Concepts for understanding fruit trees

This book presents a clear set of integrative concepts for understanding the overall physiology and growth of temperate deciduous fruit trees. The emphasis is on overarching principles rather than detailed descriptions of tree physiology or difference among numerous species of fruit trees. Although the focus is on deciduous fruit trees, many aspects apply to evergreen fruit trees and trees that grow naturally in unmanaged situations.

Understanding the long-term storage sink.

Similar to short-term starch storage in the chloroplasts of the leaves that serves to buffer growth of organs from carbohydrate shortages due to diurnal patterns of photosynthesis related to daily patterns of light and darkness, trees also have long-term storage capacity to enable them to supply the minimal respiratory needs of tissues during the winter and resume growth in the spring when trees are still leafless. This long-term storage of carbohydrates and some minerals occurs primarily in the phloem and xylem tissue of the branches, trunk and roots. While active phloem tissue has higher concentrations of stored carbohydrates than xylem tissue, the mass of active xylem storage tissue is many times the mass of the active phloem tissue. Thus, xylem tissue comprises the largest storage compartment of temperate deciduous fruit trees. This chapter deals with understanding the long-term storage sink in fruit trees.

Warmer spring temperatures in temperate deciduous forests advance the timing of tree growth but have little effect on annual woody productivity

As the climate changes, warmer spring temperatures are causing earlier leaf-out1–6 and commencement of net carbon dioxide (CO2) sequestration2,4 in temperate deciduous forests, resulting in a tendency towards increased growing season length1,4,5,7–9 and annual CO2 uptake2,4,10–14. However, less is known about how spring temperatures affect tree stem growth, which sequesters carbon (C) in wood that has a long residence time in the ecosystem15,16. Using dendrometer band measurements from 463 trees across two forests, we show that warmer spring temperatures shifted the woody growth of deciduous trees earlier but had no consistent effect on peak growing season length, maximum daily growth rates, or annual growth. The latter finding was confirmed on the centennial scale by 207 tree-ring chronologies from 108 forests across eastern North America, where annual growth was far more sensitive to temperatures during the peak growing season than in the spring. These findings imply that extra CO2 uptake in years with warmer springs10–12 is not allocated to long-lived woody biomass, where it could have a substantial and lasting impact on the forest C balance. Rather, contradicting current projections from global C cycle models2,3,17,18, our empirical results imply that warming spring temperatures are unlikely to increase the woody productivity or strengthen the CO2 sink of temperate deciduous forests.

Including Leaf Traits Improves a Deep Neural Network Model for Predicting Photosynthetic Capacity from Reflectance

Accurate knowledge of photosynthetic capacity is critical for understanding the carbon cycle under climate change. Despite the fact that deep neural network (DNN) models are increasingly applied across a wide range of fields, there are very few attempts to predict leaf photosynthetic capacity (indicated by maximum carboxylation rate, Vcmax, and maximum electron transport rate, Jmax) from reflected information. In this study, we have built a DNN model that uses leaf reflected spectra, alone or together with other leaf traits, for the reliable estimation of photosynthetic capacity, accounting for leaf types and growing periods in cool–temperate deciduous forests. Our results demonstrate that even though DNN models using only the reflectance spectra are capable of estimating both Vcmax and Jmax acceptably, their performance could nevertheless be improved by including information about other leaf biophysical/biochemical traits. The results highlight the fact that leaf spectra and leaf biophysical/biochemical traits are closely linked with leaf photosynthetic capacity, providing a practical and feasible approach to tracing functional traits. However, the DNN models developed in this study should undergo more extensive validation and training before being applied in other regions, and further refinements in future studies using larger datasets from a wide range of ecosystems are also necessary.