dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2020). Even though most populations stem from individuals that were naive to the areas they were introduced into, seasonal migrations seem to occur in most populations. These migrations appear short in distance between a low altitude wintering area and a higher summer range. Yet, populations are still poorly connected, and drastic bottlenecks and founder effects have resulted in a very low level of genetic diversity  (Biebach & Keller, 2009). Thus, effective conservation of this species and its habitats would highly benefit from better knowledge of the landscape characteristics used by ibex during migration, and from an assessment of the connectivity offered by available habitats. Owing to a unique GPS telemetry dataset from 425 ibex and 15 populations across the entire distribution range of the species, we first aimed at determining the environmental drivers of migratory tracks accounting for the many factors likely at play in an individual's choice of routes.
Study areas and GPS data We relied on a GPS dataset collected on 425 individual Alpine ibex (Capra ibex; 41% females and 59% males; 77% being adults >4 years-old) from 15 reintroduced populations. Those populations were distributed across the whole Alps (10 in France, 2 in Italy, 1 in Switzerland, and 2 in Austria; Figure  1 ; latitudinal gradient: 44-47°N, longitudinal gradient: 6°-13°E and altitudinal gradient: 1700-2700m). Alpine ibex can share habitats with northern chamois (Rupicapra rupicapra), less frequently with red deer (Cervus elaphus) and roe deer (Capreolus capreolus), and with livestock during summer (sheep, goats and cows). Grey wolf (Canis lupus) is present in most of ibex distribution range, but predation occurs rarely on ibex. Sample sizes varied between populations (minimum: 7 individuals in Hohe Tauern National Park; maximum: 117 in the Bargy massif).
, 2015)  derived from the observed step length and turning angle distributions of the used steps. We accounted for the variable step duration in our dataset by deriving specific distributions for each step duration and checked if habitat selection was similar for the different timesteps by using the method of Used Calibration Plots (see Appendix S6). We scaled habitat variables across all populations (i. e. , variables were centered and divided by their standard deviation) to make their effect size comparable in iSSA outputs and we checked for potential correlations between our variables using a Pearson correlation. Correlation coefficients were > 0. 3 for the variables forest and proximity to forest (ρ = 0. 5) and for the variables step length, log(step length) and total elevation change (ρ = 0.
For each seasonal migration (i. e. , spring or fall), we first selected habitat variables previously identified as having a significant effect in at least 8/15 populations. To assess if variables had a significant effect, we computed the 95% confidence intervals (CI) for selection coefficients resulting from the model averaging. A coefficient was significant if its CI nonoverlapped with 0. We included these variables to fit average seasonal iSSA models on three different training datasets. Second, we used those averaged seasonal iSSA models to build three different resistance maps for each population. Third, we modeled connectivity between winter and summer ranges using the Randomized Shortest Path algorithm on each resistance map  (Panzacchi et al. , 2016). Finally, we used 2 different methods to evaluate the performance of our three connectivity models by comparing their predictions to the different validation datasets previously set aside.
Sampling training and validation datasets We built three different combinations of training/validation datasets, which were then used to build resistance maps and model three corresponding connectivity maps. Specifically, we first fitted two seasonal iSSA using 90% of the whole dataset (Figure  2 ; 'leave 10% of whole data out' approach), and second, we fitted population-specific seasonal iSSA models with 90% of the population-specific dataset as a training dataset ('leave 10% of population data out' approach). For those two approaches, the remaining 10% of migratory tracks served as validation datasets and the 90%/10% sampling procedure was repeated 250 times to assess uncertainty around connectivity predictions. Third, we fitted iSSA with data from all populations but one, and used data from the discarded population for validation, and repeated this for the 15 populations ('leave one population out' approach).
We then calculated the percentage of ibex locations collected during migration included in each connectivity corridor as a metric of predictive performance of our connectivity models (representation in corridors;  Poor et al. , 2012; Zeller et al. , 2018; Goicolea et al. , 2021). As the percentage of ibex locations during migration that fall within a given corridor is strongly dependent on the area of this corridor, we also computed the proportion of locations in the corridor divided by the corridor surface to get an index of accuracy of connectivity predictions (Appendix S11). We applied both validation methods (ranking and representation in corridors) on the three validation datasets we set aside previously to validate our three approaches to compute connectivity maps.
Identification of migrant ibex Among the 1068 seasonal tracks available in our GPS dataset, we identified 337 migratory tracks (169 in spring, 168 in autumn), distributed between multiple winter and summer ranges within each population. On average, the proportion of seasonal tracks identified as migration was 45% (SD 22. 5) over the 15 populations, confirming partial migration. However, it varied greatly between populations, from 13% to 75% in the Bargy and Champsaur populations, respectively (considering populations with enough animals to estimate this proportion). On average, migrant ibex traveled 12 km (SD 8) of topographic distance, with population means that varied from 6 to 22 km and an individual maximum of 62 km. In spring, those migratory tracks lasted 3. 5 days (SD 3. 6) on average and occurred around May 27 (SD 27 days), while in autumn they lasted 6.
According to the ranking validation method, the three connectivity modeling approaches tested (i. e. , 'leave 10% of whole data out', 'leave 10% of population data out' and 'leave one population out') provided connectivity maps that predicted ibex migratory movements better than random surfaces (see Figure  5  for examples, and Appendix S12). The mean and median ranks of used steps in the three validation datasets were all>8, with values from 9 to 11 depending on the population, although variability was important (1 st quartile and 3 rd quartile ranging from 5 to 14 depending on populations; Appendix S13). However, within populations results were similar whatever the training dataset used. Comparable proportions of locations of migratory tracks were included in connectivity corridors for the three modeling approaches: between 93.
DISCUSSION Relying on a dataset assembling 337 migratory tracks collected in 15 Alpine ibex populations distributed across the Alps, we identified the environmental predictors of corridors in this endemic and emblematic short-distance and altitudinal migrant species. While consistently limiting energetically-costly elevation change, ibex migrated mostly in south-facing snow-free slopes and close to steep areas providing refuges from perceived predation risk. By contrast, neither the landmarks (ridges, tree lines, valley bottoms) hypothesized as visual cues for ibex navigation, nor human infrastructures (ski areas and roads, when present) affected ibex migratory movements. The randomized shortest path algorithm revealed an intermediate movement strategy in Alpine ibex, trading off optimization and exploration during migratory movements.
The abilities of the three modeling methods we compared to predict migratory connectivity from the results of those movement analyses, relying either on population-specific or multipopulational approaches, were comparable. They provided efficient connectivity models to inform conservation of Alpine ibex and its habitats, and a framework for future research investigating connectivity in migratory species from multi-populational datasets. In addition to spring green wave tracking, evidenced in commonly-studied long-distance migrations from North American, Scandinavian and African ungulates, we revealed other predictors, more scarcely investigated, may also drive Alpine ibex during both spring and autumn migration. By focusing on south-facing snow-free slopes, they may partly benefit from emerging vegetation during spring migration (although we did not fully investigate the green wave hypothesis, see Methods; but see  Semenzato et al.
In this context, preserving and (re-)establishing connectivity within and between ibex populations will probably be a major conservation issue in the next decades, and tools such as our connectivity models could be particularly helpful. More generally, our study also provided an original methodological framework for future research and conservation efforts dedicated to connectivity analysis and predictions of movements other than migration. Here, the three different approaches (i. e. , 'leave 10% of whole data out', 'leave 10% of population dataset out' and 'leave one population out') revealed no major differences in accuracy of corresponding connectivity predictions. Thus, our models could be used to predict migratory movements in monitored populations with either enough data, using population-specific model, or using data from all populations.
Moreover, we could predict movements in populations where no GPS data are available but seasonal range locations are known or predicted with habitat selection models. With the advent of animal tracking over the last decades, and the generalization of initiatives aiming at gathering those GPS data in common databases (e. g. Movebank, Euromammals, Biologging initiative, Global Initiative for Ungulate Migrations;  Kauffman et al. , 2021; Urbano, Cagnacci & Euromammals consortium, 2021) , multi-population analyses will develop and testing the reliability of population-specific versus multi-population connectivity predictions is crucial, particularly in a context of demand and need around conserving and restoring connectivity within species distribution ranges. A variable name is in bold type if its selection coefficient was significant in at least 8/15 populations.
ACKNOWLEDGMENTS We thank all the professionals and interns involved in the monitoring of GPS-collared ibex on all study sites, in particular,  F. Couilloud  and  le Service Départemental de la Haute-Savoie et le Service Départemental de l'Isère  from the  Office Français de la Biodiveristé , and  L. Gautero ,  V. Roggero ,  A. Rivelli ,  E. Piacenza ,  M. Dotto ,  A. Menzano. We thank the  Alcotra LEMED-ibex  and all its partners:  Ecrins NP ,  Gran Paradiso NP ,  Asters-CEN 74 ,  Vanoise NP, Autonomous Region Aosta Valley, Mercantour NP, Protected areas of Maritime Alps , Protected areas of  Cottian Alpsfor  providing GPS data collected during the program. We also thank the  GSE-AIESG :  Gruppo Stambecco Europa-Alpine Ibex European Specialist Group  and  Gran Paradiso National Park  for providing data on ibex population distribution, and in particular  A.
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is bioRxiv preprint doi: ; https://doi. org/10. 1101/2023. 03. 02. 530594 this version posted March 3, 2023. The copyright holder for this preprint. CC-BY-NC-ND 4. 0 International license made available under a procedures (i. e. , using three different pairs of training/validation datasets; Figure.
Sequence dataset construction Putative lophotrochozoans genes were identified using tBLASTn and BLASTp searches using genes from human, fly, nematode, cnidarians, and oysters as queries. Reciprocal BLAST was performed on all genes, including newly identified lophotrochozoan sequences. BLAST searching was conducted using NCBI databases for most species, and on PdumBase in addition to our recent transcriptome for annelid Platynereis dumerilii  74, 75. After identification of genes in target species, proteins were analysed with ScanProsite (ExPaSy)  76  to verify the presence of specific domains such as P20 and P10 for Caspases, CARD and Death Domains for CRADD, CARD and NB-ARC for APAF-1, and DED and Death domains for FADD. Sampling was conducted on all available lophotrochozoan genomes/transcriptomes, and for each species all Caspases and Caspase activators were explored.
Caspase family proteins are short (containing the large common P20 and the small P10 domains) with a high number of genes per species, which rapidly limits the relevance of the phylogenetic analyses. To reduce artefact branching and unreadable topologies, and to maximize phylogenetic diversity across lophotrochozoans, the dataset constructed for phylogenetic analysis with all types of Caspases was built with restricted number of representative lophotrochozoan species which recapitulate lophotrochozoan diversity. Secondly, analysis of Caspases with long pro-domains only was conducted with all species possible (for which unambiguous Caspase identifications were made). The BCL2 phylogeny was also conducted on a representative species subset to overcome the same aforementioned artefacts. FADD, CRADD, and APAF-1 analyses were conducted with all sequences identified at the lophotrochozoan scale which had the two specific domains.
Supplementary Table 1: List of sequences used to run phylogenetic analysis on all types of Caspases. Haliotis diversicolor Supplementary AYK02335 NCBI Haliotis diversicolor ABY87390 NCBI Octopus bimaculoides XP_014783442. 1 CARD NCBI Octopus bimaculoides XP_014790087. 1 CARD NCBI Octopus bimaculoides XP_014784582. 1 CARD NCBI Octopus bimaculoides XP_014780506. 1 NCBI Octopus bimaculoides XP_014783598. 1 NCBI Octopus bimaculoides XP_014775611. 1 NCBI Octopus bimaculoides XP_014775614. 1 NCBI Octopus bimaculoides XP_014789170. 1 NCBI Octopus bimaculoides XP_014775609. 1 NCBI Octopus bimaculoides XP_014768177. 1 NCBI Platynereis dumerilii comp214042_c1_seq4 CARD PdumBase Platynereis dumerilii comp218890_c0_seq4 DED PdumBase Platynereis dumerilii comp223063_c0_seq9 CARD PdumBase Platynereis dumerilii comp217549_c0_seq1 Z-BINDING PdumBase DOMAIN Platynereis dumerilii comp219364_c1_seq2 PdumBase Platynereis dumerilii comp216671_c1_seq8 PdumBase Platynereis dumerilii comp219121_c0_seq2 PdumBase Platynereis dumerilii comp214042_c1_seq4 PdumBase Platynereis dumerilii PladumEVm029732 Paré et al, 2023 Platynereis dumerilii PladumEVm031996 Paré et al, Species Accession number prodomain Data Base 2023 Aplysia californica Platynereis dumerilii XP_005113266.
1 NCBI NCBI Biomphalaria glabrata Spirometra erinaceieuropaei XP_013074008 VZI03839. 1 NCBI NCBI Biomphalaria glabrata Spirometra erinaceieuropaei XP_013065656 VZI43280. 1 NCBI NCBI Biomphalaria glabrata Spirometra erinaceieuropaei XP_013067788 VZI32601. 1 ANKYRIN NCBI NCBI Biomphalaria glabrata Taenia asiatica XP_013088924 VDK35885. 1 NCBI NCBI Echinococcus granulosus Taenia asiatica KAH9285259. 1 VDK22955. 1 CARD NCBI NCBI Echinococcus granulosus Taenia asiatica KAH9278879. 1 VDK23513. 1 ANKYRIN CARD NCBI NCBI Echinococcus granulosus Taenia asiatica KAH9283631. 1 VDK23326. 1 ANKYRIN NCBI NCBI 775 Echinococcus granulosus KAH9280007. 1 NCBI 776 Haliotis diversicolor Haliotis diversicolor QHY92978. 1 AYK02334. 1 DED CARD NCBI NCBI.
Using the MASS BANK database, we identified two putative candidates, xanthine and oxipurinol, two compounds of the purine metabolic pathway. The LC-MS/MS analysis of commercial standards of these two compounds demonstrated that the unknown compound was xanthine (Supporting Information 2, Table  1 ). Although xanthine was detected in the culture media of control cells unexposed to mancozeb (~2. 7 M at 24h of culture), its levels were significantly increased up to 10. 4 M in media of hepatocyte-like HepaRG cells incubated with 100 M mancozeb (Table  1 ). We next analyzed hypoxanthine and uric acid, two other main compounds of the purine metabolic pathway. We found that hypoxanthine was also produced by hepatocyte-like HepaRG cells incubated with mancozeb but not uric acid. Quantification of the lactate dehydrogenase (LDH) activities showed no significant differences between culture media of cells exposed or not to mancozeb (Table  1 ) indicating that the increase in hypoxanthine and xanthine levels upon mancozeb treatment was due to an active secretion rather than a passive leakage from cells resulting from loss of plasma membrane integrity in mancozeb-treated cells.
Acknowledgements This work was funded by the  Cancéropole Grand-Ouest  (projet  structurant Pesticides and Tumor Niches-PeNiCa ,  2016-2018 , coordinator  O. Herault, Tours, France) , the  Institut National de la Santé et de la Recherche Médicale (Inserm, France)  and the project  PESTIFAT (Agence Française pour la Biodiversité (AFB) , plan  ECOPHYTO  II driven by the  Ministères en charge de l'Environnement et de l'Agriculture ,  Appel à projet de Recherche en Environnement-Santé-Trvail  (PREST 2018, coordinator  B. Fromenty ,  Rennes, France ). Kilian Petitjean  received a PhD fellowship from the  Agence Française pour la Biodiversité (AFB). The authors would like to thank the  Centre de Ressources Biologiques (CRB) Santé of Rennes  for providing human hepatocytes as well as  Professor Pascal Reyner  and  Naïg Guegen  ( Pôle de Recherche et d'Enseignement en Médecine Mitochondriale-PREMMi, Centre Hospitalier Universitaire, Angers, France ).
Financial support: This work was funded by the  Cancéropole Grand-Ouest (CGO projet structurant PeNiCa) ,  Institut National de la Santé et de la Recherche Médicale (Inserm, France)  and the  "Organisme Française pour la Biodiversité (OFB) , programme  ECOPHYTO II ", project  PESTIFAT AFB/2019/87 (PR-EST ,  2019-2022 ). Kilian Petitjean  received fellowships from the  CGO  (PeNiCa, 2018) and the  OFB  ( PESTIFAT ,  2019-2022 ).
The progressive increase of marine macro-litter on the bottom of the Mediterranean Sea is an urgent problem that needs accurate information and guidance to identify those areas most at risk of accumulation. In the absence of dedicated monitoring programs, an important source of opportunistic data is fishery-independent monitoring campaigns of demersal resources. These data have long been used but not yet extensively. In this paper, MEDiterranean International Trawl Survey (MEDITS) data was supplemented with 18 layers of information related to major environmental (e. g. depth, sea water and wind velocity, sea waves) and anthropogenic (e. g. river inputs, shipping lanes, urban areas and ports, fishing effort) forcings that influence seafloor macro-litter distribution. The Random Forest (RF), a machine learning approach, was applied to: i) model the distribution of several litter categories at a high spatial resolution (i.
e. 1 km 2 ); ii) identify major accumulation hot spots and their temporal trends. Results indicate that RF is a very effective approach to model the distribution of marine macro-litter and provides a consistent picture of the heterogeneous distribution of different macro-litter categories. The most critical situation in the study area was observed in the north-eastern part of the western basin. In addition, the combined analysis of weight and density data identified a tendency for lighter items to accumulate in areas (such as the northern part of the Tyrrhenian Sea) with more stagnant currents. This approach, based on georeferenced information widely available in public databases, seems a natural candidate to be applied in other basins as a support and complement tool to field monitoring activities and strategies for protection and remediation of the most impacted areas.
Data collection Data used in the present study were collected in the framework of the MEDITS survey conducted from 2013 to 2019, in the above-mentioned GSAs. The MEDITS is the main bottom trawling survey conducted in the whole Mediterranean Sea, aiming at collecting data on demersal resources and, since 2013, it has also become a valuable source of information about seafloor macro-litter  (MEDITS working group, 2012). The data series used in this study was built taking advantage of 495 hauls performed yearly across the six GSAs considered, for a total of 3465 hauls covering 7 years (Supplementary Table  1 ). In the MEDITS protocol, hauls are located according to a depth-stratified random design with the following strata: A [0-50m); B [50-100m); C  [100-200m); D [200-500m)  and E [500-800m).
These variables were considered as reliable proxies for the main sources and drivers of the distribution of marine macro-litter. Sea bottom depth was estimated, for each cell of the grid, querying the NOAA ETOPO1 Global Relief Model using the R package "marmap"  (Pante and Simon-Bouhet, 2013). Distance from the coastline was computed for each cell of the grid, using the 'dist2Line' function of the R package "geosphere"  (Hijmans et al. , 2021). Rivers' mouth positions and catchment area (in km 2 ) were obtained from the European Environmental Agency https://www. eea. europa. eu/data-and-maps/data/european-river-catchments-1) and used to compute, for each cell of the grid, an index of the Impact of River Basins defined as the average of catchment areas weighted by the distance of the cell centre from the river mouths.
, 2012; Scornet, 2017). All 3 of these parameters are tuned by means of an iterative procedure in which a wide range of values is explored for each of them. The final values obtained from the tuning procedure and used in this study were: ntree = 1000, mtry = 7, ndsize = 5. In this study, the RF approach provided in the R package "Ran-domForest"  (Liaw and Wiener, 2002)  was applied to predict the spatial distribution of each of the categories and sub-categories, and groups listed in Table  1. All variables described above as well as the MEDITS data about litter density and weight, per each category/sub-category, were standardized over the set of 11,341 cells covering the portion of GSAs from the coastline to the 1000 m isobath. The number of cells in which at least one MEDITS sampling occurred in the period considered was 1048 (around 10% of the total domain of 11,341 cells).
For each RF model, the relative importance of each predictor was internally assessed by the randomforest function in R, using the approach described in  (Breiman, 2001). This approach was devised to assess how much removing or noising each variable reduces the accuracy of the model prediction (on the training dataset). Finally, the trained RFs were used to predict the amount of litter over the whole domain (11,341 cells), to obtain a series of spatial maps of litter categories and sub-categories in the study area.
model in which: Where W Year, cell is the weight of objects (kg km -2 ) in a given cell in a given year, N is the corresponding number of objects km -2 , and category is one of the six groups represented in Table  1. GAMs are non-parametric regression models allowing to model the associations between variables without defining the exact shape of the underlying regression function. Compared to parametric (including linear) forms of models, GAMs provide more flexibility when smooth functions are used as regressors. GAMs were applied using the R package "mgcv"  (Wood, 2023). This "naive" modelling approach was devised to assess whether the average weight of waste is significantly different across different GSAs and litter categories. In particular, the Davies' approach  (Davies, 1987)  was applied to test for statistical differences between regression parameters related to the GSAs, eventually supporting the existence of statistical differences between the slopes of the regression between GSAs (different mean weight of litter categories in different areas).