Ecology

Dookes, J. S. & Mooney, H. A. Does global change increase the success of biological invaders?. Trends Ecol. Evol. 14, 135–139 (1999).Article 

Google Scholar 
Gallien, L. & Carboni, M. The community ecology of invasive species: Where are we and what’s next?. Ecography 40, 335–352 (2017).Article 

Google Scholar 
Tylianakis, J. M., Didham, R. K., Bascompte, J. & Wardle, D. A. Global change and species interactions in terrestrial ecosystems. Ecol. Lett. 11, 1351–1363 (2008).PubMed 
Article 

Google Scholar 
Adler, P. B., Dalgleish, H. J. & Ellner, S. P. Forecasting plant community impacts of climate variability and change: When do competitive interactions matter?. J. Ecol. 100, 478–487 (2012).Article 

Google Scholar 
Cahill, A. E., Aiello-Lammens, M. E., Fisher-Reid, M. C. & Hua, X. How does climate change cause extinction?. Proc. Biol. Sci. 280, 20121890 (2013).PubMed 
PubMed Central 

Google Scholar 
Ockendon, N. et al. Mechanisms underpinning climatic impacts on natural populations: Altered species interactions are more important than direct effects. Glob. Chang. Biol. 20, 2221–2229 (2014).ADS 
PubMed 
Article 

Google Scholar 
Chu, C. et al. Direct effects dominate responses to climate perturbations in grassland plant communities. Nat. Commun. 7, 11766 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Gunderson, A. R., Tsukimura, B. & Stillman, J. H. Indirect effects of global change: From physiological and behavioral mechanisms to ecological consequences. Integr. Comp. Biol. 57, 48–54 (2017).PubMed 
Article 

Google Scholar 
Suttle, K. B., Thomsen, M. A. & Power, M. E. Species interactions reverse grassland responses to changing climate. Science 315, 640–642 (2007).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Farrer, E. C. et al. Indirect effects of global change accumulate to alter plant diversity but not ecosystem function in alpine tundra. J. Ecol. 103, 351–360 (2015).CAS 
Article 

Google Scholar 
Sentis, A., Montoya, J. M. & Lurgi, M. Warming indirectly increases invasion success in food webs. Proc. R. Soc. B. 288, 1947 (2021).Article 

Google Scholar 
Ohgushi, T. Indirect interaction webs: Herbivore-induced effects through trait change in plants. Annu. Rev. Ecol. Evol. Syst. 36, 81–105 (2005).Article 

Google Scholar 
Classen, A. et al. Direct and indirect effects of climate change on soil microbial and soil microbial-plant interactions: What lies ahead?. Ecosphere 6, 1–21 (2015).Article 

Google Scholar 
Van-der-Putten, W. H., Macel, M. & Visser, M. E. Predicting species distributions and abundance responses to climate change: Why it is essential to include biotic interactions across trophic levels. Philos. Trans. R. Soc. B. 365, 2025–2034 (2010).Article 

Google Scholar 
Rudgers, J. A. et al. Climate disruption of plant-microbe interactions. Annu. Rev. Ecol. Evol. Syst. 51, 561–586 (2020).Article 

Google Scholar 
Deltedesco, E. et al. Soil microbial community structure and function mainly respond to indirect effects in a multifactorial climate manipulation experiment. Soil Biol. Biochem. 142, 1–12 (2020).Article 

Google Scholar 
Fahey, C., Koyama, A., Antunes, P. M., Dunfield, K. & Flory, S. L. Plant communities mediate the interactive effects of invasion and drought on soil microbial communities. ISME 14, 1396–1409 (2020).Article 

Google Scholar 
Nuccio, E. E. et al. An arbuscular mycorrhizal fungus significantly modifies the soil bacterial community and nitrogen cycling during litter decomposition. Environ. Microbiol. 15, 1870–1881 (2013).CAS 
PubMed 
Article 

Google Scholar 
Bennett, J. A. & Cahill, J. F. Fungal effects on plant–plant interactions contribute to grassland plant abundances: Evidence from the field. J. Ecol. 104, 755–764 (2016).Article 

Google Scholar 
Reinhart, K. O. & Callaway, R. M. Soil biota and invasive plants. New Phytol. 170, 445–457 (2006).PubMed 
Article 

Google Scholar 
Inderjit, C. J. F. Linkages of plant–soil feedbacks and underlying invasion mechanisms. AoB Plants 7, 1–8 (2015).CAS 
Article 

Google Scholar 
Lekberg, Y. et al. Relative importance of competition and plant–soil feedback, their synergy, context dependency and implications for coexistence. Ecol. Lett. 21, 1268–1281 (2018).PubMed 
Article 

Google Scholar 
Teh, S. Y. & Koh, H. L. Climate change and soil salinization: Impact on agriculture, water, and food security. IJAFP 2, 1–9 (2016).
Google Scholar 
White, E. Restore or retreat? Saltwater intrusion and coastal management in coastal wetlands. Ecosyst. Health Sustain. https://doi.org/10.1002/ehs2.1258 (2016).Article 

Google Scholar 
Donnolly, J. P. & Bertness, M. D. Rapid shoreward encroachment of salt marsh cordgrass in response to accelerated sea-level rise. PNAS 98, 14218–14223 (2001).ADS 
Article 

Google Scholar 
Sharpe, P. J. & Baldwin, A. H. Tidal marsh plant community response to sea-level rise: A mesocosm study. Aquat. Bot. 101, 34–40 (2012).Article 

Google Scholar 
Birnbaum, C., Waryszak, P. & Farrer, E. C. Direct and indirect effects of climate change in coastal wetlands: Will climate change influence wetlands by affecting plant invasion?. Wetlands 59, 1–11 (2021).
Google Scholar 
Noto, A. E. & Shurin, J. B. Early stages of sea-level rise lead to decreased salt marsh plant diversity through stronger competition in Mediterranean climate marshes. PLoS ONE 12, 1–11 (2017).Article 

Google Scholar 
Stagg, C. L., Baustian, M. M., Perry, C. L., Carruthers, T. J. B. & Hall, C. T. Direct and indirect controls on organic matter decomposition in four coastal wetland communities along a landscape salinity gradient. J. Ecol. 106, 655–670 (2017).Article 

Google Scholar 
Neubauer, S. C., Piehler, M. F., Smyth, A. R. & Franklin, R. B. Saltwater intrusion modifies microbial community structure and decreases denitrification in tidal freshwater marshes. Ecosystems 22, 912–928 (2019).CAS 
Article 

Google Scholar 
Rath, K. M., Fierer, N., Murphy, D. V. & Rousk, J. Linking bacterial community composition to soil salinity along environmental gradients. ISME. 13, 836–846 (2019).CAS 
Article 

Google Scholar 
Meyerson, L. A., Cronin, J. T. & Pysek, P. Phragmites australis as a model organism for studying plant invasions. Biol. Invasions 18, 2421–2431 (2016).Article 

Google Scholar 
Soares, M. A. et al. Evaluation of the functional roles of fungal endophytes of Phragmites australis from high saline and low saline habitats. Biol. Invasions 18, 2689–2702 (2016).Article 

Google Scholar 
Gonzalez, M., Baldwin, A. H., Maul, J. E. & Yarwood, S. A. Dark septate endophyte improves salt tolerance of native and invasive lineages of Phragmites australis. ISME 14, 1943–1954 (2020).Article 

Google Scholar 
Farrer, E. C. et al. Plant and microbial impacts of an invasive species vary across an environmental gradient. J. Ecol. 109, 2163–2176 (2021).Article 

Google Scholar 
Callaway, R. M., Thelen, G. C., Rodriguez, A. & Holben, W. E. Soil biota and exotic plant invasion. Nature 427, 731–733 (2004).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Smith, L. M. & Reynolds, H. L. Plant–soil feedbacks shift from negative to positive with decreasing light in forest understory species. Ecology 96, 2523–2532 (2015).PubMed 
Article 

Google Scholar 
Parepa, M., Schaffner, U. & Bossdorf, O. Help from underground: Soil biota facilitate knotweed invasion. Ecosphere 4, 1–11 (2013).Article 

Google Scholar 
Larios, L. & Suding, K. N. Competition and soil resource environment alter plant-soil feedbacks for native and exotic grasses. AoB Plants 7, 1–9 (2014).
Google Scholar 
Hoeksema, J. D. Ongoing coevolution in mycorrhizal interactions. New Phytol. 187, 286–300 (2010).PubMed 
Article 

Google Scholar 
Van der Heijden, M. G. A., Martin, F. M., Selosse, M. & Sanders, I. R. Mycorrhizal ecology and evolution: The past, the present, and the future. New Phytol. 205, 1406–1423 (2015).PubMed 
Article 

Google Scholar 
Hoeksema, J. D. et al. Evolutionary history of plant hosts and fungal symbionts predicts the strength of mycorrhizal mutualism. Commun. Biol. 1(116), 2018. https://doi.org/10.1038/s42003-018-0120-9 (2018).Article 

Google Scholar 
Remke, M. J., Johnson, N. C., Wright, J., Williamson, M. & Bowker, M. A. Sympatric pairings of dryland grass populations, mycorrhizal fungi and associated soil biota enhance mutualism and ameliorate drought stress. J. Ecol. 109, 1210–1223 (2020).Article 

Google Scholar 
Farrer, E. C. & Suding, K. N. Teasing apart plant community responses to N enrichment: The roles of resource limitation, competition and soil microbes. Ecol. Lett. 19, 1287–1296 (2016).PubMed 
Article 

Google Scholar 
Hawkins, A. P. & Crawford, K. M. Interactions between plants and soil microbes may alter the relative importance of intraspecific and interspecific plant competition in a changing climate. AoB Plants. 10, 39. https://doi.org/10.1093/aobpla/ply039 (2018).Article 

Google Scholar 
Wu, Y. et al. Long-term nitrogen and sulfur deposition increased root-associated pathogen diversity and changed mutualistic fungal diversity in a boreal forest. Soil Biol. Biogeochem. 115, 108163. https://doi.org/10.1016/j.soilbio.2021.108163 (2021).CAS 
Article 

Google Scholar 
Allen, W. J., Meyerson, L. A., Flick, A. J. & Cronin, J. T. Intraspecific variation in indirect plant–soil feedbacks influences a wetland plant invasion. Ecology 99, 1430–1440 (2018).PubMed 
Article 

Google Scholar 
Crawford, K. M. & Knight, T. M. Competition overwhelms the positive plant-soil feedback generated by an invasive plant. Oecologia 183, 211–220 (2017).ADS 
PubMed 
Article 

Google Scholar 
Bertness, M. D. & Shumway, S. W. Competition and facilitation in marsh plants. Am. Nat. 142, 718–724 (1993).CAS 
PubMed 
Article 

Google Scholar 
Uddin, M. N., Robinson, R. W., Buultjens, A., Al-Harun, M. A. Y. & Shampa, S. H. Role of allelopathy of Phragmites australis in its invasion processes. J. Exp. Mar. Biol. Ecol. 486, 237–244 (2017).Article 

Google Scholar 
Howard, R. J. & Rafferty, P. S. Clonal variation in response to salinity and flooding stress in four marsh macrophytes of the northern gulf of Mexico, USA. Environ. Exp. Bot. 56, 301–313 (2006).Article 

Google Scholar 
Visser, J. M., Sasser, C. E., Chabreck, R. H. & Linscombe, R. G. Marsh vegetation types of the Mississippi River Deltaic plain. Estuaries 21, 818–828 (1998).Article 

Google Scholar 
De Wit, C. T. & van den Bergh, J. P. Competition between herbage plants. NJAS 13, 212–221 (1965).Article 

Google Scholar 
R Core Team. In r: A Language and Environment for Statistical Computing; r foundation for statistical computing: Vienna, Austria (2017).Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).MATH 
Book 

Google Scholar  More

.readcube-buybox { display: none !important;}
An endangered lemur species that lives in Madagascar’s rainforest could vanish within 25 years if deforestation on the island isn’t reduced1.

Access options

/* style specs start */
style{display:none!important}.LiveAreaSection-193358632 *{align-content:stretch;align-items:stretch;align-self:auto;animation-delay:0s;animation-direction:normal;animation-duration:0s;animation-fill-mode:none;animation-iteration-count:1;animation-name:none;animation-play-state:running;animation-timing-function:ease;azimuth:center;backface-visibility:visible;background-attachment:scroll;background-blend-mode:normal;background-clip:borderBox;background-color:transparent;background-image:none;background-origin:paddingBox;background-position:0 0;background-repeat:repeat;background-size:auto auto;block-size:auto;border-block-end-color:currentcolor;border-block-end-style:none;border-block-end-width:medium;border-block-start-color:currentcolor;border-block-start-style:none;border-block-start-width:medium;border-bottom-color:currentcolor;border-bottom-left-radius:0;border-bottom-right-radius:0;border-bottom-style:none;border-bottom-width:medium;border-collapse:separate;border-image-outset:0s;border-image-repeat:stretch;border-image-slice:100%;border-image-source:none;border-image-width:1;border-inline-end-color:currentcolor;border-inline-end-style:none;border-inline-end-width:medium;border-inline-start-color:currentcolor;border-inline-start-style:none;border-inline-start-width:medium;border-left-color:currentcolor;border-left-style:none;border-left-width:medium;border-right-color:currentcolor;border-right-style:none;border-right-width:medium;border-spacing:0;border-top-color:currentcolor;border-top-left-radius:0;border-top-right-radius:0;border-top-style:none;border-top-width:medium;bottom:auto;box-decoration-break:slice;box-shadow:none;box-sizing:border-box;break-after:auto;break-before:auto;break-inside:auto;caption-side:top;caret-color:auto;clear:none;clip:auto;clip-path:none;color:initial;column-count:auto;column-fill:balance;column-gap:normal;column-rule-color:currentcolor;column-rule-style:none;column-rule-width:medium;column-span:none;column-width:auto;content:normal;counter-increment:none;counter-reset:none;cursor:auto;display:inline;empty-cells:show;filter:none;flex-basis:auto;flex-direction:row;flex-grow:0;flex-shrink:1;flex-wrap:nowrap;float:none;font-family:initial;font-feature-settings:normal;font-kerning:auto;font-language-override:normal;font-size:medium;font-size-adjust:none;font-stretch:normal;font-style:normal;font-synthesis:weight style;font-variant:normal;font-variant-alternates:normal;font-variant-caps:normal;font-variant-east-asian:normal;font-variant-ligatures:normal;font-variant-numeric:normal;font-variant-position:normal;font-weight:400;grid-auto-columns:auto;grid-auto-flow:row;grid-auto-rows:auto;grid-column-end:auto;grid-column-gap:0;grid-column-start:auto;grid-row-end:auto;grid-row-gap:0;grid-row-start:auto;grid-template-areas:none;grid-template-columns:none;grid-template-rows:none;height:auto;hyphens:manual;image-orientation:0deg;image-rendering:auto;image-resolution:1dppx;ime-mode:auto;inline-size:auto;isolation:auto;justify-content:flexStart;left:auto;letter-spacing:normal;line-break:auto;line-height:normal;list-style-image:none;list-style-position:outside;list-style-type:disc;margin-block-end:0;margin-block-start:0;margin-bottom:0;margin-inline-end:0;margin-inline-start:0;margin-left:0;margin-right:0;margin-top:0;mask-clip:borderBox;mask-composite:add;mask-image:none;mask-mode:matchSource;mask-origin:borderBox;mask-position:0 0;mask-repeat:repeat;mask-size:auto;mask-type:luminance;max-height:none;max-width:none;min-block-size:0;min-height:0;min-inline-size:0;min-width:0;mix-blend-mode:normal;object-fit:fill;object-position:50% 50%;offset-block-end:auto;offset-block-start:auto;offset-inline-end:auto;offset-inline-start:auto;opacity:1;order:0;orphans:2;outline-color:initial;outline-offset:0;outline-style:none;outline-width:medium;overflow:visible;overflow-wrap:normal;overflow-x:visible;overflow-y:visible;padding-block-end:0;padding-block-start:0;padding-bottom:0;padding-inline-end:0;padding-inline-start:0;padding-left:0;padding-right:0;padding-top:0;page-break-after:auto;page-break-before:auto;page-break-inside:auto;perspective:none;perspective-origin:50% 50%;pointer-events:auto;position:static;quotes:initial;resize:none;right:auto;ruby-align:spaceAround;ruby-merge:separate;ruby-position:over;scroll-behavior:auto;scroll-snap-coordinate:none;scroll-snap-destination:0 0;scroll-snap-points-x:none;scroll-snap-points-y:none;scroll-snap-type:none;shape-image-threshold:0;shape-margin:0;shape-outside:none;tab-size:8;table-layout:auto;text-align:initial;text-align-last:auto;text-combine-upright:none;text-decoration-color:currentcolor;text-decoration-line:none;text-decoration-style:solid;text-emphasis-color:currentcolor;text-emphasis-position:over right;text-emphasis-style:none;text-indent:0;text-justify:auto;text-orientation:mixed;text-overflow:clip;text-rendering:auto;text-shadow:none;text-transform:none;text-underline-position:auto;top:auto;touch-action:auto;transform:none;transform-box:borderBox;transform-origin:50% 50%0;transform-style:flat;transition-delay:0s;transition-duration:0s;transition-property:all;transition-timing-function:ease;vertical-align:baseline;visibility:visible;white-space:normal;widows:2;width:auto;will-change:auto;word-break:normal;word-spacing:normal;word-wrap:normal;writing-mode:horizontalTb;z-index:auto;-webkit-appearance:none;-moz-appearance:none;-ms-appearance:none;appearance:none;margin:0}.LiveAreaSection-193358632{width:100%}.LiveAreaSection-193358632 .login-option-buybox{display:block;width:100%;font-size:17px;line-height:30px;color:#222;padding-top:30px;font-family:Harding,Palatino,serif}.LiveAreaSection-193358632 .additional-access-options{display:block;font-weight:700;font-size:17px;line-height:30px;color:#222;font-family:Harding,Palatino,serif}.LiveAreaSection-193358632 .additional-login >li:not(:first-child)::before{transform:translateY(-50%);content:””;height:1rem;position:absolute;top:50%;left:0;border-left:2px solid #999}.LiveAreaSection-193358632 .additional-login >li:not(:first-child){padding-left:10px}.LiveAreaSection-193358632 .additional-login >li{display:inline-block;position:relative;vertical-align:middle;padding-right:10px}.BuyBoxSection-683559780{display:flex;flex-wrap:wrap;flex:1;flex-direction:row-reverse;margin:-30px -15px 0}.BuyBoxSection-683559780 .box-inner{width:100%;height:100%}.BuyBoxSection-683559780 .readcube-buybox{background-color:#f3f3f3;flex-shrink:1;flex-grow:1;flex-basis:255px;background-clip:content-box;padding:0 15px;margin-top:30px}.BuyBoxSection-683559780 .subscribe-buybox{background-color:#f3f3f3;flex-shrink:1;flex-grow:4;flex-basis:300px;background-clip:content-box;padding:0 15px;margin-top:30px}.BuyBoxSection-683559780 .subscribe-buybox-nature-plus{background-color:#f3f3f3;flex-shrink:1;flex-grow:4;flex-basis:100%;background-clip:content-box;padding:0 15px;margin-top:30px}.BuyBoxSection-683559780 .title-readcube{display:block;margin:0;margin-right:20%;margin-left:20%;font-size:24px;line-height:32px;color:#222;padding-top:30px;text-align:center;font-family:Harding,Palatino,serif}.BuyBoxSection-683559780 .title-buybox{display:block;margin:0;margin-right:29%;margin-left:29%;font-size:24px;line-height:32px;color:#222;padding-top:30px;text-align:center;font-family:Harding,Palatino,serif}.BuyBoxSection-683559780 .title-asia-buybox{display:block;margin:0;margin-right:5%;margin-left:5%;font-size:24px;line-height:32px;color:#222;padding-top:30px;text-align:center;font-family:Harding,Palatino,serif}.BuyBoxSection-683559780 .asia-link{color:#069;cursor:pointer;text-decoration:none;font-size:1.05em;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:1.05em6}.BuyBoxSection-683559780 .access-readcube{display:block;margin:0;margin-right:10%;margin-left:10%;font-size:14px;color:#222;padding-top:10px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .access-asia-buybox{display:block;margin:0;margin-right:5%;margin-left:5%;font-size:14px;color:#222;padding-top:10px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .access-buybox{display:block;margin:0;margin-right:30%;margin-left:30%;font-size:14px;color:#222;opacity:.8px;padding-top:10px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .usps-buybox{display:block;margin:0;margin-right:30%;margin-left:30%;font-size:14px;color:#222;opacity:.8px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .price-buybox{display:block;font-size:30px;color:#222;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;padding-top:30px;text-align:center}.BuyBoxSection-683559780 .price-from{font-size:14px;padding-right:10px;color:#222;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .issue-buybox{display:block;font-size:13px;text-align:center;color:#222;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:19px}.BuyBoxSection-683559780 .no-price-buybox{display:block;font-size:13px;line-height:18px;text-align:center;padding-right:10%;padding-left:10%;padding-bottom:20px;padding-top:30px;color:#222;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif}.BuyBoxSection-683559780 .vat-buybox{display:block;margin-top:5px;margin-right:20%;margin-left:20%;font-size:11px;color:#222;padding-top:10px;padding-bottom:15px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:17px}.BuyBoxSection-683559780 .button-container{display:flex;padding-right:20px;padding-left:20px;justify-content:center}.BuyBoxSection-683559780 .button-container >*{flex:1px}.BuyBoxSection-683559780 .button-container >a:hover,.Button-505204839:hover,.Button-1078489254:hover,.Button-2808614501:hover{text-decoration:none}.BuyBoxSection-683559780 .readcube-button{background:#fff;margin-top:30px}.BuyBoxSection-683559780 .button-asia{background:#069;border:1px solid #069;border-radius:0;cursor:pointer;display:block;padding:9px;outline:0;text-align:center;text-decoration:none;min-width:80px;margin-top:75px}.BuyBoxSection-683559780 .button-label-asia,.ButtonLabel-3869432492,.ButtonLabel-3296148077,.ButtonLabel-1566022830{display:block;color:#fff;font-size:17px;line-height:20px;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;text-align:center;text-decoration:none;cursor:pointer}.Button-505204839,.Button-1078489254,.Button-2808614501{background:#069;border:1px solid #069;border-radius:0;cursor:pointer;display:block;padding:9px;outline:0;text-align:center;text-decoration:none;min-width:80px;max-width:320px;margin-top:10px}.Button-505204839 .readcube-label,.Button-1078489254 .readcube-label,.Button-2808614501 .readcube-label{color:#069}
/* style specs end */Subscribe to Nature+Get immediate online access to the entire Nature family of 50+ journals$29.99monthlySubscribe to JournalGet full journal access for 1 year$199.00only $3.90 per issueAll prices are NET prices.VAT will be added later in the checkout.Tax calculation will be finalised during checkout.Buy articleGet time limited or full article access on ReadCube.$32.00All prices are NET prices.

Additional access options:

doi: https://doi.org/10.1038/d41586-022-03116-6

References

Subjects

Conservation biology More

Calculation of flight-step headings and movementTerms defining flight-step movement, precision and geophysical orientation cues are listed in Table 1. Since seasonal migration nearly ubiquitously proceeds from higher to lower latitudes, it is convenient to define headings clockwise from geographic South (counter-clockwise from geographic North for migration commencing in the Southern Hemisphere). Assuming a spherical Earth, a sequence of N migratory flight-steps with corresponding headings, αi, i = 0,…, N−1, the latitudes, ∅i+1, and longitudes, λi+1, on completion of each flight-step can be calculated using the Haversine Equation76, which we approximated by stepwise planar movement using Eqs. (1) and (2). For improved computational accuracy and to accommodate within flight-step effects, we updated simulated headings and corresponding locations hourly. A migrant’s flight-step distance ({R}_{{{mathrm {step}}}}=3.6{V}_{{mathrm {a}}}{cdot n}_{{mathrm {H}}}/{R}_{{{mathrm {Earth}}}}) (in radians), depends on its flight speed, Va (m/s) relative to the mean Earth radius REarth (km), and flight-step hours, nH. With a geomagnetic in-flight compass, expected hourly geographic headings are modulated by changes in magnetic declination, i.e., the clockwise difference between geographic and geomagnetic South10,32.Formulation of compass coursesFor simplicity, we consider the case of a single inherited or imprinted heading. This can be extended to include sequences of preferred headings. Expected geographic loxodrome headings remain unchanged en route, i.e.,$${bar{{{{{{rm{alpha }}}}}}}}_{i}={bar{{{{{{rm{alpha }}}}}}}}_{0}$$
(5)
Relative to geographic axes, expected geomagnetic loxodrome headings remain unchanged relative to proximate geomagnetic South, i.e., are offset by geomagnetic declination on departure (updated hourly in simulations)$${bar{{{{{{rm{alpha }}}}}}}}_{i}={bar{{{{{{rm{alpha }}}}}}}}_{0}+{delta }_{{mathrm {m}},i}$$
(6)
As described and illustrated in detail by Kiepenheuer13, the magnetoclinic compass was hypothesized to explain the prevalence of “curved” migratory bird routes, i.e., for which local geographic headings shift gradually but substantially en route. A migrant with a magnetoclinic compass adjusts its heading at each flight-step to maintain a constant transverse component, γ′, of the experienced inclination angle, γ, so that error-free headings are (see Fig. S5 in ref. 34)$${{bar{{{{{{rm{alpha }}}}}}}}_{i}={{sin }}}^{-1}left(frac{{{tan }}{gamma }_{i}}{{{tan }}{gamma }^{{prime} }}right){={{sin }}}^{-1}left(frac{{{tan }}{gamma }_{i}{{sin }}{bar{{{{{{rm{alpha }}}}}}}}_{0}}{{{tan }}{gamma }_{0}}right).$$
(7)
In a geomagnetic dipole field, the horizontal (Bh) and vertical (Bz) field, and therefore also inclination, each depends solely on geomagnetic latitude, ∅m:(gamma ={{{tan }}}^{-1}left({B}_{{mathrm {z}}}/{B}_{{mathrm {h}}}right)={{{tan }}}^{-1}left(2{{sin }}{phi }_{{mathrm {m}}}/{{cos }}{phi }_{{mathrm {m}}}right)={{{tan }}}^{-1}left(2{{tan }}{phi }_{{mathrm {m}}}right).) The projected transverse component, therefore, becomes$${gamma }^{{prime} }={{{tan }}}^{-1}left(frac{{{tan }}{gamma }_{0}}{{{sin }}{bar{{{alpha }}}}_{0}}right)={{{tan }}}^{-1}left(frac{2{{tan }}{{{phi }}}_{{mathrm {m}},0}}{{{sin }}{bar{{{{{{rm{alpha }}}}}}}}_{0}}right),$$which can be substituted into Eq. (7) to produce a closed formula for magnetoclinic headings in a dipole as a function of geomagnetic latitude$${bar{{{{{{rm{alpha }}}}}}}}_{i}left({{{phi }}}_{{mathrm {m}},i}right)={{{sin }}}^{-1}left(frac{{{sin }}{bar{{{{{{rm{alpha }}}}}}}}_{0}}{{{tan }}{{{phi }}}_{{mathrm {m}},0}}cdot {{tan }}{{{phi }}}_{{mathrm {m}},i}right),$$
(8)
with the expected initial heading, ({bar{{{{{{rm{alpha }}}}}}}}_{0}), and initial geomagnetic latitude, ∅m,0, being constants. Equations (7) and (8) have no solution when inclination increases en route, which could occur following substantial orientation error or in strongly non-dipolar fields. We followed previous studies in allowing magnetoclinic migrants to head towards magnetic East or West until inclination decreased sufficiently33,34,46, but also included orientation error based on the modelled compass precision.To assess sun-compass sensitivity algebraically, and also to improve computational efficiency, we used a closed-form equation for sunset azimuth, θs (derived in Supplementary Note 3 and see ref. 23),$${theta }_{{mathrm {s}}}={{{cos }}}^{-1}left(frac{-{{sin }}{delta }_{{mathrm {s}}}}{{{cos }}{{phi }}}right),$$
(9)
where δs is the solar declination, which varies between −23.4° and 23.4° with season and latitude23. Sunset azimuth is the positive and sunrise azimuth is the negative solution to Eq. (9) (relative to geographic N–S).Fixed sun-compass headings represent a uniform (clockwise) offset, ({bar{{{{{{rm{alpha }}}}}}}}_{{mathrm {s}}}) to sunrise or sunset azimuth, θs,i (calculated using Eq. (9))$${{bar{{{{{{rm{alpha }}}}}}}}_{i}={bar{{{{{{rm{alpha }}}}}}}}_{{mathrm {s}}}+theta }_{{mathrm {s}},i}$$
(10)
where the preferred heading on commencement of migration, ({bar{{{{{{rm{alpha }}}}}}}}_{{mathrm {s}}}={bar{{{{{{rm{alpha }}}}}}}}_{0}-{theta }_{{mathrm {s}},0}), is presumed to be imprinted using an inherited geographic or geomagnetic heading2,10,30.With a TCSC, preferred headings relative to sun azimuth are adjusted according to the time of day. In the context of sun-compass use during migration, Alerstam and Pettersson22 related the hourly “clock-shift” induced by crossing bands of longitude (∆h = 12 ∆λ/π), to a migrant’s time-compensated adjustment given the rate of change (i.e., angular speed) of sun azimuth close to sunset$$frac{partial {theta }_{{mathrm {s}}}}{partial h}cong frac{2pi {{sin }}{{phi }}}{24},$$
(11)
resulting in a “time-compensated” offset in heading on departure ((varDelta bar{{{{{{rm{alpha }}}}}}}cong varDelta {{{{{rm{lambda }}}}}},sin phi), which Eq.(4)). Equation (4) results in near-great-circle trajectories for small ranges in latitude, ∅, until inner clocks are reset. The feasibility of TCSC courses over longer distances (latitude ranges) relies on two critical but little-explored assumptions: (1) time-compensated orientation adjustments are presumed to follow the angular speed of sun azimuth (Eq. (11)) retained from the most recent clock-reset site, and (2) to negotiate unpredictable migratory schedules, migrants are presumed to retain their preferred geographic heading on arrival at extended stopovers22.Regarding the first assumption, time-compensated adjustments could also be influenced by proximate speeds of sun azimuth even when inner clocks are not fully reset. We, therefore, use distinct indices to keep track of “reference” flight-steps for clock-resets (cref,i) and time-compensated adjustments (sref,i). TCSC flight-step headings can then be written as$${bar{{{{{{rm{alpha }}}}}}}}_{i}=left{begin{array}{cc}{bar{{{{{{rm{alpha }}}}}}}}_{{c}_{{{mathrm {ref}}},i}}+left({theta }_{{mathrm {s}},i}-{theta }_{{mathrm {s}},{c}_{{{mathrm {ref}}},i}}right)+left({{{{{{rm{lambda }}}}}}}_{i}-{{{{{{rm{lambda }}}}}}}_{{c}_{{{mathrm {ref}}},i}}right){{sin }}{phi }_{{s}_{{{mathrm {ref}}},i}}, & {i,ne, c}_{{{mathrm {ref}}},i} ; (12a)\ {{{{{{rm{alpha }}}}}}}_{i-1}, & {i=c}_{{{mathrm {ref}}},i} ; (12b)end{array}right.,$$where θs,i represents the sunset azimuth on departures, cref,i specifies the most recent clock-reset site (during which geographic headings are also retained, i.e., ({bar{{{{{{rm{alpha }}}}}}}}_{i}={{{{{{rm{alpha }}}}}}}_{i-1})), and sref,i specifies the site defining the migrant’s temporal (hourly) rate of “time-compensated” adjustments (Eq. (11)). For TCSC courses as conceived by Alerstam and Pettersson22, reference rates of adjustment to sun azimuth are reset in tandem during stopovers, i.e., ({s}_{{{mathrm {ref}}},i}={c}_{{{mathrm {ref}}},i}), but we also considered a proximately gauged TCSC, where migrants gauge their adjustments to currently experienced speed of sun azimuth, i.e., ({s}_{{{mathrm {ref}}},i}=i).Regarding the second assumption, retaining geographic headings on arrival at stopovers is not consistent with ignoring geographic headings between consecutive nightly flight-steps, and may be difficult to achieve while landing. We, therefore, examined a more parsimonious alternative (Fig. 7d, Supplementary Fig. 3) where migrants retain their (usual) TCSC heading from the first night of stopovers, i.e., as if they would have departed on the first night. This alternative also simplifies Eq. (12) to$${bar{{{{{{rm{alpha }}}}}}}}_{i}={bar{{{{{{rm{alpha }}}}}}}}_{{c}_{{{mathrm {ref}}},i}}+left({theta }_{{mathrm {s}},({t}_{i-1}+1)}-{theta }_{{mathrm {s}},{t}_{i-1}}right)+left({{{{{{rm{lambda }}}}}}}_{i}-{{{{{{rm{lambda }}}}}}}_{{c}_{{{mathrm {ref}}},i}}right){{sin }}{{{phi }}}_{{s}_{{{mathrm {ref}}},i}}$$
(12c)
where the index ti−1 here represents the departure date from the previous flight.Sensitivity of compass-course headingsSensitivity was assessed by the marginal change in expected heading from previous (imprecise) headings, (partial {bar{alpha }}_{i}/partial {alpha }_{i-1}). When this is positive, small errors in headings will perpetuate, and therefore expected errors in migratory trajectories will grow iteratively. Conversely, negative sensitivity implies self-correction between successive flight-steps. Geographic and geomagnetic loxodromes are per definition constant relative to their respective axes so have “zero” sensitivity, as long as cue-detection errors are stochastically independent.For magnetoclinic compass courses in a dipole field, sensitivity can be calculated by differentiating Eq. (8) with respect to previous headings:$$frac{{mathrm {d}}{bar{{{{{{rm{alpha }}}}}}}}_{i}}{{mathrm {d}}{{{{{{rm{alpha }}}}}}}_{i-1}}=frac{{sin bar{{{{{{rm{alpha }}}}}}}}_{0}}{tan {phi }_{{mathrm {m}},0}}cdot frac{1}{cos {bar{alpha }}_{i}{cos }^{2}{phi }_{{mathrm {m}},i}}frac{partial {phi }_{{mathrm {m}},i}}{partial {alpha }_{i-1}}=frac{{R}_{{mathrm {step}}},sin {alpha }_{i-1}{sin bar{{{{{{rm{alpha }}}}}}}}_{0}}{cos {bar{alpha }}_{i}{cos }^{2}{phi }_{{mathrm {m}},i},tan {phi }_{{mathrm {m}},0}}$$
(13)
All three terms in the denominator indicate, as illustrated in Fig. 3b, that magnetoclinic courses become unstably sensitive at both high and low latitudes, and any heading with a significantly East–West component.Sensitivity of fixed sun compass headings is non-zero due to sun azimuth dependence on location (Eq. (9)):$$frac{{mathrm {d}}{bar{{{{{{rm{alpha }}}}}}}}_{i}}{{mathrm {d}}{{{{{{rm{alpha }}}}}}}_{i-1}} = , frac{sin {delta }_{{mathrm {s}},i}}{sin {theta }_{{mathrm {s}},i}}cdot frac{sin {phi }_{i}}{{cos }^{2}{phi }_{i}}frac{partial {phi }_{i}}{partial {alpha }_{i-1}}=frac{sin {delta }_{{mathrm {s}},i}}{sin {theta }_{{mathrm {s}},i}}cdot frac{{R}_{{mathrm {step}}},sin {phi }_{i},sin {alpha }_{i-1}}{{cos }^{2}{phi }_{i}}\ = , {R}_{{mathrm {step}}}cdot ,sin {alpha }_{i-1}frac{tan {phi }_{i}}{tan {theta }_{{mathrm {s}},i}}$$
(14)
The sine factor on the right-hand side in Eq. (14) causes the sign of (partial {bar{alpha }}_{i}/partial {alpha }_{i-1}) to be opposite for East to West or West to East headings, and tan θs also change sign at the fall equinox (due to solar declination changing sign). The azimuth term in the denominator indicates heightened sensitivity closer to the summer or winter equinox and at high latitudes, and, conversely, heightened robustness to errors closer to the spring or autumnal equinox (since ({{tan }}{theta }_{{mathrm {s}},0}to pm infty)). This seasonal and directional asymmetry is illustrated in Fig. 3c, e.TCSC courses (Eq. (12)) involve up to three sensitivity terms, due to dependencies on sun azimuth, longitude and latitude:$$ frac{{mathrm {d}}{bar{{{{{{rm{alpha }}}}}}}}_{i}}{{mathrm {d}}{{{{{{rm{alpha }}}}}}}_{i-1}} = , {R}_{{{mathrm {step}}}}cdot {{sin }}{alpha }_{i-1}frac{{{tan }}{phi }_{i}}{{{tan }}{theta }_{{mathrm {s}},i}}+frac{{mathrm {d}}{lambda }_{i}}{{mathrm {d}}{{{{{{rm{alpha }}}}}}}_{i-1}}{{sin }}{{{phi }}}_{{c}_{{{mathrm {ref}}}},i}+left({{{{{{rm{lambda }}}}}}}_{i}-{{{{{{rm{lambda }}}}}}}_{{c}_{{{mathrm {ref}}}},i}right)frac{{mathrm {d}}{{sin }}{phi }_{{s}_{{{mathrm {ref}}}},i}}{{mathrm {d}}{{{{{{rm{alpha }}}}}}}_{i-1}}\ =, left{begin{array}{cc}{R}_{{{mathrm {step}}}}cdot left[{{sin }}{alpha }_{i-1}frac{{{tan }}{phi }_{i}}{{{tan }}{theta }_{{mathrm {s}},i}}-frac{{{cos }}{{{{{{rm{alpha }}}}}}}_{i-1}{{sin }}{phi }_{{s}_{{{mathrm {ref}}}},i}}{{{cos }}{phi }_{i-1}}right],hfill & {{{{{rm{classic}}}}}} ; (15{{{{{rm{a}}}}}})\ {R}_{{{mathrm {step}}}}left[{{sin }}{alpha }_{i-1}frac{{{tan }}{phi }_{i}}{{{tan }}{theta }_{{mathrm {s}},i}}-frac{{{cos }}{{{{{{rm{alpha }}}}}}}_{i-1}{{sin }}{phi }_{{s}_{{{mathrm {ref}}}},i}}{{{cos }}{phi }_{i-1}}+left({{{{{{rm{lambda }}}}}}}_{i}-{{{{{{rm{lambda }}}}}}}_{{c}_{{{mathrm {ref}}}},i}right){{sin }}{alpha }_{i-1}{{cos }}{phi }_{i}right], & {{{{{rm{proximate}}}}}} ; left(15{{{{{rm{b}}}}}}right).end{array}right.$$The first square-bracketed terms in Eqs. (15a, b) are identical to the fixed sun compass (Eq. (14)), reflecting seasonal and latitudinal dependence in sun-azimuth. For headings with a Southward component (α0  1) and nonexistent for North–South headings (G = 1, reflecting no longitude bands being crossed). We expected this factor to affect compass courses differentially according to their error-accumulating or self-correcting nature.We further modified the effective goal-area breadth to account for a (geographically) circular goal area on the sphere, i.e., effectively modulating the longitudinal component of the goal-area breadth at the arrival latitude, ∅A:$${beta }_{{mathrm {A}}}=beta sqrt{{{{{sin }}}^{2}bar{alpha }+left({{cos }}bar{alpha }/{{cos }}{{{phi }}}_{{mathrm {A}}}right)}^{2}}.$$
(19)
To account for differential sensitivity among compass-courses, we generalized the normal many-wrongs relation between performance and number of steps, (1/{hat{N}}^{eta }), from η = 0.5 (Eqs. (3) and (16)) to$$eta left({sigma }_{{step}}|s,bright)=left(0.5+bright){e}^{-s{{sigma }_{{step}}}^{2}},$$
(20)
where b  0 self-correction, and s represents a modulating exponential damping factor, consistent with the limiting circular-uniform case (as κ → 0, i.e., ({sigma }_{{{mathrm {step}}}}to infty)), where no (timely) convergence of heading is expected with an increasing number of steps.In assessing performance, we also accounted for seasonal migration constraints via a population-specific maximum number of steps, Nmax (Table 2; this became significant for the longest-distance simulations with large expected errors, i.e., small ({{{{{{rm{kappa }}}}}}}_{{{mathrm {step}}}}=1/{sigma }_{{{mathrm {step}}}}^{2})). The probability of having arrived at the goal latitude can be estimated using the Central Limit Theorem:$${p}_{{{phi }},{N}_{{max }}}cong frac{1}{2}left[1-{erf}left(left(frac{{N}_{0}}{{N}_{{max }}}-frac{{I}_{1}left({{{{{{rm{kappa }}}}}}}_{{{mathrm {step}}}}right)}{{I}_{0}left({{{{{{rm{kappa }}}}}}}_{{{mathrm {step}}}}right)}right)cdot frac{{{cos }}bar{alpha }}{{sigma }_{{mathrm {C}}}sqrt{2}}right)right],$$
(21)
where Ij is the modified Bessel function of the first kind and order j53, and σC (the standard deviation in the latitudinal component of flight-step distance) can be calculated using Bessel functions together with known properties of sums of cosines53,77 (Supplementary Note 2).Regression-estimated performanceWe fit the parameters in the spherical-geometry factor (Eq. (18)) and many-wrongs effect (Eq. (20)) according to expected performance, estimated as the product of sufficiently timely migration (Eq. (21)) and sufficiently precise migration, now generalized from Eq. (16), i.e.$${p}_{beta ,hat{N}}cong {erf}left(frac{{beta }_{{mathrm {A}}}}{{G}^{{g}}sqrt{2left({{sigma }_{{{mathrm {ind}}}}}^{2}+{sigma }_{{{mathrm {step}}}}/{hat{N}}^{n}right)}}right),$$
(22)
This resulted in up to four fitted parameters for each compass course

i.

an exponent, g, to the spherical-geometry factor (Eq. (19)), i.e., Gg, reflecting how growth or self-correction in errors between steps further augments or reduces this factor,

ii.

a baseline offset, b0, to the “normal” exponent η = 0.5, which mediates the relation between the number of steps and performance (Eq. (20)),

iii.

an exponent s reflecting how decreasing precision among flight-steps dampens the many-wrongs convergence (Eq. (20)),

iv.

for TCSC courses, a modulation, ρ, to the offset, b0, quantifying the extent to which self-correction increases with increased flight-step distance Rstep, i.e., ({{b={b}_{0}R}_{{{mathrm {step}}}}^{{prime} }}^{rho }) in Eq. (20), where ({R}_{{{mathrm {step}}}}^{{prime} })is the flight-step distance scaled by its median value among species.

Parameters were fit using MATLAB routine fitnlm based on compass course performance among species and seven error scenarios (5°, 10°, 20°, 30°, 40°, 50°, and 60° directional precision among flight-steps), for all combinations (including or excluding the four parameters). The most parsimonious combination of parameters was selected using MATLAB routine aicbic, based on the AICc, the Akaike information criterion corrected for small sample size57. Null values for the spherical-geometry parameter were set to g = 1, and for the parameters governing convergence of route-mean headings b0 = 0, s = 0, and, for TCSC courses, ρ = 0 (for loxodrome courses, ρ = 0 by default, i.e., was not fitted).Statistics and reproducibilityOur simulation results, regression fitting and AICc-model selection are reproducible using the MATLAB scripts (see the section “Code availability”).Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

Pedros-Alio C. The rare bacterial biosphere. Ann Rev Mar Sci. 2012;4:449–66. https://doi.org/10.1146/annurev-marine-120710-100948.Article 
PubMed 

Google Scholar 
Sogin ML, Morrison HG, Huber JA, Welch DM, Huse SM, Neal PR, et al. Microbial diversity in the deep sea and the underexplored “rare biosphere”. Proc Natl Acad Sci. 2006;103:12115–20. https://doi.org/10.1073/pnas.0605127103.Article 
PubMed 
PubMed Central 

Google Scholar 
Hausmann B, Pelikan C, Rattei T, Loy A, Pester M. Long-term transcriptional activity at zero growth of a cosmopolitan rare biosphere member. mBio. 2019;10:e02189–18. https://doi.org/10.1128/mBio.02189-18.Article 
PubMed 
PubMed Central 

Google Scholar 
Pester M, Bittner N, Deevong P, Wagner M, Loy AA. ‘Rare biosphere’microorganism contributes to sulfate reduction in a peatland. ISME J. 2010;4:1591–602.Article 

Google Scholar 
Rivett DW, Bell T. Abundance determines the functional role of bacterial phylotypes in complex communities. Nat Microbiol. 2018;3:767–72. https://doi.org/10.1038/s41564-018-0180-0.Article 
PubMed 
PubMed Central 

Google Scholar 
van Elsas JD, Chiurazzi M, Mallon CA, Elhottova D, Kristufek V, Salles JF. Microbial diversity determines the invasion of soil by a bacterial pathogen. Proc Natl Acad Sci USA. 2012;109:1159–64. https://doi.org/10.1073/pnas.1109326109.Article 
PubMed 
PubMed Central 

Google Scholar 
Magurran AE, Henderson PA. Explaining the excess of rare species in natural species abundance distributions. Nature. 2003;422:714–6.Article 

Google Scholar 
Rabinowitz D, Rapp JK, Dixon PM. Competitive abilities of sparse grass species: means of persistence or cause of abundance. Ecology. 1984;65:1144–54. https://doi.org/10.2307/1938322.Article 

Google Scholar 
Reinhardt K, Köhler G, Maas S, Detzel P. Low dispersal ability and habitat specificity promote extinctions in rare but not in widespread species: the Orthoptera of Germany. Ecography. 2005;28:593–602. https://doi.org/10.1111/j.2005.0906-7590.04285.x.Article 

Google Scholar 
Yenni G, Adler PB, Ernest S. Strong self-limitation promotes the persistence of rare species. Ecology. 2012;93:456–61.Article 

Google Scholar 
Jousset A, Bienhold C, Chatzinotas A, Gallien L, Gobet A, Kurm V, et al. Where less may be more: how the rare biosphere pulls ecosystems strings. The ISME J. 2017;11:853–62. https://doi.org/10.1038/ismej.2016.174.Article 
PubMed 

Google Scholar 
Thingstad TF. Elements of a theory for the mechanisms controlling abundance, diversity, and biogeochemical role of lytic bacterial viruses in aquatic systems. Limnol Oceanogr. 2000;45:1320–8. https://doi.org/10.4319/lo.2000.45.6.1320.Article 

Google Scholar 
Szekely AJ, Langenheder S. The importance of species sorting differs between habitat generalists and specialists in bacterial communities. FEMS Microbiol Ecol. 2014;87:102–12. https://doi.org/10.1111/1574-6941.12195.Article 
PubMed 

Google Scholar 
Mo Y, Zhang W, Yang J, Lin Y, Yu Z, Lin S. Biogeographic patterns of abundant and rare bacterioplankton in three subtropical bays resulting from selective and neutral processes. ISME J. 2018;12:2198–210. https://doi.org/10.1038/s41396-018-0153-6.Article 
PubMed 
PubMed Central 

Google Scholar 
Nemergut DR, Schmidt SK, Fukami T, O’Neill SP, Bilinski TM, Stanish LF, et al. Patterns and processes of microbial community assembly. Microbiol Mol Biology Rev. 2013;77:342–56. https://doi.org/10.1128/MMBR.00051-12.Article 

Google Scholar 
Vellend M. Conceptual synthesis in community ecology. Q Rev Biol. 2010;85:183–206. https://doi.org/10.1086/652373.Article 
PubMed 

Google Scholar 
Vellend M The Theory of Ecological Communities. Princeton University Pres. 2016:61-7.Jia X, Dini-Andreote F, Falcao Salles J. Community assembly processes of the microbial rare biosphere. Trends Microbiol. 2018;26:738–47. https://doi.org/10.1016/j.tim.2018.02.011.Article 
PubMed 

Google Scholar 
Stegen JC, Lin X, Fredrickson JK, Chen X, Kennedy DW, Murray CJ, et al. Quantifying community assembly processes and identifying features that impose them. ISME J. 2013;7:2069–79. https://doi.org/10.1038/ismej.2013.93.Article 
PubMed 
PubMed Central 

Google Scholar 
Stegen JC, Lin X, Fredrickson JK, Konopka AE. Estimating and mapping ecological processes influencing microbial community assembly. Front Microbiol. 2015;6:https://doi.org/10.3389/fmicb.2015.00370.Webb CO, Ackerly DD, McPeek MA, Donoghue MJ. Phylogenies and community ecology. Ann Rev Ecol Syst. 2002;33:475–505. https://doi.org/10.1146/annurev.ecolsys.33.010802.150448.Article 

Google Scholar 
Lynch MDJ, Neufeld JD. Ecology and exploration of the rare biosphere. Nat Rev Micro. 2015;13:217–29. https://doi.org/10.1038/nrmicro3400.Article 

Google Scholar 
Dini-Andreote F, Stegen JC, van Elsas JD, Salles JF. Disentangling mechanisms that mediate the balance between stochastic and deterministic processes in microbial succession. Proc Natl Acad Sci USA. 2015;112:E1326–E32. https://doi.org/10.1073/pnas.1414261112.Article 
PubMed 
PubMed Central 

Google Scholar 
Chase JM, Kraft NJB, Smith KG, Vellend M, Inouye BD. Using null models to disentangle variation in community dissimilarity from variation in α-diversity. Ecosphere. 2011;2:art24 https://doi.org/10.1890/es10-00117.1.Article 

Google Scholar 
Shade A, Jones SE, Caporaso JG, Handelsman J, Knight R, Fierer N, et al. Conditionally rare taxa disproportionately contribute to temporal changes in microbial diversity. mBio. 2014;5:e01371–14. https://doi.org/10.1128/mBio.01371-14.Article 
PubMed 
PubMed Central 

Google Scholar 
Strous M, Heijnen JJ, Kuenen JG, Jetten MSM. The sequencing batch reactor as a powerful tool for the study of slowly growing anaerobic ammonium-oxidizing microorganisms. Appl Microbiol Biotechnol. 1998;50:589–96. https://doi.org/10.1007/s002530051340.Article 

Google Scholar 
Goldfarb KC, Karaoz U, Hanson CA, Santee CA, Bradford MA, Treseder KK, et al. Differential growth responses of soil bacterial taxa to carbon substrates of varying chemical recalcitrance. Front Microbiol. 2011;2:94. https://doi.org/10.3389/fmicb.2011.00094.Article 
PubMed 
PubMed Central 

Google Scholar 
Jia X, Dini-Andreote F, Falcao Salles J. Comparing the influence of assembly processes governing bacterial community succession based on DNA and RNA Data. Microorganisms. 2020;8. https://doi.org/10.3390/microorganisms8060798.Olff H, De Leeuw J, Bakker JP, Platerink RJ, van Wijnen HJ. Vegetation succession and herbivory in a salt marsh: changes induced by sea level rise and silt deposition along an elevational gradient. J Ecol. 1997;85:799–814. https://doi.org/10.2307/2960603.Article 

Google Scholar 
Dini-Andreote F, Silva M, Triado-Margarit X, Casamayor EO, van Elsas JD, Salles JF. Dynamics of bacterial community succession in a salt marsh chronosequence: evidences for temporal niche partitioning. ISME J. 2014;8:1989–2001. https://doi.org/10.1038/ismej.2014.54.Article 
PubMed 
PubMed Central 

Google Scholar 
Dini-Andreote F, Pylro VS, Baldrian P, van Elsas JD, Salles JF. Ecological succession reveals potential signatures of marine–terrestrial transition in salt marsh fungal communities. ISME J. 2016;10:1984–97.Article 

Google Scholar 
Schrama M, Berg MP, Olff H. Ecosystem assembly rules: the interplay of green and brown webs during salt marsh succession. Ecology. 2012;93:2353–64.Article 

Google Scholar 
Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Lozupone CA, Turnbaugh PJ, et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc Natl Acad Sci. 2011;108:4516–22. https://doi.org/10.1073/pnas.1000080107.Article 
PubMed 

Google Scholar 
Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Huntley J, Fierer N, et al. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J. 2012;6:1621–4.Article 

Google Scholar 
Carini P, Marsden PJ, Leff JW, Morgan EE, Strickland MS, Fierer N. Relic DNA is abundant in soil and obscures estimates of soil microbial diversity. Nat Microbiol. 2016;2:16242. https://doi.org/10.1038/nmicrobiol.2016.242.Article 
PubMed 

Google Scholar 
Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet C, Al-Ghalith GA, et al. QIIME 2: Reproducible, interactive, scalable, and extensible microbiome data science. PeerJ Preprints. 2018;6:e27295v2. https://doi.org/10.7287/peerj.preprints.27295v2.Article 

Google Scholar 
Callahan BJ, McMurdie PJ, Holmes SP. Exact sequence variants should replace operational taxonomic units in marker-gene data analysis. ISME J. 2017;11:2639–43. https://doi.org/10.1038/ismej.2017.119.Article 
PubMed 
PubMed Central 

Google Scholar 
Yilmaz P, Parfrey LW, Yarza P, Gerken J, Pruesse E, Quast C, et al. The SILVA and “All-species Living Tree Project (LTP)” taxonomic frameworks. Nucleic Acids Res. 2014;42:D643–8. https://doi.org/10.1093/nar/gkt1209.Article 
PubMed 

Google Scholar 
Price MN, Dehal PS, Arkin AP. FastTree: computing large minimum evolution trees with profiles instead of a distance matrix. Mol Biol Evol. 2009;26:1641–50.Article 

Google Scholar 
R Core Team: R: A language and environment for statistical computing. In. Vienna, Austria: R Foundation for Statistical Computing; 2017.RStudio Team: RStudio: integrated development for R. In., vol. 42. Boston, MA: RStudio, Inc.; 2015.Wickham H. ggplot2: elegant graphics for data analysis. J Stat Softw. 2010;35:65–88.
Google Scholar 
Chen H, Boutros PC. VennDiagram: a package for the generation of highly-customizable Venn and Euler diagrams in R. BMC Bioinf. 2011;12:35.Article 

Google Scholar 
Dixon P. VEGAN, a package of R functions for community ecology. J Veg Sci. 2003;14:927–30. https://doi.org/10.1111/j.1654-1103.2003.tb02228.x.Article 

Google Scholar 
Yamamoto K, Hackley KC, Kelly WR, Panno SV, Sekiguchi Y, Sanford RA, et al. Diversity and geochemical community assembly processes of the living rare biosphere in a sand-and-gravel aquifer ecosystem in the Midwestern United States. Sci Rep. 2019;9. https://doi.org/10.1038/s41598-019-49996-z.Galand PE, Casamayor EO, Kirchman DL, Lovejoy C. Ecology of the rare microbial biosphere of the Arctic Ocean. Proc Natl Acad Sci. 2009;106:22427–32. https://doi.org/10.1073/pnas.0908284106.Article 
PubMed 
PubMed Central 

Google Scholar 
Reveillaud J, Maignien L, Murat Eren A, Huber JA, Apprill A, Sogin ML, et al. Host-specificity among abundant and rare taxa in the sponge microbiome. ISME J. 2014;8:1198–209. https://doi.org/10.1038/ismej.2013.227.Article 
PubMed 
PubMed Central 

Google Scholar 
Logares R, Audic S, Bass D, Bittner L, Boutte C, Christen R, et al. Patterns of rare and abundant marine microbial eukaryotes. Curr Biol. 2014;24:813–21. https://doi.org/10.1016/j.cub.2014.02.050.Article 
PubMed 

Google Scholar 
Campbell BJ, Yu L, Heidelberg JF, Kirchman DL. Activity of abundant and rare bacteria in a coastal ocean. Proc Natl Acad Sci. 2011;108:12776–81. https://doi.org/10.1073/pnas.1101405108.Article 
PubMed 
PubMed Central 

Google Scholar 
Hirsch JE. An index to quantify an individual’s scientific research output. Proc Natl Acad Sci USA. 2005;102:16569. https://doi.org/10.1073/pnas.0507655102.Article 
PubMed 
PubMed Central 

Google Scholar 
Haegeman B, Hamelin J, Moriarty J, Neal P, Dushoff J, Weitz JS. Robust estimation of microbial diversity in theory and in practice. ISME J. 2013;7:1092–101. https://doi.org/10.1038/ismej.2013.10.Article 
PubMed 
PubMed Central 

Google Scholar 
Paradis E, Claude J, Strimmer K. APE: analyses of phylogenetics and evolution in R language. Bioinformatics (Oxford, England). 2004;20:289–90.Article 

Google Scholar 
Kembel SW, Cowan PD, Helmus MR, Cornwell WK, Morlon H, Ackerly DD, et al. Picante: R tools for integrating phylogenies and ecology. Bioinformatics. 2010;26:1463–4.Article 

Google Scholar 
Stegen JC, Lin X, Konopka AE, Fredrickson JK. Stochastic and deterministic assembly processes in subsurface microbial communities. ISME J. 2012;6:1653–64. https://doi.org/10.1038/ismej.2012.22.Article 
PubMed 
PubMed Central 

Google Scholar 
Jiao S, Lu Y. Soil pH and temperature regulate assembly processes of abundant and rare bacterial communities in agricultural ecosystems. Environ Microbiol. 2020;22:1052–65. https://doi.org/10.1111/1462-2920.14815.Article 
PubMed 

Google Scholar 
Logares R, Lindström ES, Langenheder S, Logue JB, Paterson H, Laybourn-Parry J, et al. Biogeography of bacterial communities exposed to progressive long-term environmental change. ISME J. 2012;7:937–48. https://doi.org/10.1038/ismej.2012.168.Article 
PubMed 
PubMed Central 

Google Scholar 
Kurm V, van der Putten WH, Weidner S, Geisen S, Snoek BL, Bakx T, et al. Competition and predation as possible causes of bacterial rarity. Environ Microbiol. 2019;21:1356–68. https://doi.org/10.1111/1462-2920.14569.Article 
PubMed 
PubMed Central 

Google Scholar 
Aanderud ZT, Saurey S, Ball BA, Wall DH, Barrett JE, Muscarella ME, et al. Stoichiometric shifts in Soil C:N:P promote bacterial taxa dominance, maintain biodiversity, and deconstruct community assemblages. Front Microbiol. 2018;9:1401 https://doi.org/10.3389/fmicb.2018.01401.Article 
PubMed 
PubMed Central 

Google Scholar 
Sloan WT, Woodcock S, Lunn M, Head IM, Curtis TP. Modeling taxa-abundance distributions in microbial communities using environmental sequence data. Microb Ecol. 2007;53:443–55. https://doi.org/10.1007/s00248-006-9141-x.Article 
PubMed 

Google Scholar 
Magurran AE, McGill BJ. Biological diversity: frontiers in measurement and assessment. Oxford University Press; 2011.Richter-Heitmann T, Hofner B, Krah FS, Sikorski J, Wust PK, Bunk B, et al. Stochastic dispersal rather than deterministic selection explains the spatio-temporal distribution of soil bacteria in a temperate grassland. Front Microbiol. 2020;11:1391. https://doi.org/10.3389/fmicb.2020.01391.Article 
PubMed 
PubMed Central 

Google Scholar 
Ivanov II, Honda K. Intestinal commensal microbes as immune modulators. Cell Host Microbe. 2012;12:496–508. https://doi.org/10.1016/j.chom.2012.09.009.Article 
PubMed 
PubMed Central 

Google Scholar 
van Veelen HPJ, Falcao Salles J, Tieleman BI. Multi-level comparisons of cloacal, skin, feather and nest-associated microbiota suggest considerable influence of horizontal acquisition on the microbiota assembly of sympatric woodlarks and skylarks. Microbiome. 2017;5:156. https://doi.org/10.1186/s40168-017-0371-6.Article 
PubMed 
PubMed Central 

Google Scholar 
Warmink JA, Nazir R, Corten B, van Elsas JD. Hitchhikers on the fungal highway: The helper effect for bacterial migration via fungal hyphae. Soil Biology Biochem. 2011;43:760–5. https://doi.org/10.1016/j.soilbio.2010.12.009.Article 

Google Scholar 
Snell Taylor SJ, Evans BS, White EP, Hurlbert AH. The prevalence and impact of transient species in ecological communities. Ecology. 2018;99:1825–35. https://doi.org/10.1002/ecy.2398.Article 
PubMed 

Google Scholar 
Kurm V, Geisen S, Gera Hol WH. A low proportion of rare bacterial taxa responds to abiotic changes compared with dominant taxa. Environ Microbiol. 2019;21:750–8. https://doi.org/10.1111/1462-2920.14492.Article 
PubMed 

Google Scholar 
Wang Y, Hatt JK, Tsementzi D, Rodriguez RL, Ruiz-Perez CA, Weigand MR, et al. Quantifying the Importance of the Rare Biosphere for Microbial Community Response to Organic Pollutants in a Freshwater Ecosystem. Appl Environ Microbiol. 2017;83:e03321–16. https://doi.org/10.1128/AEM.03321-16.Article 
PubMed 
PubMed Central 

Google Scholar 
Cao J, Jia X, Pang S, Hu Y, Li Y, Wang Q. Functional structure, taxonomic composition and the dominant assembly processes of soil prokaryotic community along an altitudinal gradient. Appl Soil Ecol. 2020;155. https://doi.org/10.1016/j.apsoil.2020.103647.Meyer KM, Memiaghe H, Korte L, Kenfack D, Alonso A, Bohannan BJM. Why do microbes exhibit weak biogeographic patterns. ISME J. 2018;12:1404–13. https://doi.org/10.1038/s41396-018-0103-3.Article 
PubMed 
PubMed Central 

Google Scholar 
Anderson RE, Sogin ML, Baross JA. Biogeography and ecology of the rare and abundant microbial lineages in deep-sea hydrothermal vents. FEMS Microbiol Ecol. 2015;91:1–11. https://doi.org/10.1093/femsec/fiu016.Article 
PubMed 

Google Scholar 
Mallon CA, Le Roux X, van Doorn GS, Dini-Andreote F, Poly F, Salles JF. The impact of failure: unsuccessful bacterial invasions steer the soil microbial community away from the invader’s niche. ISME J. 2018;12:728–41. https://doi.org/10.1038/s41396-017-0003-y.Article 
PubMed 
PubMed Central 

Google Scholar 
Langenheder S, Bulling MT, Solan M, Prosser JI. Bacterial biodiversity-ecosystem functioning relations are modified by environmental complexity. PLoS One. 2010;5:e10834. https://doi.org/10.1371/journal.pone.0010834.Article 
PubMed 
PubMed Central 

Google Scholar 
Bardgett RD, Van Der Putten WH. Belowground biodiversity and ecosystem functioning. Nature. 2014;515:505.Article 

Google Scholar 
Griffiths B, Ritz K, Wheatley R, Kuan H, Boag B, Christensen S, et al. An examination of the biodiversity–ecosystem function relationship in arable soil microbial communities. Soil Biol Biochem. 2001;33:1713–22.Article 

Google Scholar 
Hooper DU, Chapin F, Ewel J, Hector A, Inchausti P, Lavorel S, et al. Effects of biodiversity on ecosystem functioning: a consensus of current knowledge. Ecol Monogr. 2005;75:3–35.Article 

Google Scholar 
Logares R, Tesson SVM, Canback B, Pontarp M, Hedlund K, Rengefors K. Contrasting prevalence of selection and drift in the community structuring of bacteria and microbial eukaryotes. Environ Microbiol. 2018;20:2231–40. https://doi.org/10.1111/1462-2920.14265.Article 
PubMed 

Google Scholar 
Zhou J, Ning D. Stochastic community assembly: does it matter in microbial ecology? Microbiol Mol Biol Rev. 2017;81:e00002–17.Article 

Google Scholar 
Logares R, Deutschmann IM, Junger PC, Giner CR, Krabberod AK, Schmidt TSB, et al. Disentangling the mechanisms shaping the surface ocean microbiota. Microbiome. 2020;8:55. https://doi.org/10.1186/s40168-020-00827-8.Article 
PubMed 
PubMed Central 

Google Scholar 
Dini-Andreote F, Brossi MJ, van Elsas JD, Salles JF. Reconstructing the genetic potential of the microbially-mediated nitrogen cycle in a salt marsh ecosystem. Front Microbiol. 2016;7:902. https://doi.org/10.3389/fmicb.2016.00902.Article 
PubMed 
PubMed Central 

Google Scholar  More

Study areaWe conducted our study in two suburbs in Wellington, New Zealand (Fig. 1). The 4.7-hectare site in the suburb of Kelburn (-41.285°S, 174.770°E) was situated on the grounds of student accommodation for Victoria University of Wellington. The site comprised bungalow houses, two accommodation halls, and access roads and paths. About half of the vegetation at the Kelburn site was a mix of tended grass lawns and gardens containing a variety of native New Zealand plant species, e.g., flax (Phormium spp.), longwood tussock (Carex comans), and cabbage tree (Cordyline australis). The other half was a mix of dense ground cover dominated by invasive weed species and native and exotic trees and shrubs, e.g., pōhutukawa (Metrosideros excelsa), common oak (Quercus robur), kawakawa (Piper excelsum), and taupata (Coprosma repens). The second suburb was Roseneath (−41.292°S, 174.801°E) on a small peninsula on the north-eastern side of Mount Victoria. The site was 8.5 hectares comprising 76 residential properties, public thoroughfares, and footpaths. We conducted fieldwork in the gardens of 25 of these properties. The vegetation varied considerably between gardens, comprising native and introduced garden plants and invasive weeds, especially blackberry (Rubus fruticosus).Figure 1(A) The study was conducted in the suburbs of Kelburn (left yellow dot) and Roseneath (right yellow dot) in the city of Wellington, New Zealand. The black polygon represents the 1475 ha area that will be targeted for ship rat (Rattus rattus) eradication in Wellington city, New Zealand. In each suburb, we radio-collared ship rats and deployed three types of devices (bait stations, chew cards, and WaxTags) to estimate home range and detection parameters. (B) In Kelburn, we radio-collared 14 rats and deployed eight devices. (C) In Roseneath, we radio-collared 16 rats and deployed 30 devices. The yellow circles indicate home range centers of individual rats, the red triangles indicate the location of bait stations and detection devices, and the small black dots indicate the telemetry locations of rats.Full size imageRat capture, radio-collaring, and field methodologyWe set 100 live-capture cage traps (custom-made, spring-loaded traps) in Kelburn from 12 July to 15 August 2020, and another 100 in Roseneath from 20 August to 20 October 2020. We baited cage traps with apple coated in chocolate spread and checked them at least once every 24 h. We set cage traps in areas with complex vegetative groundcover and understorey to maximize capture rates of ship rats (see35), and to provide shelter from inclement weather. We provided additional shelter by inserting bedding inside a tin can placed in the cage traps, along with a plastic cover over the traps to limit exposure to wind and rain. Cage traps were active for 5 days per week on average. We released all non-target species (house mice Mus musculus, European hedgehogs Erinaceus europaeus, and Eurasian blackbirds Turdus merula).We transferred any trap containing a captured rat into a sealed plastic container. Depending on the estimated size of the captured rat, we placed between one and three cotton balls soaked in isoflurane (99.9%, Attane, Piramal Critical Care Inc., Bethlehem, Pennsylvania, USA) inside the plastic container. A rat was anesthetized when it lost balance and was unable to regain balance when we gently rotated the container. We then removed the rat from the cage trap and placed it next to a heat pad with its head close to the cotton balls soaked in isoflurane to maintain anaesthesia while handling them. We fitted all rats weighing  > 110 g with a V1C 118B VHF radio-collar (Lotek, Havelock North, New Zealand). We marked each collared rat with a unique pelage code using a permanent blonde hair dye60. We also recorded biometrics, including sex, weight, and length. When processing was finished, we placed the rat into another container to recover. This container had a heating pad for warmth and an apple for food to avoid a drop in body temperature and hypoglycemia, which are common problems with anaesthesia62. When the rat appeared mobile, energetic, and behaving normally, we released it at the point of capture.We monitored radio-collared rats using a Yagi antenna (Lotek, Havelock North, New Zealand) and a Telonics R-1000 receiver (Telonics Inc., Mesa, Arizona, USA). We conducted radio-telemetry work during August–November 2020, with fixes taken during the day and night. We recorded a total of three fixes per rat per night, taken at two-hour intervals between the hours of sunset (2200 h) and sunrise (0500 h). We mostly attempted one day-time fix (1200 h); however, if a tracked rat was active (determined by a VHF signal that was moving or changing amplitude), we attempted a second fix in the afternoon. To minimize location error, we used the close approach radio-tracking method described by63. Once a successful fix was made, we used a handheld GPS unit to record the location, date, and time. Telemetry fixes were collected for each radio-collared rat for 18–97 days.After approximately one week of radiotracking an animal, we obtained an initial crude estimate of the center of each rat’s home range as the mean of all eastings and northings (based on a minimum of 15 telemetry points per rat). A bait station baited with non-toxic pellets (Protecta Sidekick bait stations, Bell Laboratories Inc., Windsor, Wisconsin, USA), a WaxTag with a peanut butter odor incorporated into the wax (PCR WaxTags, Traps.co.nz, Rolleston, New Zealand), and a chew card (a corflute card baited with peanut butter) were deployed at varying distances (max. 50 m) and cardinal directions from the estimated home range center of each individual rat. This layout maximized the likelihood of encounters with devices, compared with a regular grid-type deployment where some of the devices could fall outside a collared rat’s home range and thus never be encountered. Note that the crude estimate of the location of the home range center for each rat was only used to guide device placement, i.e., it was not used in any statistical analyses, or to describe rat home range sizes. Further, to avoid a choice-type experiment (i.e., all three devices set immediately next to each other), we randomly assigned a distance and cardinal direction to each device type within each rat’s home range but ensured all devices were deployed  > 15 m apart. The three device types were chosen because they are used by Predator Free Wellington to conduct their eradication operations.Every deployed device had a trail camera (Browning Strike Force HD Pro Micro Series, Morgan, Utah, USA) taking video of rats encountering and interacting with the device. We set cameras to take 20 s of video footage when triggered, followed by a 1 s re-trigger interval. We fixed trail cameras to trees at a height of 50 cm above ground level and placed the devices 1.5 m in front of the camera (after64). This strategy allowed accurate identification of pelage codes on marked rats. We cleared vegetation in front of and immediately behind the trail cameras to avoid accidental triggers. We used pegs to mark a 30-cm-radius circle around each device and considered a rat–device encounter when a rat entered that circle. We serviced trail camera–device pairs at least once every three days. This included adding more non-lethal bait to bait stations and peanut butter to monitoring devices, installing new WaxTags or chew cards if they had been destroyed, and replacing batteries and SD cards in trail cameras. We set up 54 trail camera–device pairs. However, due to trail camera malfunctions, we were able to retrieve footage from only 38 cameras, 8 in Kelburn and 30 in Roseneath. Trail camera–device pairs were active for 20–70 days, but we retained data from only the first 20 days for the analyses.Video processingAll video footage was viewed and interpreted by the same individual (HRM) for consistency. We extracted the following information: date and time of rat sightings, rat ID (according to the pelage code, or designated as ‘R’ for unmarked rats), the duration of the visit to a device, whether or not an encounter occurred (as defined above), and whether or not an interaction occurred. We defined an interaction as a rat either gnawing on a chew card or WaxTag or entering a bait station.Data analysisWe combined all ship rat telemetry data with the device encounter and interaction data, and developed a hierarchical Bayesian model to infer factors influencing the key parameters σ, ε0, and θ. The analytical approach builds on that described in65. For the purpose of estimating ε0 and θ, multiple encounters or interactions by the same individual with the same device on the same night were counted as a single encounter or interaction.The VHF telemetry data Zij were composed of xij (eastings) and yij (northings) locations for each individual rat i at site j (either Kelburn or Roseneath). To simplify the notation, we drop the j subscript from all subsequent equations. We modelled the probability of observing Zi as a symmetric bivariate normal variable$$P({Z}_{i})= prod_{i=1}^{{L}_{i}}Normal(Delta {x}_{i}|0,{sigma }_{i}^{2})Normal(Delta {y}_{i}|0,{sigma }_{i}^{2})$$
(1)
where σi is the standard deviation of a normal distribution with zero mean, Li is the number of location fixes for individual i, and Δxi and Δyi are the straight-line distances from the home range center of individual i to xi and yi, respectively.Home range centers can be estimated using various methods, all of which have underlying assumptions (e.g.,66,67). We calculated the home range center for each individual as the mean of all xi and yi, i.e., the centroid of all locations that we recorded for each individual ( > 30 VHF fixes in all instances). Under this formulation, the home range center is assumed to be perfectly observed, an assumption that is supported by the sample size of telemetry locations that we obtained for each individual (see Supplementary Table 266).We modelled σi as a log-normal variable with mean ln(μi), which was a function of the sex of the individual:$$lnleft({sigma }_{i}right)sim Normal(mathit{ln}left({mu }_{i}right), V)$$
(2)
$$lnleft({mu }_{i}right)= {beta }_{0}+ {beta }_{1}{sex}_{i}$$
(3)
where V is the variance of ln(σi), and ln(μi) is a linear function of a categorical variable indicating whether rat i is a male (0) or a female (1). The priors on the β coefficients and V were Normal(0, 10) and InverseGamma(0.01, 0.01), respectively.The encounter data (Eimt) across all devices m and nights t was modelled as a Bernoulli process:$${E}_{imt}sim Bernoulli({gamma }_{imt})$$
(4)
$$logitleft({gamma }_{imt}right)sim MultivariateNormal(logitleft({P}_{imt}right), varSigma )$$
(5)
where γimt is a latent variable representing the degree to which the nightly probability of rat i encountering a given device is not independent of the encounter outcomes of nearby devices, i.e., we assumed there is spatial autocorrelation in the nightly probability of encountering a device. To account for the spatial autocorrelation not explained by the covariates explicitly modelled (i.e., σ and device type, see below), we included an exponential spatial covariance error structure (Σ) as follows:$$varSigma = {nu }^{2}{e}^{-varphi r}$$
(6)
where ν2 is the variance, φ is a correlation distance parameter, and r is the distance (in m) between pairs of devices68,69. Further, because not all devices were available on all nights, Σ was calculated iteratively for each night considering only those devices that were available. We used moderately informative log-normal priors for the covariance parameters to obtain proper posteriors69: ν2 ~ logN(3,1) and φ ~ logN(1,1).The nightly probability of encounter of device m by individual i on night t (Pimt) was calculated using a half-normal detection function70:$${P}_{imt}= {{left({varepsilon }_{0, im}{e}^{left(-frac{{d}_{im}^{2}}{2{sigma }_{i}^{2}}right)}right)}^{{tau E}_{it}^{*}}}times {{left({varepsilon }_{0,im}{e}^{left(-frac{{d}_{im}^{2}}{2{sigma }_{i}^{2}}right)}right)}^{1-{E}_{it}^{*}}}$$
(7)
where ε0,im is the maximum nightly probability of encounter for device m, or the probability if device m was placed at the center of the home range of rat i. The variable σi is the standard deviation from Eq. (1) (i.e., σi is estimated jointly from the telemetry and encounter data) and dim is the distance (in m) between the home range center of rat i and device m; only devices within a distance of 3.72σi from the home range center were considered in the calculation in Eq. (7)70. Finally, τ is a strictly positive parameter (i.e., τ  > 0), measuring the degree of device-shyness, which is multiplied by an indicator variable (left({E}_{it}^{*}right)) which takes a value of 0 when individual i has not encountered a device (of any type) on nights prior to night t, or a value of 1 if it had previously encountered one, regardless of the type of device it encountered. If τ  1 then rats are ‘device-shy’ and thus more likely to avoid devices on nights following an initial encounter. ({E}_{it}^{*}) was reset to 0 after 20 days of no encounters with a device. Following65 we set the prior on τ as Gamma(0.933, 8.33) (shape and rate parameters, respectively).Values of ε0,im were predicted as a function of σi, device type, and individual effects using the following equation:$$logitleft({varepsilon }_{0, im}right)={alpha }_{0}+ {alpha }_{1}mathrm{ln}left({sigma }_{i}right)+ {alpha }_{2}{chewcard}_{m}+{alpha }_{3}{waxtag}_{m}+{delta }_{i}$$
(8)
where α2 and α3 quantify the increase or decrease in the maximal probability of encountering a chew card or a WaxTag relative to a bait station (which is the reference category). The δi parameters account for individual differences in ε0. Finally, we allowed ε0 to be a function of ln(σi) because we assumed encounter probability at home range center will decrease with increasing home range size (as suggested by71 and shown by65). The priors on the α coefficients and δ were Normal(0, 10) and Normal(0, 1), respectively.The interaction data (Iimn) across all devices m and nights n when encounters occurred was modelled as a Bernoulli process with probability θ, which was a function of device type and individual effects:$${mathrm{I}}_{imn}sim Bernoullileft({theta }_{imn}right)$$
(9)
$$logitleft({theta }_{imn}right)={lambda }_{0}+ {lambda }_{1}{chewcard}_{m}+{lambda }_{2}{waxtag}_{m}+{lambda }_{3}{I}_{in}^{*}+{rho }_{i}$$
(10)
where θimn is the probability of rat i interacting with device m given that it has encountered it on night n, and λ1 and λ2 quantify the increase or decrease in the conditional probability of interaction for a chew card or a WaxTag relative to a bait station. The λ3 parameter is analogous to τ in Eq. (7) but for the process of interaction given encounter with a device. However, by incorporating λ3 directly into a linear equation, this parameter can take negative values and thus should be interpreted differently to τ: if λ3  0 indicates that individuals become ‘device-happy’ after an initial interaction. This parameter is multiplied by an indicator variable ({(I}_{in}^{*})) which takes a value of 0 when individual i has not interacted with a device (of any type) on nights prior to night n, or a value of 1 when it has interacted with one previously, regardless of the type of device it interacted with. If a rat had not interacted with a device for 20 days, ({I}_{in}^{*}) was reset to 0. Finally, the ρi parameters account for individual differences in θ. The priors on the λ coefficients and ρ were Normal(0, 10) and Normal(0, 1), respectively. Although we explicitly modelled spatial autocorrelation in the probability of encountering a device, we did not do so for the probability of interaction given an encounter. In this instance we assumed that whether an animal chose to interact with an encountered device would depend on its previous experience (as quantified by λ3) rather than the spatial location of nearby devices.We used Markov Chain Monte Carlo (MCMC) simulation to estimate model parameters using Python programming language. The variance parameter V was sampled from the full conditional posteriors, but all other parameters were estimated using the Metropolis algorithm69. Posterior summaries were taken from four chains containing 3000 samples each (with a burn-in of 2000 and a thinning rate of 30). Convergence on posteriors was assessed by visual inspection and a scale reduction factor  More

Neo, M. L., Eckman, W., Vicentuan, K., Teo, S.L.-M. & Todd, P. A. The ecological significance of giant clams in coral reef ecosystems. Biol. Conserv. 181, 111–123 (2015).Article 

Google Scholar 
Wolfe, K. et al. Priority species to support the functional integrity of coral reefs. Oceanogr. Mar. Biol. Annu. Rev. 58, 179–318 (2020).Article 

Google Scholar 
Ip, Y. K. & Chew, S. F. Light-dependent phenomena and related molecular mechanisms in giant clam-dinoflagellate associations: A review. Front. Mar. Sci. 8, 627722 (2021).Article 

Google Scholar 
Rossbach, S. et al. Flexibility in Red Sea Tridacna maxima-symbiodiniaceae associations supports environmental niche adaption. Ecol. Evol. 11, 3393–3406 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Neo, M. L. et al. Giant clams (Bivalvia: Cardiidae: Tridacninae): A comprehensive update of species and their distribution, current threats and conservation status. Oceanogr. Mar. Biol. Annu. Rev. 55, 87–388 (2017).Article 

Google Scholar 
Armstrong, E. J., Dubousquet, V., Mills, S. C. & Stillman, J. H. Elevated temperature, but not acidification, reduces fertilization success in the small giant clam, Tridacna maxima. Mar. Biol. 167, 8 (2020).CAS 
Article 

Google Scholar 
Lokier, S., Al-Suwaidi, A. E. & Steuber, T. Stable isotope sclerochronology of Pleistocene shells of the ‘Giant Clam’ Tridacna from Abu Dhabi. Tribulus 20, 21–23 (2012).
Google Scholar 
Obura, D. The diversity and biogeography of Western Indian Ocean reef-building corals. PLoS ONE 7, e45013 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kulbicki, M. et al. Biogeography of butterflyfishes: A global model for reef fishes? In Biology of Butterflyfishes (eds Pratchett, M. S. et al.) 70–106 (CRC Press, 2013).Chapter 

Google Scholar 
DiBattista, J. D. et al. On the origin of endemic species in the Red Sea. J. Biogeogr. 43, 13–30 (2016).Article 

Google Scholar 
Kemp, J. M. Zoogeography of the coral reef fishes of the north-eastern Gulf of Aden, with eight new records of coral reef fishes from Arabia. Fauna Arabia 18, 293–321 (2000).
Google Scholar 
Sheppard, C. R. C. & Salm, R. V. Reef and coral communities of Oman, with a description of a new coral species (Order Scleractinia, genus Acanthastrea). J. Nat. Hist. 22, 263–279 (1988).Article 

Google Scholar 
Burt, J. A. et al. Biogeographic patterns of reef fish community structure in the northeastern Arabian Peninsula. ICES J. Mar. Sci. 68, 1875–1883 (2011).Article 

Google Scholar 
Torquato, F. & Møller, P. R. Physical-biological interactions underlying the connectivity patterns of coral-dependent fishes around the Arabian Peninsula. J. Biogeogr. 49, 483–496 (2022).Article 

Google Scholar 
Watanabe, T., Suzuki, A., Kawahata, H., Kan, H. & Ogawa, S. A 60-year isotopic record from a mid-Holocene fossil giant clam (Tridacna gigas) in the Ryukyu Islands: Physiological and paleoclimatic implications. Palaeogeogr. Palaeoclimatol. Palaeoecol. 212, 343–354 (2004).Article 

Google Scholar 
Elliot, M. et al. Profiles of trace elements and stable isotopes derived from giant long-lived Tridacna gigas bivalves: Potential applications in paleoclimatic studies. Palaeogeogr. Palaeoclimatol. Palaeoecol. 280, 123–142 (2009).Article 

Google Scholar 
Welsh, K., Elliot, M., Tudhope, A., Ayling, B. & Chappell, J. Giant bivalves (Tridacna gigas) as recorders of ENSO variability. Earth Planet. Sci. Lett. 307, 266–270 (2011).ADS 
CAS 
Article 

Google Scholar 
Hori, M. et al. Middle Holocene daily light cycle reconstructed from the strontium/calcium ratios of a fossil giant clam shell. Sci. Rep. 5, 8734 (2015).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Komagoe, T., Watanabe, T., Shirai, K., Yamazaki, A. & Uematu, M. Geochemical and microstructural signals in giant clam Tridacna maxima recorded typhoon events at Okinotori Island, Japan. J. Geophys. Res. Biogeosci. 123, 1460–1474 (2018).CAS 
Article 

Google Scholar 
Yuan, Y., Kusky, T. M. & Rajendran, S. Tertiary and Quaternary marine terraces and planation surfaces of northern Oman: Interaction of flexural bulge migration associated with the Arabian-Eurasian collision and eustatic sea level changes. J. Earth Sci. 27, 955–970 (2016).CAS 
Article 

Google Scholar 
Louis, V., Besseau, L. & Lartaud, F. Step in time: Biomineralisation of bivalve’s shell. Front. Mar. Sci. 9, 906085 (2022).Article 

Google Scholar 
Mossadegh, Z. K. et al. Palaeoecology of well-preserved coral communities in a siliciclastic environment from the Late Pleistocene (MIS 7), Kish Island, Persian Gulf (Iran): The development of low-relief reef frameworks (biostromes) in increasingly restricted environments. Int. J. Earth Sci. 102, 545–570 (2013).Article 

Google Scholar 
Pico, T., Creveling, J. R. & Mitrovica, J. X. Sea-level records from the U.S. mid-Atlantic constrain laurentide ice sheet extent during marine isotope stage 3. Nat. Commun. 8, 15612 (2017).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hoffmann, G. et al. Quaternary uplift along a passive continental margin (Oman, Indian Ocean). Geomorphology 350, 106870 (2020).Article 

Google Scholar 
Grant, K. M. et al. Rapid coupling between ice volume and polar temperature over the past 150,000 years. Nature 491, 744–747 (2012).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Lambeck, K., Rouby, H., Purcell, A., Sun, Y. & Sambridge, M. Sea level and global ice volumes from the Last Glacial Maximum to the Holocene. Proc. Natl. Acad. Sci. 111, 15296–15303 (2014).ADS 
CAS 
PubMed 
PubMed Central 
Article 

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

Google Scholar 
Burns, S. J., Matter, A., Frank, N. & Mangini, A. Speleothem-based paleoclimate record from northern Oman. Geology 26, 499–502 (1998).ADS 
Article 

Google Scholar 
Hoffmann, G., Rupprechter, M., Rahn, M. & Preusser, F. Fluvio-lacustrine deposits reveal precipitation pattern in SE Arabia during early MIS 3. Quat. Int. 382, 145–153 (2015).Article 

Google Scholar 
Kobashi, T. & Grossman, E. J. The oxygen isotopic record of seasonality in Conus shells and its application to understanding late middle Eocene (38 Ma) climate. Paleontol. Res. 7, 343–355 (2003).Article 

Google Scholar 
Watanabe, T. K. et al. Past summer upwelling events in the Gulf of Oman derived from a coral geochemical record. Sci. Rep. 7, 4568 (2017).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Jayaram, C. et al. Analysis of gap-free chlorophyll-α data from MODIS in Arabian Sea, reconstructed using DINEOF. Int. J. Remote Sens. 39, 7506–7522 (2018).Article 

Google Scholar 
Warter, V., Erez, J. & Müller, J. Environmental and physiological controls on daily trace element incorporation in Tridacna crocea from combined laboratory culturing and ultra-high resolution LA-ICP-MS analysis. Palaeogeogr. Palaeoclimatol. Palaeoecol. 496, 32–47 (2018).Article 

Google Scholar 
Ayouche, A. et al. Structure and dynamics of the Ras al Hadd oceanic dipole in the Arabian Sea. Oceans 2, 105–125 (2021).Article 

Google Scholar 
Sano, Y. et al. Past daily light cycle recorded in the strontium/calcium ratios of giant clam shells. Nat. Commun. 3, 761 (2012).ADS 
PubMed 
Article 

Google Scholar 
Santos, G. M. et al. Δ14C and δ13C of seawater DIC as tracers of coastal upwelling: A 5-year time series from Southern California. Radiocarbon 53, 669–677 (2011).CAS 
Article 

Google Scholar 
North Greenland Ice Core Project Members. High-resolution record of Northern Hemisphere climate extending into the last interglacial period. Nature 431, 147–151 (2004).Article 

Google Scholar 
Zhang, X. & Prange, M. Stability of the Atlantic overturning circulation under intermediate (MIS3) and full glacial (LGM) conditions and its relationship with Dansgaard-Oeschger climate variability. Quat. Sci. Rev. 242, 106443 (2020).Article 

Google Scholar 
Schulte, S. & Müller, P. J. Variations of sea surface temperature and primary productivity during Heinrich and Dansgaard-Oeschger events in the northeastern Arabian Sea. Geo-Mar. Lett. 21, 168–175 (2001).ADS 
Article 

Google Scholar 
Deplazes, G. et al. Weakening and strengthening of the Indian monsoon during Heinrich events and Dansgaard-Oeschger oscillations. Paleoceanography 29, 99–114 (2014).ADS 
Article 

Google Scholar 
Duprey, N. et al. Calibration of seawater temperature and δ18Oseawater signals in Tridacna maxima’s δ18Oshell record based on in situ data. Coral Reefs 34, 437–450 (2015).ADS 
Article 

Google Scholar 
Govil, P. & Naidu, P. D. Evaporation-precipitation changes in the eastern Arabian Sea for the last 68 ka: Implications on monsoon variability. Paleoceanography 25, 1210 (2010).ADS 
Article 

Google Scholar 
Watanabe, T. K. et al. Corals reveal an unprecedented decrease of Arabian Sea upwelling during the current warming era. Geophys. Res. Lett. 48, e2021GL092432 (2021).ADS 
Article 

Google Scholar 
Gaye, B. et al. Glacial−interglacial changes and Holocene variations in Arabian Sea denitrification. Biogeosciences 15, 507–527 (2018).ADS 
CAS 
Article 

Google Scholar 
DiNezio, P. N. et al. Glacial changes in tropical climate amplified by the Indian Ocean. Sci. Adv. 4, 9658 (2018).ADS 
Article 

Google Scholar 
Kleypas, J. A., McManus, J. W. & Menez, L. A. B. Environmental limits to coral reef development: Where do we draw the line? Am. Zool. 39, 146–159 (1999).Article 

Google Scholar 
Abram, N. J., Webster, J. M., Davies, P. J. & Dullo, W. C. Biological response of coral reefs to sea surface temperature variation: Evidence from the raised Holocene reefs of Kikai-jima (Ryukyu Islands, Japan). Coral Reefs 20, 221–234 (2001).Article 

Google Scholar 
Clemens, S. C. & Prell, W. L. A 350,000 year summer-monsoon multi-proxy stack from the Owen Ridge, Northern Arabian Sea. Mar. Geol. 201, 35–51 (2003).ADS 
Article 

Google Scholar 
Caley, T. et al. New Arabian Sea records help decipher orbital timing of Indo-Asian monsoon. Earth Planet. Sci. Lett. 308, 433–444 (2011).ADS 
CAS 
Article 

Google Scholar 
Banakar, V. K., Mahesh, B. S., Burr, G. & Chondankar, A. R. Climatology of the Eastern Arabian Sea during the last glacial cycle reconstructed from paired measurement of foraminiferal δ18O and Mg/Ca. Quat. Res. 73, 535–540 (2010).CAS 
Article 

Google Scholar 
Mattern, F. et al. Coastal dynamics of uplifted and emerged late Pleistocene near-shore coral patch reefs at Fins (eastern coastal Oman, Gulf of Oman). J. Afr. Earth Sci. 138, 192–200 (2018).Article 

Google Scholar 
Hoffmann, J. S., Clark, P. U., Parnell, A. C. & He, F. Regional and global sea-surface temperatures during the last interglaciation. Science 355, 276–279 (2017).ADS 
Article 

Google Scholar 
van de Berg, W. J., van den Broeke, M., Ettema, J., van Meijgaard, E. & Kaspar, F. Significant contribution of insolation to Eemian melting of the Greenland ice sheet. Nat. Geosci. 4, 1245 (2011).
Google Scholar 
Nicholl, J. A. L. et al. A Laurentide outburst flooding event during the last interglacial period. Nat. Geosci. 5, 901–904 (2012).ADS 
CAS 
Article 

Google Scholar 
Tzedenakis, P. C. et al. Enhanced climate instability in the North Atlantic and southern Europe during the Last Interglacial. Nat. Commun. 9, 4235 (2018).ADS 
Article 

Google Scholar 
Sandeep, N. et al. South Asian monsoon response to weakening of Atlantic meridional overturning circulation in a warming climate. Clim. Dyn. 54, 3507–3524 (2020).Article 

Google Scholar 
Rao, S. A. et al. Why is Indian Ocean warming consistently? Clim. Change 110, 709–719 (2012).ADS 
Article 

Google Scholar 
Heron, S. F., Maynard, J. A., van Hooidonk, R. & Eakin, M. Warming trends and bleaching stress of the world’s coral reefs 1985–2012. Sci. Rep. 6, 38402 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Chollett, I., Mumby, P. J. & Cortés, J. Upwelling areas do not guarantee refuge for coral reefs in a warming ocean. Mar. Ecol. Prog. Ser. 416, 47–56 (2010).ADS 
Article 

Google Scholar 
Praveen, V., Ajayamohan, R. S., Valsala, V. & Sandeep, S. Intensification of upwelling along Oman coast in a warming scenario. Geophys. Res. Lett. 43, 7581–7589 (2016).ADS 
Article 

Google Scholar 
Schulz, K. G., Hartley, S. & Eyre, B. Upwelling amplifies ocean acidification on the East Australian Shelf: Implications for marine ecosystems. Front. Mar. Sci. 6, 636 (2019).Article 

Google Scholar 
Southon, J., Kashgarian, M., Fontugne, M., Metivier, B. & Yim, W.W.-S. Marine reservoir corrections for the Indian Ocean and Southeast Asia. Radiocarbon 44, 167–180 (2002).Article 

Google Scholar 
Jochum, K. P., Stoll, B., Herwig, K. & Willbold, M. Validation of LA-ICP-MS trace element analysis of geological glasses using a new solid-state 193 nm Nd:YAG laser and matrix-matched calibration. J. Anal. At. Spectrom. 22, 112–121 (2007).CAS 
Article 

Google Scholar 
Mischel, S. A., Mertz-Kraus, R., Jochum, K. P. & Scholz, D. Termite: An R script for fast reduction laser ablation inductivity coupled plasma mass spectrometry data and its application to trace element measurements. Rapid Commun. Mass Spectrom. 31, 1079–1087 (2017).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Jochum, K. P., Willbold, M., Raczek, I., Stoll, B. & Herwig, K. Chemical characterisation of the USGS reference glasses GSA-1G, GSC-1G, GSD-1G, GSE-1G, BCR-2G, BHVO-2G and BIR-1G using EPMA, ID-TIMS, ID-ICP-MS and LA-ICP-MS. Geostand. Geoanal. Res. 29, 285–302 (2005).CAS 
Article 

Google Scholar 
Okai, T., Suzuki, A., Kawahata, H., Terashima, S. & Imai, N. Preparation of a new geological survey of Japan geochemical reference material: Coral JCp-1. Geostand. Newslett. 26, 95–99 (2002).CAS 
Article 

Google Scholar 
Sekimoto, S. et al. Neutron activation analysis of carbonate reference materials: Coral (JCp-1) and giant clam (JCt-1). J. Radioanal. Nucl. Chem. 322, 1579–1583 (2019).CAS 
Article 

Google Scholar 
Paillard, D., Labeyrie, L. & Yiou, P. Macintosh program performs time-series analysis. Eos Trans. AGU 77, 379 (1996).ADS 
Article 

Google Scholar  More

Hughes, T. P. et al. Coral reefs in the Anthropocene. Nature 546, 82–90 (2017).CAS 
PubMed 
Article 

Google Scholar 
Hatcher, B. G. Coral reef primary productivity: a beggar’s banquet. Trends Ecol. Evol. 3, 106–111 (1988).CAS 
PubMed 
Article 

Google Scholar 
Hatcher, B. G. Coral reef primary productivity. A hierarchy of pattern and process. Trends Ecol. Evol. 5, 149–155 (1990).CAS 
PubMed 
Article 

Google Scholar 
Lewis, S. M. The role of herbivorous fishes in the organization of a Caribbean reef community. Ecol. Monogr. 56, 183–200 (1986).Article 

Google Scholar 
Carpenter, R. C. Partitioning herbivory and its effects on coral reef algal communities. Ecol. Monogr. 56, 345–364 (1986).Article 

Google Scholar 
McCook, L. J. Macroalgae, nutrients and phase shifts on coral reefs: scientific issues and management consequences for the Great Barrier Reef. Coral Reefs 18, 357–367 (1999).Article 

Google Scholar 
Meyer, J. L., Schultz, E. T. & Helfman, G. S. Fish schools: an asset to corals. Science 220, 1047–1049 (1983).CAS 
PubMed 
Article 

Google Scholar 
Allgeier, J. E., Layman, C. A., Mumby, P. J. & Rosemond, A. D. Consistent nutrient storage and supply mediated by diverse fish communities in coral reef ecosystems. Glob. Change Biol. 20, 2459–2472 (2014).Article 

Google Scholar 
Shantz, A. A., Ladd, M. C., Schrack, E. & Burkepile, D. E. Fish-derived nutrient hotspots shape coral reef benthic communities. Ecol. Appl. 25, 2142–2152 (2015).PubMed 
Article 

Google Scholar 
Folke, C. et al. Regime shifts, resilience, and biodiversity in ecosystem management. Annu. Rev. Ecol. Evol. Syst. 35, 557–581 (2004).Article 

Google Scholar 
Worm, B. et al. Impacts of biodiversity loss on ocean ecosystem services. Science 314, 787–790 (2006).CAS 
PubMed 
Article 

Google Scholar 
McCann, K. S. The diversity–stability debate. Nature 405, 228–233 (2000).CAS 
PubMed 
Article 

Google Scholar 
Ives, A. R. & Carpenter, S. R. Stability and diversity of ecosystems. Science 317, 58–62 (2007).CAS 
PubMed 
Article 

Google Scholar 
Hughes, T. P. et al. Phase shifts, herbivory, and the resilience of coral reefs to climate change. Curr. Biol. 17, 360–365 (2007).CAS 
PubMed 
Article 

Google Scholar 
Graham, N. A. J. et al. Managing resilience to reverse phase shifts in coral reefs. Front. Ecol. Environ. 11, 541–548 (2013).Article 

Google Scholar 
Holbrook, S. J., Schmitt, R. J., Adam, T. C. & Brooks, A. J. Coral reef resilience, tipping points and the strength of herbivory. Sci. Rep. 6, 35817 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hughes, T. P. et al. Spatial and temporal patterns of mass bleaching of corals in the Anthropocene. Science 359, 80–83 (2018).CAS 
PubMed 
Article 

Google Scholar 
Holling, C. S. Resilience and stability of ecological systems. Annu. Rev. Ecol. Syst. 4, 1–23 (1973).Article 

Google Scholar 
Mumby, P. J. et al. Fishing, trophic cascades, and the process of grazing on coral reefs. Science 311, 98–101 (2006).CAS 
PubMed 
Article 

Google Scholar 
Green, A. L. & Bellwood, D. R. Monitoring Functional Groups of Herbivorous Reef Fishes as Indicators of Coral Reef Resilience. A Practical Guide for Coral reef Managers in the Asia Pacific Region (IUCN, 2009).Bozec, Y. M., O’Farrell, S., Bruggemann, J. H., Luckhurst, B. E. & Mumby, P. J. Tradeoffs between fisheries harvest and the resilience of coral reefs. Proc. Natl Acad. Sci. USA 113, 4536–4541 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bruno, J. F., Cote, I. M. & Toth, L. T. Climate change, coral loss, and the curious case of the parrotfish paradigm: why don’t marine protected sreas improve reef resilience? Ann. Rev. Mar. Sci. 11, 307–334 (2019).PubMed 
Article 

Google Scholar 
Brandl, S. J. et al. Coral reef ecosystem functioning: eight core processes and the role of biodiversity. Front. Ecol. Environ. 17, 445–454 (2019).Article 

Google Scholar 
Mora, C. A clear human footprint in the coral reefs of the Caribbean. Proc. Biol. Sci. 275, 767–773 (2008).PubMed 
PubMed Central 

Google Scholar 
Duffy, J. E., Godwin, C. M. & Cardinale, B. J. Biodiversity effects in the wild are common and as strong as key drivers of productivity. Nature 549, 261–264 (2017).CAS 
PubMed 
Article 

Google Scholar 
Allgeier, J. E., Layman, C. A., Mumby, P. J. & Rosemond, A. D. Biogeochemical implications of biodiversity and community structure across multiple coastal ecosystems. Ecol. Monogr. 85, 117–132 (2015).Article 

Google Scholar 
Mellin, C., Bradshaw, C. J., Fordham, D. A. & Caley, M. J. Strong but opposing beta-diversity–stability relationships in coral reef fish communities. Proc. Biol. Sci. 281, 20131993 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
Nash, K. L. et al. Herbivore cross-scale redundancy supports response diversity and promotes coral reef resilience. J. Appl. Ecol. 53, 646–655 (2016).Article 

Google Scholar 
Thibaut, L. M., Connolly, S. R. & Sweatman, H. P. Diversity and stability of herbivorous fishes on coral reefs. Ecology 93, 891–901 (2012).PubMed 
Article 

Google Scholar 
Zhang, S. Y. et al. Is coral richness related to community resistance to and recovery from disturbance? PeerJ 2, e308 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
Clements, C. S. & Hay, M. E. Biodiversity enhances coral growth, tissue survivorship and suppression of macroalgae. Nat. Ecol. Evol. 3, 178–182 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Bellwood, D. R., Hoey, A. S., Ackerman, J. L. & Depczynski, M. Coral bleaching, reef fish community phase shifts and the resilience of coral reefs. Glob. Change Biol. 12, 1587–1594 (2006).Article 

Google Scholar 
Burkepile, D. E. & Hay, M. E. Herbivore species richness and feeding complementarity affect community structure and function on a coral reef. Proc. Natl Acad. Sci. USA 105, 16201–16206 (2008).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Mumby, P. J. Phase shifts and the stability of macroalgal communities on Caribbean coral reefs. Coral Reefs 28, 761–773 (2009).Article 

Google Scholar 
Cheal, A. J., Emslie, M., MacNeil, M. A., Miller, I. & Sweatman, H. Spatial variation in the functional characteristics of herbivorous fish communities and the resilience of coral reefs. Ecol. Appl. 23, 174–188 (2013).PubMed 
Article 

Google Scholar 
Cheal, A. J. et al. Coral–macroalgal phase shifts or reef resilience: links with diversity and functional roles of herbivorous fishes on the Great Barrier Reef. Coral Reefs 29, 1005–1015 (2010).Article 

Google Scholar 
Bellwood, D. R., Hughes, T. P., Folke, C. & Nystrom, M. Confronting the coral reef crisis. Nature 429, 827–833 (2004).CAS 
PubMed 
Article 

Google Scholar 
Mouillot, D. et al. Functional over-redundancy and high functional vulnerability in global fish faunas on tropical reefs. Proc. Natl Acad. Sci. USA 111, 13757–13762 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Graham, N. A., Jennings, S., MacNeil, M. A., Mouillot, D. & Wilson, S. K. Predicting climate-driven regime shifts versus rebound potential in coral reefs. Nature 518, 94–97 (2015).CAS 
PubMed 
Article 

Google Scholar 
Steneck, R. S., Mumby, P. J., Macdonald, C., Rasher, D. B. & Stoyle, G. Attenuating effects of ecosystem management on coral reefs. Sci. Adv. 4, eaao5493 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Williams, I. D., Polunin, N. V. C. & Hendrick, V. J. Limits to grazing by herbivorous fishes and the impact of low coral cover on macroalgal abundance on a coral reef in Belize. Mar. Ecol. Prog. Ser. 222, 187–196 (2001).Article 

Google Scholar 
Harvey, C. J. et al. The importance of long-term ecological time series for integrated ecosystem assessment and ecosystem-based management. Prog. Oceanogr. 188, 102418 (2020).Article 

Google Scholar 
Robinson, J. P. W. et al. Productive instability of coral reef fisheries after climate-driven regime shifts. Nat. Ecol. Evol. 3, 183–190 (2019).PubMed 
Article 

Google Scholar 
MacNeil, M. A. et al. Water quality mediates resilience on the Great Barrier Reef. Nat. Ecol. Evol. 3, 620–627 (2019).PubMed 
Article 

Google Scholar 
Kayal, M. et al. Predator crown-of-thorns starfish (Acanthaster planci) outbreak, mass mortality of corals, and cascading effects on reef fish and benthic communities. PLoS ONE 7, e47363 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Adjeroud, M. et al. Recurrent disturbances, recovery trajectories, and resilience of coral assemblages on a South Central Pacific reef. Coral Reefs 28, 775–780 (2009).Article 

Google Scholar 
Adam, T. C. et al. How will coral reef fish communities respond to climate-driven disturbances? Insight from landscape-scale perturbations. Oecologia 176, 285–296 (2014).PubMed 
Article 

Google Scholar 
Munsterman, K. S., Allgeier, J. E., Peters, J. R. & Burkepile, D. E. A view from both ends: shifts in herbivore assemblages impact top-down and bottom-up processes on coral reefs. Ecosystems 24, 1702–1715 (2021).Article 

Google Scholar 
Newman, M. J., Paredes, G. A., Sala, E. & Jackson, J. B. Structure of Caribbean coral reef communities across a large gradient of fish biomass. Ecol. Lett. 9, 1216–1227 (2006).PubMed 
Article 

Google Scholar 
McClanahan, T. R. et al. Critical thresholds and tangible targets for ecosystem-based management of coral reef fisheries. Proc. Natl Acad. Sci. USA 108, 17230–17233 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Suchley, A., McField, M. D. & Alvarez-Filip, L. Rapidly increasing macroalgal cover not related to herbivorous fishes on Mesoamerican reefs. PeerJ 4, e2084 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Rogers, A., Blanchard, J. L. & Mumby, P. J. Vulnerability of coral reef fisheries to a loss of structural complexity. Curr. Biol. 24, 1000–1005 (2014).CAS 
PubMed 
Article 

Google Scholar 
Hempson, T. N., Graham, N. A. J., MacNeil, M. A., Hoey, A. S. & Wilson, S. K. Ecosystem regime shifts disrupt trophic structure. Ecol. Appl 28, 191–200 (2018).PubMed 
Article 

Google Scholar 
Mouillot, D. et al. Global marine protected areas do not secure the evolutionary history of tropical corals and fishes. Nat. Commun. 7, 10359 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Han, X., Adam, T. C., Schmitt, R. J., Brooks, A. J. & Holbrook, S. J. Response of herbivore functional groups to sequential perturbations in Moorea, French Polynesia. Coral Reefs 35, 999–1009 (2016).Article 

Google Scholar 
Donovan, M. K. et al. Nitrogen pollution interacts with heat stress to increase coral bleaching across the seascape. Proc. Natl Acad. Sci. USA 117, 5351–5357 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Holbrook, S. J. et al. Recruitment drives spatial variation in recovery rates of resilient coral reefs. Sci. Rep. 8, 7338 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Doropoulos, C. et al. Characterising the ecological trade-offs throughout the early ontogeny of coral recruitment. Ecol. Monogr. 86, 20–44 (2016).Article 

Google Scholar 
Russ, G. R., Questel, S. A., Rizzari, J. R. & Alcala, A. C. The parrotfish–coral relationship: refuting the ubiquity of a prevailing paradigm. Mar. Biol. 162, 2029–2045 (2015).Article 

Google Scholar 
Chung, A. E. et al. Building coral reef resilience through spatial herbivore management. Front. Mar. Sci. https://doi.org/10.3389/fmars.2019.00098 (2019).Kelly, E. L. A. et al. A budget of algal production and consumption by herbivorous fish in an herbivore fisheries management area, Maui, Hawaii. Ecosphere 8, e01899 (2017).Article 

Google Scholar 
Edwards, A. J. Reef Rehabilitation Manual (Coral Reef Targeted Research & Capacity Building for Management Program, 2010).Worm, B. et al. Rebuilding global fisheries. Science 325, 578–585 (2009).CAS 
PubMed 
Article 

Google Scholar 
Sale, P. F. et al. Transforming management of tropical coastal seas to cope with challenges of the 21st century. Mar. Pollut. Bull. 85, 8–23 (2014).CAS 
PubMed 
Article 

Google Scholar 
Schindler, D. E. & Hilborn, R. Sustainability. Prediction, precaution, and policy under global change. Science 347, 953–954 (2015).CAS 
PubMed 
Article 

Google Scholar 
Mumby, P. J. et al. Mangroves enhance the biomass of coral reef fish communities in the Caribbean. Nature 427, 533–536 (2004).CAS 
PubMed 
Article 

Google Scholar 
Walsworth, T. E. et al. Management for network diversity speeds evolutionary adaptation to climate change. Nat. Clim. Change 9, 632–636 (2019).Article 

Google Scholar 
Cote, I. M. & Darling, E. S. Rethinking ecosystem resilience in the face of climate change. PLoS Biol. 8, e1000438 (2010).PubMed 
PubMed Central 
Article 

Google Scholar 
Darling, E. S. & Cote, I. M. Seeking resilience in marine ecosystems. Science 359, 986–987 (2018).CAS 
PubMed 
Article 

Google Scholar 
Rassweiler, A. et al. Perceptions and responses of Pacific Island fishers to changing coral reefs. Ambio 49, 130–143 (2020).PubMed 
Article 

Google Scholar 
Moorea Coral Reef LTER & Carpenter, R. MCR LTER: Coral Reef: Long-term Population and Community Dynamics: Benthic Algae and Other Community Components (Environmental Data Initiative, accessed 2019); https://doi.org/10.6073/pasta/37d9c451a908e4a6f8e7ab914b93f44fBrooks, A. MCR LTER: Coral Reef: Long-term Population and Community Dynamics: Fishes (MCR, 2018).de Loma, T. L. et al. A framework for assessing impacts of marine protected areas in Moorea (French Polynesia). Pac. Sci. 62, 431–441 (2008).Article 

Google Scholar 
Nicholson, M. D. & Jennings, S. Testing candidate indicators to support ecosystem-based management: the power of monitoring surveys to detect temporal trends in fish community metrics. ICES J. Mar. Sci. 61, 35–42 (2004).Article 

Google Scholar 
Chao, A. Nonparametric estimation of the number of classes in a population. Scand. J. Stat. 11, 265–270 (1984).
Google Scholar 
Burnham, K. P. & Anderson, D. R. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach 2nd edn (Springer, 2002).R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2019). More

Burke, L., Reytar, K., Spalding, M. & Perry, A. Reefs at Risk Revisited (World Resource Institute, 2011).Bell, J. D. et al. Planning the use of fish for food security in the Pacific. Mar. Policy 33, 64–76 (2009).Article 

Google Scholar 
Gillett, R. Fisheries in the Economies of the Pacific Island Countries and Territories (Asian Development Bank, 2016).The Regional State of the Coast Report: Western Indian Ocean (UNEP, Nairobi Convention & WIOMSA, 2015).Wabnitz, C. C. C., Cisneros-Montemayor, A. M., Hanich, Q. & Ota, Y. Ecotourism, climate change and reef fish consumption in Palau: benefits, trade-offs and adaptation strategies. Mar. Policy 88, 323–332 (2018).Article 

Google Scholar 
Cinner, J. E. et al. Building adaptive capacity to climate change in tropical coastal communities. Nat. Clim. Change 8, 117–123 (2018).Article 

Google Scholar 
Thilsted, S. H. et al. Sustaining healthy diets: the role of capture fisheries and aquaculture for improving nutrition in the post-2015 era. Food Policy 61, 126–131 (2016).Article 

Google Scholar 
Beal, T., Massiot, E., Arsenault, J. E., Smith, M. R. & Hijmans, R. J. Global trends in dietary micronutrient supplies and estimated prevalence of inadequate intakes. PLoS ONE 12, e0175554 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Calder, P. C. Marine omega-3 fatty acids and inflammatory processes: effects, mechanisms and clinical relevance. Biochim. Biophys. Acta 1851, 469–484 (2015).CAS 
PubMed 
Article 

Google Scholar 
Haddad, L. et al. A new global research agenda for food. Nature 540, 30–32 (2016).CAS 
PubMed 
Article 

Google Scholar 
Golden, C. D. et al. Aquatic foods to nourish nations. Nature 598, 315–320 (2021).CAS 
PubMed 
Article 

Google Scholar 
MacNeil, M. et al. Recovery potential of the world’s coral reef fishes. Nature 520, 341–344 (2015).CAS 
PubMed 
Article 

Google Scholar 
Graham, N. A. J., Jennings, S., MacNeil, M. A., Mouillot, D. & Wilson, S. K. Predicting climate-driven regime shifts versus rebound potential in coral reefs. Nature 518, 94–97 (2015).CAS 
PubMed 
Article 

Google Scholar 
Crona, B. I., Van Holt, T., Petersson, M., Daw, T. M. & Buchary, E. Using social–ecological syndromes to understand impacts of international seafood trade on small-scale fisheries. Glob. Environ. Change 35, 162–175 (2015).Article 

Google Scholar 
Okemwa, G. M., Kaunda-Arara, B., Kimani, E. N. & Ogutu, B. Catch composition and sustainability of the marine aquarium fishery in Kenya. Fish. Res. 183, 19–31 (2016).Article 

Google Scholar 
Cinner, J. E., Folke, C., Daw, T. & Hicks, C. C. Responding to change: using scenarios to understand how socioeconomic factors may influence amplifying or dampening exploitation feedbacks among Tanzanian fishers. Glob. Environ. Change 21, 7–12 (2011).Article 

Google Scholar 
Hicks, C. C., Graham, N. A. J., Maire, E. & Robinson, J. P. W. Secure local aquatic food systems in the face of declining coral reefs. One Earth 4, 1214–1216 (2021).Article 

Google Scholar 
Albert, J. et al. Malnutrition in rural Solomon Islands: an analysis of the problem and its drivers. Matern. Child Nutr. 16, e12921 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Golden, C. D. et al. Social–ecological traps link food systems to nutritional outcomes. Glob. Food Security 30, 100561 (2021).Article 

Google Scholar 
Smale, D. A. et al. Marine heatwaves threaten global biodiversity and the provision of ecosystem services. Nat. Clim. Change 9, 306–312 (2019).Article 

Google Scholar 
Robinson, J. P. W., Wilson, S. K., Jennings, S. & Graham, N. A. J. Thermal stress induces persistently altered coral reef fish assemblages. Glob. Change Biol. 25, 2739–2750 (2019).Article 

Google Scholar 
Robinson, J. P. W. et al. Productive instability of coral reef fisheries after climate-driven regime shifts. Nat. Ecol. Evol. 3, 183–190 (2019).PubMed 
Article 

Google Scholar 
Stuart-Smith, R. D., Brown, C. J., Ceccarelli, D. M. & Edgar, G. J. Ecosystem restructuring along the Great Barrier Reef following mass coral bleaching. Nature 560, 92–96 (2018).CAS 
PubMed 
Article 

Google Scholar 
Morais, R. et al. Severe coral loss shifts energetic dynamics on a coral reef. Funct. Ecol. 34, 1507–1518 (2020).Article 

Google Scholar 
Robinson, J. P. W. et al. Habitat and fishing control grazing potential on coral reefs. Funct. Ecol. 34, 240–251 (2020).Article 

Google Scholar 
Fontoura, L. et al. Climate-driven shift in coral morphological structure predicts decline of juvenile reef fishes. Glob. Change Biol. 26, 557–567 (2020).Article 

Google Scholar 
Rogers, A., Blanchard, J. L. & Mumby, P. J. Fisheries productivity under progressive coral reef degradation. J. Appl. Ecol. 55, 1041–1049 (2018).Article 

Google Scholar 
Bates, A. E. et al. Climate resilience in marine protected areas and the ‘protection paradox’. Biol. Conserv. 236, 305–314 (2019).Article 

Google Scholar 
Darling, E. S. et al. Social–environmental drivers inform strategic management of coral reefs in the Anthropocene. Nat. Ecol. Evol. 3, 1341–1350 (2019).PubMed 
Article 

Google Scholar 
Soliño, L. & Costa, P. R. Global impact of ciguatoxins and ciguatera fish poisoning on fish, fisheries and consumers. Environ. Res. 182, 109111 (2020).PubMed 
Article 

Google Scholar 
Rogers, A. et al. Anticipative management for coral reef ecosystem services in the 21st century. Glob. Change Biol. 21, 504–514 (2015).Article 

Google Scholar 
Thiault, L. et al. Escaping the perfect storm of simultaneous climate change impacts on agriculture and marine fisheries. Sci. Adv. 5, eaaw9976 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Souter, D. et al. Status of Coral Reefs of the World: 2020 (Global Coral Reef Monitoring Network & International Coral Reef Initiative, 2021).Hicks, C. C. et al. Harnessing global fisheries to tackle micronutrient deficiencies. Nature 574, 95–98 (2019).CAS 
PubMed 
Article 

Google Scholar 
Bierwagen, S. L., Heupel, M. R., Chin, A. & Simpfendorfer, C. A. Trophodynamics as a tool for understanding coral reef ecosystems. Front. Mar. Sci. 5, 24 (2018).Article 

Google Scholar 
Flombaum, P. et al. Present and future global distributions of the marine Cyanobacteria Prochlorococcus and Synechococcus. Proc. Natl Acad. Sci. USA 110, 9824–9829 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lehane, L. & Lewis, R. J. Ciguatera: recent advances but the risk remains. Int. J. Food Microbiol. 61, 91–125 (2000).CAS 
PubMed 
Article 

Google Scholar 
Fraser, K. M. et al. Production of mobile invertebrate communities on shallow reefs from temperate to tropical seas. Proc. R. Soc. B Biol. Sci. 287, 20201798 (2020).CAS 
Article 

Google Scholar 
Ullah, H., Nagelkerken, I., Goldenberg, S. U. & Fordham, D. A. Climate change could drive marine food web collapse through altered trophic flows and cyanobacterial proliferation. PLoS Biol. 16, e2003446 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Kang, J. X. Omega-3: a link between global climate change and human health. Biotechnol. Adv. 29, 388–390 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hixson, S. M. & Arts, M. T. Climate warming is predicted to reduce omega-3, long-chain, polyunsaturated fatty acid production in phytoplankton. Glob. Change Biol. 22, 2744–2755 (2016).Article 

Google Scholar 
Tan, K., Zhang, H. & Zheng, H. Climate change and n-3 LC-PUFA availability. Prog. Lipid Res. 86, 101161 (2022).CAS 
PubMed 
Article 

Google Scholar 
Pethybridge, H. R. et al. Spatial patterns and temperature predictions of tuna fatty acids: tracing essential nutrients and changes in primary producers. PLoS ONE 10, e0131598 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Hempson, T. N., Graham, N. A. J., MacNeil, M. A., Bodin, N. & Wilson, S. K. Regime shifts shorten food chains for mesopredators with potential sublethal effects. Funct. Ecol. 32, 820–830 (2018).Article 

Google Scholar 
Bellwood, D. R., Hughes, T. & Hoey, A. S. Sleeping functional group drives coral-reef recovery. Curr. Biol. 16, 2434–2439 (2006).CAS 
PubMed 
Article 

Google Scholar 
Sunday, J. M. et al. Species traits and climate velocity explain geographic range shifts in an ocean-warming hotspot. Ecol. Lett. 18, 944–953 (2015).PubMed 
Article 

Google Scholar 
Burrows, M. T. et al. Ocean community warming responses explained by thermal affinities and temperature gradients. Nat. Clim. Change 9, 959–963 (2019).Article 

Google Scholar 
Cheung, W. W., Watson, R. & Pauly, D. Signature of ocean warming in global fisheries catch. Nature 497, 365–368 (2013).CAS 
PubMed 
Article 

Google Scholar 
Stuart-Smith, R. D., Mellin, C., Bates, A. E. & Edgar, G. Habitat loss and range shifts contribute to ecological generalization amongst reef fishes. Nat. Ecol. Evol. 5, 656–662 (2021).PubMed 
Article 

Google Scholar 
Brown, J. H., Gillooly, J. F., Allen, A. P., Savage, V. M. & West, G. B. Toward a metabolic theory of ecology. Ecology 85, 1771–1789 (2004).Article 

Google Scholar 
Du Pontavice, H., Gascuel, D., Reygondeau, G., Maureaud, A. & Cheung, W. W. L. Climate change undermines the global functioning of marine food webs. Glob. Change Biol. 26, 1306–1318 (2020).Article 

Google Scholar 
Jones, J. et al. The microbiome of the gastrointestinal tract of a range-shifting marine herbivorous fish. Front. Microbiol. 9, 2000 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Littman, R., Willis, B. L. & Bourne, D. G. Metagenomic analysis of the coral holobiont during a natural bleaching event on the Great Barrier Reef. Environ. Microbiol. Rep. 3, 651–660 (2011).CAS 
PubMed 
Article 

Google Scholar 
Robinson, J. P. W. et al. Climate-induced increases in micronutrient availability for coral reef fisheries. One Earth 5, 98–108 (2022).PubMed 
PubMed Central 
Article 

Google Scholar 
Froese, R. & Pauly, D. FishBase (FishBase, 2021); www.fishbase.orgMacNeil, M. A. NutrientFishbase dataset. GitHub https://github.com/mamacneil/NutrientFishbase (2021).Waldock, C., Stuart-Smith, R. D., Edgar, G. J., Bird, T. J. & Bates, A. E. The shape of abundance distributions across temperature gradients in reef fishes. Ecol. Lett. 22, 685–696 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Breitburg, D. et al. Declining oxygen in the global ocean and coastal waters. Science 359, eaam7240 (2018).PubMed 
Article 

Google Scholar 
Chaudhary, C., Richardson, A. J., Schoeman, D. S. & Costello, M. J. Global warming is causing a more pronounced dip in marine species richness around the equator. Proc. Natl Acad. Sci. USA 118, e2015094118 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Cheung, W. W. L., Reygondeau, G. & Frölicher, T. L. Large benefits to marine fisheries of meeting the 1.5°C global warming target. Science 354, 1591–1594 (2016).CAS 
PubMed 
Article 

Google Scholar 
Golden, C. et al. Nutrition: fall in fish catch threatens human health. Nature 534, 317–320 (2016).PubMed 
Article 

Google Scholar 
Nash, K. L. & Graham, N. A. J. Ecological indicators for coral reef fisheries management. Fish Fish. 17, 1029–1054 (2016).Article 

Google Scholar 
Pereira, H. M. et al. Essential biodiversity variables. Science 339, 277–278 (2013).CAS 
PubMed 
Article 

Google Scholar 
Brandl, S. J. et al. Coral reef ecosystem functioning: eight core processes and the role of biodiversity. Front. Ecol. Environ. 17, 445–454 (2019).Article 

Google Scholar 
Maire, E. et al. Micronutrient supply from global marine fisheries under climate change and overfishing. Curr. Biol. 31, 4132–4138 (2021).CAS 
PubMed 
Article 

Google Scholar 
Miloslavich, P. et al. Essential ocean variables for global sustained observations of biodiversity and ecosystem changes. Glob. Change Biol. 24, 2416–2433 (2018).Article 

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

Google Scholar 
Nash, K. L., Graham, N. A. J., Wilson, S. K. & Bellwood, D. R. Cross-scale habitat structure drives fish body size distributions on coral reefs. Ecosystems 16, 478–490 (2013).Article 

Google Scholar 
Pratchett, M. S. et al. in Oceanography and Marine Biology: An Annual Review Vol. 46 (eds Gibson, R. N. et al.) 251–296 (CRC Press, 2008).Graham, N. A. J. et al. Dynamic fragility of oceanic coral reef ecosystems. Proc. Natl Acad. Sci. USA 103, 8425–8429 (2006).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Richardson, L. E., Graham, N. A. J., Pratchett, M. S., Eurich, J. G. & Hoey, A. S. Mass coral bleaching causes biotic homogenization of reef fish assemblages. Glob. Change Biol. 24, 3117–3129 (2018).Article 

Google Scholar 
Graham, N. A. et al. Lag effects in the impacts of mass coral bleaching on coral reef fish, fisheries, and ecosystems. Conserv. Biol. 21, 1291–1300 (2007).PubMed 
Article 

Google Scholar 
Hempson, T., Graham, N., Macneil, A., Hoey, A. & Wilson, S. Ecosystem regime shifts disrupt trophic structure. Ecol. Appl. 28, 191–200 (2018).PubMed 
Article 

Google Scholar 
Jouffray, J.-B. et al. Identifying multiple coral reef regimes and their drivers across the Hawaiian archipelago. Phil. Trans. R. Soc. B Biol. Sci. 370, 20130268 (2015).Article 

Google Scholar 
McLean, M. et al. Trait structure and redundancy determine sensitivity to disturbance in marine fish communities. Glob. Change Biol. 25, 3424–3437 (2019).Article 

Google Scholar 
Nash, K. L., Graham, N. A. J., Jennings, S., Wilson, S. K. & Bellwood, D. R. Herbivore cross-scale redundancy supports response diversity and promotes coral reef resilience. J. Appl. Ecol. 53, 646–655 (2016).Article 

Google Scholar 
Vaitla, B. et al. Predicting nutrient content of ray-finned fishes using phylogenetic information. Nat. Commun. 9, 3742 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Kissling, W. D. et al. Towards global data products of essential biodiversity variables on species traits. Nat. Ecol. Evol. 2, 1531–1540 (2018).PubMed 
Article 

Google Scholar 
Edgar, G. J. et al. Reef Life Survey: establishing the ecological basis for conservation of shallow marine life. Biol. Conserv. 252, 108855 (2020).Article 

Google Scholar 
Pauly, D. & Zeller, D. Accurate catches and the sustainability of coral reef fisheries. Curr. Opin. Environ. Sustain. 7, 44–51 (2014).Article 

Google Scholar 
Worm, B. & Branch, T. A. The future of fish. Trends Ecol. Evol. 27, 594–599 (2012).PubMed 
Article 

Google Scholar 
McClanahan, T. R. et al. Critical thresholds and tangible targets for ecosystem-based management of coral reef fisheries. Proc. Natl Acad. Sci. USA 108, 17230–17233 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Cinner, J. E. et al. Meeting fisheries, ecosystem function, and biodiversity goals in a human-dominated world. Science 368, 307–311 (2020).CAS 
PubMed 
Article 

Google Scholar 
Robinson, J. P. W. et al. Managing fisheries for maximum nutrient yield. Fish Fish. 23, 800–811 (2022).Article 

Google Scholar 
Graham, N. A. et al. Extinction vulnerability of coral reef fishes. Ecol. Lett. 14, 341–348 (2011).PubMed 
PubMed Central 
Article 

Google Scholar 
Schartup, A. T. et al. Climate change and overfishing increase neurotoxicant in marine predators. Nature 572, 648–650 (2019).CAS 
PubMed 
Article 

Google Scholar 
Free, C. M. et al. Impacts of historical warming on marine fisheries production. Science 363, 979–983 (2019).CAS 
PubMed 
Article 

Google Scholar 
Pinsky Malin, L. et al. Preparing ocean governance for species on the move. Science 360, 1189–1191 (2018).CAS 
PubMed 
Article 

Google Scholar 
Thorson, J. T. Predicting recruitment density dependence and intrinsic growth rate for all fishes worldwide using a data-integrated life-history model. Fish Fish. 21, 237–251 (2020).Article 

Google Scholar 
Ahern, M. B. et al. Locally-procured fish is essential in school feeding programmes in sub-Saharan Africa. Foods 10, 2080 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
UNEP-WCMC, WorldFish Centre, WRI & TNC. Global Distribution of Coral Reefs. Version 4.1. Ocean Data Viewer https://doi.org/10.34892/t2wk-5t34 (UN Environment World Conservation Monitoring Centre, 2021).Morillo-Velarde, P. S. et al. Habitat degradation alters trophic pathways but not food chain length on shallow Caribbean coral reefs. Sci. Rep. 8, 4109 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Kumar, M. et al. Minerals, PUFAs and antioxidant properties of some tropical seaweeds from Saurashtra coast of India. J. Appl. Phycol. 23, 797–810 (2011).CAS 
Article 

Google Scholar 
Coleman, M. A. et al. Climate change does not affect the seafood quality of a commonly targeted fish. Glob. Change Biol. 25, 699–707 (2019).Article 

Google Scholar 
Sissener, N. H. Are we what we eat? Changes to the feed fatty acid composition of farmed salmon and its effects through the food chain. J. Exp. Biol. 221, jeb161521 (2018).PubMed 
Article 

Google Scholar 
Hadj-Hammou, J., Mouillot, D. & Graham, N. A. J. Response and effect traits of coral reef fish. Front. Mar. Sci. 8, 640619 (2021).Article 

Google Scholar 
Mouillot, D., Graham, N. A. J., Villéger, S., Mason, N. W. H. & Bellwood, D. R. A functional approach reveals community responses to disturbances. Trends Ecol. Evol. 28, 167–177 (2013).PubMed 
Article 

Google Scholar 
McMahon, K. W., Thorrold, S. R., Houghton, L. A. & Berumen, M. L. Tracing carbon flow through coral reef food webs using a compound-specific stable isotope approach. Oecologia 180, 809–821 (2016).PubMed 
Article 

Google Scholar 
McMahon, K., Hamady, L. L. & Thorrold, S. Ocean ecogeochemistry—a review. Oceanogr. Mar. Biol. 51, 327–374 (2013).
Google Scholar 
Chikaraishi, Y. et al. Determination of aquatic food-web structure based on compound-specific nitrogen isotopic composition of amino acids. Limnol. Oceanogr. Methods 7, 740–750 (2009).CAS 
Article 

Google Scholar 
Bowes, R. E. & Thorp, J. H. Consequences of employing amino acid vs. bulk-tissue, stable isotope analysis: a laboratory trophic position experiment. Ecosphere 6, 14 (2015).Article 

Google Scholar 
Blanchard, J. L., Heneghan, R. F., Everett, J. D., Trebilco, R. & Richardson, A. J. From bacteria to whales: using functional size spectra to model marine ecosystems. Trends Ecol. Evol. 32, 174–186 (2017).PubMed 
Article 

Google Scholar 
Kleiber, D., Harris, L. M. & Vincent, A. C. J. Gender and small-scale fisheries: a case for counting women and beyond. Fish Fish. 16, 547–562 (2015).Article 

Google Scholar  More