Ecology

1.
Webb, T. J. Marine and terrestrial ecology: unifying concepts, revealing differences. Trends Ecol. Evol. 27, 535–541 (2012).
PubMed  Article  Google Scholar 
2.
Calosi, P., Bilton, D. T., Spicer, J. I., Votier, S. C. & Atfield, A. What determines a species’ geographical range? Thermal biology and latitudinal range size relationships in European diving beetles (Coleoptera: Dytiscidae). J. Anim. Ecol. 79, 194–204 (2010).
PubMed  Article  Google Scholar 

3.
Sunday, J. M., Bates, A. E. & Dulvy, N. K. Thermal tolerance and the global redistribution of animals. Nat. Clim. Chang. 2, 686–690 (2012).
ADS  Article  Google Scholar 

4.
Wiens, J. J. et al. Niche conservatism as an emerging principle in ecology and conservation biology. Ecol. Lett. 13, 1310–1324 (2010).
PubMed  Article  Google Scholar 

5.
Huey, R. B. et al. Predicting organismal vulnerability to climate warming: roles of behaviour, physiology and adaptation. Philos. Trans. R. Soc. B 367, 1665–1679 (2012).
Article  Google Scholar 

6.
Wake, D. B., Roth, G. & Wake, M. H. On the problem of stasis in organismal evolution. J. Theor. Biol. 101, 211–224 (1983).
Article  Google Scholar 

7.
Hoffmann, A. A., Chown, S. L. & Clusella-Trullas, S. Upper thermal limits in terrestrial ectotherms: how constrained are they? Funct. Ecol. 27, 934–949 (2013).
Article  Google Scholar 

8.
Storch, D., Menzel, L., Frickenhaus, S. & Pörtner, H. Climate sensitivity across marine domains of life: limits to evolutionary adaptation shape species interactions. Glob. Chang. Biol. 20, 3059–3067 (2014).
ADS  PubMed  Article  Google Scholar 

9.
Addo-Bediako, A., Chown, S. L. & Gaston, K. J. Thermal tolerance, climatic variability and latitude. Proc. R. Soc. Lond. B 267, 739–745 (2000).
CAS  Article  Google Scholar 

10.
Sunday, J. M., Bates, A. E. & Dulvy, N. K. Global analysis of thermal tolerance and latitude in ectotherms. Proc. R. Soc. Lond. B 278, 1823–1830 (2011).
Google Scholar 

11.
van Berkum, F. H. Latitudinal patterns of the thermal sensitivity of sprint speed in lizards. Am. Nat. 132, 327–343 (1988).

12.
Munoz, M. M. et al. Evolutionary stasis and lability in thermal physiology in a group of tropical lizards. Proc. R. Soc. Lond. B 281, 20132433 (2014).
Google Scholar 

13.
Araújo, M. B. et al. Heat freezes niche evolution. Ecol. Lett. 16, 1206–1219 (2013).
PubMed  Article  Google Scholar 

14.
Kellermann, V. et al. Upper thermal limits of Drosophila are linked to species distributions and strongly constrained phylogenetically. Proc. Natl Acad. Sci. USA 109, 16228–16233 (2012).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

15.
Bogert, C. M. Thermoregulation in reptiles, a factor in evolution. Evolution 3, 195–211 (1949).
CAS  PubMed  Article  PubMed Central  Google Scholar 

16.
Ruddiman, W. F. Earth’s Climate: Past and Future (Macmillan, 2001).

17.
Romdal, T. S., Araújo, M. B. & Rahbek, C. Life on a tropical planet: niche conservatism and the global diversity gradient. Glob. Ecol. Biogeogr. 22, 344–350 (2013).
Article  Google Scholar 

18.
Hedges, S. B., Marin, J., Suleski, M., Paymer, M. & Kumar, S. Tree of life reveals clock-like speciation and diversification. Mol. Biol. Evol. 32, 835–845 (2015).
CAS  PubMed  PubMed Central  Article  Google Scholar 

19.
Herrando-Pérez, S. et al. Heat tolerance is more variable than cold tolerance across species of Iberian lizards after controlling for intraspecific variation. Funct. Ecol. 34, 631–645 (2020).
Article  Google Scholar 

20.
Hamilton, W. J. Life’s Color Code (New York: McGraw-Hill, 1973).

21.
Cooper, N., Thomas, G. H., Venditti, C., Meade, A. & Freckleton, R. P. A cautionary note on the use of Ornstein Uhlenbeck models in macroevolutionary studies. Biol. J. Linn. Soc. 118, 64–77 (2016).
Article  Google Scholar 

22.
Münkemüller, T., Boucher, F. C., Thuiller, W. & Lavergne, S. Phylogenetic niche conservatism—common pitfalls and ways forward. Funct. Ecol. 29, 627–639 (2015).
PubMed  PubMed Central  Article  Google Scholar 

23.
Buckley, L. B. & Huey, R. B. Temperature extremes: geographic patterns, recent changes, and implications for organismal vulnerabilities. Glob. Chang. Biol. 22, 3829–3842 (2016).
ADS  PubMed  Article  Google Scholar 

24.
Hoffmann, A. A. Physiological climatic limits in Drosophila: patterns and implications. J. Exp. Biol. 213, 870–880 (2010).
CAS  PubMed  Article  Google Scholar 

25.
Bennett, J. M. et al. GlobTherm a global database on thermal tolerances for aquatic and terrestrial organisms. Sci. Data 5, 180022 (2018).
PubMed  PubMed Central  Article  Google Scholar 

26.
Rangel, T. F. et al. Modeling the ecology and evolution of biodiversity: biogeographical cradles, museums, and graves. Science (80-.) 361, eaar5452 (2018).
Article  CAS  Google Scholar 

27.
Stephens, P. R. & Wiens, J. J. Explaining species richness from continents to communities: the time-for-speciation effect in emydid turtles. Am. Nat. 161, 112–128 (2003).
PubMed  Article  Google Scholar 

28.
Grosberg, R. K., Vermeij, G. J. & Wainwright, P. C. Biodiversity in water and on land. Curr. Biol. 22, R900–R903 (2012).
CAS  PubMed  Article  Google Scholar 

29.
Cutler, D. R. et al. Random forests for classification in ecology. Ecology 88, 2783–2792 (2007).
Article  Google Scholar 

30.
Pörtner, H. Climate change and temperature-dependent biogeography: oxygen limitation of thermal tolerance in animals. Naturwissenschaften 88, 137–146 (2001).
ADS  PubMed  Article  Google Scholar 

31.
Colwell, R. K., Brehm, G., Cardelús, C. L., Gilman, A. C. & Longino, J. T. Global warming, elevational range shifts, and lowland biotic attrition in the wet tropics. Science (80-.) 322, 258–261 (2008).
ADS  CAS  Article  Google Scholar 

32.
Tewksbury, J. J., Huey, R. B. & Deutsch, C. A. Putting the heat on tropical animals. Science (80-.) 320, 1296–1297 (2008).
CAS  Article  Google Scholar 

33.
Sinervo, B. et al. Erosion of lizard diversity by climate change and altered thermal niches. Science (80-.) 328, 894–899 (2010).
ADS  CAS  Article  Google Scholar 

34.
Barnosky, A. D. et al. Has the Earth’s sixth mass extinction already arrived? Nature 471, 51–57 (2011).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

35.
Gavrilets, S. & Vose, A. Dynamic patterns of adaptive radiation. Proc. Natl Acad. Sci. USA 102, 18040–18045 (2005).
ADS  CAS  PubMed  Article  Google Scholar 

36.
Schluter, D. & Pennell, M. W. Speciation gradients and the distribution of biodiversity. Nature 546, 48–55 (2017).
ADS  CAS  PubMed  Article  Google Scholar 

37.
Porter, W. P. & Kearney, M. Size, shape, and the thermal niche of endotherms. Proc. Natl Acad. Sci. USA 106, 19666–19672 (2009).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

38.
Rubalcaba, J. G. & Olalla‐Tárraga, M. Á. The biogeography of thermal risk for terrestrial ectotherms: scaling of thermal tolerance with body size and latitude. J. Anim. Ecol. 89, 1277–1285 (2020).

39.
Hochachka, P. W. & Somero, G. N. Biochemical Adaptation: Mechanism and Process in Physiological Evolution (Oxford University Press, 2002).

40.
Wiens, J. J. & Graham, C. H. Niche conservatism: integrating evolution, ecology, and conservation biology. Annu. Rev. Ecol. Evol. Syst. 36, 519–539 (2005).

41.
IUCN. The IUCN Red List of Threatened Species http://www.iucnredlist.org (2015).

42.
Horton, T. et al. World Register of Marine Species (WoRMS) http://www.marinespecies.org (2017).

43.
Guiry, M. D. & Guiry, G. M. AlgaeBase. World-wide electronic publication http://www.algaebase.org (2016).

44.
Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1‐km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).

45.
Assis, J. et al. Bio‐ORACLE v2. 0: extending marine data layers for bioclimatic modelling. Glob. Ecol. Biogeogr. 27, 277–284 (2018).
Article  Google Scholar 

46.
Tyberghein, L. et al. Bio‐ORACLE: a global environmental dataset for marine species distribution modelling. Glob. Ecol. Biogeogr. 21, 272–281 (2012).
Article  Google Scholar 

47.
Caspermeyer, J. New grand tree of life study shows a clock-like trend in the emergence of new species and diversity. Mol. Biol. Evol. 32, 1113 (2015).
CAS  PubMed  Article  Google Scholar 

48.
Holt, B. G. & Jønsson, K. A. Reconciling hierarchical taxonomy with molecular phylogenies. Syst. Biol. 63, 1010–1017 (2014).
PubMed  Article  Google Scholar 

49.
Ruggiero, M. A. et al. A higher level classification of all living organisms. PLoS ONE 10, e0119248 (2015).
PubMed  PubMed Central  Article  CAS  Google Scholar 

50.
Cooper, N. & Purvis, A. Body size evolution in mammals: complexity in tempo and mode. Am. Nat. 175, 727–738 (2010).
PubMed  Article  Google Scholar 

51.
Felsenstein, J. Maximum-likelihood estimation of evolutionary trees from continuous characters. Am. J. Hum. Genet. 25, 471 (1973).
CAS  PubMed  PubMed Central  Google Scholar 

52.
Zanne, A. E. et al. Three keys to the radiation of angiosperms into freezing environments. Nature 506, 89–92 (2014).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

53.
Alexander Pyron, R. & Wiens, J. J. A large-scale phylogeny of Amphibia including over 2800 species, and a revised classification of extant frogs, salamanders, and caecilians. Mol. Phylogenet. Evol. 61, 543–583 (2011).
PubMed  Article  Google Scholar 

54.
Pyron, R. A., Burbrink, F. T. & Wiens, J. J. A phylogeny and revised classification of Squamata, including 4161 species of lizards and snakes. BMC Evol. Biol. 13, 93 (2013).
PubMed  PubMed Central  Article  CAS  Google Scholar 

55.
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 (2012).
ADS  CAS  PubMed  Article  Google Scholar 

56.
Faurby, S. & Svenning, J.-C. A species-level phylogeny of all extant and late Quaternary extinct mammals using a novel heuristic-hierarchical Bayesian approach. Mol. Phylogenet. Evol. 84, 14–26 (2015).
PubMed  Article  Google Scholar 

57.
Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).
MATH  Article  Google Scholar 

58.
R Development Core Team. R: A Language and Environment for Statistical Computing (R Development Core Team, 2020).

59.
Hedges, S. B., Dudley, J. & Kumar, S. TimeTree: a public knowledge-base of divergence times among organisms. Bioinformatics 22, 2971–2972 (2006).
CAS  PubMed  Article  PubMed Central  Google Scholar 

60.
Zanne, A. E. et al. Data from: three keys to the radiation of angiosperms into freezing environments. Dryad Digit. Repos. 10, https://doi.org/10.5061/dryad.63q27 (2014).

61.
Pyron, R. A. & Wiens, J. J. Data from: a large-scale phylogeny of Amphibia including over 2800 species, and a revised classification of extant frogs, salamanders, and caecilians. https://doi.org/10.5061/dryad.vd0m7 (2011).

62.
Pyron, R. Alexander, Burbrink, Frank T., Wiens, J. J. Data from: a phylogeny and revised classification of Squamata, including 4161 species of lizards and snakes. Dryad Digit. Repos. https://doi.org/10.5061/dryad.82h0me (2013).

63.
Morales-Castilla, I. MoralesCastilla/ThermalEvolution: ThermalEvolution (Version v1.0). Zenodo https://doi.org/10.5281/zenodo.4311705 (2020). More

In most of the forest soil samples used in this survey, specimens belonging to the genus Hantzschia are quite common. Based molecular as well as on light microscopy (LM) and scanning electron microscopy (SEM) observations of 25 strains, seven different taxa were recognized. Figure 1 contains the locations of the strain’s habitats. In anticipation of the nomenclatural consequences, we are using the new names already here but will describe them formally later.
Figure 1

Map with the habitat locations of the studied strains.

Full size image

Molecular data
The obtained phylogenetic tree for representatives of the different strains Hantzschia contains several large clades, some of which are monophyletic, while others contain several different species names (Fig. 2). In the analyzed tree, the largest clade is represented by different strains of H. amphioxys, the structure of which is described in the corresponding molecular analysis section. At the same time, the most significant is that in the same clade there is strain H. amphioxys D27_008, which has been designated as epitype20. One of the largest is the clade with H. abundans, which, in addition to our strains, and some that have already been published, includes the group of strains referred to as “Hantzschia sp. 3” (Sterre6)e, (Sterre6)f from Souffreau et al.16. We propose to refer to all of these strains as H. abundans. The next clade consists of the new species of H. attractiva and three strains of Hantzschia sp. 2 (Mo1)a, (Mo1)e, (Mo1)m from Souffreau et al.16, the latter we propose to merge into the new species named H. pseudomongolica, which is sister to H. attractiva. Given the topology of the tree and the morphological features of the representatives, we can conclude that there is a close relation between H. abundans and H. attractiva plus H. pseudomongolica. A separate group consists of two clades with sufficient statistical support (likelihood bootstrap, LB 76; posterior probability, PP 100), one of which is represented by two strains of H. parva, and the other with strains of H. cf. amphioxys (Sterre1)f, (Sterre1)h. Another large clade represents a set of strains of Hantzschia sp. 1 and Hantzschia sp. 2 (Mo1)h, (Mo1)l from Souffreau et al.16, among which there are both large cells (86–89 µm length) and smaller ones (37–39 µm length); strains also differ by striation – from 18–20 striae in 10 μm (strain (Mo1)h) to 21–22 in 10 μm (strain (Ban1)h). It is possible that Hantzschia sp. 1 and Hantzschia sp. 2 (Mo1)h, (Mo1)l may be several closely related species. Besides the large clades, there are a number of separate branches in the tree, representing separate strains: Hantzschia sp. 1 (Ban1)d, and the new species H. belgica (H. cf. amphioxys (Sterre3)a from Souffreaua et al.16) and H. stepposa. Interesting is the position of the H. abundans (Tor3)c strain, which is very distant from other representatives of H. abundans and probably is a cryptic taxon, whose taxonomic status needs to be revised.
Figure 2

Bayesian tree for representatives of the different strains Hantzschia, from an alignment with 40 sequences and 1785 characters (partial rbcL gene and 28S rDNA fragments). Type strains indicated in bold. The epitype of Hantzschia amphioxys is underlined. Values above the horizontal lines (on the left of slash) are bootstrap support from RAxML analyses ( More

1.
Healy, S. D. & Rowe, C. A critique of comparative studies of brain size. Proc. R. Soc. B Biol. Sci. 274, 453–464 (2007).
Article  Google Scholar 
2.
Roth, G. & Dicke, U. Evolution of the brain and intelligence. Trends Cogn. Sci. 9, 250–257 (2005).
PubMed  Article  Google Scholar 

3.
Logan, C. J., Kruuk, L. E. B., Stanley, R., Thompson, A. M. & Clutton-Brock, T. H. Endocranial volume is heritable and is associated with longevity and fitness in a wild mammal. R. Soc. Open Sci. 3, 160622 (2016).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

4.
Dunbar, R. I. M. Neocortex size as a constraint on group size in primates. J. Hum. Evol. 22, 469–493 (1992).
Article  Google Scholar 

5.
Innocenti, G. M. & Kaas, J. H. The cortex. Trends Neurosci. 18, 371–372 (1995).
CAS  Article  Google Scholar 

6.
Kaas, J. H. The evolution of isocortex. Brain. Behav. Evol. 46, 187–196 (1995).
CAS  PubMed  Article  Google Scholar 

7.
Barton, R. A. & Harvey, P. H. Mosaic evolution of brain structure in mammals. Nature 405, 1055–1058 (2000).
ADS  CAS  PubMed  Article  Google Scholar 

8.
Reader, S. M. & Laland, K. N. Social intelligence, innovation, and enhanced brain size in primates. Proc. Natl. Acad. Sci. 99, 4436–4441 (2002).
ADS  CAS  PubMed  Article  Google Scholar 

9.
Sol, D., Székely, T., Liker, A. & Lefebvre, L. Big-brained birds survive better in nature. Proc. R. Soc. B Biol. Sci. 274, 763–769 (2007).
Article  Google Scholar 

10.
Benson-Amram, S., Dantzer, B., Stricker, G., Swanson, E. M. & Holekamp, K. E. Brain size predicts problem-solving ability in mammalian carnivores. Proc. Natl. Acad. Sci. USA 113, 2532–2537 (2016).
ADS  CAS  PubMed  Article  Google Scholar 

11.
Cartmill, M. New views on primate origins. Evol. Anthropol. Issues News Rev. 1, 105–111 (2005).
Article  Google Scholar 

12.
Allman, J., McLaughlin, T. & Hakeem, A. Brain weight and life-span in primate species. Proc. Natl. Acad. Sci. 90, 118–122 (1993).
ADS  CAS  PubMed  Article  Google Scholar 

13.
González-Lagos, C., Sol, D. & Reader, S. M. Large-brained mammals live longer. J. Evol. Biol. 23, 1064–1074 (2010).
PubMed  Article  Google Scholar 

14.
Harvey, P. H. & Bennett, P. M. Evolutionary biology: Brain size, energetics, ecology and life history patterns. Nature 306, 314–315 (1983).
ADS  CAS  PubMed  Article  Google Scholar 

15.
Aiello, L. C. & Wheeler, P. The expensive-tissue hypothesis: The brain and the digestive system in human and primate evolution. Curr. Anthropol. 36, 199–221 (1995).
Article  Google Scholar 

16.
Kudo, H. & Dunbar, R. I. M. Neocortex size and social network size in primates. Anim. Behav. 62, 711–722 (2001).
Article  Google Scholar 

17.
Schillaci, M. A. Sexual selection and the evolution of brain size in primates. PLoS ONE 1, e62 (2006).
ADS  PubMed  PubMed Central  Article  Google Scholar 

18.
Shultz, S. & Dunbar, R. I. M. The evolution of the social brain: anthropoid primates contrast with other vertebrates. Proc. R. Soc. B Biol. Sci. 274, 2429–2436 (2007).
Article  Google Scholar 

19.
King, B. J. Extractive foraging and the evolution of primate intelligence. Hum. Evol. 1, 361–372 (1986).
Article  Google Scholar 

20.
Barton, R. A. Neocortex size and behavioural ecology in primates. Proc. R. Soc. Lond. B 263, 173–177 (1996).
ADS  CAS  Article  Google Scholar 

21.
DeCasien, A. R., Williams, S. A. & Higham, J. P. Primate brain size is predicted by diet but not sociality. Nat. Ecol. Evol. 1, 0112 (2017).
Article  Google Scholar 

22.
Powell, L. E., Isler, K. & Barton, R. A. Re-evaluating the link between brain size and behavioural ecology in primates. Proc. R. Soc. B Biol. Sci. 284, 20171765 (2017).
Article  Google Scholar 

23.
Dunbar, R. I. M. & Shultz, S. Why are there so many explanations for primate brain evolution?. Philos. Trans. R. Soc. B Biol. Sci. 372, 20160244 (2017).
Article  Google Scholar 

24.
Van Schaik, C. P. Why are diurnal primates living in groups?. Behaviour 87, 120–144 (1983).
Article  Google Scholar 

25.
Van Schaik, C. P. & Van Hooff, J. A. R. A. M. On the ultimate causes of primate social systems. Behaviour 85, 91–117 (1983).
Article  Google Scholar 

26.
Wrangham, R. W. An ecological model of female-bonded primate groups. Behaviour 75, 262–300 (1980).
Article  Google Scholar 

27.
Atchley, W. R., Riska, B., Kohn, L. A. P., Plummer, A. A. & Rutledge, J. J. A quantitative genetic analysis of brain and body size associations, their origin and ontogeny: Data from mice. Evolution 38, 1165 (1984).
PubMed  Article  Google Scholar 

28.
Riska, B. & Atchley, W. R. Genetics of growth predict patterns of brain-size evolution. Science 229, 668–671 (1985).
ADS  CAS  PubMed  Article  Google Scholar 

29.
Rogers, J. et al. Heritability of brain volume, surface area and shape: An MRI study in an extended pedigree of baboons. Hum. Brain Mapp. 28, 576–583 (2007).
PubMed  PubMed Central  Article  Google Scholar 

30.
Gómez-Robles, A., Hopkins, W. D., Schapiro, S. J. & Sherwood, C. C. Relaxed genetic control of cortical organization in human brains compared with chimpanzees. Proc. Natl. Acad. Sci. 112, 14799–14804 (2015).
ADS  PubMed  Article  CAS  Google Scholar 

31.
DeCasien, A. R., Sherwood, C. C., Schapiro, S. J. & Higham, J. P. Greater variability in chimpanzee (Pan troglodytes) brain structure among males. Proc. R. Soc. B 287, 20192858 (2020).
PubMed  Article  Google Scholar 

32.
Fears, S. C. et al. Identifying heritable brain phenotypes in an extended pedigree of vervet monkeys. J. Neurosci. 29, 2867–2875 (2009).
CAS  PubMed  PubMed Central  Article  Google Scholar 

33.
Noreikiene, K. et al. Quantitative genetic analysis of brain size variation in sticklebacks: Support for the mosaic model of brain evolution. Proc. R. Soc. B Biol. Sci. 282, 20151008 (2015).
Article  Google Scholar 

34.
Kotrschal, A. et al. Artificial selection on relative brain size in the guppy reveals costs and benefits of evolving a larger brain. Curr. Biol. 23, 168–171 (2013).
CAS  PubMed  PubMed Central  Article  Google Scholar 

35.
Cheverud, J. M. et al. Heritability of brain size and surface features in rhesus macaques (Macaca mulatta). J. Hered. 81, 51–57 (1990).
CAS  PubMed  Article  Google Scholar 

36.
de Villemereuil, P. Tutorial estimation of a biological trait heritability using the animal model How to use the MCMCglmm R package. (2012).

37.
Axelrod, C. J., Laberge, F. & Robinson, B. W. Intraspecific brain size variation between coexisting sunfish ecotypes. Proc. R. Soc. B Biol. Sci. 285, 20181971 (2018).
Article  Google Scholar 

38.
Blomquist, G. E. Fitness-related patterns of genetic variation in rhesus macaques. Genetica 135, 209–219 (2009).
PubMed  Article  Google Scholar 

39.
Brent, L. J. N. et al. Personality traits in rhesus macaques (Macaca mulatta) are heritable but do not predict reproductive output. Int. J. Primatol. 35, 188–209 (2014).
PubMed  Article  Google Scholar 

40.
Dubuc, C. et al. Sexually selected skin colour is heritable and related to fecundity in a non-human primate. Proc. R. Soc. B Biol. Sci. 281, 20141602 (2014).
Article  Google Scholar 

41.
Kimock, C. M., Dubuc, C., Brent, L. J. N. & Higham, J. P. Male morphological traits are heritable but do not predict reproductive success in a sexually-dimorphic primate. Sci. Rep. 9, 19794 (2019).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

42.
Kruuk, L. E. B. Estimating genetic parameters in natural populations using the ‘animal model’. Philos. Trans. R. Soc. B 359, 873–890 (2004).
Article  Google Scholar 

43.
Falk, D., Froese, N., Sade, D. S. & Dudek, B. C. Sex differences in brain/body relationships of Rhesus monkeys and humans. J. Hum. Evol. 36, 233–238 (1999).
CAS  PubMed  Article  Google Scholar 

44.
Herndon, J. G., Tigges, J., Anderson, D. C., Klumpp, S. A. & McClure, H. M. Brain weight throughout the life span of the chimpanzee. J. Comp. Neurol. 409, 567–572 (1999).
CAS  PubMed  Article  Google Scholar 

45.
Iwaniuk, A. N. Interspecific variation in sexual dimorphism in brain size in Nearctic ground squirrels (Spermophilus spp.). Can. J. Zool. 79, 759–765 (2001).
Article  Google Scholar 

46.
Towe, A. L. & Mann, M. D. Habitat-related variations in brain and body size of pocket gophers. J. Hirnforsch. 36, 195–201 (1995).
CAS  PubMed  Google Scholar 

47.
Kotrschal, A., Räsänen, K., Kristjánsson, B. K., Senn, M. & Kolm, N. Extreme sexual brain size dimorphism in sticklebacks: A consequence of the cognitive challenges of sex and parenting?. PLoS ONE 7, e30055 (2012).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

48.
Ritchie, S. J. et al. Sex differences in the adult human brain: Evidence from 5216 uk biobank participants. Cereb. Cortex 28, 2959–2975 (2018).
PubMed  PubMed Central  Article  Google Scholar 

49.
Whitten, P. L. Diet and dominance among female vervet monkeys (Cercopithecus aethiops). Am. J. Primatol. 5, 139–159 (1983).
PubMed  Article  Google Scholar 

50.
Mori, A. Analysis of population changes by measurement of body weight in the Koshima troop of Japanese monkeys. Primates 20, 371–397 (1979).
Article  Google Scholar 

51.
Small, M. F. Body fat, rank, and nutritional status in a captive group of Rhesus Macaques. Int. J. Primatol. 2, 91–95 (1981).
Article  Google Scholar 

52.
Sade, D. S. Population dynamics in relation to social structure on Cayo Santiago. Ybk. Phys. Anthr. 20, 253–262 (1976).
Google Scholar 

53.
Silk, J. B., Clark-Wheatley, C. B., Rodman, P. S. & Samuels, A. Differential reproductive success and facultative adjustment of sex ratios among captive female bonnet macaques (Macaca radiata). Anim. Behav. 29, 1106–1120 (1981).
Article  Google Scholar 

54.
Rawlins, R. G. & Kessler, M. J. The Cayo Santiago macaques: History, behavior, and biology (SUNY Series Primatology, Suny, 1986).
Google Scholar 

55.
Kessler, M. J. & Rawlins, R. G. A 75-year pictorial history of the Cayo Santiago rhesus monkey colony. Am. J. Primatol. 78, 6–43 (2016).
PubMed  Article  Google Scholar 

56.
Widdig, A. et al. Genetic studies on the Cayo Santiago rhesus macaques: A review of 40 years of research. Am. J. Primatol. 78, 44–62 (2016).
PubMed  Article  Google Scholar 

57.
Widdig, A. et al. Low incidence of inbreeding in a long-lived primate population isolated for 75 years. Behav. Ecol. Sociobiol. 71, 18 (2017).
PubMed  Article  Google Scholar 

58.
Cheverud, J. M. Epiphyseal union and dental eruption in Macaca mulatta. Am. J. Phys. Anthropol. 56, 157–167 (1981).
CAS  PubMed  Article  Google Scholar 

59.
Turnquist, J. E. & Kessler, M. J. Free-ranging Cayo Santiago rhesus monkeys (Macaca mulatta): I. Body size, proportion, and allometry. Am. J. Primatol. 19, 1–13 (1989).
PubMed  Article  Google Scholar 

60.
Havill, L. M. Osteon remodeling dynamics in macaca mulatta: Normal variation with regard to age, sex, and skeletal maturity. Calcif. Tissue Int. 74, 95–102 (2004).
CAS  PubMed  Article  Google Scholar 

61.
Konigsberg, L. et al. External brain morphology in rhesus macaques (Macaca mulatta). J. Hum. Evol. 19, 269–284 (1990).
Article  Google Scholar 

62.
Logan, C. J. & Clutton-Brock, T. H. Validating methods for estimating endocranial volume in individual red deer (Cervus elaphus). Behav. Process. 92, 143–146 (2013).
Article  Google Scholar 

63.
Jolly, C. The classification and natural history of Theropithecus (Simopithecus) (Andrew, 1916) baboons of the African Plio-Pleistocene. (Bull. Brit. Mus. Nat. Hist., 1972).

64.
Delson, E. et al. Body mass in Cercopithecidae (Primates, mammalia): Estimation and scaling in extinct and extant taxa. (American Museum of Natural History, 2000).

65.
Hadfield, J. D., Richardson, D. S. & Burke, T. Towards unbiased parentage assignment: Combining genetic, behavioural and spatial data in a Bayesian framework. Mol. Ecol. 15, 3715–3730 (2006).
CAS  PubMed  Article  Google Scholar 

66.
Hadfield, J. D. MCMCglmm Course Notes. (2016).

67.
Morrissey, M. B. & Wilson, A. J. pedantics: An r package for pedigree-based genetic simulation and pedigree manipulation, characterization and viewing: Computer program article. Mol. Ecol. Resour. 10, 711–719 (2009).
PubMed  Article  Google Scholar 

68.
Hadfield, J. D. MCMC methods for multi-response generalized linear mixed models: The MCMCglmm R package. J. Stat. Softw. 33, 1–22 (2010).
Article  Google Scholar 

69.
Hadfield, J. D. & Nakagawa, S. General quantitative genetic methods for comparative biology: Phylogenies, taxonomies and multi-trait models for continuous and categorical characters. J. Evol. Biol. 23, 494–508 (2010).
CAS  PubMed  Article  Google Scholar 

70.
Wilson, A. J. et al. An ecologist’s guide to the animal model. J. Anim. Ecol. 79, 13–26 (2010).
PubMed  Article  Google Scholar 

71.
Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest package: Tests in linear mixed effects models. J. Stat. Softw. 82, 13 (2017).
Article  Google Scholar 

72.
Lande, R. & Arnold, S. J. The measurement of selection on correlated characters. Evolution 37, 1210–1226 (1983).
PubMed  Article  Google Scholar 

73.
Morrissey, M. B. & Sakrejda, K. Unification of regression-based methods for the analysis of natural selection. Evolution 67, 2094–2100 (2013).
PubMed  Article  Google Scholar 

74.
Stinchcombe, J., Agrawal, A., Hohenlohe, P., Arnold, S. & Blows, M. Estimating nonlinear selection gradients using quadratic regression coefficients: Double or nothing?. Evolution 62, 2435–2440 (2008).
PubMed  Article  Google Scholar 

75.
Matsumura, S., Arlinghaus, R. & Dieckmann, U. Standardizing selection strengths to study selection in the wild: A critical comparison and suggestions for the future. Bioscience 62, 1039–1054 (2012).
Article  Google Scholar  More

This proteomic survey was conducted to identify proteins that were differentially expressed in the serum of manatees affected by two distinct mortality episodes: a red tide group and an unknown mortality episode group in the IRL. These groups were compared to a control group sampled at Crystal River. The red tide group’s exposure was evidenced by the presence of the PbTx antigen, with brevetoxin values in the 4.3 to 14.4 ng/ml range. The other group did not present with clinical symptoms except for mild cold stress in some animals. Two proteomics approaches were employed, 2D-DIGE and shot gun proteomics using LC–MS/MS, which provided similar results, suggesting that several serum proteins were specifically altered in each of the manatee mortality episode groups compared to the Crystal River control group. The differentially expressed serum proteins were cautiously identified based on annotation of the manatee genome6,7 and their amino acid sequence homologies with human serum proteins. While additional work still needs to be done to confirm that the identified manatee proteins function similarly to their human homologs, possible insight on the function of the proteins can be derived from human studies.
The two proteomics methods used, 2D-DIGE and iTRAQ LC–MS/MS are complementary and both rely on LC–MS/MS for protein identification. 2D-DIGE is a top-down approach, quantifying the differentially expressed proteins at the protein level before identifying the protein by LC–MS/MS, while the iTRAQ method is a bottom-up approach, where the whole proteome is first digested with trypsin, the generated peptides are separated by chromatography and identified and measured by mass spectrometry. Mass spectrometry has become the primary method to analyze proteomes, benefitting from genomic sequences and bioinformatics tools that can translate the sequences into predicted proteins. There are excellent reviews of proteomics methods and how they may be used across species8,9.
In total, 19 of the 26 proteins identified using the 2D-DIGE method were also identified by iTRAQ (Supplementary Table 1) which showed that these findings were replicated using two complementary experimental methods. In the 2D-DIGE method, most of the proteins were found in multiple spots, suggesting that they were differentially modified. 2D-DIGE can separate proteins based on a single charge difference. Some of the spots contained multiple proteins so it was difficult to determine the fold change of each of the proteins in these spots. For example, protein C4A was identified in 7 different spots, likely representing multiple isoforms. We were not able to corroborate the different post-translational modifications (PTMs) with iTRAQ, as the experiment was not designed to look for PTMs, only total protein quantitation. A drawback of 2D-DIGE is that keratin introduced into the sample from reagents at the time of electrophoresis or through the multiple steps required for protein extraction is also seen in the gels10,11,12. It is unlikely that the keratins were from the serum samples, as blood was collected directly into vacuum tubes. Because of the issue of keratin contamination, the 2D-DIGE method is considered more qualitative in its determination and thus in this study, iTRAQ data were the primary basis for quantitation.
Pathway analysis detects groups of proteins that are linked in pathways that may be related to disease processes. We used Pathway Studio using subnetwork enrichment analysis to determine disease pathways potentially in place for the red tide and IRL manatees. The Pathway Studio database is constructed from relationships detected between proteins and diseases from articles present in Pubmed but is heavily directed towards human and rodent proteomes. To be able to use this tool, we assigned human homologs to the identified manatee proteins, assuming that based on their sequence homology the proteins would function in a similar way. There are many studies that suggest this assumption has merit, for example Nonaka and Kimura have examined the evolution of the complement system and found clear indications of homology among vertebrates13.
The top 20 pathways for the red tide group (Table 3) and the IRL group (Table 4) show the diverse set of molecular pathways that may be affected by the exposures. Many of the same pathways appeared for both groups including thrombophilia, inflammation, wounds and injuries, acute phase reaction and amyloidosis. Thrombophilia was the most upregulated pathway for the IRL group (p-value 1.10E-19) and the second most upregulated pathway for the red tide group (p-value 4.1E-19). Thrombophilia, a condition in which blood clots occur in the absence of injury, happens when clotting factors become unbalanced. We obtained proteomics information on 12 of the proteins in this pathway, with some moving in opposing directions. The dysregulated proteins that were increased for both red tide and the IRL groups were SERPIN D1 (Serpin family member D 1), CRP (C-reactive protein), and PLAT (plasminogen activator) and the ones that were decreased in both groups, were SERPIN C1 (Serpin family member C 1), F5 (coagulation factor 5), and ALB (albumin). One protein, AGT (angiotensinogen), was upregulated in the red tide group but downregulated in the IRL. HRG (histidine rich glycoprotein), PROS1 (Protein S), C4BPA (complement component 4 binding protein alpha, and F2 (coagulation factor 2, also known as prothrombin) were downregulated in the red tide group but upregulated in the IRL group. The disparate regulation of proteins in this pathway suggests that clotting was among the pathways disrupted in the affected manatees. Red tide exposed manatees often present with hemorrhagic issues in their intestines, lungs and the brain (14), suggesting that downregulation of coagulation factors may be responsible for this clinical evaluation. Interestingly HRG was upregulated in the IRL by 1.34-fold and downregulated in the red tide group by 0.56-fold, making this protein a good biomarker to distinguish the two events.
Table 3 Subnetwork enrichment pathways for serum proteins obtained from manatees exposed to red tide.
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Table 4 Subnetwork enrichment pathways for serum proteins obtained from manatees sampled in the IRL.
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Among the manatees in the red tide group, inflammation was ranked 3rd (p-value  More

Oceanic gateways play a key role in controlling global ocean circulation and climate systems1. Ancient seaways are unique environments in which a complex interplay of processes may take place (i.e., oceanic-, tidal-, bottom-, turbiditic- and wind-currents)2,3. The constricted morphology of the seaway usually funnels and amplifies the currents that shape the seafloor (i.e., tidal currents)4. Previous sedimentological studies of ancient seaways have been largely focussed on shallow counterparts (generally between 100 and 150 m of water depth)4,5,6. Few published examples of deep ancient seaways ( > 150 m) and associated deposits can be found. However, oceanographic studies have shown that deep seaways are different from shallow ones, with bottom-currents sometimes playing a dominant role7,8,9. The Rifian Corridor is one of those few examples (Fig. 1)2,3,10,11.
Figure 1

Palaeogeographic reconstruction of the late Miocene western Mediterranean with the location of the studied outcrops; red (lower) and orange (upper) arrows show palaeo-Mediterranean Outflow Water (palaeo-MOW) branches (modified from de Weger et al.2). Below, schematic sedimentary logs of the studied outcrops. Map created with Adobe Illustrator, version 22.1.0 (https://www.adobe.com/products/illustrator.html).

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During the late Miocene, the Atlantic Ocean and the Mediterranean Sea were connected by two principal gateways, with a complex morphology, sills and channels through south Iberia and north Africa —the Betic and Rifian corridors, respectively12,13. The Rifian Corridor was a main deep seaway of this network (Fig. 1). This gateway progressively closed (7.1–6.9 Ma) due to tectonically induced uplift, leading to the onset of the Mediterranean Salinity Crisis in the late Miocene13,14. During the late Tortonian, the seaway evolved into a narrow, deep corridor hosting a complex interplay of processes2,3.
Ichnological analysis comprises a wide range of tools (e.g., ichnofabric approach, ichnofacies model) that prove very useful in sedimentary basin research15. The ichnofacies model is of special interest for detailed palaeoenvironmental reconstructions and for recognizing, distinguishing, and interpreting sedimentary environments16,17,18,19. Recent steps in ichnological research have established means of recognising and characterising contouritic processes, revealing the importance of ichnology as a proxy for discerning between contourites, turbidites, hemipelagites and pelagites20,21,22,23,24, but not without scepticism25. At any rate, the relationship between deep-sea settings and trace fossils is very complex, and depends highly upon the palaeonvironmental factors that affect trace makers26.
Trace-fossil research on seaway environments has been conducted mainly on shallow marine settings, including brackish-water ecosystems (i.e., estuarine complexes, resulting in the so-called “brackish-water model”27,28), beach–shoreface complexes with evidence of tidal processes29,30, and compound dune fields31. Still, detailed trace-fossil analysis and ichnofacies characterisation of ancient deep seaways has never been carried out. The aim of this research is to conduct a detailed ichnological analysis of selected outcrops of the Rifian Corridor (Ain Kansera, Sidi Chahed, Kirmta and Sidi Harazem), as a unique opportunity to assess trace-fossil variations to interpret an ancient deep-water seaway where shallow marine processes (i.e., tidal variations), pelagic/hemipelagic settling, turbiditic supplies and contouritic flows closely (less than 20 km) interact2,3. We evaluate the importance of palaeoenvironmental factors such as nutrients, oxygenation, and flow velocity in a setting dominated by bottom currents, and their incidence on the trace maker community. The utility of the ichnofacies approach is underlined within the framework of improving high-resolution palaeoenvironmental reconstructions in different depositional environments of ancient deep gateways.
Trace-fossil assemblages at the Rifian Corridor
In both contouritic and turbiditic deposits, ichnodiversity is low (4 and 5 ichnogenera, respectively), whereas trace-fossil abundance is high in the former and moderate in the latter. Shallow marine deposits from the southern Rifian Corridor feature an abundant and moderately diverse trace-fossil assemblage (9 ichnogenera). Within the selected outcrops, the clear ichnological variability can be attributed to the different facies.
The Sidi Harazem turbiditic ichnoassemblage consists of 5 ichnogenera —Ophiomorpha (O. rudis), Planolites, Spirophyton, Thalassinoides, and Zoophycos (Fig. 3E–H)— and the thick sandstone beds are more bioturbated than the marly ones. Ophiomorpha is the most abundant ichnogenus, and appears in the thick turbiditic sandstone beds; Thalassinoides is common, Planolites rare, and Zoophycos and Spirophyton is occasionally found. The trace-fossil assemblage of marly pelagic and hemipelagic deposits from the Sidi Harazem consists of abundant undifferentiated structures and scarce Planolites-like and Thalassinoides-like trace fossils.
The sandy contourites in Kirmta and Sidi Chahed comprise a highly abundant and scarcely diverse trace-fossil assemblage (4 ichnogenera), dominated by Macaronichnus and Scolicia, and common Planolites and Thalassinoides (Fig. 2). Trace fossils were predominantly found in the planar-stratified and cross-bedded sandstone. Turbidites show an absence of discrete trace fossils. The trace = fossil assemblage of muddy contourite deposits from both outcrops consist of regular undifferentiated biogenic structures and scarce Planolites-like and Thalassinoides-like trace fossils.
Figure 2

Trace-fossil specimens from the sandy contourite deposits at Sidi Chahed (A–D) and Kirmta (E–H) outcrops. (A, B) Scolicia in the sole of sandy clastic contouritic beds of Sidi Chahed; (C) Close-up view of Macaronichnus at Sidi Chahed; (D) Planolites within the interbedding of the foresets at Sidi Chahed. (E) Scolicia and some Macaronichnus at Kirmta; (F, G) Macaronichnus isp. and some Thalassinoides in the sole of sandy clastic contouritic beds at Kirmta; (H) Close-up view of Macaronichnus at Kirmta. Macaronichnus (Ma), Planolites (Pl), Scolicia (Sc), and Thalassinoides (Th).

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The Ain Kansera section is characterised by a shallow marine ichnoassemblage with high ichnodiversity and an abundance of vertical structures, including 9 ichnogenera in the sandstone beds: Conichnus, Diplocraterion, Macaronichnus, Ophiomorpha, Parahaentzschelinia, Planolites, Scolicia, Skolithos, and Thalassinoides (Fig. 3A–D). The sandstone beds with swaley cross-stratification show a change in the trace-fossil assemblage towards the top of the outcrop. The lower sandstone beds present dominant Conichnus and Macaronichnus, common Parahaentzschelinia and Thalassinoides, and rare Diplocraterion, Planolites, and Scolicia. The upper sandstone beds record the disappearance of Conichnus and Parahaentzschelinia, while Ophiomorpha and Skolithos become dominant.
Figure 3

Trace-fossil specimens from shallow marine deposits at Ain Kansera (A–D) and turbiditic deposits at Sidi Harazem (E–H). (A) Close-up view of Macaronichnus at Ain Kansera; (B) Densely Conichnus assemblage at Ain Kansera; (C) Macaronichnus cross-cut by a Skolithos at Ain Kansera; (D) Skolithos and Ophiomorpha at Ain Kansera; (E, F) Ophiomorpha (O. rudis) at Sidi Harazem; (G) Zoophycos cross-cut by a Thalassinoides at Sidi Harazem; (H) Close-up view of Spyrophyton at Sidi Harazem. Conichnus (Co), Macaronichnus (Ma), Ophiomorpha (Op), Skolithos (Sk), Spyrophyton (Sp), Thalassinoides (Th), and Zoophycos (Zo).

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Ichnofacies characterisation
The trace-fossil assemblage of Sidi Harazem is typified by vertical burrows of Ophiomorpha rudis and some Thalassinoides. Ophiomorpha is generally but not exclusively characteristic of high-energy environments (i.e., shoreface) in well-sorted, shifting sandy substrates, constituting a common element of the Skolithos and Cruziana ichnofacies17,18. However, the appearance of Ophiomorpha in deep-sea environments is also recorded, and usually explained as an effect of transport of the trace makers by currents from shallow marine environments into the deep-sea33,34. Uchman35 proposed the Ophiomorpha rudis ichnosubfacies within the Nereites ichnofacies for the record of ichnoassemblages dominated by Ophiomorpha rudis in thick sandstone beds related with channels and proximal lobes in turbiditic systems36. Accordingly, the Sidi Harazem trace-fossil assemblage could be associated with the Ophiomorpha rudis ichnosubfacies. Ichnosubfacies/ichnofacies assignation is tentative due to the absence of other components of this ichnosubfacies (e.g., Scolicia, Nereites, graphoglyptids); this uncertainty is tied to outcrop limitations, e.g. the low exposure of turbiditic soles and difficulties in observing discrete trace fossils in the non-compact hemipelagic and pelagic deposits.
The trace-fossil assemblages of Kirmta and Sidi Chahed feature high abundance and low ichnodiversity, being dominated by horizontal trace fossils, such as Macaronichnus and Scolicia. Macaronichnus is usually interpreted as a shallow marine (up to foreshore) trace fossil37 that occasionally appears in deeper water environments38,39 and is commonly associated with the Skolithos ichnofacies17,18,19,40. Scolicia presents a wide environmental range, but is a typical element of the deep-marine Nereites and the shelfal Cruziana ichnofacies40. The proximal expression of the Cruziana ichnofacies is dominated by deposit-feeding burrows, but also includes structures of passive carnivores, omnivores, suspension feeders, as well as grazing forms41. This ichnofacies is defined as a transition between the distal expression of the Skolithos ichnofacies and the archetypal Cruziana ichnofacies41. The low ichnodiversity observed within the contourite facies from Kirmta and Sidi Chahed outcrops, together with the ubiquity of the dominant trace fossils, hamper a conclusive ichnofacies assignation. Still, though Macaronichnus is typical from high energy shallow marine environments, it may locally appear in the proximal Cruziana ichnofacies41. Considering the dominance of horizontal feeding trace fossils produced by deposit and detritus feeders over dwelling structures of suspension feeding structures, contourite ichnoassemblages at the Rifian Corridor, registered at Kirmta and Sidi Chahed outcrops, can therefore be tentatively assigned to an impoverished proximal Cruziana ichnofacies18.
The trace-fossil assemblage of Ain Kansera is characterised by moderate ichnodiversity with a dominance of vertical (Skolithos and Ophiomorpha), cylindrical or conic-shaped (Conichnus) dwelling burrows of suspension feeders and passive predators. Horizontal trace fossils produced by a mobile fauna are scarce, mainly associated with Macaronichnus trace makers. According to these ichnological features, shallow marine facies at the Rifian Corridor —represented by Ain Kansera sediments— can be clearly assigned to the Skolithos ichnofacies, with predominant burrow systems having vertical, cylindrical, or U-shaped components of suspension feeders and passive predators, and a scarcity of horizontal trace fossils17,18,19,40,42.
Ichnofacies in the Rifian Corridor seaways: hydrodynamic energy and the incidence of bottom currents
Over the past years, detailed ichnological research has revealed the major incidence of particular environmental factors (e.g., organic-matter content, oxygenation, sedimentation rate) on ichnological attributes from deep-sea environments, including ichnofacies characterisation and distribution26. The deep sea is a complex environment where several depositional processes co-exist, including pelagic/hemipelagic settling, bottom currents and gravity flows9. Trace-fossil analysis has proven useful for discerning and characterising such sedimentary environments and associated deposits21. Hydrodynamic conditions are a very significant limiting factor for trace makers, inducing variations in distribution and behaviour, hence in the preservation of trace fossils19,29,43,44. Typically, ichnoassemblages related to high energy conditions are characterised by vertical dwelling structures of infaunal suspension feeders and/or passive predators, forming low-diversity suites; ichnoassemblages related to low energy conditions are dominated by horizontal feeding trace fossils of deposit and detritus feeders, as well as higher diversity19. Ichnofacies identification is mainly based on the recognition of key features that connect biological structures with physical parameters (i.e., environmental conditions)17,18,19. Accordingly, ichnofacies reflect specific combinations of organisms´ responses to a wide range of environmental conditions.
In the case of seaways, prevailing hydrodynamic conditions are a main environmental factor, along with controlling depositional processes and sedimentation regimes6,30. Even though the number of trace-fossil studies is considerably lower than in other clastic shallow or deep marine environments, ichnological analysis has proven to be useful to characterise waves, tides or storms in shallow seaways29,30, overlooking deep seaways and their implications. Deep seaways with narrow palaeogeographical configuration, as is the case of the Rifian Corridor10, would promote higher energetic conditions than those typical of deep-sea environments. In the study area, clearly distinct sedimentary environments —in terms of hydrodynamic conditions, bathymetry, rate of sedimentation, etc.— are closely spaced2, passing from shallow marine to turbiditic slope systems in less than 20 km (Fig. 4). Such variations in palaeoenvironmental conditions are supported by ichnofacies characterisation and distribution.
Figure 4

Palaeogeographic model of the late Miocene Rifian Corridor (Morocco) with ichnofacies distribution (lower red and upper orange branches indicate palaeo-MOW location; modified from de Weger et al.2). Conichnus (Co), Diplocraterion (Di), Macaronichnus (Ma), Ophiomorpha (Op), Parahaentzschelinia (Ph), Planolites (Pl), Scolicia (Sc), Skolithos (Sk), Spyrophyton (Sp), Thalassinoides (Th), and Zoophycos (Zo).

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Turbidite deposits from Sidi Harazem, emplaced on the slope of the Rifian Corridor, are typified by vertical trace fossils, mainly by the record of Ophiomorpha rudis. These ichnological attributes are similar to those associated with particular sub-environments (e.g., channels and proximal trubiditic lobes) of the turbiditic systems, conforming the Ophiomorpha rudis ichnosubfacies inside the Nereites ichnofacies36.
Sandy contourite 2D- and 3D-dune facies (upper slope environment) (Fig. 4) from Sidi Chahed and Kirmta are related to high-energy deep-water environments. However, they are dominated by horizontal trace fossils (Macaronichnus and Scolicia) produced by mobile deposit- and detritus-feeders, discarding a direct assignation to the Skolithos ichnofacies. In this case, palaeoenvironmental conditions other than hydrodynamic energy must be considered to explain the dominance of horizontal forms and the absence of vertical biogenic structures. The record of densely Macaronichnus ichnoasemblages in these contourite sediments was recently linked to high nutrient supply provided by ancient bottom currents39,45. This agrees with the record of Scolicia: its abundance and size usually increase in conjunction with greater amounts and nutritious values of benthic food20,46. Thus, the strong palaeo-MOW bottom currents that dominated the slope may have created well-oxygenated and nutrient-rich benthic environments, favouring colonisation by trace makers that could exploit such accumulations of organic matter inside the sediment. Macaronichnus and Scolicia producers could develop an opportunistic behaviour, determining rapid and complete bioturbation, avoiding colonisation by other trace makers —including suspension feeders—these ichnological features resemble the Cruziana ichnofacies attributes. Notwithstanding, the high ichnodiversity that is characteristic of the Cruziana ichnofacies is absent here. The great abundance and low ichnodiversity observed for the contourite facies appear to indicate the absence of an archetypal Cruziana ichnofacies, but the development of the proximal Cruziana ichnofacies. Bottom currents and their associated deposits (i.e., contourites) have been previously linked to both the Cruziana and Zoophycos ichnofacies in Cyprus Miocene carbonate contourite deposits22,23, meaning that contourite deposits are not exclusively related to a single ichnofacies. The replacement from the Zoophycos to Cruziana ichnofacies was interpreted to be mainly controlled by sea level dynamics23.
The shallow marine facies from Ain Kansera (shoreface environment) are dominated by vertical, cylindrical, or U-shaped dwelling burrows (Conichnus, Ophiomorpha and Skolithos) of suspension feeders (Fig. 4). These attributes are usually related to high energetic conditions developed in shallow marine environments conforming the Skolithos ichnofacies18.
In short, at the Rifian Corridor, ichnofacies distributions from proximal to distal settings are controlled by bottom currents (palaeo-MOW), with hydrodynamic conditions being the major palaeonvironmental limiting factor. Particularly noteworthy is the development of the proximal Cruziana ichnofacies in deeper settings from the slope environments; bottom currents generated high energetic conditions similar to those of shallow/proximal areas. More

1.
Ramos-Onsins, S. E. & Rozas, J. Statistical properties of new neutrality tests against population growth. Mol. Biol. Evol. 19, 2092–2100 (2000).
Article  Google Scholar 
2.
Wan, Q.-H., Wu, H., Fujihara, T. & Fang, S.-G. Which genetic marker for which conservation genetics issue? Electrophoresis 25, 2165–2176 (2004).
CAS  PubMed  Article  Google Scholar 

3.
Selkoe, K. A. & Toonen, R. J. Microsatellites for ecologists: a practical guide to using and evaluating microsatellite markers. Ecol. Lett. 9, 615–629 (2006).
PubMed  Article  Google Scholar 

4.
Rogers, A. R. & Harpending, H. Population growth makes waves in the distribution of pairwise genetic differences. Mol. Biol. Evol. 9, 552–569 (1992).
CAS  PubMed  Google Scholar 

5.
Nabholz, B., Mauffrey, J.-F., Bazin, E., Galtier, N. & Glemin, S. Determination of mitochondrial genetic diversity in mammals. Genetics 178, 351–361 (2008).
PubMed  PubMed Central  Article  Google Scholar 

6.
Whitfield, A., Mkare, T. K., Teske, P. R., James, N. & Cowley, P. D. Life-histories explain the conservation status of two estuary-associated pipefishes. Biol. Conserv. 212, 256–264 (2017).
Article  Google Scholar 

7.
Leffler, E. M. et al. Revisiting an old riddle: what determines genetic diversity levels within species?. PLoS Biol. 10, e1001388 (2012).
CAS  PubMed  PubMed Central  Article  Google Scholar 

8.
Kimura, M. & Crow, J. F. The number of alleles that can be maintained in a finite population. Genetics 49, 725–738 (1964).
CAS  PubMed  PubMed Central  Article  Google Scholar 

9.
Kimura, M. The Neutral Theory of Molecular Evolution (Cambridge University Press, Cambridge, 1983).
Google Scholar 

10.
Romiguier, J. et al. Comparative population genomics in animals uncovers the determinants of genetic diversity. Nature 515, 261–263 (2014).
ADS  CAS  PubMed  Article  Google Scholar 

11.
Ellegren, H. & Galtier, N. Determinants of genetic diversity. Nat. Rev. Genet. 17, 422–433 (2016).
CAS  PubMed  Article  Google Scholar 

12.
Caley, M. J. et al. Recruitment and the local dynamics of open marine populations. Annu. Rev. Ecol. Syst. 27, 477–500 (1996).
Article  Google Scholar 

13.
Teske, P. R. et al. Implications of life history for genetic structure and migration rates of southern African coastal invertebrates: planktonic, abbreviated and direct development. Mar. Biol. 152, 697–711 (2007).
Article  Google Scholar 

14.
Mkare, T. K., van Vuuren, B. J. & Teske, P. R. Conservation implications of significant population differentiation in an endangered estuarine seahorse. Biodivers. Conserv. 26, 1275–1293 (2017).
Article  Google Scholar 

15.
Vandewoestijne, S., Schtickzelle, N. & Baguette, M. Positive correlation between genetic diversity and fitness in a large, well-connected metapopulation. BMC Biol. 6, 46 (2008).
PubMed  PubMed Central  Article  Google Scholar 

16.
Frankham, R. Genetic rescue of small inbred populations: meta-analysis reveals large and consistent benefits of gene flow. Mol. Ecol. 24, 2610–2618 (2015).
PubMed  Article  Google Scholar 

17.
Nussear, K. E. et al. Translocation as a conservation tool for Agassiz’s desert tortoises: survivorship, reproduction, and movements. J. Wildl. Manag. 76, 1341–1353 (2012).
Article  Google Scholar 

18.
Wright, D. J. et al. The impact of translocations on neutral and functional genetic diversity within and among populations of the Seychelles warbler. Mol. Ecol. 23, 2165–2177 (2014).
PubMed  PubMed Central  Article  Google Scholar 

19.
Whiteley, A. R., Fitzpatrick, S. W., Funk, W. C. & Tallmon, D. A. Genetic rescue to the rescue. Trends Ecol. Evol. 30, 42–49 (2015).
PubMed  Article  Google Scholar 

20.
Edmands, S. & Timmerman, C. C. Modeling factors affecting the severity of outbreeding depression. Conserv. Biol. 17, 883–892 (2003).
Article  Google Scholar 

21.
Tallmon, D. A., Luikart, G. & Waples, R. S. The alluring simplicity and complex reality of genetic rescue. Trends Ecol. Evol. 19, 489–496 (2004).
PubMed  Article  Google Scholar 

22.
Frankham, R. et al. Predicting the probability of outbreeding depression. Conserv. Biol. 25, 465–475 (2011).
PubMed  Article  Google Scholar 

23.
Miller, K. A. et al. Securing the demographic and genetic future of tuatara through assisted colonization. Conserv. Biol. 26, 790–798 (2012).
PubMed  Article  Google Scholar 

24.
Frankham, R. Genetics and extinction. Biol. Conserv. 126, 131–140 (2005).
Article  Google Scholar 

25.
Peniche, G. et al. Protecting free-living dormice: molecular identification of cestode parasites in captive dormice (Muscardinus avellanarius) destined for reintroduction. EcoHealth 14, 106–116 (2017).
PubMed  Article  Google Scholar 

26.
Pollom, R. Hippocampus capensis. The IUCN Red List of Threatened Species 2017: .T10056A54903534. http://dx.doi.org/https://doi.org/10.2305/IUCN.UK.2017-3.RLTS.T10056A54903534.en (2017).

27.
Bell, E. M., Lockyear, J. F., McPherson, J. M., Marsden, A. D. & Vincent, A. C. J. First field studies of an endangered South African seahorse, Hippocampus capensis. Environ. Biol. Fishes 67, 35–46 (2003).
Article  Google Scholar 

28.
Lockyear, J. F., Hecht, T., Kaiser, H. & Teske, P. R. The distribution and abundance of the endangered Knysna seahorse Hippocampus capensis (Pisces: Syngnathidae) in South African estuaries. Afr. J. Aquat. Sci. 31, 275–283 (2006).
Article  Google Scholar 

29.
Teske, P. R., Cherry, M. I. & Matthee, C. A. Population genetics of the endangered Knysna seahorse, Hippocampus capensis. Mol. Ecol. 12, 1703–1715 (2003).
CAS  PubMed  Article  Google Scholar 

30.
López, A., Vera, M., Planas, M. & Bouza, C. Conservation genetics of threatened Hippocampus guttulatus in vulnerable habitats in NW Spain: temporal and spatial stability of wild populations with flexible polygamous mating system in captivity. PLoS ONE 10, e0117538 (2015).
PubMed  PubMed Central  Article  CAS  Google Scholar 

31.
Pickrell, J. K. & Pritchard, J. K. Inference of population splits and mixtures from genome-wide allele frequency data. PLoS Genet. 8, e1002967 (2012).
CAS  PubMed  PubMed Central  Article  Google Scholar 

32.
Lande, R. Genetics and demography in biological conservation. Science 241, 1455–1460 (1988).
ADS  CAS  PubMed  Article  Google Scholar 

33.
Wang, J. Estimation of effective population sizes from data on genetic markers. Phil. Trans. R. Soc. B360, 1395–1409 (2005).
Article  CAS  Google Scholar 

34.
Schwartz, M. K., Luikart, G. & Waples, R. S. Genetic monitoring as a promising tool for conservation and management. Trends Ecol. Evol. 22, 25–33 (2007).
PubMed  Article  Google Scholar 

35.
Armstrong, D. P. & Seddon, P. J. Directions in reintroduction biology. Trends Ecol. Evol. 23, 20–25 (2008).
PubMed  Article  Google Scholar 

36.
Cerón-Souza, I. et al. Contrasting demographic history and gene flow patterns of two mangrove species on either side of the Central American Isthmus. Ecol. Evol. 5, 3486–3499 (2015).
PubMed  PubMed Central  Article  Google Scholar 

37.
Woodall, L. C., Koldewey, H. J., Boehm, J. T. & Shaw, P. W. Past and present drivers of population structure in a small coastal fish, the European long snouted seahorse Hippocampus guttulatus. Conserv. Genet. 16, 1139–1153 (2015).
Article  Google Scholar 

38.
Teske, P. R. et al. Molecular evidence for long-distance colonization in an Indo-Pacific seahorse lineage. Mar. Ecol. Prog. Ser. 286, 249–260 (2005).
ADS  CAS  Article  Google Scholar 

39.
Heydorn, A. E. F. & Grindley, J. R. Estuaries of the Cape: Part II Synopses of available information on individual systems. Report 30. (Associated Printing and Publishing Co. (Pty) Ltd., 1985).

40.
Turpie, J. K. & Clark, B. Development of a conservation plan for temperate South African estuaries on the basis of biodiversity importance, ecosystem health and economic costs and benefits. Report by Anchor Environmental Consultants. C.A.P.E. Regional Estuarine Management Programme. 125 (2007).

41.
Penrith, M. J. & Penrith, M. Redescription of Pandaka silvana (Barnard) (Pisces, Gobiidae). Ann. South Afr. Mus. 60, 105–108 (1972).
Google Scholar 

42.
Branch, G. M. The ecology of Patella linnaeus from the cape Peninsula, South Africa I. Zonation, movements and feeding. Zool. Afr. 6, 1–38 (1971).
Article  Google Scholar 

43.
Largier, J. L., Attwood, C. & Harcourt-Baldwin, J. L. The hydrographic character of the Knysna Estuary. Trans. R. Soc. South Afr. 55, 107–122 (2000).
Article  Google Scholar 

44.
Russell, I. A. Mass mortality of marine and estuarine fish in the Swartvlei and Wilderness lake systems, Southern Cape. South. Afr. J. Aquat. Sci. 20, 93–96 (1994).
Google Scholar 

45.
Roberts, M. J., van der Lingen, C. D., Whittle, C. & van den Berg, M. Shelf currents, lee-trapped and transient eddies on the inshore boundary of the Agulhas Current, South Africa: their relevance to the KwaZulu-Natal sardine run. Afr. J. Mar. Sci. 32, 423–447 (2010).
Article  Google Scholar 

46.
Teske, P. R., Bader, S. & Golla, T. R. Passive dispersal against an ocean current. Mar. Ecol. Prog. Ser. 539, 153–163 (2015).
ADS  CAS  Article  Google Scholar 

47.
Claassens, L. An artificial water body provides habitat for an endangered estuarine seahorse species. Estuar. Coast. Shelf Sci. 180, 1–10 (2016).
ADS  Article  Google Scholar 

48.
Wilcove, D. S., Rothstein, D., Dubow, J., Phillips, A. & Losos, E. Quantifying threats to imperiled species in the United States: Assessing the relative importance of habitat destruction, alien species, pollution, overexploitation, and disease. Bioscience 48, 607–615 (1998).
Article  Google Scholar 

49.
Hey, J. Isolation with migration models for more than two populations. Mol. Biol. Evol. 27, 905–920 (2010).
CAS  PubMed  Article  Google Scholar 

50.
Claassens, L., Barnes, R. S. K., Wasserman, J., Lamberth, S. J., Miranda, A. F., van Niekerk, L. & Adams, J. B. Knysna Estuary health: ecological status, threats and options for the future. Afr. J. Aquat. 45 (2020).

51.
Nielsen, R. & Wakeley, J. Distinguishing migration from isolation: a Markov chain Monte Carlo approach. Genetics 158, 885–896 (2001).
CAS  PubMed  PubMed Central  Google Scholar 

52.
Whitfield, A. K. Threatened fishes of the world: Hippocampus capensis Boulenger, 1900 (Syngnathidae). Environ. Biol. Fishes 44, 362–362 (1995).
Article  Google Scholar 

53.
Yue, G. H., David, L. & Orban, L. Mutation rate and pattern of microsatellites in common carp (Cyprinus carpio L.). Genetica 129, 329–331 (2007).
CAS  PubMed  Article  Google Scholar 

54.
Waples, R. S. & Do, C. Linkage disequilibrium estimates of contemporary Ne using highly variable genetic markers: a largely untapped resource for applied conservation and evolution. Evol. Appl. 3, 244–262 (2010).
PubMed  Article  Google Scholar 

55.
Do, C. et al. NeEstimator v2: re-implementation of software for the estimation of contemporary effective population size (Ne) from genetic data. Mol. Ecol. Resour. 14, 209–214 (2014).
CAS  PubMed  Article  Google Scholar 

56.
Heled, J. & Drummond, A. J. Bayesian inference of population size history from multiple loci. BMC Evol. Biol. 8, 289 (2008).
PubMed  PubMed Central  Article  CAS  Google Scholar 

57.
Drummond, A. J., Suchard, M. A., Xie, D. & Rambaut, A. Bayesian phylogenetics with BEAUti and the BEAST 1.7. Mol. Biol. Evol. 29, 1969–1973 (2012).
CAS  PubMed  PubMed Central  Article  Google Scholar 

58.
Peakall, R. & Smouse, P. E. GenAlEx 6.5: genetic analysis in excel. Population genetic software for teaching and research—an update. Bioinformatics 28, 2537–2539 (2012).
CAS  PubMed  PubMed Central  Article  Google Scholar 

59.
Lischer, H. E. L. & Excoffier, L. PGDSpider: an automated data conversion tool for connecting population genetics and genomics programs. Bioinformatics 28, 298–299 (2012).
CAS  PubMed  Article  Google Scholar 

60.
Rambaut, A., Suchard, M. A., Xie, D. & Drummond, A. J. Tracer v1.6. (2014). More

Cuba has hosted relatively small numbers of tourist groups given its size, which might have helped to keep invasive plants at bay. Credit: Roberto Machado Noa/LightRocket/Getty

Ecology
18 February 2021

Cuba’s relatively closed economy could explain why it has fewer invasive plant species per unit area than other Caribbean islands.

For more than 60 years, the rocky relationship between the United States and Cuba has helped to steer tourists and businesses away from the Caribbean island. Now, researchers have found that Cuba’s economic and political isolation might also have limited the spread of invasive plants.
Meghan Brown at Hobart and William Smith Colleges in Geneva, New York, and her colleagues estimated the number of invasive plant species on 45 Caribbean islands. The researchers found that larger islands tend to have more exotic plant species than do smaller ones. But Cuba, the biggest island in the Caribbean, is home to hundreds fewer such species than expected for its size.
Mass tourism seems to favour the introduction of invasive plants, the team found, probably because hotels plant exotic ornamental species and tourists carry seeds in their bags or on their shoes. Cuba — which has one of the region’s lowest shares of holidaymakers in comparison to its area — has about the same number of invasive species as Puerto Rico, which is one-tenth the size of Cuba but has many more visitors for its land area. More

1.
Bahrick, L. E. & Watson, J. S. Detection of intermodal proprioceptive–visual contingency as a potential basis of self-perception in infancy. Dev. Psychol. 21, 963 (1985).
Article  Google Scholar 
2.
Van Den Bos, E. & Jeannerod, M. Sense of body and sense of action both contribute to self-recognition. Cognition 85, 177–187 (2002).
Article  Google Scholar 

3.
Wilson, M. Six views of embodied cognition. Psychon. B. Rev. 9, 625–636 (2002).
Article  Google Scholar 

4.
Smith, L. & Gasser, M. The development of embodied cognition: Six lessons from babies. Artif. life 11, 13–29 (2005).
Article  Google Scholar 

5.
Shettleworth, S. J. Cognition, Evolution, and Behavior. Oxford University Press.

6.
Kohda, M. et al. If a fish can pass the mark test, what are the implications for consciousness and self-awareness testing in animals?. PLoS Biol 17, e3000021 (2019).
CAS  Article  Google Scholar 

7.
Gallup, G. G. Chimpanzees: Self-recognition. Science 167, 86–87 (1970).
ADS  Article  Google Scholar 

8.
Epstein, R., Lanza, R. P. & Skinner, B. F. “Self-awareness” in the pigeon. Science 212, 695–696 (1981).
ADS  CAS  Article  Google Scholar 

9.
Heyes, C. M. Self-recognition in primates: Further reflections create a hall of mirrors. Anim. Behav. 50, 1533–1542 (1995).
Article  Google Scholar 

10.
Suddendorf, T. & Butler, D. L. Response to Gallup et al.: Are rich interpretations of visual self-recognition a bit too rich?. Trends. Cogn. Sci. 18, 58–59 (2014).
Article  Google Scholar 

11.
Reiss, D. & Marino, L. Mirror self-recognition in the bottlenose dolphin: A case of cognitive convergence. Proc. Natl. Acad. Sci. USA 98, 5937–5942 (2001).
ADS  CAS  Article  Google Scholar 

12.
Plotnik, J. M., De Waal, F. B. & Reiss, D. Self-recognition in an Asian elephant. Proc. Natl. Acad. Sci. USA 103, 17053–17057 (2006).
ADS  CAS  Article  Google Scholar 

13.
Prior, H., Schwarz, A. & Güntürkün, O. Mirror-induced behavior in the magpie (Pica pica): Evidence of self-recognition. PLoS Biol 6, e202 (2008).
Article  Google Scholar 

14.
Bekoff, M. & Sherman, P. W. Reflections on animal selves. Trends Ecol. Evol. 19, 176–180 (2004).
Article  Google Scholar 

15.
Lenkei, R., Faragó, T., Kovács, D., Zsilák, B. & Pongrácz, P. That dog won’t fit: Body size awareness in dogs. Anim. Cogn 23, 337–350 (2019).
Article  Google Scholar 

16.
Zazzo, R. Des enfants, des singes et des chiens devant le miroir. Rev. Psychol. Appl. 29, 235–246 (1979).
Google Scholar 

17.
Cuthill, I. & Guilford, T. Perceived risk and obstacle avoidance in flying birds. Anim. Behav. 40, 188–190 (1990).
Article  Google Scholar 

18.
Khvatov, I. A., Sokolov, A. Y. & Kharitonov, A. N. Snakes Elaphe radiata may acquire awareness of their body limits when trying to hide in a shelter. Behav. Sci. 9, 67 (2019).
Article  Google Scholar 

19.
Maeda, T. & Fujita, K. Do dogs (Canis familiaris) recognize their own body size? In Proceedings of the 2nd Canine Science Forum, Vienna, Austria, 52 (2010).

20.
Dale, R. & Plotnik, J. M. Elephants know when their bodies are obstacles to success in a novel transfer task. Sci. Rep. 7, 46309 (2017).
ADS  CAS  Article  Google Scholar 

21.
Brownell, C. A., Zerwas, S. & Ramani, G. B. “So big”: The development of body self-awareness in toddlers. Child Dev. 78, 1426–1440 (2007).
Article  Google Scholar 

22.
Povinelli, D. J. & Cant, J. G. Arboreal clambering and the evolution of self-conception. Q. Rev. Biol. 70, 393–421 (1995).
CAS  Article  Google Scholar 

23.
Povinelli, D. J. Failure to find self-recognition in Asian elephants (Elephas maximus) in contrast to their use of mirror cues to discover hidden food. J. Comp. Psychol. 103, 122 (1989).
Article  Google Scholar 

24.
Topál, J. et al. The dog as a model for understanding human social behaviour. Adv. Stud. Behav. 39, 71–116 (2009).
Article  Google Scholar 

25.
Sanford, E. M., Burt, E. R. & Meyers-Manor, J. E. Timmy’s in the well: Empathy and prosocial helping in dogs. Learn. Behav. 46, 374–386 (2018).
Article  Google Scholar 

26.
Pongrácz, P., Bánhegyi, P. & Miklósi, Á. When rank counts—dominant dogs learn better from a human demonstrator in a two-action test. Behaviour 149, 111–132 (2012).
Article  Google Scholar 

27.
Huber, L., Popovová, N., Riener, S., Salobir, K. & Cimarelli, G. Would dogs copy irrelevant actions from their human caregiver?. Learn. Behav. 46, 387–397 (2018).
Article  Google Scholar 

28.
Virányi, Z. S., Topál, J., Miklósi, Á. & Csányi, V. A nonverbal test of knowledge attribution: A comparative study on dogs and children. Anim. Cogn. 9, 13–26 (2006).
Article  Google Scholar 

29.
Polgárdi, R., Topál, J. & Csányi, V. Intentional behaviour in dog-human communication: An experimental analysis of “showing” behaviour in the dog. Anim. Cogn. 3, 159–166 (2000).
Article  Google Scholar 

30.
Pongrácz, P., Hegedüs, D., Sanjurjo, B., Kővári, A. & Miklósi, Á. “We will work for you”—Social influence may suppress individual food preferences in a communicative situation in dogs. Learn. Motiv. 44, 270–281 (2013).
Article  Google Scholar 

31.
Fugazza, C., Pogány, Á. & Miklósi, Á. Recall of others’ actions after incidental encoding reveals episodic-like memory in dogs. Curr. Biol. 26, 3209–3213 (2016).
CAS  Article  Google Scholar 

32.
Horowitz, A. Smelling themselves: Dogs investigate their own odours longer when modified in an “olfactory mirror” test. Behav. Proc. 143, 17–24 (2017).
Article  Google Scholar 

33.
Moore, C., Mealiea, J., Garon, N. & Povinelli, D. J. The development of body self-awareness. Infancy 11, 157–174 (2007).
Article  Google Scholar 

34.
Howell, T. J. & Bennett, P. C. Can dogs (Canis familiaris) use a mirror to solve a problem?. J. Vet. Behav. 6, 306–312 (2011).
Article  Google Scholar 

35.
Bekoff, M. Awareness: Animal reflections. Nature 419, 255 (2002).
ADS  CAS  Article  Google Scholar 

36.
Kaplan, J. T., Aziz-Zadeh, L., Uddin, L. Q. & Iacoboni, M. The self across the senses: An fMRI study of self-face and self-voice recognition. Soc. Cogn. Affect. Neur. 3, 218–223 (2008).
Article  Google Scholar  More