Coastal wetlands, known for their remarkable productivity and diverse ecological functions, face growing threats from aquaculture expansion, which can fragment natural habitats and disrupt water flow. In this study, we focused on White-bellied Sea-Eagles Haliaeetus leucogaster along the eastern coast of Odisha, India, to see how these top predators adapt to accelerating aquaculture. Across 22 nesting sites over three breeding seasons (2021–2024), we analysed 3,319 prey items, and found that fish dominated at 66.89%, followed by birds (25.64%), reptiles (3.31%), invertebrates (3.16%), and mammals (0.99%). Using generalised additive models (GAMs), we evaluated various landscape factors influencing the proportion of fish in WBSE diets. Our results revealed that shorter distances to natural water-bodies and higher water coverage strongly influenced higher fish intake, while aquaculture-related variables did not increase fish consumption. These patterns indicate that WBSEs favour wild fish in less-disturbed wetlands, likely because of better energy returns, lower risks, and convenient perching spots. Consequently, our findings highlight the need to safeguard natural aquatic habitats for sustaining apex predators and point to ways of reconciling aquaculture growth with wetland conservation. By clarifying how the eagles respond to changes in coastal landscapes, we offer key insights for preserving biodiversity under fast-paced environmental transformation.

In this paper, we propose new Metropolis–Hastings and simulated annealing algorithms on a finite state space via modifying the energy landscape. The core idea of landscape modification rests on introducing a parameter c, such that the landscape is modified once the algorithm is above this threshold parameter to encourage exploration, while the original landscape is utilized when the algorithm is below the threshold for exploitation purposes. We illustrate the power and benefits of landscape modification by investigating its effect on the classical Curie–Weiss model with Glauber dynamics and external magnetic field in the subcritical regime. This leads to a landscape-modified mean-field equation, and with appropriate choice of c the free energy landscape can be transformed from a double-well into a single-well landscape, while the location of the global minimum is preserved on the modified landscape. Consequently, running algorithms on the modified landscape can improve the convergence to the ground state in the Curie–Weiss model. In the setting of simulated annealing, we demonstrate that landscape modification can yield improved or even subexponential mean tunnelling time between global minima in the low-temperature regime by appropriate choice of c, and we give a convergence guarantee using an improved logarithmic cooling schedule with reduced critical height. We also discuss connections between landscape modification and other acceleration techniques, such as Catoni’s energy transformation algorithm, preconditioning, importance sampling, and quantum annealing. The technique developed in this paper is not limited to simulated annealing, but is broadly applicable to any difference-based discrete optimization algorithm by a change of landscape.