dataesr/scientific-paragraphs-categorization huggingface dataset:
Such task, however, can be rather challenging due to the lack of long-term datasets  (White, 2018). Ultimately, the examination of long-term trends in fish abundance can improve understanding of population fluctuations caused by either natural variation or anthropogenic influence and further augment the ability to diagnose, predict and respond to potential stressors  (Radinger et al. , 2019). The Prespa wetland, formed by two natural lakes Mikri Prespa and Megali Prespa  (Crivelli and Catsadorakis, 1997) , is situated in south-eastern Europe between Albania, North Macedonia and Greece (40. 8N, 20. 9E). The lakes are of Tertiary origin and among the oldest ones in Europe. They are located at 850 metres above sea level (m a. s. l. ) enclosed by mountains rising to over 2500 m a. s. l. (Crivelli et al.
Active traditional fishing methods, such as the "pelaizia", have been abandoned since the early 2000s  (Crivelli et al. , 1997a; Catsadorakis et al. , 2018). Finally, several non-native species were introduced through the years  (Shumka and Apostolou, 2018)  but only two invasive ones are currently established; the Topmouth gudgeon (Pseudorasbora parva), appeared in the 1970s and its abundance had increased significantly by the 1990s  (Crivelli, 1990; Rosecchi et al. , 1993)  and the Pumpkinseed (Lepomis gibbosus) that was first observed in the lakes in 1996. The aim of this study is to summarize long-term patterns in fish biomass data of Prespa Lakes, collected over a period of 34 years, to identify the factors that play a role in shaping species-specific biomass trends and to disentangle the direct and indirect effects of various large-scale ecological stressors in the two different (ecologically and human-impacted) natural lake systems.
Sampling Data were collected for 26 monitoring years from 1984 to 2017  (1984-1985, 1990-1994, 1996-1998, 2000, 2002-2013, 2015-2017)  in Lake Mikri Prespa and for 13 monitoring years from 1996 to 2017  (1996-1998, 2007-2013, 2015-2017)  in Lake Megali Prespa. Gillnet monitoring was carried out three times per year (April, May, June) in both lakes to cover the whole spawning season. Sampling stations in each lake were located in the littoral zone; a shallower area (0. 5-5 m) with a gravelly substrate and scattered vegetation in Mikri Prespa and a station close to the shore (0. 9-2. 2 m) with a sandy substrate near a stream outlet in Megali Prespa. Due to increase in macrophyte cover that interfered with gillnet setting, from 2015 onwards the sampling station in Mikri Prespa was relocated 150 m away.
Environmental variables To investigate which environmental factor(s) may affect each species BPUE, we identified and collected data on 7 predictor variables characterizing abiotic, biotic and humaninduced processes (Tab. 1). Monthly average air temperature and monthly total precipitation from 1984 to 2017 were retrieved from the meteorological stations located between the two lakes, and run by the Power Public Corporation S. A. Hellas, the Society for the Protection of Prespa (SPP) and the Ministry of Environment, Energy and Climate Change (https:// kyy. hydroscope. gr/stations/d/200166/). Monthly data of the average water level for both lakes were collected throughout all years by the SPP as part of their long-term monitoring scheme. To quantify any predation pressure on fish abundance, we used the number of piscivorous birds (in breeding pairs) as a proxy (SPP, unpublished data): for Mikri Prespa we included numbers of Pygmy Cormorant (Microcarbo pygmaeus) whereas for Megali Prespa we included pairs of both Dalmatian Pelican (Pelecanus crispus) and Great Cormorant (Phalacrocorax carbo), since these species differentially forage in each lake  (Hatzilacou, 1993; Willems and de Vries, 1998).
, 2015). Furthermore, Tinca tinca was not considered in the analyses for Lake Megali Prespa as it was rarely caught. BPUE data can show periodic signals and non-linear patterns of temporal change, thus a General Additive Model approach (GAM) was used to model the overall and specieslevel temporal BPUE trends as a smooth nonlinear function of time. GAMs were built in the "mgcv" package  (Wood, 2018)  in R 3. 6. 2 (R Core Team, 2019) using the Restricted Maximum Likelihood (REML) estimation method. Since the relocation of the sampling station in Mikri Prespa after 2015 could have influenced BPUE trends, we tested for such effect by examining any structural changes in the mean of the monthly BPUE changes over time. The analysis was performed using the "strucchange" R package  (Zeileis et al. , 2002).
Tests against changes were conducted using the Ordinary Least Squares (OLS)-based cumulative sum (CUSUM) process and the corresponding significance test. When significant results were obtained, changes were dated by applying the function 'breakpoints' which allows for the simultaneous estimation of multiple breakpoints along with their respective 95% confidence intervals  (Zeileis et al. , 2003). The optimum number of breakpoints in each species trend was evaluated using the Bayesian Information Criterion (BIC). When a breakpoint (along with its confidence intervals) included 2013 for any species (no fishing occurred in 2014), BPUE data for years 2015-2017 were not included in subsequent analyses. The influence of environmental variables was assessed for all fish species in both lakes using an analytical approach based on random forests.
A random forest is a powerful machinelearning algorithm based on regression/classification tree ensembles that does not require assumptions such as linear or nonlinear relationships between predictors and response factors  (Breiman, 2001; Cutler et al. , 2007). Models were fitted using the R package 'randomForest'  (Liaw and Wiener, 2002)  using 500 trees and two random variables at each split. Model performance was assessed based on its respective Mean of Squared Residuals (MSR) and pseudo R 2. Furthermore, we used the R package "rfUtilities"  (Evans and Murphy, 2015)  to perform significance tests on the built models. The importance of the predictors was tested for statistical significance (p < 0. 05) using a permutation approach  (Altmann et al. , 2010)  as implemented in the R package "pRF"  (Chakravarthy, 2016).
Asymmetric predation on different age classes  (Beckmann et al. , 2006)  along with the vulnerability of fish that have been stocked in numerous occasions  (Kortan and Adámek, 2011)  could play some role but further research is needed to determine if this result is something more than a coincidental relationship, especially since Cyprinus carpio population shows an increasing overall trend. Overall, the data collected in this study provide a significant contribution to the exploration of the ecology and dynamics of fish community in an ecologically important region. The analysis showed the importance of long-term monitoring in distinguishing between natural variation and direct versus indirect effects of large-scale stressors on fish biomass as well as the improvement in our understanding of freshwater ecosystems, contributing to the preservation and management of freshwater fish populations.
A BRT model was fitted to model the number of eels swimming toward the lagoon using the 10 environmental parameters as predictors. The number of eels was log transformed to limit the influence of data collected for a few specific dates where eel count numbers were exceptionally high, on the total deviance. The BRT was fitted with a learning rate of 0. 005 and a tree complexity of three as recommended for a small dataset (250-500 lines,  [48] ). The BRT performance was assessed via the amount of deviance explained and the cross-validated correlation  [49]. The partial dependence plots for parameters with a contribution >10% were used to visualise the effect of a parameter on the number of eels swimming toward the lagoon. In dependence plots, the fitted function value superior to zero indicates a positive effect of the variable on the number of eels swimming toward the lagoon compared to the average effect of all other parameters  [48]  and vice versa for negative values.
Acknowledgments We are grateful to  Eric Schmitt  and  Fanny Raux  for their help in collecting the data, as well as to the "Plateforme serre, cultures et terrains expérimentaux - Université de Lille " for the support in plant collection maintenance. This work is a contribution to the  CPER  research project  CLIMIBIO  that funded L. M. 's salary. The authors thank the  French Ministère de l'Enseignement Supérieur et de la Recherche , the  Hauts de France Region  and the  European Funds for Regional Economical Development  for their financial support to this project.
This catchment is characterized by intense agricultural use, diverse geology, and complex terrain. WATERSED model was used as a dynamic method to evaluate the spatial distribution of hydrologic and erosive fragility during rainfall events. WATERSED was parameterized for modeling surface runoff volume, sediment yield and interrill erosion, based on monitored data from a zero-order notill catchment and literature data. Modeling results were analyzed for each land use. For fingerprinting, two sediment sampling strategies and source groupings were considered. One considered spatial sources, and the endmembers were the sub catchments, the other considered land use source types within each sub catchment. Deposited bed sediment samples were collected at the outlets of each sub catchment and the main outlet.
, 2011) , the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on Support to Operational Hydrology and Water Management (H-SAF), the National Environmental Satellite, Data, and Information Service (NESDIS) Operational Soil Moisture Products (SMOPS), the Soil Moisture and Ocean Salinity (SMOS)  (Kerr et al. , 2001)  mission, and the Soil Moisture Active Passive (SMAP) mission  (Entekhabi et al. , 2010a). Temporally and spatially continuous soil moisture datasets are commonly explored through hydrological and land surface models  (Albergel et al. , 2013; Albergel et al. , 2017; Balsamo et al. , 2018; Liang et al. , 1996; Western et al. , 2004). Such datasets are challenging to develop and validate using ground-based measurements alone  (Brocca et al.
, 2017; Mohanty et al. , 2017) , owing to the high spatial and temporal variability of soil moisture  (Crow et al. , 2012; Famiglietti et al. , 2008). The accuracy of these simulated soil moisture products depends on the quality and availability of meteorological observations, soil texture, soil hydraulic properties, and the physics of the models involved  (Montzka et al. , 2017; Reichle et al. , 2011; Rodell et al. , 2004; Walker et al. , 2003). Existing in situ soil moisture monitoring networks and databases such as the TERENO  (Zacharias et al. , 2011) , OzNet  (Smith et al. , 2012) , COSMOS-UK  (Evans et al. , 2016) , and ISMN have been instrumental for validating soil moisture derived from either model simulations or satellite retrievals. Beyond in situ measurements and model simulations, remote sensing provides another path to estimating soil moisture  (Kerr, 2007; Peng and Loew, 2017; Schmugge et al.
, 2007). Comprehensive reviews on soil moisture retrieval from remote sensing measurements are available from, e. g. ,  Wagner et al. (2007)  and  Karthikeyan et al. (2017). Currently, there are several microwave-based soil moisture products available on the global scale. Operationally produced datasets include, but are not limited to, retrievals from the Advanced Scatterometer (ASCAT)  (Bartalis et al. , 2007)  onboard the Metop satellites, the Advanced Microwave Scanning Radiometer2 (AMSR2)  (Kim et al. , 2015)  onboard the Global Change Observation Mission-Water (GCOM-W), the Soil Moisture and Ocean Salinity (SMOS) mission  (Kerr et al. , 2010) , and the Soil Moisture Active Passive (SMAP) mission  (Entekhabi et al. , 2010a). Apart from these soil moisture products, which are directly retrieved from single satellite platforms, merged long-term (40 years) soil moisture products have been produced within the ESA CCI by harmonizing and merging multiple microwave-based soil moisture products  (Dorigo et al.
, 2017; Gruber et al. , 2017; Gruber et al. , 2019). This product, hereafter referred to as ESA CCI Soil Moisture (SM), aims to extend the typically short temporal coverage of single-sensor soil moisture products. These products are currently operationally produced and distributed every 10 days in the Copernicus Climate Change Service (C3S; https://climate. copernicus. eu/). These recent global soil moisture datasets usually provide soil moisture information at coarse spatial resolution (around 25-50 km)  (Brocca et al. , 2017). A remaining challenge is the operational retrieval of high spatial resolution (0. 1-1 km) soil moisture products with comparable spatial-temporal coverage and retrieval quality  (Peng et al. , 2017b; Sabaghy et al. , 2018). Current and future satellite missions, such as the ESA Sentinel-1 European Radar Observatory, the Satélite Argentino de Observación COn Microondas (SAOCOM) mission, the NASA-ISRO Synthetic Aperture Radar (NISAR), the Radar Observing System for Europe L (ROSE-L), and the Tandem-L satellites, offer opportunities to generate high-resolution soil moisture products.
, 2019). In addition, the combined high-resolution ASCAT/Sentinel-1 (1 km) soil moisture product has also been published recently  (Bauer-Marschallinger et al. , 2019; Bauer-Marschallinger et al. , 2018). Nonetheless, there is still a need to develop models and algorithms that combine multiple datasets (e. g. , coarse and fine resolution observations from optical, thermal infrared and microwave sensors as well as in situ measurements) to generate long-term soil moisture datasets with high spatial and temporal resolution. Recent reviews by  Peng et al. (2017b)  and  Sabaghy et al. (2018)  have comprehensively summarized various downscaling approaches applied to improve the spatial resolution of existing soil moisture products. Apart from spatial resolution limitations, a major constraint on satellite-based products is that the soil moisture information provided by microwave remote sensing is representative only for the upper few centimetres of the soil  (Collow et al.
Applications of satellite-derived soil moisture datasets Table  1  lists publicly available global satellite-based soil moisture products. All of them have been comprehensively validated (e. g. ,  Albergel et al. , 2012; Brocca et al. , 2011; Chan et al. , 2016; Colliander et al. , 2017; Dorigo et al. , 2015; Draper et al. , 2009; Jackson et al. , 2010; Peng et al. , 2015b). The grid spacing shown in Table  1  refers to the spatial interval used to resample satellite observations. The grid spacing is not the actual satellite spatial resolution and is normally finer than actual spatial resolution. Fig. 1  provides an overview of the characteristic spatial and temporal resolutions of various land applications, ranging from applied hydrology to climate applications, in comparison to the characteristics of typical high-and low-resolution satellite soil moisture products.
Climate modelling and research Climate research needs information on soil moisture to improve understanding of land-atmosphere processes (e. g. ,  Loew et al. , 2013; Seneviratne et al. , 2013; Taylor et al. , 2012; Van den Hurk et al. , 2016). For example, based on AMSR-E and ASCAT soil moisture datasets,  Taylor et al. (2012)  found that afternoon precipitation occurred more frequently over dry soils across the entire globe (Fig. 3 ). Moreover, remote-sensing soil moisture products have also been used to study soil moisture-temperature and soil moisture-evapotranspiration coupling strengths  (Lei et al. , 2018) , which are relevant metrics for the occurrence of hot extremes in transitional climate regimes (e. g. ,  Dong and Crow, 2019; Hirschi et al. , 2014). Likewise, the decline of global land  evapotranspiration in the early 2000s was caused by a decrease of soil moisture in the Southern Hemisphere  (Jung et al.
Hydrometeorological disasters Hydrometeorological disasters are typically referred to as floods or droughts. In both cases, the soil moisture state plays a vital role in controlling the partitioning of surface water and energy fluxes  (Koster et al. , 2004). For flood forecasting applications, it is essential to know accurately the pre-rainstorm soil moisture conditions (in relation to the saturation level) and its spatial distribution within a watershed. Many studies have considered satellite-derived soil moisture to improve flood forecasting via data assimilation techniques (e. g. ,  Komma et al. , 2008; Li   et al. , 2018; Massari et al. , 2018; Wanders et al. , 2014). However, operational flood forecasting systems are still limited in their usage of existing satellite-derived soil moisture datasets because of: a) the lack of spatial detail, b) insufficient data continuity, c) limitations in data record lengths, and d) the lack of community acceptance.
Long-term, highresolution soil moisture records are needed to satisfy these requirements. It is thus important to motivate investment at operational flood forecasting centres to develop the appropriate infrastructure for assimilating high-resolution data. Apart from flood forecasting, satellite-derived soil moisture can be used to analyze agricultural droughts caused by the prolonged absence of precipitation or increased evapotranspiration  (Van Loon, 2015). For example, soil moisture datasets can be used to identify droughts (e. g. ,  Anderson et al. , 2012; Nicolai-Shaw et al. , 2017; Peng et al. , 2019) , to develop drought indices (e. g. ,  Carrão et al. , 2016; Martínez-Fernández et al. , 2016; Sadri et al. , 2018) , and to evaluate and improve processbased drought forecasting models (e.
, 2012; Ichoku et al. , 2016)  examined the relationship of soil moisture with wildfire events, a major contributor to the uncertainty of terrestrial carbon cycle estimates  (Friend et al. , 2014). Soil moisture dynamics after a fire are also an indicator for the severity of a fire (dependent on the depth of the organic soil layer that has been destroyed) and indicate how fast a forest can recover  (Chu and Guo, 2014; MacDonald and Huffman, 2004). High-resolution soil moisture observations together with vegetation datasets will further support the monitoring of wildfire probability and the development of appropriate fire models on regional scales. Soil moisture has also been used to monitor epidemic risk related to weather and environment conditions  (Peters et al. , 2014). Montosi et al. (2012)  found that soil moisture plays an important role in malaria dynamics.
High spatial and temporal resolutions Although several kilometre-scale soil moisture products have been released recently, the major challenge in the retrieval of such highresolution products from SAR data is how to accurately parameterize soil roughness and account for the impacts of observation angle  (Verhoest et al. , 2008; Zhu et al. , 2019). Other challenges include retrieval uncertainty quantification and data continuity. In order to make use of the existing long-term coarse-resolution soil moisture products from e. g. , ESA CCI SM, downscaling algorithms provide a means to improve the spatial resolution of soil moisture datasets. In addition to microwave observations, optical/thermal band observations have been used to either downscale microwave-based coarse-resolution soil moisture (e.
, 2017; Miralles et al. , 2011)  have released root zone soil moisture datasets  (Brocca et al. , 2012a; Mecklenburg et al. , 2016; Reichle et al. , 2017a; Reichle et al. , 2017b; Reichle et al. , 2019). These  datasets can help to better understand the role of root-zone soil moisture in climate and hydrological predictions. However, the estimation of root-zone soil moisture at high spatial resolution still needs to be investigated. One option is to use P-band SAR that has a much deeper penetration depth than C-and L-band SAR. Several studies have already successfully used airborne P-band SAR data to retrieve root zone soil moisture (e. g. ,  Crow et al. , 2018c; Sadeghi et al. , 2017). However, it is still challenging to build space-borne P-band SAR systems due to the increased antenna-length requirements and the effects of radio frequency interference.
Validation and quality traceability Validation of satellite-derived soil moisture and the provision of spatial-temporal error information are important for all applications. In general, the implementation of a thorough validation and monitoring framework for operational soil moisture products requires three main components: 1) reference data, 2) a validation guidance using appropriate metrics, and 3) validation and monitoring tools. The global in situ soil moisture networks together with the SMOS and SMAP satellite teams have made great efforts to provide extensive ground-based soil moisture measurements to the public. However, data quality, continuity, temporal legacy, gaps, and scaling errors of the reference data should be traceable, due to their fundamental role for the validation of satellite-based data.
Mission continuity Satellite mission continuity is crucial for the generation of consistent and long-term soil moisture datasets. A range of ESA, JAXA, and NASA sensors operating in C-and X-band have ensured the availability of soil moisture product since 1978. Thanks to efforts by ESA and JAXA, the continuation of these data products is largely ensured. In contrast, the ESA SMOS and NASA SMAP missions successfully provide L-band measurements and generate global soil moisture products. However, the continuity of these dedicated L-band soil moisture missions is not ensured. Based on lessons learned from SMOS and SMAP, it is therefore important to develop future L-band soil moisture missions to provide data continuity and high-resolution measurements. Currently, the above-mentioned CIMR mission plans include coarse-resolution L-band radiometer observations, and a potential SMOS follow-on mission, SMOS-HR (High-Resolution), is planned to deliver observations at 10 km  (Rodríguez-Fernández et al.
Conclusions Numerous operational soil moisture datasets, generated from satellite microwave remote sensing observations, have emerged over the last decade. Following the release of these soil moisture datasets, different science communities have made efforts to exploit the potential of these data by using them for a wide range of applications. Our review shows that there is a strong demand from various user communities for higherresolution datasets at kilometre scale. The current and future satellite observations provide opportunities to develop high spatial resolution soil moisture products featuring moderate (e. g. , daily) temporal resolution. Downscaling methods that integrate optical, thermal infrared and microwave observations based on data assimilation and machine learning provide an alternative to achieving high-resolution soil moisture products.
However, challenges regarding high temporal resolution and the accuracy of the high-resolution products still need to be addressed. Integrating observations from multi-satellite constellations such as the Metop-A, B, and C platforms might lead to the generation of a twice-daily soil moisture product. In the future, the proposed Hydroterra geosynchronous radar satellite might provide hourly soil moisture data on a fine spatial scale -albeit with reduced area coverage. Moreover, CubeSats and small satellites have great potential for providing very high temporal-spatial resolution soil moisture. Quality assured longterm high-resolution soil moisture datasets will facilitate a wide range of applications of soil moisture products in the future.