dataesr/scientific-paragraphs-categorization huggingface dataset:
2007 ). Therefore, the use of complementary techniques makes it possible to understand the use of food resources more accurately, due to the greater representativeness of the diet in the combined datasets. In the present study, we aimed to assess the variation in the brown booby diet in the southwest Atlantic Ocean and to test its association with pre-established biogeographic zonings and population structure, as well as their potential use as biological samplers of the fish communities. For this, we reviewed and compiled data on regurgitate material (hereafter 'regurgitates') and stable isotopes of carbon and nitrogen from previous publications and unpublished databases. We expect the diet to vary spatially according to environmental heterogeneity, fitting into biogeographic patterns and the population structure already described for the species in the southwestern Atlantic Ocean.
Regurgitates We gathered data on regurgitates from an active and non-systematic search in articles, book chapters, technical reports, and conference abstracts. We obtained six sources of datasets published between 2004 and 2023, two Ph. D. theses, one conference abstract, one technical report, and one unpublished database from Noronha, totaling nine breeding sites from Moleques to SPSP (Table  1 , and Table  S1 ), which were classified into at least two distinct biogeographical zones (Fig. 1b ). The prey taxonomy was updated according to Eschmeyer's Catalog of Fishes  (Fricke et al. 2022)  for fish and the World Register of Marine Species (WoRMS 2022) for other taxa (e. g. squids). Then, we extracted information on the number of regurgitates (i. e. samples), and prey abundance at the family and species levels per colony from each data Table  1  Data sources and their respective sample sizes obtained by non-systematic search that described the diet of Sula leucogaster from regurgitated material and stable isotopes in colonies along the southwestern Atlantic Ocean  source using the 'vegan'  (Oksanen et al.
2020 ) and 'plyr'  (Wickham 2011)  packages in the R software (R Core Team 2021). For descriptive metrics of prey species and families, we calculated the frequency of occurrence (FO%), relative abundance (RA%), richness (R), and α-diversity (Shannon Index)  (Magurran 2003)  from each data source separately. Prey items not identified at species and family levels were disregarded from species and family analysis, even if it was the only representative of a taxonomic lineage. Items derived from cannibalism were also removed from the analysis (as showed in A. Kohlrausch unpubl. data). To assess differences between prey communities, we used complementary statistical approaches. Due to the differences in sampling efforts between studies and databases, the dataset was converted to a binary matrix of presence/ absence.
Paired distances between colonies were estimated with the beta-sim index (β sim ) with subsequent generation of a dendrogram based on Euclidean distances using the 'betapart'  (Baselga et al. 2021 ) and 'ggdendro' (Vries and Ripley 2020) packages. The β sim was calculated according to the following equation  (Kreft and Jetz 2010) : where "a" = total number of shared species between colonies, and "b,c" = number of exclusive species of each colony. β sim varies between 0 (low dissimilarity, identical taxa list) to 1 (high dissimilarity, no shared taxa). To visually identify similarities between colonies and location (Fig. 1a ), genetic (as described below), and biogeographic criteria (Fig. 1b ), we performed a non-metric multidimensional scaling (NMDS) with the Bray-Curtis index. Additionally, we ran sim = 1 - a min(b,c) +a , a permutational analysis of variance (PERMANOVA) with 999 permutations to test differences between the prey dataset and the criteria, assuming no dietary differences as the null hypothesis  (Legendre and Legendre 2012).
For graphing, we used the 'ggplot2' package  (Wickham 2016 ). The criteria used for the analyses were categorical and considered geographic, genetic, and biogeographical aspects. Colony location concerning the continental shelf was assigned as 'coastal' or 'oceanic' whether the colony was on or off the shelf, respectively. The genetic criteria followed the previously published clustering based on microsatellites  (Nunes and Bugoni 2018) , where the K = 2 arrangement splits the colonies into two groups (SPSP; and Others), and the K = 3 suggests isolation of SPSP, in addition to a weaker gene flow between "Coastal" (Moleques, Cagarras, and Abrolhos) and "FN-RO" (Noronha and Rocas) groups. For standardization purposes, we included the coastal colonies not studied by  Nunes and Bugoni (2018)  in the "Coastal" group.
Stable isotopes We used carbon (δ 13 C) and nitrogen (δ 15 N) isotopic ratios from brown booby whole blood samples obtained from scientific articles, technical reports, and unpublished datasets (Table  1 ). All data were collected from breeding individuals and without distinction between males and females. Sample processing of the unpublished datasets consisted in place a few drops of the whole blood on tubes, which were air-dried. Blood samples were freeze-dried, ground, and homogenized. Subsamples of 1 mg were weighed into tin cups and analyzed in a mass spectrometer  (Mancini et al. 2014; Nunes et al. 2018). Standards used were Vienna Pee Dee belemnite and atmospheric air for carbon and nitrogen, respectively. Measurement precision of both δ 15 N and δ 13 C is described in each reference as well as secondary isotopic reference materials used by each laboratory (Table  1 ).
Lipids were not extracted from seabird blood due to their expected low concentration  (Bearhop et al. 2000). We calculated the mean and standard deviation of each colony using the 'FSA' package  (Ogle et al. 2021). Then, we tested univariate differences between colonies using the Kruskal-Wallis test, and Mann-Whitney U test as post-hoc, using False Discovery Rate for adjusting P values  (Benjamini and Hochberg 1995). Additionally, we calculated the two-dimensional isotopic niche breadth based on Bayesian ellipses as implemented in the 'SIBER' package  (Jackson et al. 2011).
Regurgitates In total, 877 regurgitates composed the dataset, with 4270 prey belonging to at least 105 species, 43 families, and 17 orders (Table  S2 ), showing an increasing accumulation curve of prey species relative to the number of datasets (Fig. S1 ). Different sample sizes were reported among studies, with Rocas (n = 1128) presenting the highest prey numbers and Noronha (n = 85) the lowest ones (Fig. S2 ). For the analysis at the species-level only, we considered 3727 prey belonging to 99 species. Moleques exhibited the highest species richness (R = 39) and Noronha the lowest (R = 8) (Fig. 2a ). Considering all colonies, the most frequent species were Sardinella aurita and Harengula clupeola (FO = 55. 5%), followed by Micropogonias furnieri, Ctenosciaena gracilicirrhus, Pellona harroweri, Hirundichthys affinis, Cetengraulis edentulus and Opisthonema oglinum (FO = 44.
Stable isotopes The analyzed dataset comprised 194 blood samples from seven colonies (Table  1 ). The mean carbon and nitrogen isotopic ratios obtained from the data sources ranged from -17. 7‰ in Cagarras to -16. 5‰ in Noronha for δ 13 C, and from 9. 9‰ in Noronha to 14. 3‰ in Santana for δ 15 N (Table  S6 ). Univariate differences between colonies were significant both for δ 13 C and δ 15 N values (P ≤ 0. 01). Pairdifferences between colonies resulted in significant differences for δ 15 N between all colonies, except between Noronha and Rocas (P = 0. 42). For δ 13 significant pairwise differences were identified among all colonies, except between Moleques and Abrolhos (P = 0. 27), Moleques and Rocas (P = 0. 03), Moleques and SPSP (P = 0. 03), and Abrolhos and Rocas (P = 0. 40). Differences occurred between genetic and biogeographic criteria for δ 15 N, except between Spalding's Ecoregions WTS180 and TSA76 (P = 0.
Demersal prey (e. g. Sciaenidae and Batrachoididae) discarded from shrimp trawling were frequent in the diet of boobies from southern coastal colonies  (Vianna and Almeida 2005; Branco and Verani 2006; Cattani et al. 2011; Silva et al. 2016 ) and absent in oceanic colonies, suggesting an influence of such fishery in the dietary composition of brown boobies. However, prey usually associated with discards were scarce in Cagarras, despite the occurrence of bottom trawling around the archipelago  (Moraes et al. 2013; Amorim and Monteiro-Neto 2016). It is possible that the dataset from Cagarras is biased by insufficient sampling (i. e. one breeding season)  (Torres et al. unpubl. data) , but the availability of engraulids and clupeids around the colony  (Jablonski et al. 2006; Silva-Jr. et al. 2016 ) may favor "natural prey" over discards from trawling, as observed in other Sulidae species  (Votier et al.
Publications and databases used in the present study comprised variable sampling efforts, periods, sex ratios, and researchers, which could make results biased regarding prey abundance and richness. In addition, the comparison of isotopic values requires caution, as the study covers colonies distributed over a 28° latitudinal gradient and therefore include potential differences in isotopic baselines. Temporal variation in sampling the colonies could lead to variations in isotopic information given the dynamics of primary productivity and prey availability around the colonies  (Mancini et al. 2014). However, the heterogeneity of the sample for each colony also has an important positive aspect, since the representativeness of the dietary variations in the colony is greater compared to a point-in-time sampling.
Acknowledgements We are grateful to many researchers who studied the diet of brown boobies during the last 30 years in the Atlantic Ocean. We also thank  E. E. M. Valim ,  T. P. X. Nascimento ,  L. R. M. Porto ,  G. P. Bighetti ,  F. P. Marques ,  C. Campolina ,  C. Barbraud , and  K. Delord  for their support in fieldwork. F. L. Rodrigues  provided important contribution in prey identification and classification. We also thank the two anonymous reviewers for the comments and suggestions that contribute to improving the manuscript.
Funding The unpublished datasets were obtained with funding from the IRD Young Associated Team Program (JEAI TABASCO), the IRD mixed international laboratory program (LMI TAPIOCA), CPER Celimer (France), Fundação Boticário (Brazil), the TRIATLAS project (European Union's Horizon 2020 research and innovation program, Grant No. 817578), the PADDLE project (European Union's Horizon 2020 RISE program  (Grant No. 734271) , and the "Projeto Costões Rochosos", which is an environmental offset measure established through a Consent Decree/Conduct Adjustment Agreement between Petrorio and the Brazilian Ministry for the Environment, with the Brazilian Biodiversity Fund (FUNBIO) as an implementer. PLM received a PNPD scholarship from Coordenação de Aperfeiçoamento de Pessoal de Nível Superior-Brasil (CAPES) Finance Code 001.
LB had been funded by National Research Council (CNPq; Grant No. 405497/2012-1) and is a fellowship from CNPq (No. 311409/2018-0). GTN is funded by CNPq  (Grant No 443328/2019-6). Data availability All data and the R script are publicly available through the GitHub repository: https:// github. com/ Seabi rdEco logyU FRGS/ DietB iogeo graphy.
The Cloud-Aerosol Lidar with Orthogonal Polarization PSC detection around the station are compared to DDU PSC datasets and show a good agreement despite more water-ice PSC detection. Detailed 2015 lidar measurements are presented to highlight interesting features of PSC fields above DDU. Then, combining a temperature proxy to lidar measurements, we build a trend of PSC days per year at DDU from ERA5 (the fifth generation of European ReAnalysis) and NCEP (National Centers for Environment Protection reanalysis) reanalyses fitted on lidar measurements operated at the station. This significant 14-year trend of -4. 6 PSC days per decade is consistent with recent temperature satellite measurements at high latitudes. Specific DDU lidar measurements are presented to highlight fine PSC features that are often sub-scale to global models and spaceborne measurements.
Lidar instruments, both ground-based and spaceborne, are more appropriate to study PSCs as they provide extremely high vertical and time resolution data at the cost of a heavy inversion procedure (i. e. as compared to direct particle counters) needed to retrieve the optical properties. Achtert and Tesche (2014)  presented a comprehensive review of the PSC classification schemes. Their study shows that these classifications can lead to very different outcomes when applied to a single dataset. As these classifications are often derived from a single instrument, they are likely to carry their own biases and prevent quantitative comparisons with other datasets carrying other biases related to different instrumental setups. Also, different sets of variables have been used to interpret the measurements and the lack of homogeneity in lidar data processing and in the definition of some optical properties make intercomparisons between different studies more difficult.
Following the conclusions of  Achtert and Tesche (2014) , we decided to consider three different classifications proposed by  Blum et al. (2005)  (hereafter called B05),  Pitts et al. (2011)  (hereafter called P11), and an updated version of P11  (Pitts et al. , 2018, hereafter called P18). These classifications are used to analyse the lidar measurements conducted at the French Antarctic station Dumont d'Urville (DDU, 66. 6 • S, 140. 0 • E). DDU is located on the shore of the continent and therefore often lies at the edge of the polar vortex. The station has hosted a stratospheric lidar since 1989, making it one of the very few Antarctic station with a long-term lidar data record. Considering the latest laser source replacement in 2005 and the continuous monitoring from 2006, DDU PSC dataset is compared to the spaceborne PSC measurements conducted by CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) in the vicinity of the station, from 2007 to 2020.
Two of the major roles of a ground station are to perform process studies and establish decadal trends. Such trends are highly valuable because they reflect the evolution of the stratosphere in terms of temperature and chemical compositions. In this study, DDU lidar measurements are exploited to produce a trend of PSC days per year at DDU. Section 2 presents the data and instruments exploited in this study. Section 3 introduces the PSC detection methods as well as the considered classification schemes. Section 4 exposes the results of the study. First, the outcomes of the application of the classification schemes B05, P11 and P18 to the DDU lidar data record are presented and discussed, and CALIOP and DDU PSC measurements are compared. Then the analysis of an interesting example of a long lidar session is used to illustrate the unique capabilities of lidar measurements in characterizing very fine vertical features in PSC fields.
Third, lidar measurements and temperatures from ERA5 (the fifth generation of European ReAnalysis), NCEP (National Centers for Environment Protection reanalysis) and infrared atmospheric sounding interferometers (IA-SIs) are combined to produce a PSC occurrence trend from 2007 to 2020. To support the use of these temperature data sources, they are compared to temperatures from radiosondes launched daily at DDU. Finally, some challenges of PSC parametrization in climate models are discussed before exposing the main conclusions.
Finally, signal inversion is performed using the Klett-Fernald formalism  (Klett, 1981 (Klett, , 1985;; Fernald, 1984)    R T profiles. The lidar inversion at stratospheric altitudes is highly sensitive to the molecular density and the clear-air reference altitude. ERA5 daily meteorological data are used for data processing and clear-air altitude is set between 28 and 32 km according to the signal dynamics. The total uncertainty on the R T is estimated to be around 7 % on the parallel channel R up to 28 km, with details available in  Tencé et al. (2022). On the perpendicular channel and associated R ⊥ backscatter ratio, the altitude-dependent uncertainty ranges from 10 % to 30 %. As for the depolarization ratio δ(z), assuming larger uncertainties on the aerosol depolarization ratio δ(z) rather than on the linear volume depolarization ratio at relevant altitudes in this paper we estimate the error of around 30 %  (Tencé et al.
IASI temperature product The The IASI instruments are Fourier transform spectrometers that measure spectra of the Earth and atmosphere infrared radiance between 645 and 2760 cm -1 (3. 62 and 15. 5 µm) in each pixel. The atmospheric temperature profiles can be retrieved from the radiances observed in the carbon dioxide absorption bands at 700 and 2300 cm -1. Bouillon et al. (2022)  computed atmospheric temperatures in 10 atmospheric layers between 750 and 7 hPa by first selecting IASI's most sensitive channels to temperature and then by using the radiances in these channels as input for an artificial neural network (ANN). The ANN was trained using IASI radiances as input and the matching ERA5 temperature profiles as output. The validation of the ANN output against ERA5 and radiosoundings observations showed very good agreement between the three datasets between 750 and 7 hPa.
Reanalysis products -ERA5 and NCEP As discussed in further detail in the following sections, reanalysis temperature products are often used to complement or replace local radiosonde measurements for both data processing and interpretation of ground-based lidar measurements. For this study, two reanalysis products are considered: the fifth generation of the reanalysis product of the European Centre for Medium-Range Weather Forecasts (ECMWF), hereafter called ERA5, and the reanalysis produced by the National Centers for Environmental Protection (NCEP) and the National Center for Atmospheric Research (NCAR), hereafter called NCEP. ERA5 is a four-dimensional variational data assimilation (4D-Var) product fully available since January 2019. It is based on the Integrated Forecasting System (IFS) Cy41r2 and provides records of the atmosphere, land surface and ocean waves from 1979 onwards.
A detailed description of ERA5 can be found in  Hersbach et al. (2020). The temperature product used in this study is gridded on 35 pressure levels (from 975 to 2 hPa) and is 4× daily product, meaning it is available everyday at 00:00, 06:00, 12:00 and 18:00 UTC. The product is interpolated to the DDU location and the original horizontal resolution is 0. 25 • × 0. 25 •. Since ERA5 will be compared with IASI temperature data further in this paper, it should be specified that ERA5 assimilates the measurements of IASI, among other instruments. The interpolation of ERA5 at DDU is also used to calculate the dynamic tropopause, defined as the lowest point between the 380 K potential temperature and |PV| = 2 PVU (calculated from the vorticity, the winds and the temperature from ERA5), which is the tropopause used throughout this study.
4 %. In order to compare the abundance of NAT and NAT mixtures in each classification, the relative abundance of NAT and mixed categories was summed for each scheme, as proper comparison needs to account for the differences in the schemes. For B05, NAT + MIX types weight 64. 4 %. For P11, MIX1 + MIX2 + ENH types account for 70. 7 % of all PSC types. For the distributions based on P18, NATmix + ENH categories make up for 61 % and 62. 1 % of the total for the datasets based on DDU lidar and CALIOP measurements respectively. These small differences reflect the discrepancies between the ICE proportions in all schemes. The ICE abundance presents an important variability among the four diagrams. The first reason for this variability comes directly from the thresholds considered: as it was previously highlighted, the evolution of R T ,ICE from P11 to P18 is significant and explains an important discrepancy between the resulting ICE percentages.
As a reminder B05 defines the MIX category as any measurement not belonging to any of the other classes. Finally, temperature values associated with PSC types gathered in Fig. 5  are derived from ERA5 reanalyses and are relevant for the intercomparison of the classification schemes, but they may not resolve small-scale variations or features that are important for PSC formations pathways. Also, the time of lidar PSC measurement does not exactly match ERA5 data and MLS H 2 O and HNO 3 measurements, which necessarily generates uncertainty. The relevancy of ERA5 reanalyses in the study of PSC at DDU is reviewed in Sect. 4. 3.
Temperature datasets Lidar data processing heavily relies on accurate temperature data due to Rayleigh scattering and extinction correction to get the Mie contribution out of the raw signal. PSC characterization is also closely connected to temperature thresholds, as shown in Figs. 6  and 7. In that regard, the choice of temperature dataset should be done with caution. In order to use the most adapted temperature dataset to process our PSC measurements at DDU, we compare several ones in Fig. 8 , from reanalysis to satellite observations. Figure  8a  shows the distribution of DDU radiosondes locations between 15 and 25 km, i. e. the stratospheric altitude range of most PSC occurrences, from 2010 to 2020 (radiosondes were not equipped with a GPS before 2010). Figure  8b, c  and d  present the temperature difference between ERA5 (Fig.
We basically consider three temperature datasets, two are reanalyses, ERA5 and NCEP, and the third one is an inversion product out of IASI satellite radiances. Figure  8b, c     NCEP is obviously less accurate that ERA5 and IASI, both in the troposphere and in the stratosphere. The difference between ERA5 and IASI seems not to be significant and the comparison with radiosondes exhibits altitude-dependent patterns. For ERA5, T ERA5-RS is positive in the stratosphere and negative in the upper troposphere. For IASI, a positive T IASI-RS bias is recorded below the tropopause suggesting a difficulty in assessing the tropopause height, or a vertical resolution problem (the IASI dataset is a 11-layer product between 750 and 7 hPa, whereas ERA5 has a finer resolution). To quantify the deviation of ERA5, NCEP and IASI with respect to the radiosondes, the standard deviation of T ERA5-RS , T NCEP-RS and T IASI-RS was computed between 15 and 25 km.
ERA5, NCEP and IASI temperatures are used to compile stratospheric temperature above DDU. Using these temperatures, a number of potential PSC days is computed, i. e. the number of days where PSCs could occur based only on temperature. The number of PSC days per year is the variable considered in order to circumvent the challenging issue of delimiting PSCs both in time and space. Mainly due to chemical kinetic concerns, PSCs generally form a few degrees below T NAT  (Dye et al. , 1992)   The number of PSCs detected by the lidar in Fig. 9  is consistently and logically below the one estimated by the temperature datasets. The three datasets were considered for the trend calculation to show that despite the discrepancies they all indicate an approximately similar trend. From Fig. 9 , we note a decreasing trend of -4.
The spaceborne geometry always has direct access to the stratosphere without any significant particle extinction. Investigating this correlation is an interesting perspective of this work. Correlating PSC formation temperature to the three chemical classes, ICE clouds are observed at a higher altitude than STS and NAT clouds. From typical lidar time series, we highlight the small-scale features of PSC layers as well as their temporal variability both in vertical extent and optical properties, discussing time integration influence (both from a ground and space geometry) when using thresholds to characterize cloud types. Smaller local datasets are able to support class definition by refining mesoscale observed behaviours. We also extensively consider the sensitivity of temperature datasets as small-scale proxies for PSC formation thresholds.