dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2021; Mouillot et al. , 2014) , which, in turn, should increase the functional similarities between sites  (Jarzyna & Jetz, 2018). We suggest that within a large species pool, the functional redundancy of species increases, given the limited set of trait combinations and/or available niches. Therefore, smaller species pools are more likely to have functionally unique species and lower functional similarities than larger pools. In the case of large pools, we found that taxonomic decay was often stronger than functional decay. Furthermore, because species pool size increases with study extent  (Drakare et al. , 2005; Palmer & White, 1994; Triantis et al. , 2011) , the datasets with larger extents had slower functional distance decay even along environmental gra- that filtering on a given trait might filter other traits concomitantly, and if the focal trait is not included in the analyses, a mismatch between functional composition and the environment is expected.
| The effect of latitude on the rate of distance decay In addition to our master hypothesis, we investigated whether the rate of distance decay showed consistent variation across realms, along geographical gradients and among major taxonomic groups. We did not find slower rates of decay along spatial distances in the datasets at higher latitudes, but we found a unimodal relationship with the highest decay rate at c. 30°. Similar results have been found earlier in terrestrial vertebrates when considering only the turnover component of β-diversity  (Castro-Insua et al. , 2016)  and for the total β-diversity of marine phytoplankton  (Martin et al. , 2021). It is noteworthy that our latitudinal patterns were related mainly to the replacement component for both taxonomic and functional decay (Appendix S4; Figure  S4.
7). Regarding environmental gradients, we found opposing patterns compared with spatial gradients, with the flattest rates of decay in the datasets near 50° and a notable increase from 60° towards the poles. A hump-shaped relationship between functional diversity and latitude has also been found previously for aquatic macroinvertebrates  (Múrria et al. , 2020) , also with the minimum at c. 50°. Múrria et al. (2020)  were studying patterns in functional dispersion, whereas we found here that the breakpoint was related mainly to the differences in richness for taxonomic similarities and replacement for functional similarities. Traditionally, latitudinal patterns of biodiversity have been explained by Rapoport's rule, positing that there is an increase in species range size towards high latitudes  (Stevens, 1989) , hence lower taxonomic replacement.
Strong seasonality, resource scarcity and climatic stress should select only the highly specialized taxa and modify the functional space towards the poles  (Lamanna et al. , 2014). Therefore, it is plausible that the climatic stress leads to an increase in richness differences in communities towards the poles, as observed in vertebrates elsewhere  (Castro-Insua et al. , 2016). Moreover, as environmental heterogeneity increases towards the poles, and functional clustering is expected to be stronger at higher latitudes  (Jarzyna et al. , 2021 ; but see  Kruk et al. , 2017) , we suggest that strong environmental filtering in datasets at higher latitudes (above 50°) selects for the species with different trait combinations between sites, thereby increasing the rate of functional decay. The latitudinal decrease in the rate of abundance-based functional distance decay (Appendix S3; Figure  S3.
| The effect of spatial extent on the rate of distance decay The rate of spatial distance decay was slower in the datasets covering a larger spatial extent, suggesting that regional species pools are limited and that new species are not found constantly at the same frequency when extent is larger. Lower decay rates in larger study areas could also result from repeated patterns in environmental variation; that is, environmental patchiness or natural periodicity in the environment  (Nekola & White, 1999). In agreement with our hypothesis, we also found that the rate of taxonomic decay along environmental distance was higher in the datasets covering a larger spatial extent. These findings indicate that spatial distance decay is more affected by species pool effects and dispersal processes than environmental distance decay, possibly because the latter reflects more strongly the level of local deterministic environmental filtering processes.
Similar evidence has accumulated from case studies conducted in various ecosystems  (Meynard et al. , 2011; Sokol et al. , 2011; Weinstein et al. , 2014; Zagmajster et al. , 2014). The finding that the rate of distance decay along environmental distance was higher in the datasets covering larger extents indicates the stronger environmental filtering for larger study areas. We also note that, in our BRT models, the extent, latitude and γ-diversity had by far the largest relative importance, suggesting that their interplay shapes distance decay to a great extent.
| Organismal variables and dataset features Organism size did seem to affect taxonomic or functional distance decay along spatial and environmental gradients, because the slopes typically increased with organism body size. This might be because β-diversity should be low among the small microbial taxa with efficient passive dispersal  (Soininen, McDonald, et al. , 2007). The rationale behind this idea is that efficient dispersal homogenizes communities among sites, resulting in lower β-diversity  (Mouquet & Loreau, 2003). Body size is also a key driver of the biological complexity of organisms  (Heim et al. , 2017) , and it might be that smaller organisms show a much more limited set of trait combinations than macroorganisms, leading to a lower functional redundancy among larger species. Furthermore, our knowledge about the taxonomy and functional traits of organisms is typically size dependent.
| Study design There are also some possibly influential aspects in our study design that should be discussed. Although the study is global in its extent, the availability of datasets was not evenly distributed geographically. This is a well-known problem in biodiversity research  (Titley et al. , 2017)  that calls for complementary studies to verify that these trends hold true in poorly sampled regions. Also, we relied on the suite of traits and environmental variables included in the original datasets, hence the collection of traits and environmental variables used differed somewhat among datasets even for the same focal taxonomic groups. Although traits covered mostly the same functional roles of the species, the variation in traits and environmental variables across datasets increases the uncertainty on how environmental variables filter the functional structure of communities in different contexts and how strong the community-environment relationships might be.
An alignment of key traits and environmental variables is therefore desirable but requires a suite of sister studies following the same protocol, which is, unfortunately, not yet available. Moreover, the fact that some of the biotic groups (e. g. , bryophytes, corals, foraminifera) were underrepresented in our analysis, with only a few datasets included (Figure  2 ), or the total lack of some taxa (e. g. , bacteria, and aquatic and terrestrial mammals), makes it more difficult to generalize distance decay across certain taxa.
Klink et al. , 2017; Meffert & Dziock, 2013; Pakeman, 2011; F I G U R E 2 Study design highlighting (a) a map of the study sites coloured according to the realms (FRE = freshwater; MAR = marine; TER = terrestrial); (b) the number of datasets for major biotic groups; and (c) the distribution of the datasets with respect to spatial extent, number of study sites, functional γ-diversity (log 10 hypervolume SD 3 ), taxonomic γ-diversity (number of species), number of environmental variables.
, estimated as the mean log 10 -transformed fresh weight (in grams) of the biotic group included in the dataset; (5) dispersal mode, classified as active and passive modes and organisms dispersed by seeds; (6) taxonomic γ-diversity, expressed as the total number of species in the dataset; (7) functional γ-diversity, measured as the total volume of the union of the n-dimensional hypervolumes estimated within the dataset (Mammola & Cardoso, 2020); (8) total number of study sites in the dataset; and (9) the number of environmental variables in the dataset.
Relative effects (expressed as percentages) of geographical factors on the rate of decay along (a) spatial distance decay and (b) environmental distance decay of the total component of taxonomic (TAX, light blue) and functional (FUN, orange) similarities using occurrence data across datasets. Partial dependence plots show the effects of a predictor variable on the response variable after accounting for the average effects of all other variables in the model. Positive values indicate an increase in the rate of decay (steeper slopes) compared with the mean rate, whereas negative values indicate a decrease in the rate of decay (flatter slopes) compared with the mean rate. Semitransparent lines represent the actual predicted effects; continuous lines represent LOESS fits to predicted values from boosted regression trees (BRTs).
dients, and taxonomic composition turned out to be the best descriptor of distance decay patterns. Another possible reasoning is F I G U R E 7 Relative effects (expressed as a percentage) of organismal variables and dataset features on the rate of decay along (a) spatial distance and (b) environmental distance, considering the total component of taxonomic (light blue lines) and functional (orange lines) similarities using occurrence data across datasets. Partial dependence plots show the effects of a predictor variable on the response variable after accounting for the average effects of all other variables in the model. Positive values indicate an increase in the rate of decay (steeper slopes) compared with the mean rate, whereas negative values indicate a decrease in the rate of decay (flatter slopes) compared with the mean rate.
ACK N OWLED G M ENTS Caio Graco-Roza  was funded by the  Coordination for the Improvement of Higher Education Personnel (CAPES) , the  Carlos Chagas Filho Research Support Foundation (FAPERJ)  and the  Ella and Georg Erhnrooth Foundation ;  Jan Altman  by research grants  INTER - EXCELLENCE LTAUSA19137  provided by  Czech Ministry of Education, Youth and Sports ,  20-05840Y  of the  Czech Science Foundation , and long-term research development project no. RVO 67985939  of the  Czech Academy of Sciences ;  Otso Ovaskainen  was funded by  Academy of Finland  (grant no. 309581 ),  Jane and Aatos Erkko Foundation ,  Research Council of Norway  through its  Centres of Excellence Funding Scheme  ( 223257 ), and the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement no.
856506 ; ERC-synergy project LIFEPLAN); and  Jianjun Wang  was funded by  CAS Key Research Program of Frontier Sciences  ( QYZDB-SSW-DQC043 ) and  National Natural Science Foundation of China  ( 91851117 ). The "sPlot" project was initiated by sDiv, the  Synthesis Centre of the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig , funded by the  German Research Foundation  ( DFG FZT 118 ), and is now a platform of iDiv. The study was supported by the TRY initiative on plant traits (http://www. try-db. org). We are also grateful to  Jens Kattge  and TRY database. TRY is hosted, developed and maintained at the  Max Planck Institute for Biogeochemistry (MPI-BGC ).
DATA AVA I L A B I L I T Y S TAT E M E N T All the data used in this study can be found in a Zenodo repository (https://doi. org/10. 5281/zenodo. 6406911). Code to run the analysis is available in a GitHub repository (https://github. com/graco -roza/ DISTA NCE_DECAY_2. 0). The authors declare that some datasets were compiled from sPlot (https://www. idiv. de/en/splot. html),.
FFigure Figure S4. 7a). Along environmental distance, the taxonomic replacement increased towards higher latitudes, whereas the functional replacement had a U-shaped pattern, with a decrease from low to mid-latitudes (c. 45°) and a sharp increase towards the poles (Appendix S4; Figure S4. 7b). For the richness differences component, the slopes of taxonomic similarities were steepest in the datasets at c. 45° for the spatial distance decay, whereas the slopes of functional similarities became notably steeper with latitude (Appendix S4; Figure S4. 8a). For environmental distance, slopes became flatter from low to high latitudes up to c. 50° for taxonomic similarities, whereas for functional similarities, slopes did not vary along latitude (Appendix S4; Figure S4. 8b). Both replacement and richness differences showed flatter spatial slopes with increasing spatial extent (Appendix S4; Figure S4.
F I G U R E 6 6 Relative effects (expressed as percentages) of geographical factors on the rate of decay along (a) spatial distance decay and (b) environmental distance decay of the total component of taxonomic (TAX, light blue) and functional (FUN, orange) similarities using occurrence data across datasets. Partial dependence plots show the effects of a predictor variable on the response variable after accounting for the average effects of all other variables in the model. Positive values indicate an increase in the rate of decay (steeper slopes) compared with the mean rate, whereas negative values indicate a decrease in the rate of decay (flatter slopes) compared with the mean rate. Semitransparent lines represent the actual predicted effects; continuous lines represent LOESS fits to predicted values from boosted regression trees (BRTs).
). We plotted in figures only the regressions with p < 0. 05 and when statistical analyses showed significant differences overall between tested and control leaves (see below); when overall significant differences were found but regressions were not significant, a horizontal line at the average of Δψ leaf was plotted instead. We report the mean effect sizes for Δψ leaf (MPa) ± standard errors. We used linear mixed models with Tukey's post hoc comparisons to test the differences between test and control leaves overall for each Assumption at p < 0. 05. We used the "lme()" function from the "nlme" package  (Pinheiro et al. , 2017)  and the "glht()" function from the "multcomp" package  (Hothorn et al. , 2008).
ACKNOWLEDGEMENTS This study was supported by a  FPDI research fellowship , an  Individual Excellence Stay  from the  Junta de Andalucía  and an  Individual Fellowship  from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement No. 751918-AgroPHYS  to C. M R-D; a  JAE-predoc fellowship  and a  APOSTD fellowship  from the  Generalitat Valenciana  both co-funded by the  European Union  to A. F; a  Marie Curie Fellowship  ( FP7-PEOPLE-2013-IOF-624473 ) to R. L. ; the  Austrian research agency (FWF)  project  P32203 research area "Mountain Regions " to St. M; and the  National Science Foundation  (Grant IOS-# 1457279 ) to L. S. We are grateful to  Alec Baird ,  Marvin Browne ,  Christian Henry ,  Leila Fletcher ,  Camila Medeiros , and  Marissa Ochoa  for comments on the manuscript.
European Union's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie  grant agreement No. 751918-AgroPHYS;  Marie Curie Fellowship  ( FP7-PEOPLE-2013-IOF-624473 );  Austrian research agency (FWF)  project  P32203 ;  National Science Foundation  (Grant  IOS-#1457279 ).
Model calibration The proposed rainfall discharge model counts 6 parameters (Table  1 ) and the EA simulation at the spring brings one more parameter. The conceptual representation of EA formation is considered as simple as possible to avoid overparameterization and to limit potential parametric equifinality  (Perrin et al. , 2001). Based on former studies, the recharge area was identified as Ra = 116 km 2. The model is implemented in the python environment  (Summerfield, 2010) , while the parameter estimation was performed using a particle swarm optimization (PSO) procedure  (Eberhart and Kennedy, 1995)  implemented in the "pyswarm" package  (Lee, 2014). Furthermore, a Latin Hypercube sampling (LHS) is applied to account for parametric uncertainties and investigate the parameter distribution according to the model performance criteria.
Acknowledgments The authors would like to thank the  French National Observation Services KARST  (http://sokarst. org/)initiative at the  INSU/CNRS , which aims to strengthen the dissemination of knowledge and promotes cross-disciplinary research on karst systems at the national scale, for their support of the use of the KarstMod model. The authors also thank the  Regional Natural Park of the Grands Causses (PNRGC)  for providing the discharge data as well as the  French National Observation Services H+  (http://hplus. ore. fr/) for providing meteorological data over the Durzon karst system. The  French National Observation Services KARST  and H+ are part of the Elter European Infrastructure (https://www. ltereurope. net/elter-esfri). Dissolved gas analyses were performed within the  CONDATE-EAU analytical platform , which are also acknowledged for the discussions.
Determining the turtles' nesting habitat requirements The loggerhead and green turtles, although of different sizes, have comparable requirements in terms of nesting criteria. The first phase of study focuses on the analysis of islets' characteristics in relation with turtles' nesting habitat. This concerns the geomorphology, sedimentology, land cover and the general context of the islet. Once the inventory of these factors is made, it is a matter of determining how to qualify them from direct observations (field data) and from indirect sources such as satellite images, aerial photographs or any other useful sources (geo-referenced databases). The objective is to provide an indicator value for each factor and then to carry out a multi-criteria analysis to determine a unique indicator of nesting conditions.
Reef-flat wideness A narrow reef-flat (small distance between the reef front and the nesting area) is a favourable factor because it makes access to the nesting area easier, less risky and faster than in the case of a wider shelf. The reef-flat widths surrounding the islets of the Caledonian lagoon are highly variable (Online Resource 1). The values range from a few metres to several hundred metres. Moreover, for some islets, the reef-flat width is also highly variable depending on the segment of shoreline considered. Moreover, for a given width of reef-flat, the presence of a pass leads to a local decrease in the width of the reef-flat and thus allows turtles to access the beach more easily (Online Resource 1). Based on the location of 140 nests (data acquired by WWF during 2018-2019 campaign) observed on eleven islets spread across Southern Lagoon we have analysed the reef-flat width in front of these nests.
Acknowledgements This study was carried out as part of the  OBLIC  project ( Observatoire du littoral de Nouvelle-Calédonie ) supported by the  New Caledonia Government  ( Direction de l'Industrie, des Mines et de l'Environnement de Nouvelle-Calédonie ) and the  BRGM (French Geological Survey: Bureau de Recherche Géologique et Minière) , by the  BRGM 's  Resilience  project and by the  World Wide Fund for Nature (WWF). Thanks to  Cécile Capderrey  and  Audrey Baills (BRGM) ,  Julie Mounier  and  Julie-Anne Kerandel  ( Direction des Affaires Maritimes de Nouvelle-Calédonie ) for their helpful discussions and comments, to  Claudine Jego  and  Jack Challis  for improving the English. Finally, we would like to thank the reviewers for their insightful comments and suggestions for improving this document.
In our current work, we re-analysed the raw XRF data previously acquired from the New Caledonian Cunoniaceae using an improved quantification model, and then focussed on two taxa: the Mn hyperaccumulator Pancheria reticulata and the Ni hyperaccumulator Pancheria xaragurensis, for further investigations. We used synchrotron-based X-ray fluorescence microscopy (XFM) to determine the tissue-level distribution of Mn, Ni, and other elements. We also analysed field collected plant tissue samples of both taxa and their associated soil chemistry. The aim of this study is to use the Cunoniaceae as a model system for studying the nature of the hyperaccumulation phenomenon and to identify shared mechanisms or differences in the accumulation of the two metals in two contrasting species of Pancheria.
The methods used have been described in detail elsewhere  (Gei et al. 2020b). A handheld XRF device (Thermo Fisher Scientific Niton XL3t 950 GOLDD+) was used to measure the elemental concentrations of Ni and Mn in the leaves of the herbarium specimens. Following completion of the earlier extensive herbarium XRF screening study at the Herbarium of New Caledonia (NOU)  (Gei et al. 2020b ), the calibration model for XRF measurements was further improved  (Paul et al. 2020a ). This revised calibration (which has an improved regression fit of R 2 0. 97 for Mn) was used in this study to determine the concentrations of Mn in the Cunoniaceae family with greater accuracy. For each species and element, the median rather than the mean was used, as means are more sensitive to extreme values, which are common in this dataset and could not be calculated for many species when most specimens had values below the limit of detection (LOD for Mn 455 μg g -1 and for Ni 107 μg g -1 ).
X-ray fluorescence microscopy (XFM) of P. reticulata and P. xaragurensis The X-ray fluorescence microscopy (XFM) experiment was performed at the PETRA III (Deutsches Elektronen-Synchrotron; DESY) at beamline P06 that has a cryogenically cooled double-crystal monochromator with Si(111) crystals and a K/B mirror system that focusses a beam of 10 11 photon/s down to 300 nm size in the energy range 5-21 keV  (Schroer et al. 2010). The data for P. reticulata was collected using the Maia detector, which has an annular array of 384 silicon PIN-diode elements operated at normal incidence geometry  (Ryan et al. 2018). The data for P. xaragurensis was obtaned using a silicon drift detector (Hitachi Vortex EM-90, 80 mm 2 ) operated in 90-degree geometry. The reason for using different detection systems is that the Maia detector was damaged and not operational at the time of the experiment for P.
Data processing of XFM data The XFM data collected with the Maia detector was analysed using GeoPIXE software  (Ryan and Jamieson 1993; Ryan 2000; Ryan et al. 1990 ), whereas the XFM data collected with the silicon drift detector was analysed using PyMCA software  (Solé et al. 2007) , and exported into ImageJ as greyscale 16-bit TIFF files, and visualized using ImageJ's "Fire" lookup table  (Schneider et al. 2012). The matrix file used for the spectra fitting was an assumed freeze-dried plant material composition of C 31 O 15 H 51 N 2 S 0. 8 with a density of 0. 75 g cm -3 , and for fresh samples the composition was C 7. 3 O 33 H 59 N 0. 7 S 0. 8 with a density of 0. 90 g cm -3 and considering two layers of 4 μm Ultralene (van der Ent et al. 2019c). Note that the values reported for the fresh specimens will report per unit hydrated weight and the values for the freezedried specimens as dry weight.