Warming and top‐down control of stage‐structured prey: Linking theory to patterns in natural systems

Abstract

Warming has broad and often nonlinear impacts on organismal physiology and traits, allowing it to impact species interactions like predation through a variety of pathways that may be difficult to predict. Predictions are commonly based on short‐term experiments and models, and these studies often yield conflicting results depending on the environmental context, spatiotemporal scale, and the predator and prey species considered. Thus, the accuracy of predicted changes in interaction strength, and their importance to the broader ecosystems they take place in, remain unclear. Here, we attempted to link one such set of predictions generated using theory, modeling, and controlled experiments to patterns in the natural abundance of prey across a broad thermal gradient. To do so, we first predicted how warming would impact a stage‐structured predator–prey interaction in riverine rock pools between Pantala spp. dragonfly nymph predators and Aedes atropalpus mosquito larval prey. We then described temperature variation across a set of hundreds of riverine rock pools (n = 775) and leveraged this natural gradient to look for evidence for or against our model's predictions. Our model's predictions suggested that warming should weaken predator control of mosquito larval prey by accelerating their development and shrinking the window of time during which aquatic dragonfly nymphs could consume them. This was consistent with data collected in rock pool ecosystems, where the negative effects of dragonfly nymph predators on mosquito larval abundance were weaker in warmer pools. Our findings provide additional evidence to substantiate our model‐derived predictions while emphasizing the importance of assessing similar predictions using natural gradients of temperature whenever possible.

Regional and local factors interact to shape colonization and extinction dynamics of invasive Hydrilla verticillata in a patchy landscape

“…Previous work in this system has shown how dynamic and variable these rock pools are in regards to temperature (max. temp range 20–42°C during summer months) (Davidson et al., 2024 ), dissolved oxygen (1.54–9.43 mg/L, mean = 5.70 mg/L, n = 20, Richie Dang ‐ RHD), and pH (7.18–9.15, mean = 8.30, n = 55) (Jackson, 2010 ). The same can be said about the ecosystem level processes as well in regards to gross primary productivity (0–12.45 g/m 2 /d, mean = 2.68 g/m 2 /d, n = 20, Richie Dang ‐ RHD), particulate organic nitrogen (0.35–39.43 mgN/L, mean = 13.12 mgN/L, n = 20, Richie Dang ‐ RHD), and CHLa (2.7–1130.9 μg/L, mean = 218.8 μg/L, n = 20, Richie Dang ‐ RHD).…”

A framework for understanding climate change impacts through non‐compensatory intra‐ and interspecific climate change responses

“…dependence and compensatory mortality (i.e., when extrinsic mortality reduces density-dependent mortality; Anderson & Burnham, 1976). This minimal model represents a single species and can be broken into life stages if needed as different life stages may have different responses to climate variables and accounting for stage structure may be necessary to understand overall population patterns (Coulson et al, 2001;Davidson et al, 2024;Johnson et al, 2023). It can also be connected to other species via connecting rates, such as the predator functional response, which, for example, would link the uptake rate for the predator and the extrinsic death rate for the prey (see the Interspecific Non-compensatory Effects section below).…”