1

Running Head: Methods for studying stratification 1

2

Title: Alternative methods for studying stratification dynamics on discrete and continuous time 3

scales 4

5

Katherine Hudson, Northeastern University, Marine and Environmental Sciences, 430 Nahant 6

Road, Nahant, MA 01908 7

8

2

Abstract 9

Stratification is an important driver for many biological and ecological processes across 10

benthic and pelagic habitats in the world ocean. However, stratification dynamics are still 11

undersampled due to limitations of current methods. Current methodologies rely primarily on 12

CTD and Niskin bottle data to develop stratification profiles, that are then compared over time. 13

Here, we describe two new methodologies which utilize remote sensing technologies for 14

examining stratification dynamics on discrete and continuous time scales. The first, focusing on 15

thin layers and zooplankton distributions in the water column, utilizes a Remotely Operated 16

Vehicle (OpenROV version 2.8) to record vertical transects in discrete time using a low-power 17

lens placed periodically over an HD imager. The second utilizes a customizable mooring system 18

and thermistor strings to continuously observe stratification as well as dynamic phenomena such 19

as internal waves. Using these methods, physical phenomena such as internal waves and thin 20

layers were observed with the continuous and discrete methods, respectively. These 21

methodologies allow for the observation of stratification dynamics on a variety of time and 22

spatial scales. A model was constructed in R to examine the effects of perturbations of the 23

stratified layer on downwelling that could have consequences for deeper-water pelagic and 24

benthic organisms. Understanding stratification dynamics and their impacts on water column 25

biota and the benthos across temporal and spatial scales will become increasingly important as 26

climate change impacts the dynamics of the surface layer of the world ocean. 27

Key Words: stratification, dynamics, zooplankton population dynamics, remote sensing, 28

temporal scales, internal waves, thin layers 29

Introduction 30

3

The stratification of the water column, or the distribution of bodies of water according to 31

their relative densities, has been shown to impact physical and biological phenomena throughout 32

the world ocean (Li 2002; Leichter et al. 1996; Wang et al. 2007). Changes in stratification 33

dynamics have been shown to influence species distributions, drive physical events in the water 34

column, and even influence events such as hurricanes and tropical cyclones above the ocean 35

(Greer et al. 2014; Butman et al. 2006b; Kunze et al. 2002; Holligan et al. 1985) 36

Despite the importance of stratification dynamics to species distributions across the world 37

ocean, stratification dynamics remain poorly sampled (Eickstedt et al. 2007). Sampling of ocean 38

stratification primarily occurs with CTDs, a group of ocean instruments capable of measuring 39

conductivity, temperature, and depth (Thompson and Emery 2014). These instruments can be 40

used to construct discrete temperature, salinity, and density profiles as a function of depth 41

(Thompson and Emery 2014). Data from CTD casts have been used previously to construct 42

reliable, long-term time series datasets that describe the seasonal changes in water column 43

structure and stratification (Steinberg et al. 2001). These data have been extremely influential to 44

describing the ocean circulation system present throughout the world ocean (Steinberg et al. 45

2001). However, these measurements are discrete (Thomson and Emery 2014). As a result, the 46

data they can collect are ultimately limited by their sampling frequency (Thomson and Emery 47

2014). 48

For example, the Bermuda Institute of Ocean Sciences (formally the Bermuda Biological 49

Research Station) has been following this sampling regime since 1954 with the development of 50

the Bermuda Atlantic Time-Series (BATS) study (Steinberg et al. 2001). While the data 51

collected at BATS is extremely valuable and has resulted in a wide-range of publications, the 52

sampling frequency of approximately once a month limits the researchers and scientists from 53

4

drawing concrete conclusions on what occurs at the study locations, or extrapolating those 54

results, on small time scales (Doney et al. 1996; Thompson and Emery 2014). 55

Currently, there are very few methods available for collecting data on continuous time 56

scales. One of the most popular of these are temporary mooring systems that can be deployed 57

with instrumentation specific to the needs of the researcher and the questions at hand (Butman et 58

al. 2006a). Such mooring systems have been used to study physical and biological phenomena 59

such as internal waves in Stellwagen Bank and harmful algal blooms in the Gulf of Maine 60

(Butman et al. 2006a, K. Hudson, pers. obs.). Instrument platforms and underwater vehicles, 61

autonomous or otherwise, have also been deployed to collect continuous data on the world ocean 62

(Eriksen et al. 2001). However, these systems are often only deployed for a single season and are 63

difficult to recover in inclement conditions (Pillsbury et al. 1969). 64

Another significant limitation to current stratification sampling methods is the cost of 65

instrumentation and ship time (Eriksen et al. 2001). CTD instruments, often included with 66

sampling bottle arrays, cost thousands of dollars, depending on the depth rating of the instrument 67

(Thompson and Emery 2014). Instruments capable of taking continuous measurements range can 68

cost upwards of $5,000 (Pillsbury et al. 1969). Research cruises to collect these data and deploy 69

the necessary instruments also can cost as much as $25,000 per day at sea (K. Hudson, pers. 70

obs). The high costs of both instruments and ship time often make up a significant portion of 71

grant budgets. Therefore, there is a significant need to develop relatively low-cost 72

instrumentation that can produce high quality and reliable data. 73

This study aims to address this need for data to be produced on a continuous time scale 74

and be relatively low cost when compared to traditional methods. Using northern Massachusetts 75

Bay as a study site, moorings like those used to study internal waves off Stellwagen Bank were 76

5

constructed (Butman et al. 2006). These moorings included thermistor strings of Onset HOBO 77

temperature loggers, low-cost temperature loggers ranging between $50 - $200 per device. 78

Inspired by the Massachusetts Bay Internal Wave Experiment in 1998 and work by John Witman 79

in the Gulf of Maine, three moorings were deployed off Nahant, MA and Rockport, MA to 80

observe stratification dynamics, including internal wave phenomena, during the summer months 81

of 2016 (Butman et al. 2006; Witman et al. 1993; Witman et al. 2004). 82

Internal waves occur in stratified waters and propagate along the stratification boundary 83

(Haury et al. 1979). They are formed by a disturbance in this boundary layer, which is usually 84

created by the movement of water due to tides over a large geographic feature, such as a ridge or 85

seamount (Haury et al. 1979; Helfrich and Melville 2006). These phenomena, in addition to 86

other stratification processes, have been shown to have significant impacts on plankton 87

distributions throughout the water column and can induce downwelling events (Lai et al. 2010; 88

Scotti and Pineda 2007; Shanks 1983; Witman et al. 1993). 89

In addition, an open-source remotely operate vehicle (ROV) by OpenROV was used to 90

make visual observations of zooplankton. Using the HD imager aboard the ROV and a 91

magnifying lens placed in front of the lens, the OpenROV v. 2.8 was deployed to observe how 92

zooplankton are impacted by stratification dynamics. Although this is technically discrete 93

method of observing plankton dynamics in response to changes in stratification dynamics, it is 94

hypothesized that the collaboration of these technologies provides a more holistic view of 95

stratification dynamics and their overall ecological impacts in northern Massachusetts Bay. 96

Methods 97

Continuous Observations – Thermistor String Mooring System 98

6

To continuously measure stratification dynamics, vertical moorings were constructed in 99

the spring of 2016. Moorings were constructed to hold HOBO thermistor strings and allow for 100

simple and quick recovery, data download, and redeployment. Three moorings were constructed 101

to accommodate deployment water column depths of approximately 30, 80, and 50 m. The top 102

and bottom 6 m of these moorings consisted of ¾-inch braided line to accommodate added stress 103

from the buoys and anchors. One-ft diameter Norway buoys were used through the testing phase. 104

When deployed in Rockport, these buoys were paired with 10 m of line between them in the 105

event one buoy was lost. Anchors were constructed from recycled brake pads with the line 106

looped through the center of the brake pad and passing the other end of the line through the eye 107

splice. When deployed in Nahant, only one anchor was used and weighed approximately 60 lbs. 108

In Rockport, anchors were deployed in pairs and weighed a cumulative 80 lbs to reduce the risk 109

of dragging. The remaining line between the buoy and anchor was 1/4-inch braided line. Each 110

line had an eye splice at each end, which contained a metal thimble of the appropriate diameter. 111

Sections of the mooring system were attached using ½-inch shackles. The buoys were attached to 112

the gear using a whale-friendly break away swivel (weak link), that has a breaking strength of 113

600 lbs. 114

The thermistor string was constructed using woven strap material with a width of 1 inch. 115

The strap was cut to be equal to the length of the ¼-inch line section of the mooring. When the 116

length required exceeded the length of the strap available in a single unit, sections of strap were 117

connected with bowline knots. HOBO Water Temperature Pro V2 temperature loggers were 118

connected to the line using 1-inch S-clips by making a small incision in the strap material and 119

passing one end of the clip through this incision. The other clip was passed through the eye of 120

the temperature logger. Temperature loggers were placed on the strap every five meters between 121

7

10 and 50 meters and programmed to record water temperature every minute. If the mooring 122

length exceeded 50 meters, loggers were placed every 10 meters after 50 meters. 123

To facilitate the recovery of the thermistor string without pulling the entire mooring up, a 124

5-lb dive weight was attached to the bottom of the thermistor string. A two-meter chain was 125

attached to the bottom of the string using a carabiner clip that was tied to the end of the strap 126

using a bowline knot. This chain added additional weight to the string and was wrapped around 127

the 1/4-inch line to allow the thermistor string to be pulled up without removing the entire 128

mooring system. Another carabiner clip was used to attach the top of the thermistor string to the 129

shackle at the top of the ¼-inch line. Figure 1 illustrates the instrumented mooring system. 130

Upon recovery of the thermistor string, data were uploaded to a computer using a HOBO 131

Base Station and saved using HOBOware Pro for later analysis in the R programming 132

environment. Analysis included visual interpretation of water column structure to see if events 133

such as internal waves or water column turn-over could be observed. A Fast Fourier Transform 134

(FFT) followed by a power spectral density analysis using data that were Hanning-windowed 135

was performed in Mathematica to test for the presence of periodic events such as internal waves 136

and tidal currents. A handheld CTD (YSI CastAway) was cast three times at the mooring 137

location at each recovery to collect water temperature, salinity, and density data. These data were 138

used in the calculation of a mean Brunt-Väisälä frequency to determine if physical phenomena 139

such as internal waves could occur under the stratification present at the time of the observations. 140

The mooring rated to 30 m was deployed off East Point, Nahant, MA in early May 2016 141

(Figure 2a). This mooring served as the test for the later deployments of two moorings off 142

Rockport, MA and was used to make improvements to the overall mooring system. One full 143

recovery of the entire system was made in May 2016 and four data recoveries were made 144

8

between May and July to collect the data from the temperature loggers, for a total of five 145

recoveries. An attempt to relocate this mooring to Rockport, MA in August 2016 was 146

unsuccessful. The remaining two moorings were deployed off Rockport, MA in August 2016 147

(Figure 2b). One of these moorings, in addition to the mooring design described above, also 148

included an InterOcean S4 electromagnetic current meter at the base (Figure 1). An attempt to 149

recover the moorings was made in October 2016 was unsuccessful. During the search, numerous 150

minke whales were observed surfacing in the vicinity of the mooring site, and it is suspected that 151

whale feeding activity resulted in break off of the swivel attached to the mooring buoy. 152

Mathematical Model of Internal Wave Induced Downwelling on Benthic Communities 153

A spatially-explicit, two-patch model was constructed to model the effects of internal 154

wave induced downwelling (IWID) on benthic communities. The first patch represented the 155

pelagic environment while the second represented the benthic environment. The pelagic patch 156

consisted of a single equation determining the abundance of plankton in the pelagic zone, while 157

the benthic patch consisted of two equations outlining plankton and consumer abundances. The 158

equations describing the abundances of the plankton populations were based on a continuous 159

logistic growth model, with parameters added for loss due to mortality, consumption, and the 160

effects of IWID. For the benthic consumer, the equation was based on a Rosenzweig-MacArthur 161

model for predator populations, with a density-dependent term added for mortality: 162

𝑑𝑃1

𝑑𝑡 = 𝑟1𝑃1 (1 −

𝑃1

𝐾1) − 𝑃1𝑦 − 𝑑𝑃1 (Eq. 1) 163

𝑑𝑃2

𝑑𝑡 = 𝑟2𝑃2 (1 −

𝑃2

𝐾2) −

𝑃2𝑎𝑁

(1+(𝑎+ℎ+𝑃2))+ 𝑑𝑃1 (Eq. 2) 164

𝑑𝑁

𝑑𝑡 =

𝑃2𝑎𝑏𝑁

(1+(𝑎+ℎ+𝑃2))− 𝑚𝑁 (Eq. 3) 165

9

where Pi is the abundances of pelagic and benthic plankton, respectively, and N is the abundance 166

of the benthic consumer. In Eqs. 1 and 2, ri and Ki are the intrinsic rates of growth and carrying 167

capacities of the pelagic and benthic plankton populations, respectively. The growth rates for the 168

pelagic and benthic plankton were equal to 0.75 and 0.30, while the carrying capacities were 169

equal to 20 and 5, respectively (Eqs. 1-2). General mortality and predation of the pelagic 170

plankton was represented by y = 0.2 (Eq. 1). The pelagic plankton population (𝑃1) in the pelagic 171

patch of the equation was given a higher rate of growth (𝑟1) and carrying capacity (𝐾1) than the 172

populations in the benthic patch due to the increased light availability. The benthic plankton 173

population (𝑃2) was given a lower rate of growth (𝑟2) and carrying capacity (𝐾2) due to 174

decreased light availability at depth. These values were not equal to zero due to the nutrient 175

availability at depth, which would have stimulated some growth. Pelagic plankton were given a 176

low mortality term (y) due to the high productivity that would be occurring during the summer 177

months, when internal waves would be propagating through this system. 178

Attack rate, handling time, and efficiency of the benthic consumer were equal to a = 0.5, 179

h = 0.2, and b = 0.75, respectively (Eq. 3). Mortality of the benthic consumer was represented by 180

m = 0.75 (Eq. 3). These parameters were chosen to compensate for the fact that successful 181

benthic consumers would need to consume high quantities of plankton to have enough energies 182

for their respective functions. Thus, the consumers were made to be highly efficient, with low 183

handling times and high attack rates. In addition, benthic consumers were given a high mortality 184

to compensate for the high competition for resources at depth. 185

The effect of IWID is represented by d and is calculated using the following equation: 186

𝑑 = 𝑃1 (0.5 (𝐷 sin (𝜏2𝜋 (𝑡

𝑚𝑎𝑥𝑇𝑖𝑚𝑒)))) (Eq. 4) 187

10

Where D is the relative strength of the IWID event, 𝜏 is the frequency of the event, t is the time 188

step, and maxTime is the total time that the model run was simulated during the experiments 189

(Eq. 4). A for loop was used to keep all values of this equation at or above zero, to account for 190

periods in which IWID is not occurring. In the experiments, these parameters were set to D = 191

0.25, 𝜏 = 2, and maxTime = 1000 (Eq. 4). The time step was determined by the deSolve function 192

in the R programming environment. 193

This model assumes that all plankton populations are general populations consisting of 194

both phytoplankton and zooplankton. Intraguild predation on phytoplankton by zooplankton was 195

assumed to be negligible over the time scale of the simulations which are comparable to internal 196

wave perturbations (O(hours)). It was assumed that benthic consumers were generic, non-197

specialized consumers and that competition between consumers was negligible. This model also 198

assumed that benthic consumers were only consuming benthic plankton populations. Detritus 199

inputs were not considered for this model. 200

In an R programming environment, in silico experiments were conducted with the model 201

described above, testing how benthic communities in this model were dependent on IWID and 202

how they responded to changes in downwelling intensity and frequency, both independently and 203

in conjunction with each other. The first experiment tested the dependency of the benthic 204

consumer on IWID events by simulating the model in the presence and absence of IWID. 205

Absence of IWID events was simulated by setting D = 0. Using the deSolve function in R, 206

twenty simulations were conducted with 1000 time steps each. 207

Discrete Observations – ROV imaging of plankton and particulates 208

An OpenROV v. 2.8 was used to conduct vertical video transects (Figure 3). The 209

OpenROV v. 2.8 was constructed in July 2016 according to kit instructions supplied by the 210

11

manufacturer. A 100 m neutrally buoyant tether was used during deployments. In addition, 211

external light cubes (high intensity LEDS) and an Inertial Motion Unit (IMU), purchased from 212

OpenROV, were added to the constructed OpenROV v. 2.8, according to kit instructions. 213

For this study, a magnifying lens was attached to the external surface of the electronics 214

chamber and positioned so that the HD imager of the OpenROV v. 2.8 would look through the 215

lens when in its down-most position. The support for this lens was designed in SolidWorks 216

(Dussault) and 3D-printed to securely hold the lens in place against the electronics housing. This 217

support was designed to fit snugly against the curvature of the housing and included a small hole 218

on both the top and bottom to allow for air to escape from behind the lens while deployed. To 219

allow the lens to be removed for cleaning, the lens support connects to two additional supports 220

that are mounted directly to the electronics housing. The lens support is attached to these 221

supplemental supports using a screw on each side. The lens support is described in Figure 4 and, 222

with the current configuration, the lens has a magnification of approximately 2 times. 223

Vertical transects were made with the camera positioned to look through the lens to 224

visualize zooplankton. Identifications were attempted when possible. Video was recorded using 225

the screen record function in Quick Time Player on the computer used for control of the ROV, an 226

Apple MacBook Pro. Three screenshots were taken at each half meter from the maximum depth 227

to the surface. The number of zooplankton and particulates observed in each screenshot was 228

counted using the Cell Counter plug-in in ImageJ (NIH). Particulate counts were square-root 229

transformed and regressed in a linear model against depth in R. A handheld CTD (YSI 230

CastAway) was deployed after the deployment to collect data on stratification. 231

Results 232

Continuous Observations– Thermistor String Mooring System 233

12

Although all three moorings were lost during the summer of 2016, the test mooring 234

deployed at approximately 30 m off East Point, Nahant observed a variety of stratification 235

dynamics both graphically and numerically. Temperatures off East Point ranged between 236

approximately 5 and nearly 20 degrees C over the course of the deployment period with 237

stratification increasing into July. The transition from a uniform to stratified water column can be 238

seen, especially in early June, where there are multiple instances of mixing, resulting in 239

temperature being uniform across depths. 240

On multiple occasions, there were concurrent increases or decreases in temperature 241

across the mid-depths (15 – 25 m) and an example of these events from May 14, 2016 is 242

presented in Figure 5. A spectral analysis of these data indicates that some of these oscillations 243

occur with a period of one to two hours at these same depths (Figure 6). In addition, in the FFT 244

analysis, oscillations were observed that occurred with approximately 12 hour periods (Figure 6). 245

Mathematical Model of Internal Wave Induced Downwelling on Benthic Communities 246

While data could not be collected to validate or parameterize the IWID model, model 247

simulations showed that generalist communities could be dependent on IWID events. The 248

absence of IWID events lead to the extinction of local populations, while populations fluctuated 249

but persisted in response to IWID events when present (Figure 7). 250

Discrete Observations – ROV imaging of plankton and particulates 251

Several vertical transects with the OpenROV v. 2.8 were made in mid-February 2017. 252

These transects were conducted in the same location at the mooring test site (Figure 1) to locate 253

this mooring. While the search-and-rescue mission was unsuccessful, the vertical video transects 254

from depth to the surface indicated a strong relationship with particulate matter and depth 255

(Figure 7). When these variables were linearly regressed, a strong, significant relationship was 256

13

observed (p << 0.001, R2 = 0.8035). Particulate counts increased significantly with depth, 257

ranging from as little as 4 to 1000 particulates observed (Figure 8). This increase correlated with 258

a thermocline that was observed in the corresponding CTD cast (Figure 8). In addition, seeming 259

local areas (> 0.5 m) of high and low densities of particulate matter were observed in the vertical 260

transect, particularly below the thermocline observed by the CTD cast (Figure 8). Individual 261

plankton were not able to be identified during this transect, although an abundance of larval fish 262

were observed near the bottom. 263

Discussion 264

Continuous Observations 265

While all three moorings constructed to record stratification dynamics on a continuous, 266

long-term scale were eventually lost, one mooring was successfully able to observe internal wave 267

phenomena occurring off East Point, Nahant. These events can be seen in the time series data 268

from the mooring site by concurrent, rapid changes in water temperature at 15, 20, and 25 m 269

depths, an example of which is shown in Figure 5. These events, as shown by the FFT analysis, 270

occur approximately every two hours, which equivalent to the expected frequency of internal 271

waves as seen in other systems (Figure 6) (Scotti et al. 2007; Scotti et al. 2008). The internal 272

waves, according to the data presented here, appear to occur within one to two hours of the high 273

or low tide (Figure 5), indicating that these waves are tidally generated. These waves are most 274

likely traveling from the location where they are generated from to East Point, although the exact 275

direction of the wave is unknown due to the single time point available. It is possible, due to the 276

relative shallowness of the East Point site, in comparison to other locations where internal waves 277

have been observed previously, that the internal waves are shoaling, or breaking, at East Point. 278

14

These shoaling events have been shown to cause local downwelling-events, which can 279

have significant impacts on the benthic communities (Witman et al. 1993; Lai et al. 2010). The 280

theoretical model created in conjunction with the field deployments demonstrated that under the 281

correct conditions that benthic communities could be highly dependent on these internal wave-282

induced downwelling effects and that this dependency can change depending on the frequency 283

and intensity of the events (Figure 7). Further sampling is necessary, however, to properly 284

parameterize the model to the stratification dynamics present at East Point. 285

Previous research has shown that, in addition to the effects internal waves have on the 286

benthic community through their shoaling, the internal waves that are propagating around East 287

Point may have a significant impact on the pelagic communities (Pineda et al. 2015; Scotti and 288

Pineda 2007; Greer et al. 2014). Internal waves have been shown to have significant effects on 289

the distribution of plankton in the water column in a variety of systems (Pineda et al. 2015; Scotti 290

and Pineda 2007; Greer et al. 2014). 291

To determine the directionality of the internal waves and their impact on the benthic and 292

pelagic communities, significantly more sampling is need in this area. A redeployment of a 293

reconstructed mooring system will help describe the directionality of the wave. In addition, in 294

situ pelagic and benthic sampling, either by traditional methods such as Niskin bottle sampling 295

and diver surveys of pelagic and benthic communities, respectively, or the OpenROV v. 2.8 and 296

lens design described above, will help describe the impact these waves have on the biological 297

communities. Sampling of the benthic community along with increased descriptions of the 298

internal waves that occur in this area will also help to strengthen the IWID model. 299

Discrete Observations 300

15

A vertical transect with the OpenROV v. 2.8 detected a significant increase in particulate 301

matter with depth in late February and a corresponding CTD cast observed a thermocline at 302

approximately 10m during this cast (Figure 8). This relationship was expected because many of 303

these particulates were aggregates of particulate matter, most likely consisting of dead plankton 304

and fecal pellets (MacIntyre 1995). These aggregates have been shown to sink to depth as they 305

gain mass, thus more were expected at depth (MacIntyre 1995). While aggregates such as these 306

have been observed, and photographed in situ previously, this is one of the first observations, to 307

our knowledge, to be made using an ROV such as the OpenROV v. 2.8. 308

In addition to the strong linear relationship observed between particulate matter and 309

depth, possible thin layers were observed in the vertical transect. An example of these can be 310

seen in the variance from the regression line between 5 and 10 m and at 15 m in Figure 8. Thin 311

layers are, as the name suggests, layers of plankton aggregates that occur on small spatial scales, 312

usually ranging between 0.5 m and 1 m in size, vertically (Sullivan et al. 2010). These 313

phenomena can occur on very large spatial scales in the horizontal direction and have also been 314

observed on a variety of temporal scales, ranging from hours to days (Sullivan et al. 2010). Thin 315

layers, formed by small-scale physical processes and water column stratification, have been 316

shown to have an important impact on pelagic communities throughout the food web (Clay et al. 317

2004; McManus et al. 2003). More observations are required to determine if the variances in 318

particulate matter distribution seen in this vertical transect are in fact thin layers and not natural 319

variances in particulates throughout the water column. 320

Limitations of both observation systems 321

While both observation methods were successful individually, they each have their 322

drawbacks. The mooring systems, in addition to being easily lost at sea, only have, at their 323

16

maximum, a 5 m resolution, making them incapable of measuring small scale processes such as 324

thin layers. In addition, the moorings are limited to the depths for which they were constructed 325

(30, 50, and 80 m). Although the current modular design allows sections to be added or replaced 326

with longer or shorter segments, this incurs an additional cost. The moorings are also limited by 327

their ability to store data. The HOBO water temperature v2 probes purchased for this study and 328

set to a sampling frequency of one sample per minute can only be deployed for 33 days before 329

they need to be recovered and the data uploaded. The S4 current meter, when deployed at the 330

sampling rate of one sample every four minutes, similarly, only has enough memory for a two-331

week deployment. Therefore, constant data recovery is necessary for an accurate and complete 332

time series using these systems. 333

In contrast, the OpenROV v. 2.8, which can instantly record data at much smaller spatial 334

scales, is limited primarily by the time scale at which it can sample. Although the OpenROV v. 335

2.8 is easy to deploy and manipulate in the water column under low current conditions, it can 336

only record data in discrete time. It is not suitable for long deployments. Another major 337

limitation of this sampling method is that it is limited by weather. The OpenROV v. 2.8 is 338

difficult to deploy and manipulate in rough seas or high current conditions. High currents also 339

make visualization of plankton through the magnification lens difficult, especially on screenshots 340

of the resulting video. 341

With the current design of the lens, it was also not possible to magnify observed plankton 342

and particulate matter to the correct size for identification. A second lens may help increase 343

magnification. In addition, more light in the form of more external light cubes or other light 344

sources, may help increase identifications. 345

17

Overall, there is no best method for the observation of stratification methods. However, 346

using both discrete and continuous methods in conjunction with each other to make observations 347

on a variety of temporal and spatial scales provides the most holistic view of stratification. 348

Through both the continuous and discrete methods, we observed both large (internal waves) and 349

small scale (thin layers) physical phenomena that are directly related to stratification dynamics as 350

well as seasonal changes in stratification through the summer months. 351

Understanding stratification on a variety of scales is essential to not only a greater 352

understanding of the world ocean but also critical for a more holistic view of the physical 353

processes that govern the distribution of marine species. Phenomena such as internal waves and 354

thin layers have been shown to have significant impacts on the distribution of plankton, which in 355

turn can impact the distributions of species that consume the plankton (Pineda et al. 2015; Clay 356

et al. 2004). 357

Since stratification is mostly governed by changes in water temperature, global climate 358

change will have a significant impact on stratification regimes world-wide in the coming years. 359

Models are already predicting that changes in surface temperatures and salinity, resulting from 360

increased glacial melt, will have significant impacts on stratification in nearly all the ocean 361

basins (Capotondi et al. 2012). Since stratification is one of the major drivers of the ocean 362

circulation system, this will have a significant impact on biological and physical systems 363

throughout the world ocean. 364

Acknowledgements 365

This work was funded by the Northeastern University Office of the Provost’s Advanced 366

Research/Creative Endeavors Award, Beta Beta Beta Biological Honors Society Research Grant, 367

and the Field Robotics Laboratory at Northeastern University’s Marine Science Center (MSC). 368

18

Special thanks to Dr. Tarik Gouhier for serving on the committee for this work and his assistance 369

in developing the IWID model; Drs. Brian Helmuth and Loretta Fernandez for also serving on 370

the committee for this work; the MSC’s 2016 Summer Internship program; the Grabowski lab 371

for assisting in mooring design and the donation of materials; Lydia Sylla for the construction of 372

the OpenROV v. 2.8; Ethan Edson for his support in the field and designing and 3D printing the 373

lens support; and the rest of the Field Robotics Laboratory, the MSC community, and Three Seas 374

31 for their assistance, support, and inspiration for this work. 375

19

Literature Cited 376

Butman, B., P. S. Alexander, S. P. Anderson, F. L. Lightsom, A. Scotti, and R.C. Beardsley. 377

2006. The Massachusetts Bay internal wave experiment, August 1998; Data Report: U.S. 378

Geological Survey Data Series 85, Version 2.0, DVD-ROM. 379

Butman, B., P. S. Alexander, A. Scotti, R. C. Beardsley, and S. P. Anderson. 2006. Large 380

internal waves in Massachusetts Bay transport sediments offshore. Continental Shelf 381

Research 26:2029–2049. 382

Capotondi, A., M. A. Alexander, N. A. Bond, E. N. Curchitser, and J. D. Scott. (2012). Enhanced 383

upper ocean stratification with climate change in the CMIP3 models. Journal of 384

Geophysical Research: Oceans 117(C4). 385

Clay, T. W., S. M. Bollens, A. B. Bochdansky, and T. R. Ignoffo. 2004. The effects of thin layers 386

on the vertical distribution of larval Pacific herring, Clupea pallasi. Journal of 387

Experimental Marine Biology and Ecology 305: 171-189. 388

Doney, S. C., M. D. Glover, and R. G. Najjar. 1996. A new coupled, one-dimensional biological-389

physical model for the upper ocean: Applications to the JGOFS Bermuda Atlantic Time-390

series Study (BATS) site. Deep Sea Research Part II: Topical Studies in 391

Oceanography 43:591-624. 392

Eickstedt, D. P., M. R. Benjamin, and J. Curcio. 2007. Behavior based adaptive control for 393

autonomous oceanographic sampling. In Robotics and Automation, 2007 IEEE 394

International Conference 4245-4250. 395

Eriksen, C. C., T. J. Osse, R. D. Light, T. Wen, T. W. Lehman, P. L. Sabin, J. W. Ballard, and A. 396

M. Chiodi. 2001. Seaglider: a long-range autonomous underwater vehicle for 397

oceanographic research. IEEE Journal of Oceanic Engineering 26:424 - 436. 398

20

Greer, A. T., R. K. Cowen, C. M. Guigand, J. A. Hare, and D. Tang. 2014. The role of internal 399

waves in larval fish interactions with potential predators and prey. Proceedings in 400

Oceanography 127:47-61. 401

Haury, L. R. M. G. Briscoe, and M. H. Orr. 1979. Tidally generated internal wave packets in 402

Massachusetts Bay. Nature 278:312-317. 403

Helfrich, K. R. and W. K. Melville. 2006. Long nonlinear internal waves. Annual Review of 404

Fluid Mechanics 38:395-425. 405

Holligan, P. M., R. D. Pingree, and G. T. Mardell. 1985. Oceanic solitons, nutrient pulses and 406

phytoplankton growth. Nature 314:348-350. 407

Kunze, E., L. K. Rosenfeld, G. S. Carter, and M. C. Gregg. 2002. Internal waves in Monterey 408

submarine canyon. Journal of Physical Oceanography 32:1890-1913. 409

Lai, Z., C. Chen, R. C. Beardsley, B. Rothschild, and R. Tian. 2010. Impact of high-frequency 410

nonlinear internal waves on plankton dynamics in Massachusetts Bay. Journal of Marine 411

Research 68:259-281. 412

Leichter, J. J., S. R. Wing, S. L. Miller, and M. W. Denny. 1996. Pulsed delivery of 413

subthermocline water to Conch Reef (Florida Keys) by internal tidal bores. Limnology 414

and Oceanography 41:1490-1501. 415

Li, W. K. W. 2002. Macroecological patterns of phytoplankton in the northwestern North 416

Atlantic Ocean. Nature 419:154-157. 417

MacIntyre, S., A. L. Alldredge, and C. C. Gotschalk. 1995. Accumulation of marine snow at 418

density discontinuities in the water column. Oceanography 40:449 – 468. 419

McManus, M. A., A. L. Alldredge, A. H. Barnard, E. Boss, J. F. Case, T. J. Cowles, P. L. 420

Donaghay, L. B. Eisner, D. J. Gifford, C. F. Greenlaw, C. M. Herren, D. V. Holliday, D. 421

21

Johnson, S. MacIntyre, D. M. McGehee, T. R. Osborn, M. J. Perry, R. E. Pieper, J. E. B. 422

Rines, D. C. Smith, J. M. Sullivan, M. K. Talbot, M. S. Twardowski, A. Weidemann, and 423

J. R. Zaneveld. 2003. Characteristics, distribution and persistence of thin layers over a 48 424

hour period. Marine Ecology-Progress Series 261:1-19. 425

Pillsbury, D., R. L. Smith, and R. C. Tipper. 1969. A reliable low‐cost mooring system for 426

oceanographic instrumentation. Limnology and Oceanography 14:307-311. 427

Pineda, J., V. Starczak, J. C. Silva, K. Helfrich, M. Thompson, and D. Wiley. 2015. Whales and 428

waves: Humpback whale foraging response and the shoaling of internal waves at 429

Stellwagen Bank. Journal of Geophysical Research: Oceans 120:2555-2570. 430

Scotti A. and J. Pineda. 2007. Plankton accumulation and transport in propagating nonlinear 431

internal fronts. Journal of Marine Research 65:117-145. 432

Scotti, A., R.C. Beardsley, and B. Butman. 2007. Generation and propagation of nonlinear 433

internal waves in Massachusetts Bay. Journal of Geophysical Research 112:C10001 434

Scotti, A., R. C. Beardsley, B. Butman, and J. Pineda. 2008. Shoaling of nonlinear internal waves 435

in Massachusetts Bay. Journal of Geophysical Research 113:C08031 436

Shanks, A. L. 1983. Surface slicks associated with tidally forced internal waves may transport 437

pelagic larvae of benthic invertebrates and fishes shoreward. Marine ecology progress 438

series. Oldendorf 13:311-315. 439

Sullivan, J. M., D. Van Holliday, M. McFarland, M. A. McManus, O. M. Cheriton, K. J. Benoit-440

Bird, L. Goodman, Z. Wang, J. P. Ryan, M. Stacey, and C. Greenlaw. 2010. Layered 441

organization in the coastal ocean: An introduction to planktonic thin layers and the 442

LOCO project. Continental Shelf Research 30: 1. 443

22

Steinberg, D. K., C.A. Carlson, N. R. Bates, R. J. Johnson, A. F. Michaels, and A. H. Knap. 444

2001. Overview of the US JGOFS Bermuda Atlantic Time-series Study (BATS): a 445

decade-scale look at ocean biology and biogeochemistry. Deep Sea Research Part II: 446

Topical Studies in Oceanography 48:1405-1447. 447

Thomson, R. E., and W. J. Emery. 2014. Chapter 1: Data Acquisition and Recording. In: Data 448

analysis methods in physical oceanography. Newnes. 449

Witman, J. D., J. J. Leichter, S. J. Genovese, and D. A. Brooks. 1993. Pulsed phytoplankton 450

supply to the rocky subtidal zone: influence of internal waves. Proceedings of the 451

National Academy of Sciences 90:1686-1690. 452

Witman, J. D., M. R. Patterson, and S. J. Genovese. 2004. Benthic–pelagic linkages in subtidal 453

communities: influence of food subsidy by internal waves. Pages 133-153 in G. A. Polis, 454

M. E. Power, and G. R. Huxel, editors. Food Webs at the Landscape Level. The 455

University of Chicago Press, Chicago. 456

Wang, Y. H., C. F. Dai, and Y. Y. Chen. 2007. Physical and ecological processes of internal 457

waves on an isolated reef ecosystem in the South China Sea. Geophysical Research 458

Letters 34:L18609. 459

23

Figure Legends 460

Figure 1. General design of moorings and thermistor strings used in this study, indicating the 461

primary location of HOBO V2 Water Temperature Pro temperature loggers and InterOcean S4 462

electromagnetc current meter, when deployed. 463

Figure 2. Map indicating locations of moorings at (A) the test site off East Point, Nahant, MA 464

and (B) the study site at Rockport, MA. 465

Figure 3. A photo of the OpenROV v. 2.8 used in this study with external light cubes mounted 466

laterally. 467

Figure 4. Design of lens mount attached to OpenROV v. 2.8 for the visualization of zooplankton 468

and particulate matter with (A) indicating the front (facing the water) of the lens mount and (B) 469

indicating the back (facing the ROV) with grey areas indicating cuts into the mount made to (a) 470

support the lens and (b) allow air and/or water to escape from behind the lens during 471

deployment. The removable lens support was attached to the permanent side supports using a 472

screw on each side. 473

Figure 5. (A) Water column structure and fluctuations between May 11, 2016 and August 27, 474

2016 as a function of depth. (B) An example of stratification dynamics on the short-term scale 475

from May 14, 2016 with high tide at 6:12 and 18:56 and low tide at 12:30. 476

Figure 6. Power spectral density plot of temperature oscillations between May 11 and 19, 2016. 477

Figure 7. (A) Internal wave induced downwelling model results for systems with and without 478

IWID events with P1 representing pelagic plankton populations, P2 representing benthic 479

plankton populations, and N representing benthic consumer populations. (B) Mean pelagic (P1) 480

and benthic (P2) plankton and benthic consumer (N) populations in 1000 step simulations across 481

different IWID event frequencies (tau) and intensities. 482

24

Figure 8. Particulates counted in vertical transect using the OpenROV v. 2.8 as a function of 483

depth. The blue horizontal line indicates the thermocline location as determined by a CTD cast. 484

The red line indicates the significant linear regression model between the two variables (p << 485

0.001, R2 = 0.8035). 486

25

Figures 487

488

Figure 1. 489

26

490

491

492

493

494

495

496

497

498

499

500

501

502

503

504

505

Figure 2. 506

A.

B.

1

2

3

27

507

Figure 3. 508

28

509

Figure 4. 510

A.

B.

a.

b.

Permanently

mounted side

support

b.

Removable

lens support

Permanently

mounted side

support

29

511

512

513

514

515

516

517

518

519

520

521

Figure 5. 522

A.

B.

30

523

Figure 6. 524

525

31

526

527

Figure 7. 528

0 200 400 600 800 1000

02

040

60

80

10

0

Time

Abu

nd

ance

P1

P2

N

0 200 400 600 800 10000

20

40

60

80

10

0

Time

Abu

nd

ance

A.

B.

32

529

Figure 8. 530