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5,145 results for “CO₂”
Supplementary data from: Current and past climate co-shape community-level plant species richness in the Western Siberian Arctic
<p>The Arctic ecosystems and their species are exposed to amplified climate warming and, in some regions, to rapidly developing economic activities. We used macroecological modeling to estimate the community-level species richness across the Western Siberian tundra, with climate variables and anthropogenic influence identified as main explanatory factors. Our results reveal complex spatial patterns of community-level species richness in the Western Siberian Arctic. We show that climatic factors such as temperature (including paleotemperature) and precipitation are the main drivers of plant species richness in this area, and the role of relief is clearly secondary.</p> <p>Here we present a supplementing dataset to the analysis of our paper "Current and past climate co-shape community-level plant species richness in the Western Siberian Arctic"<strong> </strong>(<a href="https://doi.org/10.1002/ece3.11140">https://doi.org/10.1002/ece3.11140</a>). Our research is based on the Western Siberian part of the Russian Arctic Vegetation Archive (AVA-RUS, <a href="http://avarus.space">http://avarus.space</a>), with 1483 Braun-Blanquet plots observed from 2005-2018.</p> <p>The dataset contains geolocated species richness data along with sampled raster data on environmental and anthropogenic predictors used for modeling. The scripts are used for paleoclimatic data sampling; testing univariate predictive performance and limited collinearity for all predictors; fitting four different modes: random forest, gradient boosting machine, generalized linear model, and generalized additive model; their validation and projection. Detailed information regarding the data structure and the applied methods could be found in the paper.</p>
Fig. 3 in Molecular phylogeny of Indonesian Lymantria Tussock Moths (Lepidoptera: Erebidae) based on CO I gene sequences
Fig. 3. Neighbor-Joining tree based on K2P distance model of all substitutions of CO I gene (Bootstrap support are shown at the nodes; ID=specimens from Indonesia).
Fig. 4. Maximum likelihood tree for 43 in Molecular phylogeny of Indonesian Lymantria Tussock Moths (Lepidoptera: Erebidae) based on CO I gene sequences
Fig. 4. Maximum likelihood tree for 43 species of Lymantria based all substitutions of CO I gene (Bootstrap support are shown at the nodes; ID=specimens from Indonesia).
◂Fig. 3 Gynoecium of C. crenata %yellow frames), C. cf. grandicalyx %blue frames) and C. sinensis %pink frames; A, B stack shot images; C–K light microscopy; G polarised light; TS in horizontal orientation). A, B Anthetic female flower, calyx and corolla partly removed. B LS of gynoecium. C LS of functionally female flower %note strongly stained peripheral tissue of corolla, anther and gynoecium). D LS of gynoecium. E, F TS of functionally female flower %note strongly stained, peripheral tissue). G TS of functionally female flower %note crystal deposition). H LS of ovule %note stalked embryo sac). J TS of functionally male flower with non-functional ovules. K LS of functionally male flower %style lacking, original position indicated by an asterisk) %LS, longisection; TS, transverse section; a,anther; bs, basal septum; c, calyx; car, carpel; co, corolla; db, dorsal bundles; es, embryo sac; fs, false septum; lb, lateral bundles; o, ovule; stg, stigma; sty, style; t, trichomes; tt, transmission tissue; ut, peripheral, strongly stained tissue; vb, ventral bundles; vs, ventral slit) in Observations on flower and fruit anatomy in dioecious species of Cordia (Cordiaceae, Boraginales) with evolutionary interpretations
◂Fig. 3 Gynoecium of C. crenata %yellow frames), C. cf. grandicalyx %blue frames) and C. sinensis %pink frames; A, B stack shot images; C–K light microscopy; G polarised light; TS in horizontal orientation). A, B Anthetic female flower, calyx and corolla partly removed. B LS of gynoecium. C LS of functionally female flower %note strongly stained peripheral tissue of corolla, anther and gynoecium). D LS of gynoecium. E, F TS of functionally female flower %note strongly stained, peripheral tissue). G TS of functionally female flower %note crystal deposition). H LS of ovule %note stalked embryo sac). J TS of functionally male flower with non-functional ovules. K LS of functionally male flower %style lacking, original position indicated by an asterisk) %LS, longisection; TS, transverse section; a,anther; bs, basal septum; c, calyx; car, carpel; co, corolla; db, dorsal bundles; es, embryo sac; fs, false septum; lb, lateral bundles; o, ovule; stg, stigma; sty, style; t, trichomes; tt, transmission tissue; ut, peripheral, strongly stained tissue; vb, ventral bundles; vs, ventral slit)
Fig. 1 in Co-invaders: The effects of alien parasites on native hosts
Fig. 1. Schematic diagram of processes involved in species invasions and coinvasions. (a) Free-living aliens. The light blue oval shape represents a new area, outside the natural range of the alien species, shown in red. Arrows indicate movement of alien species through the phases of introduction, establishment and invasion of the habitat of the native species, shown in blue. Vertical bars represent barriers to be overcome in each phase. (b) Parasitic aliens. The alien host species (in red) contains an alien parasite species. The alien parasite goes through the processes of introduction, establishment and spread with its original host and then switches to a native host species (in blue) to become a co-invader. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Fig. 2 in Co-invaders: The effects of alien parasites on native hosts
Fig. 2. (a) Relative proportions of taxa represented in 98 examples of co-introduced parasites: prokaryotes (viruses and bacteria); protozoans; helminths (platyhelminths, nematodes and acanthocephalans); arthropods (crustaceans, arachnids); and a miscellaneous group including fungi, myxozoans, annelids, molluscs and pentasomids. (b) Relative proportions of alien hosts represented in 98 examples of cointroductions: molluscs; arthropods; fishes; mammals; and other vertebrates (amphibians, reptiles and birds). (c) Number of co-introduced parasite species with direct and indirect life cycles which have switched (black bars) or not switched (white bars) from alien to native host species.
Fig. 4 in Nematode-coccidia parasite co-infections in African buffalo: Epidemiology and associations with host condition and pregnancy
Fig. 4. Predicted mean and standard error body condition scores show associations with infection presence and season, with co-infected buffalo in much lower condition in the early wet season (Table S2). Coccidia infection status is represented with C– and C+; nematode infection status is represented with N– and N+.
Fig. 5 in Nematode-coccidia parasite co-infections in African buffalo: Epidemiology and associations with host condition and pregnancy
Fig. 5. Season and co-infection differences in nematode aggregation. (a) Aggregation patterns in calves (b) and non-calves. (c) In non-calves, the distribution of nematode parasites in the late wet season shows that k is not significantly different in coccidia positive vs. negative buffalo. (d) In the early wet season coccidia positive buffalo have a truncated distribution, resulting in significantly reduced aggregation. Arrows indicate nematode intensity values in the tail of the distribution of coccidia negative buffalo. Coccidia infection status is represented with C– and C+.
Fig. 3 in Nematode-coccidia parasite co-infections in African buffalo: Epidemiology and associations with host condition and pregnancy
Fig. 3. Patterns of parasite egg/oocyst counts with co-infection for (a) nematodes and (b) coccidia. (c), the mean nematode intensity in calves is higher in early wet season than in the late wet season independent of co-infection with coccidia. (d) Co-infection with coccidia alters the seasonal patterns of nematode intensity in non-calf buffalo (>1 year, juvenile through senescent). Calf vs. non-calf division is based on model paramters (Table 1).
Fig. 2 in Nematode-coccidia parasite co-infections in African buffalo: Epidemiology and associations with host condition and pregnancy
Fig. 2. Age specific patterns of parasite prevalence with co-infection. (a) Prevalence of nematodes is higher in buffalo co-infected with coccidia (C+) compared to coccidia negative buffalo (C–) in all age categories (N = 33, 318, 166, 272, 162 for calf, juvenile, subadult, adult and senescent C– buffalo; N = 58, 237, 55, 54, 20 for C+ buffalo). (b) Prevalence of coccidia is higher in buffalo co-infected with nematodes (N+) compared to nematode negative buffalo (N–) in calf, juvenile, subadult, and senescent buffalo but not adult buffalo (N = 13, 107, 92, 144, 56 for calf, juvenile, subadult, adult and senescent N– buffalo; N = 38, 448, 129, 208, 100 for N+ buffalo).
Fig. 1 in Nematode-coccidia parasite co-infections in African buffalo: Epidemiology and associations with host condition and pregnancy
Fig. 1. Age, sex and seasonal patterns of infection. Both parasites had the highest (a) prevalence (sample size for calf, juvenile, subadult, adult, and senescent respectively: N = 91, 555, 221, 326, 182) and (b) mean intensity in calves and juveniles (nematode N = 78, 448, 129, 208, 100; coccidia N = 58, 237, 55, 60, 14). (c) Males had lower estimated nematode prevalence and (d) higher estimated coccidia intensity compared to female buffalo. (e) The estimated nematode prevalence, coccidia prevalence, and (f) mean coccidia intensity were all increased in the early wet season compared to the late wet season. ‡Indicates significant differences at p <0.05.
Fig. 2 in Past, present and future of host‾parasite co-extinctions
Fig. 2. Comparison between helminth parasite diversity (for Acantocephala, Cestoda, Monogenea, Nematoda and Trematoda) in vertebrates (amphibians, birds, fish, mammals and reptiles) estimated using, respectively, the approach by Poulin and Morand (2004) (dark grey) and the more recent approach proposed by Strona and Fattorini (2014a) (light grey). Data were obtained from Table 1 in Strona and Fattorini (2014a).
Fig. 7 in Past, present and future of host‾parasite co-extinctions
Fig. 7. Schematic representation of the possible different parasitological consequences of a biological invasion. A: The invader loses its parasite and does not get local parasites; B: The invader loses its parasites and gets new ones from native hosts; C: The invader retains its parasites and these establish new symbioses with local species; D: The invader retains its parasites and acquire new parasites from local hosts; its parasites establish new symbioses with local host species; E: The invader does not lose its parasites, does not get new ones from native hosts, and its parasites do not expand their host range.
Fig. 6 in Past, present and future of host‾parasite co-extinctions
Fig. 6. Example of asymmetry of interactions as observed in all host parasite records available from FishPest dataset (Strona and Lafferty, 2012). The graph shows the relationship between the maximum specificity of the parasites using a certain host species, and the parasite richness on that host species. Boxplots correspond to different classes of hosts identified on the basis of the maximum specificity of their parasites. Thus, the first boxplot provides information on parasite species richness of all fish species whose most specific parasite uses just one host. It is apparent that specific parasites tend to use hosts harboring many parasites, while species-poor parasitofaunas are often composed by generalist parasites. Boxes indicate first and third quartiles, whiskers indicate range values, and horizontal lines indicate median values.
Fig. 5 in Past, present and future of host‾parasite co-extinctions
Fig. 5. Graph showing the relationship between fish parasite specificity and the corresponding average vulnerability of the hosts used by those parasites. Data were obtained using the same data and procedure as in Strona et al. (2013), computing mean host vulnerability values for different parasite host range classes. Differently from Strona et al. (2013), however, classes were defined using a logarithmic progression instead of a geometric one, resulting in an even tighter relationship between log(host range) and mean host vulnerability (rs = 0.93; p <0.05).
Fig. 3 in Past, present and future of host‾parasite co-extinctions
Fig. 3. Distribution of parasite specificity expressed as the logarithm of host range size in fish (A) and terrestrial vertebrates (B). Data for fish parasites (Acantocephala, Cestoda, Monogenea, Nematoda and Trematoda) were collected from FishPest (Strona and Lafferty, 2012). Data for parasites of terrestrial vertebrates (Acantocephala, Cestoda, Nematoda and Trematoda for amphibians, birds, mammals and reptiles) were collected from the Natural Museum History database (http://www.nhm.ac.uk). Since (as to June 11th 2015) all amphibians in the database are erroneously classified as reptiles, information was corrected using Catalogue of Life (http://www.catalogueoflife.org/). Y-axes indicate parasite species numbers.
Fig. 3 in Co-infection patterns of intestinal parasites in arboreal primates (proboscis monkeys, Nasalis larvatus) in Borneo
Fig. 3. Differences in width among trichurid egg morphotypes found in proboscis monkey feces. (T1 n = 11, T2 n = 30, T3 n = 30, T4 n = 2, and T5 n = 10). Median, boxes define the 25th and 75th percentiles, whiskers extend to maximum ± 1.5 times the interquartile range (IQR = middle 50% of the records). *p = 0.05; **p = 0.001; ***p = 0.0001.
Fig. 2 in Co-infection patterns of intestinal parasites in arboreal primates (proboscis monkeys, Nasalis larvatus) in Borneo
Fig. 2. Taxonomic diversity of helminth parasites found in proboscis monkeys. The five detected helminth orders were: the order Enoplida, trichurids (morphotypes T1-T4 genus Trichuris, T5 genus Anatrichosoma), the order Strongylida (morphotypes S1 genus Trichostrongylus, S2 genus Oesophagostomum/Ternidens, S3 unknown strongylid), the order Rhabditida, genus Strongyloides (R), the order Ascaridida, genus Ascaris (with exfoliated rough brown outer shell layer) (A) and the order Oxyurida, genus Enterobius (O). Scale bars = 50 Mm. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article).
Fig. 1 in Co-infection patterns of intestinal parasites in arboreal primates (proboscis monkeys, Nasalis larvatus) in Borneo
Fig. 1. Sample collection sites along the Kinabatangan River in Borneo. The island of Borneo, South-East Asia, with position of Lot 6 on the southern riverbank in the Lower Kinabatangan Wildlife Sanctuary in Sabah, Malaysian Borneo. Map reproduced according to GPS data points collected and mapped via Garmin Map Source (version 6.16.3).
Fig. 4 in Co-infection patterns of intestinal parasites in arboreal primates (proboscis monkeys, Nasalis larvatus) in Borneo
Fig. 4. Differences in length among strongylid egg morphotypes found in proboscis monkey feces. (S1 n = 30, S2 n = 30, and S3 n = 17). Median, boxes define the 25th and 75th percentiles, whiskers extend to maximum ± 1.5 times the interquartile range (IQR = middle 50% of the records). *p = 0.05; **p = 0.001; ***p = 0.0001.
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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.