions, but not at any of the light levels. Also, seedling growth increased at high light conditions and high N and K but was not related to trait kurtosis. Our results indicate that local-scale variability of soil fertility and understory light conditions result in shifts in species ecological strategies that increase growth despite a weak trait optimization, suggesting the existence of alternative phenotypes that achieve similar high performance. Uncovering the links between abiotic factors, functional trait diversity and performance is necessary to better predict tree responses to future changes in abiotic conditions.
Tree growth periodicity in the ever-wet tropical forest of the Americas: Datasets
<p>Datasets for "Tree growth periodicity in the ever-wet tropical forest of the Americas"</p>
Data from: Does biomass growth increase in the largest trees? Flaws, fallacies and alternative analyses
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Data from: Constrained tree growth and gas-exchange of seawater exposed forests in the Pacific Northwest, USA
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Data from: Improving predictions of tropical tree survival and growth by incorporating measurements of whole leaf allocation
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Long-term logging residue loadings affect tree growth but not soil nutrients in lodgepole pine forests
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Impacts of recurrent dry and wet years alter long-term tree growth trajectories
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Data from: Stimulation of boreal tree seedling growth by wood-derived charcoal: effects of charcoal properties, seedling species and soil fertility
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Data from: Trade-offs in juvenile growth potential vs. shade tolerance among subtropical rainforest trees on soils of contrasting fertility
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Data from: The negative effect of lianas on tree growth varies with tree species and season
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Improving intra- and inter-annual GPP predictions by using individual-tree inventories and leaf growth dynamics
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Tree seedling trait optimization and growth in response to local-scale soil and light variability
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Verification of the accuracy of the recent 50 years of tree growth and long-term change in intrinsic water-use efficiency using xylem Δ14C and δ13C in trees in an aseasonal tropical rainforest
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Macrosystems VIDA Tree Growth Simulation - 100 by 100 World 30 Species
Patterns of biodiversity, such as the increase toward the tropics and the peaked curve during ecological succession, are fundamental phenomena for ecology. Such patterns have multiple, interacting causes, but temperature emerges as a dominant factor across organisms from microbes to trees and mammals, and across terrestrial, marine, and freshwater environments. However, there is little consensus on the underlying mechanisms, even as global temperatures increase and the need to predict their effects becomes more pressing. The purpose of this project is to generate and test theory for how temperature impacts biodiversity through its effect on biochemical processes and metabolic rate. A combination of standardized surveys in the field and controlled experiments in the field and laboratory measure diversity of three taxa -- trees, invertebrates, and microbes -- and key biogeochemical processes of decomposition in seven forests distributed along a geographic gradient of increasing temperature from cold temperate to warm tropical. This dataset was based on simulations run by VIDA, a software suite that attempts to model the growth of individual trees using empirically derived--or randomly chosen--values for use with allometric relationships. By modeling the behavior of an individual tree, it is possible to model population dynamics in a spatially explicit simulation space. The modeling was done by Sean Hammond at The Brown Lab (PI, Jim Brown) at the University of New Mexico as part of a macrosystems biodiversity and latitude project supported by the National Science Foundation under Cooperative Agreement DEB#1065836.
ABoVE: Photochemical Reflectance and Tree Growth, Brooks Range, Alaska, 2018-2019
This dataset provides simultaneous in-situ measurements of the photochemical reflectance index (PRI) and radial tree growth of selected white spruce trees (Picea glauca (Moench) Voss) at the northern treeline in the Brooks Range of Alaska, south of Chandalar Shelf and Atigun Pass on the east side of the Dalton Highway. PRI and dendrometer measurements were simultaneously collected on 29 trees from six plots spaced along a 5.5 km transect from south to north where tree density becomes increasingly sparse. Measurements were made throughout the 2018 and 2019 growing seasons (May 1 to September 15) with a sampling interval of 5 minutes. The data were collected to better understand the suitability of the PRI to remotely track radial tree growth dynamics.
Soil variation response is mediated by growth trajectories rather than functional traits in a widespread pioneer Neotropical tree
<p>Description of Soil_DataTrees.csv</p> <ul> <li>Tree_label: Label of trees on the field, there are 70 trees</li> <li>Tree_site: Site on which the tree has been sampled; COU: Counami; SPA: Sparouine</li> <li>Descr_date: Date of tree sampling</li> <li>Soil_type: Type of soil; FS: ferralitic soils; WS: white-sand soils</li> <li>Soil_sample: Label of soil sample</li> <li>H2Osoil: Soil water content (g kg<sup>-1</sup>)</li> <li>Clay: Soil clay content (g kg<sup>-1</sup>)</li> <li>SiltTh: Soil thin silt content (g kg<sup>-1</sup>)</li> <li>SiltCo: Soil coarse silt content (g kg<sup>-1</sup>)</li> <li>SandTh: Soil thin sand content (g kg<sup>-1</sup>)</li> <li>SandCo: Soil coarse sand content (g kg<sup>-1</sup>)</li> <li>Csoil: Soil carbon content (g kg<sup>-1</sup>)</li> <li>Nsoil: Soil nitrogen content (g kg<sup>-1</sup>)</li> <li>CNsoil: Soil carbon:nitrogen ratio</li> <li>MOsoil: Soil organic matter content (g kg<sup>-1</sup>)</li> <li>Ptotsoil: Soil total phosphorus content (g 100g<sup>-1</sup>)</li> <li>Kcec: Soil potassium:CEC[cation-exchange capacity] ratio</li> <li>Cacec: Soil calcium:CEC ratio</li> <li>Mgcec: Soil magnesium:CEC ratio</li> <li>Nacec: Soil sodium:CEC ratio</li> <li>Alcec: Soil aluminum:CEC ratio</li> <li>Fecec: Soil iron:CEC ratio</li> <li>Mncec: Soil manganese:CEC ratio</li> <li>Hcec: Soil hydrogen:CEC ratio</li> <li>pHsoil: Soil pH (cmol kg<sup>-1</sup>)</li> <li>CECsoil: Soil cation-exchange capacity (cmol kg<sup>-1</sup>)</li> <li>Indexsoil: Soil index of fertility = (K+Ca+Mg+Na)/CEC</li> </ul> <p>K, Ca, Mg, Na, Al, Fe, Mn, H were initially measured in cmol kg<sup>-1</sup></p> <p> </p> <p>Description of Trait_DataTrees.csv</p> <ul> <li>Tree_label: Label of the tree on the field. There are 70 trees</li> <li>Tree_site: Site of sampling; COU: Counami; SPA: Sparouine</li> <li>Descr_date: Date of tree sampling</li> <li>Calendar_day: Day of the year (between 1 and 365) of tree sampling</li> <li>Soil_type: Type of the soil; FS: ferralitic soils; WS: white-sand soils</li> <li>PCA1_soil: Coordinates of the trees along the first axis of PCA (principal component analysis) with soil data, used as a quantitative soil index on FS-WS soil gradient</li> <li>mesHeight: Measured tree height (m)</li> <li>Height: Tree height based on the sum of all internodes length (m)</li> <li>Dbh: Tree diameter at height breast (cm)</li> <li>Age: Tree age (year)</li> <li>Order: Number of branching order</li> <li>Brtot: Total number of branches branching from the trunk</li> <li>Leaftot: Total number of leaves</li> <li>Fltot: Total number of inflorescences</li> <li>Acrown: Total estimated crown area (m²)</li> <li>INA1: Number of trunk internodes</li> <li>Brbear: Number of A2 bearing branches</li> <li>Brdead: Number of A2 dead branches</li> <li>Br1stH: First branching height</li> <li>Fl1stH: First flowering height</li> <li>Br1stIN: First branching node rank</li> <li>Fl1stIN: First flowering node rank</li> <li>Br1stAge: First branching age</li> <li>Fl1stAge: First flowering age</li> <li>LL: Leaf lifespan (day)</li> <li>Lpet: Petiole length (cm)</li> <li>Apet: Petiole cross-sectional area (mm²)</li> <li>Nlobe: Number of leaf lobes</li> <li>LMA: Leaf mass area (g m<sup>-2</sup>)</li> <li>Thleaf: Leaf thickness (µm)</li> <li>Aleaf: Estimated individual leaf area (cm²)</li> <li>Chlleaf: Leaf chlorophyll content (mg ml<sup>-1</sup>)</li> <li>H20resleaf: Leaf residual water content (%)</li> <li>dC13leaf: δ<sup>13</sup>C content (‰)</li> <li>Cleaf: Leaf carbon content (g kg<sup>-1</sup>)</li> <li>Nleaf: Leaf nitrogen content (g kg<sup>-1</sup>)</li> <li>CNleaf: Leaf carbon:nitrogen ratio</li> <li>Pleaf: Leaf phosphorus content (g kg<sup>-1</sup>)</li> <li>Kleaf: Leaf potassium content (g kg<sup>-1</sup>)</li> <li>WSG: Wood specific gravity (g cm<sup>-3</sup>)</li> </ul> <p> </p> <p> </p> <ul> <li>Tree_label: Label of the tree</li> <li>Soil_type: Type of the soil; FS: ferralitic soils; WS: white-sand soils</li> <li>rank_base: Rank of the internode from the base of the tree</li> <li>rank_top: Rank of the internode from the apex of the tree</li> <li>phyllochron: Phyllochron, number of days for the production of one leaf</li> <li>date: Estimated date of tree germination</li> <li>nb_day_base: Number of days since estimated germination</li> <li>nb_day_top: Age of the internode in days at tree sampling</li> <li>AS_rank_base: Rank of the annual shoot from the base of the tree</li> <li>As_rank_top: Rank of the annual shoot from the apex of the tree</li> <li>AS_nodes_base: Number of internodes per annual shoot</li> <li>AS_length_base: Length of the annual shoot (cm)</li> <li>AS_br_base: Number of A2 branches on the annual shoot</li> <li>AS_flo_base: Number of inflorescences on the annual shoot</li> <li>lg_en: Internode length (cm)</li> <li>ht_en: Cumulated height of the tree based on the sum of internode length (cm)</li> <li>ma_lgen: Moving average of internode length</li> <li>resi_lgen: Residuals of internode length</li> </ul> <p> </p>
Data from: Greater growth stability of trees in marginal habitats suggests a patchy pattern of population loss and retention in response to increased drought at the rear edge
Species rear range-edges are predicted to retract as climate warms, yet evidence of population persistence is accumulating. Accounting for this disparity is essential to enable prediction and planning for species' range retractions. At the Mediterranean edge of European beech-dominated temperate forest, we tested the hypothesis that individual performance should decline at the limit of the species' ecological tolerance in response to increased drought. We sampled 40 populations in a crossed factor design of geographical and ecological marginality and assessed tree growth resilience and decline in response to recent drought. Drought impacts occurred across the rear edge, but tree growth stability was unexpectedly high in geographically isolated marginal habitat and lower than anticipated in the species' continuous range and better-quality habitat. Our findings demonstrate that, at the rear edge, range shifts will be highly uneven and characterised by reduction in population density with local population retention rather than abrupt range retractions.
Data from: A multi-decade experiment shows that fertilization by salmon carcasses enhanced tree growth in the riparian zone
As they return to spawn and die in their natal streams, anadromous, semelparous fishes such as Pacific salmon import marine‐derived nutrients to otherwise nutrient‐poor freshwater and riparian ecosystems. Diverse organisms exploit this resource, and previous studies have indicated that riparian tree growth may be enhanced by such marine‐derived nutrients. However, these studies were largely inferential and did not account for all factors affecting tree growth. As an experimental test of the contribution of carcasses to tree growth, for 20 yr, we systematically deposited all sockeye salmon (Oncorhynchus nerka) carcasses (217,055 individual salmon) in the riparian zone on one bank of a 2‐km‐long stream in southwestern Alaska, reducing carcass accumulation on one bank and enhancing it on the other. After accounting for partial consumption and movement of carcasses by brown bears (Ursus arctos) and variation in salmon abundance and body size, we estimated that 267,620 kg of salmon were deposited on the enhanced bank and 45,200 kg on the depleted bank over the 20 yr, for a 5.9‐fold difference in total mass. In 2016, we sampled needles of 84 white spruce trees (Picea glauca) the dominant riparian tree species, for foliar nitrogen (N) content and stable isotope ratios (δ15N), and took core samples for annual growth increments. Stable isotope analysis indicated that marine‐derived N was incorporated into the new growth of the trees on the enhanced bank. Analysis of tree cores indicated that in the two decades prior to our enhancement experiment, trees on the south‐facing (subsequently the depleted) bank grew faster than those on the north‐facing (later enhanced) bank. This difference was reduced significantly during the two decades of fertilization, indicating an effect of the carcass transfer experiment against the background of other factors affecting tree growth.
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Allen Brain Atlas
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