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Biology

Overfishing and climate variability interactions spell trouble for fast growing species

Article: Pinksy, M., Byler, D. 2015. Fishing, fast growth, and climate variability increase the risk of collapse. Proc. R. Soc. B. 282: 20151053. DOI: http://dx.doi.org/10.1098/rspb.2015.1053

If it wasn’t already apparent, Earth’s climate is a complicated beast. Globally, temperatures are ticking upwards at an alarming trend, obscuring some of the other complex patterns occurring in Earth’s climate such as climate variability. In large portions of Earth, climate tends to oscillate between warm and cold phases, from seasonally to sometimes even over multiple decades! These oscillations, like the Pacific Decadal Oscillation, can vary in magnitude but the underlying drivers of these changes remain unknown. These complex climate patterns are superimposed on Earth’s ecosystems (in addition to climate change as well as other disturbances), where different species respond according to their various life histories.

In many terrestrial systems, the underlying biology behind fast and slow growing species seems to indicate that faster growing species may be better suited to dealing with perturbations like climate variability. Much of this is quite intuitive – faster growing species tend to be more adaptable in the face of environmental stress (just imagine how hard cockroaches or termites are to exterminate). However, a recent study done by Dr. Malin Pinsky at Rutgers University and Dr. David Byler at Princeton University show that this might not be the case in the ocean.

Methods

Drs. Pinksy and Byler developed two quantitative models to predict the probability of fisheries collapse and the degree of depletion in stocks from around the world. Using available stock assessment data spanning from the 1950’s to today, as well as global climate data, they built two models: one for predicting the probability of fishery collapse and another for predicting the degree of stock depletion. They did so using boosted regression trees, a powerful statistical tool for making predictions. This way, they were able to look at how different factors like exploitation (magnitude of overfishing, duration of overfishing), life history traits (growth rate, reproductive output, egg size, trophic level), and climate (short term and long term variability) contribute to stock depletion and collapse. Additionally, these models are able to look at how these factors may interact with each other to produce an even greater effect than either could individually.

Results and Implications

The models showed that overfishing was the most important factor contributing the both fishery depletion and collapse (not particularly surprising). In particular, overall fishing pressure (the amount of fish removed) contributed the most to fishery collapse (Fig 1a-1f) while duration of overfishing drove the magnitude of fishery depletion (Fig 1g-1l). Behind overfishing, fast growth rate was actually the most significant determinant for fishery depletion and collapse. So fish with faster growth rates tended to be more depleted and had a higher probability of collapse, a pattern that seems to contradict inferences from terrestrial systems that fast growing species adapt to perturbations better. Among the climate parameters, seasonal variation was the most significant factor (behind overfishing and fast growth rate). Higher seasonal variability was associated with greater fishery depletion and increased probability of collapse.

fig 1.tiff

Fig 1. Model outputs showing the relationships between various model factors and probability of fishery collapse (a-f) or depletion (g-l) and 95% confidence intervals. Higher fishing pressures, faster growth rates, and greater seasonal variability were the best predictors of fishery collapse. Fecundity, egg size, and overfishing duration also contributed to collapse probability, but less so. However, overfishing duration was the number one predictor of fishery depletion, with fast growth rate and management styles coming out as also important determinants of fishery depletion as well. Management in Fig 1i corresponds to different governmental bodies that manage fisheries: Argentina (G), Australia (A), Canada (C), Europe (E), Multinational (M), New Zealand (N), South Africa (S), and United States (U).

 

The interactions between various factors were particularly important for the collapse model. Overfishing greatly increased the probability of collapse when coupled with either fast growth rate or seasonal climate variability (Fig 2). Among populations that were overfished, those that also had fast growth rates had almost three times the risk of collapsing than those that were overfished but had slower growth rates!

fig 2.tiff

Figure 2. Interaction plots show how different factors couple to create disproportionately large effects in collapse probability (a-c) or mean depletion (d-f). Maximum fishing and growth rate (a) had the largest interactions of all the factors, increasing collapse probability by 3 times.

 

While boosted regression trees are an effective means of modelling complex relationships to give good predictions, a drawback of these models is that their output is not always easily interpreted. While the models tell us that fast life growth rate and overfishing increase risk of fishery collapse, they do not tell us why that might be the case. Drs. Pinsky and Byler propose a few reasons for this unexpected pattern, the most interesting of which comes from a socio-economic point of view. Fast growing animals respond quickly to perturbations such as climate variability or overfishing, which the researchers did observe in their models (stocks that undergo pressures from overfishing or climate variability alone have low collapse probabilities). However, combining overfishing and climate variability puts stocks at greater risk, and avoiding overfishing would be the only way to avoid major depletion and collapse. However, regulatory decisions, such as fisheries closures or reduced catch limits take time to enact (societal and industry push back, time lag between data collection and analysis, etc.). This delay in action may explain why fast growing species are more sensitive to collapse than slow growing species.

These results challenge what many assume: that fishing predisposes large, slow growing species for collapse. And while that may be true, Drs. Pinsky and Byler’s work shows that, through a combination of climate, fishing, and possible socio-economic drivers, fast growing species may be the ones most at risk. Their work also highlights how the timeliness of regulatory action can play a large role in preventing collapse.

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