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Figure 3 in Prevalence of Spiroplasma and interaction with wild Glossina tachinoides microbiota
Figure 3. Prevalence of Spiroplasma and Trypanosoma (single and multiple) infections per country, location, and sex. Prevalence data were square root transformed and averaged based on location-sex and the matrix display was conducted in PRIMER version 7 + software. Tree on the left of the matrix is the similarity dendrogram based on the similarity index of the square root of the prevalence values. The color index is the square root of the prevalence values ranged 0–9 which is the square root of 0–81% prevalence.
Fig. 5 in Peculiarities of Trypanosoma rangeli KP1(-) Strains Isolated from the Wild Rodent Phyllomys dasythrix (Santa Catarina, Brazil): Comparisons with T. rangeli KP1(+) strains and Trypanosoma lewisi (Kinetoplastea, Trypanosomatidae)
Fig. 5. kDNA minicircle amplicons presented by all KP1(+) and KP1(–) Trypanosoma rangeli strains examined in this study, and obtained by polymerase chain reaction using the primers 121/122. (M) molecular marker (100-bp DNA ladder).
Fig. 4 in Peculiarities of Trypanosoma rangeli KP1(-) Strains Isolated from the Wild Rodent Phyllomys dasythrix (Santa Catarina, Brazil): Comparisons with T. rangeli KP1(+) strains and Trypanosoma lewisi (Kinetoplastea, Trypanosomatidae)
Fig. 4. Isoenzyme profiles at IDH locus presented by KP1(+) and KP1(–) Trypanosoma rangeli strains and T. lewisi.
Fig. 3 in Peculiarities of Trypanosoma rangeli KP1(-) Strains Isolated from the Wild Rodent Phyllomys dasythrix (Santa Catarina, Brazil): Comparisons with T. rangeli KP1(+) strains and Trypanosoma lewisi (Kinetoplastea, Trypanosomatidae)
Fig. 3. Diagrammatic representation of the electrophoretic patterns of malate dehydrogenase (MDH), phosphoglucomutase (PGM), glucose phosphate isomerase (GPI) and malic enzyme (ME) displayed by KP1(–) and KP1(+) Trypanosoma rangeli strains, and T. lewisi.
Fig. 1 in Peculiarities of Trypanosoma rangeli KP1(-) Strains Isolated from the Wild Rodent Phyllomys dasythrix (Santa Catarina, Brazil): Comparisons with T. rangeli KP1(+) strains and Trypanosoma lewisi (Kinetoplastea, Trypanosomatidae)
Fig. 1. Camera lucida drawings of representative bloodstream trypomastigotes of Trypanosoma rangeli KP1(–) and KP1(+) strains from experimentally infected mice by metacyclic trypomastigotes grown in DMEM medium. The position of the nucleus in each trypomastigote is indicated by arrowheads. Parasites were from Giemsa-stained smears of each strain, as seen under optical microscopy (×1,000). In a trypomastigote of the strain SC-61 are indicated the reference points for taking measurements: anterior end (A), posterior end (P), nucleus (N) and free flagellum (F).
Fig. 2 in Peculiarities of Trypanosoma rangeli KP1(-) Strains Isolated from the Wild Rodent Phyllomys dasythrix (Santa Catarina, Brazil): Comparisons with T. rangeli KP1(+) strains and Trypanosoma lewisi (Kinetoplastea, Trypanosomatidae)
Fig. 2. Comparative growth in axenic cultures of Trypanosoma rangeli strains [KP1(–) and KP1(+)] and T. lewisi. Data (106 cells/μL) are the averages and the maximum growth from the values taken at the 4th, 7th, 10th, 13th, 17th and 20th days of cultivation in liver infusion-tryptose broth supplemented with 20% fetal calf serum (LIT-20) at 27.3 ± 0.4°C.
Fig. 1 in Callicultureinducingfromredclover(Trifolium Pratense L.) Wild Accessions And Results Of Dna Extraction
Fig. 1. Results of flow cytometry: a) 2n ploidy control, b) the typical result for leaves, c) the typical result for stems, d) the typical result for roots.
Fig. 1 in Tick (Acarina: Ixodidae) species and life stages collected from Arkansas wild pigs
Fig. 1. Locations of Arkansas wild pig traps where ticks were collected Feb 2019 to Jan 2020. Collection sites are labeled by county. Two traps in Crawford County, 2 traps in Polk County, and 2 traps in Scott County were close geographically and appear as 1 location.
Data from: The importance of biotic interactions in distribution models of wild bees depends on the type of ecological relations, spatial scale and range
<p>Studies have found that biotic information can play an important role in shaping the distribution of species even at large scales. However, results from species distribution models are not always consistent among studies, and the underlying factors that influence the importance of biotic information to distribution models, are unclear. 2. We studied wild bees and plants, and cleptoparasite bees and their hosts in the Netherlands to evaluate how the inclusion of their biotic interactions affects the performance of species distribution models. We assessed model performance through spatial block cross-validation and by comparing models with interactions to models where the interacting species were randomized. Finally, we evaluated how, (i) spatial resolution, (ii) taxonomic rank (genus or species), (iii) degree of specialization, (iv) distribution of the biotic factor, (v) bee body size and (vi) type of biotic interaction, affect the importance of biotic interactions in shaping the distribution of wild bee species using generalized linear models. 3. We found that the models of wild bees improved when the biotic factor was included. The model performance improved the most for parasitic bees. Spatial resolution, taxonomic rank, distribution range of the biotic factor, and degree of specialization of the modelled species all influenced the importance of the biotic interaction to the models. 4. We encourage researchers to include biotic interactions in species distribution models, especially for specialized species and when the biotic factor has a limited distribution range. However, before adding the biotic factor we suggest considering different spatial resolutions and taxonomic ranks of the biotic factor. We recommend using single species or genus data as a biotic factor in the models of specialist species and for the generalist species, we recommend using an approximate measure of interactions, such as flower richness.</p>
Agricultural landscape simplification affects wild plant fitness indirectly through herbivore-mediated changes in floral display
<p>As natural landscapes are modified and converted into simplified agricultural landscapes, the community composition and interactions of organisms persisting in these modified landscapes are altered. While many studies examine the consequences of these changing interactions for crops, few have evaluated the effects on wild plants. Here, we examine how pollinator and herbivore interactions affect fitness for wild resident and phytometer plants at sites along a landscape gradient ranging from natural to highly simplified. We tested the direct and indirect effects of landscape composition on plant traits and fitness mediated by insect interactions. For phytometer plants exposed to herbivores, we found that greater landscape complexity corresponded with elevated herbivore damage, which reduced total flower production but increased individual flower size. Though larger flowers increased pollination, the reduction in flowers ultimately reduced plant fitness. Herbivory was also higher in complex landscapes for resident plants, but overall damage was low and therefore did not have a cascading effect on floral display and fitness. This work highlights that landscape composition directly affects patterns of herbivory with cascading effects on pollination and wild plant fitness. Further, the absence of fitness consequences for resident plants suggests that they may be adapted to their local insect community. </p>
Fig. 9 in Ad-hoc devised phototraps unravel Cerambyx miles diel activity in the wild (Coleoptera: Cerambycidae)
Fig. 9 – Diel activity (%) of Cerambyx miles in the wild during two consecutive years as assessed with phototrapping: frequency of captures in 2020 with PT1 traps (upper panel), frequency of sightings in 2021 with PT2 traps (central panel), and pooled data (bottom panel). The frequency distributions on an hourly basis show the strong prevalence of the diurnal behaviour exhibited by C. miles adults (Local time, GMT+2). Field trials were conducted both years during June-July at Montánchez mountain range, Cáceres (SW Spain). See text for additional details.
Figs 3-8 in Ad-hoc devised phototraps unravel Cerambyx miles diel activity in the wild (Coleoptera: Cerambycidae)
Figs 3-8 – Some selected images (unretouched, original frames) showing phototrapped adults of Cerambyx miles; 3, a C. miles male prowling the container of a PT1 phototrap at 19.05 h. Two marked C. welensii adults are visible within the trap. Montánchez, Cáceres, 7 July 2020; 4, a C. miles male lured by a PT1 phototrap at 19.15 h before being trapped. Montánchez, Cáceres, 8 July 2020; 5, a mated pair of C. miles prowling a PT1 phototrap at 18.25 h. Two marked C. cerdo adults are visible within the trap. Note the removable cardboard panel to protect the container from direct sunlight. Montánchez, Cáceres, 8 July 2020; 6, a C. miles female feeding in the drinker of a PT2 phototrap at 14.21 h. Montánchez, Cáceres, 29 June 2021; 7, a mated pair of C. miles (while female feeds) in the platform of a PT2 phototrap at 17.56 h. Cancho Blanco, Zarza de Montánchez, Cáceres, 1 July 2021; and 8, a C. miles male feeding in the drinker of a PT2 phototrap at 14.14 h. Montánchez, Cáceres, 29 July 2021 (Local time, GMT+2).
Fig. 10 in Ad-hoc devised phototraps unravel Cerambyx miles diel activity in the wild (Coleoptera: Cerambycidae)
Fig. 10 – Pie charts depicting the diel activity (%) of Cerambyx miles depending on the data source, either field experimental data from this study (left) or literature data (right). Captures/sightings from this study (n = 139) and literature data (n = 103 records from 79 references, Table S1) were scored into three diel activity classes as either diurnal (D), crepuscular (C) or nocturnal (N). The two frequency distributions were significantly different (see text) suggesting an underlying bias in how entomologists have perceived or described the diel activity of C. miles adults.
Figure 5 in How long do dolphins live? Survival rates and life expectancies for bottlenose dolphins in zoological facilities ťs. wild populations
Figure 5. Kaplan-Meier survival curves depicting the proportion of bottlenose dolphins in zoological care surviving to each age (calculated in days, then transformed to years) during four time periods.
Figure 2 in How long do dolphins live? Survival rates and life expectancies for bottlenose dolphins in zoological facilities ťs. wild populations
Figure 2. ASR (95% confidence intervals) of bottlenose dolphin calves <1 yr old in zoological care across historical time periods.
Figure 4 in How long do dolphins live? Survival rates and life expectancies for bottlenose dolphins in zoological facilities ťs. wild populations
Figure 4. The population age structure for bottlenose dolphins in zoological care on the last day of each time period.
Figure 3 in How long do dolphins live? Survival rates and life expectancies for bottlenose dolphins in zoological facilities ťs. wild populations
Figure 3. Survivorship to each age as calculated for age-at-death data for modern-day dolphins in zoological care and two wild populations.
Figure 1 in How long do dolphins live? Survival rates and life expectancies for bottlenose dolphins in zoological facilities ťs. wild populations
Figure 1. ASR (95% confidence intervals) of bottlenose dolphins>1 yr old in zoological care across historical time periods.
Fig. 2 in Host plant resistance in cultivated jute and its wild relatives towards jute hairy caterpillar Spilosoma obliqua (Lepidoptera: Arctiidae)
Fig. 2. Mean number of egg clusters (A) and eggs per cluster (B) laid by Spilosoma obliqua females on 6 jute species in no-choice tests.
Fig. 1 in Host plant resistance in cultivated jute and its wild relatives towards jute hairy caterpillar Spilosoma obliqua (Lepidoptera: Arctiidae)
Fig. 1. Effect of cultivated and wild jute species on Spilosoma obliqua larvae settlement (%) (A) and leaf area consumed (cm2) (B) afer 24 h in multiplechoice tests.
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.