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Figures 27–30 in Biology of the sineguelas leaf beetle, Podontia quatuordecimpunctata (L.) (Chrysomelidae: Galerucinae: Alticini), on Spondias purpurea L. (Anacardiaceae) in the Philippines
Figures 27–30. Pupae and pupal cases of sineguelas leaf beetle (SLB) (Podontia quatuordecimpunctata (L., 1767). 27) Pupal case constructed from soil by the pre-pupal larva. 28) Mature larva extracted from pupal case. 29) Pupal cases at the base of a tree of sineguelas (Spondias purpurea L.) in field. 30) Pharate adult inside pupal case. (Photographs courtesy of SLB project).
Figures 23–26 in Biology of the sineguelas leaf beetle, Podontia quatuordecimpunctata (L.) (Chrysomelidae: Galerucinae: Alticini), on Spondias purpurea L. (Anacardiaceae) in the Philippines
Figures 23–26. Sineguelas leaf beetle (Podontia quatuordecimpunctata (L., 1767) on sineguelas (Spondias purpurea L.). 23) Third instar larva. 24) Fourth instar larva. 25) instar IV larva. 26) Late instar larvae of SLB can defoliate the twig and leave only midribs and stems. (Photographs courtesy of SLB project).
Figures 1–4. Plants associated with the sineguelas leaf beetle 1 in Biology of the sineguelas leaf beetle, Podontia quatuordecimpunctata (L.) (Chrysomelidae: Galerucinae: Alticini), on Spondias purpurea L. (Anacardiaceae) in the Philippines
Figures 1–4. Plants associated with the sineguelas leaf beetle 1) A typical sineguelas tree (Spondias purpurea (L.) with foliage (June to November). 2) Fruits (April to June) in the Philippines. 3) Fruits of yellow sineguelas (Spondias dulcis Parkinson). 4) Fruits of "Libas" (Spondias pinnata (L.f.) Kurz). (Photographs courtesy of SLB project).
Figures 14–15. Leaf beetle rearing cages. 14 in Biology of the sineguelas leaf beetle, Podontia quatuordecimpunctata (L.) (Chrysomelidae: Galerucinae: Alticini), on Spondias purpurea L. (Anacardiaceae) in the Philippines
Figures 14–15. Leaf beetle rearing cages. 14) Sineguelas leaf beetle rearing cage with sineguelas (Spondias purpurea (L., 1767) seedling as food and soil at the base of the twig for pupation. 15) Rearing cages where the study of its biology was conducted. (Photographs courtesy of SLB project).
FIG. 2 in Biological activity of some Romanian and Turkish Trichoderma Pers. strains
FIG. 2. — Trichoderma Pers. hyperparasitic development on various plant pathogens: Alt., Alternaria sp. Nees; B.c., Botrytis cinerea Pers.; F.c., Fusarium culmorum (Wm.G.Sm.) Sacc.; F.g., F. graminearum Schwabe; F.o., F.oxysporum Schltdl.; F.p., F. proliferatum (Matsush.) Nirenberg ex Gerlach & Nirenberg; M.p., Macrophomina phaseolina (Tassi) Goid.; S.s., Sclerotinia sclerotiorum (Lib.) de Bary.
FIG. 1 in Biological activity of some Romanian and Turkish Trichoderma Pers. strains
FIG. 1. — In vitro antifungal activity of tested Trichoderma strains Ș4 (2), Td-Exp1 (3) and Td-Exp2 (4) against various plant pathogens (1), such as Fusarium culmorum (Wm.G.Sm.) Sacc. (A), F. oxysporum Schltdl. (B), Macrophomina phaseolina (Tassi) Goid. (C) and Sclerotinia sclerotiorum (Lib.) de Bary (D). Scale bars: 1 cm.
FIG. 3 in Biological activity of some Romanian and Turkish Trichoderma Pers. strains
FIG. 3. — Mycoparasitic activity of Trichoderma Td-Exp1 strains against plant pathogens:A, Botrytis cinerea Pers.; B, Fusarium graminearum Schwabe;C, Sclerotinia sclerotiorum (Lib.) de Bary. Scale bars: 1 cm.
FIG. 5 in Biological activity of some Romanian and Turkish Trichoderma Pers. strains
FIG. 5. — Metabolic activity in biofilm and antibiofilm effect of Trichoderma spp. against human pathogenic bacterial strains. Symbols: ns, no significant; *, p <0.05; **, p <0.01; ***, p <0.001; ****, p <0.0001.
Biological and physical controls on multidecadal acidification in a eutrophic estuary
<p>Estuaries support many ecologically and economically important resources that are especially vulnerable to ocean acidification from rising anthropogenic CO<sub>2</sub>. However, complex local processes in estuaries complicate and may disguise long-term pH trends. For example, terrestrial nutrient runoff and marine coastal upwelling may exacerbate pH variability and declines. We investigated eutrophication impacts on acidification in a central California estuary, Elkhorn Slough, which receives high nutrient loads from intensive surrounding agriculture and upwelling of the California Current System. We examined drivers of acidification including nutrients, ecosystem metabolism, and upwelling by modelling pH trends over 20 years using a Generalized Additive Mixed Model at four sites from the National Estuarine Research Reserve Systemwide Monitoring Program and collected additional water samples to calculate aragonite saturation. Our models revealed acidification trends over two decades which were more pronounced near the marine inlet. Near the marine inlet, high nutrient levels and lower buffering are associated with the greatest rate of acidification in the estuary, which was four times greater than the trend from anthropogenic CO<sub>2</sub> alone. However, tidally restricted areas experienced a different acidification pattern. A tidally restricted site recorded higher mean pH and aragonite saturation and increased pH levels associated with stronger upwelling conditions supplying marine-sourced nutrients. Therefore, variable ecosystem metabolism and tidal cycles are threats to acidity in this location. The effects of enhanced seasonal cycles or long-term trends in different zones of the estuary have implications for monitoring with a temporal frequency and scale to capture coastal acidification risks in estuaries.</p>
Dataset from the analysis of biological activity of endophytic strain Serratia quinivorans KP32, the expression of biocontrol-related genes and the activity of antioxidant enzymes in bacterial cells treated with pathogenic fungi filtrates
<p>This dataset contains the data from the analyses published in the article entitled "Genetic Determinants of Antagonistic Interactions and the Response of New Endophytic Strain <i>Serratia quinivorans</i> KP32 to Fungal Phytopathogens" in the International Journal of Molecular Sciences (https://doi.org/10.3390/ijms232415561). The data consist of results collected for studies on the antifungal activity of KP32 strain towards four fungal phytopathogens, results of primer efficiency determination and studies on the expression of genes potentially involved in biocontrol after treatment of KP32 strain with the fungal phytopathogens filtrates. Additionally, absorbances from activity tests for catalase (CAT) and superoxide dismutase (SOD) in the strain treated with fungal pathogens are included.</p>
Figure 3 in Biology of jungle perch, Kuhlia rupestris, identification of threats and knowledge gaps to improve local and global management
Figure 3. – Life cycle of K. rupestris and conservation issues. Within the cycle (blue area), the solid black line corresponds to the freshwater life phase. The lower and upper grey discontinuous lines correspond to the larval marine and estuarine phases of the species respectively. Outside the cycle, in orange, the known and suspected threats on the different life phases of the species (Gelineau et al., modified, 2015).
Figure 1 in Biology of jungle perch, Kuhlia rupestris, identification of threats and knowledge gaps to improve local and global management
Figure 1. – Distribution of K. rupestris in the Indo-Pacific zone (modified from Feutry, 2012a). In dotted line, the presumed natural range of the species and in solid lines, localities with a high likelihood of occurrence or known occurrence.
Code and example images from: recolorize: An R package for flexible color segmentation of biological images
<p>Color pattern variation provides biological information in fields ranging from disease ecology to speciation dynamics. Comparing color pattern geometries across images requires color segmentation, where pixels in an image are assigned to one of a set of color classes shared by all images. Manual methods for color segmentation are slow and subjective, while automated methods can struggle with high technical variation in aggregate image sets. We present recolorize, an R package toolbox for human-subjective color segmentation with functions for batch-processing low-variation image sets and additional tools for handling images from diverse (high variation) sources. The package also includes export options for a variety of formats and color analysis packages. This paper illustrates recolorize for three example datasets, including high variation, batch processing, and combining with reflectance spectra, and demonstrates the downstream use of methods that rely on this output.</p>
Figure 1 in Are fisheries regulations influencing the biology and reproduction of the surmullet Mullus surmuletus Linnaeus, 1758 on the south-eastern coasts of France (NW Mediterranean)?
Figure 1. – Sampling sites in West, AMA (Adjacent Marine Protected Area of Hyères Bay) and East zones on the south-eastern coast of France, NW Mediterranean Sea.
Figure 4 in Are fisheries regulations influencing the biology and reproduction of the surmullet Mullus surmuletus Linnaeus, 1758 on the south-eastern coasts of France (NW Mediterranean)?
Figure 4. – Percentages of both sexes and females:males sex-ratio of Mullus surmuletus by zone (A) and season (B).
Figure 3 in Are fisheries regulations influencing the biology and reproduction of the surmullet Mullus surmuletus Linnaeus, 1758 on the south-eastern coasts of France (NW Mediterranean)?
Figure 3. – Mean (± SE) total length (TL, cm) of Mullus surmuletus in West, AMA and East zones. N: number of analyzed individuals per sex in zones. Values with the same post-hoc letters (red for females, blue for males) are not significantly different (p> 0.05).
Figure 2 in Are fisheries regulations influencing the biology and reproduction of the surmullet Mullus surmuletus Linnaeus, 1758 on the south-eastern coasts of France (NW Mediterranean)?
Figure 2. – Percentage of individuals of Mullus surmuletus analyzed by two-cm size class (total length in cm) and by sex. N: number of individuals, F: females, M: males, Undet.: unidentified group includes immature individuals and those whose sex could not be identified.
Figure 8 in Are fisheries regulations influencing the biology and reproduction of the surmullet Mullus surmuletus Linnaeus, 1758 on the south-eastern coasts of France (NW Mediterranean)?
Figure 8. – Mean percentages of gonadal development stages (GDS) of Mullus surmuletus. A: By zone for females; B: By zone for males; C: By season for females; D: By season for males. N: number of individuals.
Figure 6 in Are fisheries regulations influencing the biology and reproduction of the surmullet Mullus surmuletus Linnaeus, 1758 on the south-eastern coasts of France (NW Mediterranean)?
Figure 6. – Mean gonado-somatic index (GSI, %) of males and females of Mullus surmuletus by (A) zone and (B) season. Values with the same post-hoc let-letters (red for females, blue for males) are not significantly different (p> 0.05).
Figure 7 in Are fisheries regulations influencing the biology and reproduction of the surmullet Mullus surmuletus Linnaeus, 1758 on the south-eastern coasts of France (NW Mediterranean)?
Figure 7. – Mean percentage of gonadal development stages of Mullus surmuletus by 2-cm size class (TL, cm) and sex for (A) females and (B) males. N: number of individuals.
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.