dataesr/scientific-paragraphs-categorization huggingface dataset:
The second one was restricted to sites having the longest fully overlapping observation period with no gap, leading to time series of 5, 12, and 11 years, for bats, birds, and butterflies, respectively. This last data selection procedure reduced the number of sites included to 87, 106, and 57, for bats, birds, and butterflies, respectively (Dataset 3). The analysis of the three datasets gave qualitatively similar results (Supplementary Figs. 7 8 9 ), confirming the robustness of our results to both the presence of gaps and the number of communities studied. Community species diversity and phylogenetic diversity. For each local community, we estimated the species diversity as the exponential of the Shannon diversity index calculated from the summed yearly abundance across all years for each species seen in a site during the survey period.
9 (Supplementary Fig. 12 ). For butterflies, we downloaded published sequences for the following genes: cytochrome oxidase c subunit 1 (COI, 657 bp), elongation factor 1 alpha (EF1α, 1239 bp), glyceraldehyde 3-phosphate dehydrogenase (GAPDH, 690 bp), ribosomal protein S5 (RPS5, 616 bp), wingless (wg, 402 bp) (Supplementary Table  4 ). Sequences were aligned using CodonCode Aligner 6. 0. 2 [http://www. codoncode. com], and the different genes were concatenated. Phylogenetic analyses were performed in a Bayesian framework using BEAST 1. 8. 1. The dataset was partitioned by gene. Unlinked GTR + Γ model of nucleotide substitution and uncorrelated lognormal clocks were implemented for all partitions. The MCMC analysis was run for 100 million generations, and sampled every 100,000 generation, which resulted in 1000 trees.
fr/fr/datasets/corine-land-cover-occupation-des-sols-en-france/] to quantify the percentages of cover occupied by five land-use categories within buffers surrounding the study sites: urban, cropland, heterogeneous agriculture, woodland and seminatural open areas (Supplementary Table  5 ). To calculate the percentage of cover associated to each of our landscape variables around each study site, we used 3 buffer sizes per taxa: buffers of radius 250, 500, and 1000 m around the transect for bat and around the garden for butterfly communities, and squares of 2, 2. 5, and 3 km of side for bird communities. These differences in size and shape among taxa accommodate for the shape of the sampled area and the scale at which landscape is known to affect those taxa  44, 57, 58. The use of either of the three buffer sizes in our statistical analyses (see below) did not change qualitatively our results (Supplementary Figs.
7 8 9 ). Second, to account for landscape complexity, we calculated a Shannon diversity index on the area of each land-use category of the level 3 of Corine land cover (Supplementary Table  5 ). To account for potential changes in the landscape during the monitoring period, we averaged the percentage areas and landscape complexity described above over the years available in the Corine land cover database that match the monitoring periods. For bird communities, we used the land cover data for the years 2000, 2006, 2012, and 2018; for bat communities, the years 2006 and 2012; and for butterfly communities, the years 2006, 2012, and 2018. Third, to account for the intensity of both urban and agricultural land uses, we calculated two indices. The area of sealed soil from the European Soil Sealing V2 [http://www.
Acknowledgements We thank  Frédéric Jiguet ,  Christian Kerbiriou , and  Jean-François Julien  for sharing their knowledge on birds and bats;  Claire de Mazancourt ,  Martin Jeanmougin ,  Emmanuelle Porcher , and  Sonia Kéfi  for insightful discussions and  Alexandre Robert  for drawing the animal motifs. Most importantly, we thank all the volunteers who contributed to the citizen science programs STOC-EPS,  Vigie-Chiro , and  Opération Papillons , run by  Vigie-Nature ,  Ligue pour la Protection des Oiseaux , and  Noé Conservation. T. O. was supported with a fellowship by the Chaire " Modélisation Mathématique et Biodiversité" of Veolia Environnement-École Polytechnique-Museum national  d'Histoire naturelle-Fondation X, E. T. and C. F. were funded by the  ANR  project  ECOSTAB  ( ANR-17-CE32-0002/ECOSTAB ).
Biological Parameters All the biological parameters for the plankton communities, with the exception of the particulate organic matter and variable Chl a measurements were collected in two size fractions by sequential filtration through 10 µm and 0. 6 µm using low vacuum pressure (<0. 5 mm Hg). Thus, the large fraction contained cells >10 µm (L-plankton, large cells) and the small fraction contained cells between 0. 6 and 10 µm (S-plankton, small cells). Data were corrected using procedural blanks and normalized to a filtered volume for all parameters. Particulate organic matter. Samples for particulate organic carbon (POC) were collected at the start and the end of the incubations. To compare the contributions of large (>10 µm) and small (<10 µm) phytoplankton, POC samples were collected for the whole phytoplankton community as well as for the small phytoplankton fraction on pre-combusted GFF filters (15 h, 200 • C, ~0.
Single turnover fluorescence induction curves consisted of a saturation phase comprising 100 flashlets on a 2 µs pitch and a relaxation phase comprising 40 flashlets on a 50 µs pitch. Excitation light was produced by a block of 450, 530, and 624 nm light-emitting diodes with intensities automatically optimized for each sample. Next to the variable Chl a fluorescence measurement, at the start and end of the long-term incubation experiments, blank measurements were made with 0. 22 µm filtered seawater. Using the software FastPro8 GUI (Chelsea Technologies Group Ltd. ), all acquisitions were fitted to the biophysical model  [29]  and relaxation phase  [30]  to determine dark-adapted minimum (F 0 ) and maximum (F m ) photosystem II (PSII) fluorescence yields and PSII functional absorption cross-section (s PSII ), the time constant for electron transport at the acceptor side of PSII (τ Qa ), and the connectivity factor (P, dimensionless).
FCM data analysis. Raw data and statistical analyses were performed using the PhenoFlow (v. 1. 1. 1; [32] ), flowCore (v. 1. 48. 1;  [33] ), and flowFDA (v. 0. 99;  [34] ) packages in R 3. 5. 3 [35]. All raw flow cytometry data and code for analysis are publicly available at https://github. com/DuhaimeLab/ps97_ plp_vlp_data_analysis. Gate selections for PLPs and VLPs were determined visually from plots of log-transformed green fluorescence (excitation 488 nm; emission 530 ± 15 nm) height data ("FL1-H") plotted against red fluorescence (>630 nm) height data ("FL3-H"). A clear break between events was identified and captured in VLP-and PLP-specific polygon gates. The coordinate (x, y) bounds for PLPs going clockwise around the polygon were (6. 15, 0), (6. 15, 5. 9375), (7. 7) and (7. 0). The coordinate (x, y) bounds for VLPs going clockwise around the polygon were (5.
4, 0), (5. 4, 5), (6. 15, 5. 9375), and (6. 15,0). Concentrations of VLPs and PLPs per sample were calculated using the event count inside each respective gate, the volume analyzed, and the sample dilution used for analysis. For each sample analyzed, a fingerprint of event kernel densities across 128 bins on the forward scatter height ('FSC-H'), side scatter height ('SSC-H'), FL1-H, and FL3-H channels was calculated using flowFDA. This fingerprint was analyzed for phenotypic alpha diversity metrics using PhenoFlow. In order to compensate for the large variation in event counts across the PLP and VLP datasets (157-1026 and 399-2603, respectively), which were below the minimum recommended threshold of 10,000 events for phenotypic analysis  [36] , each sample was randomly subsampled to the minimum number of events across the respective dataset (157 for PLPs and 399 for VLPs).
Statistical Analysis Data are given as the mean and standard deviation of the three biological replicates for all parameters except for flow cytometric data (see below) and POC production rate (n = 4). Signficant differences at the level of 0. 05 between the treatments were tested using one-way ANOVA followed by post-hoc (Holm-Sidak method) tests. Statistical analyses were performed using SigmaPlot (SysStat Software Inc. , version 14. 0 San Jose, CA, USA). For the photophysiology, Dunnett's post-hoc comparison was performed using GraphPad Prism 5 for windows. Correlations amongst parameters were explored using Pearson Product Moment Correlation with a significance level of 0. 05 using Sigma Plot (version 14. 0). For PLP and VLP abundance and diversity comparisons, linear mixed models (LMMs) were created from z-score scaled data to determine the fixed effects of experimental treatments relative to the final control treatments using the lmerTest package in R (v.
3. 1-1,  [38] ). As flow cytometric data were collected under different conditions on multiple days (identified as "analysis groups"), the LMMs were used to evaluate how the random effects of the analysis group affected the variance within the data after the assigned fixed effects were considered. In order to estimate the influence of the analysis group on the remaining variance, the variance attributed to the analysis group was divided by the total residual variance. Five LMMs were developed for both the PLP and VLP analyses: two models with all stations combined (treatment and station assigned as fixed effects, analysis group as random effects, final control and stations Bio 1 or 2 as reference; Supplementary Tables S2, S4, S8) to account for all pairwise station comparisons and one model for each of the three individual stations (treatment assigned as fixed effect, analysis group as random effect, final control samples as reference; Supplementary Tables S3, S5, S7, and S9).
Alternatively, the viruses of the blooming heterotrophic populations may have integrated into their host genomes upon infection, resulting in lysogeny rather than lysis. This alternate outcome of virus-host interactions in response to favorable growth conditions for host populations has been proposed as the "Piggyback-the-Winner" (PtW) model  [49]. Distinguishing between these two scenarios would have required measurements of intra-and extracellular viral loads throughout the experiment. Despite this uncertainty, our study supports the undisputed position that virus-microbe-environment dynamics are inherently complex, are difficult to disentangle, and will require more targeted inquiry than community level data types such as flow cytometry are able to provide.
Conceptual Framework on How EPS Could Affect SO Microbial Community Amongst our experimental treatments, the bacterial EPS was the treatment, which exhibited the greatest POC and Chl a accumulation rates as well as increases in PLP and VLP abundance. Together with reports that viral lysis affects Fe chemistry and its bioavailability through the release of cellular lysis products  [11, 12] , our data demonstrated that bacterial EPS availability likely plays a role in microbe-virus infection dynamics (see Figure  8 ). Analysis of the correlations between viral and prokaryotic parameters (Supplementary Table  S12 ) showed clear linkage with primary producers, specifically in relation to their biomass build-up. PLP abundances were positively correlated with POC accumulation rates of S-plankton (p = 0.
Conversely, for other barriers, innovation teams mentioned very generic challenges with limited precision, which resulted in broader categories. For example, we considered a single category for challenges related to the Common Agricultural Policy (CAP) environmental or sanitary regulations, even though it may embrace various fields of application and impacts. We built a matrix with the presence/absence of 46 barriers (Table  2 ) across the 25 innovation settings. The final dataset presenting barriers to crop diversification according to the characteristics of the 25 cases is available at: https://zenodo. org/record/3249967#. XQoQoo_gpPY This dataset was the basis of the following MCA. All interviewed human participants belonged to organisations which were partners of the DiverIMPACTS project.
The visual "drawing" of ideal-type boundaries and connected barriers on MCA outputs (Fig  4 ) was subject to our human interpretation to "make sense" of data (informed by the qualitative analysis of material collected with innovation teams). This exploratory approach could be enhanced by more systematic methods of clustering on a larger dataset. The impact of less frequent barriers on ideal-types could also be tested while comparing MCA outputs integrating only more frequent barriers. In our analysis, the first four dimensions (axis 1 to 4) of the MCA explain only 42% of total variance. This suggests that although innovation settings play a role in shaping the barriers to crop diversification, other factors should be considered. Studying more cases would allow us to strengthen the robustness of the analysis and also to integrate new variables that we did not integrate because of the small sample.
Analysing hybrid and intermediate food system innovation settings Our conceptual proposal of food system innovation settings is based on 3 criteria with 3 modalities, which implies 27 combinations. Of the 27 possible combinations, 16 of the 25 case studies (64% of cases) are in 4 of the 27 possible combinations (14. 8% of the possible combinations). This study was based on empirical data collected on the 25 cases of the DiverIMPACTS project which were not initially selected to cover all possible combinations of innovation settings. In those cases, there is a very strong link between local and organic chains, and between conventional farming and commodity value chains. This can be explained by the fact that organic farmers may be more likely to be involved in local value chains. Organic agriculture was developed as an alternative to dominant agriculture, and promoting shorter supply chains has historically been a strong strategy to support the development of that innovation niche  [10, 22].
Acknowledgments We are grateful to all the partners of DiverIMPACTS who have inspired us, and to the innovation teams that enthusiastically took part in the research. We especially thank  Walter Rossing ,  Pieter de Wolf ,  Jorieke Potters ,  Daniel de Jong  ( Wageningen University and Research ) who organised the co-innovation workshops where our interactions with the innovation teams took place, and the project leader  Antoine Messean (INRA ), for his support and advice. We thank the academic editor and the three reviewers for their complementary constructive feedback on our paper.
Seismic Data For this study we leverage 6 years of continuous seismic data recorded across the Observatoire Volcanologique du Piton de la Fournaise (OVPF) network shown in Figure  1. The network consists of stations equipped with short-period seismometers and broadband seismometers, recording at a sampling rate of 100Hz. The data shown in this manuscript are primarily from the seismic station of the Cratere Bory (BOR) site although the analysis described in sections 2. 2 and 2. 3 has been performed for most of the seismic stations situated in the the Enclos Fouqué belonging to the OVPF network caldera we find that data recorded at the BOR site provides the best performance for our eruptive state classifier. We note that in the period under study at BOR seismic data for the April 4 th eruption is missing, and thus this eruption does not appear in our dataset We also use data from the Enclos Sery Sud (CSS), Chateau Fort (FOR) and the Faujas.
The training data in this case constitutes approximately 30% of the total data available for the BOR station, or 12000 time windows. The trained model is blind tested on the remaining 70% of the data, outputting an estimate for the eruptive state of the volcano during this period, without ever having actually seen this data. This allows us to estimate the consistency of the characteristics of the eruptive tremor throughout this period: if the spectral characteristics of the tremor are different between the training and the testing set, the model will be unable to effectively predict the eruptive state in the testing set. Figure  2a  shows the ability of our classifier to predict whether Piton de la Fournaise is undergoing an eruption or dormant based on features generated from a single time window of the continuous seismic signal recorded at the BOR station.
The classifier was trained on the grey portion of the dataset  (2013) (2014) (2015) , and tested on the violet section  (2015) (2016) (2017) (2018) (2019). The model performs well on the test set, with a precision of 0. 99, accuracy of 0. 97 and a recall score of 0. 83. We can see from these features that the eruptive tremor detected at the BOR station occurs consistently in the 3-5Hz frequency range. This is confirmed in Figure  2d , where we see that the 99th percentile of the 3-4Hz frequency band (red trace) of the seismic signal spikes for all eruptions, as opposed to the 14-15Hz band (blue trace). We note that the slightly lower recall score, indicating there are a number of false negatives produced by our model in the test set. Examining Figure  2a  and 2d we see that during some eruptions the level of the 3-4Hz band can drop below a given threshold, resulting in false negative prediction.
Unsupervised Learning: Spectral Clustering Unsupervised learning is a term which describes a set of machine learning (ML) techniques used to learn relationships in datasets where no training labels are available  (Ghahramani, 2004). Cluster analysis, which is a subset of unsupervised learning, can be defined as the task of grouping data using similarity measures which quantify the proximity between data vectors in a given feature space (Aminzadeh & Chatterjee, 1984). In this particular case, we have no explicit labelled data concerning different types or phases of eruptive behavior that Piton de la Fournaise may exhibit, or the specific seismic fingerprints of these behaviors in our feature space, so this approach is particularly well suited for our problem. In order to tackle the investigation into the seismic signature of the eruptive dynamics of Piton de la Fournaise, we turn to a clustering algorithm known as spectral clustering.
We note that some clusters exhibit considerable overlap in the feature space visualization (C1, C5 and C4), there are also clusters that exhibit distinct separation in our feature space picked out by the algorithm (C0 and C2). In order to evaluate the effectiveness of the clustering analysis on the eruptive dynamics of the Piton de la Fournaise volcano, we examine the temporal distribution of the clusters throughout the dataset. Figure  4a  shows the Piton de la Fournaise eruptive state during the 2013-2019 period (red trace), and how the spectral clustering algorithm separated the features into 6 clusters (blue scatter plot). The colored bounding boxes are chosen to demonstrate the effectiveness of the combination of our feature generation method and spectral clustering in highlighting different eruptive behaviors of the Piton de la Fournaise volcano.
Acknowledgments C. R. , B. R. L. , P. J acknowledge institutional support (LDRD) at  Los Alamos  and the  US DOE Office of Science, Geoscience , for funding. We thank  Ian McBrearty ,  James Theiler ,  Andrew Delorey  and  Daniel Trugman  for useful discussions. The data were obtained from the the  Observatoire Volcanologique du Piton de la Fournaise (OVPF) network.
The largest levels of regional forest cover increase relative to the baseline occur across the tropics with large increases in South America, Southeast Asia, Sub-Saharan Africa, and in the temperate and boreal regions including in Russia, China, and Canada. Intensively managed plantations comprise less than 7% of the projected additional increases in forest areas globally under all price scenarios by 2055 (Supplementary Data 3). The proportion Annual mitigation (Gt CO 2 ) Annual mitigation (Gt CO 2 ) Fig. 2  Projected annual mitigation across biome, by mitigation activity (avoided deforestation, forest management, re/afforestation, and changes in rotation lengths) at a starting carbon price of $50/tCO 2 and under 1 and 3% growth scenarios. In the temperate and boreal biomes, where forest loss is predominately due to forestry activities, we include carbon gains due to avoided forest in our measure of gains due to shifts in rotation length.
To disaggregate carbon to the national level, we started with the regional estimates of carbon fluxes by activity. For rotations, we disaggregated regional sequestration using observed industrial roundwood harvests. Changes in rotations can only occur on forests that are harvested for timber purposes, and if a country has a large share of total industrial wood harvest in a region, we assumed it is responsible for a proportional share of the change due to changes in rotations. data from FAOSTAT (2019), we determined each countries' proportional contribution to its regional production of industrial roundwood, and allocated the regional estimate using that proportion. For management, we used the area of planted trees found in UN FAO (2015) to allocate the carbon gains from management. This approach makes sense given that management can only occur in places where trees are planted.
We calculated the proportion of a region's total planted tree area in a given country, and use that proportion to allocate carbon gains. There is some potential that this underestimates carbon allocation due to management in some countries if there is large potential to shift existing forests that are not managed into replanted forests. For land use change, comprising avoided forest conversion and afforestation/ reforestation, we used data on forest change from 2005-2015 from UN FAO (2015). We calculated the absolute value of annual forest change over that time period for each country and for the region. We then allocate the region's carbon flux due to land use change to each country based on the proportion of the region's land use change observed in that country. GTM isolation of private land management costs.
Ice water content is determined by the microphysics scheme, with cloud fractions then diagnosed as in  Abel (2017). Other parameterization schemes are documented in  Stratton et al. (2018, their  Table  2 ). Both CP4-A, and the driving GCM, use the Even Newer Dynamics for General Atmospheric Modelling of the Environment (ENDGame) dynamical core  (Wood et al. Accepted for publication in Journal of Climate. DOI 10. 1175/JCLI-D-19-0380. 1. 2014). The ENDGame dynamical core has been shown previously to improve the representation of key processes over West Africa, particularly the seasonal progression of monsoon rains  (Vellinga et al. 2016; Fitzpatrick et al. 2016 Appendix B). In each climate respectively, the same SSTs are prescribed for the driving GCM, CP4-A, and the parameterized model. Current climate SSTs are provided from the Reynolds daily observational dataset  (Reynolds et al.
Methods In this paper, we analyze the climatic drivers of changes in early-evening MCS extreme precipitation rates (the 99 th percentile of MCS precipitation rate) over the Accepted for publication in Journal of Climate. DOI 10. 1175/JCLI-D-19-0380. 1. Sahel. The calculation methods for extreme precipitation rate and other considered MCS-relevant metrics are presented in Table  1. We define the Sahel as 15°W-15°E, 10-18°N (displayed in Fig. 3 ); the suitability of this region for both climates is explored in Section 4. 1. The codes employed in this paper are available at http://doi. org/10. 5281/zenodo. 2560371 and http://doi. org/10. 5281/zenodo. 2560410. MCSs are classified as any contiguous structure with an area of cold-cloud (Outgoing Longwave Radiation [OLR] <= 167 Wm -2 ) of at least 25,000 km 2 simulated during July-September.