dataesr/scientific-paragraphs-categorization huggingface dataset:
646908 ) through  ERC Consolidator Grant  " S4F". Research at the Centre for Star and Planet Formation  is funded by the  Danish National Research Foundation. M. N. Drozdovskaya acknowledges the financial support of the Center for Space and Habitability (CSH)  Fellowship and the  IAU Gruber Foundation Fellowship. A. Coutens  postdoctoral grant is funded by the  ERC Starting  Grant  3DICE  (grant agreement  336474 ). This research made use of Astropy, a community-developed core Python package for Astronomy (Astropy Collaboration 2013). This research has made use of  NASA 's Astrophysics Data System.
Here, seabird space-use with respect to these two aspects is quantified to indicate appropriate spatial scales for conservation management of breeding seabirds at the family level. Existing tracking data from 52 species of ten different families collected in the Atlantic Ocean basin over the past two decades were used. These data were analysed with previously established methods  [1, 39, 40]  to quantify the broad space-use requirements and spatial aggregation patterns of adult seabirds during the breeding season, and variation among families was tested. This approach allowed an assessment of whether the patterns of taxonomically coherent groups of species are sufficiently consistent to provide guidance for marine management.
2. 1. Data collation and aggregation Seabird tracking data were collated for adult birds during the breeding season, when individuals are most constrained in their space-use due to the need to return to the nesting site on land. This constraint to return to the nest will likely accentuate differences between families and therefore facilitate a greater contrast in the space-use across the taxonomic spectrum. While protecting juvenile, immature and adult life-stages outside the breeding season is equally important for the conservation of long-lived species  [41, 42] , the movement patterns of seabirds when they are not breeding may be more affected by their latitudinal distributions than by taxonomic differences  [43, 44]. In addition, a broad taxonomic comparison of distribution patterns of juvenile, immature, and adult life stages outside the breeding season is currently difficult due to the paucity of suitably high-resolution tracking data for these stages.
Seabird tracking data from the Atlantic Ocean basin were available from the BirdLife Seabird Tracking Database  [34]  or through institutional repositories or collaborators. The selection of data used for this analysis was opportunistic and taxonomically imbalanced because seabird tracking efforts have so far focused on species and families of larger body size. However, the data represent a broad taxonomic spectrum of seabird movements during the breeding season from a large geographical region and are therefore useful to inform spatial scales for management. Only tracking data from Global Positioning System (GPS) loggers and Platform Terminal Transmitters (PTT) were used due to their high spatial accuracy, and only datasets with at least five individuals were included to minimise erroneous conclusions based on small sample size  [1, 37, 40].
During the breeding season, adult seabirds can be constrained to forage within different distances from their nest depending on whether they are incubating eggs or feeding small or large chicks  [16, [45] [46] [47]. All tracking data were therefore divided into two stages, distinguishing the incubation period from the chick-rearing period when adults regularly return to feed the chick and therefore may not travel as far. Tracking data were analysed separately for each combination of species, colony, and breeding stage, except for some species where the tracking period spanned separate breeding stages that were not distinguished because of a lack of concurrent monitoring (classified as 'unspecified breeding', Table  S1 ). Our analysis was based on 210 datasets of 52 species from ten seabird families (Tables  1  and S1 ).
Quantifying the travel distance and size of exploited area First, unrealistic locations were removed based on a species-specific speed filter  [50]  and PTT data were linearly interpolated to a regular 1 hr interval to reduce differences between GPS and PTT data due to their different temporal sampling resolution  [51]. Mean sampling schemes were one location every 17 ± 32 minutes (standard deviation, range 0. 5 -156) for GPS and one location every 65 ± 34 minutes (2. 4 -188) for PTT datasets. Tracking data were then divided into discrete foraging trips either manually or using species-and devicespecific cut-off values for minimum distances and durations implemented with standard processing routines  [1]. For each foraging trip the maximum distance from the colony (foraging range) and the total travel distance as the sum of all straight-line distances between all subsequent locations were calculated.
The median (and range) of these trip characteristics are presented for each species, based on all foraging trips from all colonies and breeding stages, to provide a general overview of travel capabilities across seabird species  [52] [53] [54]. These summaries were also calculated using just the first trip of any given individual to reduce pseudo-replication  [55, 56] , but this data reduction did not alter the broad taxonomic pattern (Table  S2 ). Because single-dimension trip characteristics do not capture the range of directionality across foraging trips from individuals in a colony, the area used by each species at each colony was also quantified. This area was calculated as the minimum convex polygon of 95% of all locations for each tracking dataset and is hereafter referred to as 'exploited area'.
Quantifying the spatial aggregation and size of area with concentrated use Foraging areas may range from widely dispersed to highly concentrated in a relatively restricted area. First, the 'core area' for each individual was calculated as the 50% kernel utilization distribution, and the extent of overlap between core areas of all individuals of a given species at a colony in a given breeding stage was then quantified. To identify the core area, the scale of the area-restricted search derived from first-passage time analysis was used as the smoothing factor in the kernel density estimator  [1, 59, 60]. Because the core area size is dependent on the smoothing factor, and area-restricted search may be difficult to detect for some species or data resolutions  [61] , an alternative approach was also used in which the smoothing factor was scaled to the median foraging range of a colony.
Results from both approaches were highly correlated and did not affect our conclusions (Table  S3 ), and only results from the former approach are presented. The overlap in core areas of individuals was quantified using Bhattacharyya's Affinity index (BA), a non-directional measure of homerange overlap that ranges between 0 (complete separation) and 1 (completely matching probability distributions), and is considered the most appropriate index for quantifying the similarity between utilisation distributions  [39, 62]. Because the BA is calculated between pairs of individuals, the BA across all pairwise comparisons was averaged for a given dataset. Individuals for which <10 locations were available were excluded from the estimation of spatial aggregation. To compare the size of the core areas of each population (hereafter 'area of concentrated use'), the 50% kernel utilisation distribution of each individual was delineated, and areas where the 50% kernels of at least 20% of tracked individuals of that population overlapped were identified  [1].
2. 3. Assessing representativeness of datasets with varying sample size Sample size can affect quantitative metrics of space-use based on tracking data  [40, 51, 63]. Because datasets ranged from 5 to 119 individuals per colony and breeding stage, the representativeness of each dataset was quantified to characterise the distribution at the level of the colony. Following the approach of Lascelles et al. [1] , each dataset was iteratively subsampled to randomly select tracking data from 3 to n-1 individuals, where n is the number of individuals tracked in that dataset. During each iteration, the 50% kernel utilisation distribution was calculated from the randomly selected data, and the proportion of the unsampled locations that fell within the 50% isopleth was assessed. If the proportion of unsampled locations contained within the 50% isopleth of the randomly selected individuals (hereafter referred to as the 'inclusion value') was ≥50%, then the dataset was considered representative for the colony because the un-sampled individuals were already properly represented by the sampled individuals  [1].
For each simulated sample size of every dataset 30 iterations were performed and the mean inclusion value across the 30 iterations was calculated for each sample size. A non-linear least-squares regression was then fitted to inclusion values to estimate the asymptote of each dataset based on the 30 iterations for each simulated sample size. The representativeness of each dataset is reported as the proportion of the estimated asymptote that the mean inclusion value of a dataset achieved at the highest sample size. If this representativeness was >70%, a dataset was adequate to describe the space-use of the population  [1, 40]. If the non-linear regression could not identify an asymptote due to a singular gradient (i. e. the area expansion had not levelled off with increasing sample size), the mean inclusion value for the largest sample size of that dataset was used.
In addition, variation at the species and colony level was accounted for by including these two variables as random intercepts to avoid pseudoreplication  [69]. Because some datasets had small sample sizes, each dataset was weighted based on the level of representativeness that was attained in the sensitivity analysis to reduce the influence of small and possibly unrepresentative datasets on the overall conclusions. The data collation of all individual foraging trips was used to test the effect of seabird family on maximum travel distance from the colony. The effect of seabird family on spatial aggregation, the size of an area of concentrated use, and the size of the exploited area was tested at a population level because the latter three measures were calculated for each unique combination of species, colony, and breeding stage.
For each of these four response variables, two models were fitted that differed only by the inclusion of seabird family as a fixed factor in one of the pair of models, while all other fixed and random factors were identical. A likelihood-ratio test was used to infer whether seabird family explained a significant amount of variation in space-use variables that was not already accounted for by other fixed or random effects  [70]. All analytical steps were conducted in R 3. 4. 2 [71] , and code to replicate the analyses is provided at https://github. com/steffenoppel/seabirds.
Results Seabird tracking data from 52 species across ten families were collected between 1998 and 2017 in 210 unique combinations of species, colony, and breeding stage (Table  S1 ). The data contained a total of 12,039 distinct foraging trips from 5419 individual birds, with a mean of 21 tracked individuals (range 5 -119) per dataset, and included >10% of the species in each family that breed in the Atlantic Ocean basin (Table  1 ). As expected, seabird species varied enormously in foraging trip characteristics, with single foraging trips ranging from <1 km to >12,000 km (Table  2 ). There was considerable variation within species and families in the foraging range, and some of this variation was explained by the breeding stage (Fig. 1 ). Despite substantial variation among breeding stages, species and colonies, there was clear evidence that foraging range varied at the family level (LR-Test  9 2 = 55.
<<<< FIGURE 1 here >>>> Seabirds also varied markedly in the extent to which they congregated at sea. The average Bhattacharyya's Affinity index for a given dataset ranged from virtually no overlap (BA < 0. 001 for four datasets; Adélie Penguin Pygoscelis adeliae, European Shag Phalacrocorax aristotelis, Common Guillemot Uria aalge, Tristan Albatross Diomedea dabbenena) to very high overlap (BA = 0. 91; Magellanic Penguin Spheniscus magellanicus). Due to the high variation in overlap among species and between breeding stages, there was no significant variation among families (LR-Test  9 2 = 12. 22, p = 0. 20). For most families there was higher overlap during chick-rearing than during incubation (Fig. 2 ). Cormorants, gulls and auks had consistently high overlap in both breeding stages, while albatrosses and frigatebirds showed consistently low overlap (Fig.
2 ). Owing to variability in travel distance and aggregation, the size of the marine area exploited by seabird populations during the breeding season varied by six orders of magnitude among families (Table  2 ; LR-Test  9 2 = 57. 91, p < 0. 001), with cormorants and penguins having generally the smallest exploited areas and albatrosses and Great Shearwaters (Ardenna gravis) the largest (Table  2 ). <<<< FIGURE  2  here >>>> Low overlap of individual core ranges can frequently lead to unrepresentative tracking data, as the foraging behaviour of untracked individuals is poorly captured by those already tracked. There were 101 (48%) datasets that did not meet the criteria for representativeness that would be required to designate marine important bird areas following Lascelles et al. (2016), with 100% of the frigatebird datasets (n = 3) and 80% of albatross datasets (n = 20) not representative at the population level.
For gulls, penguins and gannets, >60% of datasets were representative (Table  S3 ). There was a positive correlation between the number of individuals tracked and representativeness (  = 0. 332, p < 0. 01, n = 210), and of the datasets that included >50 individuals only three were not representative (all from albatrosses, Fig. S1 ). Accounting for the level of representativeness of each dataset, and simulating the size of an area of concentrated use across a range of sample sizes, there was a strong effect of family on the size of areas of concentrated use (LR-Test  8 2 = 57. 91, p < 0. 001). The largest areas of concentrated use were found in albatrosses and gannets, and the smallest in cormorants and gulls, but within each family, the size of the area of concentrated use varied by two to four orders among species and breeding stages (Fig.
For some families there may also be significant intra-family variation, which our data collation may not capture appropriately: penguins, for example, include both migrant and resident species, but our tracking data encompassed mostly migrant species, which have greater foraging ranges even during the breeding season  [78]. Nonetheless, for families that encompass few species, such as the tropicbirds and the frigatebirds, the information provided here is likely more accurate and transferrable than for the gull family which encompasses >40 species in the Atlantic Ocean basin with a diverse range of body sizes and travel capabilities  [79, 80]. Because high-resolution GPS tracking devices have only recently become small enough to track small seabirds  [81] , our data are biased towards larger-bodied species, with many storm petrels, small auks, and diving petrels not yet represented in tracking databases.
While such differences need to be considered for the implementation of specific protection measures, our broad scale analysis indicated that the differences in space-use among families were generally larger than differences within species, and our overall conclusions are unlikely to be affected by a few atypical datasets. Our results also highlight that for some families the space-use patterns vary substantially between incubation and the chick-rearing stage (Figs 1 and 2, Table  2 ), which may be relevant for seasonal site protection or other dynamic area-based management measures that aim to regulate certain activities during discrete periods  [86]. However, some of the apparent variation between breeding stages might be a consequence of varying data coverage and inter-specific differences.
For example, among gannets and boobies our results seem to indicate that birds have extremely low spatial aggregation during incubation compared to brood-guard (Fig. 2 ). This pattern is potentially because the largest gannet in our dataset, the Northern Gannet (Morus bassanus), which forages in temperate and productive waters, has mostly been tracked while feeding chicks  [87] [88] [89] , whereas the available data during incubation were from the smaller, tropical boobies, which forage in less productive waters and may therefore disperse more widely at sea  [90] [91] [92]. Nonetheless, our data clearly indicate that most seabirds have smaller foraging ranges and show greater aggregation at sea during the chick-rearing than the incubation period, which could be used to inform appropriate management approaches at different times of the breeding cycle.
The dataset and space-use metrics that was collated could also be used with various explanatory variables to understand the causes of variation and predict the likely movement scales of other species of seabirds for which no tracking data exist. Such extrapolations have been applied successfully to separate colonies within species  [83] , but if space-use requirements can also be predicted across species then some conservation management may proceed on that basis rather than await species-specific local tracking data  [93, 94]. The generality of the patterns of space use found among families could be tested with data from additional species, regions and marine systems, or life-history stages. Nonetheless, researchers considering which seabirds to track for the purpose of improving conservation management are encouraged to first critically examine the value that the collected data will add to existing knowledge  [35, 36, 95].
analysis was facilitated by the Seabird Tracking database hosted by BirdLife Paquet, Michelle Valliant, Skomer Wardens, Ben Dean, Holly Kirk, Robin Freeman, Dave Boyle, Chris Perrins, Helen Peat, Andrew Wood, Samir Martins, Pedro López, Sergi Torn, Adrià Compte, Marina Pastor, Diniz Montrond, and Artur Lópes. We appreciate the financial and logistical support of the following institutions during data collection: Enterprise St Helena, Darwin Initiative, Department for Environment, Food, and Rural Affairs (UK), St Helena Nature Conservation Group, St Helena National Trust, Falkland Islands Government, Tristan da Cunha Government, South African Department of Environmental Affairs (South African National Antarctic Programme), National Research Foundation (South Africa), University of Cape Town, Instituto Chico Mendes de Conservação da Biodiversidade (ICMBio/MMA), Ascension Island Government, MAVA Foundation, Anguilla National Trust, Jost van Dyke Preservation Society, Alderney Commission for Renewable Energy, Alderney Wildlife Trust, The Seabird Group, Wildlife Conservation Society, Department of Environment and Climate Change (Government of Canada), Bird Studies Canada, Netherlands AntArctic Program, Netherlands Organisation for Scientific Research (NWO), Australian Antarctic Division, Microsoft Research Cambridge, UK Natural Environment Research Council, Northern Ireland Environment Agency, Scottish Natural Heritage, Birds of Lundy Fund, Merton College (Oxford), Skokholm Islands Advisory Committee, Wildlife Trust for South and West Wales, Copeland Bird Observatory, Landmark Trust, National Trust for Scotland, LIFE+ program of the European Union(LIFE10/MT/090), Maltese Ministry for Sustainable Development, the Environment and Climate Change, SPEA, Ministerio de Ciencia e Innovación and Fondos FEDER (CGL2013-42585-P).
, 2006). In both plasmid and chromosomal systems, as discussed earlier, TLS events occur not within the context of the advancing replication fork but rather behind the fork as a post-replicative gap-filling reaction  (Daigaku et al. , 2010; Karras and Jentsch, 2010; Lehmann and Fuchs, 2006; Pages and Fuchs, 2003). Pol V replication activity was tracked by an increase in mutation events and unexpectedly revealed that, after completing the canonical TLS reaction (TLS patch formation), Pol V is able to reaccess the undamaged DNA template downstream of the lesion to synthesize yet another patch or patches of DNA on the template (referred to as error-prone post-TLS patch or patches), as witnessed by a high frequency of synthesis errors. The sequencing data also revealed a patch with increased error frequency upstream of the TT (6-4) lesion that we tentatively attribute to error-prone synthesis following exonuclease-mediated resection of the primer blocked at the lesion site.
Herein, the terms Melanesia and Micronesia are used in their geographical sense. We use the term Polynesia to include all islands and atolls whose inhabitants speak Polynesian languages, including 23 found throughout Melanesia and Micronesia, referred to as outlier Polynesia (Fig. 1a ). Separating the demographic histories of Polynesia and Melanesia became difficult to sustain with developments in archaeology during the second half of the 20th century. These established that the settlement of southern Melanesia (Santa Cruz, Vanuatu, New Caledonia and Fiji) and western Polynesia (Tonga, Samoa, Niue and Figure  1. Sampling locations and overview of genomic diversity. (a) Sources of population data used in the present study. The Philippine group names are abbreviated as follows: Aet (Aeta); Agt (Agta); Bat (Batak); Cas (Casiguran); Kan (Kankanaey); Taga (Tagalog); Tagb (Tagbanua); Zam (Zambales); and Phi (Philippines, incorporating all other groups from this region).
25. Detailed sample information is given in Supplementary Table  S1. The map was created using R v. 3. 4. 1 (R Core Team (2017). R: A language and environment for statistical computing. R Foundation for Statistical Computing, https://www. R-project. org/), and packages 'maps' v. 3. 2. 0 (https://cran. r-project. org/ package=maps) and 'mapdata' v. 2. 2-6 (https://cran. r-project. org/package=mapdata). (b) Inset at top right shows two alternative reconstructed sub-groupings of Polynesian languages discussed in the text. The critical differences are the position of the East Polynesian languages relative to the rest of nuclear Polynesian, and their relationship to the Central Northern Outlier languages. In the sub-grouping according to Pawley  31  all the Polynesian Outlier languages group within Samoic implying an early separation of Proto-East Polynesian from the rest of the Nuclear Polynesian languages.
Data. Here we present a new genomic dataset sampled from the Leeward Society Islands, eastern Polynesia. We report high-resolution autosomal genotyping data from 30 individuals, complemented by genotyping and/or re-sequencing of uniparental loci (mtDNA and Y) from 81 individuals, including seven Y chromosomes re-sequenced by a target-capture method. In addition, we present new uniparental data from 49 Maori individuals sampled in New Zealand (Supplementary Tables  S1-S3 ). The dataset is analyzed together with publicly available data from Island Southeast Asia, Melanesia and Polynesia (Supplementary Tables S1, S4 and S5). For detailed information about samples used in the present study, please refer to the Materials and Methods section.