Evaluation of Fish Communities in Daya Bay Using Biomass Size Spectrum and ABC Curve

Based on the data collected by four trawl surveys during 2016–2017, we applied biomass size spectrum (BSS) and abundance–biomass comparison (ABC) curve to assess the status of fish communities’ status in Daya Bay, China. Our findings indicated a unimodal pattern and biomass size ranged from −2 to 10 grain levels and the pattern of the Sheldon-type BSS of fish in Daya Bay. Moreover, fishes in the range of four to eight size class were relatively abundant. The highest peak belonged to the two to four grain level (log2 size bins), mainly consisting of Leiognathus brevirostris, Callionymus meridionalis, Callionymus koreanus, Evynnis cardinalis, Trachurus japonicus, and other small fishes. The curves of the BSS in spring and winter were relatively flat and comprised a large curvature. The summer and autumn curves were comparatively steep, and the seasonal curvature was small. The curvatures of the curve were mainly related to a large number of small Evynnis cardinalis and a small number of large-sized Harpadon nehereus and Leiognathus ruconius. In our study, it was observed that the number and the size of the breeding population, trophic levels, migration habits, and other life history characteristics, as well as anthropogenic disturbances (especially overfishing), significantly affected the peak shape, slope, or curvature of the fish BSS, with overfishing being the main factor. The ABC curve exhibited that Daya Bay was in a critical state of disturbance throughout the year. The spring, summer, and autumn were in severe disturbance, while the winter was in moderate disturbance.

Surfing the biomass size spectrum: some remarks on history, theory, and application

Charles Elton introduced the “pyramid of numbers” in the late 1920s, but this remarkable insight into body-size dependent patterns in natural communities lay fallow until the theory of the biomass size spectrum was introduced by aquatic ecologists in the mid-1960s. They noticed that the summed biomass concentration of individual aquatic organisms was roughly constant across equal logarithmic intervals of body size from bacteria to the largest predators. These observations formed the basis for a theory of aquatic ecosystems, based on the body size of individual organisms, that revealed new insights into constraints on the structure of biological communities. In this review, we discuss the history of the biomass spectrum and the development of underlying theories. We indicate how to construct biomass spectra from sample data, explain the mathematical relations among them, show empirical examples of their various forms, and give details on how to statistically fit the most robust linear and nonlinear models to biomass spectra. We finish by giving examples of biomass spectrum applications to production and fisheries ecology and offering recommendations to help standardize use of the biomass spectrum in aquatic ecology.

Models on the Slope of, and Biomass Flow up, the Biomass Size Spectrum

A comparison is made between the different models of the biomass size spectrum proposed by a number of authors. Though superficially dissimilar, the models are all mathematically compatible if the differences in their underlying assumptions are taken into account. The simplest model does not consider the complexities of food webs over food chains, somatic growth, or the continuous nature of the size spectrum. Comparison with the more complex models, however, shows that these omissions do not seriously affect the slope of the size spectrum. For example, one model predicts that the effects of somatic growth and reproduction cancel if cohort biomasses remain relatively constant as the cohorts mature. If growth rate is related to body size in an allometric relationship and reproduction is ignored, then another model gives a slightly different slope (higher by roughly 0.03). If the same assumptions are used in both models, however, they give compatible results. Some simple equations are suggested for routine application in size spectrum analysis of biomass and production data.