Philippa Kaur

1.
DiGiacomo, G., Hadrich, J., Hutchison, W. D., Peterson, H. & Rogers, M. Economic impact of spotted wing drosophila (Diptera: Drosophilidae) yield loss on Minnesota Raspberry farms: A grower survey. J. Integr. Pest Manag. 10, https://doi.org/10.1093/jipm/pmz006 (2019).
2.
Farnsworth, D. et al. Economic analysis of revenue losses and control costs associated with the spotted wing drosophila, Drosophila suzukii (Matsumura), in the California raspberry industry. Pest Manag. Sci. 73, 1083–1090. https://doi.org/10.1002/ps.4497 (2017).
CAS  Article  PubMed  Google Scholar 

3.
Cini, A., Ioriatti, C. & Anfora, G. A review of the invasion of Drosophila suzukii in Europe and a draft research agenda for integrated pest management. Bull. Insectol. 65, 149–160 (2012).
Google Scholar 

4.
Okada, T. Systematic Study of Drosophilidae and Allied Families of Japan. 95–106 (Gihodo Co. Ltd., 1956).

5.
Walsh, D. B. et al. Drosophila suzukii (Diptera: Drosophilidae): Invasive pest of ripening soft fruit expanding its geographic range and damage potential. J. Integr. Pest Manag. 2, G1–G7. https://doi.org/10.1603/Ipm10010 (2011).
Article  Google Scholar 

6.
Kanzawa, T. Studies on Drosophila suzukii mats. J. Plant Proteom. 23, 66–70, 127–132, 183–191 (1939).

7.
Bolda, M. P. & Goodhue, R. E. Spotted wing Drosophila: Potential economic impact of a newly established pest. Agric. Resour. Econ. Updates Univ. Calif. Giannini Found. 13, 5–8, https://doi.org/10.1016/j.jff.2015.04.027 (2010).

8.
Schetelig, M. F. et al. Environmentally sustainable pest control options for Drosophila suzukii. J. Appl. Entomol. 142, 3–17. https://doi.org/10.1111/jen.12469 (2017).
Article  Google Scholar 

9.
Lee, J. C. et al. Biological control of spotted-wing Drosophila (Diptera: Drosophilidae)—Current and pending tactics. J. Integr. Pest Manag. 10, https://doi.org/10.1093/jipm/pmz012 (2019).

10.
Fleury, F., Gibert, P., Ris, N. & Allemand, R. Chapter 1 Ecology and life history evolution of frugivorous Drosophila parasitoids. 70, 3–44, https://doi.org/10.1016/s0065-308x(09)70001-6 (2009).

11.
Daane, K. M. et al. First exploration of parasitoids of Drosophila suzukii in South Korea as potential classical biological agents. J. Pest Sci. 89, 823–835. https://doi.org/10.1007/s10340-016-0740-0 (2016).
ADS  Article  Google Scholar 

12.
Girod, P. et al. The parasitoid complex of D. suzukii and other fruit feeding Drosophila species in Asia. Sci. Rep. 8, 11839, https://doi.org/10.1038/s41598-018-29555-8 (2018).

13.
Girod, P. et al. Host specificity of Asian parasitoids for potential classical biological control of Drosophila suzukii. J. Pest. Sci. 2004(91), 1241–1250. https://doi.org/10.1007/s10340-018-1003-z (2018).
Article  Google Scholar 

14.
Matsuura, A., Mitsui, H. & Kimura, M. T. A preliminary study on distributions and oviposition sites of Drosophila suzukii (Diptera: Drosophilidae) and its parasitoids on wild cherry tree in Tokyo, central Japan. Appl. Entomol. Zool. 53, 47–53. https://doi.org/10.1007/s13355-017-0527-7 (2018).
Article  Google Scholar 

15.
Wang, X. G., Nance, A. H., Jones, J. M. L., Hoelmer, K. A. & Daane, K. M. Aspects of the biology and reproductive strategy of two Asian larval parasitoids evaluated for classical biological control of Drosophila suzukii. Biol. Control 121, 58–65. https://doi.org/10.1016/j.biocontrol.2018.02.010 (2018).
Article  Google Scholar 

16.
Abram, P. K. et al. New records of Leptopilina, Ganaspis, and Asobara species associated with Drosophila suzukii in North America, including detections of L. japonica and G. brasiliensis. J. Hymenoptera Res. 78, 1–17, https://doi.org/10.3897/jhr.78.55026 (2020).

17.
Puppato, S., Grassi, A., Pedrazzoli, F., De Cristofaro, A. & Ioriatti, C. First report of Leptopilina japonica in Europe. Insects 11, https://doi.org/10.3390/insects11090611 (2020).

18.
Kacsoh, B. Z. & Schlenke, T. A. High hemocyte load is associated with increased resistance against parasitoids in Drosophila suzukii, a relative of D. melanogaster. PLoS One 7, e34721, https://doi.org/10.1371/journal.pone.0034721 (2012).

19.
Chabert, S., Allemand, R., Poyet, M., Eslin, P. & Gibert, P. Ability of European parasitoids (Hymenoptera) to control a new invasive Asiatic pest, Drosophila suzukii. Biol. Control 63, 40–47. https://doi.org/10.1016/j.biocontrol.2012.05.005 (2012).
Article  Google Scholar 

20.
Nagaraja, H. in Biological Control of Insect Pests Using Egg Parasitoids (eds S. Sithanantham, Chandish R. Ballal, S. K. Jalali, & N. Bakthavatsalam) Chapter 8, 175–189 (Springer, 2013).

21.
Rossi Stacconi, M. V., Grassi, A., Ioriatti, C. & Anfora, G. Augmentative releases of Trichopria drosophilae for the suppression of early season Drosophila suzukii populations. BioControl 64, 9–19, https://doi.org/10.1007/s10526-018-09914-0 (2018).

22.
Rossi-Stacconi, M. V. et al. Multiple lines of evidence for reproductive winter diapause in the invasive pest Drosophila suzukii: Useful clues for control strategies. J. Pest Sci. 89, 689–700. https://doi.org/10.1007/s10340-016-0753-8 (2016).
Article  Google Scholar 

23.
Mazzetto, F. et al. Drosophila parasitoids in northern Italy and their potential to attack the exotic pest Drosophila suzukii. J. Pest Sci. 89, 837–850. https://doi.org/10.1007/s10340-016-0746-7 (2016).
Article  Google Scholar 

24.
Wang, X. G., Kacar, G., Biondi, A. & Daane, K. M. Foraging efficiency and outcomes of interactions of two pupal parasitoids attacking the invasive spotted wing drosophila. Biol. Control 96, 64–71. https://doi.org/10.1016/j.biocontrol.2016.02.004 (2016).
Article  Google Scholar 

25.
Kacar, G., Wang, X. G., Biondi, A. & Daane, K. M. Linear functional response by two pupal Drosophila parasitoids foraging within single or multiple patch environments. PLoS ONE 12, e0183525. https://doi.org/10.1371/journal.pone.0183525 (2017).
CAS  Article  PubMed  PubMed Central  Google Scholar 

26.
Zhu, C. J., Li, J., Wang, H., Zhang, M. & Hu, H. Y. Demographic potential of the pupal parasitoid Trichopria drosophilae (Hymenoptera: Diapriidae) reared on Drosophila suzukii (Diptera: Drosophilidae). J. Asia-Pac. Entomol. 20, 747–751. https://doi.org/10.1016/j.aspen.2017.04.008 (2017).
Article  Google Scholar 

27.
Kruitwagen, A., Beukeboom, L. W. & Wertheim, B. Optimization of native biocontrol agents, with parasitoids of the invasive pest Drosophila suzukii as an example. Evol. Appl. 11, 1473–1497. https://doi.org/10.1111/eva.12648 (2018).
Article  PubMed  PubMed Central  Google Scholar 

28.
Rossi Stacconi, M. V. et al. Host location and dispersal ability of the cosmopolitan parasitoid Trichopria drosophilae released to control the invasive spotted wing Drosophila. Biol. Control 117, 188–196, https://doi.org/10.1016/j.biocontrol.2017.11.013 (2018).

29.
Wolf, S., Boycheva-Woltering, S., Romeis, J. & Collatz, J. Trichopria drosophilae parasitizes Drosophila suzukii in seven common non-crop fruits. J. Pest Sci. 93, 627–638. https://doi.org/10.1007/s10340-019-01180-y (2019).
Article  Google Scholar 

30.
Wang, X. G. et al. Thermal performance of two indigenous pupal parasitoids attacking the invasive Drosophila suzukii (Diptera: Drosophilidae). Environ. Entomol. 47, 764–772. https://doi.org/10.1093/ee/nvy053 (2018).
Article  PubMed  Google Scholar 

31.
Rossi Stacconi, M. V. et al. Comparative life history traits of indigenous Italian parasitoids of Drosophila suzukii and their effectiveness at different temperatures. Biol. Control 112, 20–27, https://doi.org/10.1016/j.biocontrol.2017.06.003 (2017).

32.
Colombari, F., Tonina, L., Battisti, A. & Mori, N. Performance of Trichopria drosophilae (Hymenoptera: Diapriidae), a generalist parasitoid of Drosophila suzukii (Diptera: Drosophilidae), at low temperature. J. Insect Sci. 20, https://doi.org/10.1093/jisesa/ieaa039 (2020).

33.
Carton, Y., Bouletreau, M., Alphen, J. J. M. V. & Lenteren, J. C. V. in The Genetics and Biology of Drosophila Vol. 3 (eds M. Ashburner, H.L. Carson, & J.N. Thompson) Chap. 39, 348–394 (Academic Press, 1986).

34.
Wang, X. G., Kacar, G., Biondi, A. & Daane, K. M. Life-history and host preference of Trichopria drosophilae, a pupal parasitoid of spotted wing drosophila. Biocontrol 61, 387–397. https://doi.org/10.1007/s10526-016-9720-9 (2016).
CAS  Article  Google Scholar 

35.
Boycheva Woltering, S., Romeis, J. & Collatz, J. Influence of the rearing host on biological parameters of Trichopria drosophilae, a potential biological control agent of Drosophila suzukii. Insects 10, https://doi.org/10.3390/insects10060183 (2019).

36.
Yi, C. et al. Life history and host preference of Trichopria drosophilae from Southern China, one of the effective pupal parasitoids on the Drosophila species. Insects 11, https://doi.org/10.3390/insects11020103 (2020).

37.
Lynch, Z. R., Schlenke, T. A. & de Roode, J. C. Evolution of behavioural and cellular defences against parasitoid wasps in the Drosophila melanogaster subgroup. J. Evol. Biol. 29, 1016–1029. https://doi.org/10.1111/jeb.12842 (2016).
CAS  Article  PubMed  Google Scholar 

38.
Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH image to ImageJ: 25 years of image analysis. Nat. Methods 9, 671–675. https://doi.org/10.1038/nmeth.2089 (2012).
CAS  Article  PubMed  PubMed Central  Google Scholar 

39.
Otto, M. & Mackauer, M. The developmental strategy of an idiobiont ectoparasitoid, Dendrocerus carpenteri : Influence of variations in host quality on offspring growth and fitness. Oecologia 117, 353–364. https://doi.org/10.1007/s004420050668 (1998).
ADS  Article  PubMed  Google Scholar 

40.
Friard, O., Gamba, M. & Fitzjohn, R. BORIS: A free, versatile open-source event-logging software for video/audio coding and live observations. Methods Ecol. Evol. 7, 1325–1330. https://doi.org/10.1111/2041-210x.12584 (2016).
Article  Google Scholar 

41.
Bates, D., Machler, M., Bolker, B. M. & Walker, S. C. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48. https://doi.org/10.18637/jss.v067.i01 (2015).
Article  Google Scholar 

42.
R: A Language and Environment for Statistical Computing (R, Vienna, 2008).

43.
Steidle, J. L. M. & van Loon, J. J. A. in Chemoecology of Insect Eggs and Egg Deposition (eds Monika Hilker & Torsten Meiners) 291–317 (Blackwell, 2003).

44.
Romani, R., Isidoro, N., Bin, F. & Vinson, S. B. Host recognition in the pupal parasitoid Trichopria drosophilae: A morpho-functional approach. Entomol. Exp. Appl. 105, 119–128. https://doi.org/10.1046/j.1570-7458.2002.01040.x (2002).
CAS  Article  Google Scholar 

45.
Ballman, E. S., Collins, J. A. & Drummond, F. A. Pupation behavior and predation on Drosophila suzukii (Diptera: Drosophilidae) pupae in maine wild blueberry fields. J. Econ. Entomol. 110, 2308–2317. https://doi.org/10.1093/jee/tox233 (2017).
Article  PubMed  Google Scholar 

46.
Carton, Y. Biologie de pimpla instigator (Ichneumonidae: Pimplinae). Entomol. Exp. Appl. 17, 265–278. https://doi.org/10.1111/j.1570-7458.1974.tb00344.x (1974).
Article  Google Scholar 

47.
Vinson, S. B. Host selection by insect parasitoids. Annu. Rev. Entomol. 21, 109–133. https://doi.org/10.1146/annurev.en.21.010176.000545 (1976).
Article  Google Scholar 

48.
Poyet, M. et al. Resistance of Drosophila suzukii to the larval parasitoids Leptopilina heterotoma and Asobara japonica is related to haemocyte load. Physiol. Entomol. 38, 45–53. https://doi.org/10.1111/phen.12002 (2013).
Article  Google Scholar 

49.
Honti, V., Csordas, G., Kurucz, E., Markus, R. & Ando, I. The cell-mediated immunity of Drosophila melanogaster: Hemocyte lineages, immune compartments, microanatomy and regulation. Dev. Comp. Immunol. 42, 47–56. https://doi.org/10.1016/j.dci.2013.06.005 (2014).
CAS  Article  PubMed  Google Scholar 

50.
Iacovone, A., Ris, N., Poirie, M. & Gatti, J. L. Time-course analysis of Drosophila suzukii interaction with endoparasitoid wasps evidences a delayed encapsulation response compared to D. melanogaster. PLoS One 13, e0201573, https://doi.org/10.1371/journal.pone.0201573 (2018).

51.
Bozler, J., Kacsoh, B. Z. & Bosco, G. Maternal priming of offspring immune system in Drosophila. G3 (Bethesda) 10, 165–175, https://doi.org/10.1534/g3.119.400852 (2020).

52.
Charnov, E. L., Los-den Hartogh, R. L., Jones, W. T. & van den Assem, J. Sex ratio evolution in a variable environment. Nature 289, 27–33, https://doi.org/10.1038/289027a0 (1981).

53.
Sandlan, K. Sex-ratio regulation in Coccygomimus-Turionella Linnaeus (Hymenoptera, Ichneumonidae) and its ecological implications. Ecol. Entomol. 4, 365–378. https://doi.org/10.1111/j.1365-2311.1979.tb00596.x (1979).
Article  Google Scholar 

54.
King, B. H. Offspring sex-ratios in parasitoid wasps. Q. Rev. Biol. 62, 367–396. https://doi.org/10.1086/415618 (1987).
Article  Google Scholar  More

The fines content of the pellets, agglutinated with wheat starch and kraft lignin (both at 4%), was 125 higher and 75% lower than in the control, respectively (Table 1). The fines generation of the pellets in all treatments was lower than 1% (0.03 to 0.27%) and, therefore, they met the marketing standard EN 14961-232.
Table 1 Fine content (%), hardness (%), bulk density (g m−3), apparent density (g m−3) by gravimetric method and apparent density (g m−3) by X-ray densitometry of Pinus wood pellets produced with different percentages of the additives (A) corn and wheat and kraft lignin and in the control.
Full size table

The lower values of the fines content of the pellets produced with kraft lignin are possibly due to the densification process of the pellet matrix with higher contents of this additive, generating pellets with better bonding characteristics between the particles and, consequently, less fines. In addition, lignin has a cementing action between the cells9 during the pressing process, and high temperature causes this compound to reach the glass transition stage, ensuring a strong bond between the particles8,33. Pellets with lower fines production during handling and transport should be preferred commercially34. The fines content increases with the moisture level of the material, causing cracks to exhaust gases, mainly water vapor, and, consequently, reducing their mechanical resistance during handling35. On the other hand, the low moisture content makes biomass compaction difficult, due to the water’s characteristic of helping the heat transfer and promoting lignin plasticization as a natural biomass binder36. The moisture content between 8 and 12% in the dry basis is ideal for reducing fines generation to within the European standard EN 14961-232.
The hardness of the pellets was similar with the different percentages of corn starch, but it was higher with wheat starch (Table 1). The hardness increased by 22% when the percentage of kraft lignin reached 5%, in relation to the control. The hardness of the pellets with 3 and 5% of corn starch and 4% of kraft lignin was similar to the control.
The similar hardness of the pellets with the different percentages of wheat starch confirms studies that binders can reduce the mechanical properties of pellets at a higher moisture content, because water takes the place of hydrogen bonds, affecting cohesion between the particles37. Higher hardness affects pellet length, because the higher the hardness, the greater the breaking strength after contact with the pelletizing press knife15. In addition, pellets with lower hardness have points for water ingress, increasing the moisture content and consequently the breaking point and causing higher fine generation38. The higher hardness of pellets produced with 5% kraft lignin is possibly due to the decrease of their hygroscopic equilibrium moisture, due to the hydrophobic character of this compound. The kraft lignin residue is a compound of C–C and C–O–C phenylpropane units with low water relationship39. In addition, the constant pressing temperature of 120 °C plasticizes kraft lignin as an adhesive, increasing particle contact and reducing expansion due to lower hygroscopicity, consequently increasing hardness40. Kraft lignin, as an additive, facilitates the use of this residue and confers better properties to pellets by increasing their mechanical strength13,14,15.
The bulk density of pellets with 1% corn or wheat starches and 3% kraft lignin was higher than other mixtures (Table 1). The bulk density of kraft lignin pellets was higher than those with corn or wheat starch. The bulk density of pellets with 1% corn starch and 5% kraft lignin was lower than those with 3% lignin, which were denser than those with only wood (control).
The higher bulk density values for 3% kraft lignin pellets may be associated with a higher amount of lignin in the mixture (wood + additive), which plasticizes more efficiently, generating a smooth and uniform texture in the pellets and improving their density. The pelletizing matrix temperature influences the durability and bulk density of pellets36, as lignin is a natural wood binder and requires temperatures above the glass transition (75–100 °C) to produce bonding between the particles. Temperatures above 90 °C improve pellet characteristics, and require lower compaction pressure at increasing compaction matrix temperatures4,41. The lower density values of wheat starch pellets may be due to the high moisture content of the steam generated during the high temperatures in the compaction process (120 °C), causing micro-cracks in the pellet structure and reducing its density35. Starch acts as a lubricating agent in the pelletizing process, facilitating the flow of raw material through the pelletizing matrix36. The bulk density of the pellets was greater than the minimum required by the European Marketing Standard EN 14961-232, equal to or greater than 0.60 g cm−3 in all treatments. This highlights the potential use of additives in pelletizing, which should be at most 2% relative to the dry mass of primary raw material.
The apparent density of pellets varied in a fashion similar to that of bulk density (Table 1), with no effect from the type and amount of additive added to the particles mass, comparing the three different additives and considering the same proportion used, except for pellets produced with 3% wheat starch, with lower apparent density. The apparent density of pellets produced with 1 and 2% corn starch and 1, 3, 4 and 5% kraft lignin was higher, and the other treatments were similar to the control (Table 1). Lignin and corn starch promoted better connection between particles, favoring biomass compaction and increasing pellet density.
The variation in the apparent density of the pellets, similar to that of bulk density between 1.15 g m−3 (3% wheat) and 1.23 g m−3 (3% lignin), is possibly due to the wheat starch gelatinization process starting at lower temperatures (± 70 °C) than that of corn starch (± 85 °C)42. This leads to the starch adhering to the pellet feeder system wall, reducing the proportion of additive that reaches the pelletizing matrix and consequently diminishing the unit density of the pellet. The higher apparent density of pellets produced with 1 and 2% corn starch and 1, 3, 4 and 5% kraft lignin is due to the lower rate of return of the pelletizing process and the higher molecular weight of the additives, influencing the pellet density7,36. Bulk density and apparent density determine pellet storage and transport conditions, and are directly related to energy density in those with 1 and 2% corn starch and 1, 3, 4 and 5% lignin, with higher density and a higher amount of energy per volume unit43.
The apparent density of the pellets produced with additives and evaluated by X-ray densitometry ranged from 1.00 to 1.31 g m−3 in their longitudinal axis (Table 1), with the lowest value for pellets produced with 1% wheat starch, and the highest value with 1% corn starch.
The lower apparent density values of wheat starch pellets can be associated with the presence of cracks (empty spaces), directly related to the susceptibility to rupture2. Low density peaks indicate small cracks that are attributed to a moisture content of the mixture or particle sizes inadequate for pelletizing4, affecting the physical properties of biomass densification44. The average apparent density of pellets is within the range established by the German standard DIN 51731, from 1.00 to 1.40 g m−345.
Pellet density varied in longitudinal density profiles, with one uniform and one irregular pattern (Fig. 1). The apparent density variation of pellets produced without additives along the longitudinal axis (coefficient of variation of 5.29%) was higher. On the other hand, the apparent density variation of the profile (coefficient of variation of 4.19%) with additives was lower, showing greater cohesion between the particles and the additives. X-ray densitometry showed pellet density variations for all additives and in the control.
Figure 1

Longitudinal variation of pellet density with different proportions of the additives kraft lignin and corn and wheat starch.

Full size image

Uniform or irregular density patterns according to longitudinal pellet density profiles are due to variations in pellet internal density, which can be attributed to factors such as additive molecular weight, particle size, and temperature and pressure during pelletization46,47,48. Cracks are common in compacted material during pelletizing4,6, and can be attributed to inadequate pellet moisture content or particle sizes. The density of biomass varies with the moisture content44 and with the temperature strengthening the adhesion between the particles. Density profiles can explain the performance of pellets, whose cracks and high density variability affect their durability and final quality, since reductions in density are associated with cracks and, consequently, pellet breakage or rupture points, which can generate fines5. The apparent density of the pellets by gravimetric and X-ray densitometry, similar between treatments with additives, confirm that this technique, commonly used to evaluate the apparent density of materials and easier to apply than other methodologies, can be used to evaluate the quality of the pellets. Variations in the apparent density and longitudinal density profile obtained with the gravimetric and X-ray densitometry demonstrate that factors such as moisture, binder type, pressure and particle size interfere with the pelletizing process, causing variations in the material’s internal structure46,47. In addition, this technique accesses different parts of the pellet and therefore identifies point variations in the product density as reported for the 2% wheat starch pellet.
In conclusion, the additives reduced the fines content and increased the hardness and density of the pellets. Therefore, they have the potential to produce pellets with greater resistance to the transport, storage and handling processes. Apparent density along the longitudinal axis of the pellets without starch was higher. The apparent density of pellets containing starch increased the cohesion between the particles and reduced the density variation as shown by their densitometric profiles. More

Myopic reallocation improves collective wealth
Beginning from some initial extraction state, agents within a networked population of multiple common-pool resources play an iterated game in which they observe current resource conditions at each round, and incrementally shift their extraction efforts from lower-quality sources toward higher-quality sources in order to maximize their payoffs in the following round (Eq. 3). Agents’ extraction efforts are thus redirected away from over-exploited sources toward less-exploited sources so that the system approaches a steady state in which all sources equally share the burden of over-extraction. In the process, some sources increase in quality, while others are further degraded; nonetheless, the overall result of these reallocations is a net increase in collective wealth.
To show this, we consider an arbitrary initial extraction state, in which the population’s collective extraction effort is (Q=Nlangle overrightarrow{q}rangle). In this state, the initial collective payoff extracted by the population is ({F}_{0}={sum }_{sin mathbf{S}}overrightarrow{q}(s)cdot b(s)) (where we ignore cost terms, since these remain constant under reallocation), and so the population’s collective wealth per unit extraction effort is

$$frac{{F}_{0}}{Q}=frac{sum_{sin mathbf{S}}overrightarrow{q}(s)cdot left[alpha -beta (s)overleftarrow{q}(s)right]}{sum_{sin mathbf{S}}overrightarrow{q}(s)}=alpha -frac{langle beta {overrightarrow{q}}^{2}rangle }{langle overrightarrow{q}rangle }.$$
(4)

Under reallocation dynamics (Eq. 3), this total extraction (Q) is conserved, and the system will approach a steady state in which all sources share a common quality value

$${b}_{f}=alpha -frac{langle overrightarrow{q}rangle }{langle {beta }^{-1}rangle }.$$
(5)

The population’s collective wealth approaches the steady-state value

$${F}_{f}={sum }_{sin mathbf{S}}left[overrightarrow{q}(s)cdot {b}_{f}right]=Q{b}_{f}.$$
(6)

Collective wealth is increased (or at least conserved) if ({F}_{0}le {F}_{f}), or equivalently, if (frac{{F}_{0}}{Q}le {b}_{f}). Using Eqs. 4 and 5, this condition reduces to

$$langle overrightarrow{q}{rangle }^{2}le langle beta {overleftarrow{q}}^{2}rangle langle {beta }^{-1}rangle .$$
(7)

The validity of this inequality is guaranteed by the Cauchy–Schwarz inequality29, (langle XY{rangle }^{2}le langle {X}^{2}rangle langle {Y}^{2}rangle) for random variables (X) and (Y), with the identifications (X=sqrt{beta (s)}overrightarrow{q}(s)) and (Y=sqrt{beta (s{)}^{-1}}). Furthermore, equality occurs if and only if the quantity (beta left(sright)overrightarrow{q}left(sright)) shares the same value for all sources, that is, when initial conditions are already steady states where all sources share a common quality value. Reallocation dynamics thus increase collective wealth for any initial condition where sources vary from one another in quality (see Section S2.1 of the Supplementary Information). This includes Nash equilibrium initial conditions, upon which we will now focus our attention.
CPR degree heterogeneity leads to greater improvements in efficiency under myopic reallocation
In the unique Nash equilibrium state of a given network26, each agent sets its extraction at each source to the point beyond which further extraction would increase its costs more than it would increase its payoffs, given that all other agents are doing the same. In this state, no agent can increase its payoffs by unilaterally adjusting its extraction levels while other agents hold their extraction levels constant. However, when all agents simultaneously adapt their extraction levels according to the reallocation update rule (Eq. 3), under which each increase in extraction at one source is matched by an equal decrease at another source, then higher payoffs can be achieved. To quantify the extent to which reallocation alone can help alleviate the “tragedy of the commons” represented by Nash equilibrium, we now apply reallocation dynamics to Nash equilibrium initial conditions on a variety of network types, and compare the population’s collective wealth values before and after reallocation.
When network-structured populations of rational individuals extract benefits from multiple linearly-degrading CPRs, the burdens of over-exploitation tend to fall upon sources in a degree-dependent manner. Myopic reallocation tends to shift these burdens among sources of different degrees, and to distribute the resulting increases in collective wealth among individuals of different degree classes. In order to understand how these reallocations shift extraction pressure and agent payoffs among nodes of different degrees, we use a heterogeneous mean-field approach to derive estimates for these shifts. Under this perspective, the conditions defining Nash equilibrium ((frac{partial f(a)}{partial q(a,s)}=0)) lead us to estimate the expected values for extraction pressure on degree-(n) sources, (langle overrightarrow{q}{rangle }_{n}), by solving a linear system defined by

$$langle overrightarrow{q}{rangle }_{n}=frac{1}{{beta }_{n}}left[frac{n}{n+1}right]left[alpha -sum_{m=1}^{{m}_{mathrm{max}}}{P}_{mathbf{A}}left(mright)frac{m}{langle mrangle }cdot left(frac{gamma m}{[gamma mlangle {beta }^{-1}{rangle }_{m}+1]}left[alpha langle {beta }^{-1}{rangle }_{m}-sum_{{n}^{^{prime}}=1}^{{n}_{mathrm{max}}}{P}_{mathbf{S}}({n}^{^{prime}})frac{{n}^{^{prime}}}{langle nrangle }cdot langle overrightarrow{q}{rangle }_{{n}^{^{prime}}}right]right)right],$$
(8)

with one such condition for each unique source degree (nin {1,dots , {n}_{mathrm{max}}}) represented in the network, where brackets subscripted by agent degree (m) indicate expected values (langle x{rangle }_{m}={sum }_{n=1}^{{n}_{mathrm{max}}}{P}_{mathbf{S}}left(nright)frac{n}{langle nrangle }cdot {x}_{n}) and we have assumed no degree-degree correlations (see the Supplementary Information Section S3 for details). Solving this system numerically (here we use Python 3.7.3 with SciPy 1.2.130) for each of the 9 network types under consideration by inserting the corresponding ensemble degree distributions ({P}_{mathbf{A}}left(mright)) and ({P}_{mathbf{S}}left(nright)) (Fig. 1), we use the resulting values of (langle overrightarrow{q}{rangle }_{n}) to compute the expected total extraction by a degree-m agent (langle overleftarrow{q}{rangle }_{m}) at equilbrium as

$$langle overleftarrow{q}{rangle }_{m}=left(frac{m}{mgamma langle {beta }^{-1}{rangle }_{m}+1}right)left[alpha langle {beta }^{-1}{rangle }_{m}-left(sum_{n=1}^{{n}_{mathrm{max}}}{P}_{mathbf{S}}left(nright)frac{n}{langle nrangle }cdot langle overrightarrow{q}{rangle }_{n}right)right],$$
(9)

from which (langle q{rangle }_{m,n}), the expected equilibrium extraction by a degree-(m) agent from a degree-(n) source, can be computed using the Nash equilbrium condition:

$$langle q{rangle }_{m,n}=frac{alpha }{{beta }_{n}}-langle overrightarrow{q}{rangle }_{n}-frac{upgamma }{{beta }_{n}}langle overleftarrow{q}{rangle }_{m}.$$
(10)

These values are then used to compute the corresponding estimated collective wealth (i.e. the sum of all agent payoffs, (F=sum_{ain mathbf{A}}f(a))) and wealth equality (as quantified by Gini index (G)) attained at Nash equilibrium, as well as the subsequent shifts that are brought by myopic reallocation dynamics toward steady states. These values are shown in Fig. 2 for a range of values of the cost parameter (gamma), which quantifies the influence of diminishing marginal utility. The expected changes in extraction pressure for sources of different degrees, as well as the changes in agent fitness expected for agents of each degree class, are illustrated for each network type for cost-free extraction ((gamma =0)) in Fig. 3, and similarly for a representative case of costly extraction ((gamma =0.2)) in Fig. 4. The estimates presented here correspond to a uniform capacity scenario where all CPRs degrade in proportion to the total amount of extraction exerted upon their users. However, we find that qualitatively similar results also hold for a degree-proportional capacity scenario in which sources degrade in proportion to the total extraction per user that they receive (see Section S4 in the Supplementary Information).
Figure 2

Estimates of (a) Ratio of total collective wealth of equilibrium (“Eq”) states relative to efficient (“Ef”) states, ({F}_{mathrm{Eq}}/{F}_{mathrm{Ef}}); (b) increase in efficiency from equilibrium to steady states (“SS”), (({F}_{mathrm{SS}}-{F}_{mathrm{Eq}})/{F}_{mathrm{Ef}}); (c) Gini index of equilibrium states ({G}_{mathrm{Eq}}); and (d) decrease in Gini index from equilibrium to steady states, (({G}_{mathrm{Eq}}-{G}_{mathrm{SS}})), all as functions of cost parameter (gamma). Results shown correspond to a uniform capacity scenario with (alpha =beta =1).

Full size image

Figure 3

Estimated shifts in extraction patterns due to reallocation dynamics from Nash equilibrium (“Eq”) to steady states (“SS”) under cost-free extraction: (a) Change in total extraction pressure (Delta langle overrightarrow{q}{rangle }_{n}=langle overrightarrow{q}{rangle }_{n,mathrm{SS}}-langle overrightarrow{q}{rangle }_{n,mathrm{Eq}}), as a function of source degree (n); and (b) change in expected agent fitness, (Delta langle f{rangle }_{m}=langle f{rangle }_{m,mathrm{SS}}-langle f{rangle }_{m,mathrm{Eq}}) as a function of agent degree (m). Results shown correspond to a uniform capacity scenario with (alpha =beta =1) and (gamma =0). Note that results for all network types sharing a common source degree distribution type (“D”, “N”, or “PL”) are overlapping.

Full size image

Figure 4

Estimated shifts in extraction patterns due to reallocation dynamics from Nash equilibrium (“Eq”) to steady states (“SS”) under costly extraction: (a) Change in total extraction pressure (Delta langle overrightarrow{q}{rangle }_{n}=langle overrightarrow{q}{rangle }_{n,mathrm{SS}}-langle overrightarrow{q}{rangle }_{n,mathrm{Eq}}), as a function of source degree (n); and (b) change in expected agent fitness, (Delta langle f{rangle }_{m}=langle f{rangle }_{m,mathrm{SS}}-langle f{rangle }_{m,mathrm{Eq}}) as a function of agent degree (m). Results shown correspond to a uniform capacity scenario with (alpha =beta =1) and (gamma =0.2).

Full size image

In Nash equilibrium states of the uniform capacity scenario, sources with fewer users (i.e. lower degree) experience lower extraction pressure. Since all networks under comparison here share an equal number of edges, networks having greater heterogeneity among source degrees—and thus a greater abundance of low-degree sources—suffer less over-exploitation overall, and so tend to operate more efficiently at equilibrium (Fig. 2). As agents then shift their extraction away from over-burdened, lower-quality sources toward higher-quality sources, these systems approach steady states where their multiple CPR sources all share a uniform quality value. In this way, steady states of reallocation dynamics qualitatively resemble Pareto efficient extraction states, which are characterized by uniform quality among all CPR sources (though, unlike these steady states, optimal efficiency also requires uniform extraction levels among all agents regardless of degree; see Section S3.2 in the Supplementary Information). The resulting shifts in efficiency (Fig. 2b), source extraction pressure (Figs. 3a and 4a), and agent payoffs (Figs. 3b and 4b) are more pronounced for networks having greater heterogeneity among CPR source degrees due to the greater initial discrepancies among source quality values that these networks support at Nash equilibrium. When simulations of reallocation dynamics from equilbrium are performed on individual networks (see Section S6 in the Supplementary Information), then the shifts in extraction pressure and agent payoffs observed are often more exaggerated than those estimated here. Since the heterogeneous mean-field perspective treats all sources of a common degree as a single class, it does not distinguish higher-order differences among nodes that share the same degree. As a result, the model predicts no shifts under reallocation dynamics for networks in which all sources share a common degree, i.e. delta-function (“D”) source degree distributions, for example. However, on actual networks of this type, reallocation dynamics nonetheless do increase collective wealth by equalizing differences in quality among sources.
When extraction is costly ((gamma >0)), agent degree heterogeneity also plays a secondary role to source degree heterogeneity in determining equilibrium efficiency and the effects of reallocation dynamics (Figs. 2 and 4). Diminishing marginal utility motivates agents to moderate their overall extraction levels; all sources affiliated with any given agent will be affected by its tendency to reduce extraction, and the extent of this reduction will depend in turn on each source’s degree, the degrees of its other users, and so on. Higher agent degree heterogeneity is thus predicted to slightly increase equilibrium efficiency due to the presence of higher-degree agents that reduce their extraction per source by larger amounts than do lower-degree agents. While the overall gains in collective wealth expected to be achieved by way of reallocations are thus slightly reduced by the presence of these higher-degree agents, greater agent degree heterogeneity is also associated with faster times of convergence toward steady states, since high-degree agents are able to simultaneously shift efforts directly between a large number of sources, and so to more rapidly equalize source quality values (see Section S5.1 in the Supplementary Information).
Myopic reallocation from Nash equilibrium reduces wealth inequality
Since reallocation dynamics increase collective wealth, many—if not all—agents will attain improved payoffs under reallocation dynamics from suboptimal states like Nash equilibrium. We now turn our attention to how these increases in collective wealth are distributed throughout a population with respect to agent degree. Under the heterogeneous mean-field approach, we estimate that the shift in expected payoffs due to reallocations from Nash equilibrium are given by

$$Delta langle f{rangle }_{m}=mleft[left(frac{1}{langle nrangle }left[langle frac{n{b}_{n}}{{beta }_{n}}rangle {b}_{f}-langle frac{n{b}_{n}^{2}}{{beta }_{n}}rangle right]right)-upgamma langle overleftarrow{q}{rangle }_{m}left(frac{1}{langle nrangle }left[langle frac{n}{{beta }_{n}}rangle -langle frac{n{b}_{n}^{2}}{{beta }_{n}}rangle right]right)right],$$
(11)

where ({b}_{n}=alpha -{beta }_{n}langle overrightarrow{q}{rangle }_{n}) (see Section S3.1.3 in the Supplementary Information). When extraction is cost-free ((gamma =0)), the increased payoffs brought about by reallocation dynamics are expected to affect each edge in a uniform way, on average, and thus tend to be shared among agents of all degree classes in proportion to their degree (m). This is reflected in the linear increase of expected agent payoff with respect to degree (Fig. 3b), and also in the lack of change in the expected Gini index predicted for all network types under cost-free ((gamma =0)) extraction (Fig. 2d). However, when extraction is costly ((gamma >0)) and diminishing marginal utility acts to disincentivize increased extraction for higher-degree agents, the overall efficiency (Fig. 2a) and equality (Fig. 2c) of equilibrium states are increased from those observed under cost-free extraction. In these cases, reallocation dynamics also tend to increase the equality of the population’s wealth distribution, as reflected in the decreasing—and eventually negative—shifts in payoffs expected for agents of increasingly high degree (Fig. 4b), and also in the expected reductions in Gini index (Fig. 2d), caused by reallocation dynamics. This occurs because diminishing marginal utility motivates high-degree agents to exert less overall extraction effort per source at Nash equilibrium than do lower-degree agents. In the steady states subsequently reached under reallocation dynamics, all sources share a uniform quality value; each agent’s total extracted benefits then becomes strictly proportional to the overall magnitude of its extraction effort. Higher-degree agents end up receiving a smaller payoff per source than do their lower-degree counterparts in steady states. As Eq. (11) suggests, agents with higher initial extraction levels (langle overleftarrow{q}{rangle }_{m}) will experience a lower (and possibly even negative) shift in payoff per source (Delta langle f{rangle }_{m}/m) as a result of reallocations. This levelling-out of degree-based payoff inequities has its most pronounced effects at intermediate levels of the cost parameter (here, for values of (gamma approx .35), as shown in Fig. 2d). In simulations performed on specific networks, we find that reallocation dynamics lead not only to increased collective wealth, but also to increased equality, even on networks with homogeneous, “delta-function” (“D”) source degree distributions, although the heterogeneous mean-field approach predicts no such shift. Networks of other types similarly tend to undergo greater increases in equality than those predicted here due to higher-order types of heterogeneity not captured by the model (see Section S6 in the Supplementary Information). More

Molecular analyses
Results of molecular analyses
The sequences obtained are available in GenBank (Supplementary Information 1). Sequence alignments were 399 bp for COI and 587 bp for 28S including gaps.
Phylogenetic analysis
Our molecular analysis (Fig. 1) with both markers generated seven supported clusters, six of which were in agreement with the morphological determination (i.e. C. alazanicus, C. brunnicans, C. circumscriptus, C. furcillatus, C. nubeculous and C. pictipennis). However, one cluster (i.e. two species) corresponded to undistinguished C. clastrieri and C. festivipennis.
Figure 1

Block diagram of the study.

Full size image

In addition, the COI mtDNA tree shows that C. furcillatus is the sister of the “C. clastrieri/festivipennis” clade. Indeed, C. pictipennis is the sister species of C. brunnicans while C. circumscriptus is positioned between the two clades.
Moreover, the 28S rDNA tree shows that C. pictipennis is the sister of the “C. clastrieri/festivipennis” clade. The other species are positioned in several places without a clade.
Intra- and inter-specific comparison
The COI Genbank sequences show little intraspecific divergence in both C. clastrieri (0.1 ± 0.1%) and C. festivipennis (1.2 ± 0.4%). The interspecific difference between C. clastrieri and in C. festivipennis is 0.7 ± 0.2%.
Small intraspecific divergences with COI sequences were observed in our sample: C. alazanicus (1.2 ± 0.4%), C. brunnicans (0.7 ± 0.2%), C. circumscriptus (2.2 ± 0.5%), C. clastrieri (0.3 ± 0.1%), C. festivipennis (0.4 ± 0.1%), C. furcillatus (1.5 ± 0.4%), C. nubeculosus (0.2 ± 0.1%) and C. pictipennis (1.1 ± 0.3%).
Finally, C. festivipennis and C. clastrieri—grouped in the same main clade—showed small interspecific distances (0.4 ± 0.2%); these were not identified as separate species based on DNA barcodes. We therefore decided to create a new group (C. clastrieri/festivipennis clade) based on interspecific distance. The overall mean genetic distance (K2P) computed for the different species of Culicoides was found to be 16.6 ± 1.4%. Interspecific K2P values for different (Table 1) species and taxa ranged from 27.3% (between C. furcillatus and C. nubeculosus; between C. circumscriptus-and C. furcillatus) to 17.2 ± 2.1% (between C. circumscriptus and the C. clastrieri/festivipennis clade) for our samples. For the COI Genbank sequences, we observed approximatively the same proportion and the same species (Table 1). We remarked very little interspecific divergence between our sample of the C. clastrieri/festivipennis clade and the C. clastrieri/festivipennis Genbank clade (0.6 ± 0.4%).
Table 1 Estimation of pairwise distance (± SD) of the Culicoides species for the COI domain of the mtDNA and D1D2 region of the rDNA.
Full size table

Analysis from 28S rDNA sequences did not show any intraspecific divergence whatever the taxa (0.000) with the exception of C. nubeculosus (0.1 ± 0.1%) and C. festivipennis/C.clastrieri (0.1 ± 0%). The overall mean genetic distance (K2P) computed for the different species of Culicoides was found to be 2.1 ± 0.03%. Interspecific K2P values for different species (Table 1) and taxa ranged from 1.2% (between C. circumscriptus and C. furcillatus; C. furcillatus and C. brunnicans, the main C. clastrieri/festivipennis clade and C. furcillatus) to 5.3 ± 0.9% (between C. circumscriptus and C. nubeculosus).
Morphometric and morphological analyses
In all, 148 specimens identified as C. alazanicus (n = 10), C. brunnicans (n = 27), C. circumscriptus (n = 27), C. clastrieri (n = 21), C. festivipennis (n = 20), C. furcillatus (n = 14), C. nubeculosus (n = 19) and C. pictipennis (n = 20) were analysed with 11 wing landmarks/specimens (Fig. 2).
Figure 2

Trees obtained from nucleotide analysis of: (a) COI mtDNA; (b) 28S rDNA (with MP method) sequences of C. alazanicus, C. brunnicans, C. circumscriptus C. clastrieri, C. festivipennis, C. furcillatus, C. nubeculosus and C. pictipennis and bootstrap values are shown in nodes (1000 replicates).

Full size image

Principal component analyses
Principal component analysis (PCA) was used to observe possible grouping trends.
Firstly, we performed a first normed PCA using the “Wing landmarks” model. The first three axes accounted for 76%, 15% and 8% of the total variance, which suggests a weak structuration of the data. This was confirmed by a scatterplot of PCA axes 1 and 2 that was unable to separate the species (Fig. 3).
Figure 3

Principal component analysis (PCA): percentage of variance explained for each PCA dimension and results.

Full size image

Secondly, we performed a first normed PCA on the “Wing morphological characters” model. The various specimens of each species are represented by a single point suggesting a close correlation of wing morphological characters. This model, without variance, is not validated and does not permit species separation.
We studied the “Full wing (landmarks and morphological, characters)” model through a normed PCA on raw data. C. clastrieri could be clearly separated from C. festivipennis. The first five axes accounted for 40%, 25%, 12%, 10% and 5% of the total variance. The scatterplot separated unambiguously and without overlap C. clastrieri-C. festivipennis on the one hand and the six species on the other hand (Fig. 3).
Finally, we performed a first normed PCA on the “Full model” (Morphological characters—wing, head, abdomen, legs—and wing landmarks). The first nine axes accounted for 26%, 23%, 22%, 10%, 8%., 4%, 3%, 2% and 1% of the total variance, which reveals good structuration of the data. This was confirmed by a scatterplot of PCA axes 1 and 2 that presents the same topology as the wing morphological model (Fig. 3).
This supports discrimination according to the species’ wing pattern. Similarly, and some body pattern characters could be used to identify Culicoides from the clastrieri/festivipennis clade better and quicker. With that objective in mind, we performed analyses on three datasets: (1) “Wing landmarks” (11 landmarks); (2) “Full wing” (38 items) and (3) the “Full model” that includes 71 items.
Discriminant analyses
PLS-DA and sPLS-DA models were used in order to discriminate the extremes (i.e. the most sensitive and most robust groups) using the three datasets (species, models and components) as described. The accuracy and the balanced error rate (BER) for the two models were compared and are summarised in Supplementary Information 2 and Fig. 4.
Figure 4

Balanced error rate (BER) choosing the number of dimensions. Performance and ncomp selection.

Full size image

The tuning step of the number of components to select showed that 16 components were necessary to lower the BER (Fig. 4A,B) for the “Wing landmarks” data. The AUC values with 16 components are as follows: C. alazanicus (0.97, p  More

1.
Rockström, J. et al. Sustainable intensification of agriculture for human prosperity and global sustainability. Ambio 46, 4–17 (2017).
PubMed  Article  Google Scholar 
2.
García-Arenal, F. & McDonald, B. A. An analysis of the durability of resistance to plant viruses. Phytopathology 93, 941–952 (2003).
PubMed  Article  Google Scholar 

3.
Anderson, P. K. et al. Emerging infectious diseases of plants: pathogen pollution, climate change and agrotechnology drivers. Trends Ecol. Evol. 19, 535–544 (2004).
PubMed  Article  Google Scholar 

4.
Landis, D. A., Wratten, S. D. & Gurr, G. M. Habitat management to conserve natural enemies of arthropod pests in agriculture. Annu. Rev. Entomol. 45, 175–201 (2000).
CAS  PubMed  Article  Google Scholar 

5.
Stukenbrock, E. H. & McDonald, B. A. The origins of plant pathogens in agro-ecosystems. Annu. Rev. Phytopathol. 46, 75–100 (2008).
CAS  PubMed  Article  Google Scholar 

6.
Biek, R. & Real, L. A. The landscape genetics of infectious disease emergence and spread. Mol. Ecol. 19, 3515–3531 (2010).
PubMed  PubMed Central  Article  Google Scholar 

7.
Meentemeyer, R. K., Haas, S. E. & Václavík, T. Landscape epidemiology of emerging infectious diseases in natural and human-altered ecosystems. Annu. Rev. Phytopathol. 50, 379–402 (2012).
CAS  PubMed  Article  Google Scholar 

8.
Boccardo, G. & Milne, R.G. Plant Reovirus Group. Description of Plant Viruses. No. 294. CM/AAB (1984).

9.
Dovas, C. I., Eythymiou, K. & Katis, N. I. First report of maize rough dwarf virus (MRDV) on maize crops in Greece. Plant Pathol. 53, 238–238 (2004).
Article  Google Scholar 

10.
Lenardon, S. L., March, G. J., Nome, S. F. & Ornaghi, J. A. Recent outbreak of “Mal de Rio Cuarto” virus on corn in Argentina. Plant Dis. 82, 448 (1998).
CAS  PubMed  Article  Google Scholar 

11.
Zhang, H., Chen, J., Lei, J. & Adams, M. J. Sequence analysis shows that a dwarfing disease on rice, wheat and maize in China is caused by rice black-streaked dwarf virus. Eur. J. Plant Pathol. 107, 563–567 (2001).
CAS  Article  Google Scholar 

12.
Hoang, A. T. et al. Identification, characterization, and distribution of southern rice black-streaked dwarf virus in Vietnam. Plant Dis. 95, 1063–1069 (2011).
PubMed  Article  PubMed Central  Google Scholar 

13.
Achon, M. A., Serrano, L., Clemente-Orta, G. & Barcelo, A. The virome of maize rough dwarf disease: molecular genome diversification, phylogeny and selection. Ann Appl Biol. 176, 192–202 (2020).
CAS  Article  Google Scholar 

14.
Lovisolo, O. Maize Rough Dwarf Virus. Descriptions of Plant Viruses No. 72. Commonw. Mycol. Inst. Asso. Appl. Biol. (1971).

15.
Achon, M. A. & Sobrepere, M. Incidence of potyviruses in commercial maize fields and their seasonal cycles in Spain. JPDP 108, 399–406 (2001).
CAS  Google Scholar 

16.
Achon, M. A. & Alonso-Dueñas, N. Impact of 9 years of Bt-maize cultivation on the distribution of maize viruses. Transgenic Res. 18, 387–397 (2009).
CAS  PubMed  Article  PubMed Central  Google Scholar 

17.
Achon, M. A., Subira, J. & Sin, E. Seasonal occurrence of Laodelphax striatellus in Spain: effect on the incidence of Maize rough dwarf virus. Crop Prot. 47, 1–5 (2013).
Article  Google Scholar 

18.
Achon, M. A., Serrano, L., Sabate, J. & Porta, C. Understanding the epidemiological factors that intensify the incidence of maize rough dwarf disease in Spain. Ann. Appl. Biol. 166, 311–320 (2015).
CAS  Article  Google Scholar 

19.
CABI, 2017. Laodelphax striatellus. Crop protection compendium, Wallingford, UK: CAB International. https://www.cabi.org/isc/datasheet/10935 (2017).

20.
Milne, R. G. & Lovisolo, O. Maize rough dwarf and related viruses. Adv. Virus. Res. 21, 267–341 (1977).
CAS  PubMed  Article  PubMed Central  Google Scholar 

21.
Häni, A., Günthart, H. & Brunetti, R. Identifikation des Rauhverzwergungsvirus an Mais im Tessin. Landwirtschaft Schweiz 2, 131–136 (1989).
Google Scholar 

22.
Hibino, H. Biology and epidemiology of rice viruses. Annu. Rev. Phytopathol. 34, 249–274 (1996).
CAS  PubMed  Article  PubMed Central  Google Scholar 

23.
Bar-Tsur, A., Saadi, H. & Antignu, Y. Resistance of corn genotypes to maize rough darf virus. Maydica 33, 189–200 (1988).
Google Scholar 

24.
Rodriguez-Pardina, P. E., Gimenez-Pecci, M. P. & Laguna, I. G. Wheat: a new natural host for the Mal de rio cuarto virus in the endemic disease area, Rio Cuarto, Cordoba province, Argentina. Plant Dis. 82, 149–152 (1998).
Article  Google Scholar 

25.
Wang, H. D. et al. Recent rice stripe virus epidemics in Zhejiang province, China, and experiments on sowing date, disease–yield loss relationships, and seedling susceptibility. Plant Dis. 92, 1190–1196 (2008).
PubMed  Article  PubMed Central  Google Scholar 

26.
Wang, H. D. et al. Studies on the epidemiology and yield losses from rice black-streaked dwarf disease in a recent epidemic in Zhejiang province, China. Plant Pathol. 58, 815–825 (2009).
Article  Google Scholar 

27.
Cirilo, A. G. & Andrade, F. Sowing date and maize productivity: I. Crop growth and dry matter partitioning. Crop Sci. 34, 1039–1043 (1994).
Article  Google Scholar 

28.
Farnham, D. E. Row spacing, plant density, and hybrid effects on corn grain yield and moisture. Agron. J. 93, 1049–1053 (2001).
Article  Google Scholar 

29.
Kucharik, C. J. A multidecadal trend of earlier corn planting in the central USA. Agron. J. 98, 1544–1550 (2006).
Article  Google Scholar 

30.
Bruns, H. A. & Abbas, H. K. Planting date effects on Bt and non-Bt corn in the mid-south USA. Agron. J. 98, 100–106 (2006).
Article  Google Scholar 

31.
Achon, M. A. & Clemente, G. Nuevos retos en el control de las enfermedades virales del maíz. Vida rural 424, 44–50 (2017).
Google Scholar 

32.
Maresma, A., Ballesta, A., Santiveri, F. & Lloveras, J. Sowing date affects maize development and yield in irrigated Mediterranean Environments. Agriculture 9, 67 (2019).
Article  Google Scholar 

33.
Chaplin-Kramer, R. et al. A meta-analysis of crop pest and natural enemy response to landscape complexity. Ecol. Lett. 14, 922–932 (2011).
PubMed  Article  PubMed Central  Google Scholar 

34.
Harpaz, I. Maize Rough Dwarf (Israel Universities Press, Jerusalem, 1972).
Google Scholar 

35.
Conti, M. Investigations on the epidemiology of maize rough dwarf virus. I. Overwintering of virus in its planthopper vector, Acta HI Congr. Un. Fitopat. Medit., Oeiras 22–28 Outubro 1972, 11. (1972).  

36.
Thresh, J. M. The origins and epidemiology of some important plant virus diseases. Appl. Biol. 5, 1–65 (1980).
Google Scholar 

37.
Grilli, M. P. The role of landscape structure on the abundance of a disease vector planthopper: a quantitative approach. Landsc. Ecol. 25, 383–394 (2010).
Article  Google Scholar 

38.
Conti, M. Investigations on the epidemiology of maize rough dwarf virus III. Field symptoms, incidence and control. Maydica 21, 165–175 (1976).
Google Scholar 

39.
Syobu, S. I., Otuka, A. & Matsumura, M. Trap catches of the small brown planthopper, Laodelphax striatellus (Fallén) (Hemiptera: Delphacidae), in northern Kyushu district, Japan in relation to weather conditions. Appl. Entomol. Zool. 46, 41–50 (2011).
Article  Google Scholar 

40.
Clemente-Orta, G., Albajes, R. & Achon, M. A. Early planting, management of edges and non-crop habitats reduce potyvirus infection in maize. Agron. Sustain. Dev. 40, 21 (2020).
Article  Google Scholar 

41.
Clemente-Orta, G. et al. Changes in landscape composition influence the abundance of insects on maize: the role of fruit orchards and alfalfa crops. Agric. Ecosyst. Environ. 291, 106805 (2020).
CAS  Article  Google Scholar 

42.
Grilli, M. P. & Bruno, M. Regional abundance of a planthopper pest: the effect of host match area and configuration. Entomol. Exp. Appl. 122, 133–143 (2007).
Article  Google Scholar 

43.
Grilli, M. P. & Gorla, D. E. The effect of agroecosystem management on the abundance of Delphacodes kuscheli (Homopteran: Delphacidae), vector of the maize rough dwarf virus, in central Argentina. Maydica 43, 77–82 (1998).
Google Scholar 

44.
MacArthur, R. H. & Wilson, E. O. Island Biogeography (Princeton University Press, Princeton, 1967).
Google Scholar 

45.
Root, R. B. Organization of a plant-arthropod association in simple and diverse habitats: the fauna of collards (Brassica oleraceae). Ecol. Monogr. 43, 95–124 (1973).
Article  Google Scholar 

46.
Tscharntke, T. et al. Landscape moderation of biodiversity patterns and processes-eight hypotheses. Biol. Rev. 87, 661–685 (2012).
PubMed  Article  PubMed Central  Google Scholar 

47.
Trumper, E.V. Modelos de epidemiologia matemática aplicados al estudio de1 sistema Virus MRC-maiz-Delphacidae (“Ma1 de Rio Cuarto”). Tesis doctoral. Universidad National de Cordoba (1996).

48.
Cheng, J. A. Rice Planthoppers in the Past Half Century in China. Rice Planthoppers: Ecology, Management Social Economics and Policy 1–32 (Springer, Dordrecht, 2015).
Google Scholar 

49.
Liu, Z. et al. (2016) The effect of landscape composition on the abundance of Laodelphax striatellus Fallén in fragmented agricultural landscapes. Land 5, 36 (2016).
Article  Google Scholar 

50.
Clemente-Orta, G. & Álvarez, H. A. L. influencia del paisaje agrícola en el control biológico desde una perspectiva espacial. Revista Ecosistemas 28, 13–25 (2019).
Article  Google Scholar 

51.
Madeira, F. et al. Stable carbon and nitrogen isotope signatures to determine predator dispersal between alfalfa and maize. Biol. Control. 77, 66–75 (2014).
Article  Google Scholar 

52.
Cantero-Martínez, C. & Moncunill, J. Sistemas agrícolas de la Plana de Lleida: Descripción y evaluación de los sistemas de producción en el área del canal Segarra-Garrigues antes de su puesta en funcionamiento. (2012).

53.
Braun-Blanquet, J. Fitosociología. Bases para el estudio de las comunidades vegetales (Blume, Madrid, 1979).
Google Scholar 

54.
DePaulo, J. J. & Powell, C. A. Extraction of double-stranded RNA from plant tissues without the use of organic solvents. Plant Dis. 79, 246–248 (1995).
CAS  Article  Google Scholar 

55.
Albajes, R., Lumbierres, B., Pons, X. & Comas, J. Representative taxa in field trials for environmental risk assessment of genetically modified maize. Bull. Entomol. Res. 103, 724–733 (2013).
CAS  PubMed  Article  Google Scholar 

56.
Ardanuy, A., Lee, M. S. & Albajes, R. Landscape context influences leafhopper and predatory Orius spp. abundances in maize fields. Agric. Forest. Entomol. 20, 81–92 (2018).
Article  Google Scholar 

57.
Holzinger, W. E., Kammerlander, I. & Nickel, H. The Auchenorrhyncha of Central Europe. In Fulgoromorpha, Cicadomorpha Excl-Cicadellidae Vol. 1 (ed. Brill) (Brill, Leiden-Boston, 2003).
Google Scholar 

58.
ESRI. ArcGIS Desktop Version 10.3.1 (Environmental Systems Research Institute, Redlands, 2015).
Google Scholar 

59.
Bartoń, K. (2018). Package “MuMIn” Title Multi-Model Inference. In: CRAN-R. https://cran.r-project.org/web/packages/MuMIn/MuMIn.pdf

60.
Burnham, K. P. & Anderson, D. R. Multimodel inference: understanding AIC and BIC in model selection. Sociol. Methods Res. 33, 261–304 (2004).
MathSciNet  Article  Google Scholar 

61.
Paradis, E. Package “ape” Title Analyses of Phylogenetics and Evolution Depends R. https://cran.r-project.org/web/packages/ape/ape.pdf (2019).

62.
Max, K. et al. Caret: Title Classification and Regression Training. R package version: 6.0-84. https://cran.r-project.org/web/packages/caret/caret.pdf (2018).

63.
Bates, D. et al. Lme4: Linear Mixed-Effects Models using ‘Eigen’ and S4. R package version 1.1-21. https://cran.r-project.org/web/packages/lme4/lme4.pdf (2019).

64.
Zuur, A. F., Ieno, E. N. & Elphick, C. S. A protocol for data exploration to avoid common statistical problems. Methods Ecol. Evol. 1, 3–14 (2010).
Article  Google Scholar 

65.
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ R version 3.6.2. (2019). More

1.
Adeyemi, M. M. H. The potential of secondary metabolites in plant material as deterents against insect pests: a review. Afr. J. Pure Appl. Chem. 4, 243–246 (2010).
CAS  Google Scholar 
2.
Walling, L. L. The myriad plant response to herbivores. J. Plant Growth Regul. 19, 195–216 (2000).
CAS  PubMed  Article  PubMed Central  Google Scholar 

3.
Croteau, R., Kutchan, T. M. & Lewis, N. G. Natural products (Secondary metabolites). In Biochemistry & Molecular Biology of Plants (eds Buchanan, B. B. et al.) 1250–1318 (American Society of Plants Biologists, Rockville, 2000).
Google Scholar 

4.
Dewick, P. M. Medicinal Natural Products: A Biosynthetic Approach 2nd edn. (Wiley, Chichester, England, 2002).
Google Scholar 

5.
Pham, A. & Hwang, S. Chemical-based resistance of Brassica oleracea and Rorippa dubia in response to Spodoptera litura attack. J. Appl. Entomol. 144, 201–2011 (2019).
Article  CAS  Google Scholar 

6.
Niemetz, R. & Gross, G. G. Enzymology of gallotannin and ellagitannin biosynthesis. Phytochemistry 66, 2001–2011 (2005).
CAS  PubMed  Article  PubMed Central  Google Scholar 

7.
Barbosa, P. et al. Plant allelochemicals and insect parasitoids effects of nicotine on Cotesia congregata (Say) (Hymenoptera:Braconidae) and Hyposoter annulipes (Cresson) (Hymenoptera: Ichneumonidae). J. Chem. Ecol. 12, 1319–1328 (1986).
CAS  PubMed  Article  PubMed Central  Google Scholar 

8.
Reitz, S. R. & Trumble, J. T. Effects of linear furanocoumarins on the herbivore Spodoptera exigua and the parasitoid Archytas marmoratus: host quality and parasitoid success. Entomol. Exp. Appl. 84, 9–16 (1997).
CAS  Article  Google Scholar 

9.
Vinson, S. B. & Barbosa, P. Interrelationships of nutritional ecology of parasitoids. In Nutritional Ecology of Insects, Mites, Spiders, and Related Invertebrates (eds Slansky, F., Jr. & Rodriguez, J. G.) 673–695 (Wiley, New York, 1987).
Google Scholar 

10.
Vinson, S. B. & Iwantsch, G. F. Host Suitability for Insect Parasitoids. Annu. Rev. Entomol. 25, 397–419 (1980).
Article  Google Scholar 

11.
Duffey, S. S., Bloem, K. A. & Campbell, B. C. Consequences of sequestration of plant natural products in plant insect-parasitoid interactions. In Interactions of Plant Resistance and Parasitoids and Predators of Insects (eds Boethel, D. J. & Eikenbary, R. D.) 31–60 (Wiley, New York, 1986).
Google Scholar 

12.
Rowell-Rahier, M., Pasteels, J. M. Phenolglucosides and interactions at three trophic levels: Salicaceae herbivores-predators. In Insect Plant Interactions Volume 2. pp. 75–94. Boca Raton, Florida: CRC. (1990).

13.
Kester, K. M. & Barbosa, P. Behavioral and ecological constraints imposed by plants on insect parasitoids: implications for biological control. Biol. Control 1, 94–106 (1991).
Article  Google Scholar 

14.
Dhir, B. C., Mohapatra, H. K. & Senapati, B. Assessment of crop loss in groundnut due to tobacco caterpillar, Spodoptera litura (F.). Indian J. Plant Prot. 20, 215–217 (1992).
Google Scholar 

15.
Armes, N. J., Wightman, J. A., Jadhav, D. R. & Ranga-Rao, G. V. Status of insecticide resistance in Spodoptera litura in Andhra Pradesh, India. Pesticide Sci. 50, 240–248 (1997).
CAS  Article  Google Scholar 

16.
Kranthi, K. R., Jadhav, D. R., Wanjari, R. R., Ali, S. S. & Russell, D. Carbamate and organophosphate resistance in cotton pests in India, 1995 to 1999. Bull. Entomol. Res. 91, 37–46 (2001).
CAS  PubMed  PubMed Central  Google Scholar 

17.
Brower, J. H., Smith, L., Vail, P. V. & Flinn, P. W. Biological control. In Integrated Management of Insects in Stored Products (eds Subramanyam, B. & Hagstrum, D. W.) 223–286 (Marcel Dekker Inc, New York, 1996).
Google Scholar 

18.
Reinert, J. A. & King, E. W. Action of Bracon hehetor Say as a parasite of Plodia interpunctella at controlled densities. Ann. Entomol. Soc. Am. 64, 1335–1340 (1971).
Article  Google Scholar 

19.
Press, J. W., Flaherty, B. R. & McDonald, I. C. Survival and reproduction of Bracon hebetor on insecticide-treated Ephestia cautella larvae. J. Georgia Entomol. Soc. 16, 231–234 (1981).
CAS  Google Scholar 

20.
Gerling, D. & Rotary, N. Hypersensitivity, resulting from host-unsuitability, as exemplified by two parasite species attacking Spodoptera littoralis (Lepidoptera: Noctuidae). Entomophaga 18, 391–396 (1973).
Article  Google Scholar 

21.
Selin-Rani, S. et al. Toxicity and physiological effect of quercetin on generalist herbivore, Spodoptera litura Fab. and a non-target earthworm Eisenia fetida Savigny. Chemosphere 165, 257–267 (2016).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

22.
Ghumare, S. S. & Mukherjee, S. N. Performance of Spodoptera litura (Fabricius) on different host plants: influence of nitrogen and total phenolics of plants and mid-gut esterase activity of the insect. Indian J. Exp. Biol. 41, 895–899 (2003).
CAS  PubMed  PubMed Central  Google Scholar 

23
Ananthakrishnan, T. N., Gurusubramanian, G. & Gopichandran, R. Influence of chemical profiles of host plant on the infestation diversity of Retithrips syriacus (Mayet). J. Biosci. 7, 483–489 (1991).
Google Scholar 

24.
Bhattacharya, A. K. & Chenchaiah, K. C. Seed coat phenolic compounds of Cajanus cajan as chemical barrier in formulation of artificial diet of Spodoptera litura (F.). Ann. Plant Prot. Sci. 15, 92–96 (2007).
Google Scholar 

25.
Gautam, S., Samiksha, R., Arora, S. & Sohal, S. K. Chemical profiling of polyphenols in extracts from bark of Acacia nilotica (Linn.) and their efficacy against Spodoptera litura (Fab.). Arch. Phytopathol. Plant Prot. 51, 41–53 (2018).
CAS  Article  Google Scholar 

26.
Bernays, E. A., Driver, G. C. & Bilgener, M. Herbivores and plant tannins. Adv. Ecol. Res. 19, 263–302 (1989).
Article  Google Scholar 

27.
Sharma, R. & Sohal, S. K. Oviposition response of melon fruit fly, Bactrocera cucurbitae (Coquillett) to different phenolic compounds. J. Biopest. 9, 46–51 (2016).
CAS  Google Scholar 

28.
Nathan, S. S. & Kalaivani, K. Combined effects of azadirachtin and nucleopolyhedrovirus (SpltNPV) on Spodoptera litura Fabricius (Lepidoptera: Noctuidae) larvae. Biol. Control 39, 96–104 (2006).
CAS  Article  Google Scholar 

29.
Deota, P. T. & Upadhyay, P. R. Biological studies of azadirachtin and its derivatives against polyphagous pest, Spodoptera litura. Nat. Prod. Res. 19, 529–539 (2005).
CAS  PubMed  Article  PubMed Central  Google Scholar 

30.
Shu, B. et al. Azadirachtin affects the growth of Spodoptera litura Fabricius by inducing apoptosis in larval midgut. Frontiers Physiol. 9, 137 (2018).
Article  Google Scholar 

31.
De Moraes, C. M., Lewis, W. J., Pare, P. W., Alborn, H. T. & Tumlinson, J. H. Herbivore-infested plants selectively attract parasitoids. Nature 393, 570–573 (1998).
ADS  Article  Google Scholar 

32.
Camphell, B. C. & Duffey, S. S. Tomatine and parasitic wasps: potential incompatibility of plant antibiosis with biological control. Science 205, 700–702 (1979).
ADS  Article  Google Scholar 

33.
Campbell, B. C. & Duffey, S. S. Alleviation of α-tomatine-induced toxicity to the parasitoid, Hyposoter exiguae, by phytosterols in the diet of the host, Heliothis zea. J. Chem. Ecol. 7, 927–946 (1981).
CAS  PubMed  Article  PubMed Central  Google Scholar 

34.
Bloem, K. A. & Duffey, S. S. Interactive effect of protein and rutin on larval Heliothis zea and the endoparasitoid Hyposoter exiguae. Entomol. Exp. Appl. 54, 149–161 (1990).
CAS  Article  Google Scholar 

35.
El-Heneidy, A. H., Barbosa, P. & Gross, P. Influence of dietary nicotine on fall armyworm, Spodoptera frugiperda and its parasitoid, the ichneumonid wasp Hyposoter annulipes. Entomol. Exp. Appl. 46, 227–232 (1988).
CAS  Article  Google Scholar 

36.
Reitz, S. R. & Trumble, J. T. Tritrophic interactions among linear furanocoumarins, the Herbivore Trichoplusia ni (Lepidoptera: Noctuidae), and the polyembryonic parasitoid Copidosoma floridanum (Hymenoptera: Encyrtidae). Environ. Entomol. 25, 1391–1397 (1996).
Article  Google Scholar 

37.
Mondy, N. et al. Importance of sterols acquired through host feeding in synovigenic parasitoid oogenesis. J. Insect Physiol. 52, 897–904 (2006).
CAS  PubMed  Article  PubMed Central  Google Scholar 

38.
Punia, A., Chauhan, N. S., Kaur, S. & Sohal, S. K. Effect of Ellagic acid on the larvae of Spodoptera litura (Lepidoptera: Noctuidae) and its parasitoid Bracon hebetor (Hymenoptera: Braconidae). J. Asia-Pac. Entomol. 23, 660–665 (2020).
Article  Google Scholar 

39.
Barbosa, P. & Saunders, J. A. Plant allelochemicals: Linkages between herbivores and their natural enemies. Rec. Adv. Phytochem. 19, 107–137 (1985).
CAS  Google Scholar 

40.
Ode, P., Berenbaum, J. R., Zangerl, M. R. & Hardy, I. C. W. Host plant, host plant chemistry and the polyembryonic parasitoid Copidosoma sosares: indirect effects in a tritrophic interaction. Oikos 104, 388–400 (2004).
CAS  Article  Google Scholar 

41.
Narendra, G., Khokhar, S. & Ram, P. Effect of insecticides on some biological parameters of Trichogramma chilonis Ishii (Hymenoptera: Trichogrammtidae). J. Biol. Control 21, 130–134 (2013).
Google Scholar 

42.
Abedi, Z., Saber, M., Gharekhani, G., Mehrvar, A. & Kamita, S. G. Lethal and sublethal effects of azadirachtin and cypermethrin on Habrobracon hebetor (Hymenoptera: Braconidae). J. Econ. Entomol. 107, 638–645 (2014).
CAS  PubMed  Article  PubMed Central  Google Scholar 

43.
Radcliffe, E. B. Population responses of green peach aphid in Minnesota on potatoes treated with various insecticides. Proc. N Cent. Branch Entomol. Soc. Am. 27, 103–105 (1972).
Google Scholar 

44.
Flanders, S. E. Environmental resistance to the establishment of parasitic hymenoptera. Ann. Entomol. Soc. Am. 33, 245–253 (1940).
Article  Google Scholar 

45.
Kaplan, I., Carrillo, J., Garvey, M. & Ode, P. J. Indirect plant-parasitoid interactions mediated by changes in herbivore physiology. Curr. Opin. Insect Sci. 14, 112–119 (2016).
PubMed  Article  PubMed Central  Google Scholar 

46.
Ayyangar, G. S. G. & Rao, P. J. Changes in haemolymph constituents of Spodoptera litura (Fabr.) under the influence of azadirachtin. Indian J. Entomol. 52, 69–83 (1990).
Google Scholar 

47.
Zibaee, A. & Bandani, A. R. Effects of Artemisia annua L. (Asteracea) on the digestive enzymatic profiles and the cellular immune reactions of the Sunn pest, Eurygaster integriceps (Heteroptera: Scutellaridae), against Beauveria bassiana. Bull. Entomol. Res. 100, 185–196 (2009).
PubMed  Article  CAS  PubMed Central  Google Scholar 

48.
Kalyani, S. S. & Holihosur, R. S. N. Toxic effect of crude aqueous leaf extracts of Clerodendron inerme, on the total haemocyte count of sixth instar larva of Helicoverpa armigera (H). Int. J. Innov. Res. Sci. Technol. 1, 221–224 (2015).
Google Scholar 

49.
Saxena, B. P. & Tikku, K. Effect of plumbagin on haemocytes of Dysdercus koenigii F. Proc. Anim. Sci. 99, 119–124 (1990).
Article  Google Scholar 

50.
Sakihama, Y. Plant phenolic antioxidant and prooxidant activities: phenolics-induced oxidative damage mediated by metals in plants. Toxicology 177, 67–80 (2002).
CAS  PubMed  Article  PubMed Central  Google Scholar 

51.
Krishnan, N. & Sehnal, F. Compartmentalization of oxidative stress and antioxidant defense in the larval gut of Spodoptera littoralis. Arch. Insect Biochem. Physiol. 63, 1–10 (2006).
CAS  PubMed  Article  PubMed Central  Google Scholar 

52.
Lindroth, R. L. Biochemical detoxication: mechanism of differential tiger swallowtail tolerance to phenolic glycosides. Oecologia 81, 219–224 (1989).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

53.
Despres, L., David, J. P. & Gallet, C. The evolutionary ecology of insect resistance to plant chemicals. Trends Ecol. Evol. 22, 298–307 (2007).
PubMed  Article  PubMed Central  Google Scholar 

54.
Terriere, L. C. Induction of detoxication enzymes in insects. Annu. Rev. Entomol. 29, 71–88 (1984).
CAS  PubMed  Article  PubMed Central  Google Scholar 

55.
Li, X. C., Schuler, M. A. & Berenbaum, M. R. Molecular mechanisms of metabolic resistance to synthetic and natural xenobiotics. Annu. Rev. Entomol. 52, 231–253 (2007).
PubMed  Article  CAS  PubMed Central  Google Scholar 

56.
Koul, O. G., Singh, R. & Singh, J. Bioefficacy and mode-of-action of Aglaroxin B and Aglaroxin C from Aglaia elaeagmoidea (syn. A. Irox burghiana) against Helicoverpa armigera and Spodoptera litura. Biopesticides Int. 1, 54–64 (2005).
Google Scholar 

57.
Waldbauer, G. P. The Consumption and Utilization of Food by Insects. Adv. Insect Physiol. 5, 229–288 (1968).
Article  Google Scholar 

58.
Tauber, O. E. & Yeager, J. F. On total hemolymph (blood) cell counts of insects I. Orthoptera, odonata, hemiptera, and homoptera. Ann. Entomol. Soc. Am. 28, 229–240 (1935).
Article  Google Scholar 

59.
Arnold, J. W. & Hinks, C. F. Insect haemocytes under light microscopy: techniques. In Insect Haemocyte Development, Forms, Functions and Techniques (ed. Gupta, A. P.) 531–538 (Cambridge University Press, Cambridge, 1979).
Google Scholar 

60.
Livak, K. J. & Schmittgen, T. D. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods 25, 402–408 (2001).
CAS  Article  Google Scholar  More

Algicide preparation
Four batches of algicide were used for experiments, labeled Batch 3, Batch 4–5–6, Batch 7, and Batch 8, following methods used by Grasso27. For each batch, a single colony of Shewanella sp. IRI-160 was transferred from a modified LM medium plate to liquid LM medium for overnight growth, then inoculated into f/2 with 0.05% casamino acids and incubated for 10 days at room temperature with bubbling. Bacteria and other compounds greater than 60 kDa in size were filtered out using a HemoFlow HF80S 60 kDa dialysis cartridge (Fresenius Medical Care, Waltham, MA), creating a batch of sterile filtered exudate referred to as IRI-160AA. Samples of the algicide were diluted with ultrapure water, then total nitrogen (TN) was measured with a TOC-V total organic carbon analyzer equipped with a Total Nitrogen Measuring Unit (Shimadzu Corp., Kyoto, Japan). The algicide has approximately 5.02 mg/L TN. The 24-h EC50 for K. veneficum differed among batches but was always close to 1% (actual EC50s ranged from 0.93% in Batch 4–5–6 to 1.5% in Batch 3), thus a 1% concentration of the algicide was included in all invertebrate assays27. Animals were also exposed to a media control to ensure mortality was due to the algicide.
Statistical analyses
For all statistics, data were analyzed using Shapiro–Wilk normality tests and Brown-Forsythe equal variance tests. If they failed either, data were transformed and reanalyzed. If transformed data passed both tests, then analysis proceeded. If neither log or square-root transformed data passed both normality and equal variance tests, then a non-parametric test was run if possible. Specific details on statistical analyses are provided in each section below.
Copepod mortality
Mortality experiments followed established methods for determining acute toxicity in aquatic animals30,31,33,49. For A. tonsa adults, we collected animals in Fall of 2018 after sunset near the mouth of the Broadkill River (Delaware, USA) using a plankton net. Cod ends were diluted and maintained in field collected seawater with ambient food at room temperature (~ 20 °C) until use in experiments. Adults were filtered out of the bulk collection with a 500-μm mesh, then sorted for adult females. We transferred one adult female (n = 24 for 40%, 48 for 30%, and 72 for all other concentrations) into each well of a 12-well plate containing 5 mL of test solution; test solutions included a seawater control (0%); algicide mixtures prepared from Batch 3 of the IRI-160AA in 20 psu, 0.2 μm-filtered sea water collected from Indian River Inlet, DE, USA (FSW) (1%, 5%, 10%, 13.5%, 18%, 24%, 30%, and 40% v/v); and a 24% media solution as a media control. The plates were incubated at 25 °C in low-light (~ 2.37 × 1013 photons cm-2 s−1) on a 14:10 h day:night cycle for 48 h. Every 6 h for the first 24 h, and again at 48 h, we counted the number alive and dead.
For A. tonsa nauplii, adult females and males were placed in two 1 L beakers at room temperature with a 150-μm mesh placed several centimeters off the bottom (to prevent egg cannibalism), a slow bubbler (~ 2 small bubbles s−1), and ambient seawater diluted with 20 psu FSW until the water was mostly clear. Adults were allowed to mate in the beaker for approximately 24 h, after which we removed the mesh, thus removing the adults and leaving behind any nauplii and eggs. After another 24 h, the contents of the beakers were poured through a 20-μm mesh, and we extracted the nauplii and placed them into experimental treatments (0% seawater control, algicide at 1%, 5%, 10%, 13.5%, 18%, 24%, and 30% v/v concentrations, plus a 24% media control; n = 48 animals for all concentrations) following the procedure outlined above for the adult female copepods. This experiment was conducted three times; the first two mortality experiments used Batch 3 of the IRI-160AA, and the third mortality experiment used Batch 8.
From the data collected, we generated a Probit model50 and obtained a 24-h LC50. Another approach looks at mortality over several time points in order to generate a time series of survival (e.g., Robineau et al.51, Keller et al.52). This also allows the generation of an LC50 at several time points (e.g., 6, 12, 18, and 24 h), which can better inform how a certain animal may survive over time. We used SigmaPlot to generate graphs of survival over time, and R statistical software53 and the R package ecotoxicology54 for generating and graphing the Probit model and running a χ2 test to evaluate the model.
Crab mortality
We conducted mortality experiments for the blue crab (Callinectes sapidus) in larval (Z1-stage zoeae) and postlarval (megalopae) stages in a similar manner to mortality experiments with Acartia tonsa. We collected ovigerous female blue crabs during the Summer of 2018 by dip net and drop net at sunset from the Delaware Bay (similar to methods used by Kernehan55) in Cape Henlopen State Park and maintained them in a recirculating water tray containing filtered ambient seawater (~ 30 psu) at room temperature. We staged egg masses every few days55, and females predicted to hatch within ~ 3 days were moved to 7-gallon buckets in a 25 °C incubator containing ~ 30 psu sea water and a bubbler. Zoea larvae (Z1-stage) hatched from these females were kept in large finger bowls with 30 psu sea water at room temperature and were fed lab-reared rotifers (Brachionus rotundiformis, Reed Mariculture). These animals became subjects for mortality and sub-lethal experiments within approximately a day of hatching. Four experiments were conducted; three mortality experiments used Batch 4–5–6 of the IRI-160AA, while the fourth experiment (24 individuals for each concentration) used Batch 7.
Megalopae were collected by plankton net set on rising tides at night during the Summer and Fall of 2018. They were maintained in large finger bowls at room temperature and fed with Artemia nauplii and went into experiments within a few days of collection. Only megalopae in intermolt based on morphology56 were used in experiments. Megalopae experiments used Batch 3 of the IRI-160AA.
Both zoeae and megalopae were exposed to 1%, 5%, 10%, 13.5%, 18%, and 24% algicide concentrations, plus a 0% seawater control and a 24% media control (n = 84 animals for the 0% concentration and 60 for all other concentrations for zoeae, and n = 24 animals for megalopae for all concentrations). Animals were incubated at 25 °C under low-light (~ 2.37 × 1013 photons cm-2 s-1) on a 14:10 light:dark cycle for the duration of experiments. We checked on zoeae and megalopae every 6 h for 24 h; megalopae were checked at an additional 48-h time point.
Oyster mortality
Oyster larvae (eyed pediveligers of Crassostrea virginica) were provided by University of Maryland’s Horn Point Laboratory. Animals were maintained on a damp coffee filter in a sealed plastic container on ice during transport, then released into room-temperature fingerbowls containing 20 psu water and fed a locally-isolated alga (Storeatula major) at room temperature. Experiments occurred in similar fashion to those conducted on Acartia tonsa and Callinectes sapidus. Larvae were assayed in 12-well plates (n = 36 animals for all concentrations). Animals were exposed to 1%, 5%, 10%, 13.5%, 18%, and 24% algicide concentrations, plus a 0% seawater control, and 24% media control. Animals were incubated at 25 °C under a 14:10 light:dark cycle for the duration of experiments. Survival was evaluated every 6 h for 24 h and again at 48 h. Larvae were additionally examined at the start of the experiment and at the 24- and 48-h time points for an activity assay. These experiments used Batch 3 of the IRI-160AA.
Wild-type adult C. virginica were collected from the Delaware Bay near the University of Delaware Lewes Campus, while Haskins-disease-resistant strain individuals were collected from aquaculture cages maintained by the Delaware Center for the Inland Bays. On the first day, individuals were cleaned with a wire brush, and divided into two buckets containing approximately 10 L of 20 psu seawater and were fed Isochrysis galbana (~ 100,000 cells L−1). On the second day the water was changed and they were again fed. On the third day, water was changed and animals were not fed. On the fourth day, individuals were removed from the buckets, dried with a paper towel, labeled with permanent marker, and placed in pairs into forty-one 1 L plastic containers containing 1 L of various algicide solutions: 0%, 1%, 5%, 10%, 13.5%, 18%, and 24% (n = 28 for 0%, 22 for 1% and 18%, and 20 for all other concentrations). Individuals were checked every 6 h for 24 h and assessed if they were alive or dead. Closed individuals were assumed to be alive. If open individuals were observed, we gently tapped on the container to see if the individual shut its shell; animals that responded to this stimulus were marked as alive. Only animals that did not respond to repeated stimuli were scored as dead. Proportion surviving was compared across algicide concentration and strain. These experiments all used Batch 8 of the IRI-160AA.
Copepod sub-lethality
Respiration
We conducted respiration experiments on A. tonsa adult females and young nauplii in a 24-well microplate respirometer (Loligo Systems). First, we sorted animals into fingerbowls containing 100 mL of their respective algicide concentrations. After 24 h of algicide exposure, we removed animals via pipette and put one animal into each well of the respirometer plate (200 μL wells for adult females and 80 μL for nauplii) filled with 0.2 μm filtered FSW, then sealed the plate with Parafilm and a weight. Each experiment also had 4 to 6 wells with only FSW to calculate background oxygen consumption. The experiment occurred in darkness within a 25 °C incubator at night and lasted several hours (n = 26–39 animals for adult females, 11–18 for nauplii). Oxygen concentrations in each well were recorded every minute. At the end of the experiment, respiration rates were calculated in R statistical software using the respR package57 over a period of time when the animals were still in independent respiration, and a one-way ANOVA on ranks in SigmaPlot (Systat Software, San Jose, CA) compared treatments. Experiments with adult females used Batch 3 of IRI-160AA, while nauplii experiments used Batch 8.
Activity
Experiments determining effects on swimming activity utilized Locomotor Activity Monitors (LAMs; TriKinetics). Three beams of infrared light cross a 3 mL test tube containing an animal and register when the animal crosses the beams. We sorted batches of adult female A. tonsa into fingerbowls containing different algicide treatments. Animals were incubated at 25 °C in low-light conditions (~ 2.37 × 1013 photons cm−2 s−1) for 24 h on a 11:13-h light:dark cycle. Animals were pipetted into plastic test tubes (one animal per tube) containing ~ 3 mL of FSW, which then went into the LAMs (n = 21–36 animals). The experiment lasted 24 h with beam breaks summed at one-minute intervals, allowing the data to be analyzed wholly for the 24-h period as well as across different light phases to account for light:dark mediated activity rhythms. Experiments started in the afternoon and ran overnight, creating an initial light phase (L1), a dark phase (D), and a second light phase (L2). Comparing treatments across the entire time period was done using a one-way ANOVA on ranks, while analyzing the data based on the different light phases was performed via a one-way repeated-measures ANOVA. Additionally, at the end of the LAM activity experiments we collected the individuals and noted mortality. This data was analyzed via a one-way ANOVA on ranks. Copepod activity experiments used Batch 3 of the IRI-160AA. Nauplii were too small to generate a reliable signal in the LAMs and were not used in these experiments.
Crab sub-lethality
Respiration
Respiration experiments followed methods described for A. tonsa above and involved zoeae and megalopae. A one-way ANOVA on ranks was calculated using the data for each life stage. The first four zoeae experiments used Batch 4–5-6 of IRI-160AA, while the last two experiments used Batch 7. Megalopae experiments all used Batch 3.
Activity
Activity level experiments followed methods described for A. tonsa above and involved zoeae and megalopae. The 24-h data were analyzed using a one-way ANOVA on square root transformed data for zoeae, and a one-way ANOVA on ranks for megalopae. The data broken down by light phase were analyzed via one-way repeated measures ANOVA on log-transformed data for both zoeae and megalopae. These experiments all used Batch 3 of IRI-160AA.
At the end of experiments we collected the individuals and noted mortality. This data was analyzed via a one-way ANOVA for zoeae and a one-way ANOVA on ranks for the megalopae.
Metamorphosis
We sorted megalopae into finger bowls containing 100 mL of filtered estuary water with different concentrations of the IRI-160AA algicide (0%, 1%, and 17% v/v). After 24-h of exposure, we sorted animals into 12-well plates containing FSW (n = 60 individuals for each treatment). Water was changed daily, and animals were fed freshly hatched Artemia daily. Every 12 h, we counted how many megalopae had molted into first crabs until most had metamorphosed (5.5 days) and used a Kaplan–Meier Survival Analysis with a Gehan-Breslow test to determine if there was a difference in time to metamorphosis (TTM) across treatments. These experiments used Batch 3 of the IRI-160AA.
Abdomen Pumping and Grooming
Crabs with egg masses were collected from the Delaware Bay near Lewes, DE and separated into numbered baskets and maintained in a flow-through sea water table. They were fed thawed squid (Loligo opalescens) every day, and eggs were photographed every two to three days under a dissecting scope until they reached ~ 6 days until hatching (i.e., late-stage sensu Tankersley et al.)36. Homogenized egg water (seawater plus homogenized eggs, designated SW + HE, ~ 20 eggs mL−1) was utilized to induce pumping and grooming behavior and made according to Tankersley et al.36.
Ovigerous females were exposed to several sub-lethal concentrations of algicide combined with the homogenized egg solution and monitored for pumping and grooming behavior. Test solutions were diluted to 1.5 L with filtered 30 psu seawater, and 3.75 mL aliquot of a pre-prepared homogenized egg solution was added to achieve a final concentration of ~ 20 eggs/mL. These experiments used Batch 4–5–6, Batch 7, and Batch 3 of the IRI-160AA.
Between three and six crabs were tested at a time, and all crabs were staged the day of the experiment to verify that their eggs were no more than six days from hatching. All experiments were performed under dim red light to reduce disturbance. Each crab was tested in every treatment. A crab was placed into a translucent container (20.1 × 16.5 × 11.4 cm) with a given treatment condition and acclimated for 2.5 min. Then, for the following 2.5 min, the number of times the crab pumped its abdomen was recorded. Immediately following the end of the first crab’s measurement period, another crab was placed into the same treatment to begin its acclimation period. Each crab was returned to a flowing water table between treatments and remained there for at least twenty minutes before beginning the acclimation period of its next treatment. The treatment series began and ended with 30 psu seawater (SW), and proceeded through an increasing gradient of 0, 7, 11, and 17% IRI-160AA in SW + HE.
Each measurement period of the pumping experiments was filmed. The videos were reviewed later, and the time the crabs spent grooming their egg masses was recorded.
A χ2 test was performed for the 24 crabs tested to assess if the proportion of crabs performing the behaviors differed among treatments. A one-way repeated-measures ANOVA (Friedman Repeated Measures Analysis of Variance on Ranks) was used to assess trends in the number of pumps and the time spent grooming. Only crabs that performed the behavior were included in each analysis.
Oyster sub-lethality
Respiration
Respiration on oyster pediveligers following methods described for A. tonsa nauplii above. Two individuals were placed in each 80 µl well, with rates calculated per individual. Data were analyzed via a one-way ANOVA on Ranks. These experiments all used Batch 3 of IRI-160AA.
Activity
Activity experiments on pediveliger larvae were conducted in LAMs and followed similar methods to Acartia tonsa and Callinectes sapidus. The 24-h data was tested via a one-way ANOVA on ranks, while the data broken down by light phase was analyzed via a one-way repeated measures ANOVA. These experiments used Batch 3 of IRI-160AA.
An additional analysis of pediveliger activity occurred during the mortality experiment by ranking how active each animal appeared to be on a scale of 1 (High Activity, HA, animal was actively swimming), 2 (Medium Activity, MA, animal had its velum extended and cilia active, sometimes scooting across the bottom), 3 (Low Activity, LA, animal was enclosed in its shell but viscera moved when the shell was touched), and 4 (Dead/No Activity, D, animal was completely unresponsive even to repeated stimulation). Ranking occurred at the start of the experiment (where all animals scored as HA), at the 24-h mark, and at the 48-h mark. This assessment was analyzed via a χ2 test for both the 24-h and 48-h data sets. At the end of the LAM experiments, animals were analyzed in the same manner.
Activity experiments on the wild-type adult C. virginica occurred during the mortality experiments. At each 6-h time point, animals in the containers (0%, 1%, 5%, 10%, 13.5%, 18%, and 24% v/v IRI-160AA treatments) were scored as either Open (O) or Closed (C), and analyzed via a two-way repeated measures ANOVA on the proportion of animals that opened at each time point in each concentration.
Feeding
Feeding experiments occurred only on adult C. virginica. Animals and containers from the mortality experiments were rinsed to remove algicide residue, then filled with 1 L of 20 psu seawater and Isochrysis galbana at ~ 100,000 cells L−1, and one animal from each container was returned to it. Five milliliters from each container were removed immediately and in vivo chlorophyll a florescence was measured using a fluorometer (Turner Systems). Air stones were added to the containers to keep the algae in suspension, and lids were added to prevent liquid from bubbling out. After 6 h, another fluorescence reading was taken. Animals were given another 6 h to feed, and a final fluorescence reading was taken at the 12-h time point. Clearance rates (CR) were calculated according to Thessen et al.58 from time zero to six hours (initial rate, 0–6), and from six to twelve hours (end rate, 6–12), and compared across time ranges and treatments and strains using a three-way ANOVA. More

1.
Cesar, H. J. S., Burke, L. & Pet-Soede, L. The Economics of Worldwide Coral Reef Degradation. 23 (Cesar Environmental Economics Consulting: The Netherlands). https://www.icran.org/pdf/cesardegradationreport.pdf (2003).
2.
Tambutté, E. et al. Observations of the tissue-skeleton interface in the scleractinian coral Stylophora pistillata. Coral Reefs 26, 517–529 (2007).
ADS  Article  Google Scholar 

3.
Mollica, N. R. et al. Ocean acidification affects coral growth by reducing skeletal density. Proc. Natl. Acad. Sci. 115, 1754–1759 (2018).
ADS  CAS  Article  Google Scholar 

4.
Mass, T. et al. Cloning and characterization of four novel coral acid-rich proteins that precipitate carbonates in vitro. Curr. Biol. 23, 1126–1131. https://doi.org/10.1016/j.cub.2013.05.007 (2013).
CAS  Article  PubMed  Google Scholar 

5.
Guo, W. Seawater temperature and buffering capacity modulate coral calcifying pH. Sci. Rep. 9, 1–13 (2019).
Article  Google Scholar 

6.
McCulloch, M. et al. Resilience of cold-water scleractinian corals to ocean acidification: boron isotopic systematics of pH and saturation state up-regulation. Geochim. Cosmochim. Acta 87, 21–34 (2012).
ADS  CAS  Article  Google Scholar 

7.
Guo, W. et al. Ocean acidification has impacted coral growth on the great barrier reef. Geophys. Res. Lett. https://doi.org/10.1029/2019gl086761 (2020).
Article  PubMed  PubMed Central  Google Scholar 

8.
Sevilgen, D. S. et al. Full in vivo characterization of carbonate chemistry at the site of calcification in corals. Sci. Adv. https://doi.org/10.1126/sciadv.aau7447 (2019).
Article  PubMed  PubMed Central  Google Scholar 

9.
Venn, A., Tambutté, E., Holcomb, M., Allemand, D. & Tambutté, S. Live tissue imaging shows reef corals elevate pH under their calcifying tissue relative to seawater. PLoS ONE 6, e20013 (2011).
ADS  CAS  Article  Google Scholar 

10.
Cai, W.-J. et al. Microelectrode characterization of coral daytime interior pH and carbonate chemistry. Nat. Commun. 7, 1–8 (2016).
Google Scholar 

11.
Holcomb, M. et al. Coral calcifying fluid pH dictates response to ocean acidification. Sci. Rep. 4, 5207. https://doi.org/10.1038/srep05207 (2014).
CAS  Article  PubMed  PubMed Central  Google Scholar 

12.
DeCarlo, T. M., Holcomb, M. & McCulloch, M. T. Reviews and syntheses: revisiting the boron systematics of aragonite and their application to coral calcification. Biogeosciences 15, 2819–2834. https://doi.org/10.5194/bg-15-2819-2018 (2018).
ADS  CAS  Article  Google Scholar 

13.
McCulloch, M. T., D’Olivo, J. P., Falter, J., Holcomb, M. & Trotter, J. A. Coral calcification in a changing world and the interactive dynamics of pH and DIC upregulation. Nat. Commun. 8, 15686. https://doi.org/10.1038/ncomms15686 (2017).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

14.
Horvath, K. M. et al. Next-century ocean acidification and warming both reduce calcification rate, but only acidification alters skeletal morphology of reef-building coral Siderastrea siderea. Sci. Rep. 6, 29613. https://doi.org/10.1038/srep29613 (2016).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

15.
Tambutté, E. et al. Morphological plasticity of the coral skeleton under CO2-driven seawater acidification. Nat. Commun. 6, 7368. https://doi.org/10.1038/ncomms8368 (2015).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

16.
Stewart, J. A., Anagnostou, E. & Foster, G. L. An improved boron isotope pH proxy calibration for the deep-sea coral Desmophyllum dianthus through sub-sampling of fibrous aragonite. Chem. Geol. 447, 148–160. https://doi.org/10.1016/j.chemgeo.2016.10.029 (2016).
ADS  CAS  Article  Google Scholar 

17.
Allison, N., Finch, A. A. & EIMF. δ11B, Sr, Mg and B in a modern Porites coral: the relationship between calcification site pH and skeletal chemistry. Geochim. Cosmochim. Acta 74, 1790–1800 (2010).
ADS  CAS  Article  Google Scholar 

18.
Rollion-Bard, C. & Blamart, D. SIMS method and examples of applications in coral biomineralization. In Biomineralization Sourcebook: Characterization of Biominerals and Biomimetic Materials (eds DiMasi, E. & Gower, L. B.) 249–261 (CRC Press, Boca Raton, 2014).
Google Scholar 

19.
Trotter, J. et al. Quantifying the pH ‘vital effect’ in the temperate zooxanthellate coral Cladocora caespitosa: validation of the boron seawater pH proxy. Earth Planet. Sci. Lett. 303, 163–173. https://doi.org/10.1016/j.epsl.2011.01.030 (2011).
ADS  CAS  Article  Google Scholar 

20.
Krief, S. et al. Physiological and isotopic responses of scleractinian corals to ocean acidification. Geochim. Cosmochim. Acta 74, 4988–5001 (2010).
ADS  CAS  Article  Google Scholar 

21.
Hönisch, B. et al. Assessing scleractinian corals as recorders for paleo-pH: empirical calibration and vital effects. Geochim. Cosmochim. Acta 68, 3675–3685. https://doi.org/10.1016/j.gca.2004.03.002 (2004).
ADS  CAS  Article  Google Scholar 

22.
Tanaka, K. et al. Response of Acropora digitifera to ocean acidification: constraints from δ11B, Sr, Mg, and Ba compositions of aragonitic skeletons cultured under variable seawater pH. Coral Reefs 34, 1139–1149 (2015).
ADS  Article  Google Scholar 

23.
Reynaud, S., Hemming, N. G., Juillet-Leclerc, A. & Gattuso, J.-P. Effect of pCO2 and temperature on the boron isotopic composition of the zooxanthellate coral Acropora sp. Coral Reefs 23, 539–546 (2004).
Google Scholar 

24.
Anagnostou, E., Huang, K.-F., You, C.-F., Sikes, E. & Sherrell, R. Evaluation of boron isotope ratio as a pH proxy in the deep sea coral Desmophyllum dianthus: evidence of physiological pH adjustment. Earth Planet. Sci. Lett. 349, 251–260 (2012).
ADS  Article  Google Scholar 

25.
Jurikova, H. et al. Boron isotope composition of the cold-water coral Lophelia pertusa along the Norwegian margin: zooming into a potential pH-proxy by combining bulk and high-resolution approaches. Chem. Geol. 513, 143–152. https://doi.org/10.1016/j.chemgeo.2019.01.005 (2019).
ADS  CAS  Article  Google Scholar 

26.
Kasemann, S. A., Schmidt, D. N., Bijma, J. & Foster, G. L. In situ boron isotope analysis in marine carbonates and its application for foraminifera and palaeo-pH. Chem. Geol. https://doi.org/10.1016/j.chemgeo.2008.12.015 (2009).
Article  Google Scholar 

27.
Rollion-Bard, C., Chaussidon, M. & France-Lanord, C. pH control on oxygen isotopic composition of symbiotic corals. Earth Planet. Sci. Lett. 215, 275–288. https://doi.org/10.1016/S0012-821X(03)00391-1 (2003).
ADS  CAS  Article  Google Scholar 

28.
Standish, C. D. et al. The effect of matrix interferences on in situ boron isotope analysis by laser ablation multi-collector inductively coupled plasma mass spectrometry. Rapid Commun. Mass Spectrom. 33, 959–968 (2019).
ADS  CAS  Article  Google Scholar 

29.
Sadekov, A. et al. Accurate and precise microscale measurements of boron isotope ratios in calcium carbonates using laser ablation multicollector-ICPMS. J. Anal. At. Spectrom. 34, 550–560 (2019).
CAS  Article  Google Scholar 

30.
Fietzke, J. et al. Boron isotope ratio determination in carbonates via LA-MC-ICP-MS using soda-lime glass standards as reference material. J. Anal. At. Spectrom. 25, 1953–1957 (2010).
CAS  Article  Google Scholar 

31.
Oppelt, A., López, M. & Rocha, C. Biogeochemical analysis of the calcification patterns of cold-water corals Madrepora oculata and Lophelia pertusa along contact surfaces with calcified tubes of the symbiotic polychaete Eunice norvegica: evaluation of a ‘mucus’ calcification hypothesis. Deep Sea Res. I Oceanogr. Res. Pap. 127, 90–104. https://doi.org/10.1016/j.dsr.2017.08.006 (2017).
ADS  CAS  Article  Google Scholar 

32.
Fowell, S. et al. Historical trends in pH and carbonate biogeochemistry on the Belize Mesoamerican Barrier Reef System. Geophys. Res. Lett. 45, 3228–3237 (2018).
ADS  CAS  Article  Google Scholar 

33.
Runcorn, S. K. Corals as paleontological clocks. Sci. Am. 215, 26–33 (1966).
Article  Google Scholar 

34.
DeCarlo, T. M. & Cohen, A. L. Dissepiments, density bands and signatures of thermal stress in Porites skeletons. Coral Reefs 36, 749–761. https://doi.org/10.1007/s00338-017-1566-9 (2017).
ADS  Article  Google Scholar 

35.
Barnes, D. & Lough, J. On the nature and causes of density banding in massive coral skeletons. J. Exp. Mar. Biol. Ecol. 167, 91–108 (1993).
Article  Google Scholar 

36.
DeCarlo, T. M. et al. Coral Sr-U thermometry. Paleoceanography 31, 626–638 (2016).
ADS  Article  Google Scholar 

37.
Gagnon, A. C., Adkins, J. F., Fernandez, D. P. & Robinson, L. F. Sr/Ca and Mg/Ca vital effects correlated with skeletal architecture in a scleractinian deep-sea coral and the role of Rayleigh fractionation. Earth Planet. Sci. Lett. 261, 280–295. https://doi.org/10.1016/j.epsl.2007.07.013 (2007).
ADS  CAS  Article  Google Scholar 

38.
Blamart, D. et al. Correlation of boron isotopic composition with ultrastructure in the deep-sea coral Lophelia pertusa: implications for biomineralization and paleo-pH. Geochem. Geophys. Geosyst. https://doi.org/10.1029/2007GC001686 (2007).
Article  Google Scholar 

39.
Jokiel, P. L. Coral reef calcification: carbonate, bicarbonate and proton flux under conditions of increasing ocean acidification. Proc. Biol. Sci. 280, 20130031–20130031. https://doi.org/10.1098/rspb.2013.0031 (2013).
CAS  Article  PubMed  PubMed Central  Google Scholar 

40.
Galli, G. & Solidoro, C. ATP supply may contribute to light-enhanced calcification in corals more than abiotic mechanisms. Front. Mar. Sci. https://doi.org/10.3389/fmars.2018.00068 (2018).
Article  Google Scholar 

41.
Barott, K. L., Venn, A. A., Perez, S. O., Tambutté, S. & Tresguerres, M. Coral host cells acidify symbiotic algal microenvironment to promote photosynthesis. Proc. Natl. Acad. Sci. 112, 607–612. https://doi.org/10.1073/pnas.1413483112 (2015).
ADS  CAS  Article  PubMed  Google Scholar 

42.
Bernardet, C., Tambutté, E., Techer, N., Tambutté, S. & Venn, A. Ion transporter gene expression is linked to the thermal sensitivity of calcification in the reef coral Stylophora pistillata. Sci. Rep. 9, 1–13 (2019).
Article  Google Scholar 

43.
Le Goff, C. et al. In vivo pH measurement at the site of calcification in an octocoral. Sci. Rep. 7, 11210. https://doi.org/10.1038/s41598-017-10348-4 (2017).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

44.
Zoccola, D. et al. Bicarbonate transporters in corals point towards a key step in the evolution of cnidarian calcification. Sci. Rep. 5, 9983. https://doi.org/10.1038/srep09983 (2015).
CAS  Article  PubMed  PubMed Central  Google Scholar 

45.
Furla, P., Galgani, I., Durand, I. & Allemand, D. Sources and mechanisms of inorganic carbon transport for coral calcification and photosynthesis. J. Exp. Biol. 203, 3445–3457 (2000).
CAS  PubMed  Google Scholar 

46.
DeLong, K. L., Maupin, C. R., Flannery, J. A., Quinn, T. M. & Shen, C.-C. Refining temperature reconstructions with the Atlantic coral Siderastrea siderea. Palaeogeogr. Palaeoclimatol. Palaeoecol. 462, 1–15. https://doi.org/10.1016/j.palaeo.2016.08.028 (2016).
Article  Google Scholar 

47.
Castillo, K. D., Ries, J. B. & Weiss, J. M. Declining coral skeletal extension for forereef colonies of Siderastrea siderea on the Mesoamerican Barrier Reef System Southern Belize. PLoS ONE 6, e14615 (2011).
ADS  CAS  Article  Google Scholar 

48.
Castillo, K. D., Ries, J. B., Weiss, J. M. & Lima, F. P. Decline of forereef corals in response to recent warming linked to history of thermal exposure. Nat. Clim. Change 2, 756–760. https://doi.org/10.1038/nclimate1577 (2012).
Article  Google Scholar 

49.
Foster, G. L. Seawater pH, pCO2 and CO32− variations in the Caribbean Sea over the last 130 kyr: a boron isotope and B/Ca study of planktic forminifera. Earth Planet. Sci. Lett. 271, 254–266. https://doi.org/10.1016/j.epsl.2008.04.015 (2008).
ADS  CAS  Article  Google Scholar 

50.
Foster, G. L. et al. Interlaboratory comparison of boron isotope analyses of boric acid, seawater and marine CaCO3 by MC-ICPMS and NTIMS. Chem. Geol. 358, 1–14. https://doi.org/10.1016/j.chemgeo.2013.08.027 (2013).
ADS  CAS  Article  Google Scholar 

51.
le Roux P. J. et al. In situ, multiplemultiplier, laser ablation ICP‐MS measurement of boron isotopic composition (δ11B) at the nanogram level. Chem. Geol. 203(1–2), 123–138. https://doi.org/10.1016/j.chemgeo.2003.09.006 (2004).
ADS  CAS  Article  Google Scholar 

52.
Inoue, M., Nohara, M., Okai, T., Suzuki, A. & Kawahata, H. Concentrations of trace elements in carbonate reference materials coral JCp-1 and Giant Clam JCt-1 by inductively coupled plasma-mass spectrometry. Geostand. Geoanal. Res. 28, 411–416. https://doi.org/10.1111/j.1751-908X.2004.tb00759.x (2004).
CAS  Article  Google Scholar 

53.
Thil, F. et al. Development of laser ablation multi-collector inductively coupled plasma mass spectrometry for boron isotopic measurement in marine biocarbonates: new improvements and application to a modern Porites coral. Rapid Commun. Mass Spectrom. 30, 359–371 (2016).
CAS  Article  Google Scholar 

54.
Hathorne, E. C. et al. Interlaboratory study for coral Sr/Ca and other element/Ca ratio measurements. Geochem. Geophys. Geosyst. 14, 3730–3750. https://doi.org/10.1002/ggge.20230 (2013).
ADS  CAS  Article  Google Scholar 

55.
Hijmans, R. & Van Etten, J. Geographic analysis and modeling with raster data. R Package Version 2, 1–25 (2012).
Google Scholar 

56.
R: A language and environment for statistical computing (R Foundation for Statistical Computing, Vienna, Austria, 2010).

57.
Foster, G. L., von Strandmann, P. & Rae, J. W. B. Boron and magnesium isotopic composition of seawater. Geochem. Geophys. Geosyst. https://doi.org/10.1029/2010gc003201 (2010).
Article  Google Scholar 

58.
Klochko, K., Kaufman, A. J., Yao, W. S., Byrne, R. H. & Tossell, J. A. Experimental measurement of boron isotope fractionation in seawater. Earth Planet. Sci. Lett. https://doi.org/10.1016/j.epsl.2006.05.034 (2006).
Article  Google Scholar 

59.
Holcomb, M., DeCarlo, T., Gaetani, G. & McCulloch, M. Factors affecting B/Ca ratios in synthetic aragonite. Chem. Geol. 437, 67–76 (2016).
ADS  CAS  Article  Google Scholar 

60.
Dickson, A. G. Thermodynamics of the dissociation of boric acid in synthetic seawater from 273.15 to 318.15 K. Deep Sea Res. Oceanogr. Res. Pap. 37, 755–766. https://doi.org/10.1016/0198-0149(90)90004-F (1990).
ADS  CAS  Article  Google Scholar 

61.
Lee, K. et al. The universal ratio of boron to chlorinity for the North Pacific and North Atlantic oceans. Geochim. Cosmochim. Acta 74, 1801–1811 (2010).
ADS  CAS  Article  Google Scholar 

62.
Zeebe, R. E. & Wolf-Gladrow, D. A. CO2in Seawater: Equilibrium, Kinetics, Isotopes in Seawater: Equilibrium, Kinetics, Isotopes Vol. 65 (Elsevier, Amsterdam, 2001).
Google Scholar 

63.
Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9, 671–675. https://doi.org/10.1038/nmeth.2089 (2012).
CAS  Article  PubMed  PubMed Central  Google Scholar  More