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Modeling of swordtip squid
(Uroteuthis edulis) monthly
habitat preference using remote
sensing environmental data and
climate indices
Ali Haghi Vayghan
1
*, Aratrika Ray
2
, Sandipan Mondal
2,3
and Ming-An Lee
2,3,4
*
1
Department of Ecology and Aquatic Stocks Management, Artemia and Aquaculture Research
Institute, Urmia University, Urmia, Iran,
2
Department of Environmental Biology and Fishery Science,
National Taiwan Ocean University, Keelung, Taiwan,
3
Center of Excellence for Oceans, National
Taiwan Ocean University, Keelung, Taiwan,
4
Doctoral Degree Program in Ocean Resource and
Environmental Change, National Taiwan Ocean University, Keelung, Taiwan
Understanding the spatial arrangement of species in maritime settings necessitates
the study of oceanography. Hence, doing a study on the correlation between
oceanography and species dispersion is imperative, considering the impacts of
global climate change. The study used a generalized additive modeling approach to
analyze the influence of oceanographic conditions on the distribution of swordtip
squid in northeastern Taiwan, integrating fishing data, climatic oscillation and
oceanography. Among seven oceanographic characteristics, bottom sea
temperature (SSTB), sea surface height (SSH), sea surface chlorophyll (SSC), and
sea surface temperature (SST) showed significant influence in generalized additive
model (GAM) analysis (combined deviance explained: 40.30%). The monthly catch
rate of swordtip squid is influenced by six climatic oscillations, with the Pacific
Decadal Oscillation having the most significant impact, accounting for 31% of the
distribution, followed by the North Pacific Gyre Oscillation at 10.8% and the Western
Pacific Oscillation at 6.05%. From 2015 to 2019, the main areas for squid fishing were
situated in the northeastern waters of Taiwan, precisely within the geographical
coordinates of 25°N to 28°N and 121.5°E to 125°E. This study provides crucial insights
for managing swordtip squid fisheriesinTaiwan'snorthwest waters, highlighting the
importance of incorporating oceanographic conditions relating to climate change
information into decision-making to protect global ocean fisheries and their
dependent communities.
KEYWORDS
climate indices, environment remote sensing, habitat modeling, sustainable fisheries,
swordtip squid
Frontiers in Marine Science frontiersin.org01
OPEN ACCESS
EDITED BY
Stephen J. Newman,
Western Australian Fisheries and Marine
Research Laboratories, Australia
REVIEWED BY
Yeny Nadira Kamaruzzaman,
Universiti Malaysia Terengganu, Malaysia
Elaine Sabu,
REVA University, India
Ayan Chanda,
Indian Institute of Technology Kharagpur,
India
*CORRESPONDENCE
Ming-An Lee
malee@mail.ntou.edu.tw
Ali Haghi Vayghan
a.haghi@urmia.ac.ir
RECEIVED 28 October 2023
ACCEPTED 02 January 2024
PUBLISHED 22 January 2024
CITATION
Haghi Vayghan A, Ray A, Mondal S and
Lee M-A (2024) Modeling of swordtip squid
(Uroteuthis edulis) monthly habitat
preference using remote sensing
environmental data and climate indices.
Front. Mar. Sci. 11:1329254.
doi: 10.3389/fmars.2024.1329254
COPYRIGHT
© 2024 Haghi Vayghan, Ray, Mondal and Lee.
This is an open-access article distributed under
the terms of the Creative Commons Attribution
License (CC BY). The use, distribution or
reproduction in other forums is permitted,
provided the original author(s) and the
copyright owner(s) are credited and that the
original publication in this journal is cited, in
accordance with accepted academic
practice. No use, distribution or reproduction
is permitted which does not comply with
these terms.
TYPE Original Research
PUBLISHED 22 January 2024
DOI 10.3389/fmars.2024.1329254
1 Introduction
Forage fish play a pivotal role in marine ecosystems and are
highly valuable worldwide, both directly as a landed commodity
and indirectly as prey for other harvested species (Hunsicker et al.,
2010;Pikitch et al., 2014). Squid (Cephalopoda: Loginidae) plays a
critical role in sustaining trophic webs, is fast growing, has a short
life span, lives in warm continental shelf waters, is a highly
reproductive species, and is sensitive to changes in ambient
oceanic variables, which result in its wide distribution in the
Pacific Ocean from the South China Sea to Japan and through
the Java Sea and coastal waters of Indonesia, Malaysia, and Thailand
as well as the waters of the tropical and equatorial regions of Latin
America (Roper et al., 1984;Liao et al., 2006;Wang et al., 2021;
Fang et al., 2023). These characteristics rapidly increase the
population size of this commercially important species in suitable
habitats. Therefore, environmental factors play a critical role in its
life cycle (Arkhipkin et al., 2015), which results in interannual
changes in its overall yield. Cephalopods are sensitive to
environmental variables, with the water temperature (Jiajia et al.,
2020;Yamaguchi et al., 2020a;Yamaguchi et al., 2020b;Yamaguchi
et al., 2022) being the key factor affecting the population biomass
and species distribution (Cheng et al., 2022;Pang et al., 2022;Yu et
al., 2019a) in terms of spawning activity and recruitment (Agnew
et al., 2000;Postuma and Gasalla, 2010). Any change in water
temperature, chlorophyll a concentration, and salinity affects
biological characteristics (Yunrong et al., 2013;Jin et al., 2019),
migration (Chen, 2016), feeding behavior (Lin et al., 2020), and
catch status of squids in different habitats (Yu et al., 2015;Gong
et al., 2021;Zhang et al., 2022). Climate change has had large
impacts on squid catch fluctuation and habitat (Yu et al., 2015;Fang
et al., 2021;Wang et al., 2023). For example, changes in surface
water temperature have caused squid to shift northward or
southward (Chen et al., 2007), resulting in inter-annual variations
in growth, maturation, population dynamics, and fishing grounds
(Chen et al., 2022). Cephalopods have been largely exploited in
China, Taiwan, Japan, and Korea in the Northwest Pacific Ocean,
and they are also preyed upon by top predators (e.g., shark, tuna,
and billfish), marine mammals (e.g., whales, dolphins, and seals),
and seabirds (Rodhouse and Nigmatullin, 1996;Smale, 1996).
Habitat monitoring of cephalopods can indirectly provide insights
into top predators’habitat, spawning grounds, and migration
routes. Therefore, suitable habitat research can enable sustainable
fishery production.
Increasingly, the health of ocean ecosystems and aquatic
habitats has been threatened by overexploitation, global warming,
pollution, and anthropogenic-driven climate change, affecting fish
population sustainability and marine fishery (Andrews et al., 2023;
Barman et al., 2023). The distribution of fish species is significantly
influenced by various oceanographic factors, including
temperature, salinity, dissolved oxygen levels, nutrients, and water
currents, with temperature particularly affecting the distribution of
various fish species. Changes in water temperature influence fish
feeding, habitat suitability, metabolic rate, reproduction, disease,
stress, oxygen consumption, spawning habits, and migration
patterns (Alfonso et al., 2021;Teng et al., 2021;Vayghan and Lee,
2022). Fish populations and the ecosystems they are a part of may
suffer from abrupt or significant temperature fluctuations. For
example, fish that are exposed to higher temperatures have higher
metabolic rates, which in turn cause them to feed more.
Temperature variations in the water can affect which habitats are
suitable for a given species, which could result in changes to the
species’distribution and abundance. The temperature of the water
is also a major factor in fish reproduction. It may have an impact on
when spawning occurs, how eggs develop, and how long larvae
survive. Fish diseases can vary in severity depending on the
temperature. Because fish are ectothermic,theenvironment
outside affects their internal body temperature. Fish can
experience stress due to abrupt or drastic changes in water
temperature, which can weaken their immune systems and
general well-being. Finally, the timing and paths of migration can
be impacted by changes in water temperature (Chanda and Bora,
2020), which may also have an effect on the availability of food and
suitable habitats. Salinity also affects fish distribution. It is essential
for some of the most important processes, including spawning
(Bani et al., 2016;Ferreira-Martins et al., 2016), migration, and
osmoregulation (Urbina and Glover, 2015) as well as physiological
processes such as sperm activation, egg fertilization, and embryo
development (Mendoza-Portillo et al., 2023). Changes in salinity
may therefore influence the distribution of many fish species
because salinity is important for several processes, mainly
osmoregulation. In addition to breathing, oxygen is essential for
survival, growth, activity, behavior, and reproduction. Gamete
formation and hormone modulation are extremely oxygen-
dependent and are two crucial processes for attaining maturity or
spawning in albacore tuna (Sear et al., 2014). Nitrogen and
phosphorus are essential for primary productivity in water.
Phytoplankton, which form the base of the food chain, requires
nutrients to grow. Nutrient-rich environments support higher
productivity, thus attracting fish. As nutrient concentrations
fluctuate because of upwelling, runoff, or pollution, fish
distribution patterns also alter. Ocean currents influence fish
distribution, migration, and dispersal (Haghi Vayghan et al.,
2016;Teng et al., 2021;Mammel et al., 2022). They carry fish
larvae, eggs, and juveniles over long distances, which affects their
connectivity and settlement. Currents also affect the distribution of
food supplies, influencing the geographical patterns of fish
populations. Rising atmospheric CO
2
concentrations induce
ocean acidification, which decreases the pH of saltwater (Pearson
and Palmer, 2000;Jiang et al., 2019). The effects of acidity on fish
physiology and behavior may affect fish distribution. Notably, these
oceanographic characteristics interact with one another and other
environmental elements to form ecosystems that are ideal habitats
for marine organisms.
Additionally, oceanographic phenomena, including waves,
oceanic fronts, upwelling, ocean current, and climatic phenomena
such as the Pacific Decadal Oscillation (PDO) and the El Niño-
Southern Oscillation (ENSO), all contribute to the marine
environment (Johnson et al., 2020;Choi and Son, 2022).
Turbulence and vertical mixing, for instance (Barman and Bora,
2021;Chanda and Pramanik, 2023), can be brought on by wave
action at the ocean’s surface. Plankton and other small organism
Haghi Vayghan et al. 10.3389/fmars.2024.1329254
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distribution may be impacted, which may have an impact on fish
prey bases since fish distribution and prey distribution are
frequently correlated. Oceanic fronts are areas where various
water masses that have different salinity and temperature
properties come together. Variations in nutrient availability and
productivity are frequently caused by these fronts. These areas may
draw fish because of the abundance of prey linked to
higher productivity.
Habitat changes in fishery resources can affect catch rates and
habitat suitability, and these changes result in ecosystem imbalance
and socioeconomic problems (Mammel et al., 2023;Peluso et al.,
2023). Satellite remote sensing data enable the rapid screening of
aquatic species habitats; through this approach, scientists conduct
extensive surveys worldwide from different perspectives.
Subsequently, combining different technologies and scientific
management strategies, such as EBFM, contributes to favorable
sustainable fishery development in oceans.
Climate change has led to markedly elevated global temperatures,
primarily due to an increase in greenhouse gas emissions—
particularly CO
2
—especially since the Industrial Revolution
(Lønborg et al., 2020). This has been causing a continuous increase
in water temperature, which may affect the habitat of many fish and
shellfish species, ultimately damaging fisheries (Arias et al., 2021).
Consequently, SST has also increased. Seas have a critical role in
capturing and storing heat from the atmosphere (Arneth et al., 2020).
Because of its high heat capacity, seawater can absorb and store a
large amount of heat. With global warming, oceans serve as a heat
sink, absorbing a significant portion of additional heat (Suh et al.,
2020), thereby increasing SST. By 2100, ocean SST is expected to have
increased globally by 1–4°C. Consequently, the oceanographic factors
that are closely related to SST are also changing, likely altering species
habitat (Kroeker and Sanford, 2022). Thus, understanding the
present-day oceanography preferences of any species to predict the
future habitat condition is crucial. To this end, by understanding
spatiotemporal fishery resource production and managing
anthropogenic activity over oceans, ecosystem-based fishery
management (EBFM) is required for planning ocean resources and
for ensuring the sustainable yield of protein source in oceans.
A better understanding of the monthly patterns of swordtip squid
(Uroteuthis edulis)fishery by using environmental data and climate
indices can help to identify possible changes in its habitat that are
caused by environmental changes and to develop appropriate plans.
Therefore, given the commercial value of swordtip squid, habitat
trend modeling with an emphasis on hotspot regions should be
conducted to establish possible management strategies for ensuring
future sustainable fisheries and food supplies.
In this work, we used scientificfishery data collected over a
period of five years (2015 to 2019) to examine the spatial and
temporal distribution patterns of U. edulis in the East China Sea
(ECS) around Taiwan. To be precise, we created habitat models in
order to determine the primary environmental factors that
influence the distribution of species and provide predicted maps
for different months. These maps demonstrate the fluctuation in the
probability of occurrence throughout the research region and
throughout the year. This research improves the comprehension
of the geographical and temporal fluctuations in the environmental
preferences of U. edulis and establishes a significant database and
theoretical structure for the efficient management of this fishery.
2 Materials and methods
2.1 STEP I –Data collection
2.1.1 Squid fishery data
For this study, small-scale squid fishery data from January 2015
to December 2019 were obtained from Taiwan Fisheries Agency
(primarily coastal water fishing trawlers<100 gross registered
tonnages and<24 m in length). The monthly fishery data had a
spatial coverage of 20–26°N and 117–126°E with a spatial resolution
of 0.1°. The logbook included information such as year, month,
latitude, longitude, fish catch in kilograms, fishing effort in hours,
total catch weight (but not dried or wet weight), type of fishing gear
used, and vessel identification number. No information was
available regarding the duration of fishing gear soaking or
fishing depth.
2.1.2 Climatic oscillation data
The monthly climatic oscillation data were downloaded from
the National Oceanic and Atmospheric Administration website
(http://www.cpc.ncep.noaa.gov). The collected climatic oscillation
data were Southern Oscillation Index (SOI), Multivariate ENSO
Index Version 2 (MEI V2), PDO, and Dipole Modular Index
obtained from January 2014 to December 2019 (Data
access: 15.08.2023).
2.1.3 Oceanographic variable data
For the current study, seven oceanographic characteristics were
collected from various sources: sea surface temperature (SST),
bottom sea temperature (SSTB), sea surface salinity (SSS), mixed
layer depth (MLD), sea surface chlorophyll (SSC), sea surface height
(SSH), and bathymetry (BAT). SST (1-2 m) and SSTB (ocean floor)
pertain to the thermal conditions of separate oceanic strata, each
serving unique functions within the marine ecosystem (Table 1).
The CMEMS global ocean eddy-resolving reanalysis product
GLORYS12V1 (0.08° horizontal resolution and 50 vertical levels)
was utilized to acquire SST, SSS, MLD, and SSH*. The processing
level and coordinate reference system are L4 and W, respectively. As
an added benefit, we collected SSC** by using the CMEMS global
ocean biogeochemical hindcast product FREEGLORYS2V4 (0.25°
horizontal resolution, 75 vertical levels, and daily temporal
resolution; EPSG 42); the processing level and coordinate
reference system are L4 and ETRS89, respectively. BAT*** data
were collected from the website of Asia-Pacific Data Research
Center (APDRC) with zonal and meridional resolution of 0.08°.
All these data were collected between January 2015 and
December 2019 (Data access: 15.08.2023), encompassing the
geographic ranges 117°E–126°E and 20–26°N, respectively. To
match the fishery data (version 2019a), all these data were
interpolated to a spatial resolution of 0.1° by using MATLAB. In
addition, similar to oceanographic and fishery data, BAT was
interpolated to a monthly temporal resolution by using MATLAB.
Haghi Vayghan et al. 10.3389/fmars.2024.1329254
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2.2 STEP II –Impact of climatic oscillations
on squid catch
2.2.1 Wavelet analysis
Wavelet analysis helps to understand catch variability by analyzing
time series data, providing insights into population dynamics,
environmental influences, and fishing activities, ultimately
improving fisheries management practices. Following the method of
Tzeng et al. (2012), we conducted cross-wavelet coherence and phase
analyses to understand the association between the climatic
oscillations and squid catch. According to Grinsted et al. (2004),the
wavelet coherence of two time series can be defined as follows:
R2
n(s)=S½s−1WXY
n(s)
2
S(s−1WX
n(s))2·S½s−1WY
n(S)2
where Wis the wavelet transform of the time series, and Sis a
smoothing operator used to calculate average values (Torrence and
Webster, 1999).
The wavelet coherence phase can be derived as follows:
∅nSðÞ=tan−1Imaginary S(s−1WXY
n(s))
hi
Real Ss
−1WXY
n(S)ðÞ
8
<
:
9
=
;
For a more detailed calculation of the wavelet transform, please
refer to the studies of Torrence and Compo (1998) and Rouyer et al.
(2008). Additionally, MATLAB, whcih was used to calculate the
wavelet transform, and the time series analysis methods presented
by Hsieh et al. (2009) and Tzeng et al. (2012) were applied to
analyze long-term trends.
2.2.2 Generalized additive model analysis
Generalized additive models (GAMs) are valuable in oceanography
and fisheries due to their flexibility in modeling complex relationships
in data, handling non-linearity, interactions, and irregularly sampled
data. Thus, GAM analysis was performed to identify the effect of each
climatic oscillation on squid catch by using R studio version 4.2.3. Each
climatic oscillation was considered a predictor variable, whereas squid
catch was considered the response variable. We used the Gaussian
family and the generalized cross-validation (GCV) method in the
“mgcv”package. The climatic oscillations with the highest influence
were selected on the basis of the least GCV values and the highest R
2
value explained the highest deviance. Only the selected climatic
oscillation was used in further analysis.
2.3 STEP III –Standardization of
nominal CPUE
Fishery data standardization is crucial for sustainable practices,
informed decision-making, and global marine resource
conservation, ensuring reliability, comparability, and integrity in
fisheries management and research. The relative abundance of
squid was indexed as nominal CPUE (N.CPUE). N.CPUE was
calculated using the following formula:
N:CPUE =Totalcatch in Kg
Totalef f or t in hour
To obtain bias-filtered standardized CPUE (S.CPUE) data and to
diminish the dominating effects of spatial (latitude, longitude) and
temporal (year, month) factors, N.CPUE was standardized using one
of the most common methods, namely generalized linear model
(GLM). We also included the selected climatic oscillation from the
previous analysis in the standardization model construction because
many climatic oscillations have been demonstrated to significantly
contribute to species catch (Auber et al., 2015). A GLM model with
five factors was constructed in R studio version 4.2.3 by using the
mgcv program. The GLM models were constructed as follows:
GLMn:Log (N:CPUE +c)
∼Year +Month +Lat +Lon +CO +µ+€
where c has the constant value of 0.1, n is the no. of variables,
GLM
n
is the model with n factors, μ is the intercept (Year*Lat,
Year*Lon, and Lat*Lon), €is a variable with a normal distribution
and zero mean, and CO is the selected climatic oscillation.
2.4 STEP IV –Collinearity analysis
When two predictor variables are collinear, this implies that they
are highly correlated, making the accurate estimation of their
individual regression coefficients difficult or impossible. Collinearity
analysis is a crucial regression modeling tool that helps researchers
TABLE 1 Source of different oceanographic data.
Environmental data Unit Source Time period Spatial
resolution
Temporal
resolution
SST °C
CMEMS
2015-2019
0.08°
Monthly
SSTB °C
SSH meter
SSS psu
MLD meter
SSC mgm
−3
0.25°
BAT meter APDRC 0.08° Daily
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Frontiers in Marine Science frontiersin.org04
identify and address issues related to multicollinearity, thereby
improving the reliability, interpretability, and generalizability of the
models. Thus, two-step analysiswas conducted to validate collinearity
results by comparing the outcomes of two different approaches. The
two-step collinearity analysis performed were as follows:
2.4.1 Variance inflation factor analysis
In a regression study, multicollinearity is evaluated using the
variance inflation factor (VIF) measure with in the R studio of
version 4.2.3 using the “car”library and “vif”functions. In the
present study, VIF was used to test the collinearity of oceanographic
variables. Any oceanographic variable with VIF > 5 was considered
to exhibit collinearity and was excluded from further analysis.
2.4.2 Pearson correlation coefficient analysis
To validate the result of VIF analysis, Pearson correlation
coefficient analysis (Baez et al., 2020) was performed to test
collinearity between all possible pairs of oceanographic variables.
Pearson correlation coefficient analysis was performed inthe R studio
of version 4.2.3 using the “ggplot2”library and “corr”functions. The
index range for the degree of collinearity between two explanatory
variables was from −1 to 1. This indexing scale was divided into six
sections, with 0 representing no relationship, +1 or −1 representing
an ideal positive or negative correlation, +0.1 to +0.3/−0.1 to −0.3
representing a weak positive or negative correlation, +0.4 to +0.7/−0.4
to −0.7 representing a moderate positive or negative correlation, and
+0.8 to +1.0/−0.8 to −1.0 representing a strong positive or negative
correlation, respectively (Ratner, 2009). Any pair of explanatory
variables (Zuur et al., 2010)withacorrelationcoefficient of 0.75 or
more (Lezama-Ochoa et al., 2017) was considered to exhibit
collinearity and was excluded from further analysis.
2.5 STEP V –Impact of oceanographic
variables on standardized CPUE for squid
By using R 4.2.3, we performed GAM analysis to identify the effect
of each oceanographic variable on the standardized CPUE for squid.
This is because GAMs are valuable in oceanography and fisheries due
to their flexibility in modeling complex relationships in data, handling
non-linearity, interactions, and irregularly sampled data (Mondal et
al., 2023a;Mondal et al., 2023b). Each oceanographic variable was
considered as a predictor variable, whereas the standardized CPUE for
squid was considered as a response variable. We used the Gaussian
family and the GCV method in the “mgcv”package. Oceanographic
variables were ranked on the basis of the least GCV values and the
highest R
2
explained the highest deviance. Oceanographic variables
that explained a deviance of 10% or more were only selected for
further analysis. The GAM model was as follows:
GAMn:Log (S:CPUE +c)
∼s(a1)+s(a2)+s(a3)+…:+s(an)
where S.CPUE is the standardized CPUE of the long-line catch
data, sis a smoothing function of each model covariate, and a
n
is the
nth oceanographic variable.
2.6 STEP VI –Habitat prediction
The study examined the association between environmental
fluctuations and catch rate of swordtip squid from 2014 to 2019
between January to December. Furthermore, a habitat preference
model was created and generalized additive models (GAMs) were
employed to analyze potential seasonal fishing grounds. GAMs are
advantageous due to their ability to represent complex relationships
between responses and explanatory variables, including highly
nonlinear and nonmonotonic relationships. This makes GAMs
well-suited for expressing underlying relationships in ecological
systems. The GAMs were built using R version 4.2.3 with the
“mgcv”package. function. The response variable in this study was
the catch rate. The predictor variables included time variables (year
and month) and environmental factors. The GAM was formulated
in the following manner:
Log(S:CPU E +c)
=a0+s(Year)+s(Month)+s(Envir onmental factor
1)
+s(Environmental factor
2)+…s(Environmental factor
n)
c represents 10% of the average S.CPUE. To address the issue of
the log-link function’s inability to handle zeroes, a constant value c
was introduced to all catch rates. The c-value is frequently
employed in catch rate standardizations for pelagic species. All
covariates were treated as continuous variables, and the effective
degrees of freedom were estimated for each main factor. The above
equation defines the model constant, a
0
, and the spline smoothing
function, s for each covariate in the model. The interaction between
year and month was included in the analysis to consider potential
inter-annual variability in the temporal patterns of squid, which
may be influenced by environmental factors.
The model with the optimal conformation was chosen through
a stepwise procedure that relied on the lowest GCV and highest R
2
value. The final set of variables had a P-value below 0.05. The GAMs
selected were utilized to forecast the relative abundance of the
swordtip squid in the study region from 2015 to 2019. This was
achieved by employing the R function “predict.gam.”The catch
rates were predicted and mapped using MATLAB, and then
overlaid with the actual catch rates.
3 Results
3.1 Catch rate variability in the study period
Figure 1 illustrates the total catch of swordtip squid from
2015 to 2019. Compared with the catch rate in other years, the
total catch exhibited an excessively high upward trend in 2018.
The total catch rate in 2018 was approximately 4,50,000 kg. It
fluctuated downward again in 2019, with<50,000 kg of catch. The
first peak of the highest total catch rate in 2018 occurred in July,
with a second peak in September. Overall, a declining trend
was observed in the total catch rate for swordtip squid from 2015
to 2019.
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3.2 Multicollinearity among climatic indices
by VIF
Figure 2 illustrates multicollinearity among the climate
indices, specifically SOI, PDO, Western PacificOscillation
(WPO), North Pacific Gyre Oscillation (NPGO), (Multivariate El-
Niño Southern Oscillation Index version 2 (MEI_V2), and Nino 3.4.
MEI_V2 and Nino 3.4 had high collinearity (VIF > 5). Therefore,
these factors were eliminated from further analysis. The VIF values
(all<2) for the other predictor variables (SOI, PDO, WPO, and
NPGO) in the model indicate that multicollinearity was not
an issue.
FIGURE 2
Multicollinearity test among climate indices by VIF. The red dashed line represents the threshold VIF value = 5, beyond the climate indices show
severe correlation.
FIGURE 1
Total catch rate of swordtip squid from 2015 to 2019.
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3.3 Influence of climatic indices on catch
rate by GAM
The results from Figure 3 and Table 2 indicate the impact of
climatic indices on the catch rate. The significance of these findings
was determined through a GAM analysis. The highest level of
deviance was explained by PDO (31%), followed by NPGO (6.05%),
whereas SOI explained the least amount of deviance (0.87%).
3.4 Nominal catch per unit effort
standardization by GLM and
spatiotemporal variability
Using the GLM, the nominal swordtip CPUE values were
standardized to reduce the bias with various causes. Among all
climatic oscillations, PDO was used for standardization because it
had the highest influence on the catch rate of swordtip squid.
S.CPUE reached its peak in 2018 at approximately 50,000
individuals and reached its lowest point from 2015 to 2016
(Figure 4). The primary fishing season for swordtip squid occurs
between April and September. The standardized CPUE reached its
peak in June from 2015 to 2019. Specifically, catch rates increased
from early spring to late autumn and decreased in winter.
3.5 Influence of environmental parameters
The Pearson correlation analysis revealed the correlations
among all the seven environmental parameters (Figure 5).
Correlation analysis was conducted to examine the relationship
among environmental variables. SSH and SST had a higher positive
correlation than the other parameters, followed by MLD and SSC
and SSTB and SSC. However, SSS and SST exhibited the highest
negative correlation, followed by MLD and SST, SSS and SSC, and
SSTB and SSS. The other parameter combinations displayed milder
positive and negative correlation values. Therefore, the influence of
all these parameters on the catch rate was determined using
GAM analysis.
Table 3 and Figure 6 present the results of the effects of different
environmental parameters on the catch rate calculated using a
GAM analysis. SSTB explained the most variation (13%),
followed by SSH (12.50%), SSC (11%), and SST (6.48%), whereas
SSS (3.80%) and EKE (3.59%) explained the least variation.
SSTB, SSH, SSC, and SST exerted the highest influence in GAM
analysis; therefore, these four variables were included in the final
model for CPUE prediction. Figure 7 depicts the presence or absence
of multicollinearity among the final four environmental variables:
SSTB, SSH, SSC, and SST. The VIF values (all<1) for all the predictor
variables in the model suggest that multicollinearity is not a concern.
3.6 Final prediction model through GAM
The selected GAM accounted for 40.30% of the observed
deviance over the years (Table 4). Additionally, the residuals
conformed sufficiently to the assumption of a Gaussian
distribution, as indicated by the normal quantile–quantile plots
(Figure 8). All analyzed variables, including SST, SSTB, SSH, and
SSC, were significant predictors. Furthermore, the inclusion of
predictor variables at various levels increased the proportion of
explained deviance. We observed temporal variations in the catch
rates of swordtip squid, as indicated by the interaction between year
and month (Figure 9).
Catch rates were analyzed by using model-predicted relative
abundance maps to investigate temporal and spatial effects as well
as the influence of environmental factors on the distribution of
swordtip squid (Figure 10). The predicted relative abundance
displayed similar trends to catch rates and had significant high
correlations with catch rates (R = 0.64).
FIGURE 3
Impact of Pacific Decadal Oscillation (PDO), WPO, North Pacific Gyre Oscillation (NPGO), and Southern Oscillation Index (SOI) on the monthly catch
rate of swordtip squid. The solid line represents the fitted GAM function, whereas the black dot line represents 95% confidence intervals. The rug
plot on the x-axis indicates the relative density of data points.
Haghi Vayghan et al. 10.3389/fmars.2024.1329254
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3.7 Predicted geographical distribution of
swordtip squid
Figure 11 displays the monthly mean model-predicted relative
abundance in the study region from January to December, with
high spatial resolution. Swordtip squid primarily inhabits the shelf
north of Taiwan and the Kuroshio front. We observed that from
2015 to 2019, the fishing grounds were primarily located in the
northeastern waters of Taiwan, specifically between 25°N and 28°N
and between 121.5°E and 125°E. These fishing grounds exhibited
both seasonal and annual variations. In spring, the fishing grounds
were primarily concentrated at 27–28°N. In late summer and
autumn (September–November), they gradually shifted
southwestward. During winter, they shifted toward coastal waters.
Asignificant increase in relative abundance was noted in the
northern region above 27°N, especially from April to September.
However, this abundance decreased from October to March. In
August and September, a significant increase in abundance was
observed, extending southwestward to encompass the coastal waters
of mainland China, and in winter, the fishing grounds were mainly
situated in coastal waters. Certain fishing positions were specifically
found at approximately 122°E from December to March. The
relative abundance of squid was high in northeastern waters of
Taiwan during spring and autumn, with a northward shift observed
during summer.
4 Discussion
This study modeled the monthly distribution of swordtip squid
by using environmental data and climate indices to determine its
influence on the catch rate of swordtip squid in Taiwan. The
plausible reason for the highest squid total catch in 2018 could be
because the coldest phase of the PDO in the same year. This is
because negative PDO phase increases mixing of colder, deep ocean
waters with warmer surface waters causing more upwelling and
higher marine productivity. Previous studies also showed that the
PDO and changes in SST in the Northwest Pacific may influence the
anchovy population in the central Yellow Sea (Zhou et al., 2015). The
main fishing season for swordtip squid occurred between April and
September; the peak occurred in June (Figure 1). Meanwhile Figure 6
shows that among seven oceanographic characteristics, SSTB, SSH,
SSC, and SST exerted higher influence in GAM analysis. These are
the main variables affecting swordtip squid distribution. The
influence of monthly six climatic oscillations on the monthly catch
rate of swordtip squid was examined, where PDO (31%) exerted the
most influence, followed by NPGO (10.8%) and WPO (6.05%)
(Figure 3). PDO impacts marine species distribution and
abundance, affecting optimal habitats and prey species. It also
impacts atmospheric circulation, precipitation patterns, and ocean
currents, affecting the structure and dynamics of marine ecosystems.
Thus, a reliable index like PDO for species abundance and
distribution is crucial to the habitat model. Next, the key
environmental variables influencing the habitat preferences of
marine species should be identified for ensuring effective fishery
management in aquatic ecosystems (Zarkami et al., 2023). Notably,
FIGURE 4
Monthly variation in the sum of S.CPUE of swordtip squid.
TABLE 2 Relationship of climatic oscillations on the monthly catch rate
of swordtip squid.
Model R-sq adjusted Deviance explained GCV
PDO 0.245 31% 1616.6
WPO 0.0373 6.05% 1927.3
NPGO 0.0628 10.80% 1923.7
SOI -0.00836 0.87% 2003.9
The analysis was conducted using GAM.
Haghi Vayghan et al. 10.3389/fmars.2024.1329254
Frontiers in Marine Science frontiersin.org08
due to the short life span, squid abundance directly depends on the
recruitment capacity of the current year, and any changes in
environment and food availability will alter the stock status (Vila
et al., 2010). Therefore, modeling the potential effects of
oceanographic factors on species distribution is crucial for EBFM,
as management strategies implemented for one target species, such
as swordtip squid, can affect the sustainability of other target (e.g.,
tuna) or non-target species (Young et al., 2020;Pearman et al., 2020).
The effects of oceanographic factors on swordtip squid are
poorly understood. Zhang et al. (2022) reported that three
environmental variables affect the neon flying squid habitat in the
Northwest Pacific Ocean—SST, seawater temperature at 50-m
depth (T50 m), and SSC—in association with anti-cyclonic eddies
(warm core). Our data indicated that swordtip squid preferred SSTB
ranges of 15–18°C and SST of 24–28°C. Temperature is a dominant
factor affecting the distribution of different species of squid
(Postuma and Gasalla, 2010;Yu et al., 2015;Yamaguchi et al.,
2020a;Fang et al., 2021;Wang et al., 2021;Yamaguchi et al., 2022;
Fang et al., 2023;Wang et al., 2023,Yu et al., 2019b,Yu et al., 2022)
and fish (Vayghan et al., 2016;Teng et al., 2021;Mammel et al.,
FIGURE 5
Correlation between various environmental parameters.
FIGURE 6
Effects on monthly swordfish catch rates from SSTB, SSH, SSC, and SST. The fitted generalized additive model (GAM) function is shown as a solid
line, with 95% confidence intervals as black dot lines.
Haghi Vayghan et al. 10.3389/fmars.2024.1329254
Frontiers in Marine Science frontiersin.org09
2022;Vayghan and Lee, 2022;Zarkami et al., 2023). The SSTB is a
crucial environmental component that significantly impacts the
distribution, behavior, and ecological dynamics of squid. Studying
the correlation between the temperature of the ocean floor and
squid may provide significant knowledge for the management of
fisheries, conservation initiatives, and forecasts of how squid
populations would react to changes in environmental
circumstances (Wang et al., 2022). Notably, temperature changes
influence the growth and development of the squid reproductive
system (Boavida-Portugal et al., 2010;Wang et al., 2013;Pang et al.,
2020). Embryo development, hatching, and juveniles’growth
depend on water temperature (Moreno et al., 2012), thus
regulating the recruitment capacity of the squid population
(Challier et al., 2005) and affecting the CPUE for squid (Wang
et al., 2021) and fish stock (Wang et al., 2013;Porcaro et al., 2014).
In winter, squid migrates to warmer environments to ensure their
feeding and growth in southern Taiwan (Wang et al., 2021;Cheng
et al., 2022;Yamaguchi et al., 2022;Fang et al., 2023;Yu et al.,
2019c), and this species moves back northward in summer. Overall,
seawater temperature strongly influences swordtip squid habitat,
growth, maturation, and fishing ground, and this variable should be
highly considered in future habitat studies in relation to
climate change.
Many studies have used SSH, ocean circulation, and other
indices to develop a habitat model for squid (Alabia et al., 2020a;
Alabia et al., 2015;Fei et al., 2022;Yu et al., 2019d). In the present
study, the optimum SSH range for swordtip squid in the study
region varied between 0.4-0.6 m. SSH is linked to ocean’s vertical
structure, including the thermocline, which influences nutrient
distribution. Changes in SSH can affect the depth of the
thermocline, affecting nutrient availability and fish distribution.
Upwelling and down-welling processes attract fish to areas with
increased food availability and biological productivity.
Understanding the relationship between SSH and favorable
conditions is crucial for predicting fish distribution and assessing
marine ecosystem health (Mondal et al., 2022, Mondal et al., 2023).
SSC reflects the phytoplankton stock and is assumed to have an
indirect association with the spatiotemporal distribution of squid
species; thus, SSC has been frequently used as an environmental
variable in habitat models (Igarashi et al., 2018;Wang et al., 2021;
Zhang et al., 2022;Ito et al., 2023). Phytoplankton is a key element
of marine primary productivity and is a feeding source of
zooplankton and some marine organisms, reflecting the primary
productivity level of the sea (Pitchaikani and Lipton, 2016;Bacha
et al., 2017). In general, many small pelagic fishes commonly prey
on zooplankton such as copepods, which are crucial food sources
for juvenile swordtip squid (Yagi et al., 2011;Ito et al., 2023),
TABLE 3 Swordtip squid catch rates and the influence of monthly
environmental fluctuations by generalized additive model (GAM).
Parameter R-
sq adjusted
Deviance
explained
GCV
SST 0.0641 6.48% 0.9349
SSC 0.11 11% 0.88951
SSS 0.0373 3.80% 0.96177
MLD 0.0469 4.75% 0.95218
SSH 0.124 12.50% 0.87513
EKE 0.0353 3.59% 0.96369
SSTB 0.13 13.00% 0.86936
FIGURE 7
Multicollinearity test was conducted on environmental parameters by using VIF. The red dashed line represents the VIF threshold value of 5,
indicating a significant correlation.
Haghi Vayghan et al. 10.3389/fmars.2024.1329254
Frontiers in Marine Science frontiersin.org10
whereas the main prey of the adult squid is small pelagic fishes.
Furthermore, studies have applied primary production data sets in
habitat models to evaluate habitat preferences and main fishing
grounds in different ecosystem (Liu et al., 2022;Mammel et al.,
2022;Mondal et al., 2022; Mondal et al., 2023; Teng et al., 2021;
Vayghan and Lee, 2022;Semedi et al., 2023;Zarkami et al., 2023).
The fact that climate warming leads to shallowing of the mixed
layer, lower phytoplankton bloom, and overall duration (Gittings
et al., 2018) can influence the future habitat of aquatic organism
must be considered for swordtip squid habitat management.
Moreover, ocean currents have been used to monitor swordtip
squid habitat and migration in Pacific Ocean and mainly East China
Sea and Taiwan (Yamaguchi et al., 2015;Yamaguchi et al., 2018;
Yamaguchi et al., 2019;Yamaguchi et al., 2020a;Li et al., 2021;
Yamaguchi et al., 2021;Ito et al., 2023;Li et al., 2023). In this study,
the main ocean currents affecting swordtip squid were the Kuroshio
Branch Current in the western continental shelf of the East China
Sea, China coastal current, and Taiwan Warm Current (Qi et al.,
2017;Li et al., 2023). Currents may affect the life span of
cephalopods through variations in water temperature and salinity
(O'Dor et al., 2002;Wang et al., 2021), thus influencing fish
migration and feeding (Brodersen et al., 2008). In addition,
currents aid in the dispersion of fertilized eggs and paralarva,
which have weak active swimming ability in their early life,
through the upper and middle layers of ocean, thus enhancing
their early-stage development (Yamaguchi et al., 2018;Li et al.,
2021;Li et al., 2023). Monsoon greatly affected the seasonal
variations of the strength of different ocean currents, especially in
seasonal transition stages (Qi et al., 2017), and influenced the
paralarva of cephalopods as they passively transitioned through
the planktonic state. Consistent with our results, Wang et al. (2021)
reported high CPUE in areas with a velocity between 0.2–0.3 m s
-1
for ocean currents, indicating that these areas were suitable habitats.
Overall, the characteristics and seasonal variations of the currents
affected the migration routes of fishing groups (Yamaguchi et al.,
2020a;Yamaguchi et al., 2021). Li et al. (2023) provided a
comprehensive discussion of the effects of the currents on the
seasonal migration routes of swordtip squid. However, unsuitable
current velocity or the physicochemical properties of ocean currents
may result in damage to eggs and paralarva, affecting the early
growth and development of larva (Yamaguchi et al., 2021)or
increasing energy consumption in adult squids.
Climatewarminghasnumerouseffectsonthemarine
environment, inducing rapid changes from the phytoplankton
scale (Gittings et al., 2018;Lewis et al., 2020) to the scale of apex
predators (Erauskin-Extramiana et al., 2019; Mondal, et al., 2023)
and mammals (Silber et al., 2017;Albouy et al., 2020), finally
triggering the global reorganization of marine life (Alabia et al.,
2020a). In this study, we observed that the climatic oscillations
PDO, NPGO, and WPO exerted significant influence, making them
a potential sign of the effects of climate change in Pacific Ocean on
the standardized CPUE for swordtip squid. Increasing research
efforts in ocean and fishery sciences can lead to a better
understanding of how climate change can influence marine
species at different levels, such as their physiology (Farrell et al.,
2009;Hollowed et al., 2013), distribution (Kleisner et al., 2017;
Pinsky et al., 2018), and migration patterns (Dufour et al., 2010),
finally affecting ecology dynamics (Tamario et al., 2019;Hansen
et al., 2020), fishery production (Plaganyi, 2019), and marine
biodiversity (Worm and Lotze, 2021). These consequences could
decrease the efficacy of marine resource management policies, likely
also challenging the efficacy of single-species management models
(Pentz and Klenk, 2023). Therefore, the impact of climate change
extends far beyond the marine environment and the fishery sector
to food systems and food security (Dawson et al., 2016;Myers et al.,
2017), agricultural production (Müller et al., 2011), national
security and geopolitics (Greaves, 2019), global biodiversity
(Nunez et al., 2019), and many others aspects not yet discovered.
Crozier et al. (2021) stated that the population of early-life-stage
TABLE 4 Final model for predicting the distribution pattern of swordtip
squid by using the selected four remote sensing
environmental parameters.
Model R-
sq adjusted
Deviance
explained
GCV
SST+SSC
+SSH+SSTB
0.402 40.30% 0.59886
FIGURE 8
Normal quantile–quantile plot and histogram of the final GAM prediction.
Haghi Vayghan et al. 10.3389/fmars.2024.1329254
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salmon will decline rapidly in response to climate change. Huang
et al. (2021) also reviewed the effects of climate change on fish
growth and concluded its negative impact at both global and local
scales. Future poleward shifts of boreal species in response to
climate change and changes in marine biodiversity by century
end (2076–2100) have also been reported (Alabia et al., 2020b),
where a PDO shift significantly affects the favorable foraging habitat
of large oceanic squid (Alabia et al., 2020b). Therefore, habitat
modeling of swordtip squid requires the implementation of plans of
action by managers and stakeholders to balance future commercial
fishery production where stock management and fishing limitation
plans can be defined by considering climate scenarios. In 2008,
Di Lorenzo et al. (2008) developed a correlation between the NPGO
index and changes in salinity, nitrate, and SSC concentrations. This
FIGURE 9
Time series of N.CPUE and S.CPUE data as well as spatiotemporal changes in the sum of S.CPUE.
FIGURE 10
Relationship between standardized catch rate (S.CPUE) of swordtip squid and predicted relative abundance (P.CPUE) of swordtip squid in the study
region from 2015 to 2019 by using the selected generalized additive model (GAM).
Haghi Vayghan et al. 10.3389/fmars.2024.1329254
Frontiers in Marine Science frontiersin.org12
association indicates a connection between the NPGO index and
fluctuations in winds that promote upwelling. Prior research has
shown robust correlations between fish catches and climate
indicators that impact ocean productivity and eddy activity,
including NPGO, PDO, and WPO. They have significant
biological impacts in the high and mid-latitude regions of the
Northeast Pacific and Northwest Pacific.
To address the urgent need for more ambitious measures to
prevent harmful climate change, the Sustainable Development Goals
(SDGs) pertaining to climate change have been established, along
with the inclusion of climate change as a driver of disaster risk (Diz
et al., 2019). Humans benefit from a healthy ocean in many ways,
such as the supply of food and raw materials, control over regional
climates, development and job opportunities, and the preservation of
culturally significant locations (Lynch et al., 2020. The SDGs
comprise a broad range of direct objectives that can be
accomplished through the current study, including the mitigation
and sustainable use of marine resources (SDG 14) and the
establishment of regulations for climate change adaptation (SDG
13). Indirect objectives include the eradication of hunger and poverty
(SDG 1), among many others.
5 Conclusion
This study on swordtip squid in Taiwan’s waters used remote
sensing data, climate indices, and catch data to model their habitat
preferences. Four oceanographic characteristics, including bottom
sea temperature, sea surface height, and sea surface chlorophyll,
were found to affect distribution. The study also considered six
climatic oscillations, with Pacific Decadal Oscillation being the most
significant. The findings suggest that habitat trend modeling could
help establish sustainable fisheries and food supplies management
strategies. The study emphasizes the importance of considering
habitat transformation and climate change for the sustainability of
ocean resources from social and ecological perspectives. Adaptive
stock research based on habitat trend association can help reduce
bias and enable sustainable fishery management.
Data availability statement
The raw data supporting the conclusions of this article will be
made available by the authors, without undue reservation.
Author contributions
AH: Conceptualization, Supervision, Validation, Visualization,
Writing –review & editing. AR: Methodology, Project
administration, Software, Writing –original draft. SM:
Conceptualization, Data curation, Formal analysis, Validation,
Visualization, Writing –review & editing. M-AL: Funding
acquisition, Investigation, Project administration, Resources,
Supervision, Writing –review & editing.
FIGURE 11
Monthly mean distribution patterns of swordtip squid catch rates (represented by open circles) superimposed on the predicted catch rate (0.1°
resolution) in the southern East China Sea from January to December.
Haghi Vayghan et al. 10.3389/fmars.2024.1329254
Frontiers in Marine Science frontiersin.org13
Funding
The author(s) declare financial support was received for the
research, authorship, and/or publication of this article. This study
was sponsored partly by a research grant from the National Science
and Technology Council of Taiwan (NSTC 112-2611-M-019 -021
and 112-2811-M-019 -004) and the Fisheries Agency of the
Ministry of Agriculture, Executive Yuan, ROC.
Acknowledgments
The first author (AH) thanks Iran’s National Elites Foundation
and Urmia University. This manuscript was edited by Wallace
Academic Editing.
Conflict of interest
The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could be
construed as a potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the authors
and do not necessarily represent those of their affiliated
organizations, or those of the publisher, the editors and the
reviewers. Any product that may be evaluated in this article, or
claim that may be made by its manufacturer, is not guaranteed or
endorsed by the publisher.
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