Multiple trait approach to inform ecosystem service value of corals propagated for restoration on the Great Barrier Reef

doi:10.21203/rs.3.rs-2030847/v1

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Multiple trait approach to inform ecosystem service value of corals propagated for restoration on the Great Barrier Reef

https://doi.org/10.21203/rs.3.rs-2030847/v1

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Coral propagation- and planting-based reef restoration practices are accelerating globally, yet short-term “success” continues to be measured as broad metrics of coral survival and growth, even though goals are often centred on recovering broad ecosystem service values. As such, how restoration activities may impact healthy reef functioning remains uncertain. For example, trade-offs in resource acquisition and partitioning that potentially regulate growth vs survival may yield very different outcomes towards factors governing reef biogeochemical cycles. Here we considered a proof-of-concept “multi-trait” approach to capture how a broader range of functional traits reflect the expression of growth and survival for a key coral species (Acropora cf. hyacinthus) — impacted by recent mass bleaching events — propagated for restoration activities on the Great Barrier Reef (GBR), Australia. We examined a diverse array of bio-physical, bio-chemical, and skeletal traits (n = 91 traits) for wild (donor) colonies and their nursery-derived fragments from a 12-month growth period (Opal Reef, northern GBR). Nursery corals grew 20-25 times faster than their donor (wild) colonies, but both exhibited similar survivorship. Faster growth within nurseries was accompanied by more pigmented colonies (darker-coloured and with more symbionts), and higher photosynthesis, respiration, and calcification rates. However, despite these metabolic changes, biogeochemical properties of the nursery and reef corals (carbohydrates, lipids, proteins, elemental stoichiometry, and skeletal properties) remained largely the same after 12 months, suggesting the bio-energetic value to trophic transfer as well as the structural rigour of corals was unaltered by nursery propagation. Thus, a “multi-trait” approach enables more informed evaluation as to how propagation activities impact diverse ecosystem service values, highlighting the immense importance of this knowledge in choosing coral individuals for restoration. Our example provides confidence to practitioners that key ecosystem service attributes of native corals are largely retained through an intermediate nursery growth phase that can accelerate coral biomass gains.

Biological sciences/Biochemistry/Biogeochemistry

Biological sciences/Biochemistry/Bioinorganic chemistry

Biological sciences/Biochemistry/Carbohydrates

Biological sciences/Biochemistry/Dna

Biological sciences/Biochemistry/Lipidomics

Biological sciences/Biochemistry/Lipids

Biological sciences/Biochemistry/Metals

Biological sciences/Biochemistry/Proteins

Biological sciences/Biochemistry

Biological sciences/Biological techniques

Biological sciences/Molecular biology

Biological sciences/Physiology

Earth and environmental sciences/Biogeochemistry

Earth and environmental sciences/Climate sciences

Earth and environmental sciences/Ecology

Earth and environmental sciences/Environmental sciences

Earth and environmental sciences/Ocean sciences

Physical sciences/Chemistry

Coral reefs sustain immense biodiversity and essential ecosystem services to almost a billion people but are deteriorating rapidly under relentless anthropogenic pressures¹ transforming coral reefs into highly altered assemblages^2–5. In response, coral propagation and re-planting initiatives have grown over the past 5-10 years in attempts to restore coral populations (e.g.^6–8) and offset further losses as local and global stressors continue to grow^8–10. Cost-effectiveness and efficacy of coral propagation and re-planting activities to date has improved largely through collective learning of localised success and failures as more activity has established (e.g.^6,11–16). For restoration efforts to be considered successful, the repaired habitat needs to have sufficient biotic and abiotic resources to sustain its structure and function (e.g.^7,17), but other more specific measures of success may also be needed, such as increasing abundance of a specific species¹⁸.

Coral restoration success itself remains almost universally gauged in terms of two key traits — coral growth and survivorship — because of the relative ease of collecting associated data (e.g.^6,13,19,20), but also given that growth and survivorship fundamentally govern coral competitive fitness over space and time (e.g.^13,21). However, these two traits alone may not (always) be informative enough to capture how coral health and reef functioning, and hence ecosystem service contributions are impacted during coral propagation and re-planting. For example, photosynthesis is a key measure of metabolic energy generation for growth vs survivorship, but also regulates biogeochemical transformation of key elements available to other reef taxa^22,23, including CO₂ drawdown from the atmosphere²⁴. Similarly, coral morphology (and in turn, structural complexity) is central to sustaining reef biodiversity^25,26, but does not account for how biochemical signals may moderate species interactions and recovery trajectories post disturbance¹⁸. Therefore, whilst many functional coral traits (i.e. biological attributes or characteristics of a coral individual that impact its performance and thus fitness;²⁷) can individually be considered central metrics of reef health, they can carry different weighting to the perception of success depending on the ecosystem service value goals for any restoration effort⁷.

Trait-based approaches have been widely used in ecology (and evolutionary studies) to facilitate quantitative comparisons of biological form and function²⁸. Accounting for expression and trade-offs of functional traits has been particularly critical for understanding organism and ecosystem processes, functionality, productivity, and resilience^29–31 in both past and present environmental regimes needed to forecast future ecosystem function. As a result, many studies have focussed on the expression of “supertraits” — measurable properties that capture a large amount of variation for a broad range of biological, ecological, and evolutionary processes (sensu²¹ ), such as leaf mass per area in plants^32,33 and body mass in animals^34,35. In corals, colony mass per unit tissue surface area (CMA, the relationship between coral weight and area) has similarly been proposed as a supertrait where CMA likely captures long-term colony investment of resource allocation and growth²¹. Whilst such metrics are powerfully informative, they remain proxies of function for the major biological properties of interest, and thus problematic for reef restoration where specific functional indicators are often required to support decision making, such as heat tolerance^36–38. Intriguingly, Cornwell et al.³⁹ recently demonstrated that variation in heat tolerance of the coral species Acropora hyacinthus could in fact, to some extent, be explained by trade-offs amongst inherent endosymbiont load (and growth rate) — but nonetheless noted caution in unilateral use of this single trait for restoration.

Coral studies have increasingly moved towards multi-trait assessments of function to better resolve the major biological properties regulating fitness outcomes, both in terms of inherent gene expression (e.g.⁴⁰) and emergent measurable physiological outcomes (e.g.⁴¹). However, such approaches have yet to be applied in the context of coral restoration, and it remains unclear whether and how deeper mining of traits beyond growth and survival could better inform descriptions of success across the rapidly growing volume of coral restoration efforts worldwide. We therefore implemented a proof-of-concept, multi-trait approach to evaluate collective trait expression for A. cf. hyacinthus propagated in coral nurseries (vs wild parent established on the reef) over 12 months on the Great Barrier Reef (GBR). Whilst many traits can conceivably be used to describe coral performance (summarised in Table S1), we selected well-established, eco-physiological traits (Fig. 1). We examine how these various traits — and the “trait space” they collectively reveal — reflect trade-offs in resource partitioning underpinning differences in growth and/or survival potential for corals propagated in nurseries compared to the reef (see selected traits in Table S2).

Survivorship and growth

A. cf. hyacinthus survivorship after 12 months was not statistically different amongst coral groups (wild vs nursery grown corals, 100.0 ± 0.0 vs 77.8 ± 11.1 %, respectively; n = 6 for each coral group) (Wilcoxon rank-sum test, p = 0.2500, Fig. 2A). However, higher relative areal growth rate (ΔG_A; % growth in cm² y^-1) over 12 months was evident for the nursery corals (308.9 ± 52.8) compared to the wild corals (12.4 ± 11.5) (t_(5.0) = 4.2, p = 0.0083, Fig. 2B). This outcome was also observed in the relative increase in area covered by coral tissue (%), (112.4 ± 11.5 vs 309.0 ± 54.0 %, for wild vs nursery corals, respectively; n = 6 for each coral group) (Fig. 2C; t_(5.0) = 4.7, p = 0.0055). Similarly, relative linear growth rate (ΔG_L; % growth in cm y^-1) was higher for nursery compared to wild corals (84.4 ± 11.2 vs 4.5 ± 5.0; t_(5.0) = 4.3, p = 0.0076, Fig. S3A). Amongst the six colonies examined, highest areal growth rates in wild and nursery corals were consistently observed for colony #1 (50.7 and 432.0 cm² y^-1, respectively; see example in Fig. S3B-C). Overall, areal and linear growth rates observed for nursery corals were ~20-25 times higher compared against wild corals.

Physical appearance

Multiple traits were examined to describe bio-physical colouration (red [R], green [G], blue [B], hue, saturation, brightness, and bleaching state; Fig. S4). Initial RGB values for wild corals (WT0) were 103.2 ± 10.0, 89.0 ± 8.5, and 68.0 ± 9.8 for RGB, respectively (n = 6). Higher RGB and brightness values correspond to a paler coloration (i.e. closer to white). R and G values were slightly increased (138.5 ± 6.0 and 97.8 ± 3.4), and B values decreased (42.2 ± 3.9) in wild corals (n = 6) after 12 months (WT12). In contrast, the nursery corals (NT12) became darker after 12 months (RGB of 98.5 ± 5.1, 67.3 ± 4.6, and 27.8 ± 2.6, respectively; n = 6) compared to the other two coral groups. Differences in the RGB, and brightness values among WT12 and NT12 coral groups were evident (see RM-ANOVA in Fig. S4A-D) indicating different colouration between these two coral groups. We also observed similar saturation and hue levels among WT12 and NT12 (see RM-ANOVA in Fig. S4E-F). All coral groups were in a non-bleached stage (average score of 6.0 ± 0.0 for all three coral groups) based on the coral colour reference card⁴² (See Fig. S4G). In summary, at 12 months post-fragmentation, corals propagated within the nursery (NT12) were darker than those retained in the wild (WT12) compared to the start (WT0).

Photobiology

Whilst nursery-propagation resulted in darker colonies after 12 months, photobiological traits for NT12 vs WT12 were generally similar; of note, however, photobiological characteristics varied as a function of time whereby both NT12 and WT12 were largely different compared to WT0 (for 8 out of 12 photobiological traits; E_K, F_q´/F_m´_MAX, [1-C]_MAX, [1-Q]_MAX, symbiont cell density, and total pigment, Chl a, and Chl c2 density). Specifically, the light saturation coefficient (E_K, μmol photons m^–2 s^–1) was higher for WT12 and NT12 (292.8 ± 64.4 and 328.2 ± 49.7, respectively) compared to WT0 (126.4 ± 16.9) (Fig. 3A; one-way RM ANOVA, F_(1.4,7.2)= 6.2, p = 0.0337; Tukey’s test WT0 vs WT12, and WT0 vs NT12; p = 0.0447, and 0.0310, respectively). Maximum photochemical efficiency of PSII (F_q´/F_m´_MAX, dimensionless) was lower for WT12 and NT12 (0.6412 ± 0.0130 and 0.6368 ± 0.0162) compared to WT0 (0.7043 ± 0.0118) (Fig. 3B; one-way RM ANOVA, F_(1.3,6.5)= 12.2, p = 0.0091; Tukey’s test WT0 vs WT12, and WT0 vs NT12, p = 0.0134, and 0.0438, respectively). When examining the fluorescence quenching parameters, whereby photochemical quenching capacity was reduced (i.e. [1-C]_MAX values were higher) for WT12 and NT12 (0.2330 ± 0.0422 and 0.3037 ± 0.0342) compared to WT0 (0.1214 ± 0.0198) (one-way RM ANOVA, F_(2.0,9.8)= 16.8, p = 0.0007; Tukey’s test WT0 vs WT12 (p = 0.0350) and WT0 vs NT12 (p = 0.0070); Fig. 3C). Similarly, non-photochemical quenching capacity was also reduced ([1-Q]_MAX values were higher) for WT12 and NT12 (0.8539 ± 0.0345 and 0.8123 ± 0.0387) vs WT0 (0.6879 ± 0.0255) (Fig. 3D; one-way RM ANOVA, F_(1.7,8.6)= 7.1, p = 0.0169; Tukey’s test WT0 vs WT12, p = 0.0488).

Symbiont cell densities (cells x 10⁶ cm^-2) were higher for WT12 and NT12 (0.5600 ± 0.1037 and 0.7317 ± 0.1224, n = 6, respectively) compared to the initial wild colonies (WT0) (0.2525 ± 0.0368, n = 4) (Fig. 3E; one-way ANOVA, F_(2,13) = 2.5, p = 0.0331; Tukey’s test WT0 vs NT12; p = 0.0264). No differences were found among coral groups for total pigment per cell, although lowest values were observed for NT12 (8.7 ± 1.1), followed by WT12 (10.5 ± 2.2) and highest values for WT0 (13.4 ± 2.2) (Fig. 3F-H; one-way ANOVA, F_(2,13) = 1.345, p = 0.2945), consistent with higher light-acclimated symbiont photobiology (above). However, pigment per coral surface area demonstrated that NT12 (but not WT12) A. cf. hyacinthus was more pigmented than WT0 (Fig. 3I-K), consistent with the darker coloration observed for the NT12 corals (Fig. S4). Specifically, total pigment, and Chl a and Chl c₂ densities (per coral surface area) were higher for NT12 (5.9 ± 0.7, 4.2 ± 0.6, and 1.7 ± 0.2, respectively) vs WT0 (3.2 ± 0.3, 2.2 ± 0.2, and 1.0 ± 0.1, respectively; Tukey’s test, p = 0.0117, 0.0275, and 0.0026, respectively). Intriguingly, the chlorophyll ratio (a:c2) remained similar over time and environments (WT0 (2.3 ± 0.2), WT12 (2.8 ±0.2) and NT12 (2.6 ± 0.3); one-way ANOVA, F_(2,13) = 0.3, p = 0.3543, Fig. 3L). In summary, after 12 months of growth, colonies on both the nursery and reef contained more symbiont cells (and more pigments) (typical of lower-light adapted corals) but were counterintuitively acclimated to higher light intensities.

Symbiodiniaceae ITS2 identity

All samples showed an association with Cladocopium; however, the major ITS2 type profiles were highly variable across all groups (WT0, WT12, and NT12; see Fig. S5), with only one major ITS2 type profile (C50a/C3k/C50c/C3-C3b-C50f) present in all three groups. All four initial samples had unique major ITS2 type profiles that included C21/C3, C40/3, C3-C21-C3k-C3at-C3b-C3av-C3dp, and C50a/C3k/C50c/C3-C3b-C50f. The wild colonies after 12 months also all had distinct major ITS2 type profiles. The highest association of WT12 (3 out of 6 samples) was with Cladocopium of the C3k radiation, albeit three distinct C3k ITS2-type profiles observed (representative of different genotypes). Of all groups, the nursery corals had the most consistent major ITS2 type profiles, with 5 of the 6 samples associating with Cladocopium of the C3k radiation (4 unique type profiles). In summary, after 12 months of growth, both the wild and nursery colonies had distinct major ITS2 profiles relative to the initial wild colony major ITS2 type profiles.

Metabolism

Surface area-normalised gross photosynthesis rate (P_G, µmol O₂ cm^-2 h^-1) of NT12 (932.5 ± 73.8) was similar to WT0 (932.4 ± 108.8) and ~40% higher than WT12 (690.0 ± 57.5) but overall not statistically different across groups (one-way RM ANOVA, F_(1.6,7.9)= 3.4, p = 0.0941; Fig. 4A). Similarly, no differences were found in net photosynthesis (P_N, µmol O₂ cm^-2 h^-1) among coral groups (one-way RM ANOVA, F_(1.1,5.6)= 1.9, p = 0.2277; Fig. 4B), where P_N of NT12 (278.5 ± 51.1) was slightly lower than WT0 and WT12 (442.2 ± 93.8 and 332.4 ± 33.1, respectively). In contrast to P_G and P_N, respiration rates (R, µmol O₂ cm^-2 h^-1) were higher for NT12 (654.0 ± 35.4) than both WT0 and WT12 (490.2 ± 24.5 and 357.6 ± 50.6, respectively) (one-way RM ANOVA, F_(1.5,7.4)= 19.3, p = 0.0017; Tukey’s test, p = 0.0107 for WT0 vs NT12 and, p = 0.0092 for WT12 vs NT12; Fig. 4C). Consequently, the P_G:R ratio was lower for NT12 (~1.4) than WT0 (~1.9) or WT12 (~2.1) but not statistically distinguishable between coral groups (one-way RM ANOVA, F_(1.5,7.6)= 4.2, p = 0.0674; Fig. 4D). Light-dependent calcification rate (G_L, µmol CaCO₃ cm^-2 h^-1) for NT12 (0.3704 ± 0.0811; n =4) was lower than WT0 (0.6726 ± 0.1347) and higher than WT12 (0.1112 ± 0.0411; n = 4) but again not statistically different (Fig. 4E; one-way ANOVA, F_(2,11) = 6.6, p = 0.0130; but note Tukey’s test detected differences only for WT0 vs WT12, p = 0.0108). Significant differences were observed for corresponding dark calcification (G_D, µmol CaCO₃ cm^-2 h^-1) across groups. In descending order, WT12, NT12, and WT0 were -0.3876 ± 0.1178, 0.1010 ± 0.1043, and 0.2250 ± 0.0654, respectively; Fig. 4F; one-way RM ANOVA, F_(1.2,5.9)= 11.9, p = 0.0124), particularly Tukey’s test detected differences between WT12 vs WT0 and NT12, p = 0.0393 and 0.0022, respectively. The G_L:G_D ratio remained constant for all coral groups (Fig. 4G; Kruskal-Wallis, H = 6.3, p = 0.0327), and Dunn’s test revealed differences between WT0 and WT12 (p = 0.0373).

Energy reserves and tissue biomass

No differences were found for any of the 28 energetic characteristics when nursery-propagating corals were compared to their wild counterparts (NT12 vs WT12) (Fig. 5, Fig. S7 and Fig. S8). However, a time effect was observed for carbohydrate, lipid, and protein content (mg:mg dry weight [DW]) and total energy reserves (J:g DW), whereby wild corals at T12 exhibited more energetic reserves to total tissue biomass compared to WT0 (Tukey’s test WT0 vs WT12; p = 0.0747, 0.0421, 0.0110, and 0.0053, respectively) (Fig. 5A-D). Specifically, carbohydrate concentrations for WT0, WT12, and NT12 were 0.0101 ± 0.0022, 0.0252 ± 0.0023, 0.0293 ± 0.0058, respectively; lipid content was 0.0984 ± 0.0107, 0.1356 ± 0.0098, and 0.1339 ± 0.0071; and protein content 0.0254 ± 0.0034, 0.0407 ± 0.0035, and 0.0415 ± 0.0019, respectively. Energy reserves determined from the carbohydrate, lipid, and protein contents were lower for WT0 (4671.0 ± 468.5) compared to WT12 and NT12 (6770.0 ± 414.9, and 6794 ± 222.3, respectively). Note for comparability, Fig. S6A-D presents the same data normalised to ash-free dry weight and surface area, but where no differences were detected amongst coral groups. Surface area-normalised total dry weight tissue biomass (mg DW cm^-2; Fig. 5E) — but not the ash-free total dry weight biomass (mg DW cm^-2; Fig. 5F) — was greater for WT0 (21.4 ± 2.3) than WT12 and NT12 (12.1 ± 1.0, and 14.5 ± 1.0). Thus, despite the greater growth of A. cf. hyacinthus for nursery compared to reef colonies over 12 months, all colonies exhibited less tissue biomass (but more energy reserves) at 12 months compared to initial samples. A strong time effect (but not location effect) was also observed when profiling lipids in more detail via 19 fatty acid molecules (Fig. S7). In general, differences in MUFA and PUFA (mono- and poly-unsaturated fatty acids) (p = 0.0324 and 0.0099, respectively) but not in SFA (saturated fatty acids; but note p = 0.0501) were found for both nursery and reef corals at T12 compared to T0 (Fig. S8).

Elemental content

After 12 months propagation, carbon-to-nitrogen ratios (C:N) for WT0, WT12, and NT12 were similar (7.11 ± 0.18, 7.02 ± 0.24, and 6.98 ± 0.54, respectively) (Fig. 6A; ANOVA, F₍₂,₁₂₎= 0.0238, p = 0.9765). In contrast, carbon-to-phosphorus (C:P) and nitrogen-to-phosphorus (N:P) ratios for WT0 (1332.9 ± 223.5, and 189.9 ± 35.7; Fig. 6B) were 100% greater than WT12 (679.6 ± 72.0, and 94.8 ± 7.6; n = 5) and NT12 (604.7 ± 95.9, and 84.4 ± 8.55), respectively (Fig. 6C). This change over time appeared driven by lower P content in WT0 (0.0208 ± 0.0028 mmol P g sample^-1; Fig. S9D). Furthermore, C:N:P was highest for WT0 (1333:190:1) compared to W12 and NT12 (680:95:1 and 605:84:1, respectively), and all were significantly higher than the Redfield ratio of 106:16:1⁴³.

In general, no differences were detected over time (WT0 vs WT12) or at 12 months post-propagation (WT12 vs NT12) for C, N, P, and 17 major and trace elements (Fig. S9), except for Cu content of NT12 (0.8836 ± 0.1097) which was similar to WT0 (0.9543 ± 0.0624) and higher than WT12 (0.5567 ± 0.0434); one-way ANOVA, F_(2,13) = 6.8, p = 0.0096; Tukey’s test WT0 vs WT12, and WT12 vs NT12; p = 0.0159, and 0.0260, respectively; Fig. S9L.

Skeletal properties

Skeletal traits appeared largely unchanged after 12-months of propagation (WT12 vs NT12) (bulk volume, bulk density, biomineral density, pore volume, apparent (internal) porosity, hardness, and colony mass per area; Fig. 7A-G). The only exception was hardness (HD) (Fig. 7F; one-way RM ANOVA, F_(1.4,7.2)= 8.2, p = 0.0182, Tukey’s test WT0 vs WT12, p = 0.0488) where values were higher for WT0 (38.8 ± 1.5) than both WT12 or NT12 after 12 months (31.5 ± 1.1, and 35.3 ± 0.9, respectively). Thus, overall, propagation in nurseries compared to the reef did not result in a difference in skeletal properties despite the greater growth rates observed for nursery colonies (Fig. 2B).

Multi-trait assessment and “trait redundancy”

We finally performed a series of principal components analysis (PCA) to reduce the trait space and identify the main drivers of variance from amongst the 90 traits examined (Table S2). Specifically, a PCA was first conducted for each of the following seven biological properties: physical appearance, photobiology, metabolism, energy reserves, FAME, elementome, and skeletal traits; to identify the two main contributing traits to the variation (Table S3A) beyond survivorship and growth, and in doing so where “trait redundancy” (i.e. traits that have similar contribution to the separation of coral groups) existed within biological properties. This is observed via overlapped trait-based vectors and correlated variables in Fig. S10 and Table S3A, respectively. A total of 14 (of 90) traits (brightness, blue, symbiont cell density, maximum photochemical efficiency of PSII [F_q´/F_m´_MAX], dark and light calcifications [G_D and G_L, respectively], total energy reserves, ash-free dry weight biomass, methyl arachidate, methyl myristoleate, potassium [K], strontium [Sr], pore volume, and biomineral density) were identified as the strongest drivers of variation from the individual PCAs (Table S4), and were therefore subsequently selected for the final PCA (the so-called multitraits, Fig. 8). We identified three clusters corresponding to the three groups (WT0, WT12, and NT12), despite some overlap; and where WT0 was somewhat separated from WT12 and NT12 (Fig. 8). Overall, 51.6% of the coral trait variance between coral groups was accounted by the first and second principal components collectively (PC1 and PC2, respectively; Table S4B). PC1 accounted for 34.5% of the total trait variance of the coral holobiont, with the following top 4 loadings: methyl arachidate (15.41%), total energy reserves (14.49%), blue (11.47%), and K (10.47%) (see Table S4A for contributions by the rest of the traits) contributing the largest loadings to this vector (Fig. 8). A further 17.1% of the total trait variance was accounted for by PC2, with brightness (25.77%), dark calcification (15.80%), ash-free dry weight biomass (11.51%), and symbiont cell density (8.23%) followed by other traits (see Table S4A) contributing the largest loadings to this vector (Fig. 8). Differences between coral groups were dependent on both PC1 and PC2, with PC resolving separation between all three coral groups. Here, WT0 separated from NT12 by PC1 (p = 0.0237; Table S5A), whilst all three coral groups were separated from each other by PC2 (WT0 - WT12, WT0 - NT12, and WT12 – NT12; p = 0.025, 0.027, and 0.0067, respectively). However, the one-way PERMANOVA between coral groups for the main 14 traits did not return differences between coral groups (p = 0.3706, Table S6).

Most coral reef restoration project goals focus on recovery of a functionally healthy and self-sustaining reef ecosystem. However, evaluations of success towards this goal to date have largely rested on few ecological^8,9 or biological metrics (e.g. coral growth and survival;^6,44) rather than more specific metrics that inform ecosystem services outcomes^6,7,19. We therefore examined how deeper mining of coral biological multi-trait expression encompassing bio-physical, and bio-chemical properties, can potentially provide more meaningful descriptions of success beyond growth and survival alone. Our observations — using Acropora cf. hyacinthus grown over 12 months on the Great Barrier Reef (GBR) — demonstrated higher relative growth rates (but comparable survivorship) for nursery compared to wild grown colonies. However, these higher growth rates were underpinned by higher photosynthesis, respiration and calcification rates, as well as more pigmented — darker-coloured, more symbiont cells and pigments per surface area — colonies. Intriguingly, this “nursery effect” occurred against a backdrop of a different dominant symbiont ITS2 type profile with overall higher light acclimation (E_K, more quenching capacity) and more symbiont cells and pigments (per surface area), hosted by the corals after 12 months of growth for both nursery and wild colonies. Other key properties, including elemental and bio-molecular content (carbohydrates, lipids, proteins, and fatty acid methyl esters) and skeletal properties — appeared less affected by nursery propagation over 12 months. Thus collectively, compared to growth and survivorship, the multi-trait evaluation yields insight into potential changes in colony performance and reef functioning, and hence factors governing overall ecological success.

Biological responses to nursery propagation

High survivorship and relative growth rates of A. cf. hyacinthus in nurseries of our study site (Mojo, Opal Reef) are consistent with previous observations from the same nursery approach at different sites of the same reef^13,45. Specifically, Howlett et al.⁴⁵ reported A. hyacinthus areal growth rates of 1-2 cm² month^-1 (equivalent to 14.4 cm² y^-1; RayBan) to 10.8 cm² month^-1 (129.6 cm² y^-1; Blue Lagoon), where the latter site is close to our study site (Mojo, ca. 200 m away) that yielded areal growth of 30 cm² month^-1 (equivalent to >300 cm² y^-1). In water, light availability generally appears lower at RayBan than Mojo/Blue Lagoon⁴⁵, where these lower areal growth rates are consistent with those measured for A. hyacinthus in more turbid environments (e.g. Singapore reefs,⁴⁶).

Most intriguing was the higher growth (20-25 times greater) observed for our nursery grown compared to reef grown A. cf. hyacinthus. Few studies to date have evaluated the growth performance of corals in nurseries compared to their respective source colonies retained on the reef; for example, linear growth of the branching coral Acropora formosa was previously shown to be ~5-50% faster in nurseries than in the natural neighbouring reef (Malaysia;⁴⁷). Such higher growth rates in nurseries have been proposed to reflect more favourable biological and physical conditions where nurseries are often located away from the reef; these include improved light availability (lower sedimentation), increased water flow, enhanced planktonic supply, reduced intra- and interspecific competition, and controlled corallivory^46,48–51. Whilst we cannot currently verify the specific nature of our higher nursery growth rates, our nursery design is conducive to coral growth via a low surface area and reduced drag to enable relatively unimpeded water flow (e.g.⁴⁵), which is essential for coral metabolism^52,53, and supply of planktonic food for heterotrophic feeding⁵⁴. Corals on our nurseries are typically observed with their polyps out during the day, and also fish-associated nursery communities comprise few (if any) persistently present corallivore fish (All authors, Pers. Obs.). However, it is also possible that higher growth gains for nursery corals — which start as relatively small fragment sizes of 64.3 - 191.7 cm² — in part reflect more rapid growth compared to established adult colonies (e.g.⁵⁵).

Both bio-optical and metabolic traits were observed to change for nursery compared to wild-grown corals. Photographic-based traits revealed colonies became darker in the nursery over 12 months (compared to on the reef) (Fig. S4), accompanied by more pigmentation per colony surface area (Fig.3 I-K). At face value, this darker colouration might appear indicative of acclimation to lower light intensities, as increases in coral pigmentation often occur via low-light acclimation^56–58. However, the photophysiology, in fact, indicated that both nursery and wild colonies had become high-light acclimated over the growth period, as indicated by a two-fold higher E_K in these coral groups^59–61, while symbiont cells also generally contained less photosynthetic pigments⁵⁷ (Fig.3 A and F-H). Despite the high variance in major ITS2 type profiles (Fig. S5) and consequent unique genotypes, such acclimation over time might reflect that both reef and nursery major symbiont type moved to C3k predominantly over 12 months. Presumably, host-symbiont metabolic compatibility changed to match a change in the prevailing resources available (e.g. light;⁶²) — but where specific nursery growth conditions were further fundamentally altered compared to the reef. For example, lower symbiont densities have been observed under higher temperatures (e.g.^63–66) and lower inorganic nutrient supply^63,67–69. Ultimately however, the exact nature of increased symbiont density cannot be verified without further detailed assessments of specific environmental histories in the reef compared to adjacent nursery habitats.

The high ITS2 type profile variance found across all coral groups was perhaps somewhat surprising, assuming a highly connected population⁷⁰. Future work expanding the number of samples taken per colony would help resolve whether any intra-colony variance in major ITS2 type profiles explain the variance found here ⁷¹. In contrast, high homogeneity in major ITS2 type profiles was reported on the same species in the same location (Mojo reef) in February 2020 (i.e. sampling time of our donor colonies; T0) by Grima et al.⁷². However, Kriefall et al.⁷³ recently reported a high profile variance for A. hyacinthus sampled across dynamic Mo’orean reef habitats. As such, the shifts we observed over 12 months are likely indicative of a change in the prevailing local environmental conditions inherent to both reef and nursery habitats at Opal Reef. Interestingly, the nursery corals exhibited the most consistent ITS2 type profiles, with most samples associated with C3k. This type is considered a host-specific symbiont type in the genus Acropora⁷⁴ and is associated with strongly performing holobiont colonies when exposed to sub-optimum environmental conditions⁷⁵. However, careful consideration should be taken when considering the dominant symbiont type shift to be the cause of acclimation as there are many distinct ITS2 predicted profiles not previously reported in A. hyacinthus that could yield different photophysiologies given they are predicted unique genotypes⁷⁶.

More symbionts per surface area for nursery-grown corals after 12 months were accompanied by higher photosynthesis rates (per surface area). Gross photosynthesis (P_G) was ~35% higher for nursery compared to wild colonies (Fig. 4A), suggesting more gross photosynthesis per symbiont cell. However, higher P_G/cell is contrary to what is expected where inorganic C limitation constrains any potential gain to photosynthesis as symbiont densities increase⁷⁷. Instead, a key response was almost 50% higher respiration rates (R) in nursery coral compared to wild corals after 12 months growth. Higher R indicates higher basal metabolic traits^78,79 associated with greater heterotrophic activity^80,81 of either stored energy or acquisition of new energy, which in the latter case for corals may occur through enhanced feeding^80,81. Indeed, corals have previously been suggested to grow faster through enhanced capacity to feed in higher flow waters compared to the reef⁵⁴. We, therefore, propose that the greater respiratory rates would suggest heterotrophic activity, which may be a critical feature supporting higher growth rates possibly through enhanced feeding in our nurseries. However, future studies should verify this hypothesis with targeted feeding studies.

All coral groups had higher C:N:P values than 106:16:1 (typically from phytoplankton;^43,82), and might be explained by the coral-symbiont relationship and their nutrient cycling and uptake^83,84. All coral groups exhibited similar C:N (values of ~7), which is consistent with recent studies^72,85, highlighting the conservative nature of this feature in corals, but also across other taxa, e.g. phytoplankton⁸⁶. Higher C:N is indicative of elevated carbon-rich storage materials⁸⁷, so the stability of C:N despite the increase in total lipids compared to carbohydrates measured over time (Fig. 5AC and Fig. 6) could be considered surprising. However, in marine phytoplankton under N limitation , species that accumulated lipids over carbohydrates saw small changes in their C:N⁸⁸, which could perhaps explain the results observed here but clearly requires further targeted investigation in future. Our data demonstrated similar C:P and N:P for both the wild and nursery corals at the end of the experiment, despite the marked differences in their growth rates. Intriguingly, the Growth Rate Hypothesis (GRH) predicts a positive relationship between phosphorus (P) content and growth rate in invertebrates, where P content decreases due to allocation to RNA synthesis during the organism’s growth⁸⁹. Thus our observations for corals here add to the growing body of evidence refuting the GRH when P is not limiting and can be stored in excess^90–92.

Of the coral elemental profiles (n=20) in our study, corals grown in their (non-native) nursery environments exhibited higher growth but no change in elemental content suggesting that the “biochemical niche” (sensu⁷²) largely did not differ from neighbouring reef (Fig. S9). As such, biogeochemical service provision — e.g. elemental quotas available for trophic transfer — was unaltered through nursery growth. The one exception was the higher Cu for nursery corals compared to the wild colonies after 12 months. Given the higher respiration rates for nursery grown corals, it is plausible to propose that this higher Cu reflects higher metabolic requirements where Cu is an essential cofactor for aerobic respiration (it acts as a redox cofactor in the Cytochrome c Oxidase)⁹³. In Chlamydomonas, the “Cu economy” involves substituting copper-containing plastocyanin with heme-containing cytochrome; in turn, the Cu that is saved by this replacement is used instead for COX biosynthesis, prioritizing allocation and reallocation of copper to respiration vs. photosynthesis⁹⁴. Verifying this hypothesis warrants further investigation is needed (e.g. presence of transcripts encoding proteins belonging to COX).

Finally, greater metabolic potential to support enhanced growth is important to ensure that coral skeletal properties are retained, i.e. more resources to ensure faster growth does not result in a weaker skeleton. Importantly, we observed that light calcification rates per coral surface area were, in fact, >3 times higher for nursery compared to wild corals after 12 months (Fig. 4D), aligning with the higher growth rates for nursery corals (20-25 times higher). Such an outcome is perhaps expected since higher growth is accompanied by higher calcification^95,96 in other coral species reared in nurseries. Further analysis of skeletal properties (bulk volume, biomineral density, bulk density, pore volume, apparent (internal) porosity, hardness, and colony mass per area; Fig. 7) did not reveal differences between nursery and wild grown corals that overall did not impact on skeletal strength. In particular, Lizcano-Sandoval et al.⁹⁶ showed that skeletal density was maintained despite an increase in growth and calcification, as the opposite of another study⁹⁵ where there were trade-offs between skeletal density and growth, while calcification rate was conserved, depending on the nursery environment.

Towards a multi-trait approach to evaluate benefits of coral propagation

In evaluating 90 traits spread across multiple properties (bio-optics, metabolism, energy, lipid diversity, elementome, and skeleton), we demonstrate that faster-growing corals in nurseries undertake a significant change in metabolism and resource partitioning compared to wild grown corals, which could conceivably impact the interpretation of ecosystem service value beyond just areal gain in coral cover (see also⁷ ). Specifically, higher photosynthesis, respiration, and calcification inherently alter ecosystem metabolic balance⁹⁷ and, in turn, biogeochemical cycling of key elements that underpin CO₂ and O₂ inventories^98,99. Similarly, lower calcification may reduce skeletal strength^100,101 that is critical for dissipating wave energy. Based on our nursery observations, the gains observed in areal coverage (growth rate) clearly need to be balanced in terms of costs (or additional benefits) to other ecosystem services, highlighting the need to introduce multi-trait assessments to robustly determine how nursery growth may aid reef ecosystem service value(s). In our study the enhanced growth of nursery corals did not come at a cost to skeletal integrity/strength, likely as a result of the greater metabolic activity, thus ensuring corals locally re-planted are likely of a similar robustness as the original source colonies.

Whilst we used a multi-trait approach here as a proof-of-concept for a deeper examination of biological attributes, it is clear that broad examination of many traits (initially 90 but reduced to 14 in our case) is neither routinely feasible nor often warranted. The final PCA (Fig. 8). showed multiple components that are driving the variation among coral groups. Whilst these provide a diagnostic of key factors that stand out in the biological processes of this specific case (i.e. Acropora cf. hyacinthus in an optimal nursery environment in the Northern GBR), in isolation, it is hard to resolve the collective process, although it might also indicate where greater investigation of specific properties is required to unlock the underlying mechanisms at play. Therefore, deeper diving into the various properties is initially needed to understand why and how this final PCA emerges. In this regard, the individual PCAs (Fig. S10) showed how several key traits were instrumental in driving differences amongst nursery and wild grown corals, whereas other traits could be considered redundant. For example, vectors of surface area-normalised chlorophyll a and c2 and total pigments all closely aligned (as were those for the various symbiont cell density normalised pigment contents), suggesting one descriptor of pigmentation is likely sufficient to differentiate coral groups. Similarly, in the case of the PAM-based photobiology, F_q´/F_m´_MAX (derived maximum photochemical efficiency) separated the coral groups along an opposite vector to all of E_K, [1-Q], and [1-C], suggesting that F_q´/F_m´_MAX and E_K may be adequate to retain these coral groups since [1-Q] and [1-C] often (but not always) reflect strategies underpinning changes to E_K (e.g.^60,99).

Overall, fewer traits are likely sufficient to retain groupings. However, our data demonstrate that most traits provide meaningful descriptors at play (operating along different vectors). Despite the challenges of reconciling actual processes from distilling 90 traits down to 14, a multi-trait approach clearly generates deeper insight to enable more informed decision making for restoration; for example, to select species¹⁰², stress tolerance^41,60,103, growth factors¹⁰⁴, and genetic and microbiome dynamics¹⁰⁵. So, practices that quantify success based on wider traits are likely to inform how restoration may retain or alter diverse ecosystem service values.

In conclusion, by using a multi-trait proof-of-concept, we have shown how changes in the biological machinery underpin enhanced coral growth through nursery-based propagation compared to original donor wild corals. Our diverse trait-based observations collectively evidence an enhanced metabolism (photosynthetic, respiratory, and calcifying activity) and pigmentation, likely through heterotrophic feeding under nursery conditions. As such, we show that nursery grown corals carry attributes that impact the perceived ecosystem value beyond simply addition of new coral biomass to the reef; for example, changes in the biogeochemical cycling of key elements that underpin CO₂ and O₂ inventories (through respiration and photosynthesis). Our findings strongly support the notion of the need for coral restoration practices to move beyond a few “simple” metrics of growth and survivorship (e.g.⁷) as measures of success, where goals are to improve (or retain) the ecosystem service value(s).

Sampling location and experimental design

Our multi-trait assessment focussed on a key coral species for the northern Great Barrier Reef (GBR), Australia, Acropora cf. hyacinthus¹⁰⁶. We tentatively identified all source colonies as A. hyacinthus in the field, but subsequent examination (Sage Rassmussen, Pers. Comm.) indicated that one colony exhibited a lightly different radial corallite appearance (colony #1) less consistent with this species. As such, we refer to the taxon studied throughout as A. cf. hyacinthus. Colonies (average initial area: 8290.4 ± 2422.0 cm²) of A. cf. hyacinthus (n = 6) were selected, tagged, and sampled from “Mojo” site at Opal reef (5-6 m depth, collected a minimum of 10 m apart), 50–60 km from Port Douglas, Queensland (145° 53’ 50.3’’E, 16°12’ 30.4’’S) (Fig. S1), February 2020. One partial colony was made from each of the 6 original parent colonies via fragmentation (a maximum of 10% of each wild colony was removed using a hammer and a chisel). Triplicate fragments were made from each partial colony (i.e. n = 3 replicates of each colony; with an average initial area of 127.1 ± 8.2 cm²) and moved to a nearby coral nursery facility consisting of two aluminium diamond‐mesh frames (2.0 x 1.2 m) placed 1–2 m above the sand by means of 2 × 9 kg Besser blocks and a 20-L float^13,45 so that corals were suspended at 4–5 m depth. Both aluminium frames were conditioned in situ for at least two weeks prior to holding any coral fragment. An additional set of fragments (n = 3 per colony, 1 for incubation, 1 for laboratory analysis, and 1 for skeleton assessment) were sampled from the same initial partial parent colony — referred to hereafter as “Wild” — for all initial laboratory trait measurements (time zero; WT0) alongside taking pictures of the wild colonies for assessment of physical traits. Both the wild colonies and nursery retained fragments (or clones) were then all re-sampled after 12 months (February 2021; WT12 and NT12) to evaluate potential trait expression variation from propagation on the nursery vs reef. All traits were therefore measured at WT0, WT12, and NT12, with the exception of growth and survivorship which were calculated from the difference in size and total colonies/fragments remaining between time points (i.e. WT12-WT0 or NT12-WT0).

Due to technical failure environmental data were extracted from GIOVANNI online system for satellite-derived data maintained by NASA (https://giovanni.gsfc.nasa.gov/giovanni/). Sea surface temperature (SST) was obtained using monthly area-averaging bounded to 145° 53’ 50.3’’E, 16°12’ 30.4’’S (Opal Reef) between the beginning of February 2020 and the end of February 2021. We also obtained 9-month HOBO SST data from the nursery at Mojo, which was overlaid with Giovanni SST data for Opal Reef (Fig. S2). It should be noted that at the onset of the experiment — i.e. March 2020 — the GBR experienced its third (and most widespread) mass bleaching event in five years. Coral mortality from this event was relatively low throughout the GBR compared to 2016/17¹⁰⁷, and whilst A. cf. hyacinthus colonies and fragments at Opal Reef visibly paled (including on the nurseries, JE, Pers. Comm.) in the second month of the experiment no mortality of our experimental coral material was observed.

On site data were collected and consisted of measuring several metabolic traits as previously described in Camp et al.¹⁰⁸, notably endosymbiont photophysiological performance, photosynthesis, respiration, and calcification rates. Additional samples were snap-frozen in liquid N₂ for further analysis in the laboratory including symbiont cell density and pigment content (chlorophyll a and c2)¹⁰⁹, ITS2 symbiont identity¹¹⁰, bio-molecular content (carbohydrates, lipids, proteins, and fatty acid methyl esters [FAME])^111–113 and elemental composition — C:N, referring to C (Carbon), N (Nitrogen), and trace elements — of the coral holobiont (host + symbiont fractions) as per Grima et al.⁷². Data on physical traits were obtained by underwater photographs^42,114 and by storing additional sub-samples in sodium hypochlorite for later analysis of skeletal properties (e.g. bulk volume, biomineral density, bulk density, pore volume, apparent (internal) porosity, hardness, and colony mass per area)^21,100,115. All samples for the incubations (n = 6) and the skeletal assessment (n = 6) were collected for WT0. Unfortunately, two samples (designated for laboratory analysis only) were lost for this coral group (n = 4). During the second field campaign, all samples were successfully collected for NT12 and NT12 (n =6 per each analysis).

Survival and growth

Corals were photographed using an Olympus Stylus TOUGH TG-4 digital camera together with a ruler by SCUBA. Growth rates were determined as the change in size (areal and linear extension) over time (ΔG; cm² y^-1 and cm y^-1) and survival rate as the proportion of all initial coral individuals remaining over time (%). To compare growth amongst coral groups, we determined the relative growth rate as % growth y^-1 (i.e. [ΔG/G_I] x 100), where G_I corresponds to the initial size (area or diameter). Normalising DG to G_I in this way enables comparison of growth from other studies where corals with different initial sizes have been used (e.g.¹³).

Physical appearance

Each coral colony or fragment was photographed (excluding the pale growing tip) at exposure 0 and a fixed distance of 20 cm with an Olympus Stylus TOUGH TG-4 digital camera, together with two underwater colour reference cards — a black and white card¹¹⁶ and the Coral Watch bleaching card⁴². Manual settings were used to ensure the gain and white balance settings remained constant. Photographs were subsequently analysed in Adobe Photoshop (version 20.013.20074) using the histogram function to determine the hue (reflected colour, with values of 0-360°), saturation (proportion of grey in the hue), and brightness (relative lightness and darkness, both having values of 0-255, where 0 and 255 are absolute white and black, respectively) from ten selected sampling points on the coral, which were then averaged (with the black and white card as a reference). Red, green, and blue (RGB) values (values from 0 to 255 corresponding to standard white and black, respectively) were also extracted using the same card. Coral tissue colouration was further categorised according to the bleaching state by use of the Coral Watch bleaching card as per Siebeck et al.⁴², where a colour score difference > 2 is considered indicative of a significant change in symbiont density and chlorophyll a concentration, and thus bleaching state.

Photosynthesis, respiration and calcification rates, and photobiology

Light and dark calcification, net photosynthesis, and respiration rates were performed on the operations vessel (Wavelength 5). Visible non-live coral tissue such as coral skeleton or attached rock/substrate were removed from each fragment (~5 cm) from each coral group (n = 6 fragments per group) prior to incubation as per Camp et al.¹⁰⁸ (to prevent biological alteration of incubated seawater). Fragments were then incubated for 2 h in the light followed by 2 h in the dark (covered in a blackout material) within 470 mL custom-made sealable acrylic chambers filled with ambient seawater (30°C) continuously mixed via a magnetic stir plate. All chambers were positioned in a temperature-controlled water bath (ambient temperature of 30 ± 0.5 °C) to maintain the same temperature across chambers. Light intensity for incubations was set to 300 µmol photons m⁻² s⁻¹, corresponding to the average light saturation coefficient (E_K, mmol photons m^-2 s^-1) — a measure of the long-term light acclimation state (e.g.⁵⁹) — as determined via rapid light curves (RLCs) assessed by Pulse Amplitude-Modulated (PAM) fluorometer (Walz) (instrument settings detailed below). Incubation irradiance was supplied via white LED aquarium lights (Hydra 52, Aqua Illuminations, Illinois, USA) positioned directly above the chambers, and measured using a PAR meter equipped with a 4π sensor (Li-Cor LI-250A). Oxygen optodes were connected to a FireSting O₂ logger and data were acquired using the FireSting software (PyroScience GmbH, Aachen, Germany). Each optode was calibrated prior to measurements using a freshly prepared sodium thiosulfate solution (10% w/w) and air-bubbled ambient seawater at experimental temperatures for 0% and 100% air saturation values, respectively.

Oxygen concentration was measured at the beginning, during, and after 2 h (for both light and then dark phases) in each chamber until a linear change in rate was recorded for each replicate. Net photosynthesis and respiration rates (P_N, R) were calculated based on the changes in O₂ concentration over time during the light and dark incubations, respectively, corrected for any changes in parallel water-only controls (n = 2)¹⁰⁸. Gross photosynthesis (P_G) was subsequently determined from net photosynthesis adjusted for respiration (i.e. P_G = P_N- R). Rates of light and dark calcification were determined by the alkalinity anomaly technique¹¹⁷ corrected for any changes in total alkalinity (A_T) of the controls (n = 3). All rates were normalised to the incubation volume, time, and coral surface area, which was determined by the single wax dipping technique^68,118.

RLCs were used to obtain the following photobiological traits: light saturation coefficient (E_K, µmol photons m^-2 s^-1), derived maximum photochemical efficiency of PSII (F_q´/F_m´_MAX), photochemical quenching ([1-C]), and non-photochemical quenching (1- Q) as per Suggett et al.¹¹⁹ of the coral-associated Symbiodiniaceae^59,60. The PAM fluorometer was programmed as Measurement intensity: 12; Gain: 4; Saturating intensity: 10; Saturation width: 0.8s; Damping: 2; Light curve interval: 3. For each RLC, an initial dark measurement and 8 actinic light steps were applied, whereby each light intensity was applied for 20 s. The actinic light levels (verified against a factory-calibrated LI-192 quantum sensor, Li-Cor) of the RLC were 0, 117, 149, 212, 302, 434, 600, 847 and 1304 and 0, 115, 195, 265, 350, 500, 710, 1020, 1400 µmol photons m^–2 s^–1 in February 2020 and 2021, respectively. All coral material was low-light acclimated for at least 20 min prior to the RLC as per Suggett et al.¹⁰⁴. Values for [1-Q] and [1-C] were only evaluated at the maximum RLC light intensity for the purposes of comparing trait expression.

Symbiodiniaceae cell density and pigment content

Coral tissue was removed from the coral fragments with an air-gun¹²⁰ in a small Ziplock bag containing 4 mL of 0.2 µm filtered seawater (FSW) (Minisart NML 16534 syringe filters, Sartorius, Germany). An aliquot of the resulting coral slurry (1 mL) was used for cell counts. The symbiont fraction was isolated from the host tissue via centrifugation (4°C at 3500 RPM for 5 min), and the symbiont pellet resuspended in 1 mL FSW. The algal suspension was preserved with 20 µL of 2% glutaraldehyde, and cell counts were later performed using a hemacytometer under a Nikon Ti microscope at 10x magnification. Cell density was obtained by standardising the values to volume and fragment surface area (wax dipping method).

A second 1 mL aliquot of the slurry was then used to determined chlorophylls a and c2 concentration. Host and symbiont fractions were again separated via centrifugation (as described above) and the pellet resuspended in 1 mL FSW. The 1 mL-algal suspension was pelleted and the cells lysed with 3 mL of 100% acetone in a scintillation vial placed on an ice bath and sonicated for 10 min. Samples were covered with aluminium foil throughout to minimise any chlorophyll degradation. After sonication, samples were stored at -4°C to allow pigment extraction. After 24 h, samples were centrifuged to remove cellular debris and measured on a spectrophotometer (Cary 60 UV-Vis, Agilent Technologies, Santa Clara, CA, USA); l = 630, 663 & 750 nm in a 4 mL-glass cuvette. Chlorophyll a and c2 were derived from the spectrophotometric equations of Jeffrey and Humphrey¹²¹ by standardising values to cell concentration and fragment surface area (pg cell^-1 and pg cm^-2), in addition to calculating chlorophylls ratio (a:c2).

Symbiodiniaceae ITS2 identity

Small sub-samples of each fragment (~1 cm²) were air-picked with 2 mL of 0.2 µm-FSW into small Ziplock bags. The resulting slurry was then centrifuged (4°C at 3500 RPM for 5 min) to retrieve a pellet of the endosymbionts. DNA extraction of the pellet was performed using the Qiagen DNeasy Plant Mini Kit (Qiagen, Hilden, Germany), where cells were disrupted with glass beads (BioSpec). Extracted DNA quality and quantity were checked using a NanoDrop 2000C spectrophotometer (Thermo Fisher Scientific, MA, USA). Amplification of the ITS2 region was performed by PCR where ITS2-reverse and ITSintfor2 primer pairs were used¹²², following PCR conditions from Arif et al.¹²³. Sequencing of the amplicons was achieved using Illumina Miseq (2 × 300 bp) at the Ramaciotti Centre for Genomics (University of New South Wales, Australia). Output files of the Illumina sequencing were submitted to SymPortal¹²⁴ to identify Symbiodiniaceae taxa¹²⁵ from the ITS2 marker.

Energy reserves and tissue biomass

Coral tissue was air-brushed from fragment sub-samples (~10 cm) in medium Ziplock bags containing 20-30 mL of 10% Tris buffer (Sigma-Aldrich), and the resulting 10-15 mL slurry was freeze-dried. A minimum of 15 g of dry coral mass was used to determine carbohydrates, lipids, and proteins content. Soluble lipids were extracted using chloroform:methanol method as per Grottoli et al.^111,112. Briefly, the coral slurry was submerged in a 2:1 chloroform:methanol solution for 1 h, washed in 0.88% NaCl followed by 100% chloroform, and washed again with 0.88% KCl. Following lipid extraction, fatty acids were esterified into methyl esters following the acid catalysed methylation method¹²⁶ and detected through gas chromatography (GCMS-QP2020 equipped with an AOC-20is autosampler, Shimadzu Corporation) (methodology adapted from Conlan et al.¹¹³).

Soluble proteins were extracted using the bicinchoninic acid method¹²⁷ with bovine serum albumin as a standard (Pierce BCA Protein Assay Kit). Soluble carbohydrates were quantified via the phenol-sulfuric acid method¹²⁸ with glucose as a standard. Soluble holobiont carbohydrate, protein, and lipid concentrations were then standardised to dry and ash-free dry weights and surface area. Total energy reserves were calculated as the sum of lipids, protein, and carbohydrate values converted into Joules¹²⁹. Tissue biomass was measured by drying a whole coral sample (skeleton, animal tissue, and algal endosymbiont) to constant dry weight (48 h, 60°C) and burning for 6 h (450°C). The difference between dry and burned weight was the ash-free dry weight. Both dry and ash-free dry weights were standardised to the surface area of that coral fragment.

Elementome

Coral tissue was air-picked in a trace-clean laminar flow hood from small sub-samples of each fragment (< 5 cm) in previously acid-washed Ziplock bags, containing 2 mL of 10% Tris buffer (Sigma-Aldrich). The resulting coral slurry consisted of host tissue and symbiont was freeze-dried. Total dry weight material was divided into a minimum of 30 mg and 5 mg for C:N and trace elements analysis, respectively. Total elemental C and N was determined using a Leco Total Carbon and Nitrogen Analyser¹³⁰ following manufacturer methods for soil and plant material with a furnace temperature at 1200°C and a soil calibration standard. Major and trace elements (Phosphorus [P], Magnesium [Mg], Sulphur [S], Potassium [K], Calcium [Ca], Vanadium [V], Manganese [Mn], Iron [Fe], Nickel [Ni], Copper [Cu], Zinc [Zn], Strontium [Sr], Molybdenum [Mo], Cadmium [Cd], Tin (Sn), Lead (Pb), Cobalt [Co], and Selenium [Se]) were determined by acid digestion of each freeze-dried sample^72,131. Digestion and filtration steps were conducted as per Grima et al.⁷². Quantitative analysis of elements was carried out using flow injection analysis (FIA) employing an Agilent 1200 Series HPLC system coupled with an 8900 series ICP-MS/MS instrument (Agilent Technologies, Santa Clara, CA, USA) according to a method developed by Grima et al.⁷². The interface was equipped with s-lenses and Pt sampler and skimmer cones. The ICP-MS/MS instrument was operated in MS/MS mode using oxygen as cell gas (99.995%, grade 4.5, BOC, North Ryde, NSW, Australia) for mass shifting of S and P. The remaining elements were acquired on mass. Quantification was performed in an external calibration approach using diluted, matrix-matched element standards for ICP-MS obtained from Choice Analytical Pty Ltd (Thornleigh, NSW, Australia). The performance of the ICP-MS instrument was tuned daily with a solution containing 1 μg L^-1 Li, Y, Tl and 100 μg L^-1 P and S.

Skeletal properties

The buoyant method applied for corals (adapted from Bucher et al.¹³² and Jokiel et al.¹³³), combined with the Archimedes principles, were used to test bulk volume, biomineral density, bulk density, pore volume, apparent (internal) porosity, hardness, and colony mass per area on previously cleaned and dried coral skeletons (< 2 cm)¹⁰⁰. Colony mass per area (considered a supertrait;²¹) was also estimated. Briefly, the fragment was previously weighed to obtain the dry weight (m or DW; g) before being inserted in a glass vessel in a 50°C water bath for two h under vacuum. The vessel was filled with Milli-Q water to obtain the saturated weight (m_s or SW; g) of the coral followed by the measurement of the buoyant weight (m_h or BW; g). Lastly, surface area (SA) was extracted following the wax technique. Skeletal properties were extracted using the previous calculations and the density of water (ρH₂0) at 20°C (0.9982 g cm^-3) as per the following equations:

[Eq. 1] V_A = (m_s - m)/ρH₂0; the pore volume connected to the external surface (V_A)

[Eq. 2] V_B =(m_s - m_h)/ ρH₂0; the total volume occupied by the coral skeleton (called bulk volume)

[Eq 3] P_A = V_A/V_B = (m_s - m)/(m_s - m_h); the apparent porosity (or effective or connected porosity, P_A) (ratio of the pore volume connected to the external surface (V_A) to V_B)

[Eq 4] d_b = m/V_B - the bulk density (ratio of the mass to V_B)

[Eq 5] d_r = m/(V_B - V_A) - the biomineral density (ratio of the biomineral mass to biomineral volume, excluding pore volume connected to the external surface, also called real density or micro-density)

Hardness testing was conducted using a Shore D Hardness Tester (TE-271) as per Leggat et al.¹¹⁵ on a second coral fragment (< 2 cm), calibrated using reference material as per the manufacturer’s recommendation¹³⁴. Hardness was consistently determined on the base of the branch for all fragments, with 10 measurements performed for each.

Statistical analysis

Data analyses were performed using Rstudio (version 1.4.1717) and GraphPad Prism (version 9.1.2). To conduct and visualise the Principal Component Analysis (PCA), the “FactoMineR” and “factoextra” R packages were used^135,136. Assumptions of normality were assessed visually via QQ plots and a Shapiro-Wilk’s test, and equal variances were assessed using Brown-Forsythe test¹³⁷. Data were transformed to meet normality assumptions where required. In particular, arcsine (Y/100) for percentages (1-100%) such as survivorship, saturation, and brightness; log transformation was carried for % data above 100% such as relative growth rate (log(Y)) and for negative values (log (Y+a)); and sin (Y) for degree data such as hue. Paired student’s t-test (normal distributions with equal variances, or its non-parametric homolog, Wilcoxon rank-sum test) were used to evaluate differences in survival and growth rates for wild and nursery corals (WT12 and NT12) over a year. Significance was set at p < 0.05 (for all tests) and the mean ± the standard error of the mean (SEM) was reported unless specifically noted. A series of (repeated measures) analysis of variance (ANOVA) (or its non-parametric homolog, Friedman or Kruskal-Wallis tests, respectively) with post hoc tests (Tukey or Dunn) were undertaken to compare among coral groups (“control” WT0, WT12, and NT12) for the rest of the coral traits. Additionally, a PCA biplot, including a cut-off for the cos2 = 0.5, was built for each following biological section: (a) photobiology, (b) physical appearance, (c) elementome, (d) metabolism, (e) energy reserves, (f) fatty acid methyl esters (FAME), and (g) skeletal properties, in order to identify the main drivers of the variation during propagation alongside the redundant traits to construct the final multi-trait PCAs (with 14 main drivers). Lastly, differences in coral groups (“control” WT0, WT12, and NT12) were tested for significance with pairwise permutational multivariate analysis of variance (PERMANOVA; perm = 999) of Bray–Curtis dissimilarities using the “vegan” R package.

Data availability

The datasets generated and analysed during the current study are available from the corresponding author upon request.

Acknowledgements

We wish to thank the past, present, and emerging Gadigal of the Eora Nation, Yidinji, Gunggandji, Yirrganydji, and Kuku Yalanji Traditional Owners of the of the land and sea country where our research took place. Also, to Dr. Matthew Nitschke, Dr. Samantha Goyen, Dr. Jennifer Matthews, Dr. Unnikrishnan Kuzhiumparambil, Dr. Trent Haydon, Dr. Mickael Ros, Paige Strudwick, Amanda Grima, Sage Fitzgerald, Rachel Alderdice; Wavelength crew including Jenny Edmondson, Lorna Howlett, Christine Roper, Than Miller, and Climate Change Cluster and Faculty technical staff, Paul Brooks, Erin Lockhart, Scott Allchin, Kun Xiao, Helen Price, Rachel Keeys, Susan Fenech, Herbert Yuan, Taya Lapshina, Penny McCracken, and Dr Alexander Angeloski. This research was supported by an International Research Scholarship (IRS) and UTS President's Scholarship (UTSP) to CINL. Access to sites and coral material, as well as contributions of DJS, EC and JE to writing and manuscript preparation, was facilitated through funding to the Coral Nurture Program from the Australian & Queensland Governments “Solving the bottleneck of reef rehabilitation through boosting coral abundance: Miniaturising and mechanising coral out-planting” in 2020; and the partnership between the Australian Government’s Reef Trust and the Great Barrier Reef Foundation in 2021; also, the many in kind contributions from Wavelength Reef Cruises crew who have tirelessly supported the work at Opal Reef since 2018. Contribution of EFC was through the University of Technology Sydney Chancellor’s Postdoctoral Research Fellowship and ARC Discovery Early Career Research Award (DE190100142). All material was collected under permit G20/43740.1 and G21/45224.1 (2020, and 2021, respectively) from the Great Barrier Reef Marine Park Authority.

AUTHOR CONTRIBUTIONS

CINL, DJS, and EFC designed the experiment, and alongside JE conducted the field-based experimental set up and sample collection. CINL analysed the bio-physical, metabolic, photobiological, bio-elemental, and skeletal samples, and DH, UK, DC, and RGV contributed to the analysis of photobiological, and bio-elemental samples. CINL conducted the statistical analysis and prepared the figures with guidance from DJS and EFC. CINL wrote the manuscript with editorial support from DJS and EFC, and with all authors contributing their relevant expertise in methods reporting and evaluation, and overall manuscript editing.

ADDITIONAL INFORMATION

Supplementary information accompanies this paper.

Competing interests: The authors declare no competing interests.

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Multiple trait approach to inform ecosystem service value of corals propagated for restoration on the Great Barrier Reef

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