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1,445 results for “species richness.”
Figure 10 in Shifting the known richness of Paramycodrosophila Duda, 1924 (Diptera: Drosophilidae): the description of nineteen new species in the Neotropical region
Figure 10 Holotype of Paramycodrosophila boldrinii n. sp.: A, posterior end of abdomen (ventral view); B, posterior end of abdomen (lateral view); C, articulated periphallic and phallic organs (ventral view); D, articulated periphallic and phallic organs (lateral view); E, epandrium and associated sclerites (posterior view); F, epandrium and associated sclerites (posterolateral view); G, hypandrium, phallus and associated sclerites (ventral view); H, hypandrium, phallus and associated sclerites (lateroventral view); I, hypandrium, phallus and associated sclerites (lateral view). Scale bars: 0.1 mm.
Figure 13 in Shifting the known richness of Paramycodrosophila Duda, 1924 (Diptera: Drosophilidae): the description of nineteen new species in the Neotropical region
Figure 13 Holotype of Paramycodrosophila amazonensis n. sp.: A, head (anterolateral view); B, thorax (dorsal view); C, head and thorax (lateral view); D, abdomen (lateral view); E, abdomen (dorsal view); F, wing.
Figure 8 in Shifting the known richness of Paramycodrosophila Duda, 1924 (Diptera: Drosophilidae): the description of nineteen new species in the Neotropical region
Figure 8 Holotype of Paramycodrosophila rafaeli n. sp.: A, posterior end of abdomen (ventral view); B, posterior end of abdomen (lateral view); C, articulated periphallic and phallic organs (ventral view); D, articulated periphallic and phallic organs (lateral view); E, epandrium and associated sclerites (posterior view); F, epandrium and associated sclerites (ventral view); G, hypandrium, phallus and associated sclerites (ventral view); H, hypandrium, phallus and associated sclerites (lateroventral view); I, hypandrium, phallus and associated sclerites (lateral view). Scale bars: 0.1 mm.
Figure 9 in Shifting the known richness of Paramycodrosophila Duda, 1924 (Diptera: Drosophilidae): the description of nineteen new species in the Neotropical region
Figure 9 Holotype of Paramycodrosophila boldrinii n. sp.: A, head (anterolateral view); B, thorax (dorsal view); C, head and thorax (lateral view); D, abdomen (lateral view); E, abdomen (dorsal view); F, wing.
Figure 30 Paramycodrosophila marinhoi n in Shifting the known richness of Paramycodrosophila Duda, 1924 (Diptera: Drosophilidae): the description of nineteen new species in the Neotropical region
Figure 30 Paramycodrosophila marinhoi n. sp. (HBTL02): A, posterior end of abdomen (ventral view); B, posterior end of abdomen (lateral view); C, oviscapt (ventral view); D, oviscapt (lateral view); E, spermatheca (lateral view); F, choria (left, lateral view; right, dorsal view). Scale bars: 0.1 mm.
Figure 31 in Shifting the known richness of Paramycodrosophila Duda, 1924 (Diptera: Drosophilidae): the description of nineteen new species in the Neotropical region
Figure 31 Holotype of Paramycodrosophila viscondedemauaensis n. sp.: A, head (anterolateral view); B, thorax (dorsal view); C, thorax (lateral view); D, abdomen (lateral view); E, abdomen (dorsal view); F, wing.
Figure 35 in Shifting the known richness of Paramycodrosophila Duda, 1924 (Diptera: Drosophilidae): the description of nineteen new species in the Neotropical region
Figure 35 Holotype of Paramycodrosophila pedraseladensis n. sp.: A, head (anterolateral view); B, thorax (dorsal view); C, head and thorax (lateral view); D, abdomen (lateral view); E, abdomen (dorsal view); F, wing.
Figure. Map of Georgia with main administrative divisions delimited. Shade intensity indicates the richness of mayfly species for respective administrative unit. Dots indicate localities sampled for mayflies prior to this study. in The first annotated checklist of mayflies (Ephemeroptera: Insecta) of Georgia with new distribution data and a new record for the country
Figure. Map of Georgia with main administrative divisions delimited. Shade intensity indicates the richness of mayfly species for respective administrative unit. Dots indicate localities sampled for mayflies prior to this study.
Figure 2 in Life in the extreme environment: Structure and species richness of bird assemblages on Yuzhny Island of Novaya Zemlya, Russia
Figure 2. Structure of bird assemblages on Yuzhny Island of Novaya Zemlya. (A) Cluster analysis of bird assemblages. Ward's classification approach was applied to a Euclidean distance matrix, which was calculated based on bird species abundances (categorical estimations by using a five-point logarithmic scale). (B)-(C) Histograms of the distribution of bird ecological groups (number of species in each group) over the range of habitats (B) and over relative occurrence of each species through habitats (C).
Diversity, Species Richness and Community Composition of Wetland Birds in the Lowlands of Western Nepal
Open the record for dataset details and reuse information.
FIGURE 2. Species richness distribution within the 56 0.5 in Updated checklist and conservation status of Cactaceae in the state of Durango, Mexico
FIGURE 2. Species richness distribution within the 56 0.5° × 0.5° cells.
Linked collectors and determiners for: The Ceratocanthinae of Ulu Gombak: high species richness at a single site, with descriptions of three new species and an annotated checklist of the Ceratocanthinae of Western Malaysia and Singapore (Coleoptera, Scarabaeoidea, Hybosoridae).
Natural history specimen data linked to collectors and determiners held within, "The Ceratocanthinae of Ulu Gombak: high species richness at a single site, with descriptions of three new species and an annotated checklist of the Ceratocanthinae of Western Malaysia and Singapore (Coleoptera, Scarabaeoidea, Hybosoridae)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/7a68e13d-3c05-49d5-86a3-7bdb5c4af1ce">https://bionomia.net/dataset/7a68e13d-3c05-49d5-86a3-7bdb5c4af1ce</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/7a68e13d-3c05-49d5-86a3-7bdb5c4af1ce">https://gbif.org/dataset/7a68e13d-3c05-49d5-86a3-7bdb5c4af1ce</a>. Formatted as a Frictionless Data package.
Data from: Habitat amount, not patch size and isolation, drives species richness of macro‐moth communities in countryside landscapes
Aim: Our aim is to test whether species richness patterns are best explained by the effect of the total amount of habitat within the landscape, or instead by a combination of patch size and patch isolation effects. To this end, we jointly contrast the habitat amount hypothesis and countryside biogeography with patch size and isolation concepts from island biogeography. Location: Three multi-habitat landscapes in Peneda-Gerês National Park, NW Portugal. Taxon: Macro-moths (Lepidoptera). Methods: Light-trapping using a semi-nested design at 84 fixed sites which were each repeatedly sampled six times. Results: Autocovariate models show that sampling sites with a higher number of forest and meadow macro-moth species (alpha diversity) were surrounded by a higher amount of forest and meadow habitat, respectively within a 160 and 320 m radius (scale of effect). These top-ranked models, containing only habitat amount as a significant variable, had lower AIC than models (only) containing patch size and/or isolation. Complementary to this, the countryside species-area relationship (SAR) model outperforms the classic SAR model, so that the effective area of habitat explains landscape species richness (gamma diversity) across spatial scales (beta diversity) better than the classic SAR. Specifically, we show that forest macro-moths have a higher spatial turnover than meadow macro-moths and that, on average, there are more species in forest than in meadow habitat. Main conclusions: The habitat amount hypothesis predicts alpha species richness in multi-habitat landscapes better than do patch size and isolation while the countryside SAR predicts beta and gamma diversity better than the classic SAR. We suggest that evidence is mounting to revise the application of the classical approaches of island biogeography and metapopulation theory to conservation biogeography.
Data from: Biodiversity change is uncoupled from species richness trends: consequences for conservation and monitoring
1. Global concern about human impact on biological diversity has triggered an intense research agenda on drivers and consequences of biodiversity change in parallel with international policy seeking to conserve biodiversity and associated ecosystem functions. Quantifying the trends in biodiversity is far from trivial, however, as recently documented by meta-analyses which report little if any net change of local species richness through time. 2. Here, we summarize several limitations of species richness as a metric of biodiversity change and show that the expectation of directional species richness trends under changing conditions is invalid. Instead, we illustrate how a set of species turnover indices provide more information content regarding temporal trends in biodiversity, as they reflect how dominance and identity shift in communities over time. 3. We apply these metrics to three monitoring data sets representing different ecosystem types. In all data sets, nearly complete species turnover occurred, but this was disconnected from any species richness trends. Instead, turnover was strongly influenced by changes in species presence (identities) and dominance (abundances). We further show that these metrics can detect phases of strong compositional shifts in monitoring data and thus identify a different aspect of biodiversity change decoupled from species richness. 4. Synthesis and application: Temporal trends in species richness are insufficient to capture key changes in biodiversity in changing environments. In fact, reductions in environmental quality can lead to transient increases in species richness if immigration or extinction have different temporal dynamics. Thus, biodiversity monitoring programs need to go beyond analyses of trends in richness in favour of more meaningful assessments of biodiversity change.01-Jun-2017
Data from: The impact of seasonality on niche breadth, distribution range and species richness: a theoretical exploration of Janzen's hypothesis
Being invoked as one of the candidate mechanisms for the latitudinal patterns in biodiversity, Janzen's hypothesis states that the limited seasonal temperature variation in the tropics generates greater temperature stratification across elevations, which makes tropical species adapted to narrower ranges of temperatures and have lower effective dispersal across elevations than species in temperate regions. Numerous empirical studies have documented latitudinal patterns in species elevational ranges and thermal niche breadths that are consistent with the hypothesis, but the theoretical underpinnings remain unclear. This study presents the first mathematical model to examine the evolutionary processes that could back up Janzen's hypothesis and assess the effectiveness of limited seasonal temperature variation to promote speciation along elevation in the tropics. Results suggest that trade-offs in thermal tolerances provide a mechanism for Janzen's hypothesis. Limited seasonal temperature variation promotes gradient speciation not due to the reduction in gene flow that is associated with narrow thermal niche, but due to the pleiotropic effects of more stable divergent selection of thermal tolerance on the evolution of reproductive incompatibility. The proposed modelling approach also provides a potential way to test a speciation model against genetic data.
Figure 3 from: Zumstein P, Bruelheide H, Fichtner A, Schuldt A, Staab M, Härdtle W, Zhou H, Assmann T (2021) What shapes ground beetle assemblages in a tree species-rich subtropical forest? In: Spence J, Casale A, Assmann T, Liebherr JК, Penev L (Eds) Systematic Zoology and Biodiversity Science: A tribute to Terry Erwin (1940-2020). ZooKeys 1044: 907-927. https://doi.org/10.3897/zookeys.1044.63803
Figure 3 Relationships between ground beetle biomass and canopy cover (A) and herb cover (B). Black lines indicate significant relationships at p < 0.05 obtained from mixed-effects models (keeping other significant predictors fixed at their means) with grey areas indicating the 95% confidence intervals. Points (slightly jittered to improve visibility) represent observed values per trap. The fixed-effects explained 30% of the variation in ground beetle biomass.
Figure 4 from: Zumstein P, Bruelheide H, Fichtner A, Schuldt A, Staab M, Härdtle W, Zhou H, Assmann T (2021) What shapes ground beetle assemblages in a tree species-rich subtropical forest? In: Spence J, Casale A, Assmann T, Liebherr JК, Penev L (Eds) Systematic Zoology and Biodiversity Science: A tribute to Terry Erwin (1940-2020). ZooKeys 1044: 907-927. https://doi.org/10.3897/zookeys.1044.63803
Figure 4 Representatives of ground beetles from pitfall traps and flight interception traps in Gutianshan NP ACarabus kiukiangensisBCarabus davidisCLioptera erotyloidesDTricondyla macrodera.
Figure 2 from: Zumstein P, Bruelheide H, Fichtner A, Schuldt A, Staab M, Härdtle W, Zhou H, Assmann T (2021) What shapes ground beetle assemblages in a tree species-rich subtropical forest? In: Spence J, Casale A, Assmann T, Liebherr JК, Penev L (Eds) Systematic Zoology and Biodiversity Science: A tribute to Terry Erwin (1940-2020). ZooKeys 1044: 907-927. https://doi.org/10.3897/zookeys.1044.63803
Figure 2 Relationships between ground beetle species richness and canopy cover (A) and herb cover (B). Black lines indicate significant relationships at p < 0.05 obtained from mixed-effects models (keeping other significant predictors fixed at their means) with grey areas indicating the 95% confidence intervals. Points represent observed values per trap. Note that some traps had similar richness and predictor values. The fixed-effects explained 12% of the variation in ground beetle species richness.
Figure 1 from: Zumstein P, Bruelheide H, Fichtner A, Schuldt A, Staab M, Härdtle W, Zhou H, Assmann T (2021) What shapes ground beetle assemblages in a tree species-rich subtropical forest? In: Spence J, Casale A, Assmann T, Liebherr JК, Penev L (Eds) Systematic Zoology and Biodiversity Science: A tribute to Terry Erwin (1940-2020). ZooKeys 1044: 907-927. https://doi.org/10.3897/zookeys.1044.63803
Figure 1 Relationships between ground beetle abundance and canopy cover (A), herb cover (B) and pH-value of the soil (C). Black lines indicate significant relationships at p < 0.05 obtained from mixed-effects models (keeping other significant predictors fixed at their means) with grey areas indicating the 95% confidence intervals. Points represent observed values per trap. Note that some traps had similar abundance and predictor values. The fixed-effects explained 22% of the variation in ground beetle abundance.
Figure 1 from: Dole SA, Hulcr J, Cognato AI (2021) Species-rich bark and ambrosia beetle fauna (Coleoptera, Curculionidae, Scolytinae) of the Ecuadorian Amazonian Forest Canopy. In: Spence J, Casale A, Assmann T, Liebherr JК, Penev L (Eds) Systematic Zoology and Biodiversity Science: A tribute to Terry Erwin (1940-2020). ZooKeys 1044: 797-813. https://doi.org/10.3897/zookeys.1044.57849
Figure 1 A species accumulation curves for Onkone Gare B species accumulation curves for Tiputini C species richness estimators for Onkone Gare D species richness estimators for Tiputini E species accumulation curves for both sites combined F species richness estimators for both sites combined.
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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.