Postdoc, PhD
Christian Jørgensen

[ About our recent paper on Evolutionary Fisheries Management in Science ]

Evolution is the powerful process that made the living world. Variation, selection, reproduction; over and over again countless times. By reconstructing that process we are better equipped for understanding how biology works, and how it will respond to the changing environments.

Isn't it improbable that humans have evolved from bacteria?

It took some eons, sure enough, but we're here, right? Although no sane person could predict that exactly you and I, or even humans, would be the result, evolution is the closest we come to a predictive biological science. Why? It is a common misunderstanding that evolution only happens on the geological timescale of millions of years. Bacteria that are resistant to antibiotics, insects that survive DDT, and mosquitoes that evade malarial drugs are all examples that evolution can happen in just a few years. And the examples are kind of predictable: when you apply strong selection, as with antibiotics or pesticides, the most resistant strains will flourish.

Anthropogenic influences now make environments change at ever accelerating rates. The unavoidable consequence is that contemporary evolution changes species and biological interactions as we speak. To understand why and potentially predict how biology and biological interactions will change, we first need to understand the evolutionary trade-offs that shaped the living world to become the way it is.

My research focuses on three topics that all use evolutionary biology:


Fisheries-induced evolution

One dominant selective agent nowadays is fishing. Both industrial and recreational fishing can induce mortality rates so high that evolutionary responses in the harvested species are bound to happen. But, in what direction and towards what endpoint will the fish species evolve?

I use evolutionary models to investigate evolving traits. So far I have, together with several co-authors, shown that fishing can induce evolution towards earlier sexual maturation at smaller size, breakdown of mating systems in hermaphrodites, altered migration patterns and geographical distribution, increased natural mortality, and less skipped spawning. I am also interested in how fisheries management can use this type of knowledge to achieve sustainable fisheries.

Evolutionary marine ecology

What can fisheries science learn from life history theory and behavioural ecology? Theories and methodologies developed in these central disciplines of evolutionary ecology have the potential to shed new light on many aspects of marine ecology. Most population level phenomena arise from individual processes. One fruitful approach is therefore to incorporate ecological and physiological mechanisms at the individual level, and investigate their effects on populations.

Using state-dependent energy allocation models, I have investigated the life history trade-offs that underlie skipped spawning in fish. Although a lot is driven by environment, both external ecological factors and internal state-dependent dynamics influence the level of skipped spawning.

I am also interested in what happens with larvae after they have been spawned - during their pelagic drift phase. Their challenge is not only to eat and to survive, but also to drift towards benevolent settlement habitat. Surprisingly many aspects of a larva's future life can be modified by vertical migrations, and the trade-offs are almost piled on top of each other. By incorporating mechanistic representations of predation and feeding, physiological growth models, and simple rules that govern risk-sensitive behaviour, these trade-offs can be studied in physical ocean models. Experimenting with these virtual large-scale laboratories is thrilling.

Methodologies for evolutionary modelling

I am intrigued by how the rich mechanisms of physiology and behaviour cause constraints and opportunities for life history evolution. To be able to construct evolutionary models that incorporate more complex individuals, I also work on developing new methodologies. Because evolution naturally involves multidimensional feedback loops, all evolutionary modelling methodologies have to make several and often fundamental simplifications.

The black ball on top represents the population, consisting of individuals with evolving life history traits, interacting with each other behaviourally, and in a constant flux of population dynamics. The striped ball below is the physical environment and interactions with other species. Changes in the environment will have implications for the population and vice versa.

In a model, one often has to cheat by fixing some of the interactions. Frequently, one assumes that part of the environment stays fixed, or that there is only one species in the world.

Simplifying assumptions make models easier to make and easier to understand - but also less realistic. How does one know what to simplify? In short, there is no simple answer but many solutions we can learn from.

The different modelling methodologies have different strengths and weaknesses. A varied toolbox is necessary to reach generalisable conclusions. The methods I am working on are still in the phase of being developed. Hopefully they can complement and extend current methodologies.