Declining carbohydrate content of Sitka-spruce treesdying from seawater exposure

Abstract

Increasing sea levels associated with climate change threaten the survival of coastal forests, yet the mechanisms by which seawater exposure causes tree death remain poorly understood. Despite the potentially crucial role of nonstructural carbohydrate (NSC) reserves in tree survival, their dynamics in the process of death under seawater exposure are unknown. Here we monitored progressive tree mortality and associated NSC storage in Sitka-spruce (Picea sitchensis) trees dying under ecosystem-scale increases in seawater exposure in western Washington, USA. All trees exposed to seawater, because of monthly tidal intrusion, experienced declining crown foliage during the sampling period, and individuals with a lower percentage of live foliated crown (PLFC) died faster. Tree PLFC was strongly correlated with subsurface salinity and needle ion contents. Total NSC concentrations in trees declined remarkably with crown decline, and reached extremely low levels at tree death (2.4% and 1.6% in leaves and branches, respectively, and 0.4% in stems and roots). Starch in all tissues was almost completely consumed, while sugars remained at a homeostatic level in foliage. The decreasing NSC with closer proximity to death and near zero starch at death are evidences that carbon starvation occurred during Sitka-spruce mortality during seawater exposure. Our results highlight the importance of carbon storage as an indicator of tree mortality risks under seawater exposure.

Figure 1

Site location and tree crown mortality estimation. A, The site is located at Beaver Creek, Washington, along the Pacific coast of the Pacific Northwest, USA. The spruce forest in this site was dying after causeway removal in 2014, shown in the satellite images from Google Earth. B, Examples of tree (%) estimation.

Figure 2

The dynamics of tree death over time. A, The dynamics of tree PLFC during sampling periods. Data points are staggered to reveal overlapping points. Different colors of points refer to the gradient of PLFC. Central circles and bars indicate mean values with 95% confidence interval. Solid circles refer to PLFC of the initial 14 target trees; crosses refer to PLFC of the 14 more target trees added to increase sampling sizes from March 2019. Numbers near points refer to the amounts of dead trees (i.e. PLFC = 0%) within the 14 or 28 sampling sizes. Lmer analysis showed that PLFC changed significantly with time (F-value = 31.64, P < 0.001; Supplemental Table S1). B, The proportion of trees with 50% ≤PLFC ≤ 100%, 25% ≤ PLFC < 50%, and PLFC < 25% over sampling periods.

Figure 3

Relationships between the PLFC at each sampling time and relative change of PLFC compared to last sampling time. Either a linear or loess function (blue line) was chosen to better represent the relationships. Shaded areas represent 95% confidence intervals for predictions. Note that the occasional appearance of positive values of relative change of PLFC is due to the error in the estimation of PLFC. The comparisons of PLFC among sampling time for each tree are shown in Supplemental Table S2.

Figure 4

Relationships of crown mortality with needle/soil salinity. A–D, Relationships of PLFC with modeled subsurface salinity, and with needle and soil Na⁺, K⁺, and Ca²⁺ contents. E–H, Relationships of PLFC decreasing rates with subsurface salinity, needle/soil Na⁺, K⁺, and Ca²⁺ contents. PLFC, needle/soil Na⁺, K⁺, and Ca²⁺ contents for each tree were the average value from March 2019 to July 2019. Subsurface salinity was averaged from simulated values at the 5, 15, 25, 35, and 45 cm depth from March 2019 to July 2019. Decrease in rates of PLFC were calculated by the decrease in slope of PLFC during the sampling period. Shaded areas represent 95% confidence intervals for predictions from a linear model. Dashed lines indicate insignificant linear regressions (P > 0.05).

Figure 5

Shoot water potential dynamics over time. Data points are staggered to reveal overlapping points. Different colors of points refer to the PLFC. Black and blue central circles with bars indicate mean predawn and mid-day water potential with 95% confidence interval, respectively. Lmer analysis showed that predawn and mid-day water potential did not change with time (F-value = 0.30, P = 0.88 for predawn water potential; F-value = 0.24, P = 0.87 for mid-day water potential; Supplemental Table S3).

Figure 6

Correlation matrix for PLFC, tissue NSC concentrations, shoot predawn and mid-day water potentials (Ψ_pd and Ψ_md), ions (Na⁺, K⁺, Ca²⁺, Mg²⁺) contents in needles and soil, modeled subsurface salinities, and stem diameters at breast height (DBH). The color of each circle represents the correlation coefficient (scale to right of matrix). The size of the circle and asterisks represent the significance level (*P < 0.05; **P < 0.01; ***P < 0.001).

Figure 7

Relationships of crown mortality and carbohydrates. Relationships between PLFC and total NSC (A), starch (B), and soluble sugars (C) concentrations in leaves, branches, stems, and roots. Colors within each panel represent the sampling months: October (tawny), March (blue), May (orange), June (green), and July (purple), all months combined (black). The fitted model is GLMs with log links. Shaded areas represent 95% confidence intervals of the regression lines. Only significant regressions are shown (P <0.05, details of model predictions are in Table 1 and Supplemental Table S5). Values shown in each panel are the concentrations of NSC (or its components, ± se) predicted at 0% PLFC from significant regressions overall the sampling periods.

Figure 8

Starch storage in woody tissues (branches, stems, roots) in response to trees’ weeks to complete death. Trees’ weeks to complete death were estimated based on the time when 0% PLFC in each crown was found (the week of 0% PLFC was week zero). Each prior sampling period was then used to populate the regression. Only significant regressions are shown (P <0.05). Shaded areas represent 95% confidence intervals of the regression lines.