Ecology

1.2012 Recreational Water Quality Criteria. (U. S. Environmental Protection Agency, 2012).2.Lee, C. M. et al. Persistence of fecal indicator bacteria in Santa Monica Bay beach sediments. Water Res. 40, 2593–2602 (2006).CAS 
PubMed 
Article 

Google Scholar 
3.Luo, C. et al. Genome sequencing of environmental Escherichia coli expands understanding of the ecology and speciation of the model bacterial species. Proc. Natl. Acad. Sci. 108, 7200–7205 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
4.Ishii, S., Ksoll, W. B., Hicks, R. E. & Sadowsky, M. J. Presence and growth of naturalized Escherichia coli in temperate soils from lake superior watersheds. Appl. Environ. Microbiol. 72, 612–621 (2006).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
5.Rochelle-Newall, E., Nguyen, T. M. H., Le, T. P. Q., Sengtaheuanghoung, O. & Ribolzi, O. A short review of fecal indicator bacteria in tropical aquatic ecosystems: Knowledge gaps and future directions. Front. Microbiol. 6, 1–15 (2015).Article 

Google Scholar 
6.Tymensen, L. D. et al. Comparative accessory gene fingerprinting of surface water Escherichia coli reveals genetically diverse naturalized population. J. Appl. Microbiol. 119, 263–277 (2015).CAS 
PubMed 
Article 

Google Scholar 
7.Ishii, S. & Sadowsky, M. J. Escherichia coli in the environment: Implications for water quality and human health. Microbes and environments / JSME 23, 101–108 (2008).Article 

Google Scholar 
8.Surbeck, C. Q., Jiang, S. C. & Grant, S. B. Ecological control of fecal indicator bacteria in an urban stream. Environ. Sci. Technol. 44, 631–637 (2010).ADS 
CAS 
PubMed 
Article 

Google Scholar 
9.Jang, J. et al. Environmental Escherichia coli: Ecology and public health implications—A review. J. Appl. Microbiol. 123(3), 570–581. https://doi.org/10.1111/jam.13468 (2017).CAS 
Article 
PubMed 

Google Scholar 
10.Van Elsas, J. D., Semenov, A. V., Costa, R. & Trevors, J. T. Survival of Escherichia coli in the environment: Fundamental and public health aspects. ISME J. 5, 173–183 (2010).PubMed 
PubMed Central 
Article 

Google Scholar 
11.Jaureguy, F. et al. Phylogenetic and genomic diversity of human bacteremic Escherichia coli strains. BMC Genomics 9, 560 (2008).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
12.Clermont, O. et al. Characterization and rapid identification of phylogroup G in Escherichia coli, a lineage with high virulence and antibiotic resistance potential. Environ. Microbiol. 21, 3107–3117 (2019).CAS 
PubMed 
Article 

Google Scholar 
13.Clermont, O., Christenson, J. K., Denamur, E. & Gordon, D. M. The Clermont Escherichia coli phylo-typing method revisited: Improvement of specificity and detection of new phylo-groups. Environ. Microbiol. Rep. https://doi.org/10.1111/1758-2229.12019 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
14.Ratajczak, M. et al. Influence of hydrological conditions on the Escherichia coli population structure in the water of a creek on a rural watershed. BMC Microbiol. 10, 1–10 (2010).Article 
CAS 

Google Scholar 
15.Johnson, J. R. et al. Phylogenetic backgrounds and virulence associated traits of Escherichia coli isolates from surface waters and diverse animals in Minnesota and Wisconsin. Appl. Environ. Microbiol. 83, 1–33 (2017).CAS 

Google Scholar 
16.Petit, F. et al. Change in the structure of Escherichia coli population and the pattern of virulence genes along a rural aquatic continuum. Front. Microbiol. 8, 1–14 (2017).Article 

Google Scholar 
17.Kraft, N. J. B. et al. Community assembly, coexistence and the environmental filtering metaphor. Funct. Ecol. 29, 592–599 (2015).Article 

Google Scholar 
18.Berthe, T., Ratajczak, M., Clermont, O., Denamur, E. & Petit, F. Evidence for coexistence of distinct Escherichia coli populations in various aquatic environments and their survival in estuary water. Appl. Environ. Microbiol. 79, 4684–4693 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
19.Méric, G., Kemsley, E. K., Falush, D., Saggers, E. J. & Lucchini, S. Phylogenetic distribution of traits associated with plant colonization in Escherichia coli. Environ. Microbiol. 15, 487–501 (2013).PubMed 
Article 
CAS 

Google Scholar 
20.Walk, S. T. The “Cryptic” Escherichia. EcoSal Plus 6, 2 (2015).21.Ingle, D. J. et al. Biofilm formation by and thermal niche and virulence characteristics of Escherichia spp. Appl. Environ. Microbiol. 77, 2695–2700 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
22.Elmqvist, T. The Urban Planet: Knowledge Towards Sustainable Cities (Cambridge University Press, 2018).Book 

Google Scholar 
23.Hosen, J. D., Febria, C. M., Crump, B. C. & Palmer, M. A. Watershed urbanization linked to differences in stream bacterial community composition. Front. Microbiol. 8, 1–17 (2017).Article 

Google Scholar 
24.Wang, S.-Y., Sudduth, E. B., Wallenstein, M. D., Wright, J. P. & Bernhardt, E. S. Watershed urbanization alters the composition and function of stream bacterial communities. PLoS ONE 6, e22972 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
25.Bernhardt, E. S., Band, L. E., Walsh, C. J. & Berke, P. E. Understanding, managing, and minimizing urban impacts on surface water nitrogen loading. Ann. N. Y. Acad. Sci. 1134, 61–96 (2008).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
26.Hosen, J. D., McDonough, O. T., Febria, C. M. & Palmer, M. A. Dissolved organic matter quality and bioavailability changes across an urbanization gradient in headwater streams. Environ. Sci. Technol. 48, 7817–7824 (2014).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
27.Hatt, B. E., Fletcher, T. D., Walsh, C. J. & Taylor, S. L. The influence of urban density and drainage infrastructure on the concentrations and loads of pollutants in small streams. Environ. Manage. 34, 112–124 (2004).PubMed 
Article 
PubMed Central 

Google Scholar 
28.Smith, R. M., Kaushal, S. S., Beaulieu, J. J., Pennino, M. J. & Welty, C. Influence of infrastructure on water quality and greenhouse gas dynamics in urban streams. Biogeosciences 14, 2831–2849 (2017).ADS 
CAS 
Article 

Google Scholar 
29.Handler, N. B., Paytan, A., Higgins, C. P., Luthy, R. G. & Boehm, A. B. Human development is linked to multiple water body impairments along the California coast. Estuar. Coasts 29, 860–870 (2006).CAS 
Article 

Google Scholar 
30.Ishii, S. et al. Factors controlling long-term survival and growth of naturalized Escherichia coli populations in temperate field soils. Microbes Environ. 25, 8–14 (2010).PubMed 
Article 
PubMed Central 

Google Scholar 
31.Whitman, R. L. et al. Microbes in beach sands: Integrating environment, ecology and public health. Rev. Environ. Sci. Biotechnol. 13, 329–368 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
32.Kleinheinz, G. et al. Effect of aquatic macrophytes on the survival of Escherichia coli in a laboratory microcosm. Lake Reserv. Manage. 25, 149–154 (2009).Article 

Google Scholar 
33.Moreira, S. et al. Persistence of Escherichia coli in freshwater periphyton: Biofilm-forming capacity as a selective advantage. FEMS Microbiol. Ecol. 79, 608–618 (2012).CAS 
PubMed 
Article 

Google Scholar 
34.Pachepsky, Y. A. & Shelton, D. R. Escherichia coli and fecal coliforms in freshwater and estuarine sediments. Crit. Rev. Environ. Sci. Technol. 41, 1067–1110 (2011).CAS 
Article 

Google Scholar 
35.Walsh, C. J. & Kunapo, J. The importance of upland flow paths in determining urban effects on stream ecosystems. J. N. Am. Benthol. Soc. 28, 977–990 (2009).Article 

Google Scholar 
36.McLellan, S. L., Fisher, J. C. & Newton, R. J. The microbiome of urban waters. Int. Microbiol. 18, 141–149 (2015).PubMed 
PubMed Central 

Google Scholar 
37.Newton, R. J. et al. Sewage reflects the microbiomes of human populations. MBio 6, 1–9 (2015).CAS 
Article 

Google Scholar 
38.Richards, S., Paterson, E., Withers, P. J. A. & Stutter, M. Septic tank discharges as multi-pollutant hotspots in catchments. Sci. Total Environ. 542, 854–863 (2016).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
39.Sowah, R. A., Habteselassie, M. Y., Radcliffe, D. E., Bauske, E. & Risse, M. Isolating the impact of septic systems on fecal pollution in streams of suburban watersheds in Georgia, United States. Water Res. 108, 330–338 (2017).CAS 
PubMed 
Article 

Google Scholar 
40.Ly, D. K. & Chui, T. F. M. Modeling sewage leakage to surrounding groundwater and stormwater drains. Water Sci. Technol. 66, 2659–2665 (2012).PubMed 
Article 

Google Scholar 
41.Graziano, M., Giorgi, A. & Feijoó, C. Science of the Total Environment Multiple stressors and social-ecological traps in Pampean streams (Argentina): A conceptual model. Sci. Total Environ. 765, 142785 (2020).ADS 
PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
42.Graziano, M. et al. Fostering urban transformations in Latin America: Lessons around the ecological management of an urban stream in coproduction with a social movement (Buenos Aires, Argentina). Ecol. Soc. 24, 13 (2019).Article 

Google Scholar 
43.Cirelli, A. F. & Ojeda, C. Wastewater management in Greater Buenos Aires, Argentina. Desalination 218, 52–61 (2008).CAS 
Article 

Google Scholar 
44.Elordi, M. L., Lerner, J. E. C. & Porta, A. Evaluación del impacto antrópico sobre la calidad del agua del arroyo Las Piedras, Quilmes, Buenos Aires, Argentina. Acta Bioquimica Clinica Latinoamericana 50, 669–677 (2016).
Google Scholar 
45.Censo nacional de población, hogares y viviendas 2010 : censo del Bicentenario : resultados definitivos, Serie B nº 2. (Instituto Nacional de Estadística y Censos, 2012).46.Gordon, N. D., McMahon, T. A., Finlayson, B. L., Gippel, C. J. & Nathan, R. J. Stream Hydrology: An Introduction for Ecologists (Wiley, 2004).
Google Scholar 
47.Elosegui, A., Sabater, S. (eds.). Conceptos y técnicas en ecología fluvial. 243-251. (Fundación BBVa, 2009)48.Baird, R. B., Eaton, A. D., Rice, E. W., & Bridgewater, L. (eds.)Standard methods for the examination of water and wastewater, 23. (American Public Health Association, 2017).
49.Clermont, O., Bonacorsi, S., Bingen, E. & Bonacorsi, P. Rapid and simple determination of the Escherichia coli phylogenetic group. Appl. Environ. Microbiol. 66, 4555–4558 (2000).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
50.Clermont, O., Gordon, D. M., Brisse, S., Walk, S. T. & Denamur, E. Characterization of the cryptic Escherichia lineages: Rapid identification and prevalence. Environ. Microbiol. 13, 2468–2477 (2011).PubMed 
Article 
PubMed Central 

Google Scholar 
51.Lescat, M. et al. Commensal Escherichia coli strains in Guiana reveal a high genetic diversity with host-dependant population structure. Environ. Microbiol. Rep. 5, 9–57 (2013).Article 
CAS 

Google Scholar 
52.Clermont, O. et al. Evidence for a human-specific Escherichia coli clone. Environ. Microbiol. 10, 1000–1006 (2008).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
53.Congedo, L. Semi-automatic classification plugin for QGIS. Sapienza Univ, 1-25 (2013).54.Blanchet, F. G., Legendre, P. & Borcard, D. Forward selection of explanatory variables. Ecology 89, 2623–2632 (2008).PubMed 
Article 

Google Scholar 
55.Borcard, D., Gillet, F. & Lengendre, P. Numerical Ecology with R (Springer, 2018).MATH 
Book 

Google Scholar 
56.Legendre, P., Borcard, D. & Roberts, D. W. Variation partitioning involving orthogonal spatial eigenfunction submodels. Ecology 93, 1234–1240 (2012).PubMed 
Article 

Google Scholar 
57.Bivand, R. S. & Wong, D. W. S. Comparing implementations of global and local indicators of spatial association. TEST 27, 716–748 (2018).MathSciNet 
MATH 
Article 

Google Scholar 
58.Magurran, A. E. Measuring Biological Diversity (Wiley, Hoboken, 2004).
Google Scholar 
59.Oksanen, J. et al. Vegan: Ecological Diversity. R Project, 368. http://cran.r-project.org (2013)
60.Wilkinson, L. & Friendly, M. History corner the history of the cluster heat map. Am. Stat. 63, 179–184 (2009).Article 

Google Scholar 
61.Wei, T. et al. Visualization of a correlation matrix. Statistician 56, 316–324 (2017).
Google Scholar 
62.Robinaugh, D. J., Millner, A. J. & McNally, R. J. Identifying highly influential nodes in the complicated grief network. J. Abnormal Psychol. 125(6), 747 (2016).Article 

Google Scholar 
63.Peres-Neto, P. R., Legendre, P. L., Dray, S. & Borcard, D. Variation partitioning of species data matrices: Estimation and comparison of fractions. Ecology 87, 2614–2625 (2006).PubMed 
Article 
PubMed Central 

Google Scholar 
64.Legendre, P. & Gallagher, E. D. Ecologically meaningful transformations for ordination of species data. Oecologia 129, 271–280 (2001).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
65.Lepš, J. & Šmilauer, P. Multivariate Analysis of Ecological Data Using CANOCO (Cambridge University Press, Cambridge, 2003).MATH 
Book 

Google Scholar 
66.Simpson, G. Restricted permutations; using the permute package. http://cran.r-project.org (2012).67.Booth, D. B., Roy, A. H., Smith, B. & Capps, K. A. Global perspectives on the urban stream syndrome. Freshw. Sci. 35, 412–420 (2016).Article 

Google Scholar 
68.Peipoch, M., Brauns, M., Hauer, F. R., Weitere, M. & Valett, H. M. Ecological simplification: Human influences on Riverscape complexity. Bioscience 65, 1057–1065 (2015).Article 

Google Scholar 
69.Stoppe, N. D. C. et al. Worldwide phylogenetic group patterns of Escherichia coli from commensal human and wastewater treatment plant isolates. Front. Microbiol. 8, 2512 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
70.Escobar-Páramo, P. et al. Large-scale population structure of human commensal Escherichia coli isolates. Appl. Environ. Microbiol. 70, 5698–5700 (2004).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
71.Walk, S. T., Alm, E. W., Calhoun, L. M., Mladonicky, J. M. & Whittam, T. S. Genetic diversity and population structure of Escherichia coli isolated from freshwater beaches. Environ. Microbiol. 9, 2274–2288 (2007).PubMed 
Article 

Google Scholar 
72.Touchon, M. et al. Phylogenetic background and habitat drive the genetic diversification of Escherichia coli. PLoS Genet. 16, e1008866 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
73.R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2019).
Google Scholar  More

1.PlasticsEurope. Plastics the—Facts 2019: An Analysis of European Plastics Production, Demand and Waste Data (PlasticsEurope, 2019).
Google Scholar 
2.Eriksen, M. et al. Plastic pollution in the world’s oceans: More than 5 trillion plastic pieces weighing over 250,000 tons afloat at sea. PLoS ONE 9, e111913. https://doi.org/10.1371/journal.pone.0111913 (2014).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
3.Borrelle, S. B. et al. Predicted growth in plastic waste exceeds efforts to mitigate plastic pollution. Science 369, 1515–1518. https://doi.org/10.1126/science.aba3656 (2020).ADS 
CAS 
Article 
PubMed 

Google Scholar 
4.Bergmann, M., Tekman, M. B. & Gutow, L. Sea change for plastic pollution. Nature 544, 297. https://doi.org/10.1038/544297a (2017).ADS 
CAS 
Article 
PubMed 

Google Scholar 
5.Imhof, H. K. et al. Spatial and temporal variation of macro-, meso- and microplastic abundance on a remote coral island of the Maldives, Indian Ocean. Mar. Pollut. Bull. 116, 340–347. https://doi.org/10.1016/j.marpolbul.2017.01.010 (2017).CAS 
Article 
PubMed 

Google Scholar 
6.Obbard, R. W. Microplastics in polar regions: The role of long range transport. Curr. Opin. Environ. Sci. Health 1, 24–29. https://doi.org/10.1016/j.coesh.2017.10.004 (2018).Article 

Google Scholar 
7.Wessel, C. C., Lockridge, G. R., Battiste, D. & Cebrian, J. Abundance and characteristics of microplastics in beach sediments: Insights into microplastic accumulation in northern Gulf of Mexico estuaries. Mar. Pollut. Bull. 109, 178–183. https://doi.org/10.1016/j.marpolbul.2016.06.002 (2016).CAS 
Article 
PubMed 

Google Scholar 
8.Woodall, L. C. et al. The deep sea is a major sink for microplastic debris. Royal Soc. Open Sci. https://doi.org/10.1098/rsos.140317 (2014).Article 

Google Scholar 
9.Barnes, D. K. A., Galgani, F., Thompson, R. C. & Barlaz, M. Accumulation and fragmentation of plastic debris in global environments. Philos. Trans. Royal Soc. Lond. Ser. B, Biol. Sci. 364, 1985–1998. https://doi.org/10.1098/rstb.2008.0205 (2009).CAS 
Article 

Google Scholar 
10.Arthur, C., Baker, J. E. & Bamford, H. A. Proceedings of the International Research Workshop on the Occurrence, Effects, and Fate of Microplastic Marine Debris, September 9–11, 2008 (University of Washington Tacoma, 2009).
Google Scholar 
11.Hartmann, N. B. et al. Are we speaking the same language? Recommendations for a definition and categorization framework for plastic Debris. Environ. Sci. Technol. 53, 1039–1047. https://doi.org/10.1021/acs.est.8b05297 (2019).ADS 
CAS 
Article 
PubMed 

Google Scholar 
12.Lusher, A. in Marine Anthropogenic Litter (eds Bergmann, M., Gutow, L. & Klages, M.) 245–307 (Springer International Publishing, 2015).13.Cole, M., Lindeque, P., Halsband, C. & Galloway, T. S. Microplastics as contaminants in the marine environment: A review. Mar. Pollut. Bull. 62, 2588–2597. https://doi.org/10.1016/j.marpolbul.2011.09.025 (2011).CAS 
Article 
PubMed 

Google Scholar 
14.Wright, S. L., Thompson, R. C. & Galloway, T. S. The physical impacts of microplastics on marine organisms: A review. Environ. Pollut. 178, 483–492. https://doi.org/10.1016/j.envpol.2013.02.031 (2013).CAS 
Article 
PubMed 

Google Scholar 
15.de Sá, L. C., Oliveira, M., Ribeiro, F., Rocha, T. L. & Futter, M. N. Studies of the effects of microplastics on aquatic organisms: What do we know and where should we focus our efforts in the future?. Sci. Total Environ. 645, 1029–1039. https://doi.org/10.1016/j.scitotenv.2018.07.207 (2018).ADS 
CAS 
Article 
PubMed 

Google Scholar 
16.Bråte, I. L. N. et al. Mytilus spp. as sentinels for monitoring microplastic pollution in Norwegian coastal waters: A qualitative and quantitative study. Environ. Pollut. 243, 383–393. https://doi.org/10.1016/j.envpol.2018.08.077 (2018).CAS 
Article 
PubMed 

Google Scholar 
17.Lusher, A. L., Tirelli, V., O’Connor, I. & Officer, R. Microplastics in Arctic polar waters: The first reported values of particles in surface and sub-surface samples. Sci. Rep. 5, 14947. https://doi.org/10.1038/srep14947 (2015).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
18.Cózar, A. et al. The Arctic Ocean as a dead end for floating plastics in the North Atlantic branch of the thermohaline circulation. Sci. Adv. 3, e1600582. https://doi.org/10.1126/sciadv.1600582 (2017).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
19.Kanhai, L. D. K. et al. Microplastics in sub-surface waters of the Arctic Central Basin. Mar. Pollut. Bull. 130, 8–18. https://doi.org/10.1016/j.marpolbul.2018.03.011 (2018).CAS 
Article 
PubMed 

Google Scholar 
20.Tekman, M. B. et al. Tying up loose ends of microplastic pollution in the Arctic: Distribution from the sea surface through the water column to deep-sea sediments at the HAUSGARTEN observatory. Environ. Sci. Technol. 54, 4079–4090. https://doi.org/10.1021/acs.est.9b06981 (2020).ADS 
CAS 
Article 
PubMed 

Google Scholar 
21.Obbard, R. W. et al. Global warming releases microplastic legacy frozen in Arctic Sea ice. Earth’s Future 2, 315–320. https://doi.org/10.1002/2014EF000240 (2014).ADS 
Article 

Google Scholar 
22.Peeken, I. et al. Arctic sea ice is an important temporal sink and means of transport for microplastic. Nat. Commun. 9, 1505. https://doi.org/10.1038/s41467-018-03825-5 (2018).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
23.Kanhai, L. D. K., Gardfeldt, K., Krumpen, T., Thompson, R. C. & O’Connor, I. Microplastics in sea ice and seawater beneath ice floes from the Arctic Ocean. Sci. Rep. 10, 5004. https://doi.org/10.1038/s41598-020-61948-6 (2020).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
24.Bergmann, M. et al. White and wonderful? Microplastics prevail in snow from the Alps to the Arctic. J. Sci. Adv. 5, eaax1157. https://doi.org/10.1126/sciadv.aax1157 (2019).ADS 
CAS 
Article 

Google Scholar 
25.Amélineau, F. et al. Microplastic pollution in the Greenland Sea: Background levels and selective contamination of planktivorous diving seabirds. Environ. Pollut. 219, 1131–1139. https://doi.org/10.1016/j.envpol.2016.09.017 (2016).CAS 
Article 
PubMed 

Google Scholar 
26.Kühn, S. et al. Plastic ingestion by juvenile polar cod (Boreogadus saida) in the Arctic Ocean. Polar Biol. 41, 1269–1278. https://doi.org/10.1007/s00300-018-2283-8 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
27.Fang, C. et al. Microplastic contamination in benthic organisms from the Arctic and sub-Arctic regions. Chemosphere 209, 298–306. https://doi.org/10.1016/j.chemosphere.2018.06.101 (2018).ADS 
CAS 
Article 
PubMed 

Google Scholar 
28.Bråte, I. L. N. et al. Microplastics in Marine Bivalves from the Nordic Environment Vol. 504 (Nordic Council of Ministers, 2020).Book 

Google Scholar 
29.Misund, O. A. et al. Norwegian fisheries in the Svalbard zone since 1980. Regulations, profitability and warming waters affect landings. Polar Sci. 10, 312–322. https://doi.org/10.1016/j.polar.2016.02.001 (2016).ADS 
Article 

Google Scholar 
30.Jones, C. G., Lawton, J. H. & Shachak, M. Organisms as ecosystem engineers. Oikos 69, 373–386. https://doi.org/10.2307/3545850 (1994).Article 

Google Scholar 
31.Foster, M. S. Rhodoliths: Between rocks and soft places. J. Phycol. 37, 659–667 (2001).Article 

Google Scholar 
32.Fredericq, S. et al. The critical importance of rhodoliths in the life cycle completion of both macro- and microalgae, and as holobionts for the establishment and maintenance of marine biodiversity. Front. Mar. Sci. https://doi.org/10.3389/fmars.2018.00502 (2019).Article 

Google Scholar 
33.Krayesky-Self, S. et al. Eukaryotic life inhabits rhodolith-forming coralline algae (Hapalidiales, Rhodophyta), remarkable marine benthic microhabitats. Sci. Rep. 7, 45850. https://doi.org/10.1038/srep45850 (2017).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
34.Kamenos, N. A., Moore, P. G., Hall-Spencer, J. & Donnan, D. Maerl: Its value as a habitat for commercial species. Shellfish News 18, 8–9 (2004).
Google Scholar 
35.Kamenos, N. A., Moore, P. G. & Hall-Spencer, J. M. Nursery-area function of maerl grounds for juvenile queen scallops Aequipecten opercularis and other invertebrates. Mar. Ecol. Prog. Ser. 274, 183–189. https://doi.org/10.3354/meps274183 (2004).ADS 
Article 

Google Scholar 
36.Gagnon, P., Matheson, K. & Stapleton, M. Variation in rhodolith morphology and biogenic potential of newly discovered rhodolith beds in Newfoundland and Labrador (Canada). Bot. Mar. 55, 85–99 (2012).Article 

Google Scholar 
37.Teichert, S. et al. Rhodolith beds (Corallinales, Rhodophyta) and their physical and biological environment at 80°31’N in Nordkappbukta (Nordaustlandet, Svalbard Archipelago, Norway). Phycologia 51, 371–390 (2012).Article 

Google Scholar 
38.Teichert, S. et al. Arctic rhodolith beds and their environmental controls. Facies 60, 15–37. https://doi.org/10.1007/s10347-013-0372-2 (2014).Article 

Google Scholar 
39.Teichert, S. Hollow rhodoliths increase Svalbard’s shelf biodiversity. Sci. Rep. 4, 6972. https://doi.org/10.1038/srep06972 (2014).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
40.Denisenko, S. G., Denisenko, N. V., Lehtonen, K. K., Andersin, A. B. & Laine, A. O. Macrozoobenthos of the Pechora Sea (SE Barents Sea): Community structure and spatial distribution in relation to environmental conditions. Mar. Ecol. Prog. Ser. 258, 109–123 (2003).ADS 
Article 

Google Scholar 
41.Rees, H. L. & Dare, P. J. Sources of Mortality and Associated Life-Cycle Traits of Selected Benthic Species: A Review Vol. 33, 36 (CEFAS Directorate of Fisheries Research, 1993).
Google Scholar 
42.Sejr, M. K. et al. Growth and production of Hiatella arctica (Bivalvia) in a high-Arctic fjord (Young Sound, Northeast Greenland). Mar. Ecol. Prog. Ser. 244, 163–169. https://doi.org/10.3354/meps244163 (2002).ADS 
Article 

Google Scholar 
43.Witman, J. D. & Sebens, K. P. Regional variation in fish predation intensity: A historical perspective in the Gulf of Maine. Oecologia 90, 305–315. https://doi.org/10.1007/bf00317686 (1992).ADS 
Article 
PubMed 

Google Scholar 
44.Kamenos, N. A., Moore, P. G. & Hall-Spencer, J. M. Small-scale distribution of juvenile gadoids in shallow inshore waters; what role does maerl play?. ICES J. Mar. Sci. 61, 422–429 (2004).Article 

Google Scholar 
45.Teichert, S., Voigt, N. & Wisshak, M. Do skeletal Mg/Ca ratios of Arctic rhodoliths reflect atmospheric CO2 concentrations?. Polar Biol. 43, 2059–2069. https://doi.org/10.1007/s00300-020-02767-3 (2020).Article 

Google Scholar 
46.Ragazzola, F. et al. Phenotypic plasticity of coralline algae in a High CO2 world. Ecol. Evol. 3, 3436–3446. https://doi.org/10.1002/ece3.723 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
47.Teichert, S. & Freiwald, A. Polar coralline algal CaCO3-production rates correspond to intensity and duration of the solar radiation. Biogeosciences 11, 833–842. https://doi.org/10.5194/bg-11-833-2014 (2014).ADS 
Article 

Google Scholar 
48.Büdenbender, J., Riebesell, U. & Form, A. Calcification of the Arctic coralline red algae Lithothamnion glaciale in response to elevated CO2. Mar. Ecol. Prog. Ser. 441, 79–87 (2011).ADS 
Article 

Google Scholar 
49.Wisshak, M. et al. Habitat Characteristics and Carbonate Cycling of Macrophyte-Supported Polar Carbonate Factories (Svalbard)—Cruise No. MSM55—June 11–June 29, 2016—Reykjavik (Iceland)—Longyearbyen (Norway) 58 (Bremen, 2017).50.Löder, M. G. J. et al. Enzymatic purification of microplastics in environmental samples. Environ. Sci. Technol. 51, 14283–14292. https://doi.org/10.1021/acs.est.7b03055 (2017).ADS 
CAS 
Article 
PubMed 

Google Scholar 
51.Hufnagl, B. et al. A methodology for the fast identification and monitoring of microplastics in environmental samples using random decision forest classifiers. Anal. Methods 11, 2277–2285. https://doi.org/10.1039/C9AY00252A (2019).CAS 
Article 

Google Scholar 
52.Yanfang, L., Hua, Z. & Cheng, T. A review of possible pathways of marine microplastics transport in the ocean. Anthr. Coasts 3, 6–13. https://doi.org/10.1139/anc-2018-0030 (2020).Article 

Google Scholar 
53.Erni-Cassola, G., Zadjelovic, V., Gibson, M. I. & Christie-Oleza, J. A. Distribution of plastic polymer types in the marine environment; A meta-analysis. J. Hazard. Mater. 369, 691–698. https://doi.org/10.1016/j.jhazmat.2019.02.067 (2019).CAS 
Article 
PubMed 

Google Scholar 
54.Choy, C. A. et al. The vertical distribution and biological transport of marine microplastics across the epipelagic and mesopelagic water column. Sci. Rep. 9, 7843. https://doi.org/10.1038/s41598-019-44117-2 (2019).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
55.Kooi, M. et al. The effect of particle properties on the depth profile of buoyant plastics in the ocean. Sci. Rep. 6, 33882. https://doi.org/10.1038/srep33882 (2016).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
56.Vinay Kumar, B. N., Löschel, L. A., Imhof, H. K., Löder, M. G. J. & Laforsch, C. Analysis of microplastics of a broad size range in commercially important mussels by combining FTIR and Raman spectroscopy approaches. Environ. Pollut. https://doi.org/10.1016/j.envpol.2020.116147 (2020).Article 
PubMed 

Google Scholar 
57.Löder, M. G. J. & Gerdts, G. in Marine Anthropogenic Litter (eds Bergmann, M., Gutow, L. & Klages, M.) 201–227 (Springer International Publishing, 2015).58.Wisshak, M. et al. Epibenthos dynamics and environmental fluctuations in two contrasting Polar carbonate factories (Mosselbukta and Bjørnøy-Banken, Svalbard). Front. Mar. Sci. 6, 667. https://doi.org/10.3389/fmars.2019.00667 (2019).Article 

Google Scholar 
59.Frias, J. P. G. L., Lyashevska, O., Joyce, H., Pagter, E. & Nash, R. Floating microplastics in a coastal embayment: A multifaceted issue. Mar. Pollut. Bull. 158, 111361. https://doi.org/10.1016/j.marpolbul.2020.111361 (2020).CAS 
Article 
PubMed 

Google Scholar 
60.Rochman, C. M. et al. Anthropogenic debris in seafood: Plastic debris and fibers from textiles in fish and bivalves sold for human consumption. Sci. Rep. 5, 14340. https://doi.org/10.1038/srep14340 (2015).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
61.Digka, N., Tsangaris, C., Torre, M., Anastasopoulou, A. & Zeri, C. Microplastics in mussels and fish from the Northern Ionian Sea. Mar. Pollut. Bull. 135, 30–40. https://doi.org/10.1016/j.marpolbul.2018.06.063 (2018).CAS 
Article 
PubMed 

Google Scholar 
62.Santana, M. F. M., Ascer, L. G., Custódio, M. R., Moreira, F. T. & Turra, A. Microplastic contamination in natural mussel beds from a Brazilian urbanized coastal region: Rapid evaluation through bioassessment. Mar. Pollut. Bull. 106, 183–189. https://doi.org/10.1016/j.marpolbul.2016.02.074 (2016).CAS 
Article 
PubMed 

Google Scholar 
63.Gomiero, A., Strafella, P., Øysæd, K. B. & Fabi, G. First occurrence and composition assessment of microplastics in native mussels collected from coastal and offshore areas of the northern and central Adriatic Sea. Environ. Sci. Pollut. Res. Int. 26, 24407–24416. https://doi.org/10.1007/s11356-019-05693-y (2019).CAS 
Article 
PubMed 

Google Scholar 
64.Mathalon, A. & Hill, P. Microplastic fibers in the intertidal ecosystem surrounding Halifax Harbor, Nova Scotia. Mar. Pollut. Bull. 81, 69–79. https://doi.org/10.1016/j.marpolbul.2014.02.018 (2014).CAS 
Article 
PubMed 

Google Scholar 
65.Van Cauwenberghe, L., Claessens, M., Vandegehuchte, M. B. & Janssen, C. R. Microplastics are taken up by mussels (Mytilus edulis) and lugworms (Arenicola marina) living in natural habitats. Environ. Pollut. 199, 10–17. https://doi.org/10.1016/j.envpol.2015.01.008 (2015).CAS 
Article 
PubMed 

Google Scholar 
66.Li, J. et al. Using mussel as a global bioindicator of coastal microplastic pollution. Environ. Pollut. 244, 522–533. https://doi.org/10.1016/j.envpol.2018.10.032 (2019).CAS 
Article 
PubMed 

Google Scholar 
67.Woodall, L. C. et al. Using a forensic science approach to minimize environmental contamination and to identify microfibres in marine sediments. Mar. Pollut. Bull. 95, 40–46. https://doi.org/10.1016/j.marpolbul.2015.04.044 (2015).CAS 
Article 
PubMed 

Google Scholar 
68.Kowalski, N., Reichardt, A. M. & Waniek, J. J. Sinking rates of microplastics and potential implications of their alteration by physical, biological, and chemical factors. Mar. Pollut. Bull. 109, 310–319. https://doi.org/10.1016/j.marpolbul.2016.05.064 (2016).CAS 
Article 
PubMed 

Google Scholar 
69.Kooi, M., Nes, E. H. V., Scheffer, M. & Koelmans, A. A. Ups and downs in the ocean: Effects of biofouling on vertical transport of microplastics. Environ. Sci. Technol. 51, 7963–7971. https://doi.org/10.1021/acs.est.6b04702 (2017).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
70.Barrows, A. P. W., Cathey, S. E. & Petersen, C. W. Marine environment microfiber contamination: Global patterns and the diversity of microparticle origins. Environ. Pollut. 237, 275–284. https://doi.org/10.1016/j.envpol.2018.02.062 (2018).CAS 
Article 
PubMed 

Google Scholar 
71.Halsband, C. & Herzke, D. Plastic litter in the European Arctic: What do we know?. Emerg. Contam. 5, 308–318. https://doi.org/10.1016/j.emcon.2019.11.001 (2019).Article 

Google Scholar 
72.Bergmann, M., Lutz, B., Tekman, M. B. & Gutow, L. Citizen scientists reveal: Marine litter pollutes Arctic beaches and affects wild life. Mar. Pollut. Bull. 125, 535–540. https://doi.org/10.1016/j.marpolbul.2017.09.055 (2017).CAS 
Article 
PubMed 

Google Scholar 
73.von Moos, N., Burkhardt-Holm, P. & Köhler, A. Uptake and effects of microplastics on cells and tissue of the blue mussel Mytilus edulis L. after an experimental exposure. Environ. Sci. Technol. 46, 11327–11335. https://doi.org/10.1021/es302332w (2012).ADS 
CAS 
Article 

Google Scholar 
74.Kolandhasamy, P. et al. Adherence of microplastics to soft tissue of mussels: A novel way to uptake microplastics beyond ingestion. Sci. Total Environ. 610–611, 635–640. https://doi.org/10.1016/j.scitotenv.2017.08.053 (2018).ADS 
CAS 
Article 
PubMed 

Google Scholar 
75.Löder, M. G. J., Kuczera, M., Mintenig, S., Lorenz, C. & Gerdts, G. Focal plane array detector-based micro-Fourier-transform infrared imaging for the analysis of microplastics in environmental samples. J. Environ. Chem. 12, 563–581. https://doi.org/10.1071/EN14205 (2015).CAS 
Article 

Google Scholar 
76.Hammer, Ø., Harper, D. A. T. & Ryan, P. D. PAST: Paleontological statistics software package for education and data analysis. Palaeontol. Electron. 4, 9 (2001).
Google Scholar 
77.R Foundation for Statistical Computing. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2019).
Google Scholar 
78.Venables, W. N. & Ripley, B. D. Modern Applied Statistics with S 4th edn, 498 (Springer, 2002).Book 

Google Scholar 
79.Vegan: Community Ecology Package (2020). More

Freshwater fish are important contributors to human livelihoods, food and nutrition, recreation, ecosystem services, and biological diversity. Yet, they inhabit some of the most threatened ecosystems globally1, face higher declines relative to marine and terrestrial species2, and are disproportionally understudied3,4. Inland fisheries are subjected to a suite of anthropogenic stressors across aquatic-terrestrial landscapes5, including flow alterations, dams, invasive species, sedimentation, drought, and pollution6,7,8. Evaluating stressors and their impacts on global inland fisheries is essential for effective management, monitoring, and conservation6, but unlike marine fisheries, there is no standardized method to assess inland fisheries9.Data inputs for a fisheries threat assessment typically include baseline information, such as species-specific landings or in situ population data (volume and composition), size (population and landings), and biomass. In addition, multi-stressor interactions (e.g., synergistic, additive) across complex habitats often warrant cross-ecosystem and cross-sector evaluations at multiple scales10,11. However, in the case of inland fisheries, these data inputs are severely deficient and often disparate in many regions12,13, which challenges the development of a global assessment. Thus, evaluating stressors and their impacts on inland fisheries necessitates the use of additional data sources (e.g., expert knowledge) beyond those typically derived directly from fish or fish habitats12,14. Local and subject-matter expertise can provide contextualized insights where spatial data are limited or unattainable (e.g., emerging threats15) and where empirical evidence is incomplete (e.g., multi-stressor interactions).Expert elicitation (i.e., expert opinion synthesis, where opinion is the preliminary state of knowledge of an individual) is increasingly used to inform ecological evaluations and guide water infrastructure, development, food security, and conservation decision-making and assessments, especially in data-poor scenarios14,16. While spatial data can be integrated as a suite of individual stressors (i.e., input variables) within ranking systems for the development of vulnerability or habitat assessments for conservation purposes14,17, the utilization of spatial variables is limited by the method for determining relative impacts (i.e., value judgment)18. Cumulate impact scores and systematic weighted ranking of threats are often based on geographically biased, small sized, or non-representative subsets of experts’ opinions (e.g., global weight determination from eight experts5). Thus, data collection for this study was motivated by the development of a global assessment of threats to major inland fisheries, and the overarching need for tractable freshwater indicators. The data generated contribute essential relative influence scores for the assessment and provide a timely snapshot of inland fisheries as perceived by fisheries professionals. Threat composition and influence have broader potential applications to inform vulnerability and adaptation components of freshwater conservation and management targets (e.g., United Nations (UN) Sustainable Development Goals, UN International Decade “Water for Sustainable Development,” Convention on Biological Diversity, Ramsar Convention on Wetlands).This paper introduces a dataset that can help address a knowledge gap in understanding natural and human influences on inland fisheries with local, contextualized fishery evaluations. Derived from an electronic survey, data comprise perceptions from fisheries professionals (n = 536) on the relative influence and spatial associations of fishery threats, recent successes, and adaptive capacity measures within the respondent’s fishery of expertise.In the context of the survey, we use the term “threat” as a proximate human activity or process (“direct threat”) causing degradation or impairment (“stress”; e.g., reduced population size, fragmented riparian habitat) to ecological targets (e.g., species, communities, ecosystems; in this case, fishery)19. We considered only the threats most proximate and direct to the target (fishery) and excluded stresses (i.e., symptoms, degraded key attributes) and contributing factors (i.e., root causes, underlying factors). For example, we considered pollutants (direct threat) rather than the pollution source (contributing factor) or the resulting contaminated water (stress, effect). We addressed the ambiguity of the term ‘fishery’20 by allowing respondents to indicate a geographic location (specific point) within their fishery area. This allows for spatial attribution with an inclusive use of ‘fishery’ as it pertains to threats (e.g., threats to a fish population of fishery-targeted species, catch characteristics, or the habitat in which the fishery operates).We structured survey questions about the occurrence and relative influence of threats to the production and health of inland fisheries using 29 specified individual threats derived from well-studied pressures to inland fisheries in addition to pressures emerging as threats to fisheries (e.g., climate change, plastics15). We categorized individual threats into five well-established categories: habitat degradation, pollution, overexploitation, species invasion, and climate change1,7 for organizational context in the survey. We also designed survey questions specifically to understand the social adaptive capacity of fishers using five major community-level domains: fisher access to assets (e.g., financial, technological, service), fisher and institutional flexibility to adapt to changing conditions (e.g., livelihood alternatives, adaptive management), social capital and organization to enable cooperation and collective action (e.g., co-management), learning and problem-solving for responding to threats, and fishers’ sense of agency to influence and shape actions and outcomes21.This dataset can be useful as an overview assessment, on which future assessments may expand for specific temporal or spatial interests. Some data in this dataset (e.g., microplastics, invasive species disturbances) are otherwise unattainable at relevant scales from geospatial information and therefore provide novel information. Potential uses include demographic influences on threat perceptions, spatial distribution of adaptive capacity measures paired with climate change or other threats, external factors driving multi-stressor interactions, and paired geospatial and expert-derived threat analysis. These data can provide insights on fisheries as a coupled human-natural system and inform regional and global freshwater assessments. More

1.Keenan, R. J. et al. Forest ecology and management dynamics of global forest area: Results from the FAO Global Forest Resources Assessment 2015. For. Ecol. Manag. 352, 9–20 (2015).Article 

Google Scholar 
2.Siitonen, J. Forest management, coarse woody debris and saproxylic organisms: Fennoscandian boreal forests as an example. Ecol. Bull. 49, 11–41 (2001).
Google Scholar 
3.Stokland, J. N., Siitonen, J. & Jonsson, B. G. Biodiversity in Dead Wood (Cambridge University Press, 2012).Book 

Google Scholar 
4.Nordén, J., Penttilä, R., Siitonen, J., Tomppo, E. & Ovaskainen, O. Specialist species of wood-inhabiting fungi struggle while generalists thrive in fragmented boreal forests. J. Ecol. 101, 701–712 (2013).Article 

Google Scholar 
5.Tikkanen, O.-P., Martikainen, P., Hyvärinen, E., Junninen, K. & Kouki, J. Red-listed boreal forest species of Finland: Associations with forst structure, tree species, and decaying wood. Ann. Zool. Fennici 43, 373–383 (2006).
Google Scholar 
6.Sippola, A.-L., Lehesvirta, T. & Renvall, P. Effect of selective logging on coarse woody debris and diversity of wood-decaying polypores in eastern Finland. Ecol. Bull. 49, 243–254 (2001).
Google Scholar 
7.Axelsson, A. L., Östlund, L. & Hellberg, E. Changes in mixed deciduous forests of boreal Sweden 1866–1999 based on interpretation of historical records. Landsc. Ecol. 17, 403–418 (2002).Article 

Google Scholar 
8.Eriksson, S., Skånes, H., Hammer, M. & Lönn, M. Current distribution of older and deciduous forests as legacies from historical use patterns in a Swedish boreal landscape (1725–2007). For. Ecol. Manag. 260, 1095–1103 (2010).Article 

Google Scholar 
9.Wallenius, T. H., Lilja, S. & Kuuluvainen, T. Fire history and tree species composition in managed Picea abies stands in southern Finland: Implications for restoration. For. Ecol. Manag. 250, 89–95 (2007).Article 

Google Scholar 
10.Stokland, J. N. Host-tree associattions. In Biodiversity in Dead Wood (eds Stokland, J. N. et al.) 82–109 (Cambridge University Press, 2012).Chapter 

Google Scholar 
11.Kouki, J., Arnold, K. & Martikainen, P. Long-term persistence of aspen – A key host for many threatened species—Is endangered in old-growth conservation areas in Finland. J. Nat. Conserv. 12, 41–52 (2004).Article 

Google Scholar 
12.Komonen, A., Tuominen, L., Purhonen, J. & Halme, P. Landscape structure influences browsing on a keystone tree species in conservation areas. For. Ecol. Manag. 457, 117724 (2020).Article 

Google Scholar 
13.Purhonen, J. et al. Morphological traits predict host-tree specialization in wood-inhabiting fungal communities. Fungal Ecol. 46, 100863 (2020).Article 

Google Scholar 
14.Dowding, P. Nutrient uptake and allocation during substrate exploitation by fungi. In The Fungal Community. Its Organization and Role in the Ecosystems (eds Wicklow, D. T. & Carroll, G. C.) 612–636 (Marcel Dekker Inc, 1981).
Google Scholar 
15.Boddy, L., Frankland, J. & van West, P. Ecology of Saprotrophic Basidiomycetes (Elsevier Ltd, 2008).
Google Scholar 
16.Kahl, T. et al. Wood decay rates of 13 temperate tree species in relation to wood properties, enzyme activities and organismic diversities. For. Ecol. Manag. 391, 86–95 (2017).Article 

Google Scholar 
17.Abrego, N. & Salcedo, I. Variety of woody debris as the factor influencing wood-inhabiting fungal richness and assemblages: Is it a question of quantity or quality?. For. Ecol. Manag. 291, 377–385 (2013).Article 

Google Scholar 
18.Lindblad, I. Wood-inhabiting fungi on fallen logs of Norway spruce: Relations to forest management and substrate quality. Nord. J. Bot. 18, 243–255 (1998).Article 

Google Scholar 
19.Tomao, A., Antonio Bonet, J., Castaño, C. & de-Miguel, S. How does forest management affect fungal diversity and community composition? Current knowledge and future perspectives for the conservation of forest fungi. For. Ecol. Manag. 457, 1176 (2020).Article 

Google Scholar 
20.Bader, P., Jansson, S. & Jonsson, B. G. Wood-inhabiting fungi and substratum decline in selectively logged boreal spruce forests. Biol. Conserv. 72, 355–362 (1995).Article 

Google Scholar 
21.Heilmann-Clausen, J. & Christensen, M. Does size matter?. For. Ecol. Manag. 201, 105–117 (2004).Article 

Google Scholar 
22.Nordén, B., Götmark, F., Tönnberg, M. & Ryberg, M. Dead wood in semi-natural temperate broadleaved woodland: Contribution of coarse and fine dead wood, attached dead wood and stumps. For. Ecol. Manag. 194, 235–248 (2004).Article 

Google Scholar 
23.Ottosson, E. et al. Diverse ecological roles within fungal communities in decomposing logs of Picea abies. FEMS Microbiol. Ecol. 91, 1–13 (2015).Article 
CAS 

Google Scholar 
24.Juutilainen, K., Mönkkönen, M., Kotiranta, H. & Halme, P. The effects of forest management on wood-inhabiting fungi occupying dead wood of different diameter fractions. For. Ecol. Manag. 313, 283–291 (2014).Article 

Google Scholar 
25.Jönsson, M., Ruete, A., Kellner, O., Gunnarsson, U. & Snäll, T. Will forest conservation areas protect functionally important diversity of fungi and lichens over time?. Biodivers. Conserv. https://doi.org/10.1007/s10531-015-1035-0 (2016).Article 

Google Scholar 
26.Abrego, N., Norberg, A. & Ovaskainen, O. Measuring and predicting the influence of traits on the assembly processes of wood-inhabiting fungi. J. Ecol. https://doi.org/10.1111/1365-2745.12722 (2017).Article 

Google Scholar 
27.Bässler, C. et al. Functional response of lignicolous fungal guilds to bark beetle deforestation. Ecol. Indic. 65, 149–160 (2016).Article 

Google Scholar 
28.Bässler, C., Heilmann-Clausen, J., Karasch, P., Brandl, R. & Halbwachs, H. Ectomycorrhizal fungi have larger fruit bodies than saprotrophic fungi. Fungal Ecol. 17, 205–212 (2015).Article 

Google Scholar 
29.Sherwood, M. A. Convergent evolution in discomycetes from bark and wood. Bot. J. Linn. Soc. 82, 15–34 (1981).Article 

Google Scholar 
30.Unterseher, M., Otto, P. & Morawetz, W. Species richness and substrate specificity of lignicolous fungi in the canopy of a temperate, mixed deciduous forest. Mycol. Prog. 4, 117–132 (2005).Article 

Google Scholar 
31.Dawson, S. K. & Jönsson, M. Just how big is intraspecific trait variation in basidiomycete wood fungal fruit bodies?. Fungal Ecol. 46, 100865 (2020).Article 

Google Scholar 
32.Dawson, S. K. et al. Handbook for the measurement of macrofungal functional traits: A start with basidiomycete wood fungi. Funct. Ecol. 33, 372–387 (2019).Article 

Google Scholar 
33.Zanne, A. E. et al. Fungal functional ecology: Bringing a trait-based approach to plant-associated fungi. Biol. Rev. 95, 409–433 (2020).PubMed 
Article 

Google Scholar 
34.Nordén, B., Ryberg, M., Götmark, F. & Olausson, B. Relative importance of coarse and fine woody debris for the diversity of wood-inhabiting fungi in temperate broadleaf forests. Biol. Conserv. 117, 1–10 (2004).Article 

Google Scholar 
35.Stokland, J. N. & Larsson, K. Forest ecology and management legacies from natural forest dynamics : Different effects of forest management on wood-inhabiting fungi in pine and spruce forests. For. Ecol. Manag. 261, 1707–1721 (2011).Article 

Google Scholar 
36.Cajander, A. K. Forest types and their significance. Acta For. Fenn. 56, 1–69 (1949).
Google Scholar 
37.Ahti, T., Hämet-Ahti, L. & Jalas, J. Vegetation zones and their sections in northwestern Europe. Ann. Bot. Fenn. 5, 169–211 (1968).
Google Scholar 
38.Renaud, V., Innes, J. L., Dobbertin, M. & Rebetez, M. Comparison between open-site and below-canopy climatic conditions in Switzerland for different types of forests over 10 years (1998–2007). Theor. Appl. Climatol. 105, 119–127 (2011).ADS 
Article 

Google Scholar 
39.Renvall, P. Community structure and dynamics of wood-rotting Basidiomycetes on decomposing conifer trunks in northern Finland. Karstenia 35, 1–51 (1995).Article 

Google Scholar 
40.Abrego, N., Halme, P., Purhonen, J. & Ovaskainen, O. Fruit body based inventories in wood-inhabiting fungi: Should we replicate in space or time?. Fungal Ecol. 20, 225–232 (2016).Article 

Google Scholar 
41.Halme, P. & Kotiaho, J. S. The importance of timing and number of surveys in fungal biodiversity research. Biodivers. Conserv. 21, 205–219 (2012).Article 

Google Scholar 
42.Purhonen, J., Huhtinen, S., Kotiranta, H. & Kotiaho, J. S. Detailed information on fruiting phenology provides new insights on wood-inhabiting fungal detection. Fungal Ecol. 27, 175–177 (2017).Article 

Google Scholar 
43.Royal Botanic Gardens Kew, Landcare Research-NZ & Chinese Academy of Science. Index Fungorum. www.indexfungorum.org 01.03.2017 (2017).44.Barton, K. MuMIn: Multi-Model Inference. R Package Version 1.43.6. https://CRAN.R-project.org/package=MuMIn 15.11.2020 (2019).45.R Core Team. R: A Language and Environment for Statistical Computing. Available at: https://www.r-project.org/ (2017).46.Magnusson, A. et al. glmmTMB: Generalized Linear Mixed Models Using Template Model Builder. https://cran.r-project.org/web/packages/glmmTMB/glmmTMB.pdf 30.08.2018 (2018).47.Oksanen, J. et al. vegan: Community Ecology Package. R package version 2.4-4. https://cran.r-project.org/web/packages/vegan/index.html 30.12.2017 (2017).48.Abrego, N., Bässler, C., Christensen, M. & Heilmann-Clausen, J. Implications of reserve size and forest connectivity for the conservation of wood-inhabiting fungi in Europe. Biol. Conserv. 191, 469–477 (2015).Article 

Google Scholar 
49.Halme, P. et al. The effects of habitat degradation on metacommunity structure of wood-inhabiting fungi in European beech forests. Biol. Conserv. 168, 24–30 (2013).Article 

Google Scholar 
50.Edman, M., Kruys, N. & Jonsson, B. G. Local dispersal sources strongly affect colonization patterns of wood-decaying fungi on spruce logs. Ecol. Appl. 14, 893–901 (2004).Article 

Google Scholar 
51.Komonen, A. & Müller, J. Dispersal ecology of deadwood organisms and connectivity conservation. Conserv. Biol. 32, 535–545 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
52.Abrego, N. & Salcedo, I. How does fungal diversity change based on woody debris type? A case study in Northern Spain. Ekologija 57, 109–119 (2011).Article 

Google Scholar 
53.Juutilainen, K., Halme, P., Kotiranta, H. & Mönkkönen, M. Size matters in studies of dead wood and wood-inhabiting fungi. Fungal Ecol. 4, 342–349 (2011).Article 

Google Scholar 
54.Heilmann-Clausen, J. & Christensen, M. Wood-inhabiting macrofungi in Danish beech-forests ? conflicting diversity patterns and their implications in a conservation perspective. Biol. Conserv. 122, 633–642 (2005).Article 

Google Scholar 
55.Moore, D., Gange, A. C., Gange, E. G. & Boddy, L. Fruit bodies: Their production and develpoment in relation to environment. In Ecology of Saprotrophic Basidiomycetes (eds Boddy, L. et al.) (Elsevier, 2008).
Google Scholar 
56.Junninen, K., Similä, M., Kouki, J. & Kotiranta, H. Assemblages of wood-inhabiting fungi along the gradients of succession and naturalness in boreal pine-dominated forests in Fennoscandia. Ecography (Cop.) 29, 75–83 (2006).Article 

Google Scholar 
57.Agren, J. & Zackrisson, O. Age and size structure of Pinus sylvestris populations on mires in Central and Northern Sweden. J. Ecol. 78, 1049–1062 (1990).Article 

Google Scholar 
58.Niemelä, T., Wallenius, T. & Kotiranta, H. The kelo tree, a vanishing substrate of specified wood-inhabiting fungi. Polish Bot. J. 47, 91–101 (2002).
Google Scholar 
59.Venugopal, P., Julkunen-Tiitto, R., Junninen, K. & Kouki, J. Phenolic compounds in Scots pine heartwood: Are kelo trees a unique woody substrate?. Can. J. For. Res. 46, 225–233 (2016).CAS 
Article 

Google Scholar 
60.Jonsson, B. G. et al. Dead wood availability in managed Swedish forests – Policy outcomes and implications for biodiversity. For. Ecol. Manag. 376, 174–182 (2016).Article 

Google Scholar 
61.Runnel, K. & Lõhmus, A. Deadwood-rich managed forests provide insights into the old-forest association of wood-inhabiting fungi. Fungal Ecol. 27, 155–167 (2017).Article 

Google Scholar 
62.Junninen, K. & Komonen, A. Conservation ecology of boreal polypores: A review. Biol. Conserv. 144, 11–20 (2011).Article 

Google Scholar 
63.Krah, F. S. et al. Independent effects of host and environment on the diversity of wood-inhabiting fungi. J. Ecol. 106, 1428–1442. https://doi.org/10.1111/1365-2745.12939 (2018).Article 

Google Scholar 
64.Hoppe, B. et al. Linking molecular deadwood-inhabiting fungal diversity and community dynamics to ecosystem functions and processes in Central European forests. Fungal Divers. 77, 367–379 (2016).Article 

Google Scholar 
65.Kubartová, A., Ottosson, E., Dahlberg, A. & Stenlid, J. Patterns of fungal communities among and within decaying logs, revealed by 454 sequencing. Mol. Ecol. 21, 4514–4532 (2012).PubMed 
Article 

Google Scholar 
66.Kazartsev, I., Shorohova, E., Kapitsa, E. & Kushnevskaya, H. Decaying Picea abies log bark hosts diverse fungal communities. Fungal Ecol. 33, 1–12 (2018).Article 

Google Scholar 
67.von Bonsdorff, T. et al. New national and regional biological records for Finland 8. Contributions to agaricoid, gastroid and ascomycetoid taxa of fungi 5. Memo. Soc. pro Fauna Flora Fenn. 92, 120–128 (2016).
Google Scholar 
68.von Bonsdorff, T. et al. New national and regional biological records for Finland 5. Contributions to agaricoid and ascomycetoid taxa of fungi 4. Memo. Soc. pro Fauna Flora Fenn. 91, 56–66 (2015).
Google Scholar 
69.Frøslev, T. G. et al. Man against machine: Do fungal fruitbodies and eDNA give similar biodiversity assessments across broad environmental gradients?. Biol. Conserv. 233, 201–212 (2019).Article 

Google Scholar 
70.Esri. ArcMap, version 10.5.1. http://desktop.arcgis.com/en/arcmap/ 04.09.2017 (2017). Available at: http://desktop.arcgis.com/en/arcmap/. More

From SSN to PFCsWe analyzed the 1,914,171 proteins from 885 MAGs from marine plankton, recovered from 12 geographically bound assemblies of metagenomic sets corresponding to a total of 93 Tara Oceans samples from the 0.2 to 3 µm and 0.2 to 1.6 µm size fractions21. A flowchart of our bioinformatic pipeline is available in Supplementary Fig. 1. 39.6% of the MAGs’ proteins (757,457) were involved in our SSN, i.e., they had at least one similarity relationship with another protein that satisfied the chosen threshold of 80% similarity and 80% coverage (see “Methods”). In total, 51.1% of the network proteins could be annotated to 4922 unique molecular function IDs in the KEGG database37, associated with 327 distinct metabolic pathways (a full list of these pathways is available in Supplementary Data 1). In total, 85.2% of the network proteins were annotated to 17,009 eggNOG functional descriptions38,39.The SSN involved 233,756 connected components (CCs), i.e., groups of nodes (here proteins) connected together by at least one path and disconnected from the rest of the network. According to KEGG and eggNOG databases, 15.3% and 48.5% of the CCs remained without any functional annotation (i.e., all sequences from the CC were unmatched in the databases, or had a match but were not yet linked to any biological function, Table 1), and 14.8% were functionally unannotated for both databases. We ranked the functional homogeneity of CCs involving at least one functional annotation from 0 (all annotations in the CC are different) to 1 (all annotations in the CC are the same) and found mean homogeneity scores of 0.99 over 1 for KEGG annotations and 0.94 over 1 for eggNOG ones (see “Methods” for score calculation details). Only 88 (0.04%) CCs had a homogeneity score below 0.5 in both annotation databases, all with sizes below five proteins. 177 CCs (0.07%) had a score below 0.8 in both databases, all under 12 proteins in size. These CCs were kept in the analysis while tagged as poorly homogenous. We thereafter considered each CC as a PFC, numbered from #1 to #233,756.Table 1 Metrics computed on the 233,756 protein functional clusters (PFC) from the sequence similarity network of MAGs proteins.Full size tableTo check for the influence of taxonomic relationships between the MAGs on our PFCs, we computed different metrics based on MAGs taxonomic annotations provided by Delmont et al.21. (Table 1). This taxonomic annotation based on 43 single-copy core genes allowed to annotate 100% of the MAGs at the domain level, and 95% of the MAGs at the phylum level, the remaining 5% corresponding to Bacteria MAGs of unidentified phyla21. Only 1330 PFCs (0.6%) mixed proteins from the Archaea and Bacteria domains. PFCs were very homogeneous at the phylum level, then the homogeneity decreased at lower taxonomic rank, meaning that PFCs studied here were generally not specific from a single class, order, family, genus, or MAG (Table 1). In total, 7834 PFCs (3.4%) were only composed of proteins with no functional annotation in KEGG and eggNOG databases, and no taxonomic annotation under the phylum level. Their sizes ranged from 2 to 30 proteins (mean of 2.62). Their 20,552 proteins came from Euryarchaeota MAGs (12,458; 60.6%), Bacteria MAGs of unidentified phylum (2742; 13.3%), Candidatus Marinimicrobia MAGs (2451; 11.9%), Proteobacteria MAGs (1528; 7.4%), Acidobacteria MAGs (1031; 5%), Verrucomicrobia MAGs (103; 0.5%), Planctomycetes MAGs (89; 0.4%), Bacteroidetes MAGs (79; 0.4%), Chloroflexi MAGs (59; 0.3%) and Candidate Phyla Radiation MAGs (12; 0.05%). We hereafter considered these functionally and taxonomically unknown PFCs as “dark” PFCs40,41. Their nucleotidic sequences are available in separate supplementary files (see “Data availability”). The abundance of dark PFCs was significantly different from the abundance of other PFCs in 85 samples over 93 (two-sided Wilcoxon rank-sum test, p-value  More

1.Mendo, T., Semmens, J. M., Lyle, J. M., Tracey, S. R. & Moltschaniwskyj, N. Reproductive strategies and energy sources fueling reproductive growth in a protracted spawner. Mar. Biol. 163, 2 (2016).Article 

Google Scholar 
2.Stephens, P. A., Boyd, I. L., McNamara, J. M. & Houston, A. I. Capital breeding and income breeding: their meaning, measurement, and worth. Ecology 90, 2057–2067 (2009).PubMed 
Article 
PubMed Central 

Google Scholar 
3.Bonnet, X., Bradshaw, D. & Shine, R. Capital versus income breeding: An ectothermic perspective. Oikos 83, 333–342 (1998).Article 

Google Scholar 
4.Johnson, R. A. Capital and income breeding and the evolution of colony founding strategies in ants. Insectes Soc. 53, 316–322 (2006).Article 

Google Scholar 
5.Wheatley, K. E., Bradshaw, C. J., Harcourt, R. G. & Hindell, M. A. Feast or famine: Evidence for mixed capital–income breeding strategies in Weddell seals. Oecologia 155, 11–20 (2008).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
6.Tammaru, T. & Haukioja, E. Capital breeders and income breeders among Lepidoptera: Consequences to population dynamics. Oikos 77, 561–564 (1996).Article 

Google Scholar 
7.McHuron, E. A., Costa, D. P., Schwarz, L. & Mangel, M. State-dependent behavioural theory for assessing the fitness consequences of anthropogenic disturbance on capital and income breeders. Methods Ecol. Evol. 8, 552–560 (2017).Article 

Google Scholar 
8.Williams, C. T. et al. Seasonal reproductive tactics: Annual timing and the capital-to-income breeder continuum. Philos. Trans. R. Soc. B 372, 20160250 (2017).Article 

Google Scholar 
9.Kerby, J. & Post, E. Capital and income breeding traits differentiate trophic match–mismatch dynamics in large herbivores. Philos. Trans. R. Soc. B 368, 20120484 (2013).Article 

Google Scholar 
10.Zeng, Y., McLay, C. & Yeo, D. C. Capital or income breeding crabs: Who are the better invaders?. Crustaceana 87, 1648–1656 (2014).Article 

Google Scholar 
11.Griffen, B. D. The timing of energy allocation to reproduction in an important group of marine consumers. PLoS ONE 13, e0199043 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
12.Boudreau, S. A. & Worm, B. Ecological role of large benthic decapods in marine ecosystems: A review. Mar. Ecol. Prog. Ser. 469, 195–213 (2012).ADS 
Article 

Google Scholar 
13.Holland, D. S. & Kasperski, S. The impact of access restrictions on fishery income diversification of US West Coast fishermen. Coast. Manag. 44, 452–463 (2016).Article 

Google Scholar 
14.Edwards, M. & Richardson, A. J. Impact of climate change on marine pelagic phenology and trophic mismatch. Nature 430, 881–884 (2004).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
15.Anilkumar, G. Reproductive physiology of female crustaceans. Ph.D. thesis, University of Calicut, India (1980).16.Lovrich, G. A., Romero, M. C., Tapella, F. & Thatje, S. Distribution, reproductive and energetic conditions of decapod crustaceans along the Scotia Arc (Southern Ocean). Sci. Mar. 69, 183–193 (2005).Article 

Google Scholar 
17.Sainmont, J., Andersen, K. H., Varpe, Ø. & Visser, A. W. Capital versus income breeding in a seasonal environment. Am. Nat. 184, 466–476 (2014).PubMed 
Article 
PubMed Central 

Google Scholar 
18.Griffen, B. D. Metabolic costs of capital energy storage in a small-bodied ectotherm. Ecol. Evol. 7, 2423–2431 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
19.Varpe, Ø., Jørgensen, C., Tarling, G. A. & Fiksen, Ø. The adaptive value of energy storage and capital breeding in seasonal environments. Oikos 118, 363–370 (2009).Article 

Google Scholar 
20.Varpe, Ø., Jørgensen, C., Tarling, G. A. & Fiksen, Ø. Early is better: Seasonal egg fitness and timing of reproduction in a zooplankton life-history model. Oikos 116, 331–1342 (2007).Article 

Google Scholar 
21.Warner, G. F. The life history of the mangrove tree crab, Aratus pisoni. J. Zool. 153, 321–335 (1967).Article 

Google Scholar 
22.Díaz, H. & Conde, J. E. Population dynamics and life history of the mangrove crab Aratus pisonii (Brachyura, Grapsidae) in a marine environment. Bull. Mar. Sci. 45, 148–163 (1989).
Google Scholar 
23.de Arruda Leme, M. H. & Negreiros-Fransozo, M. L. Reproductive patterns of Aratus pisonii (Decapoda: Grapsidae) from an estuarine area of São Paulo northern coast, Brazil. Rev. Biol. Trop. 46, 673–678 (1998).
Google Scholar 
24.Cannizzo, Z. J., Lang, S. Q., Benitez-Nelson, B. & Griffen, B. D. An artificial habitat increases the reproductive fitness of a range-shifting species within a newly colonized ecosystem. Sci. Rep. 10, 554 (2020).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
25.Riley, M. E., Vogel, M. & Griffen, B. D. Fitness-associated consequences of an omnivorous diet for the mangrove tree crab Aratus pisonii. Aquat. Biol. 20, 35–43 (2014).Article 

Google Scholar 
26.López, B. & Conde, J. E. Dietary variation in the crab Aratus pisonii (H. Milne Edwards, 1837)(Decapoda, Sesarmidae) in a mangrove gradient in northwestern Venezuela. Crustaceana 86, 1051–1069 (2013).Article 

Google Scholar 
27.Erickson, A. A., Feller, I. C., Paul, V. J., Kwiatkowski, L. M. & Lee, W. Selection of an omnivorous diet by the mangrove tree crab Aratus pisonii in laboratory experiments. J. Sea Res. 59, 59–69 (2008).ADS 
Article 

Google Scholar 
28.Beever, J. W., Simberloff, D. & King, L. L. Herbivory and predation by the mangrove tree crab Aratus pisonii. Oecologia 43, 317–328 (1979).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
29.Riley, M. E., Johnston, C. A., Feller, I. C. & Griffen, B. D. Range expansion of Aratus pisonii (mangrove tree crab) into novel vegetative habitats. Southeast. Nat. 13, N43–N48 (2014).Article 

Google Scholar 
30.Cannizzo, Z. J. & Griffen, B. D. Changes in spatial behaviour patterns by mangrove tree crabs following climate-induced range shift into novel habitat. Anim. Behav. 121, 79–86 (2016).Article 

Google Scholar 
31.Riley, M. E. & Griffen, B. D. Habitat-specific differences alter traditional biogeographic patterns of life history in a climate-change induced range expansion. PLoS ONE 12, e0176263 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
32.Cannizzo, Z. J. & Griffen, B. D. An artificial habitat facilitates a climate-mediated range expansion into a suboptimal novel ecosystem. PLoS ONE 14, e0207416 (2019).Article 
CAS 

Google Scholar 
33.Bernardo, J. & Agosta, S. J. Evolutionary implications of hierarchical impacts of nonlethal injury on reproduction, including maternal effects. Biol. J. Linn. Soc. 86, 309–331 (2005).Article 

Google Scholar 
34.Griffen, B. D., Cannizzo, Z. J., Carver, J. & Meidell, M. Reproductive and energetic costs of injury in the mangrove tree crab. Mar. Ecol. Prog. Ser. 640, 127–137 (2020).ADS 
Article 

Google Scholar 
35.De Arruda Leme, M. H., De Sousa Soares, V. & Pinheiro, M. A. A. Population dynamics of the mangrove tree crab Aratus pisonii (Brachyura: Sesarmidae) in the estuarine complex of Cananéia-Iguape, São Paulo, Brazil. Pan-Am. J. Aquat. Sci. 9, 259–266 (2014).
Google Scholar 
36.Skov, M. W. et al. Marching to a different drummer: Crabs synchronize reproduction to a 14-month lunar-tidal cycle. Ecology 86, 1164–1171 (2005).Article 

Google Scholar 
37.Schmidt, A. J., Bemvenuti, C. E. & Diele, K. Effects of geophysical cycles on the rhythm of mass mate searching of a harvested mangrove crab. Anim. Behav. 84, 333–340 (2012).Article 

Google Scholar 
38.Dronkers, J. J. Tidal Computations in Rivers and Coastal Waters (Wiley, 1964).
Google Scholar 
39.Varpe, Ø. Life history adaptations to seasonality. Integr. Comp. Biol. 57, 943–960 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
40.Conde, J. E. et al. Population and life history features of the crab Aratus pisonii (Decapoda: Grapsidae) in a subtropical estuary. Interciencia 25, 151–158 (2000).
Google Scholar 
41.Elner, R. W. & Beninger, P. G. Multiple reproductive strategies in snow crab, Chionoecetes opilio: Physiological pathways and behavioral plasticity. J. Exp. Mar. Biol. Ecol. 193, 93–112 (1995).Article 

Google Scholar 
42.Tepolt, C. K. & Somero, G. N. Master of all trades: Thermal acclimation and adaptation of cardiac function in a broadly distributed marine invasive species, the European green crab, Carcinus maenas. J. Exp. Biol. 217, 1129–1138 (2014).PubMed 
Article 
PubMed Central 

Google Scholar 
43.Ruiz, G., Fofonoff, P., Steves, B. & Dahlstrom, A. Marine crustacean invasions in North America: a synthesis of historical records and documented impacts. In In the Wrong Place-Alien Marine Crustaceans: Distribution, Biology and Impacts 215–250. (Springer, 2011).44.Griffen, B. D. & Mosblack, H. Predicting diet and consumption rate differences between and within species using gut ecomorphology. J. Anim. Ecol. 80, 854–863 (2011).PubMed 
Article 
PubMed Central 

Google Scholar 
45.Cannizzo, Z. J., Dixon, S. R. & Griffen, B. D. An anthropogenic habitat within a suboptimal colonized ecosystem provides improved conditions for a range-shifting species. Ecol. Evol. 8, 1521–1533 (2018).PubMed 
PubMed Central 
Article 

Google Scholar  More

Here, we analyzed the burrowing mechanisms of beetle larvae. Beetle larvae were placed on the soil surface to make sure they could burrow into the soil (Fig. 1a). In order to observe the burrowing behavior, a two-dimensional (2D) observation tank (130 × 210 ×  ~ 20 mm) was constructed (Fig. 1b); we succeeded in observing the dynamics of the larvae under a 2D soil condition (Fig. 1c, Supplementary Movie 1). The larvae burrowed by rotating themselves (Fig. 1d, Supplementary Movie 1). Rotation was observed regardless of sex. All observed individuals proceeded towards the bottom and stopped when rotating at the bottom layer (Fig. 1c).Figure 1Burrowing dynamics of scarab beetle (Trypoxylus dichotomus) larva. (a) Burrowing images. After beetle is put on the soil, they can burrow in a short time ( More

1.Bradshaw, A. D. Evolutionary significance of phenotypic plasticity in plants. Adv. Genet. 155, 115–155 (1965).Article 

Google Scholar 
2.Sultan, S. E. Phenotypic plasticity and plant adaptation. Acta Bot. Neerl. 44, 363–383 (1995).Article 

Google Scholar 
3.Callaway, R. M., Pennings, S. C. & Richards, C. L. Phenotypic plasticity and interactions among plants. Ecology 84, 1115–1128 (2003).Article 

Google Scholar 
4.Fordyce, J. A. The evolutionary consequences of ecological interactions mediated through phenotypic plasticity. J. Exp. Biol. 209, 2377–2383 (2006).PubMed 
Article 
PubMed Central 

Google Scholar 
5.Owusu-Nketia, S. et al. Functional roles of root plasticity and its contribution to water uptake and dry matter production of CSSLs with the genetic background of KDML105 under soil moisture fluctuation. Plant Prod. Sci. 21, 266–277 (2018).CAS 
Article 

Google Scholar 
6.Acciaresi, H. & Guiamet, J. Below- and above-ground growth and biomass allocation in maize and Sorghum halepense in response to soil water competition. Weed Res. 50, 481–492 (2010).Article 

Google Scholar 
7.Oduor, A. M. O. Evolutionary responses of native plant species to invasive plants: A review. New Phytol. 200, 986–992 (2013).PubMed 
Article 
PubMed Central 

Google Scholar 
8.Mealor, B. & Hild, A. L. Potential selection in native grass populations by exotic invasion. Mol. Ecol. 15, 2291–2300 (2006).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
9.Ferrero-Serrano, Á., Hild, A. L. & Mealor, B. A. Can invasive species enhance competitive ability and restoration potential in native grass populations?. Restor. Ecol. 19, 545–551 (2011).Article 

Google Scholar 
10.Goergen, E. M., Leger, E. A. & Espeland, E. K. Native perennial grasses show evolutionary response to Bromus tectorum (cheatgrass) invasion. PLoS ONE 6, 1–8 (2011).Article 
CAS 

Google Scholar 
11.Melgoza, G., Nowak, R. S. & Tausch, R. J. Soil water exploitation after fire: competition between Bromus tectorum (cheatgrass) and two native species. Oecologia 83, 7–13 (1990).ADS 
PubMed 
Article 

Google Scholar 
12.Reichenberger, G. & Pyke, D. A. Impact of early root competition on fitness components of four semiarid species. Oecologia 85, 159–166 (1990).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
13.Phillips, A. J. & Leger, E. A. Plastic responses of native plant root systems to the presence of an invasive annual grass 1. Am. J. Bot. 102, 73–84 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
14.Cipollini, D., Purrington, C. B. & Bergelson, J. Costs of induced responses in plants. Basic Appl. Ecol. 4, 79–85 (2003).Article 

Google Scholar 
15.Relyea, R. Competitor-induced plasticity in tadpoles: consequences, cues, and connections to predator-induced plasticity. Ecol. Monogr. 72, 523–540 (2002).Article 

Google Scholar 
16.War, A. R., Sharma, H. C., Paulraj, M. G., War, M. Y. & Ignacimuthu, S. Herbivore induced plant volatiles: their role in plant defense for pest management. Plant Signal. Behav. 6, 1973–1978 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
17.Karban, A. R., Baldwin, I. T., Baxter, K. J., Laue, G. & Felton, G. W. Communication between plants: induced resistance in wild tobacco plants following clipping of neighboring sagebrush. Oeciologia 125, 66–71 (2000).ADS 
CAS 
Article 

Google Scholar 
18.Alexander, J. M., Diez, J. M. & Levine, J. M. Novel competitors shape species’ responses to climate change. Nature 525, 515–518 (2015).ADS 
CAS 
PubMed 
Article 
PubMed Central 

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

Google Scholar 
20.HilleRisLambers, J., Adler, P. B., Harpole, W. S., Levine, J. M. & Mayfield, M. M. Rethinking community assembly through the lens of coexistence theory. Annu. Rev. Ecol. Evol. Syst. 43, 227–248 (2012).Article 

Google Scholar 
21.Mayfield, M. M. & Levine, J. M. Opposing effects of competitive exclusion on the phylogenetic structure of communities. Ecol. Lett. 13, 1085–1093 (2010).PubMed 
Article 
PubMed Central 

Google Scholar 
22.Muthukrishnan, R., Sullivan, L. L., Shaw, A. K. & Forester, J. D. Trait plasticity alters the range of possible coexistence conditions in a competition–colonisation trade-off. Ecol. Lett. 23, 791–799 (2020).PubMed 
Article 
PubMed Central 

Google Scholar 
23.Pérez-Ramos, I. M., Matías, L., Gómez-Aparicio, L. & Godoy, Ó. Functional traits and phenotypic plasticity modulate species coexistence across contrasting climatic conditions. Nat. Commun. 10, 1–11 (2019).Article 
CAS 

Google Scholar 
24.Roscher, C., Schumacher, J., Schmid, B. & Schulze, E.-D. Contrasting effects of intraspecific trait variation on trait-based niches and performance of legumes in plant mixtures. PLoS ONE 10, 1–18 (2015).Article 
CAS 

Google Scholar 
25.Liu, B. et al. Complementarity in nutrient foraging strategies of absorptive fine roots and arbuscular mycorrhizal fungi across 14 coexisting subtropical tree species. New Phytol. 208, 125–136 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
26.Turcotte, M. M. & Levine, J. M. Phenotypic plasticity and species coexistence. Trends Ecol. Evol. 31, 803–813 (2016).PubMed 
Article 

Google Scholar 
27.Foster, B. L. Establishment, competition and the distribution of native grasses among Michigan old-fields. J. Ecol. 87, 476–489 (1999).Article 

Google Scholar 
28.James, J. J., Svejcar, T. J. & Rinella, M. J. Demographic processes limiting seedling recruitment in arid grassland restoration. J. Appl. Ecol. 48, 961–969 (2011).Article 

Google Scholar 
29.Larson, J. E., Anacker, B. L., Wanous, S. & Funk, J. L. Ecological strategies begin at germination: Traits, plasticity and survival in the first 4 days of plant life. Funct. Ecol. 34, 968–979 (2020).Article 

Google Scholar 
30.Foxx, A. Data: Induced plasticity alters responses to conspecific interactions in seedlings of a perennial grass. Mendeley Data https://doi.org/10.17632/hhpnttctth.1 (2021).Article 

Google Scholar 
31.R Core Team. R: A language and environment for statistical computing. R Found. Stat. Comput. Vienna, Austria (2020).32.Crawley, M. J. Statistics: An introduction using R (Wiley, Hoboken, 2005).MATH 
Book 

Google Scholar 
33.Kraft, N. J. B., Crutsinger, G. M., Forrestel, E. J. & Emery, N. C. Functional trait differences and the outcome of community assembly: An experimental test with vernal pool annual plants. Oikos 123, 1391–1399 (2014).Article 

Google Scholar 
34.Foxx, A. J. & Kramer, A. T. Variation in number of root tips influences survival in competition with an invasive grass. J. Arid Environ. 179, 104189 (2020).ADS 
Article 

Google Scholar 
35.McGlone, C. M., Sieg, C. H., Kolb, T. E. & Nietupsky, T. Established native perennial grasses out-compete an invasive annual grass regardless of soil water and nutrient availability. Plant Ecol. 213, 445–457 (2011).Article 

Google Scholar 
36.Liu, J. G., Mahoney, K. J., Sikkema, P. H. & Swanton, C. J. The importance of light quality in crop-weed competition. Weed Res. 49, 217–224 (2009).Article 

Google Scholar 
37.Westoby, M. A leaf-height-seed (LHS) plant ecology strategy scheme. Plant Soil 199, 213–227 (1998).CAS 
Article 

Google Scholar 
38.Gundel, P. E., Pierik, R., Mommer, L. & Ballaré, C. L. Competing neighbors: Light perception and root function. Oecologia 176, 1–10 (2014).ADS 
PubMed 
Article 

Google Scholar 
39.Poorter, H. et al. Biomass allocation to leaves, stems and roots: Meta-analyses of interspecific variation and environmental control. New Phytol. 193, 30–50 (2012).CAS 
PubMed 
Article 

Google Scholar 
40.Berendse, F. & Móller, F. Effects of competition on root-shoot allocation in Plantago lanceolata L.: Adaptive plasticity or ontogenetic drift?. Plant Ecol. 201, 567–573 (2009).Article 

Google Scholar 
41.Ghalambor, C. K., McKay, J. K., Carroll, S. P. & Reznick, D. N. Adaptive versus non-adaptive phenotypic plasticity and the potential for contemporary adaptation in new environments. Funct. Ecol. 21, 394–407 (2007).Article 

Google Scholar 
42.Bennett, J. A., Riibak, K., Tamme, R., Lewis, R. J. & Pärtel, M. The reciprocal relationship between competition and intraspecific trait variation. J. Ecol. 104, 1410–1420 (2016).Article 

Google Scholar 
43.Jupp, A. & Newman, I. Morphological and anatomical effects of severe drought on the roots of Lolium perenne L. New Phytol. 105, 393–402 (1987).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
44.Foxx, A. J. & Kramer, A. T. Hidden variation: Cultivars and wild plants differ in trait variation with surprising root trait outcomes. Restor. Ecol. https://doi.org/10.1111/rec.13336 (2020).Article 

Google Scholar 
45.Zeldin, J., Lichtenberger, T. M., Foxx, A. J., Webb Williams, E. & Kramer, A. T. Intraspecific functional trait structure of restoration-relevant species: Implications for restoration seed sourcing. J. Appl. Ecol. 57, 864–874 (2020).Article 

Google Scholar 
46.Abbott, J. M. & Stachowicz, J. J. The relative importance of trait vs genetic differentiation for the outcome of interactions among plant genotypes. Ecology 97, 84–94 (2016).PubMed 
Article 
PubMed Central 

Google Scholar 
47.Adler, P. B. et al. Competition and coexistence in plant communities: Intraspecific competition is stronger than interspecific competition. Ecol. Lett. 21, 1319–1329 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
48.Schroeder-Georgi, T. et al. From pots to plots: Hierarchical trait-based prediction of plant performance in a mesic grassland. J. Ecol. 104, 206–218 (2016).Article 

Google Scholar 
49.Taylor, D. & Aarssen, L. Complex competitive relationships among genotypes of three perennial grasses: Implications for species coexistence. Am. Nat. 136, 305–327 (1990).Article 

Google Scholar 
50.Espeland, E. K. et al. Evolution of plant materials for ecological restoration: Insights from the applied and basic literature. J. Appl. Ecol. 54, 102–115 (2017).Article 

Google Scholar 
51.Rottstock, T., Kummer, V., Fischer, M. & Joshi, J. Rapid transgenerational effects in Knautia arvensis in response to plant community diversity. J. Ecol. 105, 714–725 (2017).CAS 
Article 

Google Scholar 
52.Álvarez-Yépiz, J. C., Búrquez, A. & Dovčiak, M. Ontogenetic shifts in plant–plant interactions in a rare cycad within angiosperm communities. Oecologia 175(2), 725–735. https://doi.org/10.1007/s00442-014-2929-3 (2014).ADS 
Article 
PubMed 

Google Scholar  More