Novel ecological and climatic conditions drive rapid adaptation in invasive Florida Burmese pythons.
Daren C CardBlair W PerryRichard H AdamsDrew R SchieldAcacia S YoungAudra L AndrewTereza JezkovaGiulia I M PasquesiNicole R HalesMatthew R WalshMichael R RochfordFrank J MazzottiKristen M HartMargaret E HunterTodd A CastoePublished in: Molecular ecology (2018)
Invasive species provide powerful in situ experimental systems for studying evolution in response to selective pressures in novel habitats. While research has shown that phenotypic evolution can occur rapidly in nature, few examples exist of genomewide adaptation on short "ecological" timescales. Burmese pythons (Python molurus bivittatus) have become a successful and impactful invasive species in Florida over the last 30 years despite major freeze events that caused high python mortality. We sampled Florida Burmese pythons before and after a major freeze event in 2010 and found evidence for directional selection in genomic regions enriched for genes associated with thermosensation, behaviour and physiology. Several of these genes are linked to regenerative organ growth, an adaptive response that modulates organ size and function with feeding and fasting in pythons. Independent histological and functional genomic data sets provide additional layers of support for a contemporary shift in invasive Burmese python physiology. In the Florida population, a shift towards maintaining an active digestive system may be driven by the fitness benefits of maintaining higher metabolic rates and body temperature during freeze events. Our results suggest that a synergistic interaction between ecological and climatic selection pressures has driven adaptation in Florida Burmese pythons, demonstrating the often-overlooked potential of rapid adaptation to influence the success of invasive species.
Keyphrases
- stem cells
- type diabetes
- gene expression
- metabolic syndrome
- mesenchymal stem cells
- cardiovascular disease
- body composition
- insulin resistance
- risk assessment
- coronary artery disease
- adipose tissue
- electronic health record
- risk factors
- blood glucose
- blood pressure
- machine learning
- weight loss
- big data
- deep learning
- tissue engineering
- bioinformatics analysis
- solar cells