Philippa Kaur

1.
Fuentes, M. M. et al. Adaptive management of marine mega-fauna in a changing climate. Mitig. Adapt. Strat. Glob. Change 21, 209–224 (2016).
Article  Google Scholar 
2.
Grose, S. O., Pendleton, L., Leathers, A., Cornish, A. & Waitai, S. Climate change will re-draw the map for marine megafauna and the people who depend on them. Front. Mar. Sci. 7, 547 (2020).
Article  Google Scholar 

3.
Halley, J. M., Van Houtan, K. S. & Mantua, N. How survival curves affect populations’ vulnerability to climate change. PLoS ONE 13, e0203124 (2018).
PubMed  PubMed Central  Article  CAS  Google Scholar 

4.
Zacharias, M. A. & Roff, J. C. Use of focal species in marine conservation and management: a review and critique. Aquat. Conserv. Mar. Freshw. Ecosyst. 11, 59–76 (2001).
Article  Google Scholar 

5.
Hazen, E. L. et al. Ontogeny in marine tagging and tracking science: technologies and data gaps. Mar. Ecol. Prog. Ser. 457, 221–240 (2012).
ADS  Article  Google Scholar 

6.
Hazen, E. L. et al. Predicted habitat shifts of Pacific top predators in a changing climate. Nat. Clim. Change 3, 234–238 (2013).
ADS  Article  Google Scholar 

7.
Jorgensen, S. J. et al. Killer whales redistribute white shark foraging pressure on seals. Sci. Rep. 9, 1–9. https://doi.org/10.1038/s41598-019-39356-2 (2019).
CAS  Article  Google Scholar 

8.
Domeier, M. L. Global perspectives on the biology and life history of the white shark (CRC Press, Boca Raton, 2012).
Google Scholar 

9.
Bruce, B. D. & Bradford, R. W. Habitat use and spatial dynamics of juvenile white sharks, Carcharodon carcharias, in eastern Australia. Global perspectives on the biology and life history of the white shark, 225–254 (2012).

10.
Lowe, C. G. et al. Historic fishery interactions with white sharks in the Southern California Bight. Global Perspectives on the Biology and Life History of the White Shark’.(Ed. ML Domeier.) pp, 169–186 (2012).

11.
Villafaña, J. A. et al. First evidence of a palaeo-nursery area of the great white shark. Sci. Rep. 10, 1–8 (2020).
Article  CAS  Google Scholar 

12.
Oñate-González, E. C. et al. Importance of Bahia Sebastian Vizcaino as a nursery area for white sharks (Carcharodon carcharias) in the Northeastern Pacific: a fishery dependent analysis. Fish. Res. 188, 125–137 (2017).
Article  Google Scholar 

13.
Klimley, A. P. The areal distribution and autoecology of the white shark, Carcharodon carcharias, off the west coast of North America. Mem. Southern Calif. Acad Sci 9, 15–40 (1985).
Google Scholar 

14.
Weng, K. C. et al. Movements, behavior and habitat preferences of juvenile white sharks Carcharodon carcharias in the eastern Pacific. Mar. Ecol. Prog. Ser. 338, 211–224 (2007).
ADS  Article  Google Scholar 

15.
White, C. F. et al. Quantifying habitat selection and variability in habitat suitability for juvenile white sharks. PLoS ONE 14, e0214642. https://doi.org/10.1371/journal.pone.0214642 (2019).
Article  PubMed  PubMed Central  Google Scholar 

16.
Di Lorenzo, E. & Mantua, N. Multi-year persistence of the 2014/15 North Pacific marine heatwave. Nat. Clim. Change 6, 1042–1047 (2016).
ADS  Article  Google Scholar 

17.
Peterson, W. T. et al. The pelagic ecosystem in the Northern California Current off Oregon during the 2014–2016 warm anomalies within the context of the past 20 years. J. Geophys. Res.: Oceans 122, 7267–7290 (2017).
ADS  Article  Google Scholar 

18.
Thompson, A. et al. State of the California current: a new anchovy regime and Marine Heatwave? California Cooperative Oceanic Fisheries Investigations Reports. Calif. Cooper. Ocean. Fish. Investig. 60, 1–61 (2019).
Google Scholar 

19.
Gentemann, C. L., Fewings, M. R. & García-Reyes, M. Satellite sea surface temperatures along the West Coast of the United States during the 2014–2016 northeast Pacific marine heat wave. Geophys. Res. Lett. 44, 312–319 (2017).
ADS  Article  Google Scholar 

20.
Sanford, E., Sones, J. L., García-Reyes, M., Goddard, J. H. & Largier, J. L. Widespread shifts in the coastal biota of northern California during the 2014–2016 marine heatwaves. Sci. Rep. 9, 1–14 (2019).
ADS  Article  CAS  Google Scholar 

21.
Choy, C. A. et al. The vertical distribution and biological transport of marine microplastics across the epipelagic and mesopelagic water column. Sci. Rep. 9, 1–9 (2019).
Article  CAS  Google Scholar 

22.
Kohl, W. T., McClure, T. I. & Miner, B. G. Decreased temperature facilitates short-term sea star wasting disease survival in the keystone intertidal sea star Pisaster ochraceus. PLoS ONE 11, e0153670 (2016).
PubMed  PubMed Central  Article  Google Scholar 

23.
Laake, J. L., Lowry, M. S., DeLong, R. L., Melin, S. R. & Carretta, J. V. Population growth and status of California sea lions. J. Wildl. Manag. 82, 583–595 (2018).
Article  Google Scholar 

24.
Jones, T. et al. Unusual mortality of Tufted puffins (Fratercula cirrhata) in the eastern Bering Sea. PLoS ONE 14, e0216532 (2019).
CAS  PubMed  PubMed Central  Article  Google Scholar 

25.
Savage, K. Alaska and British Columbia large whale unusual mortality event summary report. (2017).

26.
Gravem, S. A. & Morgan, S. G. Shifts in intertidal zonation and refuge use by prey after mass mortalities of two predators. Ecology 98, 1006–1015 (2017).
PubMed  Article  PubMed Central  Google Scholar 

27.
Cheung, W. W. & Frölicher, T. L. Marine heatwaves exacerbate climate change impacts for fisheries in the northeast Pacific. Sci. Rep. 10, 1–10 (2020).
Article  CAS  Google Scholar 

28.
Kanive, P. E. et al. Size-specific apparent survival rate estimates of white sharks using mark–recapture models. Can. J. Fish. Aquat. Sci. 76, 2027–2034 (2019).
Article  Google Scholar 

29.
California_State_Senate. Budget Act of 2018. Senate Bill 840 2017–2018 (2018).

30.
Miller-Rushing, A., Primack, R. & Bonney, R. The history of public participation in ecological research. Front. Ecol. Environ. 10, 285–290 (2012).
Article  Google Scholar 

31.
Vianna, G. M., Meekan, M. G., Bornovski, T. H. & Meeuwig, J. J. Acoustic telemetry validates a citizen science approach for monitoring sharks on coral reefs. PLoS ONE 9, e95565 (2014).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

32.
Klimley, A. P., Anderson, S. D., Pyle, P. & Henderson, R. Spatiotemporal patterns of white shark (Carcharodon carcharias) predation at the South Farallon Islands, California. Copeia, 680–690 (1992).

33.
Fredston-Hermann, A., Selden, R., Pinsky, M., Gaines, S. D. & Halpern, B. S. Cold range edges of marine fishes track climate change better than warm edges. Glob. Change Biol. 26, 2908–2922 (2020).
ADS  Article  Google Scholar 

34.
Cheung, W. W., Watson, R. & Pauly, D. Signature of ocean warming in global fisheries catch. Nature 497, 365–368 (2013).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

35.
Mcclure, M. M. et al. Incorporating climate science in applications of the US Endangered Species Act for aquatic species. Conserv. Biol. 27, 1222–1233 (2013).
PubMed  Article  PubMed Central  Google Scholar 

36.
Silber, G. K. et al. Projecting marine mammal distribution in a changing climate. Front. Mar. Sci. 4, 413 (2017).
Article  Google Scholar 

37.
Cao, J., Thorson, J. T., Punt, A. E. & Szuwalski, C. A novel spatiotemporal stock assessment framework to better address fine-scale species distributions: Development and simulation testing. Fish Fish. 21, 350–367. https://doi.org/10.1111/faf.12433 (2020).
Article  Google Scholar 

38.
Dewar, H., Domeier, M. & Nasby-Lucas, N. Insights into young of the year white shark, Carcharodon carcharias, behavior in the Southern California Bight. Environ. Biol. Fishes 70, 133–143 (2004).
Article  Google Scholar 

39.
Moxley, J. H., Nicholson, T. E., Van Houtan, K. S. & Jorgensen, S. J. Non-trophic impacts from white sharks complicate population recovery for sea otters. Ecol. Evol. 9, 6378–6388. https://doi.org/10.1002/ece3.5209 (2019).
Article  PubMed  PubMed Central  Google Scholar 

40.
Cury, P. et al. Small pelagics in upwelling systems: patterns of interaction and structural changes in “wasp-waist” ecosystems. ICES J. Mar. Sci. 57, 603–618 (2000).
Article  Google Scholar 

41.
Nicholson, T. E. et al. Gaps in kelp cover may threaten the recovery of California sea otters. Ecography 41, 1751–1762 (2018).
Article  Google Scholar 

42.
Kenyon, K. W. The sea otter in the eastern Pacific Ocean. (US Bureau of Sport Fisheries and Wildlife, 1969).

43.
Tinker, M. T., Hatfield, B. B., Harris, M. D. & Ames, J. A. Dramatic increase in sea otter mortality from white sharks in California. Mar. Mammal Sci. 32, 309–326 (2016).
Article  Google Scholar 

44.
Miller, M. A. et al. Predators, disease, and environmental change in the nearshore ecosystem: mortality in Southern Sea Otters (Enhydra lutris nereis) From 1998–2012. Front. Mar. Sci. 7, 582 (2020).
Article  Google Scholar 

45.
Estes, J. A. & Palmisano, J. F. Sea otters: their role in structuring nearshore communities. Science 185, 1058–1060 (1974).
ADS  CAS  PubMed  Article  Google Scholar 

46.
Hughes, B. B. et al. Recovery of a top predator mediates negative eutrophic effects on seagrass. Proc. Natl. Acad. Sci. USA 110, 15313–15318. https://doi.org/10.1073/pnas.1302805110 (2013).
ADS  Article  PubMed  Google Scholar 

47.
Becker, S. L., Nicholson, T. E., Mayer, K. A., Murray, M. J. & Van Houtan, K. S. Environmental Factors May Drive the Post-release Movements of Surrogate-Reared Sea Otters. Frontiers in Marine Science 7, doi:https://doi.org/10.3389/fmars.2020.539904 (2020).

48.
Mayer, K. A. et al. Surrogate rearing a keystone species to enhance population and ecosystem restoration. Oryx, 1–11 (2019).

49.
Jorgensen, S. J. et al. Philopatry and migration of Pacific white sharks. Proc. R. Soc. B: Biol. Sci. 277, 679–688 (2010).
Article  Google Scholar 

50.
Breaker, L. & Broenkow, W. W. The circulation of Monterey Bay and related processes. Moss Land. Mar. Lab. Tech. Publ. 89, 114 (1989).
Google Scholar 

51.
Santora, J. A. et al. Habitat compression and ecosystem shifts as potential links between marine heatwave and record whale entanglements. Nat. Commun. 11, 536. https://doi.org/10.1038/s41467-019-14215-w (2020).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

52.
Zanna, L., Khatiwala, S., Gregory, J. M., Ison, J. & Heimbach, P. Global reconstruction of historical ocean heat storage and transport. Proc. Natl. Acad. Sci. 116, 1126–1131 (2019).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

53.
Gaines, S. D. & Denny, M. W. The largest, smallest, highest, lowest, longest, and shortest: extremes in ecology. Ecology 74, 1677–1692 (1993).
Article  Google Scholar 

54.
Hobday, A. J. et al. A hierarchical approach to defining marine heatwaves. Prog. Oceanogr. 141, 227–238 (2016).
ADS  Article  Google Scholar 

55.
Oliver, E. C. et al. Longer and more frequent marine heatwaves over the past century. Nat. Commun. 9, 1–12 (2018).
CAS  Article  Google Scholar 

56.
Worm, B. et al. Global catches, exploitation rates, and rebuilding options for sharks. Mar. Policy 40, 194–204 (2013).
Article  Google Scholar 

57.
McCauley, D. J., DeSalles, P. A., Young, H. S., Gardner, J. P. & Micheli, F. Use of high-resolution acoustic cameras to study reef shark behavioral ecology. J. Exp. Mar. Biol. Ecol. 482, 128–133 (2016).
Article  Google Scholar 

58.
Ward-Paige, C. A. & Worm, B. Global evaluation of shark sanctuaries. Global Environ. Change 47, 174–189 (2017).
Article  Google Scholar 

59.
Van Houtan, K. S. et al. Coastal sharks supply the global shark fin trade. Biol. Let. 16, 20200609 (2020).
Article  CAS  Google Scholar 

60.
Benson, J. F. et al. Juvenile survival, competing risks, and spatial variation in mortality risk of a marine apex predator. J. Appl. Ecol. 55, 2888–2897 (2018).
Article  Google Scholar 

61.
Cleveland, W. S. & Devlin, S. J. Locally weighted regression: an approach to regression analysis by local fitting. J. Am. Stat. Assoc. 83, 596–610 (1988).
MATH  Article  Google Scholar 

62.
Beck, J., Ballesteros-Mejia, L., Nagel, P. & Kitching, I. J. Online solutions and the ‘W allacean shortfall’: what does GBIF contribute to our knowledge of species’ ranges?. Divers. Distrib. 19, 1043–1050 (2013).
Article  Google Scholar 

63.
Dickinson, J. L., Zuckerberg, B. & Bonter, D. N. Citizen science as an ecological research tool: challenges and benefits. Annu. Rev. Ecol. Evol. Syst. 41, 149–172 (2010).
Article  Google Scholar 

64.
Van Horn, G. et al. in Proceedings of the IEEE conference on computer vision and pattern recognition. 8769–8778.

65.
Teo, S. L. et al. Validation of geolocation estimates based on light level and sea surface temperature from electronic tags. Mar. Ecol. Prog. Ser. 283, 81–98 (2004).
ADS  Article  Google Scholar 

66.
Handcock, M. S. Package ‘reldist’. (2016).

67.
Reynolds, R. & Banzon, V. NOAA Optimum Interpolation 1/4 Degree Daily Sea Surface Temperature (OISST) Analysis, Version 2. NOAA National Centers for Environmental Information. 10, V5SQ8XB5 (2008).

68.
Banzon, V., Smith, T. M., Chin, T. M., Liu, C. & Hankins, W. A long-term record of blended satellite and in situ sea-surface temperature for climate monitoring, modeling and environmental studies. (2016).

69.
Lyons, K. et al. The degree and result of gillnet fishery interactions with juvenile white sharks in southern California assessed by fishery-independent and-dependent methods. Fish. Res. 147, 370–380 (2013).
ADS  Article  Google Scholar 

70.
Mayer, L. et al. The Nippon Foundation—GEBCO seabed 2030 project: The quest to see the world’s oceans completely mapped by 2030. Geosciences 8, 63 (2018).
ADS  Article  Google Scholar 

71.
Tanaka, K. R. et al. Mesoscale climatic impacts on the distribution of Homarus americanus in the US inshore Gulf of Maine. Can. J. Fish. Aquat. Sci. 76, 608–625 (2019).
Article  Google Scholar 

72.
R_Core_Team. (Vienna, Austria, 2019). More

Affiliations

Ecosystem Dynamics and Forest Management Group, Technical University of Munich, Freising, Germany
Cornelius Senf & Rupert Seidl

Institute for Silviculture, University of Natural Resources and Life Sciences (BOKU), Vienna, Austria
Cornelius Senf & Rupert Seidl

Berchtesgaden National Park, Berchtesgaden, Germany
Rupert Seidl

Authors
Cornelius Senf

Rupert Seidl

Corresponding author
Correspondence to Cornelius Senf. More

1.
Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853. https://doi.org/10.1126/science.1244693 (2013).
ADS  CAS  Article  Google Scholar 
2.
Chazdon, R. L. & Guariguata, M. R. Natural regeneration as a tool for large-scale forest restoration in the tropics: prospects and challenges. Biotropica 48, 716–730. https://doi.org/10.1111/btp.12381 (2016).
Article  Google Scholar 

3.
Holl, K. D. Restoring tropical forests from the bottom up. Science 355, 455–456. https://doi.org/10.1126/science.aam5432 (2017).
ADS  CAS  Article  PubMed  Google Scholar 

4.
Brancalion, P. H. S. et al. Balancing economic costs and ecological outcomes of passive and active restoration in agricultural landscapes: the case of Brazil. Biotropica 48, 856–867. https://doi.org/10.1111/btp.12383 (2016).
Article  Google Scholar 

5.
Foley, J. A. et al. Amazonia revealed: forest degradation and loss of ecosystem goods and services in the Amazon Basin. Front. Ecol. Environ. 5, 25–32. https://doi.org/10.1890/1540-9295(2007)5[25:ARFDAL]2.0.CO;2 (2007).
Article  Google Scholar 

6.
Montibeller, B., Kmoch, A., Virro, H., Mander, Ü. & Uuemaa, E. Increasing fragmentation of forest cover in Brazil’s Legal Amazon from 2001 to 2017. Sci. Rep. 10, 5803. https://doi.org/10.1038/s41598-020-62591-x (2020).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

7.
Csillik, O., Kumar, P., Mascaro, J., O’Shea, T. & Asner, G. P. Monitoring tropical forest carbon stocks and emissions using Planet satellite data. Sci. Rep. 9, 17831. https://doi.org/10.1038/s41598-019-54386-6 (2019).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

8.
Nunes, S. et al. Uncertainties in assessing the extent and legal compliance status of riparian forests in the eastern Brazilian Amazon. Land Use Policy 82, 37–47. https://doi.org/10.1016/j.landusepol.2018.11.051 (2019).
Article  Google Scholar 

9.
Rocha, G. P. E., Vieira, D. L. M. & Simon, M. F. Fast natural regeneration in abandoned pastures in southern Amazonia. For. Ecol. Manag. 370, 93–101. https://doi.org/10.1016/j.foreco.2016.03.057 (2016).
Article  Google Scholar 

10.
Rodrigues, S. B. et al. Direct seeded and colonizing species guarantee successful early restoration of South Amazon forests. For. Ecol. Manag. 451, 117559. https://doi.org/10.1016/j.foreco.2019.117559 (2019).
Article  Google Scholar 

11.
Fearnside, P. M. Deforestation in Brazilian Amazonia: history, rates, and consequences. Conserv. Biol. 19, 680–688. https://doi.org/10.1111/j.1523-1739.2005.00697.x (2005).
Article  Google Scholar 

12.
Laurance, W. F. et al. Rain forest fragmentation and the proliferation of sucessional trees. Ecology 87, 469–482. https://doi.org/10.1890/05-0064 (2006).
Article  PubMed  Google Scholar 

13.
Poorter, L. et al. Biomass resilience of Neotropical secondary forests. Nature 530, 211–214. https://doi.org/10.1038/nature16512 (2016).
ADS  CAS  Article  PubMed  Google Scholar 

14.
Camargo, J. L. C., Ferraz, I. D. K. & Imakawa, A. M. Rehabilitation of degraded areas of central Amazonia using direct sowing of forest tree seeds. Restor. Ecol. 10, 636–644. https://doi.org/10.1046/j.1526-100X.2002.01044.x (2002).
Article  Google Scholar 

15.
Guariguata, M. R. & Ostertag, R. Neotropical secondary forest succession: changes in structural and functional characteristics. For. Ecol. Manag. 148, 185–206. https://doi.org/10.1016/S0378-1127(00)00535-1 (2001).
Article  Google Scholar 

16.
Crouzeilles, R. et al. A global meta-analysis on the ecological drivers of forest restoration success. Nat. Commun. 7, 11666. https://doi.org/10.1038/ncomms11666 (2016).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

17.
Chazdon, R. L. et al. The potential for species conservation in tropical secondary forests. Conserv. Biol. 23, 1406–1417. https://doi.org/10.1111/j.1523-1739.2009.01338.x (2009).
Article  PubMed  Google Scholar 

18.
Peres, C. A., Emilio, T., Schietti, J., Desmouliere, S. J. & Levi, T. Dispersal limitation induces long-term biomass collapse in overhunted Amazonian forests. Proc. Natl. Acad. Sci. USA 113, 892–897. https://doi.org/10.1073/pnas.1516525113 (2016).
ADS  CAS  Article  PubMed  Google Scholar 

19.
Pessoa, M. S. et al. Deforestation drives functional diversity and fruit quality changes in a tropical tree assemblage. Perspect. Plant Ecol. Evol. Syst. 28, 78–86. https://doi.org/10.1016/j.ppees.2017.09.001 (2017).
Article  Google Scholar 

20.
Bowen, M. E., McAlpine, C. A., House, A. P. & Smith, G. C. Regrowth forests on abandoned agricultural land: a review of their habitat values for recovering forest fauna. Biol. Cons. 140, 273–296. https://doi.org/10.1016/j.biocon.2007.08.012 (2007).
Article  Google Scholar 

21.
Chazdon, R. L. & Uriarte, M. Natural regeneration in the context of large-scale forest and landscape restoration in the tropics. Biotropica 48, 709–715. https://doi.org/10.1111/btp.12409 (2016).
Article  Google Scholar 

22.
Neuschulz, E. L., Mueller, T., Schleuning, M. & Böhning-Gaese, K. Pollination and seed dispersal are the most threatened processes of plant regeneration. Sci. Rep. 6, 29839. https://doi.org/10.1038/srep29839 (2016).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

23.
Stoner, K. E., Riba-Hernández, P., Vulinec, K. & Lambert, J. E. The role of mammals in creating and modifying seedshadows in tropical forests and some possible consequences of their elimination. Biotropica 39, 316–327. https://doi.org/10.1111/j.1744-7429.2007.00292.x (2007).
Article  Google Scholar 

24.
Griffiths, H. M., Bardgett, R. D., Louzada, J. & Barlow, J. The value of trophic interactions for ecosystem function: dung beetle communities influence seed burial and seedling recruitment in tropical forests. Proc. R. Soc. B 283, 20161634. https://doi.org/10.1098/rspb.2016.1634 (2016).
Article  PubMed  Google Scholar 

25.
Asquith, N. M. & Mejía-Chang, M. Mammals, edge effects, and the loss of tropical forest diversity. Ecology 86, 379–390. https://doi.org/10.1890/03-0575 (2005).
Article  Google Scholar 

26.
Beck, H., Snodgrass, J. W. & Thebpanya, P. Long-term exclosure of large terrestrial vertebrates: Implications of defaunation for seedling demographics in the Amazon rainforest. Biol. Cons. 163, 115–121. https://doi.org/10.1016/j.biocon.2013.03.012 (2013).
Article  Google Scholar 

27.
Paine, C. E., Beck, H. & Terborgh, J. How mammalian predation contributes to tropical tree community structure. Ecology 97, 3326–3336. https://doi.org/10.1002/ecy.1586 (2016).
Article  PubMed  Google Scholar 

28.
Sobral, M. et al. Mammal diversity influences the carbon cycle through trophic interactions in the Amazon. Nat. Ecol. Evol. 1, 1670–1676. https://doi.org/10.1038/s41559-017-0334-0 (2017).
Article  PubMed  Google Scholar 

29.
Bascompte, J. & Jordano, P. Plant-animal mutualistic networks: the architecture of biodiversity. Annu. Rev. Ecol. Evol. Syst. 38, 567–593. https://doi.org/10.1146/annurev.ecolsys.38.091206.095818 (2007).
Article  MATH  Google Scholar 

30.
Wunderle, J. M. The role of animal seed dispersal in accelerating native forest regeneration on degraded tropical lands. For. Ecol. Manag. 99, 223–235. https://doi.org/10.1016/S0378-1127(97)00208-9 (1997).
Article  Google Scholar 

31.
Fragoso, J. M. V. Tapir-generated seed shadows: scale-dependent patchiness in the Amazon Rain Forest. J. Ecol. 85, 519–529. https://doi.org/10.2307/2960574 (1997).
Article  Google Scholar 

32.
Hibert, F. et al. Unveiling the diet of elusive rainforest herbivores in next generation sequencing era? The tapir as a case study. PLoS ONE 8, e60799. https://doi.org/10.1371/journal.pone.0060799 (2013).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

33.
Terborgh, J. et al. Tree recruitment in an empty forest. Ecology 89, 1757–1768. https://doi.org/10.1890/07-0479.1 (2008).
Article  PubMed  Google Scholar 

34.
Wright, S. J. et al. The plight of large animals in tropical forests and the consequences for plant regeneration. Biotropica 39, 289–291. https://doi.org/10.1111/j.1744-7429.2007.00293.x (2007).
Article  Google Scholar 

35.
Molto, Q. et al. Predicting tree heights for biomass estimates in tropical forests; a test from French Guiana. Biogeosciences 11, 3121–3130. https://doi.org/10.5194/bg-11-3121-2014 (2014).
ADS  Article  Google Scholar 

36.
Letcher, S. G. & Chazdon, R. L. Rapid recovery of biomass, species richness, and species composition in a forest chronosequence in Northeastern Costa Rica. Biotropica 41, 608–617. https://doi.org/10.1111/j.1744-7429.2009.00517.x (2009).
Article  Google Scholar 

37.
Körner, C. The use of ‘altitude’ in ecological research. Trends Ecol. Evol. 22, 569–574. https://doi.org/10.1016/j.tree.2007.09.006 (2007).
Article  PubMed  Google Scholar 

38.
Beven, K. & Kirkby, M. J. A physically based, variable contributing area model of basin hydrology. Hydrol. Sci. J. 24, 43–69. https://doi.org/10.1080/02626667909491834 (1979).
Article  Google Scholar 

39.
Campling, P., Gobin, A. & Feyen, J. Logistic modeling to spatially predict the probability of soil drainage classes. Soil Sci. Soc. Am. J. 66, 1390–1401. https://doi.org/10.2136/sssaj2002.1390 (2002).
ADS  CAS  Article  Google Scholar 

40.
Nobre, A. D. et al. Height above the nearest drainage: a hydrologically relevant new terrain model. J. Hydrol. 404, 13–29. https://doi.org/10.1016/j.jhydrol.2011.03.051 (2011).
ADS  Article  Google Scholar 

41.
Schietti, J. et al. Vertical distance from drainage drives floristic composition changes in an Amazonian rainforest. Plant Ecol. Diver. 7, 241–253. https://doi.org/10.1080/17550874.2013.783642 (2014).
Article  Google Scholar 

42.
Gehring, C., Denich, M. & Vlek, P. L. G. Resilience of secondary forest regrowth after slash-and-burn agriculture in central Amazonia. J. Trop. Ecol. 21, 519–527. https://doi.org/10.1017/S0266467405002543 (2005).
Article  Google Scholar 

43.
Feldpausch, T. R., Riha, S. J., Fernandes, E. C. M. & Wandelli, E. V. Development of forest structure and leaf area in secondary forests regenerating on abandoned pastures in Central Amazônia. Earth Interact. 9, 1–22. https://doi.org/10.1175/EI140.1 (2005).
Article  Google Scholar 

44.
Luskin, M. S., Ickes, K., Yao, T. L. & Davies, S. J. Wildlife differentially affect tree and liana regeneration in a tropical forest: an 18-year study of experimental terrestrial defaunation versus artificially abundant herbivores. J. Appl. Ecol. 56, 1379–1388. https://doi.org/10.1111/1365-2664.13378 (2019).
Article  Google Scholar 

45
Lu, D., Mausel, P., Brondizio, E. & Moran, E. Classification of successional forest stages in the Brazilian Amazon basin. Forest Ecol. Manag. 181, 301–312. https://doi.org/10.1016/S0378-1127(03)00003-3 (2003).
Article  Google Scholar 

46.
Crouzeilles, R. et al. Ecological restoration success is higher for natural regeneration than for active restoration in tropical forests. Science Advances 3, e1701345. https://doi.org/10.1126/sciadv.1701345 (2017).
ADS  Article  PubMed  PubMed Central  Google Scholar 

47
de Castilho, C. V. et al. Variation in aboveground tree live biomass in a central Amazonian Forest: Effects of soil and topography. Forest Ecol. Manag. 234, 85–96. https://doi.org/10.1016/j.foreco.2006.06.024 (2006).
Article  Google Scholar 

48.
Jucker, T. et al. Topography shapes the structure, composition and function of tropical forest landscapes. Ecol. Lett. 21, 989–1000. https://doi.org/10.1111/ele.12964 (2018).
Article  PubMed  PubMed Central  Google Scholar 

49.
Fortunel, C. et al. Topography and neighborhood crowding can interact to shape species growth and distribution in a diverse Amazonian forest. Ecology 99, 2272–2283. https://doi.org/10.1002/ecy.2441 (2018).
Article  PubMed  Google Scholar 

50.
Tiessen, H., Chacon, P. & Cuevas, E. Phosphorus and nitrogen status in soils and vegetation along a toposequence of dystrophic rainforests on the upper Rio Negro. Oecologia 99, 145–150. https://doi.org/10.1007/BF00317095 (1994).
ADS  CAS  Article  PubMed  Google Scholar 

51.
Paredes, O. S. L., Norris, D., Oliveira, T. G. D. & Michalski, F. Water availability not fruitfall modulates the dry season distribution of frugivorous terrestrial vertebrates in a lowland Amazon forest. PLOS ONE 12, e0174049. https://doi.org/10.1371/journal.pone.0174049 (2017).
CAS  Article  PubMed  PubMed Central  Google Scholar 

52.
Michalski, L. J., Norris, D., de Oliveira, T. G. & Michalski, F. Ecological relationships of meso-scale distribution in 25 neotropical vertebrate species. PLoS ONE 10, e0126114. https://doi.org/10.1371/journal.pone.0126114 (2015).
CAS  Article  PubMed  PubMed Central  Google Scholar 

53.
Mendes Pontes, A. R. Tree reproductive phenology determines the abundance of medium-sized and large mammalian assemblages in the Guyana shield of the Brazilian Amazonia. Anim. Biodiver. Conserv. 43(1), 9–26. https://doi.org/10.32800/abc.2020.43.0009 (2020).
Article  Google Scholar 

54.
Arévalo-Sandi, A., Bobrowiec, P. E. D., Rodriguez Chuma, V. J. U. & Norris, D. Diversity of terrestrial mammal seed dispersers along a lowland Amazon forest regrowth gradient. PLoS ONE 13, e0193752. https://doi.org/10.1371/journal.pone.0193752 (2018).
CAS  Article  PubMed  PubMed Central  Google Scholar 

55
Arita, H. T., Robinson, J. G. & Redford, K. Rarity in Neotropical forest mammals and its ecological correlates. Conserv. Biol. 4, 181–192. https://doi.org/10.1111/j.1523-1739.1990.tb00107.x (1990).
Article  Google Scholar 

56.
Peres, C. A. & Palacios, E. Basin-wide effects of game harvest on vertebrate population densities in Amazonian forests: implications for animal-mediated seed dispersal. Biotropica 39, 304–315. https://doi.org/10.1111/j.1744-7429.2007.00272.x (2007).
Article  Google Scholar 

57.
Emmons, L. H. & Feer, F. Neotropical Rainforest Mammals: A Field Guide (The University of Chicago Press, Chicago, 1997).
Google Scholar 

58.
Michalski, F., Michalski, L. J. & Barnett, A. A. Environmental determinants and use of space by six Neotropical primates in the northern Brazilian Amazon. Stud. Neotrop. Fauna Environ. 52, 187–197. https://doi.org/10.1080/01650521.2017.1335276 (2017).
Article  Google Scholar 

59.
Laurance, W. F. et al. Ecosystem decay of Amazonian forest fragments: a 22-year investigation. Conserv. Biol. 16, 605–618. https://doi.org/10.1046/j.1523-1739.2002.01025.x (2002).
Article  Google Scholar 

60.
Norris, D., Peres, C. A., Michalski, F. & Hinchsliffe, K. Terrestrial mammal responses to edges in Amazonian forest patches: a study based on track stations. Mammalia 72, 15–23. https://doi.org/10.1515/mamm.2008.002 (2008).
Article  Google Scholar 

61.
Martínez-Ramos, M. et al. Natural forest regeneration and ecological restoration in human-modified tropical landscapes. Biotropica 48, 745–757. https://doi.org/10.1111/btp.12382 (2016).
Article  Google Scholar 

62.
Laurance, W. F., Delamônica, P., Laurance, S. G., Vasconcelos, H. L. & Lovejoy, T. E. Rainforest fragmentation kills big trees. Nature 404, 836–836. https://doi.org/10.1038/35009032 (2000).
ADS  CAS  Article  PubMed  Google Scholar 

63.
Tabarelli, M., Lopes, A. V. & Peres, C. A. Edge-effects drive tropical forest fragments towards an early-successional system. Biotropica 40, 657–661. https://doi.org/10.1111/j.1744-7429.2008.00454.x (2008).
Article  Google Scholar 

64.
Santos, B. A. et al. Drastic erosion in functional attributes of tree assemblages in Atlantic forest fragments of northeastern Brazil. Biol. Cons. 141, 249–260. https://doi.org/10.1016/j.biocon.2007.09.018 (2008).
Article  Google Scholar 

65.
Melo, F. P. L., Arroyo-Rodríguez, V., Fahrig, L., Martínez-Ramos, M. & Tabarelli, M. On the hope for biodiversity-friendly tropical landscapes. Trends Ecol. Evol. 28, 462–468. https://doi.org/10.1016/j.tree.2013.01.001 (2013).
Article  PubMed  Google Scholar 

66
Malhi, Y. et al. Error propagation and scaling for tropical forest biomass estimates. Philos. Trans. R. Soc. Lond. Ser. B Biol. Sci. 359, 409–420. https://doi.org/10.1098/rstb.2003.1425 (2004).
Article  Google Scholar 

67.
Keller, M., Palace, M. & Hurtt, G. Biomass estimation in the Tapajos National Forest, Brazil: examination of sampling and allometric uncertainties. For. Ecol. Manag. 154, 371–382. https://doi.org/10.1016/S0378-1127(01)00509-6 (2001).
Article  Google Scholar 

68.
Arévalo-Sandi, A. R. & Norris, D. Short term patterns of germination in response to litter clearing and exclosure of large terrestrial vertebrates along an Amazon forest regrowth gradient. Glob. Ecol. Conserv. 13, e00371. https://doi.org/10.1016/j.gecco.2017.e00371 (2018).
Article  Google Scholar 

69.
David, M. O. et al. Terrestrial ecoregions of the world: a new map of life on earth. Bioscience 51, 933–938. https://doi.org/10.1641/0006-3568(2001)051[0933:TEOTWA]2.0.CO;2 (2001).
Article  Google Scholar 

70
ter Steege, H. et al. An analysis of the floristic composition and diversity of Amazonian forests including those of the Guiana Shield. J. Trop. Ecol. 16, 801–828 (2000).
Article  Google Scholar 

71.
Batista, A. P. B. et al. Caracterização estrutural em uma floresta de terra firme no estado do Amapá, Brasil. Pesq. flor. bras 35, 21–33 (2015).
Article  Google Scholar 

72
Eswaran, H., Ahrens, R., Rice, T. J. & Stewart, B. A. Soil Classification: A Global Desk Reference (CRC Press, Boca Raton, 2002).
Google Scholar 

73.
Kottek, M., Grieser, J., Beck, C., Rudolf, B. & Rubel, F. World map of the Koppen–Geiger climate classification updated. Meteorol. Z. 15, 259–263. https://doi.org/10.1127/0941-2948/2006/0130 (2006).
Article  Google Scholar 

74.
ANA. Sistema de Monitoramento Hidrológico (Hydrological Monitoring System). Agência Nacional de Águas[[nl]]National Water Agency, Available at http://www.hidroweb.ana.gov.br, 2017).

75
Norris, D., Rodriguez Chuma, V. J. U., Arevalo-Sandi, A. R., Landazuri Paredes, O. S. & Peres, C. A. Too rare for non-timber resource harvest? Meso-scale composition and distribution of arborescent palms in an Amazonian sustainable-use forest. Forest Ecol. Manag. 377, 182–191. https://doi.org/10.1016/j.foreco.2016.07.008 (2016).
Article  Google Scholar 

76.
Norris, D. & Michalski, F. Socio-economic and spatial determinants of anthropogenic predation on Yellow-spotted River Turtle, Podocnemis unifilis (Testudines: Pelomedusidae), nests in the Brazilian Amazon: Implications for sustainable conservation and management. Zoologia (Curitiba) 30, 482–490. https://doi.org/10.1590/S1984-46702013000500003 (2013).
Article  Google Scholar 

77
Yirdaw, E., MongeMonge, A., Austin, D. & Toure, I. Recovery of floristic diversity, composition and structure of regrowth forests on fallow lands: implications for conservation and restoration of degraded forest lands in Laos. New Forests 50, 1007–1026. https://doi.org/10.1007/s11056-019-09711-2 (2019).
Article  Google Scholar 

78.
McElhinny, C., Gibbons, P., Brack, C. & Bauhus, J. Forest and woodland stand structural complexity: its definition and measurement. For. Ecol. Manag. 218, 1–24. https://doi.org/10.1016/j.foreco.2005.08.034 (2005).
Article  Google Scholar 

79.
Sist, P., Mazzei, L., Blanc, L. & Rutishauser, E. Large trees as key elements of carbon storage and dynamics after selective logging in the Eastern Amazon. For. Ecol. Manag. 318, 103–109. https://doi.org/10.1016/j.foreco.2014.01.005 (2014).
Article  Google Scholar 

80.
Phillips, O. L. et al. Species matter: wood density influences tropical forest biomass at multiple scales. Surv. Geophys. 40, 913–935. https://doi.org/10.1007/s10712-019-09540-0 (2019).
ADS  Article  PubMed  PubMed Central  Google Scholar 

81.
Bastin, J.-F. et al. Pan-tropical prediction of forest structure from the largest trees. Glob. Ecol. Biogeogr. 27, 1366–1383. https://doi.org/10.1111/geb.12803 (2018).
Article  Google Scholar 

82.
TEAM Network. 69 (Tropical Ecology, Assessment and Monitoring Network, Center for Applied Biodiversity Science, Conservation International., Arlington, VA, USA., 2011).

83.
Hortal, J., Borges, P. A. & Gaspar, C. Evaluating the performance of species richness estimators: sensitivity to sample grain size. J. Anim. Ecol. 75, 274–287. https://doi.org/10.1111/j.1365-2656.2006.01048.x (2006).
Article  PubMed  Google Scholar 

84.
Magurran, A. E. & McGill, B. J. in Biological diversity: frontiers in measurement and assessment (eds A. E. Magurran & B. J. McGill) Ch. 1, 1–7 (Oxford University Press, 2011).

85.
Laliberté, E. & Legendre, P. A distance-based framework for measuring functional diversity from multiple traits. Ecology 91, 299–305. https://doi.org/10.1890/08-2244.1 (2010).
Article  PubMed  Google Scholar 

86.
FD: measuring functional diversity from multiple traits, and other tools for functional ecology. R package version 1.0–12 (2014).

87.
Burnham, K. P. & Anderson, D. R. Model Selection and Multi-model Inference: A Practical Information-Theoretic Approach (Springer, Berlin, 2002).
Google Scholar 

88.
Drasgow, F. in The Encyclopedia of Statistics Vol. 7 (eds S. Kotz & N. Johnson) 68–74 (Wiley, 1986).

89.
R Foundation for Statistical Computing. R: A Language and Environment for Statistical Computing. 3.6.3 (R Foundation for Statistical Computing, Vienna, 2020).
Google Scholar 

90.
90vegan: Community Ecology Package. R package version 2.4-0. https://CRAN.R-project.org/package=vegan (2016).

91.
Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, Berlin, 2009).
Google Scholar 

92.
MuMIn: multi-model inference. R package version 1.15.6. https://CRAN.R-project.org/package=MuMIn (2016).

93.
Tweedie: Tweedie exponential family models. R package version 2.2.1. https://cran.r-project.org/web/packages/tweedie (2014). More

Characteristics of the NMs
Commercially available spherical (10, 60, and 100 nm) and rod-shaped (10 × 45 nm and 50 × 100 nm) citrate-coated Au-NMs from Nanopartz (USA) were characterized in Milli-Q (MQ) water in terms of particle size and morphology using transmission electron microscopy (TEM) (Supplementary Fig. 1). The physicochemical properties of the Au-NMs in MQ water are summarized in Supplementary Table 1. A negative zeta potential (a measure of colloidal dispersion electrostatic stability) was observed for all Au-NMs and ranged from −21 to −25 mV in MQ water and from −17 to −19 mV in the algal exposure medium (without algae). The stability of the particles against dissolution and agglomeration in the algal exposure medium without algae was monitored throughout the exposure duration (72 h). The dissolved fraction of the Au-NMs was More

1.
TTWG [Turtle Taxonomy Working Group; Rhodin, A. G. J. et al.] Turtles of the World. Annotated Checklist and Atlas of Taxonomy, Synonymy, Distribution, and Conservation Status (8th Ed.) (Chelonian Research Foundation and Turtle Conservancy, Chelonian Research Monographs 7, 2017).
2.
TEWG [Turtle Extinctions Working Group; Rhodin, A. G. J. et al.] Turtles and Tortoises of the World During the Rise and Global Spread of Humanity: First Checklist and Review of Extinct Pleistocene and Holocene Chelonians (IUCN/SSC Tortoise and Freshwater Turtle Specialist Group, Chelonian Research Monographs 5, 2015).

3.
Clausen, C. J., Cohen, A. D., Emiliani, C., Holman, J. A. & Stipp, J. J. Little Salt Spring, Florida: A unique underwater site. Science 203, 609–614 (1979).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

4.
Holman, J. A. & Clausen, C. J. Fossil vertebrates associated with Paleo-Indian artifact at Little Salt Spring, Florida. J. Vertebr. Paleontol. 4, 146–154 (1984).
Article  Google Scholar 

5.
Cantalamessa, G. et al. A new vertebrate fossiliferous site from the Late Quaternary at San José on the north coast of Ecuador: Preliminary note. J. South Am. Earth Sci. 14, 331–334 (2001).
ADS  Article  Google Scholar 

6.
Aguilera Socorro, O. Tesoros paleontológicos de Venezuela. El Cuaternario del Estado Falcón (Ministerio de la Cultura, Instituto del Patrimonio Cultural, Caracas, 2006).
Google Scholar 

7.
Zacarías, G. G., de la Fuente, M. S., Fernández, M. S. & Zurita, A. E. Nueva especie de tortuga terrestre gigante del género Chelonoidis Fitzinger, 1835 (Cryptodira: Testudinidae), del miembro inferior de la Formación Toropí/Yupoí (Pleistoceno tardío/Lujanense), Bella Vista, Corrientes, Argentina. Ameghiniana 50, 298–318 (2013).
Article  Google Scholar 

8.
Zacarías, G. G., de la Fuente, M. S. & Zurita, A. E. Testudinoidea Fitzinger (Testudines: Cryptodira) de la Formación Toropí/Yupoí (ca. 58–28 ka) en la Provincia de Corrientes, Argentina: Taxonomía y aspectos paleoambientales. Rev. Bras. Paleontol. 17, 389–404 (2014).
Article  Google Scholar 

9.
Torres Chiriboga, F. J. Histología ósea de una tortuga gigante del Pleistoceno (Testudinidae) de Ecuador continental, con comentarios del origen de las tortugas de Galápagos (Disertación previa, Pontificia Universidad Católica del Ecuador, Quito, 2016).
Google Scholar 

10.
Cadena, E. A. & Román-Carrión, J. L. A review of the fossil record of Ecuador, with insights about its challenges and future development. Ameghiniana 55, 571–591 (2018).
Article  Google Scholar 

11.
Franz, R., Albury, N. A. & Steadman, D. W. Extinct tortoises from the Turks and Caicos Islands. Florida Mus. Nat. Hist. Bull. 58, 1–38 (2020).
Google Scholar 

12.
Williams, E. E. Testudo cubensis and the evolution of Western Hemisphere tortoises. Bull. Am. Mus. Nat. Hist. 95, 1–36 (1950).
Google Scholar 

13.
Williams, E. E. A new fossil tortoise from Mona Island, West Indies, and a tentative arrangement of the tortoises of the world. Bull. Am. Mus. Nat. Hist. 99, 545–560 (1952).
Google Scholar 

14.
Auffenberg, W. Notes on West Indian tortoises. Herpetologica 23, 34–44 (1967).
Google Scholar 

15.
Franz, R. & Woods, C. A. A fossil tortoise from Hispaniola. J. Herpetol. 17, 79–81 (1983).
Article  Google Scholar 

16.
Franz, R. & Franz, S. A new fossil land tortoise in the genus Chelonoidis (Testudines: Testudinidae) from the northern Bahamas, with an osteological assessment of other Neotropical tortoises. Florida Mus. Nat. Hist. Bull. 49, 1–44 (2009).
Google Scholar 

17.
Steadman, D. W. et al. Exceptionally well preserved late Quaternary plant and vertebrate fossils from a blue hole on Abaco, The Bahamas. Proc. Natl. Acad. Sci. USA 104, 19897–19902 (2007).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

18.
Hastings, A. K., Krigbaum, J., Steadman, D. W. & Albury, N. A. Domination by reptiles in a terrestrial food web of the Bahamas prior to human occupation. J. Herpetol. 48, 380–388 (2014).
Article  Google Scholar 

19.
Kehlmaier, C. et al. Tropical ancient DNA reveals relationships of the extinct Bahamian giant tortoise Chelonoidis alburyorum. Proc. R. Soc. B 284, 20162235 (2017).
PubMed  Article  CAS  PubMed Central  Google Scholar 

20.
Steadman, D. W. et al. The paleoecology and extinction of endemic tortoises in the Bahamian Archipelago. Holocene 30, 420–427 (2020).
ADS  Article  Google Scholar 

21.
Albury, N. A., Franz, R., Rimoli, P., Lehman, P. & Rosenberger, A. L. Fossil land tortoises (Testudines: Testudinidae) from the Dominican Republic, West Indies, with a description of a new species. Am. Mus. Novit. 3904, 1–28 (2018).
Article  Google Scholar 

22.
Fulton, T. L. & Shapiro, B. Setting up an ancient DNA laboratory. In Ancient DNA: Methods and Protocols. Methods in Molecular Biology, Vol. 1963 (eds Shapiro, B. et al.), 1–13 (Humana Press, Totowa, 2019).
Google Scholar 

23.
Dabney, J. et al. Complete mitochondrial genome sequence of a Middle Pleistocene cave bear reconstructed from ultrashort DNA fragments. Proc. Natl. Acad. Sci. USA 110, 15758–15763 (2013).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

24.
Gansauge, M.-T. & Meyer, M. Single-stranded DNA library preparation for the sequencing of ancient or damaged DNA. Nat. Protoc. 8, 737–748 (2013).
PubMed  Article  CAS  PubMed Central  Google Scholar 

25.
Korlević, P. et al. Reducing microbial and human contamination in DNA extractions from ancient bones and teeth. Biotechniques 58, 87–93 (2015).
Google Scholar 

26.
Maricic, T., Whitten, M. & Pääbo, S. Multiplexed DNA sequence capture of mitochondrial genomes using PCR products. PLoS One 5, e14004 (2010).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

27.
Horn, S. Target enrichment via DNA hybridization capture. In Ancient DNA: Methods and Protocols. Methods in Molecular Biology, Vol. 840 (eds Shapiro, B. & Hofreiter, M.), 177–188 (Springer, Berlin, 2012).
Google Scholar 

28.
Jiang, H., Lei, R., Ding, S. W. & Zhu, S. Skewer: A fast and accurate adapter trimmer for next-generation sequencing paired-end reads. BMC Bioinform. 15, 182 (2014).
Article  Google Scholar 

29.
Bushnell, B., Rood, J. & Singer, E. BBMerge—accurate paired shotgun read merging via overlap. PLoS One 12, e0185056 (2017).
PubMed  PubMed Central  Article  CAS  Google Scholar 

30.
Wingett, S. W. & Andrews, S. FastQ Screen: A tool for multi-genome mapping and quality control [version 2; referees: 4 approved]. F1000Research 7, 1338 (2018).
PubMed  PubMed Central  Article  Google Scholar 

31.
Hahn, C., Bachmann, L. & Chevreux, B. Reconstructing mitochondrial genomes directly from genomic next-generation sequencing reads—a baiting and iterative mapping approach. Nucleic Acids Res. 41, 1–9 (2013).
Article  CAS  Google Scholar 

32.
Milne, I. et al. Using Tablet for visual exploration of second-generation sequencing data. Brief. Bioinform. 14, 193–202 (2013).
CAS  PubMed  Article  PubMed Central  Google Scholar 

33.
Quinlan, A. R. & Hall, I. M. BEDTools: A flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).
CAS  PubMed  PubMed Central  Article  Google Scholar 

34.
Kehlmaier, C. et al. Ancient mitogenomics clarifies radiation of extinct Mascarene giant tortoises. Sci. Rep. 9, 17487 (2019).
ADS  PubMed  PubMed Central  Article  Google Scholar 

35.
Poulakakis, N. et al. Colonization history of Galapagos giant tortoises: Insights from mitogenomes support the progression rule. J. Zool. Syst. Evol. Res. 58, 1262–1275 (2020).
Article  Google Scholar 

36.
Thompson, J. D., Higgins, D. G. & Gibson, T. J. Clustal W: Improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res. 22, 4673–4680 (1994).
CAS  PubMed  PubMed Central  Article  Google Scholar 

37.
Hall, T. A. BioEdit: A user-friendly biological sequence alignment editor and analysis program for Windows 95/98/NT. Nucleic Acids Symp. Ser. 41, 95–98 (1999).
CAS  Google Scholar 

38.
Bernt, M. et al. MITOS: Improved de novo metazoan mitochondrial genome annotation. Mol. Phylogenet. Evol. 69, 313–319 (2013).
PubMed  Article  Google Scholar 

39.
Kumar, S., Stecher, G., Knyaz, C. & Tamura, K. MEGA X: Molecular Evolutionary Genetic Analysis across computing platforms. Mol. Biol. Evol. 35, 1547–1549 (2018).
CAS  PubMed  PubMed Central  Article  Google Scholar 

40.
Stamatakis, A. RAxML version 8: A tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30, 1312–1313 (2014).
CAS  PubMed  PubMed Central  Article  Google Scholar 

41.
Ronquist, F. et al. MrBayes 3.2: Efficient Bayesian phylogenetic inference and model choice across a large model space. Syst. Biol. 61, 539–542 (2012).
PubMed  PubMed Central  Article  Google Scholar 

42.
Lanfear, R., Frandsen, P. B., Wright, A. M., Senfeld, T. & Calcott, B. PartitionFinder 2: New methods for selecting partitioned models of evolution for molecular and morphological phylogenetic analyses. Mol. Biol. Evol. 34, 772–773 (2016).
Google Scholar 

43.
Rambaut, A., Drummond, A. J., Xie, D., Baele, G. & Suchard, M. A. Posterior summarization in Bayesian phylogenetics using Tracer 1.7. Syst. Biol. 5, 901–904 (2018).
Article  CAS  Google Scholar 

44.
Drummond, A. J., Suchard, M. A., Xie, D. & Rambaut, A. Bayesian phylogenetics with BEAUti and the BEAST 1.7. Mol. Biol. Evol. 29, 1969–1973 (2012).
CAS  PubMed  PubMed Central  Article  Google Scholar 

45.
Woods, R. et al. Rapid size change associated with intra-island evolutionary radiation in extinct Caribbean “island shrews”. BMC Evol. Biol. 29, 106 (2020).
Article  CAS  Google Scholar 

46.
Geist, D., Snell, H. L., Snell, H. M., Goddard, C. & Kurz, M. Paleogeography of the Galápagos Islands and biogeographical implications. In The Galápagos: A Natural Laboratory for the Earth Sciences, Vol. 204 (eds Harpp, K., Mittelstaedt, E., d’Ozouville, N. & Graham, D.) 145–166 (American Geophysical Union, New York, 2014).
Google Scholar 

47.
Hearty, P. J., Kindler, P., Cheng, H. & Edwards, R. A +20 m middle Pleistocene sea-level highstand (Bermuda and the Bahamas) due to partial collapse of Antarctic ice. Geology 27, 375–378 (1999).
ADS  Article  Google Scholar 

48.
Bowen, D. Sea level ∼400 000 years ago (MIS 11): Analogue for present and future sea-level? Clim. Past 6, 19–29 (2010).
Article  Google Scholar 

49.
Steadman, D. W. & Franklin, J. Origin, paleoecology, and extirpation of bluebirds and crossbills in the Bahamas across the last glacial-interglacial transition. Proc. Natl. Acad. Sci. USA 114, 9924–9929 (2017).
CAS  PubMed  Article  PubMed Central  Google Scholar 

50.
Fritz, U., Široký, P., Kami, H. & Wink, M. Environmentally caused dwarfism or a valid species—Is Testudo weissingeri Bour, 1996 a distinct evolutionary lineage? New evidence from mitochondrial and nuclear genomic markers. Mol. Phylogenet. Evol. 37, 389–401 (2005).
CAS  PubMed  Article  PubMed Central  Google Scholar 

51.
Fritz, U. et al. Phenotypic plasticity leads to incongruence between morphology-based taxonomy and genetic differentiation in western Palaearctic tortoises (Testudo graeca complex; Testudines, Testudinidae). Amphibia-Reptilia 28, 97–121 (2007).
Article  Google Scholar 

52.
Fritz, U. et al. Mitochondrial phylogeography and subspecies of the wide-ranging sub-Saharan leopard tortoise Stigmochelys pardalis (Testudines: Testudinidae)—a case study for the pitfalls of pseudogenes and GenBank sequences. J. Zool. Syst. Evol. Res. 48, 348–359 (2010).
Article  Google Scholar 

53.
Fritz, U. et al. Northern genetic richness and southern purity, but just one species in the Chelonoidis chilensis complex. Zool. Scr. 41, 220–232 (2012).
Article  Google Scholar 

54.
Carlson, L. A. & Keegan, W. F. Resource depletion in the prehistoric northern West Indies. In Voyages of Discovery (ed. Fitzpatrick, S. M.) 85–107 (Praeger, Westport, 2004).
Google Scholar 

55.
Keegan, W. F. Taino Indian Myth and Practice: The Arrival of the Stranger King (University Press of Florida, Gainesville, 2007).
Google Scholar 

56.
Oswald, J. A. et al. Ancient DNA and high-resolution chronometry reveal a long-term human role in the historical diversity and biogeography of the Bahamian hutia. Sci. Rep. 10, 1373 (2020).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

57.
Loire, E. & Galtier, N. Lacking conservation genomics in the giant Galápagos tortoise. bioRxiv 101980, 1–14 (2017).
Google Scholar 

58.
Fontaine, M. C. A genomic perspective is needed for the re-evaluation of species boundaries, evolutionary trajectories, and conservation strategies of the Galápagos giant tortoises. PCI Evol. Biol. 100031, 1–3 (2017).
Google Scholar 

59.
Vargas-Ramírez, M., Maran, J. & Fritz, U. Red- and yellow-footed tortoises (Chelonoidis carbonaria, C. denticulata) in South American savannahs and forests: Do their phylogeographies reflect distinct habitats? Org. Divers. Evol. 10, 161–172 (2010).
Article  Google Scholar 

60.
Blake, S. et al. Seed dispersal by Galápagos tortoises. J. Biogeogr. 39, 1961–1972 (2012).
Article  Google Scholar 

61.
Walton, R. et al. In the land of giants: Habitat use and selection of the Aldabra giant tortoise on Aldabra Atoll. Biodiv. Conserv. 28, 3183–3198 (2019).
Article  Google Scholar  More

1.
Alin SR, Johnson TC. Carbon cycling in large lakes of the world: a synthesis of production, burial, and lake-atmosphere exchange estimates. Glob Biogeochemical Cycles. 2007;21:GB3002.
Google Scholar 
2.
Durisch-Kaiser E, Schmid M, Peeters F, Kipfer R, Dinkel C, Diem T, et al. What prevents outgassing of methane to the atmosphere in Lake Tanganyika? J Geophys Res. 2011;116:G02022.
Google Scholar 

3.
Takahashi T, Koblmüller S. The adaptive radiation of Cichlid fish in Lake Tanganyika: a morphological perspective. Int J Evolut Biol. 2011;2011:1–14.
Article  Google Scholar 

4.
Salzburger W. Understanding explosive diversification through Cichlid fish genomics. Nat Rev Genet. 2018;19:705–17.
CAS  PubMed  Article  Google Scholar 

5.
Corman JR, McIntyre PB, Kuboja B, Mbemba W, Fink D, Wheeler CW, et al. Upwelling couples chemical and biological dynamics across the littoral and pelagic zones of Lake Tanganyika, East Africa. Limnol Oceanogr. 2010;55:214–24.
CAS  Article  Google Scholar 

6.
Cabello-Yeves PJ, Zemskaya TI, Rosselli R, Coutinho FH, Zakharenko AS, Blinov VV, et al. Genomes of novel microbial lineages assembled from the sub-ice waters of Lake Baikal. Appl Environ Microbiol. 2017;84:e02132–17.
PubMed  PubMed Central  Article  Google Scholar 

7.
Cabello‐Yeves PJ, Zemskaya TI, Zakharenko AS, Sakirko MV, Ivanov VG, Ghai R, et al. Microbiome of the deep Lake Baikal, a unique oxic bathypelagic habitat. Limnol Oceanogr. 2019;65:1471–88.
Article  CAS  Google Scholar 

8.
De Wever A. Spatio-temporal dynamics in the microbial food web in Lake Tanganyika. University of Gent; 2006. p. 1–169.

9.
Pirlot S, Unrein F, Descy J-P, Servais P. Fate of heterotrophic bacteria in Lake Tanganyika (East Africa): fate of bacteria in Lake Tanganyika. FEMS Microbiol Ecol. 2007;62:354–64.
CAS  PubMed  Article  Google Scholar 

10.
Schubert CJ, Durisch-Kaiser E, Wehrli B, Thamdrup B, Lam P, Kuypers MMM. Anaerobic ammonium oxidation in a tropical freshwater system (Lake Tanganyika). Environ Microbiol. 2006;8:1857–63.
CAS  PubMed  Article  Google Scholar 

11.
Shade A, Kent AD, Jones SE, Newton RJ, Triplett EW, McMahon KD. Interannual dynamics and phenology of bacterial communities in a eutrophic lake. Limnol Oceanogr. 2007;52:487–94.
CAS  Article  Google Scholar 

12.
Nurk S, Meleshko D, Korobeynikov A, Pevzner PA. metaSPAdes: a new versatile metagenomic assembler. Genome Res. 2017;27:824–34.
CAS  PubMed  PubMed Central  Article  Google Scholar 

13.
Kang DD, Froula J, Egan R, Wang Z. MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities. PeerJ. 2015;3:e1165
PubMed  PubMed Central  Article  CAS  Google Scholar 

14.
Kang DD, Li F, Kirton E, Thomas A, Egan R, An H, et al. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ. 2019;7:e7359.
PubMed  PubMed Central  Article  Google Scholar 

15.
Wu Y-W, Simmons BA, Singer SW. MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinformatics. 2016;32:605–7.
CAS  PubMed  Article  Google Scholar 

16.
Sieber CMK, Probst AJ, Sharrar A, Thomas BC, Hess M, Tringe SG, et al. Recovery of genomes from metagenomes via a dereplication, aggregation and scoring strategy. Nat Microbiol. 2018;3:836–43.
CAS  PubMed  PubMed Central  Article  Google Scholar 

17.
Olm MR, Brown CT, Brooks B, Banfield JF. dRep: a tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication. ISME J. 2017;11:2864–68.
CAS  PubMed  PubMed Central  Article  Google Scholar 

18.
Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 2015;25:1043–55.
CAS  PubMed  PubMed Central  Article  Google Scholar 

19.
The Genome Standards Consortium, Bowers RM, Kyrpides NC, Stepanauskas R, Harmon-Smith M, Doud D, et al. Minimum information about a single amplified genome (MISAG) and a metagenome-assembled genome (MIMAG) of bacteria and archaea. Nat Biotechnol. 2017;35:725–31.
Article  CAS  Google Scholar 

20.
Bushnell B. BBMAP. https://jgi.doe.gov/data-and-tools/bbtools/bb-tools-user-guide/bbmap-guide/. 2014.

21.
Hyatt D, Chen G-L, LoCascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinform. 2010;11:119.
Article  CAS  Google Scholar 

22.
Anantharaman K, Brown CT, Hug LA, Sharon I, Castelle CJ, Probst AJ, et al. Thousands of microbial genomes shed light on interconnected biogeochemical processes in an aquifer system. Nat Commun. 2016;7:13219.
CAS  PubMed  PubMed Central  Article  Google Scholar 

23.
Eddy SR. Accelerated profile HMM searches. PLoS Comput Biol. 2011;7:e1002195.
CAS  PubMed  PubMed Central  Article  Google Scholar 

24.
Hug LA, Baker BJ, Anantharaman K, Brown CT, Probst AJ, Castelle CJ, et al. A new view of the tree of life. Nat Microbiol. 2016;1:1–6.
Article  CAS  Google Scholar 

25.
Brown AMV, Howe DK, Wasala SK, Peetz AB, Zasada IA, Denver DR. Comparative genomics of a plant-parasitic nematode endosymbiont suggest a role in nutritional symbiosis. Genome Biol Evol. 2015;7:2727–46.
CAS  PubMed  PubMed Central  Article  Google Scholar 

26.
Katoh K, Standley DM. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol Biol Evol. 2013;30:772–80.
CAS  PubMed  PubMed Central  Article  Google Scholar 

27.
Miller MA, Pfeiffer W, Schwartz Terri. Creating the CIPRES Science Gateway for inference of large phylogenetic trees. Proceedings of the Gateway Computing Environments Workshop. New Orleans, LA; 2010. p. 1–8.

28.
Parks DH, Chuvochina M, Waite DW, Rinke C, Skarshewski A, Chaumeil P-A, et al. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nat Biotechnol. 2018;36:996–1004.
CAS  PubMed  PubMed Central  Article  Google Scholar 

29.
Newton RJ, Jones SE, Eiler A, McMahon KD, Bertilsson S. A guide to the natural history of freshwater lake bacteria. Microbiol Mol Biol Rev. 2011;1:14.
Article  CAS  Google Scholar 

30.
Rohwer RR, Hamilton JJ, Newton RJ, McMahon KD. TaxAss: leveraging a custom freshwater database achieves fine-scale taxonomic resolution. mSphere. 2018;3:e00327–18.
CAS  PubMed  PubMed Central  Article  Google Scholar 

31.
Soo RM, Hemp J, Parks DH, Fischer WW, Hugenholtz P. On the origins of oxygenic photosynthesis and aerobic respiration in Cyanobacteria. Science. 2017;355:1436–40.
CAS  PubMed  Article  Google Scholar 

32.
Linz AM, He S, Stevens SLR, Anantharaman K, Rohwer RR, Malmstrom RR, et al. Freshwater carbon and nutrient cycles revealed through reconstructed population genomes. PeerJ. 2018;6:e6075.
PubMed  PubMed Central  Article  CAS  Google Scholar 

33.
Bendall ML, Stevens SL, Chan L-K, Malfatti S, Schwientek P, Tremblay J, et al. Genome-wide selective sweeps and gene-specific sweeps in natural bacterial populations. ISME J. 2016;10:1589–601.
PubMed  PubMed Central  Article  Google Scholar 

34.
Jain C, Rodriguez-R LM, Phillippy AM, Konstantinidis KT, Aluru S. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat Commun. 2018;9:5114.
PubMed  PubMed Central  Article  CAS  Google Scholar 

35.
Soo RM, Skennerton CT, Sekiguchi Y, Imelfort M, Paech SJ, Dennis PG, et al. An expanded genomic representation of the phylum cyanobacteria. Genome Biol Evolution. 2014;6:1031–45.
Article  Google Scholar 

36.
Zhou Z, Tran P, Liu Y, Kieft K, Anantharaman K. METABOLIC: a scalable high-throughput metabolic and biogeochemical functional trait profiler based on microbial genomes. bioRxiv. 2019;761643.

37.
Zhang H, Yohe T, Huang L, Entwistle S, Wu P, Yang Z, et al. dbCAN2: a meta server for automated carbohydrate-active enzyme annotation. Nucleic Acids Res. 2018;46:W95–W101.
CAS  PubMed  PubMed Central  Article  Google Scholar 

38.
Mukherjee S, Stamatis D, Bertsch J, Ovchinnikova G, Katta HY, Mojica A, et al. Genomes OnLine database (GOLD) v.7: updates and new features. Nucleic Acids Res. 2019;47:D649–59.
CAS  PubMed  Article  Google Scholar 

39.
Edmond JM, Stallard RF, Craig H, Craig V, Weiss RF, Coulter GW. Nutrient chemistry of the water column of Lake Tanganyika. Limnol Oceanogr. 1993;38:725–38.
CAS  Article  Google Scholar 

40.
Verburga P, Hecky RE. The physics of the warming of Lake Tanganyika by climate change. Limnol Oceanogr. 2009;54:2418–30.
Article  Google Scholar 

41.
Järvinen M, Salonen K, Sarvala J, Vuorio K, Virtanen A. The stoichiometry of particulate nutrients in Lake Tanganyika—implications for nutrient limitation of phytoplankton. Hydrobiologia. 1999;407:81–8.
Article  Google Scholar 

42.
Ehrenfels B, Bartosiewicz M, Mbonde AS, Baumann KBL, Dinkel C, Junker J, et al. Thermocline depth and euphotic zone thickness regulate the abundance of diazotrophic cyanobacteria in Lake Tanganyika. Preprint at https://doi.org/10.5194/bg-2020-214 (2020).

43.
Tran P, Ramachandran A, Khawasik O, Beisner BE, Rautio M, Huot Y, et al. Microbial life under ice: Metagenome diversity and in situ activity of Verrucomicrobia in seasonally ice‐covered Lakes. Environ Microbiol. 2018;20:2568–84.
CAS  PubMed  Article  Google Scholar 

44.
Martinez-Garcia M, Brazel DM, Swan BK, Arnosti C, Chain PSG, Reitenga KG, et al. Capturing single cell genomes of active polysaccharide degraders: an unexpected contribution of verrucomicrobia. PLoS ONE. 2012;7:1–11.
Google Scholar 

45.
Damrow R, Maldener I, Zilliges Y. The multiple functions of common microbial carbon polymers, glycogen and PHB, during stress responses in the non-diazotrophic Cyanobacterium Synechocystis sp. PCC 6803. Front Microbiol. 2016;7:966.
PubMed  PubMed Central  Article  Google Scholar 

46.
Paerl HW, Otten TG. Duelling ‘CyanoHABs’: unravelling the environmental drivers controlling dominance and succession among diazotrophic and non-N2-fixing harmful cyanobacteria. Environ Microbiol. 2016;18:316–24.
CAS  PubMed  Article  Google Scholar 

47.
Raymond J, Siefert JL, Staples CR, Blankenship RE. The natural history of nitrogen fixation. Mol Biol Evol. 2004;21:541–54.
CAS  PubMed  Article  Google Scholar 

48.
Berman-Frank I, Lundgren P, Falkowski P. Nitrogen fixation and photosynthetic oxygen evolution in cyanobacteria. Res Microbiol. 2003;154:157–64.
CAS  PubMed  Article  Google Scholar 

49.
Cabello-Yeves PJ, Ghai R, Mehrshad M, Picazo A, Camacho A, Rodriguez-valera F. Reconstruction of diverse verrucomicrobial genomes from metagenome datasets of freshwater reservoirs. Front Microbiol. 2017;8:2131.
PubMed  PubMed Central  Article  Google Scholar 

50.
Hansel CM, Fendorf S, Jardine PM, Francis CA. Changes in bacterial and archaeal community structure and functional diversity along a geochemically variable soil profile. Appl Environ Microbiol. 2008;74:1620–33.
CAS  PubMed  PubMed Central  Article  Google Scholar 

51.
Edlund A, Hårdeman F, Jansson JK, Sjöling S. Active bacterial community structure along vertical redox gradients in Baltic Sea sediment. Environ Microbiol. 2008;10:2051–63.
PubMed  Article  CAS  Google Scholar 

52.
Beman JM, Carolan MT. Deoxygenation alters bacterial diversity and community composition in the ocean’s largest oxygen minimum zone. Nat Commun. 2013;4:2705.
PubMed  Article  CAS  Google Scholar 

53.
Schoell M, Tietze K, Schoberth SM. Origin of methane in Lake Kivu (East-Central Africa). Chem Geol. 1988;71:257–65.
CAS  Article  Google Scholar 

54.
Bogard MJ, del Giorgio PA, Boutet L, Chaves MCG, Prairie YT, Merante A, et al. Oxic water column methanogenesis as a major component of aquatic CH4 fluxes. Nat Commun. 2014;5:5350.
CAS  PubMed  Article  Google Scholar 

55.
Vanwonterghem I, Evans PN, Parks DH, Jensen PD, Woodcroft BJ, Hugenholtz P, et al. Methylotrophic methanogenesis discovered in the archaeal phylum Verstraetearchaeota. Nat Microbiol. 2016;1:16170.
CAS  PubMed  Article  Google Scholar 

56.
Gao Q, Chen S, Kimirei IA, Zhang L, Mgana H, Mziray P, et al. Wet deposition of atmospheric nitrogen contributes to nitrogen loading in the surface waters of Lake Tanganyika, East Africa: a case study of the Kigoma region. Environ Sci Pollut Res. 2018;25:11646–60.
CAS  Article  Google Scholar 

57.
Chale FMM. Inorganic nutrient concentrations and chlorophyll in the euphotic zone of Lake Tanganyika. Hydrobiologia. 2004;523:189–97.
CAS  Article  Google Scholar 

58.
Higgins SN, Hecky RE, Taylor WD. Epilithic nitrogen fixation in the rocky littoral zones of Lake Malawi, Africa. Limnol Oceanogr. 2001;46:976–82.
CAS  Article  Google Scholar 

59.
Brion N, Nzeyimana E, Goeyens L, Nahimana D, Tungaraza C, Baeyens W. Inorganic nitrogen uptake and river inputs in northern Lake Tanganyika. J Gt Lakes Res. 2006;32:553–64.
CAS  Article  Google Scholar 

60.
Norici A, Hell R, Giordano M. Sulfur and primary production in aquatic environments: an ecological perspective. Photosynth Res. 2005;86:409–17.
CAS  PubMed  Article  Google Scholar 

61.
Botz RW, Stoffers P. Light hydrocarbon gases in Lake Tanganyika hydrothermal fluids (East-Central Africa). Chem Geol. 1993;104:217–24.
CAS  Article  Google Scholar 

62.
Tiercelin J-J, Pflumio C, Castrec M, Boulégue J, Gente P, Rolet J, et al. Hydrothermal vents in Lake Tanganyika, East African, Rift system. Geology. 1993;21:499–502.
CAS  Article  Google Scholar 

63.
Elsgaard L, Prieur D. Hydrothermal vents in Lake Tanganyika harbor spore-forming thermophiles with extremely rapid growth. J Gt Lakes Res. 2011;37:203–6.
CAS  Article  Google Scholar 

64.
Preisler A, de Beer D, Lichtschlag A, Lavik G, Boetius A, Jørgensen BB. Biological and chemical sulfide oxidation in a Beggiatoa inhabited marine sediment. ISME J. 2007;1:341–53.
CAS  PubMed  Article  Google Scholar 

65.
McAllister SM, Moore RM, Gartman A, Luther GW, Emerson D, Chan CS. The Fe(II)-oxidizing Zetaproteobacteria: historical, ecological and genomic perspectives. FEMS Microbiol Ecol. 2019;95:fiz015.
CAS  PubMed  PubMed Central  Article  Google Scholar 

66.
Carpenter SR. Phosphorus control is critical to mitigating eutrophication. Proc Natl Acad Sci. 2008;105:11039–40.
CAS  PubMed  Article  Google Scholar 

67.
Lewis WM, Jr. Causes for the high frequency of nitrogen limitation in tropical lakes. SIL Proceedings. vol. 28. 2002; p. 210–3.

68.
De Keyzer ELR, Masilya Mulungula P, Alunga Lufungula G, Amisi Manala C, Andema Muniali A, Bashengezi Cibuhira P, et al. Local perceptions on the state of the pelagic fisheries and fisheries management in Uvira, Lake Tanganyika, DR Congo. J Great Lakes Res. 2020;46:1740–53.
Article  Google Scholar 

69.
Mölsä, H. Management of fisheries on Lake Tanganyika challenges for research and the community. University of Kuopio; 2008.

70.
Foley B, Jones ID, Maberly SC, Rippey B. Long-term changes in oxygen depletion in a small temperate lake: effects of climate change and eutrophication. Freshw Biol. 2012;57:278–89.
CAS  Article  Google Scholar  More

1.
Minchella, D. J. & Scott, M. E. Parasitism: a cryptic determinant of animal community structure. Trends Ecol. Evol. 6(8), 250–254. https://doi.org/10.1016/0169-5347(91)90071-5 (1991).
CAS  Article  PubMed  Google Scholar 
2.
Dobson, A., Lafferty, K. D., Kuris, A. M., Hechinger, R. F. & Jetz, W. Homage to Linnaeus: how many parasites? How many host?. Proc. Natl. Acad. Sci. USA 105, 11482–11489. https://doi.org/10.1073/pnas.0803232105 (2008).
ADS  Article  PubMed  Google Scholar 

3.
Hatcher, M. J. & Dunn, A. M. Parasites in ecological communities: from interactions to ecosystems. https://doi.org/10.1017/CBO9780511987359 (Cambridge University Press, Cambridge, 2011).
Google Scholar 

4.
Sures, B., Nachev, M., Pahl, M., Grabner, D. & Selbach, C. Parasites as drivers of key processes in aquatic ecosystems: facts and future directions. Exp. Parasitol. 180, 141–147. https://doi.org/10.1016/j.exppara.2017.03.011 (2017).
CAS  Article  PubMed  Google Scholar 

5.
Marcogliese, D. J. & Cone, D. K. Food webs: a plea for parasites. Trends Ecol. Evol. 12, 320–325. https://doi.org/10.1016/S0169-5347(97)01080-X (1997).
CAS  Article  PubMed  Google Scholar 

6.
Thompson, R. M., Mouritsen, K. N. & Poulin, R. Importance of parasites and their life cycle characteristics in determining the structure of a large marine food web. J. Anim. Ecol. 74, 77–85. https://doi.org/10.1111/j.1365-2656.2004.00899.x (2005).
Article  Google Scholar 

7.
Hernandez, A. D. & Sukhdeo, M. V. K. Parasites alter the topology stream food web across seasons. Oecologia 156, 613–624. https://doi.org/10.1007/s00442-008-0999-9 (2008).
ADS  Article  PubMed  Google Scholar 

8.
Dick, J. T. A. et al. Parasitism may enhance rather than reduce the predatory impact of an invader. Biol. Lett. 6, 636–638. https://doi.org/10.1098/rsbl.2010.0171 (2010).
Article  PubMed  PubMed Central  Google Scholar 

9.
Buck, J. C. Indirect effects explain the role of parasites in ecosystems. Trends Parasitol. 35, 835–847. https://doi.org/10.1016/j.pt.2019.07.007 (2019).
Article  PubMed  Google Scholar 

10.
Sabadel, A. J. M., Stumbo, A. D. & MacLeod, C. D. Stable-isotope analysis: a neglected tool for placing parasites in food webs. J. Helminthol. 93, 1–7. https://doi.org/10.1017/S0022149X17001201 (2019).
CAS  Article  PubMed  Google Scholar 

11.
Barber, I., Hoare, D. & Krause, J. Effects of parasites on fish behaviour: an evolutionary perspective and review. Rev. Fish Biol. Fish. 10, 131–165. https://doi.org/10.1023/A:1016658224470 (2000).
Article  Google Scholar 

12.
Barber, I. & Wright, H.A. Effects of parasites on fish behaviour: interactions with host physiology in Fish physiology (eds. Katherine, R.W.W., Sloman, A. & Sigal, B.) 109–149. https://doi.org/10.1016/S1546-5098(05)24004-9 (Academic Press, 2005)

13.
Hughes, D. P., Brodeur, J. & Thomas, F. Host Manipulation by Parasites (Oxford University Press, Oxford, 2012).
Google Scholar 

14.
Moore, J. Parasites and Behaviour of Animals (Oxford University Press, Oxford, 2002).
Google Scholar 

15.
Shariff, M., Richards, R. H. & Sommerville, C. The histopathology of acute and chronic infections of rainbow trout Salmo gairdneri Richardson with eye flukes, Diplostomum spp. J. Fish. Dis. 3, 455–465. https://doi.org/10.1111/j.1365-2761.1980.tb00432.x (1980).
Article  Google Scholar 

16.
Stumbo, A. D. & Poulin, R. Possible mechanism of host manipulation resulting from a diel behaviour pattern of eye-dwelling parasites?. Parasitology 143, 1261–1267. https://doi.org/10.1017/S0031182016000810 (2016).
Article  PubMed  Google Scholar 

17.
Poulin, R. & Cribb, T. H. Trematode life cycles: short is sweet?. Trends Parasitol. 18, 176–183. https://doi.org/10.1016/S1471-4922(02)02262-6 (2002).
Article  PubMed  Google Scholar 

18.
Cribb, T. H., Bray, R. A., Olson, P. D. & Littlewood, D. T. J. Life cycle evolution in the Digenea: a new perspective from phylogeny. Adv. Parasitol. 54, 197–254. https://doi.org/10.1016/S0065-308X(03)54004-0 (2003).
Article  PubMed  Google Scholar 

19.
Streilein, J. W. Oculae immune privilege: the eye takes a dim but practical view of immunity and inflammation. J. Leukoc. Biol. 74, 179–185. https://doi.org/10.1189/jlb.1102574 (2003).
CAS  Article  PubMed  Google Scholar 

20.
Crowden, A. E. & Broom, D. M. Effects of the eyefluke, Diplostomum spathaceum, on the behaviour of dace (Leuciscus leuciscus). Anim. Behav. 28, 287–294. https://doi.org/10.1016/S0003-3472(80)80031-5 (1980).
Article  Google Scholar 

21.
Seppälä, O., Karvonen, A. & Valtonen, E. T. Manipulation of fish host by eye flukes in relation to cataract formation and parasite infectivity. Anim. Behav. 70, 889–894. https://doi.org/10.1016/j.anbehav.2005.01.020 (2005).
Article  Google Scholar 

22.
Seppälä, O., Karvonen, A. & Valtonen, E. T. Shoaling behaviour of fish under parasitism and predation risk. Anim. Behav. 75, 145–150. https://doi.org/10.1016/j.anbehav.2007.04.022 (2008).
Article  Google Scholar 

23.
Vivas Muñoz, J. C., Bierbach, D. & Knopf, K. Eye fluke (Tylodelphys clavata) infection impairs visual ability and hampers foraging success in European perch. Parasitol. Res. 118, 2531–2541. https://doi.org/10.1007/s00436-019-06389-5 (2019).
Article  PubMed  Google Scholar 

24.
Vivas Muñoz, J. C., Staaks, G. & Knopf, K. The eye fluke Tylodelphys clavata affects prey detection and intraspecific competition of European perch (Perca fluviatilis). Parasitol. Res. 116, 2561–2567. https://doi.org/10.1007/s00436-017-5564-1 (2017).
Article  PubMed  Google Scholar 

25.
Bergman, E. Foraging abilities and niche breadths of two percids, Perca fluviatilis and Gymnocephalus cernua, under different environmental conditions. J. Anim. Ecol. 57, 443–453. https://doi.org/10.2307/4916 (1988).
Article  Google Scholar 

26.
Diehl, S. Foraging efficiency of three freshwater fishes: effects of structural complexity and light. Oikos 53, 207–214. https://doi.org/10.2307/3566064 (1988).
Article  Google Scholar 

27.
Craig, J. F. Percid Fishes: Systematics, Ecology and Exploitation (Blackwell Science, Hoboken, 2000). https://doi.org/10.1002/9780470696033.
Google Scholar 

28.
Kennedy, C. R. & Burrough, R. Parasites of trout and perch in Malham Tarn. Fld. Stud. 4, 617–629 (1978).
Google Scholar 

29.
Kennedy, C. R. Long term studies on the population biology of two species of eye fluke, Diplostomurn gasterostei and Tylodelphys clavata (Digenea: Diplostomatidae), concurrently infecting the eyes of perch, Perca fluviatilis. J. Fish Biol. 19, 221–236. https://doi.org/10.1111/j.1095-8649.1981.tb05826.x (1981).
Article  Google Scholar 

30.
Kennedy, C. R. Interspecific interactions between larval digeneans in the eyes of perch, Perca fluviatilis. Parasitology 122, S13–S22. https://doi.org/10.1017/S0031182000016851 (2001).
Article  PubMed  Google Scholar 

31.
Valtonen, E. T., Holmes, J. C., Aronen, J. & Rautalahti, I. Parasite communities as indicators of recovery from pollution: parasites of roach (Rutilus rutilus) and perch (Perca fluviatilis) in Central Finland. Parasitology 126, S43–S52. https://doi.org/10.1017/S0031182003003494 (2003).
CAS  Article  PubMed  Google Scholar 

32.
Behrmann-Godel, J. Parasite identification, succession and infection pathways in perch fry (Perca fluviatilis): new insights through a combined morphological and genetic approach. Parasitology 140, 509–520. https://doi.org/10.1017/S0031182012001989 (2013).
CAS  Article  PubMed  Google Scholar 

33.
Soylu, E. Metazoan parasites of perch Perca fluviatilis L. from Lake Sığırcı, Ipsala. Turkey. Pak. J. Zool. 45, 47–52 (2013).
Google Scholar 

34.
Vivas Muñoz, J.C. Tylodelphys clavata in perch (Perca fluviatilis): spatial heterogeneity, impact on feeding behaviour and intraspecific competition. Master Thesis. Humboldt-Universität zu Berlin (2014)

35.
Hjelm, J., Svanbäck, R., Byström, P., Persson, L. & Wahlström, E. Diet dependent body morphology and ontogenetic reaction norms in Eurasian perch. Oikos 95, 311–323. https://doi.org/10.1034/j.1600-0706.2001.950213.x (2001).
Article  Google Scholar 

36.
Svanbäck, R. & Eklöv, P. Effects of habitat and food resources on morphology and ontogenetic growth trajectories in perch. Oecologia 131, 61–70. https://doi.org/10.1007/s00442-001-0861-9 (2002).
ADS  Article  PubMed  Google Scholar 

37.
Svanbäck, R. & Eklöv, P. Morphology dependent foraging efficiency in perch: a trade-off for ecological specialization?. Oikos 102, 273–284. https://doi.org/10.1034/j.1600-0706.2003.12657.x (2003).
Article  Google Scholar 

38.
Svanbäck, R. & Eklöv, P. Morphology in perch affects habitat specific feeding efficiency. Funct. Ecol. 18, 503–510. https://doi.org/10.1111/j.0269-8463.2004.00858.x (2004).
Article  Google Scholar 

39.
Quevedo, M. & Olsson, J. The effect of small-scale resource origin on trophic position estimates in Perca fluviatilis. J. Fish Biol. 69, 141–150. https://doi.org/10.1111/j.1095-8649.2006.01072.x (2006).
Article  Google Scholar 

40.
Quevedo, M., Svanbäck, R. & Eklöv, P. Intrapopulation niche partitioning in a generalist predator limits food web connectivity. Ecology 90, 2263–2274. https://doi.org/10.1890/07-1580.1 (2009).
Article  PubMed  Google Scholar 

41.
Frankiewicz, P. & Wojtal-Frankiewicz, A. Two different feeding tactics of young-of-the-year perch, Perca fluviatilis L., inhabiting the littoral zone of the lowland Sulejow Reservoir (Central Poland). Ecohydrol. Hydrobiol. 12, 35–41. https://doi.org/10.2478/v10104-012-0001-7 (2012).
Article  Google Scholar 

42.
Persson, L. Effects of reduced interspecific competition on resource utilization in perch (Perca fluviatilis). Ecology 67, 355–364. https://doi.org/10.2307/1938578 (1986).
Article  Google Scholar 

43.
Persson, L. & Greenberg, L. Interspecific and intraspecific size class competition affecting resource use and growth of perch, Perca fluviatilis. Oikos 59, 97–106. https://doi.org/10.2307/3545128 (1990).
Article  Google Scholar 

44.
Diehl, S. Effects of habitat structure on resource availability, diet and growth of benthivorous perch, Perca fluviatilis. Oikos 67, 403–414. https://doi.org/10.2307/3545353 (1993).
Article  Google Scholar 

45.
Svanbäck, R. & Persson, L. Individual diet specialization, niche width and population dynamics: implications for trophic polymorphisms. J. Anim. Ecol. 73, 973–982. https://doi.org/10.1111/j.0021-8790.2004.00868.x (2004).
Article  Google Scholar 

46.
Eklöv, P. & Svanbäck, R. Predation risk influences adaptive morphological variation in fish populations. Am. Nat. 167, 440–452. https://doi.org/10.1086/499544 (2006).
Article  PubMed  Google Scholar 

47.
Svanbäck, R. & Bolnick, D. I. Intraspecific competition drives increased resource use diversity within a natural population. Proc. R. Soc. B Biol. Sci. 274, 839–844. https://doi.org/10.1098/rspb.2006.0198 (2007).
Article  Google Scholar 

48.
Sharma, C. M. & Borgstrøm, R. Shift in density, habitat use, and diet of perch and roach: An effect of changed predation pressure after manipulation of pike. Fish. Res. 91, 98–106. https://doi.org/10.1016/j.fishres.2007.11.011 (2008).
Article  Google Scholar 

49.
Svanbäck, R., Eklöv, P., Fransson, R. & Holmgren, K. Intraspecific competition drives multiple species resource polymorphism in fish communities. Oikos 117, 114–124. https://doi.org/10.1111/j.2007.0030-1299.16267.x (2008).
Article  Google Scholar 

50.
Okun, N. & Mehner, T. Distribution and feeding of juvenile fish on invertebrates in littoral reed (Phragmites) stands. Ecol. Freshw. Fish 14, 139–149. https://doi.org/10.1111/j.1600-0633.2005.00087.x (2005).
Article  Google Scholar 

51.
Hyslop, E. J. Stomach content analysis: a review of methods and their application. J. Fish Biol. 17, 411–429. https://doi.org/10.1111/j.1095-8649.1980.tb02775.x (1980).
Article  Google Scholar 

52.
Peterson, B. J. & Fry, B. Stable isotopes in ecosystem studies. Annu. Rev. Ecol. Syst. 18, 293–320. https://doi.org/10.1146/annurev.ecolsys.18.1.293 (1987).
Article  Google Scholar 

53.
Beaudoin, C. P., Tonn, W. M., Prepas, E. E. & Wassenaar, L. I. Individual specialization and trophic adaptability of northern pike (Esox lucius): an isotope and dietary analysis. Oecologia 120, 386–396. https://doi.org/10.1007/s004420050871 (1999).
ADS  Article  PubMed  Google Scholar 

54.
Bolnick, D. I. et al. The ecology of individuals: incidence and implications of individual specialization. Am. Nat. 161, 1–28. https://doi.org/10.2307/3078879 (2003).
MathSciNet  Article  PubMed  Google Scholar 

55.
Bearhop, S. et al. Stable isotopes indicate sex-specific and long-term individual foraging specialization in diving seabirds. Mar. Ecol. Prog. Ser. 311, 157–164. https://doi.org/10.3354/meps311157 (2006).
ADS  Article  Google Scholar 

56.
Phillips, D. L. & Gregg, J. W. Source partitioning using stable isotopes: coping with too many sources. Oecologia 136, 261–269. https://doi.org/10.1007/s00442-003-1218-3 (2003).
ADS  Article  PubMed  Google Scholar 

57.
Parnell, A. C., Inger, R., Bearhop, S. & Jackson, A. L. Source partitioning using stable isotopes: coping with too much variation. PLoS ONE 5, e9672. https://doi.org/10.1371/journal.pone.0009672 (2010).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

58.
Parnell, A. C. et al. Bayesian stable isotope mixing models. Environmetrics 24, 387–399. https://doi.org/10.1002/env.2221 (2013).
MathSciNet  Article  Google Scholar 

59.
Bolnick, D. I. et al. Why intraspecific trait variation matters in community ecology? Trends Ecol. Evol. 26, 183–192. https://doi.org/10.1016/j.tree.2011.01.009 (2011).
Article  Google Scholar 

60.
Voutilainen, A., Figueiredo, K. & Huuskonen, H. Effects of the eye fluke Diplostomum spathaceum on the energetics and feeding of Arctic charr Salvelinus alpinus. J. Fish Biol. 73, 2228–2237. https://doi.org/10.1111/j.1095-8649.2008.02050.x (2008).
Article  Google Scholar 

61.
Padrós, F., Knuden, R. & Blasco-Costa, I. Histopathological characterisation of retinal lesions associated to Diplostomum species (Platyhelminthes: Trematoda) infection in polymorphic Arctic charr Salvelinus alpinus. Int. J. Parasito. 7, 68–74. https://doi.org/10.1016/j.ijppaw.2018.01.007 (2018).
Article  Google Scholar 

62.
Ubels, J. L. et al. Impairment of retinal function in yellow perch (Perca flavescens) by Diplostomum baeri metacercariae. Int. J. Parasitol. Parasites Wildl. 7, 171–179. https://doi.org/10.1016/j.ijppaw.2018.05.001 (2018).
Article  PubMed  PubMed Central  Google Scholar 

63.
Lemly, A. D. & Esch, G. W. Effects of the trematode Uvulifer ambloplitis on juvenile bluegill sunfish, Lepomis macrochirus: ecological implications. J. Parasit. 70, 475–492. https://doi.org/10.2307/3281395 (1984).
Article  Google Scholar 

64.
Santoro, M. et al. Parasitic infection by larval helminths in Antarctic fishes: pathological changes and impact on the host body condition index. Dis. Aquat. Org. 105, 139–148. https://doi.org/10.3354/dao02626 (2013).
CAS  Article  Google Scholar 

65.
Owen, S. F., Barber, I. & Hart, P. J. B. Low level infection by eye fluke, Diplostomum spp., affects the vision of three-spined sticklebacks, Gasterosteus aculeatus. J. Fish Biol. 42, 803–806. https://doi.org/10.1111/j.1095-8649.1993.tb00387.x (1993).
Article  Google Scholar 

66.
Pennycuick, L. Quantitative effects of three species of parasites on a population of three-spined sticklebacks, Gasterosteus aculeatus L. J. Zool. 165, 143–162. https://doi.org/10.1111/j.1469-7998.1971.tb02179.x (1971).
Article  Google Scholar 

67.
Marcogliese, D. J. et al. Spatial and temporal variations in abundance of Diplostomum spp. in walleye (Stizostedion vitreum) and white sucker (Catostomus commersoni) from the St. Lawrence River: importance the importance of gulls and fish stocks. Can. J. Zool. 79, 355–369. https://doi.org/10.1139/z00-209 (2001).
Article  Google Scholar 

68.
Dörücü, M., Dildiz, N. & Grabbe, M. C. J. Occurrence and effects of Diplostomum sp. infection in eyes of Acanthobrama marmid in Keban Dam Lake, Elazığ, Turkey. Turk. J. Vet. Anim. Sci. 26, 239–243 (2002).
Google Scholar 

69.
Machado, P. M., Takemoto, R. M. & Pavanelli, G. C. Diplostomum (Austrodiplostomum) compactum (Lutz, 1928) (Platyhelminthes, Digenea) metacercariae in fish from the floodplain of the Upper Paraná River. Brazil. Parasitol. Res. 97, 436–444. https://doi.org/10.1007/s00436-005-1483-7 (2005).
CAS  Article  PubMed  Google Scholar 

70.
Weatherley, A. H. Growth and Ecology of Fish Populations (Academic Press, London, 1972).
Google Scholar 

71.
Lagrue, C. & Poulin, R. Measuring fish body condition with or without parasites: does it matter?. J. Fish Biol. 87, 836–847. https://doi.org/10.1111/jfb.12749 (2015).
CAS  Article  PubMed  Google Scholar 

72.
Craig, J. F. A study of the food and feeding of perch, Perca fluviatilis L., inWindermere. Freshw Biol 8, 59–68. https://doi.org/10.1111/j.1365-2427.1978.tb01426.x (1978).
Article  Google Scholar 

73.
Guma’a, S.A. The food and feeding habits of young perch, Perca fluviatilis, in Windermere. Freshw Biol 8, 177–187. https://doi.org/10.1111/j.1365-2427.1978.tb01439.x (1978).
Article  Google Scholar 

74.
Wang, N. & Eckmann, R. Distribution of perch (Perca fluviatilis L.) during their first year of life in Lake Constance. Hydrobiologia 277, 135–143. https://doi.org/10.1007/BF00007295 (1994).
Article  Google Scholar 

75.
Imbock, F., Appenzeller, A. & Eckmann, R. Diel and seasonal distribution of perch in Lake Constance: a hydroacoustic study and in situ observations. J. Fish Biol. 49, 1–13. https://doi.org/10.1111/j.1095-8649.1996.tb00001.x (1996).
Article  Google Scholar 

76.
Hejlm, J., Persson, L. & Christensen, B. Growth, morphological variation and ontogenetic niche shifts in perch (Perca fluviatilis) in relation to resource availability. Oceologia 122, 190–199. https://doi.org/10.1007/PL00008846 (2000).
ADS  Article  Google Scholar 

77.
Horppila, J. et al. Seasonal changes in the diets and relative abundances of perch and roach in the littoral and pelagic zones of a large lake. J. Fish Biol. 56, 51–72. https://doi.org/10.1111/j.1095-8649.2000.tb02086.x (1999).
Article  Google Scholar 

78.
Allen, K. R. The food and migration of the perch (Perca fluviatilis) in Windermere. J Anim Ecol 4, 264–273. https://doi.org/10.2307/1016 (1935).
Article  Google Scholar 

79.
Mustamäki, N., Cederberg, T. & Mattila, J. Diet, stable isotopes and morphology of Eurasian perch (Perca fluviatilis) in littoral and pelagic habitats in the northern Baltic Proper. Environ. Biol. Fish 97, 675–689. https://doi.org/10.1007/s10641-013-0169-8 (2014).
Article  Google Scholar 

80.
Bootsma, H. A., Hecky, R. E., Hesslein, R. H. & Turner, G. F. Food partitioning among Lake Malawi nearshore fishes as revealed by stable isotope analyses. Ecology 77, 1286–1290. https://doi.org/10.2307/2265598 (1996).
Article  Google Scholar 

81.
Jakobsen, P. J., Johnsen, G. H. & Larsson, P. Effects of predation risk and parasitism on the feeding ecology, habitat use, and abundance of lacustrine threespine stickleback (Gasterosteus aculeatus). Can. J. Fish. Aq. Sci. 45, 426–431. https://doi.org/10.1139/f88-051 (1988).
Article  Google Scholar 

82.
Milinski, M. Parasites determine a predator’s optimal feeding strategy. Behav. Ecol. Sociobiol. 15, 35–37. https://doi.org/10.1007/BF00310212 (1984).
Article  Google Scholar 

83.
Barber, I. & Huntingford, F. A. The effect of Schistocephalus solidus (Cestoda: Pseudophyllidea) on the foraging and shoaling behaviour of three-spined sticklebacks, Gasterosteus aculeatus. Behaviour 132, 1223–1240. https://doi.org/10.1163/156853995X00540 (1995).
Article  Google Scholar 

84.
Van den Brink, F. W. B., Van der Velde, G. & Bij de Vaate, A. Amphipod invasion on the Rhine. Nature 352, 576. https://doi.org/10.1038/352576a0 (1991).
ADS  Article  Google Scholar 

85.
den Hartog, C., Van den Brink, F. W. B. & Van der Velde, G. Why was the invasion of the river Rhine by Corophium curvispinum and Corbicula species so successful?. J. Nat. Hist. 26, 1121–1129. https://doi.org/10.1080/00222939200770651 (1992).
Article  Google Scholar 

86.
Dick, J. T. A. & Platvoet, D. Invading predatory crustacean Dikerogammarus villosus eliminates both native and exotic species. Proc. R. Soc. Lond. B Biol. Sci. 267, 977–983. https://doi.org/10.1098/rspb.2000.1099 (2000).
CAS  Article  Google Scholar 

87.
Platvoet, D., Van Der Velde, G., Dick, J. T. A. & Li, S. Q. Flexible omnivory in Dikerogammarus villosus (Sowinsky, 1894) (Amphipoda) – Amphipod Pilot Species Project (AMPIS) Report 5. Crustaceana 82, 703–720. https://doi.org/10.1163/156854009X423201 (2009).
Article  Google Scholar 

88.
Richter, L. et al. The very hungry amphipod: the invasive Dikerogammarus villosus shows high consumption rates for two food sources and independent of predator cues. Biol. Invasions 20, 1321–1335. https://doi.org/10.1007/s10530-017-1629-4 (2018).
Article  Google Scholar 

89.
Worischka, S. et al. Food consumption of the invasive amphipod Dikerogammarus villosus in field mesocosms and its effects on leaf decomposition and periphyton. Aquat. Invasions 13, 261–275. https://doi.org/10.3391/ai.2018.13.2.07 (2018).
Article  Google Scholar 

90.
Berg, M.B. Laval food and feeding behaviour in The Chironomidae (eds. Armitage, P.D., Cranston, P.S. & Pinder, L.C.V.) 136–168. https://doi.org/10.1007/978-94-011-0715-0_7 (Springer, 1995)

91.
Henriques-Oliveira, A. L., Nessimian, J. L. & Dorvillé, L. F. M. Feeding habits of chironomid larvae (Insecta: Diptera) from a stream in the Floresta da Tijuca, Rio de janeiro, Brazil. Braz. J. Biol. 63, 269–281. https://doi.org/10.1590/S1519-69842003000200012 (2003).
CAS  Article  PubMed  Google Scholar 

92.
Post, D. M. Using stable isotopes to estimate trophic position: models, methods, and assumptions. Ecology 83, 703–718. https://doi.org/10.2307/3071875 (2002).
Article  Google Scholar 

93.
Syrovátka, V. The predatory behaviour of Monopelopia tenuicalcar (Kieffer, 1918) larvae in a laboratory experiment. J. Limnol. 77, 88–94. https://doi.org/10.4081/jlimnol.2018.1792 (2018).
Article  Google Scholar 

94.
Bernot, R. J. & Lamberti, G. A. Indirect effects of a parasite on a benthic community: an experiment with trematodes, snails and periphyton. Freshw. Biol. 53, 322–329. https://doi.org/10.1111/j.1365-2427.2007.01896.x (2008).
Article  Google Scholar 

95.
Seppälä, O., Karvonen, A. & Valtonen, E. T. Parasite-induced change in host behaviour and susceptibility to predation in an eye fluke-fish interaction. Anim. Behav. 68, 257–263. https://doi.org/10.1016/j.anbehav.2003.10.021 (2004).
Article  Google Scholar 

96.
Gopko, M., Mikheev, V. N. & Taskinen, J. Deterioration of basic components of the anti-predator behavior in fish harboring eye fluke larvae. Behav. Ecol. Sociobiol. 71, 68. https://doi.org/10.1007/s00265-017-2300-x (2017).
Article  Google Scholar 

97.
Flink, H., Behrens, J. W. & Svensson, P. A. Consequences of eye fluke infection on anti-predator behaviours in invasive round gobies in Kalmar Sound. Parasitol. Res. 116, 1653–1663. https://doi.org/10.1007/s00436-017-5439-5 (2017).
Article  PubMed  PubMed Central  Google Scholar 

98.
Scheffer, M., Hosper, S. H., Meijer, M. L., Moss, B. & Jeppesen, E. Alternative equilibria in shallow lakes. Trends Evol. Ecol. 8, 275–279. https://doi.org/10.1016/0169-5347(93)90254-M (1993).
CAS  Article  Google Scholar 

99.
Driescher, E., Behrendt, H., Schellenberger, G. & Stellmacher, R. Lake Müggelsee and its environment – natural conditions and anthropogenic impacts. Int. Revue. ges. Hydrobiol. 78, 327–343. https://doi.org/10.1002/iroh.19930780303 (1993).
CAS  Article  Google Scholar 

100.
Kozicka, J. & Niewiadomska, K. Studies on the biology and taxonomy of trematodesof the genus Tylodelphys Diesing, 1850 (Diplostomatidae). Acta Parasitol. Pol. 8, 379–400 (1960).
Google Scholar 

101.
Dönges, J. Entwicklungs- und Lebensdauer von Metacercarien. Z. Parasitenk. 31, 340–366. https://doi.org/10.1007/BF00259732 (1969).
Article  PubMed  Google Scholar 

102.
Kennedy, C. R. Long-term stability in the population levels of the eyefluke Tylodelphys podicipina(Digenea: Diplostomatidae) in perch. J. Fish Biol. 31, 571–581. https://doi.org/10.1111/j.1095-8649.1987.tb05259.x (1987).
Article  Google Scholar 

103.
Höglund, J. & Thulin, J. Identification of Diplostomumspp. in the retina of perch Perca fluviatilisand the lens of roach Rutilus rutilusfrom the Baltic Sea – an experimental study. Syst. Parasitol. 21, 1–19. https://doi.org/10.1007/BF00009910 (1992).
Article  Google Scholar 

104.
Niewiadomska, K. Rasoẑyty ryb Polski Prywry – Digenea (Polskie Towarzystwo Parazytologiczne, Warsaw, Poland, 2003).
Google Scholar 

105.
Blasco-Costa, I. et al. Fish pathogens near the Arctic Circle: molecular, morphological and ecological evidence for unexpected diversity of Diplostomum (Digenea: diplostomidae) in Iceland. Int. J. Parasitol. 44, 703–715. https://doi.org/10.1016/j.ijpara.2014.04.009 (2014).
Article  PubMed  Google Scholar 

106.
Bush, A. O., Lafferty, K. D., Lotz, J. M. & Shostak, A. W. Parasitology meets ecology on its own terms: Margolis et al revisited. J. Parasitol. 83, 575–583. https://doi.org/10.2307/3284227 (1997).
CAS  Article  PubMed  Google Scholar 

107.
Nash, R. D. M., Valencia, A. H. & Geffen, A. J. The origin of Fulton’s condition factor: setting the record straight. Fisheries 31, 236–238 (2006).
Google Scholar 

108.
Persson, L., Andersson, J., Wahlström, E. & Eklöv, P. Size–specific interactions in lake systems: predator gape limitation and prey growth rate and mortality. Ecology 77, 900–911. https://doi.org/10.2307/2265510 (1996).
Article  Google Scholar 

109.
Pinder, L. C. V. Biology of freshwater Chironomidae. Ann. Rev. Entomol. 31, 1–23. https://doi.org/10.1146/annurev.en.31.010186.000245 (1986).
Article  Google Scholar 

110.
Linzmaier, S. M., Twardochleb, L. A., Olden, J. D., Mehner, T. & Arlinghaus, R. Size-dependent foraging niches of European Perch Perca fluviatilis (Linnaeus, 1758) and North American Yellow Perch Perca flavescens (Mitchill, 1814). Environ. Biol. Fish 101, 23–37. https://doi.org/10.1007/s10641-017-0678-y (2018).
Article  Google Scholar 

111.
Nachev, M. et al. Understanding trophic interactions in host–parasite associations using stable isotopes of carbon and nitrogen. Parasit Vectors 10, 90. https://doi.org/10.1186/s13071-017-2030-y (2017).
CAS  Article  PubMed  PubMed Central  Google Scholar 

112.
Werner, R. A. & Brand, W. A. Referencing strategies and techniques in stable isotope ratio analysis. Rapid. Commun. Mass Spectrom. 15, 501–519. https://doi.org/10.1002/rcm.258 (2001).
ADS  CAS  Article  PubMed  Google Scholar 

113.
DeNiro, M. J. & Epstein, S. Influence of diet on the distribution of carbon isotopes in animals. Geochim. Cosmochim. Acta 42, 495–506. https://doi.org/10.1016/0016-7037(78)90199-0 (1978).
ADS  CAS  Article  Google Scholar 

114.
DeNiro, M. J. & Epstein, S. Influence of diet on the distribution of nitrogen isotopes in animals. Geochim. Cosmochim. Acta 45, 341–351. https://doi.org/10.1016/0016-7037(81)90244-1 (1981).
ADS  CAS  Article  Google Scholar 

115.
Fry, B. & Sherr, E. B. δ13C measurements as indicators of carbon flow in marine and freshwater ecosystems. Contrib. Mar. Sci. 27, 13–47 (1984).
CAS  Google Scholar 

116.
Minagawa, M. & Wada, E. Stepwise enrichment of 15N along food chains: Further evidence and the relation between δ15N and animal age. Geochim. Cosmochim. Acta 48, 1135–1140. https://doi.org/10.1016/0016-7037(84)90204-7 (1984).
ADS  CAS  Article  Google Scholar 

117.
Vander Zanden, M. J. & Rasmussen, J. B. Variation in δ15N and δ13C trophic fractionation: Implications for aquatic food web studies. Limnol. Oceanogr. 46, 2061–2066. https://doi.org/10.4319/lo.2001.46.8.2061 (2001).
ADS  CAS  Article  Google Scholar 

118.
Elsdon, T. S., Ayvazian, S., McMahon, K. W. & Thorrold, S. R. Experimental evaluation of stable isotope fractionation in fish muscle and otoliths. Mar. Ecol. Prog. Ser. 408, 195–205. https://doi.org/10.3354/meps08518 (2010).
ADS  CAS  Article  Google Scholar 

119.
Parnell, A. & Jackson, A. SIAR: Stable isotope analysis in R. R package ver. 4.2. http://CRAN.R-project.org/package=siar (2013)

120.
R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ (2018) More

1.
Tscharntke, T. et al. Landscape moderation of biodiversity patterns and processes: eight hypotheses. Biol. Rev. 87, 661–685 (2012).
PubMed  Article  Google Scholar 
2.
Fahrig, L. et al. Functional landscape heterogeneity and animal biodiversity in agricultural landscapes: Heterogeneity and biodiversity. Ecol. Lett. 14, 101–112 (2011).
PubMed  Article  Google Scholar 

3.
Rundlöf, M., Nilsson, H. & Smith, H. G. Interacting effects of farming practice and landscape context on bumble bees. Biol. Conserv. 141, 417–426 (2008).
Article  Google Scholar 

4.
Wamser, S., Diekötter, T., Boldt, L., Wolters, V. & Dauber, J. Trait-specific effects of habitat isolation on carabid species richness and community composition in managed grasslands: Effects of habitat isolation on carabid beetles. Insect Conser. Divers. 5, 9–18 (2012).
Article  Google Scholar 

5.
Sonnier, G., Jamoneau, A. & Decocq, G. Evidence for a direct negative effect of habitat fragmentation on forest herb functional diversity. Landsc. Ecol. 29, 857–866 (2014).
Article  Google Scholar 

6.
Wilcove, D. S. & McLellan, C. H. Habitat fragmentation in the temperate zone. Conserv. Biol. 1, 237–256 (1986).
Google Scholar 

7.
Wilcox, B. A. & Murphy, D. D. Conservation strategy: The effects of fragmentation on extinction. Am. Nat. 125, 879–887 (1985).
Article  Google Scholar 

8.
Leibold, M. A. et al. The metacommunity concept: A framework for multi-scale community ecology: The metacommunity concept. Ecol. Lett. 7, 601–613 (2004).
Article  Google Scholar 

9.
Fahrig, L. Ecological responses to habitat fragmentation per se. Annu. Rev. Ecol. Evol. Syst. 48, 1–23 (2017).
Article  Google Scholar 

10.
Fletcher, R. J. et al. Is habitat fragmentation good for biodiversity?. Biol. Conserv. 226, 9–15 (2018).
Article  Google Scholar 

11.
Fahrig, L. et al. Is habitat fragmentation bad for biodiversity?. Biol. Conserv. 230, 179–186 (2019).
Article  Google Scholar 

12.
Gámez-Virués, S. et al. Landscape simplification filters species traits and drives biotic homogenization. Nat. Commun. 6, 8568 (2015).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

13.
Perović, D. et al. Configurational landscape heterogeneity shapes functional community composition of grassland butterflies. J. Appl. Ecol. 52, 505–513 (2015).
Article  Google Scholar 

14.
Concepción, E. D. et al. Contrasting trait assembly patterns in plant and bird communities along environmental and human-induced land-use gradients. Ecography 40, 753–763 (2017).
Article  Google Scholar 

15.
Rocha-Santos, L. et al. The loss of functional diversity: A detrimental influence of landscape-scale deforestation on tree reproductive traits. J. Ecol. 108, 212–223 (2019).
Article  Google Scholar 

16.
Provost, G. L. et al. Land-use history impacts functional diversity across multiple trophic groups. PNAS 117, 1573–1579 (2020).
PubMed  Article  CAS  Google Scholar 

17.
Solé-Senan, X. O., Juárez-Escario, A., Robleño, I., Conesa, J. A. & Recasens, J. Using the response-effect trait framework to disentangle the effects of agricultural intensification on the provision of ecosystem services by Mediterranean arable plants. Agric. Ecosyst. Environ. 247, 255–264 (2017).
Article  Google Scholar 

18.
Grime, J. P. Trait convergence and trait divergence in herbaceous plant communities: mechanisms and consequences. J. Veg. Sci. 17, 255–260 (2006).
Article  Google Scholar 

19.
Macarthur, R. & Levins, R. The limiting similarity, convergence, and divergence of coexisting species. Am. Nat. 101, 377–385 (1967).
Article  Google Scholar 

20.
de Bello, F. et al. Evidence for scale- and disturbance-dependent trait assembly patterns in dry semi-natural grasslands. J. Ecol. 101, 1237–1244 (2013).
Article  Google Scholar 

21.
Muscarella, R. & Uriarte, M. Do community-weighted mean functional traits reflect optimal strategies?. Proc. R. Soc. B 283, 20152434 (2016).
PubMed  Article  Google Scholar 

22.
de Bello, F. et al. Partitioning of functional diversity reveals the scale and extent of trait convergence and divergence. J. Veg. Sci. 20, 475–486 (2009).
Article  Google Scholar 

23.
Mouchet, M. A., Villéger, S., Mason, N. W. H. & Mouillot, D. Functional diversity measures: an overview of their redundancy and their ability to discriminate community assembly rules: Functional diversity measures. Funct. Ecol. 24, 867–876 (2010).
Article  Google Scholar 

24.
Shmida, A. & Wilson, M. V. Biological determinants of species diversity. J. Biogeogr. 12, 1–20 (1985).
Article  Google Scholar 

25.
Baudry, J. & Papy, F. The role of landscape heterogeneity in the sustainability of cropping systems. In Crop Science: Progress and Prospects (eds Baudry, J. & Papy, F.) 243–249 (CABI Publishing, Oxfordshire, 2001).
Google Scholar 

26.
Duflot, R., Georges, R., Ernoult, A., Aviron, S. & Burel, F. Landscape heterogeneity as an ecological filter of species traits. Acta Oecol. 56, 19–26 (2014).
ADS  Article  Google Scholar 

27.
Cleland, E., Chuine, I., Menzel, A., Mooney, H. & Schwartz, M. Shifting plant phenology in response to global change. Trends Ecol. Evol. 22, 357–365 (2007).
PubMed  Article  PubMed Central  Google Scholar 

28.
Hendrickx, F. et al. Pervasive effects of dispersal limitation on within- and among-community species richness in agricultural landscapes. Glob. Ecol. Biogeogr. 18, 607–616 (2009).
Article  Google Scholar 

29.
Dunning, J. B., Danielson, B. J. & Pulliam, H. R. Ecological processes that affect populations in complex landscapes. Oikos 65, 169 (1992).
Article  Google Scholar 

30.
Jonason, D. et al. Weak functional response to agricultural landscape homogenisation among plants, butterflies and birds. Ecography 40, 1221–1230 (2017).
Article  Google Scholar 

31.
Kuussaari, M. et al. Extinction debt: a challenge for biodiversity conservation. Trends Ecol. Evol. 24, 564–571 (2009).
PubMed  Article  Google Scholar 

32.
Diamond, J. M. Biogeographic kinetics: estimation of relaxation times for avifaunas of Southwest Pacific Islands. Proc. Natl. Acad. Sci. 69, 3199–3203 (1972).
ADS  CAS  PubMed  Article  Google Scholar 

33.
Hanski, I. & Ovaskainen, O. Extinction debt at extinction threshold. Conserv. Biol. 16, 666–673 (2002).
Article  Google Scholar 

34.
Helm, A., Hanski, I. & Partel, M. Slow response of plant species richness to habitat loss and fragmentation. Ecol. Lett. 9, 72–77 (2005).
Google Scholar 

35.
Sang, A., Teder, T., Helm, A. & Pärtel, M. Indirect evidence for an extinction debt of grassland butterflies half century after habitat loss. Biol. Conserv. 143, 1405–1413 (2010).
Article  Google Scholar 

36.
Lindborg, R. Evaluating the distribution of plant life-history traits in relation to current and historical landscape configurations. J. Ecol. 95, 555–564 (2007).
Article  Google Scholar 

37.
Saar, L., de Bello, F., Pärtel, M. & Helm, A. Trait assembly in grasslands depends on habitat history and spatial scale. Oecologia 184, 1–12 (2017).
ADS  PubMed  Article  Google Scholar 

38.
Yamanaka, S., Akasaka, T., Yamaura, Y., Kaneko, M. & Nakamura, F. Time-lagged responses of indicator taxa to temporal landscape changes in agricultural landscapes. Ecol. Ind. 48, 593–598 (2015).
Article  Google Scholar 

39.
Piqueray, J. et al. Plant species extinction debt in a temperate biodiversity hotspot: Community, species and functional traits approaches. Biol. Conserv. 144, 1619–1629 (2011).
Article  Google Scholar 

40.
Barbaro, L. & van Halder, I. Linking bird, carabid beetle and butterfly life-history traits to habitat fragmentation in mosaic landscapes. Ecography 32, 321–333 (2009).
Article  Google Scholar 

41.
Grime, J. P. Benefits of plant diversity to ecosystems: Immediate, filter and founder effects. J. Ecol. 86, 902–910 (1998).
Article  Google Scholar 

42.
Lortie, C. J. et al. Rethinking plant community theory. Oikos 107, 433–438 (2004).
Article  Google Scholar 

43.
Turnbull, L. A., Rees, M. & Crawley, M. J. Seed mass and the competition/colonization trade-off: A sowing experiment. J. Ecol. 87, 899–912 (1999).
Article  Google Scholar 

44.
van Kleunen, M., Fischer, M. & Schmid, B. Effects of intraspecific competition on size variation and reproductive allocation in a clonal plant. Oikos 94, 515–524 (2001).
Article  Google Scholar 

45.
Zambrano, J. et al. The effects of habitat loss and fragmentation on plant functional traits and functional diversity: What do we know so far?. Oecologia 191, 505–518 (2019).
ADS  PubMed  Article  Google Scholar 

46.
Atauri, J. A. & de Lucio, J. V. The role of landscape structure in species richness distribution of birds, amphibians, reptiles and lepidopterans in Mediterranean landscapes. Landsc. Ecol. 16, 147–159 (2001).
Article  Google Scholar 

47.
Weibull, A.-C., Östman, Ö. & Granqvist, Å. Species richness in agroecosystems: the effect of landscape, habitat and farm management. Biodivers. Conserv. 12, 1335–1355 (2003).
Article  Google Scholar 

48.
Smith, H. G., Dänhardt, J., Lindström, Å. & Rundlöf, M. Consequences of organic farming and landscape heterogeneity for species richness and abundance of farmland birds. Oecologia 162, 1071–1079 (2010).
ADS  PubMed  Article  Google Scholar 

49.
Sirami, C. et al. Increasing crop heterogeneity enhances multitrophic diversity across agricultural regions. PNAS 116, 16442–16447 (2019).
CAS  PubMed  Article  Google Scholar 

50.
Redon, M., Bergès, L., Cordonnier, T. & Luque, S. Effects of increasing landscape heterogeneity on local plant species richness: How much is enough?. Landsc. Ecol. 29, 773–787 (2014).
Article  Google Scholar 

51.
Fahrig, L. Rethinking patch size and isolation effects: the habitat amount hypothesis. J. Biogeogr. 40, 1649–1663 (2013).
Article  Google Scholar 

52.
MacDonald, Z. G., Anderson, I. D., Acorn, J. H. & Nielsen, S. E. The theory of island biogeography, the sample-area effect, and the habitat diversity hypothesis: Complementarity in a naturally fragmented landscape of lake islands. J. Biogeogr. 45, 2730–2743 (2018).
Article  Google Scholar 

53.
Smart, S. M., Bunce, R. G. H., Firbank, L. G. & Coward, P. Do field boundaries act as refugia for grassland plant species diversity in intensively managed agricultural landscapes in Britain?. Agric. Ecosyst. Environ. 91, 73–87 (2002).
Article  Google Scholar 

54.
Klimesova, J., Latzel, V., de Bello, F. & van Groenendael, J. M. Plant functional traits in studies of vegetation changes in response to grazing and mowing: Towards a use of more specific traits. Preslia 80, 245–253 (2008).
Google Scholar 

55.
Fuller, R. J., Chamberlain, D. E., Burton, N. H. K. & Gough, S. J. Distributions of birds in lowland agricultural landscapes of England and Wales: How distinctive are bird communities of hedgerows and woodland?. Agric. Ecosyst. Environ. 84, 79–92 (2001).
Article  Google Scholar 

56.
Hinsley, S. A. & Bellamy, P. E. The influence of hedge structure, management and landscape context on the value of hedgerows to birds: A review. J. Environ. Manage. 60, 33–49 (2000).
Article  Google Scholar 

57.
Noh, J., Echeverría, C., Pauchard, A. & Cuenca, P. Extinction debt in a biodiversity hotspot: the case of the Chilean Winter Rainfall-Valdivian Forests. Landsc. Ecol. Eng. 15, 1–12 (2019).
Article  Google Scholar 

58.
Saar, L., Takkis, K., Pärtel, M. & Helm, A. Which plant traits predict species loss in calcareous grasslands with extinction debt? Traits predicting extinctions in grasslands. Divers. Distrib. 18, 808–817 (2012).
Article  Google Scholar 

59.
Figueiredo, L., Krauss, J., Steffan-Dewenter, I. & Cabral, J. S. Understanding extinction debts: Spatio–temporal scales, mechanisms and a roadmap for future research. Ecography 42, 1973–1990 (2019).
Article  Google Scholar 

60.
Krauss, J. et al. Habitat fragmentation causes immediate and time-delayed biodiversity loss at different trophic levels: Immediate and time-delayed biodiversity loss. Ecol. Lett. 13, 597–605 (2010).
PubMed  PubMed Central  Article  Google Scholar 

61.
With, K. A. How fast do migratory songbirds have to adapt to keep pace with rapidly changing landscapes?. Landsc. Ecol 30, 1351–1361 (2015).
Article  Google Scholar 

62.
Andrén, H. Effects of habitat fragmentation on birds and mammals in landscapes with different proportions of suitable habitat: A review. Oikos 71, 355–366 (1994).
Article  Google Scholar 

63.
Kavelaars, M. M. et al. Breeding habitat loss reveals limited foraging flexibility and increases foraging effort in a colonial breeding seabird. Mov. Ecol. 8, 45 (2020).
PubMed  PubMed Central  Article  Google Scholar 

64.
van Zanten, B. T. et al. European agricultural landscapes, common agricultural policy and ecosystem services: A review. Agron. Sustain. Dev. 34, 309–325 (2014).
Article  Google Scholar 

65.
Ramalho, C. E., Laliberté, E., Poot, P. & Hobbs, R. Effects of fragmentation on the plant functional composition and diversity of remnant woodlands in a young and rapidly expanding city. J. Veg. Sci. 29, 285–296 (2018).
Article  Google Scholar 

66.
Jackson, S. T. & Sax, D. F. Balancing biodiversity in a changing environment: Extinction debt, immigration credit and species turnover. Trends Ecol. Evol. 25, 153–160 (2010).
PubMed  Article  PubMed Central  Google Scholar 

67.
Renner, S. S. & Zohner, C. M. Climate change and phenological mismatch in trophic interactions among plants, insects, and vertebrates. Annu. Rev. Ecol. Evol. Syst. 49, 165–182 (2018).
Article  Google Scholar 

68.
Damien, M. & Tougeron, K. Prey–predator phenological mismatch under climate change. Curr. Opin. Insect Sci. 35, 60–68 (2019).
PubMed  Article  PubMed Central  Google Scholar 

69.
Lalechère, E., Archaux, F. & Jabot, F. Relative importance of landscape and species characteristics on extinction debt, immigration credit and relaxation time after habitat turnover. Popul. Ecol. 61, 383–395 (2019).
Article  Google Scholar 

70.
Ernoult, A. et al. Potential landscape drivers of biodiversity components in a flood plain: Past or present patterns?. Biol. Conserv. 127, 1–17 (2006).
Article  Google Scholar 

71.
Meeus, J. H. A., Wijermans, M. P. & Vroom, M. J. Agricultural landscapes in Europe and their transformation. Landsc. Urban Plan. 18, 289–352 (1990).
Article  Google Scholar 

72.
McGarigal, K., Cushman, S. & Ene, E. FRAGSTATS v4: Spatial Pattern Analysis Program for Categorical and Continuous Maps. Computer software program produced by the authors at the University of Massachusetts, Amherst. http://www.umass.edu/landeco/research/fragstats/fragstats.html. (2012).

73.
Duflot, R., Aviron, S., Ernoult, A., Fahrig, L. & Burel, F. Reconsidering the role of ‘semi-natural habitat’ in agricultural landscape biodiversity: A case study. Ecol. Res. 30, 75–83 (2015).
Article  Google Scholar 

74.
Bibby, C. J., Burgess, N. D., Hill, D. A. & Mustoe, S. Bird Census Techniques (Elsevier, Amsterdam, 2000).
Google Scholar 

75.
Kühn, I., Durka, W. & Klotz, S. BiolFlor: A new plant-trait database as a tool for plant invasion ecology: BiolFlor: A plant-trait database. Divers. Distrib. 10, 363–365 (2004).
Article  Google Scholar 

76.
Kleyer, M. et al. The LEDA Traitbase: a database of life-history traits of the Northwest European flora. J. Ecol. 96, 1266–1274 (2008).
Article  Google Scholar 

77.
Duquet, M. Tout sur les Oiseaux d’Europe (Delachaux, Colombes, 2015).
Google Scholar 

78.
Dormann, C. F. et al. Collinearity: A review of methods to deal with it and a simulation study evaluating their performance. Ecography 36, 27–46 (2013).
Article  Google Scholar 

79.
Garnier, E. et al. Plant functional markers capture ecosystem properties during secondary succession. Ecology 85, 2630–2637 (2004).
Article  Google Scholar 

80.
Sonnier, G., Shipley, B. & Navas, M.-L. Quantifying relationships between traits and explicitly measured gradients of stress and disturbance in early successional plant communities. J. Veg. Sci. 21, 1014–1024 (2010).
Article  Google Scholar 

81.
R Core Team. R: A Language and Environment for Statistical Computing (R Core Team, Vienna, 2020).
Google Scholar 

82.
Blomberg, S. P., Garland, T. & Ives, A. R. Testing for phylogenetic signal in comparative data: Behavioral traits are more labile. Evolution 57, 717–745 (2003).
PubMed  Article  Google Scholar 

83.
Blomberg, S. P. & Garland, T. Tempo and mode in evolution: Phylogenetic inertia, adaptation and comparative methods: Phylogenetic inertia. J. Evol. Biol. 15, 899–910 (2002).
Article  Google Scholar 

84.
Zanne, A. E. et al. Three keys to the radiation of angiosperms into freezing environments. Nature 506, 89–92 (2014).
ADS  CAS  PubMed  Article  Google Scholar 

85.
Jetz, W., Thomas, G. H., Joy, J. B., Hartmann, K. & Mooers, A. O. The global diversity of birds in space and time. Nature 491, 444–448 (2012).
ADS  CAS  PubMed  Article  Google Scholar 

86.
Revell, L. J. phytools: An R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3, 217–223 (2012).
Article  Google Scholar 

87.
de Bello, F. et al. On the need for phylogenetic ‘corrections’ in functional trait-based approaches. Folia Geobot. 50, 349–357 (2015).
Article  Google Scholar 

88.
Bernard-Verdier, M. et al. Community assembly along a soil depth gradient: Contrasting patterns of plant trait convergence and divergence in a Mediterranean rangeland. J. Ecol. 100, 1422–1433 (2012).
Article  Google Scholar 

89.
Burnham, K. P. & Anderson, D. R. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach (Springer, New York, 2002).
Google Scholar 

90.
Fox, J. & Weisberg, S. An R Companion to Applied Regression (Sage, Thousand Oaks, 2019).
Google Scholar 

91.
Fahrig, L. Effects of Habitat Fragmentation on Biodiversity. Annu. Rev. Ecol. Evol. Syst. 34, 487–515 (2003).
Article  Google Scholar  More