Ecology

1.Baker, M. R., Hofmann, H. A. & Wong, R. Y. Neurogenomics of Behavioural Plasticity in Socioecological Contexts (Wiley, 2001).Book 

Google Scholar 
2.Koolhaas, J. M. et al. Coping styles in animals: Current status in behavior and stress-physiology. Neurosci. Biobehav. Rev. 23(7), 925–935 (1999).CAS 
Article 

Google Scholar 
3.Koolhaas, J. M., de Boer, S. F., Coppens, C. M. & Buwalda, B. Neuroendocrinology of coping styles: Towards understanding the biology of individual variation. Front. Neuroendocrinol. 31(3), 307–321 (2010).CAS 
Article 

Google Scholar 
4.Øverli, Ø. et al. Evolutionary background for stress-coping styles: Relationships between physiological, behavioral, and cognitive traits in non-mammalian vertebrates. Neurosci. Biobehav. Rev. 31(3), 396–412 (2007).Article 

Google Scholar 
5.Brown, G. E. et al. Retention of acquired predator recognition among shy versus bold juvenile rainbow trout. Behav. Ecol. Sociobiol. 67(1), 43–51 (2013).Article 

Google Scholar 
6.Dougherty, L. R. & Guillette, L. M. Linking personality and cognition: a meta-analysis. Philos. Trans. R. Soc. B. Biol. Sci. https://doi.org/10.1098/rstb.2017.0282 (2018).Article 

Google Scholar 
7.Lucon-Xiccato, T. & Bisazza, A. Individual differences in cognition among teleost fishes. Behav. Process. 141, 184–195 (2017).Article 

Google Scholar 
8.Miller, N. Cognition in fishes. Behav. Process. https://doi.org/10.1016/j.beproc.2017.03.013 (2017).Article 

Google Scholar 
9.Sih, A. & Del Giudice, M. Linking behavioural syndromes and cognition: A behavioural ecology perspective. Philos. Trans. R. Soc. B. Biol. Sci. 367(1603), 2762–2772 (2012).Article 

Google Scholar 
10.Amy, M., van Oers, K. & Naguib, M. Worms under cover: Relationships between performance in learning tasks and personality in great tits (Parus major). Anim. Cogn. https://doi.org/10.1007/s10071-012-0500-3 (2012).Article 
PubMed 

Google Scholar 
11.Bolhuis, J. E., Schouten, W. G. P., De, L. J. A., Schrama, J. W. & Wiegant, V. M. Individual coping characteristics, rearing conditions and behavioural flexibility in pigs. Behav. Brain Res. 152(2), 351–360 (2004).Article 

Google Scholar 
12.DePasquale, C., Wagner, T., Archard, G. A., Ferguson, B. & Braithwaite, V. A. Learning rate and temperament in a high predation risk environment. Oecologia 176(3), 661–667 (2014).ADS 
CAS 
Article 

Google Scholar 
13.Dugatkin, L. A. & Alfieri, M. S. Boldness, behavioral inhibition and learning. Ethol. Ecol. Evol. 15(1), 43–49 (2003).Article 

Google Scholar 
14.Mazza, V., Eccard, J. A., Zaccaroni, M., Jacob, J. & Dammhahn, M. The fast and the flexible: Cognitive style drives individual variation in cognition in a small mammal. Anim. Behav. https://doi.org/10.1016/j.anbehav.2018.01.011 (2018).Article 

Google Scholar 
15.Mesquita, F. O., Borcato, F. L. & Huntingford, F. A. Cue-based and algorithmic learning in common carp: A possible link to stress coping style. Behav. Process. 115, 25–29 (2015).Article 

Google Scholar 
16.Trompf, L. & Brown, C. Personality affects learning and trade-offs between private and social information in guppies, Poecilia reticulata. Anim. Behav. 88, 99–106 (2014).Article 

Google Scholar 
17.Budaev, S. V. & Zhuikov, A. Y. Avoidance learning and “personality” in the guppy (Poecilia reticulata). J. Comp. Psychol. 112(1), 92–94 (1998).Article 

Google Scholar 
18.Exnerová, A., Svádová, K. H., Fučíková, E., Drent, P. & Štys, P. Personality matters: Individual variation in reactions of naive bird predators to aposematic prey. Proc. R. Soc. B. Biol. Sci. https://doi.org/10.1098/rspb.2009.1673 (2010).Article 

Google Scholar 
19.Miller, K. A., Garner, J. P. & Mench, J. A. Is fearfulness a trait that can be measured with behavioural tests? A validation of four fear tests for Japanese quail. Anim Behav. https://doi.org/10.1016/j.anbehav.2005.08.018 (2006).Article 

Google Scholar 
20.Øverli, Ø. & Sørensen, C. On the role of neurogenesis and neural plasticity in the evolution of animal personalities and stress coping styles. Brain Behav. Evol. https://doi.org/10.1159/000447085 (2016).Article 
PubMed 

Google Scholar 
21.Sørensen, C., Johansen, I. B. & Øverli, Ø. Neural plasticity and stress coping in teleost fishes. Gen. Comp. Endocrinol. https://doi.org/10.1016/j.ygcen.2012.12.003 (2013).Article 
PubMed 

Google Scholar 
22.Wong, R. Y., Lamm, M. S. & Godwin, J. Characterizing the neurotranscriptomic states in alternative stress coping styles. BMC Genom. 16(1), 425 (2015).Article 

Google Scholar 
23.Oswald, M. E., Singer, M. & Robison, B. D. The quantitative genetic architecture of the bold-shy continuum in zebrafish, Denio rerio. PLoS ONE 8(7), e28865. https://doi.org/10.1371/journal.pone.0068828 (2013).CAS 
Article 

Google Scholar 
24.Kfir, A. et al. Learning-induced modulation of the GABAB-mediated inhibitory synaptic transmission: Mechanisms and functional significance. J.. Neurophysiol. https://doi.org/10.1152/jn.00004.2014 (2014).Article 
PubMed 

Google Scholar 
25.Lin, Y. et al. Activity-dependent regulation of inhibitory synapse development by Npas4. Nature https://doi.org/10.1038/nature07319 (2008).Article 
PubMed 
PubMed Central 

Google Scholar 
26.Maya-Vetencourt, J. F. et al. Experience-dependent expression of NPAS4 regulates plasticity in adult visual cortex. J. Physiol. https://doi.org/10.1113/jphysiol.2012.234237 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
27.Heaney, C. F. & Kinney, J. W. Role of GABABreceptors in learning and memory and neurological disorders. Neurosci. Biobehav. Rev. https://doi.org/10.1016/j.neubiorev.2016.01.007 (2016).Article 
PubMed 

Google Scholar 
28.Ploski, J. E., Monsey, M. S., Nguyen, T., DiLeone, R. J. & Schafe, G. E. The neuronal PAS domain protein 4 (Npas4) is required for new and reactivated fear memories. PLoS ONE https://doi.org/10.1371/journal.pone.0023760 (2011).Article 
PubMed 
PubMed Central 

Google Scholar 
29.Ramamoorthi, K. et al. Npas4 regulates a transcriptional program in CA3 required for contextual memory formation. Science https://doi.org/10.1126/science.1208049 (2011).Article 
PubMed 
PubMed Central 

Google Scholar 
30.Baker, M. R. & Wong, R. Y. Contextual fear learning and memory differ between stress coping styles in zebrafish. Sci. Rep. https://doi.org/10.1038/s41598-019-46319-0 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
31.Maren, S., Phan, K. L. & Liberzon, I. The contextual brain: Implications for fear conditioning, extinction and psychopathology. Nat. Rev. Neurosci. https://doi.org/10.1038/nrn3492 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
32.Lal, P. et al. Identification of a neuronal population in the telencephalon essential for fear conditioning in zebrafish. BMC Biol. https://doi.org/10.1186/s12915-018-0502-y (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
33.Ganz, J. et al. Subdivisions of the adult zebrafish pallium based on molecular marker analysis. F1000 Res. https://doi.org/10.12688/f1000research.5595.2 (2015).Article 

Google Scholar 
34.de CarmoSilva, R. X., Lima-Maximino, M. G. & Maximino, C. The aversive brain system of teleosts: Implications for neuroscience and biological psychiatry. Neurosci. Biobehav. Rev. https://doi.org/10.1016/j.neubiorev.2018.10.001 (2018).Article 

Google Scholar 
35.Panula, P. et al. The comparative neuroanatomy and neurochemistry of zebrafish CNS systems of relevance to human neuropsychiatric diseases. Neurobiol. Dis. https://doi.org/10.1016/j.nbd.2010.05.010 (2010).Article 
PubMed 

Google Scholar 
36.Wong, R. Y. et al. Comparing behavioral responses across multiple assays of stress and anxiety in zebrafish (Danio rerio). Behaviour 149(10–12), 1205–1240 (2012).
Google Scholar 
37.Kern, E. M. A., Robinson, D., Gass, E., Godwin, J. & Langerhans, R. B. Correlated evolution of personality, morphology and performance. Anim. Behav. 117, 79–86 (2016).Article 

Google Scholar 
38.Wong, R. Y., McLeod, M. M. & Godwin, J. Limited sex-biased neural gene expression patterns across strains in Zebrafish (Danio rerio). BMC Genom. 15(1), 905. https://doi.org/10.1186/1471-2164-15-905 (2014).Article 

Google Scholar 
39.Wong, R. Y., Oxendine, S. E. & Godwin, J. Behavioral and neurogenomic transcriptome changes in wild-derived zebrafish with fluoxetine treatment. BMC Genom. 14(1), 348 (2013).CAS 
Article 

Google Scholar 
40.Goodman, A. C. & Wong, R. Y. Differential effects of ethanol on behavior and GABAA receptor expression in adult zebrafish (Danio rerio) with alternative stress coping styles. Sci. Rep. https://doi.org/10.1038/s41598-020-69980-2 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
41.Baker, M. R., Goodman, A. C., Santo, J. B. & Wong, R. Y. Repeatability and reliability of exploratory behavior in proactive and reactive zebrafish Danio rerio. Sci. Rep. https://doi.org/10.1038/s41598-018-30630-3 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
42.Johnson, Z. V. et al. Exploratory behaviour is associated with microhabitat and evolutionary radiation in Lake Malawi cichlids. Anim. Behav. https://doi.org/10.1016/j.anbehav.2019.11.006 (2020).Article 

Google Scholar 
43.Gerlai, R. Learning and memory in zebrafish (Danio rerio). Methods Cell Biol. https://doi.org/10.1016/bs.mcb.2016.02.005 (2016).Article 
PubMed 

Google Scholar 
44.Norton, W. & Bally-Cuif, L. Adult zebrafish as a model organism for behavioural genetics. BMC Neurosci. https://doi.org/10.1186/1471-2202-11-90 (2010).Article 
PubMed 
PubMed Central 

Google Scholar 
45.Oliveira, R. F. Mind the fish: Zebrafish as a model in cognitive social neuroscience. Front. Neural Circuits https://doi.org/10.3389/fncir.2013.00131 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
46.Wong, R. Y., French, J. & Russ, J. B. Differences in stress reactivity between zebrafish with alternative stress coping styles. R. Soc. Open Sci. https://doi.org/10.1098/rsos.181797 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
47.Maren, S. Neurobiology of Pavlovian fear conditioning. Annu. Rev. Neurosci. https://doi.org/10.1146/annurev.neuro.24.1.897 (2001).Article 
PubMed 

Google Scholar 
48.McCurley, A. T. & Callard, G. V. Characterization of housekeeping genes in zebrafish: Male-female differences and effects of tissue type, developmental stage and chemical treatment. BMC Mol. Biol. https://doi.org/10.1186/1471-2199-9-102 (2008).Article 
PubMed 
PubMed Central 

Google Scholar 
49.Wong, R. Y., Ramsey, M. E. & Cummings, M. E. Localizing brain regions associated with female mate preference behavior in a swordtail. PLoS ONE https://doi.org/10.1371/journal.pone.0050355 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
50.Wong, R. Y. & Cummings, M. E. Expression patterns of Neuroligin-3 and tyrosine hydroxylase across the brain in mate choice contexts in female swordtails. Brain Behav Evol https://doi.org/10.1159/000360071 (2014).Article 
PubMed 

Google Scholar 
51.Wullimann, M. F., Rupp, B. & Reichert, H. Neuroanatomy of the zebrafish brain: A topological. Atlas https://doi.org/10.1007/978-3-0348-8979-7 (1996).Article 

Google Scholar 
52.Benjamini, Y., Drai, D., Elmer, G., Kafkafi, N. & Golani, I. Controlling the false discovery rate in behavior genetics research. Behav. Brain Res. 125(1–2), 279–284 (2001).CAS 
Article 

Google Scholar 
53.Wassertheil, S. & Cohen, J. Statistical power analysis for the behavioral sciences. Biometrics https://doi.org/10.2307/2529115 (1970).Article 

Google Scholar 
54.Starkings, S. IBM SPSS statistics 19 made simple by Colin D. Gray and Paul R. Kinnear. Int. Stat. Rev. https://doi.org/10.1111/j.1751-5823.2012.00187_13.x (2012).Article 

Google Scholar 
55.Richardson, J. T. E. Eta squared and partial eta squared as measures of effect size in educational research. Educ. Res. Rev. https://doi.org/10.1016/j.edurev.2010.12.001 (2011).Article 

Google Scholar 
56.Benito, E. & Barco, A. The neuronal activity-driven transcriptome. Mol. Neurobiol. https://doi.org/10.1007/s12035-014-8772-z (2015).Article 
PubMed 

Google Scholar 
57.Vertkin, I. et al. GABA B receptor deficiency causes failure of neuronal homeostasis in hippocampal networks. Proc. Natl. Acad. Sci. https://doi.org/10.1073/pnas.1424810112 (2015).Article 
PubMed 

Google Scholar 
58.von Trotha, J. W., Vernier, P. & Bally-Cuif, L. Emotions and motivated behavior converge on an amygdala-like structure in the zebrafish. Eur. J. Neurosci. https://doi.org/10.1111/ejn.12692 (2014).Article 

Google Scholar 
59.Ganz, J. et al. Subdivisions of the adult zebrafish subpallium by molecular marker analysis. J. Comp. Neurol. https://doi.org/10.1002/cne.22757 (2012).Article 
PubMed 

Google Scholar 
60.Perathoner, S., Cordero-Maldonado, M. L. & Crawford, A. D. Potential of zebrafish as a model for exploring the role of the amygdala in emotional memory and motivational behavior. J. Neurosci. Res. https://doi.org/10.1002/jnr.23712 (2016).Article 
PubMed 

Google Scholar 
61.Qiu, J. et al. Decreased Npas4 and Arc mRNA levels in the hippocampus of aged memory-impaired wild-type but not memory preserved 11β-HSD1 deficient mice. J. Neuroendocrinol. https://doi.org/10.1111/jne.12339 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
62.Vindas, M. A. et al. How do individuals cope with stress? Behavioural, physiological and neuronal differences between proactive and reactive coping styles in fish. J. Exp. Biol. https://doi.org/10.1242/jeb.153213 (2017).Article 
PubMed 

Google Scholar 
63.Øverli, Ø., Pottinger, T. G., Carrick, T. R., Øverli, E. & Winberg, S. Brain monoaminergic activity in rainbow trout selected for high and low stress responsiveness. Brain. Behav. Evol. https://doi.org/10.1159/000047238 (2001).Article 
PubMed 

Google Scholar 
64.Walker, D. L., Toufexis, D. J. & Davis, M. Role of the bed nucleus of the stria terminalis versus the amygdala in fear, stress, and anxiety. Eur. J. Pharmacol. https://doi.org/10.1016/S0014-2999(03)01282-2 (2003).Article 
PubMed 

Google Scholar 
65.Goode, T. D. & Maren, S. Role of the bed nucleus of the stria terminalis in aversive learning and memory. Learn. Mem. https://doi.org/10.1101/lm.044206.116 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
66.Henckens, M. J. A. G. et al. CRF receptor type 2 neurons in the posterior bed nucleus of the stria terminalis critically contribute to stress recovery. Mol. Psychiatry https://doi.org/10.1038/mp.2016.133 (2017).Article 
PubMed 

Google Scholar 
67.Rink, E. & Wullimann, M. F. Connections of the ventral telencephalon (subpallium) in the zebrafish (Danio rerio). Brain Res. https://doi.org/10.1016/j.brainres.2004.03.027 (2004).Article 
PubMed 

Google Scholar 
68.Boulton, K. et al. How integrated are behavioral and endocrine stress response traits? A repeated measures approach to testing the stress-coping style model. Ecol. Evol. 5(3), 618–633 (2015).Article 

Google Scholar 
69.Baugh, A. T. et al. Corticosterone responses differ between lines of great tits (Parus major) selected for divergent personalities. Gen. Comp. Endocrinol. 175(3), 488–494 (2012).CAS 
Article 

Google Scholar 
70.Wong RY, French J, Russ JB (2018) Differences in stress reactivity between zebrafish with alternative stress coping styles. Dissertation (University of Nebraska at Omaha).71.Furukawa-Hibi, Y., Yun, J., Nagai, T. & Yamada, K. Transcriptional suppression of the neuronal PAS domain 4 (Npas4) gene by stress via the binding of agonist-bound glucocorticoid receptor to its promoter. J. Neurochem. https://doi.org/10.1111/jnc.12034 (2012).Article 
PubMed 

Google Scholar 
72.Ibi, D. et al. Social isolation rearing-induced impairment of the hippocampal neurogenesis is associated with deficits in spatial memory and emotion-related behaviors in juvenile mice. J. Neurochem. https://doi.org/10.1111/j.1471-4159.2007.05207.x (2008).Article 
PubMed 

Google Scholar 
73.Yun, J. et al. Chronic restraint stress impairs neurogenesis and hippocampus-dependent fear memory in mice: Possible involvement of a brain-specific transcription factor Npas4. J. Neurochem. https://doi.org/10.1111/j.1471-4159.2010.06893.x (2010).Article 
PubMed 

Google Scholar 
74.Sun, X. & Lin, Y. Npas4: Linking neuronal activity to memory. Trends Neurosci. https://doi.org/10.1016/j.tins.2016.02.003 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
75.Makkar, S. R., Zhang, S. Q. & Cranney, J. Behavioral and neural analysis of GABA in the acquisition, consolidation, reconsolidation, and extinction of fear memory. Neuropsychopharmacology https://doi.org/10.1038/npp.2010.53 (2010).Article 
PubMed 
PubMed Central 

Google Scholar  More

Study areaWe performed this study in San Nicolas Tecoaco village (20° 2′ 38.2ʺ N, 98° 35′ 16ʺ W), Hidalgo State, central Mexico, from March 2014 to October 2014. This location has an annual average temperature of 16 °C and an average altitude of 2600 m above sea level. The type of vegetation occurring in this area is classified as a xerophilous shrubland42.Study speciesOpuntia robusta (Cactaceae) is an endemic plant found in Meridional Altiplano, México43, which exists in the following three sexual forms: hermaphrodite, dioecious (male and female), and trioecious44. In a parallel study, Sandoval-Molina45 found that the most common herbivores of this plant were leaf-footed bugs, Chelinidea sp., Narnia sp. (Hemiptera: Coreidae), the cactus long-horned beetle, Moneilema sp. (Coleoptera: Cerambycidae), and mining insects. Before 2017, this population was considered to be gynodioecious; thus, we did not collect samples from male individuals in this study. In 2018, fewer than 15 male individuals were reportedly present in a population of more than 800, and most of these were hermaphrodites (Supplementary Information).Determination of plant sexWhite empty anthers, short style, and well-developed lobular stigma characterised female flowers, while a relatively longer style compared to that of the female and functional anthers characterised hermaphrodite individuals44.Comparison of tissue cost between female and hermaphrodite individualsIn March 2017, we undertook a census in San Nicolas Tecoaco, to identify the number of female and hermaphrodite plants with cladode and flower sprouts from the set of plants studied in the previous years. We selected 1–2 m tall plants, located 5–10 m apart for sampling. Finally, we randomly selected 19 plants (eleven female and eight hermaphrodite individuals) bearing flower buds and young cladodes on different branches for analysis and tagged the cladodes and flower sprouts using a permanent marker. We marked the flower sprouts on the adjacent side of their parental cladode surface.Between March 2017 and June 2017, we obtained sufficient data to estimate the relative growth rates of the species, in order to explore possible differences in the energy costs of cladodes and flower buds between the two sexual forms of O. robusta. We measured the length, width, and thickness of each cladode and flower bud twice during the study, once at the beginning, and once at the end of the study. Additionally, we also measured the lengths of the flowers from the base to the beginning of the sepals. Since the flower buds were spherical, we considered the thickness to be equal to the width. Subsequently, we calculated the flower volume immediately after the emergence of cladodes and flower buds, and the final volume after anthesis. We estimated the initial and final volumes (Vx) of the cladodes using the formula Vx = ((a/2))/((b/2)π)c, and those of the flowers using the formula Vx = 4/3πa2b. Here, x represents the time of measurement (initial or final), a and b represent the major and minor axes of the ellipsis, while c represents the cladode thickness. We measured all estimators to the nearest 1.0 mm and represented values in centimetres. We estimated the relative growth rate (RGR) using the formula proposed by Hunt46: RGR = (lnVf – lnVi)/(t2 – t1). Here, Vf represents the final volume [cm3], Vi represents the initial volume [cm3], t1 represents the initial time [day], and t2 represents the final time [day].We compared relative growth rate data using a generalized linear model (GLM) with gamma error distribution in the R software, using the log link function47. The explanatory variables included sex, type of structure, and their interactions. We performed partial regression using the ggeffects package in R48.We obtained meteorological variables, including total precipitation [mm], maximum temperature [°C], minimum temperature [°C], mean temperature [°C], global radiation [W(m2)−1], relative humidity [%], reference evapotranspiration [mm], and potential evapotranspiration [mm] for the Singuilucan municipality from March–October 2014, from the official Mexican Government weather station database of the Instituto Nacional de Investigaciones Forestales Agrícolas y Pecuarias49. We summed up the data for the per-day total precipitation, and that for the reference and potential evapotranspiration, from the beginning of each month through the sampling day. In the months (March, April, and May) or days when values from the meteorological database were underestimated, we averaged the values for the closest preceding and following days. If we lacked the data for more than one day and the data for such days could not be acquired, we considered a repetition of the averaged value for the days for which we lacked data, between the existing days. For July, we considered the values for the previous day (11/07/14), since we lacked the data for the days on which sampling was performed and the subsequent days. For the additive variables (total precipitation, reference, and potential evapotranspiration), we summed up data for 30 days, excluding data for one day, for the 31-day period.To determine the effects of the environmental variables on the concentration and presence/absence of secondary metabolites, we used R to formulate a structural equation model (SEM) in piecewiseSEM47,50. For concentrations, we fitted linear mixed-effects models using the nlme package51 and used the plant ID as a random factor. To evaluate the presence or absence of substances, we fitted generalized linear models with binomial error distributions and logits as the link functions. The concentration and presence/absence of 4-HBA, CGA, and QUE were dependent variables, and total precipitation, average temperature, global radiation, relative humidity, and potential evapotranspiration were explanatory variables. We analysed the sexes separately, and the substance concentration variables were log + 1 transformed. We assessed the goodness-of-fit using the Fisher function in the piecewiseSEM package50, where a larger p-value implies better data adjustment to the model. We conducted a visualisation of the SEM models using Biorender52, flaticon53, and CorelDRAW54.We estimated fruit traits (biomass [g], volume [cm3], and tissue density [g × cm−3]) and the number of fruits eaten by fructivores and compared them between the sexual forms using data reported by Janczur et al.18. The former comparison enabled the assessment of the possible differences in reproduction per fruit biomass between the sexual forms. The latter comparison enabled the assessment of the differences in preference for fruits eaten by animals in relation to the different sexual forms, and thus, the mechanisms by which this may increase the probability of seed dispersal. Higher zoochory of one sexual form may occur not only because of differences in fruit biomass density [g × cm−3], but also because of differences in the volatile substance content between the sexual forms.To test the effects of sexual form on fruit traits, we used generalized linear models in R. To analyse the number of fruits eaten, we used the negative binomial error distribution and log link function, and the Gaussian error distribution and identity link function for the other fruit traits47,55,56. We performed all post-hoc contrasts for fruit traits using the emmeans package47,57, and generated plots using the ggplot R package47,58. We compared the average number of fruits produced by the two sexual forms using the Kruskal–Wallis test.Comparison of secondary metabolite occurrence/concentration between female and hermaphrodite individualsWe obtained plant samples for secondary metabolite analysis using 100 m long Canfield lines, which were parallel to the contours of the hill and located 60 m from each other, and selected plants that were located near the lines and were 10 m apart for analysis. We randomly assigned each plant to one of the eight groups established herein, with three female plants and twelve hermaphrodite plants. The uneven number of individuals of each sex was attributable to the low proportion of females in the population. We tagged examined cladodes on their surface using a permanent marker.We used a stainless-steel punch (Ø = 0.5 cm) to remove two samples of vegetative tissue from cladodes belonging to the same order of each plant. We perforated the mid-section of the arc delimited by the border of the upper quarters of the cladodes, approximately 1 cm away from the edge. We placed samples in labelled Ziploc bags, stored them in a cooler containing ice, and then transported them to the laboratory in a portable refrigerator at − 20 °C. The samples were stored in the laboratory at − 40 °C until extraction.We performed homogenisation of approximately 1 g of the sample containing the cuticle in 35 mL of 100% methanol in an ultrasonic 6 L bath for 30 min at room temperature (21 °C). We filtered the methanol extracts, placed them in amber bottles, and stored the bottles at − 20 °C until further analysis59. We determined the types and concentrations of secondary metabolites in these tissues using high-performance liquid chromatography (HPLC), in accordance with the procedure described by Janczur and González Camarena59, using the following: Waters 717 liquid chromatograph with autosampler, Waters 2487 HPLC Absorbance UV–Vis Detector, Waters 1525 Binary HPLC Pump, Waters control module with SAT/IN Bus (Waters, Milford, MA, USA), Symmetry HPLC C18 column (particle size 5 µm, length 250 mm, internal Ø = 4.6 cm; Waters, Milford, MA, USA). We filtered the extracts using a 0.45 µm pore size nylon-membrane filter. The mobile phase consisted of 0.1% v/v acetic acid (A) together with 100% acetonitrile (B). For the mobile phase A, we dissolved 1 mL of glacial acetic acid with HPLC water, until the volume was 1 L. For the mobile phase B, we used 100% acetonitrile. We filtered both mobile phases using a 0.45 µm nylon membrane. We degasified them with an ultrasonic bath for 30 min. We set the column temperature at 25 °C, used the 254 nm UV detector, and established the flow of the mobile phase, injection volume, and run time as 0.2–0.8 mL/min, at 8 µL, and 35 min, respectively. To wash the piston seals, we used MeOH : H2O (60 : 50). To generate the calibration curves, we used standards for salicylic acid (SA), 4-hydroxybenzoic acid (4-HBA), chlorogenic acid (CGA), and quercetin (QUE) (Sigma-Aldrich). We generated the following calibration curves: yi = 1109.4xi + 481.67, yi = 296.01xi + 133.74, yi = 551.41xi + 263.64, and yi = 919.96xi + 201.64; here, yi represents the area below the absorbance curve, xi represents the concentration of the secondary metabolite, and i = 1, 2, 3, and 4 for 4-HBA, CGA, QUE, and SA, respectively. SA was not present in any of the samples tested (Table S1 online31).We used a logistic regression model to test the effect of the sexual form, month of study, cladode age category, cladode size, the number of cladodes above a given cladode, and the cladode order above the soil level, on the probability of detecting secondary metabolites in the cladodes. Since the latter data were ordinal, the sexual form and month were considered as discrete variables and treated the other traits as continuous variables60. We applied the generalized linear mixed model (GLMM) with a logit link function [ln(P/(1-P)], where P indicated the probability of detecting a given metabolite, binomial response distribution, maximum likelihood estimation technique, Newton–Raphson optimisation algorithm, and Person Chi-Square/df fit criterion. We used the GLIMMIX procedure in SAS statistical software61 (Methods S1).We used generalized linear models (GLMs) in R47 to determine the relationship between cladode length, width, thickness, months, age, cladode order from the soil, and cladodes above a given cladode, and the concentrations of the different secondary metabolites. Since many concentrations were null, we analysed only the positive concentrations (Methods S1).Comparison of damage between female and hermaphrodite individualsWe used the same plants as those used for relative growth rate analysis. We analysed the extent of damage caused by herbivorous insects on both sexes of O. robusta from March–June 2017. We selected two branches, one with flowers and the other with cladodes, from each plant. We estimated two types of damage caused by herbivores using image analysis, to determine the total percentage of tissue removed and other types of damage, such as scars or necrosis. We acquired photographs of one randomly selected face of each structure, using a Nikon D3200 with an AF-S DX NIKKOR 18–55 mm f/3.5–5.6G VR lens (Nikon Corporation, Tokyo, Japan) mounted on a tripod, using a 1-cm piece of millimetre paper as a reference for size. We analysed all images using ImageJ62 to estimate the total proportion of damaged areas.We analysed data on herbivore damage and other damages using a GLM procedure with the Gaussian error distribution and identity link function47 in R. The response variables were the logit transformed proportion of damage (ln[P/(1-P)]), where P represents the proportion of tissue damaged. In our statistical models, the transformation improved the distribution of residuals. The explanatory variables were sex, type of structure, and their interactions. We performed partial regressions using the ggeffects package in R48.Comparison of the occurrence/concentrations of secondary metabolites between younger and older vegetative tissuesWe named the oldest cladodes (closest to the soil) as ‘first-order cladodes,’ those growing on the oldest cladodes as ‘second-order cladodes’ etc. We selected each plant branch with the largest number of cladodes. We measured the length, width, and thickness of each cladode. We sampled vegetative tissues from plants belonging to each of the eight groups; the first group on the 10th March, the second group on the 12th April and so on, through the 10th May, 14th June, 12th July, 10th August, 13th September, and 11th October 2014. We measured the length and width of each cladode to the nearest 0.5 cm, using a measuring tape, and their thickness to the nearest 0.01 mm, using a calliper. We conducted the latter measurement in the apical part of the cladodes in the case of apical cladodes, or at the point of ramification of the daughter cladode when it grew on its apex.During eight years of observations prior to the commencement of this study, we observed that the age of the cladodes in the studied zone could be estimated by examining the following colour patterns of their spines: 1—yellowish, 2—yellow, white base, 3—white-yellowish, 4—white, 5—greyish, 6—black, with ‘1’ being the youngest, and ‘6’ being the oldest. We assigned each cladode to one of the classes. We used the HPLC procedure described by Janczur and González Camarena59 to determine the concentrations of different secondary metabolites in the plant tissues.To test whether different estimators of cladode age were parallel (to test whether younger cladodes were mostly apical, and thus bore fewer cladodes above), we examined the relationship between the cladode order from the soil or cladode number above a given cladode and cladode age, using ordinary least squares regression (OLS). We used a numerical algorithm applied to the SMATR software for R63. We included a test for the determination of the effects of cladode age estimators on the SMSs occurrence/concentration in the same GLM models, as described in the previous section.Trade-off between investments in defence, growth, and reproductionWe tested the relationship between cladode length and cladode order or cladode age to determine whether cladode size was parallel to cladode age. We performed OLS analysis and slope comparison between sexual forms using the Wald test (WT—test statistic) and tested the significance of differences between the intercepts. We used a numerical algorithm applied in the SMATR software63. To estimate the relative investment in growth and reproduction, we counted the number of flower and cladode buds on parental cladodes of the same plants used in the study performed by Sandoval and Janczur (Dataset online29). We used generalized linear models in R, with a negative binomial error distribution and log link function47,55,56, to test the effects of sexual form on the average number of flower and cladode buds. Significant differences between the number of flowers and cladodes for certain sexual forms implies a higher relative reproductive investment.We used the same method of quantification for the standardized major axis and GLM models for intersexual comparisons, as described in the previous Sect. 59. For example, larger relative allocations for reproduction and secondary metabolites together with lower allocation to growth in one sexual form, compared to lower allocations for reproduction and secondary metabolites, and higher allocations for growth in the other sexual form imply that the production of secondary metabolites does not compete with either growth or reproduction; rather, growth competes with reproduction, and allocation to the production of secondary metabolites is an outcome of the gain in terms of fitness from such an allocation.Effects of the existence of trade-offs between different secondary metabolites on the predictions of the plant defence hypothesisWe used ordinary least squares regression (OLS), coefficient of determination, and t-tests to determine the existence of possible trade-offs in the proportion of cladodes harbouring different secondary metabolites. We performed the t-test to determine the significance of correlation between cladode order and cladode age64.Ethics statementThis research did not involve any human or animal measurements. We obtained permission from the head of the Singuilucan municipality, State of Hidalgo, Mexico, to conduct research activities at the selected sites of the municipality. The owners of the lands permitted us to conduct the study and were informed of the permission granted by the municipality. MKJ obtained a permit (09,448/14) from the Ministry of Environment and Natural Resources of the United States of Mexico (SEMARNAT), which stated that no permission is necessary to conduct field studies on plants belonging to the genus Opuntia. The study site was not considered to be a protected area65, and O. robusta was not considered to be an endangered species66. During this study, we did not affect or involve any endangered species. As we did not sample all plants, we did not deposit specimens in a public herbarium. No plant was killed or severely damaged as a result of our research activity; the plant material used for this study was sampled at a limited scale, and therefore, the sampling presented with negligible effects on the functions of the broader ecosystem. All the methods were carried out in accordance to relevant guidelines and regulations. More

1.Sun, Y., Hu, K. L., Zhang, K. F., Jiang, L. H. & Xu, Y. Simulation of nitrogen fate for greenhouse cucumber grown under different water and fertilizer management using the EU-Rotate_N model. Agric. Water Manage. 112, 21–32 (2012).Article 

Google Scholar 
2.Vejan, P., Abdullah, R., Khadiran, T., Ismail, S. & Boyce, A. N. Role of plant growth promoting rhizobacteria in agricultural sustainability—A review. Molecules 21(5), 573. https://doi.org/10.3390/molecules21050573 (2016).3.Ferreira, C. M. H., Soares, H. & Soares, E. V. Promising bacterial genera for agricultural practices: An insight on plant growth-promoting properties and microbial safety aspects. Sci. Total Environ. 682, 779–799. https://doi.org/10.1016/j.scitotenv.2019.04.225 (2019).ADS 
CAS 
Article 
PubMed 

Google Scholar 
4.Sammauria, R., Kumawat, S., Kumawat, P., Singh, J. & Jatwa, T. K. Microbial inoculants: potential tool for sustainability of agricultural production systems. Arch. Microbiol. 202(4), 677–693 https://doi.org/10.1007/s00203-019-01795-w (2020).5.Singh, M. et al. PGPR Amelioration in Sustainable Agriculture 41–66 (Woodhead Publishing, 2019).Book 

Google Scholar 
6.Berg, G. Plant-microbe interactions promoting plant growth and health: perspectives for controlled use of microorganisms in agriculture. Appl. Microbiol. Biotechnol. 84, 11–18 (2009).CAS 
Article 

Google Scholar 
7.Olanrewaju, O. S., Glick, B. R. & Babalola, O. O. Mechanisms of action of plant growth promoting bacteria. World J. Microbiol. Biotechnol. 33(11), 197. https://doi.org/10.1007/s11274-017-2364-9 (2017).8.Ambrosini, A., de Souza, R. & Passaglia, L. M. P. Ecological role of bacterial inoculants and their potential impact on soil microbial diversity. Plant Soil 400, 193–207. https://doi.org/10.1007/s11104-015-2727-7 (2016).CAS 
Article 

Google Scholar 
9.O’Callaghan, M. Microbial inoculation of seed for improved crop performance: Issues and opportunities. Appl. Microbiol. Biotechnol. 100, 5729–5746 (2016).CAS 
Article 

Google Scholar 
10.Kaminsky, L. M., Trexler, R. V., Malik, R. J., Hockett, K. L. & Bell, T. H. The inherent conflicts in developing soil microbial inoculants. Trends Biotechnol. 37, 140–151 (2019).CAS 
Article 

Google Scholar 
11.Chen, X. H. et al. Comparative analysis of the complete genome sequence of the plant growth-promoting bacterium Bacillus amyloliquefaciens FZB42. Nat. Biotechnol. 25, 1007–1014 (2007).CAS 
Article 

Google Scholar 
12.Chowdhury, S. P., Hartmann, A., Gao, X. W. & Borriss, R. Biocontrol mechanism by root-associated Bacillus amyloliquefaciens FZB42—A review. Front. Microbiol. 6, 780. https://doi.org/10.3389/fmicb.2015.00780 (2015).13.Han, L. et al. Bacillus amyloliquefaciens B1408 suppresses Fusarium wilt in cucumber by regulating the rhizosphere microbial community. Appl. Soil Ecol. 136, 55–66 (2019).Article 

Google Scholar 
14.Wu, B. et al. Effects of Bacillus amyloliquefaciens ZM9 on bacterial wilt and rhizosphere microbial communities of tobacco. Appl. Soil Ecol. 103, 1–12 (2016).ADS 
Article 

Google Scholar 
15.Krober, M. et al. Effect of the strain Bacillus amyloliquefaciens FZB42 on the microbial community in the rhizosphere of lettuce under field conditions analyzed by whole rnetagenome sequencing. Front. Microbiol. 5, 252. https://doi.org/10.3389/fmicb.2014.00252 (2014).16.Shen, Z. Z. et al. Effect of the combination of bio-organic fertiliser with Bacillus amyloliquefaciens NJN-6 on the control of banana Fusarium wilt disease, crop production and banana rhizosphere culturable microflora. Biocontrol Sci. Technol. 25, 716–731 (2015).Article 

Google Scholar 
17.Shen, Z. Z. et al. Rhizosphere microbial community manipulated by 2 years of consecutive biofertilizer application associated with banana Fusarium wilt disease suppression. Biol. Fertility Soils 51, 553–562 (2015).CAS 
Article 

Google Scholar 
18.Li, Q. et al. Rhizosphere microbiome mediated growth-promoting mechanisms of Bacillus amyloliquefaciens FH-1 on rice. Acta Microbiol. Sin. 59, 1–17 (2019).ADS 
CAS 

Google Scholar 
19.Qin, Y. X., Shang, Q. M., Zhang, Y., Li, P. L. & Chai, Y. R. Bacillus amyloliquefaciens L-S60 reforms the rhizosphere bacterial community and improves growth conditions in cucumber plug seedling. Front. Microbiol. 8, 2620. https://doi.org/10.3389/fmicb.2017.02620 (2017).20.Idris, E. E., Iglesias, D. J., Talon, M. & Borriss, R. Tryptophan-dependent production of indole-3-acetic acid (IAA) affects level of plant growth promotion by Bacillus amyloliquefaciens FZB42. Mol. Plant-Microbe Interact. 20, 619–626. https://doi.org/10.1094/Mpmi-20-6-0619 (2007).CAS 
Article 
PubMed 

Google Scholar 
21.Mendes, R. et al. Deciphering the rhizosphere microbiome for disease-suppressive bacteria. Science 332, 1097–1100 (2011).ADS 
CAS 
Article 

Google Scholar 
22.Panke-Buisse, K., Poole, A. C., Goodrich, J. K., Ley, R. E. & Kao-Kniffin, J. Selection on soil microbiomes reveals reproducible impacts on plant function. ISME J 9, 980–989. https://doi.org/10.1038/ismej.2014.196 (2015).CAS 
Article 
PubMed 

Google Scholar 
23.de Vries, F. T., Griffiths, R. I., Knight, C. G., Nicolitch, O. & Williams, A. Harnessing rhizosphere microbiomes for drought-resilient crop production. Science 368, 270 (2020).ADS 
Article 

Google Scholar 
24.Rodriguez, P. A. et al. Systems biology of plant–microbiome interactions. Mol. Plant 12, 804–821. https://doi.org/10.1016/j.molp.2019.05.006 (2019).CAS 
Article 
PubMed 

Google Scholar 
25.Trabelsi, D. & Mhamdi, R. Microbial inoculants and their impact on soil microbial communities: A review. Biomed. Res. Int. 2013, 863240. https://doi.org/10.1155/2013/863240 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
26.Gu, Y. et al. The effect of microbial inoculant origin on the rhizosphere bacterial community composition and plant growth-promotion. Plant Soil 452, 105–117. https://doi.org/10.1007/s11104-020-04545-w (2020).CAS 
Article 

Google Scholar 
27.Ke, X. B. et al. Effect of inoculation with nitrogen-fixing bacterium Pseudomonas stutzeri A1501 on maize plant growth and the microbiome indigenous to the rhizosphere. Syst. Appl. Microbiol. 42, 248–260 (2019).CAS 
Article 

Google Scholar 
28.Barberan, A., Bates, S. T., Casamayor, E. O. & Fierer, N. Using network analysis to explore co-occurrence patterns in soil microbial communities. ISME J. 6, 343–351. https://doi.org/10.1038/ismej.2011.119 (2012).CAS 
Article 
PubMed 

Google Scholar 
29.Kong, Z. Y. et al. Co-occurrence patterns of microbial communities affected by inoculants of plant growth-promoting bacteria during phytoremediation of heavy metal contaminated soils. Ecotoxicol. Environ. Saf. 183, 109504. https://doi.org/10.1016/j.ecoenv.2019.109504 (2019).30.Newman, M. E. Modularity and community structure in networks. Proc. Natl. Acad. Sci. USA 103, 8577–8582. https://doi.org/10.1073/pnas.0601602103 (2006).ADS 
CAS 
Article 
PubMed 

Google Scholar 
31.Mendes, R. & Raaijmakers, J. M. Cross-kingdom similarities in microbiome functions. ISME J. 9, 1905–1907. https://doi.org/10.1038/ismej.2015.7 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
32.Mueller, U. G. & Sachs, J. L. Engineering microbiomes to improve plant and animal health. Trends Microbiol. 23, 606–617. https://doi.org/10.1016/j.tim.2015.07.009 (2015).CAS 
Article 
PubMed 

Google Scholar 
33.Toju, H. et al. Core microbiomes for sustainable agroecosystems. Nat. Plants 4, 247–257 (2018).Article 

Google Scholar 
34.Chowdhury, S. P. et al. Effects of Bacillus amyloliquefaciens FZB42 on lettuce growth and health under pathogen pressure and its impact on the rhizosphere bacterial community. Plos One 8(7), e68818. https://doi.org/10.1371/journal.pone.0068818 (2013).35.Correa, O. S. et al. Bacillus amyloliquefaciens BNM122, a potential microbial biocontrol agent applied on soybean seeds, causes a minor impact on rhizosphere and soil microbial communities. Appl. Soil Ecol. 41, 185–194 (2009).Article 

Google Scholar 
36.Wan, T. T., Zhao, H. H. & Wang, W. Effect of biocontrol agent Bacillus amyloliquefaciens SN16-1 and plant pathogen Fusarium oxysporum on tomato rhizosphere bacterial community composition. Biol. Control 112, 1–9 (2017).Article 

Google Scholar 
37.Wan, T. T., Zhao, H. H. & Wang, W. Effects of the biocontrol agent Bacillus amyloliquefaciens SN16-1 on the rhizosphere bacterial community and growth of tomato. J. Phytopathol. 166, 324–332 (2018).CAS 
Article 

Google Scholar 
38.Nautiyal, C. S. et al. Plant growth-promoting bacteria Bacillus amyloliquefaciens NBRISN13 modulates gene expression profile of leaf and rhizosphere community in rice during salt stress. Plant Physiol. Biochem. 66, 1–9 (2013).CAS 
Article 

Google Scholar 
39.Kumar, S., Suyal, D. C., Yadav, A., Shouche, Y. & Goel, R. Microbial diversity and soil physiochemical characteristic of higher altitude. Plos One 14(3), e0213844. https://doi.org/10.1371/journal.pone.0213844 (2019).40.Kielak, A. M., Barreto, C. C., Kowalchuk, G. A., van Veen, J. A. & Kuramae, E. E. The ecology of acidobacteria: Moving beyond genes and genomes. Front. Microbiol. 7, 744. https://doi.org/10.3389/fmicb.2016.00744 (2016).41.Ul-Hassan, A. & Wellington, E. M. Actinobacteria in Encyclopedia of Microbiology (Third Edition) (ed Schaechter, M.) 25–44 (Academic Press, 2009).42.Zhang, M., Powell, C. A., Guo, Y., Benyon, L. & Duan, Y. Characterization of the microbial community structure in Candidatus Liberibacter asiaticus-infected citrus plants treated with antibiotics in the field. BMC Microbiol. 13, 112. https://doi.org/10.1186/1471-2180-13-112 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
43.Albuquerque, L., Johnson, M. M., Schumann, P., Rainey, F. A. & da Costa, M. S. Description of two new thermophilic species of the genus Rubrobacter, Rubrobacter calidifluminis sp. nov. and Rubrobacter naiadicus sp. Nov., and emended description of the genus Rubrobacter and the species Rubrobacter bracarensis. Syst. Appl. Microbiol. 37, 235–243. https://doi.org/10.1016/j.syapm.2014.03.001 (2014).CAS 
Article 
PubMed 

Google Scholar 
44.Egas, C. et al. Complete genome sequence of the radiation-resistant bacterium Rubrobacter radiotolerans RSPS-4. Stand. Genomic. Sci. 9(3), 1062–1075. https://doi.org/10.4056/sigs.5661021 (2014).45.Ge, S. M., Zhou, M. H., Dong, X. J., Lu, Y. & Ge, S. C. Distinct and effective biotransformation of hexavalent chromium by a novel isolate under aerobic growth followed by facultative anaerobic incubation. Appl. Microbiol. Biotechnol. 97, 2131–2137 (2013).CAS 
Article 

Google Scholar 
46.Sturm, G., Jacobs, J., Sproer, C., Schumann, P. & Gescher, J. Leucobacter chromiiresistens sp. nov., a chromate-resistant strain. Int. J. Syst. Evol. Microbiol. 61, 956–960 (2011).CAS 
Article 

Google Scholar 
47.Muir, R. E. & Tan, M. W. Virulence of Leucobacter chromiireducens subsp. solipictus to Caenorhabditis elegans: Characterization of a novel host-pathogen interaction. Appl. Environ. Microbiol. 74, 4185–4198 (2008).CAS 
Article 

Google Scholar 
48.Zhang, Y. et al. Abundance and diversity of candidate division JS1-and Chloroflexi-related bacteria in cold seep sediments of the northern South China Sea. Front. Earth Sci. Prc. 6, 373–382. https://doi.org/10.1007/s11707-012-0324-0 (2012).ADS 
CAS 
Article 

Google Scholar 
49.Bennett, A. C., Murugapiran, S. K. & Hamilton, T. L. Temperature impacts community structure and function of phototrophic Chloroflexi and Cyanobacteria in two alkaline hot springs in Yellowstone National Park. Environ. Microbiol. Rep. https://doi.org/10.1111/1758-2229.12863 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
50.Devos, D. P. Gemmata obscuriglobus. Curr. Biol. 23, R705-707. https://doi.org/10.1016/j.cub.2013.07.013 (2013).CAS 
Article 
PubMed 

Google Scholar 
51.Dong, L. L. et al. Diversity and composition of bacterial endophytes among plant parts of Panax notoginseng. Chin. Med. UK 13, 41. https://doi.org/10.1186/s13020-018-0198-5 (2018).52.Ma, Q. et al. Bacterial community compositions of coking wastewater treatment plants in steel industry revealed by Illumina high-throughput sequencing. Bioresour. Technol. 179, 436–443 (2015).CAS 
Article 

Google Scholar 
53.Kepel, B. J. F., Gani, M. A. & Tallei, T. E. Comparison of bacterial community structure and diversity in traditional gold mining waste disposal site and rice field by using a metabarcoding approach. Int. J. Microbiol. 2020, 1858732. https://doi.org/10.1155/2020/1858732 (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
54.Chouari, R. et al. Molecular evidence for novel planctomycete diversity in a municipal wastewater treatment plant. Appl. Environ. Microbiol. 69, 7354–7363. https://doi.org/10.1128/aem.69.12.7354-7363.2003 (2003).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
55.Zhao, Y. et al. Endosphere microbiome comparison between symptomatic and asymptomatic roots of Brassica napus infected with Plasmodiophora brassicae. Plos One 12(10), e0185907. https://doi.org/10.1371/journal.pone.0185907 (2017).56.Banerjee, S. et al. Agricultural intensification reduces microbial network complexity and the abundance of keystone taxa in roots. ISME J. https://doi.org/10.1038/s41396-019-0383-2 (2019).Article 
PubMed 
PubMed Central 

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

Google Scholar 
58.Olesen, J. M., Bascompte, J., Dupont, Y. L. & Jordano, P. The modularity of pollination networks. Proc. Natl. Acad. Sci. USA 104, 19891–19896 (2007).ADS 
CAS 
Article 

Google Scholar 
59.Kang, Y., Shen, M., Wang, H. & Zhao, Q. A possible mechanism of action of plant growth-promoting rhizobacteria (PGPR) strain Bacillus pumilus WP8 via regulation of soil bacterial community structure. J. Gen. Appl. Microbiol. 59, 267–277 (2013).CAS 
Article 

Google Scholar 
60.Wang, J. et al. Traits-based integration of multi-species inoculants facilitates shifts of indigenous soil bacterial community. Front. Microbiol. https://doi.org/10.3389/fmicb.2018.01692 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
61.R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing (2020).62.Xiong, J. et al. Evidence of bacterioplankton community adaptation in response to long-term mariculture disturbance. Scientific Report 5, 15274. https://doi.org/10.1038/srep15274 (2015).ADS 
CAS 
Article 

Google Scholar 
63.Segata, N. et al. Metagenomic biomarker discovery and explanation. Genome Biol. 12(6), R60. https://doi.org/10.1186/gb-2011-12-6-r60 (2011).64.Jiang, Y. J. et al. Plant cultivars imprint the rhizosphere bacterial community composition and association networks. Soil Biol. Biochem. 109, 145–155 (2017).CAS 
Article 

Google Scholar 
65.Ju, F., Xia, Y., Guo, F., Wang, Z. P. & Zhang, T. Taxonomic relatedness shapes bacterial assembly in activated sludge of globally distributed wastewater treatment plants. Environ. Microbiol. 16, 2421–2432 (2014).CAS 
Article 

Google Scholar  More

1.Hoegh-Guldberg, O. et al. Coral reefs under rapid climate change and ocean acidification. Science 318, 1737–1742 (2007).ADS 
CAS 
Article 

Google Scholar 
2.Woodley, J. D. et al. Hurricane Allen’s impact on Jamaican coral reefs. Science 214, 749–755 (1981).ADS 
CAS 
Article 

Google Scholar 
3.Scoffin, T. P. The geological effects of hurricanes on coral reefs and the interpretation of storm deposits. Coral Reefs 12, 203–221 (1993).ADS 
Article 

Google Scholar 
4.Browning, T. N. et al. Widespread deposition in a Coastal Bay following three major 2017 Hurricanes (Irma, Jose, and Maria). Sci. Rep. 9, 7101. https://doi.org/10.1038/s41598-019-43062-4 (2019).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
5.Rogers, C. S. Immediate effects of hurricanes on a diverse coral/mangrove ecosystem in the US Virgin Islands and the potential for recovery. Diversity 11, 130 (2019).Article 

Google Scholar 
6.Graham, N. A. J. & Nash, K. L. The importance of structural complexity in coral reef ecosystems. Coral Reefs 32, 315–326 (2013).ADS 
Article 

Google Scholar 
7.Newman, S. P. et al. Reef flattening effects on total richness and species responses in the Caribbean. J. Anim Ecol. 84, 1678–1689 (2015).Article 

Google Scholar 
8.Burns, J. H. et al. Assessing the impact of acute disturbances on the structure and composition of a coral community using innovative 3D reconstruction techniques. Methods Oceanogr. 15, 49–59 (2016).Article 

Google Scholar 
9.Massel, S. R. & Done, T. J. Effects of cyclone waves on massive coral assemblages on the Great Barrier Reef: Meteorology, hydrodynamics and demography. Coral Reefs 12(3–4), 153–166 (1993).ADS 
Article 

Google Scholar 
10.Madin, J. S. & Connolly, S. R. Ecological consequences of major hydrodynamic disturbances on coral reefs. Nature 444(7118), 477–480 (2006).ADS 
CAS 
Article 

Google Scholar 
11.Madin, J. S., Baird, A. H., Dornelas, M. & Connolly, S. R. Mechanical vulnerability explains size-dependent mortality of reef corals. Ecol. Lett. 17(8), 1008–1015 (2014).Article 

Google Scholar 
12.Bellwood, D. R. & Hughes, T. P. Regional-scale assembly rules and biodiversity of coral reefs. Science 292, 1532–1535 (2001).ADS 
CAS 
Article 

Google Scholar 
13.Friedlander, A. et al. The state of coral reef ecosystems of the Northwestern Hawaiian Islands. The state of coral reef ecosystems of the United States and Pacific Freely Associated States. PLoS ONE 73, 263–306 (2005).
Google Scholar 
14.Houston, S. & Birchard, T. Central Pacific Hurricane Center Tropical Cyclone Report, Hurricane Walaka. https://www.nhc.noaa.gov/data/tcr/CP012018_Walaka.pdf (2020).15.Nugert, A. D. et al. Fire and rain: The legacy of Hurricane Lane in Hawai ‘i. Bull. Am. Meteorol. Soc. 101, 954–967 (2020).Article 

Google Scholar 
16.Fukunaga, A., Burns, J. H. R., Pascoe, K. H. & Kosaki, R. K. Associations between benthic cover and habitat complexity metrics obtained from 3D reconstruction of coral reefs at different resolutions. Remote Sens. 12, 1011 (2020).ADS 
Article 

Google Scholar 
17.Fukunaga, A. & Burns, J. H. R. Metrics of coral reef structural complexity extracted from 3D mesh models and digital elevation models. Remote Sens. 12, 2676 (2020).ADS 
Article 

Google Scholar 
18.Meyer, C. G., Holland, K. N. & Papastamatiou, Y. P. Seasonal and diel movements of giant trevally Caranx ignobilis at remote Hawaiian atolls: Implications for the design of marine protected areas. Mar. Ecol. Prog. Ser. 12(333), 13–25 (2007).ADS 
Article 

Google Scholar 
19.Fukunaga, A., Kosaki, R. K. & Hauk, B. B. Distribution and abundance of the introduced snapper Lutjanus kasmira (Forsskål, 1775) on shallow and mesophotic reefs of the Northwestern Hawaiian Islands. Bioinvasions Rec. 6, 259–268 (2017).Article 

Google Scholar 
20.Burns, J. H., Delparte, D., Gates, R. D. & Takabayashi, M. Integrating structure-from-motion photogrammetry with geospatial software as a novel technique for quantifying 3D ecological characteristics of coral reefs. PeerJ 3, e1077. https://doi.org/10.7717/peerj.1077 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
21.R Core Team. R: A Language and Environment for Statistical Computing, v. 3.5.3 (R Foundation for Statistical Computing, 2019). https://www.R-project.org (2019).22.Hijmans, R. J. raster: Geographic Data Analysis and Modeling, R package Version 2.9-5, https://CRAN.R-project.org/package=raster (2019)23.Bivand, R. & Rundel, C. rgeos: Interface to Geometry Engine—Open Source (GEOS), R package version 0.4-3. https://CRAN.R-project.org/package=rgeos (2019).24.Fukunaga, A., Burns, J. H. R., Craig, B. K. & Kosaki, R. K. Integrating three-dimensional benthic habitat characterization techniques into ecological monitoring of coral reefs. J. Mar. Sci. Eng. 7, 27 (2019).Article 

Google Scholar 
25.Risk, M. J. Fish diversity on a coral reef in the Virgin Islands. Atoll Res. Bull. 153, 1–4 (1972).Article 

Google Scholar 
26.Horn, B. K. P. Hill shading and the reflectance map. Proc. IEEE 69, 14–47 (1981).ADS 
Article 

Google Scholar 
27.Walbridge, S., Slocum, N., Pobuda, M. & Wright, D. J. Unified geomorphological analysis workflows with Benthic Terrain Modeler. Geosciences 8, 94 (2018).Article 

Google Scholar 
28.Sappington, J. M., Longshore, K. M. & Thompson, D. B. Quantifying landscape ruggedness for animal habitat analysis: A case study using bighorn sheep in the Mojave Desert. J. Wildl. Manage. 71, 1419–1426 (2007).Article 

Google Scholar 
29.Zevenbergen, L. W. & Thorne, C. R. Quantitative analysis of land surface topography. Earth Surf. Process Landf. 12, 47–56 (1987).ADS 
Article 

Google Scholar 
30.Couch, C. S. et al. Mass coral bleaching due to unprecedented marine heatwave in Papahānaumokuākea Marine National Monument (Northwestern Hawaiian Islands). PLoS ONE 12(9), e0185121. https://doi.org/10.1371/journal.pone.0185121 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
31.Magel, J. M. T., Burns, J. H., Gates, R. D. & Baum, J. K. Effects of bleaching-associated mass coral mortality on reef structural complexity across a gradient of local disturbance. Sci. Rep. 9, 2515. https://doi.org/10.1038/s41598-018-37713-1 (2019).ADS 
CAS 
Article 

Google Scholar 
32.Luckhurst, B. E. & Luckhurst, K. Analysis of the influence of substrate variables on coral reef fish communities. Mar. Biol. 49(4), 317–323 (1978).Article 

Google Scholar 
33.Graham, N. A. J. et al. Dynamic fragility of oceanic coral reef ecosystems. Proc. Natl. Acad. Sci. USA 103, 8425–8429 (2006).ADS 
CAS 
Article 

Google Scholar 
34.Mandelbrot, B. B. & Mandelbrot, B. B. The Fractal Geometry of Nature Vol. 1 (WH Freeman, 1982).MATH 

Google Scholar 
35.Young, G. C. et al. Cost and time-effective method for multi-scale measures of rugosity, fractal dimension, and vector dispersion from coral reef 3D models. PLoS ONE 12, e0175341. https://doi.org/10.1371/journal.pone.0175341 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar  More

Departamento de Ciencias Forestales, Universidad Nacional de Colombia Sede Medellín, Medellín, ColombiaAlvaro Duque, Miguel A. Peña & Sebastián González-CaroGrupo de Investigación en Biodiversidad, Medio Ambiente y Salud -BIOMAS – Universidad de Las Américas (UDLA), Quito, EcuadorFrancisco Cuesta, Marco Calderón-Loor & Esteban PintoDepartment of Plant and Microbial Biology, University of Minnesota, Saint Paul, MN, USAPeter KennedySchool of Geography, University of Leeds, Leeds, UKOliver L. PhillipsCentre for Integrative Ecology, School of Life and Environmental Sciences, Deakin University, Melbourne, VIC, AustraliaMarco Calderón-LoorInstituto de Ecología Regional (IER), Universidad Nacional de Tucumán (UNT) – Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Tucumán, ArgentinaCecilia Blundo, Julieta Carilla, Ricardo Grau, Agustina Malizia & Oriana Osinaga-AcostaHerbario Nacional de Bolivia (LPB), La Paz, BoliviaLeslie Cayola, Alfredo Fuentes & María I. Loza-RiveraMissouri Botanical Garden, St. Louis, MO, USALeslie Cayola, Alfredo Fuentes & María I. Loza-RiveraCenter for Conservation and Sustainable Development, Missouri Botanical Garden, St. Louis, MO, USAWilliam Farfán-Ríos, María I. Loza-Rivera & J. Sebastián TelloLiving Earth Collaborative, Washington University in Saint Louis, St. Louis, MO, USAWilliam Farfán-RíosPlant Ecology and Ecosystems Research, University of Gottingen, Gottingen, GermanyJürgen HomeierCentre of Biodiversity and Sustainable Land Use (CBL), University of Gottingen, Gottingen, GermanyJürgen HomeierEnvironmental Change Institute, School of Geography and the Environment, University of Oxford, Oxford, UKYadvinder MalhiFacultad de Ciencias Agrarias, Universidad Nacional de Jujuy, Jujuy, ArgentinaLucio MaliziaUniversité du Quebec a Montreal, Montreal, QC, CanadaJohanna A. Martínez-VillaDepartment of Biology, Washington University in St. Louis, St. Louis, MO, USAJonathan A. MyersConsorcio para el Desarrollo Sostenible de la Ecorregión Andina (CONDESAN), Quito, EcuadorManuel PeralvoColumbus State University, University System of Georgia, Columbus, GA, USAEsteban PintoCarbon Cycle and Ecosystems, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USASassan SaatchiCenter for Energy, Environment and Sustainability, Winston-Salem, NC, USAMiles SilmanCentro Jambatú de Investigación y Conservación de Anfibios, Quito, EcuadorAndrea Terán-ValdezBiology Department, University of Miami, Coral Gables, FL, USAKenneth J. Feeley More

1.Spilker, L. Cassini-Huygens’ exploration of the Saturn system: 13 years of discovery. Science 364, 1046–1051 (2019).ADS 
Article 

Google Scholar 
2.Thomas, P. et al. Enceladus’s measured physical libration requires a global subsurface ocean. Icarus 264, 37–47 (2016).ADS 
Article 

Google Scholar 
3.Waite, J. H. et al. Cassini finds molecular hydrogen in the Enceladus plume: evidence for hydrothermal processes. Science 356, 155–159 (2017).ADS 
Article 

Google Scholar 
4.Nathues, A. et al. Recent cryovolcanic activity at Occator crater on Ceres. Nat. Astron. 4, 794–801 (2020).ADS 
Article 

Google Scholar 
5.Schmidt, B. et al. Post-impact cryo-hydrologic formation of small mounds and hills in Ceres’s Occator crater. Nat. Geosci. 13, 605–610 (2020).ADS 
Article 

Google Scholar 
6.Reynolds, R. T., Squyres, S. W., Colburn, D. S. & McKay, C. P. On the habitability of Europa. Icarus 56, 246–254 (1983).ADS 
Article 

Google Scholar 
7.Martin, A. & McMinn, A. Sea ice, extremophiles and life on extra-terrestrial ocean worlds. Int. J. Astrobiol. 17, 1–16 (2018).ADS 
Article 

Google Scholar 
8.McCollom, T. M. Methanogenesis as a potential source of chemical energy for primary biomass production by autotrophic organisms in hydrothermal systems on Europa. J. Geophys. Res. Planets 104, 30729–30742 (1999).ADS 
Article 

Google Scholar 
9.Hsu, H.-W. et al. Ongoing hydrothermal activities within Enceladus. Nature 519, 207–210 (2015).ADS 
Article 

Google Scholar 
10.Glein, C. R., Baross, J. A. & Waite, J. H. Jr The pH of Enceladus’ ocean. Geochim. Cosmochim. Acta 162, 202–219 (2015).ADS 
Article 

Google Scholar 
11.Choblet, G. et al. Powering prolonged hydrothermal activity inside Enceladus. Nat. Astron. 1, 841–847 (2017).ADS 
Article 

Google Scholar 
12.Kleerebezem, R. & Van Loosdrecht, M. C. A generalized method for thermodynamic state analysis of environmental systems. Crit. Rev. Environ. Sci. Technol. 40, 1–54 (2010).Article 

Google Scholar 
13.Mousis, O. et al. Formation conditions of Enceladus and origin of its methane reservoir. Astrophys. J. Lett. 701, L39 (2009).ADS 
Article 

Google Scholar 
14.McKay, C., Khare, B. N., Amin, R., Klasson, M. & Kral, T. A. Possible sources for methane and C2–C5 organics in the plume of Enceladus. Planet. Space Sci. 71, 73–79 (2012).ADS 
Article 

Google Scholar 
15.Jannasch, H. W. & Mottl, M. J. Geomicrobiology of deep-sea hydrothermal vents. Science 229, 717–725 (1985).ADS 
Article 

Google Scholar 
16.Schrenk, M. O., Kelley, D. S., Bolton, S. A. & Baross, J. A. Low archaeal diversity linked to subseafloor geochemical processes at the Lost City Hydrothermal Field, Mid-Atlantic Ridge. Environ. Microbiol. 6, 1086–1095 (2004).Article 

Google Scholar 
17.Hedderich, R. & Whitman, W. B. in The Prokaryotes: Prokaryotic Physiology and Biochemistry (eds Rosenberg, E. et al.) 635–662 (Springer, 2013).18.Travis, B. & Schubert, G. Keeping Enceladus warm. Icarus 250, 32–42 (2015).ADS 
Article 

Google Scholar 
19.Martin, W., Baross, J., Kelley, D. & Russell, M. J. Hydrothermal vents and the origin of life. Nat. Rev. Microbiol. 6, 805–814 (2008).Article 

Google Scholar 
20.Taubner, R.-S. et al. Biological methane production under putative Enceladus-like conditions. Nat. Commun. 9, 748 (2018).ADS 
Article 

Google Scholar 
21.McKay, C. P., Porco, C. C., Altheide, T., Davis, W. L. & Kral, T. A. The possible origin and persistence of life on Enceladus and detection of biomarkers in the plume. Astrobiology 8, 909–919 (2008).ADS 
Article 

Google Scholar 
22.Catling, D. C. et al. Exoplanet biosignatures: a framework for their assessment. Astrobiology 18, 709–738 (2018).ADS 
Article 

Google Scholar 
23.Lorenz, R. D. A. Bayesian approach to biosignature detection on ocean worlds. Nat. Astron. 3, 466–467 (2019).ADS 
Article 

Google Scholar 
24.Bouquet, A., Mousis, O., Waite, J. H. & Picaud, S. Possible evidence for a methane source in Enceladus’ ocean. Geophys. Res. Lett. 42, 1334–1339 (2015).ADS 
Article 

Google Scholar 
25.Neveu, M. & Rhoden, A. R. Evolution of Saturn’s mid-sized moons. Nat. Astron. 3, 543–552 (2019).ADS 
Article 

Google Scholar 
26.Prialnik, D. & Merk, R. Growth and evolution of small porous icy bodies with an adaptive-grid thermal evolution code: I. Application to Kuiper belt objects and Enceladus. Icarus 197, 211–220 (2008).ADS 
Article 

Google Scholar 
27.Roberts, J. H. The fluffy core of Enceladus. Icarus 258, 54–66 (2015).ADS 
Article 

Google Scholar 
28.Goodman, J. C., Collins, G. C., Marshall, J. & Pierrehumbert, R. T. Hydrothermal plume dynamics on Europa: implications for chaos formation. J. Geophys. Res. Planets 109, E03008 (2004).ADS 
Article 

Google Scholar 
29.Goodman, J. C. & Lenferink, E. Numerical simulations of marine hydrothermal plumes for Europa and other icy worlds. Icarus 221, 970–983 (2012).ADS 
Article 

Google Scholar 
30.Topçuoğlu, B. D. et al. Hydrogen limitation and syntrophic growth among natural assemblages of thermophilic methanogens at deep-sea hydrothermal vents. Front. Microbiol. 7, 1240 (2016).Article 

Google Scholar 
31.Daniel, R. M. et al. The molecular basis of the effect of temperature on enzyme activity. Biochem. J. 425, 353–360 (2010).Article 

Google Scholar 
32.Tijhuis, L., Van Loosdrecht, M. C. & Heijnen, J. A thermodynamically based correlation for maintenance Gibbs energy requirements in aerobic and anaerobic chemotrophic growth. Biotechnol. Bioeng. 42, 509–519 (1993).Article 

Google Scholar 
33.Sleep, N., Meibom, A., Fridriksson, T., Coleman, R. & Bird, D. H2-rich fluids from serpentinization: geochemical and biotic implications. Proc. Natl. Acad. Sci. USA 101, 12818–12823 (2004).ADS 
Article 

Google Scholar 
34.McCollom, T. M. Abiotic methane formation during experimental serpentinization of olivine. Proc. Natl Acad. Sci. USA 113, 13965–13970 (2016).ADS 
Article 

Google Scholar 
35.Pudlo, P. et al. Reliable ABC model choice via random forests. Bioinformatics 32, 859–866 (2015).Article 

Google Scholar 
36.Krissansen-Totton, J., Olson, S. & Catling, D. C. Disequilibrium biosignatures over Earth history and implications for detecting exoplanet life. Sci. Adv. 4, eaao5747 (2018).ADS 
Article 

Google Scholar 
37.Russell, M. J. et al. The drive to life on wet and icy worlds. Astrobiology 14, 308–343 (2014).ADS 
Article 

Google Scholar 
38.Sasselov, D. D., Grotzinger, J. P. & Sutherland, J. D. The origin of life as a planetary phenomenon. Sci. Adv. 6, eaax3419 (2020).ADS 
Article 

Google Scholar 
39.Takai, K. et al. Cell proliferation at 122°C and isotopically heavy CH4 production by a hyperthermophilic methanogen under high-pressure cultivation. Proc. Natl Acad. Sci. USA 105, 10949–10954 (2008).ADS 
Article 

Google Scholar 
40.Kalirai, J. Scientific discovery with the James Webb Space Telescope. Contemp. Phys. 59, 251–290 (2018).ADS 
Article 

Google Scholar 
41.Phillips, C. B. & Pappalardo, R. T. Europa Clipper mission concept: exploring Jupiter’s ocean moon. Eos 95, 165–167 (2014).ADS 
Article 

Google Scholar 
42.Eigenbrode, J., Gold, R. E., McKay, C. P., Hurford, T. & Davila, A. Searching for life in an ocean world: the Enceladus Life Signatures and Habitability (ELSAH) mission concept. In Proc. 42nd COSPAR Scientific Assembly abstr. F3.6–3-18 (2018).43.Cable, M. L. et al. Enceladus Life Finder: The Search for Life in a Habitable Moon (NASA, JPL, 2016); https://trs.jpl.nasa.gov/handle/2014/4590544.Mitri, G. et al. Explorer of Enceladus and Titan (E2T): investigating ocean worlds’ evolution and habitability in the solar system. Planet. Space Sci. 155, 73–90 (2018).ADS 
Article 

Google Scholar 
45.Sauterey, B., Charnay, B., Affholder, A., Mazevet, S. & Ferrière, R. Co-evolution of primitive methane-cycling ecosystems and early Earth’s atmosphere and climate. Nat. Commun. 11, 2705 (2020).ADS 
Article 

Google Scholar 
46.Lever, M. A. et al. Life under extreme energy limitation: a synthesis of laboratory-and field-based investigations. FEMS Microbiol. Rev. 39, 688–728 (2015).Article 

Google Scholar 
47.Connolly, J. P. & Coffin, R. B. Model of carbon cycling in planktonic food webs. J. Environ. Eng. 121, 682–690 (1995).Article 

Google Scholar 
48.Krissansen-Totton, J. & Catling, D. C. Constraining climate sensitivity and continental versus seafloor weathering using an inverse geological carbon cycle model. Nat. Commun. 8, 15423 (2017).ADS 
Article 

Google Scholar 
49.Virtanen, P. et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat. Methods 17, 261–272 (2020).Article 

Google Scholar 
50.Gillooly, J. F., Brown, J. H., West, G. B., Savage, V. M. & Charnov, E. L. Effects of size and temperature on metabolic rate. Science 293, 2248–2251 (2001).ADS 
Article 

Google Scholar 
51.Csilléry, K., Blum, M. G., Gaggiotti, O. E. & François, O. Approximate Bayesian computation (ABC) in practice. Trends Ecol. Evol. 25, 410–418 (2010).Article 

Google Scholar 
52.Sisson, S. A., Fan, Y. & Beaumont, M. Handbook of Approximate Bayesian Computation (Chapman and Hall/CRC, 2018).53.Pedregosa, F. et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).MathSciNet 
MATH 

Google Scholar 
54.Tutolo, B. M., Seyfried, W. E. & Tosca, N. J. A seawater throttle on H2 production in Precambrian serpentinizing systems. Proc. Natl Acad. Sci. USA 117, 14756–14763 (2020).Article 

Google Scholar 
55.Glein, C. R. & Waite, J. H. The carbonate geochemistry of Enceladus’ ocean. Geophys. Res. Lett. 47, e2019GL085885 (2020).ADS 
Article 

Google Scholar 
56.Charlou, J., Donval, J., Fouquet, Y., Jean-Baptiste, P. & Holm, N. Geochemistry of high H2 and CH4 vent fluids issuing from ultramafic rocks at the Rainbow hydrothermal field (36°14’ N, MAR). Chem. Geol. 191, 345–359 (2002).ADS 
Article 

Google Scholar  More

Global scientific partnerships should generate and share knowledge equitably, but too often exploit research partners in lower-income countries, while disproportionately benefitting those in higher-income countries. Here, I outline my suggestions for more-equitable partnerships.International scientific collaboration is meant to bring together knowledge and resources to solve humanity’s most pressing problems. Scientists pursue collaborations for many different reasons, from learning, testing and integrating approaches, to sharing and developing new ideas. Collaborations can also help institutions in low- and medium-income countries to access resources they lack, build capacity and increase scientific visibility including through publications and citations. While language1 and other cultural barriers prevent an even geographical distribution of authorships, readership and editorial processes2,3, structural power imbalances in international collaborations remain largely unexplored. My goal, as a Colombia-based scientist with 23 years of experience of international collaboration, is to reflect on how these imbalances are too often embedded in scientific practices between scientists from high- and lower-income nations. These imbalances can result in extractive partnerships where benefits flow in only one direction and may even impoverish research in the disadvantaged country by removing experience and not contributing to local capacity and infrastructure. This practice has been termed ‘helicopter’, ‘parachute’ or ‘colonial’ science4. After years of observing and experiencing the effects, both positive and negative, of the ways in which science and research collaborations in the developing world unfold, and given the prevalence of many unhealthy practices, I propose some guidelines to make international collaboration more inclusive, equitable and in the end more meaningful and relevant for all. More

A study on the global burden of disease conducted by the Institute for Health Metrics and Evaluation (IHME) showed that air pollution is the fifth highest risk factor for mortality worldwide and the leading environmental risk factor; air pollution is responsible for 4.2 million deaths annually1,2. Among various air pollutants, fine particulate matter measuring 2.5 µm or less in aerodynamic diameter (PM2.5) is sufficiently small to penetrate the lungs deeply and pass into the blood stream. This may cause cardiovascular and respiratory diseases, such as lower respiratory infection (LRI), ischemic heart disease, cerebrovascular disease, chronic obstructive pulmonary disease (COPD), and lung cancer1,2,3.During the period 2000–2015, when the annual GDP growth rate in India exceeded 8%4, the number of premature deaths attributable to PM2.5 exposure increased from 857,300 to 1,090,400 people1. In 2015, PM2.5-related premature deaths in India accounted for a quarter of global deaths attributed to PM2.5, a level that was comparable to that of China, which has some of the world’s highest air pollution levels1.India’s rapid economic growth between 1995 and 2009 was mainly due to increasing fixed capital formation (i.e., final demand), and the additional capital formation (i.e., investment) was attributed to a marked increase in coal consumption in India during the same period; coal consumption is one of the major sources of PM2.5 emissions5. Thus, to reduce premature deaths related to PM2.5 emissions in India, it is considered important for Indian policymakers to develop effective demand- and supply-side policy with a focus on higher priority sectors.In 2019, the Indian government launched the National Clean Air Programme (NCAP) to achieve its sustainable development goals; the proposed national target was a 20–30% reduction in PM2.5 and PM10 levels by 20246. This is the first time-bound commitment concerning air pollution that has been promulgated in India. Although the NCAP mentioned the importance of adopting a multi-sectoral and collaborative approach6, concrete collaborative policies have not yet been developed. To develop effective demand- and supply-side policies, it is important to obtain a deeper understanding of the supply chain structure centered around a critical sector that has contributed to PM2.5 emissions—and therefore, premature deaths—in India.According to the Regional Emission Inventory in Asia (REAS) database for emissions from 2000 to 20087, the power generation sector is one of the largest contributors of PM2.5 emissions in India, accounting for 822,000 tons of PM2.5 in 2008. In addition, the emissions from the power generation sector increased consistently from 2000 to 2008. Considering energy sources for electrical power generation in India, coal-fired thermal power accounted for 68% of the total 462 TWh generated in 20078. However, coal-fired thermal power plants were responsible for more than 90% of PM2.5 emissions in the power generation sector in 20077, which means that coal-fired thermal power is the most emission-intensive sector and that it plays a critical role in the emissions-related health impact on the people of India. This study examined power generation sector including the coal-fired thermal power and oil-fired thermal power generation, biomass power generation, which account for the remaining 10% of PM2.5 emissions as a critical emission source sector.PM2.5 emissions from the electric power sector have been increasing due to the increases in electric power consumption that is directly necessary for households, and for industries that produce “final” goods and services. In addition to direct electric power use, it is also important to note that both consumers, i.e., households and industry, also indirectly consume electric power through the production of “intermediate” goods and services (including electric power) that are required to produce the final goods and services. It is also important to note that both direct and indirect electric power consumption generate PM2.5 emissions.The electric power generation sector plays an important role in the supply chain9. To effectively mitigate the health impacts related to PM2.5 emissions in India, the PM2.5 emissions associated with the indirect use of electricity (i.e., Scope 3 emissions from the electricity sector in line with the greenhouse gas [GHG] protocol10, as well as emissions associated with the direct use of electricity (i.e., Scope 2 emissions from the electricity sector in line with the GHG protocol11) need to be reduced. In other words, it is necessary to identify environmentally important supply chain paths that have the greatest mitigation potential for health impacts in India.A highly relevant study by Guttikunda and Jawahar (2014)12 focused on coal-fired power plants located in Indian states in 2010 and estimated the total annual PM2.5 emissions in India at around 580,000 tons. These authors also estimated that the annual PM2.5-induced mortalities in India were between 80,000 and 115,000. However, because the study of Guttikunda and Jawahar (2014)12 only examined “production-based” PM2.5 emissions and production-based mortality risks, these results provide a relatively limited understanding of how the final demand of countries such India affects PM2.5-induced mortality risks.Nansai et al. (2020)13 quantified the mortality-based economic losses (i.e., income loss) attributed to primary and secondary PM2.5 emissions in individual Asian countries that were induced by the final demand of the world’s five largest consuming countries. Their findings showed that in 2010, consumption in the USA, China, Japan, Germany, and the United Kingdom caused approximately 2000, 7700, 2700, 3300, and 3400 deaths in India, respectively. These deaths resulted in economic losses in India of 0.14, 0.26, 0.087, 0.11, and 0.11 billion US dollars in purchasing power parity, respectively. In India, particularly, the export of goods and services from India to these developed countries contributed considerably to PM2.5 emissions, and therefore the high number of premature deaths in India. This situation calls for an analysis of how the global supply chain is impacting health in India in terms of emission responsibility14. In addition, domestic policies need to be introduced to mitigate air pollution inside India, and demand-side policies that consider the role of consumers outside India need to be developed.Structural path analysis (SPA) is a well-known and effective method that was first introduced by Defourny and Thorbecke (1984)15 to trace important supply chain paths from complex input–output structures by decomposing matrix products into elements (paths). Previous studies addressing PM2.5 emissions have applied this method. For example, Meng et al. (2015)16 identified PM2.5 emission-intensive supply chain paths in China using SPA. However, they only considered PM2.5 emissions and did not consider the reduction potential of health impacts. Nagashima et al. (2017)17 identified critical supply chain paths that contribute toward premature deaths in East Asian countries; however, they did not include secondary PM2.5 generation, which has a marked influence on health, and they did not consider India in their analysis.This study used EXIOBASE 3 data for 2010 and applied an SPA18,19,20,21 to identify important supply chain paths driven by domestic and international demands that contribute to primary and secondary PM2.5 emissions from the power sector, which is an environmentally critical sector in India. We introduced an atmospheric transport model to fully link final demand via supply chains to the primary emitter that is the power sector in India. Finally, we linked the atmospheric transport of emissions from the emitter to the impact on health in India. To the best of our knowledge, this study is the first attempt to estimate consumption-based PM2.5 emissions as well as the consumption-based mortality risk in India by using a combined approach that is based on an environmentally extended multi-regional input–output (MRIO) analysis and an atmospheric transport model.The remainder of this manuscript is structured as follows: “Methodology” section explains our methodology, “Data and computation” section describes the data, “Results” section presents and discusses the results, and finally, “Discussion and conclusion” section contains the discussion and conclusions. More