dataesr/scientific-paragraphs-categorization huggingface dataset:
In our study, a significant time effect was observed for Proteobacteria and Spirochaetes in the rumen of the control ewes, with an increase in the relative abundance in those phyla over time. We noticed a numerical decrease in the Fibrobacter succinogenes concentration, measured by qPCR, in the rumen of the control ewes at lambing, compared to one month before parturition. This was corroborated with 16S sequencing data which showed an almost 40% decrease in the relative abundance of the Fibrobacteres phylum, which gathers only one genus, Fibrobacter. This important fibrolytic bacterial species s plays a key role in fiber digestion  [42] [43] [44]. This species has been reported to be quite sensitive to changes in the ecological conditions of the rumen  [45] , which might have occurred at parturition.
We noticed a more stable total VFA concentration across the periods in the SC group, whereas decreased levels of VFA were measured at parturition in the control group, mainly driven by acetate concentrations. These data suggest a beneficial effect of the live yeast supplementation on microbial activity in the rumen. Another interesting result was the stabilization of the bacterial fecal diversity throughout the study, as indicated by the significant differences between the groups. Indeed, the supplemented animals presented stable diversity indexes over time, while a strong decrease in the alpha diversity indexes was observed in the control group at Pa. Such a sharp decrease in diversity in the hindgut may increase the risk for opportunistic pathogens to find a free ecological niche in which to settle and trigger disease  [61].
Acknowledgments: The authors would like to thank  Catherine Dhainaut  and  Raphaele Gresse  from  UMR MEDIS  and  Martine Tourret  and  Dominique Graviou  from  UMR Herbivores  for their skilled technical assistance,  Aline Cercy ,  Loic Gaillard ,  Bruno Viallard  and  Pierre Chirent  from  UE Herbipôle  for their very valuable help in the animal experiment,  Anne de la Foye  from  UMR Herbivores  for her advice in the statistical analysis, and  Eric Chevaux ,  Emmanuelle Apper , and  Clothilde Villot  from  Lallemand SAS  for valuable discussions on the results of this study.
Several econometric models were developed, such as CEMDAP  [6]  and MORPC  [7]. A rule-based model (also called computational process model), is a computer program built on a set of rules (a production system model). Each rule has a format of (if "condition" then "action"), it is applied when its condition is met. This approach can capture the schedules constraints directly. Several rule-based models were developed, such as SCHEDULER  [8] , ALBATROSS  [9] , TASHA  [10]. The remainder of this paper is structured as follows. In Section 2, we describe previous works. In Section 3, we precisely define the problem that we are addressing. In Section 4, we introduce the proposed methodology to set the time-choice. In Section 5, we describe the travel demand. In Section 6, we describe the implementation of the time-choice approach.
This travel time is used to estimate the start-time of the next activity. Several constraints should be considered to represent a real-life activity time-choice: the activity-location has an open and close time (time-windows), and the activity itself has a preferred time. To deal with these constraints, capturing the temporal constraint of activities is necessary. In this work, we propose an approach that takes into consideration the previous constraints. The objective of this approach is to set a realistic time-choice for the activity-chain by capturing the temporal constraints of future activities and by using the start-time, endtime, and duration distributions as references. These distributions are extracted from a survey data. Survey data are first cleaned, then the different distributions are built, and the desired activity-chains are generated (or loaded).
• The typical day modeled is a day of week in a pre-COVID-19 world, and the adaptation to a post-COVID-19 scenario requires time distributions that are still unknown at the time of publication. The ENTD is the only French census publicly available describing distributions of hours of activities. It is performed once every 10 years: the last one was started in 2018-2019 and is not published yet. We made the hypothesis that these data are still relevant in a pre-COVID-19 situation in terms of hours of activities, since modes of consumption and work have not changed significantly at the time of the model. In a post-COVID-19 situation times of activities, especially work hours, can be expected to change with the introduction of work-from-home and more agile model of office and therefore the time distribution of connected secondary activities.
Survey Data Description One of the main data sources that we could find is the National Transport and Travel Survey (Enquête Nationale Transports et Déplacements) ENTD, it is a French national survey with several objectives: learning about the trips of households residing in metropolitan France and their use of both public and individual modes of transport. This survey describes all trips, regardless of the reason (origin and destination of the activity), distance, duration, mode of transport used, time of year, or time of day  [18]. Several types of information can be extracted from the ENTD trips, such as the following data distributions: (1) activity end-time, (2) activity-duration, and (3) the travel speed of each mode of transport. Others parameters can also be extracted from ENTD: (4) the typical activity duration, and two tolerance (delay) coefficients, namely (5) duration delay coefficient and (  6 ) mode-travel time delay coefficient.
To apply the time-choice process, the parameters (1-3) are necessary, along with the demand file, which contains the activity-plans for the different agents; the parameters (4-6) are optional, as are (  7 ) the activity-gap data. The required data (variables) are presented as follows: • ENTD distributions: -Duration-distribution: each activity has a duration-distribution, which represents the proportion of users who participate in this activity for a given duration. -End-time distribution: for each activity, the end-time distributions can be reconstructed from ENTD observations, regrouped, and based on a time-period. -Travel speed-distribution: in this approach, each transport mode has a travelspeed value (an approximate value), it was used to estimate the travel time from an activity to another one.
Activity-Duration: Typical Value and Distribution Two duration parameters are required: typical duration and duration distribution. The activity typical duration can be defined for each activity. In this work, the five-number summary is computed to get more information about the available data (activities data). Due the large variety of data, a first constraint is applied to restrict the duration range (minimum and maximum values). Second, the five-number summary are calculated; as result, the median value seems like the more realistic duration for an activity and is considered as the typical value. These typical durations are stored in a module called duration, which is a part of the population generator (Figure  8 ). Activity-duration distribution is built from the updated survey (ENTD) data for each activity.
Travel Speed Range by Mode The travel speed is used to calculate the real travel time between two activities. The trips-data (extracted from ENTD) contain information about the distance and travel time for each observation (trip), and the trip speed for each observation can be deduced. To generate a distribution of travel speed for each mode, the following process is applied: first, the travel speed is calculated for each trip; then, a data cleaning process is applied to remove the overlays (non defined or non realistic) data, mainly due the null speed (defined or null travel-distance). In next step, a minimum and a maximum speed value (bounds) are defined for each mode, with the aim to restrict the study data. These bounds are used to eliminate the non-realistic observations from the dataset.
Discussion The proposed approach fits well the duration of activities. For the activity time distribution, two patterns are distinguished: the primary activities work and education fit well. The output of these distributions is similar to the referenced distribution (in ENTD). However, the proposed approach shows some difficulty fitting a set of secondary activities in some parts of the day. This drawback could be the result of multiple reasons: the primary activities are first inserted into the plan, followed by the secondary activities, and the proposed approach assigns the duration and end-time for the activity by following their order in the activity-chain, which means that the time-setting of secondary activities depends mainly on the time-setting of the first activities; the second reason is that there was no consideration of shopping in the morning or at lunchtime, and the same is true for restaurant activity (at lunchtime), where the lunch activity is considered as an internal activity of work or education, providing a larger time-distribution for the secondary activities; the third reason is the difference between the national-scale and the catchment area, where restricting the time-window of activities will impact the fitting data and the performance of the approach.
Funding: This research was funded by the E3S project, a partnership between Eiffage and the I-SITE FUTURE consortium. FUTURE bénéficie d'une aide de l'État gérée par l'Agence Nationale de la Recherche (ANR) au titre du programme d'Investissements d'Avenir (référence ANR-16-IDEX-0003) en complément des apports des établissements et partenaires impliqués. FUTURE benefits from France grant managed by the National Research Agency (ANR) under the Investments for the Future program (reference ANR-16-IDEX-0003) in addition to the contributions of the establishments and partners involved.
We further address the potential connection between IPSO 25 and platelet ice formation under near-coastal fast ice, which is 90 related to the presence of near-surface ice shelf basal melt water. An intercomparison of sea ice as well as temperature reconstructions (based on GDGT analyses) with (1) sea-ice distributions obtained from satellite observations and (2) estimated sea-ice distribution and SSTs deduced from modelled data provides for an evaluation of the proxy approaches. For a more semi-quantitative sea-ice estimate, the relatively new approach of PIPSO 25 has been used to further assess the advantages and limitations of the sea-ice index as a potential tool to validate and improve numerical climate models to better understand 95 current and past trends in sea-ice development in the Southern Ocean.
Comparing biomarker data with satellite and numerical model data Here, we discuss the advantages and caveats of the sea-ice biomarker IPSO 25 and the semi-quantitative sea-ice index PIPSO 25 by comparing the proxy data to satellite and numerical model data. The main ice algae bloom in the Southern Ocean occurs during spring, when temperatures increase, sea ice starts melting, which results in the release of nutrients and stratification of the water column and the increasing solar insulation stimulates the productivity of photosynthesizing organisms  (Arrigo, 2017; 400 Belt, 2018). The sea-ice biomarker IPSO 25 is hence commonly interpreted as a spring sea-ice indicator, which is why, in the following, we compare the biomarker-based sea-ice reconstructions to satellite-derived spring SIC and modelled spring SIC.
415 IPSO 25 concentrations in the surface sediments around the Antarctic Peninsula exhibit similar trends as the satellite-derived and modelled SIC, while they differ significantly in the Amundsen and Weddell Seas, where high SIC are revealed by satellites and the model but IPSO 25 is very low concentrated. The relatively low IPSO 25 concentrations in these areas highlight the uncertainty when considering IPSO 25 as a sea-ice proxy alone, since such low concentrations are not only observed under open water conditions, but also under a severe sea-ice cover. In this case, the low concentrations of IPSO 25 are the result of the 420 latter, where limited light availability hinders ice algae growth, leading to an underestimation of sea-ice cover. As a result, IPSO 25 and satellite/model data show low correlations (R² = 0.
7b+d ) than IPSO 25 concentrations. For correlations of satellite/model data with PIPSO 25 calculated using the HBI E-triene, brassicasterol and dinosterol, respectively, we refer the reader to Fig. S4. There are, however, also limitations in the semi-430 quantitative sea-ice index PIPSO 25 , that need to be considered when interpreting this approach. A drawback may appear when the concentrations of the sea-ice proxy IPSO 25 and the phytoplankton marker are both low (due to unfavourable conditions for both ice algae as well as phytoplankton) or high (due to a significant seasonal shift in sea-ice cover and/or stable ice edge conditions), which may lead to similar PIPSO 25 values, although the sea-ice conditions are completely different from each other. This scenario was detected in five samples from the Weddell Sea (PS111/13-2, /15-1, /16-3, /29-3; /40-2; Fig.
3b+c), 435  where IPSO 25 and the HBI Z-triene concentrations are close to the detection limit, while P Z IPSO 25 values are very low, suggesting a reduced sea-ice cover. Satellite and model data, however, show that these sample locations are influenced by heavy, perennial sea-ice conditions. We conclude that biomarker concentrations of both biomarkers at or close to the detection limit, indicative of a severe ice cover, need to be treated with caution. As mentioned above, we assigned a maximum P Z IPSO 25 value of 1 to these samples and we note that such practice always needs to be made clear when applying the PIPSO 25 approach. 440 The coupling of IPSO 25 with a phytoplankton marker, nonetheless, provides the more robust and reliable sea-ice reconstructions. Regarding the above-mentioned ambiguities, we recommend to not only calculate the PIPSO 25 index, but also consider individual biomarker concentrations and, if possible, take other sea-ice measures, such as satellite data and/or wellpreserved diatom assemblage data  (Lamping et al.
As stated above, B. adeliensis is not confined to platelet ice, but is also observed in bottom ice and described as well adapted to changes in the texture of sea ice during ice melt  (Riaux-Gobin et al. , 2013). 480 In an attempt to elucidate the relationship of IPSO 25 and platelet ice more clearly, we here regard our data in connection to observed platelet ice occurrences. Elevated IPSO 25 concentrations in front of the Larsen Ice Shelves at the East Antarctic Peninsula could be linked to several processes. So far, it is hard to differentiate between in-situ IPSO 25 production or allochthonous input of IPSO 25 from drift ice. According to  Langhorne et al. (2015) , sea-ice cores retrieved from that area did not incorporate platelet ice. The high IPSO 25 485 concentrations could hence be explicable by either drift ice by the Weddell Gyre or by bottom ice production.
Conclusion Biomarker analyses focusing on IPSO 25 , HBI-trienes, phytosterols and GDGTs, in surface sediment samples from the continental shelves off West Antarctica were investigated to depict recent sea surface and temperature conditions in this climate sensitive region. Proxy-based reconstructions of the sea surface conditions were compared to (1) satellite observations and (  2  more realistic, when compared to temperature data based on the WOA13 and modelled annual mean SSTs. Further investigations of HBI synthesis, transport, sedimentation and preservation within the sediments as well as the composition of 530 its sources habitat (bottom ice, platelet ice, brine channels) and its connection to platelet ice formation via in situ or laboratory measurements are required to better constrain the proxy´s potential as a robust sea-ice biomarker.
Acknowledgements Denise Diekstall ,  Mandy Kuck  and  Jonas Haase  are kindly acknowledged for laboratory support. We thank the captains, crews and science parties of RV Polarstern cruises  PS69 ,  PS97 ,  PS104 ,  PS111  and  PS118. Especially Frank Niessen ,  Sabine Hanisch  and  Michael Schreck  are thanked for their support during  PS118. Simon Belt  is acknowledged for providing the 7-HND 545 internal standard for HBI quantification. N. L. , M. -E. V. and J. M. were funded through the  Helmholtz Research Grant   VH-NG-1101.
Avoidance learning We examined whether wing colour pattern affected learning of birds from group 1 (n=28) and group 2 (n=31) by assessing the probability of prey survival within trials. Learning curves and their confidence intervals (figure  2A ) were estimated as a function of the interaction between treatments (i. e. , groups) and trial ("ggeffects" package version 4. 0. 3 in RStudio). We performed a generalized linear mixed model (GLMM) ("lme4" package v. 1. 1. 23 in RStudio version v. 4. 0. 2) with a logit link function and binomial distribution. Survival probability of prey within trial was explained by the wing colour pattern (explanatory variable). To account for repeated measurements, bird ID nested within trial was added as random factor. Additionally, we calculated the odds ratio (OR) based on GLMM estimates and its confidence interval (CI) to assess the strength of the effect of different wing patterns.
Acknowledgments. We acknowledge  Helinä Nisu  for taking loving care of birds and  Konnevesi Research Station  for facilities to conduct this experiment. EPV and ME thank  Christine Calvas  from the  Center of Ecology and Conservation Sciences  for kindly handling the funding that allowed EPV to travel to Konnevesi. EPV thanks to  Sebastián Mena  for designing figure 2 (electronic supplementary material). PMM thank the support of the  Department of Ecology (Biology Centre, CAS ) and its head,  Vojtěch Novotný , for partly covering travel expenses to carry out the behavioural experiments. We thank the organizers of the 8th International Conference on the  Biology of Butterflies, held in Bangalore, India , in 2018, where the idea of this project emerged. Footnotes. Electronic supplementary material is available online in Figshare:.
319124), and additionally supported by the Doctoral School 227 Muséum National d'Histoire  Naturelle -Sorbonne Université  ( TRANSHUMANCE mobility grant for EPV ), and  Secretaría de Educación Superior, Ciencia, Tecnología e Innovación Senescyt  (graduate fellowship to EPV). PMM. was funded by the  Czech Science Foundation (Junior GAČR  grant  GJ20-18566Y ) and the  PPLZ program  of the  Czech Academy of Sciences  (fellowship grant  L200961951 ).
FLUXNET-CH 4 includes half-hourly and daily gap-filled and non-gap-filled aggregated CH 4 fluxes and meteorological data from 79 sites globally: 42 freshwater wetlands, 6 brackish and saline wetlands, 7 formerly drained ecosystems, 7 rice paddy sites, 2 lakes, and 15 uplands. Then, we (2) evaluate FLUXNET-CH 4 representativeness for freshwater wetland coverage globally because the majority of sites in FLUXNET-CH 4 Version 1. 0 are freshwater wetlands which are a substantial source of total atmospheric CH 4 emissions; and (3) we provide the first global estimates of the seasonal variability and seasonality predictors of freshwater wetland CH 4 fluxes. Our representativeness analysis K. B. Delwiche et al. : FLUXNET-CH 4 suggests that the freshwater wetland sites in the dataset cover global wetland bioclimatic attributes (encompassing energy, moisture, and vegetation-related parameters) in arctic, boreal, and temperate regions but only sparsely cover humid tropical regions.
Our results provide seasonality parameters for CH 4 modeling and highlight seasonality metrics that cannot be predicted by temperature or GPP (i. e. , seasonality of CH 4 peak). FLUXNET-CH 4 is a powerful new resource for diagnosing and understanding the role of terrestrial ecosystems and climate drivers in the global CH 4 cycle, and future additions of sites in tropical ecosystems and site years of data collection will provide added value to this database. All seasonality parameters are available at https://doi. org/10.
g. , US-Twt;  Knox et al. , 2016). The dataset has 1 year of urban data from site UK-LBT in London, England. UK-LBT observes CH 4 fluxes from a 190 m tall communications tower in the center of London and has a mean annual CH 4 flux of 46. 5 ± 5. 6 g C m -2 yr -1. This flux is more than twice as high as the mean annual CH 4 flux across all FLUXNET-CH 4 sites, 16. 9 g C m -2 yr -1. The London site has higher CH 4 emissions in the winter com-pared to summer, which is attributed to a seasonal increase in natural gas usage  (Helfter et al. , 2016. )  3. 1. 4   (Bartlett et al. , 1987; Poffenbarger et al. , 2011). Saltwater wetlands along the coast have unique CH 4 dynamics attributable to the presence of abundant electron acceptors, most importantly sulfates which inhibit methanogenesis  (Pattnaik et al.
The individual site DOIs are provided below in Table  2. All seasonality parameters used in these analyses are available at https://doi. org/10. 5281/zenodo. 4672601 (Delwiche et al. , 2021). The breadth and scope of CH 4 flux data in the FLUXNET-CH 4 dataset make it possible to study the global patterns of CH 4 fluxes, particularly for global freshwater wetlands which release a substantial fraction of atmospheric CH 4. To help data users understand seasonal patterns within the dataset, we provide the first global estimates of CH 4 flux patterns and predictors in CH 4 seasonality using freshwater wetland data. In the seasonality analysis, we find that, on average, the seasonal increase in CH 4 emissions begins about 3 months earlier and lasts about 4 months longer at the warmest sites compared with the coolest sites.
FLUXNET-CH 4 is an important new resource for the research community, but critical data gaps and opportunities remain. The current FLUXNET-CH 4 dataset is biased towards sites in boreal and temperate regions, which influence the relationships presented in our analyses. Tropical ecosystems are estimated to account for 64 % of potential natural CH 4 emissions (< 30 • N;  Saunois et al. , 2020)  but only account for 13 % of the FLUXNET-CH 4 sites in the dataset. Unsurprisingly, tropical sites in our network do not represent the range of bioclimatic wetland conditions present in the tropics. Therefore, while maintaining flux towers in tropical ecosystems is challenging, it is necessary to further constrain the global CH 4 cycle. Coastal wetlands are also poorly represented in FLUXNET-CH 4 even though there is evidence of substantial CH 4 emissions from these ecosystems, and so better representation across salinity gradients is warranted.
Lastly, the average time series for FLUXNET-CH 4 Version 1. 0 is relatively short, only 3. 7 site years on average compared with 7. 2 for CO 2 sites in FLUXNET  (Pastorello et al. , 2020). Adding additional site years of data from existing sites, as a complement to adding new sites, will increase the community's ability to explain interannual variability in CH 4 emissions and seasonality. Nevertheless, FLUXNET-CH 4 is an important and unprecedented resource with which to diagnose and understand drivers of the global CH 4 cycle.
B2. 1 Qualifiers: general General qualifiers indicate additional information about a variable. -_F: variable has been gap-filled by the FLUXNET-CH 4 team. Gaps in meteorological variables, including air temperature (TA), incoming shortwave (SW_IN) and longwave (LW_IN) radiation, vapor pressure deficit (VPD), pressure (PA), precipitation (P ), and wind speed (WS), were filled with ERA-Interim (ERA-I) reanalysis data  (Vuichard and Papale, 2015). Other variables were filled using the multidimensional scaling (MDS) approach in REddyProc (see  Delwiche et al. , 2021 , for more details). -_DT: variable is acquired using the flux partitioning method from  Lasslop et al. (2010) , with values estimated by fitting the light-response curve. -_NT: variable is acquired using the flux partitioning method from  Reichstein et al.