Gradients and fine-scale phytoplankton variability in near-shore mediterranean waters

(1)

Title: Gradients and fine-scale phytoplankton variability in near-shore Mediterranean waters

___________________________________________________________________________________

AUTHOR: Francisco Javier Torres Serra

____________________________________________________________________________

Master’s Thesis

Master’s degree in Marine Ecology

at the

UNIVERSITAT DE LES ILLES BALEARS

Academic year 2016 - 2017

Date: 21 July 2017

UIB Master’s Thesis Supervisor: Gotzon Basterretxea Oyarzábal _______________________________________

(2)

Gradients and fine scale variability UIB 2016-2017

1. INTRODUCTION

Phytoplankton is the base of the marine food chains and an important distributor to the planetary carbon fixation and oxygen production (Field et al. 1998).

Endogenous and exogenous processes regulate its dynamics and, hence, its distribution in the sea. As evidenced by satellite data, physical processes as such as currents, fronts and eddies determine large spatial variations in phytoplankton biomass (Yoder et al. 1993). Likewise, biological processes such as growth, mortality and grazing determine some scales of variability. These time-space ranges or scales of variability in the sea expand from seconds and millimeters –i.e. those associated with molecular processes- to the global extension and thousands of years representative of climate oscillations (Tommy D. Dickey 2003). At the open ocean, meso-scale phenomena prevail, whereas coastal areas are more dynamic and time and space scales notably shorten. Identifying dominant scales of variability reveals how marine ecosystems change in time and space while providing information about the endurance of a system.

Phytoplankton variability is a primary driver of the ecology of coastal areas because is directly involved in the quality of coastal waters, the cycling of carbon, nutrients, other elements, and the exchanges at the land-sea-air interface (Cloern and Jassby 2009). The set of processes that determine coastal phytoplankton variability have been identified since long, but common patterns of variability and predominant scales in near-shore waters are unknown yet (Levin 1992). Coastal geomorphology, water column shallowness, water column stability, sea-land interactions, are modulators of coastal biophysical dynamics and produce complex response dynamics in phytoplankton while determining key ecological processes as such as transport of nutrients, plankton, spore and larval dispersal and settlement, dispersion of contaminants and overall diversity (Leis 1982; Roy, Cury, and Kifani 1992; Slomp and Van Cappellen 2004; Siegel et al. 2008). However, despite the efforts in characterizing this environment, search for common features

(4)

governing the biological dynamics in near-shore ecosystems has been generally elusive because site-specific mechanisms tend to prevail (Cloern and Jassby 2009).

In low energy areas, such as many Mediterranean coasts, the coastal geomorphology and alongshore flow patterns can isolate the waters adjacent to the shore generating sharp cross-shore gradients in temperature, salinity and biological properties (Blythe et al. 2011). A characteristic trait of this nearshore environment is the sharp variation in phytoplankton abundance often perceived by green bands and/or foam lines delineating changes in water properties (Wieters et al. 2003; Kim, Cornuelle, and Terrill 2009; Lucas et al. 2011; Corcoran and Shipe 2011). The enhanced biomass in this zone is the response to the increased nutrients from land and exchanges with the sediment.

Away from riverine influence areas, chemically altered submarine groundwater discharges, has a capital importance in sustaining near-shore primary productivity (Rodellas et al. 2015). In particular, the nutrient content of coastal waters can be altered by diffuse fresh and recirculated seawater inputs through permeable coastal sediments (Gotzon Basterretxea et al. 2010; Tovar-Sánchez et al. 2013). This nutrient supply is able to produce changes in coastal microbial communities and stimulating microalgal growth (Garcés et al 2011). In fact, evidence from monitoring long-term programs in the Mediterranean Sea has revealed marked differences in cross-shore nutrient and phytoplankton biomass in coastal inshore waters, where near-shore chlorophyll on average exceeds six fold offshore values with high risk eutrophication associated (Flo et al. 2011).

While nutrient and plankton concentrations off the Mediterranean coast (i.e. >1 nautical mile from the shore-line) range from oligotrophic to mesotrophic status, near-shore zones are characterized by excess nutrient loading and high phytoplankton biomass frequently displaying massive microalgal blooms. Near- shore harmful algal blooms with densities of up to 10⁶ cells L^-1 with a duration spanning from days to weeks have been increasingly reported along the Mediterranean coasts for the last few decades (Garcés et al. 1999). These high

(5)

biomass events are related with favorable sea and wind conditions and thus may exhibit rapid variation with changing atmospheric and oceanographic conditions (G. Basterretxea et al. 2005).

In the present study, we focus in the cross-shore trends of surface seawater temperature and salinity and chlorophyll in near-shore waters of the Mediterranean Sea. The main purpose of this study is (1) to develop a measuring system capable of acquiring high resolution underway measurements of the near-shore environment and (2) characterizing the cross-shore variations of the measured variables (temperature, salinity and chlorophyll) and their possible drivers.

(6)

2. METHODS

2.1 INSTRUMENT DEVELOPMENT

2.1.1 Hardware

Due to the necessity of characterizing the near-shore at high resolution, a system integrating information from several available oceanographic instruments was developed as part of the present work. The Continuous Underway Hydrological REcorder (CUHRE) consisted on a portable system including a twister water pump, SBE 45 microthermosalinograph (TSG), in-line Turner fluorometer, SA 320 NMEA receiver, SBE 25 Junction Box, LISST 100X and Garmin 421S chart plotter with echo sounder (GPS). An Arduino microcontroller controls the flux and voltage sensors are installed in CUHRE and a computer controls and combines data acquisition (Figure 1). Power for the data acquisition system is supplied by a 12V- 72A DC battery whereas the water pump and is connected to the boat energy system. This enables operation for long periods and stable power supply to sampling instruments.

Figure 1. Instrumental energy supply.

(7)

Surface (i.e. 0.4 m depth) water flows through the system getting first through a flux sensor, then through the fluorimeter and, finally, through the SBE 45 (Figure 2). The fluorimeter provides continuous signal of fluorescence received from chloroplasts emission, at a frequency of 4 Hz. The dynamic range of the probe oscillates between 4 and 20 mA with a background noise of 0.02mA. Fluorescence signal and salinity require temperature correction and calibration that are performed in the data processing routines.

The SBE 45TSG measures water temperature and conductivity at 1Hz.

Conductivity is transformed to salinity units using SBE standard algorithms. Error in temperature measurements is 0.002 ºC and salinity ±0.005 psu. A NMEA receiver, placed on top of the system provides the position at the same frequency.

Both systems are connected through a SBE Junction Box that sends signal of temperature, salinity, conductivity, latitude, longitude and time to the computer. A Garmin 421S GPS SoundPlotter acquires redundant information of the position.

The echosounder transducer is located bellow the waterline adjacent to the pump.

2.1.2 Data Processing

Raw data is processed using a set of MATLAB scripts. First, the quality of the information registered by each sensor is checked. This processing step removes outliers and applies fluorescence and salinity calibration functions. It also chooses the best geolocation information from the Navigator and NMEA information files.

Figure 2. Scheme of flowing system (blue arrows), taps (orange crosses) and flow sensors (black arrows).

(8)

In a second step, time is transformed into distance and all data is merged into a single coherent matrix for each acquisition period.

For each transect a single matrix with chlorophyll concentration, temperature, salinity, depth, latitude, longitude, time and distance from shore has been achieved.

The effect of temperature on fluorescence signal has been corrected using:

- Equation 1: F(t) =f(t) - (p1·T(t)2 + p2·T(t) + p3); (see Figure 3)

Where: F= corrected fluorescence; f= raw fluorescence; T= temperature; t= time;

p1= - 1.022·10^-3; p2= - 1.718·10^-2; p3=1.425;

Confidence interval (95% of significance level):

p1= ± 2.5·10^-5; p2= ± 9.1·10^-4; p3= ± 8·10^-3;

Figure 3: Adjustment of fluorescence raw data. Black line shows a decreasing fluorescence with an increasing temperature. Blue line is the black line with correction equation applied.

Red line represents the fit line of corrected data of fluorescence.

(9)

The effect of temperature on salinity values has been corrected using:

Equation 2: S(t)=s(t)-(p1·T(t)³ + p2·T(t)² + p3·T(t)+ p4); (Figure 4)

Where: S= Corrected salinity; s= raw salinity; T= temperature; t= time;

p1= 5.972·10^-6; p2= -2.785·10^-4; p3= 5.096·10^-3; p4= -0.01395;

Confidence interval (95% of significance level):

p1= ±2.2·10^-7; p2= ±1.25·10^-5; p3= ±2.19·10^-4; p4= ±0.0012

Figure 4: Correction of salinity measurements. Black line represents one concentration of salinity in raw data (38psu). Blue line is the corrected black data line with the equation applied (Equation 2). Red line represents the fit of blue line.

(10)

2.2 SAMPLING

2.2.1 Site description and characterization

Sampling was carried out between June and July of 2014 along the coasts of Mallorca and the Catalan Coast (North-Western Mediterranean, Figure 5). A total of 47 transects, 26 horizontal transects were sampled in Mallorca and 21 in the Catalan coast. Sampling was during the summer season, consisting in a cross-shore navigation from an approximate distance of 1 nautical mile to the shoreline (Figure 6). Continuous underway automatic measurements of temperature (T), salinity (S) and fluorescence based chlorophyll (Chl) were carried out at each sampling point.

Samples were collected between 11 am and 6 pm under good weather conditions, either in the absence of breakers or with small waves breaking on the shoreline. The

Figure 5. Transect location. Cross-shore trajectories are represented by turquoise lines A) Mallorca, B, C and D) Girona, Barcelona and Tarragona (Catalan Coast).

A

C D

B

(11)

data were captured at 1 Hz and merged with both with the GPS position and with the echosounder depth. Surface (~0.4 m depth) seawater was pumped from a depth of ~25 cm at a rate of (1.5 L min⁻¹) using a submersible pump attached to the side of a boat. At each sampling site, the slow navigation of the boat navigated (2–2.5 kt) allowed a spatial resolution of roughly 1 record·m⁻¹. Discrete chlorophyll a (Chl) samples were obtained at the beginning (off-shore station) and end (near- shore station) of each transect for purposes of calibration with the fluorescence data.

240 ml were filtered through a Whatman GF/F filter and stored on ice until laboratory analysis. It has been selected relatively linear beaches typically presenting bottom slopes (β) varying between 0.006 and 0.02.

2.2.2 Continuous data processing and statistics

Continuous T, S and fluorescence records were despiked, interpolated at 1 meter intervals and fluorescence (volts) was converted into Chl (mg/m^-3) using a calibration obtained from simultaneous discrete water samples. A principal component analysis (PCA) was carried out using the continuous records of the cross-shore transect data to identify variance patterns in the data. In the PCA, the lengths of the transects were homogenized to 1500 m and the T, S and Chl values were normalized (0 to 1). Chlorophyll transects were grouped into three major clusters (C1, C2, C3) attending to the obtained variation modes and coefficients, using k-means clustering.

Autocorrelation analysis was used to assess the main length scale of phytoplankton patchiness along the transects. First, the chlorophyll data were detrended and

Shore-line

1 nm

Beach Sea

Figure 6. Sampling transect scheme.

nm= nautical mile.

(12)

periodicities were afterwards estimated using the autocorrelation function.

Decorrelation lengths were estimated by abscissa axis crossing of the autocorrelation function. Both power spectral and autocorrelation characterizations of horizontal variability are made under the assumption that the processes are comparatively static for the period of the survey. This fact should not introduce large errors, since cross-shore currents in the coastal Mediterranean are generally low, e.g. Jordi et al. (2011), as compared with sampling speed.

2.2.3 Detection of near-shore gradient limits

A wavelet decomposition method was used to identify the cross-shore extension of the near-shore waters. This method consists in identifying changes in the variance of the wavelet reconstruction coefficients of the Chl register. A first step in this method is the application of a two order low pass zero-phase digital filter (Figure 7A). Then, wavelet decomposition coefficients are extracted using level one coefficients and extremal phase wavelets (Figure 7B). A Daubechies mother

Figure 7. Example of a transect with a standard NSG and the way to perform by variance analysis by wavelet decomposition coefficients.

A B C

D E

(13)

wavelet with one vanishing moment (‘db1’) is generally used for the analysis.

However, in some cases where cross-shore gradients are complex, a smoother mother wavelet ‘db3’ was used. The next step is the replacement of the top 2%

values with the mean value of the wavelet coefficients in order to remove the most intense signal (Figure 7D). Variance change points are estimated using the wavelet coefficients obtained in the previous step (Figure 7E; Lavielle 1999). Figure 8 shows an example of complex gradient resolved by this method and shows that is capable to resolve different types of transects.

Figure 8. Example of a transect with a complex NSG and the way to perform by variance analysis of wavelet decomposition coefficients.

(14)

3. RESULTS

3.1 GENERAL CROSS-SHORE CHARACTERISTICS

Figure 9 shows the mean and envelope of the measured continuous transects.

Overall, near-shore warming (mean 0.6±0.5ºC above offshore values) was evidenced in most of the registers revealing heat accumulation with decreasing depth near the shore. Shoreline temperatures occasionally exceeded 2.4ºC offshore values, but most frequently warming was in the range of 0.5 to 1 ºC. Salinity variations were site dependent and ranged from weak (0.02 ppt) declines in the first tenths of meters from the shoreline to more intense declines in the presence of river plumes (i.e. 4.5 ppt at some transects). Chlorophyll values showed minor variations in off-shore waters (e.g. 0.45±0.28 mg·m⁻³ at 1500m). These Chlorophyll concentrations, although still low, exceeded typical open ocean values in the Western Mediterranean during summer 0.05 mg·m⁻³, thus revealing the influence of coastal conditions at the off-shore boundary of the sampled transects. At near- shore, Chlorophyll concentrations were highly variable, with median values of

Figure 9. Mean (colored line) temperature, salinity, and chlorophyll and envelope of the 47 transects (shaded area). Values are represented as absolute anomalies from offshore (2000m) value in each transect.

(15)

0.91 mg·m⁻³ but with concentrations reaching up to 10.37 mg·m⁻³ (range 0.28- 10.37 mg·m⁻³). Except in areas affected by river or urban outflow, phytoplankton biomass accumulation generally occurred in the first 200 to 400 m from the shoreline.

The Figure 10 shows the location of the along transect chlorophyll and temperature maximum and the salinity minimum. Chlorophyll maxima are found mainly in the first 250 meters from the coast, with a median location of 84 meters. The location of the thermal maxima is generally restricted to the first 140 meters from the coast, and most transects present highest values within the first 10 meters from the shore, where the water column is shallow. The distribution of the negative salinity anomalies is quite scattered, and while most transect present low near-shore values, 30% of the transects present significant salinity variations further from the shore. It is noticeable that 70% of the transects sampled at Mallorca present salinity minima in the firsts 150 meters.

Figure 10. Histogram of chlorophyll maximum, temperature maximum and salinity minimum distances from shore. Red bars are drawn as outliers if they are larger than q3 + w*iqr or smaller than q1 – w*iqr, where w is 1.5, iqr=q3-q1 and q1 and q3 are the 25th and 75th percentiles, respectively. The 1.5 multiplier corresponds to approximately ±2.7σ (where σ is standard deviation) and 99.3% coverage of the data for a normal distribution (Krzywinski and Altman 2014).

500 1000 1500 2000 2500 3000 3500 4000 4500

Dist ance from shore ( m ) 0

5 10 15 20 25 30 35 40

Number of profiles

Ch lor oph yll M a x im u m Te m pe r a t u r e M a x im u m Sa lin it y M in im u m

0 500 1000 1500 2000 2500 3000 3500 4000 45000 500 1000 1500 2000 2500 3000 3500 4000 45000

(16)

Mean cross-shore variations of temperature, salinity and chlorophyll reveal different along transect zones (Figure 11). Marked temperature and chlorophyll variations are observed near the shore in a region extending up to 250m, called High Change Region (HCR). A Transition Region (TR) extending from 250 to 1400 presents remarkable variations of salinity that are attributed to the presence of torrents and city outlets. Off from these areas (OFF) variations are generally low.

Figure 12 shows and example of a chlorophyll transect with these three sub-regions.

Figure 11. Mean cross-shore variations (colored line) of temperature, salinity, and chlorophyll and envelope of the 47 transects (shaded area). Black lines separate HCR (0- 250 m) and TR (250-1400m). Values are represented as absolute anomalies from offshore (2500m) value in each transect.

0 500 1000 1500 2000 2500 0

1 2 3 4

∆Chl (mg m-3 )

2500

0 500 1000 1500 2000 2500 -0.4

-0.2 0 0.2

∆Salinity

Distance form shoreline (m)

0 500 1000 1500 2000

0 0.2 0.4 0.6 0.8

∆Temperature (o C)

(17)

Figure 12. Transect example of NSG. Three regions can be defined (red, magenta and black). 0 meters correspond to shore-line. red- line represents High Change Region (HCR) and is the nearest region to shore-line;

magenta-line represents Transition Region (TR) and is placed between HCR and OFF.

While the mean trends presented in Figure 11 represent a large percentage of the transects, marked differences can occur at some sites. For example, some transects near the City of Barcelona present large variations off the coast that are attributed to the presence of waters from city drainage systems and rivers. Also, cross-shore gradients extending far from the shore (> 2km) were observed at other sites (Figure 13).

Figure 13. A, B & C. Examples of No-NSG transects: OFF phenomena that inhibit the formation of a differential near-shore region (Barcelona Transects). D. Example of anomalous transects:

perturbation longer than nautical mile or coastal waters length (Girona Transect). Filtered chlorophyll transect data.

A 0.

91 m g·

m

−3

bu t wi th co nc en tr ati on C 0.

91 m g·

m

−3

bu t wi th co

B b0 .9 1 m g·

m

−3

bu t wi th co nc en tr ati D 0 . 9 1 m g

· m

−3

b u

(18)

3.2 NEAR-SHORE ZONE WIDTH AND CHARACTERISTICS

The detection of the near-shore zone, where the most acute gradient occurs, presents difficulties indebted to the variety of patterns observed. As explained in the methodology section, we used a method based on detection of variance in wavelet decomposition analysis, which is capable to embrace complex transects and identify smooth transitions. Figure 14 shows the near-shore limit obtained from the chlorophyll, temperature and salinity variations. In most of the transects, the limit of the Chl gradient is restricted to a distance of 600 meters from shore. Temperature presents limit point occurring between 300 and 900 meters from shore, and salinity shows a limit that in many cases exceeds 1500m.

Figure 14. Limit point identified in chlorophyll, temperature and salinity. No-NSG transectss have been removed from total.

The boxplot in Figure 15 reveals the mean characteristics of the cross-shore zones.

A broader variation can be observed in all variables in the NSG. Median chlorophyll values are higher in NSG region. Conversely, concentrations in CW are rarely reach 0.3 mg·m^-3. Similarly, significantly higher temperatures are observed in NSG. The case of salinity is somewhat different. While absolute median values are relatively similar, high variations can be expected at some sites due to the influence of terrestrial sources.

(19)

Figure 15. Distribution of difference between NSG and CW mean for each transect with off-shore value as reference. Median value is drawn by red line, quartiles are represented by the edges of the blue box and whisker show the range of extreme values not considered as outliers.

3.4 PATTERNS OF CROSS-SHORE VARIATION

The main patterns of cross-shore variation were analyzed using PCA analysis (Figure 16). The first three components explain more than 82% of the variability of each of the four variables analyzed (T, S and Chl). The first component (c1) in temperature explains 52% of the variability, revealing a linear trend from offshore towards the shore. This linear trend was determined in ~87% of the transects. The second and third components also indicated near-shore warming. This gradient was exponential in shape in the restricted band <400 m from the shoreline (marked in gray in the figure). A TR followed from this distance extending to 1200m. All salinity components indicated salinity declines near the shore. While a quasi-linear

(20)

shoreward decreasing tendency was observed in c1, components 2 and 3 reflected different patterns of variation with decays at the near-shore (e.g. < 400m from the shoreline). The c2 and c3 trend is perturbed by variations that may either increase or decrease salinity in the TR. Similarly to temperature, the first component of Chlorophyll indicated lineal enhancement towards the coast. The c2 and c3 components of also presented a trend comparable to that of temperature. However, in the case of c2, chlorophyll was slower the transitional zone than in the offshore boundary.

Figure 16. PCA scores for temperature, salinity, chlorophyll. The variability explained by each component (c1, c2 and c3) is indicated. The patterns were adjusted to zero in the off-shore limit (1500m) to facilitate interpretation.

(21)

Figure 17 shows the mean temperature, salinity and chlorophyll transects resulting from k-means clusterization of the dataset. Most transects 42 to 49 % are included in Cluster 1 that represents exponential variation with distance from shore. Both Temperature and Chlorophyll increase in near-shore whereas a marked decline is observed in salinity. These changes mainly occur in the first 400 m from the shoreline. Other clusters such as T3, S3 and Chl2, represent transects with variations at a distance from shore (400-600m).

Figure 17. Mean cross-shore variation of temperature, salinity, and chlorophyll (normalized values) for each of the three clusters (C1, C2, C3) obtained by k-means clustering of the 47 transects analyzed. The colored line is the mean value and the gray shaded areas indicate the standard deviation.

The spatial distribution of the Chl clusters is shown in Figure 18. Cluster 1 transects are mainly located in Mallorca (PAL) and Girona (GIR). Cluster 2 and 3 corresponds to Barcelona (BCN), Tarragona (TGN) and Northern Mallorca Bays (ALC). The data suggests that wind direction can be determinant in the shape of the transect. Shoreward directed winds were related to cluster 1 transects in most of cases.

(22)

Figure 18. Map showing the chlorophyll transects categorized by types (C1, C2, C3) and main wind direction (black arrows).

We used spatial autocorrelation analysis to further examine along this phytoplankton patch scale (Fig 19A). The Chl spatial decorrelation lengths varied between 30 and 300 m, with median values of 77m and 25-75% percentiles of 60 and 120. Larger scales were observed in T (median=134m; percentiles=100-174) and S (median=142m; percentiles=95-173m). The frequency distribution of the chlorophyll crossings of the 47 transects is shown in Fig 19B, and presents a log- normal distribution.

(23)

Figure 19. A: Histogram showing the frequency of chlorophyll decorrelation lengths calculated for the 47 transects. B: Spatial autocorrelation of the same variables as a function of lag. Data are from a transect at Palma Bay.

4. DISCUSSION

The CUHRE is a powerful tool to obtain high-resolution data in near-shore waters.

It allows detecting variations in the hydrographical properties at fine scales (metric). This provides vital and accurate information for the management of coastal areas. Comparatively to routine point sampling monitoring procedures, the information provided by the instrument herein presented supposes a quantum leap in the characterization of nearshore areas. Previous studies have used similar equipment (Powell, Cloern, and Walters 1986; Jassby, Cole, and Cloern 1997;

Dustan and Pinckney 1989; Madden and Day 1992); however, the main advantage of the CUHRE is its portability and capacity to be operated in small boats. In our case, we were able to perform between 5 and 6 1.5 mile-long transects per day.

The results of this study demonstrate the existence of distinct zones in the cross- shore variation of temperature, salinity and chlorophyll. Higher abundance of phytoplankton is usually found in coastal ecosystems (Cullen 1982). We have developed a method to sample at high frequencies in a small-scale environment with a high cost/efficiency level. This method allows a high rate in vivo sampling

A B

(24)

that can help to resolve connectivity paradigms in marine ecology (Brown et al.

2016) and can improve coastal management being more critics with its implementation (Shipman and Stojanovic 2007).

As revealed by the PCA and analysis, a transition area lies between the coastal margin and offshore waters. In some transects, such as c1-type, this zone presents a gentle transition from warmer waters containing high phytoplankton biomass to the oligotrophic conditions prevailing offshore. Wind induced cross-shore advection produces important variability in this zone. In other transect types (c2, c3), variability in this transition zone is similar or exceeds nearshore variations.

These variations are generally related to alongshore features generated by river plumes, city outfalls and/or other point sources. This is clearly observed in the proximity of the Ter river at GIR, and at some sampling points near the city of Barcelona (BCN). The definition of these river-plume features fades with distance from source and it could be plausible to speculate that in some cases c3-type is indeed the result of a dampened signal produced by a distant source similar to that on c2-type. In the case of river and stream plumes the source can be tracked by changes in salinity. In some other cases, the source of these perturbations at the transition zone are more difficult to identify. Headland fronts, topographic eddies and water masses in ports, inlets and other coastal features are source of this variability.

Regarding the limits of the nearshore zone, beaches or bays, such as Pollença Bay, with low seafloor slopes present a nearshore boundary exceeding 1 km from the coast. The topographical protection at these sites from alongshelf circulation facilitates de development of a differentiated nearshore environment. Contrarily, bays or beaches poorly protected from seas, such as Es Trenc (Mallorca South) or southern area of Alcúdia Bay, present low nearshore gradients due to the higher renewal of coastal waters. The above mentioned examples evidence the local scale complexity of the sampled transects. This variety in forms and shapes can be governed by exogenous factors that become each area as a particular.

(25)

4.1 IMPLICATIONS FOR COASTAL MANAGEMENT

Cross-shore phytoplankton variations pose a challenge for the design of coastal monitoring strategies in the Mediterranean Sea. Our observations provide an explanation for the difficulties in interpreting sampling data obtained as part of most European Water Framework Directive (WFD) monitoring programs in the Mediterranean Sea, see also Camp et al. 2015. Measurements taken at a particular distance from shore may lead to misinterpretations on the overall biomass and community composition. To avoid these biases, the spatial resolution of measurements across nearshore waters should be sufficiently refined such that they capture the main patterns in the system. According to Kalikhman (2001) a field can be reconstructed properly (r²>0.70) if sampling distance/autocorrelation <1.0–1.5.

This implies that, with the spatial Chl decorrelation scales obtained in the present study, water measurements should be obtained at spatial intervals of 90 to 180 m to finely characterize the nearshore gradient. While fine spatial resolution may be unnecessary for the purpose of most monitoring programs, our results raise concern on coastal water assessment based on single point measurements.

(26)

REFERENCES

Basterretxea, G., E. Garcés, A. Jordi, M. Masó, and J. Tintoré. 2005. “Breeze Conditions as a Favoring Mechanism of Alexandrium Taylori Blooms at a Mediterranean Beach.” Estuarine, Coastal and Shelf Science 62 (1): 1–12.

doi:10.1016/j.ecss.2004.07.008.

Basterretxea, Gotzon, Antonio Tovar-Sanchez, Aaron J. Beck, Pere Masqué, Henry J. Bokuniewicz, Ruth Coffey, Carlos M. Duarte, et al. 2010. “Submarine Groundwater Discharge to the Coastal Environment of a Mediterranean Island (Majorca, Spain): Ecosystem and Biogeochemical Significance.”

Ecosystems 13 (5): 629–43. doi:10.1007/s10021-010-9334-5.

Blythe, Jonathan N., Da Silva, José C. B, and Jesús Pineda. 2011. “Nearshore, Seasonally Persistent Fronts in Sea Surface Temperature on Red Sea Tropical Reefs.” ICES Journal of Marine Science 68 (9): 1827–32.

doi:10.1093/icesjms/fsr109.

Brown, Christopher J., Alastair R. Harborne, Claire B. Paris, and Peter J. Mumby.

2016. “Uniting Paradigms of Connectivity in Marine Ecology.” Ecology 97 (9): 2447–57. doi:10.1002/ecy.1463.

Camp, Jordi, Eva Flo, Magda Vila, Laura Arin, Albert Reñé, Nagore Sampedro, Marta Manzanera, and Esther Garcés. 2015. “Pros and Cons of Biological Quality Element Phytoplankton as a Water-Quality Indicator in the NW Mediterranean Sea.” In Experiences from Ground, Coastal and Transitional Water Quality Monitoring, edited by Antoni Munné, Antoni Ginebreda, and Narcís Prat, 135–60. The Handbook of Environmental Chemistry 43.

Springer International Publishing. doi:10.1007/698_2015_392.

Cloern, James E., and Alan D. Jassby. 2009. “Patterns and Scales of Phytoplankton Variability in Estuarine–Coastal Ecosystems.” Estuaries and Coasts 33 (2):

230–41. doi:10.1007/s12237-009-9195-3.

Corcoran, Alina A., and Rebecca F. Shipe. 2011. “Inshore–offshore and Vertical Patterns of Phytoplankton Biomass and Community Composition in Santa Monica Bay, CA (USA).” Estuarine, Coastal and Shelf Science 94 (1): 24–

35. doi:10.1016/j.ecss.2011.05.007.

Cullen, John J. 1982. “The Deep Chlorophyll Maximum: Comparing Vertical Profiles of Chlorophyll a.” Canadian Journal of Fisheries and Aquatic Sciences 39 (5): 791–803. doi:10.1139/f82-108.

Dustan, Phillip, and James L. Pinckney. 1989. “Tidally Induced Estuarine Phytoplankton Patchiness.” Limnology and Oceanography 34 (2): 410–19.

doi:10.4319/lo.1989.34.2.0410.

(27)

Field, Christopher B., Michael J. Behrenfeld, James T. Randerson, and Paul Falkowski. 1998. “Primary Production of the Biosphere: Integrating Terrestrial and Oceanic Components.” Science 281 (5374): 237–40.

doi:10.1126/science.281.5374.237.

Flo, Eva, Esther Garcés, Marta Manzanera, and Jordi Camp. 2011. “Coastal Inshore Waters in the NW Mediterranean: Physicochemical and Biological Characterization and Management Implications.” Estuarine, Coastal and Shelf Science 93 (4): 279–89. doi:10.1016/j.ecss.2011.04.002.

Garces. 1999. “A Recurrent and Localized Dinoflagellate Bloom in a Mediterranean Beach.” Journal of Plankton Research 21: 2373–91.

Jassby, A. D., B. E. Cole, and J. E. Cloern. 1997. “The Design of Sampling Transects for Characterizing Water Quality in Estuaries.” Estuarine, Coastal and Shelf Science 45 (3): 285–302. doi:10.1006/ecss.1996.0199.

Kim, Sung Yong, Bruce D. Cornuelle, and Eric J. Terrill. 2009. “Anisotropic Response of Surface Currents to the Wind in a Coastal Region.” Journal of Physical Oceanography 39 (6): 1512–33. doi:10.1175/2009JPO4013.1.

Krzywinski, Martin, and Naomi Altman. 2014. “Points of Significance: Visualizing Samples with Box Plots.” Nature Methods 11 (2): 119–20.

doi:10.1038/nmeth.2813.

Lavielle, Marc. 1999. “Detection of Multiple Changes in a Sequence of Dependent Variables.” Stochastic Processes and Their Applications 83 (1): 79–102.

doi:10.1016/S0304-4149(99)00023-X.

Leis, J. M. 1982. “Nearshore Distributional Gradients of Larval Fish (15 Taxa) and Planktonic Crustaceans (6 Taxa) in Hawaii.” Marine Biology 72 (1): 89–97.

doi:10.1007/BF00393952.

Levin, Simon A. 1992. “The Problem of Pattern and Scale in Ecology: The Robert H. MacArthur Award Lecture.” Ecology 73 (6): 1943–67.

doi:10.2307/1941447.

Lucas, Andrew J., Christopher L. Dupont, Vera Tai, John L. Largier, Brian Palenik, and Peter J. S. Franks. 2011. “The Green Ribbon: Multiscale Physical Control of Phytoplankton Productivity and Community Structure over a Narrow Continental Shelf.” Limnology and Oceanography 56 (2): 611–26.

doi:10.4319/lo.2011.56.2.0611.

Madden, Christopher J., and John W. Day. 1992. “An Instrument System for High- Speed Mapping of Chlorophyll a and Physico-Chemical Variables in Surface Waters.” Estuaries 15 (3): 421–27. doi:10.2307/1352789.

Powell, Thomas M., James E. Cloern, and Roy A. Walters. 1986. “Phytoplankton Spatial Distribution in South San Francisco Bay: Mesoscale and Small- Scale Variability,” 369–83. doi:10.1016/B978-0-12-761890-6.50028-9.

Rodellas, Valentí, Jordi Garcia-Orellana, Pere Masqué, and Joan S. Font-Muñoz.

2015. “The Influence of Sediment Sources on Radium-Derived Estimates

(28)

of Submarine Groundwater Discharge.” Marine Chemistry 171 (April):

107–17. doi:10.1016/j.marchem.2015.02.010.

Roy, C., P. Cury, and S. Kifani. 1992. “Pelagic Fish Recruitment Success and Reproductive Strategy in Upwelling Areas: Environmental Compromises.”

South African Journal of Marine Science 12 (1): 135–46.

doi:10.2989/02577619209504697.

Shipman, Brian, and Tim Stojanovic. 2007. “Facts, Fictions, and Failures of Integrated Coastal Zone Management in Europe.” Coastal Management 35 (2–3): 375–98. doi:10.1080/08920750601169659.

Siegel, D. A., S. Mitarai, C. J. Costello, S. D. Gaines, B. E. Kendall, R. R. Warner, and K. B. Winters. 2008. “The Stochastic Nature of Larval Connectivity among Nearshore Marine Populations.” Proceedings of the National Academy of Sciences 105 (26): 8974–79. doi:10.1073/pnas.0802544105.

Slomp, Caroline P., and Philippe Van Cappellen. 2004. “Nutrient Inputs to the Coastal Ocean through Submarine Groundwater Discharge: Controls and Potential Impact.” Journal of Hydrology 295 (1): 64–86.

doi:10.1016/j.jhydrol.2004.02.018.

Tommy D. Dickey. 2003. “Emerging Ocean Observations for Interdisciplinary Data Assimilation Systems.” Journal of Marine Systems, The Use of Data Assimilation in Coupled Hydrodynamic, Ecological and Bio-geo-chemical Models of the Ocean. Selected papers from the 33rd International Liege Colloquium on Ocean Dynamics, held in Liege, Belgium on May 7-11th, 2001., 40–41 (April): 5–48. doi:10.1016/S0924-7963(03)00011-3.

Tovar-Sánchez, Antonio, David Sánchez-Quiles, Gotzon Basterretxea, Juan L.

Benedé, Alberto Chisvert, Amparo Salvador, Ignacio Moreno-Garrido, and Julián Blasco. 2013. “Sunscreen Products as Emerging Pollutants to Coastal Waters.” PLOS ONE 8 (6): e65451. doi:10.1371/journal.pone.0065451.

Wieters, Evie A., D. M. Kaplan, S. A. Navarrete, A. Sotomayor, J. Largier, K. J.

Nielsen, and F. Véliz. 2003. “Alongshore and Temporal Variability in Chlorophyll a Concentration in Chilean Nearshore Waters.” Marine Ecology Progress Series 249: 93–105.

Yoder, James A., Charles R. McClain, Gene C. Feldman, and Wayne E. Esaias.

1993. “Annual Cycles of Phytoplankton Chlorophyll Concentrations in the Global Ocean: A Satellite View.” Global Biogeochemical Cycles 7 (1):

181–93. doi:10.1029/93GB02358.