A central challenge in global change research is the projection of the future behavior of a system based upon past observations. Tree‐ring data have been used increasingly over the last decade to project tree growth and forest ecosystem vulnerability under future climate conditions. But how can the response of tree growth to past climate variation predict the future, when the future does not look like the past? Space‐for‐time substitution (SFTS) is one way to overcome the problem of extrapolation: the response at a given location in a warmer future is assumed to follow the response at a warmer location today. Here we evaluated an SFTS approach to projecting future growth of Douglas‐fir (Pseudotsuga menziesii ), a species that occupies an exceptionally large environmental space in North America. We fit a hierarchical mixed‐effects model to capture ring‐width variability in response to spatial and temporal variation in climate. We found opposing gradients for productivity and climate sensitivity with highest growth rates and weakest response to interannual climate variation in the mesic coastal part of Douglas‐fir's range; narrower rings and stronger climate sensitivity occurred across the semi‐arid interior. Ring‐width response to spatial versus temporal temperature variation was opposite in sign, suggesting that spatial variation in productivity, caused by local adaptation and other slow processes, cannot be used to anticipate changes in productivity caused by rapid climate change. We thus substituted only climate sensitivities when projecting future tree growth. Growth declines were projected across much of Douglas‐fir's distribution, with largest relative decreases in the semiarid U.S. Interior West and smallest in the mesic Pacific Northwest. We further highlight the strengths of mixed‐effects modeling for reviving a conceptual cornerstone of dendroecology, Cook's 1987 aggregate growth model, and the great potential to use tree‐ring networks and results as a calibration target for next‐generation vegetation models.

中文翻译：

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道格拉斯冷杉生长的基于大陆规模的树轮预测：测试时空替代的极限。

全球变化研究中的一个主要挑战是根据过去的观察结果预测系统的未来行为。在过去十年中，越来越多地使用树年轮数据来预测未来气候条件下的树木生长和森林生态系统脆弱性。但是当未来看起来不像过去时，树木生长对过去气候变化的反应如何预测未来？时空替换（SFTS）是克服外推问题的一种方法：假定在较热的将来给定位置的响应将遵循今天在较热的位置的响应。在这里，我们评估了SFTS方法来预测花旗松（Pseudotsuga menziesii）的未来增长），该物种在北美占据非常大的环境空间。我们拟合了分层的混合效应模型来捕获环宽度变异性，以响应气候的时空变化。在道格拉斯冷杉山脉中部沿海地区，我们发现生产力和气候敏感性的梯度相反，增长率最高，对年际气候变化的响应最弱。在半干旱内部，环更窄，对气候的敏感性更强。对空间温度与时间温度变化的环宽度响应在符号上是相反的，这表明由局部适应和其他缓慢过程导致的生产力空间变化不能用来预测由快速气候变化引起的生产力变化。因此，在预测未来树木的生长时，我们仅替换了对气候的敏感性。道格拉斯冷杉分布的大部分地区都预计会出现增长下降，半干旱的美国内陆西部相对下降幅度最大，而内陆太平洋西北地区的下降幅度最小。我们进一步强调了混合效应模型在恢复树状生态学概念基石，Cook 1987年总体增长模型方面的优势，以及使用树环网络和结果作为下一代植被模型的校准目标的巨大潜力。