Modeling optimal responses and fitness consequences in a changing Arctic

Area of Science:

• Ecology and evolutionary biology
• Climate change impacts on wildlife
• Animal behavior and decision-making

Background:

• Animals face trade-offs between survival and reproduction, influenced by environmental factors.
• Climate change alters environmental conditions, potentially shifting optimal decision-making strategies for wildlife.
• Stochastic dynamic programming is a powerful tool for modeling these complex trade-offs.

Purpose of the Study:

• To model the lifetime foraging and reproductive decisions of female polar bears using stochastic dynamic programming.
• To investigate how climate change, specifically reduced spring feeding periods, affects polar bear optimal strategies.
• To quantify the fitness consequences of altered decision-making under changing environmental conditions.

Main Methods:

• Developed a stochastic dynamic programming model for a female polar bear (Ursus maritimus).
• Simulated shortened spring feeding seasons to represent climate change impacts.
• Calculated optimal foraging decisions and energetic thresholds for reproduction abandonment.

Main Results:

• Shortened spring feeding periods led to predictions of riskier foraging behavior.
• Optimal reproductive thresholds increased under reduced feeding availability.
• A 1-week reduction in feeding decreased fitness by 15%; a 3-week reduction decreased fitness by 68%.

Conclusions:

• Polar bears exhibit significant fitness declines due to climate change-induced reductions in feeding periods.
• Even optimal adaptation to changing environments results in substantial fitness costs.
• This modeling approach effectively demonstrates species' responses to shifting ecological trade-offs and cumulative fitness impacts.