dataesr/scientific-paragraphs-categorization huggingface dataset:
We additionally incorporated tracks of one bird that was caught with mist nets at its wintering area in 2019 in the National Nature Reserve of Moëze-Oléron near La Rochelle (France). This bird nested in 2020 on the Wadden Sea island of Spiekeroog, which is part of our study area. It was equipped with the same tag as used in our study (cf. Jiguet et al. , 2021a). However, the tag was fitted with a silicone leg-loop harness and the bird received colour rings. All tags were programmed to record geographic positions every 10 min. Data were stored in the Movebank database (www. movebank. org). We used bill length to determine the sex of each individual according to  Summers et al. (2013). From 68 of all 86 tagged birds, we gathered additional information on nesting success based on observations in the field.
Post-breeding migration and wintering areas We defined the start of post-breeding migration (= migration departure) as the timestamp of the last location that was less than 1 km away from the recorded nest before the bird had moved at least 50 km away from the nest. We assigned all locations of postbreeding migration to four different regions: (i) the Netherlands (NL), (ii) Great Britain and Ireland (GB/IE), (iii) France including the Channel Islands (FR) and (iv) the Iberian Peninsula (Spain and Portugal, ES/PT) (overlay with the map 'countries' in R-package 'rworldmap',  South, 2011). Locations of individuals on the water were attributed to the nearest of the four regions. To determine wintering areas, we first extracted all areas after migration departure where a bird stayed within a radius of 10 km for at least 3 days.
Statistical analysis We performed all statistical analyses using R 3. 6. 1 (R Core  Team, 2021). The effects of (i) breeding success and sex on migration departure and of (ii) sex on the number of stopovers and migration duration were analysed by using generalised linear mixed-effects models (GLMM; R package 'lme4 ′ ,  Bates et al. , 2015). The effects of (i) migration distance on the number of stopovers, (ii) migration distance and the number of stopovers on migration duration and (iii) geographical latitude of the nesting site on latitude of the wintering area were assessed by using linear mixed-effects models (LMM) or GLMMs depending on the distribution of the data. For GLMMs with a discrete response variable, we applied Poisson linkage. Continuous response variables were modelled using gamma error structure.
Differences in migration distance, migration duration and the number of stopovers between the four groups of wintering areas were assessed by GLMMs using Tukey's test as a post-hoc test (R package 'multcomp',  Hothorn et al. , 2008). In all models, tagging location ('subarea', Fig. 1 ) was used as a random effect to account for potential spatial autocorrelation. Variance explained by fixed effects (marginal R 2 ) and variance explained by both fixed and random effects (conditional R 2 ) were calculated according to  Nakagawa et al. (2017)  using the function 'r. squaredGLMM'.
Acknowledgement We would like to thank  R. Vohwinkel ,  G. Müskens  and  G. Nikolaus  ( †) for their support during catching and tagging of the birds. C. Peerenboom  ( NLWKN, Hannover ) provided valuable data on the productivity of different Curlew populations in Lower Saxony. We are thankful to  Tierpark Nordhorn  and all employees of the nature conservation authorities, conservation stations and all people involved in the protection programmes for grassland-breeding waders for information about nest locations, breeding success, their tireless efforts and general support. Approval for tagging Curlews was obtained in Germany from the  Lower Saxony State Office for   Consumer Protection and Food Safety  (LAVES,  AZ 33. 19-42502-04-20/3373  and  33. 19-42502-04-21/361 ),  Senator for Labour, Women's Affairs, Health, Youth and Social Affairs in Bremen  ( AZ 33.
In parallel, we formulated a model of the microcosm functioning under a chemical stressor based on coupled ordinary differential equations (ODE) and effect functions. First, using all data we estimated model parameters, in particular those related to effect functions (Figure  1 , black boxes). Using data where species occur in isolation and where they occur as a community of species permitted to identify direct and indirect effects of cadmium on the population dynamics of the different species. We then globally analysed the perturbations of our small community (objective 1). We also extracted EC 50 for the different processes (growth, survival, and strength of interspecies interaction) (Figure  1 , orange boxes). In order to assess the relevance of certain data, we removed those data from the complete dataset to build partial datasets.
Computation Monte Carlo Markov Chain (MCMC) computations were performed using the JAGS software via the rjags R package  [Plummer, 2009 , Team, 2013] , after the model was discretised using the Euler method with a time step equal to 0. 1 as stated in  [Lamonica et al. , 2016a]. Three chains were run. A total of 20000 iterations was performed as a burn-in phase and inference was based on 100000 additional iterations for each of the three chains. To check the convergence of the estimation process, we used the Gelman and Rubin convergence diagnostic  [Gelman and Rubin, 1992]  with a cut-off of 1. 01.
18 We aim at determining which types of data could be sufficient to accurately (in terms of mode of the posterior distribution) and precisely (in terms of dispersion of the posterior distribution) estimate parameters of stress functions for the different species (Table  1 ). Our reference was the posterior distributions obtained when estimating parameters from the whole dataset, considering them as the "best possible estimates in the present case study in view of the model and all available data". To evaluate the information provided by certain data, we built partial datasets by removing these data from the whole dataset. Then, we re-estimated the model parameters using these partial datasets and compared the newly obtained estimates with the reference ones. Four partial datasets (numbered A to D) were used to estimate the stress function parameters.
They are summarized in Table  2. We evaluated only information provided by data collected under contaminated conditions, removing them successively to build the partial datasets. Regarding data without contaminant, including the controls in Experiments 4 to 6, they were kept in all partial datasets. Dataset A included all data except the "monospecific data" with contaminant. This corresponds to exclude data collected from beakers containing only one species. Thus, data from Experiment 6 and data "duckweeds alone" from Experiments 4 and 5 were not included in dataset A. Dataset B included all data except the "two species data" with contaminant. This corresponds to exclude data collected from beakers with both duckweeds and algae. Thus, duckweed and algae data collected from beakers containing both duckweeds and algae from Experiments 4 and 5 were not included in dataset B.
In dataset C, we 19 only included the "complete microcosm data", which corresponds to include only data collected from Experiment 5 with the three species. Dataset D was used to evaluate the information provided by data collected at concentrations lower than E ra and E r d (EC 50 values) for algae and duckweeds, respectively. Thus, dataset D included all data except those related to duckweeds and algae exposed to the lowest cadmium concentrations ("monospecific data" and "two species data") collected from Experiment 5. All in all, we tested the minimum necessary dataset in terms of species combinations (one, two, or three species) from datasets A, B and C, and of concentration range via dataset D. 6 Results.
Look-back on the experimental design To take into account the potential effect of correlations between parameters, we compared 95% credibility intervals of the stress functions predicted from the joint posterior distributions obtained for each partial dataset (A to D) to the 21 one obtained from the whole dataset (Figure  4 ). The 95% credibility intervals of the predicted stress functions on daphnid growth rate and survival for each partial dataset appear superimposed to the 95% credibility intervals of the predicted stress functions for all the data. This result was expected since daphnid's survival and size data were included in all datasets, the whole and partial ones. When "monospecific" data were removed (dataset A), the predicted stress functions were different from the reference ones with larger 95% credibility bands, particularly for competition parameter β.
When "two species" data were removed (dataset B) the predicted stress functions for both algal and duckweed growth rates (r a and r d ) were very close to the reference ones. On the contrary, the predicted stress function for competition parameter β was overestimated, and showed more uncertainty. When "monospecific" and "two species" data were removed (dataset C), we obtained very large 95% credibility intervals for predicted stress functions for both growth rates and the competition parameter. At last, dataset D (without data related to duckweeds and algae exposed to the lowest concentrations C 1 to C 4 ) led to very similar predicted stress functions for both algal and duckweed growth rates (r a and r d ) compared to the reference ones, while for competition parameter β the predicted stress function was overestimated with a greater uncertainty.
Moreover, microcosms are not standardised experimental tools since they are usually set up on a case-by-case basis, according to the specific objectives of the study  [Cairns Jr and Cherry, 1997, Crossland and La Point, 1992]. Thanks to our modelling approach, we were able to question the relevance of using certain data to estimate the chosen parameters. Hence, using dataset B provided stress functions similar to the reference ones based on all available data, even though there were changes in individual parameters, which needs to be taken into account when aiming at estimating EC 50 in particular. We also showed that stress function on the interspecific competition parameter (β) from partial dataset D differed from the reference one, while the stress functions on the processes related to growth (for both algae and duckweeds) remained unchanged.
Such results suggest that two-species data are not fully necessary, while a larger range of tested concentrations would be strongly recommended to estimate parameters related to effects on interactions between species. Datasets A and B were chosen in order to test if monospecific, respectively two-species, microcosms were necessary to estimate parameters, and dataset C was chosen to test if the complete microscosm alone was sufficient to estimate parameters. Similarly, dataset D was chosen to test if reducing the number of tested concentrations would affect the quality of parameter estimates. Omitting the different species combinations, as well as reducing the number of tested concentrations, would save a lot of time and experimental effort, especially since adding one concentration to the design implies adding the number of replicates times the number of species combination (and not only the number of replicates).
Our method also permitted to assess which data to include when estimating parameters of interest in a dynamic ecosystem model from a laboratory based microcosm ecotoxicity study. Such an approach could be enhanced to better foresee further experiments with microcosms based a similar model. Beyond 28 this, if parameters are simultaneously estimated a whole dataset, this makes possible to compare these reference estimates with those obtained with partial datasets. This gives knowledge on the data dependency in the modelling results. Last but not least, such a retrospective and descriptive sensitivity analysis puts light in the fact that data quality and design are more beneficial for modelling purpose than quantity. Ideally, as the use of models and big data in ecology increases  [Van Den Brink et al.
36 Table  2 : Experimental design and partial datasets. Time (days) Number of alive daphnids per beaker q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q C 0 = 0 µg/L Time (days) Number of algal cells per beaker (log 10 scale) q q q q q q q q q q q q q q q q q q q q q q q q q C 0 = 0 µg/L Time (days) Number of algal cells per beaker (log 10 scale) q q q q q q q q q q q q q q q.
The black dashed, gray dashed, black and gray rectangles refer to the data that have been removed from the entire dataset to build partial dataset A, B, C and D, license perpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint inThe copyright holder for this this version posted September 13, 2022. ; https://doi. org/10. 1101/2022. 05. 24.
Survey data The whole dataset comprised 4231 trawl hauls performed during the Mediterranean International Trawl Survey (MEDITS)  52  , which is jointly performed by the Laboratory of Marine Biology and Fishery of Fano (Italy) and the Institute of Oceanography and Fisheries of Split (Croatia) on an annual basis since 1996 generally in the late spring-summer period (May to September). Hauls were located from 10 to 500 m of depth, following a random-stratified sampling scheme where strata were defined according to depth. Further details on sampling procedures, data collection and analysis can be found in the MEDITS handbook  53. We further excluded those hauls which have occurred in the autumn-winter period (October to December) due to the potential redistribution of elasmobranch among seasons  54  , leading to a total of 4050 trawl hauls analysed.
The abundance (i. e. , the number of fish per standardized trawl haul) of 30 elasmobranch species was recorded totally. However, we excluded those species scarcely represented (i. e. , that occurred in less than 2 % of the trawl hauls or with less than 150 individuals in the dataset). The common smooth-hound Mustelus mustelus, and the blackspotted smooth-hound Mustelus punctulatus were grouped together under the category smooth-hound. The morphological identification for the Mustelus genus is not trivial  55  , and the common smooth-hound and the blackspotted smooth-hound can be often misidentified. Also, the two species co-occur in the North-Central Adriatic Sea, have a similar diet  56  , share similar habitats, and the chance of hybridization is not excluded  55, 57. The blackmouth shark, Galeus melastomus, was excluded from the analysis because it inhabits deeper water and its distribution range is likely not adequately covered by the survey.
Environmental covariates As explanatory covariates, we included the survey year, log-transformed depth, and bottom temperature as linear fixed effects and seabed substrate as a binary categorical variable (sand, mud to muddy sand). We also accounted for the variability in the sampling effort including the log-transformed swept area of each haul as a predictor. We chose environmental variables based on the availability of the data and assumed relevance to the community assemblages. We extracted the bottom temperature from the Copernicus Marine Service Data  58  and derived seabed substrate from the European Marine Observation Data Network (EMODnet) Geology Project (http://www. emodnet-geology. eu/), using the Folk 5 classification. Subsequently, we matched all environmental data to the trawl hauls.
Traits and phylogeny As species traits, we compiled data on three ecological traits related to life-histories and feeding habits that we expected to influence the spatio-temporal dynamics of the elasmobranchs, that were comparable across sharks and rays, and for which information was available in the literature. Included traits were age at first maturity, reproductive mode and trophic level (Table  1 ). Age at first maturity data were retrieved from a field guide to the Mediterranean elasmobranchs  59  and complemented with a publicly available dataset on marine fish traits  60. If data were aggregated by sex we kept the female age at first maturity, and in case data were available as a range we took the median value. Wherever age at first maturity information was not available at the species level we used inferred data from the genus level (i.
e. , for small-spottedcatshark and nursehound). Considering trophic level, we sourced data from standardised diet studies  26, 27, 61. For smooth-hound, we used trait data of Mustelus mustelus. We provide detailed references in the supplementary information (Supplementary Table  S1 ). We further ensured that traits were not highly correlated (r < 0. 7) (Supplementary Fig. S5 ). To account for phylogenetic dependencies, we included a phylogenetic correlation matrix in the model's covariance structure. However, since true phylogenetic information of species was not available, we used the as. phylo function in the ape R-package  62  to build a phylogenetic tree of the included species based on their taxonomic structure including phylum, class, order, family, genus and species, and assuming equal branch length of 1 between each node.
Statistical analysis Using the 'Hmsc' R-package  17, 29  version 3. 0-12 we fitted joint species distribution models to the elasmobranch community abundance data while also including information on traits, environmental covariates and phylogenetic constraints. Here we used spatially  63  and temporally structured latent variables to account for the hierarchical study design (while also modeling residual co-occurrence among species). Spatially structured latent variables were implemented through the predictive Gaussian process for big spatial data  64. The response matrix was the number of individuals of each elasmobranch species per trawl haul. Due to the zero-inflated nature of the data, we applied a hurdle approach, i. e. , one model for presence-absence (PA) and another model for conditional abundance (ABU).
We employed probit regression in the PA model and a log-linear regression for the abundance data in the ABU model. The raw abundance data in the ABU model were scaled to zero mean and unit variance within each species to better accommodate default priors, which are reported in the 'Hmsc' R-package paper  29. For each model, we sampled the posterior distribution with four Markov chains Monte Carlo (MCMC), each of which was run for 37 500 iterations, the first 12 500 being discarded as burn-in. The chains were thinned by 100, resulting in 250 posterior samples per chain, returning 1000 posterior samples in total. After fitting the model, we examined and ensured the convergence of the MCMC simulations by examining the potential scale reduction factors  65  (Supplementary Fig. S6-S7 ). The explanatory power of the model was evaluated by computing the coefficient of discrimination Tjur's R 266 and the Area Under the Curve (AUC)  67  for the PA model, which measures how well the model discriminates between presences and absence, and by computing the R 2 for the ABU model.
Acknowledgements We are grateful to  Otso Ovaskainen  for the critical discussion held in Jyväskylä. We thank the  MEDITS  staff involved in the scientific sampling and analysis of biological data. This study has been conceived under the  International PhD Program 'Innovative Technologies  and  Sustainable Use  of  Mediterranean Sea Fishery and Biological Resources'  (http: //www. fishmed-phd. org/).
EdU+ and/or BrdU+ cell counting An automatic cell counting procedure was established (see supplementary figure X) and performed with the Imaris software by BitPlane (version 9. 5). First, for each sample, all nuclei positions were identified and modelled thanks to the Hoechst signal by using the function "Spots" with a standardized nucleus diameter of 5µm. A Region of Interest (ROI) corresponding specifically, either to the regenerative part or to a body segment was then manually delineated with the surface tool, also thanks to the Hoechst signal and the general morphology of the structure. Then, the spots inside the ROI were sorted along the fluorescent signals of the EdU or/and BrdU with a filter "Intensity Mean" to discriminate true positive nuclei from background. This procedure allowed to determine the absolute number of nuclei inside the ROI and, among them, the number of positive nuclei for each signal; and hence to extract the proportions of EdU+, BrdU+ and EdU+/BrdU+ cells for each sample.
Acknowledgments This study is dedicated to the memory of our friend, mentor and supervisor, the late  Professor Michel Vervoort. We are grateful to all members of the  Gazave & Vervoort lab  for their support and suggestions on this study. We thank  Dr. Quentin Schenkelaars  for suggesting the use of fluorescent beads, as well as  Dr. Gabriel Krasovec  and  Dr. Lucie Laplane  for helpful comments the manuscript. We thank  Dr. Yves Clément  for his help with statistical analyses. We deeply thank the  ImagoSeine core facility of Institut Jacques Monod , a member of  France-BioImaging  ( ANR-10-INBS-04 ) and certified IBiSA.
Funding Work in our team is supported by funding from:  Labex "Who Am I" laboratory of excellence  (No. ANR-11-LABX-0071 ) funded by the  French Government  through its " Investments for the Future" program  operated by the  Agence Nationale de la Recherche  under grant No. ANR-11-IDEX-0005-01 ,  Agence Nationale de la Recherche «STEM»  ( ANR-19-CE27-0027-01 )),  Centre National de la Recherche Scientifique (CNRS) ,  INSB (Grant Diversity of Biological Mechanisms) ,  Université Paris Cité ,  Association pour la Recherche sur le Cancer  (grant  PJA 20191209482 ), and  comité départemental de Paris de la Ligue Nationale Contre le Cancer  (grant  RS20/75-20 ). LB has obtained a CDSN PhD fellowship from  ENS Lyon  and his fourth year of PhD is supported by the  Labex "Who am I ".
Measuring velocity fields is important in order to provide a database for simulation tools as well as to improve the understanding of physical phenomena. However, experimental databases including velocity fields remain limited due to the difficulty of implementing advanced measurement techniques on large-scale experiments involving fire. For fires in confined and ventilated environments, hostile environments, containment and the presence of soot are also considered. Under these conditions, obtaining experimental multidirectional velocity field data remains a challenge. Optical techniques such as particle imaging are rarely used for fire even though they could provide global measurements for multi-dimensional velocity fields. The velocity measurement techniques used in fire research remain basic and intrusive, limiting data production.
3 m and 0. 75 m from the window, depending on the size of the field of view required. Two of the room walls were replaced with standard glass panels to ensure optical accessibility. The field of view of the camera was spatially calibrated with a calibration plate. Images were captured (including laser-camera synchronization) and processed using INSIGHT4G software. In order to obtain local measurements for the flow velocity components, captured images are split into smaller square Interrogation Areas (IA), to which a cross-correlation algorithm is applied. IA are either 32 or 64 pixels wide and benefit from 50% overlap, allowing to reach a resolution of 1. 67 mm for a 210 mm × 210 mm field of view and 2. 84 mm for a 358 mm × 358 mm one. The olive oil droplets were injected with a generator (model 9307-6) into the inlet duct upstream of the flowrate measurement in order to avoid an additional air flow.