AU2021101527A4 - Habitat suitability assessment and prediction technology of Sthenoteuthis oualaniensis in the South China Sea in different seasons - Google Patents
Habitat suitability assessment and prediction technology of Sthenoteuthis oualaniensis in the South China Sea in different seasons Download PDFInfo
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Abstract
The invention discloses a technical method for habitat suitability assessment and prediction of
Sthenoteuthis oualaniensis in different seasons in the South China Sea. The present invention takes into
account the seasonal differences in the influences of marine environmental variables on the habitat
distribution of Sthenoteuthis oualaniensis in the South China Sea. It also applies a Suitability Index (SI)
model to establish a Habitat Suitability Index (HSI) model, and sets 10 different weight schemes for
environmental factors to explore the effects of different key environmental factors in different seasons on
the habitat suitability evaluation of Sthenoteuthis oualaniensis in the South China Sea, which enhances the
reliability of the model prediction.
FIGURES
113
Fishing groundft4
I ONoisalanienjis off the <
I South China Sea
4j4E 1070E I 1O 1130E 11-E 11"
Figure1I
* *ObscrcdSl *Observed S 1 obseved SI
0.8 SUC ~ . ~urv -S u -SI uv
0.6 410-6
0. ~0,4 0O.4
0.2 A 0O.2 0. 2
0 0
20 21 22 23 24 25 26 0.050.0750.10125. 15 0.17 50.2 0225 0.2 5 3.5 7 10.5 14 17.5 21 24.5 28 31.5
TempSO m (T) Chl-a ALD in
Figure 2
Description
113
Fishing groundft4
I ONoisalanienjis off the < I South China Sea
4j4E 107 0E I 1O 1130E 11-E 11"
Figure1I
* *ObscrcdSl *Observed S 1 obsevedSI 0.8 SUC ~ . ~urv -S u -SI uv
0.6 410-6 0. ~0,4 0O.4
0.2 A 0O.2 0. 2
0 0 20 21 22 23 24 25 26 0.050.0750.10125. 15 0.17 50.2 02250.2 5 3.5 7 10.5 14 17.5 21 24.5 28 31.5 TempSO m (T) Chl-a ALD in
Figure 2
Habitat suitability assessment and prediction technology of Sthenoteuthis
oualaniensis in the South China Sea in different seasons
The invention relates to a method for evaluating and predicting habitat suitability of
Sthenoteuthis oualaniensis in the South China Sea, in particular to a method for
evaluating and predicting habitat suitability of Sthenoteuthis oualaniensis in the South
China Sea based on key environmental factors in different seasons.
Sthenoteuthis oualaniensis is an oceanic economic cephalopod with large biomass,
which is widely distributed in tropical and subtropical waters of the Indian Ocean and
Pacific Ocean, with the richest resources in the northwest Indian Ocean and the South
China Sea, and it is an important fishing target for squid fisheries in China (including
Taiwan Province), Philippines, Vietnam and other countries. As an ecological species
with short life cycle, the resource abundance and spatial distribution of this species are
sensitive to environmental changes, and the catch has significant time scale differences.
In many studies on the response of squid resources and habitat suitability to the
environment, the monthly or seasonal differences in the impacts of environmental
variables on squid fishing grounds or habitat changes are mostly ignored in the analysis.
That is, the impacts of marine environmental variables on squid populations may be
different in different months or seasons. Therefore, it is of significance that the
establishment of models based on the relationship between different key environmental factors and the South China Sea Sthenoteuthis oualaniensis population within different seasons, and the assessment and prediction of their habitat suitability for fishery production in countries or regions where Sthenoteuthis oualaniensisare fished.
Since Sthenoteuthis oualaniensis has the habit of vertical movement day and night,
the present invention considers the influences of water temperature vertical structure in
addition to the influence of sea surface environmental variables. Although many studies
have found that the abundance of Sthenoteuthis oualaniensis resources or the spatial
distribution of fishing grounds are closely related to sea surface temperature (SST), sea
surface height (SSH), chlorophyll-a concentration (Chl-a) and other marine
environmental variables, however, the combined effects of vertical structure of water
temperature and sea surface environment on the abundance of squid resources are
considered in other studies. At the same time, many studies do not consider whether the
selected environmental variables are important environmental factors that have a high
degree of influence on Sthenoteuthis oualaniensis in this period, which reduces the
accuracy of fishing ground detection. The maximum entropy model was used to select
three key environmental factors from Chl-a, mixed layer depth (MLD), SSH, sea surface
salinity (SSS) and water temperature at different depths (0 m, 50 m, 100 m, 150 m, 200
m, 300 m) in spring, summer and autumn. Based on the key environmental factors in
each season, an integrated habitat model of Sthenoteuthis oualaniensisin the South China
Sea was constructed, and the performance of the model for predicting the fishing ground
can be improved. The model method can be used to guide the efficient exploration of
Chinese fishing vessels.
In this invention, considering the seasonal differences in the degree of influence of
marine environmental variables on Sthenoteuthis oualaniensis, the maximum entropy
model was used to select three key environmental factors from Chl-a, MLD, SSH, SSS
and water temperature at different depths (including 0 m, 50 m, 100 m, 150 m, 200 m,
300 m) in spring, summer and autumn which strongly affect the distribution of
Sthenoteuthis oualaniensishabitat in the South China Sea. The three key environmental
factors with higher degree of influence on the habitat distribution of Sthenoteuthis
oualaniensis,based on the fisheries data of Sthenoteuthis oualaniensis and different key
environmental factors (spring: Temp_50m, Chl-a, MLD; summer: SST, Chl-a,
Temp_100m; autumn: MLD, Chl-a, SST) in each season, a method to assess the habitat
suitability of Sthenoteuthis oualaniensis based on the key environmental factors in
different seasons is proposed.
In order to solve the above technical problems, the invention is proposed to be
realized by the following technical scheme:
The technical method for evaluating and predicting the habitat suitability of
Sthenoteuthis oualaniensis based on key environmental factors in different seasons
comprises the following steps:
S. Processing environmental factor data of Chl-a, MLD, SSH, SSS, SST, Temp_50
m, Temp_100 m, Temp_150 m, Temp_200 m, Temp_300 m, and applying the maximum
entropy model to select the three key environmental factors that have a high degree of
influence on the distribution of Sthenoteuthis oualaniensishabitat in the South China Sea
in each season;
S2. Processing the key environmental factor data of the Sthenoteuthis oualaniensis
fishery in the South China Sea in spring, summer and autumn, and matching the data with
each other to obtain fishery and environmental data at the corresponding longitude and
latitude. And fisheries data include information such as fishing operation time (year,
month, day), fishing location (latitude and longitude) and the number of fishing vessels;
S3.For Sthenoteuthis oualaniensis fisheries in the South China Sea, fishing effort is
defined as the number of fishing vessels, and the number and spatial distribution of its
location can reflect the abundance and distribution range of the Sthenoteuthis
oualaniensis resource, so the present invention correlates fishing effort, which can
indicate the location of the gravity center of fishing vessels , with different environmental
factors in each season, and uses the frequency distribution method to calculate the
suitability index based on key environmental factors within different seasons ( Suitability
Index (SI);
S4. Processing each environmental factor in spring, summer and autumn in
diffement class interval, drawing a frequency distribution graph with environmental
factors as abscissa and SI value as ordinate according to the distribution of fishing effort
in different environmental variable intervals, and performing nonlinear fitting to obtain
an equation conforming to the distribution law of SI value;
S5. Taking the environmental factors in spring, summer and autumn as independent
variables in the fitted equation and substituting them into the equation, and calculating
the dependent variable SI value corresponding to latitude and longitude and fisheries
data;
S6. Adjusting the proportion of SI values of various environmental factors in spring,
summer and autumn, setting 10 weight schemes, and calculating the HSI value of
Sthenoteuthis oualaniensison the fishing ground in the south China sea corresponding to
each scheme;
S7. Calculating the corresponding fishing effort in different HSI intervals in each
season, comparing the results and selecting the optimal weight scheme in different
seasons, and selecting the HSI model with the best fitting result as the optimal HSI
model;
S8. Prediction analysis based on optimal HSI model combined with fisheries data
and environmental data, comparison of predicted and actual distribution results and
evaluation of model prediction accuracy for habitat suitability assessment and prediction
of Sthenoteuthis oualaniensisin the South China Sea.
Preferably, in step S, the maximum entropy model is used to select three key
environmental factors with high degree of influence in each season from a plurality of
environmental variables affecting the habitat distribution of Sthenoteuthis oualaniensisin
the South China Sea as the environmental variables for constructing the comprehensive
habitat model.
Further, in the step S2, the fishing effort that can indicate the gravity center of the
fishing vessels is taken as an index for calculating the suitability index (SI). Specifically,
when the SI value is 1, the highest fishing effort in each season corresponding to each
grid position of Sthenoteuthis oualaniensisfishing ground is the sea area with the largest
distribution of squid resources in the South China Sea. When SIvalue is 0, it is the sea
area with the least distribution of Sthenoteuthis oualaniensisresources in the South China sea. And the SI model is established according to the index. According to the equation fitted by the model and the data of environmental factors in different seasons, calculate the SI values corresponding to different seasons, different spatial locations and different environmental factors. In the step S5, different from the HSI value calculated by the previous arithmetic average method or the maximum/minimum method, the invention changes the proportion of SI values of different environmental factors in different seasons, sets 10 schemes according to the weight sum of 1, compares and analyzes the influence degree of different environmental factors on the distribution of this squid in the
South China Sea, selects the scheme with the best prediction performance of the HSI
model, and enhances the scientificity and rationality of the research results. In the step
S6, the fishing effort is taken as the standard for selecting the optimal weight, and the
optimal HSI model obtained under the optimal weight scheme in each season is
synthesized. As shown in step S7, the invention combines the establishment of the model
with the prediction, verifies the accuracy and precision of the model by superimposing
the actual fishing effort and its position with the predicted HSI value, and strengthens the
reliability of the model results.
Beneficial effects:
(1) The invention adopts key environmental factors within different seasons to
assess and predict the habitat suitability of Sthenoteuthis oualaniensisin the South China
Sea, through which the invention can well detect the fishing ground of Sthenoteuthis
oualaniensis and its spatial distribution, and improve the fishing yield and efficiency for
different fishing country or region;
(2) Comparing the maximum/minimum method or arithmetic average method in the
habitat model, the HSI model is established on the basis of the SI model, and 10 different
weight schemes are given to different environmental factors in different seasons, so as to
explore the influence differences of different environmental factors on the suitability
evaluation of Sthenoteuthis oualaniensishabitat in each season in the South China Sea, so
that the model results are in good agreement with the actual situation and the prediction
effect is more accurate.
Fig. 1 shows the geographical distribution of fishing vessels fishing Sthenoteuthis
oualaniensisin the South China Sea in an embodiment of the present invention.
Fig. 2 shows the fitted SI curve of each environmental factor in spring in an
embodiment of the present invention.
Fig. 3 shows the fitted SI curve of each environmental factor in summer in an
embodiment of the present invention.
Fig. 4 shows the fitted SI curve of each environmental factor in autumn in an
embodiment of the present invention.
Fig. 5 shows the HSI in spring, summer and autumn during 2014-2016 output from
the HSI model based on the optimal weighting scheme overlapped with the distribution of
actual fishing vessels on the fishing ground of Sthenoteuthis oualaniensisin the South
China Sea in spring, summer and in an embodiment of the present invention.
Fig. 6 shows the HSI in spring, summer and autumn in 2017 output from the HSI
model based on the optimal weighting scheme overlapped with the distribution of actual fishing vessels on the fishing ground of Sthenoteuthis oualaniensis in the South China
Sea in spring, summer and in an embodiment of the present invention..
The embodiment of the present invention will be explained clearly and in detail with
reference to the attached drawings: this embodiment is implemented on the premise of the
technical scheme of the present invention, and a detailed implementation mode and
specific operation process are given, but the protection scope of the present invention is
not limited to the following embodiments, and the content is not limited to the following
embodiments only.
The following description selects the assessment and prediction of Sthenoteuthis
oualaniensis habitat in the South China Sea from March-November 2014-2017 as an
implementation case with a spatial resolution of 0.5°x0.5°, covering the range between
104°~119°E and 5-~16°N.
1. Data sources and construction of SI model
The fisheries data of Sthenoteuthis oualaniensis in the South China Sea in the
invention comes from the South China Sea Fishery Research Strategy Center. Based on
the statistical data of squid fishery in the South China Sea from 2014 to 2017, the number
of fishing vessels was summarized. The temporal resolution was season, and the spatial
resolution was 0.5°x0.5°. The maximum entropy model was used to select three key
environmental factors from Chl-a, MLD, SSH, SSS and water temperature at different
depths (including 0 m, 50 m, 100 m, 150 m, 200 m, 300 m) in spring, summer and
autumn which strongly affect the distribution of Sthenoteuthis oualaniensishabitat in the
South China Sea. The three key environmental factors with higher degree of influence on
the habitat distribution of Sthenoteuthis oualaniensis, based on the fisheries data of
Sthenoteuthis oualaniensis and different key environmental factors (spring: Temp_50m,
Chl-a, MLD; summer: SST, Chl-a, Temp_100m; autumn: MLD, Chl-a, SST) in each
season. All environmental factors data are from the Asia Pacific data research center
(http://apdrc.soest.hawaii.edu/lasOfes/v6/dataset?Catitem=71), the temporal resolution
of all the key environmental factor data was converted into season, and the spatial
resolution was converted into 0.5°x0.5° and matched with the fisheries data.
Define the latitude and longitude 0.5°x0.5° as a fishing unit, and use the fishing
effort as the index for calculating the suitability index (SI), which ranges from 0 to 1, that
is, when the SI is 1, it means that the highestfishing effort in each season corresponding
to each grid position of the Sthenoteuthis oualaniensis fishery is the sea area with the
highest distribution of Sthenoteuthis oualaniensis resources; when the SI is 0, it means
the sea area with the lowest distribution of Sthenoteuthis oualaniensis resources. The SI
model was established by segmenting each environmental factor in different seasons and
calculating the fishing effort in each interval based on the frequency distribution method,
where the SI calculation formula is as follows.
SI = Effortsi Max(Effortsi)
In this formula, Effort, is the fishing effort in interval i of different environmental
variables in each season, Max(Effortsi) is the maximum fishing effort in that interval, s
represents the season, and i is the fishing unit. Data for each environmental factor in
spring, summer and fall were input to calculate SI values for each environmental variable, and data for each environmental variable were fitted to its SI value using SPSS by least squares method with the following fitting equation.
SIx = exp [a x (X - b)'] (2)
In this formula, a, b are the model parameters estimated by the least squares method; Xi;
is the value of each environmental variable corresponding to a certain latitude and
longitude, i is longitude and j is latitude. The distribution of the fitted suitability index
(SI) curves for each environmental factor in spring, summer, and autumn are shown in
Figures 2, 3, and 4, and the fitted and statistical results of the SI model are shown in
Table 1. It was proved by statistical tests that all the SI model parameters variables for all
environmental factors in spring, summer, and autumn passed the significance test
(P<0.05), and the Root Mean Squared Error (RMSE) was low as well as the correlation
coefficient (R 2) was high.
Table 1 Suitability index model of Sthenoteuthis oualaniensisin spring, summer and autumn Season Adaptive index model R2 P RMSE SITempsom = exp(-0.445(Tsom - 22.480)2) 0.842 <0.05 0.027 Spring 5 2 9 SICl-a = exp(- 67.0 (Xcl-a- 0.09)2) 0.993 <0.05 0.002 SIMLD= exp(-0.074(XMLD- 7.097)2) 0.704 <0.05 0.028 SISST= exp(-5. 6 0 5 (TsST- 28.365)2) 0.966 <0.05 0.011 Summer SICl-a exp(-1 9 7.4 5 1(Xchla- 0.071)2) 0.958 <0.05 0.013 SI1oom= exp(-0.891(X 1 0 0 m - 17.988)2) 0.963 <0.05 0.006 SISST= exp(- 4 .3 7 4 (TsST- 27.913)2) 0.970 <0.05 0.007 Autumn SICl-a = exp(- 8 5 4.0 3 6(Xchla- 0.0962) 0.999 <0.05 0.000 SIMLD= exp(-0.011(XMLD- 23.953)2) 0.806 <0.05 0.040
2. Construction of the HSI model
On the basis of the established SI model, the environmental factors in spring,
summer and autumn are given different weights based on the arithmetic weighting method (see Table 2 for the setting of weight scheme), and a comprehensive HSI model is established. The calculation formula of the HSI value is as follows:
HSIspring = WTemp5som X SIemp50m +WChi-a X SIChi-a +WMLD X SIMLD (3) HSIsummer = WSST X SIsST+WChi-a X SIchi-a + Wemp_100m X SITemp100m (4) X HSIAutumn = WMLD X SIMLD + WChi-a SIchi-a + WSST X SIsST (5)
WTemp_5m, WCh-a, andWMLDare the weights of SIvalues for spring Temp_50m, Chl-a,
and MLD, respectively; SITemp_50m, SICh-a, andSIMLDare the SI values for Temp_50m,
Chl-a, and MLD, respectively;Wss1, WChl-a, andWemp ioom are the weights of SI values
for SST, Chl-a, and Temp_100m, respectively; SIss1, SIChl-a, and SITemploom are the
weights of SI values for SST, Chl-a, and Temp_100m, respectively.WMLD, WChl-a, Wss1
are weights of SI values of MLD, Chl-a, SST in autumn, respectively. SIMLD, SIChl-a, and
SIss1are the SI values of MLD, Chl-a, and MLD, respectively.
Table 2 Weight setting scheme of different environmental factors
Weight schemes W1 W2 W3 Scheme 1 0 1 0 Scheme 2 0 0 1 Scheme 3 0.1 0.8 0.1 Scheme 4 0.1 0.1 0.8 Scheme 5 0.25 0.5 0.25 Scheme 6 0.25 0.25 0.5 Scheme 7 0.333 0.333 0.333 Scheme 8 0.5 0.25 0.25 Scheme 9 0.8 0.1 0.1 Scheme 10 1 0 0
3. Model selection and validation
According to the HSI model with different weighting schemes established above, the
HSI values of Sthenoteuthis oualaniensisin the South China Sea in spring, summer and autumn of 2014-2016 were calculated. The HIS ranges from 0 to 1 and is defined as
O<HSI<0.2 (unsuitable habitat), 0.2<HSI<0.4 (general habitat), 0.4<HSI<0.6 (less
suitable habitat), 0.6<HSI<0.8 (suitable habitat) and 0.8<HSI<1.0 (optimal habitat)
according to their magnitudes. The number of fishing vessels operating in spring, summer
and autumn within unsuitable, suitable and optimal habitat were examined.
The proportion of fishing effort in each HSI class interval in spring, summer and
autumn was compared and analyzed. When the proportion of fishing effort in unsuitable
habitat (0<HSI<0.2) was the lowest, and the sum of the proportion of fishing effort in
suitable and optimal habitat (HSI>0.6) was the highest (i.e., the fishing effort showed an
increasing trend with the increase of HSI interval), it indicated that the model had better
prediction performance. As shown in Tables 3, 4, and 5, the optimal model weighting
scheme in spring was model 3, i.e., the SI values of Temp_50m, Chl-a, and MLD had
weights of 0.1:0.8:0.1, respectively, with Chl-a having the highest weight; the optimal
model weighting scheme in summer was model 3, i.e., the SI values of SST, Chl-a, and
Temp_100m had weights of 0.1:0.8:0.1, respectively, with Chl-a having the highest
weight. The optimal model weighting scheme in autumn is model 8, i.e., the
corresponding weights of MLD, Chl-a, and SST are 0.5:0.25:0.25, and the proportion of
MLD is the highest.
Table 3 Percentage of fishing efforts within different class interval of habitat suitability
index (HSI) in spring during 2014-2016 based on the weighted-based HSI model
HSI Casel Case2 Case3 Case4 Case5 Case6 Case7 Case8 Case9 CaselO 0.0-0.1 7.50% 40.01% 0.11% 4.41% 0.11% 0.11% 0.11% 0.11% 0.06% 11.10% 0.1-0.2 0.40% 4.24% 5.32% 34.63% 0.17% 1.37% / / 9.22% 4.92%
0.2-0.3 1.09% 3.95% 2.46% 4.06% 1.37% 14.94% 1.49% 10.02% 9.39% 11.45% 0.3-0.4 0.97% 1.43% 2.06% 6.53% 4.01% 18.66% 14.83% 10.53% 6.98% 3.32% 0.4-0.5 1.83% 7.50% 1.77% 2.86% 10.53% 16.66% 20.15% 9.56% 11.05% 12.82% 0.5-0.6 2.63% 1.14% 2.52% 5.09% 26.85% 2.18% 12.08% 22.67% 12.19% 3.78% 0.6-0.7 19.06% 2.98% 16.60% 2.12% 19.23% 9.33% 22.44% 15.51% 9.10% 7.10% 0.7-0.8 16.20% 0.69% 33.14% 4.58% 21.24% 10.76% 12.59% 13.45% 7.56% 8.64% 0.8-0.9 12.48% 10.53% 34.57% 13.11% 15.23% 24.61% 14.83% 8.01% 18.55% 3.49% 0.9-1.0 37.84% 27.53% 1.43% 22.61% 1.26% 1.37% 1.49% 10.13% 15.91% 33.37%
Table 4 Percentage of fishing efforts within different class interval of habitat suitability
index (HSI) in summer during 2014-2016 based on the weighted-based HSI model
HSI Casel Case2 Case3 Case4 Case5 Case6 Case7 Case8 Case9 CaselO 0.0-0.1 10.48% 9.10% / 0.31% / / / / / 0.38% 0.1-0.2 1.91% 7.96% 10.48% 8.03% / 0.08% / / 0.15% 11.48% 0.2-0.3 1.68% 1.76% 1.45% 6.50% 2.45% 0.99% 0.15% 0.31% 12.32% 7.73% 0.3-0.4 3.21% 12.47% 3.98% 6.96% 7.50% 6.50% 4.21% 8.26% 8.26% 4.13% 0.4-0.5 2.60% 5.74% 3.14% 12.17% 8.11% 13.70% 14.54% 16.99% 5.13% 7.27% 0.5-0.6 1.99% 10.25% 4.67% 7.65% 12.09% 24.87% 16.53% 8.80% 8.72% 4.67% 0.6-0.7 15.99% 8.95% 10.71% 14.46% 20.28% 13.62% 25.94% 25.55% 10.33% 8.88% 0.7-0.8 8.34% 12.39% 24.79% 22.26% 35.58% 30.45% 28.16% 31.14% 28.92% 25.55% 0.8-0.9 35.81% 17.67% 40.78% 15.15% 14.00% 9.79% 10.48% 8.95% 20.50% 6.12% 0.9-1.0 17.98% 13.70% / 6.50% / / / / 5.66% 23.79%
Table 5 Percentage of fishing efforts within different class interval of habitat suitability
index (HSI) in autumn during 2014-2016 based on the weighted-based HSI model
HSI Casel Case2 Case3 Case4 Case5 Case6 Case7 Case8 Case9 CaselO 0.0-0.1 7.90% 2.45% / / / / / / / 0.82% 0.1-0.2 7.36% 8.72% 5.45% 1.63% / / / / / 0.82% 0.2-0.3 3.54% 16.08% 12.53% 1.09% 0.27% 0.54% / / 1.63% 5.99% 0.3-0.4 1.91% 14.99% 0.82% 24.80% 5.99% 1.36% 0.82% 2.72% 13.35% 9.54% 0.4-0.5 6.27% 10.63% 3.81% 15.26% 11.99% 10.35% 10.90% 15.26% 6.81% 6.54% 0.5-0.6 8.45% 7.90% 5.18% 18.80% 10.08% 44.96% 18.80% 4.63% 5.45% 3.27%
0.6-0.7 13.35% 10.90% 25.61% 12.81% 37.87% 23.16% 41.96% 28.61% 1.36% 1.36% 0.7-0.8 13.35% 8.99% 13.90% 13.62% 30.52% 19.07% 25.07% 44.69% 23.43% 14.71% 0.8-0.9 5.18% 11.44% 21.25% 10.35% 3.27% 0.54% 2.45% 4.09% 42.51% 19.62% 0.9-1.0 32.70% 7.90% 11.44% 1.63% / / / / 5.45% 37.33%
Based on the optimal weighting model for spring, summer and autumn, the HSI of
Sthenoteuthis oualaniensis in the South China Sea was calculated based on the
environmental data in each season of 2017. And its spatial distribution was plotted and
superimposed with the actual fishing effort. As shown in Figure 5, the distribution of his
overlapped with the actual fishing effort of Sthenoteuthis oualaniensis fisheries in spring,
summer and autumn of 2014-2016 based on the output of the HSI model with the optimal
weighting scheme. As shown in Figure 6, the HSI values predicted by the HSI model
based on the optimal weighting scheme for spring, summer and autumn of 2017 are
shown with the superimposed distribution of their actual fishing effort. From Figures 5
and 6, it can be shown that most of the fishing efforts from 2014-2016 (the years of
model construction input) and 2017 (the years of model prediction validation) were
concentrated in the area with high HSI values. It indicates that the integrated HSI model
based on the optimal weighting scheme can better assess and predict the habitat
suitability of Sthenoteuthis oualaniensis,and the optimal weights are different in different
seasons, and the proportion of weights occupied by each environmental factor is also
different, i.e., there are significant seasonal differences in the degree of influence of
environmental factors on the habitat of the Sthenoteuthis oualaniensis.
The above shows and describes the basic principles, main features and advantages of
the present invention. It should be understood by those skilled in the art that the present
invention is not limited by the above embodiments. The above embodiments and descriptions only illustrate the principles of the present invention. Without departing from the spirit and scope of the present invention, there will be various changes and improvements in the present invention, all of which fall within the scope of the claimed invention. The claim scope of that present invention is defined by the append claims and their equivalents.
Claims (7)
1. A technical method for habitat suitability assessment and prediction of
Sthenoteuthis oualaniensisin the South China Sea based on key environmental factors in
different seasons is characterized in the following content:
S1. Processing environmental factor data of Chl-a, MLD, SSH, SSS, SST, Temp_50
m, Temp_100 m, Temp_150 m, Temp_200 m, Temp_300 m, and applying the maximum
entropy model to select the three key environmental factors that have a high degree of
influence on the distribution of Sthenoteuthis oualaniensishabitat in the South China Sea
in each season;
S2. Processing the key environmental factor data of the Sthenoteuthis oualaniensis
fisheries in the South China Sea in spring, summer and autumn, and matching the data
with each other to obtain fisheries and environmental data at the corresponding longitude
and latitude. And fisheries data include information such as fishing operation time (year,
month, day), fishing location (latitude and longitude) and the number of fishing vessels;
S3.For Sthenoteuthis oualaniensis fisheries in the South China Sea, fishing effort is
defined as the number of fishing vessels, and the number and spatial distribution of its
location can reflect the abundance and distribution range of the Sthenoteuthis
oualaniensis resource, so the present invention correlates fishing effort, which can
indicate the location of the gravity center of fishing vessel, with different environmental
factors in each season, and uses the frequency distribution method to calculate the
suitability index based on key environmental factors within different seasons;
S4. Processing each environmental factor in spring, summer and autumn in different
class interval, drawing a frequency distribution graph with environmental factors as abscissa and SI value as ordinate according to the distribution of fishing effort in different environmental variable intervals, and performing nonlinear fitting to obtain an equation conforming to the distribution law of SI value;
S5. Taking the environmental factors in spring, summer and autumn as independent
variables in the fitted equation and substituting them into the equation, and calculating
the dependent variable SI value corresponding to latitude and longitude and fisheries
data;
S6. Adjusting the proportion of SI values of various environmental factors in spring,
summer and autumn, setting 10 weight schemes, and calculating the HSI value of
Sthenoteuthis oualaniensison the fishing ground in the South China sea corresponding to
each scheme;
S7. Calculating the corresponding fishing effort in different HSI intervals in each
season, comparing the results and selecting the optimal weight scheme in different
seasons, and selecting the HSI model with the best fitting result as the optimal HSI
model;
S8. Prediction analysis based on optimal HSI model combined with fisheries data
and environmental data, comparison of predicted and actual distribution results and
evaluation of model prediction accuracy for habitat suitability assessment and prediction
of Sthenoteuthis oualaniensisin the South China Sea.
2. The technical method for assessing and predicting the habitat suitability of
Sthenoteuthis oualaniensis in the the South China Sea based on key environmental
factors within different seasons according to claim 1 is characterized in that in step S1,
the maximum entropy model is used to select three key environmental factors with high degree of influence in each season from a plurality of environmental variables affecting the habitat distribution of Sthenoteuthis oualaniensis as the environmental variables for constructing the comprehensive habitat model.
3. The technical method for assessing and predicting the habitat suitability of
Sthenoteuthis oualaniensis in the South China Sea based on key environmental factors
within different seasons according to claim 1 is characterized in that the S2 takes the
fishing effort that can indicate the location of the gravity center of fishing vessels as an
indicator for calculating SI.
4. The technical method for assessing and predicting the suitability of Sthenoteuthis
oualaniensis habitat in the South China Sea based on key environmental factors in
different seasons according to claim 1 is characterized in that S2 takes the fishing effort
that can indicate the location of the gravity center of fishing vessels as the index for
calculating the suitability index (SI). Specifically, when the SI value is 1, i.e., the highest
fishing effort in each season corresponding to each grid position of Sthenoteuthis
oualaniensisfishery in the South China Sea. When the SI value is 0, it means the sea area
with the least distribution of Sthenoteuthis oualaniensisresources in the South China Sea,
and the SI model is established according to the index; based on the equation fitted by the
model and the data of environmental factors in different seasons, the SI values
corresponding to different seasons, different spatial locations and different environmental
factors are calculated.
5. The technical method for evaluating and forecasting the habitat suitability of
Sthenoteuthis oualaniensisin the South China Sea based on key environmental factors in
different seasons according to claim 1, which is characterized in that step S5 is to adjust the proportion of SI values of various environmental factors in different seasons, set 10 weight schemes according to the weight sum of 1, compare and analyze the seasonal differences in the impacts of environmental factors on the distribution of squid resources, calculate the HSI value of squid fishing ground in the South China Sea corresponding to each scheme, and select and obtain the optimal weight scheme in each season.
6. The technical method for habitat suitability assessment and prediction of
Sthenoteuthis oualaniensisin the South China Sea based on key environmental factors in
different seasons according to claim 1, characterized in that in step S6, due to the
limitation of fisheries data information, fishing effort is taken as an index for selecting the
optimal weight model in each season.
7. The technical method for assessing and predicting the habitat suitability of
Sthenoteuthis oualaniensis in the South China Sea based on key environmental factors
within different seasons according to claim 1, characterized in that said step S7 overlays
the actual number of operating fishing vessels with the predicted HSI values to verify the
model accuracy, while adopting a combination of model construction and prediction to
strengthen the reliability of the model.
FIGURES
1/3 2021101527
Figure 1
Figure 2
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