Growth Parameters and Spawning Season Estimation of Four Important Flyingfishes in the Kuroshio Current Off Taiwan and Implications From Comparisons With Global Studies

Abstract

Growth shapes the life history of fishes. Establishing appropriate aging procedures and selecting representative growth models are important steps in developing stock assessments. Flyingfishes (Exocoetidae) have ecological, economic, and cultural importance to many coastal countries including Taiwan. There are 29 species of flyingfishes found in the Kuroshio Current off Taiwan and adjacent waters, comprising 56% of the flyingfishes taxa recorded worldwide. Among the six dominant species in Taiwan, four are of special importance. This study reviews aging data of these four species, documents major points of the aging methods to address three aging issues identified in the literature, and applies multi-model inference to estimate sex-combined and sex-specific growth parameters for each species. The candidate growth models examined included von Bertalanffy, Gompertz, Logistic, and Richards models, and the resulting optimal model tended to be the von Bertalanffy model for sex-combined data and Gompertz and von Bertalanffy models for sex-specific cases. The study also estimates hatch dates from size data collected from 2008 to 2017; the results suggest that the four flyingfishes have two spawning seasons per year. Length-weight relationships are also estimated for each species. Finally, the study combines the optimal growth estimates from this study with estimates for all flyingfishes published globally, and statistically classifies the estimates into clusters by hierarchical clustering analysis of logged growth parameters. The results demonstrate that aging materials substantially affect growth parameter estimates. This is the first study to estimate growth parameters of flyingfishes with multiple model consideration. This study provides advice for aging flyingfishes based on the three aging issues and the classification analysis, including a recommendation of using the asterisci for aging flyingfishes to avoid complex otolith processing procedures, which could help researchers from coastal countries to obtain accurate growth parameters for many flyingfishes.

Length-Weight Relationships and Otolith-Based Growth Curves for Brushtooth Lizardfish off Taiwan With Observations of Region and Aging–Material Effects on Global Growth Estimates

“…Aging material was also an important cause of variation in φ of global estimates (Supplementary Figure 3), as has been demonstrated in previous studies for other species (Chang and Maunder, 2012;Chang et al, 2022). The classification approach statistically separated the studies into different groups associated with the aging materials used (Cluster 1 against Clusters 2 and 3).…”

“…Identifying appropriate aging procedures and growth models are two important steps in the process of estimating reliable growth curves and in developing proper stock assessments for designing management plans for the fish resource (Maunder and Piner, 2015;Maunder et al, 2016;Chang et al, 2022). The first step is to provide reliable age data as the basis for estimates of growth parameters, as well as mortality rate and productivity (Dos Santos et al, 2017).…”

“…These methods are not applicable to all species. Low recapture rates, high costs, or data sampling limitations, for example, are major constraints for validation (Campana, 2001;Fisheries and Oceans Canada, 2019;The Campana lab, 2022); and thus, most studies choose the one or two methods that are most applicable (e.g., Iglesias et al, 1997;Carbonara et al, 2018;Kastelle et al, 2020;Chang et al, 2022). Among them, MI analysis is one of the most popular methods due to its moderate sampling requests and low cost (Lessa et al, 2006), although there are some considerations to make the interpretations objective and rigorous, such as randomized sampling and objective interpretation (to be discussed later) (Campana, 2001).…”

“…Other commonly used alternatives include the Gompertz model (Gompertz, 1825), the logistic model (Ricker, 1975), and the Richards model (Richards, 1959). The multi-model inference approach (MMIA) (e.g., Katsanevakis, 2006;Katsanevakis and Maravelias, 2008) is a useful way to select the most representative growth model from a suite of candidates for the species studied (e.g., Williams et al, 2012;Carbonara et al, 2020;Chang et al, 2022).…”

Length–Weight Relationships, Growth Models of Two Croakers (Pennahia macrocephalus and Atrobucca nibe) off Taiwan and Growth Performance Indices of Related Species

“…Edge analysis examines the timing of the translucent zone formation, and the ages are considered validated if only one opaque and one translucent band is formed annually [22][23][24][25]. The method is relatively easy to conduct compared to MIA, which plots the proportion of completion of the outermost increment against the month of capture to examine if it shows a yearly sinusoidal cycle [13,26,27] and can be used alone for the validation of annual periodicity of growth increment [18,24].…”