Recruitment success and variability in marine fish populations: Does age -truncation matter?

1. Recruitment success and variability in marine fish populations: Does age-truncation matter? Sarah Ann Siedlak1, John Wiedenmann21University of Miami, Coral Gables, FL 33146, 2 Institute of Marine and Coastal Sciences, Rutgers University, New Brunswick, NJ 08901 l i n e a r (0 . 0 0 6 ) 8 0 Be ve rt o n -H o l t (0 . 7 9 3 ) 0 0 R i cke r (0 . 2 0 1 ) 2 1 6 s n o i t 0 a 0 i v 0 s e 8 t i d u 4 t r n c 0 e e 0 0 R m 6 t i u r c 0 e 0 2 0 R 4 0 0 0 2 0 0 0 5000 10000 15000 20000 3.5 4.0 4.5 5.0 5.5 Sp a w n i n g b i o ma ss Me a n a g e o f sp a w n i n g b i o ma ss No Fishing n 0 o 1 i t . r 0 o p o r P 0 0 . 0 1 2 3 4 5 6 7 8 9 10 Ag e Heavy Fishing 0 3 . n 0 o i t r o 5 p 1 . o 0 r P 0 0 . 0 1 2 3 4 5 6 7 8 9 10 Ag e Abstract Methods Results Sustainable management of marine fisheries requires a better understanding of the factors controlling reproductive success (recruitment) and variability. Most fisheries typically remove the largest (and oldest) individuals in the population, resulting in a truncation of the population age structure. In this study, the effects of this age-truncation on the recruitment success and variability were explored for 33 populations in the North Atlantic using information from stock assessments. For most populations we explored, age-truncation is not predicted to affect recruitment success and variability, suggesting that calls to change harvest practices across fisheries to protect older fish are likely unwarranted. • To determine the effects of age-truncation on recruitment success and variability we used the following steps: • Collect stock assessment reports for North Atlantic fish populations in U.S. and European waters for our analyses (NEFSC 2008; ICES 2011). • Compile annual estimates of recruitment, total spawning biomass, and spawning biomass-at-age from reports. • Determine the best fitting model predicting recruitment as a function of spawning biomass (Figure 2a). • Calculate the relative recruitment deviations from 3) and the mean age of the spawners each year. • Fit linear model to relationship predicting recruitment deviations as a function of spawner age (Figure 2b). • Calculate the variability in recruitment for young and old spawners (young = lower 50%; old = upper 50%). a b Objectives • To explore the effects of age-truncation on the population dynamics of marine fishes. • Use information collected from stock assessments to test the hypotheses that populations with greater age-truncation (i.e., those comprised disproportionately of younger fish) will have less successful and more variable recruitment as suggested by Palumbi (2004) and Hsieh et al. (2006), respectively. Gulf of Maine Haddock a b Figure 3. a) Relative slopes of recruitment deviations vs. mean spawner age for all 33 stocks, colored by family. b) Ratio of recruitment variability between young and old spawners varied greatly across populations, especially for Gadidae and Pleuronectidae families. Significant for 4 different populations. * = statistically significant result, with significance for the variance ratio determined using Levene’s test (Levene 1960). Figure 1. Example of age-truncation in a population. With no fishing (top), the proportion of spawning biomass remains high at older ages (62% of biomass is age 6 or older). Under heavy fishing (bottom), the oldest fish are removed, and the spawning biomass is dominated by younger fish (only 5% of the biomass is age 6 or older). Modified from Wiedenmann and Mangel (2007). Conclusions • 45% of stocks analyzed show either a positive (9%) or negative (36%) relationship, where the age of spawners significantly impacts recruitment success (Figure 3a). • No apparent trend of relationship patterns across species or families. • Therefore, managing for age structure may not be as important as managing for sustainable harvest rates. • Recruitment variability does not appear to be affected by age-truncation for the majority of stocks we explored (Figure 3b). Figure 2. a) Example spawning biomassvs. recruitment plot for Gulf of Maine haddock showing the recruitment models (lines) fit to the data (colored circles). Model comparison was done using Akaike Information Criterion (AIC), and the best-fitting model is the one with the highest AIC weight (shown in parentheses; in this case the Beverton-Holt model). b) Relative deviations from the best-fitting model from a) as a function of the mean age of the spawning biomass. The red line represents the linear fit, with significance determined using AIC. Acknowledgments References We thank the National Science Foundation for the opportunity to participate in the Research Internships in Ocean Sciences Program at Rutgers University. Many thanks to all faculty and staff at the Rutgers University Marine Field Station. Hsieh, C.H., Beddington, J.R., Hunter, J.R., May, R.M., Reiss, C.S., Sugihara, G. (2006). Fishing elevates variability in the abundance of exploited species. Nature 443, 859-862. ICES (2011). Report of the Fisheries Working Group, 28 April–4 May 2011. ICES CM 2011/ACOM:05. Levene, H. (1960). In Contributions to Probability and Statistics: Essays in Honor of Harold Hotelling, I. Olkin et al. eds., Stanford University Press, pp. 278-292. NEFSC (2008). Assessment of 19 Northeast groundfish stocks through 2007. 2008 Report of the 3rd Groundfish Assessment Review Meeting (2008 GARM III). Woods Hole, Massachusetts, Northeast Fisheries Science Center. Palumbi, S.R. Why mothers matter. Nature, 430, 621-622 (2004). Wiedenmann, J. and M. Mangel. 2007. Rebuilding Fisheries.  Phase 2. Identifying Situations of Special Concern.  Final report to the Lenfest Ocean Program.