dataesr/scientific-paragraphs-categorization huggingface dataset:
05) explanatory variable effects at the multivariate level, we then ran univariate linear mixed-effects models (LMM) using the lme4 package (version 1. 1-21;  Bates et al. , 2019)  to test the effects of aforementioned explanatory variables on the microbial taxonomic and catabolic diversity variables. The soil microbial taxonomic diversity response variables included the ASV richness, relative abundance, and H' for: all bacteria, the bacteria functional groups, all fungi, and the fungi functional groups. LMM were also run for the ASV bacterial richness response variables, but not for the ASV fungal response variables since PERMANOVA results were not significant. The soil microbial catabolic diversity response variables included: Sum15, catabolic H', and the substrate respiration for all 15 'used' substrates (GLU, XYL, CEL, ASP, SER, LYS, GLY, GLUT, N-AC, OX, UR, MAL, CAF, SYR, and VAN).
Extreme values were removed (defined as ± 3 times the interquartile; number of removed data points never exceeded 6 % of the total number of data points) and random variables were transformed (log2) when necessary to meet normality assumptions of LMM. The plot nested within site and climate chambers were included as random variables. The LMMs were structured as such: response variable ~ Root CWM + Soil PC1 + Treatment*Tree species number + (1|Chamber) +(1|Site/-Plot). The most parsimonious model was identified using the 'dredge' function from the MuMIn package (version 1. 43. 6; (Bartoń, 2019)  which ranks all possible models with all possible combinations of the explanatory variables included in the full model by the lowest Akaike Information Criteria (AIC). To correct for family-wise error rate, p-values were corrected for multiple comparisons using the Bonferroni correction method with the function 'p.
CRediT authorship contribution statement L. M. G. , L. D. P. S. , N. F. , S. H. , A. M. , and F. B. developed the study design, planned and assisted in field sampling, and participated in article redaction. S. H. , N. F. , A. M. , and L. M. G. developed the experimental design and B. B. provided knowledge and experience for gas flux measurements. L. M. G. conducted the experiment, with assistance from A. S. , and lead data analysis and manuscript redaction. K. G. provided knowledge and experience for the microbial taxonomic measurements and analyses, while L. D. P. S. performed them. A. S. performed the soil microbial activity measurements.
Acknowledgements This research was part of the  SoilForEUROPE  project funded through the 2015-2016 BiodivERsA COFUND call for research proposals, with the national funders  Agence Nationale de la Recherche  ( ANR, France) ,  Belgian Science Policy Office (BELSPO, Belgium) ,  Deutsche Forschungsgemeinschaft (DFG, Germany) ,  Research Foundation Flanders (FWO, Belgium) , and  The Swedish Research Council (FORMAS, Sweden). We thank the site managers  Leena Finér  with the  Finnish Forest Research Institute (Metla) ,  Bogdan Jaroszewicz  with the  University of Warsaw ,  Olivier Bouriaud  with the  Forest Research and Management Institute (ICAS) , and  Filippo Bussotti  with  University of Florence  and associates, as well as the  SoilForEUROPE consortium  for their assistance with the soil sampling campaign.
This study benefited from the  CNRS  human and technical resources allocated to the  ECOTRONS Research Infrastructure as well as from the state allocation  " Investissement d'Avenir" AnaEE-France   ANR-11-INBS-0001. We thank the  Ecotron  staff and in particular  Sébastien Devidal  for the technical support during the incubation period. We thank the experimental platform PACE at  CEFE Montpellier, France  for the gas flux measurements. We also thank  Bart Muys  and  Karen Vancampenhout  at  KU Leuven University  for the soil texture data, and  Iulian Danila  at the  University of Suceava  for the tree basal data. We thank  Beatrix Schnabel  and  Anna Heintz-Buschart  for their molecular and bioinformatics contributions. The sequencing data were analyzed at the  High-Performance Computing (HPC) Cluster EVE , a joint effort between the  Helmholtz Centre for Environmental Research -UFZ  and the  German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig.
They found that cropland was not involved in terrestrial insect decline and proposed improved water quality to be a driver of the freshwater insect increase. Yet their meta-analysis raised comments regarding their data selection and methodology  (Desquilbet et al. 2020 ) leading to some corrections  (van Klink et al. 2020b) , the limitations of abundance and biomass as sole indicators of insect trends  (Jähnig et al. 2021 ) and the heterogeneity in temporal coverage with a lack of old baselines  (Duchenne et al. 2022 ). In the first place, however, the sensitive issue of insect trends and drivers requires the utmost rigor in databases intended to serve as references. We present an in-depth study of the InsectChange database  (van Klink et al. 2021)  underlying the analysis by  van Klink et al.
The different issues in the InsectChange database A first shortcoming is a lack of clear information on which invertebrate taxa are included in InsectChange. These are insects and arachnids according to the title and main text, but also entognaths, as indicated in the keywords and appendices. Considering entognaths within the scope of the data paper, we find that 17 types of problems pertaining to errors (240), inconsistencies (39), methodological issues (166) and information gaps (81) affect 154 of the 165 datasets totalizing 526 occurrences (Figure  1a ), with more problem types per study in the freshwater realm (3. 4 ± 1. 5, Among the errors, 35 datasets considered taxa other than insects, arachnids or entognaths (hereafter collectively referred to as "insects" for brevity), most often including the entire invertebrate assemblage instead of insects only, sometimes biasing the trends to the point of reversal (Figure  2a ).
The composition of the invertebrate group selected from the source datasets was misreported in 55 datasets (e. g. Figure  2b ). Insect numbers were misreported from source studies in 25 datasets because of calculation errors, inversion of numbers or species counted twice (e. g. Figures  2c  and 2d ). Variation in sampling effort was not accounted for in 17 datasets (e. g. Figure  2e ). Some datasets or plots had overlapping data for all or part of the time period (  13 datasets, e. g. Figure  2f ). Finally, the geographic coordinates of the included plots were erroneous or not precise enough given the local scale objectives of the study in 88 datasets. There were also a number of inconsistencies and information gaps. There were inconsistencies of taxa between plots of a same dataset (e. g.
Focus on the problematic inclusion of clams, snails, worms, shrimps in freshwater data In the freshwater realm, 80% (19 out of 24) of the biomass and 40% (21 out of 54) of the abundance datasets included invertebrates other than insects. They were related to 28 distinct datasets, 24 of which involved the entire freshwater invertebrate assemblage (Figure  3 , Appendix S2: Table  S1 ). The great majority of these 24 datasets included worms, mollusks and crustaceans, with notably Oligochaeta, Turbellaria and Amphipoda, often indicative of poor water quality (Appendix S2: Table  S2 ). The inclusion of these datasets is not consistent with the purpose of the database, because the dynamics of insects cannot be inferred from those of invertebrate assemblages. This is illustrated by Figure  3a  showing that insect and invertebrate assemblages can have contrasted trajectories (top), and that proliferating invasive mollusks can drive the trend of the invertebrate assemblage (bottom) (see also FreshwaterNonInsects.
xlsx). The 62 freshwater datasets, 28 including non-insects (24 the entire assemblage). The insect share, extracted from 13 of these, averaged 48. 5% with a ~35% CV. Photograph credits detailed in Appendix S2: Table  S3. Beyond these examples, we calculated that on average insects made up 48. 5% ± 32. 2% of the entire assemblage in the 13 datasets (48 plots) with information on all or part of the time records (Figure  3b , FreshwaterNonInsects. xlsx). The insect share in the assemblage was also highly variable over time (Appendix S2: Figure  S1 ) with a coefficient of variation averaging 34. 8% ± 22. 5% in the 8 datasets (42 plots) where information was available for more than one time record. Considering non-insects in the assemblage can therefore considerably alter the inferred 'insect' trend.
Inclusion of experimental or disturbed situations with internal drivers of insect change A major limitation of the InsectChange database is that users are inclined to erroneously attribute insect changes to the ecological drivers extracted from external databases, when they more directly reflect habitat changes caused by -most often omitted-internal drivers investigated in the original studies. Indeed, 88 datasets were extracted from controlled or natural experiments or from strongly disturbed contexts. In addition, the factors originally investigated were not mentioned in 69% of these 88 datasets (Problems. xlsx). Among these 88 datasets, 14 controlled experiments tested the effect of one or several treatments in different plots (Figure  4a ) and 53 natural experiments  (Diamond 1983 ) investigated the effect of a natural disturbance by comparing insect abundance in more or less disturbed plots (Figure  4b ) or before and after the disturbance in a plot (Figure  4c ).
In these experimental datasets, control plots, experimental plots or both control and experimental plots were inconsistently included in InsectChange. In the remaining 21 observational studies, a strong disturbance affected insect trends (Figure  4d ). Among the 88 datasets, the studied factor could be negative, such as a severe drought, fire or pesticide application, creating deleterious then recovery conditions for insects at the beginning, middle or end of the observation period, this timing strongly influencing insect trends (Appendix  S3 ). The factor could also be positive, such as cessation of harmful activities, remediation measures (Figure  4e ), active restoration or creation of new habitats such as ponds (Figure  4f ), reservoirs or nesting sites, favoring insect recovery or colonization.
Insects were also expected to increase in 6 studies on polluted and eutrophicated freshwaters (Appendix S3) because stress-tolerant Of these 88 datasets affected by specific internal drivers, there were almost five times more situations expected to favor an increase rather than a decrease in insects (Figure  4g , Appendix S3). Two-thirds of the freshwater datasets were affected by internal drivers, a proportion significantly higher than that (one half) of the terrestrial datasets (χ² = 5. 7, P=0. 02, Figure  4h ). Of these twothirds, insects were expected to increase in 61% of cases and decrease in only 15% of cases. We thus caution that InsectChange is unrepresentative of the habitat conditions and associated insect trends worldwide, particularly in freshwaters. In controlled experiments, it seems prudent to consider only control sites.
Methodological issues resulting in a strong overestimation of the local cropland cover Last, we found a strong overestimation of local cropland, a possible driver included in InsectChange by matching plots to land covers of the ESA CCI database (ESA 2017) via their geographic coordinates (GC). We found that the assessment of local cropland cover was inadequate for almost half (468 of 982) of the terrestrial plots, with a very uneven distribution of errors (Figure  5a , Appendix S4, CroplandCover. xlsx). Most plots assessed as having no surrounding crops were assessed (451 of 486), while most plots assessed as having surrounding crops suffered from an overestimation of the crop cover (350 of 496), two-thirds (233) having no surrounding crops. We observed a similar picture when considering only plots with distinct GC (508) (Figure  5a ).
Because of this strong bias, we argue that InsectChange cannot provide a reliable analysis of the impact of local cropland cover on insect change and could incorrectly dismiss the impact of crops on insect decline. The main reason for these errors was an inaccurate assessment of the local cropland cover from the interpretation of satellite images in the ESA-CCI database (Appendix S4), notably due to its limitation that grasslands may inaccurately be coded as croplands  (Desquilbet et al. 2020 ) and its imprecise representation of land cover when used at a local scale composed of nine 300 m × 300 m squares with a rough cropland cover assigned to each of them (Figure  5b ). Second, the GC were inadequate for estimating land cover at this scale for 228 of the 982 plots, mainly (212 plots) because the considered plots aggregated data from sampling points often several kilometers apart under the unique GC of their barycenter or of one of these points (Figure  5b ).
Conclusion The numerous problems underlying InsectChange call for corrections and an extreme vigilance in its use. They fully undermine the results obtained so far from this database, in the first place those of van  Klink et al. (2020a) , widely covered by the media, which cast unsubstantiated doubt on insect decline  (Kimbrough 2020 , McGrath 2020). We argue that InsectChange, in its current state, does not allow to study insect trends worldwide, to analyze the influence of agriculture on insects or to suggest a positive effect of water protection policies on freshwater insects. More generally, this careful review illustrates the importance of contacting dataset owners to ensure their appropriate use. It calls for vigilance to avoid transferring errors across databases, as occurred for 11 datasets incorporated from the Global Population Dynamics Database  (Prendergast et al.
Acknowledgments This work was publicly funded by the  French National Research Agency  ( ANR-17-EURE-0010  to MD,  ANR-16-IDEX-0006  to LG,  Investissements d'avenir program ). We thank  Sonja Jähnig  for helpful discussions,  Gaëlle Viennois  for double-checking the land cover analysis,  Dirk Maes  and  Hans van Dyck  for their help in analyzing biased data on Belgian lepidopterans (study 70), other co-authors of Desquilbet et al. (2020), and  Marianne Elias ,  Marilyne Laurans ,  Doyle McKey  and  Denis Bourguet  for helpful comments on earlier drafts.
community-level structure of plant recruitment interactions as a complex network can provide relevant information on ecological and evolutionary processes acting both at the species and ecosystem levels. We report a data set containing 143 plant recruitment networks in 23 countries across five continents, including temperate and tropical ecosystems. Each network identifies the species under which another species recruits. All networks report the number of recruits (i. e. , individuals) per species. The data set includes >850,000 recruiting individuals involved in 118,411 paired interactions among 3318 vascular plant species across the globe. The cover of canopy species and open ground is also provided. Three sampling protocols were used: (1) The Recruitment Network (RN) protocol (106 networks) focuses on interactions among established plants ("canopy species") and plants in their early stages of recruitment ("recruit species").
A series of plots was delimited within a locality, and all the individuals recruiting and their canopy species were identified; (2) The paired Canopy-Open (pCO) protocol (26 networks) consists in locating a potential canopy plant and identifying recruiting individuals under the canopy and in a nearby open space of the same area; (3) The Georeferenced plot (GP) protocol (11 networks) consists in using information from georeferenced individual plants in large plots to infer canopy-recruit interactions. Some networks incorporate data for both herbs and woody species, whereas others focus exclusively on woody species. The location of each study site, geographical coordinates, country, locality, responsible author, sampling dates, sampling method, and life habits of both canopy and recruit species are provided.
Similarly, the differences between back-calculated temperatures from the 14 equations were also tested using the Friedman non-parametric test (χ 2 ). The Friedman test was coupled with the Wilcoxon post hoc test to test for the significance of pairwise differences between equations. The size effect was assessed using the Kendall test (W). Kendall's W varied from 0 (weak effect) to 1 (strong effect). All analyses were performed with the software R (v. 3. 6. 2) and the libraries "stats" and "rstatix. ".
It is worth noting that the slope and intercept of the oxygen isotope fractionation equations established for the sagittae of freshwater fish are of the same order of magnitude as those we established for the lapilli of European chub (Table  2 ). To further test the generalizability of the fractionation equation developed in the present study, an independent dataset combining the oxygen isotopic signature of water and chub lapilli would be needed. These results confirm the necessity 1) of establishing specific equations to provide reliable tools for estimating the thermal habitat used by fish, and 2) of developing experiments under known environmental conditions.
Through recent improvements in retrieval algorithms,  Van Damme et al. (2021)  used the version 3 of the IASI-NH3 total columns dataset to characterize the evolution of atmospheric NH3 at global, national and regional scales over the  11-year period (2008-2018). Using a statistical trend method based on least squares regression and bootstrap resampling,  (Van Damme et al. , 2021)   Additional details on the monitoring sites can be found in the literature  (Abbadie, 2006; Adon et al. , 2010; Akpo et al. , 2015; Delmas et al. , 1995; Diawara et al. , 2014; Le Roux et al. , 2006; Ossohou et al. , 2019; Ouafo-Leumbe et al. , 2018; Yoboué et al. , 2005). To date, measurements of atmospheric and meteorological physico-chemical parameters are continuing at all the INDAAF sites. These measurements are referenced in the INDAAF database (http://indaaf.
obs-mip. fr) and in the WMO OSCAR database (https://oscar. wmo. int/surface/#/). (Ferm, 1991). The passive samplers were mounted and analyzed at the "Laboratoire d'Aérologie (LAERO)" in Toulouse (France) for all INDAAF sites. (Adon et al. , 2010; Bahino et al. , 2018; Ossohou et al. , 2020). INDAAF passive samplers have proven to be accurate, cheaper, easy to use and useful for air quality monitoring. The measurement accuracy of passive samplers, evaluated through covariance with duplicates, was estimated at 14. 3% for NH3  (Adon et al. , 2010). Detection limit for NH3 was calculated from field blanks and is equal to 0. 7±0. 2 ppb. Values below the detection limit, as well as non-valid reproducibility values, were removed from the database. Thus, the percentages of valid data in the final database for the studied period 1998/2005-2018 were 97% for Banizoumbou, 93% for Katibougou, 90% for Djougou, 94% for Lamto, 73% for Bomassa and 93% for Zoétélé.
Biomass burning emissions of NH3 The fourth version of the Global Fire Emissions Database (GFED4) provides monthly biomass burning emissions at 0. 25° resolution since 1997 from all biomass burning sources, i. e. many sectors (agricultural waste burning, boreal forest fires, peat fires, savanna fires, grassland fires, shrubland fires, temperate forest fires and tropical deforestation and degradation). Emissions of NH3 from biomass burning sources were downloaded for the 1° x 1° grid cell centered on each INDAAF site. The GFED4 emissions are based on the combination of satellite information on fire activity and vegetation productivity to estimate gridded monthly burned area and fire emissions, as well as scalars that can be used to calculate higher temporal resolution emissions  (Giglio et al.
We regridded the Level-2 IASI NH3 product to 0. 25° × 0. 25° resolution. The IASI products have been validated based on aircraft and groundbased measurements. The IASI version 3 dataset compare well with the reconstructed in-situ columns, with the reanalysed dataset used in this study presenting a better agreement  (Guo et al. , 2021). Previous validation work comparing older versions of the IASI product with ground-based Fourier transform infrared (FTIR) observations of NH3 total columns has also shown robust correlations at sites with high NH3 concentrations, but lower at sites where atmospheric concentrations approach IASI's detection limits  (Dammers et al. , 2017). Although FTIR observations are absent from Africa, earlier works have shown fair agreement between previous versions of IASI total column densities and INDAAF NH3 surface observations in West Africa  (Van Damme et al.
, 2015)  and seasonal pattern  (Hickman et al. , 2018; Ossohou et al. , 2019). In the study, the IASI NH3 reanalysed product was used for the years 2008 -the first full year of data available-to the end of 2018. We also used the Tropical Rainfall Measuring Mission (TRMM) daily precipitation product (3B42), which is based on a combination of TRMM observations, geo-synchronous infrared observations, and rain gauge observations  (Huffman et al. , 2007). Independent rain gauge observations from West Africa have been used to validate this precipitation product, with no indication of bias  (Nicholson et al. , 2003). For analyses of seasonal and interannual variability in each product, we used the mean monthly value (for NO2 and NH3) or the monthly sum (for precipitation) for a 1 x 1 grid cell containing each INDAAF site.
Trend analysis Trend analyses were carried out by using Mann-Kendall (MK)  (Kendall, 1975; Mann, 1945)  and Seasonal Kendall (SK)  (Hirsch et al. , 1982)  which are statistical non-parametric tests used to determine the increasing or decreasing trends of a random variable over some period of time. The MK and SK tests were suitable for cases with monotonic trends. The MK test allows working with no seasonal or other cycles in the data such as average annual data. The SK test follows the same principle as the MK test and is significantly robust to seasonality and was therefore applied for monthly time series. The SK test takes into account a 12-month seasonality in the time series data by computing the MK test on each dataset of "months" over the period   1998/2005-2018  separately, and then combining the results  (Tang et al.
, 2018). This study based on ground-based and satellite measurements ranging from one to two decades clearly shows a correspondence between early rainy season precipitation and NH3 concentrations over the two dry savanna sites. Moreover, the results based on our analysis of a long-term database clearly indicates that this process is reproducible every year. The temporal evolution of NH3 can be associated with two most important phenomena: (1) Possible Birch effect emissions in the early and possibly late rainy season, and (2) the overall seasonal cycle of NH3 and the reasons for this broad seasonality (separate from the Birch effect). Indeed, during the wet season (June-September), the months are wetter and cooler and give the soils more moisture than the dry season months. As a result, wet season soils are less susceptible to intense drying events than during the dry season.