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70 results for “Individual growth”
Selection in space and time: individual tree growth is adapted to tropical forest gap dynamics
<p>In the present study, we assessed genotypic diversity within closely-related sympatric tree species belonging to the widespread tropical tree species complex <em>Symphonia globulifera</em>. We addressed the fine-scale spatial and temporal genetic adaptations of individuals through differential growth strategies in response to forest gap dynamics. We finally compared the breadth of successional niches encountered by <em>Symphonia</em> species to other locally abundant species. Combining tree diameter censuses, indirect measures of light environment of the recent past and present, and single nucleotide polymorphisms (SNPs), we used population genomics, environmental association analyses, genome wide association and growth modelling to address the following questions:</p> <ul> <li>Are individual genotypes structured by the mosaic of light and competition environments resulting from forest gap dynamics?</li> <li>Is the growth of individuals determined by genotypes?</li> <li>Is there an association between genotypic adaptations to gap dynamics and to growth?</li> <li>How are genotypic adaptations to gap dynamics and to growth structured in time, i.e., across life stages?</li> <li>Are breadths of successional niches for <em>Symphonia</em> species wider than those of other locally abundant species?</li> </ul> <p>Find the analyses here : https://sylvainschmitt.github.io/GOING/introduction.html</p>
FIG. 4. Individual growth model fits for 36 in Hoplodactylus duvaucelii
FIG. 4. Individual growth model fits for 36 male Diamondback Terrapins. Blue lines represent the individual von Bertalanffy model fits for the corresponding plastron-at-age observations, which are represented with black dots. The 95% credible regions are show in the pink-shaded region.
FIG. 1. Individual growth model fits for 44 in Hoplodactylus duvaucelii
FIG. 1. Individual growth model fits for 44 female Diamondback Terrapins. Red lines represent the individual von Bertalanffy model fits for the corresponding plastron-at-age observations, which are represented with black dots. The 95% credible regions are shown in the blue-shaded region.
Individualizing Dose of Growth Hormone to Maintain Normal Growth Velocity After Fulfilled Catch up Growth in Children
ClinicalTrials.gov study NCT02879747. IPD Sharing: Not stated. Countries: 0. Publications: 3.
Individualized Treatment Based on Epidermal Growth Factor Receptor Mutations and Level of BRCA1 Expression in Advanced Adenocarcinoma
ClinicalTrials.gov study NCT00883480. IPD Sharing: Not stated. Countries: 1. Publications: 1.
The Effect of Growth Hormone Releasing Hormone on Cognitive Function in Individuals With Mild Cognitive Impairment
ClinicalTrials.gov study NCT02553603. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Data from: Subtle individual variation in indeterminate growth leads to major variation in survival and lifetime reproductive output in a long-lived reptile
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Biological and growth data of wild and hatchery-reared sea trout (Salmo trutta L.) individuals
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Data for: Effects of nutrient heterogeneity on root foraging and plant growth at the individual and community level
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Data from: Individual growth as a non-dietary determinant of the isotopic niche metrics
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Data from: Temperature- and latitude-specific individual growth rates shape the vulnerability of damselfly larvae to a widespread pesticide
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Data from: Size matters: individual variation in ectotherm growth and asymptotic size
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Data from: From the individual to the landscape and back: Time-varying effects of climate and herbivory on tree sapling growth at distribution limits
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Data from: Towards smarter harvesting from natural palm populations by sparing the individuals that contribute most to population growth or productivity
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Data from: A statistical framework to explore ontogenetic growth variation among individuals and populations: a marine fish example
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Are leaf, stem and hydraulic traits good predictors of individual tree growth? (FUN2FUN project)
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Data from: Individual and combined impacts of sulfoxaflor and Nosema bombi on bumblebee (Bombus terrestris) larval growth
<p>Sulfoxaflor is a globally important novel insecticide that can have negative impacts on the reproductive output of bumblebee (<i>Bombus terrestris</i>) colonies. However, it remains unclear as to which life-history stage is critically affected by exposure. One hypothesis is that sulfoxaflor exposure early in the colony's life cycle can impair larval development, reducing the number of workers produced, and ultimately lowering colony reproductive output. Here we assess the influence of sulfoxaflor exposure on bumblebee larval mortality and growth both when tested in insolation and when in combination with the common fungal parasite <i>Nosema bombi, </i>following a pre-registered design. We found no impact of sulfoxaflor (5ppb) or <i>N. bombi</i> exposure (50,000 spores) on larval mortality when tested in isolation but found an additive, negative effect when larvae received both stressors in combination. Furthermore, we found that sulfoxaflor and <i>N. bombi </i>exposure individually impaired larval growth but also found possible antagonistic effects when used in combination. Ultimately, our results suggest that colony-level consequences of sulfoxaflor exposure for bumblebees may be mediated through direct effects on larvae. As sulfoxaflor is licenced for use globally, our findings highlight the need to understand how novel insecticides impact non-target insects at various stages of their development. </p>
Data from: Estimation of individual growth trajectories when repeated measures are missing
Individuals in a population vary in their growth due to hidden and observed factors such as age, genetics, environment, disease, and carryover effects from past environments. Because size affects fitness, growth trajectories scale up to affect population dynamics. However, it can be difficult to estimate growth in data from wild populations with missing observations and observation error. Previous work has shown that linear mixed models (LMMs) underestimate hidden individual heterogeneity when over 25% of repeated measures are missing. Here we demonstrate a flexible and robust way to model growth trajectories. We show that state-space models (SSMs), fit using R package growmod, are far less biased than LMMs when fit to simulated datasets with missing repeated measures and observation error. This method is much faster than MCMC methods, allowing more models to be tested in a shorter time. For the scenarios we simulated, SSMs gave estimates with little bias when up to 87.5 % of repeated measures were missing. We use this method to quantify growth of Soay sheep, using data from a long-term mark-recapture study, and demonstrate that growth decreased with age, population density, weather conditions, and when individuals are reproductive. The method improves our ability to quantify how growth varies among individuals in response to their attributes and the environments they experience, with particular relevance for wild populations.
Figure 3 in Population density and diet type interactively affect individual growth of an omnivorous soil-dwelling insect (Anomala cuprea, Coleoptera: Scarabaeidae)
Figure 3. Temporal changes in the coefficient of variation (CV) of larval fresh weight at two population densities and with two diets. Error bars indicate standard error. See Tab. 3 for details of statistical results.
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>
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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.