Abstract
Understanding how organisms respond to human impacts is increasingly challenging biologists. Shortlived organisms can adapt rapidly to changes in environmental hazards, but only recently have long-lived organisms been shown to adapt to human impacts. Changes in any life-history trait, such as individual growth rates, may affect demographic model predictions and reliability of elasticity analyses that are often used to help manage and conserve long-lived organisms. The aim of this study was to test model predictions of the effect of increased recruitment and density-dependent processes to manage populations of long-lived turtles in two continents. We explored how human-induced changes in juvenile density affect population growth estimates and the strength of selection on stage-based life-history traits. Model projections undervalued the potential effect of an increase in nest survival. Sensitivity calculations indicated greatest selection intensities for juvenile growth or maturation, whereas elasticity analyses indicated that changes in adult survival have the largest proportional effect on population fitness. Long-term use of the locality of our North American population as a recreational site may have increased adult mortality of turtles and reduced the number of nest predators, inducing rapid individual growth and early maturation. The traditional static view of turtle life history and demography thus is inappropriate even over relatively short periods of time. Anthropogenically-induced changes in demographic processes can potentially induce adaptive changes to life-history processes, which can seriously impact the reliability of long-term projections from common demographic models. Management practices must account for this dynamism accordingly.
Original language | English |
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Pages (from-to) | 1951-1959 |
Number of pages | 9 |
Journal | Biological Conservation |
Volume | 143 |
Issue number | 9 |
DOIs | |
Publication status | Published - 2010 |
Keywords
- organisms
- invasive species
- turtle
- adaptive growth
- population models
- human impact