dataesr/scientific-paragraphs-categorization huggingface dataset:
72 to 120. 3 mm 2 mg -1 )  [54]  and for herbaceous plants growing on ultramafic substrates from Lesbos island (East Mediterranean) (SLA from around 10 to 45 mm 2 mg -1 ) [55]. SLA has been correlated with relative growth rate and stress tolerance, as well as a protection against herbivores  [56, 57] , thus we can conclude that our studied communities are characterized by low relative growth rates but high tolerance to stress. Community mean leaf concentrations of N, P, Ca, and Mn were slightly below the average values from TRY database and (for N and P) below the vegetation from Lesbos island  [31, 55]. In contrast, CWM values for leaf K and Mg are over the mean for TRY. High Mg CWM are understandable because Mg is the dominant element in exchange complex in serpentine-derived soils  [38, 51]  and in our samples.
Acknowledgments:  Sabah Biodiversity Centre  provided  C. Q-S. with the Access License ref no:  JKM/MBS. 1000-2/2JLD. 4(101 ). Sabah Parks  and  Sabah Forestry Department  provided specific permits for the access to sampling sites. Romain Goudon ,  Lucas Charrois , and  Stephane Colin  are acknowledged for their contribution to the analyses of soils. Two anonymous reviewers provided valuable comments for the improvement of the manuscript.
5 h during the other IOPs. In this study, the radiosondes data were averaged at a final vertical resolution of 50 m. Additionally, measurements of an ultra-high frequency (UHF) wind profiler are used to derive the NLLJ core height at a 15 min time interval  (Dione et al. , 2019). The meteorological conditions at surface (temperature, relative humidity and pressure of the air at 2 m a. g. l), and some terms of the surface energy budget (net radiative flux (R n0 ) sensible heat (SHF 0 ) and latent heat (LHF 0 ) fluxes at 4 m a. g. l) were continuously acquired  (Kohler et al. , 2016). SHF 0 and LHF 0 are deduced from high-frequency (20 Hz) measurements 5 processed with Eddy-covariance methods by using the TK3. 11 software  (Mauder et al. , 2013).
Factors contributing to the uncertainty of the cause-effect modelling processes include the limited knowledge of the spatiotemporal dynamics of ecological processes, the lack of a precise understanding of the mechanism of cumulative effects (e. g. additive, synergistic or antagonistic), and the gaps and incomplete information on the distribution, dynamics and magnitudes of pressures. To overcome these gaps, inputs have been generated from models, expert assessments, and extrapolations from patchy datasets. In some cases, efforts have been made to statistically quantify uncertainty by using sensitivity analyses  (Ansong et al. , 2017)  or by incorporating variability to ensure that model structure is adequate, such as in the Western Mediterranean case. Still, in many of the case studies a further exploration and description of sources of uncertainty through e.
Acknowledgements This article is a product of the working group on cumulative effects assessment under the framework of  COST Action 15121  "Advancing marine conservation in the European and contiguous seas" (MarCons; http://www. marcons-cost. eu;(Katsanevakis et al. , 2017)-supported by  COST (European Cooperation in Science and Technology ,  CA15121 ). Special thanks to  Nicole Stollberg  who helped to develop high quality figures. Further, we thank two anonymous reviewers for their helpful comments.
Genetic Analyses Considering the low frequency of interspecific hybridisation (one backcrossed individual out of 210 tested in Czechia and Slovakia)  [3, 4] , species determination based on phenotypes has been confirmed by sequencing the mitochondrial control region in the samples originating from the zone of sympatry (N = 27). The tissue was collected in the course of skull preparation, and fixed in 96% ethanol, prior to storage at -20 • C. DNA was extracted using a DNA Blood and Tissue Kit (Qiagen, Prague, Czech Republic). Amplification of the mtDNA control region was achieved in line with the protocol used in Bolfíková and Hulva  [3]. Sequences obtained were compared with those available in the GenBank ® database. allopatry were borrowed from the Museum für Naturkunde, Berlin (Germany; MFN) and the Institute of Vertebrate Biology, Brno (Czech Republic; IVB).
3D Geometric Morphometrics Approach and Statistical Analyses Skull sizes and shapes were assessed using three-dimensional landmark-based geometric morphometrics. A total of 47 three-dimensional coordinates were recorded corresponding to 13 landmarks on the mandible, and 34 landmarks on the cranium divided into 13 and 21 landmarks on the ventral and dorsal side, respectively (Figure  2 , Table  S2 ). Landmark coordinates were acquired using the Reflex microscope and Axel software (Reflex Measurement Ltd. , Butleigh, Somerset BA6 8SP, UK) at the Museum and Institute of Zoology of the Polish Academy of Sciences with headquarters in Warsaw. All measurements were taken by the same person (MK). The coordinates were superimposed using a generalised Procrustes analysis algorithm  [24, 25]. During this procedure, all specimens are translated, so the center of gravity of their landmark configuration coincides, normalised to the unit centroid size and rotated to minimise the squared summed distances between corresponding landmarks.
Mean CVP values are provided with a 90% confidence interval obtained with 100 resamples  [27]. Shape changes along the CVA axes were visualised by calculating shape changes along the factorial axes using multivariate regression  [28]  using the 'Morpho v2. 8 R package  [29]. Because sample sizes were relatively small compared to the large number of variables, we applied dimensionality reduction of the data prior to MANOVAs and CVAs by substitution of the primary data by the first scores of the principal component analyses (PCA) maximizing the leave-one-out cross validation between groups  [27, 30]. morphometrics. A total of 47 three-dimensional coordinates were recorded corresponding to 13 landmarks on the mandible, and 34 landmarks on the cranium divided into 13 and 21 landmarks on the ventral and dorsal side, respectively (Figure  2 , Table  S2 ).
Landmark coordinates were acquired using the Reflex microscope and Axel software (Reflex Measurement Ltd. , Butleigh, Somerset BA6 8SP, UK) at the Museum and Institute of Zoology of the Polish Academy of Sciences with headquarters in Warsaw. All measurements were taken by the same person (MK). To test for possible common differences in size and shape between allopatric and sympatric populations of the two species, we applied two-way ANOVA and MANOVA using size or shape as variables, species as the main classifier, and allopatric versus sympatric distribution as a sub-classifier factor. When necessary, p-values were adjusted for multiple comparisons after Benjamini and Hochberg  [31]. Overall phenotypic similarities between groups were depicted using neighbour-joining networks, computed based on the Mahalanobis D 2 distances  [32].
Acknowledgments: We would like to thank the  Museum für Naturkunde, Berlin (Germany)  and the  Institute of Vertebrate Biology, Brno (Czech Republic ) for a loan of the skulls. We also thank the rescue centers in the  Czech Republic, namely Vlašim, Praha, Bruntál, Jaroměř and Litoměřice , for their contribution to the study. For valuable suggestions we thank to  Dr. Vladimír Vohralík.
The data were Tukey's Ladder of Powers transformed when visual breakdowns in LME model assumptions were revealed by residual plots. The significance of the fixed effects was determined by comparing models with and without the factor of interest using LRT. When the interaction term in the model was significant, Tukey's HSD post-hoc test was carried out and a significant effect was determined at p <. 05. All statistical analysis was carried out in the R programming language 3. 4. 3 (R Development Core Team, 2018) using the additional packages, ape  (Paradis, Claude, & Strimmer, 2004) , nlme (Pinheiro, Bates, Debroy, Sarkar, & Team, 2018), plyr  (Wickham, 2011) , ggplot2  (Wickham, 2009)  and multcomp  (Hothorn, Bretz, & Westfall, 2008).
05; Figure  3 ). There was a trend towards increased topsoil K and MBC concentration (0-0. 15 m) as LTP increased. An increased LTP significantly increased AS in the 0-0. 15-m depth (p =. 05) and soil C concentration in the 0-0. 15-m and 0. 15-0. 30-m depths (p =. 002, p =. 05, respectively). In contrast, as LTP increased, soil K concentration decreased in the 0. 15-0. 30-m depth (p =. 007; Figure  3 ). MAP (i. e. , % years with manure application in 10 years) did not affect any of the soil indicators measured (pH, P, K, BD, AS, C and MBC) at any of the three depth intervals (0-0. 15, 0. 15-0. 30 and 0. 30-0. 60 m) assessed (data not shown).
Survey data were processed using Compass and Walls, and drawn using Adobe Illustrator. To capture specimens, baited traps or hand-held nets were used. Photographs were taken in the field with the help of a small glass tank and a Canon EOS100 camera. A small fin clip was taken with scissors for DNA studies from all specimens. Voucher samples were stored at the Colección Nacional de Peces, IBUNAM, Mexico. In accordance with the Ethical Guidelines for the Use of Animals in Research and collecting permit # SGPA/DGVS/1893/19 from the Secretaría del Medio Ambiente y Recursos Naturales, México issued to Patricia Ornelas-García, only five specimens were euthanized from Sótano del Toro #2, which were stored in 100% ethanol. Five other fish were kept alive in the laboratory of Patricia Ornelas-García for breeding in captivity, which can serve as a stock for future studies.
PCR amplified samples were purified with the QIAquick PCR purification kit and directly sequenced by SeqWright Genomic Services. Chromatograms obtained from the automated sequencer were read and contigs made using the sequence editing software Sequencher™ 3. 0. All external primers were excluded from the analyses. BLAST was used to identify GenBank sequences that resemble the specimens. Sequences were aligned with ClustalW2.
PCR amplified samples were purified with the QIAquick PCR purification kit and directly sequenced by SeqWright Genomic Services. Chromatograms obtained from the automated sequencer were read and contigs made using the sequence editing software SequencherTM 3. 0. All external primers were excluded from the analyses. BLAST was used to identify GenBank sequences that resemble the specimens. Sequences were aligned with ClustalW2. Sótano del Toro is the smallest and shallowest of the known eyeless Astyanax caves. It is possible to stand in the entrance sink and see the 0. 5 m wide by 2. 5 m long pool. A gallery can be seen continuing underwater. Light reaches this small pool, as can be seen in Figure  2. The cave was visited in 1969, when three days of work resulted in only about eight fish being collected.
Asymmetry is evident in some individuals (K) in which on one side they may have an eye, but on the other is eyeless (inset photo). Scale bar in A is for (A-L) and scale bar in M is for (A-O). Five troglomorphic fish from the Toro system were genotyped. DNA amplification of mitochondrial 16S rRNA produced a 572 bp sequence. All specimens presented a haplotype A (GenBank# AP011982. 1) identical in sequence to fish from Pachón, Chica, and Chiquitita caves and from the local surface Astyanax (Figure  10 ). When compared to the mitochondrial haplotypes B of fish from Rascón and Tamasopo surface streams, Toro specimens differed by 2-3 bp. When compared to the mitochondrial haplotypes B of cavefish from Sabinos, Tinaja, and Curva, they differed by 5 bp (red arrows in Figure  10 ). It is thus supported that Astyanax Toro specimens have a mitochondrial DNA most similar to haplotypes A, with specimens being identical to individuals from other southern El Abra caves like Chica and Chiquitita.
Introgression between the surface morph and the cave morph is suggested by the presence of individuals that are highly depigmented, but with eyes (G,H,K) or individuals with pigment, but reduced eyes (J). Asymmetry is evident in some individuals (K) in which on one side they may have an eye, but on the other is eyeless (inset photo). Scale bar in A is for (A-L) and scale bar in M is for (A-O). Five troglomorphic fish from the Toro system were genotyped. DNA amplification of mitochondrial 16S rRNA produced a 572 bp sequence. All specimens presented a haplotype A (GenBank# AP011982. 1) identical in sequence to fish from Pachón, Chica, and Chiquitita caves and from the local surface Astyanax (Figure  10 ). When compared to the mitochondrial haplotypes B of fish from Rascón and Tamasopo surface streams, Toro specimens differed by 2-3 bp.
When compared to the mitochondrial haplotypes B of cavefish from Sabinos, Tinaja, and Curva, they differed by 5 bp (red arrows in Figure  10 ). It is thus supported that Astyanax Toro specimens have a mitochondrial DNA most similar to haplotypes A, with specimens being identical to individuals from other southern El Abra caves like Chica and Chiquitita. In the mysid shrimp from the Toro system, the H3 fragment was 328 bp long. Two clades or haplotypes (GenBank # MH422492, MH422494) were found, in support of Kopp et al. [13]. The two mysid shrimp haplotypes differed by 36 bp (10. 9%). The first clade A included specimens from Pachón, Toro #2, and Chiquitita. The clade B included Tinaja and Sabinos. Thus, similarity of sequences among populations did not follow geographical proximity between caves.
Specimens from the northernmost (Pachón) and the southernmost (Toro #2 and Chiquitita) portions of the Sierra de El Abra were identical. Likewise, the specimens from the central Sierra de El Abra (Sabinos and Tinaja) were identical. It is thus supported that the mysid shrimp from Toro #2 cave has an H3 DNA most closely related to the clade A, with their sequence being identical to individuals from other southern El Abra caves such as Cueva Chiquitita. In the mysid shrimp from the Toro system, the H3 fragment was 328 bp long. Two clades or haplotypes (GenBank # MH422492, MH422494) were found, in support of Kopp et al. [13]. The two mysid shrimp haplotypes differed by 36 bp (10. 9%). The first clade A included specimens from Pachón, Toro #2, and Chiquitita. The clade B included Tinaja and Sabinos.
Acknowledgments: Thanks to all group members who participated in the field trips:  Gerardo Gil ,  Jessica Gordon ,  Cait McCann ,  Matt Schram ,  Raul Se Feliz ,  Geraldine Solignac ,  Juan Cancino Zapata ,  Víctor Rodríguez Ballesteros ,  Carlos Pedraza Lara ,  Morgan Jones ,  Constance Pierre ,  Stéphane Père ,  Julien Fumey ,  Julien Leclercq ,  Maryline Blin ,  Jorge Torres-Paz ,  Boudjema Imarazene ,  François Agnès. Students  from the  BIOL320 Genetics course at Marist College, NY , helped with the DNA sequencing. Proof-reading by  Sara Carbone.
Acknowledgments We thank  Benjamin Glasner  for field assistance. Research reported here are contributions to the program of the  Long-Term Socio-Ecological Research Network-Chile (LTSER-Chile)  and the  Red de Centros y Estaciones Regionales de Investigación, RECER UC, Pontificia Universidad Católica de Chile. The authors gratefully acknowledge the work of the Observatoire homme-milieux International,  Patagonia-Bahía Exploradores  (OHM-I,  INEE ,  CNRS  and the  Estación Patagonia UC, Pontificia Universidad Católica de Chile ).
Funding This study was partly funded by the  LabEx DRIIHM ,  French Programme "Investissements d'Avenir"  ( ANR-11-LABX-0010 ) which is managed by the  French National Research Agency. FDA and AS-B received support from  FONDECYT   1191865. FDA, AG & PAM were supported by Grants ICMMINECON,  P05-002  and  CONICYT AFB-170008  (Chile) to the  Institute of Ecology and Biodiversity. JLG received support from  FONDECYT   1161110. FDA was supported by  Vicerrectoría de Investigación (VRI) ,  Universidad Mayor , Chile.
Two species, the commensal Mastomys natalensis, and the non-commensal Otomys unisulcatus showed significant temporal changes in body size, with the former increasing and the latter decreasing, in relation with climate warming. Commensalism could explain the increase in size with time due to steadily increasing food availability through increased agricultural production. Apart from this, we found no general life history or habitat predictors of a temporal response in African rodents. Secondly, in order to further test this hypothesis, we incorporated our data into a meta-analysis based on published literature on temporal responses in rodents, resulting in a combined dataset for 50 species from seven families worldwide; among these, 29 species showed no significant change, eight showed a significant increase in size, and 13 showed a decline in size.
MATERIAL AND METHODS As described below, two approaches and datasets were analysed in the study. Firstly, mensural data of six African rodent species (from two sub-families) were collected from 739 voucher specimens in museum collections spanning about 100 years, and we used PCA and general linear models to analyse morphological trends in these data. Secondly, we added our own conclusions to a meta-database obtained from scanning all published studies where temporal trends in body or skull size were assessed. Adding the data from our recently analysed species to this global dataset, resulted in a final database of 50 species from seven rodent families. The binary response variable derived from the literature was simply the presence or absence of a reported significant temporal response of body size for each species.
Analysis of cranial variables for African species All statistical analyses were conducted with R (version 3. 4;  R Core Team, 2017). Firstly, the existence of sexual dimorphism in size was tested using a t -test, with PC. 1 used as an indicator of both skull and body size. None of the analyses showed significant sexual dimorphism in size, therefore the data for males and females were pooled. Both simple and multiple linear models were used to assess the relationship between PC. 1 and seven predictor variables, i. e. , relative age of specimens (tooth-wear class), year of collection, geographical (elevation, latitude and longitude) and environmental [mean annual temperature (Bio1) and annual precipitation (Bio12)] variables. Elevation, Bio1 and Bio12 were obtained from the Worldclim database  (Hijmans et al.
Meta-analysis of rodent databases The second analysis was performed on a larger species number to test the possible effect of different life history and habitat predictors on the probability of a temporal response. We then incorporated our results of the previous analysis on six African rodent species (this study) into a meta-analysis based on published literature of temporal responses in rodents around the world, resulting in a dataset for 50 species from seven families (Table  S3 ). The probability of temporal response was determined as a binary response variable based on metadata from literature, i. e. , either a reported significant temporal response (1) or no response (0). The final group of species included in the meta-analysis included a range of small rodents such as mice (11 g) to larger rodents such as squirrels and marmots (4 kg).
Meta-analysis of combined database Among 50 rodent species reviewed, 29 showed no significant change, eight showed a significant increase in size and 13 showed a decline in size (see Table  S3 ). Out of the seven families, three had no species that varied temporally, Dipodidae (two species), kangaroo rats Heteromyidae (two species) and pocket gophers Geomidae (two species). A ''mole-rat'' (Spalacidae, one species) showed a temporal response (increase), while around half the species of murids, cricetids and sciurids (squirrels) showed a temporal response. However, the direction of changes varied between families. In Muridae (n = 18), both increases (four) and decreases (four) were recorded, while in Cricetidae (n = 18), only decreases (nine) were observed, and in Sciuridae (n = 7), only increases (three) were observed.
Temporal size trends in African rodents Few data are available on temporal trends in body size in African rodents. Nengovhela, Baxter & Taylor (2015)  assessed temporal changes in skull size over 100 years in museum and seven habitat variables (10 predictors in total) used to explain variation in probability of temporal response in 50 rodents species. Rodent family was taken as a random factor to account partly for phylogenetic biases. The four quantitative variables were litter size, body mass, maximum latitude and range size area. For the six binary variables, default values were ''1'', i. e. fossoriality, habitat specialist, commensal, dessert-dwelling, high elevation, r-or k-selected (r=1, k=0). Estimates represent comparisons with state ''0'', e. g. , non-fossorial rodents (''0'') have lower probability (negative sign of estimate) of temporal change than fossorial (''1'').
Life history and habitat do not seem to predict response to climate warming Rodents have been said to display a very wide variation of life history patterns  (Samuels, 2009)  with body size reported to be related to a multitude of life history traits (e. g. , gestation length, reproductive rate, home range size, basal metabolic rate, etc,  (Webster, Gittleman & Purvis, 2004). Based on our generalized linear mixed model of our global meta-database of 50 rodent species (including also our current data), we found that none of our chosen predictors could significantly explain the probability of a temporal response. However, looking at each predictor, elevation even though insignificant, showed a 60% chance of temporal changes in high altitude species compared to 30% in low altitude species. This might be due to the fact that high elevation habitats are more prone to fragmentation  (Naya, Naya & Cook, 2017) , causing rapid morphological changes.
Body size response to climate change is not so universal in rodents Both in the new data added for six African rodents (where one species increased in size, one decreased and four did not change), and for our global dataset of 50 species, we found variable responses to general temporal changes over an approximately 100 year period, with increases, decreases and no change. These differing temporal responses in rodents have been reported in other studies, for example,  Villar & Naya (2018)  showed a temporal decrease in body size of about seven rodent species and no response in ten species. A similar trend was reported in  Eastman et al. (2012) ,  Nengovhela, Baxter & Taylor (2015) ,  Ozgul et al. (2010) ,  Pergams & Lawler (2009) , with either decreases, increases and no changes reported (Table  S3 ).
The Holozoa clade comprises animals and several unicellular lineages (choanoflagellates, filastereans, and teretosporeans). Understanding their full diversity is essential to address the origins of animals and other evolutionary questions. However, they are poorly known. To provide more insights into the real diversity of holozoans and check for undiscovered diversity, we here analyzed 18S rDNA metabarcoding data from the global Tara Oceans expedition. To overcome the low phylogenetic information contained in the metabarcoding data set (composed of sequences from the short V9 region of the gene), we used similarity networks by combining two data sets: unknown environmental sequences from Tara Oceans and known reference sequences from GenBank. We then calculated network metrics to compare environmental sequences with reference sequences.