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Figure 11 in Impact of aquatic habitat environment on the elemental composition and shell shape variability of the Beringian freshwater mussel Beringiana beringiana (Bivalvia, Unionidae)
Figure 11. Relationships between values of KAl and longitudinal cross-sectional area of shell (mm2), d Shell/Sediment revealed from the shell shape analysis of Beringiana beringiana. For the numbers of localities see caption for Figure 7.
Figure 7 in Impact of aquatic habitat environment on the elemental composition and shell shape variability of the Beringian freshwater mussel Beringiana beringiana (Bivalvia, Unionidae)
Figure 7. Relationships between Ca-normalized concentration of zinc in shells and principal component 1, (A), copper in shells and principal component 1 (B), revealed from the shell shape analysis of Beringiana beringiana: (1) Peschanoye Lake, Kunashir Island, (2) Lebedinoe Lake, Iturup Island, (3) Bolshoye Vavayskoye Lake, Sakhalin Island, (4) Kurazhechnoye Lake, Kamchatka Peninsula, (5) Vaskovskoye Lake, Primorsky Krai, (6) Khalaktyrskoye Lake, Kamchatka Peninsula.
Figure 10 in Impact of aquatic habitat environment on the elemental composition and shell shape variability of the Beringian freshwater mussel Beringiana beringiana (Bivalvia, Unionidae)
Figure 10. Relationships between values of Kd Shell/Water Al (A), Kd Shell/Water P (B), Kd Shell/Water Fe (C) and longitudinal cross-sectional area of shell (mm2), revealed from the shell shape analysis of Beringiana beringiana. For the numbers of localities see caption for Figure 7.
Figure 9 in Impact of aquatic habitat environment on the elemental composition and shell shape variability of the Beringian freshwater mussel Beringiana beringiana (Bivalvia, Unionidae)
Figure 9. Relationships between Zn: Ca – ratio in Beringiana beringiana shells and longitudinal cross-sectional area of shell (mm2), revealed from shell shape analysis of Beringiana beringiana. For the numbers of localities see caption for Figure 7.
Fig. 2 in Effects of introduced sika deer (Cervus nippon) and population control activity on the distribution of Haemaphysalis ticks in an island environment
Fig. 2. Seasonal changes in tick abundance of the dominant species (A) H. megaspinosa, (B) H. longicornis, and (C) H. cornigera on Niijima Island. (Broken line with black dot: Larvae, Dotted-dashes line with back triangles: Nymphs, Solid line with crosses: Adults).
Fig. 3 in Effects of introduced sika deer (Cervus nippon) and population control activity on the distribution of Haemaphysalis ticks in an island environment
Fig. 3. Statistical summary of the abundances of H. megaspinosa larvae. (A) Days since the last sika deer was captured in August–November, (B) days since the last sika deer was captured in August–November for the high sika deer group, and (C) days since the last sika deer was captured in August–November for the low sika deer group. (X mark inside box: mean, lower and upper side of the box: first and third quartiles, line inside box: median, lower and upper error lines 10th and 90th percentiles, respectively, circles: data falling outside 10th and 90th percentiles).
Fig. 1 in Effects of introduced sika deer (Cervus nippon) and population control activity on the distribution of Haemaphysalis ticks in an island environment
Fig. 1. Map of Niijima Island, with locations of foot snare traps (black circle), and tick collection routes for the tick survey conducted June, August, and November of 2018 and February of 2019 (orange area). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 3 in Insect visitors to the annual plant community in a xeric environment in Central Mexico
Fig. 3. Canonical correspondence analyses of the abundance of floral visitors in response to environmental variables (disturbance index, temperature, and humidity). (a) Effect of environmental variables on the abundance of each insect order of floral visitors. (b) Effect of environmental variables on the abundance of insect species displaying abundance that was significantly different between sites.
Fig. 2 in Insect visitors to the annual plant community in a xeric environment in Central Mexico
Fig. 2. Dendrogram showing similarity indexes obtained from species composition of floral visitors shared by the annual plants distributed at the low (LPD) and high (HPD) plant density sites.
Fig. 1 in Insect visitors to the annual plant community in a xeric environment in Central Mexico
Fig. 1. Abundance-rank curves of the insect species collected visiting the flowers of annual plants in sites with contrasting plant density. Relative abundance is plotted on a log10 scale, and the abscissa is the rank from the most to the least common species (James & Rathbun 1981). Closed triangles and diamonds indicate the abundance of Dasytinae species 1 (Coleoptera) and Miridae species 1 (Hemiptera), in the low and high plant density sites, respectively.
Fig. 2 in Can the environment influence varroosis infestation in Africanized honey bees in a Neotropical region?
Fig. 2. Biplot of canonical discriminator of temperature (°C), rainfall (mm), altitude (m), and Varroa destructor infestation level (%) from Bahia State mesoregions, Brazil. BA1: São Francisco Valley; BA2: Middle East; BA3: Metropolitan of Salvador; BA4: South Center; BA5: South.
Fig. 1 in Can the environment influence varroosis infestation in Africanized honey bees in a Neotropical region?
Fig. 1. Cities (black squares) where the apiaries are located from 5 different mesoregions in Bahia State, Brazil. BA1: São Francisco Valley; BA2: Middle East; BA3: Metropolitan of Salvador; BA4: South Center; BA5: South.
Fig. 3 in Can the environment influence varroosis infestation in Africanized honey bees in a Neotropical region?
Fig. 3. Contribution of the variables in the first canonical discriminant function from temperature (°C), rainfall (mm), altitude (m), and Varroa destructor infestation level (%) from 5 different mesoregions in Bahia State,Brazil.BA1: São Francisco Valley; BA2: Middle East; BA3: Metropolitan of Salvador; BA4: South Center; BA5: South.
Fig. 1 in Prevalence of avian haemosporidia among injured wild birds in Tokyo and environs, Japan
Fig. 1. Locations of the four facilities in the Kanto region that samples were collected. A. Kanagawa Prefecture Natural Conservation Center, B. Inokashira Animal Hospital, C. Gyotoku Wild Bird Hospital, D. Bird Clinic Kanesaka Animal Hospital.
Fig. 2 in Prevalence of avian haemosporidia among injured wild birds in Tokyo and environs, Japan
Fig. 2. Hemacolor ® stained blood smears from rescued birds: (a) Plasmodium sp. from Cyanopica cyanus, (b) P. reluctum from Hypisipetes amaurotis, (c) P. circumflexum from Fulica atra, (d) Haemoproteus sp. from Hypisipetes amaurotis, (e) Haemoproteus sp. from Larus canus, (f) H. minutus from Turdus cardis, (g) Leucocytozoon sp. from Anas acuta, (h) Leucocytozoon sp. from Aythya marila.
Fig. 2 in Do growth rate and survival differ between undisturbed and disturbed environments for Sceloporus spinosus Wiegmann, 1828 (Squamata: Phrynosomatidae) from Oaxaca, Mexico?
Fig. 2. Growth rate of Sceloporus spinosus. (A) Undisturbed area (UA) males, (B) Disturbed area (DA) males, (C) UA females, and (D) DA females. Black circles represent data points for individual lizards. Modeled relationships between growth and body sizes of males and females: solid lines = Von Bertalanffy, dashed lines = logistic by length, and dotted lines = logistic by mass.
Fig. 1 in Do growth rate and survival differ between undisturbed and disturbed environments for Sceloporus spinosus Wiegmann, 1828 (Squamata: Phrynosomatidae) from Oaxaca, Mexico?
Fig. 1. Map of the study area. The green polygon depicts Yagul Natural Protected Area, including the two sampling sites (UA = undisturbed area; DA = disturbed area, land use change).
Fig. 3. Means and 95 in Do growth rate and survival differ between undisturbed and disturbed environments for Sceloporus spinosus Wiegmann, 1828 (Squamata: Phrynosomatidae) from Oaxaca, Mexico?
Fig. 3. Means and 95% confidence intervals of the Asymptotic growth (A) and Characteristic growth (r) parameters obtained by 1 the Von Bertalanffy and logistic by length models for males and females of Sceloporus spinosus in both Disturbed area (DA) and Undisturbed area (UA) populations.
Fig. 3. Sporulation rates for E in Distribution of Eimeria uekii and Eimeria raichoi in cage protection environments for the conservation of Japanese rock ptarmigans (Lagopus muta japonica) in the Japanese Alps
Fig. 3. Sporulation rates for E. uekii (A) and E. raichoi (B) at temperatures of 15, 20, and 25 ◦ C.
Fig. 1 in Distribution of Eimeria uekii and Eimeria raichoi in cage protection environments for the conservation of Japanese rock ptarmigans (Lagopus muta japonica) in the Japanese Alps
Fig. 1. Location of Mt. Norikuradake (36◦06′N, 137◦33′E) in the Northern Japanese Alps (1) and Mt. Komagatake (35◦79′N, 137◦80′E) in the Central Japanese Alps (2). Three broods were transported from Mt. Norikuradake to Mt. Komagatake.
ScienceDex guides
Understand access before you commit
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.