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Historical Sea Surface Temperature (SST) data and thermal stress indices of the Tara Pacific Expedition's coral reef sampling sites, from May 1st 2002 to August 31st 2018.
<p>The Tara Pacific expedition (2016-2018) sampled coral ecosystems at 111 sampling sites around 32 islands in the Pacific Ocean, and sampled the surface of oceanic waters at 249 locations, resulting in the collection of nearly 58,000 samples (Gorsky et al. 2019, Planes et al. 2019, Flores et al. 2020). The expedition was designed to systematically study corals, fish, plankton, and seawater, and included the collection of samples for advanced biogeochemical, molecular, and imaging analysis.</p> <p>Here we provide a high-resolution historical dataset that spans from 2002 to each sites’ sampling date and gives an overview of past climate variability and heatwaves experienced by corals sampled at each site. Ocean skin temperature (11 and 12 µm spectral bands longwave algorithm) was extracted from 1km resolution level-2 MODIS-Aqua and MODIS-Terra from 2002 to the sampling date and from level-2 VIIRS-SNPP from 2012 to the sampling date. Day and night overpasses were used to maximize data recovery. Following recommendations from NASA Ocean Color (OB.DAAC), only SST products of quality 0 and 1 were used. The 9 closest pixels to the sampling sites of each scene were extracted. All the extracted pixels from the 3 satellites were then averaged daily to obtain daily SST averages and standard deviations time series for each sampling site, from 2002 to the sampling date.</p> <p>Each time series was first averaged on a Julian day basis to provide a seasonal average. This yearly seasonal average was triplicated and concatenated into a 3-year seasonal cycle to apply a digital low pass filter on the middle year without generating artifacts. A digital low pass filter (filter order 3, pass band ripple 0.1; “filfilt” function in matlab) with 36 Julian days windows was applied to the concatenated time series to remove high frequency noise. The middle year was then extracted from the concatenated time series to recover the seasonal cycle. The sea surface temperature anomaly was calculated as the SST minus the seasonal cycle over the full time series. Considering the short periods of missing data (mean of the 95th percentile of the duration of consecutive days with missing data: 9.8 ± 4.1 days), the missing values in the SST and SST anomaly time series were linearly interpolated in order to calculate thermal stress indices. The SST anomaly frequency was calculated as the number of days over the past 52 weeks when the SST anomaly is greater than or equal to 1 °C. Thermal stress indices relevant to coral reef health were then calculated using methodology developed for the Coral Reef Temperature Anomaly Database (CoRTAD) data base (Saha et al. 2019). Events of cold temperature accumulation were also reported to cause bleaching and mortality (Lirman et al. 2011; González-Espinosa & Donner 2020), therefore, the same set of indices were calculated for cold stress adapting the CoRTAD method, but using the minimum weekly climatologies.</p> <p>A condensed table containing single values associated with each sampling site was created ('TaraPacific_SST_timeseries_mean_products') extracting the minimum, maximum, sum, averages, standard deviations, and value recorded at the sampling day of each of these indices (detailed in the readme file provided with the dataset 'README_TaraPacific_historical_SST.md'). Additional metrics of the last heating and cooling events as well as the time of recovery is also provided to represent the state of thermal stress at the day of sampling.</p>
Deleterious effects of thermal and water stresses on life history and physiology: a case study on woodlouse
<p>Datasets and R source code of the article Depeux C, Branger A, Moulignier T, Moreau J, Lemaître J-F, Dechaume-Moncharmont F-X, Laverre T, Paulhac H, Gaillard J-M, Beltran-Bech S (2023) Deleterious effects of thermal and water stresses on life history and physiology: a case study on woodlouse. <strong><em>Peer Community Journal</em></strong> 3:e7 http://dx.doi.org/<a href="https://doi.org/10.24072/pcjournal.228">10.24072/pcjournal.228</a></p> <p>This article previously appeared as preprint Depeux C, Branger A, Moulignier T, Moreau J, Lemaître J-F, Dechaume-Moncharmont F-X, Laverre T, Pauhlac H, Gaillard J-M, Beltran-Bech S (2022) Deleterious effects of thermal and water stresses on life history and physiology: a case study on woodlouse. <em><strong>bioRxiv</strong>, 2022.09.26.509512 </em> https://doi.org/10.1101/2022.09.26.509512</p> <p><em>Peer-reviewed and recommended by <strong>Peer Community in Ecology</strong>: </em> Belsare A (2022) An experimental approach for understanding how terrestrial isopods respond to environmental stressors. <em>Peer Community in Ecology, 100506. </em><a href="https://doi.org/10.24072/pci.ecology.100506"><strong>https://doi.org/10.24072/pci.ecology.100506</strong></a></p>
Daily time series of 12 human thermal stress indices in Greece aggregated at commune level (1998-2022)
<p>The overview table of the dataset containing 12<strong> </strong>Human Thermal Stress Indices in Greece (<strong>HTSI-GR</strong>):</p> <div> <table> <tbody> <tr> <th> <p>Heat indices names</p> </th> <th> <p>Description</p> </th> <th> <p>Units</p> </th> <th> <p>Reference</p> </th> <th> <p>Dataset file names</p> </th> </tr> <tr> <td> <p><strong>AT</strong></p> </td> <td> <p>Apparent Temperature</p> </td> <td> <p>°C</p> </td> <td> <p>Steadman, R. G. Norms of apparent temperature in Australia. Aust. Met. Mag. 43, 1–16 (1994).</p> </td> <td> <p>AT_min_1998-01-01_2022-12-31.csv, AT_mean_1998-01-01_2022-12-31.csv, AT_max_1998-01-01_2022-12-31.csv</p> </td> </tr> <tr> <td> <p><strong>HI</strong></p> </td> <td> <p>Heat Index</p> </td> <td> <p>°C</p> </td> <td> <p>Rothfusz, L.P. Te heat index equation. National Weather Service Technical Attachment. Report No. SR 90–23 (1990).</p> </td> <td> <p>HI_min_1998-01-01_2022-12-31.csv, HI_mean_1998-01-01_2022-12-31.csv, HI_max_1998-01-01_2022-12-31.csv</p> </td> </tr> <tr> <td> <p><strong>Humidex</strong></p> </td> <td> <p>Humidity Index</p> </td> <td> <p>°C</p> </td> <td> <p>Masterson, J. & Richardson, F.A. Humidex: a method of quantifying human discomfort due to excessive heat and humidity (Environment Canada, 1979).</p> </td> <td> <p>Humidex_min_1998-01-01_2022-12-31.csv, Humidex_mean_1998-01-01_2022-12-31.csv, Humidex_max_1998-01-01_2022-12-31.csv</p> </td> </tr> <tr> <td> <p><strong>NET</strong></p> </td> <td> <p>Normal Effective Temperature</p> </td> <td> <p>°C</p> </td> <td> <p>Landsberg HE. The assessment of human bioclimate: a limited review of physical parameters. Technical Note No. 123, WMO-No. 331 (World Meteorological Organization, 1972).</p> </td> <td> <p>NET_min_1998-01-01_2022-12-31.csv, NET_mean_1998-01-01_2022-12-31.csv, NET_max_1998-01-01_2022-12-31.csv</p> </td> </tr> <tr> <td> <p><strong>WBGT</strong></p> </td> <td> <p>Wet Bulb Globe Temperature (simple)</p> </td> <td> <p>°C</p> </td> <td> <p>Australian Bureau of Meteorology. Thermal comfort observations http://bom.gov.au/info/thermal_stress/ (2020).</p> </td> <td> <p>WBGT_min_1998-01-01_2022-12-31.csv, WBGT_mean_1998-01-01_2022-12-31.csv, WBGT_max_1998-01-01_2022-12-31.csv</p> </td> </tr> <tr> <td> <p><strong>thermofeelWBGT</strong></p> </td> <td> <p>Wet Bulb Globe Temperature</p> </td> <td> <p>°C</p> </td> <td> <p>Stull, R. Wet-bulb temperature from relative humidity and air temperature. J. Appl. Meteorol. Climatol. 50, 2267–2269 (2011).</p> </td> <td> <p>thermofeelWBGT_min_1998-01-01_2022-12-31.csv, thermofeelWBGT_mean_1998-01-01_2022-12-31.csv, thermofeelWBGT_max_1998-01-01_2022-12-31.csv</p> </td> </tr> <tr> <td> <p><strong>WBT</strong></p> </td> <td> <p>Wet Bulb Temperature</p> </td> <td> <p>°C</p> </td> <td> <p>Stull, R. Wet-bulb temperature from relative humidity and air temperature. J. Appl. Meteorol. Climatol. 50, 2267–2269 (2011).</p> </td> <td> <p>WBT_min_1998-01-01_2022-12-31.csv, WBT_mean_1998-01-01_2022-12-31.csv, WBT_max_1998-01-01_2022-12-31.csv</p> </td> </tr> <tr> <td> <p><strong>WCT</strong></p> </td> <td> <p>Wind Chill Temperature</p> </td> <td> <p>°C</p> </td> <td> <p>Office of the Federal Coordinator for Meteorological services and supporting research (OFCM). Report on Wind Chill Temperature and extreme heat indices: evaluation and improvement projects. Report No. FCM-R19-2003 (U.S. Office of the Federal Coordinator for Meteorological Services and Supporting Research, 2003).</p> </td> <td> <p>WCT_min_1998-01-01_2022-12-31.csv, WCT_mean_1998-01-01_2022-12-31.csv, WCT_max_1998-01-01_2022-12-31.csv</p> </td> </tr> <tr> <td> <p><strong>MRT</strong></p> </td> <td> <p>Mean Radiant Temperature</p> </td> <td> <p>°C</p> </td> <td> <p>Weihs, P. et al. The uncertainty of UTCI due to uncertainties in the determination of radiation fluxes derived from measured and observed meteorological data. Int. J. Biometeorol. 56, 537–555 (2012).</p> </td> <td> <p>MRT_min_1998-01-01_2022-12-31.csv, MRT_mean_1998-01-01_2022-12-31.csv, MRT_max_1998-01-01_2022-12-31.csv</p> </td> </tr> <tr> <td> <p><strong>UTCI</strong></p> </td> <td> <p>Universal Thermal Climate Index (UTCI)</p> </td> <td> <p>°C</p> </td> <td> <p>Bröde, P. et al. Deriving the operational procedure for the Universal Thermal Climate Index (UTCI). Int. J. Biometeorol. 56, 481–494<br>(2012).</p> </td> <td> <p>UTCI_min_1998-01-01_2022-12-31.csv, UTCI_mean_1998-01-01_2022-12-31.csv, UTCI_max_1998-01-01_2022-12-31.csv</p> </td> </tr> <tr> <td> <p><strong>UTCI2</strong></p> </td> <td> <p>Indoor environment UTCI with 2 parameters (air temperature and humidity)</p> </td> <td> <p>°C</p> </td> <td> <p>Bröde, P. et al. Deriving the operational procedure for the Universal Thermal Climate Index (UTCI). Int. J. Biometeorol. 56, 481–494<br>(2012).</p> </td> <td> <p>UTCI2_min_1998-01-01_2022-12-31.csv, UTCI2_mean_1998-01-01_2022-12-31.csv, UTCI2_max_1998-01-01_2022-12-31.csv</p> </td> </tr> <tr> <td> <p><strong>UTCI3</strong></p> </td> <td> <p>Outdoor shaded space environment UTCI with 3 parameters (air temperature, humidity, and wind speed)</p> </td> <td> <p>°C</p> </td> <td> <p>Bröde, P. et al. Deriving the operational procedure for the Universal Thermal Climate Index (UTCI). Int. J. Biometeorol. 56, 481–494<br>(2012).</p> </td> <td> <p>UTCI3_min_1998-01-01_2022-12-31.csv, UTCI3_mean_1998-01-01_2022-12-31.csv, UTCI3_max_1998-01-01_2022-12-31.csv</p> </td> </tr> </tbody> </table> </div> <p> </p> <p>The overview table of the <strong>HTSI-GR</strong> additional resources folder containing support files and instructions for dataset replication:</p> <table> <tbody> <tr> <th> <p><strong>File names</strong></p> </th> <th> <p>Description</p> </th> </tr> <tr> <td> <p><strong>0. Calculate thermofeelWBGT.py</strong></p> </td> <td> <p>A python script that calculates the Wet Bulb Globe Temperature (WBGT) using the Thermofeel library. Processes NetCDF files containing daily meteorological data and outputs WBGT values in new NetCDF files for each day.</p> </td> </tr> <tr> <td> <p><strong>1. Merge_HI_by_max-mean-min.py</strong></p> </td> <td> <p>A python script that merges daily NetCDF files containing heat index (HI) data into three separate files based on mean, maximum and minimum values for further processing.</p> </td> </tr> <tr> <td> <p><strong>2. QGIS_zonal_statistics.py</strong></p> </td> <td> <p>A python script that calculates zonal statistics for heat indices using QGIS python console. Uses a shapefile of Greek communes and a raster NetCDF file containing daily index values, and outputs daily CSV files with computed statistics.</p> </td> </tr> <tr> <td> <p><strong>3. Zonal_format.py</strong></p> </td> <td> <p>A python script that formats the zonal statistics results into a comprehensive dataset. Combines daily CSV files into a single CSV, fills in missing data using nearest neighbour values, and produces a final formatted dataset.</p> </td> </tr> <tr> <td> <p><strong>Greek Communes.ZIP</strong></p> </td> <td> <p>Contains the shapefile of Greek communes derived from the Hellenic Statistical Authority (ELSTAT) required for zonal statistics calculations. KALCODE and Commune names are linked in the .shp.</p> </td> </tr> <tr> <td> <p><strong>Nearest Neighbour data table.csv</strong></p> </td> <td> <p>A support table to script <strong>3.Zonal</strong><strong>_format.py</strong> that lists communes with missing data and their nearest neighbour with data.</p> </td> </tr> <tr> <td> <p><strong>Read me.txt</strong></p> </td> <td> <p>Provides an overview and instructions for using the scripts. Describes the purpose of each script, lists prerequisites, and provides step-by-step instructions for replicating the dataset.</p> </td> </tr> </tbody> </table> <p> </p> <p> </p>
Biogeographic parallels in thermal tolerance and gene expression variation under temperature stress in a widespread bumble bee
<p>Global temperature changes have emphasized the need to understand how species adapt to thermal stress across their ranges. Genetic mechanisms may contribute to variation in thermal tolerance, providing evidence for how organisms adapt to local environments. We determine physiological thermal limits and characterize genome-wide transcriptional changes at these limits in bumble bees using laboratory-reared <em>Bombus vosnesenskii</em> workers. We analyze bees reared from latitudinal (35.7–45.7°N) and altitudinal (7–2154 m) extremes of the species' range to correlate thermal tolerance and gene expression among populations from different climates. We find that critical thermal minima (CT<sub>MIN</sub>) exhibit strong associations with local minimums at the location of queen origin, while critical thermal maximum (CT<sub>MAX</sub>) was invariant among populations. Concordant patterns are apparent in gene expression data, with regional differentiation following cold exposure, and expression shifts invariant among populations under high temperatures. Furthermore, we identify several modules of co-expressed genes that tightly correlate with critical thermal limits and temperature at the region of origin. Our results reveal that local adaptation in thermal limits and gene expression may facilitate cold tolerance across a species range, whereas high temperature responses are likely constrained, both of which may have implications for climate change responses of bumble bees.</p>
Data from: Stingless bee foragers experience more thermally stressful microclimates but have wider thermal tolerance breadths than other worker subcastes
<p>The current state of anthropogenic climate change is of particular concern for insects, especially in the tropics where the effects are predicted to be the most deleterious. Researching climatic tolerance in social insects is challenging because adaptations can exist at both an individual level and a societal level. However, these studies are important because social insects comprise a tremendous portion of the planet's animal biomass, biodiversity, and include many important pollinators. Considering how individual physiologies construct group-level adaptations can improve the accuracy of climate change impact assessments for a variety of social species. <em>Tetragonisca angustula</em> is a neotropical stingless bee species known to exhibit particularly high worker subcaste specialization in the form of a morphologically distinct soldier caste, a trait most commonly found and studied in ants and termites. We used this model species to investigate 1) whether age- and size-differentiated task groups differ in thermal tolerance, 2) which worker subcastes operate closest to their thermal limits, and 3) the extent to which behavioral thermoregulation via shifting active foraging times can offset thermal stress in this species. We measured the thermal tolerance (CT<sub>max</sub> and CT<sub>min</sub>) of smaller-bodied foragers, and two soldier sub-castes (hovering guards and standing guards) in <em>T. angustula</em>. Despite the difference in body size between the foragers and guards, no differences in the upper or lower thermal limits were observed. However, the average thermal tolerance breadth of foragers was significantly larger than that of guards, indicating that soldiers at the nest entrance are more thermally specialized than foragers. Temperatures at foraging sites were more variable than at nest entrances, which caused warming tolerance to be significantly lower among small-bodied foragers as compared to either hovering guards or standing guards. The magnitude of warming tolerances indicated a low risk of imminent climate change impacts in this environment, but our results suggest that as temperatures increase, foragers are likely to meet their upper thermal limits before other worker subcastes. Foragers may shift the times they are active as a form of thermoregulation which could selectively impact pollination rates for plants leading to repercussions on agriculture and ecosystem functioning. This work establishes novel approaches to predicting climatic change risk in heterogeneous cooperative societies.</p>
Data from: Corals that survive repeated thermal stress show signs of selection and acclimatization
<p>Climate change is transforming coral reefs by increasing the frequency and intensity of marine heatwaves, often leading to coral bleaching and mortality. Coral communities have demonstrated modest increases in thermal tolerance following repeated exposure to moderate heat stress, but it is unclear whether these shifts represent acclimatization of individual colonies or mortality of thermally susceptible individuals. For corals that survive repeated bleaching events, it is important to understand how past bleaching responses impact future growth potential. Here, we track the bleaching responses of 1,832 corals in leeward Maui through multiple marine heatwaves and document patterns of coral growth and survivorship over a seven-year period. While we find limited evidence of acclimatization at population scales, we document reduced bleaching over time in specific individuals, primarily in the stress-tolerant taxa <em>Porites lobata</em>, indicative of acclimatization. For corals that survived both bleaching events, we find no relationship between bleaching response and coral growth in three of four taxa studied. This decoupling between bleaching and growth suggests that coral survivorship is a better indicator of future growth than is a coral's bleaching history. Based on these results, we recommend restoration practitioners in Hawaiʻi obtain outplants from <em>Porites</em> and <em>Montipora</em> colonies with a proven track-record of growth and survivorship, rather than devote resources toward identifying and cultivating bleaching-resistant phenotypes. Survivorship followed a latitudinal thermal stress gradient, but because this gradient was small, it is likely that local environmental factors also drove differences in coral performance between sites. Efforts to reduce human impacts at low performing sites would likely improve coral survivorship in the future.</p>
Fig. 1 in The Influence Of Thermal Preadaptation On Some Oxidative Processes In The First Leaves Of Wheat Seedlings (Triticum Aestivum L.) Under Heat Stress
Fig. 1. The rate of superoxide (O2˙ˉ) production (µmol/h) in the etiolated first leaves at the early (from 4th to 5th days) and late (from 7th to 8th days) stages of seedlings development.
Fig. 2 in The Influence Of Thermal Preadaptation On Some Oxidative Processes In The First Leaves Of Wheat Seedlings (Triticum Aestivum L.) Under Heat Stress
Fig. 2. Catalase activity in the etiolated first leaves at the early (from 4th to 5th days) and late (from 7th to 8th days) stages of seedling development.
Fig. 4 in The Influence Of Thermal Preadaptation On Some Oxidative Processes In The First Leaves Of Wheat Seedlings (Triticum Aestivum L.) Under Heat Stress
Fig. 4. Catalase activity in the etiolated first leaves at the (A) early (from 4th to 5th days) and (B) late (from 7th to 8th days) stages of seedling development (C ‒ control 26oC; E ‒ 26oC → 32oC; E1 ‒ 32o→42oC; E2 ‒ 26oC → 42oC).
Fig. 3 in The Influence Of Thermal Preadaptation On Some Oxidative Processes In The First Leaves Of Wheat Seedlings (Triticum Aestivum L.) Under Heat Stress
Fig. 3. The rate of superoxide (O2˙ˉ) production (µmol/h) in the etiolated first leaves at the (A) early (from 4th to 5th days) and (B) late (from 7th to 8th days) stages of seedlings development (C ‒ control 26oC; E ‒ 26oC → 32oC; E1 ‒ 32o→42oC; E2 ‒ 26oC → 42oC).
The effects of environmental history and thermal stress on coral physiology and immunity
<p>This dataset has all data for the manuscript (Wall CB, CA Ricci, GE Foulds, LD Mydlarz, RD Gates, HM Putnam (2018) The effects of environmental history and thermal stress on coral physiology and immunity. <em>Marine Biology</em>). Data included a zipped archive shape file for creating Kāne'ohe Bay map, physical data (light and temperature) from Kāne'ohe Bay and laboratory experiments, pCO<sub>2</sub> data for Kāne'ohe Bay reef sites dowloaded from NOAA PMEL, and biological responses (PAM fluorometry, physiology, immune activity and oxidative profile). </p>
Data from: Stingless bee foragers experience more thermally stressful microclimates and have wider thermal tolerance breadths than other worker subcastes
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Differences in gene expression between high and low tolerance rainbow trout (Oncorhynchus mykiss) to acute thermal stress
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Data from: Corals that survive repeated thermal stress show signs of selection and acclimatization
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Biogeographic parallels in thermal tolerance and gene expression variation under temperature stress in a widespread bumble bee
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Data from: Incorporating local information to predict thermal stress for diverse species
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Data and coding files for: Within population plastic responses to combined thermal-nutritional stress differ from those in response to single stressors, and are genetically independent across traits in both males and females
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Data from: Does local adaptation along a latitudinal cline shape plastic responses to combined thermal and nutritional stress?
<p>Thermal and nutritional stress are commonly experienced by animals. This will become increasingly so with climate change. Whether populations can plastically respond to such changes will determine their survival. Plasticity can vary among populations depending on the extent of environmental heterogeneity. However, theory conflicts as to whether environmental heterogeneity should increase or decrease plasticity. Using three locally-adapted populations of Drosophila melanogaster sampled from a latitudinal gradient, we investigated whether plastic responses to combinations of nutrition and temperature increase or decrease with latitude for four traits: egg-adult viability, egg-adult development time, and two body size traits. Employing nutritional geometry, we reared larvae on 25 diets varying in protein and carbohydrate content at two temperatures: 18ºC and 25ºC. Plasticity varied among traits and across the three populations. Viability was highly canalized in all three populations. The tropical population showed the least plasticity for development time, the sub-tropical showed the highest plasticity for wing area, and the temperate population showed the highest plasticity for femur length. We found no evidence of latitudinal plasticity gradients in either direction. Our data highlight that differences in thermal variation and resource predictability experienced by populations along a latitudinal cline are not sufficient to predict their plasticity. </p>
Quantifying thermal exposure for migratory riverine species: phenology of Chinook salmon populations predicts thermal stress
<p>Migratory species are particularly vulnerable to climate change because habitat throughout their entire migration cycle must be suitable for the species to persist. For migratory species in rivers, predicting climate change impacts is especially difficult because there is a lack of spatially-continuous and seasonally-varying stream temperature data, habitat conditions can vary for an individual throughout its life cycle, and vulnerability can vary by life stage and season. To predict thermal impacts on migratory riverine populations, we first expanded a spatial stream network model to predict mean monthly temperature for 465,775 river km in the western U.S., and then applied simple yet plausible future stream-temperature change scenarios. We then joined stream temperature predictions to 44,396 spatial observations and life stage-specific phenology (timing) for 26 ecotypes (i.e. geographically distinct population groups expressing one of four distinct seasonal migration patterns) of Chinook salmon (<i>Oncorhynchus tshawytscha</i>), a phenotypically diverse anadromous salmonid that is ecologically and economically important but declining throughout its range. Thermal stress, assessed for each life stage and ecotype based on federal criteria, was influenced by migration timing rather than latitude, elevation, or migration distance, such that sympatric ecotypes often showed differential thermal exposure. Early-migration phenotypes were especially vulnerable due to prolonged residency in inland streams during the summer. We evaluated the thermal suitability of 31,699 stream km which are currently blocked by dams to explore reintroduction above dams as an option to mitigate the negative effects of our warmer stream temperature scenarios. Our results showed that negative impacts of stream temperature warming can be offset for almost all ecotypes if formerly occupied habitat above dams is made available. Our approach of combining spatial distribution and phenology data with spatially- and temporally-explicit temperature predictions enables researchers to examine thermal exposure of migrating populations that use seasonally-varying habitats.</p>
Data from: Haemoglobin-mediated response to hyper-thermal stress in the keystone species Daphnia magna
Anthropogenic global warming has become a major geological and environmental force driving drastic changes in natural ecosystems. Due to the high thermal conductivity of water and the effects of temperature on metabolic processes, freshwater ecosystems are among the most impacted by these changes. The ability to tolerate changes in temperature may determine species long-term survival and fitness. Therefore, it is critical to identify coping mechanisms to thermal and hyper-thermal stress in aquatic organisms. A central regulatory element compensating for changes in oxygen supply and ambient temperature is the respiratory protein haemoglobin (Hb). Here, we quantify haemoglobin (Hb) plastic and evolutionary response in D. magna (sub)populations resurrected from the sedimentary archive of a lake with known history of increase in average temperature and recurrence of heat waves. By measuring constitutive changes in crude Hb protein content among (sub)populations we assessed evolution of the haemoglobin gene family in response to temperature increase. To quantify the contribution of plasticity in the response of this gene family to hyper-thermal stress, we quantified changes in Hb content in all (sub)populations under hyper-thermal stress as compared to non-stressful temperature. Further, we tested competitive abilities of genotypes as a function of their Hb content, constitutive and induced. We found that haemoglobin (Hb)-rich genotypes have superior competitive abilities as compared to Hb-poor genotypes under hyper-thermal stress after a period of acclimation. These findings suggest that whereas long-term adjustment to higher occurrence of heat waves may require a combination of plasticity and genetic adaptation, plasticity is most likely the coping mechanism to hyper-thermal stress in the short term. Our study suggests that with higher occurrence of heat waves Hb-rich genotypes may be favoured with potential long-term impact on population genetic diversity
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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.