Ecology

1.Ottesen, E. A. et al. Pattern and synchrony of gene expression among sympatric marine microbial populations. Proc. Natl Acad. Sci. USA 110, E488–E497 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
2.Muñoz-Marín, M. D. C. et al. The transcriptional cycle is suited to daytime N2 fixation in the unicellular cyanobacterium “Candidatus Atelocyanobacterium thalassa” (UCYN-A). mBio 10, e02495-18 (2019).PubMed 
PubMed Central 

Google Scholar 
3.Vislova, A., Sosa, O. A., Eppley, J. M., Romano, A. E. & DeLong, E. F. Diel oscillation of microbial gene transcripts declines with depth in oligotrophic ocean waters. Front. Microbiol. 10, 2191 (2019).PubMed 
PubMed Central 

Google Scholar 
4.Harke, M. J. et al. Periodic and coordinated gene expression between a diazotroph and its diatom host. ISME J. 13, 118–131 (2019).CAS 
PubMed 

Google Scholar 
5.Hernández Limón, M. D. et al. Transcriptional patterns of Emiliania huxleyi in the North Pacific Subtropical Gyre reveal the daily rhythms of its metabolic potential.Environ. Microbiol. 22, 381–396 (2020).PubMed 

Google Scholar 
6.Becker, K. W. et al. Daily changes in phytoplankton lipidomes reveal mechanisms of energy storage in the open ocean. Nat. Commun. 9, 5179 (2018).PubMed 
PubMed Central 

Google Scholar 
7.Frischkorn, K. R., Haley, S. T. & Dyhrman, S. T. Coordinated gene expression between Trichodesmium and its microbiome over day–night cycles in the North Pacific Subtropical Gyre. ISME J. 12, 997–1007 (2018).PubMed 
PubMed Central 

Google Scholar 
8.Ottesen, E. A. et al. Ocean microbes. Multispecies diel transcriptional oscillations in open ocean heterotrophic bacterial assemblages. Science 345, 207–212 (2014).CAS 
PubMed 

Google Scholar 
9.Wilson, S. T. et al. Coordinated regulation of growth, activity and transcription in natural populations of the unicellular nitrogen-fixing cyanobacterium Crocosphaera. Nat. Microbiol. 2, 17118 (2017).CAS 
PubMed 

Google Scholar 
10.Saito, M. A. et al. Iron conservation by reduction of metalloenzyme inventories in the marine diazotroph Crocosphaera watsonii. Proc. Natl Acad. Sci. USA 108, 2184–2189 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
11.Strenkert, D. et al. Multiomics resolution of molecular events during a day in the life of Chlamydomonas. Proc. Natl Acad. Sci. USA 116, 2374–2383 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
12.Boysen, A. K. et al. Particulate metabolites and transcripts reflect diel oscillations of microbial activity in the surface ocean. mSystems 6, e00896-20 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
13.White, A. E., Barone, B., Letelier, R. M. & Karl, D. M. Productivity diagnosed from the diel cycle of particulate carbon in the North Pacific Subtropical Gyre: optically derived productivity. Geophys. Res. Lett. 44, 3752–3760 (2017).CAS 

Google Scholar 
14.DeLong, E. F. et al. Community genomics among stratified microbial assemblages in the ocean’s interior. Science 311, 496–503 (2006).CAS 
PubMed 

Google Scholar 
15.Sunagawa, S. et al. Ocean plankton. Structure and function of the global ocean microbiome. Science 348, 1261359 (2015).PubMed 

Google Scholar 
16.Coles, V. J. et al. Ocean biogeochemistry modeled with emergent trait-based genomics. Science 358, 1149–1154 (2017).CAS 
PubMed 

Google Scholar 
17.Walbauer, J. R., Rodrigue, S., Coleman, M. L. & Chisholm, S. W. Transcriptome and proteome dynamics of a light–dark synchronized bacterial cell cycle.PLoS ONE 7, e43432 (2012).
Google Scholar 
18.Steiner, P. A. et al. Highly variable mRNA half-life time within marine bacterial taxa and functional genes. Environ. Microbiol. 21, 3873–3884 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
19.Moran, M. A. et al. Sizing up metatranscriptomics. ISME J. 7, 237–243 (2013).CAS 
PubMed 

Google Scholar 
20.Tamames, J., Cobo-Simón, M. & Puente-Sánchez, F. Assessing the performance of different approaches for functional and taxonomic annotation of metagenomes. BMC Genomics 20, 960 (2019).21.DiTullio, G. R. & Laws, E. A. Diel periodicity of nitrogen and carbon assimilation in five species of marine phytoplankton: accuracy of methodology for predicting N-assimilation rates and N/C composition ratios. Mar. Ecol. Prog. Ser. 32, 123–132 (1986).CAS 

Google Scholar 
22.Granum, E., Kirkvold, S. & Myklestad, S. M. Cellular and extracellular production of carbohydrates and amino acids by the marine diatom Skeletonema costatum: diel variations and effects of N depletion. Mar. Ecol. Prog. Ser. 242, 83–94 (2002).CAS 

Google Scholar 
23.Lacour, T., Sciandra, A., Talec, A., Mayzaud, P. & Bernard, O. Diel variations of carbohydrates and neutral lipids in nitrogen-sufficient and nitrogen-starved cyclostat cultures of Isochrysis sp. J. Phycol. 48, 966–975 (2012).PubMed 

Google Scholar 
24.Follett, C. L., Dutkiewicz, S., Karl, D. M., Inomura, K. & Follows, M. J. Seasonal resource conditions favor a summertime increase in North Pacific diatom–diazotroph associations. ISME J. 12, 1543–1557 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
25.Chen, W.-N. U. et al. Diel rhythmicity of lipid-body formation in a coral-Symbiodinium endosymbiosis. Coral Reefs 31, 521–534 (2012).
Google Scholar 
26.Zhou, X. & Mopper, K. Photochemical production of low-molecular-weight carbonyl compounds in seawater and surface microlayer and their air-sea exchange. Mar. Chem. 56, 201–213 (1997).CAS 

Google Scholar 
27.Durham, B. P. et al. Sulfonate-based networks between eukaryotic phytoplankton and heterotrophic bacteria in the surface ocean.Nat. Microbiol. 4, 1706–1715 (2019).CAS 
PubMed 

Google Scholar 
28.Lambert, S. et al. Rhythmicity of coastal marine picoeukaryotes, bacteria and archaea despite irregular environmental perturbations. ISME J. 13, 388–401 (2019).PubMed 

Google Scholar 
29.Kolody, B. C. et al. Diel transcriptional response of a California Current plankton microbiome to light, low iron, and enduring viral infection. ISME J. 13, 2817–2833 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
30.Aylward, F. O. et al. Microbial community transcriptional networks are conserved in three domains at ocean basin scales. Proc. Natl Acad. Sci. USA 112, 5443–5448 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
31.Rusch, D. B. et al. The Sorcerer II Global Ocean Sampling expedition: northwest Atlantic through eastern tropical Pacific. PLoS Biol. 5, e77 (2007).PubMed 
PubMed Central 

Google Scholar 
32.Bork, P. et al. Tara Oceans studies plankton at planetary scale. Science 348, 873 (2015).CAS 
PubMed 

Google Scholar 
33.Delmont, T. O. et al. Nitrogen-fixing populations of Planctomycetes and Proteobacteria are abundant in surface ocean metagenomes. Nat. Microbiol. 3, 804–813 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
34.Fuhrman, J. A. et al. Annually reoccurring bacterial communities are predictable from ocean conditions. Proc. Natl Acad. Sci. USA 103, 13104–13109 (2006).CAS 
PubMed 
PubMed Central 

Google Scholar 
35.Morris, R. M. et al. Temporal and spatial response of bacterioplankton lineages to annual convective overturn at the Bermuda Atlantic Time‐series Study site. Limnol. Oceanogr. 50, 1687–1696 (2005).CAS 

Google Scholar 
36.Mende, D. R. et al. Environmental drivers of a microbial genomic transition zone in the ocean’s interior. Nat. Microbiol. 2, 1367–1373 (2017).CAS 
PubMed 

Google Scholar 
37.Keeling, P. J. et al. The Marine Microbial Eukaryote Transcriptome Sequencing Project (MMETSP): illuminating the functional diversity of eukaryotic life in the oceans through transcriptome sequencing. PLoS Biol. 12, e1001889 (2014).PubMed 
PubMed Central 

Google Scholar 
38.Kanehisa, M., Sato, Y., Kawashima, M., Furumichi, M. & Tanabe, M. KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res. 44, D457–D462 (2016).CAS 

Google Scholar 
39.Thaben, P. F. & Westermark, P. O. Detecting rhythms in time series with RAIN. J. Biol. Rhythms 29, 391–400 (2014).PubMed 
PubMed Central 

Google Scholar 
40.Cuhel, R. L., Ortner, P. B. & Lean, D. R. S. Night synthesis of protein by algae. Limnol. Oceanogr. 29, 731–744 (1984).CAS 

Google Scholar 
41.Coesel, S. N. et al. Diel transcriptional oscillations of light-sensitive regulatory elements in open-ocean eukaryotic plankton communities. Proc. Natl Acad. Sci. USA 118, e2011038118 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
42.Bolay, P., Muro-Pastor, M. I., Florencio, F. J. & Klähn, S. The distinctive regulation of cyanobacterial glutamine synthetase. Life (Basel) 8, 52 (2018).CAS 

Google Scholar 
43.Karl, D. M., Church, M. J., Dore, J. E., Letelier, R. M. & Mahaffey, C. Predictable and efficient carbon sequestration in the North Pacific Ocean supported by symbiotic nitrogen fixation. Proc. Natl Acad. Sci. USA 109, 1842–1849 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
44.Berman, T. & Bronk, D. A. Dissolved organic nitrogen: a dynamic participant in aquatic ecosystems. Aquat. Microb. Ecol. 31, 279–305 (2003).
Google Scholar 
45.Lee, C. & Bada, J. L. Amino acids in equatorial Pacific Ocean water. Earth Planet. Sci. Lett. 26, 61–68 (1975).CAS 

Google Scholar 
46.Bada, J. L. & Lee, C. Decomposition and alteration of organic compounds dissolved in seawater. Mar. Chem. 5, 523–534 (1977).CAS 

Google Scholar 
47.Poretsky, R. S., Sun, S., Mou, X. & Moran, M. A. Transporter genes expressed by coastal bacterioplankton in response to dissolved organic carbon. Environ. Microbiol. 12, 616–627 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
48.Berthelot, H. et al. NanoSIMS single cell analyses reveal the contrasting nitrogen sources for small phytoplankton. ISME J. 13, 651–662 (2019).CAS 
PubMed 

Google Scholar 
49.Moore, L. R., Post, A. F., Rocap, G. & Chisholm, S. W. Utilization of different nitrogen sources by the marine cyanobacteria Prochlorococcus and Synechococcus. Limnol. Oceanogr. 47, 989–996 (2002).CAS 

Google Scholar 
50.Hu, S. K., Connell, P. E., Mesrop, L. Y. & Caron, D. A. A hard day’s night: diel shifts in microbial eukaryotic activity in the North Pacific Subtropical Gyre. Front. Mar. Sci. https://doi.org/10.3389/fmars.2018.00351 (2018).51.Hannides, C. C. S., Popp, B. N., Choy, C. A. & Drazen, J. C. Midwater zooplankton and suspended particle dynamics in the North Pacific Subtropical Gyre: a stable isotope perspective. Limnol. Oceanogr. 58, 1931–1946 (2013).CAS 

Google Scholar 
52.Becker, K. W. et al. Combined pigment and metatranscriptomic analysis reveals highly synchronized diel patterns of phenotypic light response across domains in the open oligotrophic ocean.ISME J. 15, 520–533 (2021).CAS 
PubMed 

Google Scholar 
53.Mruwat, N. et al. A single-cell polony method reveals low levels of infected Prochlorococcus in oligotrophic waters despite high cyanophage abundances. ISME J. 15, 41–54 (2021).CAS 
PubMed 

Google Scholar 
54.Chesson, P. L. & Warner, R. R. Environmental variability promotes coexistence in lottery competitive systems. Am. Nat. 117, 923–943 (1981).
Google Scholar 
55.Shmida, A. & Ellner, S. Coexistence of plant species with similar niches. Vegetatio 58, 29–55 (1984).
Google Scholar 
56.Ellner, S. P., Snyder, R. E. & Adler, P. B. How to quantify the temporal storage effect using simulations instead of math. Ecol. Lett. 19, 1333–1342 (2016).PubMed 

Google Scholar 
57.Adler, P. B., Fajardo, A., Kleinhesselink, A. R. & Kraft, N. J. B. Trait-based tests of coexistence mechanisms. Ecol. Lett. 16, 1294–1306 (2013).PubMed 

Google Scholar 
58.Adler, P. B., HilleRisLambers, J., Kyriakidis, P. C., Guan, Q. & Levine, J. M. Climate variability has a stabilizing effect on the coexistence of prairie grasses. Proc. Natl Acad. Sci. USA 103, 12793–12798 (2006).CAS 
PubMed 
PubMed Central 

Google Scholar 
59.Cáceres, C. E. Temporal variation, dormancy, and coexistence: a field test of the storage effect. Proc. Natl Acad. Sci. USA 94, 9171–9175 (1997).PubMed 
PubMed Central 

Google Scholar 
60.Padisák, J. Identification of relevant time-scales in non-equilibrium community dynamics: conclusions from phytoplankton surveys. N. Z. J. Ecol. 18, 169–176 (1994).
Google Scholar 
61.Anderies, J. M. & Beisner, B. E. Fluctuating environments and phytoplankton community structure: a stochastic model. Am. Nat.155, 556–569 (2000).PubMed 

Google Scholar 
62.Wagg, C. et al. Functional trait dissimilarity drives both species complementarity and competitive disparity. Funct. Ecol. 31, 2320–2329 (2017).
Google Scholar 
63.Bligh, E.G. & Dyer, W. J. A rapid method of total lipid extraction and purification. Can. J. Biochem. Physiol. 37, 911–917 (1959).CAS 
PubMed 

Google Scholar 
64.Boysen, A. K., Heal, K. R., Carlson, L. T. & Ingalls, A. E. Best-matched internal standard normalization in liquid chromatography–mass spectrometry metabolomics applied to environmental samples. Anal. Chem. 90, 1363–1369 (2018).CAS 
PubMed 

Google Scholar 
65.MacLean, B. et al. Skyline: an open source document editor for creating and analyzing targeted proteomics experiments. Bioinformatics 26, 966–968 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
66.Fountoulakis, M. & Lahm, H. W. Hydrolysis and amino acid composition analysis of proteins. J. Chromatogr. A 826, 109–134 (1998).CAS 
PubMed 

Google Scholar 
67.Popendorf, K. J., Fredricks, H. F. & Van Mooy, B. A. S. Molecular ion-independent quantification of polar glycerolipid classes in marine plankton using triple quadrupole MS. Lipids 48, 185–195 (2013).CAS 
PubMed 

Google Scholar 
68.Collins, J. R., Edwards, B. R., Fredricks, H. F. & Van Mooy, B. A. S. LOBSTAHS: an adduct-based lipidomics strategy for discovery and identification of oxidative stress biomarkers. Anal. Chem. 88, 7154–7162 (2016).CAS 
PubMed 

Google Scholar 
69.Hummel, J. et al. Ultra performance liquid chromatography and high resolution mass spectrometry for the analysis of plant lipids. Front. Plant Sci. 2, 54 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
70.Smith, C. A., Want, E. J., O’Maille, G., Abagyan, R. & Siuzdak, G. XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal. Chem. 78, 779–787 (2006).CAS 
PubMed 

Google Scholar 
71.Kuhl, C., Tautenhahn, R., Böttcher, C., Larson, T. R. & Neumann, S. CAMERA: an integrated strategy for compound spectra extraction and annotation of liquid chromatography/mass spectrometry data sets. Anal. Chem. 84, 283–289 (2012).CAS 
PubMed 

Google Scholar 
72.Biller, S. J. et al. Prochlorococcus extracellular vesicles: molecular composition and adsorption to diverse microbes.Environ. Microbiol. https://doi.org/10.1111/1462-2920.15834 (2021).Article 
PubMed 

Google Scholar 
73.Aylward, F. O. et al. Diel cycling and long-term persistence of viruses in the ocean’s euphotic zone. Proc. Natl Acad. Sci. USA 114, 11446–11451 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
74.Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
75.Masella, A. P., Bartram, A. K., Truszkowski, J. M., Brown, D. G. & Neufeld, J. D. PANDAseq: paired-end assembler for illumina sequences. BMC Bioinformatics 13, 31 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
76.Joshi, N. & Fass, J. Sickle: A sliding-window, adaptive, quality-based trimming tool for FastQ files. Version 1.33. GitHub https://github.com/najoshi/sickle (2015).77.Kopylova, E., Noé, L. & Touzet, H. SortMeRNA: fast and accurate filtering of ribosomal RNAs in metatranscriptomic data. Bioinformatics 28, 3211–3217 (2012).CAS 

Google Scholar 
78.Kiełbasa, S. M., Wan, R., Sato, K., Horton, P. & Frith, M. C. Adaptive seeds tame genomic sequence comparison. Genome Res. 21, 487–493 (2011).PubMed 
PubMed Central 

Google Scholar 
79.Li, H. & Durbin, R. Fast and accurate long-read alignment with Burrows–Wheeler transform. Bioinformatics 26, 589–595 (2010).PubMed 
PubMed Central 

Google Scholar 
80.Alexander, H. et al. Functional group-specific traits drive phytoplankton dynamics in the oligotrophic ocean. Proc. Natl Acad. Sci. USA 112, E5972–E5979 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
81.Anders, S., Pyl, P. T. & Huber, W. HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).CAS 

Google Scholar 
82.Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).PubMed 
PubMed Central 

Google Scholar 
83.Meinicke, P. UProC: tools for ultra-fast protein domain classification. Bioinformatics 31, 1382–1388 (2015).CAS 
PubMed 

Google Scholar 
84.Mende, D. R., Boeuf, D. & DeLong, E. F. Persistent core populations shape the microbiome throughout the water column in the North Pacific Subtropical Gyre. Front. Microbiol. 10, 2273 (2019).PubMed 
PubMed Central 

Google Scholar 
85.White, A. E. et al. Phenology of particle size distributions and primary productivity in the North Pacific subtropical gyre (Station ALOHA). J. Geophys. Res. Oceans 120, 7381–7399 (2015).PubMed 
PubMed Central 

Google Scholar 
86.Borchers, H. W. pracma: Practical numerical math functions. R package version 2 https://cran.r-project.org/web/packages/pracma/index.html (2019).87.Maechler, M., Rousseeuw, P., Struyf, A., Hubert, M. & Hornik, K. cluster: Cluster analysis basics and extensions. R package version 1.56 (2012).88.Wehrens, R. & Buydens, L. M. C. Self- and super-organizing maps in R: the Kohonen package. J. Stat. Softw. 21, 1–19 (2007).
Google Scholar 
89.Hennig, C. fpc: Flexible procedures for clustering. R package version 2.2-9 (2010).90.Muratore, D. Code for complex marine microbial communities partition metabolism of scarce resources over the diel cycle. Zenodo https://doi.org/10.5281/zenodo.3817416 (2020). More

1.Flemming, H. C. & Wuertz, S. Bacteria and archaea on Earth and their abundance in biofilms. Nat. Rev. Microbiol. 17, 247–260 (2019).CAS 
PubMed 

Google Scholar 
2.Hou, Q. & Kolodkin-Gal, I. Harvesting the complex pathways of antibiotic production and resistance of soil bacilli for optimizing plant microbiome. FEMS Microbiol Ecol., https://doi.org/10.1093/femsec/fiaa142 (2020).3.Frost, I. et al. Cooperation, competition and antibiotic resistance in bacterial colonies. ISME J. 12, 1582–1593 (2018).MathSciNet 
CAS 
PubMed 
PubMed Central 

Google Scholar 
4.Niehus, R. & Mitri, S. Handling unpredictable ecosystems. Nat. Ecol. Evol. 2, 1207–1208 (2018).PubMed 

Google Scholar 
5.Cordero, O. X. et al. Ecological populations of bacteria act as socially cohesive units of antibiotic production and resistance. Science 337, 1228–1231 (2012).ADS 
CAS 
PubMed 

Google Scholar 
6.Zhu, H., Sandiford, S. K. & van Wezel, G. P. Triggers and cues that activate antibiotic production by actinomycetes. J. Ind. Microbiol Biotechnol. 41, 371–386 (2014).CAS 
PubMed 

Google Scholar 
7.Arakawa, K. Manipulation of metabolic pathways controlled by signaling molecules, inducers of antibiotic production, for genome mining in Streptomyces spp. Antonie Van. Leeuwenhoek 111, 743–751 (2018).CAS 
PubMed 

Google Scholar 
8.Cornforth, D. M. & Foster, K. R. Competition sensing: the social side of bacterial stress responses. Nat. Rev. Microbiol 11, 285–293 (2013).CAS 
PubMed 

Google Scholar 
9.Westhoff, S., Kloosterman, A. M., van Hoesel, S. F. A., van Wezel, G. P. & Rozen, D. E. Competition sensing changes antibiotic production in streptomyces. mBio. 12, https://doi.org/10.1128/mBio.02729-20 (2021).10.Hou, Q. et al. Weaponizing volatiles to inhibit competitor biofilms from a distance. NPJ Biofilms Microbiomes 7, 2 (2021).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
11.Shank, E. A. Considering the lives of microbes in microbial communities. mSystems 3, https://doi.org/10.1128/mSystems.00155-17 (2018).12.Shank, E. A. et al. Interspecies interactions that result in Bacillus subtilis forming biofilms are mediated mainly by members of its own genus. Proc. Natl Acad. Sci. USA 108, E1236–E1243 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
13.Lyons, N. A., Kraigher, B., Stefanic, P., Mandic-Mulec, I. & Kolter, R. A combinatorial kin discrimination system in Bacillus subtilis. Curr. Biol. 26, 733–742 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
14.Stefanic, P., Kraigher, B., Lyons, N. A., Kolter, R. & Mandic-Mulec, I. Kin discrimination between sympatric Bacillus subtilis isolates. Proc. Natl Acad. Sci. USA 112, 14042–14047 (2015).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
15.Kalamara, M., Spacapan, M., Mandic-Mulec, I. & Stanley-Wall, N. R. Social behaviours by Bacillus subtilis: quorum sensing, kin discrimination and beyond. Mol. Microbiol. 110, 863–878 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
16.Harris, K. D. & Kolodkin-Gal, I. Applying the handicap principle to biofilms: condition-dependent signalling in Bacillus subtilis microbial communities. Environ. Microbiol., https://doi.org/10.1111/1462-2920.14497 (2018).17.Dorrestein, P. C. & Kelleher, N. L. Dissecting non-ribosomal and polyketide biosynthetic machineries using electrospray ionization Fourier-Transform mass spectrometry. Nat. Prod. Rep. 23, 893–918 (2006).CAS 
PubMed 

Google Scholar 
18.Bloudoff, K. & Schmeing, T. M. Structural and functional aspects of the nonribosomal peptide synthetase condensation domain superfamily: discovery, dissection and diversity. Biochim Biophys. Acta Proteins Proteom. 1865, 1587–1604 (2017).CAS 
PubMed 

Google Scholar 
19.Butcher, R. A. et al. The identification of bacillaene, the product of the PksX megacomplex in Bacillus subtilis. Proc. Natl Acad. Sci. USA 104, 1506–1509 (2007).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
20.Tsuge, K., Ano, T. & Shoda, M. Isolation of a gene essential for biosynthesis of the lipopeptide antibiotics plipastatin B1 and surfactin in Bacillus subtilis YB8. Arch. Microbiol. 165, 243–251 (1996).CAS 
PubMed 

Google Scholar 
21.Coutte, F. et al. Effect of pps disruption and constitutive expression of srfA on surfactin productivity, spreading and antagonistic properties of Bacillus subtilis 168 derivatives. J. Appl Microbiol. 109, 480–491 (2010).CAS 
PubMed 

Google Scholar 
22.Hilton, M. D., Alaeddinoglu, N. G. & Demain, A. L. Synthesis of bacilysin by Bacillus subtilis branches from prephenate of the aromatic amino acid pathway. J. Bacteriol. 170, 482–484 (1988).CAS 
PubMed 
PubMed Central 

Google Scholar 
23.Zheng, G., Hehn, R. & Zuber, P. Mutational analysis of the sbo-alb locus of Bacillus subtilis: identification of genes required for subtilosin production and immunity. J. Bacteriol. 182, 3266–3273 (2000).CAS 
PubMed 
PubMed Central 

Google Scholar 
24.De Gonzalo, C. V. G., Zhu, L. Y., Oman, T. J. & van der Donk, W. A. NMR structure of the S-linked glycopeptide sublancin 168. Acs Chem. Biol. 9, 796–801 (2014).
Google Scholar 
25.Ongena, M. & Jacques, P. Bacillus lipopeptides: versatile weapons for plant disease biocontrol. Trends Microbiol. 16, 115–125 (2008).CAS 
PubMed 

Google Scholar 
26.Magnuson, R., Solomon, J. & Grossman, A. D. Biochemical and genetic characterization of a competence pheromone from B. subtilis. Cell 77, 207–216 (1994).CAS 
PubMed 

Google Scholar 
27.Nakano, M. M. et al. Srfa is an operon required for surfactin production, competence development, and efficient sporulation in Bacillus-Subtilis. J. Bacteriol. 173, 1770–1778 (1991).CAS 
PubMed 
PubMed Central 

Google Scholar 
28.Danevcic, T. et al. Surfactin facilitates horizontal gene transfer in Bacillus subtilis. Front. Microbiol. 12, doi:ARTN 657407 https://doi.org/10.3389/fmicb.2021.657407 (2021).29.Kluge, B., Vater, J., Salnikow, J. & Eckart, K. Studies on the biosynthesis of surfactin, a lipopeptide antibiotic from Bacillus-Subtilis Atcc-21332. Febs Lett. 231, 107–110 (1988).CAS 
PubMed 

Google Scholar 
30.Gonzalez, D. J. et al. Microbial competition between Bacillus subtilis and Staphylococcus aureus monitored by imaging mass spectrometry. Microbiology 157, 2485–2492 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
31.Rosenberg, G. et al. Not so simple, not so subtle: the interspecies competition between Bacillus simplex and Bacillus subtilis and its impact on the evolution of biofilms. NPJ Biofilms Microbiomes 2, 15027 (2016).PubMed 
PubMed Central 

Google Scholar 
32.Falardeau, J., Wise, C., Novitsky, L. & Avis, T. J. Ecological and mechanistic insights into the direct and indirect antimicrobial properties of Bacillus subtilis lipopeptides on plant pathogens. J. Chem. Ecol. 39, 869–878 (2013).CAS 
PubMed 

Google Scholar 
33.Hoe, B. C., Gorzelnik, K. V., Yang, J. Y., Hendricks, N. & Dorrestein, P. C. Enzymatic resistance to the lipopeptide surfactin as identi fi ed through imaging mass spectrometry of bacterial competition. https://doi.org/10.1073/pnas.1205586109/-/DCSupplemental.www.pnas.org/cgi/doi/10.1073/pnas.1205586109 (2012).34.Watrous, J. et al. Mass spectral molecular networking of living microbial colonies. Proc. Natl Acad. Sci. USA 109, E1743–E1752 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
35.Hu, F. X., Liu, Y. Y. & Li, S. Rational strain improvement for surfactin production: enhancing the yield and generating novel structures. Microbial Cell Factor. 18, https://doi.org/10.1186/s12934-019-1089-x (2019).36.Grau, A., Go, J. C. & Ortiz, A. A study on the interactions of surfactin with phospholipid vesicles. Biochim. Biophys. Acta. 1418, 307–319 (1999).37.Straight, P. D., Fischbach, M. A., Walsh, C. T., Rudner, D. Z. & Kolter, R. A singular enzymatic megacomplex from Bacillus subtilis. Proc. Natl Acad. Sci. USA 104, 305–310 (2007).ADS 
CAS 
PubMed 

Google Scholar 
38.Vargas-Bautista, C., Rahlwes, K. & Straight, P. Bacterial competition reveals differential regulation of the pks genes by Bacillus subtilis. J. Bacteriol. 196, 717–728 (2014).PubMed 
PubMed Central 

Google Scholar 
39.Rajavel, M., Mitra, A. & Gopal, B. Role of Bacillus subtilis BacB in the synthesis of bacilysin. J. Biol. Chem. 284, 31882–31892 (2009).CAS 
PubMed 
PubMed Central 

Google Scholar 
40.Chen, X. H. et al. Difficidin and bacilysin produced by plant-associated Bacillus amyloliquefaciens are efficient in controlling fire blight disease. J. Biotechnol. 140, 38–44 (2009).CAS 
PubMed 

Google Scholar 
41.Wu, L. M. et al. Difficidin and bacilysin from Bacillus amyloliquefaciens FZB42 have antibacterial activity against Xanthomonas oryzae rice pathogens. Sci. Rep. 5, https://doi.org/10.1038/srep12975 (2015).42.Inaoka, T., Takahashi, K., Ohnishi-Kameyama, M., Yoshida, M. & Ochi, K. Guanine nucleotides guanosine 5’-diphosphate 3’-diphosphate and GTP co-operatively regulate the production of an antibiotic bacilysin in Bacillus subtilis. J. Biol. Chem. 278, 2169–2176 (2003).CAS 
PubMed 

Google Scholar 
43.Rapp, C., Jung, G., Katzer, W. & Loeffler, W. Chlorotetain from Bacillus-Subtilis, an antifungal dipeptide with an unusual chlorine-containing amino-acid. Angew. Chem. Int Ed. 27, 1733–1734 (1988).
Google Scholar 
44.Phister, T. G., O’Sullivan, D. J. & McKay, L. L. Identification of bacilysin, chlorotetaine, and iturin a produced by Bacillus sp. strain CS93 isolated from pozol, a Mexican fermented maize dough. Appl Environ. Microbiol 70, 631–634 (2004).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
45.Tsuge, K., Ano, T., Hirai, M., Nakamura, Y. & Shoda, M. The genes degQ, pps, and lpa-8 (sfp) are responsible for conversion of Bacillus subtilis 168 to plipastatin production. Antimicrob. Agents Chemother. 43, 2183–2192 (1999).CAS 
PubMed 
PubMed Central 

Google Scholar 
46.Msadek, T., Kunst, F., Klier, A. & Rapoport, G. DegS-DegU and ComP-ComA modulator-effector pairs control expression of the Bacillus subtilis pleiotropic regulatory gene degQ. J. Bacteriol. 173, 2366–2377 (1991).CAS 
PubMed 
PubMed Central 

Google Scholar 
47.Verhamme, D. T., Kiley, T. B. & Stanley-Wall, N. R. DegU co-ordinates multicellular behaviour exhibited by Bacillus subtilis. Mol. Microbiol. 65, 554–568 (2007).CAS 
PubMed 

Google Scholar 
48.Comella, N. & Grossman, A. D. Conservation of genes and processes controlled by the quorum response in bacteria: characterization of genes controlled by the quorum-sensing transcription factor ComA in Bacillus subtilis. Mol. Microbiol. 57, 1159–1174 (2005).CAS 
PubMed 

Google Scholar 
49.Wolf, D. et al. The quorum-sensing regulator ComA from Bacillus subtilis activates transcription using topologically distinct DNA motifs. Nucleic Acids Res. 44, 2160–2172 (2016).CAS 
PubMed 

Google Scholar 
50.Koroglu, T. E., Ogulur, I., Mutlu, S., Yazgan-Karatas, A. & Ozcengiz, G. Global regulatory systems operating in Bacilysin biosynthesis in Bacillus subtilis. J. Mol. Microbiol Biotechnol. 20, 144–155 (2011).CAS 
PubMed 

Google Scholar 
51.Ceyhan, D. I., Celekli, A. & Can, C. Relationship between soil composition, diversity and antifungal properties of Bacillus spp. isolated from southeastern Anatolia. Biotechnol. Biotec. Eq. 33, 170–177 (2019).CAS 

Google Scholar 
52.Saxena, A. K., Kumar, M., Chakdar, H., Anuroopa, N. & Bagyaraj, D. J. Bacillus species in soil as a natural resource for plant health and nutrition. J. Appl. Microbiol. 128, 1583–1594 (2020).CAS 
PubMed 

Google Scholar 
53.Arnaouteli, S., Bamford, N. C., Stanley-Wall, N. R. & Kovacs, A. T. Bacillus subtilis biofilm formation and social interactions. Nat. Rev. Microbiol., https://doi.org/10.1038/s41579-021-00540-9 (2021).54.Dergham, Y. et al. Comparison of the genetic features involved in Bacillus subtilis biofilm formation using multi-culturing approaches. Microorganisms 9, https://doi.org/10.3390/microorganisms9030633 (2021).55.Oppenheimer-Shaanan, Y. et al. Spatio-temporal assembly of functional mineral scaffolds within microbial biofilms. NPJ Biofilms Microbiomes 2, 15031 (2016).PubMed 
PubMed Central 

Google Scholar 
56.Barabesi, C. et al. Bacillus subtilis gene cluster involved in calcium carbonate biomineralization. J. Bacteriol. 189, 228–235 (2007).CAS 
PubMed 

Google Scholar 
57.Marvasi, M., Visscher, P. T., Perito, B., Mastromei, G. & Casillas-Martinez, L. Physiological requirements for carbonate precipitation during biofilm development of Bacillus subtilis etfA mutant. FEMS Microbiol Ecol. 71, 341–350 (2010).CAS 
PubMed 

Google Scholar 
58.Richter, M. & Rossello-Mora, R. Shifting the genomic gold standard for the prokaryotic species definition. P Natl Acad. Sci. USA 106, 19126–19131 (2009).ADS 
CAS 

Google Scholar 
59.Goldoni, M. & Johansson, C. A mathematical approach to study combined effects of toxicants in vitro: Evaluation of the Bliss independence criterion and the Loewe additivity model. Toxicol. Vitr. 21, 759–769 (2007).60.Fan, F. & Wood, K. V. Bioluminescent assays for high-throughput screening. Assay. Drug Dev. Technol. 5, 127–136 (2007).CAS 
PubMed 

Google Scholar 
61.McLoon, A. L., Kolodkin-Gal, I., Rubinstein, S. M., Kolter, R. & Losick, R. Spatial regulation of histidine kinases governing biofilm formation in Bacillus subtilis. J. Bacteriol. 193, 679–685 (2011).CAS 
PubMed 

Google Scholar 
62.Irazoki, O., Hernandez, S. B. & Cava, F. Peptidoglycan muropeptides: release, perception, and functions as signaling molecules. Front. Microbiol. 10, https://doi.org/10.3389/fmicb.2019.00500 (2019).63.Virmani, R. et al. The Ser/Thr protein kinase PrkC imprints phenotypic memory in Bacillus anthracis spores by phosphorylating the glycolytic enzyme enolase. J. Biol. Chem. 294, 8930–8941 (2019).PubMed 
PubMed Central 

Google Scholar 
64.Lopez, D., Vlamakis, H. & Kolter, R. Generation of multiple cell types in Bacillus subtilis. FEMS Microbiol Rev. 33, 152–163 (2009).CAS 
PubMed 

Google Scholar 
65.Libby, E. A., Goss, L. A. & Dworkin, J. The eukaryotic-like Ser/Thr kinase PrkC regulates the essential WalRK two-component system in Bacillus subtilis. PLoS Genet. 11, e1005275 (2015).PubMed 
PubMed Central 

Google Scholar 
66.Rismondo, J., Percy, M. G. & Grundling, A. Discovery of genes required for lipoteichoic acid glycosylation predicts two distinct mechanisms for wall teichoic acid glycosylation. J. Biol. Chem. 293, 3293–3306 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
67.Audisio, M. C. Gram-positive bacteria with probiotic potential for the Apis mellifera L. Honey Bee: the experience in the Northwest of Argentina. Probiotics Antimicrob. Proteins 9, 22–31 (2017).CAS 
PubMed 

Google Scholar 
68.Emmert, E. A. & Handelsman, J. Biocontrol of plant disease: a (gram-) positive perspective. FEMS Microbiol. Lett. 171, 1–9 (1999).CAS 
PubMed 

Google Scholar 
69.Bais, H. P., Fall, R. & Vivanco, J. M. Biocontrol of Bacillus subtilis against infection of Arabidopsis roots by Pseudomonas syringae is facilitated by biofilm formation and surfactin production. Plant Physiol. 134, 307–319 (2004).CAS 
PubMed 
PubMed Central 

Google Scholar 
70.Raaijmakers, J. M., De Bruijn, I., Nybroe, O. & Ongena, M. Natural functions of lipopeptides from Bacillus and Pseudomonas: more than surfactants and antibiotics. FEMS Microbiol. Rev. 34, 1037–1062 (2010).CAS 
PubMed 

Google Scholar 
71.Gibbs, K. A., Urbanowski, M. L. & Greenberg, E. P. Genetic determinants of self identity and social recognition in bacteria. Science 321, 256–259 (2008).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
72.Wenren, L. M., Sullivan, N. L., Cardarelli, L., Septer, A. N. & Gibbs, K. A. Two independent pathways for self-recognition in Proteus mirabilis are linked by type VI-dependent export. mBio. 4, https://doi.org/10.1128/mBio.00374-13 (2013).73.Hou, Q. & Kolodkin-Gal, I. Harvesting the complex pathways of antibiotic production and resistance of soil bacilli for optimizing plant microbiome. FEMS Microbiol. Ecol. 96, https://doi.org/10.1093/femsec/fiaa142 (2020).74.Shivers, R. P. & Sonenshein, A. L. Activation of the Bacillus subtilis global regulator CodY by direct interaction with branched-chain amino acids. Mol. Microbiol. 53, 599–611 (2004).CAS 
PubMed 

Google Scholar 
75.Zhang, Z. R. et al. Antibiotic production in Streptomyces is organized by a division of labor through terminal genomic differentiation. Sci. Adv. 6, https://doi.org/10.1126/sciadv.aay5781 (2020).76.Vollmer, W., Blanot, D. & de Pedro, M. A. Peptidoglycan structure and architecture. FEMS Microbiol Rev. 32, 149–167 (2008).CAS 
PubMed 

Google Scholar 
77.Bhavsar, A. P. & Brown, E. D. Cell wall assembly in Bacillus subtilis: how spirals and spaces challenge paradigms. Mol. Microbiol. 60, 1077–1090 (2006).CAS 
PubMed 

Google Scholar 
78.Korgaonkar, A., Trivedi, U., Rumbaugh, K. P. & Whiteley, M. Community surveillance enhances Pseudomonas aeruginosa virulence during polymicrobial infection. Proc. Natl Acad. Sci. USA 110, 1059–1064 (2013).ADS 
CAS 
PubMed 

Google Scholar 
79.Korgaonkar, A. K. & Whiteley, M. Pseudomonas aeruginosa enhances production of an antimicrobial in response to N-acetylglucosamine and peptidoglycan. J. Bacteriol. 193, 909–917 (2011).CAS 
PubMed 

Google Scholar 
80.Sicard, J. F. et al. N-Acetyl-glucosamine influences the biofilm formation of Escherichia coli. Gut Pathog. 10, 26 (2018).PubMed 
PubMed Central 

Google Scholar 
81.Aliashkevich, A., Alvarez, L. & Cava, F. New insights into the mechanisms and biological roles of D-amino acids in complex eco-systems. Front. Microbiol. 9, 683 (2018).PubMed 
PubMed Central 

Google Scholar 
82.Rigali, S. et al. Feast or famine: The global regulator DasR links nutrient stress to antibiotic production by Streptomyces. EMBO Rep. 9, 670–675 (2008).83.Vollmer, W. Structural variation in the glycan strands of bacterial peptidoglycan. FEMS Microbiol Rev. 32, 287–306 (2008).CAS 
PubMed 

Google Scholar 
84.Kim, S. J., Chang, J. & Singh, M. Peptidoglycan architecture of Gram-positive bacteria by solid-state NMR. Biochim Biophys. Acta 1848, 350–362 (2015).CAS 
PubMed 

Google Scholar 
85.Vetsigian, K., Jajoo, R. & Kishony, R. Structure and evolution of Streptomyces interaction networks in soil and in silico. PLoS Biol. 9, e1001184 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
86.Cuthbertson, L. & Nodwell, J. R. The TetR family of regulators. Microbiol Mol. Biol. Rev. 77, 440–475 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
87.Cava, F., de Pedro, M. A., Lam, H., Davis, B. M. & Waldor, M. K. Distinct pathways for modification of the bacterial cell wall by non-canonical D-amino acids. EMBO J. 30, 3442–3453 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
88.van der Es, D., Hogendorf, W. F., Overkleeft, H. S., van der Marel, G. A. & Codee, J. D. Teichoic acids: synthesis and applications. Chem. Soc. Rev. 46, 1464–1482 (2017).PubMed 

Google Scholar 
89.Egan, A. J. F., Errington, J. & Vollmer, W. Regulation of peptidoglycan synthesis and remodelling. Nat. Rev. Microbiol. 18, 446–460 (2020).CAS 
PubMed 

Google Scholar 
90.Dubnau, D. Genetic competence in Bacillus subtilis. Microbiol Rev. 55, 395–424 (1991).CAS 
PubMed 
PubMed Central 

Google Scholar 
91.Stefanic, P. et al. Kin discrimination promotes horizontal gene transfer between unrelated strains in Bacillus subtilis. Nat. Commun. 12, 3457 (2021).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
92.Salvadori, G., Junges, R., Morrison, D. A. & Petersen, F. C. Competence in Streptococcus pneumoniae and close commensal relatives: mechanisms and implications. Front. Cell Infect. Microbiol. 9, 94 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
93.Jowett, G. H. Statistical-methods for research workers – Fisher, Ra. R. Stat. Soc. C.-Appl. 5, 68–70 (1956).
Google Scholar 
94.Farzand, A. et al. Marker assisted detection and LC-MS analysis of antimicrobial compounds in different Bacillus strains and their antifungal effect on Sclerotinia sclerotiorum. Biol. Control 133, 91–102 (2019).CAS 

Google Scholar 
95.Paksanont, S. et al. Effect of temperature on Burkholderia pseudomallei growth, proteomic changes, motility and resistance to stress environments. Sci. Rep. 8, 9167 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
96.Andreevskaya, M. et al. Food spoilage-associated Leuconostoc, Lactococcus, and Lactobacillus species display different survival strategies in response to competition. Appl. Environ. Microbiol. 84, https://doi.org/10.1128/AEM.00554-18 (2018).97.Ju, S. Y. et al. Isolation and optimal fermentation condition of the Bacillus subtilis Subsp. natto strain WTC016 for nattokinase production. Fermentation-Basel 5, https://doi.org/10.3390/fermentation5040092 (2019).98.Mouloud, G., Daoud, H., Bassem, J., Atef, I. & Hani, B. New bacteriocin from Bacillus clausii strainGM17: purification, characterization, and biological activity. Appl. Biochem. Biotech. 171, 2186–2200 (2013).CAS 

Google Scholar  More

1.Cherlet, M., et al (Eds.). World Atlas of Desertification. Luxembourg: Publication Office of the European Union (2018).2.Belnap, J. The potential roles of biological soil crusts in dryland hydrologic cycles. Hydrol. Process. 20, 3159–78 (2006).ADS 
CAS 

Google Scholar 
3.Maestre, F. T. et al. Ecology and functional roles of biological soil crusts in semi-arid ecosystems of Spain. J. Arid Environ. 75, 1282–91 (2011).ADS 
PubMed 
PubMed Central 

Google Scholar 
4.Noy-Meir, I. Desert ecosystems: environment and producers. Annu. Rev. Ecol. Evol. Syst. 4(1), 25–51 (1973).
Google Scholar 
5.Puigdefábregas, J., Sole, A., Gutierrez, L., Del Barrio, G. & Boer, M. Scales and processes of water and sediment redistribution in drylands: results from the Rambla Honda field site in Southeast Spain. Earth-Sci. Rev. 48(1–2), 39–70 (1999).ADS 

Google Scholar 
6.Puigdefábregas, J. The role of vegetation patterns in structuring runoff and sediment fluxes in drylands. Earth Surf. Process. Landf. 30(2), 133–147 (2005).ADS 

Google Scholar 
7.Berdugo, M., Soliveres, S. & Maestre, F. T. Vascular plants and biocrusts modulate how abiotic factors affect wetting and drying events in drylands. Ecosystems 17(7), 1242–1256 (2014).CAS 

Google Scholar 
8.Meza, F. J., Montes, C., Bravo-Martínez, F., Serrano-Ortiz, P. & Kowalski, A. S. Soil water content effects on net ecosystem CO2 exchange and actual evapotranspiration in a Mediterranean semiarid savanna of Central Chile. Sci. Rep. 8, 8570 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
9.Austin, A. T. et al. Water pulses and biogeochemical cycles in arid and semiarid ecosystems. Oecologia 141, 221–35 (2004).ADS 
PubMed 

Google Scholar 
10.Safirel, U & Adeel, Z. Ecosystems and human well-being: current state and trends, vol. 1. Washington, DC: Island Press (2005).11.Brocca, L., Melone, F., Moramarco, T. & Morbidelli, R. Spatial-temporal variability of soil moisture and its estimation across scales: Soil Moisture Spatiotemporal Variability. Water Resour. Res. 46, W02516 (2010).ADS 

Google Scholar 
12.Brocca, L. et al. Assimilation of surface-and root-zone ASCAT soil moisture products into rainfall–runoff modeling. IEEE Trans. Geosci. Remote Sens. 50, 2542–2555 (2012).ADS 

Google Scholar 
13.Parinussa, R. et al. Global surface soil moisture from the Microwave Radiation Imager onboard the Fengyun-3B satellite. Int. J. Remote Sens. 35, 7007–7029 (2014).
Google Scholar 
14.Cui, Y. et al. A spatio-temporal continuous soil moisture dataset over the Tibet Plateau from 2002 to 2015. Sci. Data 6, 247 (2019).PubMed 
PubMed Central 

Google Scholar 
15.Solomon, S. et al. (Eds.). Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel in Climate Change. Cambridge and New York: Cambridge University Press (2007).16.Soong, J. L., Phillips, C. L., Ledna, C., Koven, C. D. & Torn, M. S. CMIP5 models predict rapid and deep soil warming over the 21st century. J. Geophys. Res. Biogeosci. 125(2), e2019JG005266 (2020).ADS 

Google Scholar 
17.Zhou, S. et al. Soil moisture–atmosphere feedbacks mitigate declining water availability in drylands. Nat. Clim. Change 11(1), 38–44 (2021).ADS 

Google Scholar 
18.Lian, X. et al. Multifaceted characteristics of dryland aridity changes in a warming world. Nat. Rev. Earth Environ. 1–19 (2021).19.Naz, B. S., Kollet, S., Franssen, H. J. H., Montzka, C. & Kurtz, W. A 3 km spatially and temporally consistent European daily soil moisture reanalysis from 2000 to 2015. Sci. Data 7(1), 1–14 (2020).
Google Scholar 
20.Tietjen, B. et al. Effects of climate change on the coupled dynamics of water and vegetation in drylands. Ecohydrology 3, 226–237 (2010).
Google Scholar 
21.Cui, Y. et al. A spatio-temporal continuous soil moisture dataset over the Tibet Plateau from 2002 to 2015. Sci. Data 6(1), 1–7 (2019).
Google Scholar 
22.Tongway, D.J., Valentin, C., Seghieri, J. (Eds.). Banded vegetation patterning in arid and semiarid environments: ecological processes and consequences for management. Berlin: Springer (2001).23.Maestre, F. T. & Cortina, J. Spatial patterns of surface soil properties and vegetation in a Mediterranean semi-arid steppe. Plant Soil 241(2), 279–291 (2002).CAS 

Google Scholar 
24.Maestre, F.T. et al. Biogeography of global drylands. New Phytol. (2021).25.Bhark, E. W. & Small, E. E. Association between plant canopies and the spatial patterns of infiltration in shrubland and grassland of the Chihuahuan Desert, New Mexico. Ecosystems 6, 0185–96 (2003).
Google Scholar 
26.Yepez, E. A. et al. Dynamics of transpiration and evaporation following a moisture pulse in semiarid grassland: a chamber-based isotope method for partitioning flux components. Agric. For. Meteorol. 132, 359–76 (2005).ADS 

Google Scholar 
27.Eldridge, D. J. et al. Interactive effects of three ecosystem engineers on infiltration in a semi-arid Mediterranean grassland. Ecosystems 13(4), 499–510 (2010).MathSciNet 

Google Scholar 
28.Cerdà, A. The effect of patchy distribution of Stipa tenacissima L. on runoff and erosion. J. Arid Environ. 36(1), 37–51 (1997).ADS 
MathSciNet 

Google Scholar 
29.Weber, B., Büdel, B. & Belnap, J. (Eds.). Biological soil crusts: an organizing principle in drylands. Cham: Springer (2016).30.Eldridge, D. J. et al. The pervasive and multifaceted influence of biocrusts on water in the world’s drylands. Glob. Change Biol. 26(10), 6003–6014 (2020).ADS 

Google Scholar 
31.Castillo-Monroy, A. P., Delgado-Baquerizo, M., Maestre, F. T. & Gallardo, A. Biological soil crusts modulate nitrogen availability in semi-arid ecosystems: Insights from a Mediterranean grassland. Plant Soil 333, 21–34 (2010).CAS 

Google Scholar 
32.Escolar, C., Martínez, I., Bowker, M. A. & Maestre, F. T. Warming reduces the growth and diversity of biological soil crusts in a semi-arid environment: implications for ecosystem structure and functioning. Philos. T. R. Soc. B. 367(1606), 3087–3099 (2012).
Google Scholar 
33.Maestre, F. T. et al. Changes in biocrust cover drive carbon cycle responses to climate change in drylands. Glob. Change Biol. 19, 3835–3847 (2013).ADS 

Google Scholar 
34.Delgado‐Baquerizo, M. et al. Direct and indirect impacts of climate change on microbial and biocrust communities alter the resistance of the N cycle in a semiarid grassland. J. Ecol. 102(6), 1592–1605 (2014).
Google Scholar 
35.Delgado‐Baquerizo, M. et al. Differences in thallus chemistry are related to species‐specific effects of biocrust‐forming lichens on soil nutrients and microbial communities. Funct. Ecol. 29(8), 1087–1098 (2015).
Google Scholar 
36.Lafuente, A., Berdugo, M., Ladron de Guevara, M., Gozalo, B. & Maestre, F. T. Simulated climate change affects how biocrusts modulate water gains and desiccation dynamics after rainfall events. Ecohydrology 11(6), e1935 (2018).PubMed 

Google Scholar 
37.IUSS Working Group WRB. World Reference Base for Soil Resources 2006. World Soil Resources Reports No. 103. Rome, Italy: FAO (2006).38.Chamizo, S., Cantón, Y., Lázaro, R. & Domingo, F. The role of biological soil crusts in soil moisture dynamics in two semiarid ecosystems with contrasting soil textures. J. Hydrol. 489, 74–84 (2013).ADS 

Google Scholar 
39.Chamizo, S., Cantón, Y., Rodríguez‐Caballero, E. & Domingo, F. Biocrusts positively affect the soil water balance in semiarid ecosystems. Ecohydrol. 9(7), 1208–1221 (2016).
Google Scholar 
40.Dalton, M., Buss, P., Treijs, A. & Portmann, M. in Irrigation Australia Limited Regional Conference (Penrith Panthers, 2015).41.Francesca, V., Osvaldo, F., Stefano, P. & Paola, R. P. Soil moisture measurements: Comparison of instrumentation performances. J. Irrig. Drain. Eng. 136(2), 81–89 (2010).
Google Scholar 
42.Payero, J. O., Nafchi, A. M., Davis, R. & Khalilian, A. An Arduino-based wireless sensor network for soil moisture monitoring using Decagon EC-5 sensors. Open J. soil Sci. 7(10), 288 (2017).
Google Scholar 
43.Payero, J. O., Qiao, X., Khalilian, A., Mirzakhani-Nafchi, A. & Davis, R. Evaluating the effect of soil texture on the response of three types of sensors used to monitor soil water status. JWARP 9(06), 566 (2017).
Google Scholar 
44.Sakaki, T., Limsuwat, A., Smits, K.M. & Illangasekare, T.H. Empirical two‐point α‐mixing model for calibrating the ECH2O EC‐5 soil moisture sensor in sands. Water Resour. Res. 44(4) (2008).45.Sharma, H., Shukla, M. K., Bosland, P. W. & Steiner, R. Soil moisture sensor calibration, actual evapotranspiration, and crop coefficients for drip irrigated greenhouse chile peppers. Agric. Water Manag. 179, 81–91 (2017).
Google Scholar 
46.Castillo-Monroy, A. P., Maestre, F. T., Rey, A., Soliveres, S. & García-Palacios, P. Biological soil crust microsites are the main contributor to soil respiration in a semiarid ecosystem. Ecosyst. 14(5), 835–847 (2011).CAS 

Google Scholar 
47.Steven, B., Gallegos-Graves, L. V., Belnap, J. & Kuske, C. R. Dryland soil microbial communities display spatial biogeographic patterns associated with soil depth and soil parent material. FEMS Microbiol. Ecol. 86(1), 101–113 (2013).CAS 
PubMed 

Google Scholar 
48.Ding, J. & Eldridge, D. J. Biotic and abiotic effects on biocrust cover vary with microsite along an extensive aridity gradient. Plant Soil 450(1), 429–441 (2020).CAS 

Google Scholar 
49.Rodríguez-Caballero, E. et al. Ecosystem services provided by biocrusts: from ecosystem functions to social values. J. Arid Envion. 159, 45–53 (2018).ADS 

Google Scholar 
50.Zaady, E., Eldridge, D.J. & Bowker, M.A. in Biological soil crusts: An organizing principle in drylands (Springer, 2016).51.R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ (2019).52.Ushey, K. renv: Project Environments. R package version 0.13.2. https://CRAN.R-project.org/package=renv (2021).53.Dowle, M. & Srinivasan, A. data.table: Extension of ‘data.frame’. R package version 1.14.0. https://CRAN.R-project.org/package=data.table (2021).54.Firke, S. janitor: Simple Tools for Examining and Cleaning Dirty Data. R package version 2.1.0. https://CRAN.R-project.org/package = janitor (2021).55.Wickham, H. et al. Welcome to the tidyverse. J. Open Source Softw. 4(43), 1686 (2019).ADS 

Google Scholar 
56.Zhu, H. kableExtra: Construct Complex Table with ‘kable’ and Pipe Syntax. R package version 1.3.4. https://CRAN.R-project.org/package=kableExtra (2021).57.Microsoft & Weston, S. foreach: Provides Foreach Looping Construct. R package version 1.5.1. https://CRAN.R-project.org/package=foreach (2020).58.Microsoft Corporation & Weston, S. doParallel: Foreach Parallel Adaptor for the ‘parallel’ Package. R package version 1.0.16. https://CRAN.R-project.org/package=doParallel (2020).59.Wickham, H. & Hester, J. readr: Read Rectangular Text Data. R package version 1.4.0. https://CRAN.R-project.org/package=readr (2020).60.Ooms, J. writexl: Export Data Frames to Excel ‘xlsx’ Format. R package version 1.4.0. https://CRAN.R-project.org/package=writexl (2021).61.Müller, K., Wickham, H., James, D.A. & Falcon, S. RSQLite: ‘SQLite’ Interface for R. R package version 2.2.7. https://CRAN.R-project.org/package=RSQLite (2021).62.Csárdi, G., Podgórski, K. & Geldreich, R. zip: Cross-Platform ‘zip’ Compression. R package version 2.1.1. https://CRAN.R-project.org/package=zip (2020).63.Xie, Y. knitr: A General-Purpose Package for Dynamic Report Generation in R. R package version 1.31. https://CRAN.R-project.org/package=knitr (2021).64.R Special Interest Group on Databases (R-SIG-DB), Wickham, H. & Müller, K. DBI: R Database Interface. R package version 1.1.1. https://CRAN.R-project.org/package=DBI (2021).65.Moreno, J. et al. The MOISCRUST dataset. figshare https://doi.org/10.6084/m9.figshare.14748384 (2021).66.Topp, G. C. & Davis, J. L. Measurement of soil water content using time-domain reflectometry (TDR): a field evaluation. Soil Sci. Soc. Am. J. 49, 19–24 (1985).ADS 

Google Scholar 
67.Cantón, Y., Solé-Benet, A. & Domingo, F. Temporal and spatial patterns of soil moisture in semiarid badlands of SE Spain. J. Hydrol. 285, 199–214 (2004).ADS 

Google Scholar 
68.Breshears, D. D. & Barnes, F. J. Interrelationships between plant functional types and soil moisture heterogeneity for semiarid landscapes within the grassland/forest continuum: a unified conceptual model. Landsc. Ecol. 14, 465–78 (1999).
Google Scholar 
69.D’Odorico, P., Caylor, K., Okin, G. S. & Scanlon, T. M. On soil moisture–vegetation feedbacks and their possible effects on the dynamics of dryland ecosystems. J. Geophys. Res. 112, G04010 (2007).ADS 

Google Scholar  More

Génomique Métabolique, Genoscope, Institut François Jacob, CEA, CNRS, Univ Evry, Université Paris-Saclay, 91057, Evry, FranceTom O. Delmont, Patrick Wincker & Eric PelletierResearch Federation for the study of Global Ocean systems ecology and evolution, FR2022/Tara GOsee, Paris, FranceTom O. Delmont, Juan José Pierella Karlusich, Chris Bowler, Patrick Wincker & Eric PelletierInstitut de Biologie de l’ENS (IBENS), Département de biologie, École normale supérieure, CNRS, INSERM, Université PSL, 75005, Paris, FranceJuan José Pierella Karlusich & Chris BowlerGraduate Program in Biophysical Sciences, University of Chicago, Chicago, IL, 60637, USAIva VeseliDepartment of Medicine, University of Chicago, Chicago, IL, 60637, USAJessika Fuessel & A. Murat ErenBay Paul Center, Marine Biological Laboratory, Woods Hole, MA, 02543, USAA. Murat ErenDepartment of Ecology, Environment and Plant Sciences, Stockholm University Stockholm, Stockholm, 10691, SwedenRachel A. Foster More

1.Eckehard, G. et al. Forest biodiversity, ecosystem functioning and the provision of ecosystem services. Biodivers. Conserv. 26, 3005–3035. https://doi.org/10.1007/s10531-017-1453-2 (2017).Article 

Google Scholar 
2.Bastrup-Birk, A., Reker, J., Zal, N., Romao, C. & Cugny-Seguin, M. (2016) European Forest Ecosystems: State and Trends Technical Report No 5 (Publications Office of the European Union, European Environment Agency, 2016).
Google Scholar 
3.Aznar-Sánchez, J. A., Belmonte-Ureña, L. J., López-Serrano, M. J. & Velasco-Muñoz, J. F. Forest ecosystem services: An analysis of worldwide research. Forests 9, 453. https://doi.org/10.3390/f9080453 (2018).Article 

Google Scholar 
4.Masiero, M. et al. Valuing Forest Ecosystem Services: A Training Manual for Planners and Project Developers. Forestry Working Paper No. 11 216 (FAO, 2019).
Google Scholar 
5.Maes, J. et al. Mapping and Assessment of Ecosystems and their Services: An Analytical Framework for Ecosystem Condition (Publications Office of the European Union, 2018).
Google Scholar 
6.Pastur, G. M., Perera, A. H., Peterson, U. & Iverson, L. R. Ecosystem services from forested landscapes: An overview. In Ecosystem Services from Forest Landscapes: Broadscale Considerations (eds Perera, A. H. et al.) 1–10 (Springer International, 2018).
Google Scholar 
7.Jenkins, M. & Schaap, B. Background Analytical Study Forest Ecosystem Services, by, Background study prepared for the thirteenth session of the United Nations Forum on Forests (2018).8.Lellia, C. et al. Biodiversity response to forest structure and management: Comparing species richness, conservation relevant species and functional diversity as metrics in forest conservation. For. Ecol. Manage. 432, 707–717. https://doi.org/10.1016/j.foreco.2018.09.057 (2019).Article 

Google Scholar 
9.van der Plas, F. et al. Jack-of-all-trades effects drive biodiversityecosystem multifunctionality relationships in European forests. Nat. Commun. 7, 11109. https://doi.org/10.1038/ncomms11109 (2016).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
10.van der Plas, F. et al. Continental mapping of forest ecosystem functions reveals a high but unrealized potential for forest multifunctionality. Ecol. Lett. 21, 32–42. https://doi.org/10.1111/ele.12868 (2017).Article 

Google Scholar 
11.Onyekwelu, J. C. & Olabiwonnu, A. A. Can forest plantations harbour biodiversity similar to natural forest ecosystems over time?. Int. J. Biodivers. Sci. Ecosyst. Serv. Manage. 12, 108–115. https://doi.org/10.1080/21513732.2016.1162199 (2016).Article 

Google Scholar 
12.Saikia, P. et al. Plant diversity patterns and conservation status of eastern Himalayan forests in Arunachal Pradesh, Northeast India. For. Ecosyst. 4, 28. https://doi.org/10.1186/s40663-017-0117-8 (2017).Article 

Google Scholar 
13.Mishra, B. P., Tripathi, O. & Laloo, R. C. Community characteristics of a climax subtropical humid forest of Meghalaya and population structure of ten important tree species. Trop. Ecol. 46, 241–251 (2005).
Google Scholar 
14.de Gouvenain, R. C. & Silander, J. Temperate Forests. Reference Module in Life Sciences (Elsevier, 2017).
Google Scholar 
15.FAO. 2016. Global Forest Resources Assessment 2015: How Are the World’s Forests Changing? Second Edition. Rome, Italy: FAO [www document]. http://www.fao.org/3/a-i4793e.pdf (2015).16.Durigan, M. R. et al. Soil organic matter responses to anthropogenic forest disturbance and land use change in the Eastern Brazilian Amazon. Sustainability 9, 379. https://doi.org/10.3390/su9030379 (2017).CAS 
Article 

Google Scholar 
17.Mukhortova, L., Schepaschenko, D., Shvidenko, A., McCallum, I. & Kraxner, F. Soil contribution to carbon budget of Russian forests. Agric. For. Meteorol. 200, 97–108. https://doi.org/10.1016/j.agrformet.2014.09.017 (2015).ADS 
Article 

Google Scholar 
18.Justine, M. F. Y. et al. Biomass stock and carbon sequestration in a chronosequence of Pinus massoniana plantations in the upper reaches of the Yangtze River. Forests 6, 3665–3682. https://doi.org/10.3390/f6103665 (2015).Article 

Google Scholar 
19.Hansson, K. Impact of tree species on carbon in forest soils. Doctoral Thesis, Swedish University of Agricultural Sciences. Faculty of Natural Resources and Agricultural Sciences (2011).20.Zhang, Y., Duan, B., Xian, J., Korpelainen, H. & Li, C. Links between plant diversity, carbon stocks and environmental factors along a successional gradient in a subalpine coniferous forest in Southwest China. For. Ecol. Manage. 262, 361–369. https://doi.org/10.1016/j.foreco.2011.03.042 (2011).Article 

Google Scholar 
21.Sing, L., Metzger, M. J., Paterson, J. S. & Ray, D. A review of the effects of forest management intensity on ecosystem services for northern European temperate forests with a focus on the UK. Forestry 91, 151–164. https://doi.org/10.1093/forestry/cpx042 (2018).Article 

Google Scholar 
22.Ruiz-Benito, P. et al. Diversity increases carbon storage and tree productivity in Spanish forests. Glob. Ecol. Biogeogr. 23, 311–322. https://doi.org/10.1111/geb.12126 (2014).Article 

Google Scholar 
23.Ricketts, T. H. et al. Disaggregating the evidence linking biodiversity and ecosystem services. Nat. Commun. 7, 13106. https://doi.org/10.1038/ncomms13106 (2016).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
24.Jarzyna, M. A. & Jetz, W. Taxonomic and functional diversity change is scale dependent. Nat. Commun. 9, 2565. https://doi.org/10.1038/s41467-018-04889-z (2018).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
25.Madrigal-González, J. et al. Climate reverses directionality in the richness–abundance relationship across the World’s main forest biomes. Nat. Commun. 11, 5635. https://doi.org/10.1038/s41467-020-19460-y (2020).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
26.Kendie, G., Addisu, S. & Abiyu, A. Biomass and soil carbon stocks in different forest types, Northwestern Ethiopia. Int. J. River Basin Manag. 19(1), 123–129. https://doi.org/10.1080/15715124.2019.159318 (2021).Article 

Google Scholar 
27.Omoro, L. M. A., Starr, M. & Pellikka, P. K. E. Tree biomass and soil carbon stocks in indigenous forests in comparison to plantations of exotic species in the Taita Hills of Kenya. Silva Fenn. 47, 935. https://doi.org/10.14214/sf.935 (2013).Article 

Google Scholar 
28.Zhang, G., Zhang, P., Peng, S., Chen, Y. & Cao, Y. The coupling of leaf, litter, and soil nutrients in warm temperate forests in northwestern China. Sci. Rep. 7, 11754. https://doi.org/10.1038/s41598-017-12199-5 (2017).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
29.Kerdraon, D. et al. Litter traits of native and non-native tropical trees influence soil carbon dynamics in timber plantations in panama. Forests 10, 209. https://doi.org/10.3390/f10030209 (2019).Article 

Google Scholar 
30.Novara, A. et al. Litter contribution to soil organic carbon in the processes of agriculture abandon. Solid Earth 6, 425–432. https://doi.org/10.5194/se-6-425-2015 (2015).ADS 
Article 

Google Scholar 
31.Capellesso, E. S. et al. Effects of forest structure on litter production, soil chemical composition and litter–soil interactions. Acta Bot. Bras. 30(3), 329–335. https://doi.org/10.1590/0102-33062016abb0048 (2016).Article 

Google Scholar 
32.Castle, S. C. & Neff, J. C. Plant response to nutrient availability across variable bedrock geologies. Ecosystems 12, 101–113. https://doi.org/10.1007/s10021-008-9210-8 (2009).CAS 
Article 

Google Scholar 
33.Gerdol, R., Marchesini, R. & Iacumin, P. Bedrock geology interacts with altitude in affecting leaf growth and foliar nutrient status of mountain vascular plants. Plant Ecol. 10, 839–850. https://doi.org/10.1093/jpe/rtw092 (2017).Article 

Google Scholar 
34.Sieber, I., Borges, P. & Burkhard, B. Hotspots of biodiversity and ecosystem services: The Outermost Regions and Overseas Countries and Territories of the European Union. One Ecosyst. 3, e24719. https://doi.org/10.3897/oneeco.3.e24719 (2018).Article 

Google Scholar 
35.Iranah, P., Lal, P., Wolde, B. T. & Burli, P. Valuing visitor access to forested areas and exploring willingness to pay for forest conservation and restoration finance: The case of small island developing state of Mauritius. J. Environ. Manage. 223, 868–877. https://doi.org/10.1016/j.jenvman.2018.07.008 (2018).Article 
PubMed 

Google Scholar 
36.Balzan, M. V., Potschin-Young, M. & Haines-Young, R. Island ecosystem services: insights from a literature review on case-study island ecosystem services and future prospects. Int. J. Biodivers. Sci. Ecosyst. Serv. Manage. 14, 71–90. https://doi.org/10.1080/21513732.2018.1439103 (2018).Article 

Google Scholar 
37.Wardle, D. A. Islands as model systems for understanding how species affect ecosystem properties. J. Biogeogr. 29, 583–591. https://doi.org/10.1046/j.1365-2699.2002.00708.x (2002).Article 

Google Scholar 
38.Wardle, D. A., Zackrisson, O., Hornberg, G. & Gallet, C. The influence of island area on ecosystem properties. Science 277, 1296–1299. https://doi.org/10.1126/science.277.5330.1296 (1997).CAS 
Article 

Google Scholar 
39.Santamarta, J. C., Rodríguez-Martín, J. & Neris, J. Water resources management and forest engineering in volcanic islands. IERI Procedia 9, 129–134. https://doi.org/10.1016/j.ieri.2014.09.052 (2014).Article 

Google Scholar 
40.Fontes, J. C., Pereira, L. S. & Smith, R. E. Runoff and erosion in volcanic soils of Azores: Simulation with OPUS. CATENA 56, 199–212. https://doi.org/10.1016/j.catena.2003.10.011 (2004).Article 

Google Scholar 
41.Rodrigues, F. & Rodrigues, A. F. Distribution of environmental isotopes in precipitation on a small oceanic island (Terceira-Azores): Some particularities based on preliminary results. Arquipélago. Agrarian Sci. Environ. 1, 1–6 (2002).
Google Scholar 
42.Dias, E. & Melo, C. Factors influencing the distribution of Azorean mountain vegetation: Implications for nature conservation. Biodivers. Conserv. 19, 3311–3326. https://doi.org/10.1007/s10531-010-9894-x (2010).Article 

Google Scholar 
43.Louvat, P. & Allègre, C. J. Riverine erosion rates on Sao Miguel volcanic island, Azores archipelago. Chem. Geol. 148, 177–200. https://doi.org/10.1016/S0009-2541(98)00028-X (1998).ADS 
CAS 
Article 

Google Scholar 
44.Malheiro, A. Geological hazards in the Azores archipelago: Volcanic terrain instability and human vulnerability. J. Volcanol. Geotherm. Res. 156, 158–171. https://doi.org/10.1016/j.jvolgeores.2006.03.012 (2006).ADS 
CAS 
Article 

Google Scholar 
45.Marques, R., Zêzere, J., Trigo, R., Gaspar, J. & Trigo, I. Rainfall patterns and critical values associated with landslides in Povoação County (São Miguel Island, Azores): Relationships with the North Atlantic Oscillation. Hydrol. Process. https://doi.org/10.1002/hyp.6879 (2008).Article 

Google Scholar 
46.Lopes, F. & Amaral, B. The value of forest recreation in Azorean public parks. Rev. Econ. Sociol. Rural https://doi.org/10.1590/1806-9479.2021.238884 (2021).Article 

Google Scholar 
47.Pavão, D. C. et al. Land cover along hiking trails in a nature tourismdestination: the Azores as a case study. Environ. Dev. Sustain. https://doi.org/10.1007/s10668-021-01356-6 (2021).Article 

Google Scholar 
48.Florestas.pt The Navigator Company Madeira de criptoméria: inovar para reforçar valor (https://florestas.pt/valorizar/madeira-de-criptomeria-inovar-para-reforcar-valor/) 07 de abril 202149.Marcelino, J. A. P., Silva, L., Garcia, P. V., Weber, E. & Soares, A. O. Using species spectra to evaluate plant community conservation value along a gradient of anthropogenic disturbance. Environ. Monit. Assess. 185, 6221–6233. https://doi.org/10.1007/s10661-012-3019-9 (2013).Article 
PubMed 

Google Scholar 
50.Marcelino, J. A. P., Weber, E., Silva, L., Garcia, P. V. & Soares, A. O. Expedient metrics to describe plant community change across gradients of anthropogenic influence. Environ. Manage. 54, 1121–1130. https://doi.org/10.1007/s00267-014-0321-z (2014).ADS 
Article 
PubMed 

Google Scholar 
51.Abreu, P. M. R. Contributo da Criptoméria Para o Sequestro de carbono nos Açores 128 (Tese de Mestrado, Universidade de Aveiro, 2011).
Google Scholar 
52.Vergílio, M., Fjøsneb, K., Nistorab, A. & Calado, H. Carbon stocks and biodiversity conservation on a small island: Pico (the Azores, Portugal). Land Use Policy 58, 196–207. https://doi.org/10.1016/j.landusepol.2016.07.020 (2016).Article 

Google Scholar 
53.Borges Silva, L. et al. Development allometric equations for estimating above-ground biomass of woody plants invaders: The Pittosporum undulatum the Azores archipelago. In Modeling, Dynamics, Optimization and Bioeconomics II. DGS 2014. Springer Proceedings in Mathematics & Statistics Vol. 195 (eds Pinto, A. & Ziberman, D.) 463–484 (Springer, 2017).
Google Scholar 
54.Borges Silva, L., Teixeira, A., Alves, M., Elias, R. B. & Silva, L. Tree age determination in the widespread woody plant invader Pittosporum undulatum. For. Ecol. Manage. 400, 457–467. https://doi.org/10.1016/j.foreco.2017.06.027 (2017).Article 

Google Scholar 
55.Borges Silva, L. et al. Biomass valorization in the management of woody plant invaders: The case of Pittosporum undulatum in the Azores. Biomass Bioenergy 109, 155–165. https://doi.org/10.1016/j.biombioe.2017.12.025 (2018).Article 

Google Scholar 
56.Mendonça, E. F. E. P. Serviços dos Ecossistemas na Ilha Terceira: estudo preliminar com ênfase no sequestro de carbono e na biodiversidade 147 (Tese de Mestrado, Universidade dos Açores, 2012).
Google Scholar 
57.Cruz, A. & Benedicto, J. Assessing socio-economic benefits of Natura 2000: A case study on the ecosystem service provided by SPA Pico da Vara/Ribeira do Guilherme. Output of the project Financing Natura 2000: Cost estimate and benefits of Natura 2000, 43 (2009).58.Cruz, A., Benedicto, J. & Gil, A. Socio-economic benefits of Natura 2000 in Azores Islands – a Case Study approach on ecosystem services provided by a Special Protected Area. J. Coast Res. 64, 1955–1959 (2011).
Google Scholar 
59.Borges, P. A. V. et al. (eds) A List of the Terrestrial and Marine Biota from the Azores 432 (Princípia, 2010).
Google Scholar 
60.Silva, L., Moura, M., Schaefer, H., Rumsey, F. & Dias, E. F. Vascular Plants (Tracheobionta). In A List of the Terrestrial and Marine Biota from the Azores (eds Borges, P. A. V. et al.) 117–146 (Princípia, 2010).
Google Scholar 
61.Elias, R. B. et al. Natural zonal vegetation of the Azores Islands: characterization and potential distribution. Phytocoenologia 46, 107–123. https://doi.org/10.1127/phyto/2016/0132 (2016).Article 

Google Scholar 
62.Borges, P. A. V. et al. Community structure of woody plants on islands along a bioclimatic gradient. Front. Biogeogr. 10, 1–31. https://doi.org/10.21425/F5FBG40295 (2018).Article 

Google Scholar 
63.Fimbel, R. A. & Fimbel, C. A. The role of exotic conifer plantations in rehabilitating degraded tropical forest lands: A case study from the Kibale forest in Uganda. For. Ecol. Manage. 81, 215–226. https://doi.org/10.1016/0378-1127(95)03637-7 (1996).Article 

Google Scholar 
64.Omoro, L. M. A., Pellikka, P. K. E. & Rogers, P. C. Tree species diversity, richness, and similarity between exotic and indigenous forests in the cloud forests of Eastern Arc Mountains, Taita Hills, Kenya. J. For. Res. 21, 255–264. https://doi.org/10.1007/s11676-010-0069-0 (2010).Article 

Google Scholar 
65.Tenzin, J. & Hasenauer, H. Tree species composition and diversity in relation to anthropogenic disturbances in broad-leaved forests of Bhutan. Int. J. Biodivers. Sci. Ecosyst. Serv. Manage. 12, 274–290. https://doi.org/10.1080/21513732.2016.1206038 (2016).Article 

Google Scholar 
66.Braun, A. C. Taxonomic diversity and taxonomic dominance: The example of forest plantations in south-central Chile. Open J. Ecol. 5, 199–212. https://doi.org/10.4236/oje.2015.55017 (2015).Article 

Google Scholar 
67.Cordeiro, N. & Silva, L. Seed production and vegetative growth of Hedychium gardnerianum Ker-Gawler (Zingiberaceae) in São Miguel Island (Azores). Arquipélago. Life Mar. Sci. 20A, 31–36 (2003).
Google Scholar 
68.Ricketts, T. H. Tropical forest fragments enhance pollinator activity in nearby coffee crops. Conserv. Biol. 18, 1262–1271. https://doi.org/10.1111/j.1523-1739.2004.00227.x (2004).Article 

Google Scholar 
69.Bunker, D. E. et al. Species loss and above-ground carbon storage in a tropical forest. Science 310, 1029–1031. https://doi.org/10.1126/science.11176821029-1031 (2005).ADS 
CAS 
Article 
PubMed 

Google Scholar 
70.Phillpott, S. M. et al. Functional richness and ecosystem services: bird predation on arthropods in tropical agroecosystems. Ecol. Appl. 19, 1858–1867. https://doi.org/10.1890/08-1928.1 (2009).Article 

Google Scholar 
71.Ifo, S. A. et al. Tree species diversity, richness, and similarity in intact and degraded forest in the tropical rainforest of the Congo Basin: Case of the Forest of Likouala in the Republic of Congo. Int. J. For. Res. 2016, 1–12. https://doi.org/10.1155/2016/7593681 (2016).Article 

Google Scholar 
72.Borges, P. A. V., Santos, A. M. C., Elias, R. B. & Gabriel, R. The Azores Archipelago: Biodiversity erosion and conservation biogeography. In Encyclopedia of the World’s Biomes-Earth Systems and Environmental Sciences. Reference Module in Earth Systems and Environmental Sciences (eds Scott, E. et al.) 1–13 (Elsevier, 2019).
Google Scholar 
73.Lourenço, P., Medeiros, V., Gil, A. & Silva, L. Distribution, habitat and biomass of Pittosporum undulatum, the most important woody plant invader in the Azores Archipelago. For. Ecol. Manage. 262, 178–187. https://doi.org/10.1016/j.foreco.2011.03.021 (2011).Article 

Google Scholar 
74.Gabriel, R. & Bates, J. W. Bryophyte community composition and habitat specificity in the natural forests of Terçeira, Azores. Plant Ecol. 177, 125–144. https://doi.org/10.1007/s11258-005-2243-6 (2005).Article 

Google Scholar 
75.Elias, R. B., Dias, E. & Pereira, F. Disturbance, regeneration and the spatial pattern of tree species in Azorean mountain forests. Community Ecol. 12, 23–30. https://doi.org/10.1556/ComEc.12.2011.1.4 (2011).Article 

Google Scholar 
76.Elias, R. B. & Dias, E. The effects of landslides on the mountain vegetation of Flores Island, Azores. J. Veg. Sci. 20, 706–717. https://doi.org/10.1111/j.1654-1103.2009.01070.x (2009).Article 

Google Scholar 
77.Gleadow, R. M., Rowan, K. S. & Ashton, D. H. Invasion by Pittosporum undulatum of the forests of Central Victoria IV. Shade tolerance. Aust J. Bot. 31, 151–160. https://doi.org/10.1071/BT9830151 (1983).Article 

Google Scholar 
78.Bradstock, R. A., Tozer, M. G. & Keith, D. A. Effects of high frequency fire on floristic composition and abundance in a fire-prone heathland near Sydney. Aust. J. Bot. 45, 641–655. https://doi.org/10.1071/BT96083 (1997).Article 

Google Scholar 
79.Gleadow, R. M. & Ashton, D. H. Invasion by Pittosporum undulatum of the forests of Central Victoria. I. Invasion patterns and plant morphology. Aust. J. Bot. 29, 705–720. https://doi.org/10.1071/BT9810705 (1981).Article 

Google Scholar 
80.Ramos, J. A. Introduction of exotic tree species as a threat to the azores bullfinch population. J. Appl. Ecol. 33, 710–722 (1996).
Google Scholar 
81.Silva, L., Ojeda-Land, E. & Rodríguez-Luengo, J. L. Invasive terrestrial flora and fauna of Macaronesia. Top 100 in Azores, Madeira and Canaries 546 (ARENA, 2008).
Google Scholar 
82.Castro, S. A. et al. Floristic homogenization as a teleconnected trend in oceanic islands. Divers. Distrib. 16, 902–910. https://doi.org/10.1111/j.1472-4642.2010.00695.x (2010).Article 

Google Scholar 
83.Kueffer, C. et al. Magnitude and form of invasive plant impacts on oceanic islands: A global comparison. Perspect. Plant Ecol. Evol. Syst. 12, 145–161. https://doi.org/10.1016/j.ppees.2009.06.002 (2010).Article 

Google Scholar 
84.Gil, A., Lobo, A., Abadi, M., Silva, L. & Calado, H. Mapping invasive woody plants in Azores Protected Areas by using very high-resolution multispectral imagery. Eur. J. Remote. Sens. 46, 289–304. https://doi.org/10.5721/EuJRS20134616 (2013).Article 

Google Scholar 
85.DRRF. Plano de Gestão Florestal-Perímetro Florestal e Matas Regionais da Ilha de São Miguel. Direção Regional dos Recursos Florestais. Secretaria Regional da Agricultura e Florestas. Região Autónoma dos Açores. (http://drrf.azores.gov.pt/areas/cert/Documents/PGF_do_Perimetro_Florestal_e_Matas_Regionais_da_Ilha_de_Sao_Miguel_2017.pdf) (2017).86.Dutra Silva, L., Azevedo, E. B., Elias, R. B. & Silva, L. Species distribution modeling: Comparison of fixed and mixed effects models using INLA. Int. J. Geogr. Inf. Sci. 6, 1–35. https://doi.org/10.3390/ijgi6120391 (2017).Article 

Google Scholar 
87.Dutra Silva, L., Azevedo, E. B., Reis, F. V., Elias, R. B. & Silva, L. Limitations of species distribution models based on available climate change data: a case study in the Azorean forest. Forests 10, 575. https://doi.org/10.3390/f10070575 (2019).Article 

Google Scholar 
88.Hortal, J., Borges, P. A. V., Jiménez-Valverde, A., Azevedo, E. B. & Silva, L. Assessing the areas under risk of invasion within islands through potential distribution modelling: The case of Pittosporum undulatum in São Miguel, Azores. J. Nat. Conserv. 18, 247–257. https://doi.org/10.1016/j.jnc.2009.11.002 (2010).Article 

Google Scholar 
89.Gil, A., Yu, Q., Abadi, M. & Calado, H. Using ASTER multispectral imagery for mapping woody invasive species in Pico da Vara Natural Reserve (Azores Islands, Portugal). Revista Árvore. 38, 391–401 (2014).
Google Scholar 
90.Magurran, A. E. Ecological Diversity and Its Measurement 178 (Croom Helm, 1988).
Google Scholar 
91.Dias, E., Elias, R. B., Melo, C. & Mendes, C. O elemento insular na estruturação das florestas da Macaronésia. In Árvores e Florestas de Portugal. Volume 6. Açores e Madeira. A Floresta das ilhas 362 (Público, Comunicação Social, SA. Fundação Luso-Americana para o Desenvolvimento, 2007).
Google Scholar 
92.Dias, E., Elias, R. B., Melo, C. & Mendes, C. O elemento insular na estruturação das florestas da Macaronésia. Açores Madeira 6, 15–48 (2007).
Google Scholar 
93.Kacholi, D. S. Analysis of structure and diversity of the Kilengwe forest in the Morogoro Region, Tanzania. Int. J. Biodivers. 2014, 1–8. https://doi.org/10.1155/2014/516840 (2014).Article 

Google Scholar 
94.Jögren, E. Recent changes in the vascular flora and vegetation of the Azores Islands, Memórias da Sociedade Broteriana. Agric. For. 22, 1–113 (1973).
Google Scholar 
95.Silva, L. & Smith, C. W. A quantitative approach to the study of non- indigenous plants: An example from the Azores Archipelago. Biodivers. Conserv. 15, 1661–1679. https://doi.org/10.1007/s10531-004-5015-z (2006).Article 

Google Scholar 
96.Szmyt, J. Structural diversity of selected oak stands (Quercus robur L.) on the Krotoszyn Plateau in Poland. For. Res. Pap. 78, 4–27. https://doi.org/10.1515/frp-2017-0002 (2017).Article 

Google Scholar 
97.Lillo, E. P., Fernando, E. S. & Lillo, M. J. R. Plant diversity and structure of forest habitat types on Dinagat Island, Philippines. J. Asia Pac. Biodivers. 12, 83–105. https://doi.org/10.1016/j.japb.2018.07.003 (2018).Article 

Google Scholar 
98.Morin, X., Fahse, L., Scherer-Lorenzen, M. & Bugmann, H. Tree species richness promotes productivity in temperate forests through strong complementarity between species. Ecol. Lett. 14, 1211–1219. https://doi.org/10.1111/j.1461-0248.2011.01691.x (2011).Article 
PubMed 

Google Scholar 
99.Park, J., Kim, H. S., Jo, H. K. & Jung, B. The influence of tree structural and species diversity on temperate forest productivity and stability in Korea. Forests https://doi.org/10.3390/f10121113 (2019).Article 

Google Scholar 
100.Yang, Y., Luo, Y. & Finzi, A. Carbon and nitrogen dynamics during forest stand development: A global synthesis. New Phytol. 190, 977–989. https://doi.org/10.1111/j.1469-8137.2011.03645.x (2011).CAS 
Article 
PubMed 

Google Scholar 
101.Houghton, R. A., Hall, F. & Goetz, S. J. Importance of biomass in the global carbon cycle. J. Geophys. Res. 114, G00E03. https://doi.org/10.1029/2009JG000935 (2009).ADS 
Article 

Google Scholar 
102.Matos, B. et al. Linking dendrometry and dendrochronology in the Dominant Azorean Tree Laurus azorica (Seub.) Franco. Forests 10, 538. https://doi.org/10.3390/f10070538 (2019).Article 

Google Scholar 
103.Keith, H. et al. Evaluating nature-based solutions for climate mitigation and conservation requires comprehensive carbon accounting. Sci. Total Environ. 769, 144341. https://doi.org/10.1016/j.scitotenv.2020 (2021).ADS 
CAS 
Article 
PubMed 

Google Scholar 
104.Luyssaert, S. et al. Old-growth forests as global carbon sinks. Nature 455, 213–215. https://doi.org/10.1038/nature07276 (2008).ADS 
CAS 
Article 
PubMed 

Google Scholar 
105.Pavão, D. C. et al. Dendrochronological potential of the Azorean endemic gymnosperm Juniperus brevifolia. Dendrochronologica 71, 125901. https://doi.org/10.1016/j.dendro.2021.125901 (2022).Article 

Google Scholar 
106.Fernández-Palácios, J. M., Garcia Esteban, J. J., López, R. J. & Luzardo, M. C. Aproximación a la estima de la biomassa y producción primaria neta aéreas en una estación de la Laurisilva tinerfeña. Vieraea 20, 11–20 (1991).
Google Scholar 
107.Brown, S. & Lugo, A. E. Biomass of tropical forests: A new estimate based on forest volumes. Science 223, 1290–1293. https://doi.org/10.1126/science.223.4642.1290 (1984).ADS 
CAS 
Article 
PubMed 

Google Scholar 
108.Silva, J. Açores e Madeira: A Floresta das Ilhas Vol. 6, 362 (Coleção Árvores e florestas de Portugal,1ª Edição, Fundação Luso-Americana para o Desenvolvimento, 2007).
Google Scholar 
109.Fukuda, M., Iehara, T. & Matsumoto, M. Carbon stock estimates for Sugi and Hinoki forests in Japan. For. Ecol. Manage. 184, 1–16. https://doi.org/10.1016/S0378-1127(03)00146-4 (2003).Article 

Google Scholar 
110.Sasaki, N. & Kim, S. Biomass carbon sinks in Japanese forests: 1966–2012. Forestry 82, 105–115. https://doi.org/10.1093/forestry/cpn049 (2009).Article 

Google Scholar 
111.Dar, J. A. & Sundarapandian, S. M. Soil organic carbon stock assessment in two temperate forest types of western Himalaya of Jammu and Kashmir, India. For. Res. 3, 114. https://doi.org/10.4172/2168-9776.1000114 (2013).Article 

Google Scholar 
112.Gilliam, F. S. Excess nitrogen in temperate forest ecosystems decreases herbaceous layer diversity and shifts control from soil to canopy structure. Forests 10, 66. https://doi.org/10.3390/f10010066 (2019).Article 

Google Scholar 
113.Li, P., Wang, Q., Endo, T., Zhao, X. & Kakubari, Y. Soil organic carbon stock is closely related to vegetation properties in cold-temperate mountainous forests. Geoderma 154, 407–415. https://doi.org/10.1016/j.geoderma.2009.11.023 (2010).ADS 
CAS 
Article 

Google Scholar 
114.Diaz-Pines, E., Rubio, A., Miegroet, H. V., Montes, F. & Benito, M. Does tree species composition control soil organic carbon pools in Mediterranean mountain forests. For. Ecol Manage. 262, 1895–1904. https://doi.org/10.1016/j.foreco.2011.02.004 (2011).Article 

Google Scholar 
115.Berg, B. Litter decomposition and organic matter turnover in northern forest soils. For. Ecol. Manage. 133, 13–22. https://doi.org/10.1016/S0378-1127(99)00294-7 (2000).Article 

Google Scholar 
116.Boring, L. R. & Hendricks, J. J. Litter quality of native herbaceous legumes in a burned pine forest of the Gerogia Piedmont. Can. J. For. Res. 22, 2007–2010. https://doi.org/10.1139/x92-263 (1992).Article 

Google Scholar 
117.Thuille, A. & Schulze, E. D. Carbon dynamics in successional and afforested spruce stands in Thuringia and the Alps. Glob. Chang. Biol. 6, 325–342. https://doi.org/10.1111/j.1365-2486.2005.01078.x (2006).ADS 
Article 

Google Scholar 
118.Jandl, R. et al. How strongly can forest management influence soil carbon sequestration?. Geoderma 137, 253–268. https://doi.org/10.1016/j.geoderma.2006.09.003 (2007).ADS 
CAS 
Article 

Google Scholar 
119.van Wesemael, B. & Veer, M. A. C. Soil organic matter accumulation, litter decomposition and humus forms in Mediterranean forests of southern Tuscany, Italy. J. Soil Sci. 43, 133–144. https://doi.org/10.1111/j.1365-2389.1992.tb00125.x (1992).Article 

Google Scholar 
120.Kavvadias, V. A., Alifragis, D. A., Tsiontsis, A., Brofas, G. & Stamatelos, G. Litterfall, litter accumulation and litter decomposition rates in four forest ecosystems in northern Greece. For. Ecol Manage. 144, 113–127. https://doi.org/10.1016/S0378-1127(00)00365-0 (2001).Article 

Google Scholar 
121.Rahman, M. M., Tsukamoto, J., Tokumoto, Y. & Ashikur, R. S. The role of quantitative traits of leaf litter on decomposition and nutrient cycling of the forest ecosystems. J. For. Sci. 29, 38–48. https://doi.org/10.7747/JFS.2013.29.1.38 (2013).Article 

Google Scholar 
122.Bowden, R. et al. Litter input controls on soil carbon in a temperate deciduous forest. Soil Sci. Soc. Am. J. 78, S66–S75. https://doi.org/10.2136/sssaj2013.09.0413nafsc (2014).Article 

Google Scholar 
123.Madeira, M. et al. (eds) Soils of Volcanic Regions in Europe (Springer, 2007).
Google Scholar 
124.Arnalds, O. et al. (eds) Soils of Volcanic Regions in Europe (Springer, 2007).
Google Scholar 
125.Zheng, X., Wei, X. & Zhang, S. Tree species diversity and identity effects on soil properties in the Huoditang area of the Qinling Mountains, China. Ecosphere 8, e01732. https://doi.org/10.1002/ecs2.1732 (2017).Article 

Google Scholar 
126.Duan, L., Huang, Y., Hao, J., Xie, S. & Hou, M. Vegetation uptake of nitrogen and base cations in China and its role in soil acidification. Sci. Total Environ. 330, 187–198. https://doi.org/10.1016/j.scitotenv.2004.03.035 (2004).ADS 
CAS 
Article 
PubMed 

Google Scholar 
127.Heath, L. S., Kimble, J. M., Birdsey, R. A. & Lal, R. The potential of U.S. forest soils to sequester carbon. In The Potential of U.S. Forest Soils to Sequester Carbon and Mitigate the Greenhouse Effect (eds Kimble, J. M. et al.) 385–394 (CRC Press, 2003).
Google Scholar 
128.D’Amore, D. & Kane, E. Climate Change and Forest Soil Carbon. U.S. Department of Agriculture, Forest Service, Climate Change Resource Center. www.fs.usda.gov/ccrc/topics/forest-soil-carbon (2016).129.Ramade, F. Ecology of Natural Resources (Wiley, 1981).
Google Scholar 
130.Osman, K. T. Physical properties of forest soils. In Forest Soils 19–44 (Springer, 2013).
Google Scholar 
131.Sanchez, P. A. & Logan, T. J. Myths and science about the chemistry and fertility of soils in the tropics. In Myths and Science of Soils of the Tropics Vol. 29 (eds Lal, R. & Sanchez, P. A.) 35–46 (SSSA, 1992).
Google Scholar 
132.Sibrant, A. L. R. et al. Morpho-structural evolution of a volcanic island developed inside an active oceanic rift: S. Miguel Island (Terceira rift, Azores). J. Volcanol. Geotherm. Res. 301, 90–106. https://doi.org/10.1016/j.jvolgeores.2015.04.011 (2015).ADS 
CAS 
Article 

Google Scholar 
133.Hildenbrand, A., Weis, D., Madoreira, P. & Marques, F. O. Recent plate reorganization at the Azores triple junction: Evidence from combined geochemical and geochronological data on Faial, S. Jorge and Terceira volcanic islands. Lithos 210–211, 27–39. https://doi.org/10.1016/j.lithos.2014.09.009 (2014).ADS 
CAS 
Article 

Google Scholar 
134.Demand, J., Fabriol, R., Gerard, F., Lundt, F. & Chovelon, P. Prospection Géothermique, íles de Faial et de Pico (Açores). Rapport géologique, geochimique et gravimétrique. Technical report, BRGM 82 SGN 003 GTH (1982).135.Elias, R. B. & Dias, E. Ecologia das florestas de Juniperus dos Açores Cadernos de Botânica nº5 (Herbário da Universidade dos Açores, 2008).
Google Scholar 
136.DRRF. Avaliação da Biomassa Disponível em Povoamentos Florestais na Região Autonoma dos Açores (Evaluation of Available Biomass in Forestry Stands in the Azores Autonomic Region) 8 (Inventário Florestal da Regiao Autonoma dos Açores Direcção Regional dos Recursos Florestais, Secretaria Regional da Agricultura e Florestas da Região Autonoma dos Açores, 2007).
Google Scholar 
137.Silva, L. & Smith, C. W. A characterization of the non-indigenous flora of the Azores Archipelago. Biol. Invasions 6, 193–204. https://doi.org/10.1023/B:BINV.0000022138.75673.8c (2004).Article 

Google Scholar 
138.Fernandes, A. & Fernandes, R. B. Iconographia Selecta Florae Azoricae Vol. I, 131 (Fasc. 1. Coimbra, 1980).
Google Scholar 
139.Fernandes, A. & Fernandes, R. B. Iconographia Selecta Florae Azoricae Vol. II, 178 (Fasc. 1 Edição da Secretaria Regional da Cultura da Região Autónoma dos Açores, 1983).
Google Scholar 
140.Mengistu, B. & Asfaw, Z. Woody species diversity and structure of agroforestry and adjacent land uses in Dallo Mena District, South-East Ethiopia. Nat. Resour. 7, 515–534. https://doi.org/10.4236/nr.2016.710044 (2016).Article 

Google Scholar 
141.Liu, X. et al. Tree species richness increases ecosystem carbon storage in subtropical forests. Proc. Biol. Sci. 285, 20181240. https://doi.org/10.1098/rspb.2018.1240 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
142.Lou, J. Entropy & diversity. Oikos 113, 363–375. https://doi.org/10.1111/j.2006.0030-1299.14714.x (2006).Article 

Google Scholar 
143.Whittaker, R. H. Communities and Ecosystems 162 (MacMillan, 1970).
Google Scholar 
144.Mori, A. S., Isbell, F. & Seidl, R. β-diversity, community assembly, and ecosystem functioning. Trends Ecol. Evol. 33, 549–564. https://doi.org/10.1016/j.tree.2018.04.012 (2018).Article 
PubMed 

Google Scholar 
145.Oksanen, J. et al. Community Ecology Package. Vegan Tutorial (2018).146.Pavão, D. C., Elias, R. E. & Silva, L. Comparison of discrete and continuum community models: Insights from numerical ecology and Bayesian methods applied to Azorean plant communities. Ecol. Model. 402, 93–106. https://doi.org/10.1016/j.ecolmodel.2019.03.021 (2019).Article 

Google Scholar 
147.Legendre, P. & Legendre, L. Numerical Ecology 2nd edn, 853 (Elsevier, 1998).MATH 

Google Scholar 
148.Oksanen F.G. et al. Vegan: Community Ecology Package. R Package Version 2.4-2 (2017).149.Dufrêne, M. & Legendre, P. Species assemblages and indicator species: The need for a flexible asymmetrical approach. Ecol. Monogr. 67, 345–366. https://doi.org/10.2307/2963459 (1997).Article 

Google Scholar 
150.Silva, L., Le Jean, F., Marcelino, J. & Soares, A. O. Using bayesian inference to validate plant community assemblages and determine indicator species. In Modeling, Dynamics, Optimization and Bioeconomics II. DGS 2014. Springer Proceedings in Mathematics & Statistics Vol. 195 (eds Pinto, A. & Zilberman, D.) (Springer, 2017).
Google Scholar 
151.van Rensburg, B. J., McGeoch, M. A., Chown, S. L. & van Jaarsveld, A. S. Conservation of heterogeneity among dung beetles in the Maputaland Centre of Endemism, South Africa. Biol. Conserv. 88, 145–153. https://doi.org/10.1016/S0006-3207(98)00109-8 (1999).Article 

Google Scholar 
152.Solomou, A. D. & Sfougaris, A. I. Herbaceous plant diversity and identification of indicator species in olive groves in Central Greece. Commun. Soil Sci. Plant Anal. 44, 320–330. https://doi.org/10.1080/00103624.2013.741926 (2013).CAS 
Article 

Google Scholar 
153.De Caceres, M. & Jansen, F. Indicspecies: Relationship Between Species and Groups of Sites. R package version 1.7.5. (2016).154.Aboal, J., Arévalo, J. R. & Fernández, Á. Allometric relationships of different tree species and stand above ground biomass in the Gomera laurel forest (Canary Islands). Flora 200, 264–274. https://doi.org/10.1016/j.flora.2004.11.001 (2005).Article 

Google Scholar 
155.Lim, K. H., Lee, K.-H., Lee, K. H. & Park, I. H. Biomass expansion factors and allometric equations in an age sequence for Japanese cedar (Cryptomeria japonica) in southern. J. For. Res. 18, 316–322. https://doi.org/10.1007/s10310-012-0353-2 (2013).CAS 
Article 

Google Scholar 
156.Paul, K. I. et al. Development and testing of allometric equations for estimating above-ground biomass of mixed-species environmental plantings. For. Ecol. Manage. 310, 483–494. https://doi.org/10.1016/j.foreco.2013.08.054 (2013).Article 

Google Scholar 
157.Acosta-Mireles, M., Vargas-Hernández, J., Velázquez-Martínez, A. & Etchevers-Barra, J. D. Aboveground biomass estimation by means of allometric relationships in six hardwood species in Oaxaca, México. Agrociência 36, 725–736 (2002).
Google Scholar 
158.Zianis, D. & Mencuccini, M. On simplifying allometric analyses of forest biomass. For. Ecol. Manage. 187, 311–332. https://doi.org/10.1016/j.foreco.2003.07.007 (2004).Article 

Google Scholar 
159.IPCC. Guidelines for National Greenhouse Gas Inventories Vol. 4 (Intergovernmental Panel on Climate Change (IPCC), Agriculture, Forestry and Other Land Use (AFLOLU), Institute for Global Environmental Strategies, 2006).
Google Scholar 
160.Mokany, K., Raison, J. R. & Prokushkin, A. S. Critical analysis of root: shoot ratios in terrestrial biomes. Glob. Chang. Biol. 12, 84–96. https://doi.org/10.1111/j.1365-2486.2005.001043.x (2006).ADS 
Article 

Google Scholar 
161.Lamlom, S. & Savidge, R. A. A reassessment of carbon content in wood: Variation within and between 41 North American species. Biomass Bioenergy. 25, 381–388. https://doi.org/10.1016/S0961-9534(03)00033-3 (2003).CAS 
Article 

Google Scholar 
162.Jew, E. K. K., Dougill, A. J., Sallu, S. M., O’Connell, J. & Benton, T. G. Miombo woodland under threat: consequences for tree diversity and carbon storage. For. Ecol. Manage. 361, 144–153. https://doi.org/10.1016/j.foreco.2015.11.0110378-1127 (2016).Article 

Google Scholar 
163.Hetland, J., Yowargana, P., Leduc, S. & Kraxner, F. Carbon-negative emissions: systemic impacts of biomass conversion: A case study on CO2 capture and storage options. Int. J. Greenh. Gas Control. 49, 330–342 (2016).CAS 

Google Scholar 
164.Macías, C. A. S., Orihuela, J. C. A. & Abad, S. I. Estimation of above-ground live biomass and carbon stocks in different plant formations and in the soil of dry forests of the Ecuadorian coast. Food Energy Secur. 6, e115. https://doi.org/10.1002/fes3.115 (2017).Article 

Google Scholar 
165.Yigini, Y. et al. Soil Organic Carbon Mapping Cookbook 2nd edn, 220 (FAO, 2018).
Google Scholar 
166.Azevedo, E. B. & Pereira, L. S. Modelling the local climate in island environments: Water balance applications. Agric. Water Manag. 40, 393–403 (1999).
Google Scholar 
167.Costa, H. et al. Predicting successful replacement of forest invaders by native species using species distribution models: The case of Pittosporum undulatum and Morella faya in the Azores. For. Ecol. Manage. 279, 90–96. https://doi.org/10.1016/j.foreco.2012.05.022 (2012).Article 

Google Scholar 
168.Costa, H., Medeiros, V., Azevedo, E. B. & Silva, L. Evaluating the ecological-niche factor analysis as a modelling tool for environmental weed management in island systems. Weed Res. 53, 221–230. https://doi.org/10.1111/wre.12017 (2013).Article 

Google Scholar  More

1.Eisner, R., Seabrook, L. M. & McAlpine, C. A. Are changes in global oil production influencing the rate of deforestation and biodiversity loss?. Biol. Conserv. 196, 147–155. https://doi.org/10.1016/j.biocon.2016.02.017 (2016).Article 

Google Scholar 
2.Fahrig, L. Effects of habitat fragmentation on biodiversity. Annu. Rev. Ecol. Evol. Syst. 34, 487–515. https://doi.org/10.1146/132419 (2003).Article 

Google Scholar 
3.Pfeifer, M. et al. Creation of forest edges has a global impact on forest vertebrates. Nature 551, 187–191. https://doi.org/10.1038/nature24457 (2017).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
4.Tilman, D., May, R., Lehman, C. & Nowak, M. Habitat destruction and the extinction debt. Nature 371, 65–66. https://doi.org/10.1038/371065a0 (1994).ADS 
Article 

Google Scholar 
5.Fisher, J. T. & Burton, C. A. Wildlife winners and losers in an oil sands landscape. Front Ecol. Environ. https://doi.org/10.1002/fee.1807 (2018).Article 

Google Scholar 
6.Heim, N., Fisher, J. T., Volpe, J., Clevenger, A. P. & Paczkowski, J. Carnivore community response to anthropogenic landscape change: species-specificity foils generalizations. Landscape Ecol. 34, 2493–2507. https://doi.org/10.1007/s10980-019-00882-z (2019).Article 

Google Scholar 
7.Pereira, H. M., Navarro, L. & Martins, I. Global biodiversity change: the bad, the good, and the unknown. Annu. Rev. Environ. Resour. https://doi.org/10.1146/annurev-environ-042911-093511 (2012).Article 

Google Scholar 
8.Northrup, J. M., Anderson, C. R. Jr. & Wittemyer, G. Quantifying spatial habitat loss from hydrocarbon development through assessing habitat selection patterns of mule deer. Glob Change Biol. 21, 3961–3970. https://doi.org/10.1111/gcb.13037 (2015).ADS 
Article 

Google Scholar 
9.Holbrook, S. J. & Schmitt, R. J. The combined effects of predation risk and food reward on patch selection. Ecology 69, 125–134. https://doi.org/10.2307/1943167 (1988).Article 

Google Scholar 
10.Moody, A. L., Houston, A. I. & McNamara, J. M. Ideal free distributions under predation risk. Behav. Ecol. Sociobiol. 38, 131–143 (1996).Article 

Google Scholar 
11.Dietz, H. & Edwards, P. J. Recognition that causal processes change during plant invasion helps explain conflicts in evidence. Ecology 87, 1359–1367 (2006).Article 

Google Scholar 
12.Hobbs, R. J. & Huenneke, L. F. Disturbance, diversity, and invasion: implications for conservation. Conserv. Biol. 6, 324–337 (1992).Article 

Google Scholar 
13.Van der Graaf, S., Stahl, J., Klimkowska, A. & Drent, J. P. B. Surfing on a green wave—How plant growth drives spring migration in the Barnacle Goose Branta leucopsis. Ardea -Wageningen- 94, 567 (2006).
Google Scholar 
14.Parker, I. M. et al. Impact: toward a framework for understanding the ecological effects of invaders. Biol. Invasions 1, 3–19. https://doi.org/10.1023/A:1010034312781 (1999).Article 

Google Scholar 
15.Pimentel, D., Zuniga, R. & Morrison, D. Update on the environmental and economic costs associated with alien-invasive species in the United States. Ecol. Econ. 52, 273–288. https://doi.org/10.1016/j.ecolecon.2004.10.002 (2005).Article 

Google Scholar 
16.Shackelford, N. et al. Primed for change: developing ecological restoration for the 21st Century. Restor. Ecol. 21, 297–304. https://doi.org/10.1111/rec.12012 (2013).Article 

Google Scholar 
17.Pickell, P. D., Pickell, P. D., Andison, D. W., Coops, N. C. & Gergel, S. E. The spatial patterns of anthropogenic disturbance in the western Canadian boreal forest following oil and gas development. Can. J. For. Res. 45, 732–743. https://doi.org/10.1139/cjfr-2014-0546 (2015).Article 

Google Scholar 
18.Fisher, J. T. & Wilkinson, L. The response of mammals to forest fire and timber harvest in the North American boreal forest. Mammal Rev. 35, 51–81 (2005).Article 

Google Scholar 
19.Wittische, J., Heckbert, S., James, P. M. A., Burton, A. C. & Fisher, J. T. Community-level modelling of boreal forest mammal distribution in an oil sands landscape. Sci. Total Environ. 755, 142500. https://doi.org/10.1016/j.scitotenv.2020.142500 (2021).ADS 
CAS 
Article 
PubMed 

Google Scholar 
20.Hewitt, D. G. Biology and management of white-tailed deer (CRC Press, Boca Raton, 2011).Book 

Google Scholar 
21.McCabe, R. E. & McCabe, T. R. in White tailed deer: ecology and management Ch. Chapter 2, 19–72 (Stackpole, A Wildlife Management Institute Book, 1984).22.Webb, R. The range of white-tailed deer in Alberta (Alberta Fish and Wildlife Division Edmonton, Alberta, 1967).
Google Scholar 
23.Dawe, K. L. & Boutin, S. Climate change is the primary driver of white-tailed deer (Odocoileus virginianus) range expansion at the northern extent of its range; land use is secondary. Ecol. Evol. 6, 6435–6451. https://doi.org/10.1002/ece3.2316 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
24.DeCesare, N. J., Hebblewhite, M., Robinson, H. S. & Musiani, M. Endangered, apparently: the role of apparent competition in endangered species conservation. Anim. Conserv. 13, 353–362. https://doi.org/10.1111/j.1469-1795.2009.00328.x (2010).Article 

Google Scholar 
25.Latham, A. D. M., Latham, M. C., McCutchen, N. A. & Boutin, S. Invading white-tailed deer change wolf-caribou dynamics in northeastern Alberta. J. Wildl. Manag. 75, 204–212. https://doi.org/10.1002/jwmg.28 (2011).Article 

Google Scholar 
26.Latham, A. D. M., Latham, M. C., Boyce, M. C. & Boutin, S. Movement responses by wolves to industrial linear features and their effect on woodland caribou in northeastern Alberta. Ecol. Appl. 21, 11 (2011).Article 

Google Scholar 
27.Fisher, J. T., Burton, A. C., Nolan, L. & Roy, L. Influences of landscape change and winter severity on invasive ungulate persistence in the Nearctic boreal forest. Sci. Rep. 10, 8742. https://doi.org/10.1038/s41598-020-65385-3 (2020).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
28.Dabros, A., Pyper, M. & Castilla, G. Seismic lines in the boreal and arctic ecosystems of North America: environmental impacts, challenges, and opportunities. Environ. Rev. 26, 214–229. https://doi.org/10.1139/er-2017-0080 (2018).Article 

Google Scholar 
29.Dickie, M., Serrouya, R., McNay, R. S., Boutin, S. & du Toit, J. Faster and farther: wolf movement on linear features and implications for hunting behaviour. J. Appl. Ecol. 54, 253–263. https://doi.org/10.1111/1365-2664.12732 (2017).Article 

Google Scholar 
30.Finnegan, L., MacNearney, D. & Pigeon, K. E. Divergent patterns of understory forage growth after seismic line exploration: implications for caribou habitat restoration. For. Ecol. Manag. 409, 634–652. https://doi.org/10.1016/j.foreco.2017.12.010 (2018).Article 

Google Scholar 
31.Prokopenko, C. M., Boyce, M. S., Avgar, T. & Tulloch, A. Characterizing wildlife behavioural responses to roads using integrated step selection analysis. J. Appl. Ecol. 54, 470–479. https://doi.org/10.1111/1365-2664.12768 (2017).Article 

Google Scholar 
32.Waring, G. H., Griffis, J. L. & Vaughn, M. E. White-tailed deer roadside behavior, wildlife warning reflectors, and highway mortality. Appl. Anim. Behav. Sci. 29, 215–223. https://doi.org/10.1016/0168-1591(91)90249-W (1991).Article 

Google Scholar 
33.Bowman, J., Ray, J. C., Magoun, A. J., Johnson, D. S. & Dawson, F. N. Roads, logging, and the large-mammal community of an eastern Canadian boreal forest. Can. J. Zool. 88, 454–467. https://doi.org/10.1139/z10-019 (2010).Article 

Google Scholar 
34.Munro, K. G., Bowman, J. & Fahrig, L. Effect of paved road density on abundance of white-tailed deer. Wildl. Res. 39, 478. https://doi.org/10.1071/wr11152 (2012).Article 

Google Scholar 
35.Fisher, J. T. & Burton, A. C. Spatial structure of reproductive success infers mechanisms of ungulate invasion in Nearctic boreal landscapes. Ecol. Evol. 11, 900–911. https://doi.org/10.1002/ece3.7103 (2021).Article 
PubMed 

Google Scholar 
36.Kie, J. G. Optimal foraging and risk of predation effects on behavior and social structure in ungulates. J. Mammal. 80, 1114–1129 (1999).Article 

Google Scholar 
37.Brown, J. S., Laundré, J. W. & Gurung, M. The ecology of fear: optimal foraging, game theory, and trophic interactions. J. Mammal. 80, 385–399. https://doi.org/10.2307/1383287 (1999).Article 

Google Scholar 
38.Kittle, A. M., Fryxell, J. M., Desy, G. E. & Hamr, J. The scale-dependent impact of wolf predation risk on resource selection by three sympatric ungulates. Oecologia 157, 163–175. https://doi.org/10.1007/s00442-008-1051-9 (2008).ADS 
Article 
PubMed 

Google Scholar 
39.Moen, A. N. Energy conservation by white-tailed deer in the winter. Ecology 57, 192–198. https://doi.org/10.2307/1936411 (1976).Article 

Google Scholar 
40.Schmidt, K. Winter ecology of nonmigratory Alpine red deer. Oecologia 95, 226–233. https://doi.org/10.1007/BF00323494 (1993).ADS 
Article 
PubMed 

Google Scholar 
41.Kilgo, J. C., Ray, H. S., Vukovich, M., Goode, M. J. & Ruth, C. Predation by coyotes on white-tailed deer neonates in South Carolina. J. Wildl. Manag. https://doi.org/10.1002/jwmg.393 (2012).Article 

Google Scholar 
42.Laurent, M., Dickie, M., Becker, M., Serrouya, R. & Boutin, S. Evaluating the mechanisms of landscape change on white-tailed deer populations. J. Wildl. Manag. 85, 340–353. https://doi.org/10.1002/jwmg.21979 (2020).Article 

Google Scholar 
43.Schneider, R. R., Hauer, G., Adamowicz, W. L. & Boutin, S. Triage for conserving populations of threatened species: the case of woodland caribou in Alberta. Biol. Conserv. 143, 1603–1611. https://doi.org/10.1016/j.biocon.2010.04.002 (2010).Article 

Google Scholar 
44.Kilkenny, C., Browne Wj Fau – Cuthill, I. C., Cuthill Ic Fau – Emerson, M., Emerson M Fau – Altman, D. G. & Altman, D. G. Improving bioscience research reporting: the ARRIVE guidelines for reporting animal research. PLoS biol. 8(6), e1000412 (2010).45.DelGiudice, G. D., Mangipane, B. A., Sampson, B. A. & Kochanny, C. O. Chemical immobilization, body temperature, and post-release mortality of white-tailed deer captured by clover trap and net-gun. Wildl. Soc. Bull. (1973-2006) 29, 1147–1157 (2001).
Google Scholar 
46.Droge, E., Creel, S., Becker, M. S. & M’Soka, J. Risky times and risky places interact to affect prey behaviour. Nat. Ecol. Evol. 1, 1123–1128. https://doi.org/10.1038/s41559-017-0220-9 (2017).Article 
PubMed 

Google Scholar 
47.Kunkel, K. E. & Mech, L. D. Wolf and bear predation on white-tailed deer fawns in northeastern Minnesota. Can. J. Zool. 72, 1557–1565 (1994).Article 

Google Scholar 
48.Latham, A., Latham, M., Knopff, K., Hebblewhite, M. & Boutin, S. Wolves, white-tailed deer, and beaver: Implications of seasonal prey switching for woodland caribou declines. Ecography https://doi.org/10.1111/j.1600-0587.2013.00035.x (2013).Article 

Google Scholar 
49.Alberta Environment and Sustainable Resource Development. Alberta Vegetation Index. Accessed October 2016. https://geodiscover.alberta.ca/50.Manly, B., McDonald, L., Thomas, D., McDonald, T. & Erickson, W.Resource selection by animals: statistical design and analysis for field studies. Vol. 63, pp. 1-10 (Springer Science & Business Media, 2007).51.Boyce, M. S., Vernier, P. R., Nielsen, S. E. & Schmiegelow, F. K. A. Evaluating resource selection functions. Ecol. Model. 157, 281–300. https://doi.org/10.1016/S0304-3800(02)00200-4 (2002).Article 

Google Scholar 
52.Hijmans, R. & van Etten, J. Raster: Geographic data analysis and modeling. CRAN R package 2 (2016).53.R: A language and environment for statistical computing. (Vienna, Austria, 2013).54.Zuur, A., Hilbe, J. & Ieno, E. A Beginner’s Guide to GLM and GLMM with R: a frequentist and Bayesian perspective for ecologists. (Highland Statistics, 2013).55.Gillies, C. S. et al. Application of random effects to the study of resource selection by animals. J. Anim. Ecol. 75, 887–898. https://doi.org/10.1111/j.1365-2656.2006.01106.x (2006).Article 
PubMed 

Google Scholar 
56.Craney, T. A. & Surles, J. G. Model-dependent variance inflation factor cutoff values. Qual. Eng. 14, 391–403. https://doi.org/10.1081/QEN-120001878 (2002).Article 

Google Scholar 
57.Akaike, H. Information theory and an extension of the maximum likelihood principle. Selected papers of hirotugu akaike 199–213 (Springer, New York, 1998).Book 

Google Scholar 
58.Burnham, K. P. & Anderson, D. R. Multimodel inference: understanding AIC and BIC in model selection. Sociol. Methods Res. 33, 261–304. https://doi.org/10.1177/0049124104268644 (2004).MathSciNet 
Article 

Google Scholar 
59.Boulanger, Y. et al. Climate change impacts on forest landscapes along the Canadian southern boreal forest transition zone. Landscape Ecol. 32, 1415–1431. https://doi.org/10.1007/s10980-016-0421-7 (2017).Article 

Google Scholar 
60.Sulla-Menashe, D., Woodcock, C. E. & Friedl, M. A. Canadian boreal forest greening and browning trends: an analysis of biogeographic patterns and the relative roles of disturbance versus climate drivers. Environ. Res. Lett. 13, 014007. https://doi.org/10.1088/1748-9326/aa9b88 (2018).ADS 
Article 

Google Scholar 
61.St-Pierre, F., Drapeau, P. & St-Laurent, M.-H. Drivers of vegetation regrowth on logging roads in the boreal forest: Implications for restoration of woodland caribou habitat. For. Ecol. Manag. 482, 118846. https://doi.org/10.1016/j.foreco.2020.118846 (2021).Article 

Google Scholar 
62.Berger, J. Fear, human shields and the redistribution of prey and predators in protected areas. Biol. Let. 3, 620–623. https://doi.org/10.1098/rsbl.2007.0415 (2007).Article 

Google Scholar 
63.Heyes, A., Leach, A. & Mason, C. F. The economics of Canadian oil sands. Rev. Environ. Econ. Policy 12, 242–263. https://doi.org/10.1093/reep/rey006 (2018).Article 

Google Scholar 
64.Komers, P. E. & Stanojevic, Z. Rates of disturbance vary by data resolution: implications for conservation schedules using the Alberta boreal forest as a case study. Global Change Biol. 19, 2916–2928 (2013).ADS 
CAS 
Article 

Google Scholar 
65.Hebblewhite, M. & Merrill, E. H. Trade-offs between predation risk and forage differ between migrant strategies in a migratory ungulate. Ecology 90, 3445–3454. https://doi.org/10.1890/08-2090.1 (2009).Article 
PubMed 

Google Scholar 
66.Mech, D. L. & Boitani, L. Wolves: behavior, ecology, and conservation Vol. 57 (University of Chicago Press, Chicago, 2004).
Google Scholar 
67.Creel, S., Winnie, J. A., Christianson, D. & Liley, S. Time and space in general models of antipredator response: tests with wolves and elk. Anim. Behav. 76, 1139–1146. https://doi.org/10.1016/j.anbehav.2008.07.006 (2008).Article 

Google Scholar 
68.Steenweg, R. et al. Scaling-up camera traps: monitoring the planet’s biodiversity with networks of remote sensors. Front. Ecol. Environ. 15, 26–34. https://doi.org/10.1002/fee.1448 (2017).Article 

Google Scholar 
69.Hebblewhite, M. Billion dollar boreal woodland caribou and the biodiversity impacts of the global oil and gas industry. Biol. Cons. 206, 102–111. https://doi.org/10.1016/j.biocon.2016.12.014 (2017).Article 

Google Scholar 
70.Côté, S. D., Rooney, T. P., Tremblay, J.-P., Dussault, C. & Waller, D. M. Ecological impacts of deer overabundance. Annu. Rev. Ecol. Evol. Syst. 35, 113–147 (2004).Article 

Google Scholar 
71.McCullough, D. R. Evaluation of night spotlighting as a deer study technique. J. Wildl. Manag. 46, 963–973. https://doi.org/10.2307/3808229 (1982).Article 

Google Scholar 
72.Preston, T., Wildhaber, M., Green, N., Albers, J. & Debenedetto, G. Enumerating white-tailed deer using unmanned aerial vehicles. Wildlife Soc. Bull. https://doi.org/10.1002/wsb.1149 (2021).Article 

Google Scholar 
73.Parks, A. E. Provincial woodland caribou range plan. 212 (Edmonton, Alberta, 2017).74.Tattersall, E. R., Burgar, J. M., Fisher, J. T. & Burton, A. C. Boreal predator co-occurrences reveal shared use of seismic lines in a working landscape. Ecol. Evol. 10, 1678–1691. https://doi.org/10.1002/ece3.6028 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
75.Diaz, S. et al. Pervasive human-driven decline of life on Earth points to the need for transformative change. Science (New York N.Y.) https://doi.org/10.1126/science.aax3100 (2019).Article 
PubMed Central 

Google Scholar 
76.Bayoumi, T. & Muhleisen, M. Energy, the exchange rate, and the economy: macroeconomic benefits of Canada’s oil sands production (International Monetary Fund, Washington, 2006).
Google Scholar 
77.Zhu, K., Song, Y. & Qin, C. Forest age improves understanding of the global carbon sink. Proc. Natl. Acad. Sci. 116, 3962. https://doi.org/10.1073/pnas.1900797116 (2019).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar  More

1.Zhang QQ, Ying GG, Pan CG, Liu YS, Zhao JL. Comprehensive evaluation of antibiotics emission and fate in the river basins of China: source analysis, multimedia modeling, and linkage to bacterial rResistance. Environ Sci Technol. 2015;49:6772–82.CAS 
Article 

Google Scholar 
2.Zhao WX, Wang B, Yu G. Antibiotic resistance genes in China: occurrence, risk, and correlation among different parameters. Environ Sci Pollut R. 2018;25:21467–82.CAS 
Article 

Google Scholar 
3.Han XM, Hu HW, Chen QL, Yang LY, Li HL, Zhu YG, et al. Antibiotic resistance genes and associated bacterial communities in agricultural soils amended with different sources of animal manures. Soil Biol Biochem. 2018;126:91–102.CAS 
Article 

Google Scholar 
4.Huerta B, Marti E, Gros M, López P, Pompêo M, Armengol J, et al. Exploring the links between antibiotic occurrence, antibiotic resistance, and bacterial communities in water supply reservoirs. Sci Total Environ. 2013;456:161–70.Article 

Google Scholar 
5.Martinez JL, Sánchez MB, Martínez-Solano L, Hernandez A, Garmendia L, Fajardo A, et al. Functional role of bacterial multidrug efflux pumps in microbial natural ecosystems. Fems Microbiol Rev. 2009;33:430–49.CAS 
Article 

Google Scholar 
6.Wright GD. The antibiotic resistome: the nexus of chemical and genetic diversity. Nat Rev Microbiol. 2007;5:175–86.CAS 
Article 

Google Scholar 
7.Meng F, Yang S, Wang X, Chen T, Wang X, Tang X, et al. Reclamation of Chinese herb residues using probiotics and evaluation of their beneficial effect on pathogen infection. J Infect Public Health. 2017;10:749–54.Article 

Google Scholar 
8.Zhou Y, Selvam A, Wong JWC. Chinese medicinal herbal residues as a bulking agent for food waste composting. Bioresour Technol. 2018;249:182–8.CAS 
Article 

Google Scholar 
9.Wu HW, Sun XQ, Liang BW, Chen JB, Zhou XF. Analysis of livestock and poultry manure pollution in China and its treatment and resource utilization. J Agro-Environ Sci. 2020;39:1168–76.
Google Scholar 
10.Chen J, Yu Z, Michel FC Jr., Wittum T, Morrison M. Development and application of real-time PCR assays for quantification of erm genes conferring resistance to macrolides-lincosamides-streptogramin B in livestock manure and manure management systems. Appl Environ Microbiol. 2007;73:4407–16.CAS 
Article 

Google Scholar 
11.Duan M, Gu J, Wang X, Li Y, Zhang S, Yin Y, et al. Effects of genetically modified cotton stalks on antibiotic resistance genes, intI1, and intI2 during pig manure composting. Ecotoxicol Environ Saf. 2018;147:637–42.CAS 
Article 

Google Scholar 
12.Cui E, Wu Y, Zuo Y, Chen H. Effect of different biochars on antibiotic resistance genes and bacterial community during chicken manure composting. Bioresour Technol. 2016;203:11–7.CAS 
Article 

Google Scholar 
13.Ma Y, Wilson CA, Novak JT, Riffat R, Aynur S, Murthy S, Pruden A. Effect of various sludge digestion conditions on sulfonamide, macrolide, and tetracycline Resistance Genes and Class I Integrons. Environ Sci Technol. 2011;45:7855–61.CAS 
Article 

Google Scholar 
14.Tien YC, Li B, Zhang T, Scott A, Murray R, Sabourin L, et al. Impact of dairy manure pre-application treatment on manure composition, soil dynamics of antibiotic resistance genes, and abundance of antibiotic-resistance genes on vegetables at harvest. Sci Total Environ. 2017;581-582:32–9.CAS 
Article 

Google Scholar 
15.Zhang L, Sun XY. Effects of waste lime and Chinese medicinal herbal residue amendments on physical, chemical, and microbial properties during green waste composting. Environ Sci Pollut Res. Int. 2018;25:31381–95.CAS 
Article 

Google Scholar 
16.Wang YQ, Wu XQ, Zhu TT, Ma QG, Chen HG. Study on utilization of solid slag compost of Chinese medicinal herbal. J Chin Medicinal Mater. 2008;31:1622–4.CAS 

Google Scholar 
17.Wu DL, Liu P, Luo YZ, Tian GM, Mahmood Q. Nitrogen transformations during co-composting of herbal residues, spent mushrooms, and sludge. J Zhejiang Univ Sci B. 2010;11:497–505.Article 

Google Scholar 
18.Ward T, Larson J, Meulemans J, Hillmann B, Lynch J, Sidiropoulos D, et al. BugBase predicts organism-level microbiome phenotypes. bioRxiv. 2017;133462.19.Chao A. Nonparametric estimation of the number of classes in a population. Scand J Stat. 1984;11:265–70.
Google Scholar 
20.Chao A, Yang MCK. Stopping rules and estimation for recapture debugging with unequal failure rates. Biometrika. 1993;80:193–201.Article 

Google Scholar 
21.Shannon CE. A mathematical theory of communication. Bell Syst Tech J. 1948;27:623–56.Article 

Google Scholar 
22.Simpson EH. Measurement of diversity. Nature 1949;163:688.Article 

Google Scholar 
23.Huang K, Xia H, Wu Y, Chen J, Cui G, Li F, et al. Effects of earthworms on the fate of tetracycline and fluoroquinolone resistance genes of sewage sludge during vermicomposting. Bioresour Technol. 2018;259:32–9.CAS 
Article 

Google Scholar 
24.Qian X, Sun W, Gu J, Wang XJ, Sun JJ, Yin YN, et al. Variable effects of oxytetracycline on antibiotic resistance gene abundance and the bacterial community during aerobic composting of cow manure. J Hazard Mater. 2016;315:61–9.CAS 
Article 

Google Scholar 
25.Zhang R, Gu J, Wang X, Li Y, Zhang K, Yin Y, Zhang X. Contributions of the microbial community and environmental variables to antibiotic resistance genes during co-composting with swine manure and cotton stalks. J Hazard Mater. 2018;358:82–91.CAS 
Article 

Google Scholar 
26.Wang H, Sangwan N, Li HY, Su JQ, Oyang WY, Zhang ZJ, et al. The antibiotic resistome of swine manure is significantly altered by association with the Musca domestica larvae gut microbiome. Isme J. 2017;11:100–11.Article 

Google Scholar 
27.Li J, Xin Z, Zhang Y, Chen J, Yan J, Li H, Hu H. Long-term manure application increased the levels of antibiotics and antibiotic resistance genes in a greenhouse soil. Appl Soil Ecol. 2017;121:193–200.Article 

Google Scholar 
28.Su JQ, Wei B, Ou-Yang WY, Huang FY, Zhao Y, Xu HJ, et al. Antibiotic resistome and its association with bacterial communities during sewage sludge composting. Environ Sci Technol. 2015;49:7356–63.CAS 
Article 

Google Scholar 
29.Li H, Duan M, Gu J, Zhang Y, Qian X, Ma J, et al. Effects of bamboo charcoal on antibiotic resistance genes during chicken manure composting. Ecotoxicol Environ Saf. 2017;140:1–6.Article 

Google Scholar 
30.Zhang J, Lin H, Ma J, Sun W, Yang Y, Zhang X. Compost-bulking agents reduce the reservoir of antibiotics and antibiotic resistance genes in manures by modifying bacterial microbiota. Sci Total Environ. 2019;649:396–404.CAS 
Article 

Google Scholar 
31.Ghosh S, Ramsden SJ, LaPara TM. The role of anaerobic digestion in controlling the release of tetracycline resistance genes and class 1 integrons from municipal wastewater treatment plants. Appl Microbiol Biotechnol. 2009;84:791–6.CAS 
Article 

Google Scholar 
32.Selvam A, Xu D, Zhao Z, Wong JW. Fate of tetracycline, sulfonamide and fluoroquinolone resistance genes and the changes in bacterial diversity during composting of swine manure. Bioresour Technol. 2012;126:383–90.CAS 
Article 

Google Scholar 
33.Antunes P, Machado J, Sousa JC, Peixe L. Dissemination of sulfonamide resistance genes (sul1, sul2, and sul3) in Portuguese Salmonella enterica strains and relation with integrons. Antimicrob Agents Chemother. 2005;49:836–9.CAS 
Article 

Google Scholar 
34.Zhu YG, Johnson TA, Su JQ, Qiao M, Guo GX, Stedtfeld RD, et al. Diverse and abundant antibiotic resistance genes in Chinese swine farms. Proc Natl Acad Sci USA. 2013;110:3435–40.CAS 
Article 

Google Scholar 
35.Chen Q, An X, Li H, Su J, Ma Y, Zhu YG. Long-term field application of sewage sludge increases the abundance of antibiotic resistance genes in soil. Environ Int. 2016;92-93:1–10.CAS 
Article 

Google Scholar  More

The GCBD is stored at figshare23. Below we describe 20 Tables (also see Fig. 3 schematic) that comprise the GCBD: (1) Site_Info_tbl, (2) Sample_Event_tbl, (3) R_Scripts_tbl, (4) Cover_tbl, (5) Bleaching_tbl, (6) Environmental_tbl, (7) Authors_LUT, (8) Bleaching_Level_LUT, (9) City_Town_Name_LUT, (10) Country_Name_LUT, (11) Data_Source_LUT, (12) Ecoregion_Name_LUT, (13) Exposure_LUT, (14) Ocean_Name_LUT, (15) Realm_Name_LUT, (16) State_Island_Province_Name_LUT, (17) Substrate_Type_LUT, (18) Relevant_Papers_tbl, (19) Severity_Code_LUT, and (20) Bleaching_Prevalence_Score_LUT, where LUT stands for look-up table.

1)

Site Information (Site_Info_tbl)
Latitude_Degrees: latitude coordinates in decimal degrees.
Longitude_Degrees: longitude coordinates in decimal degrees.
Ocean_Name: the ocean in which the sampling took place.
Realm_Name: identification of realm as defined by the Marine Ecoregions of the World (MEOW)12.
Ecoregion_Name: identification of the Ecoregions (150) as defined by Veron et al.13.
Country_Name: the country where sampling took place.
State_Island_Province_Name: the state, territory (e.g., Guam) or island group (e.g., Hawaiian Islands) where sampling took place.
City_Town_Name: the region, city, or nearest town, where sampling took place.
Site_Name: the accepted name of the site or the name given by the team that sampled the reef.
Distance_to_Shore: the distance (m) of the sampling site from the nearest land.
Exposure: a site was considered exposed if it had >20 km of fetch, if there were strong seasonal winds, or if the site faced the prevailing winds. Otherwise, the site was considered sheltered or ‘sometimes’. ‘Sometimes’ refers to a few sites with a >20 km fetch through a narrow geographic window, and therefore we considered that the site was potentially exposed during cyclone seasons. We left the category ‘sometimes’ in the database because those sites were not clearly exposed sites, nor were they clearly sheltered sites, and future researchers may be interested in temporary exposure.
Turbidity: kd490 with a 100-km buffer.
Cyclone_Frequency: number of cyclone events from 1964 to 2014.
Comments: comments of any issues with the site or additional information.

2)

Sample Event Information (Sample_Event_tbl)
Site_ID: site ID field from Site_Info_tbl.
Reef_ID: name of reef site that was adopted by sampling group (from ReefCheck).
Quadrat_No: quadrat number (from McClanahan et al.)20.
Date_Day: the date of the sampling event.
Date_Month: the month of sampling event.
Date_Year: the year of sampling event.
Depth: depth (m) of sampling site. Comments: comments of any issue or additional information of sampling event.

3)

R Code (R_Scripts_tbl)
Relevant_Papers_ID: relevant papers ID field from Relevant_Papers_tbl.
Project name: name of project associated with R code.
Paper_Title: title of paper where R code was published.
Code_Name: name of R code file.
Description: description of the R code.
Data_Source: data source ID field from Data_Source_LUT.
R_Code: attachment of R code file.
URL: hyperlink to R code or link to github.

4)

Coral Cover Information (Cover_tbl)
Sample_ID: sampled ID field from Sample_Event_tbl.
Substrate_Type: substrate type ID field from Substrate_LUT.
S1: Reef Check breaks down transects into four 20 m × 5 m segments, point data from segment one of transect.
S2: Reef Check breaks down transects into four 20 m × 5 m segments, point data from segment two of transect.
S3: Reef Check breaks down transects into four 20 m × 5 m segments, point data from segment three of transect.
S4: Reef Check breaks down transects into four 20 m × 5 m segments, point data from segment four of transect.
Perc_hardcoral: percent hard coral cover from McClanahan et al.20 data source.
Perc_macroalgae: percent macroalgae cover from McClanahan et al.20 data source.
Average_Ellipse_Transect: calculated percent hard coral cover per 10 m × 1 m transect using ellipse equation.
Average_Ellipse_Site: calculated percent hard coral cover per site using ellipse equation.
Comments: comments of any issue or additional information of sampling event

5)

Bleaching Information (Bleaching_tbl)
Sample_ID: sample ID field from Sample_Event_tbl.
Bleaching_Level: Reef Check data, coral population or coral colony.
S1: Reef Check breaks down transects into four 20 m × 5 m segments, percent bleaching from segment one of transect.
S2: Reef Check breaks down transects into four 20 m × 5 m segments, percent bleaching from segment two of transect.
S3: Reef Check breaks down transects into four 20 m × 5 m segments, percent bleaching from segment three of transect.
S4: Reef Check breaks down transects into four 20 m × 5 m segments, percent bleaching from segment four of transect.
Percent_Bleaching_RC_Old_Method: old method of determining percent bleaching from Reef_Check.
Severity_Code: coded range of bleaching severity from Donner et al.10.
Percent_Bleached: percent of coral bleaching.
Number_Bleached_colonies: number of bleached corals from McClanahan et al.20 data source.
Bleaching_intensity: from McClanahan et al.20 data source.
Bleaching_Prevalence_Score: coded range of bleaching prevalence from Safaie et al.21.

6)

Environmental Parameter Information (Environmental_tbl)
Sample_ID: sample ID field from Sample_Event_tbl.
ClimSST: CoRTAD. [Climatological Sea-Surface Temperature (SST)] based on weekly SSTs for the study time frame, created using a harmonics approach.
Temperature_ Kelvin: CoRTAD. SST in Kelvin.
Temperature_Mean: CoRTAD. Mean SST in degrees Celsius.
Temperature_Minimum: CoRTAD. Minimum SST in degrees Celsius.
Temperature_Maximum: CoRTAD. Maximum SST in degrees Celsius.
Temperature_Kelvin_Standard_Deviation: CoRTAD. Standard deviation of SST in Kelvin.
Windspeed: CoRTAD. meters per hour.
SSTA: CoRTAD. (Sea-Surface Temperature Anomaly) weekly SST minus weekly climatological SST.
SSTA_Standard_Deviation: CoRTAD. The Standard Deviation of weekly SSTA in degrees Celsius over the entire period.
SSTA_Mean: CoRTAD. The mean SSTA in degrees Celsius over the entire period.
SSTA_Minimum: CoRTAD. The minimum SSTA in degrees Celsius over the entire period.
SSTA_Maximum: CoRTAD. The maximum SSTA in degrees Celsius over the entire period.
SSTA_Frequency: CoRTAD. (Sea Surface Temperature Anomaly Frequency) number of times over the previous 52 weeks that SSTA  >  = 1 degree Celsius.
SSTA_Frequency_Standard_Deviation: CoRTAD. The standard deviation of SSTA Frequency in degrees Celsius over the entire time period of 40 years.
SSTA_FrequencyMax: CoRTAD. The maximum SSTA Frequency in degrees Celsius over the entire time period.
SSTA_FrequencyMean: CoRTAD. The mean SSTA Frequency in degrees Celsius over the entire time period of 40 years.
SSTA_DHW: CoRTAD. (Sea Surface Temperature Degree Heating Weeks) sum of previous 12 weeks when SSTA  >  = 1 degree Celsius.
SSTA_DHW_Standard_Deviation: CoRTAD. The standard deviation SSTA DHW in degrees Celsius over the entire period.
SSTA_DHWMax: CoRTAD. The maximum SSTA DHW in degrees Celsius over the entire time period of 40 years.
SSTA_DHWMean: CoRTAD. The mean SSTA DHW in degrees Celsius over the entire time period of 40 years.
TSA: CoRTAD. (Thermal Stress Anomaly) weekly SSTs minus the maximum of weekly climatological SSTs in degrees Celsius.
TSA_Standard_Deviation: CoRTAD. The standard deviation of TSA in degrees Celsius over the entire time period of 40 years.
TSA_Minimum: CoRTAD. The minimum TSA in degrees Celsius over the entire time period of 40 years.
TSA_Maximum: CoRTAD. The maximum TSA in degrees Celsius over the entire time period of 40 years.
TSA_Mean: CoRTAD. The mean TSA in degrees Celsius over the entire time period of 40 years.
TSA_Frequency: CoRTAD. The number of times over previous 52 weeks that TSA  >  = 1 degree Celsius.
TSA_Frequency_Standard_Deviation: CoRTAD. The standard deviation of frequency of TSA in degrees Celsius over the entire time period of 40 years.
TSA_FrequencyMax: CoRTAD. The maximum TSA frequency in degrees Celsius over the entire time period of 40 years.
TSA_FrequencyMean: CoRTAD. The mean TSA frequency in degrees Celsius over the entire time period of 40 years.
TSA_DHW: CoRTAD. (Thermal Stress Anomaly Degree Heating Weeks) sum of previous 12 weeks when TSA  >  = 1 degree Celsius.
TSA_DHW_Standard_Deviation: CoRTAD. The standard deviation of TSA DHW in degrees Celsius over the entire time period of 40 years.
TSA_DHWMax: CoRTAD. The maximum TSA DHW in degrees Celsius over the entire time period of 40 years.
TSA_DHWMean: CoRTAD. The mean TSA DHW in degrees Celsius over the entire time period of 40 years.

7)

Author Names (Authors_LUT)
Last_Name: author’s last name.
First_Name: author’s first name.
Middle_Initial: author’s middle initial.

8)

Bleaching Level Information (Bleaching_Level_LUT)
Bleaching_Level: Reef Check data, coral population or coral colony.

9)

City, Town Names (City_Town_Name_LUT)
City_Town_Name: the region, city, or town, where sampling took place.

10)

Country names (Country_Name_LUT)
Country_Name: name of the country where sampling took place.

11)

Data Source Information (Data_Source_LUT)
Data_Source: name of source of original data set.
Sample_Method: Description of the sampling methods used to collect the data. If more than one method was used then we stated that an amalgamation of methods were used to collect the data, and the original papers are found in “Relevant_Papers_tbl”, and can be referenced therein.

12)

Ecoregion Names (Ecoregion_Name_LUT)
Ecoregion_Name: name of Ecoregion from Veron et al.13.

13)

Exposure Type (Exposure_LUT)
Exposure_Type: site exposure to fetch.

14)

Ocean Name Information (Ocean_Name_LUT)
Ocean_Name: name of ocean where sampling took place.

15)

Name of Realm (Realm_Name_LUT)
Realm_Name: name of realm as identified by the Marine Ecoregions of the World (MEOW)12.

16)

State, Island, Province Name (State_Island_Province_Name_LUT)
State_Island_Province_Name, Name of the state, territory (e.g. Guam) or island group (e.g. Hawaiian Islands) where sampling took place.

17)

Substrate Type (Substrate_Type_LUT)
Substrate_Type: type of substrate from Reef Check data.

18)

Relevant Publications (Relevant_Papers_tbl)
Data_Source: source associated with publication.
Author_ID: author ID field from Authors_LUT.
Title: title of published work.
Journal_Name: name of publication journal.
Year_Published: year of publication.
Volume: volume number of journal.
Issue: issue number of journal.
Pages: page range of publication.
URL: hyperlink to publication.
DOI: DOI number of publication.
pdf: pdf attachment of publication.

19)

Severity Index Code (Severity_Code_LUT)
Severity_Code: coded range of bleaching severity from Donner et al.10.

20)

Bleaching Prevalence Code (Bleaching_Prevalence_Score_LUT)

Bleaching_Prevalence_Score: coded range of bleaching prevalence from Safaie et al. 21. More