Recruitment in a changing environment: the 2000s North Sea herring recruitment failure

Environmentally induced change appears to be impacting the recruitment of North Sea herring (Clupea harengus). Despite simultaneously having a large adult population, historically low exploitation, and Marine Stewardship Council accreditation (implying sustainability), there have been an unprecedented 6 sequential years of poor juvenile production (recruitment). Analysis suggests that the poor recruitment arises during the larval overwintering phase, with recent survival rates greatly reduced. Contemporary warming of the North Sea has caused significant changes in the plankton community, and a recently identified regime shift around 2000 shows close temporal agreement with the reduced larval survival. It is, therefore, possible that we are observing the first consequences of this planktonic change for higher trophic levels. There is no indication of a recovery in recruitment in the short term. Fishing mortality is currently outside the agreed management plan, and forecasts show a high risk of the stock moving outside safe biological limits soon, potentially precipitating another collapse of the stock. However, bringing the realized fishing mortality back in line with the management plan would likely alleviate the problem. This illustrates again that recruitment is influenced by more than just spawning-stock biomass, and that changes in other factors can be of equal, or even greater, importance. In such dynamically changing environments, recent management success does not necessarily guarantee future sustainability.

Beyond long-term averages: making biological sense of a rapidly changing world

Biological responses to climate change are typically communicated in generalized terms such as poleward and altitudinal range shifts, but adaptation efforts relevant to management decisions often require forecasts that incorporate the interaction of multiple climatic and nonclimatic stressors at far smaller spatiotemporal scales. We argue that the desire for generalizations has, ironically, contributed to the frequent conflation of weather with climate, even within the scientific community. As a result, current predictions of ecological responses to climate change, and the design of experiments to understand underlying mechanisms, are too often based on broad-scale trends and averages that at a proximate level may have very little to do with the vulnerability of organisms and ecosystems. The creation of biologically relevant metrics of environmental change that incorporate the physical mechanisms by which climate trains patterns of weather, coupled with knowledge of how organisms and ecosystems respond to these changes, can offer insight into which aspects of climate change may be most important to monitor and predict. This approach also has the potential to enhance our ability to communicate impacts of climate change to nonscientists and especially to stakeholders attempting to enact climate change adaptation policies.

Evaluating machine-learning techniques for recruitment forecasting of seven North East Atlantic fish species

The effect of different factors (spawning biomass, environmental conditions) on recruitment is a subject of great importance in the management of fisheries, recovery plans and scenario exploration. In this study, recently proposed supervised classification techniques, tested by the machine-learning community, are applied to forecast the recruitment of seven fish species of North East Atlantic (anchovy, sardine, mackerel, horse mackerel, hake, blue whiting and albacore), using spawning, environmental and climatic data. In addition, the use of the probabilistic flexible naive Bayes classifier (FNBC) is proposed as modelling approach in order to reduce uncertainty for fisheries management purposes. Those improvements aim is to improve probability estimations of each possible outcome (low, medium and high recruitment) based in kernel density estimation, which is crucial for informed management decision making with high uncertainty. Finally, a comparison between goodness-of-fit and generalization power is provided, in order to assess the reliability of the final forecasting models. It is found that in most cases the proposed methodology provides useful information for management whereas the case of horse mackerel is an example of the limitations of the approach. The proposed improvements allow for a better probabilistic estimation of the different scenarios, i.e. to reduce the uncertainty in the provided forecasts.