dataesr/scientific-paragraphs-categorization huggingface dataset:
Identifying associations between phenotypes and environmental parameters is crucial for understanding how natural selection acts at the individual level. In this context, genetically isolated populations can be useful models for identifying the forces selecting fitness-related traits. Here, we use a comprehensive dataset on a genetically and ecologically isolated population of the strictly marine bird, the brown booby Sula leucogaster, at the tropical and remote Saint Peter and Saint Paul Archipelago, mid-Atlantic Ocean, in order to detect phenotypic adjustments from interindividual differences in diet, foraging behaviour, and nest quality. For this, we took biometrics of all individuals of the colony breeding in 2014 and 2015 and tested their associations with nest quality, diet parameters, and foraging behaviour.
7% for the large TTFs, 7. 2% for the intermediate TTFs, and 23. 1% for the BHs, while for males they were 59. 1%, 5. 9%, and 35%, respectively (Fig. 1 ; Table  1 ; Supplementary Fig. 2 ). Foraging behaviour was recorded for 97 brown boobies (52 females and 45 males), summing to 258 trips. Mean (±1 standard deviation) trip duration was 57 ± 27. 5 min, while mean maximum distance from the colony and the mean total trip length were 7. 1 ± 4. 3 km and 27. 0 ± 13. 7 km, respectively. Nest content had no significant effect on the foraging trip parameters and isotopic data for males or females; therefore, no individual was removed from the male and female datasets in subsequent analyses (Supplementary Fig. 3 ). In total, all the 304 nests were sampled for their landscape characteristics. Colony area was calculated using a minimum convex polygon and resulted in 601 m 2 , representing a density of 0.
Nest characterization. All the active and non-active nests (i. e. , those only containing nest material such as small stones and feathers) were georeferenced using the Real-Time Kinematic (RTK) method with a preprocessed base point and a rover station (Topcon Positioning Systems, Inc. ), which was placed at the central point of each nest. Non-active nests are also easily identified by the configuration of the nest material, making it possible to locate the centre of the nest to take the point. Radio-based communication between base and rover stations provided real-time corrections when triangulating to GPS satellites so that approximately 20 mm-resolution latitude, longitude, and altitude data were obtained for each nest. After recording data for 304 points (i. e. , all the active and non-active nests) in 1580 m 2 , a three-dimensional elevation map was built with a resolution of 0.
Body mass was obtained to the nearest 20 g using Pesola ® spring scales, and morphometrics were measured as follows: culmen length (exposed culmen) and tarsus length (from middle of the midtarsal joint to the distal end of the tarsometatarsus) with Vernier calipers (0. 01 mm) and wing chord (carpal joint to the tip of the longest primary; unflattened wing) with a metal ruler with stop (1 mm). The gender was determined by differences in the skin coloration around the eyes, and the adults were distinguished from juveniles by plumage coloration  28. After sampling, each booby was identified with a numbered tarsal metal ring to avoid resampling and aiming monitoring demographic parameters. Because there is a strong and reverse sexual size dimorphism in brown boobies  28  , during the fieldwork carried out in 2015, boobies for sampling were divided into subgroups based on their body size and each gender was treated as a distinct dataset.
e. , a male of 1185 g), as recommended for seabirds  63. After logger recovery, data were downloaded using the dedicated GiPSy-4 Utility software (Technosmart, Rome, Italy). Logger deployment and recovery (when biological samples were taken) lasted ~5 and 10 min, respectively, and in both handling procedures, the bird was returned immediately to its nest to minimize stress. Diet parameters. Diet of boobies was studied during the same aforementioned period of tracking by analysing stable isotopes of carbon and nitrogen in the blood serum and the regurgitated material. For serum isolation, 3-ml blood samples were taken from the tarsal vein during GPS recovery using a sterile syringe/needle and transferred to a non-heparinized tube, which was kept undisturbed at room temperature for 10-30 min to allow for blood clotting.
Statistical analyses. The accuracy of GPS positions used for tracking was <10 m in more than 95% of the location fixes, allowing reconstruction of the foraging trips and extraction of high-resolution foraging parameters, such as the total distance travelled ('D' , km), maximum distance to the colony ('Dmax' , km), time spent at sea ('T' , h), sinuosity of the path ('Sin' , defined as the ratio D. 2Dmax -1 ), and average flight speed ('FS' , km. h -1 Carbon and nitrogen isotopic ratios were used for calculating the isotopic niche width through the Bayesian framework implemented in SIBER package  68. Standard ellipse areas (‰ 2 ) corrected for small sample sizes (SEAc) were estimated for each body size category along with the pairwise overlap percentage value between ellipses. Proportions of prey contribution to the diet of each group were estimated with Bayesian mixing models as implemented in MixSIAR package  69.
Differences of boobies body size between the categories of nest quality (considering both approaches to classify nests) were tested with univariate Analysis of Variance (ANOVA), and normality and homoscedasticity of the residuals were tested with Shapiro-Wilk's and Levene's tests, respectively. Similarly, the intrasexual differences between the foraging parameters and stable isotopes were tested with the nonparametric Kruskal-Wallis test. As intrasexual relationships between body size and behavioural and diet variables were expected to be non-linear, two distinct approaches were used to test for the association between the isotopic data and foraging trip parameters with PC1. First, intrasexual differences of foraging behaviour and diet were tested with the nonparametric Kruskal-Wallis test by using body size groups as factors.
The second approach was based on correlations between the continuous body size index and behavioural and diet variables by using the nonparametric Kendall's tau (τ), which measures the correlation between two ranked variables  73. Finally, regression trees were used to identify the importance of the body size groups by explaining the variance in each foraging and diet parameter. For classifying variables according to their importance at each regression tree, the Random Forest algorithm implemented in the R package randomForest was applied  74  by generating 1000 regression trees with 20 per-tree permutations for out-of-bag data. The classification procedure was carried out by ranking variables according to the resulting error rate caused when each variable was removed from the model (i. e.
Acknowledgements We thank  Fernanda P. Marques  and  Patrícia L. Mancini  for their support in fieldwork and laboratory procedures, as well as  Arel Hadi  and the  LABSERCA/Unisinos team  for assistance in obtaining and processing GIS data. We also thank  Marinha do Brasil  and  Comissão Interministerial para os Recursos do Mar  for providing logistical support for fieldwork in Saint Peter and  Saint Paul Archipelago. Instituto Chico Mendes de Conservação da Biodiversidade (ICMBio)  granted sampling permit No. 38723  and  CEMAVE/ICMBio  provided metal rings and permits for banding boobies. The manuscript greatly benefited from comments and suggestions by  Eduardo R. Secchi ,  Luciano Dalla Rosa ,  Alexandre M. Garcia , and  Flávio Quintana , and two anonymous reviewers. Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)  granted fund No.
Most cases of anisakidosis are reported in Japan, Spain, the Netherlands and Germany  [25]. The annual incidence in most European countries is around 20 cases/year  [11]. In France, in 1989, Hubert et al. [16]  reported an incidence of 8 cases/year (21 cases between January 1, 1985 and September  30, 1987 ) by surveying all Parasitology laboratories at University hospitals in mainland France. In 1995, Bourée et al. reported 25 cases that they had personally observed and identified 30 previously published cases  [3]. Sporadic cases of anisakidosis are still regularly diagnosed but few are published  [4, 8, 34]. Former studies had shown that the prevalence of infestation of marine fishes by anisakids was variable, but may be high  [3, 22, 23]. These data were confirmed by the recent program "Fish-Parasites" (ANR-10-ALIA-004), which evaluated the prevalence of anisakids in the 15 most consumed fish species in France  [26, 31].
These aspects are well known in Spain or in Italy  [1, 21]. In France, allergic manifestations to anisakids have been reported  [10, 22] , but their precise incidences still need to be evaluated. The analysis of a nationwide hospital medical information database (PMSI) database identified, between 1997 and 1999, 19 cases of anisakidosis for which the disease was recorded as the main reason for hospitalization  [14]. The analysis of the same database (PMSI) during the period 2010-2014 identified 19 hospitalized cases for which anisakidosis was reported as the main diagnosis of hospitalization  [9]. Therefore, the incidence of anisakidosis had decreased between these two periods: an average of 6. 3 cases/year in the period 1997-1999 vs 3. 8 cases/year in the period 2010-2014. This tendency to a decrease in cases had already been found by Petithory in 2007  [22].
Acknowledgments. We would like to thank the following members of the French association of hospital practitioners and teachers in  Parasitology-Mycology "ANOFEL"  for their help in supplying information about the patients with anisakidosis who were included:  Bernard Cimon  (Angers),  Frédéric Grenouillet (Besançon) ,  Laurence Delhaes (Bordeaux) ,  Dorothée Quinio  (Brest),  Michel Vergnaud (Caen), Françoise Botterel (Créteil), Frédéric Dalle (Dijon) ,  Hélène Fricker-Hidalgo  (Grenoble),  Marie-Laure Dardé  (Limoges),  Martine Wallon  (Lyon),  Coralie L'Ollivier  ( Marseille) ,  Marie Machouart (Nancy) ,  Pierre Marty (Nice) ,  Fabrice Le Pape (Nantes), Sandrine Houze (Paris-Bichat), Luc Paris (Paris-Pitié Salpêtrière), Christophe Hennequin (Paris-Saint Antoine), Nisen Abuaf (Paris-Tenon ),  Dominique Toubas  (Reims),  Florence Robert-Gangneux (Rennes), Loïc Favennec (Rouen) ,  Pierre Flori  ( Saint-Etienne ),  Odile Villard  (Strasbourg),  Judith Fillaux  (Toulouse),  Jacques Chandenier  (Tours) and  Patrice Bourée (Paris).
pendent data sets. The simulation adequately reproduced the latitudinal gradient in fire frequency in Manitoba and the longitudinal gradient from Manitoba towards southern Ontario, as well as the temporal patterns present in independent fire histories. However, the simulation led to the underestimation and overestimation of fire frequency at both the northern and southern limits of the boreal forest in Québec. The general pattern of simulated total tree biomass also agreed well with observations, with the notable exception of overestimated biomass at the northern treeline, mainly for PFT Picea. In these northern areas, the predictive ability of LPJ-LMfire is likely being affected by the low density of weather stations, which leads to underestimation of the strength of fireweather interactions and, therefore, vegetation consumption during extreme fire years.
Simulation protocol LPJ-LMfire was run monthly from 1901 to 2012 on 10 × 10 km equal-area grids covering eastern boreal Canada from Manitoba to Newfoundland. Driver data sets were prepared in netCDF format and are described in Table  1. Climate data were compiled at a monthly time step, while atmospheric CO 2 concentrations were compiled at an annual time step (see Sect. 2. 4). A 1120-year spin-up period was prescribed to equilibrate vegetation and carbon pools with climate at the beginning of the study period  (Smith et al. , 2001)  and to ensure that forest biomass and fire disturbances were in stable condition  (Tang et al. , 2010). This spin-up run was made using linearly detrended 1901-2012 climate data and repeated 10 times.
Climate Monthly means of temperature, diurnal temperature range, precipitation, number of days with precipitation, and wind speed were extracted for the 1901-2012 period from Environment Canada's historical climate database (Environment Canada, 2013) using BioSIM software (v. 10. 3. 2;. Régnière et al. , 2014). Gridded climate data were prepared in BioSIM by interpolating weather data from the four weather stations that were closest to each 10 × 10 km grid, adjusted for elevation and location differentials with regional gradients, and averaged using inverse distance weighting (1/d 2 , where d is distance). Missing wind speed values between 1901 and 1968 and those for 2010-2012 were set to the monthly 1969-2010 averages. Monthly means of total cloud cover percentage for the entire atmosphere and convective available potential energy (CAPE) were interpolated on our grid from the NOAA-CIRES 20th Century Reanalysis v2 data set at a ∼ 2.
Soils The volume fraction of coarse fragments together with the 0-100 cm deep soil texture fractions of sand and clay were interpolated on the 10 × 10 km grids from the 1 km resolution ISRIC -World Soil Information data set  (Hengl et al. , 2014). For topography, we interpolated the 30 arcsec gridded digital elevation model (DEM) of Canada  (Natural Resources Canada, 2007). We calculated slopes in degrees at 30 arcsec with the DEM map using ArcGIS 10. 4. 1 and interpolated the data to our 10 × 10 km grids. To calculate the percentage of land (i. e. , removing lakes and water course areas) in each grid cell, we rasterized the water fraction of the National Hydro Network (NHN) data set at 100 m resolution (Natural Resources Canada, 2010). We calculated the water fraction at a 10 km resolution from 100 m resolution grid cells that had a percentage of water fraction > 50 %.
PFT definitions and LPJ-LMfire model modifications LPJ-LMfire was calibrated for four PFTs that corresponded to the predominant tree genera currently present in the boreal forest of Canada: Picea, Abies, Pinus, and Populus. PFTrelated parameters, e. g. , fraction of roots in the upper soil layer or minimum and maximum temperatures of the coldest month for establishment, were assigned values from the published literature or global databases (see Table  S1  in Supplement S2 for further details).
Fire activity The simulated burned area fraction was evaluated against three fire data products. First, annual burn rates for 1980-2012 were compiled from the Natural Resources Canada fire database (M. A. Parisien, personal communication, 2016) using Canada's national fire polygons with the hexagonal cells approach from  Héon et al. (2014) , but extended to our study area. We used 365 hexagonal cells to cover our study area and to compute the 1980-2012 simulated mean annual burn rates with 95 % CI for each hexagonal cell. The second fire data product originated from stand-replacing fire history studies. Here, historical annual proportions of burned areas were obtained for 26 locations (Fig. S3 ) using post-fire stand initiation reconstructions based upon field and archival data that were digitized and included in GIS databases  (Girardin et al.
, 2013b; Héon et al. , 2014; Portier et al. , 2016). Using a 100 km radius around each location centroid, we calculated the simulated mean annual burn rates between 1911 and 2012, together with the 95 % CI. Differences between our simulated 95 % CI estimates and these two fire data products were considered qualitatively as "not different" if the observed annual burn rate fell within the 95 % CI of the simulated mean burn rate. Note that as the period covered by the historical fire data often extended further back in time into the 19th or 18th centuries for southern locations (Table  S2 ), some important differences could be expected in the comparison process. Finally, a third validation of fire simulations was made by comparing time series of total simulated annual burned areas in boreal forests of Manitoba, Ontario, and Québec with provincial fire statistics (point data) from the Canadian National Fire Database (CNFDB; Canadian Forest Service, 2016) covering the 1959-2012 period.
, 2006). In the present study, simulations are limited by the relatively low accuracy of soil attributes in databases for Canada's boreal forest  (Hengl et al. , 2014). The input data set of soil attributes that was used in our simulations tended to underestimate clay and sand percentages in our study area when compared to point observations (Fig. S10 ). These effects add up to other weaknesses in physiological constraints, such as cold climate not being sufficiently restrictive and allowing Picea to become overly abundant in the simulation runs. While a previous study showed that the abundance of Picea decreases with latitude in the tundra region and is coupled with the occurrence of dwarf shrubs in the Ericaceae and herbs  (Gajewski et al. , 1993) , such species were not parametrized in the current version of LPJ-LMfire due to a lack of information on their physiological and biogeographical preferences.
Spatiotemporal patterns were well captured, based upon our climate data inputs. Despite an overarching CO 2 -induced enhancement of NPP in LPJ-LMfire, aboveground biomass was relatively stable because of the compensatory effects of increasing fire activity. This study helps reduce uncertainties in our knowledge regarding fire patterns in the recent past and confirms that fires have been a dominant driver of boreal forest in eastern Canada during the last century. We further provide a new tool to re-.
Acknowledgements. This study was made possible thanks to the financial support provided by the  European IRSES   NEWFOREST  project, the  Forest Complexity Modelling (FCM) program , and the  NSERC Strategic and Discovery programs. Jed Kaplan  was supported by the  European Research Council  ( COEVOLVE 313797 ). This research was conducted as part of the  International Associated Laboratory MONTABOR (LIA France-Canada ) and the International Research Group on Cold Forests. We thank  Melanie Desrochers  and  Xiao Jing Guo  for their help with mapping and computation for this project. We also thank  Daniel Stubbs  from  Calcul Québec and Compute Canada  for help with the Fortran language and server space facilities for running  LPJ-LMfire. We also thank  William F. J. Parsons  and  Isabelle Lamarre  for English language editing of a previous version of the paper and the two anonymous reviewers for comments on an earlier version.
E. Chaste et al. :  The pyrogeography of eastern boreal Canada fine predictions of future fire risks and effects of ongoing climate change in these forests to better inform management and improve risk mitigation strategies. Code availability. The source code of LPJ-LMfire is available at https://github. com/ARVE-Research/LPJ-LMfire/tree/v1. 3  (Kaplan et al. , 2018). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/bg-15-1273-2018-supplement.
Results Annotation and sequence analysis. Ostrea edulis. The comparison of O. edulis transcriptome sequence data with Crassostrea gigas protein data base allowed identifying 46 091 genes among which sequences showing more than 25% of covering were selected for gene ontology analysis. The analysis was done under Blast2GO using GO terms related to the apoptotic pathway: "apoptotic process" (GO:0006915), "execution phase of apoptosis" (GO:0097194), "regulation of execution phase of apoptosis" (GO:1900117), "negative regulation of execution phase of apoptosis" (GO:1900118), "positive regulation of execution phase of apoptosis" (GO:1900119). After filtration, 358 contigs corresponding to 72 genes appeared related to the apoptotic pathway (Supplementary Table  1 ). Additionally, considering the central role of caspases in the apoptosis pathway and other genes like those in Bcl-2 family, a manual screening was also done.
Intrinsic apoptotic pathway. Expression of bcl2 and endoG appeared slightly modulated by the presence of parasites. Significant downregulation (p < 0. 05) of endoG was observed after 20 h of contact with B. ostreae and a significant overexpression (p < 0. 05) of bcl2 was noticed after 4 h of incubation (Fig. 4a ). Bonamia ostreae gene expression profile. Housekeeping genes. Expression stability (M) of two candidate housekeeping genes was tested in our experimental conditions using two softwares: GeNorm and NormFinder. Ct values were distributed between 16. 21 ± 0. 27 for 18 s and 21. 26 ± 0. 2 for GAPDH. A low M value translates a highly stable expression in the tested conditions. Stability of these two genes was good and appeared similar using GeNorm (M = 0. 03) and NormFinder (M = 0. 01).
Two essential components of the apoptosome, cytochrome C and the Apaf-1 were also present among filtered genes. Interestingly, at least 14 IAPs were present in tested O. edulis transcriptome. Like in the genome of C. gigas where more than 40 IAP genes were identified, the regulation system of the apoptosis pathway seems to be an important adaptation system for the flat oyster  15, 28. The absence of some apoptotic genes in our data set compared to other species is probably explained by the fact that the current analysis was carried out using transcriptomic sequence data and not genomic data like for C. gigas. Apoptosis is generally activated in response of environmental stressful conditions or diseases. Investigated O. edulis transcriptome was obtained from animals collected in natural beds and not exposed to particular stressful conditions.
Methods Sequence analysis and annotation. Ostrea edulis. Transcriptome data previously obtained from an equimolar pool of total RNA from various tissues (muscle, mantle, gills and digestive gland) of eight individual flat oysters collected from natural beds  48, 49  were used to identify apoptosis related genes. First, these sequences were compared with Crassostrea gigas data base using ngKLAST-LE (v4. 7) and the KLASTx algorithm  50  with threshold E-Value < 10 -3. In a second step, sequences showing more than 25% of covering were selected and Gene Ontology (GO) was assigned using Blast2Go 51 software. Bonamia ostreae. Transcriptome data was obtained from purified parasites B. ostreae. The cDNA libraries were prepared from 3 µg of total RNA with Illumina Tru-Seq RNA Sample Prep Kits (Illumina) for 100 bp paried-end reads according to the manufacturer's instructions.
Expression level of selected genes was analyzed using the method described by  58. Host gene expression was normalized using gene encoding elongation factor-1α (EF-1α) according to Morga et al. 59. Calibrator consisted in control hemocytes. B. ostreae housekeeping gene was determined in our conditions by testing the most stable expression gene between two genes, 18 s  60  and GAPDH (from the present study), using Normfinder  61  and GeNorm softwares  62.
As shown in a previous report  15  , a strong gradient of soil pH was observed along our eight sampling locations, with values ranging from The software used to create the map was QGIS 2. 10 using STRM30  54, 55  elevation model (Data: SIO, NOAA, U. S. Navy, NGA, GEBCO) and Landsat 8 Satellite image (Data available from the U. S. Geological Survey). 8. 8 (site 1; lower elevation) to 5. 7 (site 8; higher elevation) (Fig. 1 ; Supplementary information Table  1 ). Nutrients in the form of total C, P, K and Ca correlated significantly with changes in pH (Spearman's rho p < 0. 05), concordant with previous observations at this site  15  (Supplementary information Table  1 ). Total C (0. 37-1. 1%) and total N contents (0. 02-0. 06%) were higher than values reported for soils located near our transect  19  , whereas P contents (2.