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22 results for “macro-invertebrates”
Figure 4 in Patterns of spatial variability of mobile macro-invertebrate assemblages within a Posidonia oceanica meadow
Figure 4. Non-metric multidimensional scaling (nMDS) ordination on macro-invertebrate assemblages of Pianosa Island. S = shallow, I = intermediate, D = deep; e = east, s = south, w = west.
Figure 3 in Patterns of spatial variability of mobile macro-invertebrate assemblages within a Posidonia oceanica meadow
Figure 3. (a) Mean species number and (b) number of individuals per sample of mobile macroinvertebrate assemblages of Posidonia oceanica meadow (mean ± standard error, SE; n = 24).
Figure 2 in Patterns of spatial variability of mobile macro-invertebrate assemblages within a Posidonia oceanica meadow
Figure 2. (a) Shoot density and (b) mean leaf length of Posidonia oceanica meadow of Pianosa Island (mean ± standard error, SE; n = 120).
Figure 2 in Using deep-learning for automatic identification of images of marine benthic macro-invertebrate bycatch: a proof of concept
Figure 2. – Three images of organisms obtained by cropping images of lots; from left to right: Chalinidae (Porifera), Polyclinidae (Chordata), Hormatidae (Cnidaria).
Figure 3 in Using deep-learning for automatic identification of images of marine benthic macro-invertebrate bycatch: a proof of concept
Figure 3. – Image of a batch of macro-invertebrate bycatch organisms from Kerguelen Exclusive Economical Zone (Poker 4 survey, 2017), including corals, a crinoïd, an ophiurid, a sea urchin and a brachiopoda; organisms are incomplete and have been quickly spread out over a small plate to take the picture.
Figure 6 in Using deep-learning for automatic identification of images of marine benthic macro-invertebrate bycatch: a proof of concept
Figure 6. – Example of detection and classification obtained with an image including an Ophiuroid, a piece of coral and a sea star with network 2; red squares and annotations have been provided by the computer with no human action.
Figure 5 in Using deep-learning for automatic identification of images of marine benthic macro-invertebrate bycatch: a proof of concept
Figure 5. – Example of detection and classification obtained with an image including Ascidians and a sea star with network 2; red squares and annotations have been provided by the computer with no human action.
FIGURE 2 in Exotic Terrestrial Macro-Invertebrate Invaders in California from 1700 to 2015: An Analysis of Records
FIGURE 2. Total number of invertebrates detected in Cal- FIGURE 3. Plot of CUSUM values for counts of exotic ifornia 1935–2010. arthropods recorded in California over the period 1935 to 2010.
FIGURE 1 in Exotic Terrestrial Macro-Invertebrate Invaders in California from 1700 to 2015: An Analysis of Records
FIGURE 1 (left). Examples of non-native macro-terrestrial arthropods that have been detected in California. A. Avocado thrips, Scirtothrips perseae Nakahara (Thysanoptera: Thripidae). (Detected 1992). B. Brown widow spider, Latrodectus geometricus Koch (Araneae: Theridiidae) (detected 2003). C. Glassy-winged sharpshooter, Homalodisca vitripennis (Germar) (Hemiptera: Cicadellidae) (detected 1994). D. Brown marmorated stink bug, Halyomorpha halys Stål (Hemiptera: Pentatomidae) (detected 2005). E. Citrus leafminer, Phyllocnistis citrella Stainton (Lepidoptera: Gracillariidae) (detected 1999). F. Asian citrus psyllid, Diaphorina citri Kuwayama (Hemiptera: Psyllidae) (detected 2008). G. Palm weevil, Rhynchophorus vulneratus (Panzer) (Coleoptera: Curculionidae) (detected 2010) (eradicated 2015). H. Gold spotted oak borer, Agrilus auroguttatus Waterhouse (Coleoptera: Buprestidae) (detected 2004).
Figure 2 in Bendima: a database for marine macro-invertebrate bycatch data designed to improve reproducibility in benthic ecology
Figure 2. – The proportion of the various phyla represented in the Bendima database, based on the number of single organisms or colonies.
Figure 1 in Bendima: a database for marine macro-invertebrate bycatch data designed to improve reproducibility in benthic ecology
Figure 1. – Images and samples collection; sorting of the caught organisms (A), full photographing (B), taxa identification/counting/measurement and storage in the Bendima database in the form of cropped images (C), conservation of representative sub-samples for taxonomy and DNA barcoding (D).
Figure 3 in Bendima: a database for marine macro-invertebrate bycatch data designed to improve reproducibility in benthic ecology
Figure 3. – The geographical coverage of the Bendima database; the location of the zoomed area is in provided in the inset world map; stations are aggregated into presence data according to a grid of cells of 1°; the letters and num- bers relate to the name of the French Economic Exclusive Zones (EEZ) and various geomorphic structures located in international waters: A: Kerguelen EEZ, B: Crozet EEZ, C: Saint-Paul et Amsterdam EEZ, 1: Del Cano ridge, 2: Elan Bank, 3: Ob et Lena Bank, 4 and 5: Antarctica shelf.
Figure 1 in Patterns of spatial variability of mobile macro-invertebrate assemblages within a Posidonia oceanica meadow
Figure 1. Map of Pianosa Island. Stars indicate the study areas.
Data from: Resource stoichiometry and availability modulate species richness and biomass of tropical litter macro-invertebrates
1. The high biodiversity and biomass of soil communities is crucial for litter decomposition in terrestrial ecosystems such as tropical forests. However, the leaf litter that these communities consume is of particularly poor quality as indicated by elemental stoichiometry. The impact of resource quantity, quality, and other habitat parameters on species richness and biomass of consumer communities is often studied in isolation, although much can be learned from simultaneously studying both community characteristics. 2. Using a data set of 780 macro-invertebrate consumer species across 32 sites in tropical lowland rainforest and agricultural systems on Sumatra, Indonesia, we investigated the effects of basal resource stoichiometry (C:X ratios of N, P, K, Ca, Mg, Na, S in local leaf litter), litter mass (basal resource quantity and habitat space), plant species richness (surrogate for litter habitat heterogeneity), and soil pH (acidity) on consumer species richness and biomass across different consumer groups (i.e., three feeding guilds and ten selected taxonomic groups). 3. In order to distinguish the most important predictors of consumer species richness and biomass, we applied a standardised model averaging approach investigating the effects of basal resource stoichiometry, litter mass, plant species richness, and soil pH on both consumer community characteristics. This standardised approach enabled us to identify differences and similarities in the magnitude and importance of such effects on consumer species richness and biomass. 4. Across consumer groups, we found litter mass to be the most important predictor of both species richness and biomass. Resource stoichiometry had a more pronounced impact on consumer species richness than on their biomass. As expected, taxonomic groups differed in which resource and habitat parameters (basal resource stoichiometry, litter mass, plant species richness, and pH) were most important for modulating their community characteristics. 5. The importance of litter mass for both species richness and biomass indicates that these tropical consumers strongly depend on habitat space and resource availability. Our study supports previous theoretical work indicating that consumer species richness is jointly influenced by resource availability and the balanced supply of multiple chemical elements in their resources.
Data from: Resource stoichiometry and availability modulate species richness and biomass of tropical litter macro-invertebrates
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Figure 5 in Patterns of spatial variability of mobile macro-invertebrate assemblages within a Posidonia oceanica meadow
Figure 5. Percentage pseudo-variance components of mobile macro-invertebrate assemblages of Posidonia oceanica meadow at (a) shallow, (b) intermediate and (c) deep stands.
Figure 7 in Using deep-learning for automatic identification of images of marine benthic macro-invertebrate bycatch: a proof of concept
Figure 7. – Example of detection and classification obtained with an image including Crinoïds, a Gastropod and pieces of seaweed with network 2; red squares and annotations have been provided by the computer with no human action.
Figure 1 from: Vannevel R, Brosens D, De Cooman W, Gabriels W, Lavens F, Mertens J, Vervaeke B (2018) The inland water macro-invertebrate occurrences in Flanders, Belgium. ZooKeys 759: 117-136. https://doi.org/10.3897/zookeys.759.24810
Figure 1 Location of the region of Flanders in Belgium, its main rivers (Yser, Scheldt, Meuse) and the subunits of the WFD river basin districts.
Figure 2 from: Vannevel R, Brosens D, De Cooman W, Gabriels W, Lavens F, Mertens J, Vervaeke B (2018) The inland water macro-invertebrate occurrences in Flanders, Belgium. ZooKeys 759: 117-136. https://doi.org/10.3897/zookeys.759.24810
Figure 2 The VMM monitoring network for surface water quality assessments.
Figure 5 from: Vannevel R, Brosens D, De Cooman W, Gabriels W, Lavens F, Mertens J, Vervaeke B (2018) The inland water macro-invertebrate occurrences in Flanders, Belgium. ZooKeys 759: 117-136. https://doi.org/10.3897/zookeys.759.24810
Figure 5 Sampling effort of the sediment quality network (number of samples per year).
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.