dataesr/scientific-paragraphs-categorization huggingface dataset:
For acute effect assessment, such data are collected from standard toxicity tests, from which the 50% lethal or effective concentration (LC 50 or EC 50 , respectively) is generally estimated at the end of the exposure period, meaning that not all observations over time are used. In addition, classical dose-response models implicitly assume that the exposure concentration remains constant throughout the experiment, which makes it difficult to extrapolate the results to more realistic scenarios with time-variable exposure profiles combining different heights, widths and frequencies of contaminant pulses  [6] [7] [8] [9]. To overcome this limitation at the organism level, the use of mechanistic models, such as toxicokinetic-toxicodynamic (TKTD) models, is now promoted to describe the effects of a substance of interest by integrating the dynamics of the exposure  1, 10, 11.
Posterior predictive check (PPC). The posterior predictive check consists of comparing replicated data drawn from the joint posterior predictive distribution to observed data. A measure of goodness-of-fit is the percentage of observed data falling within the 95% predicted credible intervals  27. So the better fit is at a %PCC around 95. WAIC and LOO-CV. Information criteria such as the WAIC and LOO-CV are common measures of predictive precision also used to compare models (i. e. the lower is the value, the better is the fit). The WAIC is the sum of the log predictive density computed for every point, to which a bias is added to take into account the number of parameters. The LOO-CV method uses the log predictive density estimated from a training subset and applies it to another one  27. Both the WAIC and LOO-CV criteria were computed with the R package bayesplot  28.
For validation, we calibrated the model on a data set A to then predict another data set B. As a result, regardless of the measure of goodness-of-fit, the predictions are always better when the calibration is carried out using data of time-variable exposure profiles to then predict data from constant exposure profiles than when the inverse was carried out, that is, calibration using data from testing under constant exposure profiles to then predict data from testing under time-variable exposure profiles. Table  2  shows that the GUTS-RED-SD and GUTS-RED-IT models are similar in the quality of their fits. However, the GUTS-RED-IT model particularly underperforms for carbendazim and dimethoate under time-variable exposure profiles. Nonetheless, under time-variable exposure profiles for the malathion and propiconazole data sets, the 95% credible interval for the GUTS-RED-IT model is large (see figures in the Supplementary Material).
This difference is confirmed in the Supplementary Material for the malathion and propiconazole data sets. Since the cumulative amount of  contaminant is not changed, we do not see any effect of contaminant depuration (Eq. (  3 ) and Fig. 3 ), which could help individuals recover under a lower frequency of peaks. The comparison between constant and time-variable exposure profiles (Fig. 4  and Supplementary Material) suggests that uncertainty is smaller when calibration is performed with data collected under a time-variable exposure profile. This result is counter-intuitive, especially since the number of time series was higher for the constant exposure profiles, which would reduce the uncertainties of parameter estimates. If this result is confirmed, then it would be better to predict variable exposure profiles with parameters calibrated from time-variable exposure data sets.
Discussion tracking uncertainties for environmental quality standards. Regardless of the scientific field, risk assessment is by definition linked to the notion of probability, characterized by different uncertainties such as the variability among organisms and noise in observations. In this sense, tracking how uncertainty propagates into models from collected data to model calculations of toxicity endpoints that are finally used for EQSs derivation is of fundamental interest for ERA  15. For ERA, achieving good fits of experimental data is not enough. Instead, the key objective is the application of these fits to predict adverse effects under real environmental exposure profiles and to derive robust EQSs  1, 5, 6, 12, 16. In this context, as we have shown in this paper, calibrated TKTD models allow predictions of regulatory toxicity endpoints under any type of exposure profile  30.
Acknowledgements The authors are very grateful for inputs from  Theo Brock  on an earlier version of the manuscript. We thank  Andreas Focks  and two anonymous reviewers for their valuable suggestions. The authors also thank the  French National Agency for Water and Aquatic Environments  (ONEMA, now the  French Agency for Biodiversity)  for its financial support. This manuscript has not been submitted for publication in another journal, but a pre-print.
, 2018 : supplementary text 1). We retained inferences with a level of confidence > 95% and which were fully congruent in at least three runs. The pedigree contained 1696 individuals (born between 1996 and 2012), with 675 maternal links (48%) and 645 paternal links (46%) (from 360 different dams and 304 different sires). From this, we could infer which female conceived with which male. Overall, both parents were known for a total of 511 fawns. However, for each litter with full siblings (N = 51), we retained only one fawn, because we were interested in the mating event; therefore, our database finally included 460 mating events.
For the sake of accuracy, we did not include those mating pairs for which the age was estimated for both the mother and the father (N = 118), but note that results were very similar when they were included (for a summary of the results with the full dataset, N = 460 mating events, see Supporting Information, Tables  S1  and S2 ; Fig. S1 ). In addition, to minimize the risk of error in age-class attribution, we excluded mating pairs for which one or both of the parents had an estimated age of 7 or 8 years (N = 26), because the risk of misclassification of individuals was higher when the estimated age was close to the threshold of 7. 5 years old. Our analyses were therefore performed on 316 mating events. We attributed a 'quality index' for age estimation to each mating pair based on the degree of confidence we had when assigning individuals to either the 'prime-aged' or the 'old' group.
More specifically, if the estimated age of either the mother or the father during the mating year was 5 or 6 years old, the parent that had an estimated age was classified as 'prime-aged', with a quality index for the mating pair of three (i. e. lowest quality). If the estimated age of either the mother or the father during the mating year was < of known age (i. e. first caught as newborn fawns or as 8-month-old fawns). The dataset (including a total of 316 mating events) is available in the Supporting Information (Table  S3 ). Sample sizes for the different combinations of age-specific mating pairs (i. e. primeaged female-prime-aged male, old female-prime-aged male, prime-aged female-old male and old female-old male) in relationship to the age estimation quality index for the pair are provided in Table  1.
STATISTICAL ANALYSES We analysed the age-specific variation in female mating tactics by studying the covariation between maternal age and the age of their mate. More specifically, we evaluated whether the probability that a female roe deer mated with a prime-aged male depended on its own age class, using generalized linear mixed-effect models with a binomial family and the glmer function implemented in the R lme4 package  (Bates et al. , 2015). The most general model included the additive effect of maternal age class (fitted as a two-modality factor: prime-aged vs. old) and age estimation quality index (fitted as a three-modality factor; see previous subsection) as fixed effects. In the most general model (maternal age class + quality index; see Supporting Information, Table  S4 ), we therefore tested whether the probability that a female roe deer mated with a prime-aged male depended on its own age class and whether the 'quality index' for age estimation for each mating pair (i.
e. the degree of confidence in age-class assignment) affected the results. We compared this model with the model including only the maternal age class effect (excluding the quality index effect) to test whether the probability that a female roe deer mated with a prime-aged male depended on its own age class irrespective of the quality index for the mating pair. These two models were compared with the constant model. Given that an individual female could feature several times in the dataset, both within (18 females featured more than once within a given year owing to multiple paternity) and among (36, 26, three and one female featured two, three, four and five times among years, respectively) fawn cohorts, we included maternal identity and the fawn birth cohort as random effects to control for pseudo-replication issues.
e. during the rut). Relationship between paternal and maternal age (in years) in the European roe deer population of Chizé based on mating events between parents of known age only (N = 98; A) and based on all mating events in which at least one parent was of known age (N = 316; B). The size of each data point is proportional to sample size. The vertical and horizontal grey females and old males, although we expected between three and four mating events between old males and old females according to the population age structure (see Supporting Information, Table  S5 ). When analysing the full dataset, including all mating events where both parents were of known age or where one of the two parents was of known age and the other had an estimated age (N = 316 mating events; see Fig. 1B ), the selected model of variation in the probability that a female mated with a prime-aged male included the effect of maternal age class only (ΔAICc compared with the constant model = -2.
DISCUSSION We found that mating events between old females and prime-aged males were much more frequent than mating events between prime-aged females and primeaged males in roe deer. A likely hypothesis to explain this pattern is that old females are more selective than prime-aged females in order to avoid mating with old males. Indeed, mating events between old females and old males were rarely observed in our dataset (N = 3 out of 316 mating events with the dataset combining parents of known and estimated age, and N = 0 out of 98 mating events when we restricted the dataset to parents of known age only). Importantly, this result is very unlikely to be influenced by the inter-annual variations in sex-age structure within the population. Indeed, except for one specific year (2005), the observed yearly probability of mating events between old females and old males in our population was always lower than the expected yearly probability of mating events between old females and old males under the assumption of random mating (for an analysis considering the yearly availabilities of prime-aged vs.
Therefore, the most likely interpretation of our results is that direct mate choice drives the avoidance of old mates among old roe deer females. dotted lines represent the threshold values for maternal and paternal age classes, respectively, fixed at 7. 5 years. Maternal and paternal ages were defined as the age of the mother and the father, respectively, at fawn conception (i. e. during the rut). Note that in B, owing to uncertainty in age estimation, the age of some individuals was estimated to be 0 or 1 year. Mating events between old females and old males were rarely observed in our dataset (N = 0 out of 98 mating events in A; N = 3 out of 316 mating events in B). Two types of evidence are required to demonstrate that such non-random mating is a consequence of adaptive female mate choice.
Given the assumption that a similar decline also occurs in roe deer bucks, a lower frequency of mating events between old females and old males compared with mating events between old females and prime-aged males is likely to be an indication of mate choice, with old females avoiding copulation with old males. However, in the present study, we used reproductive success as a proxy for mating success, because direct observations of mating in roe deer are extremely rare, especially in forest habitat, such as Chizé. Although roe deer females are monoestrous, they may mate with multiple males during a single oestrous period  (Danilkin & Hewison, 1996; Vanpé et al. , 2009b). As a result, some of the males they mated with could go undetected in our dataset because of age-dependent post-copulatory sperm selection.
ACKNOWLEDGEMENTS We thank the numerous co-workers and volunteers for their assistance during roe deer captures, and  Maxime Galan  at  Centre de Biologie pour la Gestion des Populations -Institut National de la Recherche Agronomique  for genetic analyses. C. V. was funded by the ' AGEX '  JCJC ANR  project ( ANR-15-CE32-0002-01 ) awarded to J. -F. L. The field study was supported by grants from  ONCFS. Genetic data were produced thanks to the support of the ' PATCH '  RPDOC ANR  project ( ANR-12-PDOC-0017-01 ) awarded to C. V. We are grateful to  Luca Montana ,  Jason Airst  and one anonymous referee for their helpful comments on a previous draft of this work.
NDVI) values in the Globe-LFMC dataset was incorrectly stated as >80% of values in a given window missing due to cloud and snow masking. This has now been corrected in the HTML and PDF versions. In addition, due to a datum assignment error, values included in Table  5  were incorrectly reported. Table  5  has been corrected in the HTML and PDF versions, and the underlying data corrected in the associated figshare record.
On the other hand, a rather atheoretical exploratory literature departs from the representative economy assumption and examines instead the entire distribution of the variable of interest, typically income, using tools such as Markov chains and distribution dynamics. With the "regional turn" that this literature has taken from the end of the 90s starting inter alia with  Rey & Montouri (1999)  and  Lopez-Bazo et al. (1999) , and because regions typically experience greater openness and heterogeneity than national economies, issues arising from the presence of spatial dependence and spatial heterogeneity in regional growth datasets have been largely explored (see for instance  Rey & Le Gallo (2009)  for a review). While confirmatory approaches use spatial econometric methods to tackle these issues, exploratory approaches have been developed to analyse the spatial and space-time mobility of income distributions  (Rey et al.
Further, as our first synthetic factor can be interpreted as economic development, we compare the results obtained for this factor to those obtained for GDP per capita and show that there are indeed substantial temporal and spatial differences. The remainder of the paper is organized as follows. In section 2, we present the database that we use in section 3 to perform the multiple factor analysis. In section 4, we analyze regional inequalities and their dynamics using the first component and contrast the results with those obtained with GDP per capita. Both a global and a local analysis are undertaken. In section 5, we briefly present the results obtained for three other meaningful factors. Section 6 provides some concluding comments and suggests some avenues for future research.
Data description Table  1  presents the socio-economic variables collected for the empirical analysis. They are grouped into five broad categories: demography, economy, employment, education, and health. Economy variables (by economic sector) come from the Cambridge Econometrics' European Regional Database (ERD) and the remaining ones from the Eurostat Database "REGIO". The list of variables in Table  1  includes 19 out of the 24 main regional indicators published in the third report on economic and social cohesion  (European Commission, 2004 )-variables with (*) in Table  1. 1 As in Del  Campo et al. (2008) , we exclude the remaining five variables as they do not meet the requirement necessary to undertake the empirical analysis, i. e. availability for all EU-28 countries 2 and expression as ratio or means to avoid scale problems.
To this end, each group of variables is normalized by dividing all its elements by first singular value (matrix equivalent of the standard deviation). Then, the normalized data tables are concatenated into a unique data table which is submitted in the second step to PCA. As MFA boils down to a PCA on the concatenated-normalized data tables, the usual PCA outputs (coordinates, cosine, contributions, etc. ) are available. Moreover, some specific-MFA outputs can also be derived to quantify the importance of each group in the common solution. We apply MFA to extract a few principal components accounting for the major proportion of the total variance present in the dataset. Since each variable appears sixteen times, as much correlation coefficients can be computed with the factors. However, as the correlation coefficients between factors and the yearly versions of each of the variables have a stable sign, we present the average correlation with factors for each variable.
The mobility index (M ) can also be decomposed as follows: M = φM W + (1 -φ)M W , with M W = [τ W -1]/(-2). We report in Table  4  the values for the global measures of spatial autocorrelation and rank concordance over 2000-2015. The spatial weight matrix used to construct these statistics is based on k-nearest neighbors calculated from the great circle distance between regions' centroids. Since this weighting scheme avoids the problem of isolated regions having non neighbors, it is very useful for our case with on a dataset composed of some islands. The k-nearest neighbors weight matrix is defined as follows:        w ij (k) = 0 if i = j, ∀k w ij (k) = 1 if d ij ≤ d i (k) w ij (k) = 0 if d ij > d i (k) (4) where d i (k) is the k th order smallest distance between regions i and j such that region i has exactly k neighbors.
Conclusion We analyze in this paper socioeconomic disparities at work in a sample of 275 regions in EU-28 from a dynamic perspective  (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014) (2015). Starting from a wide set of socioeconomic indicators from Cambridge Econometrics' European Regional and Eurostat "REGIO" databases, we show.
Going local: a closer look at spatial dependence and its dynamics We continue the investigation of the spatial dynamics at work in the distribution of ECO-DEV over time at a local level. Indeed, since global Moran's I statistic and the global indicator of mobility association τ W yield a single result for the entire dataset for a given year, they may mask more complex local dynamics. In our case of positive global autocorrelation, Moran's I fails to discriminate between a spatial clustering of low values and a spatial clustering of high values. Therefore, we use local indicators of spatial association (LISA) in conjunction with Moran scatterplots for a closer view of the spatial dependence and its dynamics, firstly between initial and final periods (Directional LISA) and secondly between a sequence of many periods (Markov LISA).
4. 2. 2 Going local: a closer look at spatial dependence and its dynamics We continue the investigation of the spatial dynamics at work in the distribution of ECO-DEV over time at a local level. Indeed, since global Moran's I statistic and the global indicator of mobility association τ W yield a single result for the entire dataset for a given year, they may mask more complex local dynamics. In our case of positive global autocorrelation, Moran's I fails to discriminate between a spatial clustering of low values and a spatial clustering of high values. Therefore, we use local indicators of spatial association (LISA) in conjunction with Moran scatterplots for a closer view of the spatial dependence and its dynamics, firstly between initial and final periods (Directional LISA) and secondly between a sequence of many periods (Markov LISA).
Here, we investigated the functional resilience of the fish community by studying how disturbances affect the whole fish community and its major trophic guilds. After describing the changes in the fish community composition, we studied the variations of abundance and species synchrony for the whole community and its different trophic guilds. As investigating temporal synchrony requires long-term data, our 35-year-long dataset is not only adapted for such analyses but also rare among coral reef monitoring programs  (Moritz et al. , 2018; Wilkinson, Nowak, Miller, & Baker, 2013). As coral reef fish species are highly dependent on benthic composition, we hypothesized that the various trophic guilds were differently affected by varying substrate compositions (i. e. , hard coral and other important benthic components).
| Data collection The benthic composition and the fish community of Tiahura fore reef were monitored each year since 1987  (Galzin et al. , 2016). The benthic composition was monitored using four fixed 50-metre transects parallel to the barrier reef located at 6, 12, 20 and 25 m depth every year. Substrate per cent cover for eight categories (coral, macroalgae, pavement, crustose coralline algae, rubble, sand, turf and "others") was evaluated using the point intercept transect method by identifying substrate every metre along the transect  (Hill & Wilkinson, 2004). We used the data of a previous study  (Bouchon, 1985)  to obtain the coral cover of Tiahura fore reef in 1983. The fish community was monitored by visual census using a 2 × 50 m benthic transect located at 12 m deep parallel to the barrier reef.
Data sets Three main types of data sets were used: satellite images, bathymetric (lagoon depth), and hydrological measurements (tidal level). Cloud free Landsat images covering a period of thirty years were acquired from the U. S. Geological Survey (https://earthexplorer. usgs. gov). All selected Landsat scenes were acquired between August and September assuring nearly similar weather conditions (Table  1 ). The images were selected during similar tide magnitudes in order to avoid tidal fluctuations affecting the water body surface change. An additional Sentinel 2 image, in accordance with Landsat acquisition conditions, was acquired for validation. Tide data were acquired from online sources that use XTide. XTide is a tide prediction program commonly used in scientific work  (Berthot & Pattiaratchi, 2005; Kilcher & Nash, 2010; Verhoeven et al.
Sequence Data Processing The sequencing platform performed demultiplexing and adapter removal. Data were provided in form of separate fastq files for each sample. Bioinformatic processing was performed with Mothur Software  (Schloss et al. , 2009)  as proposed by  Vasselon et al. (2017a). Low quality sequences (read length below 250 bp, Phred quality score below 23 over a moving window of 25 bp, more than one mismatch in the primer sequence, homopolymer over 8 bp or presence of ambiguous base) were removed and obtained quality filtered sequences from all samples were analyzed jointly. Removal of chimera using Uchime algorithm  (Edgar et al. , 2011)  was performed after pre-clustering (applied for denoising of sequencing error based on cluster generation by reads with only one nucleotide difference).
Diatom Richness, Diversity, and Community Structure Chao1 richness and Simpson diversity were calculated for all sampling dates and locations. NMDS was performed with Mothur Software on Bray-Curtis dissimilarity matrix to compare diatom communities. Differences between sites and seasons were tested with PERMANOVA (p < 0. 05)  (Anderson, 2001; McArdle and Anderson, 2001 ) made using the vegan package  (Oksanen et al. , 2017).