dataesr/scientific-paragraphs-categorization huggingface dataset:
In order to address the first question, "which traits have been investigated in relation to responding to disturbances (response traits) or affecting ecosystem processes (effect traits), and how many papers have studied each of these links?, " a weighted and directed tripartite network diagram was produced using the R package "igraph"  (Csárdi, 2019). The diagram illustrates the number of papers, displayed as thickness of the lines (edges), linking traits to disturbances or processes (nodes). To address the second research question, "which traits are being used together?, " the R packages "igraph"  (Csárdi, 2019)  and "CINNA"  (Ashtiani et al. , 2019)  were used to produce an undirected, weighted network diagram. This diagram shows the links between traits used together in papers, where the size of the nodes shows the number of papers using the trait, and the size of the edges illustrates the frequency of trait combinations being included together in papers.
, 2012). The median rank for the centrality metrics and the individual metric values were plotted using the R package "ggplot2"  (Wickam, 2016). To illustrate the traits being used together to generate functional diversity and related metrics, a Principal Coordinate Analysis (PCoA)  (Kenkel, 2006)  was carried out using the R package "ape"  (Paradis et al. , 2019). The analysis was conducted on a Jaccard's dissimilarity matrix based on a presence/absence matrix  (Cheetham and Hazel, 1969)  of traits within papers looking at functional diversity metrics. Two principal component axes, explaining 49% of the variation in the distance matrix, are illustrated. The number of clusters of traits within the plot was selected using the R package "NbClust. " This package determines the optimum number of clusters based on 30 indices  (Charrad et al.
"Predation" captured papers assessing prey traits, predator traits, or the interaction between both. The most studied disturbance-response trait links were between size and fishing (n = 16), size and climate change (n = 15), diet and fishing (n = 11), and diet and climate change (n = 8). The most studied effect trait-process links were diet and herbivory (n = 9), size and predation (n = 8), and size and herbivory (n = 6). Size and diet had the greatest number of papers that link the traits both to disturbances and processes, making them both well-studied response and effect traits (Figure  1 ; Supplementary Figure  2 ). The network analysis plot, based on the traits of all papers included in the database, illustrates three key findings. Firstly, size and diet, followed by space use, gregariousness, and growth rate, are the most commonly used traits in the literature (Figure  2 ).
, 2010; Quimbayo et al. , 2021 ) (Figure  3 ), are not as easily available, and as such have typically been used in studies conducted on historical records, at a small geographical scale, or for few species (e. g. ,  Munday et al. , 2011; Fox and Bellwood, 2013; Streit et al. , 2015). Thus, there is scope to build upon morphological trait databases, so that ecosystem functioning can be better understood at a larger scale  (Kiørboe et al. , 2018). If such traits become more readily available, they might start being used in conjunction with behavioral and life history traits to bridge together research being conducted on the impact of disturbances and ecosystem functioning. Although this review provides a broad overview of coral reef fish traits through a response-and-effect framework, it should be noted that it does not extensively cover all of the literature dealing with traits.
In addition to being a super-trait, size provides an avenue for investigating the importance of intraspecific variation. Intraspecific variation is a burgeoning field of research within functional ecology  (Albert et al. , 2011; Allgeier et al. , 2017; Des Roches et al. , 2018). Where substantial variability exists within species, it may no longer be sufficient to use species-level trait data  (Bolnick et al. , 2011). One example of the importance of intraspecific variability is illustrated in a paper by  Barneche et al. (2018). The authors show that there is hyperallometric scaling in reproductive output; larger coral reef fish mothers, within the same species, have a far greater reproductive output than smaller mothers. The functional trait of an individual fish might also depend on its sex, age, or size at which it was sampled.
For example, many species undergo ontogenetic shifts in their diet; it has been found that some piscivores are able to expand their diet breadth as they grow older and bigger, and their gape size can accommodate larger prey  (Dunic and Baum, 2017). This diversity of size amongst individuals of a species, can be easily recorded. The size and species identity of fish is often collected using routine visual surveying techniques  (Caldwell et al. , 2016). Indeed, total length is a relatively simple trait to measure observationally and non-invasively in situ  (Villéger et al. , 2017). Diet, on the other hand, is not typically measured for each fish during a survey, unless it is one of the explicit aims of the research. Rather, species are assigned a diet category post data collection using expert knowledge, published literature, or databases such as Fishbase (e.
Bat activity may vary greatly between night this mainly because weather conditions are the main drivers bat activity  (Bolliger et al. 2020) , this design (i. e. 2 pairs, 4 points sampled the same night, Fig. 1b ) allow to control for weather conditions and to evaluate with accuracy the effect of different variables (presence of streetlight, distance to the streetlight). As it is impossible to identify individual bats from their echolocation calls, we calculated bat activity as the number of bat passes per species. A bat pass is defined as the occurrence of a single or several echolocation calls of the same bat species during a 5-second interval  (Millon et al. , 2015). Using the software TADARIDA  (Bas et al. , 2017)  in its latest version (online repository (https://github. com/YvesBas), echolocation calls were detected and classified to the most accurate taxonomic level.
In addition, for each echolocation TADARIDA provided a confidence score for each automated identification. We follow the approach developed by  Barre et al. (2019)  for accounting for automated identification errors in acoustic surveys. We used an independent dataset comprising 8405 bat passes recorded throughout France as part of the Vigie-Chiro bat-monitoring program and both checked manually and ran through TADARIDA to perform a logistic regression between the success/failure of automatic species assignation and the confidence index provided by the software. We could hence associate each confidence index with an identification success probability and calculate for each species the minimum confidence index required to tolerate a given maximum error risk, i. e. confidence threshold (methodology detailed in Appendix D).
We used the confidence thresholds calculated on the national dataset to create two subsets of this study's dataset: one with a 0. 5 Maximum Error Risk Tolerance (MERT) and another subset with a 0. 1 maximum error risk tolerance. We performed the analysis on both subsets and found similar results. Hereafter we show results for a 0. 5 MERT while results for a 0. 1 MERT are presented in Appendix E. An overlap of the detection volumes of the two recording points of a pair could occur depending on the distance between the points and the species maximum distance of detectability. For example, Rhinolophus hipposideros can be detected at a maximum of 5 m, hence for this species the volumes of detection overlapped for only 11% of pairs. For Tadarida teniotis which can be detected at 150 m, volumes of detection overlapped for all pairs.
Statistical analysis 2. 5. 1. Effect of the presence of streetlight We tested the effect of the treatment (experimental lit vs control dark) to measure the effect of the presence of a streetlight on bat activity. For each species and group of species, we performed a generalized linear mixed model (GLMM) (R package glmmTMB v. 0. 2. 0; Brooks et al. , 2017)  using the number of bat passes as the response variable and the recording point type (four levels: street-lit, hedge-lit, street-dark and hedge-dark) as a fixed effect. To account for the paired design in the species models, we used the site as a random effect because we expect some variations of bat activity between night, and each night we sample a site including four points. Models for the aerial and clutter groups contained a random effect on the recording point nested within the site and a second random effect -species identity-to account for the species effect (an approach regularly used to account for differences in abundances between species, see  Kerbiriou et al.
Species activity ~ recording point type + PC1 + PC2 + (1|site) Group activity ~ recording point type + PC1 + PC2 + (1|species) + (1|site/recording point) They were applied on data for all recording points (n = 112). We performed a post-hoc Tukey HSD test to obtain comparison between experimental lit and control dark points at the street and at the hedge (package lsmeans v. 2. 27-67; Lenth, 2016).
Bat monitoring In the dataset allowing for a maximum error risk of 0. 5, there was a total of 187 885 bat passes belonging to 20 species. Five species were not included in the analysis as the number of bat passes recorded was too low (n < 50). The most abundant species were P. kuhlii (n = 89 773), P. pipistrellus (n = 45 194) and P. pygmaeus (n = 32 380) (Table  1 ). Almost all bat passes were attributed to aerial species with only 1% of bat passes being of clutter species (n = 1277) (see Appendix E and G for more details).
Effect of the presence of the streetlight The presence of a streetlight had a significant positive effect on all aerial species activity except for P. pipistrellus, T. teniotis and E. serotinus and on the aerial group activity at the street points compared to a similar environment with no light (Fig. 2a ). At the hedge, this positive effect was significant but weaker for the aerial group and for P. nathusii, H. savii, M. schreibersii and N. leisleri (Fig. 2b ). All these effects were also significant for the 0. 1 maximum error risk tolerance (MERT) dataset except for P. nathusii. This species number of bat passes dropped drastically from 5122 at the 0. 5 MERT to 74 at the 0. 1 MERT due to difficulties in its acoustic identification. A significant negative effect was detected on the activity of M.
Effect of the light illuminance on bat activity Light illuminance had a significant positive effect on the aerial group activity both at the street and hedge points (Fig. 3 ). At the species level, illuminance had a significant positive effect on P. kuhlii, P. pygmaeus, P. nathusii, M. schreibersii and N. leisleri but  these effect disappeared for the 0. 1 MERT dataset for P. pygmaeus and P. nathusii. This positive effect was also present and stronger at the hedge for P. kuhlii, P. nathusii and N. leisleri. Again, it was not significant for P. nathusii when using the 0. 1 MERT dataset. The illuminance had a negative impact on the clutter group activity at the hedge. At the species level, the illuminance only had a significant negative effect on R. hipposideros at street points but it was at least twice as strong as the higher positive effect for aerial species.
Effect of the streetlight characteristics on bat activity The lamp height had a negative effect on P. nathusii activity at the streetlight but this effect was not significant with the 0. 1 MERT dataset (Appendix C, Table  C. 3). HPS lamps had a significant negative impact on H. savii activity at the streetlight compared to LED lamps. There was no other effect of the lamp height or type for any other species or for the two groups either at the streetlight or at the hedge. The distance to the streetlight had a significant negative effect on all aerial species activity except for T. teniotis and E. serotinus and it was also significant for the aerial group (Fig. 4 ). The distance to the streetlight had no significant effect on clutter species and group.
Light diffusing outside urban areas and onto potential habitats for bats can decrease their quality and this might alter protected areas capacity to preserve species with conservation issues. We performed all the models (n = 125) on two datasets including a different maximum error risk tolerance (0. 5 and 0. 1). This allowed us to confirm the robustness of our findings except for five models. Four of these were for P. nathusii which acoustic identification is difficult notably due to confusions with P. kuhlii  (Obrist and Fluckiger, 2004) , moreover P. kuhlii is more common than P. nathusii in the study area , hence results for this species should be considered with caution.
Streetlight characteristics We found a negative impact of the distance to streetlights on seven out of nine aerial species activity. Similarly, a study undertaken in a protected area in the North of France, showed that the positive effect of light on P. nathusii, P. kuhlii, P. pipistrellus and N. leisleri activity disappeared rapidly with increasing distance steps (no effects at more than 10 m from the streetlight;  Azam et al. , 2018). However here, while considering distances between 7 and 192 m, we detect a linear negative effect for most aerial species. This is consistent with the positive effect of illuminance on aerial activity as the illuminance decreases proportionally to the inverse of the distance squared (R 2 = 0. 9 in our dataset). We did not find any effect of the distance on clutter species either at the species or group level and  Azam et al.
The idea of statistical bias correction is to find a mathematical transformation that makes climate simulations have similar statistical properties as a reference dataset over the historical period, and then apply this transformation for the modeled projection. Such transformations may be determined with statistical models based on either perfect prognosis (PP) or model output statistics (MOS) approaches  (Maraun et al. 2010). The PP approach consists in determining the statistical link between a variable of interest from references (predictand) and one or several observed variables (predictors) occurring at the same time. Simultaneous values of predictand and predictors are indeed required to implement the PP approach and learn the (synchronous) relationships between them. By applying these relationships to predictors from climate simulations, this approach implicitly makes the assumption that these predictors are realistically simulated  (Wilks 2006 ).
In parallel, i. e. , in contexts other than bias correction, over the last decades, machine learning techniques have emerged as a promising approach to model highly nonlinear and complex relationships between statistical variables. Major improvements have been obtained with Deep Learning models (see the overview of  Schmidhuber 2015) , which have proved to be efficient to extract high-level feature information from various datasets. In particular, convolutional neural networks (CNNs, see e. g. ,  Lecun and Bengio 1995)  showed that they can capture with great performances complex spatial structures. Initially developed for computer vision problems (e. g. ,  Szegedy et al. 2015; He et al. 2016) , they found numerous applications in climate sciences: for instance for weather forecast prediction uncertainty  (Scher and Messori 2018) , emulations of atmospheric dynamics  (Shi et al.
2015; Scher and Messori 2019; Chapman et al. 2019) , detection of extreme weather events  (Liu et al. 2016; Racah et al. 2017 ) and statistical downscaling  (Vandal et al. 2017; Rodrigues et al. 2018; Baño-Medina et al. 2020). A recent overview of Deep Learning applications for Earth system science is offered by  Reichstein et al. (2019). Recently, a new class of artificial neural networks, named Generative Adversarial Networks (GANs;  Goodfellow et al. 2014) , has led to tremendous interests due to their ability to infer high dimensional probability distributions. Initially, this machine-learning method has been developed for estimating the distribution of images from a target dataset, with the aim of sampling new (and unseen) images from this distribution. GANs, implemented with deep convolutional neural networks, have achieved impressive results in computer vision problems (e.
g. ,  Radford et al. 2016)  and are a subject of active research to improve computing architectures (e. g. ,  Salimans et al. 2016; Karras et al. 2018; Menick and Kalchbrenner 2018)  and optimization techniques (e. g. ,  Mao et al. 2017; Arjovsky et al. 2017; Roth et al. 2017). Conditional formulations of GANs have also been developed, for which additional information, such as class labels or images, can serve as inputs to condition the generation of the new images (e. g. ,  Mirza and Osindero 2014; Gauthier 2014; Denton et al. 2015; Kim et al. 2017; Isola et al. 2017). In particular, image-conditional GANs permit to perform image-to-image translation tasks by learning how to map the statistical distribution of one set of images (source dataset) to the statistical distribution of another set (target dataset).
Depending on the correspondence between images of the source and target datasets, different versions of image-conditional GANs have been developed. When all the images are paired (i. e. , there is a known one-to-one correspondence between every images of the source and target datasets), conditional GANs are trained by supervised learning  (Yoo et al. 2016; Isola et al. 2017). When only a few images are paired, semi-supervised is used  (Gan et al. 2017)  and when all points are unpaired, only unsupervised learning can be applied  (Kim et al. 2017; Yi et al. 2017; Zhu et al. 2017). Due to the stochastic and high-dimensionality nature of many physical processes of the Earth system, GANs and conditional GANs are particularly appealing for atmospheric science problems. Recently, they have been used for various Earth-science related applications: for instance for statistical downscaling  (Leinonen et al.
e. , calibration) and future (i. e. , projection) periods. One univariate quantilemapping-based BC method and two MBC algorithms are included in the study in order to gain a better understanding of the performances of MBC-CycleGAN concerning univariate, spatial and temporal properties. The paper is organized as follows: Section 2 presents the model and reference data used, and Sect. 3 describes the MBC-CycleGAN algorithm. Then, Sect. 4 displays the experimental setup used in this study, and results are provided in Sect. 5. Conclusions, discussions and perspectives for future research are finally proposed in Sect. 6.
Reference and model data In this study, the dataset employed as reference for the bias correction is the "Système d'Analyse Fournissant des Renseignements Atmosphèriques á la Neige" (SAFRAN) reanalysis  (Vidal et al. 2010 ) with an approximate 8 km × 8 km spatial resolution. Daily temperature and precipitation time series from 1 January 1979 to 31 December 2016 are extracted over the region of Paris, France  ([47. 878, 49. 830  • N] × [0. 949,3. 947 • E]), which corresponds to a domain with 28 × 28 = 784 continental grid cells. For the climate simulations data to be corrected, daily temperature and precipitation time series are taken from runs of the IPSL-CM5A-MR Earth system model  (Marti et al. 2010; Dufresne et al. 2013 ) with a 1. 25 • × 2. 5 • spatial resolution over the same region of Paris.
For the 1979-2005 period, a historical run is extracted and concatenated with a run under RCP 8. 5 scenario (i. e. , the scenario with highest CO 2 concentration) for the 2006-2016 period, to obtain the desired 1979-2016 period. To perform a bias correction, a one-to-one correspondence between model and reference grid cells is needed, i. e. , spatial resolutions between reference and model data have to be the same. Hence, IPSL data are regridded to the SAFRAN spatial resolution with a bilinear interpolation for both temperature and precipitation. More data are required for this study, in particular for the implementation of the PP approach and to assess the influence of nonstationary properties of climate simulations on the performance of the proposed MBC method. For sake of clarity and make reading easier, these data will be introduced thereafter in the appropriate sections.
GAN In its most basic formulation, a generative adversarial network consists of two neural networks that are trained conjointly: a generator and a discriminator. We first consider one random variable living in ℝ d , with a probability dis- tribution denoted ℙ. This random variable characterizes the available data, such as images of the target dataset (i. e. , references), and hence takes its values in a high-dimensional space. We assume to have at hand samples 1 , … , n drawn according to the density ℙ on ℝ d. The generator, denoted G, is a function from ℝ d ′ to ℝ d and is intended to be applied to a d ′ -dimensional random variable , usually multivariate Gaussian random noise (with d ′ < < d ), such that the ran- dom variable G( ) follows the law of , i. e. ℙ = ℙ ( ). Let 1 , … , n be a sample drawn from the distribution of.
CycleGAN for unsupervised image-to-image translation CycleGAN  (Zhu et al. 2017 ) is a particular image-conditional GANs that is commonly used for unsupervised image-to-image translation. In the original application, CycleGAN has been applied with great success to transform photographs into the styles of master paintings by modifying colour information (i. e. , RGB colour channels and/or spatial features of colours) of the photographs. Instead of the random noise , we introduce another random variable , with probability distribution ℙ , living in the same dimensional space as (i. e. , ℝ d ). This random variable characterizes the images of the source dataset (i. e. , biased simulations to correct). The CycleGAN approach consists in learning a mapping (i. e. , a generator) G → ∶ ℝ d → ℝ d such that the random variable G → ( ) follows the law of (i.
Adaptation of CycleGAN for MBC The main idea of the proposed methodology, named MBC-CycleGAN, is to adapt the CycleGAN approach so that it turns daily maps of a simulated variable with spatial features inappropriate compared to a reference dataset, to more realistic maps. Here, MBC-CycleGAN is developed in the context of the "marginal/dependence" MBC category, i. e. , correcting separately marginal distributions and dependence relationships. In addition to marginal distributions, we consider the adjustment of spatial dependence structures. The algorithm is trained on a historical period (i. e. , calibration) for which both climate simulations and reference datasets are available. Once the adversarial neural network has converged, adjustment of climate simulations over a projection period (e.
′ ) independ- ent and identically distributed copy of (resp. ) and ‖. ‖ the Euclidean distance. The corresponding energy statistic of E between two k-dimensional statistical samples and can be computed as follows: Fig. 3  Flowchart for the MBC-CycleGAN method to adjust climate simulations for the projection period References and model simulations datasets for calibration (0) period. References and model simulations datasets for projection (1) period. • Adjust each variable using the univariate QQ method for period 0. • Adjust each variable using the univariate QQ method for period 1. Step 1: Correction of univariate properties • Apply a pointwise min-max normalization of QQ and references data for period 0. • Train the CycleGAN model to adjust for spatial biases. Step 2: Training of the CycleGAN • Apply a pointwise min-max normalization of QQ data for period 1.