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

Ecological niche model-based assessment method for habitat of large-eye tuna in pacific ocean area Download PDF

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CN111583051A
CN111583051A CN202010400942.4A CN202010400942A CN111583051A CN 111583051 A CN111583051 A CN 111583051A CN 202010400942 A CN202010400942 A CN 202010400942A CN 111583051 A CN111583051 A CN 111583051A
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周成
王禹程
许柳雄
万荣
王学昉
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Abstract

The invention discloses a pacific sea area large-eye tuna habitat evaluation method based on an ecological niche modellevelSea level height value scoring SSHlevelAnd the score CHL of the marine chlorophyll concentrationlevelAmbient temperature score Trange0And oxygen content rating DO0/1(ii) a And multiplying the scores to obtain the quality score of the comprehensive habitat. The selection of various parameter characteristics employed in the present invention is more closely matched to the habits of large-eyed tuna, which are not generally altered, so that the model of the present invention is temporally comparable to a statistical-based modelThe space and spatial directions have better universality and can be suitable for the evaluation of the bullseye tunas in other areas and other times.

Description

Ecological niche model-based assessment method for habitat of large-eye tuna in pacific ocean area
Technical Field
The invention relates to the field of aquatics, in particular to a pacific sea area macroreticular tuna habitat evaluation method based on an ecological niche model.
Background
Giant-eyed tunas (thunnusobeus) are the target species for the pacific tropical longline fishing industry, with a capture of about 10 million tons per year, and are shipped primarily to the high-quality fresh and frozen tuna markets in asia, north america, and other regions. The pacific area seine fishery also catches large-eyed tunas. In the western pacific, the catch is about 5% and the east pacific catch is 10%. Since the mid 1990 s, the annual fishing production has typically exceeded 12 million tons, and the number of floating artificial fish gathering devices used in the pacific seine fishery has also increased substantially.
Although the latest evaluation results of large-eye fish stocks are optimistic, the evaluation results are greatly different due to differences in growth curves and structures of regions used, and thus may be considered as overfishing. In the east pacific, the latest egg production was estimated at 20% of the undeveloped level. These evaluations, like the resource evaluations of tropical tunas in other regions, rely primarily on the data of the seine and longline fisheries. Therefore, understanding the vulnerability of macrogol fish to fishing gear, environmental drivers including population changes, is essential to explain catch rates, size composition and other characteristics of the data, which requires a method of assessing the habitat of macrogol fish.
Disclosure of Invention
The invention aims to provide a method for evaluating the habitat of the tuna in the pacific sea area based on the ecological niche model according to the defects of the prior art, and the method obtains the method for evaluating the habitat of the tuna according to the ecological niche model and the analysis result of the habit of the tuna.
The purpose of the invention is realized by the following technical scheme:
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 valuelevelSea level height value scoring SSHlevelAnd the score CHL of the marine chlorophyll concentrationlevelAmbient temperature score Trange0And oxygen content rating DO0/1；
(S3) calculating a composite habitat quality score based on the scores; the calculation formula is as follows:
Habitat＝SSTlevel·SSHlevel·CHLlevel·Trange0·DO0/1
wherein: habitat is the composite Habitat quality score.
The invention has the further improvement that each grading mapping function of the surface environmental factor data is obtained by training fishery data and marine surface biological/non-biological environmental data by adopting a hierarchical clustering method, and the method specifically comprises the following steps:
(S21) acquiring a sea surface temperature range flat value SSTA, a sea surface height value SSH and a sea surface chlorophyll concentration CHL of each sea area from the sea surface biological/non-biological environment data, and acquiring a 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.
The invention is further improved in that in the process of solving the grading mapping function of the environmental temperature, the inhabitation proportion of the large-eye tuna at different temperatures is obtained from the marking release data and is fitted into a probability distribution function with the input of the environmental temperature and the output value of 0-1.
In a further development of the invention, the threshold value for the oxygen content is 1 ml/L.
The invention has the further improvement that the grading mapping function of the oxygen content is a binary function, and the threshold value of the grading mapping function is determined according to the dissolved oxygen physiological requirement of the bullseye 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.
The invention has the advantages that: the ecological niche model utilizes known distribution data and relevant environment variables of the species, constructs the model according to certain algorithm operation, judges ecological requirements of the species, and predicts actual distribution and potential distribution of the species. The establishment of the ecological niche model requires the reference of a large amount of species ecological knowledge and experience, and the algorithm is determined according to different species, has stronger pertinence and initiative and does not highly depend on statistical results. The selection of various parameter characteristics adopted in the invention is more fit with the habit of the large-eye tuna, and the habit of the animal is not changed generally, so that compared with a statistic-based model, the model disclosed by the invention has better universality in both time and space directions, and can be suitable for the evaluation of the large-eye tuna in other areas and other times.
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FIG. 1 is a flow chart of a pacific sea area macroreticular tuna habitat evaluation method based on an ecological niche model;
FIG. 2 is a graph of the percentage of perch time at different ambient temperatures for large-eyed tuna during the day and at night;
fig. 3 is a diagram illustrating a score mapping function of surface environmental factor data.
Detailed Description
The features of the present invention and other related features are described in further detail below by way of example in conjunction with the following drawings to facilitate understanding by those skilled in the art:
example (b): as shown in fig. 1, an embodiment of the present invention includes a method for evaluating the habitat of large-eyed tuna in pacific ocean area based on an ecological niche model, which includes 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 average 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 valuelevelSea level height value scoring SSHlevelAnd the score CHL of the marine chlorophyll concentrationlevelAmbient temperature score Trange0And oxygen content rating DO0/1. Wherein, Trange0Is a continuous variable between 0 and 1; DO0/1A binary variable of 0 or 1; scoring of sea surface temperature range flat value SSTAlevelSea level height value scoring SSHlevelAnd the score CHL of the marine chlorophyll concentrationlevelAll are discrete variables between 0 and 1.
(S3) calculating a composite habitat quality score based on the scores; the calculation formula is as follows:
Habitat＝SSTlevel·SSHlevel·CHLlevel·Trange0·DO0/1
wherein: the Habitat is the quality score of the comprehensive Habitat, the value range of the Habitat is 0-1, and the larger the value of the Habitat is, the more suitable the large-eye tuna inhabits the sea area to be evaluated. The score is referred to the actual exploration result of the sea area to be evaluated, so that the actual fishing degree of the large-eye tunas in the sea area to be evaluated can be evaluated.
The present embodiment uses the niche model to model the habitat of the bullseye tuna. The modeling process typically includes four steps: (1) determining the main behavior and ecological characteristics of the bullseye tuna; (2) collecting and processing geographic distribution data, yield and environmental covariates of the bullseye tunas; (3) obtaining an environment variable range and geographical distribution classification related to the ecology of the bulleye tunas through cluster analysis to describe different productivity habitat characteristics of the bulleye tunas and finally grading a single environment variable; (4) and calculating the habitat adaptability of the grid unit by using the model to perform comprehensive scoring as the habitat quality of the geographic unit.
Specifically, the present embodiment adopts a sea surface temperature range flat value SSTA, a sea surface altitude value SSH, a sea surface chlorophyll concentration CHL, an ambient temperature T, and an oxygen content DO as input parameters of the tuna macroopter habitat evaluation model. The basis is as follows:
(1) large-eyed tuna are considered opportunistic carnivores and visual predators. The large-eyed tuna tends to stay in the clear water body to increase the efficiency of visual predation and to select the appropriate target. Clear water bodies are generally less nutritious, meaning that the chlorophyll concentration in the water body is low. Secondly, chlorophyll can play a key role in the marine ecosystem, being an energy source for nutrient level cycling and therefore can be considered as an indicator of the degree of food enrichment. Many temperate tunas, such as tuna longipes and tuna atlantic bluefin, are reported to aggregate near the chlorophyll front. The present study assumes that the bullseye tuna is also attracted by chlorophyll, as chlorophyll represents one of the main characteristics of primary production, sufficient to maintain zooplankton productivity and upper trophic levels. Therefore, this example uses the marine chlorophyll concentration CHL as one of the characteristics of the macroopter tuna feeding habitat.
(2) Some studies have found the correlation of the physical habitat of the ocean basin with sea level height, for example, positive and negative sea level anomalies are associated with ocean vortices (anti-cyclone/gas vortex), respectively, describing the aggregation and dispersion of regional water masses. The sea level height value SSH is used to detect the presence of scale vortices throughout the investigation region. In the southern Pacific, the subtropical radial zone (STCZ) near New Zealand and the high shear zone at the vortex edge of the American Samoya EEZ are considered important habitat for fish in the middle and upper layers, especially for tuna in the long fin. In the northwest atlantic region, the capture of bluefin tuna is highest in anticyclonic vortices, while the capture of yellowfin tuna and bullseye tuna is highest in gas vortices; whereas tropical tunas and long-fin tunas prefer slight positive or negative values of SSH. Yellow-fin and long-fin tunas are more resistant to SSH than bonito and large-eyed tunas. Therefore, the sea surface height value SSH is also used as one of the characteristics of the macroopter tuna feeding habitat.
(3) Macroreticular tuna is believed to have extensive water temperature tolerance, even at night, and the perched water layer of macroreticular tuna is generally more than 50m deep, and thus, Sea Surface Temperature (SST) seems to have less impact on macroreticular tuna distribution and abundance. In contrast, the present study selected the sea surface temperature range average value (SSTA) as an ecologically relevant environmental factor for large-bore tunas, where SSTA reflects the presence of vortices, e.g., warmer central water mass for convergent vortices and colder central water mass for divergent vortices. For a radial vortex, an upwelling is formed in the center of the vortex, which carries the macronutrients to the upper middle layers of the ocean, increasing the micro and middle layer zooplankton productivity in these areas. Therefore, sea surface temperature range leveling (SSTA) is also one of the characteristics of macroreticular tuna feeding habitats.
(4) Dissolved oxygen is also a key feature. Regarding the physiological demand of the macroreticular tuna for dissolved oxygen, the minimum dissolved oxygen required by the macroreticular tuna is 1ml/l, and the vertical movement of the Atlantic macroreticular tuna is limited by the 1ml/l oxygen jump layer; additional physiological observations indicate that cardiac performance of large-eyed tuna decreases below 2.1ml/l of dissolved oxygen; the detection of long-line fishing catches also shows that adult large-bore tunas are rarely captured in water with the dissolved oxygen range of 1.0-1.4 ml/l. In the mid-west pacific tropical and subtropical waters, the dissolved oxygen concentration is higher in the temperature range preferred by the bullseye tuna, and therefore it seems unlikely that the dissolved oxygen concentration would limit its vertical distribution in this region. In contrast, at a certain depth, the concentration of dissolved oxygen in the east pacific is much lower than in the west pacific. For this reason, the present study defines a threshold value for dissolved oxygen concentration of 1ml/l, below which water areas are considered to be unfavorable habitat environments.
(5) The bait for large-eye tunas is typically composed of a variety of organisms, such as fish, crustaceans, squid, and jelly-like organisms, which are commonly found in the marine deep-scattering layer (DSL). DSL is mainly composed of weak swimming life forms that can dive into specific depth layers between 250 and 500 meters during the day, depending on temperature and lighting conditions. Continuous observation provides evidence for the large-eye tuna to reflect the diurnal vertical movement of DSL.
Archival tagged distribution of large tunas has greatly improved understanding of the pacific tuna's horizontal and vertical motion, habitat utilization, and population structure over the past 20 years. At the same time, other physiological property studies provide an insight into the feeding and behavior patterns of bullseye tunas. The unique physiological adaptation of macroreticular tunas makes them able to tolerate low ambient temperatures and dissolved oxygen regions, making it possible to follow DSL depths for migration during the day. The bullseye tuna balances the use of cold, low-oxygen water during the day by warming the muscle tissue at the sea surface. The typical swimming behavior of large-eyed tuna, which is located in deeper waters for most of the day and migrates to the surface for temperature regulation, has been confirmed by studies of acoustic tracking technology and labeling technology. Due to changes in environmental conditions, changes in DSL depth may affect the time that bullseye tunas live in deep water. Therefore, the ambient temperature T is one of the characteristics of the feeding habitat of the macroreticular tuna.
In this embodiment, each score mapping function of the surface environmental factor data is obtained by training fishery data and surface biological/non-biological environmental data by a hierarchical clustering method, which specifically includes 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 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.
Specifically, fishery data comes from two regional fishery management organizations: the tuna committee (WCPFC) and the intermodal tuna americana committee (IATTC). The research selects the data of the longline fishing operation in Japan and Taiwan in China as the fishery data of the research because the data quality is generally superior to that of other fleets, the fishing yield and the effort of the two fleets are the largest components of the pacific longline fishing fishery, and the time sequence and the fishing range are wider than those of the fleets in other countries. The data sets were acquired in 1997 to 2010 time series with a pacific basin (50 ° N-50 ° S, 140 ° E-70 ° W) spatial resolution of 5 ° x 5 ° and temporal resolution of months. The data set includes the working area (latitude and longitude units), the working time (year/month), the fishing effort (total number of hooks thrown), and the yield (number of large-eye tunas caught).
For marine surface biological/non-biological environmental data, the marine surface temperature range flatness values are derived from a Kaplan Extended SST (version V2) which is generated from the MOHSST5 version of the GOSTA dataset, uk, and processed by taking SST data as input values, processing methods including EOF projection, optimal interpolation, kalman filter prediction, KF analysis and optimal smoother. These techniques use spatial patterns and temporal interpolation to fill in the missing data. The data set was stored on a 5 ° × 5 ° grid, containing the monthly balance values of 1856 years to date.
The sea surface altitude values are derived from the French space agency (French space agency), the data set contains absolute dynamic mapping (related to ground level), and are linked and averaged in every 1 ° grid per month. Dynamic mapping is derived from sea surface height reference geohorics measured by satellites such as Envisat, Topex/Poseidon, Jason-1, and OSTM/Jason-2.
The marine chlorophyll data is derived from the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) Orbview-2 satellite, Goddard Space light Center (GSFC) of the american Space agency, which distributes scientific quality chlorophyll-a concentration data with a spatial resolution of 0.1 ° × 0.1 ° and a monthly temporal resolution through the marine water color net.
Hierarchical clustering is an existing algorithm, in the clustering process. The matrix including surface environmental factors (SSH, SSTA, CHL) and CPUE is analyzed, and thus it is possible to classify ecological regions of similar environmental conditions and catch rates. Through repeated tests, 15 groups are finally reserved as different ecological categories, at the moment, the number of geographic units of each category is not greatly different, and the ecological characteristics of each category can be more conveniently explained. Further, a category containing fewer elements (considered as an abnormal category) or a category that may be wrongly classified is culled.
For SSHA and CHL, selecting several groups with CPUE obviously higher than other categories and grading, selecting the 15 th quantile and the 85 th quantile in each category as the classification boundary of each environmental factor/grade, and keeping the range of each grade not overlapped. And selecting the 5 th quantile and the 95 th quantile of all the data of all the categories as the threshold value of the environmental factor so as to determine the extreme environmental boundary of the favorable habitat.
For SSTA, we only select several classes of high CPUEs as the first class, select the 15 th quantile and the 85 th quantile as the classification limits of this class, and classify into the second class beyond this threshold range, so SSTA has only two major classes, i.e. medium-quality habitats and high-quality habitats.
Fig. 3 shows a diagram of a score mapping function of the surface environmental factor data (SSTA, SSH, CHL), wherein SSTA has two levels in total, and the score is 1 and 0.3, respectively, and SSH and CHL have four levels, and the score is 0.3, 0.8, 0.9, and 1, respectively. The threshold settings for each level of each variable are shown in table 1.
TABLE-1 different level of threshold settings for variables
Figure BDA0002489444860000061
As shown in table-1 and fig. 3, the table may be used as a score mapping function for the corresponding parameters. In the process of evaluating the habitat of the bulleye tuna, the SSTA, the SSH and the CHL are respectively substituted into the table 1 to obtain corresponding grades, and the corresponding grades are mapped according to the grades. Levels 1 to 4 map 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 ambient temperature, the inhabitation proportion of the bullseye tuna at different temperatures is obtained from the tag release data, and the obtained value is fitted to a probability distribution function with the input of the ambient temperature and the output value of 0 to 1. Table-2 shows the marked play data used in this example.
As shown in table 2, available habitat usage data were extracted from these documents, and the vertical downstream water layer and duration of swimming of macroreticular tuna were influenced by marine environment, in particular dissolved oxygen and ambient temperature, in addition to bait organisms and own physiological conditions. Thus, the extracted data includes the temperature of the water layer where the macroopter tuna is located during the daytime and nighttime and the percentage of perching time.
As shown in fig. 2, from the above data, it can be seen that the ambient temperature where the macroreticular tuna inhabit for the longest time is about 11 ℃, the ambient temperature where the macroreticular tuna inhabits is not less than 5 ℃, the percentage of the inhabitation time at 19 ℃ is relatively minimal during the day, and that a peak occurs at 25 ℃ in warm water above 20 ℃. At night, the longest perching water layer of the macroreticular tuna is about 25 ℃. The finally obtained plot of the inhabitation time distribution shows peaks at 11 deg.C (day) and 25 deg.C (night), respectively.
Table-2 source of tagged release data
Figure BDA0002489444860000071
In this embodiment, after the two curves need to be subjected to comprehensive statistics, the two curves are fitted to a probability distribution function with an input of the ambient temperature and an output value of 0 to 1, and this process can be implemented by using a common statistical tool. Substituting the ambient temperature T into the probability distribution function to obtain a probability value corresponding to the ambient temperature T, where the probability value can be used as a score T of the ambient temperature in this embodimentrange0。
In this embodiment, the score mapping function of the oxygen content is a binary function, and the threshold value is determined according to the physiological requirement of the bulleye tuna for dissolved oxygen; 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. In one embodiment, the threshold oxygen level is 1 ml/L.
The above embodiments of the present invention do not limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in 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 valuelevelSea level height value scoring SSHlevelAnd the score CHL of the marine chlorophyll concentrationlevelAmbient temperature score Trange0And oxygen content rating DO0/1；
(S3) calculating a composite habitat quality score based on the scores; the calculation formula is as follows:
Habitat＝SSTlevel·SSHlevel·CHLlevel·Trange0·DO0/1
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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