dataesr/scientific-paragraphs-categorization huggingface dataset:
Hence, the taxonomy-based implementation strategy for these monitoring programmes will require improvements of molecular protocols and reference databases, to generate EG data that better fit the current standards, or an adaptation of the currently implemented assessment criteria to fit the specificities of EG data  (Hering et al. , 2018). Monitoring programmes relying on the screening of established bioindicators for the computation of BI values are proposed as being compatible with an implementation of EG  (Hering et al. , 2018; Pawlowski et al. , 2018). Indeed, this compatibility is greatly facilitated by the fact that the assessment criteria, i. e. , BIs, are not meant to strictly rely on taxonomic inventories but rather on the autecology of bioindicators. Hence, for the taxonomy-based strategy, the BI formulations can compensate the impact of taxonomic mismatches between morphology and EG and databases incompleteness to some extent, because multiple taxa are ascribed identical autecological values, conveying similar ecological signal  (Keck, Vasselon, Rimet, Bouchez, & Kahlert, 2018).
, 2017; Cordier et al. , 2017; Tapolczai, Vasselon, et al. , 2019). Hence, these approaches are less sensitive to the biological and technical issues mentioned above, because the ecological signal (autecology) is calibrated directly on the specificities of EG data. From an implementation perspective, this de novo strategy thus may represent the most direct path towards implementation of EG into monitoring programmes relying on BIs (Figure  2 ). Though somewhat unintuitive, this is because inferred BI values with a de novo strategy convey the same ecological meaning as they do with current methodologies, which is not the case when BIs values are inferred from bioindicators composition profiles depicted by EG data, as their autecological values were calibrated only on morphology-based data. Thus, the de novo strategy assures a better continuity with previous BIs data and time series and expand the range of possible bioindicators to virtually any taxa or sequence.
• A collection of reference morphological and molecular data in parallel, at least in a subset of reference points or during a transition period, will assure backward and forward compatibility of time series data sets, regardless of the envisioned implementation strategy to be decided in future monitoring campaigns. • The efforts to complete reference sequence databases need to be sustained, by adding more representatives of the known biodiversity, with a wider geographical coverage. • A reference database framework for de novo strategies needs to be established. A key requirement is the ability to reliably compare OTUs or ASVs identified in monitoring programmes to formally establish knowledge about their sensitivity to disturbance. • The taxonomic resolution level (haplotype, species, genus, family, order, class) at which HTS reads are most informative as genetic bioindicators for a given situation remains to be identified.
Nationale de la Recherche, Grant/ Award Number: ANR-17-CE32-011; ERA-NET C-IPM BioAWARE; Office Français de la Biodiversité (OFB); UK Natural Environment Research Council, Grant/Award Number: NE/N003756/1 and NE/N006216/1; German Science Foundation (DFG), Grant/Award Number: STO414/15-1; New Zealand Ministry for Business, Innovation and Employment, Grant/Award Number: CAWX1904 and C05X1707; DNAqua-Net COST Action, Grant/Award Number: CA15219; European Union; IKERBASQUE (Basque Foundation for Science); Basque Government (project microgAMBI) environmental genomics-based methods, applied to the biomonitoring of ecosystems. By taking a general overview, without narrowing our perspective to particular habitats or groups of organisms, this paper aims to compare, review and discuss the strengths and limitations of four general implementation strategies of environmental genomics for monitoring: (a) Taxonomy-based analyses focused on identification of known bioindicators or described taxa; (b) De novo bioindicator analyses; (c) Structural community metrics including inferred ecological networks; and (d) Functional community metrics (metagenomics or metatranscriptomics).
The de novo strategy provides numerous advantages over the taxonomy-based one. First, it can reduce or bypass the dependence on reference sequence databases for taxonomic assignments of HTS reads to known bioindicators. Instead, new ecological knowledge is hypothesised de novo during the calibration of OTUs or ASVs autecological values (IndVal) or during the supervised training of a model (SML). Second, it can leverage powerful but previously inaccessible groups of bioindicators among prokaryotes, protists, meiofauna and mesozooplankton, that are widespread and may react both faster.
tural and functional community metrics-based implementation strategy. Such effort will probably contribute to the establishment of a more broadly applicable monitoring framework and less constrained by the database and geographical coverage limitations. ACK N OWLED G EM ENTST. C. , J. P. , and L. A. P. G. were supported by the Swiss National Science Foundation (grant 31003A_179125). T. C. , J. P. , L. A. P. G. , and A. B. were supported by the European Cross-Border Cooperation Programme (Interreg France-Switzerland 2014-2020, SYNAQUA project). L. A. S. was funded by a 'Ramón y Cajal' contract (RYC-2012-11404) from the Spanish Ministry of Economy and Competitiveness. E. A. is funded by the Saudi Aramco-KAUST Center for Marine Environmental Observations. D. A. B. would like to acknowledge the financial support of the French Agence Nationale de la Recherche project NGB (ANR-17-CE32-011) and the ERA-NET C-IPM BioAWARE.
A. B. , and F. K. were supported by the Office Français de la Biodiversité (OFB). S. C. benefitted from the UK Natural Environment Research Council Grants NE/N003756/1 and NE/N006216/1. T. S. , and L. F. were supported by the German Science Foundation (DFG) under grant STO414/15-1. XP is supported by the New Zealand Ministry for Business, Innovation and Employment contracts CAWX1904 (Biosecurity Toolbox) and C05X1707 (Lakes380). A. B. , and F. L. were supported by DNAqua-Net COST Action CA15219 'Developing new genetic tools for bioassessment of aquatic ecosystems in Europe' funded by the European Union. A. L. is supported by IKERBASQUE (Basque Foundation for Science) and the Basque Government (project microgAMBI). We thank the members of DNAqua-net COST Action for helpful discussions. F I G U R E 2 Strengths and limitations of the currently envisioned implementation strategies of environmental genomics for the monitoring of ecosystems, and their ability to fulfill the criteria of existing monitoring programmes.
The resulting relations must be simple enough to be used operationally by practitioners. They can be established simply by using mobility indicators such as the Heim's ratio µ H or the effective friction coefficient µ e f f  [5] : µ H = H ∆L , (1) µ e f f = tan(θ) + H 0 ∆L. (2) H is the drop height, ∆L the horizontal travel distance along flow path, θ the average slope along flow path, H 0 the initial mass maximum height and ∆L the travel distance along topography from the collapse scar toe (Figure  1 ). µ e f f is less straight-forward to use than µ H as it takes into account the geometry of the initial mass, which is not always easy to estimate. The empirical estimations relating the landslide volume to µ H or µ e f f rely on large datasets that are not, in general, site specific. Thus, the particular topographic setting of a given study site is not taken into account.
To answer this question, we will derive site-specific power laws relating the horizontal travel distance ∆L to the unstable volume V, using a purely empirical approach, a numerical approach and combining empirical data with numerical modeling. Empirical data is drawn from two landslides databases (  [3, 5, 31] ). Numerical modeling is carried out with the SHALTOP shallow-water numerical model  [32] [33] [34]. We will use a simple Coulomb friction law that proved, with SHALTOP, to reproduce successfully real landslides deposits  [5, 29, [35] [36] [37] [38]. Besides, this rheology involves a single parameter, which simplifies uncertainty analysis. We compare the prediction and associated uncertainties for three documented case studies: rock/sand avalanches from the Samperre cliff in Martinique, Lesser Antilles, the Frank Slide rock avalanche in Canada and the Fei Tsui Road debris slide in Hong-Kong.
It is assumed that the development of a 1 to 4 m high perched aquifer above the kaolinite-rich layer favored the initiation of the landslide as a translational slide. As a result of low cohesion values  [43] , the material then desegregated rapidly. The debris slide traveled about 65 m (30 m from the scar toe) with limited spreading, with some of the deposits piled up against the corner of a building. Table  1. Description of the case studies considered in this work. We give the characteristics of the documented landslides and of the simulation databases. µ 1 e f f (V) gives the effective friction coefficient estimated from the landslide volume, using the power law deduced for DB1 (see Figure  5b ). For the Samperre case study, we give the estimated volumes and travel distances for the 2009 and 2018 collapses (date in brackets).
Landslide Databases We use two databases to estimate empirically travel distances. The first one (DB1) is drawn from  [5]. It includes 43 dense and rapid landslides from Earth, Mars, Iapetus (Saturn's satellite) and Io (Jupiter's satellite). Both the Frank Slide and the Fei Tsui Road debris slide are included in DB1. The second database (DB2) combines 44 rockfalls, rockfalls avalanches, debris flows, debris slides and debris avalanches drawn from  [3] , as well as 49 rock avalanches drawn from  [31]. From these databases we fit two power laws relating µ H to the landslide volume V: µ H = µ 1 H (V) for DB1 and µ H = µ 2 H (V) for DB2. Only DB1 could be used to derive a relation between µ e f f and V, µ e f f = µ 1 e f f (V), because the measures field observations required to compute µ e f f are not available in DB2.
Simulation Databases For each case study, we construct a simulation database with the SHALTOP shallow-water numerical code  [32] [33] [34]. This is done by considering multiple initial unstable geometries and friction coefficients µ S. For each case study, we model destabilized volumes ranging from 1/3 one third to about twice the volumes involved in documented events. The range of tested µ S encompasses realistic coefficients estimated from expert knowledge, in regard of the volumes considered. In particular, the value µ e f f = µ 1 e f f (V) (with V the volume of the real landslide, see Table  1 ) is also tested: it has been shown to be a good estimate of the friction coefficient µ S needed to reproduce the landslide dynamics and deposits  [5, 36, 37]. The lowest value µ S is also constrained by practical considerations: for small µ S , the landslide does not stop within the simulation grid and no travel distance can be measured.
The main characteristics of the simulations are given in Table  1. For the Samperre cliff case study, several topographic surveys were carried out over the years: they allow to quantify the evolution of the cliff after collapses episodes, but no data are available to constrain precisely individual events. Thus, we have inferred a total of 10 initial volumes/geometries spanning volumes from 0. 25 × 10 6 m 3 to 3. 7 × 10 6 m 3 : two of them are used to reproduce the August 2009 (1 × 10 6 m 3 ) and January 2018 1. 5 × 10 6 m 3 major events. More details about the definition of collapse scenarios are given in Appendix A. Friction coefficients are chosen between µ S = tan(10 • ) = 0. 18 and µ S = tan(15 • ) = 0. 27 every 1 • , and every 2. 5 • up to µ S = tan(35 • ) = 0. 70. This results in a total of 165 simulations.
Estimation of Horizontal Travel Distances We use three different methods to estimate the horizontal travel distance ∆L directly from the landslide volume V. They are summarized in Figure  6. We use empirical data from DB1 or DB2, site-specific numerical simulations or a combination of the two. For the sake of clarity and simplicity, we do not explicit the functions that will be mentioned hereafter. It can be any kind of statistical model fitted to the landslide travel distances databases, or to the numerical simulations results, provided the associated uncertainties can be estimated. In this work, we use power law relations that are fitted by transforming the variables on a logarithm scale and by fitting a linear regression model using Ordinary Least Square (OLS) regressions (see more details in Appendix B).
If the topography is convex, which is the case in our three case studies, it is possible to invert the previous relation to derive ∆L from μH : ∆L = ∆L ( μH ). Note that μH is computed geometrically. We relate it to realistic landslide mobilities by choosing μH = µ H , where µ H is the Heim's ratio measured on real landslides. Using the volume dependent relations deduced from the empirical databases DB1 and DB2, µ H = µ 1 H (V) and µ H = µ 2 H (V), we get: ∆L = ∆L ( μH ) = ∆L (µ 1 H (V)), (4) ∆L = ∆L ( μH ) = ∆L (µ 2 H (V)), (5) which we will write more simply  The superscripts 1 and 2 refer to the empirical database that is used for a given relation (DB1 and DB2, respectively). In this study, all functionals are power laws. ∆L = f 1 emp (V), (6) ∆L = f 2 emp (V). (7).
Numerical runout estimation In comparison, it is more difficult to use µ e f f for direct empirical estimation as its definition includes a more complex topography description (the mean slope along flow path) and the initial geometry and volume (through the initial height H 0 ). Thus, it is not straight-forward to invert (2) to get a relation ∆L = ∆L (µ e f f , V). However, this can be done through simulations, as will be explained in Section 2. 2. 3. Not that we could also have used empirical power laws ∆L = F emp (V) derived directly from landslide databases, and apply them directly on each case study. We choose not to, because such a method does not yield a site-specific power law. In comparison, µ H can be deemed to be, in a first approximation, a good mobility indicator, that can then be used on a chosen site.
Numerical/Empirical Runout Estimation In the relation ∆L = f S (V, µ S ) deduced from a simulation database, we can choose µ S as a function of the volume. As a matter of fact, it has been shown that lower friction coefficients are needed to model larger landslides. This can be done by back-analysis of multiple events with various volumes on a same site, but we do not have enough data for that. The other possibility is to consider that the empirical mobility indicators µ e f f and µ H are good estimates for µ S and setting, respectively µ S = µ 1 H (V), µ S = µ 2 H (V) or µ S = µ 1 e f f (V). Combining these relation with ∆L = f S (V, µ S ), we derive the volume dependent relations: ∆L = f 1 S,µ H (V) = f S (V, µ 1 H (V)), (9) ∆L = f 2 S,µ H (V) = f S (V, µ 2 H (V)), (10) ∆L = f 1 S,µ e f f (V) = f S (V, µ 1 e f f (V)).
( 11 ) Another possibility is not to consider µ S , and use only the simulations results. As a matter of fact, we can compute for each simulated landslide the mobility indicators μH and μe f f. The indicates that they are given by simulation results, when µ H and µ e f f are computed on real landslides. Their respective geometrical definitions are however the same. From the simulation database, we can thus estimate a relation ∆L = ∆L ( μH ), which is the same as for empirical runout estimation (see Section 2. 2. 1). To be consistent with the definition (2) of µ e f f that includes both the initial mass geometry and the travel distance, we derive from the simulations results a relation ∆L = g S (V, μe f f ). This is done with the tuples ( μe f f , V) for which μe f f lies within the 95% prediction interval of the empirical relation μe f f = µ 1 e f f (V).
Quality of Power Law Regressions In this work, we have used simple power laws to derive statistical models both from empirical databases and simulations results. Their quality can be at first hand assesed from the adjusted R2, but other indicators are needed to assess the reliability of uncertainty estimation. The details of the statistical analyses, with the coefficients and quality indicators, are given in Table  A1. The behaviour of residuals depending on predicted values is given in Appendix A (Figures  A1  and A2 ). The site-specific power laws are associated with very good R2: 0. 91 for g S (V, μe f f ) for the Samperre case study, and more than 0. 97 for other power laws. In comparison, empirical power laws feature R2 values between 0. 68 (for DB2) and 0. 84 (for DB1). g S (V, μe f f ) and f S (V, µ S ) have a Variance Inflation Factor (VIF) below 1.
Besides, most of the uncertainty can be recovered by considering only the deviation between the best-fit power laws, and the data used to derive them. In comparison, the uncertainty on the power law coefficients increases σ by at most 3% (compare orange and green bars in Figure  8 ). This can be expected, as we compute σ for volumes within the range of simulated volumes. If we considered larger or smaller volumes, the uncertainty on coefficients would have more significant effects. Table  2. Estimations of travel distance ∆L depending on destabilized volume V, following a power law ∆L = αV β. We give the value of the exponent β along with the standard deviation of its estimation σ β , and the normalized standard deviation σ of the prediction ∆L see (  13 ) and  (14). ∆L is the averaged predicted travel distance for volumes between the minimum and maximum volumes used in the simulation databases, in each case study.
Fei Tsui Road Case Study The results for the Fei Tsui case study are presented in Figure  10. As previously, the direct empirical estimations are coherent with observations (Figure  10a, c ). The estimation f 1 emp , that uses the database DB1, only slightly under-estimates the estimation by about 10%. When µ e f f and µ H are used to estimate µ S in numerical simulations, the observations are all well reproduced (Figure  10b, d, f ). To the contrary, the estimation with g S,µ e f f over-estimates the real travel distance by 45%. A major difference between the empirical estimations and other estimations is the value of the power law exponent β. For f 1 emp and f 2 emp we compute β = 0. 09, but we get β = 0. 38 or β = 0. 39 for empirical/numercial estimations. A similar value (β = 0. 34) is derived for the back-analysis estimation f f it S.
Quality of Empirical Power Laws Uncertainty Related to Dispersion in Empirical Power Laws Empirical laws can be improved by considering a database of landslides sharing similar characteristics with the case study. For instance, if we construct DB2 by taking only the debris flows, debris slides and debris avalanches from  [3] , we derive a new relations µ H = µ 2 * H (V) with R2 = 0. 8 that can be used for the Fei Tsui Road case study (see Figures  A5  and A6 ). As a result, the standard deviation σ of the travel distance estimation f 2 * emp is about 0. 25. In comparison, we had σ = 0. 35 for f 2 emp (that is, when we included rockfalls and rock avalanches in DB2). However, removing rockfalls and rock avalanches from DB2 also leads to a 15% over-estimation of the observed distance (though it remains in the 95% prediction interval).
Topography Description in Empirical Power Laws Uncertainty Related to Topography Description To estimate more finely the travel distance in empirical relations, it may be worth using statistical model that do not depend only on the volume. For instance in  [31] , a relation ∆L = a 0 10 a 3 C V a 1 H a 2 × 10 ( 15 ) is derived from a database of rock avalanches, with R2 = 0. 8. Here, C is an indicator variable with C = 1 if lateral confinement is observed, and C = 0 otherwise. In comparison, with the same data, they derive a power law µ H = a 0 V a 1 with R2 = only. In our work, combining their data with the data of  [3]  allows to increase the R2 to 0. 68 because the resulting database DB2 spans a wider range of volumes. By estimating the probabilistic distribution of the residues in  (15) , it is then possible to estimate, for a given topography and volume, the probability that the landslide goes further than a chosen point M on the topography.
Thus, it does not take into account lateral spreading that can limit travel distance: it has been observed empirically that lateral confinement increases travel distances  [8, 31]. Besides, the propagation of landslides on complex topographies with non constant slope is not predicted either. In  [5] , the formal expression of µ e f f is extended empirically to non constant slopes by considering the average slope θ along the landslide path. As discussed previously, it proved to yield conclusive results at the scale of the database, but it may not be precise enough when considering forward prediction in a specific case study. Indeed, when we try using µ e f f directly to estimate travel distances (that is, with the function ∆L = g S (V, μe f f )), we get somehow unstable results. Though the estimation is good for the Samperre Cliff (Figure  7e ), high uncertainties are obtained for the Frank Slide case study.
In the latter case, the dependence to volume seems also over-estimated in comparison with other estimations (Figure  9e ). Finally for the Fei Tsui Road case study, the travel distance is over-estimated by 45%. Thus, further investigations are needed to try and derive formulas relating landslides deposits and topography geometry to µ S. This could be done by modeling the propagation of landslides on synthetic topographies with, for instance, a slope break or an exponential profiles, and analyze the correlations between μH and μe f f derived from simulations results, and µ S. Another possibility is to disregard empirical mobility estimations, and consider instead directly the friction coefficient µ S needed to reproduce each event from a database of at least, typically, 30 landslides, which is necessary to evaluate correctly uncertainty.
We have indeed β = 0. 38 or β = 0. 39 in the former estimations, and β = 0. 34 for f it S , with a constant friction coefficient derived by back-analysis. In comparison, for the Frank Slide case study, the exponent β in f f it S is less than half the exponent in f 1 S,µ H , f 2 S,µ H and f 1 S,µ e f f. Indeed, in this case, the travel distance depends mainly on the friction coefficient µ S , such that choosing a constant value of µ S instead of a volume dependent value has more impact than in the Fei Tsui Road case study. These results may seem contradictory with empirical power laws ∆L = F emp (V) derived directly from landslide databases. The exponent β varies between 0. 25 and 0. 39 in  [4]  and does not seem to depend on the range of volumes considered. In  [5] , β = 0. 22 for V < 10 6 m 3 , β = 0.
Conclusions In this work, we derived operational and site-specific power laws to predict the horizontal travel distance ∆L of a landslide from its volume V. Such simple relations are indeed of prior importance for landslide hazard assessment, in particular for crisis management when travel distance estimations must be provided quickly by practitioners. We have compared three methodologies to derive such power laws, using (i) a purely empirical approach, (ii) a database of simulations along with the back-analysis of a documented event, and (iii) combining an empirical estimation of mobility with simulations. We show that: 1. The best results, in terms of prediction uncertainty, are obtained with numerical estimations of travel distances, with friction coefficient deduced from back-analysis.