Ecology

1.
Cardinale, B. J. et al. Biodiversity loss and its impact on humanity. Nature 486, 59–67 (2012).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 
2.
WWF. Living Planet Report – 2018: Aiming higher. (WWF International, 2018).

3.
Kelly, R. P. et al. Harnessing DNA to improve environmental management. Science 344, 1455–1456 (2014).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

4.
Bonar, S. A., Hubert, W. A. & Willis, D. W. Standard methods for sampling North American freshwater fishes (2009).

5.
Wheeler, Q. D., Raven, P. H. & Wilson, E. O. Taxonomy: impediment or expedient?. Science (New York, NY) 303, 285 (2004).
CAS  Article  Google Scholar 

6.
Kelly, R. P., Port, J. A., Yamahara, K. M. & Crowder, L. B. Using environmental DNA to census marine fishes in a large mesocosm. PLoS ONE 9, e86175 (2014).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

7.
Valentini, A. et al. Next-generation monitoring of aquatic biodiversity using environmental DNA metabarcoding. Mol. Ecol. 25, 929–942 (2016).
CAS  PubMed  Article  PubMed Central  Google Scholar 

8.
McDevitt, A. D. et al. Environmental DNA metabarcoding as an effective and rapid tool for fish monitoring in canals. J. Fish Biol. 95, 679–682 (2019).
CAS  PubMed  Article  PubMed Central  Google Scholar 

9.
Taberlet, P., Coissac, E., Hajibabaei, M. & Rieseberg, L. H. Environmental DNA. Mol. Ecol. 21, 1789–1793 (2012).
CAS  PubMed  Article  PubMed Central  Google Scholar 

10.
Deiner, K. et al. Environmental DNA metabarcoding: transforming how we survey animal and plant communities. Mol. Ecol. 26, 5872–5895 (2017).
PubMed  Article  PubMed Central  Google Scholar 

11.
Nobile, A. B. et al. DNA metabarcoding of neotropical ichthyoplankton: enabling high accuracy with lower cost. Metabarcoding Metagenomics 3, e35060 (2019).
Article  Google Scholar 

12.
Mariac, C. et al. Metabarcoding by capture using a single COI probe (MCSP) to identify and quantify fish species in ichthyoplankton swarms. PLoS ONE 13, e0202976 (2018).
CAS  PubMed  PubMed Central  Article  Google Scholar 

13.
Leray, M., Meyer, C. P. & Mills, S. C. Metabarcoding dietary analysis of coral dwelling predatory fish demonstrates the minor contribution of coral mutualists to their highly partitioned, generalist diet. PeerJ 3, e1047 (2015).
PubMed  PubMed Central  Article  CAS  Google Scholar 

14.
Shokralla, S. et al. Massively parallel multiplex DNA sequencing for specimen identification using an IlluminaMiSeq platform. Sci. Rep. 5, 9687 (2015).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

15.
Kitano, T., Umetsu, K., Tian, W. & Osawa, M. Two universal primer sets for species identification among vertebrates. Int. J. Legal Med. 121, 423–427 (2007).
PubMed  Article  PubMed Central  Google Scholar 

16.
Stoeckle, M. Y., Soboleva, L. & Charlop-Powers, Z. Aquatic environmental DNA detects seasonal fish abundance and habitat preference in an urban estuary. PLoS ONE 12, e0175186 (2017).
PubMed  PubMed Central  Article  CAS  Google Scholar 

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

18.
Bylemans, J. et al. An environmental DNA-based method for monitoring spawning activity: a case study, using the endangered Macquarie perch (Macquaria australasica). Methods Ecol. Evol. 8, 646–655 (2017).
Article  Google Scholar 

19.
De Souza, L. S., Godwin, J. C., Renshaw, M. A. & Larson, E. Environmental DNA (eDNA) detection probability is influenced by seasonal activity of organisms. PLoS ONE 11, e0165273 (2016).
PubMed  PubMed Central  Article  CAS  Google Scholar 

20.
Dejean, T. et al. Improved detection of an alien invasive species through environmental DNA barcoding: the example of the American bullfrog Lithobates catesbeianus. J. Appl. Ecol. 49, 953–959 (2012).
Article  Google Scholar 

21.
Cilleros, K. et al. Unlocking biodiversity and conservation studies in high-diversity environments using environmental DNA (eDNA): a test with Guianese freshwater fishes. Mol. Ecol. Resour. 19(1), 27–46. https://doi.org/10.1111/1755-0998.12900 (2018).
CAS  Article  PubMed  PubMed Central  Google Scholar 

22.
Sales, N. G., Wangensteen, O. S., Carvalho, D. C. & Mariani, S. Influence of preservation methods, sample medium and sampling time on eDNA recovery in a neotropical river. Environ. DNA 1(2), 119–130. https://doi.org/10.1002/edn3.14 (2019).
Article  Google Scholar 

23.
Sales, N. G. et al. Assessing the potential of environmental DNA metabarcoding for monitoring Neotropical mammals: a case study in the Amazon and Atlantic Forest, Brazil. Mamm. Rev. 50, 221–225 (2020).
Article  Google Scholar 

24.
Dejean, T. et al. Persistence of environmental DNA in freshwater ecosystems. PLoS ONE 6, e23398 (2011).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

25.
Gomes, L. C., Pessali, T. C., Sales, N. G., Pompeu, P. S. & Carvalho, D. C. Integrative taxonomy detects cryptic and overlooked fish species in a neotropical river basin. Genetica 143, 581–588 (2015).
PubMed  Article  PubMed Central  Google Scholar 

26.
Pugedo, M. L., de Andrade Neto, F. R., Pessali, T. C., Birindelli, J. L. O. & Carvalho, D. C. Integrative taxonomy supports new candidate fish species in a poorly studied neotropical region: the Jequitinhonha River Basin. Genetica 144, 341–349 (2016).
PubMed  Article  PubMed Central  Google Scholar 

27.
Ramirez, J. L. et al. Revealing hidden diversity of the underestimated NeotropicalIchthyofauna: DNA barcoding in the recently described genus Megaleporinus (Characiformes: Anostomidae). Front. Genet. 8, 1–11 (2017).
Article  CAS  Google Scholar 

28.
Carvalho, D. C. et al. Deep barcode divergence in Brazilian freshwater fishes: the case of the São Francisco River basin. Mitochondrial DNA 22, 80–86 (2011).
PubMed  Article  CAS  PubMed Central  Google Scholar 

29.
Collins, R. A. et al. Non-specific amplification compromises environmental DNA metabarcoding with COI. Methods Ecol. Evol. 10, 1985–2001 (2019).
Article  Google Scholar 

30.
Shaw, J. L. A. et al. Comparison of environmental DNA metabarcoding and conventional fish survey methods in a river system. Biol. Conserv. 197, 131–138 (2016).
Article  Google Scholar 

31.
Yamamoto, S. et al. Environmental DNA metabarcoding reveals local fish communities in a species-rich coastal sea. Sci. Rep. 7, 40368 (2017).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

32.
Miya, M. et al. MiFish, a set of universal PCR primers for metabarcoding environmental DNA from fishes: detection of more than 230 subtropical marine species. R. Soc. Open Sci. 2, 150088 (2015).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

33.
MacDonald, A. J. & Sarre, S. D. A framework for developing and validating taxon-specific primers for specimen identification from environmental DNA. Mol. Ecol. Resour. 17, 708–720 (2017).
CAS  PubMed  Article  PubMed Central  Google Scholar 

34.
Aljanabi, S. M. & Martinez, I. Universal and rapid salt-extraction of high quality genomic DNA for PCR-based techniques. Nucleic Acids Res. 25, 4692–4693 (1997).
CAS  PubMed  PubMed Central  Article  Google Scholar 

35.
Thomsen, P. F. et al. Environmental DNA from seawater samples correlate with trawl catches of subarctic deepwater fishes. PLoS ONE 11, e0165252 (2016).
PubMed  PubMed Central  Article  CAS  Google Scholar 

36.
Larkin, M. A. et al. Clustal W and Clustal X version 2.0. Bioinformatics 23, 2947–2948 (2007).
CAS  PubMed  Article  PubMed Central  Google Scholar 

37.
Kumar, S., Stecher, G. & Tamura, K. MEGA7: molecular evolutionary genetics analysis version 7.0 for bigger datasets. Mol. Biol. Evol. 33, 1870–1874 (2016).
CAS  Article  Google Scholar 

38.
Kimura, M. A simple method for estimating evolutionary rates of base substitutions through comparative studies of nucleotide sequences. J. Mol. Evol. 16, 111–120 (1980).
ADS  CAS  Article  Google Scholar 

39.
Saitou, N. & Nei, M. The neighbor-joining method: a new method for reconstructing phylogenetic trees’. Mol. Biol. Evol. 4, 406–425 (1987).
CAS  PubMed  PubMed Central  Google Scholar 

40.
Felsenstein, J. Evolutionary trees from gene frequencies and quantitative characters: finding maximum likelihood estimates. Evolution (N. Y.) 35, 1229–1242 (1981).
Google Scholar 

41.
Kearse, M. et al. Geneious Basic: an integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics 28, 1647–1649 (2012).
PubMed  PubMed Central  Article  Google Scholar 

42.
Proutski, V. & Holmes, E. SWAN: sliding window analysis of nucleotide sequence variability. Bioinformatics 14, 467–468 (1998).
CAS  PubMed  Article  PubMed Central  Google Scholar 

43.
Brown, S. D. J. et al. Spider: an R package for the analysis of species identity and evolution, with particular reference to DNA barcoding. Mol. Ecol. Resour. 12, 562–565 (2012).
PubMed  Article  PubMed Central  Google Scholar 

44.
R Core Team. R: A Language and Environment for Statistical Computing (2020).

45.
Meusnier, I. et al. A universal DNA mini-barcode for biodiversity analysis. BMC Genomics 9, 214 (2008).
PubMed  PubMed Central  Article  CAS  Google Scholar 

46.
Ye, J. et al. Primer-BLAST: a tool to design target-specific primers for polymerase chain reaction. BMC Bioinform. 13, 134 (2012).
CAS  Article  Google Scholar 

47.
Fujisawa, T. & Barraclough, T. G. Delimiting species using single-locus data and the generalized mixed yule coalescent approach: a revised method and evaluation on simulated data sets. Syst. Biol. 62, 707–724 (2013).
PubMed  PubMed Central  Article  Google Scholar 

48.
Zhang, J., Kapli, P., Pavlidis, P. & Stamatakis, A. A general species delimitation method with applications to phylogenetic placements. Bioinformatics 29, 2869–2876 (2013).
CAS  PubMed  PubMed Central  Article  Google Scholar 

49.
Puillandre, N., Lambert, A., Brouillet, S. & Achaz, G. ABGD, Automatic Barcode Gap Discovery for primary species delimitation. Mol. Ecol. 21, 1864–1877 (2012).
CAS  PubMed  Article  PubMed Central  Google Scholar 

50.
Drummond, A. J. & Rambaut, A. BEAST: Bayesian evolutionary analysis by sampling trees. BMC Evol. Biol. 7, 214 (2007).
PubMed  PubMed Central  Article  CAS  Google Scholar 

51.
Rambaut, A., Suchard, M. A., Xie, D. & Drummond, A. J. Tracer 1.6 http://beast.bio.ed.ac.uk/tracer (2014).

52.
Rambaut, A. & Drummond, A. J. TreeAnnotator, version 1.7. 5. Available beast. bio. ed. ac. uk/TreeAnnotator (accessed 15 April 2010) (2012).

53.
Ward, R. D. DNA barcode divergence among species and genera of birds and fishes. Mol. Ecol. Resour. 9, 1077–1085 (2009).
CAS  PubMed  Article  PubMed Central  Google Scholar 

54.
Hajibabaei, M. et al. A minimalist barcode can identify a specimen whose DNA is degraded. Mol. Ecol. Notes 6, 959–964 (2006).
CAS  Article  Google Scholar 

55.
Yu, H.-J. & You, Z.-H. Comparison of DNA truncated barcodes and full-barcodes for species identification. in International Conference on Intelligent Computing 108–114 (Springer, 2010).

56.
Harper, L. R. et al. Environmental DNA (eDNA) metabarcoding of pond water as a tool to survey conservation and management priority mammals. Biol. Conserv. 238, 108225 (2019).
Article  Google Scholar  More

1.
Gabriel, W. & Bürger, R. Survival of small populations under demographic stochasticity. Theor. Popul. Biol. 41, 44–71 (1992).
CAS  PubMed  MATH  Article  Google Scholar 
2.
Lande, R. Risks of population extinction from demographic and environmental stochasticity and random catastrophes. Am. Nat. 142, 911–927 (1993).
PubMed  Article  Google Scholar 

3.
Melbourne, B. A. & Hastings, A. Extinction risk depends strongly on factors contributing to stochasticity. Nature 454, 100–103 (2008).
ADS  CAS  PubMed  Article  Google Scholar 

4.
Frankham, R. Genetics and extinction. Biol. Conserv. 126, 131–140 (2005).
Article  Google Scholar 

5.
Spielman, D., Brook, B. W. & Frankham, R. Most species are not driven to extinction before genetic factors impact them. Proc. Natl. Acad. Sci. USA 101, 15261–15264 (2004).
ADS  CAS  PubMed  Article  Google Scholar 

6.
Wright, S. Evolution in mendelian populations. Genetics 16, 97 (1931).
CAS  PubMed  PubMed Central  Google Scholar 

7.
Nunney, L. The influence of mating system and overlapping generations on effective population size. Evolution 47, 1329–1341 (1993).
PubMed  Article  Google Scholar 

8.
Ardren, W. R. & Kapuscinski, A. R. Demographic and genetic estimates of effective population size (Ne) reveals genetic compensation in steelhead trout. Mol. Ecol. 12, 35–49 (2003).
CAS  PubMed  Article  Google Scholar 

9.
Turner, T. F., Osborne, M. J., Moyer, G. R., Benavides, M. A. & Alò, D. Life history and environmental variation interact to determine effective population to census size ratio. Proc. R. Soc. B Biol. Sci. 273, 3065–3073 (2006).
Article  Google Scholar 

10.
Luikart, G., Ryman, N., Tallmon, D. A., Schwartz, M. K. & Allendorf, F. W. Estimation of census and effective population sizes: the increasing usefulness of DNA-based approaches. Conserv. Gen. 11, 355–373 (2010).
CAS  Article  Google Scholar 

11.
Palstra, F. P. & Fraser, D. J. Effective/census population size ratio estimation: a compendium and appraisal. Ecol. Evol. 2, 2357–2365 (2012).
PubMed  PubMed Central  Article  Google Scholar 

12.
Palstra, F. P. & Ruzzante, D. E. Genetic estimates of contemporary effective population size: what can they tell us about the importance of genetic stochasticity for wild population persistence?. Mol. Ecol. 17, 3428–3447 (2008).
PubMed  Article  Google Scholar 

13.
Nunney, L. Measuring the ratio of effective population size to adult numbers using genetic and ecological data. Evolution 49, 389–392 (1995).
PubMed  Article  Google Scholar 

14.
Hauser, L. & Carvalho, G. R. Paradigm shifts in marine fisheries genetics: ugly hypotheses slain by beautiful facts. Fish Fish. 9, 333–362 (2008).
Article  Google Scholar 

15.
Ruggeri, P. et al. Coupling demographic and genetic variability from archived collections of European anchovy (Engraulis encrasicolus). PLoS ONE 11, e0151507 (2016).
PubMed  PubMed Central  Article  CAS  Google Scholar 

16.
Waples, R. S., Grewe, P. M., Bravington, M. W., Hillary, R. & Feutry, P. Robust estimates of a high Ne/N ratio in a top marine predator, southern bluefin tuna. Sci. Adv. 4, eaar7759 (2018).
ADS  PubMed  PubMed Central  Article  Google Scholar 

17.
Dudgeon, C. L. & Ovenden, J. R. The relationship between abundance and genetic effective population size in elasmobranchs: an example from the globally threatened zebra shark Stegostoma fasciatum within its protected range. Conserv. Gen. 16, 1443–1454 (2015).
Article  Google Scholar 

18.
Portnoy, D. S., McDowell, J. R., McCandless, C. T., Musick, J. A. & Graves, J. E. Effective size closely approximates the census size in the heavily exploited western Atlantic population of the sandbar shark, Carcharhinus plumbeus. Conserv. Gen. 10, 1697–1705 (2009).
Article  Google Scholar 

19.
Waples, R. S., Luikart, G., Faulkner, J. R. & Tallmon, D. A. Simple life-history traits explain key effective population size ratios across diverse taxa. Proc. R. Soc. B Biol. Sci. 280, 20131339 (2013).
Article  Google Scholar 

20.
Chevolot, M., Hoarau, G., Rijnsdorp, A. D., Stam, W. T. & Olsen, J. L. Phylogeography and population structure of thornback rays (Raja clavata L., Rajidae). Mol. Ecol. 15, 3693–3705 (2006).
CAS  PubMed  Article  Google Scholar 

21.
Nunney, L. & Elam, D. R. Estimating the effective population size of conserved populations. Conserv. Biol. 8, 175–184 (1994).
Article  Google Scholar 

22.
Grimm, A., Gruber, B., Hoehn, M., Enders, K. & Henle, K. A model-derived short-term estimation method of effective size for small populations with overlapping generations. Methods Ecol. Evol. 7, 734–743 (2016).
Article  Google Scholar 

23.
Blower, D. C., Riginos, C. & Ovenden, J. R. neogen: a tool to predict genetic effective population size (Ne) for species with generational overlap and to assist empirical Ne study design. Mol. Ecol. Resour. 19, 260–271 (2019).
PubMed  Article  Google Scholar 

24.
Pazmiño, D. A., Maes, G. E., Simpfendorfer, C. A., Salinas-de-León, P. & van Herwerden, L. Genome-wide SNPs reveal low effective population size within confined management units of the highly vagile Galapagos shark (Carcharhinus galapagensis). Conserv. Genet. 18, 1151–1163 (2017).
Article  Google Scholar 

25.
Reid-Anderson, S., Bilgmann, K. & Stow, A. Effective population size of the critically endangered east Australian grey nurse shark Carcharias taurus. Mar. Ecol. Prog. Ser. 610, 137–148 (2019).
ADS  CAS  Article  Google Scholar 

26.
Vignaud, T. M. et al. Genetic structure of populations of whale sharks among ocean basins and evidence for their historic rise and recent decline. Mol. Ecol. 23, 2590–2601 (2014).
CAS  PubMed  Article  Google Scholar 

27.
Bansemer, C. S. & Bennett, M. B. Retained fishing gear and associated injuries in the east Australian grey nurse sharks (Carcharias taurus): implications for population recovery. Mar. Freshw. Res. 61, 97–103 (2010).
Article  Google Scholar 

28.
Irigoyen, A. & Trobbiani, G. Depletion of trophy large-sized sharks populations of the Argentinean coast, south-western Atlantic: insights from fishers’ knowledge. Neotrop. Ichthyol. 14, e150081 (2016).
Article  Google Scholar 

29.
Chiaramonte, G., Domingo, A. & Soto, J. Carcharias taurus (Southwest Atlantic subpopulation). The IUCN Red List of Threatened Species. e.T63163A12625032 (2007).

30.
Pollard, D., Gordon, I., Williams, S., Flaherty, A. & McAuley, R. Carcharias taurus (East coast of Australia subpopulation). The IUCN Red List of Threatened Species. e.T44070A10854830 (2003).

31.
Walls, A. & Soldo, A. Carcharias taurus (Mediterranean subpopulation). The IUCN Red List of Threatened Species: e.T3854A48947509 (2015).

32.
Green, M., Ganassin, C. & Reid, D. Report into the NSW Shark Meshing (Bather Protection) Program. New South Wales Department of Primary Industries (2009).

33.
Reid, D. D., Robbins, W. D. & Peddemors, V. M. Decadal trends in shark catches and effort from the New South Wales, Australia, Shark Meshing Program 1950–2010. Mar. Freshw. Res. 62, 676–693 (2011).
CAS  Article  Google Scholar 

34.
Dudley, S. F. J. & Simpfendorfer, C. A. Population status of 14 shark species caught in the protective gillnets off KwaZulu-Natal beaches, South Africa, 1978–2003. Mar. Freshw. Res. 57, 225 (2006).
Article  Google Scholar 

35.
Dicken, M. L., Smale, M. J. & Booth, A. J. Long-term catch and effort trends in Eastern Cape Angling Week competitions. Afr. J. Mar. Sci. 34, 259–268 (2012).
Article  Google Scholar 

36.
Dicken, M., Booth, A. J. & Smale, M. J. Estimates of juvenile and adult raggedtooth shark (Carcharias taurus) abundance along the east coast of South Africa. Can. J. Fish. Aquat. Sci. 65, 621–632 (2008).
Article  Google Scholar 

37.
Klein, J. D., Bester-van der Merwe, A. E., Dicken, M. L., Mmonwa, K. L. & Teske, P. R. Reproductive philopatry in a coastal shark drives age-related population structure. Mar. Biol. 166, 26 (2019).
Article  Google Scholar 

38.
Van Oosterhout, C., Hutchinson, W. F., Wills, D. P. M. & Shipley, P. MICRO-CHECKER: software for identifying and correcting genotyping errors in microsatellite data. Mol. Ecol. Notes 4, 535–538 (2004).
Article  CAS  Google Scholar 

39.
Rousset, F. GENEPOP’007: a complete re-implementation of the GENEPOP software for Windows and Linux. Mol. Ecol. Res. 8, 103–106 (2008).
Article  Google Scholar 

40.
Benjamini, Y. & Yekutieli, D. The control of the false discovery rate in multiple testing under dependency. Ann. Stat. 29, 1165–1188 (2001).
MathSciNet  MATH  Article  Google Scholar 

41.
R Core Team. R: a language and environment for statistical computing. R Foundation for Statistical Computing (2017).

42.
Chikhi, L., Sousa, V. C., Luisi, P., Goossens, B. & Beaumont, M. A. The confounding effects of population structure, genetic diversity and the sampling scheme on the detection and quantification of population size changes. Genetics 186, 983–995 (2010).
PubMed  PubMed Central  Article  Google Scholar 

43.
Leblois, R. et al. Maximum-likelihood inference of population size contractions from microsatellite data. Mol. Biol. Evol. 31, 2805–2823 (2014).
CAS  PubMed  Article  Google Scholar 

44.
Peakall, R. & Smouse, P. E. GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research-an update. Bioinformatics 28, 2537–2539 (2012).
CAS  PubMed  PubMed Central  Article  Google Scholar 

45.
Cornuet, J. M. et al. DIYABC v2.0: a software to make approximate Bayesian computation inferences about population history using single nucleotide polymorphism, DNA sequence and microsatellite data. Bioinformatics 30, 1187–1189 (2014).
CAS  PubMed  Article  Google Scholar 

46.
Goldman, K. J., Branstetter, S. & Musick, J. A. A re-examination of the age and growth of sand tiger sharks, Carcharias taurus, in the western North Atlantic: the importance of ageing protocols and use of multiple back-calculation techniques. Environ. Biol. Fish. 77, 241–252 (2006).
Article  Google Scholar 

47.
Garza, J. C. & Williamson, E. G. Detection of reduction in population size using data from microsatellite loci. Mol. Ecol. 10, 305–318 (2001).
CAS  PubMed  Article  Google Scholar 

48.
Cornuet, J.-M. et al. Inferring population history with DIY ABC: a user-friendly approach to approximate Bayesian computation. Bioinformatics 24, 2713–2719 (2008).
CAS  PubMed  PubMed Central  Article  Google Scholar 

49.
de Irio, M. D. & Griffiths, R. C. Importance sampling on coalescent histories. I. Adv. Appl. Prob. 36, 417–433 (2004).
MathSciNet  MATH  Article  Google Scholar 

50.
de Iorio, M. D. & Griffiths, R. C. Importance sampling on coalescent histories. II: subdivided population models. Adv. Appl. Prob. 36, 434–454 (2004).
MathSciNet  MATH  Article  Google Scholar 

51.
Cornuet, J. M. & Beaumont, M. A. A note on the accuracy of PAC-likelihood inference with microsatellite data. Theor. Popul. Biol. 71, 12–19 (2007).
CAS  PubMed  MATH  Article  Google Scholar 

52.
Nance, H. A., Klimley, P., Galván-Magaña, F., Martínez-Ortíz, J. & Marko, P. B. Demographic processes underlying subtle patterns of population structure in the scalloped hammerhead shark, Sphyrna lewini. PLoS ONE 6, e21459 (2011).
ADS  PubMed  PubMed Central  Article  Google Scholar 

53.
Vignaud, T. M. et al. Blacktip reef sharks, Carcharhinus melanopterus, have high genetic structure and varying demographic histories in their Indo-Pacific range. Mol. Ecol. 23, 5193–5207 (2014).
PubMed  Article  Google Scholar 

54.
Cabrera, A. A. & Palsbøll, P. J. Inferring past demographic changes from contemporary genetic data: a simulation-based evaluation of the ABC methods implemented in DIYABC. Mol. Ecol. Res. 17, e94–e110 (2017).
CAS  Article  Google Scholar 

55.
Blunier, T. & Brook, E. J. Timing of millennial-scale climate change in Antarctica and Greenland during the last glacial period. Science 291, 109–112 (2001).
ADS  CAS  PubMed  Article  Google Scholar 

56.
Fraser, C. I., Nikula, R., Ruzzante, D. E. & Waters, J. M. Poleward bound: biological impacts of southern hemisphere glaciation. Trends Ecol. Evol. 27, 462–471 (2012).
PubMed  Article  Google Scholar 

57.
Provan, J. & Bennett, K. Phylogeographic insights into cryptic glacial refugia. Trends Ecol. Evol. 23, 564–571 (2008).
PubMed  Article  Google Scholar 

58.
Tolley, K., Groeneveld, J., Gopal, K. & Matthee, C. Mitochondrial DNA panmixia in spiny lobster Palinurus gilchristi suggests a population expansion. Mar. Ecol. Prog. Ser. 297, 225–231 (2005).
ADS  CAS  Article  Google Scholar 

59.
O’Brien, S. M., Gallucci, V. F. & Hauser, L. Effects of species biology on the historical demography of sharks and their implications for likely consequences of contemporary climate change. Conserv. Gen. 14, 125–144 (2013).
Article  Google Scholar 

60.
Portnoy, D. S. et al. Contemporary population structure and post-glacial genetic demography in a migratory marine species, the blacknose shark, Carcharhinus acronotus. Mol. Ecol. 23, 5480–5495 (2014).
CAS  PubMed  Article  Google Scholar 

61.
Domingues, R. R., Hilsdorf, A. W. S., Shivji, M. M., Hazin, F. V. H. & Gadig, O. B. F. Effects of the Pleistocene on the mitochondrial population genetic structure and demographic history of the silky shark (Carcharhinus falciformis) in the western Atlantic Ocean. Rev. Fish Biol. Fish. 28, 213–227 (2018).
Article  Google Scholar 

62.
Portnoy, D. S. et al. Population structure, gene flow, and historical demography of a small coastal shark (Carcharhinus isodon) in US waters of the Western Atlantic Ocean. ICES J. Mar. Sci. 73, 2322–2332 (2016).
Article  Google Scholar 

63.
Smale, M. J., Booth, A. J., Farquhar, M. R., Meÿer, M. R. & Rochat, L. Migration and habitat use of formerly captive and wild raggedtooth sharks (Carcharias taurus) on the southeast coast of South Africa. Mar. Biol. Res. 8, 115–128 (2012).
Article  Google Scholar 

64.
Bradshaw, C. J. A., Peddemors, V. M., McAuley, R. B. & Harcourt, R. G. Population viability of eastern Australian grey nurse sharks under fishing mitigation and climate change. Final Report to the Commonwealth of Australia, Department of the Environment, Water, Heritage and the Arts (2008).

65.
Ahonen, H., & Stow, A. Population size and structure of grey nurse shark in east and west Australia. Final Report to Department of Marine and Freshwater Environment, Water, Heritage and the Arts (2009).

66.
Otway, N. M. & Burke, A. L. Mark-recapture population estimate and movements of grey nurse sharks. NSW Fisheries Final Report Series. No. 63 (2004).

67.
Belmar-Lucero, S. et al. Concurrent habitat and life history influences on effective/census population size ratios in stream-dwelling trout. Ecol. Evol. 2, 562–573 (2012).
PubMed  PubMed Central  Article  Google Scholar 

68.
Frankham, R. Effective population size/adult population size ratios in wildlife: a review. Genet. Res. 66, 95 (1995).
Article  Google Scholar 

69.
Hedgecock, D. Does variance in reproductive success limit effective population sizes of marine organisms. In Genetics and Evolution of Aquatic Organisms (ed. Beaumont, A.) 122–134 (Springer, Berlin, 1994).
Google Scholar 

70.
Gilmore, R., Putz, O. & Dodrill, J. Oophagy, intrauterine cannibalism and reproductive strategy in lamnoid sharks. In Reproductive Biology and Phylogeny of Chondrichthyes (ed. Hamlett, W.) 435–463 (Science Publishers Inc., Enfield, 2005).
Google Scholar 

71.
Franklin, I. R. Evolutionary change in small populations. In Conservation Biology: An Evolutionary-Ecological Perspective (eds Soulé, M. E. & Wilcox, B. M.) 135–150 (Sunderland, Sinauer, 1980).
Google Scholar 

72.
Frankham, R. Challenges and opportunities of genetic approaches to biological conservation. Biol. Conserv. 143, 1919–1927 (2010).
Article  Google Scholar 

73.
Franklin, I. R. & Frankham, R. How large must populations be to retain evolutionary potential?. Anim. Conserv. 1, 69–70 (1998).
Article  Google Scholar 

74.
Frankham, R., Bradshaw, C. J. A. & Brook, B. W. Genetics in conservation management: revised recommendations for the 50/500 rules, Red List criteria and population viability analyses. Biol. Conserv. 170, 56–63 (2014).
Article  Google Scholar 

75.
Department of the Environment. Recovery plan for the grey nurse shark (Carcharias taurus) in Australia (2014).

76.
van Sittert, L. The marine fisheries of South Africa in Oxford Research Encyclopedia of African History 1–15 (2017).

77.
RSA (Republic of South Africa). Marine living resources act (act no. 18 of 1998). 1–67 (1998). More

1.
Ceballos, G. et al. Accelerated modern human-induced species losses: Entering the sixth mass extinction. Sci. Adv. 1, e1400253 (2015).
ADS  PubMed  PubMed Central  Article  Google Scholar 
2.
Ceballos, G. & Ehrlich, P. R. Mammal population losses and the extinction crisis. Science 296, 904–907 (2002).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

3.
Estes, J. A. et al. Trophic downgrading of planet earth. Science 333, 301 (2011).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

4.
Ripple, W. J. et al. Status and ecological effects of the world’s largest carnivores. Science 343, 1241484 (2014).
PubMed  Article  CAS  Google Scholar 

5.
Durant, S. M. et al. The global decline of cheetah Acinonyx jubatus and what it means for conservation. Proc. Natl. Acad. Sci. 114, 528–533 (2017).
CAS  Article  Google Scholar 

6.
6Woodroffe, R. & Sillero Zubiri, C. Lycaon pictus. The IUCN Red List of Threatened Species. e.T12436A16711116 (Gland, Switzerland, 2012).

7.
Riggio, J. et al. The size of savannah Africa: A lion’s (Panthera leo) view. Biodivers. Conserv. 22, 17–35 (2013).
Article  Google Scholar 

8.
MacArthur, R. H. & Wilson, E. O. The Theory of Island Biogeography (Princeton University Press, Princeton, 1967).
Google Scholar 

9.
Hanski, I. & Gilpin, M. Metapopulation dynamics: Brief history and conceptual domain. Biol. J. Lin. Soc. 42, 3–16 (1991).
Article  Google Scholar 

10.
Haddad, N. M. et al. Habitat fragmentation and its lasting impact on Earth’s ecosystems. Sci. Adv. 1, e1500052 (2015).
ADS  PubMed  PubMed Central  Article  Google Scholar 

11.
Prugh, L. R., Hodges, K. E., Sinclair, A. R. E. & Brashares, J. S. Effect of habitat area and isolation on fragmented animal populations. Proc. Natl. Acad. Sci. 105, 20770 (2008).
ADS  CAS  PubMed  Article  Google Scholar 

12.
Kareiva, P. Habitat fragmentation and the stability of predator–prey interactions. Nature 326, 388–390 (1987).
ADS  Article  Google Scholar 

13.
Levey, D. J. & Stiles, F. G. Evolutionary precursors of long-distance migration: Resource availability and movement patterns in Neotropical landbirds. Am. Nat. 140, 447–476 (1992).
Article  Google Scholar 

14.
Durant, S. M. Competition refuges and coexistence: An example from Serengeti carnivores. J. Anim. Ecol. 67, 370–386 (1998).
Article  Google Scholar 

15.
Durant, S. M. Living with the enemy: Avoidance of hyenas and lions by cheetahs in the Serengeti. Behav. Ecol. 11, 624–632 (2000).
Article  Google Scholar 

16.
Creel, S. & Creel, N. M. Limitation of African wild dogs by competition with larger carnivores. Conserv. Biol. 10, 526–538 (1996).
Article  Google Scholar 

17.
Creel, S. & Creel, N. M. The African wild dog: Behavior, ecology and conservation (Princeton University Press, Princeton, 2002).
Google Scholar 

18.
Swanson, A. et al. Cheetahs and wild dogs show contrasting patterns of suppression by lions. J. Anim. Ecol. 83, 1418–1427 (2014).
Article  Google Scholar 

19.
Dröge, E., Creel, S., Becker, M. S. & Msoka, J. Spatial and temporal avoidance of risk within a large carnivore guild. Ecol. Evol. 7, 189–199 (2017).
PubMed  Article  Google Scholar 

20.
Gorman, M. L., Mills, M. G., Raath, J. P. & Speakman, J. R. High hunting costs make African wild dogs vulnerable to kleptoparasitism by hyaenas. Nature 391, 479–481 (1998).
ADS  CAS  Article  Google Scholar 

21.
Broekhuis, F., Cozzi, G., Valeix, M., McNutt, J. W. & Macdonald, D. W. Risk avoidance in sympatric large carnivores: Reactive or predictive?. J. Anim. Ecol. 82, 1098–1105 (2013).
Article  Google Scholar 

22.
Creel, S. et al. Carnivores, competition and genetic connectivity in the Anthropocene. Sci. Rep. 9, 16339. https://doi.org/10.1038/s41598-019-52904-0 (2019).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

23.
Frid, A. & Dill, L. M. Human-caused disturbance stimuli as a form of predation risk. Conserv. Ecol. 6, 11 (2002).
Article  Google Scholar 

24.
Broekhuis, F. Natural and anthropogenic drivers of cub recruitment in a large carnivore. Ecol. Evol. 8, 6748–6755 (2018).
PubMed  PubMed Central  Article  Google Scholar 

25.
Broekhuis, F., Madsen, E. K. & Klaassen, B. Predators and pastoralists: how anthropogenic pressures inside wildlife areas influence carnivore space use and movement behaviour. Anim. Conserv. 22, 404–416. https://doi.org/10.1111/acv.12483 (2019).
Article  Google Scholar 

26.
Klaassen, B. & Broekhuis, F. Living on the edge: Multiscale habitat selection by cheetahs in a human-wildlife landscape. Ecol. Evol. 8, 7611–7623 (2018).
PubMed  PubMed Central  Article  Google Scholar 

27.
Dolrenry, S., Stenglein, J., Hazzah, L., Lutz, R. S. & Frank, L. A metapopulation approach to African lion (Panthera leo) conservation. PLoS ONE 9, e88081 (2014).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

28.
Petkova, D., Novembre, J. & Stephens, M. Visualizing spatial population structure with estimated effective migration surfaces. Nat. Genet. 48, 94 (2015).
PubMed  PubMed Central  Article  CAS  Google Scholar 

29.
Mills, M. G. & Gorman, M. L. Factors affecting the density and distribution of wild dogs in the Kruger National Park. Conserv. Biol. 11, 1397–1406 (1997).
Article  Google Scholar 

30.
Fuller, T., Mills, M. L., Borner, M., Laurenson, M. & Kat, P. Long distance dispersal by African wild dogs in East and South Africa. Revue de zoologie africaine 106, 535–537 (1992).
Google Scholar 

31.
Frame, L. H., Malcolm, J. R., Frame, G. W. & Van Lawick, H. Social organization of African Wild Dogs (Lycaon pictus) on the Serengeti Plains, Tanzania 1967–1978 1. Zeitschrift für Tierpsychologie 50, 225–249 (1979).
Article  Google Scholar 

32.
Woodroffe, R. et al. Dispersal behaviour of African wild dogs in Kenya. Afr. J. Ecol. 58, 46–57 (2020).
Article  Google Scholar 

33.
Creel, S. & Creel, N. M. Communal hunting and pack size in African wild dogs, Lycaon pictus. Anim. Behav. 50, 1325–1339 (1995).
Article  Google Scholar 

34.
Creel, S., Creel, N. M., Creel, A. & Creel, B. Hunting on a hot day: Effects of temoperature on interactions between African wild dogs and their prey. Ecology 97, 2910–2916 (2016).
PubMed  MATH  Article  Google Scholar 

35.
Woodroffe, R. Ranging behaviour of African wild dog packs in a human-dominated landscape. J. Zool. 283, 88–97 (2011).
Article  Google Scholar 

36.
Creel, S. Four factors modifying the effect of competition on carnivore population dynamics as illustrated by African wild dogs. Conserv. Biol. 15, 271–274 (2001).
Article  Google Scholar 

37.
Cushman, S. A. et al. Prioritizing core areas, corridors and conflict hotspots for lion conservation in southern Africa. PLoS ONE 13, e0196213 (2018).
PubMed  PubMed Central  Article  CAS  Google Scholar 

38.
Cushman, S. A., Elliot, N. B., Macdonald, D. W. & Loveridge, A. J. A multi-scale assessment of population connectivity in African lions (Panthera leo) in response to landscape change. Landsc. Ecol. 31, 1337–1353 (2016).
Article  Google Scholar 

39.
Short Bull, R. A. et al. Why replication is important in landscape genetics: American black bear in the Rocky Mountains. Mol. Ecol. 20, 1092–1107 (2011).
Article  Google Scholar 

40.
Zeller, K. A. et al. Using step and path selection functions for estimating resistance to movement: Pumas as a case study. Landsc. Ecol. 31, 1319–1335 (2016).
Article  Google Scholar 

41.
Elliot, N. B., Cushman, S. A., Macdonald, D. W. & Loveridge, A. J. The devil is in the dispersers: Predictions of landscape connectivity change with demography. J. Appl. Ecol. 51, 1169–1178 (2014).
Article  Google Scholar 

42.
Oriol-Cotterill, A., Macdonald, D. W., Valeix, M., Ekwanga, S. & Frank, L. G. Spatiotemporal patterns of lion space use in a human-dominated landscape. Anim. Behav. 101, 27–39 (2015).
Article  Google Scholar 

43.
Cozzi, G. et al. African Wild dog dispersal and implications for management. J. Wildl. Manag. 84, 614–621 (2020).
Article  Google Scholar 

44.
Cozzi, G., Broekhuis, F., McNutt, J. W. & Schmid, B. Comparison of the effects of artificial and natural barriers on large African carnivores: implications for interspecific relationships and connectivity. J. Anim. Ecol. 82, 707–715 (2013).
PubMed  Article  Google Scholar 

45.
McRae, B. H. Isolation by resistance. Evolution 60, 1551–1561 (2006).
Article  Google Scholar 

46.
Whittington-Jones, B. M., Parker, D. M., Bernard, R. T. & Davies-Mostert, H. T. Habitat selection by transient African wild dogs (Lycaon pictus) in northern KwaZulu-Natal, South Africa: Implications for range expansion. South Afr. J. Wildlife Res. 44, 135–147 (2011).
Article  Google Scholar 

47.
47Becker, M. Zambian Carnivore Programme 2019 Annual Report. 40 (Zambian Carnivore Programme, Mfuwe, Zambia, 2020).

48.
Abrahms, B. et al. Lessons from integrating behaviour and resource selection: activity-specific responses of African wild dogs to roads. Anim. Conserv. 19, 247–255 (2016).
Article  Google Scholar 

49.
Venter, O. et al. Sixteen years of change in the global terrestrial human footprint and implications for biodiversity conservation. Nat. Commun. 7, 12558 (2016).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

50.
Venter, O. et al. Global terrestrial Human Footprint maps for 1993 and 2009. Sci. Data 3, 160067 (2016).
PubMed  PubMed Central  Article  Google Scholar 

51.
Tucker, M. A. et al. Moving in the Anthropocene: Global reductions in terrestrial mammalian movements. Science 359, 466–469 (2018).
ADS  CAS  PubMed  Article  Google Scholar 

52.
van Beest, F. M. et al. Classifying grey seal behaviour in relation to environmental variability and commercial fishing activity-a multivariate hidden Markov model. Sci. Rep. 9, 1–14 (2019).
Article  CAS  Google Scholar 

53.
McClintock, B. T. & Michelot, T. momentuHMM: R package for generalized hidden Markov models of animal movement. Methods Ecol. Evol. 9, 1518–1530 (2018).
Article  Google Scholar 

54.
Michelot, T., Langrock, R. & Patterson, T. A. moveHMM: An R package for the statistical modelling of animal movement data using hidden Markov models. Methods Ecol. Evol. 7, 1308–1315 (2016).
Article  Google Scholar 

55.
Langrock, R. et al. Flexible and practical modeling of animal telemetry data: Hidden Markov models and extensions. Ecology 93, 2336–2342 (2012).
PubMed  Article  Google Scholar 

56.
Zucchini, W., MacDonald, I. L. & Langrock, R. Hidden Markov models for time series: an introduction using R (CRC Press, Boca Raton, 2017).
Google Scholar 

57.
57DNPW. National Conservation Action Plan for Cheetah and African Wild Dog for Zambia, 2019–2023. (Zambia Department of National Parks and WIldlife, Chilanga, Zambia, 2019).

58.
Chomba, C., Mwenya, A. N. & Nyirenda, N. Wildlife legislation and institutional reforms in Zambia for the period 1912–2011. J. Sustain. Dev. Afr. 13, 2 (2011).
Google Scholar 

59.
Astle, W., Webster, R. & Lawrance, C. Land classification for management planning in the Luangwa Valley of Zambia. J. Appl. Ecol. 6, 143–169 (1969).
Article  Google Scholar 

60.
Rosenblatt, E. et al. Detecting declines of apex carnivores and evaluating their causes: An example with Zambian lions. Biol. Cons. 180, 176–186 (2014).
Article  Google Scholar 

61.
Rosenblatt, E. et al. Effects of a protection gradient on carnivore density and survival: An example with leopards in the Luangwa valley Zambia. Ecol. Evol. 6, 3772–3785 (2016).
PubMed  PubMed Central  Article  Google Scholar 

62.
Rosenblatt, E. et al. Do protection gradients explain patterns in herbivore densities? An example with ungulates in Zambia’s Luangwa Valley. PLoS ONE 14, e0224438. https://doi.org/10.1371/journal.pone.0224438 (2019).
CAS  Article  PubMed  PubMed Central  Google Scholar 

63.
Watson, F. G., Becker, M. S., Milanzi, J. & Nyirenda, M. Human encroachment into protected area networks in Zambia: Implications for large carnivore conservation. Reg. Environ. Change 15, 415–429 (2015).
Article  Google Scholar 

64.
Watson, F., Becker, M. S., McRobb, R. & Kanyembo, B. Spatial patterns of wire-snare poaching: Implications for community conservation in buffer zones around National Parks. Biol. Cons. 168, 1–9 (2013).
Article  Google Scholar 

65.
Lloyd, C. T. et al. Global spatio-temporally harmonised datasets for producing high-resolution gridded population distribution datasets. Big Earth Data 3, 108–139. https://doi.org/10.1080/20964471.2019.1625151 (2019).
Article  PubMed  PubMed Central  Google Scholar 

66.
Creel, S. et al. What explains variation in the strength of behavioral responses to predation risk? A standardized test with large carnivore and ungulate guilds in three ecosystems. Biol. Cons. 232, 164–172 (2019).
Article  Google Scholar 

67.
67Masenga, E. H. Behavioural ecology of free-ranging and reintroduced African wild dog (Lycaon pictus) packs in the Serengeti ecosystem, Tanzania PhD thesis, Norwegian University of Science and Technology, (2017).

68.
Marsden, C. D. et al. Spatial and temporal patterns of neutral and adaptive genetic variation in the endangered African wild dog (Lycaon pictus). Mol. Ecol. 21, 1379–1393 (2012).
PubMed  Article  PubMed Central  Google Scholar 

69.
Becker, M. Zambian Carnivore Programme 2016 Annual Report (Mfuwe, Zambia, 2016).
Google Scholar 

70.
momentuHMM: R package for analysis of telemetry data using generalized multivariate hidden Markov models of animal movement (CRAN, Vienna, Austria, 2020). More

1.
Snyder, N. & Snyder, H. The California Condor: A Saga of Natural History and Conservation (Academic Press, San Diego, 2000).
Google Scholar 
2.
Emslie, S. D. Age and diet of fossil California Condors in Grand Canyon, Arizona. Science 237, 768–770. https://doi.org/10.1126/science.237.4816.768 (1987).
ADS  CAS  Article  PubMed  Google Scholar 

3.
Mee, A. & Snyder, N.F.R. California Condors in the 21st century-Conservation problems and solutions (eds. Mee, A. & Hall, L.S. & Grantham, J.). California Condors in the 21st Century. 243–272 (Series in Ornithology, no. 2. American Ornithologists’ Union and Nuttall Ornithological Club, Washington, DC, 2007).

4.
Parish, C.N., Heinrich, W.R. & Hunt, W.G. Lead exposure, diagnosis, and treatment in California Condors released in Arizona, (eds. Mee, A. & Hall, L.S. & Grantham, J.). California Condors in the 21st Century. 97–108 (Series in Ornithology, no. 2. American Ornithologists’ Union and Nuttall Ornithological Club,Washington, DC, 2007).

5.
Brandt, J. & Astell, M. California Condor Recovery Program 2017 Annual Report (eds. Weprin, N., Cook, D. & Ledig, D.). 1–62. (Hopper Mountain National Wildlife Refuge Complex. US Fish and Wildlife Service, Ventura, CA, 2019).

6.
Parish, C.N., Hunt, W.G., Feltes, E., Sieg, R. & Orr., K. Lead exposure among a reintroduced population of California Condors in northern Arizona and southern Utah (eds. Watson, R.T., Fuller, M., Pokras & M., Hunt, W.G.). Ingestion of lead from spent ammunition: Implications for wildlife and humans. 259–264. (The Peregrine Fund, Boise, Idaho, 2009). DOI https://doi.org/10.4080/ilsa.2009.0217.

7.
Koford, C. B. The California Condor. Nat. Audubon Res. Rep. 4, 1–154 (1953).
Google Scholar 

8.
Wilbur, S.R. The California Condor, 1966–76: a look at its past and future. U. S. Fish & Wildlife Service North American Fauna 72, 1–136 (1978).

9.
D’Elia, J., Haig, S. M., Mullins, T. D. & Miller, M. P. Ancient DNA reveals substantial genetic diversity in the California Condor (Gymnogyps californianus) prior to a population bottleneck. Condor 118, 703–714. https://doi.org/10.1650/CONDOR-16-35.1 (2016).
Article  Google Scholar 

10.
Ralls, K., Ballou, J. D., Rideout, B. A. & Frankham, R. Genetic management of chondrodystrophy in California Condors. Anim. Conserv. 3, 145–153. https://doi.org/10.1111/j.1469-1795.2000.tb00239.x (2000).
Article  Google Scholar 

11.
Ralls, K. & Ballou, J. D. Genetic status and management of California Condors. Condor 106, 215–228 (2004).
Article  Google Scholar 

12.
Aguilar, R. F., Yoshicedo, J. N. & Parish, C. N. Ingluviotomy tube placement for lead-induced crop stasis in the California condor (Gymnogyps californianus). J. Avian Med. Surg. 26, 176–181. https://doi.org/10.1647/2010-029R2.1 (2012).
Article  PubMed  Google Scholar 

13.
Plaza, P. I. & Lambertucci, S. A. What do we know about lead contamination in wild vultures and condors? A review of decades of research. Sci. Total. Environ. 654, 409–417. https://doi.org/10.1016/j.scitotenv.2018.11.099 (2019).
ADS  CAS  Article  PubMed  Google Scholar 

14.
Cade, T.J., Osborn, S.A.H., Hunt & W.G., Woods, C.P. Commentary on released California Condors Gymnogyps californianus in Arizona, (eds. Chancellor, R.D. & Meyburg, B.U.). Raptors worldwide: Proceedings of the VI world conference on birds of prey and owls. 11–25. (World Working Group on Birds of Prey and Owls/MME-Birdlife 11–25, Hungary, 2004).

15.
Hunt, W. G., Parish, C. N., Orr, K. & Aguilar, R. F. Lead poisoning and the reintroduction of the California condor in northern Arizona. J. Avian Med. Surg. 23, 145–150. https://doi.org/10.1647/2007-035.1 (2009).
Article  PubMed  Google Scholar 

16.
Richner, H., Christe, P. & Opplinger, A. Paternal investment affects prevalence of malaria. Proc. Natl. Acad. Sci. USA 92, 1192–1194. https://doi.org/10.1073/pnas.92.4.1192 (1995).
ADS  CAS  Article  PubMed  Google Scholar 

17.
Forrester, D.J. & Spalding, M.G. Parasites and diseases of wild birds in Florida. (University Press of Florida, 2003).

18.
Webb, S. L., Fedynich, A. M., Yeltatzie, S. K., De Vault, T. L. & Rhodes, O. E. Jr. Survey of blood parasites in black vultures and turkey vultures from South Carolina. Southeast Nat. 4, 355–360 (2005).
Article  Google Scholar 

19.
Greiner, E. C., Fedynich, A. M., Webb, S. L., DeVault, T. L. & Rhodes, O. E. Jr. Hematozoa and a new haemoproteid species from Cathartidae (New World Vulture) in South Carolina. J Parasitol. 97, 1137–1139. https://doi.org/10.1645/GE-2332.1 (2011).
Article  PubMed  Google Scholar 

20.
Yabsley, M. J. et al. Parasitaemia data and molecular characterization of Haemoproteus catharti from New World vultures (Cathartidae) reveals a novel clade of Haemosporida. Malar J. 17, 12. https://doi.org/10.1186/s12936-017-2165-5 (2018).
CAS  Article  PubMed  PubMed Central  Google Scholar 

21.
Wetmore, P. W. Blood parasites of birds of the District of Columbia and Patuxent Research Refuge vicinity. J. Parasitol. 27, 379–393 (1941).
Article  Google Scholar 

22.
Love, G. J., Wilkin, S. A. & Goodwin, M. H. Incidence of blood parasites in birds collected in southwestern Georgia. J. Parasitol. 39, 52–57 (1953).
CAS  Article  Google Scholar 

23.
Halpern, N. & Bennett, G. F. Haemoproteus and Leucocytozoon infections in birds of the Oklahoma City Zoo. J. Wildl. Dis. 19, 330–332 (1983).
CAS  Article  Google Scholar 

24.
Wahl, M. Blood-borne parasites in the Black Vulture Coragyps atratus in northwestern Costa Rica. Vulture News. 64, 21–30 (2013).
Google Scholar 

25.
Chagas, C. R. F. et al. Diversity and distribution of avian malaria and related haemosporidian parasites in captive birds from a Brazilian megalopolis. Malar J. 16, 83. https://doi.org/10.1186/s12936-017-1729-8 (2017).
Article  PubMed  PubMed Central  Google Scholar 

26.
Walther, E. L. et al. Description, molecular characterization, and patterns of distribution of a widespread New World avian malaria parasite (Haemosporida: Plasmodiidae), Plasmodium (Novyella) homopolare sp. nov. Parasitol. Res. 113, 3319–3332. https://doi.org/10.1007/s00436-014-3995-5 (2014).
Article  PubMed  Google Scholar 

27.
Altschul, S. F. et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucl. Acids Res. 125, 3389–3402. https://doi.org/10.1093/nar/25.17.3389 (1997).
Article  Google Scholar 

28.
Bensch, S., Hellgren, O. & Pérez-Tris, J. MalAvi: a public database of malaria parasites and related haemosporidians in avian hosts based on mitochondrial cytochrome b lineages. Mol. Ecol. Resour. 9, 1353–1358. https://doi.org/10.1111/j.1755-0998.2009.02692.x (2009).
Article  PubMed  Google Scholar 

29.
Walther, E. L. et al. First molecular study of prevalence and diversity of avian haemosporidia in a Central California songbird community. J. Ornithol. 157, 549–564 (2016).
Article  Google Scholar 

30.
Pacheco, M. A., Escalante, A. A., Garner, M. M., Bradley, G. A. & Aguilar, R. F. Haemosporidian infection in captive masked bobwhite quail (Colinus virginianus ridgwayi), an endangered subspecies of the northern bobwhite quail. Vet. Parasitol. 182, 113–120. https://doi.org/10.1016/j.vetpar.2011.06.006 (2011).
Article  PubMed  PubMed Central  Google Scholar 

31.
Ishak, H. D. et al. Blood parasites in owls with conservation implications for the Spotted Owl (Strix occidentalis). PLoS ONE 3, e2304. https://doi.org/10.1371/journal.pone.0002304 (2008).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

32.
Remple, J. D. Intracellular hematozoa of raptors: a review and update. J. Avian Med. Surg. 18, 75–88 (2004).
Article  Google Scholar 

33.
Valkiunas, G. & Iezhova, T. A. Keys to the avian malaria parasites. Malar. J. 17, 212. https://doi.org/10.1186/s12936-018-2359-5 (2018).
Article  PubMed  PubMed Central  Google Scholar 

34.
Gonzalez-Serrano, P., et al. Climate change and risks for mountain species. Mosquito vectors and circulation of West Nile virus and avian malaria in territories of Bearded vultures (Gypaetus barbatus). First Iberian Congress of Applied Science on Game Resources (CICARC) Ciudad Real, (Spain, 1–4 July 2019).

35.
Garnham, P. C. C. Malaria parasites and other Haemosporidia (Blackwell Scientific Publications, Oxford, 1966).
Google Scholar 

36.
Valkiunas, G. Avian Malaria Parasites and Other Haemosporidia (CRC Press, New York, 2005).
Google Scholar 

37.
Atkinson, C. T. & Samuel, M. D. Avian malaria Plasmodium relictum in native Hawaiian forest birds: epizootiology and demographic impacts on ‘apapane Himatione sanguinea. J. Avian Biol. 41, 357–366 (2010).
Article  Google Scholar 

38.
Cornet, S., Nicot, A., Rivero, A. & Gandon, S. Evolution of plastic transmission strategies in avian malaria. PLoS Pathog. 10(9), e1004308. https://doi.org/10.1371/journal.ppat.1004308 (2014).
CAS  Article  PubMed  PubMed Central  Google Scholar 

39.
Pacheco, M. A. et al. Primers targeting mitochondrial genes of avian haemosporidians: PCR detection and differential DNA amplification of parasites belonging to different genera. Int. J. Parasitol. 48, 657–670. https://doi.org/10.1016/j.ijpara.2018.02.003 (2018).
CAS  Article  PubMed  PubMed Central  Google Scholar 

40.
Smith, K. F., Sax, D. F. & Lafferty, K. D. Evidence for the role of infectious disease in species extinction and endangerment. Conserv. Biol. 20, 1349–1357. https://doi.org/10.1111/j.1523-1739.2006.00524.x (2006).
Article  PubMed  Google Scholar 

41.
Mace, M.E. California Condor (Gymnogyps californianus) international studbook. (Zoological Society of San Diego, San Diego Wild Animal Park, Escondido, California, 2005).

42.
Pacheco, M. A., García-Amado, M. A., Manzano, J., Matta, N. E. & Escalante, A. A. Blood parasites infecting the Hoatzin (Opisthocomus hoazin), a unique neotropical folivorous bird. PeerJ 7, e6361. https://doi.org/10.7717/peerj.6361 (2019).
Article  PubMed  PubMed Central  Google Scholar 

43.
Gouy, M., Guindon, S. & Gascuel, O. SeaView version 4: a multiplatform graphical user interface for sequence alignment and phylogenetic tree building. Mol. Biol. Evol. 272, 221–224. https://doi.org/10.1093/molbev/msp259 (2010).
CAS  Article  Google Scholar 

44.
Pacheco, M. A. et al. Mode and rate of evolution of haemosporidian mitochondrial genomes: Timing the radiation of avian parasites. Mol. Biol. Evol. 35, 383–403. https://doi.org/10.1098/rstb.2015.0128 (2018).
CAS  Article  PubMed  Google Scholar 

45.
Benson, D. A. et al. GenBank. Nucl. Acids Res. 41, D36–D42. https://doi.org/10.1093/nar/gks1195 (2012).
CAS  Article  PubMed  Google Scholar 

46.
Ronquist, F. & Huelsenbeck, J. P. MrBayes 3: Bayesian phylogenetic inference under mixed models. Bioinformatics 1912, 1572–1574. https://doi.org/10.1093/bioinformatics/btg180 (2003).
CAS  Article  Google Scholar 

47.
Kumar, S., Stecher, G. & Tamura, K. MEGA7: Molecular Evolutionary Genetics Analysis version 7.0 for bigger datasets. Mol. Biol. Evol. 33, 1870–1874. https://doi.org/10.1093/molbev/msw054 (2016).
CAS  Article  Google Scholar  More

1.
Kroeker, K. J. et al. Impacts of ocean acidification on marine organisms: quantifying sensitivities and interaction with warming. Glob. Change Biol. 19, 1884–1896 (2013).
Article  Google Scholar 
2.
Pfister, C. A. et al. Historical baselines and the future of shell calcification for a foundation species in a changing ocean. Proc. Royal Soc. B-Biol. Sci. 283, https://doi.org/10.1098/rspb.2016.0392 (2016).

3.
Przeslawski, R., Byrne, M. & Mellin, C. A review and meta-analysis of the effects of multiple abiotic stressors on marine embryos and larvae. Glob. Change Biol. 21, 2122–2140 (2015).
Article  Google Scholar 

4.
Waldbusser, G. G. & Salisbury, J. E. Ocean acidification in the coastal zone from an organism’s perspective: multiple system parameters, frequency domains, and habitats. Annu. Rev. Mar. Sci. 6, 221–247 (2014).
Article  Google Scholar 

5.
Helmuth, B., Mieszkowska, N., Moore, P. & Hawkins, S. J. Living on the edge of two changing worlds: forecasting the responses of rocky intertidal ecosystems to climate change. Annu. Rev. Ecol. Evolution Syst. 37, 373–404 (2006).
Article  Google Scholar 

6.
Kordas, R. L., Donohue, I. & Harley, C. D. G. Herbivory enables marine communities to resist warming. Sci. Adv. 3, https://doi.org/10.1126/sciadv.1701349 (2017).

7.
Kroeker, K. J., Kordas, R. L. & Harley, C. D. G. Embracing interactions in ocean acidification research: confronting multiple stressor scenarios and context dependence. Biol. Lett. 13, https://doi.org/10.1098/rsbl.2016.0802 (2017).

8.
Jellison, B. M. & Gaylord, B. Shifts in seawater chemistry disrupt trophic links within a simple shoreline food web. Oecologia 190, 955–967 (2019).
Article  Google Scholar 

9.
Wootton, J. T., Pfister, C. A. & Forester, J. D. Dynamic patterns and ecological impacts of declining ocean pH in a high-resolution multi-year dataset. Proc. Natl Acad. Sci. USA 105, 18848–18853 (2008).
CAS  Article  Google Scholar 

10.
Burrows, M. T. et al. Global-scale species distributions predict temperature-related changes in species composition of rocky shore communities in Britain. Glob. Change Biol. 26, 2093–2105 (2020).
Article  Google Scholar 

11.
Pershing, A. J. et al. Slow adaptation in the face of rapid warming leads to collapse of the Gulf of Maine cod fishery. Science 350, 809–812 (2015).
CAS  Article  Google Scholar 

12.
Wang, Z. H. A. et al. The marine inorganic carbon system along the Gulf of Mexico and Atlantic coasts of the United States: insights from a transregional coastal carbon study. Limnol. Oceanogr. 58, 325–342 (2013).
CAS  Article  Google Scholar 

13.
Colton, H. S. On some varieties of Thais lapillus in the Mount Desert region, a study of individual ecology. Proc. Acad. Natl Sci. Phila. 68, 440–454 (1916).
Google Scholar 

14.
Fisher, J. A. D. Exploring ecology’s attic: overlooked ideas on intertidal food webs. Bull. Ecol. Soc. Am. 86, 145–151 (2005).

15.
Connell, J. H. Effects of competition, predation by Thais lapillus, and other factors on natural populations of barnacle Balanus balanoides. Ecol. Monogr. 31, 61–104 (1961).
Article  Google Scholar 

16.
Lubchenco, J. Plant species diversity in a marine intertidal community—importance of herbivore food preference and algal competitive abilities. Am. Naturalist 112, 23–39 (1978).
Article  Google Scholar 

17.
Lubchenco, J. & Menge, B. A. Community development and persistence in a low rocky intertidal zone. Ecol. Monogr. 48, 67–94 (1978).
Article  Google Scholar 

18.
Petraitis, P. S. & Dudgeon, S. R. Variation in recruitment and the establishment of alternative community states. Ecology 96, 3186–3196 (2015).
CAS  Article  Google Scholar 

19.
Lord, J. P., Harper, E. M. & Barry, J. P. Ocean acidification may alter predator-prey relationships and weaken nonlethal interactions between gastropods and crabs. Mar. Ecol. Prog. Ser. 616, 83–94 (2019).
CAS  Article  Google Scholar 

20.
Sorte, C. J. B. et al. Long-term declines in an intertidal foundation species parallel shifts in community composition. Glob. Change Biol. 23, 341–352 (2017).
Article  Google Scholar 

21.
Salisbury, J. E. & Jönsson, B. F. Rapid warming and salinity changes in the Gulf of Maine alter surface ocean carbonate parameters and hide ocean acidification. Biogeochemistry 141, 401–418 (2018).
CAS  Article  Google Scholar 

22.
Southward, A. J. 40 years of changes in species composition and population density of barnacles on a rocky shore near Plymouth. J. Mar. Biol. Assoc. UK 71, 495–513 (1991).
Article  Google Scholar 

23.
Hawkins, S. J., Southward, A. J. & Genner, M. J. Detection of environmental change in a marine ecosystem—evidence from the western English Channel. Sci. Total Environ. 310, 245–256 (2003).
CAS  Article  Google Scholar 

24.
Hawkins, S. J. et al. Consequences of climate-driven biodiversity changes for ecosystem functioning of North European rocky shores. Mar. Ecol. Prog. Ser. 396, 245–259 (2009).
Article  Google Scholar 

25.
Mieszkowska, N., Burrows, M. T., Pannacciulli, F. G. & Hawkins, S. J. Multidecadal signals within co-occurring intertidal barnacles Semibalanus balanoides and Chthamalus spp. linked to the Atlantic Multidecadal Oscillation. J. Mar. Syst. 133, 70–76 (2014).
Article  Google Scholar 

26.
Findlay, H. S., Kendall, M. A., Spicer, J. I. & Widdicombe, S. Future high CO2 in the intertidal may compromise adult barnacle Semibalanus balanoides survival and embryonic development rate. Mar. Ecol. Prog. Ser. 389, 193–202 (2009).
Article  Google Scholar 

27.
Thomas, A. C., Townsend, D. W. & Weatherbee, R. Satellite-measured phytoplankton variability in the Gulf of Maine. Continental Shelf Res. 23, 971–989 (2003).
Article  Google Scholar 

28.
Commito, J. A., Jones, B. R., Jones, M. A., Winders, S. E. & Como, S. After the fall: legacy effects of biogenic structure on wind-generated ecosystem processes following mussel bed collapse. Diversity-Basel 11, https://doi.org/10.3390/d11010011 (2019).

29.
Beal, B. F. et al. Spatial variability in recruitment of an infaunal bivalve: experimental effects of predator exclusion on the softshell clam (Mya arenaria L.) along three tidal estuaries in southern Maine, USA. J. Shellfish Res. 37, 27 (2018).
Article  Google Scholar 

30.
Roman, J. Diluting the founder effect: cryptic invasions expand a marine invader’s range. Proc. R. Soc. B-Biol. Sci. 273, 2453–2459 (2006).
Article  Google Scholar 

31.
Vadas, R. L.,Sr. & Elner, R. W. Plant-animal interactions in the north-west Atlantic. In Plant-Animal Interactions in the Marine Benthos (eds. John, D. M., Hawkins, S. J. & Price, J. H.). Systematics Association Special Vol. 46, 33–60 (Oxford: Clarendon Press, 1992).

32.
Wethey, D. S. et al. Response of intertidal populations to climate: effects of extreme events versus long term change. J. Exp. Mar. Biol. Ecol. 400, 132–144 (2011).
Article  Google Scholar 

33.
Maggs, C. A. et al. Evaluating signatures of glacial refugia for North Atlantic benthic marine taxa. Ecology 89, S108–S122 (2008).
Article  Google Scholar 

34.
Wares, J. P. & Cunningham, C. W. Phylogeography and historical ecology of the North Atlantic intertidal. Evolution 55, 2455–2469 (2001).
CAS  Article  Google Scholar 

35.
Chapman, J. W., Carlton, J. T., Bellinger, M. R. & Blakeslee, A. M. H. Premature refutation of a human-mediated marine species introduction: the case history of the marine snail Littorina littorea in the Northwestern Atlantic. Biol. Invasions 9, 995–1008 (2007).
Article  Google Scholar 

36.
Cloern, J. E. & Jassby, A. D. Drivers of changes in estuarine-coast ecosystems: discoveries from four decades of study in San Francisco Bay. Rev. Geophys. 50, 33 (2012).
Article  Google Scholar 

37.
Kemp, W. M. et al. Eutrophication of Chesapeake Bay: historical trends and ecological interactions. Mar. Ecol. Prog. Ser. 303, 1–29 (2005).
Article  Google Scholar 

38.
Kübler, J. E. & Dudgeon, S. R. Prediciting effects of ocean acidification and warming on algae lacking carbon concentrating mechanisms. PLoS ONE 10, https://doi.org/10.1371/journal.pone.0132806 (2015).

39.
Petraitis, P. S., Liu, H. & Rhile, E. C. Densities and cover data for intertidal organisms in the Gulf of Maine, USA, from 2003 to 2007. Ecology 89, 588 http://esapubs.org/archive/ecol/E089/032 (2008).
Article  Google Scholar 

40.
Petraitis, P. S., Liu, H. & Rhile, E. C. Barnacle, fucoid, and mussel recruitment in the Gulf of Maine, USA, from 1997 to 2007. Ecology 90, 571 http://esapubs.org/archive/ecol/E090/039/ (2009).
Article  Google Scholar 

41.
Petraitis, P. S. & Vidargas, N. A. Marine intertidal organisms found in experimental clearings on sheltered shores in the Gulf of Maine, USA. Ecology 87, 796 http://esapubs.org/archive/ecol/E087/047/ (2006).
Article  Google Scholar 

42.
Lewis, J. R. The Ecology of Rocky Shores (English University Press, 1964).

43.
Horton, T. et al. World register of marine species (WoRMS). http://www.marinespecies.org (2010).

44.
Petraitis, P. S. & Dudgeon, S. R. Data and R scripts for analyses of declines in invertebrate species from the Gulf of Maine, USA, 1997–2018 ver 1. Environmental Data Initiative https://doi.org/10.6073/pasta/7ad4e0c85ff585199cb2a007eb291c9d (2020).

45.
Hadfield, J. D. MCMC methods for multi-response generalized linear mixed models: the MCMCglmm R package. J. Stat. Softw. 33, 1–22 (2010).
Article  Google Scholar 

46.
Burnham, K. P. & Anderson, D. R. Model Selection and Multi-Model Inference: A Practical Information-Theoretic Approach, Second edn. (Springer, 2002).

47.
Bartón, K. MuMIn: multi-model inference R package version 1.40.0. https://CRAN.R-project.org/package=MuMIn (2019).

48.
Honaker, J., King, G. & Blackwell, M. Amelia II: a program for missing data. J. Stat. Softw. 45, 1–47 (2011).
Article  Google Scholar 

49.
Choirat, C., Honaker, J., Imai, K., King, G. & Lau, O. Zelig: everyone’s statistical software. version 5.1.6.1. http://zeligproject.org (2008).

50.
Imai, K., King, G. & Lau, O. Toward a common framework for statistical analysis and development. J. Computational Graph. Stat. 17, 892–913 (2008).
Article  Google Scholar  More

The goal of this study was to examine the fluid flows directly adjacent to propulsor surfaces in order to better understand how metachronal propulsors interact with fluids for thrust production. Based on the direct comparison of the mean contributions of pulling vs. pushing forces throughout the power stroke of replicate individual propulsors (Fig. 4, which are generated by negative vs. positive pressure fields, respectively) we suggest that the propulsors of the animals examined rely predominantly on negative pressure for generating thrust. The assertion that these propulsor level observations to apply to the movement of the whole animal requires the assumptions that, first, the propulsors we quantified are representative of the all the other propulsors contributing to swimming thrust, and second, that the thrust generated for the whole animal is due to accumulated total thrust generated by individual propulsors. Although our data addresses the first of these assumptions by replicating individual propulsors, we cannot document the second assumption that the total thrust represents the sum of all individual propulsor elements. While this second assumption is intuitively appealing, our data is confined to the small spatial and temporal scales around individual propulsor elements. Confirmation that the whole-organism thrust results from of the summation of individual contributions requires experiments at different scales than those used in the current study.
Thrust generated by a propulsor is ultimately determined by the overall pressure gradient across the propulsor. So does it matter whether that gradient is dominated by negative or positive pressure? We believe that this distinction is fundamental for understanding why animal propulsors bend in a surprisingly characteristic and narrow range. Rigid paddle designs are dominated by positive pressure pushing against a fluid, which in turn, generates thrust pushing a body forward. Bending at propulsor margins encourages vortex formation on the lee side of the propulsor (Figs. 2, 3) that differs from rigid propulsors. Counter-rotating vortices formed on the lee side of a bending propulsor accelerate fluid at the intersection of the vortices12,15. The fluid thus accelerated relative to the leading edge of the propulsor is the basis of the pressure gradient across the propulsor surface. In turn, this elevated pressure gradient generates high thrust and is the reason for the dominant contribution of suction thrust to natural bending propulsors. More generally, negative pressure fields are a fundamental feature of vortices which are universally formed around objects moving in fluids (except at the lowest Reynolds numbers). Lift, a different propulsive mode that relies on negative pressure, is a well-known example that illustrates how kinematics and morphology can enhance negative pressure for thrust2. Lift occurs when a foil separates flow traveling over and under the foil surface. With the correct foil shape and kinematics, the separation of flow can generate strong negative pressure fields above the foil leading to an upward pulling thrust on the foil. This lift relies on the negative pressure field and foil shape.
To be clear, the thrust generated by limbs and ctenes in this study is not lift because the forces generated by lift are directed perpendicular to the direction of flow and the forces we describe are oriented in the direction of flow (Fig. 3). However, like lift, we suggest that paddles must move with prescribed kinematics to generate enhanced negative pressure fields. Bending kinematics in particular have been shown to greatly enhance vorticity and along with that, negative pressure11,16,17. Rigid, non-bending paddles generate different hydrodynamic structures than we observed13,14 and do not generate strong negative pressure fields16,17,18,19. Therefore, the kinematics of bending appear to be important for generating strong negative pressure fields around moving propulsors.
Until recently, technical constraints have limited our ability to investigate the scope of the benefits of using negative pressure for thrust. However several numerical studies, and a few experimental studies, have compared rigid to flexible propulsors. These studies have demonstrated that first, bending enhances negative pressure fields, second, bending generates elevated thrust, and third, bending enhances hydrodynamic efficiency12,16,17,20,21,22,23,24. The hydrodynamic patterns around bending propulsors show that negative pressure fields associated with bends generate significantly greater flow velocities than positive pressure fields (Fig. 2e11,12,16,21). This would lead to enhanced momentum transfer and explain the enhanced thrust observed for bending propulsors. The similar bending kinematics between the limbs and ctenes in this study and the swimming and flying animals from Lucas et al.1 suggests that these small paddling swimmers may employ similar hydrodynamic features as flying birds and swimming fish. If these bending patterns are predominately used to generate negative pressure fields for thrust, it follows that there is a need for greater focus on negative pressure around bending propulsors in order to understand the extent of the benefits of animals experience by pulling rather than pushing themselves through fluids.
Despite the vast difference in scale and Reynolds number, the results of this study suggest that the small metachronal paddles of swimming invertebrates may produce some similar effects as flapping wings in birds and insects. For example, there are similarities in the degree of bending and location of bending for the paddles in this study and the spanwise flexibility of birds and insects1. Such spanwise flexibility was found to be beneficial and yielded an increase in thrust coefficient, and a small decrease in power-input requirement, resulting in higher efficiency25.
In addition to the benefits for single propulsors, negative pressure fields can facilitate the movement and coordination of multiple propulsors which have antiplectic metachronal wave kinematics. During an antiplectic metachronal wave, a leading propulsor will begin the power stroke and, after it has initiated its stroke, the propulsor immediately behind it will initiate its own power stroke. This sequential pattern will continue for all the subsequent propulsors in the antiplectic wave. The predominately negative pressure on the leeward of each propulsor can serve to facilitate the kinematics of the adjacent propulsor by reducing the hydrodynamic resistance necessary to initiate and complete its power stroke26. In addition, the negative pressure in the gap between adjacent propulsors can serve as a cue for the adjacent propulsor to initiate its power stroke. It has been suggested that the ctenes of ctenophores require such cues to coordinate the metachronal kinematics26,27,28. At lower Reynolds numbers (Re  More

1.
IPCC. Climate change 2013: the Physical Science Basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. In: Stocker TF, Qin D, Plattner G-K, Tignor M, Allen SK, Boschung J, et al. editors. Cambridge, United Kingdom and New York, USA: Cambridge University Press; 2013. p. 1535.
2.
De Azevedo TR, Junior CC, Junior AB, dos Santos Cremer M, Piatto M, Tsai DS, et al. SEEG initiative estimates of Brazilian greenhouse gas emissions from 1970 to 2015. Sci Data. 2018;5:180045.
PubMed  PubMed Central  Article  CAS  Google Scholar 

3.
West TAP, Börner J, Fearnside PM. Climatic benefits from the 2006-2017 avoided deforestation in Amazonian Brazil. Front For Glob Change. 2019;2:52.
Article  Google Scholar 

4.
Malhi Y, Roberts JT, Betts RA, Killeen TJ, Li W, Nobre CA. Climate change, deforestation, and the fate of the Amazon. Science. 2008;319:169–73.
CAS  PubMed  Article  Google Scholar 

5.
Curtis PG, Slay CM, Harris NL, Tyukavina A, Hansen MC. Classifying drivers of global forest loss. Science. 2018;361:1108–11.
CAS  PubMed  Article  Google Scholar 

6.
Paula FS, Rodrigues JLM, Zhou J, Wu L, Mueller RC, Mirza BS, et al. Land use change alters functional gene diversity, composition and abundance in Amazon forest soil microbial communities. Mol Ecol. 2014. https://doi.org/10.1111/mec.12786.

7.
Evans PN, Boyd JA, Leu AO, Woodcroft BJ, Parks DH, Hugenholtz P, et al. An evolving view of methane metabolism in the Archaea. Nat Rev Microbiol. 2019;17:219–32.
CAS  PubMed  Article  Google Scholar 

8.
Serrano-Silva N, Sarria-Guzmán Y, Dendooven L, Luna-Guido M. Methanogenesis and methanotrophy in soil: a review. Pedosphere. 2014;24:291–307.
CAS  Article  Google Scholar 

9.
Zinder SH. Physiological ecology of methanogens. In: Ferry JG, editor. Methanogenesis: ecology, physiology, biochemistry and genetics. New York, NY, USA: Chapman & Hall; 1993. p. 128–206.

10.
Schink B, Stams AJM. Syntrophism among prokaryotes. In: Rosenberg E, DeLong F, Lory S, Stackebrandt E, Thompson F, editors. The prokaryotes: prokaryotic communities and ecophysiology. Berlin: Springer; 2013. p. 471–93.

11.
Costa KC, Leigh JA. Metabolic versatility in methanogens. Curr Opin Biotechnol. 2014;29:70–5.
CAS  PubMed  Article  Google Scholar 

12.
Semrau JD, DiSpirito AA, Yoon S. Methanotrophs and copper. FEMS Microbiol Rev. 2010;34:496–531.
CAS  PubMed  Article  Google Scholar 

13.
van Teeseling MCF, Pol A, Harhangi HR, van der Zwart S, Jetten MSM, Op den Camp HJM, et al. Expanding the verrucomicrobial methanotrophic world: description of three novel species of Methylacidimicrobium gen. nov. Appl Environ Microbiol. 2014;80:6782–92.
PubMed  PubMed Central  Article  CAS  Google Scholar 

14.
Zheng Y, Hou L, Chen F, Zhou J, Liu M, Yin G, et al. Denitrifying anaerobic methane oxidation in intertidal marsh soils: Occurrence and environmental significance. Geoderma. 2020;357:113943.
CAS  Article  Google Scholar 

15.
Zheng Y, Wang H, Liu Y, Zhu B, Li J, Yang Y, et al. Methane-dependent mineral reduction by aerobic methanotrophs under hypoxia. Environ Sci Technol Lett. 2020;7:606–12.
CAS  Article  Google Scholar 

16.
Murrell JC, Dalton H. Nitrogen fixation in obligate methanotrophs. J Gen Microbiol. 1983;129:3481–6.
CAS  Google Scholar 

17.
Graham DW, Chaudhary JA, Hanson RS, Arnold RG. Factors affecting competition between type I and type II methanotrophs in two-organism, continuous-flow reactors. Microb Ecol. 1993;25:1–17.
CAS  PubMed  Article  Google Scholar 

18.
Amaral JA, Knowles R. Growth of methanotrophs in methane and oxygen counter gradients. FEMS Microbiol Lett. 1995;126:215–20.
CAS  Article  Google Scholar 

19.
Dunfield PF, Liesack W, Henckel T, Knowles R, Conrad R. High-affinity methane oxidation by a soil enrichment culture containing a type II methanotroph. Appl Environ Microbiol. 1999;65:1009–14.
CAS  PubMed  PubMed Central  Article  Google Scholar 

20.
Bull ID, Parekh NR, Hall GH, Ineson P, Evershed RP. Detection and classification of atmospheric methane oxidizing bacteria in soil. Nature. 2000;405:175–8.
CAS  PubMed  Article  Google Scholar 

21.
Meyer KM, Klein AM, Rodrigues JLM, Nüsslein K, Tringe SG, Mirza BS, et al. Conversion of Amazon rainforest to agriculture alters community traits of methane-cycling organisms. Mol Ecol. 2017;26:1547–56.
CAS  PubMed  Article  Google Scholar 

22.
Baani M, Liesack W. Two isoenzymes of particulate methane monooxygenase with different methane oxidation kinetics are found in Methylocystis sp. strain SC2. Proc Natl Acad Sci USA. 2008;105:10203–8.
CAS  PubMed  Article  Google Scholar 

23.
Sharp CE, Smirnova AV, Graham JM, Stott MB, Khadka R, Moore TR, et al. Distribution and diversity of Verrucomicrobia methanotrophs in geothermal and acidic environments. Environ Microbiol. 2014;16:1867–78.
CAS  PubMed  Article  Google Scholar 

24.
Keller M, Goreau TJ, Wofsy SC, Kaplan WA, McElroy MB. Production of nitrous oxide and consumption of methane by forest soils. Geophys Res Lett. 1986;10:1156–9.
Article  Google Scholar 

25.
Steudler PA, Melillo JM, Feigl BJ, Neill C, Piccolo MC, Cerri CC. Consequence of forest-to-pasture conversion on CH4 fluxes in the Brazilian Amazon Basin. J Geophys Res Atmos. 1996;101:18547–54.
CAS  Article  Google Scholar 

26.
Fearnside PM. Greenhouse gases from deforestation in Brazilian Amazonia: net committed emissions. Climatic Change. 1997;35:321–60.
CAS  Article  Google Scholar 

27.
Fernandes SAP, Bernoux M, Cerri CC, Feigl BJ, Piccolo MC. Seasonal variation of soil chemical properties and CO2 and CH4 fluxes in unfertilized and P-fertilized pastures in an Ultisol of the Brazilian Amazon. Geoderma. 2002;107:227–41.
CAS  Article  Google Scholar 

28.
Wick B, Veldkamp E, de Mello WZ, Keller M, Crill P. Nitrous oxide fluxes and nitrogen cycling along a pasture chronosequence in Central Amazonia, Brazil. Biogeosciences. 2005;2:175–87.
CAS  Article  Google Scholar 

29.
Tveit AT, Urich T, Svenning MM. Metatranscriptomic analysis of active peat soil microbiome. Appl Environ Microbiol. 2014;80:5761–72.
CAS  PubMed  PubMed Central  Article  Google Scholar 

30.
Singer E, Wagner M, Woyke T. Capturing the genetic makeup of the active microbiome in situ. ISME J. 2017;11:1949–63.
CAS  PubMed  PubMed Central  Article  Google Scholar 

31.
Keiblinger KM, Wilhartitz IC, Schneider T, Roschitzki B, Schmid E, Eberl L, et al. Soil metaproteomics—comparative evaluation of protein extraction protocols. Soil Biol Biochem. 2012;54:14–24.
CAS  PubMed  PubMed Central  Article  Google Scholar 

32.
Kroeger ME, Nüsslein K. Stable isotope probing—detection of active microbes in soil. In: Elsas JDV, editor. Modern soil microbiology, 3rd ed. Boca Raton: CRC Press; 2019.

33.
Esson K, Lin X, Kumaresan D, Chanton JP, Murrell JC, Kostka JE. Alpha- and gammaproteobacterial methanotrophs codominate the active methane-oxidizing communities in an acidic boreal peat bog. Appl Environ Microbiol. 2016;82:2363–71.
CAS  PubMed  PubMed Central  Article  Google Scholar 

34.
Dumont MG, Radajewski SM, Miguez CB, McDonald IR, Murrell JC. Identification of a complete methane monooxygenase operon from soil by combining stable isotope probing and metagenomic analysis. Environ Microbiol. 2006;8:1240–50.
CAS  PubMed  Article  Google Scholar 

35.
Lu Y, Leuders T, Friedrich MW, Conrad R. Detecting active methanogenic populations on rice roots using stable isotope probing. Environ Microbiol. 2005;7:326–36.
CAS  PubMed  Article  Google Scholar 

36.
Neufeld JD, Vohra J, Dumont MG, Lueders T, Manefield M, Friedrich MW, et al. DNA stable-isotope probing. Nat Protoc. 2007;2:860–6.
CAS  PubMed  Article  Google Scholar 

37.
Holmes AJ, Costello A, Lidstrom ME, Murrell JC. Evidence that particulate methane monooxygenase and ammonia monooxygenase may be evolutionarily related. FEMS Microbiol Lett. 1995;132:203–8.
CAS  PubMed  Article  Google Scholar 

38.
Costello A, Lidstrom ME. Molecular characterization of functional and phylogenetic genes from natural populations of methanotrophs in lake sediments. Appl Environ Microbiol 1999;65:5066–74.
CAS  PubMed  PubMed Central  Article  Google Scholar 

39.
Steinberg LM, Regan JM. Phylogenetic comparison of the methanogenic communities from an acidic, oligotrophic fen and an anaerobic digester treating municipal wastewater sludge. Appl Environ Microbiol. 2008;74:6663–71.
CAS  PubMed  PubMed Central  Article  Google Scholar 

40.
Meyer KM, Morris AH, Webster K, Klein AM, Kroeger ME, Meredith LK, et al. Belowground changes to community structure alter methane-cycling dynamics in Amazonia. 2020. https://www.biorxiv.org/content/10.1101/2020.03.10.984807v1. in review.

41.
Navarrete AA, Tsai SM, Mendes LW, Faust K, de Hollander M, Cassman NA, et al. Soil microbiome responses to the short-term effects of Amazonian deforestation. Mol Ecol. 2015;24:2433–48.
CAS  PubMed  Article  Google Scholar 

42.
Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: high resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–3.
CAS  PubMed  PubMed Central  Article  Google Scholar 

43.
Bokulich NA, Kaehler BD, Rideout JR, Dillon M, Bolyen E, Knight R, et al. Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin. Microbiome. 2018;6:90.
PubMed  PubMed Central  Article  Google Scholar 

44.
Keegan KP, Glass EM, Meyer F. MG-RAST, a metagenomics service for analysis of microbial community structure and function. Methods Mol Biol. 2016;1399:207–33.
CAS  PubMed  Article  Google Scholar 

45.
R Core Team. R: a language and environment for statistical computing. Vienna Austria: R Foundation for Statistical Computing; 2018. https://www.r-project.org/.

46.
Oksanen JF, Blanchet G, Friendly M, Kindt R, Legendre P, McGlinn D, et al. vegan: community ecology package. R package version. 2018;2:4–6. https://CRAN.R-project.org/package=vegan.

47.
Parks DH, Tyson GW, Hugenholtz P, Beiko RG. STAMP: statistical analysis of taxonomic and functional profiles. Bioinformatics. 2014;30:3123–4.
CAS  PubMed  PubMed Central  Article  Google Scholar 

48.
Wickham H. ggplot2: elegant graphics for data analysis. New York: Spring-Verlag; 2016.
Google Scholar 

49.
Parks DH, Tyson GW, Hugenholtz P, Beiko RG. STAMP: statistical analysis of taxonomic and functional profiles. Bioinformatics. 2014;30:3123–4.
CAS  PubMed  PubMed Central  Article  Google Scholar 

50.
Pinto AJ, Raskin L. PCR biases distort bacterial and archaeal community structure in pyrosequencing datasets. PLoS ONE. 2012;7:e43093.
CAS  PubMed  PubMed Central  Article  Google Scholar 

51.
Huse SM, Welch DM, Morrison HG, Sogin ML. Ironing out the wrinkles in the rare biosphere through improved OTU clustering. Environ Microbiol. 2010;12:1889–98.
CAS  PubMed  PubMed Central  Article  Google Scholar 

52.
Quince C, Walker AW, Simpson JT, Loman NJ, Segata N. Shotgun metagenomics, from sampling to sequencing and analysis. Nat Biotechnol. 2017;35:833–44.
CAS  PubMed  Article  Google Scholar 

53.
Coyotzi S, Pratscher J, Murrell JC, Neufeld JD. Targeted metagenomics of active microbial populations with stable-isotope probing. Curr Opin Biotechnol. 2016;41:1–8.
CAS  PubMed  Article  Google Scholar 

54.
Eyice Ö, Namura M, Chen Y, Mead A, Samavedam S, Schäfer H. SIP metagenomics identifies uncultivated Methylophilaceae as dimethylsulphide degrading bacteria in soil and lake sediment. Int Soc Microb Ecol J. 2015;9:2336.
CAS  Google Scholar 

55.
Cardoso D, Sarkinen T, Alexander S, Amorim AM, Bittrich V, Celis M, et al. Amazon plant diversity revealed by a taxonomically verified species list. Proc Natl Acad Sci USA. 2017;114:10695–700.
CAS  PubMed  Article  Google Scholar 

56.
Sgouridis F, Ullah S. Soil greenhouse gas fluxes, environmental controls, and the partitioning of N2O sources in UK natural and seminatural land use types. J Geophys Res Biogeosciences. 2017. https://doi.org/10.1002/2017JG003783.

57.
Wanyama I, Pelster DE, Butterbach-Bahl K, Verchot LV, Martius C, Rufino MC. Soil carbon dioxide and methane fluxes from forests and other land use types in an African tropical montane region. Biogeochemistry. 2019;143:171–90.
CAS  Article  Google Scholar 

58.
Lackner N, Hintersonnleitner A, Wagner AO, Illmer P. Hydrogenotrophic methanogenesis and autotrophic growth of Methanosarcina thermophila. Archaea. 2018;2018:4712608.
PubMed  PubMed Central  Article  CAS  Google Scholar 

59.
Ferry JG. Fundamentals of methanogenic pathways that are key to the biomethanation of complex biomass. Curr Opin Biotechnol. 2011;22:351–7.
CAS  PubMed  PubMed Central  Article  Google Scholar 

60.
Welander PV, Metcalf WW. Loss of the mtr operon in Methanosarcina blocks growth on methanol, but not methanogenesis, and reveals an unknown methanogenic pathway. Proc Natl Acad Sci USA. 2005;102:10664–9.
CAS  PubMed  Article  Google Scholar 

61.
Mand TD, Metcalf WW. Energy conservation and hydrogenase function in methanogenic archaea, in particular the genus Methanosarcina. Microbiol Mol Biol Rev. 2019;83:e00020–19.
CAS  PubMed  PubMed Central  Article  Google Scholar 

62.
Lammel DR, Feigl BJ, Cerri CC, Nusslein K. Specific microbial gene abundances and soil parameters contribute to C, N, and greenhouse gas process rates after land use change in Southern Amazonian Soils. Front Microbiol. 2015;6:1057.
PubMed  PubMed Central  Article  Google Scholar 

63.
Kroeger ME, Delmont TO, Meyer KM, Guo J, Khan K, Rodrigues JLM, et al. New biological insights into how deforestation in Amazonia affects soil microbial communities using metagenomics and metagenome-assembled genomes. Front Microbiol. 2018;9:1635.
PubMed  PubMed Central  Article  Google Scholar 

64.
Pedrinho A, Mendes LW, Merloti LF, de Cassia da Fonseca M, de Souza Cannavan F, Tsai SM. Forest-to-pasture conversion and recovery based on assessment of microbial communities in Eastern Amazon rainforest. FEMS Microbiol Ecol. 2019;95:fiy236.
CAS  PubMed  Article  Google Scholar 

65.
Carini P, Marsden PJ, Leff JW, Morgan EE, Strickland MS, Fierer N. Relic DNA is abundant in soil and obscures estimates of soil microbial diversity. Nat Microbiol. 2017;2:16242.
CAS  Article  Google Scholar 

66.
Lennon JT, Muscarella ME, Placella SA, Lehmkuhl BK. How, when, and where relic DNA affects microbial diversity. MBio. 2018;9:e00637–18.
PubMed  PubMed Central  Google Scholar 

67.
Sirois SH, Buckley DH. Factors governing extracellular DNA degradation dynamics in soil. Environ Microbiol Rep. 2019;11:173–84.
CAS  PubMed  Article  Google Scholar 

68.
Mancinelli RL. The regulation of methane oxidation in soil. Annu Rev Microbiol. 1995;49:581–605.
CAS  PubMed  Article  Google Scholar 

69.
Guggenheim C, Brand A, Bürgmann H, Sigg L, Wehrli B. Aerobic methane oxidation under copper scarcity in a stratified lake. Sci Rep. 2019;9:4817.
PubMed  PubMed Central  Article  CAS  Google Scholar 

70.
Knapp CW, Fowle DA, Kulczycki E, Roberts JA, Graham DW. Methane monooxygenase gene expression mediated by methanobactin in the presence of mineral copper sources. Proc Natl Acad Sci USA. 2007;104:12040–5.
CAS  PubMed  Article  Google Scholar 

71.
Rodrigues JLM, Pellizari VH, Mueller R, Baek K, da C Jesus E, Paula FS, et al. Conversion of the Amazon rainforest to agriculture results in biotic homogenization of soil bacterial communities. Proc Natl Acad Sci USA. 2013;110:988–93.
CAS  PubMed  Article  Google Scholar 

72.
Hoehler T, Gunsalus RP, McInerney MJ. Environmental Constraints that Limit Methanogenesis. In Timmis KN, editor. Handbook of Hydrocarbon and Lipid Microbiology. Berlin, Heidelberg: Springer; 2010. p. 635–54.

73.
Elshahed MS, Bhupathiraju VK, Wofford NQ, Nanny MA, McInerney MJ. Metabolism of benzoate, cyclohex-1-ene carboxylate, and cyclohexane carboxylate by “Syntrophus aciditrophicus” strain SB in syntrophic association with H2-using microorganisms. Appl Environ Microbiol. 2001;67:1728–38.
CAS  PubMed  PubMed Central  Article  Google Scholar 

74.
Zehnder AJB, Stumm W. Geochemistry and biogeochemistry of anaerobic habitats. In Zehnder AJB editor. Biology of Anaerobic Microorganisms. New York, NY, USA: John Wiley & Sons; 1988. p. 1–38.

75.
Wolfe RS. My kind of biology. Annu Rev Microbiol. 1991;45:1–35.
CAS  PubMed  Article  Google Scholar 

76.
Strittmatter AW, Liesegang H, Rabus R, Decker I, Amann J, Andres S, et al. Genome sequence of Desulfobacterium autotrophicum HRM2, a marine sulfate reducer oxidizing organic carbon completely to carbon dioxide. Environ Microbiol. 2009;11:1038–55.
CAS  PubMed  PubMed Central  Article  Google Scholar 

77.
Badziong W, Ditter B, Thauer RK. Acetate and carbon dioxide assimilation by Desulfovibrio vulgaris (Marburg), growing on hydrogen and sulfate as sole energy source. Arch Microbiol. 1979;123:301–5.
CAS  Article  Google Scholar 

78.
Watson SW, Waterbury JB. Characteristics of two marine nitrite oxidizing bacteria, Nitrospina gracilis nov. gen. nov. sp. and Nitrococcus mobilis nov. gen. nov. sp. Arch Microbiol. 1971;77:203–30.
Google Scholar 

79.
Sorokin DY, Tourova TP, Lysenko AM, Mityushina LL, Kuenen JG. Thioalkalivibrio thiocyanoxidans sp. nov. and Thioalkalivibrio paradoxus sp. nov., novel alkaliphilic, obligately autotrophic, sulfur-oxidizing bacteria capable growth on thiocyanate, from soda lakes. IJSEM. 2002;52:657–64.
CAS  PubMed  Google Scholar 

80.
Kukla J, Whitfeld T, Cajthaml T, Baldrian P, Veselá-Šimáčková H, Novotný V, et al. The effect of traditional slash-and-burn agriculture on soil organic matter, nutrient content, and microbiota in tropical ecosystems of Papua New Guinea. Land Degrad Dev. 2018;30:166–77.
Article  Google Scholar 

81.
Banat IM, Marchant R. Geobacillus activities in soil and oil contamination remediation. In: Logan NA, De Vos P, editors. Endospore-forming soil bacteria, soil biology 27, Berlin Heidelberg: Springer-Verlag; 2011. p. 259–70.

82.
Durrer A, Margenot AJ, Silva LCR, Bohannan BJM, Nüsslein K, van Haren J, et al. Beyond total carbon: long-term effect of deforestation on Amazonian soils. Biogeochemistry BIOG-D-19-00139. in review.

83.
Nazina TN, Tourova TP, Poltaraus AB, Novikova EV, Grigoryan AA, Ivanova AE, et al. Taxonomic study of aerobic thermophilic bacilli: descriptions of Geobacillus subterraneus gen. nov., sp. nov. and Geobacillus uzenensis sp. nov. from petroleum reservoirs and transfer of Bacillus thermoleovorans, Bacillus kaustophilus, Bacillus thermodenitrificans to Geobacillus as the new combinations G. stearothermophilus, G.th. IJSEM. 2001;51:433–46.
CAS  PubMed  Google Scholar 

84.
Schnürer A, Schink B, Svensson BH. Clostridium ultunense sp. nov., a mesophilic bacterium oxidizing acetate in syntrophic association with a hydrogenotrophic methanogenic bacterium. IJSEM. 1996;46:1145–52.
Google Scholar 

85.
Myhr S, Torsvik T. Denitrovibrio acetiphilus, a novel genus and species of dissimilatory nitrate-reducing bacterium isolated from an oil reservoir model column. IJSEM. 2000;50:1611–9.
CAS  PubMed  Google Scholar 

86.
Yao H, Conrad R, Wassmann R, Neue HU. Effect of soil characteristics on sequential reduction and methane production in sixteen rice paddy soils from Chine, the Philippines, and Italy. Biogeochemistry. 1999;47:269–95.
CAS  Article  Google Scholar 

87.
Achtnich C, Bak F, Conrad R. Competition for electron donors among nitrate reducers, ferric iron reducers, sulfate reducers, and methanogens in anoxic paddy soil. Biol Fertil Soils. 1995;19:65–72.
CAS  Article  Google Scholar 

88.
Verchot LV, Davidson EA, Cattânio JH, Ackerman IL. Land-use change and biogeochemical controls of methane fluxes in soils of Eastern Amazonia. Ecosystems. 2000;3:41–56.
CAS  Article  Google Scholar 

89.
Wagner R, Zona D, Oechel W, Lipson D. Microbial community structure and soil pH correspond to methane production in Arctic Alaska soils. Environ Microbiol. 2017;19:3398–410.
CAS  PubMed  Article  Google Scholar 

90.
Feigl BJ, Melillo J, Cerri CC. Changes in the origin and quality of soil organic matter after pasture introduction in Rôndonia (Brazil). Plant Soil. 1995;175:21–9.
CAS  Article  Google Scholar 

91.
Salimon CI, Davidson EA, Victoria RL, Melo AWF. CO2 flux from soil in pastures and forests in southwestern Amazonia. Glob Change Biol. 2004;10:833–43.
Article  Google Scholar 

92.
Wilhelm RC, Singh R, Eltis LD, Mohn WW. Bacterial contributions to delignification and lignocellulose degradation in forest soils with metagenomic and quantitative stable isotope probing. ISME J. 2019;13:413–8.
CAS  PubMed  Article  Google Scholar 

93.
Pepe-Ranney C, Campbell AN, Koechli CN, Berthrong S, Buckley DH. Unearthing the ecology of soil microorganisms using a high-resolution DNA-SIP approach to explore cellulose and xylose metabolism in soil. Front Microbiol. 2016;7:703.
PubMed  PubMed Central  Article  Google Scholar 

94.
Murase J, Hordijk K, Tayasu I, Bodelier PLE. Strain-specific incorporation of methanotrophic biomass into eukaryotic grazers in a rice field soil revealed by PLFA-SIP. FEMS Microbiol Ecol. 2011;75:284–90.
CAS  PubMed  Article  Google Scholar 

95.
Radajewski S, Ineson P, Parekh NR, Murrell JC. Stable-isotope probing as a tool in microbial ecology. Nature. 2000;403:646–9.
CAS  PubMed  Article  Google Scholar 

96.
Cébron A, Bodrossy L, Chen Y, Singer AC, Thompson IP, Prosser JI, et al. Identity of active methanotrophs in landfill cover soil as revealed by DNA-stable isotope probing. FEMS Microbiol Ecol. 2007;62:12–23.
PubMed  Article  CAS  Google Scholar 

97.
Mathew RP, Feng Y, Githinji L, Ankumah R, Balkcom KS. Impact of no-tillage and conventional tillage systems on soil microbial communities. Appl Environ Soil Sci. 2012;2012:548620.
Article  Google Scholar 

98.
Eilers KG, Debenport S, Anderson S, Fierer N. Digging deeper to find unique microbial communities: the strong effect of depth on the structure of bacterial and archaeal communities in soil. Soil Biol Biochem. 2012;50:58–65.
CAS  Article  Google Scholar 

99.
Zhou J, Wu L, Deng Y, Zhi X, Jiang Y-H, Tu Q, et al. Reproducibility and quantitation of amplicon sequencing-based detection. ISME J. 2011;5:1303–13.
CAS  PubMed  PubMed Central  Article  Google Scholar 

100.
Wen C, Wu L, Qin Y, Van Nostrand JD, Ning D, Sun B, et al. Evaluation of the reproducibility of amplicon sequencing with Illumina MiSeq platform. PLoS ONE. 2017. https://doi.org/10.1371/journal.pone.0176716.

101.
Berenguer E, Gardner TA, Ferreira J, Aragao LE, Camargo PB, Cerri CE, et al. Developing cost-effective field assessments of carbon stocks in human-modified tropical forests. PLoS ONE. 2015;10:e0133139.
PubMed  PubMed Central  Article  CAS  Google Scholar  More

Global food demand will rise substantially over the coming decades. Meeting this demand while decreasing the environmental footprint of agriculture is one of largest challenges of the twenty-first century1,2,3. A growing world population and changing diets are projected to double4 meat and dairy consumption between 2000 and 2050. As one of the main feed sources for livestock, grasslands play a key role in meeting this demand. With over 33 million km2, permanent grasslands account for ~ 25% of the world’s land cover. Over two thirds of this area is utilised for agriculture, making it the most dominant land use5. Sustainably increasing grassland productivity is therefore crucial to ensure future global food security6,7.
Phosphorus (P) is an essential nutrient, often limiting plant growth8. P fertilisation is therefore needed to sustain productivity in agricultural systems across the world. Because the world’s P reserves are decreasing, the importance of judicious P use will increase over the coming century. Although estimates of global P reserves vary, the costs of high quality P fertilisers will increase, as will the global demand for these fertilisers9,10,11,12. Differences in climate, geography, agricultural development, and fertilisation practices have led to great global imbalances of P in agricultural land13,14,15,16. In parts of Europe, North America, and China, historical applications of manure and fertilisers have resulted in positive P balances and increased risk of eutrophication of surface waters17. In many other regions, predominantly in tropical areas, farmers struggle to maintain soil P availability to sustain optimal rates of crop production18. Recent predictions suggest that global P inputs in grasslands will have to increase fourfold to support an 80% increase in grass yield projected for 205015, which implies an urgent need to increase use efficiency of P fertiliser sources.
The large diversity in agronomic P status of soils across the world and the projected increase in cost and demand of P fertilisers necessitate a rethink of the use of P resources: are we applying fertilisers at the right rates to the right soils? The success of fertiliser application depends on conditions created by climate and management19,20 and is strongly governed by soil properties such as pH and concentrations of metal oxides and Ca in soil that can impact P availability to plants8,21,22. However, data for these relationships are fragmentary and country- or region-specific, and global assessments are lacking23,24. Here we use a meta-analysis on a global database of 67 studies and 1227 observations with a wide range of soil properties and climatic conditions to assess the general effect of P fertilisation on grassland production across the world. Furthermore, we identify soil-related driving factors that determine the success of fertiliser applications.
Our dataset included data from field grasslands all over the world (Supplementary Fig. 1). Most studies originated from Europe and North America, but due to several studies with many observations from the Australian continent, there were almost as many observations from Oceania. We analysed our dataset using two different metrics: the response ratio (RR) as measure for the relative increase in dry matter yield as a result of P fertilisation, and P agronomic efficiency (PAE) expressing the absolute yield increase per unit of P applied.
Factors controlling the success of phosphorus fertilisation
P fertilisation increased grassland yield by 37% (95% confidence interval: 33 to 40%; Fig. 1; Supplementary Table 3) averaged over all grasslands, soil types, and fertility levels, resulting in a PAE of 32 kg kg−1 (Fig. 2; Supplementary Table 4). In other words, dry matter yields increased by 32 kg per kg of P applied on average. Yield responses to P additions increased with P application rates: rates below 25 kg P ha−1 increased yields by 40% on average, whereas applying over 100 kg P ha−1 increased grass yield by 65%. An exception to this pattern were grasslands fertilised with 25–50 kg P ha−1, which responded to a lesser extent than those in other categories. This is likely an artefact due to a relatively high average soil P status of studies included in this category (Supplementary Fig. 3), which may have led to high yields in the control treatments. The PAE, on the other hand, decreased with P application rates (Supplementary Table 4): yields increased by 53 kg per kg P applied at rates lower than 25 kg P ha−1, but only by 12 kg kg−1 P at rates higher than 100 kg P ha−1. This indicates that finding a balance between P input and yield response is crucial for optimising fertiliser effectivity, as the agronomic efficiency decreases with higher application rates.
Figure 1

Impact of phosphorus (P) fertilisation for the controlling factors crop, P rate, climate, and mean annual temperature expressed as relative yield increase per category. The 95% confidence intervals are represented by the error bars, and the number of studies and observations per category are between parentheses; *,**,***Significant controlling factor effect at an α of 0.05, 0.01 and 0.001, respectively.

Full size image

Figure 2

The Phosphorus agronomic efficiency (PAE) for different controlling factors per subgroup. The effect is expressed for crop, climate, and P status (Olsen-equivalent) × P rate (c). Low SPT: ≤ 10 mg P kg−1; high SPT:  > 10 mg P kg−1; low rate: P rate ≤ 50 kg P ha−1; high rate: P rate  > 50 kg P ha−1 The 95% confidence intervals are represented by the error bars, and the number of studies and observations per category are between parentheses; *, **,***Significant controlling factor effect at an α of 0.05, 0.01 and 0.001, respectively.

Full size image

Systems that included legumes responded more strongly to P fertilisation than systems without legumes (Fig. 1). On average, P fertiliser increased yield in grass/legume systems by 54%, but only by 25% in grassland systems without legumes. These numbers corresponded with a PAE of 46 kg kg−1 for grass/legume and 22 kg kg−1 for grass-only systems, meaning that P fertilisation was roughly twice as effective in grasslands with legumes than in those without legumes. Legumes like alfalfa and clover are regularly included in grassland mixtures, mainly because they provide extra N inputs to the plant-soil system by establishing a symbiosis with N-fixing microorganisms23. These results likely reflect that legumes generally require more P than grasses, and can acquire it less easily due to thicker roots and shorter root hairs11,25,26.
In our database, more than half (36) of the studies included more than one N treatment. Overall, the N application rate had little effect on the response of grasslands to P fertilisation. There was no significant effect of N rate on the PAE (Supplementary Table 4). Yield responses to P fertiliser at N application rates over 200 kg N ha−1 were slightly but significantly smaller than at lower N rates (Supplementary Table 3). However, if N limitation of the grasslands would have played a prominent role, a general increase in response to P fertiliser with increasing N rate would have been observed. These results suggest that differences in yield responses were mainly driven by a response to P fertilisation rather than to N fertilisation.
Geographical variation in responses
P application increased grassland yields in tropical regions (i.e. latitudes ≤ 35°) significantly more strongly than in temperate grasslands (Fig. 1, Supplementary Table 3). However, because yields of tropical grasslands were relatively low, the PAE of fertiliser application did not differ significantly between the two regions (34 and 31 kg kg−1 for tropical and temperate regions, respectively; Fig. 2, Supplementary Table 4). These results likely reflect that soils in (sub)tropical regions are often highly weathered, nutrient-poor, and have a low P availability due to high abundancy of adsorbents like Al and Fe oxides8. In contrast, decades of manure and fertiliser applications have resulted in a build-up of soil P levels well beyond crop requirements and a corresponding decrease in yield response to P fertiliser application17,27 in many temperate regions (e.g. North America, Europe, and New Zealand). The differences in response of temperate and tropical grasslands are also reflected in the results for mean annual temperature (MAT; Fig. 1, Supplementary Table 3), with grasslands in colder regions (MAT  20 °C reacting the strongest. Higher temperatures may lead to more rapid plant production and to an increase in mineralisation of organic matter. Correlation analysis of the controlling factors showed that MAT and latitude among our studies were strongly correlated (Supplementary Fig. 4; Spearman’s ρ = -0.95).
Yield responses to P fertilisation were significantly smaller in Asia, North America, and Europe (+ 15 to + 29%) than in South America, Oceania, and Africa (+ 58 to + 94%). The PAE ranged from 12 kg kg−1 for studies in Asia to 74 kg kg−1 for studies in Oceania and even 117 kg kg−1 for the one African study included in our dataset (Supplementary Table 4). The continents with grasslands that showed a strong response to P fertilisation roughly coincide with the areas that have relatively low P inputs and outputs, as modelled by Sattari et al.15. Taken together, these results imply that Africa and Oceania with low P inputs responded strongly to P fertilisation whereas grasslands in Europe, North America and Asia with relatively high P inputs over the past decades, showed a weak response to P fertilisation.
Do we apply phosphorus fertilisers to the right soils?
We used various soil parameters as controlling factors (Table 1) to identify what soil properties drive differences in yield response to P fertilisation. One of the most important parameters is the agronomic P status of the soil, which is commonly determined with a soil P test (SPT). Because soil type, climate, and crop response vary considerably across the world, each country and sometimes even region has its own SPT method and classification system28,29. Given this large variety of SPT procedures (and resulting P concentrations) in use, we applied conversion formulas published in peer-reviewed papers to express reported SPT values in our database as ‘Olsen-equivalent’ P values wherever possible (see Supplementary Methods).
Table 1 Controlling factors and categories distinguished in the meta-analysis.
Full size table

Grasslands on soils with low SPT values (≤ 5 mg P kg−1) responded strongest to P fertilisation with a yield increase of 110% on average (Fig. 3, Supplementary Table 3). Conversely, P additions to soils with SPT values  > 5 mg P kg−1 increased yields by 7–25%. Although yield response decreased dramatically with increasing SPT values, the responses at relatively high SPT values (10–25 and  > 25 mg P kg−1) were still statistically significant. Critical values (that is, SPT levels for which the yield is 95% of the maximum yield) for grass of 23–25 mg kg−1 Olsen P have been reported previously for English grasslands30, which coincides with the limited yield response for soils in the highest SPT category. A study of 25 Spanish soils also showed an average critical SPT of 24 mg Olsen P kg−1 for ryegrass, although there was a wide spread for individual soils, ranging from 11 to 46 mg kg−131. For a range of Australian grassland species, however, lower critical SPT values (between 9 and 15 mg kg−1) have been determined32. This variety of critical SPT values found in literature illustrates that the effect of P fertilisation is strongly dependent on soil, climate, and even grassland species. Therefore, our results here do not give a hard SPT limit beyond which further P applications are rendered ineffective, but do indicate a strong decrease in effectiveness at higher SPT values.
Figure 3

Effect of different soil characteristics on the impact of phosphorus (P) fertilisation. The effect is expressed for soil P status based on Olsen P-equivalent, soil pH, organic matter content, and clay content. The 95% confidence intervals are represented by the error bars, and the number of studies and observations per category are between parentheses; *, **,***Significant controlling factor effect at an α of 0.05, 0.01 and 0.001, respectively.

Full size image

The strong yield response to P fertilisation on soils with low SPT values was not merely the result of a low yield of the control treatments. PAE was also highest (75 kg kg−1) for soils with SPT ≤ 5 mg P kg−1 (Supplementary Table 4) and fertilisation on these soils was 3 to 8 times as effective as on soils with higher SPT values in terms of absolute yield increases. Without correcting for the P application rate, absolute yield responses (average yield of treated plots minus average yield of control plots) to P fertilisation varied substantially (− 2.7 to 11.3 tonnes ha−1; Supplementary Fig. 5). The largest response (on average 2.7 tonnes ha−1 increase) and variation to P fertilisation were found for soils in the lowest SPT category. The yield response decreased with higher SPT (Supplementary Fig. 5). Figure 2 shows that both relatively low (≤ 50 kg P ha−1) and relatively high ( > 50 kg P ha−1) P application rates on soils with a low P status (≤ 10 mg P kg−1 Olsen-equivalent) were more effective than any P fertilisation rate on soils with a relatively high P status ( > 10 mg P kg−1 Olsen-equivalent). The high PAE of large application rates on soils with a low P status (Fig. 2) may be the result of the binding behaviour of P in soil: in soils with a low P status (where relatively more P adsorption occurs), relatively high P inputs are required to raise the level of plant-available P, so grassland on these soils will benefit relatively more from high application rates. Conversely, applying large amounts of P to soils with a relatively high P status ( > 10 mg P kg−1) showed a low PAE.
Yield responses to P applications were highest on grasslands with a soil pH of 5–6 (60% yield increase; Fig. 3) whereas lower and higher pH levels resulted in lower (11–26%) yield responses. We observed the same pattern for PAE, where studies with a pH of 5 to 6 had a 50 kg yield increase per kg of P fertilised, whereas for soils with a pH above 7 this was only 11 kg (Supplementary Table 4). Soil pH is a crucial parameter in determining the availability of P to crops8. In acidic mineral soils, binding of P to Fe and Al (hydr)oxides is often the main factor that governs the level of plant available P. In contrast, in soils with pH values above 7, P is more likely to form poorly soluble Ca-P precipitates, decreasing plant available P. The relative availability of soil P is highest at soil pH levels of 5 to 733,34, which would imply that around this pH fertiliser P application would yield the strongest responses.
We found a positive correlation between the soil organic matter (OM) content and yield response to P fertilisation (Fig. 3). On average, P application increased yield by only 11% on soils with an OM content below 2% (PAE was 7.2 kg kg−1 on average and this effect was not statistically significant). Yield responses were much higher (41–80%) in soils with an OM content of  > 5%. The PAE was 9 times as high in soils with  > 5% OM as in soils with  More