Yiping Ren

Implementing a multispecies size-spectrum model in a data-poor ecosystem

Multispecies ecological models have been used for predicting the effects of fishing activity and evaluating the performance of management strategies. Size-spectrum models are one type of physiologically-structured ecological model that provide a feasible approach to describing fish communities in terms of individual dietary variation and ontogenetic niche shift. Despite the potential of ecological models in improving our understanding of ecosystems, their application is usually limited for data-poor fisheries. As a first step in implementing ecosystem-based fisheries management (EBFM), this study built a size-spectrum model for the fish community in the Haizhou Bay, China. We describe data collection procedures and model parameterization to facilitate the implementation of such size-spectrum models for future studies of data-poor ecosystems. The effects of fishing on the ecosystem were exemplified with a range of fishing effort and were monitored with a set of ecological indicators. Total community biomass, biodiversity index, W-statistic, LFI (Large fish index), MeanW (mean body weight) and Slope (slope of community size spectra) showed a strong non-linear pattern in response to fishing pressure, and largest fishing effort did not generate the most drastic responses in certain scenarios. We emphasize the value and feasibility of developing size-spectrum models to capture ecological dynamics and suggest limitations as well as potential for model improvement. This study aims to promote a wide use of this type of model in support of EBFM.

Optimization of sampling effort for a fishery-independent survey with multiple goals

Fishery-independent surveys are essential for collecting high quality data to support fisheries management. For fish populations with low abundance and aggregated distribution in a coastal ecosystem, high intensity bottom trawl surveys may result in extra mortality and disturbance to benthic community, imposing unnecessarily large negative impacts on the populations and ecosystem. Optimization of sampling design is necessary to acquire cost-effective sampling efforts, which, however, may not be straightforward for a survey with multiple goals. We developed a simulation approach to evaluate and optimize sampling efforts for a stratified random survey with multiple goals including estimation of abundance indices of individual species and fish groups and species diversity indices. We compared the performances of different sampling efforts when the target estimation indices had different spatial variability over different survey seasons. This study suggests that sampling efforts in a stratified random survey can be reduced while still achieving relatively high precision and accuracy for most indices measuring abundance and biodiversity, which can reduce survey mortality. This study also shows that optimal sampling efforts for a stratified random design may vary with survey objectives. A postsurvey analysis, such as this study, can improve survey designs to achieve the most important survey goals.

Spatio-seasonal patterns of fish diversity, Haizhou Bay, China

Spatial-seasonal patterns in fish diversity in Haizhou Bay were studied based on stratified random surveys conducted in 2011. Principal component analysis was conducted to distinguish different diversity components, and the relationships among 11 diversity indices were explored. Generalized additive models were constructed to examine the environmental effects on diversity indices. Eleven diversity indices were grouped into four components: (1) species numbers and richness, (2) heterogeneous indices, (3) evenness, and (4) taxonomic relatedness. The results show that diversity indices among different components are complementary. Spatial patterns show that fish diversity was higher in coastal areas, which was affected by complex bottom topography and spatial variations of water mass and currents. Seasonal trends could be best explained by the seasonal migration of dominant fish species. Fish diversity generally declined with increasing depth except for taxonomic distinctness, which increased with latitude. In addition, bottom temperature had a significant effect on diversity index of richness. These results indicate that substrate complexity and environmental gradients had important influences on fish diversity patterns, and these factors should be considered in fishery resource management and conservation. Furthermore, diversity in two functional groups (demersal/pelagic fishes) was influenced by different environmental factors. Therefore, the distribution of individual species or new indicators in diversity should be applied to examine spatio-seasonal variations in fish diversity.

Use of random forests and support vector machines to improve annual egg production estimation

The delta-generalized additive model (Delta_GAM) is commonly used for analyzing zero-inflated continuous data, and has been widely applied in egg production methods (EPMs). It consists of two GAMs: one with a binomial distribution to estimate the probability of non-zero values, and the other with a log-normal distribution (Delta_LN model) or a gamma distribution (Delta_LG model) to model the continuous non-zero values. However, the rather restrictive distribution assumptions are not fulfilled for egg production data. In this study, we modified the Delta_GAMs using two machine learning techniques: random forest (Delta_RF) and support vector machines (Delta_SVM). We applied the tenfold cross-validation procedure to compare the performance of these four models using root mean square error (RMSE) and the EPM survey data of small yellow croaker Larimichthys polyactis, mullet Liza haematocheilus and gizzard shad Konosirus punctatus from Haizhou Bay, China. Both the Delta_RF and Delta_SVM models showed superior performance to that of the Delta_LN and Delta_LG models. Predicted spatial and temporal distributions varied among the models, although predictive performance varied little. The annual egg production was predicted and estimated with large uncertainty. We propose that machine learning techniques such as RFs and SVMs be used to model zero-inflated continuous data from EPM surveys, which tend to provide a more reliable estimation of annual egg production (AEP).

A mass balanced model of trophic structure and energy flows of a semi-closed marine ecosystem

The marine ecosystem of the Jiaozhou Bay has degraded significantly in fisheries productivity and its ecological roles as spawning and nursery ground for many species of commercial importance has been declining in recent years. A mass-balanced trophic model was developed using Ecopath with Ecosim to evaluate the trophic structure of the Jiaozhou Bay for improving ecosystem management. The model were parameterized based on the fisheries survey data in the Jiaozhou Bay in 2011, including 23 species groups and one detritus group according to their ecological roles. The trophic levels of these ecological groups ranged from 1 (primary producers and detritus) to 4.3 (large demersal fishes). The estimated total system throughput was 12 917.10 t/(km2·a), with 74.59% and 25.41% contribution of the total energy flows from phytoplankton and detritus, respectively. Network analyses showed that the overall transfer efficiency of the ecosystem was 14.4%, and the mean transfer efficiency was 14.5% for grazing food chain and 13.9% for detritus food chain. The system omnivory index (SOI), Finn’s cycled index (FCI) and connectance index (CI) were relatively low in this area while the total primary production/total respiration (TPP/TR) was high, indicating an immature and unstable status of the Jiaozhou Bay ecosystem. Mixed trophic impact analysis revealed that the cultured shellfish had substantial negative impacts on most functional groups. This study contributed to ecosystem-level evaluation and management planning of the Jiaozhou Bay ecosystem.

Optimization of stratification scheme for a fishery-independent survey with multiple objectives

Fishery-independent surveys are often used for collecting high quality biological and ecological data to support fisheries management. A careful optimization of fishery-independent survey design is necessary to improve the precision of survey estimates with cost-effective sampling efforts. We developed a simulation approach to evaluate and optimize the stratification scheme for a fishery-independent survey with multiple goals including estimation of abundance indices of individual species and species diversity indices. We compared the performances of the sampling designs with different stratification schemes for different goals over different months. Gains in precision of survey estimates from the stratification schemes were acquired compared to simple random sampling design for most indices. The stratification scheme with five strata performed the best. This study showed that the loss of precision of survey estimates due to the reduction of sampling efforts could be compensated by improved stratification schemes, which would reduce the cost and negative impacts of survey trawling on those species with low abundance in the fishery-independent survey. This study also suggests that optimization of a survey design differed with different survey objectives. A post-survey analysis can improve the stratification scheme of fishery-independent survey designs.

Annual variations of biogenic element contents of manila clam (Ruditapes philippinarum) bottom-cultivated in Jiaozhou Bay, China

Manila clam (Ruditapes philippinarum) was monthly sampled from its benthic aquaculture area in Jiaozhou Bay from May 2009 to June 2010. The annual variations of major elemental composition, organic content, fatness and element ratio of Manila clam were examined. The element removal effect of clam farming in Jiaozhou Bay was analyzed based on natural mortality and clam harvest. The results indicated that the variation trend of carbon content in shell (Cshell) was similar to that in clam (Cclam). Such a variation was higher in summer and autumn than in other seasons, which ranged from 9.10 ± 0.13 to 10.38 ± 0.09 mmol g−1 and from 11.28 ± 0.29 to 12.36 ± 0.06 mmol g−1, respectively. Carbon content of flesh (Cflesh) showed an opposite variation trend to that of shell in most months, varying from 29.42 ± 0.05 to 33.64 ± 0.62 mmol g−1. Nitrogen content of shell (Nshell) and flesh (Nflesh) changed seasonally, which was relatively low in spring and summer. Nshell and Nflesh varied from 0.07 ± 0.009 to 0.14 ± 0.009 mmol g−1 and from 5.46 ± 0.12 to 7.39 ± 0.43 mmol g−1, respectively. Total nitrogen content of clam ranged from 0.50 ± 0.003 to 0.76 ± 0.10 mmol g−1 with a falling tend except for a high value in March 2010. Phosphorus content of clam (Nclam) fluctuated largely, while phosphorus content of shell (Pshell) was less varied than that of flesh (Pflesh). Pshell varied from 0.006 ± 0.001 to 0.016 ± 0.001 mmol g−1; while Pflesh fluctuated between 0.058 ± 0.017 and 0.293 ± 0.029 mmol g−1. Pclam ranged from 0.015 ± 0.002 to 0.041 ± 0.006 mmol g−1. Carbon and nitrogen content were slightly affected by shell length, width or height. Elemental contents were closely related to the reproduction cycle. The removal amounts of carbon, nitrogen and phosphorus from clam harvest and natural death in Jiaozhou Bay were 2.92×104t, 1420 t and 145 t, respectively. The nutrient removal may aid to reduce the concentrations of nitrogen and phosphorus, the main causes of eutrophication, and to maintain the ecosystem health of Jiaozhou Bay.

Age determination for whitespotted conger Conger myriaster through somatic and otolith morphometrics

It is difficult to determine ages of eels via otoliths, because multiple alternating translucent and opaque zones in the otoliths are hard to identify. In this study, we developed an efficient age determination method for whitespotted conger (Conger myriaster), using random forest models with otolith weight and length, total body length, capture location and season as predictors. 409 specimens were collected from six locations in Yellow and East China Sea between October 2016 and December 2017. Overall OOB error rate was 17.36% compared with 16.26% for the external cross-validation dataset, and the error of age was within one year. Otolith weight and total length were the most important predictors, followed by otolith length, capture locations and seasons. There were no significant differences between the results derived from otolith/somatic morphometrics and otoliths annuli in the estimation of age composition and von Betalanffy Growth Functions growth curve. Our results demonstrated that random forest model with otolith and somatic morphometrics is an efficient and reliable approach for age determination of C. myriaster, which may also be applied to other eel species.

Using a new framework of two-phase generalized additive models to incorporate prey abundance in spatial distribution models of juvenile slender lizardfish in Haizhou Bay, China

ABSTRACT The predictive skill of species distribution models depends on the quality and quantity of input information. In addition to the physical environmental variables, prey availability is also one of the main drivers regulating spatial distribution of marine species. However, prey distribution data have rarely been considered in habitat models due to the lack of information on non-commercial prey species. This may lead to an incomplete view of species distributions and biased model predictions. In this study, we developed a new framework of two-phase generalized additive models (GAMs) based on the Tweedie distribution to incorporate the predicted prey abundance as covariates in habitat models, and applied this framework to juvenile slender lizardfish Saurida elongata in Haizhou Bay, China. This study demonstrated that the predictive skill of habitat models could be greatly improved through incorporating prey abundance as explanatory variables. The importance of prey distribution data in the habitat model confirms the essentiality of including prey data while modelling species distribution. Spatial overlap and GAM analysis demonstrated that not all dominant prey can be selected as potential explanatory variables and only those prey species showing high spatiotemporal occurrences with predators should be incorporated. The framework derived in this study could be extended to other marine organisms to improve the predictive skill of habitat models and enhance our understanding of the ecological mechanisms underlying the distribution of marine species.

Comparing the prediction of joint species distribution models with respect to characteristics of sampling data

Biotic interactions have been rarely included in traditional species distribution models, wherein joint species distribution models (JSDMs) emerge as a feasible approach to incorporate environmental factors and interspecific interactions simultaneously, making it a powerful tool for analyzing the structure and assembly processes of biotic communities. However, the predictability and statistical robustness of JSDMs are largely unknown because of the lack of research efforts for those newly developed models. This study systematically evaluated the performances of five JSDMs in predicting the occurrence and biomass of multiple species, with a particular focus on diverse characteristics of sampling data, including type of response variables, number of sampling sites, and the number of species included in models. In general, most models yielded satisfactory performances on fitting to observed data and on the estimation of environmental effects; however, they showed less well performances in evaluating species associations, and their predictability had large variations. The JSDMs showed inconsistent performances between the goodness-of-fit and predictability in cross-validation, and the Boral model was relatively robust than others. The predictability of JSDMs was less influenced by sample sizes and substantially improved by incorporating rare species. This study contributes to an appropriate model selection and application of JSDMs.