Macquarie Island (54°50^′ S, 158°85^′ E) is a small sub-Antarctic island (128 km2) located in the Southern Ocean just north of the polar front, 1200 km south-west of New Zealand and 1300 km from the Antarctic continent (Fig. 1). It is one of the few landmasses within the Polar Frontal Zone and modern core SHW belt (50–55°S; Fig. 1), making it ideally suited to study past and current changes in the SHW, temperature, and precipitation. It has a harsh, cool, wet, oceanic climate with low seasonality and high wind velocities throughout the year. Together, these represent the influence the SHW have on climate in the region (Selkirk et al., 1990). The SHW prevail almost exclusively from the west and north-west with a mean annual wind speed of 35 kmh-1 and gusts reaching 185 kmh-1 (between 1948–2025; BOM 2025). The continual dominance of the SHW drive environmental and ecosystems responses on a west-to-east gradient across the island (Chau et al., 2019; Meredith et al., 2022), including the deposition and accumulation of wind-blown inputs such as sea spray and minerogenic aerosols (Buckney and Tyler, 1974; Saunders et al., 2009). Mean annual temperature ranges from 3.1–6.6 °C, and the island experiences high annual rainfall of > 1000 mm with > 317 rainydaysyr-1 (between 1948–2025; BOM 2025). Rainfall has increased in recent decades with a higher frequency of intense rainfall events, mostly occurring during winter, which are then accompanied by drier, windier summers (Andersen et al., 2009; Kong et al., 2025). Persistent cloud cover over the island results in low light levels and sunshine hours per day (average 2.4 h from 1948–2022; BOM, 2025).

Macquarie Island is geologically unique, being the only location worldwide where an intact marine ophiolite sequence of oceanic crust and upper mantle is exposed above sea level (Davis, 1987). The island is composed mostly of pillow basalts with interspersed flows of massive basalt (Selkirk et al., 1990). Dolerite, ultrabasics and intrusives are also present but are confined to the northern third of the island (Mawson, 1943). As widespread glaciation did not occur during the Last Glacial Maximum (26–20 ka), marine, periglacial and subaerial erosional, rather than glacial processes, shaped the island as well as lake formation and ontogeny. The island is fringed by a low coastal terrace leading to steep-sided slopes (20–40°) that rise to form the island plateau sitting at ∼ 200–400 m a.s.l. (Selkirk et al., 1990; McBride and Selkirk, 1998).

The island has numerous shallow and deep lakes and ponds across the plateau and coastal terrace (Fig. 2). High accumulation of surface water and a high water-table at or very near the surface lead to the formation of extensive mires across the island (Löffler, 1984). While lake edges can form thick ice cover during winter, complete freezing of the lakes is not typically observed (Evans, 1970; Selkirk-Bell and Selkirk, 2013). The island is vegetated by bryophytes, tussock grass, herbs and sedges, with no shrub or tree species present (Selkirk et al., 1990).

Surface sediments and water samples were collected from lakes on Macquarie Island during the 2022–2023 austral summer (referred to as 2022). Sites were selected to replicate those sampled in 2018 that were published by Meredith et al. (2022).

Lake surface sediments were collected from 30 plateau (inland) sites for diatom analyses (lake ID = LK), representing conditions more than 10 years post-rabbit and rodent eradication. Surface sediments (top 2 cm) were collected from each site using a long-handled scoop from < 1.5 m water depth. This method for sediment collection was selected for its logistical feasibility. Sediment mixing was minimised by visually assessing sampling depth and subsampling where necessary to retain only the upper ∼ 2 cm. Sediments were generally well consolidated and remained intact during collection. Based on available lead-210 (210Pb) chronologies from Macquarie Island lake cores, this interval represents approximately 10 years of accumulation (Saunders et al., 2013; Saunders et al., 2018), comparable to surface sediment sampling approaches used in previous studies (e.g. Saunders et al., 2009). An additional 17 coastal and five plateau sites sampled in 2006 (lake ID = S; Saunders et al., 2009) were included in the diatom dataset to extend the EC and nutrient gradients of the updated dataset, totalling 52 samples (Fig. 2a). Two lakes were replicated in the 2006 and 2022 seasons (S9 = LK40, S18 = LK2; Fig. 2a).

Lake water general parameters were measured in-situ at each site, including temperature, EC, dissolved oxygen (DO), and pH using a YSI ProQuatro Multiparameter Meter, with calibration performed prior to every sampling trip. DO was calibrated in water-saturated air following YSI manufacturer protocols. EC was calibrated using a 1413 µScm-1 standard solution, and pH was calibrated using a three-point procedure with pH 4.0, 7.0, and 10.0 buffer solutions. Water samples were collected at all diatom sampling sites to measure total oxidised nitrogen (TON), phosphate (PO43-), and silica (Si). Additional water samples were collected from 40 plateau sites for water chemistry analysis (Fig. 2b), including major ions and stable water isotopes (oxygen [δ18O] and hydrogen [δ2H]). Each site was sampled three times across the 2022–2023 season (November, December to January, and February). All water samples were collected from ∼ 20 cm below the water surface and were filtered in-situ with 0.45 µm polyethersulpone filters into High Density Poly-Ethylene (HDPE) bottles following the method described by Meredith et al. (2009). Water samples were refrigerated (4 °C) until analysis.

Major ions, and oxygen (δ18O) and hydrogen (δ2H) stable isotopes were analysed at the Australian Nuclear Science and Technology Organisation (ANSTO). Cations and anions were analysed using inductively coupled plasma-atomic emission spectrometry (ICP-AES). δ18O and δ2H stable isotopes were analysed with a Picarro L2130-i Cavity Ring-Down Spectrometer. Values were reported as per mill (‰) deviations relative to the international standard V-SMOW (Vienna Standard Mean Ocean Water), with a reproducible precision of ± 0.2 ‰ and ± 1.0 ‰, respectively.

Nutrient data from 2006 samples (filtered at 0.45 µm) measured soluble reactive phosphate (SRP), TON (nitrate [NO3] + nitrite [NO2]), and silicate (Si) using an Alpkem Autoanalyser (Continuous Flow Solution Analyser), representing the operationally defined dissolved inorganic (and therefore readily bioavailable) fractions of total N, P, and Si. In contrast, the 2022 dataset measured TON, PO43-, and Si ions using ICP-AES at ANSTO on filtered, undigested waters, with results reported as the corresponding inorganic species and concentrations consistently at or near detection limits. Despite methodological differences, the two approaches yield consistently low and broadly comparable concentrations in the two replicate lakes across sampling trips: for S9/LK40, PO43- concentrations were 0.002 mgL-1 (autoanalyser) and < 0.01 mgL-1 (ICP-AES), and TON concentrations were 0.006 mgL-1 (autoanalyser) and < 0.06 mgL-1 (ICP-AES); for S18/LK2, PO43- concentrations were 0.004 mgL-1 (autoanalyser) and < 0.01 mgL-1 (ICP-AES), and TON concentrations were 0.005 mgL-1 (autoanalyser) and < 0.06 mgL-1 (ICP-AES). Furthermore, if the 2022 analyses targeted the same reactive fractions measured in 2006, the results would still fall below or close to detection limits.

Diatom preparation followed methods described by McBride (2009). Cleaned diatom solutions were mounted onto slides using Norland Optical Adhesive 61. At least 300–400 frustules were counted per sample, using Differential Interference Contrast (DIC) and oil immersion at 1000× magnification on a Zeiss Axioskope microscope, mounted with a TOUPTEK camera (U3CMOS). Species identification was primarily based on sub-Antarctic taxonomy described in Van de Vijer et al. (2002); Sterken et al. (2015); Sabbe et al. (2019); and Van de Vijver (2019). Species were photographed and documented (see Supplement for an illustrated species catalogue).

Analyses of 40 plateau lakes on Macquarie Island showed that lake water general parameters (EC, pH and DO), and nutrients, did not vary significantly (p > 0.01) across the 2022 sampling events (E2–4; Table 1). Temperature varied, being significantly lower in E2 compared to E3 and E4 (p < 0.01). Lakes were moderately acidic (pH 5.7) to alkaline (pH 9.14). Mean EC ranged from 126–261 µScm-1, with a decrease in EC from west to east across the island. Lakes were oxic (DO = 8.64–12.61 mgL-1) and oligotrophic, with PO43- and TON concentrations under or close to detection limits (< 0.01–0.02 mgL-1 and < 0.06–0.1 mgL-1, respectively). Similarly, comparison with data from plateau lakes sampled in 2018 showed no significant difference (p > 0.01) across all lake water general parameters, excluding temperature, indicating generally stable conditions in plateau lakes across years. However, a comparison between plateau lakes measured in 2022 and coastal lakes in 2006 did show significant differences (p < 0.01). Coastal sites in 2006 were generally eutrophic with higher nutrient ranges (TON = 0.007–4.636 mgL-1 and PO43- = 0.1–9.9 mgL-1), and higher EC (406–1482 µScm-1), while temperature, DO, pH, Si were not significantly different (p > 0.01) (Table 1; see Table S1 and S2 in the Supplement for full results).

Major ion analysis of the 40 plateau lakes showed that, although dilute in concentration, Cl (1.1–3.7 mmolL-1) and Na (0.9–2.6 mmolL-1) dominate the ionic composition of all lake waters (Fig. 3; see Table S3 for full cation and anion results). All lakes showed similar ionic ratios to seawater for SO4, Cl, Mg, and Na, suggesting a marine origin. Seawater ionic ratios diverged for K, Ca and F for some lakes, while SiO2 was higher in all lakes, suggesting additional sources for these ions (Fig. 4).

Statistical analyses showed no significant (p > 0.005) differences in major ion concentrations across 2022 sampling events (E2–4). Broader changes were detected between 2018 and 2022, with Cl, SO4, Br, and Mg all showing significantly higher mean concentrations (p < 0.005) in 2018 compared to all 2022 sampling events (E2–4). Fe, Na, K, Ca, F, SiO2, and Al did not significantly vary (p > 0.005) between sampling events. All ions that show significant variation (p < 0.005) have predominantly marine sources.

A PCA shows the relationship between lake water chemistry parameters, with samples grouped by lake type and sampling year (Fig. 5). Together, PC1 and PC2 captured 53 % of the total variance in the dataset. PC1 represents a salinity and sea-spray gradient with variability in EC, distance from the west coast, Na, Cl, Br, Ca, Mg, and K captured. PC2 represents an altitude and terrestrial ion gradient with variability in elevation, temperature, SiO2, Ca, Fe, and F captured. Lakes cluster according to environmental processes (groups derived from Meredith et al., 2022), with SSA influenced lakes having positive PC1 scores, which suggests higher concentrations of marine derived ions. The grouping of samples influenced by catchment processes and those influenced by rainfall is driven by PC2 with catchment influenced lakes having lower elevation and higher ion concentrations. SSA and rainfall influenced lakes cluster based on the year that they were sampled with greater ion concentrations in 2018.

δ2H and δ18O were measured in the 2022 samples and ranged from -38.3 ‰ to -9.2 ‰ and -38.3 ‰ to -0.67 ‰, respectively. Lake waters in 2018 and 2022 fell below the Global and Cape Grim (northwest Tasmania) Meteoric Water Lines (MWLs), suggesting slight isotopic enrichment in Macquarie Island's lakes (Fig. 6). In 2018, lake waters were significantly higher in δ2H (mean -24.8 ‰) and δ18O (mean -2.83 ‰) compared to 2022 (mean δ2H = -24.8 ‰ and δ18O = -3.55 ‰). Significant isotopic enrichment of δ2H and δ18O (p < 0.001) can be seen in the data at the beginning of the 2022 austral summer (E2–3; Fig. 6a). SSA influenced lakes tended to have higher isotopic values, while catchment influenced lakes had lower values (Fig. 6b), with a significant difference between all lake types detected (p < 0.001). LK20 and LK21 from E2 were outliers, plotting above the MWLs with lower δ18O values. Cl concentrations appeared to be related to δ2H and δ18O values (Fig. 6c), however the correlation between the parameters was low (R2 ≤ 0.24). This lack of relationship was consistent across lake types and sampling events.

The seasonal hydrochemistry dataset presented in this study support the 2018 baseline assessment (Meredith et al., 2022) that lake water chemistry is controlled by SSAs, terrestrial catchment processes, elevation and rainfall dilution. The seasonal water chemistry data, which is presented for the first time in this study, shows that there is no significant variation in major ions across the 2022–2023 austral summer. Comparison of 2018 and 2022 data shows that significant variation (p < 0.005), in some major ions is evident, with higher concentrations in 2018 of Br and Cl, SO4, and Mg associated with SSAs. Although not statistically significant (p > 0.005), other sea-spray derived ion mean values (Na, Ca, K) were higher in 2018 (Fig. 3; Table S4). However, not all ions increased in concentration, and terrestrially derived ions such as Fe, F, Si, and Al were lower in 2018, suggesting that lakes in 2022 had stronger SSA influence.

Despite these changes, PCA of the 2018 and 2022 lake water chemistry datasets (Fig. 5) show that lakes typically cluster by the lake types identified in Meredith et al. (2022) (i.e., as SSA, catchment and rainfall influenced). This indicates that the major hydrogeochemical processes influencing Macquarie Island lakes are consistent between years, with no major environmental shifts occurring in weathering and erosion of the island's geology, suggesting these processes are stable on an annual time scale. Identifying hydrogeochemical stability is important for identifying lake sites suitable for diatom–conductivity inference models. It also strengthens palaeoclimate interpretations by suggesting that local lake dynamics are relatively constant, supporting the hypothesis that in sea-spray dominated lakes, proxies primarily record externally forced changes driven by the SHW rather than internal hydrological or geochemical dynamics (Saunders et al., 2018; Perren et al., 2020). This further emphasises that water chemistry characteristics are critical to consider in site-selection to develop reliable SHW reconstructions on Macquarie Island.

Interpreting environmental proxies as direct indicators of climate variability can be challenging, as multiple processes may produce similar signals (Molén, 2024). When using diatom–conductivity models to infer past SHW variability, it is essential to consider how near surface evaporation has the potential to concentrate ions in surface waters and mimic the effects of other processes such as increasing lake water salinity from SSA deposition due to stronger winds. Although this study and previous studies (Evans, 1970; Buckney and Tyler, 1974; Meredith et al., 2022) have demonstrated that SHW-driven SSA inputs are a dominant control on lake water chemistry on Macquarie Island, the role of evaporation in amplifying these signals remains unclear.

To explore this, we analysed δ2H and δ18O values from 2018 and across the 2022–2023 Austral summer. Isotopic enrichment is evident across the 2022–2023 season (Fig. 6a), indicating a strengthening evaporative signal through summer. Lake stable water isotopes sampled in 2018 were significantly more enriched than the 2022 mean, as expected given that the 2018 samples were collected in late summer, when evaporative effects are strongest and cumulative due to warmer temperatures throughout summer. This is supported by lake water temperatures being significantly lower in early summer (E2) compared to late summer (E3 and E4; Table 1). Given Macquarie Island's persistently high cloud cover, humidity, and low sunshine hours (BOM, 2025), solar evaporation is likely limited and confined to the summer season. Evaporation can produce heavy-isotope enrichment and the residual lake water becomes progressively enriched in heaver isotopes (δ2H and δ18O), moving away from the Global MWL (Gat, 1996). Comparisons between E1 (2018) and E4 (2022), which were sampled at the same time of the year in January–February provide a valuable comparison of potential interannual variability in lake water chemistry and processes (Fig. 3, Table S4). These two sampling events show near identical mean isotopic composition (p > 0.01; δ2H = -20.7 ‰ and δ18O = -2.8 ‰ in 2022, and δ2H = -20.1 ‰ and δ18O = -2.8 ‰ in 2018), suggesting broadly stable summer evaporative conditions between years. Furthermore, SSA influenced lakes on the plateau are in proximity to the west coast and have the greatest exposure to the SHW (Fig. 2b). These lakes have significantly higher isotopic enrichment (Fig. 5b), providing further evidence that wind is likely the primary driver of evaporation in plateau lake waters across Macquarie Island, particularly in lakes located on the west coast, which may be most suitable for reconstructions of SHW dynamics (Saunders et al., 2018). As both wind-enhanced evaporation and wind-driven SSA transport and deposition contributes to the concentration of SSA ions in these lakes, both ion deposition and concentration reflect a SHW signal.

Cl- is a robust tracer of hydrogeochemical processes and, together with δ2H and δ18O values, can be used to better understand evaporation (Kirchner et al., 2010). While the isotopic enrichment observed generally indicates an evaporative signal, the absence of a correlation between Cl- and δ2H or δ18O (Fig. 6c) suggests that isotopes are capturing short-term (summer) evaporation rather than sustained evaporative concentration sufficient to increase Cl- concentration in the lake waters like those in environments driven primarily by solar evaporation (Meredith et al., 2009). On Macquarie Island, wind-driven SSA deposition and rainfall dilution therefore likely remain the primary drivers of Cl- variability. Consequently, EC in lake waters of Macquarie Island remains a robust proxy for interpreting variations in SHW strength.

Diatom analysis showed that typical sub-Antarctic genera (Van de Vijver, 2019; Goeyers et al., 2022), including Psammothidum, Planothidium, and Fragilaria, dominated lake diatom communities on Macquarie Island. Across 52 lakes, 141 taxa were identified, indicating intermediate species diversity relative to previous studies on the island which reported 102 (McBride, 2009) and 208 (Saunders et al., 2009) species. Consistent with these earlier studies we have demonstrated that diatoms on Macquarie Island exhibit clear and distinct ecological preferences. Combined with the pronounced environmental gradients among lakes, these species-environment relationships provide a strong basis for using diatoms as indicators of limnological conditions and environmental change.

Psammothidium species are characteristic of low EC sites (Van de Vijer et al., 2002), and while abundance and dominance of key Psammothidium species showed variation along the lower end of the EC gradient (Fig. 10), they dominated low EC sites. N. bergstromiana is considered endemic to Macquarie Island and was commonly found with dominant Psammothidium species at low EC sites, typically occurring where EC was < 200 µScm-1, consistent with what has previously been reported (Sabbe et al., 2019). A. principissa, previously identified as Aulacoseria distans (Ehrenberg) Simonsen on Macquarie Island (McBride, 2009; Saunders et al., 2009), was also common at low EC (Fig. 10). This taxa is commonly found on sub-Antarctic Islands and is suggested to prefer very low conductance values < 80 µScm-1 (Van de Vijver, 2012), while EC was not observed < 160 µScm-1 in this dataset, A. principissa may be an indicator of very low EC conditions.

P. lanceolatum was a dominant high EC, high nutrient taxa. While it has been found to dominate flora elsewhere, this contrasts previous studies where it has been reported to be characteristic of oligotrophic conditions (Van de Vijer et al., 2002). F. capucina, P. delicatulum, and unknown sp. 111 were more commonly dominant at high EC. F. capucina was found across the EC gradient displaying a bimodal distribution (Fig. 10), consistent with its cosmopolitan and ecologically tolerant nature (Van de Vijer et al., 2002).

A key aim of this study was to update and improve existing quantitative diatom models for Macquarie Island. While the dominant taxa identified here are consistent with those reported by Saunders et al. (2009), the strength of some diatom–environment relationships differ. EC and pH remain strong explanatory variables for diatom variation, whereas PO43- and Si showed limited influence in the present study (Tables 3 and 4). This reduced explanatory power likely reflects the low nutrient variability across plateau lakes, where concentrations were generally below detection limits.

Sub-Antarctic lakes are characteristically oligotrophic; high nutrient levels do occur, but they are associated with peatlands or animal colonies, as is the case with coastal lakes on Macquarie Island (Selkirk et al., 1990). Saunders et al. (2009) recorded greater nutrient variability across plateau sites at the lower end of the EC gradient, attributed to enhanced organic inputs during periods of high ecological disturbance from invasive rabbits. These differences suggest that the dataset in the present study represents post-eradication limnological conditions that are more reflective of pre-invasion or near-natural states, providing an updated basis for developing robust diatom–environment models that are less influenced by disturbance. The use of field parameters collected across multiple sampling events increases confidence that the developed models reflect representative environmental conditions (Goldenberg Vilar et al., 2018; Kennedy and Buckley, 2021). Such repeated-sampling approaches are rarely achieved in diatom transfer function development, particularly in remote regions such as the sub-Antarctic.

Widespread recovery of vegetation communities provides evidence for catchment scale ecosystem recovery across the island (Springer, 2018; Fitzgerald et al., 2021). While quantitative runoff or nutrient time series are not available to directly extend this to limnological conditions, we are able to provide site-specific evidence from one sedimentary diatom record. At Emerald Lake (LK6), downcore diatom assemblages show a clear ecological shift coincident with the introduction of rabbits (1878 CE) and their establishment on Macquarie Island, with F. capucina and P. abundans dominating downcore intervals (Saunders et al., 2013). In contrast, diatom assemblages in recent (2022) surface sediments from this site exhibit higher diversity (48 species) and greater similarity in assemblage composition to pre-rabbit sediment intervals rather than assemblages from previously collected 2006 surface sediments (15 species), which were dominated by F. capucina (48 % relative abundance). Notably, F. capucina was absent from the 2022 surface sample. Together, these lines of evidence suggest that the modern calibration dataset is less influenced by organic inputs and erosion associated with the rabbit invasion period. Accordingly, we interpret the 2022 dataset as representing post-eradication recovery conditions that are moving toward, but do not necessarily fully reflect, pre-disturbance baseline states. However, further studies are needed to assess how widespread this is across the island.

EC was shown to be the major independent driver of diatom assemblages, with strong performance in all CCA models, individually accounting for almost 50 % of the variance in the full CCA model and strong explanatory power as indicted by λ1/λ2 (Table 3). Although EC reflects a high proportion of shared variance (Fig. 9), this is consistent with its role as an integrative measure of ionic strength and catchment inputs. The shared component primarily reflects its covariation with major ions and nutrient variables (TON and PO43-), which are typically correlated with EC in these systems. Despite this overlap, EC retained a strong and highly significant independent effect (p = 0.001), confirming its dominant ecological influence on diatom distributions.

While PO43-, TON, and Si each explained significant but moderate portions of individual variance (Table 3), they were no longer significant once covariation was controlled for (Table 4), meaning their explanatory power is mostly shared variance with other environmental gradients, primarily EC and each other, with negligible unique variance. This interpretation is supported by simple linear correlations between EC and nutrient variables in coastal lakes, which show weak or absent relationships (Fig. S3). These results indicate that although nutrients and EC co-occur in coastal systems, nutrient variability is not strongly or systematically coupled to EC. Together with the VIFs (< 3), variance partitioning and partial CCA results (Table 4), this supports the interpretation that the weak unique nutrient signal reflects an ecological reality in which EC exerts a first-order control on diatom assemblages, rather than a statistical artefact of collinearity. Similarly, while pH explained a major and independent gradient in diatom variation within plateau lakes (Fig. 8), it did not capture assemblage changes across high-EC, high-nutrient sites. This was indicated by individual CCA results, which were less than half of the variance explained by EC (Table 3). This, paired with the widespread oligotrophic nature of plateau lakes on Macquarie Island lends strength to the independent explanatory power of EC across the whole dataset.

Furthermore, pH showed poor predictive performance as a transfer function, with the lowest Rboot2 = 0.26, and high RMSEP = 0.6 from the WAPLS-2 model (Table 5). While this is surprising due to the strong pH gradient across plateau sites, most diatoms were found across the pH gradient (Fig. 11) with some species showing broad tolerance and pH ranges. EC had the strongest performance with the WA and ML models producing the highest Rboot2 (0.74 and 0.80, respectively) and comparable RMSEP. WAINV and WAPLS-1 showed identical performance, with no benefit from additional WAPLS components, which progressively increased predictive error and decreased Rboot2, suggesting overfitting. WA was therefore chosen over WAPLS as the simpler model.

The WA EC transfer function performed better than the previously published Macquarie Island diatom-conductivity transfer function (Saunders et al., 2009), with higher Rboot2. However, some caution is warranted when predicting across the upper EC range, where greater predictive error is evident (Fig. 12). This can be attributed to lower species turnover, higher variability in PO43-, TON, and Si, and fewer sites at the upper end of the nutrient and EC gradients. Further refinement of the EC transfer functions could be achieved with more evenly distributed sampling across the environmental gradient. The ML model, with higher Rboot2, appears more capable of addressing these issues and maintains more consistent predictive power across the EC range. This is likely due to its explicit curve-fitting approach. By estimating individual species optima and tolerances, ML can better represent asymmetric or skewed response curves (Birks, 2012). The ML transfer function is therefore considered to be the preferred model, although both WA and ML are robust based on comparable RMSEP.

The conceptual link between large-scale wind regimes and long-term limnological and ecological responses in Southern Hemisphere lake systems is well established in previous studies (e.g., Saunders et al., 2009, 2015, 2018; Perren et al., 2020, 2025; Van Nieuwenhuyze, 2020; Humphries et al., 2021; Meredith et al., 2022). The data presented here builds on this existing framework and demonstrates that incorporating diatom data with seasonal and multi-year hydrogeochemical data provides a unique opportunity to comprehensively understand diatom-environment responses. By quantifying temporal variability in hydrogeochemical processes, including the role of evaporation, this study strengthens confidence that EC reflects SHW-driven sea-spray inputs rather than local lake hydrogeochemical processes. This hydrological context is critical for interpreting diatom–environment relationships and ensuring the reliability of EC as a proxy for past SHW behaviour, providing a strong foundation for future palaeoclimate reconstructions. The resulting diatom–conductivity model provides a robust and ecologically grounded framework for reconstructing long-term SHW variability on Macquarie Island and establishes an important benchmark for sub-Antarctic palaeoclimate comparisons across the region. This model will be applied in future studies to reconstruct past variability in the SHW and associated hydroclimatic changes on Macquarie Island. Any future time-series monitoring of lake EC paired with local wind speed records would further allow direct assessment of the relationship between wind speed and lake salinity, including the rate of hydrogeochemical response and any wind speed thresholds required to drive measurable change.

By capturing post-eradication and near-natural ecological conditions, the EC model developed in this study offers an improved foundation for assessing long-term wind-driven variability, as it reduces ecological noise associated with past disturbance. When applied in parallel with other proxies, such as isotopic or geochemical indicators, these reconstructions will contribute to a more comprehensive understanding of past SHW dynamics and their role in modulating Southern Hemisphere mid-high latitude climate, thereby providing context for understanding future changes.

Furthermore, a multiproxy approach will be valuable for independently reconstructing key climatic drivers, including precipitation, temperature, and atmospheric circulation, thereby improving interpretations of past SHW variability and helping to assess how hydroclimatic processes may modify EC signals (e.g., through dilution and enrichment). On Macquarie Island, geochemical indicators of sea-spray and dust inputs (e.g., S, Br, Ti) can help distinguish marine aerosol delivery from catchment-derived material, while glycerol dialkyl glycerol tetraethers (GDGT)-biomarker reconstructions can provide an independent constraint on temperature variability. Both approaches are currently being undertaken on Macquarie Island lake sediment cores by our research group. Mercury (Hg) concentrations and isotopes offer an independent proxies for atmospheric transport and Hg deposition linked to large-scale circulation, precipitation, and the influence of seabirds, all particularly well suited to the remote setting of Macquarie Island (Schneider et al., 2022; Guédron et al., 2018), which is also the focus of ongoing work (e.g. Schneider et al., 2024; Li et al., 2026). Although isotope (δ2H, δ18O) palaeo-records are not currently available for Macquarie Island, they represent an important avenue for future research to constrain precipitation–evaporation balance. Together, these complementary proxies provide a framework to separate the relative influence of atmospheric circulation, hydroclimate, and temperature on lake systems, providing more comprehensive palaeoclimate records and interpretations.