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ShareScore release 0.9.0
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718 results for “functional traits”
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.
FuTRES (Functional Trait Resource for Environmental Studies) data store archival copy - 5/21/2022
<p> </p> <p>The Functional Trait Resource for Environmental Studies (FuTRES) project is a collaborative project among four universities (University of Oregon, University of Arizona, University of Florida, and Howard University). The key deliverables of FuTRES are a workflow for assembling functional trait data measured at the specimen level, a database to serve that data, and scientific publications demonstrating the utility of the assembled data. This dataset represents the FuTRES datastore as of 5/21/2022, providing an archive that is timestamped and providing all data that is not currently embargoed by providers. The column headers for FuTRES data are: basisOfRecord,catalogNumber,class,collectionCode,country,decimalLatitude,decimalLongitude,diagnosticID,eventID,family,genus,individualID,institutionCode,lifeStage,locality,mapped_project,materialSampleID,maximumChronometricAge,maximumChronometricAgeReferenceSystem,maximumElevationInMeters,measurementMethod,measurementSide,measurementType,measurementUnit,measurementValue,minimumChronometricAge,minimumChronometricAgeReferenceSystem,minimumElevationInMeters,observationID,occurrenceID,occurrenceRemarks,order,reproductiveCondition,samplingProtocol,scientificName,sex,specificEpithet,stateProvince,verbatimElevation,verbatimEventDate,verbatimLatitude,verbatimLocality,verbatimLongitude,verbatimMeasurementUnit,yearCollected,projectID,inferred_traits. The traits available and number of records for each trait: </p> <ul> <li><a href="https://futres-data-interface.netlify.app/">length (1,790,883)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tail length (520,281)</a></li> <li><a href="https://futres-data-interface.netlify.app/">body length (456,165)</a></li> <li><a href="https://futres-data-interface.netlify.app/">pes length (413,668)</a></li> <li><a href="https://futres-data-interface.netlify.app/">ear length to notch (397,073)</a></li> <li><a href="https://futres-data-interface.netlify.app/">external ear length (397,073)</a></li> <li><a href="https://futres-data-interface.netlify.app/">body mass (373,949)</a></li> <li><a href="https://futres-data-interface.netlify.app/">weight (373,949)</a></li> <li><a href="https://futres-data-interface.netlify.app/">width (7,429)</a></li> <li><a href="https://futres-data-interface.netlify.app/">talus width (1,705)</a></li> <li><a href="https://futres-data-interface.netlify.app/">metacarpal bone of digit 3 proximal articular breadth (783)</a></li> <li><a href="https://futres-data-interface.netlify.app/">molar tooth 1 occlusal surface width (782)</a></li> <li><a href="https://futres-data-interface.netlify.app/">metacarpal bone of digit 3 breadth (716)</a></li> <li><a href="https://futres-data-interface.netlify.app/">metacarpal bone of digit 3 depth (706)</a></li> <li><a href="https://futres-data-interface.netlify.app/">talus length (649)</a></li> <li><a href="https://futres-data-interface.netlify.app/">long bone length (637)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper secondary molar tooth 1 occlusal surface width (605)</a></li> <li><a href="https://futres-data-interface.netlify.app/">talus trochlea breadth (596)</a></li> <li><a href="https://futres-data-interface.netlify.app/">epiphysis width (581)</a></li> <li><a href="https://futres-data-interface.netlify.app/">talus breadth (560)</a></li> <li><a href="https://futres-data-interface.netlify.app/">talus medial depth (549)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tooth row length (498)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower tooth row length (425)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tibia distal width (413)</a></li> <li><a href="https://futres-data-interface.netlify.app/">molar tooth 1 occlusal surface length (402)</a></li> <li><a href="https://futres-data-interface.netlify.app/">premolar tooth 4 occlusal surface width (361)</a></li> <li><a href="https://futres-data-interface.netlify.app/">premolar tooth 3 occlusal surface length (343)</a></li> <li><a href="https://futres-data-interface.netlify.app/">femur width (301)</a></li> <li><a href="https://futres-data-interface.netlify.app/">humerus length (297)</a></li> <li><a href="https://futres-data-interface.netlify.app/">humerus width (293)</a></li> <li><a href="https://futres-data-interface.netlify.app/">premolar tooth 2 occlusal surface width (261)</a></li> <li><a href="https://futres-data-interface.netlify.app/">premolar tooth 2 occlusal surface length (260)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tibia diaphysis width (260)</a></li> <li><a href="https://futres-data-interface.netlify.app/">molar tooth 2 occlusal surface length (242)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tibia length (228)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tibia distal breadth (208)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tibia distal depth (201)</a></li> <li><a href="https://futres-data-interface.netlify.app/">molar tooth 2 occlusal surface width (197)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper secondary premolar tooth 4 occlusal surface length (187)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper secondary premolar tooth 4 occlusal surface width (185)</a></li> <li><a href="https://futres-data-interface.netlify.app/">premolar tooth 1 occlusal surface length (184)</a></li> <li><a href="https://futres-data-interface.netlify.app/">premolar tooth 1 occlusal surface width (184)</a></li> <li><a href="https://futres-data-interface.netlify.app/">premolar tooth 3 occlusal surface width (184)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper secondary canine tooth to premolar tooth 3 length (184)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper secondary premolar tooth 3 occlusal surface length (184)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper secondary premolar tooth 3 occlusal surface width (184)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper secondary molar tooth 1 occlusal surface length (180)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower secondary molar tooth 1 occlusal surface length (177)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower secondary molar tooth 1 occlusal surface width (177)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower secondary premolar tooth 4 occlusal surface length (176)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower secondary premolar tooth 4 occlusal surface width (176)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tibia proximal width (162)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower secondary premolar tooth 3 occlusal surface length (159)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper secondary premolar tooth 2 occlusal surface length (155)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper secondary premolar tooth 2 occlusal surface width (155)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tibia diaphysis breadth (145)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower secondary molar tooth 2 occlusal surface length (120)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower secondary molar tooth 2 occlusal surface width (119)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tibia diaphysis depth (111)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower secondary premolar tooth 2 occlusal surface width (106)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower secondary premolar tooth 2 occlusal surface length (105)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper premolar tooth 1 occlusal surface length (105)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper secondary premolar tooth 1 occlusal surface length (105)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper secondary premolar tooth 1 occlusal surface width (105)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tibia proximal breadth (85)</a></li> <li><a href="https://futres-data-interface.netlify.app/">molar tooth 3 occlusal surface length (81)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower secondary premolar tooth 1 occlusal surface length (79)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower secondary premolar tooth 1 occlusal surface width (79)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper secondary molar tooth 2 occlusal surface length (78)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper secondary molar tooth 2 occlusal surface width (78)</a></li> <li><a href="https://futres-data-interface.netlify.app/">humerus trochlea breadth (76)</a></li> <li><a href="https://futres-data-interface.netlify.app/">humerus medial trochlear height (74)</a></li> <li><a href="https://futres-data-interface.netlify.app/">humerus trochlear height at sagittal crest (74)</a></li> <li><a href="https://futres-data-interface.netlify.app/">humerus trochlear sulcus height (74)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tibia proximal depth (73)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper molar tooth 1-2 length (73)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper tooth row length (73)</a></li> <li><a href="https://futres-data-interface.netlify.app/">anterior tibial tuberosity length (70)</a></li> <li><a href="https://futres-data-interface.netlify.app/">humerus distal depth (69)</a></li> <li><a href="https://futres-data-interface.netlify.app/">femur length (67)</a></li> <li><a href="https://futres-data-interface.netlify.app/">ulna width (67)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tibia medial length (63)</a></li> <li><a href="https://futres-data-interface.netlify.app/">humerus diaphysis breadth (57)</a></li> <li><a href="https://futres-data-interface.netlify.app/">humerus diaphysis depth (54)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower secondary molar tooth 3 occlusal surface length (51)</a></li> <li><a href="https://futres-data-interface.netlify.app/">trochlea tali length (49)</a></li> <li><a href="https://futres-data-interface.netlify.app/">femur diaphysis breadth (45)</a></li> <li><a href="https://futres-data-interface.netlify.app/">forelimb zeugopod bone length (45)</a></li> <li><a href="https://futres-data-interface.netlify.app/">femur distal breadth (44)</a></li> <li><a href="https://futres-data-interface.netlify.app/">ulna length (42)</a></li> <li><a href="https://futres-data-interface.netlify.app/">femur proximal breadth (40)</a></li> <li><a href="https://futres-data-interface.netlify.app/">humerus length from trochlea to caput (38)</a></li> <li><a href="https://futres-data-interface.netlify.app/">calcaneus length (37)</a></li> <li><a href="https://futres-data-interface.netlify.app/">femur caput depth (37)</a></li> <li><a href="https://futres-data-interface.netlify.app/">humerus length from trochlea to ventral tubercle (37)</a></li> <li><a href="https://futres-data-interface.netlify.app/">humerus proximal breadth (37)</a></li> <li><a href="https://futres-data-interface.netlify.app/">femur diaphysis depth (36)</a></li> <li><a href="https://futres-data-interface.netlify.app/">femur distal depth (36)</a></li> <li><a href="https://futres-data-interface.netlify.app/">femur trochlea breadth (32)</a></li> <li><a href="https://futres-data-interface.netlify.app/">femur proximal depth (31)</a></li> <li><a href="https://futres-data-interface.netlify.app/">molar tooth 3 occlusal surface width (30)</a></li> <li><a href="https://futres-data-interface.netlify.app/">talus lateral length (30)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper molar tooth 3 occlusal surface length (30)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper molar tooth 3 occlusal surface width (30)</a></li> <li><a href="https://futres-data-interface.netlify.app/">femur length from caput to lateral condyle (28)</a></li> <li><a href="https://futres-data-interface.netlify.app/">femur length from greater trochanter to medial condyle (28)</a></li> <li><a href="https://futres-data-interface.netlify.app/">body length with tail (25)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower molar tooth 1 occlusal surface length (23)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower molar tooth 2 occlusal surface length (23)</a></li> <li><a href="https://futres-data-interface.netlify.app/">ulna depth across the process anaconaeus (23)</a></li> <li><a href="https://futres-data-interface.netlify.app/">ulna proximal articular breadth (23)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper molar tooth 1 occlusal surface length (22)</a></li> <li><a href="https://futres-data-interface.netlify.app/">olecranon depth (21)</a></li> <li><a href="https://futres-data-interface.netlify.app/">olecranon length (21)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper molar tooth 2 occlusal surface length (21)</a></li> <li><a href="https://futres-data-interface.netlify.app/">breadth of calcaneal body (18)</a></li> <li><a href="https://futres-data-interface.netlify.app/">calcaneus width (18)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tibia lateral length (14)</a></li> <li><a href="https://futres-data-interface.netlify.app/">femur length from caput to medial condyle (5)</a></li> <li><a href="https://futres-data-interface.netlify.app/">radius distal width (3)</a></li> <li><a href="https://futres-data-interface.netlify.app/">radius length (3)</a></li> <li><a href="https://futres-data-interface.netlify.app/">radius proximal articular width (3)</a></li> <li><a href="https://futres-data-interface.netlify.app/">radius proximal width (3)</a></li> <li><a href="https://futres-data-interface.netlify.app/">radius width (3)</a></li> <li><a href="https://futres-data-interface.netlify.app/">body height (1)</a></li> <li><a href="https://futres-data-interface.netlify.app/">height (1)</a></li> </ul>
The Biomass and Plant Functional Traits of Leymus chinensis Affected by Genotypic Diversity and Soil Nitrogen Addition through a Two-year Experiment, Tianjin, China, 2021-2023
In order to investigate the effects of soil nitrogen addition on the genotypic diversity of Leymus chinensis, 12 genotypes of Leymus chinensis were used as plant material and a two-factor experimental design was carried out in this study. Factor one was genotypic diversity of L. chinensis, including three levels: mono-genotype (G1), three genotypes (G3), and six genotypes (G6). Factor two was the soil nitrogen addition level, which included four levels: no nitrogen addition (N0), 2.5 g N/(m²·a) nitrogen application (N2.5), 5 g N/(m²·a) nitrogen application (N5), and 10 g N/(m²·a) nitrogen application (N10). Each treatment had 12 combinations as replicates, and 12 genotypes of L. chinensis were used. The frequency of each genotype was standardized across all treatment levels of genotypic diversity × soil nitrogen addition. The experiment commenced in September 2021 and soil nitrogen was applied every 2 months. Plants were cultivated in the experimental field at Nankai University, but were moved to a greenhouse for overwintering from November to February each year. During the experiment, there were no stresses or disturbances such as shading, drought, or insect feeding; weeds were regularly removed.
Functional Trait Measurements of Macroalgal Communities in the Santa Barbara Channel
This dataset contains trait and elemental composition data for macroalgal samples collected across depth gradients at multiple sites in the Santa Barbara Channel, California. Each sample represents an individual specimen characterized by morphological measurements (e.g., blade thickness, stipe diameter, total height), biomass of anatomical parts (blade, stipe, holdfast, reproductive tissue), and anchoring strength. In addition, biochemical traits—including carbon (C), nitrogen (N), and hydrogen (H) content—were measured from tissue samples analyzed in the analytical laboratory. These data support a trait-based modeling approach to macroalgal community structure and distribution, contributing to our understanding of functional diversity and ecosystem dynamics in temperate marine systems. Accompanying metadata include collection site, date, time, depth, location coordinates, and substrate type, providing context for environmental variation across samples.
Zooplankton functional trait database for Canadian lakes
<p>This dataset contains functional traits of 102 crustacean zooplankton taxa sampled in 624 Canadian lakes (Pelagic sample), as well as 58 sub-fossil cladoceran taxa (Sediments sample) sampled in 101 lakes. Lakes were sampled across Canada as part of the NSERC Canadian LakePulse Network. The traits used are: feeding type (B(<em>Bosmina</em>)-filtration, C(<em>Chydorus</em>)-filtration, D(<em>Daphia</em>)-filtration, S(<em>Sidae</em>)-filtration, stationary suspension or raptorial), habitat (littoral, pelagic or intermediate) and trophic group (carnivore, herbivore, omnivore, or a combination of these). Pelagic species length of up to 10 individuals per taxon per lake were measured by BSA Environmental Services (Ohio, U.S.A.), and averaged for each taxon. Sediments sample lengths were either obtained from the literature (Demott & Kerfoot, 1982; Barnett et al., 2007; Griffiths et al., 2019), or from the Pelagic sample length data. Feeding type, habitat and trophic group functional traits were obtained from literature (Demott & Kerfoot, 1982; Barnett et al., 2007; Hébert et al., 2016; Griffiths et al., 2019).</p> <p>References</p> <p>Barnett, A. J., Finlay, K., & Beisner, B. (2007). Functional diversity of crustacean zooplankton communities: Towards a trait-based classification. <em>Freshwater Biology</em>, <em>52</em>(5), 796–813. https://doi.org/10.1111/j.1365-2427.2007.01733.x</p> <p>Demott, W. R., & Kerfoot, W. C. (1982). Competition among cladocerans: nature of the interaction between Bosmina and Daphnia. <em>Ecology</em>, <em>63</em>(6), 1949–1966. https://doi.org/10.2307/1940132</p> <p>Griffiths, K., Winegardner, A. K., Beisner, B. E., & Gregory-Eaves, I. (2019). Cladoceran assemblage changes across the Eastern United States as recorded in the sediments from the 2007 National Lakes Assessment, USA. <em>Ecological Indicators</em>, <em>96</em>, 368–382. 061</p> <p>Hébert, M.-P., Beisner, B. E., & Maranger, R. (2016). A compilation of quantitative functional traits for marine and freshwater crustacean zooplankton. Ecology. https://doi.org/10.1890/15-1275</p>
Functional traits database for North American birds
<p>Estimation of functional diversity in biological communities requires extensive and complete data on numerous functional traits of species or even individuals. When estimating functional diversity at large scales, this fact possesses an issue that may be hard to overcome: for many species, there might not be sufficient data on their functional traits. In such cases, even if there is missing information on functional trait value for one species in a community, this makes the trait impossible to use for the estimation of the functional diversity of a community. On the other hand, there are available datasets on the functional traits of all extant species within certain lineages across the world, but such datasets are often limited to very few functional traits, missing some dimensions of species' ecological niches. In this dataset, I compiled the available data from various sources that describe 23 functional traits of 703 bird species that occur in Canada, the United States, and Mexico. These functional traits include the following: diet type, diurnal and nocturnal feeding, diet items, feeding methods, feeding substrate, nest type, nest substrates, breeding system, chick development at hatching, nest aggregation, clutch size, first breeding age, number of clutches a year, breeding success, adult annual survival, mean biomass, maximum lifespan, hand-wing index, kleptoparasitism, nest parasitism, and the extent of dependency on other species for building a nest.</p>
American Residential Macrosystems - Leaf functional traits and raw data in five major metropolitan areas, 2012-2013
"We used leaf functional traits in residential yards and nearby natural areas to assess biotic ecological homogenization in five cities across the U.S. that span major ecological biomes and climatic regions: Baltimore, MD, Boston, MA, Los Angeles, CA, Miami, FL, and Minneapolis-St. Paul, MN."
Niwot plant functional traits, 2008 - 2018.
Globally, human activities are altering environmental conditions through changes to disturbance regimes, climate, and primary productivity. Attempts to generalize changes in biodiversity in response to altered environmental conditions have had mixed success and the mechanisms that causes differences in responses are poorly understood. These contingent responses represent one of the largest obstacles to synthesis in community ecology and global change biology. Over the past decade, much progress has been made in resolving these ecological contingencies by shifting from a taxonomic perspective on community assembly and global change to a focus on functional traits. Functional traits are any morphological, physiological, or phenological characteristic of a species linked to fitness and have proven to be effective for understanding species interactions, biogeographic processes, and environmental change. Between 2008 and 2018, we measured plant height, specific leaf area, leaf size, chlorophyll content, stomatal conductance, leaf dry matter content, and leaf chemical composition (nitrogen, carbon, δ13C, δ15N) on 2689 individual plants from 141 species (not all traits on all individuals or species). These measurements were collected to capture intraspecific trait variation among the key habitat types on Niwot Ridge and to examine how species traits respond to global change in the ITEX experiment.
How is tree growth rate linked to root functional traits in phylogenetically related poplar hybrids?
<p>Fine roots play a crucial role in soil nutrient and water acquisition, significantly contributing to tree growth. Fine roots with a high specific root length (SRL) and small diameter are often considered to help trees grow fast. However, inconsistencies in the literature do not provide a clear basis on the effect of root functional traits, such as SRL or root mass density (RMD), on tree growth rate in phylogenetically related trees. Our aim was to examine relationships between tree growth rate and root functional traits, using clones displaying different growth rates in a hybrid poplar plantation located in New Liskeard, ON, Canada. Fine roots (diameter < 2 mm) samples were collected using soil cores at depths of 0–20, 20–40 and 40–60 cm, and analyzed for morphological, chemical and architectural traits. High SRL and thin fine roots were associated with the least productive clones, which is not consistent with the root economics spectrum (RES) theory. However, the most productive clone had larger fine root diameter and higher root lignin concentrations, probably reducing root construction and maintenance costs and C losses. Therefore, at the 0–20 and 20–40 cm depths, tree growth rates showed positive correlations with root diameter and root lignin concentrations, but negative correlations with SRL and root soluble compounds concentration. Increasing RMD at the 0–20 cm depth promoted tree growth rates, showing the importance of soil exploration in the topsoil for tree growth. We conclude that fine root variation does not always follow the RES hypothesis and argue that the rapid growth rate of trees may also be driven by fine root growth in diameter and mass in phylogenetically related trees.</p>
Supplementary Data Files for the paper "Intrinsically disordered compositional bias in proteins: Sequence traits, region clustering, and generation of hypothetical functional associations"
<div> <div> <div> <div> <p><strong>Supplementary data files relating to <a href="https://doi.org/10.1177/11779322241287485">https://doi.org/10.1177/11779322241287485. </a></strong></p> <p><strong><span>Suppl. File 1: Protein Family Clusters.</span></strong></p> <p><strong><span>Suppl. File 2: Cluster GO enrichments/depletions. </span></strong></p> <p><strong><span>Suppl. File 3: The raw ID-CBR data with annotations. </span></strong></p> <p><strong><span>Suppl. File 4: ­ID-CBR Cluster membership.</span></strong></p> <p><strong><span>Each file has an explanatory header. </span></strong></p> <p> </p> </div> </div> </div> </div>
Dataset and R code: Above and belowground functional trait coordination in the Neotropical understory genus Costus
<p>Dataset and R code accompanying the paper "Above and belowground functional trait coordination in the Neotropical understory genus <em>Costus</em>" published by AoB Plants. </p>
CarniTraits: Functional traits of the worlds Late Quaternary terrestrial mammalian predators
<p><em>CarniTraits, </em>a comprehensive functional trait database of all late Quaternary (~130,000 ybp) terrestrial mammalian carnivore species (149 species, >1kg body mass). The database contains the body mass, diet, locomotion, cooperative hunting, hunting habitat, hunting method, bone use, and hunting temporal activity patterns of all carnivores over the last ~130,000 years. We also include the IUCN status for all extant species and ground the database in a modern phylogeny and is thus compatible and easily interlinked with range maps published in PHYLACINE v1.2.1. CarniTraits is broadly applicable to assisting in local and macroecological studies, meta-analytic research, global syntheses, and paleoecological research.</p> <p><em>CarniTraits</em> includes data on:</p> <ul> <li>Body Mass</li> <li>Diet</li> <li>Scavenging behaviour</li> <li>Bone Consumption</li> <li>Locomotion</li> <li>Cooperative hunting</li> <li>Hunting habitat</li> <li>Hunting method</li> <li>Hunting time</li> <li>Brain mass</li> <li>Encephalisation Quotient</li> </ul> <p>Each of these traits is fundamental to the ecological impact that carnivores have on terrestrial ecosystems. Trait data compiled represents the best available knowledge on the functional traits of late Quaternary hypercarnivorous mammals. As such, <em>CarniTraits</em> provides a tool for the analysis of carnivore functional diversity both past and present, as well as their effects on ecosystem dynamics.</p> <p>Each trait is accompanied by columns that describes the confidence in the data (notes), whether it was inferred and what level it was inferred from and the reference that the data was collected from.</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>
Supplementary Files for "Extinction debt and functional traits mediate community saturation over large spatiotemporal scales"
<p><strong>Supplementary Files for "Extinction debt and functional traits mediate community saturation over large spatiotemporal scales"</strong></p> <p><strong>Supplementary Tables:</strong></p> <p><strong>Supplementary Table S1.</strong> Species composition data of the 67 sites included herein from members of the Dipsadidae.</p> <p><strong>Supplementary Table S2.</strong> Scores for the Principal Component (PC) Axes corresponding to the PC analyses performed with the climatic variables of the sites included in this work.</p> <p><strong>Supplementary Table S3.</strong> Species composition data of the 67 sites included herein from species from families different from Dipsadidae.</p> <p><strong>Supplementary Table S4.</strong> Functional data corresponding to each of the species found in the 67 sites included in this work.</p> <p><strong>Supplementary Table S5.</strong> Functional data corresponding to each of the species from families different from Dipsadidae found in the 67 sites included in this work.</p> <p><strong>Supplementary Table S6.</strong> GenBank accession numbers of each of the sequences used for constructing the timetree used for this work.</p> <p><strong>Supplementary Table S7.</strong> Table indicating the areas inhabited by each of the species of the Dipsadidae included in the Bayesian timetree used for the ancestral area estimation performed herein.</p> <p><strong>References used for constructing Supplementary Tables S4 and S5</strong></p> <p> </p> <p><strong>Supplementary Figures:</strong></p> <p><strong>Supplementary Figure S1.</strong> Results of the ancestral estimations as recovered by ‘BioGeoBEARS’</p>
Seasonal trends in leaf level physiological parameters, obtained through gas exchange, reflectance spectroscopy and, functional trait analysis
This data package contains leaf level gas exchange, reflectance spectroscopy, and functional trait measurements collected in six common deciduous tree species across the full 2021 growth season (May -October) at the Black Rock Forest in Cornwall, New York, USA. Branches were sampled predawn using the shotgun method of branch retrieval, and re-cut under water to preserve hydraulic function before transport to the lab. Gas exchange data included in this package are stomatal response curves (irradiance response) which can be used to estimate stomatal slope and intercept. Spectroscopic data are full-range (350 – 2500 nm) leaf reflectance spectra collected on all leaves sampled for gas exchange and traits. Leaf level trait measurements include leaf mass per area (LMA), leaf dry matter content (LDMC), elemental nitrogen and carbon expressed on a per mass basis, and fitted values of Asat, Vcmax, and Rdark scaled to a reference temperature of 25C. Data from these three data tables (stomatal responce, spectra, leaf traits) can be cross referenced using the unique SampleID. Additional data tables include stomatal anatomy (stomatal density, length, and width of the guard cells), hydraulic properties estimated from pressure volume curves (relative water deficit at the turgor loss point), and predawn water potential for all sampled branches. Site level data includes the dGPS location of each sampled tree, its species, and DBH. Each tabular data file (*.csv) is accompanied by a data description (*_dd.csv) which includes relevant metadata (unit, definition, data type). Copies of all raw instrument output (spectroradiometer, LICOR, pressure chamber) and included as .zip files.
Plant functional traits in the black sand extended growing season experiment, 2023.
Climate change is predicted to affect the alpine at a more rapid rate than other ecosystems. One predicted consequence is earlier snowmelt, which the Niwot Ridge LTER site Black Sand experiment aimed to replicate. Black sand was hand-spread across the early snowmelt treatment to cause a more rapid snowmelt than in the control plots. Sand was still spread across the control plots after snowmelt to control for the effects of the sand on vegetation. Each treatment also contained ITEX chambers which raised the temperature of individual plots. Within this larger experiment, we measured plant functional traits to see if they varied across the four treatments: control, control + ITEX, early snowmelt, and early snowmelt + ITEX.
Functional traits—not nativeness—shape the effects of large mammalian herbivores on plant communities
<p>Large mammalian herbivores (megafauna) have experienced extinctions and declines since prehistory. Introduced megafauna have partly counteracted these losses yet are thought to have unusually negative effects compared to native megafauna. Using a meta-analysis of 3,995 plot-scale plant abundance and diversity responses from 221 studies, we found no evidence that megafauna impacts were shaped by nativeness, 'invasiveness', 'feralness', coevolutionary history, or functional and phylogenetic novelty. Nor was there evidence that introduced megafauna facilitate introduced plants more than native megafauna. Instead, we found strong evidence that functional traits shaped megafauna impacts, with larger-bodied and bulk-feeding megafauna promoting plant diversity. Our work suggests that trait-based ecology provides better insight into interactions between megafauna and plants than concepts of nativeness.</p>
Data from: Tree functional traits across Caribbean island dry forests are remarkably similar
<p>Delineation of potential dry forest and estimated actual dry forest on Caribbean islands. Potential dry forest is delineated based on CHELSA climate data (<a href="http://www.chelsa-climate.org/">www.chelsa-climate.org</a>) and the FAO definition of dry forest. Estimated actual dry forest is corrected for land cover using data from Hansen et al. (2022) <a href="https://doi.org/10.1088/1748-9326/ac46ec">https://doi.org/10.1088/1748-9326/ac46ec</a>. Areas of potential dry forest, estimated actual dry forest, and area of built-up land covers are summarized by islands and joined to CHELSA bioclimatic variables for selected islands where data on functional traits are available. Trait values by sites are also included. The package consists of data outputs and R scripts to reproduce the data outputs from identified publicly available data sources.</p>
Distance functions of carabids in crop fields depend on functional traits, crop type and adjacent habitat: a synthesis
<p>Natural pest and weed regulation are essential for agricultural production, but the spatial distribution of natural enemies within crop fields and its drivers are mostly unknown. Using 28 datasets comprising 1,204 study sites across eight Western and Central European countries, we performed a quantitative synthesis of carabid richness, activity densities and functional traits in relation to field edges (i.e. distance functions). We show for the first time that distance functions of carabids strongly depend on carabid functional traits, crop type and, to a lesser extent, adjacent non-crop habitats. Richness of both predators and granivores and activity densities of small and granivorous species decreased towards field interiors, whereas the densities of large species increased. We found strong distance decays in maize and vegetables whereas richness and densities remained more stable in cereals, oilseed crops and legumes. We conclude that carabid assemblages in agricultural landscapes are driven by the complex interplay of crop types, adjacent non-crop habitats and further landscape parameters with great potential for targeted agroecological management. In particular, our synthesis indicates that a higher edge-interior ratio can counter the distance decay of carabid richness per field and thus likely benefits natural pest and weed regulation, hence contributing to agricultural sustainability.</p>
Drivers of plant diversity, community composition, functional traits and soil processes along an alpine gradient in the central Chilean Andes
<p>The datasets in this repository include plant community surveys, hyperspectral reflectance data at the leaf and canopy level, leaf trait data, and soil chemistry data collected at five sites along an elevation gradient of 2400m-3500m in the Chilean Andes (33°S, 70°W). The purpose of this study was to evaluate the environmental drivers of community assembly processes along the elevation gradient.</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.