The first chapter of my doctoral dissertation was recently published in the Canadian Journal of Fisheries and Aquatic Sciences. The article entitled Coho salmon escapement and trends in migration timing to a data-poor river: estimates from a Bayesian hierarchical model has recently been uploaded to the CJFAS website in publication format.
As fisheries management agencies consider shifting towards more ecosystem based approaches to managing fisheries, they need to manage species which have historically garnered limited interest from fishers and researchers. As such, there is generally limited information on the basic ecology of these species in the systems which are being managed. In the Chignik salmon fishery on the Alaska Peninsula, fisheries management and harvest has historically focused on sockeye salmon. The system also supports a population of coho salmon, but they are not managed or directly targeted for harvest due to low economic value and logistic factors. However, previous research (Ruggerone and Rogers 1992) has estimated that juvenile coho salmon consume over half of the emerging sockeye salmon fry in the rearing lakes annually, presenting a potential predation bottleneck to the productivity of the sockeye salmon fishery. Therefore, there is increasing interest in managing the coho salmon population for increased harvest in order to reduce this predation pressure on sockeye salmon.
As there has been limited interest in coho salmon historically, there are limited data available to estimate the numbers of coho salmon returning to the system each year. Sockeye salmon are enumerated at a seasonal weir. However, due to a later spawning migration time, coho salmon are just starting to return to the system when the weir is removed for the season. Therefore, only the beginning of the coho salmon run is counted. In this paper, co-author Daniel Schindler (UW) and I used a Bayesian hierarchical modeling approach to estimate the number of adult coho salmon returning to the system in years for which limited data are available. The Bayesian hierarchical model structure assumes that there is a river-level mean escapement date, migration duration, and escapement size, and that the peak escapement date, migration duration, and escapement size in any given year are drawn from a distribution around these river-level means. This allows the model to use years with more escapement data to inform years with less data available. Our estimates of escapement were more precise in years for which more daily escapement counts were available, and less precise when fewer data were available, relying more heavily on the historical mean values than the few observations in those years. The Bayesian hierarchical model structure also provides estimates of uncertainty around the annual escapement estimates.
Additionally, we examined the trends in peak escapement timing over time and in relation to broad-scale environmental conditions. We found that coho salmon escapement is negatively correlated with PDO index, being earlier in positive PDO years, and that it is getting later over time. However, the significance of these trends depends on the assumptions made about the shape of the spawning migration arrival timing. If we assume normally distributed arrival timing, only the relationship with PDO was significant. If we assume a gamma distributed arrival timing with a long descending limb, only the relationship with time is significant.
Overall, our results have implications for the management of any future coho salmon fishery that may be implemented. The escapement estimates allow for the calculation of escapement goals, under either single-species or multi-species management frameworks. Further, knowing the productivity of the coho salmon populations allows us to simulate the fishery dynamics under different harvest scenarios, as well as under different environmental and economic conditions. Such simulations are important to provide stakeholders with knowledge about the viability of alternative harvest strategies for their fishery. The relationships of peak escapement timing with time and environmental conditions can aid managers and fishers with in-season decisions about when to allow fishing and when the run has likely peaked. Finally, the precision of our annual escapement estimates (or lack thereof in data poor years) highlights the importance of monitoring data if coho salmon populations are to be effectively managed.
Walsworth TE and Schindler DE (In press) Coho salmon escapement and trends in migration timing to a data-poor river: estimates from a Bayesian hierarchical model. Canadian Journal of Fisheries and Aquatic Sciences. Accepted July 25, 2015. DOI: 10.1139/cjfas-2014-0554.