Ecology

1.Waldbusser, G. G. & Salisbury, J. E. Ocean acidification in the coastal zone from an Organism’s perspective: Multiple system parameters, frequency domains, and habitats. Annu. Rev. Mar. Sci. 6, 221–247 (2014).ADS 
Article 

Google Scholar 
2.Duarte, C. M. et al. Is ocean acidification an open-ocean syndrome? Understanding anthropogenic impacts on seawater pH. Estuaries Coasts 36, 221–236 (2013).CAS 
Article 

Google Scholar 
3.Johnson, Z. I. et al. Dramatic variability of the carbonate system at a temperate coastal ocean site (Beaufort, North Carolina, USA) is regulated by physical and biogeochemical processes on multiple timescales. PLoS ONE 8, e85117 (2013).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
4.Baumann, H. & Smith, E. M. Quantifying metabolically driven ph and oxygen fluctuations in US nearshore habitats at diel to interannual time scales. Estuaries Coasts 41, 1102–1117 (2018).CAS 
Article 

Google Scholar 
5.Carstensen, J. & Duarte, C. M. Drivers of pH variability in coastal ecosystems. Environ. Sci. Technol. 53, 4020–4029 (2019).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
6.Clark, H. R. & Gobler, C. J. Diurnal fluctuations in CO2 and dissolved oxygen concentrations do not provide a refuge from hypoxia and acidification for early-life-stage bivalves. Mar. Ecol. Prog. Ser. 558, 1–14 (2016).ADS 
CAS 
Article 

Google Scholar 
7.Mangan, S., Urbina, M. A., Findlay, H. S., Wilson, R. W. & Lewis, C. Fluctuating seawater pH/pCO2 regimes are more energetically expensive than static pH/pCO2 levels in the mussel Mytilus edulis. Proc. R. Soc. B Biol. Sci. 284, 20171642 (2017).Article 
CAS 

Google Scholar 
8.Hauri, C., Gruber, N., McDonnell, A. M. P. & Vogt, M. The intensity, duration, and severity of low aragonite saturation state events on the California continental shelf. Geophys. Res. Lett. 40, 3424–3428 (2013).ADS 
Article 

Google Scholar 
9.Pacella, S. R., Brown, C. A., Waldbusser, G. G., Labiosa, R. G. & Hales, B. Seagrass habitat metabolism increases short-term extremes and long-term offset of CO2 under future ocean acidification. Proc. Natl. Acad. Sci. U. S. A. 115, 3870–3875 (2018).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
10.Adelsman, H. & Whitely Binder, L. Ocean Acidification: From Knowledge to Action, Washington State’s Strategic Response. Washington State Blue Ribbon Panel on Ocean Acidification. (Washington Department of Ecology, Olympia, Washington, 2012).11.Nielsen, K. et al. Emerging Understanding of the Potential Role of Seagrass and Kelp as an Ocean Acidification Management Tool in California (California Ocean Science Trust, Oakland, CA, 2018).12.Ekstrom, J. A. et al. Vulnerability and adaptation of US shellfisheries to ocean acidification. Nat. Clim. Change 5, 207–214 (2015).ADS 
Article 

Google Scholar 
13.Koweek, D. A. et al. Expected limits on the ocean acidification buffering potential of a temperate seagrass meadow. Ecol. Appl. 28, 1694–1714 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
14.Miller, C. A., Yang, S. & Love, B. A. Moderate increase in TCO2 enhances photosynthesis of seagrass Zostera japonica, but not Zostera marina: Implications for acidification mitigation. Front. Mar. Sci. 4, 2 (2017).Article 

Google Scholar 
15.Cyronak, T. et al. Short-term spatial and temporal carbonate chemistry variability in two contrasting seagrass meadows: Implications for pH buffering capacities. Estuaries Coasts 41, 1282–1296 (2018).CAS 
Article 

Google Scholar 
16.Unsworth, R. K. F., Collier, C. J., Henderson, G. M. & McKenzie, L. J. Tropical seagrass meadows modify seawater carbon chemistry: Implications for coral reefs impacted by ocean acidification. Environ. Res. Lett. 7, 024026 (2012).ADS 
Article 
CAS 

Google Scholar 
17.Hendriks, I. E. et al. Photosynthetic activity buffers ocean acidification in seagrass meadows. Biogeosciences 11, 333–346 (2014).ADS 
Article 
CAS 

Google Scholar 
18.Greiner, C. M., Klinger, T., Ruesink, J. L., Barber, J. S. & Horwith, M. Habitat effects of macrophytes and shell on carbonate chemistry and juvenile clam recruitment, survival, and growth. J. Exp. Mar. Biol. Ecol. 509, 8–15 (2018).CAS 
Article 

Google Scholar 
19.Groner, M. L. et al. Oysters and eelgrass: Potential partners in a high pCO2 ocean. Ecology 99, 1802–1814 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
20.Waldbusser, G. G. et al. Ocean acidification has multiple modes of action on bivalve larvae. PLoS ONE 10, e0128376 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
21.Hurd, C. L. et al. Ocean acidification as a multiple driver: How interactions between changing seawater carbonate parameters affect marine life. Mar. Freshw. Res. 71, 263–274 (2020).CAS 
Article 

Google Scholar 
22.Hales, B., Suhrbier, A., Waldbusser, G. G., Feely, R. A. & Newton, J. A. The carbonate chemistry of the “Fattening Line,” Willapa Bay, 2011–2014. Estuaries Coasts 1–14 (2016).23.Ricart, A. M. et al. Coast-wide evidence of low pH amelioration by seagrass ecosystems. Glob. Change Biol. 27, 2580–2591 (2021).ADS 
Article 

Google Scholar 
24.Hoppe, C. J. M., Langer, G., Rokitta, S. D., Wolf-Gladrow, D. A. & Rost, B. Implications of observed inconsistencies in carbonate chemistry measurements for ocean acidification studies. Biogeosciences 9, 2401–2405 (2012).ADS 
CAS 
Article 

Google Scholar 
25.Buapet, P., Gullström, M. & Björk, M. Photosynthetic activity of seagrasses and macroalgae in temperate shallow waters can alter seawater pH and total inorganic carbon content at the scale of a coastal embayment. Mar. Freshw. Res. 2, 2 (2013).
Google Scholar 
26.Waldbusser, G. G. et al. Saturation-state sensitivity of marine bivalve larvae to ocean acidification. Nat. Clim. Change 5, 273–280 (2015).ADS 
CAS 
Article 

Google Scholar 
27.Comeau, S., Carpenter, R. C. & Edmunds, P. J. Coral reef calcifiers buffer their response to ocean acidification using both bicarbonate and carbonate. Proc. R. Soc. B-Biol. Sci. 280, 20122374 (2013).CAS 
Article 

Google Scholar 
28.Kawahata, H. et al. Perspective on the response of marine calcifiers to global warming and ocean acidification—Behavior of corals and foraminifera in a high CO2 world “hot house”. Prog. Earth Planet. Sci. 6, 5 (2019).Article 

Google Scholar 
29.Ries, J. B. A physicochemical framework for interpreting the biological calcification response to CO2-induced ocean acidification. Geochim. Cosmochim. Acta 75, 4053–4064 (2011).ADS 
CAS 
Article 

Google Scholar 
30.Raven, J. A., Giordano, M., Beardall, J. & Maberly, S. C. Algal evolution in relation to atmospheric CO2: Carboxylases, carbon-concentrating mechanisms and carbon oxidation cycles. Philos. Trans. R. Soc. B Biol. Sci. 367, 493–507 (2012).CAS 
Article 

Google Scholar 
31.Vieira, S., Cartaxana, P., Máguas, C. & Marques da Silva, J. Photosynthesis in estuarine intertidal microphytobenthos is limited by inorganic carbon availability. Photosynth. Res. 128, 85–92 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
32.Egleston, E. S., Sabine, C. L. & Morel, F. M. M. Revelle revisited: Buffer factors that quantify the response of ocean chemistry to changes in DIC and alkalinity. Glob. Biogeochem. Cycles 24, GB1002 (2010).ADS 
Article 
CAS 

Google Scholar 
33.Cyronak, T., Santos, I. R., McMahon, A. & Eyre, B. D. Carbon cycling hysteresis in permeable carbonate sands over a diel cycle: Implications for ocean acidification. Limnol. Oceanogr. 58, 131–143 (2013).ADS 
CAS 
Article 

Google Scholar 
34.Burdige, D. J. & Zimmerman, R. C. Impact of sea grass density on carbonate dissolution in Bahamian sediments. Limnol. Oceanogr. 47, 1751–1763 (2002).ADS 
CAS 
Article 

Google Scholar 
35.Chou, W.-C. et al. A Unique Diel Pattern in Carbonate Chemistry in the Seagrass Meadows of Dongsha 1 Island: implications for ocean acidification buffering. ESSOAr. https://doi.org/10.1002/essoar.10504715.1 (2020).36.Su, J. et al. Chesapeake Bay acidification buffered by spatially decoupled carbonate mineral cycling. Nat. Geosci. 13, 441–447 (2020).ADS 
CAS 
Article 

Google Scholar 
37.Enríquez, S. & Schubert, N. Direct contribution of the seagrass Thalassia testudinum to lime mud production. Nat. Commun. 5, 3835 (2014).ADS 
PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
38.Currin, C., Brewer, J. & Delano, P. Tide Flat Microphytobenthos: Biomass Distribution, Community Composition and Trophic Role in a Macrotidal Alaskan Estuary (National Centers for Coastal Ocean Science, Beaufort, NC, 2002).39.Martin, S. et al. Comparison of Zostera marina and maerl community metabolism. Aquat. Bot. 83, 161–174 (2005).ADS 
CAS 
Article 

Google Scholar 
40.Ries, J. B., Ghazaleh, M. N., Connolly, B., Westfield, I. & Castillo, K. D. Impacts of seawater saturation state (ΩA = 0.4–4.6) and temperature (10, 25 °C) on the dissolution kinetics of whole-shell biogenic carbonates. Geochim. Cosmochim. Acta 192, 318–337 (2016).ADS 
CAS 
Article 

Google Scholar 
41.Brewer, P. G. & Goldman, J. C. Alkalinity changes generated by phytoplankton growth1. Limnol. Oceanogr. 21, 108–117 (1976).ADS 
CAS 
Article 

Google Scholar 
42.Gazeau, F., Urbini, L., Cox, T. E., Alliouane, S. & Gattuso, J.-P. Comparison of the alkalinity and calcium anomaly techniques to estimate rates of net calcification. Mar. Ecol. Prog. Ser. 527, 1–12 (2015).ADS 
Article 
CAS 

Google Scholar 
43.Wolf-Gladrow, D. A., Zeebe, R. E., Klaas, C., Körtzinger, A. & Dickson, A. G. Total alkalinity: The explicit conservative expression and its application to biogeochemical processes. Mar. Chem. 106, 287–300 (2007).CAS 
Article 

Google Scholar 
44.Cai, W.-J., Wang, Y. & Hodson, R. E. Acid-base properties of dissolved organic matter in the estuarine waters of Georgia, USA. Geochim. Cosmochim. Acta 62, 473–483 (1998).ADS 
CAS 
Article 

Google Scholar 
45.Ko, Y. H., Lee, K., Eom, K. H. & Han, I.-S. Organic alkalinity produced by phytoplankton and its effect on the computation of ocean carbon parameters. Limnol. Oceanogr. 61, 1462–1471 (2016).ADS 
Article 

Google Scholar 
46.Invers, O., Zimmerman, R. C., Alberte, R. S., Pérez, M. & Romero, J. Inorganic carbon sources for seagrass photosynthesis: an experimental evaluation of bicarbonate use in species inhabiting temperate waters. J. Exp. Mar. Biol. Ecol. 265, 203–217 (2001).CAS 
Article 

Google Scholar 
47.Sand-Jensen, K. & Gordon, D. M. Differential ability of marine and freshwater macrophytes to utilize HCO3- and CO2. Mar. Biol. 80, 247–253 (1984).CAS 
Article 

Google Scholar 
48.Larkum, A. W. D., Davey, P. A., Kuo, J., Ralph, P. J. & Raven, J. A. Carbon-concentrating mechanisms in seagrasses. J. Exp. Bot. 68, 3773–3784 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
49.Rubio, L. et al. Direct uptake of HCO3- in the marine angiosperm Posidonia oceanica (L.) Delile driven by a plasma membrane H+ economy. Plant Cell Environ. 40, 2820–2830 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
50.Fernández, J. A., García-Sánchez, M. J. & Felle, H. H. Physiological evidence for a proton pump and sodium exclusion mechanisms at the plasma membrane of the marine angiosperm Zostera marina L. J. Exp. Bot. 50, 1763–1768 (1999).
Google Scholar 
51.Berg, P. et al. Dynamics of benthic metabolism, O2, and pCO2 in a temperate seagrass meadow. Limnol. Oceanogr. 64, 2586–2604 (2019).ADS 
CAS 
Article 

Google Scholar 
52.Buapet, P., Rasmusson, L. M., Gullstrom, M. & Bjork, M. Photorespiration and carbon limitation determine productivity in temperate seagrasses. PLoS ONE 8, e83804 (2013).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
53.Garcı́a-Sánchez, M. J., Jaime, M. P., Ramos, A., Sanders, D. & Fernández, J. ,. Sodium-dependent nitrate transport at the plasma membrane of leaf cells of the marine higher plant Zostera marina L. Plant Physiol. 122, 879–886 (2000).Article 

Google Scholar 
54.Drechsler, Z. & Beer, S. Utilization of inorganic carbon by Ulva lactuca. Plant Physiol. 97, 1439–1444 (1991).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
55.Ribas-Ribas, M. et al. Effects of upwelling, tides and biological processes on the inorganic carbon system of a coastal lagoon in Baja California. Estuar. Coast. Shelf Sci. 95, 367–376 (2011).ADS 
CAS 
Article 

Google Scholar 
56.Omarjee, A., Taljaard, S., Weerts, S. P. & Adams, J. B. The influence of mouth status on pH variability in small temporarily closed estuaries. Estuar. Coast. Shelf Sci. 246, 107043 (2020).CAS 
Article 

Google Scholar 
57.McCutcheon, M. R., Staryk, C. J. & Hu, X. Characteristics of the carbonate system in a semiarid estuary that experiences summertime hypoxia. Estuaries Coasts. 42, 1509–1523 (2019).CAS 
Article 

Google Scholar 
58.Miller, C. A. & Kelley, A. L. Seasonality and biological forcing modify the diel frequency of nearshore pH extremes in a subarctic Alaskan estuary. Limnol. Oceanogr. 66, 1475–1491 (2021).ADS 
CAS 
Article 

Google Scholar 
59.Moxham, R. M. & Nelson, A. E. Trace Elements Reconnaissance in the Jakolof Bay Area, Southern Alaska (United States Department of Interior, Geological Survey, 1950).60.Hartwell, S. I., Dasher, D. & Lomax, T. Characterization of Benthic Habitats and Contaminant Assessment in Kenai Peninsula Fjords and Bays (NOAA Technical Memorandum NOS NCCOS, 2016).61.Lueker, T. J., Dickson, A. G. & Keeling, C. D. Ocean pCO2 calculated from dissolved inorganic carbon, alkalinity, and equations for K1 and K2: Validation based on laboratory measurements of CO2 in gas and seawater at equilibrium. Mar. Chem. 70, 105–119 (2000).CAS 
Article 

Google Scholar 
62.Dickson, A. Thermodynamics of the Dissociation of Boric-Acid in Synthetic Seawater from 273.15-K to 318.15-K. Deep-Sea Res. Part Oceanogr. Res. Pap. 37, 755–766 (1990).ADS 
CAS 
Article 

Google Scholar 
63.Uppström, L. R. The boron/chlorinity ratio of deep-sea water from the Pacific Ocean. Deep Sea Res. Oceanogr. Abstr. 21, 161–162 (1974).ADS 
Article 

Google Scholar  More

Angeloni F, Ouborg NJ, Leimu R (2011) Meta-analysis on the association of population size and life history with inbreeding depression in plants. Biol Conserv 144:35–43Article 

Google Scholar 
Armbruster P, Reed DH (2005) Inbreeding depression in benign and stressful environments. Heredity 95:235–242CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Barrett SCH (2010) Understanding plant reproductive diversity. Philos Trans R Soc B 365:99–109Article 

Google Scholar 
Bierne N, Tsitrone A, David P (2000) An inbreeding model of associative overdominance during a population bottleneck. Genetics 155:1981–1990CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bijlsma R, Bundgaard J, van Putten WF (1999) Environmental dependence of inbreeding depression and purging in Drosophila melanogaster. J Evol Biol 12:1125–1137Article 

Google Scholar 
Brown KE, Kelly JK (2019) Severe inbreeding depression is predicted by the “rare allele load” in Mimulus guttatus. Evolution 74:587–596PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
Byers DL, Waller DM (1999) Do plant populations purge their genetic load? Effects of population size and mating history on inbreeding depression. Annu Rev Ecol Syst 30:479–513Article 

Google Scholar 
Campbell SA, Halitschke R, Thaler JS, Kessler A (2014) Plant mating systems affect adaptive plasticity in response to herbivory. Plant J 78:481–490CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Carleial S, van Kleunen M, Stift M (2017) Relatively weak inbreeding depression in selfing but also in outcrossing populations of North American Arabidopsis lyrata. J Evol Biol 30:1994–2004CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Charlesworth B (2018) Mutational load, inbreeding depression and heterosis in subdivided populations. Mol Ecol 27:4991–5003PubMed 
Article 

Google Scholar 
Charlesworth D, Willis JH (2009) The genetics of inbreeding depression. Nat Rev Genet 10:783–796CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Cheptou PO, Imbert E, Lepart J, Escarre J (2000) Effects of competition on lifetime estimates of inbreeding depression in the outcrossing plant Crepis sancta (Asteraceae). J Evol Biol 13:522–531Article 

Google Scholar 
Cheptou P-O, Donohue K (2011) Environment-dependent inbreeding depression. Its ecological and evolutionary significance. N. Phytol 189:395–407Article 

Google Scholar 
Cheptou P-O, Lepart J, Escarré J (2001) Inbreeding depression under intraspecific competition in a highly outcrossing population of Crepis sancta (Asteraceae). Evidence for frequency‐dependent variation. Am J Bot 88:1424–1429CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Colmer TD, Voesenek LACJ (2009) Flooding tolerance. Suites of plant traits in variable environments. Funct Plant Biol 36:665CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Crow JF (1958) Some possibilities for measuring selection intensities in man. Hum Biol 30:1–13CAS 
PubMed 
PubMed Central 

Google Scholar 
Darwin C (1878) The effects of cross and self fertilisation in the vegetable kingdom, 2nd edn. John Murray, London, UKBook 

Google Scholar 
Diekmann M (2003) Species indicator values as an important tool in applied plant ecology—a review. Basic Appl Ecol 4:493–506Article 

Google Scholar 
Dudash MR (1990) Relative fitness of selfed and outcrossed progeny in a self-compatible, protandrous species, Sabatia angularis L. (Gentianaceae). A comparison in three environments. Evolution 44:1129–1139PubMed 
Article 
PubMed Central 

Google Scholar 
Eckert CG, Barrett SCH (1994) Inbreeding depression in partially self-fertilizing Decodon verticillatus (Lythraceae). Population-genetic and experimental analyses. Evolution 48:952–964PubMed 
Article 
PubMed Central 

Google Scholar 
Ellenberg H, Weber HE, Dull R, Wirth V, Werner W, Paulissen D (2001) Zeigerwerte von Pflanzen in Mitteleuropa, 3rd edn. Erich Goltze, Göttingen
Google Scholar 
Fox CW, Reed DH (2011) Inbreeding depression increases with environmental stress. An experimental study and meta-analysis. Evolution 65:246–258PubMed 
Article 
PubMed Central 

Google Scholar 
Glémin S (2003) How are deleterious mutations purged? Drift versus nonrandom mating. Evolution 57:2678–2687PubMed 
Article 
PubMed Central 

Google Scholar 
González AM, Ron AM, de, Lores M, Santalla M (2014) Effect of the inbreeding depression in progeny fitness of runner bean (Phaseolus coccineus L.) and it is implications for breeding. Euphytica 200:413–428Article 

Google Scholar 
Hedrick PW, Kalinowski ST (2000) Inbreeding depression in conservation biology. Annu Rev Ecol Syst 31:139–162Article 

Google Scholar 
Hilbig W (1975) Übersicht über die Pflanzengesellschaften des südlichen Teiles der DDR XII – Die Großseggenrieder. Hercynia 12:341–356
Google Scholar 
Husband BC, Schemske DW (1996) Evolution of the magnitude and timing of inbreeding depression in plants. Evolution 50:54–70PubMed 
Article 
PubMed Central 

Google Scholar 
Ivey CT, Carr DE (2005) Effects of herbivory and inbreeding on the pollinators and mating system of Mimulus guttatus (Phrymaceae). Am J Bot 92:1641–1649PubMed 
Article 
PubMed Central 

Google Scholar 
Keller LF, Grant PR, Grant BR, Petren K (2002) Environmental conditions affect the magnitude of inbreeding depression in survival of Darwin’s finches. Evolution 56:1229–1239PubMed 
Article 
PubMed Central 

Google Scholar 
Keller LF, Waller DM (2002) Inbreeding effects in wild populations. Trends Ecol Evol 17:230–241Article 

Google Scholar 
Kéry M, Matthies D, Spillmann H-H (2000) Reduced fecundity and offspring performance in small populations of the declining grassland plants Primula veris and Gentiana lutea. J Ecol 88:17–30Article 

Google Scholar 
Kondrashov AS, Houle D (1997) Genotype—environment interactions and the estimation of the genomic mutation rate in Drosophila melanogaster. Proc R Soc Lond B Biol Sci 258:221–227
Google Scholar 
Li Y, van Kleunen M, Stift M (2019) Sibling competition does not magnify inbreeding depression in North American Arabidopsis lyrata. Heredity 123:723–732PubMed 
PubMed Central 
Article 

Google Scholar 
Lynch M, Walsh B (1998) Genetics and analysis of quantitative traits. Sinauer Assoc., Sunderland, MA,
Google Scholar 
O’Grady JJ, Brook BW, Reed DH, Ballou JD, Tonkyn DW, Frankham R (2006) Realistic levels of inbreeding depression strongly affect extinction risk in wild populations. Biol Conserv 133:42–51Article 

Google Scholar 
Oberdorfer E, Schwabe A (2001) Pflanzensoziologische Exkursionsflora. Für Deutschand und angrenzende Gebiete, 8th edn. E. Ulmer, Stuttgart
Google Scholar 
Reed DH, Fox CW, Enders LS, Kristensen TN (2012) Inbreeding-stress interactions. Evolutionary and conservation consequences. Ann N. Y Acad Sci 1256:33–48PubMed 
Article 
PubMed Central 

Google Scholar 
Rehling F, Matthies D, Sandner TM (2019) Responses of a legume to inbreeding and the intensity of novel and familiar stresses. Ecol Evol 9:1255–1267PubMed 
PubMed Central 
Article 

Google Scholar 
Rosche C, Hensen I, Lachmuth S (2018) Local pre-adaptation to disturbance and inbreeding-environment interactions affect colonisation abilities of diploid and tetraploid Centaurea stoebe. Plant Biol 20:75–84CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Sandner TM, Matthies D (2016) The effects of stress intensity and stress type on inbreeding depression in Silene vulgaris. Evolution 70:1225–1238PubMed 
Article 
PubMed Central 

Google Scholar 
Sandner TM, Matthies D (2017) Interactions of inbreeding and stress by poor host quality in a root hemiparasite. Ann Bot 119:143–150PubMed 
Article 
PubMed Central 

Google Scholar 
Sandner TM, Matthies D (2018) Inbreeding limits responses to environmental stress in Silene vulgaris. Environ Exp Bot 147:86–94Article 

Google Scholar 
Schmitt J, Eccleston J, Ehrhardt DW (1987) Dominance and suppression, size-dependent growth and self-thinning in a natural Impatiens capensis population. J Ecol 75:651Article 

Google Scholar 
Schmitt J, Ehrhardt DW (1990) Enhancement of inbreeding depression by dominance and suppression in Impatiens capensis. Evolution 44:269–278PubMed 
Article 
PubMed Central 

Google Scholar 
Schrieber K, Schweiger R, Kröner L, Müller C, Campbell H (2019) Inbreeding diminishes herbivore-induced metabolic responses in native and invasive plant populations. J Ecol 107:923–936Article 

Google Scholar 
Truscott A-M, Soulsby C, Palmer SCF, Newell L, Hulme PE (2006) The dispersal characteristics of the invasive plant Mimulus guttatus and the ecological significance of increased occurrence of high-flow events. J Ecol 94:1080–1091Article 

Google Scholar 
Walisch TJ, Colling G, Poncelet M, Matthies D (2012) Effects of inbreeding and interpopulation crosses on performance and plasticity of two generations of offspring of a declining grassland plant. Am J Bot 99:1300–1313PubMed 
Article 
PubMed Central 

Google Scholar 
Waller DM (1985) The genesis of size hierarchies in seedling populations of Impatiens capensis Meerb. N. Phytol 100:243–260Article 

Google Scholar 
Waller DM, Dole J, Bersch AJ (2008) Effects of stress and phenotypic variation on inbreeding depression in Brassica rapa. Evolution 62:917–931PubMed 
Article 
PubMed Central 

Google Scholar 
Waples RS (2020) An estimator of the Opportunity for Selection that is independent of mean fitness. Evolution 74:1942–1953PubMed 
Article 
PubMed Central 

Google Scholar 
Weigel KA (2001) Controlling inbreeding in modern breeding programs. J Dairy Sci 84:E177–E184CAS 
Article 

Google Scholar 
Weiner J (1985) Size hierarchies in experimental populations of annual plants. Ecology 66:743–752Article 

Google Scholar 
Willi Y, Dietrich S, van Kleunen M, Fischer M (2007) Inter-specific competitive stress does not affect the magnitude of inbreeding depression. Evol Ecol Res 9:959–974
Google Scholar 
Willis JH (1993) Partial self-fertilization and inbreeding depression in two populations of Mimulus guttatus. Heredity 71:145–154Article 

Google Scholar 
Wright S (1922) Coefficients of inbreeding and relationship. Am Nat 56:330–338Article 

Google Scholar 
Yun L, Agrawal AF (2014) Variation in the strength of inbreeding depression across environments. Effects of stress and density dependence. Evolution 68:3599–3606PubMed 
Article 

Google Scholar  More

Genome skimming provides robust phylogenyPioneering molecular phylogenetic studies in Sepiolida that used short regions of a few mitochondrial and nuclear genes failed to resolve the relationship of major clades9,22,23. To increase the number of phylogenetically informative sites, Sanchez et al.11 sequenced and analyzed the transcriptomes of multiple species of Euprymna Steenstrup, 1887, related bobtail squids including Sepiola parva Sasaki, 1913 and Sepiola birostrata Sasaki, 1918, and several bottletail squids. They found that S. parva grouped with the Euprymna species to the exclusion of S. birostrata, and further morphological analysis led to the formal redefinition of the genus Euprymna and the reassignment of S. parva Sasaki, 1913 to Euprymna parva11. The following year, in an exhaustive study of hectocotylus structure, Bello19 proposed that Euprymna be split back into the original Euprymna Steenstrup, 1998 and a newly defined genus, Eumandya Bello 2020 that contains E. parva Sasaki, 1913 and E. pardalota Reid 2011, two taxa whose arms have two rows of suckers rather than four as in other Euprymna species. Similarly, Bello introduced a new genus, Lusepiola Bello, 2020 that has the effect of renaming Sepiola birostrata Sasaki, 1918 as Lusepiola birostrata. For clarity, we adopt the finer-grained nomenclature of Bello below, but happily note that E. parva and E. pardalota have the same abbreviations in both the notation of Sanchez et al.11 and Bello19.Sanchez et al.11 also emphasized the need for more taxon sampling, careful species assignment, and the inclusion of more informative sites when studying this group of cephalopods. However, the distribution and lifestyle of many lineages of Sepiolida makes the collection of fresh tissue for RNA sequencing very challenging. To overcome this limitation, we sequenced the genomic DNA of several Sepiolida species at shallow coverage up to 3.6× and accessed by this way several mitochondrial and nuclear loci. Most of our samples were carefully identified at the species level based on morphological characters.We recovered the mitochondrial genomes of the species targeted in this study and annotated the 13 protein-coding genes, 22 tRNAs, and two rRNAs (although only the conserved region of the large and small rRNA was obtained for Rondeletiola minor Naef, 1912).Additionally, we also downloaded the complete mitochondrial genomes of S. austrinum and Idiosepius sp., and the transcriptome of E. tasmanica available in the NCBI database. The transcriptome of E. tasmanica was used to extract its complete set of mitochondrial protein-coding genes. We could reconstruct the mitochondrial gene order for all species with complete mtDNA genomes, but we observed no re-arrangement for members of Sepiolidae, and only Sepiadarium austrinum deviated from the arrangement seen in all other Sepiadariidae (Fig. S1).To complement the mitochondrial-based evolutionary history, we also annotated several nuclear loci. As ribosomal gene clusters are present in numerous copies, they were successfully retrieved for almost all the species, except for 28S of the Sepiadariidae sp. specimen, which appeared problematic and was excluded.By mapping reads to the reference genome of E. scolopes, we obtained 3,279,410 loci shared between at least two species and further selected 5215 loci presented in most of our Sepiolidae species, but allowed some missing data in the Euprymna + Eumandya clade. This was done because the phylogenetic relationships of the Euprymna + Eumandya species were previously described in detail in Sanchez et al.11 using transcriptome data. Out of the 5215 loci, 5164 loci had a per-site coverage ranging between two and five. After trimming and removal of regions without informative sites, 577 loci remained. These ultraconserved loci had lengths ranging between 10 and 690 base pairs (bp), with an average of 65 bp. Our alignment matrix had a length of 37,512 bp and consisted of 16,495 distinct site patterns, and variable sites between 1 and 130 bp with an average value of 7 bp. We expected a low value of variable sites because these regions are highly conserved.We considered resolved nodes to be those with the ultrafast bootstrap support and posterior probability larger than 95% and 0.9, respectively. Only the very unresolved nodes were found based on the mito_nc matrix (Fig. 1). However, among the species in these nodes, Adinaefiola ligulata Naef, 1912 was well supported with amino acid sequences from mitochondrial genes (posterior probability of 1 and 94% bootstrap support) and partially by the ultraconserved loci (posterior probability of 1, but only 85% bootstrap support) as sister to the Sepiola clade (Figs. 2 and S2). Moreover, compared to the mito_nc matrix and with identical topology, mito_aa and UCEbob fully resolved the relationship of the Indo-Pacific and Mediterranean Sea Sepiolinae. The tree generated by the nuclear_rRNA produces a topology with most nodes unsupported (Fig. S3), suggesting these markers are too conserved for assessing the relationships among this group.Fig. 1: Phylogeny of Sepiolida based on nucleotide sequences from the mitochondria (mt_nc matrix).The topology of the maximum likelihood tree is shown. Numbers by the nodes indicate bootstrap support and the Bayesian posterior probabilities. Values of bootstrap support and posterior probabilities above 95% and 0.95, respectively, are not shown. (*) indicates that the node was resolved with the mito_aa and UCEbob matrices. (+) indicate that A. ligulata is sister to Sepiola using mito_aa with ultrafast bootstrap support of 94% and a posterior probability of 1. Abbreviations: IP, Indo-Pacific Ocean; MA, Mediterranean Sea, and the Atlantic Ocean.Full size imageFig. 2: Phylogenetic tree of Sepiolida based on conserved nuclear loci (UCEbob matrix).The topology of the maximum likelihood tree is shown. Numbers in by the nodes indicate the bootstrap support and the Bayesian posterior probability. Values of bootstrap support and posterior probabilities above 95% and 0.95, respectively, are not shown. IP, Indo-Pacific Ocean; MA, Mediterranean Sea, and the Atlantic Ocean.Full size imageUsing the UCEbob matrix, the topology and supported relationships of Euprymna + Eumandya species resemble those reported in Sanchez et al.11 using transcriptome sequences, proving our protocol valid when using low coverage sequencing and when a reference genome of the closest related species is available.The position of R. minor showed discordance between mitochondrial and nuclear datasets. Using the mitochondrial matrices, R. minor rendered the Sepietta Naef, 1912 clade paraphyletic, whereas using the UCEbob and rRNA_nc matrices, R. minor appeared sister to the Sepietta clade. These relationships were resolved in both mitochondrial and nuclear-based trees and require further investigation with more DNA markers and a wider population sampling.Molecular systematics of Sepiolida cladesUsing the complete mitochondrial genome, ribosomal nuclear genes, and ultraconserved loci, we recovered the monophyly of the two families of the order Sepiolida—Sepiadariidae and Sepiolidae9,24—and the monophyly of the three described subfamilies of the family Sepiolinae. However, contrary to what is proposed based on morphology in Young24, the Rossinae is not sister to all the remaining Sepiolidae but rather is sister to Heteroteuthinae, although this is unresolved in the UCE phylogeny. With the lack of systematic work on these subfamilies, our robust phylogenetic backbone in Sepiolida using new samples carefully identified by morphology and with museum vouchers, represents a notable advance to clarify the evolution of morphological traits in major clades within the family.Based on morphological characters of the hectocotylus, Bello19 recently split the polyphyletic Sepiola Leach 1817 into Lusepiola, Adinaefiola, and Boletzkyola, reserving Sepiola for the S. atlantica group sensu Naef 1923. These newly defined clades are consistent with our molecular phylogeny here and in Sanchez11, who also noted the polyphyly of Sepiola in the Indo-Pacific lineage.We find that Sepiolinae can be robustly split into two geographically distinct tribes: one that comprises species with known distribution in the Indo-Pacific region (tribe Euprymmini new tribe, defined as Sepiolinae with a closed bursa copulatrix, type genus Euprymna) and the other including all the Mediterranean and Atlantic species (tribe Sepiolini Appellof, 1989, defined here as Sepiolinae with an open bursa copulatrix, type genus Sepiola). Our molecular relationship is consistent with 13 of the 15 apomorphies used in the cladogram shown in Fig. 21 in Bello19. The other two proposed apomorphies in Bello (his apomorphic characters 4 and 6) group two IP lineages, Lusepiola and Inioteuthis, in a clade with species from the Mediterranean and Atlantic. Such relationships contradict our Euprymmini-Sepiolini sister relationship. Moreover, according to our phylogeny, apomorphy 6 of Bello, characterized by the participation of ventral and dorsal pedicels in the formation of the hectocotylus copulatory apparatus, implies that the male ancestor of Sepiolinae had a more developed hectocotylus that was simplified in the Euprymna and Eumandya clades.Among euprymins, we confirmed the monophyly of Euprymna Steenstrup 1887 as found previously by transcriptome analysis11. We also support the monophyly of Eumandya Bello, 2020 (Figs. 1 and 2), grouping the type species E. pardalota with E. parva along with the unnamed “Type 1” Ryukyuan species of Sanchez et al.11, for which only hatchlings were available. The phylogenomic grouping of Ryukyuan “Type 1” with Eumandya suggests that when its adults are found (or hatchlings are raised to maturity), its arms will carry two rows of suckers. We also found an adult of a Ryukyuan “Type 4” (extending the notation of Sanchez et al.11 in the coastal waters of Kume Island, that groups with E. scolopes from Hawaii, suggesting a divergence based on geographic isolation in the North Pacific. We also find that Lusepiola birostrata (formerly Sepiola birostrata) is grouped with Inioteuthis japonica as sister to a clade containing Euprymna, Eumandya, and an unnamed sepioline from Port Kembla, at the northeast of Martin Island in Australian waters.Among the sepiolins, we confirm the monophyly of Sepietta (only for nuclear-genome-based trees, see below). Adinaefiola, another genus erected by Bello19, with Sepiola ligulata Naef 1912 as its type species; was found sister to the Sepiola clade, but only in the tree based on amino acid mitochondrial sequences (mito_aa matrix) with a bootstrap value of 94% and a posterior probability of 1 (Fig. S2).Outside the sepiolines, members of the subfamily Heteroteuthinae are the most elusive and underrepresented in studies of cephalopod systematics due to their oceanic lifestyles. The placement of several heteroteuthin remains controversial. Lindgren et al.9, with six nuclear and four mitochondrial genes downloaded from GenBank found that Sepiolina Naef, 1912 was sister to Heteroteuthis Gray, 1849 + Rossia Owen, 1834+ Stoloteuthis Verril, 1881; rendering the subfamily Heteroteuthinae polyphyletic. In contrast, our work supports the monophyly of Heteroteuthinae by including Stoloteuthis and Heteroteuthis in this subfamily, while Rossia was placed within the Rossinae (Figs. 1, 2, S2). Members of Heteroteuthinae included in this study formed a sister group to a monophyletic Rossinae (Figs. 1, 2, S2). Semirossia, however, rendered the Rossia clade paraphyletic. Further discussion about the position of Semirossia is difficult because of the lack of information about the original source of this specimen in Kawashima et al.25.The light organ and luminescence evolutionBobtail squids are thought to use the bioluminescence of their light organ to camouflage them from predators while foraging and swimming at night through a mechanism called counter-illumination. This has been researched extensively using E. scolopes as a model system26,27,28. Unfortunately, the limited number of sequences available and the misidentification of bobtail squids in the GenBank database11,29,30 have hindered our understanding of the light organ evolution in the whole taxon.Our robust phylogeny and Bayesian reconstruction of ancestral bioluminescence clarify how the light organ and its luminescence have evolved in the family Sepiolidae. Members of Sepiolinae comprise neritic and benthic adults with bilobed light organs, except for two genera: Inioteuthis from the Indo-Pacific region, and the Sepietta species from the Mediterranean Sea and the Atlantic waters. The ancestor of the Sepiolinae very likely possessed a bilobed light organ that harbored luminescent symbiotic bacteria (Fig. 3). This character persisted until the ancestor of the euprymnins and sepiolins. Assuming that R. minor is sister to the Sepietta clade (as shown with the nuclear-based dataset, Fig. 2), it is clear that the bilobed light organ was lost once in Inioteuthis and Sepietta, and simplified to a rounded organ in R. minor. The alternative scenario, where R. minor renders the Sepietta clade paraphyletic (based on mitochondrial matrices, Fig. 1), is less plausible as it implies that the light organ was lost twice in the Sepietta group, once in S. obscura and then in the ancestor of S. neglecta and S. oweniana; or alternatively that it was lost in the ancestor of Sepietta-Rondelentiola followed by a reversion of this character in the lineage of Rondelentiola.Fig. 3: Ancestral character reconstruction (ASR).ASR of (a) the shape of the light organ and (b) the origin of luminescence in the Sepiolida. The posterior probability of each state is shown as a pie chart, mapped tree generated in BEAST (based on mito_nt matrix, see below), with the outgroups removed.Full size imageThe light organ is also present in all members of Heteroteuthinae. These bobtails are pelagic as adults, and their light organ appears as a single visceral organ rather than the bilobed form found in nektobenthic Sepiolinae. In contrast to the bacteriogenic luminescence of the light organ in E. scolopes31, previous studies in H. dispar3 failed to detect symbiotic bacteria and suggested that the luminescence has an autogenic origin. Thus, it seems plausible that the monophyly of Heteroteuthinae found in our study supports the findings in Lindgren et al.9 for convergent evolution of autogenic light organs associated with pelagic lifestyle in many squid, octopus, and Vampyroteuthis Chun, 19039,32.Divergence time of SepiolidaThe absence of fossils for this group limited our calculations of divergence time to the use of secondary calibrations. These calibrations can provide more accurate estimates depending on the type of primary calibrations that are used33. We retrieved secondary calibrations from previous estimations in Tanner et al.15, who used eleven fossil records spanning from coleoids to gastropods in transcriptome-based phylogenetic trees. Specifically, we used the time for the splits of Sepia esculenta and S. officinalis (~91 Mya), Idiosepiidae, and Sepiolida (~132 Mya) and the origin of the Decapodiformes (root age, ~174 Mya) (Fig. 4). These calibrations and our robust phylogenetic trees allow us to investigate the events that shape the divergence of some clades of the order Sepiolida (Figs. 4,  S4).Fig. 4: A chronogram of sepiolids using complete mitochondrial genes.Red dots indicate the nodes with secondary calibrations. K-Pg, refers to the Cretaceous-Paleogene boundary and MSC, to the Messinian salinity crisis.Full size imageSepiolida appeared before the Cretaceous-Paleogene extinction event34, during the middle Mesozoic around 94 Mya (95% HPD = 60.61–130.72). This time frame coincides with the rapid diversification of several oegopsida lineages15,35. Our molecular estimates also indicate that radiation of Sepiolidae and Sepidariidae occurred around the Cretaceous-Paleogene boundaries and is concurrent with the rapid diversification of modern marine percomorph fishes around the globe, after the extinction of Mesozoic fishes36,37.Among the species of Sepiolinae collected in the Mediterranean Sea for this study, only Sepiola robusta Naef, 1912, and Sepiola affinis Naef, 1912 are endemic to the Mediterranean Sea38. The distribution of the other species includes the Mediterranean Sea, North Atlantic Ocean, East Atlantic Ocean, and/or up to the Gulf of Cadiz. The confidence intervals for the split between the Mediterranean-Atlantic and Indo-Pacific lineages, and their diversification, overlap during the early Eocene to the beginning of the Oligocene (Figs. 4 and  S4). This time interval coincides with the end of the Tethys Sea, which separated the Indo-Pacific from the Mediterranean and Atlantic region through the Indian-Mediterranean Seaway39,40. This separation also influenced the divergence of loliginid clades, coinciding with the split between the Eastern Atlantic plus Mediterranean clade (Loligo, Afrololigo, Alloteuthis) and Indo-Pacific clade (Uroteuthis and Loliolus) (~55 Mya based on Fig. 2 in Anderson and Marian41).Our chronogram indicates that the ancestor of Sepiolinae arose prior to the early Eocene around 46 Mya (95% HPD = 25.16–69.49) (Fig. 4), already possessing a bilobed light organ hosting luminescent bacteria (Fig. 3). We estimate that the split between S. affinis and S. intermedia occurred around 2.62 Mya (95% HPD = 0.3–7.4) (Fig. 4) during the end of the Zanclean period, when the Atlantic Ocean refilled the Mediterranean after the Messinian salinity crisis42,43. While S. affinis is a coastal species with a narrow depth limit, S. intermedia inhabits a wider range of deeper waters. It is possible that two populations of their ancestor, each adapted to a different ecological niche and diverged sympatrically in Mediterranean waters, and, after the speciation, S. intermedia extended its distribution outside the Mediterranean to the Gulf of Cadiz44.We also estimate that the split between H. dispar Rüppell, 1844 and H. hawaiiensis (Berry, 1909) occurred around 2.4 Mya (95% HPD = 0.46–5.88), coinciding with the closure of the Isthmus of Panama around 2.8 Mya45. Surveys of these species found H. hawaiiensis in the North Pacific and H. dispar in the North Atlantic Ocean and Mediterranean Sea46. A recent speciation event might be the reason for the lack of morphological differences between the two species46. Thus, these species may be rendered as cryptic species, a phenomenon increasingly reported in oceanic cephalopods47. The sister species of this cryptic species complex, H. dagamensis Robson, 1924, appeared before, around 6 Mya, and is reported with broad distribution in the South Atlantic Ocean off South Africa, the Gulf of Mexico, North Atlantic Ocean between Ireland and Newfoundland in Canada, and the South Pacific Ocean off New Zealand48,49,50.The origin of the Heteroteuthis ancestor of H. dispar, H. hawaiiensis, and H. dagamensis can be placed in the Pacific Ocean. After the formation of the Isthmus of Panama, the northern population of Heteroteuthis might have split into H. hawaiiensis in North Pacific and H. dispar in the Atlantic Ocean (from where it also migrated to the Mediterranean Sea). Meanwhile, the formation of the equatorial currents isolated the southern population of Heteroteuthis and gave rise to H. dagamensis. Then, H. dagamensis extended its distribution from the Southern Pacific to the South Atlantic Ocean, the North Atlantic waters, and the Gulf of Mexico. Analysis of molecular species delimitation, however, suggests that H. dagamensis includes cryptic lineages among Atlantic and New Zealand populations30.While the origin of Heteroteuthis might also be in the Atlantic Ocean, the higher diversity of heteroteuthins in the Pacific (H. hawaiiensis, H. dagamensis, H. ryukyuensis Kubodera, Okutani and Kosuge, 2009, H. nordopacifica Kubodera and Okutani, 2011, and an unknown H. sp. KER (only known from molecular studies49)) than at the Atlantic (H. dispar and H. dagamensis), make its origin at the Atlantic less plausible. Moreover, the Atlantic Heteroteuthis were found nested within Heteroteuthinae species from the Pacific, supporting Pacific Ocean origin (Figs. 1, 4).By sequencing the genomic DNA of sepiolids at low coverage, we recovered complete mitochondrial genomes and nuclear ribosomal genes for most of our collections. Furthermore, mapping reads to the reference genome of E. scolopes allowed us to retrieve additional nuclear-ultraconserved regions. We demonstrate that these nuclear and mitochondrial loci are useful to reconstruct robust phylogenetic trees, especially when the transcriptomes of specimens are difficult to collect, as for sepiolids inhabiting oceanic environments. Finally, our study integrated genomic DNA sequencing with confident morphological identification, which helped to reconstruct the ancestral character of the light organ and its luminescence in sepiolids, and clarify how major lineages have evolved, establishing the existence of distinct Indo-Pacific and Mediterranean-Atlantic subfamilies of Sepiolinae. Our collections and genomically anchored phylogenies will provide a reliable foundation classification of sepiolids for future studies. More

To determine whether exposure to (Z)-11-16:Ald induced detectable defence-related responses in B. nigra, we investigated the amount of feeding damage to plants and the volatile emissions of plants exposed to volatilised pure compound. Contrary to our hypothesis, P. xylostella larvae fed more on plants previously exposed to (Z)-11-16:Ald at biologically relevant levels than on controls, suggesting that exposure to (Z)-11-16:Ald might increase the susceptibility of plants to future herbivory. Earlier work investigating the responses of plants to insect sex pheromone showed that exposure primed defences14,16, resulting in higher volatile emissions13, and lower feeding12. Our results are different to these findings and suggest alternative ecological effects of detecting insect pheromone to those reported earlier.The greater feeding on (Z)-11-16:Ald-exposed plants compared to controls could relate to the differences in volatile emissions of the differently treated plants. Although there were no detectable differences in volatile emissions between exposed plants and controls immediately after (Z)-11-16:Ald-exposure, there were differences after a subsequent 24 h of feeding. The difference could be due to (Z)-11-16:Ald-induced changes in the plant directly affecting herbivore-induced volatile emission, or could be related to altered plant nutrition or defences leading to an increase in the leaf area consumed by herbivores and a consequent difference in induction of volatile emissions. Thus (Z)-11-16:Ald could potentially alter plant defences and in doing so increase the survival of eggs and future hatched larvae. It has earlier been shown in Arabidopsis thaliana that application of Pieris brassicae or Spodoptera littoralis egg extract onto leaves reduced the induction of genes related to defence against insects after caterpillar feeding23. However, at this stage our data does not allow us to further explore these ecological hypotheses. Additional studies would need to test in greater detail the deposition of eggs, the hatching of eggs, and performance and survival of developing larvae of plants exposed to (Z)-11-16:Ald.The observation of changes in feeding amount and plant volatile responses prompted us to examine the early and late signaling events in response to (Z)-11-16:Ald exposure. The results showed that exposure to (Z)-11-16:Ald induced a transmembrane depolarisation by plants. The depolarisation of the plasma membrane potential is known to be the first detectable event in the detection of a biotic or abiotic stress19. We estimated the detection threshold of the pheromone to be a concentration between 25 and 10 ppm. A lower level of transmembrane depolarisation was observed when plants were exposed to (Z)-11-16:Ac, hence (Z)-11-16:Ald appears to be the most phytoactive component of the P. xylostella sex pheromone. However, it remains unclear how specific the plant response is to the (Z)-11-16:Ald, which should be further elucidated by comparison with aldehydes that have a more similar chemical structures.The plasma membrane is the only cellular structure in direct contact with the environment, which makes it critical for sensing environmental stimuli and initiating a cascade of events that eventually leads to a specific response21. As demonstrated in Arabidopsis, Vm depolarisation depends on a cascade of events that include changes in [Ca2+]cyt and the production of ROS24. We found that 50 ppm and 100 ppm of (Z)-11-16:Ald increased both the [Ca2+]cyt and the production of ROS. Moreover, we demonstrated that the increased ROS detected by CLSM were associated with the increased expression of reactive oxygen species (ROS)-mediated genes and ROS-scavenging enzyme activity. ROS participate in cell oxidation, during which H2O2 is produced and later regulated by ROS-scavenging enzymes involved in its degradation to protect cells from oxidative stress25,26. The production of H2O2 is potentially harmful and can result in oxidation in the cells of aerobic living organisms27. It is also an important component of the signalling network in plants28,29 and takes part in plant defence in response to environmental stress30,31. Several plant species trigger localized cell death by producing ROS at oviposition sites on leaves, which has been shown to be associated with an increase in egg mortality or a reduction of larval survival rate32. Recently, Bittner and colleagues demonstrated that when plants are previously exposed to the female pine sawfly (D. pini) sex pheromone, they produce H2O2 and induce defence-related genes faster after egg deposition on leaves, compared to plants that have not been exposed to the pheromone15. They suggested that the sex pheromone acted as an environmental cue indicating to plants that there would be future egg deposition on needles and subsequent herbivory. Plants then responded to it by producing H2O2, which formed necrotic tissue and reduced survival of eggs. Taken together with our observations, it is possible that the (Z)-11-16:Ald also primes B. nigra plant defences by producing H2O2 as a defensive mechanism to limit future egg deposition. It was shown that B. nigra plants induce the necrosis of cells located at the oviposition site of Pieris rapae and Pieris napi33, which can support this hypothesis. Contrary to the hypothesis of defence induction, B. nigra plants exposed to (Z)-11-16:Ald received more herbivore-feeding damage than control plants, but further investigations are needed to determine whether the production of H2O2 following exposure to (Z)-11-16:Ald would reduce subsequent egg deposition or hatching.Interestingly, early signalling events following the exposure to (Z)-11-16:Ald are analogous to the responses induced by biotic stress such as herbivore-wounding20,22, and in response to HIPVs21. For example, exposure to HIPVs resulted in a depolarisation of the plasma membrane (Vm)34,35 due to the entrance of calcium (Ca2+) into the cytosol of cells34. The detection of HIPVs results in transcriptional36,37, metabolic and physiological changes in plants38. Several studies have shown that plants perceiving HIPVs deploy faster and stronger chemical defences upon subsequent stress8,9, which can negatively affect herbivorous insects39. The observed action of (Z)-11-16:Ald is typical to that of insect and mite elicitors21,40,41. However, it is notable that to determine if (Z)-11-16:Ald induced detectable responses in B. nigra plants, whole plants were exposed to vapourised (Z)-11-16:Ald for 24 h, while to determine early and late signalling events following exposure to (Z)-11-16:Ald we applied 100 ppm in aqueous state for 30 min to 1 h (Table S2). While we used a biologically realistic scenario with a realistic concentration of pheromone for the whole plant responses the in vitro experiments focussed on mechanisms do not represent biologically accurate scenarios and utilised high concentrations of pheromone. Future studies should bridge this methodological gap by utilising more biologically realistic scenarios in mechanism elucidation.(Z)-11-16:Ald has been found to be a main constituent of pheromones in many moth species from the Noctuidae family including the corn earworm Helicoverpa zea42, which is the second-most important economic pest species in North America43. Many plants that have co-evolved with the Noctuidae could have the ability to detect (Z)-11-16:Ald. Prior to this study, the ability of plants to detect insect-emitted volatiles had been reported for two species: a perennial plant, S. altissima, and a tree, P. sylvestris. We can now tentatively add an annual plant, B. nigra, to the list. These studies suggest that the ability to detect insect-emitted volatiles has widely evolved in a large variety of plant families, and highlight the need to determine how widespread this trait is. Further studies should also determine the threshold and distance of detection and the ecological consequences of this detection.In summary our results indicate for the first time that exposing B. nigra plants to volatile (Z)-11-16:Ald increases the susceptibility of plants to feeding by P. xylostella larvae and induces an alteration in herbivore-induced volatile emissions. Further mechanistic experiments conducted in vitro using high doses of pheromone indicated that exposure to (Z)-11-16:Ald induces responses in receiver plants that are characterised by a depolarisation of Vm, an increase in [Ca2+]cyt and production of H2O2 leading to an increase in ROS-mediated gene expression and ROS scavenging-enzyme activity, which are typical responses to insect elicitors. This study supports recent findings showing that plants can detect insect-emitted volatiles. However, further research should be conducted to determine an accurate dose response of whole plants to volatile pheromone and the specificity of the response to this particular aldehyde.Materials and methodsThe study complied with local and national regulations.PlantsBrassica nigra (black mustard) seeds were collected from wild populations in the Netherlands (supplied by E. Poelman, Wageningen University). For experiment 1, conducted in Kuopio (Finland), plants were grown in plastic pots (8 × 8 cm) containing a mix of peat, soil and sand (3:1:1), in plant growth chambers (Weiss Bio 1300 m, Germany) with a 16L: 8D and light intensity of 250 µmol m−2 s−1. The temperature was maintained at 21 °C with 60% relative humidity during the day and decreased to 16 °C with 80% RH during the night time. Plants were watered every day and fertilized twice per week with a 0.1% solution containing nitrogen, phosphorous and potassium in a 19:4:20 ratio (Kekkilä Oyj, Finland).For experiments conducted in Turin (Italy) (Experiments 2 to 4), plants were grown in plastic pots (8 × 8 cm) containing a mix of peat, soil (Klasmann-Deilmann, Germany), sand (Vimark, Italy) and vermiculite (Unistara, Italy), in a climate-controlled room at 22 ± 1 °C, 16L:8D with light intensity of 250 µmol m−2 s−1. Three and four-week-old plants were used for all the experiments.Insects and synthetic compoundsPlutella xylostella were reared on broccoli (B. oleracea var. italica) with an artificial light–dark cycle of 16L:8D at 22 ± 0.5 °C.Synthetic (Z)-11-hexadecenal [(Z)-11-16:Ald] and (Z)-11-hexadecenyl acetate [(Z)-11-16:Ac] (isomeric purity 93%), were purchased from Pherobank (Wageningen, The Netherlands).Exposure of plants to treatmentsA 100 ppm solution of synthetic (Z)-11-16:Ald diluted in dichloromethane was prepared and 100 µl of the solution was injected into a rubber septum (7 mm O.D; Sigma-Aldrich) and left for 30 min for the dichloromethane to evaporate. As a solvent control, 100 µl of dichloromethane was deposited on a rubber septum, and left to evaporate for 30 min. A second control, without the rubber septum and dichloromethane, was also set up. Either treatment or control septa were enclosed in 0.5 L glass jars connected with Teflon tubing to plastic bags (Polyethylene terephthalate; overall dimensions 28 × 35 cm; Look Isopussi Eskimo oy, Finland). Plants were enclosed in the PET bags, that were previously baked for 1 h at 120 °C. For 24 h, a clean air flow was passed into the treatment or control glass jars and then into the bags containing the plants (Fig. S1 and Table S2).Volatile collections and feeding assaysAfter 24 h of exposure to the treatments, jars containing the rubber septa were disconnected from the plants, and a first VOC sampling was made before adding 22 first and second instar P. xylostella larvae were added to each plant for 24 h. After 24 h of feeding, volatile compounds were collected by dynamic headspace sampling (Fig. S1). VOCs were trapped by pulling clean air at 0.22 L min−1 for one hour through steel tubes filled with 200 mg Tenax TA 60/80 adsorbent (Markes International Ltd, UK) using a vacuum pump (KNF, Germany). The collected volatiles were thermally desorbed and analysed by gas chromatography-mass spectrometry (Agilent 7890A GC, and Agilent MS model 5975C VL MSD; New York, USA). Trapped compounds were desorbed with a thermal desorption unit (TD-100; Markes International Ltd, Llantrisant, UK) at 250 °C for 10 min, and cryofocused at − 10 °C in splitless mode onto an HP‐5 capillary column (50 m, 0.2 mm i.d, 0.5 μm film thickness; Hewlett‐Packard). The oven temperature programme was held at 40 °C for 1 min and then ramped 5 °C min−1 to 210 °C, and then further ramped at 20 °C min−1 to 290 °C. The carrier gas was helium, the transfer line temperature to the MSD was 300 °C, the ionization energy was 70 eV, and the full scan range was 29–355 m/z. We identified volatiles by comparison with a series of analytical standards (Sigma-Aldrich, Germany) and by comparison of their mass spectra to those in the NIST and Wiley 275 mass spectral libraries. Compound quantification was based on Total Ion Chromatograms (TIC) and according to the responses of analytical standards. Emission rates (ER) were calculated with the formula ER = X*Ai/Dw*t*Ao. ER was expressed in ng gDW−1 h−1, X was the compound quantity (ng), Ai and Ao were the inlet and outlet air flows (mL min-1), respectively, t was the sampling time of one hour and Dw is the dry weight of the plant sampled (g).After the volatile collection, we placed leaves of plants on A4 paper for scaling and digitally photographed them. Plants were then dried in paper bags in an oven at 60 °C for 3 days. The leaf area consumed by larvae was calculated using the LeafAreaAnalyzer software (https://github.com/EmilStalvinge/LeafAreaAnalyser; emilstalvinge@gmail.com).Transmembrane potential (Vm) measurementsLeaf segments (0.5 cm2) of three individual B. nigra plants were placed in Eppendorf tubes for 1 h with either 1 ml of (Z)-11-16:Ald solution, (Z)-11-16:Ac solution or control (Table S2). Vm was determined by inserting an electrode into a plant leaf segment following a method detailed earlier44. Vm variations were measured upon perfusion of four concentrations: 10, 25, 50, and 100 ppm diluted in MES buffer (pH 6.5) + 0.1% Tween 20 (V/V). A solution of MES buffer (pH 6.5) + 0.1% Tween 20 (V/V) was used as control. Vm values were recorded every five seconds.Determination of intracellular calcium variations using confocal laser scanning microscopy (CLSM) and Calcium OrangeCalcium Orange dye (stock solution in DMSO, Molecular Probes, Leiden, The Netherlands) was diluted in 5 mM MES-Na buffer (pH 6.0) to a final concentration of 5 µM. This solution was applied to B. nigra leaves attached to the plant as previously reported20,24,45. After 1 h incubation with Calcium Orange, the leaf was mounted on a Leica TCS SP2 (Leica Microsystems Srl, Milan, Italy) multiband confocal laser scanning microscope (CLSM) stage, without separating the leaf from the plant, in order to assess the basic fluorescence levels as control. A 50 µl application of either 50 or 100 ppm (Z)-11-16:Ald was made and after 30 min the calcium signature was observed. The microscope operates with a Krypton/Argon laser at 543 nm and 568 nm wavelengths: the first wavelength excites Calcium Orange, resulting in green fluorescence and the second mainly excites chlorophyll, resulting in red fluorescence. All images were obtained with an objective HCX APO 40 × immersed in water with a numeric aperture (NA) of 0.8. The scan speed was set at 400 Hz (Hz). The microscope pinhole was set at 0.064 mm and the average size depth of images was between 65 and 70 µm; the average number of sections per image was 25. The image format was 1024 × 1024 pixels, 8 bits per sample and 1 sample per pixel.CLSM Subcellular localization of H2O2 and active peroxidases using 10-acetyl-3,7-dihydroxyphenoxazine (Amplex Red)B. nigra leaves from rooted potted plants were treated with 50 µl of either 50 or 100 ppm of (Z)-11-16:Ald (Table S2) after incubation with the dye 10-acetyl-3,7-dihydroxyphenoxazine (Amplex Red) as described earlier22. The Molecular Probes Amplex Red Hydrogen Peroxide/Peroxidase Assay kit (A-22188) was used and dissolved in MES-Na buffer 50 mM (pH6.0) containing 0.5 mM calcium sulfate to obtain a 50 μM solution. Leaves where then mounted on a Leica TCS SP2 miscroscope as described above. Scannings were recorded after 180 min using the HCX PL APO 63x/1.20 W Corr/0.17CS objective. The microscope was operated with a Laser Ar (458 nm/5 mW; 476 nm/5 mW; 488 nm/20 mW; 514 nm/20 mW); a Laser HeNe 543 nm/1,2 mW and a Laser HeNe 633 nm/10 mW.ROS-scavenging enzyme activities and soluble protein determinationLeaves were collected immediately after 30 min of exposure to either 100 ppm of (Z)-11-16:Ald or control (Table S2). Intact leaves of two pooled plants were frozen in liquid N2 and stored at -80ºC before enzyme extraction. Frozen leaves were used for extraction of ROS scavenger enzymes following the method described in Maffei et al.22. All steps were carried out at 4 °C. Plant material was ground with a mortar and pestle under liquid nitrogen in cold 50 mM sodium phosphate buffer, pH 7.5, containing 250 mM sucrose, 1.0 mM EDTA, 10 mM KCl, 1 mM MgCl2, 0.5 mM phenylmethylsulfonyl fluoride (PMSF), 0.1 mM dithiothreitol (DTT), and 1% (w/v) polyvinylpolypyrrolidone (PVPP) in a 1:10 proportion (weight of plant material to buffer volume). The homogenate was then centrifuged at 25,000 g for 20 min at 4 °C and the supernatant was used directly for measurement of enzyme activity. The soluble protein concentration was measured using the method established by Bradford46 using bovine serum albumin as a standard.Catalase (CAT) activity was assayed spectrophotometrically by monitoring the absorbance change at 240 nm due to the decreased absorption of H2O2 (ɛ = 39.4 mM−1 cm−1). The reaction mixture in 1 mL final volume contained 50 mM Na-P, pH 7.0, 15 mM H2O2, and the enzyme extract. The reaction was initiated by addition of H2O2.Peroxidase (POX) activity was measured by detecting the oxidation of guaiacol (ɛ = 26.6 mM−1 cm−1) in the presence of H2O2. The reaction mixture contained 50 mM Na-P, pH 7.0, 0.33 mM guaiacol, 0.27 mM H2O2, and the enzyme extract in a 1.0 mL final volume. The reaction was started by addition of guaiacol and measured spectrophotometrically at 470 nm.Superoxide dismutase (SOD) activity was measured by reduction of nitro blue tetrazolium due to a photochemically generated superoxide anion. One ml of assay mixture consisted of 50 mM Na-P buffer, pH 7.8, 13 mM methionine, 75 μM nitro blue tetrazolium (NBT), 2 μM riboflavin, 0.1 mM EDTA, and the enzyme extract. Riboflavin was added as the last reagent. Samples were placed 30 cm below a light source (60 µmol m−1 s−1), and the reaction was allowed to run for 15 min. The reaction was stopped by switching off the light. A non irradiated reaction mixture, which was run in parallel, did not develop colour and served as a control. The absorbance was read at 560 nm.Quantitative gene expression analysis by Real-time PCRTotal RNA was isolated from control or treatment leaf tissues using RNA Isolation mini Kit (Machery-Nagel, Germany), and RNase- Free DNase according to the manufacturer’s protocols. The quality of RNA was checked in 1% agarose gel and the final yield was checked with a Spectrophotometer (Pharmacia Biotech Ultrospec 3000, United States). The cDNA synthesis was performed starting from 1 µg RNA using the High Capacity cDNA Reverse Transcription kit (Applied Biosystem, United States). Primers for real-time PCR were designed using the Primer 3.0 software47 and the relative sequences are listed in Supplementary Table S3. The real-time PCR was performed on an Mx3000P Real-Time System (Agilent Technologies, United States) using SYBR green I with the dye ROX as an internal loading standard. The reaction mixture was 10 µl in volume and comprised 5 µl of 2 × Maxima SYBR Green qPCR Master Mix (Thermo Fisher Scientific), 0.5 ml of cDNA, and 100 nM of primers (Integrated DNA Technologies, United States). The thermal conditions were as follows: 10 min at 95 °C, 40 cycles of 15 s at 95 °C, 20 s at 57 °C, and 30 s at 72 °C. Fluorescence was read after each annealing and extension phase. All runs were followed by a melting curve analysis from 55 to 95 °C. Two reference genes, ACT1 and eEF1Balpha2, were used to normalize the results. The sequences of the primers used in this work for CAT1, CuZnSOD1, PER4, ACT1 and eEF1Balpha2 are reported in Table S3. All amplification plots were analyzed with the MX3000P software (Agilent Technologies, United States) to obtain Ct values. Real-time PCR data are expressed as fold change of the treatment with respect to the control.Statistical analysisArea consumed by larvae, Vm measurements, enzyme activity and gene expression data were analysed using SPSS 25 software (IBM Corp. Armonk, USA). The normality of data and homogeneity of variances were checked and log transformed when the data did not meet assumptions for parametric analyses. Because we observed no significant differences between the control and the solvent control, we directly compared the control with (Z)-11-16:Ald for all analyses. Differences between treatments were analysed using T-tests for the fed area, gene expression and enzyme activity. Volatile emission rates were log transformed and auto-scaled (mean-centered and divided by the standard deviation of each variable). Partial Least Squares – Discriminant Analysis (PLS-DA) was performed on emission rates with the R software (v. 3.4.3) with the package vegan and RVAideMemoire with cross validation based on 50 submodels (fivefold outer loop and fourfold inner loop). A pairwise test was performed, based on PLS-DA with 999 permutations, to highlight the differences between treatments. The PLS-DA graphics were done with metaboanalyst (https://www.metaboanalyst.ca/). More

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

Google Scholar 
2.Saatchi, S. S. et al. Benchmark map of forest carbon stocks in tropical regions across three continents. Proc. Natl Acad. Sci. USA 108, 9899–9904 (2011).CAS 
Article 

Google Scholar 
3.Veldkamp, E., Schmidt, M., Powers, J. S. & Corre, M. D. Deforestation and reforestation impacts on soils in the tropics. Nat. Rev. Earth Environ. 1, 590–605 (2020).Article 

Google Scholar 
4.Ceccherini, G. et al. Abrupt increase in harvested forest area over Europe after 2015. Nature 583, 72–77 (2020).CAS 
Article 

Google Scholar 
5.Mitchard, E. T. A. The tropical forest carbon cycle and climate change. Nature 559, 527–534 (2018).CAS 
Article 

Google Scholar 
6.Curran, L. M. et al. Lowland forest loss in protected areas of Indonesian Borneo. Science 303, 1000–1003 (2004).CAS 
Article 

Google Scholar 
7.Friedl, A. et al. MODIS Collection 5 Global Land Cover: Algorithm Refinements and Characterization of New Datasets, 2001–2012 Collection 5.1 (Boston University, 2010).8.Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).CAS 
Article 

Google Scholar 
9.Margono, B. A., Potapov, P. V., Turubanova, S., Stolle, F. & Hansen, M. C. Primary forest cover loss in Indonesia over 2000–2012. Nat. Clim. Change 4, 730–735 (2014).Article 

Google Scholar 
10.Turubanova, S. et al. Ongoing primary forest loss in Brazil, Democratic Republic of the Congo, and Indonesia. Environ. Res. Lett. 13, 074028 (2018).Article 

Google Scholar 
11.Searchinger, T. et al. Creating a Sustainable Food Future: A Menu of Solutions to Feed Nearly 10 Billion People by 2050 (World Resources Institute, 2019).12.Gibbs, H. K. et al. Tropical forests were the primary sources of new agricultural land in the 1980s and 1990s. Proc. Natl Acad. Sci. USA 107, 16732–16737 (2010).CAS 
Article 

Google Scholar 
13.Tyukavina, A. et al. Congo Basin forest loss dominated by increasing smallholder clearing. Sci. Adv. 4, eaat2993 (2018).Article 

Google Scholar 
14.Achard, F. et al. Determination of tropical deforestation rates and related carbon losses from 1990 to 2010. Glob. Change Biol. 20, 2540–2554 (2014).Article 

Google Scholar 
15.Baccini, A. et al. Tropical forests are a net carbon source based on aboveground measurements of gain and loss. Science 358, 230–234 (2017).CAS 
Article 

Google Scholar 
16.Aide, T. M. et al. Woody vegetation dynamics in the tropical and subtropical Andes from 2001 to 2014: satellite image interpretation and expert validation. Glob. Change Biol. 25, 2112–2126 (2019).Article 

Google Scholar 
17.Song, X. P. et al. Global land change from 1982 to 2016. Nature 560, 639–643 (2018).CAS 
Article 

Google Scholar 
18.Körner, C. et al. A global inventory of mountains for bio-geographical applications. Alp. Bot. 127, 1–15 (2017).Article 

Google Scholar 
19.Zeng, Z. et al. Highland cropland expansion and forest loss in Southeast Asia in the twenty-first century. Nat. Geosci. 11, 556–562 (2018).CAS 
Article 

Google Scholar 
20.Zeng, Z., Gower, D. B. & Wood, E. F. Accelerating forest loss in Southeast Asian Massif in the 21st century: a case study in Nan Province, Thailand. Glob. Change Biol. 24, 4682–4695 (2018).Article 

Google Scholar 
21.Zarin, D. J. et al. Can carbon emissions from tropical deforestation drop by 50% in 5 years? Glob. Change Biol. 22, 1336–1347 (2016).Article 

Google Scholar 
22.Spracklen, D. & Righelato, R. Tropical montane forests are a larger than expected global carbon store. Biogeosciences 11, 2741–2754 (2014).CAS 
Article 

Google Scholar 
23.Miettinen, J., Shi, C. & Liew, S. C. Deforestation rates in insular Southeast Asia between 2000 and 2010. Glob. Change Biol. 17, 2261–2270 (2011).Article 

Google Scholar 
24.Austin, K. G. et al. What causes deforestation in Indonesia? Environ. Res. Lett. 14, 024007 (2019).Article 

Google Scholar 
25.Hansen, M. et al. Response to comment on ‘high-resolution global maps of 21st-century forest cover change’. Science 344, 981–981 (2014).CAS 
Article 

Google Scholar 
26.Chan, N., Xayvongsa, L. & Takeda, S. in Environmental Resources Use and Challenges in Contemporary Southeast Asia (eds Lopez, M. I. & Suryomenggolo, J.) 231–246 (Springer, 2018).27.Thompson, J. R., Carpenter, D. N., Cogbill, C. V. & Foster, D. R. Four centuries of change in northeastern United States forests. PLoS ONE 8, e72540 (2013).CAS 
Article 

Google Scholar 
28.Lawrence, D. & Vandecar, K. Effects of tropical deforestation on climate and agriculture. Nat. Clim. Change 5, 27–36 (2015).Article 

Google Scholar 
29.Zeng, Z. et al. Deforestation-induced warming over tropical mountain regions regulated by elevation. Nat. Geosci. 14, 23–29 (2021).CAS 
Article 

Google Scholar 
30.Senior, R. A., Hill, J. K., Benedick, S. & Edwards, D. P. Tropical forests are thermally buffered despite intensive selective logging. Glob. Change Biol. 24, 1267–1278 (2018).Article 

Google Scholar 
31.Senior, R. A., Hill, J. K., González del Pliego, P., Goode, L. K. & Edwards, D. P. A pantropical analysis of the impacts of forest degradation and conversion on local temperature. Ecol. Evol. 7, 7897–7908 (2017).Article 

Google Scholar 
32.Sodhi, N. S. et al. The state and conservation of Southeast Asian biodiversity. Biodivers. Conserv. 19, 317–328 (2010).Article 

Google Scholar 
33.Ahrends, A. et al. Current trends of rubber plantation expansion may threaten biodiversity and livelihoods. Glob. Environ. Change 34, 48–58 (2015).Article 

Google Scholar 
34.Edwards, D. P. et al. Degraded lands worth protecting: the biological importance of Southeast Asia’s repeatedly logged forests. Proc. R. Soc. B 278, 82–90 (2011).Article 

Google Scholar 
35.Srinivasan, U., Elsen, P. R. & Wilcove, D. S. Annual temperature variation influences the vulnerability of montane bird communities to land-use change. Ecography 42, 2084–2094 (2019).Article 

Google Scholar 
36.Rahbek, C. et al. Humboldt’s enigma: what causes global patterns of mountain biodiversity? Science 365, 1108–1113 (2019).CAS 
Article 

Google Scholar 
37.Guo, F., Lenoir, J. & Bonebrake, T. C. Land-use change interacts with climate to determine elevational species redistribution. Nat. Commun. 9, 1315 (2018).Article 
CAS 

Google Scholar 
38.Elsen, P. R., Monahan, W. B. & Merenlender, A. M. Topography and human pressure in mountain ranges alter expected species responses to climate change. Nat. Commun. 11, 1974 (2020).CAS 
Article 

Google Scholar 
39.Spracklen, D. V., Arnold, S. R. & Taylor, C. M. Observations of increased tropical rainfall preceded by air passage over forests. Nature 489, 282–285 (2012).CAS 
Article 

Google Scholar 
40.Spracklen, D. V. & Garcia-Carreras, L. The impact of Amazonian deforestation on Amazon Basin rainfall. Geophys. Res. Lett. 42, 9546–9552 (2015).Article 

Google Scholar 
41.Cheng, L. et al. Quantifying the impacts of vegetation changes on catchment storage–discharge dynamics using paired-catchment data. Water Resour. Res. 53, 5963–5979 (2017).Article 

Google Scholar 
42.Chappell, A., Baldock, J. & Sanderman, J. The global significance of omitting soil erosion from soil organic carbon cycling schemes. Nat. Clim. Change 6, 187–191 (2015).Article 
CAS 

Google Scholar 
43.Yue, Y. et al. Lateral transport of soil carbon and land–atmosphere CO2 flux induced by water erosion in China. Proc. Natl Acad. Sci. USA 113, 6617–6622 (2016).CAS 
Article 

Google Scholar 
44.Ziegler, A. D. et al. Carbon outcomes of major land-cover transitions in SE Asia: great uncertainties and REDD+ policy implications. Glob. Change Biol. 18, 3087–3099 (2012).Article 

Google Scholar 
45.Brinck, K. et al. High resolution analysis of tropical forest fragmentation and its impact on the global carbon cycle. Nat. Commun. 8, 14855 (2017).CAS 
Article 

Google Scholar 
46.Fox, J., Castella, J. C. & Ziegler, A. D. Swidden, rubber and carbon: can REDD+ work for people and the environment in montane mainland Southeast Asia? Glob. Environ. Change 29, 318–326 (2014).Article 

Google Scholar 
47.Harris, N. L. et al. Global maps of twenty-first century forest carbon fluxes. Nat. Clim. Change https://doi.org/10.1038/s41558-020-00976-6 (2021).48.Tachikawa, T., Hato, M., Kaku, M. & Iwasaki, A. Characteristics of ASTER GDEM version 2. In Proc. IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 3657–3660 (IEEE, 2011).49.Burrough, P. A., McDonnell, R., McDonnell, R. A. & Lloyd, C. D. Principles of Geographical Information Systems (Oxford Univ. Press, 2015).50.Mokany, K., Raison, R. J. & Prokushkin, A. S. Critical analysis of root : shoot ratios in terrestrial biomes. Glob. Change Biol. 12, 84–96 (2006).Article 

Google Scholar 
51.Tyukavina, A. et al. Aboveground carbon loss in natural and managed tropical forests from 2000 to 2012. Environ. Res. Lett. 10, 074002 (2015).Article 
CAS 

Google Scholar 
52.Ryan, S. E. & Porth, L. S. A Tutorial on the Piecewise Regression Approach Applied to Bedload Transport Data (CreateSpace, 2015).53.Toms, J. D. & Lesperance, M. L. Piecewise regression: a tool for identifying ecological thresholds. Ecology 84, 2034–2041 (2003).Article 

Google Scholar 
54.Zeng, Z. et al. A reversal in global terrestrial stilling and its implications for wind energy production. Nat. Clim. Change 9, 979–985 (2019).Article 

Google Scholar 
55.Zaiontz, C. Real Statistics Using Excel (accessed 16 June 2021); http://www.real-statistics.com/ More

A combination of airborne and satellite remote sensing data were used to quantify changes in mangrove forest structure and function from Hurricane Irma (Supplementary Fig. 1). Findings based on multi-sensor airborne data were scaled to the entire study area using estimates of forest structure and vegetation phenology derived from satellite data.G-LiHT Airborne campaignDuring April 2017, NASA Goddard’s Lidar, Hyperspectral, and Thermal (G-LiHT) airborne imager conducted an extensive airborne campaign in South Florida covering >130,000 ha. The same flight lines were resurveyed with G-LiHT eight months later, during November and December of 2017, to quantify structural changes in coastal forests of South Florida and Everglades National Park (ENP) following Hurricane Irma (Fig. 1). Lidar data was collected with two VQ-280i (Riegl USA) and synced during flight using RiACQUIRE version 2.3.7. The plane flew at a nominal height of 335 m above ground level at a pulse repetition frequency of 300 kHz to collect ~12 laser pulses per square meter. The analysis of pre- and post-hurricane conditions used 1-m resolution lidar data products (Supplementary Fig. 2) and 3-cm resolution stereo aerial and ground photos to estimate changes in vegetation structure, fractional cover, and terrain heights across the study domain. G-LiHT lidar canopy height models, digital terrain models, and estimates of fractional vegetation canopy cover (FVC) were produced using standard processing methodology21. All Level 1 through 3 lidar data products and fine-resolution imagery are openly shared through the G-LiHT webpage (https://gliht.gsfc.nasa.gov/).High resolution stereo maps of canopy heightStereo imagery from high-resolution commercial satellites can be used to estimate canopy and terrain surfaces42,43. Here, we derived digital surface models (DSMs) from DigitalGlobe’s WorldView 2 Level 1B imagery. DigitalGlobe provides these data to U.S. Government agencies and non-profit organizations that support U.S. interests via the NextView license agreement44. The spatial resolution of these data depends on the degree of off-nadir pointing for each acquisition. In this study, image resolution ranged from 0.5 to 0.7 m. We selected along-track stereopairs within the study domain to identify stereo image strips (each ~17 km × 110 km) that were nominally cloud-free over the forested domain of interest for years 2012–2013, the most recent cloud-free stereo data available for the study region prior to Hurricane Irma. The DSMs were produced using the Ames Stereo Pipeline (ASP) v. 2.5.1 on the NASA Center for Climate Simulation’s Advanced Data Analytics Platform at Goddard Space Flight Center (ADAPT, https://www.nccs.nasa.gov/services/adapt). The Worldview DSMs have been shown to accurately estimate mangrove canopy height when compared to airborne lidar and radar interferometry42,43. The processing workflow was adapted from ref. 45, and was implemented semi-global matching algorithms with a 5 × 5 correlation kernel, and a 3 × 3 median-filter applied to the output point cloud prior to producing a 1 m DSM using a weighted average gridding rule46. The ASP processing yielded five DSMs at 1-m resolution that were used to capture pre-storm canopy surface elevations.Each of the five Worldview DSMs were individually calibrated using overlapping pre-storm G-LiHT lidar data to estimate mangrove canopy heights across the study region (Supplementary Fig. 1). We sampled 1000 points within the mangrove forest cover (see mangrove classification, below) to develop a bias-correction equation between G-LiHT lidar-derived canopy heights and stereo DSM elevations (Supplementary Fig. 6). The bias-corrected canopy height models from high-resolution stereo imagery were mosaicked together to generate a 1-m resolution CHM for the entire study region (Supplementary Fig. 7). A pre-storm canopy volume was calculated by summing the 1 m × 1 m WorldView CHM for the entire region of interest. Similarly, a post-storm canopy volume was derived using the canopy damage model (see the section below), the relationship between the pre-storm CHM and the max wind speed. This analysis was conducted in ArcMap 10.7.1.Landsat mangrove forest classificationLandsat 8 Operational Land Imager (OLI) imagery was used to map mangrove cover for the southern Florida study region. The imagery was preprocessed to surface reflectance47 and clouds were masked following methods outlined in ref. 48. The Surface Reflectance Tier 1 product in Google Earth Engine was used to create a cloud-free image mosaic for 2016 based on the median values of all cloud-free images for the year for all bands (Supplementary Fig. 1).Training points were hand-selected using contemporary Google Earth imagery, field photos, and expert knowledge of the region. Twenty-four polygons covering a mangrove area of 1243 ha and 17 polygons covering a non-mangrove area of 2759 ha were identified for training regions. Within each of the two classes (i.e., mangrove and non-mangrove), 100,00 points were sampled and used for the training data in a Random Forest Classification implemented in Google Earth Engine49. The Random Forest model used 20 trees and a bag fraction of 0.5. The Landsat-based mangrove map was validated using the Region 3 species land cover map developed by the National Park Service for Everglades National Park50. The National Park species map was reclassified into mangrove and non-mangrove land cover, and 500 randomly generated points were sampled within each of the two land cover classes. The resulting error matrix indicated an overall accuracy of 90.6%.Post-storm canopy coverTime series of Landsat data were used to estimate hurricane damages of mangrove forest cover through December 31, 2017. We combined data from Landsat 7 ETM+ and Landsat 8 OLI to create a dense time series of cloud-free observations. All images were pre-processed to surface reflectance and masked for clouds using the same methods as the mangrove classification. Landsat 7 and Landsat 8 data were then harmonized to account for differences in the sensor specifications following51. We calculated the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) for each image in the collection. We calculated two reference maps from the time series of Landsat imagery (Supplementary Fig. 1). A pre-storm reference was calculated as the median value for each reflectance and index band for all cloud-free imagery in the two years prior Hurricane Irma, August 31, 2015 through August 31, 2017. Similarly, a post-storm median mosaic image was made using Landsat data between October 1, 2017 and December 31, 2017.Pre- and post-storm wall-to-wall Fractional Vegetation Cover (FVC) maps were generated using a combination of lidar-based FVC metrics and Landsat imagery (Supplementary Fig. 1). First, lidar-based FVC was binned into five classes; 0–20%, 20–40%, 40–60%, 60–80%, and 80–100% (Supplementary Fig. 7). We then collected 1000 randomly generated points in each of the five FVC classes, a total of 5000 points, to be used as training data in the Landsat classification. Here, we implemented a Random Forest Classifier using 100 trees and a bag fraction of 0.5. These steps were applied to both the pre-storm and post-storm lidar-derived FVC and Landsat mosaic image metrics. Changes between the pre- and post-storm FVC were then calculated based on the five different FVC classes (Supplementary Fig. 7). For example, a pixel with pre-storm FVC of 80–100% and a post-storm FVC of 20–40%, a reduction of three FVC classes, was assigned a drop in FVC of 40–60% (Fig. 1).Recovery times and resilienceWe estimated the time to full recovery of pre-storm mangrove green canopy cover using the time series of Landsat NDVI during the first 15-months following Hurricane Irma. The pre-storm mean NDVI layer was used as a reference, as described in the previous section. Next we calculated the NDVI anomaly for each image during the post-storm period, September 17, 2017 through December 31, 2018 (Supplementary Fig. 1). We then summed the individual anomaly values from each Landsat image and normalized by the total number of valid pixels (i.e., pixels meeting quality control measures) to estimate the average change in NDVI within the 15 months after the storm. We used anomaly values to identify mangrove forests with large decreases in the 15 months after the storm using a threshold of 0.2 for the 15-month NDVI average anomaly19,52. These areas suffered large losses of canopy material and limited new growth during the post-storm period. We used the slope in NDVI values for each pixel during 2018 to estimate the time in years to full recovery to pre-storm NDVI values, excluding data from October to December 2017 to remove delayed browning of damaged vegetation and spurious NDVI values from surface water features following the storm. Areas with a negative NDVI slope were not assigned a recovery time.We used a combination of the NDVI slope, estimated time to full NDVI recovery, and the average change in NDVI between the pre- and post-storm periods to categorize mangrove forest resilience, the potential for mangroves to rebound to pre-disturbance conditions. The specific criteria for mangrove recovery rates and mangrove damage thresholds were adapted from field and remote sensing studies, respectively6,19,25. Regions of high resilience (a combination of high resistance and resilience) were identified based on rapid recovery and/or little to no immediate impact from the storm: (1) areas that were observed to recover to pre-disturbance conditions during 2018, (2) areas that were predicted to recover within 5 years regardless of the post-storm drop in NDVI6, and (3) regions with a post-storm change in NDVI 15 years or a negative NDVI slope that occurred in regions with the largest ( >0.2) post-storm drop in average NDVI25 (Supplementary Fig. 9). The resilience class map is available online for download53.Mangrove species and elevationWe used species level maps developed by the National Park Service for Everglades National Park50 to characterize the impact of Hurricane Irma on different mangrove species. For that study, dominant species were identified through photo-interpretation of stereoscopic, color-infrared aerial imagery. Grid cells of 50 m × 50 m covering an area (Region 3) of ~100,000 ha in southwest Florida were interpreted based on the majority cover type and validated using field observations. A total of 169 vegetation cover classes were identified in this region, however, only five mangrove cover classes were considered for these analyses: Avicennia germinans (Black Mangrove), Laguncularia racemosa (White Mangrove), Rhizophora mangle (Red Mangrove), Conocarpus erectus (Buttonwood), and a single mixed species mangrove class. Mangrove forest communities were defined as the dominant diagnostic species in the upper-most stratum50. The mangrove species data were reprojected to match the Landsat resolution and the resilience maps. We used the intersection of the resilience and species extent maps to estimate the proportion of each resilience class by dominant species.The USGS National Elevation Dataset (NED) was used to estimate the soil elevation across southwest Florida. The 1/9 arc second (~3 m × 3 m) products were acquired from NED, and reprojected to Landsat resolution to estimate the proportion of each resilience class by soil elevation.Additional data and analysisModeled maximum storm surge data for Hurricane Irma were acquired from Coastal Emergency Risks Assessment data portal. Storm surge is derived from the ADCIRC Prediction System that solves for time dependent, circulation, and transport in multiple dimensions54. Maximum sustained hurricane wind speed was modeled hourly at a 5 km × 5 km resolution for 2017 by NASA’s Global Modeling and Assimilation Office (GMAO)55. The storm maximum wind speed for each 5 km × 5 km grid cell was calculated and binned into six discrete classes of wind speeds at 5 m s−1 increments: 26–30, 31–35, 36–40, 41–45, 46–50, and >50.Statistical analysesCanopy height losses measured from NASA G-LiHT data were grouped by five pre-storm canopy height classes (0–5 m, 5–10 m, 10–15 m, 15–20 m, and >20 m). All valid pixels within the lidar footprint was used to calculate the mean, standard error, and area (sum of 1 m × 1 m pixels) for each class (Supplementary Table 1). These results were then tested for significant differences between canopy height losses and pre-storm canopy height classes between using a one-way ANOVA analysis with a post-hoc Tukey test in R (version 4.0.3). For testing the significance between environmental variables (i.e., pre-storm canopy height, canopy height loss, percent canopy height loss, surface elevation, and storm surge water level above ground) we employed a two-sided Kolmogorov–Smirnov test56 implemented in R (version 4.0.3). First, we created a multi-band stacked image which included each of the variable layers. Within each resilience class (i.e., Low, Intermediate, and High) with randomly selected 10,000–20,000 points using Google Earth Engine to sample from the environmental variables images. From that sample set we then randomly selected 500 samples within each of the resilience classes. Each class combination (1) Low-Intermediate, (2) Low-High, and (3) Intermediate-High were compared using the Kolmogorov–Smirnov test. We repeated this procedure using 5000 iterations in order to provide a robust estimate of the Kolmogorow–Smirnov statistic, including the mean and first and third quartiles, which were then compared to the critical value (Supplementary Table 2).Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

1.Arim, M. & Marquet, P. A. Intraguild predation: A widespread interaction related to species biology. Ecol. Lett. 7, 557–564. https://doi.org/10.1111/j.1461-0248.2004.00613.x (2004).Article 

Google Scholar 
2.Polis, G. A., Myers, C. A. & Holt, R. D. The ecology and evolution of intraguild predation: Potential competitors that eat each other. Annu. Rev. Ecol. Syst. 20, 297–330. https://doi.org/10.1146/annurev.es.20.110189.001501 (1989).Article 

Google Scholar 
3.Rosenheim, J. A., Kaya, H. K., Ehler, L. E., Marois, J. J. & Jaffee, B. A. Intraguild predation among biological-control agents: Theory and evidence. Biol. Control 5, 303–335. https://doi.org/10.1006/bcon.1995.1038 (1995).Article 

Google Scholar 
4.Rosenheim, J. A. & Harmon, J. P. The influence of intraguild predation on the suppression of a shared prey population: An empirical reassessment. In Trophic and Guild in Biological Interactions Control (eds Brodeur, J. & Boivin, G.) 1–20 (Springer, 2006) https://doi.org/10.1007/1-4020-4767-3_1.Chapter 

Google Scholar 
5.Fonseca, M. M. et al. How to evaluate the potential occurrence of intraguild predation. Exp. Appl. Acarol. 72, 103–114. https://doi.org/10.1007/s10493-017-0142-x (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
6.Ferguson, K. I. & Stiling, P. Non-additive effects of multiple natural enemies on aphid populations. Oecologia 108, 375–379 (1996).ADS 
Article 

Google Scholar 
7.Hindayana, D., Meyhöfer, R., Scholz, D. & Poehling, H.-M. Intraguild predation among the hoverfly Episyrphus balteatus de Geer (Diptera: Syrphidae) and other aphidophagous predators. Biol. Control 20, 236–246 (2001).Article 

Google Scholar 
8.Denoth, M., Frid, L. & Myers, J. H. Multiple agents in biological control: Improving the odds?. Biol. Control 24, 20–30. https://doi.org/10.1016/S1049-9644(02)00002-6 (2002).Article 

Google Scholar 
9.Muştu, M., Kilinçer, N., Ülgentürk, S. & Kaydan, M. B. Feeding behavior of Cryptolaemus montrouzieri on mealybugs parasitized by Anagyrus pseudococci. Phytoparasitica 36, 360–367 (2008).Article 

Google Scholar 
10.Lucas, E. Intraguild predation among aphidophagous predators. Eur. J. Entomol. 102, 351–364 (2005).
Google Scholar 
11.Muştu, M. & Kilinçer, N. Intraguild predation of Planococcus ficus parasitoids Anagyrus pseudococci and Leptomastix dactylopii by Nephus kreissli. Biocontrol Sci. Technol. 24, 257–269. https://doi.org/10.1080/09583157.2013.856866 (2014).Article 

Google Scholar 
12.Diehl, S. & Feißel, M. Effects of enrichment on three-level food chains with omnivory. Am. Nat. 155, 200–218 (2000).Article 

Google Scholar 
13.Holt, R. D. & Polis, G. A. A theoretical framework for intraguild predation. Am. Nat. 149, 745–764 (1997).Article 

Google Scholar 
14.Kuijper, L. D. J., Kooi, B. W., Zonneveld, C. & Kooijman, S. A. L. M. Omnivory and food web dynamics. Ecol. Modell. 163, 19–32 (2003).Article 

Google Scholar 
15.Morin, P. Productivity, intraguild predation, and population dynamics in experimental food webs. Ecology 80, 752–760 (1999).Article 

Google Scholar 
16.Mylius, S. D., Klumpers, K., de Roos, A. M. & Persson, L. Impact of intraguild predation and stage structure on simple communities along a productivity gradient. Am. Nat. 158, 259–276 (2001).CAS 
Article 

Google Scholar 
17.Polis, G. A. & Holt, R. D. Intraguild predation: The dynamics of complex trophic interactions. Trends Ecol. Evol. 7, 151–154 (1992).CAS 
Article 

Google Scholar 
18.Janssen, A. et al. Intraguild predation usually does not disrupt biological control. In Trophic and Guild in Biological Interactions Control (eds Brodeur, J. & Boivin, G.) 21–44 (Springer, 2006) https://doi.org/10.1007/1-4020-4767-3_2.Chapter 

Google Scholar 
19.Skalski, G. T. & Gilliam, J. F. Functional responses with predator interference: Viable alternatives to the Holling type II model. Ecology 82, 3083–3092 (2001).Article 

Google Scholar 
20.de Villemereuil, P. B. & López-Sepulcre, A. Consumer functional responses under intra- and inter-specific interference competition. Ecol. Modell. 222, 419–426. https://doi.org/10.1016/j.ecolmodel.2010.10.011 (2011).Article 

Google Scholar 
21.Sutherland, W. J. From Individual Behaviour to Population Ecology (Oxford Series in Ecology and Evolution, 1996).
Google Scholar 
22.Pedersen, B. S. & Mills, N. J. Single vs. multiple introduction in biological control: The roles of parasitoid efficiency, antagonism and niche overlap. J. Appl. Ecol. 41, 973–984 (2004).Article 

Google Scholar 
23.Godfray, H. C. J. & Godfray, H. C. J. Parasitoids: Behavioral and Evolutionary Ecology Vol. 67 (Princeton University Press, 1994).Book 

Google Scholar 
24.Harvey, J. A., Poelman, E. H. & Tanaka, T. Intrinsic inter-and intraspecific competition in parasitoid wasps. Annu. Rev. Entomol. 58, 333–351 (2013).CAS 
Article 

Google Scholar 
25.Peri, E., Cusumano, A., Amodeo, V., Wajnberg, E. & Colazza, S. Intraguild interactions between two egg parasitoids of a true bug in semi-field and field conditions. PLoS ONE 9(6), e99876. https://doi.org/10.1371/journal.pone.0099876 (2014).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
26.Bruzzone, O. A., Logarzo, G. A., Aguirre, M. B. & Virla, E. G. Intra-host interspecific larval parasitoid competition solved using modelling and bayesian statistics. Ecol. Modell. 385, 114–123 (2018).Article 

Google Scholar 
27.Triapitsyn, S. V. et al. Complex of primary and secondary parasitoids (Hymenoptera: Encyrtidae and Signiphoridae) of Hypogeococcus spp. mealybugs (Hemiptera: Pseudococcidae) in the New World. Florida Entomol. 101, 411–434. https://doi.org/10.1653/024.101.0320 (2018).Article 

Google Scholar 
28.Aguirre, M. B. et al. Analysis of biological traits of Anagyrus cachamai and Anagyrus lapachosus to assess their potential as biological control candidate agents against Harrisia cactus mealybug pest in Puerto Rico. Biocontrol 64, 539–551. https://doi.org/10.1007/s10526-019-09956-y (2019).CAS 
Article 

Google Scholar 
29.Poveda-Martínez, D. et al. Species complex diversification by host plant use in an herbivorous insect: The source of Puerto Rican cactus mealybug pest and implications for biological control. Ecol. Evol. 10, 10463–10480. https://doi.org/10.1002/ece3.6702 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
30.Poveda-Martínez, D. et al. Untangling the Hypogeococcus pungens species complex (Hemiptera: Pseudococcidae) for Argentina, Australia, and Puerto Rico based on host plant associations and genetic evidence. PLoS ONE 14(7), e0220366. https://doi.org/10.1371/journal.pone.0220366 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
31.Thurstone, L. L. A law of comparative judgment. Psychol. Rev. 34, 273 (1927).Article 

Google Scholar 
32.Bradley, R. A. & Terry, M. E. Rank analysis of incomplete block designs: I. The method of paired comparisons. Biometrika 39, 324–345. https://doi.org/10.2307/2334029 (1952).MathSciNet 
Article 
MATH 

Google Scholar 
33.Gelman, A., Carlin, J. B., Stern, H. S. & Rubin, D. B. Bayesian data analysis. In Texts Stat. Sci. 2nd ed, 661 (CRC Press, 2003).34.Stevens, S. S. On the Theory of Scales of Measurement, vol. 103, 677–680 (1946).35.Patil, A., Huard, D. & Fonnesbeck, C. J. PyMC: Bayesian stochastic modelling in Python. J. Stat. Softw. 35, 1 (2010).Article 

Google Scholar 
36.Zwölfer, H. The structure and effect of parasite complexes attacking phytophagous host insects. In Proc. Adv. Study Inst. Dyn. Numbers Popul. 405–418 (1971).37.Zwölfer, H. Strategies and counterstrategies in insect population systems competing for space and food in flower headsand plant galls. Fortschr. Zool. 25(2/3), 331–353 (1979).
Google Scholar 
38.Vance, R. R. The stable coexistence of two competitors for one resource. Am. Nat. 126, 72–86 (1985).Article 

Google Scholar 
39.Fellers, J. H. Interference and exploitation in a guild of woodland ants. Ecology 68, 1466–1478 (1987).Article 

Google Scholar 
40.Cusumano, A., Peri, E., Vinson, S. B. & Colazza, S. Intraguild interactions between two egg parasitoids exploring host patches. Biocontrol 56, 173–184 (2011).Article 

Google Scholar 
41.Mizutani, N. Interspecific larval competition among three egg parasitoid species on the host, Riptortus clavatus (Thunberg) (Heteroptera: Alydidae). Proc. Assoc. Plant Prot. Kyushu 40, 106–110 (1994).Article 

Google Scholar 
42.Weber, C. A., Smilanick, J. M., Ehler, L. E. & Zalom, F. G. Ovipositional behavior and host discrimination in three scelionid egg parasitoids of stink bugs. Biol. Control 6, 245–252 (1996).Article 

Google Scholar 
43.Alim, M. A. & Lim, U. T. Interspecific larval competition between two egg parasitoids in refrigerated host eggs of Riptortus pedestris (Hemiptera: Alydidae). Biocontrol Sci. Technol. 21, 395–407 (2011).Article 

Google Scholar 
44.De Moraes, C. M. & Lewis, W. J. Analyses of two parasitoids with convergent foraging strategies. J. Insect Behav. 12, 571–583 (1999).Article 

Google Scholar 
45.Mackauer, M. Host discrimination and larval competition in solitary endoparasitoids. Crit. Issues Biol. Control, Intercept, Andover, Hants, UK. xvii + 330 pp (1990).46.Strand, M. R. & Godfray, H. C. J. Superparasitism and ovicide in parasitic Hymenoptera: Theory and a case study of the ectoparasitoid Bracon hebetor. Behav. Ecol. Sociobiol. 24, 421–432 (1989).Article 

Google Scholar 
47.Abram, P. K., Brodeur, J., Urbaneja, A. & Tena, A. Nonreproductive effects of insect parasitoids on their hosts. Annu. Rev. Entomol. 64, 259–276 (2019).CAS 
Article 

Google Scholar 
48.Abram, P. K., Brodeur, J., Burte, V. & Boivin, G. Parasitoid-induced host egg abortion: An underappreciated component of biological control services provided by egg parasitoids. Biol. Control 98, 52–60 (2016).Article 

Google Scholar 
49.Abram, P. K., Gariepy, T. D., Boivin, G. & Brodeur, J. An invasive stink bug as an evolutionary trap for an indigenous egg parasitoid. Biol. Invasions 16, 1387–1395 (2014).Article 

Google Scholar 
50.Steiner, A. L. Stinging behaviour of solitary wasps. In Venoms of the Hymenoptera. Biochemical, Pharmacological and Behavioural Aspects (ed Piek, T.) 63–148 (Academic Press, London, 1986) https://doi.org/10.1016/b978-0-12-554770-3.50008-5.51.Feng, Y., Wratten, S., Sandhu, H. & Keller, M. Interspecific competition between two generalist parasitoids that attack the leafroller Epiphyas postvittana (Lepidoptera: Tortricidae). Bull. Entomol. Res. 105, 426–433 (2015).CAS 
Article 

Google Scholar 
52.De Moraes, C. M. & Mescher, M. C. Intrinsic competition between larval parasitoids with different degrees of host specificity. Ecol. Entomol. 30, 564–570 (2005).Article 

Google Scholar 
53.Desneux, N., Barta, R. J., Hoelmer, K. A., Hopper, K. R. & Heimpel, G. E. Multifaceted determinants of host specificity in an aphid parasitoid. Oecologia 160, 387–398 (2009).ADS 
Article 

Google Scholar 
54.Brodeur, J. & Boivin, G. Functional ecology of immature parasitoids. Annu. Rev. Entomol. 49, 27–49 (2004).CAS 
Article 

Google Scholar 
55.Rogers, D. Random search and insect population models. J. Anim. Ecol. 41, 369–383 (1972).Article 

Google Scholar 
56.Holling, C. S. Some characteristics of simple types of predation and parasitism. Can. Entomol. 91, 385–398. https://doi.org/10.4039/Ent91385-7 (1959).Article 

Google Scholar  More

1.Zhao, F. et al. Diversity and species abundance patterns of the early Cambrian (Series 2, Stage 3) Chengjiang Biota from China. Paleobiology 40, 50–69 (2014).Article 

Google Scholar 
2.Zhu, M.-Y., Zhang, J.-M. & Li, G.-X. Sedimentary environments of the early Cambrian Chengjiang biota: sedimentology of the Yu’anshan Formation in Chengjiang County, eastern Yunnan. Acta Palaeontol. Sin. 40, 80–105 (2001).
Google Scholar 
3.Hu, S.-X. Taphonomy and palaeoecology of the early Cambrian Chengjiang Biota from eastern Yunnan, China. Berl. Palobiologische Abhandlungen 7 (2005).4.Hou, X. et al. The Cambrian Fossils of Chengjiang, China. The Flowering of Early Animal Life 2nd edn (John Wiley & Sons, 2017).5.Zhang, W.-T. & Hou, X.-G. Preliminary notes on the occurrence of the unusual trilobite Naraoia in Asia. Acta Palaeontol. Sin. 24, 591–595 (1985).
Google Scholar 
6.Luo, H.-L, Hu, S.-X, Chen, L.-Z, Zhang, S.-S & Tao, Y.-H. Early Cambrian Chengjiang Fauna from Kunming Region, China (Yunnan Science and Technology Press, 1999).7.Chen, J.-Y The Dawn of Animal World (Jiangsu Science and Technology Press, China, 2004).8.Duan, Y. et al. Reproductive strategy of the bradoriid arthropod Kunmingella douvillei from the lower Cambrian Chengjiang Lagerstätte, South China. Gondwana Res. 25, 983–990 (2014).Article 

Google Scholar 
9.Zhao, F.-C., Zhu, M.-Y. & Hu, S.-X. Community structure and composition of the Cambrian Chengjiang biota. Sci. China Earth Sci. 53, 1784–1799 (2010).Article 

Google Scholar 
10.Liu, Y. et al. Three-dimensionally preserved minute larva of a great-appendage arthropod from the early Cambrian Chengjiang biota. Proc. Natl Acad. Sci. USA 113, 5542–5546 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
11.Ou, Q. et al. Evolutionary trade-off in reproduction of Cambrian arthropods. Sci. Adv. 6, 33–76 (2020).
Google Scholar 
12.Dornbos, S. Q. & Chen, J.-Y. Community palaeoecology of the Early Cambrian Maotianshan Shale biota: ecological dominance of priapulid worms. Palaeogeogr. Palaeoclimatol. Palaeoecol. 258, 200–212 (2008).Article 

Google Scholar 
13.Fu, D. et al. The Qingjiang biota—a Burgess Shale-type fossil Lagerstätte from the early Cambrian of South China. Science 363, 1338–1342 (2019).CAS 
PubMed 
Article 

Google Scholar 
14.Caron, J.-B. & Jackson, D. A. Paleoecology of the Greater Phyllopod Bed community, Burgess Shale. Palaeogeogr. Palaeoclimatol. Palaeoecol. 258, 222–256 (2008).15.Nanglu, K., Caron, J.-B. & Gaines, R. R. The Burgess Shale paleocommunity with new insights from Marble Canyon, British Columbia. Paleobiology 46, 58–81 (2020).Article 

Google Scholar 
16.Gaines, R. R. in Reading and Writing of the Fossil Record: Preservational Pathways to Exceptional Fossilization Vol. 20 (eds Laflamme, M. et al.) 123–146 (Paleontological Research Institution, 2014).17.Zhai, D. et al. Variation in appendages in early Cambrian bradoriids reveals a wide range of body plans in stem-euarthropods. Commun. Biol. 2, 329 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
18.Isaevaa, V. V., Ozernyukc, N. D. & Rozhnov, S. V. Evidence for evolutionary changes in ontogeny: paleontological, comparative morphological, and molecular aspects. Biol. Bull. 40, 243–252 (2013).Article 

Google Scholar 
19.Liu, Y., Haug, J. T., Haug, C., Briggs, D. E. G. & Hou, X.-G. A 520 million-year-old chelicerate larva. Nat. Commun. 5, 4440 (2014).CAS 
PubMed 
Article 

Google Scholar 
20.Chipman, A. D. An embryological perspective on the early arthropod fossil record. BMC Evol. Biol. 15, 285 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
21.Wolfe, J. M. Metamorphosis is ancestral for crown euarthropods, and evolved in the Cambrian or earlier. Integr. Comp. Biol. 57, 499–509 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
22.Haug, T. J. Why the term “larva” is ambiguous, or what makes a larva? Acta Zool. 101, 167–188 (2018).Article 

Google Scholar 
23.Fu, D., Zhang, X., Budd, G. E., Liu, W. & Pan, X. Ontogeny and dimorphism of Isoxys auritus (Arthropoda) from the Early Cambrian Chengjiang biota, South China. Gondwana Res. 25, 975–982 (2014).Article 

Google Scholar 
24.Yang, X.-F., Kimmig, J., Lieberman, B. S. & Peng, S.-C. A new species of the deuterostome Herpetogaster from the early Cambrian Chengjiang biota of South China. Sci. Nat. 107, 37 (2020).CAS 
Article 

Google Scholar 
25.Zhai, D. Y. et al. Fine-scale appendage structure of the Cambrian trilobitomorph Naraoia spinosa and its ontogenetic and ecological implications. Proc. R. Soc. B 286, 20192371 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
26.Hughes, N. C. et al. Articulated trilobite ontogeny: suggestions for a methodological standard. J. Paleont. 95, 298–304 (2021).Article 

Google Scholar 
27.Chen, J.-Y. & Zhou, G.-Q. Biology of the Chengjiang fauna. Bull. Natl Mus. Nat. Sci. 10, 11–106 (1997).
Google Scholar 
28.Haug, J. T., Caron, J.-B. & Haug, C. Demecology in the Cambrian: synchronized moulting in arthropods from the Burgess Shale. BMC Biol. 11, 64 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
29.Robison, R. A., Babcock, L. E. & Gunther, V. G. Exceptional Cambrian fossils from Utah: A Window into the Age of Trilobites (Utah Geological Survey, 2015).30.Kimmig, J., Strotz, L. C., Kimmig, S. R., Egenhoff, S. O. & Lieberman, B. S. The Spence Shale Lagerstätte: an important window into Cambrian biodiversity. J. Geol. Soc. Lond. 176, 609–619 (2019).Article 

Google Scholar 
31.Paterson, J. R. et al. The Emu Bay Shale Konservat-Lagerstätte: a view of Cambrian life from East Gondwana. J. Geol. Soc. Lond. 173, 3107 (2016).32.Du, K. et al. A new early Cambrian Konservat-Lagerstätte expands the occurrence of Burgess Shale-type deposits on the Yangtze Platform. Earth Sci. Rev. 211, 103409 (2020).Article 

Google Scholar 
33.Harper, D. A. T. et al. The Sirius Passet Lagerstätte of North Greenland: a remote window on the Cambrian explosion. J. Geol. Soc. Lond. 176, 1023–1037 (2019).Article 

Google Scholar 
34.Chen, L. Z et al. Early Cambrian Chengjiang Fauna in Eastern Yunnan, China (Yunnan Science and Technology Press, 2002).35.Zhao, F. C., Caron, J.-B., Hu, S. X. & Zhu, M. Y. Quantitative analysis of taphofacies and paleocommunities in the Early Cambrian Chengjiang Lagerstätte. PALAIOS 24, 826–839 (2009).CAS 
Article 

Google Scholar 
36.Beck, M. K. et al. The identification, conservation, and management of estuarine and marine nurseries for fish and invertebrates. BioScience 51, 633–641 (2001).Article 

Google Scholar 
37.Botton, M. L. & Loveland, R. E. Abundance and dispersal potential of horseshoe crab (Limulus polyphemus) larvae in the Delaware estuary. Estuar. Coasts 26, 1472–1479 (2003).Article 

Google Scholar 
38.Watson, W. H. & Chabot, C. C. High resolution tracking of adult horseshoe crabs Limulus polyphemus in a New Hampshire estuary using a fixed array ultrasonic telemetry. Curr. Zool. 56, 599–610 (2010).Article 

Google Scholar 
39.Perry, F. A. et al. Habitat partitioning in Antarctic krill: spawning hotspots and nursery areas. PLoS ONE 14, e0219325 (2019).40.Nagelkerken, I. in Ecological Connectivity among Tropical Coastal Ecosystems (ed. Nagelkerken, I.) 357–399 (Springer, 2009).41.Kanciruk, P. in The Biology and Management of Lobsters Vol. 2 (eds Cobb, J. S. & Phillips, B. F.) 59–96 (Academic Press, 1980).42.Sandt, V. J. & Stoner, A. W. Ontogenetic shift in habitat by early juvenile queen conch, Strombus gigas: patterns and potential mechanisms. Fish. Bull. 91, 516–525 (1993).
Google Scholar 
43.Pedrotti, M. L. & Fenaux, L. Dispersal of echinoderm larvae in a geographical area marked by upwelling (Ligurian Sea, NW Mediterranean). Mar. Ecol. Prog. Ser. 87, 217–227 (1992).Article 

Google Scholar 
44.Zhai, D. et al. Spatial heterogeneity of the population age structure of the ostracode Limnocythere inopinata in Hulun Lake, Inner Mongolia and its implications. Hydrobiologia 716, 29–46 (2013).CAS 
Article 

Google Scholar 
45.Baillon, S., Hamel, J. F., Wareham, V. E. & Mercier, A. Deep cold-water corals as nurseries for fish larvae. Front. Ecol. Environ. 10, 351–356 (2012).Article 

Google Scholar 
46.Treude, T., Kiel, S., Linke, P., Peckmann, J. & Goedert, J. Elasmobranch egg capsules associated with modern and ancient cold seeps: a nursery for marine deep-water predators. Mar. Ecol. Prog. Ser. 437, 175–181 (2011).Article 

Google Scholar 
47.Rooper, C. N., Boldt, J. L. & Zimmermann, M. An assessment of juvenile Pacific Ocean perch (Sebastes alutus) habitat use in a deepwater nursery. Estuar. Coast. Shelf Sci. 75, 371–380 (2007).Article 

Google Scholar 
48.Pimiento, C., Ehret, D. J., MacFadden, B. J. & Hubbell, G. Ancient nursery area for the extinct giant shark Megalodon from the Miocene of Panama. PLoS ONE 5, e10552 (2010).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
49.Villafaña, J. A. et al. First evidence of a palaeo-nursery area of the great white shark. Sci. Rep. 10, 8502 (2020).50.Paterson, J. R., Jago, J. B., Brock, G. A. & Gehling, J. G. Taphonomy and palaeoecology of the emuellid trilobite Balcoracania dailyi (early Cambrian, South Australia). Palaeogeogr. Palaeoclimatol. Palaeoecol. 249, 302–321 (2007).Article 

Google Scholar 
51.Hartnoll, R. G. in Physiology and Behaviour of Marine Organisms (eds McLusky, D. S. & Berry, A. J.) 349–358 (Pergamon Press, 1978).52.Hartnoll, R. G. & Bryant, A. D. Size-frequency distributions in decapod Crustacea—the quick, the dead and the cast-offs. J. Crust. Biol. 10, 14–19 (1990).Article 

Google Scholar 
53.Sheldon, P. R. Trilobite size-frequency distributions, recognition of instars, and phyletic size changes. Lethaia 21, 293–306 (1988).Article 

Google Scholar 
54.Herrnkind, W. F. in The Biology and Management of Lobsters Vol. 1 (eds Cobb, J. S. & Phillips B. F.) 349–407 (Academic Press, 1980)55.Linnane, A., Dimmlich, W. & Ward, T. Movement patterns of the southern rock lobster, Jasus edwardsii, of South Australia. NZ J. Mar. Freshw. Res. 39, 335–346 (2005).Article 

Google Scholar 
56.Blazejowski, B. et al. Ancient animal migration: a case study of eyeless, dimorphic Devonian trilobites from Poland. Palaeontology 59, 743–751 (2016).Article 

Google Scholar 
57.Hughes, N. C., Kříž, J., Macquaker, J. H. S. & Huff, W. D. The depositional environment and taphonomy of the Homerian “Aulacopleura shales” fossil assemblage near Loděnice, Czech Republic (Prague Basin, Perunican microcontinent). Bull. Geosci. 89, 219–238 (2014).Article 

Google Scholar 
58.Whitaker, A. F. & Kimmig, J. Anthropologically introduced biases in natural history collections, with a case study on the invertebrate paleontology collections from the middle Cambrian Spence Shale Lagerstätte. Palaeontol. Electron. 23, a58 (2020).
Google Scholar 
59.Conway Morris, S. The community structure of the Middle Cambrian phyllopod bed (Burgess Shale). Palaeontology 29, 423–467 (1986).
Google Scholar 
60.Caron, J.-B., Gaines, R. R., Aria, C., Mángano, M. G. & Streng, M. A new phyllopod bed-like assemblage on from the Burgess Shale of the Canadian Rockies. Nat. Commun. 5, 3210 (2014).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
61.Ihaka, R. R. & Gentleman, R. A language for data analysis and graphics. J. Comput. Graph. Stat. 5, 299–314 (1996).
Google Scholar  More