dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements The authors are grateful to the three anonymous reviewers for their important comments and suggestions. We deeply thank  D Huang  (nus) for his constructive comments on the first manuscript draft. We thank  Lueter C  (zmb) for information provided about the holotype of Caryophyllia glabrescens. New Caledonia data and specimens were obtained during the ird coralcal1 (http://dx. doi. org/10. 17600/7100020), coralcal2 (http:// dx. doi. org/10. 17600/8100050), coralcal3 (http://dx. doi. org/10. 17600/9100010 ), coral-cal4 (http://dx. doi. org/10. 17600/12100060 ), bibelot (http://dx. doi. org/10. 17600/14003700 ), and coralcal5 (http://dx. doi. org/10. 17600/ 15004300 ) expeditions on the RV Alis. Material from Madagascar was collected during the mad (http://dx. doi. org/10. 17600/16004700) expedition on the  RV Antea.
It is noteworthy that the 16S rRNA alignment did not include any sequences of Pachyseris because no 16S rRNA sequences of this genus were available on GenBank at the time of the analyses. Within Euphylliidae, the phylogeny reconstructions inferred from 12S rRNA and 16S rRNA recovered Fimbriaphyllia (group V-A sensu Luzon 10. 1163/18759866-bja10041 | arrigoni et al. Downloaded from Brill. com03/21/2023 08:13:58AM via Ifremer Centre de Bretagne and IFREMER rRNA phylogeny hypothesis.
For sampling in the Red Sea, we wish to thank the captain and crew of the mv Dream-Master, the kaust Coastal and Marine Resources Core Lab, and A Gusti (kaust). We are grateful to E Dutrieux, CH Chaineau (Total SA), R Hirst, M Abdul Aziz (ylng), and M Pichon (mtq) for allowing and supporting research in Yemen. We are grateful to E Karsenti (embl) and E Bougois (Tara Expeditions), the OCEANS Consortium for allowing sampling during the Tara Oceans expedition in Djibouti and Mayotte. We thank the commitment of the following people and additional sponsors who made this singular expedition possible: cnrs, embl, Genoscope/cea, vib, Stazione Zoologica Anton Dohrn, unimib, anr (projects poseidon/anr-09-blan-0348, biomarks/ anr-08-bdva-003, prometheus/anr-09genm-031, and tara-girus/anr-09-pcsgenm-218), EU FP7 (MicroB3/No.
, 2014). Technical grade insecticides were used for all laboratory trials (purity >98%; Chemservice, West Chester, PA, USA). Dose-response studies should ideally be linked to relevant field concentrations so that their ecological relevance can be evaluated. To select experimental concentrations for each chemical and to assess the ecological relevance of the derived LC 50 values, we followed the methods of  Halstead et al. (2015). Briefly, we used the United States Environmental Protection Agency (US EPA) Surface Water Calculator software to generate 150 simulated annual peak estimated environmental concentrations (EECs) in a standardized lentic waterbody. In the software, the waterbody is a set distance from the field where the insecticide was applied according to the instructions on the product label.
The software calculates an EEC based on inputs of pesticide traits (e. g. half-life and adsorption), application amount and frequency, and soil and climatic characteristics (based on a region of interest; see Table  S1  for details on the parameters used in the model). In risk assessments conducted by the US EPA, EECs are compared against toxicity values (e. g. LC 50 ) to characterize the likelihood of toxicity at a given level of exposure  (USEPA, 2004; USEPA, 1992; Rohr et al. , 2016). Evaluation of EECs in this way informs the development of environmental standards, policies, guidelines, and regulations, as well as the registration and reregistration of chemicals for legal use  (USEPA, 2004). Additionally, this model has been validated against actual field concentrations  (USEPA, 2004).
Data analyses We used the drc package  (Ritz et al. , 2015)  in R 4. 0. 4 statistical software  (RCoreTeam, 2018)  to generate dose-response curves and estimate LC 50 values. Two-parameter logistic models were used to estimate 96-h and 10-d LC 50 values, and we approximated 95% confidence intervals around each LC 50 value using the variance of the estimate and then back-transforming from the log scale  (Ritz et al. , 2015). We then used our LC 50 estimates to determine the proportion of the simulated EEC values that exceeded the US EPA's level of concern, which is defined as ≥ 50 percent of the LC 50 value (0. 5 x LC 50 ) (USEPA, 2020). Insecticides with EECs that exceeded the level of concern are more likely to enter water bodies at levels toxic to the focal species when applied at their label rates (see Table  S1  for label rates).
To determine if differences in prawn survival were more associated with either individual chemicals or chemical class, we performed a Cox mixed effects model for the survival of prawns across all treatments using the R package survival  (Therneau, 2020). We converted all concentrations to toxic units (TUs) using SPEAR Calculator software (v0. 8. 1, Department System Ecotoxicology -Helmoltz Center for  Ehrsam et al. , 2016)  to account for variation in absolute toxicity among chemicals, as described by  Halstead et al. (2015). Standardized chemical concentration was used as a continuous fixed effect in the model, and random intercepts for each chemical were nested within their respective chemical classes (pyrethroid or organophosphate). Coefficients of the random effects were used to determine the contribution of each chemical and chemical class to overall mortality risk.
Insecticide use We conducted a survey of 663 households at the 16 study villages in 2016 to collect data on the area of crop land where different types of insecticides were used. A respondent from each household was asked to report the area of cultivated land they controlled as well as their use of insecticide on their land. We then calculated the total area on which each class of insecticide was applied in each village. Household surveys were approved by Internal Review Board of the University of California, Santa  676)  and in Senegal by the National Committee of Ethics for Health Research from the Republic of Senegal (Protocol #SEN14/33).
Data analyses All statistical analyses were conducted with R 4. 0. 4 statistical software  (RCoreTeam, 2018). To predict prawn survival, we performed a Cox proportional hazards regression analysis using the survival package in R and including a single survival time value for each prawn (n = 225 total prawns). A Cox model estimates regression coefficients for each predictor and exponentiated coefficients or hazard ratios. A positive regression coefficient suggests that mortality increases with a given predictor. Hazard ratios give the effect sizes, with values > 1, <1, and equal to 1 representing predictors that increase, decrease, or have no effect on mortality, respectively. Additionally, 95% confidence intervals can be estimated for the hazard ratios. One assumption of the Cox model is that risk is consistent throughout the study.
Lab study We found that pyrethroid insecticides were generally an order of magnitude more toxic (lower estimated LC 50 values) than organophosphate insecticides (Table  1 ). Overall, the LC 50 (95% CI) of the most toxic pyrethroid and organophosphate were 0. 25 μg/L (0. 07-0. 43) for esfenvalerate and 16. 73 μg/L (7. 86-25. 60) for chlorpyrifos, respectively (Table  1 ). Greater toxicity of pyrethroid compared to organophosphate insecticides for M. rosenbergii in our laboratory experiment is consistent with data from the US EPA's Ecotox database for other Macrobrachium species, for example, LC 50 values for M. nipponense and M. rosenbergii exposed to pyrethroids were 0. 05 and 0. 03 μg/L (Table  1 , Table  S3 ). However, we encourage caution in interpreting the terbufos LC 50 estimate because we did not have a concentration that exceeded 50% mortality.
1 ; Table  1 ). A Cox mixed-effects survival model indicated that insecticide class accounted for 70. 7% of the variance in prawn mortality. After 96 h of exposure, the three most deadly insecticides (i. e. , highest hazard ratios or risk per μg/l) were the pyrethroids esfenvalerate and λ-cyhalothrin, and the organophosphate chlorpyrifos (Table  S5 , Table  2 ). Converting all concentrations to toxic units (TUs) using the SPEAR Calculator software revealed that, on average, pyrethroid insecticides led to 275% more mortality than organophosphates (Table  S5 ). The coefficients of random effects suggest that variation among individual insecticides within the organophosphate class was largely driven by the relatively low risk presented by malathion (Tables  1  and 2 ).
Additionally, previous laboratory studies support our finding of greater toxicity of pyrethroid than organophosphate insecticides to crayfish and Macrobrachium prawns  (Bajet et al. , 2012; Halstead et al. , 2015; Halstead et al. , 2018). Pyrethroids, including deltamethrin, the most common pyrethroid reported in our household surveys in Senegal, had such a high toxicity to M. lar in the Philippines that pyrethroid environmental concentrations actually exceeded LC 50 values in the laboratory  (Bajet et al. , 2012). Environmental concentrations simulated in our study using EPA software showed patterns consistent within insecticide class and with previous studies  (Halstead et al. , 2015). However, we are the first to provide evidence from nature supporting all of these laboratory findings, thus providing evidence of toxicity at relevant exposures  (Rohr et al.
Although the median U concentration was significantly increased in underweight participants, the multiple analysis with mutual adjustment showed, at most a suggestive, inverse association. This result is in line with findings from the National Health and Nutrition Examination Survey (NHANES) on the US children and adolescents showing negative association between obesity and urinary heavy metals such as cadmium, cobalt and lead with endocrine disruptive properties  [31]. Literature data on association between exposure to U and BMI is limited and warrants further research. We used hair samples in the study to estimate U exposure levels. Hair is considered to be a good bio-indicator of acute or protracted exposure reflecting the exposures that happened even weeks to months before the measurement as compared to urine, blood or faeces, which only reflect recent exposure.
The main strength of our study includes individual measurements of U concentration in hair samples of children and adults with information collected from personal interviews to estimate the potential effects of different factors on the U hair levels. Household-based sampling allowed to recruit study subjects with relatively stable residence in the area reducing impacts of a high mobility prevalent among migrant workers and informal settlers  [33, 34]. The study provides additional information about this under-researched population. The study has some limitations including the size of the study area that spread up to only 5 km away from the mine tailings, which might be insufficient to capture heterogeneity in exposure levels. We used the hair samples and questionnaire data that were collected in a cross-sectional survey conducted by the South African Medical Research Council assessing contaminants other than uranium, e.
Acknowledgment The authors gratefully acknowledge support from the  South African National Research Foundation  (grant number  90332 ) and from the  South African Medical Research Council (Baseline Fund   001/2016 ). The study was supported by the  Ministry of Health, Labour and Welfare of Japan  (grant agreement no. 2012-02-21-01 ). The funding source had no involvement in the content or preparation of the manuscript. The authors thank  Jana Seibt  and  Heidrun Neubert  for their experimental support in the processing of the hair analyses as well as  Sabrina Beutner  and  Stephanie Bachmann  (all from  Helmholtz-Zentrum Dresden-Rossendorf ) for ICP-MS measurements.
a b s t r a c t Northern peatlands store globally-important amounts of carbon in the form of partly decomposed plant detritus. Drying associated with climate and land-use change may lead to increased fire frequency and severity in peatlands and the rapid loss of carbon to the atmosphere. However, our understanding of the patterns and drivers of peatland burning on an appropriate decadal to millennial timescale relies heavily on individual site-based reconstructions. For the first time, we synthesise peatland macrocharcoal records from across North America, Europe, and Patagonia to reveal regional variation in peatland burning during the Holocene. We used an existing database of proximal sedimentary charcoal to represent regional burning trends in the wider landscape for each region. Long-term trends in peatland burning appear to be largely climate driven, with human activities likely having an increasing influence in the late Holocene.
, 2020; Kasin et al. , 2013)  that we cannot account for in this study. Similarly, particularly severe in-situ smouldering fires can consume much of the charcoal they produce and cause some loss of the peatland archive  (Zaccone et al. , 2014). To enable a comparison of localised peatland burning to that in the wider regional landscape, we selected records that were proximal to our peatland sites (see Fig. S1 ) from the Global Charcoal Database (GCD) v. 4. 0. 7 using the Paleofire package v. 1. 2. 4  (Blarquez et al. , 2014 ) in R v. 4. 0. 5 (R Core Team, 2021). These records from the GCD include microcharcoal and macrocharcoal and are from a variety of sedimentary archives, excluding those listed as a bog, fen or mire. These records are assumed to provide a record of regional biomass burning  (Marlon et al.
, 2016). We ensured that the wider landscape sites that pertained to each sub-region were within a convex hull defined by the loci of the peatland sites in that subregion, or no more than 200 km outside it (150 km in central Europe to avoid duplication of sites). Further details of the wider landscape records selected from the GCD can be found in Fig. S1  and Table  S2. Where peatland macrocharcoal records from the GCD met our quality control criteria, we included them in our peatland burning dataset (see Table  S1 ).
Age-depth modelling In order to standardise the methodology used for age-depth modelling, we produced new Bayesian age-depth models (Figs. S2eS38) for each peatland record from chronological information such as 14 C, 210 Pb, tephra layers and spheroidal carbonaceous particles (SCPs), using the rbacon package v. 2. 5. 7  (Blaauw et al. , 2021)  in R (R Core Team, 2021). We established quality control criteria that required cores to comprise at least ten sample depths, to have a chronology spanning at least 500 years, and to have a date (e. g. 210 Pb, 14 C or tephra) at least every 2500 years on average. Prior information on accumulation rate and its memory or variability can be found for each age-depth model in Figs. S2eS38.
Resampling and transformation The peatland macrocharcoal records that we used have been compiled using a variety of methods (e. g. particle counts, area measurements and relative abundances) and a range of particle size fractions (e. g. >125 mm, >150 mm and >0. 5 mm). This variability in measurement approach can result in values that differ by orders of magnitude and therefore data standardisation is required to compare relative changes between records over time  (Power et al. , 2010). Furthermore, owing to varying accumulation rates within and among cores, standardisation of temporal resolution via binning or smoothing is required to avoid an inflated influence of high-resolution samples on any subsequent analyses. Macrocharcoal occurs in 47. 8% (12,321 out of 25,758) of pre-binned peat samples.
Proportional relative scaling and presence/absence For the PRS and presence/absence analyses, we developed a new method to resample the temporal resolution of raw charcoal data proportionally into equal 50-year time bins using depth intervals calculated from age-depth models (Fig. S39 ). This approach minimises potential distortion from non-contiguous sampling, particularly for records with infrequent sampling (Fig. S40 ). The proportion of samples containing charcoal is important for calculating both PRS and presence/absence; therefore, we applied the depth binning approach to resample the data prior to calculation of PRS and presence/absence. We calculated presence/absence for each resampled record, and the percentage of sites containing charcoal for each 50-year period in North America, Europe, Patagonia and sub-regions of interest.
Comparison of peatland burning to the wider landscape The wider landscape charcoal records represent the regional fire signal proximal to our peatland records, while excluding data specifically from peatlands (see Section 2. 2 for further details). Sufficient chronological information was not available from the GCD to apply our depth binning approach to sites representing biomass burning in the wider landscape. Therefore, we used an established method from major composite analyses of sedimentary charcoal records from the GCD that involves a Box-Cox, a min-max and a z-score transformation  (Daniau et al. , 2012; Marlon et al. , 2016 Marlon et al. , , 2008)). We applied the Box-Cox, min-max and z-score transformations to our peatland dataset (Table  S1 ) and the wider landscape dataset from the GCD (Table  S2 ) using the Paleofire package  (Blarquez et al.
We must also consider the possibility that researchers may have avoided disturbed peatland areas affected by recent fires when collecting cores, but this is unlikely to be the sole factor at play here. Our European composite record shows more peatland burning in the early Holocene from 10 to 8 ka, a period during which our database is composed primarily of records from central Europe and southern Scandinavia and the Baltics (Fig. 2 ). Relatively constant levels of peatland burning are observed after 8 ka, but with marked increases at ~5 ka and from 1. 5 to 0. 5 ka (Fig. 2 ). Burning in the wider landscape of Europe increases conspicuously from 6. 5 to 5. 5 ka and, unlike the peat record, shows an overall increase in the last four centuries.
5 ka onwards  (Camill et al. , 2012; Hargan et al. , 2020; Hobbs et al. , 2017). These warmer conditions in the mid-Holocene were associated with less frequent intrusions of cool, dry Pacific or Arctic air masses, resulting in fewer periods of late-spring or summer drought that are conducive to fire activity  (Carcaillet and Richard, 2000; Edwards et al. , 1996). We observe increasing levels of peatland burning from 8 to 4. 5 ka in the western Hudson Bay, along with increased burning in peatlands and the wider landscape in the eastern Hudson Bay from 7 to 4. 5 ka (Fig. 3 ). However, it should be noted that there are spatial gaps in our dataset with few records from Hudson Bay Lowlands and to the west of James Bay (Fig. 1 ). During the mid-Holocene many peatlands in the Hudson Bay region were transitioning from wet fens to drier bogs and this reduction in surface wetness and increased potential for the build-up of woody biomass likely made peatlands more susceptible to fire, especially if the sites became forested  (Camill et al.
5 ka. Shifting land management practices, including peatland drainage and prescribed burning of moorlands from ~1850 CE  (Holden et al. , 2007) , are coincident with widespread peatland drying across Britain and Ireland since ~1800 CE  (Swindles et al. , 2019). These recent human impacts may explain the uptick in the proportion of sites burning in the last two centuries (Fig. 4A ). Central European sites in our database are characterised by greater peatland burning at ~9 ka, before relatively constant levels of burning until the late Holocene, with decreased burning at ~2 ka and an increase from 1 ka to present (Fig. 4D  and E ). Burning in the wider landscape during the Holocene generally showed a slow increase before 1. 5 ka, followed by a steeper increase to present (Fig. 4F ). Summer temperatures increased until ~8 ka, before stabilising and showing a general decrease from ~6 ka for the majority of the Holocene  (Davis et al.
Recommendations for future research This study represents a clear step forward in our understanding of the patterns of peatland fire on a continental scale and has allowed us to consider the drivers and controls. Nevertheless, to better quantify the controls on peatland fire, comparison of in-situ peatland charcoal records to datasets of past climate, human population/density and peatland vegetation and moisture conditions is needed. This comparison needs to be made at suitable spatial and temporal scales. For example, a study linking local ecohydrological proxy data (e. g. testate amoebae and plant macrofossils) with accompanying macrocharcoal data for multiple sites (regionally or globally) would be particularly useful for furthering our understanding of peatland resilience and long-term fire dynamics.
Acknowledgements This work developed from the  PAGES (Past Global Changes) C-PEAT  ( Carbon in Peat on EArth through Time ) working group. PAGES has been supported by the  U. S. National Science Foundation ,  Swiss National Science Foundation ,  Swiss Academy of Sciences and Chinese Academy of Sciences. We acknowledge the following financial support:  U. K. Natural Environment Research Council Training Grants   NE/L002574/1  (T. G. S. ) and  NE/S007458/1  (R. E. F. );  Dutch Foundation for the Conservation of Irish Bogs, Quaternary Research Association and Leverhulme Trust   RPG-2021-354  (G. T. S. ); the  Academy of Finland  (M. V. );  PAI/SIA 77180002  and  FONDECYT Iniciaci on 11220705 -ANID , Chile (C. A. M. );  R20F0002 (PATSER) ANID Chile  (R. D. M. );  Swedish Strategic Research Area (SRA) MERGE  (ModElling the Regional and Global Earth system) (M.
J. G. );  Polish National Science Centre (NCN)  Grant numbers  2018/29/B/ST10/ 00120  (K. A. ),  2021/41/B/ST10/00060  (M. L. ),  2018/31/B/ST10/02498  and  2019/35/O/ST10/02903  (M. S. );  Russian Science Foundation  Grant No. 19-14-00102  (Y. A. M. );  University of Latvia  Grant No. AAp2016/B041/Zd2016/AZ03  and the  Estonian Science Council  grant  PRG323  (TrackLag) (N. S. and A. M. );  U. S. Geological Survey Land Change Science/Climate Research & Development Program  (M. J. , L. A. , and D. W. );  German Research Foundation (DFG) , grant  MA 8083/2e1  (P. M. ) and grant  BL 563/19e1  (K. H. K. );  German Academic Exchange Service (DAAD) , grant no. 57044554 ,  Faculty of Geosciences, University of Münster , and  Bavarian University Centre for Latin America (BAYLAT)  (K. H. K. ). Records from the Global Charcoal Database supplemented this work and therefore we would like to thank the contributors and managers of this open-source resource.
, 2021; Cormier-Salem, 1999; Mukherjee et al. , 2014; Walters et al. , 2008). Although databases of mangrove areas exist (GMW, 2022; USGS, 2016), they are highly inaccurate, particularly in West Africa due to persistent cloud cover  (Bunting et al. , 2018). The authors also mention that fringing and fragmented mangroves are sometimes omitted, even though they are frequently encountered in West-Africa  (Liu et al. , 2021). Mapping the extent of mangroves has been the subject of many studies  (Kuenzer et al. , 2011; Wang et al. , 2019) , and various mapping methods have been developed to produce local data. SPOT data has successfully mapped the extent of mangroves in West Africa over several years with an object-based image analysis (OBIA) approach  (Conchedda et al. , 2008). Landsat data has also led to the production of accurate maps using a pixel approach combined with linear spectral unmixing  (Han et al.