dataesr/scientific-paragraphs-categorization huggingface dataset:
05 and the adjusted R 2 of the final RDA model should not be greater than its respective global model. When both spatial and environmental global models were significant, we ran a pRDA using the selected  All analyses were undertaken in R (The R Core Team 2012). Dissimilarities and RDAs were obtained using functions from the vegan  (Oksanen et al. 2012 ) and betapart  (Baselga et al. 2013)  packages. Candidate models were compared and model averaging performed using functions of the MuMIn package (Barto n, 2014).
Explaining dissimilarity-based beta diversity across drainage basins Based on the Sørensen coefficient, beta diversity values showed high variation across the 95 datasets, ranging from 0. 06 to 0. 94 for the average of pairwise dissimilarities and from 0. 14 to 0. 99 for its multiple-sites version. Beta diversity quantified as the average of pairwise dissimilarities was strongly correlated to the average of distances to the metacommunity centroid (r = 0. 99). The average of pairwise dissimilarities was also correlated to the multiple-sites version, although to a lesser degree (r = 0. 75). The correlation structure among the three beta diversity metrics based on the Simpson coefficient and among the three nestedness-resultant beta diversity metrics was similar to that found for the Sørensen coefficient.
18). Regardless of the measure of beta diversity used, the most important explanatory variables were more strongly related to matrix dimensions (number of sites and number of species) and dataset characteristics (especially matrix fill) than to biological, ecological, or geographical factors (Table  2 ). There was also substantial uncertainty regarding the best models, as indicated by their Akaike weights and the evidence ratio between the best models (Table  1 ). However, the weighted average of the estimates based on model uncertainty also suggested a greater importance of data properties as compared to the other explanatory variables (Table  2 ).
Explaining metacommunity variation within drainage basins: variance partitioning RDAs indicated that about half of the insect datasets were associated with environmental or spatial predictors. Global RDA models for environmental predictors were significant (P < 0. 05) for 28 of the 61 metacommunities. For spatial predictors, global RDA models were significant for 13 metacommunities. In only nine cases was metacommunity structure related to both environmental and spatial predictors (Fig. 3 ). Among these, pRDA models obtained by forward selection indicated that the percentages of variation explained by the environmental variables (fraction [a]) and spatial variables (fraction [c]) were on average (mean AE SD) 13. 0 AE 6. 8% and 6. 1 AE 3. 1%, respectively. The average amount of variance shared by environmental and spatial variables [b] was 10.
6 AE 6. 2%. Total explained variance ([a] + [b] + [c]) by the forwardselected pRDA models was 29. 7 AE 11. 5% (Fig. 3 ). The structures of 19 metacommunities (28-9 = 19) were associated exclusively with environmental predictors, and four datasets (13-9 = 4) exclusively with spatial predictors (Fig. 3 ). Among the former, environment explained on average 22. 6 AE 10. 0%. For the four datasets exclusively related to spatial predictors, variation explained was 16. 7 AE 7. 9%. After exclusion of environmental variables with variance inflation factors >10, the numbers of variables per dataset used in the RDA analyses were reduced to an average of 10. 9 AE 3. 0 (range: 4-16) with a total of 61 variables. The final environmental RDA models included 2. 6 AE 0. 9 predictor variables per dataset and a total of 30 variables.
Site elevation and stream width were usually important in explaining variation within metacommunities. However, some variables available in many datasets were never (e. g. , SO 4 and dead wood material) or seldom selected in the models (e. g. , total phosphorus and conductivity; Appendix S5). On the other hand, multivariate models selected a few variables more often than would have been expected from their proportional availability (e. g. , bank modification, clogging, and stream order). These variables, however, were available for few datasets (<4), making it difficult to determine their general importance in structuring the metacommunities.
Discussion Our findings showed that variation in the beta diversity of stream insects across the 95 metacommunities (i. e. , spatial level 1) was not well explained by ecological predictors, such as insect group, spatial extent, latitude, and altitudinal range. In addition, environmental, and spatial variables were poor predictors of assemblage composition within most metacommunities (i. e. , spatial level 2), although environmental variables tended to be better predictors than spatial ones. Because our findings were consistent across a large number of datasets from around the world, we believe that they represent worldwide patterns in stream insect metacommunities. Below, we will discuss our finding in the context of spatial extent, environmental heterogeneity, latitude, and predictability.
Environmental and spatial predictors were both important for nine metacommunities and all four fractions obtained from partial redundancy analysis (pRDA) models are presented. Environmental or spatial predictors were important for 19 and 4 metacommunities, respectively, and their explained fractions obtained from RDA models. In all cases, a forward selection procedure was used to select predictor variables. A salient finding was that dataset characteristics were the main predictors of variation in beta diversity across the 95 metacommunities. Although it seems obvious that matrix fill should be negatively related to beta diversity, our results clearly demonstrate the importance of taking this data property into account when attempting to find correlates of beta diversity. This issue has rarely been considered and we suggest that taking dataset characteristics into account as covariables, before conjecturing about ecological explanations, should be standard practice in future comparative studies.
A combination of low matrix fill (i. e. , numerous absences in the site-by-species matrix) and large numbers of rare species typically results in high beta diversity values  (Podani and Schmera 2011). Rare species are a typical property of stream insect datasets  (Malmqvist et al. 1999; Heino 2005 ). This may be a result of the high variability in stream hydrology. Such variability makes the distributions of individual rare species more difficult to model than the distributions of individual common species  (Soininen et al. 2013 ) and, hence, variation in species composition may likewise be highly challenging to model at the assemblage level. Streams are dynamic systems due to recurrent high or low flows  (Resh et al. 1988; Townsend 1989; McGarvey 2014) , and this variability in flow affects habitat conditions.
Although our predictor variables were important in about half of the datasets and the proportion of variation explained in all cases was low (<50%), environmental variables tended to have a more important influence on stream insect metacommunity structure than spatial variables. This result agrees with previous studies restricted to a single or a few drainage basins  (Landeiro et al. 2012; Al-Shami et al. 2013a; G€ othe et al. 2013; Gr€ onroos et al. 2013). Furthermore, among the many environmental variables included in our study, we found that some easily obtainable variables had particular explanatory power, including stream elevation and stream width. On the other hand, some frequently measured variables (Appendix S3 and S5) were never selected in the models and thus only seem relevant in very specific regional contexts.
g. ,  Townsend et al. 2000). Obtaining such site-level variables that are strictly comparable across datasets, however, was impossible given our broad-scale approach. Third, we were limited by data availability to considering only five taxonomic groups, all of which shared some characteristics (e. g. , all had flying adult stages). We cannot predict how patterns of beta diversity may play out for groups that are restricted to in-stream dispersal (e. g. , fish, mollusks, crayfish, and shrimps). An important consideration related to the second and third limitations of our study is that spatial variables derived from watercourse and overland distances might have different predictive power in explaining variation in species composition within each drainage basin  (Jacobson and Peres-Neto 2010; Altermatt 2013; Altermatt et al.
Acknowledgments The writing of this manuscript was supported by grants from the  Academy of Finland  to JH and the  Brazilian Council of Science and Technology (CNPq)  to ASM and LMB. SAAS and CSMR were funded by  FRGS of Ministry of Higher Education, Malaysia. NB acknowledges the  GUADALMED  project ( HID98-0323-C05  and  REN2001-3438-C07 ) funded by the  Spanish Ministry of Science and Technology  and the  EU -funded project  BioFresh  (7th FWP contract No  226874 ). CB and LMM were supported by  PIP 5733 CONICET. MC and RL were funded by  Companhia Energ etica de Minas Gerais  ( Peixe Vivo Program )  & P&D ANEEL/CEMIG GT-487  and by  Fundac ßão de Amparo a Pesquisa de Minas Gerais  ( FAPEMIG PPM-00077/13 ). NH and RTM received financial support from  PRONEX-CNPq-FAPEAM ,  CNPq  and  INPA.
Methods We outline here the principal datasets and techniques used in this multi-disciplinary study. In addition to those described below, we have incorporated datasets obtained from operational Ozone Monitoring Instrument (OMI) SO 2 retrievals  (Yang et al. 2007; Sealing 2013 ) and a Comprehensive Nuclear Test-Ban Treaty Organisation (CTBTO) infrasound station (IS32) in Nairobi, 1,708 km from Nabro  (Fee et al. 2013). Additionally, we report in the Overview of the petrology of the 2011 lavas and tephra section a digest of conventional XRF analyses of the whole-rock compositions of samples of lava and tephra erupted at Nabro in 2011.
Combined, the data from these networks enable us to characterise the seismicity before, during and after the Nabro eruption (Fig. 1 ). We focus our analysis on events spanning the period from 23 February to 17 September 2011 (3. 5 months before the eruption to 3 months after). We note that in August 2011, eight broadband stations were deployed around the volcano  (Hamlyn et al. 2014 ). These provided a valuable 14-month dataset that is especially suited to studies of the relaxation of the system after the eruption. We did not include these data in the present analysis. We used HYPOINVERSE-2000  (Klein 2002 ) to locate events from measurements of P-and S-wave arrival times, drawing on a 1D velocity model previously used for investigations in the Afar region  (Keir et al. 2009 ). This model is based on the crustal structure deduced from wide-angle controlled-source seismology  (Makris and Ginzburg 1987).
Data We have analysed spaceborne synthetic aperture radar (SAR) imagery to characterise surface changes associated with the Nabro eruption. We examined backscatter images acquired through time to track the evolution of lava flows and tephra deposits, and of large-scale topographic changes, and to measure surface motion based on interferometric processing and image correlation. Data were acquired from the X-band SAR sensors TerraSAR-X (operated by DLR) and COSMO-SkyMed (operated by ASI). The satellites provide SAR images with nominal spatial resolutions of 2 m for TerraSAR-X ('Stripmap mode') and about 1 m for COSMO-SkyMed ('Spotlight' mode). Our differential analyses require identical acquisition geometry for the SAR images, to avoid geometric distortions and artefacts arising from differences in illumination directions.
Ground deformation analysis We have analysed ground deformation associated with the eruption in two ways. First, the line-of-sight component of ground displacement is retrieved via interferometric SAR (InSAR) analysis of pairs of SAR images spanning the eruption. We used the NSBAS software, a modified version of ROI_PAC adapted to processing of time series SAR images  (Rosen et al. 2004; Doin et al. 2011). The InSAR method requires preservation of surface properties of the ground-it thus fails where tephra deposits or lava flows have accumulated between image pairs. Thus, we only obtain meaningful InSAR measurements beyond Nabro's caldera rim. InSAR is sensitive only to the component of ground motion that is directed towards or away from the satellite, in the line-of-sight (LOS) direction, about 30°from nadir.
Accordingly, we computed interferograms on descending and ascending overpasses (looking from ESE and WSW, respectively) to evaluate the importance of horizontal and vertical components of ground motion (Fig. 3 ). For SAR data captured after the eruption began, we selected scenes that were acquired within a short time of each other to ensure that the InSAR pairs captured comparable deformation fields. We focus here on two pairs of SAR images acquired by TerraSAR-X: 11 June 2010 and 1 July 2011 for the descending pass and 14 May 2010 and 6 July 2011 for the ascending pass. An analysis of ground deformation spanning the period July 2011 to September 2012 (after the main phase of the eruption) is provided by  Hamlyn et al. (2014). In addition to InSAR, we performed a sub-pixel correlation of SAR amplitude images using the 'ampcor' routine of ROI_PAC (Fig.
We interpret the MODIS, AVHRR and ALI images visually. They contain useful spatial information but are acquired infrequently. In contrast, the SEVIRI images enable nearcontinuous monitoring albeit at comparatively coarse spatial resolution. We extract the volcanogenic thermal signal from the SEVIRI dataset following  Barnie and Oppenheimer (2015a)  as follows. Independent component analysis (ICA) is applied to the time series of radiances associated with each non-saturated band 4 (3. 9 μm) pixel, in a 9×9 pixel widow surrounding the eruption, to find statistically independent contributing sources. The magnitude of the contribution of each source to each pixel is found, again excluding saturated hotspots. Volcanic sources are identified by a high temperature event (HTE) threshold (the product of the absolute skewness of the source and per-pixel contributions).
The main flow appears to be at its maximum extent, though the 'spillway' at the vent that sources it remains 'hot', suggesting sustained lava effusion, in the shortwave infrared channels. Comparing the SAR images acquired on 18 June and 4 July indicates that the main flow only advanced a further 400-500 m in the intervening period, reaching 750 m elevation, c Composite plot showing RSAM at stations KOZE (blue) and FAME (yellow), SO 2 emission rate computed from OMI data (red) and infrasound signal recorded at the Nairobi station (green). SO 2 emission rates were derived using a plume traverse technique (e. g. Theys et al. 2013) , assuming SO 2 advection in a constant wind field, with wind speed derived from a trajectory model. The magnitude and temporal variation of our OMIbased SO 2 fluxes are broadly consistent with analyses of other satellite SO 2 measurements for Nabro  (Theys et al.
g. Walter et al. 2014)  or potentially interconnection between magma reservoirs (e. g. Albino and Sigmundsson 2014). The former explanation seems more likely based on the observation that post-eruption seismicity (31 August-7 October 2011) beneath Mallahle had significantly lower b values than that associated with Nabro  (Hamlyn et al. 2014). Optical imagery indicates vigorous activity at the vent region, likely characterised by a lava lake sourcing a steamrich, low-altitude plume. SO 2 was still detected by OMI but burdens and emission rates were reduced, suggesting lower lava effusion rates and consistent with generally low explosivity at the vent(s). Explosive activity on 18 and 21/ 22 June is apparent in the Nairobi infrasound data and coincides with a pulse in RSAM and SO 2 output. By 29 June, the optical shortwave infrared images (ALI) indicate diminished lava effusion.
Acknowledgments This research was funded by the  NERC  (project no. NE/J012297/1  ' Mechanisms and implications of the 2011 eruption of Nabro volcano, Eritrea ') with contributions from JOSH's  NERC  fellowship ( NE/I020342/1 ) and supported with access to the  NERC Ion Microprobe Facility. Additional funding was received in the form of grant  NE/L013932/1  held by  DK  and the support of several of the authors by the  NERC-funded Centre for Observation and Modelling of Earthquakes, Volcanoes and Tectonics (COMET). We acknowledge funding for BG from the  EPSRC  and the  School of Earth Sciences  at the  University of Bristol ; for AD by the  Leverhulme Trust and Isaac Newton Trust ; for SAC from  NASA  through the  Aura Science Team  (grant  NNX11AF42G ) and  MEaSUREs  (grant  NNX13AF50G ) programmes; and  DF  from  NSF  grant  EAR-1113294.
218 A Hellinger--transformed testate amoebae community matrix was used as a 219 response matrix  (Legendre and Gallagher, 2001). Redundancy analyses (RDA) were 220 employed to relate abiotic variables (water table, pH and conductivity) to biotic 221 communities. The significance of the model, axes, and variables was tested using a 222 Monte Carlo test with 999 permutations. Computations were performed in R 3. 0. 1 223  (Team, 2013) , using the vegan package  (Oksanen et al. , 2011). (Juggins and Birks, 2012). Prior to analysis, species present in 230 less than three samples were removed. The best performing model was assessed using 231 RMSEP, R 2 , and maximum bias criteria. Then, the model was used for the quantitative 232 inference of the water table. Sample specific errors of the reconstruction were calculated 233 using bootstrapping  (Birks, 1995).
399 A characteristic feature of this Siberian peatland is that minerotrophic species are 400 absent, as the habitat is purely organic and very acidic. An advantage of our study is that 401 the training set was taken from a pristine bog, large enough to exclude any bordering 402 effect, and then applied to a core also representing pristine conditions. In Europe, in 403 contrast, testate amoebae training sets are often collected from raised bogs that have 404 experienced disturbance, possibly biasing reconstructions derived from them. We think 405 that the new calibration data set from Siberia is important for its inferences concerning paleohydrological dynamics in order to better assess the processes that drive carbon 407 accumulation. In our application of the testate amoebae transfer function to the 1300--408 year--long core from Mukhrino mire, the composition of the modern communities was 409 similar to that of the fossil assemblages, and thus the transfer function could be used to 410 reconstruct past hydrological conditions with confidence  (Juggins and Birks, 2012).
, 431 2011). Similarly, the peatlands of the British Isles indicate moist conditions during the 432 LIA (Charman et al. , 2006). Changes in temperature may also be involved in the shifts 433 observed in the Mukhrino mire record. Cool temperatures could have limited peat 434 accumulation, generating changes in our proxies that are suggestive of a dry phase. The 435 extensive carbon accumulation database from northern latitudes reveals such a trend 436 during the LIA, with carbon sequestration rate declining during the transition from the 437 MWP to the LIA. This was probably because the LIA featured the combination of lower temperatures and increased cloudiness, suppressing net primary productivity (Charman 439 et al. , 2013). However, given the distance between western Europe and Siberia, it may 440 be the case that the climatic causes of dry conditions during the LIA at eastern sites were 441 different from those operating further to the west.
Synchrotron x-ray diffraction was performed on the CRISTAL beamline (Soleil Synchrotron, Saint-Aubin). The powder sample was put in a glass capillary tube of 0. 3 mm inner diameter, and rotated during the experiment. The data were collected at 300 K, using a wavelength λ = 0. 620 70 Å. Electron diffraction (ED) observations were carried out at room temperature, using a JEOL 200 CX microscope, equipped with a tilt-rotation sample holder (±60 • ). Highresolution electron microscopy was performed with a 2010 FEG microscope. Powder specimens were first crushed in ethanol in an agate mortar; drops of the suspension were subsequently deposited onto a holey carbon grid. Neutron diffraction experiments versus temperature (in the range 10-730 K) were carried out on the D20 diffractometer with wavelength λ = 2.
41 Å, in a vanadium can. Rietveld refinements of the powder diffraction data were performed with programs of the FULLPROF suite  [15]. The LuFe 2 O 4 -hp sample used for transport measurements was prepared at Laboratoire Magmas et Volcans: a pressed pellet of LuFe 2 O 4 was placed in a pressure medium consisting of semisintered Cr-doped MgO, and inserted between eight tungsten carbide anvils with a 1500-ton press. The compression ramp lasted 2 h and the sample was kept at 17 GPa for 2 h. Decompression was achieved in 10 h. Slow decompression is mandatory in order to avoid anvil failure and sample loss. Electrical measurements were performed on a 4 mm × 1. 1 mm × 1 mm sample of LuFe 2 O 4 -hp. The four electrical contacts located on the boundary of the sample have been made with silver epoxy, and the resistivity value of the sample was measured by a standard dc method in the van der Pauw geometry, taking the average of all current configurations.
ACKNOWLEDGMENTS The authors acknowledge  G. Hearne  ( University of Johannesburg ) and  A. Maignan  ( CRISMAT ) for discussions. The multianvil apparatus of Laboratoire Magmas et Volcans is financially supported by the  Centre National de la Recherche Scientifique (Instrument National de l'INSU). Financial support for this work was partially provided by the  French Agence Nationale de la Recherche , Grants No. ANR-08-BLAN-0005-01  and No. JC08-331297.
Research of the first author was supported by grants  BSF 2010466 , and  ISF 104/11. The second author was supported by the  CNRS - Mastodons  project  GARGANTUA , and the  LabEx PERSYVAL-Lab  ( ANR-11-LABX-0025 ). Research of the third author was supported by  NSF  grants  CMMI-1232623 ,  CMMI-1262063 ,  CCF-1415498 ,  CCF-1523768.
Data source Researchers documented episodes of care extracted from 79 clinical records of FHSS users in Nunavik, who were aged 14 years or older on 1 January 2007 and who had depression or an anxiety disorder. Data were collected in 2009 in 10 Nunavik communities as part of a research program on the quality of mental healthcare  (21). The data were recorded in an Access database and described all the consultations of individuals presenting with suspected or confirmed CMDs over a 2-year observation period. This period was established by using the last mental health consultation in 2007 as point of reference and then adding the previous and the following 12 months. The presence of a CMD was confirmed after considering the diagnoses documented during the observation period or, in their absence, by establishing research diagnoses from signs and symptoms presented by the individual during this period.
Methods Sand fly experiments were conducted from early July to late August 2012 in an agricultural area along the flood plain of the Ourika River and nearby hills south of Marrakech, Morocco. The Ourika River, draining from the High Atlas Mountains, creates a fertile valley full of mosaic orchards and fields. The nearby hills are with distance to the plain increasingly arid with sparse dry steppe vegetation. This area is known for its high mosquito  [22]  and sand fly biting pressure [unpublished data of authors].