FINAL REPORT December 2018

Project Name: A Rapid Assessment Protocol for Eelgrass Monitoring in Estuarine Embayments Project Partners: Jill Carr1, Kathryn Ford1, Prassede Vella2, Sara Grady2,3

1Massachusetts Division of Marine Fisheries 2MassBays National Estuary Program

3North and South Rivers Watershed Association Project Summary Eelgrass (Zostera marina) beds have experienced severe declines over several decades in the Duxbury-Kingston-Plymouth (DKP) embayment, and there is a need and local interest in tracking these changes. The Massachusetts Division of Marine Fisheries (DMF) previously investigated potential causes for eelgrass loss in DKP1, and has conducted limited acoustic mapping surveys in the embayment since 2014. DMF most recently completed an acoustic eelgrass survey in 2017 for this project, funded by the Massachusetts Bays National Estuary Program (MassBays) under FFY17 U.S. EPA grant number 00A00436. This grant also included the development and testing of an eelgrass monitoring protocol that could be implemented by trained citizen scientists with the goal of establishing rapid assessment standards that can be executed annually or biennially by volunteers. To supplement existing mapping efforts, scientists from MassBays and DMF worked alongside local citizen scientists to monitor eelgrass extent and condition in DKP using a novel sampling methodology in August 2018. Sampling stations were selected using a stratified random design, where strata included Massachusetts Department of Environmental Protection’s (MassDEP) 305b sub-embayments as well as persistence of eelgrass based on the MassDEP eelgrass polygons. The overall sampling area was determined based on bathymetry to include areas not historically identified as eelgrass but that may be suitable habitat. Over the course of six sea days, 250 stations were sampled for eelgrass presence and percent cover using an underwater camera mounted to a standardized 0.25m2 PVC quadrat frame, in addition to depth and water clarity data. Of the 250 stations, 100 “indicator” stations, or stations within the strata of highest eelgrass persistence, were chosen to also include eelgrass shoot collection to measure plant height, width, disease and epiphytic cover. Tasks and Deliverables Acoustic Mapping The first phase of this project included three days of acoustic eelgrass mapping conducted in DKP on June 26, June 28 and September 7, 2017. The objective of this effort was to check the boundaries and confirm presence or absence of discrete eelgrass beds, and compare those results to acoustic surveys conducted in 2014 and 2016. The 2017 survey followed DMF’s Standard Operating Procedure for Acoustic Mapping of Eelgrass and targeted the same survey tracks as in 2016 and included the addition of several new tracks across the southern Clarks Island bed (Fig 1). The tracks that cross relatively persistent beds (e.g. Clarks Island) and beds thought to be in decline (e.g. Northwest Duxbury Bay)(Fig 1) provide a helpful snapshot against which future surveys can be assessed with a reduced effort compared to mapping the entire embayment. Sonar data were processed in SonarTRX and mosaics were exported to ArcGIS10.3. Data were analyzed for the visual signature of eelgrass at a scale of 1:1,500. Underwater photos collected during the survey were interpreted to groundtruth the sonar data. Where possible, areas adjacent to the survey tracks were assessed for eelgrass using 2016 USGS NAIP aerial imagery and May 2016 and April 2017 Google Earth imagery. The 2017 acoustic survey found an overall loss of up to 87.5 acres (8.4% of the total eelgrass area) since 2014 and a loss of up to 64.6 acres (6.2%) since 2016 (Fig 2) for the beds that were examined. An additional 68.5 acres

(6.6%) of eelgrass in northwest Duxbury Bay were classified as “2017 Probable Loss/Inconclusive” because the sonar imagery was extremely difficult to interpret in this area, limited groundtruthing data were collected, and available satellite imagery for 2017 was not taken at an advantageous tide. There is a high probability that these areas were either lost or experienced severe declines in density between 2016 and 2017, making any lasting eelgrass undetectable in sonar and satellite imagery. However since low-density eelgrass is difficult to detect in both sonar and aerial imaging methods, it is possible that these numbers overestimate actual losses.

Protocol Development The next phase of the project was the development of an eelgrass sampling protocol designed to be conducted by trained citizen scientists. An expert advisory group was convened on 1/31/2018, where DMF and the North and South Rivers Watershed Association (NSRWA) presented potential eelgrass monitoring methods to 21 colleagues working in the field of eelgrass sampling and ecological monitoring. Using the group’s input, the protocol was drafted by DMF staff, followed by an extensive equipment-testing process to hone the specific sampling techniques. The draft protocol was shared with the group for review. Seven edited versions were received and incorporated into a final document, which was then provided to project participants (i.e. citizen scientists, who were coordinated by NSRWA) a week prior to the sampling event. The final protocol entitled Eelgrass Monitoring: Development of a Citizen Scientist Monitoring Method is in Appendix A and includes notes from the 1/31/18 meeting and extensive details on the protocol development process, such as the site selection methodology and rationale for the chosen sampling strategy. Protocol Implementation The third phase of the project was to implement the protocol using volunteer citizen scientists and other volunteers from the partner agencies. Volunteers were recruited by NSRWA through email outreach to a subset of an existing volunteer database and local contacts. Implementation took place in a bioblitz format (dubbed “Eelgrass Week”) over six days from August 19 to August 29, 2018. On the first day, a two hour training session was conducted on land to familiarize volunteers with the equipment and run mock-sampling drills. Four groups were created such that each group contained one DMF staff and up to three volunteers. Groups were assigned to the four available boats (3 DMF and 1 State employee’s personal boat) and each boat was assigned a sampling region containing pre-determined sampling stations. Sampling regions were delineated in such a way that reduced the team’s travel time between stations, and resulted in an equal number of stations across teams (Fig 3). After training on 8/19, groups practiced sampling on the water. The majority of the data collected that day were deemed practice data since the water was too rough and turbid to collect suitable imagery. Marginal weather continued into the following day and sporadically that week, thus the schedule was delayed and sampling took place over five days on 8/21, 8/23, 8/27, 8/28 and 8/29. Over the course of the bioblitz, teams sampled 250 stations for eelgrass presence and percent cover using an underwater camera mounted to a standardized 0.25m2 PVC quadrat frame. Of those stations, 100 “indicator” stations received additional sampling if eelgrass was present. Sampling included the collection of eelgrass shoots to measure plant height, width, disease and epiphytic cover. Refer to Appendix A for detailed protocols. On days where adequate volunteer participation was not available, employees from partner State agencies were recruited to fill gaps as their availability allowed. A total of 28 participants collected data: 12 were citizen scientist volunteers (including their paid local coordinator) and 16 were State employees. Seven of the 28 participants also participated in the authoring of the protocol. Data were collected on paper datasheets, and later entered into an Excel database by NSRWA. Data quality control was conducted by DMF and NSRWA. Both groups have complete data archives including electronic copies of the datasheets and digital underwater photography from each station. DMF continues to work toward the development of a web-based app for data collection for use with this protocol.

Data Analysis The data provide a wealth of meaningful information about the presence and condition of eelgrass in DKP, as well as a strong baseline dataset. The drop-frame system successfully provided seafloor and eelgrass imagery in a variety of conditions (Fig 4). DMF scientists reported strong agreement in the assignment of eelgrass percent cover recordings between staff and volunteers interpreting the images. For each station, percent cover bin data from the four subsamples were converted to the bin’s midpoint value, subsamples were averaged, and the resulting value assigned back into a percent cover bin. None of the stations in northern Duxbury Bay, western Duxbury Bay or inner Kingston Bay contained eelgrass (Fig 5), even in areas that were previously identified by MassDEP as eelgrass. The only substantial beds observed in this survey were around Clarks Island, in Plymouth Harbor outside of the jetty, and a small meadow in central Kingston Bay. Clusters, outliers, and hotspots in the percent cover data were analyzed using ArcGIS Spatial Analyst tools. As expected, a significant cluster of low values (zeros) exists in northern Duxbury Bay, and significant high value clusters exist around Clarks Island, in Plymouth Harbor, and at the small bed remaining in Kingston Bay (Fig 6). Outliers of low values amidst high values demonstrate patchiness (Fig 6). Hotspot analysis shows a cold spot in northern Duxbury Bay and hotspots around Clarks Island, western Plymouth Harbor and the small bed in Kingston Bay (Fig 7). The photo data help to corroborate some of the acoustic survey findings, such as losses in western Duxbury Bay and the most southern bed off Clarks Island (Fig 2). Other bed locations could not be corroborated because sampling stations did not align with acoustic transects. One interesting case, however, is the “Cowyard Shoal” bed (Fig 1) in south-central Duxbury Bay that disappeared in MassDEP’s most recent mapping. While none of the acoustic surveys detected eelgrass in this location, the photo sampling detected sparse grass (0-10%) at one station directly within the sidescan swath. This may speak to the difficulty of mapping patchy and low-density eelgrass using remote sensing methods. Several other bed-specific results were surprising. For one, the absence of eelgrass north of Clarks Island was surprising because it was observed in recent satellite imagery and acoustic data. The bed off Plymouth’s Cordage site (Fig 1) contained several stations on its shallow edge, all of which were devoid of eelgrass. However, the sampling crew noted dense, continuous eelgrass throughout the deeper portions of the bed (absent of sampling stations) as viewed over the side of the boat. The long, narrow bed in the central area of the embayment thought to be lost in 2017 (Fig 2, shown in red) could not specifically be confirmed with the photo data, however qualitative notes from an adjacent station documented an area of small, dense patches amidst sand waves. In agreement with previous descriptions from stakeholders, areas south of Clarks Island have become dense mussel beds mixed with filamentous and macro algae. Large, dense areas of Chorda sp. were also observed near Clarks Island. Since Chorda can be mistaken as eelgrass due to its long whip-like appearance, future efforts should include reassessing past survey data to determine if Chorda areas were misidentified as eelgrass in previous mapping efforts (Fig 4). The proportion of stations that contained eelgrass was tabulated by persistence strata, MassDEP 305b waterway and DMF’s sampling region (Tables 1 and 2 respectively; Fig 3). Eelgrass was found at 0% of stations in the “never identified” stratum, 4% of stations in the “identified 1-3 times” stratum, and 32% of the “identified 4-5 times” stratum (Table 1). Duxbury Bay had eelgrass at 10% of its stations, and Plymouth Harbor had eelgrass at 37%; with a total of 14% of stations in the entire embayment containing eelgrass (Table 1). In areas that were expected to contain the most persistent eelgrass (“identified 4-5 times” stratum, containing all of the indicator stations), this monitoring found only 24% of this stratum in Duxbury Bay and 62% of this stratum in Plymouth Harbor contained eelgrass (Table 1). Since these beds have been persistent in previous mapping events, it was surprising how few stations had eelgrass present.

Thirty indicator stations were successfully sampled for eelgrass shoots using either the anchor or snorkel method. The snorkel method was not in the protocol, but ended up being an effective method for crew who were interested in snorkeling given the high air temperatures during sampling. Using this method, snorkelers donned masks and snorkels and free-dived to the bottom where three individual shoots were harvested by pinching and breaking off the plant just below the sediment. An average leaf area was calculated per station from up to four subsamples (each containing three shoots) of leaf length and width measurements. Average leaf area ranged from a minimum of 10.4cm2 to a maximum of 48.83cm2 with a standard error of 2.06. Individual values range from a length of 12cm to 125cm, and a width of 2mm to 8mm. The majority of wasting disease data indicated low levels of infection, and epiphytic cover data indicated low to moderate growth (Table 3). The distance between the designated station coordinates and the actual sampling location was analyzed using code based on the Pythagorean equation2. The SOP specified that monitoring take place within a 10 m radius from the designated coordinates. The average distance from the designated coordinates was 13.3 m, which includes two outliers that were likely caused by faulty GPS fix or recording error. With the two outliers removed, the average distance was 8.9 m. Of the 250 stations, 165 stations (66%) were sampled in the correct radius, 78 (31%) were within 25 m and five stations (2%) exceeded 25 m. Sampling beyond the assigned radius was likely due to the difficulty of getting on-station in strong currents and wave chop, as experienced in the first several days of sampling. After the sampling event, DMF sent a SurveyMonkey poll to all participants and received nine responses. Parsing the responses to include only those from citizen scientists, it was felt that the SOP and training made them feel extremely prepared for the project. 80% of citizen scientists felt extremely confident in their data collection abilities for the project, and 20% were very confident. They felt the experience was very well organized, and extremely enjoyable. All citizen scientists agreed that this monitoring should continue for future years; 60% are extremely likely and 40% are very likely to volunteer for this project in the future. Open-ended feedback included that the camera equipment was awkward on a small boat, that it’s critical to watch the tides, and that we should find ways to increase accountability for volunteers to avoid last-minute cancellations. Budget DMF’s implementation budget is in Table 4. The expert workshop had no cost. NSRWA and MassBays expenses were not available. The primary driver of expenditure overages was the need to use State employee time and vessels for the field work. Lessons Learned and Recommendations Though the sampling effort was successfully completed, it was not without challenges. Per the original scope of work, DMF was responsible for the training of volunteers who would then conduct most of the sampling. However due to a lack of local volunteers with available watercraft and re-scheduling issues caused by several days of inclement weather, DMF’s involvement was higher than anticipated both in staff time and resources (boats, vehicles, fuel). In order to be a sustainable and effective monitoring program, local watercraft resources are required, as are more volunteers. Volunteer scheduling should include “extra” volunteers who can be available in the event of last-minute cancellations, and coverage should be sought for the anticipated sampling week as well as for several rain-dates. From a more technical aspect, some elements of the sampling protocol were challenging. For one, this embayment is characterized by very shallow sandbars bifurcated by narrow drainage channels at low tide. Many stations were inaccessible an hour before and after low tide, and it was critical that teams planned the day with that in mind. Many teams found it difficult and very time consuming to collect eelgrass samples using the provided 3lb. sampling anchor, especially in low density beds. In fact, when the weather allowed, some teams

preferred to simply snorkel to collect samples. While the equipment-testing for this method did include trying multiple rake and anchor options, this part of the protocol still needs work as equipment efficacy varies in different sediment and depth conditions. Another sampling challenge was collection of top-side photos of the collected eelgrass shoots to help confirm size, epiphyte and wasting disease records. The mounted underwater camera is not of high enough resolution (and is situated too high in its frame) to capture the desired level of detail on the plants being processed. DMF scientists attempted to capture such photos on their mobile phones instead, however phone availability and image quality varied. For that reason, data on leaf length, epiphytes and wasting disease could not be quality controlled post-sampling. We recommend the incorporation of high resolution cameras (i.e. GoPros or similar) to collect eelgrass shoot sample photos, as well as the development of written standards for photo collection. We recommend that the data be processed with a power analysis to determine the minimum number of stations that should be monitored going forward while still providing a statistically robust sample size. Furthermore, many stations where eelgrass was expected were actually bare, and all stations that were not previously identified as eelgrass were bare. An alternative site selection should be developed for years where volunteers or boats are in short supply, where stations with confirmed eelgrass receive the same level of sampling while stations found to be bare, or a subset of such stations, receive reduced or no sampling. Other means of modifying the sampling plan if resources are limited include targeting one discrete area of the embayment each year and rotating through the areas (i.e. a repeated multi-year sampling design); or reducing the field effort by eliminating the time-consuming raked sample collection. Since any modification can affect the value and significance of the time series data, organizers in DKP or other embayments should carefully consider their data goals and available resources in advance of taking on this effort. That said, continued sampling is recommended in DKP. Sampling should take place ideally annually or at least every other year to detect acute changes to eelgrass beds, and the anniversary dates (i.e. late August) should be targeted to reduce seasonal variability. We are very pleased with the outcomes of this study, and hope to see the protocol implemented in other embayments where eelgrass concerns exist and citizens are engaged. Four underwater photo-frame sampling kits will remain available for loan, and will be stored at DMF’s Annisquam River Marine Fisheries Field Station in Gloucester. Acknowledgements This work would not have been possible without the support of the North and South Rivers Watershed Association and the Massachusetts Bays National Estuary Program. The acoustic survey was conducted by Steven Voss, John Logan and Justin Fleming (DMF), and interpretation of the data was done by Alex Boeri (DMF). We thank the following experts for their attendance and input at the advisory meeting: Susan Bryant, Cohasset Center for Student Coastal Research; Phil Colarusso, EPA; Bill Doyle, MFAC/Plymouth Rock Oyster Growers; Tay Evans, Kate Frew and Alex Boeri, DMF; Joe Grady, Duxbury Conservation; Randall Hughes and Forest Schenck, Northeastern University; Raymond Kane, MFAC/Comm. ASMFC; Michael McHugh, MassDEP; Gregg Morris, Two Rocks Oyster Farm; Alyssa Novak, Boston University; Ann Priester, Island Creek Oysters; Kaitlyn Shaw, Town of Nantucket; Juliet Simpson, MIT Sea Grant; Dante Torio, Jackson Lab UNH; and Kim Tower, Town of Plymouth. The project partners thank the other protocol authors, including Tay Evans, Kate Frew, and Alex Boeri. The protocol implementation relied on citizen scientists who volunteered their time and State employees who swiftly rearranged their schedules to fill in gaps as needed. The project partners thank the following citizen scientists: Chris Howie, Janis Owens, Jackie Roux, Paul Hall, Kelly Swart, Susan and John Bryant, Angeline Graham, David Heacock, Steph Lines, Georgia Rousseau; and the following State employees: Tay Evans, Mark

Rousseau, Kate Frew, Alex Boeri, Kristen Schmicker, John Logan, Steven Voss, Eileen Feeney, Shaun Wallace, Sam Andrews, Kevin Creighton and Pooja Potti. We especially thank Bob Boeri (CZM) for his time and the use of his personal watercraft. Tay Evans, Mark Rousseau, Alex Boeri, Kate Frew and Prassede Vella improved the content of this report with their able editing.

References 1Ford, K. and J. Carr (2016) Eelgrass loss over time in Duxbury, Kingston and Plymouth Bays, Massachusetts. Final Report to Massachusetts Bays National Estuary Program, dated May 9, 2016. 2https://gis.stackexchange.com/questions/88484/performing-distance-calculation-using-excel, accessed on 12/3/2018.

Figures and Tables

Figure 1: Location of 2017 acoustic surveys Figure 2: Estimated eelgrass losses in 2016 and 2017 based on acoustic

imagery

Figure 3: Delineation of DEP 305b Waterbodies and DMF sampling regions.

Figure 4: Examples of underwater imagery collected during the sampling week showing (in

clockwise order from top left): eelgrass with macroalgae, very dense eelgrass, shell hash and gravel

substrate, moderate density eelgrass, mussel bed with filamentous algae, and long brown algae

(Chorda sp.)

Figure 5. Average eelgrass percent cover per station based on August 2018 sampling data

Figure 6. Cluster and Outliers analysis of percent cover data Figure 7. Hotspot analysis of percent cover data

Table 1. Percent of Stations Containing Eelgrass by persistence strata and MassDEP Waterbody

never identified identified 1-3 times

identified 4-5 times

Grand Total

Duxbury Bay 0.0% 2.2% 24.1% 10.0%

Plymouth Harbor 0.0% 20.0% 61.9% 36.6%

Grand Total 0.0% 4.0% 32.0% 14.4%

Table 2. Percent of Stations Containing Eelgrass by persistence strata and DMF Sampling Region

never identified identified 1-3 times

identified 4-5 times

Grand Total

Central Duxbury 0.0% 9.1% 35.0% 18.4%

Kingston 0.0% 0.0% 15.0% 6.0%

NE Duxbury 0.0% 0.0% 38.1% 16.0%

NW Duxbury 0.0% 0.0% 0.0% 0.0%

Plymouth 0.0% 15.4% 63.6% 34.8%

Grand Total 0.0% 4.0% 32.0% 14.4%

Table 3. Percent of indicator station subsamples with observed epiphyte cover and wasting disease

Epiphyte Cover

Wasting Disease

None 15% 8%

Low 53% 84%

Medium 24% 8%

High 8% 0%

Table 4. DMF Implemented Budget

Item Budgeted Match Budgeted

Total Actual

Staff time $6,500 $9,795 $32,3401

Field investigation equipment (5 photo-video kits)

$11,000 $9,639

Indirect charges: 14.65% on salaries & contracts

$1,391 $5,557 $4,689

Vessels (1 boat, 5 days @ $600/day)

$3,000 $10,8002

Acoustic survey instrumentation (sss, u/w camera, processing software)

$1,150 $1,150

Total requested $18,891 $58,618

1 Personnel total was 98 days @ $330/day

2 Boat use: 3 days in 2017 and 5 days with 3 boats in 2018. 18 boat days @ $600/day

Appendix A

Eelgrass Monitoring: Development of a Citizen Scientist Monitoring Method

A Pilot Study in Duxbury-Kingston-Plymouth Bay

Prepared by Jillian Carr1, Kathryn Ford1, Tay Evans1, Kate Frew1, Alex Boeri1, Prassede Vella2, and Sara Grady2

1Massachusetts Division of Marine Fisheries2Massachusetts Bays National Estuary Program

David Pierce Director

August 2018

Pam DiBonaExecutive Director

Contents Summary .....................................................................................................................................................................1

Background .................................................................................................................................................................1

Goals ...........................................................................................................................................................................3

Quadrat-based Photo Monitoring ..............................................................................................................................3

What? .................................................................................................................................................................3

Who? ..................................................................................................................................................................3

When? ................................................................................................................................................................4

Where? ...............................................................................................................................................................4

How? ...................................................................................................................................................................7

Data management ..................................................................................................................................................8

Other Recommendations ........................................................................................................................................ 10

Closing Remarks & Acknowledgements .................................................................................................................. 10

References ............................................................................................................................................................... 11

Appendix A: Broad-scale Monitoring ...................................................................................................................... 15

Appendix B: Meso-scale Monitoring ....................................................................................................................... 18

Appendix C: Fine-scale Monitoring ......................................................................................................................... 20

Appendix D: Stakeholder/Expert Meeting Minutes (1/31/18) ................................................................................ 21

Appendix E: Habitat Suitability Maps ...................................................................................................................... 22

Appendix F: Site Selection Map ............................................................................................................................... 23

Appendix G: Standard Operating Procedure ........................................................................................................... 21

Appendix H: Monitoring Kit Contents ..................................................................................................................... 26

Appendix I: Field Datasheet ..................................................................................................................................... 28

Summary

With funding from the Massachusetts Bays National Estuary Program (MassBays) and with input from local

experts, the Massachusetts Division of Marine Fisheries (MA DMF) developed an eelgrass monitoring protocol

to be implemented by trained citizen scientists to track changes in eelgrass extent and condition. The protocol

will be tested in the Duxbury-Kingston-Plymouth embayment (DKP), where aerial monitoring programs and

acoustic surveys have detected substantial declines in eelgrass extent and density over the last several

decades. Data collected in this effort will supplement existing mapping programs and enhance our

understanding of the embayment.

Background Seagrasses are submerged, rooted and flowering marine plants found in shallow nearshore areas worldwide.

Seagrass meadows enhance biodiversity (Hughes et al. 2002; Lubbers et al. 1990; Wyda et al. 2002), attenuate

wave energy (Fonseca and Cahalan 1992; Koch 2001), stabilize sediments (Fonseca and Fisher 1986), sequester

carbon and nutrients (Pedersen et al. 1999; Touchette and Burkholder 2000), oxygenate sediments and filter the

water column (Short and Short 1984). Meadows and patches create important coastal habitats, providing

shelter and forage for many marine fish species (Heck et al. 1989; Lubbers et al. 1990). As estuarine plants,

seagrasses are useful indicators of estuarine health as they are subject to anthropogenic and environmental

stresses. These stresses include light limitation caused by nutrient loading (Lee et al. 2007, Short et al. 1995,

Dennison et al. 1993), development-related habitat loss (Short and Burdick 1996, Landry and Golden 2017),

disease (Bull et al. 2012, Muehlstein 1989), and climate change (Thom et al. 2014, Echavarria-Heras et al. 2006).

Seagrasses are used as an indicator of estuary health, therefore monitoring programs help to enhance the

protection and management of seagrass beds (Orth et al. 2006).

Because of the importance and vulnerability of seagrasses, programs that monitor their extent or health have

been increasing world-wide during the last two decades (Orth et al. 2006). The predominant seagrass in New

England is Zostera marina, known commonly as eelgrass. Currently, the primary eelgrass survey effort in

Massachusetts is a fixed-wing aerial photography survey conducted by the Massachusetts Department of

Environmental Protection (DEP) (Costello and Kenworthy 2011). The DEP Eelgrass Mapping Project is conducted

yearly in selected embayments and sections of the coastline, providing nearly coast-wide coverage

approximately every five years. Their survey includes aerial photo interpretation along with groundtruthing with

underwater video to spot-check the aerial imagery analysis. Since the DEP eelgrass survey only provides updates

1

every five years, and the imagery is captured at a 1:20,000 scale, it is not well suited for tracking detailed spatial

patterns within seagrass meadows, especially in patchy dynamic beds or in estuaries that are turbid. This can

make it difficult to address specific environmental issues that may lead to uncertainty in the rate of change in

eelgrass meadows and the causes of meadow decline (Neckles et al. 2012, Valle et al. 2015). Other mapping

efforts include ad-hoc sidescan acoustic surveys conducted by MA DMF which also include underwater photo

groundtruthing, and also have their limitations. In general, seagrass surveys can be divided into three scale

categories:

1. Broad-scale remote sensing studies using fixed-wing aerial photography, Landsat satellite imagery

(Hogrefe et al. 2014, O’Neill and Costa 2013), drone imagery (Duffy et al. 2018), or acoustic mapping are

typically designed to assess regional changes in spatial distribution. See Appendix A for a literature

review of broad-scale survey methods.

2. Meso-scale surveys are designed to assess single embayment and/or meadow-level changes in eelgrass

spatial distribution and some can roughly track health-related changes like density, percent cover and

canopy height. Examples of meso-scale monitoring include acoustic surveys (Vandermeulen 2014,

Sonoki et al. 2016), randomized quadrat-based surveys (Neckles et al. 2012, Raposa and Bradley 2010),

underwater camera and benthic grab surveys (McKenzie 2003), and towed video surveys (Berry et al.

2003). See Appendix B for a literature review of meso-scale survey methods.

3. Fine-scale surveys use high resolution monitoring to identify meadow and patch-level changes and

responses to stressors to inform system-wide trends. These programs often require SCUBA or snorkel

work at permanent monitoring sites, which cover a very small study area in comparison to broad and

meso-scale surveys (Short et al. 2006, Neckles et al. 2012, McKenzie et al. 2003). See Appendix C for a

literature review of fine-scale survey methods.

In the Duxbury-Kingston-Plymouth embayment (DKP), broad-scale surveys have detected large declines in

eelgrass extent and density over the last several decades, with further losses documented using acoustic surveys

done in between aerial survey time periods (Costello and Kenworthy 2011, Ford and Carr 2016). However, the

cause and longevity of these losses are unclear, though likely associated with a mix of geomorphological

processes, physical impacts, climate, and water quality. Therefore, in DKP more frequent meadow-level data

collection is needed to supplement existing mapping programs and inform the overall understanding of the

embayment.

2

Informed by extensive expert input, this protocol was developed to provide a methodology that can be applied

by an engaged group of citizen scientists. Citizen science has recently expanded into the field of seagrass

research, and volunteers can help collect, process and analyze data in a variety of ways (Jones et al. 2017).

Marine benthic projects that rely on citizen scientists include Seagrass-Watch (McKenzie et al. 2003), Reef Life

Survey (Stuart-Smith et al. 2017) and Seagrass Spotter (seagrassspotter.org). Monitoring methods include

satellite photo analysis (blog.floatingforests.org), use of submersible cameras (Raoult et al. 2016, Wright et al.

2015) and data collection by divers (Stuart-Smith et al. 2017, McKenzie et al. 2003, Roelfsema et al. 2016, Sailing

for Seagrass).

Goals The overarching goal is to better understand and track changes to eelgrass bed extent and condition in DKP.

When these datasets are considering alongside potential stressors, managers and stewards are in a better

position to effectively manage and protect the resource. The specific goals of the citizen science monitoring

protocol include documentation of eelgrass presence or absence and percent cover across the embayment, and

assessment of eelgrass health in terms of canopy height, epiphyte coverage, and wasting disease presence at

discrete indicator beds.

Quadrat-based Photo Monitoring

What?

This sampling protocol is focused on creating an annual assessment of eelgrass using underwater photo

monitoring, and has been informed by Neckles et al. (2012). This protocol is designed to be used by volunteers

and scientists, focusing on meso-scale survey data collection that provides a rapid assessment of eelgrass

coverage and plant characteristics throughout the embayment, with more intensive sampling at indicator

areas. A meeting convening eelgrass experts took place on 1/31/18 in Kingston to discuss monitoring

methodology options in DKP (meeting notes in Appendix D). This protocol is based upon feedback received

during that meeting, and the final document was reviewed by the expert group to ensure consensus with the

methodology.

Who?

Monitoring will be conducted by trained volunteers with no previous data collection experience necessary.

Volunteers will be solicited by the local watershed group, North & South Rivers Watershed Association

(NSRWA), which also hosts the South Shore Regional Coordinator for MassBays. The first year of protocol

implementation will be dedicated to training and testing the method while collecting data. Prior to sampling, an

3

onsite training session will be provided by MA DMF and MassBays. Teams of 2-3 volunteers will work aboard

watercraft under supervision and operation of MA DMF and MassBays in addition to any volunteer-provided

watercraft. In future years, it is the intent that volunteer-provided watercraft can accommodate all sampling

needs. Most powerboats and small engineless boats nine feet or greater can accommodate the sampling gear.

Sampling from a kayak or small canoe is not recommended due to the lack of deck space and stability.

When?

Sampling will be conducted over a single sampling week (a blitz) to reduce environmental or other fluctuations

as much as possible. In the first year, the blitz will be a multi-day effort where supervised volunteer teams are

deployed to sample all of the designated monitoring stations. In order to ensure proper data collection and

management, and since sampling is targeting a single point in time instead of season-wide, a blitz approach was

determined to be the most efficient sampling scheme. The first year of sampling will inform the level of effort

(e.g. number of people, boats and sea-days) needed for future years.

Monitoring should take place during the peak growing season for eelgrass when leaf biomass is at its highest,

preferably in August. If possible, the same approximate dates should be targeted each year. While no designated

tidal stage is required, it is important for samplers to consider depth at tidal stage in their sampling area, as some

monitoring stations may be too shallow or too deep to access at certain tides. Time of day should also be

considered, as water clarity monitoring via secchi disk must be done between 10am and 4pm. Monitoring of all

stations should take place ideally annually but at least every other year to detect acute changes to eelgrass beds.

Where?

The DKP embayment was first divided into the two major sub-embayments of Duxbury and Plymouth Harbors as

defined in the DEP 305b Integrated List of Waters layer (available on MassGIS). Within the two sub-

embayments, a stratified random site selection was done using eelgrass persistence and sub-embayment as the

strata. At the 1/31/2018 meeting, experts agreed that randomized site selection within suitable habitat areas

would give a monitoring program the greatest statistical strength, and would help to answer the questions of

where eelgrass still exists, if it returns to areas it has previously disappeared from, and if gross changes to

coverage and condition are observable across the embayment.

Other site selection models were investigated including randomly located transects, sampling based on a

tessellated or grid pattern, and simple random point sampling. These methods were ultimately disregarded

because they required too many sampling locations, resulted in over- or under-sampling certain areas of

4

interest, or were not conducive to a volunteer boat and camera based program. Stratified random sampling can

provide population estimates for each of the strata, allowing comparison between them, and has the potential

to increase precision due to lower standard errors (Levy and Lemeshow 2008).

We first looked at relevant layers pertaining to habitat suitability including bathymetry, sediment type, and

presence of previously mapped eelgrass (Appendix E). Other datasets relevant to habitat suitability that were

not incorporated due to lack of data include fetch and long-term water quality. All analyses were completed in

ArcGIS 10.5. The stratification was focused by sub-embayment (Duxbury and Plymouth Harbors) and presence

of previously mapped eelgrass. Bathymetry was used to limit the study area to depths suitable for eelgrass.

Sediment type wasn’t used since 95% of the embayment has the same sediment type (sand).

To limit the study area to depths suitable for eelgrass, USGS LIDAR data (downloaded from

www.sciencebase.gov) were extracted as a raster and converted to points where eelgrass had been previously

mapped by DEP’s mapping program. Using these points, a histogram was generated to assess the minimum and

maximum depths where eelgrass had been identified. Depth contours were then generated using the Create

Contour tool at these minimum and maximum depths and used to clip the study area. Mapped aquaculture

grant sites were removed from the study area, as were portions of the embayment that are particularly difficult

to sample (e.g. very shallow innermost harbors). Manual smoothing of the boundary was done since some

boundaries were excessively complicated as a result of the contour line drawing on the raster LIDAR surface.

Three strata were then delineated using an assessment of the frequency in which eelgrass had been identified at

a location in the past. The DEP eelgrass polygons from 1995, 2001, 2006, 2012, and 2015 were used to generate

raster layers at 27 m2 cell resolution (all rasters were snapped to the 1995 base layer) and each layer had a cell

value of “1” for an area with eelgrass and “NoData” for an area without eelgrass. These layers were then

summed using the Cell Statistics tool. The resulting map indicated cells that had been identified as eelgrass

once, twice, three times, four times, or five times by the DEP mapping program. This map was merged with the

study area map, and all remaining areas were coded as “Never Identified.” The three primary stratification units

were defined where eelgrass was “Identified 4-5 Times”, “Identified 1-3 Times”, and “Never Identified”.

Because the “Identified 4-5 Times” stratum indicates high eelgrass persistence across DEP’s survey, sampling

stations selected in this stratum are categorized as “indicator sites”, suggesting that changes at these stations

may be indicative of changes to the greater meadow or embayment.

5

In order to distribute random samples into each of the six strata (the three eelgrass frequencies in both

Plymouth and Duxbury Bays), it was decided to split the samples by each eelgrass frequency unit as follows:

% of all samples # of samples Stratum

20% 30 Never Identified

40% 60 Identified 1-3 Times

40% 60 Identified 4-5 Times (indicator beds)

The least degree of variation in eelgrass presence/absence is expected to occur in the stratum where eelgrass

has never been identified but habitat may be suitable, therefore this stratum received the lowest proportion of

samples (n=30, or 20% of the total samples). To populate the remaining strata, several different split options

were explored. Ultimately, while variation has the potential to be highest in the beds of questionable

persistence (identified 1-3 times), it is important to adequately sample the most persistent beds since they will

act as indicators of change. Therefore, the remaining samples (n=120 or 80% of the total) were equally

distributed between these two strata.

The samples were then distributed into Duxbury and Plymouth Bays based on the stratum area within each sub-

embayment:

Sub-embayment

Eelgrass Persistence and Indicator Status

# of samples

% of stratum

area

# samples

Duxbury Bay Never Identified 30

80% 24

Plymouth Bay Never Identified 20% 6

Duxbury Bay Identified 4-5 Times (indicator beds) 60

80% 48

Plymouth Bay Identified 4-5 Times (indicator beds) 20% 12

Duxbury Bay Identified 1-3 Times 60

90% 54

Plymouth Bay Identified 1-3 Times 10% 6

The ET GeoWizards tool Random Points in Polygons was used to distribute the samples with a minimum

distance to the boundary of each polygon of 25 meters to account for unreliable edge data. The resulting

sampling stations

6

(n=150) appear well distributed and meet our goals for safety, access and randomness (Appendix F). The same

stations will be sampled each survey year to detect changes in eelgrass presence or condition. In the first year,

sampling will take place at up to an additional 100 locations to allow for a post-sampling power analysis that

will help determine if a statistically suitable number of stations have been sampled.

The organizer may decide to assign specific stations to specific sampling teams based on the volunteer

team’s available resources (boat type, access, preferred launch site, etc). This approach may also serve to

increase ownership of the stations and those eelgrass beds.

How?

Training will be provided prior to sampling, and all monitoring will be done in accordance with the Division of

Marine Fisheries Standard Operating Procedures (SOP) for Citizen Science Eelgrass Monitoring (Appendix G).

Volunteers (and organizers, in year one) will provide a stable vessel with standard safety equipment.

Monitoring kits will be provided by MassBays/MA DMF to each volunteer team and will contain all equipment

needed for sampling (Appendix H). Targeting the hours between 10 am and 4 pm during August, volunteers will

navigate to each of the stations using provided GPS coordinates. Once anchored, volunteers will follow the SOP

to record water clarity using a secchi disk, collect photos of the eelgrass in a quadrat placed on the seafloor, and

record percent cover. The photo-sampling will be repeated at four corners of the boat to create replicates and

improve statistical strength of the survey. At designated indicator stations, additional sampling will include

conducting four plant grabs to record leaf measurements, collecting sample photos and assessing disease and

epiphytes. See Appendix G for a detailed step-by-step field protocol with visual guides, and Appendix I for Field

Datasheets.

Data obtained from the underwater photographs and eelgrass samples taken at indicator stations include

presence/absence of eelgrass, percent cover, and canopy height, as well as presence or absence of wasting

disease and epiphytes. Additional data to be collected include topside information (date, time, names of

volunteers, and weather conditions), water depth and secchi depth readings. All data will be recorded on the

paper data sheets provided, which will then be submitted along with photos to the appropriate location per

the Data Management section below. To assist in the collection of these data, laminated SOPs for all methods,

visual guides and equipment are available in the monitoring kit.

The protocol in Appendix G uses commonly measured parameters as indicators of eelgrass structure and

function (Evans and Short 2005). Eelgrass canopy height changes seasonally and responds to water quality and

periods of low light. Therefore, changes in canopy height may be indicative of stress in a system (Olesen and

7

Sand-Jensen 1993). Likewise, epiphyte coverage and wasting disease both limit photosynthesis and therefore

can serve as a proxy for plant fitness (Broderson et al. 2015; Burdick et al. 1993). Tracking wasting disease

coverage may help researchers understand certain trends in eelgrass loss. A secchi disk is used to measure

water transparency, which is important as it relates to light available for plant growth. The secchi depth is the

depth at which a weighted black and white disk is just visible through the water column, corresponding to the

depth at which approximately 10% of the surface light remains (Wetzel 1983). The relationship between secchi

depth and the light attenuation of Photosynthetically Active Radiation, the portion of the light spectrum used by

plants, is nonlinear and dependent on particles and color in the water column that effects how light is absorbed

or scattered (Holmes 1970). However, secchi depth commonly serves as an index of water quality. Light

availability is recognized as the most important factor regulating eelgrass depth limits (Olsen and Sand-Jensen

1993) and eelgrass requires between 11 and 33% of surface light to thrive (Ochieng et al. 2009). Therefore,

secchi depth measurements may help inform if changes in light conditions are taking place spatially or

temporally. The use of a “view bucket” or “look box” in combination with the secchi disc helps to reduce glare

and light refraction off the water leading to a more accurate and consistent secchi depth reading.

Data management

For the first year of sampling, data will be collected using the provided datasheets (Appendix I), provided in the

monitoring kit. For future years, an app may be developed that will facilitate data collection via a tablet or

smartphone. Photos are collected onto the SD card in the camera. The organizer will meet the sampling teams at

the beginning of each day to provide kits and blank SD cards.

At the end of each sampling day, paper datasheets and camera SD cards will be turned in to the organizer (Sara

Grady at NSRWA/MassBays). The organizer will scan and save the sheets and file the paper copies as soon as

possible. The photos will be downloaded from the SD cards into a designated electronic folder. Photo file dates

and times will be verified to be accurate. If they are not accurate, they must be placed in a subfolder named

with the correct date and the sampling team (e.g. 08202018_team4) to minimize file confusion.

Other processing steps that should be done soon after sampling are as follows:

1. Data from the paper datasheets is entered in an Excel database and each entry checked for quality

control (QC) by a second person.

2. Review the photos from each station. Delete extra photos (no more than two representative photos for

each sample are recommended) and rename photos with the date, station and sample number (e.g.

8

Station5quad_Sample2_082018, or Station5anchor_Sample2_08202018). There will be at least four

quadrat photos at every station plus an additional four anchor-collected sample photos taken at

certain stations, so proper file organization is important to avoid confusion.

3. Back up all datasheets and photos to an online cloud storage service such as Dropbox, OneDrive, or

Google Drive.

All of these tasks can be completed by the organizer or a volunteer specifically trained in the management of

these files.

Another important form of QC is the periodic spot-checking of the collected data by visually comparing the

eelgrass percent cover reported on the datasheet to the underwater photos collected during sampling. In the

first year, QC should be done for all data collected at all stations. Subsequent years that involve repeat-

volunteers might require less QC, at the organizer’s discretion. If concerning mischaracterizations are found

during QC, retraining should take place with the appropriate samplers, and the organizer should determine

whether the images should be reprocessed and the data updated.

When all data are entered, analysis can take many forms: a spatial display of presence and absence across the

embayment, analysis of variables by indicator bed, and integration with other mapping and water quality data.

Neckles et al. (2012) ran an inverse distance-weighted spatial interpolation in ArcGIS to show bay-wide patterns,

and used general linear models to quantitatively compare variables over time. While the data will be housed at

NSRWA/MassBays, MA DMF recommends shared access and partnership in data analysis and distribution. The

use of cloud storage backup will enable easy sharing.

As with all sampling, it is important to have a way to measure the success of an effort. In this project, success

of the first year of sampling will be evaluated based on whether or not all stations were sampled as described

and all parameters were able to be sampled. Volunteers will be solicited for feedback based on their

experience. Following the first year, completion of a power analysis will help determine if an adequate number

of stations were sampled. Protocol issues that may not be improved through experience will necessitate a re-

evaluation of the methodology.

9

Other Recommendations In addition to the citizen science protocol described above and in the appendices, we provide these additional

recommendations regarding eelgrass monitoring in DKP:

Broad-scale recommendations: MA DMF recommend the continuation of the long-running, carefully

standardized survey that DEP’s eelgrass mapping program oversees for broad-scale surveying of eelgrass spatial

extent in DKP. Other methods should continue to be experimentally explored (e.g. U.S. EPA satellite imagery

studies). If a more efficient methodology is developed, it should be standardized to the DEP mapping methods.

Meso-scale recommendations: MA DMF recommends a meso-scale survey effort in DKP that combines

periodic acoustic surveys and the quadrat-based photo survey described herein. Acoustic surveys should be

performed biennially or as funding allows, following the MA DMF Guidelines (available upon request). It is

likely that the quadrat-based survey can indicate areas of interest where the acoustic survey should be focused.

Fine-scale recommendations: The recommended fine-scale strategy for the DKP embayment is the

establishment of a SeagrassNet site. SeagrassNet is a scientific global seagrass monitoring program that

combines plant collection, underwater surveys, and photography into a standardized protocol (Short et al.

2006). By utilizing this standardized protocol, acute changes in specific beds of interest in DKP may be identified

and compared to changes to other eelgrass beds monitored with the same methodology locally, regionally and

internationally.

Closing Remarks & Acknowledgements This document and its appendices are intended to guide the monitoring of eelgrass in the Duxbury, Kingston and

Plymouth embayment. As this is a pilot study, this methodology may be amended over time to incorporate

lessons learned. It is hoped that these methods can be easily applied to other embayments where eelgrass

monitoring is needed and citizen scientists are actively engaged.

This work is part of the project A Rapid Assessment Protocol for Eelgrass Monitoring in Estuarine Embayments

funded from the MassBays National Estuary Program under FFY17 U.S. EPA grant number 00A00436. The

authors sincerely thank all those who attended stakeholder and expert meetings. Thoughtful edits and

improvements to this document were provided by Phil Colarusso, Michael McHugh, Randall Hughes, and Forest

Schenck. Helpful guidance and discussions about methodology were provided by Hilary Neckles and Holly

Plaisted.

10

References Andrade, F. and M.A. Ferreira. 2011. A method for monitoring shallow seagrass meadows (Zostera spp.) using terrestrial oblique large-scale photography. Aquatic Botany 95:103-109. Barrell, J. and J. Grant. 2015. High-resolution, low-altitude aerial photography in physical geography. A case study characterizing eelgrass (Zostera marina L.) and blue mussel (Mytilus edulis L.) landscape mosaic

structure. Progress in Physical Geography 39(4): 440-459. Barrell, J., J. Grant, A. Hanson, and M. Mahoney. 2015. Evaluating the complementarity of acoustic and

satellite remote sensing for seagrass landscape mapping. Int. J. Remote Sens. 36:4069-4094. Berry, H.D., A.T. Sewell, S. Wyllie-Echeverria, B.R. Reeves, T.F. Mumford Jr., J.R. Skalski, R.C. Zimmerman and J.

Archer. 2003. Puget Sound submerged vegetation monitoring project: 2000-2002 monitoring report. Nearshore Habitat Program, Washington State Department of Resources. Olympia, WA. 60pp plus appendices.

Brodersen, K.E., M. Lichtenberg, L. Paz and M. Kuehl. 2015. Epiphyte-cover on seagrass (Zostera marina L.) leaves impedes plant performance and radial O2 loss from the below-ground tissue. Frontiers in Marine Science 2:58. doi: 10.3389/fmars.2015.00058

Brooks, R.P., D.H. Wardrop, and J.A. Bishop. 2004. Assessing wetland condition on a watershed basis in the Mid-Atlantic region using synoptic land-cover maps. Environmental Monitoring and Assessment 94: 9–22.

Bull, J.C., E.J. Kenyon and K.J. Cook. 2012. Wasting disease regulates long-term population dynamics in a threatened seagrass. Oecologica 169:135-142.

Burdick, D.M., F.T. Short and J. Wolf. 1993. An index to assess and monitor the progression of the wasting disease in eelgrass, Zostera marina. Marine Ecology Progress Series 94:83-90.

Chandler, J., P. Ashmore, C. Paola, M. Gooch, and F. Varkaris. 2002. Monitoring river channel change using terrestrial oblique digital imagery and automated digital photogrammetry. Ann. Assoc. Am. Geogr. 92,

631–644. Cho, H.J., D. Mishra, and J.S. Wood. 2012. Remote Sensing of Submerged Aquatic Vegetation. In: Escalante-Ramierez, B. (ed), Remote Sensing – Applications. Costello, C. and W.J. Kenworthy. 2011. Twelve-year mapping and change analysis of eelgrass (Zostera marina)

areal abundance in Massachusetts (USA) identifies statewide declines. Estuaries and Coasts 34(2):232-242. DOI 10.1007/s12237-010-9371-5.

Dennison, W.C., R.J. Orth, K.A. Moore, J.C. Stevenson, V. Carter, S. Kollar, P.W. Bergstrom and R.A. Batiuk. 1993. Assessing water quality with submerged aquatic vegetation: habitat requirements as barometers of Chesapeake Bay health. Bioscience 43:86–94.

Dobson, J.E., E.A. Bright, R.L. Ferguson, D.W. Field, L.L. Wood, K. D. Haddad, H. Iredale, III, J.R. Jensen, V.V. Klemas, R.J. Orth, and J.P. Thomas. 1995. NOAA Coastal Change Analysis Program (C-CAP): guidance for regional implementation. NOAA Technical Report NMFS 123.

Duffy, J.P., L. Pratt, K. Anderson, P.E. Land and J.D. Shutler. 2018. Spatial assessment of intertidal seagrass meadows using optical imaging systems and a lightweight drone. Estauarine, Coastal and Shelf Science 200: 169-180.

Echavarria-Heras, H.A., E. Solana-Arellano and E. Franco-Vizcaino. 2006. The role of increased sea surface temperature on eelgrass leaf dynamics: onset of El Nino as a proxy for global climate change in San Quintin Bay, Baja California. Bulletin of the Southern California Academy of Sciences 105(3).

Evans, N. T. & Short, F. T. Functional trajectory models for assessment of transplanted eelgrass, Zostera marina L., in the Great Bay Estuary, New Hampshire. Estuaries 28, 936–947 (2005).

11

Ferguson R.L., L.L. Wood, and D.B. Graham. 1993. Monitoring spatial change in seagrass habitat with aerial photography. Photogramm Eng Remote Sens 59:1033-1038 Finkbeiner, M., B. Stevenson, and R. Seaman. 2001. Guidance for benthic habitat mapping: an aerial aerial photographic approach. NOAA/CSC/20117-PUB. Fonseca, M.S., P.E. Whitfield, N.M. Kelly, and S.S. Bell. 2002. Modeling seagrass landscape pattern and

associated ecological attributes. Ecol Appl 12:218–237 Fonseca, M.S. and J.A. Cahalan. 1992. A preliminary evaluation of wave attenuation by four species of seagrass.

Estuarine Coastal and Shelf Science 35:565–576. Fonseca, M.S. and J.S. Fisher. 1986. A comparison of canopy friction and sediment movement between four

species of seagrass with reference to their ecology and restoration. Mar. Ecol. Prog. Ser. 29:15-22, 1986 Ford, K. and J. Carr. 2016. Eelgrass loss over time in Duxbury, Kingston, and Plymouth Bays, Massachusetts. Final

Report to the Massachusetts Bays Program. 40pp plus appendices. Haddad, K.D. and B.A. Harris. 1985. Use of remote sensing to assess estuarine habitats. In: Magoon, O.T.;

Converse, H.; Miner, D.; Clark, D., and Tobin, L.T. (eds.), Coastal Zone '85, Proceedings 4th Symposium Coastal and Ocean Management. New York: American Society of Civil Engineers, pp. 662-6

Heck, Jr. K.L., K.W. Able, M.P. Fahay, and C.T. Roman. 1989. Fishes and decapod crustaceans of Cape Cod eelgrass meadows: Species composition, seasonal abundance patterns and comparison with unvegetated substrates. Estuaries 12:59-65.

Hogrefe, K.R., D.H. Ward, T.F. Donnelly and N. Dau. 2014. Establishing a baseline for regional scale monitoring of eelgrass (Zostera marina) habitat on the Lower Alaskan Peninsula. Remote Sens. 6:12447-12477.

Holmes, R.W. 1970. The Secchi disk in turbid coastal waters, Limnology and Oceanography. Vol. 15(5):688. Hossain, M.S., J.S. Bujang, M.H. Zakaria and M. Hashim. 2014. The application of remote sensing to

seagrass ecosystems: an overview and future research prospects. Int. J. Remote Sens. 36: 61-114. Hughes, J.E., L.A. Deegan, J.C. Wyda, M.J. Weaver, A. Wright. 2002. The effects of eelgrass habitat loss on

estuarine fish communities of southern New England. Estuaries 25: 235-249. Jensen, J.R., E.W. Ramsey, H.E. Mackey, E. Christensen, and R. Sharitz. 1987. Inland wetland change detection using aircraft MSS data. Photogramm. Eng. Remote Sens. 53:521-529. Johnson, S.W. and J.F. Thedinga. 2005. Fish use and size of eelgrass meadows in southeastern Alaska: A baseline

for long-term assessment of biotic change. Northwest Sci. 79:141–155. Jones, B.L., R.K.F. Unworth, L.J. McKenzie, R.L. Yoshida and L.C. Cullen-Unsworth. 2017. Crowdsourcing

conservation: The role of citizen science in securing a future for seagrass. Marine Pollution Bulletin. DOI: 10.1016/j.marpolbul.2017.11.005

Klemens, K.W. 2017. Development and Evaluation of a USV Based Mapping System for Remote Sensing of Eelgrass Extent in Southern California. Thesis 70pp.

Knudby, A., C. Newman, Y. Shaghude, and C. Muhando. 2010. Simple and effective monitoring of historic changes in nearshore environments using the free archive of Landsat imagery. Int. J. Appl. Earth Obs. Geoinf 12:116e122.

Koch, E.W. 2001. Beyond light: physical, geological and geochemical parameters as possible submerse aquatic vegetation habitat requirements. Estuaries 24:1-17.

Landry, J.B. and R.R. Golden. 2017. In situ effects of shoreline type and watershed land use on submerged aquatic vegetation habitat quality in the Chesapeake and Mid-Atlantic coastal bays. Estuaries and Coasts pp1-13.

Larsen, P.F., S. Barker, J. Wright, and C.B. Erickson. 2004. Use of cost effective remote sensing to map and measure marine intertidal habitats in support of ecosystem modeling efforts: Cobscook Bay, Maine. Northeast. Nat. 11 (SI2), 225–242.

Lee, K.S., S.R. Park and Y.K. Kim. 2007. Effects of irradiance, temperature, and nutrients on growth dynamics of seagrasses: A review. Journal of Experimental Marine Biology and Ecology 350(1-2):144–175.

12

Levy, P.S. and S. Lemeshow. 2008. Sampling of Populations: Methods and Applications. Wiley Series in Survey Methodology, 4th ed. 616pp.

Lubbers, L., W.R. Boynton, and W.M. Kemp. 1990. Variations in structure of estuarine fish communiries in relation to abundance of submersed vascular plants. Marine Ecology Progress Series 65:1-14.

Lyons, M., C. Roelfsema, E. Kovacs, J. Samper-Villarreal, M. Saunders, P. Maxwell, and S. Phinn. 2015. Rapid monitoring of seagrass biomass using a simple linear modelling approach, in the field and from space. Mar. Ecol. Prog. Ser. 530:1e14.

McKenzie, L.J., S.J. Campbell and C.A. Roder. 2003. Seagrass-Watch: Manual for mapping and monitoring seagrass resources by community (citizen) volunteers. 2nd edition. QFS, NFC, Cairns. 100pp. http://www.seagrasswatch.org/manuals.html

McKenzie, L.J. 2003. Draft guidelines for the rapid assessment of seagrass habitats in the western Pacific. Queensland Department of Primary Industries, Northern Fisheries Centre, Cairns. 43pp.

Moller, T. and G. Martin. 2007. Distribution of the eelgrass Zostera marina L. in the coastal waters of Estonia, NE Baltic Sea. Proc. Estonian Acad. Sci. Biol. Ecol. 56(4):270-277.

Muehlstein, L.K. 1989. Perspectives on the wasting disease of eelgrass Zostera marina. Disease of AquaticOrganisms 7:211-221.

Nahirnick, N, P. Hunter, S. Schroeder, M. Costa, T. Sharma. 2017. Unmanned Aerial Photographic Mapping of Eelgrass. (http://marinesurvivalproject.com/wp-content/uploads/EOS2017_UAV_poster.pdf) Accessed May 31, 2018.

Neckles, H.A., B.S. Kopp, B.J. Peterson and P.S. Pooler. 2012. Integrating scales of seagrass monitoring to meet conservation needs. Estuaries and Coasts 35:23-46.

Norris J.G., S. Wyllie-Echeverria, J.R. Skalski, and R.C. Zimmerman. 2001. Eelgrass Monitoring In Puget Sound: Methods and Preliminary Results of the Submerged Vegetation Monitoring Project. Washington State Department of Natural Resources.

Ochieng, C.A., Short, F.T. & Walker, D.I. 2009. Photosynthetic and morphological responses of eelgrass (Zosteramarina L.) to a gradient of light conditions. Journal of Experimental Marine Biology and Ecology 382:117–124.

Olesen, B. & Sand-Jensen, K. 1993. Seasonal acclimatization of eelgrass Zostera marina growth to light. MarineEcology Progress Series 94:91–99.

O’Neill, J.D. and M. Costa. 2013. Mapping eelgrass (Zostera marina) in the Gulf Islands National Park Reserve of Canada using high spatial resolution satellite and airborne imagery. Remote Sensing of Environment 133: 152-167.

Orth, R.J., T.J.B. Carruthers, W.C. Dennison, C.M. Duarte, J.W. Fourqurean, K.L. Heck, A.R. Hughes, G.A. Kendrick, W.J. Kenworthy, S. Olyarnik, F.T. Short, M. Waycott, S.L. Williams. 2006. A Global Crisis for Seagrass Ecosystems. BioScience. 56(12):987-996.

Pedersen, A. U., B. Jørgen, and B.A. Lomstein. 1999. The effect of eelgrass decomposition on sediment carbon and nitrogen cycling: A controlled laboratory experiment. Limnol. Oceanogr., 44(8):1978–1992.

Raoult, V., P.A. David, S.F. Dupont, C.P. Mathewson, S.J. O’Neill, N.N. Powell and J.E. Williamson. 2016. GoPros as an underwater photogrammetry tool for citizen science. PeerJ 4:e1960; DOI 10.7717/peerj.1960

Raposa, K.B. and M. Bradley. 2010. A method for rapidly assessing the distribution and cover of submersed aquatic vegetation in Rhode Island estuaries. Narragansett Bay Research Reserve Technical Report Series 2010:1, 13pp.

Roelfsema, C.M., M. Lyons, E.M. Kovacs, P. Maxwell, M.I. Saunders, J. Samper-Villarreal, and S.R. Phinn. 2014. Multi-temporal mapping of seagrass cover, species and biomass: A semi-automated object-based image analysis approach. Remote Sens. Environ. 150:172e187.

Roelfsema, C., R. Thurstan, M. Beger, C. Dudgeon, J. Loder, E. Kovacs, M. Gallo, J. Flower, K-le Gomez Cabrera, J. Ortiz, A. Lea and D. Kleine. 2016. A citizen science approach: a detailed ecological assessment of subtropical reefs and Point Lookout, Australia. PLoS ONE 11(10): e0163407.

13

Short, F.T., L.J. McKenzie, R.G. Coles, K.P. Vidler and J.L. Gaeckle. 2006. SeagrassNet manual for scientific monitoring of seagrass habitat, worldwide edition. University of New Hampshire Publication. 75 pp.

Short, F.T. and Coles, R.G. 2001. Global Seagrass Research Methods Elsevier 493 pp. Short, F.T. and C.A. Short. 1984. The seagrass filter: purification of coastal water. In: Kennedy, V.S. (Ed.), The

Estuary as a filter. Academic Press, pp. 395–413. Short, F.T., D.M. Burdick, and J.E. Kaldy. 1995. Mesocosm experiments quantifying the effects of

eutrophication on eelgrass, Zostera marina. Limnology and Oceanography 40:740-749. Short, F.T. and D.M. Burdick. 1996. Quanitifying eelgrass habitat loss in relation to housing development and

nitrogen loading in Waquoit Bay, Massachusetts. Estuaries 19:730-739 Sonoki, S., H. Shao, Y. Morita, K. Minami, J. Shoji, M. Hori and K. Miyashita. 2016. Using acoustics to determine

eelgrass bed distribution and to assess the seasonal variation of ecosystem service. PLoS One 11(3): e0150890.

Stuart-Smith, R.D., G.J. Edgar, N.S. Barrett, A. E. Bates, S.C. Baker, N.J. Bax, M.A. Becerro, J. Berkhout, J.L. Blanchard, D.J. Brock, G.F. Clark, A.T. Cooper, T.R. Davis, P.B. Day, J.E. Duffy, T.H. Holmes, S.A. Howe, A. Jordan, S. Kininmonth, N.A. Knott, J.S. Lefcheck, S.D. Ling, A. Parr, E. Strain, H.Sweatman and R. Thomson. 2017. Assessing National Biodiversity Trends for Rocky and Coral Reefs through the Integration of Citizen Science and Scientific Monitoring Programs.BioScience 67:2 (134-146).

Thom, R., S. Southard, A. Borde. 2014.Climate-linked mechanisms driving spatial and temporal variation in eelgrass (Zostera marina L.) growth and assemblage structure in Pacific Northwest estuaries, U.S.A. Journal of Coastal Research, suppl. An International forum for the littoral sciences; Fort Lauderdale. Vol. SI, Iss. 68 pp1-11.

Touchette, B.W. and J.M. Burkholder. 2000. Overview of the physiological ecology of carbon metabolism in seagrasses. Journal of Exper Mar Biol and Ecol 250:169–205.

Valle, M., V. Pala, V. Lafon, A. Dehouck, J.M. Garmendia, A. Borja and G. Chust. 2015. Mapping estuarine habitats using airborne hyperspectral imagery, with special focus on seagrass meadows. Estuar. Coast. Shelf Sci. 164: 433e442.

Vandermeulen, H. 2014. Bay-scale assessment of eelgrass beds using sidescan and video. Helgol Mar Res 68: 559-569.

Wetzel, R.G. 1983. Limnology, 2nd Ed. Saunders Coll. Publishing, Philadelphia. 767pp. Wolf, P.R. 1980. Definitions and terms used in photogrammetry. In: Chester, C., Slama (Eds.), Manual of Photogrammetry. , 4th ed. American Society of Photogrammetry, pp.995–1048. Wright, N., L. Boyer and K. Erikson. 2015. Final Report – Nearshore Eelgrass Inventory. Island Trust Fund and

SeaChange publication, 107pp. http://www.islandstrustfund.bc.ca/media/75994/islands-trust-fund-final-eelgrass-mapping-report-2012-2014-for-web.pdf

Wyda, J.C., L.A. Deegan, J.E. Hughes, M.J. Weaver. 2002. The Response of Fishes to Submerged Aquatic Vegetation Complexity in Two Eindicatorgions of the Mid-Atlantic Bight: Buzzards Bay and Chesapeake Bay. Estuaries 25: 86-100.

14

Appendix A: Broad-scale Monitoring Broad-scale monitoring is typically designed to answer questions about gross changes to eelgrass in an

embayment or a region, such as presence/absence over time. Images collected by remote sensing or aerial

photography methods are often used as a primary method to collect and interpret seagrass distribution and

meadow size over a broad area over time. Aerial photographs taken at low altitude first captured by balloons in

1855 are now captured by airplanes and drones (Barrell and Grant 2015, Nahirnick et al. 2017). Remote sensing

using satellites started in the 1960s by the military has expanded to methods also using aircrafts and drones.

There are advantages and disadvantages to both aerial photographs and satellite imagery of subtidal habitats.

While satellite imagery is more cost effective and time efficient for rapid, repeated observations over large

regions, it typically provides lower resolution than aerial photographs (Larsen et al. 2004; Chandler et al. 2002)

and there is less control over the ambient environmental conditions. The interpreter of satellite images has to

account for atmospheric interferences, variability in water depth and bottom albedo, water column attenuation

by scattering and absorption, and bottom reflectance variability (Cho et al. 2012; Haddad and Harris 1985;

Ferguson et aI. 1993; Jensen et al. 1987). Similarly the interpreter for aerial photography needs to account for

environmental conditions such as low altitude atmospheric conditions, sea state, water clarity, and water depth

(Dobson et al. 1995). Aerial photography lends itself to more flexibility in scheduling around the time of day,

sensor altitude and flight line placement, unlike satellite imagery which is more opportunistic (Dobson et al.

1995).

Since 1995, DEP’s Eelgrass Mapping Program has collected aerial photographs and field-groundtruthing data to

map eelgrass coverage in coastal waters and therefore characterize overall eelgrass trends. DEP’s survey is

conducted yearly at select embayments on a rotating schedule, therefore individual embayments are revisited

every 3 to 5 years. DEP standardizes image collection to ensure maximum eelgrass visibility, following the

Coastal Change Analysis Program (C-CAP) standards for aerial photographic surveys for seagrasses (Costello and

Kenworthy 2011; Dobson et al. 1995; Finkbeiner et al. 2001). True color aerial photography taken at a scale of

1:20,000 between May and early August requires specific environmental conditions including near to low tide,

sun angle <25°, winds <5 mph, minimal cloud cover, no haze, fog, or rainfall or high winds within the previous 48

hours (Costello and Kenworthy, 2011). DEP collected aerial imagery in DKP in 1995, 2001, 2006, 2012, and 2015.

Delineations based on the aerial photographs are groundtruthed with underwater video surveys. In addition,

DEP utilized Massachusetts Department of Transportation (MassDOT) photos in an effort to establish an eelgrass

15

baseline for 1951. The 1951 photos were not taken using the same specifications as applied by DEP, but were

the earliest and best images available at that time (Ford and Carr 2016).

In Massachusetts, a mix of non-profit, Federal, and State agencies have collected aerial photographs to monitor

eelgrass beds to provide supplemental perspective of areas of interest seen in DEP images and also help

qualitatively assess specific beds between DEP mapping years (Ford and Carr 2016). These images are all taken

with a variety of cameras aboard a variety of platforms, and methods are not standardized across different

survey groups. The Center for Coastal Studies (CCS) in Provincetown has conducted aerial photo surveys at least

three times per year since 2007 to collect eelgrass imagery over Cape Cod Bay embayments. Surveys are

conducted during March/April (pre-growing season), July/August (peak growing season), and

October/November (post-growing season). The United States Geological Survey (USGS) collected aerial

photography in 2013/2014. MA DMF conducted qualitative aerial photo fly-overs of DKP and several Buzzards

Bay and North Shore embayments in September 2014 using a volunteer pilot service. The U.S. EPA is currently

investigating the use of satellite imagery for eelgrass mapping in select Massachusetts embayments on Cape

Cod (Phil Colarusso, pers. comm.).

Other methods to monitor eelgrass include using satellite imagery, terrestrial oblique true color large-scale

photography or optical imaging systems, and lightweight drones (Andrade and Ferreira 2011; Duffy et al. 2018).

These methods are relatively low-cost alternative to aerial photography but have their own limitations. Satellite

imagery methods involve the interpretation of satellite-acquired aerial imagery for eelgrass signatures. Available

imagery has varied resolution ranging from 30m for Landsat Thematic Mapper, 4m for IKONOS, 2.44m for

QuickBird multispectral data, and 1m or less for airborne hyperspectral data (Cho et al. 2012; Knudby et al.

2010; Lyons et al. 2015; Roelfsema et al. 2014). Resolution can be a potential hindrance, as can the timing of

satellite fly-overs, cloud cover, waves, tidal conditions and other environmental factors. The terrestrial oblique

method uses photographs taken monthly during low-water spring tides, from a fixed elevated point.

Orthophotographs, with the metric qualities of a true map, are produced by correcting the oblique images that

had distortions due to relief and photo-tilt and lens effects (Wolf 1980; Chandler et al. 2002). Finding an

elevated point overlooking the area of interest is the main limitation for this method. A study by Duffy et al.

(2018) used a drone and consumer grade cameras to produce very high spatial resolution (~4 mm pixel -1)

mosaics of intertidal sites in Wales, UK. Drones allow for higher resolution with additional flexibility in

deployment capabilities and customization (Duffy et al. 2018). Limitations similar to satellite and aerial

photography include turbidity, waves, cloud cover, weather (e.g. rain or fog) and water column height (Andrade

and Ferreira 2011; Duffy et al. 2018). Also, drones typically have a short flight time and regulatory operational

limitations, so can only be used over small areas.

16

Appendix B: Meso-scale Monitoring

Meso-scale monitoring usually involves ground-based assessment of a specific embayment or meadow. This

level of monitoring can be used to estimate the coverage and quality of seagrass beds at a higher resolution and

can include physical, chemical, or biological parameters of the bed (Brooks et al. 2004, Neckles et al. 2012,

Hogrefe et al. 2014, Vandermeulen 2014). Detected changes might include density, percent cover, canopy

height, sediment characteristics, or observation of changes to algae communities. The information can be used

to monitor meadow-scale changes in extent, and to predict or explain changes in areal extent (Hogrefe et al.

2014, Johnson and Thedinga 2005). Examples of meso-scale surveys include acoustic remote sensing surveys

(Ford and Carr 2016, Barrell et al. 2015, Vandermeulen 2014, Sonoki et al. 2016), randomized quadrat-based

surveys

(Neckles et al. 2012, Raposa and Bradley 2010), systematic point sampling (Hogrefe et al. 2014), towed

underwater video surveys (Berry et al. 2003, Fonseca et al. 2002, Vandermeulen 2014), unmanned surface

vehicles (USV) equipped with sidescan sonar sensors (Klemens 2017), and benthic grab surveys (Short and Coles

2001, Norris et al. 2001, Moller and Martin 2007, McKenzie 2003).

Remote sensing methods that utilize active sonar (e.g. by emitting pulses of sound) can collect fine-scaled

measurements of large seagrass regions, even in deep coastal water (Hossain et al. 2014), while remaining

relatively cost-effective and easily repeatable (Barrell et al. 2015, Neckles et al. 2012). Numerous sonar

arrangements are available including echosounders, side-scan sonar and seismic hydrophone systems, of various

frequencies and beam counts. Acoustic methods are less affected by water clarity and turbidity compared to

photo interpretation methods, and some researchers have been able to produce algorithms to compute canopy

height of seagrass beds (Hossain et al. 2014). Generally though, acoustic methods require specialized

equipment, trained staff to collect and process the data, and additional time and resources to groundtruth the

survey area. Since 2014, MA DMF has conducted three acoustic surveys in DKP. The 2014 survey utilized both

echosounder and sidescan sonar equipment and sampled at least one transect through every bed in DKP.

Follow-up surveys in 2016 and 2017 utilized only side scan sonar and operated as more of a spot-checking effort,

where certain beds of concern were targeted with one to two transects. Ideally, transects should be spaced so

that adjacent image swaths overlap by at least 50%, and cover the entirety of the bed. A survey of this

magnitude has not been conducted in DKP.

Quadrat-based assessments or systematic point sampling from a boat at permanent sampling stations provide

an estimate of percent cover and canopy height within a bed and therefore allow users to establish trends in the

health and condition of eelgrass beds over time (Neckles et al. 2012, Hogrefe et al. 2014). To measure percent

17

cover an underwater camera takes a picture of a quadrat on the seafloor that is analyzed either in real-time or

later in the lab for eelgrass coverage. To measure canopy height, a benthic grab survey is done in which a small

anchor is used to collect samples to measure leaf length. The time and resources required for quadrat-based

assessments depend on the size of the eelgrass meadow and number of sampling points. Underwater video

surveys with 10cm scaling lasers provide accurately scaled images that can be used to estimate percent cover

and health of the meadow (Vandermeulen 2014). Underwater videography is useful for deeper and/or larger

beds and is better at discerning eelgrass from other vegetation than remote sensing or aerial imagery methods

(Berry et al. 2003). However, it requires a trained observer to assess hours of video tape.

18

Appendix C: Fine-scale Monitoring

Fine-scale monitoring can include many approaches including plant collection, underwater surveys, and

photography. Collecting data at this resolution can help identify stressors and their particular impacts on

eelgrass. While these efforts cover a very small study area and often require SCUBA and other advanced

training, repeated monitoring of plant and habitat parameters provides baseline information and a means of

comparison to assess changes in eelgrass (Neckles et al. 2012). Through these approaches, detailed information

related to potential impacts can be gathered such as morphological and reproductive condition of plants,

presence of grazing, disease, and any other environmental variables (Short & Coles 2001).

SeagrassNet is a scientific global seagrass monitoring program that combines plant collection, underwater

surveys, and photography into a standardized protocol (Short et al. 2006). By utilizing this standardized protocol,

acute changes in specific beds of interest may be identified and compared to changes to other eelgrass beds

monitored with the same methodology locally, regionally and internationally. A SeagrassNet site is made up of

three 50-meter transects (deep, mid, and shallow depths) each with 12 predetermined randomized quadrats,

where data collected include: percent cover, shoot density, canopy height, number of reproductive shoots,

presence of invasive species, evidence of grazing, collection of a digital photograph, and collection of a biomass

indicator. Transect level data collection includes water and sediment samples. Water samples are collected in a

sealed vial from the canopy of the eelgrass at each transect, and are then processed with a refractometer to

obtain salinity readings. Sediment samples are taken once at each transect to detect changes to the primary and

secondary sediment types. Post-monitoring lab work involves processing the indicator samples taken at each

quadrat to obtain the dried biomass of each shoot’s leaves, stem, and root.

Site selection is a crucial component to the success of SeagrassNet monitoring. The ideal site lies within an

eelgrass meadow that is representative of the location (Short et al. 2006). Because SeagrassNet sites are

permanent, it is imperative that ease of access should be considered in the site selection process. Locating the

permanent transects in a range of conditions from pristine to stressed within a single site is optimal to

thoroughly monitor changes to the eelgrass bed. Targeted locations should have consistent eelgrass presence

currently and historically.

19

Eelgrass Experts Monitoring Methodology Meeting January 31, 2018, 12:30-3:30pm

Kingston Town Hall

Hosted by: NSRWA/MassBays, DMF

Attendance:

Alex Boeri, MassDMF

Susan Bryant, Cohasset Center for Student Coastal Research

Jill Carr, MassDMF

Phil Colarusso, EPA

Bill Doyle, MFAC, Plymouth Rock Oyster Growers

Tay Evans, MassDMF

Kate Frew, MassDMF

Joe Grady, Duxbury Conservation

Sara Grady, MassBays/NSRWA

Randall Hughes, Northeastern University (by Skype)

Raymond Kane, MFAC, Comm. ASMFC

Michael McHugh, MassDEP

Gregg Morris, Two Rocks Oyster Farm/fisheries researcher

Alyssa Novak, Boston University

Ann Priester, Island Creek Oysters

Forest Schenck, Northeastern University

Kaitlyn Shaw, Town of Nantucket (on phone)

Juliet Simpson, MIT Sea Grant

Dante Torio, Jackson Lab UNH

Kim Tower, Plymouth DMEA

Prassede Vella, MassBays/CZM

Sara Grady led introductions around the room and summarized the agenda. Susan Bryant recorded the meeting

with audio and video equipment.

Eelgrass Trends

Jill Carr described 60 years of eelgrass trends in mapping data from DKP. Severe losses have been observed in

aerial (DEP) and acoustic (DMF) mapping programs. Losses have occurred gradually and constantly, from both

the shallow and deep edges, and across the embayment. Stressors were briefly discussed. The group was asked to

identify questions they have about the embayment that can be answered through eelgrass monitoring. Over the

course of the meeting, the group came up with the following:

Where does suitable habitat exist? Consider identifying habitat suitability and use that to select

monitoring sites. Define areas of historic eelgrass and add any areas where eelgrass could recruit into

today where it wasn’t in the past. Use this in monitoring site selection.

How does system-wide health compare to specific bed health (and which is our goal?)

Can sediment cores reveal changes in nitrogen over time and help describe losses? Maybe do a single-

time deep coring, and then volunteers could collect surface sediment samples to create a bathymetry map

of the embayment

Can we track a rebound? Recent recoveries in Chesapeake Bay and Great Bay are encouraging

Are changes in density predictive of loss, as it appears to be in remote sensing analysis?

Is reproductive success changing or becoming a limiting factor? (fewer repro shoots, poor seed dispersal)

How are plants responding to stressors (and what are those stressors? Need continued work on

determining cause for loss and current stressors).

When grass disappears, does it ever come back? (historic areas)

Appendix D: Stakeholder/Expert Meeting Minutes (1/31/18)

20

Are light conditions favorable, and can monitoring incorporate light and other WQ? (put sensors on the

drop frames)

Can monitoring the wrack line tell us anything about the cause of loss? (e.g. roots present or clipped)

Will dividing up the survey areas by volunteer group help increase stewardship and regularity of sampling

at that site?

Discussion about eelgrass changes and stressors in DKP:

In 2014, only 987 acres remained in an embayment that may have supported 3400ac (1951). 70% loss in

60 years, 50% loss in last 5 years.

Aquaculturists noted a huge mussel set in all of DKP in 2015-2016 could be why mussels have moved in,

as evident in the photo from Saquish Head. Spat comes from upper harbor and can be very episodic

depending on weather.

Interesting discussion: Can mussels be harvested and eelgrass planted in pockets within the mussel bed?

Utilize the structure of the mussel bed to protect the eelgrass and dissipate the bottom sheer stress.

Bill Doyle- Recent observations from locals around Clarks Island show heavy erosion at location of

mussel beds, with previous sand bottoms now appearing to be hard clay. Super-hot summer days last year

may have killed mussels and allowed for scour. Discussed how variable mussel beds can be, sometimes a

lot – sometimes none.

1920s-1970s: Clarks Island was surrounded by mussel beds. Check for historic maps or DMP estuary

reports showing mussels?

Observations of increased Ulva and red/brown algae recently, and increased tunicates/sponges. Ulva is

heavily fouling oyster gear. Especially last 10 years. Concerning given the water quality implications.

Many observations of green crabs, but no historic data.

Aquaculturist observation: Some of the flats that used to have eelgrass are now getting deeper due to

scour. The grass is no longer binding the sediment. (specific to Duxbury beds)

Potential increase of nutrients in bay (N +P)

o Major increase of Ulva recently

o Tunicates also covering a great deal of eelgrass – however Phil does not think it would be enough

to cause major loss of eelgrass

o K. Shaw - Water quality at the CCS monitoring site in 2012 was 0.5 mg/l total nitrogen and the

eelgrass threshold is 0.37mg/l, so there may have been stress from increased nitrogen in the

system – look into again, I think DMF found no significance.

Kaitlyn Shaw: Could look at contaminants in the water (pharmaceuticals and others). We have also heard

this in the past from aquaculturists – interest in looking into herbicides, pesticides, fertilizers.

o CFCS looked at contaminants in Cape Cod Bay, but not DKP

Weather (Bill Doyle):

o Harbor had 18” of ice in 2014

o Two 100 year storm events in the last few years (Hurricane Sandy, Blizzard’17)

o Winds and storm surge caused increased water level for extended period of time and fast

outgoing currents

o When water receded, extreme currents may have removed sediment/grass

o DMF: Wind direction has shifted over time: measurable in weather data, but needs more work

Bathymetry

o Dramatic shifts in sediment recently – some locations gaining 2-3 inches of sediment a year (per

aquaculturists)

o Scouring observed in some locations

o Some areas of loss now appear to be exposed at mean low – could this have caused some loss?

o Changes in bathymetry can occur rapidly when eelgrass/mussels leave an area

Alyssa Novak: Ammonia production from oysters is toxic to eelgrass and could be a contributing factor to

the eelgrass decline. No one has looked into this for DKP but we should

21

Plausible that losses are a result of recruitment issues in the embayment. Should quantify reproductive

shoots in monitoring. Maybe it didn’t recover due to lack of seeding – Phil

Loss might be due to all of the above – possibly storms knocked out the eelgrass and then it couldn’t

rebound due to changes in the habitat suitability, either because of poor water quality or changes in

hydrodynamics or other.

80% daily flushing in the system, some think that means it is unlikely to be a nutrient loading issue

Green crabs were noted as a nuisance – local aquaculturist comment

Chlorophyll data are available from landsat analysis – viewed map of DKP showing hot spots (Dante

Torio)

Mapping

Sara Grady presented various mapping approaches seen in the literature. Some studies identify DKP as a macro-

scale site based on its size (around 39km2), and monitoring in an embayment of this size should be limited to

remote-sensing methods. However, we have observed large losses that have raised large questions, which may

become elucidated with Tier 2 and 3 monitoring.

The tiered approach was presented as a goal, but the floor was opened to other monitoring strategies or methods.

There was some discussion about doing multiple transects (using towable cameras or snorkelers) rather than

point-locations. Tay Evans also suggested a protocol that would give flexibility to the samplers, for example the

drop-frame camera would be the bare minimum, but if samplers were able and interested, they could expand to

collect more data by doing snorkel work along transects.

There was discussion about installing acoustic mapping equipment on volunteer boats and having them run

surveys during their normal operations. While this would collect more data, it would have a heavy training,

QA/QC and data processing element, and may result in more sporadic data collection over various spatial and

time scales, which would be difficult to interpret. Acoustic surveys require vessels to run around 3-4kts in calm

weather conditions, and often multiple passes over the same area may be needed for adequate data – this can be

challenging if volunteers are trying to tack the effort onto their other boating needs that day.

In general, there was good agreement around using the following tiered approach:

Tier 1 – continued DEP aerial mapping (every 5 yrs)

Tier 2 –Neckles (2012) hexagonal grid sampling method with drop-frame camera PLUS

continued DMF (or other) acoustic mapping (annually, biennially?)

Tier 3 – establish one SeagrassNet site (3 transects) at a relatively stable bed

Other Mapping Thoughts

Fish finder sonar – could volunteers do it instead of DMF? (Juliet Simpson)

o Issues with this include standardization of equipment, method of scan, areas scanned etc. see

above

Put HOBO’s out in the Bay to observe temperature changes (Phil)

o According to aquaculturists, Plymouth already has tidbit sensors deployed – collaborate with

towns if possible

o Check to see if towns would volunteer/contribute to the project at all

o Mentioned that Providence Center for Coastal Studies monitors water temp

Tier 3 sites – more than one necessary for best results?

o One real SGN site would allow for results to be given to SGN

o Two would allow for a healthy site and a stressed site to see changes over time (Phil)

Volunteers can be used to look at eelgrass that has washed ashore for existence of roots etc. Would this be

helpful to the monitoring effort?

Connect with Jarrett Burns at UMB, doing citizen science with kelp

22

Keep in mind that satellite imagery is constantly improving, and may become more of a tool for eelgrass

in the future (Phil C is starting a new project on this)

DEP (Mike McHugh) finds this info very helpful – ground efforts can help inform their aerial survey

delineations (need more info on what format and what info would be helpful)

Consider having volunteers use YSI’s at each site, or have YSI data collection on a separate sampling day

Consider incorporating coring and surface sediment collections. Coring in areas with existing AND

historic eelgrass will be very telling. Don’t core in bare areas since sediments may have shifted (Alyssa)

Check in on dye study in Plymouth – hasn’t happened yet

Susan/Dante – consider ArcGIS online data entry form (but keep access and security in mind)

Include in protocol how to deal with drifting vs anchoring at a site

Input provided post-meeting

2/1/18 phone call between J.Carr and Hilary Neckles:

Perfectly OK to reduce total sampling area to eliminate channels and other unsuitable areas.

The Neckles et al (2012) methodology was easier to conduct at Pleasant Bay than Great South Bay due to

embayment size. The smaller, the easier. DKP is of similar size to Pleasant Bay.

The method is working really well for them. It is tracking changes in the embayment.

The method is repeated every 3 years. Adding in the index site was very helpful; it identified losses along

the deep edge recently. Also ID’d ulva colonization.

At first, it took 3 people about a week to sample 170 hexagons (about half of which were vegetated).

Now, with more routine, it takes 2 people just 3 days. Hilary estimates it may be a 6 day task in DKP, to

start.

The method would vary depending on the tidal cycle and visibility. In shallow water, they could simply

look over the side. Up around 1m deep, they used a view scope. Beyond that, underwater camera. They

also incorporated some “drive-overs”, which were quick checks of areas that were already known to be

bare, and quickly confirmed without anchoring and conducting full sampling.

Site selection involved overlaying 500m wide hexagons, and then randomly generating a sampling point

within each cell. These sites are revisited with each sampling effort.

For rake sampling, at least 3 terminal shoots were grabbed for canopy height measurements. At deeper

sites (need more info – how deep is too deep?), the rake is challenging and the anchor could sometimes be

helpful.

Data can inform density changes, and can also produce a % cover map of the embayment.

Target peak canopy height (July/Aug), and target the right tides for the site (ideal timing depends on what

depth you are targeting).

2/2/18 email from Forest Schenck to J.Carr

Having citizens collect density data may be problematic due to accuracy and access, but the data could be

really informative in understanding bed changes across the embayment

Citizens doing presence/absence, depth and WQ testing along with other ongoing WQ work could help

predict seagrass suitability. But are suitability models useful? It’s not as though restoration is an

immediate plan.

Interesting idea to test suitability by doing test transplants throughout embayment, which may get at

questions of habitat suitability as well as responses to stressors. Seeding could be an easy citizen program

2/1/18 email from D. Torio to J.Carr

Dante provided powerpoint of his citizen science protocol for monitoring eelgrass in Canada

Shared chlorophyll, temperature, NIR (Near infrared/Red Reflectance, a vegetation index) data for GIS

Use of ipads to collect data in the field, data go directly into a webmap

23

Appendix E: Habitat Suitability Maps

24

Appendix F: Site Selection Map

25

Appendix G: Standard Operating Procedure

Massachusetts Division of Marine Fisheries Standard Operating Procedure Citizen Scientist Eelgrass Monitoring

OBJECTIVE: Volunteer monitoring of eelgrass extent and condition annually in DKP. Volunteers will take measurements at fixed stations assigned throughout the embayment using a stratified repeated random design, in accordance with the document titled “Eelgrass Monitoring: Development of a Citizen Scientist Monitoring Method - Pilot Study in Duxbury-Kingston-Plymouth Bay”. Sampling will be performed at peak biomass in August according to the following procedure.

I. GEAR LIST: Shallow draft vessel Coast guard required safety gear Boat anchor GPS unit with accuracy of 4 m or better Monitoring Kit contents:

Clipboard, datasheets, pencils, laminated SOPs Underwater digital camera, reel, and case 0.25 m2 PVC quadrat drop-frame, line SD card and charged battery for camera Secchi disk, line Measuring tape View Scope bucket Small Danforth anchor and small mushroom anchor, line Misc: zip ties, duct tape

II. SUMMARYAt all stations:

Navigate to the station using GPS coordinates and anchor the boat, record actual coordinates and other topsideinformation.

Record secchi disk measurements at two locations on the sunny side of the boat using the view bucket. At four cardinal directions around the boat, use the drop-frame to take a sample picture and estimate the

percent cover within the quadrat using the visual guides. Review data to ensure accuracy. If there are any changes, cross out the original and initial the change. If not an “indicator” station, raise the anchor and navigate to the next station.Additional sampling at indicator eelgrass stations: At each of the four cardinal directions around the boat where eelgrass was observed, use the Danforth anchor

to take a bottom grab sample, collecting at least three shoots per sample. Identify the longest leaf from each shoot. Measure the leaves and estimate coverage of wasting disease and

epiphytes, and record. Lay the shoots on the tote cover and fan the leaves, collect photos of the sample using the underwater camera. Raise the anchor and navigate to the next station.

Date Changes

to streamline; photos communications

Point of Contact: Jillian.Carr@state.ma.us

MA DMF Annisquam River Field Station 30 Emerson Ave.

Gloucester, MA 01930 978-282-0308

26

Version 1, Created by T. Evans and J.Carr, 08/2018

III. DETAILED METHODS:1. Navigating to the station

Volunteers navigate using their boat’s GPS (or a hand held unit if necessary) to get as close tothe monitoring station as possible. Stations are defined as the area within a 10-m radius circleof the GPS location, accounting for boat swing and GPS error.

Once on station, turn the boat into the wind or current, whichever is strongest. Anchor the boatby lowering the anchor off of the bow. Let out the necessary scope.

2. Data collection at all stations: Secchi diskA Secchi disk is a weighted 20 cm diameter disk painted black and white with an attached line. Ideal weather conditions for accurate secchi data collection include sunny or partly sunny skies; calm winds (≤10 knots) and little to no chop (waves on the water). Collect secchi measurements between 10 am and 4 pm. Ideally, water level should be about 50% greater than the secchi depth so that it is viewed through the water column rather than against bottom-reflected light. This may not always be possible in DKP. If the disk hits the bottom, record “bottom” under secchi depth with the water depth indicated.

Record the time, weather observations, water depth and other trip information on thedatasheet.

Remove your sunglasses, as they will give you an inaccurate reading (but be sure to wear regularcorrective lenses if you need them).

Unwind several meters of the Secchi disk rope from the holder. Lean over the sunny side of the boat and submerge the bottom 1-2” of the view bucket into the

water. Another volunteer slowly lowers the secchi disk into the water until the viewer can no longer

see it. Slowly raise the disk. When the secchi disk reappears, mark the rope at the surface of thewater with a clothespin.

Bring the secchi disk back on board and measure the length of the line from the disk to theclothespin location with your measuring tape and record this measurement on your data sheet.Repeat from another location on the boat and record.

If you need to re-take a measurement, don’t erase the old one, just cross out and initial thesuspect data so that it can be used if needed to troubleshoot later.

If two different people will regularly be making secchi measurements, both should take the firstfew measurements to ensure that the results are similar.

Useful website with tips: http://www.secchidipin.org/?s=secchi+disk

3. Camera set up and operation: Follow the laminated camera guide included in the camera case.

4. Data collection at all stations: pictures and percent cover data Four samples will be collected off the four corners of the boat. Write the station number and sample ID on the frame labeler (e.g. “101_1” for the first

sample at station 101). Beginning on the windward and up-current side of the boat, with the camera on, gently lower

the drop-frame over the side. Once it hits the bottom leave it there for 10 seconds to allowsediment to settle. View the camera screen to ensure the quadrat landed flat.

27

Look at the monitor and ensure that the image is of the whole quadrat and the bottom (and/oreelgrass) is clearly visible. On the DVR unit, press the center “OK” button to take a picture. Ifyou are unsure if a picture was taken, press the “Preview” button on the DVR unit to view thelast image captured.

Record the timestamp from the picture. Record sediment type as mud, clay, sand,

gravel and/or cobble and note otherbenthic characteristics (mussels, debris,algae or other observation) on thedatasheet.

Estimate the percent cover of eelgrassusing the following bins (0%, 1-10%, 10-30%, 30-75%, 75-100%) and the providedcoverage guide (right).

Repeat at the remaining 3 corners of theboat, be sure to update the labeler.

If this is an indicator station, continue tostep (5).

1-10% 10-30%

30-75% 75-100%

10-30%

28

5. Additional data collection at indicator stations: Eelgrass length and width anchor sample measurements If eelgrass was present at a given sample location (e.g. corner of the boat), collect a sample by

tossing the anchor out about 5 feet from the boat and gently dragging it several feet, attempting to collect at least three eelgrass shoots. Slowly pull it up, deploying again as necessary. This will be repeated at each of the four corners of the boat to generate four samples, each containing three shoots.

From the sample, select three intact shoots and place the shoots on the white tote lid, fanningthe leaves. Place the Station label in the field of view. Slide the lid under the frame and collectas many pictures as needed to capture the entire sample.

Identify the longest leaf in each of the three sample plants. Measure the length and width of theleaf using the measuring tape. Length is measured from the meristem to the leaf tip (see below),and width is measured across the widest part of the leaf. Ifthe tallest leaf is broken indicate this with an asterisk ( * ).Record the measurements on the datasheet.

Estimate cover of epiphytes (encrusting algae or tunicates)on the three leaf samples by looking over all of the leavesfor all of the shoots and assigning none, low, med. or highfor the entire sample (see guide below).

Estimate cover of wasting disease on the three leaf sampleby looking over all of the leaves for all of the shoots andassigning a none, low, med. or high category for the entiresample (see below).

Discard plants overboard and repeat at remaining corners.(Note: If colleagues or scientists request samplingcollection, samples should be placed in clean, clearlylabeled zip-lock bags and stored on ice in a cooler untiltransfer to the requester).

Wasting disease (left) and epiphyte coverage (right) on eelgrass. Photos from Cornell Cooperative Extension/SeagrassLI.org

29

Image altered from Burdick et al. 1993.

6. Cleaning and storage At the end of each field day, inspect all equipment to ensure everything is accounted for and in

similar condition to when it was received at the beginning of the day. If any items are missing,damaged, or altered in any way, note the change(s) and inform the organizer.

Rinse all gear that came in contact with salt water, taking particular care with the camera andlowering frame. Soak the camera in a tub of warm water.

Be careful not to allow any cables, connections, or electronic equipment from the waterproofbox to come into contact with water. The two plugs attached to the camera cable reel must alsoremain clean and dry at all times.

Inspect the camera case to make sure it has remained clean and dry after each use. Ifnecessary, carefully clean that monitor screen with a paper towel. If water is present in the box,remove it as soon as possible with a dry paper towel and inspect all electronic equipment toensure no damage occurred.

Allow all gear to dry and store in a cool, dry place. Recharge batteries if needed, and give SD card and data sheets to the organizer.

INDEX FOR MEASURING COVERAGE OF WASTING DISEASE AND EPIPHYTES

30

Appendix H: Monitoring Kit Contents

Each monitoring kit is self-contained inside a clear plastic container and includes all equipment needed to

complete volunteer monitoring (see table below). The operating instructions for each item, along with

maintenance information, can be found in the Standard Operating Procedure document.

The drop camera frame is built entirely from PVC pipe and has predetermined specifications to ensure accurate

data collection and correct field-of-view for the underwater camera. The base of the frame consists of a square

with an inside measurement of 49.5cm (19.5”) x 49.5cm (19.5”). This base creates the 0.25 meter square (m2)

quadrat used to assess percent cover in a standardized area. The total height of the frame is about 93cm

(36.5”), with four pipes extending vertically at a slight angle from each side of the base up to a smaller PVC

square at the top of the frame where the underwater camera can be mounted. Instructions and materials for

building the frame are below.

Monitoring Kit contents

Item Model Accessories

“Kit” Tote Clear weathertight storage case

White Cover

Frame PVC 25 ft. line with white buoy, label slate, washable markers for labelling

Camera Splashcam Deep Blue Pro Waterproof electronics box, 50 ft. cable, cable reel, cords

Secchi Disk Fieldmaster Pre-Built 50 ft. line, circle weight

Viewscope Pre-Built 5 gallon bucket with interior painted, Plexiglas view

Measuring Tape Stanley LeverLock, meters

Danforth Anchor Seachoice 4lbs 30 ft. rope with white buoy

Mushroom Anchor Seachoice 8lbs 30 ft. rope with white buoy

Paper datasheets, SOP In clipboard pencils

Clipboard Black plastic

Laminated list of contents In tote

Misc In ziplock bag Zip ties, Duct Tape

31

Drop Camera Frame Construction

Starting Components

Equipment Length Quantity

3/4" PVC pipe 10' 2

1/4" rebar 10' 1

3/4" PVC T-bar 8

3/4" PVC 90° angle 8

3" PVC coupling 1

PVC cement 1

Finished Product:

Cut Components

Equipment Length Quantity

3/4" PVC pipe 36” 4

3/4" PVC pipe 9” 8

3/4" PVC pipe 1.25" 8

1/4" rebar 20" 4

3/4" PVC T-bar 8

3/4" PVC 90° angle 8

3" PVC coupling 1

PVC cement 1

32

Eelgrass Monitoring Datasheet for All Stations

Trip InformationDate: Crew Names: Boat Name:

Sampling Station Number (i.e. #1-250, #9000):

Actual Lat.: Actual Long: GPS Device:

Wind Direction (circle one): N NE E SE S SW W NWWind Speed, kts (circle one): 0-5 5-10 10-15 15-20 20+Sea State (circle one): glass-calm small ripples small waves moderate waves high wavesCloud cover (circle one): 0% 1-25% 25- 50% 50-100%Tide (circle one): flooding high slack high ebbing low slack low

Secchi Sampling

Water Depth (feet): Time of Sampling: Secchi Depth (meters) #1:(meters):

Drop-Frame Data CollectionPicture taken

Picture Timestamp

NotesEelgrass Percent cover Sediment (all that apply)

mud clay sand gravel cobble 0 1-10 11-30 30-75 75-100

mud clay sand gravel cobble 0

75-100mud clay sand gravel cobble

1-10 11-30 30-75 75-100

Y / N

Y / N

Y / N

Y / N

1-10 11-30 30-75 75-100

0 1-10 11-30 30-75

SAMPLE 1

SAMPLE 2

SAMPLE 3

SAMPLE 4 0mud clay sand gravel cobble

Appendix I: Field Datasheet

33

Secchi Depth (meters) #2:

Additional Eelgrass Monitoring Datasheet for Indicator Stations

Station Number (i.e. #9000)

Sample 1

Wasting disease

Epiphyte cover

length (cm) width (mm) length (cm) width (mm) length (cm) width (mm)

Sample 2

Wasting disease

Epiphyte cover

length (cm) width (mm) length (cm) width (mm) length (cm) width (mm)

Sample 3

Wasting disease

Epiphyte cover

length (cm) width (mm) length (cm) width (mm) length (cm) width (mm)

Sample 4

Wasting disease

Epiphyte cover

length (cm) width (mm) length (cm) width (mm) length (cm) width (mm)

Notes:

Shoot 1 Shoot 2 Shoot 3

none, low, med., high

Shoot 1 Shoot 2 Shoot 3

none, low, med., high

none, low, med., high

none, low, med., high

Shoot 1 Shoot 2 Shoot 3

Shoot 1 Shoot 2 Shoot 3

34