Philippa Kaur

1.Haddaway, N. R., Styles, D. & Pullin, A. S. Environmental impacts of farm land abandonment in high altitude/mountain regions: A systematic map of the evidence. Environ. Evid. 2(1), 18. https://doi.org/10.1186/2047-2382-2-18 (2013).Article 

Google Scholar 
2.Bender, O., Boehmer, H. J., Jens, D. & Schumacher, K. P. Analysis of land-use change in a sector of Upper Franconia (Bavaria, Germany) since 1850 using land register records. Landsc. Ecol. 20(2), 149–163. https://doi.org/10.1007/s10980-003-1506-7 (2005).Article 

Google Scholar 
3.Biró, M., Szitár, K., Horváth, F., Bagi, I. & Molnár, Z. Detection of long-term landscape changes and trajectories in a Pannonian sand region: Comparing land-cover and habitat-based approaches at two spatial scales. Community Ecol. 14(2), 219–230. https://doi.org/10.1556/ComEc.14.2013.2.12 (2013).Article 

Google Scholar 
4.Schulp, C. J. E., Levers, C., Kuemmerle, T., Tieskens, K. F. & Verburg, P. H. Mapping and modelling past and future land use change in Europe’s cultural landscapes. Land Use Policy 80, 332–344. https://doi.org/10.1016/j.landusepol.2018.04.030 (2019).Article 

Google Scholar 
5.González Díaz, J. A., Celaya, R., Fernández García, R., Osoro, K. & Rosa García, R. Dynamics of rural landscapes in marginal areas of northern Spain: Past, present, and future. Land Degrad. Dev. 30, 141–150. https://doi.org/10.1002/ldr.3201 (2018).Article 

Google Scholar 
6.Hersperger, A. M., Gennaio, M.-P., Verburg, P. H., & Buergi, M. Linking land change with driving forces and actors: Four conceptual models. Ecol. Soci. 15(4) (2010). Retrieved from http://apps.webofknowledge.com.infozdroje.czu.cz/full_record.do?product=UA&search_mode=GeneralSearch&qid=1&SID=R1PQ9zBCVnF6m2Wcyo8&page=1&doc=2.7.Khromykh, V. & Khromykh, O. Analysis of spatial structure and dynamics of Tom Valley landscapes based on GIS, digital elevation model and remote sensing. Proc. Soc. Behav. Sci. 120, 811–815. https://doi.org/10.1016/j.sbspro.2014.02.165 (2014).Article 

Google Scholar 
8.Kienast, F. Analysis of historic landscape patterns with a geographical information system? A methodological outline. Landsc. Ecol. 8(2), 103–118. https://doi.org/10.1007/BF00141590 (1993).Article 

Google Scholar 
9.Matsushita, B., Xu, M. & Fukushima, T. Characterizing the changes in landscape structure in the Lake Kasumigaura Basin, Japan using a high-quality GIS dataset. Landsc. Urban Plan. 78(3), 241–250. https://doi.org/10.1016/j.landurbplan.2005.08.003 (2006).Article 

Google Scholar 
10.Plieninger, T. et al. Patterns and drivers of scattered tree loss in agricultural landscapes: Orchard meadows in Germany (1968–2009). PLoS ONE https://doi.org/10.1371/journal.pone.0126178 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
11.Swetnam, R. D. Rural land use in England and Wales between 1930 and 1998: Mapping trajectories of change with a high resolution spatio-temporal dataset. Landsc. Urban Plan. 81(1–2), 91–103. https://doi.org/10.1016/j.landurbplan.2006.10.013 (2007).Article 

Google Scholar 
12.Eurostat. Life in Cities. Urban Europe—Statistics on Cities, Towns and Suburbs. (Publications Office of the European Union, 2016). https://doi.org/10.2785/9112013.Fernández Ales, R. Effect of economic development on landscape structure and function in the Province of Seville (SW Spain) and its consequences on conservation. In Land Abandonment and Its Role in Conservation, Options Mediterraneennes, Serie A, Seminaires Mediterraneens Vol. 15 (eds Baudry, J. & Bunce, R. G. H.) 61–69 (Options Méditerranéennes, 1991).
Google Scholar 
14.Falcucci, A., Maiorano, L. & Boitana, L. Changes in land-use/land-cover patterns in Italy and their implications for biodiversity conservation. Landsc. Ecol. 22, 617–631 (2007).Article 

Google Scholar 
15.Antrop, M. Why landscapes of the past are important for the future. Landsc. Urban Plan. https://doi.org/10.1016/j.landurbplan.2003.10.002 (2004).Article 

Google Scholar 
16.Janečková Molnárová, K., Skřivanová, Z., Kalivoda, O. & Sklenička, P. Rural identity and landscape aesthetics in exurbia: Some issues to resolve from a Central European perspective. Morav. Geogr. Rep. 25(1), 2–12. https://doi.org/10.1515/mgr-2017-0001 (2017).Article 

Google Scholar 
17.Bičík, I., Jeleček, L. & Štěpánek, V. Land-use changes and their social driving forces in Czechia in the 19th and 20th centuries. Land Use Policy 18(1), 65–73. https://doi.org/10.1016/S0264-8377(00)00047-8 (2001).Article 

Google Scholar 
18.Lipsky, Z. The changing face of the Czech rural landscape. Landsc. Urban Plan. 31(1–3), 39–45. https://doi.org/10.1016/0169-2046(94)01034-6 (1995).Article 

Google Scholar 
19.Prishchepov, A. V., Radeloff, C. V., Baumann, M., Kemmerle, T. & Müller, D. Effects of institutional changes on land use: Agricultural land abandonment during the transition from state-command to market-driven economies in post-Soviet Eastern Europe. Enivron. Res. Lett. 7, 024021. https://doi.org/10.1088/1748-9326/7/2/024021 (2012).Article 

Google Scholar 
20.Forman, T. T. R. & Godron, M. Landscape Ecology (Wiley, 1986).
Google Scholar 
21.Míchal, I. Ekologická Stabilita (Veronica, 1994).
Google Scholar 
22.Bucala, A. The impact of human activities on land use and land cover changes and environmental processes in the Gorce Mountains (Western Polish Carpathians) in the past 50 years. J. Environ. Manag. 138, 4–14 (2014).Article 

Google Scholar 
23.Kozak, J. Forest cover change in the western Carpathians in the past 180 years. A case study in the Orawa region in Poland. Mt. Res. Dev. 23(4), 369–375 (2003).Article 

Google Scholar 
24.Latocha, A. Land-use changes and longer-term human–environment interactions in a mountain region (Sudetes Mountains, Poland). Geomorphology 108(1), 48–57. https://doi.org/10.1016/j.geomorph.2008.02.019 (2009).ADS 
Article 

Google Scholar 
25.Prauser, S. & Rees, A. The expulsion of the “German” communities from Eastern Europe at the end of the Second World War (Badia Fiesolana, 2004).
Google Scholar 
26.Petráš, R. Reforma správy a národní otázka na počátku první republiky. Historický Obzor: Časopis pro Výuku Dějepisu a Popularizaci Historie 13(5), 133–137 (2002).
Google Scholar 
27.Bičík, I. & Kabrda, J. Land use changes in Czech border regions (1845–2000). AUC Geogr. 42, 23–52 (2007).
Google Scholar 
28.Staněk, T. & von Arburg, A. Vysídlení Němců a proměny českého pohraničí 1945–1951 (I.) (Vysídlení) (Zdeněk Susa, 2010).
Google Scholar 
29.Weber, M. Dědictví krajiny jako výzva pro její současné obyvatele. In Proměny sudetské krajiny, Antikomple (ed. Spurnný, M.) (Nakladatelství Českého lesa, 2006).
Google Scholar 
30.Romportl, D., Chuman, T. & Lipsky, Z. Landscape typology of Czechia. Geografie 118(1), 16–39 (2013).Article 

Google Scholar 
31.Estel, S. et al. Mapping farmland abandonment and recultivation across Europe using MODIS NDVI time series. Remote Sens. Environ. 163, 312–325. https://doi.org/10.1016/J.RSE.2015.03.028 (2015).ADS 
Article 

Google Scholar 
32.Levers, C., Schneider, M., Prishchepov, A. V., Estel, S. & Kuemmerle, T. Spatial variation in determinants of agricultural land abandonment in Europe. Sci. Total Environ. 644, 95–111. https://doi.org/10.1016/J.SCITOTENV.2018.06.326 (2018).ADS 
CAS 
Article 
PubMed 

Google Scholar 
33.Zelinka, V. Continuity and extinction of agricultural land in the sudetes—A case study in the landscape of highlands and mountains. J. Landsc. Ecol. 11(2), 53–66. https://doi.org/10.2478/jlecol-2018-0006 (2018).Article 

Google Scholar 
34.ESRI. ArcGIS 10.6 (2017).35.Statistický lexikon obcí v republice Československé. (I). (Ministerstvo vnitra a Státní úřad statistický.Orbis, 1934).36.CENIA. Historické letecké snímky—50. léta. https://www.cenia.cz (2012).37.Bičík, I. et al. Land use changes in the Czech republic 1845–2010 (Springer, 2015).Book 

Google Scholar 
38.Demková, K. & Lipský, Z. Změny nelesní dřevinné vegetace v jihozápadní části bílých karpat v letech 1949–2011. Geogr. Sb. CGS 120(1), 64–83 (2015).
Google Scholar 
39.Forejt, M., Skalos, J., Pereponova, A., Plieninger, T. & Vojta, J. Changes and continuity of wood-pastures in the lowland landscape in Czechia. Appl. Geogr. 79, 235–244. https://doi.org/10.1016/j.apgeog.2016.12.016 (2017).Article 

Google Scholar 
40.ČÚZK. Geoportál ČÚZK—přístup k mapovým produktům a službám resortu. https://geoportal.cuzk.cz/ (2016).41.Skokanová, H. Application of methodological principles for assessment of land use changes trajectories and processes in South-eastern Moravia for the period 1836–2006 Eastern Moravia for the period 1836–2006. Acta Pruhon. 91, 15–21 (2015).
Google Scholar 
42.Grossmann, E. B. & Mladenoff, D. J. Open woodland and savanna decline in a mixed-disturbance landscape (1938 to 1998) in the Northwest Wisconsin (USA) Sand Plain. Landsc. Ecol. 22, 43–55 (2007).Article 

Google Scholar 
43.Skaloš, J. et al. What are the transitions of woodlands at the landscape level? Change trajectories of forest, non-forest and reclamation woody vegetation elements in a mining landscape in North-western Czech Republic. Appl. Geogr. 58, 206–216. https://doi.org/10.1016/j.apgeog.2015.02.003 (2015).Article 

Google Scholar 
44.Raška, P., Zábranský, V., Brázdil, R. & Lamková, J. The late Little Ice Age landslide calamity in North Bohemia: Triggers, impacts and post-landslide development reconstructed from documentary data (case study of the Kozí vrch Hill landslide). Geomorphology 255, 95–107. https://doi.org/10.1016/j.geomorph.2015.12.009 (2016).ADS 
Article 

Google Scholar 
45.Amici, V. et al. Anthropogenic drivers of plant diversity: Perspective on land use change in a dynamic cultural landscape. Biodivers. Conserv. 24(13), 3185–3199. https://doi.org/10.1007/s10531-015-0949-x (2015).Article 

Google Scholar 
46.Bürgi, M. et al. Processes and driving forces in changing cultural landscapes across Europe. Landsc. Ecol. 32(11), 2097–2112. https://doi.org/10.1007/s10980-017-0513-z (2017).Article 

Google Scholar 
47.Raška, P., Hruška, V. & Kolektiv, A. Adaptabilita a Resilience (Univerzita J.E. Purkyně v Ústí nad Labem, 2014).
Google Scholar 
48.CENIA. CORINE Land Cover 2012 Databáze České Republiky (2014).49.Juřena, J. Soumrak nad českým Podkrkonoším: Německá opkuace Novopacka a Jilemnicka v roce 1938. (FORTprint, 2013).50.Czech Geological Survey. Soil Map of Czech Republic (2018).51.Meduna, P. & Sádlo, J. Bezdězsko—Dokesko. Krajina mezi odolností a stagnací. Hist. Geogr. 35(1), 147–160 (2009).
Google Scholar 
52.Novák, J., Sádlo, J. & Svobodová-svitavská, H. Unusual vegetation stability in a lowland pine forest area (Doksy region, Czech Republic). Holocene 22(8), 947–955. https://doi.org/10.1177/0959683611434219 (2012).ADS 
Article 

Google Scholar 
53.Klápště, J. Proměna českých zemí ve středověku. (Lidové noviny, 2012).54.Engstová, B. & Petříček, V. Landscape and vegetation in a military area—Past and present. J. Landsc. Stud. 1, 91–102 (2008).
Google Scholar 
55.Turner, M. G. & Gardner, R. H. Landscape Ecology in Theory and Practice 2nd edn. (Springer, 2015).
Google Scholar 
56.Chytrý, M. (ed.) Vegetace České republiky 1. Travinná a keříčková vegetace (Academia, 2007).
Google Scholar 
57.Chytrý, M., Kučera, T., Kočí, M., Grulich, V. & Lustyk, P. Katalog biotopů České republiky (Agentura ochrany přírody a krajiny ČR, 2010).
Google Scholar  More

1.Pafilis, P. et al. Reproductive biology of insular reptiles: marine subsidies modulate expression of the ‘island syndrome’. Copeia 2011, 545–552 (2011).Article 

Google Scholar 
2.Ruttenberg, B. I., Haupt, A. J., Chiriboga, A. I. & Warner, R. R. Patterns, causes and consequences of regional variation in the ecology and life history of a reef fish. Oecologia 145, 394–403 (2005).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
3.Hilderbrand, G. V. et al. The importance of meat, particularly salmon, to body size, population productivity, and conservation of North American brown bears. Can. J. Zool. 77, 132–138 (1999).Article 

Google Scholar 
4.Gust, N. Variation in the population biology of protogynous coral reef fishes over tens of kilometres. Can. J. Fish. Aquat. Sci. 61, 205–218 (2004).Article 

Google Scholar 
5.Clifton, K. Asynchronous food availability on neighboring Caribbean coral reefs determines seasonal patterns of growth and reproduction for the herbivorous parrotfish Scarus iserti. Mar. Ecol. Prog. Ser. 116, 39–46 (1995).ADS 
Article 

Google Scholar 
6.Goldstein, E. D., D’Alessandro, E. K. & Sponaugle, S. Demographic and reproductive plasticity across the depth distribution of a coral reef fish. Sci. Rep. 6, 34077 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
7.Pérez-Ruzafa, A., Pérez-Marcos, M. & Marcos, C. From fish physiology to ecosystems management: keys for moving through biological levels of organization in detecting environmental changes and anticipate their consequences. Ecol. Ind. 90, 334–345 (2018).Article 

Google Scholar 
8.Agrawal, A. A. Phenotypic plasticity in the interactions and evolution of species. Science 294, 321–326 (2001).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
9.Leslie, H. M., Breck, E. N., Chan, F., Lubchenco, J. & Menge, B. A. Barnacle reproductive hotspots linked to nearshore ocean conditions. Proc. Natl. Acad. Sci. 102, 10534–10539 (2005).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
10.Polis, G. A., Anderson, W. B. & Holt, R. D. Toward an integration of landscape and food web ecology: the dynamics of spatially subsidized food webs. Annu. Rev. Ecol. Syst. 28, 289–316 (1997).Article 

Google Scholar 
11.Lundberg, J. & Moberg, F. Mobile link organisms and ecosystem functioning: implications for ecosystem resilience and management. Ecosystems 6, 87–98 (2003).Article 

Google Scholar 
12.Glazier, D. S. Trade-offs between reproductive and somatic (storage) investments in animals: a comparative test of the Van Noordwijk and De Jong model. Evol. Ecol. 13, 539–555 (1999).Article 

Google Scholar 
13.Zera, A. J. & Harshman, L. G. The physiology of life history trade-offs in animals. Annu. Rev. Ecol. Syst. 32, 95–126 (2001).Article 

Google Scholar 
14.Stearns, S. C. The Evolution of Life Histories (OUP Oxford, 1992).
Google Scholar 
15.Otero, X. L., Peña-Lastra, S. D. L., Pérez-Alberti, A., Ferreira, T. O. & Huerta-Diaz, M. A. Seabird colonies as important global drivers in the nitrogen and phosphorus cycles. Nat. Commun. 9, 246 (2018).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
16.Jones, H. P. et al. Severity of the effects of invasive rats on seabirds: a global review. Conserv. Biol. 22, 16–26 (2008).PubMed 
Article 
PubMed Central 

Google Scholar 
17.Graham, N. A. J. et al. Seabirds enhance coral reef productivity and functioning in the absence of invasive rats. Nature 559, 250–253 (2018).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
18.Benkwitt, C. E., Wilson, S. K. & Graham, N. A. J. Biodiversity increases ecosystem functions despite multiple stressors on coral reefs. Nat. Ecol. Evol. 4, 919–926 (2020).PubMed 
Article 
PubMed Central 

Google Scholar 
19.Clements, K. D., German, D. P., Piché, J., Tribollet, A. & Choat, J. H. Integrating ecological roles and trophic diversification on coral reefs: multiple lines of evidence identify parrotfishes as microphages. Biol. J. Linn. Soc. 120, 729–751 (2017).
Google Scholar 
20.Nicholson, G. M. & Clements, K. D. Resolving resource partitioning in parrotfishes (Scarini) using microhistology of feeding substrata. Coral Reefs 39, 1313–1327 (2020).Article 

Google Scholar 
21.Bonaldo, R., Hoey, A. & Bellwood, D. The ecosystem roles of parrotfishes on tropical reefs. Oceanogr. Mar. Biol. 52, 81–132 (2014).Article 

Google Scholar 
22.Hoey, A. S. Feeding in parrotfishes: the influence of species, body size, and temperature. In Biology of Parrotfishes (ed Hoey, A. S. & Bonaldo, R. M) 119–133 (CRC Press, 2018).Chapter 

Google Scholar 
23.Lange, I. D. et al. Site-level variation in parrotfish grazing and bioerosion as a function of species-specific feeding metrics. Diversity 12, 379 (2020).Article 

Google Scholar 
24.Lokrantz, J., Nyström, M., Thyresson, M. & Johansson, C. The non-linear relationship between body size and function in parrotfishes. Coral Reefs 27, 967–974 (2008).ADS 
Article 

Google Scholar 
25.Mumby, P. J. et al. Fishing, trophic cascades, and the process of grazing on coral reefs. Science 311, 98–101 (2006).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
26.Green, A. L. et al. Larval dispersal and movement patterns of coral reef fishes, and implications for marine reserve network design. Biol. Rev. 90, 1215–1247 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
27.Graham, N. A. J. & McClanahan, T. R. The last call for marine wilderness? Bioscience 63, 397–402 (2013).Article 

Google Scholar 
28.Hays, G. C. et al. A review of a decade of lessons from one of the world’s largest MPAs: conservation gains and key challenges. Mar. Biol. 167, 159 (2020).Article 

Google Scholar 
29.Sheppard, C. R. C. et al. Reefs and islands of the Chagos Archipelago, Indian Ocean: why it is the world’s largest no-take marine protected area. Aquat. Conserv. Mar. Freshwat. Ecosyst. 22, 232–261 (2012).CAS 
Article 

Google Scholar 
30.Carr, P. et al. Status and phenology of breeding seabirds and a review of Important bird and biodiversity areas in the British Indian Ocean Territory. Bird Conserv. Int. 31,  14–34 (2021).Article 

Google Scholar 
31.Hilton, G. M. & Cuthbert, R. J. The catastrophic impact of invasive mammalian predators on birds of the UK Overseas Territories: a review and synthesis. Ibis 152, 443–458 (2010).Article 

Google Scholar 
32.Stoddart, D. R. Rainfall on Indian Ocean coral islands. Atoll Res. Bull. 147, 1–21 (1971).Article 

Google Scholar 
33.Perry, C. T., Kench, P. S., O’Leary, M. J., Morgan, K. M. & Januchowski-Hartley, F. Linking reef ecology to island building: parrotfish identified as major producers of island-building sediment in the Maldives. Geology 43, 503–506 (2015).ADS 
Article 

Google Scholar 
34.Yeager, L. A., Marchand, P., Gill, D. A., Baum, J. K. & McPherson, J. M. Marine Socio-Environmental Covariates: queryable global layers of environmental and anthropogenic variables for marine ecosystem studies. Ecology 98, 1976–1976 (2017).PubMed 
Article 

Google Scholar 
35.Taylor, B. M., Trip, E. D. L. & Choat, J. H. Dynamic demography: investigations of life-history variation in the parrotfishes. In Biology of Parrotfishes (eds Hoey, A. S. & Bonaldo, R. M.) 69–98 (CRC Press, 2018).Chapter 

Google Scholar 
36.Colin, P. L. & Bell, L. J. Aspects of the spawning of labrid and scarid fishes (Pisces: Labroidei) at Enewetak Atoll, Marshall Islands with notes on other families. Environ. Biol. Fishes 31, 229–260 (1991).Article 

Google Scholar 
37.Bay, L. K., Choat, J. H., van Herwerden, L. & Robertson, D. R. High genetic diversities and complex genetic structure in an Indo-Pacific tropical reef fish (Chlorurus sordidus): evidence of an unstable evolutionary past?. Mar. Biol. 144, 757–767 (2004).CAS 
Article 

Google Scholar 
38.Meyer, C. G., Papastamatiou, Y. P. & Clark, T. B. Differential movement patterns and site fidelity among trophic groups of reef fishes in a Hawaiian marine protected area. Mar. Biol 157, 1499–1511 (2010).Article 

Google Scholar 
39.Brown-Peterson, N. J., Wyanski, D. M., Saborido-Rey, F., Macewicz, B. J. & Lowerre-Barbieri, S. K. A standardized terminology for describing reproductive development in fishes. Mar. Coast. Fish. 3, 52–70 (2011).Article 

Google Scholar 
40.Benkwitt, C. E., Wilson, S. K. & Graham, N. A. J. Seabird nutrient subsidies alter patterns of algal abundance and fish biomass on coral reefs following a bleaching event. Glob. Change Biol. 25, 2619–2632 (2019).ADS 
Article 

Google Scholar 
41.Froese, R. & Pauly, D. FishBase (World Wide Web Electronic Publication, 2018).
Google Scholar 
42.Taylor, B. M. et al. Synchronous biological feedbacks in parrotfishes associated with pantropical coral bleaching. Glob. Change Biol. 26, 1285–1294 (2020).ADS 
Article 

Google Scholar 
43.Polunin, N. V. C. & Roberts, C. M. Greater biomass and value of target coral-reef fishes in two small Caribbean marine reserves. Mar. Ecol. Prog. Ser. 100, 167–176 (1993).ADS 
Article 

Google Scholar 
44.Wilson, S. K., Graham, N. A. J. & Polunin, N. V. C. Appraisal of visual assessments of habitat complexity and benthic composition on coral reefs. Mar. Biol. 151, 1069–1076 (2007).Article 

Google Scholar 
45.McCauley, D. J. et al. From wing to wing: the persistence of long ecological interaction chains in less-disturbed ecosystems. Sci. Rep. 2, 409 (2012).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
46.Savage, C. Seabird nutrients are assimilated by corals and enhance coral growth rates. Sci. Rep. 9, 4284 (2019).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
47.Kurita, Y., Meier, S. & Kjesbu, O. S. Oocyte growth and fecundity regulation by atresia of Atlantic herring (Clupea harengus) in relation to body condition throughout the maturation cycle. J. Sea Res. 49, 203–219 (2003).ADS 
Article 

Google Scholar 
48.Hoey, J., McCormick, M. I. & Hoey, A. S. Influence of depth on sex-specific energy allocation patterns in a tropical reef fish. Coral Reefs 26, 603–613 (2007).ADS 
Article 

Google Scholar 
49.Tootell, J. S. & Steele, M. A. Distribution, behavior, and condition of herbivorous fishes on coral reefs track algal resources. Oecologia 181, 13–24 (2015).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
50.Francis, R. I. C. C. Are growth parameters estimated from tagging and age-length data comparable?. Can. J. Fish. Aquat. Sci. 45, 936–942 (1988).Article 

Google Scholar 
51.Choat, J. H. & Robertson, D. R. Age-based studies. In Coral Reef Fishes: Dynamics and Diversity in a Complex Ecosystem (ed Sale, P. F.) 57–80 (Academic Press, 2002).Chapter 

Google Scholar 
52.McElreath, R. Statistical Rethinking: A Bayesian Course with Examples in R and Stan (CRC Press, 2020).Book 

Google Scholar 
53.Bürkner, P. C. brms: An R package for Bayesian multilevel models using Stan. J. Stat. Softw. 80, 1–28 (2017).Article 

Google Scholar 
54.Bürkner, P. C. Advanced Bayesian multilevel modeling with the R package brms. R J. 10, 395–411 (2018).Article 

Google Scholar 
55.Hoenig, J. M. Empirical use of longevity data to estimate mortality rates. Fish. Bull. 82, 898–903 (1983).
Google Scholar 
56.Taylor, B. M. et al. Bottom-up processes mediated by social systems drive demographic traits of coral-reef fishes. Ecology 99, 642–651 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
57.Taylor, B. M. Drivers of protogynous sex change differ across spatial scales. Proc. Roy. Soc. B: Biol. Sci. 281, 20132423 (2014).Article 

Google Scholar 
58.Ruttenberg, B. I. et al. Predator-induced demographic shifts in coral reef fish assemblages. PLoS ONE 6, e21062 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
59.Gust, N., Choat, J. & Ackerman, J. Demographic plasticity in tropical reef fishes. Mar. Biol. 140, 1039–1051 (2002).Article 

Google Scholar 
60.Friedlander, A. & DeMartini, E. Contrasts in density, size, and biomass of reef fishes between the northwestern and the main Hawaiian islands: the effects of fishing down apex predators. Mar. Ecol. Prog. Ser. 230, 253–264 (2002).ADS 
Article 

Google Scholar 
61.Richardson, K. M., Iverson, J. B. & Kurle, C. M. Marine subsidies likely cause gigantism of iguanas in the Bahamas. Oecologia 189, 1005–1015 (2019).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
62.Croll, D. A., Maron, J. L., Estes, J. A., Danner, E. M. & Byrd, G. V. Introduced predators transform subarctic islands from grassland to tundra. Science 307, 1959–1961 (2005).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
63.Briggs, A. A. et al. Effects of spatial subsidies and habitat structure on the foraging ecology and size of geckos. PLoS ONE 7, e41364 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
64.Sterner, R. W. & Elser, J. J. Ecological Stoichiometry: the Biology of Elements from Molecules to the Biosphere (Princeton University Press, 2002).
Google Scholar 
65.Kolb, G., Ekholm, J. & Hambäck, P. Effects of seabird nesting colonies on algae and aquatic invertebrates in coastal waters. Mar. Ecol. Prog. Ser. 417, 287–300 (2010).ADS 
Article 

Google Scholar 
66.Lorrain, A. et al. Seabirds supply nitrogen to reef-building corals on remote Pacific islets. Sci. Rep. 7, 3721 (2017).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
67.Plass-Johnson, J. G., McQuaid, C. D. & Hill, J. M. Stable isotope analysis indicates a lack of inter- and intra-specific dietary redundancy among ecologically important coral reef fishes. Coral Reefs 32, 429–440 (2013).ADS 
Article 

Google Scholar 
68.Mizutani, H. & Wada, E. Nitrogen and carbon isotope ratios in seabird rookeries and their ecological implications. Ecology 69, 340–349 (1988).Article 

Google Scholar 
69.Erler, D. V. et al. Has nitrogen supply to coral reefs in the south Pacific Ocean changed over the past 50 thousand years?. Paleoceanogr. Paleoclimatol. 34, 567–579 (2019).ADS 
Article 

Google Scholar 
70.Benstead, J. P. et al. Coupling of dietary phosphorus and growth across diverse fish taxa: a meta-analysis of experimental aquaculture studies. Ecology 95, 2768–2777 (2014).Article 

Google Scholar 
71.McBride, R. S. et al. Energy acquisition and allocation to egg production in relation to fish reproductive strategies. Fish Fish. 16, 23–57 (2015).Article 

Google Scholar 
72.Sogard, S. M. Size-selective mortality in the juvenile stage of teleost fishes: a review. Bull. Mar. Sci. 60, 1129–1157 (1997).
Google Scholar 
73.Walsh, S. M., Hamilton, S. L., Ruttenberg, B. I., Donovan, M. K. & Sandin, S. A. Fishing top predators indirectly affects condition and reproduction in a reef-fish community. J. Fish Biol. 80, 519–537 (2012).CAS 
PubMed 
Article 

Google Scholar 
74.DeMartini, E., Friedlander, A. & Holzwarth, S. Size at sex change in protogynous labroids, prey body size distributions, and apex predator densities at NW Hawaiian atolls. Mar. Ecol. Prog. Ser. 297, 259–271 (2005).ADS 
Article 

Google Scholar 
75.Taylor, B. M. et al. Synchronous biological feedbacks in parrotfishes associated with pantropical coral bleaching. Glob. Change Biol. 26, 1285–1294 (2020).ADS 
Article 

Google Scholar 
76.Morais, R. A. & Bellwood, D. R. Principles for estimating fish productivity on coral reefs. Coral Reefs 39, 1221–1231 (2020).Article 

Google Scholar 
77.Hixon, M. A., Johnson, D. W. & Sogard, S. M. BOFFFFs: on the importance of conserving old-growth age structure in fishery populations. ICES J. Mar. Sci. 71,  2171–2185 (2014).Article 

Google Scholar 
78.Barneche, D. R., Robertson, D. R., White, C. R. & Marshall, D. J. Fish reproductive-energy output increases disproportionately with body size. Science 360, 642–645 (2018).ADS 
CAS 
PubMed 
Article 

Google Scholar 
79.Buckner, E. V., Hernández, D. L. & Samhouri, J. F. Conserving connectivity: Human influence on subsidy transfer and relevant restoration efforts. Ambio 47, 493–503 (2018).PubMed 

Google Scholar 
80.Jones, H. P. et al. Invasive mammal eradication on islands results in substantial conservation gains. PNAS 113, 4033–4038 (2016).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
81.Benkwitt, C. E., Gunn, R. L., Le Corre, M., Carr, P., Graham, N. A. J. Rat eradication restores nutrient subsidies from seabirds across terrestrial and marine ecosystems. Curr. Biol. https://doi.org/10.1016/j.cub.2021.03.104 (2021).Article 
PubMed 
PubMed Central 

Google Scholar  More

1.Ibisch, P. L. et al. A global map of roadless areas and their conservation status. Science 354, 1423–1427 (2016).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
2.Torres, A., Jaeger, J. A. G. & Alonso, J. C. Assessing large-scale wildlife responses to human infrastructure development. PNAS 113, 8472–8477 (2016).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
3.Bennett, V. J. Effects of road density and pattern on the conservation of species and biodiversity. Curr. Landsc. Ecol. Rep. 2, 1–11 (2017).Article 

Google Scholar 
4.Coffin, A. W. From roadkill to road ecology: a review of the ecological effects of roads. J. Transp. Geogr. 15, 396–406 (2007).Article 

Google Scholar 
5.Fahrig, L. & Rytwinski, T. Effects of roads on animal abundance: an empirical review and synthesis. Ecol. Soc. 14, 21 (2009).Article 

Google Scholar 
6.Forman, R. T. & Alexander, L. E. Roads and their major ecological effects. Annu. Rev. Ecol. Syst. 29, 207–231 (1998).Article 

Google Scholar 
7.Trombulak, S. C. & Frissell, C. A. Review of ecological effects of roads on terrestrial and aquatic communities. Conserv. Biol. 14, 18–30 (2000).Article 

Google Scholar 
8.Ascensão, F. et al. Disentangle the causes of the road barrier effect in small mammals through genetic patterns. PLoS ONE 11, e0151500 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
9.Dyer, S. J., O’Neill, J. P., Wasel, S. M. & Boutin, S. Quantifying barrier effects of roads and seismic lines on movements of female woodland caribou in northeastern Alberta. Can. J. Zool. 80, 839–845 (2002).Article 

Google Scholar 
10.Glista, D. J., DeVault, T. L. & DeWoody, J. A. A review of mitigation measures for reducing wildlife mortality on roadways. Landsc. Urban Plan. 91, 1–7 (2009).Article 

Google Scholar 
11.Taylor, B. D. & Goldingay, R. L. Roads and wildlife: impacts, mitigation and implications for wildlife management in Australia. Wildl. Res. 37, 320–331 (2010).Article 

Google Scholar 
12.Tigas, L. A., Van Vuren, D. H. & Sauvajot, R. M. Behavioral responses of bobcats and coyotes to habitat fragmentation and corridors in an urban environment. Biol. Conserv. 108, 299–306 (2002).Article 

Google Scholar 
13.Herrmann, H.-W., Pozarowski, K. M., Ochoa, A. & Schuett, G. W. An interstate highway affects gene flow in a top reptilian predator (Crotalus atrox) of the Sonoran Desert. Conserv. Genet. 18, 911–924 (2017).CAS 
Article 

Google Scholar 
14.Holderegger, R. & Di Giulio, M. The genetic effects of roads: a review of empirical evidence. Basic Appl. Ecol. 11, 522–531 (2010).Article 

Google Scholar 
15.Keller, L. F. & Waller, D. M. Inbreeding effects in wild populations. Trends Ecol. Evol. 17, 230–241 (2002).Article 

Google Scholar 
16.Benítez-López, A., Alkemade, R. & Verweij, P. A. The impacts of roads and other infrastructure on mammal and bird populations: a meta-analysis. Biol. Conserv. 143, 1307–1316 (2010).Article 

Google Scholar 
17.Kerley, L. L. et al. Effects of roads and human disturbance on Amur tigers. Conserv. Biol. 16, 97–108 (2002).Article 

Google Scholar 
18.Roedenbeck, I. A. & Voser, P. Effects of roads on spatial distribution, abundance and mortality of brown hare (Lepus europaeus) in Switzerland. Eur. J. Wildl. Res. 54, 425–437 (2008).Article 

Google Scholar 
19.Clark, R. W., Brown, W. S., Stechert, R. & Zamudio, K. R. Roads, interrupted dispersal, and genetic diversity in timber Rattlesnakes. Conserv. Biol. 24, 1059–1069 (2010).PubMed 
Article 
PubMed Central 

Google Scholar 
20.Niko, B. & Waits, L. P. Molecular road ecology: exploring the potential of genetics for investigating transportation impacts on wildlife. Mol. Ecol. 18, 4151–4164 (2009).Article 

Google Scholar 
21.Frankham, R., Briscoe, D. A. & Ballou, J. D. Introduction to Conservation Genetics (Cambridge University Press, 2002).Book 

Google Scholar 
22.Amos, W. & Balmford, A. When does conservation genetics matter?. Heredity 87, 257–265 (2001).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
23.Frankham, R. Genetics and extinction. Biol. Cons. 126, 131–140 (2005).Article 

Google Scholar 
24.Teixeira, J. C. & Huber, C. D. The inflated significance of neutral genetic diversity in conservation genetics. Proc. Natl. Acad. Sci. U. S. A. 118, e2015096118 (2021).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
25.Keller, I. & Largiadèr, C. R. Recent Habitat fragmentation caused by major roads leads to reduction of gene flow and loss of genetic variability in ground beetles. Proc. Biol. Sci. 270, 417–423 (2003).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
26.Noël, S., Ouellet, M., Galois, P. & Lapointe, F.-J. Impact of urban fragmentation on the genetic structure of the eastern red-backed salamander. Conserv. Genet. 8, 599–606 (2007).Article 
CAS 

Google Scholar 
27.Marsh, D. M. et al. Effects of roads on patterns of genetic differentiation in red-backed salamanders, Plethodon cinereus. Conserv. Genet. 9, 603–613 (2008).Article 

Google Scholar 
28.Brehme, C. S., Tracey, J. A., Mcclenaghan, L. R. & Fisher, R. N. Permeability of roads to movement of scrubland lizards and small mammals. Conserv. Biol. 27, 710–720 (2013).PubMed 
Article 
PubMed Central 

Google Scholar 
29.Ford, A. T. & Clevenger, A. P. Factors affecting the permeability of road mitigation measures to the movement of small mammals. Can. J. Zool. 97, 379–384 (2018).Article 

Google Scholar 
30.Claireau, F. et al. Major roads have important negative effects on insectivorous bat activity. Biol. Conserv. 235, 53–62 (2019).Article 

Google Scholar 
31.Jacobson, S. L., Bliss-Ketchum, L. L., Rivera, C. E. & Smith, W. P. A behavior-based framework for assessing barrier effects to wildlife from vehicle traffic volume. Ecosphere 7, e01345 (2016).Article 

Google Scholar 
32.Assis, J. C., Giacomini, H. C. & Ribeiro, M. C. Road permeability index: evaluating the heterogeneous permeability of roads for wildlife crossing. Ecol. Ind. 99, 365–374 (2019).Article 

Google Scholar 
33.Lesbarrères, D., Primmer, C. R., Lodé, T. & Merilä, J. The effects of 20 years of highway presence on the genetic structure of Rana dalmatina populations. Écoscience 13, 531–538 (2006).Article 

Google Scholar 
34.Landguth, E. L. et al. Quantifying the lag time to detect barriers in landscape genetics: quantifying the lag time to detect barriers in landscape genetics. Mol. Ecol. 19, 4179–4191 (2010).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
35.Epps, C. W. & Keyghobadi, N. Landscape genetics in a changing world: disentangling historical and contemporary influences and inferring change. Mol. Ecol. 24, 6021–6040 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
36.Blair, C. et al. A simulation-based evaluation of methods for inferring linear barriers to gene flow. Mol. Ecol. Resour. 12, 822–833 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
37.Mona, S., Ray, N., Arenas, M. & Excoffier, L. Genetic consequences of habitat fragmentation during a range expansion. Heredity 112, 291–299 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
38.Weyer, J., Jørgensen, D., Schmitt, T., Maxwell, T. J. & Anderson, C. D. Lack of detectable genetic differentiation between den populations of the Prairie Rattlesnake (Crotalus viridis) in a fragmented landscape. Can. J. Zool. 92, 837–846 (2014).Article 

Google Scholar 
39.Frankham, R. Relationship of genetic variation to population size in wildlife. Conserv. Biol. 10, 1500–1508 (1996).Article 

Google Scholar 
40.Ehrich, D. & Jorde, P. E. High genetic variability despite high-amplitude population cycles in lemmings. J. Mammal. 86, 380–385 (2005).Article 

Google Scholar 
41.Gauffre, B. et al. Short-term variations in gene flow related to cyclic density fluctuations in the common vole. Mol. Ecol. 23, 3214–3225 (2014).PubMed 
Article 
PubMed Central 

Google Scholar 
42.Schweizer, M., Excoffier, L. & Heckel, G. Fine-scale genetic structure and dispersal in the common vole (Microtus arvalis). Mol. Ecol. 16, 2463–2473 (2007).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
43.Keane, B., Ross, S., Crist, T. O. & Solomon, N. G. Fine-scale spatial patterns of genetic relatedness among resident adult prairie voles. J. Mammal. 96, 1194–1202 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
44.Boyce, C. C. K. & Boyce, J. L. Population biology of Microtus arvalis. II. Natal and breeding dispersal of females. J. Anim. Ecol. 57, 723–736 (1988).Article 

Google Scholar 
45.Luque-Larena, J. J. et al. Recent large-scale range expansion and outbreaks of the common vole (Microtus arvalis) in NW Spain. Basic Appl. Ecol. 14, 432–441 (2013).Article 

Google Scholar 
46.Salamolard, M., Butet, A., Leroux, A. & Bretagnolle, V. Responses of an avian predator to variations in prey density at a temperate latitude. Ecology 81, 2428–2441 (2000).Article 

Google Scholar 
47.Krebs, C. J. & Myers, J. H. Population cycles in small mammals. In Advances in Ecological Research Vol. 8 (ed. MacFadyen, A.) 267–399 (Academic Press, 1974).
Google Scholar 
48.Gerlach, G. & Musolf, K. Fragmentation of landscape as a cause for genetic subdivision in bank voles. Conserv. Biol. 14, 1066–1074 (2000).Article 

Google Scholar 
49.Rico, A., Kindlmann, P. & Sedláček, F. Can the barrier effect of highways cause genetic subdivision in small mammals?. Acta Theriol. 54, 297–310 (2009).Article 

Google Scholar 
50.Nei, M., Maruyama, T. & Chakraborty, R. The bottleneck effect and genetic variability in populations. Evolution 29, 1–10 (1975).PubMed 
PubMed Central 
Article 

Google Scholar 
51.Motro, U. & Thomson, G. On heterozygosity and the effective size of populations subject to size changes. Evolution 36, 1059–1066 (1982).PubMed 
Article 
PubMed Central 

Google Scholar 
52.Kirkpatrick, M. & Jarne, P. The effects of a bottleneck on inbreeding depression and the genetic load. Am. Nat. 155, 154–167 (2000).PubMed 
Article 
PubMed Central 

Google Scholar 
53.Xenikoudakis, G. et al. Consequences of a demographic bottleneck on genetic structure and variation in the Scandinavian brown bear. Mol. Ecol. 24, 3441–3454 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
54.Parra, G. J. et al. Low genetic diversity, limited gene flow and widespread genetic bottleneck effects in a threatened dolphin species, the Australian humpback dolphin. Biol. Conserv. 220, 192–200 (2018).Article 

Google Scholar 
55.Berthier, K., Charbonnel, N., Galan, M., Chaval, Y. & Cosson, J.-F. Migration and recovery of the genetic diversity during the increasing density phase in cyclic vole populations. Mol. Ecol. 15, 2665–2676 (2006).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
56.Norén, K. & Angerbjörn, A. Genetic perspectives on northern population cycles: bridging the gap between theory and empirical studies: GENETIC structure in cyclic populations. Biol. Rev. 89, 493–510 (2014).PubMed 
Article 
PubMed Central 

Google Scholar 
57.Jangjoo, M., Matter, S. F., Roland, J. & Keyghobadi, N. Demographic fluctuations lead to rapid and cyclic shifts in genetic structure among populations of an alpine butterfly, Parnassius smintheus. J. Evol. Biol. 33, 668–681 (2020).PubMed 
Article 
PubMed Central 

Google Scholar 
58.ESRI. ArcGIS desktop: release 10.3 Environmental Systems Research Institute, Redlands, CA (2015).59.Saunders, S. C., Mislivets, M. R., Chen, J. & Cleland, D. T. Effects of roads on landscape structure within nested ecological units of the Northern Great Lakes Region, USA. Biol. Conserv. 103, 209–225 (2002).Article 

Google Scholar 
60.Fahrig, L. Effects of habitat fragmentation on biodiversity. Annu. Rev. Ecol. Evol. Syst. 34, 487–515 (2003).Article 

Google Scholar 
61.Schlaepfer, D. R., Braschler, B., Rusterholz, H.-P. & Baur, B. Genetic effects of anthropogenic habitat fragmentation on remnant animal and plant populations: a meta-analysis. Ecosphere 9, e02488 (2018).Article 

Google Scholar 
62.Charlesworth, B. Effective population size and patterns of molecular evolution and variation. Nat. Rev. Genet. 10, 195–205 (2009).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
63.Compton, B. W., McGarigal, K., Cushman, S. A. & Gamble, L. R. A resistant-Kernel model of connectivity for amphibians that breed in vernal pools. Conserv. Biol. 21, 788–799 (2007).PubMed 
Article 
PubMed Central 

Google Scholar 
64.Balkenhol, N., Cushman, S., Storfer, A. & Waits, L. Landscape Genetics: Concepts, Methods, Applications (Wiley, 2015).Book 

Google Scholar 
65.Aguilar, R., Quesada, M., Ashworth, L., Herrerias-Diego, Y. & Lobo, J. Genetic consequences of habitat fragmentation in plant populations: susceptible signals in plant traits and methodological approaches. Mol. Ecol. 17, 5177–5188 (2008).PubMed 
Article 
PubMed Central 

Google Scholar 
66.Lloyd, M. W., Campbell, L. & Neel, M. C. The Power to Detect Recent Fragmentation Events Using Genetic Differentiation Methods. PLoS ONE 8, e63981 (2013).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
67.Smouse, P. E., Long, J. C. & Sokal, R. R. Multiple regression and correlation extensions of the mantel test of matrix correspondence. Syst. Zool. 35, 627–632 (1986).Article 

Google Scholar 
68.Nowak, R. M. Walker’s Mammals of the World (Johns Hopkins University Press, 1999).
Google Scholar 
69.García, J. T. et al. A complex scenario of glacial survival in Mediterranean and continental refugia of a temperate continental vole species (Microtus arvalis) in Europe. J. Zool. Syst. Evol. Res. 58, 459–474 (2020).Article 

Google Scholar 
70.Martínková, N. et al. Divergent evolutionary processes associated with colonization of offshore islands. Mol. Ecol. 22, 5205–5220 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
71.Keyghobadi, N. K. The genetic implications of habitat fragmentation for animals. Can. J. Zool. 85(10), 1049–1064 (2007).Article 

Google Scholar 
72.Vandergast, A. G., Bohonak, A. J., Weissman, D. B. & Fisher, R. N. Understanding the genetic effects of recent habitat fragmentation in the context of evolutionary history: phylogeography and landscape genetics of a southern California endemic Jerusalem cricket (Orthoptera: Stenopelmatidae: Stenopelmatus). Mol. Ecol. 16, 977–992 (2007).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
73.Zellmer, A. J. & Knowles, L. L. Disentangling the effects of historic vs. contemporary landscape structure on population genetic divergence. Mol. Ecol. 18, 3593–3602 (2009).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
74.Delaney, K. S., Riley, S. P. D. & Fisher, R. N. A rapid, strong, and convergent genetic response to urban habitat fragmentation in four divergent and widespread vertebrates. PLoS ONE 5, e12767 (2010).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
75.Tkadlec, E. & Stenseth, N. C. A new geographical gradient in vole population dynamics. Proc. R. Soc. Lond. Ser. B Biol. Sci. 268, 1547–1552 (2001).CAS 
Article 

Google Scholar 
76.Lambin, X., Bretagnolle, V. & Yoccoz, N. G. Vole population cycles in northern and southern Europe: Is there a need for different explanations for single pattern?. J. Anim. Ecol. 75, 340–349 (2006).PubMed 
Article 
PubMed Central 

Google Scholar 
77.Ehrich, D. & Stenseth, N. C. Genetic structure of Siberian lemmings (Lemmus sibiricus) in a continuous habitat: large patches rather than isolation by distance. Heredity 86, 716–730 (2001).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
78.Gauffre, B., Estoup, A., Bretagnolle, V. & Cosson, J. F. Spatial genetic structure of a small rodent in a heterogeneous landscape. Mol. Ecol. 17, 4619–4629 (2008).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
79.Galliard, J.-F.L., Rémy, A., Ims, R. A. & Lambin, X. Patterns and processes of dispersal behaviour in arvicoline rodents. Mol. Ecol. 21, 505–523 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
80.Gauffre, B., Petit, E., Brodier, S., Bretagnolle, V. & Cosson, J. F. Sex-biased dispersal patterns depend on the spatial scale in a social rodent. Proc. R. Soc. B Biol. Sci. 276, 3487 (2009).CAS 
Article 

Google Scholar 
81.Lidicker, W.Z. Jr. The role of dispersal in the demography of small mammals, in Small Mammals: their production and population dynamics. Eds F.B. Golley, K. Petrusewicz and L. Ryszkowski, 103–28 (Cambridge University Press, London, 1975).82.Gaines, M. S. & McClenaghan, L. R. Dispersal in small mammals. Annu. Rev. Ecol. Syst. 11, 163–196 (1980).Article 

Google Scholar 
83.Swingland, I. R. & Greenwood, P. J. The Ecology of Animal Movement (Clarendon Press, 1983).
Google Scholar 
84.Burton, C., Krebs, C. J. & Taylor, E. B. Population genetic structure of the cyclic snowshoe hare (Lepus americanus) in southwestern Yukon, Canada. Mol. Ecol. 11, 1689–1701 (2002).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
85.Plante, Y., Boag, P. T. & White, B. N. Microgeographic variation in mitochondrial DNA of meadow voles (Microtus pennsylvanicus) in relation to population density. Evolution 43, 1522–1537 (1989).PubMed 
PubMed Central 

Google Scholar 
86.Encuesta sobre superficies y rendimientos del cultivo 2012. Catálogo de publicaciones de la Administración General del Estado (MAGRAMA, 2013).87.Oñate, J. J. et al. Programa piloto de acciones de conservación de la biodiversidad en sistemas ambientales con usos agrarios en el marco del desarrollo rural. Convenio de colaboración entre la Dirección General para la Biodiversidad (Ministerio de Medio Ambiente) y el Departamento Interuniversitario de Ecología (Universidad Autónoma de Madrid, 2003).88.Ji, S. et al. Impact of different road types on small mammals in Mt. Kalamaili Nature Reserve. Transp. Res. Part D Transp. Environ. 50, 223–233 (2017).Article 

Google Scholar 
89.Vignieri, S. N. Streams over mountains: influence of riparian connectivity on gene flow in the Pacific jumping mouse (Zapus trinotatus): connectivity patterns in pacific jumping mice. Mol. Ecol. 14, 1925–1937 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
90.Russo, I.-R.M., Sole, C. L., Barbato, M., von Bramann, U. & Bruford, M. W. Landscape determinants of fine-scale genetic structure of a small rodent in a heterogeneous landscape (Hluhluwe-iMfolozi Park, South Africa). Sci. Rep. 6, 29168 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
91.Mougeot, F., Lambin, X., Rodríguez-Pastor, R., Romairone, J. & Luque-Larena, J.-J. Numerical response of a mammalian specialist predator to multiple prey dynamics in Mediterranean farmlands. Ecology 100, e02776 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
92.Schwartz, M. K. & McKelvey, K. S. Why sampling scheme matters: the effect of sampling scheme on landscape genetic results. Conserv. Genet. 10, 441 (2009).Article 

Google Scholar 
93.Strauss, W. M. Preparation of genomic DNA from Mammalian tissue. Curr. Protoc. Mol. Biol. 42, 1–3 (1998).Article 

Google Scholar 
94.Ishibashi, Y. et al. Polymorphic microsatellite DNA markers in the field vole Microtus montebelli. Mol. Ecol. 8, 163–164 (1999).CAS 
PubMed 
PubMed Central 

Google Scholar 
95.Gauffre, B., Galan, M., Bretagnolle, V. & Cosson, J. Polymorphic microsatellite loci and PCR multiplexing in the common vole, Microtus arvalis. Mol. Ecol. Notes 7, 830–832 (2007).CAS 
Article 

Google Scholar 
96.Johnson, P. C. D. & Haydon, D. T. Software for quantifying and simulating microsatellite genotyping error. Bioinform. Biol. Insights 1, 71–75 (2009).PubMed 
PubMed Central 

Google Scholar 
97.Paradis, E. pegas: an R package for population genetics with an integrated–modular approach. Bioinformatics 26, 419–420 (2010).CAS 
Article 

Google Scholar 
98.Brookfield, J. F. A simple new method for estimating null allele frequency from heterozygote deficiency. Mol. Ecol. 5, 453–455 (1996).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
99.Adamack, A. T. & Gruber, B. PopGenReport: simplifying basic population genetic analyses in R. Methods Ecol. Evol. 5, 384–387 (2014).Article 

Google Scholar 
100.Jones, O. R. & Wang, J. COLONY: a program for parentage and sibship inference from multilocus genotype data. Mol. Ecol. Resour. 10, 551–555 (2010).PubMed 
Article 
PubMed Central 

Google Scholar 
101.Keenan, K., McGinnity, P., Cross, T. F., Crozier, W. W. & Prodöhl, P. A. diveRsity: An R package for the estimation and exploration of population genetics parameters and their associated errors. Methods Ecol. Evol. 4, 782–788 (2013).Article 

Google Scholar 
102.Coulon, A. genhet: an easy-to-use R function to estimate individual heterozygosity. Mol. Ecol. Resour. 10, 167–169 (2010).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
103.Coltman, D. W., Pilkington, J. G. & Pemberton, J. M. Fine-scale genetic structure in a free-living ungulate population. Mol. Ecol. 12, 733–742 (2003).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
104.Amos, W. et al. The influence of parental relatedness on reproductive success. Proc. R. Soc. Lond. Ser. B Biol. Sci. 268, 2021–2027 (2001).CAS 
Article 

Google Scholar 
105.Aparicio, J. M., Ortego, J. & Cordero, P. J. What should we weigh to estimate heterozygosity, alleles or loci? Estimating heterozygosity from neutral markers. Mol. Ecol. 15, 4659–4665 (2006).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
106.Kamvar, Z. N., Tabima, J. F. & Grünwald, N. J. Poppr: an R package for genetic analysis of populations with clonal, partially clonal, and/or sexual reproduction. PeerJ 2, e281 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
107.Peakall, R. & Smouse, P. E. GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research—an update. Bioinformatics 28, 2537–2539 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
108.Hedrick, P. W. A standardized genetic differentiation measure. Evolution 59, 1633–1638 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
109.Jost, L. GST and its relatives do not measure differentiation. Mol. Ecol. 17, 4015–4026 (2008).PubMed 
Article 
PubMed Central 

Google Scholar 
110.Meirmans, P. G. & Hedrick, P. W. Assessing population structure: FST and related measures. Mol. Ecol. Resour. 11, 5–18 (2011).PubMed 
Article 
PubMed Central 

Google Scholar 
111.Nei, M. Analysis of gene diversity in subdivided populations. PNAS 70, 3321–3323 (1973).ADS 
CAS 
PubMed 
MATH 
Article 
PubMed Central 

Google Scholar 
112.Nei, M. & Chesser, R. K. Estimation of fixation indices and gene diversities. Ann. Hum. Genet. 47, 253–259 (1983).CAS 
PubMed 
MATH 
Article 
PubMed Central 

Google Scholar 
113.Bowcock, A. M. et al. High resolution of human evolutionary trees with polymorphic microsatellites. Nature 368, 455–457 (1994).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
114.Takezaki, N. & Nei, M. Genetic distances and reconstruction of phylogenetic trees from microsatellite DNA. Genetics 144, 389–399 (1996).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
115.Smouse, P. E. & Peakall, R. Spatial autocorrelation analysis of individual multiallele and multilocus genetic structure. Heredity 82, 561–573 (1999).PubMed 
Article 
PubMed Central 

Google Scholar 
116.Queller, D. C. & Goodnight, K. F. Estimating relatedness using genetic markers. Evolution 43, 258–275 (1989).PubMed 
Article 
PubMed Central 

Google Scholar 
117.Ritland, K. Estimators for pairwise relatedness and individual inbreeding coefficients. Genet. Res. 67, 175–185 (1996).Article 

Google Scholar 
118.Lynch, M. & Ritland, K. Estimation of pairwise relatedness with molecular markers. Genetics 152, 1753–1766 (1999).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
119.Jombart, T. adegenet: a R package for the multivariate analysis of genetic markers. Bioinformatics 24, 1403–1405 (2008).CAS 
Article 

Google Scholar 
120.Legendre, P. Spatial autocorrelation: Trouble or new paradigm?. Ecology 74, 1659–1673 (1993).Article 

Google Scholar 
121.Zuur, A., Ieno, E. N. & Smith, G. M. Analyzing Ecological Data (Springer, Berlin, 2007).MATH 
Book 

Google Scholar 
122.Hedges, L. V. & Olkin, I. Statistical Methods for Meta-Analysis (Academic Press, 2014).MATH 

Google Scholar 
123.Kirby, K. N. & Gerlanc, D. BootES: an R package for bootstrap confidence intervals on effect sizes. Behav. Res. Methods 45, 905–927 (2013).PubMed 
Article 
PubMed Central 

Google Scholar 
124.Cohen, J. A power primer. Psychol. Bull. 112, 155–159 (1992).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
125.Cohen, J. Statistical Power Analysis for the Behavioral Sciences (Academic Press, 2013).MATH 
Book 

Google Scholar 
126.González-Esteban, J. & Villate I. Microtus arvalis Pallas, 1778. In: Atlas y Libro Rojo de los Mamíferos Terrestres de España, Palomo, L.J., Gisbert, J. & Blanco J.C. (Eds.), Dirección General para la Biodiversidad-SECEM-SECEMU, 426-428 (2007). More

1.Yamauchi, A., Ikegawa, Y., Ohgushi, T. & Namba, T. Density regulation of co-occurring herbivores via two indirect effects mediated by biomass and non-specific induced plant defenses. Thyroid Res. https://doi.org/10.1007/s12080-020-00479-2 (2020).Article 

Google Scholar 
2.Wootton, J. T. Indirect effects in complex ecosystems: recent progress and future challenges. J. Sea Res. 48, 157–172. https://doi.org/10.1016/S1385-1101(02)00149-1 (2002).ADS 
Article 

Google Scholar 
3.Wootton, J. T. The nature and consequences of indirect effects in ecological communities. Ann. Rev. Ecol. Syst. 25, 443–466. https://doi.org/10.1146/annurev.es.25.110194.002303 (1994).Article 

Google Scholar 
4.Comeault, A. A. & Matute, D. R. Temperature-dependent competitive outcomes between the fruit flies Drosophila santomea and Drosophila yakuba. Am. Nat. 197, 312–323. https://doi.org/10.1086/712781 (2021).Article 
PubMed 

Google Scholar 
5.Darwin, C. On the Origin of Species by Means of Natural Selection or the Preservation of Favoured Races in the Struggle for Life (Murray, 1859).
Google Scholar 
6.Kankaanpää, T. et al. Parasitoids indicate major climate-induced shifts in arctic communities. Global Chang. Biol. https://doi.org/10.1111/gcb.15297 (2020).Article 

Google Scholar 
7.Nagelkerken, I., Goldenberg, S. U., Ferreira, C. M., Ullah, H. & Connell, S. D. Trophic pyramids reorganize when food web architecture fails to adjust to ocean change. Science 369, 829–832. https://doi.org/10.1126/science.aax0621 (2020).ADS 
CAS 
Article 
PubMed 

Google Scholar 
8.Kaur, T. & Dutta, P. S. Persistence and stability of interacting species in response to climate warming: the role of trophic structure. Thyroid Res. https://doi.org/10.1007/s12080-020-00456-9 (2020).Article 

Google Scholar 
9.Han, P. et al. Global change-driven modulation of bottom–up forces and cascading effects on biocontrol services. Curr. Opin. Insect Sci. 35, 27–33. https://doi.org/10.1016/j.cois.2019.05.005 (2019).Article 
PubMed 

Google Scholar 
10.Waring, G. L. & Cobb, N. S. in Insect-Plant Interactions, Vol. 4 (ed. Bernays, E. A.) 167–226 (CRC Press, 1992).11.Zavala, J. A., Gog, L. & Giacometti, R. Anthropogenic increase in carbon dioxide modifies plant-insect interactions. Ann. Appl. Biol. https://doi.org/10.1111/aab.12319 (2016).Article 

Google Scholar 
12.Bezemer, T. M. & Jones, T. H. Plant-insect herbivore interactions in elevated atmospheric CO2: quantitative analyses and guild effects. Oikos 82, 212. https://doi.org/10.2307/3546961 (1998).Article 

Google Scholar 
13.Navarro, E. C., Lam, S. K. & Trebicki, P. Elevated carbon dioxide and nitrogen impact wheat and its aphid pest. Front. Plant Sci. https://doi.org/10.3389/fpls.2020.605337 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
14.Moreno-Delafuente, A., Viñuela, E., Fereres, A., Medina, P. & Trębicki, P. Simultaneous increase in CO2 and temperature alters wheat growth and aphid performance differently depending on virus infection. Insects 11, 459. https://doi.org/10.3390/insects11080459 (2020).Article 
PubMed Central 

Google Scholar 
15.Shiferaw, B. et al. Crops that feed the world 10. Past successes and future challenges to the role played by wheat in global food security. Food Sec. 5, 291–317. https://doi.org/10.1007/s12571-013-0263-y (2013).Article 

Google Scholar 
16.Minks, A. K. & Harrewijn, P. Aphids. Their Biology, Natural Enemies and Control Vol. A (Elsevier, 1987).
Google Scholar 
17.Trebicki, P., Dader, B., Vassiliadis, S. & Fereres, A. Insect-plant-pathogen interactions as shaped by future climate: effects on biology, distribution, and implications for agriculture. Insect Sci. 24, 975–989. https://doi.org/10.1111/1744-7917.12531 (2017).CAS 
Article 
PubMed 

Google Scholar 
18.Aradottir, G. I. & Crespo-Herrera, L. Host plant resistance in wheat to barley yellow dwarf viruses and their aphid vectors: a review. Curr. Opin. Insect Sci. https://doi.org/10.1016/j.cois.2021.01.002 (2021).Article 
PubMed 

Google Scholar 
19.Yin, W. et al. Microhabitat separation between the pest aphids Rhopalosiphum padi and Sitobion avenae: food resource or microclimate selection?. J. Pest. Sci. https://doi.org/10.1007/s10340-020-01298-4 (2020).Article 

Google Scholar 
20.Noble, D. in Perspectives on Organisms. Biological Time, Symmetries and Singularities Lecture Notes in Morphogenesis (eds. Longo, G. & Montévil, M.) VII–X (Springer, 2014).21.Sadras, V. O. Effective phenotyping applications require matching trait and platform and more attention to theory. Front. Plant Sci. https://doi.org/10.3389/fpls.2019.01339 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
22.West-Eberhard, M. J. Developmental Plasticity and Evolution (Oxford University Press, 2003).Book 

Google Scholar 
23.Sadras, V. O. et al. Aphid resistance: an overlooked ecological dimension of nonstructural carbohydrates in cereals. Front. Plant Sci. 11, 937. https://doi.org/10.3389/fpls.2020.00937 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
24.Bogaert, F. et al. How the use of nitrogen fertiliser may switch plant suitability for aphids: the case of Miscanthus, a promising biomass crop, and the aphid pest Rhopalosiphum maidis. Pest Manag. Sci. 73, 1648–1654. https://doi.org/10.1002/ps.4505 (2017).CAS 
Article 
PubMed 

Google Scholar 
25.Radin, J. W. Control of plant growth by nitrogen: differences between cereals and broadleaf species. Plant Cell Environ. 6, 65–68 (1983).
Google Scholar 
26.Fereres, A., Lister, R. M., Araya, J. E. & Foster, J. E. Development and reproduction of the English grain aphid (Homoptera, Aphididae) on wheat cultivars infected with barley yellow dwarf virus. Environ. Entomol. 18, 388–393. https://doi.org/10.1093/ee/18.3.388 (1989).Article 

Google Scholar 
27.Pompon, J., Quiring, D., Goyer, C., Giordanengo, P. & Pelletier, Y. A phloem-sap feeder mixes phloem and xylem sap to regulate osmotic potential. J. Insect Physiol. 57, 1317–1322. https://doi.org/10.1016/j.jinsphys.2011.06.007 (2011).CAS 
Article 
PubMed 

Google Scholar 
28.Tjallingii, W. F. Electronic recording of penetration behaviour by aphids. Entomol. Exp. Appl. 24, 721–730. https://doi.org/10.1111/j.1570-7458.1978.tb02836.x (1978).Article 

Google Scholar 
29.Mattson, W. J. Herbivory in relation to plant nitrogen content. Ann. Rev. Ecol. Syst. 11, 119–161. https://doi.org/10.1146/annurev.es.11.110180.001003 (1980).Article 

Google Scholar 
30.White, T. C. R. The Inadequate Environment—Nitrogen and the Abundance of Animals (Springer Verlag, 1993).
Google Scholar 
31.Aqueel, M. A. & Leather, S. R. Effect of nitrogen fertilizer on the growth and survival of Rhopalosiphum padi (L.) and Sitobion avenae (F.) (Homoptera: Aphididae) on different wheat cultivars. Crop Prot. 30, 216–221. https://doi.org/10.1016/j.cropro.2010.09.013 (2011).Article 

Google Scholar 
32.Sadras, V. O. & Lemaire, G. Quantifying crop nitrogen status for comparisons of agronomic practices and genotypes. Field Crops Res. 164, 54–64 (2014).Article 

Google Scholar 
33.Gastal, F., Lemaire, G., Durand, J. L. & Louarn, G. in Crop Physiology: Applications for Genetic Improvement and Agronomy (eds. Sadras, V. O. & Calderini, D. F.) 161–206 (Academic Press, 2015).34.Lemaire, G. & Millard, P. An ecophysiological approach to modelling resource fluxes in competing plants. J. Exp. Bot. 50, 15–28. https://doi.org/10.1093/jexbot/50.330.15 (1999).CAS 
Article 

Google Scholar 
35.Ainsworth, E. A. & Long, S. P. What have we learned from 15 years of free-air CO2 enrichment (FACE)? A meta-analytic review of the responses of photosynthesis, canopy properties and plant production to rising CO2. New Phytol. 165, 351–372. https://doi.org/10.1111/j.1469-8137.2004.01224.x (2004).Article 

Google Scholar 
36.Sun, Y. C., Chen, F. J. & Ge, F. Elevated CO2 changes interspecific competition among three species of wheat aphids: Sitobion avenae, Rhopalosiphum padi, and Schizaphis graminum. Environ. Entomol. 38, 26–34. https://doi.org/10.1603/022.038.0105 (2009).CAS 
Article 
PubMed 

Google Scholar 
37.Oehme, V., Högy, P., Zebitz, C. P. W. & Fangmeier, A. Effects of elevated atmospheric CO2 concentrations on phloem sap composition of spring crops and aphid performance. J. Plant Interact. 8, 74–84. https://doi.org/10.1080/17429145.2012.736200 (2013).CAS 
Article 

Google Scholar 
38.Oehme, V., Hogy, P., Franzaring, J., Zebitz, C. P. W. & Fangmeier, A. Response of spring crops and associated aphids to elevated atmospheric CO2 concentrations. J. Appl. Bot. Food Qual. 84, 151–157 (2011).CAS 

Google Scholar 
39.Dáder, B., Fereres, A., Moreno, A. & Trębicki, P. Elevated CO2 impacts bell pepper growth with consequences to Myzus persicae life history, feeding behaviour and virus transmission ability. Sci. Rep. 6, 19120. https://doi.org/10.1038/srep19120 (2016).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
40.Olin, S. et al. Modelling the response of yields and tissue C:N to changes in atmospheric CO2 and N management in the main wheat regions of western Europe. Biogeosciences 12, 2489–2515. https://doi.org/10.5194/bg-12-2489-2015 (2015).ADS 
Article 

Google Scholar 
41.Justes, E., Mary, B., Meynard, J. M., Machet, J. M. & Thelierhuche, L. Determination of a critical nitrogen dilution curve for winter wheat crops. Ann. Bot. 74, 397–407 (1994).CAS 
Article 

Google Scholar 
42.Evans, J. R. & Clarke, V. C. The nitrogen cost of photosynthesis. J. Exp. Bot. 70, 7–15. https://doi.org/10.1093/jxb/ery366 (2019).CAS 
Article 
PubMed 

Google Scholar 
43.Mittler, T. E., Dadd, R. H. & Daniels, S. C. Utilization of different sugars by aphid Myzus persicae. J. Insect Physiol. 16, 1873–2000. https://doi.org/10.1016/0022-1910(70)90234-9 (1970).CAS 
Article 

Google Scholar 
44.Douglas, A. E. et al. Sweet problems: insect traits defining the limits to dietary sugar utilisation by the pea aphid, Acyrthosiphon pisum. J. Exp. Biol. 209, 1395–1403. https://doi.org/10.1242/jeb.02148 (2006).CAS 
Article 
PubMed 

Google Scholar 
45.Bloom, A. J., Chapin, F. S. I. & Mooney, H. A. Resource limitation in plants—an economic analogy. Ann. Rev. Ecol. Syst. 16, 363–392 (1985).Article 

Google Scholar 
46.Chapin, F. S. I., Schulze, E. D. & Mooney, H. A. The ecology and economics of storage in plants. Annu. Rev. Ecol. Syst. 21, 423–447 (1990).Article 

Google Scholar 
47.Ovenden, B. et al. Selection for water-soluble carbohydrate accumulation and investigation of genetic x environment interactions in an elite wheat breeding population. Theor. Appl. Gen. 130, 2445–2461. https://doi.org/10.1007/s00122-017-2969-2 (2017).CAS 
Article 

Google Scholar 
48.del Pozo, A. et al. Physiological traits associated with wheat yield potential and performance under water-stress in a Mediterranean environment. Front. Plant Sci. https://doi.org/10.3389/fpls.2016.00987 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
49.Dixon, A. F. G. in Aphids Their Biology, Natural Enemies and Control, Vol. A (eds. Minks, A. K. & Harrewijn, P.) (Elsevier, 1987).50.Brabec, M., Honek, A., Pekar, S. & Martinkova, Z. Population dynamics of aphids on cereals: digging in the time-series data to reveal population regulation caused by temperature. PLoS ONE https://doi.org/10.1371/journal.pone.0106228 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
51.Cid, M., Ávila, A., García, A., Abad, J. & Fereres, A. New sources of resistance to lettuce aphids in Lactuca spp. Arthropod.-Plant Interact. 6, 655–669. https://doi.org/10.1007/s11829-012-9213-4 (2012).Article 

Google Scholar 
52.Collado-Gonzalez, J. et al. Effects of water deficit during maturation on amino acids and jujube fruit eating quality. Maced. J. Chem. Chem. Eng. 33, 103–117. https://doi.org/10.20450/mjcce.2014.375 (2014).Article 

Google Scholar 
53.Riga, P. et al. Diffuse light affects the contents of vitamin C, phenolic compounds and free amino acids in lettuce plants. Food Chem. 272, 227–234. https://doi.org/10.1016/j.foodchem.2018.08.051 (2019).CAS 
Article 
PubMed 

Google Scholar 
54.Nagumo, Y. et al. Rapid quantification of cyanamide by ultra-high-pressure liquid chromatography in fertilizer, soil or plant samples. J. Chromatogr. A 1216, 5614–5618. https://doi.org/10.1016/j.chroma.2009.05.067 (2009).CAS 
Article 
PubMed 

Google Scholar 
55.Cerrillo, I. et al. Effect of fermentation and subsequent pasteurization processes on amino acids composition of orange juice. Plant Foods Hum. Nutr. 70, 153–159. https://doi.org/10.1007/s11130-015-0472-y (2015).CAS 
Article 
PubMed 

Google Scholar 
56.Salazar, C., Armenta, J. M., Cortés, D. F. & Shulaev, V. Combination of an AccQ·Tag-ultra performance liquid chromatographic method with tandem mass spectrometry for the analysis of amino acids. Methods Mol. Biol. (Clifton N. J.) 828, 13–28. https://doi.org/10.1007/978-1-61779-445-2_2 (2012).CAS 
Article 

Google Scholar 
57.Nadezdha, P. T., Pascal, B. A., Annick, M. & Panteley, D. P. HPLC analysis of mono- and disaccharides in food products. In Scientific Works Volume LX, 765–791 (Food Science, Engineering and Technology, 2013).58.Greenland, S. Valid p-values behave exactly as they should: some misleading criticisms of p-values and their resolution with s-values. Am. Stat. 73, 106–114. https://doi.org/10.1080/00031305.2018.1529625 (2019).MathSciNet 
Article 

Google Scholar 
59.Sarria, E., Cid, M., Garzo, E. & Fereres, A. Excel workbook for automatic parameter calculation of EPG data. Comput. Electron. Agric. 67, 35–42. https://doi.org/10.1016/j.compag.2009.02.006 (2009).Article 

Google Scholar 
60.Venables, W. N. & Ripley, B. D. Modern Applied Statistics with S 4th edn. (Springer, 2002).Book 

Google Scholar 
61.Garzo, E., Rizzo, E., Fereres, A. & Gomez, S. K. High levels of arbuscular mycorrhizal fungus colonization on Medicago truncatula reduces plant suitability as a host for pea aphids (Acyrthosiphon pisum). Insect Sci. 27, 99–112. https://doi.org/10.1111/1744-7917.12631 (2020).CAS 
Article 
PubMed 

Google Scholar 
62.Potvin, C., Lechowicz, M. J. & Tardif, S. The statistical analysis of ecological response curves obtained from experiments involving repeated measures. Ecology 71, 1389–1400 (1990).Article 

Google Scholar  More

1.Bradshaw, A. D. Evolutionary significance of phenotypic plasticity in plants. Adv. Genet. 13, 115–155 (1965).Article 

Google Scholar 
2.Weiss, L. C. & Tollrian, R. Predator induced defenses in Crustacea. in The Natural History of Crustacea: Life Histories, Volume 5 (eds. Welborn, G. & Thiel, M.) 303–321 (Oxford University Press, 2018).
Google Scholar 
3.Tollrian, R. Predator-induced helmet formation in Daphnia cucullata (Sars). Arch. für Hydrobiol. 119, 191–196 (1990).
Google Scholar 
4.Krueger, D. A. & Dodson, S. I. Embryological induction and predation ecology in Daphnia pulex. Limnol. Oceanogr. https://doi.org/10.4319/lo.1981.26.2.0219 (1981).Article 

Google Scholar 
5.Grant, J. W. G. & Bayly, I. A. E. Predator induction of crests in morphs of the Daphnia carinata King complex. Limnol. Oceanogr. https://doi.org/10.4319/lo.1981.26.2.0201 (1981).Article 

Google Scholar 
6.Macháček, J. Indirect effect of planktivorous fish on the growth and reproduction of Daphnia galeata. Hydrobiologia https://doi.org/10.1007/BF00028397 (1991).Article 

Google Scholar 
7.Stibor, H. & Luning, J. Predator-induced phenotypic variation in the pattern of growth and reproduction in Daphnia hyalina (Crustacea: Cladocera). Funct. Ecol. https://doi.org/10.2307/2390117 (1994).Article 

Google Scholar 
8.Dodson, S. I., Tollrian, R. & Lampert, W. Daphnia swimming behaviour during vertical migration. J. Plankton Res. 19, 969–978 (1997).Article 

Google Scholar 
9.Tollrian, R., Duggen, S., Weiss, L. C., Laforsch, C. & Kopp, M. Density-dependent adjustment of inducible defenses. Sci. Rep. 5, 12736 (2015).ADS 
CAS 
Article 

Google Scholar 
10.Miyakawa, H. et al. Gene up-regulation in response to predator kairomones in the water flea Daphnia pulex. BMC Dev. Biol. https://doi.org/10.1186/1471-213X-10-45 (2010).Article 
PubMed 
PubMed Central 

Google Scholar 
11.Oda, S., Kato, Y., Watanabe, H., Tatarazako, N. & Iguchi, T. Morphological changes in Daphnia galeata induced by a crustacean terpenoid hormone and its analog. Environ. Toxicol. Chem. https://doi.org/10.1002/etc.378 (2011).Article 
PubMed 

Google Scholar 
12.Miyakawa, H., Sato, M., Colbourne, J. K. & Iguchi, T. Ionotropic glutamate receptors mediate inducible defense in the water flea Daphnia pulex. PLoS ONE 10, 1–12 (2015).Article 

Google Scholar 
13.Weiss, L. C., Kruppert, S., Laforsch, C. & Tollrian, R. Chaoborus and Gasterosteus anti-predator responses in Daphnia pulex are mediated by independent cholinergic and gabaergic neuronal signals. PLoS ONE 7, e36879 (2012).ADS 
CAS 
Article 

Google Scholar 
14.Weiss, L. C., Leese, F., Laforsch, C. & Tollrian, R. Dopamine is a key regulator in the signalling pathway underlying predatorinduced defences in Daphnia. Proc. R. Soc. B Biol. Sci. https://doi.org/10.1098/rspb.2015.1440 (2015).Article 

Google Scholar 
15.Weiss, L. C., Leimann, J. & Tollrian, R. Predator-induced defences in Daphnia longicephala: location of kairomone receptors and timeline of sensitive phases to trait formation. J. Exp. Biol. 218, 2918–2926 (2015).Article 

Google Scholar 
16.Bullock, T. & Horridge, G. A. Structure and function in the nervous systems of invertebrates. (San Francisco, 1965).17.Fritsch, M., Kaji, T., Olesen, J. & Richter, S. The development of the nervous system in Laevicaudata (Crustacea, Branchiopoda): insights into the evolution and homologies of branchiopod limbs and ‘frontal organs’. Zoomorphology 132, 163–181 (2013).Article 

Google Scholar 
18.Kolb, B. & Whishaw, I. Q. Brain plasticity and behavior. Annu. Rev. Psychol. https://doi.org/10.1146/annurev.psych.49.1.43 (1998).Article 
PubMed 

Google Scholar 
19.Turner, A. M. & Greenough, W. T. Differential rearing effects on rat visual cortex synapses. I. Synaptic and neuronal density and synapses per neuron. Brain Res. https://doi.org/10.1016/0006-8993(85)90525-6 (1985).Article 
PubMed 

Google Scholar 
20.Woodley, S. K., Mattes, B. M., Yates, E. K. & Relyea, R. A. Exposure to sublethal concentrations of a pesticide or predator cues induces changes in brain architecture in larval amphibians. Oecologia 179, 655–665 (2015).ADS 
Article 

Google Scholar 
21.Gronenberg, W., Heeren, S. & Hölldobler, B. Age-dependent and task-related morphological changes in the brain and the mushroom bodies of the ant Camponotus floridanus. J. Exp. Biol. 199, 2011–2019 (1996).CAS 
Article 

Google Scholar 
22.Barth, M. & Heisenberg, M. Vision affects mushroom bodies and central complex in Drosophila melanogaster. Learn. Mem. https://doi.org/10.1101/lm.4.2.219 (1997).Article 
PubMed 

Google Scholar 
23.Barth, M., Hirsch, H. V. B., Meinertzhagen, I. A. & Heisenberg, M. Experience-dependent developmental plasticity in the optic lobe of Drosophila melanogaster. J. Neurosci. https://doi.org/10.1523/jneurosci.17-04-01493.1997 (1997).Article 
PubMed 
PubMed Central 

Google Scholar 
24.van Dijk, L. J. A., Janz, N., Schäpers, A., Gamberale-Stille, G. & Carlsson, M. A. Experience-dependent mushroom body plasticity in butterflies: Consequences of search complexity and host range. Proc. R. Soc. B Biol. Sci. 284, 0–7 (2017).
Google Scholar 
25.Withers, G. S., Fahrbach, S. E. & Robinson, G. E. Selective neuroanatomical plasticity and division of labour in the honeybee. Nature https://doi.org/10.1038/364238a0 (1993).Article 
PubMed 

Google Scholar 
26.Heisenberg, M., Heusipp, M. & Wanke, C. Structural plasticity in the Drosophila brain. J. Neurosci. https://doi.org/10.1523/jneurosci.15-03-01951.1995 (1995).Article 
PubMed 
PubMed Central 

Google Scholar 
27.Niven, J. E. & Laughlin, S. B. Energy limitation as a selective pressure on the evolution of sensory systems. J. Exp. Biol. https://doi.org/10.1242/jeb.017574 (2008).Article 
PubMed 

Google Scholar 
28.Berlucchi, G. & Buchtel, H. A. Neuronal plasticity: historical roots and evolution of meaning. Exp. Brain Res. https://doi.org/10.1007/s00221-008-1611-6 (2009).Article 
PubMed 

Google Scholar 
29.Zhai, R. G. & Bellen, H. J. The architecture of the active zone in the presynaptic nerve terminal. Physiology https://doi.org/10.1152/physiol.00014.2004 (2004).Article 
PubMed 

Google Scholar 
30.Horn, G., Bradley, P. & McCabe, B. J. Changes in the structure of synapses associated with learning. J. Neurosci. https://doi.org/10.1523/jneurosci.05-12-03161.1985 (1985).Article 
PubMed 
PubMed Central 

Google Scholar 
31.Beaulieu, C. & Colonnier, M. Richness of environment affects the number of contacts formed by boutons containing flat vesicles but does not alter the number of these boutons per neuron. J. Comp. Neurol. https://doi.org/10.1002/cne.902740305 (1988).Article 
PubMed 

Google Scholar 
32.Anderson, B. J. Plasticity of gray matter volume: The cellular and synaptic plasticity that underlies volumetric change. Dev. Psychobiol. https://doi.org/10.1002/dev.20563 (2011).Article 
PubMed 

Google Scholar 
33.Tyagarajan, S. K. & Fritschy, J.-M. Gephyrin: a master regulator of neuronal function?. Nat. Rev. Neurosci. 15, 141–156 (2014).CAS 
Article 

Google Scholar 
34.Dutertre, S., Becker, C. M. & Betz, H. Inhibitory glycine receptors: an update. J. Biol. Chem. https://doi.org/10.1074/jbc.R112.408229 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
35.Fritschy, J. M., Harvey, R. J. & Schwarz, G. Gephyrin: where do we stand, where do we go?. Trends Neurosci. https://doi.org/10.1016/j.tins.2008.02.006 (2008).Article 
PubMed 

Google Scholar 
36.Choii, G. & Ko, J. Gephyrin: a central GABAergic synapse organizer. Exp. Mol. Med. https://doi.org/10.1038/emm.2015.5 (2015).Article 
PubMed 

Google Scholar 
37.Phillips-Portillo, J. & Strausfeld, N. J. Representation of the brain’s superior protocerebrum of the flesh fly, Neobellieria bullata, in the central body. J. Comp. Neurol. https://doi.org/10.1002/cne.23094 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
38.Schindelin, J. et al. Fiji: An open-source platform for biological-image analysis. Nat. Methods https://doi.org/10.1038/nmeth.2019 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
39.de Reuille, P. B. et al. MorphoGraphX: a platform for quantifying morphogenesis in 4D. Elife https://doi.org/10.7554/eLife.05864 (2015).Article 

Google Scholar 
40.Cignoni, P. et al. MeshLab: An open-source 3D mesh processing tool. In 6th Eurographics Italian Chapter Conference 2008 – Proceedings (2008).
Google Scholar 
41.Horstmann, M. et al. Scan, extract, wrap, compute—a 3D method to analyse morphological shape differences. PeerJ 2018, 1–20 (2018).
Google Scholar 
42.R Development Core Team, R. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. https://doi.org/10.1007/978-3-540-74686-7 (2011).Article 

Google Scholar 
43.Wickham, H. et al. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag. New York (2016).44.Ohyama, T. et al. A multilevel multimodal circuit enhances action selection in Drosophila. Nature 520, 633–639 (2015).ADS 
CAS 
Article 

Google Scholar 
45.Simpson, J. H. Chapter 3 Mapping and Manipulating Neural Circuits in the Fly Brain. Advances in Genetics. https://doi.org/10.1016/S0065-2660(09)65003-3 (2009).Article 

Google Scholar 
46.Boyan, G., Williams, L. & Liu, Y. Conserved patterns of axogenesis in the panarthropod brain. Arthropod Struct. Dev. https://doi.org/10.1016/j.asd.2014.11.003 (2015).Article 
PubMed 

Google Scholar 
47.Cayre, M., Strambi, C. & Strambi, A. Neurogenesis in an adult insect brain and its hormonal control. Nature https://doi.org/10.1038/368057a0 (1994).Article 

Google Scholar 
48.Harzsch, S. & Dawirs, R. R. Neurogenesis in the developing crab brain: Postembryonic generation of neurons persists beyond metamorphosis. J. Neurobiol. https://doi.org/10.1002/(SICI)1097-4695(199603)29:3%3c384::AID-NEU9%3e3.0.CO;2-5 (1996).Article 
PubMed 

Google Scholar 
49.Sandeman, R., Clarke, D., Sandeman, D. & Manly, M. Growth-related and antennular amputation-induced changes in the olfactory centers of crayfish brain. J. Neurosci. https://doi.org/10.1523/jneurosci.18-16-06195.1998 (1998).Article 
PubMed 
PubMed Central 

Google Scholar 
50.Harzsch, S., Miller, J., Benton, J. & Beltz, B. From embryo to adult: Persistent neurogenesis and apoptotic cell death shape the lobster deutocerebrum. J. Neurosci. https://doi.org/10.1523/jneurosci.19-09-03472.1999 (1999).Article 
PubMed 
PubMed Central 

Google Scholar 
51.Letourneau, J. G. Addition of sensory structures and associated neurons to the crayfish telson during development. J. Comp. Physiol. A https://doi.org/10.1007/BF00656778 (1976).Article 

Google Scholar 
52.Sandeman, D. C. Organization of the central nervous system. in The Biology of Crustacea. Vol. 3. Neurobiology: Structure and Function 1–61 (Academic Press, 1982).
Google Scholar 
53.Laverack, M. S. The numbers of neurones in decapod Crustacea. J. Crustac. Biol. 8, 1–11 (1988).Article 

Google Scholar 
54.Moss, S. J. & Smart, T. G. Constructing inhibitory synapses. Nat. Rev. Neurosci. 2, 240–250 (2001).CAS 
Article 

Google Scholar 
55.Bliss, T. V. P. & Collingridge, G. L. A synaptic model of memory: long-term potentiation in the hippocampus. Nature https://doi.org/10.1038/361031a0 (1993).Article 
PubMed 

Google Scholar 
56.Collingridge, G. L., Isaac, J. T. R. & Yu, T. W. Receptor trafficking and synaptic plasticity. Nat. Rev. Neurosci. https://doi.org/10.1038/nrn1556 (2004).Article 
PubMed 

Google Scholar 
57.Atwood, H. L. & Wojtowicz, J. M. Short-term and long-term plasticity and physiological differentiation of crustacean motor synapses. Int. Rev. Neurobiol. 28, 275–362 (1986).CAS 
Article 

Google Scholar 
58.Liu, G. Local structural balance and functional interaction of excitatory and inhibitory synapses in hippocampal dendrites. Nat. Neurosci. https://doi.org/10.1038/nn1206 (2004).Article 
PubMed 
PubMed Central 

Google Scholar 
59.Hao, J., Wang, X. D., Dan, Y., Poo, M. M. & Zhang, X. H. An arithmetic rule for spatial summation of excitatory and inhibitory inputs in pyramidal neurons. Proc. Natl. Acad. Sci. USA. https://doi.org/10.1073/pnas.0912022106 (2009).Article 
PubMed 

Google Scholar 
60.Fu, A. K. & Ip, N. Y. Regulation of postsynaptic signaling in structural synaptic plasticity. Curr. Opin. Neurobiol. https://doi.org/10.1016/j.conb.2017.05.016 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
61.Chater, T. E. & Goda, Y. The role of AMPA receptors in postsynaptic mechanisms of synaptic plasticity. Front. Cell. Neurosci. https://doi.org/10.3389/fncel.2014.00401 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
62.Velazquez, J. L., Thompson, C. L., Barnes, E. M. & Angelides, K. J. Distribution and lateral mobility of GABA/benzodiazepine receptors on nerve cells. J. Neurosci. 9, 2163–2169 (1989).CAS 
Article 

Google Scholar 
63.Gaiarsa, J. L., Caillard, O. & Ben-Ari, Y. Long-term plasticity at GABAergic and glycinergic synapses: mechanisms and functional significance. Trends Neurosci. https://doi.org/10.1016/S0166-2236(02)02269-5 (2002).Article 
PubMed 

Google Scholar 
64.Nusser, Z., Hájos, N., Somogyi, P. & Mody, I. Increased number of synaptic GABA(A) receptors underlies potentiation at hippocampal inhibitory synapses. Nature https://doi.org/10.1038/25999 (1998).Article 
PubMed 

Google Scholar  More

1.Polis, G. A. & Strong, D. R. Food web complexity and community dynamics. Am. Nat. 147, 813–846 (1996).Article 

Google Scholar 
2.Lindeman, R. L. The trophic-dynamic aspect of ecology. Ecology 23, 399–417 (1942).Article 

Google Scholar 
3.Coleman, D. C., Andrews, R., Ellis, J. E. & Singh, J. S. Energy flow and partitioning in selected man-managed and natural ecosystems. Agro-Ecosyst. 3, 45–54 (1976).Article 

Google Scholar 
4.Steffan, S. A. et al. Unpacking brown food-webs: Animal trophic identity reflects rampant microbivory. Ecol. Evol. 7, 3532–3541 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
5.Coleman, D. C. Energetics of detritivory and microbivory in soil in theory and practice. In Food Webs (eds. Polis G. A. & Winemiller K. O.) 39–50 (Springer, 1996).Chapter 

Google Scholar 
6.Moore, J. C. et al. Detritus, trophic dynamics and biodiversity. Ecol. Lett. 7, 584–600 (2004).ADS 
Article 

Google Scholar 
7.Hagen, E. M. et al. A meta-analysis of the effects of detritus on primary producers and consumers in marine, freshwater, and terrestrial ecosystems. Oikos 121, 1507–1515 (2012).Article 

Google Scholar 
8.Danovaro, R., Snelgrove, P. V. R. & Tyler, P. Challenging the paradigms of deep-sea ecology. Trends Ecol. Evol. 29, 465–475 (2014).PubMed 
Article 

Google Scholar 
9.Smith, C. R., De Leo, F. C., Bernardino, A. F., Sweetman, A. K. & Arbizu, P. M. Abyssal food limitation, ecosystem structure and climate change. Trends Ecol. Evol. 23, 518–528 (2008).PubMed 
Article 

Google Scholar 
10.Gage, J. D. & Tyler, P. A. Deep-Sea Biology: A Natural History of Organisms at the Deep-Sea Floor. (Cambridge University Press, 1991).Book 

Google Scholar 
11.De La Rocha, C. L. & Passow, U. Factors influencing the sinking of POC and the efficiency of the biological carbon pump. Deep Res. Part II Top. Stud. Oceanogr. 54, 639–658 (2007).Article 

Google Scholar 
12.Smith, K. L., Ruhl, H. A., Huffard, C. L., Messié, M. & Kahru, M. Episodic organic carbon fluxes from surface ocean to abyssal depths during long-term monitoring in NE Pacific. Proc. Natl. Acad. Sci. USA. 115, 12235–12240 (2018).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
13.Ramirez-Llodra, E. et al. Deep, diverse and definitely different: Unique attributes of the world’s largest ecosystem. Biogeosciences 7, 2851–2899 (2010).ADS 
Article 

Google Scholar 
14.Ruhl, H. A. Abundance and size distribution dynamics of abyssal epibenthic megafauna in the northeast Pacific. Ecology 88, 1250–1262 (2007).PubMed 
Article 
PubMed Central 

Google Scholar 
15.Billett, D. S. M. Deep-sea holothurians. Oceanogr. Mar. Biol. An Annu. Rev. 29, 259–317 (1991).
Google Scholar 
16.Bett, B. J., Malzone, M. G., Narayanaswamy, B. E. & Wigham, B. D. Temporal variability in phytodetritus and megabenthic activity at the seabed in the deep northeast Atlantic. Prog. Oceanogr. 50, 349–368 (2001).ADS 
Article 

Google Scholar 
17.Durden, J. M. et al. Response of deep-sea deposit-feeders to detrital inputs: A comparison of two abyssal time-series sites. Deep. Res. Part II Top. Stud. Oceanogr. 173, 104677 (2020).18.Khripounoff, A. & Sibuet, M. L. nutrition d’echinodermes abyssaux I. Alimentation des holothuries. Mar. Biol. 60, 17–26 (1980).Article 

Google Scholar 
19.Roberts, D., Gebruka, A., Levin, V. & Manship, B. A. D. Feeding and digestive strategies in deposit-feeding holothurians. Oceanogr. Mar. Biol. Annu. Rev. 38, 257–310 (2000).
Google Scholar 
20.FitzGeorge-Balfour, T., Billett, D. S. M., Wolff, G. A., Thompson, A. & Tyler, P. A. Phytopigments as biomarkers of selectivity in abyssal holothurians; interspecific differences in response to a changing food supply. Deep. Res. Part II Top. Stud. Oceanogr. 57, 1418–1428 (2010).21.Miller, R. J., Smith, C. R., Demaster, D. J. & Fornes, W. L. Feeding selectivity and rapid particle processing by deep-sea megafaunal deposit feeders : A 234 Th tracer approach. J. Mar. Res. 58, 653–673 (2000).Article 

Google Scholar 
22.Witte, U. et al. In situ experimental evidence of the fate of a phytodetritus pulse at the abyssal sea floor. Lett. Nat. 424, 763–766 (2003).CAS 
Article 

Google Scholar 
23.Moore, H., Manship, B. & Roberts, D. Gut structure and digestive strategies in three species of abyssal holothurians. in Echinoderm Research 111–119 (Balkema, 1995).24.Deming, J. W. & Colwell, R. R. Barophilic bacteria associated with digestive tracts of abyssal holothurians. Appl. Environ. Microbiol. 44, 1222–1230 (1982).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
25.Amaro, T., Luna, G. M., Danovaro, R., Billett, D. S. M. & Cunha, M. R. High prokaryotic biodiversity associated with gut contents of the holothurian Molpadia musculus from the Nazaré Canyon (NE Atlantic). Deep. Res. Part I Oceanogr. Res. Pap. 63, 82–90 (2012).26.Roberts, D. et al. Sediment distribution, hydrolytic enzyme profiles and bacterial activities in the guts of Oneirophanta mutabilis, Psychropotes longicauda and Pseudostichopus villosus: What do they tell us about digestive strategies of abyssal holothurians?. Prog. Oceanogr. 50, 443–458 (2001).ADS 
Article 

Google Scholar 
27.Sibuet, M., Khripounoff, A., Deming, J., Colwell, R. & Dinet, A. Modification of the gut contents in the digestive tract of abyssal holothurians. In Proceedings of the International Echinoderm Conference. Tampa Bay. (ed Lawrence, J. M.) 421–428 (Balkema, 1982).
Google Scholar 
28.Bradley, C. J. et al. Trophic position estimates of marine teleosts using amino acid compound specific isotopic analysis. Limnol. Oceanogr. Methods 13, 476–493 (2015).Article 

Google Scholar 
29.Ohkouchi, N. et al. Advances in the application of amino acid nitrogen isotopic analysis in ecological and biogeochemical studies. Org. Geochem. 113, 150–174 (2017).CAS 
Article 

Google Scholar 
30.Popp, B. N. et al. Stable isotopes as indicators of ecological change. Terr. Ecol. 1, 173–190 (2007).
Google Scholar 
31.Chikaraishi, Y. et al. Determination of aquatic food-web structure based on compound-specific nitrogen isotopic composition of amino acids. Limnol. Oceanogr. Methods 7, 740–750 (2009).CAS 
Article 

Google Scholar 
32.McClelland, J. W. & Montoya, J. P. Trophic relationships and the nitrogen isotopic composition of amino acids in plankton. Ecology 83, 2173–2180 (2002).Article 

Google Scholar 
33.Steffan, S. A. et al. Microbes are trophic analogs of animals. Proc. Natl. Acad. Sci. U. S. A. 112, 15119–15124 (2015).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
34.Yamaguchi, Y. T. et al. Fractionation of nitrogen isotopes during amino acid metabolism in heterotrophic and chemolithoautotrophic microbes across Eukarya, Bacteria, and Archaea: Effects of nitrogen sources and metabolic pathways. Org. Geochem. 111, 101–112 (2017).CAS 
Article 

Google Scholar 
35.Iken, K., Brey, T., Wand, U., Voigt, J. & Junghans, P. Food web structure of the benthic community at the Porcupine Abyssal Plain (NE Atlantic): A stable isotope analysis. Prog. Oceanogr. 50, 383–405 (2001).ADS 
Article 

Google Scholar 
36.Romero-Romero, S. et al. Seasonal pathways of organic matter within the Avilés submarine canyon: Food web implications. Deep. Res. Part I. Oceanogr. Res. Pap. 117, 1–10 (2016).ADS 
CAS 
Article 

Google Scholar 
37.Reid, W., Wigham, B., McGill, R. & Polunin, N. Elucidating trophic pathways in benthic deep-sea assemblages of the Mid-Atlantic Ridge north and south of the Charlie-Gibbs fracture zone. Mar. Ecol. Prog. Ser. 463, 89–103 (2012).ADS 
Article 

Google Scholar 
38.Drazen, J. et al. Bypassing the abyssal benthic food web: Macrourid diet in the eastern North Pacific inferred from stomach content and stable isotopes analyses. Limnol. Oceanogr. 53, 2644–2654 (2008).ADS 
Article 

Google Scholar 
39.Post, D. Using stable isotopes to estimate trophic position: Models, methods, and assumptions. Ecology 83, 703–718 (2002).Article 

Google Scholar 
40.Witbaard, R., Duineveld, G. C. A., Kok, A., Van Der Weele, J. & Berghuis, E. M. The response of Oneirophanta mutabilis (Holothuroidea) to the seasonal deposition of phytopigments at the Porcupine Abyssal Plain in the Northeast Atlantic. Prog. Oceanogr. 50, 423–441 (2001).ADS 
Article 

Google Scholar 
41.Wigham, B. D., Hudson, I. R., Billett, D. S. M. & Wolff, G. A. Is long-term change in the abyssal Northeast Atlantic driven by qualitative changes in export flux? Evidence from selective feeding in deep-sea holothurians. Prog. Oceanogr. 59, 409–441 (2003).ADS 
Article 

Google Scholar 
42.Hudson, I. R., Wigham, B. D., Billett, D. S. M. & Tyler, P. A. Seasonality and selectivity in the feeding ecology and reproductive biology of deep-sea bathyal holothurians. Prog. Oceanogr. 59, 381–407 (2003).ADS 
Article 

Google Scholar 
43.Lauerman, L. M. L., Smoak, J. M., Shaw, T. J., Moore, W. S. & Smith, K. L. 234Th and 210Pb evidence for rapid ingestion of settling particles by mobile epibenthic megafauna in the abyssal NE Pacific. Limnol. Oceanogr. 42, 589–595 (1997).ADS 
CAS 
Article 

Google Scholar 
44.Roberts, D., Billett, D. S. M., McCartney, G. & Hayes, G. E. Procaryotes on the tentacles of deep-sea holothurians: A novel form of dietary supplementation. Limnol. Oceanogr. 36, 1447–1451 (1991).ADS 
Article 

Google Scholar 
45.Larsen, T., Lee Taylor, D., Leigh, M. B. & O’Brien, D. M. Stable isotope fingerprinting: A novel method for identifying plant, fungal, or bacterial origins of amino acids. Ecology 90, 3526–3535 (2009).46.Plante, C. J., Jumars, P. A. & Baross, J. A. Digestive associations between marine detritivores and bacteria. Annu. Rev. Ecol. Syst. 21, 93–127 (1990).Article 

Google Scholar 
47.Drazen, J. C., Phleger, C. F., Guest, M. A. & Nichols, P. D. Lipid, sterols and fatty acid composition of abyssal holothurians and ophiuroids from the North-East Pacific Ocean: Food web implications. Comp. Biochem. Physiol. – B Biochem. Mol. Biol. 151, 79–87 (2008).48.Amaro, T. et al. Possible links between holothurian lipid compositions and differences in organic matter (OM) supply at the western Pacific abyssal plains. Deep. Res. Part I Oceanogr. Res. Pap. 152, (2019).49.Kharlamenko, V. I., Maiorova, A. S. & Ermolenko, E. V. Fatty acid composition as an indicator of the trophic position of abyssal megabenthic deposit feeders in the Kuril Basin of the Sea of Okhotsk. Deep. Res. Part II Top. Stud. Oceanogr. 154, 374–382 (2018).50.Ginger, M. L. et al. Organic matter assimilation and selective feeding by holothurians in the deep sea: Some observations and comments. Prog. Oceanogr. 50, 407–421 (2001).ADS 
Article 

Google Scholar 
51.McMahon, K. W., Thorrold, S. R., Houghton, L. A. & Berumen, M. L. Tracing carbon flow through coral reef food webs using a compound-specific stable isotope approach. Oecologia 180, 809–821 (2016).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
52.Kaufmann, R. S. & Smith, K. L. Activity patterns of mobile epibenthic megafauna at an abyssal site in the eastern North Pacific: Results from a 17-month time-lapse photographic study. Deep. Res. Part I Oceanogr. Res. Pap. 44, 559–579 (1997).53.Kuhnz, L. A., Ruhl, H. A., Huffard, C. L. & Smith, K. L. Rapid changes and long-term cycles in the benthic megafaunal community observed over 24 years in the abyssal northeast Pacific. Prog. Oceanogr. 124, 1–11 (2014).ADS 
Article 

Google Scholar 
54.Vardaro, M. F., Ruhl, H. A. & Smith, K. L. Climate variation, carbon flux, and bioturbation in the abyssal north pacific. Limnol. Oceanogr. 54, 2081–2088 (2009).ADS 
Article 

Google Scholar 
55.Ziegler, A., Mooi, R., Rolet, G. & De Ridder, C. Origin and evolutionary plasticity of the gastric caecum in sea urchins (Echinodermata: Echinoidea). BMC Evol. Biol. 10, 313 (2010).PubMed 
PubMed Central 
Article 

Google Scholar 
56.McCarthy, M. D., Benner, R., Lee, C. & Fogel, M. L. Amino acid nitrogen isotopic fractionation patterns as indicators of heterotrophy in plankton, particulate, and dissolved organic matter. Geochim. Cosmochim. Acta 71, 4727–4744 (2007).ADS 
CAS 
Article 

Google Scholar 
57.Calleja, M. L., Batista, F., Peacock, M., Kudela, R. & McCarthy, M. D. Changes in compound specific δ15N amino acid signatures and d/l ratios in marine dissolved organic matter induced by heterotrophic bacterial reworking. Mar. Chem. 149, 32–44 (2013).CAS 
Article 

Google Scholar 
58.Smith, K. L., Ruhl, H. A., Kaufmann, R. S. & Kahru, M. Tracing abyssal food supply back to upper-ocean processes over a 17-year time series in the northeast Pacific. Limnol. Oceanogr. 53, 2655–2667 (2008).ADS 
Article 

Google Scholar 
59.Huffard, C. L., Kuhnz, L. A., Lemon, L., Sherman, A. D. & Smith, K. L. Demographic indicators of change in a deposit-feeding abyssal holothurian community (Station M, 4000 m). Deep. Res. Part I Oceanogr. Res. Pap. 109, 27–39 (2016).60.Hannides, C. C. S., Popp, B. N., Anela Choy, C. & Drazen, J. C. Midwater zooplankton and suspended particle dynamics in the North Pacific Subtropical Gyre: A stable isotope perspective. Limnol. Oceanogr. 58, 1931–1936 (2013).61.Neto, R. R., Wolff, G. A., Billett, D. S. M., Mackenzie, K. L. & Thompson, A. The influence of changing food supply on the lipid biochemistry of deep-sea holothurians. Deep. Res. Part I Oceanogr. Res. Pap. 53, 516–527 (2006).62.Amaro, T., Witte, H., Herndl, G. J., Cunha, M. R. & Billett, D. S. M. Deep-sea bacterial communities in sediments and guts of deposit-feeding holothurians in Portuguese canyons (NE Atlantic). Deep Res. Part I Oceanogr. Res. Pap. 56, 1834–1843 (2009).ADS 
Article 

Google Scholar 
63.Silfer, J. A., Engel, M. H., Macko, S. A. & Jumeau, E. J. Stable carbon isotope analysis of amino acid enantiomers by conventional isotope ratio mass spectrometry and combined gas chromatography/isotope ratio mass spectrometry. Anal. Chem. 63, 370–374 (1991).CAS 
Article 

Google Scholar 
64.Jarman, C. L. et al. Diet of the prehistoric population of Rapa Nui (Easter Island, Chile) shows environmental adaptation and resilience. Am. J. Phys. Anthropol. 164, 343–361 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
65.Dauwe, B., Middelburg, J. J., Herman, P. M. J. & Heip, C. H. R. Linking diagenetic alteration of amino acids and bulk organic matter reactivity. Limnology 44, 1809–1814 (1999).CAS 

Google Scholar 
66.McCarthy, M. D., Benner, R., Lee, C., Hedges, J. I. & Fogel, M. L. Amino acid carbon isotopic fractionation patterns in oceanic dissolved organic matter: An unaltered photoautotrophic source for dissolved organic nitrogen in the ocean?. Mar. Chem. 92, 123–134 (2004).CAS 
Article 

Google Scholar 
67.R: A Language and Environment for Statistical Computing. https://www.R-project.org (R Core Team. R Foundation for Statistical Computing, 2020). More

1.Pereira, H. M. et al. Scenarios for global biodiversity in the 21st century. Science 330, 1496–1501 (2010).ADS 
CAS 
PubMed 
Article 

Google Scholar 
2.Pereira, H. M. et al. Essential biodiversity variables. Science 339, 277–278 (2013).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
3.Stem, C., Margoluis, R., Salafsky, N. & Brown, M. Monitoring and evaluation in conservation: a review of trends and approaches. Conserv. Biol. 19, 295–309 (2005).Article 

Google Scholar 
4.Atwood, T. B. et al. Herbivores at the highest risk of extinction among mammals, birds, and reptiles. Sci. Adv. 6, eabb8458 (2020).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
5.Cardillo, M. et al. Human population density and extinction risk in the world’s carnivores. PLoS Biol 2, e197 (2004).PubMed 
PubMed Central 
Article 

Google Scholar 
6.Ripple, W. J. et al. Status and ecological effects of the world’s largest carnivores. Science 343, 1241484 (2014).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
7.Wolf, C. & Ripple, W. J. Prey depletion as a threat to the world’s large carnivores. R. Soc. Open Sci. 3, 160252 (2016).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
8.Kissling, W. D. et al. Building essential biodiversity variables (EBV s) of species distribution and abundance at a global scale. Biol. Rev. 93, 600–625 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
9.Nesshöver, C., Livoreil, B., Schindler, S. & Vandewalle, M. Challenges and solutions for networking knowledge holders and better informing decision-making on biodiversity and ecosystem services. Biodivers. Conserv. 25, 1207–1214 (2016).Article 

Google Scholar 
10.Gardner, T. A. et al. The cost-effectiveness of biodiversity surveys in tropical forests. Ecol. Lett. 11, 139–150 (2008).PubMed 
Article 
PubMed Central 

Google Scholar 
11.Field, S. A., Tyre, A. J. & Possingham, H. P. Optimizing allocation of monitoring effort under economic and observational constraints. J. Wildl. Manag. 69, 473–482 (2005).Article 

Google Scholar 
12.Braunisch, V. & Suchant, R. Predicting species distributions based on incomplete survey data: The trade-off between precision and scale. Ecography 33, 826–840 (2010).Article 

Google Scholar 
13.Taberlet, P., Coissac, E., Hajibabaei, M. & Rieseberg, L. H. Environmental DNA. Mol. Ecol. 21, 1789–1793 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
14.Deiner, K. et al. Long-range PCR allows sequencing of mitochondrial genomes from environmental DNA. Methods Ecol. Evol. 8, 1888–1898 (2017).Article 

Google Scholar 
15.Deiner, K., Walser, J.-C., Mächler, E. & Altermatt, F. Choice of capture and extraction methods affect detection of freshwater biodiversity from environmental DNA. Biol. Conserv. 183, 53–63 (2015).Article 

Google Scholar 
16.Tsuji, S., Takahara, T., Doi, H., Shibata, N. & Yamanaka, H. The detection of aquatic macroorganisms using environmental DNA analysis—A review of methods for collection, extraction, and detection. Environ. DNA 1, 99–108 (2019).Article 

Google Scholar 
17.Sales, N. G. et al. Fishing for mammals: Landscape-level monitoring of terrestrial and semi-aquatic communities using eDNA from riverine systems. J. Appl. Ecol. 57, 707–716 (2020).CAS 
Article 

Google Scholar 
18.Sales, N. G. et al. Assessing the potential of environmental DNA metabarcoding for monitoring Neotropical mammals: a case study in the Amazon and Atlantic Forest, Brazil. Mammal Rev. 50, 221–225 (2020).Article 

Google Scholar 
19.Barnes, M. A. & Turner, C. R. The ecology of environmental DNA and implications for conservation genetics. Conserv. Genet. 17, 1–17 (2016).CAS 
Article 

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

Google Scholar 
21.Leempoel, K., Hebert, T. & Hadly, E. A. A comparison of eDNA to camera trapping for assessment of terrestrial mammal diversity. Proc. R. Soc. B Biol. Sci. 287, 20192353 (2020).CAS 
Article 

Google Scholar 
22.Harper, L. R. et al. Environmental DNA (eDNA) metabarcoding of pond water as a tool to survey conservation and management priority mammals. Biol. Conserv. 238, 108225 (2019).Article 

Google Scholar 
23.Rodgers, T. W. & Mock, K. E. Drinking water as a source of environmental DNA for the detection of terrestrial wildlife species. Conserv. Genet. Resour. 7, 693–696 (2015).Article 

Google Scholar 
24.Ushio, M. et al. Environmental DNA enables detection of terrestrial mammals from forest pond water. Mol. Ecol. Resour. 17, e63–e75 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
25.Williams, K. E., Huyvaert, K. P., Vercauteren, K. C., Davis, A. J. & Piaggio, A. J. Detection and persistence of environmental DNA from an invasive, terrestrial mammal. Ecol. Evol. 8, 688–695 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
26.Merkes, C. M., McCalla, S. G., Jensen, N. R., Gaikowski, M. P. & Amberg, J. J. Persistence of DNA in carcasses, slime and avian feces may affect interpretation of environmental DNA data. PLoS ONE 9, e113346 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
27.Pont, D. et al. Environmental DNA reveals quantitative patterns of fish biodiversity in large rivers despite its downstream transportation. Sci. Rep. 8, 1–13 (2018).ADS 
CAS 
Article 

Google Scholar 
28.Zinger, L. et al. Advances and prospects of environmental DNA in neotropical rainforests. Adv. Ecol. Res. 62, 331–373 (2020).Article 

Google Scholar 
29.Withers, P. C., Cooper, C. E., Maloney, S. K., Bozinovic, F. & Cruz-Neto, A. P. Ecological and Environmental Physiology of Mammals Vol. 5 (Oxford University Press, 2016).Book 

Google Scholar 
30.Bicudo, J. E. P., Buttemer, W. A., Chappell, M. A., Pearson, J. T. & Bech, C. Ecological and Environmental Physiology of Birds Vol. 2 (Oxford University Press, 2010).Book 

Google Scholar 
31.Naidoo, R. & Burton, A. C. Relative effects of recreational activities on a temperate terrestrial wildlife assemblage. Conserv. Sci. Pract. 2, e271 (2020).
Google Scholar 
32.Cantera, I. et al. Optimizing environmental DNA sampling effort for fish inventories in tropical streams and rivers. Sci. Rep. 9, 1–11 (2019).CAS 
Article 

Google Scholar 
33.Tarboton, D. G., Bras, R. L. & Rodriguez-Iturbe, I. The fractal nature of river networks. Water Resour. Res. 24, 1317–1322 (1988).ADS 
Article 

Google Scholar 
34.Ishige, T. et al. Tropical-forest mammals as detected by environmental DNA at natural saltlicks in Borneo. Biol. Conserv. 210, 281–285 (2017).Article 

Google Scholar 
35.Joseph, L. N., Field, S. A., Wilcox, C. & Possingham, H. P. Presence–absence versus abundance data for monitoring threatened species. Conserv. Biol. 20, 1679–1687 (2006).PubMed 
Article 
PubMed Central 

Google Scholar 
36.Lacy, R. C. Lessons from 30 years of population viability analysis of wildlife populations. Zoo Biol. 38, 67–77 (2019).PubMed 
Article 

Google Scholar 
37.Gärdenfors, U., Hilton-Taylor, C., Mace, G. M. & Rodríguez, J. P. The application of IUCN Red List criteria at regional levels. Conserv. Biol. 15, 1206–1212 (2001).Article 

Google Scholar 
38.Munro, R., Nielsen, S. E., Price, M., Stenhouse, G. & Boyce, M. S. Seasonal and diel patterns of grizzly bear diet and activity in west-central Alberta. J. Mammal. 87, 1112–1121 (2006).Article 

Google Scholar 
39.Deiner, K. & Altermatt, F. Transport distance of invertebrate environmental DNA in a natural river. PLoS ONE 9, e88786 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
40.Hunter, M. E. et al. Detection limits of quantitative and digital PCR assays and their influence in presence–absence surveys of environmental DNA. Mol. Ecol. Resour. 17, 221–229 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
41.Roussel, J.-M., Paillisson, J.-M., Treguier, A. & Petit, E. The downside of eDNA as a survey tool in water bodies. J. Appl. Ecol. 52, 823–826 (2015).CAS 
Article 

Google Scholar 
42.Williams, B. K., Nichols, J. D. & Conroy, M. J. Analysis and Management of Animal Populations (Academic Press, 2002).
Google Scholar 
43.Burton, A. C. et al. Wildlife camera trapping: A review and recommendations for linking surveys to ecological processes. J. Appl. Ecol. 52, 675–685 (2015).Article 

Google Scholar 
44.Morris, W. F. et al. Quantitative Conservation Biology (Sinauer Sunderland, 2002).
Google Scholar 
45.Parks, B. C. South Chilcotin Mountains Park and Big Creek Park Management Plan (2019).46.McLellan, M. L. et al. Divergent population trends following the cessation of legal grizzly bear hunting in southwestern British Columbia, Canada. Biol. Conserv. 233, 247–254 (2019).Article 

Google Scholar 
47.Kays, R. et al. Camera traps as sensor networks for monitoring animal communities. In 2009 IEEE 34th Conference on Local Computer Networks 811–818. https://doi.org/10.1109/LCN.2009.5355046 (IEEE, 2009).48.Hendry, H. & Mann, C. Camelot–intuitive software for camera trap data management. BioRxiv 203216 (2017).49.Valentini, A. et al. Next-generation monitoring of aquatic biodiversity using environmental DNA metabarcoding. Mol. Ecol. 25, 929–942 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
50.Taberlet, P., Bonin, A., Zinger, L. & Coissac, E. Environmental DNA: For biodiversity research and monitoring (Oxford University Press, Oxford, 2018).Book 

Google Scholar 
51.Boyer, F. et al. obitools: A unix-inspired software package for DNA metabarcoding. Mol. Ecol. Resour. 16, 176–182 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
52.De Barba, M. et al. DNA metabarcoding multiplexing and validation of data accuracy for diet assessment: Application to omnivorous diet. Mol. Ecol. Resour. 14, 306–323 (2014).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
53.Schnell, I. B., Bohmann, K. & Gilbert, M. T. P. Tag jumps illuminated–reducing sequence-to-sample misidentifications in metabarcoding studies. Mol. Ecol. Resour. 15, 1289–1303 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
54.Wearn, O. & Glover-Kapfer, P. Camera-trapping for conservation: A guide to best-practices. WWF Conserv. Technol. Ser. 1, 2019–2104 (2017).
Google Scholar 
55.R Core Team. R: A language and environment for statistical computing (R Foundation for Statistical Computing, 2020).
Google Scholar 
56.Plummer, M., et al.. JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling. In Proceedings of the 3rd International Workshop on Distributed Statistical Computing vol. 124, 1–10 (Vienna, Austria, 2003).57.Tuomisto, H. A diversity of beta diversities: Straightening up a concept gone awry. Part 1. Defining beta diversity as a function of alpha and gamma diversity. Ecography 33, 2–22 (2010).Article 

Google Scholar 
58.Ahumada, J. A. et al. Community structure and diversity of tropical forest mammals: data from a global camera trap network. Philos. Trans. R. Soc. B Biol. Sci. 366, 2703–2711 (2011).Article 

Google Scholar 
59.Samejima, H., Ong, R., Lagan, P. & Kitayama, K. Camera-trapping rates of mammals and birds in a Bornean tropical rainforest under sustainable forest management. For. Ecol. Manag. 270, 248–256 (2012).Article 

Google Scholar 
60.Parsons, A. W. et al. Do occupancy or detection rates from camera traps reflect deer density?. J. Mammal. 98, 1547–1557 (2017).Article 

Google Scholar 
61.Sollmann, R., Mohamed, A., Samejima, H. & Wilting, A. Risky business or simple solution–Relative abundance indices from camera-trapping. Biol. Conserv. 159, 405–412 (2013).Article 

Google Scholar 
62.Villette, P., Krebs, C. J., Jung, T. S. & Boonstra, R. Can camera trapping provide accurate estimates of small mammal (Myodes rutilus and Peromyscus maniculatus) density in the boreal forest?. J. Mammal. 97, 32–40 (2016).Article 

Google Scholar 
63.Feldhamer, G. A., Thompson, B. C. & Chapman, J. A. Wild Mammals of North America: Biology, Management, and Conservation (The Johns Hopkins University Press, 2003).
Google Scholar 
64.Burnham, K. P. & Anderson, D. R. A Practical Information-Theoretic Approach. Model Selection Multimodel Inference 2nd edn, Vol. 2 (Springer, Berlin, 2002).MATH 

Google Scholar  More

Metacommunity model and asymptotic community assemblyWe built a large set of model metacommunities (detailed in full in “Methods”) describing competitive dynamics within a single guild of species across a landscape. Each metacommunity consisted of a set of patches, or local communities, randomly placed in a square arena and linked by a spatial network. The dynamics of each population are governed by three processes: inter- and intraspecific interactions, heterogeneous responses to the environment and dispersal between adjacent patches (Fig. 1). Competition coefficients between species are drawn at random and the population dynamics within each patch are described by a Lotka-Volterra competition model. We control the level of environmental heterogeneity across the network directly by generating an intrinsic growth rate for each species at each patch from a random, spatially correlated distribution. To ensure any turnover is purely autonomous, we keep the environment fixed throughout simulations. Dispersal between neighbouring patches declines exponentially with distance between sites. This formulation allows precise and independent control of key properties of the metacommunity–the number of patches, the characteristic dispersal length and the heterogeneity of the environment.Fig. 1: Elements of the Lotka-Volterra metacommunity model and the emergence of autonomous population dynamics.Environmental heterogeneity, represented by the intrinsic growth rate matrix R, is modelled using a spatially autocorrelated Gaussian random field. A random spatial network, represented by the dispersal matrix D, defines the spatial connectivity of the landscape. The network of species interactions, represented by the competitive overlap matrix A, is modelled by sampling competition coefficients at random (perpendicular bars indicate recipients of a deleterious competitive impact). The resulting dynamics of local population biomasses, given by the colour-coded equation, are numerically simulated. The Hadamard product ‘∘’ represents element-wise matrix multiplication. For large metacommunities, local populations exhibit persistent dynamics despite the absence of external drivers. In the 3D boxes, typical simulated biomass dynamics of dominating species are plotted on linear axes over 2500 unit times. The graphs illustrate the complexity of the autonomous dynamics and the propensity for compositional change (local extinction and colonisation).Full size imageTo populate the model metacommunities, we iteratively introduced species with randomly generated intrinsic growth rates and interspecific interaction coefficients. Between successive regional invasions we simulated the model dynamics, and removed any species whose abundance fell below a threshold across the whole network. Through this assembly process and the eventual onset of ecological structural instability, both average local diversity, the number of species coexisting in a given patch, and regional diversity, the total number of species in the metacommunity, eventually saturate and then fluctuate around an equilibrium value—any introduction of a new species then leads on average to the extinction of one other species (Supplementary Fig. 1). In these intrinsically regulated metacommunities we then studied the phenomenology of autonomous community turnover in the absence of regional invasions or abiotic change.In our metacommunity model, local community dynamics and therefore local limits on species richness depend on a combination of biotic and abiotic filtering (non-uniform responses of species to local conditions)33,34,35 and immigration from adjacent patches, generating so called mass effects in the local community36,37,38. Biotic filtering via interspecific competition is encoded in the interaction coefficients Aij, while abiotic filtering occurs via the spatial variation of intrinsic growth rates Rix. For simplicity, and since predator-prey dynamics are known to generate oscillations39 through mechanisms distinct from those we report here, we restrict our analysis to competitive communities for which all ecological interactions are antagonistic. The off-diagonal elements of the interaction matrix A describe how one species i affects another species j. These are sampled independently from a discrete distribution, such that the interaction strength Aij is set to a constant value in the range 0 to 1 (in most cases 0.5) with fixed probability (connectance, in most cases 0.5) and otherwise set to zero. Intraspecific competition coefficients Aii are set to 1 for all species. This discrete distribution of the interaction terms was chosen for its relative efficiency. In the Supplementary discussion (and Supplementary Fig. 2) we show that outcomes remain unaffected when more complex distributions are modelled. Intrinsic growth rates Rix are sampled from spatially correlated normal distributions with mean 1, autocorrelation length ϕ and variance σ2 (Supplementary Fig. 3).Dispersal is modelled via a spatial connectivity matrix with elements Dxy. The topology of the model metacommunity, expressed through D, is generated by sampling the spatial coordinates of N patches from a uniform distribution ({mathcal{U}}(0,sqrt{N})times {mathcal{U}}(0,sqrt{N})), i.e., an area of size N. Thus, under variation of the number of patches, the inter-patch distances remain fixed on average. Spatial connectivity is defined by linking these patches through a Gabriel graph40, a planar graph generated by an algorithm that, on average, links each local community to four close neighbours41. Avoidance of direct long-distance dispersal and the sparsity of the resulting dispersal matrix permit the use of efficient numerical methods. The exponential dispersal kernel defining Dxy is tuned by the dispersal length ℓ, which is fixed for all species.The dynamics of local population biomasses Bix = Bix(t) are modelled using a system of spatially coupled Lotka-Volterra (LV) equations that, in matrix notation, takes the form23$$frac{d{bf{B}}}{dt}={bf{B}}circ ({bf{R}}-{bf{A}}{bf{B}})+{bf{B}}{bf{D}},$$
(1)
with ∘ denoting element-wise multiplication. Hereafter this formalism is referred to as the Lotka-Volterra Metacommunity Model (LVMCM). Further technical details are provided in Methods and the Supplementary Discussion.In order to numerically probe the impacts of ℓ, ϕ and σ2 on the emergent temporal dynamics, we initially fixed N = 64 and varied each parameter through multiple orders of magnitude (Supplementary Fig. 4). In order to obtain a full characterisation of autonomous turnover in the computationally accessible spatial range (N ≤ 256), we then selected a parameter combination found to generate substantial fluctuations for further analysis. Thereafter we assembled metacommunities of 8–256 patches (Fig. 2a) until regional diversity limits were reached (with tenfold replication) and generated community time series of 104 unit times from which the phenomenology of autonomous turnover could be explored in detail. We found no evidence to suggest that the phenomenology described below depends on this specific parameter combination. While future results may confirm or refute this, autonomous turnover arises over a wide range of parameters (Supplementary Fig. 4) and as such the phenomenon is robust.Fig. 2: Autonomous turnover in model metacommunities.a Typical model metacommunities: a spatial network with N nodes representing local communities (or patches) and edges, channels of dispersal. Patch colour represents the number of clusters in local community state space detected over 104 unit times t using hierarchical clustering of the Bray-Curtis (BC) dissimilarity matrix, Supplementary Fig. 6. b Colour coded matrices of pairwise temporal BC dissimilarity corresponding to the circled patches in (a). Insets represent 102 unit times. For small networks (N = 8) local compositions converge to static fixed points. As metacommunity extent increases, however, persistent dynamics emerge. Initially this autonomous turnover is oscillatory in nature with communities fluctuating between small numbers of states which can be grouped into clusters (16 ≤ N ≤ 32). Intermediate metacommunities (32 ≤ N ≤ 64) manifest “Clementsian” temporal turnover, characterised by sharp transitions in composition, implying species turn over in cohorts. Large metacommunities (N ≥ 128) turn over continuously, implying “Gleasonian” assembly dynamics in which species’ temporal occupancies are independent. c The mean number of local compositional clusters detected for metacommunities of various numbers of patches N (error bars represent standard deviation across all replicated simulations). While the transition from static to dynamic community composition at the local scale is sharp (see text), non-uniform turnover within metacommunities (a) blurs the transition at the regional scale. Aij = 0.5 with probability 0.5, ϕ = 10, σ2 = 0.01, ℓ = 0.5.Full size imageAutonomous turnover in model metacommunitiesFor small (N ≤ 8) metacommunities assembled to regional diversity limits, populations attain equilibria, i.e., converge to fixed points, implying the absence of autonomous turnover23. With increasing metacommunity size N, however, we observe the emergence of persistent population dynamics (Supplementary Fig. 5 and external video) that can produce substantial turnover in local community composition. This autonomous turnover can be represented through Bray-Curtis42 (BC) dissimilarity matrices comparing local community composition through time (Fig. 2b), and quantified by the number of compositional clusters detected in such matrices using hierarchical cluster analysis (Fig. 2a, c).At intermediate spatial scales (Fig. 2, 16 ≤ N ≤ 32) we often find oscillatory dynamics, which can be perfectly periodic or slightly irregular. With increasing oscillation amplitude, these lead to persistent turnover dynamics where local communities repeatedly transition between a small number of distinct compositional clusters (represented in Fig. 2 by stripes of high pairwise BC dissimilarity spanning large temporal ranges). At even larger scales (N ≥ 64) this compositional coherence begins to break down, and for very large metacommunities (N ≥ 128) autonomous dynamics drive continuous acyclic change in community composition. The number of compositional clusters detected over time typically varies within a given metacommunity (Fig. 2a node colour), however we find a clear increase in the average number of compositional clusters, i.e., an increase in turnover, with increasing total metacommunity size (Fig. 2c).Metacommunities in which the boundaries of species ranges along environmental gradients are clumped are termed Clementsian, while those for which range limits are independently distributed are  referred to as Gleasonian43. We consider the block structure of the temporal dissimilarity matrix at intermediate N to represent a form of Clementsian temporal turnover, characterised by sudden significant shifts in community composition. Metacommunity models similar to ours have been found to generate such patterns along spatial gradients44, potentially via an analogous mechanism45. Large, diverse model metacommunities manifest Gleasonian temporal turnover. In such cases, species colonisations and extirpations are largely independent and temporal occupancies predominantly uncorrelated, such that compositional change is continuous, rarely, if ever, reverting to the same state.Mechanistic explanation of autonomous turnoverSurprisingly, the onset and increasing complexity of autonomous turnover as system size N increases (Fig. 2) can be understood as a consequence of local community dynamics alone. To explain this, we first recall relevant theoretical results for isolated LV communities. Then we demonstrate that, in presence of weak propagule pressure, these results imply local community turnover dynamics, controlled by the richness of potential invaders, that closely mirror the dependence on system size seen in full LV metacommunities.Application of methods from statistical mechanics to models of large isolated LV communities with random interactions revealed that such models exhibit qualitatively distinct phases46,47,48. If the number of modelled species, S, interpreted as species pool size, lies below some threshold value determined by the distribution of interaction strengths (Supplementary Fig. 7), these models exhibit a unique linearly stable equilibrium (Unique Fixed Point phase, UFP). Some species may go extinct, but the majority persists48. When pool size S exceeds this threshold, there appear to be no more linearly stable equilibrium configurations. Any community formed by a selection from the S species is either unfeasible (there is no equilibrium with all species present), intrinsically linearly unstable, or invadable by at least one of the excluded species. This has been called the multiple attractor (MA) phase47. However, the implied notion that this part of the phase space is in fact characterised by multiple stable equilibria may be incorrect.Population dynamical models with many species have been shown to easily exhibit attractors called stable heteroclinic networks49, which are characterised by dynamics in which the system bounces around between several unstable equilibria, each corresponding to a different composition of the extant community, implying indefinite, autonomous community turnover (Fig. 3, red line). As these attractors are approached, models exhibit increasingly long intermittent phases of slow dynamics, which, when numerically simulated, can give the impression that the system eventually reaches one of several ‘stable’ equilibria, suggesting that turnover comes to a halt. We demonstrate in the Supplementary discussion that the MA phase of isolated LV models is in fact characterised by such stable heteroclinic networks (Supplementary Figs. 8 and 9). Note, we retain the MA terminology here because the underlying complete heteroclinic networks, interpreted as a directed graph50,51 (Fig. 3, inset), might have multiple components that are mutually unreachable through dynamic transitions52, each representing a different attractor.Fig. 3: Approximate heteroclinic networks underlie autonomous community turnover.The main panel shows two trajectories in the state space of a community of three hypothetical species (population biomasses B1, B2, B3) that are in non-hierarchical competition with each other, such that no species can competitively exclude both others (a “rock-paper-scissors game”17). Without propagule pressure, the system has three unstable equilibrium points (P1, P2, P3) and cycles between these (red curve), coming increasingly close to the equilibria and spending ever more time in the vicinity of each. The corresponding attractor is called a heteroclinic cycle (dashed arrows). Under weak extrinsic propagule pressure (blue curve), the three equilibria and the heteroclinic cycle disappear, yet the system closely tracks the original cycle in state space. Such a cycle can be represented as a graph linking the dynamically connected equilibria (inset). With more interacting species, these graphs can become complex “heteroclinic networks”49,50,51 with trajectories representing complex sequences of species composition during autonomous community turnover.Full size imageIf one now adds to such isolated LV models terms representing weak propagule pressure for all S species (Supplementary Eq. (2)), dynamically equivalent to mass effects occurring in the full metacommunity model (Eq. (1)), then none of the S species can entirely go extinct. The weak influx of biomass drives community states away from the unstable equilibria representing coexistence of subsets of the S species and the heteroclinic network connecting them (blue line in Fig. 3). Typically, system dynamics then still follow trajectories closely tracking the original heteroclinic networks (Fig. 3), but now without requiring boundless time to transition from the vicinity of one equilibrium to the next.The nature and complexity of the resulting population dynamics depend on the size and complexity of the underlying heteroclinic network, and both increase with pool size S. In simulations (Supplementary Fig. 10) we find that, as S increases, LV models with weak propagule pressure pass through the same sequence of states as we documented for LVMCM metacommunities in Fig. 2: equilibria, oscillatory population dynamics, Clementsian and finally Gleasonian temporal turnover.Above we introduced the number of clusters detected in Bray-Curtis dissimilarity matrices of fixed time series length as a means of quantifying the approximate number of equilibria visited during local community turnover. As shown in Fig. 4a, b, this number increases in LV models with S in a manner strikingly similar to its increase in the LVMCM with the number of species present in the ecological neighbourhood of a given patch. Thus, dynamics within a patch are controlled not by N directly but rather by neighbourhood species richness. For a given neighbourhood, species richness depends on the number of connected patches, the total area and therefore total abiotic heterogeneity encompassed, and the connectivity, all of which can vary substantially within a metacommunity of a given size N. As illustrated in Fig. 4b, there is a tendency for neighbourhood richness to be larger in larger metacommunities, leading indirectly to the dependence of metacommunity dynamics on N seen in Fig. 2.Fig. 4: Ecological mass effects drive autonomous turnover.a The number of compositional clusters detected, plotted against the size of the pool of potential invaders S for an isolated LV community using a propagule pressure ϵ of 10−10 and 10−15, fit by a generalised additive model87. For S 1 compositional cluster) occurs at a pools size of around S = 35 species, consistent with the theoretical prediction47 of the transition between the UFP and MA phases (Supplementary Discussion). Close inspection of this threshold reveals an important and hitherto unreported relationship between the transition into the MA phase and local ecological limits set by the onset of ecological structural instability, which is known to regulate species richness in LV systems subject to external invasion pressure23,24: in the Supplementary Discussion we show that the boundary between the UFP and MA phases47 coincides precisely with the onset of structural instability24 (Supplementary Eqs. (3)–(9)).For LVMCM metacommunities, this relationship (demonstrated analytically in the Supplementary Discussion) is numerically confirmed in Fig. 5. During assembly, local species richness increases until it reaches the limit imposed by local structural instability. Further assembly occurs via the “regionalisation” of the biota53—a collapse in average range sizes23 and associated increase in spatial beta diversity—until regional diversity limits are reached23. The emergence of autonomous turnover coincides with the onset of species saturation at the local scale. Autonomous turnover can therefore serve as an indirect indication of intrinsic biodiversity regulation via local structural instability in complex communities.Fig. 5: The emergence of temporal turnover during metacommunity assembly.a Local species richness, defined by reference to source populations only (({overline{alpha }}_{text{src}}), grey) and regional diversity (γ black) for a single metacommunity of N = 32 coupled communities during iterative regional invasion of random species. We quantify local source diversity ({overline{alpha }}_{text{src}}) as the metacommunity average of the number αsrc of non-zero equilibrium populations persisting when immigration is switched off (off-diagonal elements of D set to zero), since this is the component of a local community subject to strict ecological limits to biodiversity. Note the log scale chosen for easy comparison of local and regional species richness. b Increases in regional diversity beyond local limits arise via corresponding increases in spatial turnover (({overline{beta }}_{text{s}}), black). Autonomous temporal turnover (({overline{beta }}_{text{t}}), grey) sets in (crosses a threshold mean Bray Curtis (BC) dissimilarity of 10−2) precisely when average local species richness ({overline{alpha }}_{text{src}}) has reached its limit, reflecting the equivalence of the transition to the MA phase space and the onset of local structural instability. In both panels, the dashed line marks the point at which autonomous temporal turnover was first detected. Aij = 0.3 with probability 0.3, ϕ = 10, σ2 = 0.01, ℓ = 0.5. Both spatial and temporal turnover computed as the mean BC dissimilarity. In each iteration of the assembly model (regional invasion event), 0.1S + 1 species were introduced. Dynamics were simulated for 2 × 104 unit times, with the second 104 unit times analysed for autonomous turnover, and a total of 104 invasions were modelled.Full size imageThus, we have shown that propagule pressure perturbs local communities away from unstable equilibria and drives compositional change. In order to invade, however, species need to be capable of passing through biotic and abiotic filters33,34,35. We would expect, therefore, that turnover would be suppressed in highly heterogeneous or poorly connected environments where mass effects are weak. Indeed, by manipulating the autocorrelation length ϕ and variance σ2 of the abiotic filter represented by the matrix R and the characteristic dispersal length ℓ, we observe a sharp drop-off in temporal turnover in parameter regimes that maximise between-patch community dissimilarity (short environmental correlation or dispersal lengths, Supplementary Fig. 11). Thus, we conclude that it is not species richness or spatial dissimilarity per se that best predict temporal turnover, but the size of the pool of species with positive invasion fitness, i.e., those not repelled by the combined effects of biotic and abiotic filters.The macroecology of autonomous turnoverWe find good correspondence between temporal and spatio-temporal biodiversity patterns emerging in model metacommunities in the absence of external abiotic change and in empirical data (Fig. 6), with quantitative characteristics lying within the ranges observed in natural ecosystems.Fig. 6: Macroecological signatures of autonomous compositional change.A bimodal distribution in temporal occupancy observed in North American birds54 (a) and in simulations (e N = 64, ϕ = 5, σ2 = 0.01, ℓ = 0.5). Intrisically regulated species richness observed in estuarine fish species59 (b) and in simulations (f N = 64, ϕ = 5, σ2 = 0.01, ℓ = 0.5, 1000 unit times t). The decreasing slopes of the STR with increasing sample area12 (c), and the SAR with increasing sample duration12 (d) for various communities and in simulations (g and h N = 256, ϕ = 10, σ2 = 0.01, ℓ = 0.5, spatial window ΔA, temporal windo ΔT). In (c) and (d) we have rescaled the sample area/duration by the smallest/shortest reported value and coloured by community (see original study for details). In (g) and (h) we study the STAR in metacommunities of various size N, represented by colour. Limited spatio-temporal turnover in the smallest metacommunties (blue colours) greatly reduces the exponents of the STAR relative to large metacommunities (red colours). Aij = 0.5 with probability 0.5 in all cases.Full size imageTemporal occupancyThe proportion of time in which species occupy a community tends to have a bi-modal empirical distribution54,55,56 (Fig. 6a). The distribution we found in simulations (Fig. 6e) closely matches the empirical pattern.Community structureTemporal turnover has been posited to play a stabilising role in the maintenance of community structure57,58. In an estuarine fish community59, for example, species richness (Fig. 6b) and the distribution of abundances were remarkably robust despite changes in population biomasses by multiple orders of magnitude. In model metacommunities with autonomous turnover we found, likewise, that local species richness exhibited only small fluctuations around the steady-state mean (Fig. 6f, three random local communities shown) and that the macroscopic structure of the community was largely time invariant (Supplementary Fig. 12). In the light of our results, we propose the absence of temporal change in community properties such as richness or the abundance distribution despite potentially large fluctuations in population abundances59 as indicative of autonomous compositional turnover.The species-time-area-relation, STARThe species-time-relation (STR), typically fit by a power law of the form S ∝ Tw 12,60,61, describes how observed species richness increases with observation time T. The exponent w of the STR has been found to be consistent across taxonomic groups and ecosystems12,13,62, indicative of some general population dynamical mechanism. However, the exponent of the STR decreases with increasing sampling area12, and the exponent of the empirical Species Area Relation (SAR) (S ∝ Az) consistently decreases with increasing sampling duration12 (Fig. 6c, d). We tested for these patterns in a large simulated metacommunity with N = 256 patches by computing the species-time-area-relation (STAR) for nested subdomains and variable temporal sampling windows (see “Methods”). We observed exponents of the nested SAR in the range z = 0.02–0.44 and for the STR a range w = 0.01–0.44 (Supplementary Fig. 13). We also found a clear decrease in the rate of species accumulation in time as a function of sample area and vice-versa (Fig. 6g, h), consistent with the empirical observations. Meta-analyses of these patterns in nature have reported exponents which are remarkably consistent, with z typically in the range 0.1–0.363, and w typically in the range 0.2–0.413, in both cases largely independent of location or taxonomic group13.Thus, the distribution of temporal occupancy, the time invariance of key macroecological structures and the STAR in our model metacommunities match observed patterns. This evidence suggests that such autonomous dynamics cannot be ruled out as an important driver of temporal compositional change in natural ecosystems.Turnover rate in simulated metacommunitiesHow do the turnover rates that we find in our model compare with those observed? Our current analytic understanding of autonomous turnover is insufficient for estimating the rates directly from parameters, but the simulation results provide some indication of the expected order of magnitude, that can be compared with observations. Key for such a comparison is the fact that, because the elements of R are 1 on average, the time required for an isolated single population to reach carrying capacity is ({mathcal{O}}(1)) unit times. Supplementary Fig. 12b suggests that transitions between community states occur at the scale of around 10–50 unit times. This gives a holistic, rule-of-thumb estimate for the expected rate of autonomous turnover, depending on the typical reproductive rates of the guild of interest. In the case of macroinvertebrates, for example, the time required for populations to saturate in population biomass could be of the order of a month or less. By our rule of thumb, this would mean that autonomous community turnover would occur on a timescale of years. In contrast, for slow growing species like trees, where monoculture stands can take decades to reach maximum population biomass, the predicted timescale for autonomous turnover would be on the order of centuries or more. Indeed, macroinvertebrate communities have been observed switching between community configurations with a period of a few years64,65, while the proportional abundance of tree pollen and tree fern spores fluctuates in rain forest bog deposits with a period of the order of 103 years66—suggesting that the predicted autonomous turnover rates are biologically plausible.ConclusionsCurrent understanding of the mechanisms driving temporal turnover in ecological communities is predominantly built upon phenomenological studies of observed patterns2,67,68,69 and is unquestionably incomplete10,59. That temporal turnover can be driven by external forces—e.g., seasonal or long term climate change, direct anthropogenic pressures—is indisputable. A vitally important question is, however, how much empirically observed compositional change is actually due to such forcing. Recent landmark analyses of temporal patterns in biodiversity have detected no systematic change in species richness or structure in natural communities, despite rates of compositional turnover greater than predicted by stochastic null models1,70,71,72. Here we have shown that empirically realistic turnover in model metacommunities can occur via precisely the same mechanism as that responsible for regulating species richness at the local scale. While the processes regulating diversity in natural communities remain insufficiently understood, our theoretical work suggests local structural instability may explain these empirical observations in a unified and parsimonious way. Therefore, we advocate for the application of null models of metacommunity dynamics that account for natural turnover in ecological status assessments and predictions based on ancestral baselines. Future work will involve fitting the model described here to observations by estimating abiotic and biotic parameters from empirical datasets. In the Supplementary Discussion we show how different combinations of parameters lead to different quantitative outcomes (Supplementary Fig. 4), likely representing different types of empirical metacommunities. Understanding where in this parameter space natural systems exist may provide the foundation for a quantitative null model, a baseline expectation of turnover against which observations can be compared.Our simulations revealed a qualitative transition from “small” metacommunities, where autonomous turnover is absent or minimal, to “large” metacommunities with pronounced autonomous turnover (Fig. 2). The precise location of the transition between these cases depends on details such as dispersal traits, the ecological interaction network, and environmental gradients (Supplementary Fig. 4). Taking, for simplicity, regional species richness as a measure of metacommunity size suggests that both ‘small’ and ‘large’ communities in this sense are realised in nature. In our simulations, the smallest metacommunities sustain 10s of species, while the largest have a regional diversity of the order 103, which is not large comparable to the number of tree species in just 0.25 km2 of tropical rainforest (1100–1200 in Borneo and Ecuador73) or of macroinvertebrates in the UK ( >32,00074). Within the ‘small’ category, where autonomous turnover is absent, we would therefore expect to be, e.g., communities of marine mammals or large fish, where just a few species interact over ranges that can extend across entire climatic niches, implying that the effective number of independent “patches” is small and providing few opportunities for colonisation by species from neighbouring communities. Likely to belong to the ‘large’ category are communities of organisms that occur in high diversity with range sizes that are small compared to climatic niches, such as macroinvertebrates. For these, autonomous turnover of local communities can plausibly be expected based on our findings. Empirically distinguishing between these two cases for different guilds will be an important task for the future.For metacommunities of intermediate spatial extent, autonomous turnover is characterised by sharp transitions between cohesive states at the local scale. To date, few empirical analyses have reported such coherence in temporal turnover, perhaps because the taxonomic and temporal resolution required to detect such patterns is not yet widely available. Developments in biomonitoring technologies75 are likely to reveal a variety of previously undetected ecological dynamics, however and by combining high resolution temporal sampling and metagenetic analysis of community composition, a recent study demonstrated cohesive but short-lived community cohorts in coastal plankton76. Such Clementsian temporal turnover may offer a useful signal of autonomous compositional change in real systems.Thus, overcoming previous computational limits to the study of complex metacommunities11,77, we have discovered the existence of two distinct phases of metacommunity ecology—one characterised by weak or absent autonomous turnover, the other by continuous compositional change even in the absence of external drivers. By synthesising a wide range of established ecological theory11,23,24,47,48,49, we have heuristically explained these phases. Our explanation implies that autonomous turnover requires little more than a diverse neighbourhood of potential invaders, a weak immigration pressure, and a complex network of interactions between co-existing species. More