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opennotspecifiedDec 1916View details →

Pelvic arcade and vertebral structure of the second chief specimen of Tyrannosaurus rex, Amer. Mus. 5027, discovered in 1908. An orthogonal projection executed on a very large scale and reproduced one-twelfth natural size. C 1-C 10 cervical series, D 1-D 13 dorsal or thoracic series, S 1-S 5 sacral series, Cd 1- Cd 53 caudal series. The caudals actually preserved are shaded; those drawn in outline are conjectural and restored. The total number of caudals is conjectural in Skeletal Adaptations of Ornitholestes, Struthiomimus, Tyrannosaurus

Pelvic arcade and vertebral structure of the second chief specimen of Tyrannosaurus rex, Amer. Mus. 5027, discovered in 1908. An orthogonal projection executed on a very large scale and reproduced one-twelfth natural size. C 1-C 10 cervical series, D 1-D 13 dorsal or thoracic series, S 1-S 5 sacral series, Cd 1- Cd 53 caudal series. The caudals actually preserved are shaded; those drawn in outline are conjectural and restored. The total number of caudals is conjectural

Fig. S7 in Supplementary Material for The global tree restoration potential

Fig. S7. Global forest restoration potential. The global potential forest cover is illustrated on (A), representing an area of 8.7 billion hectares of forest cover. Forests are defined as pixels with a forest cover ≥ 10%. The global potential forest cover available for restoration is illustrated in (B) using cropland from Globcover and in (C) using Cropland from Fritz and colleagues (2015). These are calculated from the global potential forest cover (A) subtracting existing forest cover and removing agricultural and urban areas. This global tree restoration potential represents an area of 1.8 billion hectares of forest (Globcover; Table S2) or of 1.7 billion hectares of forest (Fritz and colleagues (2015); Table S2).

Fig. S4. K in Supplementary Material for The global tree restoration potential

Fig. S4. K-fold cross-validation (A) procedure; (B) density plot and (C) boxplots of observed versus predicted tree cover estimates. See Methods for detailed description of panel a.

Fig. 1 in The global tree restoration potential

Fig. 1. Predicted vs. observed tree cover. (A and B) The predicted tree cover (x axes) compared with the observed tree cover (y axes). (A) Results as a density plot, with the 1:1 line in dotted black and the regression line in continuous black (intercept = –2% forest cover; slope = 1.06; R 2 = 0.86), which shows that the model is un-biased. (B) Results as boxplots, to illustrate the quality of the prediction in all tree cover classes.

Fig. 2 in The global tree restoration potential

Fig. 2. The current global tree restoration potential. (A) The global potential tree cover representing an area of 4.4 billion ha of canopy cover distributed across the world. (B and C) The global potential tree cover available for restoration. Shown is the global potential tree cover (A), from which we subtracted existing tree cover (15) and removed agricultural and urban areas according to (B) Globcover (16) and (C) Fritz et al. (17). This global tree restoration potential [(B) and (C)] represents an area of 0.9 billion ha of canopy cover (table S2).

Fig. S1 in Supplementary Material for The global tree restoration potential

Fig. S1. Observed tree cover across the world's protected areas. Spatial distribution of the 0.5 hectare plots located in protected areas (9), for which we photo-interpreted tree cover using very high spatial resolution images. Small captions represent the different forest types in protected areas as seen from very high spatial resolution images, including boreal, dry, temperate and tropical forests.

Fig. S3 in Supplementary Material for The global tree restoration potential

Fig. S3. Tree cover distribution. Histogram illustrating the relative frequency of tree cover, distributed by bins of 10 %. The U-shaped distribution shows a dominance of 0 and 100% of tree cover in the world, when tree cover is photo-interpreted at very high spatial resolution in protected areas, on 0.5-hectare plots and independently of model-based approaches.

Fig. 3 in The global tree restoration potential

Fig. 3. Risk assessment of future changes in potential tree cover. (A) Illustration of expected losses in potential tree cover by 2050, under the "business as usual" climate change scenario (RCP 8.5), from the average of three Earth system models commonly used in ecology (cesm1cam5, cesm1bgc, and mohchadgem2es). (B) Quantitative numbers of potential gain and loss are illustrated by bins of 5° along a latitudinal gradient.

Fig. S6 in Supplementary Material for The global tree restoration potential

Fig. S6. Uncertainty map of the prediction of potential tree cover. The uncertainty is calculated at the pixel-level as the standard deviation among the k tree cover layers predicted from the k models developed from the k-fold cross-validation. The bimodal distribution of the standard deviation is illustrated within caption (A), showing a peak at 0% and another at 7% of standard deviation in tree cover. The resulting map (A) shows higher uncertainty in regions with intermediate potential tree cover and low uncertainty in regions with low (e.g. desert) or high (rainforest) tree cover levels. One exception remains, with higher levels of uncertainty in tropical wet forests of Central Africa (C) vs. other tropical wet forests (B).

Fig. S5. Uncertainty from k in Supplementary Material for The global tree restoration potential

Fig. S5. Uncertainty from k-fold cross validation. The uncertainty is expressed as the standard deviation of the tree cover predicted from the k potential tree cover layers computed during the k-fold crossvalidation. (A) Summary of the procedure. (B) Uncertainty (standard deviation) vs. mean predicted tree cover at the pixel level. The relationship shows that the level of uncertainty is greater at intermediate tree cover classes, reaching 15% of tree cover variation at 50% of the predicted potential tree cover.

Fig. S2 in Supplementary Material for The global tree restoration potential

Fig. S2. Distribution of the World's protected areas among the main Ecoregions of the world. The World Database on Protected Areas (WDPA) is developed by the United Nations Environmental Program (UNEP) and the International Union for Conservation of Nature (IUCN). The WDPA is the most comprehensive global database of marine and terrestrial protected areas. The ecoregions of the world are provided by the Nature Conservancy and defined by the World Wide Fund for Nature.

opencc-zeroOct 2019View details →

Supplementary material 1 from: Valkó O, Tóth K, Kelemen A, Miglécz T, Radócz S, Sonkoly J, Tóthmérész B, Török P, Deák B (2018) Cultural heritage and biodiversity conservation – plant introduction and practical restoration on ancient burial mounds. Nature Conservation 24: 65-80. https://doi.org/10.3897/natureconservation.24.20019

Supplementary material 1 from: Valkó O, Tóth K, Kelemen A, Miglécz T, Radócz S, Sonkoly J, Tóthmérész B, Török P, Deák B (2018) Cultural heritage and biodiversity conservation – plant introduction and practical restoration on ancient burial mounds. Nature Conservation 24: 65-80. https://doi.org/10.3897/natureconservation.24.20019

opencc-by-4.0Aug 2024View details →

Engineering the plant intracellular immune receptor Sr50 to restore recognition of the AvrSr50 escape mutant

The datasets for 'Engineering the plant intracellular immune receptor Sr50 to restore recognition of the AvrSr50 escape mutant'.   All_docking_models.zip: molecular docking models used to derive the initial structural hypothesis (Model I, Alternative models I and II).  Zdock1.AF2.zip: includes refined, relaxed structures of Model I.  Zdock5.AF2.zip: includes refined, relaxed structures of Alternative model II.   Zdock15.AF2.zip: includes refined, relaxed structures of Alternative moel I. Model_II.zip: includes ColabDock outputs used as Model II. Model_III.zip: includes ColabDock outputs used as Model III. Model_IV.zip: includes ColabFold outputs used as Model IV. Sr50*.AF.zip: predicted structures by ColabFold for the indicated pairs of the receptors and effectors.    To read more about the workflow, please refer to https://github.com/s-kyungyong/Sr50-AvrSr50

FIGURE 3. Suaeda glauca, SEM micrographs. A. A in Restoration of Suaeda sect. Helicilla (Chenopodiaceae) and typification of its related taxa

FIGURE 3. Suaeda glauca, SEM micrographs. A. A regular, sub-spherical seed. B. Detail of seed coat. C. An irregular, disc-shaped seed. D. S. linifolia, detail of seed coat. E. S. glauca, distribution map: Most Chinese records from Qin Hai-Ning & Ma Ke-Ping (2016). Photos and map by M. Lomonosova.

FIGURE 2. Suaeda glauca. A in Restoration of Suaeda sect. Helicilla (Chenopodiaceae) and typification of its related taxa

FIGURE 2. Suaeda glauca. A. Habitat on sandy shore of the Sea of Japan, Southern Primoriye (Russian Far East), the species grows scattered among the predominant Phragmites australis var. humilis (De Not) Tzvelev. B. Habit and habitat (sandy pockets among lava rocks) near Seogwipo, Jeju island (S Korea). C. Flowering branch, note the carinate tepals; from greenhouse in Novosibirsk. D. Fruiting branches, note strongly carinate, flattened fruits bearing disc-shaped seeds; from Southern Primoriye. E. Cross section of stem. F. Cross section of a leaf. Photos by H. Freitag (B, F); M. Lomonosova (A, C, E); molbiol.ru (D).

FIGURE 1 in Restoration of Suaeda sect. Helicilla (Chenopodiaceae) and typification of its related taxa

FIGURE 1. Abbreviated ITS maximum likelihood tree of Suaedoideae / Salicornioideae including key species of the different sections, based on Schütze et al. (2003), Kapralov et al. (2006) and Schütze (2011). Tree computed by R. Brandt.

FIGURE 4 in Restoration of Suaeda sect. Helicilla (Chenopodiaceae) and typification of its related taxa

FIGURE 4. Lectotype of Suaeda stauntonii Moq., Herbarium Firenze (FI-017377, herb. Webb no. 157134). Photo by courtesy of the Herbarium Firenze.

opencc-by-4.0Sep 2024View details →

Microbes in reconstructive restoration: Divergence in constructed and natural tree island soil fungi affects tree growth

Project folder for publication "Microbes in reconstructive restoration: Divergence in constructed and natural tree island soil fungi affects tree growth" containing: 1) scripts for data processing, 2) intermediate and final files output by scripts, and 3) RMarkdown files used to perform statistical analyses and generate figures. Descriptions of files, scripts, and folders in README files. Manuscript Abstract: As ecosystems face unprecedented change and habitat loss, pursuing comprehensive and resilient habitat restoration will be integral to protecting and maintaining natural areas and the services they provide. Microbiomes offer an important avenue for improving restoration efforts as they  are integral to ecosystem health and functioning. Despite microbiomes’ importance, unresolved knowledge gaps hinder their inclusion in restoration efforts. Here, we address two critical gaps in understanding microbial roles in restoration – fungal microbiomes’ importance in “reconstructive” restoration efforts and how management and restoration decisions interactively impact fungal communities and their cascading effects on trees. We combined field surveys, microbiome sequencing, and greenhouse experiments to determine how reconstructing an iconic landscape feature – tree islands – in the highly imperiled Everglades impacts fungal microbiomes and fungal effects on native tree species compared to their natural  counterparts under different proposed hydrological management regimes. Constructed islands used in this research were built from peat soil and limestone collected from deep sloughs and levees nearby the restoration sites in 2003, providing 18 years for microbiome assembly on  constructed islands. We found that while fungal microbiomes from natural and constructed tree islands exhibited similar diversity and richness, they differed significantly in community composition. These compositional differences arose mainly from changes to which fungal taxa were present on the islands rather than changes in relative abundances. Surprisingly, ~50% of fungal hub taxa (putative keystone fungi) from natural islands were missing on constructed islands, suggesting that differences in community composition of constructed island could be important for microbiome stability and function. The differences in fungal composition between natural and constructed islands had important consequences for tree growth. Specifically, these compositional differences interacted with hydrological regime (treatments simulating management strategies) to affect woody growth across the four tree species in our experiment. Taken together, our results demonstrate that reconstructing a landscape feature without consideration of microbiomes can result in diverging fungal communities that are likely to interact with management decisions leading to meaningful consequences for foundational primary producers. Our results recommend cooperation between restoration practitioners and ecologists to evaluate opportunities for active management and restoration of microbiomes during future reconstructive restoration.

opencc-by-4.0Sep 2024View details →

Optimizing Sustainable Dairy Farming: A Techno-Economic Analysis of Graphene Ranch Restoration

This dataset, titled Standardized Dairy Farm Cost Output Table, contains financial and production information related to the establishment and operation of a proposed dairy farm. It includes initial investment costs such as stock cows, fixed assets, and working capital, as well as production capacity and budget projections over multiple years. Key sections include: Initial Cost of Investment: Covers items like stock cows and fixed assets. Production Capacity: Provides figures on dairy farm output over the years. Budget Projections: Displays financial allocations and expected expenditures over different time periods. The dataset consists of multiple columns spanning projected years and various financial indicators to help estimate the cost-output relationship in dairy farming operations.