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828 results for “species trait”
Functional Traits of Selected Tree Species in Harvard Forest, New Hampshire, and Southern Quebec 2015
Increasing evidence suggests that species' phenological responses may predict their performance with warming, but this work has generally ignored whether phenology is correlated with other traits known to drive plant performance. This is perhaps surprising given that interest in functional traits has also increased in recent decades, yet within the functional traits literature there has been an equally limited consideration of phenology, perhaps because robustly estimating it is time-intensive, and simple field estimates will show extreme variation across sites of different latitudes and climate regimes. Here we collected a suite of trait data on the same species for which we collected phenological data (see related dataset HF314, Leaf and Flower Phenology of Woody Plant Species at Harvard Forest and Southern Quebec 2015) to help address this gap. We focused on populations of trees in temperate forests in the Northeast face, which face different environmental conditions across their ranges. This project measured functional traits of trees at two to four sites, to provide a foundation for studies on the relationship between range shift, phenology, and functional traits.
PGT01 Konza prairie grass species trait
Evolutionary history plays a key role driving patterns of trait variation across plant species. For scaling and modeling purposes, grass species are typically organized into C3 versus C4 plant functional types (PFTs). PFT groupings may obscure important functional differences among species. Rather, grouping grasses by evolutionary lineage may better represent grass functional diversity. We measured 11 structural and physiological traits in situ from 75 grass species within the North American tallgrass prairie. We tested whether traits differed significantly among photosynthetic pathways or lineages (tribe) in annual and perennial grass species. We hypothesized that tribe would be the best predictor of traits, more so than photosynthetic pathway. We further hypothesized that there would be substantial variation of traits in species among the seven C4 lineages represented at our site.
Floral traits of animal-pollinated Sevilleta plant species
Concern about pollinator populations is widespread, with bees documented to be in decline due to factors including habitat loss, disease, and pesticides. In addition, climate change may be an important cause of bee population losses, but few studies have examined bee abundance relationships with climate variables. Importantly, bees may respond directly to climate or may exhibit indirect responses to climate via changes in plant phenology or community composition. This study collected floral trait data to complement the Sevilleta LTER pollinator monitoring, plant phenology, and plant biomass datasets, with the aim of examining whether floral resource availability mediates bee responses to climate. For 71 common, animal-pollinated flowering plant species, we measured floral traits relevant to pollination in June–October 2018 and April–August 2019 within sites representing four ecosystem types at the Sevilleta National Wildlife Refuge: Plains grassland, Chihuahuan Desert grassland, Chihuahuan Desert shrubland, and piñon-juniper woodland. On a minimum of 5 individuals per plant species, we recorded the total number of open flowers and the corolla width of flowers, along with plant height and vegetative cover. These data may be used in combination with the Sevilleta LTER pollinator monitoring, phenology, and biomass datasets to examine how bee and floral resource abundance, diversity, and phenology vary across years and whether these changes correspond with one another, as well as to consider relationships among climate, floral resource abundance/diversity, and bee abundance/diversity.
Regional and local variation in chemical, structural, and physical leaf traits for tree species in the northeastern United States, 2016-2023.
This dataset is a compilation of leaf trait measurements for 25 different Northern American tree species in the northeastern United States collected between 2016 and 2023 by the Terrestrial Ecosystems Analysis Lab at the University of New Hampshire. Currently, this dataset contains measurements for 2,006 samples across 18 chemical, physical, and structural traits. Measured traits include stable isotopes for carbon (C) and nitrogen (N), chlorophyll estimates, leaf and petiole dimensions, and leaf and petiole water content. Traits have been measured at plots spanning a wide range of latitude, longitude, elevation, and forest types. A simple table containing these plot descriptions has been included. Additional leaf physiological and optical traits have been measured concurrently on many of these samples and have been or will be published separately. This is a continuous dataset that will be updated on an as needed basis.
CoRRE Trait Data: A collection of 17 categorical and continuous traits for more than 4000 grassland species worldwide
In our changing world, it is critical to understand and predict plant community responses to global change drivers. Plant functional traits promise to be a key predictive tool for many ecosystems, including grasslands, however their use requires both complete plant community and functional trait data. Yet, representation of these data in global databases is incredibly sparse, particularly beyond a handful of most used traits and common species. Here we present the CoRRE Trait Database, spanning 17 traits (9 categorical, 8 continuous) anticipated to predict species’ responses to global change for 4,079 vascular plant species across 173 plant families present in 390 grassland experiments from around the world. The database contains complete categorical trait records for all 4,079 plant species, obtained from a comprehensive literature search. Additionally, the database contains nearly complete coverage (99.97%) of species mean values for continuous traits for a subset of 2,927 plant species, predicted from observed trait data drawn from TRY and a variety of other plant trait databases using Bayesian Probabilistic Matrix Factorization (BHPMF) and multivariate imputation using chained equations (MICE). These data will shed light on mechanisms underlying population, community, and ecosystem responses to global change in grasslands worldwide.
Species trait tissue chemistry: Biodiversity II: Effects of Plant Biodiversity on Population and Ecosystem Processes
Biodiversity II (E120) is designed to determine how the number of plant species affects the dynamics of ecological processes at the population, community, and ecosystem levels. By experimentally manipulating the number of species and the kinds of species, the amount of plant growth and the change from year to year, that result can be examined. Plots are large (9m x 9m actively maintained) and well-replicated, allowing responses of plant pathogens, insect herbivores, seed predators, soil parameters, invasive plant species and other variables to also be studied. Plots were seeded in May 1994 to have 1, 2, 4, 8, or 16 species, with roughly 30 replicates of each diversity level. The species composition of each plot was chosen by random draw from a pool of 18 grassland perennials that included four warm-season (C4) grasses, four cool-season (C3) grasses, four legumes, four non-legume forbs, and two woody species. All species occur in monoculture allowing comparison of responses of each species in monoculture to combinations of these same species. The experiment was established in 1994 by the lead investigators David Tilman, Peter Reich, Johannes Knops, and David Wedin. Experiment 120 is similar to Experiment 123, but it uses larger plots to provide a large capacity for long-term subexperiments.
Categorical traits for macroalgae species of the Santa Barbara Channel
These data describe 32 categorical traits for 50 species of macroalgae found in the Santa Barbara Channel. Data are contained in one table, including a list of species, trait states for any given trait, and citations for where this information was found.
Potential Metabolic Activity, Catalase Activity, Performance traits and Morphological variables of 94 individuals belonging to Podarcis muralis species used in the analysis
<p>Potential Metabolic Activity (ETS26_P, ETS31_P, ETS36_P), Catalase Activity (CAT_P), Performance traits (BITE, SPRINT,CLIMB, MANO) and Morphological variables (snout-vent length (SVL), trunk length (TRL), pileus length (PL), head length (HL), head width (HW), head height (HH), fore limb length (FLL) and hind limb length (HLL) of 94 individuals belonging to <em>Podarcis muralis</em> species. The data was used in the analysis of the paper entitled: Is It Function or Fashion? An Integrative Analysis of Morphology, Performance, and Metabolism in a Colour Polymorphic Lizard, by authors Verónica Gomes, Anamarija Žagar, Guillem Pérez i de Lanuza, Tatjana Simčič and Miguel A. Carretero, published in the journal Diversity 2022, 14, 116. <a href="https://doi.org/10.3390/d14020116">https://doi.org/10.3390/d14020116</a></p>
Data set: Variations in water economy traits in two Sphagnum species across their distribution boundaries
<p><em>Sphagnum</em> trait data collected (2016-2017) across a climatic gradient in Sweden. Trait data for both shoot and canopy traits. Data for <em>Sphagnum cuspidatum</em> and <em>Sphagnum lindbergii</em>. Also contains data on species occurrence records in Sweden and output from speceis distribution modelling. See published paper for more information.</p> <p>Files contain (i) processed data ("calculated_trait_data...cvs"), (ii) raw data ("Campbell_etal_clim_traits_...cvs"), (iii) their readme files, and (iv) R-scripts to run the analyses. Note that you need the files in the zip-file to run the analyses in the R-script. The zip-file contains all raw data (climate, traits, species occurences), MaxEnt output, and raster files from photogrammetry.</p> <p>More info in paper: <a href="https://doi.org/10.1002/ajb2.16347" target="_blank" rel="noopener">https://doi.org/10.1002/ajb2.16347</a></p>
Data_Schönauer et al. (2023)_Root and branch hydraulic functioning and trait coordination across organs in drought-deciduous and evergreen tree species of a subtropical highland forest
<p>Data used in</p> <p>Schönauer, M., Hietz, P., Schuldt, B., and Rewald, B. (2023). Root and branch hydraulic functioning and trait coordination across organs in drought-deciduous and evergreen tree species of a subtropical highland forest. Frontiers in plant science 14, 1127292. doi: 10.3389/fpls.2023.1127292</p>
LPT01 Leaf physiological and structural traits of encroaching shrub species at Konza Prairie
A variety of leaf-level physiological and structural traits were collected on seven species of encroaching shrubs at Konza Prairie Biological Station during the summer of 2022. Shrub species spanned an order of magnitude in abundance. These data were used to assess if the most abundant encroaching species at Konza Prairie have common growth forms and physiology or unique traits that differentiate their carbon- and water-use strategies. Measurements included A-Ci response curves, light response curves, pressure-volume curves, specific leaf area, leaf dry matter content, leaf carbon and nitrogen content, leaf 13C and wood density. All measurements were collected on the same shrub individuals.
Effects of plant hydraulic traits on the flammability of live fine canopy fuels in 62 Australian plant species
<ol> <li><span>Plant species vary in how they regulate moisture and this has implications for their flammability during wildfires. We explored how fuel moisture is shaped by variation within six hydraulic traits: saturated moisture content, cell wall rigidity, cell solute potential, symplastic water fraction and tissue capacitance.</span></li> <li><span>Using pressure-volume curves, we measured these hydraulic traits distal shoots (<i>i.e.</i> twigs + leaves) in 62 plant species across four wooded communities in south-eastern Australia. For a subset of 30 of those species, we also measured hydraulic traits of twigs using moisture-release curves. Moisture content of fine fuels was then estimated for circumstances typical of fire weather. These projections were made assuming that under the hot, dry, windy conditions typical of large wildfires, leaves and fine twigs would function at internal water pressures close to wilting point (<i>i.e. </i>turgor loss point, TLP). The effect of different moisture contents at TLP on ignition time was then modelled using a fully mechanistic, finite element model of biomass ignition based on standard principles of physical chemistry.</span></li> <li><span>We also measured predawn water potential, an indication of plant access to soil water that is influenced by root architecture. These data were used to model how root traits influence fuel moisture and ignition time.</span></li> </ol>
Data from: Soil organic carbon stability in forests: distinct effects of tree species identity and traits
Rising atmospheric CO2 concentrations have increased interest in the potential for forest ecosystems and soils to act as carbon (C) sinks. While soil organic C contents often vary with tree species identity, little is known about if, and how, tree species influence the stability of C in soil. Using a 40‐year‐old common garden experiment with replicated plots of eleven temperate tree species, we investigated relationships between soil organic matter (SOM) stability in mineral soils and 17 ecological factors (including tree tissue chemistry, magnitude of organic matter inputs and their turnover, microbial community descriptors, and soil physico‐chemical properties). We measured five SOM stability indices, including heterotrophic respiration, C in aggregate‐occluded particulate organic matter (POM) and mineral‐associated SOM, and bulk SOM δ15N and ∆14C. The stability of SOM varied substantially among tree species and this variability was independent of the amount of organic C in soils. Thus, when considering forest soils as C sinks, the stability of C stocks must be considered in addition to their size. Further, our results suggest tree species regulate soil C stability via the composition of their tissues, especially roots. Stability of SOM appeared to be greater (as indicated by higher δ15N and reduced respiration) beneath species with higher concentrations of nitrogen and lower amounts of acid‐insoluble compounds in their roots, while SOM stability appeared to be lower (as indicated by higher respiration and lower proportions of C in aggregate‐occluded POM) beneath species with higher tissue calcium contents. The proportion of C in mineral‐associated SOM and bulk soil ∆14C, though, were negligibly dependent on tree species traits, likely reflecting an insensitivity of some SOM pools to decadal‐scale shifts in ecological factors. Strategies aiming to increase soil C stocks may thus focus on particulate C pools, which can more easily be manipulated and are most sensitive to climate change.
Growth traits of a tropical timber species at Southeast Asia, Shorea macrophylla, and scripts for genome wide association study and genomic prediction
<p><em><span>Shorea macrophylla</span></em><span> is a commercially important tropical tree species grown for timber and oil. It is amenable to plantation forestry due to its fast initial growth. Genomic selection (GS) has been used in tree breeding studies to shorten long breeding cycles but has not previously been applied to <em>S. macrophylla</em>. To build genomic prediction models for GS, leaves and growth trait data were collected from a half-sib progeny population of <em>S. macrophylla</em> in Sari Bumi Kusuma forest concession, central Kalimantan, Indonesia. 18037 SNP markers were identified in two ddRAD-seq libraries. Genomic prediction models based on these SNPs were then generated for breast height and total height in the 7th year from planting (D7 and H7). These traits were chosen because of their relatively high narrow-sense genomic heritability and because seven years was considered long enough to assess initial growth. Genomic prediction models were built using 12 methods with the full set of identified SNPs and subsets of 48, 96, and 192 SNPs selected based on the results of a genome-wide association study (GWAS). The GBLUP and RKHS methods gave the highest predictive ability (PA) for D7 and H7 and showed that D7 has an additive genetic architecture while H7 has an epistatic genetic architecture. LightGBM and CNN1D also achieved high PA for D7 with 48 and 96 selected SNPs, and for H7 with 96 and 192 selected SNPs, showing that gradient boosting decision trees and deep learning can be useful in genomic prediction. For almost all methods and both traits, PA was higher when SNPs were selected based on their GWAS P-values than when using the full set of SNPs. These results suggest that GS with GWAS-based SNP selection could be used in <em>S. macrophylla </em>breeding to improve initial growth and reduce genotyping costs for next generation seedlings.</span></p>
Shortgrass prairie (Colorado, USA) and northern mixedgrass prairie (Wyoming, USA) species traits
<p>These data support Mueller et al. 2023, entitled "Coordination of leaf, root, and seed traits shows the importance of whole plant economics in two semiarid grasslands."</p> <p>Uncertainty persists within trait-based ecology, partly because few studies assess multiple axes of functional variation and their effect on plant performance. For 55 species from two semiarid grasslands, we quantified: i) covariation between economic traits of leaves and absorptive roots, ii) covariation among economic traits, plant height, leaf size, and seed mass, and iii) relationships between these traits and species' abundance. Pairs of analogous leaf and root traits were at least weakly positively correlated (e.g., SLA and SRL). Two pairs of such traits, N content and DMC of leaves and roots, were at least moderately correlated (r>0.5) whether species were grouped by site, taxonomic group and growth form, or life history. Root diameter was positively correlated with seed mass for all groups of species except annuals and monocots. Species with higher LDMC tended to be more abundant (r=0.63). Annuals with larger seeds were more abundant (r=0.69). Compared to global-scale syntheses with many observations from mesic ecosystems, we observed stronger correlations between analogous leaf and root traits, weaker correlations between SLA and leaf N, and stronger correlations between SRL and root N. In dry grasslands, plant persistence may require coordination of above- and belowground traits, and dense tissues may facilitate dominance.</p>
Dataset for: Climate and shared evolutionary history drive trait variation among species of Neotropical understory monocots
<p>Dataset and p-values form phylogenetically generalized least square models accompanying the manuscript "Climate and shared evolutionary history drive trait variation among species of Neotropical understory monocots".</p>
Thermal performance of Aedes sierrensis life history traits for populations collected across the species range
<p>How mosquitoes may respond to rapid climate warming remains unknown for most species, but will have major consequences for their future distributions, with cascading impacts on human well-being, biodiversity, and ecosystem function. We investigated the adaptive potential of a wide-ranging mosquito species, <em>Aedes sierrensis</em>, across a large climatic gradient by conducting a common garden experiment measuring the thermal limits of mosquito life history traits. Although field-collected populations originated from vastly different thermal environments that spanned over 1,200 km, we found limited variation in upper thermal tolerance between populations. In particular, the upper thermal limits of all life history traits varied by <3°C across the species range and, for most traits, did not differ significantly between populations. For one life history trait—pupal development rate—we did detect significant variation in upper thermal limits between populations, and this variation was strongly correlated with source temperatures, providing evidence of local thermal adaptation for pupal development. However, we found that maximum environmental temperatures across most of the species' range already regularly exceed the highest upper thermal limits estimated under constant temperatures. This result suggests that strategies for coping with and/or avoiding thermal extremes are likely key components of current and future mosquito thermal tolerance.</p>
Resources for: Spatio-temporal integrated Bayesian species distribution models reveal lack of broad relationships between traits and range shifts
<p><strong>Aim</strong>: Climate change and habitat loss or degradation are some of the greatest threats that species face today, often resulting in range shifts. Species traits have been discussed as important predictors of range shifts, with the identification of general trends being of great interest for conservation efforts. However, studies reviewing relationships between traits and range shifts have questioned the existence of such generalized trends, due to mixed results and weak correlations, as well as analytical shortcomings. The aim of this study was to test this relationship empirically, using analytical approaches that account for common sources of bias when assessing range trends.<br><strong>Location</strong>: Tanzania, East Africa.<br><strong>Time period</strong>: 1980-1999 and 2000-2020.<br><strong>Major taxa studied</strong>: 57 savannah specialist birds found in Tanzania, belonging to 26 families and 11 orders.<br><strong>Methods</strong>: We applied recently developed integrated spatio-temporal species distribution models in R-INLA, combining citizen science and bird atlas data to estimate ranges of species, quantify range shifts, and test the predictive power of traditional trait groups, as well as exposure-related and sensitivity traits. We based our study on 40 years of bird observations in East African savannahs, a biome that has experienced increasing climatic and non-climatic pressures over recent decades. We correlated patterns of change with species traits.<br><strong>Results</strong>: We find indications of relationships identified by previous research, but low average explanatory power of traits from an ecological perspective, confirming the lack of meaningful general associations. However, our analysis finds compelling species-specific results.<br><strong>Main conclusions</strong>: We highlight the importance of individual assessments, while demonstrating the usefulness of our analytical approach for analyses of range shifts.</p>
Traits data of naturalized and non-naturalized alien species of four Indonesian Botanic Gardens
<p>The establishment of new botanic gardens in tropical regions highlights a need for weed risk assessment tools suitable for tropical ecosystems. The relevance of plant traits for invasion into tropical rainforests has not been well studied. </p> <p>Working in and around four botanic gardens in Indonesia where 590 alien species have been planted, we estimated the effect of four plant traits, plus time since species introduction, on: a) the naturalization probability and b) abundance (density) of naturalized species in adjacent native tropical rainforests; and c) the distance that naturalized alien plants have spread from the botanic gardens. </p> <p>We found that specific leaf area (SLA) strongly differentiated 23 naturalized from 78 non-naturalized alien species (randomly selected from 577 non-naturalized species) in our study. These trends may indicate that exotics with high SLA benefit from at least two factors when establishing in tropical forests: high growth rates and occupation of forest gaps. We also found that height was unrelated to naturalization probability, but naturalized aliens were having high SLA and were short. </p> <p>Exotic species that were present in the gardens for over 30 years and those with small seeds also had higher probabilities of becoming naturalized, indicating that garden plants can invade the understorey of closed canopy tropical rainforests, especially when invading species are shade-tolerant and have sufficient time to establish.</p> <p>On average, exotic species that were not animal dispersed spread 78 m further into the forests than animal-dispersed species. We did not detect relationships between the measured traits and estimated density of naturalized exotics in the adjacent forests.</p> <p>Synthesis: Traits were able to differentiate exotic species from botanic gardens that naturalized in native forest from those that did not; this is promising for developing trait-based risk assessment in the tropics. To limit the risk of invasion and spread into adjacent native forests, we suggest tropical botanic gardens avoid planting exotic species with fast carbon capture strategies and those that are shade tolerant.</p>
Fig. 2. – Species prediction for a grid cell. A in Geographical patterns of woody plants' functional traits in Burkina Faso
Fig. 2. – Species prediction for a grid cell. A. Average of maximal plant size; B. Percentage of spinescent species; C. Percentage of species containing latex; D. Percentage of species with compound leaves.
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.