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165 results for “environmental trait”

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zenodo52/100

FuTRES (Functional Trait Resource for Environmental Studies) data store archival copy - 5/21/2022

<p>&nbsp;</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.&nbsp; 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.&nbsp; &nbsp;The column headers for FuTRES data are:&nbsp;&nbsp;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.&nbsp; The traits available and number of records for each trait:&nbsp;</p> <ul> <li><a href="https://futres-data-interface.netlify.app/">length&nbsp;(1,790,883)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tail length&nbsp;(520,281)</a></li> <li><a href="https://futres-data-interface.netlify.app/">body length&nbsp;(456,165)</a></li> <li><a href="https://futres-data-interface.netlify.app/">pes length&nbsp;(413,668)</a></li> <li><a href="https://futres-data-interface.netlify.app/">ear length to notch&nbsp;(397,073)</a></li> <li><a href="https://futres-data-interface.netlify.app/">external ear length&nbsp;(397,073)</a></li> <li><a href="https://futres-data-interface.netlify.app/">body mass&nbsp;(373,949)</a></li> <li><a href="https://futres-data-interface.netlify.app/">weight&nbsp;(373,949)</a></li> <li><a href="https://futres-data-interface.netlify.app/">width&nbsp;(7,429)</a></li> <li><a href="https://futres-data-interface.netlify.app/">talus width&nbsp;(1,705)</a></li> <li><a href="https://futres-data-interface.netlify.app/">metacarpal bone of digit 3 proximal articular breadth&nbsp;(783)</a></li> <li><a href="https://futres-data-interface.netlify.app/">molar tooth 1 occlusal surface width&nbsp;(782)</a></li> <li><a href="https://futres-data-interface.netlify.app/">metacarpal bone of digit 3 breadth&nbsp;(716)</a></li> <li><a href="https://futres-data-interface.netlify.app/">metacarpal bone of digit 3 depth&nbsp;(706)</a></li> <li><a href="https://futres-data-interface.netlify.app/">talus length&nbsp;(649)</a></li> <li><a href="https://futres-data-interface.netlify.app/">long bone length&nbsp;(637)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper secondary molar tooth 1 occlusal surface width&nbsp;(605)</a></li> <li><a href="https://futres-data-interface.netlify.app/">talus trochlea breadth&nbsp;(596)</a></li> <li><a href="https://futres-data-interface.netlify.app/">epiphysis width&nbsp;(581)</a></li> <li><a href="https://futres-data-interface.netlify.app/">talus breadth&nbsp;(560)</a></li> <li><a href="https://futres-data-interface.netlify.app/">talus medial depth&nbsp;(549)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tooth row length&nbsp;(498)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower tooth row length&nbsp;(425)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tibia distal width&nbsp;(413)</a></li> <li><a href="https://futres-data-interface.netlify.app/">molar tooth 1 occlusal surface length&nbsp;(402)</a></li> <li><a href="https://futres-data-interface.netlify.app/">premolar tooth 4 occlusal surface width&nbsp;(361)</a></li> <li><a href="https://futres-data-interface.netlify.app/">premolar tooth 3 occlusal surface length&nbsp;(343)</a></li> <li><a href="https://futres-data-interface.netlify.app/">femur width&nbsp;(301)</a></li> <li><a href="https://futres-data-interface.netlify.app/">humerus length&nbsp;(297)</a></li> <li><a href="https://futres-data-interface.netlify.app/">humerus width&nbsp;(293)</a></li> <li><a href="https://futres-data-interface.netlify.app/">premolar tooth 2 occlusal surface width&nbsp;(261)</a></li> <li><a href="https://futres-data-interface.netlify.app/">premolar tooth 2 occlusal surface length&nbsp;(260)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tibia diaphysis width&nbsp;(260)</a></li> <li><a href="https://futres-data-interface.netlify.app/">molar tooth 2 occlusal surface length&nbsp;(242)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tibia length&nbsp;(228)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tibia distal breadth&nbsp;(208)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tibia distal depth&nbsp;(201)</a></li> <li><a href="https://futres-data-interface.netlify.app/">molar tooth 2 occlusal surface width&nbsp;(197)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper secondary premolar tooth 4 occlusal surface length&nbsp;(187)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper secondary premolar tooth 4 occlusal surface width&nbsp;(185)</a></li> <li><a href="https://futres-data-interface.netlify.app/">premolar tooth 1 occlusal surface length&nbsp;(184)</a></li> <li><a href="https://futres-data-interface.netlify.app/">premolar tooth 1 occlusal surface width&nbsp;(184)</a></li> <li><a href="https://futres-data-interface.netlify.app/">premolar tooth 3 occlusal surface width&nbsp;(184)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper secondary canine tooth to premolar tooth 3 length&nbsp;(184)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper secondary premolar tooth 3 occlusal surface length&nbsp;(184)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper secondary premolar tooth 3 occlusal surface width&nbsp;(184)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper secondary molar tooth 1 occlusal surface length&nbsp;(180)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower secondary molar tooth 1 occlusal surface length&nbsp;(177)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower secondary molar tooth 1 occlusal surface width&nbsp;(177)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower secondary premolar tooth 4 occlusal surface length&nbsp;(176)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower secondary premolar tooth 4 occlusal surface width&nbsp;(176)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tibia proximal width&nbsp;(162)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower secondary premolar tooth 3 occlusal surface length&nbsp;(159)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper secondary premolar tooth 2 occlusal surface length&nbsp;(155)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper secondary premolar tooth 2 occlusal surface width&nbsp;(155)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tibia diaphysis breadth&nbsp;(145)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower secondary molar tooth 2 occlusal surface length&nbsp;(120)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower secondary molar tooth 2 occlusal surface width&nbsp;(119)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tibia diaphysis depth&nbsp;(111)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower secondary premolar tooth 2 occlusal surface width&nbsp;(106)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower secondary premolar tooth 2 occlusal surface length&nbsp;(105)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper premolar tooth 1 occlusal surface length&nbsp;(105)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper secondary premolar tooth 1 occlusal surface length&nbsp;(105)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper secondary premolar tooth 1 occlusal surface width&nbsp;(105)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tibia proximal breadth&nbsp;(85)</a></li> <li><a href="https://futres-data-interface.netlify.app/">molar tooth 3 occlusal surface length&nbsp;(81)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower secondary premolar tooth 1 occlusal surface length&nbsp;(79)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower secondary premolar tooth 1 occlusal surface width&nbsp;(79)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper secondary molar tooth 2 occlusal surface length&nbsp;(78)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper secondary molar tooth 2 occlusal surface width&nbsp;(78)</a></li> <li><a href="https://futres-data-interface.netlify.app/">humerus trochlea breadth&nbsp;(76)</a></li> <li><a href="https://futres-data-interface.netlify.app/">humerus medial trochlear height&nbsp;(74)</a></li> <li><a href="https://futres-data-interface.netlify.app/">humerus trochlear height at sagittal crest&nbsp;(74)</a></li> <li><a href="https://futres-data-interface.netlify.app/">humerus trochlear sulcus height&nbsp;(74)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tibia proximal depth&nbsp;(73)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper molar tooth 1-2 length&nbsp;(73)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper tooth row length&nbsp;(73)</a></li> <li><a href="https://futres-data-interface.netlify.app/">anterior tibial tuberosity length&nbsp;(70)</a></li> <li><a href="https://futres-data-interface.netlify.app/">humerus distal depth&nbsp;(69)</a></li> <li><a href="https://futres-data-interface.netlify.app/">femur length&nbsp;(67)</a></li> <li><a href="https://futres-data-interface.netlify.app/">ulna width&nbsp;(67)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tibia medial length&nbsp;(63)</a></li> <li><a href="https://futres-data-interface.netlify.app/">humerus diaphysis breadth&nbsp;(57)</a></li> <li><a href="https://futres-data-interface.netlify.app/">humerus diaphysis depth&nbsp;(54)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower secondary molar tooth 3 occlusal surface length&nbsp;(51)</a></li> <li><a href="https://futres-data-interface.netlify.app/">trochlea tali length&nbsp;(49)</a></li> <li><a href="https://futres-data-interface.netlify.app/">femur diaphysis breadth&nbsp;(45)</a></li> <li><a href="https://futres-data-interface.netlify.app/">forelimb zeugopod bone length&nbsp;(45)</a></li> <li><a href="https://futres-data-interface.netlify.app/">femur distal breadth&nbsp;(44)</a></li> <li><a href="https://futres-data-interface.netlify.app/">ulna length&nbsp;(42)</a></li> <li><a href="https://futres-data-interface.netlify.app/">femur proximal breadth&nbsp;(40)</a></li> <li><a href="https://futres-data-interface.netlify.app/">humerus length from trochlea to caput&nbsp;(38)</a></li> <li><a href="https://futres-data-interface.netlify.app/">calcaneus length&nbsp;(37)</a></li> <li><a href="https://futres-data-interface.netlify.app/">femur caput depth&nbsp;(37)</a></li> <li><a href="https://futres-data-interface.netlify.app/">humerus length from trochlea to ventral tubercle&nbsp;(37)</a></li> <li><a href="https://futres-data-interface.netlify.app/">humerus proximal breadth&nbsp;(37)</a></li> <li><a href="https://futres-data-interface.netlify.app/">femur diaphysis depth&nbsp;(36)</a></li> <li><a href="https://futres-data-interface.netlify.app/">femur distal depth&nbsp;(36)</a></li> <li><a href="https://futres-data-interface.netlify.app/">femur trochlea breadth&nbsp;(32)</a></li> <li><a href="https://futres-data-interface.netlify.app/">femur proximal depth&nbsp;(31)</a></li> <li><a href="https://futres-data-interface.netlify.app/">molar tooth 3 occlusal surface width&nbsp;(30)</a></li> <li><a href="https://futres-data-interface.netlify.app/">talus lateral length&nbsp;(30)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper molar tooth 3 occlusal surface length&nbsp;(30)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper molar tooth 3 occlusal surface width&nbsp;(30)</a></li> <li><a href="https://futres-data-interface.netlify.app/">femur length from caput to lateral condyle&nbsp;(28)</a></li> <li><a href="https://futres-data-interface.netlify.app/">femur length from greater trochanter to medial condyle&nbsp;(28)</a></li> <li><a href="https://futres-data-interface.netlify.app/">body length with tail&nbsp;(25)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower molar tooth 1 occlusal surface length&nbsp;(23)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower molar tooth 2 occlusal surface length&nbsp;(23)</a></li> <li><a href="https://futres-data-interface.netlify.app/">ulna depth across the process anaconaeus&nbsp;(23)</a></li> <li><a href="https://futres-data-interface.netlify.app/">ulna proximal articular breadth&nbsp;(23)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper molar tooth 1 occlusal surface length&nbsp;(22)</a></li> <li><a href="https://futres-data-interface.netlify.app/">olecranon depth&nbsp;(21)</a></li> <li><a href="https://futres-data-interface.netlify.app/">olecranon length&nbsp;(21)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper molar tooth 2 occlusal surface length&nbsp;(21)</a></li> <li><a href="https://futres-data-interface.netlify.app/">breadth of calcaneal body&nbsp;(18)</a></li> <li><a href="https://futres-data-interface.netlify.app/">calcaneus width&nbsp;(18)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tibia lateral length&nbsp;(14)</a></li> <li><a href="https://futres-data-interface.netlify.app/">femur length from caput to medial condyle&nbsp;(5)</a></li> <li><a href="https://futres-data-interface.netlify.app/">radius distal width&nbsp;(3)</a></li> <li><a href="https://futres-data-interface.netlify.app/">radius length&nbsp;(3)</a></li> <li><a href="https://futres-data-interface.netlify.app/">radius proximal articular width&nbsp;(3)</a></li> <li><a href="https://futres-data-interface.netlify.app/">radius proximal width&nbsp;(3)</a></li> <li><a href="https://futres-data-interface.netlify.app/">radius width&nbsp;(3)</a></li> <li><a href="https://futres-data-interface.netlify.app/">body height&nbsp;(1)</a></li> <li><a href="https://futres-data-interface.netlify.app/">height&nbsp;(1)</a></li> </ul>

opencc-by-4.0May 2022View details →
edi48/100

Environmental, community and trait data of small water bodies in Zijin Mountain, Nanjing, Jiangsu, China, 2022

Small water bodies (SWBs) are vulnerable to drought and play a vital role in the conservation of aquatic biodiversity. Currently climate change is intensifying the seasonal drought of SWBs in monsoonal east Asia. However, little is known about the response of benthic macroinvertebrates of small ponds and streams that simultaneously suffer from climate-induced extreme drought. This study aimed to explore the taxonomic and functional response of macroinvertebrates in ponds and streams, either respectively or jointly, to extreme summer drought. We calculated taxonomic and functional diversity indices of communities in 11 streams and 12 ponds across three seasons: spring, summer and winter in 2022. We performed a permutational multivariate analysis of variance, Moran’s eigenvector maps, Moran Spectral Randomization based variation partitioning and convex hull analysis of trait space to examine temporal compositional and functional, as well as trait changes, and the contributions of environmental and spatial factors in shaping communities. The responses of taxonomic and functional diversity in ponds and streams were contrasting during the summer drought. Ponds showed increased taxonomic richness (TR), functional richness (FRic), functional richness (FRed) and trait space volume, while streams experienced decreased TR, FRic and trait space volume but increased FRed. The taxonomic and functional increases of ponds were driven by an influx of generalist taxa from streams, while the increase of FRed in streams resulted from the loss of species with strong dispersal and lentic adaptation traits. Dispersal played a more significant role than environmental filtering in shaping community structure during the drought, especially for streams lacking hydrological connectivity. This study provides the first insights into the complex response of macroinvertebrates to summer drought of SWBs in east Asia monsoonal region. Our results underscore the refuge effect of ponds during summer

openCC (other)Dec 2024View details →
edi48/100

Periphyton Abundance and Structural Traits, Diatom Taxa Relative Abundance, and Associated Environmental Data from Samples Collected from the Greater Everglades, Florida, USA from September 2005 - ongoing

This data package contains benthic algae (periphyton) and environmental data collected annually during the wet season between 2005 and 2021 from sites distributed throughout the greater Everglades ecosystem. This project is part of the Comprehensive Everglades Restoration Program's Monitoring and Assessment Plan (CERP MAP) intended to document baseline variability in periphyton attributes for assessing the effectiveness of restoration projects. A total of 200 primary sampling units (PSU) of 800 m x 800 m are nested in 32 landscape units (LSU) and each year, random coordinates are 'drawn' within each PSU and one draw is visited in each sampleable PSU. Sampled periphyton is processed for aggregate structural traits (i.e., biomass, chlorophyll-a, organic content, and phosphorus concentration) and for diatom taxa. For diatoms, slides are prepared, and at least 500 frustules are enumerated and identified to the lowest possible taxonomic resolution per slide. Taxon abundances are then relativized to the total count. These data accompany environmental and spatial data for each sampled draw. In addition to the CERP MAP data, this dataset also includes data on the same variables collected from up to 21 primary sampling units in the Broward County Water Preserve Area beginning in 2020. The data in this package replace and supersede those in package knb-lter-fce.1210 (https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-fce&identifier=1210).

openCC (other)Sep 2025View details →
dryad40/100

Effects of genetic vs. environmental quality on condition-dependent morphological and life history traits in a neriid fly

<p>Condition is assumed to reflect both genes and environment, enabling condition-dependent signals to reveal genetic quality. However, because the phenotypic effects of variation in genetic quality could be masked by environmental heterogeneity, the contribution of genetic quality to phenotypic variation in fitness-related traits and condition-dependent signals remains unclear. We compared effects of ecologically relevant manipulations of environmental quality (nutrient dilution in the larval diet) and genetic quality (one generation of inbreeding) on male and female morphology, life history and reproductive performance in the neriid fly <em>Telostylinus angusticollis</em>. We found that larval diet quality had strong, positive effects on male and female body size, male secondary sexual traits, and aspects of male and female reproductive performance. By contrast, inbreeding had weak effects on most traits, and no trait showed clear and consistent effects of both environmental and genetic quality. Indeed, inbreeding effects on body size and male competitive performance were of opposite sign in rich vs. poor larval diet treatment groups. Our results suggest that environmental quality strongly affects condition, but the effects of genetic quality are subtle and environment-dependent in this species. These findings raise questions about the genetic architecture of condition and the potential for condition-dependent traits to function as signals of genetic quality.</p>

opencc-zeroApr 2022View details →
zenodo40/100

Dataset: Environmental conditions and male quality traits simultaneously explain variation of multiple colour signals in male lizards

<p>Dataset and R code associated with the following publication:</p> <p>Badiane et al.&nbsp;(2022),&nbsp;Environmental conditions and male quality traits simultaneously explain variation of multiple colour signals in male lizards. Journal of Animal Ecology, in press</p> <p>This dataset includes the following files:</p> <p>- An excel file containing the reflectance spectra of all individuals from all the study populations</p> <p>- An excel file containing the variables collected at the individual and population levels</p> <p>- Two R scripts corresponding to the analyses performed in the publication</p>

opencc-by-4.0Jun 2022View details →
zenodo40/100

Data used in the paper: Historical and current environmental selection on functional traits of trees in the Atlantic Forest biodiversity hotspot

<p>This repository contains phylogenetic and functional trait data, raster files, and tables with sampling information and references used in the article &quot;Historical and current environmental selection on functional traits of trees in the Atlantic Forest biodiversity hotspot&quot; by Silva, J.L.A., Souza, A., and Vit&oacute;ria, A.P. Journal of Vegetation Science, <a href="https://doi.org/10.1111/jvs.13049">https://doi.org/10.1111/jvs.13049</a> .</p> <p>Description of files:</p> <p>(1) &quot;Species-level_Trait_Data_Silva_et_al._2021.csv&quot;: This file contains species-specific mean trait values and the plant growth form of the 2,122 studied species, whenever available. Trait values were compiled from public sources such as original papers, master and doctoral dissertations, and global trait databases.</p> <p>(2) &quot;Phylogenetic_Tree_Silva_et_al._2021.txt&quot;: This file contains the phylogenetic tree of the 2,122 studied species.</p> <p>(3) &quot;CWM_Trait_Maps.zip&quot;: This file contains seven rasters of spatially contiguous surfaces produced by Ordinary Kriging Interpolation using Community-Weighted Means (CWM) of each functional trait.</p> <p>(4) &quot;References-abundance-data.csv&quot;: This file contains sampling details and the references used to compile species abundance data for each studied site.</p> <p>(5) &quot;References-trait-data.csv&quot;: This file contains sampling details and the references used to compile functional trait data.</p> <p>&nbsp;</p>

opencc-by-4.0Jun 2021View details →
zenodo40/100

Leaf functional traits and environmental conditions of four common nymphaeid species

<p>This dataset includes measures of 21 leaf functional traits of 48 wild populations of four species of nymphaeids:&nbsp;<em>Nuphar lutea</em>,&nbsp;<em>Nelumbo nucifera</em>,&nbsp;<em>Nymphaea alba</em>&nbsp;and&nbsp;<em>Nymphoides peltata</em>. For each population (plot) coordinates, water depth, pH, specific conductivity, nitrate, dissolved inorganic carbon, dissolved reactive silica, soluble reactive phosphorus, sediment organic matter content and sediment total phosphorus content and sediment density are provided as well. The populations were located in different lake systems in Italy. The dataset includes a legend sheet with information on single traits and environmental variables, including units of measurement.</p> <p>Structural traits (area, fresh and dry weight, dry matter content and specific area) were measured on 5 leaves from different individuals, while leaf pigments and phosphorus content were measured on 3 leaves. Leaf carbon and nitrogen content were measured on one aliquot of the pooled dried material of all leaves. Therefore, the missing values in the dataset derive from the sampling design of the study. All further information on the methodology used to collect and measure the traits and environmental variables can be found in the related paper ("Ecological and functional niches comparison reveals differentiated resource-use strategies and ecological thresholds in four key floating-leaved macrophytes", Dalla Vecchia et al. 2024, Limnology and Oceanography).</p>

opencc-by-4.0Jun 2024View details →
zenodo40/100

Figure 1 in Using of fluctuating asymmetry in adult Pelophylax ridibundus (Amphibia: Anura: Ranidae) meristic traits as a method for assessing developmental stability of population and environmental quality of their habitat: industrial area in southern Bulgaria

Figure 1. An indicative map of the sites in southern Bulgaria where P. ridibundus individuals were captured in 2019.

opencc-by-4.0Feb 2022View details →
zenodo40/100

Figure 2 in Using of fluctuating asymmetry in adult Pelophylax ridibundus (Amphibia: Anura: Ranidae) meristic traits as a method for assessing developmental stability of population and environmental quality of their habitat: industrial area in southern Bulgaria

Figure 2. Photos of some asymmetric P. ridibundus individuals from site 1: the Chaya River in southern Bulgaria. Legend: a–d: asymmetric morphological traits on the back of the body and hind limbs of frogs, e–f: asymmetric morphological traits on the fingers of frogs. Trait 1 – number of stripes on the dorsal side of the thigh (femur); trait 2 – number of spots on the dorsal side of the thigh; trait 3 – number of stripes on the dorsal side of the shank (crus); trait 4 – number of spots on the dorsal side of the shank; trait 5 – number of stripes on the foot (pes); trait 6 – number of spots on the foot; trait 7 – number of stripes and spots on the back (dorsum); trait 8 – number of white spots on the ventral side of the second finger of the hind leg; trait 9 – number of white spots on the ventral side of the third finger of the hind leg; trait 10 – number of white spots on the ventral side of the fourth finger of the hind leg.

opencc-by-4.0Feb 2022View details →
zenodo40/100

Fig. 3 in Adaptations, life-history traits and ecological mechanisms of parasites to survive extremes and environmental unpredictability in the face of climate change

Fig. 3. Flow chart outlining factors that can influence the response of parasites to climate change.

opencc-by-4.0Aug 2020View details →
zenodo40/100

FIGURE 3 in Habitat modification driven by land use as an environmental filter on the morphological traits of neotropical stream fish fauna

FIGURE 3 | Representation of significant associations (p &lt;0.05) identified by the fourth-corner method in the factorial map of the RLQ analysis. Red denotes a positive relationship between morphological traits and environmental variables, blue indicates a negative relationship, and grey represents nonsignificant relationships. Codes: Cond: Conductivity, Rock: Rocky substrate, Woody: Woody debris, Turb: Turbidity, Backw: Backwater, DO: Dissolved Oxygen, Temp: Temperature. See acronyms for the morphological traits in Tab. S3.

opencc-by-4.0Apr 2024View details →
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FIGURE 1 in Habitat modification driven by land use as an environmental filter on the morphological traits of neotropical stream fish fauna

FIGURE 1 | Study area. Location of sampling sites according with land use covers: S1 -Manoel Gomes, S2 - Pedregulho, S3 - Arquimedes, S4 - Bom Retiro, S5 - Rio da Paz, S6 - Nene, S7 - Cascavel, S8 - Afluente do Quati, and S9 - Quati.

opencc-by-4.0Apr 2024View details →
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FIGURE 2 in Habitat modification driven by land use as an environmental filter on the morphological traits of neotropical stream fish fauna

FIGURE 2 | Relationship between morphological traits and environmental variables of the first two axes of the RLQ of the species along the lower Iguaçu River. The figures of the fish were added to illustrate the species. Codes: Woody: Woddy debris, Cond: Conductivity, Rocky: Rocky substrate, Turb: Turbidity, Backw: Backwater, DO: Dissolved Oxygen, Temp: Temperature, Anc: Ancystrus sp., Syn: Synbranchus sp., Hyp: Hypostomus sp., Hep: Heptapterus sp., Cam: Cambeva sp., Cor: Corydoras sp., Rha: Rhamdia sp., Geo: Geophagus sp., Ast: Astyanax sp., Psa: Psalidodon sp., Bry: Bryconamericus sp., Gym: Gymnotus sp., Hop: Hoplias sp., Pha: Phalloceros sp., Poe: Poecilia sp.

opencc-by-4.0Apr 2024View details →
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F I G U R E 1 A in Linking plant functional traits to biodiversity under environmental change

F I G U R E 1 A diagram of the linkage between functional traits and biodiversity at multiple ecological scales. Across different scales, the diagram includes the response of functional traits to different environmental conditions, interactions under shifts in community structure and ecosystem functioning, which affect species' survival or extinction, and thus biodiversity. The diagram is constructed following a bottom‐up logic based on individual traits and aims to facilitate future trait‐diversity models to predict biodiversity responses under environmental change.

opencc-by-4.0Apr 2024View details →
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Data from: Environmental conditions during development affect sexual selection through trait-fitness relationships

<p>Sexual selection can be shaped by spatial variation in environmental features among populations. Differences in sexual selection among populations generated through the effects of the environment could be shaped via four paths: differences in mean absolute fitness, differences in the means or variances of phenotypes, or differences in the absolute fitness-trait function relationship. Because sexual selection only occurs during the adult life stage, most studies have focused on identifying environmental features that influence these metrics of fitness and trait distributions among adults. However, these adult features could also be affected by environmental factors experienced in early life stages that then shape the trajectory for sexual selection during the adult life stage. Here we investigated how among-population variation in environmental conditions during the juvenile (larval) stage of two species of <i>Enallagma</i> damselflies shapes sexual selection on male body size. We found that environmental factors related to predation pressures, lake primary productivity, and habitat availability have a role in shaping spatial variation in sexual selection. This acts mainly through how the environment affects absolute fitness-body size associations, not spatial variation in mean fitness or body size means and variances. These results demonstrate that the underpinnings of sexual selection in the wild can arise from environmental conditions during pre-reproductive life stages.</p>

opencc-zeroAug 2021View details →
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Data for: Combining environmental niche models, multi-grain analyses, and species traits identifies pervasive effects of land use on butterfly biodiversity across Italy

<p><span>Understanding how species respond to human activities is paramount to ecology and conservation science, one outstanding question being how large-scale patterns in land use affect biodiversity. To facilitate answering this question, we propose a novel analytical framework that combines Environmental Niche Models, multi-grain analyses, and species traits. We illustrate the framework capitalizing on the most extensive dataset compiled to date for the butterflies of Italy (106,514 observations for 288 species), assessing how agriculture and urbanization have affected biodiversity of these taxa from landscape to regional scales (3–48 km grains) across the country while accounting for its steep climatic gradients.</span></p> <p><span>Multiple lines of evidence suggest pervasive and scale-dependent effects of land use on butterflies in Italy. While land use explained patterns in species richness primarily at grains ≤ 12 km, idiosyncratic responses in species highlighted "winners" and "losers" across human-dominated regions. Detrimental effects of agriculture and urbanization emerged from landscape (3-km grain) to regional (48-km grain) scales, disproportionally affecting small butterflies and butterflies with a short flight curve. Human activities have therefore reorganized the biogeography of Italian butterflies, filtering out species with poor dispersal capacity and narrow niche breadth not only from local assemblages but also from regional species pools. </span></p> <p><span>These results suggest that global conservation efforts neglecting large-scale patterns in land use risk falling short of their goals, even for taxa typically assumed to persist in small natural areas (e.g., invertebrates). Our study also confirms that consideration of spatial scales will be crucial to implementing effective conservation actions in the Post-2020 Global Biodiversity Framework. In this context, applications of the proposed analytical framework have broad potential to identify which mechanisms underlie biodiversity change at different spatial scales. </span></p> <p><span><em>Funding statement: </em>FR is supported by the PROBAE project "Protect butterflies across Europe through climate refugia" funded by the European Commission through Horizon 2020, Marie Skłodowska-Curie Actions (MSCA) individual fellowship, reintegration panel (Grant agreement ID: 101024579). Open Access Funding provided by Universita degli Studi di Torino within the CRUI-CARE Agreement.</span></p>

opencc-zeroJan 2023View details →
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Environmental context and herbivore traits mediate the strength of associational effects in a meta-analysis of crop diversity

<p>1. Crop diversification offers a promising solution to meet expanding global food demands while maintaining ecosystem services. Diversification strategies that employ mixed planting to reduce pest damage (e.g., intercropping), termed "associational effects" (AE) in the ecological literature, can decrease (associational resistance) or increase (associational susceptibility) herbivore abundance on a focal plant. While application of AE to agroecosystems typically reduces pest abundance, the range of outcomes varies widely.</p> <p>2. We conducted a meta-analysis using 272 estimates of insect herbivore abundance on crops neighbored by a conspecific or heterospecific from 44 studies undertaken on six continents. We focus on four agricultural crops well-represented from sites across the globe to test hypotheses related to understanding how herbivore traits (diet breadth, feeding guild, origin), plant traits (crop type, phylogenetic distance to neighbor), and environmental context (climate, experimental design) contribute to variation in the outcomes of associational effects.</p> <p>3. Overall, bicultures provided a strong reduction of insect abundance on the focal crop. Climate and interactions between herbivore traits, particularly diet breadth and origin, and plant traits or environmental context mediated the strength of associational effects.</p> <p>4. Bicultures provided the strongest reductions in insect abundance at low latitudes and this effect decreased at higher latitudes, but only for insects with certain traits. Abundance of generalist herbivores and globally distributed pests tended to be most strongly negatively affected by bicultures, under certain contexts, whereas specialist herbivores and native pests were less affected by neighbors.</p> <p>5. Synthesis and application. This meta-analysis highlights that variability in AE is determined in part by the interactive effects of herbivore traits and environmental context and provides guidance for incorporating beneficial ecological interactions into integrated pest management strategies.</p>

opencc-zeroFeb 2023View details →
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The trait-mediated trade-off between growth and survival depends on tree sizes and environmental conditions

<p><span>Interspecific relationships between growth and survival are critical determinants of tree species diversity maintenance in forests. The trade-offs between growth and survival in co-occurring tree species are believed to arise along a continuum of life-history strategies. For example, co-occurring species range from those that grow slowly and survive well in resource-poor environments to those that grow quickly but have low survival rates in resource-rich environments. However, uncertainties remain regarding how growth-survival trade-offs are related to species traits, tree sizes, or environmental conditions.</span></p> <p><span>We examined how the relationships between species traits and growth–survival relationships shift in response to changes in stem sizes and across census periods with different climate conditions (frequency of strong winds, drought intensity) across 45 co-occurring tree species based on 23 years of growth and survival records in a warm temperate rain forest on Yakushima Island, Japan. We developed hierarchical Bayesian models of relative growth and survival rates, including leaf traits, wood density, and 95-percentile maximum stem diameter as explanatory variables. We tested the relationships between estimated trait-mediated growth–survival relationships and the intensities of climate events during five census periods.</span></p> <p><span>Each trait's effects on growth–survival relationships differed across the five census periods in response to climate conditions. Interspecific growth–survival relationships affected by a single trait axis for leaves or wood tended to be negative. In contrast, those affected by the maximum stem diameter tended to be positive. Such trends </span><span>increased with more frequent </span><span>strong winds or more intense droughts. The single-trait effects on growth–survival relationships were stronger for smaller sizes than for larger sizes. For all traits combined, we found a significant growth–survival trade-off only for small-sized stems in three of five census periods.</span></p> <p><span>Synthesis: Our results indicate that the effect of species traits on the growth–survival relationships depended on tree sizes, the census periods, or both in response to the frequency or intensity of climate events. We argue the importance of incorporating spatial and temporal variations in environmental conditions into long-term data from tree census to predict forest dynamics.</span></p>

opencc-zeroMay 2023View details →
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Facilitation strength across environmental and beneficiary trait gradients in stream communities

<p>Ecosystem engineers modify habitats in ways that facilitate other community members by ameliorating harsh conditions. The strength of such facilitation is predicted to be influenced by both beneficiary traits and abiotic context. One key trait of animals that could control the strength of facilitation is beneficiary body size because it should determine how beneficiaries fit within and exploit stress ameliorating habitat modifications. However, few studies have measured how beneficiary body size relates to facilitation strength along environmental gradients. We examined how the strength of facilitation by net-spinning caddisflies on invertebrate communities in streams varied along an elevation gradient and based on traits of the invertebrate beneficiaries. We measured whether use of silk retreats as habitat concentrated invertebrate density and biomass compared to surrounding rock-surface habitat and whether the use of retreat habitat varied across body sizes of community members along the gradient. We found that retreats substantially concentrated the densities of a diversity of taxa including eight different Orders, and this effect was greatest at high elevations. Caddisfly retreats also concentrated invertebrate biomass more as elevation increased. Body size of invertebrates inhabiting retreats was lower than that of surrounding rock habitats at low elevation sites, however body size between retreats and rocks converged at higher elevation sites. Additionally, the body size of invertebrates found in retreats varied within and across taxa. Specifically, caddisfly retreats functioned as a potential nursery for taxa with large maximal body sizes. However, the patterns of this taxa-specific nursery effect were not influenced by elevation unlike the patterns observed based on community level body size.Collectively, our results indicate that invertebrates use retreats in earlier life stages or when they are smaller in body size independent of life stage. Furthermore, our analysis suggests that facilitation strength intensifies as elevation increases within stream invertebrate communities. Further consideration of how trait variation and environmental gradients interact to determine the strength and direction of biotic interactions will be important as species ranges and environmental conditions continue to shift. </p>

opencc-zeroJun 2023View details →
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Data from: Environmental modulation of plant mycorrhizal traits in the global flora

<p>Mycorrhizal symbioses are known to strongly influence plant performance, structure plant communities and shape ecosystem dynamics. Plant mycorrhizal traits, such as those characterizing mycorrhizal type (arbuscular (AM), ecto-, ericoid, or orchid mycorrhiza) and status (obligately (OM), facultatively (FM), or non-mycorrhizal) offer valuable insight into plant belowground functionality. Here, we compile available plant mycorrhizal trait information and global occurrence data (~100 million records) for 11,770 vascular plant species. Using a plant phylogenetic mega-tree and high-resolution climatic and edaphic data layers, we assess phylogenetic and environmental correlates of plant mycorrhizal traits. We find that plant mycorrhizal type is more phylogenetically conserved than plant mycorrhizal status, while environmental variables (both climatic and edaphic; notably soil texture) explain more variation in mycorrhizal status, especially FM. The previously underestimated role of environmental conditions has far-reaching implications for our understanding of ecosystem functioning under changing climatic and soil conditions. </p>

opencc-zeroDec 2022View details →

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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.

allen-brain-atlas
neuroscienceopenDocumentation, web resources, and API references are available online.
Last verified 2026-04-30Open record

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.

abode-home-cage
behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.
Last verified 2026-04-30Open record

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.

dandi-nwb
electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.
Last verified 2026-04-30Open record

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.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
Last verified 2026-04-29Open record

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.

openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record

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