Compensatory density dependence in fish populations: importance, controversy, understanding and prognosis
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Density-dependent processes such as growth, survival, reproduction and movement are compensatory if their rates change in response to variation in population density (or numbers) such that they result in a slowed population growth rate at high densities and promote a numerical increase of the population at low densities. Compensatory density dependence is important to fisheries management because it operates to offset the losses of individuals. While the concept of compensation is straightforward, it remains one of the most controversial issues in population dynamics. The difficulties arise when going from general concepts to specific populations. Compensation is usually quantified using some combination of spawner-recruit analysis, long-term field monitoring or manipulative studies, and computer modelling. Problems arise because there are limitations to each of these approaches, and these limitations generally originate from the high uncertainty associated with field measurements. We offer a hierarchical approach to predicting and understanding compensation that ranges from the very general, using basic life-history theory, to the highly site-specific, using detailed population models. We analyse a spawner-recruit database to test the predictions about compensation and compensatory reserve that derive from a three-endpoint life-history framework designed for fish. We then summarise field examples of density dependence in specific processes. Selected long-term field monitoring studies, manipulative studies and computer modelling examples are then highlighted that illustrate how density-dependent processes led to compensatory responses at the population level. Some theoretical and empirical advances that offer hope for progress in the future on the compensation issue are discussed. We advocate an approach to compensation that involves process-level understanding of the underlying mechanisms, life-history theory, careful analysis of field data, and matrix and individual-based modelling. There will always be debate if the quantification of compensation does not include some degree of understanding of the underlying mechanisms.
