Philippa Kaur

1.
Owen, A. W. Trilobite abnormalities. Earth Environ. Sci. Trans. R. Soc. Edinb. 76(2–3), 255–272 (1985).
Google Scholar 
2.
Daley, A. C. & Drage, H. B. The fossil record of ecdysis, and trends in the moulting behaviour of trilobites. Arthropod Struct. Dev. 45(2), 71–96 (2016).
ADS  PubMed  Google Scholar 

3.
Clarkson, E. N. On the schizochroal eyes of three species of Reedops (Trilobita: Phacopidae) from the Lower Devonian of Bohemia. Earth Environ. Sci. Trans. R. Soc. Edinb. 68(8), 183–205 (1969).
Google Scholar 

4.
Henningsmoen, G. Moulting in trilobites. Fossils Strata. 4(1), 79–200 (1975).
Google Scholar 

5.
Howe, N. R. Partial molting synchrony in the giant Malaysian prawn, Macrobrachium rosenbergii: A chemical communication hypothesis. J. Chem. Ecol. 7(3), 487–500 (1981).
PubMed  CAS  Google Scholar 

6.
Drage, H. B. Quantifying intra-and interspecific variability in trilobite moulting behaviour across the Palaeozoic. Paleontol. Electron. 22(2) (2019).

7.
Pates, S. & Bicknell, R. D. Elongated thoracic spines as potential predatory deterrents in olenelline trilobites from the lower Cambrian of Nevada. Palaeogeogr. Palaeoclimatol. Palaeoecol. 516, 295–306 (2019).
Google Scholar 

8.
Webster, S. G. Seasonal anecdysis and moulting synchrony in field populations of Palaemon elegans (Rathke). Estuar. Coast. Shelf Sci. 15(1), 85–94 (1982).
ADS  Google Scholar 

9.
Leinaas, H. P. Synchronized moulting controlled by communication in group-living Collembola. Science 219(4581), 193–195 (1983).
ADS  PubMed  CAS  Google Scholar 

10.
Stone, R. P. Mass molting of tanner crabs Chionoecetes bairdi in a Southeast Alaska-Estuary. Alaska Fish. Res. Bull. 6(1), 19–28 (1999).
Google Scholar 

11.
Kim, K. W. Social facilitation of synchronized molting behavior in the spider Amaurobius ferox (Araneae, Amaurobiidae). J. Insect Behav. 14(3), 401–409 (2001).
CAS  Google Scholar 

12.
Haug, J. T., Caron, J. B. & Haug, C. Demecology in the Cambrian: Synchronized molting in arthropods from the Burgess Shale. BMC Biol. 11(1), 64 (2013).
PubMed  PubMed Central  Google Scholar 

13.
Braddy, S. J. Eurypterid palaeoecology: Palaeobiological, ichnological and comparative evidence for a ‘mass–moult–mate’ hypothesis. Palaeogeogr. Palaeoclimatol. Palaeoecol. 172(1–2), 115–132 (2001).
Google Scholar 

14.
Karim, T. & Westrop, S. R. Taphonomy and paleoecology of Ordovician trilobite clusters, Bromide Formation, south-central Oklahoma. Palaios 17, 394–402 (2002).
ADS  Google Scholar 

15.
Vrazo, M. B. & Braddy, S. J. Testing the ‘mass-moult-mate’hypothesis of eurypterid palaeoecology. Palaeogeogr. Palaeoclimatol. Palaeoecol. 311(1–2), 63–73 (2011).
Google Scholar 

16.
Paterson, J. R., Jago, J. B., Brock, G. A. & Gehling, J. G. Taphonomy and palaeoecology of the emuellid trilobite Balcoracania dailyi (early Cambrian, South Australia). Palaeogeogr. Palaeoclimatol. Palaeoecol. 249(3–4), 302–321 (2007).
Google Scholar 

17.
Błażejowski, B., Brett, C. E., Kin, A., Radwański, A. & Gruszczyński, M. Ancient animal migration: A case study of eyeless, dimorphic Devonian trilobites from Poland. Palaeontology 59(5), 743–751 (2016).
Google Scholar 

18.
Vannier, J. et al. Collective behaviour in 480-million-year-old trilobite arthropods from Morocco. Sci. Rep. 9(1), 1–10 (2019).
CAS  Google Scholar 

19.
Pocock, K. J. The Emuellidae, a new family of trilobites from the Lower Cambrian of South Australia. Palaeontology 13(4), 522–562 (1970).
Google Scholar 

20.
Esker, G. C. New species of trilobites from the Bromide Formation (Pooleville Member) of Oklahoma. Oklahoma Geology Notes. 24(9), 195–209 (1964).
Google Scholar 

21.
Thoral, M. Contribution à l’étude paléontologique de l’Ordovicien inférieur de la Montagne Noire et révision sommaire de la faune cambrienne de la Montagne Noire. (Imprimerie de la Charité, Montpellier, 1935).

22.
Passano, L. M. Molting and its control. In Metabolism and Growth (1960).

23.
Webster, M., Gaines, R. R. & Hughes, N. C. Microstratigraphy, trilobite biostratinomy, and depositional environment of the “lower Cambrian” Ruin Wash Lagerstätte, Pioche Formation, Nevada. Palaeogeogr. Palaeoclimatol. Palaeoecol. 264(1–2), 100–122 (2008).
Google Scholar 

24.
Esteve, J. & Zamora, S. Enrolled agnostids from Cambrian of Spain provide new insights about the mode of life in these forms. Bull. Geosci. 89(2), 283–291 (2014).
Google Scholar 

25.
Speyer, S. E. Comparative taphonomy and palaeoecology of trilobite lagerstätten. Alcheringa 11(3), 205–232 (1987).
Google Scholar 

26.
Geyer, G. & Peel, J. S. The Henson Gletscher Formation, North Greenland, and its bearing on the global Cambrian Series 2–Series 3 boundary. Bull. Geosci. 86(3), 465–534 (2011).
Google Scholar 

27.
Zhou, T. M., Liu, Y. R., Meng, X. S & Sun, Z. H. Palaeontological atlas of central and southern China. In Early Palaeonzoic, vol. 1 (eds. Hubei Institute of Geological Sciences, Geological Bureau of Henan Province, Geological Bureau of Hubei Province, Geological Bureau of Hunan Province, Geological Bureau of Guangdong Province & Geological Bureau of Guangxi Province) 104–266 (Geological Publishing House, Beijing, 1977).

28.
Yuan, J. L. & Esteve, J. The earliest species of Burlingia Walcott, 1908 (Trilobita) from South China: Biostratigraphical and palaeogeographical significance. Geol. Mag. 152(2), 358–366 (2015).
ADS  Google Scholar 

29.
Hughes, N. C., Minelli, A. & Fusco, G. The ontogeny of trilobite segmentation: A comparative approach. Paleobiology. 32(4), 602–627 (2006).
Google Scholar 

30.
Brett, C. E. & Baird, G. C. Taphonomic approaches to temporal resolution in stratigraphy: Examples from Paleozoic marine mudrocks. Short Courses Paleontol. 6, 251–274 (1993).
Google Scholar 

31.
Brandt, D. S. Taphonomic grades as a classification for fossiliferous assemblages and implications for paleoecology. Palaios 4(4), 303–309 (1989).
ADS  Google Scholar 

32.
Schäfer, W. & Oertel, I. Ecology and Palaeoecology of Marine Environments (University of Chicago Press, Illinois, 1972).
Google Scholar 

33.
Brett, C. E. & Baird, G. C. Comparative taphonomy: A key to paleoenvironmental interpretation based on fossil preservation. Palaios 1(3), 207–227 (1986).
ADS  Google Scholar 

34.
Plotnick, R. E. Taphonomy of a modern shrimp: Implications for the arthropod fossil record. Palaios. 286–293 (1986).

35.
Plotnick, R. E., Baumiller, T. & Wetmore, K. L. Fossilization potential of the mud crab, Panopeus (Brachyura: Xanthidae) and temporal variability in crustacean taphonomy. Palaeogeogr. Palaeoclimatol. Palaeoecol. 63(1–3), 27–43 (1988).
Google Scholar 

36.
Babcock, L. E. & Chang, W. Comparative taphonomy of two nonmineralized arthropods: Naraoia (Nektaspida; Early Cambrian, Chengjiang Biota, China) and Limulus (Xiphosurida; Holocene, Atlantic Ocean). Collect. Res. 10, 233–250 (1997).
Google Scholar 

37.
Speyer, S. E. & Brett, C. E. Clustered trilobite assemblages in the Middle Devonian Hamilton group. Lethaia. 18(2), 85–103 (1985).
Google Scholar 

38.
Paterson, J. R. et al. Trilobite clusters: What do they tell us? A preliminary investigation. Adv. Trilobite Res. 9, 313–318 (2008).
Google Scholar 

39.
Gaines, R. R. & Droser, M. L. Paleoecology of the familiar trilobite Elrathia kingii: An early exaerobic zone inhabitant. Geology 31(11), 941–944 (2003).
ADS  Google Scholar 

40.
Gutiérrez-Marco, J. C., Sá, A. A., García-Bellido, D. C., Rábano, I. & Valério, M. Giant trilobites and trilobite clusters from the Ordovician of Portugal. Geology 37(5), 443–446 (2009).
ADS  Google Scholar 

41.
Esteve, J., Hughes, N. C. & Zamora, S. Purujosa trilobite assemblage and the evolution of trilobite enrollment. Geology 39(6), 575–578 (2011).
ADS  Google Scholar 

42.
Brett, C. E., Zambito, J. J. IV., Schindler, E. & Becker, R. T. Diagenetically-enhanced trilobite obrution deposits in concretionary limestones: The paradox of “rhythmic events beds”. Palaeogeogr. Palaeoclimatol. Palaeoecol. 367, 30–43 (2012).
Google Scholar 

43.
Hoare, B. Animal Migration: Remarkable Journeys in the Wild. (University of California Press, 2009).

44.
Chatterton, B. D. E. & Fortey, R. A. Linear clusters of articulated trilobites from Lower Ordovician (Arenig) strata at Bini Tinzoulin, North Zagora, Southern Morocco. Adv. Trilobite Res. (Cuadernos del Museo Geominero) 9, 73–77 (2008).

45.
Trenchard, H., Brett, C. E. & Perc, M. Trilobite ‘pelotons’: Possible hydrodynamic drag effects between leading and following trilobites in trilobite queues. Palaeontology 60(4), 557–569 (2017).
Google Scholar 

46.
Kim, K. W. & Horel, A. Matriphagy in the spider Amaurobius ferox (Araneidae, Amaurobiidae): an example of mother-offspring interactions. Ethology 104(12), 1021–1037 (1998).
Google Scholar 

47.
Kim, K. W. & Roland, C. Trophic egg laying in the spider, Amaurobius ferox: mother–offspring interactions and functional value. Behav. Proc. 50(1), 31–42 (2000).
CAS  Google Scholar 

48.
Drage, H. B., Holmes, J. D., García-Bellido, D. C. & Daley, A. C. An exceptional record of Cambrian trilobite moulting behaviour preserved in the Emu Bay Shale, South Australia. Lethaia 51(4), 473–492 (2018).
Google Scholar 

49.
Zhao, Y. L. et al. Balang section, Guizhou, China: Stratotype section for the Taijiangian Stage and candidate for GSSP of an unnamed Cambrian Series. Camb. Syst. China Korea Guide Field Excursions 62–83 (2005).

50.
Zhao, Y. L. et al. Kaili Biota: A taphonomic window on diversification of metazoans from the basal Middle Cambrian: Guizhou, China. Acta Geol. Sin.-English Ed. 79(6), 751–765 (2005).
Google Scholar 

51.
Yang, X. L., Zhao, Y. L., Peng, J., Yang, Y. N. & Yang, K. D. Discovery of Oryctocephalid trilobites from the Tsinghsutung Formation (Duyunian Stage, Qiandongian Series, Cambrian), Jianhe County, Guizhou Province. Geol. J. China Univ. 16(3), 309–316 (2010).
Google Scholar 

52.
Yuan, J. L., Esteve, J. & Ng, T. W. Articulation, interlocking devices and enrolment in Monkaspis daulis (W alcott, 1905) from the Guzhangian, middle Cambrian of North China. Lethaia. 47(3), 405–417 (2014).
Google Scholar 

53.
Zhao, Y. L., Yuan, J. L., Esteve, J. & Peng, J. The oryctocephalid trilobite zonation across the Cambrian Series 2-Series 3 boundary at Balang, South China: A reappraisal. Lethaia. 50(3), 400–406 (2017).
Google Scholar 

54.
Abràmoff, M. D., Magalhães, P. J. & Ram, S. J. Image processing with ImageJ. Biophoton. Int. 11(7), 36–42 (2004).
Google Scholar 

55.
Esteve, J., Zhao, Y. L., Maté-González, M. A., Gómez-Heras, M. & Peng, J. A new high-resolution 3-D quantitative method for analysing small morphological features: An example using a Cambrian trilobite. Sci. Rep. 8(1), 1–10 (2018).
CAS  Google Scholar 

56.
Lask, P. B. The hydrodynamic behavior of sclerites from the trilobite Flexicalymene meeki. Palaios, 219–225 (1993).

57.
Hesselbo, S. P. The biostratinomy of Dikelocephalus sclerites: implications for the use of trilobite attitude data. Palaios. 605–608 (1987).

58.
Mikulic, D. G. The arthropod fossil record: biologic and taphonomic controls on its composition. Short Courses Paleontol. 3, 1–23 (1990).
Google Scholar 

59.
Speyer, S. E. & Donovan, S. K. Trilobite taphonomy: A basis for comparative studies of arthropod preservation, functional anatomy and behaviour. Processes Fossil., 194–219 (1991).

60.
Speyer, S. E. & Brett, C. E. Trilobite taphonomy and Middle Devonian taphofacies. Palaios., 312–327 (1986).

61.
Schumacher, G. A. & Shrake, D. L. Paleoecology and comparative taphonomy of an Isotelus (Trilobita) fossil lagerstätten from the Waynesville Formation (Upper Ordovician, Cincinnatian Series) of southwestern Ohio. In Paleontological Events: Stratigraphic, Ecological, and Evolutionary Implications. 131–161 (Columbia University Press, New York, 1997).

62.
Hickerson, W. J. Middle Devonian (Givetian) trilobite clusters from eastern Iowa and northwestern Illinois. In Paleontological Events: Stratigraphic, Ecological, and Evolutionary Implications. 224–246 (Columbia University Press, New York, 1997).

63.
Hughes, N. C. & Cooper, D. L. Paleobiologic and taphonomic aspects of the “granulosa” trilobite cluster, Kope Formation (Upper Ordovician, Cincinnati region). J. Paleontol. 73(2), 306–319 (1999).
Google Scholar 

64.
Hunda, B. R., Hughes, N. C. & Flessa, K. W. Trilobite taphonomy and temporal resolution in the Mt. Orab shale bed (Upper Ordovician, Ohio, USA). Palaios. 21(1), 26–45 (2006).

65.
Hunter, J. D. Matplotlib: A 2D graphics environment. Comput. Sci. Eng. 9(3), 90–95 (2007).
Google Scholar 

66.
Davis, J. C. Statistics and data analysis In Geology 289–291 (Wiley, New York, 1986).

67.
Roubeyrie, L. & Celles, S. Windrose: A Python Matplotlib, Numpy library to manage wind and pollution data, draw windrose. J Open Source Softw. 3(29), 268 (2018).
ADS  Google Scholar 

68.
Sun, H.-J., Zhao, Y.-L., Peng, J. & Yang, Y.-N. New Wiwaxia material from the Tsinghsutung Formation (Cambrian Series 2) of Eastern Guizhou, China. Geol. Mag. 151(2), 339–348 (2014).
ADS  CAS  Google Scholar  More

1.
Dai, A. Increasing drought under global warming in observations and models. Nat. Clim. Change 3, 52–58 (2013).
Google Scholar 
2.
Cook, B. I., Smerdon, J. E., Seager, R. & Coats, S. Global warming and 21st century drying. Clim. Dynam. 43, 2607–2627 (2014).
Google Scholar 

3.
Trenberth, K. E. et al. Global warming and changes in drought. Nat. Clim. Change 4, 17–22 (2014).
Google Scholar 

4.
Webb, J. K., Brook, B. W. & Shine, R. What makes a species vulnerable to extinction? Comparative life‐history traits of two sympatric snakes. Ecol. Res. 17, 59–67 (2002).
Google Scholar 

5.
Clark, M. E. & Martin, T. E. Modeling tradeoffs in avian life history traits and consequences for population growth. Ecol. Model. 209, 110–120 (2007).
Google Scholar 

6.
MacArthur, R. H. & Wilson, E. O. The Theory of Island Biogeography (Princeton Univ. Press, 1967).

7.
Bennett, P. M. & Owens, I. P. F. Variation in extinction risk among birds: chance or evolutionary predisposition? Proc. R. Soc. Lond. B 264, 401–408 (1997).
Google Scholar 

8.
Pfister, C. A. Patterns of variance in stage-structured populations: evolutionary predictions and ecological implications. Proc. Natl Acad. Sci. USA 95, 213–218 (1998).
CAS  Google Scholar 

9.
Nelson, R. J. Simulated drought affect male reproductive function in deer mice (Permoyscus maniculatus bairdii). Phys. Zool. 66, 99–114 (1993).
Google Scholar 

10.
Winne, C. T., Willson, J. D. & Gibbons, J. W. Income breeding allows an aquatic snake Seminatrix pygaea to reproduce normally following prolonged drought-induced aestivation. J. Anim. Ecol. 75, 1352–1360 (2006).
Google Scholar 

11.
Boag, P. T. & Grant, P. R. Intense natural selection in a population of Darwin’s finches (Geospizinae) in the Galapagos. Science 214, 82–85 (1981).
CAS  Google Scholar 

12.
Grant, P. R., Grant, B. R., Keller, L. F. & Petren, K. Effect of El Niño events on Darwin’s finch productivity. Ecology 81, 2442–2457 (2000).
Google Scholar 

13.
Cruz-McDonnell, K. K. & Wolf, B. O. Rapid warming and drought negatively impact population size and reproductive dynamics of an avian predator in the arid southwest. Glob. Change Biol. 22, 237–253 (2016).
Google Scholar 

14.
Sperry, J. H. & Weatherhead, P. J. Prey-mediated effects of drought on condition and survival in a terrestrial snake. Ecology 89, 2770–2776 (2008).
Google Scholar 

15.
Allen, C. D. et al. A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. For. Ecol. Manage. 259, 660–684 (2010).
Google Scholar 

16.
Calow, P. The cost of reproduction—a physiological approach. Biol. Rev. 54, 23–40 (1979).
CAS  Google Scholar 

17.
Reznick, D. Costs of reproduction: an evaluation of the empirical evidence. Oikos 44, 257–267 (1985).
Google Scholar 

18.
Flatt, T. Survival costs of reproduction in Drosophila. Exp. Geron. 46, 369–375 (2011).
Google Scholar 

19.
Forbes, M. R. L., Clark, R. G., Weatherhead, P. J. & Armstrong, T. Risk-taking by female ducks: intra- and interspecific tests of nest defense theory. Behav. Ecol. Sociobiol. 34, 79–85 (1994).
Google Scholar 

20.
Ghalambor, C. K. & Martin, T. E. Fecundity-survival trade-offs and parental risk-taking in birds. Science 292, 494–497 (2001).
CAS  Google Scholar 

21.
Møller, A. P. & Liang, W. Tropical birds take small risks. Behav. Ecol. 24, 267–272 (2012).
Google Scholar 

22.
Deutsch, C. A. et al. Impacts of climate warming on terrestrial ectotherms across latitude. Proc. Natl Acad. Sci. USA 105, 6668–6672 (2008).
CAS  Google Scholar 

23.
Tewksbury, J. J., Huey, R. B. & Deutsch, C. A. Putting the heat on tropical animals. Science 320, 1296–1297 (2008).
CAS  Google Scholar 

24.
Martin, T. E., Riordan, M. M., Repin, R., Mouton, J. C. & Blake, W. M. Apparent annual survival estimates of tropical songbirds better reflect life history variation when based on intensive field methods. Glob. Ecol. Biogeogr. 26, 1386–1397 (2017).
Google Scholar 

25.
Martin, T. E. Age-related mortality explains life history strategies of tropical and temperate songbirds. Science 349, 966–970 (2015).
CAS  Google Scholar 

26.
Martin, T. E., Oteyza, J. C., Boyce, A. J., Lloyd, P. & Ton, R. Adult mortality probability and nest predation rates explain parental effort in warming eggs with consequences for embryonic development time. Am. Nat. 186, 223–236 (2015).
Google Scholar 

27.
Arslan, N. Ş. & Martin, T. E. Reproductive biology of Grey-breasted Wood-Wren (Henicorhina leucophrys): a comparative study of tropical and temperate wrens. Wilson J. Ornithol. 131, 1–11 (2019).
Google Scholar 

28.
Stutchbury, B. J. & Morton, E. S. Behavioral Ecology of Tropical Birds Ch. 5 (Academic Press, 2001).

29.
Collar, N. in Handbook of the Birds of the World Alive (eds del Hoyo, J. et al.) (Lynx Edicions, 2019); https://doi.org/10.2173/bow.borwht1.01

30.
Caswell, H. Matrix Population Models: Construction, Analysis, and Interpretation (Sinauer, 2001).

31.
Wisdom, M. J., Mills, L. S. & Doak, D. F. Life stage simulation analysis: estimating vital-rate effects on population growth for conservation. Ecology 81, 628–641 (2000).
Google Scholar 

32.
Muñoz, A. P., Kéry, M., Martins, P. V. & Ferraz, G. Age effects on survival of Amazon forest birds and the latitudinal gradient in bird survival. Auk 135, 299–313 (2018).
Google Scholar 

33.
Lloyd, P. & Martin, T. E. Fledgling survival increases with development time and adult survival across north and south temperate zones. Ibis 158, 135–143 (2016).
Google Scholar 

34.
Ropelewski, C. F. & Jones, P. D. An extension of the Tahiti-Darwin Southern Oscillation Index. Mon. Weather Rev. 115, 2161–2165 (1987).
Google Scholar 

35.
Aiba, S. I. & Kitayama, K. Effects of the 1997–98 El Nino drought on rain forests of Mount Kinabalu, Borneo. J. Trop. Ecol. 18, 215–230 (2002).
Google Scholar 

36.
Hirshfield, M. F. & Tinkle, D. W. Natural selection and the evolution of reproductive effort. Proc. Natl Acad. Sci. USA 72, 2227–2231 (1975).
CAS  Google Scholar 

37.
Martin, T. E., Ton, R. & Oteyza, J. C. Adaptive influence of extrinsic and intrinsic factors on variation of incubation periods among tropical and temperate passerines. Auk 135, 101–113 (2018).
Google Scholar 

38.
Wilmers, C. C. & Post, E. Predicting the influence of wolf-provided carrion on scavenger community dynamics under climate change scenarios. Glob. Change Biol. 12, 403–409 (2006).
Google Scholar 

39.
Lenssen, N. et al. Improvements in the GISTEMP uncertainty model. J. Geophys. Res. Atmos. 124, 6307–6326 (2019).
Google Scholar 

40.
IPCC Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) 1311–1393 (Cambridge Univ. Press, 2013).

41.
Taylor, I. H. et al. The impact of climate mitigation on projections of future drought. Hydrol. Earth Syst. Sci. 17, 2339–2358 (2013).
Google Scholar 

42.
Kitayama, K. An altitudinal transect study of the vegetation on Mount Kinabalu, Borneo. Vegetation 102, 149–171 (1992).
Google Scholar 

43.
Blake, J. G. & Loiselle, B. A. Enigmatic declines in bird numbers in lowland forest of eastern Ecuador may be a consequence of climate change. Peer J. 3, e1177 (2015).
Google Scholar 

44.
Mitchell, A. E., Tuh, F. & Martin, T. E. Breeding biology of an endemic Bornean turdid, the Fruithunter (Chlamydochaera jefferyi), and life history comparisons with Turdus species of the world. Wilson J. Ornithol. 129, 36–45 (2017).
Google Scholar 

45.
White, G. C. & Burnham, K. P. Program MARK: survival estimation from populations of marked animals. Bird Study 46, 120–139 (1999).
Google Scholar 

46.
Shaffer, T. L. A unified approach to analyzing nest success. Auk 121, 526–540 (2004).
Google Scholar 

47.
Burnham, K. P. & Anderson, D. R. Model Selection and Multimodel Inference: A Practical Information–Theoretic Approach (Springer-Verlag, 2002).

48.
Pradel, R., Hines, J. E., Lebreton, J. D. & Nichols, J. D. Capture–recapture survival models taking account of transients. Biometrics 53, 60–72 (1997).
Google Scholar 

49.
Burnham, K. P., Anderson, D. R., White, G. C., Brownie, C. & Pollock, K. H. Design and Analysis Methods for Fish Survival Experiments Based on Release–recapture (Amer Fisheries Society, 1987).

50.
Felsenstein, J. Phylogenies and the comparative method. Am. Nat. 125, 1–15 (1985).
Google Scholar 

51.
Orme, D. The caper package: comparative analysis of phylogenetics and evolution in R. R package version 3.5.0 http://cran.r-project.org/web/packages/caper/vignettes/caper.pdf (2013).

52.
Jetz, W., Thomas, G. H., Joy, J. B., Hartmann, K. & Mooers, A. O. The global diversity of birds in space and time. Nature 491, 444–448 (2012).
CAS  Google Scholar 

53.
Hackett, S. J. et al. A phylogenomic study of birds reveals their evolutionary history. Science 320, 1763–1768 (2008).
CAS  Google Scholar 

54.
Maddison, W. P. & Maddison, D. R. Mesquite: a modular system for evolutionary analysis. R package version 2.75 http://mesquiteproject.org (2011).

55.
Pagel, M. D. A method for the analysis of comparative data. J. Theor. Biol. 156, 431–442 (1992).
Google Scholar 

56.
Symonds, M. R. & Blomberg, S. P. in Modern Phylogenetic Comparative Methods and their Application in Evolutionary Biology (eds Garamszegi, L. Z.) Ch. 5 (Springer, 2014).

57.
Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
Google Scholar  More

TM7x has restricted host range
In this study we expanded the number of Actinomyces host species/strains that were previously tested on TM7x infection [8] and conducted thorough phenotypic and comparative genomic analyses. TM7x cells were isolated apart from their original co-cultivated bacterial host XH001 (Actinomyces odontolyticus strain) and added back to cultures of diverse Actinomyces strains (n = 27) that span the Actinomyces lineage, as well as other common oral bacterial strains (n = 10) in an established re-infection assay (see methods, Table S1). By 16S phylogeny, Actinomyces lineages are divided into two major clades (clade-1 and −2), with XH001 in clade-2, agreeing with previous study [8] (Fig. 1). TM7x did not grow on any clade-1 Actinomyces strains after multiple passages, nor the common oral bacteria; while all tested strains (12 in addition to XH001) in clade-2 were infected with TM7x over multiple passages (Fig. 1) based on imaging techniques and PCR. These results suggest that the tested Actinomyces species fall into two major groups: resistant or susceptible to TM7x infection (Fig. S1a).
Different phenotypic responses of bacterial hosts to TM7x infection
Infection of naïve XH001 cells by TM7x induces a “growth-crash”, in which host cell density drops precipitously, followed by recovery in their bacterial hosts (Fig. S1b) [8]. This is analogous to a previously hypothesized cyclically-recurring population crash during parasite-host dynamics [30, 31], but interestingly in our case only a single crash was observed followed by stable growth. Recovered XH001 were found to have single-nucleotide variants relative to their naïve ancestors, presumably imparting the observed regain of fitness [7, 8]. The host growth is measured by cell density (OD600) whereas TM7x abundance is scored visually by phase-contrast imaging (see methods; [8]. These methods assess the host and TM7x abundances qualitatively but rapidly and accurately [8].
To further investigate the initial response to TM7x infection, the re-infection assay was conducted by adding TM7x to the 12 susceptible Actinomyces strains with a three-to-one TM7x-to-host cell ratio, and their growth was monitored by OD600 and TM7x scores (Fig. 2). Nine of these hosts displayed varying growth/crash/recovery patterns, and all of these included a clear crash phase and thus are referred to as “permissive” hosts (Fig. 2a–i). However, the remaining three hosts (F0311, ICM47, ICM58) lacked a discernable crash phase, hereafter referred to as “nonpermissive” hosts (Figs. 2j–l, S1c). Furthermore, three of the nine permissive hosts (ATCC17982, F0543, W712) had extended, 4–5 passage-long growth-crash phases before recovery while the rest of the hosts had only one passage-long growth-crashes (Fig. 2a–c). TM7x scoring was consistent with the observed host growth-crashes. When initial increase of the TM7x score was plotted for all hosts (Figs. 2, S1d), the three nonpermissive hosts (F0311, ICM47, ICM58) had a late increase in TM7x score compared to the rest of the hosts. F0310 was the only permissive host to have very late TM7x increase and growth-crash at passage twelve (Fig. 2i).
Fig. 2: Re-infection of susceptible bacteria by TM7x.

a–l Isolated TM7x cells from XH001-TM7x coculture were added to the 12 susceptible host cells at passage 0, and cell density (blue, circles) and TM7x scores (red, squares) were monitored during subsequent passages. Host alone control is shown in gray triangles. a–i Host strains where cell density drops precipitously are referred to as ‘permissive’ hosts. j–l Three strains that do not have growth-crash are termed ‘nonpermissive’ hosts. Host strain names are labeled on the top right corner of each graph.

Full size image

Previously, during the growth-crash phase, both attached and free-floating TM7x cells were observed, with individual XH001 cells often infected with multiple TM7x cells [8]. This induced host cell swelling and elongation, both common morphological stress responses, with XH001 cell length increasing from ~1.7 µm in monoculture to ~3.7 µm in cocultures, and eventually led to cell death [17]. Phase-contrast imaging illustrated similar results, with increased numbers of attached and free-floating TM7x observed for all nine permissive hosts and one nonpermissive host (Fig. S2a–i, j). However, two of the nonpermissive hosts (ICM47, IMC58) did not display an increased level of TM7x bacteria on their surfaces, nor increased cell length (Fig. S2k, l). To assess cell length quantitatively, we measured the cell length for all 12 bacterial hosts after infection. All hosts had significantly increased cell length (Figs. S1e, S3a, b, d–j) except two nonpermissive (ICM47, ICM58) and one permissive (W712) strains maintained or even slightly decreased their cell length after TM7x infection (Fig. S3c, k, l). The decrease in W712 cell length could be a result of W712 having the longest cells before TM7x infection or an inherent limitation in the image analysis of long cells (see methods). Nevertheless, W712 cells were swollen when they were infected with TM7x (Fig. S2c). Furthermore, although F0311 is a nonpermissive host, it did show many TM7x bacteria on its surface during the infection (Fig. S2j), which could be contributing to its increased cell length. Our findings suggest that TM7x-susceptible hosts divide into two broad categories (Fig. S1a): permissive and nonpermissive, though the permissive strains do present a spectrum of crash intensity and duration (Fig. 2).
Host sensitivity to TM7x infection
Our data showed that even though similar TM7x-to-host ratios were used in re-infection experiments, different hosts displayed drastically different crash/recovery dynamics (Fig. 2), suggesting these hosts have differential sensitivity to TM7x. Notably, a rapid increase of TM7x abundance within the first two passages was observed for three strains: A. odontolyticus ATCC17982 and two A. meyeri strains (W712 and ATCC35568) (Fig. 2a, c, f). To investigate this differential sensitivity further, dose-dependent TM7x infection of naïve XH001 cells was carried out. Results showed that the passage at which XH001 crashed, referred to as the ‘crash point’, was TM7x concentration dependent —with increasing TM7x, we observed earlier crash points (Figs. 3a, S4). Total colony forming units and irregular colony numbers, reflecting the number of total viable hosts and the TM7x infected hosts, respectively [8], were also determined during all passages. By these measurements, the crash points were dependent on the number of TM7x added to the assay. TM7x was able to infect at extremely low concentrations (three TM7x per 4.5 × 106 XH001 cells), and able to completely inhibit XH001 at higher concentrations (2.7 × 108 TM7x per 4.5 × 106 XH001 cells). A similar pattern of TM7x and XH001 growth dynamics were observed at each TM7x concentration (Fig. S4). During the XH001 crash phase (by OD600 or total cfu), the amount of TM7x (by TM7x score or irregular colony) always increased to a maximum and then decreased during XH001 recovery. The crash points determined by total colony forming unit always occurred ~1–1.5 passages before the OD600 crash point, which was consistent with our previous study [8]. This passage difference may be explained by the fact that dead cells can contribute to the cell density measurements.
Fig. 3: Host sensitivity determined by varying TM7x dosage.

Isolated TM7x cells were added to host cells XH001 a, W712 b and ICM47 c in increasing concentrations. For each concentration of TM7x, shown as a TM7x to XH001 ratio, cell density (column one) and total colony forming units (column two) were determined, and only the region leading up to the growth-crash point is graphed. The full data are shown in Figs. S4–6. Total colony forming units were determined in triplicate and error bars indicate the standard deviation.

Full size image

The sensitivity of A. meyeri strain W712 to TM7x was similarly tested. Remarkably, while dose-dependent growth-crash was also observed (Figs. 3b, S5), it took close to tenfold fewer TM7x cells (3.5 × 107 TM7x per 4.5 × 106 W712 cells) to completely inhibit the initial growth of W712 compared to XH001 (Fig. 3b), suggesting that the sensitivity of W712 to TM7x allows faster TM7x growth at the expense of W712. This was reflected by both the OD600/TM7x score and total/irregular colony measurements (Fig. S5). Again, similar to what was observed in the initial coculture experiment (Fig. 2c), all growth-crashes in W712 had prolonged growth-crashes (Fig. S5). In contrast, the nonpermissive strain ICM47 was completely resistant to growth-crash even at the TM7x-to-ICM47 ratio of 4.9 × 108:4.5 × 106 (Figs. 3c, S6). Despite TM7x infection and growth on ICM47, no growth-crash was observed by cell density measurement and total colony forming units. ICM47 strains also did not form obvious irregular colony morphology, suggesting TM7x does not stress or damage host growth as with the other strains.
TM7x has unique cell localization on the nonpermissive ICM58
TM7x and XH001 have various morphological cell shapes depending on growth conditions and nutrient availability (Fig. S7a) [17]. For all permissive and nonpermissive strains, we observed normally shaped TM7x bacteria growing on the cell surface of the host bacteria by FISH (Figs. 4, S7). Consistent with our previous findings, TM7x attached to bacterial hosts had simple dot/cocci or teardrop-like morphology, shown in green (Figs. 4, S7) [17]. Remarkably, compared to all tested bacterial hosts, only on ICM58, many TM7x localized to the cell poles (Fig. 4f). The polar localization was previously not observed in the close relatives of TM7x, but was shown in a distant lineage (HMT-351) that grows on Actinomyces sp. HMT-897 [32]. Exactly how and why pole localization occurs is yet to be determined. Typically, gram-positive bacteria have significant long-axis polarization in terms of protein composition and cell wall structure [33], and TM7x could be targeting those areas. The polar localization of TM7x on ICM58 suggests a different mechanism for attachment compared to other hosts.
Fig. 4: TM7x localization on ICM58.

FISH imaging was carried out for all permissive (a–c, see Fig. S7) and all nonpermissive (d–f) bacterial hosts. TM7x (green) was visualized using a Saccharibacteria-specific DNA probe tagged with the Cy5 fluorescent molecule. The host bacteria were visualized by universal nucleic acid stain syto9, which also stains TM7x. Only sample strains are shown in this figure, and the complete set can be found in Fig. S7, including a few of the resistant strains visualized by FISH. Scale bars are 5 μm.

Full size image

Genome content separates permissive and nonpermissive hosts
As genomes of twenty-three out of the twenty-seven tested Actinomyces strains are publicly available, we downloaded them for comparative genomic analyses. To place the currently unnamed genomes (e.g., Actinomyces sp. F0310) in context with named species, we first related genomes by average AAI and constructed a phylogenomic tree from concatenated core genes (Fig. S8a, b). From the AAI data, clear patterns emerged: the thirteen TM7x-susceptible genomes, including XH001, span the two closely-related species A. odontolyticus and A. meyeri ( >83% AAI to XH001) and a few unnamed strains ranging from 74 to 85% AAI to XH001 (Fig. S8a). These relationships were confirmed by a phylogenomic tree generated with PhyloPhlAn based on 387 concatenated core genes (Fig. S8b). The phylogenomic tree revealed an A. odontolyticus clade including four A. odontolyticus strains and A. sp. ICM39, which is sister to a monophyletic clade of the three nonpermissive strains, and another clade containing two A. meyeri strains and A. sp F0310.
We then performed a pangenome analysis to compare the genome content of these strains (Fig. 5) to identify genomic signatures associated with different susceptibility to TM7x infection. By grouping genomes based on gene content (Fig. 5, top right dendrogram), the resistant strains (concentric layers colored red) are clearly separated from the susceptible strains (permissive (blue) and nonpermissive (purple); Fig. 5), agreeing closely with the phylogenomic tree (Fig. S8b). Remarkably, within the susceptible strains the nonpermissive strains (purple) form an internal subgroup distinct from permissive strains (blue) (Fig. 5). All phylogenomic analyses and AAI are consistent with the observed separation of groups (heatmap in Fig. 5), while the 16S rRNA gene phylogeny fails to indicate that the purple group of nonpermissive hosts is distinct (Fig. 1). As the susceptible strains span at least two phylogenetically classified species (A. odontolyticus and A. meyeri) and potentially other closely related but unnamed species, the genome grouping by gene content broadly reflects the previously observed phylogenomic and AAI distinctions (Figs. 5, S8). F0310 was the only strain that shifted places from being similar to A. meyeri species based on genome sequence (in phylogenomic tree) to being in middle of the A. odontolyticus species. Based on the gene content and phylogenomic tree, the nonpermissive genomes are a genetically distinct group most closely related to A. odontolyticus and less so to A. meyeri.
Fig. 5: Pangenome of the experimentally tested Actinomyces strains.

The central, radial dendrogram arranges each of 12,372 unique gene clusters (groups of putatively homologous genes) according to their presence/absence across genomes. Each concentric 270˚ layer represents a different genome, colored according to TM7x susceptibility, and is filled or left unfilled to mark which gene clusters are found in each genome. Layers are arranged by frequency of gene clusters, displayed as a dendrogram on the top righthand side of plot. Extending off the end of the plot show bar charts reporting various statistics for each genome and a heatmap showing average amino acid identity. The heatmap is also shown in Fig. S7 in higher magnification. Sets of key gene clusters are highlighted with a labeled arc spanning gene clusters of interest.

Full size image

Furthermore, core gene clusters for the various groups can be readily discerned, with 346 gene clusters forming the core of all 23 genomes, 464 exclusively shared by all susceptible strains, and 51 and 28 gene clusters exclusively shared by the resistant and nonpermissive strains, respectively (Fig. 5, Table S2). For context, each genome contains ~1700–2800 gene clusters (Fig. 5, light gray bar chart on right). While most genomes are estimated to be nearly complete and a handful are closed, most of the genomes are not closed and may be missing genes for methodological rather than biological reasons (Fig. 5, bar charts of genome statistics). Yet, the correlation of gene content with response to TM7x raises the possibility that certain shared genomic features may underly the observed phenotypes.
Comparative genomics reveal functional characteristics of different groups
We observed clades of strains defined by phylogeny and response to TM7x, e.g., permissive hosts. Ranking the predicted functions found across genomes for each TM7x response category (permissive, nonpermissive, or resistant) and combinations thereof can reveal functions enriched for each response type. The differentially enriched functions for these groups span multiple functional categories, from central metabolism to cell wall synthesis to regulation and recombination (Table 1).
Table 1 Enriched Pfam functions in resistant, susceptible, permissive, nonpermissive, and nonpermissive/resistant genomes. Only the top five gene functions are shown.
Full size table

For resistant vs. susceptible Actinomyces, numerous gene functions were exclusive to each (Table S2), potentially due to the strong genetic distinction between the two groups. Most pronounced of all functions were cell wall modification associated genes. Within the top five scored genes, we found Mur ligase family [34] and bacitracin resistance [35] proteins associated with resistant strains, and glycosyl transferase family [36] and O-mannosyltrasferase [37] proteins from susceptible strains (Table 1). These genes may directly or indirectly influence the TM7x attachment to the host. In addition, a key gene in the arginine deaminase (ADI) pathway, amidinotransferase arcA, was found in all ten of the resistant strains but none of the susceptible strains (Table 1). The ADI pathway can facilitate growth in acidic environments by increasing the pH, raising the possibility that TM7x, which encode a complete ADI pathway, could complement their ADI-less hosts [38]. Given the drastic oral pH shifts [39, 40] as well as localized pH stress from streptococcal neighbors [41], pH modulation and tolerance could be key for oral Actinomyces [40].
Permissive and nonpermissive genomes also contained distinctive functions (Table 1). For example, permissive strains are enriched for a GlcNAc-PI de-N-acetylase [42] and family 4 glycosyl hydrolase [43], which could be putatively involved in the hydrolysis of cell envelope glycoproteins, and may have the potential to regulate TM7x attachment levels. Interestingly, resistant and nonpermissive strains also share some functions not found in any permissive strains, such as a cytidine triphosphate (CTP) synthase.
Amino acid variants reveal genes phylogenetically correlated with TM7x response
While comparing gene presence can reveal major traits that may be involved in the observed phenotypes, it cannot distinguish between subtle but potentially critical variations in the sequence of shared proteins. If TM7x susceptibility is not due to clade-specific genes but instead distinct sequence variants of certain core genes, those sequence variants should correlate with TM7x sensitivity.
Thus, we employed a phylogenetic approach to look for core genes with sequence variants that match the observed phenotypes. This is a powerful way to identify shared genes in a pangenome that are correlated with an ecological phenotype [44], though sometimes prone to false positives and noise. From each of the 291 and 419 gene clusters with a single copy in each of the 23 genomes and the 13 susceptible genomes, respectively (Fig. 6a), we created a phylogenetic tree and compared it against topologies that differentiated sequence variants from nonpermissive (purple) vs. permissive (blue) vs. resistant (red). Fifteen gene clusters produced such topologies that distinguished each response type (Fig. 6b–d). While some are almost certainly noise (e.g., ribosomal protein rplR), many functionally interesting genes are identified including several cell envelope-associated proteins like the protein translocase secA, the ABC transporter sn-glycerol-3-phosphate ugpC, and an L,D-transpeptidase (Fig. 6b–d). The genes listed here represent a relatively short list of hypotheses that await future experimental investigation before any confident assertions can be made about their relevance to Actinomyces/TM7x associations.
Fig. 6: Gene trees from core gene clusters reveal gene variants that correlate with TM7x susceptibility.

a Cartoon showing a simplified topology of the genome similarity dendrogram from Fig. 5, with the blue, purple, and red clades representing the permissive, nonpermissive, and resistant genomes respectively. Single-copy core gene clusters, those with only one gene sequence from each genome, core to all 23 genomes (first column of boxes, 291 gene clusters) and core to susceptible genomes (second column of boxes, 419 genes) were identified. For each gene cluster a phylogenetic tree was created and compared against three topologies of interest; gene clusters core to all genomes (b and c), and gene clusters core to susceptible genomes (d). Gene clusters core to all genomes could reveal each observed clade to be monophyletic with variable relationships (b) or place resistant sequences sister to those from nonpermissive hosts (c). The number over each arrow reports the number of gene clusters producing the illustrated topology. Polytomies represent either real polytomies or an unspecified hierarchy that preserves the monophyly of the illustrated clades. The text lists the predicted Pfam functions for each gene cluster.

Full size image More

1.
Sanmartín, I., Enghoff, H. & Ronquist, F. Patterns of animal dispersal, vicariance and diversification in the Holarctic. Biol. J. Linn. Soc. 73, 345–390 (2001).
Google Scholar 
2.
Sanmartín, I. & Ronquist, F. Southern hemisphere biogeography inferred by event-based models: Plant versus animal patterns. Syst. Biol. 53, 216–243 (2004).
PubMed  Google Scholar 

3.
Shaw, A. J. Biogeographic patterns and cryptic speciation in bryophytes. J. Biogeogr. 28, 253–261 (2001).
Google Scholar 

4.
Feldberg, K. et al. Phylogenetic biogeography of the leafy liverwort Herbertus (Jungermanniales, Herbertaceae) based on nuclear and chloroplast DNA sequence data: correlation between genetic variation and geographical distribution. J. Biogeogr. 34, 688–698 (2007).
Google Scholar 

5.
Shaw, A. J. et al. Intercontinental genetic structure in the amphi-Pacific peatmoss Sphagnum miyabeanum (Bryophyta: Sphagnaceae). Biol. J. Linn. Soc. 111, 17–37 (2014).
Google Scholar 

6.
Vanderpoorten, A., Devos, N., Goffinet, B., Hardy, O. J. & Shaw, A. J. The barriers to oceanic island radiation in bryophytes: Insights from the phylogeogaphy of the moss Grimmia montana. J. Biogeogr. 35, 654–663 (2008).
Google Scholar 

7.
Ono, F. Moss spore can tolerate ultra-high pressure. In High pressure Bioscience (eds Akasaka, K. & Matsuki, H.) 443–466 (Springer, New York, 2015).
Google Scholar 

8.
van Zanten, B. O. Experimental studies on trans-oceanic long-range dispersal of moss spores in the Southern Hemisphere. J. Hattori Bot. Lab. 44, 455–482 (1978).
Google Scholar 

9.
Muñoz, J., Felicísimo, ÁM., Cabezas, F., Burgaz, A. R. & Martínez, I. Wind as a long-distance dispersal vehicle in the Southern Hemisphere. Science 304, 1144–1147 (2004).
ADS  PubMed  Google Scholar 

10.
van Zanten, B. O. & Gradstein, S. R. Experimental dispersal geography of neotropical liverworts. Beih. Nova Hedwigia 90, 41–94 (1988).
Google Scholar 

11.
Kyrkjeeide, M. O. et al. Long-distance dispersal and barriers shape genetic structure of peatmosses (Sphagnum) across the Northern Hemisphere. J. Biogeogr. 43, 1215–1226 (2016).
Google Scholar 

12.
Patiño, J., Goffinet, B., Sim-Sim, M. & Vanderpoorten, A. Is the sword moss (Bryoxiphium) a preglacial Tertiary relict?. Mol. Phylogenet. Evol. 96, 200–206 (2016).
PubMed  Google Scholar 

13.
Bechteler, J. et al. Geographical structure, narrow species ranges, and Cenozoic diversification in a pantropical clade of epiphyllous leafy liverworts. Ecol. Evol. 7, 638–653 (2017).
PubMed  Google Scholar 

14.
Carter, B. E. et al. Species delimitation and biogeography of a southern hemisphere liverwort clade, Frullania subgenus Microfrullania (Frullaniaceae, Marchantiophyta). Mol. Phylogenet. Evol. 107, 16–26 (2017).
PubMed  Google Scholar 

15.
Scheben, A. et al. Multiple transoceanic dispersals and geographical structure in the pantropical leafy liverwort Ceratolejeunea (Lejeuneaceae, Porellales). J. Biogeogr. 43, 1739–1749 (2016).
Google Scholar 

16.
Patiño, J. et al. The anagenetic world of spore-producing land plants. New Phytol. 201, 305–311 (2014).
PubMed  Google Scholar 

17.
Norhazrina, N., Vanderpoorten, A., Hedenäs, L. & Patiño, J. What are the evolutionary mechanisms explaining the similar species richness patterns in tropical mosses? Insights from the phylogeny of the pantropical genus Pelekium. Mol. Phylogenet. Evol. 105, 139–145 (2016).
PubMed  Google Scholar 

18.
Puttik, M. N. et al. The interrelationships of land plants and the nature of the ancestral embryophyte. Curr. Biol. 28, 733–745 (2018).
Google Scholar 

19.
Qiu, Y. L., Cho, Y. R., Cox, J. C. & Palmer, J. D. The gain of three mitochondrial introns identifies liverworts as the earliest land plants. Nature 394, 671–674 (1998).
ADS  CAS  PubMed  Google Scholar 

20.
Wickett, N. J. et al. Phylotranscriptomic analysis of the origin and early diversification of land plants. Proc. Natl. Acad. Sci. U.S.A. 111, E4859–E4868 (2014).
CAS  PubMed  PubMed Central  Google Scholar 

21.
Morris, J. L. et al. The timescale of early land plant evolution. Proc. Natl. Acad. Sci. USA 115, 2274–2283 (2018).
Google Scholar 

22.
Heinrichs, J. et al. Lejeuneaceae (Marchantiophyta) from a species-rich taphocoenosis in Miocene Mexican amber, with a review of liverworts fossilised in amber. Rev. Palaeobot. Palynol. 221, 59–70 (2015).
Google Scholar 

23.
Wilson, R., Heinrichs, J., Hentschel, J., Gradstein, S. R. & Schneider, H. Steady diversification of derived liverworts under tertiary climatic fluctuations. Biol. Lett. 3, 566–569 (2007).
PubMed  PubMed Central  Google Scholar 

24.
Gradstein, S. R. The Liverworts and Hornworts of Colombia and Ecuador 1–880 (Springer, New York, 2020).
Google Scholar 

25.
Lee, G. E. A systematic revision of the genus Lejeunea Lib. (Marchantiophyta: Lejeuneaceae) in Malaysia. Cryptogam. Bryol. 34, 381–484 (2013).
Google Scholar 

26.
Lee, G. E., Bechteler, J. & Heinrichs, J. A revision of unrevised taxon names of Taxilejeunea (Marchantiophyta: Lejeuneaceae) from Asia. Phytotaxa 358, 226–248 (2018).
Google Scholar 

27.
Heinrichs, J. et al. Molecular phylogeny of the leafy liverwort Lejeunea (Porellales): Evidence for a neotropical origin, uneven distribution of sexual systems and insufficient taxonomy. PLoS ONE 8, e82547 (2013).
ADS  PubMed  PubMed Central  Google Scholar 

28.
Heinrichs, J. et al. Crown group Lejeuneaceae and pleurocarpous mosses in early eocene (Ypresian) Indian amber. PLoS ONE 8, e82547 (2016).
Google Scholar 

29.
Tiffney, B. H. The eocene north atlantic land bridge: Its importance in Tertiary and modern phytogeography of the Northern Hemisphere. J. Arnold Arbor. 66, 243–273 (1985).
Google Scholar 

30.
Tiffney, B. H. & Manchester, S. R. The use of geological and paleontological evidence in evaluating plant phylogeographic hypotheses in the Northern Hemisphere tertiary. Int. J. Plant Sci. 162, 3–17 (2001).
Google Scholar 

31.
Brikiatis, L. The De Geer, Thulean and Beringia routes: Key concepts for understanding early Cenozoic biogeography. J. Biogeogr. 41, 1036–1054 (2014).
Google Scholar 

32.
Laenen, B. et al. Increased diversification rates follow shifts to bisexuality in liverworts. New Phytol. 210, 1121–1129 (2016).
PubMed  Google Scholar 

33.
Erwin, D. H. Climate as a driver of evolutionary change. Curr. Biol. 19, R575-583 (2009).
CAS  PubMed  Google Scholar 

34.
Condamine, F. L., Rolland, J. & Morlon, H. Assessing the causes of diversification slowdowns: Temperature-dependent and diversity-dependent models receive equivalent support. Ecol. Lett. 22, 1900–1912 (2019).
PubMed  Google Scholar 

35.
Reiner-Drehwald, M. E. Catalogue of the genus Lejeunea Lib. (Hepaticae) of Latin America. Bryophyt. Bibl. 54, 1–101 (1999).
Google Scholar 

36.
Lee, G. E. et al. The leafy liverwort genus Lejeunea (Porellales, Jungermanniopsida) in Miocene Domican amber. Rev. Palaeobot. Palynol. 238, 144–150 (2017).
Google Scholar 

37.
Lee, G. E., Schäfer-Verwimp, A., Schmidt, A. R. & Heinrichs, J. Transfer of the miocene Lejeunea palaeomexicana grolle to Ceratolejeunea. Cryptogam. Bryol. 36, 335–341 (2015).
Google Scholar 

38.
Denk, T., Grimsson, F., Zetter, R. & Simonarson, L. The Biogeographic history of Iceland – The North Atlantic Land Bridge revisited. in Late Cainozoic floras of Iceland, 15 million years of vegetation and climate history in the northern North Atlantic, 647–666 (Springer, 2011).

39.
Graham, A. The role of land bridges, ancient environments, and migrations in the assembly of the North America flora. J. Syst. Evol. 56, 405–429 (2018).
Google Scholar 

40.
Jiang, D. et al. Asymmetric biotic interchange across the Bering land bridge between Eurasia and North America. Natl. Sci. Rev. 6, 739–745 (2019).
Google Scholar 

41.
Morley, R. J. Why are there so many primitive angiosperms in the rain forests of Asia-Australia? In Floral and Faunal Migrations and Evolution in SE Asia-Australia (eds Metcalfe, I. et al.) 185–200 (Swetz & Zeitliner, Lisse, 2001).
Google Scholar 

42.
Couvreur, T. L. P. et al. Early evolutionary history of the flowering plant family Annonaceae: Steady diversification and boreotropical geodispersal. J. Biogeogr. 38, 664–680 (2011).
Google Scholar 

43.
Davis, C. C., Bell, C. D., Mathews, S. & Donoghue, M. J. Laurasian migration explains Gondwanan disjunctions: Evidence from Malpighiaceae. Proc. Natl. Acad. Sci. U.S.A. 99, 6833–6837 (2002).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

44.
Muellner, A. N., Savolainen, V., Samuel, R. & Chase, M. W. The mahogamy family “out of Africa”: Divergence time estimation, global biogeographic patterns inferred from plastid rbcL DNA sequences, extant, and fossil distribution of diversity. Mol. Phylogenet. Evol. 40, 236–250 (2006).
CAS  PubMed  Google Scholar 

45.
Schneider, H. et al. Chloroplast phylogeny of asplenioid ferns based on rbcL and trnL-F spacer sequences (Polypodiidae, Aspleniaceae) and its implications for biogeography. Syst. Bot. 29, 260–274 (2004).
Google Scholar 

46.
Wei, R. et al. Eurasian origin, boreotropical migration and transoceanic dispersal in the pantropical fern genus Diplazium (Athyriaceae). J. Biogeogr. 42, 1809–1819 (2015).
Google Scholar 

47.
Hennequin, S., Hovenkamp, P., Christenhusz, M. J. M. & Schneider, H. Phylogenetics and biogeography of Nephrolepis—A tale of old settlers and young tramps. Bot. J. Linn. Soc. 164, 113–127 (2010).
Google Scholar 

48.
Wen, J., Nie, Z. L. & Ickert-Bond, S. M. Intercontinental disjunctions between eastern Asia and western North America in vascular plants highlight the biogeographic importance of the Bering land bridge from late Cretaceous to Neogene. J. Syst. Evol. 54, 469–490 (2016).
Google Scholar 

49.
Shaw, A. J. et al. Pleistocene survival, regional genetic structure and interspecific gene flow among three northern peat-mosses: Sphagnum inexspectatum, S. orientale and S. miyabeanum. J. Biogeogr. 42, 364–376 (2014).
Google Scholar 

50.
Bosboom, R. E. et al. Late Eocene sea retreat from the Tarim Basin (west China) and concomitant Asian paleoenvironmental change. Palaeogeogr. Palaeoclimatol. Palaeoecol. 299, 385–398 (2011).
Google Scholar 

51.
Chmielewski, M. W. & Eppley, S. M. Forest passerines as a novel dispersal vector of viable bryophyte propagules. Proc. R. Soc. B 286, 20182253 (2019).
CAS  PubMed  Google Scholar 

52.
Heinken, T., Lees, R., Raudnitschka, D. & Rung, S. Epizoochorous dispersal of bryophytes stem fragments by roe deer (Capreoluscapreolus) and wild boar (Susscrofa). J. Bryol. 23, 293–300 (2001).
Google Scholar 

53.
Pérez-Escobar, O. A. et al. Recent origin and rapid speciation of neotropical orchids in the world’s richest plant biodiversity hotspot. New Phytol. 215, 891–905 (2017).
PubMed  PubMed Central  Google Scholar 

54.
Nie, Z. L. et al. Recent assembly of the global herbaceous flora: Evidence from the paper daisies (Asteraceae: Gnaphalieae). New Phytol. 209, 1795–1806 (2016).
CAS  PubMed  Google Scholar 

55.
Morley, R. J. Cretaceous and Tertiary climate change and the past distribution of megathermal rainforests. In Tropical Rainforest Responses to Climate Change (eds Bush, M. B. et al.) 1–34 (Springer, New York, 2011).
Google Scholar 

56.
Jaramillo, C., Rueda, M. J. & Mora, G. Cenozoic plant diversity in the Neotropics. Science 311, 1893–1896 (2006).
ADS  CAS  PubMed  Google Scholar 

57.
Kong, H. et al. Both temperature fluctuations and East Asian monsoons have driven plant diversification in the karst ecosystems from southern China. Mol. Ecol. 26, 6414–6429 (2017).
PubMed  Google Scholar 

58.
Tada, R., Zheng, H. & Clift, D. Evolution and variability of the Asian monsoon and its potential linkage with uplift of the Himalaya and Tibetan Plateau. Prog. Earth Planet Sci. 3, 4–26 (2016).
ADS  Google Scholar 

59.
Proctor, M. C. F. et al. Desiccation-tolerance in bryophytes. Bryologist 110, 595–621 (2007).
CAS  Google Scholar 

60.
McDaniel, S. F., Atwood, J. & Burleigh, J. G. Recurrent evolution of dioecy in bryophytes. Evolution 67, 567–572 (2012).
PubMed  Google Scholar 

61.
van Zanten, B. O. & Pócs, T. Distribution and dispersal of bryophytes. Adv. Bryol. 1, 479–562 (1981).
Google Scholar 

62.
Laenen, B. et al. Geographical range in liverworts: Does sex really matter?. J. Biogeogr. 43, 627–635 (2016).
Google Scholar 

63.
Lee, G. E., Bechteler, J., Pócs, T., Schäfer-Verwimp, A. & Heinrichs, J. Molecular and morphological evidence for an intercontinental range of the liverwort Lejeunea pulchriflora (Marchantiophyta: Lejeuneaceae). Org. Divers. Evol. 16, 13–21 (2016).
Google Scholar 

64.
Hall, T. A. BioEdit: a user-friendly biological sequence alignment editor and analysis program for Windows 95/98/NT. Nucleic Acids Symp. Ser. 41, 95–98 (1999).
CAS  Google Scholar 

65.
Janssen, T. et al. Neoendemism in Madagascan scaly tree ferns results from recent, coincident diversification bursts. Evolution 62, 1876–1889 (2008).
PubMed  Google Scholar 

66.
Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: Improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).
CAS  PubMed  PubMed Central  Google Scholar 

67.
Lanfear, R., Calcott, B., Ho, S. Y. W. & Guindon, S. PartitionFinder: Combined selection of partitioning schemes and substitution models for phylogenetic analyses. Mol. Biol. Evol. 29, 1695–1701 (2012).
CAS  PubMed  Google Scholar 

68.
Stamatakis, A. RAxML Version 8: A tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30, 1312–1313 (2014).
CAS  PubMed  PubMed Central  Google Scholar 

69.
Mason-Gamer, R. J. & Kellogg, E. A. Testing for phylogenetic conflict among molecular data sets in the tribe Triticeae (Gramineae). Syst. Biol. 45, 524–545 (1996).
Google Scholar 

70.
Ronquist, F. et al. MrBayes 3.2: Efficient Bayesian phylogenetic inference and model choice across a large model space. Syst. Biol. 61, 539–542 (2012).
PubMed  PubMed Central  Google Scholar 

71.
Rambaut, A., Drummond, A. J., Xie, D., Baele, G. & Suchard, M. A. Posterior summarization in Bayesian phylogenetics using tracer 1.7. Syst. Biol. 67, 901–904 (2018).
CAS  PubMed  PubMed Central  Google Scholar 

72.
Larget, B. & Simon, D. L. Markov chain Monte Carlo algorithms for the Bayesian analysis of phylogenetic trees. Mol. Biol. Evol. 16, 750–759 (1999).
CAS  Google Scholar 

73.
Drummond, A. J., Suchard, M. A., Xie, D. & Rambaut, A. Bayesian phylogenetics with BEAUti and the BEAST 1.7. Mol. Biol. Evol. 29, 1969–1973 (2012).
CAS  PubMed  PubMed Central  Google Scholar 

74.
Nagori, M. L., Khosla, S. C. & Jakhar, S. R. middle eocene ostracoda from the tadkeshwar lignite mine, Camba Basin, Gujarat. J. Geol. Soc. India 81, 514–520 (2013).
Google Scholar 

75.
Benton, M. J. & Donoghue, P. C. J. Paleontological evidence to date the tree of life. Mol. Biol. Evol. 24, 26–53 (2007).
CAS  PubMed  Google Scholar 

76.
Donoghue, P. C. J. & Benton, M. J. Rocks and clocks: Calibrating the tree of life using fossils and molecules. Trends Ecol. Evol. 22, 424–431 (2007).
PubMed  Google Scholar 

77.
Ho, S. Y. W. & Phillips, M. J. Accounting for calibration uncertainty in phylogenetic estimation of evolutionary divergence times. Syst. Biol. 58, 367–380 (2009).
PubMed  Google Scholar 

78.
Graur, D. & Martin, W. Reading the entails of chickens: Molecular timescales of evolution and the illusion of precision. Trends Genet. 20, 80–86 (2004).
CAS  PubMed  Google Scholar 

79.
Reisz, R. R. & Müller, J. Molecular timescales and the fossil record: A paleontological perspective. Trends Genet. 20, 237–241 (2004).
CAS  PubMed  Google Scholar 

80.
Palmer, J. D. Plastid chromosome, structure and evolution. In The Molecular Biology of Plastids (eds Bogorad, L. & Vasil, I. K.) 5–53 (Academic Press, Cambridge, 1991).
Google Scholar 

81.
Villarreal, J. C. & Renner, S. S. Hornwort pyrenoids, a carbon-concentrating mechanism, evolved and were lost at least five times during the last 100 million years. Proc. Natl. Acad. Sci. U.S.A. 109, 18873–18878 (2012).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

82.
Les, D. H., Crawford, D. J., Kimball, R. T., Moody, M. L. & Landolt, E. Biogeography of discontinuously distributed hydrophytes, a molecular appraisal of intercontinental disjunctions. Int. J. Plant Sci. 164, 917–932 (2003).
Google Scholar 

83.
Villarreal, J. C. & Renner, S. S. A review of molecular-clock calibrations and substitution rates in liverworts, mosses, and hornworts, and a timeframe for a taxonomically cleaned-up genus Nothoceros. Mol. Phylogenet. Evol. 78, 25–35 (2014).
PubMed  Google Scholar 

84.
Drummond, A. J., Ho, S. Y. M., Phillips, M. J. & Rambaut, A. Relaxed phylogenetics and dating with confidence. PLoS Biol. 4, e88 (2006).
PubMed  PubMed Central  Google Scholar 

85.
Stadler, T. On incomplete sampling under birth-death models and connections to the sampling-based coalescent. J. Theor. Biol. 261, 58–66 (2009).
MathSciNet  PubMed  MATH  Google Scholar 

86.
Lartillot, N. & Philippe, H. Computing Bayes factor using thermodynamic integration. Syst. Biol. 55, 195–207 (2006).
PubMed  Google Scholar 

87.
Xie, W., Lewis, P. O., Fan, Y., Kuo, L. & Chen, M. H. Improving marginal likelihood estimation for Bayesian phylogenetic model selection. Syst. Biol. 60, 150–160 (2011).
PubMed  Google Scholar 

88.
Baele, G. et al. Improving the accuracy of demographic and molecular clock model comparison while accommodating phylogenetic uncertainty. Mol. Biol. Evol. 29, 2157–2167 (2012).
CAS  PubMed  PubMed Central  Google Scholar 

89.
Baele, G., Lemey, P. & Vansteelandt, S. Make the most of your samples: Bayes factor estimators for high-dimensional models of sequence evolution. BMC Bioinform. 14, 85 (2013).
Google Scholar 

90.
Matzke, N. J. Model selection in historical biogeography reveals that founder-event speciation in a crucial process in island clades. Syst. Biol. 63, 951–970 (2014).
PubMed  Google Scholar 

91.
Ree, R. H. & Smith, S. A. Maximum likelihood inference of geographic range evolution by dispersal, local extinction, and cladogenesis. Syst. Biol. 57, 4–14 (2008).
PubMed  Google Scholar 

92.
Ree, R. H. & Sanmartín, I. Conceptual and statistical problems with the DEC+J model of founder-event speciation and its comparison with DEC via model selection. J. Biogeogr. 45, 741–749 (2018).
Google Scholar 

93.
Condamine, F. L., Rolland, J. & Morlon, H. Macroevolutionary perspectives to environmental change. Ecol. Lett. 16, 72–85 (2013).
PubMed  Google Scholar 

94.
Zachos, J. C., Dickens, G. R. & Zeebe, R. E. An early cenozoic perspective on greenhouse warming and carbon-cycle dynamics. Nature 451, 279–283 (2008).
ADS  CAS  PubMed  Google Scholar 

95.
Maddison, W. P., Midford, P. E. & Otto, S. P. Estimation a binary character’s effect on speciation and extinction. Syst. Biol. 56, 701–710 (2007).
PubMed  Google Scholar 

96.
FitzJohn, R. G. Diversitree: Comparative phylogenetic analyses of diversification in R. Methods Ecol. Evol. 3, 1084–1092 (2012).
Google Scholar 

97.
Rabosky, D. L. & Goldberg, E. E. Model inadequacy and mistaken inferences of trait-dependent speciation. Syst. Biol. 64, 340–355 (2015).
CAS  PubMed  Google Scholar 

98.
Beaulieu, J. M. & O’Meara, B. C. Detecting hidden diversification shifts in models of trait-dependent speciation and extinction. Syst. Biol. 65, 583–601 (2016).
PubMed  Google Scholar 

99.
Caetano, D. S., O’Meara, B. C. & Beaulieu, J. M. Hidden state models improve state-dependent diversification approaches, including biogeographical models. Evolution 72, 2308–2324 (2018).
PubMed  Google Scholar  More

1.
Iuliano, A. D. et al. Estimates of global seasonal influenza-associated respiratory mortality: a modelling study. Lancet 391, 1285–1300. https://doi.org/10.1016/s0140-6736(17)33293-2 (2018).
Article  PubMed  Google Scholar 
2.
Ahmed, S. M. et al. Global prevalence of norovirus in cases of gastroenteritis: a systematic review and meta-analysis. Lancet Infect. Dis. 14, 725–730. https://doi.org/10.1016/s1473-3099(14)70767-4 (2014).
Article  PubMed  Google Scholar 

3.
Robert-Koch-Institute (Germany). Infektionsepidemiologisches Jahrbuch für 2018.

4.
Saunders-Hastings, P. & Krewski, D. Reviewing the history of pandemic influenza: Understanding patterns of emergence and transmission. Pathogens 5, 66. https://doi.org/10.3390/pathogens5040066 (2016).
Article  PubMed Central  Google Scholar 

5.
de Picoli Junior, S. et al. Spreading patterns of the influenza a (h1n1) pandemic. PLoS ONE 6, e17823. https://doi.org/10.1371/journal.pone.0017823 (2011).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

6.
Lofgren, E., Fefferman, N. H., Naumov, Y. N., Gorski, J. & Naumova, E. N. Influenza seasonality: Underlying causes and modeling theories. J. Virol. 81, 5429–5436. https://doi.org/10.1128/jvi.01680-06 (2006).
Article  PubMed  PubMed Central  Google Scholar 

7.
Bjørnstad, O. N. & Viboud, C. Timing and periodicity of influenza epidemics. Proc. Natl. Acad. Sci. 113, 12899–12901. https://doi.org/10.1073/pnas.1616052113 (2016).
CAS  Article  PubMed  Google Scholar 

8.
Liu, X.-X. et al. Seasonal pattern of influenza activity in a subtropical city, China, 2010–2015. Sci. Rep. 7, https://doi.org/10.1038/s41598-017-17806-z (2017).

9.
Bozick, B. A. & Real, L. A. The role of human transportation networks in mediating the genetic structure of seasonal influenza in the united states. PLOS Pathog. 11, e1004898. https://doi.org/10.1371/journal.ppat.1004898 (2015).
CAS  Article  PubMed  PubMed Central  Google Scholar 

10.
Dahlgren, F. S. et al. Patterns of seasonal influenza activity in U.S. core-based statistical areas, described using prescriptions of oseltamivir in medicare claims data. Epidemics 26, 23–31. https://doi.org/10.1016/j.epidem.2018.08.002 (2019).
Article  PubMed  Google Scholar 

11.
Lopman, B. et al. Increase in viral gastroenteritis outbreaks in europe and epidemic spread of new norovirus variant. Lancet 363, 682–688. https://doi.org/10.1016/s0140-6736(04)15641-9 (2004).
Article  PubMed  Google Scholar 

12.
Bloom-Feshbach, K. et al. Latitudinal variations in seasonal activity of influenza and respiratory syncytial virus (RSV): A global comparative review. PLoS ONE 8, e54445. https://doi.org/10.1371/journal.pone.0054445 (2013).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

13.
Ahmed, S. M., Lopman, B. A. & Levy, K. A systematic review and meta-analysis of the global seasonality of norovirus. PLoS ONE 8, e75922. https://doi.org/10.1371/journal.pone.0075922 (2013).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

14.
Petrova, V. N. & Russell, C. A. The evolution of seasonal influenza viruses. Nat Rev Microbiol 16, 47–60. https://doi.org/10.1038/nrmicro.2017.118 (2018).
CAS  Article  PubMed  Google Scholar 

15.
Su, S., Fu, X., Li, G., Kerlin, F. & Veit, M. Novel influenza d virus: Epidemiology, pathology, evolution and biological characteristics. Virulence 8, 1580–1591. https://doi.org/10.1080/21505594.2017.1365216 (2017).
Article  PubMed  PubMed Central  Google Scholar 

16.
Conrad, O. et al. System for automated geoscientific analyses (SAGA) v. 2.1.4.. Geosci. Model Dev. 8, 1991–2007. https://doi.org/10.5194/gmd-8-1991-2015 (2015).
ADS  Article  Google Scholar 

17.
BKG. Geobasis-de/bkg dl-de/by-2-0. Database. http://www.bkg.bund.de (2019).

18.
OriginLab Corporation, Northampton. Origin(Pro) 2019b. Website https://www.originlab.com/ (2019).

19.
Rajao, D. S., Vincent, A. L. & Perez, D. R. Adaptation of human influenza viruses to swine. Front. Vet. Sci. 5, https://doi.org/10.3389/fvets.2018.00347 (2019).

20.
Brankston, G., Gitterman, L., Hirji, Z., Lemieux, C. & Gardam, M. Transmission of influenza a in human beings. Lancet Infect. Dis. 7, 257–265. https://doi.org/10.1016/s1473-3099(07)70029-4 (2007).
Article  PubMed  Google Scholar 

21.
Ward, J. W. Twelve diseases that changed our world. Emerg. Infect. Dis. 14, 866a–8866. https://doi.org/10.3201/eid1405.080072 (2008).
Article  Google Scholar 

22.
Rao, S., Nyquist, A.-C. & Stillwell, P. C. 27 – influenza. In Wilmott, R. W. et al. (eds.) Kendig’s Disorders of the Respiratory Tract in Children (Ninth Edition), 460–465, https://doi.org/10.1016/B978-0-323-44887-1.00027-4 (2019).

23.
Pauly, M. D., Procario, M. & Lauring, A. S. The mutation rates and mutational bias of influenza a virus. eLifehttps://doi.org/10.1101/110197 (2017).

24.
Gregorio, E. D. & Rappuoli, R. From empiricism to rational design: a personal perspective of the evolution of vaccine development. Nat. Rev. Immunol. 14, 505–514. https://doi.org/10.1038/nri3694 (2014).
CAS  Article  PubMed  PubMed Central  Google Scholar 

25.
Shah, M. P. & Hall, A. J. Norovirus illnesses in children and adolescents. Infect. Dis. Clin. N. Am. 32, 103–118. https://doi.org/10.1016/j.idc.2017.11.004 (2018).
Article  Google Scholar 

26.
Patel, M. M. et al. Systematic literature review of role of noroviruses in sporadic gastroenteritis. Emerg Infect Dis 14, 1224–31. https://doi.org/10.3201/eid1408.071114 (2008).
Article  PubMed  PubMed Central  Google Scholar 

27.
Lanata, C. F. et al. Global causes of diarrheal disease mortality in children More

1.
Dethlefsen, L. & Relman, D. A. Proc. Natl Acad. Sci. USA 108 (Suppl. 1), 4554–4561 (2011).
CAS  Article  Google Scholar 
2.
Gibbons, S. M., Kearney, S. M., Smillie, C. S. & Alm, E. J. PLoS Comput. Biol. 13, e1005364 (2017).
Article  Google Scholar 

3.
Ng, K. M. et al. Cell Host Microbe 26, 650–665 (2019).
CAS  Article  Google Scholar 

4.
Suez, J. et al. Cell 174, 1406–1423 (2018).
CAS  Article  Google Scholar 

5.
Chng, K. R. et al. Nat. Ecol. Evol. https://doi.org/10.1038/s41559-020-1236-0 (2020).

6.
Paine, R. T. Conserv. Biol. 9, 962–964 (1995).
Article  Google Scholar 

7.
Raymond, F. et al. ISME J. 10, 707–720 (2016).
CAS  Article  Google Scholar 

8.
Zaura, E. et al. mBio 6, e01693-15 (2015).
Article  Google Scholar 

9.
Duvallet, C., Gibbons, S. M., Gurry, T., Irizarry, R. A. & Alm, E. J. Nat. Commun. 8, 1784 (2017).
Article  Google Scholar 

10.
Horn, H. S. Annu. Rev. Ecol. Syst. 5, 25–37 (1974).
Article  Google Scholar  More

Climate change and agriculture: an institutional perspective
In Pakistan, public institutions are considered among the key stakeholders in irrigated agriculture due to their importance in providing a range of services, i.e., surface irrigation, on-farm water management, pest and disease management, advisory, credit, and marketing services12. Hence it is pertinent to understand how these institutions perceive climate variability and its impacts in the study area.
Regarding observation on changes in climate, the majority of the office bearers reported substantial changes in temperature, rainfall, and cropping season expansion over the past 2 decades (Table 3). Notably, a significant increase in temperature and a decrease in rainfall is observed. Specifically, many respondents were of the view that summer seasons have become warmer. In contrast, monsoon rains, which account for two-thirds of the annual precipitation, has significantly decreased (shifting to late summer months). These observations are in line with the historical temperature and rainfall trends in the study area1,3. Further, respondents also indicated a variation in the duration of both Rabi (winter) and Kharif (summer) cropping seasons. An official from DoAE described that during the past few years, winter wheat cultivation is merged nearly a month to the summer season due to which the next crop faces delays in sowing and subsequent yield losses.
Table 3 Perceived climate changes and impacts at the farm level.
Full size table

In terms of climate-induced impact, the findings show that most of the effects reported are biophysical (droughts, floods, and water resources) and biological (insect, diseases, and weeds) in nature. Officials from PID and OFWM reported increasing water scarcity due to the reduced surface water flows and critical depletion of groundwater reserves that lead to the overall reduction in cultivated area under rice crop. Further, increased incidents of extreme temperature during early crop growth stages and intensive rainfall during harvesting seasons have severely affected rice yield. Heavy rain in late monsoon season leads to flooding in plain areas of Punjab and poses a severe threat to the sustainability of agriculture in the province.
Further, officials indicated that high temperatures and heatwaves have resulted in an increase in crop water requirements due to high evapotranspiration. Similarly, changing patterns of rainfall and extreme temperature events have increased the presence of fungal diseases, insect and weed attacks. Similar findings have been reported by a recent study showing a significant increase in the incidence and severity of climate-induced biological and biophysical risk in Pakistan5. Moreover, an official from DoAE reported a 100–150 kg/ha in general and 150–200 kg/ ha (in worst case scenario) reduction in wheat and rice yields due to increases in weed germination. Several respondents revealed that due to excessive use of insecticides and pesticides, aiming to control pests and diseases, the penetration of various harmful chemicals has alarmingly increased in both soil and water and resulted in degradation of water and soil quality.
In general, various respondents also highlighted the increase in unrest among farmers due to decreasing profit margins on account of the increasing cost of production and productivity decline due to climate change. Many farmers have been switched to non-farm businesses, and this lacking interest may further risk the national goal of sustainable food self-sufficiency and security.
Institutional capacities regarding CCA/ CRM in agriculture
This study further analyzed the capabilities of agricultural institutions using seven indicators-based index approach. Results of the selected indicators are given in Table 4, which shows a medium level of preparedness and capacities of the selected institutions. Specifically, the results of each indicator are explained in the following.
Table 4 Institutional Capacities Index (ICI).
Full size table

Perception and knowledge
Literature shows that stakeholders’ perception and knowledge of climate change and its impact are among the key factors that define the level of intentions to make efforts regarding CCA/CRM19. These attributes allow an actor to formulate practices based on their knowledge and beliefs, which leads towards adequate risk management support19,37. Hence, officials’ perception and understanding of climate change impacts and risk management strategies were selected as the first indicator of institutional capacities assessment. Results (Table 4) show that overall, this indicator’s index maintained a good score, which is highest amongst all indicators. Specifically, most of the respondents had a significant perception of climate change and its induced impacts at the farm level. However, their knowledge and beliefs on adaptation strategies and their effectiveness are limited. Most of the respondents with negative beliefs about climate change adaptation were mainly from research and credit institutions. As reported by Farani37, a vigilant understanding of climate change is imperative to implement risk management mechanisms. Hence these findings imply to mainstream the climate change agenda across all agricultural institutions as they are part of the same institutional chain. This may lead to an equal understanding of climate-smart practices and hence improve institutions’ tendency to design and implement risk management mechanisms at the local level. A study reports similar findings on public health institutions, which also indicated the positive behavior of supervisors as an essential determinant of effective risk management services38.
Training and expertise
Institution’s technical resources, such as professional training and expertise, are also considered as crucial elements while dealing with climate hazards19. Such training helps office bearers to be well prepared and respond to catastrophes39. Current findings show that public institutions attained a medium level of training and expertise, as only 39% of the respondents possessed some knowledge regarding CCA/CRM. Similarly, two-third of the officials did not have any prior experience in climate risk management. Similarly, results show that only 12% of the officials received appropriate training related to CCA and CRM. However, one of the officials reported that since the last few years, some understanding of climate change had been developed at their department, and more officials are being invited for climate change-related training. Low training and expertise of agricultural office bearers in dealing with climatic risks may be translated into little support from public institutions to farming communities and hence may further increase the vulnerability of agriculture. Roosli39 was also of the view that skilled human resource is a pivotal attribute of institutions’ risk management capacity, as they have exceptional ability to provide technical aid to the disaster-prone communities by integrating and effectively using available resources. Fideldman19 has also raised the importance of staff’s skills in terms of integrating and implementing knowledge and mobilizing available resources against the environmental uncertainties. Further, professional knowledge and expertise not only improve the emergency response against climatic catastrophes but also improve the farmers’ and peers’ skills39.
Human resources
According to the Gupta’s Adaptive Capacity Wheel (ACW) framework, human resource has critical significance in determining the institutions’ abilities while dealing with climate risks16. Following ACW, human resources were also chosen as an indicator to assess institutional capacities. According to the findings, the HR index of the institutions reported a deficient value of 0.44. Sub-indicators further revealed that only 31% of institutions had sufficient human resources, and particularly only 26% of the institutions had adequate human resources to meet the operational requirement dealing with risk management emergencies. Officials from DoAE, OFWM, and PID indicated a severe shortage of skilled human resources to meet climate change challenges in the field operations. An official from PID described that, in case of any extreme climate event such as canal breakage, windstorm, or extreme hailing, sometimes quick response and technical support was not provided or possible due to limited skilled human resources.
These findings revealed that lack of human resources in public institutions might lead to limited risk management support and hence may further increase the vulnerability of farming communities to climate change. These results are supported by a study conducted in Congo, where forest institutions lacked in human resources in terms of climate change response15. Gupta was also of this view that institutions with adequate human resources have a greater ability to mobilize climate change adaption and risk management processes in agriculture. These findings conclude that sufficient human resources in public institutions are the prerequisite of active risk management support.
Plan and priorities
Institutions’ priorities, planning, and emergency response mechanism are widely reported as important factors in dealing with the environmental uncertainties10,17,38. According to our findings, public institutions attained a satisfactory score regarding this indicator (0.66). Specifically, one-third of the office barriers indicated climate change as an important agenda for their department. Similarly, in terms of programs and initiatives regarding climate change, 42% of the institutions reported that they are carrying related initiatives and programs. While one-third of the respondents were of the view that they are planning to add CCA/CRM in their priorities. Further, 35% of the institutions, mainly the field institutions such as PID, DoAE, DoAF, and CRS, indicated having an active emergency response mechanism dealing with climatic catastrophes.
Wenger40 reported that effective risk management response is closely associated with emergency planning within the institutions. Huq10, has also stressed the significance of defined objectives and plan among the key factors of successful implementation of adaptation and risk management response to flood disasters. Hence our study implies further strengthening the planning infrastructure by removing existing gaps, which will increase the institution’s abilities in dealing with environmental catastrophes.
Coordination and collaboration
A wide range of literature shows that coordination between different stakeholders is among the critical determinants of the institution’s adaptive and risk management capacities15,16,28 and often support collective action and decision making regarding climate change adaptation15,41. The CCI value of 0.45 showed that institutions had a minimal level of coordination with other stakeholders. For instance, in terms of community interaction, one-third of the institutions reported direct coordination with the farmers, indicating a reduced level of cooperation between the farmers and institutions. The officials who indicated coordination with farmers were mainly from the field institutions (PID, OFWM, DoAE, and SWTL). However, the research institutions had also acknowledged the significance of institution-community coordination. An official from a research institution (FTAR) stated that it is very pertinent for all institutions to have interactive communication with the farmers. However, most of the research institutions have a deficient level of community coordination, due to which most of the contingency plans and alerts (which usually go through the filed institutions) do not reach to the farmers timely. There is a need to develop such a communication system that could connect agricultural institutions and the farmers on a single communication platform.
In terms of inter-departmental collaboration, 27% of the respondents indicate that their respective institutions have a coordination mechanism with other public sector institutions. In comparison, merely 6% of them stated coordination with the private sector’s institutions. However, a decent level of coordination (63%) was indicated within the same institution. A minimal level of coordination, particularly between public and private institutions, is worrisome, as non-governmental bodies of Pakistan, which are already at the emerging stage, could face further marginalization12. Literature also advocates smooth coordination between the public and private organizations for effective adaptation and risk management support in agriculture16. Brown15 stated that a well-coordinated network between the actors of the same institution chain is critical for an active response to a challenge like climate change. Hence these findings conclude that a well-coordinated institutional setup may be more capable in coping with agricultural hazards.
Financial resources
Financial resources are also widely quoted among the significant determinants of institutional adaptive capacity16,28. Financial resources of the institution facilitate the actors’ preparedness and emergency response-ability towards natural disasters42. However, in the current study, the financial resources of the agricultural institutions were severely deficient (FRI 0.36). Findings revealed that only 15% of the institutions indicated funds availability for the CCA/CRM related operations. A significant majority of the officials (85%) reported the insufficiency of the financial resources available for climate change. Overall, a gap of nearly 40% was reported in terms of funds availability and requirement.
The respondents who indicated the availability of funds, particularly for CCA/CRM, were mainly from the research intuitions such as FTAR, SWTR, PWQP. Even though field institutions such as DoAE, DoAF, OFWM, PID have significant importance to carry community-level activities did not indicate enough financial support specified for CCA/CRM related operation. For instance, an official from DoAE reported a severe shortage of funds for launching emergency awareness campaigns and training seminars during the period of extreme weather events such as droughts, floods, heavy rains, and insect attacks. Due to financial constraints, such activities have been restricted to a few official visits or small gatherings in a few villages.
Apart from the field institutions, some credit providing institutions have also raised similar concerns. An official from ZTBL mentioned that in some situations when a cropping season faces unexpected yield losses due to rainfall or insect and disease attack. Farmers, particularly the smallholders, desperately need a loan to cultivate the next crop, and due to the unavailability of credit for such emergencies, the institution is unable to offer credit to these farmers.
Our findings are in line with the studies conducted in Cambodia28 and Cameron15, where institutions reported similar challenges while implementing climate response strategies. As argued by Gupta16, institutions’ financial resources are among the foremost determinants of effective adaptive and risk management in agriculture. These findings imply that the institutions, which are farmers’ first line of defense in an emergency, need to be strengthened in such a significant resource.
Physical resources
Access to adequate physical resources is considered as another critical component to define their role in supporting farmers to manage climate risks at the community level15,43. In terms of physical resources, availability of vehicles, machinery (harvesters, bulldozers, cranes), communication equipment, and hardware are considered for the capacity assessment of field and market institutions. In contrast, instruments, apparatuses, and laboratory equipment are considered for research institutions.
According to the results, the critical index value of the physical resources (0.39) indicates insufficient availability of infrastructure and physical resources in public institutions. Results of sub-indicators further revealed a vast gap (51%) between the availability and actual requirement of these resources. Only 21% of institutions indicated enough availability of machinery and hardware for extreme climatic conditions and emergencies. These figures are alarming as physical resources are pivotal elements while providing community support against catastrophes. Field intuitions, particularly the DoAE, DoAF, and PID, have indicated the critical shortage of these resources.
The officials from DoAE and PID have specifically indicated the lack of vehicles as the critical constraint limiting their efficiency while conducting the field operations. An official from DoAE revealed that most of the available vehicles are either very old or non-functional, which means filed staff has to wait hours and days to complete assigned field operations. Similar challenges were reported in terms of communication infrastructure as the officials from the DoAE highlighted a huge communication gap between farmers and their department due to the unavailability of contemporary communication tools. Previous studies43 have also reported similar findings of lacking logistic and communication resources and urged the provision of these resources for capacitated community support regarding natural disasters. In a nutshell, the physical resources of agricultural institutions are deficient in terms of meeting catastrophic challenges and seek serious consideration from concerned authorities.
Institutional capacities across different types of institutions
To have a comprehensive understanding of institutional capacities across different types of Institutions, ICI was compared by categorizing the agricultural institutions into three categories, i.e., research, field, and market and credit institutions. Cumulative ICI values (Fig. 1) across these categories show that research institutions have attained higher index value, while credit and market, and field institutions are among the low capacitated institutions. The ICI values further show that perception and knowledge were high in case of field institutions, which could be due to their more field experience and interaction with farming communities. Such communication enables them to have a better understanding of climatic risks and farm level CCA/CRM practices. Moreover, financial resources showed the lowest value across all types of institutions. In terms of plans and priorities regarding CCA/CRM, research institutions maintained a higher index value.
Figure 1

Institutional capacities index (ICI) across different categories of institutions.

Full size image

In contrast, field, and credit and market institutions lacked in this indicator, highlighting the need for planning and prioritizing climate change agenda among these institutions. In terms of physical resources, which are regarded among the most critical resources, revealed alarming indications as both research and field institutions had a deficient amount of machinery and hardware resources. These findings imply that focus should be given to these institutions as they play a more crucial role (in terms of community support) when compared to credit and market institutions. Field institutions were also found lacking in terms of human resources, which could constraint the efficiency of these institutions in managing farm-level activities.
Gaps and solutions
After exploring institutions’ capacities in the selected indicators, officials were asked to indicate existing gaps and related solutions, which are essential to increase the capacities in the context of climate governance and CCA/CRM in agriculture. The following gaps and solutions were identified and prioritized.
Need for an effective administrative mechanism
An effective administration and coordination mechanism has been listed as a top priority by most of the office-bearers to enhance the institutional capacity in managing climate risks. Officials also highlighted the importance of ensuring effective administrative mechanisms to implement and monitor the individual and collective performances in ongoing projects. That will improve the output of resources being invested at various levels. Fidelman and Madan19 have also indicated a sound administrative system among the critical components of the institution’s capacity dealing with CCA/CRM. Bettini raised the importance of constructing such a rule system that identifies accountability and defines boundaries and hierarchy in water management institutions18. Hence it is needed to develop or customize such institutional arrangements that are interactive, effectively administered, and target oriented.
Need for physical and financial resources
The second suggested measure is the provision of physical and financial resources required to support farm-level adaptation. Officials indicated that the current state of these resources is not enough to meet the institutional operational requirements to conduct CCA/CRM related operations. Brown has also identified similar gaps among the Congo’s forest institutions dealing with climate risk management15; however, Grecksch14 reported a higher level of physical and financial resources among the German institutions. Officials suggested that an appropriate amount of financial support should be specified for extreme climate events, along with emphasizing the need for communication and logistic resource. Literature also ranks these resources among the pertinent element of effective risk management44. The institutions equipped with such crucial resources would be more likely to overcome the climatic challenges. For instance, at the farm level, well-equipped institutions may have a better ability to reach farmers’ knowledge as well as technical requirements, to reduce the actual and potential losses. Similarly, the research institutions having contemporary technology apparatuses and instruments may create better innovation, i.e., climate-resilient farm inputs (seeds, water-efficient measures) that will ultimately reduce the farmers’ vulnerability of climate risks.
Need for professional training
Thirdly, a considerable portion of the respondent indicated the training need of staff regarding CCA and CRM. Institutions reported that human resources generally in the non-administrative and research positions, while particularly in field operations, are in much need of training. As indicated by Roosli that stakeholders may enhance the skilled humane resource by launching a series of training and disaster management programs that may lead to effective risk management response39. This study stresses that departmental training courses could be launched where indigenous and research knowledge could be integrated. Field staff should particularly be trained regarding emergency response in extreme climate events such as excessive rains, floods, wind storms. At the same time, the researcher’s skills should be enhanced in terms of the development of climate-smart practices and modeling farm-level risks and vulnerability.
Need for enhanced support
The last indicated challenge by the public institutions was the lack of support from the higher authorities. Institutions urged the need for a shared understanding and realization of agricultural vulnerability to climate change at both policy and higher administrative levels, which may put the energy into the local level. Similar capacity recommendations were identified by Brown15, where institutions reported a need for a common understanding between the stakeholders of forest communities for effective climate response. More

1.
Pimm, S. L. et al. The biodiversity of species and their rates of extinction, distribution, and protection. Science 344, 1246752 (2014).
CAS  Article  Google Scholar 
2.
Newbold, T. et al. Global effects of land use on local terrestrial biodiversity. Nature 520, 45–50 (2015).
CAS  Article  Google Scholar 

3.
Alvarez-Filip, L., Dulvy, N. K., Gill, J. A., Côté, I. M. & Watkinson, A. R. Flattening of Caribbean coral reefs: region-wide declines in architectural complexity. Proc. Biol. Sci. 276, 3019–3025 (2009).
Article  Google Scholar 

4.
Millennium Ecosystem Assessment Ecosystems and Human Well-Being: Synthesis (Island Press, 2005).

5.
Schulze, E. D. & Mooney, H. A. Biodiversity and Ecosystem Function (Springer, 1993).

6.
Pimm, S. L. The complexity and stability of ecosystems. Nature 307, 321–326 (1984).
Article  Google Scholar 

7.
Morse, D. R., Lawton, J. H., Dodson, M. M. & Williamson, M. H. Fractal dimension of vegetation and the distribution of arthropod body lengths. Nature 314, 731–733 (1985).
Article  Google Scholar 

8.
McCoy, E. D. & Bell, S. S. in Habitat Structure: the Physical Arrangement of Objects in Space (eds Bell, S. S. et al.) 3–27 (Springer, 1991).

9.
Beck, M. W. Separating the elements of habitat structure: independent effects of habitat complexity and structural components on rocky intertidal gastropods. J. Exp. Mar. Biol. Ecol. 249, 29–49 (2000).
CAS  Article  Google Scholar 

10.
Kovalenko, K. E., Thomaz, S. M. & Warfe, D. M. Habitat complexity: approaches and future directions. Hydrobiologia 685, 1–17 (2012).
Article  Google Scholar 

11.
Arrhenius, O. Species and area. J. Ecol. 9, 95–99 (1921).
Article  Google Scholar 

12.
MacArthur, R. H. & Wilson, E. O. The Theory of Island Biogeography (Princeton Univ. Press, 1967).

13.
Mandelbrot, B. B. The Fractal Geometry of Nature (W. H. Freeman, 1983).

14.
Tokeshi, M. & Arakaki, S. Habitat complexity in aquatic systems: fractals and beyond. Hydrobiologia 685, 27–47 (2012).
Article  Google Scholar 

15.
Johnson, M. P., Frost, N. J., Mosley, M. W. J., Roberts, M. F. & Hawkins, S. J. The area-independent effects of habitat complexity on biodiversity vary between regions. Ecol. Lett. 6, 126–132 (2003).
Article  Google Scholar 

16.
Chesson, P. Mechanisms of maintenance of species diversity. Annu. Rev. Ecol. Syst. 31, 343–366 (2000).
Article  Google Scholar 

17.
Pianka, E. R. Evolutionary Ecology (Harper and Row, 1988).

18.
Sugihara, G. & May, R. M. Applications of fractals in ecology. Trends Ecol. Evol. 5, 79–86 (1990).
CAS  Article  Google Scholar 

19.
Jones, C. G., Lawton, J. H. & Shachak, M. Positive and negative effects of organisms as physical ecosystem engineers. Ecology 78, 1946–1957 (1997).
Article  Google Scholar 

20.
Brown, J. H. et al. The fractal nature of nature: power laws, ecological complexity and biodiversity. Phil. Trans. R. Soc. B 357, 619–626 (2002).
Article  Google Scholar 

21.
Graham, N. A. J. & Nash, K. L. The importance of structural complexity in coral reef ecosystems. Coral Reefs 32, 315–326 (2013).
Article  Google Scholar 

22.
Madin, J. S. et al. Cumulative effects of cyclones and bleaching on coral cover and species richness at Lizard Island. Mar. Ecol. Prog. Ser. 604, 263–268 (2018).
Article  Google Scholar 

23.
Hurlbert, S. H. The nonconcept of species diversity: a critique and alternative parameters. Ecology 52, 577–586 (1971).
Article  Google Scholar 

24.
Hata, T. et al. Coral larvae are poor swimmers and require fine-scale reef structure to settle. Sci. Rep. 7, 2249 (2017).
Article  Google Scholar 

25.
Madin, J. S. & Connolly, S. R. Ecological consequences of major hydrodynamic disturbances on coral reefs. Nature 444, 477–480 (2006).
CAS  Article  Google Scholar 

26.
Alvarez-Filip, L. et al. Drivers of region-wide declines in architectural complexity on Caribbean reefs. Coral Reefs 30, 1051–1060 (2011).
Article  Google Scholar 

27.
Allouche, O., Kalyuzhny, M., Moreno-Rueda, G., Pizarro, M. & Kadmon, R. Area-heterogeneity tradeoff and the diversity of ecological communities. Proc. Natl Acad. Sci. USA 109, 17495–17500 (2012).
CAS  Article  Google Scholar 

28.
Paxton, A. B., Pickering, E. A., Adler, A. M., Taylor, J. C. & Peterson, C. H. Flat and complex temperate reefs provide similar support for fish: evidence for a unimodal species-habitat relationship. PLoS ONE 12, e0183906 (2017).
Article  Google Scholar 

29.
Huston, M. A. Patterns of species diversity on coral reefs. Annu. Rev. Ecol. Syst. 16, 149–177 (1985).
Article  Google Scholar 

30.
Loke, L. H. L., Todd, P. A., Ladle, R. J. & Bouma, T. J. Creating complex habitats for restoration and reconciliation. Ecol. Eng. 77, 307–313 (2015).
Article  Google Scholar 

31.
Young, G. C., Dey, S., Rogers, A. D. & Exton, D. Cost and time-effective method for multi-scale measures of rugosity, fractal dimension, and vector dispersion from coral reef 3D models. PLoS ONE 12, e0175341 (2017).
CAS  Article  Google Scholar 

32.
Friedman, A., Pizarro, O., Williams, S. B. & Johnson-Roberson, M. Multi-scale measures of rugosity, slope and aspect from benthic stereo image reconstructions. PLoS ONE 7, e50440 (2012).
CAS  Article  Google Scholar 

33.
Weiher, E. & Keddy, P. A. Ecological Assembly Rules: Perspectives, Advances, Retreats (Cambridge Univ. Press, 2001).

34.
Bartholomew, A., Diaz, R. J. & Cicchetti, G. New dimensionless indices of structural habitat complexity: predicted and actual effects on a predator’s foraging success. Mar. Ecol. Prog. Ser. 206, 45–58 (2000).
Article  Google Scholar 

35.
Strain, E. M. A. et al. Eco-engineering urban infrastructure for marine and coastal biodiversity: which interventions have the greatest ecological benefit? J. Appl. Ecol. 55, 426–441 (2018).
Article  Google Scholar 

36.
Dubuc, B., Zucker, S. W., Tricot, C., Quiniou, J. F. & Wehbi, D. Evaluating the fractal dimension of surfaces. Proc. R. Soc. Lond. A Math. Phys. Sci. 425, 113–127 (1989).
CAS  Google Scholar 

37.
Zhou, G. & Lam, N. S.-N. A comparison of fractal dimension estimators based on multiple surface generation algorithms. Comput. Geosci. 31, 1260–1269 (2005).
Article  Google Scholar 

38.
Johnson‐Roberson, M. et al. High‐resolution underwater robotic vision‐based mapping and three‐dimensional reconstruction for archaeology. J. Field Robot. 34, 625–643 (2017).
Article  Google Scholar 

39.
Pizarro, O., Friedman, A., Bryson, M., Williams, S. B. & Madin, J. A simple, fast, and repeatable survey method for underwater visual 3D benthic mapping and monitoring. Ecol. Evol. 7, 1770–1782 (2017).
Article  Google Scholar 

40.
Mahon, I., Williams, S. B., Pizarro, O. & Johnson-Roberson, M. Efficient view-based SLAM using visual loop closures. IEEE Trans. Robot. 24, 1002–1014 (2008).
Article  Google Scholar 

41.
Bryson, M. et al. Characterization of measurement errors using structure‐from‐motion and photogrammetry to measure marine habitat structural complexity. Ecol. Evol. 7, 5669–5681 (2017).
Article  Google Scholar 

42.
Zawada, D. G. & Brock, J. C. A multi-scale analysis of coral reef topographic complexity using Lidar-derived bathymetry. J. Coast. Res. 10053, 6–15 (2009).
Article  Google Scholar 

43.
Bivand, R. S., Pebesma, E. & Gómez-Rubio, V. Applied Spatial Data Analysis with R (Springer, 2013).

44.
Wood, S. N., Pya, N. & Säfken, B. Smoothing parameter and model selection for general smooth models. J. Am. Stat. Assoc. 111, 1548–1563 (2016).
CAS  Article  Google Scholar 

45.
R Core Team R: A Language and Environment For Statistical Computing (R Foundation for Statistical Computing, 2019). More