Ecology

Faisal, A. A., Selen, L. P. J. & Wolpert, D. M. Noise in the nervous system. Nat. Rev. Neurosci. 9, 292–303 (2008).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Tsetsos, K. et al. Economic irrationality is optimal during noisy decision making. Proc. Natl. Acad. Sci. 113, 3102–3107 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bushnell, P. J. Behavioral approaches to the assessment of attention in animals. Psychopharmacology 138, 231–259 (1998).CAS 
PubMed 
Article 

Google Scholar 
Katsuki, F. & Constantinidis, C. Bottom-up and top-down attention: Different processes and overlapping neural systems. Neuroscientist 20, 509–521 (2014).PubMed 
Article 

Google Scholar 
Moore, T. & Zirnsak, M. Neural mechanisms of selective visual attention. Annu. Rev. Psychol. 68, 47–72 (2017).CAS 
PubMed 
Article 

Google Scholar 
Ferguson, K. I. & Stiling, P. Non-additive effects of multiple natural enemies on aphid populations. Oecologia 108, 375–379 (1996).ADS 
PubMed 
Article 

Google Scholar 
Sih, A., Englund, G. & Wooster, D. Emergent impacts of multiple predators on prey. Trends Ecol. Evol. 13, 350–355 (1998).CAS 
PubMed 
Article 

Google Scholar 
Soluk, D. A. & Collins, N. C. Synergistic interactions between fish and stoneflies: Facilitation and interference among stream predators. Oikos. 52, 94–100 (1988).
Article 

Google Scholar 
Cooper, W. E., Pérez-Mellado, V. & Hawlena, D. Number, speeds, and approach paths of predators affect escape behavior by the Balearic lizard, Podarcis lilfordi. J. Herpetol. 41, 197–204 (2007).Article 

Google Scholar 
Relyea, R. A. How prey respond to combined predators: A review and an empirical test. Ecology 84, 1827–1839 (2003).Article 

Google Scholar 
Krupa, J. J. & Sih, A. Fishing spiders, green sunfish, and a stream-dwelling water strider: Male–female conflict and prey responses to single versus multiple predator environments. Oecologia 117, 258–265 (1998).ADS 
PubMed 
Article 

Google Scholar 
Nityananda, V. Attention-like processes in insects. Proc. R. Soc. B Biol. Sci. 283, 20161986 (2016).Article 

Google Scholar 
Amo, L., López, P. & Martín, J. in Annales Zoologici Fennici, 671–679 (JSTOR).Bagheri, Z. M., Donohue, C. G. & Hemmi, J. M. Evidence of predictive selective attention in fiddler crabs during escape in the natural environment. J. Exp. Biol. 223, 234963 (2020).Article 

Google Scholar 
Geist, C., Liao, J., Libby, S. & Blumstein, D. T. Does intruder group size and orientation affect flight initiation distance in birds?. Anim. Biodivers. Conserv. 28, 69–73 (2005).
Google Scholar 
McIntosh, A. R. & Peckarsky, B. L. Criteria determining behavioural responses to multiple predators by a stream mayfly. Oikos. 554–564 (1999).Hemmi, J. M. & Tomsic, D. The neuroethology of escape in crabs: From sensory ecology to neurons and back. Curr. Opin. Neurobiol. 22, 194–200 (2012).CAS 
PubMed 
Article 

Google Scholar 
Zeil, J. & Hemmi, J. M. The visual ecology of fiddler crabs. J. Comp. Physiol. A. 192, 1–25 (2006).ADS 
Article 

Google Scholar 
Nalbach, H.-O., Nalbach, G. & Forzin, L. Visual control of eye-stalk orientation in crabs: Vertical optokinetics, visual fixation of the horizon, and eye design. J. Comp. Physiol. A. 165, 577–587 (1989).Article 

Google Scholar 
Zeil, J. & Al-Mutairi, M. The variation of resolution and of ommatidial dimensions in the compound eyes of the fiddler crab Uca lactea annulipes (Ocypodidae, Brachyura, Decapoda). J. Exp. Biol. 199, 1569–1577 (1996).CAS 
PubMed 
Article 

Google Scholar 
Howard, J. & Snyder, A. W. Transduction as a limitation on compound eye function and design. Proc. R. Soc. Lond. Series B Biol. Sci. 217, 287–307 (1983).ADS 

Google Scholar 
Land, M. F. Visual acuity in insects. Annu. Rev. Entomol. 42, 147–177 (1997).CAS 
PubMed 
Article 

Google Scholar 
Land, M. F. & Nilsson, D.-E. Animal Eyes (OUP, 2012).Book 

Google Scholar 
Bagheri, Z. M. et al. A new method for mapping spatial resolution in compound eyes suggests two visual streaks in fiddler crabs. J. Exp. Biol. 223, 210195 (2020).Article 

Google Scholar 
Smolka, J. & Hemmi, J. M. Topography of vision and behaviour. J. Exp. Biol. 212, 3522–3532 (2009).PubMed 
Article 

Google Scholar 
Land, M. & Layne, J. The visual control of behaviour in fiddler crabs. J. Comp. Physiol. A. 177, 91–103 (1995).Article 

Google Scholar 
Layne, J., Land, M. & Zeil, J. Fiddler crabs use the visual horizon to distinguish predators from conspecifics: A review of the evidence. J. Mar. Biol. Assoc. UK. 77, 43–54 (1997).Article 

Google Scholar 
Hemmi, J. M. Predator avoidance in fiddler crabs: 1. Escape decisions in relation to the risk of predation. Animal Behav. 69, 603–614 (2005).Article 

Google Scholar 
Layne, J. E. Retinal location is the key to identifying predators in fiddler crabs (Uca pugilator). J. Exp. Biol. 201, 2253–2261 (1998).CAS 
PubMed 
Article 

Google Scholar 
Nalbach, H.-O. Frontiers in Crustacean Neurobiology 165–172 (Springer, 1990).Book 

Google Scholar 
Smolka, J., Zeil, J. & Hemmi, J. M. Natural visual cues eliciting predator avoidance in fiddler crabs. Proc. R. Soc. B Biol. Sci. 278, 3584–3592 (2011).Article 

Google Scholar 
Hemmi, J. M. Predator avoidance in fiddler crabs: 2. The visual cues. Animal Behav. 69, 615–625 (2005).Article 

Google Scholar 
Hemmi, J. M. & Pfeil, A. A multi-stage anti-predator response increases information on predation risk. J. Exp. Biol. 213, 1484–1489 (2010).PubMed 
Article 

Google Scholar 
Smolka, J., Raderschall, C. A. & Hemmi, J. M. Flicker is part of a multi-cue response criterion in fiddler crab predator avoidance. J. Exp. Biol. 216, 1219–1224 (2013).PubMed 

Google Scholar 
How, M. J., Pignatelli, V., Temple, S. E., Marshall, N. J. & Hemmi, J. M. High e-vector acuity in the polarisation vision system of the fiddler crab Uca vomeris. J. Exp. Biol. 215, 2128–2134 (2012).PubMed 
Article 

Google Scholar 
Paulk, A. C. et al. Selective attention in the honeybee optic lobes precedes behavioral choices. Proc. Natl. Acad. Sci. 111, 5006–5011 (2014).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Tang, S. & Juusola, M. Intrinsic activity in the fly brain gates visual information during behavioral choices. Nat. Precedings. 1–1 (2010).Bagheri, Z. M., Cazzolato, B. S., Grainger, S., O’Carroll, D. C. & Wiederman, S. D. An autonomous robot inspired by insect neurophysiology pursues moving features in natural environments. J. Neural Eng. 14, 046030 (2017).ADS 
PubMed 
Article 

Google Scholar 
Chancán, M., Hernandez-Nunez, L., Narendra, A., Barron, A. B. & Milford, M. A hybrid compact neural architecture for visual place recognition. IEEE Robot. Automat. Lett. 5, 993–1000 (2020).Article 

Google Scholar 
Colonnier, F., Ramirez-Martinez, S., Viollet, S. & Ruffier, F. A bio-inspired sighted robot chases like a hoverfly. Bioinspir. Biomim. 14, 036002 (2019).ADS 
PubMed 
Article 

Google Scholar 
Medan, V., Oliva, D. & Tomsic, D. Characterization of lobula giant neurons responsive to visual stimuli that elicit escape behaviors in the crab Chasmagnathus. J. Neurophysiol. 98, 2414–2428 (2007).PubMed 
Article 

Google Scholar 
Oliva, D. & Tomsic, D. Computation of object approach by a system of visual motion-sensitive neurons in the crab Neohelice. J. Neurophysiol. 112, 1477–1490 (2014).PubMed 
Article 

Google Scholar 
Oliva, D. & Tomsic, D. Object approach computation by a giant neuron and its relationship with the speed of escape in the crab Neohelice. J. Exp. Biol. 219, 3339–3352 (2016).PubMed 

Google Scholar 
Sztarker, J., Strausfeld, N. J. & Tomsic, D. Organization of optic lobes that support motion detection in a semiterrestrial crab. J. Comparat. Neurol. 493, 396–411 (2005).Article 

Google Scholar 
Medan, V., De Astrada, M. B., Scarano, F. & Tomsic, D. A network of visual motion-sensitive neurons for computing object position in an arthropod. J. Neurosci. 35, 6654–6666 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Tomsic, D. & Sztarker, J. in Oxford Research Encyclopedia of Neuroscience (2019).Sztarker, J. & Tomsic, D. Neuronal correlates of the visually elicited escape response of the crab Chasmagnathus upon seasonal variations, stimuli changes and perceptual alterations. J. Comp. Physiol. A. 194, 587–596 (2008).Article 

Google Scholar 
Tomsic, D., de Astrada, M. B. & Sztarker, J. Identification of individual neurons reflecting short-and long-term visual memory in an arthropodo. J. Neurosci. 23, 8539–8546 (2003).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Layne, J. E., Barnes, W. J. P. & Duncan, L. M. J. Mechanisms of homing in the fiddler crab Uca rapax 1. Spatial and temporal characteristics of a system of small-scale navigation. J. Exp. Biol. 206, 4413–4423 (2003).PubMed 
Article 

Google Scholar 
Dahmen, H., Wahl, V. L., Pfeffer, S. E., Mallot, H. A. & Wittlinger, M. Naturalistic path integration of Cataglyphis desert ants on an air-cushioned lightweight spherical treadmill. J. Exp. Biol. 220, 634–644 (2017).PubMed 
Article 

Google Scholar 
Hemmi, J. M. & Merkle, T. High stimulus specificity characterizes anti-predator habituation under natural conditions. Proc. R. Soc. B Biol. Sci. 276, 4381–4388 (2009).Article 

Google Scholar 
Scarano, F. & Tomsic, D. Escape response of the crab Neohelice to computer generated looming and translational visual danger stimuli. J. Physiol.-Paris 108, 141–147 (2014).PubMed 
Article 

Google Scholar 
Ryan, T. P. & Morgan, J. P. Modern experimental design. J. Stat. Theory Practice 1, 501–506 (2007).MATH 
Article 

Google Scholar 
Hemmi, J. M. & Zeil, J. Burrow surveillance in fiddler crabs I. Description of behaviour. J. Exp. Biol. 206, 3935–3950 (2003).PubMed 
Article 

Google Scholar 
Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. (2014).emmeans: Estimated Marginal Means, aka Least-Squares Means. v. R package version 1.5.2-1. (2020).Cremers, J. Bpnreg: Bayesian projected normal regression models for circular data. R Package Version 1, 3 (2018).
Google Scholar 
Cremers, J. & Klugkist, I. One direction? A tutorial for circular data analysis using R with examples in cognitive psychology. Front. Psychol. 2040 (2018).Oliva, D., Medan, V. & Tomsic, D. Escape behavior and neuronal responses to looming stimuli in the crab Chasmagnathus granulatus (Decapoda: Grapsidae). J. Exp. Biol. 210, 865–880 (2007).PubMed 
Article 

Google Scholar 
Gabbiani, F., Krapp, H. G. & Laurent, G. Computation of object approach by a wide-field, motion-sensitive neuron. J. Neurosci. 19, 1122–1141 (1999).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Simultaneous Inference in General Parametric Models. v. R package version v1.4-10 (2019).Avargues-Weber, A., Deisig, N. & Giurfa, M. Visual cognition in social insects. Annu. Rev. Entomol. 56, 423–443 (2011).CAS 
PubMed 
Article 

Google Scholar 
Avarguès-Weber, A. & Giurfa, M. Conceptual learning by miniature brains. Proc. R. Soc. B Biol. Sci. 280, 20131907 (2013).Article 

Google Scholar 
De Bivort, B. L. & Van Swinderen, B. Evidence for selective attention in the insect brain. Curr. Opin. Insect Sci. 15, 9–15 (2016).PubMed 
Article 

Google Scholar 
Klapoetke, N. C. et al. Ultra-selective looming detection from radial motion opponency. Nature 551, 237–241 (2017).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Von Reyn, C. R. et al. A spike-timing mechanism for action selection. Nat. Neurosci. 17, 962–970 (2014).Article 
CAS 

Google Scholar 
Fotowat, H. & Gabbiani, F. Collision detection as a model for sensory-motor integration. Annu. Rev. Neurosci. 34, 1–19 (2011).CAS 
PubMed 
Article 

Google Scholar 
Strausfeld, N. J. & Olea-Rowe, B. Convergent evolution of optic lobe neuropil in Pancrustacea. Arthropod. Struct. Dev. 61, 101040 (2021).PubMed 
Article 

Google Scholar 
Tomsic, D. Visual motion processing subserving behavior in crabs. Curr. Opin. Neurobiol. 41, 113–121 (2016).CAS 
PubMed 
Article 

Google Scholar 
Giribet, G. & Edgecombe, G. D. The phylogeny and evolutionary history of arthropods. Curr. Biol. 29, R592–R602 (2019).CAS 
PubMed 
Article 

Google Scholar 
Christian, E. V. Sprung der Collembolen. Zoologische Jahrbucher. Abteilung fur Systematik, Okologie und Geographie der Tiere (1979).Brackenbury, J. Regulation of swimming in the Culex pipiens (Diptera, Culicidae) pupa: Kinematics and locomotory trajectories. J. Exp. Biol. 202, 2521–2529 (1999).CAS 
PubMed 
Article 

Google Scholar 
Domenici, P. & Blake, R. W. Escape trajectories in angelfish (Pterophyllum eimekei). J. Exp. Biol. 177, 253–272 (1993).Article 

Google Scholar 
Kimura, H. & Kawabata, Y. Effect of initial body orientation on escape probability of prey fish escaping from predators. Biol. Open. 7, bio023812 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Martín, J. & López, P. The escape response of juvenile Psammodromus algirus lizards. J. Comp. Psychol. 110, 187 (1996).Article 

Google Scholar 
Lancer, B. H., Evans, B. J. E., Fabian, J. M., O’Carroll, D. C. & Wiederman, S. D. A target-detecting visual neuron in the dragonfly locks on to selectively attended targets. J. Neurosci. 39, 8497–8509 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Nityananda, V. & Pattrick, J. G. Bumblebee visual search for multiple learned target types. J. Exp. Biol. 216, 4154–4160 (2013).PubMed 

Google Scholar 
Pollack, G. S. Selective attention in an insect auditory neuron. J. Neurosci. 8, 2635–2639 (1988).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Rossel, S. Binocular vision in insects: How mantids solve the correspondence problem. Proc. Natl. Acad. Sci. 93, 13229–13232 (1996).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Wiederman, S. D. & O’Carroll, D. C. Selective attention in an insect visual neuron. Curr. Biol. 23, 156–161 (2013).CAS 
PubMed 
Article 

Google Scholar 
Jackson, R. R. & Cross, F. R. Spider cognition. Adv. Insect Physiol. 41, 115–174 (2011).Article 

Google Scholar 
Jackson, R. R. & Li, D. One-encounter search-image formation by araneophagic spiders. Anim. Cogn. 7, 247–254 (2004).PubMed 
Article 

Google Scholar 
Guest, B. B. & Gray, J. R. Responses of a looming-sensitive neuron to compound and paired object approaches. J. Neurophysiol. 95, 1428–1441 (2006).PubMed 
Article 

Google Scholar 
Eliassen, S., Jørgensen, C., Mangel, M. & Giske, J. Quantifying the adaptive value of learning in foraging behavior. Am. Nat. 174, 478–489 (2009).PubMed 
Article 

Google Scholar 
Eliassen, S., Andersen, B. S., Jørgensen, C. & Giske, J. From sensing to emergent adaptations: Modelling the proximate architecture for decision-making. Ecol. Model. 326, 90–100 (2016).Article 

Google Scholar 
Gigerenzer, G. Why heuristics work. Perspect. Psychol. Sci. 3, 20–29 (2008).PubMed 
Article 

Google Scholar  More

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.This is a summary of: Wu, L. et al. Reduction of microbial diversity in grassland soil is driven by long-term climate warming. Nat. Microbiol. https://doi.org/10.1038/s41564-022-01147-3 (2022). More

Mu, Q. Y., Zhou, C. & Ng, C. W. W. Compression and wetting induced volumetric behavior of loess: Macro- and micro-investigations. Transp. Geotech. 23, 100345 (2020).Article 

Google Scholar 
Pan, L., Zhu, J. G. & Zhang, Y. F. Evaluation of structural strength and parameters of collapsible loess. Int. J. Geomech. 21, 04021066 (2021).Article 

Google Scholar 
He, S. X., Bai, H. B. & Xu, Z. W. Evaluation on tensile behavior characteristics of undisturbed loess. Energies 11, 1974 (2018).Article 
CAS 

Google Scholar 
He, S. X. & Bai, H. B. Elastic-plastic behavior of compacted loess under direct and cyclic tension. Adv. Mater. Sci. Eng. 2019, 1–12 (2019).
Google Scholar 
Wu, X. Y., Niu, F. J., Liang, Q. G. & Li, G. Y. Study on tensile strength and tensile-shear coupling mechanism of loess around Lanzhou and Yanan city in china by unconfined penetration test. KSCE J. Civ. Eng. 23, 1–12 (2019).Article 

Google Scholar 
You, Z. L., Zhang, M. Y., Liu, F. & Ma, Y. M. Numerical investigation of the tensile strength of loess using discrete element method. Eng. Fract. Mech. 247, 107610 (2021).Article 

Google Scholar 
Zhang, F. Y., Pei, X. J. & Yan, X. D. Physicochemical and mechanical properties of lime-treated loess. Geotech. Geol. Eng. 36, 685–696 (2018).Article 

Google Scholar 
Gu, K. & Chen, B. Loess stabilization using cement, waste phosphogypsum, fly ash and quicklime for self-compacting rammed earth construction. Constr. Build. Mater. 231, 117195–117195 (2020).CAS 
Article 

Google Scholar 
Xue, Z. F., Cheng, W. C., Wang, L. & Song, G. Y. Improvement of the shearing behaviour of loess using recycled straw fiber reinforcement. KSCE J. Civ. Eng. 25, 3319–3335 (2021).Article 

Google Scholar 
Chu, F., Luo, J. B. & Deng, G. H. Experimental study of dynamic deformation and strength properties and seismic subsidence characteristics of fiber yarn reinforced loess. J. Rock. Mech. Geotech. 39, 2306–2320 (2020).
Google Scholar 
Liu, W., Wang, Q., Lin, G. C. & Tian, X. X. Variations of suction and suction stress of unsaturated loess due to changes in lignin content and sample preparation method. J. Mt. Sci. Engl. 18, 16 (2021).
Google Scholar 
Wang, X. G., Liu, K. & Lian, B. Q. Experimental study on ring shear properties of fiber-reinforced loess. Bull. Eng. Geol. Environ. 80, 5021–5029 (2021).Article 

Google Scholar 
Lian, B. Q., Peng, J. B., Zhan, H. B. & Wang, X. G. Mechanical response of root-reinforced loess with various water contents. Soil. Tillage Res. 193, 85–94 (2019).Article 

Google Scholar 
Xu, J. et al. Triaxial shear behavior of basalt fiber-reinforced loess based on digital image technology. KSCE J. Civ. Eng. 1, 1–13 (2021).
Google Scholar 
Li, J. D. et al. Study on strength characteristics and mechanism of loess stabilized by F1 ionic soil stabilizer. Arab. J. Geosci. 14, 1162 (2021).Article 

Google Scholar 
Lv, Q. F., Chang, C. R., Zhao, B. H. & Ma, B. Loess soil stabilization by means of SiO2 nanoparticles. Soil Mech. Found. Eng. 54, 409–413 (2018).Article 

Google Scholar 
Ma, W. J., Wang, B. L., Wang, X., Jiang, D. J. & Li, Z. Y. Experimental study on mechanical properties of modified loess. Water. Resour. Hydropower Eng. 49, 150–156 (2018).
Google Scholar 
Hou, Y. F., Li, P. & Wang, J. D. Review of chemical stabilizing agents for improving the physical and mechanical properties of loess. Bull. Eng. Geol. Environ. 80, 9201–9215 (2021).Article 

Google Scholar 
Liu, X. J., Fan, J. Y., Yu, J. & Gao, X. Solidification of loess using microbial induced carbonate precipitation. J. Mt. Sci. Engl. 18, 265–274 (2021).Article 

Google Scholar 
Chang, I., Im, J. & Cho, G. C. Introduction of microbial biopolymers in soil treatment for future environmentally-friendly and sustainable geotechnical engineering. Sustainability 8, 251 (2016).Article 

Google Scholar 
Jang, J. A review of the application of biopolymers on geotechnical engineering and the strengthening mechanisms between typical biopolymers and soils. Adv. Mater. Sci. Eng. 2020, 1465709 (2020).Article 
CAS 

Google Scholar 
Chang, I., Lee, M., Tran, T., Lee, S. & Cho, G. C. Review on biopolymer-based soil treatment (BPST) technology in geotechnical engineering practices. Transp. Geotech. 24, 100385 (2020).Article 

Google Scholar 
Mendonça, A., Morais, P. V., Pires, A. C., Chung, A. P. & Oliveira, P. V. A review on the importance of microbial biopolymers such as xanthan gum to improve soil properties. Appl. Sci. 11, 170 (2020).Article 
CAS 

Google Scholar 
Rosalam, S. & England, R. Review of xanthan gum production from unmodified starches by Xanthomonas campestris sp. Microb. Technol. 39, 197–207 (2006).CAS 
Article 

Google Scholar 
Moghal, A. A. B. & Vydehi, K. V. State-of-the-art review on efficacy of xanthan gum and guar gum inclusion on the engineering behavior of soils. Innov. Infrastruct. Solut. 6, 1–14 (2021).Article 

Google Scholar 
Shimizu, Y. et al. Viscosity measurement of Xanthan–Poly(vinyl alcohol) mixture and its effect on the mechanical properties of the hydrogel for 3D modeling. Sci. Rep. 8, 16538 (2018).PubMed 
PubMed Central 
Article 
ADS 
CAS 

Google Scholar 
Kumar, S. A. & Sujatha, E. R. An Appraisal of the Hydro-mechanical behaviour of polysaccharides, xanthan gum, guar gum and β-glucan amended soil. Carbohyd. Polym. 265, 118083 (2021).Article 
CAS 

Google Scholar 
Chang, I., Prasidhi, A. K., Im, J., Shi, H. D. & Cho, G. C. Soil treatment using microbial biopolymers for anti-desertification purposes. Geoderma 253–254, 39–47 (2015).Article 
ADS 
CAS 

Google Scholar 
Fatehi, H., Ong, D. E. L., Yu, J. & Chang, I. Biopolymers as green binders for soil improvement in geotechnical applications: A review. Geosciences (Switzerland). 11, 291 (2021).CAS 
ADS 

Google Scholar 
Lee, S., Chang, I., Chung, M. K., Kim, Y. & Kee, J. Geotechnical shear behavior of xanthan gum biopolymer treated sand from direct shear testing. Geomech. Eng. 12, 831–847 (2017).Article 

Google Scholar 
Lee, S., Im, J., Cho, G. C. & Chang, I. Laboratory triaxial test behavior of xanthan gum biopolymer treated sands. Geomech. Eng. 17, 445–452 (2019).
Google Scholar 
Chang, I., Im, J., Prasidhi, A. K. & Cho, G. C. Effects of xanthan gum biopolymer on soil strengthening. Constr. Build. Mater. 74, 65–72 (2015).Article 

Google Scholar 
Liu, J. E. et al. The impact of natural polymer derivatives on sheet erosion on experimental loess hillslope. Soil. Tillage Res. 139, 23–27 (2014).Article 

Google Scholar 
Pu, S. et al. Stabilization behavior and performance of loess using a novel biomass-based polymeric soil stabilizer. Environ. Eng. Geosci. 25, 103–114 (2019).Article 

Google Scholar 
Zhang, X. C., Zhong, Y. J., Pei, X. J. & Duan, Y. Y. A cross-linked polymer soil stabilizer for hillslope conservation on the loess plateau. Front. Earth Sci. 9, 771316 (2021).Article 

Google Scholar 
Ni, J., Li, S. S., Ma, L. & Geng, X. Y. Performance of soils enhanced with eco-friendly biopolymers in unconfined compression strength tests and fatigue loading tests. Constr. Build. Mater. 263, 120039 (2020).CAS 
Article 

Google Scholar 
Kameda, J. & Yohei, H. Influence of biopolymers on the rheological properties of seafloor sediments and the runout behavior of submarine debris flows. Sci. Rep. 11, 1493 (2021).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
Ramani, S., Atchaya, S., Sivasaran, A. & Keerdthe, R. S. Enhancing the geotechnical properties of soil using xanthan gum—An eco-friendly alternative to traditional stabilizers. Bull. Eng. Geol. Environ. 80, 1157–1167 (2020).
Google Scholar 
Cabalar, A. F., Awraheem, M. H. & Khalaf, M. M. Geotechnical properties of a low-plasticity clay with biopolymer. J. Mater. Civ. Eng. 30, 04018170 (2018).Article 

Google Scholar 
Reddy, J. J. & Varaprasad, B. J. S. Long-term and durability properties of xanthan gum treated dispersive soils—An eco-friendly material. Mater. Today. 44, 309–314 (2021).CAS 

Google Scholar 
Joga, J. R. & Varaprasad, B. J. S. Effect of xanthan gum biopolymer on dispersive properties of soils. J. Eng. Technol. 17, 563–571 (2020).CAS 

Google Scholar 
Muguda, S. et al. Mechanical properties of biopolymer-stabilised soil-based construction materials. Géotech. Lett. 7, 309–314 (2017).Article 

Google Scholar 
Muguda, S., et al. Cross-linking of biopolymers for stabilizing earthen construction materials. Build. Res. Inf. 1–13 (2021).Soldo, A., Miletić, M. & Auad, M. L. Biopolymers as a sustainable solution for the enhancement of soil mechanical properties. Sci. Rep. 10, 267 (2020).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
Jiang, T., et al. Diametric splitting tests on loess based on PIV technique. Rock Soil Mech. 42, 2120–2126+2140 (2021).Zhang, J. R., Wang, L. J., Jiang, T., Ren, M. & Wei, M. Diametric splitting tests on unsaturated expansive soil with different dry densities based on the particle image velocimetry technique. Acta Geotech. Slov. 18, 15–27 (2021).Article 

Google Scholar 
Qureshi, M. U., Chang, I. & Al-Sadarani, K. Strength and durability characteristics of biopolymer-treated desert sand. Geomech. Eng. 12, 785–801 (2017).Article 

Google Scholar 
Ng, C. W. W. et al. Influence of biopolymer on gas permeability in compacted clay at different densities and water contents. Eng. Geol. 272, 105631 (2020).Article 

Google Scholar 
Kwon, Y. M., Ham, S. M., Kwon, T. H., Cho, G. C. & Chang, I. Surface-erosion behaviour of biopolymer-treated soils assessed by EFA. Géotech. Lett. 10, 106–112 (2020).Article 

Google Scholar 
Ramachandran, A. L., Dubey, A. A., Dhami, N. K. & Mukherjee, A. Multiscale study of soil stabilisation using bacterial biopolymers. J. Geotech. Geoenviron. Eng. 147, 04021074 (2021).CAS 
Article 

Google Scholar 
Nugent, R. A., Zhang, G. & Gambrell, R. P. Effect of exopolymers on the liquid limit of clays and its engineering implications. Transp. Res. Rec. 2101, 34–43 (2009).Article 

Google Scholar 
Wang, Y., Li, T. L., Zhao, C. X., Hou, X. K. & Zhang, Y. G. A study on the effect of pore and particle distributions on the soil water characteristic curve of compacted loess. Environ. Earth. Sci. 80, 764 (2021).Article 

Google Scholar 
Gao, Y., Sun, D. A., Zhu, Z. C. & Xu, Y. F. Hydromechanical behavior of unsaturated soil with different initial densities over a wide suction range. Acta. Geotech. 14, 417–428 (2018).Article 

Google Scholar 
Li, B. & Chen, Y. L. Influence of dry density on soil-water retention curve of unsaturated soils and its mechanism based on mercury intrusion porosimetry. Trans. Tianjin Univ. 22, 268–272 (2016).CAS 
Article 

Google Scholar 
Xu, W. S., Li, K. S., Chen, L. X., Kong, W. H. & Liu, C. X. The impacts of freeze-thaw cycles on saturated hydraulic conductivity and microstructure of saline-alkali soils. Sci. Rep. 11, 18655 (2021).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
Li, Z. Q., Qi, Z. Y., Qi, S. W., Zhang, L. X. & Hou, X. H. Microstructural changes and micro-macro-relationships of an intact, compacted and remolded loess for land-creation project from the Loess Plateau. Environ. Earth. Sci. 80, 593 (2021).Article 

Google Scholar  More

Variable names, units and timestampsStreamflow is runoff routed along a drainage network, in m3/s, also known as discharge, which is the variable name used in the files. Water temperature is given in units of Kelvin. Filenames include the variable name, GCM, scenario (hist for historical, or one of the RCPs) and the time period (years). The timestamps in the files reflect the last date of the period over which the output was averaged, so the first timestamp of the weekly averages is January 7th 1976.Ecologically-relevant variablesThe ecologically-relevant streamflow and water temperature variables derived from the weekly values are established based on a combination of classification frameworks, i.e., indicators of hydrologic alteration19, terrestrial bioclimatic variables in the worldclim dataset20 as well as the CMCC-BioClimInd dataset21, aggregated accordingly: 1976–2005 (1979–2005 for E2O); 2021–2040; 2041–2060; 2061–2080; 2081–2099. The scripts used to compute these derived variables can be found under Code Availability.For files containing information on timing (see Tables 2–3), note that the counting is 0-indexed. So week numbers run from 0 through 51, months from 0 to 11. For timing of quarters, 0 is DJF, 1 is MAM, 2 is JJA, 3 is SON. The week number (for WT-wmin, WT-wmax, Q-wmin, Q-wmax) is determined as the mode, i.e. the most frequent week number within a period. For each period (20, 25 or 30 years) we looked for the week number in which the minimum or maximum water temperature or discharge occurs. If that happens most often in week X, that week number is stored. It can however occur that a certain minimum/maximum temperature or discharge occurs equally often in multiple weeks – then we assign a missing value.The variables Q-bfi and Q-vi are calculated according to Pastor et al.30. The baseflow index is an indicator of the importance of stored sources; a high index indicates that flow is mostly sustained by stored sources such as groundwater.Scripts used to create the derived variables are available through the FutureStreams GitHub repository (see Code Availability below).Multi-model set-upWe provide future scenarios for four RCPs (representative concentration pathways; 2.6, 4.5, 6.0 and 8.5 W/m2 in 2100) for the five ISI-MIP GCMs. Projections differ across RCPs due to differences in greenhouse gas forcing, and across GCMs due to differences in e.g model parameterization and resolution. Generally the spread across GCMs is larger than that across RCPs7,31. When interested in the general effect of climate change, users are advised to use the mean or median across the GCMs, rather than selecting a specific GCM. When interested in the spread across GCMs, users can explore or represent that in various ways, such as color intensity indicating agreement amongst models5, bar or violin plots7 etc.Warming levelsTo facilitate assessments and comparisons of streamflow and water temperature at a certain air temperature rise rather than specific years5,7, we provide a table with the years in which each GCM/RCP reaches the global mean temperature rises 1.5°, 2.0°, 3.2°, 4.5° compared to pre-industrial temperatures (as used by Barbarossa et al.7) with our scripts (see Code Availability). These years represent the central value of a 30-year running mean, so users should evaluate the 30-year mean (or other statistic) of discharge or water temperature centered around the year that a certain warming level is reached, which is specific to each RCP and GCM combination. For instance, if 1.5° warming is reached in 2040, the 30-year period 2025–2054 should be considered.GCMs, bias-correction and reanalysis dataThe majority of our simulations are forced with meteorological time series from GCMs. Those are bias-corrected27 before being applied to impact models such as PCR-GLOBWB, which corrects for systematic deviations of the simulated historical data from observations. For instance, for temperature the offset in average temperature in the historical GCM simulation with respect to observations is subtracted from temperatures in all scenarios of that GCM. The bias-corrected GCM forcing should thus well represent climatology, but not necessarily timing of actual events such as floods and droughts. Reanalysis data is created by assimilating observations into weather models, to obtain consistent and globally complete time series. The output of the simulation forced with meteorological time series from the (E2O) reanalysis data should therefore reflect not only the average streamflow and water temperatures, but also timing of actual events such as droughts.If users want to check for themselves how the GCM-forced historical simulations discussed here deviate from reanalysis-forced simulations, they can use the output from the E2O-forced simulation provided here, the monthly output linked to Wanders et al.13 (see also Code Availability) or the daily output of those simulations which are available from Niko Wanders upon request. The latter are forced with ERA-40/ERA-Interim reanalysis data.Notes of cautionBeware of temperature in grid cells where streamflow is low, which can cause temperatures to become unrealistically high due to strong fluctuations in the water level. The computational timesteps currently implemented in DynWat are not sufficiently small to provide stable solutions for these conditions. For some lakes and reservoirs we observe a similar problem when lakes expand or shrink as a result of water levels changes. These locations can be masked and we can assume that water temperature follows the air temperature for these very shallow water layers. A file with locations of lakes and reservoirs is provided in the data repository (under indicators/mask) so users can mask these if desired.Furthermore, we provide masks for each GCM-RCP-period which users can apply to the derived variables if desired. These masks are based on Q-mean and WT-mean and thresholds of 10 m3/s and 350 K, respectively. They can be found in the data repository (i.e. indicators/waterTemperature/WT-mask). The scripts used to create these masks are provided through the FutureStreams GitHub repository (see Code Availability below), which can be used to create masks with different thresholds. These scripts are called mask_unrealistic_values.py and maskFunctions.py.We also provide scripts to mask out unrealistic values directly in the weekly Q and WT files, these scripts are mask_unrealistic_values_weekly.py and maskFunctions_weekly.py. In all these scripts the threshold for discharge is set to 10 m3/s and for water temperature to 350 K, but users can change those to their preferred values. The threshold value will be included in the resulting output file name.Furthermore, we encountered spin-up issues in some pixels for the future RCP simulations. Instead of following the temperatures from the end of the historical simulation, temperatures drop at the beginning of the future simulation, so the first few weeks of 2006 temperatures can be unrealistically low. In Fig. 2, output of the year 2007 is used for the year 2006 .Fig. 2Water temperature [°C] anomaly. The maps show the difference between the mean water temperature over the period 2070–2099 (RCP8p5) and the historical period 1975–2005. The map shows values only for rivers with streamflow greater than 50 m3/s and the width of the rivers is scaled based on the streamflow values for clarity of representation. Insets below the map show the original gridded resolution at 5 arcminute for cells with streamflow values greater than 10 m3/s. The bottom insets show water temperature time series sampled at specific grid-cell locations (white crosses in the insets) for the Amazon (−57.2083° longitude, −2.625° latitude), Danube (20.125° lon, 45.2083° lat) and Ganges (88.375° lon, 24.375° lat). Time series are represented for each GCM and RCP available within FutureStreams; thin lines represent weekly values, thick lines represent 10 year rolling means.Full size imageFig. 3Streamflow [m3/s] anomaly. The maps show the difference between the log10 transformed mean streamflow over the period 2070–2099 (RCP8p5) and the log10 transformed mean streamflow over historical period 1975–2005. The map shows values only for rivers with streamflow values greater than 50 m3/s and the width of the rivers is scaled based on the streamflow values for clarity of representation. Insets below the map show the original gridded resolution at 5 arcminute for cells with streamflow values greater than 10 m3/s. The bottom insets show water temperature time series sampled at specific grid-cell locations (white crosses in the insets) for the Amazon (−57.2083° longitude, −2.625° latitude), Danube (20.125° lon, 45.2083° lat) and Ganges (88.375° lon, 24.375° lat). Time series are represented for each GCM and RCP available within FutureStreams; thin lines represent weekly values and thick lines represent 10 year rolling means.Full size imageFig. 4Anomalies for selected ecologically relevant derived variables (bioclimatic indicators) for the same areas in the Amazone (left), Danube (middle) and Ganges (right) basins as used in Figs. 2 and 3. Differences are shown between RCP8.5 2080–2099 and 1976–2005. WT-cq is the water temperature of the coldest quarter, WT-range is temperature range, Q-max is maximum streamflow, Q-dm is streamflow of the driest month (see also Tables 2 and 3 below). For streamflow we show the difference between log10-transformed flow.Full size image More

Tolussi, C. E., Hilsdorf, A. W. S., Caneppele, D. & Moreira, R. G. The effects of stocking density in physiological parameters and growth of the endangered teleost species piabanha, Brycon insignis (Steindachner, 1877). Aquaculture 310, 221–228 (2010).
Google Scholar 
Wang, Y. et al. Effects of stocking density on growth, serum parameters, antioxidant status, liver and intestine histology and gene expression of largemouth bass (Micropterus salmoides) farmed in the in-pond raceway system. Aquac. Res. 51, 5228–5240 (2020).CAS 

Google Scholar 
Zahedi, S., Akbarzadeh, A., Mehrzad, J., Noori, A. & Harsij, M. Effect of stocking density on growth performance, plasma biochemistry and muscle gene expression in rainbow trout (Oncorhynchus mykiss). Aquaculture 498, 271–278 (2019).CAS 

Google Scholar 
Yousefi, M., Paktinat, M., Mahmoudi, N., Pérez-Jiménez, A. & Hoseini, S. M. Serum biochemical and non-specific immune responses of rainbow trout (Oncorhynchus mykiss) to dietary nucleotide and chronic stress. Fish Physiol. Biochem. 42, 1417–1425 (2016).CAS 
PubMed 

Google Scholar 
Duan, Y., Dong, X., Zhang, X. & Miao, Z. Effects of dissolved oxygen concentration and stocking density on the growth, energy budget and body composition of juvenile Japanese flounder, Paralichthys olivaceus (Temminck et Schlegel). Aquac. Res. 42, 407–416 (2011).CAS 

Google Scholar 
Castillo-Vargasmachuca, S. et al. Effect of stocking density on growth performance and yield of subadult pacific red snapper cultured in floating sea cages. N. Am. J. Aquac. 74, 413–418 (2012).
Google Scholar 
Upadhyay, A. et al. Stocking density matters in open water cage culture: influence on growth, digestive enzymes, haemato-immuno and stress responses of Puntius sarana (Ham, 1822). Aquaculture 547, 737445 (2021).
Google Scholar 
Kumar, V. et al. Assessment of the effect of sub-lethal acute toxicity of Emamectin benzoate in Labeo rohita using multiple biomarker approach. Toxicol. Rep. 9, 102–110 (2022).CAS 
PubMed 
PubMed Central 

Google Scholar 
Rebl, A. et al. The synergistic interaction of thermal stress coupled with overstocking strongly modulates the transcriptomic activity and immune capacity of rainbow trout (Oncorhynchus mykiss). Sci. Rep. 10, 1–15 (2020).ADS 

Google Scholar 
Braun, N., de Lima, R. L., Baldisserotto, B., Dafre, A. L. & de Oliveira Nuñer, A. P. Growth, biochemical and physiological responses of Salminus brasiliensis with different stocking densities and handling. Aquaculture 301, 22–30 (2010).CAS 

Google Scholar 
Refaey, M. M., Tian, X., Tang, R. & Li, D. Changes in physiological responses, muscular composition and flesh quality of channel catfish Ictalurus punctatus suffering from transport stress. Aquaculture 478, 9–15 (2017).CAS 

Google Scholar 
Liu, G. et al. Influence of stocking density on growth, digestive enzyme activities, immune responses, antioxidant of Oreochromis niloticus fingerlings in biofloc systems. Fish Shellfish Immunol. 81, 416–422 (2018).CAS 
PubMed 

Google Scholar 
Kumar, G. & Engle, C. R. Technological advances that led to growth of shrimp, salmon, and tilapia farming. Rev. Fish. Sci. Aquac. 24, 136–152 (2016).
Google Scholar 
Sundin, L. Hypoxia and blood flow control in fish gills. In Biology of tropical fishes (eds Val, A. L. & Almeida-Val, V. M. F.) 353–362 (Manaus INPA, 1999).
Google Scholar 
Beveridge, M. C. M. Cage Aquaculture Vol. 5 (John Wiley & Sons, 2008).
Google Scholar 
Valenti, W. C., Barros, H. P., Moraes-Valenti, P., Bueno, G. W. & Cavalli, R. O. Aquaculture in Brazil: past, present and future. Aquac. Rep. 19, 100611 (2021).
Google Scholar 
Das, A. K., Meena, D. K. & Sharma, A. P. Cage farming in an Indian Reservoir. World Aquac. 45, 56–59 (2014).
Google Scholar 
Sarkar, U. K. et al. Status, prospects, threats, and the way forward for sustainable management and enhancement of the tropical Indian reservoir fisheries: an overview. Rev. Fish. Sci. Aquac. 26, 155–175 (2018).
Google Scholar 
Singh, A. K. & Lakra, W. S. Culture of Pangasianodon hypophthalmus into India: impacts and present scenario. Pakistan J. Biol. Sci. 15, 19 (2012).CAS 

Google Scholar 
Jena, J. et al. Evaluation of growth performance of Labeo fimbriatus (Bloch), Labeo gonius (Hamilton) and Puntius gonionotus (Bleeker) in polyculture with Labeo rohita (Hamilton) during fingerlings rearing at varied densities. Aquaculture 319, 493–496 (2011).
Google Scholar 
Liu, B., Jia, R., Han, C., Huang, B. & Lei, J.-L. Effects of stocking density on antioxidant status, metabolism and immune response in juvenile turbot (Scophthalmus maximus). Comp. Biochem. Physiol. Part C Toxicol. Pharmacol. 190, 1–8 (2016).CAS 

Google Scholar 
Wu, F. et al. Effect of stocking density on growth performance, serum biochemical parameters, and muscle texture properties of genetically improved farm tilapia, Oreochromis niloticus. Aquac. Int. 26, 1247–1259 (2018).CAS 

Google Scholar 
Andrade, T. et al. Evaluation of different stocking densities in a Senegalese sole (Solea senegalensis) farm: implications for growth, humoral immune parameters and oxidative status. Aquaculture 438, 6–11 (2015).CAS 

Google Scholar 
Qi, C. et al. Effect of stocking density on growth, physiological responses, and body composition of juvenile blunt snout bream, Megalobrama amblycephala. J. World Aquac. Soc. 47, 358–368 (2016).CAS 

Google Scholar 
Shao, T. et al. Evaluation of the effects of different stocking densities on growth and stress responses of juvenile hybrid grouper♀ Epinephelus fuscoguttatus×♂ Epinephelus lanceolatus in recirculating aquaculture systems. J. Fish Biol. 95, 1022–1029 (2019).CAS 
PubMed 

Google Scholar 
Adineh, H., Naderi, M., Hamidi, M. K. & Harsij, M. Biofloc technology improves growth, innate immune responses, oxidative status, and resistance to acute stress in common carp (Cyprinus carpio) under high stocking density. Fish Shellfish Immunol. 95, 440–448 (2019).CAS 
PubMed 

Google Scholar 
Fazelan, Z., Vatnikov, Y. A., Kulikov, E. V., Plushikov, V. G. & Yousefi, M. Effects of dietary ginger (Zingiber officinale) administration on growth performance and stress, immunological, and antioxidant responses of common carp (Cyprinus carpio) reared under high stocking density. Aquaculture 518, 734833 (2020).CAS 

Google Scholar 
Hoseini, S. M., Yousefi, M., Hoseinifar, S. H. & Van Doan, H. Effects of dietary arginine supplementation on growth, biochemical, and immunological responses of common carp (Cyprinus carpio L.), stressed by stocking density. Aquaculture 503, 452–459 (2019).CAS 

Google Scholar 
Adineh, H., Naderi, M., Nazer, A., Yousefi, M. & Ahmadifar, E. Interactive effects of stocking density and dietary supplementation with nano selenium and garlic extract on growth, feed utilization, digestive enzymes, stress responses, and antioxidant capacity of grass carp, Ctenopharyngodon idella. J. World Aquac. Soc. 52, 789–804 (2021).CAS 

Google Scholar 
Zhao, H. et al. Transcriptome and physiological analysis reveal alterations in muscle metabolisms and immune responses of grass carp (Ctenopharyngodon idellus) cultured at different stocking densities. Aquaculture 503, 186–197 (2019).CAS 

Google Scholar 
Frisso, R. M., de Matos, F. T., Moro, G. V. & de Mattos, B. O. Stocking density of Amazon fish (Colossoma macropomum) farmed in a continental neotropical reservoir with a net cages system. Aquaculture 529, 735702 (2020).CAS 

Google Scholar 
Tammam, M. S., Wassef, E. A., Toutou, M. M. & El-Sayed, A.-F.M. Combined effects of surface area of periphyton substrates and stocking density on growth performance, health status, and immune response of Nile tilapia (Oreochromis niloticus) produced in cages. J. Appl. Phycol. 32, 3419–3428 (2020).CAS 

Google Scholar 
Zaki, M. A. A. et al. The impact of stocking density and dietary carbon sources on the growth, oxidative status and stress markers of Nile tilapia (Oreochromis niloticus) reared under biofloc conditions. Aquac. Reports 16, 100282 (2020).
Google Scholar 
Rowland, S. J., Mifsud, C., Nixon, M. & Boyd, P. Effects of stocking density on the performance of the Australian freshwater silver perch (Bidyanus bidyanus) in cages. Aquaculture 253, 301–308 (2006).
Google Scholar 
Mohler, J. W., King, M. K. & Farrell, P. R. Growth and survival of first-feeding and fingerling Atlantic sturgeon under culture conditions. N. Am. J. Aquac. 62, 174–183 (2000).
Google Scholar 
Mirghaed, A. T., Hoseini, S. M. & Ghelichpour, M. Effects of dietary 1, 8-cineole supplementation on physiological, immunological and antioxidant responses to crowding stress in rainbow trout (Oncorhynchus mykiss). Fish Shellfish Immunol. 81, 182–188 (2018).
Google Scholar 
Hoseini, S. M., Mirghaed, A. T., Iri, Y. & Ghelichpour, M. Effects of dietary cineole administration on growth performance, hematological and biochemical parameters of rainbow trout (Oncorhynchus mykiss). Aquaculture 495, 766–772 (2018).CAS 

Google Scholar 
Barton, B. A., Morgan, J. D. & Vijayan, M. M. Physiological and condition-related indicators of environmental stress in fish. In Biological Indicators of Aquatic Ecosystem Stress (ed. Adams, S. M.) 111–148 (American Fisheries Society, 2002).
Google Scholar 
Varela, J. L. et al. Dietary administration of probiotic Pdp11 promotes growth and improves stress tolerance to high stocking density in gilthead seabream Sparus auratus. Aquaculture 309, 265–271 (2010).CAS 

Google Scholar 
Costas, B., Aragão, C., Dias, J., Afonso, A. & Conceição, L. E. C. Interactive effects of a high-quality protein diet and high stocking density on the stress response and some innate immune parameters of Senegalese sole Solea senegalensis. Fish Physiol. Biochem. 39, 1141–1151 (2013).CAS 
PubMed 

Google Scholar 
Long, L. et al. Effects of stocking density on growth, stress, and immune responses of juvenile Chinese sturgeon (Acipenser sinensis) in a recirculating aquaculture system. Comp. Biochem. Physiol. Part C Toxicol. Pharmacol. 219, 25–34 (2019).CAS 

Google Scholar 
Sadhu, N., Sharma, S. R. K., Joseph, S., Dube, P. & Philipose, K. K. Chronic stress due to high stocking density in open sea cage farming induces variation in biochemical and immunological functions in Asian seabass (Lates calcarifer, Bloch). Fish Physiol. Biochem. 40, 1105–1113 (2014).CAS 
PubMed 

Google Scholar 
Zahran, E., Risha, E., AbdelHamid, F., Mahgoub, H. A. & Ibrahim, T. Effects of dietary Astragalus polysaccharides (APS) on growth performance, immunological parameters, digestive enzymes, and intestinal morphology of Nile tilapia (Oreochromis niloticus). Fish Shellfish Immunol. 38, 149–157 (2014).CAS 
PubMed 

Google Scholar 
Aruoma, O. I. Free radicals, oxidative stress, and antioxidants in human health and disease. J. Am. Oil Chem. Soc. 75, 199–212 (1998).CAS 
PubMed 
PubMed Central 

Google Scholar 
Haridas, H. et al. Enhanced growth and immuno-physiological response of genetically improved farmed Tilapia in indoor biofloc units at different stocking densities. Aquac. Res. 48, 4346–4355 (2017).CAS 

Google Scholar 
Ruane, N. M., Carballo, E. C. & Komen, J. Increased stocking density influences the acute physiological stress response of common carp Cyprinus carpio (L.). Aquac. Res. 33, 777–784 (2002).
Google Scholar 
Wang, X. et al. Effects of stocking density on growth, nonspecific immune response, and antioxidant status in African catfish (Clarias gariepinus). (2013).Johnson, K. M. & Lema, S. C. Tissue-specific thyroid hormone regulation of gene transcripts encoding iodothyronine deiodinases and thyroid hormone receptors in striped parrotfish (Scarus iseri). Gen. Comp. Endocrinol. 172, 505–517 (2011).CAS 
PubMed 

Google Scholar 
El-Khaldi, A. T. F. Effect of different stress factors on some physiological parameters of Nile tilapia (Oreochromis niloticus). Saudi J. Biol. Sci. 17, 241–246 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
Sharma, A., Devi, S., Singh, K. & Prabhakar, P. K. Correlation of body mass index with thyroid-stimulating hormones in thyroid patient. Asian J. Pharm. Clin. Res. 11, 65–68 (2018).
Google Scholar 
Li, D., Liu, Z. & Xie, C. Effect of stocking density on growth and serum concentrations of thyroid hormones and cortisol in Amur sturgeon, Acipenser schrenckii. Fish Physiol. Biochem. 38, 511–520 (2012).CAS 
PubMed 

Google Scholar 
Park, J.-W. et al. The thyroid endocrine disruptor perchlorate affects reproduction, growth, and survival of mosquitofish. Ecotoxicol. Environ. Saf. 63, 343–352 (2006).CAS 
PubMed 

Google Scholar 
Refaey, M. M. et al. High stocking density alters growth performance, blood biochemistry, intestinal histology, and muscle quality of channel catfish Ictalurus punctatus. Aquaculture 492, 73–81 (2018).CAS 

Google Scholar 
Reinecke, M. et al. Growth hormone and insulin-like growth factors in fish: where we are and where to go. Gen. Comp. Endocrinol. 142, 20–24 (2005).CAS 
PubMed 

Google Scholar 
Salas-Leiton, E. et al. Dexamethasone modulates expression of genes involved in the innate immune system, growth and stress and increases susceptibility to bacterial disease in Senegalese sole (Solea senegalensis Kaup, 1858). Fish Shellfish Immunol. 32, 769–778 (2012).CAS 
PubMed 

Google Scholar 
Dyer, A. R. et al. Correlation of plasma IGF-I concentrations and growth rate in aquacultured finfish: a tool for assessing the potential of new diets. Aquaculture 236, 583–592 (2004).CAS 

Google Scholar 
Kajimura, S. et al. Dual mode of cortisol action on GH/IGF-I/IGF binding proteins in the tilapia, Oreochromis mossambicus. J. Endocrinol. 178, 91–99 (2003).CAS 
PubMed 

Google Scholar 
Ren, Y., Wen, H., Li, Y. & Li, J. Stocking density affects the growth performance and metabolism of Amur sturgeon by regulating expression of genes in the GH/IGF axis. J. Oceanol. Limnol. 36, 956–972 (2018).ADS 
CAS 

Google Scholar 
Salas-Leiton, E. et al. Effects of stocking density and feed ration on growth and gene expression in the Senegalese sole (Solea senegalensis): potential effects on the immune response. Fish Shellfish Immunol. 28, 296–302 (2010).CAS 
PubMed 

Google Scholar 
Vijayan, M. M., Aluru, N. & Leatherland, J. F. Stress response and the role of cortisol. Fish Dis. Disord. 2, 182–201 (2010).
Google Scholar 
Hegazi, M. M., Attia, Z. I. & Ashour, O. A. Oxidative stress and antioxidant enzymes in liver and white muscle of Nile tilapia juveniles in chronic ammonia exposure. Aquat. Toxicol. 99, 118–125 (2010).CAS 
PubMed 

Google Scholar 
Kpundeh, M. D., Xu, P., Yang, H., Qiang, J. & He, J. Stocking densities and chronic zero culture water exchange stress’ effects on biological performances, hematological and serum biochemical indices of GIFT tilapia juveniles (Oreochromis niloticus). J. Aquac. Res. Dev. 4, 2 (2013).
Google Scholar 
Tan, C. et al. Effects of stocking density on growth, body composition, digestive enzyme levels and blood biochemical parameters of Anguilla marmorata in a recirculating aquaculture system. Turk. J. Fish. Aquat. Sci. 18, 9–16 (2018).
Google Scholar 
Ni, M. et al. The physiological performance and immune responses of juvenile Amur sturgeon (Acipenser schrenckii) to stocking density and hypoxia stress. Fish Shellfish Immunol. 36, 325–335 (2014).CAS 
PubMed 

Google Scholar 
Abdel-Tawwab, M. Effects of dietary protein levels and rearing density on growth performance and stress response of Nile tilapia, Oreochromis niloticus (L.). Int. Aquat. Res. 4, 1–13 (2012).
Google Scholar 
Chatterjee, N. et al. Effect of stocking density and journey length on the welfare of rohu (Labeo rohita Hamilton) fry. Aquac. Int. 18, 859–868 (2010).
Google Scholar 
Pakhira, C., Nagesh, T. S., Abraham, T. J., Dash, G. & Behera, S. Stress responses in rohu, Labeo rohita transported at different densities. Aquac. Rep. 2, 39–45 (2015).
Google Scholar 
Tahmasebi-Kohyani, A., Keyvanshokooh, S., Nematollahi, A., Mahmoudi, N. & Pasha-Zanoosi, H. Effects of dietary nucleotides supplementation on rainbow trout (Oncorhynchus mykiss) performance and acute stress response. Fish Physiol. Biochem. 38, 431–440 (2012).CAS 
PubMed 

Google Scholar 
Montero, D. et al. Effect of vitamin E and C dietary supplementation on some immune parameters of gilthead seabream (Sparus aurata) juveniles subjected to crowding stress. Aquaculture 171, 269–278 (1999).CAS 

Google Scholar 
Urbinati, E. C., de Abreu, J. S., da Silva Camargo, A. C. & Parra, M. A. L. Loading and transport stress of juvenile matrinxã (Brycon cephalus, Characidae) at various densities. Aquaculture 229, 389–400 (2004).
Google Scholar 
Evans, D. H. Cell signaling and ion transport across the fish gill epithelium. J. Exp. Zool. 293, 336–347 (2002).CAS 
PubMed 

Google Scholar 
McCormick, S. D. Endocrine control of osmoregulation in teleost fish. Am. Zool. 41, 781–794 (2001).CAS 

Google Scholar 
Postlethwaite, E. & McDonald, D. Mechanisms of Na+ and C-regulation in freshwater-adapted rainbow trout (Oncorhynchus mykiss) during exercise and stress. J. Exp. Biol. 198, 295–304 (1995).CAS 
PubMed 

Google Scholar 
Liu, P., Du, Y., Meng, L., Li, X. & Liu, Y. Metabolic profiling in kidneys of Atlantic salmon infected with Aeromonas salmonicida based on 1H NMR. Fish Shellfish Immunol. 58, 292–301 (2016).CAS 
PubMed 

Google Scholar 
Hosfeld, C. D., Hammer, J., Handeland, S. O., Fivelstad, S. & Stefansson, S. O. Effects of fish density on growth and smoltification in intensive production of Atlantic salmon (Salmo salar L.). Aquaculture 294, 236–241 (2009).
Google Scholar 
Wagner, E. I., Miller, S. A. & Bosakowski, T. Ammonia excretion by rainbow trout over a 24-hour period at two densities during oxygen injection. Progress. Fish-Culturist 57, 199–205 (1995).
Google Scholar 
Dong, J. et al. Effect of stocking density on growth performance, digestive enzyme activities, and nonspecific immune parameters of Palaemonetes sinensis. Fish Shellfish Immunol. 73, 37–41 (2018).CAS 
PubMed 

Google Scholar 
Wang, Y. et al. Effects of stocking density on the growth performance, digestive enzyme activities, antioxidant resistance, and intestinal microflora of blunt snout bream (Megalobrama amblycephala) juveniles. Aquac. Res. 50, 236–246 (2019).CAS 

Google Scholar 
Trenzado, C. E. et al. Effect of dietary lipid content and stocking density on digestive enzymes profile and intestinal histology of rainbow trout (Oncorhynchus mykiss). Aquaculture 497, 10–16 (2018).CAS 

Google Scholar 
Li, X., Liu, Y. & Blancheton, J.-P. Effect of stocking density on performances of juvenile turbot (Scophthalmus maximus) in recirculating aquaculture systems. Chin. J. Oceanol. Limnol. 31, 514–522 (2013).ADS 
CAS 

Google Scholar 
Ezhilmathi, S. et al. Effect of stocking density on growth performance, digestive enzyme activity, body composition and gene expression of Asian seabass reared in recirculating aquaculture system. Aquac. Res. https://doi.org/10.1111/are.15725 (2022).Article 

Google Scholar 
Bolasina, S., Tagawa, M., Yamashita, Y. & Tanaka, M. Effect of stocking density on growth, digestive enzyme activity and cortisol level in larvae and juveniles of Japanese flounder, Paralichthys olivaceus. Aquaculture 259, 432–443 (2006).CAS 

Google Scholar 
Hoseini, S. M., Hoseinifar, S. H. & Van Doan, H. Effect of dietary eucalyptol on stress markers, enzyme activities and immune indicators in serum and haematological characteristics of common carp (Cyprinus carpio) exposed to toxic concentration of ambient copper. Aquac. Res. 49, 3045–3054 (2018).CAS 

Google Scholar 
Ni, M. et al. Effects of stocking density on mortality, growth and physiology of juvenile Amur sturgeon (Acipenser schrenckii). Aquac. Res. 47, 1596–1604 (2016).CAS 

Google Scholar 
Abdel-Tawwab, M., Hagras, A. E., Elbaghdady, H. A. M. & Monier, M. N. Dissolved oxygen level and stocking density effects on growth, feed utilization, physiology, and innate immunity of Nile Tilapia, Oreochromis niloticus. J. Appl. Aquac. 26, 340–355 (2014).
Google Scholar 
Toko, I., Fiogbe, E. D., Koukpode, B. & Kestemont, P. Rearing of African catfish (Clarias gariepinus) and vundu catfish (Heterobranchus longifilis) in traditional fish ponds (whedos): effect of stocking density on growth, production and body composition. Aquaculture 262, 65–72 (2007).
Google Scholar 
Suárez, M. D. et al. Influence of dietary lipids and culture density on rainbow trout (Oncorhynchus mykiss) flesh composition and quality parameter. Aquac. Eng. 63, 16–24 (2014).
Google Scholar 
Santín, A., Grinyó, J., Bilan, M., Ambroso, S. & Puig, P. First report of the carnivorous sponge Lycopodina hypogea (Cladorhizidae) associated with marine debris, and its possible implications on deep-sea connectivity. Mar. Pollut. Bull. 159, 111501 (2020).PubMed 

Google Scholar 
Jørpeland, G., Imsland, A., Stien, L. H., Bleie, H. & Roth, B. Effects of filleting method, stress, storage and season on the quality of farmed Atlantic cod (Gadus morhua L.). Aquac. Res. 46, 1597–1607 (2015).
Google Scholar 
Bulow, F. J. RNA-DNA ratios as indicators of growth in fish: a review. In The Age and growth of fish (eds Summerfelt, R. C. & Hall, G. E.) 45–64 (Iowa State University Press, Ames, Iowa, 1987).
Google Scholar 
Regnault, M. & Luquet, P. Study by evolution of nucleic acid content of prepuberal growth in the shrimp Crangon vulgaris. Mar. Biol. 25, 291–298 (1974).CAS 

Google Scholar 
Tanaka, H. K. M. et al. High resolution imaging in the inhomogeneous crust with cosmic-ray muon radiography: the density structure below the volcanic crater floor of Mt. Asama, Japan. Earth Planet. Sci. Lett. 263, 104–113 (2007).ADS 
CAS 

Google Scholar 
Gwak, W. S. & Tanaka, M. Developmental change in RNA: DNA ratios of fed and starved laboratory-reared Japanese flounder larvae and juveniles, and its application to assessment of nutritional condition for wild fish. J. Fish Biol. 59, 902–915 (2001).CAS 

Google Scholar 
Ali, M., Iqbal, R., Rana, S. A., Athar, M. & Iqbal, F. Effect of feed cycling on specific growth rate, condition factor and RNA/DNA ratio of Labeo rohita. African J. Biotechnol. 5, 1551–1556 (2006).CAS 

Google Scholar 
Zehra, S. & Khan, M. A. Dietary lysine requirement of fingerling Catla catla (Hamilton) based on growth, protein deposition, lysine retention efficiency, RNA/DNA ratio and carcass composition. Fish Physiol. Biochem. 39, 503–512 (2013).CAS 
PubMed 

Google Scholar 
Misra, H. P. & Fridovich, I. The role of superoxide anion in the autoxidation of epinephrine and a simple assay for superoxide dismutase. J. Biol. Chem. 247, 3170–3175 (1972).CAS 
PubMed 

Google Scholar 
Takahara, S. et al. Hypocatalasemia: a new genetic carrier state. J. Clin. Invest. 39, 610–619 (1960).CAS 
PubMed 
PubMed Central 

Google Scholar 
Rick, W. & Stegbauer, H. P. α-Amylase measurement of reducing groups. In Methods of Enzymatic Analysis (ed. Bergmeyer, H. S.) 885–890 (Elsevier, 1974).
Google Scholar 
Cherry, I. S. & Crandall, L. A. Jr. The specificity of pancreatic lipase: its appearance in the blood after pancreatic injury. Am. J. Physiol. Content 100, 266–273 (1932).CAS 

Google Scholar 
Drapeau, G. R. [38] Protease from Staphyloccus aureus. In Methods in Enzymology (eds Jura, N. & Murphy, J. M.) 469–475 (Elsevier, 1976).
Google Scholar 
AOAC. Official Methods of Analysis of AOAC International. (Association of Official Analytical Chemists Washington, DC, 2005).Bosworth, B. G., Small, B. C. & Mischke, C. Effects of transport water temperature, aerator type, and oxygen level on channel catfish Ictalurus punctatus fillet quality. J. World Aquac. Soc. 35, 412–419 (2004).
Google Scholar 
Ma, L. Q., Qi, C. L., Cao, J. J. & Li, D. P. Comparative study on muscle texture profile and nutritional value of channel catfish (Ictalurus punctatus) reared in ponds and reservoir cages. J. Fish. China 38, 532–537 (2014).
Google Scholar 
APHA. Standard Methods for the Examination of Water and Wastewater. (American Public Health Association, American Water Works Association, Water Environment Federation, 2012). More

Danovaro, et al. Ecological variables for developing a global deep-ocean monitoring and conservation strategy. Nat. Ecol. Evol. 4(2), 181–192. https://doi.org/10.1038/s41559-019-1091-z (2020).Danovaro, R., Snelgrove, P. V. R. & Tyler, P. Challenging the paradigms of deep-sea ecology. Trends Ecol. Evol. 29(8), 465–475. https://doi.org/10.1016/j.tree.2014.06.002 (2014).Article 
PubMed 

Google Scholar 
Collin, S. P. The neuroecology of cartilaginous fishes: sensory strategies for survival. Brain Behav. Evol. 80(2), 80–96. https://doi.org/10.1159/000339870 (2012).Article 
PubMed 

Google Scholar 
Carrier, J. C., Musick, J. A., & Heithaus, M. R. (Eds.). Biology of sharks and their relatives. CRC (2012).Musick, J. A. & Cotton, C. F. Bathymetric limits of chondrichthyans in the deep sea: a re-evaluation. Deep Sea Res. Part II 115, 73–80. https://doi.org/10.1016/j.dsr2.2014.10.010 (2015).Article 

Google Scholar 
Treberg, J. R. & Speers-Roesch, B. Does the physiology of chondrichthyan fishes constrain their distribution in the deep sea?. J. Exp. Biol. 219(5), 615–625. https://doi.org/10.1242/jeb.128108 (2016).Article 
PubMed 

Google Scholar 
Didier, D. A., Kemper, J. M. & Ebert, D. A. Phylogeny, biology and classification of extant holocephalans. In Biology of Sharks and Their Relatives, 2nd edn (Carrier, J. C., Musick, J. A. & Heithaus, M. R., eds), pp. 97–124. New York, NY: CRC Pres. (2012).Weigmann, S. Annotated checklist of the living sharks, batoids and chimaeras (Chondrichthyes) of the world, with a focus on biogeographical diversity. J. Fish Biol. 88(3), 837–1037. https://doi.org/10.1111/jfb.12874 (2016).CAS 
Article 
PubMed 

Google Scholar 
Coates, M. I., Gess, R. W., Finarelli, J. A., Criswell, K. E. & Tietjen, K. A symmoriiform chondrichthyan braincase and the origin of chimaeroid fishes. Nature 541(7636), 208–211. https://doi.org/10.1038/nature20806 (2017).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Lisney, T. J. A review of the sensory biology of chimaeroid fishes (Chondrichthyes; Holocephali). Rev. Fish Biol. Fisheries 20(4), 571–590. https://doi.org/10.1007/s11160-010-9162-x (2010).Article 

Google Scholar 
Finucci, B. et al. Ghosts of the deep–biodiversity, fisheries, and extinction risk of ghost sharks. Fish Fish. 22(2), 391–412. https://doi.org/10.1111/faf.12526 (2021).Article 

Google Scholar 
Newton, K. C., Gill, A. B. & Kajiura, S. M. Electroreception in marine fishes: chondrichthyans. J. Fish Biol. 95(1), 135–154. https://doi.org/10.1111/jfb.14068 (2019).Article 
PubMed 

Google Scholar 
Crampton, W. G. Electroreception, electrogenesis and electric signal evolution. J. Fish Biol. 95(1), 92–134. https://doi.org/10.1111/jfb.13922 (2019).Article 
PubMed 

Google Scholar 
Whitehead, D. L. Ampullary organs and electroreception in freshwater Carcharhinus leucas. J. Physiol.-Paris 96(5–6), 391–395. https://doi.org/10.1016/S0928-4257(03)00017-2 (2002).Article 
PubMed 

Google Scholar 
Raschi, W. G., & Gerry, S. Adaptations in the elasmobranch electroreceptive system. Fish Adaptations. Enfield, NH: Scientific Publishers, 233–258 (2003).Atkinson, C. J. L. & Bottaro, M. Ampullary pore distribution of Galeus melastomus and Etmopterus spinax: possible relations with predatory lifestyle and habitat. J. Mar. Biol. Assoc. UK 86(2), 447–448. https://doi.org/10.1017/S0025315406013336 (2006).Article 

Google Scholar 
Kempster, R. M. & Collin, S. P. Electrosensory pore distribution and feeding in the basking shark Cetorhinus maximus (Lamniformes: Cetorhinidae). Aquat. Biol. 12(1), 33–36. https://doi.org/10.3354/ab00328 (2011).Article 

Google Scholar 
Kempster, R. M., McCarthy, I. D. & Collin, S. P. Phylogenetic and ecological factors influencing the number and distribution of electroreceptors in elasmobranchs. J. Fish Biol. 80(5), 2055–2088. https://doi.org/10.1111/j.1095-8649.2011.03214.x (2012).CAS 
Article 
PubMed 

Google Scholar 
Whitehead, D. L., Gauthier, A. R., Mu, E. W., Bennett, M. B. & Tibbetts, I. R. Morphology of the Ampullae of Lorenzini in juvenile freshwater Carcharhinus leucas. J. Morphol. 276(5), 481–493. https://doi.org/10.1002/jmor.20355 (2015).Article 
PubMed 

Google Scholar 
Gauthier, A. R. G., Whitehead, D. L., Tibbetts, I. R., Cribb, B. W. & Bennett, M. B. Morphological comparison of the Ampullae of Lorenzini of three sympatric benthic rays. J. Fish Biol. 92(2), 504–514. https://doi.org/10.1111/jfb.13531 (2018).CAS 
Article 
PubMed 

Google Scholar 
Fields, R. D., Bullock, T. H. & Lange, G. D. Ampullary sense organs, peripheral, central and behavioral electroreception in Chimeras (Hydrolagus, Holocephali, Chondrichthyes). Brain Behav. Evol. 41(6), 269–289. https://doi.org/10.1159/000113849 (1993).CAS 
Article 
PubMed 

Google Scholar 
Didier, D.A. Phylogenetic systematics of extant chimaeroid fishes (Holocephali, Chimaeroidei). American Museum Novitates; n. 3119 (1995).Serena, F. Field identification guide to the sharks and rays of the Mediterranean and Black Sea (Food and Agriculture Organization, 2005).
Google Scholar 
Holt, R. E., Foggo, A., Neat, F. C. & Howell, K. L. Distribution patterns and sexual segregation in chimaeras: implications for conservation and management. ICES J. Mar. Sci. 70(6), 1198–1205. https://doi.org/10.1093/icesjms/fst058 (2013).Article 

Google Scholar 
Ragonese, S., Vitale, S., Dimech, M., & Mazzola, S. Abundances of demersal sharks and chimaera from 1994–2009 scientific surveys in the central Mediterranean Sea. PloS one, 8(9). https://doi.org/10.1371/journal.pone.0074865 (2013).Vacchi, M., & Orsi, L. R. Alimentazione di Chimaera monstrosa L. sui fondi batiali liguri. Atti della Società Toscana di Scienze Naturali, Memorie serie B, 86, 388–391 (1979).Macpherson, E. Food and feeding of Chimaera monstrosa, Linnaeus, 1758, in the western Mediterranean. ICES J. Mar. Sci. 39(1), 26–29. https://doi.org/10.1093/icesjms/39.1.26 (1980).Article 

Google Scholar 
Mauchline, J. & Gordon, J. D. M. Diets of the sharks and chimaeroids of the Rockall Trough, northeastern Atlantic Ocean. Mar. Biol. 75(2–3), 269–278. https://doi.org/10.1007/BF00406012 (1983).Article 

Google Scholar 
Albo-Puigserver, et al. Feeding ecology and trophic position of three sympatric demersal chondrichthyans in the northwestern Mediterranean. Mar. Ecol. Prog. Ser. 524, 255–268. https://doi.org/10.3354/meps11188( (2015).ADS 
Article 

Google Scholar 
Priede, I. G. Deep-sea fishes: biology, diversity, ecology and fisheries. Cambridge University Press (2017).Ferrando, S. et al. First description of a palatal organ in Chimaera monstrosa (Chondrichthyes, Holocephali). Anat. Rec. 299(1), 118–131. https://doi.org/10.1002/ar.23280 (2016).Article 

Google Scholar 
Garza-Gisholt, E., Hart, N. S., & Collin, S. P. Retinal morphology and visual specializations in three species of chimaeras, the deep-sea R. pacifica and C. lignaria, and the Vertical Migrator C. milii (Holocephali). Brain, behavior and evolution, 92(1–2), 47–62. https://doi.org/10.1159/000490655 (2018).Pethybridge, H., Daley, R. K. & Nichols, P. D. Diet of demersal sharks and chimaeras inferred by fatty acid profiles and stomach content analysis. J. Exp. Mar. Biol. Ecol. 409(1–2), 290–299. https://doi.org/10.1016/j.jembe.2011.09.009 (2011).Article 

Google Scholar 
Rivera-Vicente, A. C., Sewell, J. & Tricas, T. C. Electrosensitive spatial vectors in elasmobranch fishes: implications for source localization. PLoS ONE 6(1), e16008. https://doi.org/10.1371/journal.pone.0016008 (2011).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Kajiura, S. M., Cornett, A. D. & Yopak, K. E. Sensory adaptations to the environment: electroreceptors as a case study. Biol. Sharks Relatives 2, 393–434 (2010).Article 

Google Scholar 
Raschi, W. A morphological analysis of the Ampullae of Lorenzini in selected skates (Pisces, Rajoidei). J. Morphol. 189(3), 225–247. https://doi.org/10.1002/jmor.1051890303 (1986).Article 
PubMed 

Google Scholar 
Jordan, L. K. et al. Linking sensory biology and fisheries bycatch reduction in elasmobranch fishes: a review with new directions for research. Conserv. Physiol. 1(1), cot002. https://doi.org/10.1093/conphys/cot002 (2013).Wueringer, B. E., Peverell, S. C., Seymour, J., Squire Jr, L., Kajiura, S. M., & Collin, S. P. Sensory systems in sawfishes. 1. The ampullae of Lorenzini. Brain, behavior and evolution, 78(2), 139–149. https://doi.org/10.1159/000329515 (2011).Bird C.S. The tropho-spatial ecology of deep-sea sharks and chimaeras from a stable isotope perspective. PhD thesis – University of Southampton, UK (2017).Andres, K. H. & Von Düring, M. Comparative anatomy of vertebrate electroreceptors. Prog Brain Res 74, 113–131. https://doi.org/10.1016/S0079-6123(08)63006-X (1998).Article 

Google Scholar 
Crooks, N. & Waring, C. P. A study into the sexual dimorphisms of the Ampullae of Lorenzini in the lesser-spotted catshark, Scyliorhinus canicula (Linnaeus, 1758). Environ. Biol. Fishes 96(5), 585–590. https://doi.org/10.1016/S0079-6123(08)63006-X (2013).Article 

Google Scholar 
Didier, D. A. Phylogeny and classification of extant Holocephali. Biol. Sharks Relatives 4, 115–138 (2004).Article 

Google Scholar 
Wueringer, B. E. & Tibbetts, I. R. Comparison of the lateral line and ampullary systems of two species of shovelnose ray. Rev. Fish Biol. Fisheries 18(1), 47–64. https://doi.org/10.1007/s11160-007-9063-9 (2008).Article 

Google Scholar 
Theiss, S. M., Collin, S. P. & Hart, N. S. Morphology and distribution of the ampullary electroreceptors in wobbegong sharks: implications for feeding behaviour. Mar. Biol. 158(4), 723–735. https://doi.org/10.1007/s00227-010-1595-1 (2011).Article 

Google Scholar 
Schäfer, B. T. et al. Morphological observations of Ampullae of lorenzini in Squatina guggenheim and S. occulta (Chondrichthyes, Elasmobranchii, Squatinidae). Microscopy Res Tech. 75(9), 1213–1217. https://doi.org/10.1002/jemt.22051 (2012).Brown, B. R. Sensing temperature without ion channels. Nature 421(6922), 495–495. https://doi.org/10.1038/421495a (2003).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Fields, R. D., Fields, K. D. & Fields, M. C. Semiconductor gel in shark sense organs?. Neurosci. Lett. 426(3), 166–170. https://doi.org/10.1016/j.neulet.2007.08.064 (2007).CAS 
Article 
PubMed 
PubMed Central 
MATH 

Google Scholar 
Brown, B. R. Temperature response in electrosensors and thermal voltages in electrolytes. J. Biol. Phys. 36(2), 121–134. https://doi.org/10.1007/s10867-009-9174-8 (2010).Article 
PubMed 

Google Scholar 
Josberger, E. E. et al. Proton conductivity in Ampullae of Lorenzini jelly. Sci. Adv. 2(5), e1600112. https://doi.org/10.1126/sciadv.1600112 (2016).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Froese, R. and Pauly D. https://www.fishbase.de/ (2021).Sims, D. W. The biology, ecology and conservation of elasmobranchs: recent advances and new frontiers. J. Fish Biol. 87(6), 1265–1270. https://doi.org/10.1111/jfb.12861 (2015).CAS 
Article 
PubMed 

Google Scholar 
Heithaus, M. R., Frid, A., Wirsing, A. & Worm, B. Predicting ecological consequences of marine top predator declines. Trends Ecol. Evol. 23, 202–210. https://doi.org/10.1016/j.tree.2008.01.003 (2008).Article 
PubMed 

Google Scholar 
Dymek, J., Muñoz, P., Mayo-Hernández, E., Kuciel, M. & Żuwała, K. Comparative analysis of the olfactory organs in selected species of marine sharks and freshwater batoids. Zool. Anz. 294, 50–61. https://doi.org/10.1016/j.jcz.2021.07.013 (2021).Article 

Google Scholar 
Bellono, N. W., Leitch, D. B. & Julius, D. Molecular tuning of electroreception in sharks and skates. Nature 558(7708), 122. https://doi.org/10.1038/s41586-018-0160-9 (2018).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Luchetti, E. A., Iglésias, S. P., & Sellos, D. Y. Chimaera opalescens n. sp., a new chimaeroid (Chondrichthyes: Holocephali) from the north‐eastern Atlantic Ocean. J. Fish Biol., 79(2), 399–417. https://doi.org/10.1111/j.1095-8649.2011.03027.x (2011).Marranzino, A. N. & Webb, J. F. Flow sensing in the deep sea: the lateral line system of stomiiform fishes. Zool. J. Linn. Soc. 183(4), 945–965. https://doi.org/10.1093/zoolinnean/zlx090 (2018).Article 

Google Scholar 
Yopak, K. E. & Montgomery, J. C. Brain organization and specialization in deep-sea chondrichthyans. Brain Behav. Evol. 71(4), 287–304. https://doi.org/10.1159/000127048 (2008).Article 
PubMed 

Google Scholar 
Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9(7), 671–675. https://doi.org/10.1038/nmeth.2089 (2012).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
R Core Team, R. A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ (2021).Wickham, H. ggplot2: Elegant Graphics for Data Analysis. Springer, New York (2016). More

Carvalho, G. R. & Hauser, L. Molecular genetics and the stock concept in fisheries. In Molecular Genetics in Fisheries (eds Carvalho, G. R. & Pitcher, T. J.) 55–79 (Springer Netherlands, 1995). https://doi.org/10.1007/978-94-011-1218-5_3.Chapter 

Google Scholar 
Avise, J. C. Conservation genetics in the marine realm. J. Hered. 89, 377–382 (1998).Article 

Google Scholar 
Waples, R. S. Separating the wheat from the chaff: Patterns of genetic differentiation in high gene flow species. J. Hered. 89, 438–450 (1998).Article 

Google Scholar 
Pecoraro, C. et al. The population genomics of yellowfin tuna (Thunnus albacares) at global geographic scale challenges current stock delineation. Sci. Rep. 8, 13890 (2018).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Nikolic, N. et al. Connectivity and population structure of albacore tuna across southeast Atlantic and southwest Indian Oceans inferred from multidisciplinary methodology. Sci. Rep. 10, 15657 (2020).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Anderson, G., Lal, M., Hampton, J., Smith, N. & Rico, C. Close kin proximity in yellowfin tuna (Thunnus albacares) as a driver of population genetic structure in the tropical western and central Pacific Ocean. Front. Mar. Sci. 6, 341 (2019).Article 

Google Scholar 
Collette, B. B. & Nauen, C. E. Scombrids of the World: An Annotated and Illustrated Catalogue of Tunas, Mackerels, Bonitos, and Related Species Known to date v.2 (FAO, 1983).
Google Scholar 
Majkowski, J., Arrizabalaga, H. & Carocci, F. C1. Tuna and Tuna-like Species. Review of the state of World Fisheries Resources (FAO, 2005).Mahon, R. Fisheries and research for tunas and tuna-like species in the Western Central Atlantic: implications of the agreement for the implementation of the provisions of the United Nations Convention on the Law of the Sea of the 10 December 1982 relating to the conservation and management of straddling fish stocks and highly migratory fish stocks. (FAO Fisheries Technical Paper, 1996).Doray, M., Stéquert, B. & Taquet, M. Age and growth of blackfin tuna (Thunnus atlanticus ) caught under moored fish aggregating devices, around Martinique Island. Aquat. Living Resour. 17, 13–18 (2004).Article 

Google Scholar 
Arocha, F., Barrios, A. & Marcano, J. Blackfin tuna (Thunnus atlanticus) in the Venezuelan fisheries. Collect. Vol. Sci. Pap ICCAT 68(3), 1253–1260 (2012).
Google Scholar 
Mathieu, H., Pau, C. & Reynal, L. Chapter 2.1.10.7 THON A NAGEOIRES NOIRES. ICCAT ICCAT Manual. International Commission for the Conservation of Atlantic Tuna. 15 (2013).Maghan, W. B. & Rivas, L. R. The blackfin tuna (Thunnus atlanticus) as an underutilized fishery resource in the tropical western Atlantic Ocean. FAO Fish. Rep. 71(2), 163–172 (1971).
Google Scholar 
De Sylva, D. P., Rathjen, W. F. & Higman, J. B. Fisheries development for underutilized Atlantic tunas: Blackfin and little tunny. NOAA Technical Memorandum NMFS-SEFC-191 (1987).Richardson, D. E., Llopiz, J. K., Guigand, C. M. & Cowen, R. K. Larval assemblages of large and medium-sized pelagic species in the Straits of Florida. Prog. Oceanogr. 86, 8–20 (2010).ADS 
Article 

Google Scholar 
Freire, K. M. F., Lessa, R. & Lins-Oliveira, J. E. Fishery and biology of blackfin tuna Thunnus atlanticus off northeastern Brazil. Gulf Caribb. Res. 17, 15–24 (2005).Article 

Google Scholar 
Vieira, K. R., Oliveira, J. E. L. & Barbalho, M. C. Aspects of the dynamic population of blackfin tuna (Thunnus atlanticus-Lesson, 1831) caught in the Northeast Brazil. Collect. Vol. Sci. Pap ICCAT 58(5), 1623–1628 (2005).
Google Scholar 
FJ Mather, I. I. I. Tunas (genus Thunnus) of the western North Atlantic. Part III. Distribution and behavior of Thunnus species. World Sci. Meeting Biol. Tunas Exper. Pap. Vol. 8, 1–23 (1962)Cornic, M. & Rooker, J. R. Influence of oceanographic conditions on the distribution and abundance of blackfin tuna (Thunnus atlanticus) larvae in the Gulf of Mexico. Fish. Res 201, 1–10 (2018).Article 

Google Scholar 
Block, B. A. et al. Electronic tagging and population structure of Atlantic bluefin tuna. Nature 434, 1121–1127 (2005).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Luckhurst, B. E., Trott, T. & Manuel, S. Landings, seasonality, catch per unit effort, and tag-recapture results of yellowfin tuna and blackfin tuna at Bermuda. Am. Fish. Soc. Symp. 25, 225–234 (2001).
Google Scholar 
Singh-Renton, S. & Renton, J. CFRAMP’s large pelagic fish tagging program. Gulf Caribb. Res. Vol 19, (2007).Cermeño, P. et al. Electronic tagging of Atlantic bluefin tuna (Thunnus thynnus, L.) reveals habitat use and behaviors in the Mediterranean Sea. PLoS ONE 10, e0116638 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Begg, G. A., Friedland, K. D. & Pearce, J. B. Stock identification and its role in stock assessment and fisheries management: An overview. Fish. Res 43, 1–8 (1999).Article 

Google Scholar 
Saxton, B. Historical demography and genetic population structure of theBlackfin tuna (Thunnus atlanticus) from the Northwest Atlantic Ocean and the Gulf of Mexico. Texas A&M University (2009).Antoni, L., Luque, P. L., Naghshpour, K., Reynal, L. & Saillant, E. A. Development and characterization of microsatellite markers for blackfin tuna (Thunnus atlanticus) with the use of Illumina paired-end sequencing. Fish. Bull. 112, 322–325 (2014).Article 

Google Scholar 
Weir, B. S. & Cockerham, C. C. Estimating F-statistics for the analysis of population structure. Evolution 38, 1358 (1984).CAS 
PubMed 

Google Scholar 
Goudet, J. FSTAT (Version 1.2): A computer program to calculate F-statistics. J. Hered 86, 485–486 (1995).Article 

Google Scholar 
Rousset, F. Genepop’007: A complete re-implementation of the genepop software for Windows and Linux. Mol. Ecol. Resour. 8, 103–106 (2008).PubMed 
Article 

Google Scholar 
Guo, S. W. & Thompson, E. A. Performing the exact test of Hardy-Weinberg proportion for multiple alleles. Biometrics 48, 361–372 (1992).CAS 
PubMed 
MATH 
Article 

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

Google Scholar 
Excoffier, L., Smouse, P. E. & Quattro, J. M. Analysis of molecular variance inferred from metric distances among DNA haplotypes: Application to human mitochondrial DNA restriction data. Genetics 131, 479–491 (1992).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Excoffier, L. & Lischer, H. E. L. Arlequin suite ver 3.5: A new series of programs to perform population genetics analyses under Linux and Windows. Mol. Ecol. Resour. 10, 564–567 (2010).PubMed 
Article 

Google Scholar 
Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B (Methodol.) 57, 289–300 (1995).MathSciNet 
MATH 

Google Scholar 
Pritchard, J. K., Stephens, M. & Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 155, 945–959 (2000).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Falush, D., Stephens, M. & Pritchard, J. K. Inference of population structure using multilocus genotype data: Linked loci and correlated allele frequencies. Genetics 164, 1567–1587 (2003).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hubisz, M. J., Falush, D., Stephens, M. & Pritchard, J. K. Inferring weak population structure with the assistance of sample group information. Mol. Ecol. Resour. 9, 1322–1332 (2009).PubMed 
PubMed Central 
Article 

Google Scholar 
Jombart, T., Devillard, S. & Balloux, F. Discriminant analysis of principal components: A new method for the analysis of genetically structured populations. BMC Genet. 11, 94 (2010).PubMed 
PubMed Central 
Article 

Google Scholar 
Dupanloup, I., Schneider, S. & Excoffier, L. A simulated annealing approach to define the genetic structure of populations. Mol. Ecol. 11, 2571–2581 (2002).CAS 
PubMed 
Article 

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

Google Scholar 
Smouse, P. E. & Peakall, R. Spatial autocorrelation analysis of individual multiallele and multilocus genetic structure. Heredity 82(Pt 5), 561–573 (1999).PubMed 
Article 

Google Scholar 
Rousset, F. Genetic differentiation and estimation of gene flow from F-statistics under isolation by distance. Genetics 145, 1219–1228 (1997).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bezerra, N. P. A. et al. Reproduction of Blackfin tuna Thunnus atlanticus (Perciformes: Scombridae) in Saint Peter and Saint Paul Archipelago, Equatorial Atlantic, Brazil. Rev. Biol. Trop. 61, 1327–1339 (2013).PubMed 
Article 

Google Scholar 
Fitzpatrick, B. M. Power and sample size for nested analysis of molecular variance. Mol. Ecol. 18, 3961–3966 (2009).PubMed 
Article 

Google Scholar 
Ely, B. et al. Consequences of the historical demography on the global population structure of two highly migratory cosmopolitan marine fishes: The yellowfin tuna (Thunnus albacares) and the skipjack tuna (Katsuwonus pelamis). BMC Evol. Biol. 5, 19 (2005).PubMed 
PubMed Central 
Article 

Google Scholar 
Alvarado Bremer, J. R., Viñas, J., Mejuto, J., Ely, B. & Pla, C. Comparative phylogeography of Atlantic bluefin tuna and swordfish: The combined effects of vicariance, secondary contact, introgression, and population expansion on the regional phylogenies of two highly migratory pelagic fishes. Mol. Phylogenet. Evol. 36, 169–187 (2005).CAS 
PubMed 
Article 

Google Scholar 
Hedgecock, D., Barber, P. & Edmands, S. Genetic approaches to measuring connectivity. Oceanography 20, 70–79 (2007).Article 

Google Scholar 
Pruett, C. L., Saillant, E. & Gold, J. R. Historical population demography of red snapper (Lutjanus campechanus) from the northern Gulf of Mexico based on analysis of sequences of mitochondrial DNA. Mar. Biol. 147, 593–602 (2005).CAS 
Article 

Google Scholar 
Saillant, E., Bradfield, S. C. & Gold, J. R. Genetic variation and spatial autocorrelation among young-of-the-year red snapper (Lutjanus campechanus) in the northern Gulf of Mexico. ICES J. Mar. Sci 67, 1240–1250 (2010).Article 

Google Scholar 
Robledo-Arnuncio, J. J. & Rousset, F. Isolation by distance in a continuous population under stochastic demographic fluctuations. J. Evol. Biol. 23, 53–71 (2010).CAS 
PubMed 
Article 

Google Scholar 
Rocha, L. A., Craig, M. T. & Bowen, B. W. Phylogeography and the conservation of coral reef fishes. Coral Reefs 26, 501–512 (2007).ADS 
Article 

Google Scholar 
Vasconcellos, A. V., Vianna, P., Paiva, P. C., Schama, R. & Solé-Cava, A. Genetic and morphometric differences between yellowtail snapper (Ocyurus chrysurus, Lutjanidae) populations of the tropical West Atlantic. Genet. Mol. Biol. 31, 308–316 (2008).CAS 
Article 

Google Scholar 
Vieira, K. R., Oliveira, J. E. L. & Barbalho, M. C. Reproductive characteristics of blackfin tuna Thunnus atlanticus (Lesson, 1831), in northeast Brazil. Collect. Vol. Sci. Pap ICCAT 58, 1629–1634 (2005).
Google Scholar 
Nielsen, E. E. et al. Genomic signatures of local directional selection in a high gene flow marine organism; the Atlantic cod (Gadus morhua). BMC Evol. Biol. 9, 276 (2009).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Lamichhaney, S. et al. Population-scale sequencing reveals genetic differentiation due to local adaptation in Atlantic herring. Proc. Natl. Acad. Sci. USA 109, 19345–19350 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Latch, E. K., Dharmarajan, G., Glaubitz, J. C. & Rhodes, O. E. Relative performance of Bayesian clustering software for inferring population substructure and individual assignment at low levels of population differentiation. Conserv. Genet. 7, 295–302 (2006).Article 

Google Scholar 
Brophy, D., Rodríguez-Ezpeleta, N., Fraile, I. & Arrizabalaga, H. Combining genetic markers with stable isotopes in otoliths reveals complexity in the stock structure of Atlantic bluefin tuna (Thunnus thynnus). Sci. Rep. 10, 14675 (2020).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar  More

Spalding, M. D. & Grenfell, A. M. New estimates of global and regional coral reef areas. Coral Reefs 16, 225–230 (1997).Article 

Google Scholar 
Moberg, F. & Folke, C. Ecological goods and services of coral reef ecosystems. Ecol. Econ. 29, 215–233 (1999).Article 

Google Scholar 
Roberts, C. M. et al. Marine Biodiversity Hotspots and Conservation Priorities for Tropical Reefs. Science 295, 1280–1284 (2002).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Johannes, R., Wiebe, W., Crossland, C., Rimmer, D. & Smith, S. Latitudinal limits of coral reef growth. Mar. Ecol. Prog. Ser. 11, 105–111 (1983).ADS 
Article 

Google Scholar 
Kleypas, J. A., Mcmanus, J. W. & Meñez, L. A. B. Environmental Limits to Coral Reef Development: Where Do We Draw the Line? Am. Zool. 39, 146–159 (1999).Article 

Google Scholar 
Yamano, H., Hori, K., Yamauchi, M., Yamagawa, O. & Ohmura, A. Highest-latitude coral reef at Iki Island, Japan. Coral Reefs 20, 9–12 (2001).Article 

Google Scholar 
Guan, Y., Hohn, S. & Merico, A. Suitable Environmental Ranges for Potential Coral Reef Habitats in the Tropical Ocean. PLOS ONE 10, e0128831 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Bellwood, D. R. & Hughes, T. P. Regional-Scale Assembly Rules and Biodiversity of Coral Reefs. Science 292, 1532–1535 (2001).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Connolly, S. R., Bellwood, D. R. & Hughes, T. P. Indo-Pacific Biodiversity of Coral Reefs: Deviations from a Mid-Domain Model. Ecology 84, 2178–2190 (2003).Article 

Google Scholar 
Bellwood, D. R., Hughes, T. P., Connolly, S. R. & Tanner, J. Environmental and geometric constraints on Indo‐Pacific coral reef biodiversity. Ecol. Lett. 8, 643–651 (2005).Article 

Google Scholar 
Kiessling, W., Simpson, C., Beck, B., Mewis, H. & Pandolfi, J. M. Equatorial decline of reef corals during the last Pleistocene interglacial. Proc. Natl Acad. Sci. 109, 21378–21383 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Veron, J. E. N. et al. Delineating the Coral Triangle. Galaxea. J. Coral Reef. Stud. 11, 91–100 (2009).Article 

Google Scholar 
Briggs, J. C. Marine Longitudinal Biodiversity: Causes and Conservation. Divers. Distrib. 13, 544–555 (2007).Article 

Google Scholar 
Renema, W. et al. Hopping Hotspots: Global Shifts in Marine Biodiversity. Science 321, 654–657 (2008).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Kiessling, W. Paleoclimatic significance of Phanerozoic reefs. Geology 29, 751–754 (2001).ADS 
Article 

Google Scholar 
Wallace, C. & Rosen, B. Diverse staghorn corals (Acropora) in high-latitude Eocene assemblages: Implications for the evolution of modern diversity patterns of reef corals. Proc. Biol. Sci. 273, 975–982 (2006).PubMed 
PubMed Central 

Google Scholar 
Perrin, C. & Kiessling, W. Latitudinal trends in Cenozoic reef patterns and their relationship to climate. Carbonate Syst. Oligocene–Miocene Clim. Transit. 17–33 (Wiley-Blackwell, 2010).Kiessling, W. Habitat effects and sampling bias on Phanerozoic reef distribution. Facies 51, 24–32 (2005).Article 

Google Scholar 
Kiessling, W. Reef expansion during the Triassic: Spread of photosymbiosis balancing climatic cooling. Palaeogeogr. Palaeoclimatol. Palaeoecol. 290, 11–19 (2010).Article 

Google Scholar 
Ziegler, A. M., Hulver, M. L., Lotts, A. L. & Schmachtenberg, W. F. Uniformitarianism and palaeoclimates: inferences from the distribution of carbonate rocks. In: Fossils and Climate (ed. Brenchley, P. J.), 3–25 (Wiley, Chichester, 1984).Crame, J. A. & Rosen, B. R. Cenozoic palaeogeography and the rise of modern biodiversity patterns. Geol. Soc. Lond. Spec. Publ. 194, 153–168 (2002).ADS 
Article 

Google Scholar 
Leprieur, F. et al. Plate tectonics drive tropical reef biodiversity dynamics. Nat. Commun. 7, 1–8 (2016).Article 
CAS 

Google Scholar 
Zaffos, A., Finnegan, S. & Peters, S. E. Plate tectonic regulation of global marine animal diversity. Proc. Natl Acad. Sci. U. S. A. 114, 5653–5658 (2017).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Roberts, G. G. & Mannion, P. D. Timing and periodicity of Phanerozoic marine biodiversity and environmental change. Sci. Rep. 9, 6116 (2019).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Valentine, J. W. & Moores, E. M. Global Tectonics and the Fossil Record. J. Geol. 80, 167–184 (1972).ADS 
Article 

Google Scholar 
Pellissier, L., Heine, C., Rosauer, D. F. & Albouy, C. Are global hotspots of endemic richness shaped by plate tectonics? Biol. J. Linn. Soc. 123, 247–261 (2017).Article 

Google Scholar 
Chittaro, P. M. Species-area relationships for coral reef fish assemblages of St. Croix, US Virgin Islands. Mar. Ecol. Prog. Ser. 233, 253–261 (2002).ADS 
Article 

Google Scholar 
Tittensor, D. P., Micheli, F., Nyström, M. & Worm, B. Human impacts on the species–area relationship in reef fish assemblages. Ecol. Lett. 10, 760–772 (2007).PubMed 
Article 

Google Scholar 
Tittensor, D. P. et al. Global patterns and predictors of marine biodiversity across taxa. Nature 466, 1098–1101 (2010).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Huntington, B. E. & Lirman, D. Species-area relationships in coral communities: evaluating mechanisms for a commonly observed pattern. Coral Reefs 31, 929–938 (2012).ADS 
Article 

Google Scholar 
Kiessling, W., Simpson, C. & Foote, M. Reefs as cradles of evolution and sources of biodiversity in the Phanerozoic. Science 327, 196–198 (2010).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Pandolfi, J. M. et al. Global Trajectories of the Long-Term Decline of Coral Reef Ecosystems. Science 301, 955–958 (2003).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Hoegh-Guldberg, O. Coral reef ecosystems and anthropogenic climate change. Reg. Environ. Change 11, 215–227 (2011).Article 

Google Scholar 
Hughes, T. P. et al. Global warming and recurrent mass bleaching of corals. Nature 543, 373–377 (2017).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Kim, S. W. et al. Refugia under threat: Mass bleaching of coral assemblages in high-latitude eastern Australia. Glob. Change Biol. 25, 3918–3931 (2019).ADS 
Article 

Google Scholar 
Pörtner, H.-O. et al. IPCC special report on the ocean and cryosphere in a changing climate. IPCC Intergov. Panel Clim. Change Geneva Switz. 1, 1–755 (2019).Sully, S., Burkepile, D. E., Donovan, M. K., Hodgson, G. & van Woesik, R. A global analysis of coral bleaching over the past two decades. Nat. Commun. 10, 1264 (2019).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Couce, E., Ridgwell, A. & Hendy, E. J. Future habitat suitability for coral reef ecosystems under global warming and ocean acidification. Glob. Change Biol. 19, 3592–3606 (2013).ADS 
Article 

Google Scholar 
Hoegh-Guldberg, O., Poloczanska, E. S., Skirving, W. & Dove, S. Coral Reef Ecosystems under Climate Change and Ocean Acidification. Front. Mar. Sci. 4, 1–20 (2017).O’Neill, B. C. et al. The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6. Geosci. Model Dev. 9, 3461–3482 (2016).ADS 
Article 

Google Scholar 
Precht, W. F. & Aronson, R. B. Climate flickers and range shifts of reef corals. Front. Ecol. Environ. 2, 307–314 (2004).Article 

Google Scholar 
Greenstein, B. J. & Pandolfi, J. M. Escaping the heat: range shifts of reef coral taxa in coastal Western Australia. Glob. Change Biol. 14, 513–528 (2008).ADS 
Article 

Google Scholar 
Pellissier, L. et al. Quaternary coral reef refugia preserved fish diversity. Science 344, 1016–1019 (2014).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Vilhena, D. A. & Smith, A. B. Spatial Bias in the Marine Fossil Record. PLoS ONE 8, 1–7 (2013).Article 
CAS 

Google Scholar 
Close, R. A., Benson, R. B. J., Saupe, E. E., Clapham, M. E. & Butler, R. J. The spatial structure of Phanerozoic marine animal diversity. Science 368, 420–424 (2020).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Jones, L. A., Dean, C. D., Mannion, P. D., Farnsworth, A. & Allison, P. A. Spatial sampling heterogeneity limits the detectability of deep time latitudinal biodiversity gradients. Proc. R. Soc. B Biol. Sci. 288, 20202762 (2021).Article 

Google Scholar 
Jones, L. A. & Eichenseer, K. Uneven spatial sampling distorts reconstructions of Phanerozoic seawater temperature. Geology (2021) https://doi.org/10.1130/G49132.1.Stolarski, J. et al. The ancient evolutionary origins of Scleractinia revealed by azooxanthellate corals. BMC Evol. Biol. 11, 1–11 (2011).Article 

Google Scholar 
Frankowiak, K. et al. Photosymbiosis and the expansion of shallow-water corals. Sci. Adv. 2, e1601122 (2016).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
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 (2017).Article 

Google Scholar 
Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190, 231–259 (2006).Article 

Google Scholar 
Swets, J. A. Measuring the accuracy of diagnostic systems. Science 240, 1285–1293 (1988).ADS 
MathSciNet 
CAS 
PubMed 
MATH 
Article 

Google Scholar 
Boyce, M. S., Vernier, P. R., Nielsen, S. E. & Schmiegelow, F. K. A. Evaluating resource selection functions. Ecol. Model. 157, 281–300 (2002).Article 

Google Scholar 
Hirzel, A. H., LeLay, G., Helfer, V., Randin, C. & Guisan, A. Evaluating the ability of habitat suitability models to predict species presences. Ecol. Model. 199, 142–152 (2006).Article 

Google Scholar 
Elith, J., Kearney, M. & Phillips, S. The art of modelling range-shifting species. Methods Ecol. Evol. 1, 330–342 (2010).Article 

Google Scholar 
Miller, K. G. et al. The Phanerozoic Record of Global Sea-Level Change. Science 310, 1293–1298 (2005).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Hallam, A., Grose, J. A. & Ruffell, A. H. Palaeoclimatic significance of changes in clay mineralogy across the Jurassic-Cretaceous boundary in England and France. Palaeogeogr. Palaeoclimatol. Palaeoecol. 81, 173–187 (1991).Article 

Google Scholar 
Gröcke, D. R., Price, G. D., Ruffell, A. H., Mutterlose, J. & Baraboshkin, E. Isotopic evidence for Late Jurassic–Early Cretaceous climate change. Palaeogeogr. Palaeoclimatol. Palaeoecol. 202, 97–118 (2003).Article 

Google Scholar 
Royer, D. L., Berner, R. A., Montañez, I. P., Tabor, N. J. & Beerling, D. J. CO2 as a primary driver of Phanerozoic climate. GSA Today 14, 1–10 (2004).
Google Scholar 
Grabowski, J. et al. Magnetic susceptibility and spectral gamma logs in the Tithonian–Berriasian pelagic carbonates in the Tatra Mts (Western Carpathians, Poland): Palaeoenvironmental changes at the Jurassic/Cretaceous boundary. Cretac. Res. 43, 1–17 (2013).Article 

Google Scholar 
Vickers, M. L. et al. The duration and magnitude of Cretaceous cool events: Evidence from the northern high latitudes. GSA Bull. 131, 1979–1994 (2019).CAS 
Article 

Google Scholar 
Hay, W. W. & Floegel, S. New thoughts about the Cretaceous climate and oceans. Earth-Sci. Rev. 115, 262–272 (2012).ADS 
CAS 
Article 

Google Scholar 
Tennant, J. P., Mannion, P. D. & Upchurch, P. Sea level regulated tetrapod diversity dynamics through the Jurassic/Cretaceous interval. Nat. Commun. 7, 12737 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Schouten, S. et al. Onset of long-term cooling of Greenland near the Eocene-Oligocene boundary as revealed by branched tetraether lipids. Geology 36, 147 (2008).ADS 
Article 

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

Google Scholar 
Crame, J. A. Taxonomic diversity gradients through geological time. Divers. Distrib. 7, 175–189 (2001).
Google Scholar 
Mannion, P. D., Upchurch, P., Benson, R. B. J. & Goswami, A. The latitudinal biodiversity gradient through deep time. Trends Ecol. Evol. 29, 42–50 (2014).PubMed 
Article 

Google Scholar 
Fenton, I. S. et al. The impact of Cenozoic cooling on assemblage diversity in planktonic foraminifera. Philos. Trans. R. Soc. B Biol. Sci. 371, 1–12 (2016).Article 
CAS 

Google Scholar 
Saupe, E. E. et al. Climatic shifts drove major contractions in avian latitudinal distributions throughout the Cenozoic. Proc. Natl Acad. Sci. 116, 12895–12900 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hall, R. Cenozoic geological and plate tectonic evolution of SE Asia and the SW Pacific: computer-based reconstructions, model and animations. J. Asian Earth Sci. 20, 353–431 (2002).ADS 
Article 

Google Scholar 
Hall, R. Southeast Asia’s changing palaeogeography. Blumea 54, 148–161 (2009).Article 

Google Scholar 
Gaboriau, T. et al. Ecological constraints coupled with deep-time habitat dynamics predict the latitudinal diversity gradient in reef fishes. Proc. R. Soc. B Biol. Sci. 286, 20191506 (2019).Article 

Google Scholar 
Saupe, E. E. et al. Extinction intensity during Ordovician and Cenozoic glaciations explained by cooling and palaeogeography. Nat. Geosci. 13, 65–70 (2020).ADS 
Article 
CAS 

Google Scholar 
Lunt, D. J. et al. DeepMIP: model intercomparison of early Eocene climatic optimum (EECO) large-scale climate features and comparison with proxy data. Clim 17, 203–227 (2021).ADS 

Google Scholar 
Freeman, L. A., Kleypas, J. A. & Miller, A. J. Coral Reef Habitat Response to Climate Change Scenarios. PLoS ONE 8, 1–14 (2013).
Google Scholar 
Foster, G. L., Royer, D. L. & Lunt, D. J. Future climate forcing potentially without precedent in the last 420 million years. Nat. Commun. 8, 1–8 (2017).ADS 
Article 
CAS 

Google Scholar 
Farnsworth, A. et al. Past East Asian monsoon evolution controlled by paleogeography, not CO2. Sci. Adv. 5, 1–13 (2019).Article 
CAS 

Google Scholar 
Zhang, L. et al. Consensus Forecasting of Species Distributions: The Effects of Niche Model Performance and Niche Properties. PLoS ONE 10, 1–18 (2015).
Google Scholar 
Harrison, S. P. et al. Evaluation of CMIP5 palaeo-simulations to improve climate projections. Nat. Clim. Change 5, 735–743 (2015).ADS 
Article 

Google Scholar 
Seo, C., Thorne, J. H., Hannah, L. & Thuiller, W. Scale effects in species distribution models: implications for conservation planning under climate change. Biol. Lett. 5, 39–43 (2009).PubMed 
Article 

Google Scholar 
Couce, E., Ridgwell, A. & Hendy, E. J. Environmental controls on the global distribution of shallow-water coral reefs. J. Biogeogr. 39, 1508–1523 (2012).Article 

Google Scholar 
Laborel, J. West African reef corals: an hypothesis on their origin. in Proceedings of the Second International Coral Reef Symposium vol. 1 425–443 (Great Barrier Reef Committee Brisbane, 1974).Spalding, M., Spalding, M. D., Ravilious, C. & Green, E. P. World Atlas of Coral Reefs. (University of California Press, 2001).Block, S. et al. Where to Dig for Fossils: Combining Climate-Envelope, Taphonomy and Discovery Models. PLoS ONE 11, 1–16 (2016).Jones, L. A. et al. Coupling of palaeontological and neontological reef coral data improves forecasts of biodiversity responses under global climatic change. R. Soc. Open Sci. 6, 182111 (2019).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kusumoto, B. et al. Global distribution of coral diversity: Biodiversity knowledge gradients related to spatial resolution. Ecol. Res. 35, 315–326 (2020).Article 

Google Scholar 
Muir, P. R., Wallace, C. C., Done, T. & Aguirre, J. D. Limited scope for latitudinal extension of reef corals. Science 348, 1135–1138 (2015).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Sillero, N. & Barbosa, A. M. Common mistakes in ecological niche models. Int. J. Geogr. Inf. Sci. 35, 213–226 (2021).Article 

Google Scholar 
Valdes, P. J. et al. The BRIDGE HadCM3 family of climate models:HadCM3@Bristol v1.0. Geosci. Model Dev. 10, 3715–3743 (2017).ADS 
CAS 
Article 

Google Scholar 
Sheppard, C. R. C. Predicted recurrences of mass coral mortality in the Indian Ocean. Nature 425, 294–297 (2003).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Saupe, E. E. et al. Macroevolutionary consequences of profound climate change on niche evolution in marine molluscs over the past three million years. Proc. R. Soc. B Biol. Sci. 281, 1–9 (2014).
Google Scholar 
Haywood, A. M. et al. What can Palaeoclimate Modelling do for you? Earth Syst. Environ. 3, 1–18 (2019).Article 

Google Scholar 
Sellwood, B. W. & Valdes, P. J. Mesozoic climates: General circulation models and the rock record. Sediment. Geol. 190, 269–287 (2006).ADS 
Article 

Google Scholar 
Waterson, A. M. et al. Modelling the climatic niche of turtles: a deep-time perspective. Proc. R. Soc. B Biol. Sci. 283, 1–9 (2016).
Google Scholar 
Chiarenza, A. A. et al. Ecological niche modelling does not support climatically-driven dinosaur diversity decline before the Cretaceous/Paleogene mass extinction. Nat. Commun. 10, 1–14 (2019).CAS 
Article 

Google Scholar 
Dunne, E. M., Farnsworth, A., Greene, S. E., Lunt, D. J. & Butler, R. J. Climatic drivers of latitudinal variation in Late Triassic tetrapod diversity. Palaeontology 64, 101–117 (2020).Article 

Google Scholar 
Lyster, S. J., Whittaker, A. C., Allison, P. A., Lunt, D. J. & Farnsworth, A. Predicting sediment discharges and erosion rates in deep time—examples from the late Cretaceous North American continent. Basin Res. 1–27 (2020) https://doi.org/10.1111/bre.12442.Lunt, D. J. et al. Palaeogeographic controls on climate and proxy interpretation. Clim 12, 1181–1198 (2016).ADS 

Google Scholar 
Vasquez, V. L., de Lima, A. A., dos Santos, A. P. & Pinto, M. P. Influence of spatial extent on habitat suitability models for primate species of Atlantic Forest. Ecol. Inform. 61, 101179 (2021).Article 

Google Scholar 
Collins, D. S. et al. Controls on tidal sedimentation and preservation: Insights from numerical tidal modelling in the Late Oligocene–Miocene South China Sea, Southeast Asia. Sedimentology 65, 2468–2505 (2018).Article 

Google Scholar 
Dean, C. D., Collins, D. S., van Cappelle, M., Avdis, A. & Hampson, G. J. Regional-scale paleobathymetry controlled location, but not magnitude, of tidal dynamics in the Late Cretaceous Western Interior Seaway, USA. Geology 47, 1083–1087 (2019).ADS 
CAS 
Article 

Google Scholar 
Markwick, P. J. & Valdes, P. J. Palaeo-digital elevation models for use as boundary conditions in coupled ocean–atmosphere GCM experiments: a Maastrichtian (late Cretaceous) example. Palaeogeogr. Palaeoclimatol. Palaeoecol. 213, 37–63 (2004).Article 

Google Scholar 
Elith, J. et al. Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29, 129–151 (2006).Article 

Google Scholar 
Sillero, N. What does ecological modelling model? A proposed classification of ecological niche models based on their underlying methods. Ecol. Model. 222, 1343–1346 (2011).Article 

Google Scholar 
Guisan, A., Thuiller, W. & Zimmermann, N. E. Habitat suitability and distribution models: with applications in R. (Cambridge University Press, 2017).Kearney, M. R., Wintle, B. A. & Porter, W. P. Correlative and mechanistic models of species distribution provide congruent forecasts under climate change. Conserv. Lett. 3, 203–213 (2010).Article 

Google Scholar 
Owens, H. L. et al. Constraints on interpretation of ecological niche models by limited environmental ranges on calibration areas. Ecol. Model. 263, 10–18 (2013).Article 

Google Scholar 
Franklin, J. Mapping Species Distributions: Spatial Inference and Prediction. (Cambridge University Press, 2010). https://doi.org/10.1017/CBO9780511810602.Liu, C., White, M. & Newell, G. Selecting thresholds for the prediction of species occurrence with presence-only data. J. Biogeogr. 40, 778–789 (2013).Article 

Google Scholar 
Liu, C., Newell, G. & White, M. On the selection of thresholds for predicting species occurrence with presence-only data. Ecol. Evol. 6, 337–348 (2016).PubMed 
Article 

Google Scholar 
Kiessling, W. & Krause, M. C. PARED—An online database of Phanerozoic reefs. https://www.paleo-reefs.pal.uni-erlangen.de/ (2021).Jones, L. A., Mannion, P. D., Farnsworth, A., Bragg, F. & Lunt, D. J. Code from ‘Climatic and tectonic drivers shaped the tropical distribution of coral reefs’. Zenodo (2022) https://doi.org/10.5281/zenodo.6458366. More