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OPEN

Spatial and economic
quantification of provisioning
service by eelgrass beds in Lake
Notoro, Hokkaido, Japan
Keizo Ito 1*, Shiori Sonoki 2, Kenji Minami 3, Susumu Chiba 4, Hokuto Shirakawa 5,
Toshifumi Kawajiri 6, Yanhui Zhu 3 & Kazushi Miyashita 3
Eelgrass beds provide a habitat for many high-value fishery resources, and provisioning services,
one of the ecosystem services, need to be quantified. However, few examples have been evaluatedspatially. We determined the distribution of eelgrass beds in Lake Notoro, a marine lagoon in
Hokkaido, Japan, and quantified the provisioning services by the eelgrass beds in relation to Pandalus
latirostris, a fishery resource. Acoustic measurement surveys of the eelgrass beds and catch surveysof the shrimp were conducted in July and August 2015. The relationship between catch per unit effort(CPUE) of shrimp and the distribution of eelgrass beds was shown. The estimated distribution area of
eelgrass beds was 7.07 ­km2. Shrimp was frequently caught at water depths of 3–5 m, approximately
200 m from the edge of the eelgrass beds. The expected catch of shrimp in the fishing area of Lake
Notoro in 2015 was 25.37 tons and US$ 463.6 thousand. Eelgrass beds were found to affect thefisheries production not only on the inside but also at the edge and outside. The entire coastal spaceshould be evaluated, while considering the effect of the distribution of eelgrass beds, to quantify theprovisioning services.
In the Millennium Ecosystem Assessment, ecosystem services are defined as the benefits that humans receivefrom ­ecosystems1. Ecosystem services are divided into four categories based on their roles: provisioning, regulating, supporting, and cultural s­ ervices1. Ecosystem services are often expressed their economic value in monetary­units2. The economic valuation of ecosystem services is important because placing an economic value on nature
makes it easier for public and policy makers to recognize its i­ mportance2–4. The value of ecosystem services on
Earth is US$ 33.2680 trillion per year, of which US$ 20.9490 trillion per year is the value of ecosystem services
in marine a­ reas5. Marine areas are divided into open ocean and coastal zones. The value of coastal zones is US$
12.5680 trillion per year though their area is one-tenth that of the open ­ocean5. The coastal zones are divided
into four biomes, of which the value of the ecosystem services of seagrass and algae beds is high at US$ 3.8010
trillion, although the area occupied by seagrass and algal beds is 6.4% of the coastal z­ ones5.
Seagrasses are marine flowering plants that inhabit shallow coastal zones on all continents, except Antarctica,
and form extensive ­meadows6,7. The ecosystem services provided by seagrasses were reviewed by Nordlund
et al.8 and range from providing food for humans and habitats to providing food for aquatic organisms, carbon
sequestration, coastal protection, and primary production. Seagrass beds provide habitat and food for aquaticorganisms, serving not only as nurseries for juveniles of commercially exploited species but also as fishinggrounds for ­adults9. Seagrass beds contribute to fishing ­production10, and the economic valuation of provisioning
services, which are ecosystem services, is important. Seagrass beds have been reported to influence the densityand biomass of fish populations due to their complex canopy s­ tructure11. Studies on the influence of seagrass
bed size and density on the provision of ecosystem services are also ­required8. Quantification of the spatial provisioning services by seagrass beds is hence necessary. However, there are few examples of such studies. Eelgrass
1

Graduate School of Environmental Science, Hokkaido University, 20‑5 Benten‑cho, Hakodate,
Hokkaido 040‑0051, Japan. 2Kushiro Fisheries Research Institute, Hokkaido Research Organization, 2‑6 Hama‑cho,
Kushiro, Hokkaido 085‑0024, Japan. 3Field Science Center for Northern Biosphere, Hokkaido University, 20‑5
Benten‑cho, Hakodate, Hokkaido 040‑0051, Japan. 4Faculty of Bioindustry, Tokyo University of Agriculture,
196 Yasaka, Abashiri, Hokkaido 099‑2493, Japan. 5Fisheries Research Institute, Japan Fisheries Research and
Education Agency, 1‑5939‑22, Suido‑cho, Chuo‑ku, Niigata, Niigata 951‑8121, Japan. 6Nishi-Abashiri Fishermen’s
Association, 1‑7‑1, Omagari, Abashiri, Hokkaido 093‑0045, Japan. *email: itoh.keizo.l8@elms.hokudai.ac.jp
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beds are one of the most widely distributed seagrass beds in most of the world’s o­ ceans6. Eelgrass beds have
many important roles, such as increasing fisheries production, improving water quality, and uptake of carbon
and ­nitrogen12–15. Eelgrass beds are distributed in Japan from Hokkaido to the Ryukyu ­Islands16. Eelgrass bedsin the coastal zone of Japan have seasonality in biomass and shoot density, with the highest values in springand summer and the lowest values in fall and w­ inter16. The provision of habitat and food for aquatic organisms
by eelgrass beds have been reported in several studies in J­ apan17,18. Thus, it is important to quantify the spatialand economic provisioning of services during the thriving season, when they are distributed over a wide area.
Lake Notoro, located in Abashiri City in eastern Hokkaido, Japan, is a marine lagoon where eelgrass beds
are formed by Zostera caespitosa, Zostera marina, and Zostera japonica16. Z. japonica is found at depths shallower than 1 m, whereas Z. caespitosa and Z. marina are found at depths as low as 10 ­m16. The eelgrass beds in
Lake Notoro provide habitats for fishery species such as fish, shrimp, and sea urchins, and provide provisioning­services18. Among them, Pandalus latirostris is one of the most valuable fishery resources in Lake N­ otoro19, with−1
an average value of US$ 24.67 ­kg caught in 10 years, excluding the closed season from 2012 to 2021, assumingthat 1 US$ is 130 Japanese y­ en20. Shrimp have utilized eelgrass beds throughout their life h­ istory21. The shrimp
fishery in Lake Notoro is limited to summer and the shrimps are caught in shrimp cages. Shrimp cages are used
to selectively catch only large female shrimps by limiting their mesh ­size19,22. The Nishi-Abashiri Fishermen’s
Association, which oversees the fisheries in Lake Notoro, has been implementing positive management measures for shrimp resource ­conservation23. However, with a decline in eelgrass beds in recent years, the amountof shrimp caught in Lake Notoro has also been declining since its peak of 74 tons in ­200020. The relationshipbetween the density of eelgrass beds and that of shrimp was positive in the yearling group but not in the 2 years
group which becomes a fishery r­ esource24. We can contribute to the sustainable development of fisheries andlocal communities in Lake Notoro by identifying where shrimp are most likely to be caught in eelgrass beds.
From the above, demonstration of the spatial and economic value of provisioning services using eelgrass bedsin Lake Notoro is necessary. However, no such studies have been conducted so far.
Acoustic measurement methods are effective in quantifying the spatial distribution of eelgrass ­beds25. Thesemethods can continuously measure the presence and depth of objects in the water by emitting ultrasonic waves
into the water and receiving the reflected waves that bounce back from the ­objects26. Acoustic measurement
methods can provide information on a wide range of distribution more easily and quickly than direct observation­methods27. These methods are also less affected by water clarity, and more effective than optimal remote sensing
with aircraft, satellites, and unmanned aerial vehicles (UAVs) in determining the spatial structure of seagrassbeds in coastal ­waters28. The quantitative mapping of eelgrass beds using acoustic measurement methods has
been actively ­pursued29–32. Spatial seagrass structure and depth have been found to affect fish abundance in small­scales33. Accordingly, potential catch of fishery resources by eelgrass beds can be visualized for the entire coastal
area by combining the spatial distribution of eelgrass beds over a wide area obtained by acoustic measurement
methods and the relationship between eelgrass beds and fishery resources. This allows a spatial and economic
evaluation of the provisioning services by eelgrass beds over a wide area, and has a significant impact on the
management of eelgrass beds and fisheries.
In this study, we aimed to spatially and economically evaluate eelgrass beds by quantifying their provisioning
services using the relationship between eelgrass beds and P. latirostris in Lake Notoro as a model case. Threetasks were performed to achieve these goals. First, we mapped the thickness of the eelgrass beds and estimatedtheir distribution areas using acoustic measurement methods. Second, we examined the shrimp catch using
shrimp cages and tested a generalized additive model (GAM) for the characteristics of the fishery grounds where
P. latirostris is frequently caught, based on the distribution of eelgrass beds and CPUE of the shrimp. Finally,
we mapped the potential CPUE of shrimp using the above model, calculated the potential catch of shrimp, andevaluated the provisioning services provided by eelgrass beds spatially and economically in Lake Notoro.

Materials and methods
Field surveys

In July and August, when the eelgrass beds were in full bloom, acoustic measurement surveys of the eelgrassbeds and catch surveys of the shrimp were conducted (Fig. 1). Acoustic measurement surveys of line transects
in Lake Notoro were conducted from July 16 to 17, 2015, to estimate the distribution area of the eelgrass beds
using a quantitative echo sounder, KCE-300, with a split beam transducer (120 kHz, Sonic Co., Japan, Table 1).
Quantitative echo sounders can assess biomass quantitatively and are different from typical commercial fisheries
echo ­sounders34,35. Quantitative echo sounders are also applied to eelgrass ­beds32. We followed a previous study
and adopted a high-frequency transducer to prevent resonance by small gases in the s­ eawater32. The eelgrass bedsin Lake Notoro inhabit water depths up to 10 ­m16. In addition, Z. japonica is a small eelgrass found at depths shallower than 1 m and is not distributed within the area navigable by ships in Lake Notoro. Therefore, Z. caespitosa
and Z. marina were selected as target species for our study. The survey area was 16.24 k­ m2 on the shoreside, up
to an area navigable by ship, and on the offshore side, up to a water depth of 10 m (Fig. 1). The transducer of thequantitative echo sounder was attached to the outside of the ship, such that the surface of the transducer was at
a depth of 50 cm from the sea surface to avoid the influence of bubbles. Acoustic measurements were made bynavigating the ship at 3 knots on the survey transect lines set at 500 m intervals. The bathymetry of the survey
area was estimated from acoustic data measured by ­kriging36, a spatial interpolation method.
Catch survey of the shrimp was conducted on August 8–11, 2015, under the same conditions as the catchmethod used in the fishery of Lake Notoro, and CPUE of the shrimp was calculated. Shrimp cages were conicalwith a base diameter of 70 cm, height of 30 cm, mesh size of 1 cm, and two entrances with a diameter of 5 cm.
Approximately 500 g of frozen Pacific saury bait per cage was folded in half, which is commonly used in shrimp
fisheries. The survey was conducted on the same timescale as that of normal fisheries, with cages placed at 4:00 am

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Figure 1.  The study area, Lake Notoro, in eastern Hokkaido, Japan. The solid lines show the acoustic survey
transects. The closed circles show the points of the catch surveys of P. latirostris. The map was generated by
ArcGIS 10.8 (https://​www.​esri.​com) and does not require any permission from anywhere.

KCE-300 with T-182 transducer
Frequency (kHz)120

Pulse length (ms)

0.6

Beam width (°)

8.5

Resolution (cm)3.5

Ping rate (­ s−1)

5Beam type

Split beam

Weight of transducer (kg)8.0

Diameter of transducer (cm)13.0

Table 1.  Settings of quantitative echo sounder, KCE-300 with T-182 transducer (Sonic Co.).

and retrieved at 4:00 am the next day. The northwest area of Lake Notoro is one of the common shrimp-fishinggrounds. Acoustic measurement surveys in July were also found that the eelgrass beds were widely distributedin the northwest, while they were less distributed toward east, with some areas having no eelgrass beds. Thus,
the cages were placed northwest to show relationship between distribution of the eelgrass beds and CPUE of
the shrimp. The cages were placed at intervals of approximately 350 m, with a fixed line set perpendicular to thenearest shoreline on the line of the acoustic measurement survey of the eelgrass beds. Five lines were preparedwith three cages per fixed line, and 15 cages were placed per day for four consecutive days (Fig. 1). The cages wereretrieved, the caught shrimps were transferred to a fish container prepared onboard, measured on a platform
scale. The caught shrimps were quickly released at the same points where the cages were retrieved.

Data analysis

Acoustic data from the quantitative echo sounder were analyzed using Echoview 11.1 (Echoview Software Pty
Ltd., Australia). In eelgrass beds, the acoustic backscattering strength can identify seawater, eelgrass beds, andthe sea bottom (Fig. 2). The boundary of the surface acoustic scattering l­ayer37 was excluded from the analysis
according to a previous ­study32. The layer with the strongest acoustic backscattering strength was defined as
the lower edge, the boundary between the sea bottom and seagrass beds, and no eelgrass beds less than 45 cm
below the sea bottom were c­ onsidered32, to account for the influence of a dead zone near the sea b­ ottom38. A
histogram of the reflection intensity was obtained to extract the eelgrass beds from the echogram, which was
created from the reflection intensity every 2 dB over a 20 m horizontal range, excluding the acoustic scattering
layer and the dead zone, and was bimodal: seawater and eelgrass ­beds32. The presence of eelgrass beds can be
determined using the mean of the lowest frequency as the boundary between the two m­ odes32. In this study, thisScientific Reports |(2024) 14:3742 |https://doi.org/10.1038/s41598-024-54348-7

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Figure 2.  Representative echogram of an eelgrass bed. The white line is the maximum acoustic reflection
intensity, the thin black line is raised 45 cm from the white line to account for the influence of a dead zone near
the sea bottom, and the thick black line is the upper threshold of the eelgrass beds.

mean value was calculated at five random sites where eelgrass beds were present and was defined as the thresholdfor the upper edge of the eelgrass beds. The thickness of the eelgrass beds was defined as the length from theupper edge, defined as the threshold, to the lower edge and the boundary between the sea bottom and eelgrassbeds. The mean threshold of reflection intensity between the eelgrass beds and seawater was set at − 53.0 dB.
Acoustic data were extracted at 2 m horizontal distance intervals. The presence or absence and thickness of theeelgrass beds were mapped using ArcGIS 10.8 (ESRI). We estimated the distribution of eelgrass beds between linetransects using ­kriging36, a spatial interpolation method for obtaining a value at a point without direct observation from neighboring observations using spatial autocovariance. Because the environment differs among areas,even within the same lake, in this study, the study area was divided into four areas (north, south, east, and west),
and kriging was conducted with the parameters set for each area. The presence or absence of eelgrass beds wasestimated using indicator kriging, which was used to provide a probability map from a binary ­function39. Next,
the thickness of the eelgrass beds in the study area was estimated using ordinary kriging to consider the internal
structure of the eelgrass beds, and their distribution area was calculated. Ordinary kriging predicts the value ofan unsampled point by a linear combination of neighboring o­ bservations40.
The CPUE of shrimp, calculated from the catch survey, was defined as the mean of the four days of the CPUE
for each site. GAM was used to determine the relationship between the CPUE of the shrimp and eelgrass beds.
The response variable was the shrimp CPUE, and the explanatory variables were thickness of the eelgrass beds,
patch length of the eelgrass beds, water depth, and distance from the edge of the eelgrass beds obtained from theacoustic data. Acoustic data for the explanatory variables were used in the analysis by dividing the acoustic databy 10 m to represent the eelgrass beds in a detailed range while preserving the characteristics of the patches. A
patch of eelgrass beds was defined as one continuously measured eelgrass beds community, and the patch length
was the mean of the lengths of each patch. Distance from the edge of the eelgrass beds was defined as the distancefrom each site in the catch survey to the nearest eelgrass bed edge-derived kriging, with a positive value inside
the eelgrass bed and a negative value outside. Analysis was performed using the ‘mgcv’ package of the free R­ 41,
and the best model was determined based on Akaike’s Information Criterion (AIC).

Quantification of the provisioning services

The best model in the AIC and acoustic data from the eelgrass beds in July 2015 were used to estimate thepotential CPUE of shrimp in Lake Notoro. Acoustic data were separated every 10 m and the thickness of theeelgrass beds, patch length of the eelgrass beds, water depth, and distance from the edge of the eelgrass beds
were extracted to use spatial analysis. The location information of the data within 10 m of the acoustic data
was averaged and used as the representative location information for the point. The shrimp CPUE at each site
was estimated using the best model. The potential CPUE of the shrimp was mapped every 0.5 kg ­cage−1 fromthe CPUE of each point using ordinary kriging. Fishermen in Lake Notoro often use water depths of 3–5 m as
shrimp fishing grounds. In recent years, the eastern area of Lake Notoro has been set as a prohibited fishing area
because of a decline in shrimp catch. Therefore, areas with water depths of 3–5 m, excluding the eastern area,
were extracted as actual fishing areas from the potential CPUE map. Potential CPUE in the actual fishing areas
was calculated by multiplying the estimated CPUE by their respective areas, then summing them and dividing by
the total area of the actual fishing areas. This potential CPUE was multiplied by the actual number of fishermen,
the number of shrimp cages per fisherman per day, and the mean number of fishing days in the past 3 years to
obtain the expected catch of shrimp in Lake Notoro in 2015. To quantify the provisioning service of the eelgrassbeds, the expected catch of shrimp was estimated by multiplying the catch by the amount per kilogram. The
amount per kilogram of shrimp was calculated using the mean of the amounts for 10 years prior to 2014 (2375Japanese yen k­ g−1) when 1 US$ was 130 Japanese ­yen20.

Results

Distribution area of the eelgrass beds

The thickness of eelgrass beds in Lake Notoro in July 2015 was 0.76 ± 0.12 m. The total distribution area of theeelgrass beds estimated by kriging was 7.07 ­km2 and they were present in 43.53% of the survey area (Fig. 3). No
eelgrass beds were observed at depths greater than 6 m in the estimated distribution. By sea area, the largest

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Figure 3.  Estimated distribution area of eelgrass beds in July 2015. The map was generated by ArcGIS 10.8
(https://​www.​esri.​com) and does not require any permission from anywhere.

distribution area was 2.78 ­km2 in the west, 2.13 ­km2 in the south, 1.97 ­km2 in the north, and 0.18 ­km2 in theeast (Fig. 3).

Spatial relationship between the CPUE of P. latirostris and eelgrass beds

The mean CPUE of the shrimp cages at each of the 15 sites in August ranged from 0.07 to 3.10 kg (Table 2). The
GAM results showed that the model with the smallest AIC and the best model included the thickness of theeelgrass beds, patch length of the eelgrass beds, water depth, and distance from the edge of the eelgrass beds
(Table 3). The catch of the shrimp was low on the shallow shoreside where the eelgrass beds were densely distributed, increased as it approached the edge, and decreased as it moved away from the margin and the water
depth became deeper. In particular, shrimp were most likely to be caught at depths of 3–5 m, approximately
200 m from the edge of the eelgrass beds (Fig. 4).

Calculation of the provisioning services by the eelgrass beds

From the potential map spatially interpolated by kriging using the best model, the distribution area of shrimp
was 4.95 ­km2 in the actual fishing areas at water depths of 3–5 m, excluding a prohibited fishing area (Fig. 5). The
potential CPUE, obtained by multiplying the estimated CPUE by their respective areas, then summing them and
CPUE (kg c­ age−1)
Point

LatitudeLongitude

8-Aug

9-Aug

10-Aug11-AugMean (kg c­ age−1)

A1

44.0727144.1151

1.26

1.95

1.48

1.42

1.53

A2

44.0728

144.1194

1.50

2.55

2.84

2.66

2.39

A3

44.0729144.1244

0.44

0.10

0.74

0.86

0.54

B1

44.0906

144.1255

0.06

0.70

1.22

0.32

0.58

B2

44.0875

144.1264

2.20

1.30

2.40

2.46

2.09

B3

44.0843

144.1270

2.90

2.24

2.80

2.78

2.68

C1

44.0929144.1380

0.90

1.30

1.10

0.77

1.02

C2

44.0895

144.1386

2.40

2.30

1.84

2.64

2.30

C3

44.0862144.1395

5.00

4.14

2.50

0.74

3.10

D1

44.0937

144.1507

1.70

2.40

1.34

1.04

1.62

D2

44.0907144.1506

2.10

2.00

4.00

4.54

3.16

D3

44.0873

144.1503

4.00

2.40

2.50

1.16

2.52

E1

44.0935

144.1633

0.50

0.64

1.06

1.54

0.94

E2

44.0917144.1635

2.26

3.90

2.98

1.26

2.60

E3

44.0885

144.1639

0.08

0.14

0.02

0.04

0.07

Table 2.  CPUE of P. latirostris from the catch survey for 4 days in August 2015.

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FamilyGamma

Link functionlog

Adjusted R-squared

0.92

Deviance explained (%)

98.3

Parametric coefficients

Estimate

Std. Errort valuePr( >|t|)Intercept

1.01

0.25

4.06

0.0082Thickness of eelgrass bed− 1.030.39− 2.680.0400Distance of eelgrass bed− 0.030.03− 1.130.3067

Smooth terms

edf

Ref.dfF

p values(Water depth)

2.63

3.08

18.05

0.0044s(Distance from the edge of eelgrass bed)

3.92

3.99

27.78

0.0012

Table 3.  Summary results for the parametric coefficients and smooth terms of the final GAM selected tomodel the CPUE of P. latirostris.

Figure 4.  GAM-predicted smooth splines of the response variable CPUE of P. latirostris as a function of the
explanatory variables (a) water depth and (b) distance from the edge of the eelgrass bed. The tick marks inside
the x-axis are observed data points of each variable. The y-axis represents the partial effect of each variable.
The degrees of freedom for non-linear fits are in parenthesis on the y-axis. The dotted lines represent the 95%
confidence intervals of the smooth spline functions.

Figure 5.  Map of the estimated potential CPUE of P. latirostris obtained by extracting a range of 3–5 m indepth, to be used in the actual fishery. The map was generated by ArcGIS 10.8 (https://​www.​esri.​com) and does
not require any permission from anywhere.

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dividing by the total area of the actual fishing areas, was calculated to be 3.11 kg ­cage−1. Multiplying the CPUE
per unit area by the actual number of fishermen (32), the number of shrimp cages per fisherman per day (15),and the mean number of fishing days in the past 3 years (17), the expected catch of shrimp in Lake Notoro was
calculated to be 25.37 tons. Multiplying the catch by the amount per kilogram of shrimp, the expected catchof shrimp in Lake Notoro in 2015 was calculated to be 60.26 million Japanese yen. At an exchange rate of 130
Japanese yen to US$, the catch was valued at US$ 463.6 thousand.

Discussion

The distribution area of eelgrass beds in July 2015, obtained using a quantitative echo sounder was 7.07 ­km2, andno eelgrass beds were identified at depths greater than 6 m. A spot survey by scuba diving in July 1996 revealed
that the eelgrass beds were mainly distributed at depths of 0.6–6.5 ­m42. The distribution area of eelgrass bedsin Lake Notoro was estimated to be 10.05 ­km2 based on a survey conducted in July 1­ 99642; compared to thisstudy, eelgrass beds have decreased over the last 19 years to 2015, although the measurement methods differ.
Results from June 2013 at the same site as the 1996 survey reported that eelgrass beds were not identified at
depths greater than 4 m and that the biomass of the eelgrass beds had decreased compared to that in ­199618. In
particular, at the three sites surveyed in the eastern area, the eelgrass beds found at all the points in 1996 were
not found in ­201318. In this study, the area of eelgrass beds in the eastern was also 0.18 ­km2, almost undistributed.
The increase in the number of days with strong winds from the northwest in spring may be one of the ­reasons18.
The surface of Lake Notoro is covered with sea ice during winter and the surface ice thawing in spring causes­scouring43,44. Strong winds from northwest, which often blow in spring, have increased since ­199618, and thisstudy also suggests that they have had a negative impact on the distribution of eelgrass beds in recent years ineastern and some southern areas.
The distribution of eelgrass beds in July 2015 varied by area, with the eastern area having a smaller distribution
area than the other areas. The slope of sea bottoms in Lake Notoro is steep on the east side, while it is gentle on
the north and west sides. The habitat range of eelgrass beds is narrow on the east side and wide on the north andwest sides. Strong slope of sea bottoms causes sand surface fluctuations in eelgrass beds, which can lead to theoutflow and burial of seeds and newly recruited p­ lants45. On the other hand, eelgrass can be more abundant in46
gentle terrain than in steep ­terrain . The strong slope of the eastern part of Lake Notoro is thought to negatively
affect growth. In addition to the different areas of the bathymetric zone where eelgrass beds could be distributed,
differences in the sea bottom slope also caused differences in the distribution in each area.
The CPUE of the shrimp was related to the distribution of eelgrass beds and water depth and could be modeled. The model was able to reveal where shrimp was likely to be caught based on distance from the edge of theeelgrass beds, spatial structure of the eelgrass beds, and water depth. Shrimps were frequently caught near theedge of the eelgrass beds at water depths of 3–5 m, they were caught less frequently in the eelgrass beds closer
to the shore and were absent in the offshore eelgrass beds. In Notsuke Bay, which is located in the eastern partof Hokkaido, similar to Lake Notoro, the relationship between the density of shrimp and that of eelgrass beds is
positively correlated in the yearling group, with the shrimp inhabiting high-density eelgrass beds, but there wasno correlation in the 2 years group, and the dependence on eelgrass bed density becomes weaker with g­ rowth24.
Shrimp changes the spatial use of eelgrass beds at different growth stages. High-density eelgrass beds provide
shelter from predators and are used as hiding sites by younger shrimp g­ roups24. In Lake Notoro, the shrimp
prefers seagrass beds with high shoot density and tend to prefer Z. caespitosa to Z. marina18. This suggests thatthe higher density serves as a shelter and Z. caespitosa with its canopy structure is p
­ referred18. In experiments
using artificial seagrass units, mature shrimp has been reported to congregate at the edges of eelgrass ­patches47.
In addition, mature shrimp leaves the eelgrass patches at night to utilize unvegetated areas, owing to reduced
predation pressure and food r­ equirements47. In other words, mature shrimp uses not only the interior of eelgrass patches but also the edges and unvegetated areas of the patches. Offshore eelgrass beds were patchy anddiscontinuous in distribution. The patchy distribution created a sparse space between the eelgrass beds on theoffshore side. In this study, we conducted research under the same conditions as those in an actual fishery. The
shrimp cages used can selectively catch large ­shrimp19,22. Therefore, we suggested that the catch of large shrimp
was low in the high-density eelgrass beds on the shoreside and high in the eelgrass beds near the edge on theoffshore side in the best model. We also suggested that the catch of large shrimp was decreased offshore where
there were no eelgrass beds to avoid predators.
Based on the acoustic data of the eelgrass beds measured by the quantitative echo sounder and the best modelby the GAM, we estimated the potential CPUE of shrimp and created a potential map of shrimp in Lake Notoro
in 2015. Considering the actual number of fishermen, the number of shrimp cages per fisherman per day, andthe mean number of fishing days in the past 3 years, we were able to estimate the expected catch (25.37 tons) andcatch (US$ 463.6 thousand) of the shrimp in 2015, quantifying the provisioning service of the eelgrass beds. The
actual catches of the shrimp from 2012 to 2014 in Lake Notoro were 17.4, 17.1, and 15.4 tons, respectively, andthe catches were US$ 283.9, 345.5, and 387.1 thousand, respectively, at 130 Japanese yen to the US$20. Fishermen
decide where to place shrimp cages based on their own experience; the main fishing grounds are near the edgesof the eelgrass beds. In this study, we found that shrimp tended to be caught frequently near the edge of theeelgrass beds but also outside the eelgrass beds. Therefore, the catches estimated in this study were higher thanthe historical catches. In other lagoons, quantification of provisioning services from the biomass of several commercial fish in vegetated and unvegetated areas of eelgrass beds has been ­reported48. In this study, a significant
feature is that the spatial distribution relationship between eelgrass beds and shrimp was shown and the potential
map was created to calculate the provisioning services. A spatial assessment of the entire coastal area, including
areas where eelgrass beds do not exist, is important for quantifying the provisioning services of eelgrass beds.

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In this study, using the eelgrass beds of Lake Notoro and shrimp as a model case, the relationship between theeelgrass beds and shrimp was spatially clarified, and the value of the provisioning services was estimated. This
study, which combines an acoustic measurement survey of seagrass beds and a fishing survey of fishery resources
associated with seagrass beds, is extremely valuable for fisheries in coastal areas. As mentioned, the distribution
area of the eelgrass beds in Lake Notoro decreased over the 19 years from 1996 to 2015. Seagrass beds, including
eelgrass beds, have been disappearing globally at a rate of 110 ­km2 per year since ­198049. In many areas, the lossof seagrass beds has been reported to decrease fishery production, whereas the recovery of seagrass beds has
been reported to increase fishery ­production50,51. The Nishi-Abashiri Fishermen’s Association, which oversees thefisheries of Lake Notoro, calculates shrimp abundance using the distribution area of eelgrass beds. However, thearea of eelgrass beds was based on the area measured in 1996 by Hokkaido Regional Development B­ ureau42, andin light of the results of this study, the abundance of shrimp may have been overestimated. The shrimp catch has
been on a downward trend since 2­ 00020. The potential map of CPUE for shrimp spatially presented in this study
will be useful for fishermen not only to develop new fishing grounds with fishing potential and improve fishingefficiency but also to conserve the fishing grounds of shrimp and eelgrass beds to recover resources. Eelgrassbeds should be continuously monitored for distribution, and ecosystem services should be monitored as the
distribution changes. The quantitative echo sounder used in this study can measure a wide area of eelgrass beds
in a short period and estimate their horizontal and vertical distributions by extracting the height of the eelgrassbeds from the acoustic data. The productivity of an eelgrass population is closely related to its three-dimensional
horizontal ­structure52. The complex canopy structure of eelgrass beds provides habitat and food for a wide range
of vertebrates and ­invertebrates53,54. Furthermore, eelgrass beds, as in this study, have been shown to affect fisheryproduction outside their habitat. The spatial assessment of eelgrass beds conducted in this study is expected to
become more important in the future and will lead to the assessment of various ecosystem services.

Data availability

The datasets are available from the corresponding author on reasonable request.
Received: 16 October 2023; Accepted: 12 February 2024

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Acknowledgements

We would like to thank Dr. Takashi Yusa, at Tokyo University of Agriculture (currently at Fisheries ResearchInstitute, Aomori Prefectural Industrials Technology Research Center), and all those who accompanied us on thisresearch. We would like to thank the fishermen of Nishi-Abashiri Fishermen’s Association for their cooperationin the survey. We would like to thank the staff of Abashiri Fisheries Science Center and Abashiri City Hall for
their cooperation. We would like to thank the members of Laboratory of Marine Ecosystem Change Analysis,
Hokkaido University. We also thank Editage (https://​www.​edita​ge.​jp/) for English language editing.

Author contributions

K.I., S.S., and K.MIY. conceptualized and designed the study. S.S., S.C., H.S. and T.K. investigated and acquired
the data. K.I., S.S., K.MIN., H.S. and Y.Z. analyzed the data. K.I. drafted the manuscript. All authors discussed
the results and reviewed the manuscript.

Competing interests

The authors declare no competing interests.

Additional information

Correspondence and requests for materials should be addressed to K.I.
Reprints and permissions information is available at www.nature.com/reprints.

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