dataesr/scientific-paragraphs-categorization huggingface dataset:
In Africa, the humid forests lie between the drier biomes of the Sahel to the north and savanna biomes to the Input data sets. The SCI was derived from three data sets: (1) global tree cover in 2010 25 , (2) forest loss between 2000-2017  25  , and (3) canopy height in 2012  26  (Table  2 ). Tree cover and forest loss were derived from growing-season Landsat 7 and 8 Enhanced Thematic Mapper Plus (ETM+) data processed in Google Earth Engine (GEE)  24. Training data to relate to the Landsat metrics were derived from image interpretation methods, including mapping of crown/no crown categories using very high spatial resolution data such as Quickbird imagery, lidar canopy heights derived from spaceborne Geoscience Laser Altimetry System (GLAS) and airborne lidar data, existing percent tree cover layers derived from Landsat data, and global MODIS percent tree cover, rescaled using the higher spatial resolution percent tree cover data sets.
On-screen image interpretation was used to delineate change and no change training data for forest cover loss and gain. Percent tree cover, forest loss and forest gain training data were related to the Landsat time-series metrics using a decision tree classifier. Outputs include percent tree cover, forest loss, and forest gain from 2000 to 2017 at a 30 m spatial resolution. Trees were defined as all vegetation taller than 5 m in height. Forest loss was defined as a stand-replacement disturbance or the complete removal of tree cover canopy at the Landsat pixel scale. More specifically, pixels changing from >50% crown cover to 0% crown cover were defined as loss plots. Selective removals from within forested stands that did not lead to a non-forest state, however, were not included in the change characterization.
(2016) regressed GLAS height data on Landsat time-series multi-spectral data to estimate tree height from a set of multi-temporal metrics from Landsat 7 and 8 for the 2013 and 2014 calendar years in Sub-Saharan Africa  26. The regression model was validated by using reserved calibration data to determine the ability of the Landsat inputs to recreate the GLAS height calibration data, including height distributions across the study area. The regression tree algorithm accurately reproduced the GLAS-derived height training data with an overall mean absolute error (MAE) for tree height estimation of 2. 45 m. Significant underestimations were quantified for tall tree cover (MAE of 4. 65 m for >20 m heights) and overestimations for low/no tree cover (MAE 1. 61 for <5 m heights). We integrated a recently updated global terrestrial human footprint map for 2013  6.
Remotely-sensed and survey information were compiled for built environments, population density, electric infrastructure, crop lands, pasture lands, roads, railways, and navigable waterways. To facilitate comparison across these forms of human pressure, each was transformed to a 0-10 scale and weighted within that range according to estimates of their relative levels of human pressure following Sanderson et al. 28. The resulting standardized pressures were then summed together to create the human footprint map. The results were validated by comparison with data from visual interpretation of human pressure from high resolution images in 3460 × 1 km 2 sample plots randomly located across the Earth's non-Antarctic land areas  6. Strong agreement was found between the human footprint measure of pressure and pressures scored by visual interpretation of the high-resolution imagery.
Data Records The SCI and FSII data sets are distributed as GeoTIFF files through figshare  33. Each individual input layer (i. e. , canopy cover, loss year, canopy height, and human footprint) are also provided in the data record should users wish to alter the weights assigned to derive SCI and FSII in accordance with their particular needs or region. All data are clipped to the moist broadleaf biome of the humid tropics. In addition, the United Nations Development Programme will also make these data available through the UN Biodiversity Lab (www. unbiodiversitylab. org) to enable governments from nearly 140 countries to visualize them in a web-based platform.
technical Validation We performed a quantitative assessment of the extent to which the SCI represents a gradient in forest structure by comparing the SCI values with foliage height diversity (FHD) estimates derived from airborne lidar measurements. Lidar data were acquired by the Sustainable Landscapes Brazil project supported by the Brazilian Agricultural Research Corporation, the US Forest Service, USAID, and the US Department of State. The geographic area of the validation was limited to Brazil Sustainable Landscapes lidar transects (Fig. 3 ) because they are consistent, high quality, and publicly available. As similar lidar datasets become available elsewhere, the geographic scope of the validation will be extended. Patterns of forest structural development in the humid tropics are closely related to disturbance history and seral stage  28.
Thus, the gradient of forest structure in Brazil is representative of that across the humid tropics. Evidence in support of this comes from the patterns of the SCI input data sets across the study area. The range of tree cover, canopy height, and time since disturbance were similar in South America, Africa, and Asia. We downloaded 72 transect boundaries from the Sustainable Landscapes website (http://www. paisagenslidar. cnptia. embrapa. br/webgis/) and calculated the number of pixels of each SCI class in each transect. For each SCI class, we selected between 5 and 10 transects, with replacement, in which that class was represented. Some classes were rare in the dataset and were only represented by one or two transects. Only transects that were sampled by airborne lidar since 2010 were included in the validation to best match the sampling period for the SCI.
The FHD data set was then reprojected using bilinear interpolation to align with the SCI grids. The SCI layer was filtered to omit cells with a loss year greater than the year of lidar acquisition. Cell alignment between FHD grids in their native projections and SCI grids varied by transect. We corrected for alignment issues by selecting cells within unique homogenous patches of SCI using a minimum patch size of 3 × 3 cells and removing cells within 90 m of the patch perimeter. The result is that validations samples are core areas of homogeneous SCI patches. All point cloud processing was performed with the lidR package in R, v. 3. 5. 2. We estimated a baseline Ordinary Least Squares (OLS) model and subsequent linear mixed effects models to determine the strength of relationship between SCI and FHD.
All models were estimated using the full sample of 114,253 observations. For the mixed effects models, both lidar transect and SCI patch were included as random effects. Lidar transect and SCI patch were identified as random effects due to the hierarchical structure and associated spatial dependencies of the validation data -observations were nested within patches which were grouped into transects. We estimated separate linear mixed effects models to evaluate how imposing alternative random effect structures affected predictive capability. One model included only a random intercept for transect, while the second included a nested random intercept of SCI patch within transect. We report the Akaike Information Criterion (AIC) and R 2 values a as measures of goodness-of-fit across model specifications.
We additionally evaluated the extent to which the SCI differed among forests of known or inferred stand history. Classes of forest include tree plantations, older secondary forest, and primary forest. Tree plantations have been mapped in Brazil  34. Defined as "the biophysical presence of trees that are clearly planted and managed    51. a A pseudo-R 2 for the linear mixed effects models using the r2GLMM package in R v. 3. 5. 2. by humans", plantations were located largely based on forest loss and gain data  24  and confirmed with 2013-14 Landsat imagery and high-resolution imagery. Primary forests were mapped  35  based on texture analyses of 2001 Landsat data. Primary forest was defined as "mature natural humid tropical forest cover that has not been completely cleared and regrown in recent history".
Usage Notes Mapping structural condition and human pressure on forests represents measures of forest quality that are relevant for studies of species and communities that are habitat specialists 1. As such, the SCI and FSII maps can be used to inform assessment of remaining forest pattern and conservation planning. These data may be particularly useful for countries that are assessing forest condition with regards to the ABTs under the CBD's 2020 Strategic Plan for Biodiversity. They are also potentially useful for studies of carbon budgets and hydrology because it is thought that forests with high integrity hold an exceptional confluence of globally significant carbon and hydrological values relative to forests that have experienced damaging human actions. More generally, our approach for quantifying forest integrity based on stand structure may motivate integration of global spatial data layers to represent forest functional integrity and forest compositional integrity  [37] [38] [39].
Readers should note that the University of Maryland canopy height data used to develop the SCI and FSII has only been validated in the African tropics as reported in the primary publication  26. Validation of canopy height  for multiple years in 2000-2016 is underway for the three continental tropical areas and is expected to be available in the coming year. Another important caveat about SCI is that it has an unknown but likely higher level of error at forest/nonforest edges than in patch interiors. This is likely both because of edge effects reducing the classification accuracy of the input data layers and because of error in georegistration of the lidar data set and the SCI data set in the validation. We restricted the validation to non-edge plots to avoid these errors. A current limitation of the SCI and FSII data sets is that they are static in time (centered on 2013) and cannot currently be used to assess change over time.
Efforts are in progress, however, to update the input data layers to allow change analysis for the 2000-2017 period. A second limitation is that FSII has a coarser 1 km resolution compared with the 30 m SCI. This is because of the resolution of the global human footprint data set used to generate FSII. Some countries, such as Colombia, are now using the human footprint methodology to generate national 30 m maps of human pressure and include them in their reports to the CBD on national progress towards the ABTs. When completed, these data could be used to generate FSII at this finer spatial resolution for use in national conservation planning and policy development. A third limitation is that the accuracy of the height data used to generate SCI necessitates height classes being resolved in 5 m height intervals.
Code availability The SCI and FSII were generated in GEE. The Hansen Global Forest Change v1. 5 (2000-2017)  data set used to obtain loss year is served by GEE. Tree cover for 2010 was uploaded as a GEE asset from GLAD at the University of Maryland. This was done for each continental portion of the study area using the Python API Earth Engine shell on a parallel processor computer at Northern Arizona University. The same procedure was used for the canopy height data set. Human footprint data were uploaded using the GEE Asset Upload procedure. The SCI weights were generated in GEE by assigning a mask of 0 where conditions are not met and 1 when they are met. An example conditional statement is: var sciImage1 = lossYear. gte (13). and(lossYear. lte (17) ). or(forest. lt (25) ). or(height. lte( 5 )).
This formulation was then used to multiply the cells with mask = 1 times the appropriate SCI weight. This was done for each combination of input data values for each SCI weight. The resulting maps for each category of SCI weight were combined into a single map using the GEE add command. Similar coding was used for the FSII classification. The resulting SCI and FSII maps for each continent were exported from GEE to Google Drive. GEE code for the classifications are available at Africa: https://code. earthengine. google. com/f46e40ba74164a595f9be5067a7b26cf SE Asia: https://code. earthengine. google. com/e6595f8040af90a7b30a9b89e42c12f6 South America: https://code. earthengine. google. com/285ecd4784d7d4e0bc9a6e6aa966b54c.
Overview Agaricomycetes (Basidiomycota) phylogeny -part 1 Consensus phylogram (50 % majority rule) of 3 602 trees resulting from a Bayesian analysis of the LSU sequence alignment (115 sequences including outgroup; 764 aligned positions; 427 unique site patterns) using MrBayes v. 3. 2. 6  (Ronquist et al. 2012). Bayesian posterior probabilities (PP) > 0. 84 are shown at the nodes and thickened lines represent nodes with PP = 1. 00. The scale bar represents the expected changes per site. Families and orders are indicated with coloured blocks to the right of the tree. GenBank accession and/or Fungal Planet numbers are indicated behind the species names. The tree was rooted to Candida broadrunensis (GenBank KY106372. 1) and the taxonomic novelties described in this study for which LSU sequence data were available are indicated in bold face.
Overview Lecanoromycetes and Pezizomycetes phylogeny Consensus phylogram (50 % majority rule) of 3 002 trees resulting from a Bayesian analysis of the LSU sequence alignment (26 sequences including outgroup; 760 aligned positions; 264 unique site patterns) using MrBayes v. 3. 2. 6  (Ronquist et al. 2012). Bayesian posterior probabilities (PP) > 0. 84 are shown at the nodes and thickened lines represent nodes with PP = 1. 00. The scale bar represents the expected changes per site. Families, orders and classes are indicated with coloured blocks to the right of the tree. GenBank accession or Fungal Planet numbers are indicated behind the species names. The tree was rooted to Pilidium pseudoconcavum (GenBank NG_058050. 1) and the taxonomic novelties described in this study for which LSU sequence data were available are indicated in bold face.
The alignment and tree were deposited in TreeBASE (Submission ID S25229). Consensus phylogram (50 % majority rule) of 1 502 trees resulting from a Bayesian analysis of the LSU sequence alignment (35 sequences including outgroup; 798 aligned positions; 242 unique site patterns) using MrBayes v. 3. 2. 6  (Ronquist et al. 2012). Bayesian posterior probabilities (PP) > 0. 84 are shown at the nodes and thickened lines represent nodes with PP = 1. 00. The scale bar represents the expected changes per site. Families and orders are indicated with coloured blocks to the right of the tree. GenBank accession and/or Fungal Planet numbers are indicated behind the species names. The tree was rooted to Saccharata proteae (GenBank EU552145. 1) and the taxonomic novelties described in this study for which LSU sequence data were available are indicated in bold face.
The alignment and tree were deposited in TreeBASE (Submission ID S25229). Consensus phylogram (50 % majority rule) of 6 002 trees resulting from a Bayesian analysis of the LSU sequence alignment (93 sequences including outgroup; 825 aligned positions; 405 unique site patterns) using MrBayes v. 3. 2. 6  (Ronquist et al. 2012). Bayesian posterior probabilities (PP) > 0. 84 are shown at the nodes and thickened lines represent nodes with PP = 1. 00. The scale bar represents the expected changes per site. Families and orders are indicated with coloured blocks to the right of the tree. GenBank accession and/or Fungal Planet numbers are indicated behind the species names. The tree was rooted to Saccharata proteae (GenBank EU552145. 1) and the taxonomic novelties described in this study for which LSU sequence data were available are indicated in bold face.
The alignment and tree were deposited in TreeBASE (Submission ID S25229). Consensus phylogram (50 % majority rule) of 25 278 trees resulting from a Bayesian analysis of the LSU sequence alignment (117 sequences including outgroup; 899 aligned positions; 248 unique site patterns) using MrBayes v. 3. 2. 6  (Ronquist et al. 2012). Bayesian posterior probabilities (PP) > 0. 84 are shown at the nodes and thickened lines represent nodes with PP = 1. 00. The scale bar represents the expected changes per site. Families and orders are indicated with coloured blocks to the right of the tree. GenBank accession and/or Fungal Planet numbers are indicated behind the species names. The tree was rooted to Saccharata proteae (GenBank EU552145. 1) and the taxonomic novelties described in this study for which LSU sequence data were available are indicated in bold face.
, 30  Mar. 2017 , A. L. van Iperen, HPC 2058 (holotype CBS H-24151 , culture ex-type CPC 33589 = CBS 146025, ITS, LSU and tef1 sequences GenBank MN562097. 1, MN567605. 1 and MN556828. 1, MycoBank MB832856). Notes -Pseudobeltrania was recently treated by  Rajeshkumar et al. (2016). Pseudobeltrania lauri is closely related to P. ocoteae (conidia (21-)23 -27(-29) × (9 -)10(-11) μm), but is distinct in having larger conidia. Based on a megablast search of NCBIs GenBank nucleotide database, the closest hits using the ITS sequence had highest similarity to Subsessila turbinata (strain MFLUCC 15-0831, GenBank NR_148122. 1; Identities = 495/521 (95 %), 1 gap (0 %)), Porobeltraniella porosa (strain NFCCI 3995, GenBank KX519519. 1; Identities = 531/559 (95 %), 5 gaps (0 %)), and Pseudobeltrania ocoteae (strain CPC 26219, GenBank NR_138416.
1; Identities = 552/584 (95 %), 6 gaps (1 %)). Closest hits using the LSU sequence are Porobeltraniella porosa (strain NFCCI 3996, GenBank KX519526. 1; Identities = 857/864 (99 %), 1 gap (0 %)), Pseudobeltrania ocoteae (strain CPC 26219, GenBank KT950870. 1; Identities = 863/871 (99 %), no gaps), and Subsessila turbinata (strain MFLUCC 15-0831, GenBank NG_059724. 1; Identities = 815/828 (98 %), 2 gaps (0 %)). Closest hits using the tef1 sequence had highest similarity to Subsessila turbinata (strain MFLUCC 15-0831, GenBank KX762291. 1; Identities = 422/435 (97 %), no gaps), Neopestalotiopsis samarangensis (as Pestalotiopsis sp. SSNM-2012c, strain SS010, GenBank JQ968611. 1; Identities = 413/ 428 (96 %), no gaps), and Pestalotiopsis portugalica (strain LC4370, GenBank KX895226. 1; Identities = 408/423 (96 %), no gaps).
On PDA surface and reverse olivaceous grey. On OA surface iron-grey. Typus. new Zealand, Auckland, Rotorua, leaf spots on Pittosporum tenuifolium (Pittosporaceae), 25 Aug. 2017, R. Thangavel (holotype CBS H-24152, culture ex-type T17_03008A = CPC 34462 = CBS 146026, ITS and LSU sequences GenBank MN562098. 1 and MN567606. 1, MycoBank MB832857). Pedro W. Crous & Johannes Z. Groenewald, Westerdijk Fungal Biodiversity Institute, P. O. Box 85167, 3508 AD Utrecht, The Netherlands; e-mail: p. crous@wi. knaw. nl & e. groenewald@wi. knaw. nl Raja Thangavel, Plant Health and Environment Laboratory, Ministry for Primary Industries, P. O. Box 2095, Auckland 1140, New Zealand; e-mail: thangavel. raja@mpi. govt. nz Colour illustrations. Pittosporum hedge Parafenestella pittospori was isolated from. Conidiomata on synthetic nutrient poor agar; conidiomatal ostiole and wall; conidiogenous cells; conidia.
Scale bars = 120 µm (conidio mata), 20 µm (ostiole), 10 µm (all others). Notes -Parafenestella was recently treated  (Jaklitsch et al. 2018 , Valenzuela-Lopez et al. 2018 , Crous et al. 2019b) , and shown to have phoma-like asexual morphs. Within the genus Parafenestella, P. pittospori is phylogenetically distinct from other species known from DNA sequence data. Based on a megablast search of NCBIs GenBank nucleotide database, the closest hits using the ITS sequence had highest similarity to Ochrocladosporium elatum (strain 17C006, GenBank MH734786. 1; Identities = 421/437 (96 %), 3 gaps (0 %)), Neocucurbitaria vachelliae (strain CBS 142397, Gen-Bank NR_156363. 1; Identities = 412/428 (96 %), 3 gaps (0 %)), and Ochrocladosporium frigidarii (strain CZ549, GenBank FJ755255. 1; Identities = 406/423 (96 %), 3 gaps (0 %)).
Closest hits using the LSU sequence are Parafenestella tetratrupha (strain C304, GenBank MK356319. 1; Identities = 900/906 (99 %), no gaps), Parafenestella salicum (strain C311, GenBank MK356318. 1; Identities = 900/906 (99 %), no gaps), and Parafenestella rosacearum (strain C320, GenBank MK356315. 1; Identities = 900/906 (99 %), no gaps), as well as species of Neocucurbitaria, such as Neocucurbitaria unguis-hominis (strain CNM-CM 8717, GenBank LT966028. 1; Identities = 881/887 (99 %), no gaps) and Neocucurbitaria keratinophila (strain CBS 121759, GenBank LT623215. 1; Identities = 884/891 (99 %), no gaps). Classification -Microthyriaceae, Microthyriales, Dothideomycetes. Mycelium consisting of brown, smooth to warty, 2. 5-3 µm diam hyphae. Conidiophores dimorphic. Microconidiophores reduced to conidiogenous loci on hyphae, subcylindrical to doliiform with truncate apex, 5 -10 × 5 -6 µm.
2018, A. J. Carnegie, HPC 2432 (holotype CBS H-24156, culture ex-type CPC 35763 = CBS 146028, ITS and LSU sequences GenBank MN562099. 1 and MN567607. 1, MycoBank MB832858). Notes -Neoanungitea eucalyptorum is closely related to N. eucalypti (conidia (0 -)3-septate, (13 -)15 -17(-22) × (3. 5 -)4 -5 μm) described from leaves of Eucalyptus obliqua collected in Australia  (Crous et al. 2017a ). The two species can be distinguished in that N. eucalyptorum has longer conidia. Based on a megablast search of NCBIs GenBank nucleotide database, the closest hits using the ITS sequence had highest similarity to Neoanungitea eucalypti (strain CBS 143173, GenBank NR_156383. 1; Identities = 477/526 (91 %), 18 gaps (3 %)), Anungitopsis speciosa (strain CBS 181. 95, GenBank EU035401. 1; Identities = 387/467 (83 %), 26 gaps (5 %)), and Anungitopsis lauri (strain CBS 145067, GenBank NR_161129.
1; Identities = 414/507 (82 %), 30 gaps (5 %)). Closest hits using the LSU sequence are Anungitopsis speciosa (strain CBS 181. 95, GenBank EU035401. 1; Identities = 763/790 (97 %), no gaps), Spirosphaera beverwijkiana (strain CBS 470. 66, Gen-Bank MH870500. 1; Identities = 727/796 (91 %), 8 gaps (1 %)), and Microthyrium quercus (strain HKAS 92487, GenBank KY911453. 1; Identities = 726/795 (91 %), 6 gaps (0 %)). Classification -Chaetomellaceae, Chaetomellales, Leotiomycetes. Conidiomata sporodochial, superficial, separate, 180-300 µm diam, red-brown, becoming cupulate; wall of red-brown textura angularis. Conidiophores hyaline, smooth, branched, septate, filiform, giving rise to terminal and intercalary conidiogenous cells and paraphyses, up to 100 µm long, 2 -2. 5 µm wide. Conidiogenous cells monophialidic, subcylindrical, straight to curved, smooth, hyaline, with periclinal thickening and minute collarette, 4-15 × 1-1.
5 µm. Conidia hyaline, smooth, aseptate, cymbiform, guttulate, ends acute, (9-)10-12(-14) × (1. 5-)2 µm. Culture characteristics -Colonies flat, spreading, with moderate aerial mycelium and smooth, lobate margin, reaching 60 mm diam after 2 wk at 25 °C. On MEA surface cinnamon, reverse sepia. On PDA surface buff, reverse isabelline. On OA surface buff. Typus. new Zealand, Auckland, 21 Mullagh place, Phoenix sp. , 4 Feb. 2018, R. Thangavel (holotype CBS H-24157, culture ex-type T18_00344D = CPC 35872 = CBS 146029, ITS and LSU sequences GenBank MN562100. 1 and MN567608. 1, MycoBank MB832859). Notes -Pilidium was treated by  Rossman et al. (2004)  and  Marin-Felix et al. (2017). Pilidium novae-zelandiae (conidia (9-) 10 -12(-14) × (1. 5-)2 µm) is phylogenetically closely related to P. anglicum (Eucalyptus sp.