Modeling the impact of climate change on a rare color morph in fish

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Abstract

Species are typically comprised of a large number of genotypes, some of which are rare. Rare genotypes that are temperature-sensitive will predictably be impacted by climate change more profoundly than common genotypes for which gene expression does not depend on temperature. Computer simulations were used in this study to analyze the impact of changing temperatures on a very rare melanic morph that expresses a temperature-sensitive phenotype and is found in an abundant fish species (Gambusia holbrooki). This numerical model assesses the frequency of the two natural color morphs, silver and black-spotted, over time and iterates in each generation, incorporating survival estimates for different life stages based upon data collected during empirical experiments. Using the current projections for mean surface temperature increase, which are 2.5° F to 10.4° F by the year 2100, the impact of warming environment on the mosquitofish population was analyzed. Three different climate warming scenarios were used to address annual temperature fluctuations and incorporate predicted temperature rise at correspondingly minimal, mid, and maximum levels. Our results indicate that increasing temperatures will seriously affect the rare color morph and in some cases will result in extinction. The minimal climate-warming scenario produced lower extinction risks for the rare morph; however, it still decreased the frequency of the rare genotype to minimal values. The mid-range warming scenario produced a higher risk of extinction, while the maximum warming scenario resulted in the extinction of the rare color morph. The results of this study shed light on the possibility that the effects of climate change may have important ramifications for rare genotypes in nature and will likely drive some rare species genotypes to extinction.

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Panayotova, I. N., & Horth, L. (2018). Modeling the impact of climate change on a rare color morph in fish. Ecological Modelling, 387, 10–16. https://doi.org/10.1016/j.ecolmodel.2018.08.008

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