dataesr/scientific-paragraphs-categorization huggingface dataset:
Non-metric multidimensional scaling (NMDS; package vegan) was used to ordinate terrestrial and aquatic insects using rank-order dissimilarities (i. e. , Bray-Curtis distances). Convergent solutions were not possible in two dimensions; therefore, data was ordinated in three dimensions but presented in two dimensions using the first two axis. Hierarchical clustering was conducted on average FA mass fractions of insect orders with more than three replicates (hclust; R package stats). Analysis of similarity (ANOSIM, R package vegan; Oksanen et al. , 2013) with 100,000 permutations was used to determine differences between the overall FA profile due to the difference in insect origins. The test statistic, R, is an indicator of similarity where groups can be classified as well separated (R > 0. 75), separated but some overlapping (0.
5 < R ≤ 0. 75), separated but strongly overlapping (0. 25 < R ≤ 0. 5), and not separated (R < 0. 25;  Clarke et al. , 2014). Similarity percentages (SIMPER; package vegan) with 1,000 permutations were used to quantify the contribution of individual FA to the overall Bray-Curtis dissimilarity between aquatic and terrestrial insects. The FA profile of semi-aquatic insects overlapped with that of both aquatic and terrestrial insects and therefore removed from the 10. 3389/fevo. 2022. 952292 SIMPER analysis. Regression tree analysis was conducted using all aquatic and terrestrial insect orders and the 19 FA that were significant in SIMPER analysis and all PUFA of interest (i. e. , ALA, LIN, ARA, EPA, and DHA). Explanatory variables were FA percentages of insect taxa. Each split (non-terminal nodes) was labeled with the FA percentage that determined the split, while the leaves (terminal nodes) were labeled with insect order  (De'Ath and Fabricius, 2000).
NMDS and similarity percentages breakdown was conducted on aquatic and terrestrial insects sampled in each month (from April to October). Sufficient replicates of semi-aquatic insects were not possible for each month so semi-aquatic insects were removed from the analysis. The within-group monthly separation of aquatic and terrestrial insect FA profile was tested using ANOSIM. PERMANOVA pairwise comparisons (PPC; package vegan) of within-group FA profile differences in aquatic and terrestrial insects revealed little differences in adjacent months (Supplementary Table  3 ). Therefore, months were grouped into seasons: spring (April, May), summer (June, July, August), and autumn (September and October). SIMPER with 1,000 permutations were used to quantify the contribution of individual FA to the within-group Bray-Curtis dissimilarity between aquatic and terrestrial insects collected in spring, summer, and autumn.
18:1n-7, 16:1n-7, 18:4n-3 (stearidonic acid; SDA), ARA, and 14:0 (Table  1 ). Aquatic insects had higher abundance of EPA, 18:1n-7, SDA, ARA, and 14:0, whereas terrestrial insects had significantly higher abundance of 18:1 and LIN (Table  1 ). Although DHA was not a significant driver in the global dissimilarity in this dataset, DHA (≥ 1. 8%) separated Chaoborus (order: Diptera) from all other terrestrial and aquatic taxa (Figure  3 ). The majority of aquatic insect taxa (i. e. , Chironomidae, Trichoptera, and Ephemeroptera) were separated from terrestrial insect taxa by EPA ≥ 11. 1% (Figure  3 ). Two additional clusters are formed with aquatic, semi-aquatic, and terrestrial insect taxa separated by ARA. Aquatic Hymenoptera, semi-aquatic Psychodidae, and terrestrial Lepidoptera, Hemiptera, and Coleoptera were categorized with ARA < 0.
Seasonality of the aquatic/terrestrial insect fatty acid profile dissimilarity Multivariate comparisons of similarity for each sampling month revealed a significant difference in FA profiles between aquatic and terrestrial insects (ANOSIM, p < 0. 0001; Figures  4A-G ). However, the similarity (i. e. , ANOSIM) differed throughout the year (Figure  4H ). April, May, and July had similar ANOSIM values compared to the ANOSIM value of the entire dataset (Figure  4H ). June and August had higher ANOSIM value suggesting a greater dissimilarity between aquatic and terrestrial insect FA profiles, whereas September and October had lower ANOSIM values, suggesting greater similarity between aquatic and terrestrial insect FA profiles (Figure  4H ). In all months, EPA was a significant driver of the FA dichotomy between aquatic and terrestrial taxa, with the highest contribution to the dissimilarity (SIMPER, p < 0.
(2015)  attempted to quantify the dissimilarity between aquatic and terrestrial insect FA profiles so far. In their global meta-analysis, the FA profiles of aquatic and terrestrial insects were different but overlapped strongly (ANOSIM R = 0. 34; see  Hixson et al. , 2015). In our dataset, aquatic and terrestrial insect FA profiles were clearly separated with only some overlapping (ANOSIM R = 0. 54). The higher dissimilarity observed in the current study may be due to sampling in a single system with replicates for each insect taxa and thus reduced variability associated with comparing insect FA profiles collected from various climate zones. Sampling in one ecosystem also revealed temporal differences in the dissimilarity between aquatic and terrestrial insect FA profiles, potentially impacting riparian predators that can feed on both aquatic and terrestrial prey.
Asterisk represent significant ANOSIM (Analysis of Similarities) comparisons; "***" represent p < 0. 0001. (H) ANOSIM value representing the similarity between aquatic and terrestrial insects (i. e. , higher value means greater separation between groups) for each sampling month. Dashed line represents the ANOSIM value for the entire dataset. Fatty abbreviations: linoleic acid (LIN; 18:2n-6), α-linolenic acid (ALA; 18:3n-3), arachidonic acid (ARA; 20:4n-6), eicosapentaenoic acid (EPA; 20:5n-3), and docosahexaenoic acid (DHA; 22:6n-3). aquatic insects comprised 74% of riparian spider diet, whereas spiders at 500 and 1,000 m distance from Lake Mindelsee hardly consumed any aquatic insects  (Twining et al. , 2021b). Along a river ecosystem, the diet of web-building spiders has been demonstrated to contain higher proportions of aquatic-derived material in spring and summer than in autumn, based on analysis of similarity and mixing models  (Kowarik et al.
Acknowledgments We thank  Kai-Steffen Frank  and  Verena Medinger  ( BUND ) for their great support and access to sampling sites. We would also like to thank  Martin Wolf  for help designing and building traps and to  Petra Merkel  for help with the FA analysis. We further thank  Marie Huchler ,  Anna-Caterina Eberhard ,  Hannah Bodmer ,  Johanna Roller ,  Luca Kehrt , and  Frauke Schmidt  for their field and/or laboratory assistance. We would also like to thank the handling editor,  Jeff Wesner , and the three reviewers for their thoughtful comments and efforts towards improving our manuscript.
Conclusions For the sole purpose of a taxon-based vegetation classification, the most important thing is to collect enough data on species co-occurrences. This means that collecting many species co-occurrences, even if not particularly complete or accurate, is more useful than collecting few extremely accurate and comprehensive ones. Science will produce useful and essential knowledge only when it classifies objects and makes predictions based on statistically significant datasets, analysed according to adequate protocols. Field data collection should exercise the art of the feasible more than the art of the possible: the adoption of a sampling protocol aimed at recording all macroscopic photoautotrophic organisms co-occurring in the plot would require time and a whole series of tests to essay its pros and cons against a sampling approach chasing higher plot numbers more than plot completeness.
The last values of the experimental accuracy and precision of GRASP-CAM AOD have been globally obtained using all available data, but the results could vary as a function of AOD, as suggested in Sect. 3. 2. In order to obtain a more detailed description of the accuracy and precision, the differences between AOD from GRASP-CAM and AERONET ( AOD) have been calculated for different AOD bins (±0. 025 AOD bins). The amount of available data, the median and the standard deviation of all these distributions are represented in Fig. 10  as a function of AOD. The amount of data available per AOD bin is much higher for AOD values below 0. 25 (N > 1000), as can also be observed in Fig. 9 ; hence, the results will be more representative for the bins of lower AOD. The median of the AOD differences decreases with AOD, and it is within ±0.
GRASP-CAM and AERONET values look well correlated and with similar values for VCF, VCC, and RF parameters but not for the rest. The mentioned correlation between GRASP-CAM and AERONET size distribution parameters can be better observed in Fig. 13 , which also presents the total aerosol volume concentration (VCT). This figure shows the density scatter plots of the GRASP-CAM values as a function of the AERONET ones. The GRASP-CAM-AERONET data pairs have been matched up by pairing the AERONET retrievals with the closest GRASP-CAM data within ±2. 5 min. In addition, AERONET retrievals with AOD at 440 nm below 0. 05 have been discarded in order to have a minimum of aerosol load for the retrieval. A total of 1853 data pairs have been obtained. The highest correlations between GRASP-CAM and AERONET appear for VCT and VCC, both with r 2 above 0.
Indeed, the identification of these climatic zones for building energy efficiency proposals is a key element of the national's programs and policies in order to improve the thermal performances of the buildings. In Spain, a country zoning on twelve climatic zones have been adopted based on a climatic "severity" defined taking into account the degree-days and the annual solar radiation in the zone  3. In addition, within each of these twelve zones, there may be subclimatic zones depending on their altitude regarding the one of the region city capital. While in Morocco, it has been decided to establish a zoning that is the result of a dynamic simulation of a unique standard building carried out considering several Moroccan localities climate conditions using the Trnsys Software 4. Consequently, six different climatic zones have been defined throughout the country where, relevant differences in term of annual heating and cooling needs have been observed.
In Morocco In this study, the selection of building materials used in Morocco will be based on a database of building materials of which the thermophysical properties measurement have been mentioned Most of these materials there are current used in Moroccan in a National level. However, among them, there are some specific locally used materials in the Northern Morocco. Identifying the lasts allows one to identify a chain of local construction and insulation material that may be used as biosourced material in the frame of transborder regions that constitutes the climatic zone Z3 and A2 respectively in Morocco and in Spain. The building materials used at the national level in construction and building sector are given as follows [15]: -Concrete according to the classes used in Morocco (B20, B25, and B30) In the following parts, a selection is made of the candidate materials to be a part of an eco-material chain in the aforementioned transborder context.
METHODS AND SITE PROPERTIES Kathmandu is located on an intermontane basin in the midland of the Lesser Himalayas  (Sakai et al. 2002). Here we study a 2-D east-west cross-section that extends in the east-west direction. We first created the 3-D geometry of the Kathmandu basin by combining the subsurface images of  Piya (2004)  with geotechnical data. Piya (2004)  developed a database of subsurface geometry for liquefaction hazard assessment; we processed these images with the geotechnical dataset of SAFER  (Gilder et al. 2020 ) and obtained a 3-D model of the basin geometry. For the numerical models in this study, we selected a 2-D cross-section that covers the locations of the stations that were deployed by  Takai et al. (2016) , as indicated by the red line in Fig. 1(a). We set three sediment layers for the basin and consider that the shallowest layer is non-linear.
(2003)  and  Régnier et al. (2018)  in the absence of local data. We denote the viscoelastic cases as 'linear' cases throughout the manuscript. Soil non-linearity is based on the viscoelastoplasticity implementation of  Oral (2016) ;  Oral et al. (2017) , such that the non-linear soil behaviour gets closer to viscoelastic response at lower strains. We numerically modelled seismic wave propagation in linear and non-linear media in 2-D with P-SV polarization (in-plane). We used the spectral element method (e. g. Komatitsch & Vilotte 1998; Chaljub et al. 2007 ) implemented in the software SEM2DPACK for 2-D seismic wave propagation  (Ampuero et al. 2002 ; Ampuero 2012) including soil non-linearity  (Oral et al. 2019 ; see 'Data and Resources' section for software availability). The implemented model of soil non-linearity follows the  Iwan (1967)  method and is based on the formulation of  Joyner (1975) , as detailed in  Oral et al.
(2019). To achieve a good resolution of the wavefield up to 10 Hz, accounting for possible velocity reduction due to soil non-linearity, we set the element size such that the node spacing ranges between 0. 2 and 85 m. We prepared an unstructured mesh with the CUBIT mesh generation software. The mesh is made of conforming elements that follow the material interfaces. In particular, the size of elements at an interface is dictated by the material with lower wave speed, to satisfy the minimum-wavelength resolution criterion. The time step equals 1. 5 × 10 -4 s. We set the boundary conditions as periodic on the sides, free surface on top, and absorbing  (Clayton & Engquist 1977)  at the bottom, for the vertical incidence. For oblique incidence, we again use free surface on top, and absorbing layers elsewhere.
D ATA A N D R E S O U RC E S All data needed to reproduce this work are available online: 2-D wave propagation modelling tools can be found at https://github. com/jpampuero/sem2dpack. The updates can be followed in the same address. Please note: Oxford University Press is not responsible for the content or functionality of any supporting materials supplied by the authors. Any queries (other than missing material) should be directed to the corresponding author for the paper.
A C K N O W L E D G M E N T S This work was supported by the  French National Research Agency (ANR)  through project  FAULTS R GEMS  (grant  ANR-17-CE31-0008 ) and Investments-in-the- Future  project  UCAJEDI  (grant  ANR-15-IDEX-01 ). We acknowledge the comments of the Editor Víctor M. Cruz-Atienza ,  Kim Olsen  and an anonymous reviewer that improved the quality of this manuscript.
On the other hand, their application remains restricted to hindcasting of the fluxes, whereas prognostic models are capable of forecasts as well. Though diagnostic models have a less complex model structure, the surface fluxes of energy, water and carbon can be simulated coherently. For example, the GPP predictions in the LSA SAF portfolio rely on estimates of ET from the same framework (amongst other variables;  Martínez et al. , 2020). Diagnostic models are frequently used as a reference or validation dataset in LSM studies (e. g. Decharme et al. , 2019; Guimberteau et al. , 2018).
Models Three well-established models were used to simulate the intrinsically coupled fluxes of water, energy and carbon from terrestrial vegetation: a diagnostic model based on the LSA SAF algorithms (hereafter referred to as DiagMod), ISBA and ORCHIDEE. Each model has a different approach to represent plant phenology. Whereas ISBA has a fairly simple biomass allocation scheme to represent the phenological cycle, ORCHIDEE relies on a dedicated phenology module, and DiagMod is driven by remotesensed forcing variables, such as LAI. Simulations were performed for a wide range of hydro-climatic biomes and plant functional types at local scale (i. e. a single grid point). The simulated fluxes were validated using eddy-covariance measurements, and the simulated phenology was compared to remote-sensed observations of LAI.
Tower: Fluxtower observations from FLUXNET2015 dataset  (Pastorello et al. , 2020)  and the ICOS '2018 drought initiative' dataset  (Drought 2018 Team and ICOS Ecosystem Thematic Centre, 2019) , TRENDY  (Sitch et al. , 2015,  https://sites. exeter. ac. uk/trendy), CGLS: Copernicus Global Land Service  (Camacho et al. , 2013) , ECMWF: soil texture used in the ECMWF Integrated Forecast System (https://apps. ecmwf. int/codes/grib/param-db?id=43), HWSD: harmonized world soil database  (Nachtergaele et al. , 2010) , FAO/USDA: USDA texture map based on FAO digital Soil Map of the World  (Reynolds et al. , 2000)  in coherent surface fluxes. Contrary to ISBA and ORCHIDEE, the calculations for LE and GPP in diagnostic model do not share parameters like stomatal resistance. Instead, the GPP calculations are coupled to LE by using the actual evapotranspiration as an input variable.
ISBA 120 Within the Surfex (SURFace Externalisée) land surface model, ISBA (Interactions between Soil, Biosphere and Atmosphere) is the component dedicated to modeling the exchange of water, energy and carbon fluxes between the soil-vegetation-snow continuum and the atmosphere  (Masson et al. , 2013; Le Moigne et al. , 2018). In this case, a configuration of ISBA with interactive carbon cycling is used, i. e. ISBA-CC  (Gibelin et al. , 2008; Delire et al. , 2020). The fluxes of water and carbon from the vegetation are coupled through the stomatal resistance. This shared parameter is calculated through the A-gs surface 125 scheme, and largely depends on soil moisture stress and air temperature  (Calvet et al. , 2004). The parametrization for this scheme is based on plant traits derived from the TRY-database  (Kattge et al.
Test sites The performance of the models was evaluated at field-scale, using observations from flux towers. A selection of sites was made to ensure adequate data quality, homogeneous land cover and limited disturbance due to management, resulting in the 56 sites listed in Table  2. 33 of these sites are dominated by forest land cover, whereas 18 are dominated by herbaceous vegetation and 5 are crop sites (the models are configured to run without management practices). The data was collected from the FLUXNET2015 dataset  (Pastorello et al. , 2020)  and the ICOS '2018 drought initiative' dataset  (Drought 2018 Team and ICOS Ecosystem Thematic Centre, 2019) , resulting in a total of 526 simulation years. Both data products had been pre-processed with the ONEFLUX processing pipeline  (Pastorello et al.
Validation The simulated H and LE were validated with the observed daily mean fluxes from flux towers. The non-closure of the energy balance is a well-known issue in the eddy covariance observations  (Cui and Chui, 2019). The turbulent fluxes in the FLUXNET and ICOS dataset were corrected for this, under the assumption that the measured Bowen ratio is correct  (Pastorello et al. , 2020). Due to missing observations of the ground heat flux, this correction was not possible for all sites. The validation of H and LE was only performed for the sites where all fluxes were available. The mean correction of LE of each site is listed in Table  2. Similarly, the simulated GPP was validated with the FLUXNET/ICOS GPP data. The net ecosystem exchange (NEE) observed at the fluxtower was partitioned into its ecosystem respiration (RECO) and GPP components using the daytime fluxes and constant friction velocity (USTAR) threshold method  (Pastorello et al.
In this study, the corrected fluxes from the FLUXNET/ICOS dataset were used as a reference. They were corrected to close the energy balance of the observations with the assumption that observed Bowen ratio was accurate. If the non-corrected fluxes were used instead, the bias in LE was reduced, but the simulated H was overestimated (not shown here). This points at the significant uncertainty associated with the observed fluxes from eddy-covariance measurements. The estimated observation uncertainty of the turbulent fluxes (associated with random measurement errors and energy balance correction) had the same order of magnitude as the model errors. The Taylor diagrams in Fig. 2  show that the average variability of the simulated LE and GPP was in fair agreement with the observations. After removal of the mean seasonal cycle, the performance of the models decreased (r ANOM ,NS ANOM ), but the mean variability of the anomalies is reasonably accurate.
Observation uncertainties With the emergence of freely available data from eddy covariance network, the use of local datasets is an increasingly standardized approach to evaluate the performance of land surface models  (Balzarolo et al. , 2014; Napoly et al. , 2017; Williams et al. , 2020; Chen et al. , 2018; Joetzjer et al. , 2015). However, the eddy covariance observations notoriously suffer from substantial biases and non-closure of the energy balance  (Foken, 2008; Mauder et al. , 2020). The non-closure of the energy balance is attributed to 1) large advective fluxes caused by surface, 2) systematic measurement errors due to mismatch in observation footprint or inadequate sample rate, 3) thermal processes, such as heat storage or vegetation metabolism  (Mauder et al. , 2020; Chu et al.
LAI The errors in the surface fluxes were strongly correlated to errors in LAI for both prognostic models, even though their sensitivity to LAI reflects the observed sensitivity reasonably well (compared to the sensitivity to soil moisture). This seemed to indicate that the source of the errors in the fluxes lies in the feedback mechanism between GPP and LAI (i. e. biomass allocation and phenology), rather than in the forward link between GPP and LAI (i. e. photosynthesis and leaf to canopy upscaling). The prognostic simulation of LAI in ISBA was introduced by  Gibelin et al. (2006)  and uses a fairly simple scheme. The latest update was the revision of plant trait parameters according to the TRY database  (Kattge et al. , 2011; Delire et al. , 2020). It has frequently been reported that the seasonal cycle of the simulated LAI in ISBA is delayed by a month or more  (Lafont et al.
Such dynamics are implemented in ORCHIDEE: once certain phenological criteria are fulfilled, the carbon in a reserve pool is allocated to leaf biomass to kickstart the phenological cycle. Still, despite the dedicated phenology module, non-structural carbohydrates reserve dynamics, and a more advanced leaf demography, simulating LAI remained challenging in ORCHIDEE. The timing of the phenological cycle was more accurate in ORCHIDEE, though the accuracy of the simulated LAI was significantly poorer than ISBA. This was the case in particular for herbaceous vegetation. This tendency towards delayed phenology (and in particular a delayed leaf senescence) is found in most earth system models in CMIP5 and CMIP6  (Park and Jeong, 2021). The discrepancy in complexity between the modelling of photosynthesis and that of the biomass allocation has been highlighted by several authors  (Fatichi et al.
, 2019; MacBean et al. , 2020). The further development of ground water dynamics in LSM is indispensable for the accurate coupling of energy, water and carbon in forest vegetation and its response to severe drought events. Several efforts have already explored the potential of improving soil moisture dynamics in LSM. Substantial improvements to soil moisture have indeed been obtained by calibrating the pedotransfer functions, or soil physical parameters. Yet, the impact thereof on the surface fluxes has been found to be relatively limited  (Pinnington et al. , 2021) , or in some cases even negative  (Raoult et al. , 2021). Though many parameters in ISBA and ORCHIDEE are derived from databases  (Delire et al. , 2020) , the LSM have been calibrated to produce accurate surface fluxes using (amongst others) eddy covariance observations.