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1,478 results for “coral reefs”
Despite plasticity, heatwaves are costly for a coral reef fish
<p>Climate change is intensifying extreme weather events, including marine heatwaves, which are prolonged periods of anomalously high sea surface temperature that pose a novel threat to aquatic animals. Tropical animals may be especially vulnerable to marine heatwaves because they are adapted to a narrow temperature range. If these animals cannot acclimate to marine heat waves, the extreme heat could impair their behavior and fitness. Here, we investigated how marine heatwave conditions affected the performance and thermal tolerance of a tropical predatory fish, arceye hawkfish (<em>Paracirrhites arcatu</em>s), across two seasons in Mo'orea, French Polynesia. We found that the fish's daily activities, including recovery from burst swimming and digestion, were more energetically costly in fish exposed to marine heatwave conditions across both seasons, while their aerobic capacity remained the same. Given their constrained energy budget, these rising costs associated with warming may impact how hawkfish prioritize activities. Additionally, hawkfish that were exposed to hotter temperatures exhibited cardiac plasticity by increasing their maximum heart rate but were still operating within a few degrees of their thermal limits. With more frequent and intense heatwaves, hawkfish, and other tropical fishes must rapidly acclimate, or they may suffer physiological consequences that alter their role in the ecosystem. <strong><br></strong></p>
Coral reef carbonate production simulations with iLOVECLIM-iCORAL
<p><strong>ECS :</strong></p> <p>File ECS_iLOVECLIM.txt contains alpha and ECS (°C).</p> <p> </p> <p><strong>Global carbonate production evolution:</strong></p> <p>Files Coral_production_aX_sspY_refbleachZ.nc contain coral carbonate production (<span>Pg CaCO<sub>3</sub> yr<sup>-1</sup>)</span> for the different simulations:</p> <p>X=alpha value corresponding to the ECS</p> <p>Y=SSP scenario</p> <p>Z=fixed: no adaptation for bleaching</p> <p>Z=var: adaptation to bleaching</p> <p> </p> <p><strong>2D coral distribution:</strong></p> <p>File Coral_accretion2D_coral_1850_a1.5.nc contains the coral reef location for the Pre-Industrial with:</p> <p>0= no coral reef</p> <p>1= coral reef</p> <p> </p> <p>Files Coral_accretation2D_aX_SSPY_refbleach_Z_YEAR.nc with YEAR= year of simulation</p> <p>contain the coral reef locations with:</p> <p>3= accreting coral reefs</p> <p>2=re-accreting coral reefs</p> <p>1=non-accreting coral reefs</p> <p> </p> <p> </p> <p> </p> <p> </p>
Assessing key ecosystem functions through soundscapes: a new perspective from coral reefs (Acoustic dataset)
<p>Acoustic dataset associated to "Assessing key ecosystem functions through soundscapes: a new perspective from coral reefs" - article published in Ecological Indicators (2019) <a href="https://doi.org/10.1016/j.ecolind.2019.105623">https://doi.org/10.1016/j.ecolind.2019.105623</a></p> <p>All details about sampling are available in the Material and Methods section.</p> <p>Sound sample names are coded as follows : SITE_acq_hydro_100k_DDMMYY_HHMMSS.wav ; with HHMMSS in Local Time (UTC+3)</p>
Video of mobbing behavior by coral-reef fishes
<p>Video of mobbing behavior by coral-reef fishes from article in CORAL, "Trophic Mobbing in Fishes". </p> <p>1. A school of Convict Tang, <em>Acanthurus triostegus </em>mobbing the territory of the Lavendar Tang, <em>Acanthurus nigrofuscus </em>at Pupukea, Oahu (R.K. Whitton)</p> <p>2. A group of Millet-seed butterfly fish, <em>Chaetodon miliaris</em>, feeding on the eggs of<em> Abudefduf abdominalis</em> - Kahe Point, Oahu (J.L. Earle)</p> <p>3. The Reticulated Butterflyfish <em>Chaetodon reticulatus</em> on the outer reef of Avatoru Pass of Rangiroa Atoll, Tuamotu Archipelago foraging on colonies of <em>Pocillopora meandrina</em> in a loose aggregation which became a tight cluster when an area defended by <em>Plectroglyphidodon johnstonianus</em> was encountered (J.L. Earle).</p> <p>4. The Ornate Butterflyfish, <em>Chaetodon ornatissimus</em>, mobbing the territory of the Blackbar Devil Damselfish, <em>Plectroglyphidodon dickii </em>at Kiritimati, Line Islands (J.L. Earle and R.K. Whitton).</p>
Data for: Bivalve δ15N isoscapes provide a baseline for urban nitrogen footprint at the edge of a World Heritage coral reef
<p>This dataframe presents the d<sup>15</sup>N signature of 348 long-lived benthic bivalves from 12 species. Individuals were trapped at 27 sites in 2012 around the Peninsula of Nouméa, New Caledonia. For each bivalve specimen, muscle tissues were dissected and stored frozen. Muscles were freeze-dried, ground into powder, and weighed in tin cups for isotopic analysis (about 1mg in 4 × 6 mm tin cups). Muscle samples were analyzed using a Thermo Delta Advantage mass spectrometer in continuous flow mode connected to a Costech Elemental Analyzer via a ConFlo IV at Union College (Schenectady, NY, USA). Ammonium sulfate [IAEA-N-2], caffeine [IAEA-600], and an in-house acetanilide were used as standards; measurements of δ<sup>15</sup>N are reported to atmospheric nitrogen. The uncertainty for δ<sup>15</sup>N measurements was ±0.15‰ based on repeated analysis of an in-house acetanilide standard.</p> <p>The data base is made of 348 raws corresponding to specimens, and 5 columns presenting an ID, the collection site, the position within the laggon , the species and the measured D15N value. </p>
Figure 1 in Marine benthic diatoms in the coral reefs of Reunion and Rodrigues Islands, West Indian Ocean
Figure 1. Map showing location of Reunion and Rodrigues Islands, Indian Ocean and sampling sites.
Figure 7 in Substratum stability and coral reef resilience: insights from 90 years of disturbances on a reef in American Samoa
Figure 7. Acropora muricata colony having stability by being wedged into the loose substratum.
Figure 5 in Substratum stability and coral reef resilience: insights from 90 years of disturbances on a reef in American Samoa
Figure 5. Density of Acropora spp. along the Aua transect from 1917 to 2007.
Figure 6 in Substratum stability and coral reef resilience: insights from 90 years of disturbances on a reef in American Samoa
Figure 6. Acropora nana recruited abundantly to the solid reef crest in the late 1990s.
Figure 4 in Substratum stability and coral reef resilience: insights from 90 years of disturbances on a reef in American Samoa
Figure 4. Density of Porites cylindrica colonies along the Aua transect from 1917 to 2007.
Dataset: Coral high molecular weight carbohydrates support opportunistic microbes in bacterioplankton from an algae-dominated reef
<p>This dataset contains raw data for figures 5 (genus-level microbial community compositions) and 6 (predicted metabolic functions, pathway types), R code for PERMANOVAs (Table 3), DESeq2 and random forest (rfpermute) analyses, and R code to generate figures 5, 6b, S5 & S6.</p> <p>Overview of .txt files:</p> <table> <tbody> <tr> <td> <p>Genus_16S_Counts.txt</p> </td> <td> <p>Counts data used for DESeq2 analysis (Fig. 5c).</p> </td> </tr> <tr> <td> <p>Genus_16S_relAbund.txt</p> </td> <td> <p>Relative abundance data used for Fig. 5a, b & d.</p> </td> </tr> <tr> <td> <p>MicFunPred_MetaCyc_types_all</p> </td> <td> <p>Predicted pathway abundance data for all pathway types used for DESeq2 (Fig. 6b), PERMANOVA (Table 3) and column clustering of Fig. 6b.</p> </td> </tr> <tr> <td> <p>MicFunPred_MetaCyc_AA_types.txt</p> </td> <td> <p>Amino acids (Fig. 6b)</p> </td> </tr> <tr> <td> <p>MicFunPred_MetaCyc_CH_types.txt</p> </td> <td> <p>Carbohydrates (Fig. 6b)</p> </td> </tr> <tr> <td> <p>MicFunPred_MetaCyc_EM _types.txt</p> </td> <td> <p>Energy metabolism (Fig. 6b)</p> </td> </tr> <tr> <td> <p>MicFunPred_MetaCyc_FAL _types.txt</p> </td> <td> <p>Fatty acids and lipids (Fig. 6b)</p> </td> </tr> <tr> <td> <p>MicFunPred_MetaCyc_SM _types.txt</p> </td> <td> <p>Secondary metabolism (Fig. 6b)</p> </td> </tr> <tr> <td> <p>MicFunPred_MetaCyc_OBiosyn _types.txt</p> </td> <td> <p>Other biosynthesis (Fig. S6)</p> </td> </tr> <tr> <td> <p>MicFunPred_MetaCyc_ODeg _types.txt</p> </td> <td> <p>Other degradation (Fig. S6)</p> </td> </tr> </tbody> </table>
Data for: A snapshot of sediment dynamics on an inshore coral reef
Open the record for dataset details and reuse information.
Fig. 4 in Ninh Hai waters (south Vietnam): a hotspot of reef corals in the western South China Sea
Fig. 4. Diagram showing covers of corals at 15 study sites along Ninh Hai district.
Fig. 5 in Ninh Hai waters (south Vietnam): a hotspot of reef corals in the western South China Sea
Fig. 5. Marine zoning of the Nui Chua National Park.
Fig. 1 in Among-Genotype Variation For Sediment Rejection In The Reef-Building Coral Diploastrea Heliopora (Lamarck, 1816)
Fig. 1. Location of sampling site.
Diminishing potential for tropical reefs to function as coral diversity strongholds under climate change conditions
<p><b>Aim</b>: Forecasting the influence of climate change on coral biodiversity and reef functioning is important for informing policy decisions. Dominance shifts, tropicalisation and local extinctions are common responses of climate change, but uncertainty surrounds the reliability of predicted coral community transformations. Here, we use species distribution models (SDMs) to assess changes in suitable coral habitat and associated patterns in biodiversity across Western Australia (WA) under present-day and future climate scenarios (RCP 2.6 and RCP 8.5).</p> <p><b>Location:</b> Coral reef systems in WA.</p> <p><b>Methods:</b> We developed SDMs with model prediction uncertainty analyses, using specimen-based occurrence records of 188 hermatypic scleractinian coral species and seven variables to estimate present-day and future changes to coral species distribution and biodiversity patterns in WA under climate change conditions.</p> <p class="MsoCommentText"><b>Results: </b>We found that suitable habitat is predicted to increase across all regions in WA under RCP<sub>2.6 </sub><sup>2050</sup>, RCP<sub>8.5 </sub><sup>2050</sup> and RCP<sub>2.6</sub><sup> 2100</sup> scenarios with all tropical and subtropical regions remaining coral biodiversity strongholds. Under the extreme RCP<sub>8.5</sub><sup> 2100</sup> scenario however, a clear tropicalisation trend could be observed with coral species expanding their range to mid-higher latitude regions, while a substantial drop in coral species richness was predicted at low latitude tropical coral reefs, such as the inshore Kimberley and offshore NW reefs. Despite the predicted expansion south, we identified a net decline in biodiversity across the WA coastline.</p> <p class="MsoCommentText"><b>Main Conclusions: </b>Results from the models predicted higher net biodiversity loss at low latitude tropical regions compared to net gains at mid-high latitude regions under RCP<sub>8.5</sub><sup> 2100</sup>. These results are likely to be representative of latitudinal trends across the southern hemisphere and highlight that increases in habitat suitability at higher latitudes may not lead to equivalent biodiversity benefits. Urgent action is needed to limit climate change to prevent spatial erosion of tropical coral communities, extinction events and loss of tropical ecosystem services.</p>
Going against the flow: barriers to gene flow impact patterns of connectivity in cryptic coral reef gobies throughout the western Atlantic
<p class="CxSpFirst"><b>Aim</b>: Complex oceanographic features have historically caused difficulty in understanding gene flow in marine taxa. Here, we evaluate the impact of potential phylogeographic barriers to gene flow and assess demography and evolutionary history of a coral reef goby species complex. Specifically, we test how the Amazon River outflow and ocean currents impact gene flow.</p> <p class="CxSpMiddle"><b>Location</b>: Western Atlantic.</p> <p class="CxSpMiddle"><b>Taxon</b>: The bridled goby (<i>Coryphopterus glaucofraenum</i>) and sand-canyon goby (<i>C. venezuelae</i>) species complex.</p> <p class="CxSpMiddle"><b>Methods</b>: We used mitochondrial DNA and 2401 genomic SNPs to investigate evolutionary history and test hypotheses of how major barriers impact species-level differentiation. We used clustering algorithms and pairwise <i>F</i><sub>ST</sub> to assess population differentiation caused by minor barriers within and among regions. Finally, we tested alternate hypotheses of demographic history via coalescent simulations to determine the most plausible spread across the Western Atlantic.</p> <p class="CxSpMiddle"><b>Results</b>: We found two unique clades of <i>C. glaucofraenum</i> along the Brazilian coast and Atol das Rocas (AR) that are more closely related to <i>C. venzuelae</i>. Further genetic structure within the Caribbean and separately along the Brazilian coast led to at least two distinct populations in each location. Coalescent simulations indicated that an ancestral population of <i>C. venezuelae</i> split from <i>C. glaucofraenum</i> in the Caribbean, dispersed to Brazil, then spread to AR.</p> <p class="CxSpMiddle"><b>Main Conclusions</b>: Species-level genetic differentiation has resulted from the Amazon River outflow and isolation of AR. Population differentiation within the Caribbean matched previous studies indicating an east-west pattern of divergence. Brazilian population differentiation was impacted by the cold-water upwelling at Cabo Frio. Overall, this research highlights how barriers to gene flow impact speciation and genetic structure within western Atlantic gobies and provides insight into the role oceanographic features have in the speciation process of fishes.</p>
Data from: Integrating a UAV-derived DEM in object-based image analysis increases habitat classification accuracy on coral reefs
<p>Very shallow coral reefs (< 5 m deep) are naturally exposed to strong sea surface temperature variations, UV radiation and other stressors exacerbated by climate change, raising great concern over their future. As such, accurate and ecologically informative coral reef maps are fundamental for their management and conservation. Since traditional mapping and monitoring methods fall short in very shallow habitats, shallow reefs are increasingly mapped with Unmanned Aerial Vehicles (UAVs). UAV-imagery is commonly processed with Structure-from-Motion (SfM) to create orthomosaics and Digital Elevation Models (DEMs) spanning several hundred metres. Techniques to convert these SfM products to ecologically relevant habitat maps are still relatively underdeveloped. Here we demonstrate that incorporating geomorphometric variables (the DEM and its derivatives) in addition to spectral information (the orthomosaic) can greatly enhance the accuracy of automatic habitat classification. Therefore, we mapped three very shallow reef areas off KAUST on the Saudi Arabian Red Sea coast with an RTK-ready UAV. Imagery was processed with SfM, and classified through Object-Based Image Analysis (OBIA). Within our OBIA workflow, we observed overall accuracy increases of up to 11% when training a Random Forest classifier on both spectral and geomorphometric variables as opposed to traditional methods that only use spectral information. Our work highlights the potential of incorporating a UAV's DEM in OBIA for benthic habitat mapping, a promising but still scarcely exploited asset.</p>
Coral bleaching due to cold stress on a central Red Sea reef flat
<p>Ocean warming is leading to more frequent coral bleaching events. However, cold stress can also induce bleaching in corals. Here, we report observations of a boreal winter bleaching event in Jan 2020 in the central Red Sea, mainly within a population of the branching coral <em>Stylophora pistillata </em>on an offshore reef flat. Sea surface temperatures rarely fall below 24°C in this region, but data loggers deployed on several nearby reef flats recorded overnight seawater temperatures as low as 18°C just three days before the observations. The low temperatures coincided with an extremely low tide and cool air temperatures, likely resulting in the aerial exposure of the corals during the nighttime low tide event. The risk of aerial exposure is rare in winter months, as the Red Sea exhibits seasonal fluctuations in sea level with winter values typically 0.3-0.4m higher than in summer. These observations are notable for a region typically characterized as a high-temperature sea, and highlight the need for long-term monitoring programs as this rare event may have gone unnoticed.</p>
The oceanographic isolation of the Ogasawara Islands and genetic divergence in a reef-building coral
<p><strong><span>Aim:</span></strong><span> Due to their spatial isolation, oceanic islands are natural systems to study evolutionary divergence. The Ogasawara Islands belong to the most isolated archipelagos on Earth and are well-known for their high terrestrial endemicity, however, less is known about the marine realm. Here, we analyze the degree of oceanographic isolation of the archipelago based on genetic data of a reef-building coral and a biophysical dispersal model.</span></p> <p><strong><span>Location: </span></strong><span>North-Western Pacific (Ogasawara, Ryukyu, Daito Islands, Guam)</span></p> <p><strong><span>Taxon: </span></strong><em><span>Galaxea fascicularis </span></em><span>L.</span></p> <p><strong><span>Method:</span></strong><span> Three to 15 specimens were sampled at several sites in Ogasawara and its closest potential migration sources in southern Japan and the Mariana Islands (Guam) and RAD-sequenced. 108 specimens from the common Pacific lineages 'L' (Ryukyu- and Daito Islands, Guam) and 'Ogasawara' (Ogasawara) were analyzed with population genetics and demographic modeling<em>.</em> Oceanographic dispersal was investigated by inverse particle tracking using a Lagrangian particle advection simulation based on ROMS and applying biological dispersal parameters of <em>G. fascicularis</em>.</span></p> <p><strong><span>Results:</span></strong><span> The <em>G. fascicularis </em>population in Ogasawara is genetically highly differentiated from the next closest reefs in the region and has diverged from the Ryukyu Islands under very little, asymmetric, eastward migration. Inverse particle tracking confirmed the oceanographic isolation of Ogasawara and showed that the islands are rarely but most likely reached by settlers from the Ryukyu Islands by long-distance-dispersal of exceptionally long-lived larvae (>44 days), with no dispersal <em>vice versa</em>. </span></p> <p><strong><span>Main conclusions: </span></strong><span>Ogasawara is a dispersal sink location and t</span><span>he high degree of genetic differentiation in <em>Galaxea </em>has resulted from strong oceanographic isolation.</span> <span>This research highlights how oceanographic features impact species-level genetic differentiation even in well-dispersed taxa such as broadcast-spawning corals, and they are likely even more pronounced in less vagile organisms<em>.</em> These findings suggest the Ogasawaran Archipelago should be considered an important priority for marine conservation, alongside its high importance for terrestrial conservation. </span></p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.