dataesr/scientific-paragraphs-categorization huggingface dataset:
Underwater video methods have been used for marine ecological studies since the 1950s, and more recently for monitoring marine biodiversity (see  Mallet and Pelletier, 2014  for a review). Video methods have a number of advantages over conventional underwater visual census and fishing based methods for monitoring fish populations including their non-destructive nature, the fact that they cause minimal disturbance to marine life and their potential for high spatial and temporal replication. A rotating, unbaited camera system, STAVIRO (STAtionVideoROtative, in French), has been used since 2007 to survey a number of reefs in close proximity to Nouméa, New Caledonia  (Pelletier et al. , 2012). The resulting dataset includes fish count data for a wide range of species collected across a range of short-term temporal factors (tide height, time of day, tidal state, lunar cycle) and provides an opportunity to investigate the potential effects of temporal variables on monitoring fish counts.
Underwater video sampling and image analysis Information on the fish and habitats in the study areas were obtained using a remote, unbaited, rotating video system the STAVIRO (STAtionVideoROtative). A brief description of the method is given here but for more details see  Pelletier et al. (2012). The STAVIRO consists of an HD video camera enclosed in a waterproof housing attached to a motor programmed to rotate the camera housing by 60 • every 30 s (1 rotation takes ∼3 min). It is relatively lightweight (∼6 kg) and designed so that it can be easily lowered onto the seabed from a small boat (∼6 m) in depths of approximately 1-40 m. The data used in this analysis were collected over a number of outings between February and August in 2008, 2009 and 2010. In total, 442 video drops were carried out but only 197 were used in the final analysis as drops on soft bottom habitats (silt/seagrass beds) were excluded (see details of habitat analysis below).
Habitat stratification As the aims of this study were to determine the effects of temporal rather than spatial factors, the stations were classified according to substrate type prior to the analysis. Habitat stratification was carried out in R using the FactoMineR package  (Le et al. , 2008)  in two stages. A Principal Component Analysis (PCA) was performed on the 442 stations described by 7 substrate variables (%cover: ''sand'', ''rubble'', ''rock'', ''boulder''; ''slab'', ''live coral'', ''dead coral'') followed by an ascending hierarchical clustering procedure based on Ward's criterion to determine the optimal number of clusters. The results of this analysis were used to select video drops that were on hard substrates for the analysis (see supplementary materials S2). This resulted in 197 video drops that were used in the analysis.
Time of day was divided up into four bins roughly equivalent to two-hour time slots. Tide height was divided up into four bins equivalent to approximately 400 mm. Data was subset according to the levels of the temporal predictor and the metric of interest was then evaluated between protected and unprotected areas using a one factor generalized linear model (log link) with 'protection status' as a factor. To ensure a minimum number of replicates for the comparisons the GLM was not performed if there were fewer than six camera drops either inside or outside the MPAs for any of the bins. Alternate distributions were trialed (Poisson and negative binomial) and the results presented for the distribution that resulted in the lowest AIC statistic. The GLM results were plotted with the 95% CI. Data analyses were performed in R version 3.
Lunar cycle The lunar cycle significantly influenced the number of Serranidae recorded, with lower abundances observed around the full moon than during the rest of the lunar cycle. A number of studies have found changes in fish abundance including serranids related to changes in the lunar cycle  (Galzin, 1987; Letourneur, 1996; Samoilys, 1997). These have been primarily attributed to the migration of individuals to spawning aggregation sites at particular times during the lunar cycle. This behaviour has been documented for a number of grouper species including coral trout, Plectropomus leopardus, one of the most common serranids in our study area. However, we believe it is unlikely the pattern that we observed was due to movements to spawning sites as our data were not collected during the reproduction season.
Acknowledgements Thank you to the many people who helped out during field work, and image analysis, in particular  Gilles Hervé ,  Kévin Leleu ,  Nicolas Guilpart ,  Miguel Clarke ,  Napoléon Colombani ,  Jérémie Drelon , and  Matthieu Boureau. Data were collected with funding from the  ZONECO program of New Caledonia , the  PAMPA  project,  IRD  and  IFREMER  support. The present study is part of the  AMBIO  project funded by  IFREMER  and by the Government, the  Provinces , and the  Conservatoire des Espaces Naturels , of  New Caledonia. Delphine Mallet 's Ph. D. was funded by  IFREMER  and by the  French Marine Protected Area Agency. Funding for Abigail Powell 's postdoc was provided by the  SIMEO  project  (Station Instrumentale de Monitoring dans l'Océan ).
Statistics The presented results are the means and standard deviations of the replicated microcosms for each litter treatment. The litter chemical characteristics, enzyme efficiencies, and enzyme activity ratios were compared across litter treatments using a one-way ANOVA and Tukey's HSD (α = 0. 05) tests. To avoid false discovery related to multiple testing, we corrected all of the P-values using the Benjamini and Hochberg false discovery rate control  (Verhoeven et al. , 2005). Stepwise regressions were performed using the software Sigmaplot (Version 12. 0, Systat Software, Inc. , Germany) to identify the best model for describing the relationships between the significant litter chemical characteristics and metabolic responses of P. chrysosporium growing on these litters (CUE, CO 2 , C f at day 14).
A multivariate redundancy analysis (RDA) was performed using the software XLSTAT-ADA (version 2015. 2, Addinsoft, Paris, France) to extract and summarize the variation in a set of metabolic and enzymatic response variables (of P. chrysosporium growing on maize litter) that can be explained by a set of explanatory variables for the litter chemical composition  (Buttigieg and Ramette, 2014). All variables were centerreduced to perform the analysis. We used the main chemical characteristics as the independent variables, and we used the cumulative mineralized and recovered C in the fungus (in mg C g -1 initial litter C), βGs, EGs, EXs, LAPs, NAGs, LACs activity (in µmol min -1 mg -1 fungal C), natural logarithms ratio of (βG + EX + EG) activity versus (LAP + NAG) activity (in µmol min -1 g -1 DM), and enzymatic C decomposition efficiency for βG activity (in µg decomposed C mol -1 ) as the response variables.
ACKNOWLEDGMENTS We thank  L. Lesage-Meessen  at  INRA  for fungi selection. We thank  G. Alavoine  and  S. Dobosz  for their collaboration in the protocol development,  S. Millon ,  O. Delfosse ,  M. Gaddi ,  A. N. Blaise  for their technical assistance in the laboratory, and  A. Habrant  and  D. Crônier  for their collaboration in the chemical.
Acknowledgments We would like to thank the participants, who gave their time voluntarily and without which this work would not have been possible. The work was supported by  ESRC CASE  award  ES/ I026088/1 , entitled '' Multi-criteria approaches to public health decision making '', with  NICE  as the nonacademic partner. The  ESRC  had no role in preparing this document. Full ethics approval for the project was given in advance by the  School of Health and Related Research (ScHARR) ethics committee in the University of Sheffield. The authors have no competing interests. Open Access This article is distributed under the terms of the Creative Commons Attribution 4. 0 International License (http://creativecommons. org/licenses/by/4. 0/),which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
A C K N O W L E D G E M E N T S Thanks are due to the  Agence Nationale de la Recherche (ANR)  ( ANR-08-BLAN-0234-01 ) for financial support. TA acknowledges support from the program PNP of INSU. MB acknowledges support from  ENS de Lyon  for a visiting fellowship to  Lyon. RD  gratefully acknowledges support from grant  ANR-12-PDOC-0015-01  of the  ANR. We thank  Marc Moulin ,  Patrick La Rizza  and  Jean-Paul Masson  for the experimental device development. We thank  Ikuro Sumita  for the insightful discussions. LH thanks  Stéphanie Durand  and  Benoit Tauzin  for the insightful discussions about the seismic attenuation.
There are a limited number of comprehensive worldwide lake datasets  (Halbfass 1914; Meybeck 1995; Lehner and Do ¨ll 2004; Downing et al. 2006; McDonald et al. 2012 ) that contain information on location, extent, and other basic characteristics of open water bodies and wetland areas on a global scale. The recent developments in the field of remote sensing have improved these datasets and promise global land cover images of increasing quality and resolution, including the possibility to monitor spatiotemporal changes in lake and wetland extents. Verpoorter et al. (2014)  made a recent inventory of global lakes using satellite imagery with production of contours for all lakes that are greater than 0. 002 km 2 , which is currently the most complete existing database. They counted more than 117 million lakes for a total surface area of 5,000,000 km 2.
Since the middle of the 1990's, satellite radar altimetry has been a successful technique for monitoring height variations of continental surface water, such as lakes and rivers  (Morris and Gill 1994; Alsdorf et al. 2001; Calmant et al. 2008; Abarca-del-Rio et al. 2012; Sima et al. 2013; Yi et al. 2013; Silva et al. 2014; Pandey et al. 2014; Tarpanelli et al. 2015 and many others). The surface water level is measured within a terrestrial reference frame with repeatability varying from 10 to 35 days depending on the orbit cycle of the satellite. Although data acquisition is independent from weather conditions, radar altimetry can have a few limitations; i. e. , lake-shore topography may affect elevation calculations from the altimeter. In some extreme cases of mountain lakes, echoes can be foreseen and tracked several kilometers before and after the satellite passes causing slant range estimation  (Arsen et al.
gosic. org/sites/default/ files/ECVT4-Lakes. pdf). For that reason, a data collection center (called Hydrolare, see http://hydrolare. net/database. php) has been created under the sponsorship of GCOS and GTOS (Global Terrestrial Observing System) and is hosted by the State Hydrological Institute of St Petersburg in Russia. Its function is to maintain a Web database for delivery of the ECVs of the lakes in the GTN-L list (see http://www. wmo. int/pages/prog/gcos/ documents/GTN-L_List. pdf) based on synergy of in situ gauges (working with national hydrological services and other institutions and agencies providing and holding data on lakes and reservoirs) and remote sensing data, principally satellite altimeter measurements. The Hydroweb database (http://www. legos. obs-mip. fr/en/soa/hydrologie/hydroweb/) was created by Legos (Toulouse, France) in 2003, and delivers the water level of 230 lakes and volume of about 100 lakes and is officially associated with Hydrolare.
Hydroweb products are completely based on satellite altimetry and imagery. In Sect. 2 of this paper, we provide a general introduction to satellite altimetry. In Sect. 3 we concentrate on satellite imagery, and in Sect. 4 we demonstrate how to calculate lake water storage changes by a combination of both these techniques, and the Hydroweb database is briefly presented. In Sect. 5 we illustrate how to apply these techniques to a regional case study of lakes covering the Tibetan Plateau (TP). The first satellite altimeter to be launched was  GEOS-III in 1975 , followed in 1978 by  Seasat. The first use of satellite altimetry for hydrology was with GEOSAT launched in 1985  (Koblinsky et al. 1993). However, thanks to substantial progress in orbit determination from geodetic systems such as Doris, the use of GPS and laser started with the launch of the US/French TOPEX/Poseidon (T/P) satellite in 1992.
This has been applied within the automatic near-real-time altimetry data processing to all flying altimetry missions and delivered through the ''Riv-er&lake'' Website (http://www. tethys. eaprs. cse. dmu. ac. uk/RiverLake), as well as to the historical missions. Another algorithm was developed for automatic calculation of lake and river time series of the DAHITI database (http://www. dahiti. dgfi. tum. de: Schwatke et al. 2015). All retracking algorithms are potentially affected by biases because they do not measure the same physical quantity (some track the very nadir point, others process spatial average within the footprint). Taking into account these biases is essential when combining ranges from different trackers of a mission, or when comparisons between different products are made  (Cretaux et al.
On January 17, 2016, the Jason-3 mission (same instrumental and orbital characteristics as Jason-1 and Jason-2 satellites) was launched. On January 12, 2003, the first and only laser altimetry instrument GLAS was launched by NASA  (Zwally et al. 2003). It emitted at a wavelength of 1064 and 532 nm on the orbiting ICESat and had a near polar, near circular orbit of 91-day repeat cycle at an altitude of 590 km. GLAS provides laser footprint geolocation and surface elevation above ellipsoid with a dedicated algorithm after corrections for tides and atmospheric effects  (Brenner et al. 2000). Altimetry from GLAS is derived from the 1064-nm beam measurement. The laser footprint is *70 m and the data spacing is *170 m. Because the laser beam suffered from technical problems, it was only turned on for short periods (typically 2-month windows twice a year).
It was designed to study polar ice sheets but has also been used for lake level measurements. Thus, many authors have used ICESat products, particularly over the Tibetan Plateau. Although the satellite's repeat cycle and longevity were was not optimal for hydrology, spatial coverage was very high and had the potential to measure water level over a large set of lakes, as shown over the Tibetan Plateau where more than 100 lakes have been studied  (Zhang et al. 2013; Phan et al. 2011; Kropac ˇek et al. 2012; Song et al. 2013; Wang et al. 2013). It was also used to monitor river water level variations  (Baghdadi et al. 2011; Jarihani et al. 2013 ). Furthermore, it ensured accuracy of up to a few centimeters due to the signal's very small footprint, and data provided by the National Snow and Ice Data Center (NSIDC) needed no additional corrections.
7 cm/km of accuracy allowing monitoring of river discharge. Water height of lakes, reservoirs, and floodplains will be measured with 10 cm accuracy for 1000 mm 9 1000 m areas (1 km 2 area), 20 cm accuracy for 250 m 9 250 m areas (0. 0625 km 2 ) and 45 cm for 100 mm 9 100 m areas (0. 01 km 2 ). The mission will provide a large range of applications for water management and the scientific study of hydrology and once the majority of the Earth's lakes, reservoirs, and rivers have been mapped every 10 days by SWOT many components of the water cycle can be measured, for example, global discharge of rivers to the ocean, water storage changes in natural and artificial reservoirs, and floodplain dynamics. When the 3-year mission is completed, the seasonal global water cycle can then be measured by SWOT and the data assimilated into models.
Combination of Multi-Satellite Data In many cases a lake can be covered by the tracks of different satellites, sometimes with overlapping periods and sometimes without a common period of observation. The main advantage of this is the possibility to perform a multi-satellite data processing approach for a lake's water height variations over a long period of time. For example, Lake Ziling was observed by ERS2, Envisat, SARAL/AltiKa, ICESat, and Cryosat-2 satellites from 1994 until 2015. In this framework, the use of ICESat and Cryosat-2 data was fundamental since they allow calculating relative biases between each of the missions. The calculated water level time series from each satellite are biased for two main reasons: First, there is an instrumental bias of each altimeter: This is well documented in the literature  (Bonnefond et al.
2015). fundamental when it is easier to calculate volume variations. To perform this variable, we can use a combination of a lake's water height and water surface extent at different dates and then establish a relationship between these variables and water volume changes. This calculation has been used in many published studies (for an example see  Gao et al. 2012; Duan and Bastiaanssen 2013; Arsen et al. 2014). The first step relates to using satellite imagery to compute several lakes' water mask therein corresponding to concomitant water heights. However, the methodology to extract water mask from satellite imagery is not straightforward and is why there is a description below. The Hydroweb database uses this approach to determine the water surface of Tibetan lakes and other lakes throughout the world.
With a fixed threshold of 0. 4 NDWI, it can then be possible to map open water pixels of Lake Zhari Namco in all three cases. From our experience, the use of MNDWI or NDWI clearly depends of the study zone. Most of the lakes can be accurately mapped with NDWI. In some cases the use of MNDWI may be also interesting. Within the Hydroweb database, the areal extent, i. e. , surface time series, may vary slightly from other studies. This situation is perfectly understandable. As there is no clear definition of where a lake starts or ends, each author is able to perform their own delineation. However, the discrepancy between results may increase due to low-resolution bias. Low-resolution bias is the inaccuracy introduced by the differences in spatial resolution between high and low-resolution data  (Boschetti et al.
2014). This surface bias must be estimated in order to properly translate results obtained with one resolution to another (e. g. ,  Arsen et al. 2014). For the Hydroweb database, the hypsometry of lakes is estimated with no more than 10-15 images (information given). In the frame of Hydroweb, the following steps are used in Landsat image processing: • Overview of study zone before delineation. Several factors should be estimated: minimum and maximum water extent, appearing/disappearing islands, flooding areas, the presence of vegetation/aquatic vegetation components, and in situ data availability. • Image selection. If possible strictly limited to cloud free, ice-free and inundation-free scenes in order to reduce difficulties in interpretation. • Definition of region of interest (ROI) based on maximum and minimum water extension, cutoff of unnecessary parts (mountains, rivers) to reduce difficulties in interpretation, and safety margin for water rise.
This implies that, in Hydroweb, we consider the polynomial coefficient to determine surface variation from height variations only in the domain of validity of the hypsometry, so we don't extrapolate the water surface for water height outside of the maximum and minimum values used to determine polynomial coefficients. As a comparison, in  Song et al. (2013)  the same calculation was made for a set of 12 Tibetan lakes, and we see that for Lake Ayakkum they established a linear function of area versus height (Area = 89. 2857 9 height -345602) because only ICESat data from 2003 to 2009 was used, which could lead to some errors in water surface extrapolation for similar cases. Indeed, for a low lake water height of 3880 m, the two sets of polynomial coefficients both give an area of about 820-830 km 2.
In contrast, for a high lake water height of 3882 m (reached in 2011-2012), the second-order polynomial development leads to 896 km 2 , while in  Song et al. (2013)  with a one-order polynomial development, it reaches more than 1000 km 2. In Hydroweb, the time series for about 20 lakes of the Tibetan Plateau are given for these three variables, H, A, and dV over at least one decade. We use the multi-satellite approach which is described in Sect. 2. 4. The data from ERS2, T/P, Jason-2, Envisat, ICESat, Cryosat-2, and SARAL/AltiKa have been used to perform the calculation of water level variations of these lakes. We therefore provide long decadal time series for some of these lakes, and we show in the next section the results obtained for a list of 11 large lakes in the southern TP, with the majority of them having more than 20 years' worth of data.
Results A large number of recent studies have assumed that the lakes of the TP are globally in a phase of expansion as a consequence of marked climate change observed over many years. However, the majority of these studies were based on a short time period of a few years at the beginning of the twenty-first century  (Zhang et al. 2011a; Phan et al. 2011; Song et al. 2013; Wu et al. 2014 ). The authors have shown that between 2003 and 2009 (using ICESat data) from about 100 lakes monitored, the water height of a large proportion of them increased on average by about 20 cm/year. In  Song et al. (2013) , the authors also calculated water storage changes for a large number of lakes of the TP, but they only used ICESat data to determine the hypsometry. Their study only covered the period up to 2011.
5 m and the volume by 33. 5 km 3. They also compared and validated their model's results to altimetry data for the period 2005-2009. Liu et al. (2009b)  used satellite imagery (Landsat and CBERS) to measure areal extent variations of this lake since 1976. They tried to link these changes to variations of E, P, and R. They revealed that the wetter and warmer winter climate since the late 1980's and mid-1990's has increased the ground temperature which has in turn enhanced spring surface runoff and glacier melting. Meanwhile, it has decreased the seasonal underground frost (from 2. 35 to 1. 93 m on average) leading to acceleration of the permafrost contribution to the lake's water balance. Liu et al. (2009b)  also detected the years when abrupt changes were observed in different climate variables (E, P, R, air temperature) in the watershed of Lake Namco.
Results from their analysis show that 1996 was a key year for runoff changes, with a 20 % increase in annual discharge, leading the lake to expand. In another study, Kropac ˇek et al. (  2012 ) processed the satellite altimetry data from Geosat Follow On (GFO), Envisat and ICESat, from 2000 to 2009, and satellite images from Landsat satellites over the period 1976-2009. They observed an expansion of the lake of 82 km 2 (also confirmed in  Zhang et al. (2011b) ) and an increase in the water level of 0. 31 m/year during the period of measurements. This coincides quite well with the results obtained by  Krause et al. (2010) , although they mentioned stabilization of the lake level after 2005. Zhang et al. (2011b) estimated Lake Namco water storage changes and its link with climate change using different sources of information: meteorological data from 1976 to 2009, satellite images, and lake bathymetry.