**5. Conclusion**

This chapter has reviewed important aspects of pre and Postoperative evaluation of hemodialysis AVF. USG and CDI are principal imaging modality due to various advantages like easy availability, non invasive, non-ionizing and cheap modality. A detailed protocol for performing and interpreting USG and CDI studies for VA has been developed through our vast experience and we have tried to provide a proper performa for VA evaluation.


**7. Summary**

108 Biomedical Science, Engineering and Technology

the damaging effects of AGEs. We and others have found positive correlations between plasma sRAGE or esRAGE and AGEs 11, 114-116, 123. Significant positive correlation between plasma esRAGE and pentosidine was observed both in hemodialysis and non-hemodialysis subjects 11. However, plasma CML did not significantly correlated with plasma esRAGE both in hemodialysis and non-hemodialysis subjects. AGEs-mediated regulation of soluble RAGE is also supported by the findings that the suppression of sRAGE expression in diabetic rat kidney is reversed by blockade of AGEs accumulation with alagebrium 143. Other inflammatory mediators, such as S100, tumor necrosis factor α, and C-reactive protein, could also be potential candidates for regulation of the plasma level of soluble RAGE in humans 120, 142, 144. Moreover, Geroldi et al 145 showed that high serum sRAGE is associated with extreme longevity, suggesting that understanding the intrinsic regulation of RAGE and soluble RAGE is important for longevity/anti-aging strategies. Without doubt, further understanding of the regulation of soluble RAGE will be most helpful in delineating

It would be essential to determine whether currently available pharmacological agents can regulate plasma sRAGE or esRAGE. Potential agents that may affect circulating soluble RAGE include the angiotensin-converting enzyme (ACE) inhibitor 146, thiazolidinediones (TZD) 147 and statins 148-150, which are known to modulate the AGEs-RAGE system in culture. Forbes et al 146 showed that inhibition of angiotensin-converting enzyme (ACE) in rats increased renal expression of sRAGE, and that this was associated with decreases in expression of renal full-length RAGE protein. They also showed that plasma sRAGE levels were significantly increased by inhibition of ACE in both diabetic rats and in human subjects with type 1 diabetes. Thus, one attractive scenario is that the protective effect of ACE inhibition against progression of renal dysfunction is mediated through regulation of

Tam et al recently reported changes in serum levels of sRAGE and esRAGE in archived serum samples from a previous randomized double-blind placebo-controlled clinical trial that explored the cardiovascular effects of atorvastatin in hypercholesterolemic Chinese type 2 diabetic patients, and found that atorvastatin can increase circulating esRAGE levels 150.

For thiazolidinedione, a randomised, open-label, parallel group study was performed with 64 participants randomised to receive add-on therapy with either rosiglitazone or sulfonylurea to examine the effect on plasma soluble RAGE 151. At 6 months, both rosiglitazone and sulfonylurea resulted in a significant reduction in HbA1c, fasting glucose and AGE. However, significant increases in total sRAGE and esRAGE were only seen in the rosiglitazone group. In a recent study in type 2 diabetes mellitus patients, pioglitazone, but not rosiglitazone, significantly raised sRAGE levels 152, suggesting that all thiazolidinedione may not act similarly. Nevertheless, thiazolidinedione could be one promising candidate which increase circulating levels of esRAGE and sRAGE, and RAGE/soluble RAGE regulation may be involved in thiazolidinedione-mediated improvement of insulin resistance. Finally, we have started the randomized clinical trial comparing the effect of

potential targets for therapeutic application of soluble RAGE.

**A. Angiotensin-converting enzyme inhibitor**

RAGE versus soluble RAGE production.

**B. Statin**

**C. Thiazolidinedione**

**6.3 Pharmacological agents regulate circulating sRAGE and esRAGE**

The findings discussed here implicated pivotal role of RAGE system in initiation and progression of metabolic syndrome, insulin resistance and atherosclerosis. Provided that continuous RAGE activation represents the concept of "metabolic memory", metabolic syndrome might be conceptualized as memorized long-term subtle inflammation and oxidative stress using RAGE as an inflammatory scaffold. In this system, endogenous inflammatory RAGE ligands may be profoundly involved (Figure 7). Further, sRAGE or esRAGE could serve as a biomarker as well as a therapeutic target for these disease conditions. Obviously there are many missing parts to be veiled to further understand the role of RAGE/soluble RAGE axis in metabolic syndrome and insulin resistance. However, we believe our findings and this concept would open up a new research field which could further precede our understanding of the RAGE biology.

Fig. 7. Metabolic syndrome may be an aspect of "metabolic memory" conceptualized as prolonged RAGE activation through subclinical information.


**1. Introduction**

Hip fracture is the second most common fragility fracture after wrist fracture [1]. Between 28% and 35% of people aged ≥65 years have at least one fall at the same height per year that can potentially end in a fracture, and this incidence increases with age. It is called "multi-fall syndrome", which affects 30–50% of the institutionalized elderly population [2]. The incidence of hip fracture in Spain was 2.1% each year between 1997 and 2010, a year in which it was 325 cases in men and 766 in women for every 105 inhabitants, and it affects more significantly those aged 85 years or older [3].

The most pessimistic information about the percentage of elderly people who recover their previous function after suffering a hip fracture is 23% [4], but the most optimistic estimate that it can reach more than half, in which case the functional deficit baseline, 25-hydroxy-vitamin D deficiency and complication with "delirium" [5,6] are the most limiting factors for mobility recovery.

**Citation:** González Marcos, E.; González García, E.; González-Santos, J.; González-Bernal, J.J.; del Pilar Martín-Rodríguez, A.; Santamaría-Peláez, M. Determinants of Lack of Recovery from Dependency and Walking Ability Six Months after Hip Fracture in a Population of People Aged 65 Years and Over. *J. Clin. Med.* **2022**, *11*, 4467. https://doi.org/10.3390/ jcm11154467

Academic Editor: Gianluca Testa

Received: 16 June 2022 Accepted: 27 July 2022 Published: 31 July 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

Among the instruments used to standardize the measurement of the physical health of the elderly related to the activities of daily living (ADL), the Barthel Index (BI) [7] in its Spanish version [8] has been chosen as it is widely used in geriatrics.

In previous studies, there are multiple scales used to assess mobility and gait. The Tinetti scale [9] is one of them, the "Cumulated Ambulation Score" (CAS), described by Foss N.B., et al. in 2006 [10] and used by Danish authors [11]. Other scales are gait-specific, such as the aforementioned FIM scale [12], which has a module that assesses gait function. The "Functional Ambulation Classification" (FAC) was also initially described more than three decades ago for the evaluation of walking ability in stroke patients [13,14], but it has been used in elderly patients with hip fractures, too, is used in this studio [15,16], and has the advantage of its simplicity in clinical application. It is the one that we will apply in a summarized way, as it is exposed in material and methods.

Much research studies the recovery of patients in the context of rehabilitation programs. However, the results are not conclusive and more research is required [17]. However, there are not so many who study the factors that may limit the recovery of these patients.

A greater fear of falling after a hip fracture is related to the female sex, polypharmacy, poor physical functioning and daily activities, and depressive symptoms one year after the fracture occurred [18]. Frölich et al., in a prospective cohort study, found that those who were the frailest patients were the ones who failed to return to their independent living, but they consider that the majority of the community-dwelling patients returned to independent living only with a minor increase in care needs; they also consider that standing within 24 h from hip fracture surgery was vital in maximizing short-term functional recovery [19]. One systematic review proposed the hand grip strength and frailty as emerging significant predictors of poor functional outcomes and mortality in the literature, in addition to other predictors grouped in medical factors (comorbidity, anesthesia, sarcopenia), surgical factors (delay in intervention, type of fracture), socio-economic factors (age, sex, ethnicity) and system factors including lower case-volume centers [20]. Age, male sex, trochanteric fracture, preoperative delay, postoperative drainage use, serum albumin, and ADL at discharge and internal fixation are related to functional recovery [21,22]. Some of these factors can also influence mortality after hip fracture as advanced age, male sex, living in a rural area, diabetes, tumor, preoperative delay, and postoperative drainage use [22].

This research aims to study which factors exist in our population of patients aged ≥65 years, which limit, and to what degree, the recovery of the situation of independence (BI), as well as their ability to walk prior to suffering a hip fracture.

The hypothesis of this study is based on the fact that the factors that denote poor basal functioning, as well as the presence of health problems and other complications, will be factors that may influence the recovery of the baseline situation.

#### **2. Materials and Methods**

#### *2.1. Study Design—Participants*

In a retrospective longitudinal study, all patients were treated at the University Hospital of Burgos (HUBU). Inclusion criteria: Patients aged 65 years or older who, by a low energy mechanism, suffered a hip fracture in the biennium 14 March 2019–14 March 2021. All patients admitted to the HUBU with these characteristics were included in the study, followed after discharge from the outpatient clinics of the Orthopedic Surgery and Traumatology Service of the same hospital through face-to-face and non-face-to-face consultations through interviews with the patients, their families, and/or responsible caregivers. Exclusion criteria: Patients with peri-prosthetic fractures, peri-synthesis fractures, and pathological fractures, that is, on bones affected by primary tumor or metastasis, were excluded from the study; likewise, patients who were referred to other hospitals without completing the treatment or follow-up period for any cause, except death. Data collection was carried out on all patients who were admitted to the emergency room for hip fractures and underwent surgery by the Orthopedic Surgery and Traumatology Service.

#### *2.2. Sample Size*

The sample size was estimated following the procedure for finite populations, using the formula *n* = *N*×(*Zα*=1.96) 2×*p*×*q δ*2×(*N*−1)+(1.96)2×*p*×*q* . The known population reported by the National Institute of Statistics (INE) (https://www.ine.es/jaxiT3/Tabla.htm?t=2852, accessed on 25 May 2022) and a similar study [23] was taken into account, establishing a proportion of hip fractures in the population of 0.389% (*p* = 0.000398, and its complementary *q* = 0.99602) and assuming a sampling error of 1% (*δ*2 = 0.01). Based on this, it was concluded that the sample should be made up of 152 patients with hip fractures under care by the HUBU.

#### *2.3. Main Outcomes—Instruments*

The head of the Traumatology Section of the Orthopedic Surgery and Traumatology (OST) Service was responsible for collecting the data from each participant's electronic medical record for further analysis. In order to study variables that may influence cognitive impairment, sociodemographic data such as age (dichotomized in <85 and ≥85 years) and sex (woman/man) and clinical data such as the type of fracture (intracapsular/extracapsular), the type of treatment (surgical/conservative), the surgical technique (arthroplasty/synthesis), complications during admission such as "delirium" or constipation, the surgical risk assessed according to the American Society of Anesthesiologists Physical Status Classification (ASA) [24], prescription of different drugs before admission and after hospital discharge, and concomitant pathologies at the time of admission. The main variable refers to ambulation capacity according to the functional ambulation classification (FAC) [10,11] (categorized their levels 4–5 as "good", 3–4 as "regular", and 0–1 as "bad" walking ability).

There are multiple ways to standardize the measurement of the physical health of the elderly: the activities of daily living (ADL) index ("Activities of Daily Living" or "ADL") [7] and the instrumental activities of daily living (IADL) [8]. In specific questions of mobility, the functional independence measure (FIM) [9] is available, which is fundamentally validated for patients with neurological diseases, and its application is complex. The Barthel Index (BI) [10] in its Spanish version [11] has been chosen because it is the most widely used tool in the functional assessment of elderly patients suffering from hip fracture [12–16]. The categorization of the BI has been performed in four: "1" (BI = 100): fully independent, "2" (100 < BI ≥ 90): slightly dependent, "3" (90 < BI ≥ 60): moderately dependent and "4" (BI < 60): severely or totally dependent. "BI Recovery" is the difference between the BI (variable with four categories 1 to 4) at the income and at the sixth month, so that, if the value is negative, it is understood that they did not recover. "Walking ability recovery" is the category difference in "walking ability" at admission and at the sixth month so that "they do not recover" if said difference is a negative value. The cognitive impairment was assessed using Pfeiffer Scale (PS) [25]. It is a questionnaire that collects the number of errors of the evaluated patient when ten simple questions are posed and establishes four categories of the definition of cognitive impairment depending on the dependence of people in the intellectual area: 0–2 errors is the absence of deterioration or autonomy in the intellectual area, 3–4 errors is slight impairment and help of other people in intellectually complex matters, 5–7 errors is moderate deterioration and require help on a regular basis but not always, and 8–10 errors denote severe deterioration and continuous supervision. In the present study, cognitive impairment according to PS is expressed as a dichotomous variable: absence of cognitive impairment or mild impairment (PS ≤ 4 errors) and moderate or severe cognitive impairment (PS ≥ 5 errors). Data on FAC, BI, PS, and institutionalization prior to admission, at discharge, and at 6 months if the patient survives is collected. All clinical or sociodemographic information is obtained in the emergency department, on the hospitalization floor, or in face-to-face or telematic consultations after hospital discharge.

#### *2.4. Statistical Analysis*

To characterize the sample, the mean and standard deviation (SD) were used in the case of continuous variables and absolute frequencies and percentages if the variables were categorical. Both categorical variables from more than two categories and continuous variables were dichotomized based on previous studies and tended to obtain groups as homogeneous as possible. Bivariate analyses were performed to study the relationship between clinical features at "BI Recovery" and "Walking ability recovery", 6 months using the Pearson independence test (χ2), as well as the likelihood ratio. In the analyses with significant results, the ratio of advantages or "odds" (OR) with its limits (lower/upper) was also obtained. In addition, in order to quantify the magnitude of relationships of bivariate analysis and identify possible predictive factors of main variables at 6 months, depending on the different clinical characteristics, an analysis was performed using binary logistic regression, where dichotomous dependent variables are "BI Recovery" and "Walking ability recovery". All the significant variables obtained in the previous bivariate analysis were included as independent in the referred multivariate study, and the OR = eβi∗(±Δi) with its limits (lower/upper) was also obtained too.

Statistical analysis was performed with SPSS software version 25 (IBM-Inc., Chicago, IL, USA). For the analysis of statistical significance, a *p*-value < 0.05 was established.

### **3. Results**

#### *3.1. Recovery of the Initial Situation*

The study sample consisted of 665 people, 128 of whom died during the 6 months after hip fracture. The age of the participants was between 65 and 102 years, with a mean of 86.2 years, 76.7% women (*n* = 510) and 23.3% men (*n* = 155) (Figure 1). In the group of surviving patients in the series, 36.1% did not regain independence at the sixth month, nor did 44% regain walking ability prior to the fracture.

**Figure 1.** Participants flow.

*3.2. Influence on Lack of Recovery by 6th Month of the Category of the BI Prior to Admission* 3.2.1. Regarding the Previous Situation or Admission

In the bivariate analysis carried out between the BI recovery variable (yes/no) with the variables studied that take into account the situation before the patient was admitted (Table 1), an association was found with age ≥ 85 years, type of extracapsular fracture, also comorbidities such as chronic renal failure and high blood pressure, likewise the use of antihypertensive drugs, all of which are risk factors for non-recovery. There is also a relationship with independence (BI at admission ≥ 60, BI at admission ≥ 90), better cognitive status (PS at admission ≤ 4), and better gait (FAC category ≤ 2). No association was found with age, sex, or institutionalization prior to admission.

**Table 1.** Bivariate analysis recovery of the BI (yes/no); significant factors prior to or at admission.

BI: Barthel Index; FAC: functional ambulation classification; RO: Odds Ratio; PS: Pfeiffer Scale.

Binary logistic regression (Table 2) (Nagerkelke's R2 = 0.289) finds (in bold) age in completed years, surgical risk (ASA), independence (highest BI: 0–100), and cognitive impairment (number of errors in the EP) as risk factors for non-recovery of BI.

**Table 2.** Binary logistic regression of recovery of the BI (yes/no); situation prior to admission.

BI: Barthel Index; ASA: American Society of Anesthesiologists Physical Status Classification; FAC: functional ambulation classification; PS: Pfeiffer Scale.


*Article* **Physicochemical Control of Caribbean Coral Calcification Linked to Host and Symbiont Responses to Varying** *p***CO2 and Temperature**

**Robert A. Eagle 1,2,\*, Maxence Guillermic 1,2,3, Illian De Corte 1,2, Blanca Alvarez Caraveo 1, Colleen B. Bove 4,5, Sambuddha Misra 6,7, Louise P. Cameron 8, Karl D. Castillo 4,5 and Justin B. Ries 8**

**Abstract:** It is thought that the active physiological regulation of the chemistry of a parent fluid is an important process in the biomineralization of scleractinian corals. Biological regulation of calcification fluid pH (pHCF) and other carbonate chemistry parameters ([CO3 2−]CF, DICCF, and ΩCF) may be challenged by CO2 driven acidification and temperature. Here, we examine the combined influence of changing temperature and CO2 on calcifying fluid regulation in four common Caribbean coral species—*Porites astreoides*, *Pseudodiploria strigosa*, *Undaria tenuifolia*, and *Siderastrea siderea*. We utilize skeletal boron geochemistry (B/Ca and δ11B) to probe the pHCF, [CO3 2−]CF, and DICCF regulation in these corals, and δ13C to track changes in the sources of carbon for calcification. Temperature was found to not influence pHCF regulation across all *p*CO2 treatments in these corals, in contrast to recent studies on Indo-Pacific pocilloporid corals. We find that [DIC]CF is significantly lower at higher temperatures in all the corals, and that the higher temperature was associated with depletion of host energy reserves, suggesting [DIC]CF reductions may result from reduced input of respired CO2 to the DIC pool for calcification. In addition, δ13C data suggest that under high temperature and CO2 conditions, algal symbiont photosynthesis continues to influence the calcification pool and is associated with low [DIC]CF in *P. strigosa* and *P. astreoides*. In *P. astreoides* this effect is also associated with an increase in chlorophyll a concentration in coral tissues at higher temperatures. These observations collectively support the assertion that physicochemical control over coral calcifying fluid chemistry is coupled to host and symbiont physiological responses to environmental change, and reveals interspecific differences in the extent and nature of this coupling.

**Keywords:** Caribbean; coral; calcification; pH regulation; boron isotopes; B/Ca; carbon isotopes; photosynthesis; bleaching; symbiont

**Citation:** Eagle, R.A.; Guillermic, M.; De Corte, I.; Alvarez Caraveo, B.; Bove, C.B.; Misra, S.; Cameron, L.P.; Castillo, K.D.; Ries, J.B. Physicochemical Control of Caribbean Coral Calcification Linked to Host and Symbiont Responses to Varying *p*CO2 and Temperature. *J. Mar. Sci. Eng.* **2022**, *10*, 1075. https:// doi.org/10.3390/jmse10081075

Academic Editor: Tom Spencer

Received: 13 January 2022 Accepted: 25 July 2022 Published: 5 August 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

#### **1. Introduction**

Corals are foundational for some of earth's most biologically productive and diverse ecosystems, many of which are threatened by a combination of rising ocean temperatures, disease, ocean acidification (OA), and other pressures. Ocean warming can cause a breakdown in the symbiotic relationship between corals and their algal symbionts leading to coral "bleaching" and eventual mortality [1] and represents a significant threat to corals [2]. In addition, as atmospheric carbon dioxide partial pressure continues to rise, so does uptake of CO2 into seawater, which reduces seawater pH (pHSW) and calcium carbonate saturation state (Ω), thereby making seawater less chemically favorable for organisms that produce CaCO3 shells and skeletons [3]. However, it is understood that some species of marine calcifying organisms are resilient to external acidification or may even benefit by utilizing the additional dissolved inorganic carbon (DIC) available for shell building, and in some cases photosynthesis [4–7].

Coral calcification responses to ocean acidification are known to be diverse, with observations of negative skeletal growth responses [8–20]. Coupled ocean acidification and temperature stress has revealed a variety of responses due to interactions with temperature stress, including negative synergistic [14,21–24].

Whilst interactive effects of temperature and acidification on corals are known, the underlying physiological causes of those effects are less well understood. In two Indo-Pacific pocilloporid coral species, *Pocillopora damicornis* and *Stylophora pistillata*, we observed that corals cultured at 28 ◦C exhibit increased net calcification with increasing *p*CO2, but a shift to negligible or negative net calcification at 31 ◦C [7]. At the 28 ◦C treatment, CO2 fertilization of symbiont photosynthesis appeared to be occurring, which supplied the energy resources for corals to increase calcification [7,25]. At 31 ◦C, loss of symbiont density was observed from coral color analyses [7,25]. Using two independent approaches, pH microelectrodes and skeletal boron geochemistry, it was found that temperature compromised the corals' ability to maintain biological regulation of its internal parent fluids for calcification (calcifying fluids; CF), with pHCF and [CO3 2−]CF reduced at 31 ◦C and more sensitive to changes in the carbonate chemistry of external seawater [7]. Similar observations using pH sensitive dyes [26] and geochemical proxies [27–29] have been reported in field-collected corals with different thermal exposure histories. As it is thought that biological upregulation of the pHCF, [CO3 2−]CF, [DIC]CF, and the saturation state of aragonite (ΩAr)CF is important in allowing corals to calcify [30–35], loss of this control on thermal stress may explain how temperature and *p*CO2-induced ocean acidification could interact to negatively impact coral calcification [7]. Both laboratory experiments and observations on field collected corals have their limitations. In culture experiments, the question is how well are natural physiological responses to stressors simulated by the experiment and, in field collected corals, it can be difficult to constrain and disentangle complex and covarying relationships between the seawater carbonate system and their geochemical proxies [36]. Whilst coral calcifying chemistry regulation in response to acidification has been widely investigated in Indo-Pacific species, it has not to our knowledge been examined in depth in culture experiments on the Caribbean species studied here, with the exception of a study by [37] on *S. siderea* using a spatially resolved laser ablation approach.

Here, we further explore the independent and combined effect of temperature and *p*CO2 on coral and algal symbiont physiology in a study on four species of common Caribbean scleractinian corals *Porites astreoides*, *Pseudodiploria strigosa*, *Undaria tenuifolia*, and *Siderastrea siderea* (Figure 1). These corals were recovered from the Belize Mesoamerican Barrier Reef System, cultured at 28 ◦C and 31 ◦C, and at ~280 to 3300 μatm *p*CO2, and characterized for their structural and physiological responses to these conditions [38,39]. The coral species exhibited interspecific variations in host and symbiont responses to changing environmental conditions in the experiment, which allows us to investigate the link between host and symbiont physiology and the control of calcification and calcification fluid chemistry [38,39]. It was found that all four coral species exhibited nonlinear decreases in net calcification rate with increasing *p*CO2 [38]. The 31 ◦C treatment reduced calcification

rate in only one species, *P. strigosa*, although *U. tenuifolia* exhibited too low survival rates at 31 ◦C to be assessed [38]. *Siderastrea siderea* was the most resilient coral maintaining positive rates of net calcification under all conditions, even when seawater was undersaturated with respect to aragonite [38]. *Siderastrea siderea* was found to maintain relatively unaltered symbiosis under experimental conditions, and relatively constant host energy reserves, consistent with its resilient calcification response [39]. *Pseudodiploria strigosa* did not maintain indicators of coral symbiont physiology under warming, such as chlorophyll content and symbiont cell density, and was the most bleached coral in the experiment [39]. Conversely, *P. astreoides* exhibited improved symbiont physiological indicators and chlorophyll content at 31 ◦C, which were reduced under the acidification conditions [39], although the 31 ◦C condition was characterized by lower survival rates [38]. *Pseudodiploria strigosa* and *S. siderea* showed stronger correlations between symbiont density and host energy reserves, consistent with a tighter coupling between these physiological indicators than were found in *P. astreoides* [39]. It was concluded in previous work that this may result from *P. astreoides* symbionts being more efficient in delivering autotrophically derived carbon to the host, whereas *P. strigosa* and *S. siderea* require relatively greater concentrations of their symbionts to support the host [39]. Therefore, this experiment represents an opportunity to expand on our previous work [7] to explore the underlying mechanisms behind the interaction of temperature and *p*CO2 on corals and, in particular, how symbiont and host physiological responses could influence corals' ability to exert active biological control on physicochemical parameters, such as pHCF, [DIC]CF, and [CO3 2−]CF. We applied a combined approach of boron geochemistry to constrain pHCF, [DIC]CF, and [CO3 2−]CF and δ13C to trace changes in the source of carbon for calcification linked to coral and symbiont physiological responses to environmental changes.

**Figure 1.** Sample photographs of the coral species cultured in the experiment of Bove et al. (2019) [38] and investigated here: (**A**) *Porites astreoides*, (**B**) *Pseudodiploria strigosa*, (**C**) *Undaria tenuifolia*, and (**D**) *Siderastrea siderea*.

#### **2. Materials and Methods**

#### *2.1. Sample Collection*

Samples from the tropical Caribbean Scleractinian coral species *Siderastrea siderea*, *Pseudodiploria strigosa*, *Porites astreoides*, and *Undaria tenuifolia* (Figure 1) were collected from inshore and offshore reef environments along the southern portion of the Mesoamerican

Barrier Reef System (MBRS) off the coast of Belize in June 2015, at depths of 3–5 m [38]. For each species, six colonies were collected from both an inshore and offshore reef environment, totaling 48 colonies (4 species × 6 colonies × 2 reef environments), as described in Supplementary Table S3. Samples were then transported to a natural seawater flow-through aquarium system at Northeastern University's Marine Science Center in Nahant, MA, USA, where each colony was sectioned into eight fragments to be acclimated and subsequently undergo the 93-day experiment [38]. New data from a second experiment on Indo-Pacific corals are also presented here, in this experiment specimens of *Stylophora pistillata*, were obtained from DeJong MarineLife (Lingewaal, The Netherlands) and cultured as described previously [7,25].

#### *2.2. Experimental Culturing*

Following a ~70 day conditioning period, fragmented coral colonies from *S. siderea*, *P. strigosa*, *P. astreoides*, and *U. tenuifolia* were reared for 93 days under four *p*CO2 conditions corresponding to preindustrial (311 μatm), present-day control (405 μatm), end-of-century (701 μatm), and extreme (3309 μatm) carbon dioxide partial pressures, and under two temperature conditions corresponding to control (28 ◦C, as determined by in situ reef temperature records near the collection site) and elevated temperature treatments (31 ◦C), as predicted for end-of-century mean sea-surface temperature near the collection site [40]. The extreme *p*CO2 condition allows for an exploration of the physiological limits of the species, as well as simulates conditions in past greenhouse periods in earth history which are relevant to understand coral evolution. Coral fragments within each tank were fed every other day with a mixture of ca. 6 g frozen adult *Artemia* sp. and 250 mL concentrated newly hatched live *Artemia* sp. (500 mL−1) to satisfy any heterotrophic feeding by each species. Supplementary Tables S1 and S2 reproduce the measured and calculated experimental parameters from the original study [38].

#### *2.3. Net Calcification Rate, Coral Symbiont and Host Physiological Measurements*

Net coral calcification rates from this experiment were estimated from surviving coral fragments using the buoyant weight method, as described in [38], and reported in units of mg cm−2 d−1. Algal symbiont cell density was assessed using Lugol's iodine staining, with cell counts standardized to total coral tissue volume to yield values in cells per cm2 as described in [39]. Chlorophyll a (Chl a) was measured on a Turner Design 10-AU fluorometer and expressed in units of g of pigment per cm2 of coral tissue surface area [39]. Coral host protein, host lipids, and host carbohydrate were determined by spectrophotometry and recorded in units of mg per cm2 of coral tissue surface area. Whilst these data have been reported in previous publications [38,39], they are reproduced here in Table S3 to provide easy reference for interpreting the geochemical data.

#### *2.4. Coral Sample Preparation for Geochemical Analyses*

New skeletal growth was identified relative to a calcein marker emplaced in the coral skeleton at the start of the experiment, which was visualized using fluorescent light microscopy [38]. Powdered and homogenized samples underwent clay-removal and oxidative cleaning following the method from Barker et al. [41]. Sample material was then dissolved in 1 M HCl (80–100 μL).

#### *2.5. Boron Isotopic Analysis*

Boron was purified via microdistillation [42,43] following similar methods described in our previous work [7,44]. Measurements were carried out on a Thermo Scientific ®Neptune Plus MC-ICP-MS at the University of Cambridge equipped with 1013 Ω resistors [45]. Boron isotopic composition is reported in standard δ11B per mil (‰) notation with respect to the NIST SRM 951a boric acid standard (Catanzaro et al., 1970). A laboratory coral standard (NEP; *Porites* sp.) from the University of Western Australia and the Australian National University was analyzed for assessing internal reproducibility between analytical sessions as well as external reproducibility with other labs.

Sample blanks typically contained less than 0.6 ng-B (<5 ppb B). The δ11B composition of the NEP (δ11BNEP) was measured at 25.6 ± 1.0‰ (2 SD, n = 20) across 13 analytical sessions (Table S4), with each number representing an ab initio processed sample from the present study, which are within analytical uncertainty of published values for the same inhouse standard of 25.96 ± 0.30‰, 26.2 ± 0.88‰, n = 27, (25.8 ± 0.89‰, n = 6, 25.71 ± 0.79‰, n = 27, from [46–48]. The proposed international standard JCP−1 [49] was not analyzed across our analytical sessions due to lack of availability, nevertheless, previous work [7,44] was performed over the same period and report values within analytical uncertainty of published data, thereby validating the method utilized in the present study [7,49].

#### *2.6. B/Ca Analysis*

Elemental ratios were analyzed on a Thermo Scientific® Element HR ICP-MS at Cambridge University, UK and at European Institute for Marine Studies (IUEM), France, after calcium concentration checks on the Agilent® ICP-OES at Cambridge University, UK and at IUEM, France.  External reproducibility is reported relative to the consistency standard CamWuellestorfi, as published by [43] and measurements of the NEP coral. Our data were within one standard error of published values (Table S5). The analytical uncertainties (2sd) on the B/Ca ratios were 15 μmol/mol.

#### *2.7. Modelled pHCF*

The pH of the calcifying fluid (pHCF) was calculated from the boron isotopic composition of the coral skeleton following rationale from Hemming and Hanson [50] and using the MATLAB code provided in De Carlo et al. [51], where δ11BSW represents the boron isotopic composition of seawater (δ11BSW = 39.61‰) [52] and δ11BCARB represents the boron isotopic composition of the coral skeleton. The isotopic fractionation factor between the boron forms from Klochko et al. (2006) was used, α(B3-B4) = 1.0272. The dissociation constant of boron (KB) was calculated from salinity, temperature and pressure [53], and the boron concentration was calculated from salinity after Lee et al., 2010 [54].

$$\mathbf{p}\mathbf{H}\_{\rm CF} = \mathbf{p}\mathbf{K}\_{\rm B} - \log \| - \left[ \delta^{11} \mathbf{B}\_{\rm SW} - \delta^{11} \mathbf{B}\_{\rm CARE} \right] / \left[ \delta^{11} \mathbf{B}\_{\rm SW} - \mathfrak{a}\_{\rm (B3-BA)} \ast \delta^{11} \mathbf{B}\_{\rm CARE} - \varepsilon\_{\rm (B3-BA)} \right] \rangle \tag{1}$$

[H+]SW and [H+]CF were calculated based on pHSW and pHCF with [H+] = 10−pH. The proton gradient [H+]SW − [H+]CF was evaluated to better visualize the scale of pH regulation in comparison to a logarithmic scale.

#### *2.8. Modelled [CO3 2*−*]CF, DICCF, and* Ω*CF*

DICCF and [CO3 2−]CF were calculated using the method and Matlab© code from DeCarlo et al. [51]. This study uses combined B/Ca and δ11B to calculate [CO3 2−]CF, where:

$$\left[\mathrm{CO}\_{3}^{2-}\right]\_{\mathrm{CF}}\right] = \mathrm{K}\_{\mathrm{D}} \, \* \, \frac{\left[\mathrm{B}\,\mathrm{(OH)}\_{4}^{-}\right])}{\mathrm{B}/\mathrm{Ca}}\tag{2}$$

We use the KD defined in McCulloch et al. [33], KD = 0.00297\*exp(−0.0202\*[H+]), where [H+] is determined based on pHCF as calculated from δ11B following Equation (1). Given pHCF and [CO3 2−]CF, DICCF can be calculated using the known dissociation constants for carbonate speciation at known temperatures and pressure. ΩCF can also be calculated but requires and additional assumption on the [Ca2+] of the calcifying fluid. The saturation state of aragonite in the calcifying medium (ΩCF) defined following Equation (3)

is dependent on the accuracy of the [CO3 2−]CF and is limited to the additional assumption on the [Ca2+] of the calcifying fluid.

$$\left[\Omega\right]\_{\text{CF}} = \frac{\left[\text{CO}\_3^{2-}\right]\_{\text{CF}}\right] \times \left[\text{Ca}^{2+}\right]\_{\text{CF}}}{\text{Ksp}} \tag{3}$$

We calculated the saturation state of aragonite of the calcifying fluid using the [Ca2+]sw determined using the salinity of the culture condition ([Ca2+]sw (mol/kg sw) = 0.02128/40.1\* ((Salinity − 0.03)/1.805)).

#### *2.9. Carbon Stable Isotope Analyses*

Analyses on Caribbean corals were conducted on two Nu Perspective isotope ratio mass spectrometers (IRMS) at UCLA [55]. Most samples were analyzed using a Nu Carb specific sample preparation system. This system reacts 0.48 mg (±0.03 mg) of pure calcium carbonate material, for 20 min at 70 ◦C in individual reaction vials, thus eliminating any potential memory effects that are associated with analyses using a common acid bath system. Gases released by acid digestion of CaCO3 are purified in a series of liquid N2 cooled, temperature controlled cold-fingers, an Adsorption Trap (AdTrap), an in-line, short Gas Chromatograph (GC) column packed with Porapak Type-QTM50/80 and silver wool, before introduction to the mass-spectrometer dual inlet for isotope analysis. The second digestion system is the common acid bath (CAB) system [55]. Analyses of *Stylophera pistillata* skeletal δ13C and δ18O was carried out on a GasBench II coupled to a Delta V mass spectrometer at the stable isotope facility of Pôle spectrometrie Océan (PSO), Plouzané, France. Results were calibrated to the Vienna Pee Dee Belemnite (V-PDB) scale and referenced to the international standard NBS19.

#### *2.10. Statistical Methods*

For elemental data, the most restrictive false discovery rate (Q = 0.1%) was used to thin data using the ROUT method set, meaning that <0.1% of identified outliers would have been falsely identified as such. To test for statistically significant changes in net calcification, we performed a one-way ANOVA followed by Dunnett's multiple comparisons tests on skeletal B/Ca composition, δ11B, δ13C, pHCF, and ΩCF across experimental treatment conditions relative to the control condition (28 ◦C, 400 μatm *p*CO2). To determine the best fit by linear or second-order polynomial (quadratic) regressions, we used a combined linear and quadratic fit test, using the Akaike's Information Criteria (AIC). We compared the best-fit regressions across temperature conditions by first determining if the slopes of the two regressions are statistically different, and if the slopes are not significantly different (*p*-value > 0.05), we tested to see if the intercepts were significantly different (*p*-value < 0.05). This was done using an Analysis of Covariance (ANCOVA) method. The statistical analyses above were carried out GraphPad Prism version 9.0.0 for macOS, GraphPad Software, San Diego, CA, USA.

δ11B, B/Ca, and δ13C data were tested to see if it met assumptions of normality using the Shapiro–Wilks test (*p* > 0.05). At the level *p* < 0.05 we found that most of the data were not normally distributed. In order to perform a two-way AVOVA test, we transformed the data using the Tukey ladder of powers method to reduce negative and positive skew of non-normal data methodologically [56]. Method until it met the test of normality. Table S6 gives information about whether data were transformed and how they were transformed. Following confirmation that the data were normal, we analyzed isotope and elemental data using a two-way ANOVA to test the influence of the individual and combined effects of *p*CO2 and temperature. The same approach was taken to analyze the calculated parameters pHCF, [CO3 2−]CF, and DICCF and the coral and symbiont physiological data; calcification rate, symbiont cell density, chlorophyll a, and total host energy data from [38,39]. All statistical analysis was conducted using the 'MASS' package in R version 4.0.2 (R Core Development Team 2016, Vienna, Austria).

As coral species were collected from inshore and offshore reef environments, so we also tested whether there were significant effects of temperature, *p*CO2, and reef environment on geochemical and physiological parameters in a three-way ANOVA. As this analysis did not resolve a significant effect of reef environments, it justified the pooling of data across reef environments to conduct the two-way ANOVA tests described above.

#### *2.11. Data Compilation*

Data from previous publications was compiled to construct synthesis figures on coral calcifying fluid chemistry derived from geochemistry and other approaches such as electrodes and pH sensitive dyes. Literature data used for this metanalysis is summarized in Table S12 and was derived from the following publications [7,17,32,34,35,47,57–70]. In order to include the data from Allison et al., 2014 [32] on the carbonate chemistry of the calcification fluid from *P. damicornis* cultured under different concentrations of the ruthenium red (RR, 3.7 and 5.3 μM) inhibitor of Ca-ATPase activity, new calculations of the original data were performed using the framework described in this paper. Carbonate chemistry of the cultured experiments were calculated from alkalinity and pH provided in a previous publication [71] and using the CO2sys program [72].

#### **3. Results**

#### *3.1. Analyses of (δ11B), B/Ca, and δ13C*

Mean boron geochemistry (δ11B and B/Ca) and δ13C for each experimental condition of the Caribbean coral experiment are reported in Table 1, individual coral specimen data are reported in Table S7. B/Ca (±SD) measurements for the control culturing conditions were averaged for each species under the experimental control conditions of 28 ◦C and 405 μatm *p*CO2: *P. astreoides* (437 ± 31 μmol/mol), *P. strigosa* (495 ± 70 μmol/mol), *S. siderea* (436 ± 18 μmol/mol), *U. tenuifolia* (518 ± 32 μmol/mol). Under the experimental control culturing conditions, coral skeletal aragonite δ11B compositions (±SD) were averaged for each species: *P. astreoides* (24.54 ± 0.74‰), *P. strigosa* (24.40 ± 1.40‰), *U. tenuifolia* (24.63 ± 0.68‰), and *S. siderea* (23.87 ± 0.47‰). Mean δ13C (±SD) for each species under the control temperature and *p*CO2 experimental condition (28 ◦C, 405 μatm *p*CO2) was: *P. astreoides* (−2.58 ± 0.92‰), *P. strigosa* (−1.11 ± 1.51‰), *S. siderea* (−3.94 ± 0.61‰), and *U. tenuifolia* (−2.65 ± 0.34‰) (Table 1).

Boron geochemical data for *S. pistillata* have previously been reported in Guillermic et al. Here, we report δ13C and δ18O data from *S. pistillata* skeletal aragonite from this additional experiment on Indo-Pacific corals, given in Table S8. The principal reason for presenting these data in this study is that they provide context for the interpretation of data from the Caribbean corals, as described in more detail below.

Trends in measured geochemical parameters as a function of culture seawater pH (pHSW) were explored using Akaike's Information Criteria (AIC) to determine whether a linear or second-order polynomial (quadratic) regression best fit the data (Figure 2; Table S9). In all cases, a linear regression was found to fit the data better, with the exception of δ11B and B/Ca data for *S. siderea*, where a nonlinear fit was preferred (Figure 2, Table S9). In terms of geochemical proxy development for historical seawater pH reconstruction, the three species that exhibited relationships between δ11B and pHSW that were best described by linear regressions seem best suited for this endeavor. *P. strigosa* showing the greatest sensitivity of δ11B to pHSW, with a regression slope of 2.15 compared to 1.46 in *P. astreoides* and *U. tenuifolia* at the 28 ◦C temperature condition. However, *S. siderea* showed the greatest sensitivity with a slope of the linear regression between δ11B and pHSW of 2.52 but was best fit by a nonlinear regression. At 31 ◦C, it is notable that the relationship between δ11B and pHSW within *P. strigosa* was no longer statistically significant, indicating a potential influence of temperature on δ11B in this species, although this was not significant in the ANOVA tests described below (Figure 2, Tables S9 and S11).

**Table 1.** Average Geochemical Data per Experimental Condition.

\* number of specimens analyzed; 1 NIST; 2 V-PDB.

To further constrain trends in the data, two-way ANOVA tests were used to explore the individual and combined effects of *p*CO2 and temperature on geochemical parameters. For *P. astreoides*, *p*CO2 alone had a significant individual effect on δ11B and δ13C, while temperature had no effect on geochemical parameters. However, the combined effect of temperature and *p*CO2 was found to significantly impact B/Ca. For *P. strigosa*, *p*CO2 had a significant effect on δ11B and δ13C, and temperature only influenced B/Ca. In *S. siderea*, *p*CO2 had a significant effect on δ11B, δ13C, and B/Ca, whereas temperature had an effect on δ13C alone. Lastly, in *U. tenuifolia*, only *p*CO2 had a significant effect on δ13C, and no temperature effects were observed. Results of these analyses are presented in Tables 2 and S10.

Coral specimens were collected from both the inshore and offshore reef environments (Supplementary Table S3). Although previous work showed that reef environment had an effect on the physiological response of the corals [39], we found no significant effect of reef environment on the geochemical data or on the carbonate system parameters calculated for the coral calcifying fluid (Table S10). A lack of effect of local reef environment in skeleton boron and carbon geochemistry is a positive feature in terms of using the species as palaeoceanographic archives.

**Figure 2.** Measured δ11B (**A**), δ13C (**B**), and B/Ca (**C**) composition of coral aragonite following the 93-day culturing experiment. Large triangular symbols represent the mean value (±1SD) for each treatment condition. Blue symbols represent the control temperature (28 ◦C) treatment condition and red represents the high temperature (31 ◦C) treatment condition. Linear versus centered quadratic fit was determined using the Akaike's Information Criteria test (Table S8), with shaded areas representing the 95% confidence interval. Dashed blue and red curves represent the expected δ11B composition of borate ion (B(OH)4−) in solution at 28 ◦C and 31 ◦C, respectively. \* indicates a significant difference between 28 ◦C and 31 ◦C data for a given *p*CO2 treatment as demonstrated by a *p*-value < 0.05 in a Welch's T test.

It is important to note that the principal driver of trends in δ13C data across variable *p*CO2 and pHSW treatments is the isotopic composition of the CO2 gas used to manipulate the *p*CO2 of the treatment seawater. This gas is combustion sourced and therefore has a relatively negative δ13C composition that influences the δ13C of DIC more in cultures with higher *p*CO2. Despite these trends being influenced by the δ13C of the source gas, there is potential to meaningfully interpret the difference between the δ13C values between the two temperature treatments for a given *p*CO2. For example, Welch's T-Tests indicate a statistically significant difference in δ13C data between the 28 ◦C and 31 ◦C treatments at the highest *p*CO2 treatment for *P. astreoides* and *P. strigosa*, but not at the lowest *p*CO2 condition (Figure 3). Additionally, ANCOVA tests reveal the linear regressions through δ13C data as a function of *p*CO2 for both *P. astreoides* and *P. strigosa* have significantly different slopes at 28 ◦C versus 31 ◦C (*p* < 0.0001 and *p* = 0.0021, respectively), while *S. siderea* did not exhibit a significant change in slope across temperature conditions (*p* = 0.102). These trends in δ13C are likely to reflect changes in the sources of DIC for calcification linked to the coral host and symbiont physiological responses to temperature and *p*CO2, as further explored in the following sections.

**Figure 3.** (**A**) Measured net calcification rate (mg cm−2 d−1), replotted from Bove et al. [38]. (**B**) δ11B-derived pHCF. Triangle symbols represent the mean value (±1 SD) for each treatment condition, blue and red symbols represent the 28 ◦C and 31 ◦C treatments, respectively. Linear versus centered quadratic fit was determined using the Akaike's Information Criteria test (Table S8), with shading representing the 95% confidence interval. \* indicates a significant difference between 28 ◦C and 31 ◦C data for a given *p*CO2 treatment, as demonstrated by a *p*-value < 0.05 in a Welch's T-test.

**Table 2.** 2-way ANOVA output testing the influence of temperature and *p*CO2 on measured geochemical parameters.

#### *3.2. Net Calcification, pHCF, [CO3 2*−*]CF, and DICCF*

Net calcification rates for the Caribbean corals were reported in Bove et al. [38], which found that all four coral species showed non-linear reductions in net calcification with increasing *p*CO2. Corals showed interspecific differences, with *S. siderea* maintaining positive net calcification under all experimental conditions, *P. strigosa* exhibited a shift to negative net calcification at 31 ◦C only, and *P. astreoides* exhibited an increase in calcification with elevated temperature at the lower *p*CO2 condition, but a shift toward negative calcification (i.e., net dissolution) under the higher *p*CO2 treatments (Figure 3).

The pH of the coral calcifying fluid (pHCF) was calculated from δ11B data using canonical methods from the literature, as described in the methods section. Although the δ11B approach to calculating pHCF in corals has been independently verified based on comparison with pH sensitive dye and pH microelectrode approaches [7,30,47,61,73], significant differences in sensitivities in those approaches appear to exist, and different hypotheses have been advanced to explain these differences [7,47]. At the control temperature and *p*CO2 experimental condition (28 ◦C, 405 μatm *p*CO2), mean δ11B-derived pHCF (total scale, ±1 SD) for each species was as follows: *P. astreoides* (8.45 ± 0.06), *P. strigosa* (8.50 ± 0.11), *S. siderea* (8.47 ± 0.03), *U. tenuifolia* (8.49 ± 0.04) (Table 3).

The [CO3 2−]CF and DICCF were calculated from δ11B and B/Ca data using the KD of McCulloch et al. [74] and the approach of DeCarlo et al. [51] (Table 3), as described in in the methods section. The B/Ca proxy is still prone to uncertainties [70,75] and would benefit from additional validation. One critique is that the proxy is based on limited inorganic precipitation datasets [76,77] which have been difficult to reconcile due to different experimental conditions and from varying [Ca2+] in the experiment leading in turn to varying saturation states of aragonite (Ω) within the precipitation experiments [70]. This leads to potential difficulties when translating to highly biologically controlled fluids (such as the calcifying fluid in corals). Additionally, it is possible that coral biomineralization involves transformation of amorphous precursor phases, as evidenced by the detection of amorphous calcium carbonate (ACC) in corals [78], although it is unclear whether this is the universal pathway by which corals calcify. Amorphous or metastable phases of calcium carbonate may have different KD, which could contribute to differences in biomineral chemistry [79,80], although given that ACC transformation seems to occur via dissolution and reprecipitation into crystalline phases [81], precursor phase KD may not be relevant for the final mineral. Despite these uncertainties, results of Guillermic et al. [7] showed that the KD formulations used by DeCarlo et al. [51] (Termed D18); McCulloch et al. [74] (M17); Holcomb et al. [77] (H16) resulted in similar reconstructed [CO3 2−]CF among biological replicates and that the outcome was within error of independent measurements from [CO3 2−] microelectrodes as reported by Sevilgen et al. [35] (S19) for the same coral species (*S. pistillata*) (M17: 909 ± 313 (SD, n = 8), H16: 757 ± 342 (SD, n = 8), D18: 848 ± 392 (SD, n = 7), S19: 679 ± 183 (SD); at 311 ppm *p*CO2, 28 ◦C) [7].

Regression analysis of net calcification, calculated pHCF, [CO3 2−]CF, and DICCF as a function of pHSW were explored using an AIC approach (Figures 3 and 4; Table S9). pHCF as a function of pHSW follows a similar pattern to the originating δ11B data (Figure 3; Table S9). Patterns in [CO3 2−]CF as a function of pHSW differ from patterns in pHCF as a function of pHSW, with only *P. strigosa* and *S. siderea* showing statistically significant regressions under the 28 ◦C treatment (Figure 4; Table S9).

In addition, regression analyses of net calcification as a function of pHCF, [CO3 2−]CF, and [DIC]CF were explored using the AIC approach. Here, linear regressions were typically favored but rarely reached statistical significance (Table S9). This result is not surprising given the previous findings of Liu et al. [69] that pHCF is not necessarily tightly coupled to net calcification responses to seawater acidification in a wide range of marine calcifiers, including the temperate coral *Oculina arbuscula*. Net calcification represents a balance of dissolution and calcification and will be influenced by both physicochemical and other controls over calcification, all of which may confound a clear relationship with pHCF.

Two-way ANOVA tests were again used to explore the individual and combined effects of *p*CO2 and temperature on calculated calcifying fluid parameters of pHCF, [CO3 2−]CF, and DICCF. In *P. astreoides*, *p*CO2 had a significant effect on pHCF and [CO3 2−]CF. Temperature and the combined effect of temperature and *p*CO2 had significant effects on DICCF. Similarly, in *P. strigosa*, *p*CO2 had a significant effect on pHCF and [CO3 2−]CF, but temperature only had an individual effect on DICCF. In *S. siderea*, the calculated pHCF data did not meet assumptions of normality after transformation and, consequently, ANOVA results are not presented. There was a significant effect of *p*CO2 on [CO3 2−]CF of *S. siderea* and significant individual effects of both *p*CO2 and temperature on DICCF. Lastly, U. tenuifolia displayed no significant effects of either *p*CO2 or temperature on the calculated calcifying fluid parameters. Results of these analyses are presented in Table 4.

**Figure 4.** (**A**) Measured net calcification rate (mg cm−2 d−1) replotted from Bove et al. [38] (**B**) B/Ca-derived carbonate ion concentration of the coral calcifying fluid ([CO3 2−]CF, μmol/mol), and (**C**) combined δ11B and B/Ca-derived dissolved inorganic carbon concentration of the coral calcifying fluid (DICCF, μmol/mol). Triangle symbols (±1 SD) represent the mean value for each treatment condition, blue and red symbols represent the 28 ◦C and 31 ◦C treatment conditions, respectively. Linear versus centered quadratic fit was determined using the Akaike Information Criteria test (Table S8), with shading representing the 95% confidence interval. \* indicates a significant difference between 28 ◦C and 31 ◦C data for a given *p*CO2 treatment, as demonstrated by a *p*-value < 0.05 in a Welch's-T test.

**Table 3.** Net calcification data and calculated calcifying fluid pH and carbonate system parameters.

#### **Table 3.** *Cont.*

1 Refers to number of specimens analyzed; 2 ΩCF values calculated using the assumption of [Ca]CF = [Ca]SW.

**Table 4.** 2-way ANOVA output testing the influence of temperature and *p*CO2 on calculated calcifying fluid carbonate system parameters and coral and symbiont physiological parameters.

#### **Table 4.** *Cont.*

**\*** Data did not reach normality even after transformation steps, so ANOVA test was not performed.

We combined our data with a compilation of coral data from the literature using different approaches (geochemistry, microelectrode, pH-sensitive dye; Table S11). It is clear from almost all published studies that pHCF, [CO3 2−]CF, and DICCF are differentiated from seawater values and that this is an active compensatory process as pHCF, and [CO3 2−]CF values are increasingly elevated relative to seawater values under acidified conditions (Figures 5 and 6).

**Figure 5.** Compilation of δ11B-derived calcification fluid pHCF data from shallow-water zooxanthellate coral culturing experiments. We include data from cold-water azooxanthellate corals as a point of reference, as well as ranges of pHCF data as determined by SNARF pH-sensitive dyes and pH microelectrode analyses. 'RR' label denotes corals treated with the Ca-ATPase inhibitor ruthenium red [32]. Previously published data used in the figure are available in Table S12 alongside citations for data sources.

**Figure 6.** Compilation of combined δ11B and B/Ca-derived DICCF and [CO3 2−]CF measurements from available published data, as well as published microelectrode data. We include cultured shallow-water zooxanthellate corals, cold-water azooxanthellate corals, as well as a range of DICCF values as determined by SNARF-1 pH-sensitive dyes and pH microelectrode analyses. (**A**) DICCF as a function of pHSW (total scale), (**B**) DICCF as a function of DICSW, and (**C**) [CO3 2−]CF as a function of [CO3 2−]SW. 'RR' label denotes corals treated with the Ca-ATPase inhibitor ruthenium red [32]. Previously published data used in the figure are available in Table S12 alongside citations for data sources.

#### *3.3. Biological Compensation for External pH Changes, and the Importance of Coral and Symbiont Physiology*

Changes in pHCF across experimental conditions are relatively small, with values changing, for example, from 8.52 to 8.26 in the most sensitive coral species *S. siderea*. However, it is important to note that this represents a substantial compensation or buffering by the coral with respect to the much larger changes in pHSW, which range from 8.3 to 7.3 in the experiment.

To illustrate the chemical effects of this compensation, we provide calculations of the proton differential ([H+]SW − [H+]CF), maintained by the coral with respect to seawater under all experimental conditions (Figure 7, Table S9), for which larger values represent a greater difference in proton concentration in the calcification fluid relative to seawater. For example, for *P. astreoides* at 28 ◦C, the comparison between the 311 μatm *p*CO2 and the ~33,309 μatm *p*CO2 treatments show a proton differential of 0.5 × 10−8 mol/L at 400 μatm *p*CO2 and 6.3 × 10−8 mol/L at 3000 μatm, which represents a differential 12.4 times higher at the elevated *p*CO2 condition. At 31 ◦C for *P. astreoides*, the differential is 13.4 times higher between the ~400 μatm and ~3000 μatm *p*CO2 conditions. The same comparison shows a proton differential 11.4 and 13.4 times higher for *P. strigosa* at 28 ◦C and 31 ◦C, 11.9 and 13.9 times higher for *S. siderea* at 28 ◦C and 31 ◦C and 11.5 times higher for *U. tenuifolia* at 28 ◦C.

**Figure 7.** Proton differentials maintained by the coral species between seawater and their calcifying fluid ([H+]SW − [H+]CF). Values are given for preindustrial, present day, mid-century, and extreme (~3300 ppm) *p*CO2 conditions, with precise *p*CO2 values given in Table S1. Calculated values are provided in Table S9.

Using the compilation of pHCF data from the literature and from the present study, we compare the slopes of the relationships between pHCF and pHSW (Figure 8), with a slope of 1 indicating that changes in pHCF perfectly track changes in pHSW, and a slope of 0 indicating that the coral is perfectly compensating for external changes in pHSW and that pHCF is invariant across the experimental conditions. This compilation demonstrates that although there are cases where corals perfectly compensate for external pHSW changes, in most cases corals exhibit a modest decrease in pHCF with decreasing pHSW (Figure 8). Furthermore, δ11B-based estimates of pHCF were generally lower than that found by the microelectrode approach (Figure 8). We hypothesized in previous work that this could reflect different time intervals being recorded by these approaches [7], but it cannot be ruled out that there are systematic differences in the techniques, such as recording different microenvironments.

**Figure 8.** The sensitivity of pHCF to pHSW within the coral species studied here and reported in the literature. Y-Axis is the slope of the relationship between pHCF and pHSW, with a value of 1 showing that change in pHCF directly tracks the magnitude of the change in pHSW, and a value of 0 indicating perfect control over coral pHCF amidst variable pHSW. On X axis, numbers in parenthesis are the temperature of the experiment. Previously published data used in the figure are available in Table S12 alongside citations for data sources.

It should ultimately be ΩCF that is the most influential factor in coral calcification. If pHCF modestly decreases, but ΩCF remains high enough to support efficient coral calcification, then this may explain how corals continue calcifying despite acidification of their calcifying fluid. Calculating ΩCF requires an additional assumption due to the lack of constraints on [Ca2+]CF. We conducted a set of calculations where [Ca2+]CF is assumed to be equivalent to [Ca2+]SW (Table 2 and Table S7). The calculated saturation states were then plotted against coral calcification data (Figure 9). Our compilation of ΩCF exhibits inter-species variation, but none of the four species (*S. siderea*, *P. strigosa*, *P. astreoides* and *U. tenuifolia*) exhibit significantly different values from each other at under control temperature and *p*CO2 conditions (ANOVA test, *p* = 0.2), but were significantly higher compared to that calculated for *S. pistillata* [7], A. yongei (*p* < 0.05) and *P. damicornis* (*p* < 0.05) [7,67] using the same assumptions. Species-specific threshold values of ΩCF, where calcification is outweighed by dissolution, can be estimated from Figure 9, but care should be taken interpreting those absolute values due to the lack of constraints on [Ca2+]CF. There is a possibility that [Ca2+]CF could differ between species, as highlighted by previous work on A. yongei and *P. damicornis* [51]. In addition, the highest calculated ΩCF values do not translate to highest calcification rates for the corals, which may reflect prior observations that coral calcification is complex process beyond simple physicochemical manipulation of the calcifying fluid [78,82].

**Figure 9.** Relationships between boron isotope derived ΩCF and net calcification rates. ΩCF is calculated assuming that [Ca2+] of seawater is equivalent to [Ca2+] of the calcifying fluid. Published data used in this figure is given in Table S12 with citations for data sources.

Statistical analyses of coral and symbiont physiological data from this experiment have been explored elsewhere [39]. Here, we perform additional 2-way ANOVA tests on the same data to produce statistical tests that are handled identically between the geochemical and physiological data. In *P. astreoides*, we found significant individual and combined effects of *p*CO2 and temperature on Chl a, and total host energy reserves, but not on symbiont cell density. Significantly, and as noted in previous work of Bove et al. [39], the significant effect of temperature on Chl a in *P. astreoides* is actually to increase levels, whereas for all other significant effects reported from our ANOVA test they are characterizing decreases. However, it is important to note that temperature actually improved symbiont physiological parameters in this species, rather than repressing them as reported previously for this species [39]. In *P. strigosa*, only temperature had an effect on both symbiont cell density and total host energy reserves. Significant individual effects of both temperature and *p*CO2 on both symbiont cell density and Chl a were observed for *S. siderea*, whilst only temperature had an effect on total host energy for this species. For *U. tenuifolia*, only *p*CO2

had an effect on Chl a. Results of these analyses are presented in Table 4. Specimens of *S. siderea* were collected from both the inshore and offshore environments and previous work showed that reef environment affected physiological response; however, our results show a nonsignificant effect of reef environment on physiological data (Table S10), which likely reflects the difference in statistical approaches, with ANOVA tests employed here on single parameters versus the principal component analyses conducted by Bove et al. [39].

These physiological data are relevant for understanding how host and symbionts responses to environmental change may influence the chemistry of the calcifying fluid. It is notable that in the three coral species examined (excluding *U. tenuifolia*), temperature was found to influence total host energy reserves, or the total of the lipid, carbohydrate, and protein concentrations for a given area of coral host tissue. Temperature was not found to influence pHCF and [CO3 2−]CF, but in all three corals was found to reduce [DIC]CF (Table 4). Whilst this observation does not prove cause and effect, it is consistent with temperature stress causing depletion of host energy reserves, and reduced host input of respiration produced CO2 into the DIC pool for calcification. As temperature increase was associated with reduced symbiont cell density and Chl a in some of the corals, this is consistent with a scenario where the symbiont was less efficiently supplying energy to the host, leading to the observed decrease in energy reserves.

To further explore the relationship between these host and symbiont physiological changes and the source of carbon in the calcification fluid, we examined Δδ13C (δ13C31◦C − δ13C28◦C) for each *p*CO2 treatment. In Figure 10, Δδ13C is plotted against Δ[DIC] ([DIC]31◦C − [DIC]28◦C). This analysis shows that, on one hand, there is a significant difference in the source of carbon for calcification between *S. siderea* and *S. pistillata*, for which temperature did not influence δ13C at the higher *p*CO2 conditions. On the other hand, it shows that at higher *p*CO2 conditions, Δδ13C of *P. astreoides* and *P. strigosa* (Figure 10) shifted to more positive values and that Δ[DIC] shifted towards more negative values compared to the other corals, with the overall effect most pronounced in *P. astreoides*. The potential mechanisms driving these trends are discussed below.

**Figure 10.** Combined influence of temperature and *p*CO2 on the δ13C and [DIC] of the coral calcifying fluid. Data are expressed as Δδ13C (δ13C 31◦C − Δδ13C 28◦C) and Δ[DIC] ([DIC]31◦C − [DIC]28◦C) for each *p*CO2 treatment.

#### **4. Discussion**

Collectively, the data from this study and the literature suggest that most corals experience a degree of acidification of their internal calcification fluid media in response to external seawater acidification. Nonetheless, considerable biological compensation is observed for the studied species, which maintain a larger proton differential between the calcifying fluid and seawater under elevated *p*CO2. Therefore, the slight decreases in pHCF under elevated *p*CO2 may represent either a practical tradeoff by the coral, or a biological limit to controlling calcifying fluid chemistry that still allows the coral to maintain [CO3 2−]CF and ΩCF at a level that supports efficient calcification. The hypothesis that this is an active and energy-consuming adaptation by corals is supported by the finding that the ability of corals to regulate pH and carbonate chemistry of their calcifying fluid can be impaired by thermal stress and symbiont loss [7,25]. In contrast to our studies of *S. pistillata* and *P. damicornis* [7,25], we found that temperature did not result in a statistically significant change in pHCF regulation across all conditions in the Caribbean species investigated here, although it did under a subset of the experimental conditions in *S. siderea*. This may reflect that the highest temperature treatment in the present experiment [38,39], did not elicit as severe an effect on the corals and their symbionts as in the prior experiment [7,25], with the exception of *U. tenuifolia* that had too high a mortality at elevated temperature to be examined. Notably, the Ca-ATPase inhibitor experiments of Allison et al. [32], and the heat stress experiments of Guillermic et al. [7], on *Pocillopora damicornis* are the only experiments

where corals appeared to almost completely lose the ability to elevate pHCF relative to seawater, as shown in Figure 5.

Furthermore, we are able to examine associations between coral and symbiont physiological responses and calcifying fluid regulation. This revealed a significant depletion in total host energy reserves at elevated temperature, as well as a reduction in [DIC]CF in all three corals examined. The reduction in calculated [DIC]CF with temperature increase was also seen in the *S. pistillata* and *P. damicornis* experiment described in Guillermic et al. [7]. This result is consistent with a reduction in the energy transfer between the symbiont and host leading to the host depleting its energy reserves and/or the host consuming more energy in response to temperature stress. Although we cannot prove cause and effect, changes in the energy balance between host and symbiont could then lead to a reduction in the production of metabolic CO2 that is contributed to the DIC pool for calcification, which is consistent with the reduction we see in calculated [DIC]CF in these corals. The story is also more complex, however, with additional interactions apparent from the δ13C data.

Although the δ13C data did not indicate major differences in host and symbiont physiological influence on [DIC]CF for the different coral species under the control CO2 conditions, significant differences in δ13C emerged under elevated temperature and CO2 amongst the species, with shifts towards positive δ13C associated with amplified reductions in [DIC]CF in two of the coral species studied.

It is thought that changes in the flux of carbon to and from photosynthesis and respiration, respectively, may change the δ13C of the DIC pool used for calcification. Photosynthesis preferentially consumes 12C, which may then result in 13C enrichment in the DIC pool for calcification if this pool acts as a source of CO2 for photosynthesis [83]. As the rate of photosynthesis increases and/or the rate of respiration decreases, the DIC pool should have more positive δ13C resulting in more positive coral skeletal δ13C [83,84]. In the present experiment, we can see that host respiration input is likely decreasing as the host consumes energy reserves at elevated temperature. There is also an overall reduction in [DIC]CF, which is likely due to a reduction in CO2-input from respiration. One way to explain the relationship between the Δδ13C and Δ[DIC] data in Figure 10 is that, in addition to the reduction in respiration-sourced CO2 to the DIC pool for calcification, photosynthetic drawdown of 12CO2 by the symbiont is significantly enriching the δ13C of the DIC pool in *P. astreoides* and *P. strigosa* at highest temperature and *p*CO2 conditions, which could further deplete [DIC]CF and drive more positive Δδ13C. This effect was not seen in a similar experiment on *S. pistillata* [7] where symbiont loss and bleaching of the coral occurred [25], which suggests that photosynthetic influence over the DIC pool was minimal at the higher temperature and *p*CO2 conditions. This interpretation is also congruent with observations from this experiment that the symbiont in *P. astreoides* is not negatively impacted by increased temperature [39] and is evidence for a divergent host and symbiont physiological response to environmental parameters in this coral species. Conversely, the effects of host respiration and symbiont photosynthesis remained relatively balanced in terms of their influence on the source of DIC for calcification in *S. siderea*. It has been previously noted that *P. astreoides*, *S. siderea*, and *P. strigosa* host differing algal symbiont communities which may result in differing carbon allocation between the host and symbiont [39]. For example, *S. siderea* mainly hosts *Cladocopium* and *Breviolum* whereas *P. astreoides* hosts *Breviolum* and *Symbiodinium* [39]. The potential changes in carbon flux between the host and symbiont is interesting to consider in the context of the debate on the extent of mutualism between the coral host and symbiont, and whether the symbiont develops features that are more parasitic in nature under environmental change [39,85,86].

Overall, the results presented here and in previous work on these corals [7,38,39,87] highlight the considerable complexity in host and symbiont responses to ocean warming and acidification, demonstrating that these responses are closely coupled to a coral's physicochemical control of their calcification process. The combined physiological data from this study, preceding studies [38,39,87], as well as the work of other groups (e.g., [88]), converge on the conclusion that *S. siderea* is a relatively resilient species to environmental

change and so may be amongst the least impacted by future warming and acidification, compared to a number of more sensitive Caribbean species studied such as *O. faveolata*, *M. cavernosa*, and *U tenuifolia* [38,88]. Some differences in observations on *P. astreoides* between studies exist in its temperature sensitivity [38,88] which may reflect intraspecific differences in host or symbiont physiology, with the evidence here suggesting thermal sensitivity can be relatively low for the symbiont of at least some Caribbean *P. astreoides* but that reductions in net calcification can still occur in non-bleached corals in acidified seawater.

The type of data presented here provides useful insights into the underlying physiological mechanisms of coral adaptation, and the variation in responses between species in an experiment. Whilst difficult to make regional or global predictions from single studies, a recent metanalyses of experimental data suggested that at the levels predicted over the next century acidification is less of a threat to Caribbean corals compared to warming but that there is evidence for variation across the region and considerable data gaps [89]. Despite this finding, the evidence presented in this paper suggest acidification resilient Caribbean corals maintain increasingly large proton differentials with seawater under acidification, highlighting that there may be an energetic cost to this adaption that corals would have to maintain indefinitely in the future. The work here also highlights that warming can have complex influences on the calcification processes even outside a severe bleaching event through changes to the partitioning of carbon between the host, symbiont, and the pool of ions used for calcification. The sustainability of these adaptations to acidification and warming are largely unexplored in Caribbean corals, but is highly relevant given findings from this experiment that even if positive net calcification is maintained in environmental challenge experiments corals could be depleting energy reserves [39]. Therefore, it will be important for future research to attempt to determine whether even the relatively resilient species such as *S. siderea* can maintain their short-term adaptive responses over the longer term.

Overall, the results presented here and in previous work on these corals [7,38,39,87] highlight the considerable complexity in host and symbiont responses to ocean warming and acidification, and demonstrate that these responses are closely coupled to a coral's physicochemical control of their calcification process.

**Supplementary Materials:** The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/jmse10081075/s1, Table S1: Measured Culture Conditions in the 8 Experimental Setups from Bove et al., 2019; Table S2: Calculated Culture Conditions in the 8 Experimental Setups from Bove et al., 2019; Table S3: Physiological data of the host and the symbionts originally published in Bove et al. (2021) from the same corals used in this study; Table S4: Boron isotope standard reproducibility; Table S5: Reproducibility of B/Ca analyses of CamWuellestorfi and NEP standards; Table S6: Functions used for data transformations according the method of Tukey et al., 1977; Table S7: Geochemical data and calculated parameters from individual Caribbean coral specimens, of which the average per experimental condition are given in main text Tables 1 and 2; Table S8: Average and individual coral specimen geochemical data and calculations from *Stylophera pistillata*; Table S9: AIC results for Caribbean coral geochemical data as a function of the pH of seawater in the experiment and net calcification as a function of calculated CF parameter data; Table S10: Calculations of pH gradient between seawater and the calcifying fluid; Table S11: ANOVA results for Caribbean coral geochemical and physiological data; Table S12: Compilation of published data used in the main text.

**Author Contributions:** Conceptualization, R.A.E., K.D.C. and J.B.R.; methodology, R.A.E., S.M., K.D.C. and J.B.R.; formal analysis, C.B.B., L.P.C., S.M., B.A.C., M.G., I.D.C. and R.A.E.; investigation, C.B.B., L.P.C., M.G., I.D.C., K.D.C., R.A.E. and J.B.R.; resources, R.A.E., K.D.C. and J.B.R.; data curation, M.G. and R.A.E.; writing—original draft preparation, R.A.E. and I.D.C.; writing—review and editing, all authors; visualization, M.G., I.D.C. and R.A.E.; supervision, R.A.E., K.D.C. and J.B.R.; project administration, R.A.E., K.D.C. and J.B.R.; funding acquisition, R.A.E., K.D.C. and J.B.R. All authors have read and agreed to the published version of the manuscript.

**Funding:** RAE and JBR acknowledge support from National Science Foundation grants OCE-1437166 and OCE-1437371. KDC acknowledges support from NSF grant OCE-1459522. RAE, MG, ID, and BA were also supported by the "Laboratoire d'Excellence" LabexMER (ANR-10-LABX-19) Chair to RAE, co-funded by a grant from the French government under the program "Investissements d'Avenir" and MG was supported by an IAGC student grant 2017. RAE acknowledges support from the Pritzker Endowment to UCLA IoES, and JBR acknowledges support from the ZMT and the Hanse-Wissenschaftskolleg Fellowship Program. BA was supported by an NSF GRFP. M.G., I.D., and BA were supported by the Center for Diverse Leadership in Science, funded by NSF, the Packard Foundation, Sloan Foundation, and Silicon Valley Community Foundation.

**Data Availability Statement:** Data will be archived in the BCO-DMO archive on publication of this article (https://www.bco-dmo.org/, accessed on 24 July 2022).

**Acknowledgments:** We thank Belize Fisheries Department for all associated permits, the Toledo Institute for Development and Environment (TIDE), and the Southern Environmental Association (SEA) for their continued support, as well as Garbutt's Marine for assistance in the field.

**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

#### **References**


**2.2 Aluminium production**

Bauxite production has increased 50% in the past decade to an all time high of over 200 million tonnes worldwide; with Australia the largest producer, followed by China and Brazil. Four tonnes of bauxite are used to produce two tonnes of alumina, which can then produce one tonne of aluminium. Recycling of aluminium requires 95% less energy than for primary aluminium production. In order to meet the mechanical and corrosion performance requirements for many alloys as required under performance specifications, much of the recycled metal must be blended or diluted with primary metal to reduce impurity levels. The result is that, in many cases, recycled metal tends to be used primarily for lower grade casting alloys and products (Polmear 2004), however with ~35% of Al being produced from recycled material, the future ramifications for corrosion will need addressing.

### **2.3 Physical metallurgy of aluminium alloys**

The functional properties of aluminium alloys (mechanical, physical, and chemical) depend on alloy composition and alloy microstructure as determined by casting conditions and

their roles in corrosion of alloys. The physico-chemical studies have led to a better characterisation of the composition of intermetallic particles, and, importantly of their spatial distributions. The convergence of the electrochemical and physicochemical approaches, in combination with modelling, is leading to a statistical basis for understanding the influence of the intermetallic particles on corrosion of aluminium alloys. This chapter therefore aims to summarise our current understanding of the microstructure of high strength aluminium alloys, particularly the more microstructurally complex alloys such as the 2xxx, and 7xxx series alloys. It then looks at how different components of the microstructure contribute to corrosion processes and finishes by examining the principles of protection of aluminium alloys using traditional and newer techniques used to assess the

While aluminium alloy microstructure, for some specific alloys, is relatively well known, the microstructures for some high strength aluminium alloys, particularly the older AA2*xxx* alloys, is not well described or understood in the scientific literature, particularly the corrosion literature. This is partially due to manufacturing processing conditions which do not realise the intended microstructure and partially due to quasi or non-equilibrium microstructure existing in real alloys because of the difficulty of obtaining full thermodynamic equilibrium. Typical examples of common high strength alloys used in aircraft manufacture, for example, include AA2024-T3, AA7075-T6 and AA6061-T6. This section, therefore, provides a general

Processing can significantly alter the bulk microstructure, resulting in microstructural gradients and zones with different characteristics. A good example of these changes can be found in wrought alloy sheet product. First there is a gradient in grain size and constituent particle size across the sheet. Second, shear deformation, resulting from rolling, creates a surface layer called a near surface deformed layer (NSDL) with a very fine microstructure which may have a different degree of precipitation compared to the bulk depending on the

Bauxite production has increased 50% in the past decade to an all time high of over 200 million tonnes worldwide; with Australia the largest producer, followed by China and Brazil. Four tonnes of bauxite are used to produce two tonnes of alumina, which can then produce one tonne of aluminium. Recycling of aluminium requires 95% less energy than for primary aluminium production. In order to meet the mechanical and corrosion performance requirements for many alloys as required under performance specifications, much of the recycled metal must be blended or diluted with primary metal to reduce impurity levels. The result is that, in many cases, recycled metal tends to be used primarily for lower grade casting alloys and products (Polmear 2004), however with ~35% of Al being produced from

The functional properties of aluminium alloys (mechanical, physical, and chemical) depend on alloy composition and alloy microstructure as determined by casting conditions and

recycled material, the future ramifications for corrosion will need addressing.

overview of the relationship between processing and microstructure.

degree of protection.

**2. Microstructure**

heat treatments.

**2.2 Aluminium production**

**2.3 Physical metallurgy of aluminium alloys**

**2.1 Aluminium alloys in general**

thermomechanical processing. Only a small number of metals have sufficient solubility to serve as major alloying elements (Das 2006) and alloys derived from these few form the basis of the present classes of commodity Al-alloys. Magnesium, zinc, copper and silicon have significant solubility, whilst additional elements (of <1% solubility) are also used to confer improvements to alloy properties, namely grain refinement, and such elements include manganese, chromium, zirconium, titanium and less commonly (due to cost) scandium (Hatch 1984; Polmear 2004). Alloying of Manganese with Fe-containing intermetallic particles reduces the electrochemical activity of these Fe-containing particles thus improving the corrosion resistance of the alloy (Polmear 1995)

The low strength of pure aluminium (~10 MPa) mandates alloying. The simplest strengthening technique is solution hardening, whereby alloying additions have appreciable solid solubility over a wide range of temperatures and remain in solution after many thermal cycles.

The most significant increase in strength for aluminium alloys is derived from age hardening (often called precipiation hardening) which can result in strengths as high as 800 MPa. The principal of age hardening requires that the solid solubility of alloying elements decreases with temperature. The age hardening process can be summarised by the following stages:

The fundamental aspects of decomposition of a supersaturated solid solution are complex (Raviprasad, Hutchinson et al. 2003; Kovarik, Miller et al. 2006; Winkelman, Raviprasad et al. 2007). Typically however, Guinier-Preston (GP) zones and intermediate phases are formed as precursors to the equilibrium precipitate phase (Hatch 1984) (Figure 1 reveals a

Fig. 1. Dark field scanning transmission electron micrograph of coarse Al2CuMg precipitate particles in an AA2xxx (Al-Cu-Mg) alloy - imaged down <100> zone axis

High Strength Al-Alloys: Microstructure, Corrosion and Principles of Protection 227

(Hatch 1984)). The temper designation system adopted by the Aluminium Association is

Copper is one of the most common alloying additions, since it has appreciable solubility and a significant strengthening effect by its promotion of an age hardening response. These alloys were the foundation of the modern aerospace construction industry and, for example AA2024 (Al-4.4Cu-1.5Mg-0.8Mn), can achieve strengths of up to 520MPa depending on temper. The microstructure of this series is considered further below. Cu, however, is one of the nobler alloying elements and therefore supports a high rate of oxygen reduction which drives one half of the galvanic reaction. The cell is completed by the dissolution of any element less noble, particularly Al thereby facilitating the onset and propagation of

The Al-Zn-Mg alloy system provides a range of commercial compositions, primarily where strength is the key requirement. Al-Zn-Mg-Cu alloys have traditionally offered the greatest potential for age hardening and as early as 1917 a tensile strength of 580MPa was achieved, however, such alloys were not suitable for commercial use until their high susceptibility to stress corrosion cracking could be moderated. Military and commercial aerospace needs led to the introduction of a range of high strength aerospace alloys of which AA7075 (Al-5.6Zn-2.5Mg-1.6Cu-0.4Si-0.5Fe-0.3Mn-0.2Cr-0.2Ti) is perhaps the most well known, and which is now essentially wholly superseded by AA7150 (or the 7x50 family). The high strength 7xxx series alloys derive their strength from the precipitation of η-phase (MgZn2) and its precursor forms. The heat treatment of the 7xxx series alloys is complex, involving a range of heat treatments that have been developed to balance strength and stress corrosion cracking performance, comprising secondary (or more) heat treatments that can include

The surface layers of aluminium alloys can be altered during processing and storage environments, which adds complexity to the surface finishing and corrosion performance (Fishkis and Lin 1997). These effects include the formation of near surface deformed layers (NSDL) during mechanical processing, the elongation of crystalline structure during rolling and extrusion, breakup of brittle intermetallic particles, differences in surface roughness and

Casting from the melt is the first processing step.  Sand moulds are gravity fed whereas the metal moulds used in permanent mould casting are either gravity fed or by using air or gas pressure to force metal into the mould. In high pressure die castings, parts up to approximately 5 kg are made by injecting molten aluminium alloy into a metal mould

For large production scale, direct chill (DC) casting is a semi-continuous process used for the production of rectangular ingots or slab for rolling to plate, sheet, foil and cylindrical ingots or billet for extruded rods, bars, shapes, hollow sections, tube and wire. DC casting is the first step in the production of Al alloys prior to the thermomechanical treatments, and whilst it may appear to be a topic not requiring discussion in such a chapter, it is important

porosity, and the segregation of specific alloying elements to the surface.

similar for both wrought and cast aluminium alloys.

retrogression and re-aging (Sprowls 1978).

under substantial pressure using a hydraulic ram.

**2.5 Processing of aluminium alloys**

**2xxx**

corrosion.

**7xxx**

typical micrograph showing precipitate particles). GP zones are formed when solute atoms (e.g. Cu, Zn and Mg) accumulate along preferred crystal directions in the Al lattice and form a strengthening phase.

Properties can be enhanced further by careful thermo-mechanical processing that may include heat treatments like duplex aging and retrogression and re-aging. Maximum hardening in commercial alloys is often achieved when the alloy is cold worked by stretching after quenching and before aging, increasing dislocation density and providing more heterogeneous nucleation sites for precipitation. Whilst only moderate increases in strength can be obtained in Al-alloys by exploiting the Hall-Petch1 relationship (Polmear 1995), refinement is important for a range of properties including fracture and toughness. Grain refinement in aluminium alloys is achieved by additions of small amounts of low solubility elements such as Ti and B to provide grain nuclei, and by recrystallisation control using precipitates called dispersoids (typically 40 x 200 nm) which are formed from aluminium and alloying additions such as Cu, Cr, Zr or Mn to promote insoluble particles which subsequently can restrict or pin grain growth.

The microstructures developed in aluminium alloys are complex and incorporate a combination of equilibrium and non-equilibrium phases. Non-equilibrium phases exist in essentially all high-strength alloys, and as such, their properties are very temperature dependent.

Typical commercial alloys can have a chemical composition incorporating as many as ten alloying additions (with a number of these additions being unavoidable impurities). As such, from a corrosion perspective, one must understand the role of impurity elements on microstructure. Whilst not of major significance to alloy designers, impurity elements such as Fe, Mn and Si can form insoluble compounds called constituent particles. These are comparatively large and irregularly shaped with characteristic dimensions ranging from 1 to ~ 50 μm. These particles are formed during alloy solidification and are not appreciably dissolved during subsequent thermo-mechanical processing. Rolling and extrusion tend to break-up and align constituent particles within the alloy. Often constituents are found in clusters made up of several different intermetallic compound types. Because these particles are rich in alloying elements, their electrochemical behaviour can be significantly different to the surrounding matrix phase. In most alloys pitting is associated with specific constituent particles present in the alloy (Buchheit 1995; Liao, Olive et al. 1998; Wei, Liao et al. 1998; Guillaumin and Mankowski 1999; Park, Paik et al. 1999; Ilevbare, Schneider et al. 2004; Schneider, Ilevbare et al. 2004; Lacroix, Ressier et al. 2008; Lacroix, Ressier et al. 2008; Boag, Taylor et al. 2010. These are discussed below.

### **2.4 Alloy classification**

The International Alloy Designation System (IADS) gives each wrought alloy a four-digit number of which the first digit is assigned on the basis of the major alloying element(s) (Polmear 1995; Winkelman, Raviprasad et al. 2007)). The main alloying element for AA2xxx is Cu and for AA7xxx is Zn, with Mg playing a important role is both classes of alloys.

For cast aluminium alloys, alloy designations principally adopt the notation of the Aluminium Association System. The casting compositions are described by a four-digit system that incorporates three digits followed by a decimal (described in more detail in

1 The Hall-Petch relationship states that the yield strength is proportional to the inverse square root of the grain size.

(Hatch 1984)). The temper designation system adopted by the Aluminium Association is similar for both wrought and cast aluminium alloys.