In this chapter, I reviewed some technical ground about probability theory: fre- quentist and Bayesian inferences. Bayesian inference theories have been widely used in artiﬁcial intelligence and expert systems since the late 1950s; but, un- fortunately, the solution obtained by these models is rarely achievable in an an- alytical closed form. To simplify the computation of the posterior distribution, one may recourse to exponential families on the choice of priors; but for the majority of cases the posterior distributions are approximated by functional and MCMC approximation techniques. Since graphical models are at the heart of every probability model, I reviewed some basic concepts of graphical models that are necessary in understanding the probabilistic models and inference algorithms. Finally I discussed the advantage of Gaussian Process models and how one may ﬁt a time series model to a process that evolves over time.
Identiﬁcation and quantiﬁcation
of heteroscedasticity in
stock-recruitment relationshipsNon-constant variance (heteroscedasticity) in the stock-recruitment (S-R) rela- tionship is proposed as an important factor in sustainable ﬁsheries management, but its reliable estimation from noisy populations is problematic. I developed methods for both frequentist and Bayesian approaches to test whether I can accurately estimate the degree of heteroscedasticity in 90 published S-R popu- lations. The conﬁdence interval for the heteroscedastic regression model is es- timated via a parametric bootstrap approach, and the credible interval for the Bayesian method via a Markov chain Monte Carlo sampling algorithm. I found strong evidence of negative heteroscedasticity in several stocks, regardless of the statistical paradigm, the details of density dependence, and the methods used to generate the original populations. This statistical framework provides an eﬃ- cient and reliable setting for assessing heteroscedasticity of the S-R relationship in ﬁsheries.
The objectives of this Chapter are the following:
• To examine whether the additional parameter η1 can provide a better ﬁt to
• To examine whether the parameter η1 can be reliably estimated.
Much of the work in this chapter has recently been published by the author and colleagues (Panikian et al., 2015), printed in Appendix H.
Reliable mathematical modelling and prediction of ﬁsh populations is of great importance socially and economically, as well as being a necessary ingredient in the conservation of biodiversity. Various natural and anthropogenic factors af- fect ﬁsh populations, with the life of juvenile ﬁsh typically being characterised by enormous mortality rates (Hilborn and Walters, 1992). Newly hatched ﬁsh larvae have very low probability of reaching adulthood (Pitchford et al., 2005). Mortality is due to variability in food supply, migration, predation, starvation, poisonous pollutants, and ﬁshing activities (Steele et al., 1977): resulting in an unpredictable relationship between the adult population (‘stock’) and the juve- niles (‘recruitment’) that will successfully survive to enter the adult population in the future. Understanding the stock-recruitment (S-R) relationship therefore requires careful statistical techniques forming a crucial ingredient in the sustain- able management of these exploited natural resources.
There are, of course, limits to growth in populations. For instance, climatic changes, environmental conditions and natural disasters are classiﬁed as density- independent factors that inﬂuence larval survival and recruitment size directly but do not regulate variability of juvenile mortality, except for some ﬂatﬁsh species (Myers and Cadigan, 1993b; Leggett and Deblois, 1994). In contrast, in- traspeciﬁc competition, predation and disease are classiﬁed as density-dependent limiting factors that might regulate the recruitment variation of ﬁsh popula- tions (Myers and Cadigan, 1993a). At low population there would be very little density-dependent mortality during the juvenile stage, but when the population size increases a strong density-dependent mortality usually occurs. Myers and Cadigan (1993b) found that the interannual variability in juvenile survival ap- pears to be the most important source of variability in abundance; but it is attenuated by density-dependent mortality in the juvenile stage. From another perspective, Spencer (2008) studied the eﬀect of both density-dependent and density-independent factors in determining the spatial distribution of six ﬂatﬁsh species living in the eastern Bering Sea. This distribution is found to be shifting northward toward colder habitats in response to increasing temperatures caused by global warming.
Understanding the stock-recruitment (S-R) relationship therefore requires careful statistical techniques forming a crucial ingredient in the sustainable management
3.1. Introduction 77
of these exploited natural resources. From the point of view of sustainable man- agement, Shepherd and Cushing (1990) studied plausible regulatory processes for analysing ﬁsh populations and argued that increased variability at low stock sizes might prevent the collapse of stocks subject to high mortality rates, because in this case the variability acts to produce depensatory rather than compensatory density-dependence, a theme echoed by Minto et al. (2008). Hsieh et al. (2006) presented the ﬁrst empirical evidence that ﬁshing could increase the survival variability (a proxy for recruitment variability) in an exploited population and advocated that increased variability of exploited populations favours a precau- tionary management approach.
Heteroscedastic models (i.e. statistical models using non-constant variance) have gained much interest in recent years to explain the regulatory mechanisms in ﬁsh populations. Minto et al. (2008) developed a stochastic method applied to a meta-analysis of 147 ﬁsheries populations to argue that survival variability is inversely proportional to stock size. Their model was inspired by Peterman (1981) who argued that random variation in marine survival rates tends to follow a log-normal distribution; but the novelty of their method was to incorporate a functional form of non-constant recruitment variability over adult abundance. More recently, Burrow et al. (2012) investigated the feasibility of applying het- eroscedastic models in practice, using two North Sea stocks as examples. They uncovered a weakness of using a heteroscedastic regression model by showing it to be statistically unreliable to ﬁt the parameters based on small S-R popula- tions (containing 40 or 50 data points); but made a mistake while deﬁning the log-likelihood function (i.e. missing a square term and a factor of 0.5) and re- stricting their analysis to only two populations. The use of heteroscedastic models is controversial because previous research engaged in interpreting non-constant variance has failed to provide a clear-cut answer about its reliable estimation for ﬁsheries management.
The aims of this study were: (1) to develop frequentist and Bayesian methods for accurately identifying the non-constant variance exhibited in a density-dependent model, and (2) to test the reliability of these methods on 90 S-R populations. Since none of the S-R populations are direct observations, I select populations estimated by virtual population analysis (VPA) type assessments so as to ensure that the recruitment estimates are derived from the catch-at-age data, which is not dependent on the estimate of the spawning stock biomass. I found it useful
to analyse the edge eﬀects at the beginning and end of the time series data to test whether VPA methods have an impact upon our results. In this work, I employed the two dominant approaches to inference, known as Bayesian and fre- quentist statistical methods, to determine whether one can reliably estimate the non-constant variance. I conclude that within either the frequentist or Bayesian paradigm, the reliability of determining the existence of a negative η1 values (the
coeﬃcient of heteroscedasticity) can only be assessed on a case-by-case basis.