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Fig. 26 in Catalogue of type specimens of braconid wasps (Hymenoptera: Braconidae) deposited in the National Museum, Prague, Czech Republic

Fig. 26. Holotype of Opius propodealis Fischer, 1958 with its labels (scale bar = 2.5 mm).

Fig. 16 in Catalogue of type specimens of braconid wasps (Hymenoptera: Braconidae) deposited in the National Museum, Prague, Czech Republic

Fig. 16. Allotype of Opius capeki Fischer, 1963 with its labels (scale bar = 2.5 mm).

Fig. 4 in Catalogue of type specimens of braconid wasps (Hymenoptera: Braconidae) deposited in the National Museum, Prague, Czech Republic

Fig. 4. Holotype of Dendrosotinus similis Bouček, 1955 with its labels (scale bar = 2.5 mm).

Fig. 31 in Catalogue of type specimens of braconid wasps (Hymenoptera: Braconidae) deposited in the National Museum, Prague, Czech Republic

Fig. 31. Paratypus of Orgilus tobiasi Taeger, 1989 (female) (scale bar = 2.5 mm).

Fig. 30 in Catalogue of type specimens of braconid wasps (Hymenoptera: Braconidae) deposited in the National Museum, Prague, Czech Republic

Fig. 30. Paratypus of Orgilus oehlkei Taeger, 1989 (scale bar = 2.5 mm).

Fig. 21 in Catalogue of type specimens of braconid wasps (Hymenoptera: Braconidae) deposited in the National Museum, Prague, Czech Republic

Fig. 21. Holotype of Opius magnicauda Fischer, 1958 with its labels (scale bar = 2.5 mm).

Fig. 2 in Catalogue of type specimens of braconid wasps (Hymenoptera: Braconidae) deposited in the National Museum, Prague, Czech Republic

Fig. 2. Holotype of Phanerotoma bicolor Šnoflák, 1958 with its labels (scale bar = 2.5 mm).

Fig. 1 in Catalogue of type specimens of braconid wasps (Hymenoptera: Braconidae) deposited in the National Museum, Prague, Czech Republic

Fig. 1. Holotype of Triaspis algiricus Šnoflák, 1958 with its labels (scale bar = 2.5 mm).

opencc-zeroFeb 2023View details →

dryad36/100

Shift of seed mass and fruit type spectra along longitudinal gradient: High water availability and growth allometry

CE1 Propagule traits vary among biomes along geographical gradients such as longitude, but the mechanisms that underlie these variations remain unclear. This study aims to explore seed mass variation patterns of different biome types along a longitudinal gradient and their underlying variation mechanisms by involving an in-depth analysis on the variation of seed mass, fruit type spectra, growth forms and dispersal mode spectra in Inner Mongolia and northeastern China. Plant community characterization and seed collection were conducted in 26 sites spreading over five vegetation types and covering 622 species belonging to 66 families and 298 genera. We found there are significantly declining trends for mean seed mass, vertebrate-dispersed species richness and fleshy-fruited species richness along a longitudinal gradient from forests to desert grasslands. However, we also found the lowest average seed mass and the smallest proportion of species dispersed by vertebrates occurring at typical grasslands in the five biomes. The variations of average seed mass display high congruence CE2 with transition of growth form spectra. The selection for these propagule attributes is driven mainly by climatic factors such as precipitation, temperature, soil moisture and evaporation, as well as by internal biotic factors such as growth forms, canopy coverage and leaf area. A hypothesis was provided that environmental factors and botanical traits that favor greater water availability lead to emergence (or speciation) of species with large seeds or fleshy fruits with high water content. Due to greater water availability and increasing leaf area, much more photosynthate (photosynthesis production) and allometric growth then ultimately increase the biome average seed mass from west to east. Phylogenetic signal or diversity are not found to be significantly involved in the effect on the patterns. A novel mechanistic framework and mathematical model are provided to expound seed variation among species or biomes.

dryad36/100

Data from: A real data-driven simulation strategy to select an imputation method for mixed-type trait data

Missing observations in trait datasets pose an obstacle for analyses in myriad biological disciplines. Considering the mixed results of imputation, the wide variety of available methods, and the varied structure of real trait datasets, a framework for selecting a suitable imputation method is advantageous. We invoked a real data-driven simulation strategy to select an imputation method for a given mixed-type (categorical, count, continuous) target dataset. Candidate methods included mean/mode imputation, k-nearest neighbour, random forests, and multivariate imputation by chained equations (MICE). Using a trait dataset of squamates (lizards and amphisbaenians; order: Squamata) as a target dataset, a complete-case dataset consisting of species with nearly completed information was formed for the imputation method selection. Missing data were induced by removing values from this dataset under different missingness mechanisms: missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR). For each method, combinations with and without phylogenetic information from single gene (nuclear and mitochondrial) or multigene trees were used to impute the missing values for five numerical and two categorical traits. The performances of the methods were evaluated under each missing mechanism by determining the mean squared error and proportion falsely classified rates for numerical and categorical traits, respectively. A random forest method supplemented with a nuclear-derived phylogeny resulted in the lowest error rates for the majority of traits, and this method was used to impute missing values in the original dataset. Data with imputed values better reflected the characteristics and distributions of the original data compared to complete-case data. However, caution should be taken when imputing trait data as phylogeny did not always improve performance for every trait and in every scenario. Ultimately, these results support the use of a real data-driven simulation strategy for selecting a suitable imputation method for a given mixed-type trait dataset.

opencc-zeroFeb 2023View details →

Fig. 51 in Primary types in the collection of molluscs in the KwaZulu-Natal Museum: Polyplacophora

Fig. 51. Leptochiton (L.) meiringae, holotype D265/T3046: spicules of girdle.

Fig. 52 in Primary types in the collection of molluscs in the KwaZulu-Natal Museum: Polyplacophora

Fig. 52. Chaetopleura (C.) natalensis, holotype D4842/T20: fragments of valves. Scale bar = 1 mm.

Figs 46, 47 in Primary types in the collection of molluscs in the KwaZulu-Natal Museum: Polyplacophora

Figs 46, 47. Leptochiton (L.) meiringae, holotype D265/T3046: lateral views. Scale bar = 1 mm.

Figs 25, 26. Callochiton herberti, holotype D5394 in Primary types in the collection of molluscs in the KwaZulu-Natal Museum: Polyplacophora

Figs 25, 26. Callochiton herberti, holotype D5394/T16: lateral views. Scale bar = 1 mm.

Fig. 11 in Primary types in the collection of molluscs in the KwaZulu-Natal Museum: Polyplacophora

Fig. 11. Lepidochitona (L.) dicksae, holotype V6152/T1539: spicules of the girdle.

Fig. 4 in Primary types in the collection of molluscs in the KwaZulu-Natal Museum: Polyplacophora

Fig. 4. Tonicia (Lucilina) carnosa, holotype H2576/T2395. Scale bar = 1 mm.

Fig. 10 in Primary types in the collection of molluscs in the KwaZulu-Natal Museum: Polyplacophora

Fig. 10. Lepidochitona (L.) dicksae, holotype V6152/T1539: radula.

Fig. 39. Acanthochitona jugotenuis, holotype H2565 in Primary types in the collection of molluscs in the KwaZulu-Natal Museum: Polyplacophora

Fig. 39. Acanthochitona jugotenuis, holotype H2565/T2396. Scale bar = 1 mm.

opencc-by-4.0Apr 2007View details →

Fig. 3. Isolated type A coprolites viewed using transmitted-light microscopy. Scale bars 100 in The ecological role of immature phantom midges (Diptera: Chaoboridae) in the Eocene Lake Messel, Germany

Fig. 3. Isolated type A coprolites viewed using transmitted-light microscopy. Scale bars 100 µm.