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310 results for “Tree growth”
Data from: Interactions among trees: a key element in the stabilising effect of species diversity on forest growth
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Data from: Light availability predicts mortality probability of conifer saplings in Swiss mountain forests better than radial growth and tree size
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Data from: Shoot growth of woody trees and shrubs is predicted by maximum plant height and associated traits
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Data from: Marcescence and prostrate growth in tree ferns are adaptations to cold tolerance
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New tree‐level temperature response curves document sensitivity of tree growth to high temperatures across a US‐wide climatic gradient
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Are leaf, stem and hydraulic traits good predictors of individual tree growth? (FUN2FUN project)
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Data from: Combined effects of cold snaps and agriculture on the growth rates of Tree Swallows (<em>Tachycineta bicolor</em>)
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Removing climbers more than doubles tree growth and biomass in degraded tropical forests
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Data from: Reduced aboveground tree growth associated with higher arbuscular mycorrhizal fungal diversity in tropical forest restoration
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Tree regeneration after fire: Aspen removal experiment, biomass of 2002 growth of transplanted seedlings
This research was intended to address the general question of whether asexual stem regeneration of trembling aspen (Populus tremuloides Michx.) reduces rates of establishment and growth of potential invading conifer species during the initial years following fire. Interactions between aspen and conifers were studied under natural conditions in a burned aspen stand with a high potential for aspen re-sprouting. The study contributes to our understanding of whether competitive interactions between tree seedlings are likely to help maintain deciduous stands across disturbance cycles by reducing the potential for successful conifer establishment. Data are biomass (g dry weight) of current year growth (2002, leaves + stems) of transplanted seedlings. Values are averages of up to 3 seedlings per plot (fewer where seedlings died).
Tree Growth in Gentry Subplots for Six Experimental Sites (NWT, CWT, HJA, HFR, LUQ, and BCI)
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 contains growth records of plants (trees, shrubs, and liana) using the measures of diameter at breast height and/or diameter and ground height at five Gentry subplots at each experimental site. These plots were installed by the Enquist Lab and the University of Arizona as part of this macrosystems biodiversity and latitude project supported by the National Science Foundation under Cooperative Agreement DEB#1065836.
Investigating the relationship between tree stem respiration and growth rate
<b>Description: </b><p>This study was conducted at the Maliau Basin Conservation area (4.747°, 116.970°) in an old-growth forest, the Belian plot. The sample consisted of ten trees of variety of species and a range of growth rates. Sampling took place over 3 consecutive days (18/02/2020 - 20/02/2020) to compile a 24-hour cycle due to logistical constrains impeding continuous measurement. Stem Respiration was measured hourly using a closed chamber EGM-4 Infrared Gas Analyser. Each tree has a 5cm long PVC collar with a 10.6cm internal diameter sealed with glue at 1.1m height. For each measurement, the chamber was flushed and collar fanned to remove stagnant air. The chamber was then sealed onto the PVC collar and Rs measured for 120 seconds. The raw measurements provides CO2 concentration (ppm), the results were then downloaded and filtered to remove initial stabilisation period and outliers using the R package "egm_r_tools" (see https://github.com/davidorme/egm_r_tools), with the ideal measurement being a linear slope with minimal residuals. The filtered efflux slope for each tree was then scaled to hourly and summed to give a daily total per tree. Out of the 24-hour period, two hour slots were missing (14:00 and 04:00) due to logistical constraints .</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/152"><b>MRes Tropical Forest Ecology Field Course</b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3952832">here</a></p><p><b>Files: </b>This consists of 1 file: Stem_Resp_TFE2020.xlsx</p><p><b>Stem_Resp_TFE2020.xlsx</b></p><p>This file contains dataset metadata and 4 data tables:</p><ol><li><p><b>Respiration_data_corrected</b> (described in worksheet Respiration_data_corrected)</p><p>Description: 24 hour observations of CO2 flux patterns corrected using egm-r-tools R package</p><p>Number of fields: 7</p><p>Number of data rows: 240</p><p>Fields: </p><ul><li><b>date</b>: Calendar date (Field type: date)</li><li><b>hour</b>: Hour in which the measurement was taken and represents (Field type: time)</li><li><b>tree_tag</b>: ID number of tree sampled (Field type: id)</li><li><b>corrected_flux</b>: EGM corrected flux using egm_r_tools R package (Field type: numeric)</li><li><b>subplot</b>: Subplot number within Belian Plot (Field type: id)</li><li><b>Subplot_code</b>: Subplot (Field type: location)</li><li><b>EGM_unit</b>: Number of EGM unit used (Field type: id)</li></ul></li><li><p><b>Respiration_data_uncorrected</b> (described in worksheet Respiration_data_uncorrected)</p><p>Description: Raw uncorrected 24 hour observations of CO2 flux patterns from EGM machine</p><p>Number of fields: 5</p><p>Number of data rows: 219</p><p>Fields: </p><ul><li><b>date</b>: Calendar date (Field type: date)</li><li><b>Time</b>: time respiration measurement was taken (Field type: time)</li><li><b>tree_tag</b>: ID number of tree sampled (Field type: id)</li><li><b>EGM_Record_No</b>: file number of measurement taken on EGM (Field type: numeric)</li><li><b>CO2_Concentration</b>: Measured CO2 (Field type: numeric)</li></ul></li><li><p><b>Tree_data</b> (described in worksheet Tree_data)</p><p>Description: Description of tree information for sampled trees</p><p>Number of fields: 10</p><p>Number of data rows: 10</p><p>Fields: </p><ul><li><b>Plot</b>: SAFE Project plot code (Field type: location)</li><li><b>tree_tag</b>: ID number of tree sampled (Field type: id)</li><li><b>Species</b>: Species of tree (Field type: taxa)</li><li><b>WoodDensity</b>: Estimated wood density of tree (Field type: numeric)</li><li><b>Census_date1</b>: Calendar date of most recent census (Field type: date)</li><li><b>D.POM_cm1</b>: Diamater of tree at measurement point at census 1 (Field type: numeric)</li><li><b>Height_m</b>: Height of tree (Field type: numeric)</li><li><b>H.POM_m</b>: Height at which tree diameter is measured (Field type: numeric)</li><li><b>Census_date2</b>: Calendar date of previous census (Field type: date)</li><li><b>D.POM_cm2</b>: Diamater of tree at measurement point at census 2 (Field type: numeric)</li></ul></li><li><p><b>Hourly_data</b> (described in worksheet Hourly_data)</p><p>Description: Hourly records of temperature and weather observations during the study</p><p>Number of fields: 4</p><p>Number of data rows: 24</p><p>Fields: </p><ul><li><b>Date</b>: Calendar date (Field type: date)</li><li><b>Hour</b>: Hour of which measurments were taken (Field type: time)</li><li><b>Temperature (DegC)</b>: Air temperature (Field type: numeric)</li><li><b>Weather_Observations</b>: weather observations (Field type: comments)</li></ul></li></ol><p><b>Date range: </b>2018-08-21 to 2020-02-20</p><p><b>Latitudinal extent: </b>4.7467 to 4.7480</p><p><b>Longitudinal extent: </b>116.9693 to 116.9706</p><p><b>Taxonomic coverage: </b><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p><div> -  Plantae <br> -  -  Tracheophyta <br> -  -  -  Magnoliopsida <br> -  -  -  -  Ericales <br> -  -  -  -  -  Ebenaceae <br> -  -  -  -  -  -  <i>Diospyros</i> <br> -  -  -  -  -  -  -  <i>Diospyros pilosanthera</i> <br> -  -  -  -  -  Sapotaceae <br> -  -  -  -  -  -  <i>Payena</i> <br> -  -  -  -  -  -  -  <i>Payena microphylla</i> <br> -  -  -  -  Laurales <br> -  -  -  -  -  Lauraceae <br> -  -  -  -  -  -  <i>Eusideroxylon</i> <br> -  -  -  -  -  -  -  <i>Eusideroxylon zwageri</i> <br> -  -  -  -  Malvales <br> -  -  -  -  -  Dipterocarpaceae <br> -  -  -  -  -  -  <i>Dryobalanops</i> <br> -  -  -  -  -  -  -  <i>Dryobalanops lanceolata</i> <br> -  -  -  -  -  -  <i>Shorea</i> <br> -  -  -  -  -  -  -  <i>Shorea faguetiana</i> <br> -  -  -  -  Magnoliales <br> -  -  -  -  -  Annonaceae <br> -  -  -  -  -  -  <i>Maasia</i> <br> -  -  -  -  -  -  -  <i>Maasia sumatrana</i> <br> -  -  -  -  Sapindales <br> -  -  -  -  -  Meliaceae <br> -  -  -  -  -  -  <i>Aglaia</i> <br> -  -  -  -  -  -  -  <i>Aglaia odoratissima</i> <br> -  -  -  -  -  -  -  <i>Aglaia silvestris</i> <br></div><p></p>
Data from: Improving predictions of tropical tree survival and growth by incorporating measurements of whole leaf allocation
<p><span><span><span><span><span><span><span><span><span><span><span>1. Individual-level demographic outcomes should be predictable upon the basis of traits. However, linking traits to tree performance has proven challenging likely due to a failure to consider physiological traits (i.e., hard-traits) and the failure to integrate organ-level and whole plant-level trait information. </span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>2. Here, we modeled the survival rate and relative growth rate of trees while considering crown allocation, hard-traits, and local-scale biotic interactions, and compared these models to more traditional trait-based models of tree performance. </span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>3. We found that an integrative trait, total tree-level photosynthetic mass (estimated by multiplying specific leaf area and crown area) results in superior models of tree survival and growth. These models had a lower AIC than those including the effect of initial tree size or any other combination of the traits considered. Survival rates were positively related to higher values of crown area and photosynthetic mass, while relative growth rates were negatively related to the photosynthetic mass. Relative growth rates were negatively related to a neighbourhood crowding index. Furthermore, none of the hard-traits used in this study provided an improvement in tree performance models. </span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>4. <i>Synthesis</i>. Overall, our results highlight that models of tree performance can be greatly improved by including crown area information to generate a better understanding of plant responses to their environment. Additionally, the role of the hard-traits in improving models of tree performance is likely dependent upon the level of stress (e.g. drought stress), micro-environmental conditions, or short-term climatic variations that a particular forest experiences.</span></span></span></span></span></span></span></span></span></span></span></p>
Impacts of recurrent dry and wet years alter long-term tree growth trajectories
<p>Climate extremes, such as abnormally dry and wet conditions, generate abrupt shifts in tree growth, a situation which is expected to increase under predicted climate conditions. Thus, it is crucial to understand factors determining short- and long-term tree performance in response to higher frequency and intensity of climate extremes.</p> <p>We evaluated how three successive droughts and wet years influenced short- and long -term growth of six dominant Iberian tree species. Within species variation in growth response to repeated dry and wet years was evaluated as a function of individual traits related to resource and water use (diameter at breast height (DBH), wood density (WD) and specific leaf area (SLA)) and tree-to-tree competition across climatically contrasted populations. Furthermore, we assessed how short-term accumulated impacts of the repeated dry and wet years influenced long-term growth performance.</p> <p>All species showed strong short-term growth decreases and enhancements due to repeated dry and wet years. However, patterns of accumulated growth decreases (AcGD) and enhancements (AcGE) across climatically contrasting populations were species-specific. Furthermore, individual trait data were weakly associated to either AcGD or AcGE and the few relevant associations were found for conifers. Intraspecific variations in tree growth responses to repeated climates extremes were large, and not explained by intraspecific variability in SLA and WD. Accumulated impacts of repeated dry and wet years were related to long-term growth trends, showing how the recurrence of climate extremes can determine growth trajectories. The relationships of AcGD and AcGE with long-term growth trends were more common in conifers species.</p> <p>Synthesis. Repeated climate extremes do not only cause short-term growth reductions and enhancements, but also determine long-term tree growth trajectories. This result shows how repeated droughts can lead to growth decline. Conifers were more susceptible to the accumulated effects of extreme weather events indicating that in the future, more intense and frequent climate extremes will alter growth performance in forests dominated by these species.</p>
Long-term logging residue loadings affect tree growth but not soil nutrients in lodgepole pine forests
<p>Both above- and below-ground characteristics are affected by logging residue loadings. Long-term monitoring of tree growth and soil nutrients was conducted. We found that tree growth but not soil nutrients were affected. There were dynamic relationships between tree growth and logging residue loadings.</p>
Data from: Stimulation of boreal tree seedling growth by wood-derived charcoal: effects of charcoal properties, seedling species and soil fertility
1. Fire is a major disturbance in many ecosystems worldwide including the boreal forest, and significant quantities of charcoal can be input to the soil from fire. Some recent studies have provided evidence that wood-derived charcoal produced by fire can significantly stimulate plant growth. However, the mechanisms by which charcoal affects plant growth are poorly understood, and little is known about how charcoal effects on plant growth are influenced by charcoal type, soil type and plant species. 2. Seedlings from four common boreal tree species, two evergreen gymnosperms and two deciduous angiosperms, were grown in each of two soils of contrasting nutrient availability amended with charcoal with each of nine charcoal types (each produced from wood from a different plant species) in a greenhouse experiment. We also measured several functional traits for each of the charcoal types, as well as of the wood used to prepare the charcoal. 3. Charcoal addition had either positive or neutral effects on seedling growth, with great variability among charcoal types. The charcoal types that had the strongest positive effect were those that had the greatest concentrations of phosphate and total phosphorus, and in some cases were derived from woods that had the highest total phosphorus concentration. Addition of charcoal on average had a stronger positive effect on plant growth on soil with the lowest levels of phosphate and total phosphorus. 4. Generally, charcoal derived from angiosperms stimulated seedling growth more than charcoal from gymnosperms. Further, angiosperm seedlings were on average stimulated more by charcoal addition than were gymnosperm seedlings. These results indicate that charcoal produced by fire could contribute to the initial dominance of angiosperm trees in post-fire succession, and suggests a possible feedback whereby charcoal from angiosperm tree species favors growth of angiosperm seedlings. 5. This study highlights a new means by which functional trait variation among tree species could potentially exert "after-life" effects in forested ecosystems through influencing traits (and notably phosphate concentrations) of the charcoal that they produce following wildfire, with potentially important consequences for plant growth and community and ecosystem properties during post-fire succession.
Data from: Trade-offs in juvenile growth potential vs. shade tolerance among subtropical rainforest trees on soils of contrasting fertility
Plant adaptation to gradients of light availability involves a well-studied functional trade-off, as does adaptation to gradients of nutrient availability. However, little is known about how these two major trade-offs interact, and thus, it remains unclear whether and how the nature of the growth–shade tolerance trade-off differs on soils of contrasting fertility. We asked whether juvenile growth–shade tolerance trade-offs differed in slope and elevation between tree assemblages on nutrient-rich basalt and nutrient-poor rhyolite soils in an Australian subtropical rain forest. We measured the growth of, and the range of light environments occupied by, juveniles (40–120 cm tall) of eight basalt specialists, six rhyolite specialists, and one generalist that was common on both substrates. In situ minimum light requirements were estimated from the 5th percentile of the distribution of naturally regenerated juveniles in relation to daily light transmittance. Stem growth was measured for 12–16 months across a wide range of light environments to estimate the light compensation point of growth of each species. Light compensation points of growth showed nearly a 1 : 1 correspondence with in situ minimum light requirements of species, indicating that whole-plant carbon balance is a key driver of ecological success in low light. Minimum light requirements were negatively correlated with relative growth rate in low light, but correlated positively with growth in high light. Soil type had no effect on either the slope or the elevation of this trade-off, all species aligning around a common growth–shade tolerance trade-off, but our results do show a wider range of growth rates and shade tolerance on the nutrient-rich basalt soil than on the nutrient-poor rhyolite. Our results suggest that adaptation to light availability involves fundamentally similar trade-offs on these two substrates of differing fertility. However, a wider range of growth rates and shade tolerance on the nutrient-rich basalt soil than on the nutrient-poor rhyolite may help to explain the higher species richness and greater structural complexity of forest stands on the former substrate.
Data from: The negative effect of lianas on tree growth varies with tree species and season
<p>Lianas reduce tree growth, reproduction, and survival in tropical forests. Liana competition can be particularly intense in isolated forest fragments, where liana densities are high, and thus host tree infestation is common. Furthermore, lianas appear to grow particularly well during seasonal drought, when they may compete particularly intensely with trees. Few studies, however, have experimentally quantified the seasonal effects of liana competition on multiple tree species in tropical forests. We used a liana-removal experiment in a forest fragment in southeastern Brazil to test whether the effects of lianas on tree growth varies with season and tree species identity. We conducted monthly diameter measurements using dendrometer bands on 88 individuals of five tree species for 24 months. We found that lianas had a stronger negative effect on some tree species during the wet season compared to the dry season. Furthermore, lianas significantly reduced the diameter growth of two tree species but had no effect on the other three tree species. The strong negative effect of lianas on some trees, particularly during the wet season, indicates that the effect of lianas on trees varies both seasonally and with tree species identity.</p>
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
<p>Growth analysis based on tree-ring chronology is difficult in trees in aseasonal tropical rain forests, because annual growth rings may be unclear or completely absent. Fortunately, tree growth history recorded in xylem tissue is capable of providing valuable information on the responses of trees and forests to past and present environmental changes, including global warming.</p> <p>We have developed a new technique for aseasonal tropical forest trees which derives their growth rates from xylem Δ<sup>14</sup>C, and verified its accuracy. We also determined, from xylem δ<sup>13</sup>C, the intrinsic water-use efficiency (iWUE) in the past 50 years. We analyzed changes in xylem Δ<sup>14</sup>C and δ<sup>13</sup>C in 23 canopy trees of 12 species in 6 families growing in Pasoh Forest Reserve, Malaysia; each stem diameter at breast height (DBH) was recorded 14 times from 1969 to 2011.</p> <p>We found a significant positive relationship between the growth rates determined by <sup>14</sup>C dating and the past DBH data. On the other hand, leaf-internal CO<sub>2</sub> (C<sub>i</sub>) content did not change with increasing atmospheric CO<sub>2</sub> (C<sub>a</sub>). Thus, the iWUE increased significantly over the last 50 years in all the families and species tested.</p> <p>This study showed that the simultaneous measurements of xylem Δ<sup>14</sup>C and δ<sup>13</sup>C could reveal a long-term change in tree growth and iWUE during the past 50 years with high accuracy in various species and/or individuals in aseasonal tropical rainforests exhibiting high species diversity.</p>
Survival and growth data for tree species planted to reforest degraded tropical peat swamp forests and functional trait data for peat swamp forest species across Southeast Asia
<p>Degraded tropical peat swamp forests are harsh environments so difficult to restore. Evidence from past restoration projects can inform selection of species for planting. As part of a systematic review, we collated and synthesised survival and growth monitoring data on trees planted in degraded tropical peat swamp forests across Southeast Asia. A key aim of the systematic review and meta-analysis was to determine which tree species survive best when planted to restore tropical peat swamp forests. We also investigated the impact of seedling and site treatments and climatic conditions (El Niño-Southern Oscillation) on tree seedling survival and growth and the potential to use plant functional traits to predict survival and growth. </p> <p>Full methodological details of the systematic review, including: search strategy, article screening and inclusion criteria, critical appraisal of screened articles, data processing and data analysis can be found in the published article and supporting information stated below.</p> <p>Smith SW, Rahman NEB, Harrison ME, Shiodera S, Giesen W, Lampela M, Wardle DA, Chong KY, Randi A, Wijedasa LS, Teo PY, Fatimah, YA, Teng NT, Joanne YKQ, Alam MJ, Brugues Sintes P, Darusman T, Graham LLB, Katoppo DR, Kojima K, Kusin K, Lestari DP, Metali F, Morrogh-Bernard HC, Nahor MB, Napitupulu RRP, Nasir D, Nath TK, Nilus R, Norisada M, Rachmanadi D, Rachmat HH, Ripoll Capilla B, Salahuddin, Santosa PB, Sukri RS, Tay B, Tuah W, Wedeux, BMM, Yamanoshita T, Yokoyama EY, Yuwati TW, Lee JSH. Tree species that ‘live slow, die older’ enhance tropical peat swamp restoration: evidence from a systematic review. <em>Journal of Applied Ecology. </em>DOI:<a href="https://doi.org/10.1111/1365-2664.14232">10.1111/1365-2664.14232</a></p> <p>In this data repository, we have uploaded the following data used in the meta-analysis to generate the findings presented in the systematic review, specifically:</p> <ul> <li>Screening sheets of eligible articles across languages (English, Indonesian, Japanese and German) read in detailed by multiple authors on the review</li> <li>Survival monitoring data, including predicted half-life (duration until 50% mortality) derived from functional line-fitting</li> <li>Height monitoring data, including standardized relative growth rates (cm × cm<sup>-1 </sup>month<sup>-1</sup>) derived from functional line-fitting</li> <li>Plant functional traits, selected leaf nutrient contents and wood densities for those species used in the functional trait analyses</li> </ul> <p>Each data file has an associated meta-data file explaining the column headers and variables. Please note, data contributors from some studies wished to retain control over access to their monitoring data, but are willing to share this data on request. The relevant study-site code those studies used in the analyses in our systematic review can be found in the meta-data sheets. Details given include study-site code (used in the systematic review), site name and location, author name(s), author contact email(s). All these details have been provided with permission from relevant data contributor co-author(s). </p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.