dataesr/scientific-paragraphs-categorization huggingface dataset:
Rice Of the TOAR sites located in rice growing areas, 97% are in either EUR (21. 4%), NAM (11. 8%) or EAS (64. 2%, comprising data almost exclusively from Japan and the Republic of Korea, Tables 6 and S-5). Unfortunately, TOAR's data coverage for the world's largest rice producers, China (4 sites), India (1 site), Indonesia (1 site), Bangladesh (0 sites) and Vietnam (0 sites) is very sparse, which limits the conclusions that can be drawn about the global risks posed to this moderately ozone-sensitive crop.
Correlations between metrics The data from wheat and rice growing areas were explored for patterns in the correlation between metrics (Figure  14 ). For both vegetation types, AOT40 and W126 are strongly correlated (r 2 = 0. 94 and 0. 96 for wheat and rice, respectively, polynomial function), with W126 becoming increasingly higher at the highest AOT40 values, reflecting the weighting aspect of the metric. Both W126 and AOT40 are >0 at M12 values above approximately 20 ppb, with values increasing rapidly at M12 values above 40 ppb. Using these relationships, it can be seen that the CLRTAP_CL_Crops(AOT40 of 3000 ppb h) is equivalent to a W126 of 2578 ppb-hrs. Similarly, a W126 of 17000 ppb-hrs for NAAQS_Ecosystems, is equivalent to an AOT40 of 15695 ppb h; a W126 of 15000 ppb-hrs for NAAQS_Crops is equivalent to an AOT40 of 14181 ppb h; a W126 of 10000 ppb-hrs for NAAQS_Injury is equivalent to an AOT40 of 10079 ppb h; and a W126 of 7000 ppb-hrs for NAAQS_Trees is equivalent to an AOT40 of 7400 ppb h.
Effect of monitoring inlet height on metric value AOT40 is calculated from ozone observations at canopy height (1 m for crops and grassland, 20 m for forests) after adjustment for monitoring inlet height (methods provided in CLRTAP, 2017). In contrast, W126 is calculated from the ozone values at the monitoring inlet height, which can vary from site to site (e. g. it varies across the USA from ≤5 m through to ≥ 10 m, as described earlier). As inlet height was not included in the TOAR database, it has not been possible to standardise for the effect of this variation across sites. To illustrate the uncertainty introduced by measurement height, 3-and 6-month M12, AOT40 and W126 were calculated for a range of heights using a tabulated gradient for an "artificial" crop and tree species (CLRTAP, 2017).
Spatial representativeness of analysis TOAR-Vegetation provides the first major attempt to use observations rather than modelling data to assess the ozone exposure of vegetation on a global scale. Sites meeting the criteria for inclusion for perennial vegetation, wheat, and/or rice metrics were most abundant in NAM, EUR and EAS, allowing detailed assessments of present-day ozone distributions and long-term trends for these regions. Thus, much of the ozone data are from the NH mid-high latitudes of the developed world (20 to 60° N), with very limited data available for rapidly developing countries of Africa, South America, and parts of SAS/SEA. TOAR has strong representation in areas of NAM and EUR predicted to have high ozone impacts on crops  (Avnery et al. , 2011; Van Dingenen et al. , 2009; Mills et al.
producing countries in the database (535 sites), with the Republic of Korea, ranked 14 th also being well represented (190 sites). There are only 25 other sites included in TOAR in the remaining 6 highest wheat producing countries and 19 sites in the remaining 9 highest rice producing countries within the TOAR dataset. It is notable that data from only a handful of sites was available for TOAR from India (3 sites) and China (9 sites), the highest wheat and rice producing countries in the world. Other under-represented crop growing areas include parts of SEA, SSA and SAM where crops such as soybean and maize are also commonly grown. Expanding the analysis to include these two crops could have added <5 extra sites for SAM and SAF and 2 for SEA. As there are no vegetation standards that have been developed specifically for these two crops and the response function for maize is only based on data for three cultivars, exposed to ozone in the USA in the 1980s and early 1990s  (Mills et al.
, submitted) , soybean and maize were not included in the TOAR-Vegetation analysis. It is also important to note that of the other top 10 global staple food crops (FAOSTAT), only potato has been extensively studied for ozone response and is considered moderately sensitive  (Mills and Harmens, 2011a/b). One study has indicated that sweet potato is sensitive to ozone while very little/no information exists on the sensitivity of cassava, sorghum, yams and plantain. Although potato is usually harvested a little later in the season than wheat, we considered that the overlap in growing areas for the two crops was sufficient to use wheat in TOAR-Vegetation as an indicator of ozone risk to temperate annual crops. Overall, due to the shortage of ozone monitoring data in some major crop growing areas, together with a lack of information on ozone sensitivity of many staple food crops, we were unable to fully meet the first aim of this study, which was to conduct a global analysis of crop-relevant metrics.
However, we were able to meet the third aim of identifying those areas of the world where more ozone data are required. Currently, an assessment of the global impacts of ozone on crop yield that covers all growing regions remains reliant on modelling, based on either ozone concentration (e. g. Avnery et al. , 2011; Van Dingenen et al. , 2009)  or stomatal ozone uptake (e. g. Mills et al. , 2018; Mills et al. , submitted). For perennial vegetation, there is good representation of data in mid-latitudes of the Northern Hemisphere where grassland and forested areas provide grazing, harvestable biomass and other ecosystem services. However, many areas of globally important perennial vegetation are under-represented in the dataset, particularly those that are likely to have high conservation value.
, 2018). Current satellite products and ozonesondes cannot, as yet, provide the hourly data at vegetation heights needed to calculate concentration-based vegetation-metrics for ozone (see TOAR-Climate for further details). In defining crop growing areas based on production data, TOAR-Vegetation identified that crops are often produced in urban-rural fringes (see discussion of uncertainties in Section 4. 6). In many such areas, peri-urban agriculture can be of large importance, especially for people living on a low income. These areas may experience very high ozone exposure. TOAR-Vegetation Recommendation (2) is that impacts on peri-urban agriculture should be specifically considered with respect to ozone effects on food security in future investigations. Mountain/upland areas often experience higher ozone levels than adjacent areas at low altitude and are important conservation areas.
Present-day distribution of ozone metrics of relevance to vegetation Across all three metrics, the highest exposure of vegetation to ozone is in areas where high emissions and climatic conditions together promote ozone formation. These are primarily in the following mid-latitude areas of the NH: S USA; S Europe including parts of Spain, Italy, France and Greece; N India; NW and E China; Republic of Korea and Japan. Conversely, the lowest ozone metric values are in Australia and New Zealand, southern parts of SAM, and northern areas of EUR and Canada. For most SH sites, the metric values are considerably lower than in the NH, although SH sites are under-represented in the database. Ozone metric values in some coastal regions, e. g. the Pacific coast of the USA, Atlantic coast of Europe and some coastal areas of Japan are frequently lower than in adjacent inland sites.
Exceedance of air quality standards for vegetation TOAR-Vegetation has identified the areas of the world where existing air quality standards relevant to vegetation are currently exceeded, based on the CLRTAP metric AOT40 and the levels of concern developed by the US EPA for W126. Whilst these two metrics are strongly correlated within the dataset (Figure  14 ), the CLRTAP_CL_Crops appears to be much more extensively exceeded globally (82. 2% of sites) than the NAAQS_Crops (2. 1% of sites), even though both are set to protect crops against a 5% yield loss. Thus, the CLRTAP_CL_Crops (AOT40 of 3000 ppb h) appears to be more stringent than the NAAQS_ Crops, which from the functions in Figure  14  based on the wheat dataset, is equivalent to an AOT40 of 14181 ppb h. This finding is in agreement with a comparison of exceedance of air quality standards for central Italy, where the CLRTAP_CL_Crops was exceeded at 98% of monitoring sites in durum wheat-growing areas whilst the NAAQS_Crops was exceeded at only 33% of sites (determined from Figure  2  of De  Marco et al.
Exceedance of the NAAQS_Crops occurs occasionally in the areas with the highest AOT40 values. There are currently no policy-related thresholds in use for vegetation in EAS or SEA, with air pollution reduction initiatives being focussed only on health impacts (see TOAR-Health). Nevertheless, TOAR-Vegetation shows extensive exceedance of the CLRTAP_CL_Perennials in Japan and the Republic of Korea, often by more than three times, with similar levels of exceedance occurring at monitoring sites in mainland China, N India and Taiwan. The potential risk to wheat in the Republic of Korea was not included as the GAEZ dataset indicated no production of the crop in 2000, the most recent year available for crop distribution data. This is supported by FAO (Food and Agriculture Organisation) statistics showing that wheat production in this country was very low in 2000 at 2,339 tonnes, although production had increased to 39,116 tonnes by 2010 (FAOSTAT).
Comparison with modelled distributions of ozone At the coarsest resolution, the distribution of areas identified in TOAR-Vegetation as having vegetation at low or high risk of damage from ozone largely matches that predicted by global modelling of M12, AOT40 or W126  (Avnery et al. , 2011; Chuwah et al. , 2015; Fuhrer et al. , 2016; Hollaway et al. , 2012; Van Dingenen et al. , 2009; ). However, the high values for ozone metrics in TOAR-Vegetation at SEA sites tend to be underestimated in modelling studies. It is more difficult though to align measurements included in TOAR-Vegetation with modelled data due to uncertainties introduced by comparing site-specific data with grid square values presented at resolutions of 2. 8° × 2. 8°  (Avnery et al. , 2011; Hollaway et al. , 2012)  or 3° × 2°  (Chuwah et al.
, 2015). Differences in the years used also confound detailed comparisons as the global modelling studies tend to have used earlier years such as 2000  (Avnery et al. , 2011; Fuhrer et al. , 2016; Hollaway et al. , 2012)  or 2005  (Chuwah et al. , 2015)  whilst TOAR-Vegetation provides 5-year means for the period 2010-2014. To gain some impression of how well global or regional modelling represents the current measured ozone distribution, we focus on AOT40 for wheat in southern EUR. Here, the TOAR dataset indicates that values are frequently in the range 9000-15000 ppb h, and occasionally > 15000 ppb h. Whereas there are differences in the spatial patterns of measured and modelled values, modelled AOT40 values were in a similar range in southern France and Italy to the TOAR observations in two studies (10000-15000 ppb h,  Avnery et al.
, 2011, and 7000-15000 ppb h, Chuwah et al. , 2015). In another global study, however, little spatial variation in AOT40 was predicted over Europe, with crop AOT40 values approximately in the range 2000-4500 ppb h  (Hollaway et al. , 2012). At the regional scale, predictions using the EMEP MSC-W model  (Simpson et al. , 2012)  for 3 m height (more closely aligned with the data in the TOAR database than canopy height ozone usually used by EMEP when using the CLRTAP recommendations for AOT40) provide better spatial agreement with the TOAR dataset for EUR  (Simpson et al. , 2007). This latter modelling study predicted AOT40 values in southern Europe in the year 2000 to be mostly in the range 6000-12000 ppb h, rising to 12000-18000 ppb h in N Italy. From a model validation perspective, it would be useful for the next phase of TOAR to include canopy height measurements, and data from simultaneous measurements of NO x and meteorological factors affecting ozone deposition.
5. Improved scientific understanding of the impact of ozone on vegetation across broad spatial scales and over long time-periods (decadal or more) would require the development of new vegetation monitoring networks and the strengthening of existing networks. 6. Height of the monitoring inlet should be included in the next phase of TOAR to facilitate conversion of ozone metrics to canopy height to add further certainty to the risks identified. 7. The TOAR community should consider how to include stomatal uptake-based indicators in the next phase of work for the programme, including collation of additional meteorological and NO x data within the database. 8. A more complete global assessment of ozone impacts on vegetation should be conducted in the future, using an expanded TOAR dataset and an innovative integration of observations and modelling, including stomatal uptake of ozone.
, 2017; CLRTAP, 2016; Paoletti et al. , 2014; Simon et al. , 2015; TOAR-Climate; TOAR-Metrics). The consequences of this shift in ozone profile for vegetation require further study as almost all published experimental studies have exposed plants to simulated high peak, low background diurnal profiles. Where the effects of different profiles have been studied, increased background profiles had a similar effect on biomass to low background/medium peak treatments in grassland species  (Hayes et al. , 2010)  and yield in wheat  (Harmens et al. , 2018) , with the latter study showing a strong correlation between crop loss and accumulated stomatal flux of ozone, regardless of ozone profile. Globally, positive ozone trends are most prevalent in EAS across all three metrics. Data availability in TOAR-Vegetation is almost exclusively from Japan for the longest time-period, with sufficient data from the Republic of Korea available for the 2000-2014 trend assessments.
Model projections of surface ozone mixing ratios in 2030 indicate that if emissions continue to increase, generally following the RCP8. 5 scenario, then annual mean surface ozone will increase by 2-5 ppb across most of South and East Asia, with even greater increases across India  (Wild et al. , 2012; Young et al. , 2013). Given that the monitoring data available in TOAR Vegetation indicates that some of the highest vegetation ozone exposures globally currently occur in these regions, such model projections suggest that the most severe ozone exposures in the future (through 2030) will most likely occur in these region. Over the 15-and 20-year time-periods included in TOAR-Vegetation, data availability was limited for SH trends analysis. In Australia, small increases in M12 were evident at several sites with the perennials W126 decreasing at some sites.
Sources of uncertainty in the TOAR-Vegetation analysis After a comprehensive global effort, the TOAR dataset includes all currently available ozone monitoring data in vegetated areas around the world. Sources of systematic errors and uncertainty associated with the dataset are described in detail in TOAR-Surface Ozone Database. Whilst preparing the data for the vegetation metrics presented in this paper, we made several assumptions and used selection criteria that have some influence on the conclusions drawn. An important source of uncertainty for the vegetation metrics arises from the fact that the ozone monitor inlets are not always at the ideal height for different types of vegetation and that this height varies from site to site and is not documented in TOAR. Ozone levels near the ground exhibit a significant logarithmic gradient as a function of height, especially over vegetation surfaces with low roughness such as crops  (Pleijel et al.
, 1995; Sofiev and Tuovinen, 2001). This gradient has particularly marked effects on accumulated metrics such as AOT40 and W126 where a consistent small change in ozone can have a magnified effect on the final value  (Pleijel et al. , 1995; Sofiev and Tuovinen, 2001). For trees, the difference between the measured ozone level at a monitoring station over grassland and the level at the top of a 30m tree canopy varies according to atmospheric stability, time of day and growing season  (Gerosa et al. , 2017). As we did not know the measurement height at the TOAR sites, we could not correct all data to one height per vegetation type using standard methods (e. g. CLRTAP, 2017) and could not quantify how much of an impact this source of uncertainty has on the data presented here. It is likely that at measurement heights of 3-10 m, metrics for crops and low-growing grassland are overestimating the risk of effects at canopy height, whilst those for trees are underestimating risk.
, 2017). Our selection criteria for site inclusion for each vegetation type may have also introduced some uncertainty. Use of all nonurban categories for perennial vegetation also includes some sites that are "unclassified" within TOAR that could be considered urban (see TOAR-Surface Ozone Database). For crop production data, we used the GAEZ dataset for the year 2000, whilst present-day ozone values are averaged for the period 2010-2014. As discussed in Section 4. 3, this meant that no wheat sites were included for the Republic of Korea. Where there has been urban spread since the year 2000, this approach may also have introduced some sites into the crop datasets that are in urban fringes that could be strongly influenced by local emissions. For example, some of the sites in Japan with high rice production fall into the urban category as defined in TOAR-Surface Ozone Database.
, 2011a). In particular, stomatal flux provides a better indication of ozone effects in the northern third of Europe where moderate ozone concentrations coincided with conditions highly conducive to ozone uptake and the AOT40-based CL was not exceeded. In the TOAR-Vegetation dataset, the highest AOT40 and W126 values were found in warm temperate dry climates in NAM (e. g. mean of 21470 ppb h and 22987 ppb-hrs for perennials, respectively) and EUR (e. g. mean of 15888 ppb h and 14814 ppb-hrs for perennials, respectively). Here, the highest risk would be to irrigated crops whilst nonirrigated crops and perennial species might have reduced risk in these climates due to partial stomatal closure. In SEA, almost all of the perennial sites are in warm temperate moist climates where there might be less restriction on ozone uptake for several months of the growing season, and mean AOT40 and W126 values of 16073 ppb h and 18609 ppb-hrs, respectively for the perennial metrics might provide a good indication of potential risk.
These sources of uncertainty should be considered in the next phase of TOAR with the aim of progressing beyond quantifying the extent of exceedance of CLs/values of concern to quantifying the magnitude of effects predicted for each site. To achieve this, TOAR Vegetation has two further recommendations (Table  7 ). TOAR-Vegetation Recommendation (  7 ) is for the TOAR community to consider how to include stomatal uptake-based indicators in the next phase of the programme, including collation of additional meteorological and NO x data within the database. Such an approach would require additional stomatal conductance measurements to be made in some regions of the world to provide regional or local flux model parameterisation. TOAR-Vegetation recommendation (  8 ) is that a more complete global assessment of ozone impacts on vegetation should be conducted in the future, using an expanded TOAR dataset and an innovative integration of observations and modelling, including the stomatal uptake of ozone.
Summary and conclusions TOAR-Vegetation reports on the present-day global distribution of ozone at over 3300 vegetated sites across the world and long-term trends at nearly 1200 sites. As such, the TOAR database provides a unique opportunity to assess the potential threat from ozone pollution to vegetation based on measurements rather than modelling. The highest values for ozone metrics related to vegetation are in mid-latitudes of the NH where the density of ozone monitoring has also been greatest, reflecting long-standing national concerns about the potential impacts of the pollutant. Some countries of the NH such as China and India where modelling has indicated an impact of ozone on vegetation, have only been able to provide limited ozone monitoring data to TOAR for rural areas. SH data availability is also very sparse, limiting the regional scope of any conclusions drawn for this hemisphere.
| Bioinformatics Read quality was assessed with FastQC (http://www. bioinformatics. babraham. ac. uk/projects/fastqc). Demultiplexing, removal of reads with adapter contamination and trimming to 113 bp were performed with process_radtags from the stacks pipeline v1. 35  (Catchen, Amores, Hohenlohe, Cresko, & Postlethwait, 2011; Catchen, Hohenlohe, Bassham, Amores, & Cresko, 2013). We also used process_radtags to remove any read pairs with uncalled bases, a low quality score and/or a barcode or cut site with more than one mismatch. King and emperor penguin reads were aligned to the published emperor penguin reference genome (http://gigadb. org/ dataset/100005; scaffold-level assembly) while Adélie, chinstrap and gentoo penguin reads were aligned to the published Adélie penguin reference genome (http://gigadb.
org/dataset/100006; scaffold-level assembly) using bwa-mem  (Li, 2013). Terminal alignments were prevented by enforcing a clipping penalty of 100, and reads with more than five mismatches, multiple alignments and/or more than two indels were removed using a custom python script F I G U R E 1 Breeding distributions (grey) and the locations of colonies sampled in this study (coloured triangles) for (a) king penguins, which were sampled both north and south of the Antarctic Polar Front  (Borboroglu & Boersma, 2013) ; (b) emperor penguins  (LaRue et al. , 2015) ; (c) chinstrap penguins  (Borboroglu & Boersma, 2013) ; (d) Adélie penguins  (Schwaller et al. , 2013) ; and (e) gentoo penguins, which were sampled both north and south of the Antarctic Polar Front  (Borboroglu & Boersma, 2013). The number of individuals genotyped at each colony is indicated [Colour figure can be viewed at wileyonlinelibrary.
com] (filter. py, available from https://doi. org/10. 5061/dryad. 7c0q8). PCR duplicates were removed with Picard Tools (http://broadinstitute. github. io/picard). We called and filtered SNPs separately for each species using the Stacks pipeline, following many of the suggestions outlined in  Benestan et al. (2016). All of the settings and filters were applied in the same way for each species. We ran the modules of the pipeline separately: pstacks -cstacks -sstacks -rxstacks -cstacks -sstacks -populations. Briefly, in pstacks we required a minimum stack depth (-m) of six reads mapping to the same location and used the bounded SNP model (significance level of α = 0. 05, upper bound = 0. 1, lower bound = 0. 00041 corresponding to the highest sequencing error rate recorded by phiX spikes). We found that setting the stack depth to six gave sufficient numbers of polymorphic loci in all species after downstream steps had been performed, while maintaining sufficient (≥ 6X) depth at each locus to reliably call heterozygotes.