Ecology

1.Hubbell, S. P. The Unified Neutral Theory of Biodiversity and Biogeography (Princeton Univ. Press, 2001).2.Wills, C. et al. Nonrandom processes maintain diversity in tropical forests. Science 311, 527–531 (2006).ADS 
CAS 
PubMed 
Article 

Google Scholar 
3.Chesson, P. Mechanisms of maintenance of species diversity. Annu. Rev. Ecol. Evol. Syst. 31, 343–366 (2000).Article 

Google Scholar 
4.Ollerton, J. Pollinator diversity: distribution, ecological function, and conservation. Annu. Rev. Ecol. Evol. Syst. 48, 353–376 (2017).Article 

Google Scholar 
5.Vamosi, J. C. et al. Pollination decays in biodiversity hotspots. Proc. Natl Acad. Sci. USA 103, 956–961 (2006).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
6.Bennett, J. M. et al. Land use and pollinator dependency drives global patterns of pollen limitation in the Anthropocene. Nat. Commun. 11, 3999 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
7.Potts, S. G. et al. Global pollinator declines: trends, impacts and drivers. Trends Ecol. Evol. 25, 345–353 (2010).PubMed 
Article 

Google Scholar 
8.Vamosi, J. C., Magallon, S., Mayrose, I., Otto, S. P. & Sauquet, H. Macroevolutionary patterns of flowering plant speciation and extinction. Annu. Rev. Plant Biol. 69, 685–706 (2018).CAS 
PubMed 
Article 

Google Scholar 
9.Ollerton, J., Winfree, R. & Tarrant, S. How many flowering plants are pollinated by animals? Oikos 120, 321–326 (2011).Article 

Google Scholar 
10.Rodger, J. G. et al. 2021 Widespread vulnerability of plant seed production to pollinator decline. Sci. Adv. (in the press).11.Pimm, S. L., Jones, H. L. & Diamond, J. On the risk of extinction. Am. Nat. 132, 757–785 (1988).Article 

Google Scholar 
12.Sargent, R. D. & Ackerly, D. D. Plant–pollinator interactions and the assembly of plant communities. Trends Ecol. Evol. 23, 123–130 (2008).PubMed 
Article 

Google Scholar 
13.Benadi, G. & Pauw, A. Frequency dependence of pollinator visitation rates suggests that pollination niches can allow plant species coexistence. J. Ecol. 106, 1892–1901 (2018).Article 

Google Scholar 
14.Bruno, J. F., Stachowicz, J. J. & Bertness, M. D. Inclusion of facilitation into ecological theory. Trends Ecol. Evol. 18, 119–125 (2003).Article 

Google Scholar 
15.Benadi, G., Bluthgen, N., Hovestadt, T. & Poethke, H. J. Population dynamics of plant and pollinator communities: stability reconsidered. Am. Nat. 179, 157–168 (2012).PubMed 
Article 

Google Scholar 
16.Moeller, D. A. Facilitative interactions among plants via shared pollinators. Ecology 85, 3289–3301 (2004).Article 

Google Scholar 
17.Bergamo, P. J., Susin Streher, N., Traveset, A., Wolowski, M. & Sazima, M. Pollination outcomes reveal negative density-dependence coupled with interspecific facilitation among plants. Ecol. Lett. 23, 129–139 (2020).PubMed 
Article 

Google Scholar 
18.Barrett, S. C. H. The evolution of plant sexual diversity. Nat. Rev. Genet. 3, 274–284 (2002).CAS 
PubMed 
Article 

Google Scholar 
19.Ashman, T. L. & Arceo-Gómez, G. Toward a predictive understanding of the fitness costs of heterospecific pollen receipt and its importance in co-flowering communities. Am. J. Bot. 100, 1061–1070 (2013).PubMed 
Article 

Google Scholar 
20.Moreira-Hernández, J. I. & Muchhala, N. Importance of pollinator-mediated interspecific pollen transfer for angiosperm evolution. Annu. Rev. Ecol. Evol. Syst. 50, 191–217 (2019).Article 

Google Scholar 
21.Ashman, T. L. et al. Pollen limitation of plant reproduction: ecological and evolutionary causes and consequences. Ecology 85, 2408–2421 (2004).Article 

Google Scholar 
22.Tur, C., Saez, A., Traveset, A. & Aizen, M. A. Evaluating the effects of pollinator-mediated interactions using pollen transfer networks: evidence of widespread facilitation in south Andean plant communities. Ecol. Lett. 19, 576–586 (2016).CAS 
PubMed 
Article 

Google Scholar 
23.Levin, D. A. & Anderson, W. W. Competition for pollinators between simultaneously flowering species. Am. Nat. 104, 455–467 (1970).Article 

Google Scholar 
24.Ashman, T. L., Alonso, C., Parra-Tabla, V. & Arceo-Gómez, G. Pollen on stigmas as proxies of pollinator competition and facilitation: complexities, caveats and future directions. Ann. Bot. 125, 1003–1012 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
25.Lloyd, D. G. Some reproductive factors affecting the selection of self-fertilization in plants. Am. Nat. 113, 67–79 (1979).MathSciNet 
Article 

Google Scholar 
26.Sargent, R. D. & Otto, S. P. The role of local species abundance in the evolution of pollinator attraction in flowering plants. Am. Nat. 167, 67–80 (2006).PubMed 
Article 

Google Scholar 
27.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 
Article 

Google Scholar 
28.Armbruster, W. S. The specialization continuum in pollination systems: diversity of concepts and implications for ecology, evolution and conservation. Funct. Ecol. 31, 88–100 (2017).Article 

Google Scholar 
29.Minnaar, C., Anderson, B., de Jager, M. L. & Karron, J. D. Plant–pollinator interactions along the pathway to paternity. Ann. Bot. 123, 225–245 (2019).PubMed 
Article 

Google Scholar 
30.Kantsa, A. et al. Disentangling the role of floral sensory stimuli in pollination networks. Nat. Commun. 9, 1041 (2018).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
31.Fang, Q. & Huang, S. Q. A directed network analysis of heterospecific pollen transfer in a biodiverse community. Ecology 94, 1176–1185 (2013).PubMed 
Article 

Google Scholar 
32.Baldwin, B. G. Origins of plant diversity in the California floristic province. Annu. Rev. Ecol. Evol. Syst. 45, 347–369 (2014).Article 

Google Scholar 
33.Bascompte, J., Jordano, P., Melian, C. J. & Olesen, J. M. The nested assembly of plant–animal mutualistic networks. Proc. Natl Acad. Sci. USA 100, 9383–9387 (2003).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
34.Thomson, J. D., Fung, H. F. & Ogilvie, J. E. Effects of spatial patterning of co-flowering plant species on pollination quantity and purity. Ann. Bot. 123, 303–310 (2019).PubMed 
Article 

Google Scholar 
35.Rezende, E. L., Lavabre, J. E., Guimaraes, P. R., Jordano, P. & Bascompte, J. Non-random coextinctions in phylogenetically structured mutualistic networks. Nature 448, 925–928 (2007).ADS 
CAS 
PubMed 
Article 

Google Scholar 
36.Song, C. L., Rohr, R. P. & Saavedra, S. Why are some plant–pollinator networks more nested than others? J. Anim. Ecol. 86, 1417–1424 (2017).PubMed 
Article 

Google Scholar 
37.Hegland, S. J., Nielsen, A., Lazaro, A., Bjerknes, A. L. & Totland, O. How does climate warming affect plant–pollinator interactions? Ecol. Lett. 12, 184–195 (2009).PubMed 
Article 

Google Scholar 
38.Ohlemuller, R. et al. The coincidence of climatic and species rarity: high risk to small-range species from climate change. Biol. Lett. 4, 568–572 (2008).PubMed 
PubMed Central 
Article 

Google Scholar 
39.Arceo-Gómez, G., Kaczorowski, R. L. & Ashman, T.-L. A network approach to understanding patterns of coflowering in diverse communities. Int. J. Plant Sci. 179, 569–582 (2018).Article 

Google Scholar 
40.Koski, M. H. et al. Plant–flower visitor networks in a serpentine metacommunity: assessing traits associated with keystone plant species. Arthropod Plant Interact. 9, 9–21 (2015).Article 

Google Scholar 
41.Arceo-Gómez, G. et al. Patterns of among- and within-species variation in heterospecific pollen receipt: the importance of ecological generalization. Am. J. Bot. 103, 396–407 (2016).PubMed 
Article 
CAS 

Google Scholar 
42.Chao, A. et al. Rarefaction and extrapolation with Hill numbers: a framework for sampling and estimation in species diversity studies. Ecol. Monogr. 84, 45–67 (2014).Article 

Google Scholar 
43.R Core Team. R: A Language and Environment for Statistical Computing, https://www.R-project.org/ (R Foundation for Statistical Computing, 2019).44.Arceo-Gómez, G., Alonso, C., Ashman, T.-L. & Parra-Tabla, V. Variation in sampling effort affects the observed richness of plant–plant interactions via heterospecific pollen transfer: implications for interpretation of pollen transfer networks. Am. J. Bot. 105, 1601–1608 (2018).PubMed 
Article 

Google Scholar 
45.Hayes, R. A., Cullen N., Kaczorowski R. L., O’Neill E. M. & Ashman T-L. A community-wide description and key of pollen from co-flowering plants of the serpentine seeps of Mclaughlin Reserve. Madrono (in the press).46.Dafni, A. Pollination Ecology: a Practical Approach (Oxford Univ. Press, 1992).47.McMurdie, P. J. & Holmes, S. Waste NOT, want not: why rarefying microbiome data is inadmissible. PLoS Comp. Biol. 10, e1003531 (2014).ADS 
Article 
CAS 

Google Scholar 
48.Qian, H. & Jin, Y. An updated megaphylogeny of plants, a tool for generating plant phylogenies and an analysis of phylogenetic community structure. J. Plant Ecol. 9, 233–239 (2016).Article 

Google Scholar 
49.Zanne, A. E. et al. Three keys to the radiation of angiosperms into freezing environments. Nature 506, 89–92 (2014).ADS 
CAS 
PubMed 
Article 

Google Scholar 
50.Hinchliff, C. E. et al. Synthesis of phylogeny and taxonomy into a comprehensive tree of life. Proc. Natl Acad. Sci. USA 112, 12764–12769 (2015).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
51.Paradis, E. & Schliep, K. ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics 35, 526–528 (2019).CAS 
PubMed 
Article 

Google Scholar 
52.Michonneau, F., Brown, J. W. & Winter, D. J. rotl: an R package to interact with the Open Tree of Life data. Methods Ecol. Evol. 7, 1476–1481 (2016).Article 

Google Scholar 
53.Revell, L. J. phytools: an R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3, 217–223 (2012).Article 

Google Scholar 
54.Le, S., Josse, J. & Husson, F. FactoMineR: an R package for multivariate analysis. J. Stat. Softw. 25, 1–18 (2008).Article 

Google Scholar 
55.Dormann, C. F., Gruber, B. & Fruend, J. Introducing the bipartite package: analysing ecological networks. R News 8, 8–11 (2008).
Google Scholar 
56.Feinsinger, P., Spears, E. E. & Poole, R. W. A simple measure of niche breadth. Ecology 62, 27–32 (1981).Article 

Google Scholar 
57.Horn, H. S. Measurement of “overlap” in comparative ecological studies. Am. Nat. 100, 419–424 (1966).Article 

Google Scholar 
58.Almeida-Neto, M., Guimaraes, P., Guimaraes, P. R., Loyola, R. D. & Ulrich, W. A consistent metric for nestedness analysis in ecological systems: reconciling concept and measurement. Oikos 117, 1227–1239 (2008).Article 

Google Scholar 
59.Csardi, G. & Nepusz, T. The igraph software package for complex network research. InterJournal 1695, 1–9 (2006).
Google Scholar 
60.Patefield, W. Algorithm AS 159: an efficient method of generating random R × C tables with given row and column totals. Appl. Stat. 30, 91–97 (1981).MATH 
Article 

Google Scholar 
61.Oksanen, J. et al. vegan: Community Ecology Package. R package version 2.5–5, https://CRAN.R-project.org/package=vegan (2019).62.Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).63.Bastian, M., Heymann, S. & Jacomy, M. Gephi: an open source software for exploring and manipulating networks. Presented at the Third international AAAI Conference on Weblogs and Social Media (2009).64.Arceo-Gómez, G., Kaczorowski, R. L., Patel, C. & Ashman, T. L. Interactive effects between donor and recipient species mediate fitness costs of heterospecific pollen receipt in a co-flowering community. Oecologia 189, 1041–1047 (2019).ADS 
PubMed 
Article 

Google Scholar 
65.Keck, F., Rimet, F., Bouchez, A. & Franc, A. phylosignal: an R package to measure, test, and explore the phylogenetic signal. Ecol. Evol. 6, 2774–2780 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
66.Orme, D. et al. caper: comparative analyses of phylogenetics and evolution in R. R package version 1.0.1, https://CRAN.R-project.org/package=caper (2018).67.Barrett, S. C. H. The evolution of plant sexual diversity. Nat. Rev. Genet. 3, 274–284 (2002).CAS 
PubMed 
Article 

Google Scholar 
68.Fort, H., Vazquez, D. P. & Lan, B. L. Abundance and generalisation in mutualistic networks: solving the chicken-and-egg dilemma. Ecol. Lett. 19, 4–11 (2016).PubMed 
Article 

Google Scholar 
69.Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D. & R Core Team. nlme: linear and nonlinear mixed effects models. R package version 3.1-143, https://CRAN.R-project.org/package=nlme (2019).70.Lefcheck, J. S. & Freckleton, R. piecewiseSEM: piecewise structural equation modelling in R for ecology, evolution, and systematics. Methods Ecol. Evol. 7, 573–579 (2015).Article 

Google Scholar 
71.Fox, J. & Weisberg, S. An R companion to Applied Regression, 3rd edition (Sage, 2019).72.Blüthgen, N., Menzel, F. & Blüthgen, N. Measuring specialization in species interaction networks. BMC Ecol. 6, 9 (2006).PubMed 
PubMed Central 
Article 

Google Scholar 
73.Shipley, B. The AIC model selection method applied to path analytic models compared using a d-separation test. Ecology 94, 560–564 (2013).PubMed 
Article 

Google Scholar 
74.van der Bijl, W. phylopath: easy phylogenetic path analysis in R. PeerJ 6, e4718 (2018).PubMed 
PubMed Central 
Article 

Google Scholar  More

NEWS AND VIEWS
08 September 2021

Pollination advantage of rare plants unveiled

An analysis of plant–pollinator interactions reveals that the presence of abundant plant species favours the pollination of rare species. Such asymmetric facilitation might promote the coexistence of species in diverse plant communities.

Marcelo A. Aizen

 ORCID: http://orcid.org/0000-0001-9079-9749

0

Marcelo A. Aizen

Marcelo A. Aizen is at the Research Institute for Biodiversity and the Environment (INIBIOMA), National University of Comahue – CONICET, 8400 San Carlos de Bariloche, Río Negro, Argentina, and at the Institute for Advanced Study, Berlin, Germany.

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Species diversification results from the balance between the formation of new species (speciation) and the loss of existing ones (extinction). The tremendous proliferation of different life forms on Earth can be attributed to both high rates of speciation and low rates of extinction. Flowering plants — a group called angiosperms — are one of the most diverse groups of non-mobile organism. There are approximately 352,000 plant species, nearly 90% of which depend, to various extents, on insects and other animals for pollination and seed production1. These animal pollinators have been key to the unstoppable diversification of the angiosperms, starting at least 120 million years ago, with pollinators promoting speciation by acting as potent selection agents for a plethora of flower traits2,3. Pollinators also aid species persistence by enabling pollen transfer between relatively distant individuals in sparse plant populations4. Writing in Nature, Wei et al.5 report that, for plant species that flower at the same time, pollinators mediate interactions that might facilitate species coexistence in diverse plant communities.

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doi: https://doi.org/10.1038/d41586-021-02375-z

References1.Ollerton, J., Winfree, R. & Tarrant, S. Oikos 120, 321–326 (2011).Article 

Google Scholar 
2.Hernández-Hernández, T. & Wiens, J. J. Am. Nat. 195, 948–963 (2020).PubMed 
Article 

Google Scholar 
3.van der Niet, T. & Johnson, S. D. Trends Ecol. Evol. 27, 353–361 (2012).PubMed 
Article 

Google Scholar 
4.Bawa, K. S. Trends Ecol. Evol. 10, 311–312 (1995).PubMed 
Article 

Google Scholar 
5.Wei, N. et al. Nature https://doi.org/10.1038/s41586-021-03890-9 (2021).Article 

Google Scholar 
6.Hegland, S. J., Grytnes, J.-A. & Totland, Ø. Ecol. Res. 24, 929–936 (2009).Article 

Google Scholar 
7.Sargent, R. D. & Ackerly, D. D. Trends Ecol. Evol. 23, 123–130 (2008).PubMed 
Article 

Google Scholar 
8.Ghazoul, J. J. Ecol. 94, 295–304 (2006).Article 

Google Scholar 
9.Tur, C., Sáez, A., Traveset, A. & Aizen, M. A. Ecol. Lett. 19, 576–586 (2016).PubMed 
Article 

Google Scholar 
10.Bergamo, P. J., Streher, N. S., Traveset, A., Wolowski, M. & Sazima, M. Ecol. Lett. 23, 129–139 (2020).PubMed 
Article 

Google Scholar 
11.Morales, C. L. & Traveset, A. Crit. Rev. Plant Sci. 27, 221–238 (2008).Article 

Google Scholar 
12.Silvertown, J., Franco, M., Pisanty, I. & Mendoza, A. J. Ecol. 81, 465–476 (1993).Article 

Google Scholar 
13.IPBES. The Assessment Report on Pollinators, Pollination and Food Production of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES, 2016).
Google Scholar 
14.Knight, T. M. et al. Annu. Rev. Ecol. Syst. 36, 467–497 (2005).Article 

Google Scholar 
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All analysis was undertaken in Great Britain and associated islands over 20 km2. All prioritisations were undertaken at a 10 × 10 km landscape-scale on cells with greater than half land coverage. We considered designations ‘protected for biodiversity’ to be Sites of Special Scientific Interest (SSSI) and National Nature Reserves (NNR); and landscape protection designations to include National Parks (NP), Areas of Outstanding Natural Beauty (AONB), and Scottish National Scenic Areas (NSA). Different cell protection ‘cutoffs’ were tested at 30, 40, 50, 60, and 70% (Supplementary Table 1). Hence cells were considered to be ‘protected for biodiversity’ at the landscape-scale if SSSI/NNR coverage was above the percentage land cutoff, e.g. at least 40% IUCN IV protection (Fig. 1: black cells). ‘Protected landscapes’ were 10 × 10 km cells with total coverage from all of the designations above the cutoff, e.g. at least 40% IUCN V (or greater) protection, but under 40% level IV protection (Fig. 1b: grey cells). Results were qualitatively similar for all cutoffs (Supplementary Tables 2 and 3). The joint proportion of cells protected for biodiversity and protected landscapes were most similar to the actual coverage at the 40% ‘cutoff’ (27.80% of 10 × 10 km cells ‘protected’ compared to 26.71% actual area coverage), and this is presented in the main text. All designation data used is publicly available from the respective national spatial data repositories for England21 (SSSI/NNR/NP/AONB), Scotland22 (SSSI/NNR/NP/NSA), and Wales23 (SSSI/NNR/NP/AONB).We used the recorded distributions of 445 priority species listed under the Section 41 (Natural Environment and Rural Communities Act, 2006), provided by Butterfly Conservation (BC), Biological Records Centre (BRC); and breeding bird atlas data from British Trust for Ornithology (BTO)24. BTO bird atlas data are only available at the 10 × 10 km scale, which limited the spatial resolution of the analysis. We used all priority species that we were able to acquire from the above recording bodies between 2000 and 2014 (Supplementary Data 1). We used the raw distribution records for 156 species that were very localised (10 or fewer presence records) and for a further 77 species which could not be modelled (most of which were also very rare, and for which models did not converge). For the remaining 212 species with over 10 presence records, we interpolated their range using Integrated Nested Laplace Approximations (INLA) in the inlabru R package25. We used a joint model predicting distribution while accounting for recording effort, including biologically relevant covariates: seasonality, growing degree days, water availability, winter cold26, and soil pH from the Countryside Survey 2007 dataset27. These covariates were calculated from monthly means of weather data (mean temperature, sunshine and rainfall) for the decade to 2014 provided by the Met Office28. We also included soil moisture in the calculation of water availability29. We used raw data records from all 445 species, along with broad habitat layers extracted from the Land Cover Map 201530, in a Frescalo analysis31 to estimate recorder effort. See Supplementary Methods for further details of modelling.We carried out a spatial prioritisation using Core Area Zonation32, whereby cells are removed iteratively, first removing those that contribute the smallest cell value: the maximum proportion of species distributions within the remaining cells. In this way cells remaining longer within the solution complement species representation of other cells to a greater extent, and hence contribute most to underrepresented species’ distributions. However, priorities were constrained by masking or ‘locking in’ different relevant areas to each scenario such that all other cells must be removed first; reducing overall solution optimality but ensuring complementarity to masked areas. Scenario 1 only masked cells protected for biodiversity and didn’t consider other designations beyond that. Scenario 2 also masked cells protected for biodiversity but, corresponding to the 30by30 pledge, additionally masked protected landscapes.We undertook a parallel analysis additionally incorporating opportunity costs calculated from agricultural land classification and urban areas33,34,35 (Supplementary Fig. 2, Supplementary Table 4). Although urban areas are often excluded from SCP analyses, it is important to consider species complementarity of all landscapes (the government 30% target applies to the entire land surface). Since some urban/near-urban areas contain nationally rare species, we include urban areas, albeit imposing the maximum opportunity cost in these cells. In this analysis, cell value was calculated as the maximum proportion of species distributions within the remaining cells divided by the mean opportunity cost of the cell (Supplementary Fig. 3, Supplementary Tables 2 and 3).Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

Data collectionSelection of the case cities and city functional componentsTo make the research results more universal, we set the criteria for the selection of case cities as follows. (1) Large cities: cities in which the built-up area exceeded 1000 km2. We chose Beijing, Shanghai, and Tianjin. Beijing is China’s capital and political centre, Shanghai is China’s largest economic centre, and Tianjin is one of China’s four municipalities directly governed by the Central Government; (2) medium cities: cities in which the built-up area varied between 400 and 1000 km2. We chose two provincial capital cities in central China, Wuhan and Hefei, and an economically developed coastal city, Ningbo; (3) small cities: cities in which the built-up area was smaller than 400 km2. Small cities need to have a complete urban form and functions. We selected three economically developed small cities Changzhou, Nantong and Jiaxing.The selection of city functional component types should cover typical city functional components related to the coupling between humans and the city in urban systems, including production, processing, circulation, decomposition and other functions: Kentucky Fried Chicken (KFC) and McDonald’s (McD), two of the most popular western fast-food restaurants in China; Lanzhou Noodles (LZN) and Shaxian Snacks (SXS), two of the most popular Chinese fast-food restaurants in China; Agricultural Bank of China (ABC), one of the four most widely distributed banks in China; swimming pool (SP), a type of indoor sports venue popular in recent years; Shunfeng (SF) and Shentong (STO) express outlets, two of the most commonly used express service components in China; China National Petroleum Corporation (CNPC) and China Petroleum and Chemical Corporation (Sinopec) gas stations, two gas station enterprises accounting for more than half of the total number of gas stations in China; WTP, a type of waste treatment component; GH, a type of primary biological production component; and DF, a type of secondary biological processing component.Acquisition of city functional component dataLatitude and longitude data of the above city functional components were obtained through electronic maps and remote sensing images and verified through field investigation. AutoNavi and Baidu electronic maps are the two most widely used map suppliers in China due to their high accuracy and practicality46. In particular, the location of service city functional components can be accurately obtained through electronic maps. WTPs have detailed lists and location data on the government websites, and GHs can be accurately identified in Google Earth images due to their unique appearance31. Therefore, these three types of raw data are listed as the main sources of location data for functional components.Latitude and longitude data of the KFC, McD, ABC, SP, LZN, SXS, SF, STO, CNPC, Sinopec and DF locations were retrieved from AutoNavi and Baidu historical electronic maps through Python 3.5 software (https://www.python.org/). The 2012 and 2015 historical electronic map data originated from the East China Normal University Humanities and Social Sciences Big Data Platform47, and the 2018 historical electronic map data originated from the Peking University Open Research Data Platform48. Based on AutoNavi and Baidu, each individual component was strictly filtered by name and type. Please refer to the Supplementary Table S3 for a summary of the detailed filtering conditions.Accurate WTP latitude and longitude data were obtained by using the WTP name and address to query the AutoNavi map coordinate picking system [the WTP name and address were acquired from the Ministry of Ecology and Environment of the People’s Republic of China (www.mee.gov.cn), China Environment Network (www.cenews.com.cn) and Beijing Municipal Ecology and Environment Bureau (sthjj.beijing.gov.cn)]. GH latitude and longitude data were determined via a method commonly used in community ecology, which has previously been reported31. Briefly, ArcGIS 10.3 software was employed to generate grids covering the entire city (the size of each grid was 0.5 × 0.5 km), and these grids were then converted into the keyhole markup language (KML) format and imported into Google Earth for GH visual interpretation. The GHs were characterized as (a) bright white or bluish-white, (b) rectangular-shaped objects, (c) oriented in rows or separated by paths or bare areas. If a GH occurred in a specific grid, the centre of the grid was marked with the landmark tool to obtain the corresponding latitude and longitude data.Land price and housing price are affected by location factors such as population, employment, transportation, and amenities and are important indicators to determine whether a city is monocentric or polycentric49,50. Land price was also used as a determining indicator in our study. The concentric circle model was first established by Von Thünen51 to study the order of agricultural land use from urban to rural areas, and it is still an important method to explore research topics along the urban–rural gradient32,52.To obtain the land price distribution curve along the urban–rural gradient, all the standard land parcel information in each case city through the real-time land price query function provided by the China Land Price Information Service Platform (www.landvalue.com.cn), including land price, latitude and longitude, was obtained, and the parcel with highest land price was defined as the city centre. Concentric circles with an increasing radius of 1-km intervals were generated by adopting the city centre as the circle centre, and the average land price of all standard land parcels in each concentric ring was considered as the land price of the ring. We found that in all the case cities, the land price exhibited an obvious monotonous downward trend from the centre to the edge of the city (Supplementary Fig. S7). Therefore, we assumed a monocentric city model and used the concentric circles to define the urban–rural gradient.To acquire density distribution curves of the city functional components along urban–rural gradients, the latitude and longitude data of the KFC, McD, ABC, SP, LZN, SXS, SF, STO, CNPC, Sinopec, GH, WTP and DF components were applied for map labelling purposes. Concentric circles with the increasing radius of 1-km intervals were generated by adopting the city centre as the circle centre, and the number of each type of component in each concentric ring was counted. Since the overall number of WTPs and DFs was smaller, the concentric circle radius was increased at 5- and 10-km intervals, respectively, and the number of WTPs or DFs in each concentric ring was determined, while the component density in each ring was calculated by dividing the number by the area of the ring.To calculate the ecosystem services per unit area for each type of city functional component, the revenue of each component in the current year was determined. KFC and McD revenue data were retrieved from Yum China Holdings and Askci Corporation, respectively. ABC revenue data originated from the Agricultural Bank of China, Ltd., and SF and STO revenue data were acquired from SF Holding Corporation, Ltd., and STO Express Corporation, Ltd., respectively, while CNPC and Sinopec revenue data were retrieved from PetroChina Company, Ltd., and Sinopec Corporation, respectively. Moreover, LZN and SXS revenue data were obtained via field investigation. Environmental impact data of the KFC, McD, CNPC and Sinopec components originated from the Ministry of Ecology and Environment of the People’s Republic of China (www.mee.gov.cn), while LZN and SXS environmental impact data were obtained via field investigation. The costs of the KFC, McD, LZN, SXS, CNPC and Sinopec environmental impacts were converted according to the Environmental Protection Tax Law, 2018. The WTP ecosystem services were retrieved from Liu et al.53, and the GH ecosystem services originated from Chang et al.54, while the DF ecosystem services were obtained from Fan et al.55. The cultural services of all components were determined through field investigation.Data processingTo intuitively describe the density changes of city functional components along the urban–rural gradient, the density of the components in the above concentric rings were adopted as the ordinate, the distance from the city centre to the edge of the ring was adopted as the abscissa, and scatter plots were created. To compare the characteristic values of the density distribution of each type of component more clearly, a distribution model was used to fit the scatter plots35,36.Fitting of the density distribution curve of the city functional componentsThrough the nonlinear fitting function in OriginPro 2019 software (https://www.originlab.com/), the Gumbel model56,57 was considered to fit the above scatter plots to generate density distribution curves of all city functional components. The goodness-of-fit (choosing the 13 types of components in Beijing as examples) is shown in the Supplementary Fig. S2.The component density (P, individual components km−2) at a given distance from the city centre (d, km) along the urban–rural gradient is calculated as follows:$${P} = {P_{max}} {cdot} {{e^{-{e}}}^{-frac{{{d}}-{d^{*}}}{{w}} , – , frac{{{d}}-{d^{*}}}{{w}} , + , {1}}}$$
(1)
where Pmax (individual components km−2) is the peak value of the curve, d* (km) is the peak position of the curve, and w (km) is a parameter controlling the width of the curve.Calculation of the niche width of the density distribution curve of the city functional componentsTo intuitively compare the distance spanned by the density distribution curve of the city functional components, the difference in the abscissa between a density value of 10% of the Pmax value on the density distribution curve was adopted as the niche width W (km).Calculation of the skewness and kurtosis of the density distribution curve of the city functional componentsThe skewness and kurtosis are calculated according to the following equation58:$$text{skewness } = frac{frac{1}{{{n}}}{sum }_{{{i}}= {1}}^{{n}} ,{left({{x}}_{{i}}-{bar{x}}right)}^{3}}{{left(frac{1}{{{n}}}{sum}_{{{i}}= {1} }^{{n}} ,{left({{x}}_{{i}}-{bar{x}}right)}^{2}right)}^{frac{3}{{2}}}}$$
(2)
$$text{kurtosis } = frac{frac{1}{{{n}}}{sum }_{{{i}}= {1}}^{{n}} ,{left({{x}}_{{i}}-{bar{x}}right)}^{4}}{left(frac{1}{n}sumnolimits_{i=1}^{n} ,{left({{x}}_{{i}}-{bar{x}}right)}^{2}right)^2}-3$$
(3)

where xi (km) is the distance from each individual type of component to the city centre, and ‾x (km) is the average of the distances from all individual types of components to the city centre.Correlation analysis between the characteristic values of the density distribution curveLinear and nonlinear regression analyses in Microsoft Excel 2019 were implemented to study the relationship between the characteristic values of the density distribution curve, and the regression form with the best R2 value was selected.Correlation analysis between the characteristic values of the density distribution curve and the city sizeLinear and nonlinear regression analyses in Microsoft Excel 2019 were implemented to study the relationship between the characteristic values of the density distribution curve and the city size, and the regression form with the best R2 value was selected.Framework for ecosystem service assessment of the city functional componentsAccording to the classification of the Millennium Ecosystem Assessment (MA), ecosystem services include provisioning, regulating, cultural and supporting services59. In this study, the ecosystem services (goods and services) provided by the city functional components (artificial ecosystems) were divided into target and accompanied services (Supplementary Fig. S6), both of which may include provisioning, regulating and cultural services.In this study, the target services of the KFC, McD, LZN, SXS, CNPC, Sinopec, GH, and DF components were provisioning services, the target services of the ABC, SF, STO, and WTP components were regulating services, and the target services of component SP were cultural services. According to the guidance of Liu et al.53, the above regulating and cultural services were divided into positive and negative services (dis-services).The net service (NES, USD m−2 yr−1) is the sum of the positive services (target services + positive regulating services + positive cultural services) and dis-services (negative regulating services + negative cultural services):$${NES} = sum_{{i} = 1}^{n}{ES}_{i}$$
(4)

where ESi (USD m−2 yr−1) is the value of a given type of ecosystem service involved in this study, and n is the number of ecosystem service types involved in this study.The ecological index (γ) is calculated as follows:$${gamma } = {TGS}/ |EDS|$$
(5)

where TGS (USD m−2 yr−1) denotes the target services of the city functional components, and EDS (USD m−2 yr−1) denotes the dis-services of the city functional components.Calculation of the ecosystem services of the city functional componentsThe calculation methods are provided in the supplementary materials. More

1.Levin, L. A. et al. The function of marine critical transition zones and the importance of sediment biodiversity. Ecosystems 4, 430–451 (2001).CAS 
Article 

Google Scholar 
2.Sarathchandra, C. et al. Significance of mangrove biodiversity conservation in fishery production and living conditions of coastal communities in Sri Lanka. Diversity 10, 20 (2018).Article 

Google Scholar 
3.Brown, C. J. et al. The assessment of fishery status depends on fish habitats. Fish Fish. 20, 1–14 (2019).CAS 
Article 

Google Scholar 
4.Kathiresan, K. & Bingham, B. L. Biology of mangroves and mangrove ecosystems. Adv. Mar. Biol. 40, 84–254 (2001).
Google Scholar 
5.De La Morinière, E. C., Pollux, B., Nagelkerken, I. & Van der Velde, G. Post-settlement life cycle migration patterns and habitat preference of coral reef fish that use seagrass and mangrove habitats as nurseries. Estuar. Coast. Shelf Sci. 55, 309–321 (2002).ADS 
Article 

Google Scholar 
6.Asaad, I., Lundquist, C. J., Erdmann, M. V. & Costello, M. J. Delineating priority areas for marine biodiversity conservation in the Coral Triangle. Biol. Conserv. 222, 198–211 (2018).Article 

Google Scholar 
7.Myers, N., Mittermeier, R. A., Mittermeier, C. G., Da Fonseca, G. A. & Kent, J. Biodiversity hotspots for conservation priorities. Nature 403, 853 (2000).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
8.Chong, V. C., Lee, P. K. & Lau, C. M. Diversity, extinction risk and conservation of Malaysian fishes. J. Fish Biol. 76, 2009–2066. https://doi.org/10.1111/j.1095-8649.2010.02685.x (2010).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
9.Wong, S. L. Matang Mangroves: A Century of Sustainable Management (Sasyaz Holdings Private Ltd., Forestry Department Peninsular Malaysia, 2004).
Google Scholar 
10.Ong, J. et al. Hutan paya laut Merbok, Kedah: Pengurusan hutan, persekitaran fizikal dan kepelbagaian flora. In Siri Kepelbagaian Biologi Hutan Vol. 23 (eds Ku-Aman, K. A. et al.) 21–33 (Jabatan Perhutanan Semenanjung Malaysia, 2015).
Google Scholar 
11.Jamaluddin, J. A. F. et al. DNA barcoding of shrimps from a mangrove biodiversity hotspot. Mitochondrial DNA Part A 30, 618–625. https://doi.org/10.1080/24701394.2019.1597073 (2019).CAS 
Article 

Google Scholar 
12.Mansor, M., Mohammad-Zafrizal, M., Nur-Fadhilah, M., Khairun, Y. & Wan-Maznah, W. Temporal and spatial variations in fish assemblage structures in relation to the physicochemical parameters of the Merbok estuary, Kedah. J. Nat. Sci. Res. 2, 110–127 (2012).
Google Scholar 
13.Hookham, B., Shau-Hwai, A. T., Dayrat, B. & Hintz, W. A baseline measure of tree and gastropod biodiversity in replanted and natural mangrove stands in Malaysia: Langkawi Island and Sungai Merbok. Trop. Life Sci. Res. 25, 1 (2014).PubMed 
PubMed Central 

Google Scholar 
14.Mansor, M., Najamuddin, A., Mohammad-Zafrizal, M., Khairun, Y. & Siti-Azizah, M. Length-weight relationships of some important estuarine fish species from Merbok estuary, Kedah. J. Nat. Sci. Res. 2, 8–19 (2012).
Google Scholar 
15.Zainal Abidin, D. H. et al. Ichthyofauna of Sungai Merbok Mangrove Forest Reserve, northwest Peninsular Malaysia, and its adjacent marine waters. Check List 17, 601–631 (2021).Article 

Google Scholar 
16.Lim, H. C., Zainal Abidin, M., Pulungan, C. P., de Bruyn, M. & Mohd Nor, S. A. DNA barcoding reveals high cryptic diversity of the freshwater halfbeak genus Hemirhamphodon from Sundaland. PLoS ONE 11, e0163596 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
17.Mennesson, M. I., Bonillo, C., Feunteun, E. & Keith, P. Phylogeography of Eleotris fusca (Teleostei: Gobioidei: Eleotridae) in the Indo-Pacific area reveals a cryptic species in the Indian Ocean. Conserv. Genet. 19, 1025–1038 (2018).Article 

Google Scholar 
18.Gomes, L. C., Pessali, T. C., Sales, N. G., Pompeu, P. S. & Carvalho, D. C. Integrative taxonomy detects cryptic and overlooked fish species in a neotropical river basin. Genetica 143, 581–588 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
19.Iyiola, O. A. et al. DNA barcoding of economically important freshwater fish species from north-central Nigeria uncovers cryptic diversity. Ecol. Evol. 8, 6932–6951 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
20.Stern, N., Rinkevich, B. & Goren, M. Integrative approach revises the frequently misidentified species of Sardinella (Clupeidae) of the Indo-West Pacific Ocean. J. Fish Biol. 89, 2282–2305 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
21.Hebert, P. D., Ratnasingham, S. & De Waard, J. R. Barcoding animal life: Cytochrome c oxidase subunit 1 divergences among closely related species. Proc. R. Soc. Lond. Ser. B Biol. Sci. 270, S96–S99 (2003).CAS 

Google Scholar 
22.Ward, R. D., Zemlak, T. S., Innes, B. H., Last, P. R. & Hebert, P. D. DNA barcoding Australia’s fish species. Philos. Trans. R. Soc. B Biol. Sci. 360, 1847–1857 (2005).CAS 
Article 

Google Scholar 
23.Xu, L. et al. Assessment of fish diversity in the South China Sea using DNA taxonomy. Fish. Res. 233, 105771 (2020).Article 

Google Scholar 
24.Lakra, W. et al. DNA barcoding Indian marine fishes. Mol. Ecol. Resour. 11, 60–71 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
25.Hubert, N. et al. Cryptic diversity in Indo-Pacific coral-reef fishes revealed by DNA-barcoding provides new support to the centre-of-overlap hypothesis. PLoS ONE 7, e28987 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
26.Adibah, A. & Darlina, M. Is there a cryptic species of the golden snapper (Lutjanus johnii)?. Genet. Mol. Res. 13, 8094–8104 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
27.Bakar, A. A. et al. DNA barcoding of Malaysian commercial snapper reveals an unrecognized species of the yellow-lined Lutjanus (Pisces: Lutjanidae). PLoS ONE 13, e0202945 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
28.Farhana, S. N. et al. Exploring hidden diversity in Southeast Asia’s Dermogenys spp. (Beloniformes: Zenarchopteridae) through DNA barcoding. Sci. Rep. 8, 1–11 (2018).
Google Scholar 
29.Jaafar, T. N. A. M., Taylor, M. I., Nor, S. A. M., de Bruyn, M. & Carvalho, G. R. DNA barcoding reveals cryptic diversity within commercially exploited Indo-Malay Carangidae (Teleosteii: Perciformes). PLoS ONE 7, e49623 (2012).ADS 
Article 
CAS 

Google Scholar 
30.Azmir, I., Esa, Y., Amin, S., Salwany, M. & Zuraina, M. DNA barcoding analysis of larval fishes in Peninsular Malaysia. J. Environ. Biol. 41, 1295–1308 (2020).CAS 
Article 

Google Scholar 
31.Chu, C. et al. Using DNA barcodes to aid the identification of larval fishes in tropical estuarine waters (Malacca Straits, Malaysia). Zool. Stud. 58, e30 (2019).PubMed 
PubMed Central 

Google Scholar 
32.Hubert, N., Delrieu-Trottin, E., Irisson, J.-O., Meyer, C. & Planes, S. Identifying coral reef fish larvae through DNA barcoding: A test case with the families Acanthuridae and Holocentridae. Mol. Phylogenet. Evol. 55, 1195–1203 (2010).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
33.Ko, H.-L. et al. Evaluating the accuracy of morphological identification of larval fishes by applying DNA barcoding. PLoS ONE 8, e53451 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
34.Chin, T. C., Adibah, A., Hariz, Z. D. & Azizah, M. S. Detection of mislabelled seafood products in Malaysia by DNA barcoding: Improving transparency in food market. Food Control 64, 247–256 (2016).Article 
CAS 

Google Scholar 
35.Hubert, N. et al. Identifying Canadian freshwater fishes through DNA barcodes. PLoS ONE 3, e2490 (2008).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
36.Landi, M. et al. DNA barcoding for species assignment: The case of Mediterranean marine fishes. PLoS ONE 9, e106135 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
37.Russell, D., Thuesen, P. & Thomson, F. A review of the biology, ecology, distribution and control of Mozambique tilapia, Oreochromis mossambicus (Peters 1852) (Pisces: Cichlidae) with particular emphasis on invasive Australian populations. Rev. Fish Biol. Fish. 22, 533–554 (2012).Article 

Google Scholar 
38.Hebert, P. D., Cywinska, A. & Ball, S. L. Biological identifications through DNA barcodes. Proc. R. Soc. Lond. B Biol. Sci. 270, 313–321 (2003).CAS 
Article 

Google Scholar 
39.Puillandre, N., Lambert, A., Brouillet, S. & Achaz, G. ABGD, automatic barcode gap discovery for primary species delimitation. Mol. Ecol. 21, 1864–1877 (2012).CAS 
PubMed 
Article 

Google Scholar 
40.Meier, R., Zhang, G. & Ali, F. The use of mean instead of smallest interspecific distances exaggerates the size of the “barcoding gap” and leads to misidentification. Syst. Biol. 57, 809–813 (2008).PubMed 
Article 
PubMed Central 

Google Scholar 
41.Ortiz, D. & Francke, O. F. Two DNA barcodes and morphology for multi-method species delimitation in Bonnetina tarantulas (Araneae: Theraphosidae). Mol. Phylogenet. Evol. 101, 176–193 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
42.Hajibabaei, M., Singer, G. A., Hebert, P. D. & Hickey, D. A. DNA barcoding: How it complements taxonomy, molecular phylogenetics and population genetics. Trends Genet. 23, 167–172 (2007).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
43.Mecklenburg, C. W., Møller, P. R. & Steinke, D. Biodiversity of arctic marine fishes: taxonomy and zoogeography. Mar. Biodivers. 41, 109–140 (2011).Article 

Google Scholar 
44.Puckridge, M., Andreakis, N., Appleyard, S. A. & Ward, R. D. Cryptic diversity in flathead fishes (Scorpaeniformes: Platycephalidae) across the Indo-West Pacific uncovered by DNA barcoding. Mol. Ecol. Resour. 13, 32–42 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
45.Thirumaraiselvi, R. & Thangaraj, M. Genetic diversity analysis of Indian Salmon, Eleutheronema tetradactylum from South Asian countries based on mitochondrial COI gene sequences. Not. Sci. Biol. 7, 417–422 (2015).CAS 
Article 

Google Scholar 
46.Delrieu-Trottin, E. et al. Biodiversity inventory of the grey mullets (Actinopterygii: Mugilidae) of the Indo-Australian Archipelago through the iterative use of DNA-based species delimitation and specimen assignment methods. Evol. Appl. 13, 1451–1467 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
47.Durand, J.-D., Hubert, N., Shen, K.-N. & Borsa, P. DNA barcoding grey mullets. Rev. Fish Biol. Fish. 27, 233–243 (2017).Article 

Google Scholar 
48.Alavi-Yeganeh, M. S., Khajavi, M. & Kimura, S. A new ponyfish, Deveximentum mekranensis (Teleostei: Leiognathidae), from the Gulf of Oman. Ichthyol. Res. 68, 437–444. https://doi.org/10.1007/s10228-020-00794-y (2021).Article 

Google Scholar 
49.Carpenter, K. E. & Niem, V. FAO Species Identification Guide for Fishery Purposes. The Living Marine Resources of the Western Central Pacific. Bony Fishes Part 4 (Labridae to Latimeriidae), Estuarine Crocodiles, Sea Turtles, Sea Snakes and Marine Mammals Vol. 6 (FAO Library, 2001).
Google Scholar 
50.Chen, W., Ma, X., Shen, Y., Mao, Y. & He, S. The fish diversity in the upper reaches of the Salween River, Nujiang River, revealed by DNA barcoding. Sci. Rep. 5, 1–12 (2015).
Google Scholar 
51.Guimarães-Costa, A. J. et al. Fish diversity of the largest deltaic formation in the Americas-a description of the fish fauna of the Parnaíba Delta using DNA Barcoding. Sci. Rep. 9, 1–8 (2019).Article 
CAS 

Google Scholar 
52.Hupało, K. et al. An urban Blitz with a twist: Rapid biodiversity assessment using aquatic environmental DNA. Environ. DNA 3, 200–213 (2020).Article 

Google Scholar 
53.Zainal Abidin, D. H. & Noor Adelyna, M. A. Universities as Living Labs for Sustainable Development 211–225 (Springer, 2020).
Google Scholar 
54.Ratnasingham, S. & Hebert, P. D. BOLD: The barcode of life data system. Mol. Ecol. Notes 7, 355–364 (2007).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
55.Benson, D. A. et al. GenBank. Nucleic Acids Res. 46, D41–D47 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
56.Mansor, M. I. et al. Field Guide to Important Commercial Marine Fishes of the South China Sea (SEAFDEC/MFRDMD, 1998).
Google Scholar 
57.Nuruddin, A. A. & Isa, S. M. Trawl Fisheries in Malaysia-Issues, Challenges and Mitigating Measures (Fisheries Research Institute, Department of Fisheries Malaysia, 2013).
Google Scholar 
58.Kumar, S., Stecher, G., Li, M., Knyaz, C. & Tamura, K. MEGA X: Molecular evolutionary genetics analysis across computing platforms. Mol. Biol. Evol. 35, 1547–1549 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
59.Kimura, M. A simple method for estimating evolutionary rates of base substitutions through comparative studies of nucleotide sequences. J. Mol. Evol. 16, 111–120 (1980).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
60.Bouckaert, R. et al. BEAST 2: A software platform for Bayesian evolutionary analysis. PLoS Comput. Biol. 10, e1003537 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
61.Edler, D., Klein, J., Antonelli, A. & Silvestro, D. raxmlGUI 2.0: A graphical interface and toolkit for phylogenetic analyses using RAxML. Methods Ecol. Evol. 12, 373–377 (2021).Article 

Google Scholar 
62.Lanfear, R., Frandsen, P. B., Wright, A. M., Senfeld, T. & Calcott, B. PartitionFinder 2: New methods for selecting partitioned models of evolution for molecular and morphological phylogenetic analyses. Mol. Biol. Evol. 34, 772–773 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
63.Miller, M. A., Pfeiffer, W. & Schwartz, T. In Proceedings of the 2011 TeraGrid Conference: Extreme digital discovery 1–8 (2011).64.Rambaut, A. FigTree v1.4.4. Available from: http://tree.bio.ed.ac.uk/software/figtree/ (2018).65.Ratnasingham, S. & Hebert, P. D. A DNA-based registry for all animal species: The Barcode Index Number (BIN) system. PLoS ONE 8, e66213 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
66.Pons, J. et al. Sequence-based species delimitation for the DNA taxonomy of undescribed insects. Syst. Biol. 55, 595–609 (2006).PubMed 
Article 
PubMed Central 

Google Scholar 
67.Glez-Pena, D., Gomez-Blanco, D., Reboiro-Jato, M., Fdez-Riverola, F. & Posada, D. ALTER: Program-oriented conversion of DNA and protein alignments. Nucleic Acids Res. 38, W14–W18 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
68.Team, R. RStudio: integrated development for R (RStudio Inc., 2015).
Google Scholar 
69.Fujisawa, T. & Barraclough, T. G. Delimiting species using single-locus data and the Generalized Mixed Yule Coalescent approach: A revised method and evaluation on simulated data sets. Syst. Biol. 62, 707–724 (2013).PubMed 
PubMed Central 
Article 

Google Scholar  More

Narrowing crop diversity in the world’s food supplies is a potential threat to food security25; however, there have been few empirical studies to link crop diversity to system-level nutritional measures, especially beyond dietary intake at the household level9. Here we develop a method to link crops to specific micronutrients using a network approach and assess the role of crop production and imports on nutritional stability outcomes in 184 countries between 1961 and 2016. Similar to other scholars25,26, we find that crop diversity has increased over time in many regions, but that in many cases these gains are due to imports. Despite this increase in crop diversity, nutritional stability has remained stagnant or decreased in all regions except Asia, a trend largely attributed to our finding that gains in crop diversity coincide with fewer new nutritional links in a given food system.The general relationship between crop diversity and nutritional stability is contextualized by changes in crop degree and explains why stability does not mirror diversification trends. Improving crop diversity will always increase the size of the crop-nutrient network, but stability depends on the number and pattern of links within this network. As in other diversity–stability relationships functional identity matters, and declines in crop degree could reflect shifts toward networks with less nutrient-rich crops. For example, production-based crop diversity in Senegal increased by 29%, while crop degree dropped by 19% as the composition of its food supply shifted from staples (e.g., millet, groundnuts, sweet potatoes) to include less nutrient-dense crops (e.g., sugar cane, watermelon, cabbage). In light of on-going homogenization of crop diversity26, attaining the benefits of nutritional stability will require further understanding of the topology of crop-nutrient networks.By considering both production and nutritional diversity, our approach advances the quantification of food system resilience—the capacity over time of a food system and its units at multiple levels, to provide sufficient, appropriate, and accessible food to all, in the face of various and even unforeseen disturbances27. Our results have many implications for our understanding of nutritional measures and their relationship to crop diversity. First, our work reaffirms the existing body of research demonstrating that crop diversity is important for agricultural resilience11, and it does so at a national scale. Previous work has examined patterns of crop or nutritional diversity at global scales15,28 or linked crop diversity and nutrition-relevant outcomes at the field or landscape levels9. Our work answers recent calls8 to explore crop diversity and nutrition-relevant outcomes at a larger scale through a country-level analysis and incorporates both production and imports, the latter of which has been significant for driving an increase in the types of crops available in a given country over time. To be clear, we are measuring the relationships of crop diversity to nutrients and their susceptibility to disturbance; we are not measuring nutritional outcomes such as dietary intake, dietary diversity, or other health-related outcomes that are the result of nutrition. Just as nutritional status cannot be determined from dietary intake alone, nutritional stability does not determine the availability, let alone utilization, of nutrients. This is a natural area to expand this work moving forward.Second, our work establishes a functional relationship between crop diversity and nutritional stability. We suggest that this non-linear relationship has important implications for thinking about the types of crops grown or imported in a given region and how they ensure nutrient availability. A foundation shared by ecology and nutrition is that diversity can improve long-term functioning of complex biological systems29,30. Like other ecological diversity–resilience relationships, we observe that diversity loss can result in rapid loss of function31. In countries where diversity is already low, our results indicate that crop failures, either through production failure or an inability to import such crops, could lead to rapid reductions in nutrient availability within a country. Moreover, multiple failures of highly important regional crops, as might occur during a drought or other extreme events, could have catastrophic nutritional impact. Such countries are thus vulnerable to a variety of potential global challenges both ecological (e.g., climate change) and economic (e.g., trade wars).Third, that nutritional stability is stagnant or decreased over time in all regions but Asia highlights that increasing crop diversity—at least at the national level—does not necessarily lead to more stability. Instead, the wide variability in nutritional stability across countries highlights clear vulnerabilities both across and within regions. Africa has the greatest inter-regional variability, demonstrating that in some cases neighboring countries have very different stabilities of crop nutrients in their food supply chain in any given year. This variability is likely driven by multiple factors including the capacity of a country to trade32, in country food availability as a result of war or political/social unrest33,34,35, or exposure to climate-induced disasters36.Finally, the important role of imports in many regions highlights that crop diversity and nutritional stability are market exposed. While trade can positively affect food security37, it can also hinder nutrition efforts38 and could be a vulnerability if imports comprise a significant portion of nutritional stability for a given population. Countries with a high reliance on imports are thus subject to trade wars, market shifts, and price shocks that can occur for a variety of reasons39. Such countries may be more likely to experience increased variability in the future, especially as climate change is expected to affect agricultural production, markets, and trade40.The use of these results could help inform high-level discussions within countries and regions about the key crops for a given place and their availability via import or domestic production. Scenario development using our metric could help target country-specific crop additions that would maximize nutritional stability. Our approach could also be used to identify potential tradeoffs in production and import outcomes, at least as it relates to the availability of a given amount of nutrients in a certain place. In the context of policy interventions, this system-level metric could be applied in panel-type designs to diagnose whether initiatives (e.g., promoting or increasing food production, trade and storage) at different scales of organization (e.g., household, community, national) will effectively promote food system resilience programs41.Such potential applications also highlight the importance of identifying several caveats and important limitations. First, although we are addressing the nutrients available in a given country in a given time, we are not equating this with food security. This “availability” is only one component of food security, with access, utilization, and stability being other critical pillars. Thus, even though nutritional stability is generally high in most regions and remained stagnant (or increased in Asia), this does not mean that people are not food insecure. Adequate food and nutritional security comprises much more than the factors captured in our analysis, which provides a relative measure of nutrient availability not an absolute metric of adequacy. In the present study, we focused on nutrients available from crops, because animal-based products are rarely resolved to the species level and there is large interspecies variability in crop micronutrient composition. Animal-based products nonetheless play a critical role in providing some nutrients, thus there may be greater variability between countries when accounting for animal-based foods. There are also some methodological limitations. Crops are likely to vary in their loss susceptibility according to exogenous factors, such as market value or climate change vulnerability or pest pressure or simply abundance. In our current approach, all crops have equal removal probability; crop removal scenarios that account for these differential vulnerabilities is an exciting next step. Our current approach considers only nutrient presence or absence and may underestimate nutritional stability because ultimately the vulnerability of nutrient provision will also depend on how much of that nutrient is produced. Considering fractional crop loss or removal probabilities based on production levels could add realistic complexity in future analyses. Furthermore, complex system modeling of trade dynamics could explore to what extent import-based network re-orientation rescues nutritional stability by allowing for network rewiring via crop substitutability42,43. Finally, there are recognized shortcomings with the existing FAO data, especially in many low-income countries44. Nevertheless, to our knowledge, it is the best available data of its kind and scale available, so we utilize it knowing that there are many opportunities to improve this work moving forward.Despite these caveats, this work advances a method to assess the relationship between crop diversity and nutrient availability globally over the past 55 years. Future research could expand this work in multiple ways by combining crop-nutrient availability data with nutritional intake data to better assess whether available nutrients in the supply chain are making their way into household consumption. This would more completely link crop diversity with food and nutritional security outcomes, rather than just food availability as this work has done. Furthermore, our network tolerance method could be advanced by exploring the importance of certain crops for a given country or region by considering non-random loss of crops. Finally, with climate change expected to affect the yields of many globally important crops45 and potentially cause multiple crop failures at once36, this type of analysis could advance our understanding of food system vulnerability to specific crop failures and provide guidance on climate adaptation efforts or crop diversification strategies to safeguard against climate change.Resilience is now a central paradigm in many sectors—humanitarian aid, disaster risk reduction, climate change adaptation, social protection. Most analyses of resilience in food systems occur at household or community scales17 or focus on broader patterns of food production and distribution18,39. Erosion of biological diversity typically leads to loss of ecosystem functioning and services, likewise loss of crop diversity may to lead to potentially drastic shifts in nutritional stability. Together this and future analyses have the potential to direct the protection or restoration of crop diversity so as to best support nutrient availability that is stable to current and future challenges. More

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Polar bears in Norway have undergone a staggering loss in genetic diversity in recent decades, as a result of the decline of Arctic sea ice.

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References1.Maduna, S. N. et al. Proc. R. Soc. B 288, 20211741 (2021).PubMed 
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1.Laine RA. A calculation of all possible oligosaccharide isomers both branched and linear yields 1.05×10(12) structures for a reducing hexasaccharide – the isomer-barrier to development of single-method saccharide sequencing or synthesis systems. Glycobiology. 1994;4:759–67.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
2.Varki A. Biological roles of glycans. Glycobiology. 2017;27:3–49.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
3.Teeling H, Fuchs BM, Becher D, Klockow C, Gardebrecht A, Bennke CM, et al. Substrate-controlled succession of marine bacterioplankton populations induced by a phytoplankton bloom. Science. 2012;336:608–11.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
4.Myklestad SM. Release of extracellular products by phytoplankton with special emphasis on polysaccharides. Sci Total Environ. 1995;165:155–64.CAS 
Article 

Google Scholar 
5.Field CB, Behrenfeld MJ, Randerson JT, Falkowski P. Primary production of the biosphere: integrating terrestrial and oceanic components. Science. 1998;281:237–40.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
6.Wetz MS, Wheeler PA. Release of dissolved organic matter by coastal diatoms. Limnol Oceanogr. 2007;52:798–807.CAS 
Article 

Google Scholar 
7.Reintjes G, Fuchs BM, Scharfe M, Wiltshire KH, Amann R, Arnosti C. Short-term changes in polysaccharide utilization mechanisms of marine bacterioplankton during a spring phytoplankton bloom. Environ Microbiol. 2020;22:1884–900.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
8.Vidal-Melgosa S, Sichert A, Francis TB, Bartosik D, Niggemann J, Wichels A, et al. Diatom fucan polysaccharide precipitates carbon during algal blooms. Nat Commun. 2021;12:1150.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
9.Becker S, Tebben J, Coffinet S, Wiltshire K, Iversen MH, Harder T, et al. Laminarin is a major molecule in the marine carbon cycle. P Natl Acad Sci USA. 2020;117:6599–607.CAS 
Article 

Google Scholar 
10.Engel A, Thoms S, Riebesell U, Rochelle-Newall E, Zondervan I. Polysaccharide aggregation as a potential sink of marine dissolved organic carbon. Nature. 2004;428:929–32.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
11.Aluwihare LI, Repeta DJ, Chen RF. A major biopolymeric component to dissolved organic carbon in surface sea water. Nature. 1997;387:166–9.CAS 
Article 

Google Scholar 
12.Hedges JI, Baldock JA, Gelinas Y, Lee C, Peterson M, Wakeham SG. Evidence for non-selective preservation of organic matter in sinking marine particles. Nature. 2001;409:801–4.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
13.Meador TB, Aluwihare LI. Production of dissolved organic carbon enriched in deoxy sugars representing an additional sink for biological C drawdown in the Amazon River plume. Glob Biogeochem Cycles. 2014;28:1149–61.CAS 
Article 

Google Scholar 
14.Teeling H, Fuchs BM, Bennke CM, Krüger K, Chafee M, Kappelmann L, et al. Recurring patterns in bacterioplankton dynamics during coastal spring algae blooms. Elife. 2016;5:e11888.PubMed 
PubMed Central 
Article 

Google Scholar 
15.Sichert A, Corzett CH, Schechter MS, Unfried F, Markert S, Becher D, et al. Verrucomicrobia use hundreds of enzymes to digest the algal polysaccharide fucoidan. Nat Microbiol. 2020;5:1026–39.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
16.Martinez-Garcia M, Brazel DM, Swan BK, Arnosti C, Chain PS, Reitenga KG, et al. Capturing single cell genomes of active polysaccharide degraders: an unexpected contribution of Verrucomicrobia. PLoS ONE. 2012;7:e35314.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
17.Cardman Z, Arnosti C, Durbin A, Ziervogel K, Cox C, Steen AD, et al. Verrucomicrobia are candidates for polysaccharide-degrading bacterioplankton in an arctic fjord of Svalbard. Appl Environ Microbiol. 2014;80:3749–56.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
18.Spring S, Bunk B, Sproer C, Schumann P, Rohde M, Tindall BJ, et al. Characterization of the first cultured representative of Verrucomicrobia subdivision 5 indicates the proposal of a novel phylum. ISME J. 2016;10:2801–16.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
19.Cabello-Yeves PJ, Ghai R, Mehrshad M, Picazo A, Camacho A, Rodriguez-Valera F. Reconstruction of diverse verrucomicrobial genomes from metagenome datasets of freshwater reservoirs. Front Microbiol. 2017;8:2131.PubMed 
PubMed Central 
Article 

Google Scholar 
20.He S, Stevens SL, Chan L-K, Bertilsson S, del Rio TG, Tringe SG, et al. Ecophysiology of freshwater Verrucomicrobia inferred from metagenome-assembled genomes. mSphere. 2017;2:e00277–17.PubMed 
PubMed Central 
Article 

Google Scholar 
21.Tran P, Ramachandran A, Khawasik O, Beisner BE, Rautio M, Huot Y, et al. Microbial life under ice: Metagenome diversity and in situ activity of Verrucomicrobia in seasonally ice-covered Lakes. Environ Microbiol. 2018;20:2568–84.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
22.Sizikov S, Burgsdorf I, Handley KM, Lahyani M, Haber M, Steindler L. Characterization of sponge‐associated Verrucomicrobia: microcompartment‐based sugar utilization and enhanced toxin–antitoxin modules as features of host‐associated Opitutales. Environ Microbiol. 2020;22:4669–88.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
23.Chafee M, Fernàndez-Guerra A, Buttigieg PL, Gerdts G, Eren AM, Teeling H, et al. Recurrent patterns of microdiversity in a temperate coastal marine environment. ISME J. 2018;12:237–52.PubMed 
Article 
PubMed Central 

Google Scholar 
24.Francis TB, Kruger K, Fuchs BM, Teeling H, Amann RI. Candidatus Prosiliicoccus vernus, a spring phytoplankton bloom associated member of the Flavobacteriaceae. Syst Appl Microbiol. 2019;42:41–53.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
25.Kruger K, Chafee M, Ben Francis T, Glavina Del Rio T, Becher D, Schweder T, et al. In marine Bacteroidetes the bulk of glycan degradation during algae blooms is mediated by few clades using a restricted set of genes. ISME J. 2019;13:2800–16.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
26.Francis TB, Bartosik D, Sura T, Sichert A, Hehemann JH, Markert S, et al. Changing expression patterns of TonB-dependent transporters suggest shifts in polysaccharide consumption over the course of a spring phytoplankton bloom. ISME J. 2021;15:2336–50.CAS 
Article 

Google Scholar 
27.Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M, Kulikov AS, et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol. 2012;19:455–77.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
28.Alneberg J, Bjarnason BS, de Bruijn I, Schirmer M, Quick J, Ijaz UZ, et al. Binning metagenomic contigs by coverage and composition. Nat Methods. 2014;11:1144–6.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
29.Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 2015;25:1043–55.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
30.Varghese NJ, Mukherjee S, Ivanova N, Konstantinidis KT, Mavrommatis K, Kyrpides NC, et al. Microbial species delineation using whole genome sequences. Nucleic Acids Res. 2015;43:6761–71.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
31.Jain C, Rodriguez RL, Phillippy AM, Konstantinidis KT, Aluru S. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat Commun. 2018;9:5114.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
32.Smoot ME, Ono K, Ruscheinski J, Wang P-L, Ideker T. Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics. 2010;27:431–2.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
33.Chaumeil PA, Mussig AJ, Hugenholtz P, Parks DH. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics. 2019;36:1925–7.PubMed Central 

Google Scholar 
34.Eren AM, Esen OC, Quince C, Vineis JH, Morrison HG, Sogin ML, et al. Anvi’o: an advanced analysis and visualization platform for ‘omics data. PeerJ. 2015;3:e1319.PubMed 
PubMed Central 
Article 

Google Scholar 
35.Seemann T. Prokka: rapid prokaryotic genome annotation. Bioinformatics. 2014;30:2068–9.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
36.Parks DH, Chuvochina M, Waite DW, Rinke C, Skarshewski A, Chaumeil PA, et al. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nat Biotechnol. 2018;36:996–1004.CAS 
PubMed 
Article 

Google Scholar 
37.Orellana LH, Ben Francis T, Kruger K, Teeling H, Muller MC, Fuchs BM, et al. Niche differentiation among annually recurrent coastal Marine Group II Euryarchaeota. ISME J. 2019;13:3024–36.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
38.Orellana LH, Rodriguez RL, Konstantinidis KT. ROCker: accurate detection and quantification of target genes in short-read metagenomic data sets by modeling sliding-window bitscores. Nucleic Acids Res. 2017;45:e14.PubMed 
PubMed Central 

Google Scholar 
39.Rodriguez RL, Tsementzi D, Luo C, Konstantinidis KT. Iterative subtractive binning of freshwater chronoseries metagenomes identifies over 400 novel species and their ecologic preferences. Environ Microbiol. 2020;22:3394–412.Article 
CAS 

Google Scholar 
40.Delmont TO, Quince C, Shaiber A, Esen OC, Lee ST, Rappe MS, et al. Nitrogen-fixing populations of Planctomycetes and Proteobacteria are abundant in surface ocean metagenomes. Nat Microbiol. 2018;3:804–13.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
41.Sievers F, Higgins DG. Clustal Omega for making accurate alignments of many protein sequences. Protein Sci. 2018;27:135–45.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
42.Price MN, Dehal PS, Arkin AP. FastTree 2-approximately maximum-likelihood trees for large alignments. PLoS ONE 2010;5:e9490.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
43.Letunic I, Bork P. Interactive tree of life (iTOL) v3: an online tool for the display and annotation of phylogenetic and other trees. Nucleic Acids Res. 2016;44:W242–5. W1CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
44.Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41:D590–6. Database issueCAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
45.Pruesse E, Peplies J, Glockner FO. SINA: accurate high-throughput multiple sequence alignment of ribosomal RNA genes. Bioinformatics. 2012;28:1823–9.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
46.Stamatakis A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 2014;30:1312–3.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
47.Huerta-Cepas J, Szklarczyk D, Forslund K, Cook H, Heller D, Walter MC, et al. eggNOG 4.5: a hierarchical orthology framework with improved functional annotations for eukaryotic, prokaryotic and viral sequences. Nucleic Acids Res. 2016;44:D286–93. D1CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
48.Selengut JD, Haft DH, Davidsen T, Ganapathy A, Gwinn-Giglio M, Nelson WC, et al. TIGRFAMs and Genome Properties: tools for the assignment of molecular function and biological process in prokaryotic genomes. Nucleic Acids Res. 2007;35:D260–4. Database issueCAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
49.El-Gebali S, Mistry J, Bateman A, Eddy SR, Luciani A, Potter SC, et al. The Pfam protein families database in 2019. Nucleic Acids Res. 2019;47:D427–D32. D1CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
50.Rawlings ND, Barrett AJ, Thomas PD, Huang X, Bateman A, Finn RD. The MEROPS database of proteolytic enzymes, their substrates and inhibitors in 2017 and a comparison with peptidases in the PANTHER database. Nucleic Acids Res. 2018;46:D624–D32. D1CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
51.Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, Bealer K, et al. BLAST+: architecture and applications. BMC Bioinforma. 2009;10:421.Article 
CAS 

Google Scholar 
52.Saier MH Jr, Reddy VS, Tsu BV, Ahmed MS, Li C, Moreno-Hagelsieb G. The Transporter Classification Database (TCDB): recent advances. Nucleic Acids Res. 2016;44:D372–9. D1CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
53.Lombard V, Golaconda Ramulu H, Drula E, Coutinho PM, Henrissat B. The carbohydrate-active enzymes database (CAZy) in 2013. Nucleic Acids Res. 2014;42:D490–5. Database issueCAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
54.Eddy SR. Accelerated Profile HMM Searches. PLoS Comput Biol. 2011;7:e1002195.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
55.Yin Y, Mao X, Yang J, Chen X, Mao F, Xu Y. dbCAN: a web resource for automated carbohydrate-active enzyme annotation. Nucleic Acids Res. 2012;40:W445–51. Web Server issueCAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
56.Zhang H, Yohe T, Huang L, Entwistle S, Wu P, Yang Z, et al. dbCAN2: a meta server for automated carbohydrate-active enzyme annotation. Nucleic Acids Res. 2018;46:W95–W101. W1CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
57.Jones P, Binns D, Chang HY, Fraser M, Li W, McAnulla C, et al. InterProScan 5: genome-scale protein function classification. Bioinformatics. 2014;30:1236–40.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
58.Emms DM, Kelly S. OrthoFinder: solving fundamental biases in whole genome comparisons dramatically improves orthogroup inference accuracy. Genome Biol. 2015;16:157.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
59.Thiele S, Fuchs B, Amann R. Identification of microorganisms using the ribosomal RNA approach and fluorescence in situ hybridization. In: Wilderer PA, editor. Treatise on Water Science. Elsevier Science; Oxford, United Kingdom; 2011. p. 171–89.60.Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
61.Zverlov VV, Hertel C, Bronnenmeier K, Hroch A, Kellermann J, Schwarz WH. The thermostable alpha-L-rhamnosidase RamA of Clostridium stercorarium: biochemical characterization and primary structure of a bacterial alpha-L-rhamnoside hydrolase, a new type of inverting glycoside hydrolase. Mol Microbiol. 2000;35:173–9.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
62.Miyata T, Kashige N, Satho T, Yamaguchi T, Aso Y, Miake F. Cloning, sequence analysis, and expression of the gene encoding Sphingomonas paucimobilis FP2001 alpha-L -rhamnosidase. Curr Microbiol. 2005;51:105–9.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
63.Ndeh D, Rogowski A, Cartmell A, Luis AS, Basle A, Gray J, et al. Complex pectin metabolism by gut bacteria reveals novel catalytic functions. Nature. 2017;544:65–70.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
64.Li B, Lu F, Wei X, Zhao R. Fucoidan: structure and bioactivity. Molecules. 2008;13:1671–95.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
65.Ale MT, Mikkelsen JD, Meyer AS. Important determinants for fucoidan bioactivity: a critical review of structure-function relations and extraction methods for fucose-containing sulfated polysaccharides from brown seaweeds. Mar Drugs. 2011;9:2106–30.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
66.Katayama T, Sakuma A, Kimura T, Makimura Y, Hiratake J, Sakata K, et al. Molecular cloning and characterization of Bifidobacterium bifidum 1,2-alpha-L-fucosidase (AfcA), a novel inverting glycosidase (glycoside hydrolase family 95). J Bacteriol. 2004;186:4885–93.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
67.Nagae M, Tsuchiya A, Katayama T, Yamamoto K, Wakatsuki S, Kato R. Structural basis of the catalytic reaction mechanism of novel 1,2-alpha-L-fucosidase from Bifidobacterium bifidum. J Biol Chem. 2007;282:18497–509.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
68.Anderson KL, Salyers AA. Biochemical evidence that starch breakdown by Bacteroides thetaiotaomicron involves outer membrane starch-binding sites and periplasmic starch-degrading enzymes. J Bacteriol. 1989;171:3192–8.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
69.Bjursell MK, Martens EC, Gordon JI. Functional genomic and metabolic studies of the adaptations of a prominent adult human gut symbiont, Bacteroides thetaiotaomicron, to the suckling period. J Biol Chem. 2006;281:36269–79.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
70.Grondin JM, Tamura K, Dejean G, Abbott DW, Brumer H. Polysaccharide Utilization Loci: fueling microbial communities. J Bacteriol. 2017;199:e00860–16.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
71.Barbeyron T, Brillet-Gueguen L, Carre W, Carriere C, Caron C, Czjzek M, et al. Matching the diversity of sulfated biomolecules: Creation of a classification database for sulfatases reflecting their substrate specificity. PLoS ONE 2016;11:e0164846.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
72.Silchenko AS, Rasin AB, Zueva AO, Kusaykin MI, Zvyagintseva TN, Kalinovsky AI, et al. Fucoidan sulfatases from marine bacterium Wenyingzhuangia fucanilytica CZ1127(T). Biomolecules. 2018;8:98.PubMed Central 
Article 
CAS 

Google Scholar 
73.Reisky L, Prechoux A, Zuhlke MK, Baumgen M, Robb CS, Gerlach N, et al. A marine bacterial enzymatic cascade degrades the algal polysaccharide ulvan. Nat Chem Biol. 2019;15:803–12.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
74.Hettle AG, Vickers C, Robb CS, Liu F, Withers SG, Hehemann JH, et al. The molecular basis of polysaccharide sulfatase activity and a nomenclature for catalytic subsites in this class of enzyme. Structure. 2018;26:747–58.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
75.Erbilgin O, McDonald KL, Kerfeld CA. Characterization of a planctomycetal organelle: a novel bacterial microcompartment for the aerobic degradation of plant saccharides. Appl Environ Microbiol. 2014;80:2193–205.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
76.Axen SD, Erbilgin O, Kerfeld CA. A taxonomy of bacterial microcompartment loci constructed by a novel scoring method. PLoS Comput Biol. 2014;10:e1003898.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
77.Sutter M, Melnicki MR, Schulz F, Woyke T, Kerfeld CA. A catalog of the diversity and ubiquity of bacterial microcompartments. Nat Commun. 2021;12:3809.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
78.Engel A, Goldthwait S, Passow U, Alldredge A. Temporal decoupling of carbon and nitrogen dynamics in a mesocosm diatom bloom. Limnol Oceanogr. 2002;47:753–61.CAS 
Article 

Google Scholar 
79.Yew WS, Fedorov AA, Fedorov EV, Rakus JF, Pierce RW, Almo SC, et al. Evolution of enzymatic activities in the enolase superfamily: L-fuconate dehydratase from Xanthomonas campestris. Biochemistry. 2006;45:14582–97.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
80.Arredondo-Alonso S, Willems RJ, van Schaik W, Schurch AC. On the (im)possibility of reconstructing plasmids from whole-genome short-read sequencing data. Micro Genom. 2017;3:e000128.
Google Scholar 
81.Murray AE, Freudenstein J, Gribaldo S, Hatzenpichler R, Hugenholtz P, Kampfer P, et al. Roadmap for naming uncultivated Archaea and Bacteria. Nat Microbiol. 2020;5:987–94.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
82.Bowers RM, Kyrpides NC, Stepanauskas R, Harmon-Smith M, Doud D, Reddy TBK, et al. Minimum information about a single amplified genome (MISAG) and a metagenome-assembled genome (MIMAG) of bacteria and archaea. Nat Biotechnol. 2017;35:725–31.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
83.Konstantinidis KT, Rossello-Mora R, Amann R. Uncultivated microbes in need of their own taxonomy. ISME J. 2017;11:2399–406.PubMed 
PubMed Central 
Article 

Google Scholar 
84.Alejandre-Colomo C, Harder J, Fuchs BM, Rossello-Mora R, Amann R. High-throughput cultivation of heterotrophic bacteria during a spring phytoplankton bloom in the North Sea. Syst Appl Microbiol. 2020;43:126066.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar  More