Greater variability in environmental stress favours trees that prioritise storage of carbohydrate reserves over growth: a modelling analysis

Stefaniak, Elisa Z.; Tissue, David T.; Falster, Daniel S.; Medlyn, Belinda E.

doi:10.5194/egusphere-2026-1474

Preprints

https://doi.org/10.5194/egusphere-2026-1474

Preprints

10 Apr 2026

| 10 Apr 2026

Status: this preprint is open for discussion and under review for Biogeosciences (BG).

Greater variability in environmental stress favours trees that prioritise storage of carbohydrate reserves over growth: a modelling analysis

Elisa Z. Stefaniak, David T. Tissue, Daniel S. Falster, and Belinda E. Medlyn

Abstract. Trees use reserves of non-structural carbohydrates (NSC) to help them survive and recover from stress periods. However, accumulation of reserves is at the expense of growth, resulting in a growth-storage trade-off. Tree species may pursue different storage strategies to optimise fitness in environments with differing degrees of stress, but it is not clear which storage strategies provide a competitive advantage in which environments.

We use a forest gap model to explore competitive outcomes among idealised tree species with different combinations of two carbon storage-related traits: carbon utilisation rate (fast-slow spectrum) and switch time from growth to storage (risky-safe spectrum). We investigate the competitive success of alternative growth vs storage strategies in simplified environments which have a non-specific annual stress period. We vary stress intensity (the mean stress duration) and stress stochasticity (the variance of stress duration) to determine the effect of increased stress on composition outcomes.

Community composition shifted from growth-prioritising strategies to storage-prioritising strategies with increasing stress intensity and stochasticity. The major driver of this shift in community composition was increased mortality, due to depletion of carbon reserves, in species with growth-prioritising strategies.

Our results demonstrate that considering carbon storage strategies can provide new insights into tree survival and adaptation of tree communities to increasing stress caused by climate change.

Received: 22 Mar 2026 – Discussion started: 10 Apr 2026

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.

Download & links

Preprint (PDF, 1732 KB)

Supplement (2711 KB)

Download & links

Elisa Z. Stefaniak, David T. Tissue, Daniel S. Falster, and Belinda E. Medlyn

Status: open (until 31 Jul 2026)

Post a comment Subscribe to comment alert

RC1:
'Comment on egusphere-2026-1474', Anonymous Referee #1, 17 Jul 2026 reply
This manuscript addresses the ecologically important trade-off between carbon allocation to growth and to non-structural carbohydrate storage. The authors extend the plant trait-, size- and patch-structured forest model with an active non-structural-carbohydrate (NSC) storage pool. Carbon-allocation strategy is represented by two traits: the timing of the switch from growth to storage and the rate at which stored carbon is utilised for growth. Four idealised strategies (Fast-Risky, Fast-Safe, Slow-Risky, and Slow-Safe) are allowed to compete in 100-year simulations across eight environmental treatments, combining two mean annual stress durations with four levels of stress stochasticity. The extended model also incorporates storage-dependent mortality, productivity-dependent mortality, and an allometric recalibration mechanism that allows plants to restore imbalances among tissues following stress. The main results show that seasonal stress favours slower carbon-utilisation strategies, whereas risk-taking strategies are more advantageous in the absence of stress because of their longer growing periods. Overall, the study provides a useful modelling perspective on how carbon storage strategies may mediate tree survival, competitive interactions, and forest community responses under climate change, particularly as droughts, heatwaves, and other extreme events become more variable and severe. My general comments are provided below.
The experiment crosses only two mean stress intensities (54.75 and 91.25 days; t_crit=0.85 and 0.75 yr) with four levels of stochasticity. The Supplementary Information shows that these same two stress durations were also used in the single-plant sensitivity analyses from which the four allocation strategies were selected, but I could not find an ecological or empirical justification for choosing these particular values. With only two mean-stress levels, it is difficult to characterise a stress-intensity gradient or to assess whether responses are linear, nonlinear, or threshold-like. This also makes it difficult to evaluate how robust the conclusion is that the effect of mean stress intensity is inconsistent across stochasticity treatments (Lines 316–325). Would an additional sensitivity analysis across a broader gradient of mean stress duration help distinguish a genuinely weak or inconsistent intensity effect?

The key mortality coefficients (𝛼_𝑑P1 and 𝛼_𝑑P2) in the productivity-dependent mortality function and 𝛼_𝑑S1 and 𝛼_𝑑S2 in the storage-dependent mortality function are listed as being derived from simulations (Supplementary Table 2) rather than constrained by empirical data. This is important because differential mortality is presented as the primary mechanism driving the competitive outcomes (Line 373) and storage-dependent mortality in particular determines mortality during the imposed stress period (Lines 343-344). The qualitative shift from Slow-Risky to Slow-Safe may therefore depend on the selected baseline and risk coefficients. I consider a sensitivity analysis across plausible ranges of these mortality parameters to be important for establishing the robustness of the central conclusion.

Specific
The definition of the productivity-dependent mortality driver X appears inconsistent between the main text and Supplementary Table S1. In the main text, X is defined as B/A_l and as net carbon uptake per unit leaf area (Lines 168–170). In Supplementary Table S1, however, the argument of the exponential appears as (dB/dt)/dA_l (page 2 under ‘Mortality- Productivity-dependent mortality rate’) while dB/dt is separately defined as net mass production (‘Storage allocation’ section). Could the authors clarify whether the intended driver is B/A_l, (dB/dt)/A_l, or a derivative ratio involving dA_l/dt? Based on the verbal definition, I suspect that the intended expression is (dB/dt)/A_l

A paragraph is duplicated in the Supplementary Information (p. 5). “From these simulations, two values for each parameter were chosen for a total of four contrasting allocation...” appears twice in immediate succession, remove the duplicate.

Reply
Citation: https://doi.org/10.5194/egusphere-2026-1474-RC1

Elisa Z. Stefaniak, David T. Tissue, Daniel S. Falster, and Belinda E. Medlyn

Supplement

https://doi.org/10.5194/egusphere-2026-1474-supplement

Data sets

Simulation results for "Greater variability in environmental stress favours trees that prioritise storage of carbohydrate reserves over growth: a modelling analysis" Stefaniak et al. https://doi.org/10.5281/zenodo.17552724

Model code and software

plant Stefaniak and Falster https://github.com/foxeswithdata/plant

storage plant model simulation analysis Stefaniak https://github.com/foxeswithdata/storage_plant_simulation_analysis

Elisa Z. Stefaniak, David T. Tissue, Daniel S. Falster, and Belinda E. Medlyn

Viewed

Total article views: 334 (including HTML, PDF, and XML)

HTML	PDF	XML	Total	Supplement	BibTeX	EndNote
235	80	19	334	44	30	40

HTML: 235
PDF: 80
XML: 19
Total: 334
Supplement: 44
BibTeX: 30
EndNote: 40

Views and downloads (calculated since 10 Apr 2026)

Month	HTML	PDF	XML	Total
Apr 2026	161	44	11	216
May 2026	52	23	2	77
Jun 2026	11	4	1	16
Jul 2026	11	9	5	25

Cumulative views and downloads (calculated since 10 Apr 2026)

Month	HTML	PDF	XML	Total
Apr 2026	161	44	11	216
May 2026	52	23	2	77
Jun 2026	11	4	1	16
Jul 2026	11	9	5	25

Viewed (geographical distribution)

Total article views: 326 (including HTML, PDF, and XML) Thereof 326 with geography defined and 0 with unknown origin.

Country	#	Views	%

Latest update: 26 Jul 2026

Short summary

Tree growth-storage trade-off occurs as trees require storage for stress survival. We simulate a forest of idealised tree species to explore the competitive success of alternative growth vs storage strategies. Composition shifted from growth- to storage-prioritising strategies with increasing stress intensity and stochasticity largely due to higher mortality in growth-prioritising strategies. We show new insights into tree survival and adaptation of tree communities to increasing stress.


Total:	0
HTML:	0
PDF:	0
XML:	0