Wilson Steven F
EcoLogic Research, Nanaimo, British Columbia, Canada.
Ecol Appl. 2025 Apr;35(3):e70022. doi: 10.1002/eap.70022.
Forecasting the benefits of management interventions intended to improve ecological conditions requires a causal understanding of the factors that lead to system change. The causal attribution of a factor is defined as the difference between the outcome observed in the presence of the factor and the outcome that would have been observed in the factor's absence, that is, the counterfactual condition. Estimating this contrast is relatively straightforward, where matched or randomized controls are available to approximate the counterfactual condition. However, researchers must reason retrospectively from observational data where matched or randomized controls are not available. In this case, the challenge of establishing causal attribution is in estimating the true counterfactual, that is, the outcome that would have resulted from the absence of the factor, given that it was present. Causal analysis permits the estimation of counterfactuals from observational data, assuming that the model captures all common causes between exposure and outcome, that the exposure is independent of other factors in the model (i.e., exogenous), and that the exposure causes the same directional change for all units (i.e., monotonic). I estimated retrospectively the causal attribution of habitat-related factors to recruitment rates in Canada's boreal population of woodland caribou (Rangifer tarandus caribou). Aggregate habitat disturbance had low causal attribution (17.6%). Attribution was greater (29.5%) when habitat disturbance was disaggregated into different factors associated with different pathways of caribou decline. The causal attribution of all habitat factors considered nevertheless rarely exceeded 50%, suggesting that there are other systematic and/or stochastic factors that can limit the effectiveness of current habitat-related recovery actions. More effort is required to understand these factors and how they might be managed to improve the probability of successful caribou recovery.
预测旨在改善生态状况的管理干预措施的效益，需要对导致系统变化的因素有因果关系的理解。一个因素的因果归因被定义为在该因素存在时观察到的结果与在该因素不存在时（即反事实条件）会观察到的结果之间的差异。在有匹配或随机对照可用来近似反事实条件的情况下，估计这种对比相对简单。然而，研究人员必须从没有匹配或随机对照的观测数据进行回顾性推理。在这种情况下，建立因果归因的挑战在于估计真正的反事实，即考虑到该因素存在，在其不存在时会产生的结果。因果分析允许从观测数据中估计反事实，假设模型捕捉了暴露与结果之间的所有共同原因，暴露与模型中的其他因素无关（即外生），并且暴露对所有单位产生相同方向的变化（即单调）。我回顾性地估计了加拿大北方林地驯鹿（Rangifer tarandus caribou）种群中与栖息地相关因素对补充率的因果归因。总体栖息地干扰的因果归因较低（17.6%）。当将栖息地干扰分解为与驯鹿数量下降的不同途径相关的不同因素时，归因更大（29.5%）。然而，所考虑的所有栖息地因素的因果归因很少超过50%，这表明存在其他系统和/或随机因素可能会限制当前与栖息地相关的恢复行动的有效性。需要付出更多努力来了解这些因素以及如何对其进行管理，以提高驯鹿成功恢复的可能性。
