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 developed and evaluated a SFTS approach to projecting future growth of Douglas-fir (Pseudotsuga menziesii [Mirb.] Franco), a species that occupies an exceptionally large environmental and geographic space in North America – from 17°N to 55°N in latitude, and across mean annual temperatures ranging from -0.5 to 19.5°C and cumulative annual precipitation from 300 to 4800 mm. We compiled a dataset of 30,388 tree-ring time series from 2,699 sampling sites, totaling 2,706,098 ring widths over the 1902-2016 period of analysis. We then fit a hierarchical mixed effects model to these data to evaluate variation in individual tree growth in response to spatial and temporal variation in climate.
Results/Conclusions
We found opposing gradients of productivity and climate sensitivity, with largest growth rings and weakest response to interannual climate variation in the mesic coastal part of Douglas-fir’s range vs. narrower rings and stronger climate sensitivity across the semi-arid interior. Ring width variation in response to spatial vs. temporal temperature variation was opposite in sign: across space, average ring width was greater at warmer locations, but at a given location, ring widths were almost always smaller in response to warmer-than-average temperatures. This suggests that spatial variation in productivity, which is caused at least partly by local adaptation and other slow processes, cannot appropriately be used to anticipate changes in productivity caused by rapid climate change. We thus adopted an approach substituting only climate sensitivities when projecting future tree growth. Under this approach to SFTS, 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. Better understanding of the limits of SFTS is critical for ecological forecasting in a nonstationary world.