dataesr/scientific-paragraphs-categorization huggingface dataset:
Distance to coast was computed as the shortest Haversine distance to 0 m isobath, on a 15 min resolution. The bathymetric data from the ETOPO1 database (https:// ngdc. noaa. gov/ mgg/ global/ global. html) were used and obtained through the marmap R package  58. Zooplankton abundance and size estimates. Zooplankton samples were analyzed using the ZooScan imaging system  21. Zooplankton images classification was performed using an automatic recognition algorithm and validated into taxonomic groups by a posteriori expert inspection. Organisms were classified into coarse taxonomic groups on Ecotaxa  59  , generally at the class or order-level except for copepods which were identified down to the family level whenever possible. For our spatial analyses (see below), 36 taxonomic groups were retained, including total zooplankton, Copepoda, Chaetognatha, Cnidaria, Tunicata (mainly appendicularians, salps and doliolids, which present a range in size, due to change in clade), Eumalacostraca (mainly amphipods, decapods and euphausiids), Rhizaria (mainly foraminifers and radiolarians), Pteropoda and small crustacean grazers (Cladocera, Ostracoda and nauplii larvae).
DTW stretches the data series locally to have one match the other(s) as much as possible. Then, the Euclidean distance between the data series is computed by summing the distances of the aligned data points. The modelled smooth curves were projected on a scale of 1 to 100 values scaling the range observed for each covariate. All the covariate-specific smooth curves were out together in a list that can be assimilated to a multivariate data series even in lengths for each retained GAM. DTW distances were then computed and partitioning around medoids (PAM) clustering  63  was performed to cluster the GAMs (i. e. a zooplankton group + a net type) into two to ten clusters. Five different indices (Calinski-Harabasz, Dunn's, Silhouettes, classic and modified Davies Bouldin indices) were examined to choose the optimal number of cluster.
Four clusters were retained for the median-ESD based GAMs, and four for the abundance-based ones (Supplementary Fig. S15 ). Hierarchical clustering approaches were also examined but they yielded unclear performance indices so PAM was preferred. Every modelled covariate-specific smooth curves used for the DTW clustering are reported in Supplementary Figs. S10 and S11 for the median ESD responses and the abundance models, respectively. To summarize such large amount of information and illustrate the similarities between the zooplankton groups, the inter-group distance matrix issued from the DTW algorithm was projected onto to a two dimensional space based on classical multidimensional scaling. The analyses were performed with the R v3. 5. 2  64  environment and with MATLAB R2017a. All maps presented were plotted in R v3.
Acknowledgements Tara Oceans  (which includes both the Tara Oceans and  Tara Oceans Polar Circle  expeditions) would not exist without the leadership of the  Tara Expeditions Foundation  and the continuous support of 23 institutes (http:// oceans. tarae xpedi tions. org). We further thank the commitment of the following sponsors:  CNRS  (in particular Groupement de Recherche GDR3280 and the  Research Federation for the study of Global Ocean Systems Ecology and Evolution ,  FR2022/Tara Oceans-GOSEE ),  European Molecular Biology Laboratory  (EMBL),  Genoscope/CEA , The  French Ministry of Research , and the  French Government 'Investissements d' Avenir' programmes OCEANOMICS  ( ANR-11-BTBR-0008 ),  FRANCE GENOMIQUE  ( ANR-10-INBS-09-08 ),  MEMO LIFE  ( ANR-10-LABX-54 ), and  PSL Research University  ( ANR-11-IDEX-0001-02 ).
M. C. B. acknowledges postdoc fellowships from the  Coordination for the Improvement of Higher Education Personnel of Brazil (CAPES)  ( 99999. 000487/2016-03 )and the  Fonds Français pour l'Environnement Mondial (FFEM). F. B. received support from  ETH Zürich  and from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement n°SEP-210591007. We also thank the support and commitment of Agnès b. and  Etienne Bourgois , the  Prince Albert II de Monaco Foundation , the  Veolia Foundation ,  Region Bretagne, Lorient Agglomeration ,  Serge Ferrari ,  World Courier , and  KAUST. The global sampling effort was enabled by countless scientists and crew who sampled aboard the Tara from 2009-2013, and we thank  MERCATOR-CORIOLIS  and  ACRI-ST  for providing daily satellite data during the expeditions.
We are also grateful to the countries who graciously granted sampling permissions. The authors declare that all data reported herein are fully and freely available from the date of publication, with no restrictions, and that all of the analyses, publications, and ownership of data are free from legal entanglement or restriction by the various nations whose waters the Tara Oceans expeditions sampled. The following people were involved in plankton image sorting:  B. Serranito ,  N. Monferrer ,  C. Merland  and  F. Roullier. We thank the  EMBRC platform PIQv  for image analysis. This work was supported by  EMBRC-France , whose French state funds are managed by the  ANR  within the  Investments of the Future program  under reference  ANR-10-INBS-02. We are grateful to  Meike Vogt  for sharing her thoughts regarding the potential use of the present observations for zooplankton modelling.
Data availability Median ESD and abundance values by zooplankton groups are available at https:// doi. org/ 10. 17632/ nwvjw ccgvh. 1. Zooplankton imaging datasets from the Tara Oceans expeditions are available through the collaborative web Ecotaxa application and repository under the addresses: https:// ecota xa. obs-vlfr. fr/ prj/ 377, https:// ecota xa. obs-vlfr. fr/ prj/ 2245, https:// ecota xa. obs-vlfr. fr/ prj/ 378 for the WP2 net; https:// ecota xa. obs-vlfr. fr/ prj/ 397, https:// ecota xa. obs-vlfr. fr/ prj/ 398, https:// ecota xa. obs-vlfr. fr/ prj/ 395 for the Bongo net; https:// ecota xa. obs-vlfr. fr/ prj/ 415, https:// ecota xa. obs-vlfr. fr/ prj/ 409, https:// ecota xa. obs-vlfr. fr/ prj/ 408, https:// ecota xa. obs-vlfr. fr/ prj/ 411, https:// ecota xa. obs-vlfr. fr/ prj/ 412 for the Régent net.
Whale Tracking Data We compiled published and unpublished satellite tracking data from 378 individual humpback whales, totaling 291,628 location records. Argos satellite-linked telemetry tags were deployed on humpback whales in their breeding areas and Antarctic foraging areas from 2002-2018  [31] [32] [33] [34] [35] [36] [37] [38] [39] [40] [41] [42] [43] [44] [45] [46] [47] [48]  (Supplementary Table  S1 ). Adult individuals were selected for tagging in these study areas indiscriminately; specific individuals were not targeted. Details of tag types, ethics clearance, and permits are provided in the above references. This comprehensive dataset represents the movements of whales from seven Southern Hemisphere "breeding stocks"  [30]  across different ocean basins in and around the Southern Ocean.
Argos locations are recorded at irregular time intervals due to differences in the programming of telemetry tags, and because locations can be obtained only when the tag is not underwater. Furthermore, the spatial accuracy of each location may vary, as indicated by the quality class assigned to it. To estimate whale locations at regular time intervals while taking into account these different accuracies, we used the foieGras package  [50, 51]  to fit a random walk state-space model  [52] [53] [54] [55]  with a 24-h time step to the Argos tracking data. Before fitting the model, we excluded tracks with fewer than 10 locations, and we filtered the data using a speed-distance-angle filter from the argosfilter package  [56] , with a speed threshold of 5 m/s to exclude unlikely locations. We used a less conservative speed threshold than some other humpback whale studies (e.
For the mean covariates among these (ICE, ICEDIST, SST, and SSTFRONT), the temporal average of these rasters was calculated. For variance covariates among these (ICEVAR, SSTVAR), the temporal variance was calculated within each season (November-March of a given austral summer), and the mean of these variance values was then calculated. After resampling covariates onto the study area grid, we filled any remaining data gaps in the mean layers by taking the mean value from a 13 cell by 13 cell (1. 3 • by 1. 3 • ) neighborhood focused on the given cell. For model fitting, the values of these covariates were extracted at locations of the real and simulated tracks. For spatial prediction, we predicted according to covariate values across the whole study area grid (Figure  2 ). For these calculations we used mainly tools in the raster  [65] , raadtools  [66] , and grec  [62]  packages.
M1-A Naive Circumpolar Model Here, we fit a case-control classification model where the response (or output) is whether a given location in the dataset is an observed track location (case) or a simulated track location (control), and the features (or inputs) are a set of environmental covariates at that location, as described above. M1 is fitted using tracking data plus simulations from all regions together, with no information on region identity, and the fitted model is predicted circumpolarly. We thus refer to this as the "naive circumpolar model". This circumpolar prediction is the probability (0-1) in each grid cell of that grid cell containing an observed (as opposed to a simulated) track location, which we interpret as a measure of habitat selection (e. g. ,  [19, 26, 27] ).
M4-Stacked Generalization Here, we fit an additional model (the "second-level model") where the response is whether a given location in the dataset is an observed track location or a simulated track location, and the features are predictions from the five regional models (the "first-level models"). No environmental covariates are included among the features. We then predict this model circumpolarly. This kind of model is referred to as a "stacked generalization"  [67] , "meta-learner", or "meta-model"  [68] , and is a widely used method for creating ensembles of models that attempt to account for different model performance  [21] , overall and/or locally.
M5-Hybrid Generalization Finally, we fit a new model where the response is whether a given location in the dataset is an observed track location or a simulated track location, and the features are environmental covariates plus predictions from the five regional models. The model is thus additionally "informed" by the predictions from the regional models, and we term this "hybrid generalization" to reflect that it combines approaches from the M1 (the naive circumpolar model), Mr (the regional models), and M4 (stacked generalization) models.
e. , selected among) three potential values of the number of covariates to possibly split at in each node (parameter "mtry"): 2, 3, and 4, these being near the recommended default of the (rounded down) square root of the number of covariates  [71, 73]. The number of simulated locations is much higher than the number of observed locations in our dataset (observed:simulated = 1:50) and such a class imbalance can result in classifiers achieving high performance by simply predicting the majority class. We addressed this by "undersampling"  [77] -randomly downsampling the simulated locations in any modelfitting iteration to match the number of observed locations. For both the model tuning (model parameter selection) and performance evaluation (model assessment) steps, we calculated the area under the receiver operating characteristics curve (AUC) during 10-fold cross-validation, wherein, across 10 iterations, one of the folds is held out to serve as a validation (test) set, while the remaining nine folds are used as the fitting (training) set  [73, 78].
Independent Validation Data Predictive models should ideally give accurate predictions when they are confronted with new data that were not used to train the models; this is the model's generalization performance  [71] , or transferability  [84, 85]. This is particularly important to avoid the problem of overfitting, where a model fits the training data too closely, resulting in high performance for that particular dataset, but poor performance when predicting new data (the model has low bias but high variance)  [71, 86]. Cross-validation (as described above) tries to deal with this problem, aiming at more generalized models, but the gold standard for validation is independent testing data that are never seen by the models during training  [71]. This is often achieved by setting aside a proportion of the data before modelling, but doing so can be unrealistic when the data for modelling are already limited, as is often the case in animal tracking studies.
To estimate the generalization performance of our models, we used two independent datasets on humpback whale occurrence that were not acquired via satellite telemetry, since in compiling our tracking dataset we aimed to collate essentially all available humpback whale tracking data for the Southern Ocean. First, we used whaling catch records of humpback whales from the International Whaling Commission (IWC) catch databases, Version 6. 1  [87]. We filtered the catch dataset to exclude records from land-based whaling stations, and to include only catches recorded with a spatial accuracy to the nearest degree or better, south of 40 • S and taken in November-March. This resulted in 45,161 catch records from 1928-1973 (Figure  3a ). Second, we used humpback whale sightings from the IWC International Decade of Cetacean Research/Southern Ocean Whale and Ecosystem Research (IDCR/SOWER) sightings database.
Regional Models Internal cross-validation scores for the regional models (Mr) were good, with the AUC ± SD ranging from 0. 711 ± 0. 081-0. 886 ± 0. 042 (Table  2 ). However, when predicting regional models circumpolarly, they performed worse than any of the circumpolar models, according to validation with the full tracking dataset (AUC range = 0. 628-0. 743) and with the catches and sightings dataset (AUC range = 0. 596-0. 702) (Table  2 ). Neither the validation performance for the full tracking dataset (Spearman's rank correlation, ρ = -0. 7) nor that for the catches and sightings dataset (ρ = -0. 1) were positively correlated with the number of tracks used for each regional model. Univariate extrapolation percentages ranged from 0. 10%-8. 15%, and the combinatorial extrapolation percentages were 0.
00-0. 66% (Table  2 ). Regional model performance was weakly correlated with univariate extrapolation percentage (ρ = 0. 3) and with combinatorial extrapolation percentage (ρ = -0. 1). Variable importance varied among models, but ICEDIST had the highest mean variable importance across the models (90. 7), followed by SLOPEDIST (79. 9), SST (79. 4), and SHELFDIST (62. 3) (Figure  4 ). Response curves indicate differences in the relationships between habitat selection and environmental covariates among regions (Supplementary Figure  S1 ). For ICEDIST, partial dependence for the East Pacific contrasted with other regions. For SST, habitat selection decreased with increasing SST in the East Pacific and West Pacific models, contrasting with the increasing relationship in the Atlantic, East Indian, Model performance for these independent validation data was assessed using the probability predictions from each model in each grid cell, and whether or not a catch and/or sighting was recorded in that grid cell, with the AUC calculated using the pROC package  [88].
Regional Models Internal cross-validation scores for the regional models (Mr) were good, with the AUC ± SD ranging from 0. 711 ± 0. 081-0. 886 ± 0. 042 (Table  2 ). However, when predicting regional models circumpolarly, they performed worse than any of the circumpolar models, according to validation with the full tracking dataset (AUC range = 0. 628-0. 743) and with the catches and sightings dataset (AUC range = 0. 596-0. 702) (Table  2 ). Neither the validation performance for the full tracking dataset (Spearman's rank correlation, ρ = -0. 7) nor that for the catches and sightings dataset (ρ = -0. 1) were positively correlated with the number of tracks used for each regional model. Univariate extrapolation percentages ranged from 0. 10-8. 15%, and the combinatorial extrapolation percentages were 0.
Discussion We how methods for incorporating regional habitat selection for a given species that is broadly distributed across a range of environmental gradients can yield models with higher predictive performance than a range-wide or large-scale model that pools the available data. In our case study using satellite telemetry data for humpback whales across the Southern Ocean, we show that three approaches lead to more accurate predictions of an independent validation dataset of humpback whale catches and sightings, compared to the naive circumpolar model. We suggest that these approaches-mean ensembles, stacked generalization, and an approach that we call hybrid generalization-can be used when more accurate predictions are sought across regional populations of animals that may show variation in habitat selection.
Extrapolating the regional models to the whole circumpolar area led to worse predictive performance on the independent validation dataset than the naive circumpolar model M1 and the ensemble models M2-M5, illustrating how regional habitat selection models may not be sufficient in situations where large-scale predictions are sought, especially when a high degree of extrapolation is required. These findings concur with studies showing limited transferability of regional habitat selection models in other large marine vertebrates (e. g. ,  [8, 9, 28] ). Each of these studies identified that habitat selection models fitted to animals in a given region may not adequately predict the habitat selection of animals in another region. Further, the performance of regional models in our study shows that generalization performance was not related to the amount of data or the degree of extrapolation in a straightforward way, meaning that in situations where no independent validation data are available, poor generalization performance may also go undetected, since the amount of data used, the amount of extrapolation, or the internal model performance do not indicate the generalization performance (see also  [89, 90] ).
Additionally, smaller important areas are highlighted in the Sub-Antarctic. The predictions also show that key humpback whale habitats are not homogeneously distributed around Antarctica-something already indicated by whaling records  [87] , sightings data (e. g. ,  [99, 100] ), tracking data  [31] [32] [33] [34] [35] [36] [37] [38] [39] [40] [41] [42] [43] [44] [45] [46] [47] [48] , and other habitat modelling  [99]. Similar to our validation approach, but excluding the whaling dataset, Bombosch et al. [99]  used IWC sightings data corresponding with their study period as independent valuation of their predictions, achieving an AUC value of 0. 877 (compared with our result of 0. 821 for model M5). However, it is difficult to compare AUC values for models with different spatial extents  [101].
Furthermore, this score is for monthly predictions using matching monthly validation data, versus our seasonal predictions, and our tracking dataset includes some migratory behaviors that may lower model performance slightly. Being much older than the sightings and tracking data, the whaling data present a more difficult validation challenge for models, since this involves greater temporal extrapolation. For example, there is the possibility that intensive whaling may have affected humpback whale habitat selec-tion via changes to the Southern Ocean ecosystem, resulting from the removal of millions of krill predators. These could be considered drawbacks of the whaling validation data, but the positive counterpoint is that the whaling data represent a more rigorous test of model generalization.
Specific biases in the IWC dataset are: (1) pelagic whaling did not primarily target humpback whales, biasing catches towards regions of higher density for other species; (2) catch effort is not equally distributed spatially, and early coastal whaling of humpbacks is not included because no at-sea positions are available for these catches; (3) catch effort varied temporally; (4) both catches and sightings are limited to relatively open waters accessible to vessels (although this latter bias is less in the case of humpback whales, which prefer areas with lower sea ice concentration [Supplementary Figure  S1 ]); and (5) the IWC IDCR/SOWER surveys mainly targeted areas south of 60 • S  [99, 100, 102]. The IWC dataset has two general "sampling" features that affect the apparent model performance (validation AUC scores).
First, as stated above, a given cell in the study area may not have been surveyed, or whaling may not have been attempted. Second, even if a given cell was sampled (surveyed or whaled), the detection of whales (sighting or catching) is imperfect. Both sampling features mean that the IWC dataset contains false absences, where a given cell contains humpback whales that are not recorded. False presences in these data are much less likely (but could increase when temporal effects, such as shifts in distribution over time, are considered). The effect of imperfect detection on species distribution models is discussed in, for example, Guillera-Arroita  [103]. The false absences in the validation data we used can drive the apparent model performance downwards, when a model correctly predicts a presence in a given cell, but this correct prediction is not validated because the cell was not sampled, or because whales were missed (i.
Conclusions We have shown how ensemble approaches commonly used to combine different algorithms for a single region can be reframed, treating regional habitat selection models as the candidate models. This allows the incorporation of regional variation when fitting predictive models of animal habitat selection across large ranges. We tested this approach using satellite telemetry data from 168 humpback whales across five geographic regions in the Southern Ocean and an independent validation dataset of humpback whale sightings and catches. Multiregional ensemble approaches (unweighted ensembles, stacked generalization, and hybrid generalization) resulted in models with higher predictive performance than a circumpolar naive model. These approaches can be used to incorporate regional variation in animal habitat selection when fitting range-wide predictive models using machine learning algorithms.
Model performance. Summary of the model results for (a) five circumpolar and (b) five regional models of humpback whale habitat selection. Model performance (area under the receiver operating characteristic curve: AUC) was measured by (1) internal cross-validation (CV); (2) validation against the full tracking dataset; and (3) validation against an independent dataset of whale catches and sightings. The latter represents a measure of each model's generalization performance, and the overall model rank is based on this score. Extrapolation refers to the percentages of covariate values in the whole study area that were outside the univariate or combinatorial range of covariate values during model fitting. SD: standard deviation.
In order to fight the spread of the global COVID-19 pandemic, most of the world's countries have taken control measures such as lockdowns during a few weeks to a few months. These lockdowns had significant impacts on economic and personal activities in many countries. Several studies using satellite and surface observations have reported important changes in the spatial and temporal distributions of atmospheric pollutants and greenhouse gases. Global and regional chemistry-transport model studies are being performed in order to analyze the impact of these lockdowns on the distribution of atmospheric compounds. These modeling studies aim at evaluating the impact of the regional lockdowns at the global scale. In order to provide input for the global and regional model simulations, a dataset providing adjustment factors (AFs) that can easily be applied to current global and regional emission inventories has been developed.