dataesr/scientific-paragraphs-categorization huggingface dataset:
g. , BH), low vulnerability (regions of low threat, purely proactive, e. g. , LW), or high irreplaceability (e. g. , G200). We selected three NGO frameworks of global biodiversity conservation prioritizations (29) (SI Appendix, Fig. S5 ) encompassing the three major groups of the irreplaceability-vulnerability gradient. Specifically, we selected the BH (ref. 90  and Conservation International), G200  (91) , and LW  (128). BH prioritizes high vulnerability, LW prioritizes low vulnerability, and G200 prioritizes high irreplaceability. A detailed description of the three selected frameworks can be found in SI Appendix. The BH data layer was obtained from ref. 129 ; the upgraded LW data layer was obtained from ref. 130 ; and the G200 terrestrial ecoregion layer was from the World Wildlife Fund (https://www.
1); moderate (0. 1 < HMI ≤ 0. 4); and high to very high (0. 4 < HMI ≤ 1. 0). Prioritization Analyses. We used Zonation systematic conservation prioritization software, version 4  (59, 131)  to identify global priority areas for tree diversity in each of the three biodiversity dimensions (taxonomic, phylogenetic, and functional). Zonation is based on the principle of complementarity, by balancing a set of biodiversity features to jointly achieve the most effective representation in a given region, and evaluating the spatial priority areas through the priority ranking  (59). We used the core-area Zonation (CAZ) algorithm as a cell removal rule. The CAZ ranking algorithm emphasizes species (or any biodiversity feature) rarity to ensure high-quality locations for all features, even if these features occur in species-poor areas  (58).
In addition, we also performed a sensitivity analysis by comparing the resulting priority areas with those derived from simply combining the priority areas from the prioritizations on the separate diversity dimensions. We found the two analyses generated similar results, namely similar percentages of overlap with existing PAs and conservation priority frameworks (Fig. 5  vs. SI Appendix, Fig. S14 ), and similar HMI values of the priority areas inside and outside existing PAs (SI Appendix, Fig. S15  vs. Fig. S16 ). Thus, we report the joint results in the main text (Figs. 3, 4 , and 5), that is, given that only these adhere to the 17 and 50% PA targets. Spatial Analysis. The relationships among the three priority areas were tested after accounting for spatial autocorrelation using the SpatialPack package  (132).
These steps were repeated also treating the top 17% priority areas and the top 50% areas as PAs, respectively. The Sankey diagrams were plotted via the ggalluvial package  (133)  in Fig. 3  and SI Appendix, Fig. S3. Congruence between PAs, top-priority areas, and NGO frameworks was assessed through gap analyses. We overlaid the three layers (i. e. , the top-priority areas, PAs, and each conservation priority framework) to calculate the level of protection in PAs, potential protection in the priority framework, and shared protection of the two global high-priority areas (both 17 and 50%). Data Availability. All species occurrences reported in this article have been deposited in the BIEN database (https://bien. nceas. ucsb. edu/bien/) and can be accessed via the RBIEN package. In addition, species' alpha-hull ranges at the 110-km resolution, which were the input data for analyses in the study, have been deposited in GitHub (https://github.
com/wyeco/TC_conservation), together with the relevant R codes. The phylogeny and imputed trait data and phylogeny are available in Datasets S2 and S3. All other study data are included in the article and/or supporting information. funded by VILLUM FONDEN. We thank Brad Boyle for valuable database and informatics assistance and advice, and TRY contributors for sharing their data. This work was conducted as a part of the BIEN Working Group, 2008 to 2012. We thank all the data contributors and numerous herbaria who have contributed their data to various data-compiling organizations for the invaluable data and support provided to BIEN. We thank the New York Botanical Garden, the Missouri Botanical Garden, Utrecht Herbarium, UNC Herbarium, GBIF, REMIB, and SpeciesLink. The staff at CyVerse provided critical computational assistance.
C. B. was supported by a  National Research Foundation of Korea (NRF)  grant funded by the  Korean government (MIST)  ( 2022R1A2C1003504 ). A. S. M. was supported by the  Environment Research and Technology Development Fund  ( S-14 ) of the  Ministry of the Environment, Japan. J. Pisek  was supported by  Estonian Research Council  Grants  PUT 1355  and  PRG 1405. J. Peñuelas  was funded by  European Research Council Synergy  Grant  ERC-2013-SyG-610028 IMBALANCE-P. A. G. G. was funded by  National Fund for Scientific and Technological Development (FONDECYT)  grant  1200468  and  Agencia Nacional de Investigaci on y Desarrollo  ( ANID/BASAL) FB210006. V. D. P. was funded by  Conselho Nacional de Desenvolvimento Cient ıfico e Tecnol ogico (CNPq) ,  Brazil  (grant  307689/2014-0 ). The BIEN Working Group was supported by the  National Center for Ecological Analysis and Synthesis , a center funded by  NSF   EF-0553768  at the  University of California, Santa Barbara  and the  State of California.
Transient astronomy Time-domain astronomy is one of the most active branches of modern astrophysics. In a few years, new observational facilities, specifically designed with the goal of securing high-cadence observations of large fractions of the nightly sky, will become operational. Equally important, "big-data" algorithms, in a full machine-learning scenario, are increasingly being applied and developed to manage the large amount of data provided by these facilities. All in all, we are facing an impressive technological and conceptual effort that will allow the research community to identify thousands-to-millions of new or recurrent transient sources routinely. The discovery space opened by rare or peculiar transients is highly relevant, involving all categories of sources, from Galactic novae and unusual variables to supernovae, GRBs, and gravitational-wave electromagnetic counterparts, not to mention possible entirely new classes of objects.
Software The CUBES instrument benefits from an entire "software ecosystem" built around it, whose individual packages cooperate to support the users from the proposal preparation to the science-grade spectra: • The Exposure Time Calculator (ETC): a web application used to predict the CUBES exposure time required to achieve a given SNR (see Fig. 9 ); • The Observation Preparation Software (OPS): a list of tools aimed to help the users identify the best instrument settings to achieve a scientific goal; • The Instrument Control Software (ICS): devoted to the instrument devices and detectors control, as well as to the execution of the high level procedures aimed to coordinate the science exposures and produce the final files to be ingested in the archive; • The Data Reduction Software (DRS): a collection of C++ and Python recipes aimed to remove instrument artifacts from the science exposures, and produce the final 1D spectrum;   • The End-to-end Simulator (E2E): a package able to simulate realistic science exposures of a given science target, by taking into account the CUBES instrumental effects, and allowing early testing of the data reduction pipelines, as well as early validation of design decisions (Figs.
10 and 11). A relevant feature of many of the above mentioned packages is that they will be developed according to the recently published ELT software standards, and will be based on the ELT Instrument Framework libraries and tools. The CUBES instrument will, however, operate in a VLT environment (the Paranal Observatory) hence a special component is being developed by ESO to allow proper communication between the two environments. Such component, named VLT/ELT Gateway, will also be used to communicate with the UVES instrument and coordinate the CUBES+UVES simultaneous exposures from the CUBES control software. The CUBES software ecosystem is described in detail in 23.
The PI represents the leading technical institute, INAF. Each country is represented in the managerial structure by one Co-PI; altogether form the CUBES Executive Board (EB). The EB is a strong but hands-off high-level overview committee. The managerial aspects are delegated by the EB to the Project Manager (PM). The scientific aspects are delegated by the EB to the Project Scientist (PS). The project manager is supported by a System Engineer (SE) and by a Software System Engineer (SSE) who are in charge to supervise the overall system design. The SE and SSE work in close contact with the Instrument Scientist (IS) who makes sure that the adopted technical solutions match the foreseen instrument scientific needs. The overall work is organized in work-packages coordinated by the CUBES system engineers.
Public Engagement CUBES is an ambitious research program, and some of the scientific topics are related to the hottest open questions in modern astrophysics. Considering the vast discovery potential of the project, and the remarkable research and development technological activities, we consider good public communication as particularly important. Dissemination of science and technology is a fundamental part of our project and since the beginning we have included a work package devoted to public outreach. During phase A we have mainly communicated the progress of our project and the main scientific topics. We have prepared a web page that is also a useful tool for the project as a whole (https://cubes. inaf. it/home), and profiles in the main social media, i. e. Facebook, Twitter etc. A series of short video interviews with some of the people in the CUBES consortium have been prepared and made available on the web (https://www.
ACKNOWLEDGMENTS R. S. acknowledges support by the  Polish National Science Centre  through project  2018/31/B/ST9/01469. We gratefully acknowledge support from the  German Federal Ministry of Education and Research (BMBF)  through project  05A20VHA. B. B. acknowledges the  FAPESP  grant  2014/18100-4. For the purpose of open access, the author has applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising from this submission.
Here we derived a unique 20-year time series of land-sea human impacts and other environmental factors known to drive coral reef ecosystem processes across our study region in the Hawaiian Islands (Fig. 1a ). Human impacts included urban runoff, wastewater pollution, nutrient loading, sediment input, and local restrictions on fishing gear types. Environmental factors included peak and annual rainfall, wave exposure, variability in ocean temperatures and heat stress, irradiance, and phytoplankton biomass. We also incorporated multiple fish biomass metrics that represent the critical role reef fish play in maintaining coral reef ecosystem dynamics  [22] [23] [24]  (see Table  S1  for full list of factors). We combined this dataset with recurring, permanently marked and site-specific underwater survey data on coral reef benthic communities (Fig.
In short, underwater visual surveys of benthic assemblages were collated from three monitoring programs for the following years: 2003, 2007, 2011, and annually from 2014 -2019. All benthic surveys used permanently marked pins to ensure the same area of reef was surveyed over time. High resolution photographs were collected via photoquadrats at 1 m intervals along 25 m belttransects (N = 26 photographs per transect). Thirty to fifty random points were overlaid on each photograph and the benthic component under each point was identified to the lowest possible taxonomic level. Per cent cover of the major functional groups were used in this analysis, namely hard coral, crustose coralline algae, macroalgae, and turf algae. All data were averaged among each transect and then among all transects for each site (1 -4 transects per site, per year, depending on the monitoring program).
5 million, comprised of 11 species. The distribution of individual counts in the entire database for those 11 species was then used to identify observations that fell above the 99. 0% quantile of counts for each species individually. These observations were adjusted to the 99. 0% quantile for analysis. Human population. We quantified human population density for each year from 2000 -2019 using NASA Gridded Population of the World version 4 (GPWv4  57  ). The dataset is available at 1 km resolution at 5 yr intervals from 2000 -2020. The island of Hawai'i was extracted from the global GPWv4 dataset and projected to UTM at 907. 5 m resolution. The 'Focal Statistics' tool was used to calculate the total population within 15 km of each 100 m grid in our study region (Fig. S5 ). Linear interpolation was used to generate rasters for missing years and produce annual time steps of human population density for years 2000 -2019.
Wastewater pollution. We mapped wastewater effluent (gal/ha/yr) and nitrogen input (kg/ha/yr) from onsite sewage disposal systems (e. g. , cesspools and septic tanks) and injection wells (collectively OSDS) in coastal waters at 100 m resolution from 2000 -2017. Wastewater effluent and nutrient input were estimated based on the number of bedrooms and bathrooms that they service and the type of system in use (cesspool, septic tank, aerobic treatment unit, etc. )  44. Discharge rates and nutrient loading were recently updated specifically to the study region  58. OSDS locations were based on centroids of Hawai'i County Tax Map Key parcel data and dates that they went into service were based on available County and State records. OSDS inclusion was limited to those within a 1 year ground water travel time of the coast, based on 3D ground water modelling with MODFLOW and MODPATH  44.
OSDS input was assumed to flow to the nearest point along the shoreline. We then used a Gaussian decay function to estimate OSDS input offshore, approaching zero at 2 km (Figs  S6 -S8 ). Values from 2017 were used for 2018 and 2019. Nutrient input. We calculated nutrient input (kg/ha/yr) at 100 m resolution from 2001 -2017 as the combination of total nitrogen from OSDS and golf courses. See Wastewater pollution section above for quantifying nutrient input associated OSDS. For golf courses, the total golf course area was derived from NOAA Coastal Change Analysis Program (CCAP) land-use/land-cover data and Landsat cloud-free composite images created with Google Earth Engine. Golf course area was multiplied by a nitrogen application rate of 585 kg/ha/yr  59, 60  and then by a leaching rate of 32%  [61] [62] [63]  to estimate nitrogen that either runs off or reaches the groundwater.
We quantified the total area (km 2 ) of impervious surfaces within 10 km of the coastline by watershed at 100 m resolution for each year from 2000 -2017. Impervious surface cover was extracted from NOAA CCAP land use land cover data from 1992, 2001, 2005, and 2010. We also digitized 2017 impervious surface cover from a single cloud-free Landsat 8 image (courtesy of USGS) (30 m resolution, 15 m pansharpened). We filled missing years by linear interpolation. Values from 2017 were used for years 2018 and 2019. We used sub-watershed catchment data  64  to aggregate the impervious surface area by drainage to coastal source pixels and used a Gaussian decay function to estimate dispersal offshore, approaching zero at 2 km (Figs. S12 -S13 ). Rainfall. We quantified annual rainfall (mm/yr) and peak rainfall (maximum 3-day rainfall total, mm/yr) at 100 m resolution from 2000 -2019.
Daily rainfall data was provided by Dr. Ryan Longman following  65, 66. Rainfall from each rain station (Fig. S14 ) was used to derived interpolated surfaces at annual time steps using Empirical Bayesian Kriging in ArcGIS. Subwatershed catchment data  64  were clipped to 0 -10 km from the coast and used to calculate rainfall per drainage area. The resulting values were dispersed offshore from coastal line segments of watersheds using a Gaussian decay function with distance from shore, approaching zero at 2 km offshore (Fig. S15 ). Sediment Input. We quantified sediment input (tons/ha/yr) at 100 m resolution from 2000 -2019. We used the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) sediment delivery model to derive long-term annual average sediment input reaching the coast  [67] [68] [69].
We then modulated the long-term annual average sediment over time by watershed based on discharge calculated from peak rainfall data (see Rainfall above). Discharge by watershed was calculated following  70. Sediment load was assumed to scale with discharge according to approximate ratings curve following 71. We then dispersed coastal sediment values offshore following  72  (Figs. S16 -S17 ). Fishing Gear. We created a categorical value for local fishing gear restrictions using regulation information and marine managed area boundary designations from  72. All regulations were evaluated for the following gear categories or types of reef fin fishing over time: no fishing, line fishing, lay nets, spear fishing, and aquarium collection. We derived ranked fishing gear categories as follows: (1) Full no-take; (2) No lay net, spear, and aquarium, (3) No lay net and aquarium; (4) No lay net (5) No aquarium; (6) Open to all gear types.
To assign fishing gear rank to each reef survey site, we subset the survey data by date and used the Spatial Join tool with the fishing gear layer corresponding to the dates in the subset. We used the exact position coordinates of each reef survey site to determine whether a site was inside a gear restricted area. In cases where reef surveys fell within overlapping management areas, we assigned the most restrictive fishery regulations by using a 25 m search distance to assign values to the more restrictive area (Table  S5 , Fig. S18 ). Sea Surface Temperature (SST): We quantified the mean and variability in SST for each year from 2000 -2019. The summertime mean and summertime variability SST metrics were calculated by taking the mean and standard deviation over a 90-day window centred on maximum value of the 7-day moving window average for each SST pixel.
All pixels that directly overlapped with land were removed prior to assigning values to reef survey sites. All data were NOAA's Coral Reef Watch version 3. 1 available daily at 5 km resolution  28  (Fig. S19 ). Mean regional temperature (Fig. 3a ) was calculated by taking the mean of all coastal pixels across our study region for each time step. Heat stress: We explored numerous metrics to quantify the heat stress endured by reefs across our study region during the 2015 marine heatwave. These included maximum SST, maximum SST anomaly, maximum 'HotSpot', and maximum Degree Heating Week (DHW). Ultimately, we found that all of the above metrics were highly correlated (r>0. 8). As such, we used DHWaccumulation of daily heat stress above the maximum climatological monthly mean over a 12week periodgiven it is widely used by coral reef scientists across the world.
All data were NOAA's Coral Reef Watch version 3. 1, available daily at 5 km resolution  28. See Fig. S1  for a map of maximum DHW in 2015 across our study region. Phytoplankton biomass. We used satellite derived chlorophyll-a (mg m -3 ; a proxy for phytoplankton biomass) from two sources for our 20-year data set. We first used the long-term mean from 2002 -2013 in 8-day, 4 km data obtained from  72. These data were used to quantify differences in phytoplankton biomass between reefs with positive versus negative trajectories shown in Fig. 2. For all other figures and analyses, we used VIIRS (Visible Infrared Imaging Radiometer Suite) provided by NOAA's Coral Reef Watch. VIIRS provides very high spatial (750 m) and temporal (daily) resolution data starting in 2014. High cloud cover or other atmospheric interference resulted in no data available for numerous days across portions of our study region.
Wave exposure. We computed wave power at 50 m resolution for each year from 2000 -2019. Wave power (kW/m) combines wave height and period and provides a more representative metric of wave exposure than wave height alone  74. Annual data were generated for each 50 m grid cell by taking the mean of the top 97. 5% in daily maximum wave power data obtained from  75  and updated for this study. In short, a series of nested grids (from global to 50 m) using the third generational spectral wave models WAVEWATCH III  76  and Simulating Waves Nearshore (SWAN)  77  were used to quantify wave transformation over the reef environment at 50 m, hourly intervals across our study region. The modelled wave dataset was validated with available measurements from offshore and nearshore buoys and satellite altimeters (Fig.
S2 ), removing one of each pair of highly correlated (r>0. 7) predictors (except Human Population and Urban Runoff that were correlated at 0. 72, see Supplementary Information). We incorporated a random spatial factor to account for the possible influence of a change in an underlying variable along the coastline not quantified in this study. This was done by breaking the coastline up into discrete 10-km sections running north to south. Section size was determined using hierarchical clustering based on pairwise Euclidean distances between reefs and identifying an inflection point in the intra-group variance (sensu  20  ; Fig. S3 ). We fitted GAMMs for all possible combinations of the remaining predictor variables using the UGamm wrapper function, in combination with the dredge function in the MuMIn package 80.
Non-linear smoothness in the models was determined using penalized cubic regression splines, with the number of knots (limited to 4 to reduce overfitting) spread evenly throughout each covariate. All possible candidate models were computed (unique permutations of the predictor variables) but limiting the total number of predictors in any given candidate model to 6 to reduce overfitting. We used Akaike's Information Criterion with a bias correction for small sample sizes  81  (AICc) for model comparison and all models within ΔAICc ≤ 2 of the top model (ΔAICc = 0) are presented in Table  S2. To visualise the effect of predictor terms on coral cover change, we averaged the coefficients from top models (i. e. , ΔAICc ≤ 2) to generate a predicted dataset and set all other predictor terms to their median value.
A fundamental component of this model is the assumption of proportional odds, or parallel regression, which implies Bk values are independent of the logit level j. The validity of this parallel regression assumption was ascertained using Brant's Wald test  82  , as well as a likelihood ratio test (α = 0. 05). Like the GAMMs, some predictors were transformed and highly collineated variables removed prior to model fitting (Fig. S4 ). All possible candidate models were computed while limiting the total number of predictors in any given candidate model to four to reduce overfitting. Models were computed using the multinomial logistic regression function mnrfit in MATLAB. We again used AICc for model comparison and all models within ΔAICc ≤ 2 of the top model (ΔAICc = 0) are presented in Table  S4.
Acknowledgments We thank Amanda K. Dillon of Aline Designs for graphics support, the numerous divers, boat drivers, and support staff from the Hawaiʻi Division of Aquatic Resources, National Park Service, and The Nature Conservancy for logistical and data collection support, and Dr. Jason Link and Dr. Jameal Samhouri for their comments and suggestions on this manuscript. This research was supported by NOAA's Integrated Ecosystem Assessment Program (contribution no. 2022_X), NOAA's Fisheries And The Environment Program, NOAA's Pacific Islands Fisheries Science Center, and grants from the NOAA Coral Reef Conservation Program (#NA16NOS4820059), and National Marine Fisheries Service, Office of Habitat Conservation (#EA133F17SE1203 and NA17NMF4630301), and the Harold K. L. Castle Foundation. Author Contributions J.