dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We are very grateful to ISblue for funding this educational project that involved students in the  SML master's program  (" Sciences de la Mer et du littoral, mention Chimie de l'environnement Marin "). We also thank the teachers who participate in the training of the master's students in related fields,  Peggy Rimmelin  ( UMS 3113-IUEM ) for the lending of sensors and  Nicolas Floc'h (BMO)  for providing the water level data for the RPR. A special mention to  Thilo Berhends  from the  University of Utrecht  for inspiring this educational project. The objective of this project was to design and run a teaching module covering the different aspects of research work applied to estuarine processes. Constructed collaboratively, the first step was to identify an original research question and to establish a reasonable study strategy in accordance with the students' knowledge and technical skills.
MATERIALS AND METHODS Data used for this study originated from 43 adult dromedaries: 27 Sahraoui animals (13 females and 14 males) and 16 Targui animals (nine females and seven males) corresponding to a total number of 22 females and 21 males. These individuals were over five years old and were slaughtered at Ouargla slaughterhouse in Algeria, between November 2010 and September 2014. For each individual, two biometric measure ments were made prior to slaughter: the height at the withers (HW) and body weight (BW). Following slaughter, the rear left and right cannon bones were collected. A total of 86 metatarsals thus constituted the material for the study. The morpho-biometric differences between the two breeds (Sahraoui and Targui) are not explored in this study as there are already many other papers on the subject  (Babelhadj et al.
Firstly, the relationship between body weight (BW), height at the withers (HW) and other different measurements was analysed by a PCA (PCA2) using only the data from the "right" side. The surface variables (NC1, NC2, GC, PC) and the linear variables (BpT, DpT) were considered as the active variables, whereas the height at the withers, the body weight, SA, the breed and sex were treated as supplementary variables. A 95% confidence interval was set for each breed and sex group. Secondly, a multilinear regression of BW and HW based on surface and linear measurements was done using a "step by step" procedure to select the best bivariate model (RegBest of the FactomineR package), resulting in a predictive equation for each of the two biological variables to be estimated. The statistical analyses were carried out using the free R software, Rstudio.
influence of asyMMetry l versus r The study was conducted on the whole population (n = 86; 43 left and 43 right metatarsal bones). Results of linear mixed effects models show significantly different averages for both sides only on GC and BpT measurements (p-value = 0. 0276, and p = 0. 0439 respectively) (Table  2 ). However, the power of these two models was less than 0. 80, respectively 0. 6 and 0. 528, which means that the false rate risk was too high to conclude a clear, significant difference. Considering these results, the study was then conducted on the right side dataset.
2016 Babelhadj et al. , 2017;; Guintard et al. 2018). It might also be interesting to see whether young individuals (five to seven years) have significantly different values from older animals, since bone growth in this species is relatively late and ends at about seven years of age. The actual data regarding the age was not avaible in this study. Age was estimated by examination of the dentition by experienced breeders and butchers. Despite being the most common method for camels, it is not of exact accuracy. Similarly, the weight variations observed between individuals do not reflect differences in overweight status, feeding or watering of animals, parameters known to vary significantly with the total mass of an animal; but weight values were obtained by the barymetry method. A study based on the individual weighing of animals would probably be more informative, but this kind of data was not available to us because it is difficult to set up in the field.
Abstract. Regional-level applications of dynamic vegetation models are challenging because they need to accommodate the variation in plant functional diversity, which requires moving away from broadly defined functional types. Different approaches have been adopted in the last years to incorporate a trait-based perspective into modeling exercises. A common parametrization strategy involves using trait data to represent functional variation between individuals while discarding taxonomic identity. However, this strategy ignores the phylogenetic signal of trait variation and cannot be employed when predictions for specific taxa are needed, such as in applications to inform forest management planning. An alternative strategy involves adapting the taxonomic resolution of model entities to that of the data source employed for large-scale initialization and estimating functional parameters from available plant trait databases, adopting diverse solutions for missing data and non-observable parameters.
Here we report the advantages and limitations of this second strategy according to our experience in the development of MED-FATE (version 2. 9. 3), a novel cohort-based and trait-enabled model of forest dynamics, for its application over a region in the western Mediterranean Basin. First, 217 taxonomic entities were defined according to woody species codes of the Spanish National Forest Inventory. While forest inventory records were used to obtain some empirical parameter estimates, a large proportion of physiological, morphological, and anatomical parameters were matched to measured plant traits, with estimates extracted from multiple databases and averaged at the required taxonomic level. Estimates for non-observable key parameters were obtained using metamodeling and calibration exercises.
Missing values were addressed using imputation procedures based on trait covariation, taxonomic averages or both. The model properly simulated observed historical changes in basal area, with a per-Published by Copernicus Publications on behalf of the European Geosciences Union. formance similar to an empirical model trained for the same region. While strong efforts are still required to parameterize trait-enabled models for multiple taxa, and to incorporate intra-specific trait variability, estimation procedures such as those presented here can be progressively refined, transferred to other regions or models and iterated following data source changes by employing automated workflows. We advocate for the adoption of trait-enabled and population-structured models for regional-level projections of forest function and dynamics.
, 2013; Morin et al. , 2021; Prentice and Cowling, 2013; Dufrêne et al. , 2005) , with the corresponding decrease in ecological realism. However, including functional trait variation has been proven to substantially impact simulation outcomes  (Verheijen et al. , 2015)  and comprehensive global trait databases represent a key source of information to boost parameter estimation  (Kattge et al. , 2020). Different strategies have been adopted to incorporate plant functional diversity into vegetation modeling exercises  (Zakharova et al. , 2019; Berzaghi et al. , 2020). One possibility is to sample individual-level trait values from the distribution observed in a given forest area  (Fyllas et al. , 2014). This stochastic sampling of trait combinations can preserve trait covariation and relies on competition processes implemented in the model to filter trait distributions in each simulated patch  (Sakschewski et al.
, 2015; Thonicke et al. , 2020; Pavlick et al. , 2013). Another approach consists of combining a trait-based description of individuals with genetic and demographic processes included in the model to simulate trait inheritance and, hence, eco-evolutionary processes  (Scheiter et al. , 2013; Scheiter and Higgins, 2009). For simulations over large spatial extents, yet another possibility is to use climate-trait relationships, calibrated using global trait databases, to prescribe the variation across space of parameter values  (Verheijen et al. , 2013 (Verheijen et al. , , 2015)). All these approaches focus on representing the continuum of traits at the individual level, either ignoring plant taxonomy for parametrization, or using broadly defined functional types. Even though models implementing these approaches are valuable tools, disregarding the information provided by species taxonomic identity entails some trade-offs.
, 2020; Morin et al. , 2021; Ruffault et al. , 2022). While not solving the issue of intra-specific trait variability, a taxon-based parameter estimation strategy can be employed for large-scale simulation exercises in combination with systematic forest inventory data, the latter defining the maximum resolution of taxonomic entities  (Vanderwel et al. , 2013; Caspersen et al. , 2011; Dijak et al. , 2017; Morin et al. , 2021). To date, however, there are few examples of trait-enabled models of vegetation dynamics including parameterization for a large number of taxa based on plant trait databases  (Christoffersen et al. , 2016). Hence, it is important to appropriately examine the different challenges to address if this approach is adopted because, even with the current wealth of plant trait data sources, information is still insufficient for less common species that have not received much attention and for key model parameters that are difficult to measure or cannot be matched to any observable trait.
The main objectives of this manuscript are as follows: (1) to present MEDFATE (version 2. 9. 3), a cohort-based and trait-enabled model of forest function and dynamics designed for regional-scale applications, and (2) to illustrate the challenges encountered in the process of estimating parameter values for multiple taxa in trait-enabled models and provide suggestions to address them. We begin by describing the design and formulation of MEDFATE, which evolved from two preceding models  (De Cáceres et al. , 2015 , 2021 ) that now constitute alternative sub-models for transpiration and photosynthesis processes. We then detail the different procedures we adopted to estimate taxon parameter values for the application of MEDFATE over a target region in the Mediterranean Basin, including the imputation procedures used to address missing values.
2. 3 to 2. 5), because they are newly introduced in the model version presented here. Table  1  includes a description of all symbols mentioned in the text. MEDFATE has been coded in C++ and linked to a R user interface  (R Core Team, 2023). The model is implemented in a modular way, so that different R functions can be used to execute different sub-models (Fig. 1 ). An extended model description, including process formulation, can be found at https://emf-creaf. github. io/medfatebook/ (last access: 30 May 2023).
Three surveys of Spanish National Forest Inventory (SNFI) are available for the region  (SNFI2, 1989 (SNFI2, -1991;; SNFI3, 2000 SNFI3, -2001;; and SNFI4, 2013-2016). These were conducted using a systematic sampling scheme, with 10 820 plots in SNFI2, 11 314 plots in SNFI3 (average density of 1 plot per km 2 in both cases) and 5500 plots in SNFI4 (0. 5 plot per km 2 ). Plot sampling involves a variable radius with circular nested subplots, where trees of different diameter classes are identified to the species level and their DBH and height is measured. Shrub sampling consists in determining mean height and percent cover by species. Since field soil data are absent in forest inventory plots, soil physical properties (i. e. , texture, bulk density, organic matter content) in plot locations were drawn from the global database SoilGrids  (Hengl et al.
, 2017) , complemented by rock fragment content estimates derived from surface stoniness measurements made during SNFI surveys. Soil water retention and conductivity curves followed the  van Genuchten (1980)  model, with parameters estimated following  Tóth et al. (2015). Historical daily weather data used for parameter estimation and model evaluation exercises were obtained via interpolation of weather records from Catalan and Spanish surface station networks on forest plot locations using the R package "meteoland"  (De Cáceres et al. , 2018).
Parameter estimation We reduced the tree and shrub species classification codes included in the SNFI to 217 taxon entities, of which 144 taxa were at the species level and 73 were aggregated at the genus level based on their frequency and the availability of species-specific data. Upper taxonomic levels (genus, family, order) were filled using the R package "taxize" (Chamberlain and Szocs, 2013). MEDFATE (version 2. 9. 3) requires taxon-specific estimates for 117 different parameters (including qualitative variables and allometric coefficients) to be run. The different procedures employed for parameter estimation are summarized in Table  2  and are detailed in the following subsections. Table  S1  includes the strategy used to estimate taxon values for each parameter. The final taxon parameter estimates are given in Table  S3.
Parameter estimates obtained from allometric and plant trait databases We used available allometric relationships for shrub and tree leaf biomass and crown base height assembled from multiple sources  (Hasenauer, 1997; Burriel et al. , 2004; De Cáceres et al. , 2019)   , 2020). TRY data sets were complemented by additional databases, global analysis papers and personal compilations for phenology, anatomy and morphology  (Tavşanoglu and Pausas, 2018; Zanne et al. , 2009; Morris et al. , 2016) , plant hydraulics  (Mencuccini et al. , 2019; Sanchez-Martinez et al. , 2020; Martin-StPaul et al. , 2017; Choat et al. , 2012) , tissue water content  (Bartlett et al. , 2012) , and stomatal or cuticular conductance  (Hoshika et al. , 2018; Duursma et al. , 2018)  (see Table  S2 ). Transpiration and photosynthesis functions of the advanced sub-model were readily parameterizable with measured traits, although hydraulic parameters were often missing for leaf and root segments.
Estimation of transpiration and photosynthesis parameters of the basic sub-model As indicated above, eight key parameters regulating transpiration and photosynthesis in the basic sub-model could not be estimated from plant trait databases. To make transpiration, gross photosynthesis and growth predictions obtained by the basic sub-model as similar as possible to those produced by the advanced sub-model, we conducted a meta-modeling exercise in which the results of simulations with the advanced sub-model were used to provide estimates for the eight parameters of the basic sub-model. The meta-modeling exercise was conducted separately for each of 12 prioritized species on the basis of their importance in the study area. These were Pinus halepensis Mill. vised by experts prior to conducting the meta-modeling exercise.
Parameters regulating the maximum growth rates of leaves and fine roots were deemed less important, given the allocation constraints to the formation of these organs. Tree ring data have been previously used for the calibration of growth parameters in other models  (Fyllas et al. , 2017) , so we adopted the same approach. The tree ring data set used for the calibration exercise was sampled in 75 SNFI plots, located in pure stands whose dominant species are F. sylvatica, P. halepensis, P. nigra, P. sylvestris or Q. pubescens, and selected to encompass a range of climatic aridity  (Rosas et al. , 2019). We took annual basal area increments of each tree as the observations to be matched by model predictions of sapwood growth. Simulations of the calibration exercise were performed using the basic sub-model only, for computational reasons, but we compared the performance of the two sub-models with calibrated parameter values.
These growth rate estimates were obtained using data from both the SNFI2-3 and SNFI3-4 periods in the Catalan territory (but estimates for different SNFI periods and the entire Spain are provided in Table  S4 ). We finally obtained estimates of RGR cambiummax for all tree taxa by using the calculated mean annual relative growth rates as input in the fitted linear models. We took the averages of calibrated SR sapwood and RER sapwood values as defaults for all taxa.
Evaluation with SNFI data While complex models can be applied to a range of purposes, model suitability should be assessed with respect to intended application  (Planque et al. , 2022). Our aim to use MED-FATE to project forest dynamics at the regional level required evaluating the capacity of the model to reproduce observed changes in forest dynamics. We therefore compared simulated forest dynamics between surveys of the SNFI in Catalonia against observations from repeated SNFI plots. Specifically, we selected 1779 permanent plots (i. e. , common to all three surveys) without signs of management and avoiding large decreases (i. e. , > 10 %) in stand basal area, which could indicate the effect of disturbances. Three sets of simulations were performed: (i) between SNFI2 and SNFI3 (∼ 10 years), (ii) between SNFI3 and SNFI4 (∼ 15 years), and (iii) between SNFI2 and SNFI4 (∼ 25 years).
Unlike matrix-based models, the IPM methodology ) predicted for different processes (growth, mortality, ingrowth) and taking into account all of them (overall). Results are shown for the two sub-models and for simulations spanning different periods between forest inventory surveys (SNFI2-3, SNFI3-4 and SNFI2-4). Note that calibration of growth, mortality and recruitment parameters was performed using data for the SNFI2-3 and SNFI3-4 periods. Figure  A5  shows model evaluation results for the SNFI2-3 and SNFI3-4 periods with parameters calibrated using data from a different period. does not classify the population of trees into discrete stage classes. Rather, each tree population is described by a continuous distribution as a function of a continuous variable-like size  (Easterling et al. , 2000).
suber), but, since the model does not account for resprouting at present, these predictions should be interpreted as above-ground mortality. 6 Discussion 6. 1 Accounting for functional diversity in regional-level forest models Species-specific parameter estimation in complex dynamic vegetation models is often done by focusing on one species at a time (e. g. ,  Davi and Cailleret, 2017; Guillemot et al. , 2017) , but this becomes impractical when the number of taxonomic entities increases. The wealth of information in global plant trait databases and forest inventory data facilitates dealing with functional diversity, but our experience shows that model parametrization using these resources has its own challenges. Clearly, this strategy becomes more useful the larger the fraction is of taxon-specific parameters that can be conceptually matched to observable traits, which needs to be accounted for during model design and formulation.
Even though plant trait databases continue to increase in size  (Kattge et al. , 2020) , the finer the taxonomic resolution is, the less information there is available. This leads to a trade-off between the taxonomic resolution of the model entities vs. the amount of missing parameter values in the final parameter table. When parsing trait databases we took both species-or genus-level averages as valid estimates to reduce the frequency of missing values in the parameter table, but this entails a potential loss of accuracy at the species level. Regardless of this decision, and no matter how much effort is put in parameter estimation procedures, parameter imputation will always be needed to fill information gaps in models representing multiple taxa. We recommend im-plementing imputation procedures in initialization routines, keeping them separated from taxon parameter tables because the large number of missing values in the table reveals information gaps to be addressed with additional data gathering.
, 2017; Thonicke et al. , 2020). In our opinion, more research is needed around the imputation procedures to fill in missing taxon parameter values. For example, future research could evaluate the phylogenetic signal of multiple traits, compare the relative performance of different imputation alternatives, analogously to the comparison of alternative biomass allometries  (Ameztegui et al. , 2022) , or evaluate the loss of accuracy of model predictions caused by imputations. As for databases of biomass or volume allometric equations, the development of databases documenting bi-or multivariate trait relationships and their domain of application could help modelers to implement adequate imputation strategies. We addressed specific-and supra-specific trait variability in our study, but we did not consider intra-specific variability, which can amount to 25 % of overall phenotypic variation  (Funk et al.
, 2017; Siefert et al. , 2015). Berzaghi et al. (2020)  describe three main strategies to account for intraspecific trait variability in vegetation models, among which the first two could be combined with our parameter estimation approach. First, prescribed (e. g. , non-plastic) intraspecific trait variation may be approximated in models using within-species environmental-trait relationships or trait covariation  (Rosas et al. , 2019; Poyatos et al. , 2007). To implement this strategy, intra-specific functional trait databases are needed, but they are presently scarce  (López et al. , 2021). These relationships may be implemented as inbuilt estimation rules to be applied at the time of initialization. Second, intra-specific variation can be considered in vegetation models by dynamically changing parameter values (e.
Reproducibility and transferability of parameter estimation strategies Given the iterative nature of model development, taxonspecific model parameters need to be re-estimated (and model performance re-evaluated) in many situations, such as when the design or mathematical formulation of the model is modified, when taxonomic entities are redefined, or when updates of existing trait data sources occur. Unlike models developed for single or few species, parameter re-estimation for multiple-taxa requires that procedures are reproducible and automated. All the procedures mentioned in the previous section, except manual tuning, are reproducible and were repeated several times during the development of MEDFATE. The code to implement parameter estimation and evaluation procedures should be continuously integrated into workflows coupled to modeling development cycles, in the same way as model evaluation procedures for all the key outputs related to the most important applications.
In our opinion, this aspect of trait-enabled model development deserves more attention and should be one of the foci of collaboration efforts between modelers, with the long-term aim of achieving community modeling cyber infrastructures  (Fer et al. , 2021). Another practical issue is the transferability of parameter estimates and estimation procedures, initially developed for a given trait-enabled model and region, to new target regions and/or other models. Even though intra-taxon variability is neglected, taxon-average estimates derived from global plant trait databases are, in principle, as valid for our target region as they are for other regions, assuming that processes where they operate have been represented in a proper way. Moreover, trait-enabled forest models could share databases of taxon-based parameter estimates thanks to parameter definitions being matched to the same measurable entities.