Philippa Kaur

Hughes, J. & Macdonald, D. W. A review of the interactions between free-roaming domestic dogs and wildlife. Biol. Cons. 157, 341–351 (2013).Article 

Google Scholar 
Doherty, T. S. et al. The global impacts of domestic dogs on threatened vertebrates. Biol. Cons. 210, 56–59 (2017).Article 

Google Scholar 
Young, J. K., Olson, K. A., Reading, R. P., Amgalanbaatar, S. & Berger, J. Is wildlife going to the dogs? Impacts of feral and free-roaming dogs on wildlife populations. Bioscience 61, 125–132 (2011).Article 

Google Scholar 
Ritchie, E. G., Dickman, C. R., Letnic, M., Vanak, A. T. & Gommper, M. Dogs as predators and trophic regulators. Free-ranging dogs and wildlife conservation, 55–68 (2014).Gompper, M. E. In Free-ranging dogs and wildlife conservation, Oxford University Press (2014).Somaweera, R., Webb, J. K. & Shine, R. It’sa dog-eat-croc world: Dingo predation on the nests of freshwater crocodiles in tropical Australia. Ecol. Res. 26, 957–967 (2011).Article 

Google Scholar 
Weston, M. A. & Stankowich, T. In Free-Ranging Dogs and Wildlife Conservation. ME Gompper (ed.) (ed Matthew E Gompper) Ch. 4, 94–113 (Oxford University Press, 2013).Zapata-Ríos, G. & Branch, L. C. Altered activity patterns and reduced abundance of native mammals in sites with feral dogs in the high Andes. Biol. Cons. 193, 9–16 (2016).Article 

Google Scholar 
Donadio, E. & Buskirk, S. W. Diet, morphology, and interspecific killing in Carnivora. Am. Nat. 167, 524–536 (2006).PubMed 
Article 

Google Scholar 
Gingold, G., Yom-Tov, Y., Kronfeld-Schor, N. & Geffen, E. Effect of guard dogs on the behavior and reproduction of gazelles in cattle enclosures on the Golan Heights. Anim. Conserv. 12, 155–162 (2009).Article 

Google Scholar 
Fernández-Juricic, E. & Tellería, J. L. Effects of human disturbance on spatial and temporal feeding patterns of Blackbird Turdus merula in urban parks in Madrid, Spain. Bird Study 47, 13–21 (2000).Article 

Google Scholar 
Vanak, A. T. & Gompper, M. E. Dogs Canis familiaris as carnivores: Their role and function in intraguild competition. Mammal Rev. 39, 265–283 (2009).Article 

Google Scholar 
Silva-Rodríguez, E. A. & Sieving, K. E. Domestic dogs shape the landscape-scale distribution of a threatened forest ungulate. Biol. Cons. 150, 103–110 (2012).Article 

Google Scholar 
Banks, P. B. & Bryant, J. V. Four-legged friend or foe? Dog walking displaces native birds from natural areas. Biol. Let. 3, 611–613 (2007).Article 

Google Scholar 
Langston, R., Liley, D., Murison, G., Woodfield, E. & Clarke, R. What effects do walkers and dogs have on the distribution and productivity of breeding European Nightjar Caprimulgus europaeus?. Ibis 149, 27–36 (2007).Article 

Google Scholar 
Lenth, B. E., Knight, R. L. & Brennan, M. E. The effects of dogs on wildlife communities. Nat. Areas J. 28, 218–227 (2008).Article 

Google Scholar 
Weston, M. A. & Stankowich, T. Dogs as agents of disturbance. Free-Ranging Dogs and Wildlife Conservation. ME Gompper (ed.), 94–113 (2013).Letnic, M., Ritchie, E. G. & Dickman, C. R. Top predators as biodiversity regulators: The dingo Canis lupus dingo as a case study. Biol. Rev. 87, 390–413 (2012).PubMed 
Article 

Google Scholar 
Maguire, G. S., Miller, K. K. & Weston, M. A. In Impacts of Invasive Species on Coastal Environments 397–412 (Springer, 2019).Cardinale, B. J. et al. Biodiversity loss and its impact on humanity. Nature 486, 59–67 (2012).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Delgado-Baquerizo, M. et al. Microbial diversity drives multifunctionality in terrestrial ecosystems. Nat. Commun. 7, 1–8 (2016).Article 
CAS 

Google Scholar 
Rodriguez, L. F. Can invasive species facilitate native species? Evidence of how, when, and why these impacts occur. Biol. Invasions 8, 927–939 (2006).Article 

Google Scholar 
Rosenfeld, J. S. Functional redundancy in ecology and conservation. Oikos 98, 156–162 (2002).Article 

Google Scholar 
Díaz, S., Fargione, J., Chapin, F. S. III. & Tilman, D. Biodiversity loss threatens human well-being. PLoS Biol 4, e277 (2006).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Hooper, D. U. et al. A global synthesis reveals biodiversity loss as a major driver of ecosystem change. Nature 486, 105–108 (2012).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Barbier, E. B. et al. The value of estuarine and coastal ecosystem services. Ecol. Monogr. 81, 169–193. https://doi.org/10.1890/10-1510.1 (2011).Article 

Google Scholar 
Nel, R. et al. The status of sandy beach science: Past trends, progress, and possible futures. Estuar. Coast. Shelf Sci. 150, 1–10 (2014).ADS 
Article 

Google Scholar 
Schlacher, T. A. et al. Golden opportunities: A horizon scan to expand sandy beach ecology. Estuar. Coast. Shelf Sci. 157, 1–6 (2015).ADS 
Article 

Google Scholar 
Schlacher, T. A. et al. Key ecological function peaks at the land–ocean transition zone when vertebrate scavengers concentrate on ocean beaches. Ecosystems 23, 1–11 (2019).MathSciNet 

Google Scholar 
Lockwood, J. L. & Maslo, B. In Coastal Convervation (eds Brooke Maslo & JL Lockwood) 1–10 (Cambridge University Press, 2014).Morin, D. J., Lesmeister, D. B., Nielsen, C. K. & Schauber, E. M. The truth about cats and dogs: Landscape composition and human occupation mediate the distribution and potential impact of non-native carnivores. Glob. Ecol. Conserv. 15, e00413 (2018).Article 

Google Scholar 
Cortés, E. I., Navedo, J. G. & Silva-Rodríguez, E. A. Widespread presence of domestic dogs on sandy beaches of Southern Chile. Animals 11, 161 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Burger, J., Jeitner, C., Clark, K. & Niles, L. J. The effect of human activities on migrant shorebirds: Successful adaptive management. Environ. Conserv. 31, 283–288 (2004).Article 

Google Scholar 
Dowling, B. & Weston, M. A. Managing a breeding population of the Hooded Plover Thinornis rubricollis in a high-use recreational environment. Bird Conserv. Int. 9, 255–270 (1999).Article 

Google Scholar 
Vanak, A. T. & Gompper, M. E. Interference competition at the landscape level: The effect of free-ranging dogs on a native mesocarnivore. J. Appl. Ecol. 47, 1225–1232 (2010).Article 

Google Scholar 
Marzluff, J. M., McGowan, K. J., Donnelly, R. & Knight, R. L. In Avian ecology and conservation in an urbanizing world 331–363 (Springer, 2001).Handler, A., Lonsdorf, E. V. & Ardia, D. R. Evidence for red fox (Vulpes vulpes) exploitation of anthropogenic food sources along an urbanization gradient using stable isotope analysis. Can. J. Zool. 98, 79–87 (2020).Article 

Google Scholar 
Prange, S., Gehrt, S. D. & Wiggers, E. P. Demographic factors contributing to high raccoon densities in urban landscapes. The J. Wildlife Manag. 67, 324–333 (2003).Article 

Google Scholar 
Méndez, A. et al. Adapting to urban ecosystems: unravelling the foraging ecology of an opportunistic predator living in cities. Urban Ecosyst. 23, 1117–1126 (2020).Article 

Google Scholar 
Rees, J., Webb, J., Crowther, M. & Letnic, M. Carrion subsidies provided by fishermen increase predation of beach-nesting bird nests by facultative scavengers. Anim. Conserv. 18, 44–49 (2015).Article 

Google Scholar 
Kimber, O. et al. The fox and the beach: Coastal landscape topography and urbanisation predict the distribution of carnivores at the edge of the sea. Glob. Ecol. Conserv. 23, e01071 (2020).Article 

Google Scholar 
Ruxton, G. D. & Houston, D. C. Obligate vertebrate scavengers must be large soaring fliers. J. Theor. Biol. 228, 431–436 (2004).ADS 
MathSciNet 
PubMed 
MATH 
Article 

Google Scholar 
Cortés-Avizanda, A., Jovani, R., Donázar, J. A. & Grimm, V. Bird sky networks: How do avian scavengers use social information to find carrion?. Ecology 95, 1799–1808 (2014).PubMed 
Article 

Google Scholar 
Harel, R., Spiegel, O., Getz, W. M. & Nathan, R. Social foraging and individual consistency in following behaviour: Testing the information centre hypothesis in free-ranging vultures. Proc. Royal Soc. B: Biol. Sci. 284, 20162654 (2017).Article 

Google Scholar 
Soulsbury, C. D., Iossa, G., Baker, P. J., White, P. C. & Harris, S. Behavioral and spatial analysis of extraterritorial movements in red foxes (Vulpes vulpes). J. Mammal. 92, 190–199 (2011).Article 

Google Scholar 
Johnson, C. N. & VanDerWal, J. Evidence that dingoes limit abundance of a mesopredator in eastern Australian forests. J. Appl. Ecol. 46, 641–646 (2009).Article 

Google Scholar 
Polis, G. A., Anderson, W. B. & Holt, R. D. Toward an integration of landscape and food web ecology: The dynamics of spatially subsidized food webs. Ann. Rev. Ecol. Syst. 28, 289–316 (1997).Article 

Google Scholar 
Barton, P. S., Cunningham, S. A., Lindenmayer, D. B. & Manning, A. D. The role of carrion in maintaining biodiversity and ecological processes in terrestrial ecosystems. Oecologia 171, 761–772 (2013).ADS 
PubMed 
Article 

Google Scholar 
Schlacher, T. A., Strydom, S. & Connolly, R. M. Multiple scavengers respond rapidly to pulsed carrion resources at the land–ocean interface. Acta Oecologica 48, 7–12 (2013).ADS 
Article 

Google Scholar 
Dunbrack, T. R. & Dunbrack, R. L. Why take your dog on a picnic: presence of a potential predator (Canis lupus familiaris) reverses the outcome of food competition between northwestern crows (Corvus caurinus) and glaucous-winged gulls (Larus glaucescens). Northwest. Nat. 91, 94–98 (2010).Article 

Google Scholar 
Jiménez, J. et al. Restoring apex predators can reduce mesopredator abundances. Biol. Cons. 238, 108234 (2019).Article 

Google Scholar 
Bhadra, A. et al. The meat of the matter: A rule of thumb for scavenging dogs?. Ethol. Ecol. Evol. 28, 427–440 (2016).Article 

Google Scholar 
Turner, K. L., Abernethy, E. F., Conner, L. M., Rhodes, O. E. Jr. & Beasley, J. C. Abiotic and biotic factors modulate carrion fate and vertebrate scavenging communities. Ecology 98, 2413–2424 (2017).PubMed 
Article 

Google Scholar 
Ogada, D., Torchin, M., Kinnaird, M. & Ezenwa, V. Effects of vulture declines on facultative scavengers and potential implications for mammalian disease transmission. Conserv. Biol. 26, 453–460 (2012).CAS 
PubMed 
Article 

Google Scholar 
O’Bryan, C. J. et al. The contribution of predators and scavengers to human well-being. Nat. Ecol. & Evol. 2, 229–236 (2018).Article 

Google Scholar 
Gómez-Serrano, M. Á. Four-legged foes: Dogs disturb nesting plovers more than people do on tourist beaches. Ibis 163, 338–352 (2021).Article 

Google Scholar 
Stantial, M., Cohen, J., Darrah, A., Farrell, S. & Maslo, B. The effect of top predator removal on the distribution of a mesocarnivore and nest survival of an endangered shorebird. Avian Conserv. Ecol. https://doi.org/10.5751/ACE-01806-160108 (2021).Article 

Google Scholar 
Mahon, P. S. Targeted control of widespread exotic species for biodiversity conservation: The red fox (Vulpes vulpes) in New South Wales, Australia. Ecol. Manag. Restor. 10, S59–S69 (2009).ADS 
Article 

Google Scholar 
Colwell, M. A. In The Population Ecology and Conservation of Charadrius Plovers 127–147 (CRC Press, 2019).Huijbers, C. M. et al. Limited functional redundancy in vertebrate scavenger guilds fails to compensate for the loss of raptors from urbanized sandy beaches. Divers. Distrib. 21, 55–63 (2015).Article 

Google Scholar 
Huijbers, C. M., Schlacher, T. A., Schoeman, D. S., Weston, M. A. & Connolly, R. M. Urbanisation alters processing of marine carrion on sandy beaches. Landsc. Urban Plan. 119, 1–8 (2013).Article 

Google Scholar 
Meek, P. et al. Recommended guiding principles for reporting on camera trapping research. Biodivers. Conserv. 23, 2321–2343 (2014).Article 

Google Scholar 
Kolowski, J. M. & Forrester, T. D. Camera trap placement and the potential for bias due to trails and other features. PLoS ONE 12, e0186679 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Burton, A. C. et al. Wildlife camera trapping: a review and recommendations for linking surveys to ecological processes. J. Appl. Ecol. 52, 675–685 (2015).Article 

Google Scholar 
Selva, N. & Fortuna, M. A. The nested structure of a scavenger community. Proc. Royal Soc. B: Biol. Sci. 274, 1101–1108 (2007).Article 

Google Scholar 
Olson, Z. H., Beasley, J. C. & Rhodes, O. E. Jr. Carcass type affects local scavenger guilds more than habitat connectivity. PLoS ONE 11, e0147798 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Anderson, M. J. Permutation tests for univariate or multivariate analysis of variance and regression. Can. J. Fish. Aquat. Sci. 58, 626–639 (2001).Article 

Google Scholar 
Team, R. D. C. R: A language and environment for statistical computing. R Foundation for statistical computing, Vienna, Austria (2013).Dixon, P. VEGAN, a package of R functions for community ecology. J. Veg. Sci. 14, 927–930 (2003).Article 

Google Scholar 
Schlacher, T. A. et al. Conservation gone to the dogs: When canids rule the beach in small coastal reserves. Biodivers. Conserv. 24, 493–509 (2015).Article 

Google Scholar 
Lewin, W.-C., Freyhof, J., Huckstorf, V., Mehner, T. & Wolter, C. When no catches matter: Coping with zeros in environmental assessments. Ecol. Ind. 10, 572–583 (2010).Article 

Google Scholar 
Burnham, K. P. & Anderson, D. R. Model selection and multimodel inference: a practical information-theoretic approach. 488 (Springer Science & Business Media, 2002).Bolker, B. & Team, R. (R package version 0.9, 2010).Barton, K. & Barton, M. K. Package ‘mumin’. Version 1, 439 (2015).Wickham, H., Francois, R., Henry, L. & Müller, K. dplyr: A grammar of data manipulation. R package version 0.4. 3. R Found. Stat. Comput., Vienna. https://CRAN. R-project. org/package= dplyr (2015).Wickham, H., Chang, W. & Wickham, M. H. Package ‘ggplot2’. Create Elegant Data Visualisations Using the Grammar of Graphics. Version 2, 1–189 (2016). More

Watson, J. B. & Morgan, J. J. B. Emotional reactions and psychological experimentation. Am. J. Psychol. 28, 163–174 (1917).Article 

Google Scholar 
Watson, J. B. & Rayner, R. Conditioned emotional reactions. J. Exp. Psychol. 3, 1–14 (1920).Article 

Google Scholar 
LeDoux, J. Fear and the brain: where have we been, and where are we going. Biol. Psychiatry 44, 1229–1238 (1998).CAS 
PubMed 
Article 

Google Scholar 
Fendt, M. & Fanselow, M. S. The neuroanatomical and neurochemical basis of conditioned fear. Neurosci. Biobehav. Rev. 23, 743–760 (1999).CAS 
PubMed 
Article 

Google Scholar 
Maren, S. & Quirk, G. J. Neuronal signalling of fear memory. Nat. Rev. Neurosci. 5, 844–852 (2004).CAS 
PubMed 
Article 

Google Scholar 
Bouton, M. E., Mineka, S. & Barlow, D. H. A modern learning theory perspective on the etiology of panic disorder. Psychol. Rev. 108, 4–32 (2001).CAS 
PubMed 
Article 

Google Scholar 
Kim, J. J. & Jung, M. W. Neural circuits and mechanisms involved in Pavlovian fear conditioning: a critical review. Neurosci. Biobehav. Rev. 30, 188–202 (2006).PubMed 
Article 

Google Scholar 
Watson, J. B. Psychology as the behaviorist views it. Psychological Rev. 20, 158–177 (1913).Article 

Google Scholar 
Pavlov, I. P. Conditioned Reflexes: An Investigation of the Physiological Activity of the Cerebral Cortex (Oxford University Press, 1927).Guthrie, E. R. Conditioning as a principle of learning. Psychological Rev. 37, 412–428 (1930).Article 

Google Scholar 
Kamin, L. J. in Miami Symposium on the Prediction of Behavior (ed. Jones, M. R.) 9–33 (University of Miami Press, 1968).Rescorla, R. A. Probability of shock in the presence and absence of CS in fear conditioning. J. Comp. Physiol. Psychol. 66, 1–5 (1968).CAS 
PubMed 
Article 

Google Scholar 
Wagner, A. R., Logan, F. A., Haberlandt, K. & Price, T. Stimulus selection in animal discrimination learning. J. Exp. Psychol. 76, 171–180 (1968).CAS 
PubMed 
Article 

Google Scholar 
Rescorla, R. A. & Wagner, A. R. A Theory of Pavlovian Conditioning: Variations in the Effectiveness of Reinforcement and Nonreinforcement 64–99 (Appleton-Century-Crofts, 1972).Josselyn, S. A. & Tonegawa, S. Memory engrams: recalling the past and imagining the future. Science 367, https://doi.org/10.1126/science.aaw4325 (2020).Tovote, P., Fadok, J. P. & Luthi, A. Neuronal circuits for fear and anxiety. Nat. Rev. Neurosci. 16, 317–331 (2015).CAS 
PubMed 
Article 

Google Scholar 
Haubensak, W. et al. Genetic dissection of an amygdala microcircuit that gates conditioned fear. Nature 468, 270–276 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Foa, E. B. & Rothbaum, B. O. Treating the Trauma of Rape: Cognitive Behavioral Therapy for PTSD (Guilford Press, 1998).Butler, A. C., Chapman, J. E., Forman, E. M. & Beck, A. T. The empirical status of cognitive-behavioral therapy: a review of meta-analyses. Clin. Psychol. Rev. 26, 17–31 (2006).PubMed 
Article 

Google Scholar 
Delgado, M. R., Olsson, A. & Phelps, E. A. Extending animal models of fear conditioning to humans. Biol. Psychol. 73, 39–48 (2006).CAS 
PubMed 
Article 

Google Scholar 
Mahan, A. L. & Ressler, K. J. Fear conditioning, synaptic plasticity and the amygdala: implications for posttraumatic stress disorder. Trends Neurosci. 35, 24–35 (2012).CAS 
PubMed 
Article 

Google Scholar 
Craske, M. G. et al. What is an anxiety disorder? Focus 9, 20 (2011).
Google Scholar 
LeDoux, J. E. The Emotional Brain: the Mysterious Underpinnings of Emotional Life (Simon & Schuster, 1996).Fanselow, M. S. From contextual fear to a dynamic view of memory systems. Trends Cogn. Sci. 14, 7–15 (2010).PubMed 
Article 

Google Scholar 
Lima, S. L. & Dill, L. M. Behavioral decisions made under the risk of predation—a review and prospectus. Can. J. Zool. 68, 619–640 (1990).Article 

Google Scholar 
Bednekoff, P. A. Foraging in the Face of Danger 305–329 (University of Chicago Press, 2007).Stephens, D. W. Decision ecology: foraging and the ecology of animal decision making. Cogn. Affect Behav. Neurosci. 8, 475–484 (2008).PubMed 
Article 

Google Scholar 
Beckers, T., Krypotos, A. M., Boddez, Y., Effting, M. & Kindt, M. What’s wrong with fear conditioning? Biol. Psychol. 92, 90–96 (2013).PubMed 
Article 

Google Scholar 
Mobbs, D. & Kim, J. J. Neuroethological studies of fear, anxiety, and risky decision-making in rodents and humans. Curr. Opin. Behav. Sci. 5, 8–15 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Pellman, B. A. & Kim, J. J. What can ethobehavioral studies tell us about the Brain’s fear system. Trends Neurosci. 39, 420–431 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Thorndike, E. Biological Lectures from the Marine Laboratory at Woods’ Holl, USA, for 1899. Nature 62, 411 (1900).Bolles, R. C. Species-specific defense reactions and avoidance learning. Psychol. Rev. 77, 32–48 (1970).Choi, J. S. & Kim, J. J. Amygdala regulates risk of predation in rats foraging in a dynamic fear environment. Proc. Natl Acad. Sci. USA 107, 21773–21777 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zambetti, P. R., Schuessler, B. P. & Kim, J. J. Sex differences in foraging rats to naturalistic aerial predator stimuli. iScience 16, 442–452 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Yilmaz, M. & Meister, M. Rapid innate defensive responses of mice to looming visual stimuli. Curr. Biol. 23, 2011–2015 (2013).CAS 
PubMed 
Article 

Google Scholar 
Papes, F., Logan, D. W. & Stowers, L. The vomeronasal organ mediates interspecies defensive behaviors through detection of protein pheromone homologs. Cell 141, 692–703 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Tolman, E. C. Cognitive maps in rats and men. Psychol. Rev. 55, 189–208 (1948).CAS 
PubMed 
Article 

Google Scholar 
Wilensky, A. E., Schafe, G. E. & LeDoux, J. E. The amygdala modulates memory consolidation of fear-motivated inhibitory avoidance learning but not classical fear conditioning. J. Neurosci. 20, 7059–7066 (2000).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lee, T. & Kim, J. J. Differential effects of cerebellar, amygdalar, and hippocampal lesions on classical eyeblink conditioning in rats. J. Neurosci. 24, 3242–3250 (2004).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Stiedl, O. & Spiess, J. Effect of tone-dependent fear conditioning on heart rate and behavior of C57BL/6N mice. Behav. Neurosci. 111, 703–711 (1997).CAS 
PubMed 
Article 

Google Scholar 
Guimaraes, F. S., Hellewell, J., Hensman, R., Wang, M. & Deakin, J. F. Characterization of a psychophysiological model of classical fear conditioning in healthy volunteers: influence of gender, instruction, personality and placebo. Psychopharmacology 104, 231–236 (1991).CAS 
PubMed 
Article 

Google Scholar 
Mackintosh, N. J. The Psychology of Animal Learning (Academic Press, 1974).Bouton, M. E. Learning and Behavior (Sinauer Associates 2007).Sheafor, P. J. “Pseudoconditioned” jaw movements of the rabbit reflect associations conditioned to contextual background cues. J. Exp. Psychol. Anim. Behav. Process 1, 245–260 (1975).CAS 
PubMed 
Article 

Google Scholar 
Rescorla, R. A. Behavioral studies of Pavlovian conditioning. Annu. Rev. Neurosci. 11, 329–352 (1988).CAS 
PubMed 
Article 

Google Scholar 
Thompson, R. F. & Krupa, D. J. Organization of memory traces in the mammalian brain. Annu. Rev. Neurosci. 17, 519–549 (1994).CAS 
PubMed 
Article 

Google Scholar 
Fanselow, M. S. & Wassum, K. M. The origins and organization of vertebrate pavlovian conditioning. Cold Spring Harb. Perspect. Biol. 8, a021717 (2015).PubMed 
Article 

Google Scholar 
Lee, H. J., Berger, S. Y., Stiedl, O., Spiess, J. & Kim, J. J. Post-training injections of catecholaminergic drugs do not modulate fear conditioning in rats and mice. Neurosci. Lett. 303, 123–126 (2001).CAS 
PubMed 
Article 

Google Scholar 
Palgi, Y., Gelkopf, M. & Berger, R. The inoculating role of previous exposure to potentially traumatic life events on coping with prolonged exposure to rocket attacks: a lifespan perspective. Psychiatry Res. 227, 296–301 (2015).PubMed 
Article 

Google Scholar 
Somer, E. et al. Israeli civilians under heavy bombardment: prediction of the severity of post-traumatic symptoms. Prehosp. Disaster Med. 24, 389–394 (2009).PubMed 
Article 

Google Scholar 
Alexander, B. K., Beyerstein, B. L., Hadaway, P. F. & Coambs, R. B. Effect of early and later colony housing on oral ingestion of morphine in rats. Pharm. Biochem. Behav. 15, 571–576 (1981).CAS 
Article 

Google Scholar 
Gage, S. H. & Sumnall, H. R. Rat Park: how a rat paradise changed the narrative of addiction. Addiction 114, 917–922 (2019).PubMed 
Article 

Google Scholar 
Fanselow, M. S. & Lester, L. S. A Functional Behavioristic Approach to Aversively Motivated Behavior: Predatory Imminence as a Determinant of the Topography of Defensive Behavior 185–212 (Lawrence Erlbaum Associates Inc, 1988).Cain, C. & LeDoux, J. Brain mechanisms of Pavlovian and instrumental aversive conditioning. Handb. Behav. Neurosci. 17, 103–124 (2008).Article 

Google Scholar 
Choi, J. S., Cain, C. K. & LeDoux, J. E. The role of amygdala nuclei in the expression of auditory signaled two-way active avoidance in rats. Learn Mem. 17, 139–147 (2014).Article 

Google Scholar 
Steimer, T. The biology of fear- and anxiety-related behaviors. Dialogues Clin. Neurosci. 4, 231–249 (2002).PubMed 
PubMed Central 
Article 

Google Scholar 
Fanselow, M. S. The role of learning in threat imminence and defensive behaviors. Curr. Opin. Behav. Sci. 24, 44–49 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Fanselow, M. S. Associative vs topographical accounts of the immediate shock freezing deficit in rats—implications for the response selection-rules governing species-specific defensive reactions. Learn. Motiv. 17, 16–39 (1986).Article 

Google Scholar 
Landeira-Fernandez, J., DeCola, J. P., Kim, J. J. & Fanselow, M. S. Immediate shock deficit in fear conditioning: effects of shock manipulations. Behav. Neurosci. 120, 873–879 (2006).CAS 
PubMed 
Article 

Google Scholar 
Hull, C. L. A functional interpretation of the conditioned reflex. Psychol. Rev. 36, 498–511 (1929).Article 

Google Scholar 
Lazarus, A. A. Behavior Therapy and Beyond (McGraw-Hill Companies, 1971).Öhman, A. & Mineka, S. Fears, phobias, and preparedness: toward an evolved module of fear and fear learning. Psychol. Rev. 108, 483–522 (2001).PubMed 
Article 

Google Scholar 
Lee, H. & Kim, J. J. Amygdalar NMDA receptors are critical for new fear learning in previously fear-conditioned rats. J. Neurosci. 18, 8444–8454 (1998).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Mathis, A. et al. DeepLabCut: markerless pose estimation of user-defined body parts with deep learning. Nat. Neurosci. 21, 1281–1289 (2018).CAS 
PubMed 
Article 

Google Scholar  More

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Samuel, B. S., Rowedder, H., Braendle, C., Félix, M. A. & Ruvkun, G. Caenorhabditis elegans responses to bacteria from its natural habitats. Proc. Natl. Acad. Sci. USA 113, E3941–E3949 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Oliver, K. M., Smith, A. H. & Russell, J. A. Defensive symbiosis in the real world: Advancing ecological studies of heritable, protective bacteria in aphids and beyond. Funct. Ecol. 28, 341–355 (2014).
Google Scholar 
King, K. C. Defensive symbionts. Curr. Biol. 29, R78–R80 (2019).CAS 
PubMed 

Google Scholar 
Foster, K. R., Schluter, J., Coyte, K. Z. & Rakoff-Nahoum, S. The evolution of the host microbiome as an ecosystem on a leash. Nature 548, 43–51 (2017).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Ford, S. A., Kao, D., Williams, D. & King, K. C. Microbe-mediated host defence drives the evolution of reduced pathogen virulence. Nat. Commun. 7, 13430 (2016).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Litvak, Y. et al. Commensal Enterobacteriaceae protect against Salmonella colonization through oxygen competition. Cell Host Microbe 25, 128–139 (2019).CAS 
PubMed 

Google Scholar 
Pimentel, A. C., Cesar, C. S., Martins, M. & Cogni, R. The antiviral effects of the symbiont bacteria Wolbachia in insects. Front. Immunol. 11, 626329 (2021).PubMed 
PubMed Central 

Google Scholar 
Becker, M. H., Brucker, R. M., Schwantes, C. R., Harris, R. N. & Minbiole, K. P. C. The bacterially produced metabolite violacein is associated with survival of amphibians infected with a lethal fungus. Appl. Environ. Microbiol. 75, 6635–6638 (2009).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Bates, K. A., Bolton, J. S. & King, K. C. A globally ubiquitous symbiont can drive experimental host evolution. Mol. Ecol. 30, 3882–3892 (2021).CAS 
PubMed 

Google Scholar 
Dahan, D., Preston, G. M., Sealey, J. & King, K. C. Impacts of a novel defensive symbiosis on the nematode host microbiome. BMC Microbiol. 20, 1–10 (2020).
Google Scholar 
Banerjee, S., Schlaeppi, K. & van der Heijden, M. G. A. Keystone taxa as drivers of microbiome structure and functioning. Nat. Rev. Microbiol. 16, 567–576 (2018).CAS 
PubMed 

Google Scholar 
Zheng, Y. et al. Exploring biocontrol agents from microbial keystone taxa associated to suppressive soil: A new attempt for a biocontrol strategy. Front. Plant Sci. 12, 655673 (2021).PubMed 
PubMed Central 

Google Scholar 
Tudela, H., Claus, S. P. & Saleh, M. Next generation microbiome research: Identification of keystone species in the metabolic regulation of host-gut microbiota interplay. Front. Cell Dev. Biol. 9, 719072 (2021).PubMed 
PubMed Central 

Google Scholar 
Mateos-Hernández, L. et al. Anti-tick microbiota vaccine impacts Ixodes ricinus performance during feeding. Vaccine 8, 1–21 (2020).
Google Scholar 
Mateos-Hernández, L. et al. Anti-microbiota vaccines modulate the tick microbiome in a taxon-specific manner. Front. Immunol. 12, 704621 (2021).PubMed 
PubMed Central 

Google Scholar 
Dirksen, P. et al. The native microbiome of the nematode Caenorhabditis elegans: Gateway to a new host-microbiome model. BMC Biol. 14, 38 (2016).PubMed 
PubMed Central 

Google Scholar 
Berg, M. et al. Assembly of the Caenorhabditis elegans gut microbiota from diverse soil microbial environments. ISME J. 10, 1998–2009 (2016).PubMed 
PubMed Central 

Google Scholar 
Zhang, F. et al. Caenorhabditis elegans as a model for microbiome research. Front. Microbiol. 8, 485 (2017).PubMed 
PubMed Central 

Google Scholar 
King, K. C. et al. Rapid evolution of microbe-mediated protection against pathogens in a worm host. ISME J. 10, 1915–1924 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Faust, K. & Raes, J. Microbial interactions: From networks to models. Nat. Rev. Microbiol. 10, 538–550 (2012).CAS 
PubMed 

Google Scholar 
Layeghifard, M., Hwang, D. M. & Guttman, D. S. Disentangling interactions in the microbiome: A network perspective. Trends Microbiol. 25, 217–228 (2017).CAS 
PubMed 

Google Scholar 
Röttjers, L. & Faust, K. From hairballs to hypotheses–biological insights from microbial networks. FEMS Microbiol. Rev. 42, 761–780 (2018).PubMed 
PubMed Central 

Google Scholar 
Agler, M. T. et al. Microbial hub taxa link host and abiotic factors to plant microbiome variation. PLoS Biol. 14, e1002352 (2016).PubMed 
PubMed Central 

Google Scholar 
Douglas, G. M. et al. PICRUSt2 for prediction of metagenome functions. Nat. Biotechnol. 38, 685–688 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Hou, Y. et al. Hierarchical microbial functions prediction by graph aggregated embedding. Front. Genet. 11, 608512 (2021).PubMed 
PubMed Central 

Google Scholar 
Montalvo-Katz, S., Huang, H., Appel, M. D., Berg, M. & Shapira, M. Association with soil bacteria enhances p38-dependent infection resistance in Caenorhabditis elegans. Infect. Immun. 81, 514–520 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 7, 852–857 (2019).
Google Scholar 
Bokulich, N. A. et al. Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin. Microbiome 6, 1–17 (2018).
Google Scholar 
Yarza, P. et al. Uniting the classification of cultured and uncultured bacteria and archaea using 16S rRNA gene sequences. Nat. Rev. Microbiol. 12, 635–645 (2014).CAS 
PubMed 

Google Scholar 
Friedman, J. & Alm, E. J. Inferring correlation networks from genomic survey data. PLoS Comput. Biol. 8, e1002687 (2012).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
RStudio Team. RStudio: Integrated Development for R (RStudio, PBC, 2020).
Google Scholar 
Bastian, M., Heymann, S. & Jacomy, M. Gephi: An open-source software for exploring and manipulating networks. Third International AAAI Conference on Weblogs and Social Media (2009).Lhomme, S. NetSwan: Network Strengths and Weaknesses Analysis. R Pack Version (2015).Peschel, S., Müller, C. L., von Mutius, E., Boulesteix, A. L. & Depner, M. NetCoMi: Network construction and comparison for microbiome data in R. Brief Bioinform. 22, bbaa290 (2021).PubMed 

Google Scholar 
Kanehisa, M. Goto, S, KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 28, 27–30 (2000).CAS 
PubMed 
PubMed Central 

Google Scholar 
Tatusov, R. L., Galperin, M. Y., Natale, D. A. & Koonin, E. V. The COG database: A tool for genome-scale analysis of protein functions and evolution. Nucleic Acids Res. 28, 33–36 (2000).CAS 
PubMed 
PubMed Central 

Google Scholar 
Caspi, R. et al. The MetaCyc database of metabolic pathways and enzymes. Nucleic Acids Res. 46, D633–D639 (2018).CAS 
PubMed 

Google Scholar 
Fernandes, A. D. et al. Unifying the analysis of high-throughput sequencing datasets: Characterizing RNA-seq, 16S rRNA gene sequencing and selective growth experiments by compositional data analysis. Microbiome 2, 15 (2014).PubMed 
PubMed Central 

Google Scholar 
Lin, H. & Peddada, S. D. Analysis of microbial compositions: A review of normalization and differential abundance analysis. npj Biofilms Microbiomes 6, 60 (2020).PubMed 
PubMed Central 

Google Scholar 
Ploner, A. Heatplus: Heatmaps with Row and/or Column Covariates and Colored Clusters. R package version 3.2. (2021).Shannon, C. E. A mathematical theory of communication. Bell Syst. Tech. J. 27, 379–423, 623–656 (1948).Pielou, E. C. The measurement of diversity in different types of biological collections. J. Theor. Biol. 13, 131–144 (1966).ADS 

Google Scholar 
Fisher, R. A., Corbet, A. S. & Williams, C. B. The relation between the number of species and the number of individuals in a random sample of an animal population. J. Anim. Ecol. 12, 42 (1943).
Google Scholar 
Ford, S. A. & King, K. C. Harnessing the power of defensive microbes: Evolutionary implications in nature and disease control. PLoS Pathog. 12, e1005465 (2016).PubMed 
PubMed Central 

Google Scholar 
Gibbons, S. M. Keystone taxa indispensable for microbiome recovery. Nat. Microbiol. 5, 1067–1068 (2020).CAS 
PubMed 

Google Scholar 
Wu-Chuang, A. et al. Thermostable keystone bacteria maintain the functional diversity of the Ixodes scapularis microbiome under heat stress. Microb. Ecol. https://doi.org/10.1007/s00248-021-01929-y (2021).Article 
PubMed 

Google Scholar 
Ford, S. A. & King, K. C. In vivo microbial coevolution favors host protection and plastic downregulation of immunity. Mol. Biol. Evol. 38, 1330–1338 (2021).CAS 
PubMed 

Google Scholar 
Banerjee, S. et al. Agricultural intensification reduces microbial network complexity and the abundance of keystone taxa in roots. ISME J. 13, 1722–1736 (2019).PubMed 
PubMed Central 

Google Scholar 
Gao, Q. et al. The microbial network property as a bio-indicator of antibiotic transmission in the environment. Sci. Total Environ. 758, 143712 (2021).ADS 
CAS 
PubMed 

Google Scholar 
de Vries, F. T. et al. Soil bacterial networks are less stable under drought than fungal networks. Nat. Commun. 9, 3033 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
de Morais, U. L. A look at the way we look at complex networks’ robustness and resilience. https://arxiv.org/ftp/arxiv/papers/1909/1909.06448.pdf (2017).Carlson, J. M. & Doyle, J. Complexity and robustness. Proc. Natl. Acad. Sci. USA 99, 2538–2545 (2002).ADS 
PubMed 
PubMed Central 

Google Scholar 
Estrada-Peña, A., Cabezas-Cruz, A. & Obregón, D. Resistance of tick gut microbiome to anti-tick vaccines, pathogen infection and antimicrobial peptides. Pathogens 9, 309 (2020).PubMed Central 

Google Scholar 
Neelakanta, G., Sultana, H., Fish, D., Anderson, J. F. & Fikrig, E. Anaplasma phagocytophilum induces Ixodes scapularis ticks to express an antifreeze glycoprotein gene that enhances their survival in the cold. J. Clin. Investig. 120, 3179–3190 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
Dey, A. K., Gel, Y. R. & Poor, H. V. What network motifs tell us about resilience and reliability of complex networks. Proc. Natl. Acad. Sci. USA 116, 19368–19373 (2019).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Nemergut, D. R. et al. Patterns and processes of microbial community assembly. Microbiol. Mol. 77, 342–356 (2013).
Google Scholar 
Coyte, K. Z., Rao, C., Rakoff-Nahoum, S. & Foster, K. R. Ecological rules for the assembly of microbiome communities. PLoS Biol. 19, e3001116 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Coyte, K. Z., Schluter, J. & Foster, K. R. The ecology of the microbiome: Networks, competition, and stability. Science 350, 663–666 (2015).ADS 
CAS 
PubMed 

Google Scholar 
McLoughlin, K., Schluter, J., Rakoff-Nahoum, S., Smith, A. L. & Foster, K. R. Host selection of microbiota via differential adhesion. Cell Host Microbe 19, 550–559 (2016).CAS 
PubMed 

Google Scholar 
Sheridan, K. J. et al. Ergothioneine biosynthesis and functionality in the opportunistic fungal pathogen, Aspergillus fumigatus. Sci. Rep. 6, 1–17 (2016).
Google Scholar 
Rothfork, J. M. et al. Inactivation of a bacterial virulence pheromone by phagocyte-derived oxidants: New role for the NADPH oxidase in host defense. Proc. Natl. Acad. Sci. USA 101, 13867–13872 (2004).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Gaupp, R., Ledala, N. & Somerville, G. A. Staphylococcal response to oxidative stress. Front. Cell. Infect. Microbiol. Microbiol. 2, 33 (2012).
Google Scholar 
Matchado, M. S. et al. Network analysis methods for studying microbial communities: A mini review. Comput. Struct. Biotechnol. J. 19, 2687–2698 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Jiang, D. et al. Microbiome multi-omics network analysis: Statistical considerations, limitations, and opportunities. Front. Genet. 10, 995 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Gao, C. et al. Co-occurrence networks reveal more complexity than community composition in resistance and resilience of microbial communities. Nat. Commun. 13, 3867 (2022).CAS 
PubMed 
PubMed Central 

Google Scholar 
Mammeri, M. et al. Cryptosporidium parvum infection depletes butyrate producer bacteria in goat kid microbiome. Front. Microbiol. 16, 548737 (2020).
Google Scholar 
Foo, J. L., Ling, H., Lee, Y. S. & Chang, M. W. Microbiome engineering: Current applications and its future. Biotechnol. J. 12, 1600099 (2017).Inda, M. E., Broset, E., Lu, T. K. & de la Fuente-Nunez, C. Emerging frontiers in microbiome engineering. Trends Immunol. 40, 952–973 (2019). More

Brand, L. E., Campbell, L. & Bresnan, E. Karenia: The biology and ecology of a toxic genus. Harmful Algae 14, 156–178 (2012).
Google Scholar 
Hetland, R. D. & Campbell, L. Convergent blooms of Karenia brevis along the Texas coast. Geophys. Res. Lett. 34, 1–5 (2007).
Google Scholar 
Liu, G., Janowitz, G. S. & Kamykowski, D. A biophysical model of population dynamics of the autotrophic dinoflagellate Gymnodinium breve. Mar. Ecol. Prog. Ser. 210, 101–124 (2001).ADS 
CAS 

Google Scholar 
Walsh, J. J. et al. Red tides in the Gulf of Mexico: Where, when, and why?. J. Geophys. Res. 111, C11003 (2006).ADS 

Google Scholar 
Bidle, K. D. The molecular ecophysiology of programmed cell death in marine phytoplankton. Ann. Rev. Mar. Sci. 7, 341–375 (2015).PubMed 

Google Scholar 
Bidle, K. D. & Bender, S. J. Iron starvation and culture age activate metacaspases and programmed cell death in the marine diatom Thalassiosira pseudonana. Eukaryot. Cell 7, 223–236 (2008).CAS 
PubMed 

Google Scholar 
Bidle, K. D., Haramaty, L., Barcelos, R. J. & Falkowski, P. Viral activation and recruitment of metacaspases in the unicellular coccolithophore, Emiliania huxleyi. Proc. Natl. Acad. Sci. 104, 6049–6054 (2007).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Vardi, A. et al. Programmed cell death of the dinoflagellate Peridinium gatunense is mediated by CO2 limitation and oxidative stress. Curr. Biol. 9, 1061–1064 (1999).CAS 
PubMed 

Google Scholar 
Zuppini, A., Andreoli, C. & Baldan, B. Heat stress: An inducer of programmed cell death in Chlorella saccharophila. Plant Cell Physiol. 48, 1000–1009 (2007).CAS 
PubMed 

Google Scholar 
Britt, A. B. DNA damage and repair in plants. Annu. Rev. Plant Physiol. Plant Mol. Biol. 47, 75–100 (1996).CAS 
PubMed 

Google Scholar 
Jimenez, C. et al. Different ways to die: Cell death modes of the unicellular chlorophyte Dunaliella viridis exposed to various environmental stresses are mediated by the caspase-like activity DEVDase. J. Exp. Bot. 60, 815–828 (2009).CAS 
PubMed 
PubMed Central 

Google Scholar 
Moharikar, S., D’Souza, J. S., Kulkarni, A. B. & Rao, B. J. Apoptotic-like cell death pathway is induced in unicellular chlorophyte chlamydomonas reinhardtii (Chlorophyceae) cells following UV irradiation: Detection and functional analyses. J. Phycol. 42, 423–433 (2006).CAS 

Google Scholar 
Li, Z., Wakao, S., Fischer, B. B. & Niyogi, K. K. Sensing and responding to excess light. Annu. Rev. Plant Biol. 60, 239–260 (2009).CAS 
PubMed 

Google Scholar 
Niyogi, K. K. Photoprotection revisited: Genetic and molecular approaches. Annu. Rev. Plant Physiol. Plant Mol. Biol. 50, 333–359 (1999).CAS 
PubMed 

Google Scholar 
Apel, K. & Hirt, H. Reactive oxygen species: Metabolism, Oxidative Stress, and Signal Transduction. Annu. Rev. Plant Biol. 55, 373–399 (2004).CAS 
PubMed 

Google Scholar 
Müller, P., Li, X. & Niyogi, K. K. Non-photochemical quenching. A response to excess light energy. Plant Physiol. 125, 1558–1566 (2001).PubMed 
PubMed Central 

Google Scholar 
Bidle, K. D. Programmed cell death in unicellular phytoplankton. Curr. Biol. 26, R594–R607 (2016).CAS 
PubMed 

Google Scholar 
McKay, L., Kamykowski, D., Milligan, E., Schaeffer, B. & Sinclair, G. Comparison of swimming speed and photophysiological responses to different external conditions among three Karenia brevis strains. Harmful Algae 5, 623–636 (2006).CAS 

Google Scholar 
Miller-Morey, J. S. & Van Dolah, F. M. Differential responses of stress proteins, antioxidant enzymes, and photosynthetic efficiency to physiological stresses in the Florida red tide dinoflagellate, Karenia brevis. Comp. Biochem. Physiol. Part C Toxicol. Pharmacol. 138, 493–505 (2004).
Google Scholar 
Tilney, C. L., Shankar, S., Hubbard, K. A. & Corcoran, A. A. Is Karenia brevis really a low-light-adapted species?. Harmful Algae 90, 101709 (2019).CAS 
PubMed 

Google Scholar 
Yuasa, K., Shikata, T., Kuwahara, Y. & Nishiyama, Y. Adverse effects of strong light and nitrogen deficiency on cell viability, photosynthesis, and motility of the red-tide dinoflagellate Karenia mikimotoi. Phycologia 57, 525–533 (2018).CAS 

Google Scholar 
Krause, G. H. & Jahns, P. Non-photochemical energy dissipation determined by chlorophyll fluorescence quenching: Characterization and function. In Chlorophyll a Fluorescence 463–495 (Springer, Netherlands, Cham, 2004).
Google Scholar 
Evens, T. J. Photophysiological responses of the toxic red-tide dinoflagellate Gymnodinium breve (Dinophyceae) under natural sunlight. J. Plankton Res. 23, 1177–1194 (2001).CAS 

Google Scholar 
Heil, C. A. et al. Influence of daylight surface aggregation behavior on nutrient cycling during a Karenia brevis (Davis) G. Hansen & Ø Moestrup bloom: Migration to the surface as a nutrient acquisition strategy. Harmful Algae 38, 86–94 (2014).CAS 

Google Scholar 
Errera, R. Response of the Toxic Dinoflagellate Karenia Brevis to Current and Projected Environmental Conditions. (Texas A&M University, PhD dissertation, 2013).Guillard, R. R. L. & Hargraves, P. E. Stichochrysis immobilis is a diatom, not a chrysophyte. Phycologia 32, 234–236 (1993).
Google Scholar 
Dingman, J. E. & Lawrence, J. E. Heat-stress-induced programmed cell death in Heterosigma akashiwo (Raphidophyceae). Harmful Algae 16, 108–116 (2012).
Google Scholar 
Lin, Q. et al. Differential cellular responses associated with oxidative stress and cell fate decision under nitrate and phosphate limitations in Thalassiosira pseudonana: Comparative proteomics. PLoS ONE 12(9), e0184849 (2017).PubMed 
PubMed Central 

Google Scholar 
Choi, C. J., Brosnahan, M. L., Sehein, T. R., Anderson, D. M. & Erdner, D. L. Insights into the loss factors of phytoplankton blooms: The role of cell mortality in the decline of two inshore Alexandrium blooms. Limnol. Oceanogr. 62, 1742–1753 (2017).ADS 
PubMed 
PubMed Central 

Google Scholar 
Johnson, J. G., Janech, M. G. & Van Dolah, F. M. Caspase-like activity during aging and cell death in the toxic dinoflagellate Karenia brevis. Harmful Algae 31, 41–53 (2014).CAS 
PubMed 

Google Scholar 
Jauzein, C. & Erdner, D. L. Stress-related responses in Alexandrium tamarense cells exposed to environmental Changes. J. Eukaryot. Microbiol. 60, 526–538 (2013).CAS 
PubMed 

Google Scholar 
Severin, T. & Erdner, D. L. The phytoplankton taxon-dependent oil response and its microbiome: Correlation but not causation. Front. Microbiol. 10, 1–14 (2019).
Google Scholar 
Ralph, P. J. & Gademann, R. Rapid light curves: A powerful tool to assess photosynthetic activity. Aquat. Bot. 82, 222–237 (2005).CAS 

Google Scholar 
Suzuki, N. & Mittler, R. Reactive oxygen species and temperature stresses: A delicate balance between signaling and destruction. Physiol. Plant. 126, 45–51 (2006).CAS 

Google Scholar 
Krause, G. H. & Weis, E. Chlorophyll fluorescence and photosynthesis: The basics. Annu. Rev. Plant Physiol. Plant Mol. Biol. 42, 313–349 (1991).CAS 

Google Scholar 
Gechev, T. S. & Hille, J. Hydrogen peroxide as a signal controlling plant programmed cell death. J. Cell Biol. 168, 17–20 (2005).CAS 
PubMed 
PubMed Central 

Google Scholar 
Miller, G., Suzuki, N., Ciftci-Yilmaz, S. & Mittler, R. Reactive oxygen species homeostasis and signalling during drought and salinity stresses. Plant. Cell Environ. 33, 453–467 (2010).CAS 
PubMed 

Google Scholar 
Purvis, A. C. Role of the alternative oxidase in limiting superoxide production by plant mitochondria. Physiol. Plant. 100, 165–170 (1997).CAS 

Google Scholar 
Demmig-Adams, B. & Adams Iii, W. W. Photoprotection and other responses of plants to high light stress. Annu. Rev. Plant Biol. 43, 599–626 (1992).CAS 

Google Scholar 
Cui, Y., Zhang, H. & Lin, S. Enhancement of non-photochemical quenching as an adaptive strategy under phosphorus deprivation in the Dinoflagellate Karlodinium veneficum. Front. Microbiol. 8, 1–14 (2017).
Google Scholar 
Cassell, R. T., Chen, W., Thomas, S., Liu, L. & Rein, K. S. Brevetoxin, the dinoflagellate neurotoxin, localizes to thylakoid membranes and interacts with the light-harvesting complex II (LHCII) of photosystem II. ChemBioChem 16, 1060–1067 (2015).CAS 
PubMed 

Google Scholar 
Milne, A., Davey, M. S., Worsfold, P. J., Achterberg, E. P. & Taylor, A. R. Real-time detection of reactive oxygen species generation by marine phytoplankton using flow injection-chemiluminescence. Limnol. Oceanogr. Methods 7, 706–715 (2009).CAS 

Google Scholar 
Berman-Frank, I. et al. Segregation of nitrogen fixation and oxygenic photosynthesis in the marine cyanobacterium trichodesmium. Science (80-) 294, 1534–1537 (2001).ADS 
CAS 

Google Scholar 
Triantaphylidès, C. et al. Singlet oxygen is the major reactive oxygen species involved in photooxidative damage to plants. Plant Physiol. 148, 960–968 (2008).PubMed 
PubMed Central 

Google Scholar 
Gao, Y. & Erdner, D. L. Dynamics of cell death across growth stages and the diel cycle in the dinoflagellate Karenia brevis. J. Eukaryot. Microbiol. https://doi.org/10.1111/jeu.12874 (2021).Article 
PubMed 

Google Scholar 
Xu, K., Jiang, H., Juneau, P. & Qiu, B. Comparative studies on the photosynthetic responses of three freshwater phytoplankton species to temperature and light regimes. J. Appl. Phycol. 24, 1113–1122 (2012).CAS 

Google Scholar 
Yamori, W., Makino, A. & Shikanai, T. A physiological role of cyclic electron transport around photosystem I in sustaining photosynthesis under fluctuating light in rice. Sci. Rep. 6, 20147 (2016).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Berman-Frank, I., Bidle, K. D., Haramaty, L. & Falkowski, P. G. The demise of the marine cyanobacterium, Trichodesmium spp., via an autocatalyzed cell death pathway. Limnol. Oceanogr. 49, 997–1005 (2004).ADS 

Google Scholar  More

Uchida, H. & Baba, O. Fishery management and the pooling arrangement in the Sakura ebi fishery in Japan, 175–189. https://www.fao.org/3/a1497e/a1497e16.pdf (2008).Omori, M. The biology of a sergestid shrimp Sergestes lucens Hansen. Bull. Ocean Res. Inst. Univ. Tokyo 4, 1–83 (1969).
Google Scholar 
Gurney, R. & Lebour, M. V. Larvae of decapod crustacea. Part VI. The genus Sergestes. Discov. Rep. 20, 1–68 (1940).
Google Scholar 
Holthuis, L. B. FAO species catalogue. Vol. 1. Shrimps and prawns of the world. An annotated catalogue of species of interest to fisheries. FAO Fish. Synop. Vol. 125, 1–271 (1980).Omori, M., Ukishima, Y. & Muranaka, F. New record of occurrence of Sergia lucens (Hansen) (Crustacea, Sergestidae) off Tung-kang, Taiwan, with special reference to phylogeny and distribution of the species. J. Oceanogr. Soc. Jpn. 44, 261–267 (1988) (in Japanese with English abstract).Article 

Google Scholar 
Isshiki, T. & Tajima, Y. The research of a sergestid shrimp, Sergia lucens (Hansen) in the mouth of Tokyo Bay I. The seasonal distribution of adult and the distribution of eggs. Bull. Kanagawa Pref. Fish. Exp. Stn. 13, 73–78 (1992) (in Japanese with English abstract).
Google Scholar 
Lee, D. A., Wu, S. H., Liao, I. C. & Yu, H. P. On three species of commercially important sergestid shrimps (Decapoda: Sergestidae) in the coastal waters of Taiwan. J. Taiwan Fish. Res. Inst. 4, 1–19 (1996) (in Chinese with English abstract).CAS 

Google Scholar 
Yinji, L. & Ratana, C. Governing in an uncertain time: The case of Sakura shrimp fishery, Japan. Marit. Stud. 20, 115–126 (2021).Article 

Google Scholar 
Isono, R. S., Kita, J. & Setoguma, T. Acute effects of kaolinite suspension on eggs and larvae of some marine teleosts. Comp. Biochem. Physiol. Part C 120, 449–455 (1998).CAS 
Article 

Google Scholar 
Aoki, S. & Oinuma, K. Distribution of clay minerals in surface sediments of Suruga Bay, central Japan. J. Geol. Soc. Jpn. 87(7), 429–438 (1981) (in Japanese with English abstract).Article 

Google Scholar 
Nasnodkar, M. R. & Ganapati, N. N. Clay mineralogy and chemistry of mudflat core sediments from Sharavathi and Gurupur estuaries: Source and processes. Indian J. Geo-Mar. Sci. 48(3), 379–388 (2019).
Google Scholar 
Capper, N. The effects of suspended sediment on the aquatic organisms Daphnia magna and Pimephales promelas. All Theses. 2. https://tigerprints.clemson.edu/all_theses/2 (2006).Boyd, M. B. et al. Disposal of dredge spoil, problem identification and assessment and research program development. Technical report H-72–8, U.S. army engineer waterways experiment station, CE, Vicksburg, Miss. (1972).McFarland, V. A. & Peddicord, R. K. Lethality of a suspended clay to a diverse selection of marine and estuarine macrofauna. Arch. Environ. Contam. Toxicol. 9, 733–741 (1980).CAS 
Article 

Google Scholar 
Arakawa, H. et al. The influence of suspended particles on larval development in the Manila clam Ruditapes philippinarum. Sci. Postp. 1, e00028. https://doi.org/10.14340/spp.2014.08A0002 (2014).Article 

Google Scholar 
Davis, H. C. Effects of turbidity-producing materials in sea water on eggs and larvae of the clam (Venus (Mercenaria) mercenaria). Biol. Bull. 118, 48–54 (1960).Article 

Google Scholar 
Tabata, A., Morinaga, T. & Arakawa, H. Influences of concentration, particle-size and kind of inorganic suspended matter on feed caught by Manila clam, Ruditapes philippinarum. La Mer 37, 163–171 (2000).CAS 

Google Scholar 
Annisa, Dwiatmoko, M. U., Saismana, U. & Maulanai, R. Characteristics of kaolin clay on Alluvial formation subdistrict mataraman based on physical properties and chemical properties. In MATEC Web of Conferences Vol. 280, 03009. https://doi.org/10.1051/matecconf/201928003009 (2019).Murray, H. H. Structure and composition of clay minerals and their physical and chemical properties. Dev. Clay Sci. 2, 7–31. https://doi.org/10.1016/S1572-4352(06)02002-2 (2006).Article 

Google Scholar 
Kumari, N. & Mohan, C. Basics of clay minerals and their characteristic properties. Clay Clay Miner. 1–29 (2021).Lively, J. S., Kaufman, Z. & Carpenter, E. J. Phytoplankton ecology of a barrier island estuary: Great South Bay, New York. Estuar. Coast. Shelf Sci. 16(1), 51–68 (1983).ADS 
Article 

Google Scholar 
Lloyd, D. S. Turbidity as a water quality standard for salmonid habitats in Alaska. N. Am. J. Fish. Manag. 7, 34–45 (1987).Article 

Google Scholar 
Kirk, K. L. Effects of suspended clay on Daphnia body growth and fitness. Freshw. Biol. 28, 103–109 (1992).Article 

Google Scholar 
McCabe, G. D. & O’Brien, W. J. The effects of suspended silt on feeding and reproduction of Daphnia pulex. Am. Midl. Nat. 110, 324–337 (1983).Article 

Google Scholar 
Kirk, K. L. & Gilbert, J. J. Suspended clay and the population dynamics of planktonic Rotifers and Cladocerans. Ecology 71, 1741–1755 (1990).Article 

Google Scholar 
Loosanoff, V. L. Effects of turbidity on some larval and adult bivalves. Proc. Gulf. Carib. Fish. Inst. 14, 80–95 (1961).
Google Scholar 
Arruda, J. A., Marzolf, G. R. & Faulk, R. T. The role of suspended sediments in the nutrition of zooplankton in turbid reservoirs. Ecology 64, 1225–1235 (1983).Article 

Google Scholar 
Kathyayani, S. A., Muralidhar, M., Kumar, T. S. & Alavandi, S. V. Stress quantification in Penaeus vannamei exposed to varying levels of turbidity. J. Coast. Res. 86, 177–183 (2019).CAS 
Article 

Google Scholar 
Wilber, D. H. & Clarke, D. G. Biological effects of suspended sediments: A review of suspended sediment impacts on fish and shellfish with relation to dredging activities in estuaries. N. Am. J. Fish. Manag. 21, 855–875 (2001).Article 

Google Scholar 
Lin, H., Charmantier, G., Thuet, P. & Trilles, J. Effects of turbidity on survival, osmoregulation, and gill Na+-K+ ATPase in juvenile shrimp Penaeus japonicus. Mar. Ecol. Prog. Ser. 90, 31–37 (1992).ADS 
CAS 
Article 

Google Scholar 
Davis, H. C. & Hidu, H. Effects of turbidity-producing substances in sea water on eggs and larvae of three genera of bivalve mollusks. Veliger 11, 316–323 (1969).
Google Scholar 
Nimmo, D. R., Hamaker, T. L., Matthews, E. & Young, W. T. The long-term effects of suspended particulates on survival and reproduction of the mysid shrimp, Mysidopsis bahia, in the laboratory. In Proceedings of a Symposium on the Ecological Effects of Environmental Stress, New York, 413–422 (1979).Peddicord, R. & McFarland, V. Effects of suspended dredged material on the commercial crab, Cancer magister. In Proceedings of the Specialty Conference on Dredging and Its Environmental Effects, Mobile, Alabama, 633–644 (1976).Peddicord, R. K. Direct Effects of Suspended Sediments on Aquatic Organisms. Contaminants and Sediments. Volume 1. Fate and Transport, Case Studies, Modeling, Toxicity 501–536 (Ann Arbor Science Publishers, 1980).
Google Scholar 
Wakeman, T., Peddicord, R. & Sustar, J. Effects of suspended solids associated with dredging operations on estuarine organisms. In Ocean 75 conference, 431–436 (1975).Gebauer, P., Walter, I. & Anger, K. Effects of substratum and conspecific adults on the metamorphosis of Chasmagnathus granulata (Dana) (Decapoda: Grapsidae) megalopae. J. Exp. Mar. Biol. Ecol. 223, 185–198 (1998).Article 

Google Scholar 
Carvalho, L. & Calado, R. Trade-offs between timing of metamorphosis and grow out performance of a marine caridean shrimp juveniles and its relevance for aquaculture. Aquaculture 492, 97–102 (2018).Article 

Google Scholar 
Calado, R. et al. The physiological consequences of delaying metamorphosis in the marine ornamental shrimp Lysmata seticaudata and its implications for aquaculture. Aquaculture 546, 737391. https://doi.org/10.1016/j.aquaculture.2021.737391 (2022).Article 

Google Scholar 
Murphy, R. C. Factors affecting the distribution of the introduced bivalve, Mercenaria mercenaria, in a California lagoon—The importance of bioturbation. J. Mar. Res. 43, 673–692 (1985).Article 

Google Scholar 
Bricelj, V. M. & Malouf, R. E. Influence of algal and suspended sediment concentration on the feeding physiology of the hard clam Mercenaria mercenaria. Mar. Biol. 84, 155–165 (1984).Article 

Google Scholar 
Wenger, A. S., Jacob, J. L. & Jones, G. P. Increasing suspended sediment reduces foraging, growth, and condition of a planktivorous damselfish. J. Exp. Mar. Biol. Ecol. 428, 43–48 (2012).Article 

Google Scholar 
Robinson, W. E., Wehling, W. E. & Morse, M. P. The effect of suspended clay on feeding and digestive efficiency of the surf clam Spisula solidissima (Dillwyn). J. Exp. Mar. Biol. Ecol. 74, 1–12 (1984).CAS 
Article 

Google Scholar 
Turner, E. J. & Miller, D. C. Behavior and growth of Mercenaria mercenaria during simulated storm events. Mar. Biol. 111, 55–64 (1991).Article 

Google Scholar 
Grant, J. & Thorpe, B. Effects of suspended sediment on growth, respiration, and excretion of the soft-shelled clam (Mya arenaria). Can. J. Fish. Aquat. Sci. 48, 1285–1292 (1991).Article 

Google Scholar 
Gleason, R. A., Euliss, N. H., Hubbard, D. E. & Duffy, W. G. Effects of sediment load on emergence of aquatic invertebrates and plants from wetland soil egg and seed banks. Wetlands 23, 26–34 (2003).Article 

Google Scholar 
Jacek, R., Anna, S. & Miroslaw, S. The effect of lake sediment on the hatching success of Daphnia ephippial eggs. J. Limnol. 75, 597–605 (2016).
Google Scholar 
Newcombe, C. P. & McDonald, D. D. Effects of suspended sediment on aquatic ecosystems. N. Am. J. Fish. Manag. 11, 77–82 (1991).Article 

Google Scholar 
Chutter, F. M. The effects of silt and sand on the invertebrate fauna of streams and rivers. Hydrobiologia 34, 57–76 (1968).Article 

Google Scholar 
Hellawell, J. M. Biological indicators of freshwater pollution and environmental management. In Pollution Monitoring Series (ed. Melanby, K.) https://doi.org/10.1007/978-94-009-4315-5 (1986).Makita, M. & Kondo, M. Rearing of the larvae of Seigia Lucens (Hansen). Bull. Shizuoka Pref. Fish. Exp. Stn. 16, 97–105 (1982) (in Japanese).
Google Scholar  More

Influence of independent variables on the extent of stereotyped behaviourThe overall level of stereotypy we observed was low, suggesting that the pandas in our study were not seriously stressed. The variables that we found to be correlated with stereotypy are consistent with what we know of pandas’ natural history. Our study reports that variables like logs on the ground, nest, sociality, zoo, tree density, age and tree height used by pandas are the driving force for stereotypy in captive pandas involved in the study.Making the captive environment more naturalistic by integrating enrichment into the enclosure seems to be a promising way of alleviating stress and improving both welfare and reintroduction success41. It also helps to improve reproductive rate and overall health39. Improved health reduces stress and gives greater control over the environment increasing the chances of survival and longevity both in captivity and following release into the wild5. It is generally accepted that enrichment of the captive environment increases animals’ ability to cope with challenges and positive use of the environment reduces or eliminates aberrant behaviour23. Lack of enclosure enrichments and less complex enclosures can cause stereotypy and other atypical behaviours24, while providing enrichment increases the frequency of natural behaviours25 and thereby reduces stress, which in turn decreases stereotypy27. But enrichment needs to be appropriate for the species of animal concerned. Abnormal behaviours are often associated with captive conditions that deviate greatly from the species’ natural environment. Consistent with this argument we found that though dead and fallen logs on the ground are one of the important characteristics of the panda habitats in the wild42,43,44,45, merely providing them in captivity does not ensure the species’ welfare: in fact, stereotypy increased with log density in our study subjects. This could be due to the fact that four individuals that showed more stereotypy were housed in the small barren enclosures with no trees but more logs as a part of enrichment. Without those four individuals, the linear relation between stereotypy and log density was not statistically significant. This clearly suggested that merely providing logs in the small enclosures does not maintain welfare.
When animals are housed in enclosures designed to resemble their natural habitat by considering their natural history (provision of vegetation, shelter, pool, etc.), there is a reduction or elimination of abnormal patterns of behaviour such as stereotypies, increased fitness and improved health, all of which may influence reproduction25,46,47,48. For many species, nests, shelter or burrows in enclosures will serve as retreat and hiding places, which are essential to cope with environmental stressors10. Gerbils, mice and rabbits have all shown less stereotyped behaviour when retreats are provided9,49,50,51. Such retreats can mitigate the effects of zoo visitors, who can serve as a source of stress for species that rarely interact with humans in the wild. Consistent with these previous results, we found that with provision of nests, the extent of stereotypy decreased in captive pandas. Many species prefer nests both for rearing the young as well as for resting and shelter, and pandas follow this pattern, so providing nests in adequate numbers will supports their natural behaviour as well as provide relief from environmental stressors. Zidar recommends providing one more nest than there are individuals in an enclosure52.Although pandas are an asocial species, our study showed that pandas show more stereotyped behaviour when housed alone than when with another individual or in group. Being a solitary species in the wild might encourage management to house them singly in captivity, but not every activity and habit of species in the wild can be used in captivity. For example, polar bears are also a solitary species, and it was at one time thought best to manage them alone, but it was found that managing them in a social setting reduces stereotypic pacing behaviour53, consistent with this study. Importantly, managers of zoo should note that living in group is greatly influenced by the individuals’ compatibility and hence this should be kept in mind while pairing.Similarly, we found that the presence of trees, and greater mean tree height use by pandas, reduced stereotypy. Pandas’ preferred high elevation habitat is favourable for taller trees20, and Shrestha et al. found that canopy cover was an important factor in habitats for pandas in the wild54. In European zoos, pandas spend 90% of their time off the ground37. Consistent with these previous findings, our study reveals that more and taller trees support natural behaviours in panda. The Central Zoo Authority (CZA) of India enrichment manual recommends taller tree provision in panda enclosures, and again we provide empirical support for its recommendation.We found that with increasing age stereotypy increased in pandas. The older the individuals the more time spent in captivity with its associated risks of stereotypic behaviour. The same trend has been observed in other species: for example in captive bears stereotypic behaviour increased with age55. In another study Asiatic black bear and sun bear showed more stereotypy with age56.Influence of independent variables on behavioural diversityAs noted in the “Introduction” section, in a species like the panda, high daytime behavioural diversity is not necessarily a positive indication of good welfare. However, our comparison of behavioural diversity with stereotypy showed a negative trend (though not significant), suggesting that low behavioural diversity might be associated with poorer welfare.Nonetheless, we found some results that suggested that lower diversity might in fact be associated with a more natural lifestyle. Because of the amount of time that wild pandas spend foraging57 and sleeping or inactive, they cannot show much behavioural diversity, and in our sample of captive individuals, they showed the same trend. For example, behavioural diversity was lower when pandas were provided with more trees in the enclosure. This suggests that when appropriate conditions are maintained in captivity, panda prefer to be inactive during the day, as is consistent with their natural history57. As pandas are essentially arboreal mammals, naturally they also spend most of the time inactive (e.g. sleeping) on the trees57. Indeed, providing larger trees would promote inactive behaviours and hence lower behaviour diversity in captivity, this captures their natural behaviour. This is consistent with our results where increased tree height used by pandas decreased behavioural diversity.We found behavioural diversity was greater when there are more logs in the enclosure. In the Yele Reserve in Sichuan, China, Wei et al. found 107 of 185 panda dropping sites (57.8%) on shrub branches, 49 (26.5%) on fallen logs, and only 29 (15.7%) on the forest floor44. Droppings were found mostly on elevated structures ranging from 1 to 3 m above the forest floor and occasionally on trees over 12 m. Moreover, microhabitats selected by pandas were also characterized by fallen logs and tree stumps42,45. Wei and Zhang mention that to access bamboo leaves easily, pandas usually use some elevated objects, such as shrub branches, fallen logs, or tree stumps to lift their body43. Hence, providing tree logs in the vicinity supports their natural behaviour. But at the same time management should keep in mind that merely providing logs in the enclosure would not guarantee species welfare, as discussed in previous section with respect to stereotypy.Temperature is an important element of microclimate for animals, and influences the activity level of captive animals10. When temperature rises, many species show distress in captivity10. The red panda inhabits low-temperature areas20, so it is unlikely that higher temperatures would support natural behaviours. We found that with increased temperature behavioural diversity decreased in captive pandas. Similarly, we found that pandas showed higher behavioural diversity in the winter season, where temperatures are low as compared to summer season.Studies that have tried to relate behavioural diversity and stereotypy in captive animals have varied in their interpretation; many have found significantly inverse relationships between the two19. In this study our multivariate model suggested that behavioural diversity is negatively influenced by stereotypy in captive pandas, confirming previous research.Other factors associated with variations in behavioural diversity are less easy to identify with welfare, positive or negative. Behavioural diversity also decreases with age of pandas and increases with distance to cage mate, number of visitors and quantum of bamboo provided, though these effects were not significant in the REVS model.Taken together, these results suggest that higher behavioural diversity is not a straightforward indicator of better welfare in all captive animals. The overall non-significant relationship between stereotyped behaviour and diversity we observed could well be the result of a mixture of factors operating in opposite directions. To interpret diversity correctly, it would be helpful to know what level of diversity the species shows in the wild, and such data are rarely available—a limitation of our study as of many others. Although there are dissenting voices58, arguably what matters most both in terms of welfare and in terms of potential reintroduction to the wild, is that a captive animal’s time budget approximates as closely as possible that of a wild animal. It is not diversity as such that is important, but the behaviours that the animal exhibits.Differences between zoosOur study showed that both the extent of stereotyped behaviour and behavioural diversity varied significantly among zoos. However, Zoo 2, an important breeding centre, housed only a female and her two cubs; this may lead to many factors being confounded and is thus a limitation to our study. Captive animals rely on the zoo environment, its routine and husbandry practices to limit their stress levels, and any failure to provide suitable resources will certainly disturb them and lead to distress10. Controlling such variables appropriately will help reduce stress among captive animals, and we can rely to some extent on our knowledge of the species’ natural history to guide us through this challenge. Our study was able to identify some of the factors that are associated with better welfare, but even with these factors taken into account, significant differences among the three zoos remained. These are presumably due to subtler variations in the zoos’ environment or management regimes. Since the panda is endemic to high elevations, we considered whether differences between the elevations of the zoos might be relevant, but the biggest differences were between Zoos 1 and 3, which are at essentially the same elevation.In Zoo 1 pandas showed lower stereotypy and higher behavioural diversity then the other two zoos. Again, these differences may be due to subtle differences between the management regimes in the three zoos; possibilities include keepers’ attitudes and the zoo’s experience in managing pandas. It is notable that Zoo 1 has longer and wider experience in the management of red pandas than the other two zoos, which have joined the captive breeding programme more recently and have fewer animals. Other notable differences were that in Zoo 1, pandas are fed twice a day as compared to the other two zoos where feed is given all at one time (both bamboo and supplementary diet); and that in Zoo 1 the enclosures were of a good size for a small mammal like the red panda, and were well maintained with much natural vegetation. The other two zoos had a large enclosure with poor vegetation (trees and grass), or a small enclosure with a barren floor and no trees at all. Location of the enclosure also needs to be considered: in two of the enclosures at Zoo 3 the sun shone directly on the animals with no shade as such, keeping the temperature higher than would be natural for pandas. Any of these factors could be the reason the pandas performed comparatively well in Zoo 1, and it would be necessary to study a wider (and, therefore, cross-national) sample of zoos holding pandas to identify which of them are the most important. More

This study presents a comprehensive quantification of carbon sequestration as well as CO2/CH4/N2O emissions reductions from terrestrial ecosystems based on multiple sources of data from literature, inventories, public databases and documents. The pathways considered ecosystem restoration and protection from being converted into cropland or built-up areas, reforestation, management with improved nitrogen use in cropland, restricted deforestation, grassland recovery, reducing risk from forest wildfire and others. Here we describe the cross-cutting methods that apply across all 16 NCS pathways. The definitions, detailed methods and data sources for evaluating individual pathways can be found in the Supplementary Information.Cross-cutting methodsBaseline settingWe set 2000 as the base year because the large-scale national ecological projects, such as the Grain for Green Project, were started since then. We first evaluate the historical mitigation capacity during 2000–2020, which is the first 20 years of implementing the projects. From this procedure we can determine how much mitigation capacity has been realized through the previous projects in the past two decades and to what extent additional actions can be made after 2020. Relative to the baseline 2000–2020, we then evaluate the maximum potentials of the NCS mitigation in the future 10 (2020–2030) and 40 (2020–2060) years, corresponding to the timetable of China’s NDCs: carbon peak before 2030 and carbon neutrality by 2060.The settings of baseline in this study are different from the existing assessments (2000s–2010s as a baseline and 2010–2025/2030/2050 as scenarios)1,22,23,27,28. Baseline sets the temporal and spatial reference for NCS pathway scenarios, which may have a great impact on the NCS estimates. Notably, NCS actions during 2000–2020 will have a great impact in the future periods, which we refer to as the ‘legacy effect’. The legacy effect itself, mainly reforestation, is independent of being assessed, but it is conceptually attributed to natural flux and excluded from future NCS potential estimates.Maximum potentialThe MAMP refers to the additional CO2 sequestration or avoided GHG emissions measured in CO2 equivalents (CO2e) at given flux rates in a period on the maximum extent to which the stewardship options are applied (numbers are expressed as TgCO2e yr−1 for individual pathways and PgCO2e yr−1 for national total) (Extended Data Fig. 1 and Supplementary Table 2). ‘Additional’ means mitigation outcomes due to human actions taken beyond business-as-usual land-use activities (since 2020) and excluding existing land fluxes not attributed to direct human activities1. The MAMP of CH4 and N2O are accounted by three cropland and wetland pathways (cropland nutrient management, improved rice cultivation and peatland restoration). We adopt 100 yr global warming potential to calculate the warming equivalent for CH4 (25) and N2O (298), respectively38,39 because these values are used in national GHG inventories, although some researchers have argued that using the fixed 100 yr global warming potential to calculate the warming equivalents may be problematic because they cannot differentiate the contrasting impacts of the long- and short-lived climate pollutants39. Because the flux rate of the GHG by ecosystems may vary with the time of recovery or growth, the MAMP may also change for different periods even given the same extent.The ‘maximum’ is constrained by varied factors across the NCS pathways. We constrain forest and grassland restoration by the rate of implementation, farmland red line and tree surviving rate (Extended Data Fig. 2). Surviving rate here is the ratio of the area with increased vegetation cover due to reforestation to the total reforestation area. The farmland red line refers to ‘the minimum area of cultivated land’ given by the Ministry of Land and Resources of China. It defines the lowest limit, and the current red line is ~120 Mha. It is a rigid constraint below which the total amount of cultivated land cannot be reduced. From this total amount, there is provincial farmland red line. This red line sets a constraint on the implementation of the NCS pathways associated with land-use change. We set the future scenario of farmland area that can be used for grassland or forest restoration on the basis of the provincial farmland red line. Basic farmland is closely related to national food security. By 2050, China’s population is predicted to decrease slightly, but with economic development, the per capita demand for food may increase40. We assume that the food production in the future can meet the food demand via increasing agricultural investment and technological advancement. The N fertilizer reduction scenario is set to be below the level 60%, under which crop yield is not significantly affected19, because N fertilizer is surplus in many Chinese croplands. For timber production, we assume that the demand for timber can be met if the production level is maintained at the level of 2010–2020 (83.31 million m3 yr−1). As deforestation of natural forests is 100% forbidden since 2020, the future timber will come mainly from tree plantations. For grazing optimization, we assume that livestock production is not affected by grassland fencing due to refined livestock management such as improving feed nutrient and fine-seed breeding41.The areas of historical NCS implementation during 2000–2020 were estimated using statistical data, published literature and public documents, with a supplement from remote-sensing data. The flux rates were obtained either by directly using the values from multiple literature sources or from estimates using the empirical formulae. For the estimates of future NCS potential, the flux rate and extent of the pathway were determined on the basis of the baseline (2000–2020). The extent is assumed to be achieved by using the same rate but limited by the multiple constraints stated in the preceding unless the implementation scopes have been reported in national planning documents. We estimate the legacy effect by multiplying the implementation area in the past by the flux rates in the future two periods.SaturationThe future mitigation potential that we estimate for 2030 and 2060 will not persist indefinitely because the finite potential for natural ecosystems to store additional carbon will saturate. For each NCS pathway, we estimate the expected duration of the potential for sequestration at the maximum rate (Supplementary Table 3). Forests can continue to sequester carbon for 70–100 years or more. Restored grasslands and fenced grasslands can continue to sequester carbon for >50 years. Forest-fire management and cover crops can continue to sequester carbon for 40–50 years or more. Sea grasses and peatlands can continue to sequester carbon for millennia. Avoided pathways do not saturate as long as the business-as-usual cases indicate that there are potential areas for avoided losses of ecosystems. In this case, sea grass and salt marsh would disappear entirely after 64 years, but it would be 100–300 years or more for forest, grassland and peatland.Estimation of uncertaintiesThe extent (area or biomass amount) and flux (sequestration or reduced emission per area or biomass amount in unit time) are considered to estimate uncertainty of the historical mitigation capacity or future potential for each NCS pathway. We use the IPCC approaches to combine uncertainty42. Where mean and standard deviation can be estimated from collected literature, 95% CIs are presented on the basis of multiple published estimates. Where a sample of estimates is not available but only a range of a factor, we report uncertainty as a range and use Monte Carlo simulations (with normal distribution and 100,000 iterations) to combine the uncertainties of extent and flux (IPCC Approach 2). The overall uncertainties of the 16 NCS pathways were combined using IPCC Approach 142. If the extent estimate is based on a policy determination, rather than an empirical estimate of biophysical potential, we do not consider it a source of uncertainty.MACsThe economic/cost constraints refer to the amount of NCS that can be achieved at a given social cost. The MAC curve is fitted according to the total publicly funded investment and total mitigation capacity or potential during a period. The MAC curves are drawn to estimate the historical mitigation or MAMP at the cost thresholds of US$10, US$50 and US$100 (MgCO2e)−1, respectively. The trading price in China’s current carbon market is ~US$10 USD (as the minimum cost43), and the cost-effective price point44,45 to achieve the Paris Agreement goal of limiting global warming to below 2 °C above pre-industrial levels is US$100 (as the maximum cost). A carbon price of US$50 is regarded as a medium value1,46. For the pathways of reforestation, avoided grassland conversion, grazing optimization and grassland restoration, we collected the statistical data of investments in China from 2000 to 2020 and estimated the affordable MAMP below the three mitigation costs. Due to data limitations, the points used for fitting the MAC curve are values for cost (invested funds) and benefit (mitigation capacity) in each of the provinces. We rank the ratio of benefit to cost in a descending order to obtain the maximum marginal benefit for MAC by assuming that NCS measures are first implemented in the region with the highest cost/benefit rate. We refer to the investment standard before 2020 as the benchmark and estimate the cost of each pathway for the future periods with discount rates of 3% and 5%, respectively. The social discount rate 4–6% is usually used as a benchmark discount value in carbon price studies in China compared with lower scenarios (for example, 3.6%)46,47. In a global study for estimating country-level social cost of carbon, 3% and 5% are used for scenario analysis48. Note that the mean value from the two discount rates was used in presenting the results. For the other pathways where investment data cannot be obtained, we refer to relevant references to estimate MAC. All the cost estimates are expressed in 2015 dollars, transformed on the basis of the Renminbi and US dollar exchange rate of the same year. The year 2015 represents a relatively stable condition of economic increase over the past decade (2011–2020) in China (the increase rate of gross domestic product (GDP) in 2015 is similar to the 10 yr mean). In the cases when the MAC curves exceed the estimated maximum potentials in the period, we identify the historical capacity or the MAMP as limited by the biophysical estimates.Additional mitigation required to meet Paris Agreement NDCsOn 28 October 2021, China officially submitted ‘China’s Achievements, New Goals and New Measures for Nationally Determined Contributions’ (‘New Measures 2021’ hereafter) and ‘China’s Mid-Century Long-Term Low Greenhouse Gas Emission Development Strategy’ to the Secretariat of the United Nations Framework Convention on Climate Change as an enhanced strategy to China’s updated NDCs (first submission in 2015). The goal of China’s updated NDCs is to strive to peak CO2 emissions before 2030 and achieve carbon neutralization by 2060. It specified the goals to include the following: before 2030, China’s carbon dioxide emissions per unit of GDP are expected be more than 65% lower than that in 2005, and the forest stock volume is expected to be increased by around 6.0 (previously 4.5) billion m3 over the 2005 level. In the ‘New Measures 2021’9 and ‘Master Plan of Major Projects of National Important Ecosystem Protection and Restoration (2021–2035)’5, many NCS-related opportunities are proposed to consolidate the carbon sequestration of ecosystems and increase the future NCS potential, including protecting the existing ecosystems, implementing engineering to precisely improve forest quality, continuously increasing forest area and stock volume, strengthening grassland protection and recovery and wetland protection and improving the quality of cultivated land and the agricultural carbon sinks.Industrial CO2 emissionsThe historical CO2 emissions data from 2000 to 201749,50 are used as the benchmark of industrial CO2 emissions during 2000–2020. For future projections, we use the peak value of the A1B2C2 scenario (in the range of 10,000 to 12,000 Mt) in 2030 from ref. 11. We assume that CO2 emission increases linearly from 2017 to 2030.Characterizing co-benefitsNCS activities proposed in the future measures or plans may enhance co-benefits. Four generalized types of ecosystem services are identified: improving biodiversity, water-related, soil-related and air-related ecosystem services (Fig. 1). Biodiversity benefits refer to the increase in different levels of diversity (alpha, beta and/or gamma diversity)51. Water, soil and air benefits refer to flood regulation and water purification, improved fertility and erosion prevention, and improvements in air quality, respectively, as defined in the Millennium Ecosystem Assessment52. The evidence that each pathway produces co-benefits from one or more peer-reviewed publications was collected through reviewing the literature (see the details for co-benefits of each pathway in Supplementary Information).Mapping province-level mitigationThe data for extent of implementing forest pathways are obtained from the statistical yearbook and reported at the province level. To be consistent with the forest pathways, the other pathways were also aggregated to the provincial-level estimate from the spatial data. If the flux data were available in different climate regions, the provinces are first assigned to climate regions. When a province spans multiple climate zones, the weight value is set according to the proportion of area, and finally an estimated value of rate was calculated (for fire management, some grassland and wetland pathways). For the forest pathways, we first collected the flux-rate data from reviewing literature and then averaged these flux rates to region/province. The flux rates for reforestation and natural forest management were calculated separately by province and age group. Similarly, specified flux rates are applied for different times after ecosystem restoration or conversion for other pathways.Classification of NCS typesThree types of NCS pathways were classified: protection (of intact natural ecosystems), improved management (on managed lands) and restoration (of native cover)35. In our study, four (AVFC, AVGC, AVCI, AVPI), eight (IMP, NFM, FM, BIOC, CVCR, CRNM, IMRC, GROP) and four (RF, GRR, CWR, PTR) NCS pathways were identified as protection, management and restoration types, respectively (Supplementary Table 1). These pathways can be further divided into groups of ‘single’ type or ‘mixed’ type according to their contribution to individual pathways. Specifically, in a certain area, when the mitigation capacity of a certain pathway accounts for more than 50% of the total, it is regarded as a single or dominant NCS type; if no single pathway accounts for more than 50%, it is a mixed type, named by the top pathways whose NCS sum exceeds 50% of the total mitigation capacity. More