Retrospective analysis of wood anatomical traits and tree-ring isotopes suggests site-specific mechanisms triggering Araucaria araucana drought-induced dieback.

In 2010-2018 Northern Patagonia featured the longest severe drought of the last millennium. This extreme dry spell triggered widespread growth decline and forest dieback. Nonetheless, the roles played by the two major mechanisms driving dieback, hydraulic failure and carbon starvation, are still not clear and understudied in this seasonally dry region. Here, for the 1800-2017 period, we apply a retrospective analysis of radial growth, wood anatomical traits (lumen area, cell-wall thickness) and δ13 C and δ18 O stable isotopes to assess dieback causes of the iconic conifer Araucaria araucana. We selected three stands where declining (defoliated) and non-declining (not defoliated) trees coexisted along a precipitation gradient from the warm-dry Coastal Range to the cool-wet Andes. At all sites declining trees showed lower radial growth and lower theoretical hydraulic conductivity, suggesting a long-lasting process of hydraulic deterioration in their water transport system compared to non-declining, coexisting trees. Wood anatomical traits evidenced that this divergence between declining and non-declining trees started at least seven decades before canopy dieback. In the drier stands, declining trees showed higher water-use efficiency throughout the whole period, which we attributed to early stomatal closure, suggesting a greater carbon starvation risk consistent with thinner cell walls. In the wettest stand, we found the opposite pattern. Here, a reduction in water-use efficiency coupled with thicker cell walls suggested increased carbon assimilation rates and exposure to drought-induced hydraulic failure. The δ18 O values indicated different strategies of gas-exchange between sites which are likely a consequence of microsite conditions and water sources. Multi-proxy, retrospective quantifications of xylem anatomical traits and tree-ring isotopes provide a robust tool to identify and forecast which stands or trees will show dieback or, on the contrary, which will likely withstand and be more resilient to future hotter droughts.