dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2013; Morin et al. , 2021; Prentice and Cowling, 2013; Dufrêne et al. , 2005) , with the corresponding decrease in ecological realism. Nowadays, comprehensive global trait databases represent a key source of information to boost parameter estimation  (Kattge et al. , 2020)  and including functional trait variation has been proven to substantially impact simulation outcomes  (Verheijen et al. , 2015). 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 coordination 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 in combining a trait-based description of individuals with genetic and demographic processes included in the model to simulate trait inheritance and, hence, ecoevolutionary processes  (Scheiter et al. , 2013; Scheiter and Higgins, 2009). For simulations over large 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 ignore taxonomic information, or continue to use broadly-defined functional types, and focus on representing the continuum of traits at the individual level. Even though models implementing these approaches are valuable tools, disregarding taxonomic information entails some limitations.
, 2020; Morin et al. , 2021; Ruffault et al. , 2022). This taxon-based parameter estimation strategy can be employed in large-scale simulation exercises if combined with systematic forest inventory data, which defines the maximum resolution of taxonomic entities and provides empirical data to estimate parameters modulating forest dynamics  (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 being extensively parameterized for a large number of taxa using plant trait databases  (Christoffersen et al. , 2016). Hence, it is important to appropriately address the different challenges to overcome 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: (1) to present MEDFATE (ver. 2. 8. 1), a cohort-based and trait-enabled model of forest function and dynamics designed for regional applications; and (2) to illustrate the challenges encountered in the process of estimating parameter values for multiple-taxa in trait-enabled models. We begin by describing the design and formulation of MEDFATE, focusing on new processes that have been added to two preceding models  (De Cáceres et al. , 2015 , 2021)  that are now considered as alternative sub-models. We then detail the different procedures we employed 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. Once fully parameterized, we evaluate the model in terms of predicted forest dynamics at the regional level and compare its performance with that of an alternative empirical model  (García-Callejas et al.
Sub-sections 2. 1 and 2. 2 briefly describe the design of the two sub-models. We then present the design and formulation of the carbon balance, growth, mortality and recruitment processes with some more detail, because they are new to MEDFATE. Table  1  includes a description of all symbols mentioned in the text. MEDFATE is implemented in a modular way, so that different R functions can be used to execute different sub-models (Fig. 1 ). A full model description, including process formulation, can be found at https:// emf-creaf. github. io /medfatebook/. A complete list of model parameter definitions and units is given in Table  B1. i l•m -2 •d -1 basic Ei Actual daily transpiration for plant cohort i l•m -2 •d -1 Ag,i Daily gross photosynthesis of plant cohort i g C•m -2 •d -1 NPP Annual net primary production g C•m - Fig.
Three different 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 11,314 plots in SNFI3 (average density of 1 plot/km 2 ) and 5,500 plots in SNFI4 (0. 5 plot/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 is absent in forest inventory plots, soil physical properties (texture, bulk density, organic matter content) corresponding to plot locations were drawn from the global database SoilGrids  (Hengl et al.
, 2017) , complemented by rock fragment content estimates derived from surface stoniness measurements 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 was 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. 144 taxa were at the species level, whereas 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 R package 'taxize'  (Chamberlain and Szocs, 2013). MEDFATE.
Parameter estimates obtained from allometric and plant trait databases We used available allometric relationships for shrub/tree leaf biomass and crown base height assembled from multiple sources  (Hasenauer, 1997; Burriel et al. 2004; De Cáceres et al. , 2019)  to populate 19 (16%) of model parameters. In turn, 49 (42%) model parameters were mapped to plant trait definitions from existing plant trait databases (see Table  B1 ). Among those, 23 (47%) model parameters were matched to plant traits drawn from TRY public data sets (ver 5. 0; https://www. trydb. org/)  (Kattge et al. , 2020). TRY data sets were complemented with additional data bases, global analysis papers and personal compilations for phenology, anatomy and morphology  (Tavşanoǧlu and Pausas, 2018; Zanne et al. , 2009; Morris et al.
Most plant traits were mapped directly, sometimes requiring homogenizing measurement units, whereas others required the use of transforming functions. For example, RGGS was estimated by monotonically re-scaling an ordinal (0-5) shade tolerance index  (Niinemets and Valladares, 2006)  to a proportion [0-1], assuming that a higher degree of shade tolerance implied a stronger prioritization of leave/fine root maintenance over stem growth. Parameters for plant taxa were estimated using species-level averages or genus-level averages (for genus taxa or when the target species was not found in the data base).
Tree ring data has 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. Further details of the calibration procedure and results are given in Appendix C. 3. We did not found any significant relationship between calibrated estimates and species identity for RGRcambiummax, SRsapwood and RERsapwood.
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 MEDFATE 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 the third and fourth surveys of the SNFI in Catalonia against observations from repeated SNFI plots. Specifically, we selected 2,949 permanent plots (between SNFI3 and SNFI4) without signs of recent management and with that of an empirical alternative, we took an Integral Projection Model (IPM) calibrated using SNFI data for the whole of Spain and evaluated its predictive performance over the same target region.
Since the spatial resolution of climate model predictions was 0. 1º (~ 9km), we used empirical quantile mapping to bias-correct and downscale weather to the forest plot scale, taking interpolated historical records  (1976-2005 period)  as reference data  (De Cáceres et al. , 2018). Annual [CO2] series under RCP 4. 5 and RCP 8. 5 scenarios were obtained from  Meinshausen et al (2011). A climate scenario assuming constant climate (No CC) was also evaluated for comparison with RCP scenarios, by repeating historical 2001-2020 weather over the century and assuming [CO2] = 386 ppm. Forest management and natural disturbances such as wildfires or biotic attacks were not considered for simplicity. MEDFATE runs were conducted using the basic sub-model, which nevertheless required 72h of computation on 20 parallel threads to process each scenario (we estimate 1728h would be required with the advanced model).
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. Resorting on plant trait databases and forest inventory data facilitates dealing with functional diversity, but our experience shows that it has its own challenges. First, this strategy becomes more useful the larger the fraction of taxonspecific parameters that can be conceptually matched to observable traits, which needs to be accounted during model design and formulation. In other words, the model should be 'trait-enabled'. The design of MEDFATE allowed us to match up to 49 parameters with functional trait definitions (50% of parameters if we exclude allometric coefficients).
Moreover, it is unlikely to have the appropriate calibration data (e. g. tree ring series) for all woody taxa occurring in the target region. We circumvented these problems by fitting a relationship between calibrated growth or senescence rates (RGRcambiummax and SRsapwood) and observed annual growth rates relative to cambium perimeter, which can be empirically estimated using resampled plot data for all species included in the forest inventory. Another alternative estimation procedure that we adopted to populate non-observable key parameters was meta-modeling. Computational limitations of the "advanced" sub-model for regional applications led us to conduct improvements in the "basic" sub-model, including increased sensitivity to environmental variables, and a meta-modeling exercise to obtain estimates for non-observable transpiration and photosynthesis parameters for the "basic" sub-model.
This estimation procedure can be applied to any taxon, but requires confidence on the trait parameter estimates of the model used as reference (here, the "advanced" sub-model), which we partially got from stand-level evaluation exercises  (De Cáceres et al. , 2021). Developers should always avoid manual tuning, due to its non-reproducibility, and it is a slow process for multiple taxa. We only resorted on this procedure for baseline mortality rates, forced by the mixture of mechanistic and empirical design in mortality modeling, although we acknowledge that calibration exercises could be used instead (e. g. Hartig et al. , 2012). Another important limitation when estimating parameters for multiple taxa is, obviously, plant trait data availability. Even though plant trait databases continue to increase in size  (Kattge et al.
, 2020) , the finer the taxonomic resolution is, the less information is available, which leads to a trade-off between the taxonomic resolution in 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 implementing 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 missing taxon parameter values. For example, future research could evaluate the phylogenetic signal of multiple traits; or 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. In the same way that there are databases of biomass or volume allometric relationships, the development of databases documenting bi-or multivariate trait relationships and their domain of application could help modelers to implement sound imputation strategies. We addressed specific-and supra-specific trait variability in our study, but did not consider intra-specific variability, which can account to 25% of overall phenotypic variation  (Funk et al.
, 2017; Siefert et al. , 2015). Berzaghi et al. (2020)  describe three main strategies to accounting for intra-specific trait variability in vegetation models, among-which the first two could be combined with our parameter estimation approach. First, prescribed (i. e. non-plastic) intra-specific trait variation may be approximated in models using within-species environmental-trait relationships or trait coordination  (Rosas et al. , 2019; Poyatos et al. , 2007). To implement this strategy, intra-specific functional trait databases are needed, but 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 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, taxon-specific model parameters need to be re-estimated (and model performance re-evaluated) in many situations, such as whenever the design or mathematical formulation of the model is modified; taxonomic entities are redefined; updates of existing trait data sources occur. Unlike models developed for single or few species, parameter re-estimation for multiple-taxa requires that procedures be 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 focus 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 a given region as they are for other regions. Moreover, trait-enabled forest models could share databases of taxon-based parameter estimates thanks to parameter definitions being matched to the same measurable entities.
Being derived from global data, imputation procedures based on among-species trait coordination or family-level averages should be equally valid in different regions. Procedures for extracting taxon-specific parameter estimates from (global) trait databases can also be used for new taxa or other models, which points to the possibility of sharing these procedures among developers, although the information available will differ widely depending on the target region. In contrast, empirical parameters obtained from forest inventory data are unlikely to be valid in different areas  (Thonicke et al. , 2020) , and the procedures to derive them may need to be tailored to idiosyncratic aspects of national forest inventories (plot spatial arrangement and temporal replication, taxonomic treatment, field sampling protocols), unless harmonized forest inventory data is used.
The value of trait-enabled models for the projection of forest dynamics at the regional level Mechanistic models are often regarded as having a larger degree of uncertainty than empirical models -due to their larger number of parameters  (Adams et al. , 2013) , but mechanistic models of vegetation dynamics can achieve good performance when calibrated for specific stands and parameter estimates are carefully chosen for target species  (de Wergifosse et al. , 2022; Forrester et al. , 2021). In the case of mechanistic trait-enabled models, one can expect substantial biases with respect to the prediction forest dynamics because key parameters of demographic processes are unlikely to be matched by observable database traits. In contrast, we achieved a small bias in overall basal area changes by introducing key empirical elements in the model design and parameter estimation -annual relative growth rates, mortality rates, bioclimatic limits and probabilities of ingrowth -, all of them from SNFI data.
, 2021; Ruffault et al. , 2022). MEDFATE has some unique features (i. e. the link between xylem cavitation and leaf defoliation, together with the need to build new sapwood tissue to recover water transport capacity) that make it suitable to study drought legacy effects. Overall, we think MEDFATE can be an attractive tool to study forest function and dynamics under increasing water limitations, such as those of Mediterranean climate. Besides the value of the model, we illustrated the process of determining parameter estimates for a large number of taxa in trait-enabled models by extensively using plant trait databases. Hence, our efforts allow illustrating challenges that arise when adopting this approach and provide potential strategies to address them, which can be useful for the parametrization of other trait-enabled models.
C. 2 Details of the estimation of transpiration and photosynthesis parameters of the basic sub-model The six key parameters regulating transpiration (aTmax, bTmax; eq. (C. 2)) and photosynthesis (WUEmax, WUEPAR, WUECO2 and WUEVPD; eq. (C. 3)) in the basic sub-model cannot be estimated from plant trait databases. Assuming transpiration (E) and gross photosynthesis (Ag) predictions of the advanced sub-model are more accurate, we adopted a meta-modelling approach make Ag predictions obtained by the basic sub-model as similar as possible to those produced by the advanced sub-model (both for present-day and projected climate). For each target species, we first revised the parameters of the advanced submodel that regulated plant transpiration and photosynthesis (Table  C1 ): Table  C1 : Revised estimates of the parameters regulating photosynthesis and transpiration in the advanced sub-model, for the set of twelve prioritized species.
C. 3 Details of the calibration of growth and senescence parameters 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). Tree ring series were available for 5 trees per plot, with samples taken in December 2015. More information on tree-ring methods can be found in Serra-Maluquer et al. (  2018 ) and González de Andrés et al (2021). We took annual basal area increments of each tree as the observations to be matched by model predictions of sapwood growth. Simulations were performed using the basic sub-model and daily weather data for each target plot for the 2001 -2015 period.
We calibrated the three target parameters for the dominant species and rock fragment content in each plot using a genetic algorithm implemented in R package 'GA'  (Scrucca, 2013). The objective function to be minimized was the average, across cohorts with tree ring data, of the relative mean absolute error resulting from comparing observed and predicted annual BAI series. Population size for the genetic algorithm was set to 40 individuals, a maximum of 25 iterations were allowed, and the procedure stopped if the best parameter combination did not change during 5 consecutive iterations. The distribution of final values of the objective function is shown in Fig. C2. We did not find significant correlations between pairs of calibrated parameters, except for a weak relationship between SRsapwood and rock fragment content (Table  C3 ).
Acknowledgments This research was supported by the  Spanish Ministry of Economy and Competitiveness  through project  DRESS  ( CGL2017-89149-C2-2-R ) and by a  Severo Ochoa grant  ( CEX-2018-000828-S ) funded by  MCIN/AEI. We thank the ' Agencia Estatal de Meteorología (AEMET)'  and ' Servei Meteorològic de Catalunya' (SMC)  for providing daily weather station data.
Code availability Core model functions are coded in C++ and linked to a R user interface. MEDFATE is distributed via the R package 'medfate', which is available at CRAN (https://cran. r-project. org/package=medfate) and GitHub (https://github. com/emfcreaf/medfate). The model code to run MEDFATE (ver. 2. 8. 1) is available at https://doi. org/10. 5281/zenodo. 7129635 (De  Cáceres et al. , 2022).