dataesr/scientific-paragraphs-categorization huggingface dataset:
We focused on hibernation emergence dates (ED) for individual ground squirrels from four neighboring meadows monitored over a period ranging from 18 to 28 years, depending on the meadow. Our objectives were to (1) examine spatial (among and within the four meadows) and temporal (over the years) variation of ground squirrel ED and to (2) determine how this variation might be explained by local and temporal variation in microclimatic conditions (scale of tens of meters, as experienced by the animals). Columbian ground squirrels are colonial ground-dwelling sciurid rodents inhabiting burrow systems in subalpine meadows of the northern regions of the Rocky Mountains. Columbian ground squirrels are an interesting model system to investigate changes in phenology in relation to microclimates. These are relatively long-lived animals (the oldest squirrel in our data set lived up to 14 years) that hibernate for $8 months of the year, and are only active for a short summer period during which reproduction occurs  (Dobson et al.
Emergence was recorded as an ordinal date (number of days after 1 January each year). Emergence data were not collected in 2006 on Meadow  DOT, in 2003 -2006 on MA, and in 2002 -2003   was recorded at a AE1. 0-m resolution, on 10 Â 10 m Cartesian grids of color-flagged wires at each meadow. Hibernacula are usually used by the animals during a few days after emergence until collective burrows are reexcavated for the active season. First capture locations should thus provide a reasonable approximation of the area where individuals hibernated. Recorded Cartesian coordinates were converted to GPS coordinates using a database of GPS and Cartesian coordinates collected in 2018-2019. We estimated the error associated with the conversion of Cartesian to GPS coordinates at a median (AESD) of 5. 5 (AE3.
Spatial clustering To evaluate spatial heterogeneity in ED within a meadow, we investigated the spatial autocorrelation of ED. Specifically, we examined whether correlation between ED at two locations depended on the distance between them. To do so, we used variograms  (Matheron, 1963)  that measured the value of the spatial variance at different distance intervals. For any two data points, we measured their spatial distance and estimated the variance in ED. The variance was then averaged over bins of distance intervals. Empirical variograms were based on all emergence locations, but with pairs of points considered only if points were in the same year and within the same meadow. We modeled variograms using a least square method  (Rivoirard et al. , 2008). The empirical variogram of ED exhibited spatial structure (i.
Microclimate data We calculated hourly microclimatic conditions using the  Kearney et al. (2020)  microclimate model, at a fine-scale resolution (5 Â 5 m grid) for each meadow during the entire monitoring period (from 1992 to 2019). The modeling function (micro_ncep from NicheMapR package;  Kearney et al. , 2020)  downscales global atmospheric climate-forcing data (NCEP Reanalysis, historical data from the National Centre for Environmental Predictions,  Kalnay et al. , 1996 , provided by the NOAA/OAR/ESRL PSL, Boulder, CO, USA, from their website at https://psl. noaa. gov/), using terrain-specific corrections, including spatial variations in slopes (from the Mapzen elevation and Mapzen terrain service, via "elevatr" package in R;  Hollister et al. , 2020) , soil composition and hydrological properties (acquired from the "SoilGrids" database in R;  Hengl et al.
, 2017) , orientation, and hill shade. The microclimate model increases the quality and resolution of large-scale weather data and has been empirically validated  (Kearney, 2020; Lembrechts & Lenoir, 2020; Maclean, 2020; Saleeba et al. , 2020). Because Columbian ground squirrels live in open grass meadows with adjacent trees (see Figure  1 ), we estimated climatic variables without specifying shade due to vegetation cover. Our model estimated snow depths (in centimeters) on the ground as well as temperatures (in degrees Celsius), wind speed (in meters per second), and relative humidity (in %) at 10 cm above ground (roughly the height of a ground squirrel). In addition, we estimated temperature at various soil depths below ground  (0, 2. 5, 5, 25, 50, 75, 100, 125, 150, and 200 cm). Hibernating burrows are usually around 70 cm in depth and rarely exceed 1 m  (Young, 1990a (Young, , 1990b)).
Soil temperatures at various depths were highly correlated (see Appendix S1: Figure  S2 ). Thus, we used 1-m-deep temperatures as representative of hibernaculum conditions. There was no reasonably close weather station with records of climate date for the entire study period (the closest one being 58 km away at lower altitude in the plains). In addition, weather stations record temperature and precipitation variables at a standard height of 2 m, which is not representative of what a ground squirrel might experience a few centimeters above ground. Thus, using the microclimate model enabled us to obtain climate data over the entire study period at a high resolution and at the height of the animal, and further allowed us to measure snow cover (see Appendix S1: Figure  S3  for a validation of the microclimate model).
To estimate spatial and temporal variation of climate, we averaged the model-generated time series at two different spatial scales for each meadow: (1) over the entire meadow and (2) separately within each cluster. Hourly data were averaged over 24-h periods. Based on the average ordinal day of first hibernation emergence (older males) across all years (15 April AE2 days) and previous studies showing that immergence occurs close to the beginning of August  (Neuhaus, 2000; Young, 1990b) , we further divided the year in two phases: the active season (15 April-1 August) and the inactive (hibernation) season for the remainder of the year. This allowed us to test for possible effects of climate conditions during the winter and the previous summer on individual EDs. Besides air temperature (in degrees Celsius), soil temperature at 1 m depth (in degrees Celsius), relative humidity (in %), wind speed (in meters per second), and snow depth (in centimeters), we considered two additional climate variables: first day of the calendar year without snow cover on the ground (over the entire meadow or within each given spatial cluster, in ordinal date), and number of days with snow falls during the active period for investigation of late snowstorms on ED.
Spatial variation in climate variables To investigate microclimates, we assessed correlations among the 11 previously defined climate variables (air temperature during the active and inactive period, soil temperature at 1 m depth during the active and inactive period, relative humidity during the active and inactive period, wind speed during the active and inactive period, snow depth during inactive period, first day of calendar year without snow, and number of days with snow during the active period), using a principal component analysis (PCA) on data averaged by year and by cluster on each meadow. The objective was to identify principal variations related to spatial variables (clusters and meadows) given the large amount of climate data at a microscale within the meadows. Spatial differences in climate were analyzed from resulting components (PCs), through LMs at two different scales: PCs $ meadow ð7Þ PCs $ cluster ð8Þ Based on results (see below), only climate variables expected to affect ED were kept for subsequent analyses.
Statistics Statistical analyses were done in R version 4. 0. 3 (R Core Team, 2019). All tests were conducted with a probability error threshold of 5%. Results presented are means AE SE, along with the number of observations (n) and the number of individuals (N). Where appropriate (for LMMs and LMs), we ensured model residuals were normally distributed by visual inspection of density distributions, Q-Q plots, cumulative distribution functions, and P-P plots using the "fitdistrplus" package in R (Delignette-  Muller & Dutang, 2015). When the assumptions of a normal distribution of residuals were not met, we applied a Box-Cox transformation (MASS R package;  Box & Cox, 1964)  to the data (monotonous transformation), or removed extreme values from the data (i. e. , EDs later than the 22nd of May for which we likely missed the actual ED, representing 2.
ACKNOWLEDGMENTS We are grateful to  Edward A. Johnson  (Director of the  Biogeosciences Institute, University of Calgary ),  Judy Mappin-Buchannan  and  Adrienne Cunnings  (Managers, Kananaskis Field Stations), and  Kathreen Ruckstuhl  (faculty member responsible for the R. B. Miller Field Station) for housing and facilities during fieldwork. The fieldwork was aided by many volunteers and students over the years, and we thank them for their excellent efforts. We are specifically indebted to  Jan O. Murie  for initiating the long-term research on Columbian ground squirrels, and to  David A. Boag  for his earlier work on the species. Jan O. Murie  provided insightful comments on the manuscript, and we are grateful for his continued advice, help, and friendship over the years. We would like to thank  Jérôme Pansanel  for his invaluable contribution to run the microclimate model.
This research was supported by a  USA National Science Foundation  grant ( DEB-0089473 ) and a fellowship grant from the  Institute of Advanced Studies of the University of Strasbourg  to  F. Stephen Dobson , a  Natural Sciences and Engineering Research Council of Canada  grant to  Jan O. Murie , a  CNRS Projet International de Coopération Scientifique grant  ( PICS-07143 ), an  AXA   Postdoctoral Research Fellowship , a  Fyssen Foundation Postdoctoral Fellowship , and a  Fyssen Foundation  research grant to  Vincent A. Viblanc , and an  Initiative d'Excellence (IDEX attractivité ) research grant to  Claire Saraux. F. Stephen Dobson  thanks the  Région Grand Est  and the  Eurométropole de Strasbourg  for the award of a Gutenberg Excellence Chair during the time of writing. Anouch Tamian was supported by a PhD scholarship from the  Ministère de l'Enseignement Supérieur, de la Recherche et de l'Innovation.
Funding information American Society of Mammalogists , Grant/Award Number:  Grant-in-Aid of Research ;  Centre National de la Recherche Scientifique , Grant/Award Number:  PICS 07143  to VAV;  Ministère de l'Enseignement supérieur, de la Recherche et de l'Innovation , Grant/ Award Number: Doctoral contract of AT;  National Institute of Advanced Studies of University of Strasbourg , USIAS to FSD and VAV;  National Science Foundation of the USA , Grant/Award Number:  DEB-0089473  to FSD;  Région Grand Est  and the  Eurométropole de Strasbourg , Grant/ Award Number: Gutenberg Excellence Chair;  Sigma Xi Grants-in-Aid of Research, Grant/Award Numbers: Grant/Award Number :  IdEX program  to CS;  Initiative d'Excellence (IDEX attractivité) research grant ;  Fyssen Foundation  research grant;  Fyssen Foundation  Postdoctoral Fellowship; AXA Postdoctoral Research Fellowship; Natural Sciences and  Engineering Research Council of Canada  grant.
In nature, living organisms produce a wide variety of specialized metabolites to perform many biological functions. Among these specialized metabolites, some carry halogen atoms on their structure, which can modify their chemical characteristics. Research into this type of molecule has focused on how organisms incorporate these atoms into specialized metabolites. Several families of enzymes have been described gathering metalloenzymes, flavoproteins, or S-adenosyl-L-methionine (SAM) enzymes that can incorporate these atoms into different types of chemical structures. However, even though the first halogenation enzyme was discovered in a fungus, this clade is still lagging behind other clades such as bacteria, where many enzymes have been discovered. This review will therefore focus on all halogenation enzymes that have been described in fungi and their associated metabolites by searching for proteins available in databases, but also by using all the available fungal genomes.
Instead, iodide and fluoride are more concentrated in SR than in seawater  [5, 6] , but compounds containing iodine and fluorine are rarer. This can be related with that fact that chloride and bromide are 60 and 20 times, respectively, more concentrated in seawater than in sedimentary rocks (SR) found in terrestrial environments ([Cl]: 19,000 mg/kg for seawater against 320 mg/kg for SR, [Br]: 65 mg/kg against 3 mg/kg in SR). Instead, iodide and fluoride are more concentrated in SR than in seawater  [5, 6] , but compounds containing iodine and fluorine are rarer. For fungi, chlorinated compounds are the most represented (approximatively 80%). According to The Natural Products Atlas database  [7] , 705 compounds contain at least one For fungi, chlorinated compounds are the most represented (approximatively 80%).
According to The Natural Products Atlas database  [7] , 705 compounds contain at least one chlorine among the 848 halogenated metabolites described. Only 133 contain at least one bromine, which represents 15% of the described halogenated molecules. However, it should be noted that marine species of fungi have been less studied compared to their terrestrial counterparts. In fact, we now know that fungi are ubiquitous, they are both found in terrestrial and marine environments. They can live on a wide variety of marine habitats including the water column, sediments, and in association to other living organisms like invertebrates, algae or marine mammals. They have been found in many marine areas from the deep environments to the surface water. Many marine strains have their equivalent in the terrestrial environment suggesting strong adaptive capabilities to their living environment  [8, 9].
Halogenation Enzymes 2. 1. Methodology Gathering of available data about halogenation enzymes in fungi was done using the Universal Protein Resource (UniProt) database. Research on UniProt was carried out in April 2021 with keywords and restraining the queries in the fungal kingdom ("heme haloperoxidase", "flavin-dependent halogenase", "vanadium haloperoxidase" and "nonheme halogenase") (Table  1 ). UniProt database included 225,013,025 protein sequences from all kingdoms of life, with 565,928 sequences manually annotated and characterized from experimental results. The rest of the sequences corresponds to high quality computationally analysed sequences with automatic annotation and classification. For this study, Blastp analysis was also performed on the MycoCosm database from Joint Genome Institute which included 2172 fungal genomes available for comparative genomics analysis in February 2022  [23, 24].
According to Blastp, the expected value (E) is a parameter that describes the number of hits one can "expect" to see by chance when searching a database of a particular size. E value describes the random background noise and the closer to zero, the more significant the match is. Hits found on MycoCosm database are linked with a predictive Protein family code (Pfam) which are available on the InterPro database. Protein Data Bank (PDB) has been used to obtain the 3D structure of crystallized proteins. To carry out sequence alignments, the software Mega X was used  [25]. The PyMOL Molecular Graphics System, Version 2. 0 Schrödinger, LLC was used to represent 3D structures. On the PDB, structures were viewed and illustrated using Mol*  [26]  and RCSB PDB viewer. During the 80s, to detect gene encoding halogenation enzyme in fungal strains, PCR methods were used.
hHPOs in Fungi To evaluate the hHPOs presence in fungi, research on UniProt database has been made using the filters: heme and haloperoxidase and fungi. There were 5088 hits in fungal organisms but only 6 sequences had been studied with the protein function being biochemically characterized in 2022. The other sequences remain uncharacterized and have only been computationally detected in fungal genomes. Only one out of the six characterized proteins has been studied for its halogenation capacities, the CPO from C. fumago (Table  1 ). Among the 6 studied proteins, were the aromatic peroxygenases AaP from Agrocybe aegerita and the lignin peroxidase LiP from Phanerochaete chrysosporium. These enzymes have been described to possess a secondary haloperoxidase activity, after positive reaction to the monochlorodimedone (MCD) assay, explained hereafter  [80, 81].
Indeed, the AaP peroxygenase can use bromine to halogenate MCD during the MCD assay. It shares 21% identity with CPO from C. fumago, and a crystal structure is available for this enzyme (PDB ID: 2YOR). Piontek et al. who described this activity for AaP thought it was physiologically questionable because of the rarity of bromine in the terrestrial environment  [82]. In a second approach, a Blastp analysis was performed using the CPO sequence from C. fumago on MycoCosm. With an E-value threshold of 1. 0 × 10 -25 , only 6 hits were found. The percentage of identity ranged from 38% to 57%. Interestingly, when the same research was carried out with an E-value threshold of 1. 0 × 10 -10 , there were 2360 hits. This high number of sequences is explained because of the multiple presence of genes encoding heme-dependent haloperoxidase in one fungal genome  [83].
The general feature of all these hits is that they possess the more or less conserved heme-binding pocket which class them into the peroxidase family 2 (Protein family code Pfam: PF01328). This enzyme family gathers the particular peroxidases, with low similarity with other peroxidases. This family is represented by the hCPO of C. fumago and the aromatic peroxygenase from A. aegerita.
Structure The only crystal structure of a fungal vHPO is from C. inaequalis (Table  1 ). As said previously, vHPOs stabilize a required vanadate prosthetic group. vHPOs share a relatively low level of overall protein sequence identity between the different taxa (algae, fungi, marine bacteria). Only two short domains in the vanadate binding pocket are highly conserved  [90] , as presented hereafter. (1) PxYxSGHA (2) LxxxxAxxRxxxGxHxxxD Inside this catalytic pocket, vanadate is coordinated in a trigonal bipyramidal and forms a metallic interaction with the imidazole chain of the highly conserved histidine residue (H496 in CivCPO) (Figure  7a ). In the Protein Data Bank, vCPO 1VNC (and its mutants 1VNE, F, G and H) is the first crystal structure in 1996 and 1999 for its mutants  [54, 91]. Another structure, the 1IDQ, represents the crystal structure of native vanadium-containing chloroperoxidase from C.
inaequalis without the azide molecule present in 1VNC  [57]. The only crystal structure of a fungal vHPO is from C. inaequalis (Table  1 ). As said previously, vHPOs stabilize a required vanadate prosthetic group. vHPOs share a relatively low level of overall protein sequence identity between the different taxa (algae, fungi, marine bacteria). Only two short domains in the vanadate binding pocket are highly conserved  [90] , as presented hereafter. (1) PxYxSGHA (2) LxxxxAxxRxxxGxHxxxD Inside this catalytic pocket, vanadate is coordinated in a trigonal bipyramidal and forms a metallic interaction with the imidazole chain of the highly conserved histidine residue (H496 in CivCPO) (Figure  7a ). In the Protein Data Bank, vCPO 1VNC (and its mutants 1VNE, F, G and H) is the first crystal structure in 1996 and 1999 for its mutants  [54, 91].
Another structure, the 1IDQ, represents the crystal structure of native vanadiumcontaining chloroperoxidase from C. inaequalis without the azide molecule present in 1VNC  [57]. Oxygens in the equatorial plan of vanadate are stabilized by multiple hydrogen bonds with conserved residues (for vCPO PDB ID: 1VNC or 1IDQ-Lys353, Arg360, Ser402, Gly403 and Arg490)  [88, 92]. Figure  7b  shows the active site highly conserved in vHPO from different taxa (algae and fungi here). There is a perfect overlay of the residues which stabilize the vanadate cofactor  [90]. In the vCPO from C. inaequalis, another histidine residue is proposed to be part of the catalytic cycle (H404). This same histidine residue is observed in the vBPO from A. nodosum. The intermediate peroxovanadate formed during catalysis is oriented toward Phe397.
Oxygens in the equatorial plan of vanadate are stabilized by multiple hydrogen bonds with conserved residues (for vCPO PDB ID: 1VNC or 1IDQ-Lys353, Arg360, Ser402, Gly403 and Arg490)  [88, 92]. Figure  7b  shows the active site highly conserved in vHPO from different taxa (algae and fungi here). There is a perfect overlay of the residues which stabilize the vanadate cofactor  [90]. In the vCPO from C. inaequalis, another histidine residue is proposed to be part of the catalytic cycle (H404). This same histidine residue is observed in the vBPO from A. nodosum. The intermediate peroxovanadate formed during catalysis is oriented toward Phe397. In fungi, there is one equivalent of vanadium per enzyme. In contrast with vHPOs from algae which revealed multimerization, vCPO from C. inaequalis is composed by only one monomer.
vHPOs in Fungi Up to now, there are 288 sequences (UniProt database) associated to fungal vHPOs but only two have been characterized, e. g. , the one from C. inaequalis and another from Alternaria didymospora (previously named Embellisia didymospora). The first one is the only one which has been crystallized  [91]. Other sequences have been only computationally detected and automatically classified as vHPOs. For computationally detected vHPOs, UniProt database is not adapted. There are a lot of duplicated and statistically unvalidated sequences and possible confusions with PAP2 enzymes can occur. To refine this result, a Blastp analysis was done on MycoCosm database using vCPO from C. inaequalis as query sequence  [23]. For this analysis, the E-value threshold was fixed at 1. 0 × 10 -50. 105 sequences corresponded to proteins belonging to the superfamily "Acid phosphatase/Vanadium-dependent haloperoxidase" gathering approximatively 70 different fungal species with an important Alternaria genus cluster (including 20 different species).
vHPOs in Fungi Up to now, there are 288 sequences (UniProt database) associated to fungal vHPOs but only two have been characterized, e. g. , the one from C. inaequalis and another from Alternaria didymospora (previously named Embellisia didymospora). The first one is the only one which has been crystallized  [91]. Other sequences have been only computationally detected and automatically classified as vHPOs. For computationally detected vHPOs, UniProt database is not adapted. There are a lot of duplicated and statistically unvalidated sequences and possible confusions with PAP2 enzymes can occur. To refine this result, a Blastp analysis was done on MycoCosm database using vCPO from C. inaequalis as query sequence  [23]. For this analysis, the E-value threshold was fixed at 1. 0 × 10 -50. 105 sequences corresponded to proteins belonging to the superfamily "Acid phosphatase/Vanadiumdependent haloperoxidase" gathering approximatively 70 different fungal species with an important Alternaria genus cluster (including 20 different species).
The vCPO of A. didymospora shares 68% identity with the CivCPO  [29]. This is the first characterized vHPO from a marine fungus, which has a higher affinity for bromine (Km = 5 µM) compared to chlorine (Km = 1. 2 mM) at pH 5. 2, this enzyme has been natively purified  [29]. Another vHPO has also been detected in Botrytis cinerea, a fungal pathogen of plants. Authors have detected intracellular haloperoxidase activity using o-dianisidine assay  [99]. They have shown that it was a vHPO by inactivating the protein using EDTA and reactivating it using ortho-vanadate, but the sequence of the specific protein is still not available on databases such as UniProt  [99]. An interesting case is the vBPO described from the terrestrial lichen Xanthoria parietina, a symbiont between the green alga Trebouxia and an ascomycete.