dataesr/scientific-paragraphs-categorization huggingface dataset:
| Functional diversity We expressed functional diversity as the functional dispersion index (FDis, sensu  Laliberté & Legendre, 2010). For each litter mixture, FDis was calculated using the fdisp function in the fd package in r  (Laliberté et al. , 2014)  on a species-by-species distance matrix (Gower dissimilarity matrix) computed from the measured functional traits. We calculated and tested the effect of two different FDis values, based on different groups of traits. The included traits were selected to represent, on the one hand, well-documented traits that correlate with litter decomposition generally  (Canessa et al. , 2021; Makkonen et al. , 2012; Zhang et al. , 2008) , and on the other hand, a direct link to the proposed mechanisms behind litter mixture effects  (Bonanomi et al. , 2014; Hättenschwiler et al.
This procedure was performed using the dredge function in the MuMIn r package  (Barton, 2018). As several models were selected, model averaging was performed based on AICc weights, using the model. avg function in the same package. Model residuals were checked for normality. All predictors were standardized prior to analyses. This allowed us to calculate the relative importance of each predictor using the ratio between its model parameter estimate and the sum of all parameter estimates, expressed in %. Marginal and conditional R 2 values (R 2 m and R 2 c, respectively) were calculated using the Nakagawa and Schielzeth's method, where mR 2 corresponds to the variance explained by the fixed factors, and cR 2 corresponds to the variance explained by the fixed and random factors together  (Nakagawa & Schielzeth, 2013).
The aim was to provide a synoptic view of several lagoons functioning using field measurements, satellite observations and high-resolution hydrodynamic model and to characterize inter-lagoon heterogeneity. This project focused on physical processes occurring in coastal ecosystems and especially on the interactions and exchanges along the land-lagoon-ocean continuum. In this framework, field observations have been undertaken in a variety of unmonitored lagoons, with a focus on austral summer, i. e. cyclone season to identify mean circulation and forcings, as well as to highlight the scales of variability. Data presented in this paper are a contribution to the knowledge and understandings of the diversity of physical processes at stake in New Caledonia lagoons. It represents an essential database for improved realism of numerical modelling development and experiments.
8 mm recorded at Koné station, source Météo France) and gust of winds upper than 100 km h -1 in Koumac. A month later, the TD "Gretel" followed the western coast and induced 150 mm of precipitation in 48 hours. The last field experiment of the PRESENCE data set, called NEMO was conducted in Moindou's lagoon during 2020-2021 summer. Observations covered a period of 8 months separated in two legs. Observational strategy was thought to capture the main forcings and circulation features described above (see Fig. 2 ). As the Nouméa lagoon, Moindou lagoon has a funnelshaped geomorphology, it becomes wider southward (2 km on the northern part until 7 km near Moindou passage). The lagoon is mainly shallow (< 6m) but includes some reticulated deeper "holes". The surrounding semi-continuous reef barrier includes two shallow passes on the reef crest.
Hydrological patterns: CTD profilers Two versions of Sea-Bird SeaCAT profilers (SBE 19plus and SBE 19plus V2) (https://www. seabird. com/sbe-19plus-v2seacat-profiler-ctd/product?id=60761421596) were used to describe the vertical and cross-shore patterns in temperature and salinity. Two auxiliary sensors also equipped our CTD profilers: the WET Labs ECO-FLNTU acquiring Fluorescence and Turbidity and the Biospherical/Licor for PAR acquisition. The SBE19plus (used for SPHYNX and NOUMEA campaigns) (Fig. 3. F) and the SBE19 plus V2 exploited during CADHYAK and NEMO surveys are both acquired data at 4Hz. All water column profiles were done manually from coastal boats on the water column (surface to bottom). For avoiding effects due to the drifting of the boat, only descent data were kept for final processing.
2. All data were carefully viewed using owner's softwares to check for bad data (e. g. out of water, outlier spikes) and find the exact date and time of first and last good observations. 3. Data were then exported in the most convenient formats (depending on the software) to be read with Python 3. These formats were mostly Ascii or Matlab files. 4. Python 3 was used to process data when required and stored them into NetCDF (Network Common Data Form) files. Metadata thus contain information on: beginning and end dates, positions, depth, instrument model and serial number, project, publisher, contacts, processing done…etc. During this step, data were also plotted and examined to ensure the quality of obtained time-series. 5. Converted NetCDF data were finally plotted using Ferret to perform a last visual check.
Specific processing steps For each instrument types specific processing steps were conducted. For SBE56, raw data were averaged to provide final NetCDF time-series at 1 min. JFE data were averaged over the 10 samples of each burst, so that the final period is 10 or 15 min. For RBR data (pressure), no atmospherical correction was applied and signals were filtered (using linear theory reconstruction of wave parameters) giving final output files of two kinds. One file containing temperature & level dynamics at 1 min resolution and the second at 1 hour resolution offering computed wave parameters. ADCP data were filtered to avoid contamined surface cells within the NetCDF data. Final temporal resolution is thus the nominal resolution of the bursts. Marotte inclinometers data were averaged at 1 min sampling and orientation conventions was changed to be congruent with oceanographic currents convention.
No specific treatment was applied to Pacific Gyre Reef Drifters so the nominal frequency (10 min) is given in the final data. Drifter's speed was also derived from the positions and included in the final data. For CTD casts, classic processing steps using SeatermV2 software were applied, in order: data conversion in cnv, low-pass filtering, align-CTD, derive and a bin averaging at 0. 5. The final data collection consists of 5 individual data sets, one for each sampled lagoon (see Section 7).
Conclusion The PRESENCE data set presented in this paper offers a remarkable and unique opportunity to investigate lagoon hydrodynamics both by being freely accessible and composed of observations on five different lagoons around New Caledonia Grande Terre during cyclonic seasons. Eight major atmospheric events have enameled our observations period  (2014) (2015) (2016) (2017) (2018) (2019) (2020) (2021)  and sampling strategies deployed enabled to capture their signatures in various lagoons as well as on their external reef slopes (e. g incident swells). It represents a considerable contribution for the knowledge on lagoon dynamics around New Caledonia and will allow investigating easier and further on assessment of numerical model experiments or satellite derived parameters. The spatial extent of all sampling strategies offers an interesting synoptic view of those distinct lagoons functioning and dynamics, in a way complements the existing Reeftemps network.
Furthermore, even though the five lagoons were not sampled simultaneously, this data set is an important milestone about knowledge on processes at stake during cyclonic events. Interesting features were also highlighted thanks to this long-term observational strategy, whether in terms of thermal dynamics (e. g. internal waves, upwelling) as well as surface circulation (e. g SW lagoon gyre, wind dependency drift), land-lagoon continuum variability through plumes dynamics or hydrologic structuration, quantification of ocean-lagoon exchanges over reef crests or through passes. Finally, the PRESENCE data set will provide a unique opportunity to go deeper into the apprehension of coastal vulnerability or impacts of Marine Heat Waves on coastal New Caledonian lagoons.
Co-registration between individual scan positions was performed with RIEGL's RiSCAN PRO software first with the help of the retro-reflectors for coarse registration, then using the Multi-Station Adjustment (MSA) routine for fine registration  (Wilkes et al. , 2017). This routine first searches for planar surfaces in the scan data, which can be tree trunk surfaces or ground patches. Then, these surfaces are used as registration targets to adjust the scan position of all individual scans of the scan project. Due to lack of reliable global navigation satellite system (GNSS) reception below the forest canopy, initial registration of the TLS project was performed in the project coordinate system. Additionally, the registered point clouds were cleaned from noisy points by removing points exceeding pulse deviation threshold  (Wilkes et al.
At LF, one flight had to be flown manually due to an unsuccessful flight plan upload to the platform, resulting in an uneven distribution of flight lines. However, due to the open canopy structure at this savanna site canopy exploration was still very favourable for further analysis. Further details on the flight campaigns can be found in Table  1. The UAV-LS processing followed standard procedures  (Brede et al. , 2017). First, the collected raw GNSS and IMU data were processed to flight trajectories with Applanix POSPac Mobile Mapping Suite 8. 3 and under correction of GNSS logs received from permanent base stations. Second, LiDAR waveforms were processed to discrete return ranging data in the scanner's own coordinate system with RIEGL's RiPROCESS software. During this step all returns that would result in a reflectance of less than 20 dB were removed.
Wood density Wood density estimates were retrieved from two global databases and a compilation study in order to estimate AGB based on individual tree volume estimates. Data for the NL temperate site were collected from the Wood Economics Spectrum database  (Chave et al. , 2009; Zanne et al. , 2009) , where data are stored as species level-averages. Since trees from the family Fagaceae could not be identified to the species level based on the UAV-LS data, they were summarised at the family level by averaging wood densities of the species Fagus sylvatica, Quercus robur and Quercus petraea (Table  3 ). All other species were treated with species-specific wood densities. Data for the tropical sites FG and LF were extracted from the Pantropical Tree Harvest database  (Chave et al. , 2014) , where data are stored at single tree level.
Subsequently, semi-automatic foliage segmentation was applied  (Wang, 2020)  with manual parameter tuning based on 10-20 trees per site and visual assessment. Finally, defoliated tree point clouds were used to build QSMs, and derive tree trunk and branch volume with TreeQSM v2. 3. TreeQSM is a geometric fitting routine that models geometrically explicit trees by identifying the branching architecture, and fitting cylinders to represent trunks and branches  (Raumonen et al. , 2013). QSM-derived tree volume has been shown to be an unbiased predictor for individual tree AGB  (Calders et al. , 2015) , especially for large trees  (Gonzalez de Tanago et al. , 2018; Momo Takoudjou et al. , 2018). The latest version of TreeQSM is available at (https://github. com/InverseTampere/TreeQSM). For automatic optimisation of TreeQSM parameters, a grid-search optimisation was applied on an individual tree basis.
Strategy Ib: globally calibrated UAV-LS-based ASM Strategy Ib was similar to strategy Ia in the sense that a calibrated ASM was used. In this case, this ASM was built on metrics that are possible to measure from ALS and UAV-LS, hence, scaling of AGB esti-mates would be possible with airborne LiDAR technology. Jucker et al. (2017)  presented an ASM trained on a global database of inventory measured individual tree metrics and destructive harvest-derived AGB of 2395 trees. The proposed ASM was built on the metrics tree height H100 and crown diameter CD. The model differentiates angiosperm and gymnosperm species due to their structural differences: AGB = (0. 016 × α G ) × (H × CD) 2. 013+β G × 1. 021026 (4) with AGB in kg, H100 and CD in m, and α G = 0. 093 and β G = -0. 223 for gymnosperms, with both parameters set to zero for angiosperms.
Strategy IIb: locally calibrated UAV-LS-based ASM Strategy IIb used a per-site calibrated ASM based on H100 and CD that can be extracted from ALS and UAV-LS. This strategy assumed the presence of TLS and UAV-LS data for the same site, with synchronised scanning campaigns. The general ASM form of  Jucker et al. (2017)  was used and as in strategy IIa models were fitted on log-transformed, binned data: ln(AGB) = α + β ln(H × CD) (6) with AGB in kg, H100 and CD in m, and α and β model coefficients that were fitted separately for angiosperm and gymnosperm species  (Jucker et al. , 2017).
Strategy IIc: locally calibrated UAV-LS-based non-parametric model Strategy IIc assumed the same conditions as strategy IIb, i. e. , availability of TLS and UAV-LS data, but strategy IIc made use of a wider range of UAV-LS based metrics and a non-parametric modelling approach. In particular, all metrics presented in Section 3. 3 plus taxonomic information, i. e. , coniferous vs deciduous species, were considered for the modelling process to build local AGB prediction models for UAV-LS data. The rationale behind the use of multiple metrics was to possibly mitigate errors in retrieval of single metrics and optimise the prediction power of individual metrics in different forest structures. The non-parametric modelling approach adopted was random forest (RF) for regression  (Breiman, 2001)  implemented in the R ranger package  (Wright and Ziegler, 2017).
RF has gained popularity in remote sensing applications because of its robustness to multi-dimensional and co-linear data  (Belgiu and Drȃgu, 2016). In this study, RF was trained to predict individual tree volume based on all available tree metrics (Section 3. 3). Subsequently, volume was multiplied with wood density to derive AGB based on the same species information as for the TLS estimates (Sections 2. 4, 3. 1). Hyperparameter tuning via cross-validation showed low sensitivity to hyper-  parameter choices in terms of volume prediction performance root mean square error (RMSE). Therefore, default values were implemented: number of regression trees to grow of 500, minimal node size of 5 and number of metrics to possibly split at in each node of 3. Additionally, the RF importance metrics were analysed in order to gain understanding in the relative importance of metrics within and across sites.
Impact of calibration sample size on AGB prediction The collection of calibration data with TLS in the field  (Wilkes et al. , 2017)  and subsequent manual preparation of TLS trees are time consuming procedures. Therefore, the effect of the calibration sample size on the prediction errors was analysed for locally calibrated strategies IIa, IIb and IIc, and independently for each site. This was achieved by varying the number of the samples available for model calibration. Specifically, for each individual tree in the TLS database a stratified random sample of n samples was drawn from the remaining trees, whereby n was varied from 10 to the maximum available number of TLS trees minus the tree for prediction per site in steps of five. In this way predictions were always performed on all individual trees, while the number of calibration samples was varied according to n.
The stratification was implemented with five strata and based on total QSM wood volume. The rationale behind the stratification is that when selecting calibration samples in the field, field crews would be able to distinguish five size classes of trees according to their height and DBH as proxies for total tree wood volume, and with knowledge of the range of tree sizes in the area. Additionally, stratification is recommended for ASM model calibration  (Jucker et al. , 2017; Duncanson et al. , 2015). For the NL site, where taxonomic groups were distinguished in strategy IIa and IIb (Section 3. 4), the calibration models were built independently for the taxonomic groups. For this case, the number of randomly drawn samples was proportionate to the proportion of the respective taxonomic group in the full TLS database.
This effectively increased the number of samples n needed in the sampled database to built models for the site, so that at least 25 and 50 samples were necessary for strategy IIa and IIb, respectively. Based on the resulting calibration database a model according to strategies IIa, IIb and IIc was built (Section 3. 4), and the individual tree AGB was estimated. Then, RMSE (Eq. (  7 )) and relative bias (Eq. (  10 )) were calculated. Subsequently, this procedure was applied to all individual trees for a given sample size n. The procedure was repeated 20 times, and the mean and standard deviation of RMSE (Eq. (  7 )) and relative bias (Eq. (  10 )) were calculated. The resulting statistics enabled assessment of the performance of the modelling strategies under different sizes for the calibration database.
Impact of calibration sample size on AGB prediction When comparing the different AGB modelling strategies that were based on local calibration, strategy IIa consistently provided the lowest RMSE of all strategies across sites with on average 0. 42, 0. 56, 0. 70, 0. 30 Mg for n ≥ 50 for NL, FG, RC and LF, respectively (Fig. 14 ). Moreover, there were no significant trends with changing TLS database size for n ≥ 50. Since strategy IIa was the only one involving DBH as an independent variable, this result highlights the importance of DBH in AGB estimation of single trees. Strategies IIb and IIc showed comparable, decreasing trends in RMSE with n at NL and FG, and divergent trends at RC and LF. Specifically, the RMSE of strategy IIb increased monotonically with increasing n from 1. 38 Mg (n = 30) to 1.
database. The stratified sampling approach implemented in the drawing produced relatively balanced calibration databases at small n, but due to the abundance of small trees (Fig. 5 ) more and more small trees were included with increasing n, and dominated the calibration. Particularly at RC, where the UAV-LS point density was low compared to the other dense tropical site FG (Table  1  and Fig. 3 ), the segmentation quality for small understorey trees was lower, which negatively affected the model calibration. The results in terms of relative bias showed a different picture than the individual tree RMSE results. For strategies IIa and IIb, the largest possible TLS database did not result in the bias closest to 0%. In fact, for strategy IIa optimal n in terms of bias was 50, 85, 150 and 65 for NL, FG, RC and LF, respectively.
Assuming that the used TLS estimates that were used for comparison here exhibit a bias of ±10% as judged by past studies (Section 1), the modelling strategies possibly induced a larger bias in the population estimates than the TLS estimation process. A problem here is that the TLS measured DBH and H100 exhibit different error distributions than their field-measured equivalents. Wang et al. (2019)  highlighted the differences and possible biases between inventory and ALS measured individual tree heights, and concluded that ALS is more reliable in tall trees, while inventory measurements underestimate tall tree height. Coomes et al. (2017)  found biases between inventory and ALS measured CA. This crossing of data modalities would make the application of field-calibrated ASM on LiDAR derived metrics problematic.
Calibration of traditional ASMs with TLS Strategy IIa provided the overall lowest individual tree AGB RMSE for all sites except at LF. This underlines the potential of locally calibrated ASM. Additionally, it highlights the role of DBH as a powerful metric for individual tree AGB estimation  (Chave et al. , 2014; Forrester et al. , 2017; Lau et al. , 2019). Strategy IIb also included locally calibrated models, but implemented H100 and CD, which were not as well related to AGB (Fig. 10 ). Jucker et al. (2017)   Considering implementation opportunities, TLS has been discussed as a possible source for unbiased, non-destructive calibration data to supplement or even replace databases based on field measurements, especially with respect to large trees  (Calders et al. , 2015; Momo Takoudjou et al.
In fact, Martin-Ducup et al. (  2021 ) identified the segmentation step as the weak link in automatic TLS based tree-centric volume estimation, which can also be expected for UAV-LS. The reason why the full accounting was not performed here is that a complete segmentation of the TLS point cloud with high quality is still extremely labour-intense. Even automatic approaches always require manual result control. For NL, FG and RC, this means several months more of human-hour input. However, such a full segmentation of the TLS data would enable the validation of plot based AGB, and full inter-comparison of area and tree-centric AGB modelling approaches, and should be tackled in future studies. Nonetheless, the accumulation of TLS trees to populations already showed very encouraging results that give a preview of full plot-scale results (Fig.
UAV-LS for tree-centric AGB estimation: challenges and opportunities Among the modelling strategies applied in this study, strategies IIb/c make use of UAV-LS in combination with TLS data. The data acquisition with both sensor technologies has the highest requirements in terms of initial infrastructure investments, technology, logistics and legal requirements, primarily driven by the organisation of unoccupied aerial vehicleUAV flights. Flights might also not be possible at all forest sites particularly in dense canopies where the plots are far from take-off areas. This needs to be mitigated by targeting only suitable sites. As UAVs become more important in calibration and validation activities other than AGB  (Brede et al. , 2018; Origo et al. , 2021; Niro et al. , 2021) , site maintainers will also have an interest in accommodating UAV flights by, for example, preparing take-off areas.
, 2020) , structural features  (Terryn et al. , 2020) , and combined hyperspectral and structural features  (Shi et al. , 2018)  derived from UAV acquired data alongside UAV-LS could contribute to usable methods for this challenge. Finally, in order to draw conclusions on plot-based estimation capabilities of UAV-LS, a comprehensive analysis of UAV-LS segmentation performance needs to be conducted. This requires a full plot scale, quality controlled TLS individual tree segmentation and wood volume estimates. So far, TLS is the only means to adequately trace occupied canopy space back to individuals, thereby enabling to judge segmentation quality in 3D, but the process of TLS data preparation is very laborious and time-consuming.
This strategy could use TLS for calibration and field inventories for coverage of a supersite. On the other hand, local calibration based on UAV-LS derived metrics and nonparametric modelling resulted in relatively high individual tree AGB RMSE, but consistently low population bias within ±5%. A low bias is an important property when summing up individual tree AGB, as is necessary for satellite calibration and validation. This bias level was reached with 15-55 training samples, depending on site conditions, which can be relatively quickly collected with TLS. This approach can rapidly acquire hectare-scale data necessary for supersite characterisation. Compared to area-based ALS estimation approaches, the employed individual tree AGB modelling strategies have the advantage of being traceable to the individual tree and allow aggregation to arbitrary scales for flexible preparation of calibration and validation data for spaceborne missions.