dataesr/scientific-paragraphs-categorization huggingface dataset:
Occurrence datasets: historical compilation Biological sampling in the SO began with the first expeditions of the HMS Challenger (1873-1876). Sampling effort has considerably increased over the second part of the twentieth century and during recent decades in particular, following technological advances that have enabled the access to remote regions and sample processing (Fig. 2 ). This long-lasting and irregular effort in biogeographical (occurrence) data collection has had an impact on data compilation and has resulted in heterogeneous datasets, as observed in several data papers and associated Integrated Publishing Toolkit (IPT) databases such as  Guillaumot et al. (2016 ), Fabri-Ruiz et al. (2017)  or  Moreau et al. (2018) , or in the general platform biodiversity. aq web portal. The historical compilation of biological data includes 1) taxon misidentifications and taxonomic inconsistencies due to the various taxonomic revisions published through time, 2) errors in the georeferencing of occurrence records due to contrasting nomenclatures used to report latitude and longitude, 3) the accumulation of errors in metadata through the different generations of curation and 4) errors due to the use of different coordinate projection systems.
Finally, in cases where species distributions may have shifted with time, species environmental preferences may have changed or non-contemporaneous environmental or occurrence datasets are used, discrepancies between occurrence records and environmental conditions can be present and violate the environment-occurrence equilibrium assumption necessary to generate SDMs. All of these side effects were reviewed in detail by  Newbold (2010). The impacts on species niche definition and SDM predictions have been reported in many works  (Ensing et al. 2012 , Lahoz-Monfort et al. 2014 , Monk 2014 , Aguiar et al. 2015 , Tessarolo et al. 2017 , Guillaumot et al. 2018a ) that all advise us to thoroughly check datasets for quality management prior to running models.
Occurrence datasets: spatial aggregation Most species occurrence data were collected in the vicinity of research bases or their surroundings or along recurrent maritime routes, leading to clear spatial aggregation patterns in biological datasets (Fig. 3 )  (Griffiths et al. 2014 , Guillaumot et al. 2019). Spatial aggregation can affect model accuracy, as aggregated presence records do not fully and homogeneously represent the entire environment that is occupied by given species. This aggregation also violates an initial assumption of SDMs that requires independence between records (Araújo &  Guisan 2006 , Hijman 2012). This may bias model predictions  (Luoto et al. 2005 , Segurado et al. 2006 , Dormann 2007 , Kühn 2007 , Crase et al. 2012) , leading to statistical artefacts and generating inaccurate patterns  (Bahn & McGill 2007 , Currie 2007).
Occurrence datasets: presence-only records SDMs based on presence/absence data are recognized as having better predictive performance than models using presence-only data  (Zaniewski et al. 2002 , Brotons et al. 2004 , Wisz & Guisan 2009 , Lobo et al. 2010 , Smith 2013 , Carvalho et al. 2015 , Peel et al. 2019). However, except for some local-scale studies (e. g. Robinson et al. 2011) , in most oceanographic studies species absence records are usually not available for SDMs, and working with presence-only records is the only alternative  (Lobo et al. 2010). SDMs are then built by associating presence-only records with a random selection of background records that will be used to characterize the full environmental conditions  (Franklin 2010 , Barbet-Massin et al. 2012). Background records should not be mistaken for pseudo-absence records that are artificial absence data, where the species is supposed (but not confirmed) to be absent.
Pseudo-absence records do not represent the overall conditions of the study area. Presence/pseudo-absence models represent another modelling approach, predicting occupied and unoccupied habitats rather than suitable and less suitable habitats for presence/background modelling  (Sillero & Barbosa 2021). Presence-only datasets may contain several uncertainties that can bias model predictions. 1) Working on rare or cryptic species is generally prone to taxonomic misidentifications that may either contract or, alternatively, expand the extent of predicted species distributions  (Costa et al. 2015 , Aubry et al. 2017). Such biases due to taxonomic errors were shown to be highly variable and to depend on experts identifying specimens, as suggested by  Beale & Lennon (2012) , who worked on a compilation of several collections.
2) Sampling gear may have an impact on species detection. Inaccurate species observations may generate false-positive results (species predicted as being present when they were not sampled or observed in the field) and false-negative results (species predicted as being absent when they were sampled or observed in the field) during model initialization (Guillera-Arroita 2016). Species presence records should be carefully scrutinized prior to modelling  (Lozier et al. 2009 ), or at least records should be categorized into different subsets of data verifiability  (Aubry et al. 2017). 3) Georeferencing errors are a frequent issue in databases  (Murphey et al. 2004 , Maldonaldo et al. 2015). This is especially the case in large databases compiling independent datasets using species presences recorded with varying levels of precision  (Graham et al.
2008 , Bloom et al. 2018). Several studies have simulated virtual random georeferencing errors and have shown that these errors lead to significant drops in model performance and inconsistencies in the respective contributions of environmental descriptor contributions, influencing model interpretation  (Graham et al. 2008 , Osborne & Leitão 2009 , Naimi et al. 2011). These side effects seem to be minimized in local-scale models, here again advocating for the use of local-scale models whenever possible  (Mitchell et al. 2017). Occurrence datasets: dealing with small datasets Usually, the number of species presence records available for modelling is relatively limited considering the wide geographical extent of the SO  (De Broyer et al. 2014 ). Generating SDMs with small datasets may include many pitfalls: 1) It reduces the potential of SDMs to transfer in space and time  (Hernandez et al.
2018 ) and 8) it leads to a reduction in model accuracy because presence and background datasets would not differ markedly  (Luoto et al. 2005). Alternatives are being developed to produce more accurate models based on a limited amount of presence records. One solution is generating several models performed on restricted areas and datasets with more detailed information and then averaging them with a weighted ensemble approach. This 'ensemble of small models' approach showed improved performance compared to single models  (Lomba et al. 2010 , Breiner et al. 2015 , 2018). Another alternative is to restrict the prediction area according to where occurrence records are found and ensuring upstream that the number of records is sufficient to precisely characterize the species environmental preferences: trivial advice that is surprisingly neglected, as recently pointed out by  Morales et al.
(2017)  and  Araújo et al. (2019). Occurrence datasets: definition of species-occupied environmental space Spatial aggregation, along with heterogeneity, limited size and uncertainties in datasets, can strongly bias the quantification of the species-occupied environmental space  (Hortal et al. 2008 , Newbold 2010 , Tessarolo et al. 2017). However, accurately defining species-occupied space is the cornerstone of SDM initialization  (Elith et al. 2006 , Boulanger et al. 2018). Moreover, SDMs suppose that species are in equilibrium with the environmental conditions that they inhabit. SDMs do not take into consideration potential vagrants that have dispersed out of their usual environmental range or populations that could momentarily survive in unsuitable habitats because doing so violates the equilibrium assumption between species distribution and environmental conditions (Beale & Lennon 2012).
These elements should be cautiously considered when preparing datasets prior to generating models by removing any atypical records. Over the last two decades, field data acquisition has expanded through the use of biologging technology with electronic devices attached to seabirds and marine mammals in order to access the positions of species all year long  (Raymond et al. 2015 , Ropert-Coudert et al. 2020). These data uncover the hidden behaviours of marine animals and constitute a powerful way of better estimating species-occupied space; they can also be used to validate and refine our understanding of the environmental conditions prevailing in those species distribution areas  (Arthur et al. 2017 , Nachtsheim et al. 2017 , Hindell et al. 2020).
e. have good abilities to correctly transfer predictions to other geographic space and time periods;  Randin et al. 2006 ) and that they limit overfitting (i. e. mitigate model complexity) while being flexible in integrating complex environmental relationships. Machine-learning algorithms (e. g. maximum entropy (MaxEnt), boosted regression trees (BRTs), random forests (RFs), support vector machines (SVMs);  Vapnik 1998 , Breiman 2001 , Elith et al. 2008 , 2011)  give access to important aspects of computing performance  (Zhou 2012)  and are relevant approaches for handling complex relationships between species occurrences and the environment  (Olden et al. 2008 , Elith & Leathwick 2009). The BRT and RF algorithms are particularly suited to complex and heterogeneous datasets (Fig. 4 ;  Guillaumot et al.
This is the main limitation to the approach and could offset the gains in model performance  (Crimmins et al. 2013 , Zhu & Peterson 2017 , Hao et al. 2020). Such gains were contested, especially since model evaluation was often performed without using an independent evaluation dataset  (Hao et al. 2019). Combining predictions of different models generated with contrasting assumptions is therefore tricky when interpreting results (Perrault-Hébert 2019). Optimizing the parameterization of a single algorithm (which could be correctly evaluated) may therefore constitute a more valuable approach (Perrault-Hébert 2019). Comparing the performance of different algorithms can be helpful in the first stage of the modelling process in order to select the most suitable algorithm and to calibrate the models  (Massada et al.
Correcting spatial biases Generating a model based on spatially aggregated presence-only records may bias predictions with a higher probability of occurrence predicted in highly sampled areas  (Dormann 2007 , Guillaumot et al. 2018a ). To compensate for such a bias, a first approach is to sample background records according to the spatial bias introduced by the aggregated presence records themselves  (Phillips et al. 2009). The background dataset is used to define the environmental background: its boundaries and variability constitute essential information for building and projecting model outputs  (Wisz & Guisan 2009 , Barbet-Massin et al. 2012). The choice of the number of background records to be sampled and the extent of their distribution should be considered carefully when calibrating a model because it can strongly influence model predictions  (Chefaoui & Lobo 2008 , Lobo et al.
Taxonomic bias and population variability SDMs are usually parameterized using all presence records available for a species and all environmental conditions prevailing in the species records  (Elith & Leathwick 2009). When modelling species distribution at a broad spatial scale, it is often assumed that all populations of a species have the same relationship to environmental conditions over the entire distribution area  (Pierrat et al. 2012 , Xavier et al. 2016 , Guillaumot et al. 2018b , Fabri-Ruiz et al. 2019). However, occurrence datasets may include a set of populations with different phenotypic plasticities  (Chevin et al. 2010 ), transgenerational adaptations (Dixon et al. 2015)  or simply different habitat selection in the case of vagile species. Therefore, the modelled species can actually present different abilities to respond to environmental changes.
6  are here coloured according to the descriptor responsible for extrapolation; i. e. for each pixel, the predictor in question lies outside the calibration range). In this case study, 14 environmental descriptors are responsible for extrapolation, with depth being the main contributor. White pixels correspond to areas where the model does not extrapolate (the corresponding model predictions are shown in Fig. 6 ). POC stands for 'particulate organic carbon' and Chl a is the concentration in chlorophyll a on the sea surface. Generated from https://cran. r-project. org/package=SDMPlay. occupied by species is limiting and the resolution of environmental descriptors relatively low (see the 'Quality of datasets' section). As a consequence, SDM projections sometimes include wide extrapolation areas that may cover > 75% of the predicted regions (Fig.
e. some expert-driven decision) was proven to be efficient at reducing extrapolation  (Kearney & Porter 2009 , Hare et al. 2012 , De Villiers et al. 2013 , Guillaumot et al. 2020b). Such a strategy is transitional until complementary samples and more comprehensive occurrence datasets are made available to better define the species-occupied space  (Guillaumot et al. 2020b).
Some common metrics for the evaluation of model predictions Once models are generated, the accuracy of their predictions must be assessed in order to evaluate the validity of the models with regards to scientific issues to address, to compare different model outputs and to allow for the formulation of reliable interpretations  (Zurell et al. 2020). Several metrics were developed in order to evaluate the performance of models  (Fielding & Bell 1997 , Allouche et al. 2006). Most of them are based on the calculation of an error matrix (or confusion matrix) that displays the proportion of presence and absence records that are correctly predicted by the model  (Allouche et al. 2006). In most biological studies focused on the SO benthos, absence records are usually unavailable and SDMs are generated based on a set of presence/background records (see the 'Occurrence datasets' section).
As a consequence, the statistics that are commonly used for presence/ absence datasets may not be appropriate for model evaluation  (Wiley et al. 2003 , Phillips et al. 2006 , Braunish et al. 2013) , such as the κ statistic  (Allouche et al. 2006). In contrast, the area under the curve or area under the receiver operating curve (AUC) is one of the most used and appropriate metrics for measuring the performance of model predictions based on presence/ background data  (Hand 2009). The AUC is an objective measure that remains stable with low-prevalence datasets (i. e. low frequency of occurrences with regards to the projection space) and is not sensitive to threshold effects  (Manel et al. 2001 , Hand 2009 , van Proosdij et al. 2016). However, for presence/background models, specificity (the fraction of correctly predicted absences) might be overestimated when the number of background records is much higher than the number of presence-only records or when background and presences are associated with very different environmental values.
This incidentally inflates AUC scores  (Phillips et al. 2006 , Raes & ter Steege 2007 , Lobo 2008 , Jiménez-Valverde 2012)  and invalidates the relevance of the AUC metrics  (van Proosdij et al. 2016). Even when properly employed, the AUC cannot be used to compare models when SDMs are generated for different species based on different environmental descriptors or projected on distinct regions because the values depend on the relative size of suitable areas and prevalence scores may contrast (see the 'Correcting spatial biases' section)  (Wisz et al. 2008 , Anderson & Gonzalez 2011). The AUC metrics must be used as a simple measure of the relative ranking of model predictions associated with a specific dataset (El-Gabbas & Dormann 2018). Overall, each statistic is characterized by specific advantages and potential biases, so that it is recommended that one uses several statistics for evaluating model predictions  (Allouche et al.
Cross-validation procedures Cross-validation procedures are aimed at evaluating model predictions using a subset of presence or absence records retrieved from the initial dataset used for modelling in order to assess how well the test data match with the modelled predictions  (Bahn & McGill 2013). When working with presence-only datasets, two subsets of presence records are used: one subset is used to train the model (the training group) and the second subset is used to test the model (the test group). Test data and training data must be spatially independent from each other  (Hijmans 2012 , Bahn & McGill 2013). In most modelling exercises, standard cross-validation procedures are commonly used, in which the initial  Several spatial cross-validation procedures have been proposed (Fig. 8 ), and the most appropriate one can be determined by comparing the different procedures in order to define the one that is most suitable for the study  (Muscarella et al.
Conclusions and future prospects This review summarizes some points and issues to be considered during SDM construction for modelling the distribution of SO species (Fig. 1 ). It shows that accurate and efficient SDMs can be produced for SO species when considering potential common biases and issues and correcting for their side effects. Proposed corrections must be adjusted to each case study: no consensus method nor implementation procedure always perform best -each case study requires proper analyses in order to generate the most relevant and accurate predictions. This means that, for each model, several procedures to implement the model should be tested in order to select the most suitable one, ideally giving priority to the availability of independent datasets for evaluating the models.
We showed that SDMs perform best when the species-occupied space is accurately described, using extensive occurrence datasets with both presence and absence records, and when data are checked for positioning and georeferencing errors. A good knowledge of species' ecology, life history traits and populational variations within the overall species distribution and environmental range help to improve model quality  (Fois et al. 2018). The compilation, examination and preparation of datasets prior to modelling are essential steps in generating efficient models. Estimating and communicating the uncertainties associated with model predictions are also important tasks to be highlighted. This process may include a 'simple' interpretation of the ecological relevance of SDM outputs by experts  (Merow et al.
Financial support This work was supported by 'Fonds pour la formation à la Recherche dans l'Industrie et l'Agriculture' (FRIA) and 'Bourse fondation de la mer' grants to C. Guillaumot. This is contribution no. 47 to the vERSO project (www. versoproject. be), funded by the Belgian Science Policy Office (BELSPO, contract n°BR/132/A1/vERSO). Research was also financed by the 'Refugia and Ecosystem Tolerance in the Southern Ocean' project (RECTO; BR/ 154/A1/RECTO) funded by the Belgian Science Policy Office (BELSPO), this study being contribution number 24.
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Funding K. R. H. was supported by the  National Science Foundation MacroSystems Biology program  ( DEB-DEB-1340856 ). W. A. H. was supported by the  National Science Foundation  (grant # IOS-1755055 ). M. A. P. received partial support from  FutureMARES (Climate Change and Future Marine Ecosystem Services and Biodiversity, EU H2020 , award no. 869300 ). L. S. is funded by the  National Science Foundation  (grants  1951244  and  2017949 ).
Muscle strength A Grip Strength Meter (Bioseb) was used to measure forelimb and hindlimb grip strength  (19). The test was repeated three consecutive times within the same session, and the mean value was recorded as the maximal grip strength for each mouse. In situ isometric tibialis anterior muscle contraction in response to nerve stimulation was performed as described  (20). In brief, tibialis distal tendons were attached to an isometric transducer (Harvard Bioscience). Sciatic nerves were proximally crushed and distally stimulated by a bipolar silver electrode using supramaximal square wave pulses of 0. 1 ms. Data were analyzed on a microcomputer using a Pow-erLab system (4SP, AD Instruments).
RNA extraction and analysis Muscles and C2C12 cells were homogenized in TRIzol reagent (Life Technologies, Darmstadt, Germany). RNA was isolated using a standard phenol/chloroform extraction protocol, and quantified by spectrophotometry (Nanodrop, Thermo Fisher). cDNA was prepared by reverse transcription of 2 g of total RNA using SuperScript IV (Life Technologies) and oligo(dT) primers according to the supplier's protocol. cDNA was diluted hundred times and quantitative PCR was performed with a Lightcycler 480 II (Roche) using the SYBR ® Green PCR kit (Roche) according to the supplier's protocol (2 l cDNA, 4. 8 l H 2 O, 5 l Syber Green 2x mix and 0. 2 l of 100 M primer mix). Primers are described in Supplementary Table  S2. 18S, 36b4 and Gapdh were used as internal controls. Data were analyzed using the standard curve  (22)  and Ct methods  (23).
jhu. edu/software/tophat/ index. shtml) (24) and Bowtie2 2. 3. 4. 3 (http://bowtie-bio. sourceforge. net/bowtie2/index. shtml/) (25). Only uniquely aligned reads were retained for further analyses. Quantification of gene expression was performed using HTSeq 0. 11. 0  (26). For comparison among datasets, the transcripts with more than 50 raw reads were considered. Differentially expressed genes (DEGs) were identified using the Bioconductor libraries EdgeR and DESeq  (27, 28). A P-value <10 -5 and a fold change excluding values between 0. 77 and 1. 3 were used as a threshold for DEGs. For microarray analysis, gene expression profiling was performed on total RNA isolated from gastrocnemius and soleus muscles of 9-week-old control and GR (i)skm-/-mice. Biotinylated single strand cDNA targets were prepared with the Ambion WT Expression Kit and the Affymetrix GeneChip ® WT Terminal Labeling Kit according to Affymetrix recommendations.
ChIP-seq libraries were sequenced with an Illumina Hiseq 4000 as paired-end 100 bp reads for Ctcf and H3K4me3 or single-end 50 bp reads for the other sequencing datasets, and mapped to the mm10 reference genome using Bowtie 2  (25). Uniquely mapped reads were retained for further analysis. MACS2 algorithm (https:// github. com/taoliu/MACS/) (36)  was used for the peak calling with non-immunoprecipitated chromatin as control. All peaks with a FDR >0. 01 were excluded from further analysis. The genome-wide intensity profiles were visualized using the IGV genome browser (http://software. broadinstitute. org/software/igv/) (37). HOMER was used to annotate peaks and for motif searches  (38). Genomic features (promoter/TSS, 5 UTR, exon, intron, 3 UTR, TTS and intergenic regions) were defined and calculated using Refseq and HOMER according to the distance to the nearest TSS.