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1. Data accompanying "Drought Characterization with GPS: Insights into Groundwater and Reservoir Storage in California" [Young et al., (2024)]

   https://doi.org/10.6084/m9.figshare.25898044.v1  

   Young, Zachary M; Martens, Hilary R; Hoylman, Zachary H; Gardner, W Payton (June 2024, figshare)

   The data provided here accompany the publication "Drought Characterization with GPS: Insights into Groundwater and Reservoir Storage in California" [Young et al., (2024)] which is currently under review with Water Resources Research. (as of 28 May 2024)Please refer to the manuscript and its supplemental materials for full details. (A link will be appended following publication)File formatting information is listed below, followed by a sub-section of the text describing the Geodetic Drought Index Calculation. The longitude, latitude, and label for grid points are provided in the file "loading_grid_lon_lat".Time series for each Geodetic Drought Index (GDI) time scale are provided within "GDI_time_series.zip".The included time scales are for 00- (daily), 1-, 3-, 6-, 12- 18- 24-, 36-, and 48-month GDI solutions.Files are formatted following...Title: "grid point label L****"_"time scale"_monthFile Format: ["decimal date" "GDI value"]Gridded, epoch-by-epoch, solutions for each time scale are provided within "GDI_grids.zip".Files are formatted following...Title: GDI_"decimal date"_"time scale"_monthFile Format: ["longitude" "latitude" "GDI value" "grid point label L****"]2.2 GEODETIC DROUGHT INDEX CALCULATION We develop the GDI following Vicente-Serrano et al. (2010) and Tang et al. (2023), such that the GDI mimics the derivation of the SPEI, and utilize the log-logistic distribution (further details below). While we apply hydrologic load estimates derived from GPS displacements as the input for this GDI (Figure 1a-d), we note that alternate geodetic drought indices could be derived using other types of geodetic observations, such as InSAR, gravity, strain, or a combination thereof. Therefore, the GDI is a generalizable drought index framework. A key benefit of the SPEI is that it is a multi-scale index, allowing the identification of droughts which occur across different time scales. For example, flash droughts (Otkin et al., 2018), which may develop over the period of a few weeks, and persistent droughts (>18 months), may not be observed or fully quantified in a uni-scale drought index framework. However, by adopting a multi-scale approach these signals can be better identified (Vicente-Serrano et al., 2010). Similarly, in the case of this GPS-based GDI, hydrologic drought signals are expected to develop at time scales that are both characteristic to the drought, as well as the source of the load variation (i.e., groundwater versus surface water and their respective drainage basin/aquifer characteristics). Thus, to test a range of time scales, the TWS time series are summarized with a retrospective rolling average window of D (daily with no averaging), 1, 3, 6, 12, 18, 24, 36, and 48-months width (where one month equals 30.44 days). From these time-scale averaged time series, representative compilation window load distributions are identified for each epoch. The compilation window distributions include all dates that range ±15 days from the epoch in question per year. This allows a characterization of the estimated loads for each day relative to all past/future loads near that day, in order to bolster the sample size and provide more robust parametric estimates [similar to Ford et al., (2016)]; this is a key difference between our GDI derivation and that presented by Tang et al. (2023). Figure 1d illustrates the representative distribution for 01 December of each year at the grid cell co-located with GPS station P349 for the daily TWS solution. Here all epochs between between 16 November and 16 December of each year (red dots), are compiled to form the distribution presented in Figure 1e. This approach allows inter-annual variability in the phase and amplitude of the signal to be retained (which is largely driven by variation in the hydrologic cycle), while removing the primary annual and semi-annual signals. Solutions converge for compilation windows >±5 days, and show a minor increase in scatter of the GDI time series for windows of ±3-4 days (below which instability becomes more prevalent). To ensure robust characterization of drought characteristics, we opt for an extended ±15-day compilation window. While Tang et al. (2023) found the log-logistic distribution to be unstable and opted for a normal distribution, we find that, by using the extended compiled distribution, the solutions are stable with negligible differences compared to the use of a normal distribution. Thus, to remain aligned with the SPEI solution, we retain the three-parameter log-logistic distribution to characterize the anomalies. Probability weighted moments for the log-logistic distribution are calculated following Singh et al., (1993) and Vicente-Serrano et al., (2010). The individual moments are calculated following Equation 3. These are then used to calculate the L-moments for shape (), scale (), and location () of the three-parameter log-logistic distribution (Equations 4 – 6). The probability density function (PDF) and the cumulative distribution function (CDF) are then calculated following Equations 7 and 8, respectively. The inverse Gaussian function is used to transform the CDF from estimates of the parametric sample quantiles to standard normal index values that represent the magnitude of the standardized anomaly. Here, positive/negative values represent greater/lower than normal hydrologic storage. Thus, an index value of -1 indicates that the estimated load is approximately one standard deviation dryer than the expected average load on that epoch. *Equations can be found in the main text. 

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2. High species richness in the lichen genus Peltigera (Ascomycota, Lecanoromycetes): 34 species in the dolichorhizoid and scabrosoid clades of section Polydactylon, including 24 new to science

   https://doi.org/10.3767/persoonia.2023.51.01  

   Magain, N.; Miadlikowska, J.; Goffinet, B.; Goward, T.; Pardo-De la Hoz, C.J.; Jüriado, I.; Simon, A.; Mercado-Díaz, J.A.; Barlow, T.; Moncada, B.; et al (August 2023, Persoonia - Molecular Phylogeny and Evolution of Fungi)

   Applying molecular methods to fungi establishing lichenized associations with green algae or cyanobacteria has repeatedly revealed the existence of numerous phylogenetic taxa overlooked by classical taxonomic approaches. Here, we report taxonomical conclusions based on multiple species delimitation and validation analyses performed on an eight-locus dataset that includes world-wide representatives of the dolichorhizoid and scabrosoid clades in section Polydactylon of the genus Peltigera . Following the recommendations resulting from a consensus species delimitation approach and additional species validation analysis (BPP) performed in this study, we present a total of 25 species in the dolichorhizoid clade and nine in the scabrosoid clade, including respectively 18 and six species that are new to science and formally described. Additionally, one combination and three varieties (including two new to science) are proposed in the dolichorhizoid clade. The following 24 new species are described: P. appalachiensis , P. asiatica , P. borealis , P. borinquensis , P. chabanenkoae , P. clathrata , P. elixii , P. esslingeri , P. flabellae , P. gallowayi , P. hawaiiensis , P. holtanhartwigii , P. itatiaiae , P. hokkaidoensis , P. kukwae , P. massonii , P. mikado , P. nigriventris , P. orientalis , P. rangiferina , P. sipmanii , P. stanleyensis , P. vitikainenii and P. willdenowii ; the following new varieties are introduced: P. kukwae var. phyllidiata and P. truculenta var. austroscabrosa ; and the following new combination is introduced: P. hymenina var. dissecta . Each species from the dolichorhizoid and scabrosoid clades is morphologically and chemically described, illustrated, and characterised with ITS sequences. Identification keys are provided for the main biogeographic regions where species from the two clades occur. Morphological and chemical characters that are commonly used for species identification in the genus Peltigera cannot be applied to unambiguously recognise most molecularly circumscribed species, due to high variation of thalli formed by individuals within a fungal species, including the presence of distinct morphs in some cases, or low interspecific variation in others. The four commonly recognised morphospecies: P. dolichorhiza , P. neopolydactyla , P. pulverulenta and P. scabrosa in the dolichorhizoid and scabrosoid clades represent species complexes spread across multiple and often phylogenetically distantly related lineages. Geographic origin of specimens is often helpful for species recognition; however, ITS sequences are frequently required for a reliable identification. 

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3. NEON Tree Crowns Dataset

   https://doi.org/10.5281/zenodo.3765871  

   Weinstein, Ben; Marconi, Sergio; Zare, Alina; Bohlman, Stephanie; Graves, Sarah; Singh, Aditya; White, Ethan (January 2020, Zenodo)

   {"Abstract":["Abstract<\/strong><\/p>\n\nThe NeonTreeCrowns dataset is a set of individual level crown estimates for 100 million trees at 37 geographic sites across the United States surveyed by the National Ecological Observation Network\u2019s Airborne Observation Platform. Each rectangular bounding box crown prediction includes height, crown area, and spatial location. <\/p>\n\nHow can I see the data?<\/strong><\/p>\n\nA web server to look through predictions is available through idtrees.org<\/p>\n\nDataset Organization<\/strong><\/p>\n\nThe shapefiles.zip contains 11,000 shapefiles, each corresponding to a 1km^2 RGB tile from NEON (ID: DP3.30010.001). For example "2019_SOAP_4_302000_4100000_image.shp" are the predictions from "2019_SOAP_4_302000_4100000_image.tif" available from the NEON data portal: https://data.neonscience.org/data-products/explore?search=camera. NEON's file convention refers to the year of data collection (2019), the four letter site code (SOAP), the sampling event (4), and the utm coordinate of the top left corner (302000_4100000). For NEON site abbreviations and utm zones see https://www.neonscience.org/field-sites/field-sites-map. <\/p>\n\nThe predictions are also available as a single csv for each file. All available tiles for that site and year are combined into one large site. These data are not projected, but contain the utm coordinates for each bounding box (left, bottom, right, top). For both file types the following fields are available:<\/p>\n\nHeight: The crown height measured in meters. Crown height is defined as the 99th quartile of all canopy height pixels from a LiDAR height model (ID: DP3.30015.001)<\/p>\n\nArea: The crown area in m^{2<\/sup> of the rectangular bounding box.<\/p>\n\n}Label: All data in this release are "Tree".<\/p>\n\nScore: The confidence score from the DeepForest deep learning algorithm. The score ranges from 0 (low confidence) to 1 (high confidence)<\/p>\n\nHow were predictions made?<\/strong><\/p>\n\nThe DeepForest algorithm is available as a python package: https://deepforest.readthedocs.io/. Predictions were overlaid on the LiDAR-derived canopy height model. Predictions with heights less than 3m were removed.<\/p>\n\nHow were predictions validated?<\/strong><\/p>\n\nPlease see<\/p>\n\nWeinstein, B. G., Marconi, S., Bohlman, S. A., Zare, A., & White, E. P. (2020). Cross-site learning in deep learning RGB tree crown detection. Ecological Informatics<\/em>, 56<\/em>, 101061.<\/p>\n\nWeinstein, B., Marconi, S., Aubry-Kientz, M., Vincent, G., Senyondo, H., & White, E. (2020). DeepForest: A Python package for RGB deep learning tree crown delineation. bioRxiv<\/em>.<\/p>\n\nWeinstein, Ben G., et al. "Individual tree-crown detection in RGB imagery using semi-supervised deep learning neural networks." Remote Sensing<\/em> 11.11 (2019): 1309.<\/p>\n\nWere any sites removed?<\/strong><\/p>\n\nSeveral sites were removed due to poor NEON data quality. GRSM and PUUM both had lower quality RGB data that made them unsuitable for prediction. NEON surveys are updated annually and we expect future flights to correct these errors. We removed the GUIL puerto rico site due to its very steep topography and poor sunangle during data collection. The DeepForest algorithm responded poorly to predicting crowns in intensely shaded areas where there was very little sun penetration. We are happy to make these data are available upon request.<\/p>\n\n# Contact<\/p>\n\nWe welcome questions, ideas and general inquiries. The data can be used for many applications and we look forward to hearing from you. Contact ben.weinstein@weecology.org. <\/p>"],"Other":["Gordon and Betty Moore Foundation: GBMF4563"]} 

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4. The Genus Paronychia (Caryophyllaceae) in South America: Nomenclatural Review and Taxonomic Notes with the Description of a New Species from North Peru

   https://doi.org/10.3390/plants12051064  

   Iamonico, Duilio; Montesinos_Tubée, Daniel B (March 2023, Plants)

   All the names in Paronychia described from South America are investigated. Five names (P. arbuscula, P. brasiliana subsp. brasiliana var. pubescens, P. coquimbensis, P. hieronymi, and P. mandoniana) are lecto- or neotypified on specimens preserved at GOET, K, LP, and P. The typification of nine names, first proposed by Chaudhri in 1968 as the “holotype” are corrected according to Art. 9.10 of ICN. Three second-step typifications (Art. 9.17 of ICN) are proposed for P. camphorosmoides, P. communis, and P. hartwegiana. The following nomenclatural changes are proposed: P. arequipensis comb. et stat. nov. (basionym: P. microphylla subsp. microphylla var. arequepensis), P. compacta nom. nov. pro P. andina (Philippi non Gray; Art. 53.1 of ICN), P. jujuyensis comb. et stat. nov. (basionym: P. hieronymi subsp. hieronymi var. jujuyensis), P. compacta subsp. boliviana comb. nov. (basionym: P. andina subsp. boliviana), and P. compacta subsp. purpurea comb. nov. (basionym: P. andina subsp. purpurea). A new species (P. glabra sp. nov.) is proposed based on our examination of live plants and herbarium specimens. P. johnstonii subsp. johnstonii var. scabrida is synonymized (syn. nov.) with P. johnstonii. Finally, P. argyrocoma subsp. argyrocoma is excluded from South America since it was based on misidentified specimens (deposited at MO) of P. andina subsp. andina. A total of 30 species (43 taxa including subspecies, varieties, subvarieties, and forms) are recognized, highlighting that for some (Paronychia chilensis, P. communis, P. setigera) we provisionally accept Chaudhri’s infraspecific classification, since the high phenotypic variability of these taxa is quite complicated and further investigations need to solve their taxonomy. 

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5. A sensitive fluorescent assay for measuring carbon‐phosphorus lyase activity in aquatic systems

   https://doi.org/10.1002/lom3.10418  

   Granzow, Benjamin N.; Sosa, Oscar A.; Gonnelli, Margherita; Santinelli, Chiara; Karl, David M.; Repeta, Daniel J. (January 2021, Limnology and Oceanography: Methods)

   Abstract In the oligotrophic ocean where inorganic phosphate (P_i) concentrations are low, microorganisms supplement their nutrient requirements with phosphorus (P) extracted from dissolved organic matter (DOM). Most P in DOM is bound as phosphate esters, which are hydrolyzed by phosphoesterases to P_i. However, a large fraction of DOM‐P occurs as phosphonates, reduced organophosphorus compounds with a CP bond that do not yield P_ithrough simple ester hydrolysis alone. Phosphonates require an additional step that cleaves the CP bond and oxidizes P(III) to P(V) to yield P_i. Most phosphonates are metabolized by the C‐P lyase pathway, which cleaves CP bonds and oxidizes phosphonates to P_i, enabling microbial assimilation. While the activity of common phosphoesterases such as alkaline phosphatase and phosphodiesterase can be measured by a fluorescent assay, a comparable method to assess C‐P lyase activity (CLA) in natural water samples does not exist. To address this, we synthesized a dansyl‐labeled phosphonate compound, and measured its hydrolysis by C‐P lyase using high performance liquid chromatography. We found that laboratory cultures of marine bacteria expressing the C‐P lyase pathway are able to hydrolyze the dansyl phosphonate, while bacteria expressing other phosphonate degradation pathways do not. Finally, we performed several field tests of the assay to measure water column profiles of CLA at Sta. ALOHA in the North Pacific Subtropical Gyre. Activity was elevated near the deep chlorophyll maximum suggesting high levels of phosphonate degradation in that region. 

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