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FIGURE 2. Lysimachia porcatisepala S. R in Lysimachia porcatisepala, a new species of Lysimachia (Primulaceae) from Chongqing, China

FIGURE 2. Lysimachia porcatisepala S. R. Yi. A. Habit. B. Inflorescence (adaxially). C. Inflorescence (abaxially). D. Corolla. E. Corolla opened. F. Flower bud. G. Leaf blade. H. Bract. I. Pistil. J. Calyx lobes. K. Stamen.

FIGURE 11 in The taxonomic status of Ruschia stricta var. turgida and R. promontorii and a new name for R. vaginata (Ruschieae, Aizoaceae)

FIGURE 11. Habitat of Ruschia promontorii, rocky outcrops among fynbos. Cape Point Nature Reserve, August 2006, South Africa (photo by N. Helme).

FIGURE 2 in The taxonomic status of Ruschia stricta var. turgida and R. promontorii and a new name for R. vaginata (Ruschieae, Aizoaceae)

FIGURE 2. Ruschia stricta var. turgida, cult. Vanrhynsdorp, Frames sub BOL 19029; (drawing by B.O. Carter).

FIGURE 3 in The taxonomic status of Ruschia stricta var. turgida and R. promontorii and a new name for R. vaginata (Ruschieae, Aizoaceae)

FIGURE 3. Ruschia turgida (= R. stricta var. turgida) showing the bright green leaves with translucent margins along the edges and the 6-locular fruits. Farm Moedverloren, Knersvlakte, South Africa, October 2019 (photo by U. Schmiedel).

FIGURE 6 in The taxonomic status of Ruschia stricta var. turgida and R. promontorii and a new name for R. vaginata (Ruschieae, Aizoaceae)

FIGURE 6. Monotypic stands of Ruschia turgida (= R. stricta var. turgida) in broad valleys surrounded by low, quartz-gravel covered hills. Farm Moedverloren, Knersvlakte, South Africa, October 2019 (photo by U. Schmiedel).

FIGURE 4 in The taxonomic status of Ruschia stricta var. turgida and R. promontorii and a new name for R. vaginata (Ruschieae, Aizoaceae)

FIGURE 4. Fruit of Ruschia turgida (=Ruschia stricta var. turgida). Top of open fruit and side view of closed fruit, Klak 2614. Scale: Distances between white lines indicate 1 mm.

FIGURE 9 in The taxonomic status of Ruschia stricta var. turgida and R. promontorii and a new name for R. vaginata (Ruschieae, Aizoaceae)

FIGURE 9. Ruschia promontorii, Cape Point, Western Cape, South Africa, Rohland sub NBG 1543/20 (drawing by M.M. Page).

FIGURE 10 in The taxonomic status of Ruschia stricta var. turgida and R. promontorii and a new name for R. vaginata (Ruschieae, Aizoaceae)

FIGURE 10. Amphibolia laevis, Saldanha Bay, Western Cape, South Africa, Hutchinson 288 (BOL) (drawing by B.O. Carter).

opencc-by-4.0Sep 2024View details →

R Data for CPBS Report 23SDSU01 - Urban Demographic Shift of Pedestrian and Bicyclist Collisions, Equity, and Police Enforcement

Data for the statistical program R.

opennotspecifiedJun 2020View details →

FIGURE 1. Ceratopteris shingii Y.H. Yan & R in Ceratopteris shingii, a new species of Ceratopteris with creeping rhizomes from Hainan, China

FIGURE 1. Ceratopteris shingii Y.H. Yan & R. Zhang. Drawn by Wen Shao based on the holotype Fern 09770 (CSH). A. Habit; B. Enlarged fertile frond anatomy/portion for sporangium; C. Scale; D. Spore.

Análisis de microbiomas en R / Microbiome Analysis in R

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opencc-by-4.0Sep 2024View details →

Data and R File - The Role of Nutrient and Energy Limitation on Microbial Decomposition of Deep Podzolized Carbon: A Priming Experiment

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Neutral-current DIS event samples generated with MadGraph5 at leading order with $E_e = 27.5$ GeV, $E_p = 820$ GeV, and $\mu^2_\mathrm{F} = \mu^2_\mathrm{R} = Q^2$

Neutral-current deep inelastic scattering event samples at leading order, generated with MadGraph5 version 3.5.5 and saved in the Les Houches Event File format as a tarball. Beam energies of 27.5 GeV for the electron, 820 GeV for the proton. Renormalization and factorization scales (squared) set to photon virtuality $Q^2$. All cuts and parameter values are specified in the header of the event files.

The data of the mesh used in: Pan M, Zou R, Jüttler B. Algorithms and Data Structures for Cs-smooth RMB-splines of Degree 2s+ 1. Computer Aided Geometric Design, 2024: 102389.

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Data for "The Dust Extinction Curve: Beyond R(V)"

24 million dust extinction curves, detemined from Gaia XP spectra, as described in <a href="https://ui.adsabs.harvard.edu/abs/2024arXiv241022537G/abstract">Green, Zhang & Zhang (2025)</a>. We represent the extinction curves using a set of 16 basis vectors. For each star, there are 16 coefficients, which can be used to reconstruct the extinction curves. After loading <code>A_zp</code> and <code>G_subspace</code> from the file <code>G_subspace.json</code>, and <code>coeffs</code> from the file <code>coeffs.h5</code>, the extinction curves can be reconstructed using:     <code>A = A_zp + np.sum(coeffs[None,:] * G_subspace[:,:], axis=1)</code> The output <code>A</code> will have shape (star, wavelength). The wavelengths at which <code>A</code> is sampled are stored in the field <code>wavelengths_nm</code> (in nanometers), in <code>G_subspace.json</code>. The covariance matrix of the coefficients for each star is stored in the files <code>coeffs_cov_?.h5</code>. We store the diagonals and the upper triangles of the covariances separately. They can be reconstructed using the function <code>reconstruct_symm_matrices</code> from <code>symm_matrix_utils.py</code>:     <code>from symm_matrix_utils.py import reconstruct_symm_matrices</code>     <code>cov = reconstruct_symm_matrices(cov_diag, cov_triu_wo_diag)</code> Additionally, we store the inverse covariance matrices in the files <code>coeffs_icov_?.h5</code>, in the same manner as the covariance matrices. The file <code>source_info.h5</code> contains a few useful Gaia fields and parameter estimates (with corresponding uncertainties) from <a href="https://ui.adsabs.harvard.edu/abs/2024arXiv240714594Z/abstract">Zhang & Green (2025)</a>. The file <code>feature_EW.h5</code> contains the equivalent widths (in nanometers) of the VBS and the 770 and 850 nm extinction features. Every file contains the Gaia DR3 <code>source_id</code> of every star, labeled <code>gdr3_source_id</code>.

Rendered HOTSSea model output for the pacea R package

This files are for the pacea R package https://github.com/pbs-assess/pacea and are automatically downloaded to your local machine when running the function pacea::hotssea_all_variables() . See the hotssea vignette for details.  The Hindcast of the Salish Sea (HOTSSea) is a physical ocean model that recreates conditions throughout the Salish Sea from 1980 to 2018, filling in the gaps in patchy measurements. See pacea for reference.

opencc-by-4.0Nov 2024View details →

Dataset and R code for "Relationship between wind speed and plant hydraulics at the global scale"

Data collection       Plant hydraulic traits and height data were obtained from three sources: (1) field measurements of plant hydraulics for 210 forest species in China; (2) the TRY Plant Traits Database (https://www.try-db.org/TryWeb/Home.php; Kattge et al., 2020); and (3) published literature. For the latter we conducted searches on Web of Science, Google Scholar, and China National Knowledge Infrastructure (http://www.cnki.net) using keywords such as “hydraulic traits,” “xylem hydraulic conductivity,” “xylem vulnerability,” “water potential at 50% loss of hydraulic conductivity,” “xylem embolism resistance,” and “plant water conductivity.” A substantial portion of data in our study were obtained from published literature (Choat et al., 2012; Gleason et al., 2016) and the Xylem Functional Traits Database (XFT; <a href="https://xylemfunctionaltraits.org/">https://xylemfunctionaltraits.org</a>). To minimize ontogenetic and methodological variation, we only included data that met the following criteria: (a) plants were grown in natural ecosystems, excluding greenhouse and common garden experiments; (b) measurements were made on adult plants and not on seedlings; (c) hydraulic traits were measured on terminal stem or branch segments in the sapwood at the crown; (d) trait data were calculated as the mean value for each species at the same site when data were from multiple sources; and (e) data values > 3 SD (standard deviation) were removed to reduce the effect of outliers (Carmona et al., 2021); (f) height data were reported at the same site where plant hydraulic traits were measured.  Climate data were obtained either from the original reports or from WorldClim version 2 (http://worldclim.org/version2; Fick & Hijmans, 2017; Table 1) if the original data were not available. The following variables measured at ~1 km2 scale were extracted from WorldClim: mean annual wind speed (μ), mean annual precipitation, mean annual temperature, precipitation seasonality, temperature seasonality, wind seasonality (μS; coefficient of variation across monthly measurements × 100), precipitation of driest month, and minimum temperature of coldest month. The VPD data were extracted from the TerraClimate dataset (http://www.climatologylab.org/terraclimate.html; Abatzoglou et al., 2018). Annual PET (potential evapotranspiration) data were extracted from the CGIAR-CSI consortium (http://www.cgiar-csi.org/data; Zomer et al., 2008). Moisture index (MI), which is the ratio of precipitation to PET.  Data analysis Trait and environment data were log10-transformed to achieve approximate normality, except for P50 and temperature data. We first calculated correlations among all climatic variables and for subsequent analyses retained only those variables with correlation coefficients lower than |0.7| (Dormann et al., 2013). We then ran independent multiple linear models for each trait of interest using the retained climatic variables. Model selection based on a corrected Akaike information criterion and using the R package glmulti (Calcagno & de Mazancourt, 2010), identified the best linear model for each trait. The R package ‘visreg’ (Breheny & Burchett, 2017) was used to visualize the partial relationships between wind speed and hydraulic traits. Two-dimensional contour plots were then used to explore and visualise how plant hydraulic traits varied simultaneously with wind speed and moisture index. To quantify the strength of wind effects on plant hydraulics, models with wind parameters μ and μS included were compared to those without these wind parameters.  To test for differences in the relationship between hydraulic traits and wind speed among species grouped into different climatic regions (i.e., dry vs. wet sites, and tropical vs. temperate regions), we used standardized major axis (SMA) analyses using the R package ‘smatr’ (Warton et al., 2012). A grouping factor was added in each SMA to test whether species groups share a common slope, with p > 0.05 indicating species groups share a common slope. Variance partitioning analysis was performed using the ‘rdacca. hp’ R package to quantify the degree to which the effect of wind speed was independent from other climatic variables (Lai et al., 2022). The individual contribution of each predictor was estimated in this analysis. This analysis also helped to illustrate the significant values of climatic variables on plant hydraulics. A Random Forest machine-learning algorithm (implemented using the R package ‘randomForest’) was utilized to further assess the relative importance of environmental variables for each plant hydraulic trait (Breiman, 2001). To avoid multicollinearity, this analysis only included variables with correlation coefficients lower than |0.7|. A higher value of the mean decrease in accuracy (%IncMSE) indicates the increased importance of a variable (e.g., a %IncMSE value of 50 indicates that the overall mean square error would increase by 50% if that variable were to be excluded from the analysis). This provides a measure of a variable's importance in estimating the value of the target variable across the trees in the forest.

dryad32/100

Data from: Regional variation in drivers of connectivity for two frog species (Rana pretiosa and R. luteiventris) from the U.S. Pacific Northwest

Comparative landscape genetics has uncovered high levels of variation in which landscape factors affect connectivity among species and regions. However, the relative importance of species traits vs. environmental variation for predicting landscape patterns of connectivity is unresolved. We provide evidence from a landscape genetics study of two sister taxa of frogs, the Oregon spotted frog (Rana pretiosa) and the Columbia spotted frog (R. luteiventris) in Oregon and Idaho, USA. Rana pretiosa is relatively more dependent on moisture for dispersal than R. luteiventris for dispersal, so if species traits influence connectivity, we predicted that connectivity among R. pretiosa populations would be more positively associated with moisture than R. luteiventris. However, if environmental differences are important drivers of gene flow, we predicted that connectivity would be more positively related to moisture in arid regions. We tested these predictions using eight microsatellite loci and gravity models in two R. pretiosa regions and four R. luteiventris regions (n = 1,168 frogs). In R. pretiosa, but not R. luteiventris, connectivity was positively related to mean annual precipitation, supporting our first prediction. In contrast, connectivity was not more positively related to moisture in more arid regions. Various temperature metrics were important predictors for both species and in all regions, but the directionality of their effects varied. Our results indicate that connectivity in R. pretiosa may be negatively impacted by reduction in mean annual precipitation. Overall, the pattern of variation in drivers of connectivity was consistent with predictions based on species traits rather than on environmental variation.

opencc-zeroDec 2017View details →

opennotspecifiedJun 2021View details →

FIGURES 7-13 in Description of two new species of Rhamphus related to R. oxyacanthae (Curculionidae, Curculioninae, Rhamphini) from Italy based on a morphological study supported by molecular data

FIGURES 7-13. Head in dorsal view. Tubercles (shadow) at antennal base in (7) Rhamphus oxyacanthae; (8) R. bavierai. Vertex of head (shadow) of (9) Rhamphus oxyacanthae (flat); (10) R. bavierai (moderately convex). Uncus of mesotibia of (11) Rhamphus oxyacanthae; (12) R. monzinii; (13) R. pulicarius.