CN111583051A - Ecological niche model-based assessment method for habitat of large-eye tuna in pacific ocean area - Google Patents

Abstract

The invention discloses a method for evaluating the habitat of bigeye tuna in the Pacific Ocean based on an ecological niche model. In the method, surface environmental factor data, environmental temperature data and oxygen content data of the sea area to be evaluated are respectively brought into corresponding scores The mapping function is used to obtain the score SSTA _level of the sea surface temperature anomaly, the score SSH _level of the sea surface height value, the score CHL _level of the sea surface chlorophyll concentration, the score T _range0 of the ambient temperature and the score DO _0/1 of the oxygen content; The scores were multiplied to obtain a comprehensive habitat quality score. The selection of various parameter features used in the present invention is more in line with the habits of bigeye tuna, and the habits of animals usually do not change. Therefore, compared with the model based on statistics, the model of the present invention has more temporal and spatial directions. Good generality, applicable to the evaluation of bigeye tuna in other regions and eras.

Description

Habitat assessment method for bigeye tuna in the Pacific Ocean based on a niche model

technical field

The invention relates to the field of aquaculture, in particular to a habitat assessment method for bigeye tuna in the Pacific Ocean based on an ecological niche model.

Background technique

Bigeye tuna (Thunnusobesus) is the target species for tropical longline fisheries in the Pacific Ocean, with an annual catch of about 100,000 tonnes, mainly destined for high-quality fresh and frozen tuna markets in Asia, North America and elsewhere. Pacific purse seine fisheries also fish for bigeye tuna. In the Western Pacific, the catch is about 5 percent, while in the Eastern Pacific it is 10 percent. Since the mid-1990s, annual catches have typically exceeded 120,000 tonnes, while the number of drifting artificial fish-gathering devices used in the Pacific purse seine fishery has increased significantly.

Although the most recent assessments of bigeye stock are optimistic, the assessments vary widely depending on the growth curve and the area structure used, so it is still possible to consider overfishing. In the Eastern Pacific, the latest estimates of spawning are 20% of unexploited levels. As with other regional assessments of tropical tuna stocks, these assessments rely primarily on data from purse seine and longline fisheries. Therefore, understanding the vulnerability of bigeye tuna to fishing gear, including environmental drivers of population change, is extremely necessary to interpret catch rates, size composition, and other characteristics of the data, which requires an approach to assessing bigeye tuna habitats .

SUMMARY OF THE INVENTION

The object of the present invention is to provide a method for evaluating the habitat of bigeye tuna in the Pacific Ocean based on the niche model according to the deficiencies of the above-mentioned prior art. A method for assessing tuna habitat was obtained.

The realization of the object of the present invention is accomplished by the following technical solutions:

A method for evaluating the habitat of bigeye tuna in the Pacific Ocean based on a niche model is characterized by comprising the following steps:

(S1) obtaining surface layer environmental factor data, ambient temperature data and oxygen content data of the sea area to be assessed; the surface layer environmental factor data includes sea surface temperature anomalies, sea surface height values, and sea surface chlorophyll concentration;

(S2) Bring the surface environmental factor data, ambient temperature data and oxygen content data of the sea area to be assessed into the corresponding score mapping function respectively, and obtain the score SSTA _level of the sea surface temperature anomaly value, the score SSH _level of the sea surface height value, The score of sea surface chlorophyll concentration CHL _level , the score of ambient temperature T _range0 and the score of oxygen content DO _0/1 ;

(S3) Calculate the comprehensive habitat quality score according to each score; the calculation formula is:

Habitat=SST _level ·SSH _level ·CHL _level ·T _range0 ·DO _0/1

Among them: Habitat is the comprehensive habitat quality score.

A further improvement of the present invention is that each score mapping function of the surface environmental factor data is obtained by training fishery data and sea surface biological/abiotic environmental data using a hierarchical clustering method, which specifically includes the following steps:

(S21) Obtain the sea surface temperature anomaly value SSTA, the sea surface height value SSH and the sea surface chlorophyll concentration CHL of each sea area from the sea surface biological/abiotic environment data, and obtain the corresponding unit fishing from the fishery data Effort Catch CPUE;

(S22) Perform hierarchical clustering on the sea surface temperature anomaly value SSTA, the sea surface height value SSH and the sea surface chlorophyll concentration CHL with the catch per unit fishing effort CPUE as the target parameter; in the clustering process, the sea surface temperature anomaly The value SSTA is divided into two grades, the sea surface height value SSH and the sea surface chlorophyll concentration CHL are divided into four grades, and the upper and lower thresholds of each grade are obtained after the clustering process.

A further improvement of the present invention is that in the process of obtaining the score mapping function of the ambient temperature, the habitat ratio of the bigeye tuna at different temperatures is obtained from the marked release data, and the input is the ambient temperature and the output value is fitted. is a probability distribution function between 0 and 1.

A further improvement of the present invention is that the threshold value of the oxygen content is 1 ml/L.

A further improvement of the present invention is that the score mapping function of the oxygen content is a binary function, and the threshold value is determined according to the dissolved oxygen physiological demand of bigeye tuna; when the oxygen content is greater than the threshold value, the score of the oxygen content is determined. The output of the mapping function is 1, otherwise the output is 0.

The advantages of the invention are: the niche model uses the known distribution data of species and relevant environmental variables to construct a model according to certain algorithm operation, judges the ecological needs of species, and predicts the actual distribution and potential distribution of species. The establishment of a niche model requires a large amount of species ecological knowledge and experience for reference, and its algorithm is determined according to the different species, with stronger pertinence and initiative, and is not highly dependent on statistical results. The selection of various parameter features used in the present invention is more in line with the habits of bigeye tuna, and the habits of animals usually do not change. Therefore, compared with the model based on statistics, the model of the present invention has more temporal and spatial directions. Good generality, applicable to the evaluation of bigeye tuna in other regions and eras.

Description of drawings

Figure 1 is a flow chart of the habitat assessment method for the Pacific Ocean bigeye tuna based on the niche model;

Figure 2 shows the percentage of habitat time of bigeye tuna at different ambient temperatures during the day and night;

FIG. 3 is a schematic diagram of a score mapping function of surface environmental factor data.

Detailed ways

Below in conjunction with the accompanying drawings, the features of the present invention and other related features will be described in further detail by embodiments, so as to facilitate the understanding of those skilled in the same industry:

Embodiment: As shown in FIG. 1 , an embodiment of the present invention includes a method for evaluating the habitat of bigeye tuna in the Pacific Ocean based on a niche model, which includes the following steps:

(S1) Acquire surface environmental factor data, ambient temperature data, and oxygen content data of the sea area to be assessed; the surface environmental factor data includes sea surface temperature anomalies, sea surface height values, and sea surface chlorophyll concentration.

(S2) Bring the surface environmental factor data, ambient temperature data and oxygen content data of the sea area to be assessed into the corresponding score mapping function respectively, and obtain the score SSTA _level of the sea surface temperature anomaly value, the score SSH _level of the sea surface height value, The score of sea surface chlorophyll concentration CHL _level , the score of ambient temperature T _range0 and the score of oxygen content DO _0/1 . Among them, T _range0 is a continuous variable between 0 and 1; DO _0/1 is a binary variable of 0 or 1; SSTA _level for sea surface temperature anomalies, SSH _level for sea surface height, and SSH level The chlorophyll concentration score CHL _level is a discrete variable between 0 and 1.

(S3) Calculate the comprehensive habitat quality score according to each score; the calculation formula is:

Habitat=SST _level ·SSH _level ·CHL _level ·T _range0 ·DO _0/1

Among them: Habitat is the comprehensive habitat quality score, and its value ranges from 0 to 1. The larger the value of Habitat, the more suitable the sea area to be assessed is for the habitat of bigeye tuna. Comparing this score with the actual exploration results in the sea area to be assessed can help to evaluate the actual fishing degree of bigeye tuna in the sea area to be assessed.

This example uses the niche model to model the habitat of bigeye tuna. The modeling process usually consists of four steps: (1) identifying the main behavioral and ecological characteristics of bigeye tuna; (2) collecting and processing the geographic distribution data, yield and environmental covariates of bigeye tuna; (3) obtaining by cluster analysis The range of environmental variables related to the ecology of bigeye tuna and the classification of geographical distribution to describe the characteristics of different productive habitats of bigeye tuna, and finally to rank individual environmental variables; (4) Use the model to calculate the habitat adaptability of grid cells A composite score was made as the habitat quality for that geographic unit.

Specifically, this embodiment adopts the sea surface temperature anomaly value SSTA, the sea surface height value SSH, the sea surface chlorophyll concentration CHL, the ambient temperature T and the oxygen content DO as the input parameters of the bigeye tuna habitat assessment model. It is based on:

(1) Bigeye tuna are considered opportunistic carnivores and visual predators. Bigeye tuna prefer to stay in clear water to increase the efficiency of visual predation and select appropriate targets. Clear water usually has fewer nutrients, meaning the water has a low concentration of chlorophyll. Second, chlorophyll can play a key role in marine ecosystems as a source of energy for cycling nutrient levels and can therefore be considered an indicator of food enrichment. Many temperate tuna, such as albacore and Atlantic bluefin, have been reported to aggregate near chlorophyll fronts. This study hypothesized that bigeye tuna were also attracted to chlorophyll, as chlorophyll represents a major feature of primary production and is sufficient to maintain zooplankton productivity and upper trophic levels. Therefore, in this example, the sea surface chlorophyll concentration CHL is used as one of the characteristics of the feeding habitat of bigeye tuna.

(2) Some studies have found correlations between physical habitats in oceanic watersheds and sea surface heights, for example, positive and negative sea surface height anomalies are respectively associated with oceanic eddies (anticyclones/cyclonic eddies), describing the aggregation and divergence of regional water masses Happening. Sea surface height values SSH were used to detect the presence of mesoscale eddies throughout the study area. In the South Pacific, the Subtropical Convergence Zone (STCZ) near New Zealand and the high shear zone at the edge of the EEZ vortex in American Samoa are considered important pelagic habitats, especially with pronounced effects on albacore tuna. In the Northwest Atlantic region, bluefin tuna catches are highest in anticyclonic vortices, while yellowfin and bigeye tuna catches are highest in cyclonic eddies; tropical tuna and albacore tuna prefer slightly positive SSH or negative value. Yellowfin and albacore tuna are more tolerant to SSH than bonito and bigeye tuna. Therefore, the sea surface height value SSH was also used as one of the characteristics of the feeding habitat of bigeye tuna.

(3) Bigeye tuna is considered to have a wide range of water temperature tolerance. Even at night, the habitat of bigeye tuna generally exceeds 50m depth. Therefore, sea surface temperature (SST) seems to affect the distribution and abundance of bigeye tuna. Less affected. On the contrary, this study selected the sea surface temperature anomaly (SSTA) as an environmental factor related to the ecology of bigeye tuna. SSTA can reflect the existence of eddies. For example, the water mass in the center of the convergent vortex is warmer, and the center of the divergent vortex is warmer. The water mass is cold. For divergent vortices, upwellings form at their centers, carrying large amounts of nutrients to the upper and mid-ocean layers, increasing the productivity of micro- and mesoplankton in these regions. Therefore, the sea surface temperature anomaly (SSTA) was also used as one of the characteristics of the feeding habitat of bigeye tuna.

(4) Dissolved oxygen is also a key feature. Regarding the physiological needs of bigeye tuna for dissolved oxygen, the minimum dissolved oxygen required by bigeye tuna is 1ml/l, and the vertical movement of eastern Pacific bigeye tuna is limited by 1 ml/l oxygencline; there are other physiological observations. showed that the cardiac performance of bigeye tuna decreased when the dissolved oxygen was lower than 2.1ml/l; the detection of longline catches also showed that adult bigeye tuna rarely had a dissolved oxygen in the range of 1.0 to 1.4ml/l. captured in water. In tropical and subtropical waters of the central and western Pacific, dissolved oxygen concentrations are higher in the temperature range preferred by bigeye tuna, so it seems unlikely that dissolved oxygen concentrations in this region will limit their vertical distribution. In contrast, at certain depths, the dissolved oxygen concentration in the eastern Pacific is much lower than in the western Pacific. To this end, this study defines a threshold for dissolved oxygen concentration of 1ml/l, and waters below this limit are considered unfavorable habitats.

(5) The bait for bigeye tuna usually consists of a variety of organisms, such as fish, crustaceans, squid, and gelatinous organisms, which are usually found in the deep scattering layer (DSL) of the ocean. DSLs are mainly composed of weak swimmers that dive to specific depths between 250m and 500m during the day, depending on temperature and light conditions. Continuous observations provide evidence that the bigeye tuna reflects the vertical movement of DSL day and night.

Archival tagged releases of large tuna over the past 20 years have greatly improved our understanding of the horizontal and vertical movement, habitat use, and population structure of Pacific tuna. At the same time, studies of other physiological properties have provided an in-depth understanding of the feeding and behavioral patterns of bigeye tuna. The unique physiological adaptations of bigeye tuna allow them to tolerate regions of low ambient temperature and dissolved oxygen, potentially following DSL depth migration during the day. Bigeye tuna balances daytime use of icy, low-oxygen water by warming muscle tissue at the sea surface. The typical swimming behavior of bigeye tuna, which spends most of the day in deeper waters and migrates to the sea surface for thermoregulation, has been confirmed by studies of sonic tracking and tagging techniques. Variation in DSL depth may affect the time the bigeye tuna roosts in deep water due to changes in environmental conditions. Therefore, the ambient temperature T was taken as one of the characteristics of the feeding habitat of bigeye tuna.

In the present embodiment, each scoring mapping function of the surface environmental factor data is obtained by training fishery data and sea surface biological/abiotic environmental data using a hierarchical clustering method, which specifically includes the following steps:

(S21) Obtain the sea surface temperature anomaly value SSTA, sea surface height value SSH and sea surface chlorophyll concentration CHL of each sea area from the sea surface biotic/abiotic environment data, and obtain the corresponding unit fishing effort from the fishery data. amount CPUE;

(S22) Perform hierarchical clustering on the sea surface temperature anomaly value SSTA, the sea surface height value SSH and the sea surface chlorophyll concentration CHL with the catch per unit fishing effort CPUE as the target parameter; in the clustering process, the sea surface temperature anomaly The value SSTA is divided into two grades, the sea surface height value SSH and the sea surface chlorophyll concentration CHL are divided into four grades, and the upper and lower thresholds of each grade are obtained after the clustering process.

Specifically, fishery data were obtained from two regional fisheries management organizations: the Western and Central Pacific Tuna Commission (WCPFC) and the Inter-American Tropical Tuna Commission (IATTC). This study selects Japan and Taiwan longline fishing data as the fishery data for this study because the quality of the data is generally better than other fleets, and the catch and effort of these two fleets are the same as those of Pacific longline fishing. The largest component of the fishery, with a wider time series and fishing range than other countries' fleets. The time series of the obtained dataset is from 1997 to 2010, the spatial range is the Pacific Basin (50°N～50°S, 140°E～70°W), the spatial resolution is 5°×5°, and the temporal resolution is monthly . The dataset includes operation area (latitude and longitude units), operation time (year/month), fishing effort (total hooks), and catch (bigeye tuna catch).

For sea surface biotic/abiotic environmental data, SST anomalies were derived from Kaplan Extended SST (version V2), which was generated from the MOHSST5 version of the UK GOSTA dataset and processed by taking SST data as input, Processing methods include EOF projection, optimal interpolation, Kalman filter prediction, KF analysis and optimal smoother. These techniques use spatial patterns as well as temporal interpolation to fill in missing data. The dataset is stored on a 5° × 5° grid and contains monthly anomalies from 1856 to the present.

The sea surface height values are sourced from the French space agency, and this dataset contains absolute dynamic mapping (relative to the geoid) and is merged and averaged in every 1° grid per month. The dynamic mapping is derived from the sea level reference geoid measured by satellites such as Envisat, Topex/Poseidon, Jason-1 and OSTM/Jason-2.

The sea surface chlorophyll data comes from the Orbview-2 satellite of the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS), and the NASA Goddard Space Flight Center (GSFC) distributes scientific-quality chlorophyll a concentration data through the ocean water color network. The data have a spatial resolution of 0.1° × 0.1° and a monthly temporal resolution.

Hierarchical clustering is an existing algorithm in the clustering process. A matrix including surface environmental factors (SSH, SSTA, CHL) and CPUE was analysed, thus enabling the classification of ecoregions with similar environmental conditions and catch rates. After repeated tests, 15 groups were finally retained as different ecological categories. At this time, the number of geographical units in each category would not be much different, and the ecological characteristics of each category could be more easily explained. Also, classes with fewer elements (considered as abnormal classes) or classes that may be misclassified are eliminated.

For SSHA and CHL, several groups with significantly higher CPUE than other categories were selected and graded, and we selected the 15th quantile and the 85th quantile in each category as the classification boundaries for each environmental factor/grade, The ranges of each level remain non-overlapping. The 5th and 95th quantiles of all data in all categories were selected as the thresholds for this environmental factor, so as to determine the extreme environmental boundaries of favorable habitats.

For SSTA, we only select several categories with high CPUE as the first level, and select the 15th quantile and the 85th quantile as the classification boundaries of this level, and the range beyond this threshold is classified as the second category, so , SSTA has only two categories, namely medium-quality habitats and high-quality habitats.

Figure 3 shows a schematic diagram of the score mapping function of the surface environmental factor data (SSTA, SSH, CHL). Among them, SSTA has two levels, which are scored as 1 and 0.3, respectively, and SSH and CHL have four levels, respectively, scored as 0.3, 0.8, 0.9, 1. The threshold settings for each level of each variable are shown in Table 1.

Table-1 Threshold settings for different levels of each variable

As shown in Table-1 and Figure 3, the table can be used as a scoring mapping function for the corresponding parameters. In the process of evaluating the habitat of bigeye tuna, SSTA, SSH, and CHL were respectively substituted into Table 1 to obtain the corresponding grades, which were mapped into corresponding scores according to the grades. Grades 1 to 4 are mapped to scores of 0.3, 0.8, 0.9, 1.0, respectively.

In this embodiment, in the process of obtaining the score mapping function of the environmental temperature, the habitat ratio of bigeye tuna at different temperatures is obtained from the marked release data, and the input is the ambient temperature and the output value is 0. A probability distribution function between 1 and 1. Table-2 shows the marked discharge data used in this example.

As shown in Table 2, the available habitat use data were extracted from these literatures, in addition to the prey organisms and their own physiological conditions, the vertical downstream water layer and duration of swimming of bigeye tuna are affected by the marine environment, especially dissolved oxygen and ambient temperature. Therefore, the extracted data included the temperature of the water layer in which the bigeye tuna was located during the day and night and the percentage of roost time.

As shown in Figure 2, according to the above data, it can be concluded that the ambient temperature with the longest habitation time for bigeye tuna during the day is about 11°C, the ambient temperature for bigeye tuna habitat is not lower than 5°C, and the habitat time at 19°C The percentage is relatively minimal, while in warm water above 20°C, a peak occurs at 25°C. At night, the temperature of the longest habitat for bigeye tuna is around 25°C. The resulting habitat time distribution curves showed peaks at 11°C (day) and 25°C (night), respectively.

Table-2 Sources of Marked Release Data Used

In this embodiment, the above two curves need to be comprehensively counted, and then fitted into a probability distribution function whose input is ambient temperature and whose output is between 0 and 1. This process can be implemented by using common statistical tools. By substituting the ambient temperature T into the above probability distribution function, the probability value corresponding to the ambient temperature T can be obtained, and the probability value can be used as the score T _range0 of the ambient temperature in this embodiment.

In this embodiment, the score mapping function of the oxygen content is a binary function, and the threshold is determined according to the dissolved oxygen physiological demand of bigeye tuna; when the oxygen content is greater than the threshold, the output of the oxygen content score mapping function is 1 , otherwise the output is 0. In a specific embodiment, the threshold for oxygen content is 1 ml/L.

The above embodiments of the present invention do not constitute a limitation on the protection scope of the present invention. Any modifications, substitutions and improvements made within the spirit and principle of the present invention should be included within the protection scope of the present invention.

Claims (5)

1. A pacific sea area macroreticular tuna habitat evaluation method based on an ecological niche model is characterized by comprising the following steps:

(S1) acquiring surface environmental factor data, environmental temperature data and oxygen content data of the sea area to be evaluated; the surface environmental factor data comprises a sea surface temperature distance flat value, a sea surface height value and a sea surface chlorophyll concentration;

(S2) respectively bringing the surface environmental factor data, the environmental temperature data and the oxygen content data of the sea area to be evaluated into corresponding scoring mapping functions to obtain the scoring SSTA of the sea surface temperature distance average value_levelSea level height value scoring SSH_levelAnd the score CHL of the marine chlorophyll concentration_levelAmbient temperature score T_range0And oxygen content rating DO_0/1；

(S3) calculating a composite habitat quality score based on the scores; the calculation formula is as follows:

Habitat＝SST_level·SSH_level·CHL_level·T_range0·DO_0/1

wherein: habitat is the composite Habitat quality score.

2. The method for evaluating the habitat of tuna in pacific sea areas based on the ecological niche model according to claim 1, wherein the scoring mapping functions of the surface environmental factor data are obtained by training fishery data and surface biological/non-biological environmental data by a hierarchical clustering method, and specifically comprises the following steps:

(S21) obtaining sea surface temperature range flat value SSTA, sea surface height value SSH and sea surface chlorophyll concentration CHL of each sea area from the sea surface biological/non-biological environment data, and obtaining corresponding unit fishing effort fishing yield CPUE from the fishery data;

(S22) carrying out hierarchical clustering on the sea surface temperature range flat value SSTA, the sea surface height value SSH and the sea surface chlorophyll concentration CHL by taking the fishing yield CPUE of unit fishing effort as target parameters; in the clustering process, the sea surface temperature flat value SSTA is divided into two grades, the sea surface height value SSH and the sea surface chlorophyll concentration CHL are divided into four grades, and an upper threshold value and a lower threshold value of each grade are obtained after the clustering process is finished.

3. The method as claimed in claim 1, wherein in the step of obtaining the score mapping function of the environmental temperature, the habitat proportions of the bullseye tuna at different temperatures are obtained from the tag release data and are fitted to a probability distribution function with the input of the environmental temperature and the output value of 0 to 1.

4. The method for evaluating the habitat of large-eye tuna in the pacific ocean area based on the ecological niche model according to claim 1, wherein the score mapping function of the oxygen content is a binary function, and the threshold value of the score mapping function is determined according to the physiological demand of dissolved oxygen of the large-eye tuna; when the oxygen content is larger than the threshold value, the output of the grading mapping function of the oxygen content is 1, otherwise, the output is 0.

5. The method for evaluating the habitat of tuna in pacific sea areas based on the ecological niche model according to claim 4, wherein the threshold value of the oxygen content is 1 ml/L.

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