Philippa Kaur

1.Anton, A. et al. Global ecological impacts of marine exotic species. Nature Ecology & Evolution 3, 787–800, https://doi.org/10.1038/s41559-019-0851-0 (2019).Article 

Google Scholar 
2.Solgaard Thomsen, M. Indiscriminate data aggregation in ecological meta-analysis underestimates impacts of invasive species. Nat. Ecol. Evol. 4, 312–314, https://doi.org/10.1038/s41559-020-1117-6 (2020).Article 
PubMed 

Google Scholar 
3.Tsiamis, K., Zenetos, A., Deriu, I., Gervasini, E. & Cardoso, A. C. The native distribution range of the European marine non-indigenous species. Aquat. Invasions 13, https://doi.org/10.3391/ai.2018.13.2.01 (2018).4.Korpinen, S. et al. Multiple pressures and their combined effects in Europe’s seas. ETC/ICM Technical Report 4/2019. Report No. 3944280652, 164 (European Topic Centre Inland, Coastal and Marine waters (ETC/ICM) 2020).5.Ojaveer, H. et al. Ten recommendations for advancing the assessment and management of non-indigenous species in marine ecosystems. Mar. Policy 44, 160–165, https://doi.org/10.1016/j.marpol.2013.08.019 (2014).Article 

Google Scholar 
6.Trebitz, A. S. et al. Early detection monitoring for aquatic non-indigenous species: optimizing surveillance, incorporating advanced technologies, and identifying research needs. J. Environ. Manage. 202, 299–310, https://doi.org/10.1016/j.jenvman.2017.07.045 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
7.Cardeccia, A. et al. Assessing biological invasions in European Seas: biological traits of the most widespread non-indigenous species. Estuar. Coast. Shelf Sci. 201, 17–28, https://doi.org/10.1016/j.ecss.2016.02.014 (2018).ADS 
Article 

Google Scholar 
8.Carboneras, C. et al. A prioritised list of invasive alien species to assist the effective implementation of EU legislation. J. Appl. Ecol. 55, 539–547, https://doi.org/10.1111/1365-2664.12997 (2018).Article 

Google Scholar 
9.Köck, W. & Magsig, B.-O. In Handbook on Marine Environment Protection: Science, Impacts and Sustainable Management (eds Markus Salomon & Till Markus) 905-918 (Springer International Publishing, 2018).10.Tsiamis, K. et al. Prioritizing marine invasive alien species in the European Union through horizon scanning. Aquat. Conserv. 30, 794–845, https://doi.org/10.1002/aqc.3267 (2020).Article 

Google Scholar 
11.Witman, J. D. & Roy, K. Marine Macroecology (University of Chicago Press, 2009).12.Peterson, A. T. et al. Ecological Niches and Geographic Distributions (Princeton University Press, 2011).13.Culham, A. & Yesson, C. in Climate Change, Ecology and Systematics (eds T. Hodkinson, M. Jones, S. Waldren, & J. Parnell) 131-242 (Cambridge University Press, 2011).14.Lockwood, J. L., Hoopes, M. F. & Marchetti, M. P. Invasion Ecology (John Wiley & Sons, 2013).15.Katsanevakis, S. et al. Unpublished Mediterranean records of marine alien and cryptogenic species. Bioinvasions Rec. 9, 165–182, https://doi.org/10.3391/bir.2020.9.2.01 (2020).Article 

Google Scholar 
16.Meyer, C., Weigelt, P. & Kreft, H. Multidimensional biases, gaps and uncertainties in global plant occurrence information. Ecol. Lett. 19, 992–1006, https://doi.org/10.1111/ele.12624 (2016).Article 
PubMed 

Google Scholar 
17.Moudrý, V. & Devillers, R. Quality and usability challenges of global marine biodiversity databases: an example for marine mammal data. Ecol. Inform. 56, 101051, https://doi.org/10.1016/j.ecoinf.2020.101051 (2020).Article 

Google Scholar 
18.Assis, J. et al. A fine-tuned global distribution dataset of marine forests. Sci. Data 7, 119, https://doi.org/10.1038/s41597-020-0459-x (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
19.Millikin, M. R. & Williams, A. B. Synopsis of biological data on blue crab, Callinectes sapidus Rathbun (FAO Fisheries Synopsis 38, 1984).20.Johnson, D. S. The savory swimmer swims north: a northern range extension of the blue crab Callinectes sapidus? J. Crust. Biol. 35, 105–110, https://doi.org/10.1163/1937240X-00002293 (2015).Article 

Google Scholar 
21.Mancinelli, G. et al. On the Atlantic blue crab (Callinectes sapidus Rathbun 1896) in southern European coastal waters: Time to turn a threat into a resource? Fish. Res. 194, 1–8, https://doi.org/10.1016/j.fishres.2017.05.002 (2017).Article 

Google Scholar 
22.Hines, A. H. in The Blue Crab: Callinectes sapidus (eds V. S. Kennedy & L. E. Cronin) 565-654 (Maryland Sea Grant College, 2007).23.Mancinelli, G. et al. The trophic position of the Atlantic blue crab Callinectes sapidus Rathbun 1896 in the food web of Parila Lagoon (South Eastern Adriatic, Croatia): a first assessment using stable isotopes. Mediterr. Mar. Sci. 17, 634–643, https://doi.org/10.12681/mms.1724 (2016).Article 

Google Scholar 
24.Mancinelli, G. et al. Trophic flexibility of the Atlantic blue crab Callinectes sapidus in invaded coastal systems of the Apulia region (SE Italy): A stable isotope analysis. Estuar. Coast. Shelf Sci. 198, 421–431, https://doi.org/10.1016/j.ecss.2017.03.013 (2017).ADS 
CAS 
Article 

Google Scholar 
25.Baird, D. & Ulanowicz, R. E. The seasonal dynamics of the Chesapeake Bay ecosystem. Ecol Monogr 59, 329–364, https://doi.org/10.2307/1943071 (1989).Article 

Google Scholar 
26.Silliman, B. R. & Bertness, M. D. A trophic cascade regulates salt marsh primary production. Proc. Natl. Acad. Sci. USA 99, 10500–10505, https://doi.org/10.1073/pnas.162366599 (2002).CAS 
Article 
PubMed 

Google Scholar 
27.Boudreau, S. A. & Worm, B. Ecological role of large benthic decapods in marine ecosystems: a review. Mar. Ecol. Prog. Ser. 469, 195–213, https://doi.org/10.3354/meps09862 (2012).ADS 
Article 

Google Scholar 
28.Bouvier, E. L. Sur un Callinectes sapidus M. Rathbun trouvé à Rocheford. Bull. Mus. Hist. Nat. (Paris) 7, 16–17 (1901).
Google Scholar 
29.Giordani Soika, A. Il Neptunus pelagicus (L.) nell’alto Adriatico. Natura 42, 18–20 (1951).
Google Scholar 
30.Nehring, S. In In the Wrong Place – Alien Marine Crustaceans: Distribution, Biology and Impacts Vol. 6 Invading Nature – Springer Series in Invasion Ecology (eds Bella S. Galil, Paul F. Clark, & James T. Carlton) 607–624 (Springer Netherlands, 2011).31.Enzenroß, R., Enzenroß, L. & Bingel, F. Occurrence of blue crab, Callinectes sapidus (Rathbun, 1896) (Crustacea, Brachyura) on the Turkish Mediterranean and the adjacent Aegean coast and its size distribution in the bay of Iskenderun. Turk. J. Zool. 21, 113–122, https://doi.org/10.1163/001121610X538859 (1997).Article 

Google Scholar 
32.Mancinelli, G. et al. The Atlantic blue crab Callinectes sapidus in southern European coastal waters: distribution, impact and prospective invasion management strategies. Mar. Pollut. Bull. 119, 5–11, https://doi.org/10.1016/j.marpolbul.2017.02.050 (2017).CAS 
Article 
PubMed 

Google Scholar 
33.Labrune, C. et al. The arrival of the American blue crab, Callinectes sapidus Rathbun, 1896 (Decapoda: Brachyura: Portunidae), in the Gulf of Lions (Mediterranean Sea). Bioinvasions Rec. 8, 876–881, https://doi.org/10.3391/bir.2019.8.4.16 (2019).Article 

Google Scholar 
34.Cerri, J. et al. Using online questionnaires to assess marine bio-invasions: a demonstration with recreational fishers and the Atlantic blue crab Callinectes sapidus (Rathbun, 1986) along three Mediterranean countries. Mar. Pollut. Bull. 156, 111209, https://doi.org/10.1016/j.marpolbul.2020.111209 (2020).CAS 
Article 
PubMed 

Google Scholar 
35.Katsanevakis, S. et al. Impacts of invasive alien marine species on ecosystem services and biodiversity: a pan-European review. Aquat. Invasions 9, 391–423, https://doi.org/10.3391/ai.2014.9.4.01 (2014).Article 

Google Scholar 
36.Zenetos, A. et al. Annotated list of marine alien species in the Mediterranean with records of the worst invasive species. Mediterr. Mar. Sci. 6, 63–118, https://doi.org/10.12681/mms.186 (2005).Article 

Google Scholar 
37.Vilà, M. & Hulme, P. E. Impact of Biological Invasions on Ecosystem Services (Springer, 2017).38.Morais, P. et al. The Atlantic blue crab Callinectes sapidus Rathbun, 1896 expands its non-native distribution into the Ria Formosa lagoon and the Guadiana estuary (SW-Iberian Peninsula, Europe). Bioinvasions Rec. 8, 123–133, https://doi.org/10.3391/bir.2019.8.1.14 (2019).Article 

Google Scholar 
39.Vasconcelos, P. et al. Recent and consecutive records of the Atlantic blue crab (Callinectes sapidus Rathbun, 1896): rapid westward expansion and confirmed establishment along the Southern Coast of Portugal. Thalassas 35, 485–494, https://doi.org/10.1007/s41208-019-00163-1 (2019).Article 

Google Scholar 
40.Pezy, J. P., Raoux, A., Baffreau, A. & Dauvin, J. C. A well established population of the Atlantic blue crab Callinectes sapidus (Rathbun, 1896) in the English Channel? Cah. Biol. Mar. 60, 205–209 (2019).
Google Scholar 
41.Zenetos, A., Koutsogiannopoulos, D., Ovalis, P. & Poursanidis, D. The role played by citizen scientists in monitoring marine alien species in Greece. Cah. Biol. Mar. 54, 419–426 (2013).
Google Scholar 
42.Chandler, M. et al. Contribution of citizen science towards international biodiversity monitoring. Biol. Conserv. 213, 280–294, https://doi.org/10.1016/j.biocon.2016.09.004 (2017).Article 

Google Scholar 
43.Latombe, G. et al. A vision for global monitoring of biological invasions. Biol. Conserv. 213, 295–308, https://doi.org/10.1016/j.biocon.2016.06.013 (2017).Article 

Google Scholar 
44.Lucy, F. E. et al. INVASIVESNET towards an international association for open knowledge on invasive alien species. Manag. Biol. Invasion 7, 131–139, https://doi.org/10.3391/mbi.2016.7.2.01 (2016).Article 

Google Scholar 
45.Cardoso, A. C. et al. Citizen science and open data: A model for invasive alien species in Europe. RIO 3, e14811, https://doi.org/10.3897/rio.3.e14811 (2017).Article 

Google Scholar 
46.R Development Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/ (2020).47.Chamberlain, S., Ram, K. & Hart, T. spocc: Interface to Species Occurrence Data Sources. R package version 1.0.8. http://cran.r-project.org/web/packages/spocc (2020).48.Zizka, A. et al. CoordinateCleaner: Standardized cleaning of occurrence records from biological collection databases. Methods Ecol. Evol. 10, 744–751, https://doi.org/10.1111/2041-210x.13152 (2019).Article 

Google Scholar 
49.Freitag, A., Meyer, R. & Whiteman, L. Strategies employed by citizen science programs to increase the credibility of their data. Citiz. Sci. Theory Pr. 1, 1–11, https://doi.org/10.5334/cstp.6 (2016).Article 

Google Scholar 
50.Zenetos, A. et al. ELNAIS: A collaborative network on Aquatic Alien Species in Hellas (Greece). Manag. Biol. Invasion 6, 185–196, https://doi.org/10.3391/mbi.2015.6.2.09 (2015).Article 

Google Scholar 
51.Williams, A. B. The swimming crabs of the genus Callinectes (Decapoda: Portunidae). Fish. Bull. 72, 685–798 (1974).
Google Scholar 
52.Aydin, M. First record of Blue Crab Callinectes sapidus (Rathbun 1896) from the Middle Black Sea Coast. Turk. J. Marit. Mar. Sci. 3, 121–124 (2017).
Google Scholar 
53.Harzing, A. W. Publish or Perish, available from https://harzing.com/resources/publish-or-perish (2007).54.Mancinelli, G., Bardelli, R. & Zenetos, A. An updated dataset of the global occurrence of the Atlantic blue crab Callinectes sapidus Rathbun, 1896 (Brachyura: Portunidae). figshare https://doi.org/10.6084/m9.figshare.12896309 (2020).55.Buckley, M. W., DelSole, T., Lozier, M. S. & Li, L. Predictability of North Atlantic sea surface temperature and upper-ocean heat content. J. Clim. 32, 3005–3023, https://doi.org/10.1175/JCLI-D-18-0509.1 (2019).ADS 
Article 

Google Scholar 
56.Dizdarević, S., Gajić, A., Kahrić, A. & Tomanić, J. Prvi nalaz plavog raka, Callinectes sapidus Rathbun, 1896 (Malacostraca: Portunidae), u Bosni I Hercegovini. Uzaz 12, 5–9 (2016).
Google Scholar 
57.Pebesma, E. et al. Sf: Simple Features for R. R package version 0.7-7. https://cran.r-project.org/web/packages/sf (2019).58.Tyberghein, L. et al. Bio-ORACLE: a global environmental dataset for marine species distribution modelling. Global Ecol. Biogeogr. 21, 272–281 (2012).Article 

Google Scholar 
59.McLusky, D. S. & Elliott, M. Transitional waters: a new approach, semantics or just muddying the waters? Estuar. Coast. Shelf Sci. 71, 359–363, https://doi.org/10.1016/j.ecss.2006.08.025 (2007).ADS 
Article 

Google Scholar 
60.Sbrocco, E. J. & Barber, P. H. MARSPEC: ocean climate layers for marine spatial. ecology. Ecology 94, 979–979, https://doi.org/10.1890/12-1358.1 (2013).Article 

Google Scholar 
61.Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Clim. 37, 4302–4315, https://doi.org/10.1002/joc.5086 (2017).Article 

Google Scholar 
62.Domisch, S., Amatulli, G. & Jetz, W. Near-global freshwater-specific environmental variables for biodiversity analyses in 1 km resolution. Sci. Data 2, 150073, https://doi.org/10.1038/sdata.2015.73 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
63.Venter, O. et al. Global terrestrial Human Footprint maps for 1993 and 2009. Sci. Data 3, 160067, https://doi.org/10.1038/sdata.2016.67 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
64.Aiello-Lammens, M. E., Boria, R. A., Radosavljevic, A., Vilela, B. & Anderson, R. P. spThin: an R package for spatial thinning of species occurrence records for use in ecological niche models. Ecography 38, 541–545, https://doi.org/10.1111/ecog.01132 (2015).Article 

Google Scholar 
65.Aiello-Lammens, M. E. et al. spThin: Functions for Spatial Thinning of Species Occurrence Records forUse in Ecological Models. R package version 0.2.0. http://cran.r-project.org/web/packages/spThin (2019). More

1.Simberloff, D. et al. Impacts of biological invasions: what’s what and the way forward. Trends Ecol. Evol. 28, 58–66 (2013).PubMed 
Article 
PubMed Central 

Google Scholar 
2.Vilà, M. & Hulme, P. (eds) Impact of Biological Invasions on Ecosystem Services (Springer International Publishing, Berlin, 2017).
Google Scholar 
3.Gaertner, M., Den Breeyen, A., Hui, C. & Richardson, D. M. Impacts of alien plant invasions on species richness in Mediterranean-type ecosystems: a meta-analysis. Prog. Phys. Geogr. Earth Environ. 33, 319–338 (2009).Article 

Google Scholar 
4.Belnap, J., Phillips, S. L., Sherrod, S. K. & Moldenke, A. Soil biota can change after exotic plant invasion: does this affect ecosystem processes?. Ecology 86, 3007–3017 (2005).Article 

Google Scholar 
5.Liao, C. et al. Altered ecosystem carbon and nitrogen cycles by plant invasion: a meta-analysis. New Phytol. 177, 706–714 (2008).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
6.Boscutti, F. et al. Cascading effects from plant to soil elucidate how the invasive Amorpha fruticosa L. impacts dry grasslands. J. Veg. Sci. 31(4), 667–677 (2020).Article 

Google Scholar 
7.Pejchar, L. & Mooney, H. A. Invasive species, ecosystem services and human well-being. Trends Ecol. Evol. 24, 497–504 (2009).PubMed 
Article 
PubMed Central 

Google Scholar 
8.Vilà, M. & Ibáñez, I. Plant invasions in the landscape. Landsc. Ecol. 26, 461–472 (2011).Article 

Google Scholar 
9.Blackburn, T. M. et al. A proposed unified framework for biological invasions. Trends Ecol. Evol. 26(7), 333–339 (2011).PubMed 
Article 
PubMed Central 

Google Scholar 
10.Kowarik, I. On the role of alien species in urban flora and vegetation. Plant invasions: general aspects and special problems. In SPB (eds Pysek, P. et al.) 85–103 (Academic Publishing, Amsterdam, 1995).
Google Scholar 
11.Hulme, P. E. Climate change and biological invasions: evidence, expectations, and response options. Biol. Rev. 92(3), 1297–1313 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
12.Richardson, D. M. & Pyšek, P. Naturalization of introduced plants: ecological drivers of biogeographical patterns. New Phytol. 196(2), 383–396 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
13.Alexander, J. M. et al. Assembly of nonnative floras along elevational gradients explained by directional ecological filtering. Proc. Natl. Acad. Sci. 108, 656–661 (2011).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
14.Hulme, P. E. Relative roles of life-form, land use and climate in recent dynamics of alien plant distributions in the British Isles. Weed Res. 49(1), 19–28 (2009).Article 

Google Scholar 
15.Milbau, A., Stout, J. C., Graae, B. J. & Nijs, I. A hierarchical framework for integrating invasibility experiments incorporating different factors and spatial scales. Biol. Invasions 11(4), 941–950 (2009).Article 

Google Scholar 
16.González-Moreno, P., Diez, J. M., Ibáñez, I., Font, X. & Vilà, M. Plant invasions are context-dependent: multiscale effects of climate, human activity and habitat. Divers. Distrib. 20(6), 720–731 (2014).Article 

Google Scholar 
17.Bradley, B. A., Wilcove, D. S. & Oppenheimer, M. Climate change increases risk of plant invasion in the Eastern United States. Biol. Invasions 12(6), 1855–1872 (2010).Article 

Google Scholar 
18.Cao, Y., Zhang, S. & Hu, W. Simulated warming enhances biological invasion of Solidago canadensis and Bidens frondosa by increasing reproductive investment and altering flowering phenology pattern. Sci. Rep. 8(1), 1–8 (2018).ADS 

Google Scholar 
19.Molina-Montenegro, M. A. & Naya, D. E. Latitudinal patterns in phenotypic plasticity and fitness-related traits: assessing the climatic variability hypothesis (CVH) with an invasive plant species. PLoS ONE 7(10), e47620 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
20.Gritti, E. S., Smith, B. & Sykes, M. T. Vulnerability of Mediterranean Basin ecosystems to climate change and invasion by exotic plant species. J. Biogeogr. 33(1), 145–157 (2006).Article 

Google Scholar 
21.Colautti, R. I. & Barrett, S. C. Rapid adaptation to climate facilitates range expansion of an invasive plant. Science 342(6156), 364–366 (2013).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
22.Vitti, S., Pellegrini, E., Casolo, V., Trotta, G. & Boscutti, F. Contrasting responses of native and alien plant species to soil properties shed new light on the invasion of dune systems. J. Plant Ecol. 13, 667–675 (2020).Article 

Google Scholar 
23.Vilà, M., Pino, J. & Font, X. Regional assessment of plant invasions across different habitat types. J. Veg. Sci. 18, 35–42 (2007).Article 

Google Scholar 
24.Lambdon, P. W. et al. Alien flora of Europe: species diversity, temporal trends, geographical patterns and research needs. Preslia 80, 101–149 (2008).
Google Scholar 
25.Botham, M. S. et al. Do urban areas act as foci for the spread of alien plant species? An assessment of temporal trends in the UK. Divers. Distrib. 15, 338–345 (2009).Article 

Google Scholar 
26.Boscutti, F., Sigura, M., De Simone, S. & Marini, L. Exotic plant invasion in agricultural landscapes: A matter of dispersal mode and disturbance intensity. Appl. Veget. Sci. 21(2), 250–257 (2018).Article 

Google Scholar 
27.González-Moreno, P. et al. Quantifying the landscape influence on plant invasions in Mediterranean coastal habitats. Landsc. Ecol. 28(5), 891–903 (2013).Article 

Google Scholar 
28.Catford, J. A., Vesk, P. A., White, M. D. & Wintle, B. A. Hotspots of plant invasion predicted by propagule pressure and ecosystem characteristics. Divers. Distrib. 17(6), 1099–1110 (2011).Article 

Google Scholar 
29.McKinney, M. L. Urbanization, biodiversity, and conservation. The impacts of urbanization on native species are poorly studied, but educating a highly urbanized human population about these impacts can greatly improve species conservation in all ecosystems. Bio. Sci. 52, 883–890 (2002).
Google Scholar 
30.Mattingly, W. B. & Orrock, J. L. Historic land use influences contemporary establishment of invasive plant species. Oecologia 172(4), 1147–1157 (2013).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
31.Chytrý, M. et al. Separating Habitat Invasibility by Alien Plants from the Actual Level of Invasion. Ecology 89, 1541–1553 (2008).PubMed 
Article 
PubMed Central 

Google Scholar 
32.Jauni, M. & Hyvönen, T. TInvasion level of alien plants in semi-natural agricultural habitats in boreal region. Agric. Ecosyst. Environ. 138, 109–115 (2010).Article 

Google Scholar 
33.Carboni, M., Thuiller, W., Izzi, F. & Acosta, A. Disentangling the relative effects of environmental versus human factors on the abundance of native and alien plant species in Mediterranean sandy shores. Divers. Distrib. 16(4), 537–546 (2010).Article 

Google Scholar 
34.O’Reilly-Nugent, A. et al. Landscape effects on the spread of invasive species. Curr. Landsc. Ecol. Rep. 1, 107–114 (2016).Article 

Google Scholar 
35.Stohlgren, T. J. et al. Species richness and patterns of invasions in plants, birds and fishes in the United States. Biol. Invasions 8, 427–444 (2006).Article 

Google Scholar 
36.Chytrý, M. et al. Habitat invasions by alien plants: a quantitative comparison among Mediterranean, subcontinental and oceanic regions of Europe. J. Appl. Ecol. 45, 448–458 (2008).Article 

Google Scholar 
37.Pyšek, P. et al. Disentangling the role of environmental and human pressures on biological invasions across Europe. Proc. Natl. Acad. Sci. 107(27), 12157–12162 (2010).ADS 
PubMed 
Article 

Google Scholar 
38.Szymura, T. H., Szymura, M., Zając, M. & Zając, A. Effect of anthropogenic factors, landscape structure, land relief, soil and climate on risk of alien plant invasion at regional scale. Sci. Total Environ. 626, 1373–1381 (2018).ADS 
CAS 
PubMed 
Article 

Google Scholar 
39.Marini, L. et al. Alien and native plant life-forms respond differently to human and climate pressures. Global Ecol. Biogeogr. 21, 534–544 (2012).Article 

Google Scholar 
40.Buccheri, M., Boscutti, F., Pellegrini, E. & Martini, F. Alien flora in Friuli Venezia Giulia. Gortania 40, 7–78 (2019) (in Italian).
Google Scholar 
41.Barros, A. & Pickering, C. M. Non-native plant invasion in relation to tourism use of Aconcagua Park, Argentina, the highest protected area in the Southern Hemisphere. Mt. Res. Dev. 34(1), 13–26 (2014).Article 

Google Scholar 
42.Boscutti, F. et al. Conservation tillage affects species composition but not species diversity: a comparative study in northern Italy. Environ. Manag. 55(2), 443–452 (2015).ADS 
Article 

Google Scholar 
43.Galasso, G. et al. An updated checklist of the vascular flora alien to Italy . Plant Biosyst Int. J. Deal. Asp. Plant Biol. 152, 556–592 (2018).
Google Scholar 
44.Gao, T. et al. Evaluating the feasibility of using candidate DNA barcodes in discriminating species of the large Asteraceae family. BMC Evol. Biol. 10(1), 324 (2010).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
45.Gallagher, R. V., Randall, R. P. & Leishman, M. R. Trait differences between naturalized and invasive plant species independent of residence time and phylogeny. Conserv. Biol. 29(2), 360–369 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
46.Hamilton, M. A. et al. Life-history correlates of plant invasiveness at regional and continental scales. Ecol. Lett. 8, 1066–1074 (2005).Article 

Google Scholar 
47.Ahern, R. G., Landis, D. A., Reznicek, A. A. & Schemske, D. W. Spread of exotic plants in the landscape: the role of time, growth habit, and history of invasiveness. Biol. Invasions 12(9), 3157–3169 (2010).Article 

Google Scholar 
48.Ohlemüller, R., Walker, S. & Bastow Wilson, J. Local vs regional factors as determinants of the invasibility of indigenous forest fragments by alien plant species. Oikos 112, 493–501 (2006).Article 

Google Scholar 
49.Zhu, L., Sun, O. J., Sang, W., Li, Z. & Ma, K. Predicting the spatial distribution of an invasive plant species (Eupatorium adenophorum) in China. Landsc. Ecol. 22(8), 1143–1154 (2007).Article 

Google Scholar 
50.Timsina, B., Shrestha, B. B., Rokaya, M. B. & Münzbergová, Z. Impact of Parthenium hysterophorus L. invasion on plant species composition and soil properties of grassland communities in Nepal. Flora-Morphol. Distrib. Funct. Ecol. Plants 206(3), 233–240 (2011).Article 

Google Scholar 
51.Francis, A. P. & Currie, D. J. A globally consistent richness-climate relationship for angiosperms. Am. Nat. 161, 523–536 (2003).PubMed 
Article 
PubMed Central 

Google Scholar 
52.Currie, D. J. et al. Predictions and tests of climate-based hypotheses of broad-scale variation in taxonomic richness. Ecol. Lett. 7, 1121–1134 (2004).Article 

Google Scholar 
53.Tordoni, E. et al. Climate and landscape heterogeneity drive spatial pattern of endemic plant diversity within local hotspots in South-Eastern Alps. Perspect. Plant. Ecol. 43, 125512 (2020).Article 

Google Scholar 
54.Alpert, P., Bone, E. & Holzapfel, C. Invasiveness, invisibility and the role of environmental stress in the spread of non-native plants. Perspect. Plant Ecol. Evol. Syst. 3, 52–66 (2000).Article 

Google Scholar 
55.Richardson, D. & Pyšek, P. Plant invasions: merging the concepts of species invasiveness and community invasibility. Prog. Phys. Geogr. 30, 409 (2006).Article 

Google Scholar 
56.Marini, L. et al. Beta diversity and alien plant invasion. Global Ecol. Biogeogr. 22, 450–460 (2013).Article 

Google Scholar 
57.Haider, S. et al. Mountain roads and non-native species modify elevational patterns of plant diversity. Global Ecol. Biogeogr. 27, 667–678 (2018).Article 

Google Scholar 
58.Qian, H. & Ricklefs, R. E. The role of exotic species in homogenizing the North American flora. Ecol. Lett. 9(12), 1293–1298 (2006).PubMed 
Article 
PubMed Central 

Google Scholar 
59.Roy, D. B., Hill, M. O. & Rothery, P. Effects of urban land cover on the local species pool in Britain. Ecography 22, 507–515 (1999).Article 

Google Scholar 
60.McIntyre, S. & Lavorel, S. Predicting richness of native, rare, and exotic plants in response to habitat and disturbance variables across a variegated landscape. Conserv. Biol. 8(2), 521–531 (1994).Article 

Google Scholar 
61.Aikio, S., Duncan, R. P. & Hulme, P. E. The vulnerability of habitats to plant invasion: disentangling the roles of propagule pressure, time and sampling effort. Glob. Ecol. Biogeogr. 21, 778–786 (2012).Article 

Google Scholar 
62.Cilliers, S. S., Williams, N. S. G. & Barnard, F. J. Patterns of exotic plant invasions in fragmented urban and rural grasslands across continents. Landsc. Ecol. 23, 1243–1256 (2008).Article 

Google Scholar 
63.Pyšek, P. Alien and native species in Central European urban floras: a quantitative comparison. J. Biogeogr. 25, 155–163 (1998).Article 

Google Scholar 
64.Hulme, P.E. Nursery crimes: agriculture as victim and perpetrator in the spread of invasive species. Crop Sci. Technol. 733–740 (2005).65.McDougall, K. L. et al. Running off the road: roadside non-native plants invading mountain vegetation. Biol. Invasions 20, 3461–3473 (2018).Article 

Google Scholar 
66.Groves, R. H., Austin, M. P. & Kaye, P. E. Competition between Australian native and introduced grasses along a nutrient gradient. Austral. Ecol. 28, 491–498 (2003).Article 

Google Scholar 
67.Dupouey, J. L., Dambrine, E., Laffite, J. D. & Moares, C. Irreversible impact of past land use on forest soils and biodiversity. Ecology 83(11), 2978–2984 (2002).Article 

Google Scholar 
68.Foster, D. et al. The importance of land-use legacies to ecology and conservation. Bioscience 53(1), 77–88 (2003).Article 

Google Scholar 
69.Spooner, P. G. & Lunt, I. D. The influence of land-use history on roadside conservation values in an Australian agricultural landscape. Aust. J. Bot. 52, 445–458 (2004).Article 

Google Scholar 
70.Lindborg, R., Plue, J., Andersson, K. & Cousins, S. A. O. Function of small habitat elements for enhancing plant diversity in different agricultural landscapes. Biol. Conserv. 169, 206–213 (2014).Article 

Google Scholar 
71.Dorrough, J. & Scroggie, M. P. Plant responses to agricultural intensification. J. Appl. Ecol. 45(4), 1274–1283 (2008).Article 

Google Scholar 
72.Stoate, C. et al. Ecological impacts of arable intensification in Europe. J. Environ. Manag. 63, 337–365 (2001).CAS 
Article 

Google Scholar 
73.Deutschewitz, K., Lausch, A., Kühn, I. & Klotz, S. Native and alien plant species richness in relation to spatial heterogeneity on a regional scale in Germany. Glob. Ecol. Biogeogr. 12(4), 299–311 (2003).Article 

Google Scholar 
74.Grime, J. P. Plant Strategies and Vegetation Processes (Wiley, Chichester, 1979).
Google Scholar 
75.Molino, J. F. & Sabatier, D. Tree diversity in tropical rain forests: a validation of the intermediate disturbance hypothesis. Science 294(5547), 1702–1704 (2001).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
76.Tscharntke, T., Klein, A. M., Kruess, A., Steffan-Dewenter, I. & Thies, C. Landscape perspectives on agricultural intensification and biodiversity-ecosystem service management. Ecol. Lett. 8, 857–874 (2005).Article 

Google Scholar 
77.Carulli, G.B. Carta geologica del Friuli Venezia Giulia (scale 1:150000) (Geological Map of Friuli Venezia Giulia, scale 1:150000). Ed. S.E.L.C.A. Firenze (2006).78.Gortani, L. & Gortani, M. Flora friulana con particolare riguardo alla Carnia. Udine: ed. Tipografia Doretti (in Italian) (1906).79.Bonfanti, P., Fregonese, A. & Sigura, M. Landscape analysis in areas affected by land consolidation. Landsc. Urban Plan. 37(1–2), 91–98 (1997).Article 

Google Scholar 
80.Ehrendorfer, F. & Hamann, U. Vorschläge zu einer floristischen Kartierung von Mitteleuropa. Berichte der Deutschen Botanischen Gesellschaft (in German) (1965).81.Bartolucci, F. et al. An updated checklist of the vascular flora native to Italy . Plant Biosyst. Int. J. Deal. Asp. Plant Biol. 152, 179–303 (2018).
Google Scholar 
82.Engelen, G., Lavalle, C., Barredo, J. I., Van der Meulen, M. & White, R. The moland modelling framework for urban and regional land-use dynamics. In Modelling Land-Use Change: Progress and Applications (eds Koomen, E. et al.) 297–320 (Springer , Berlin, 2007).
Google Scholar 
83.Quantum GIS Development Team. QGIS Geographic Information System. Open Source Geospatial Foundation Project (2017).84.Dormann, C. F. et al. Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography 36, 27–46 (2013).Article 

Google Scholar 
85.Fox, J. & Weisberg, S. An R Companion to Applied Regression (Sage Publications , Thousand Oaks, 2011).
Google Scholar 
86.Dormann, C. F. et al. Effects of landscape structure and land-use intensity on similarity of plant and animal communities. Glob. Ecol. Biogeogr. 16, 774–787 (2007).Article 

Google Scholar 
87.Pinheiro, J. C. & Bates, D. M. Mixed-Effects Models in S and S-Plus (Springer , Berlin, 2000).
Google Scholar 
88.R Core Team R. A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, 2019).
Google Scholar 
89.Barton, K. MuMIn: Multi-model inference. R package version 1.15.6 (2016).90.Pinheiro, J., Bates, D., Debroy, S., Sarkar, D. & R core team nlme: Linear and Nonlinear Mixed Effects Models. R package version 3.1–131 (2017).91.Burham, K. P. & Anderson, D. R. Model Selection and Multimodel Inference—A Pratical Information-Theoretic Approach (Springer , Berlin, 2002).
Google Scholar  More

Preparation of positive controls for molecular testingZIKV strains MR766, PRVABC59, and DakAR41525 were separately propagated on Vero cells (ATCC CCL-81). Cell supernatant was harvested 72 hpi, and RNA extraction was performed using Trizol. Due to undetectable RNA concentration, the maximum input volume of 11 µL was used for cDNA generation using the SuperScript IV First-Strand Synthesis System with random hexamers (Thermo Fisher Scientific, Waltham, MA, United States). A ten-fold dilution series of RNA was generated for each strain to validate detection of phylogenetically divergent strains of ZIKV using our primer set. For all molecular assays, 3 µL of 10−3 of MR766 was used experimentally as the positive control. Propagation of ZIKV was conducted under CSU biosafety protocol 17-059B.Infection protocol, RNA Extraction, and cDNA synthesis for A129 mice and Jamaican fruit batsAll animal studies were carried out in accordance with ARRIVE guidelines and all procedures approved by and carried out under the Colorado State University Institutional Animal Care and Use Committee (protocol 15-6677AA). Three sub-adult male A129 mice and three female Jamaican fruit bats (Artibeus jamaicensis) were obtained from their respective breeding colonies at Colorado State University. Mice were subcutaneously inoculated with 1 × 103 PFU supernatant from PRVABC59-infected Vero cells, and bats were subcutaneously inoculated with 7.5 × 105 PFU supernatant from Vero cells infected with one of three strains (either PRVABC59, MR766, or DakAR41525; one strain per individual). Mice were euthanized at 7 days post-infection (dpi). The bat infected with ZIKV strain MR766 was euthanized at 28 dpi, while the two bats infected with strains PRVABC59 and DakAR41525 were euthanized at 45 dpi to provide a broader of time window in which to characterize sfRNA persistence. Organs and blood were harvested and placed into DMEM supplemented with 1% penicillin/streptomycin (Thermo Fisher Scientific, Waltham, MA, United States) and 10% FBS (Atlas Biologicals, Fort Collins, CO, United States) and stored at − 80 °C until RNA extraction using the Mag-Bind Viral DNA/RNA 96 kit (Omega Bio-Tek Inc., Norcross, GA, United States) on the KingFisher Flex Magnetic Particle Processor (Thermo Fisher Scientific, Waltham, MA, United States). RNA was eluted in 30 µL nuclease-free water.Droplet digital PCR (ddPCR) to detect ZIKV sfRNATo detect ZIKV sfRNA, primers were designed to target the 3′ UTR of multiple strains of ZIKV according to recommended ddPCR primer design guidelines, resulting in an amplicon 123 bp in length (F: TTCCCCACCCTTYAATCTGG and R: TGGTCTTTCCCAGCGTCAAT). Each reaction consisted of 50 ng cDNA, 125 nM foward primer, 125 nM reverse primer, and 10 µL QX200 ddPCR EvaGreen Supermix (Bio-Rad Laboratories, Hercules, CA, United States). Following reaction preparation, 20 µL of reaction and 60 µL of QX200 Droplet Generation Oil for EvaGreen (Bio-Rad Laboratories, Hercules, CA, United States) were loaded into a DG8 Cartridge for droplet generation in the QX200 Droplet Generator (Bio-Rad Laboratories, Hercules, CA, United States). Following droplet generation, plates were sealed in the PX1 PCR Plate Sealer (Bio-Rad Laboratories, Hercules, CA, United States). PCR was performed on a T100 Thermal Cycler (Bio-Rad Laboratories, Hercules, CA, United States), using the following cycling parameters: 95 °C for 5 min, 40 cycles of 95 °C for 30 s followed by 57.5 °C for 1 min, 4 °C for 5 min, 90 °C for 5 min, and held at 4 °C until reading the plate. Plates were read on the QX200 Droplet Reader (Bio-Rad Laboratories, Hercules, CA, United States). Analysis was performed by two individuals using QuantaSoft Software (Bio-Rad Laboratories, Hercules, CA, United States) to determine results.Gradient PCR was performed to identify the optimal annealing temperature, resulting in selection of 57.5 °C (Fig. S1). At this annealing temperature, the ddPCR reaction using the 3′ UTR primers successfully amplified ZIKV strains MR766, DakAR41525, and PRVABC59 (Fig. S2). As an additional and more biologically relevant sample type, 50 ng cDNA from the organs of A129 mice experimentally infected with ZIKV PRVABC59 were tested using this same assay; successful ZIKV sfRNA amplification was obtained from mouse kidney and spleen (Fig. S2). Blood and tissue samples from the three female Jamaican fruit bats were tested in duplicate on the QX200 Droplet Digital (ddPCR) System (Bio-Rad Laboratories, Hercules, CA, United States) using the ZIKV sfRNA primers as described above.Testing of archived samples from free-ranging Ugandan batsThis study utilized archived tissue samples from bats previously captured in Uganda from 2009 to 201318,26 (Table 1). Bats were captured using harp traps or mist nets, identified using a field guide specific to East African bats, and placed in holding bags prior to anesthesia via halothane and euthanasia by cervical dislocation27. This study used historic archived samples from a previous study, in which all bat captures and sampling were conducted under the approval of CDC IACUC protocols 1731AMMULX and 010-015 and carried out according to ARRIVE guidelines. RNA was extracted from frozen tissue homogenates (spleen, and in some cases both spleen and liver separately) using the MagMax 96 total RNA isolation kit (Applied Biosystems, Foster City, CA, United States), and cDNA generation was performed as above. To confirm RNA integrity via amplification of a housekeeping gene, we used previously published primers demonstrated to amplify GAPDH from two Old World bat species (black flying fox and Egyptian rousette bat) and one New World bat species (common vampire bat) (F: GTCGCCATCAATGACCCCTTC and R: TTCAAGTGAGCCCCAGCC)31. For samples with undetectable RNA concentration on the Qubit RNA HS assay, 6 µL cDNA was used as input. ddPCR was performed as above, except that an annealing temperature of 60˚C was used. Plates were read as above, and only samples deemed ‘suspect’ or ‘positive’ for GAPDH amplification were subjected to ddPCR testing with ZIKV sfRNA (3′ UTR). For these samples, the same volume of input cDNA was used to test for the presence of ZIKV sfRNA in duplicate; results were analyzed by two individuals.Table 1 All bat species and trap sites collected from 2009 to 201318,26.Full size tableSequence confirmationTo confirm specific amplification of GAPDH sequence for each of the 8 Old World species, the same primers were used in a conventional PCR assay using GoTaq HotStart Polymerase (Promega corporation, Madison, WI, United States). Cycling parameters were as follows: 95 °C for 2 min; 35 cycles of 95 °C for 1 min, 57.5 °C for 1 min, and 72 °C for 30 s; followed by 72 °C for 5 min and samples were held at 4 °C until being analyzed for the presence of a 248-bp amplicon via gel electrophoresis. Amplicons were verified by Sanger sequencing (GENEWIZ, Inc., South Plainfield NJ, United States). Results obtained from Sanger sequencing were subjected to quality analysis prior to aligning forward and reverse reads, and the consensus read was subjected to a BLAST search.Confirmation of ZIKV sfRNA ddPCR results in Ugandan bat samples using conventional PCR and sequencingSamples deemed ‘suspect’ via screening on the ddPCR system with ZIKV 3′ UTR primers were subjected to additional PCR and Sanger sequencing using the same primer set targeting the 3′ UTR of ZIKV. ZIKV strain MR766 was used as a positive control in these assays. Samples were considered ‘suspect’ if (1) the automatically-defined threshold yielded ≥ 1 positive droplet in the same 1D amplitude as the positive control cDNA (ZIKV MR766) or (2) the negative droplet populations existed in the same 1D amplitude region of positive control droplets and thus, precluded the ability to differentiate positive and negative populations. The cDNA from these samples was amplified using the GoTaq HotStart system (Promega corporation, Madison, WI, United States), with each reaction consisting of 50 ng cDNA, 25 µL GoTaq HotStart Master Mix, 400 nM forward primer, 400 nM reverse primer, and 1 M Betaine. Cycling parameters were as follows: 95 °C for 2 min; 35 cycles of 95 °C for 1 min, 57.5 °C for 1 min, and 72 °C for 30 s; followed by 72 °C for 5 min and samples were held at 4 °C until being analyzed for the presence of a 123-bp amplicon via gel electrophoresis. Positive samples were verified by Sanger sequencing (GENEWIZ, Inc., South Plainfield NJ, United States). Results obtained from Sanger sequencing were subjected to quality analysis prior to BLAST search and subsequent alignment of forward and reverse reads with the 3′ UTR of ZIKV MR766 in Geneious v11.1.5 (www.geneious.com).Comparison of detection sensitivity between sfRNA and NS5 in field-caught samplesThe four samples from which ZIKV sfRNA was amplified were subjected to cPCR amplification with GoTaq HotStart MasterMix as described above and primers designed for this study targeting NS5 from MR766, PRVABC59, and DakAR41525 in order to compare detection sensitivity (F: TGC CGC CAC CAA GAT GAA CT, R: CAT TCT CCC TTT CCA TGG ATT GAC C). Cycling parameters were as follows: 95 °C for 2 min; 35 cycles of 95 °C for 1 min, 57.5 °C for 1 min, and 72 °C for 30 s; followed by 72 °C for 5 min and samples were held at 4 °C. cDNA from ZIKV MR766 was used as a positive control. Results were sent for Sanger sequencing if a band was present. All methods in this study were carried out in accordance with relevant guidelines and regulations. More

Red Sea Research Center (RSRC), King Abdullah University of Science and Technology, Thuwal, Saudi ArabiaCarlos M. Duarte, Susana Agusti & Milica PredragovicArctic Research Centre, Department of Biology, Aarhus University, Aarhus, DenmarkCarlos M. DuarteComputational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology, Thuwal, Saudi ArabiaCarlos M. DuarteDepartment of Economics, Colorado State University, Fort Collins, CO, USAEdward BarbierDepartment of Earth, Atmospheric, and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, MA, USAGregory L. BrittenDepartamento de Ecología, Facultad de Ciencias Biológicas and Centro Interdisciplinario de Cambio Global, Pontificia Universidad Católica de Chile, Santiago, ChileJuan Carlos CastillaLaboratoire d’Océanographie de Villefranche, Sorbonne Université, CNRS, Villefranche-sur-Mer, FranceJean-Pierre GattusoInstitute for Sustainable Development and International Relations, Sciences Po, Paris, FranceJean-Pierre GattusoMonegasque Association on Ocean Acidification, Prince Albert II of Monaco Foundation, Monaco, MonacoJean-Pierre GattusoDepartment of Earth & Environment, Boston University, Boston, MA, USARobinson W. FulweilerDepartment of Biology, Boston University, Boston, MA, USARobinson W. FulweilerAustralian Research Council Centre of Excellence for Coral Reef Studies, James Cook University, Townsville, Queensland, AustraliaTerry P. HughesNational Museum of Natural History, Smithsonian Institution, Washington, DC, USANancy KnowltonSchool of Biological Sciences, The University of Queensland, St Lucia, Queensland, AustraliaCatherine E. LovelockDepartment of Biology, Dalhousie University, Halifax, Nova Scotia, CanadaHeike K. Lotze & Boris WormAlfred Wegener Institute, Integrative Ecophysiology, Bremerhaven, GermanyElvira PoloczanskaDepartment of Environment and Geography, University of York, York, UKCallum Roberts More

AffiliationsCAS Key Laboratory for Plant Diversity and Biogeography of East Asia, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming, ChinaHong QianResearch and Collections Center, Illinois State Museum, Springfield, IL, USAHong QianDepartment of Biology, University of Missouri–St. Louis, St. Louis, MO, USARobert E. RicklefsUniv. Grenoble Alpes, Univ. Savoie Mont Blanc, CNRS, LECA, Laboratoire d’Ecologie Alpine, Grenoble, FranceWilfried ThuillerAuthorsHong QianRobert E. RicklefsWilfried ThuillerCorresponding authorCorrespondence to
Hong Qian. More

1.Wagner, G. P., Pavlicev, M. & Cheverud, J. M. The road to modularity. Nat. Rev. Genet. 8, 921–931 (2007).CAS 
PubMed 
Article 

Google Scholar 
2.Wagner, G. P. & Altenberg, L. Complex adaptations and the evolution of evolvability. Evolution 50, 967–976 (1996).PubMed 
Article 

Google Scholar 
3.Draghi, J. A. & Whitlock, M. C. Phenotypic plasticity facilitates mutational variance, genetic variance, and evolvability along the major axis of environmental variation. Evolution 66, 2891–2902 (2012).PubMed 
Article 

Google Scholar 
4.Cheverud, J. M. Quantitative genetics and developmental constraints on evolution by selection. J. Theor. Biol. 110, 155–171 (1984).CAS 
PubMed 
Article 

Google Scholar 
5.Phillips, P. C. & Arnold, S. J. Visualizing multivariate selection. Evolution 43, 1209–1266 (1989).PubMed 
Article 

Google Scholar 
6.Sinervo, B. & Svensson, E. Correlational selection and the evolution of genomic architecture. Heredity 16, 948–955 (2002).
Google Scholar 
7.Blows, M. W. & Brooks, R. Measuring nonlinear selection. Am. Nat. 162, 815–820 (2003).PubMed 
Article 

Google Scholar 
8.Blows, M. W., Brooks, R. & Kraft, P. G. Exploring complex fitness surfaces: multiple ornamentation and polymorphism in male guppies. Evolution 57, 1622–1630 (2003).PubMed 
Article 

Google Scholar 
9.Jones, A. G., Arnold, S. J. & Bürger, R. Stability of the G-matrix in a population experiencing pleiotropic mutation, stabilizing selection, and genetic drift. Evolution 57, 1747–1760 (2003).PubMed 
Article 

Google Scholar 
10.Jones, A. G., Arnold, S. J. & Bürger, R. Evolution and stability of the G-matrix on a landscape with a moving optimum. Evolution 58, 1639–1654 (2004).PubMed 
Article 

Google Scholar 
11.Jones, A. G., Arnold, S. J. & Bürger, R. The mutation matrix and the evolution of evolvability. Evolution 61, 727–745 (2007).PubMed 
Article 

Google Scholar 
12.Jones, A. G., Bürger, R. & Arnold, S. J. Epistasis and natural selection shape the mutational architecture of complex traits. Nat. Commun. 5, 3709 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
13.Lande, R. The genetic covariance between characters maintained by pleiotropic mutations. Genetics 94, 203–215 (1980).CAS 
PubMed 
PubMed Central 

Google Scholar 
14.Armbruster, W. S., Pélabon, C., Hansen, T. F. & Mulder, C. P. H. in Phenotypic Integration: Studying the Ecology and Evolution of Complex Phenotypes (eds Pigliucci, M. & Preston, K.) 23–49 (Oxford Univ. Press, 2004).15.Bell, A. M. & Sih, A. Exposure to predation generates personality in threespined sticklebacks (Gasterosteus aculeatus). Ecol. Lett. 10, 828–834 (2007).PubMed 
Article 

Google Scholar 
16.Dingemanse, N. J., Barber, I. & Dochtermann, N. A. Non-consumptive effects of predation: does perceived risk strengthen the genetic integration of behaviour and morphology in stickleback? Ecol. Lett. 23, 107–118 (2020).PubMed 
Article 

Google Scholar 
17.Hansen Wheat, C., Fitzpatrick, J. L., Rogell, B. & Temrin, H. Behavioural correlations of the domestication syndrome are decoupled in modern dog breeds. Nat. Commun. 10, 2422 (2019).18.Hurst, L. D., Pál, C. & Lercher, M. J. The evolutionary dynamics of eukaryotic gene order. Nat. Rev. Genet. 5, 299–310 (2004).PubMed 
Article 
CAS 

Google Scholar 
19.Lande, R. & Arnold, S. J. The measurement of selection on correlated characters. Evolution 37, 1210–1226 (1983).PubMed 
Article 

Google Scholar 
20.Schluter, D. & Nychka, D. Exploring fitness surfaces. Am. Nat. 143, 597–616 (1994).Article 

Google Scholar 
21.Siepielski, A. M. et al. Precipitation drives global variation in natural selection. Science 355, 959–962 (2017).CAS 
PubMed 
Article 

Google Scholar 
22.Roff, D. A. & Fairbairn, D. J. A test of the hypothesis that correlational selection generates genetic correlations. Evolution 66, 2953–2960 (2012).PubMed 
Article 

Google Scholar 
23.Svensson, E. I., McAdam, A. G. & Sinervo, B. Intralocus sexual conflict over immune defense, gender load, and sex-specific signaling in a natural lizard population. Evolution 63, 3124–3135 (2009).PubMed 
Article 

Google Scholar 
24.McGlothlin, J. W., Parker, P. G., Nolan, V. & Ketterson, E. D. Correlational selection leads to genetic integration of body size and an attractive plumage trait in dark-eyed juncos. Evolution 59, 658–671 (2005).PubMed 
Article 

Google Scholar 
25.Duckworth, R. A. & Kruuk, L. E. B. Evolution of genetic integration between dispersal and colonization ability in a bird. Evolution 63, 968–977 (2009).PubMed 
Article 

Google Scholar 
26.Brodie, E. D. III Correlational selection for color pattern and antipredator behavior in the garter snake Thamnophis ordinoides. Evolution 46, 1284–1298 (1992).PubMed 
Article 

Google Scholar 
27.Wise, M. J. & Rausher, M. D. Costs of resistance and correlational selection in the multiple-herbivore community of Solanum carolinense. Evolution 70, 2411–2420 (2016).PubMed 
Article 

Google Scholar 
28.Fenster, C. B., Reynolds, R. J., Williams, C. W., Makowsky, R. & Dudash, M. R. Quantifying hummingbird preference for floral trait combinations: the role of selection on trait interactions in the evolution of pollination syndromes. Evolution 69, 1113–1127 (2015).PubMed 
Article 

Google Scholar 
29.Arnegard, M. E. et al. Genetics of ecological divergence during speciation. Nature 511, 307–311 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
30.Martin, C. H. & Wainwright, P. C. Multiple fitness peaks on the adaptive landscape drive adaptive radiation in the wild. Science 339, 208–211 (2013).CAS 
PubMed 
Article 

Google Scholar 
31.Phillips, P. C. Epistasis – the essential role of gene interactions in the structure and evolution of genetic systems. Nat. Rev. Genet. 9, 855–867 (2008).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
32.Steppan, S. J., Phillips, P. C. & Houle, D. Comparative quantitative genetics: evolution of the G matrix. Trends Ecol. Evol. 17, 320–327 (2002).Article 

Google Scholar 
33.Blows, M. W. & McGuigan, K. The distribution of genetic variance across phenotypic space and the response to selection. Mol. Ecol. 24, 2056–2072 (2015).PubMed 
Article 

Google Scholar 
34.Schluter, D. Adaptive radiation along genetic lines of least resistance. Evolution 50, 1766–1774 (1996).PubMed 
Article 

Google Scholar 
35.Lande, R. The maintenance of genetic variability by mutation in a polygenic character with linked loci. Genet. Res. 26, 221–235 (1976).Article 

Google Scholar 
36.Lande, R. The genetic correlation between characters maintained by selection, linkage and inbreeding. Genet. Res. 44, 309–320 (1984).CAS 
PubMed 
Article 

Google Scholar 
37.Bulmer, M. G. The effect of selection on genetic variability: a simulation study. Genet. Res. 28, 101–117 (1976).CAS 
PubMed 
Article 

Google Scholar 
38.Lynch, M. & Walsh, B. Genetics and Analysis of Quantitative Traits (Sinauer Associates, 1998).39.Guillaume, F. & Whitlock, M. C. Effects of migration on the genetic covariance matrix. Evolution 61, 2398–2409 (2007).PubMed 
Article 

Google Scholar 
40.Noble, D. W. A., Radersma, R. & Uller, T. Plastic responses to novel environments are biased towards phenotype dimensions with high additive genetic variation. Proc. Natl Acad. Sci. USA 116, 13452–13461 (2019).CAS 
PubMed 
Article 

Google Scholar 
41.Houle, D., Bolstad, G. H., van der Linde, K. & Hansen, T. F. Mutation predicts 40 million years of fly wing evolution. Nature 548, 447–450 (2017).CAS 
PubMed 
Article 

Google Scholar 
42.Svensson, E. I. & Berger, D. The role of mutation bias in adaptive evolution. Trends Ecol. Evol. 34, 422–434 (2019).PubMed 
Article 

Google Scholar 
43.Schweizer, G. & Wagner, A. Genotype networks of 80 quantitative Arabidopsis thaliana phenotypes reveal phenotypic evolvability despite pervasive epistasis. PLoS Comput. Biol. 16, e1008082 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
44.Delph, L. F., Steven, J. C., Anderson, I. A., Herlihy, C. R. & Brodie, E. D. III Elimination of a genetic correlation between the sexes via artificial correlational selection. Evolution 65, 2872–2880 (2011).PubMed 
Article 

Google Scholar 
45.Conner, J. K. Genetic mechanisms of floral trait correlations in a natural population. Nature 420, 407–410 (2002).CAS 
PubMed 
Article 

Google Scholar 
46.Wagner, G. P. & Zhang, J. The pleiotropic structure of the genotype–phenotype map: the evolvability of complex organisms. Nat. Rev. Genet. 12, 204–213 (2011).CAS 
PubMed 
Article 

Google Scholar 
47.Barton, N. H., Etheridge, A. M. & Véber, A. The infinitesimal model: definition, derivation, and implications. Theor. Popul. Biol. 118, 50–73 (2017).CAS 
PubMed 
Article 

Google Scholar 
48.Orr, H. A. The population genetics of adaptation: the distribution of factors fixed during adaptive evolution. Evolution 52, 935–948 (1998).PubMed 
Article 

Google Scholar 
49.Flint, J. & Mackay, T. F. C. Genetic architecture of quantitative traits in mice, flies, and humans. Genome Res. 19, 723–733 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
50.Stinchcombe, J. R., Weinig, C., Heath, K. D., Brock, M. T. & Schmitt, J. Polymorphic genes of major effect: consequences for variation, selection and evolution in Arabidopsis thaliana. Genetics 182, 911–922 (2009).PubMed 
PubMed Central 
Article 

Google Scholar 
51.Orr, H. A. The genetics of species differences. Trends Ecol. Evol. 16, 343–350 (2001).Article 

Google Scholar 
52.Nadeau, N. J. et al. The gene cortex controls mimicry and crypsis in butterflies and moths. Nature 534, 106–110 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
53.Visscher, P. M. et al. 10 years of GWAS discovery: biology, function, and translation. Am. J. Hum. Genet. 101, 5–22 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
54.Pitchers, W. et al. A multivariate genome-wide association study of wing shape in Drosophila melanogaster. Genetics 211, 1429–1447 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
55.Mackay, T. F. C. Epistasis and quantitative traits: using model organisms to study gene–gene interactions. Nat. Rev. Genet. 15, 22–33 (2014).CAS 
PubMed 
Article 

Google Scholar 
56.Sailer, Z. R. & Harms, M. J. Detecting high-order epistasis in nonlinear genotype–phenotype maps. Genetics 205, 1079–1088 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
57.Hill, W. G. “Conversion” of epistatic into additive genetic variance in finite populations and possible impact on long-term selection response. J. Anim. Breed. Genet. 134, 196–201 (2017).CAS 
PubMed 
Article 

Google Scholar 
58.Gienapp, P. et al. Genomic quantitative genetics to study evolution in the wild. Trends Ecol. Evol. 32, 897–908 (2017).PubMed 
Article 

Google Scholar 
59.Nosil, P. et al. Ecology shapes epistasis in a genotype–phenotype–fitness map for stick insect colour. Nat. Ecol. Evol. 4, 1673–1684 (2020).60.Blount, Z. D., Lenski, R. E. & Losos, J. B. Contingency and determinism in evolution: replaying life’s tape. Science 362, eaam5979 (2018).PubMed 
Article 
CAS 

Google Scholar 
61.Bolnick, D. I., Barrett, R. D. H., Oke, K. B., Rennison, D. J. & Stuart, Y. E. (Non)parallel evolution. Annu. Rev. Ecol. Evol. Syst. 49, 303–330 (2018).Article 

Google Scholar 
62.Therkildsen, N. O. et al. Contrasting genomic shifts underlie parallel phenotypic evolution in response to fishing. Science 365, 487–490 (2019).CAS 
PubMed 
Article 

Google Scholar 
63.Stern, D. L. & Orgogozo, V. Is genetic evolution predictable? Science 323, 746–751 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
64.Colosimo, P. F. et al. Widespread parallel evolution in sticklebacks by repeated fixation of Ectodysplasin alleles. Science 307, 1928–1933 (2005).CAS 
PubMed 
Article 

Google Scholar 
65.Archambeault, S. L., Bärtschi, L. R., Merminod, A. D. & Peichel, C. L. Adaptation via pleiotropy and linkage: association mapping reveals a complex genetic architecture within the stickleback Eda locus. Evol. Lett. 4, 282–301 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
66.van Rheenen, W., Peyrot, W. J., Schork, A. J., Lee, S. H. & Wray, N. R. Genetic correlations of polygenic disease traits: from theory to practice. Nat. Rev. Genet. 20, 567–581 (2019).PubMed 
Article 
CAS 

Google Scholar 
67.Stapley, J., Feulner, P. G. D., Johnston, S. E., Santure, A. W. & Smadja, C. M. Variation in recombination frequency and distribution across eukaryotes: patterns and processes. Phil. Trans. R. Soc. B 372, 20160455 (2017).PubMed 
Article 

Google Scholar 
68.Choudhury, R. R., Rogivue, A., Gugerli, F. & Parisod, C. Impact of polymorphic transposable elements on linkage disequilibrium along chromosomes. Mol. Ecol. 28, 1550–1562 (2019).CAS 
PubMed 
Article 

Google Scholar 
69.Thompson, M. J. & Jiggins, C. D. Supergenes and their role in evolution. Heredity 113, 1–8 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
70.Yeaman, S. Genomic rearrangements and the evolution of clusters of locally adaptive loci. Proc. Natl Acad. Sci. USA 110, E1743–E1751 (2013).CAS 
PubMed 
Article 

Google Scholar 
71.Faria, R., Johannesson, K., Butlin, R. K. & Westram, A. M. Evolving inversions. Trends Ecol. Evol. 34, 239–248 (2019).PubMed 
Article 

Google Scholar 
72.Tuttle, E. M. et al. Divergence and functional degradation of a sex chromosome-like supergene. Curr. Biol. 26, 344–350 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
73.Kupper, C. et al. A supergene determines highly divergent male reproductive morphs in the ruff. Nat. Genet. 48, 79–83 (2016).CAS 
PubMed 
Article 

Google Scholar 
74.Lamichhaney, S. et al. Structural genomic changes underlie alternative reproductive strategies in the ruff (Philomachus pugnax). Nat. Genet. 48, 84–88 (2016).CAS 
PubMed 
Article 

Google Scholar 
75.Huu, C. N., Keller, B., Conti, E., Kappel, C. & Lenhard, M. Supergene evolution via stepwise duplications and neofunctionalization of a floral-organ identity gene. Proc. Natl Acad. Sci. USA 117, 23148–23157 (2020).CAS 
PubMed 
Article 

Google Scholar 
76.Merrill, R. M. et al. Genetic dissection of assortative mating behavior. PLoS Biol. 17, e2005902 (2019).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
77.Whitlock, M. C., Phillips, P. C., Moore, F. B.-G. & Tonsor, S. J. Multiple fitness peaks and epistasis. Annu. Rev. Ecol. Syst. 26, 601–629 (1995).Article 

Google Scholar 
78.Dudley, S. A. The response to selection on plant physiological traits: evidence for local adaptation. Evolution 50, 103–110 (1996).PubMed 
Article 

Google Scholar 
79.Kirkpatrick, M. & Ravigné, V. Speciation by natural and sexual selection: models and experiments. Am. Nat. 159, S22–S35 (2002).PubMed 
Article 

Google Scholar 
80.Hohenlohe, P. A., Bassham, S., Currey, M. & Cresko, W. A. Extensive linkage disequilibrium and parallel adaptive divergence across threespine stickleback genomes. Phil. Trans. R. Soc. B 367, 395–408 (2012).81.Hench, K., Vargas, M., Höppner, M. P., McMillan, W. O. & Puebla, O. Inter-chromosomal coupling between vision and pigmentation genes during genomic divergence. Nat. Ecol. Evol. 3, 657–667 (2019).PubMed 
Article 

Google Scholar 
82.Gienapp, P., Calus, M. P. L., Laine, V. N. & Visser, M. E. Genomic selection on breeding time in a wild bird population. Evol. Lett. 3, 142–151 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
83.McGuigan, K., Collet, J. M., Allen, S. L., Chenoweth, S. F. & Blows, M. W. Pleiotropic mutations are subject to strong stabilizing selection. Genetics 197, 1051–105 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
84.McGuigan, K. et al. The nature and extent of mutational pleiotropy in gene expression of male Drosophila serrata. Genetics 196, 911–921 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
85.Hine, E., Runcie, D. E., McGuigan, K. & Blows, M. W. Uneven distribution of mutational variance across the transcriptome of Drosophila serrata revealed by high-dimensional analysis of gene expression. Genetics https://doi.org/10.1534/genetics.118.300757 (2018).86.Estes, S., Ajie, B. C., Lynch, M. & Phillips, P. C. Spontaneous mutational correlations for life-history, morphological and behavioral characters in Caenorhabditis elegans. Genetics 170, 645–653 (2005).PubMed 
PubMed Central 
Article 

Google Scholar 
87.Houle, D. & Fierst, J. Properties of spontaneous mutational variance and covariance for wing size and shape in Drosophila melanogaster. Evolution 67, 1116–1130 (2013).PubMed 
Article 

Google Scholar 
88.Ovaskainen, O., Karhunen, M., Zheng, C., Arias, J. M. C. & Merilä, J. A new method to uncover signatures of divergent and stabilizing selection in quantitative traits. Genetics 189, 621–632 (2011).PubMed 
PubMed Central 
Article 

Google Scholar 
89.Csilléry, K. et al. Adaptation to local climate in multi-trait space: evidence from silver fir (Abies alba Mill.) populations across a heterogeneous environment. Heredity 124, 77–92 (2020).PubMed 
Article 

Google Scholar 
90.Berg, J. J. & Coop, G. A population genetic signal of polygenic adaptation. PLoS Genet. 10, e1004412 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
91.Orr, H. A. Adaptation and the cost of complexity. Evolution 54, 13–20 (2000).CAS 
PubMed 
Article 

Google Scholar 
92.Fisher, R. A. The Genetical Theory of Natural Selection (Clarendon, 1930).93.Pavlicev, M. & Hansen, T. F. Genotype–phenotype maps maximizing evolvability: modularity revisited. Evol. Biol. 38, 371–389 (2011).Article 

Google Scholar 
94.Hine, E., McGuigan, K. & Blows, M. W. Evolutionary constraints in high-dimensional trait sets. Am. Nat. 184, 119–131 (2014).PubMed 
Article 

Google Scholar 
95.Melo, D. & Marroig, G. Directional selection can drive the evolution of modularity in complex traits. Proc. Natl Acad. Sci. USA 112, 470–475 (2015).CAS 
PubMed 
Article 

Google Scholar 
96.Kashtan, N. & Alon, U. Spontaneous evolution of modularity and network motifs. Proc. Natl Acad. Sci. USA 102, 13773–13778 (2005).CAS 
PubMed 
Article 

Google Scholar 
97.Espinosa-Soto, C. & Wagner, A. Specialization can drive the evolution of modularity. PLoS Comput. Biol. 6, e1000719 (2010).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
98.Ancel, L. W. & Fontana, W. in Modularity: Understanding the Development and Evolution of Natural Complex Systems (eds Callebaut, W. & Rasskin-Gutman, D.) 129–141 (MIT Press, 2009).99.Wagner, G. P. & Mezey, J. G. in Modularity in Development and Evolution (eds Schlosser, G. & Wagner, G. P.) 338–358 (Univ. Chicago Press, 2004).100.Fokkens, L. & Snel, B. Cohesive versus flexible evolution of functional modules in eukaryotes. PLoS Comput. Biol. 5, e1000276 (2009).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
101.Huang, W. et al. Genetic basis of transcriptome diversity in Drosophila melanogaster. Proc. Natl Acad. Sci. USA 112, E6010–E6019 (2015).CAS 
PubMed 
Article 

Google Scholar 
102.Schweizer, R. M. et al. Physiological and genomic evidence that selection on the transcription factor Epas1 has altered cardiovascular function in high-altitude deer mice. PLoS Genet. 15, e1008420 (2019).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
103.Hämälä, T. et al. Gene expression modularity reveals footprints of polygenic adaptation in Theobroma cacao. Mol. Biol. Evol. 37, 110–123 (2020).PubMed 
Article 
CAS 

Google Scholar 
104.Collet, J. M., McGuigan, K., Allen, S. L., Chenoweth, S. F. & Blows, M. W. Mutational pleiotropy and the strength of stabilizing selection within and between functional modules of gene expression. Genetics 208, 1601–1616 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
105.Jiménez, A., Cotterell, J., Munteanu, A. & Sharpe, J. A spectrum of modularity in multi‐functional gene circuits. Mol. Syst. Biol. 13, 925 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
106.Verd, B., Monk, N. A. & Jaeger, J. Modularity, criticality, and evolvability of a developmental gene regulatory network. eLife 8, e42832 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
107.Pallares, L. F. et al. Mapping of craniofacial traits in outbred mice identifies major developmental genes involved in shape determination. PLoS Genet. 11, e1005607 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
108.Arnold, S. J., Pfrender, M. E. & Jones, A. G. The adaptive landscape as a conceptual bridge between micro- and macroevolution. Genetica 112–113, 9–32 (2001).PubMed 
Article 

Google Scholar 
109.Rockman, M. V. The QTN program and the alleles that matter for evolution: all that’s gold does not glitter. Evolution 66, 1–17 (2012).PubMed 
Article 

Google Scholar 
110.Shikov, A. E., Skitchenko, R. K., Predeus, A. V. & Barbitoff, Y. A. Phenome-wide functional dissection of pleiotropic effects highlights key molecular pathways for human complex traits. Sci. Rep. 10, 1037 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
111.Sella, G. & Barton, N. H. Thinking about the evolution of complex traits in the era of genome-wide association studies. Annu. Rev. Genom. Hum. Genet. 20, 461–493 (2019).CAS 
Article 

Google Scholar 
112.Walsh, B. & Blows, M. W. Abundant genetic variation plus strong selection = multivariate genetic constraints: a geometric view of adaptation. Annu. Rev. Ecol. Evol. Syst. 40, 41–59 (2009).Article 

Google Scholar 
113.Teplitsky, C. et al. Assessing multivariate constraints to evolution across ten long-term avian studies. PLoS ONE 9, e90444 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
114.Pavlicev, M. & Cheverud, J. M. Constraints evolve: context dependency of gene effects allows evolution of pleiotropy. Annu. Rev. Ecol. Evol. Syst. 46, 413–434 (2015).115.Wei, X. & Zhang, J. Environment-dependent pleiotropic effects of mutations on the maximum growth rate r and carrying capacity K of population growth. PLoS Biol. 17, e3000121 (2019).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
116.Parter, M., Kashtan, N. & Alon, U. Facilitated variation: how evolution learns from past environments to generalize to new environments. PLoS Comput. Biol. 4, e1000206 (2008).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
117.Lande, R. Quantitative genetic analysis of multivariate evolution, applied to brain:body size allometry. Evolution 33, 402–416 (1979).PubMed 
Article 

Google Scholar 
118.Bolstad, G. H. et al. Complex constraints on allometry revealed by artificial selection on the wing of Drosophila melanogaster. Proc. Natl Acad. Sci. USA 112, 13284–13289 (2015).CAS 
PubMed 
Article 

Google Scholar 
119.Tsuboi, M. et al. Breakdown of brain–body allometry and the encephalization of birds and mammals. Nat. Ecol. Evol. 2, 1492–1500 (2018).PubMed 
Article 

Google Scholar 
120.White, C. R. et al. The origin and maintenance of metabolic allometry in animals. Nat. Ecol. Evol. 3, 598–603 (2019).PubMed 
Article 

Google Scholar 
121.Mullon, C., Keller, L. & Lehmann, L. Social polymorphism is favoured by the co-evolution of dispersal with social behaviour. Nat. Ecol. Evol. 2, 132–140 (2018).PubMed 
Article 

Google Scholar 
122.Schweizer, R. M. et al. Natural selection and origin of a melanistic allele in North American gray wolves. Mol. Biol. Evol. 35, 1190–1209 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
123.Hämälä, T., Gorton, A. J., Moeller, D. A. & Tiffin, P. Pleiotropy facilitates local adaptation to distant optima in common ragweed (Ambrosia artemisiifolia). PLoS Genet. 16, e1008707 (2020).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
124.Roda, F., Walter, G. M., Nipper, R. & Ortiz-Barrientos, D. Genomic clustering of adaptive loci during parallel evolution of an Australian wildflower. Mol. Ecol. 26, 3687–3699 (2017).CAS 
PubMed 
Article 

Google Scholar 
125.Sinervo, B. & Lively, C. M. The rock–paper–scissors game and the evolution of alternative male strategies. Nature 380, 240–243 (1996).CAS 
Article 

Google Scholar 
126.Hughes, K. A., Houde, A. E., Price, A. C. & Rodd, F. H. Mating advantage for rare males in wild guppy populations. Nature 503, 108–110 (2013).CAS 
PubMed 
Article 

Google Scholar 
127.Marques, D. A., Jones, F. C., Di Palma, F., Kingsley, D. M. & Reimchen, T. E. Experimental evidence for rapid genomic adaptation to a new niche in an adaptive radiation. Nat. Ecol. Evol. 2, 1128–1138 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
128.Brodie, E. D. III Genetic correlations between morphology and antipredator behaviour in natural populations of the garter snake Thamnophis ordinoides. Nature 342, 542–543 (1989).PubMed 
Article 

Google Scholar 
129.Auinger, H.-J. et al. Model training across multiple breeding cycles significantly improves genomic prediction accuracy in rye (Secale cereale L.). Theor. Appl. Genet. 129, 2043–2053 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
130.Xie, K. T. et al. DNA fragility in the parallel evolution of pelvic reduction in stickleback fish. Science 363, 81–84 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
131.Slate, J. Quantitative trait locus mapping in natural populations: progress, caveats and future directions. Mol. Ecol. 14, 363–379 (2005).CAS 
PubMed 
Article 

Google Scholar 
132.Brieuc, M. S. O., Waters, C. D., Drinan, D. P. & Naish, K. A. A practical introduction to random forest for genetic association studies in ecology and evolution. Mol. Ecol. Resour. 18, 755–766 (2018).PubMed 
Article 

Google Scholar 
133.Nielsen, R. Molecular signatures of natural selection. Annu. Rev. Genet. 39, 197–218 (2005).CAS 
PubMed 
Article 

Google Scholar 
134.Barghi, N., Hermisson, J. & Schlötterer, C. Polygenic adaptation: a unifying framework to understand positive selection. Nat. Rev. Genet. 21, 769–781 (2020).135.Lemos, B., Araripe, L. O. & Hartl, D. L. Polymorphic Y chromosomes harbor cryptic variation with manifold functional consequences. Science 319, 91–93 (2008).CAS 
PubMed 
Article 

Google Scholar 
136.Haddad, R., Meter, B. & Ross, J. A. The genetic architecture of intra-species hybrid mito-nuclear epistasis. Front. Genet. 9, 481 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
137.Long, A., Liti, G., Luptak, A. & Tenaillon, O. Elucidating the molecular architecture of adaptation via evolve and resequence experiments. Nat. Rev. Genet. 16, 567–582 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
138.Bollback, J. P., York, T. L. & Nielsen, R. Estimation of 2Nes from temporal allele frequency data. Genetics 179, 497–502 (2008).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
139.Svensson, E. I. & Calsbeek, R. The Adaptive Landscape in Evolutionary Biology (Oxford Univ. Press, 2012). More

Experimental data of Russian wheat aphid (RWA) development and survivalThe RWA development and survival data are from our laboratory experiments which involve the observations of 1800 RWA individuals in a factorial arrangement of five temperature and five barley plant-growth stage regimes with a total of 25 treatments. Each treatment has 72 RWA individuals as replicates. The experiment was designed to investigate the influence of temperature and barley plant-growth stage treatments on RWA development, survival and reproduction in controlled environment growth chambers. Temperature treatments were 8–1 °C, 17–10 °C, 23–16 °C, 28–21 °C, and 33–26 °C, fluctuating on a 14:10-h rectangular-wave cycle. The photoperiod was 14 vs. 10 (light vs. dark) for all treatments, with the higher constant temperature during the light phase and the lower temperature during the dark phase. Mean temperatures weighted by photoperiod were 5.1 °C, 14.1 °C, 20.1 °C, 25.1 °C and 30.1 °C, respectively. Barley plant-growth stages were two-leaf, tillering, flag leaf, inflorescence and soft dough, respectively corresponding to 12, 23, 39, 59, and 85 on the Zadoks (1974) scale38. More detailed information on the experiment design can also be found in Ma (1997) and Ma & Bechinski (2008a, 2008b)1,2,39.For analysis, we divided the life cycle of the RWA into 9 stages: first to fifth instar (abbreviated as 1st-5th), pre-reproductive period (from the time of last molting until the first nymphal production, designated Pre_R), immature period (1st + 2nd + 3rd + 4th + 5th, designated immature), mature (immature + R_age, designated mature), adult (from the time of last molting until death, designated adult). We also treat lifespan as a special variable, i.e., time from birth to death, designated lifespan. There is a response time T associated with each RWA stage and the lifespan; T is either development time (for individuals that successfully developed from one stage to the next), or death time (for individuals that died within the stage), depending on the state indication variable (short as ‘state variable’ or ‘state’). The unit for time (T) is calendar day (24 h). For stages other than adult and lifespan, if the state indication variable takes a value 1, then T is developmental time; if state is 0, then T is the death time or other censored time (e.g. lost accidentally in observation). In contrast, for the adult and lifespan stages, if state is 1, then T is death time of an individual; if state is 0, then T records the time when observation stopped due to some laboratory handling accident before the individual naturally died. Further information on the laboratory experiments is also described in Ma (1997), and Ma & Bechinski (2008a, 2008b)1,2,39.Unified survival analysis approach to insect development and survivalIssues of censoring in entomological researchCensoring occurs when the failure times of some individuals within the observation sample cannot be observed. Censoring is often unavoidable in time-to-event studies. A patient in a clinical trial may be withdrawn from the study after a period of participation; similarly, insects under observation may be lost tracks due to accidental events such as operational faults. Such kind of censoring belongs to the so-termed random censoring. In other cases, observing all individuals for the full time course to failure (such as the occurrence of death) is too costly or unacceptable, leading to the so-terms right censoring. In other situations, the process may have been going on but unnoticed prior to formal study, and consequently a starting point has to be selected, such as the exposure to some newly discovered risks, or the occurrence of a new infectious disease. This last category of censoring is known as left censoring.All three censorings exemplified above may occur in entomological experiments. Whereas the censorings discussed so far might be avoided or minimized, we realize that, in the study of insect populations, even with a perfect experiment design being perfectly executed, at least two types of natural censoring mechanisms seem uncontrollable thanks to the very nature of insect development and instarship. Two examples are presented here: (i) In a life table study, when a cohort of insects is observed, the insect development (molting, emergence, etc.) or survival (death) are typical examples of time-to-event or failure time random variable. This is not the focal point of our arguments. The point is that some insect individuals may die and never emerge from the observed instar or stage. From the perspective of observing insect development, the data may be censored due to the “premature” death events. How long it would have taken for those prematurely dead individuals to complete their developments is hardly knowable. (ii) It is well known that the number of instars in an insect species may be different among individuals of the same population; one may never know the exact number of instars an individual can potentially experience if it dies prior to reaching adult stage. For example, in the case of RWA, the majority of individuals has 4 instars, but 2, 3, 5 are also possible. If a RWA nymph died before reaching the adult stage, we may never know how many instars this prematurely dead individual would pass through. Hence, unless zero mortality in immature stages is possible, censoring in studies of insect developments occurs naturally and is incontrollable. Therefore, insect development and survival are dependent in the sense that in order to develop to the next stage, an insect individual must survive through the current stage. Such kind of dependence can be addressed ideally with conditional probability models in survival analysis including Cox proportional hazards models, which is applied to modeling the development and survival of RWA in this paper.With most statistical methods other than survival analysis, censoring presents a dilemma. If a researcher chooses to exclude the censored observations, the resulting sample may be too small to conduct proper statistical analysis. Even if the resulting sample is large enough, the parameters estimated are biased, and there is no well-established statistical procedure to quantify the bias, because there is no guarantee that the censored individuals can be represented by the remaining sample. On the other hand, if the censored individuals are included, although compartmental modeling (using a probabilistic approach, or invoking differential equations) might be useful so that the numbers of individuals in the different instars (compartments) are modeled over time with estimation of the rates of transition, there are no objective procedures to process their “partial” lifetime information, which again may introduce bias without even knowing the degree of bias introduced.How significant can the difference be between the two schemes—one with censored individuals excluded before applying survival analysis, and the other processed with survival analysis directly (i.e., the partial information of the censored individual is preserved)? Ma (1997, 2010)1,15 and Ma & Bechinski (2008b, 2009b)2,14 treated the prematurely dead RWA individuals as censored with survival analysis approach, and found that the difference between two treatments: survival analysis vs. excluding the premature dead individuals range from 4%–25% in the estimate of median development, depending on the severity of censoring (death rates)15. The treatment resolves the previously identified dilemma because survival analysis has developed effective procedures and methods (based on the asymptotical theory or more recently on the counting stochastic process) that can properly extract the partial information in those censored data.While the previous type of censoring due to premature death or variable instarships seems more likely to occur in insect demography and phenology studies under laboratory conditions, there is another subtle but hardly avoidable censoring mechanism, which is termed as interval censoring in survival analysis and it may be more likely to be encountered in field insect research such as life table study by sampling insect population periodically. This type of censoring is required because sampling is discrete and linear, whereas the process under investigation is continuous and possibly nonlinear with respect to time, which makes the precise recording of the survival times for all individuals in an experiment impossible. With interval censored data, each event time is then only known to lie in some interval, and the precise time to an event is often not known due to the limited sampling points.Ma & Bechinski (2008b, 2009b, Ma 1997, 2010) argued that1,2,14,15, whenever time-to-event data is in concern, survival analysis can be harnessed to perform the two fundamental statistical analyses in lieu of traditional statistical methods, that is: (i) hypothesis testing—replacing procedures such as significance testing, ANOVA, life tables analysis etc.; (2) model-parameter estimation—replacing conventional regression modeling. The prevalence of time-to-event data as one of the two major categories of time-dependent process data (the other is time-series data as noted previously), as well as the fact that survival analysis is developed to study time-to-event variables with observation censoring, make a very strong case for entomologists to adopt survival analysis as an appropriate statistical tool. In a previous study, we demonstrated the use of survival analysis for hypothesis testing and life table analysis (Ma 2010)15. In the present paper, we demonstrate the second area—model-parameter estimation. Specifically, we try to show that survival analysis offers a unified approach to model both insect development and survival.Survival Analysis and Proportional Hazards Model (PHM)Survivor, hazards and probability density functionsGiven response time (survival or failure time) T of a subject, three functions are usually used to describe the random variable (T): the survivor function, the probability density function, and the hazard function.The survivor function S(t) is defined as the probability that T is at least as great as a value t; that is,$$S(t) = P(T ge t)quad t > 0.$$
(1)
The survivor function is actually 1′s complement of the distribution function of random variable (T), that is, S(t) = 1–F(t), where F(t) is the distribution function of T.The probability density function (p.d.f) of T is$$f(t) = mathop {lim }limits_{{Delta t to 0^{ + } }} frac{{P(t le T le t + Delta t)}}{{Delta t}} = – frac{{dS(t)}}{{dt}}.$$
(2)
Conversely,(S(t) = int_{t}^{infty } {f(u)du}) and f(t) ≥ 0 with (int_{0}^{infty } {f(t)dt = 1.})The hazard function specifies the instantaneous rate of failure at T = t, conditional upon survival to time t. It is defined as$$lambda (t) = mathop {lim }limits_{{Delta t to 0^{ + } }} frac{{P(t le T < t + Delta t|T ge t)}}{{Delta t}} = frac{{f(t)}}{{S(t)}}.$$ (3) The relationships among S(t), f(t), and λ(t) are expressed as follows:$$lambda (t) = frac{ - dlog S(t)}{{dt}}$$ (4) $$S(t) = exp left( { - int_{0}^{t} {lambda (u)du} } right)$$ (5) $$f(t) = lambda (t)exp left( { - int_{0}^{t} {lambda (u)du} } right).$$ (6) Proportional hazards model (PHM)Cox (1972, 1975) proposed the proportional hazards model (PHM)16,40$$lambda (t,,z) = lambda_{0} (t)exp (zbeta ) = lambda_{0} (t)exp (beta_{1} z_{1} + beta_{2} z_{2} + ...beta_{n} z_{n} ),$$ (7) where λ(t, z) denotes the hazard function at time t for an individual with the characteristic represented by the covariate vector z of n elements. In entomological research, examples of z may include environmental factors (temperature and plant growth stage in this paper) that influence the development and survival of insects. Here λ0(t) is an arbitrary unspecified baseline hazard function for continuous time t. The hazards function λ(t, z) is a product of an underlying age-dependent risk, λ0(t) (baseline hazard function) and another factor, exp(zβ), which depends on covariates z and the vector β of parameters. Baseline hazard function λ0(t) is the hazard function for individuals on which covariates have “neutral effect”—the values of covariates are equal to either zero or to their averages (an example is shown later) depending on the model form adopted. The PHM estimates the risks of other groups in relation to this baseline. Other specifications of the hazard relationship are possible (e.g., λ(t, z) = λ0(t) + zβ), but the problem with these alternatives is the mathematical possibility of predicting negative hazard rates, which then requires extra constraints on estimation procedures to ensure positive values.The PHM invokes two assumptions. The first is the proportionality assumption, that there is a multiplicative relationship between the underlying hazard function and the log-linear function of the covariates such that the ratio of hazard functions for two individuals with different sets of covariates is constant in time (from which the PHM derived its name). The second assumption is that effects of covariates on the hazard function are log-linear.The conditional (with respect to the covariate vector z) probability density function of T given z for the PHM is$$f(t;,z) = lambda _{0} (t)exp (zbeta )exp left[ { - exp (zbeta )int_{0}^{t} {mathop lambda nolimits_{0} (u)du} } right].$$ (8) where λ0(t) is the baseline hazard function as explained previously, z is the vector of covariates (e.g.. air temperature and crop growth state in this study), and β is the vector of Cox’s PHM regression coefficients (parameters).The conditional survivor function (or simply called the survivor function) of T given z for the PHM is$$S(t;,z) = [S_{0} (t)]^{exp (zbeta )} ,$$ (9) where$$S_{0} (t) = exp left[ { - int_{0}^{t} {lambda _{0} (u)du} } right].$$ (10) S0(t) is called the baseline survivor function; it is computed for the default categories of the covariates (e.g., average temperature and plant growth stage in the case of this study). Therefore, the survivor function of t for a covariate vector z is obtained by raising the baseline survivor function S0(t) to a power. The usefulness of Eq. (9) is that one can predict survivor probabilities under different covariate values.If λ0(t) is arbitrary, this model is sufficiently flexible for many applications. There are two important generalizations that do not substantially complicate the estimation of β, but broadly expanding their applications: the stratified proportional hazards model and the proportional hazards model with time-dependent covariates.In the stratified version, the function λ0(t) is allowed to vary in specific subsets of the data. In particular, the population is divided into r strata wherein the hazard λj(t; z) in the j-th stratum depends on an arbitrary shape function λ0j(t). The model can be written as$$lambda_{j} (t,,z) = lambda_{0j} (t)exp (zbeta )quad j = {1},{ 2}, ldots ,r.$$ (11) This generalization is useful when the covariates do not seem to have a multiplicative effect on the hazard function. Here the range of those variables can be divided into strata where only the remaining regression variables contribute to the exponential factor in Eq. (11).The second generalization to the PHM is to allow the variable z to depend on time itself, without (Eq. 12) or with (Eq. 13) stratification:$$lambda [t,,z(t)] = lambda_{0} (t)exp [z(t)beta ],$$ (12) $$lambda_{j} [t,,z(t)] = lambda_{0j} (t)exp [z(t)beta ]quad j = 1,2, ldots ,r.$$ (13) The estimation of β depends only on the rank ordering of the variable vector z and is invariant with respect to the monotonic transformation on the dependent variable, i.e., survival time. The procedure used to estimate β is to maximize the so-called partial likelihood functions as described by Cox (1975), Kalbfleisch & Prentice (1980, 2002) and Kleinbaum and Klein (2012)19,20,30,40. BMDP™ 2L program, Survival Analysis with Covariates (BMDP 1993)41, was used to construct the proportional hazards models. The input data set was as described in Ma (1997)1. Finally, as an example, we use 1989 air temperature and barley plant-growth stage data from Moscow, ID., reported by Elberson (1992)42, as inputs to run the proportional hazards model for survival of RWA during the entire life cycle (i.e., model for LifeSpan stage). We further used coxphf function in the Survival package of open source R-Project to cross-verify the results from BMDP software. The information of Survival package, which is the cornerstone of R implementation of survival analysis, can be accessed at: (https://cran.r-project.org/web/views/Survival.html). More

1.Spalding, M. D. et al. Marine ecoregions of the world: a bioregionalization of coastal and shelf areas. BioScience 57, 573–583 (2007).Article 

Google Scholar 
2.Awad, A. A., Griffiths, C. L. & Turpie, J. K. Distribution of South African marine benthic invertebrates applied to the selection of priority conservation areas. Divers. Distrib. 8, 129–145 (2002).Article 

Google Scholar 
3.Pecl, G. T. et al. Biodiversity redistribution under climate change: impacts on ecosystems and human well-being. Science https://doi.org/10.1126/science.aai9214 (2017).4.Sunday, J. M., Bates, A. E. & Dulvy, N. K. Thermal tolerance and the global redistribution of animals. Nat. Clim. Change 2, 686–690 (2012).Article 

Google Scholar 
5.Wallace, A. R. The Geographical Distribution of Animals, with a Study of the Relations of Living and Extinct Faunas as Elucidating the Past Changes of the Earth’s Surface (Macmillan, 1876).6.Holt, B. G. et al. An update of Wallace’s zoogeographic regions of the world. Science 339, 74–78 (2013).CAS 
PubMed 
Article 

Google Scholar 
7.Ficetola, G. F., Mazel, F. & Thuiller, W. Global determinants of zoogeographical boundaries. Nat. Ecol. Evol. 1, 89 (2017).PubMed 
Article 

Google Scholar 
8.Kocsis, A. T., Reddin, C. J. & Kiessling, W. The stability of coastal benthic biogeography over the last 10 million years. Glob. Ecol. Biogeogr. 27, 1106–1120 (2018).Article 

Google Scholar 
9.Zaffosa, A., Finnegan, S. & Peters, S. E. Plate tectonic regulation of global marine animal diversity. Proc. Natl Acad. Sci. USA 114, 5653–5658 (2017).Article 
CAS 

Google Scholar 
10.Costello, M. J. et al. Marine biogeographic realms and species endemicity. Nat. Commun. https://doi.org/10.1038/s41467-017-01121-2 (2017).11.Beck, J. et al. What’s on the horizon for macroecology? Ecography 35, 673–683 (2012).Article 

Google Scholar 
12.Sunagawa, S. et al. Ocean plankton: structure and function of the global ocean microbiome. Science 348, 1261359 (2015).PubMed 
Article 
CAS 

Google Scholar 
13.Shade, A. et al. Macroecology to unite all life, large and small. Trends Ecol. Evol. 33, 731–744 (2018).PubMed 
Article 

Google Scholar 
14.Djurhuus, A. et al. Environmental DNA reveals seasonal shifts and potential interactions in a marine community. Nat. Commun. 11, 254 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
15.Richter, D. J. et al. Genomic evidence for global ocean plankton biogeography shaped by large-scale current systems. Preprint at bioRxiv https://doi.org/10.1101/867739 (2019).16.Naeem, S., Duffy, J. E. & Zavaleta, E. The functions of biological diversity in an age of extinction. Science 336, 1401–1406 (2012).CAS 
PubMed 
Article 

Google Scholar 
17.Tilman, D., Isbell, F. & Cowles, J. M. Biodiversity and ecosystem functioning. Annu. Rev. Ecol. Evol. Syst. 45, 471–493 (2014).Article 

Google Scholar 
18.Finderup Nielsen, T., Sand-Jensen, K., Dornelas, M. & Bruun, H. H. More is less: net gain in species richness, but biotic homogenization over 140 years. Ecol. Lett. 22, 1650–1657 (2019).PubMed 
Article 

Google Scholar 
19.Blowes, S. A. et al. The geography of biodiversity change in marine and terrestrial assemblages. Science 366, 339–345 (2019).CAS 
PubMed 
Article 

Google Scholar 
20.Dornelas, M. et al. Assemblage time series reveal biodiversity change but not systematic loss. Science 344, 296–299 (2014).CAS 
PubMed 
Article 

Google Scholar 
21.Olden, J. D. & Rooney, T. P. On defining and quantifying biotic homogenization. Glob. Ecol. Biogeogr. 15, 113–120 (2006).Article 

Google Scholar 
22.Stuart-Smith, R. D. et al. Integrating abundance and functional traits reveals new global hotspots of fish diversity. Nature 501, 539–542 (2013).CAS 
PubMed 
Article 

Google Scholar 
23.Mouillot, D. et al. Rare species support vulnerable functions in high-diversity ecosystems. PLoS Biol. 11, e1001569 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
24.Bernardo-Madrid, R. et al. Human activity is altering the world’s zoogeographical regions. Ecol. Lett. 22, 1297–1305 (2019).PubMed 

Google Scholar 
25.Capinha, C., Essl, F., Seebens, H., Moser, D. & Pereira, H. M. The dispersal of alien species redefines biogeography in the Anthropocene. Science 348, 1248–1251 (2015).CAS 
PubMed 
Article 

Google Scholar 
26.Azam, F. & Malfatti, F. Microbial structuring of marine ecosystems. Nat. Rev. Microbiol. 5, 782–791 (2007).CAS 
PubMed 
Article 

Google Scholar 
27.Deiner, K. et al. Environmental DNA metabarcoding: transforming how we survey animal and plant communities. Mol. Ecol. 26, 5872–5895 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
28.Emanuel, B. P., Bustamante, R. H., Branch, G. M., Eekhout, S. & Odendaal, F. J. A zoogeographic and functional approach to the selection of marine reserves on the west coast of South Africa. South Afr. J. Mar. Sci. 12, 341–354 (1992).Article 

Google Scholar 
29.Griffiths, C. L., Robinson, T. B., Lange, L. & Mead, A. Marine biodiversity in South Africa: an evaluation of current states of knowledge. PLoS ONE 5, e12008 (2010).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
30.Griffiths, C. L. et al. Impacts of human activities on marine animal life in the Benguela: a historical overview. Oceanogr. Mar. Biol. Annu. Rev. 42, 303–392 (2004).
Google Scholar 
31.Kaluza, P., Kolzsch, A., Gastner, M. T. & Blasius, B. The complex network of global cargo ship movements. J. R. Soc. Interface 7, 1093–1103 (2010).PubMed 
PubMed Central 
Article 

Google Scholar 
32.Rapacciuolo, G., Beman, J. M., Schiebelhut, L. M. & Dawson, M. N. Microbes and macro-invertebrates show parallel β-diversity but contrasting α-diversity patterns in a marine natural experiment. Proc. R. Soc. B 286, 20190999 (2019).CAS 
PubMed 
Article 

Google Scholar 
33.Astorga, A. et al. Distance decay of similarity in freshwater communities: do macro- and microorganisms follow the same rules? Glob. Ecol. Biogeogr. 21, 365–375 (2012).Article 

Google Scholar 
34.Wang, J. et al. Patterns of elevational beta diversity in micro- and macroorganisms. Glob. Ecol. Biogeogr. 21, 743–750 (2012).CAS 
Article 

Google Scholar 
35.Tittensor, D. P. et al. Global patterns and predictors of marine biodiversity across taxa. Nature 466, 1098–U1107 (2010).CAS 
PubMed 
Article 

Google Scholar 
36.Herlemann, D. P. et al. Transitions in bacterial communities along the 2000 km salinity gradient of the Baltic Sea. ISME J. 5, 1571–1579 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
37.Broman, E. et al. Salinity drives meiofaunal community structure dynamics across the Baltic ecosystem. Mol. Ecol. 28, 3813–3829 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
38.Shochat, E., Warren, P. S., Faeth, S. H., McIntyre, N. E. & Hope, D. From patterns to emerging processes in mechanistic urban ecology. Trends Ecol. Evol. 21, 186–191 (2006).PubMed 
Article 

Google Scholar 
39.Halpern, B. S. et al. Recent pace of change in human impact on the world’s ocean. Sci. Rep. 9, 11609 (2019).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
40.Kelly, R. P. et al. Genetic signatures of ecological diversity along an urbanization gradient. PeerJ 4, e2444 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
41.Blouin, D., Pellerin, S. & Poulin, M. Increase in non-native species richness leads to biotic homogenization in vacant lots of a highly urbanized landscape. Urban Ecosyst. 22, 879–892 (2019).Article 

Google Scholar 
42.Holman, L. E. et al. Detection of introduced and resident marine species using environmental DNA metabarcoding of sediment and water. Sci. Rep. 9, 11559 (2019).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
43.Lima-Mendez, G. et al. Determinants of community structure in the global plankton interactome. Science 348, 1262073 (2015).PubMed 
Article 
CAS 

Google Scholar 
44.Baas-Becking, L. G. M. Geobiologie; of inleiding tot de milieukunde (WP Van Stockum & Zoon NV, 1934).45.Hanson, C. A., Fuhrman, J. A., Horner-Devine, M. C. & Martiny, J. B. Beyond biogeographic patterns: processes shaping the microbial landscape. Nat. Rev. Microbiol. 10, 497–506 (2012).CAS 
PubMed 
Article 

Google Scholar 
46.Farjalla, V. F. et al. Ecological determinism increases with organism size. Ecology 93, 1752–1759 (2012).PubMed 
Article 

Google Scholar 
47.Wu, W. X. et al. Contrasting the relative importance of species sorting and dispersal limitation in shaping marine bacterial versus protist communities. ISME J. 12, 485–494 (2018).PubMed 
Article 

Google Scholar 
48.Hellweger, F. L., van Sebille, E. & Fredrick, N. D. Biogeographic patterns in ocean microbes emerge in a neutral agent-based model. Science 345, 1346–1349 (2014).CAS 
PubMed 
Article 

Google Scholar 
49.Balint, M. et al. Environmental DNA time series in ecology. Trends Ecol. Evol. 33, 945–957 (2018).PubMed 
Article 

Google Scholar 
50.He, K. S. et al. Will remote sensing shape the next generation of species distribution models? Remote Sens. Ecol. Conserv. 1, 4–18 (2015).Article 

Google Scholar 
51.Rius, M. et al. Range expansions across ecoregions: interactions of climate change, physiology and genetic diversity. Glob. Ecol. Biogeogr. 23, 76–88 (2014).Article 

Google Scholar 
52.Spens, J. et al. Comparison of capture and storage methods for aqueous macrobial eDNA using an optimized extraction protocol: advantage of enclosed filter. Methods Ecol. Evol. 8, 635–645 (2017).Article 

Google Scholar 
53.Leray, M. et al. A new versatile primer set targeting a short fragment of the mitochondrial COI region for metabarcoding metazoan diversity: application for characterizing coral reef fish gut contents. Front. Zool. 10, 34 (2013).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
54.Zhan, A. et al. High sensitivity of 454 pyrosequencing for detection of rare species in aquatic communities. Methods Ecol. Evol. 4, 558–565 (2013).Article 

Google Scholar 
55.Takahashi, S., Tomita, J., Nishioka, K., Hisada, T. & Nishijima, M. Development of a prokaryotic universal primer for simultaneous analysis of Bacteria and Archaea using next-generation sequencing. PLoS ONE 9, e105592 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
56.Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17, 10–12 (2011).Article 

Google Scholar 
57.Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
58.R Core Team R: A Language and Environment for Statistical Computing v.3.6.1 (R Foundation for Statistical Computing, 2019).59.Frøslev, T. G. et al. Algorithm for post-clustering curation of DNA amplicon data yields reliable biodiversity estimates. Nat. Commun. 8, 1188 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
60.Wang, Q., Garrity, G. M., Tiedje, J. M. & Cole, J. R. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73, 5261–5267 (2007).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
61.Porter, T. M. & Hajibabaei, M. Automated high throughput animal CO1 metabarcode classification. Sci. Rep. 8, 4226 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
62.Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2013).CAS 
PubMed 
Article 

Google Scholar 
63.Edgar, R. C. Updating the 97% identity threshold for 16S ribosomal RNA OTUs. Bioinformatics 34, 2371–2375 (2018).CAS 
PubMed 
Article 

Google Scholar 
64.Burki, F., Roger, A. J., Brown, M. W. & Simpson, A. G. The new tree of eukaryotes. Trends Ecol. Evol. 35, 43–55 (2020).PubMed 
Article 

Google Scholar 
65.GHRSST Level 4 G1SST Global Foundation Sea Surface Temperature Analysis (JPL_OurOceanProject, 2010); https://doi.org/10.5067/GHG1S-4FP0166.Zweng, M. M. et al. World Ocean Atlas 2018, Volume 2: Salinity NOAA Atlas NESDIS 82 (ed. Mishinov, A.) (NESDIS/US Department of Commerce, NOAA, 2019).67.Ocean Colour Climate Change Initiative Dataset Version 4.2 (European Space Agency, 2020).68.Anderson, M. J. in Wiley Stats Ref: Statistics Reference Online (eds Balakrishnan, N. et al.) 1–15 (John Wiley & Sons, 2014).69.Oksanen, J. et al. Vegan: Community ecology package. R package version 2.5–6 (2011).70.Kreft, H. & Jetz, W. A framework for delineating biogeographical regions based on species distributions. J. Biogeogr. 37, 2029–2053 (2010).Article 

Google Scholar 
71.Salazar, G. EcolUtils: Utilities for community ecology analysis. R package version 0.1 (2018).72.Anderson, M. J. Distance-based tests for homogeneity of multivariate dispersions. Biometrics 62, 245–253 (2006).PubMed 
Article 

Google Scholar 
73.Crabot, J., Clappe, S., Dray, S. & Datry, T. Testing the Mantel statistic with a spatially-constrained permutation procedure. Methods Ecol. Evol. 10, 532–540 (2019).Article 

Google Scholar 
74.McArdle, B. H. & Anderson, M. J. Fitting multivariate models to community data: a comment on distance-based redundancy analysis. Ecology 82, 290–297 (2001).Article 

Google Scholar  More