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.

中文翻译：

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竞争性人口统计模型的回顾性比较使苏格兰松鸡沼泽的“混乱”管理变得清晰

描述有关系统功能的竞争假设的预测模型的回顾性比较可以阐明自适应资源管理框架内的监管机制。我们将这种方法应用于一项为期 28 年的红松鸡 ( Lagopus lagopus scotica ) 研究) 在苏格兰，目的是减少关于松鸡种群动态重要驱动因素的不确定性，并评估使用季节性与年度模型评估的有效性。我们开发了三套模型来预测繁殖前和繁殖后的松鸡密度，以匹配地面上松鸡数量的时间。我们本着实时、自适应管理程序的精神，随时间更新条件和管理，并使用贝叶斯模型权重更新过程将模型预测与经验松鸡密度进行比较。前两个模型集涉及育种前或育种后计数的单年度更新；第三套每年更新两次。每个模型集包含七个模型，代表关于松鸡潜在重要驱动因素的日益复杂的假设：基线模型包括天气和寄生虫对生产力、射击损失和密度依​​赖性越冬生存的影响；随后的模型结合了栖息地增加/减少（HAB）、未受保护的捕食者（NPP）的控制和受保护的母鸡的捕食（Circus cyaneus , HH) 和秃鹰 ( Buteo buteo, BZ). 模型集的证据权重是一致的，在 10 年内解决了鹞 (NPP + HH)、秃鹰 (NPP + HH + BZ) 和秃鹰 + 栖息地 (NPP + HH + BZ + HAB) 模型，并降低了基线+ 栖息地、非保护捕食者和非保护捕食者 + 栖息地模型。到研究结束时，只有秃鹰和秃鹰 + 栖息地模型保留了相当大的权重，强调了系统的动态复杂性。栖息地纳入未能改善模型预测，这意味着在本研究期间，栖息地数量对于确定松鸡数量并不重要。比较每年和每半年评估的模型集，主要区别在于基线模型，其重量在每年评估时增加或保持稳定，但在每半年评估时下降。