Back

Growth sensitivity to water availability as potential indicator of drought-induced tree mortality in Mediterranean Pinus sylvestris forests

Herrero, A.; Gonzalez-Gascuena, R.; Gonzalez-Diaz, P.; Ruiz-Benito, P.; Andivia, E.

2023-05-21 ecology
10.1101/2023.05.18.541207 bioRxiv
Show abstract

Drought-associated tree mortality has worldwide increased in the last decades, impacting structure and functioning of forest ecosystems, with implications for energy, carbon and water fluxes. However, the understanding of the factors underlying this mortality are still limited, especially at stand scale. We aim to identify the factors that triggered the mortality of the widely distributed Pinus sylvestris in an extensive forest area in central Spain. We compared radial growth patterns in pairs of live and recently dead individuals that co-occur in close proximity and present similar age and size, thereby isolating the effects of size and environment from the mortality process. Temporal dynamics of growth, growth synchrony, and growth sensitivity to water availability (P-PET) were compared between live and recently dead trees. Over the last 50 years, we observed an increase in the growth synchrony and sensitivity to water availability as drought conditions intensified consistent to prior research. However, no differences were found in radial growth between live and dead individuals 15 years before mortality, and dead individuals showed lower growth synchrony and sensitivity to water availability than live ones for much of the period studied. This suggests a decoupling between trees growth responses and climatic conditions, which could increase vulnerability to hydraulic failure and/or carbon starvation. Overall, our results point to an important role of growth sensitivity to water availability in tree mortality for P. sylvestris at its southern distribution limit.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.