Introduction
The Arctic Ocean is considered to be the most vulnerable ecosystem to ocean
acidification (OA) due to the combined effects of low temperature, which increases
the solubility of CO2 and, in places, dilution of the buffering capacity
of seawater by freshwater inputs (Fabry et al., 2009; AMAP, 2013). Indeed,
large-scale assessments of pH in combination with saturation states for
aragonite (Ωarag) < 1 have led to the notion that the
Arctic Ocean is already close to a corrosive state (Fabry et al.,
2009). However, whereas this has been documented for offshore waters, the
Arctic contains a massive coastline where the regulation of pH and
Ωarag is far more complex than that offshore (Hofmann et al., 2011;
Duarte et al., 2013). In coastal waters, the role of air–sea CO2
exchange in regulating pH operates along with watershed effects driven by the
discharge of freshwater and the effects of metabolically intense communities
on pH (Duarte et al., 2013). The Greenland Ice Sheet is melting at a rate
that has more than doubled in the last decade (Helm et al., 2014) and
Greenland fjords are hence potentially among the most susceptible to the
effects of freshening and acidification.
(a) Location of Kobbefjord, Nuuk. (a) Location of
sampling sites in Kobbefjord: fjord-scale sites (CTD, CT,
AT: filled circles; CTD: open circles), vegetated subtidal sites
(open circles #1–3), and intertidal sites (open circles #4).
(c) Photopanel of benthic habitats: a typical kelp forest habitat
(dominated by Saccharina longicruris) and habitat colonized by
microalgae/scattered filamentous algae (example from site #1,
representative of sites #1–3 in map) and a vegetated intertidal pool and
the adjacent vegetated shore dominated by Ascophyllum nodosum and
Fucus spp. (site #4 in map).
As most calcifiers occupy coastal habitats, the assessment of risks of Arctic
acidification to these vulnerable species cannot be derived from
extrapolation of the current and forecasted offshore conditions alone, requiring instead an understanding of the regimes of pH and Ωarag in
the coastal habitats they occupy, and the same is true regarding potential
effects of ocean acidification on coastal phototrophs (calcifying or
non-calcifying) (Mercado and Gordillo, 2011). Such information is currently
largely lacking for the Arctic in general and for Greenland in particular,
which calls for efforts to understand variability in pH in the coastal zone
informing on the factors controlling pH and ultimately determining the
sensitivity of the coastal Arctic Ocean ecosystem to ocean acidification.
Greenland has a vast and highly indented coastline, extending approximately
44 000 km and representing ca. 12 % of the world's coastline
(Krause-Jensen and Duarte, 2014). This coastline forms a complex network of
fjords and open coasts that contains multiple features contributing to
heterogeneity, such as continental ice and freshwater discharge at the
headwaters, variable slopes and substrates, differential water residence time
conducive to widely distinct temperature regimes within neighbouring areas
(Olesen et al., 2015), and tides that generate intertidal habitats and force
flow patterns. In addition, Greenland fjords often support highly productive
kelp forests (Krause-Jensen et al., 2012) and intertidal seaweed communities
(Høgslund et al., 2014), which have been suggested to have the capacity to
affect pH and Ωarag locally (Krause-Jensen and Duarte, 2014).
Such effects have been demonstrated for Antarctic and temperate
kelp/macroalgal ecosystems (Middelboe and Hansen 2007; Delille et al., 2009;
Cornwall et al., 2013a) as well as for subtropical and tropical seagrass
meadows (e.g. Hofmann et al., 2011; Hendriks et al., 2014). Calcifiers such
as bivalves, brittle stars and sea urchins, which are potentially vulnerable
to OA, are ecologically important as they contribute significantly to carbon
cycling in both the subarctic and Arctic Greenland, where their distribution
ranges from the intertidal zone to > 300 m depth (Sejr et al., 2002;
Blicher et al., 2007, 2009, 2013; Blicher and Sejr, 2011). Phototrophs such
as kelps, while being able to affect the pH regime, may also respond to OA,
which has been shown to stimulate their growth (Olischläger et al., 2012)
and affect the competition between kelps and understory red algae (Connell
and Russell 2010).
Although the variability in pH and Ωarag in Greenland fjords
has not been reported, available oceanography and environmental surveys
suggest that this may be substantial. For instance, in Young Sound, Sejr et
al. (2011) found that the extent of sea-ice cover and inputs of glacial melt
water affect seawater pCO2 levels and sea–air exchange at spatial,
seasonal and interannual scales. Seasonal dynamics of autotrophic and
heterotrophic plankton metabolism have also been found to markedly affect
pCO2 levels in Kobbefjord, a subarctic fjord in SW Greenland (Sejr et
al., 2014). However, information on scales of variability in pH and Ωarag in Greenland fjords is still lacking, precluding the assessment
of their current and future vulnerability to ocean acidification.
Here we quantify pH variability in Kobbefjord, SW Greenland. This subarctic
fjord supports dense and productive subtidal kelp forests, intertidal
macroalgal habitats and high abundance of bivalves and sea urchins with
important roles in the ecosystem (Blicher et al., 2009; Krause-Jensen et al.,
2012). We hypothesize that Kobbefjord contains a mosaic of pH environments
nested across a range of scales of variability and that primary production in
general, and by macroalgae in particular, may be an important driver of pH
variability relevant for benthic calcifiers. We first assess seasonal and
spatial variability in the open-water pH at the kilometre scale along the horizontal
extension and at 100 m scale vertically in the fjord. We then examine diel
variability in pH within subtidal benthic habitats colonized by kelp forest
or microalgae/scattered filamentous algae as well as in vegetated tidal pools
and adjacent vegetated intertidal shores, with the distance between parallel
deployments at the 10–100 m scale. We further explore the pH variability
three-dimensionally at centimetre to metre scale within the kelp forest ecosystem and at
millimetre scale across the diffusive boundary layer (i.e. the layer in which
molecular diffusion is the dominant transport mechanism for dissolved
material; see e.g. de Beer and Larkum, 2001) of key macrophyte species.
Whereas our assessment focuses on pH, we also discuss the associated
variability in Ωarag.
Methods
Study area
Kobbefjord is located in the extensive Godthåbsfjord system in southwest
Greenland (Fig. 1a). The fjord is 17 km long and 0.8–2 km wide and has a
maximum depth of 150 m. It is subjected to marked exchange of coastal water
driven by a tidal range of 1–4.5 m (Richter et al., 2011) and receives
freshwater mainly from a river in the innermost part of the fjord, leading to
a salinity gradient in the surface water. Sea ice usually covers the inner
part of the fjord from December to early May, but the outer part of the fjord
is permanently ice-free. Light attenuation in the water column has been
reported to range from 0.083 m-1 in February to 0.197 m-1 in
May to 0.135 m-1 in September (Sejr et al., 2014). Whereas the
phytoplankton community is the main primary producer in the central parts of
the fjord (Sejr et al., 2014), subtidal macroalgae, dominated by
Saccharina longicruris and Agarum clathratum, form
productive benthic habitats along the shores to water depths of ca. 40 m
(Krause-Jensen et al., 2012) interspaced with communities of benthic
microalgae (Glud et al., 2010; Attard et al., 2014) as well as with scattered
eelgrass (Zostera marina) meadows at 1–3 m depth (Olesen et al.,
2015). Communities of intertidal macroalgae, dominated by Fucus spp.
and Ascophyllum nodosum, are prominent in the intertidal zone, where
they form an important habitat for blue mussels, for example (Blicher et al., 2013).
Three field campaigns targeting seasonal- and fjord-scale variability in pH
in the pelagic zone were conducted in the spring (19 April), midsummer
(18 July) and late summer (3 September) of 2013 (Fig. 1b). The late summer
survey was associated with an intensive campaign
(27 August–6 September 2013) exploring pH variability in shallow subtidal
kelp habitats and neighbouring habitats colonized by benthic microalgae and
scattered filamentous algae (Fig. 1c). A final late summer campaign
(22–30 August 2014) addressed pH variability in vegetated tidal pools and
surface waters of adjacent vegetated shores (Fig. 1c). All pH data from
fjord-scale to microscale are reported on the total pH scale.
Fjord- and seasonal-scale pH variation
To determine the large-scale spatial and seasonal variation in physical and
chemical parameters in the water column of Kobbefjord, vertical profiles were
performed at 11 stations located along a longitudinal gradient following the
main central axis of the fjord on 19 April, 18 July, and 3 September 2013
(Fig. 1b). We used a Sea-Bird CTD (SBE 19plus) equipped with sensors for
temperature, conductivity, fluorescence (Seapoint chlorophyll fluorometer),
oxygen (SBE 43, Sea-Bird) and pH (SBE 18, Sea-Bird). Alongside CTD profiles,
water samples were collected using a 5 L Niskin bottle at 1, 5, 10, 20, 30,
and 40 m depth. Water was collected for dissolved oxygen measurement using
Winkler titration (Parsons et al., 1984), which was used to calibrate the CTD
oxygen optode. The pH sensor was calibrated using NIST buffers and a seawater
Tris buffer prepared according to Dickson (2007). Unfiltered water was
transferred to 150 mL borosilicate glass bottles for pH analysis. The
samples were poisoned with a saturated mercuric chloride solution, cooled and
stored in darkness until arrival. Back in the lab, pH was measured
potentiometrically using a glass reference electrode (Orion, Ross Ultra
pH/ATC Triode) calibrated with NIST buffers and a seawater Tris buffer
prepared according to Dickson (2007). The measurements were used to correct
the offset of the SBE 18 pH measurements.
For estimation of the saturation state of aragonite (Ωarag),
samples for analyses of dissolved inorganic carbon (CT) and total
alkalinity (AT) were collected at five stations on one occasion
(3 September 2013). Triplicate 12 mL samples were collected at 5, 10, 20,
30, and 40 m depth and near the bottom. Samples were carefully siphoned
through Tygon tubing from Niskin bottles to 12 mL septum-capped glass vial
(Exetainers), allowing the water to overflow for two volume changes. The
samples were poisoned with 100 µL 5 % HgCl2 to avoid
biological alteration. CT was analysed with a CT
analyser (AS-C3, Apollo SciTech Inc). The accuracy of the analysis was
2.4 µmol kg-1 (average numerical deviation from the reference
material value) and the precision was 1.4 µmol kg-1 (average
standard deviation of triplicate samples). AT was analysed on an
alkalinity titrator (AS-ALK2 from Apollo SciTech), with verification against
the same certified reference material used for pH measurements or a Metrohm
Titrando 808 by open-cell titration (Dickson et al., 2007) using Batch 136
supplied by the Andrew Dickson lab at UC San Diego for verification. Average
analysis accuracy was 2.9 µmol kg-1 (average numerical
deviation from the reference material value). Relationships between the point
samples of AT and salinity (S) were used to verify the
published relationship for the Godthåbsfjord system
(TA = 159 + 63 S; Meire et al., 2015), which was subsequently applied
for estimation of AT for the full September data set.
Ωarag and pCO2 were calculated from AT and pH
using the CO2SYS Excel program version 2.1 (Pierrot et al., 2006) with
the K1 and K2 constants from Mehrbach et al. (1973), as modified by
Dickson and Millero (1987).
Small-scale and diurnal-scale pH variation
To measure small-scale and diurnal-scale variation in pH and physico-chemical
variables in kelp forests and adjacent subtidal habitats colonized by
microalgae and scattered filamentous algae we constructed metal frames
measuring approximately 0.90 m × 0.90 m × 1.10 m. Each
frame was equipped with instruments that allowed continuous measurements of
temperature, salinity, water level, oxygen concentration, photosynthetically
active radiation (PAR) and pH at ca. 50 cm above the seafloor
(Fig. A1). Measurements were made every
10 min or less. We selected three dense (close to 100 % cover) kelp beds
located in shallow water (average depth 2–5 m) in different sites of the
fjord. All kelp beds were dominated by S. longicruris with
co-occurrence of A. clathratum and were surrounded by habitats
colonized by microalgae and varying amounts of scattered filamentous algae.
We conducted parallel deployments of frames with loggers in kelp beds vs.
surrounding non-kelp habitats in each of the three sites, with each
deployment lasting about 48 h. The typical distance between kelp and
non-kelp habitats at each site was approximately 100 m. Conductivity,
temperature and water level were measured with a Hydrolab DS5X and a MicroCAT
(SBE37, Sea-Bird). Oxygen concentration was measured using miniDOT oxygen
loggers (Precision Measurement Engineering) and a Hydrolab
DS5X. PAR was measured using Odyssey PAR loggers from Dataflow Systems
Pty Limited. pH was measured using Hydrolab DS5X and SeaFET pH loggers from
Satlantic. Hydrolab DS5X pH sensors were calibrated with a routine two-point
calibration using NIST buffers of pHNBS 7.0 and 10.0. Before and
after each deployment all instruments were placed in a 50 L tank with
seawater to intercalibrate sensors. All pH loggers were offset to the same
newly calibrated high-precision SeaFET pH sensor, calibrated at the Satlantic
facility (www.satlantic.com) on the total scale using single-point
calibration. Oxygen sensors were calibrated to O2 concentrations of the
tank as determined from Winkler titrations.
To monitor three-dimensional pH variations on a metre scale within the kelp
canopy, we deployed a custom-built multi-sensor array, consisting of an
autonomous data logger (dataTaker DT85) in a watertight housing (custom-built by Albatros Marine Technologies S.I.) with 16 pre-amplified pH
electrodes (Omega, PHE-1304-NB). The pH sensors were attached to the
submersible logger by 5 m long cables to allow for adjusting their position
as needed (Fig. A1 in Appendix). The sensors were configured in situ in a
three-dimensional array on the metal frame occupying a volume of
approximately 1 m3, with four sensors at 0.1 m from the bottom, four sensors
at 0.2 m, four sensors just underneath the canopy and four above the canopy, which
typically extended about 0.75 m above the seafloor. All pH sensors were
calibrated with a three-point calibration using NIST buffers of
pHNBS 4.0, 7.0 and 10.0, allowing at least 5 min between every
reading for the sensors to stabilize. All pH loggers were offset to the same
newly calibrated high-precision SeaFET pH sensor as mentioned above. On
several occasions triplicate samples for determination of CT and
AT were collected and analysed as described above to allow
calculation of carbonate chemistry and Ωarag.
pH variation in vegetated tidal pools dominated by Ascophyllum nodosum and adjacent intertidal habitats on the shore also dominated by
A. nodosum and Fucus spp. was quantified over a diurnal
cycle through sampling at low tide just after pool formation and prior to
pool inundation during day and night. pH and Ωarag were
calculated from CT and AT samples collected and
analysed as described above and computed using the CO2SYS program (Pierrot et
al., 2006) with in situ information on temperature and salinity. Salinity was
analysed from water samples based on measurements of conductivity (Orion 3-Star conductivity benchtop), while oxygen concentration and water temperature
were determined using a portable meter (Hack, HQ40d).
Microscale pH variation
pH variations at a millimetre scale were measured in the laboratory on six
different species of macrophytes (the intertidal brown macroalgae
Ascophyllum nodosum and Fucus vesiculosus, the kelps
Saccharina longicruris and Agarum clathratum, the green
alga Ulva lactuca, and the seagrass Zostera marina)
occurring in Kobbefjord and collected either there or, for logistic reasons,
in another branch of the Godthåbsfjord system. From each species, a piece
of approximately 5 × 2 cm was cut and mounted on a microscope slide
in an aquarium with seawater before measurements. The setup was mounted in
an aquarium in a climate-controlled room with temperature kept at
2–3 ∘C. By gently blowing the water surface above the mounted slide
with air supplied by an aquarium pump, we generated a stable low current
velocity of approximately 0.28 ± 0.02 (SE) mm s-1 in our
observational area. We measured pH from a point close to the leaf surface up
until out of the diffusive boundary layer, where the pH was stable. We used
UNISENSE micro-pH sensors with 25 or 50 µm tips, connected to a
volt meter with 1-decimal precision for millivolt measurements (Consort, R362). pH
sensors were calibrated with a three-point calibration using NIST buffers of
pHNBS 4.0, 7.0 and 10.0 before each series of measurements. After
each change in species or replica, a resting period of > 15 min was
observed to allow the diffusive boundary layer to be fully developed before
measurements. A USB microscope (DinoCapture) connected to a PC with on-screen
visualization software aided in visually establishing the lowest point of the
measurements, as close to the macrophyte surface as possible without breaking
the tip of the electrode. A scaled picture from this lowest point allowed for
back-calculating the actual distance to the leaf surface afterwards. We
allowed readings at this lowest point to stabilize for > 5 min after
which the millivolt value was written down manually. The microsensor was then raised
20 µm with a precise 1-D micromanipulator and thereafter
30 µm, after which we continued with 50 µm increments and
then 100 and 500 µm increments until a stable pH was obtained for
three measurements or more and we considered we were outside the diffusive boundary
layer; between subsequent points the sensor was allowed to stabilize for at
least 5 min. We evaluated 3 replicas of each species at a irradiance of
200 µmol photons m-2 s-1, and calculated the Δ
pH across the diffusive boundary layer (defined from the tissue surface to
where pH was at 0.99 × water-column pH).
Fjord-scale pH variability in Kobbefjord on 19 April, 18 July and 3
September 2013.
Results
Data are available in digital form (Krause-Jensen et al., 2015).
Fjord-scale and seasonal pH variability
Large seasonal and spatial variability was observed in pH values along the
longitudinal gradient centrally in the fjord (Fig. 2a). pHT in
surface water increased in April due to CO2 consumption by the spring
bloom as evidenced by a very high fluorescence (Fig. A2), to a maximum value
of almost 8.50, most pronounced in the mouth of the fjord with values of
around 8.25 in the inner part (Fig. 2). Accordingly, a horizontal gradient of
around 0.25 pH units was observed along the main axis of the fjord.
pHT values in upper layers decreased during the summer to around
8.35 in July and with the maximum observed towards the inner part of the
fjord. A further decrease in pH was observed in September, with more
homogenous values in surface waters along the fjord gradient resulting in a
horizontal range of only 0.05 pH units. Vertical gradients in pH from the
surface to the deeper waters of the fjord ranged from only 0.1 units in
April, when the fjord was vertically mixed, to 0.15 units in September to
0.25 pH units in July, when maximum pHT values of 8.35 occurred in
a subsurface algal bloom in the inner parts of the fjord with waters
supersaturated in oxygen (up to 120 % saturation, Figs. A2, A3) and
minimum values of pHT 8.1 were measured in the deeper sectors
(Fig. 2a). Seasonally pH varied between 0.2 and 0.3 units in both surface and
deep waters over the 5 months. Ωarag values were closely
coupled to pH and ranged from minimum values of 1.6, observed in the bottom
waters of the inner part of the fjord, to maximum values of 2.5 in the
subsurface waters in September (Krause-Jensen et al., 2015). Corresponding
pCO2 levels ranged from 162 to 325 µatm, in the range of
values recently reported for the fjord (Sejr et al., 2014).
Oxygen saturation at the fjord-scale ranged greatly from 85 to 127 % and
was strongly related to pH for each of the three periods (Fig. 3a), pointing
at strong biological control of pH variability within the fjord. The slope of
the pH versus O2 relationship was steepest for the April survey, when the
highest pH levels were observed. Examination of pH values in relation to
fluorescence and temperature also showed that the warmest waters, of up to
10 ∘C, observed in July, supported intermediate pH, while the
highest pH was observed in the coldest waters, corresponding to the April
survey, when temperatures were uniformly low across the fjord (Fig. 3b). On a
vertical scale, the cold bottom waters with low fluorescence generally
supported the lowest pH values across seasons. Hence, overall, pH showed much
tighter correlation with O2 levels than with water temperature, and the
correlation between pH and O2 implied a similar close correlation
between Ωarag and O2 levels.
Fjord-scale relationships in Kobbefjord between pH and oxygen
(a), and between temperature and fluorescence with associated
pH levels shown with symbol colour (b), on three sampling occasions:
19 April, 18 July and 3 September 2013.
Small-scale and diurnal pH variability in kelp forests and benthic habitats colonized by microalgae/scattered filamentous algae
The three parallel deployments in kelp forest and habitats colonized by
microalgae and scattered filamentous algae encompassed six complete diurnal
cycles which exhibited peak pHT levels during the day of 8.11
(8.04–8.19) (avg. (SD)) and 8.08 (8.02–8.16), respectively, as opposed
to minimum pHT levels during night of 8.02 (7.97–8.06) and 8.01
(7.94–8.09), respectively, with no significant difference between habitats
(t test, p> 0.05). The diurnal range of minimum night pH to maximum day
pH was slightly higher in the kelp forest
(avg. ± SD = 0.098 ± 0.061) than above the
microalgae/filamentous algae (0.073 ± 0.052) (paired, one-tailed t
test, p=0.041).
Diurnal variability in pH, O2, water depth (all measured by
Hydrolab) and irradiance (measured by Odyssey loggers) at ca. 50 cm above
the seafloor in kelp forests (a–c) and habitats colonized by
microalgae/filamentous algae (e–f) during three parallel deployment
in Kobbefjord, Nuuk, 27–30 August, 30 August–2 September, and 2–5 September
2013. The deployments represent the benthic sites (#1–3, respectively)
shown on the map (Fig. 1).
There were large differences in the extent of diel fluctuations in pH among
deployments dependent on incident irradiance and the shifting phase of tidal
state and the solar cycle (Fig. 4). Diel pH fluctuations were small during
dark, cloudy days and when high tide coincided with peak solar radiation,
thereby reducing incident irradiance on the benthic habitat. In contrast,
diel pH fluctuations were amplified in deployments during sunny days when low
tide coincided with peak solar radiation (Fig. 4). Hence, the interaction
between tide and the solar cycle controlled incident radiation and thereby
induced fluctuations in photosynthetic activity and pH. This was particularly
apparent in kelp forests where peak daily pH increased as a function of
maximum daily photosynthetic solar radiation reaching the habitat during the
day, whereas this relationship was not significant in the water column above
the microalgae/filamentous algae (Fig. 5). Indeed, biologic control of pH was
also reflected in strong relationships between pH and O2 concentration
within each deployment in the kelp forests (R2=0.64–0.76), particularly
during high irradiance, as opposed to weaker pH versus O2 relationships
for the microalgae/filamentous algae sites
(R2=0.05–0.15) which also showed much
smaller variability in O2 levels (98–114 % saturation) than did the
kelp forest (92–128 % saturation) (Fig. 6). The diurnal range of O2
concentrations in the kelp forest matched the range recorded at pelagic fjord
scale on a seasonal basis (85–127 %, Fig. 3).
Maximum daily pH in a kelp forest (green dots) and above
microalgae/filamentous algae (blue dots) as a function of maximum daily
incident light over 6 full days as measured during three parallel deployments
in Kobbefjord, Nuuk, 27–30 August, 30 August–2 September, and 2–5 September
2013. Linear fit and coefficient of determination are shown for the significant
relationship for the kelp forest.
Tidal changes in water masses, reflected by changes in salinity and
temperature, also contributed to variations in pH and O2 levels. This
was visible as incidences of sudden changes in pH paralleling fluctuations in
salinity and also as differences in pH levels between deployments in water
masses of different salinity (Fig. 4). However, salinity explained much less
of the variation in pH than did O2, except in one deployment in the
microalgae/filamentous algae habitat when salinity explained 51 % of the
variation in pH as opposed to 15 % explained by O2
(R2= 0.04–0.33 in kelp forest; R2= 0.04–0.51 in
microalgae/filamentous algae, data not shown). Thus, overall biological
activity had a much stronger influence on pH than had exchange of water
masses.
The observed diurnal pH variability also translated into important
fluctuations in Ωarag, involving 0.18 ± 0.06 units (from
maximum day levels of 1.77 ± 0.21 to minimum night levels of
1.60 ± 0.17) in the kelp forest and 0.14 ± 0.07
Ωarag units (from maximum day levels of 1.72 ± 0.30 to
minimum night levels of 1.58 ± 0.26) at the microalgae/filamentous
algae sites. Corresponding pCO2 levels ranged from 238 to
536 µatm at the kelp sites and from 258 to 515 µatm at
the microalgal/filamentous algal sites.
pH vs. O2 concentration for three parallel deployments
(#1–3 shown by increasing colour intensity) in subtidal habitats colonized
by kelp forests (top panel) or microalgae/scattered filamentous algae (bottom
panels) in Kobbefjord, Nuuk, August–September 2013. Each deployment
represents 10 min loggings by multiloggers (Hydrolab) over ca. two diurnal
cycles. Linear fits and coefficients of determination are shown.
Metre- to millimetre-scale pH variability in kelp forests
Examination of the variability in pH within 1 m3 kelp forest, sampled
from the bottom of the canopy to the overlying water column, using the
multi-electrode array, showed very large concurrent pH variability involving
about 0.2 to 0.3 pH unit differences at any given time and with a total
pHT range of 7.76–8.36 across deployments (Fig. 7). In general,
pH tended to be highest at the top of the canopy and in the water just above
the canopy, reflecting that the canopy top is the most photosynthetically
active layer, while pH was generally lower in the shaded bottom part of the
canopy (Fig. 7), where photosynthetic biomass and incident irradiance are
lower and respiration rates higher. The range of pH within 1 m3 of kelp
forest at any one point in time was comparable among deployments, despite the
different light conditions, although the absolute values of pH differed among
deployments, with highest levels observed at peak incident irradiance
(Fig. 7). This small-scale variability in pH also translated into a
variability in Ωarag of about 0.20 units in 1 m3 of
habitat at any time.
pH variability within 1 m3 of kelp forest in Kobbefjord, Nuuk,
during three deployments in late August–September 2013. Sixteen pH sensors were
configured in situ in a 3-day array with four sensors at 0.1 m from the bottom,
four sensors at 0.2 m, four sensors just underneath the canopy and four in the water
column above the canopy, which typically extended about 0.75 m above the
seafloor.
pH also varied significantly within the diffusive boundary layer of the six
macrophyte species examined in the light (Fig. 8a), with pH increasing by
0.07–0.85 units, depending on species, from the top of the 0.3–2.2 mm
thick diffusive boundary layer to the surface of the plants (Fig. 8b).
pH variability in intertidal pools
pH and oxygen concentration showed important diel variability in vegetated
intertidal pools, with oxygen supersaturation (up to 176 %) during the
day and undersaturation (down to 11 %) at night, compared to far more
uniform concentrations in the surface waters on the adjacent vegetated shore
(89–111 % saturation, Fig. 9). Accordingly, pHT changed
greatly in intertidal pools, reaching maximum values of 9.0 during the day
and minimum values of 7.4 during night periods, i.e. a diel range of ca. 1.6
pH units. Diel pH fluctuations in the surface waters of the adjacent shore
were much smaller (8.0–8.5) but still high, reflecting the metabolic
activity of the intertidal vegetation growing on the shore (Fig. 9). The
difference in pH between vegetated intertidal pools and adjacent shores
provided an additional example of variability in pH between adjacent
habitats.
Microscale pH variability across diffusive boundary layers of blades
of six different macrophyte species illuminated by 200 µmol photons
m-2 s-1: the kelps Saccharina longicruris and
Agarum clathratum, the intertidal brown macroalgae Fucus vesiculosus and Ascophyllum nodosum, the green macroalga
Ulva lactuca, and the seagrass Zostera marina. (a)
pH levels (mean of 2–3 replicate measurements) across blade diffusive
boundary layers fitted by an exponential model (y=y0+a×exp-b×x, R2 > 0.90 for all individual fits).
(b) pH range across the diffusive boundary layer of the various
species.
Discussion
Our results highlight the nested scales of variability in pH present in the
Kobbefjord ecosystem involving (1) seasonal variability, largely driven by
the phytoplankton spring bloom as a major event affecting pH; (2) diel
variability acting through complex changes in submarine irradiance modulating
rates of photosynthesis and respiration of benthic vegetation driven by the
interaction of the solar and the tidal cycles; (3) large-scale variability
along horizontal and vertical fjord gradients reflecting gradients in
metabolic activity in combination with movement of water masses; (4)
variability between subtidal habitats with and without kelp forests and
between vegetated tidal pools and adjacent vegetated shores reflecting
variable degrees of biological control; (5) small-scale three-dimensional
variability due to heterogeneity in metabolic processes and mixing in
vegetated habitats; and (6) microscale variability across the diffusive
boundary layer of macrophytes (Fig. 10).
O2 concentration and pH in vegetated tidal pools and in surface
waters of neighbouring vegetated intertidal shores measured at low tide during
day and night just after pool formation and before pool inundation.
Conceptual summary of nested scales of temporal and spatial
variability in pH in Kobbefjord, Nuuk. The figure shows the maximum pH range
at the various scales examined. From lower left to upper right: (1)
microscale variability across macrophyte diffusive boundary layers, (2)
small-scale variability within kelp forests, (3) diurnal variability in
vegetated subtidal habitats and intertidal pools/adjacent shores and
variability between habitats at the 100 m scale, and (4) seasonal and
fjord-scale horizontal variability.
Overall, metabolic processes played a fundamental role in driving pH
variability across scales, as reflected in strong relationships between
oxygen concentration and pH at the fjord scale and at both diel and seasonal
scales. Primary producers played a major role in the regulation of pH
variability, both in the pelagic zone, where in particular the intense spring
bloom characteristic of Arctic ecosystems (Takahashi et al., 2003; Sejr et
al., 2014) induced high pH in the subsurface layers while the respiratory
process in the bottom waters reduced pH, and in the nearshore benthic
environment, where the presence of subtidal kelp forests and intertidal
macroalgae induced marked spatial and diurnal variability in
pH. The mosaics of pH reflected that the density of the primary
producers, and the spatio-temporal separation of photosynthesis and ecosystem
respiration in combination with mixing of water masses were key drivers of
the variability in both planktonic and benthic communities. Hence, the
vertical gradient of declining pH from upper illuminated to lower shaded
habitats varied from the 10–100 m scale in the planktonic community where
the density of primary producers is relatively low to the centimetre to metre
scale in dense kelp forests. The same is true on a temporal scale where the
diurnal pH variation in the benthic vegetation matches the seasonal
variability in pH in the planktonic community.
The scale of seasonal pH variability in the planktonic community (Fig. 10)
compared well with previous reports for the Arctic, showing the spring bloom
as a prevalent driver of pCO2 (Sejr et al., 2011; Meire et al., 2015).
Though a multitude of factors including water depth, light regime, season,
seawater retention time, density and plant species may all affect pH
variability in vegetated habitats, our results match evidence from other
latitudes of strong pH variability in macroalgal forests and seagrass
meadows. Hence, marked diel pH variability has also been reported from a
Californian kelp forest (Frieder et al., 2012), from a Mediterranean seagrass bed
(Hendriks et al., 2014), and, in an extreme case, from a temperate, shallow, dense
algal bed (diel range: ca. 1 unit; Middelboe and Hansen, 2007) and kelp forest
(diel range: ca. 0.6–0.8 pH units; Cornwall et al., 2013a). Our pH
measurements in benthic habitats neighbouring the kelp forest also carried a
biological signal, though less distinct, likely reflecting the combined
signal of the benthic primary producers at the site, of the neighbouring kelp
forests, and of the planktonic community in the water masses exchanged with
tidal currents. The marked biological control of pH in kelp forests suggests
that diel pH may be even more pronounced during sunny days with more intense
photosynthesis than during the generally overcast conditions of our survey.
Thus, while the identified pH range and pH vs. O2 relationships for the
planktonic community covered the full growth season, they solely represented
a few overcast September days in the benthic habitats and would likely
involve markedly higher levels had they covered the full growth season. For
subantarctic giant kelp forests, the diel amplitude in pCO2 and
CT (Delille et al., 2009) during spring and summer, as well as the
seasonal amplitude in pH, CT and pCO2 (Delille et al.,
2000), was reported to be markedly higher within kelp forests as compared with
unvegetated habitats, underlining the kelps' strong biological control of pH.
We further show, for the first time, significant 3-D variability in pH within
1 m3 of kelp forest, with pH ranging about 0.2–0.3 pH units at any one
point in time and a total variability across deployments of 7.76–8.36
pHT, resembling the range recorded across the entire growth
season in the pelagic. Levels of pH were dependent on the position in the
kelp canopy, with the highest pH generally appearing at the top of the canopy
and decreasing toward the seafloor, likely reflecting the vertical structure
of photosynthetic activity in the kelp bed. The fast rates of metabolic
activity in combination with reduced flow in such densely vegetated habitats
make these 3-D patterns appear in spite of the marked exchange of water
masses resulting from the 1–4.5 m tidal range.
Changes in pH were particularly pronounced in small tidal pools, where
photosynthesis of dense seaweed stands of primarily Ascophyllum nodosum and Fucus spp. drove O2 levels to large
supersaturation levels (176 %) and forced pH to extremes of up to
pHT 9.0 at low tide during sunny days, corresponding to
Ωarag of 4.14 and pCO2 of 13 µatm compared to
night values of pHT 7.4, Ωarag of 0.27 and
pCO2 of 1647 µatm driven by community respiration, which
almost depleted O2 in the pools (11 % saturation). In surface waters
of adjacent densely vegetated intertidal shores, we observed a maximum
pHT of 8.5 with corresponding Ωarag 2.23 and
pCO2 of 96 µatm during the day and a minimum pHT
of 8.0, with corresponding Ωarag of 0.54 and pCO2 of
243 µatm during the night. While intertidal brown macroalgae thrive
in such habitats when regularly flushed as in the current study, apparently
only Ulva (Enteromorpha) intestinalis occurs in isolated, rarely
flushed rock pools, where it can drive pH to levels > 10 (Björk et al.,
2004).
At the microscale, pH also showed considerable variability with a range of
up to 0.85 pH units across the diffusive boundary layer of the key species of
the vegetated shallow ecosystems, with high pH levels at the tissue surface
declining towards the bulk water during daytime (Fig. 8). There was
substantial variability among species, with intertidal macroalgae
(Ascophyllum and Fucus) showing the largest pH range. The
interspecific differences likely related to the species' photosynthetic rates
as well as to their morphology, which affect the thickness of the diffusive
boundary layer (Hurd and Pilditch, 2011). This microscale pH variability
across the diffusive boundary layer compared well with previous observations for
the calcifying alga Halimeda discoidea (pH range of 0.7 across
diffusive boundary layer; de Beer and Larkum, 2001) as well as for the
coralline algae Sporolithon durum (light–dark pH change at tissue
surface 0.9; Hurd et al., 2011) and Arthrocardia corymbosa (pH range
across diffusive boundary layer 0.4, for example, depending on flow; Cornwall et al.,
2013b). The pH range across the diffusive boundary layer of Ulva was
surprisingly low considering the ability of Ulva to elevate pH to high levels
(Björk et al., 2004), but it was probably the combination of low water
temperature and limited nutrient supply that limited the photosynthetic rate. The
diffusive boundary layer thickness as well as the pH range across it depends
markedly on flow conditions. Reduced flows as present in dense vegetation
increase the diffusive boundary layer thickness and consequently the pH range
(Hurd et al., 2011; Cornwall et al., 2013b). The current experiment was,
hence, conducted at reduced flow and, importantly, at the same flow for all
species. Exchange of water masses with different salinity and temperature
also added to the variability in pH as indicated for both pelagic (Fig. 3b)
and benthic (Fig. 4) systems but showed much weaker correlation to pH than
did O2 concentrations reflecting the biological control.
The processes above resulted in nested scales of pH variability in the
Kobbefjord ecosystem (Fig. 10), with variability ranging 0.2–0.85 units
across spatial scales and 0.2–1.6 units over diurnal to seasonal scales.
This variability provides a dynamic mosaic of niches for organisms. Niches of
high pH may be particularly important for the more vulnerable larval and
juveniles stages of calcifiers under conditions of low pH as projected for
the future (Kroecker et al., 2013). The suitability for calcifiers is best
represented by Ωarag, where calcifiers should be favoured by
high Ωarag values. The Kobbefjord ecosystems host a number of calcifying species, including
bivalves such as blue mussels, scallops and snails; echinoderms such as
green sea urchins; crustaceans such as Pseudobalanus balanoides; and calcareous algae and foraminifers. Overall, the identified Ωarag conditions were well
above 1, particularly in illuminated habitats with intense photosynthesis
and, hence, indicated favourable conditions for calcification. The
phytoplankton spring bloom, depleting CO2 and driving
Ωarag to values close to 3, would also provide adequate
conditions for pelagic calcifiers, as it would provide the double benefit of
adequate environments for aragonite deposition and food supply to support
growth and the energetic demands of calcifiers. Canopies of kelp and
intertidal seaweed environments may also provide adequate niches for
calcifiers during summer, when Ωarag values would be highest
through the cumulative action of the processes upregulating pH and
Ωarag values discussed above. Indeed, most calcifiers spawn
and recruit in early summer (Arendt et al., 2013), when pCO2 remains
low, warmer water temperatures lead to higher Ωarag and high
solar radiation and a long photoperiod allow seaweeds to draw down CO2
further (Delille et al., 2000).
The upregulating effect of primary producers on pH is counterbalanced by the
opposite effect of respiration and decomposition prevailing in shaded and
deeper basins and periods as illustrated by the large-scale seasonal
variability in the pelagic community (Fig. 2), and paralleled in kelp forests
outside the productive period (Delille et al., 2009) as well as during night-time and in shaded layers of the kelp forest (Fig. 7) and tidal pools
(Fig. 9). These shaded habitats with diurnally low Ωarag could
be challenging habitats for calcifiers. Interestingly, however, blue mussels
grew in close association with macroalgae even in intertidal pools, where
they would experience maximum Ωarag values of up to 4.28 when
low tide occurred at noon as opposed to levels as low as 0.28 during night
(Fig. 9). Blue mussels have indeed been observed to abound in intertidal
macroalgal habitats (Blicher et al., 2013) and along with other calcifiers to
be trophically linked with habitat-forming algae such as Ascophyllum
(Riera et al., 2009), and have also been reported to tolerate high
pCO2 concentrations when food is abundant (Thomsen et al., 2013).
The recurring periods of high Ωarag in combination
with adequate food supply can likely compensate for the potential problems of low
Ωarag during night. Laboratory experiments have demonstrated
that semidiurnal fluctuations of 0.3 pH units may compensate for negative
effects of constantly low pH on the development of mussel larvae (Frieder et
al., 2014). Calcareous epiphytic organisms, such as encrusted algae and
bryozoans, would also experience high variability in Ωarag at
the surface of the plant tissue, where periodically high Ωarag
values favours calcification, as elegantly demonstrated by de Beer and
Larkum (2001).
The existence of a mosaic of environments in the Kobbefjord underlines the
importance of metabolic processes along with habitat configuration and
interactions among community constituents in affecting pH in coastal
ecosystems as opposed to the simpler situation in the open ocean (Duarte et
al., 2013; Hendriks et al., 2014). This pronounced influence of metabolic
processes occurs in spite of Kobbefjord being a macrotidal area with marked
exchange of water masses with the coastal region and is probably also the
case in many other shallow coastal areas in the Arctic, as has also been
highlighted for areas in the temperate zone (Duarte et al., 2013). While the
current study explored pH in benthic habitats under overcast situations in
the early autumn of the subarctic, kelp forests are likely to induce much
more pronounced increases in pH and Ωarag in midsummer, when
irradiances are higher and the photoperiod longer, and further north, during
high-Arctic midsummer, when the sun does not set for months. Under scenarios
of ocean acidification such vegetated habitats may gain increased importance
as local refuges for calcifiers. The projected poleward expansion of
macrophytes into the Arctic with warming and reduced sea-ice cover
(Müller et al., 2009; Jueterbock et al., 2013) has been hypothesized to
provide such niches of elevated pH and Ωarag during summer
(Krause-Jensen et al., 2014). Similarly, increased pelagic primary production
as forecasted for parts of the Arctic Ocean (Arrigo et al., 2008; Slagstad et
al., 2011; Popova et al., 2012) may also create local niches of high pH.