Ecology

Mosquito abundance, biting rate and morphological identificationsA total of 3920 Anopheles sp. females, 1167 and 2753 during the dry and rainy seasons respectively, were captured on a total of 60 collection days. Overall 81% (3187/3920) of the samples were collected in the cow odor-baited double net traps (CBNTs) and while this relative abundance was rather consistent between different collection sites for the CBNTs, 67% (490/733) of the Anopheles from the human odor-baited double net traps (HBNTs) were collected in the forest sites (Table S1).The biting rate (# of females/trap/day) for the HBNTs was consistently higher in the forest sites compared to all other locations during both the rainy and the dry seasons (Table 1). However, for the CBNTs, while the biting rate was the highest in the forest sites during the dry season, the tendency changed during the rainy season with a higher biting rate in the villages and the forests near the villages compared to the forest sites (Table 1).Table 1 Biting and infectious rates of Anopheles mosquitoes collected by HBNTs and CBNTs across sites and seasons.Full size tableA total of 3131 females were morphologically identified as 14 different Anopheles species or complexes of morphologically indistinguishable sibling species. Based on these morphological identifications, species thought to be primary vectors comprised only 10.2% of the collected mosquitoes: Anopheles dirus s.l. (8.1%, n = 319), A. minimus s.l. (0.4%, n = 15) and A. maculatus s.l. (1.7%, n = 67). The most abundant species (represented by more than a hundred individuals in our collection) constituted 75.8% of the collected Anopheles mosquitoes and were represented by 6 species complexes: A. barbirostris (21.2%, n = 831), A. philippinensis (14.6%, n = 571), A. hyrcanus (13.6%, n = 535), A. kochi (10.5%, n = 412), A. dirus (8.1%), A. aconitus (7.7%, n = 303).Molecular determination of mosquito speciesA total of 844 females were molecularly characterized for species in the random subset and represent 26 distinct Anopheles species as determined by ITS2 and CO1 (Table S2). The most abundant species (representing ≥ 5% of the samples; n ≥ 42) comprise 77.8% of the molecularly typed individuals and represent 8 species from 6 different species complexes. These most abundant species included A. dirus (13.2%, n = 112) from the Dirus complex. From the Barbirostris complex; A. dissidens (13.2%, n = 112), and A. campestris-wejchoochotei (8.1%, n = 69). From the Hyrcanus Group, A. peditaeniatus (12.8%, n = 108), and A. nitidus (5.7%, n = 48). The Annularis, Funestus, and Kochi Groups were each represented by a single species A. nivipes (9.2%, n = 78), A. aconitus (6.7%, n = 57), and A. kochi (8.7%, n = 74), respectively. The 18 less abundant species, represent by fewer than 42 samples and in many cases just a handful of samples included A. philippinensis (n = 17) and A. annularis (n = 1) from the Annularis Group, A. jamesii (n = 16), A. pseudojamesi (n = 1), and A. splendidus (n = 1) from the Jamesii Group and A. saeungae (n = 29) and A. barbirostris (n = 2) from the Barbirostris Group. From the Hyrcanus Group A. crawfordi (n = 40), A. argyropus (n = 1), An. nigerrimus (n = 28), and A. sinensis (n = 3) were sampled. Anopheles maculatus (n = 22), A. sawadwongporni (n = 4), and A. rampae (n = 2) from the Maculatus Group. Anopheles tessellatus from the Tessellatus Group and A. interruptus from the Asiaticus Group were each sampled once. A. vagus (n = 12) and A. karwari (n = 3) were also present. There were 2 mosquitoes that had 99.9% identical ITS2 and 99.4% identical CO1 sequences but matched no species in the NCBI database. In addition to the random subset, 79 Plasmodium sp. infected samples were molecularly characterized for species which resulted in a total of 29 Anopheles species as determined by ITS2 and CO1.Day biting rateOverall 20.2 ± 1.2% of the Anopheles females were captured during the daytime (between 06:00 and 18:00). Indeed, while the majority of Anopheles mosquitoes bite at night, an important proportion was active during the day (Fig. 2). Excluding species with extremely low sample sizes and unidentified samples, day biting behaviour was observed for all the Anopheles species and varied from 13 to 68% (Table S3).Figure 2Average number of Anopheles females collected per trap per hour in the different collection sites in the HBNTs and the CBNTs.Full size imageThe day biting rate in the HBNTs was not different across collection sites (19.6 ± 2.9%; χ2 = 3.6, df = 3, p = 0.3; Fig. 3a) but was higher during the dry season (25.9 ± 4.6%) compared to the rainy season (13.8 ± 3.5%; χ2 = 19.08, df = 1, p  More

1.Grossnickle, D. M., Smith, S. M. & Wilson, G. P. Untangling the multiple ecological radiations of early mammals. Trends Ecol. Evol. 34, 936–949 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
2.Maor, R., Dayan, T., Ferguson-Gow, H. & Jones, K. E. Temporal niche expansion in mammals from a nocturnal ancestor after dinosaur extinction. Nat. Ecol. Evol. 1, 1889–1895 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
3.Faurby, S. et al. PHYLACINE 1.2: the phylogenetic atlas of mammal macroecology. Ecology 99, 2626–2626 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
4.Refinetti, R. The diversity of temporal niches in mammals. Biol. Rhythm Res. 39, 173–192 (2008).Article 

Google Scholar 
5.DeCoursey, P. J. Diversity of function of SCN pacemakers in behavior and ecology of three species of sciurid rodents. Biol. Rhythm Res. 35, 13–33 (2004).Article 

Google Scholar 
6.Hut, R. A., Kronfeld-Schor, N., van der Vinne, V. & De la Iglesia, H. In search of a temporal niche: environmental factors. In Neurobiology of Circadian Timing, Vol. 199 (eds. Kalsbeek, A., Merrow, M., Roenneberg, T. & Foster, R. G.) 281–304 (Elsevier, 2012).7.Pianka, E. R., Vitt, L. J., Pelegrin, N., Fitzgerald, D. B. & Winemiller, K. O. Toward a periodic table of niches, or exploring the lizard niche hypervolume. Am. Nat. 190, 601–616 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
8.Violle, C. et al. Functional rarity: the ecology of outliers. Trends Ecol. Evol. 32, 356–367 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
9.Cooke, R. S. C., Bates, A. E. & Eigenbrod, F. Global trade-offs of functional redundancy and functional dispersion for birds and mammals. Glob. Ecol. Biogeogr. 28, 484–495 (2019).Article 

Google Scholar 
10.Flynn, D. F. B. et al. Loss of functional diversity under land use intensification across multiple taxa. Ecol. Lett. 12, 22–33 (2009).PubMed 
Article 
PubMed Central 

Google Scholar 
11.Brum, F. T. et al. Global priorities for conservation across multiple dimensions of mammalian diversity. Proc. Natl Acad. Sci. USA 114, 7641–7646 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
12.Cooke, R. S. C., Eigenbrod, F. & Bates, A. E. Projected losses of global mammal and bird ecological strategies. Nat. Commun. 10, 2279 (2019).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
13.Hutchinson, G. E. Concluding remarks. Cold Spring Harb. Symp. Quant. Biol. 22, 415–427 (1957).Article 

Google Scholar 
14.Mouillot, D. et al. Niche overlap estimates based on quantitative functional traits: a new family of non-parametric indices. Oecologia 145, 345–353 (2005).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
15.Gaynor, K. M., Hojnowski, C. E., Carter, N. H. & Brashares, J. S. The influence of human disturbance on wildlife nocturnality. Science 360, 1232–1235 (2018).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
16.Levy, O., Dayan, T., Porter, W. P. & Kronfeld-Schor, N. Time and ecological resilience: can diurnal animals compensate for climate change by shifting to nocturnal activity? Ecol. Monogr. 89, e01334 (2019).Article 

Google Scholar 
17.Ankel-Simons, F. & Rasmussen, D. T. Diurnality, nocturnality, and the evolution of primate visual systems. Am. J. Phys. Anthropol. 137, 100–117 (2008).Article 

Google Scholar 
18.Russo, D., Maglio, G., Rainho, A., Meyer, C. F. J. & Palmeirim, J. M. Out of the dark: diurnal activity in the bat Hipposideros ruber on São Tomé island (West Africa). Mamm. Biol. 76, 701–708 (2011).Article 

Google Scholar 
19.Halle, S. Ecological relevance of daily activity patterns. In Activity Patterns in Small Mammals: An Ecological Approach (eds. Halle, S. & Stenseth, N. C.) 67–90 (Springer, 2000).20.Mammola, S. Assessing similarity of n-dimensional hypervolumes: which metric to use? J. Biogeogr. 46, 2012–2023 (2019).Article 

Google Scholar 
21.Langerhans, R. B. & DeWitt, T. J. Shared and unique features of evolutionary diversification. Am. Nat. 164, 335–349 (2004).PubMed 
Article 
PubMed Central 

Google Scholar 
22.Sibly, R. M. & Brown, J. H. Effects of body size and lifestyle on evolution of mammal life histories. Proc. Natl Acad. Sci. USA 104, 17707–17712 (2007).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
23.Bielby, J. et al. The fast-slow continuum in mammalian life history: an empirical reevaluation. Am. Nat. 169, 748–757 (2007).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
24.Cardillo, M. et al. Multiple causes of high extinction risk in large mammal species. Science 309, 1239–1241 (2005).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
25.Bennie, J. J., Duffy, J. P., Inger, R. & Gaston, K. J. Biogeography of time partitioning in mammals. Proc. Natl Acad. Sci. USA 111, 13727–13732 (2014).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
26.Bonebrake, T. C., Rezende, E. L. & Bozinovic, F. Climate change and thermoregulatory consequences of activity time in mammals. Am. Nat. 196, 45–56 (2020).PubMed 
Article 
PubMed Central 

Google Scholar 
27.Roll, U., Dayan, T. & Kronfeld-Schor, N. On the role of phylogeny in determining activity patterns of rodents. Evol. Ecol. 20, 479–490 (2006).Article 

Google Scholar 
28.Díaz, S. et al. The global spectrum of plant form and function. Nature 529, 167–171 (2016).ADS 
PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
29.Wilson, G. P. et al. Adaptive radiation of multituberculate mammals before the extinction of dinosaurs. Nature 483, 457–460 (2012).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
30.Anderson, S. R. & Wiens, J. J. Out of the dark: 350 million years of conservatism and evolution in diel activity patterns in vertebrates. Evolution 71, 1944–1959 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
31.Pei, Y., Valcu, M. & Kempenaers, B. Interference competition pressure predicts the number of avian predators that shifted their timing of activity. Proc. R. Soc. B 285, 20180744 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
32.Donati, G. & Borgognini-Tarli, S. M. From darkness to daylight: cathemeral activity in primates. J. Anthropol. Sci. 84, 7–32 (2006).
Google Scholar 
33.Veilleux, C. C. & Cummings, M. E. Nocturnal light environments and species ecology: implications for nocturnal color vision in forests. J. Exp. Biol. 215, 4085 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
34.le Roux, A., Cherry, M. I., Gygax, L. & Manser, M. B. Vigilance behaviour and fitness consequences: comparing a solitary foraging and an obligate group-foraging mammal. Behav. Ecol. Sociobiol. 63, 1097–1107 (2009).Article 

Google Scholar 
35.Voigt, C. C. & Lewanzik, D. Trapped in the darkness of the night: thermal and energetic constraints of daylight flight in bats. Proc. R. Soc. B 278, 2311–2317 (2011).PubMed 
Article 
PubMed Central 

Google Scholar 
36.Rydell, J. & Speakman, J. R. Evolution of nocturnality in bats: potential competitors and predators during their early history. Biol. J. Linn. Soc. 54, 183–191 (1995).Article 

Google Scholar 
37.Kronfeld-Schor, N. & Dayan, T. Partitioning of time as an ecological resource. Annu. Rev. Ecol. Evol. Syst. 34, 153–181 (2003).Article 

Google Scholar 
38.Gaston, K. J. Nighttime ecology: the “nocturnal problem” revisited. Am. Nat. 193, 481–502 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
39.Muul, I. & Lim, B. L. Comparative morphology, food habits, and ecology of some Malaysian arboreal rodents. In The Ecology of Arboreal Folivores (ed. Montgomery, G. G.) 361–368 (Smithsonian Institution, 1978).40.Hall, M. I., Kamilar, J. M. & Kirk, E. C. Eye shape and the nocturnal bottleneck of mammals. Proc. R. Soc. B 279, 4962–4968 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
41.Heesy, C. P. & Hall, M. I. The nocturnal bottleneck and the evolution of mammalian vision. Brain. Behav. Evol. 75, 195–203 (2010).PubMed 
Article 
PubMed Central 

Google Scholar 
42.Buckley, L. B., Hurlbert, A. H. & Jetz, W. Broad-scale ecological implications of ectothermy and endothermy in changing environments. Glob. Ecol. Biogeogr. 21, 873–885 (2012).Article 

Google Scholar 
43.Frey, S., Volpe, J. P., Heim, N. A., Paczkowski, J. & Fisher, J. T. Move to nocturnality not a universal trend in carnivore species on disturbed landscapes. Oikos 129, 1128–1140 (2020).Article 

Google Scholar 
44.Benítez-López, A. Animals feel safer from humans in the dark. Science 360, 1185–1186 (2018).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
45.Rabaiotti, D. & Woodroffe, R. Coping with climate change: limited behavioral responses to hot weather in a tropical carnivore. Oecologia 189, 587–599 (2019).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
46.Gaston, K. J., Visser, M. E. & Hölker, F. The biological impacts of artificial light at night: the research challenge. Philos. Trans. R. Soc. B 370, 20140133 (2015).Article 

Google Scholar 
47.McCain, C. M. & King, S. R. B. Body size and activity times mediate mammalian responses to climate change. Glob. Change Biol. 20, 1760–1769 (2014).ADS 
Article 

Google Scholar 
48.Cox, D. T. C., Maclean, I. M. D., Gardner, A. S. & Gaston, K. J. Global variation in diurnal asymmetry in temperature, cloud cover, specific humidity and precipitation and its association with leaf area index. Glob. Change Biol. 26, 7099–7111 (2020).ADS 
Article 

Google Scholar 
49.Campbell, G. S. & Norman, J. M. Animals and their environment. In An Introduction to Environmental Biophysics (eds. Campbell, G. S. & Norman, J. M.) 185–207 (Springer, 1998).50.Estes, J. A. et al. Trophic downgrading of planet earth. Science 333, 301–306 (2011).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
51.Shores, C. R., Dellinger, J. A., Newkirk, E. S., Kachel, S. M. & Wirsing, A. J. Mesopredators change temporal activity in response to a recolonizing apex predator. Behav. Ecol. 30, 1324–1335 (2019).Article 

Google Scholar 
52.Carter, N., Jasny, M., Gurung, B. & Liu, J. Impacts of people and tigers on leopard spatiotemporal activity patterns in a global biodiversity hotspot. Glob. Ecol. Conserv. 3, 149–162 (2015).Article 

Google Scholar 
53.Cooke, R. S. C., Eigenbrod, F. & Bates, A. E. Ecological distinctiveness of birds and mammals at the global scale. Glob. Ecol. Conserv. 22, e00970 (2020).Article 

Google Scholar 
54.Petchey, O. L. & Gaston, K. J. Extinction and the loss of functional diversity. Proc. R. Soc. Lond. B 269, 1721–1727 (2002).Article 

Google Scholar 
55.Thuiller, W. et al. Conserving the functional and phylogenetic trees of life of European tetrapods. Philos. Trans. R. Soc. B 370, 20140005 (2015).Article 

Google Scholar 
56.Larsen, T. H., Williams, N. M. & Kremen, C. Extinction order and altered community structure rapidly disrupt ecosystem functioning. Ecol. Lett. 8, 538–547 (2005).PubMed 
Article 
PubMed Central 

Google Scholar 
57.Holker, F., Wolter, C., Perkin, E. K. & Tockner, K. Light pollution as a biodiversity threat. Trends Ecol. Evol. 25, 681–682 (2010).PubMed 
Article 
PubMed Central 

Google Scholar 
58.Mittermeier, R., Rylands, A., Lacher, T. & Wilson, D. Handbook of the Mammals of the World, Vol. 1–4 & 6–9 (Lynx Edicions, 2001).59.R Core Team. R: A Language and Environment for Statistical Computing (R Core Team, 2019).60.Pineda-Munoz, S. & Alroy, J. Dietary characterization of terrestrial mammals. Proc. R. Soc. B 281, 20141173 (2014).PubMed 
Article 
PubMed Central 

Google Scholar 
61.Villéger, S., Mason, N. W. H. & Mouillot, D. New multidimensional functional diversity indices for a multifaceted framework in functional ecology. Ecology 89, 2290–2301 (2008).PubMed 
Article 
PubMed Central 

Google Scholar 
62.Blonder, B., Lamanna, C., Violle, C. & Enquist, B. J. The n-dimensional hypervolume. Glob. Ecol. Biogeogr. 23, 595–609 (2014).Article 

Google Scholar 
63.Penone, C. et al. Imputation of missing data in life-history trait datasets: which approach performs the best? Methods Ecol. Evol. 5, 961–970 (2014).Article 

Google Scholar 
64.Taugourdeau, S., Villerd, J., Plantureux, S., Huguenin-Elie, O. & Amiaud, B. Filling the gap in functional trait databases: use of ecological hypotheses to replace missing data. Ecol. Evol. 4, 944–958 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
65.Filho, J. A. F. D., Rangel, T. F., Santos, T. & Bini, L. M. Exploring patterns of interspecific variation in quantitative traits using sequential phylogenetic eigenvector regressions. Evolution 66, 1079–1090 (2012).Article 

Google Scholar 
66.Duong, T. & Hazelton, M. Plug-in bandwidth matrices for bivariate kernel density estimation. J. Nonparametr. Stat. 15, 17–30 (2003).MathSciNet 
MATH 
Article 

Google Scholar 
67.Blonder, B. Hypervolume concepts in niche- and trait-based ecology. Ecography 41, 1441–1455 (2018).Article 

Google Scholar 
68.Cornwell, W. K., Schwilk, D. W. & Ackerly, D. D. A trait-based test for habitat filtering: convex hull volume. Ecology 87, 1465–1471 (2006).PubMed 
Article 
PubMed Central 

Google Scholar 
69.Wilman, H. et al. EltonTraits 1.0: Species-level foraging attributes of the world’s birds and mammals. Ecology 95, 2027–2027 (2014).Article 

Google Scholar  More

In this work, we performed stochastic simulations of a cyclic nonhierarchical system composed of 5 species. To this purpose, we implemented a standard numerical algorithm largely used to study spatial biological systems11,13,41. We considered a generalisation of the rock-paper-scissors game for 5 species, whose rules are illustrated in Fig. 1a. The arrows indicate a cyclic dominance among the species. Accordingly, individuals of species i beat individuals of species (i+1), with (i=1,2,3,4,5).The dynamics of individuals’ spatial organisation occurs in a square lattice with periodic boundary conditions, following the rules: selection, reproduction, and mobility. We assumed the May-Leonard implementation so that the total number of individuals is not conserved43. Each grid point contains at most one individual, which means that the maximum number of individuals is ({mathcal {N}}), the total number of grid points.Initially, the number of individuals is the same for all species, i.e., (I_i,=,{mathcal {N}}/5), with (i=1,2,3,4,5) (there are no empty spaces in the initial state). We prepared the initial conditions by distributing each individual at a random grid point. At each timestep, one interaction occurs, changing the spatial configuration of individuals. The possible interactions are:

Selection: (i j rightarrow i otimes ,), with (j = i+1), where (otimes) means an empty space; every time one selection interaction occurs, the grid point occupied by the individual of species (i+1) vanishes.

Reproduction: (i otimes rightarrow i i,); when one reproduction is realised an individual of species i fills the empty space.

Mobility: (i odot rightarrow odot i,), where (odot) means either an individual of any species or an empty site; an individual of species i switches positions with another individual of any species or with an empty space.

In our stochastic simulations, selection, reproduction, and mobilities interactions occur with the following probabilities: s, r and m, respectively. We assumed that individuals of all species have the same probabilities of selecting, reproducing and moving. The interactions were implemented by assuming the von Neumann neighbourhood, i.e., individuals may interact with one of their four nearest neighbours. The simulation algorithm follows three steps: i) sorting an active individual; ii) raffling one interaction to be executed; iii) drawing one of the four nearest neighbours to suffer the sorted interaction (the only exception is the directional mobility, where the neighbour is chosen according to the movement tactic). If the interaction is executed, one timestep is counted. Otherwise, the three steps are redone. Our time unit is called generation, defined as the necessary time to ({mathcal {N}}) timesteps to occur.In our model, individuals of one out of the species can move into the direction with more individuals of a target species. The choice is based on the strategy assumed by species. We assumed three sorts of directional movement tactics:

Attack tactic: an individual of species i moves into the direction with more individuals of species (i+1);

Anticipation tactic: an individual of species i goes towards the direction with a larger number of individuals of species (i+2);

Safeguard tactic: an individual of species i walk into the direction with a larger concentration of individuals of species (i-2).

In the standard model, individuals of all species move randomly.We considered that only individuals of species 1 perform the directional movement tactics, as illustrated in Fig. 1b. The solid, dashed, and dashed-dotted lines represent the Attack, Anticipation, and Safeguard tactics, respectively. The concentric circumference arcs show that individuals of species 2, 3, 4, and 5 always move randomly. For implementing a directional movement, the algorithm follows the steps: i) it is assumed a disc of radius R (the perception radius), in the active individual’s neighbourhood; ii) it is defined four circular sectors in the directions of the four nearest neighbours; iii) according to the movement tactic, the target species is defined: species 2, 3, and 4, for Attack, Anticipation, and Safeguard tactics, respectively; iv) it is counted the number of individuals of the target species within each circular sector. Individuals on the borders are assumed to be part of both circular sectors; v) the circular sector that contains more individuals of the target species is chosen. In the event of a tie, a draw between the tied directions is made; vi) the active individual switches positions with the immediate neighbour in the chosen direction. The swap is also executed in case of the neighbour grid point is empty.To observe the spatial patterns, we first performed a single simulation for the standard model, Attack, Anticipation, and Safeguard tactics. The realisations run in square lattices with (500^2) grid points, for a timespan of 5000 generations. We captured 500 snapshots of the lattice (in intervals of 10 generations), that were used to make the videos of the dynamics of the spatial patterns showed in https://youtu.be/Ndvk6Rg57m4 (standard), https://youtu.be/JGhkDAHSo74 (Attack), https://youtu.be/ZZp9QlOfv2Q (Anticipation), and https://youtu.be/eFxWdLhIOuQ (Safeguard). The final snapshots were depicted in Fig. 2a–d. Individuals of species 1, 2, 3, 4, and 5 are identified with the colours ruby, blue, pink, green, and yellow, respectively; while white dots represent empty spaces. The simulations were performed assuming selection, reproduction, and mobility probabilities: (s = r = m = 1/3). The perception radius was assumed to be (R=3).The population dynamics were studied by means of the spatial density (rho _i), defined as the fraction of the grid occupied by individuals of species i at time t, i.e., (rho _i = I_i/{mathcal {N}}), where (i=0) stands for empty spaces and (i=1,…,5) represent the species 1, 2, 3, 4, and 5. The temporal changes in spatial densities of the simulations showed in Fig. 2 were depicted in Fig. 3, where the grey, ruby, blue, pink, green, and yellow lines represent the densities of empty spaces and species 1, 2, 3, 4, and 5, respectively. We also computed how the selection risk of individuals of species i changes in time. To this purpose, the algorithm counts the total number of individuals of species i at the beginning of each generation. It is then counted the number of times that individuals of species i are killed during the generation. The ratio between the number of selected individuals and the initial amount is defined as the selection risk of species i, (zeta _i). The results were averaged for every 50 generations. Figure 4 shows (zeta _i,(%)) as a function of the time for the simulations presented in Fig. 2. The ruby, blue, pink, green, and yellow lines show the selection risks of individuals of species 1, 2, 3, 4, and 5, respectively.To quantify the spatial organisation of the species, we studied the spatial autocorrelation function. This quantity measures how individuals of a same species are spatially correlated, indicating spatial domain sizes. Following the procedure carried out in literature41,42,44,45,46, we first calculated the Fourier transform of the spectral density as (C({{vec{r^{prime}}}}) = {mathcal{F}}^{{ – 1}} { S({{vec{k}}})} /C(0)), where the spectral density (S({{vec{k}}})) is given by (S({{vec{k}}}) = sumlimits_{{k_{x} ,k_{y} }} {mkern 1mu} varphi ({{vec{kappa }}})), with (varphi ({{vec{kappa }}}) = {mathcal{F}}{mkern 1mu} { phi ({{vec{r}}}) – langle phi rangle }). The function (phi ({{vec{r}}})) represents the species in the position ({{vec{r}}}) in the lattice (we assumed 0, 1, 2, 3, 4, and 5, for empty sites, and individuals of species 1, 2, 3, 4, and 5, respectively). We then computed the spatial autocorrelation function as$$C(vec{r^{prime}}) = sumlimits_{{|{{vec{r^{prime}}}}| = x + y}} {frac{{C({{vec{r^{prime}}}})}}{{min (2N – (x + y + 1),(x + y + 1))}}}.$$Subsequently, we found the scale of the spatial domains of species i, defined for (C(l_i)=0.15), where (l_i) is the characteristic length for species i.We calculated the autocorrelation function by running 100 simulations using lattices with (500^2) grid points, assuming (s = r = m = 1/3) and (R=3). Each simulation started from different random initial conditions. We then captured each species spatial configuration after 5000 generations to calculate the autocorrelation functions. Finally, we averaged the autocorrelation function in terms of the radial coordinate r and calculated the characteristic length for each species. We also calculated the standard deviation for the autocorrelation functions and the characteristic lengths. Figure 4 shows the comparison of the results for Attack, Anticipation, and Safeguard strategies with the standard model. The ruby, blue, pink, green, and yellow circles indicate the mean values for species 1, 2, 3, 4, and 5, respectively. In the case of standard model, the mean values are represented by grey circles, which are the same for all species. The error bars show that standard deviation. The horizontal black line represents (C(l_i), =, 0.15).To further explore the numerical results, we studied how the perception radius R influences species spatial densities and selection risks. We calculated the mean value of the spatial species densities, (langle , rho _i,rangle) and the mean value of selection risks, (langle , zeta _i,rangle) from a set of 100 simulations in lattices with (500^2) grid points, starting from different initial conditions for (R=1,2,3,4,5). We used (s,=r,=,m,=1/3) and a timespan of (t=5000) generations. The mean values and standard deviation were calculated using the second half of the simulations, thus eliminating the density fluctuations inherent in the pattern formation process. The results were shown in Fig. 6, where the circles represent the mean values and error bars indicate the standard deviation. The colours are the same as in Figs. 3 and 4. Furthermore, to verify the precision of the statistical results, we calculated the variation coefficient – the ratio between the standard deviation and the mean value. Supplementary Tables S1 and S2 show statistical outcomes for species densities and selections risks, respectively.We studied a more realistic scenario where not all individuals of species 1 can perform the directional movement tactics. For this reason, we defined the conditioning factor (alpha), with (0,le ,alpha ,le ,1), representing the proportion of individuals of species 1 that moves directionally. For (alpha =0) all individuals move randomly while for (alpha =1) all individuals move directionally. This means that every time an individual of species 1 is sorted to move, there is a probability (alpha) of the algorithm implementing the directional movement tactic, instead of randomly choosing one of its four immediate neighbours to switch positions. To understand the effects of the conditioning factor, we observed how the density of species 1 changes for the entire range of (alpha), with intervals of (Delta alpha = 0.1). The simulations were implemented for (R=3) and (s,=r,=,m,=1/3). It was computed the mean value of the spatial density of species 1, (langle , rho _1,rangle), and its standard deviation from a set of 100 different random initial conditions. The results were depicted in Fig. 7, where the green, red, and blue dashed lines show (langle , rho _1,rangle) as a function of (alpha). The error bars indicate the standard deviation.Finally, we aimed to investigate how the directional movement tactics jeopardise species coexistence for a wide mobility probability range. Because of this, we run 2000 simulations in lattices with (100^2) grid points for (0.05, More

1.Scholik, A. R. & Yan, H. Y. Effects of boat engine noise on the auditory sensitivity of the fathead minnow, Pimephales promelas. . Environ. Biol. Fish. 63, 203–209. https://doi.org/10.1023/A:1014266531390 (2002).Article 

Google Scholar 
2.Simpson, S. D. et al. Anthropogenic noise increases fish mortality by predation. Nat. Commun. 7, 10544. https://doi.org/10.1038/ncomms10544 (2016).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
3.Slabbekoorn, H. et al. A noisy spring: the impact of globally rising underwater sound levels on fish. Trends. Ecol. Evol. 25, 419–427. https://doi.org/10.1016/j.tree.2010.04.005 (2010).Article 
PubMed 

Google Scholar 
4.Halfwerk, W. & Slabbekoorn, H. Pollution going multimodal: the complex impact of the human-altered sensory environment on animal perception and performance. Biol. Lett. 11, 20141051. https://doi.org/10.1098/rsbl.2014.1051 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
5.Baker, C. F. & Montgomery, J. C. Sensory deficits induced by cadmium in banded kokopu, Galaxias fasciatus, juveniles. Environ. Biol. Fish. 62, 455–464. https://doi.org/10.1023/A:1012290912326 (2001).Article 

Google Scholar 
6.O’Connor, J. J. et al. Sediment pollution impacts sensory ability and performance of settling coral-reef fish. Oecologia 180, 11–21. https://doi.org/10.1007/s00442-015-3367-6 (2016).ADS 
Article 
PubMed 

Google Scholar 
7.Tierney, K. B., Sampson, J. L., Ross, P. S., Sekela, M. A. & Kennedy, C. J. Salmon olfaction is impaired by an environmentally realistic pesticide mixture. Environ. Sci. Tech. 42, 4996–5001. https://doi.org/10.1021/es800240u (2008).CAS 
Article 

Google Scholar 
8.Ward Ashley, J. W., Duff Alison, J., Horsfall Jennifer, S. & Currie, S. Scents and scents-ability: pollution disrupts chemical social recognition and shoaling in fish. Proc. R Soc. B Biol. Sci. 275, 101–105. https://doi.org/10.1098/rspb.2007.1283 (2008).CAS 
Article 

Google Scholar 
9.Besson, M. et al. Exposure to agricultural pesticide impairs visual lateralization in a larval coral reef fish. Sci. Rep. 7, 9165. https://doi.org/10.1038/s41598-017-09381-0 (2017).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
10.Vasconcelos, R. O., Amorim, M. C. P. & Ladich, F. Effects of ship noise on the detectability of communication signals in the Lusitanian toadfish. J. Exp. Biol. 210, 2104–2112. https://doi.org/10.1242/jeb.004317 (2007).Article 
PubMed 

Google Scholar 
11.Bruintjes, R. et al. Rapid recovery following short-term acoustic disturbance in two fish species. R. Soc. Open Sci. 3, 150686. https://doi.org/10.1098/rsos.150686 (2016).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
12.Radford, A. N., Lèbre, L., Lecaillon, G., Nedelec, S. L. & Simpson, S. D. Repeated exposure reduces the response to impulsive noise in European seabass. Glob. Chang. Biol. 22, 3349–3360. https://doi.org/10.1111/gcb.13352 (2016).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
13.Briffa, M., de la Haye, K. & Munday, P. L. High CO2 and marine animal behaviour: Potential mechanisms and ecological consequences. Mar. Pollut. Bull. 64, 1519–1528. https://doi.org/10.1016/j.marpolbul.2012.05.032 (2012).CAS 
Article 
PubMed 

Google Scholar 
14.Simpson, S. D. et al. Ocean acidification erodes crucial auditory behaviour in a marine fish. Biol. Lett. https://doi.org/10.1098/rsbl.2011.0293 (2011).Article 
PubMed 
PubMed Central 

Google Scholar 
15.van der Sluijs, I. et al. Communication in troubled waters: responses of fish communication systems to changing environments. Evol. Ecol. 25, 623–640. https://doi.org/10.1007/s10682-010-9450-x (2011).Article 

Google Scholar 
16.Wysocki, L. E., Dittami, J. P. & Ladich, F. Ship noise and cortisol secretion in European freshwater fishes. Biol. Conserv. 128, 501–508. https://doi.org/10.1016/j.biocon.2005.10.020 (2006).Article 

Google Scholar 
17.Montgomery, J. C., Jeffs, A., Simpson, S. D., Meekan, M. & Tindle, C. Sound as an orientation cue for the pelagic larvae of reef fishes and decapod crustaceans. Adv. Mar. Biol. 51, 143–196. https://doi.org/10.1016/S0065-2881(06)51003-X (2006).Article 
PubMed 

Google Scholar 
18.Burke, L., Reytar, K., Spalding, M. & Perry, A. Reefs at risk revisited. 130, 1 (2011).
Google Scholar 
19.Collin, S. P. & Hart, N. S. Vision and photoentrainment in fishes: The effects of natural and anthropogenic perturbation. Integr. Zool. 10, 15–28. https://doi.org/10.1111/1749-4877.12093 (2015).Article 
PubMed 

Google Scholar 
20.Brodie, J. E. et al. Terrestrial pollutant runoff to the Great Barrier Reef: An update of issues, priorities and management responses. Mar Pollut Bull 65, 81–100. https://doi.org/10.1016/j.marpolbul.2011.12.012 (2012).CAS 
Article 
PubMed 

Google Scholar 
21.Marshall, N. J. in Animal Signals. Signalling and signal design in animal communication (eds Y. Espmark, Y. Amundsen, & G. Rosenqvist) 83–120 (Tapir Academic Press, 2000).22.Marshall, J. Vision and lack of vision in the ocean. Curr. Biol. 27, R494–R502. https://doi.org/10.1016/j.cub.2017.03.012 (2017).CAS 
Article 
PubMed 

Google Scholar 
23.Cortesi, F. et al. Visual system diversity in coral reef fishes. Semin. Cell Dev. Biol. 106, 31–42. https://doi.org/10.1016/j.semcdb.2020.06.007 (2020).CAS 
Article 
PubMed 

Google Scholar 
24.Collier, C., Waycott, M. & Ospina, A. G. Responses of four Indo-West Pacific seagrass species to shading. Mar. Pollut. Bull. 65, 342–354. https://doi.org/10.1016/j.marpolbul.2011.06.017 (2012).CAS 
Article 
PubMed 

Google Scholar 
25.De’ath, G. & Fabricius, K. Water quality as a regional driver of coral biodiversity and macroalgae on the Great Barrier Reef. Ecol. Appl. 20, 840–850. https://doi.org/10.1890/08-2023.1 (2010).Article 
PubMed 

Google Scholar 
26.Fabricius, K. E. Effects of terrestrial runoff on the ecology of corals and coral reefs: review and synthesis. Mar. Pollut. Bull. 50, 125–146. https://doi.org/10.1016/j.marpolbul.2004.11.028 (2005).CAS 
Article 
PubMed 

Google Scholar 
27.Morgan, K. M., Perry, C. T., Johnson, J. A. & Smithers, S. G. Nearshore turbid-zone corals exhibit high bleaching tolerance on the Great Barrier Reef following the 2016 ocean warming event. Front. Mar. Sci. 4, 1–13. https://doi.org/10.3389/fmars.2017.00224 (2017).Article 

Google Scholar 
28.Schartau, J. M., Sjögreen, B., Gagnon, Y. L. & Kröger, R. H. H. Optical plasticity in the crystalline lenses of the cichlid fish Aequidens pulcher. Curr. Biol. 19, 122–126. https://doi.org/10.1016/j.cub.2008.11.062 (2009).CAS 
Article 
PubMed 

Google Scholar 
29.Kröger, R. H. H., Braun, S. C. & Wagner, H.-J. Rearing in different photic and chromatic environments modifies spectral responses of cone horizontal cells in adult fish retina. Vis. Neuro Sci. 18, 857–864. https://doi.org/10.1017/S0952523801186025 (2001).Article 

Google Scholar 
30.Kröger, R. H. H., Knoblauch, B. & Wagner, H.-J. Rearing in different photic and spectral environments changes the optomotor response to chromatic stimuli in the cichlid fish Aequidens pulcher. J. Exp. Biol. 206, 1643–1648. https://doi.org/10.1242/jeb.00337 (2003).Article 
PubMed 

Google Scholar 
31.Borner, K. K. et al. Turbidity affects social dynamics in Trinidadian guppies. Behav. Ecol. Sociobiol. 69, 645–651. https://doi.org/10.1007/s00265-015-1875-3 (2015).Article 

Google Scholar 
32.Kimbell, H. S. & Morrell, L. J. Turbidity influences individual and group level responses to predation in guppies Poecilia reticulata. . Anim. Behav. 103, 179–185. https://doi.org/10.1016/j.anbehav.2015.02.027 (2015).Article 

Google Scholar 
33.Johansen, J. L. & Jones, G. P. Sediment-induced turbidity impairs foraging performance and prey choice of planktivorous coral reef fishes. Ecol. Appl. 23, 1504–1517. https://doi.org/10.1890/12-0704.1 (2013).CAS 
Article 
PubMed 

Google Scholar 
34.Chamberlain, A. C. & Ioannou, C. C. Turbidity increases risk perception but constrains collective behaviour during foraging by fish shoals. Anim. Behav. 156, 129–138. https://doi.org/10.1016/j.anbehav.2019.08.012 (2019).Article 

Google Scholar 
35.Gregory, R. S. Effect of turbidity on the predator avoidance behaviour of juvenile Chinook salmon (Oncorhynchus tshawytscha). Can. J. Fish. Aquat. Sci. 50, 241–246. https://doi.org/10.1139/f93-027 (1993).Article 

Google Scholar 
36.Hess, S. et al. Enhanced fast-start performance and anti-predator behaviour in a coral reef fish in response to suspended sediment exposure. Coral Reefs 38, 103–108. https://doi.org/10.1007/s00338-018-01757-6 (2019).ADS 
Article 

Google Scholar 
37.Miner, J. G. & Stein, R. A. Detection of predators and habitat choice by small Bluegills: Effects of turbidity and alternative prey. T Am. Fish. Soc. 125, 97–103. https://doi.org/10.1577/1548-8659(1996)125%3c0097:DOPAHC%3e2.3.CO;2 (1996).Article 

Google Scholar 
38.Utne-Palm, A. C. Visual feeding of fish in a turbid environment: Physical and behavioural aspects. Mar. Freshw. Behav. Phys. 35, 111–128. https://doi.org/10.1080/10236240290025644 (2002).Article 

Google Scholar 
39.Fiksen, Ø., Aksnes, D., Flyum, H. & M. & Giske, J. ,. The influence of turbidity on growth and survival of fish larvae: A numerical analysis. Hydrobiologia 484, 49–59. https://doi.org/10.1023/A:1021396719733 (2002).Article 

Google Scholar 
40.Gregory, R. S. & Levings, C. D. The effects of turbidity and vegetation on the risk of juvenile salmonids, Oncorhynchus spp, to predation by adult cutthroat trout, O. clarkii. Environ Biol Fish 47, 279–288. https://doi.org/10.1007/BF00000500 (1996).Article 

Google Scholar 
41.Wenger, A. S., McCormick, M. I., McLeod, I. M. & Jones, G. P. Suspended sediment alters predator–prey interactions between two coral reef fishes. Coral Reefs 32, 369–374. https://doi.org/10.1007/s00338-012-0991-z (2013).ADS 
Article 

Google Scholar 
42.Asaeda, T., Kyung Park, B. & Manatunge, J. Characteristics of reaction field and the reactive distance of a planktivore, Pseudorasbora parva (Cyprinidae), in various environmental conditions. Hydrobiologia 489, 29–43. https://doi.org/10.1023/A:1023298823106 (2002).Article 

Google Scholar 
43.Barrett, J. C., Grossman, G. D. & Rosenfeld, J. Turbidity-induced changes in reactive distance of rainbow trout. Trans. Am. Fish. Soc. 121, 437–443. https://doi.org/10.1577/1548-8659(1992)121%3c0437:TICIRD%3e2.3.CO;2 (1992).Article 

Google Scholar 
44.Sweka, J. A. & Hartman, K. J. Reduction of reactive distance and foraging success in smallmouth bass, Micropterus dolomieu, exposed to elevated turbidity levels. Environ. Biol. Fish. 67, 341–347. https://doi.org/10.1023/A:1025835031366 (2003).Article 

Google Scholar 
45.Suriyampola, P. S., Cacéres, J. & Martins, E. P. Effects of short-term turbidity on sensory preference and behaviour of adult fish. Anim. Behav. 146, 105–111. https://doi.org/10.1016/j.anbehav.2018.10.014 (2018).Article 

Google Scholar 
46.De Robertis, A., Ryer, C. H., Veloza, A. & Brodeur, R. D. Differential effects of turbidity on prey consumption of piscivorous and planktivorous fish. Can. J. Fish. Aquat. Sci. 60, 1517–1526. https://doi.org/10.1139/f03-123 (2003).Article 

Google Scholar 
47.Huenemann, T. W., Dibble, E. D. & Fleming, J. P. Influence of turbidity on the foraging of largemouth bass. Trans. Am. Fish. Soc. 141, 107–111. https://doi.org/10.1080/00028487.2011.651554 (2012).Article 

Google Scholar 
48.Sekhar, M. A., Singh, R., Bhat, A. & Jain, M. Feeding in murky waters: acclimatization and landmarks improve foraging efficiency of zebrafish (Danio rerio) in turbid waters. Biol. Lett. 15, 20190289. https://doi.org/10.1098/rsbl.2019.0289 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
49.Wenger, A. S., Johansen, J. L. & Jones, G. P. Increasing suspended sediment reduces foraging, growth and condition of a planktivorous damselfish. J. Exp. Mar. Biol. Ecol. 428, 43–48. https://doi.org/10.1016/j.jembe.2012.06.004 (2012).Article 

Google Scholar 
50.Wenger, A. S. et al. Suspended sediment prolongs larval development in a coral reef fish. J. Exp. Biol. 217, 1122–1128. https://doi.org/10.1242/jeb.094409 (2014).Article 
PubMed 

Google Scholar 
51.Ehlman, S. M., Sandkam, B. A., Breden, F. & Sih, A. Developmental plasticity in vision and behavior may help guppies overcome increased turbidity. J. Comput. Physiol. 201, 1125–1135. https://doi.org/10.1007/s00359-015-1041-4 (2015).Article 

Google Scholar 
52.Hazelton, P. D. & Grossman, G. D. Turbidity, velocity and interspecific interactions affect foraging behaviour of rosyside dace (Clinostomus funduloides) and yellowfin shiners (Notropis lutippinis). Ecol. Freshw. Fish 18, 427–436. https://doi.org/10.1111/j.1600-0633.2009.00359.x (2009).Article 

Google Scholar 
53.Hecht, T. & van der Lingen, C. D. Turbidity-induced changes in feeding strategies of fish in estuaries. S. Afr. J. Zool. 27, 95–107. https://doi.org/10.1080/02541858.1992.11448269 (1992).Article 

Google Scholar 
54.Sweka, J. A. & Hartman, K. J. Effects of turbidity on prey consumption and growth in brook trout and implications for bioenergetics modeling. Can. J. Fish. Aquat. Sci. 58, 386–393. https://doi.org/10.1139/f00-260 (2001).Article 

Google Scholar 
55.Burt de Perera, T. & Macías Garcia, C. Amarillo fish (Girardinichthys multiradiatus) use visual landmarks to orient in space. Ethology 109, 341–350. https://doi.org/10.1046/j.1439-0310.2003.00876.x (2003).Article 

Google Scholar 
56.Warburton, K. The use of local landmarks by foraging goldfish. Anim. Behav. 40, 500–505. https://doi.org/10.1016/s0003-3472(05)80530-5 (1990).Article 

Google Scholar 
57.Burt de Perera, T. & Guilford, T. C. Rapid learning of shelter position in an intertidal fish, the shanny Lipophrys pholis L. J. Fish. Biol. 72, 1386–1392. https://doi.org/10.1111/j.1095-8649.2008.01804.x (2008).Article 

Google Scholar 
58.Hughes, R. N. & Blight, C. M. Two intertidal fish species use visual association learning to track the status of food patches in a radial maze. Anim. Behav. 59, 613–621. https://doi.org/10.1006/anbe.1999.1351 (2000).CAS 
Article 
PubMed 

Google Scholar 
59.Huntingford, F. A. & Wright, P. J. How sticklebacks learn to avoid dangerous feeding patches. Behav. Process. 19, 181–189. https://doi.org/10.1016/0376-6357(89)90040-5 (1989).CAS 
Article 

Google Scholar 
60.Reese, E. Orientation behavior of butterflyfishes (family Chaetodontidae) on coral reefs: spatial learning of route specific landmarks and cognitive maps. Environ. Biol. Fish. 25, 79–86. https://doi.org/10.1007/bf00002202 (1989).Article 

Google Scholar 
61.Silveira, M. M., Oliveira, J. J. & Luchiari, A. C. Dusky damselfish Stegastes fuscus relational learning: evidences from associative and spatial tasks. J. Fish. Biol. 86, 1109–1120. https://doi.org/10.1111/jfb.12618 (2015).CAS 
Article 
PubMed 

Google Scholar 
62.Cyrus, D. P. & Blaber, S. J. M. Turbidity and salinity in a tropical northern Australian estuary and their influence on fish distribution. Estuar. Coast Shelf. S 35, 545–563. https://doi.org/10.1016/S0272-7714(05)80038-1 (1992).ADS 
CAS 
Article 

Google Scholar 
63.Macdonald, R. K., Ridd, P. V., Whinney, J. C., Larcombe, P. & Neil, D. T. Towards environmental management of water turbidity within open coastal waters of the Great Barrier Reef. Mar. Pollut. Bull. 74, 82–94. https://doi.org/10.1016/j.marpolbul.2013.07.026 (2013).CAS 
Article 
PubMed 

Google Scholar 
64.Wenger, A. S. et al. A critical analysis of the direct effects of dredging on fish. Fish Fish 18, 967–985. https://doi.org/10.1111/faf.12218 (2017).Article 

Google Scholar 
65.Wenger, A. S. & McCormick, M. I. Determining trigger values of suspended sediment for behavioral changes in a coral reef fish. Mar. Pollut. Bull. 70, 73–80. https://doi.org/10.1016/j.marpolbul.2013.02.014 (2013).CAS 
Article 
PubMed 

Google Scholar 
66.Gardner, M. B. Mechanisms of size selectivity by planktivorous fish: a test of hypotheses. Ecology 62, 571–578. https://doi.org/10.2307/1937723 (1981).Article 

Google Scholar 
67.Shoup, D. E. & Wahl, D. H. The effects of turbidity on prey selection by piscivorous largemouth bass. Trans. Am. Fish. Soc. 138, 1018–1027. https://doi.org/10.1577/T09-015.1 (2009).Article 

Google Scholar 
68.Cheung, A., Zhang, S., Stricker, C. & Srinivasan, M. V. Animal navigation: the difficulty of moving in a straight line. Biol. Cybern. 97, 47–61. https://doi.org/10.1007/s00422-007-0158-0 (2007).MathSciNet 
Article 
PubMed 
MATH 

Google Scholar 
69.McLean, D. J. & Skowron Volpani, M. A. trajr: An R package for characterisation of animal trajectories. Ethology 124, 40–448. https://doi.org/10.1111/eth.12739 (2018).Article 

Google Scholar 
70.Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48. https://doi.org/10.18637/jss.v067.i01 (2015).Article 

Google Scholar 
71.jtools: Analysis and presentation of social scientific data v. R package version 2.0.1 (2019).72.Gradall, K. S. & Swenson, W. A. Responses of brook trout and creek chubs to turbidity. Trans. Am. Fish. Soc. 111, 392–395. https://doi.org/10.1577/1548-8659(1982)111%3c392:ROBTAC%3e2.0.CO;2 (1982).Article 

Google Scholar 
73.Berg, L. & Northcote, T. G. Changes in territorial, gill-flaring, and feeding behavior in juvenile Coho salmon (Oncorhynchus kisutch) following short-term pulses of suspended sediment. Can. J. Fish. Aquat. Sci. 42, 1410–1417. https://doi.org/10.1139/f85-176 (1985).Article 

Google Scholar 
74.Paris, C. B. et al. Reef odor: A wake up call for navigation in reef fish larvae. PLoS ONE 8, e72808. https://doi.org/10.1371/journal.pone.0072808 (2013).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
75.Siebeck, U. E., Parker, A. N., Sprenger, D., Mathger, L. M. & Wallis, G. A species of reef fish that uses ultraviolet patterns for covert face recognition. Curr. Biol. 20, 407–410. https://doi.org/10.1016/j.cub.2009.12.047 (2010).CAS 
Article 
PubMed 

Google Scholar 
76.Cheney, K. L., Grutter, A. S., Blomberg, S. P. & Marshall, N. J. Blue and yellow signal cleaning behavior in Coral Reef Fishes. Curr. Biol. 19, 1283–1287. https://doi.org/10.1016/j.cub.2009.06.028 (2009).CAS 
Article 
PubMed 

Google Scholar 
77.Brown, C. & Braithwaite, V. A. Size matters: a test of boldness in eight populations of the poeciliid Brachyraphis episcopi. Anim. Behav. 68, 1325–1329. https://doi.org/10.1016/j.anbehav.2004.04.004 (2004).Article 

Google Scholar 
78.Harborne, A. R., Rogers, A., Bozec, Y.-M. & Mumby, P. J. Multiple stressors and the functioning of coral reefs. Annu. Rev. Mar. Sci. 9, 445–468. https://doi.org/10.1146/annurev-marine-010816-060551 (2017).ADS 
Article 

Google Scholar  More

1.Kettle, D.S. Medical and Veterinary Entomology 2nd edn (CAB International, 1995).2.Falleroni, D. Fauna anofelica italiana e suo “habitat” (paludi, risaie, canali). Metodi di lotta contro la malaria. Riv. Malariol. 5, 553–559 (1926).
Google Scholar 
3.Severini, F., Toma, L., Di Luca, M. & Le, R. R. Zanzare Italiane: generalità e identificazione degli adulti (Diptera, Culicidae). Fragmenta Entomologica. 41(2), 213–372. https://doi.org/10.4081/FE.2009.92 (2009).Article 

Google Scholar 
4.Beebe, N. W. DNA barcoding mosquitoes: advice for potential prospectors. Parasitology 145(5), 622–633. https://doi.org/10.1017/S0031182018000343 (2018).CAS 
Article 
PubMed 

Google Scholar 
5.Manguin, S. et al. Biodiversity of Malaria in the World (John Libbey Eurotext, 2008).6.Linton, Y. M., Smith, L. & Harbach, E. Observations on the taxonomic status of Anopheles subalpinus Hackett & Lewis and An. melanoon Hacket. Eur. Mosq. Bull. 13, 1–7 (2002).
Google Scholar 
7.Boccolini, D., Di Luca, M., Marinucci, M. & Romi, R. Further molecular and morphological support for the formal synonymy of Anopheles subalpinus Hackett & Lewis with An. melanoon Hackett. Eur. Mosq. Bull. 16, 1–5 (2003).
Google Scholar 
8.Andreeva, I. V., Sibataev, A. K., Rusakova, A. M. & Stegniĭ, V. N. Morpho-cytogenetic characteristic of the mosquito Anopheles artemievi (Diptera: Culicidae), a malaria vector from the complex maculipennis. Parazitologiia. 41(5), 348–363 (2007).PubMed 

Google Scholar 
9.Artemov, G. N. et al. A standard photomap of ovarian nurse cell chromosomes and inversion polymorphism in Anopheles beklemishevi. Parasites Vectors 11(1), 211. https://doi.org/10.1186/s13071-018-2657-3 (2018).MathSciNet 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
10.Naumenko, A. N. et al. Chromosome and genome divergence between the cryptic Eurasian malaria vector-species Anopheles messeae and Anopheles daciae. Genes (Basel) 11(2), 165. https://doi.org/10.3390/genes11020165 (2020).CAS 
Article 

Google Scholar 
11.Nicolescu, G., Linton, Y. M., Vladimirescu, A., Howard, T. M. & Harbach, R. E. Mosquitoes of the Anopheles maculipennis group (Diptera: Culicidae) in Romania, with the discovery and formal recognition of a new species based on molecular and morphological evidence. Bull. Entomol. Res. 94(6), 525–535. https://doi.org/10.1079/ber2004330 (2004).CAS 
Article 
PubMed 

Google Scholar 
12.Gordeev, M. I., Zvantsov, A. B., Goriacheva, I. I., Shaĭkevich, E. V. & Ezhov, M. N. Description of the new species Anopheles artemievi sp.n. (Diptera, Culicidae). Med. Parazitol. (Mosk). 2, 4–5 (2005).
Google Scholar 
13.Djadid, N. D. et al. Molecular identification of Palearctic members of Anopheles maculipennis in northern Iran. Malar. J. 6, 6. https://doi.org/10.1186/1475-2875-6-6 (2007).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
14.Bietolini, S., Candura, F. & Coluzzi, M. Spatial and long term temporal distribution of the Anopheles maculipennis complex species in Italy. Parassitologia 48(4), 581–608 (2006).CAS 
PubMed 

Google Scholar 
15.Romi, R. et al. Status of malaria vectors in Italy. J. Med. Entomol. 34(3), 263–271. https://doi.org/10.1093/jmedent/34.3.263 (1997).CAS 
Article 
PubMed 

Google Scholar 
16.Zamburlini, R. & Cargnus, E. Residual mosquitoes in the northern Adriatic seacoast 50 years after the disappearance of malaria. Parassitologia 40, 431–437 (1998).CAS 
PubMed 

Google Scholar 
17.Gratz, N. G. Vector- and Rodent-Borne Diseases in Europe and North America: Distribution, Public Health Burden, and Control (Cambridge University Press, 2006).18.Zahar, A. R. The WHO European region and the two Eastern Mediterranean Region. Applied field studies. In Vector Bionomics in the Epidemiology and Control of Malaria. Part II. WHO/VBC/90.1 (World Health Organization, 1990).19.NPHO Annual Epidemiological Surveillance Report Malaria in Greece, 2019. https://eody.gov.gr/wp-content/uploads/2019/01/MALARIA_ANNUAL_REPORT_2019_ENG.pdf (2019).20.Romi, R. et al. Assessment of the risk of malaria re-introduction in the Maremma plain (Central Italy) using a multi-factorial approach. Malar. J. 11, 98. https://doi.org/10.1186/1475-2875-11-98 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
21.Baldari, M. et al. Malaria in Maremma, Italy. Lancet 351(9111), 1246–1247. https://doi.org/10.1016/S0140-6736(97)10312-9 (1998).CAS 
Article 
PubMed 

Google Scholar 
22.Romi, R., Boccolini, D., Menegon, M. & Rezza, G. Probable autochthonous introduced malaria cases in Italy in 2009–2011 and the risk of local vector-borne transmission. Euro Surveill. 17(48), 20325 (2012).PubMed 

Google Scholar 
23.Boccolini, D. et al. Non-imported malaria in Italy: paradigmatic approaches and public health implications following an unusual cluster of cases in 2017. BMC Public Health 20(1), 857. https://doi.org/10.1186/s12889-020-08748-9 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
24.European Centre for Disease Prevention and Control. Multiple reports of locally-acquired malaria infections in the EU—20 September 2017. (ECDC, 2017).25.Lilja, T., Eklöf, D., Jaenson, T. G. T., Lindström, A. & Terenius, O. Single nucleotide polymorphism analysis of the ITS2 region of two sympatric malaria mosquito species in Sweden: Anopheles daciae and Anopheles messeae. Med. Vet. Entomol. 34(3), 364–368. https://doi.org/10.1111/mve.12436 (2020).CAS 
Article 
PubMed 

Google Scholar 
26.Di Luca, M., Boccolini, D., Marinucci, M. & Romi, R. Intrapopulation polymorphism in Anopheles messeae (An. maculipennis complex) inferred by molecular analysis. J. Med. Entomol. 41(4), 582–586. https://doi.org/10.1603/0022-2585-41.4.582 (2004).Article 
PubMed 

Google Scholar 
27.Scharlemann, J. P. et al. Global data for ecology and epidemiology: a novel algorithm for temporal Fourier processing MODIS data. PLoS ONE 3(1), e1408. https://doi.org/10.1371/journal.pone.0001408 (2008).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
28.Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37(12), 4302–4315 (2017).Article 

Google Scholar 
29.Batovska, J., Cogan, N. O., Lynch, S. E. & Blacket, M. J. Using Next-Generation Sequencing for DNA Barcoding: Capturing Allelic Variation in ITS2. G3 (Bethesda) 7(1), 19–29. https://doi.org/10.1534/g3.116.036145 (2017).CAS 
Article 

Google Scholar 
30.Novikov, Iu. M. & Kabanova, V. M. Adaptive association of inversions in a natural population of the malaria mosquito Anopheles messeae Fall. Genetika 15(6), 1033–1045 (1979).PubMed 

Google Scholar 
31.Vaulin, O. V. & Novikov, Y. M. Polymorphism and interspecific variability of cytochrome oxidase subunit I (COI) gene nucleotide sequence in sibling species of A and B Anopheles messeae and An. beklemishevi (Diptera: Culicidae). Russ. J. Genet. Appl. Res. 2(6), 421–429. https://doi.org/10.1134/S2079059712060159 (2012).Article 

Google Scholar 
32.Bezzhonova, O. V. & Goryacheva, I. I. Intragenomic heterogeneity of rDNA internal transcribed spacer 2 in Anopheles messeae (Diptera: Culicidae). J. Med. Entomol. 45(3), 337–341. https://doi.org/10.1603/0022-2585(2008)45[337:ihorit]2.0.co;2 (2008).CAS 
Article 
PubMed 

Google Scholar 
33.Novikov, Y. M. & Shevchenko, A. I. Inversion polymorphism and the divergence of two cryptic forms of Anopheles messeae (Diptera, Culicidae) at the level of genomic DNA repeats. Russ. J. Genet. 37, 754–763 (2001).CAS 
Article 

Google Scholar 
34.Kitzmiller, J. B., Frizzi, G. & Baker, R. Evolution and speciation within the Maculipennis complex of the genus Anopheles. In Genetics of Insect Vectors of Disease ed. (ed. Wright, J.W. & Pal, R.) (Elsevier Publishing, 1967).35.De Queiroz, K. Species concepts and species delimitation. Syst. Biol. 56(6), 879–886. https://doi.org/10.1080/10635150701701083 (2007).Article 
PubMed 

Google Scholar 
36.Alquezar, D. E., Hemmerter, S., Cooper, R. D. & Beebe, N. W. Incomplete concerted evolution and reproductive isolation at the rDNA locus uncovers nine cryptic species within Anopheles longirostris from Papua New Guinea. BMC Evol. Biol. 10, 392. https://doi.org/10.1186/1471-2148-10-392 (2010).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
37.Mallet, J., Besansky, N. & Hahn, M. W. How reticulated are species?. BioEssays 38(2), 140–149. https://doi.org/10.1002/bies.201500149 (2016).Article 
PubMed 

Google Scholar 
38.Fouet, C., Kamdem, C., Gamez, S. & White, B. J. Genomic insights into adaptive divergence and speciation among malaria vectors of the Anopheles nili group. Evol Appl. 10(9), 897–906. https://doi.org/10.1111/eva.12492 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
39.Pombi, M. et al. Dissecting functional components of reproductive isolation among closely related sympatric species of the Anopheles gambiae complex. Evol. Appl. 10(10), 1102–1120. https://doi.org/10.1111/eva.12517 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
40.Cohuet, A., Harris, C., Robert, V. & Fontenille, D. Evolutionary forces on Anopheles: What makes a malaria vector?. Trends Parasitol. 26(3), 130–136. https://doi.org/10.1016/j.pt.2009.12.001 (2010).Article 
PubMed 

Google Scholar 
41.Jetten, T. H., Takken, W. Anophelism Without Malaria in Europe: A Review of the Ecology and Distribution of the Genus Anopheles in Europe. (Wageningen Agricultural University, 1994).42.Becker, N. et al. Mosquitoes and Their Control 2nd edn (Springer Science & Business Media, 2010).43.Mosca, A., Balbo L., Grieco C. & Roberto P. Rice-field mosquito control in Northern Italy. In Proc. of 14th E-SOVE Int. Conf. 98 (2010).44.Daskova, N. G. & Rasnicyn, S. P. Review of data on susceptibility of mosquitoes in the USSR to imported strains of malaria parasites. Bull. World Health Organ. 60(6), 893–897 (1982).CAS 
PubMed 
PubMed Central 

Google Scholar 
45.de Zulueta, J., Ramsdale, C. D. & Coluzzi, M. Receptivity to malaria in Europe. Bull. World Health Organ. 52(1), 109–111 (1975).PubMed 
PubMed Central 

Google Scholar 
46.Ramsdale, C. D. & Coluzzi, M. Studies on the infectivity of tropical African strains of Plasmodium falciparum to some southern European vectors of malaria. Parassitologia 17(1–3), 39–48 (1975).CAS 
PubMed 

Google Scholar 
47.Teodorescu, C., Ungureanu, E., Mihai, M. & Tudose, M. Contributions to the study of the receptivity of the vector A. labranchiae atroparvus to two strains of P. vivax. Revista Medico-Chirurgicala din Iasi 52(1), 73–75 (1978).
Google Scholar 
48.Sousa, C. A. G. Malaria Vectorial Capacity and Competence of Anopheles atroparvus Van Thiel, 1927 (Diptera, Culicidae): Implications for the Potential Re-emergence of Malaria in Portugal (Thesis, Universidade Nova de Lisboa, Instituto de Higiene e Medicina Tropical, 2008).49.Toty, C. et al. Malaria risk in Corsica, former hot spot of malaria in France. Malar. J. 9, 231. https://doi.org/10.1186/1475-2875-9-231 (2010).Article 
PubMed 
PubMed Central 

Google Scholar 
50.Calzolari, M. et al. West Nile virus surveillance in 2013 via mosquito screening in Northern Italy and the influence of weather on virus circulation. PLoS ONE 10(10), e0140915. https://doi.org/10.1371/journal.pone.0140915 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
51.Marinucci, M., Romi, R., Mancini, P., Di Luca, M. & Severini, C. Phylogenetic relationships of seven palearctic members of the maculipennis complex inferred from ITS2 sequence analysis. Insect. Mol. Biol. 8(4), 469–480. https://doi.org/10.1046/j.1365-2583.1999.00140.x (1999).CAS 
Article 
PubMed 

Google Scholar 
52.Jalali, S., Ojha, R. & Venkatesan, T. DNA barcoding for identification of agriculturally important insects. In New Horizons in Insect Science: Towards Sustainable Pest Management (ed. Chakravarthy, A. K.) (Springer, 2015).53.Lühken, R. et al. Distribution of individual members of the mosquito Anopheles maculipennis complex in Germany identified by newly developed real-time PCR assays. Med. Vet. Entomol. 30, 144–154. https://doi.org/10.1111/mve.12161 (2016).Article 
PubMed 

Google Scholar 
54.Katoh, K., Rozewicki, J. & Yamada, K. MAFFT online service: multiple sequence alignment, interactive sequence choice and visualization. Brief Bioinform. 20(4), 1160–1166. https://doi.org/10.1093/bib/bbx108 (2019).CAS 
Article 
PubMed 

Google Scholar 
55.Guindon, S. & Gascuel, O. A simple, fast, and accurate algorithm to estimate large phylogenies by maximum likelihood. Syst. Biol. 52(5), 696–704. https://doi.org/10.1080/10635150390235520 (2003).Article 
PubMed 

Google Scholar 
56.Darriba, D., Taboada, G. L., Doallo, R. & Posada, D. jModelTest 2: more models, new heuristics and parallel computing. Nat. Methods 9(8), 772. https://doi.org/10.1038/nmeth.2109 (2012).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
57.Letunic, I. & Bork, P. Interactive Tree Of Life (iTOL) v4: recent updates and new developments. Nucl. Acids Res. 47(W1), W256–W259. https://doi.org/10.1093/nar/gkz239 (2019).CAS 
Article 
PubMed 

Google Scholar 
58.Phillips, S. J., Anderson, R. P., Dudík, M., Schapire, R. E. & Blair, M. E. Opening the black box: an open-source release of Maxent. Ecography 40, 887–893. https://doi.org/10.1111/ecog.03049 (2017).Article 

Google Scholar 
59.Elith, S. J. et al. A statistical explanation of MaxEnt for ecologists. Divers. Distrib. 17, 43–57. https://doi.org/10.1111/j.1472-4642.2010.00725.x (2011).Article 

Google Scholar 
60.Warren, D. L., Glor, R. E. & Turelli, M. ENMTools: a toolbox for comparative studies of environmental niche models. Ecography 33, 607–611. https://doi.org/10.1111/j.1600-0587.2009.06142.x (2010).Article 

Google Scholar 
61.Phillips, S., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190(3–4), 231–259. https://doi.org/10.1016/j.ecolmodel.2005.03.026 (2006).Article 

Google Scholar  More

1.Yesson, C., Clark, M. R., Taylor, M. L. & Rogers, A. D. The global distribution of seamounts based on 30 arc seconds bathymetry data. Deep. Res. Part I Oceanogr. Res. Pap. 58, 442–453 (2011).ADS 
Article 

Google Scholar 
2.Preez, CDu., Curtis, J. M. R. & Clarke, M. E. The structure and distribution of benthic communities on a shallow seamount (Cobb Seamount, Northeast Pacific Ocean). PLoS ONE 11, 1–29 (2016).Article 
CAS 

Google Scholar 
3.Auster, P. J. et al. Definition and detection of vulnerable marine ecosystems on the high seas: problems with the ‘move-on’ rule. ICES J. Mar. Sci. 68, 254–264 (2011).Article 

Google Scholar 
4.Watling, L. & Auster, P. J. Seamounts on the high seas should be managed as vulnerable marine ecosystems. Front. Mar. Sci. 4, 1–4 (2017).Article 

Google Scholar 
5.Cho, W. W. Faunal Biogeography, Community Structure, and Genetic Connectivity of North Atlantic Seamounts (Massachusetts Institute of Technology & Woods Hole Oceanographic Institution, 2008).6.Rogers, A. D. The Biology of Seamounts: 25 Years on. Advances in Marine Biology vol. 79 (Elsevie, 2018).7.Wagner, D. et al. The Salas y Gómez and Nazca ridges: a global diversity hotspot in need of protection. 28 (2020).8.Kvile, K. O., Taranto, G. H., Pitcher, T. J. & Morato, T. A global assessment of seamount ecosystems knowledge using an ecosystem evaluation framework. Biol. Conserv. 173, 108–120 (2014).Article 

Google Scholar 
9.Victorero, L., Robert, K., Robinson, L. F., Taylor, M. L. & Huvenne, V. A. I. Species replacement dominates megabenthos beta diversity in a remote seamount setting. Sci. Rep. 8, 1–11 (2018).CAS 
Article 

Google Scholar 
10.Yesson, C. et al. Improved bathymetry leads to 4000 new seamount predictions in the global ocean. UCL Open Environ. Preprint, 1–12 (2020).11.Gálvez Larach, M. Montes submarinos de Nazca y Salas y Gómez: una revisión para el manejo y conservación. Lat. Am. J. Aquat. Res. 37, 479–500 (2009).Article 

Google Scholar 
12.Jarrard, R. D. & Clague, D. A. Implications of Pacific Island and seamount ages for the origin of volcanic chains. Rev. Geophys. 15, 57–76 (1977).ADS 
Article 

Google Scholar 
13.Chave, E. H. & Jones, A. T. Deep-water megafauna of the Kohala and Haleakala slopes, Alenuihaha Channel Hawaii. Deep Sea Res. Part A Oceanogr. Res. Pap. 38, 781–803 (1991).ADS 
Article 

Google Scholar 
14.Kitchingman, A., Lai, S., Morato, T. & Pauly, D. How many seamounts are there and where are they located? In Seamounts: Ecology, Fisheries & Conservation, Series 12 (eds Pitcher, T. J. et al.) 26–40 (Blackwell Publishing, 2008). https://doi.org/10.1002/9780470691953.ch2.
Google Scholar 
15.Parin, N. V., Mironov, A. N. & Nesis, K. M. Biology of the Nazca and Sala y Gómez submarine ridges, an outpost of the Indo-West Pacific fauna in the eastern Pacific ocean: composition and distribution of the fauna, its communities and history. Advances in Marine Biology vol. 32 (1997).16.Samadi, S., Schlacher, T. & Richer de Forges, B. Seamount benthos. In Seamounts: Ecology, Fisheries and Conservation (eds Pitcher, T. et al.) 119–140 (Wiley-Blackwell, 2007).
Google Scholar 
17.Mironov, A. N., Molodtsova, T. N. & Parin., N. V. Soviet and Russian studies on seamount biology. (2006).18.Fernández, M., Pappalardo, P., Rodríguez-Ruiz, M. C. & Castilla, J. C. Síntesis del estado del conocimiento sobre la riqueza de especies de macroalgas, macroinvertebrados y peces en aguas costeras y oceánicas de Isla de Pascua e Isla Salas y Gómez. Lat. Am. J. Aquat. Res. 42, 760–802 (2014).Article 

Google Scholar 
19.Easton, E. E. et al. Chile and the Salas y Gómez Ridge. In Mesophotic Coral Ecosystems 477–490 (Springer, 2019). https://doi.org/10.1007/978-3-319-92735-0_27.20.Friedlander, A. M. et al. Marine biodiversity in Juan Fernández and Desventuradas islands, Chile: global endemism hotspots. PLoS ONE 11, e0145059 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
21.Sellanes, J., Salisbury, R. A., Tapia, J. M. & Asorey, C. M. A new species of Atrimitra Dall, 1918 (Gastropoda: Mitridae) from seamounts of the recently created Nazca-Desventuradas Marine Park Chile. PeerJ 2019, 1–16 (2019).
Google Scholar 
22.Gaymer, C. F. et al. Plan General de Administración y su Valoración Económica. Informe final proyecto FIPA 2016–31 ‘Bases técnicas para la gestión del Parque Marino Nazca-Desventuradas y propuesta de Plan General de Administración’ (2018).23.Clark, M. R. et al. The ecology of seamounts: structure, function, and human impacts. Ann. Rev. Mar. Sci. 2, 253–278 (2010).PubMed 
Article 

Google Scholar 
24.Henry, L. A. et al. Environmental variability and biodiversity of megabenthos on the Hebrides Terrace Seamount (Northeast Atlantic). Sci. Rep. 4, 1–10 (2014).
Google Scholar 
25.Jones, C. G., Lawton, J. H. & Shachak, M. Organisms as ecosystem engineers. Oikos 69, 373 (1994).Article 

Google Scholar 
26.Morgan, N. B., Goode, S., Roark, E. B. & Baco, A. R. Fine scale assemblage structure of benthic invertebrate megafauna on the North Pacific Seamount Mokumanamana. Front. Mar. Sci. 6, 1–21 (2019).Article 

Google Scholar 
27.Davies, J. S. et al. Benthic assemblages of the Anton Dohrn Seamount (NE Atlantic): defining deep-sea biotopes to support habitat mapping and management efforts with a focus on vulnerable marine ecosystems. PLoS ONE 10, 33 (2015).
Google Scholar 
28.Auster, P. J., Malatesta, R. J. & Larosa, S. C. Patterns of microhabitat utilization by mobile megafauna on the southern New England (USA) continental shelf and slope. Mar. Ecol. Prog. Ser. 127, 77–85 (1995).ADS 
Article 

Google Scholar 
29.Uzmann, J. R., Cooper, R. A., Theroux, R. B. & Wigley, R. L. Synoptic comparison of three sampling techniques for estimating abundance and distribution of selected megafauna: submersible vs. camera sled vs. otter trawl. Mar. Fish. Rev. 39, 11–19 (1977).
Google Scholar 
30.Valentine, J. P. & Edgar, G. J. Impacts of a population outbreak of the urchin Tripneustes gratilla amongst Lord Howe Island coral communities. Coral Reefs 29, 399–410 (2010).ADS 
Article 

Google Scholar 
31.Greene, H. et al. A classification scheme for deep seafloor habitats. Oceanol. Acta 22, 663–678 (1999).Article 

Google Scholar 
32.Greene, H., O’Connell, V., Brylinsky, C. & Reynolds, J. Marine Benthic Habitat classification: What’s Best for Alaska? In Marine Habitat Mapping Technology for Alaska (eds Reynolds, J. & Greene, H. G.) 169–184 (Alaska Sea Grant College Program University of Alaska Fairbanks, 2008). https://doi.org/10.4027/mhmta.2008.12.
Google Scholar 
33.Naar, D. F., Johnson, K. P., Wessel, D., Duncan, P. & Mahoney, J. Rapa Nui. 2001: Cruise report for Leg 6 of the Drift expedition aboard the R/V Revelle (2001).34.Haase, K. M., Stoffers, P. & Garbe-Schönberg, C. D. The petrogenetic evolution of lavas from Easter Island and neighbouring seamounts, near-ridge hotspot volcanoes in the SE pacific. J. Petrol. 38, 785–813 (1997).ADS 
CAS 
Article 

Google Scholar 
35.Woods, M. T. & Okal, E. A. The structure of the Nazca Ridge and Sala y Gomez seamount chain from the dispersion of Rayleigh waves. Geophys. J. Int. 117, 205–222 (1994).ADS 
Article 

Google Scholar 
36.Rodrigo, C., Foucher, N., Philippi, N. & Lara, L. E. Morfoestructuras volcánicas y sedimentarias de los montes submarinos de la región de las islas Desventuradas, basadas en el análisis de datos acústicos. 110–115 (2017).37.Mecho, A. et al. Environmental drivers of mesophotic echinoderm assemblages of the Southeastern Pacific Ocean. Front Mar. Sci. 8, 1–15 (2021).Article 

Google Scholar 
38.VLC media player – Open Source Multimedia Framework and Player.39.Dyer, B. S. & Westneat, M. W. Taxonomía y biogeografía de los peces costeros del Archipiélago de Juan Fernández y de las islas Desventuradas Chile. Rev. Biol. Mar. Oceanogr. 45, 589–617 (2010).Article 

Google Scholar 
40.Pequeño, G. & Lamilla, J. The Littoral Fish Assemblage of the Desventuradas Islands (Chile) Has Zoogeographical Affinities with the Western Pacific. Glob. Ecol. Biogeogr. 9, 431–437 (2000).Article 

Google Scholar 
41.Raines, B. & Huber, M. Biodiversity Quadrupled-Revision of Easter Island and Salas y Gómez Bivalves. Zootaxa 106 (2012).42.Retamal, M. A. & Moyano, H. I. Zoogeografía de los crustáceos decápodos chilenos marinos y dulceacuícolas. Lat. Am. J. Aquat. Res. 38, 302–328 (2010).
Google Scholar 
43.Sysoev, A. B. Gastropods of the family Turridae (Gastropoda:Toxoglosa) of the Nasca and Sala y Gómez underwater ridges. 124, 245–260 (1990).44.Zarenkov, N. A. Crabs of the familiy Leucosiidae (subfamilies Ebalinae an Iliinae) collected in tropical water of Indian and Pacific oceans waters of Indian and Pacific oceans. Bol. Nauk. 10, 16–26 (1969).
Google Scholar 
45.Zarenkov, N. A. Decapods (Stenopodidea, Brachyura, Anomura) of the underwater Nazca and Salas y Gómez Ridges. Tr. Instituta Okeanol. AN USSR 124, 218–244 (1990).
Google Scholar 
46.Barriga, E., Salazar, C., Palacios, J., Romero, M. & Rodriguez, A. Distribucion, abundancia y estructura poblacional del langostino rojo de profundidad Haliporoides diomedeae (Crustacea: Decapoda: Solenoceridae). Lat. Am. J. Aquat. Res. 37, 371–380 (2009).
Google Scholar 
47.R Core Team. R Core Team (2020). R: A language and environment for statistical computing. version 4.0.3. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ (2019).48.Oksanen J et al. vegan: Community Ecology Package.R package version 2.5-7. https://cran.r-project.org/package=vegan (2020).49.Jones, D. & Frid, C. L. J. Altering intertidal sediment topography: effects on biodiversity and ecosystem functioning. Mar. Ecol. 30, 83–96 (2009).ADS 
Article 

Google Scholar 
50.Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2009).
Google Scholar 
51.National Geographic & Oceana. Islas Desventuradas. Biodiversidad marina y propuesta de conservación. 58 (2013).52.Levin, L. A. & Nittrouer, C. A. Textural characteristics of sediment on deep seamounts in the eastern Pacific Ocean between 10°N and 30°N. In Seamounts, Islands and Atolls, 43 (eds Keating, B. et al.) 187–203 (Geophysical Monograph, 1987).
Google Scholar 
53.Lourido, A., Parra, S. & Serrano, A. Preliminary Results on the Composition and Structure of Soft-Bottom Macrobenthic Communities of a Seamount: the Galicia Bank (NE Atlantic Ocean). Thalassas 35, 1–9 (2019).Article 

Google Scholar 
54.Flach, E., Muthumbi, A. & Heip, C. Meiofauna and macrofauna community structure in relation to sediment composition at the iberian margin compared to the goban spur (NE atlantic). Prog. Oceanogr. 52, 433–457 (2002).ADS 
Article 

Google Scholar 
55.Levin, L. A. & Gooday, A. The deep Atlantic Ocean floor. In Ecosystems of the Deep Oceans (ed. Tyler, P.) 187–203 (Elsevier, 2003).
Google Scholar 
56.Thistle, D. The deep-sea floor: an overview. In Ecosystems of the World, Ecosystems of the Deep Sea (ed. Tyler, P. A.) 5–37 (Elsevier, 2003).
Google Scholar 
57.Louzao, M. et al. Historical macrobenthic community assemblages in the Avilés Canyon, N Iberian Shelf: Baseline biodiversity information for a marine protected area. J. Mar. Syst. 80, 47–56 (2010).Article 

Google Scholar 
58.Kon, K., Tsuchiya, Y., Sato, T., Shinagawa, H. & Yamada, Y. Role of microhabitat heterogeneity in benthic faunal communities in sandy bottom sediments of Oura Bay, Shimoda Japan. Reg. Stud. Mar. Sci. 2, 71–76 (2015).Article 

Google Scholar 
59.Clark, M. R., Schlacher, T. A., Rowden, A. A., Stocks, K. I. & Consalvey, M. Science priorities for Seamounts: research links to conservation and management. PLoS ONE 7, e29232 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
60.Zeppilli, D., Pusceddu, A., Trincardi, F. & Danovaro, R. Seafloor heterogeneity influences the biodiversity-ecosystem functioning relationships in the deep sea. Sci. Rep. 6, 1–12 (2016).Article 
CAS 

Google Scholar 
61.de la Torriente, A. et al. Benthic habitat modelling and mapping as a conservation tool for marine protected areas: a seamount in the western Mediterranean. Aquat. Conserv. Mar. Freshw. Ecosyst. 29, 732–750 (2019).Article 

Google Scholar 
62.Gallardo, M., Macpherson, E., Tapia-Guerra, J. M., Asorey, C. M. & Sellanes, J. A new species of Munida Leach, 1820 (Crustacea: Decapoda: Anomura: Munididae) from seamounts of the Nazca-Desventuradas Marine Park. PeerJ https://doi.org/10.7717/peerj.10531 (2021).Article 

Google Scholar 
63.Castilla, J. C. Islas oceánicas chilenas: conocimiento científico y necesidades de investigación (Ediciones Universidad Católica de Chile, 1987).64.Bahamonde, N. San Félix y San Ambrosio, las islas llamadas Desventuradas 85–99 (1987).65.Díaz-Díaz, O., Bone, D., Rodríguez, C. T. & Delgado-Blas, V. H. Poliquetos de Sudamérica. Especial d, 149 (2017).66.Díaz-Díaz, O. F., Rozbaczylo, N., Sellanes, J. & Tapia-Guerra, J. M. A new species of Eunice Cuvier, 1817 (Polychaeta: Eunicidae) from the slope of the Desventuradas Islands and seamounts of the Nazca Ridge, southeastern Pacific Ocean. A New Species Cuscus 4860, 211–226 (2020).
Google Scholar 
67.Kantor, Y. & Sysoev, A. Latiaxis (Babelomurex) naskensis, a new species of Coralliophilidae (Gastropoda) from South-Eastern Pacific. Ruthenica 2, 163–167 (1992).
Google Scholar 
68.Sepulveda, J. I. Peces de las Islas Oceánicas Chilenas. In Islas Oceánicas Chilenas: Conocimiento científico y necesidades de Investigaciones. (ed. Castilla, J.) 225–246 (Ediciones Universidad Católica de Chile, 1987).69.Mironov, A. & Detinova., N. Bottom fauna of the Nazca and Sala y Gomez ridges. Plankton and benthos from the Nazca and Sala y Gomez Submarine Ridges 269–278 (1990).70.Lundsten, L. et al. Benthic invertebrate communities on three seamounts off southern and central California USA. Mar. Ecol. Prog. Ser. 374, 23–32 (2009).ADS 
Article 

Google Scholar 
71.Rex, M. A. et al. Global bathymetric patterns of standing stock and body size in the deep-sea benthos. Mar. Ecol. Prog. Ser. 317, 1–8 (2006).ADS 
Article 

Google Scholar 
72.QGIS.org. QGIS Geographic Information System.QGIS Association. Version 3.10. https://www.qgis.org (2020). More

1.Sharrow, S. H. & Ismail, S. Carbon and nitrogen storage in agroforests, tree plantations, and pastures in western Oregon, USA. Agrofor. Syst. 60(2), 123–130 (2004).Article 

Google Scholar 
2.Yang, L. L. et al. Carbon and nitrogen storage and distribution in four forest ecosystems in Liupan Mountains, Northwestern China. Acta. Ecol. Sin. 35(15), 5215–5227 (2015).
Google Scholar 
3.Watson, R. T. et al. Land use, land-use change, and forestry. In: Published for the Intergovernmental Panel on Climate Change. Cambridge University Press, pp. 308 (2000).4.Zhao, M. M. et al. Estimation of China’s forest stand biomass carbon sequestration based on the continuous biomass expansion factor model and seven forest inventories from 1977 to 2013. For. Ecol. Manag. 448, 528–534 (2019).Article 

Google Scholar 
5.Dale, V. H. et al. Climate change and forest disturbances. Bioscience 51, 723–734 (2001).Article 

Google Scholar 
6.Gunderson, P. Carbon—Nitrogen Interactions in Forest Ecosystems—Final Report. Danish Centre for Forest, Landscape and Planning, Denmark (2006).7.Hook, P. B. & Burke, I. C. Biogeochemistry in a shortgrass landscape: control by topography, soil texture, and microclimate. Ecology 81, 2686–2703 (2000).Article 

Google Scholar 
8.Vourlitis, G. L., Zorba, G., Pasquini, S. C. & Mustard, R. Carbon and nitrogen storage in soil and litter of southern Californian semi-arid shrublands. J. Arid Environ. 70, 164–173 (2007).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
9.Bonan, G. B. Forests and climate change: forcings, feedbacks, and the climate benefits of forests. Science 320, 1444–1449 (2008).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
10.Liu, G. H., Fu, B. & Fang, J. Y. Carbon dynamics of Chinese forests and its contribution to global carbon balance. Acta. Ecol. Sin. 20(5), 733–740 (2000).
Google Scholar 
11.IPCC. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge (2007).12.Phillips, J. et al. Live aboveground carbon stocks in natural forests of Colombia. For. Ecol. Manag. 374, 119–128 (2016).Article 

Google Scholar 
13.Gibbs, H. K., Brown, B., Niles, J. O. & Foley, J. A. Monitoring and estimating tropical forest carbon stocks: making REDD a reality. Environ. Res. Lett. 2(4), 1–13 (2007).
Google Scholar 
14.Aragão, L. et al. Above- and below-ground net primary productivity across ten Amazonian forests on contrasting soils. Biogeosciences 6, 2759–2778 (2009).ADS 
Article 

Google Scholar 
15.Malhi, Y. et al. Comprehensive assessment of carbon productivity, allocation and storage in three Amazonian forests. Glob. Chang. Biol. 15, 1255–1274 (2009).ADS 
Article 

Google Scholar 
16.Post, W. M. & Kwon, K. C. Soil carbon sequestration and land use change: processes and potential. Glob. Chang. Biol. 6, 317–327 (2000).ADS 
Article 

Google Scholar 
17.Ma, J. et al. Ecosystem carbon storage distribution between plant and soil in different forest types in Northeastern China. Ecol. Eng. 81, 353–362 (2015).Article 

Google Scholar 
18.Davidson, E. A., Trumbore, S. E. & Amundson, R. Biogeochemistry—soil warming and organic carbon content. Nature 408, 789–790 (2000).ADS 
CAS 
PubMed 
Article 

Google Scholar 
19.Chaturvedi, R. K. & Raghubanshi, A. S. Aboveground biomass estimation of small diameter woody species of tropical dry forest. New For. 44, 509–519 (2013).Article 

Google Scholar 
20.Wen, D. & He, N. P. Forest carbon storage along the north-south transect of eastern china: spatial patterns, allocation, and influencing factors. Ecol. Indic. 61, 960–967 (2016).CAS 
Article 

Google Scholar 
21.Fan, S. et al. A large terrestrial carbon sink in North America implied by atmospheric andoceanic carbon dioxide data and models. Science 282, 442–446 (1998).ADS 
CAS 
PubMed 
Article 

Google Scholar 
22.Gough, C. M., Vogel, C. S., Schmid, H. P. & Curtis, P. S. Controls on annual forest carbon storage: lessons from the past and predictions for the future. Bioscience 58, 609–622 (2008).Article 

Google Scholar 
23.Van Deusen, P. Carbon sequestration potential of forest land: Management for products and bioenergy versus preservation. Biomass Bioenerg. 34, 1687–1694 (2010).Article 

Google Scholar 
24.Bradford, J. B., Jensen, N. R., Domke, G. M. & D’Amato, A. W. Potential increases in natural disturbance rates could offset forest management impacts on ecosystem carbon stocks. For. Ecol. Manag. 308, 178–187 (2013).Article 

Google Scholar 
25.Park, A. Carbon storage and stand conversion in a pine-dominated boreal forest landscape. For. Ecol. Manag. 340, 70–81 (2015).Article 

Google Scholar 
26.Wang, S. J., Zhao, J. X. & Chen, Q. B. Controlling factors of soil CO2 efflux in Pinusyunnanensis across different stand ages. PLoS ONE 10(5), e0127274. https://doi.org/10.1371/journal.pone.0127274 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
27.Liu, J. et al. Distinct soil bacterial communities in response to the cropping system in a Mollisol of northeast China. Appl. Soil Ecol. 119, 407–416 (2017).Article 

Google Scholar 
28.Kavvadias, V. A. et al. Litterfall, litter accumulation and litter decomposition rates in four forest ecosystems in northern Greece. For. Ecol. Manag. 144, 113–127 (2001).Article 

Google Scholar 
29.Dai, W. et al. Spatial pattern of carbon stocks in forest ecosystems of a typical subtropical region of Southeastern China. For. Ecol. Manag. 409, 288–297 (2018).Article 

Google Scholar 
30.Liu, S. et al. Carbon and nitrogen storage and distribution in different forest ecosystems in the subalpine of western Sichuan. Acta. Ecol. Sin. 37(4), 1074–1083 (2017).CAS 
Article 

Google Scholar 
31.Kern, J., Giani, L., Teixeira, W., Lanza, G. & Glaser, B. What can we learn from ancient fertile anthropic soil (Amazonian Dark Earths, shell mounds, Plaggen soil) for soil carbon sequestration?. CATENA 172, 104–112 (2019).CAS 
Article 

Google Scholar 
32.Zhang, Z. H., Wang, L. C., Luo, J. X. & Zheng, D. R. Study on tree biomass models of Pinus Yunnanensis Faranch in Northwest Yunnan Province. J. Shandong For. Sci. Technol. 4, 4–6 (2011) ((in Chinese)).ADS 

Google Scholar 
33.Chen, C. Biomass and production of the Arbor-Layers in Pinus armandii forests. J. Northwestern Coll. For. 1, 1–18 (1984) ((in Chinese)).
Google Scholar 
34.Liu, S. R., Su, Y. M., Cai, X. H. & Ma, Q. Y. Aboveground biomass of quercus aquifolioides shrub community and its responses to altitudinal gradients in balangshan mountain, Shichuan province. Sci. Silvae. Sin. 42, 1–7 (2006) ((in Chinese)).
Google Scholar 
35.Li, J. L., Liang, S. C. & Chen, S. Z. A preliminary study on the biomass models of keteleeria davidiana var chien-peii colony in qingyan town of Guizhou province. J. Guizhou Normal Univ. 15, 7–12 (1997) ((in Chinese)).CAS 

Google Scholar 
36.Yang, L. L. et al. Carbon and nitrogen storage and distribution in four forest ecosystems in Liupan Mountains, northwestern China. Acta. Ecol. Sin. 35, 5215–5227 (2015) ((in Chinese)).
Google Scholar 
37.Xie, S. C., Liu, W. Y., Li, S. C. & Yang, G. P. Preliminary studies on the biomass of middle-mountain moist evergreen broadleaved forests in Ailao Mountain, Yunnan. Acta Phytoecol. Sin. 20, 167–176 (1996) ((in Chinese)).
Google Scholar 
38.Shen, Y., Tian, D. L., Yan, W. D. & Xiao, Y. Biomass and its distribution of natural secondary quercus fabri + sassafras tsumu+ cunninghamia lanceolata community in Yuanling county, Hunan province. J. Cent. South Univ. For. Technol. 31, 44–51 (2011) ((in Chinese)).CAS 

Google Scholar 
39.Guo, L. B. & Gifford, R. M. Soil carbon stocks and land use change: a meta analysis. Global Change Biol. 8, 345–360 (2002).ADS 
Article 

Google Scholar 
40.Zhou, Y. R., Yu, Z. L. & Zhao, S. D. Carbon storage and budget of major Chinese forest types. Acta. Phytoecol. Sin. 24, 518–522 (2000) ((in Chinese)).
Google Scholar 
41.Eslamdoust, J. & Sohrabi, H. Carbon storage in biomass, litter, and soil of different native and introduced fast-growing tree plantations in the South Caspian Sea. J. For. Res. 29, 449–457 (2018).CAS 
Article 

Google Scholar 
42.He, Y. J. et al. Carbon storage capacity of monoculture and mixed-species plantations in subtropical China. For. Ecol. Manag. 295, 193–198 (2013).Article 

Google Scholar 
43.Ren, H. et al. Spatial and temporal patterns of carbon storage from 1992 to 2002 in forest ecosystems in Guangdong, Southern China. Plant Soil 363, 123–138 (2013).CAS 
Article 

Google Scholar 
44.Ali, F., Khan, N., Ahmad, A. & Khan, A. A. Structure and biomass carbon of Olea ferruginea forests in the foot hills of Malakand division, Hindukush range mountains of Pakistan. Acta. Ecol. Sin. 39, 261–266 (2019).Article 

Google Scholar 
45.Ren, Y. et al. Potential for forest vegetation carbon storage in Fujian Province, China, determined from forest inventories. Plant Soil 345, 125–140 (2011).CAS 
Article 

Google Scholar 
46.Fu, W. J. et al. Spatial variation of biomass carbon density in a subtropical region of Southeastern China. Forests 6, 1966–1981 (2015).Article 

Google Scholar 
47.Fonseca, W., Alice, F. E. & Rey-Benayas, J. M. Carbon accumulation in aboveground and belowground biomass and soil of different age native forest plantations in the humid tropical lowlands of Costa Rica. New For. 43, 197–211 (2012).Article 

Google Scholar 
48.Nelson, A., Saunders, M., Wagner, R. & Weiskittel, A. Early stand production of hybrid poplar and white spruce in mixed and monospecific plantations in eastern Maine. New For. 43, 519–534 (2012).Article 

Google Scholar 
49.Gao, Y., Cheng, J., Ma, Z., Zhao, Y. & Su, J. Carbon storage in biomass, litter, and soil of different plantations in a semiarid temperate region of northwest China. Ann. For. Sci. 71, 427–435 (2014).Article 

Google Scholar 
50.Fortier, J., Gagnon, D., Truax, B. & Lambert, F. Biomass and volume yield after 6 years in multiclonal hybrid poplar riparian buffer strips. Biomass Bioenerg. 34, 1028–1040 (2010).Article 

Google Scholar 
51.González-Rodríguez, H. et al. Litterfall deposition and leaf litter nutrient return in different locations at Northeastern Mexico. Plant Ecol. 212, 1747–1757 (2011).Article 

Google Scholar 
52.Pan, Y. et al. A large and persistent carbon sink in the world’s forests. Science https://doi.org/10.1126/science.1201609 (2011).Article 
PubMed 

Google Scholar 
53.Bradford, J. B., Birdsey, R. A., Joyce, L. A. & Ryan, M. G. Tree age, disturbance history and carbon stocks and fluxes in subalpine rocky mountain forests. Global Change Biol. 14, 2882–2897 (2008).ADS 
Article 

Google Scholar 
54.Zhang, C. N., Yan, X. D. & Yang, J. H. Estimation of nitrogen reserves in forest soils of China. J. Southwest Agric. Univ. 26, 572-575+579 (2004) ((in Chinese)).
Google Scholar 
55.Lee, K. L., Ong, K. H., King, P. J. H., Chubo, J. K. & Su, D. S. A. Stand productivity, carbon content, and soil nutrients in different stand ages of Acacia mangium in Sarawak, Malaysia. Turk. J. Agric. For. 39, 154–161 (2015).CAS 
Article 

Google Scholar 
56.Cao, B., Domke, G. M., Russell, M. B. & Walters, B. F. Spatial modeling of litter and soil carbon stocks on forest land in the conterminous United States. Sci. Total Environ. 654, 94–106 (2019).ADS 
CAS 
PubMed 
Article 

Google Scholar 
57.Deng, L., Wang, K. B., Chen, M. L., Shangguan, Z. P. & Sweeney, S. Soil organic carbon storage capacity positively related to forest succession on the Loess Plateau, China. CATENA 110, 1–7 (2013).CAS 
Article 

Google Scholar 
58.Zhu, B. et al. Altitudinal changes in carbon storage of temperate forests on Mt Changbai, Northeast China. J. Plant Res. 123, 439–452 (2010).PubMed 
Article 

Google Scholar 
59.Xie, X. L., Sun, B., Zhou, H. Z. & Li, A. B. Soil organic carbon storage in China. Pedosphere 14, 491–500 (2004).CAS 

Google Scholar 
60.Leuschner, C., Moser, G., Bertsch, C., Röderstein, M. & Hertel, D. Large altitudinal increase in tree root/shoot ratio in tropical mountain forests of Ecuador. Basic Appl. Ecol. 8, 219–230 (2007).Article 

Google Scholar 
61.Singh, S. P., Adhikari, B. S. & Zobel, D. B. Biomass, productivity, leaf longevity, and forest structure in the central Himalaya. Ecol. Monog. 64, 401–421 (1994).Article 

Google Scholar 
62.Kirschbaum, M. U. F. Will changes in soil organic carbon act as a positive or negative feedback on global warming?. Biogeochemistry 27, 753–760 (2000).Article 

Google Scholar 
63.Raich, J. W., Russel, A. E., Kitayama, K., Parton, W. J. & Vitousek, P. M. Temperature influences carbon accumulation in moist tropical forests. Ecology 87, 76–87 (2006).PubMed 
Article 

Google Scholar  More

1.Endler, J. A. Signals, signal conditions, and the direction of evolution. Am. Nat. 139, S125–S153 (1992).Article 

Google Scholar 
2.Endler, J. A. Some general comments on the evolution and design of animal communication systems. Philos. Trans. R. Soc. Lond. B. Biol. Sci. 340, 215–225 (1993).ADS 
CAS 
PubMed 
Article 

Google Scholar 
3.Fleishman, L. J. The influence of the sensory system and the environment on motion patterns in the visual displays of anoline lizards and other vertebrates. Am. Nat. 139, S36–S61 (1992).Article 

Google Scholar 
4.Lythgoe, J. N. The Ecology of vision (Oxford University Press, 1979).
Google Scholar 
5.Bradbury, J. W. & Vehrencamp, S. L. Principles of Animal Communication 2nd edn. (Sinauer Associates, 1998).
Google Scholar 
6.Morton, E. S. Ecological sources of selection on avian sounds. Am. Nat. 109, 17–34 (1975).ADS 
Article 

Google Scholar 
7.Endler, J. A. On the measurement and classification of colour in studies of animal colour patterns. Biol. J. Linn. Soc. Lond. 41, 315–352 (1990).Article 

Google Scholar 
8.Wiley, R. H. & Richards, D. G. Adaptations for acoustic communication in birds: Sound transmission and signal detection. In Ecology and Evolution of Acoustic Communication in Birds (eds Kroodsma, D. E. & Miller, E. H.) 131–181 (Academic Press, 1983).
Google Scholar 
9.Bernard, G. D. & Remington, C. L. Color vision in Lycaena butterflies: Spectral tuning of receptor arrays in relation to behavioral ecology. Proc. Natl. Acad. Sci. USA 88, 2783–2787 (1991).ADS 
CAS 
PubMed 
Article 

Google Scholar 
10.Peters, R. A., Clifford, C. W. G. & Evans, C. S. Measuring the structure of dynamic visual signals. Anim. Behav. 64, 131–146 (2002).Article 

Google Scholar 
11.Narins, P. M. Seismic communication in anuran amphibians. Bioscience 40, 268–274 (1990).Article 

Google Scholar 
12.Fleishman, L. & Persons, M. The influence of stimulus and background colour on signal visibility in the lizard Anolis cristatellus. J. Exp. Biol. 204, 1559–1575 (2001).CAS 
PubMed 

Google Scholar 
13.Brumm, H. & Slabbekoorn, H. Acoustic communication in noise. Adv. Study Behav. 35, 151–209 (2005).Article 

Google Scholar 
14.Peters, R. A., Hemmi, J. M. & Zeil, J. Signaling against the wind: modifying motion-signal structure in response to increased noise. Curr. Biol. 17, 1231–1234 (2007).CAS 
PubMed 
Article 

Google Scholar 
15.Ord, T. J. & Stamps, J. A. Alert signals enhance animal communication in “noisy” environments. Proc. Natl. Acad. Sci. USA 105, 18830–18835 (2008).ADS 
CAS 
PubMed 
Article 

Google Scholar 
16.Komers, P. E. Behavioural plasticity in variable environments. Can. J. Zool. 75, 161–169 (1997).Article 

Google Scholar 
17.Ord, T. J., Charles, G. K., Palmer, M. & Stamps, J. A. Plasticity in social communication and its implications for the colonization of novel habitats. Behav. Ecol. 27b, 341–351 (2015).
Google Scholar 
18.Marten, K. & Marler, P. Sound transmission and its significance for animal vocalization. Behav. Ecol. Sociobiol. 2, 271–290 (1977).Article 

Google Scholar 
19.Ryan, M. J., Cocroft, R. B. & Wilczynski, W. The role of environmental selection in intraspecific divergence of mate recognition signals in the cricket frog, Acris crepitans. Evolution 44, 1869–1872 (1990).PubMed 
Article 

Google Scholar 
20.Leal, M. & Fleishman, L. J. Differences in visual signal design and detectability between allopatric populations of Anolis lizards. Am. Nat. 163, 26–39 (2004).PubMed 
Article 

Google Scholar 
21.McNett, G. D. & Cocroft, R. B. Host shifts favor vibrational signal divergence in Enchenopa binotata treehoppers. Behav. Ecol. 19, 650–656 (2008).Article 

Google Scholar 
22.Ferguson, G. W. Variation and evolution of the push-up displays of the side-blotched lizard genus Uta (Iguanidae). Syst. Zool. 20, 79–101 (1971).Article 

Google Scholar 
23.Martins, E. P., Bissell, A. N. & Morgan, K. K. Population differences in a lizard communicative display: evidence for rapid change in structure and function. Anim. Behav. 56, 1113–1119 (1998).CAS 
PubMed 
Article 

Google Scholar 
24.Martins, E. P. & Lamont, J. Estimating ancestral states of a communicative display: A comparative study of Cyclurarock iguanas. Anim. Behav. 55, 1685–1706 (1998).CAS 
PubMed 
Article 

Google Scholar 
25.Bloch, N. & Irschick, D. An analysis of inter-population divergence in visual display behavior of the green anole lizard (Anolis carolinensis). Ethology 112, 370–378 (2006).Article 

Google Scholar 
26.Barquero, M. D., Peters, R. & Whiting, M. Geographic variation in aggressive signalling behaviour of the Jacky dragon. Behav. Ecol. Sociobiol. 69, 1501–1510 (2015).Article 

Google Scholar 
27.Bian, X., Chandler, T., Laird, W., Pinilla, A. & Peters, R. Integrating evolutionary biology with digital arts to quantify ecological constraints on vision-based behaviour. Methods Ecol. Evol. 9, 544–559 (2018).Article 

Google Scholar 
28.Fleishman, L. J. Motion detection in the presence and absence of background motion in an Anolis lizard. J. Comp. Physiol. A 159, 711–720 (1986).CAS 
PubMed 
Article 

Google Scholar 
29.Fleishman, L. J. Sensory and environmental influences on display form in Anolis auratus, a grass anole from Panama. Behav. Ecol. Sociobiol. 22, 309–316 (1988).
Google Scholar 
30.Eckert, M. P. & Zeil, J. Towards an ecology of motion vision. In Motion Vision (eds Zanker, J. M. & Zeil, J.) 333–369 (Springer, 2001).
Google Scholar 
31.Peters, R. A. & Evans, C. S. Design of the Jacky dragon visual display: Signal and noise characteristics in a complex moving environment. J. Comp. Physiol. A 189, 447–459 (2003).CAS 
Article 

Google Scholar 
32.Peters, R. A. Noise in visual communication: Motion from wind-blown plants. In Animal Communication and Noise. Animal Signals and Communication (ed. Brumm, H.) 311–330 (Springer, 2013).
Google Scholar 
33.Ramos, J. A. & Peters, R. A. Motion-based signaling in sympatric species of Australian agamid lizards. J. Comp. Physiol. A 203, 661–671 (2017).CAS 
Article 

Google Scholar 
34.Ramos, J. A. & Peters, R. A. Habitat-dependent variation in motion signal structure between allopatric populations of lizards. Anim. Behav. 126, 69–78 (2017).Article 

Google Scholar 
35.Ramos, J. A. & Peters, R. A. Quantifying ecological constraints on motion signaling. Front. Ecol. Evol. 5, 9 (2017).Article 

Google Scholar 
36.Bian, X., Chandler, T., Pinilla, A. & Peters, R. Now you see me, now you don’t: Environmental conditions, signaler behavior, and receiver response thresholds interact to determine the efficacy of a movement-based animal signal. Front. Ecol. Evol. 7, 130 (2019).Article 

Google Scholar 
37.Posner, M. I., Snyder, C. R. & Davidson, B. J. Attention and the detection of signals. J. Exp. Psychol. 109, 160–174 (1980).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
38.Zeil, J. & Zanker, J. M. A glimpse into crabworld. Vis. Res. 37, 3417–3426 (1997).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
39.Koch, C. & Ullman, S. Shifts in selective visual attention: Towards the underlying neural circuitry. in Matters of Intelligence. Conceptual Structures in Cognitie Neuroscience (ed. Vaina, L. M.) 115–142 (Springer, 1987).40.Itti, L., Koch, C. & Niebur, E. A model of saliency-based visual attention for rapid scene analysis. IEEE Trans. Pattern Anal. Mach. Intell. 20, 1254–1259 (1998).Article 

Google Scholar 
41.Harel, J., Koch, C. & Perona, P. Graph-based visual saliency. Adv. Neural Inf. Proc. Sys. 19, 545–552 (2006).
Google Scholar 
42.Koch, C. Biophysics of Computation: Information Processing in Single Neurons (Oxford University Press, 1998).
Google Scholar 
43.Tatler, B. W., Hayhoe, M. M., Land, M. F. & Ballard, D. H. Eye guidance in natural vision: Reinterpreting salience. J. Vis. 11, 5 (2011).PubMed 
PubMed Central 
Article 

Google Scholar 
44.Wilson, S. & Swan, G. A Complete Guide to Reptiles of Australia 2nd edn. (Reed New Holland, 2013).
Google Scholar 
45.Heatwole, H. & Firth, B. T. Voluntary maximum temperature of the jacky lizard, Amphibolurus muricatus. Copeia 1982, 824–829 (1982).Article 

Google Scholar 
46.Harlow, P. S. & Taylor, J. E. Reproductive ecology of the jacky dragon (Amphibolurus muricatus): An agamid lizard with temperature-dependent sex determination. Aust. Ecol. 25, 640–652 (2000).Article 

Google Scholar 
47.Ord, T. J. & Evans, C. S. Display rate and opponent assessment in the Jacky dragon (Amphibolurus muricatus): An experimental analysis. Behaviour 140, 1495–1508 (2003).Article 

Google Scholar 
48.Warner, D. A. & Shine, R. Interactions among thermal parameters determine offspring sex under temperature-dependent sex determination. Proc. R. Soc. Lond. B. Biol. Sci. 278, 256–265 (2010).
Google Scholar 
49.Carpenter, C. C., Badham, J. A. & Kimble, B. Behavior patterns of three species of Amphibolurus (Agamidae). Copeia 1970, 497–505 (1970).Article 

Google Scholar 
50.Peters, R. A. & Ord, T. J. Display response of the Jacky Dragon, Amphibolurus muricatus (Lacertilia : Agamidae), to intruders: A semi-Markovian process. Aust. Ecol. 28, 499–506 (2003).Article 

Google Scholar 
51.Peters, R. A. & Evans, C. S. Introductory tail-flick of the Jacky dragon visual display: Signal efficacy depends upon duration. J. Exp. Biol. 206, 4293–4307 (2003).PubMed 
Article 

Google Scholar 
52.Carpenter, C. C. A comparison of the patterns of display of Urosaurus, Uta, and Streptosaurus. Herpetologica 18, 145–152 (1962).
Google Scholar 
53.Cogger, H. Reproductive cycles, fat body cycles and socio-sexual behaviour in the mallee dragon, Amphibolurus fordi (Lacertilia: Agamidae). Aust. J. Zool. 26, 653–672 (1978).Article 

Google Scholar 
54.Garcia, J. E., Rohr, D. & Dyer, A. G. Trade-off between camouflage and sexual dimorphism revealed by UV digital imaging: The case of Australian Mallee dragons (Ctenophorus fordi). J. Exp. Biol. 216, 4290–4298 (2013).PubMed 
Article 

Google Scholar 
55.Ramos, J. A. & Peters, R. A. Dragon wars: Movement-based signalling by Australian agamid lizards in relation to species ecology. Aust. Ecol. 41, 302–315 (2016).Article 

Google Scholar 
56.Gibbons, J. R. H. Comparative ecology and behaviour of lizards of the Amphibolurus decresii species complex. PhD dissertation, University of Adelaide, Adelaide, South Australia (1977).57.McLean, C. A., Moussalli, A., Sass, S. & Stuart-Fox, D. Taxonomic assessment of the Ctenophorus decresii complex (Reptilia: Agamidae) reveals a new species of dragon lizard from western New South Wales. Rec. Aust. Mus. 65, 51–63 (2013).Article 

Google Scholar 
58.Osborne, L. Information content of male agonistic displays in the territorial tawny dragon (Ctenophorus decresii). J. Ethol. 23, 189–197 (2005).Article 

Google Scholar 
59.Gibbons, J. R. The hind leg pushup display of the Amphibolurus decresii species complex (Lacertilia: Agamidae). Copeia 1979, 29–40 (1979).Article 

Google Scholar 
60.Chouinard-Thuly, L. et al. Technical and conceptual considerations for using animated stimuli in studies of animal behavior. Curr. Zool. 63, 5–19 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
61.Akagi, Y. & Kitajima, K. Computer animation of swaying trees based on physical simulation. Comput. Graph. 30, 529–539 (2006).Article 

Google Scholar 
62.Itti, L., Dhavale, N. & Pighin, F. Realistic avatar eye and head animation using a neurobiological model of visual attention. In Proc. SPIE 48th Annual International Symposium on Optical Science and Technology Vol. 5200 (eds Bosacchi, B. et al.) 64–78 (SPIE Press, Bellingham, 2003).
Google Scholar 
63.Fleishman, L. J. & Pallus, A. C. Motion perception and visual signal design in Anolis lizards. Proc. R. Soc. B. 277, 3547–3554 (2010).PubMed 
Article 

Google Scholar 
64.Oksanen, J., Blanchet, F. G., Friendly, M., Kindt, R., Legendre, P., McGlinn, D., Minchin, P. R., O’Hara, R. B., Simpson, G. L., Solymos, P., Stevens, M. H. H., Szoecs, E. & Wagner, H. (2019). Vegan: Community Ecology Package. R package version 2.5-4. https://CRAN.R-project.org/package=vegan65.R Core Team. (2018). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.66.Blamires, S. Circumduction and head bobbing in the agamid lizard Lophognathus temporalis. Herpetofauna 28, 51–52 (1998).
Google Scholar 
67.Carpenter, C. C. Aggression and social structure in iguanid lizards. In Lizard Ecology: A Symposium (ed. Milstead, W. W.) (University of Missouri Press Columbia, 1967).
Google Scholar 
68.Carpenter, C. Ritualistic social behaviors in lizards. in Behavior and Neurology of Lizards, An Interdisciplinary Colloquium, 253–267. (National Institute of Mental Health, 1978).69.Peters, R. A., Hemmi, J. & Zeil, J. Image motion environments: Background noise for movement-based animal signals. J. Comp. Physiol. A 194, 441–456 (2008).Article 

Google Scholar 
70.Hunter, M. L. & Krebs, J. R. Geographical variation in the song of the great tit (Parus major) in relation to ecological factors. J. Anim. Ecol 48, 759–785 (1979).Article 

Google Scholar 
71.Harmon, L. J., Kolbe, J. J., Cheverud, J. M. & Losos, J. B. Convergence and the multidimensional niche. Evolution 59, 409–421 (2005).PubMed 
Article 

Google Scholar 
72.Fleishman, L. J. Sensory influences on physical design of a visual display. Anim. Behav. 36, 1420–1424 (1988).Article 

Google Scholar 
73.Ord, T. J., Peters, R. A., Clucas, B. & Stamps, J. A. Lizards speed up visual displays in noisy motion habitats. Proc. R. Soc. Lond. B. Biol. Sci. 274, 1057–1062 (2007).
Google Scholar 
74.Hasson, O. Pursuit-deterrent signals: Communication between prey and predator. Trends Ecol. Evol. 6, 325–329 (1991).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
75.Hebets, E. A. & Uetz, G. W. Female responses to isolated signals from multimodal male courtship displays in the wolf spider genus Schizocosa (Araneae: Lycosidae). Anim. Behav. 57, 865–872 (1999).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar  More