Ecology

Pourret, O. & Hursthouse, A. It’s time to replace the term “heavy metals” with “potentially toxic elements” when reporting environmental research. IJERPH 16, 4446 (2019).CAS 
PubMed Central 

Google Scholar 
Wuana, R. A. & Okieimen, F. E. Heavy metals in contaminated soils: A review of sources, chemistry, risks and best available strategies for remediation. ISRN Ecol. 2011, 1–20 (2011).
Google Scholar 
Mahar, A. et al. Challenges and opportunities in the phytoremediation of heavy metals contaminated soils: A review. Ecotoxicol. Environ. Saf. 126, 111–121 (2016).CAS 
PubMed 

Google Scholar 
Vangronsveld, J. et al. Phytoremediation of contaminated soils and groundwater: Lessons from the field. Environ. Sci. Pollut. Res. 16, 765–794 (2009).CAS 

Google Scholar 
Desjardins, D., Nissim, W. G., Pitre, F. E., Naud, A. & Labrecque, M. Distribution patterns of spontaneous vegetation and pollution at a former decantation basin in southern Québec, Canada. Ecol. Eng. 64, 385–390 (2014).
Google Scholar 
Marchiol, L. et al. Gentle remediation at the former “Pertusola Sud” zinc smelter: Evaluation of native species for phytoremediation purposes. Ecol. Eng. 53, 343–353 (2013).
Google Scholar 
van Oort, F. et al. Les pollutions métalliques d’un site industriel et des sols environnants : distributions hétérogènes des métaux et relations avec l’usage des sols. In: Contaminations métalliques des agrosystèmes et écosystèmes péri-urbains 15–44 (Editions Quae, 2009).Hodge, A. Plastic plants and patchy soils. J. Exp. Bot. 57, 401–411 (2006).CAS 
PubMed 

Google Scholar 
Huber-Sannwald, E. & Jackson, R. B. Heterogeneous soil-resource distribution and plant responses—from individual-plant growth to ecosystem functioning. In Progress in Botany Vol. 62 (eds Esser, K. et al.) 451–476 (Springer, 2001).
Google Scholar 
Loecke, T. D. & Philip Robertson, G. Soil resource heterogeneity in the form of aggregated litter alters maize productivity. Plant Soil 325, 231–241 (2009).CAS 

Google Scholar 
Reynolds, H. L., Hungate, B. A., Iii, F. S. C. & D’Antonio, C. M. Soil Heterogeneity and Plant Competition in an Annual Grassland. 16 (2021).Maestre, F. T., Cortina, J., Bautista, S., Bellot, J. & Vallejo, R. Small-scale environmental heterogeneity and spatiotemporal dynamics of seedling establishment in a semiarid degraded ecosystem. Ecosystems 6, 630–643 (2003).
Google Scholar 
Shutcha, M. N. et al. Three years of phytostabilisation experiment of bare acidic soil extremely contaminated by copper smelting using plant biodiversity of metal-rich soils in tropical Africa (Katanga, DR Congo). Ecol. Eng. 82, 81–90 (2015).
Google Scholar 
Testiati, E. et al. Trace metal and metalloid contamination levels in soils and in two native plant species of a former industrial site: Evaluation of the phytostabilization potential. J. Hazard. Mater. 248–249, 131–141 (2013).PubMed 

Google Scholar 
Cabrera, F., Clemente, L., Díaz Barrientos, E., López, R. & Murillo, J. M. Heavy metal pollution of soils affected by the Guadiamar toxic fiood. Sci. Total Environ. 242, 117–129 (1999).CAS 
PubMed 

Google Scholar 
Imperato, M. et al. Spatial distribution of heavy metals in urban soils of Naples city (Italy). Environ. Pollut. 124, 247–256 (2003).CAS 
PubMed 

Google Scholar 
Gallagher, F. J., Pechmann, I., Bogden, J. D., Grabosky, J. & Weis, P. Soil metal concentrations and vegetative assemblage structure in an urban brownfield. Environ. Pollut. 153, 351–361 (2008).CAS 
PubMed 

Google Scholar 
Gallagher, F. J., Pechmann, I., Holzapfel, C. & Grabosky, J. Altered vegetative assemblage trajectories within an urban brownfield. Environ. Pollut. 159, 1159–1166 (2011).CAS 
PubMed 

Google Scholar 
Heckenroth, A. et al. Selection of native plants with phytoremediation potential for highly contaminated Mediterranean soil restoration: Tools for a non-destructive and integrative approach. J. Environ. Manag. 183, 850–863 (2016).CAS 

Google Scholar 
Dickinson, N. M., Turner, A. P. & Lepp, N. W. How do trees and other long-lived plants survive in polluted environments?. Funct. Ecol. 5, 5 (1991).
Google Scholar 
Partida-Martínez, L. P. & Heil, M. The microbe-free plant: Fact or artifact?. Front. Plant Sci. 2, 100 (2011).PubMed 
PubMed Central 

Google Scholar 
Giller, K. E., Witter, E. & Mcgrath, S. P. Toxicity of heavy metals to microorganisms and microbial processes in agricultural soils: A review. Soil Biol. Biochem. 30, 1389–1414 (1998).CAS 

Google Scholar 
Kabata-Pendias, A. & Pendias, H. Trace Elements in Soils and Plants (CRC Press, 2001).
Google Scholar 
Tyler, G. Heavy metal pollution and mineralisation of nitrogen in forest soils. Nature 255, 701–702 (1975).CAS 

Google Scholar 
Seshadri, B., Bolan, N. S. & Naidu, R. Rhizosphere-induced heavy metal(loid) transformation in relation to bioavailability and remediation. J. Soil Sci. Plant Nutr. https://doi.org/10.4067/S0718-95162015005000043 (2015).Article 

Google Scholar 
Kidd, P. et al. Trace element behaviour at the root–soil interface: Implications in phytoremediation. Environ. Exp. Bot. 67, 243–259 (2009).CAS 

Google Scholar 
Rivera-Becerril, F. Cadmium accumulation and buffering of cadmium-induced stress by arbuscular mycorrhiza in three Pisum sativum L. genotypes. J. Exp. Bot. 53, 1177–1185 (2002).CAS 
PubMed 

Google Scholar 
Krupa, P. & Kozdrój, J. Ectomycorrhizal fungi and associated bacteria provide protection against heavy metals in inoculated pine (Pinus sylvestris L.) seedlings. Water Air Soil Pollut. 182, 83–90 (2007).CAS 

Google Scholar 
Janoušková, M., Pavlíková, D. & Vosátka, M. Potential contribution of arbuscular mycorrhiza to cadmium immobilisation in soil. Chemosphere 65, 1959–1965 (2006).PubMed 

Google Scholar 
Leyval, C., Turnau, K. & Haselwandter, K. Effect of heavy metal pollution on mycorrhizal colonization and function: Physiological, ecological and applied aspects. Mycorrhiza 7, 139–153 (1997).CAS 

Google Scholar 
Zhang, Y., Zhang, Y., Liu, M., Shi, X. & Zhao, Z. Dark septate endophyte (DSE) fungi isolated from metal polluted soils: Their taxonomic position, tolerance, and accumulation of heavy metals in vitro. J. Microbiol. 46, 624–632 (2008).PubMed 

Google Scholar 
Krumins, J. A., Goodey, N. M. & Gallagher, F. Plant–soil interactions in metal contaminated soils. Soil Biol. Biochem. 80, 224–231 (2015).CAS 

Google Scholar 
Glick, B. R. Phytoremediation: Synergistic use of plants and bacteria to clean up the environment. Biotechnol. Adv. 21, 383–393 (2003).CAS 
PubMed 

Google Scholar 
Heckenroth, A. et al. What are the potential environmental solutions for diffuse pollution ? In Pollution of Marseille’s Industrial Calanques—The Impact of the Past on the Present 291–328 (REF2C, 2016).Li, M. S. Ecological restoration of mineland with particular reference to the metalliferous mine wasteland in China: A review of research and practice. Sci. Total Environ. 357, 38–53 (2006).CAS 
PubMed 

Google Scholar 
Mendez, M. O. & Maier, R. M. Phytoremediation of mine tailings in temperate and arid environments. Rev. Environ. Sci. Biotechnol. 7, 47–59 (2008).CAS 

Google Scholar 
Yaalon, D. H. Soils in the Mediterranean region: What makes them different?. CATENA 28, 157–169 (1997).CAS 

Google Scholar 
Li, S. et al. A comprehensive survey on the horizontal and vertical distribution of heavy metals and microorganisms in soils of a Pb/Zn smelter. J. Hazard. Mater. 400, 123255 (2020).CAS 
PubMed 

Google Scholar 
Pérez-de-Mora, A. et al. Microbial community structure and function in a soil contaminated by heavy metals: Effects of plant growth and different amendments. Soil Biol. Biochem. 38, 327–341 (2006).
Google Scholar 
Keller, C. et al. Root development and heavy metal phytoextraction efficiency: Comparison of different plant species in the field. Plant Soil. 249, 67–81 (2003).CAS 

Google Scholar 
Lambrechts, T. et al. Comparative analysis of Cd and Zn impacts on root distribution and morphology of Lolium perenne and Trifolium repens: Implications for phytostabilization. Plant Soil 376, 229–244 (2014).CAS 

Google Scholar 
Pauwels, M., Frérot, H., Bonnin, I. & Saumitou-Laprade, P. A broad-scale analysis of population differentiation for Zn tolerance in an emerging model species for tolerance study: Arabidopsis halleri (Brassicaceae). J. Evol. Biol. 19, 1838–1850 (2006).CAS 
PubMed 

Google Scholar 
Padilla, F. M. & Pugnaire, F. I. The role of nurse plants in the restoration of degraded environments. Front. Ecol. Environ. 4, 196–202 (2006).
Google Scholar 
Robles, A. B., Allegretti, L. I. & Passera, C. B. Coronilla juncea is both a nutritive fodder shrub and useful in the rehabilitation of abandoned Mediterranean marginal farmland. J. Arid Environ. 50, 381–392 (2002).
Google Scholar 
Grime, J. P. Plant Strategies and Vegetation Processes (Wiley, 1979).
Google Scholar 
Laffont-Schwob, I. et al. Diffuse and widespread present-day pollution. In Pollution of Marseille’s industrial Calanques—The Impact of the Past on the Future 204–249 (REF2C, 2016).Gelly, R. et al. Lead, zinc, and copper redistributions in soils along a deposition gradient from emissions of a Pb-Ag smelter decommissioned 100 years ago. Sci. Total Environ. 665, 502–512 (2019).CAS 
PubMed 

Google Scholar 
Tóth, G. et al. Soils of the European Union. JRC Scientific and Technical Reports 85 (2008).IUSS Working Group WRB. Base de référence mondiale pour les ressources en sols 2014, Mise à jour 2015. Système international de classification des sols pour nommer les sols et élaborer des légendes de cartes pédologiques. Rapport sur les ressources en sols du monde. Vol. 106 (2015).Dias, T. et al. Ammonium as a driving force of plant diversity and ecosystem functioning: Observations based on 5 years’ manipulation of n dose and form in a Mediterranean ecosystem. PLoS ONE 9, e92517 (2014).PubMed 
PubMed Central 

Google Scholar 
Remon, E. et al. Soil characteristics, heavy metal availability and vegetation recovery at a former metallurgical landfill: Implications in risk assessment and site restoration. Environ. Pollut. 137, 316–323 (2005).CAS 
PubMed 

Google Scholar 
Baumberger, T. et al. Plant community changes as ecological indicator of seabird colonies’ impacts on Mediterranean Islands. Ecol. Ind. 15, 76–84 (2012).
Google Scholar 
Navas, M.-L., Roumet, C., Bellmann, A., Laurent, G. & Garnier, E. Suites of plant traits in species from different stages of a Mediterranean secondary succession: Plant traits and succession. Plant Biol. 12, 183–196 (2010).CAS 
PubMed 

Google Scholar 
Guillamot, F., Calvert, V., Millot, M.-V. & Criquet, S. Does antimony affect microbial respiration in Mediterranean soils? A microcosm experiment. Pedobiologia 57, 119–121 (2014).
Google Scholar 
Wang, A., He, M., Ouyang, W., Lin, C. & Liu, X. Effects of antimony (III/V) on microbial activities and bacterial community structure in soil. Sci. Total Environ. 789, 148073 (2021).CAS 
PubMed 

Google Scholar 
Oleńska, E. et al. Trifolium repens-associated bacteria as a potential tool to facilitate phytostabilization of zinc and lead polluted waste heaps. Plants 9, 1002 (2020).PubMed Central 

Google Scholar 
Stambulska, U. Y., Bayliak, M. M. & Lushchak, V. I. Chromium(VI) toxicity in legume plants: Modulation effects of rhizobial symbiosis. BioMed Res. Int. 2018, 1–13 (2018).
Google Scholar 
Karthika, K. S., Rashmi, I. & Parvathi, M. S. Biological functions, uptake and transport of essential nutrients in relation to plant growth. In Plant Nutrients and Abiotic Stress Tolerance 1–49 (Springer Singapore, 2018). https://doi.org/10.1007/978-981-10-9044-8_1.Dary, M., Chamber-Pérez, M. A., Palomares, A. J. & Pajuelo, E. “In situ” phytostabilisation of heavy metal polluted soils using Lupinus luteus inoculated with metal resistant plant-growth promoting rhizobacteria. J. Hazard. Mater. 177, 323–330 (2010).CAS 
PubMed 

Google Scholar 
Reichman, S. M. The potential use of the legume–rhizobium symbiosis for the remediation of arsenic contaminated sites. Soil Biol. Biochem. 39, 2587–2593 (2007).CAS 

Google Scholar 
Parraga-Aguado, I., Querejeta, J.-I., González-Alcaraz, M.-N., Jiménez-Cárceles, F. J. & Conesa, H. M. Usefulness of pioneer vegetation for the phytomanagement of metal(loid)s enriched tailings: Grasses vs. shrubs vs. trees. J. Environ. Manag. 133, 51–58 (2014).CAS 

Google Scholar 
Jones, C. G., Lawton, J. H. & Shachak, M. Organisms as ecosystem engineers. Oikos 69, 373 (1994).
Google Scholar 
Carrasco, L., Azcón, R., Kohler, J., Roldán, A. & Caravaca, F. Comparative effects of native filamentous and arbuscular mycorrhizal fungi in the establishment of an autochthonous, leguminous shrub growing in a metal-contaminated soil. Sci. Total Environ. 409, 1205–1209 (2011).CAS 
PubMed 

Google Scholar 
Padilla, F. M., Ortega, R., Sánchez, J. & Pugnaire, F. I. Rethinking species selection for restoration of arid shrublands. Basic Appl. Ecol. 10, 640–647 (2009).
Google Scholar 
Ilunga wa Ilunga, E. et al. Plant functional traits as a promising tool for the ecological restoration of degraded tropical metal-rich habitats and revegetation of metal-rich bare soils: A case study in copper vegetation of Katanga, DRC. Ecol. Eng. 82, 214–221 (2015).
Google Scholar 
Salducci, M.-D. et al. How can a rare protected plant cope with the metal and metalloid soil pollution resulting from past industrial activities? Phytometabolites, antioxidant activities and root symbiosis involved in the metal tolerance of Astragalus tragacantha. Chemosphere 217, 887–896 (2019).CAS 
PubMed 

Google Scholar 
Kachout, S. S. et al. Accumulation of Cu, Pb, Ni and Zn in the halophyte plant Atriplex grown on polluted soil. J. Sci. Food Agric. 92, 336–342 (2012).CAS 
PubMed 

Google Scholar 
Schaeffer, A. et al. The impact of chemical pollution on the resilience of soils under multiple stresses: A conceptual framework for future research. Sci. Total Environ. 568, 1076–1085 (2016).CAS 
PubMed 

Google Scholar 
Tosini, L. et al. Gain in biodiversity but not in phytostabilization after 3 years of ecological restoration of contaminated Mediterranean soils. Ecol. Eng. 157, 105998 (2020).
Google Scholar 
Michelaki, C. et al. An integrated phenotypic trait-network in thermo-Mediterranean vegetation describing alternative, coexisting resource-use strategies. Sci. Total Environ. 672, 583–592 (2019).CAS 
PubMed 

Google Scholar 
Affholder, M.-C. et al. Transfer of metals and metalloids from soil to shoots in wild rosemary (Rosmarinus officinalis L.) growing on a former lead smelter site: Human exposure risk. Sci. Total Environ. 454–455, 219–229 (2013).PubMed 

Google Scholar 
Affholder, M.-C. et al. As, Pb, Sb, and Zn transfer from soil to root of wild rosemary: Do native symbionts matter?. Plant Soil 382, 219–236 (2014).CAS 

Google Scholar 
Ellili, A. et al. Decision-making criteria for plant-species selection for phytostabilization: Issues of biodiversity and functionality. J. Environ. Manag. 201, 215–226 (2017).CAS 

Google Scholar 
Laffont-Schwob, I. et al. Insights on metal-tolerance and symbionts of the rare species Astragalus tragacantha aiming at phytostabilization of polluted soils and plant conservation. ecmed 37, 57–62 (2011).
Google Scholar 
Rabier, J. et al. Heavy metal and arsenic resistance of the halophyte Atriplex halimus L. along a gradient of contamination in a French Mediterranean spray zone. Water Air Soil Pollut. 225, 1993 (2014).
Google Scholar 
Quevauviller, Ph. et al. Interlaboratory comparison of EDTA and DTPA procedures prior to certification of extractable trace elements in calcareous soil. Sci. Total Environ. 178, 127–132 (1996).CAS 

Google Scholar 
Anderson, J. P. E. & Domsch, K. H. A physiological method for the quantitative measurement of microbial biomass in soils. Soil Biol. Biochem. 10, 215–221 (1978).CAS 

Google Scholar 
R Development Core Team.pdf.Dray, S., Dufour, A. B. & Chessel, D. The ade4 package—II: Two-table and K-table methods. R News 7, 6 (2007).
Google Scholar  More

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

Green, P. A., Brandley, N. C. & Nowicki, S. Categorical perception in animal communication and decision-making. Behav. Ecol. 31, 859–867 (2020).
Google Scholar 
Petak, I. Ritualization. In Encyclopedia of Animal Cognition and Behavior (eds Vonk, J. & Shackelford, T.) 1–4 (Springer International Publishing, Cham, 2019).
Google Scholar 
Fernandez, A. A., Fasel, N., Knörnschild, M. & Richner, H. When bats are boxing: Aggressive behaviour and communication in male Seba’s short-tailed fruit bat. Anim. Behav. 98, 149–156 (2014).
Google Scholar 
van Schaik, J. et al. Bats swarm where they hibernate: Compositional similarity between autumn swarming and winter hibernation assemblages at five underground sites. PLoS ONE 10, e0130850 (2015).PubMed 
PubMed Central 

Google Scholar 
Vaughan, T. & O’Shea, T. Roosting ecology of the Pallid bat, Antrozous pallidus. J. Mammal. 57, 19–42 (1976).
Google Scholar 
Kunz, T. H. Roosting ecology. In Ecology of Bats (ed. Kunz, T. H.) (Plennum Press, 1982).
Google Scholar 
Kaňuch, P. Evening and morning activity schedules of the noctule bat (Nyctalus noctula) in Western Carpathians. Mammalia 71, 126–130 (2007).
Google Scholar 
Naďo, L. & Kaňuch, P. Swarming behaviour associated with group cohesion in tree-dwelling bats. Behav. Processes. 120, 80–86 (2015).PubMed 

Google Scholar 
Zelenka, Z., Kasanický, T., Budinská, I. & Kaňuch, P. An agent-based algorithm resembles behaviour of tree-dwelling bats under fission–fusion dynamics. Sci. Rep. 10, 16793 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Aureli, F. et al. Fission-fusion dynamics: New research frameworks. Curr. Anthropol. 49, 627–654 (2008).
Google Scholar 
Willis, C. K. R. & Brigham, R. M. Roost switching, roost sharing and social cohesion: Forest-dwelling big brown bats, Eptesicus fuscus, conform to the fission-fusion model. Anim. Behav. 68, 495–505 (2004).
Google Scholar 
Dietz, C. & Kiefer, A. Bats of Britain and Europe (Bloomsbury Publishing, 2016).
Google Scholar 
Kerth, G., Weissmann, G. & König, B. Day roost selection in female Bechstein’s bats. Oecologia 126, 1–9 (2001).ADS 
PubMed 

Google Scholar 
Reckardth, K. & Kerth, G. Roost selection and roost switching of female Bechstein’s bats. Oecologia 154, 581–588 (2007).ADS 

Google Scholar 
Mikula, P., Hromada, M. & Tryjanowski, P. Bats and swifts as food of the European kestrel (Falco tinnunculus) in small town in Slovakia. Ornis Fennica 90, 178–185 (2013).
Google Scholar 
Popa-Lisseanu, A. G., Bontadina, F., Mora, O. & Ibáñez, C. Highly structured fission-fusion societies in an aerial-hawking carnivorous bat. Anim. Behav. 75, 471–482 (2008).
Google Scholar 
Patriquin, K. J., Palstra, F., Leonard, M. L. & Broders, H. G. Female northern myotis (Myotis septentrionalis) that roost together are related. Behav. Ecol. 24, 949–954 (2013).
Google Scholar 
Sherman, P. W. Nepotism and the evolution of alarm calls. Science 197, 1246–1253 (1977).ADS 
CAS 
PubMed 

Google Scholar 
Kerth, G., Almasi, B., Ribi, N., Thiel, D. & Lüpold, S. Social interactions among wild female Bechstein’s bats (Myotis bechsteinii) living in a maternity colony. Acta Ethol. 5, 107–114 (2003).
Google Scholar 
Dietz, M. & Kalko, E. K. V. Reproduction affects flight activity in female and male Daubenton’s bats, Myotis daubentonii. Can. J. Zool. 85, 653–664 (2007).
Google Scholar 
Nelson, R. J. & Kriegsfeld, L. J. An Introduction to Behavioral Endocrinology (Sinauer Associates, 2017).
Google Scholar 
Choleris, E. & Kavaliers, M. Social learning in animals: Sex differences and neurobiological analysis. Pharmacol. Biochem. Behav. 64, 767–776 (1999).CAS 
PubMed 

Google Scholar 
McCracken, G. F. & Wilkinson, G. S. Bat mating systems. In Reproductive Biology of Bats (eds Crichton, E. G. & Krutzsch, P. H.) 321–362 (Academic Press, 2000).
Google Scholar 
Safi, K. Social bats: The males’ perspective. J. Mammal. 89, 1342–1350 (2008).
Google Scholar 
Linton, D. M. & Macdonald, D. W. Roost composition and sexual segregation in a lowland population of Daubenton’s bats (Myotis daubentonii). Acta Chiropterol. 21, 129–137 (2019).
Google Scholar 
Ružinská, R. & Kaňuch, P. Adult males in maternity colonies of Daubenton’s bat, Myotis daubentonii: What are they?. Mammalia 85, 551–556 (2021).
Google Scholar 
Barale, C. L., Rubenstein, D. I. & Beehner, J. C. Juvenile social relationships reflect adult patterns of behavior in wild geladas. Am. J. Primatol. 77, 1086–1096 (2015).PubMed 

Google Scholar 
McFarland, D. A Dictionary of Animal Behaviour (Oxford University Press, 2006).
Google Scholar 
Ratcliffe, J. & Hofstede, H. Roosts as information centres: Social learning of food preferences in bats. Biol. Lett. 1, 72–74 (2005).PubMed 
PubMed Central 

Google Scholar 
Fernandez, A. A., Burchardt, L. S., Nagy, M. & Knörnschild, M. Babbling in a vocal learning bat resembles human infant babbling. Science 373, 923–926 (2021).ADS 
CAS 
PubMed 

Google Scholar 
Wilkinson, G. S. Information transfer at evening bat colonies. Anim. Behav. 44, 501–518 (1992).
Google Scholar 
Vesterinen, E. J. et al. What you need is what you eat? Prey selection by the bat Myotis daubentonii. Mol. Ecol. 25, 1581–1594 (2016).CAS 
PubMed 

Google Scholar 
Todd, V. L. G. & Waters, D. A. Small scale habitat preferences of Myotis daubentonii, Pipistrellus pipistrellus, and potential aerial prey in an upland river valley. Acta Chiropterol. 19, 255–272 (2017).
Google Scholar 
Kaňuch, P. Roosting and population ecology of three syntopic tree-dwelling bat species (Myotis nattereri, M. daubentonii and Nyctalus noctula). Biologia 60, 579–587 (2005).
Google Scholar 
Lučan, R. K. & Hanák, V. Population ecology of Myotis daubentonii (Mammalia: Chiroptera) in South Bohemia: Summary of two long-term studies: 1968–1984 and 1999–2009. Acta Soc. Zool. Bohem. 75, 67–85 (2011).
Google Scholar 
Patriquin, K. J. & Ratcliffe, J. M. Should I stay or should I go? Fission-fusion dynamics in bats. In Sociality in Bats (ed. Ortega, J.) 65–104 (Springer, 2016).
Google Scholar 
Bogdanowicz, W. Myotis daubentonii. Mamm. Species 475, 1–9 (1994).
Google Scholar 
Rigby, E. L., Aegerter, J., Brash, M. & Altringham, J. D. Impact of PIT tagging on recapture rates, body condition and reproductive success of wild Daubenton’s bats (Myotis daubentonii). Vet. Rec. 170, 101 (2012).CAS 
PubMed 

Google Scholar 
Henry, M., Thomas, D. W., Vaudry, R. & Carrier, M. Foraging distances and home range of pregnant and lactating Little brown bats (Myotis lucifugus). J. Mammal. 83, 767–774 (2002).
Google Scholar 
Brunet-Rossinni, A. K. & Wilkinson, G. S. Methods for age estimation and the study of senescence in bats. In Ecological and Behavioral Methods for the Study of Bats (eds Kunz, T. H. & Parsons, S.) 315–325 (Johns Hopkins University Press, 2009).
Google Scholar 
Richardson, P. W. A new method of distinguishing Daubenton’s bats (Myotis daubentonii) up to one year old from adults. J. Zool. 233, 307–344 (1994).
Google Scholar 
Haarsma, A. & van Alphen, J. Chin-spot as an indicator of age in pond bats. Lutra 52, 97–107 (2009).
Google Scholar 
Burland, T. M., Barratt, E. M. & Racey, P. A. Isolation and characterization of microsatellite loci in the brown long-eared bat, Plecotus auritus, and cross-species amplification within the family Vespertilionidae. Mol. Ecol. 7, 136–138 (1998).CAS 

Google Scholar 
Castella, V. & Ruedi, M. Characterization of highly variable microsatellite loci in the bat Myotis myotis (Chiroptera: Vespertilionidae). Mol. Ecol. 9, 1000–1002 (2000).CAS 
PubMed 

Google Scholar 
Kerth, G., Safi, K. & König, B. Mean colony relatedness is a poor predictor of colony structure and female philopatry in the communally breeding Bechstein’s bat (Myotis bechsteinii). Behav. Ecol. Sociobiol. 52, 203–210 (2002).
Google Scholar 
Jan, C., Dawson, D. A., Altringham, J. D., Burke, T. & Butlin, R. K. Development of conserved microsatellite markers of high cross-species utility in bat species (Vespertilionidae, Chiroptera, Mammalia). Mol. Ecol. Resour. 12, 532–548 (2012).CAS 
PubMed 

Google Scholar 
Gruber, B. & Adamack, A. PopGenReport: A simple framework to analyse population and landscape genetic data. R package version 3.04. https://cran.r-project.org/package=popgenreport (2019).R Core Team. R: A language and environment for statistical computing (R Foundation for Statistical Computing, 2020).Dowd, C. twosamples: Fast permutation based two sample tests. R package version 1.1.1. https://cran.r-project.org/package=twosamples (2020).Kampstra, P. Beanplot: A boxplot alternative for visual comparison of distributions. J. Stat. Soft. Code Snippets 28, 1–9 (2008).
Google Scholar 
Kampstra, P. beanplot: Visualization via beanplots (like boxplot/stripchart/violin plot). R package version 1.2. https://cran.r-project.org/package=beanplot (2014).Ogle, D. H., Wheeler, P. & Dinno, A. FSA: Fisheries stock analysis. R package version 0.8.30. https://github.com/droglenc/FSA (2020).Jombart, T. adegenet: A R package for the multivariate analysis of genetic markers. Bioinformatics 24, 1403–1405 (2008).CAS 
PubMed 

Google Scholar 
Kassambara, A. (2020) ggpubr: ‘ggplot2’ based publication ready plots. R package version 0.4.0. https://cran.r-project.org/package=ggpubr (2020).Animal Behaviour. Guidelines for the treatment of animals in behavioural research and teaching. Anim. Behav. 159, I–X (2020).Russo, D. L., Cistrone, L., Jones, G. & Mazzoleni, S. Roost selection by barbastelle bats (Barbastella barbastellus, Chiroptera: Vespertilionidae) in beech woodlands of central Italy: Consequences for conservation. Biol. Conserv. 117, 73–81 (2004).
Google Scholar 
Arnold, B. D. & Wilkinson, G. S. Female natal philopatry and gene flow between divergent clades of pallid bats (Antrozous pallidus). J. Mammal. 96, 531–540 (2015).
Google Scholar 
Barclay, R. M. R. & Harder, L. D. Life histories of bats: Life in the slow lane. In Bat Ecology (eds Kunz, T. H. & Fenton, M. B.) 209–253 (University of Chicago Press, 2003).
Google Scholar 
Sun, D. et al. Behavioural patterns and postnatal development in pups of the Asian parti-coloured bat, Vespertilio sinensis. Animals 10, 1325 (2020).CAS 
PubMed Central 

Google Scholar 
Mavrodiev, P., Fleischmann, D., Kerth, G. & Schweitzer, F. Quantifying individual influence in leading-following behavior of Bechstein’s bats. Sci. Rep. 11, 1–12 (2021).
Google Scholar 
Bekoff, M. The development of social interaction, play, and metacommunication in mammals: An ethological perspective. Q. Rev. Biol. 47, 412–434 (1972).
Google Scholar 
Dunbar, R. I. M. & Shultz, S. Bondedness and sociality. Behaviour 147, 775–803 (2010).
Google Scholar 
Kerth, G., Perony, N. & Schweitzer, F. Bats are able to maintain long-term social relationships despite the high fission-fusion dynamics of their groups. Proc. R. Soc. B 278, 2761–2767 (2011).PubMed 
PubMed Central 

Google Scholar 
Hamilton, W. D. The genetical evolution of social behaviour. I. J. Theor. Biol. 7, 1–16 (1964).ADS 
CAS 
PubMed 

Google Scholar 
Ruczyński, I. & Bartoń, K. A. Seasonal changes and the influence of tree species and ambient temperature on the fission-fusion dynamics of tree-roosting bats. Behav. Ecol. Sociobiol. 74, 63 (2020).
Google Scholar 
Červený, J. & Bürger, P. Density and structure of the bat community occupying an old park at Žihobce (Czechoslovakia). In European Bat Research 1987 (eds Hanák, V. et al.) (Charles University Press, 1989).
Google Scholar 
Ripperger, S. et al. Proximity sensors on common noctule bats reveal evidence that mothers guide juveniles to roosts but not food. Biol. Lett. 15, 20180884 (2019).PubMed 
PubMed Central 

Google Scholar  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