ABSTRACT Marine macroalgae are important indicators of healthy nearshore groundwater dependent ecosystems (GDEs), which are emergent global conservation priorities. Submarine groundwater

discharge (SGD) supports abundant native algal communities in GDEs via elevated but naturally derived nutrients. GDEs are threatened by anthropogenic nutrient inputs that pollute SGD above

ambient levels, favoring invasive algae. Accordingly, this case study draws on the GDE conditions of Kona, Hawai‘i where we evaluated daily photosynthetic production and growth for two

macroalgae; a culturally valued native (_Ulva lactuca_) and an invasive (_Hypnea musciformis_). Manipulative experiments—devised to address future land-use, climate change, and water-use

in SGD via climate change decreases in rainfall or increased water-use from the aquifer may relax physiological constraints on _H. musciformis_. Collectively, this study reveals divergent

physiologies of a native and an invasive macroalga to SGD and highlights the importance of maintaining SGD quantity and quality to protect nearshore GDEs. SIMILAR CONTENT BEING VIEWED BY

OTHERS INTEGRATED PHYSIOLOGICAL RESPONSE BY FOUR SPECIES OF RHODOPHYTA TO SUBMARINE GROUNDWATER DISCHARGE REVEALS COMPLEX PATTERNS AMONG CLOSELY-RELATED SPECIES Article Open access 09

October 2024 ALKALINITY CYCLING AND CARBONATE CHEMISTRY DECOUPLING IN SEAGRASS MYSTIFY PROCESSES OF ACIDIFICATION MITIGATION Article Open access 29 June 2021 SALINITY AND TEMPERATURE

INCREASE IMPACT GROUNDWATER CRUSTACEANS Article Open access 23 July 2020 INTRODUCTION Across the tropical Pacific, native marine algae often have high cultural, ecological, nutritional, and

economic value1,2,3 and can be important indicators of healthy nearshore groundwater dependent ecosystems (GDEs)3, 4. Submarine groundwater discharge (SGD) to intact coastlines of tropical

high islands delivers a nearly ceaseless supply of nutrient-rich freshwater that sustains GDEs and their primary producers5,6,7. In relatively unperturbed watersheds, groundwater delivers

naturally elevated levels of nutrients at a rate of discharge and salinity that varies with hydrological seasons and sea levels through tidal cycles8. The off-shore waters that surround

these islands often have nutrient levels so low that dissolved inorganic nitrogen (e.g., nitrate as primary N type in SGD) and dissolved inorganic phosphate—essential macronutrients for

growth by all plants—are undetectable by standard methods5, 7. In this context, natural SGD is a critical source of nutrients that has allowed for evolutionary diversity and moderate

abundances of marine macroalgae within GDEs4. Although most macroalgae species do not tolerate freshwater6, 9, research from Hawaiʻi shows that some native species tolerate and even thrive

in lower salinities characteristic of natural SGD plumes6, 10, 11. Anthropogenic inputs to natural SGD can add large loads of nutrient pollution to coastal ecosystems, while remaining

generally unseen and infrequently measured8. Chronic imbalances resulting from these inputs increase biomass of invasive algae12 and when coupled with climate change impacts such as

reduction of rainfall, may threaten important SGD-dependent communities. The Kona coastline of West Hawaiʻi Island is a GDE hotspot with over 50 groundwater discharge points13 that sustain

the local biota. Because of the potential stress on GDEs from increased urban development and excessive groundwater withdrawal, the Keauhou aquifer, which feeds into Kona GDEs, is a priority

area for watershed conservation initiatives14. This area is also characterized by an abundance of cesspools15 and other On-Site Disposal Systems (OSDS) that currently contribute untreated

wastewater to coasts via the SGD pathway16. These anthropogenic shifts in the quality of SDG and links to macroalgae are unexplored in the context of the Hawaiʻi State Water Code, which

requires that water be managed as a public trust and includes protections for native species, i.e., _Ulva lactuca_, critical to cultural and ecological values embedded within GDEs17, 18.

Invasive micro- and macroalgal blooms are a top concern for the management and health of coastal ecosystems worldwide (e.g., 19,20,21,22,23). On tropical coral reefs, some invasive algae are

known to overgrow sessile coral, attenuating over 90% of solar irradiance required for coral growth24,25,26,27. On unperturbed reefs, turf, crustose corallines, and macroalgae compete with

coral for habitat but are kept in balance by herbivory and nutrient limitation28, 29. These natural balances are threatened globally from anthropogenic nutrient inputs30, herbivore

overharvesting31, and climate change32. In GDEs, climate change perturbations and/or land-based nutrient pollution from agriculture and urban development15 can shift the quality of SGD and

may create conditions where invasive macroalgae become dominant12, 33,34,35. In Hawai‘i, over a dozen species of macroalgae have been introduced, intentionally and accidentally, and several

have become ecological dominants in native coastal habitats22, 24, 25, 36. Importantly, there is growing concern over the potential establishment of _H. musciformis_ along the nearshore,

SGD-fed coral reefs of Kona, as SGD conditions change. Despite the cultural and ecological importance of native marine algae1, 37, and the persistent threat posed by invasive macroalgae in

Hawai‘i22, 38, 39, there is a paucity of research on the growth and physiologies of these organisms to shifting SGD conditions, limiting informed land and water management decisions. To help

fill this gap, we quantified the changes in photosynthesis and growth of a culturally and ecologically important native Hawaiian species, _Ulva lactuca_, as well as the highly invasive

macroalgae _Hypnea musciformis_. These species were specifically selected to help inform land and water management decisions on the Kona coast of Hawaiʻi Island because the native is

abundant whereas the invasive is not yet present in the Kona GDE. _Ulva lactuca_ Linnaeus (limu pālahalaha) is a common marine green algal species with wide global distribution40. Limu

pālahalaha is a beloved native alga and is used extensively in Native Hawaiian cultural practices and food preparation37, 40. Limu pālahalaha’s ability to respond rapidly to increased

nutrients allows it to thrive in natural, as well as degraded, high nutrient Hawaiian island habitats40. In contrast, _Hypnea musciformis_ (Wulfen) Lamouroux is a weedy Atlantic species that

was intentionally introduced to O‘ahu as a commercial carrageenan source39. Although extraction plans were quickly abandoned, _H. musciformis_ rapidly spread and is now abundant—up to 80%

cover in Mā‘alaea Bay, Maui agriculture and wastewater outputs—and in other nutrient-rich areas of Maui, O‘ahu, and Kaua‘i8, 22, 39, 41. This highly invasive species uses apical hooks to

attach to other plants and substrate, fragments easily22, and forms large, floating, shallow-water mats that strand on beaches leading to substantial revenue losses42. It is also apparently

absent in the Kona GDE habitats41, 43. Whereas native _U. lactuca_ and invasive _H. musciformis_ occupy some of the same bloom habitats44, it is unknown whether _H. musciformis_ can co-exist

in the full range of native habitat parameters where _U. lactuca_ thrives. The objective of this study was to probe the performance of these two algae under SGD conditions that simulated

the salinity/nutrient gradient from oligotrophic ocean to naturally-occurring nutrient-rich groundwater in Kona10. For these, we used the ecological gradients defined in previous hydrologic

modeling studies10, 13, 15, 45. To achieve this objective, we examined whether _Ulva lactuca_ and _Hypnea musciformis_ (hereafter referred to as _Ulva_ and _Hypnea_) growth and

photosynthesis respond differently under six salinity (‰) / nitrate (μM) / phosphate (μM) treatments (T0: 35 / 0.5 / 0.005, T1: 35 / 14.3 / 0.005, T2: 28 / 27 / 0.15, T2.5: 28 / 53 / 1.64,

T3: 18 /53 / 1.64, T4: 11 / 80 / 3.8) at three temperatures (~ 20, 27, and 30 °C; Table 1). Specifically, we tested the hypothesis that _Hypnea_ growth and photosynthesis would decrease as

conditions became more similar to SGD discharge points (e.g., like T4), in contrast to the increase expected in _Ulva_. We further explored the role of saturated photosynthesis rates

combined with quantitation of diurnal irradiance, at the individual plant level, to determine whether this integrated characterization of algal physiology could improve our ability to model

algal growth. Such models would be useful tools for future eco-hydrology work, using algae as proxies for health and habitat studies, and would expand our ability to better understand

current and forecast future disturbances and population shifts. The ability to forecast changes in marine ecosystems is particularly important considering the predicted effects of future

land-use and climate change. Changes in SGD quantity and/or quality could significantly reduce physiological barriers to entry for any fully-marine invasive algae, particularly those with

successful fragmentation and attachment strategies as found for this species of _Hypnea_. RESULTS This investigation generated data via replicated in vitro assessments of photosynthesis and

growth of two contrasting species over nine day runs, for 16 replicates each across three water temperatures (~ 20, 27, 30 °C) and six combinations of salinity and nutrients (T0–T4; See

above or Table 1). We documented daily ambient irradiances during each run across several seasons. The variance explained by the linear mixed-effect models (LMM) for all dependent variables

analyzed (maximum photosynthesis, Pmax; irradiance saturation, Ek; diurnal saturation time, Hsat; diurnal saturated photosynthesis index, DSPI; rel-Hsat [data in Supplemental material];

9-day growth), increased with the addition of the chosen random effects, emphasizing the importance of inherent plant variability (plant ID), irradiance changes over time (run), and other

unmeasured variables. These data are reported in R2m and R2c columns in Table 2. Overall, the LMM was a better fit for _Ulva_ than for _Hypnea_ for fixed effects (treatment and temperature),

explaining up to 62% of the variance in _Ulva_ and 38% in _Hypnea_. Both of these highest values were from the Ek model fit. With the addition of random effects, the explained variance

increased to a maximum 86% in _Ulva_ and 87% in _Hypnea—_both from the DSPI model fit. In _Ulva_, treatment had a significant effect on all dependent variables tested, whereas temperature

only affected the outcome in one analysis (Ek). The significant results were more complex in _Hypnea_ as treatment was an important factor affecting the outcomes in four of the six dependent

variables, but temperature affected outcomes in all but one (growth). All significant test results, model coefficients of determination, and random effects used in each analysis are

provided in Table 2. Both _Ulva_ and _Hypnea_ exhibited a consistently larger difference in means between T0 and T1 than between any other adjacent treatment pairs along the gradient—for

most dependent variables. This highlights the large nutrient increase (+ 2700% nitrate) from T0 (oligotrophic) to T1 (oceanic salinity + elevated nutrients) but may also reflect some reduced

variability in individual plants. T0 plants were all collected in February (_Ulva_) and October (_Hypnea_) 2022, and _Hypnea_ T2.5 individuals were collected in April 2022, whereas T1–4

were made up of a combination of plants from time periods with a broader range of diurnal irradiance. Plant variability was accounted for in the model’s random effects and trends remained

consistent even when T0 was removed from the analysis. Accordingly, the results are herein presented primarily as changes in elevated nutrient treatments T1–T4 versus oligotrophic T0 because

this is the most relevant comparison for our study. MAXIMUM PHOTOSYNTHETIC RATE (PMAX) Pmax is a value that describes the maximum irradiance-dependent photosynthetic rate. In _Ulva_, Pmax

linearly increased and more than doubled from T0 to T4. The treatment with the highest nutrient input and lowest salinity (4) also had the highest measured Pmax at 74.3 μmols electrons m−2 

s−1. Because T0 was the baseline oceanic salinity/ nutrient treatment, we found that all Ts 1–4 affected Pmax (χ2 (4) = 130.1, p < 0.001), increasing it by a maximum of 43.5 (± 3.5 std.

err.) μmols electrons m−2 s−1 (Fig. 1A; Table 3). Temperature did not have a significant effect on Pmax (χ2 (2) = 3.8, p = 0.151). Treatment affected Pmax in _Hypnea_ (χ2 (5) = 15.8, p = 

0.008) increasing it by a maximum of 14.7 μmols electrons m−2 s−1 (± 11.5 std. err.) in T2.5. The effect of temperature was also significant (χ2 (2) = 40.1, p < 0.001) decreasing Pmax by

17.3 (± 3.1 std. err.) at 27 °C and by 19.4 (± 2.7 std. err.) at 30 °C (Fig. 1B). Notably, 27 °C is close to ambient seawater temperature. IRRADIANCE SATURATION (EK) Ek denotes the

irradiance at which an individual transitions from irradiance-limited to substrate-limited photosynthetic performance. Ek ranged from 27.2 (T0) to 110.4 (T4) μmols photons m−2 s−1 in _Ulva_

indicating a strong, increasing relationship with increasing SGD conditions (Fig. 1C). Salinity/nutrient treatments affected Ek, (χ2 (4) = 140.7, p < 0.001) increasing Ek by up to 84.3 (±

 7.2 std. err.), in T4. Temperature also affected _Ulva_ (χ2 (2) = 6.8, p = 0.033), decreasing Ek by 10.5 ± 4.8 std. err. (at 27 °C) and 10.6 ± 4.4 std. err. (30 °C) μmols photons m−2 s−1.

Ek in _Hypnea_ ranged from 51.8 (T0) to 93.8 (T2.5). Similar to Pmax in _Hypnea_, Ek was affected by temperature (χ2 (2) = 54.5, p < 0.001) but not by treatment (χ2 (5) = 10.8, p = 0.056;

Fig. 1D). Ek decreased by 35.8 (± 4.2 std. err.) and 39.4 (± 4.2 std. err.) μmols photons m−2 s−1 at 27 and 30 °C, respectively, relative to 20 °C. Ek was the only dependent variable that

was significantly affected by temperature in _Ulva_, whereas temperature was almost always a significant factor affecting _Hypnea_. DIURNAL SATURATION TIME (HSAT) Diurnal saturation time

describes the mean time (in minutes) plants spent in irradiance ≥ Ek. The mean diurnal saturation time for _Ulva_ treatments increased from 333 (T4) to 507 (T0) minutes. Hsat was most

affected by the salinity/nutrient combination in T4 (χ2 (4) = 91.3, p < 0.001; Fig. 2A), which lowered it 183 min (± 50 std. err.) relative to T0. Temperature did not have a significant

effect on _Ulva_ Hsat (χ2 (2) = 2.0, p = 0.363). The range for mean Hsat in _Hypnea_ treatments was narrower than in _Ulva_ and spanned 394 to 469 min (Table 3). Both treatment (χ2 (5) = 

23.4, p < 0.001; Fig. 2B) and temperature (χ2 (2) = 13.6, p = 0.001) affected Hsat, increasing it by a maximum of 41 min (± 11 std. err.) at 27 °C relative to 20 °C individuals. NATURAL

IRRADIANCE CHANGES DURING EXPERIMENTAL PERIOD The initial experimental period ranged from September to early November 2021. The mean day length for September 20–29 (run1) was 654 min and

decreased to 611 min by run4. Because we did not use artificial light, the requirement to conduct subsequent experiments on days of similar duration was imposed, thus mid-year months were

avoided. In February and October 2022, mean day length ranged from 626 to 641 min. The day length maximum in April 2022 was 688 min—more than 30 min longer than the September 2021 maximum.

This time differential exposed a potential inconsistency that was addressed by normalizing Hsat data relative to the total minutes available for photosynthesis (≥ 1 μmols photons m−2 s−1;

rel-Hsat; see supplemental materials). Daily irradiance for each run is shown with total day length in minutes (Fig. 3). Mean Ek values for both species show the general direction of change

and are based on D1 and D5 measurements. DIURNAL SATURATED PHOTOSYNTHESIS INDEX (DSPI) The diurnal saturated photosynthesis index represents a simplified irradiance-only component of gross

photosynthetic production and combines the maximal photosynthetic rate (Pmax) multiplied by the time in saturated irradiance (Hsat), iterated every three days (or fraction thereof) for the

9-day experiment. Our results show that only treatment affected DSPI (χ2 (4) = 14.0, p = 0.007) in _Ulva,_ increasing DSPI by 1.1 mol m−2 (± 0.8 std. err.) in T4 relative to the baseline T0

(Fig. 2C). Temperature did not significantly affect DSPI (χ2 (2) = 1.3, p = 0.517). The inverse occurred in _Hypnea_ as only temperature affected DSPI (χ2 (2) = 11.1, p = 0.004) decreasing

it by 0.3 mol m−2 (± 0.1 std. err.) in both 27 and 30 °C (Fig. 2D). Treatment had no effect (χ2 (5) = 7.8, p = 0.171) on _Hypnea_ DSPI. ALGAL GROWTH To determine whether photosynthesis

correlates well with growth in these species, we first modeled growth as a function of salinity/nutrient combinations and temperature. As was the case in all other photosynthesis variables

for _Ulva_, treatment affected growth (χ2 (4) = 69.9, p < 0.001), strongly increasing the 9-day growth to a maximum of 49.3% (± 6.3 std. err.) in T4 relative to baseline T0 (Fig. 4).

Again, the effect of temperature was not significant (χ2 (2) = 0.1, p = 0.952). For _Hypnea_, treatment affected growth (χ2 (5) = 49.8, p < 0.001) but temperature did not (χ2 (2) = 2.5, p

 = 0.282). Nine-day growth in treatments 1 and 2.5 was virtually the same and increased by 15.3% (± 4.1 std. err.) and 16.0% (± 4.5 std. err.), respectively, relative to the baseline T0. We

then ran linear regressions with growth as the dependent variable and Pmax, Ek, Hsat, and DSPI as predictors. These showed that nearly all affected growth in _Ulva_ (p-values for predictors

in order: < 0.001*, < 0.001*, 0.290, < 0.001*). The photosynthesis variables were less effective for _Hypnea_ (p-values in order: 0.038*, 0.091, 0.076, < 0.001*). DSPI AS A

PREDICTOR OF GROWTH To better understand the relationship between DSPI and growth, we used an LMM adding treatment as a random effect and likelihood ratio tests as described in methods. DSPI

was significantly correlated with growth in _Ulva_ (χ2 (1) = 36.6, p < 0.001) increasing it 12.2% (± 1.9 std. err) and in _Hypnea_ (χ2 (1) = 13.6, p < 0.001), increasing it by 4.1% (±

 1.1 std. err; Fig. 5). R2c for combined fixed and random effects was 0.26 for _Ulva_ and 0.15 for _Hypnea,_ indicating potential species-specific factors for growth that remain unidentified

(e.g., apical cell growth vs. diffuse cell division)_._ DISCUSSION Few physiological studies, to date, have examined native or invasive algae production as a function of habitat parameters

in submarine groundwater discharge (SGD)-influenced groundwater dependent ecosystems (GDEs) that are common in oceanic high islands across the central Pacific region5,6,7,8. The Hawaiian

native _Ulva lactuca_ and invasive seaweed _Hypnea musciformis_ were selected because they have contrasting ecologies and possess different evolutionary histories, yet their physiology and

growth parameters in SGD conditions remain understudied. Our in-depth investigation of photosynthetic parameters of these important algae details several facets of algal physiology, and

highlights informative known and new parameters, i.e., Ek, Hsat, and DSPI, to yield an intuitively satisfying assessment for the first time. We further integrate the use of the irradiance

saturation value, the value of photosynthetically active radiation (PAR) needed to attain steady state rates of photosynthesis, also known as Ek46,47,48, and of maximal rates of

photosynthesis coupled with irradiance, to estimate overall production in response to SGD conditions. Calculating the amount of time that a plant is at or above Ek irradiance during the

experiments gives the novel ability to determine lengths of time individuals experienced steady state rates of maximal photosynthesis each day (Hsat49). Because natural irradiance fluctuated

throughout the experimental periods, and importantly, T0 and T2.5 were added subsequent to the initial experimental runs, we calculated Hsat relative to the day length (rel-Hsat) to account

for potential differences (see supplementary materials). Following Zimmerman et al. (1994)49 oxygen electrode-based work with the temperate seagrass _Zostera marina_, we multiplied Hsat and

Pmax to derive the product, a novel diurnal saturated photosynthetic index, DSPI, as a new parameter using fluorescence-based PAM measurements to estimate daily maximum photosynthetic

productivity in each individual. DSPI, thus represents a time-integrated maximal photosynthesis value that expands our ability to compare algal physiologies. Further, our results show that

DSPI and growth are significantly correlated but DSPI falls short in explaining the high variability in _Ulva_ and _Hypnea_ growth. One explanation for the high variability in _Hypnea_

growth may be its allocation of energy to survival and maintenance rather than growth when in new and nutrient-rich habitats38. Overall, these results highlight the need to better understand

energy allocation in usually nutrient-limited marine algae so we may adequately assess the risks of nutrient increases to important ecosystems such as GDEs. _Ulva_ growth increased as

treatments became more similar to SGD conditions near discharge points. The opposite relationship was found for _Hypnea_. The response dynamics were markedly different between species,

however, because growth increased consistently for _Ulva_ with each step towards hyposalinity, whereas _Hypnea_ growth appeared to reach a maximum at 28‰. With an increase from 27 to 53

μmols N at 28‰, _Hypnea_ was able to increase growth, but at the same nutrient level and a decrease in salinity to 18‰, _Hypnea_ growth stalled and further declined as treatments extended

further into hyposalinity/elevated nutrient scenarios. These divergent physiological responses allow us to better understand some of the mechanisms behind broad distributional patterns that

_Ulva_ exhibits in the field and emphasizes the strong marine environmental conditions under which invasive _Hypnea_ blooms40, 44. The LMM predicting growth as a function of DSPI showed that

the relationship was significant, but the model fit was not as strong as expected, revealing that some irradiance and plant variabilities remain unexplained. Although they added to the

variability in the response, irradiance changes experienced during the months-long experiment emerged as an unexpected positive attribute of the experimental design. In this manner, we were

able to run the experiments over an extensive period to increase replication while also capturing the irradiance changes and distributing their effects across the treatments and

temperatures. Furthermore, we did not address non-photochemical quenching in this study but recognize its potential to broaden our understanding of these photosynthetic physiologies50. In

summary, _Ulva_ T4 individuals in 20 °C, representing the strongest, natural SGD conditions (lowest salinity, highest nutrients), had the highest rates of Pmax, the highest Ek, and they

spent the least time in Hsat (even when adjusted to rel-Hsat%; see supplemental information). They also showed the highest DSPI of all treatments. Because growth was also highest in T4,

these photosynthetic parameters appear to be optimized for life near SGD discharge points along basaltic coastlines. _Hypnea_ underperformed with decreasing salinity regardless of nutrient

inputs, suggesting an intolerance to salinity 18‰ and below. The gap between 28‰ and 18‰ is wide open for future studies to narrow down and better define the salinity range that supports

_Hypnea_, assuming an adequate nutrient subsidy. Temperature affected all photosynthetic parameters. Pmax, Ek, and DSPI were highest and Hsat was lowest at 20 °C relative to 27 and 30 °C,

which were highly similar. These temperature results likely reflect the optimization of this species to sub-tropical Florida before its introduction to Hawai‘i22 but also suggests that

_Hypnea_ could survive in SGD-types of temperatures in reef regions where salinities are moderately decreased but nutrients are elevated. Based on climate change predictions of reduced

rainfall, such “sweet spots” are likely to occur because decreases in rain volume will increase salinity, whereas elevated nutrient inputs are likely to continue. These combined may create

an SGD output that favors _Hypnea_’s physiology. GDEs that are fed primarily by natural SGD sources, such as in coastal Kona, are fundamentally different from those on other islands in the

Hawaiian archipelago because many of these receive large, anthropogenically-derived inputs that mix with the natural flows6, 13. The Kona coast is well known for high volumes of SGD without

stream or overland inputs13, 45; the numerous, large, point-sourced SGD plumes and smaller diffuse flows discharge nutrient-rich fresh water as the only source of new nutrients for the

native plant community13. Natural SGD discharge points along the Kona coastline support dense populations of _Ulva,_ whereas _Hypnea_ is apparently absent from the area. By contrast,

abundant _Hypnea_ biomass is found on other islands where excess nutrient loads from agriculture and/or wastewater feed into SGD flows8, 38, 51. Thus, changes that may impact the quality and

quantity of SGD in Kona, are cause for concern. Current stressors, i.e., increasing OSDS densities, together with predicted increases in urban development and groundwater withdrawal, have

made Kona’s Keauhou aquifer a priority for watershed conservation initiatives14. And although Hawai‘i law mandates that cesspools must be upgraded by the year 2050, water quality

improvements have been slow and spatially variable15. In addition, centralized wastewater treatment plants that dispose of treated effluent in shallow coastal waters remain a threat to GDE

health and may be in violation of the U.S. Clean Water Act. Experiments that test wastewater enrichment scenarios are underway but were not included in this study. Climate change is expected

to deliver less rain to already dry areas of the Hawaiian Islands, leading to decreasing SGD inputs to coastal regions52. Our results show that _Hypnea_ is not well-suited to the current

conditions of Kona coastal SGD habitats, but _Ulva_ thrives in low salinity, high nutrient SGD ecosystems. We thus anticipate that with reduced freshwater input, coastal salinity will rise,

increasing the potential for _Hypnea_ to successfully establish in Kona. Predicted nutrient pollution of SGD will further enhance that likelihood. Localized anthropogenic eutrophication,

persistent invasive macroalgal and phytoplankton blooms, as well as decreased reef biodiversity are features of these new GDE seascapes8, 10, 12, 44, 53. Once an invasive species is

established in a new area, it can be extremely difficult to eradicate20, 54, 55 and can increase competitive interactions among invasives and natives in their now-changed ecosystem. Our

study focused on the Kona coast SGD ecosystem, but similar conditions and vulnerabilities are found globally56. Importantly, each island, its SGD, and the composition of its biological

community, may represent a continuum of SGD-biological species interactions7. Clearly, much more research is needed to better provide adaptive management of water resources that protect

native species and minimize the establishment of aggressive invasives. These results are also important in the context of subsistence collecting of limu pālahalaha (_Ulva_), and possibly

other native limu, i.e., limu manauea (_Gracilaria coronopifolia_) and limu huluhuluwaena (_Grateloupia filicina_), that thrive and may have evolved under SGD conditions41. Protection of

these organisms for the public trust is mandated by law, increasing the importance of protecting SGD quality and quantity to ensure native traditional practices are preserved. Wise

stewardship of GDEs and their recharge would require improved land and water management decisions including more permeable urban surfaces, native ecosystem protection, and sustainable

groundwater withdrawal rates, but would help to ensure groundwater dependent native communities are protected, to the benefit of all. MATERIALS AND METHODS KONA SGD COASTLINE HABITAT _Ulva_

commonly co-occurs in persistent coastal blooms with the highly branched, cylindrical rhodophyte _Hypnea,_ which is now found at numerous sites around the Main Hawaiian Islands, especially

in Maui coastal waters12, 44. In Hawaiian ecosystems, both species are readily grazed—exhibiting similar top-down vulnerabilities57—and both have similar uptake rates of limiting

macronutrients44. Strikingly, _Hypnea_ has not been found in the intertidal region of the Kona coast22, despite numerous vectors for delivery. We calibrated our experimental treatment

parameters to the natural intertidal SGD conditions at Kona, Hawai‘i to test the physiological responses of these two species to a range of salinity/nutrient combinations following a

gradient from full oceanic to groundwater seep locations. ALGAL MANIPULATIVE EXPERIMENTS Eight experimental runs were conducted from September to November 2021 to arrive at 16 total

replicates per treatment and temperature of the two co-occurring algal species. Each run began with the collection of six individual plants of _Hypnea_ and _Ulva_ in the intertidal zone

during a minus tide at Wāwāmalu Beach and Ka‘alawai, O‘ahu. Plants and seawater were stored in a cooler and transported to the Marine Macrophyte Lab greenhouse at UH-Mānoa to acclimate and

consume tissue nutrients for eight days before beginning the experiment. Acclimating plants were kept in approximately 3 L of unfiltered seawater in covered and aerated 10 L aquaria in a

partially shaded area of the lab outdoor terrace. Tanks were divided in half with white plastic egg crate to keep plants—one of each species—separate, and seawater was not changed during

this drawdown phase. Plant placement within tanks was rotated every two days to encourage homogeneous acclimations by both species. Plants were sectioned into four replicate pieces—each

0.28–0.3 g in wet weight—and returned to the tanks with the extra plant biomass removed. The experimental run commenced the following morning at 08:00 with photographs, rapid light curves

(RLCs), and wet weighing for each individual (24 per species). RLCs were run on randomly chosen replicates with a Junior PAM-III using WinControl version 3.29 software (Walz, GmbH, Germany).

Optimal settings were pre-determined for concurrent use with both species: Actinic light length was 0:30, intensity values (Epar) were set at 0, 65, 90, 125, 190, 285, 420, 625, and 820

μmols photons m−2 s−1, and the measuring light intensity was set to 8. Ft was offset to fluctuate around zero in seawater before beginning RLCs and probe location was chosen when the Ft

value was in the normal range for each species and/or was in a consistent range at a minimum of three locations on the replicate. Each RLC took ~ 5 min and all 48 samples were completed

before noon to avoid mid-day depressions in photosynthetic performance. These time constraints precluded multiple measurements on individuals. After each RLC, replicates were carefully dried

with paper towel three times and weighed with a Sartorius TE214S digital analytical balance (precision to 0.0001 g; Sartorius AG, Göttingen, Germany) then placed in 700 mL of treatment

seawater in an individual 0.946 L (1 qt) glass jar per plant after Dailer et al. (2012)51. Seawater was pre-filtered with a 0.22 μm Millepore Stericup® filtration system (Millepore Sigma,

Darmstadt, Germany), diluted with deionized water to reach the treatment salinity dilutions, and stored in darkened 20 L carboys. After all replicates were placed in clear-lidded jars, each

received the appropriate addition of nitrate and phosphate (Table 1) and were fitted with air tubes that slightly agitated and aerated the water. Eight jars were nested in clear, plastic,

water-filled bins, and were raised on white plastic egg crate above a 500-W titanium aquarium heater (Finnex TH-05005; Illinois, USA). Three daytime temperatures (~ 20, 27, and 30 °C) were

maintained within the bins from 06:00 to 18:30 every day during the experiment. Heaters were scheduled to shut off at 18:30 to allow evening and nighttime temperatures to drop to ambient (~ 

17–20 °C) in the air-conditioned greenhouse. Bins were aligned under a PVC pipe structure covered in shade cloth that reduced ambient light by ~ 50% with minimal obstruction. Six treatments

of paired salinity and nutrient concentrations simulated a gradient from coastal ocean to coastal spring conditions with decreasing salinity ranging from 35–11 ppt and increasing

macronutrient ranging from 14–80 μmol nitrate and 0.005 to 3.79 μmol phosphate (T0–4; Table 1). Water changes and fresh inoculations were done every two days before noon and to avoid bin

effects, each set of jars was moved counterclockwise to the adjacent bin and their positions within the bins were shifted from front to back. Temperatures were adjusted accordingly as

described below. Wet weights were measured on Day 1 (D1), Day 5 (D5) and on Day 9 (D9), which concluded the experimental run and yielded a total of two replicates for each treatment,

temperature, and species combination. The entire process was repeated seven more times with a final total of 16 replicates. Data gaps for both species were later identified, and each species

underwent additional treatments to fill those gaps. The first added treatment (T0) was needed to test the nutrient-enriched treatments against oligotrophic oceanic conditions. The second

added treatment combined the salinity of treatment T2 and the nutrients of treatment T3 to test a refinement of our hypothesis that increasing SGD was the primary cause of decrease in growth

and photosynthesis in _Hypnea_. In these later iterations, all steps were repeated except the replication procedure. A total of 48 plants of each species were collected, acclimated, and one

0.28–0.3 g piece per plant was placed in 700 mL of seawater to commence the experiment, as above. At the end of the 9-day run, the number of replicates per treatment/temperature/species was

16. These new treatments were run in early Feb 2022 (_Ulva_ only as wild collections of _Hypnea_ were unavailable), Apr 2022 (when _Hypnea_ became available), and Oct 2022 when day length

was similar to that of late 2021 (Sep–Nov) when the first experiments were conducted. See supplementary materials Fig. S1 for a graphical illustration of the full experimental design. Water

temperature inside the bins was regulated by aerating and maintaining the same water levels as in the individual jars. Bin temperatures were recorded every 10 min with HOBO TidbiT MX Data

loggers (MX2203, Onset Computer Co., Bourne, MA). Natural irradiance within the shaded experimental environment was measured with a spherical LI-COR 4π sensor (LI-COR Biosciences, Lincoln,

NE) submerged upwards in a clear plastic, four-quart container placed centrally and adjacent to the water bins. The sensor was connected to a LI-COR LI-1500 light sensor logger that recorded

irradiance (μmols photons m−2 s−1) every minute from before sunrise (05:00) to after sundown (19:30) for the duration of each experimental run. The irradiance saturation index, Ek,

describes the point at which Epar is optimal and is no longer limiting the individual’s ability to run electron transport at maximum capacity. This parameter alone is informative but to

derive more ecologically relevant information, we put Ek values into the context of minutes per day each individual experiences Epar ≥ Ek (termed Hsat) during the nine-day experiment. We

further normalized the total daily Hsat relative to the day length (defined as total minutes when Epar ≥ 1 μmol of photons m−2 s−1). See supplementary materials for details on rel-Hsat

results. As the last step, we developed a diurnal saturated photosynthetic index (DSPI) that incorporates Hsat and Pmax. DATA MANAGEMENT AND STATISTICAL ANALYSES Photosynthesis data from

WinControl were first cleaned of extraneous information and formatted in Python 3 script using PyCharm (v 2021.2.4 Community Edition). The output was imported into R Statistical Software

(v2021.09.0 R Core Team, 2012) and used the fitWebb model58 in the Phytotools package59 to calculate alpha (α) and Ek. This irradiance-normalized PE model was used to avoid the inherent

irradiance dependency of the quantum yield measurements59, which are commonly used to calculate ETR. We calculated photosynthesis max (Pmax) via the simple equation Pmax = α * Ek following

Silsbe and Kromkamp (2012)59. We also calculated relative ETRmax (absorption factors for each species were not determined), choosing the highest value during the RLC that satisfied the

condition ΦPSII > 0.1 (PSII = photosystem II), following Beer and Axelsson (2004)6660 protocol for reliable evolved O2 to ETR ratios. We compared statistical model fit between Pmax and

rETRmax and found Pmax coefficients of determination were higher for _Hypnea_ whereas they were the same for _Ulva_. Thus, we used Pmax for analysis because it does not violate statistical

assumptions of independence and it was a better fit for our statistical models (see supplementary materials for further details). Day 9 values for both Pmax and Ek were used for analyses.

Additional R scripts were written to calculate the time (in minutes) each individual experienced saturating irradiance (Hsat) during each day of the experiment49. For this calculation the

9-day experiment irradiance/Ek comparisons were separated into three periods: P1 = days 1–3, P2 = days 4–5, P3 = days 6–9, where Ek values from D1 were used with irradiance in P1 to

calculate daily Hsat, D5 Ek was used for P2, and D9 Ek for P3. A mean was then calculated from these three periods and was used for analysis. Total potential irradiance was shorter for D1

and D9 because the experiments began at 08:00 on D1 and concluded by 12:00 on D9. For the experiment runs in October 2022, D5, RLCs were not run for 48 _Hypnea_ (T0) replicates and the

comparisons were modified as follows: D1 Ek with irradiance on days 1–4 and D9 Ek with days 5–9 irradiance. In all cases, irradiance began at the time of the individual’s D1 RLC and ended at

the time of its final RLC. All _Ulva_ replicates in T2.5 were removed from analysis after many of these experienced high tissue losses associated with lunar cycle effects of reproduction.

Two _Hypnea_ individuals lacked pigmentation on D9 and were unable to sustain an Ft sufficient to run an RLC. These individuals were also removed from the analyses. To account for differing

amounts of daylight throughout the months-long experimental period, day length was calculated from a condensed LI-COR dataset as time Epar was ≥ 1 μmol photons m−2 s−1. This large dataset

was condensed first by taking a 10-min moving average and then by excluding all but the values at times :00, :10, :20, :30, :40, and :50 from the analysis. Then, the mean number of minutes

in saturated photosynthesis, Hsat, for each individual each day, was divided by the mean day length for each day. These values were averaged to arrive at a proportion (%) of diurnal daylight

each spent in saturating irradiance (rel-Hsat; see supplementary materials). A NEW PHOTOSYNTHESIS APPROACH A diurnal saturated photosynthesis index (DSPI) was developed to represent the

total daily photosynthetic production based on the total time spent in saturating irradiances and assuming each replicate plant ran photosynthesis at a maximum rate the entire time in Hsat.

The DSPI was calculated for each individual using a simplified equation (per Zimmerman et al. 1994 49): Pmax * Hsat. It is important to note that the data for this approach are derived from

PAM measurements that do not calculate diurnal losses from respiration or other processes not related to irradiance. The units of measurement for Pmax were first converted from seconds to

minutes to match Hsat units. The equation was used to calculate daily values for each period as previously described, that were then averaged for a final DSPI expressed in mol m−2.

Relationships between treatment and temperature (fixed effects) and photosynthesis parameters (Pmax, Ek) and iterations of these (Hsat, rel-Hsat, DSPI), and growth were analyzed using linear

mixed-effects model (LMM) packages lme4 and lmerTest in R (v1.1–31, 61). Fixed effects included treatment and temperature for all analyses and for both species. Random effects included: (a)

plant ID (to account for natural plant variability), (b) run (potential irradiance changes over time), (c) RLC order (replicate measurement order), and (d) lunar phase (affects _Ulva

lactuca_ reproduction and growth62, 63). All or a subset of these random effects were used for each analysis to achieve the best model fit and avoid issues of singularity. Plant ID was used

in all analyses. Residual plots were visually examined to ensure no deviations from normality or homoscedasticity occurred. The Performance package in R (v 0.10.2 64) was used to visualize

important model assumptions, i.e., collinearity, influential observations, linearity of residuals, etc. Marginal (fixed effects only; R2m) and conditional (fixed + random effects; R2m)

coefficients of determination were used to determine goodness of model fit. P-values were obtained via likelihood ratio tests that compared the full model with a null model that lacked the

effect being tested. Lastly, we ran basic linear regressions with growth as the dependent variable and Pmax, Ek, Hsat, rel-Hsat, and DSPI as predictors to gauge importance as predictors.

From this, DSPI as a predictor of growth was modeled with LMM as above using treatment as the random effect. Graphs were plotted using ggplot2 graphic package (v3.3.6, 65). ALGAL GROWTH

Growth was calculated using the equation $$\left(\frac{{w}_{f}- {w}_{i}}{{w}_{i}}\right)*100$$ where f = final (D9) and i = initial wet weight (w; D1). This equation was used to eliminate

the need for assumptions of steady or exponential growth because such assumptions have not been well tested or documented in _Ulva_ or _Hypnea_66. The parasite _Hypneocolax stellaris_ was

occasionally found on _Hypnea_ and was removed and biomass was quantified and subtracted from the plant weight. Details in supplementary materials. RESEARCH COMPLIANCE STATEMENT All

experiments were conducted in accordance with University of Hawai‘i at Mānoa (UHM) policies and regulations and all relevant lab personnel were trained and in compliance with (UHM) lab

safety requirements. SPECIMEN COLLECTION STATEMENT The State of Hawai‘i does not restrict nor require permission for the collection of algae except for within marine protected areas (MPAs)

or if collecting select taxa in the genus _Gracilaria_. Accordingly, all _Ulva_ and _Hypnea_ specimens were collected outside of the MPAs and are therefore in compliance with State

requirements. DATA AVAILABILITY All supporting data are available at author’s (ARD) github repository with username: angelardhawaii. https://github.com/angelardhawaii?tab=repositories.

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_Seaweed Ecology and Physiology_ (Cambridge University Press, 1994). Book  Google Scholar  Download references ACKNOWLEDGEMENTS The authors would like to acknowledge and thank Scott van de

Verg for help with algae collection and in the lab. Mahalo (thank you) to Veronica Gibson and Beatrice Pujol for lab help during experiments, and the rest of our team: Henrietta Dulai,

Kimberley Burnett, Christopher Wada, Aly El-Kadi, Jade Delevaux, Kosta Stamoulis, Meghan Dailer, Megan Donahue, and Brytne Okuhata for their invaluable input during the project. Mahalo to

Jamie and Iain Caldwell for their advice on statistical analysis, and Alessandro Donà for his expertise and help in writing scripts in Python and R and for building the experiment

structures. This is contributed paper WRRC-CP-2023-10 of the Water Resources Research Center and paper No. 198 for the School of Life Sciences, University of Hawai‘i at Mānoa. The authors

express their gratitude to the stewards of the lands and ocean of Kona, who graciously shared their knowledge about the site, granted access, and provided field help over the years. FUNDING

Funding for this project came from the USGS Water Resources Research Institute Program grant number G16AP00049 BY5 to LLB and from Hawaii Invasive Species Council PO #C21591 and National

Fish and Wildlife Foundation Award #0810.20.068602 to CMS. AUTHOR INFORMATION AUTHORS AND AFFILIATIONS * School of Life Sciences, University of Hawai‘i at Mānoa, Honolulu, HI, USA Angela

Richards Donà & Celia M. Smith * University of Hawai‘i Economic Research Organization, University of Hawai‘i at Mānoa, Honolulu, HI, USA Leah L. Bremer * Water Resources Research Center,

University of Hawai‘i at Mānoa, Honolulu, HI, USA Leah L. Bremer Authors * Angela Richards Donà View author publications You can also search for this author inPubMed Google Scholar * Celia

M. Smith View author publications You can also search for this author inPubMed Google Scholar * Leah L. Bremer View author publications You can also search for this author inPubMed Google

Scholar CONTRIBUTIONS All authors worked on the conceptualization, writing of the original and revised manuscripts, provided resources, and participated in the investigation. A.R.D. and

C.M.S. devised the methodology and analyzed the data. A.R.D. ran the experiments, collected and curated the data, and created the graphics. C.M.S. and L.L.B. supervised and acquired the

funding for the project. L.L.B. was the project administrator. CORRESPONDING AUTHOR Correspondence to Angela Richards Donà. ETHICS DECLARATIONS COMPETING INTERESTS The authors declare no

competing interests. ADDITIONAL INFORMATION PUBLISHER'S NOTE Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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