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Protected areas buffer against harvest selection and rebuild phenotypic complexity.

Albert Fernández-ChacónDavid Villegas-RíosEven MolandMarissa L BaskettEsben M OlsenStephanie M Carlson
Published in: Ecological applications : a publication of the Ecological Society of America (2020)
Harvest mortality typically truncates the harvested species' size structure, thereby reducing phenotypic complexity, which can lead to reduced population productivity, increased population variability, and selection on an array of life history traits that can further alter these demographic processes. Marine protected areas (MPAs) are a potential tool to protect older, larger individuals and therefore mitigate such ecological and evolutionary effects of harvest, depending on the degree of connectivity among areas. Such MPA protection relies on a shift in size-dependent mortality, the measurement of which can therefore serve as an early indicator of whether MPAs might achieve the desired longer-term ecological and evolutionary responses. We directly measured MPA effects on size-selective mortality and associated size structure using mark-recapture data on European lobster (Homarus gammarus) collected at three MPA-control area pairs in southern Norway during one decade (n = 5,943). Mark-recapture modeling, accounting for variation in recapture probabilities, revealed (1) that annual mean survival was higher inside MPAs (0.592) vs. control areas (0.298) and (2) that significant negative relationships between survival and body size occurred at the control areas but not in the MPAs, where the effect of body size was predominantly positive. Additionally, we found (3) that mean and maximum body size increased over time inside MPAs but not in control areas. Overall, our results suggest that MPAs can rebuild phenotypic complexity (i.e., size structure) and provide protection from harvest selection.
Keyphrases
  • cardiovascular events
  • genome wide
  • climate change
  • risk factors
  • gene expression
  • high resolution
  • human health
  • preterm infants
  • high throughput
  • deep learning
  • artificial intelligence