dataesr/scientific-paragraphs-categorization huggingface dataset:
Richness from genus surrogacy As an alternative method for estimating richness, we used the "higher taxon surrogacy" approach (79) by modeling species richness as a function of genus richness for each set of grid cell predictions, with the assumption that species richness is more undersampled than genus richness. As ants are undersampled in general, particularly in certain regions of the globe, modeling the mean of richness would likely be an underestimate, and thus, we used quantile regression to model the upper bound. We estimated genus-level richness by first aggregating the GABI species occurrence data to the genus level, then applying the same methodology we used to estimate richness at the species level (i. e. , stacking polygonal range estimates and SDM predictions). Aside from the necessity for the response to be monotonically increasing and convex in shape, we lacked a theoretical basis for specifying any particular parametric model for this relationship.
We thus fit nonparametric additive regression models with the quantile (tau) set at 0. 9 and additionally with constraints "increasing" and "convex" using the R package quantreg  (80). We fit separate models for each biogeographic realm  (81)  with our estimates of species richness as the response variable and genus richness as the sole predictor variable, exploring a range of very small to relatively large lambda values for penalizing complexity (0. 01, 0. 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10). For each realm, we selected the model with the lowest Akaike information criterion (AIC) value. When multiple models had delta AIC values (model AIC -minimum AIC across all models) equal to or less than 2, we chose the simplest model from this subset (i. e. , the model with the minimum lambda value). We combined the predictions for each biogeographic realm to make a global map of richness estimates based on genus surrogacy (fig.
Macroecological model using GLAD community database For the macroecological modeling approach, we took advantage of the GLAD  (24)  dataset to model ant species richness based on environmental variables. Using the methodology explained in ''Comparison of richness estimates to community-level point observations,'' we assigned maximum GLAD richness to 10-arc min grid cells. This resulted in 268 unique grid cells with maximum GLAD richness estimates with global representation (Africa, 28; Asia, 38; Europe, 40; North America, 43; Oceania, 46; and South America, 73). We modeled GLAD ant richness as a function of bioclimatic and biogeographic predictor variables. We selected a subset of the original 19 bioclimatic variables from WorldClim 2. 0 (59) with low collinearity [variance inflation factor less than 3 (82)] using the R package usdm  (83).
We used a conservative threshold as regression models without regularization are sensitive to dependence between predictor variables caused by high collinearity  (84). The six variables we retained for modeling were mean diurnal range (bio2), mean temperature of wettest quarter (bio8), mean temperature of driest quarter (bio9), precipitation seasonality (bio15), precipitation of warmest quarter (bio18), and precipitation of coldest quarter (bio19). We also used a dataset of global biogeographic realms based on vertebrate distributions and phylogenies  (81). We first fit a generalized linear model with Poisson error distribution and log link function with the bioclimatic and biogeographic predictor variables, but as this model showed overdispersion, we then fit all subsequent models with a negative binomial error distribution.
We performed an exhaustive model selection procedure with the R package MuMIn  (85)  and used the sample-size corrected Akaike information criterion (AICc) to determine the optimal combination of predictor variables. The optimal model included all the input predictor variables, and all other Downloaded from https://www. science. org on August 16, 2022 models were suboptimal as they had delta AICc >2. As some predictor variable ranges for the globe included values outside those used for model training, we made "clamped" predictions (fig. S3 ) that used modified versions of the original predictor variables that were constrained to their respective ranges from the training data (86) using the function clamp. vars() in ENMeval 2. 0. 0  (31). Thus, our predictions do not make extrapolations beyond the bounds of the training data.
Ultimately, there were few major differences between the unclamped and clamped predictions that extended mainly to very small areas. We do note that some regions that our range-stacking approach found were richness centers for ants, such as the African Mediterranean, Central Africa, Madagascar, and parts of Southeast Asia, have relatively poor coverage in the GLAD database, which can perhaps explain why this approach had the least correlation with the other methods. Of these regions, only Central Africa had a high richness prediction that was generally congruent with the other methods.
Terrestrial vertebrate data We compared the stacked estimates of ant richness and rarity to corresponding maps we made using existing range datasets of terrestrial vertebrates: amphibians, birds, mammals, reptiles, and these groups combined (henceforth "vertebrates"). These data come from different sources: International Union for Conservation of Nature (IUCN) range maps for amphibians and mammals (2010) and BirdLife International NatureServe breeding ranges for birds (2011) are described in Jenkins et al. (27)  (2013), and range maps for reptiles were developed by Roll et al. (28). To match our methodology for GABI polygonal range estimates, we overlaid the polygonal range estimates for each taxon and summed the overlapping polygons for each 10-arc min grid cell. Although we buffered range estimates for ants (30 km) to account for geographic uncertainty, we chose not to buffer those for vertebrates.
Tests of sensitivity to spatial resolution As our richness and rarity estimates for ants were conducted at the relatively fine resolution of 10 arc min at a global scale, we sought to determine how robust the observed diversity patterns were to coarser spatial grains. Compared with richness estimates derived from range maps, those derived from SDMs have been shown to reveal greater heterogeneity in diversity between regions when spatial grain is increased  (88). However, global diversity estimates made at fine resolutions may nonetheless have higher uncertainty, and thus, we examined how consistent our diversity patterns were over a range of increasing resolutions  (75) , similar to the methodology in "Comparison of richness estimates to community-level point observations. " For all taxa, we first coarsened each species' range estimate raster (either polygonal range estimate or SDM prediction, based on data availability) to coarser resolutions of 20, 50, 100, 200, and 500-arc min resolutions (with bilinear resampling to the maximum value to ensure that maximum richness per cell did not change) using the Python package rasterio  (89).
Predicting changes to diversity centers under a high-sampling scenario We trained Random Forest models on our estimates of ant diversity to make predictions of undiscovered ant diversity under a scenario of equally high sampling across the globe. Specifically, we built separate models predicting our global estimates of ant richness and rarity (excluding Antarctica) as a function of climatic variables [19 bioclimatic variables from WorldClim 2. 0 (59)], topography [Shuttle Radar Topography Mission (SRTM) elevation from WorldClim 2. 0], categorical biogeographic realms  (81) , the GABI ant sampling density, and summed vertebrate richness or rarity, all at 10-arc min resolution. To avoid spurious variable importance values, we removed variables with high collinearity using the vifcor() function from the R package usdm  (83)  with a threshold of 0.
7. We used slightly less restrictive criteria here than for the macroecological model (see "Alternative methods to estimate species richness"), as Random Forest is known to be relatively robust to multicollinearity  (90) , but this only resulted in the addition of one more bioclimatic variable [precipitation of the driest month (bio14)]. The biogeographical realms shapefile dataset we used from Holt et al. (81)  lacked coverage of many smaller islands (particularly in Oceania) and some coastal areas, so we first converted the polygons to a raster and then performed a k-nearest neighbors classification with the R package spatialEco  (91)  to classify cells without data to the most proximal cell with a biogeographical classification. For our ant sampling density grid (see "Species range estimates"), low density reflects either lack of field collection and/or taxonomic work to process existing collections.
The vertebrate diversity estimates were projected from the equal-area Eckert IV projected coordinate system to WGS 1984 (with bilinear resampling to the maximum value) to match the geographic coordinate system of the other datasets. We included vertebrate richness and rarity predictors for two reasons. First, it lets us determine the extent of predictive power that vertebrate biodiversity patterns hold beyond that captured by climate and region. Second, since these data represent the current state of knowledge of vertebrate biodiversity centers, they allow us to assess whether the ant data alone identify novel regions of richness and rarity even after including important regions for vertebrates. We tuned model complexity and evaluated models using spatial cross-validation to optimize transferability to new conditions.
Using the R package ranger (92), we built Random Forest models with different values of the hyperparameter "mtry," which controls how many predictor variables are randomly sampled at each split within a tree. We used a range of 1 to 10-higher values indicate more variable interactions are allowed, leading to higher model complexity (93)-with other hyperparameters left at their defaults. After removing highly collinear predictor variables, we built models with the following variables: mean diurnal temperature range (bio2), mean temperature of wettest quarter (bio8), mean temperature of driest quarter (bio9), precipitation of driest month (bio14), precipitation seasonality (bio15), precipitation of warmest quarter (bio18), precipitation of coldest quarter (bio19), elevation, biogeographic realm, ant sampling density, and vertebrate richness or rarity.
We log-transformed rarity values to avoid problems with model convergence due to extremely small values. To evaluate models, we implemented spatial cross-validation using a fivefold systematic 10 × 14 checkerboard partitioning scheme (fig. S6 ) using the R package blockCV  (94)  and then calculated the average mean square error (MSE) over the folds for each model. We performed model selection using spatial cross-validation as it tends to result in models with better transferability to new conditions  (38). We selected optimal model complexity settings that resulted in the minimum average MSE (mtry = 4 for richness and mtry = 2 for rarity; fig. S7 ) and then trained models with these settings on the full dataset. For these models, we made maps to show the average root mean square error per block labeled by their spatial fold, which highlights areas that had high prediction error when the model was trained on the other spatial folds but missing the data from the fold represented by each block (fig.
S7 ). We calculated variable importance values for each selected model with the permutation importance option in ranger (fig. S7 ). We then made extrapolations of richness and rarity for the globe under a scenario of equally high sampling by setting all cells with values in the sampling density grid to 1, representing the highest observed sampling density in our dataset (Figs. 3 to 6 ). Last, we overlaid the high-sampling scenario maps with the original diversity maps for ants and vertebrates and calculated percent overlap between them (Figs. 3 to 6 ).
Global protected area coverage To determine how much diversity center area is currently protected, we calculated the total area of global protected areas located within our diversity center estimations for each taxon (Fig. 7 ). We downloaded the World Database on Protected Areas 1. 6 (95) file geodatabase from <www. protectedplanet. net> (downloaded 11 March 2021) and retained only designated and national protected area polygons from the feature dataset to match the methodology of Jenkins et al. (27). We then fully dissolved the multifeature polygon layer to a univalue, single feature without overlap and projected it to the equal-area coordinate system Eckert IV. We converted the diversity center rasters for all taxa and sampling extrapolations for ants into univalue polygons and then used them to mask the protected areas layer.
Acknowledgments: We thank all the naturalists, collectors, curators, and data wranglers whose efforts over the past 250 years of ant research contributed to the body of information that made this work possible. We additionally thank all those who have contributed data, corrections, and feedback to one of the underlying datasets over the years. The OIST Scientific Data and Computing Section supported this work with computational resources. We also thank  R. Feitosa  for discussions and  N. Narula  and  K. Deligkaris  for database administration. Funding: This work was supported by subsidy funding to the  Okinawa Institute of Science and Technology Graduate University , the  Japan Society for the Promotion of Science KAKENHI   17 K15180  (E. P. E. ), Japan Society for the Promotion of Science Postdoctoral Fellowships for Foreign Researchers Program  (J.
Thus, transmitters recorded locations every 5 min during daylight (05:00-22:00 UTC +5) and lasted 0-100 days (average: 48 days). To ensure enough data to accurately estimate home ranges and characterize behaviour, we excluded individuals whose transmitters collected <24 h of continuous data from further analysis; all remaining transmitters were active for at least 6 days. Receiving antennas were placed near loafing sites, in a yellow-nosed albatross colony on Entrecasteaux cliff, and on the margin of the skua breeding area. Because of difficulties accessing skua nests in a strictly protected area, breeding success could not be directly monitored; however, tracking data confirmed that all individuals captured at nests continued attending their nest sites after release. Experimental design was approved by the Regional Animal Experimentation Ethical Committee (French Ministry of Research permit #10257-2018011712301381v6) and by the Comité de l'Environnement Polaire  (A-2017-97, A-2017-111, A-2018-123, A 2018-139, A-2019-69, 2019-121, A-2019-132 and A-2019-135).
, 2020). To assess infection status at capture, total nucleic acids were extracted from oral and cloacal swabs using the QuantiNova Probe PCR kit (QIAGEN, USA) and screened for Pm DNA by real-time polymerase chain reaction (RT-PCR) using primers and protocols from  Townsend et al. (1998). To compare the current study with previous epidemiology results, we incorporated an additional dataset of antibody titres and Pm DNA presence from breeding and nonbreeding skuas on Amsterdam Island sampled between 2011 and 2017  (Gamble et al. , 2019) , collected and analysed using the same methods  (Gamble et al. , 2020). Since sex of skuas could not be determined visually, we sexed individuals using DNA extracted from red blood cells using the DNeasy Blood and Tissue Kit (QIAGEN, USA). We tested for the presence of the W-chromosome-linked (i.
| Data processing and breeding status inference We conducted all data analysis in R 4. 2. 0 (R Core Team, 2022). We cleaned data by regularizing tracks to consistent 5-min intervals during each daylight tracking period using the ltraj function in package adehabitatLT  (Calenge, 2006)  and removed erroneous locations using a speed filter of 12 km/h, or 1 km between successive locations  (van Bemmelen et al. , 2018). To test for differences in Pm antibody levels and infections among capture locations, we constructed generalized linear mixed models (GLMM) in the nlme package  (Pinheiro et al. , 2022)   We considered fixed effects to differ among groups if the 95% confidence interval of the coefficient estimate did not overlap zero. To determine the breeding status of skuas captured at clubs, we used the find_nests function in the nestR package  (Picardi et al.
We considered a predictor variable to significantly improve model fit if the 95% confidence interval of its coefficient estimate did not overlap zero. To obtain final models, we successively dropped nonsignificant terms from models until confidence intervals for all remaining coefficients differed from zero, or until dropping an additional term did not decrease the AIC value by >2 points. We further assessed differences among groups using post-hoc general linear hypothesis tests in the multcomp package  (Hothorn et al. , 2008). Tracking data and individual metadata used in this analysis are archived on Zenodo: https://doi. org10. 5281/zenodo. 7362496 (Lamb et al. , 2022).
13652435, 2023, 3, Downloaded from https://besjournals. onlinelibrary. wiley. com/doi/10. 1111/1365-2435. 14240 by University Of Glasgow, Wiley Online Library on [23/03/2023]. See the Terms and Conditions (https://onlinelibrary. wiley. com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License sufficient data for analysis (i. e. functioned continuously for ≥24 h), including 6/8 transmitters deployed at nest sites and 35/42 transmitters deployed at clubs (see Table.
ACK N OWLED G EM ENTS We thank  C. Barbraud  and  K. Delord  for discussions,  J. Dechartre  and  A. Le Nozahic  for field assistnace,  M. -P. Dubois  for help with sexing and the technical team at  Ceva Biovac  for micro-agglutination analyses. This work was funded by the  French Polar Institute  ( IPEV ECOPATH-1151 ),  ANR ECOPATHS  ( ANR-21-CE35-0016 ),  Réserve Naturelle Nationale des Terres Australes Françaises, Zone Atelier Antarctique (ZATA)  and  OSU OREME ECOPOP. We also acknowledge funding support for  REMOVE_DISEASE project selected as part of the BiodiveRestore joint call from Biodiversa and Water JPI  ( ANR-21-BIRE-0006 ). This paper is a contribution of the  Plan National d'Action Albatros d'Amsterdam. Juliet Lamb  was funded by a  Marie Curie Individual Fellowship  ( H2020-MSCA-IF-2018  # 843470 ),  Amandine Gamble  by a  French Ministry of Research PhD fellowship , and  Jeremy Tornos  by  Ceva Biovac  and  ANRT  for a CIFRE PhD fellowship.
Study Design SERAPHIN (NCT00660179) was a global, multicentre, double-blind, randomised, placebocontrolled event-driven, phase 3 study, which assessed the safety and efficacy of macitentan in patients with PAH  [6]. Patients were randomly assigned in a 1:1:1 ratio to receive placebo, macitentan 3 mg, or macitentan 10 mg once daily. Patients received double-blind treatment until occurrence of a primary endpoint event of morbidity or mortality, premature discontinuation or end of study, whichever occurred first. Components of the primary endpoint were worsening of PAH, initiation of treatment with intravenous or subcutaneous prostanoids, lung transplantation, atrial septostomy or death. Worsening of PAH was defined as the combination of a decrease in 6-min walk distance (6MWD) of at least 15% from baseline, worsening of PAH symptoms and the need for new PAH treatment(s).
All primary endpoint events were independently adjudicated by a clinical event committee. SERAPHIN OL (NCT00667823) (first patient, first visit: 17 October 2008; last patient, last visit: 7 December 2020) was a long-term, multicentre, single-arm, non-comparative OL study that evaluated long-term safety and tolerability of macitentan 10 mg in patients with symptomatic PAH (Supplementary Fig. 1 ). Patients enrolled in SERAPHIN could enter SERAPHIN OL either after completing the double-blind treatment period of SERAPHIN without experiencing a primary endpoint event or after experiencing a morbidity event during SERAPHIN. Patients received macitentan 10 mg orally once daily in SERAPHIN OL until one of the following occurred: macitentan became commercially available in this indication in the patient's country, the sponsor decided to stop the study or the patient, investigator or study sponsor decided to discontinue macitentan.
Statistical Analyses All analyses were descriptive in nature. Two separate analysis sets were used for the safety analyses. The first analysis set (the OL safety set) included all patients who were enrolled in SERAPHIN OL and received at least one dose of macitentan 10 mg. The observation period for each patient was from first intake of macitentan 10 mg in SERAPHIN OL and continued up to end of treatment (EOT) plus 28 days. The second analysis set (the long-term safety/survival set) included all patients randomised to macitentan 10 mg in SERAPHIN, irrespective of whether they enrolled into SERAPHIN OL. This set was used for the analysis of additional longterm safety data as well as for the post-hoc analysis of survival. Use of this set for the survival analysis ensured a robust and non-biased approach, as treatment with macitentan had been initiated in all patients at the same time point.
DISCUSSION This article summarises the long-term safety, tolerability and survival data of PAH patients treated with macitentan 10 mg in SERAPHIN and/or its open-label extension study (SERAPHIN OL) over an observation period of up to 9 years. The results of this analysis complement and are reflective of the safety profile of macitentan as reported previously in SERAPHIN For the patients with an event, the time up to first event is counted; otherwise, the time is censored up to EOT b All adverse events leading to discontinuation of macitentan are reported here and not only those considered the primary reason for discontinuation as presented in Table  1  c Sixty-four patients had AE listed as their reason for discontinuation (Table  1 ) but two of these patients are excluded here as they did not have any respective AEs reported as leading to discontinuation d Thirty-eight patients had AE listed as their reason for discontinuation (Table  1 ).
Here, one additional patient is included who discontinued treatment in SERAPHIN due to an AE but died during SERAPHIN OL and had ' 'death' ' recorded as the reason for discontinuation in Table  1  e Occurring in C 10% of patients in either set and REPAIR  [6, 10]  and also in a real-world clinical setting  [11]. Two partially overlapping sets were included in the analysis of safety and tolerability. The first one comprised all patients who were treated with macitentan 10 mg in SERAPHIN OL regardless of treatment assignment in SERAPHIN (OL safety set, N = 550). With 550 patients and a total of 2047. 7 patient-years, this analysis set provided long-term safety data on a large PAH population, especially given the rarity of PAH. For the OL safety set, the observation period began from the start of OL, and as such two thirds of patients had already received macitentan 3 mg or 10 mg in the double-blind study.
The second set comprised all patients who were randomised to macitentan 10 mg in SERAPHIN (long-term safety/survival set, N = 242), with the observation period starting at randomisation. Encompassing both SERAPHIN and its OL, this analysis set, although smaller, provided safety data on a population that was naı ¨ve to macitentan at the start of the observation period and hence a more homogeneous population. For this same reason, it also allowed for a robust and non-biased analysis of survival, providing data for the longest follow-up period published to date for a PAH therapy. The two analysis sets provided complementary data on the safety of macitentan in PAH and showed that macitentan 10 mg was well tolerated by PAH patients. There were proportionally more patients with severe disease in the OL safety set at baseline (19% of patients were in WHO FC IV) compared with the long-term safety/survival set (2% in WHO FC IV)  [6].