dataesr/scientific-paragraphs-categorization huggingface dataset:
Trade-off When the provision of one service is reduced as a consequence of increased use of another, such as the case of crop production diminishing water quality. Inferred from negative spatial overlaps. Win-win A situation (or area) where a synergy occurs. Step 1: Aggregation and harmonisation of ES data governance (in most areas of Europe) where many decisions regarding planning and landscape management are taken  (Hamann et al. 2015; Queiroz et al. 2015). The selected grain for multi-ES research is also likely to have been driven by data availability; municipalities often are the finest scale at which some ES (such as provisioning ES) and potential social data are available (e. g. census data). We consider the potential limitations of municipality-level analyses in the discussion. Following collation and aggregation of multi-ES datasets, data are usually harmonised to a common range and unit to allow for comparison prior to data analysis.
2015)  Thereofore, studies that have bundled different ES, or measured or modelled ES in different ways, are not straightforward to compare, or necessarily generalisable to other regions. We attempted to overcome both issues in our French Alps case study by comparing two regions using the same ES and social-ecological datasets, and do so using a widely used method to analyseES bundles. However, we show that even within the French Alps, there is enormous variation in the degree to which different social-ecological variables can explain the distributions of ES bundles (See Appendix S4 for more discussion on the findings from the case study). Importantly, our case studywhich is based on the current state of the sciencedoes not enable us to identify why the explanatory power of different social and ecological variables considered here differs so much between our two regions.
2016). Another well-documented scale effect related to spatial unit is the modifiable area unit problem, in which statistical results can depend on the size and shape of spatial units in which a variable is aggregated  (Openshaw & Taylor, 1979). Grain size-dependence in the direction of correlations of ES has been demonstrated in several studies (e. g. Naidoo et al. , 2008; Anderson et al. 2009). Various processes can cause this phenomenon. Aggregation obscures ES trade-offs particularly when ES compete for space. For example, different crop competing for productive floodplain soils could be seen as spatially concurrent in aggregated datasets, thereby suggesting a synergistic relationship  (Tomscha & Gergel, 2016). When administrative units are used, the degree of variation in the grain size among units is likely to be an issue for the interpretation of relationships, as the mechanisms essential to an ES at one grain can be less important or absent at another.
Careful selection of social-ecological variables in multi-ES analyses is critical for attribution There have been several calls for ES analysts to improve understanding of ES associations, to allow for knowledge of how to minimize trade-offs and enhance synergies  (Bennett et al. 2009; Bennett et al. 2015). This understanding requires identifying key social-ecological variables that determine the co-variation in ES. Other authors have suggested the potential benefit of predicting ES associations from widely available social-ecological datasets, that are not necessarily causal  (Meacham et al. 2015). If widely accessible data on social-ecological drivers (such as land use and population density) can predict ES associations, this may overcome problems associated with complex and data-intensive models that are required to produce ES maps in data scarce regions  (Meacham et al.
Issues relevant to using cluster analysis for modelling ES associations Cluster analysis is considered a useful first step when no prior knowledge about existing relationships in a multivariate dataset exists  (Bennett et al. 2009; Dheng et al. 2016 ). However, its exploratory nature makes it unsuitable for understanding causality in ES assocations. Cluster analysis requires somewhat subjective decisions including the clustering algorithm and the number of clusters, which is not straightforward  (Legendre & Legendre 2012). The clustering solution is also entirely dependent on the input variables, rendering the results ungeneralizable to other regions. In summary, the subjectivity of cluster analysis makes it poorly suited to cross-study comparisons that are required for understanding general socio-ecological causes of ES associations.
The consideration of temporal changes in ES and drivers Inferring interactions from spatial co-incidence is loosely analogous to a space-for-time substitution in that spatial relationships are used to infer dynamics over time  (Tomscha & Gergel, 2016). A major limitation of this approach is that most spatial studies use ES snapshot data to assess ES associations and relationships with drivers. Mismatches in the timing between change in a driver (including demand) and the supply of an ES may cause relationships to be misinterpreted or overlooked, particularly in transitioning landscapes. This can also be due to mismatches in the time series of available datasets. ES are not static but spatially and temporally dynamic in terms of their delivery and associations with other services. ; Municipalities have been found to change in the bundles of services they provide over time raising concerns about using snapshots of ES provision to build understanding of ES relationships in complex and dynamic social-ecological systems  (Renard et al.
Acknowledgements A  University of Southampton IfLS Research Stimulus Fund  to RS, MS, KP, JMB and FE supported RS. RS, RL and FE received support from  ERC   Starting Grant 'SCALEFORES' (Grant number   680176 ). RS and JMB were funded by  CEH  project  NEC05264. RL, EC and SL received funding from  OPERAs  (grant number  FP7-ENV-2012-two-stage-308393 ). MS received funding from an  ESPA   Early Career Fellowship Grant  (grant number  FELL-2014-104 ) with support from the  Ecosystem Services for Poverty Alleviation (ESPA)  programme. The ESPA programme is funded by the  Department for International Development (DFID) , the  Economic and Social Research Council (ESRC)  and the  Natural Environment Research Council (NERC ). PV received support from  ERC Grant  ' GLOLAND ' (grant number  311819 ).
Funding for MGT was provided by the  US National Science Foundation  (grant numbers  DEB-1038759 ,  DEB-1440297 , and  DEB-1440485 ). EB received funding from  Natural Sciences and Engineering Research Council of Canada  (grant number  RGPIN 327077-2013 ) and  NSERC   EWR Steacie Fellowship. GDP was funded by  Social-ecological dynamics of ecosystem services in the Norrström basin (SEEN)  project, financed by the  Swedish Research Council Formas  (grant number  2012-1058 ) and  Swedish Research Council MISTRA  (through a  core grant  to the  Stockholm Resilience Centre ). We are grateful for constructive feedback from two anonymous reviewers.
The number of PCA axes deemed sufficient to characterize the non-random structure in the data in both the N and S ES datasets was selected according to the Kaiser-Guttman criterion, which selects the axes whose eigenvalues are greater than the mean of all eigenvalues  (Legendre and Legendre, 1998; Turner et al. 2015). For both regions, the first three components were selected and accounted for 61% and 57% of the total variation in ES in the N and S, respectively. As a precursor to cluster analysis, PCA can serve to separate signal from noise and lead to a more stable clustering solution, with the first axes extracting the essential information while the latter are restricted to noise  (Husson et al. 2010 ). Clustering of PCA axes i. e. uncorrelated components also means that correlated services are not counted more than once or more heavily weighted  (Turner et al.
2015). We applied k-means clustering to the relevant PCA axes to delineate ES bundles with 1000 random starts and 10,000 iterations to find a solution with the lowest within-cluster sum of squares according to the relevant PCA axes. K-means clusters municipalities so that the composition of ES values are more alike within than between clusters. Three clusters was deemed appropriate for both the N and S datasets according to a hierarchical cluster analysis using Ward's method and squared euclidean distance (with the FactoMineR package;  Lê et al. 2008 ) and qualitative assessment of ES bundles. Following  Renard et al. (2015) , we quantified the effective number of ES provided in each bundle using a transformation (H) of the Gini-Simpson's index (S): for a bundle with N ES with varying proportions p of each service (i).
2010; Crossman et al. 2013). ES maps often vary in the units, range of output values, and spatial resolution. To enable bivariate or multivariate analyses, ES datasets have been aggregated to a common resolution. While studies have mapped ES at scales ranging from local to global (see Crossman et al. 2013 and Malinga et al. 2015 for recent reviews), studies mapping ES bundles tend to be conducted for parts of countries at the spatial resolution of administrative boundaries, typically the smallest political units such as municipalities (Table 1). The use of administrative boundaries has been advocated as relevant for multi-ES studies (Raudsepp-Hearne et al. 2010), as municipalities represent the smallest scale of e. g. qualitative interpretation of maps of ES associations (through maps of PCA site scores or cluster identity) -e.
Metagenomics has emerged as a strategic approach to explore unculturable microbes through the sequencing and analysis of DNA extracted from environmental samples, as well as using such experimental methods as DNA hybridization, gene expression, proteomics, metabolomics, and enzymatic screening  [4] [5] [6] [7]. The remarkable contribution of this approach to global DNA sequencing efforts has been demonstrated by several large scale metagenomic projects including the Sargasso Sea sampling (over one million novel protein encoding genes), the Global Ocean Survey (over six million genes), and human gut microbiome studies (over 3 million genes)  [8] [9] [10] [11]. Due to the progress in DNA sequencing technology, the number of sequenced genomes and protein sequences in public databases has expanded exponentially.
is estimated that the global protein universe of microorganisms exceeds  10 12  proteins indicating that we know astonishingly little about microbial proteins and enzymes  12, 13. Even more, based on conservative estimates, over 50% of the sequences available in the databases have uncertain (general), unknown, or incorrectly annotated functions  14. Therefore, the direct experimental determination of protein function or enzyme activity for millions of biochemically uncharacterized proteins or genes of unknown function represents one of the major challenges in postgenomic biology. In addition to sequence similarity-based and comparative genomics methods of gene function prediction, there are several experimental approaches to annotation including analysis of gene or protein interactions, gene expression, gene knockouts, protein localization, and protein structures  [15] [16] [17] [18] [19].
Here we present the results of enzymatic screening of 16 metagenomic gene libraries from different environments for novel carboxylesterases. We have identified over 700 positive clones, from which 80 selected genes were expressed in E. coli, and their esterase activities were confirmed using additional assays. Four enzymes representing unknown (DUF3089) and hypothetical (MGS0084) proteins, cyclase-like enzymes (PF04199), as well as polyester hydrolyzing and lipolytic enzymes were characterized biochemically including substrate and temperature profiles. The active site residues of new enzymes were identified using site-directed mutagenesis, and the crystal structure of a metal-dependent cyclase-like esterase provided insight into the molecular mechanisms of its activity.
1d ). Three enzymes (MGS0084, MGS0156 and GEN0160) out of 80 tested clones were found to have lipase activity (Fig. 1 ), consistent with previous metagenome screens where low frequency of lipase activity was reported  7. Sequence analysis and enzyme families of identified metagenomic esterases. BLASTp searches of the NCBI database using 80 validated metagenomic esterases as queries indicated that most of these proteins represent genuine metagenomic enzymes with just 11 sequences from known genomes including Alcanivorax borkumensis, Cycloclasticus sp. 78-ME, Marinobacter hydrocarbonoclasticus, Parvibaculum lavamentivorans, and Serratia fonticola (99-100% identity) (Fig. 2 , Supplementary Table  S2 ). Sixty-nine remaining esterases showed 28-98% sequence identity to sequences from the NCBI database with most sequences within the range of 50-80% identity.
Analysis of phylogenetic distribution of the 80 validated metagenomic esterases revealed that these proteins and their top sequence homologues are present in a broad range of Gram-positive and Gram-negative microorganisms with most proteins found in Proteobacteria (52 proteins), Terrabacteria (11 proteins), and the Fibrobacteres, Chlorobi and Bacteroidetes (FCB) group (10 proteins) (Fig. 2 ). Based on sequence analysis, the 80 validated esterases belong to 17 protein families (Fig. 3 ). A majority of these enzymes are predicted to belong to the α/β hydrolase superfamily (59 proteins), which represents one of the largest groups of structurally related proteins (148 families in the ESTHER database) with diverse catalytic and non-catalytic functions including hydrolases, dehalogenases, haloperoxidases, and hydroxynitrile lyases  36, 38, 42, 43.
Previously, several metagenomic β-lactamase-like esterases have been characterized revealing high esterase activity against shorter chain p-nitrophenyl esters (C2-C5) and detectable hydrolytic activity against the β-lactamase substrates nitrocefin and cephalosporin  31, 51. All ten identified β-lactamase-like esterases are serine hydrolases with a conserved Ser-x-x-Lys catalytic motif typical for the class C β-lactamases. Interestingly, the α/β-hydrolase-like esterase GEN0169 has an additional Metallo-β-lactamase domain (PF00753). This is a domain commonly found in class B β-lactamases, a structurally unrelated enzyme family also capable of hydrolyzing β-lactam antibiotics. The remaining hydrolase-like proteins share sequence similarity with Patatin-like phospholipases (5 proteins), SGNH-hydrolases (3 proteins), and 3-hydroxybutyrate oligomer hydrolase (one protein, PF10605).
The unknown protein MGS0084 has only eight homologous sequences in the Uniprot and non-redundant GenBank databases. A protein sequence alignment of MGS0084 with its homologues shows a conserved Gly-His-Ser-His-(Ala/Gly)-Gly motif, which resembles Gly-x-Ser-x-Gly commonly found in α/β hydrolases suggesting that these proteins may represent a new branch of this superfamily (Supplementary Fig. S1 ). MGS0169 belongs to the PF04199 family of putative cyclase-like enzymes, which contain a conserved His-x-Gly-Thr-His-x-Asp-x-Pro-x-His motif predicted to form part of the active site. This motif is only partially conserved in MGS0169 and its closest homologues with the two first His residues replaced by Gln (Supplementary Fig. S1 ). Several cyclase-like proteins from different bacteria have been shown to exhibit metal-dependent amidohydrolase activity against formylkynurenine and isatin  [52] [53] [54]  , but carboxylesterase activity of PF04199 proteins has not been reported before.
This protein showed detectable esterase activity against pNP-palmitate (C16), which is a representative substrate for lipases (Fig. 4 ). This is in line with the presence of hydrolytic activity of this enzyme toward olive oil (Fig. 1 ). In contrast to the other three proteins, esterase activity of MGS0169 was greatly stimulated by the addition of divalent metal cations (Mn 2+ > Mg 2+ > Co 2+ ≫ Ni 2+ ) (Supplementary Fig. S2 ). Several biochemically characterized members of the cyclase-like protein family (PF04199) exhibited metal ion dependent amidohydrolase activity against formylkynurenine and isatin 52-54. MGS0169 also showed detectable metal dependent amidohydrolase activity against isatin (k cat /K M 0. 1 × 10 3 M -1 s -1 ), but its esterase activity against pNP-acetate was at least three orders of magnitude higher (k cat /K M ~ 0.
2 × 10 6 M -1 s -1 ) (Fig. 4 , Table  2 ). Previously, the presence of metal ion-stimulated esterase activity was demonstrated in the amidohydrolase proteins from the phosphotriesterase family (PF02126) including Rsp3690 from Rhodobacter sphaeroides and Pmi1525 from Proteus mirabilis  55, 56. However, these enzymes have different structural folds and active sites. Thus, MGS0169 and homologous proteins from the PF04199 family do indeed represent a novel group of metal-dependent esterases from the amidohydrolase superfamily. The purified metagenomic esterases showed saturation kinetics and high catalytic efficiencies with low K M values toward the tested model esterase substrates (Table  2 ). The ester substrate profiles of four metagenomic esterases were determined using a library of 89 various monoesters including alkyl and aryl esters (Supplementary Fig.
A Dali search for MGS0169 structural homologues identified several protein structures as the best matches including the isatin hydrolase IH-b from Labrenzia aggregata (PDB codes 4J0N and 4M8D; Z-score 21. 3, rmsd 2. 5 Å), the three microbial kynurenine formamidases KynB (PDB codes 4COB, 4COG, and 4CO9; Z-score 19. 4-19. 9, rmsd 2. 1-2. 7 Å), and the uncharacterized predicted hydrolase P84132_GEOSE from Geobacillus stearothermophilus (PDB code 3KRV; Z-score 18. 6, rmsd 2. 9 Å). These proteins share low sequence similarity to MGS0169 (17-22% sequence identity), and the two biochemically characterized enzymes (IH-b and KynB) have metal-dependent amidohydrolase activity against isatin and N-formylkynurenine  53, 54. The active site of MGS0169. The location of the MGS0169 active site is indicated by the unknown electron density with a tetrahedral-like geometry located in the narrow cavity formed mainly by α-helices near the end of the central β-barrel (Fig.
6 , Supplementary Fig. S2 ). Based on its shape, this density might represent a molecule of acetyl-phosphate, probably captured by the enzyme from E. coli cells. The bottom part of the ligand is positioned close to the side chains of the conserved Gln127 (5. 3 Å), Gln131 (4. 3 Å), Asp133 (3. 8 Å), and His137 (2. 6 Å) (Fig. 6 ). These residues represent a modified cyclase-like motif Gln-x-x-x-Gln-x-Asp-x-x-x-His found in several cyclase-like proteins (PF04199) including the trans-dienelactone hydrolase from Pseudomonas reinekei MT1  60  and are likely to be involved in metal ion binding. The bound ligand also interacts with the side chains of the conserved Arg87 (2. 9 Å), Glu299 (2. 6 Å), and His286 (2. 6 Å). The MGS0169 substrate binding site is less conserved with only two residues (Phe84 and His286) identical to the isatin hydrolase substrate binding site (Phe41 and His212, PDB code 4J0N) (Fig.
6 ). In the crystal structures of isatin hydrolase IH-b (PDB code 4M8D) and kynurenine formamidase KynB (PDB code 4COB), which contain the classical cyclase-like motif His-x-x-x-His-x-Asp-x-x-x-His, the side chains of the motif residues coordinate one Mn 2+ ion (IH-b) or two Zn 2+ ions (KynB), which are required for the enzymatic activity of these enzymes  53, 54. However, no metal ion was found in the corresponding site of the uncharacterized cyclase-like protein MJ0783 from Methanocaldococcus jannaschii (PDB code 2B0A), or in MGS0169. We propose that metal ion binding to MGS0169 was prevented by the presence of the bound acetylphosphate-like ligand, and the active site of the catalytically active MGS0169 contains one or two metal ions (probably Mn 2+ , based on the MGS0169 metal ion profile), as was suggested for the the trans-dienelactone hydrolase from Pseudomonas reinekei MT1  61.
Conclusions The discovery of new enzymes in environmental bacteria contributes greatly to our fundamental knowledge of protein structure-function relationships, expands the biocatalytic toolbox of enzymes for metabolic engineering and synthetic biology, and improves the quality of gene annotation in public databases, on which bioinformatics tools rely. Enzymatic screening of environmental gene libraries presented in this work revealed a huge sequence and biochemical diversity with identification of 80 esterases from 17 different enzyme families (Fig. 3 ). These enzymes exhibit diverse substrate preferences, from short to long acyl chain esters with a significant number of enzymes possessing polyesterase activity against polylactic acid and polycaprolactone. The differences in their sensitivities to temperature and salt conditions likely reflect environmental adaptations.
Lambda-ZAP clones were sequenced by primer walking, while fosmids were sequenced as mixed pools using Illumina or Roche 454 platforms (at TCAG, Genome Quebec and Genoscope). Reads were dereplicated and assembled into contigs using the Velvet algorithm  67  in Geneious 68 version 6. 0. 6, and contigs were mapped to specific fosmids using Sanger sequenced fosmid end sequences. Contigs were submitted to the MG-RAST 69 pipeline for gene annotation. In parallel, open reading frames were predicted using the Glimmer algorithm  70  , and translated protein sequences were annotated through BLAST searches of UniProt and the non-redundant GenBank protein database  71. Genes predicted as esterases, lipases, or hydrolases were selected for recombinant expression in E. coli. Where no such gene was found, smaller esterase positive genetic fragments were identified by subcloning, and all predicted genes were cloned and rescreened.
Proteins with confirmed esterase activity (Supplementary Table  S2 ) were classified into families through sequence analysis using HMMER  72  searches against the Pfam database and BLAST searches of the COG database  73  , with an E-value cut-off of 1E-5 unless otherwise indicated. Where an enzyme had a significant score to more than one protein family, the family with the smaller E-value and/or larger sequence coverage was assigned. Multiple sequence alignments were generated using MUSCLE  74. The phylogenetic tree was produced using the NCBI taxonomy of the closest sequence homologues and the PhyloT tree generator (http://phylot. biobyte. de/) and visualized using the iTOL v3 online tool  75. Enzymatic assays with purified proteins. Carboxylesterase activity of purified proteins against p-nitrophenyl (pNP) or α-naphthyl esters of various fatty acids was measured spectrophotometrically as described previously  31.
01-5. 0 mM). Kinetic parameters were calculated by non-linear regression analysis of raw data fit (to the Michaelis-Menten or Hill functions) using GraphPad Prism software (version 4. 00 for Windows). Crystallization and structure determination of MGS0169. The selenomethionine substituted MGS0169 (21-341 aa) was crystallized at 22 °C using the sitting-drop vapor diffusion method by mixing 0. 5 μl of the purified protein (20 mg/ml) with 0. 5 μl of the crystallization solution containing 0. 2 M ammonium acetate, 0. 1 M Tris-HCl (pH 8. 0), and 30% (w/v) PEG 2KMME. The crystals were stabilized by cryoprotection in Paratone-N prior to flash-freezing in liquid nitrogen. Diffraction data were collected at the beamline 19-ID with an ADSC Quantum 315 R detector of the Structural Biology Center, Advanced Photon Source, Argonne National Laboratory  76, 77.