dataesr/scientific-paragraphs-categorization huggingface dataset:
All the IBI scenarios were performed with the same model parametrization and forcing datasets, excluding the freshwater forcing, as the CMEMS-IBI operational configuration. A spin-up run of 110 days was performed to generate the same initial condition for all model scenarios. The model started from rest (the 6 September 2017) with initial three-dimensional temperature and salinity fields taken from the CMEMS-IBI operational outputs. After this spin-up period, the model solution was evaluated as stable, showing on the 1 January 2018 velocity fields similar in magnitude and pattern to the CMEMS-IBI operational solution. All the outputs from these IBI model scenario runs are delivered at hourly frequency for the sea surface, and at a daily frequency for the rest of the water column. Thus, to validate model salinity outputs, surface hourly model data have been used for comparison with mooring observed timeseries to take advantage of the higher temporal frequency of these available in-situ salinity observations.
In this case, observations f Table  7. Mean difference (Bias) and Root Mean Squared Error (RMSE) between observed salinity (from the M13 mooring buoy and the IPMA campaign) and simulated one (from the IBI_REF, IBI_LAM, IBI_CLM, IBI_NOR model scenarios), over the respective length of the simulations, in the CADIZ area. The smallest model bias and RMSE for each dataset are in bold. The mooring data have hourly frequency and IPMA data was measured every 10 min. N is the number of measurements. While the salinity at the M13 shelf-break station features a higher variability from May to August (Figure  12 ), the model scenario simulations reproduce instabilities over a longer period (March to July) and much fresher waters (up to 5 PSU of bias with respect to the measured salinity). The most significant error occurs in March, where all simulations except the one forced by the climatology reproduce a significative drop of salinity at the shelf-break.
Thus, a non-optimal representation of coastal and river freshwater signal in the ocean model set-ups can certainly lead to biased simulated ocean patterns, jeopardizing the reliability of ocean forecasts in these ROFI areas. Simultaneously to this need of operational ocean models to count with an optimal land freshwater forcing, there is a steady effort by the hydrology community to enhance monitoring of rivers and runoff rates. Several multidisciplinary R&D projects and initiatives are working on building new improved freshwater river discharge databases (combining hydrological models and observational data sources) that can be useful to improve ocean model forecasting. With the objective of quantifying the impacts that coastal and river freshwater contributions have on IBI regional ocean model simulations, the new LAMBDA river discharge database (LAMBDA Project  [27] , Campuzano et al.
This dataset tends to represent salinity gradients and on-shelf small-scale features in the IBI area more realistically than the SMOS operational products (which are currently far from being fit for model validation purposes in coastal IBI waters). Thus, a higher frequency update of this reprocessed SMOS product (i. e. , on a quarterly or monthly basis) can positively impact on the routine validation processes of operational models in the IBI area. Nevertheless, the use of these observational data sources as main references limits the operational model salinity assessments to offshore deep waters (where Argo floats' drifts and SMOS products show their highest representativeness), underrepresenting the model validation on coastal and on-shelf waters. This limitation in the operational model validation can be partially overcome thanks to the availability of in-situ observations at some buoys moored along the shelf-break, on-shelf and coastal IBI waters.
Model Scenario Validation: Are Available In-Situ Observations Adequate to Evaluate Model Sensitivity? The impacts on IBI model salinity related to changes in the river and coastal freshwater forcing are assessed by means of comparing the modelled salinity fields with in-situ salinity observations. To this purpose, a multi-platform observational salinity database (including profiles from CTDs, XBTs and surface observations from operational mooring buoys and a thermo-salinograph campaign) was compiled and used as a reference in the validation of the proposed regional model scenarios. The spatial observational coverage is illustrated in Figure  2. In-situ salinity observations on shelf and along the shelf-break have been prioritized, searching for observational coverage on ROFI areas. This approach showcases that prevailing effects of river plumes and coastal freshwater-induced fronts in such ROFI areas can be analyzed and the response of each model scenario run to simulate them can be assessed.
4. 4. How Different Are the River Flow Estimations Used as Forcing in the Ocean Model Scenarios? Significant differences in terms of total daily river discharge contributions were identified among the different datasets imposed as freshwater forcing in each sensitivity model run. Table  3  shows how the 2018 daily mean flow (averaged over the whole IBBIS study domain) of the new LAMBDA river forcing reaches up to 8910 m 3 s -1 , meaning a 70% higher contribution than the river forcing used in the IBI reference control run (5223 m 3 s -1 ) and 128% more than the pure climatological river discharge forcing (3893 m 3 s -1 ). Furthermore, the extra climatological coastal runoff, added to compensate lacks in the IBI freshwater signal, represents in the IBBIS region a yearly contribution of 843 m 3 s -1.
It is worth noting that in the first months of 2018, the variability and intensity are equivalent in both the reference and the new LAMBDA datasets, with main peaks of freshwater in January and March. However, from April onwards, the reference data, mainly based on climatology, shows a smooth decrease in total river debit, whereas LAMBDA features a minimum in summer too, associated with a reduced variability. It also shows two important peaks in June and November, that do not exist in the IBI (climatological at that time) reference forcing (Figure  3 ). The river forcing plays a significant role in the regional simulation of the IBI sea surface salinity, as it is seen when looking at the modelled salinity fields resulting from the different IBI scenario runs (Figure  6 ). As expected, due to the higher river discharge of LAMDBA (as mentioned above, overall 70% higher than the reference one), the sensitivity runs based on this forcing (IBI_LAM and IBI_NOR) are fresher than the control one.
This would mean that salinity budget at this coastal zone off the Rias is rather controlled by the extension of the western Iberian buoyancy plume (mostly fed by freshwater contribution from these three major rivers) than by the local runoff from the nearby Rias itself. Further south, the model scenario assessment performed with the IPMA SSS campaign along the Portuguese shelf shows how all runs feature a similar salinity pattern. This suggests that, in this zone, variations in the imposed river discharge data may not be the main cause of salinity model errors on the shelf freshwater budget. This is so even nearby the Tejo river mouth, where all runs show a similar pattern of salinity differences, pointing out other dynamical factors as more determinant to explain this consistent model behavior.
Conclusions and Future Research Directions In conclusion, the effect of varying imposed river outflows in the IBI operational ocean model system was investigated, showcasing the potential impacts that a new river freshwater model database (such as the LAMBDA one) can have on regional operational forecasts. Some enhancements in model capabilities to better represent salinity and especially baroclinic frontal structures linked to coastal and river freshwater buoyancy plumes have been demonstrated. Although major impacts were identified on ROFI areas associated with bigger river discharges (i. e. , the French shelf in the Gulf of Biscay or the Northwestern Iberian coast), it is found that in some other regions (such as the Portuguese shelf) or in areas with lower riverine freshwater contribution along the study year (such as the Gulf of Cadiz), these impacts related to changes in the imposed river inflows are lower, with other dynamical factors playing a more important role in governing the modelled salinity field.
Major impacts are found during spring and summer in the BISCA subregion, when IBI simulations forced by LAMBDA feature fresher water masses on shelf, that extend up to open waters. On the contrary, the simulation forced by a climatology is saltier than the other simulations. The SSS differences between the various simulations are in general more noticeable along the coast and on shelf waters, and especially obvious on the main IBI ROFI areas. Significant differences in terms of total daily river discharge contributions were identified among the different river flow datasets imposed as freshwater forcing in each sensitivity model run. The use of "realistic" daily updated model-derived river inputs can benefit operational ocean models, especially improving their ability to capture salinity variability, and as demonstrated through the model sensitivity tests performed, their uses can be an option to avoid, or at least to minimize, the use of more static climatological approaches (such as the coastal climatological correction currently applied in the IBI model set-up).
Furthermore, some regions, identified here as sensitive to changes in the freshwater forcing data, are of special interest to upgrade their river freshwater contribution, replacing the current freshwater climatological approach. The most noticeable case is the northern Iberian coast, where the current IBI operational model set-up does not count with any river source (with all the regional freshwater contribution coming from the coastal runoff correction applied), despite the existence of numerous small rivers in the area whose cumulated freshwater contribution into the ocean is not negligible (all year round). A full network of coastal salinity observations would be optimal to evaluate and tune the contribution of freshwater forcing data in ocean simulations. This need of an enhanced in-situ observational coverage should be met for on-shelf and coastal zones (and especially in ROFI areas).
Recovered ancillary databases or specific campaigns' data (such as the RECOPESCA or IPMA data used here) can be useful to study cases, but sustained operational monitoring (i. e. , the moored buoys) or routine periodic repetition of salinity observations at specific locations (such as the INTECMAR data) make it possible to analyze the salinity variability. Improvements of satellite-based products are also desired. the with the SMOS products available for coastal uses being mostly reprocessed ones (with an intense use of in-situ observations for calibration and filling gap purposes), again, the in-situ component arises as critical. It should be highlighted that enhancement of the river hydrological observation component is also a key issue: without river flow observations, calibration of hydrological models is limited, decreasing the accuracy of freshwater river flow estimations available to force ocean models.
Mean difference (Bias) and Root Mean Squared Error (RMSE) between observed salinity (from mooring buoys nd RECOPESCA XBT profiles) and simulated one (from the IBI_REF, IBI_LAM, IBI_CLM, IBI_NOR model scenarios), ver the respective length of the simulations, in the BISCA area. The smallest model bias and RMSE for each dataset are n bold. The mooring data have hourly frequency and RECOPESCA data daily frequency. N is the number of measureents.
Mean (Bias) and Root Mean Squared Error (RMSE) between observed salinity (from mooring buoys and RECOPESCA XBT profiles) and simulated one (from the IBI_REF, IBI_LAM, IBI_CLM, IBI_NOR model scenarios), over the respective length of the simulations, in the BISCA area. The smallest model bias and RMSE for each dataset are in bold. The mooring data have hourly frequency and RECOPESCA data daily frequency. N is the number of measurements.
Mean difference (Bias) and Root Mean Squared Error (RMSE) between observed salinity (from mooring buoys, an IPMA campaign with thermo-salinograph and INTECMAR CTD stations) and simulated one (from the IBI_REF, IBI_LAM, IBI_CLM, IBI_NOR model scenarios), over the respective length of the simulations, in the WISHE area. The smallest model bias and RMSE for each dataset are in bold. The mooring data have hourly frequency, the IPMA data was measured every 10 min and INTECMAR measurements at each CTD station have a weekly frequency. N is the number of measurements.
Acknowledgments: The authors are grateful to the following experts (and institutions) for their support to this research:  Pedro Montero  ( INTECMAR ) for supporting with the  INTECMAR CTD data, Guillaume Charria (Ifremer)  for supporting with the RECOPESCA data,  Diogo André Reis de Sousa  and  Paulo Oliveira  (both from the  Instituto Português do Mar e da Atmosfera)  for the IPMA campaign data. Other MyCoast ocean modelers:  Juan Taboada  (MeteoGalicia),  Joao Sobrino  (IST),  Tomasz Dabrowski  (IMI). This study has been conducted using E. U. Copernicus Marine Service Information. The authors especially thank the  CMEMS Insitu-TAC  (for the moored buoys and Argo profilers) and the  CMEMS IBI-MFC  (for their IBI operational model products and validation assessments).
Data Availability Statement: The following publicly available datasets were analyzed in this study: Model data: The CMEMS IBI MFC operational forecast product can be found in the CMEMS catalogue (IBI-MFC forecast analysis product: https://resources. marine. copernicus. eu/?option=com_csw& view=details&product_id=IBI_ANALYSISFORECAST_PHY_005_001 (accessed on 8 April 2021)); LAMBDA river data can be found in: http://www. cmems-lambda. eu/#data-portal (accessed on 8 April 2021); data from the different ocean model scenarios analyzed in the study can be available on request from the corresponding author. Observational data: The data from in-situ buoys, Argo floats and Recopesca can be found in the CMEMS catalogue (Insitu-TAC Near-Real-Time observational product: https://resources. marine. copernicus.
eu/?option=com_csw&view=details&product_id= INSITU_IBI_NRT_OBSERVATIONS_013_033 (accessed on 8 April 2021)). The salinity data from SMOS (the low-resolution level 3 SSS product computed with smoothening spatial window of 50km radius) can be found in the BEC public ftp catalogue: http://bec. icm. csic. es/bec-ftp-service/ (accessed on 8 April 2021); Finally, the INTECMAR and IPMA observational salinity datasets used in this study are 3rd Party Data, and restrictions are applied to their availability (contact with the institutions owner of these datasets would be required for access permission).
able 5. 5 Mean difference (Bias) and Root Mean Squared Error (RMSE) between observed salinity (from mooring buoys nd RECOPESCA XBT profiles) and simulated one (from the IBI_REF, IBI_LAM, IBI_CLM, IBI_NOR model scenarios), ver the respective length of the simulations, in the BISCA area. The smallest model bias and RMSE for each dataset are n bold. The mooring data have hourly frequency and RECOPESCA data daily frequency. N is the number of measureents.
Pampa grassland dry-matter production Grassland dry-matter production (DM) was assessed based on literature dataset from 1998  (Gatiboni et al. , 2000 ), 2008 -2010 (Tiecher et al. , 2014 ) and 2015 -2019 (Somavilla et al. , 2021b). DM production was analyzed based on the sum of historic DM production (ΣDM) in RP6, TP6 and WP. Phosphorus use efficiency (PUE, kg kg -1 ) was estimated based on DM increase per unit of applied P: PUE = (ΣDM F -ΣDM WP ) F (1) Wherein: ΣDM F is the sum of fertilized DM (kg ha -1 ); ΣDM WP is the sum of non-fertilized DM (kg ha -1 ) and F is the amount of applied P (kg ha -1 ).
Statistical analysis Treatments were considered a qualitative factor and subjected to analysis of variance and mean test. P lability in bulk soil and relative P contents in each particle size were analyzed based on two-factorial completely randomized block design (split-plot) with three repetitions (Eq. 4), wherein the plot corresponded to the treatment and the splitplot corresponded to P lability fraction or to particle fraction. Total soil P and bulk soil P extracted through AER and Mehlich-1 were analyzed based on completely randomized block design (Eq. 5). Analysis of variance and mean test (Scott-Knott test, p ≤ 0. 05) were carried out in SISVAR 5. 6 software. Y ijk = μ + B i + T j + error a(j, i) + R k + (TR) ij + error b(i, j, k) (4) Y ijk = μ + B i + T j + error(j, i) (5) Wherein Y is the observation; μ is the overall experimental mean; B represents the blocks (i = 1, 2, 3); T represents the treatments (j = 1, 2, 3, 4, 5); R is the particle fraction or P lability (k = 1, 2, 3) and error is the experimental error.
Solver configuration The results of the simulation presented in this study have been carried out with ANSYS Fluent 16 CFD code  [2]. This software uses a 3D finite volume method to solve the incompressible Navier-Stokes equations using a cell-centered data arrangement. A control-volume-based technique is used here. It consists of the division of the domain into discrete control volumes (grid), the integration of the governing equations on the control volume to construct algebraic equations for the discrete equations and the linearization of the discretized equations and solution of the resultant linear equation system. Let us consider the steady-state continuity  (10)  and momentum  (11)  equations in integral form: ρ - → v. d - → A = 0 (10) ρ - → v - → v. d - → A = -pI. d - → A + τ. d - → A + V - → F dV ( 11 ) where I is the identity matrix, τ is the viscous stress tensor and -→ F is the force vector.
ACKNOWLEDGEMENTS The authors thank the three anonymous reviewers for their constructive comments and suggestions. We acknowledge the  World Climate Research Programme , which, through its Working Group on Coupled Modelling, coordinated and promoted  CMIP6. We thank the climate modelling groups for producing and making available their model output, the  Earth System Grid Federation (ESGF)  for archiving the data and providing access and the multiple funding agencies who support  CMIP6  and  ESGF.
Tracking fish movements throughout their life cycle, and especially during early stages of life, offers a unique opportunity to tackle such complexities. Earlier studies of fish movement mechanisms have used boats to follow individual fish marked by a tethered float  [111] , ultrasonic  [112] , or radio  [14]  tags, resulting in relatively limited datasets of few individuals tracked at low frequency and for short durations. Although these studies made some important propositionsthat wild white bass (Morone chrysops) can swim directly homeward in open water presumably by using a sun compass  [111]  and other cues  [112] , and that wild carps can quickly learn and remember the location of new food resources  [14]  more conclusive insights and more in-depth investigation of the mechanisms underlying the observed tracks were still rather limited.
2 ). Critical to this is considering scale by contrasting results from lakes of different size: we will likely find increased sympatry and decreased connectivity with increasing habitat size, a factor that can easily be investigated in these closed systems  [133]. We envision replicated whole-lake experiments that specifically investigate multi-species dynamics in habitat use and the nature of connectivity within lake ecosystems. Instrumented individuals moving within an array of acoustic receivers will reveal patterns and drivers of movement across spatial, temporal, and ontogenic scales. Spatial overlap of individuals and species can be calculated using kernel density or convex hulls from two-or three-dimensional positions within arrays (e. g. [104] ; Fig. 2 ). Detection data from acoustic receivers can be investigated using network analysis (Fig.
How much does the physical environment influence movement? Ecologists are continually searching for fundamental patterns of movement that are predictable across organisms and scales  [269]. One encompassing pattern deals with how much an environment influences movement patterns, and whether collected trajectories are representative of an animal's full potential for movement  [12, 21, 35]. Movement data for such comparative problems are typically collected from a wide range of environments that are often assumed comparable rather than explicitly tested. These limitations are an artefact of early movement tracking technologies and their relatively small sample sizes, whereas contemporary technology allows for greater scalability and replication. Many of the largest lakes on the planet have hosted extensive tracking networks, suggesting that the gap between technology and scale-appropriate studies continues to narrow.
However, the temporal extent of these measurements will dictate their efficiency. Macrophyte cover will vary seasonally and changes in water chemistry can change on short time scales, especially where point-source pollutants are present. Investigators using telemetry should be aware of how changes in water temperature, density, salinity, gas saturation, plant biomass, and other factors will affect the detection probability and the ability to confidently resolve presence or absence of animals at a given location at a specific time. Biological properties such as plankton or fish larvae can be sampled by hydroacoustic monitors, which can provide context about prey biomass in an area to be linked to the arrival/departure of fish based on detections on acoustic arrays. For long-distance migrations of freshwater fish, it has been shown that grid arrays are more efficient and statistically robust than line arrays for tracking seasonal movements and yielding data that are useful for testing hypotheses about long-distance movements  [155].
Acknowledgments Development of this manuscript was supported by the project " Multi-Lake Research of Fish Ecology and Management using High-Resolution 3D Telemetry Systems ", funded by  ALTER-Net  within the  Multi Site Research (MSR) initiative. IJ was supported by  J. E. Purkyně Fellowship of the Czech Academy of Sciences. MŠ was supported by  TAČR   ZÉTA  project (No. TJ02000012 ). We are very grateful for illustration of Fig. 1 by  Zuzana Sajdlová. The work was supported from  ERDF/ESF  project  Biomanipulation  as a tool for improving water quality of dam reservoirs (No. CZ. 02. 1. 01/0. 0/0. 0/16_025/0007417 ).
Funding The project "Multi-Lake Research of Fish Ecology and Management using High-Resolution 3D Telemetry Systems", funded by ALTER-Net within the Multi Site Research (MSR) initiative. This publication is also based upon work from COST Action CA18102 -The European Aquatic Animal Tracking Network, supported by COST (European Cooperation in Science and Technology). Lennox was supported by the Norwegian Research Council project LaKES (320726).
Global anthropogenic mercury (Hg) emissions to the atmosphere since industrialization are widely considered to be responsible for a significant increase in surface ocean Hg concentrations. Still unclear is how those inputs are converted into toxic methylmercury (MeHg) then transferred and biomagnified in oceanic food webs. We used a unique long-term and continuous dataset to explore the temporal Hg trend and variability of three tropical tuna species (yellowfin, bigeye, and skipjack) from the southwestern Pacific Ocean between 2001 and 2018 (n = 590). Temporal trends of muscle nitrogen (δ15N) and carbon (δ13C) stable isotope ratios, amino acid (AA) δ15N values and oceanographic variables were also investigated to examine the potential influence of trophic, biogeochemical and physical processes on the temporal variability of tuna Hg concentrations.