dataesr/scientific-paragraphs-categorization huggingface dataset:
• • Fig. 2. Workflow, including input data sets (blue boxes) and analyses (green boxes) to obtain a spatial data set with estimates of the monthly distribution of 6 pelagic seabird species breeding in the Northeast Atlantic. IRMA: informed random movement algorithm colony. Based on the distance rules, we predicted the habitat use of each colony covered by the SEA -TRACK design. Finally, we weighted the estimates with population size, yielding monthly maps of the abundance of Northeast Atlantic breeding seabirds. We show how the maps of the predicted distributions can be used in (1) population management applications, by identifying the year-round distribution of vulnerable or threatened populations; and (2) spatial management applications, by identifying the populations potentially affected by area-specific human stressors or management efforts.
MATERIALS AND METHODS The study area covered the North Atlantic Ocean from 78°W to 80°E and from 35-85°N. Position data sets were given as time and geographical coordinates. Environmental variables were aggregated on a 0. 25 × 0. 25° geographic grid covering the study area, and model predictions were conducted on the same grid. All distances used in the analyses were calculated as great-circle distances in km, and when relevant, the distances were calculated around land masses. Maps presented are shown in a stereographic projection with the origin at 90°N and 0°E. All analyses were conducted in R v. 3. 6. 3 (R Development Core Team 2020).
Data sets 2. 1. 1. GLS data GLS data from 6 species of pelagic seabirds (northern fulmar, black-legged kittiwake, common murre, thick-billed murre, little auk and Atlantic puffin) were obtained from 25 seabird colonies (Fig. 1 ) from 2006-2019. Note that data be fore 2014 were collected as part of projects other than the SEATRACK project. The number of tracks, birds, years with data and positions are given in Table  S1  in the Supplement at www. int-res. com/ articles/ suppl/ m676 p255_ supp. pdf for each species, colony and month. See Table  1  for overall sample sizes for each species. In total, the data set comprised 2356 tracked birds, resulting in 4827 bird-tracking years. Adult breeding birds were equipped during the breeding season and recaptured to retrieve the logger in following breeding seasons.
The protocol for obtaining positions from the raw light measurement, including calibration of different GLS models and filtering procedures, is described in  Bråthen et al. (2021). In addition to the light sensor, the GLS loggers include a salinity sensor ('wet/dry') that records periods when the logger is submerged in saltwater. These data were used to determine when the birds departed and arrived at the breeding colony and whether they were mostly sitting on their nest (dry) or at sea (wet) during the breeding season (see below). The precision of the GLS tracks is relatively low and likely to change throughout the tracking period  (Lisovski et al. 2020 ). Thus, it is only possible to identify habitat use on a relatively coarse spatial scale (i. e. 100s of km)  (Phillips et al. 2004 ).
Environmental data Environmental data were used as predictors in the SDMs. Ideally, the predictors should encompass features with a direct link to the species' habitat selection. However, the available predictors were limited to data sets covering the entire study area. For dynamic environmental variables, these were restricted to parameters measured by remote sensing which, in many cases, will serve as proxies for more direct drivers of habitat selection such as food availability. Specification of the environmental variables and data sources are given in Table  S2. SST (SST ) is an im portant parameter often used to differentiate water masses and to model the distribution of seabirds  (Tremblay et al. 2009). We used monthly values of SST as predictors in the SDMs. Seabirds are often attracted to frontal areas where marine production is concentrated  (Fauchald 2009).
Finally, seabirds are visual predators, and we hypothesized that short daylength could be a limiting factor in the northernmost areas during the darkest winter months. Daylength (Daylen) was therefore included as a predictor from November through January. We assumed that the seabirds did not use heavily sea-ice covered areas, and we compiled monthly data on sea ice to mask areas with sea ice concentration > 80%. All predictors were adjusted to fit the spatial raster covering the study area and with a pixel resolution of 0. 25 × 0. 25°. Data with a lower spatial resolution were either disaggregated or resampled with a bilinear local interpolation using the 'raster' package v. 3. 4-5 (Hijmans 2020).
Seabird population data Population count data were compiled from different sources to generate a representative data set of the breeding populations of the 6 seabird species in the Northeast Atlantic. Data sources are given in Table  S3. Note that in the present definition of Northeast Atlantic populations, we excluded the relatively small seabird populations from the Baltic Sea, Kattegat, Germany, France and the southern portions of the UK and Ireland. The spatial resolution of the defined 'colonies' differed among data sources, and for several regions, the data set comprised a large number of small proximate breeding sites. To obtain a homogeneous and manageable data set, the original colony data set was aggregated using a spatial hierarchical cluster analysis using the 'hclust' procedure from 'stats' package v.
Such gaps include the lack of registration of positions during equinoxes and polar night/day or removal of 'false' positions over land and fast sea-ice. To alleviate these biases, we used an informed random movement algorithm (IRMA) to model new locations to fill in these non-random gaps  (Fauchald et al. 2019). IRMA builds on a method originally proposed by  Technitis et al. (2015)  to generate plausible locations using additional information available: (1) conductivity data, to determine attendance at the colony; (2) land masks, to constrain positions over the ocean; (3) longitudes (that can be estimated during the equinoxes) and (4) light levels, indicating if the birds were north or south of the Arctic Circle (i. e. continuous day/night recorded by the loggers). In addition, because we focused on pelagic species, we assumed that all GLS positions occurring over land areas were wrong, and the locations were thus removed before running IRMA through the entire data set.
We assumed that the seabirds did not occur over land and fast sea-ice, and we accordingly removed areas covered by land and areas with more than 80% sea-ice concentration. For each model, we drew 20 000 background points from the defined potential habitat and extracted the relevant environmental data at each location. SDMs of presencebackground data for each species, colony and month were fitted using the 'gam' function in the 'mgcv' package v. 1. 8-33 (Wood 2017). The probability of presence/background was modelled using a logitlink function with a binomial distribution.
Model performance The performance of habitat models is usually assessed by dividing the data set into a training data set, used to fit the model, and an independent test data set, used to evaluate the predictions from the model (i. e. cross-validation). We first evaluated the within-colony performance by withholding data from one year at a time as a test data set and used the data from the remaining years to fit the model (i. e. between-year transferability;  Péron et al. 2018 ). The fitted model was used to predict the distribution in the test year, and model performance was evaluated by comparing model predictions and the test data set. All combinations of test and training years were pooled in the performance statistics. Secondly, we evaluated the between-colony performance by using the model from one colony to predict the distribution of occurrences of birds from other colonies.
Accordingly, the AUC-ROC measure is sensitive to the definition of the area from where the background points were drawn. By expanding the area outside the core area of the species, the AUC-ROC value will increase. The AUC-ROC value should accordingly be assessed relative to the definition of the potential habitat. In the present study, this area was defined by the extent of all observations of the species in the data set. Accordingly, all models of the same species used the same area when drawing background points. Moreover, the same procedure was used when defining the potential habitat of all 6 species. Therefore, we argue that the AUC-ROC values obtained in this study can be compared within species and also, with some limitations, across species. It is, however, important to note that the AUC scores should be interpreted relative to the extent of the area occupied by the Northeast Atlantic populations of the species.
AUC-ROC values were calculated using the 'auc' procedure in the 'spatstat  ' package v. 1. 64-1 (Baddeley et al. 2015). Boyce et al. (2002)  suggested a performance indicator based on the relationship between the number of occurrences and the corresponding binned values of suitability (Boyce index)  (Hirzel et al. 2006 ). The relationship is measured by the Spearman rank correlation coefficient. A Boyce index value close to 1 suggests a close relationship between the number of occurrences and the predicted suitability; a value equal to 0 suggests no relationship; while a negative value suggests an inverse relationship between the predicted suitability and number of occurrences. Contrary to the AUC-ROC, the Boyce index is independent of the area of the defined available habitat and has therefore been suggested to be an appropriate measure for the performance of presence-only SDMs  (Hirzel et al.
For each SDM, we built 400 bootstrap samples of occurrences by resampling at the level of individual tracks. For each bootstrap sample, we fitted the corresponding GAM and predicted the suitability values in the habitat. From the 400 bootstrap habitat predictions, we estimated 95% CIs for the predictions using the 2. 5-97. 5 th percentile intervals for the predicted logit values. The cluster bootstrap procedure accounts for the spatial dependencies of positions within tracks. However, the sample included birds tracked over more than 1 yr, giving more than one track for these individuals. Overall, the data set included 4827 tracks of 2356 individuals (Table  1 ), giving on average 2 tracking years per individual bird. If individuals show site fidelity among years, there will be a spatial dependency among tracks of the same individual.
Assigning breeding population data to SDMs To generate population maps from the habitat models, it was necessary to link the seabird population data set to the SDMs of the model colonies. To this end, we used the SDM from the nearest model colony. Distance between colonies was measured as the shortest ocean distance (i. e. around land masses) using the function 'gridDistance' in the 'raster' package v. 3. 4-5 (Hijmans 2020). A cut-off distance was used to exclude colonies not adequately covered by the spatial design of model colonies. The cut-off distance was justified by analyses of model performance among colonies (see Section 2. 4). The accuracy of models in predicting the occurrences of birds from other colonies decreased with increasing distance (for Boyce index, see Fig. 3 ; For AUC-ROC, see Fig.
9 birds km -2. Compared to the breeding period, the pelagic seabirds of the Northeast Atlantic are more widely distributed during winter, i. e. they occur at lower densities over wider ocean areas. In this period, the pelagic seabirds are distributed across the ice-free part of the North Atlantic, from 50° N up to 75° N in the Barents Sea (Fig. 5C ). Irrespective of season, the 3 areas holding the highest densities of birds were the Barents Sea, the Denmark Strait and the southern Norwegian Sea (i. e. the ocean areas around the Faroe Islands, Shetland and Orkney Islands). 3. 4. 2. Important ocean areas for Norwegian blacklegged kittiwake populations In the second example, we used the data set to identify important nonbreeding habitats for blacklegged kittiwakes from the Norwegian mainland.
Spatial management applications The data set can be used to identify the populations present within a given area and hence the populations im pacted by a spatially restricted disturbance (e. g. accidental oil spill) or spatial management measures (e. g. MPA). In the following example, we used the data set to assess the populations of Northeast Atlantic pelagic seabirds that could be impacted by the recently proposed MPA in the mid-Atlantic (NACE). The estimated number of birds (including bootstrap estimates) present within the NACE area were extracted for each colony and month. Four of the species in the data set (black-legged kittiwake, Atlantic puffin, northern fulmar and thick-billed murre) included colonies with more than 1% of the population within the NACE area in any month during the year (Fig.
Equally important as a representative sampling of individuals  (Carneiro et al. 2020 ) is the design with respect to the sampling of breeding colonies. Be cause different colonies might use different habitats, a study based on individuals from colonies that represent only a small portion of the population could give biased results. It is therefore essential to link the tracking data to the population that the data represent. In the present study, we used the network of SEATRACK colonies to assess the representativity of the tracking data. The analyses showed that birds from colonies close to each other (< 500 km) tended to use the same habitats, while for distant colonies (>1000 km), the nonbreeding habitats were population-specific, and in many cases, non-overlapping. This result is corroborated by several North Atlantic multi-colony tracking studies demonstrating colony-specific and partly non-overlapping winter habitats for black-legged kittiwakes  (Frederiksen et al.
Acknowledgements. We thank all the fieldworkers for their hard work collecting data. Funding for this study was provided by the  Norwegian Ministry for Climate  and the Environment, the  Norwegian Ministry of Foreign Affairs  and the  Norwegian Oil  and Gas Association along with 8 oil companies through the  SEATRACK  project (www. seapop. no/en/ seatrack). Fieldwork in Norwegian colonies (incl. Svalbard and  Jan Mayen ) was supported by the  SEAPOP program  (www. seapop. nogrant number  192141 ). The  French Polar Institute Paul Emile Victor  funded the  ORNITHO-ENDO -CRINO program  ( IPEV program   330  to O. C. ) on Kongsfjord kittiwakes. Russian field operations were enabled by the crew on  SY 'Alter Ego'  and  Open Ocean  project team. A. V. E. and Y. V. K. were supported by project  No 0228-2019-0004  'Ornithofauna of the northern seas: peculiarities of the nonbreeding season' with the framework of MMBI research plan and the state order for 2019-2021.
Material Scope of the Nagoya Protocol and of ABS Legislation As introduced above, the Nagoya Protocol sets minimal standards, but national ABS laws can be more or less encompassing. In the formal scope of the CBD and the Nagoya Protocol are "genetic resources", defined as "genetic material of actual or potential value"  [1]. "Genetic material", in turn, is defined as "any material of plant, animal, microbial or other origin containing functional units of heredity". These definitions make it quite clear that only tangible biological materials are in the intended scope of the CBD and the Nagoya Protocol. Still, due to rapid advances in DNA sequencing and in digital technologies, claims have been made that, in the spirit of the main objectives of the CBD, also (intangible) information related to genetic resources (such as DNA sequence data stored in public, open-access databases) should be understood to be included in the formal scope of ABS obligations under the Nagoya Protocol.
Experimental sites We compiled data from 14 LTEs in arable cropping systems across Europe (Fig. 1 ), where a total of 46 treatments with increased C inputs to the soil were performed, and one control plot in each experiment was implemented (Table  1 ). The experiments lasted between 11 and 53 years (median value of 16 years) in the period from 1956 to 2018. Most of the experiments had at least three replicates, except for the Italian site Foggia, the French site Champ Noël 3 and the British site Broadbalk, where no replicates were available. We selected experiments where dry matter (DM) yields and SOC had been measured at several dates. C inputs at all sites, except for control plots and all plots in Foggia, included exogenous organic matter (EOM) addition, e. g. , animal manure, household waste, sewage sludge or compost additions.
Climate forcing Mean temperature of the sites ranged from a minimum of 5. 7 • C to a maximum of 15. 5 • C, while mean soil humidity to approximately 20 cm depth ranged between 20. 2 and 24. 6 kg H 2 O m -2 soil in the dataset (Table  2 ). When available, observed daily air temperature was used as an approximation of soil temperature. Otherwise, the land-atmosphere model ORCHIDEE was used to simulate soil surface temperature and soil humidity at the site scale  (Krinner et al. , 2005). ORCHIDEE simulations were run over each site using a 3hourly global climate dataset at 0. 5 • (GSWP3 http://hydro. iis. u-tokyo. ac. jp/GSWP3/, last access: 1 September 2020). Plant cover was set to C 3 plant functional type (PFT) for agriculture.
OsR: oilseed rapeseed; FR: fodder rapeseed; F: green fallow; C: chickpeas; T: tomato; RG: ray grass. b Optimal amounts of mineral fertilizers added to the control plot and to all other treatments in the experiment. c In Foggia, additional carbon inputs from organic treatments were calculated for each rotation as the difference between C inputs in the rotation and the reference wheat-only rotation. ), clay (%) and initial SOC stocks in the control plots (Mg C ha -1 ) at the experimental sites. Reference papers for each site are indicated. 2019 ), but they were not selected for the final modeling work of this latter study. For more information, see also  Bouthier et al. (2014). b For Arazuri, data were directly provided by the Spanish Mancomunidad de la Comarca de Pamplona.
Site 5. 85 in Le Rheu 1 to a maximum of 8. 33 in Colmar. The average bulk density (BD) in the control plots was 1. 38 g cm -3. SOC stocks (Mg C ha -1 ) were calculated at each site using the following equation: SOC (Mg C ha -1 ) = SOC (%) • BD (g cm -3 ) • sampling depth (cm), (1) where SOC (%) is the concentration of organic C in the soil, and BD is the average bulk density of the experimental plot. It should be noted that the application of EOMs might induce differences in BD with time, which in turn affects the calculations of SOC stocks. No adjustment was made in this sense, since data on the evolution of BD were available only for a few sites. This might explain differences between the SOC stocks calculated for Broadbalk in this paper and those found by  Powlson et al. (2012)  at the same site, by adjusting soil weights to observed decreases in top soil BD due to accumulating farmyard manure (FYM).
, 2007). We assumed that the values used in  Clivot et al. (2019)  for the HI compiled from French experimental sites were applicable to all the sites in our dataset, which mainly include temperate sites over Europe. When these values were not available for some crops, they have been directly derived from  Bolinder et al. (2007)  or other sources in the literature (S : R ratio for fallow from  Mekonnen et al. , 1997, and tomato from Lovelli et al. , 2012). When straw was exported from the field, we considered that only a fraction of C S was left on the soil. This fraction was set to 0. 4 for all sites and to 0. 2 in Ultuna, where almost no stubble was left on the soil, since plots were harvested by hand and crops were cut at the soil surface. We considered a C content of 0. 44 g C (g DM) -1 in the aboveground (AG) plant material  (Redin et al.
Model calibration: optimization of the metabolic : structural fractions of the litter inputs In the Century model, AG and BG carbon inputs are further separated into metabolic and structural fractions, according to the lignin-to-nitrogen (L : N) ratio. Because the L : N ratio was not available for all the crops in the database, we fitted model simulations to observed SOC dynamics for the control plot of each site, i. e. the reference plot without additional C inputs, in order to get the metabolic : structural (M : S) fraction of the AG and BG carbon inputs. We used the sequential least-squares quadratic programming function in Python (SciPy v1. 5. 1, scipy. optimize package with method, SLSQP), a nonlinear constrained, gradient-based optimization algorithm  (Fu et al. , 2019). We successfully performed the optimization at 13 sites, where at least three measures of SOC stocks were available.
Model calibration: optimization of temperature dependency parameters We optimized the Q 10 and daily soil reference temperature parameters, which affect SOC decomposition. The Q 10 factor is fixed to 2 in Century. However, many authors have shown that Q 10 measurements vary with pedoclimatic conditions and vegetation activity  (Craine et al. , 2010; Lefèvre et al. , 2014; Meyer et al. , 2018; Wang et al. , 2010). For this reason, and to correctly reproduce interregional variations among the sites in the dataset, we optimized both the Q 10 and reference temperature parameters to better fit the SOC dynamics ( Mg C ha -1 ) of each agricultural site at the control plot. We decided to bind the Q 10 between 1 and 5, following the variation in Q 10 found by  Wang et al. (2010)  over 384 samples collected in the Northern Hemisphere.
Virtual vs. actual C inputs in the experimental carbon treatments In Fig. 9  we compare the C inputs required to reach the 4p1000 target to the actual inputs used across the 46 treatments of additional C. The additional C (Mg C ha -1 yr -1 ) shown in the graph for all experimental treatments refers to exogenous organic amendments, plus additional C due to increased crop yields, relative to the control plot. The most striking result emerging from the data is that modeled additional C inputs are systematically lower or similar to at least one treatment of additional C at all sites, except for Foggia. In the Foggia experiment, different crop rotations were compared, and no additional EOM was incorporated to the soil. Here, none of the rotations had sufficient additional C content (compared to the control wheat-only treatment) to meet the required C input level predicted by Century for a 4p1000 increase rate.
Simulated carbon inputs and experimental carbon addition treatments Different types of organic C treatments were considered in this study and compared to Century simulations of C inputs required to reach the 4p1000. At all experimental sites with additional EOM inputs, at least one treatment employed higher amounts of C inputs compared to the simulated C inputs required for a 4 ‰ annual target. In Foggia, C inputs from different crop rotations were studied, but none employed sufficient amounts of additional C to reach the 4p1000, as predicted by Century. Model results in Foggia had a high standard error, mainly due to the fact that the variability of crop yields for this site was not available. Thus, for this site, we calculated model uncertainty using the average relative variability across the whole dataset, which could have increased the uncertainty of model outputs.