Philippa Kaur

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

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

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

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

Bastin, J. F. et al. The global tree restoration potential. Science 364, 76–79 (2019).Article 
CAS 

Google Scholar 
Lewis, S. L., Wheeler, C. E., Mitchard, E. T. A. & Koch, A. Restoring natural forests is the best way to remove atmospheric carbon. Nature 568, 25–28 (2019).CAS 
Article 

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

Google Scholar 
Beer, C. et al. Terrestrial gross carbon dioxide uptake: global distribution and covariation with climate. Science 329, 834–838 (2010).CAS 
Article 

Google Scholar 
Vitousek, P. M., Porder, S., Houlton, B. Z. & Chadwick, O. A. Terrestrial phosphorus limitation: mechanisms, implications, and nitrogen-phosphorus interactions. Ecol. Appl. 20, 5–15 (2010).Article 

Google Scholar 
Camenzind, T., Httenschwiler, S., Treseder, K. K., Lehmann, A. & Rillig, M. C. Nutrient limitation of soil microbial processes in tropical forests. Ecol. Monogr. 88, 4–21 (2018).Article 

Google Scholar 
Hou, E., Luo, Y., Kuang, Y., Chen, C. & Wen, D. Global meta-analysis shows pervasive phosphorus limitation of aboveground plant production in natural terrestrial ecosystems. Nat. Commun. 11, 1–9 (2020).Article 
CAS 

Google Scholar 
Du, E. et al. Global patterns of terrestrial nitrogen and phosphorus limitation. Nat. Geosci. 13, 221–226 (2020).CAS 
Article 

Google Scholar 
Sinsabaugh, R. L. & Follstad Shah, J. J. Ecoenzymatic stoichiometry and ecological theory. Annu. Rev. Ecol. Evol. Syst. 43, 313–343 (2012).Article 

Google Scholar 
Houghton, R. A. Balancing the global carbon budget. Annu. Rev. Earth Planet. Sci. 35, 313–347 (2007).CAS 
Article 

Google Scholar 
Chen, J. et al. Differential responses of carbon-degrading enzyme activities to warming: implications for soil respiration. Global Change Biol. 24, 4816–4826 (2018).Article 

Google Scholar 
Waring, B. G., Weintraub, S. R. & Sinsabaugh, R. L. Ecoenzymatic stoichiometry of microbial nutrient acquisition in tropical soils. Biogeochemistry 117, 101–113 (2014).CAS 
Article 

Google Scholar 
Mori, T., Lu, X., Aoyagi, R. & Mo, J. Reconsidering the phosphorus limitation of soil microbial activity in tropical forests. Funct. Ecol. 32, 1145–1154 (2018).Article 

Google Scholar 
Gallardo, A. & Schlesinger, W. H. Factors limiting microbial biomass in the mineral soil and forest floor of a warm-temperate forest. Soil Biol. Biochem. 26, 1409–1415 (1994).Article 

Google Scholar 
Feng, J. et al. Coupling and decoupling of soil carbon and nutrient cycles across an aridity gradient in the drylands of northern China: Evidence from ecoenzymatic stoichiometry. Global Biogeochem. Cycles. 33, 559–569 (2019).CAS 

Google Scholar 
Cui, Y. et al. Patterns of soil microbial nutrient limitations and their roles in the variation of soil organic carbon across a precipitation gradient in an arid and semi-arid region. Sci. Total Environ. 658, 1440–1451 (2019).CAS 
Article 

Google Scholar 
Jing, X. et al. Soil microbial carbon and nutrient constraints are driven more by climate and soil physicochemical properties than by nutrient addition in forest ecosystems. Soil Biol. Biochem. 141, 107657 (2020).CAS 
Article 

Google Scholar 
Fernández-Martínez, M. et al. Nutrient availability as the key regulator of global forest carbon balance. Nat. Clim. Change 4, 471–476 (2014).Article 
CAS 

Google Scholar 
Zhou, L. et al. Soil extracellular enzyme activity and stoichiometry in China’s forests. Funct. Ecol. 34, 1461–1471 (2020).Article 

Google Scholar 
Fang, J., Chen, A., Peng, C., Zhao, S. & Ci, L. Changes in forest biomass carbon storage in China between 1949 and 1998. Science 292, 2320–2322 (2001).CAS 
Article 

Google Scholar 
Zhu, J. et al. Carbon stocks and changes of dead organic matter in China’s forests. Nat. Commun. 8, 1–10 (2017).Article 
CAS 

Google Scholar 
Fang, J., Yu, G., Liu, L., Hu, S. & Chapin, F. S. Climate change, human impacts, and carbon sequestration in China. Proc. Natl. Acad. Sci. USA 115, 4015–4020 (2018).CAS 
Article 

Google Scholar 
Sinsabaugh, R. L. et al. Stoichiometry of soil enzyme activity at global scale. Ecol. Lett. 11, 1252–1264 (2008).Article 

Google Scholar 
Sinsabaugh, R. L., Hill, B. H. & Follstad Shah, J. J. Ecoenzymatic stoichiometry of microbial organic nutrient acquisition in soil and sediment. Nature 462, 795–798 (2009).CAS 
Article 

Google Scholar 
Moorhead, D. L., Sinsabaugh, R. L., Hill, B. H. & Weintraub, M. N. Vector analysis of ecoenzyme activities reveal constraints on coupled C, N and P dynamics. Soil Biol. Biochem. 93, 1–7 (2016).CAS 
Article 

Google Scholar 
Cui, Y. et al. Stoichiometric models of microbial metabolic limitation in soil systems. Global Ecol. Biogeogr. 30, 2297–2311 (2021).Article 

Google Scholar 
Elser, J. J. et al. Global analysis of nitrogen and phosphorus limitation of primary producers in freshwater, marine, and terrestrial ecosystems. Ecol. Lett. 10, 1135–1142 (2007).Article 

Google Scholar 
Schulte-Uebbing, L. & Vries, W. D. Global-scale impacts of nitrogen deposition on tree carbon sequestration in tropical, temperate, and boreal forests: a meta-analysis. Global Change Biol. 24, 416–431 (2017).Article 

Google Scholar 
Richardson, S. J., Peltzer, D. A., Allen, R. B. & Parfitt, M. G. L. Rapid development of phosphorus limitation in temperate rainforest along the Franz josef soil chronosequence. Oecologia 139, 267–276 (2004).Article 

Google Scholar 
Augusto, L., Achat, D. L., Jonard, M., Vidal, D. & Ringeval, B. Soil parent material-a major driver of plant nutrient limitations in terrestrial ecosystems. Global Change Biol. 23, 3808–3824 (2017).Article 

Google Scholar 
Yao, Q. et al. Community proteogenomics reveals the systemic impact of phosphorus availability on microbial functions in tropical soil. Nat. Ecol. Evol. 2, 499–509 (2018).Article 

Google Scholar 
Philippot, L., Raaijmakers, J. M., Lemanceau, P. & Putten, W. H. Going back to the roots: the microbial ecology of the rhizosphere. Nat. Rev. Microbiol. 11, 789–799 (2013).CAS 
Article 

Google Scholar 
Kuzyakov, Y. & Xu, X. Competition between roots and microorganisms for nitrogen: mechanisms and ecological relevance. New Phytol. 198, 656–669 (2013).CAS 
Article 

Google Scholar 
Cui, Y. et al. Ecoenzymatic stoichiometry and microbial nutrient limitation in rhizosphere soil in the arid area of the northern Loess Plateau, China. Soil Biol. Biochem. 116, 11–21 (2018).CAS 
Article 

Google Scholar 
Cui, Y. et al. Soil moisture mediates microbial carbon and phosphorus metabolism during vegetation succession in a semiarid region. Soil Biol. Biochem. 147, 107814 (2020).CAS 
Article 

Google Scholar 
Johnson, J. et al. The response of soil solution chemistry in european forests to decreasing acid deposition. Global Change Biol. 24, 3603–3619 (2018).Article 

Google Scholar 
Janssens, I. A. et al. Reduction of forest soil respiration in response to nitrogen deposition. Nat. Geosci. 3, 315–322 (2010).CAS 
Article 

Google Scholar 
Penuelas, J. et al. Human-induced nitrogen-phosphorus imbalances alter natural and managed ecosystems across the globe. Nat. Commun. 4, 1–10 (2013).
Google Scholar 
Yu, G. et al. Stabilization of atmospheric nitrogen deposition in china over the past decade. Nat. Geosci. 12, 424–429 (2019).CAS 
Article 

Google Scholar 
Cui, Y. et al. Decreasing microbial phosphorus limitation increases soil carbon release. Geoderma 419, 115868 (2022).CAS 
Article 

Google Scholar 
Sinsabaugh, R. L., Moorhead, D. L., Xu, X. & Litvak, M. E. Plant, microbial and ecosystem carbon use efficiencies interact to stabilize microbial growth as a fraction of gross primary production. New Phytol. 214, 1518–1526 (2017).CAS 
Article 

Google Scholar 
Craig, M. E., Mayes, M. A., Sulman, B. N. & Walker, A. P. Biological mechanisms may contribute to soil carbon saturation patterns. Global Change Biol. 27, 2633–2644 (2021).CAS 
Article 

Google Scholar 
Friggens, N. L., Hester, A. J., Mitchell, R. J., Parker, T. C. & Wookey, P. A. Tree planting in organic soils does not result in net carbon sequestration on decadal timescales. Global Change Biol. 26, 5178–5188 (2020).Article 

Google Scholar 
Jiang, M. et al. The fate of carbon in a mature forest under carbon dioxide enrichment. Nature 580, 227–231 (2020).CAS 
Article 

Google Scholar 
Rosinger, C., Rousk, J. & Sandén, H. Can enzymatic stoichiometry be used to determine growth-limiting nutrients for microorganisms?-A critical assessment in two subtropical soils. Soil Biol. Biochem. 128, 115–126 (2019).CAS 
Article 

Google Scholar 
Mori, T. Does ecoenzymatic stoichiometry really determine microbial nutrient limitations? Soil Biol. Biochem. 146, 107816 (2020).CAS 
Article 

Google Scholar 
Allison, S. D., Wallenstein, M. D. & Bradford, M. A. Soil-carbon response to warming dependent on microbial physiology. Nat. Geosci. 3, 336–340 (2010).CAS 
Article 

Google Scholar 
Saiya-Cork, K. R., Sinsabaugh, R. L. & Zak, D. R. The effects of long term nitrogen deposition on extracellular enzyme activity in an acer saccharum, forest soil. Soil Biol. Biochem. 34, 1309–1315 (2002).CAS 
Article 

Google Scholar 
German, D. P. et al. Optimization of hydrolytic and oxidative enzyme methods for ecosystem studies. Soil Biol. Biochem. 43, 1387–1397 (2011).CAS 
Article 

Google Scholar 
Lindstrom, M. J. & Bates, D. M. Newton-Raphson and EM Algorithms for Linear Mixed-Effects Models for Repeated-Measures Data. J. Am. Stat. Assoc. 83, 1014–1022 (1988).
Google Scholar 
Legendre, P. & Legendre, L. Numerical ecology, 2nd English edition. Elsevier Science BV, Amsterdam (1998).Muggeo, V. M. R. Segmented: an R package to fit regression models with broken-line relationships. R News 8/1, 20–25 (2008).
Google Scholar 
Toms, J. D. & Lesperance, M. Piecewise regression: a tool for identifying ecological thresholds. Ecology 84, 2034–2041 (2003).Article 

Google Scholar 
Borcard, D., Legendre, P. & Drapeau, P. Partialling out the spatial component of ecological variation. Ecology 73, 1045–1055 (1992).Article 

Google Scholar 
Breiman, L. Random forests. Machine Learning 45, 5–32 (2001).Article 

Google Scholar 
Sanchez, G., Trinchera, L. & Russolillo, G. plspm: Tools for Partial Least Squares Path Modeling (PLS-PM). R package version 0.4.7 edn (2016).Development Core Team R. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria (2016). More

Scheiner, S. M. Genetics and evolution of phenotypic plasticity. Annu. Rev. Ecol. Syst. 24, 35–68 (1993).Article 

Google Scholar 
Via, S. et al. Adaptive phenotypic plasticity: consensus and controversy. Trends Ecol. Evol. 10, 212–217 (1995).CAS 
PubMed 
Article 

Google Scholar 
Ghalambor, C. K. et al. Adaptive versus non‐adaptive phenotypic plasticity and the potential for contemporary adaptation in new environments. Funct. Ecol. 21, 394–407 (2007).Article 

Google Scholar 
King, J. G. & Hadfield, J. D. The evolution of phenotypic plasticity when environments fluctuate in time and space. Evol. Lett. 3, 15–27 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Newman, R. A. Genetic variation for phenotypic plasticity in the larval life history of spadefoot toads (Scaphiopus couchii). Evolution 48, 1773–1785 (1994).PubMed 

Google Scholar 
Nussey, D. H. et al. Selection on heritable phenotypic plasticity in a wild bird population. Science 310, 304–306 (2005).CAS 
PubMed 
Article 

Google Scholar 
Scheiner, S. Selection experiments and the study of phenotypic plasticity 1. J. Evol. Biol. 15, 889–898 (2002).Article 

Google Scholar 
Ghalambor, C. K. et al. Non-adaptive plasticity potentiates rapid adaptive evolution of gene expression in nature. Nature 525, 372–375 (2015).CAS 
PubMed 
Article 

Google Scholar 
Reger, J. et al. Predation drives local adaptation of phenotypic plasticity. Nat. Ecol. Evol. 2, 100–107 (2018).PubMed 
Article 

Google Scholar 
Sommer, R. J. Phenotypic plasticity: from theory and genetics to current and future challenges. Genetics 215, 1–13 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Brakefield, P. M. & Reitsma, N. Phenotypic plasticity, seasonal climate and the population biology of Bicyclus butterflies (Satyridae) in Malawi. Ecol. Entomol. 16, 291–303 (1991).Article 

Google Scholar 
Rountree, D. & Nijhout, H. Hormonal control of a seasonal polyphenism in Precis coenia (Lepidoptera: Nymphalidae). J. Insect Physiol. 41, 987–992 (1995).CAS 
Article 

Google Scholar 
Scheiner, S. M. & Holt, R. D. The genetics of phenotypic plasticity. X. Variation versus uncertainty. Ecol. Evol. 2, 751–767 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
Bonamour, S. et al. Phenotypic plasticity in response to climate change: the importance of cue variation. Philos. Trans. R. Soc. B 374, 20180178 (2019).Article 

Google Scholar 
Fox, R.J., Donelson, J. M., Schunter, C., Ravasi, T. & Gaitán-Espitia, J. D. Beyond buying time: the role of plasticity in phenotypic adaptation to rapid environmental change. Philos. Trans. R. Soc. B https://doi.org/10.1098/rstb.2018.0174 (2019).Auld, J. R., Agrawal, A. A. & Relyea, R. A. Re-evaluating the costs and limits of adaptive phenotypic plasticity. Proc. R. Soc. B 277, 503–511 (2010).PubMed 
Article 

Google Scholar 
Murren, C. J. et al. Constraints on the evolution of phenotypic plasticity: limits and costs of phenotype and plasticity. Heredity 115, 293–301 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Yampolsky, L. Y., Schaer, T. M. & Ebert, D. Adaptive phenotypic plasticity and local adaptation for temperature tolerance in freshwater zooplankton. Proc. R. Soc. B 281, 20132744 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
Schmid, M. & Guillaume, F. The role of phenotypic plasticity on population differentiation. Heredity 119, 214–225 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Charlesworth, B., Lande, R. & Slatkin, M. A neo-Darwinian commentary on macroevolution. Evolution 36, 474–498 (1982).PubMed 

Google Scholar 
Lynch, M. The rate of morphological evolution in mammals from the standpoint of the neutral expectation. Am. Nat. 136, 727–741 (1990).Article 

Google Scholar 
Kingsolver, J. G. & Pfennig, D. W. Patterns and power of phenotypic selection in nature. Bioscience 57, 561–572 (2007).Article 

Google Scholar 
West-Eberhard, M. J. Developmental plasticity and the origin of species differences. Proc. Natl Acad. Sci. USA 102, 6543–6549 (2005).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Turelli, M. & Barton, N. Polygenic variation maintained by balancing selection: pleiotropy, sex-dependent allelic effects and G × E interactions. Genetics 166, 1053–1079 (2004).PubMed 
PubMed Central 
Article 

Google Scholar 
Charlesworth, B. Causes of natural variation in fitness: evidence from studies of Drosophila populations. Proc. Natl Acad. Sci. USA 112, 1662–1669 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Noble, D. W., Radersma, R. & Uller, T. Plastic responses to novel environments are biased towards phenotype dimensions with high additive genetic variation. Proc. Natl Acad. Sci. USA 116, 13452–13461 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Draghi, J. A. & Whitlock, M. C. Phenotypic plasticity facilitates mutational variance, genetic variance, and evolvability along the major axis of environmental variation. Evolution 66-9, 2891–2902 (2012).Article 

Google Scholar 
Houle, D. How should we explain variation in the genetic variance of traits? Genetica 102, 241–253 (1998).PubMed 
Article 

Google Scholar 
Tollrian, R. Predator‐induced morphological defenses: costs, life history shifts, and maternal effects in Daphnia pulex. Ecology 76, 1691–1705 (1995).Article 

Google Scholar 
Agrawal, A. A., Laforsch, C. & Tollrian, R. Transgenerational induction of defences in animals and plants. Nature 401, 60–63 (1999).CAS 
Article 

Google Scholar 
Tollrian, R. Neckteeth formation in Daphnia pulex as an example of continuous phenotypic plasticity: morphological effects of Chaoborus kairomone concentration and their quantification. J. Plankton Res. 15, 1309–1318 (1993).Article 

Google Scholar 
Dennis, S. et al. Phenotypic convergence along a gradient of predation risk. Proc. R. Soc. B 278, 1687–1696 (2011).CAS 
PubMed 
Article 

Google Scholar 
Hammill, E. & Beckerman, A. P. Reciprocity in predator–prey interactions: exposure to defended prey and predation risk affects intermediate predator life history and morphology. Oecologia 163, 193–202 (2010).PubMed 
Article 

Google Scholar 
Hammill, E., Rogers, A. & Beckerman, A. P. Costs, benefits and the evolution of inducible defences: a case study with Daphnia pulex. J. Evol. Biol. 21, 705–715 (2008).CAS 
PubMed 
Article 

Google Scholar 
Barnard-Kubow, K. et al. Polygenic variation in sexual investment across an ephemerality gradient in Daphnia pulex. Mol. Bio. Evol. 39, msac121 (2022).Article 

Google Scholar 
Deng, H.-W. & Lynch, M. Inbreeding depression and inferred deleterious-mutation parameters in Daphnia. Genetics 147, 147–155 (1997).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Seyfert, A. L. et al. The rate and spectrum of microsatellite mutation in Caenorhabditis elegans and Daphnia pulex. Genetics 178, 2113–2121 (2008).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Xu, S. et al. High mutation rates in the mitochondrial genomes of Daphnia pulex. Mol. Biol. Evol. 29, 763–769 (2012).CAS 
PubMed 
Article 

Google Scholar 
Collyer, M. L. & Adams, D. C. Phenotypic trajectory analysis: comparison of shape change patterns in evolution and ecology. Hystrix 24, 75 (2013).
Google Scholar 
Adams, D.C., Collyer, M., Kaliontzopoulou, A. & Sherratt, E. et al. Geomorph: software for geometric morphometric analyses (University of New England, 2016); https://hdl.handle.net/1959.11/21330Adams, D. C. & Collyer, M. L. Comparing the strength of modular signal, and evaluating alternative modular hypotheses, using covariance ratio effect sizes with morphometric data. Evolution 73, 2352–2367 (2019).PubMed 
Article 

Google Scholar 
Richards, C. L., Bossdorf, O. & Pigliucci, M. What role does heritable epigenetic variation play in phenotypic evolution? BioScience 60, 232–237 (2010).Article 

Google Scholar 
Latta, L. C. IV et al. The phenotypic effects of spontaneous mutations in different environments. Am. Nat. 185, 243–252 (2015).PubMed 
Article 

Google Scholar 
Lind, M. I. et al. The alignment between phenotypic plasticity, the major axis of genetic variation and the response to selection. Proc. R. Soc. B 282, 20151651 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Laforsch, C. & Tollrian, R. Inducible defenses in multipredator environments: cyclomorphosis in Daphnia cucullata. Ecology 85, 2302–2311 (2004).Article 

Google Scholar 
Weiss, L. C., Leimann, J. & Tollrian, R. Predator-induced defences in Daphnia longicephala: location of kairomone receptors and timeline of sensitive phases to trait formation. J. Exp. Biol. 218, 2918–2926 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Tollrian, R. & Harvell, C.D. The Ecology and Evolution of Inducible Defenses (Princeton Univ. Press, 1999).Lande, R. Adaptation to an extraordinary environment by evolution of phenotypic plasticity and genetic assimilation. J. Evol. Biol. 22, 1435–1446 (2009).PubMed 
Article 
CAS 

Google Scholar 
Via, S. & Lande, R. Genotype–environment interaction and the evolution of phenotypic plasticity. Evolution 39, 505–522 (1985).PubMed 
Article 

Google Scholar 
Kvist, J. et al. Temperature treatments during larval development reveal extensive heritable and plastic variation in gene expression and life history traits. Mol. Ecol. 22, 602–619 (2013).CAS 
PubMed 
Article 

Google Scholar 
Siepielski, A. M. et al. Differences in the temporal dynamics of phenotypic selection among fitness components in the wild. Proc. R. Soc. B 278, 1572–1580 (2011).PubMed 
Article 

Google Scholar 
Muschick, M. et al. Adaptive phenotypic plasticity in the Midas cichlid fish pharyngeal jaw and its relevance in adaptive radiation. BMC Evol. Biol. 11, 116 (2011).Salzburger, W. Understanding explosive diversification through cichlid fish genomics. Nat. Rev. Genet. 19, 705–717 (2018).CAS 
PubMed 
Article 

Google Scholar 
Halligan, D. L. & Keightley, P. D. Spontaneous mutation accumulation studies in evolutionary genetics. Annu. Rev. Ecol. Evol. Syst. 40, 151–172 (2009).Article 

Google Scholar 
Houle, D., Morikawa, B. & Lynch, M. Comparing mutational variabilities. Genetics 143, 1467–1483 (1996).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Eberle, S. et al. Hierarchical assessment of mutation properties in Daphnia magna. G3 Genes Genomes Genetics 8, 3481–3487 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
Stenseth, N. C. et al. Ecological effects of climate fluctuations. Science 297, 1292–1296 (2002).CAS 
PubMed 
Article 

Google Scholar 
Burgmer, T., Hillebrand, H. & Pfenninger, M. Effects of climate-driven temperature changes on the diversity of freshwater macroinvertebrates. Oecologia 151, 93–103 (2007).CAS 
PubMed 
Article 

Google Scholar 
Yan, N. D. et al. Long-term trends in zooplankton of Dorset, Ontario, lakes: the probable interactive effects of changes in pH, total phosphorus, dissolved organic carbon, and predators. Can. J. Fish. Aquat. Sci. 65, 862–877 (2008).CAS 
Article 

Google Scholar 
Reed, T. E., Schindler, D. E. & Waples, R. S. Interacting effects of phenotypic plasticity and evolution on population persistence in a changing climate. Conserv. Biol. 25, 56–63 (2011).PubMed 
PubMed Central 
Article 

Google Scholar 
ASTM, Standard Guide for Conducting Acute Toxicity Tests with Fishes, Macroinvertebrates, and Amphibians (American Society for Testing and Materials, 1988).Baym, M. et al. Inexpensive multiplexed library preparation for megabase-sized genomes. PLoS ONE 10, e0128036 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zhang, J. et al. PEAR: a fast and accurate Illumina Paired-End reAd mergeR. Bioinformatics 30, 614–620 (2014).CAS 
PubMed 
Article 

Google Scholar 
Li, H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. Preprint at https://arxiv.org/abs/1303.3997 (2013).MarkDuplicates v.2.20 (Broad Institute, 2019); http://broadinstitute.github.io/picardMcKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Poplin, R. et al. Scaling accurate genetic variant discovery to tens of thousands of samples. Preprint at bioRxiv https://doi.org/10.1101/201178 (2018).Zheng, X. et al. A high-performance computing toolset for relatedness and principal component analysis of SNP data. Bioinformatics 28, 3326–3328 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Manichaikul, A. et al. Robust relationship inference in genome-wide association studies. Bioinformatics 26, 2867–2873 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Beckerman, A. P., Rodgers, G. M. & Dennis, S. R. The reaction norm of size and age at maturity under multiple predator risk. J. Anim. Ecol. 79, 1069–1076 (2010).PubMed 
Article 

Google Scholar 
Naraki, Y., Hiruta, C. & Tochinai, S. Identification of the precise kairomone-sensitive period and histological characterization of necktooth formation in predator-induced polyphenism in Daphnia pulex. Zool. Sci. 30, 619–625 (2013).Article 

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

Google Scholar 
Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).CAS 
PubMed 
Article 

Google Scholar 
Scrucca, L. et al. mclust 5: clustering, classification and density estimation using Gaussian finite mixture models. R J. 8, 289 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Fox, J. & Weisberg, S. An R Companion to Applied Regression (Sage, 2018).Ben-Shachar, M. S., Lüdecke, D. & Makowski, D. effectsize: estimation of effect size indices and standardized parameters. J. Open Source Softw. 5, 2815 (2020).Article 

Google Scholar 
Collyer, M. L. & Adams, D. C. RRPP: an r package for fitting linear models to high‐dimensional data using residual randomization. Methods Ecol. Evol. 9, 1772–1779 (2018).Article 

Google Scholar 
Collyer, M., Adams, D. & and Collyer, M.M. RRPP: linear model evaluation with randomized residuals in a permutation procedure. R package version 1.3 https://CRAN.R-project.org/package=RRPP (2021).Smirnov, P. robcor: Robust correlations. R package version 0.1-6.1 https://CRAN.R-project.org/package=ropcor (2014).Hadfield, J. D. MCMC methods for multi-response generalized linear mixed models: the MCMCglmm R package. J. Stat. Softw. 33, 1–22 (2010).Article 

Google Scholar 
Yang, J. et al. GCTA: a tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 88, 76–82 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2017).Villanueva, R., Chen, Z. & Wickham, H. ggplot2: Elegant Graphics for Data Analysis Using the Grammar of Graphics (Springer-Verlag, 2016).Wilke, C. cowplot: Streamlined plot theme and plot annotations for ‘ggplot2’. R package version 0.9. 2 https://CRAN.R-project.org/package=cowplot (2020).Dowle, M. et al. data.table: Extension of ‘data.frame‘. R package version 1.14.0 https://CRAN.R-project.org/package=data.table (2021).Daniel, M. foreach: Provides foreach looping construct. R package version 1.5.1 https://CRAN.R-project.org/package=foreach (2020).Weston, S. doMC: Foreach parallel adaptor for ‘parallel’. R package version 1.3.7 https://CRAN.R-project.org/package=doMC (2020).Clarke, E. & Sherrill-Mix, S. Ggbeeswarm: Categorical scatter (violin point) plots. R package version 0.6. 0 https://CRAN.R-project.org (2017).Garnier, S. et al. viridis: Default color maps from ‘matplotlib’. R package version 0.5.1 (2018). More

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

Floyd, T. J., Mech, L. D. & Jordan, P. A. Relating wolf scat content to prey consumed. J. Wildl. Manag. 42, 528 (1978).Article 

Google Scholar 
Ackerman, B. B., Lindzey, F. G. & Hemker, T. P. Cougar food habits in Southern Utah. J. Wildl. Manag. 48, 147 (1984).Article 

Google Scholar 
Carbone, C., Mace, G. M., Roberts, S. C. & Macdonald, D. W. Energetic constraints on the diet of terrestrial carnivores. Nature 402, 286–288 (1999).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Klare, U., Kamler, J. F. & Macdonald, D. W. A comparison and critique of different scat-analysis methods for determining carnivore diet: Comparison of scat-analysis methods. Mammal Rev. 41, 294–312 (2011).Article 

Google Scholar 
Hatton, I. A. et al. The predator-prey power law: Biomass scaling across terrestrial and aquatic biomes. Science 349, aac6284 (2015).PubMed 
Article 
CAS 

Google Scholar 
Monterroso, P. et al. Feeding ecological knowledge: The underutilised power of faecal DNA approaches for carnivore diet analysis. Mammal Rev. 49, 97–112 (2019).Article 

Google Scholar 
Hayward, M. W., O’Brien, J., Hofmeyr, M. & Kerley, G. I. H. Prey preferences of the African wild dog Lycaon Pictus (Canidae: Carnivora): Ecological requirements for conservation. J. Mammal. 87, 1122–1131 (2006).Article 

Google Scholar 
Crawford, K., Mcdonald, R. A. & Bearhop, S. Applications of stable isotope techniques to the ecology of mammals. Mammal Rev. 38, 87–107 (2008).Article 

Google Scholar 
Crossey, B., Chimimba, C., du Plessis, C., Ganswindt, A. & Hall, G. African wild dogs ( Lycaon pictus ) show differences in diet composition across landscape types in Kruger National Park, South Africa. J. Mammal. 102, 1211–1221 (2021).Article 

Google Scholar 
Ceballos, G. & Ehrlich, P. R. Mammal population losses and the extinction crisis. Science 296, 904–907 (2002).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Treves, A. & Karanth, K. U. Human-carnivore conflict and perspectives on carnivore management worldwide. Conserv. Biol. 17, 1491–1499 (2003).Article 

Google Scholar 
Swihart, R. K., Gehring, T. M., Kolozsvary, M. B. & Nupp, T. E. Responses of ‘resistant’ vertebrates to habitat loss and fragmentation: The importance of niche breadth and range boundaries. Divers. Distrib. 9, 1–18 (2003).Article 

Google Scholar 
Kamler, J. F. et al. Cuon alpinus. IUCN Red List Threat. Spec. https://doi.org/10.2305/IUCN.UK.2015-4.RLTS.T5953A72477893.en (2015).Article 

Google Scholar 
Johnsingh, A. J. T. Distribution and status of dhole Cuon alpinus Pallas, 1811 in South Asia. Mammalia 49, (1985).Acharya, B. B. Dissertation submitted to Saurashtra University, Rajkot, Gujarat, for the award of the Degree of Doctor of Philosophy in Wildlife Science. 133.Sillero-Zubiri, E. C., Hoffmann, M. & Macdonald, D. W. Canids: Foxes, Wolves, Jackals and Dogs. 443.Wolf, C. & Ripple, W. J. Range contractions of the world’s large carnivores. R. Soc. Open Sci. 4, 170052 (2017).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Karanth, K. K., Nichols, J. D., Karanth, K. U., Hines, J. E. & Christensen, N. L. The shrinking ark: Patterns of large mammal extinctions in India. Proc. R. Soc. B Biol. Sci. 277, 1971–1979 (2010).Article 

Google Scholar 
Srivathsa, A., Karanth, K. K., Jathanna, D., Kumar, N. S. & Karanth, K. U. On a dhole trail: Examining ecological and anthropogenic correlates of dhole habitat occupancy in the Western Ghats of India. PLoS ONE 9, e98803 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Newsome, T. M. & Ripple, W. J. A continental scale trophic cascade from wolves through coyotes to foxes. J. Anim. Ecol. 84, 49–59 (2015).PubMed 
Article 

Google Scholar 
Fleming, P. J. S. et al. Roles for the Canidae in food webs reviewed: Where do they fit?. Food Webs 12, 14–34 (2017).Article 

Google Scholar 
Van Valkenburgh, B. Iterative evolution of hypercarnivory in canids (Mammalia: Carnivora): Evolutionary interactions among sympatric predators. Paleobiology 17, 340–362 (1991).Article 

Google Scholar 
Clements, H. S., Tambling, C. J., Hayward, M. W. & Kerley, G. I. H. An objective approach to determining the weight ranges of prey preferred by and accessible to the five large african carnivores. PLoS ONE 9, e101054 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Hayward, M. W., Lyngdoh, S. & Habib, B. Diet and prey preferences of dholes ( C uon alpinus ): Dietary competition within A sia’s apex predator guild. J. Zool. 294, 255–266 (2014).Article 

Google Scholar 
Srivathsa, A., Sharma, S. & Oli, M. K. Every dog has its prey: Range-wide assessment of links between diet patterns, livestock depredation and human interactions for an endangered carnivore. Sci. Total Environ. 714, 136798 (2020).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Cohen, J. A. Cuon alpinus. Mamm. Spec. https://doi.org/10.2307/3503800 (1978).Article 

Google Scholar 
Srivathsa, A., Sharma, S., Singh, P., Punjabi, G. A. & Oli, M. K. A strategic road map for conserving the Endangered dhole Cuon alpinus in India. Mammal Rev. 50, 399–412 (2020).Article 

Google Scholar 
Ghaskadbi, P., Nigam, P. & Habib, B. Stranger Danger: Differential response to strangers and neighbors by a social carnivore, the Asiatic wild dog (Cuon alpinus). Behav. Ecol. Sociobiol. 76, 86. https://doi.org/10.1007/s00265-022-03188-4 (2022). Article 

Google Scholar 
Ghaskadbi, P., Das, J., Mahadev, V. & Habib, B. First record of mixed species association between dholes and a wolf from Satpura Tiger Reserve, India. Canid Biol. Conserv. 23(4): 15–17. http://www.canids.org/CBC/23/Dhole_wolf_association.pdf (2021).Wachter, B. et al. An advanced method to assess the diet of free-ranging large carnivores based on scats. PLoS ONE 7, e38066 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Edgaonkar, A. Satpura National Park, India. 135.Borah, J., Deka, K., Dookia, S. & Gupta, R. P. Food habits of dholes (Cuon alpinus) in Satpura Tiger Reserve. Madhya Pradesh, India. 73, 85–88 (2009).
Google Scholar 
Karanth, K. U. & Sunquist, M. E. Behavioural correlates of predation by tiger ( Panthera tigris ), leopard ( Panthera pardus ) and dhole ( Cuon alpinus ) in Nagarahole, India. J. Zool. 250, 255–265 (2000).Article 

Google Scholar 
Krishna, Y. C., Clyne, P. J., Krishnaswamy, J. & Kumar, N. S. Distributional and ecological review of the four horned antelope. Tetracerus quadricornis. 73, 1–6 (2009).
Google Scholar 
Sharma, K., Chundawat, R. S., Van Gruisen, J. & Rahmani, A. R. Understanding the patchy distribution of four-horned antelope Tetracerus quadricornis in a tropical dry deciduous forest in Central India. J. Trop. Ecol. 30, 45–54 (2014).Article 

Google Scholar 
Rahman, D. A., Syamsudin, M., Firdaus, A. Y. & Afriandi, H. T. Photographic record of Dholes predating on a young Banteng in southwestern Java, Indonesia. J. Threat. Taxa 13, 20278–20283 (2021).Article 

Google Scholar 
Durbin, L. S., Venkataraman, A., Hedges, S. & Dukworth, W. South Asia—south of th e Himalaya (oriental). In Canids: Foxes, Wolves, Jackals and Dogs . Status Survey and Conserva- tion Action Plan. (IUCN Canid Specialist Group, 2004).Bashir, T., Bhattacharya, T., Poudyal, K., Roy, M. & Sathyakumar, S. Precarious status of the Endangered dhole Cuon alpinus in the high elevation Eastern Himalayan habitats of Khangchendzonga Biosphere Reserve, Sikkim, India. Oryx 48, 125–132 (2014).Article 

Google Scholar 
Yoshimura, H., Hirata, S. & Kinoshita, K. Plant-eating carnivores: Multispecies analysis on factors influencing the frequency of plant occurrence in obligate carnivores. Ecol. Evol. 11, 10968–10983 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Snake-in-the-diet-of-Cuon-alpinus-Pallas-1811-in-Kalakad-Mundanthurai-Tiger-Reserve-Tamil-Nadu.pdf.Habib, B. et al. Status of Tigers, Co-Predator and Prey in Tadoba Andhari Tiger Reserve (TATR)— Phase IV Monitoring Report and Report on Collaring of Leopards. (2014). 26 (2015).Habib, B. et al. Status of Tigers, Co-Predator and Prey in Tadoba Andhari Tiger Reserve (TATR) (2015). 62 (2016).Habib, B. et al. Status of Tigers, Co-Predator and Prey in Tadoba Andhari Tiger Reserve (TATR) (2016). 27 (2017).Habib, B. et al. Status of Tigers, Co-Predator and Prey in Tadoba Andhari Tiger Reserve (TATR) (2017). 44 (2018).Habib, B. et al. Status of Tigers, Co-Predator and Prey in Tadoba Andhari Tiger Reserve (TATR) (2018). 41 (2019).Habib, B. et al. Status of Tigers, Co-Predator and Prey in Tadoba Andhari Tiger Reserve (TATR) (2019). 47 https://ntca.gov.in/assets/uploads/Reports/WII/TATR%20Phase%20IV%202019.pdf (2020).Jhala, Y. V., Qureshi, Q. & Nayak, A. K. Status of tigers, co-predators and prey in India 2018. 656 https://ntca.gov.in/assets/uploads/Reports/AITM/Tiger_Status_Report_2018.pdf (2019).Bagchi, S., Goyal, S. P. & Sankar, K. Prey abundance and prey selection by tigers (Panthera tigris) in a semi-arid, dry deciduous forest in western India. J. Zool. 260, 285–290 (2003).Article 

Google Scholar 
Woodroffe, R., Lindsey, P. A., Romañach, S. S. & Ranah, S. M. K. African Wild Dogs ( Lycaon pictus ) Can Subsist on Small Prey: Implications for Conservation. J. Mammal. 88, 181–193 (2007).Article 

Google Scholar 
Merrill, E. et al. Building a mechanistic understanding of predation with GPS-based movement data. Philos. Trans. R. Soc. B Biol. Sci. 365, 2279–2288 (2010).Article 

Google Scholar 
Pitman, R. T., Mulvaney, J., Ramsay, P. M., Jooste, E. & Swanepoel, L. H. Global Positioning System-located kills and faecal samples: A comparison of leopard dietary estimates. J. Zool. 292, 18–24 (2014).Article 

Google Scholar 
Jansen, C., Leslie, A. J., Cristescu, B., Teichman, K. J. & Martins, Q. Determining the diet of an African mesocarnivore, the caracal: Scat or GPS cluster analysis?. Wildl. Biol. 2019, wlb.00579 (2019).Article 

Google Scholar 
Leighton, G. R. M. et al. An integrated dietary assessment increases feeding event detection in an urban carnivore. Urban Ecosyst. 23, 569–583 (2020).Article 

Google Scholar 
Studd, E. K. et al. The Purr-fect Catch: Using accelerometers and audio recorders to document kill rates and hunting behaviour of a small prey specialist. Methods Ecol. Evol. 12, 1277–1287 (2021).Article 

Google Scholar 
Bhandari, A., Ghaskadbi, P., Nigam, P. & Habib, B. Dhole pack size variation: Assessing the effect of Prey availability and Apex predator. Ecol. Evol. 11, 4774–4785 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Hubel, T. Y. et al. Additive opportunistic capture explains group hunting benefits in African wild dogs. Nat. Commun. 7, 11033 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Parker, D. M., Vyver, D. B. & Bissett, C. The influence of an apex predator introduction on an already established subordinate predator. J. Zool. 313, 224–235 (2021).Article 

Google Scholar 
Johnsingh, A. J. T. Prey selection in three large sympatric carnivores in Bandipur. Mammalia 56, (1992).Marucco, F., Pletscher, D. H. & Boitani, L. Accuracy of scat sampling for carnivore diet analysis: Wolves in the Alps as a case study. J. Mammal. 89, 665–673 (2008).Article 

Google Scholar 
Martins, Q., Horsnell, W. G. C., Titus, W., Rautenbach, T. & Harris, S. Diet determination of the Cape Mountain leopards using global positioning system location clusters and scat analysis. J. Zool. 283, 81–87 (2011).Article 

Google Scholar 
Champion, S. H. G. & Seth, S. K. A Revised Survey of the Forest Types of India (Manager of Publications, 1968).
Google Scholar 
Thinley, P. et al. Seasonal diet of dholes (Cuon alpinus) in northwestern Bhutan. Mamm. Biol. 76, 518–520 (2011).Article 

Google Scholar 
Modi, S., Habib, B., Ghaskadbi, P., Nigam, P. & Mondol, S. Standardization and validation of a panel of cross-species microsatellites to individually identify the Asiatic wild dog (Cuon alpinus). PeerJ 7, e7453 (2019).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Modi, S., Mondol, S., Nigam, P. & Habib, B. Genetic analyses reveal demographic decline and population differentiation in an endangered social carnivore, Asiatic wild dog. Sci. Rep. 11, 16371 (2021).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Putman, R. J. Facts from faeces. Mammal Rev. 14, 79–97 (1984).Article 

Google Scholar 
Kohn, M. H. & Wayne, R. K. Facts from feces revisited. Trends Ecol. Evol. 12, 223–227 (1997).CAS 
PubMed 
Article 

Google Scholar 
Mukherjee, S., Goyal, S. P. & Chellam, R. Standardisation of scat analysis techniques for leopard (Panthera pardus) in Gir National Park, Western India. Mammalia 58, (1994).Bahuguna, A., Sahajpal, V., Goyal, S. P., Mukherjee, S. & Thakur, V. Species Identification from Guard Hair of Selected Indian Mammals: A Reference Guide. Wildlife Institute of India (Wildlife Institute of India, 2010).
Google Scholar 
Leopold, B. D. & Krausman, P. R. Diets of 3 Predators in Big Bend National Park, Texas. J. Wildl. Manag. 50, 290 (1986).Article 

Google Scholar 
Van Ballenberghe, V., Erickson, A. W. & Byman, D. Ecology of the Timber Wolf in Northeastern Minnesota. Wildl. Monogr. 3–43 (1975).Ciucci, P., Boitani, L., Pelliccioni, E. R., Rocco, M. & Guy, I. A comparison of scat-analysis methods to assess the diet of the wolf Canis lupus. Wildl. Biol. 2, 37–48 (1996).Article 

Google Scholar 
Weaver, J. L. Refining the equation for interpreting prey occurrence in Gray wolf scats. J. Wildl. Manag. 57, 534–538 (1993).Article 

Google Scholar 
Chakrabarti, S. et al. Adding constraints to predation through allometric relation of scats to consumption. J. Anim. Ecol. 85, 660–670 (2016).PubMed 
Article 

Google Scholar 
Lumetsberger, T. et al. Re-evaluating models for estimating prey consumption by leopards. J. Zool. 302, 201–210 (2017).Article 

Google Scholar 
Jacobs, J. Quantitative measurement of food selection: A modification of the forage ratio and Ivlev’s electivity index. Oecologia 14, 413–417 (1974).ADS 
PubMed 
Article 

Google Scholar 
Karanth, K. U. & Nichols, J. D. Distribution and Dynamics of Tiger and Prey Populations in Maharashtra, India Final Technical Report (October 2001 to August 2005). (2005).19 LIVESTOCK CENSUS-2012 ALL INDIA REPORT. https://d1wqtxts1xzle7.cloudfront.net/56129012/6ESSJan-6098P-with-cover-page-v2.pdf?Expires=1644491741&Signature=Apc1rT2raxYnUyrRJ64NqOd6oUEpnF2AiRQVPB-9gS2W2TIrOcInF3KnBJVA2dPxzfbIz8ap9IPe-l24mpYs9i8xEZAvsxRVnDhSHT8H9C9fd0voDxyUwl3gUyJgDDzLO-204J95UuopJQw5Df6xTNmTOs5Oiadk0Fkf9Fk-QRVajisuRjzyX2eLmrBH4LyTJFu5irffnKwnluqHl53KoMAQ6nTKi7dlqI4pdFIVCtisXpkSsI44xV1mYX6KC67zmKCZlvjpTxTuHCFV4nmfpgZpPXh4sIOE-0utbwcf5g~UdmRtVVhaXfjZ2iw0gOm7-bIuQILDldPr3OnNUqXbSw__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA (2012).The Measurement of Niche Overlap and Some Relatives – Hurlbert – 1978 – Ecology – Wiley Online Library. https://esajournals.onlinelibrary.wiley.com/doi/abs/https://doi.org/10.2307/1936632.Habib, B., Ghaskadbi, P., Khan, S., Hussain, Z. & Nigam, P. Not a cakewalk: Insights into movement of large carnivores in human-dominated landscapes in India. Ecol. Evol. 11, 1653–1666 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Neu, C. W., Byers, C. R. & Peek, J. M. A technique for analysis of utilization-availability data. J. Wildl. Manag. 38, 541–545 (1974).Article 

Google Scholar  More