A method for estimating and predicting cold-water fish population resources in the Qinghai-Tibet Plateau

Abstract

The present invention relates to a method for estimating and predicting the resources of cold-water fish populations on the Qinghai-Tibet Plateau, comprising the following steps: S1 sorting out the data of cold-water fish on the Qinghai-Tibet Plateau, establishing the Earth Vitality Index of cold-living fish in the permafrost zone of the Qinghai-Tibet Plateau, and establishing the Red List Index of cold-living fish in the permafrost zone of the Qinghai-Tibet Plateau; S2 establishing a model using a Bayesian state-space framework to estimate the relative abundance trend of cold-water fish; S3 classifying cold-water fish according to their living areas, generation time, and species characteristics, and further calculating their Earth Vitality Index; S4 constructing a time-varying trend model of the Red List of cold-water fish on the Qinghai-Tibet Plateau; S5 establishing a Red List change image between species according to the year of assessment; S6 analyzing the relationship between related trend changes and variables through multi-image comparison. The present invention can better understand and monitor the health status of fish ecosystems and promptly discover the trends and causes of ecological degradation by accurately estimating the trends of cold-water fish population resources.

Description

Technical Field

[0001] The invention relates to the technical field of cold-water fish population resource prediction in the Qinghai-Tibet Plateau, and in particular to a method for estimating and predicting cold-water fish population resources in the Qinghai-Tibet Plateau. Background Art

[0002] The Qinghai-Tibet Plateau is the youngest plateau in the world and the highest plateau in altitude. It is known as the "Roof of the World". The Qinghai-Tibet Plateau is also the place with the most concentrated lakes in the world, with more than 1,500 lakes of various sizes. The total area of ​​lakes in the region accounts for about 50% of the total area of ​​lakes in my country. The complex water network on the plateau, with rivers and lakes crisscrossing and lack of oxygen at high altitudes, has bred a group of unique plateau fish. The fish on the Qinghai-Tibet Plateau are the fish species with the highest altitude distribution in the world. Its fish composition also has its own characteristics and is an indispensable key species in the ecosystem. With the rapid development of the tourism industry, the number of tourists has increased significantly, and the demand for fish in the consumer market has increased year by year. Affected by human activities and other reasons, the distribution area of ​​fish on the Qinghai-Tibet Plateau has shrunk and the natural habitat has been destroyed. Downstream overfishing is the main factor affecting the biodiversity of fish on the Qinghai-Tibet Plateau. The impact of a large amount of resource consumption on fish ecology is huge, especially the slow growth rate of plateau cold-water fish, late sexual maturity, low reproductive capacity, and relatively weak population regeneration ability. Once its population is destroyed, it is difficult to recover in the short term. Fish are also an important food source for waterfowl and other organisms. The reduction in their number will produce a butterfly effect, which will threaten the stability of the ecosystem. Therefore, in-depth research on cold-water fishes in the Qinghai-Tibet Plateau not only has important scientific significance for the systematic evolution and biogeography of fishes, but is also crucial to maintaining the safety and biodiversity of the water, fish and bird ecological chain in lake basins.

[0003] However, since the Qinghai-Tibet Plateau is located at a high altitude and is sparsely populated, it is difficult to conduct research and extremely difficult to carry out field work. Detailed investigations and studies on changes in fish biodiversity have problems such as large survey scope, high cost, and small amount of data. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a method for estimating and predicting the population resources of cold-water fish in the Qinghai-Tibet Plateau with improved accuracy.

[0005] To solve the above problems, a method for estimating and predicting the cold-water fish population resources in the Qinghai-Tibet Plateau according to the present invention comprises the following steps:

[0006] S1 sorted out the data of cold-water fish on the Qinghai-Tibet Plateau, established the Earth Vitality Index of cold-water fish in the permafrost zone of the Qinghai-Tibet Plateau, and established the Red List Index of cold-water fish in the permafrost zone of the Qinghai-Tibet Plateau, ranging from 0 to 1, where 1 means no extinction risk and 0 means that all species have become extinct;

[0007] S2 used a Bayesian state-space framework to develop the following model to estimate the relative abundance trends of cold-water fish;

[0008]

[0009] Where: μ t+1 is the relative abundance index of each cold-water fish in year t+1; μ t is the logarithm of the expected abundance in year t; r t is a mean The annual change rate of the normal distribution; log(y t ) represents the logarithm of the abundance value in year t; y t represents the abundance value in year t; ε t is the observation error, which follows a normal distribution, ε t ~N(0,σ ε 2 );σ ε 2 is the process variance, which follows the inverse gamma distribution and the σ of the prior distribution 2 ~IG(0.001,0.001),σ 2 is the observed variance; r is the average annual rate of change, Fuzzy normal prior is adopted;

[0010] S3 classifies cold-water fish according to their living areas, generation time, and species characteristics, and further estimates their Earth Vitality Index:

[0011] The annual change rate d of each species in a region t Aggregated to provide a single annual rate of change for each region, the Living Planet Index (LPI) was calculated for each cold-water fish species separately, based on catch data and time series with similar ecological lifestyles or life history characteristics, the Living Planet Index trend change consists of trajectories for each region and species, cold-water fish body size, cold-water fish generation time, and indicators of functional groups with similar ecological or life history characteristics, and through data transformation, the log-transformed values ​​were converted to a linear scale;

[0012]

[0013] Where: d t I represents the annual change rate of each cold-water fish in a region, which is the logarithm of the time series growth rate in a given year t; t represents the posterior distribution of the estimated abundance trend for a given year t obtained from the output of the Bayesian state-space model; I t-1 represents the estimated abundance trend distribution in year t-1; LPI represents the Earth Vitality Index of cold-water fish, LPI t is the LPI for a given year t; t-1The LPI represents the Earth Vitality Index of cold-water fish in year t-1. When the given year t is set to 1, t＝1 The value of is equal to 1; d t It represents the average annual rate of change for all cold-water fish in a region;

[0014] S4 Construction of temporal trend model of cold-water fish red list in the Qinghai-Tibet Plateau:

[0015] Establish the red list of cold-water fishes in the Qinghai-Tibet Plateau over time based on the Red List of the World Conservation Union, and further clarify the changes in threatened species;

[0016]

[0017] Where: RLI t Indicates the Red List value for a specific year, ranging from 0 to 1; ∑ s W c(t,s) represents the sum of weights W of all cold-water fish categories in year t; W EX represents the category weight W of cold-water fish; the category weight W ranges from 0 to 5, where 0 represents Least Concern, 1 represents Near Threatened, 2 represents Vulnerable, 3 represents Endangered, 4 represents Critically Endangered, and 5 represents Extinct; N represents the number of species;

[0018] S5 Analysis of the trend model of the Red List Index of cold-water fish in the Qinghai-Tibet Plateau, and establishment of a Red List change map among species according to the assessment year;

[0019] S6 compares multiple images to analyze relevant trend changes and the relationship between variables.

[0020] Compared with the prior art, the present invention has the following advantages:

[0021] 1. The present invention is based on the characteristic attributes of the abundance of cold-water fish in the Qinghai-Tibet Plateau. By fitting an earth vitality function for each cold-water fish separately in the Bayesian population state space method, the parameter differences in region, time (year), zone, volume, generation time, and species when fitting the earth vitality index function are taken into account, thereby improving the accuracy of the prediction of the resource status of cold-water fish in the Qinghai-Tibet Plateau.

[0022] 2. The present invention collects indicators such as the Earth Vitality Index, Red List Index, biological characteristics, fishing intensity, and spatial distribution of cold-water fish in the Qinghai-Tibet Plateau for fusion analysis, focusing on estimating the relative abundance trend of cold-water fish in the Qinghai-Tibet Plateau. The logarithmic exponential growth formula defined by the state process equation is used to link the logarithm of the observed relative abundance index with the logarithm of the true expected population trend. The interannual variation pattern and resource status of cold-water fish populations in the Qinghai-Tibet Plateau are explored, reflecting the reasons for changes in the resource status of different cold-water fish species.

[0023] 3. The present invention uses population dynamics modeling and runs three Monte Carlo Markov chains (MCMC) on each data set with different initial values ​​based on the Bayesian population state space model (Just Another RedList Assessment, JARA) method, introduces process errors and observation errors, solves the problem of conditional probability distortion, and improves the accuracy of model prediction.

[0024] 4. The present invention can better understand and monitor the health of fish ecosystems and timely discover the trends and causes of ecological degradation by accurately estimating the trend of cold-water fish population resources. This is of great significance for ecological protection, fish resource recovery and sustainable management, and supporting local economic development and social stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The specific implementation modes of the present invention are further described in detail below in conjunction with the accompanying drawings.

[0026] Figure 1 It is the work flow chart of the present invention.

[0027] Figure 2 This is a result diagram of the estimation and prediction model of the cold-water fish resource status in the Qinghai-Tibet Plateau based on the Bayesian inter-layer structure proposed in the embodiment of the present invention. Among them: the black box represents the observed LPI and the blue box represents the estimated LPI, the average value of (dt) between different species in the same region represents the annual change rate (yyrc) between species in the same region, and the annual change rate dt of each species is summarized to obtain the annual change rate of a region.

[0028] Figure 3 This is a graph of the relationship between the Earth Vitality Index and the years of cold-water fish on the Qinghai-Tibet Plateau based on the Bayesian inter-layer structure proposed in the embodiment of the present invention. Among them: the Earth Vitality Index LPI of cold-water fish on the Qinghai-Tibet Plateau from 1980 to 2018 is represented in black, and the index extrapolated to 2020 is represented in blue; the black and blue thick lines represent the estimated and extrapolated LPI averages, respectively; the white and blue thin lines represent the intervals of 95% credible estimated LPI and extrapolated LPI, respectively; the gray line represents each iteration of the estimated LPI.

[0029] Figure 4 A graph showing the annual rate of change of cold-water fish in the Qinghai-Tibet Plateau over time based on the Bayesian inter-hierarchical structure proposed in an embodiment of the present invention. The annual percentage change is calculated from the posterior values ​​of the estimated LPI (grey) and the extrapolated LPI (blue) around the last evaluation year relative to the posterior value in 1970; the vertical line during 1970-2018 represents the median of the estimated and extrapolated LPI.

[0030] Figure 5 The red list status change diagram of cold-water fish in the Qinghai-Tibet Plateau from 1980 to 2018 in the embodiment of the present invention. The letters represent the status level, CR: Critically Endangered; EN: Endangered; VU: Vulnerable; NT: Near Threatened; LC: Least Concern. DETAILED DESCRIPTION

[0031] A method for estimating and predicting cold-water fish population resources in the Qinghai-Tibet Plateau comprises the following steps:

[0032] S1 sorted out the data of cold-water fish on the Qinghai-Tibet Plateau, established the Earth Vitality Index of cold-water fish in the permafrost zone of the Qinghai-Tibet Plateau, and established the Red List Index of cold-water fish in the permafrost zone of the Qinghai-Tibet Plateau, ranging from 0 to 1, where 1 means no extinction risk and 0 means that all species have become extinct;

[0033] S2 used a Bayesian state-space framework to develop the following model to estimate the relative abundance trends of cold-water fish;

[0034] μ t+1 =μ t +r t ; log(y t )=μ t +ε t ;

[0035] Where: μ t+1 is the relative abundance index of each cold-water fish in year t+1; μ t is the logarithm of the expected abundance in year t; r t is a mean The annual change rate of the normal distribution; log(y t ) represents the logarithm of the abundance value in year t; y t represents the abundance value in year t; ε t is the observation error, which follows a normal distribution, ε t ~N(0,σ ε 2 );σ ε 2 is the process variance, which follows the inverse gamma distribution and the σ of the prior distribution 2 ~IG(0.001,0.001),σ 2 is the observed variance; is the average annual rate of change, Fuzzy normal prior is adopted;

[0036] S3 classifies cold-water fish according to their living areas, generation time, and species characteristics, and further estimates their Earth Vitality Index:

[0037] The annual change rate d of each species in a region tAggregated to provide a single annual rate of change for each region, the Living Planet Index (LPI) was calculated for each cold-water fish species separately, based on catch data and time series with similar ecological lifestyles or life history characteristics, the Living Planet Index trend change consists of trajectories for each region and species, cold-water fish body size, cold-water fish generation time, and indicators of functional groups with similar ecological or life history characteristics, and through data transformation, the log-transformed values ​​were converted to a linear scale;

[0038]

[0039] Where: d t I represents the annual change rate of each cold-water fish in a region, which is the logarithm of the time series growth rate in a given year t; t represents the posterior distribution of the estimated abundance trend for a given year t obtained from the output of the Bayesian state-space model; I t-1 represents the estimated abundance trend distribution in year t-1; LPI represents the Earth Vitality Index of cold-water fish, LPI t is the LPI for a given year t; t-1 The LPI represents the Earth Vitality Index of cold-water fish in year t-1. When the given year t is set to 1, t＝1 The value of is equal to 1; It represents the average annual rate of change for all cold-water fish in a region;

[0040] S4 Construction of temporal trend model of cold-water fish red list in the Qinghai-Tibet Plateau:

[0041] Establish the red list of cold-water fishes in the Qinghai-Tibet Plateau over time based on the Red List of the World Conservation Union, and further clarify the changes in threatened species;

[0042]

[0043] Where: RLI t Indicates the Red List value for a specific year, ranging from 0 to 1; ∑ s W c(t,s) represents the sum of weights W of all cold-water fish categories in year t; W EX represents the category weight W of cold-water fish; the category weight W ranges from 0 to 5, where 0 represents Least Concern, 1 represents Near Threatened, 2 represents Vulnerable, 3 represents Endangered, 4 represents Critically Endangered, and 5 represents Extinct; N represents the number of species;

[0044] S5 Analysis of the trend model of the Red List Index of cold-water fish in the Qinghai-Tibet Plateau, and establishment of a Red List change map among species according to the assessment year;

[0045] S6 compares multiple images to analyze relevant trend changes and the relationship between variables.

[0046] Example

[0047] A method for estimating and predicting cold-water fish population resources in the Qinghai-Tibet Plateau comprises the following steps:

[0048] S1 compiled data on cold-water fish on the Qinghai-Tibet Plateau, established the Living Planet Index (LPI) for cold-water fish in the permafrost zone of the Qinghai-Tibet Plateau, and established the Red List Index (RLI) for cold-water fish in the permafrost zone of the Qinghai-Tibet Plateau, ranging between 0 and 1, with 1 indicating no risk of extinction and 0 indicating that all species have become extinct.

[0049] This paper compiled survey data of 31 cold-water fish species in the Qinghai-Tibet Plateau estimated from 1970 to 2018, and estimated the relative abundance trends of 18 cold-water fish species (Relative abundance index, n = 57). The data sources are published journals, literature and government reports, including catch data and standardized unit fishing effort catch data, which come from 57 time series data sets.

[0050] The Living Planet Index (LPI) is an index concept for measuring the ecological status of the earth. It aims to measure biodiversity by collecting population data of various vertebrate species and calculating the average changes in population numbers over a period of time.

[0051] The Red List Index (RLI) of Coldwater Fishes on the Qinghai-Tibet Plateau provides a standardized assessment of the extinction risk of each coldwater fish, covering information such as the survival range, population size, habitat, trends, threats faced, and urgent conservation actions of the relevant coldwater fish. It is the most comprehensive and authoritative list of the conservation status of coldwater fish on the Qinghai-Tibet Plateau and an important health indicator of biodiversity on the Qinghai-Tibet Plateau.

[0052] S2 used a Bayesian state-space framework to develop models to estimate trends in the relative abundance of cold-water fish.

[0053] In order to evaluate the resources of cold-water fish in the Qinghai-Tibet Plateau, the Bayesian hierarchical structure was used to estimate the relative abundance trends of 18 cold-water fish species. The Bayesian method is based on the original observations, which means that the unknown parameters are regarded as random variables and the posterior distribution of the unknown parameters is obtained by analysis.

[0054] Markov chain Monte Carlo (MCMC) was used to estimate the mean, credible interval and other statistics of the posterior distribution through the drawn samples, and the posterior distribution of parameters (such as annual change rate, etc.) was simulated. Assuming that each relative abundance index (or time series) follows the exponential growth defined by the state process equation, the process error, observation error and prior information were considered at the same time in the study. The Monte Carlo (MCMC) simulation method was used to run each data set with different initial values ​​3 times. The posterior probability distribution of the model parameters μ, r, and ε, and the MCMC process were calculated by R version 3.5.2 and JAGS ('Just Another Gibbs Sampler' v.4.3.0). Each Markov chain was drawn from a normal distribution in logarithmic space by assuming that the initial population size in the first year was drawn from a normal distribution with a mean equal to the logarithm of the first available count (y1) and a standard deviation of 1000. During the MCMC iterative calculation, each parameter was calculated 50,000 times in total. The results of the first 5,000 times were discarded. In the remaining 50,000 iterations, 10,000 times were selected for posterior inference, and the results were stored every 5 times. Therefore, the posterior distribution iteration was estimated starting from 30,000.

[0055] μ t+1 =μ t +r t (1)

[0056] log(y t )=μ t +ε t (2)

[0057]

[0058] Where: μ t+1 is the relative abundance index of each cold-water fish in year t+1; μ t is the logarithm of the expected abundance in year t; r t is a mean The annual change rate of the normal distribution; log(y t ) represents the logarithm of the abundance value in year t; y t represents the abundance value in year t; ε t is the observation error, which follows a normal distribution, ε t ~N(0,σ ε 2 );σ ε 2 is the process variance, which follows the inverse-gamma (IG) distribution and the σ of the prior distribution 2~IG(0.001,0.001),σ 2 is the observed variance; is the average annual rate of change, A fuzzy normal prior is adopted.

[0059] S3 classifies cold-water fish according to their living areas, generation times, and species characteristics, and further estimates their Earth Vitality Index (based on abundance trends).

[0060] Because the basis of the abundance model of cold-water fish in the Qinghai-Tibet Plateau in the present invention is a Bayesian state space model, it is divided into three layers, namely, region, time and species to construct the cause attribution exploration model of the Earth Vitality Index. Using the estimated abundance trend, the Earth Vitality Index LPI of cold-water fish in the Qinghai-Tibet Plateau from the reference year 1970 (set to 1) to 2018 was calculated, and then each time series was extrapolated to 2020 by level to obtain the data of the "Aichi Biodiversity Conservation Target" (the world's first 10-year biodiversity conservation target) assessment year (2020), and the annual change rate of a cold-water fish in each time series was summarized by region, and the annual change rate of cold-water fish in a region was summarized, and then the annual change rate of cold-water fish in the Qinghai-Tibet Plateau was summarized.

[0061] To calculate the LPI of cold-water fish in the Qinghai-Tibet Plateau, the annual change rate d of each species in a region was calculated. t Aggregated to provide a single annual rate of change for each region (e.g. Figures 2-3 The Living Planet Index (LPI) was also calculated for each cold-water fish species. The trend of the Living Planet Index (LPI) was composed of the trajectory of each region and species, the body size of cold-water fish, the generation time of cold-water fish, and the functional group indicators with similar ecological or life history characteristics through fishery resource surveys, and the logarithmic-transformed values ​​were converted to a linear scale through data transformation, based on catch data and time series with similar ecological lifestyles or life history characteristics.

[0062]

[0063]

[0064] Where: d t I represents the annual change rate of each cold-water fish in a region, which is the logarithm of the time series growth rate in a given year t; t represents the posterior distribution of the estimated abundance trend for a given year t obtained from the output of the Bayesian state-space model; I t-1 represents the estimated abundance trend distribution in year t-1; LPI represents the Earth Vitality Index of cold-water fish, LPI t is the LPI for a given year t; t-1The LPI represents the Earth Vitality Index of cold-water fish in year t-1. When the given year t is set to 1, t＝1 The value of is equal to 1; Represents the average annual rate of change for all cold-water fish in a region.

[0065] To investigate potential drivers of the declining abundance trends of cold-water fish and to explain the disproportionately high catches of some cold-water fish, we calculated relative fishing pressure (RFP), which is the change in catch from 1970 to 2014 (as of the time data were available), relative to the abundance (LPI) during the same period and converted to (and scaled to) the RFP in 1970. We also used the LPI of 14 of the 18 cold-water fish species. t The RFP was calculated and the results showed that the RFP of 14 cold-water fish species was not significantly different from the RFP of all 18 cold-water fish species.

[0066]

[0067] Where: RFP t is the relative fishing pressure of cold-water fish in year t; LPI t is the LPI of 18 cold-water fish species in year t; ∑ sp catch t is the sum of the catch of all cold-water fish in year t, LPI t＝1970 is the Earth Vitality Index of cold-water fish in 1970; Σ sp catch t＝1970 It is the total catch of cold-water fish in 1970.

[0068] S4 Construction of temporal trend model for the Red List of cold-water fish in the Qinghai-Tibet Plateau.

[0069] All 31 species of cold-water fish in the Qinghai-Tibet Plateau were evaluated according to the Red List Index (RLI) of the World Conservation Union (IUCN) to establish the temporal trend of the Red List Index (RLI) of cold-water fish in the Qinghai-Tibet Plateau and further clarify the changes in threatened species. Figure 5 shown.

[0070] The RLI was calculated by the proportion of 31 cold-water fish species in each category of the IUCN Red List Index (RLI) at three time points: 1980, 2005, and 2018. The IUCN Red List divides cold-water fish species into nine levels, from high to low severity: extinct (EX), extinct in the wild (EW), critically endangered (CR), endangered (EN), vulnerable (VU), near threatened (NT), least concern (LC), data deficient (DD), and not assessed (NE). This set of standards can be used to determine whether the ecosystem within this range is collapsed, vulnerable, endangered, or critically endangered, and whether the ecosystem has experienced area loss, degradation, or other major changes.

[0071]

[0072] Where: s W c(t,s) W represents the sum of the weights W of all cold-water fish categories in year t (the category weight W ranges from 0 to 5, with 0 representing Least Concern, 1 representing Near Threatened, 2 representing Vulnerable, 3 representing Endangered, 4 representing Critically Endangered, and 5 representing Extinct); W EX represents the category weight (W) of cold-water fish; N represents the number of species.

[0073] RLI t The RLI value for a particular year is expressed (range 0–1), which is obtained by multiplying the number of cold-water fish in each Red List category by the category weight (W) (0 for Least Concern, 1 for Near Threatened, 2 for Vulnerable, 3 for Endangered, 4 for Critically Endangered and 5 for Extinct), then summing the products and dividing by the maximum possible product (number of species (N) multiplied by the maximum weight 5), which is then subtracted from 1, resulting in an index between 0 (all species are extinct (EX)) and 1 (all species are Least Concern (LC)).

[0074] S5 Analysis of the Red List Index trend model for cold-water fish in the Qinghai-Tibet Plateau, and establishment of a Red List change map among species according to the assessment year.

[0075] A comparative analysis was conducted on the Red List of Cold-water Fishes of the Qinghai-Tibet Plateau at three time points. Due to the different assessment criteria in 1980, 2005 and 2018, in order to account for the uncertainty of the retrospective assessment, a bootstrap-like method was used in the present invention to iteratively resample 10,000 times from the retrospectively assigned status of each species, either a better category or a worse category, to assess data quality and check whether the data were consistent with the model assumptions.

[0076] Different species face different proportions of extinction threats. Comparative analysis of the Red List Index (RLI) of 31 species of cold-water fish on the Qinghai-Tibet Plateau assessed at three time points: 1980, 2005, and 2018, as well as the Red List Index (RLI) of mammals, birds, amphibians, reef-building corals, and cycads; Comparison of the changes in the Red List status of cold-water fish on the Qinghai-Tibet Plateau from 1980 to 2018, understanding the current status of cold-water fish population resources on the Qinghai-Tibet Plateau, and understanding the threatened status of biodiversity can better measure the risks faced by the ecosystem, provide basic data support for strengthening biodiversity conservation, and provide guidance for the rational use of fish population resources.

[0077] S6 compares multiple images to analyze relevant trend changes and the relationship between variables.

[0078] Combining the trends of the Earth Vitality Index for 18 cold-water fish species in the Qinghai-Tibet Plateau, the index can be decomposed into each region, geographic zone, body size, generation time and species trajectory in the time series, and functional groups with similar ecological or life history characteristics. By drawing Kobe diagrams, the resource status of cold-water fish populations is judged. Over time, the relative fishing intensity (relative fishing pressure, RFP) of the region continues to increase (measured by the change in catch rate relative to the change in LPI). Overfished cold-water fish populations have a biomass or abundance below the level that can produce maximum sustainable yield (the equilibrium state where the fished population maintains maximum yield (i.e., catch rate) for a long time).

[0079] According to the statistics of FAO, the fishing data of 14 species out of 18 key species were selected, and the RFP was calculated through the catch data of the 14 cold-water fish in the Qinghai-Tibet Plateau and the catch of longline fishing and purse seine fishing gear. The results showed that there was no significant difference in the RFP of these 14 cold-water fish and all 18 cold-water fish, indicating that the cold-water fish in the Qinghai-Tibet Plateau are in a state of overfishing.

Claims

1. A method for estimating and predicting the population resources of cold-water fish in the Qinghai-Tibet Plateau, comprising the following steps: S1 sorted out the data of cold-water fish on the Qinghai-Tibet Plateau, established the Earth Vitality Index of cold-water fish in the permafrost zone of the Qinghai-Tibet Plateau, and established the Red List Index of cold-water fish in the permafrost zone of the Qinghai-Tibet Plateau, ranging from 0 to 1, where 1 means no extinction risk and 0 means that all species have become extinct; S2 used a Bayesian state-space framework to develop the following model to estimate the relative abundance trends of cold-water fish; Where: μ t+1 is the relative abundance index of each cold-water fish in year t+1; μ t is the logarithm of the expected abundance in year t; r t is a mean The annual change rate of the normal distribution; log(y t ) represents the logarithm of the abundance value in year t; y t represents the abundance value in year t; ε t is the observation error, which follows a normal distribution, ε t ~N(0,σ ε 2 );σ ε 2 is the process variance, which follows the inverse gamma distribution and the σ of the prior distribution 2 ~IG(0.001,0.001),σ 2 is the observed variance; is the average annual rate of change, Fuzzy normal prior is adopted; S3 classifies cold-water fish according to their living areas, generation time, and species characteristics, and further estimates their Earth Vitality Index: The annual change rate d of each species in a region t Aggregated to provide a single annual rate of change for each region, the Living Planet Index (LPI) was calculated for each cold-water fish species separately, based on catch data and time series with similar ecological lifestyles or life history characteristics, the Living Planet Index trend change consists of trajectories for each region and species, cold-water fish body size, cold-water fish generation time, and indicators of functional groups with similar ecological or life history characteristics, and through data transformation, the log-transformed values ​​were converted to a linear scale; Where: d t I represents the annual change rate of each cold-water fish in a region, which is the logarithm of the time series growth rate in a given year t; t represents the posterior distribution of the estimated abundance trend for a given year t obtained from the output of the Bayesian state-space model; I t-1 represents the estimated abundance trend distribution in year t-1; LPI represents the Earth Vitality Index of cold-water fish, LPI t is the LPI for a given year t; t-1 The LPI represents the Earth Vitality Index of cold-water fish in year t-1. When the given year t is set to 1, t＝1 The value of is equal to 1; It represents the average annual rate of change for all cold-water fish in a region; S4 Construction of temporal trend model of cold-water fish red list in the Qinghai-Tibet Plateau: Establish the red list of cold-water fishes in the Qinghai-Tibet Plateau over time based on the Red List of the World Conservation Union, and further clarify the changes in threatened species; Where: RLI t Indicates the Red List value for a specific year, ranging from 0 to 1; ∑ s W c(t,s) represents the sum of weights W of all cold-water fish categories in year t; W EX represents the category weight W of cold-water fish; the category weight W ranges from 0 to 5, where 0 represents Least Concern, 1 represents Near Threatened, 2 represents Vulnerable, 3 represents Endangered, 4 represents Critically Endangered, and 5 represents Extinct; N represents the number of species; S5 Analysis of the trend model of the Red List Index of cold-water fish in the Qinghai-Tibet Plateau, and establishment of a Red List change map among species according to the assessment year; S6 compares multiple images to analyze relevant trend changes and the relationship between variables.

Images