Retrospective comparisons of competing demographic models give clarity from “messy” management on a Scottish grouse moor

Retrospective comparison of predictive models that describe competing hypotheses regarding system function can shed light on regulatory mechanisms within the framework of adaptive resource management. We applied this approach to a 28‐year study of red grouse (Lagopus lagopus scotica) in Scotland, with the aims of reducing uncertainty regarding important drivers of grouse population dynamics, and of evaluating the efficacy of using seasonal versus annual model assessments. We developed three sets of models that predicted pre‐breeding and post‐breeding grouse density, matching the timing of grouse counts on the ground. We updated conditions and management through time in the spirit of a real‐time, adaptive management program and used a Bayesian model weight updating process to compare model predictions with empirical grouse densities. The first two model sets involved single annual updates from either pre‐breeding or post‐breeding counts; the third set was updated twice a year. Each model set comprised seven models representing increasingly complex hypotheses regarding potentially important drivers of grouse: the baseline model included weather and parasite effects on productivity, shooting losses and density‐dependent overwinter survival; subsequent models incorporated the effect of habitat gain/loss (HAB), control of non‐protected predators (NPP) and predation by protected hen harriers (Circus cyaneus, HH) and buzzards (Buteo buteo, BZ). The weight of evidence was consistent across model sets, settling within 10 years on the harrier (NPP + HH), buzzard (NPP + HH + BZ) and buzzard + habitat (NPP + HH + BZ + HAB) models, and downgrading the baseline + habitat, non‐protected predator, and non‐protected predator + habitat models. By the end of the study only the buzzard and buzzard + habitat models retained substantial weights, emphasizing the dynamical complexity of the system. Habitat inclusion failed to improve model predictions, implying that over the period of this study habitat quantity was unimportant in determining grouse abundance. Comparing annually and biannually assessed model sets, the main difference was in the baseline model, whose weight increased or remained stable when assessed annually, but collapsed when assessed biannually. Our adaptive modeling approach is suitable for many ecological situations in which a complex interplay of factors makes experimental manipulation difficult.