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Performance evaluation of fixed-station sampling design for a fishery-independent survey with multiple objectives
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Jing Wang1, Binduo Xu2, *, Ying Xue2, Chongliang Zhang2, Mingkun Li2, Yiping Ren2, 3
Acta Oceanologica Sinica | 2022, 41(3) : 94 - 102
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Acta Oceanologica Sinica | 2022, 41(3): 94-102
• Marine Technology •
Performance evaluation of fixed-station sampling design for a fishery-independent survey with multiple objectives
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Jing Wang1, Binduo Xu2, *, Ying Xue2, Chongliang Zhang2, Mingkun Li2, Yiping Ren2, 3
Affiliations
  • 1 School of Fishery, Zhejiang Ocean University, Zhoushan 316000, China
  • 2 Fisheries College, Ocean University of China, Qingdao 266003, China
  • 3 Laboratory for Marine Fisheries Science and Food Production Processes, Pilot National Laboratory for Marine Science and Technology (Qingdao), Qingdao 266237, China
Published: 2022-03-25 doi: 10.1007/s13131-021-1932-x
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Fixed-station sampling design was widely used in fishery-independent surveys because of its characteristics of convenient sampling station setting, but the non-probabilistic (fixed) nature made it more uncertainty of drawing inferences on population. The performance of fixed-station sampling design for multispecies survey has not been evaluated, and we are uncertain if the design could detect the temporal trends of different populations in multispecies fishery-independent survey. In this study, spatial distribution of abundance indices for three species with different spatial distribution patterns including small yellow croaker (Larimichthys polyactis), whitespotted conger (Conger myriaster) and Fang’s blenny (Enedrias fangi) were simulated using ordinary kriging interpolation as the “true” population distribution. The performance of fixed-station sampling design was compared with simple random sampling design by resampling the simulated “true” populations in this simulation study. The results showed that the fixed-station sampling design had the power to detect the seasonal trends of species abundance. The effectiveness of fixed-station sampling design were different in different species distribution patterns. When the species had even distribution, fixed-station sampling design could get high quality abundance data; when the distribution was uneven with heterogeneity or patchiness, fixed-station sampling design tended to underestimate or overestimate the abundance. Evidently, the estimates of abundance index based on the fixed-station sampling design must be calibrated cautiously while applying them for fisheries stock assessment and management. This study suggested that fixed-station sampling design could catch the temporal dynamics of population abundance, but the abundance estimates from the fixed-station sampling design could not be treated as the absolute estimates of populations.

computer simulation  /  fixed-station sampling design  /  precision and accuracy  /  ordinary kriging interpolation
Jing Wang, Binduo Xu, Ying Xue, Chongliang Zhang, Mingkun Li, Yiping Ren. Performance evaluation of fixed-station sampling design for a fishery-independent survey with multiple objectives[J]. Acta Oceanologica Sinica, 2022 , 41 (3) : 94 -102 . DOI: 10.1007/s13131-021-1932-x
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1 Introduction
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Fishery-independent surveys provide reliable data on relative abundance and life history parameters for a wide range of fishery species. And the survey data are used to assess species distribution, biological interactions, and ecosystem structure and to make effective decisions for scientific fisheries managements. The quality and precision of these estimates depend, in part, on adequate sampling of all species distribution types, even it is more expensive and labor-intensive (Andrew and Mapstone, 1987; Field, 2005). One of the main goals of sampling designs is to keep a balance between estimates precision, accuracy and budget limitations.
Standardized sampling designs, at its simplest in fishery, are of two different methods: probabilistic (random) and non-probabilistic (fixed) (Bethlehem, 2009; Bonar et al., 2009). And the debate on the advantages and disadvantages of random-station versus fixed-station sampling designs has never stopped (Warren, 1994; Quist et al., 2006). The former one is commonly and typically the most statistically sound survey method for collecting fisheries data, such as simple random sampling, stratified random sampling and system sampling, as well as some more advanced sampling design derived from these traditional methods (Bonar et al., 2009; Liu et al., 2011; Yu et al., 2012; Nelson, 2014; Zhang et al., 2019; Sun and Wang, 2020). Fixed sampling can be biased by site selection, but carefully selected sampling locations can increase catches and monitor trends of target species at specific sample locations over time (Hubbard and Miranda, 1986; McClelland and Sass, 2012).
The performance of sampling design also depends on the target species distribution. Many marine species showed aggregated or patchy patterns in spatial distribution structured by water temperature, salinity, depth and other environmental variables (Mazzoni and Iglesias-Rios, 2002; Guan et al., 2017). And the spatial distribution of many populations would shift with season and climate change as well as the fishing activities (Tang et al., 2011; Vanderwal et al., 2013; Xu et al., 2017), and changes in estimates were confounded by these distribution shift (Beare et al., 2004; Perry et al., 2005). Multispecies trawl surveys often intended to cover the range of all the populations and obtain the spatiotemporal distribution data with high precision. The sampling design was inevitably a compromise, with the distribution of target populations being most concerned in the survey.
A sound fisheries management requires information on the status of fisheries ecosystems and the impacts of various pressures such as climate change, fishing and anthropogenic perturbation on fisheries ecosystems (Pikitch et al., 2004). Further, there is increasing uncertainty in fisheries management, resulting from the impact of climate change on marine ecosystem components (Littell et al., 2011; Payne et al., 2016). These challenges and the expanding objectives for fisheries management need high quality data to provide tactical and strategic decisions on multiple spatial and temporal scales. Currently, there are considerable capacity shortfalls in data collection that are hampering effective fisheries management (Dorner et al., 2015; Graham et al., 2011; Simmonds et al., 2011). According to the considerably increasing demand for reliable survey data for scientific management, it is necessary to evaluate the performance of sampling design.
Fishery-independent monitoring program can provide vital information for understanding the status of fishery resource and the recruitment dynamics of target species. Such a program can collect data about species density or abundance and associated environmental and spatial variables at selected sites simultaneously. In this study, the bottom trawl fishery-independent monitoring program is a quarterly survey which has monitored the fish assemblage of Shandong adjacent sea area during 2016−2017 using a fixed site sampling design.
The non-probabilistic nature of fixed sampling design would make it providing potential biased information for drawing inferences on a population. Simple random sampling design tends to yield unbiased estimates and is often used as a basic design to evaluate the survey precision, efficiency, and accuracy of other survey designs (Skibo et al., 2008; Cao et al., 2014; Li et al., 2015). The performance of fixed-station sampling design was evaluated in this simulation study. The main objectives of this study are: (1) to determine whether the fixed-station sampling design could capture the temporal dynamics of species with different spatial distribution patterns over seasons; (2) to compare the performances between fixed-station sampling design and simple random sampling design in quantifying the spatial and temporal variability in relative abundance of fish species.
2 Materials and methods
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2.1 Survey data
Initiated in 2016, this fishery-independent monitoring program covers four seasons monitoring time series, in the coastal waters of Shandong, China. The multispecies fishery-independent survey data were collected in autumn (October, 2016), winter (January, 2016), spring (May, 2017) and summer (August, 2017). In this program, the fixed stations were chosen in a systematic fashion to spread sampling effort evenly across the study area. The survey area was divided into 15′ × 15′ grids in the inshore waters (43 sampling stations), and 30′ × 30′ grids in the offshore area (20 sampling stations) (Fig. 1). The trawl was towed for about 1 h in duration with the speed at 2−3 kn. Fish samples were identified to species, counted for abundance and measured for biological traits. The catch data in each sampling station were standardized to the relative abundance (kg/h) with the towing speed of 3 kn and duration of 1 h.
Intuitively, more information of species distribution will improve the understanding of the variability and be helpful in specifying models to describe their abundance. There are no significant differences among the effectiveness of different sampling designs to detect population abundance estimates for even and random spatial distribution species, and vice versa (Blanchard et al., 2008). We select different species with different spatial distribution patchiness levels as the objectives in this study to assess the effectiveness of fixed-station sampling design to detect the true trends of abundance compared with random sampling design.
As the importance of species abundance in ecology, we select species abundance index as the survey evaluation objective. Small yellow croaker (Larimichthys polyactis, LP), whitespotted conger (Conger myriaster, CM) and Fang’s blenny (Enedrias fangi, EF) were selected as target species in this study. These three fish species are commercially or ecologically important species with different spatial distribution patterns in the waters. Fang’s blenny, typically small-sized demersal fish, distributing in the Yellow Sea and Bohai Sea, is an important prey species with high abundance (Li et al., 2014). Small yellow croaker is a demersal species that is widely distributed throughout the Bohai Sea, Yellow Sea, and East China Sea, supporting important demersal fisheries with annual landing of more than 300 000 t (Guo et al., 2006; Kim et al., 2010). Whitespotted conger, a species with high economic and nutritional value, is widespread from the Northwest Pacific from the East China Sea to the coastal waters around Korea and Japan (Zhang, 2010; Kawazu et al., 2015).
The log transformation, one of the data preparation methods, can be used to make highly skewed distributions less skewed. This can be valuable both for making patterns in the data more interpretable and for helping to meet the assumptions of inferential statistics (García et al., 2000). In this study, we used log-transformed abundance as the species abundance index. Tables 1 and 2 listed the mean and coefficient of variation (CV) of abundance (log-transformed) indices as well as the presence/absence ratio of each target species based on the survey data over seasons in the coastal waters of Shandong, showing different spatial distribution patterns.
2.2 Simulated data
Ordinary kriging interpolation (OKI) is one of the most commonly used geostatistical interpolation methods to estimate the values at unsurveyed locations using the combination of weights and values at surveyed locations (Schabenberger and Gotway, 2005). The weights are estimated according to semi-variance among known values (Oliver and Webster, 1990; Pokhrel et al., 2013). The geostatistical interpolation method is based on the spatial autocorrelation analysis, which is often used to interpolate data at potential sampling units according to the patterns of spatial variability and is not influenced by the current survey design. In this study, we used OKI method to interpolate the species abundance index data in the four seasons, respectively. The unit size of the interpolated abundance maps was 3′ × 3′, and there were 3800 potential sampling units in the waters. The interpolated abundance based on OKI for each specie in each sampling unit were regarded as the “true” values in the simulation study. Fixed-station sampling design and simple random sampling design were conducted based on the “true” population distribution. To quantify the accuracy of the “true” value from OKI, leave-one-out cross-validation was used to check the performance of the interpolation.
2.3 Simulation study
In this study, the bottom trawl surveys with fixed-station design were conducted seasonally and provided estimates of fishery species abundance for stock assessments. But it is uncertain if the fixed-station could detect the spatial and seasonal variations in abundance indices of the main fishery species with different spatial distribution patterns. Since the effectiveness of this fixed-station survey design needs to be examined, we use the random sampling design to assess the fixed-station survey design effectiveness.
As for the fixed-station sampling design, 63 stations out of the 3800 potential sampling stations that were closest to the 63 fixed stations used in the actual settlement survey were selected. For simple random sampling design, 63 sampling stations out of the 3800 potential sampling sites were randomly selected. Both fixed-station sampling and simple random sampling design were repeated for 1000 times in each season. Relative estimation error (REE) and relative bias (RB) were used to measure the precision and accuracy of mean estimation values for the two sampling designs in the simulation study. The process was repeated for 100 times to capture the variability in the simulation and get the distribution of performance indices. The process of evaluating the effectiveness for fixed-station sampling design was showed in the flowchart (Fig. 2).
2.4 Performances of the two survey designs
REE (Eq. (1)) was used to quantify the accuracy and precision of estimated indices (Chen, 1996). RB (Eq. (2)) could estimate the accuracy and efficiency of estimated indices, indicating whether a survey design tended to underestimate or overestimate the “true” values (Paloheimo and Chen, 1996; Jiao et al., 2004).
$ {\text{REE}} = \frac{{\sqrt {\displaystyle\sum\limits_{i = 1}^R {{{\left( {Y_{i,\;{\rm{estimated}}} - {Y_{{\rm{true}}}}} \right)}^2}/R} } }}{{{Y_{{\rm{true}}}}}} \times 100\%, $
$ \mathrm{RB}=\frac{\displaystyle\sum\limits_{j=1}^{R} Y_{j,\;{\rm{estimated}} } / R-Y_{{{\rm{true}} }}}{Y_{{{\rm{true}} }}} \times 100 \%, $
where Y true was the “true” value calculated based on the “true” population, Y i, estimated was the ith estimated value calculated according to resampling data, R was the number of repetitions (1000 times in this study).
In this study, the fixed stations were chosen in a systematic fashion to spread sampling effort, so the mean equation is $\mathop X\limits^ - =\displaystyle{{\displaystyle\sum\limits_{k = 1}^n {{X_k}} }}/n$ based on fixed-station, which is same with the equation based on simple random sampling design. Where X k was the abundance value in the kth sampled site.
As the formula shows, the REE values could reflect the difference between resampling results and true value for every species over seasons and measure both bias and variation in the evaluation. And the RB values could measure the estimation bias. Therefore faced with alternative estimators for a given objective, it is generally reasonable to use the one with the smallest REE and RB values.
3 Results
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3.1 Model evaluation
The adjusted R 2 values of leave-one-out cross-validation for three target species over seasons runs varied from 0.072 to 0.490. There was a positive relationship between predicted and observed abundance values (Fig. 3). EF fitted better (R 2: 0.12−0.49), while LP in spring and winter fitted worse (R 2=0.072 and 0.089, respectively).
3.2 Predicted distribution of target species
The predicted abundance (log-transformed) distributions by OKI method were different for the three target species during the four seasons. The abundance distribution of EF was chosen as an example to show the performance of OKI in predicting the spatial distribution of fish species abundance (Fig. 4). The spatial distribution of Fang’s blenny abundance varied over different seasons. The abundances in spring and summer were higher than those in autumn and winter. The hotspots with high abundance distributed evenly throughout the survey area in spring, however the hotspots occurred in the deeper waters in summer. The abundance was low in the shallow waters in autumn, and low in the waters between 35.5°N and 36°N in winter (Fig. 4). The abundance distribution of LP and CM were set in supplementary materials (Figs S1 and S2).
In order to evaluate the effectiveness of fixed-station sampling design in capturing the temporal variability of abundance index, the probability density distribution of the observed and simulated abundance indices were calculated, respectively. As for the same species in each season, the two probability density distribution range was similar, but the positions of peak-value in probability density distribution for observed data were different from those for simulated data, and the probabilities of the peak values in observed data were less than those for simulated data (Fig. 5).
3.3 Mean abundance indices
The mean log-transformed abundance indices based on fixed-station sampling and simple random sampling designs showed similar trends for three selected fish species in different seasons (Fig. 6). However, the deviations of estimates for the fixed-station sampling were much greater than those for the random sampling design. The fixed-station sampling would underestimate or overestimate the abundance indices of the fish populations in some seasons. The mean abundance indices from simple random sampling were the same with those from the “true” value, suggesting that the random sampling design could yield unbiased estimates for the target fish species. However, the random sampling design yielded a larger variance than that of the fixed-station sampling design.
3.4 REE and RB
The REE and RB values for two sampling designs showed that fixed-station sampling design had higher precision but lower accuracy than simple random sampling design in estimating abundance indices of selected fish species (Fig. 7). The REE values of abundance index estimates of each species from fixed-station sampling were relatively high but with low variance compared with simple random sampling in four seasons.
The random sampling design was unbiased with RB values distributing around zero. However, the fixed-station sampling design was biased. Most of the RB values of abundance index estimates of the target species were significantly greater or less than zero in different seasons. The absolute RB values of simple random sampling were less than 0.6%, which were much less than those of fixed-station sampling design (Fig. 7). As for the abundance index of LP in autumn and abundance index of EF in winter for fixed-station sampling design, the RB values were around zero, and the REE values were small, which showed that fixed-station sampling design could have both higher precision and accuracy for LP in autumn and EF in winter.
The mean REE values of abundance index estimates over seasons were calculated from the 100 replications in the simulation study. The mean REE values showed increasing trends with coefficient of variation of the original survey data of abundance indices for target fish species in four seasons, which indicated that the effectiveness of sampling design was influenced by the variance of the original survey data. Fixed-station sampling design was influenced by variability of spatial distribution of abundance data more than simple random sampling design (Fig. 8).
4 Discussion
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Fisheries scientists and managers require accurate information on fish communities to make effective management decisions (Quist et al., 2006), and they typically rely on standardized sampling data to make population inferences (Bonar et al., 2009). Fixed-station sampling design was widely used in fishery-independent surveys because of its characteristics of convenient sampling station setting (Questel et al., 2013; Kiraly et al., 2014; Li et al., 2015). The fishery monitoring program is designed to monitor seasonal changes and population distribution of multispecies. In this study, we selected three species with different spatial distribution patterns to evaluate the effectiveness of the fixed-station sampling design for monitoring different population abundance in a fishery-independent survey. It showed that fixed-station sampling design could detect the seasonal trend of mean abundance of multispecies with high precision but low accuracy. The fixed-station sampling design would underestimate or overestimate the population abundance for some fish species in some seasons, while the abundance index estimates of LP in autumn and EF in winter were unbiased with high precision. The comparison of calculated species mean abundance between fixed and random sample sites found that fixed sampling sites may be acceptable due to ease of sampling and accessibility when monitoring temporal changes instead of absolute estimation.
If population attributes are estimated incorrectly, the results, and conclusions on which they are based, will be misleading and may impact unnecessarily on fishery resources if used in making management decisions. Seasonal variations in spatial distribution and different spatial distribution patterns of fish species all affected the effectiveness of survey designs in estimating abundance indices. Spatial distribution patterns of many commercial fish species have also changed due to the high-intensity fishing activity (Blanchard et al., 2008; Tang et al., 2011; VanDerWal et al., 2013; Xu et al., 2017). It would be improper even dangerous to make decisions for fishery management based on the biased data from fixed-station sampling design which overestimated or underestimated the abundance of target fish species in the long term run. For example, the concept of optimum yield at intermediate levels of fishing (the so called maximum sustainable yield) now is enshrined in legislation as a key objective of stock assessment, which is calculated based on fishery and fishery-independent survey data, including target species abundance and yield indices. If the fishery species were overfishing or even overfished, the misperception of stock status would accelerate the decline in abundance of fish populations. Therefore, it is necessary and important to make sure whether the fixed-station sampling design can provide unbiased estimation for a multi-species survey, especially when the survey data was the only information available for fishery management in the offshore waters. And the biased estimate must be calibrated cautiously before it can be used. Such as the underestimated EF mean abundance in summer (Fig. 6), it should be increased by nearly 20% and could be used in stock assessment.
The variability level in spatial distribution of fish population had large effect on statistical power of fixed-station sampling design in estimating abundance for target fish species. When spatial distribution variability level was low, fixed-station sampling design had high power to acquire the mean abundance of the true population with high precision and accuracy due to the advantage of continuity effect of fixed-station sampling (Questel et al., 2013; Kiraly et al., 2014). In this study, due to the relatively even distribution patterns for LP in autumn and EF in winter, fixed-station sampling design was superior to simple random sampling design in estimating the abundance indices of the two species. The high variability level of spatial distribution indicated that the fish distribution was heterogeneous or aggregated, and we might yield biased estimates based on fixed-station sampling design. EF mainly distributed in the deeper waters in summer, and the estimated abundance by fixed-station sampling design was about 20% lower than the mean abundance calculated based on the “true” value. On the contrary, simple random sampling design was unbiased in estimating fish population abundance. Because of the ability to get unbiased estimates but instability, simple random sampling design is commonly used as a null design for comparing the efficiency of different sampling schemes rather than as the optimal practical fishery-independent survey design (Pooler and Smith, 2005; Skibo et al., 2008).
OKI is widely used in mining, soil, environmental sciences, meteorological and medical research (Oliver and Webster, 1990; Pokhrel et al., 2013). The spatial distribution of fish species showed a certain spatial autocorrelation. So this method could be used to simulate target fish species population distribution over seasons. Though the accuracy of OKI varied depending on the original data structure, the kriging interpolated data could be regarded as the “true” values for abundance indices of fish populations in this simulation study. OKI might not have enough power to predict absolute values of species abundance, but it was sufficient for simulating the spatial patterns of fish abundance.
Fish species have various environmental preferences, showing obvious heterogeneity with water temperature and depth in different seasons in the offshore waters. The fixed-station sampling design is biased, misestimating the species population abundance. For a long-term fishery monitoring program which provides vital information for fishery management, a better sampling design is needed instead of the currently one. Stratified random sampling design would be a good option, which can overcome the characteristic of species stratified-distribution or patch distribution.
In summary, this study aimed to examine the performance of fixed-station sampling design compared with simple random station sampling design for multispecies survey in different seasons. Three fish species with different spatial distribution patterns were selected as target populations, and OKI method was applied to simulate the “true” population distribution. This simulation study suggested that the fixed-station sampling design had the power to detect the seasonal trends of species abundance. The effectiveness of fixed-station sampling design were different in different species distribution patterns. When the species had even distribution, fixed-station sampling design could get high quality data, but conversely, it might be biased. Evidently, the estimates based on the fixed-station sampling design must be calibrated cautiously while applying them for fisheries stock assessment and management.
Acknowledgement
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The authors are grateful for the RV crew and lab mates for their assistance with data collection.
Funding
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  • The Marine S&T Fund of Shandong Province for Pilot National Laboratory for Marine Science and Technology (Qingdao) under contract No. 2018SDKJ0501-2; the National Key Research and Development Program of China under contract No. 2019YFD0901304.
References
Less
Andrew N L, Mapstone B D. 1987. Sampling and the description of spatial pattern in marine ecology. Oceanography and Marine Biology, 25: 39−90.
Beare D J, Burns F, Greig A, et al. 2004. Long-term increases in prevalence of North Sea fishes having southern biogeographic affinities. Marine Ecology Progress Series, 284: 269–278, doi: 10.3354/meps284269
Bethlehem J. 2009. Estimators. In: Bethlehem J, ed. Applied Survey Methods: A Statistical Perspective. Hoboken: John Wiley & Sons, Inc., 15–42
Blanchard J, Maxwell D L, Jennings S. 2008. Power of monitoring surveys to detect abundance trends in depleted populations: the effects of density-dependent habitat use, patchiness, and climate change. ICES Journal of Marine Science, 65(1): 111–120, doi: 10.1093/icesjms/fsm182
Bonar S A, Contreras-Balderas S, Iles A C. 2009. An introduction to standardized sampling: chapter 1. In: Bonar S A, Hubert W A, Willis D W, eds. Standard Methods for Sampling North American Freshwater Fishes. Bethesda, Maryland, USA: American Fisheries Society, 1–12
Cao Jie, Chen Yong, Chang J H, et al. 2014. An evaluation of an inshore bottom trawl survey design for American lobster (Homarus americanus) using computer simulations. Journal of Northwest Atlantic Fishery Science, 46: 27–39, doi: 10.2960/J.v46.m696
Chen Y. 1996. A Monte Carlo study on impacts of the size of subsample catch on estimation of fish stock parameters. Fisheries Research, 26(3–4): 207–223
Dorner H, Graham N, Bianchi G, et al. 2015. From cooperative data collection to full collaboration and co-management: a synthesis of the 2014 ICES symposium on fishery-dependent information. ICES Journal of Marine Science, 72(4): 1133–1139, doi: 10.1093/icesjms/fsu222
Field A P. 2005. Is the meta-analysis of correlation coefficients accurate when population correlations vary?. Psychol Methods, 10(4): 444−67.
García S, Luengo J, Herrera F. 2000. Data Preprocessing in Data Mining. Cham: Springer, 39–57
Graham N, Grainger R, Karp W A, et al. 2011. An introduction to the proceedings and a synthesis of the 2010 ICES Symposium on Fishery-Dependent Information. ICES Journal of Marine Science, 68(8): 1593–1597, doi: 10.1093/icesjms/fsr136
Guan Lisha, Chen Yong, Wilson J A. 2017. Evaluating spatio-temporal variability in the habitat quality of Atlantic cod (Gadus morhua) in the Gulf of Maine. Fisheries Oceanography, 26(1): 83–96, doi: 10.1111/fog.12188
Guo Xupeng, Jin Xianshi, Dai Fangqun. 2006. Growth variations of small yellow croaker (Pseudosciaena polyactis Bleeker) in the Bohai Sea. Journal of Fishery Sciences of China, 13(2): 243–249
Hubbard W D, Miranda L E. 1986. Competence of non-random electrofishing sampling in assessment of structural indices. Proceedings of the Annual Conference of the Southeastern Association of Fish and Wildlife Agencies, 40: 79–84
Jiao Yan, Chen Yong, Schneider D, et al. 2004. A simulation study of impacts of error structure on modeling stock-recruitment data using generalized linear models. Canadian Journal of Fisheries and Aquatic Sciences, 61(1): 122–133, doi: 10.1139/f03-149
Kawazu M, Kameda T, Kurogi H, et al. 2015. Biological characteristics of Conger myriaster during the initial stage of spawning migration in the East China Sea. Fisheries Science, 81(4): 663–671, doi: 10.1007/s12562-015-0893-4
Kim J K, Kim Y H, Kim M J, et al. 2010. Genetic diversity, relationships and demographic history of the small yellow croaker, Larimichthys polyactis (Pisces: Sciaenidae) from Korea and China inferred from mitochondrial control region sequence data. Animal Cells and Systems, 14(1): 45–51, doi: 10.1080/19768351003764973
Kiraly I A, Coghlan Jr S M, Zydlewski J, et al. 2014. Comparison of two sampling designs for fish assemblage assessment in a large river. Transactions of the American Fisheries Society, 143(2): 508–518, doi: 10.1080/00028487.2013.864706
Li Bai, Cao Jie, Chang J H, et al. 2015. Evaluation of effectiveness of fixed-station sampling for monitoring American lobster settlement. North American Journal of Fisheries Management, 35(5): 942–957, doi: 10.1080/02755947.2015.1074961
Li Shiyan, Han Dongyan, Ma Qiuyun, et al. 2014. Feeding habits of Enedrias fangi in Jiaozhou Bay based on carbon and nitrogen stable isotope analysis. Journal of Fishery Sciences of China, 21(6): 1220–1226
Littell J S, McKenzie D, Kerns B K, et al. 2011. Managing uncertainty in climate-driven ecological models to inform adaptation to climate change. Ecosphere, 2(9): 1–19
Liu Yong, Chen Yong, Cheng Jiahua, et al. 2011. An adaptive sampling method based on optimized sampling design for fishery-independent surveys with comparisons with conventional designs. Fisheries Science, 77(4): 467–478, doi: 10.1007/s12562-011-0355-6
Mazzoni R, Iglesias-Rios R. 2002. Distribution pattern of two fish species in a coastal stream in southeast Brazil. Brazilian Journal of Biology, 62(1): 171–178, doi: 10.1590/S1519-69842002000100019
McClelland M A, Sass G G. 2012. Assessing fish collections from random and fixed site sampling methods on the Illinois River. Journal of Freshwater Ecology, 27(3): 325–333, doi: 10.1080/02705060.2012.658213
Nelson G A. 2014. Cluster sampling: a pervasive, yet little recognized survey design in fisheries research. Transactions of the American Fisheries Society, 143(4): 926–938, doi: 10.1080/00028487.2014.901252
Oliver M A, Webster R. 1990. Kriging: a method of interpolation for geographical information systems. International Journal of Geographical Information System, 4(3): 313–332, doi: 10.1080/02693799008941549
Paloheimo J E, Chen Y. 1996. Estimating fish mortalities and cohort sizes. Canadian Journal of Fisheries and Aquatic Sciences, 53(7): 1572–1579, doi: 10.1139/f96-077
Payne J L, Bush A M, Heim N A, et al. 2016. Ecological selectivity of the emerging mass extinction in the oceans. Science, 353(6305): 1284–1286, doi: 10.1126/science.aaf2416
Perry A L, Low P J, Ellis J R, et al. 2005. Climate change and distribution shifts in marine fishes. Science, 308(5730): 1912–1915, doi: 10.1126/science.1111322
Pikitch E K, Santora C, Babcock E A, et al. 2004. Ecosystem-based fisheries management. Science, 305(5682): 346–347, doi: 10.1126/science.1098222
Pokhrel R M, Kuwano J, Tachibana S. 2013. A kriging method of interpolation used to map liquefaction potential over alluvial ground. Engineering Geology, 152(1): 26–37, doi: 10.1016/j.enggeo.2012.10.003
Pooler P S, Smith D R. 2005. Optimal sampling design for estimating spatial distribution and abundance of a freshwater mussel population. Journal of the North American Benthological Society, 24(3): 525–537, doi: 10.1899/04-138.1
Questel J M, Clarke C, Hopcroft R R. 2013. Seasonal and interannual variation in the planktonic communities of the northeastern Chukchi Sea during the summer and early fall. Continental Shelf Research, 67: 23–41, doi: 10.1016/j.csr.2012.11.003
Quist M C, Gerow K G, Bower M R, et al. 2006. Random versus fixed-site sampling when monitoring relative abundance of fishes in headwater streams of the upper Colorado River basin. North American Journal of Fisheries Management, 26(4): 1011–1019, doi: 10.1577/M05-153.1
Schabenberger O, Gotway C A. 2005. Statistical Methods for Spatial Data Analysis: Texts in Statistical Science. Boca Raton: Chapman and Hall, 511–513
Simmonds E J, Döring R, Daniel P, et al. 2011. The role of fisheries data in the development evaluation and impact assessment in support of European fisheries plans. ICES Journal of Marine Science, 68(8): 1689–1698, doi: 10.1093/icesjms/fsr067
Skibo K M, Schwarz C J, Peterman R M. 2008. Evaluation of sampling designs for Red Sea Urchins Strongylocentrotus franciscanus in British Columbia. North American Journal of Fisheries Management, 28(1): 219–230, doi: 10.1577/M06-293.1
Sun Chunyang, Wang Yingbin. 2020. Impacts of the sampling design on the abundance index estimation of Portunus trituberculatus using bottom trawl. Acta Oceanologica Sinica, 39(6): 48–57, doi: 10.1007/s13131-020-1607-z
Tang Fenghua, Shen Xinqiang, Wang Yunlong. 2011. Dynamics of fisheries resources near Haizhou Bay waters. Fisheries Science, 30(6): 335–341
VanDerWal J, Murphy H T, Kutt A S, et al. 2013. Focus on poleward shifts in species’ distribution underestimates the fingerprint of climate change. Nature Climate Change, 3(3): 239–243, doi: 10.1038/nclimate1688
Warren W G. 1994. The potential of sampling with partial replacement for fisheries surveys. ICES Journal of Marine Science, 51(3): 315–324, doi: 10.1006/jmsc.1994.1032
Xu Lili, Xue Ying, Jiao Yan, et al. 2017. Population structure and spatial distribution of Oratosquilla oratoria in Haizhou Bay and adjacent waters. Periodical of Ocean University of China, 47(4): 28–36
Yu Hao, Jiao Yan, Su Zhenming, et al. 2012. Performance comparison of traditional sampling designs and adaptive sampling designs for fishery-independent surveys: a simulation study. Fisheries Research, 113(1): 173–181, doi: 10.1016/j.fishres.2011.10.009
Zhang Chunguang. 2010. Fauna Sinica: Osteichthyes Anguilliformes Notacanthiformes. Beijing: Science Press, 199–203
Zhang Yiming, Neelakantan A, Park C, et al. 2019. Adaptive sampling with varying sampling cost for design space exploration. AIAA Journal, 57(3): 1032–1043, doi: 10.2514/1.J057470
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doi: 10.1007/s13131-021-1932-x
  • Receive Date：2021-01-25
  • Online Date：2025-11-21
  • Published：2022-03-25
Article Data
Affiliations
History
  • Received：2021-01-25
  • Accepted：2021-03-04
Funding
The Marine S&T Fund of Shandong Province for Pilot National Laboratory for Marine Science and Technology (Qingdao) under contract No. 2018SDKJ0501-2; the National Key Research and Development Program of China under contract No. 2019YFD0901304.
Affiliations
    1 School of Fishery, Zhejiang Ocean University, Zhoushan 316000, China
    2 Fisheries College, Ocean University of China, Qingdao 266003, China
    3 Laboratory for Marine Fisheries Science and Food Production Processes, Pilot National Laboratory for Marine Science and Technology (Qingdao), Qingdao 266237, China

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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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