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opencc-by-4.0Aug 2024View details →

Field data used in "A field-based estimation of the variability of particle entrainment in coarse-bed rivers"

This file is a companion file to "A field-based estimation of the variability of particle entrainment in coarse-bed rivers ", co-authored by Daniel Vázquez-Tarrío, Estrella Carrero-Carralero, Raúl López, Fanny Ville, Damià Vericat and Ramon J. Batalla . This is a paper published in the Journal of Geophysical Research: Earth Surface, 129 (10) (https://doi.org/10.1029/2024JF007695). This Excel file contains all the raw data used in this research.

Fig. 4 in Estimating parasite-condition relationships and potential health effects for fallow deer (Dama dama) and red deer (Cervus elaphus) in Denmark

Fig. 4. Body condition scores (BCS) of fallow deer (Dama dama) and red deer (Cervus elaphus) individuals in relation to the sum of endoparasite taxa detected. Black solid lines show the mean predicted relation for each deer species and grey areas are the 95% confidence intervals. Colours and shapes of points indicate the classification of individuals into groups (two for fallow deer and four for red deer) based on their (dis)similarity in total endoparasite load (Pload) and BCS as determined by k-means cluster analyses (see text for details). (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 Estimating parasite-condition relationships and potential health effects for fallow deer (Dama dama) and red deer (Cervus elaphus) in Denmark

Fig. 1. Map of Denmark and its location within Europe (see red square in inset) showing the distribution of the four study sites (Dyrehaven – pink square; Gyldensteen strand – green circle, Høstemark – blue triangle; Tofte – purple diamond) where fallow deer (Dama dama; n = 20) and/or red deer (Cervus elaphus; n = 21) were sampled. (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 Estimating parasite-condition relationships and potential health effects for fallow deer (Dama dama) and red deer (Cervus elaphus) in Denmark

Fig. 2. Barplots showing the sum of endoparasite taxa detected in each individual fallow deer (Dama dama; n = 20) and red deer (Cervus elaphus; n = 21) sampled across four areas in Denmark. Note that individuals were also screened for presence of Cryptosporidium spp. and two species of liver flukes (Table 1), but none were detected and thus are not placed here. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

opencc-by-4.0Apr 2023View details →

Fig. 1 in Estimating parasite infrapopulation size given imperfect detection: Proof-of-concept with ectoparasitic fleas on prairie dogs

Fig. 1. Left: Frequency histogram of raw (field) flea count indices from prairie dogs. Middle and right: Huggins closed captures model estimates for fleas combed from prairie dogs, including a histogram of estimated flea counts (infrapopulation size = ̂N) and a positive correlation between Julian date and individual flea detection probability (here, p from the first combing occasion within primary trapping occasions). In the histograms, counts of 0 fleas (gray bars) are presented for illustration; those data were not analyzed herein, because the Huggins closed captures models 'condition' on primary occasions with at least 1 flea being detected (black bars). Model output is from the top model in Table 1. On the right, dotted lines are 95% confidence intervals.

Fig. 3 in Estimating parasite-condition relationships and potential health effects for fallow deer (Dama dama) and red deer (Cervus elaphus) in Denmark

Fig. 3. Body condition scores (BCS) of individual a) fallow deer (Dama dama) and b) red deer (Cervus elaphus) in relation to the absence (0) and presence (1) of 11 endoparasite taxa. Panels outlined in red indicate a significantly (p <0.05) lower BCS with presence of that endoparasite taxon, while panels outlined in green indicate a trend (p <0.1) for higher BCS with presence of that endoparasite taxon. Size of presence data points is scaled relative to the number of larvae, eggs or (oo) cyst excreted per gram faeces except for Babesia divergens and Toxoplasma gondii, which were either present (1) or absent (0). Note that all individuals were also screened for presence of Cryptosporidium spp. and two species of liver flukes (Table 1), but none were detected and thus not plotted here. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 5 in Estimating parasite-condition relationships and potential health effects for fallow deer (Dama dama) and red deer (Cervus elaphus) in Denmark

Fig. 5. Differences in serum chemistry between groups of individuals with varying body condition scores (BCS) and endoparasite load (Pload) for fallow deer (Dama dama; a,b,c) and red deer (Cervus elaphus; d,e,f). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

FIGURE 5 in A simulation-based examination of residual diversity estimates as a method of correcting for sampling bias

FIGURE 5. The performance of different implementations of the residual diversity estimate when a specific bias is forced to be the dominant influence. (5.1) Mean Spearman's rho values of four implementations of the RDE using the Smith and McGowan method. PFORM and PTAPH are set at 0.9 to minimise their influence, PLOC is variable. PMIST set at 0.1. (5.2) Mean Spearman's rho values of four implementations of the RDE using the Smith and McGowan method. LOC and PTAPH are set at 0.9 to minimise their influence, PFORM is variable. PMIST set at 0.1. The dashed red line indicates the critical value at p=0.05. Abbreviations as in Table 1.

FIGURE 7 in A simulation-based examination of residual diversity estimates as a method of correcting for sampling bias

FIGURE 7. Sample simulation comparing the results of the taxic, phylogenetic and residual diversity estimates to the true diversity. PFROM, PLOC and PTAPH set at 0.25. PMIST set at 0.1. Black box highlights instance where the Signor Lipps effect has been exaggerated by the PDE; the TDE and RDE both identify the rapid diversity decrease present in the true diversity. Abbreviations as in Table 1.

FIGURE 6 in A simulation-based examination of residual diversity estimates as a method of correcting for sampling bias

FIGURE 6. The performance of the phylogenetic diversity estimate when errors are introduced to the phylogeny. Mean Spearman's rho values of the PDE, TDE and the best performing implementation of the RDE. PLOC, PFORM and PTAPH set at 0.25. PMIST variable. The dashed red line indicates the critical value at p=0.05. Abbreviations as in Table 1.

FIGURE 4 in A simulation-based examination of residual diversity estimates as a method of correcting for sampling bias

FIGURE 4. The performance of different implementations of the residual diversity estimate examining faunas with varying degrees of homogeneity. PFORM, PLOC and PTAPH are set at 0.25. The rate of dispersal is increased relative to the rate of local extinction to increase the homogeneity of the faunas. The dashed red line indicates the critical value at p=0.05. Abbreviations as in Table 1.

FIGURE 1 in A simulation-based examination of residual diversity estimates as a method of correcting for sampling bias

FIGURE 1. An illustration of the taphonomic filter in the simulation, shown applied to a single taxon in a single time bin. The taxon is originally present in every locality in each region it occupies, but the taphonomic filter removes it from randomly selected localities

FIGURE 3 in A simulation-based examination of residual diversity estimates as a method of correcting for sampling bias

FIGURE 3. The performance of different implementations of the residual diversity estimate (RDE) under different sampling regimes. (3.1) Mean Spearman's rho values of four implementations of the RDE using Formations as a proxy, with values of PFORM, PLOC and PTAPH variable but equal. (3.2) Mean Spearman's rho values of four implementations of the RDE using Localities as a proxy. (3.3) Mean Spearman's rho values of four implementations of the RDE, all using the Smith and McGowan method. (3.4) Mean Spearman's rho values of the taxic and phylogenetic diversity estimate compared to those of the optimum implementation of the RDE. The dashed red line indicates the critical value at p=0.05. Abbreviations as in Table 1.

FIGURE 2 in A simulation-based examination of residual diversity estimates as a method of correcting for sampling bias

FIGURE 2. An illustration of how sampling proxies are generated in this simulation. This schematic illustrates which formations and localities in a single time bin contain fossils of at least one species of the simulated clade after application of the taphonomic filter. Formations and localities are removed at random, representing a lack of sampling. Note that the number of clade-bearing formations and localities does not necessarily equal the number of formations and localities sampled, allowing the generation of four sampling proxies.

opencc-by-4.0Aug 2024View details →

Spatial Feature Engineering Dataset for Forest Aboveground Biomass Estimation Using Landsat Imagery

Study Area: The dataset covers forested regions in Oregon, Washington, Idaho, and eastern Montana, characterized by diverse climatic conditions due to orographic effects. The forests in the Coast Range and western slopes of the Cascades, with high precipitation (800-3000 mm annually), contrast with the drier forests in Idaho and Montana, which receive over 400 mm annually. The dataset includes highly productive Douglas-fir and western hemlock forests, with aboveground biomass (AGB) densities exceeding 1200 Mg ha⁻¹, as well as fire-adapted lodgepole and ponderosa pine forests in the rainshadow regions. LiDAR AGB Estimates: The dataset includes 176 lidar-derived AGB maps from 2002 to 2016, covering various regions in Oregon, Washington, Idaho, and Montana. A Random Forest (RF) model was used to estimate AGB at a 30m² resolution, utilizing lidar height features, DEM features, and climate data. Non-forested areas and buildings were masked using binary forest cover maps from the LCMS dataset and the Microsoft Building Footprints dataset. Reference Dataset: A composited AGB map, derived from the 176 lidar maps, was created to develop Landsat-based AGB models, covering 9,361,622 ha of forested land. The AGB layer was stratified into 30 bins, and training, development, and testing sets were constructed for model validation. The dataset includes 7500 test samples and 300,000 training and development samples, with a 500m buffer around test set locations to prevent spatial autocorrelation. Landsat Satellite Imagery: Landsat imagery from 1990 to 2022 was utilized, with three time series derived: all scenes, scenes from May to November, and annual medoid composites. The imagery was processed using the Google Earth Engine (GEE) platform, focusing on periods of maximum phenological activity. Feature Engineering: Extensive feature engineering was performed, generating spectral, spatial, temporal, and topographic features from Landsat imagery and DEM data. Features were extracted over the reference AGB map's domain, synchronized with the lidar acquisition dates. <ul> <li>LandTrendr Fitted Imagery: Spectral features were derived from LandTrendr-fitted imagery, smoothing variations in the time series.</li> <li>LandTrendr Disturbance and Recovery Features: Temporal features were derived from LandTrendr models, characterizing disturbance and recovery events.</li> <li>CCDC Disturbance and Recovery Features: CCDC algorithm-derived features characterized disturbances and recovery using harmonic models.</li> <li>Buffer Features: Local variations were captured using buffer statistics around each pixel.</li> <li>GLCM Features: GLCM texture features summarized the joint distribution of gray-tone values.</li> <li>Edge Detectors: Various edge detection operators captured spatial derivatives and edges.</li> <li>Morphological Operations: Morphological features were derived using multi-channel image processing techniques.</li> <li>Neighborhood Vectorization: Direct vectorization of satellite measurements in pixel neighborhoods.</li> <li>Neighborhood Similarity: Similarity features characterized the relationship between pixel neighborhoods and their centroids.</li> <li>Topographic Features: Topography was characterized using elevation, slope, aspect embeddings, and hillshade layers from the NED DEM.</li> </ul> This comprehensive dataset enables robust analysis of AGB models and their performance across diverse forested landscapes in the Pacific Northwest

opencc-by-4.0Jun 2016View details →

FIGURE 4. Body mass estimate regressions for Agerinia roselli. 1 in New dental material and redescription of Agerinia roselli (Primates, Adapiformes) from Les Saleres (early Eocene, NE Iberian Peninsula)

FIGURE 4. Body mass estimate regressions for Agerinia roselli. 1, derived from the area of the M1; 2, derived from the area of the M2. Black dots represent different molars of A. roselli. Black line indicates regression based on extant prosimian data from Egi et al. (2004).

opencc-by-4.0Apr 2024View details →

supporting files for "What to Choose for Estimating Leaf Water Status - Spectral Reflectance or in vivo Chlorophyll Fluorescence?"

Fig. 1 Workflow and parameters used for comparative analysis. Fig. 2 Representative spectra of diffusive reflectance measured on the adaxial side (RD) of fresh (RWC = 98%), partially desiccated (RWC = 52% or 54%) and severely desiccated leaves (RWC = 5%) of tobacco (A) and barley (E). Comparison of RD and RB (R from the abaxial leaf side) in fresh and severely desiccated leaves of tobacco (B) and barley (F). Water index (WI = R900/R970) and relative decrease of R in the 800-1100 nm region (ΔR) estimated from RD (indexed by "D") and RB (indexed by "B") in desiccating leaves of tobacco (C, D) and barley (G, H). Fig. 3 Water index (WISWIR = R1000/R1450) estimated from measurement of directional R from adaxial side of desiccating leaf samples of tobacco (A) and barley (B). Fig. 4 Equivalent water thickness (EWT) during desiccation of tobacco and barley leaf samples within the RWC interval 100-50%. Fig. 5 Normalized difference vegetation index (NDVI = (R780-R630)/(R780+R630)) estimated from measurement of diffusive R from the abaxial (NDVIB) and adaxial side (NDVID) of desiccating leaves of tobacco (A) and barley (D). SPAD-values of desiccating leaves of tobacco (B) and barley (E). Relative SPAD and NDVI΄D (estimated from measurement of directional R from the adaxial side) of a representative leaf of tobacco (C) and barley (F) during its desiccation (in % of the value measured immediately after leaf detachment). For selected data points, the time after the leaf detachment is indicated. Fig. 6 Chlorophyll fluorescence parameters of desiccating tobacco and barley leaf samples. (A, D) The maximum quantum yield of PSII photochemistry in the dark-adapted state (FV/FM) and the effective quantum yield of PSII photochemistry in the light-adapted state (ΦPSIIst). (B, E) The non-photochemical quenching of chlorophyll fluorescence after 1 min of exposure to actinic light (NPQ1). (C, F) The non-photochemical quenching of chlorophyll fluorescence at steady state (NPQst). Fig. 7 Coefficient of reliability (CR), coefficient of sensitivity (CS) and coefficient of inaccuracy (CI) of parameters measured in desiccating leaf samples of tobacco and barley within the RWC interval 100-50%. The parameters have been divided into 5 groups according to the type of leaf characteristics they reflect. A horizontal line in CR plot indicates the reliability threshold (CR = 0.4). Table 1 Parameters measured on desiccating tobacco and barley leaves ranked according to the value of their coefficient of reliability (CR) within the RWC interval 100-50% and the corresponding values of the coefficient of determination (R2). Fig. S1 Decrease in relative water content (RWC) of leaf samples of tobacco and barley with time after their detachment Fig. S2 Micrographs of leaf structure of fresh tobacco (A) and barley (B) leaves. Fig. S3 WISWIR images of representative desiccating tobacco and barley leaf samples. Numbers above the samples indicate their RWC in %. Fig. S4 (A) Leaf area (in % of the area of fresh leaves) and (B) equivalent water thickness (EWT) during desiccation of tobacco and barley leaf samples. Fig. S5 Imaging of chlorophyll fluorescence parameters of representative desiccating tobacco and barley leaf samples. The maximum quantum yield of PSII photochemistry (FV/FM), the effective quantum yield of PSII photochemistry in the light-adapted state (ΦPSIIst), the non-photochemical quenching of chlorophyll fluorescence after 1 min of exposure to actinic light (NPQ1), and the non-photochemical quenching of Chl fluorescence at steady state (NPQst). Fig. S6 Dependencies of measured parameters on RWC (in interval 100-50%) in desiccating tobacco leaves and segments. The parameters are ranked from most to least reliable according to their coefficient of reliability (CR). All parameters are normalized to their mean value (ȳ). Fig. S7 Dependencies of measured parameters on RWC (in interval 100-50%) in desiccating barley leaves and segments. The parameters are ranked from most to least reliable according to their coefficient of reliability (CR). All parameters are normalized to their mean value (ȳ). Fig. S8 Leaf water potential measured by psychrometry (Ψpsy) and by pressure chamber (Ψpress) in desiccating leaves of tobacco (A) and barley (B). Table S1 Parameters measured on desiccating leaf samples ranked according to their coefficient of sensitivity (CS) within the RWC interval 100-50% in tobacco and barley. Table S2 Parameters measured on desiccating leaf samples ranked according to their coefficient of inaccuracy (CI) within the RWC interval 100-50% in tobacco and barley. Table S3 Ranking of measured parameters according to their coefficient of reliability (CR), sensitivity (CS) and inaccuracy (CI) in desiccating leaf samples of tobacco and barley within the RWC interval 100-50%. The parameters have been divided into 5 groups (the first column) according to the type of leaf characteristics they reflect. Table S4 Approximate time required for the measurement of the parameters used in the study and the destructiveness/non-destructiveness of the measurement. The parameters that were used for the comparison according to their coefficients of reliability (CR), sensitivity (CS) and inaccuracy (CI) are written in bold. Fig. 2_spectra of diffusive reflectance - source data for each panel (A-H) of Fig. 2: Representative spectra of diffusive reflectance measured on the adaxial side (RD) of fresh (RWC = 98%), partially desiccated (RWC = 52% or 54%) and severely desiccated leaves (RWC = 5%) of tobacco (A) and barley (E). Comparison of RD and RB (R from the abaxial leaf side) in fresh and severely desiccated leaves of tobacco (B) and barley (F). Water index (WI = R900/R970) and relative decrease of R in the 800-1100 nm region (ΔR) estimated from RD (indexed by "D") and RB (indexed by "B") in desiccating leaves of tobacco (C, D) and barley (G, H). Fig.3_water index WISWIR - source data for each panel (A+B) of Fig. 3: Water index (WISWIR = R1000/R1450) estimated from measurement of directional R from adaxial side of desiccating leaf samples of tobacco (A) and barley (B). Fig.4_equivalent water thickness - source data for Fig. 4: Equivalent water thickness (EWT) during desiccation of tobacco and barley leaf samples within the RWC interval 100-50%. Fig.5_NDVI - source data for each panel (A-F) of Fig. 5: Normalized difference vegetation index (NDVI = (R780-R630)/(R780+R630)) estimated from measurement of diffusive R from the abaxial (NDVIB) and adaxial side (NDVID) of desiccating leaves of tobacco (A) and barley (D). SPAD-values of desiccating leaves of tobacco (B) and barley (E). Relative SPAD and NDVI΄D (estimated from measurement of directional R from the adaxial side) of a representative leaf of tobacco (C) and barley (F) during its desiccation (in % of the value measured immediately after leaf detachment). Fig.6_chlorophyll fluorescence parameters - source data for each panel (A-F) of Fig. 6: Chlorophyll fluorescence parameters of desiccating tobacco and barley leaf samples. (A, D) The maximum quantum yield of PSII photochemistry in the dark-adapted state (FV/FM) and the effective quantum yield of PSII photochemistry in the light-adapted state (ΦPSIIst). (B, E) The non-photochemical quenching of chlorophyll fluorescence after 1 min of exposure to actinic light (NPQ1). (C, F) The non-photochemical quenching of chlorophyll fluorescence at steady state (NPQst). Fig.7_coefficients of reliability, sensitivity, inaccuracy - Coefficient of reliability (CR), coefficient of sensitivity (CS) and coefficient of inaccuracy (CI) of parameters measured in desiccating leaf samples of tobacco and barley within the RWC interval 100-50%. Fig.S1_relative water content - source data for supplementary Fig. 1: Relative water content (RWC) of leaf samples of tobacco and barley with time after their detachment. Fig.S4_leaf area and equivalent water thickness - source data for supplementary Fig. 4: Leaf area (A) and equivalent water thickness (EWT; B) during desiccation of tobacco and barley leaf samples. Fig.S6_dependencies of parameters on RWC in tobacco - source data for each panel (A-N) of supplementary Fig.6: Dependencies of measured parameters (water potential, NPQ, NDVI, reflectance, SPAD, Fv/Fm, water indexes) on RWC (in interval 100-50%) in desiccating tobacco leaves and segments. Fig.S7_dependencies of parameters on RWC in barley - source data for each panel (A-N) of supplementary Fig.7: Dependencies of measured parameters (water potential, NPQ, NDVI, reflectance, SPAD, Fv/Fm, water indexes) on RWC (in interval 100-50%) in desiccating barley leaves and segments. Fig.S8_leaf water potential - source data for supplementary Fig. 8: Leaf water potential measured by psychrometry (Ψpsy) and by pressure chamber (Ψpress) in desiccating leaves of tobacco (A) and barley (B).

opencc-by-4.0Aug 2024View details →

Demographic data of US Counties Estimation 2019

US  government demographic data for all US counties estimated in 2019 including state, county, region, area land, area water, coordinates, birth rate, death rate, immigrants,

opencc-by-4.0May 2022View details →

FIGURE 4 in Estimated richness and environmental correlates of miniature fish assemblages in the rio Jacundá, Brazil

FIGURE 4 | Pairwise Pearson correlations in species abundance across the 20 sampling locations showing positive correlations in blue and negative correlations in red. Species codes are Aama (Ammoglanis amapaensis), Amin (Ammocryptocharax minutus), Epul (Elachocharax pulcher), Fpal (Fluviphylax palikur), Haft (Hemigrammus aff. tridens), Mwei (Microcharacidium weitzmani), Pana (Physopyxis ananas), Phas (Potamoglanis hasemani) and Tsp (Tyttobrycon sp.).