CN111583051B - Ecological niche model-based method for evaluating habitat of large-eye tuna in Pacific ocean area - Google Patents
Ecological niche model-based method for evaluating habitat of large-eye tuna in Pacific ocean area Download PDFInfo
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Abstract
The invention discloses a method for evaluating the habitat of large-eye tuna in the Pacific ocean area based on an ecological niche model, which comprises the steps of respectively bringing surface layer environment factor data, environment temperature data and oxygen content data of the sea area to be evaluated into corresponding scoring mapping functions to obtain scoring SSTA of a sea surface Wen Juping value level Scoring SSH of sea level altitude values level Score of sea surface chlorophyll concentration CHL level Score T of ambient temperature range0 Scoring DO of oxygen content 0/1 The method comprises the steps of carrying out a first treatment on the surface of the Multiplying the scores to obtain the comprehensive habitat quality score. The selection of various parameter characteristics adopted in the invention is more compatible with the habit of the large-eye tuna, but the habit of animals is not changed generally, so that compared with a model based on statistics, the model has better universality in time and space directions, and can be suitable for the evaluation of the large-eye tuna in other areas and other times.
Description
Technical Field
The invention relates to the field of aquatic science, in particular to a method for evaluating the habitat of large-eye tuna in Pacific sea area based on an ecological niche model.
Background
Large eye tunas (thundertubes) are the target fish species for the pacific tropical longline fishing industry, with annual harvest of about 10 ten thousand tons, mainly to the high quality fresh and frozen tuna market in asia, north america and other areas. The pacific regional seiner industry also catches large eye tuna. In the western pacific, the fishing yield is about 5%, while the eastern pacific fishing yield is 10%. Since the mid 1990 s, the annual fishing gains have generally exceeded 12 ten thousand tons, and the number of drifting artificial fish gathering devices used in the pacific seiner industry has increased substantially.
Although the most recent evaluation of large-eye fish populations, while optimistic, the evaluation varies greatly depending on the growth curve and the region structure used, and thus may still be considered over-fishing. In the east Pacific, the latest estimated egg laying amount was 20% of the undeveloped level. As with tropical tuna resource evaluations in other areas, these evaluations rely primarily on seine and longline data. Thus, understanding the vulnerability of large-eye tuna to fishing gear, including environmental drivers of population variation, is extremely necessary to account for other features of fish yield, size composition, and data, which requires a method of assessing large-eye tuna habitat.
Disclosure of Invention
According to the defects of the prior art, the invention provides an ecological niche model-based method for evaluating the habitat of large-eye tuna in the Pacific ocean, and the method is used for evaluating the habitat of the tuna according to the ecological niche model and analysis results of habits of the large-eye tuna.
The invention is realized by the following technical scheme:
the method for evaluating the habitat of the large-eye tuna in the pacific ocean area based on the ecological niche model is characterized by comprising the following steps of:
(S1) acquiring surface layer environmental factor data, environmental temperature data and oxygen content data of a sea area to be evaluated; the surface environmental factor data comprise a sea surface Wen Juping value, a sea surface height value and a sea surface chlorophyll concentration;
(S2) surface Ring of sea area to be evaluatedRespectively bringing the environmental factor data, the ambient temperature data and the oxygen content data into corresponding scoring mapping functions to obtain scoring SSTA of sea level Wen Juping value level Scoring SSH of sea level altitude values level Score of sea surface chlorophyll concentration CHL level Score T of ambient temperature range0 Scoring DO of oxygen content 0/1 ；
(S3) calculating a comprehensive habitat quality score from the scores; the calculation formula is as follows:
Habitat＝SST level ·SSH level ·CHL level ·T range0 ·DO 0/1
wherein: habitat is a comprehensive Habitat quality score.
The invention further improves that each scoring mapping function of the surface environmental factor data is trained by adopting a hierarchical clustering method by fishery data and marine creature/non-creature environmental data, and the method specifically comprises the following steps:
(S21) obtaining sea surface Wen Juping value SSTA, sea surface altitude value SSH and sea surface chlorophyll concentration CHL for each sea area from the sea surface biological/non-biological environmental data, and obtaining corresponding unit fishing effort fishing gain CPUE from the fishery data;
(S22) hierarchical clustering is carried out on sea surface Wen Juping value SSTA, sea surface height value SSH and sea surface chlorophyll concentration CHL by taking unit fishing effort fishing gain CPUE as target parameters; in the clustering process, the sea surface Wen Juping 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.
In the process of solving the scoring mapping function of the environment temperature, the invention further improves that the perching proportion of the large-eye tuna at different temperatures is obtained from the marked release data and is fitted into a probability distribution function with the input of the environment temperature and the output value of 0 to 1.
A further improvement of the invention is that the threshold value of the oxygen content is 1ml/L.
The invention is further improved in that the scoring mapping function of the oxygen content is a binary function, and the threshold value is determined according to the dissolved oxygen physiological requirement of the large-eye tuna; when the oxygen content is larger than the threshold value, the scoring mapping function of the oxygen content is output as 1, and otherwise, the scoring mapping function of the oxygen content is output as 0.
The invention has the advantages that: the ecological niche model utilizes the distribution data and related environmental variables of known species, constructs a model according to certain algorithm operation, judges the ecological requirements of the species, and predicts the actual distribution and potential distribution of the species. The establishment of the ecological niche model requires reference of a large amount of species ecology knowledge and experience, and the algorithm is determined according to different species, so that the ecological niche model has stronger pertinence and initiative and does not depend on statistical results highly. The selection of various parameter characteristics adopted in the invention is more compatible with the habit of the large-eye tuna, but the habit of animals is not changed generally, so that compared with a model based on statistics, the model has better universality in time and space directions, and can be suitable for the evaluation of the large-eye tuna in other areas and other times.
Drawings
FIG. 1 is a flow chart of a method for evaluation of the habitat of large-eye tuna in the pacific ocean based on an niche model;
FIG. 2 is a graph showing the percentage of perch time of large-eye tuna at different ambient temperatures during the day and at night;
FIG. 3 is a schematic diagram of scoring mapping functions for skin 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:
examples: as shown in fig. 1, an embodiment of the present invention includes a method for estimating habitat of large-eye tuna in pacific ocean based on an ecological niche model, comprising the steps of:
(S1) acquiring surface layer environmental factor data, environmental temperature data and oxygen content data of a sea area to be evaluated; the surface environmental factor data includes sea level Wen Juping values, sea level altitude values, and sea level chlorophyll concentrations.
(S2) respectively bringing the surface layer environment factor data, the environment temperature data and the oxygen content data of the sea area to be evaluated into corresponding scoring mapping functions to obtain scoring SSTA of the sea surface Wen Juping value level Scoring SSH of sea level altitude values level Score of sea surface chlorophyll concentration CHL level Score T of ambient temperature range0 Scoring DO of oxygen content 0/1 . Wherein T is range0 A continuous variable between 0 and 1; DO (DO) 0/1 A binary variable of 0 or 1; score SSTA for sea table Wen Juping value level Scoring SSH of sea level altitude values level Score of sea surface chlorophyll concentration CHL level Are all discrete variables between 0 and 1.
(S3) calculating a comprehensive habitat quality score from the scores; the calculation formula is as follows:
Habitat＝SST level ·SSH level ·CHL level ·T range0 ·DO 0/1
wherein: habitat is comprehensive perching quality score, the value range is between 0 and 1, and the larger the value of Habitat is, the more suitable the sea area to be evaluated is for perching of large-eye tuna. The score is compared with the actual exploration result of the sea area to be assessed, so that the actual fishing degree of the large-eye tuna in the sea area to be assessed can be assessed.
The present embodiment uses an niche model to model the habitat of large-eye tuna. The modeling process typically includes four steps: (1) Determining main behaviors and ecological characteristics of large-eye tuna; (2) Collecting and processing geographic distribution data, yield and environmental covariates of large-eye tuna; (3) Obtaining an environment variable range and geographic distribution classification related to the ecology of the large-eye tuna through cluster analysis so as to describe different productivity habitat characteristics of the large-eye tuna, and finally classifying single environment variables; (4) And calculating the habitat adaptability of the grid unit by using the model to comprehensively score as the habitat quality of the geographic unit.
Specifically, the present embodiment adopts sea surface Wen Juping value SSTA, sea surface altitude value SSH, sea surface chlorophyll concentration CHL, ambient temperature T, and oxygen content DO as input parameters of the large-eye tuna habitat assessment model. The basis is as follows:
(1) Large-eye tuna is considered an opportunistic carnivore and visual predator. Large-eye tuna is more prone to stay in clear water to increase the efficiency of visual predation and to select an appropriate target. Clear water is generally less nutritious, meaning that the chlorophyll concentration in the water is low. Second, chlorophyll can play a key role in the marine ecosystem, it is an energy source for the circulation of nutrient levels and thus can be considered an indicator of the degree of enrichment of food. Many temperate tunas, such as long fin tuna and atlantic fin tuna, are reported to gather near the chlorophyll front. The present study hypothesizes that large-eye tuna is also attracted to chlorophyll, as chlorophyll represents a major feature of primary production, sufficient to maintain zooplankton productivity and upper nutrient levels. Therefore, this example uses the sea surface chlorophyll concentration CHL as one of the characteristics of the feeding habitat of large-eye tuna.
(2) Some studies have found that the correlation of physical habitat of ocean current with sea surface altitude, e.g., positive and negative sea surface altitude anomalies are related to ocean vortices (anti-cyclone/gas vortices), respectively, describing regional water mass aggregation and dispersion. The sea level height value SSH is used to detect the presence of scale vortices throughout the region of investigation. In the south pacific, the subtropical radiation zone (STCZ) near new zealand and the high shear region of the vortex edge of samasia EEZ of the genus america are considered important mid-upper fish habitats, particularly with significant impact on long fin tuna. In the north-west atlantic region, the capture of blue-fin tuna is highest in anti-cyclone vortex, while the capture of yellow-fin tuna and large-eye tuna is highest in cyclone vortex; whereas tropical tunas and long fin tunas prefer slightly positive or negative values of SSH. Yellow and long fin tunas are more resistant to SSH than bonito and large eye tunas. Thus, sea level height value SSH is also one of the characteristics of the feeding habitat of large-eye tuna.
(3) Large-eye tuna is considered to have extensive water temperature tolerance, even at night, where the perching layer of large-eye tuna is generally over 50m deep, and thus, sea Surface Temperature (SST) appears to have less impact on the distribution and abundance of large-eye tuna. In contrast, the present study selected the sea surface Wen Juping value (SSTA) as an ecologically relevant environmental factor for large-eye tuna, SSTA being able to reflect the presence of vortices, e.g., a central water mass of polymeric vortices being warmer and a central water mass of diffuse vortices being colder. For the radial vortex, the center of the radial vortex forms an upward flow, so that a great amount of nutrient substances are carried to the middle upper layer of the ocean, and the micro-zooplankton productivity and the middle layer zooplankton productivity in the areas are increased. Therefore, the sea surface Wen Juping value (SSTA) is also one of the characteristics of the feeding habitat of large-eye tuna.
(4) Dissolved oxygen is also a key feature. Regarding the physiological requirement of large-eye tuna for dissolved oxygen, the minimum dissolved oxygen required by large-eye tuna is 1ml/l, and the vertical movement of eastern Pacific large-eye tuna is limited by 1ml/l oxygen jump layer; additional physiological observations indicate that cardiac performance of large-eye tuna decreases at dissolved oxygen below 2.1 ml/l; detection of the long-line fishing catch also shows that adult large-eye tuna is rarely captured in water with dissolved oxygen ranging from 1.0 ml/l to 1.4 ml/l. In the tropical and subtropical water areas of the middle and western pacific, the dissolved oxygen concentration is higher in the temperature range preferred by large-eye tuna, and thus it seems unlikely that the dissolved oxygen concentration limits its vertical distribution in this area. In contrast, at depth, the dissolved oxygen concentration in the eastern pacific is much lower than in the western pacific. For this purpose, the present study defines a threshold value for dissolved oxygen concentration of 1ml/l, with waters below this limit considered to be a hostile habitat.
(5) The bait for large-eye tuna is typically composed of a variety of organisms, such as fish, crustaceans, squid and gelatinous organisms, which are commonly found in marine Deep Scattering Layers (DSL). DSL consists mainly of weak swimming organisms that can submerge to specific depth layers between 250 and 500 meters during the day, depending on temperature and lighting conditions. Continuous observation provides evidence that large-eye tuna reflects DSL diurnal vertical motion.
Archive mark release of large tuna has greatly improved knowledge of the horizontal and vertical movements, habitat utilization and population structure of pacific tuna over the last 20 years. At the same time, other physiological property studies provide a deep understanding of the feeding and behavioral patterns of large-eye tunas. The unique physiological adaptation of large-eye tuna's enables them to tolerate low ambient temperatures and areas of dissolved oxygen, potentially following DSL depth migration during the day. Large eye tuna balances daytime use of ice-cold hypoxic water by warming the muscle tissue at the sea surface. The typical swimming behavior of large-eye tuna, which is in a deeper water area for most of the day time and migrates to the sea surface for temperature regulation, has been confirmed by studies of acoustic wave tracking technology and marking technology. Due to changes in environmental conditions, changes in DSL depth may affect the time that large eye tuna inhabits in deep water. Therefore, the ambient temperature T is one of the characteristics of the feeding habitat of large-eye tuna.
In this embodiment, each scoring mapping function of the surface environmental factor data is trained by adopting a hierarchical clustering method from fishery data and marine creature/non-creature environmental data, and specifically includes the following steps:
(S21) acquiring a sea surface Wen Juping value SSTA, a sea surface height value SSH and a sea surface chlorophyll concentration CHL of each sea area from sea surface biological/non-biological environment data, and acquiring a corresponding unit fishing effort fishing gain CPUE from fishery data;
(S22) hierarchical clustering is carried out on sea surface Wen Juping value SSTA, sea surface height value SSH and sea surface chlorophyll concentration CHL by taking unit fishing effort fishing gain CPUE as target parameters; in the clustering process, the sea surface Wen Juping 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, the fishery data comes from two regional fishery management organizations: the chinese-western pacific tuna committee (WCPFC) and the inter-american tropical tuna committee (IATTC). The acquired data set time series is 1997 to 2010, the space range is Pacific river basin (50 DEG N-50 DEG S,140 DEG E-70 DEG W), the space resolution is 5 DEG x 5 DEG, and the time resolution is month. The data sets include work area (latitude and longitude units), work time (years/months), catch effort (total number of hooks thrown), yield (number of large-eye tuna catches).
For marine/non-biological environmental data, the marine Wen Juping values are derived from Kaplan Extended SST (version V2) which is generated by the MOHSST5 version of the GOSTA dataset in the united kingdom and processed by taking SST data as input values, including EOF projection, optimal interpolation, kalman filter prediction, KF analysis and optimal smoother. These techniques use spatial modes and temporal interpolation to fill in lost data. The dataset was stored on a 5 x 5 grid containing the month flat values of 1856 years to date.
Sea level altitude values originate from the french space agency (French space agency), and the dataset contains absolute dynamic mapping (related to ground level) and is combined and averaged in a monthly 1 grid. Dynamic mapping is derived from sea surface altitude reference ground level measured by satellites such as Envisat, topex/Poseidon, jason-1 and OSTM/Jason-2.
Sea surface chlorophyll data were derived from Orbview-2 satellite of Sea-Viewing Wide Field-of-View Sensor (SeaWiFS), and the United states space agency Goddard Space Flight Center (GSFC) distributed chlorophyll a concentration data of scientific quality over a Sea water color grid with 0.1 DEG x 0.1 DEG spatial resolution and month time resolution.
Hierarchical clustering is an existing algorithm, and is performed in the clustering process. The matrix comprising surface environmental factors (SSH, SSTA, CHL) and CPUE is analyzed and thus can be classified by ecological area of similar environmental conditions and fishing yield. Through repeated tests, 15 groups are finally reserved as different ecological categories, so that the geographic unit numbers of each category are not greatly different, and the ecological characteristics of each category can be explained more conveniently. In addition, the class containing less elements (as if it were an abnormal class) or the class that may be misclassified is culled.
For SSHA and CHL, several groups with CPUE significantly higher than other classes are selected and ranked, we select the 15 th quantile and 85 th quantile in each class as classification limits for each environmental factor/class, the ranges of each class remain non-overlapping. The 5 th quantile and 95 th quantile of all data of all categories are selected as thresholds for the environmental factors, thereby determining extreme environmental boundaries of the favorable habitat.
For SSTA we only select a few categories of high CPUE as the first class, and 15 th quantile and 85 th quantile as the classification limits for this class, beyond which the threshold range is clustered into the second class, so SSTA has only two major categories, medium quality habitat and high quality habitat.
FIG. 3 shows a schematic diagram of the scoring mapping function of the surface environmental factor data (SSTA, SSH, CHL), wherein SSTA has two levels, respectively scored 1 and 0.3, and SSH and CHL have four levels, respectively scored 0.3, 0.8, 0.9, and 1. The threshold settings for each level of the variables are shown in table 1.
Table-1 different levels of threshold settings for variables
As shown in table-1 and fig. 3, the table may serve as a scoring mapping function for the corresponding parameters. In the course of evaluating habitat of large-eye tuna, SSTA, SSH, CHL was substituted into table 1, respectively, to obtain corresponding grades, and 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 scoring mapping function of the environmental temperature, the perching ratio of the large-eye tuna at different temperatures is obtained from the marked release data, and is fitted to a probability distribution function with the input of the environmental temperature and the output value of 0 to 1. Table-2 shows the signature release data used in this example.
As shown in table 2, available habitat usage data were extracted from these documents, and apart from bait organisms and own physiological conditions, the vertically downstream aqueous layer and the duration of swimming of large-eye tuna were affected by marine environments, in particular dissolved oxygen and ambient temperature. Thus, the extracted data includes the temperature of the water layer in which the large eye tuna is located in the daytime and nighttime, and the percentage of habitat time.
As shown in fig. 2, from the above data, it can be found that the longest ambient temperature of the large-eye tuna is about 11 ℃ during the daytime, the ambient temperature of the large-eye tuna is not lower than 5 ℃, the percentage of the perching time at 19 ℃ is relatively minimum, and a peak occurs at 25 ℃ in warm water of 20 ℃ or higher. At night, the longest perching layer temperature of large-eye tuna is about 25 ℃. The resulting perch time profile peaked at 11 ℃ (day) and 25 ℃ (night), respectively.
Table-2 marked sources of stream data
In this embodiment, after the two curves are comprehensively counted, a probability distribution function with an input of an ambient temperature and an output value of 0 to 1 is fitted, and the 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, wherein the probability value can be used as the score T of the ambient temperature in the embodiment range0 。
In the embodiment, the scoring mapping function of the oxygen content is a binary function, and the threshold value is determined according to the dissolved oxygen physiological requirement of the large-eye tuna; when the oxygen content is larger than the threshold value, the scoring mapping function of the oxygen content is output as 1, and otherwise, the scoring mapping function of the oxygen content is output as 0. In one embodiment, the threshold of oxygen content is 1ml/L.
The above embodiments of the present invention do not limit the scope of the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
Claims (2)
1. The method for evaluating the habitat of the large-eye tuna in the pacific ocean area based on the ecological niche model is characterized by comprising the following steps of:
(S1) acquiring surface layer environmental factor data, environmental temperature data and oxygen content data of a sea area to be evaluated; the surface environmental factor data comprise a sea surface Wen Juping value, a sea surface height value and a sea surface chlorophyll concentration;
(S2) respectively bringing the surface layer environment factor data, the environment temperature data and the oxygen content data of the sea area to be evaluated into corresponding scoring mapping functions to obtain scoring SSTA of the sea surface Wen Juping value level Scoring SSH of sea level altitude values level Score of sea surface chlorophyll concentration CHL level Score T of ambient temperature range0 Scoring DO of oxygen content 0/1 ；
(S3) calculating a comprehensive habitat quality score from the scores; the calculation formula is as follows:
Habitat＝SST level ·SSH level ·CHL level ·T range0 ·DO 0/1
wherein: habitat is a comprehensive Habitat quality score;
each scoring mapping function of the surface layer environment factor data is trained by fishery data and marine surface biological/non-biological environment data by adopting a hierarchical clustering method, and specifically comprises the following steps of:
(S21) obtaining sea surface Wen Juping value SSTA, sea surface altitude value SSH and sea surface chlorophyll concentration CHL for each sea area from the sea surface biological/non-biological environmental data, and obtaining corresponding unit fishing effort fishing gain CPUE from the fishery data;
(S22) hierarchical clustering is carried out on sea surface Wen Juping value SSTA, sea surface height value SSH and sea surface chlorophyll concentration CHL by taking unit fishing effort fishing gain CPUE as target parameters; in the clustering process, the sea surface Wen Juping 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;
in the process of solving the scoring mapping function of the environmental temperature, the perching proportion of the large-eye tuna at different temperatures is obtained from the marking release data, the perching time distribution curve of the large-eye tuna in the daytime and the perching time distribution curve of the large-eye tuna at night are comprehensively counted, and the probability distribution function with the input of the environmental temperature and the output value of 0 to 1 is fitted;
the scoring mapping function of the oxygen content is a binary function, and the threshold value of the scoring mapping function is determined according to the dissolved oxygen physiological requirement of the large-eye tuna; when the oxygen content is larger than the threshold value, the scoring mapping function of the oxygen content is output as 1, and otherwise, the scoring mapping function of the oxygen content is output as 0.
2. A method for evaluation of habitat of large-eye tuna in pacific ocean area based on an niche model according to claim 1, characterized in that the threshold value of oxygen content is 1ml/L.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104809479A (en) * | 2015-05-18 | 2015-07-29 | 上海海洋大学 | Fish HIS (habitat suitability index) modeling method based on SVM (support vector machine) |
| JP2015139439A (en) * | 2014-01-30 | 2015-08-03 | 富士通株式会社 | Program, method and device for evaluation of habitat suitability |
| WO2017002533A1 (en) * | 2015-06-29 | 2017-01-05 | 国立研究開発法人海洋研究開発機構 | Fishing-ground prediction device, fishing-ground prediction system, marine-environmental-data sharing device, and marine-environmental-data sharing system |
| CN107563610A (en) * | 2017-08-14 | 2018-01-09 | 水利部交通运输部国家能源局南京水利科学研究院 | A kind of quantitative analysis method that gate dam regulation and control influence on Habitat for Fish spatial character |
| CN107609691A (en) * | 2017-08-29 | 2018-01-19 | 上海海洋大学 | Mauritanian siphonopods fishing ground forecasting procedure based on habitat suitability index |
| CN108960523A (en) * | 2018-07-18 | 2018-12-07 | 上海海洋大学 | A method of utilizing the feeding ground habitat of two step Generalized Additive Models prediction squid class |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US11452286B2 (en) * | 2016-07-22 | 2022-09-27 | Shanghai Ocean University | Method of predicting central fishing ground of flying squid family ommastrephidae |
-
2020
- 2020-05-13 CN CN202010400942.4A patent/CN111583051B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2015139439A (en) * | 2014-01-30 | 2015-08-03 | 富士通株式会社 | Program, method and device for evaluation of habitat suitability |
| CN104809479A (en) * | 2015-05-18 | 2015-07-29 | 上海海洋大学 | Fish HIS (habitat suitability index) modeling method based on SVM (support vector machine) |
| WO2017002533A1 (en) * | 2015-06-29 | 2017-01-05 | 国立研究開発法人海洋研究開発機構 | Fishing-ground prediction device, fishing-ground prediction system, marine-environmental-data sharing device, and marine-environmental-data sharing system |
| CN107563610A (en) * | 2017-08-14 | 2018-01-09 | 水利部交通运输部国家能源局南京水利科学研究院 | A kind of quantitative analysis method that gate dam regulation and control influence on Habitat for Fish spatial character |
| CN107609691A (en) * | 2017-08-29 | 2018-01-19 | 上海海洋大学 | Mauritanian siphonopods fishing ground forecasting procedure based on habitat suitability index |
| CN108960523A (en) * | 2018-07-18 | 2018-12-07 | 上海海洋大学 | A method of utilizing the feeding ground habitat of two step Generalized Additive Models prediction squid class |
Non-Patent Citations (4)
| Title |
|---|
| THE IMPACT OF ENVIRONMENTAL CHANGING, FOOD AVAILABILITY AND ANTROPOGENIC PRESSURE ON SARDINE (Sardinella lemuru) CPUE IN BALI STRAIT WATERS;R. Puspasari，PF. Rachmawati，E. Susilo 等;JURNAL SEGARA;第14卷(第02期);全文 * |
| 拉尼娜期间中西太平洋鲣栖息地分布特征;晏然;陈新军;陈作志;;海洋学报;第40卷(第04期);全文 * |
| 栖息地适宜性指数模型在鱼类生境评价中的应用进展;周为峰, 李英雪, 程田飞 等;渔业信息与战略;第35卷(第01期);全文 * |
| 西北太平洋柔鱼栖息地适宜性变动研究;易倩, 余为, 陈新军;海洋渔业;第41卷(第03期);全文 * |
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