Quantile regression models for fish recruitment–environment relationships: four case studies

Abstract

Understanding and modelling the environmental control of fish recruitment has been a central question in fish population ecology for the last century. Most environment -recruitment models have primarily been developed to model mean recruitment using conventional regression techniques which assume that all environmental parameters are included and that the residual unexplained variability is unstructured. However, the complexity of environmental controls and the empirical evidence that many relationships have failed when retested suggest that these assumptions are generally not met. Most environmental controls may be considered as limiting factors to recruitment and act in interaction with other factors (often not measured or not known). We used quantile regression modelling, which is specifically designed to model limiting relationships, to reanalyse environment -recruitment relationships that have been published for 4 fish stocks: (1) Northeast Arctic cod (Barents Sea), (2) Atlanto-Scandian herring, (3) Bay of Biscay anchovy and (4) Pacific sardine. The method was adapted to the specific case of autocorrelated time series, a common feature of most environmental signals. The results from quantile regression were not straightforward extensions of conventional regressions. For Northeast Arctic cod and Pacific sardine, the original relationships with temperature were not statistically significant in the quantile model. For Atlanto-Scandian herring the relationship was confirmed and temperature clearly appeared as a limiting factor to recruitment. The published relationship for the Bay of Biscay anchovy with upwelling was not confirmed, but the previously undetected relationship with river runoff was established. In this specific case, it was only by using a quantile model that the relationship could be detected as statistically significant. These results confirm the ability of quantile regression models to provide robust interpretation of environment -recruitment relationships and to produce environmentally based advance warning when recruitment is expected to be low.

The response of nematodes to deep-sea CO2 sequestration: A quantile regression approach

“…Quantile regression has been demonstrated as a useful tool in ecological studies for detecting limiting factors (Cade et al, 1999;Cade and Noon, 2003;Planque and Buffaz, 2008). To understand if a factor is limiting, it is helpful to model the maximum response instead of a mean.…”

A multi-model approach to evaluate the role of environmental variability and fishing pressure in sardine fisheries

“…In most marine systems and for many fish populations, relationships between environment and recruitment have been proposed, but they have often been contested or have failed when retested with new and longer sets of observations (see Myers, 1998). The multiplicity and complexity of the environmental controls limit our ability to adequately understand and model environmentrecruitment relationships (Planque and Buffaz, 2008). Studies showed that a variety of environmental and oceanographic variables (local scale effects, such was SST) affect the same species (and or species stocks) differently across inter and intra geographical scales (Planque and Frédou, 1999;Ullah et al, 2012;Teixeira et al, 2013).…”

Climate change impacts on coastal and pelagic environments in the southeastern Bay of Biscay

“…Factors such as the NAO (albacore, Arregui et al 2006;sardine, Alheit & Hagen 1997; several fish stocks, mammals and seabirds, Hemery et al 2008), EA pattern (anchovy, Borja et al 2008); temperature anomaly (hake, Fernandes et al 2010), upwelling strength (anchovy, Borja et al 2008), turbulence (anchovy, mackerel and hake, Borja et al 1998, Allain et al 2007, Fernandes et al 2010, river discharge (anchovy, Planque & Buffaz 2008) and Ekman transport (anchovy, Irigoien et al 2008) have been shown to have significant effects on the recruitment or abundance of these species.…”