A hotter climate makes trees grow faster and produce lighter wood
the verdict
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Evidence regarding the relationship between climate, tree growth, and wood density is mixed. Some sources note that trees have grown faster alongside lighter wood characteristics over the past century, while global analyses indicate that warmer conditions tend to be associated with denser wood.
2018 study found that trees grow faster due to increased carbon dioxide levels; however, the trees are also 8–12 percent lighter and denser since 1900. The
Climate change is already now altering biomes, adversely affecting terrestrial and marine ecosystems. Climate change represents long-term changes in temperature and average weather patterns. This leads to a substantial increase in both the frequency and the intensity of extreme weather events. As a region's climate changes, a change in its flora and fauna follows. For instance, out of 4000 species
In the western U.S., since 1986, longer, warmer summers have resulted in a fourfold increase in major wildfires and a sixfold increase in the area of forest burned, compared to the period from 1970 to 1986. While fire suppression policies have played a substantial role as well, both healthy and unhealthy forests now face an increased risk of forest fires because of the warming climate.
A 2018 study found that trees grow faster due to increased carbon dioxide levels; however, the trees are also 8–12 percent lighter and denser since 1900. The authors note, "Even though a greater volume of wood is being produced today, it now contains less material than just a few decades ago."
Boreal forests, also known as taiga, are warming at a faster rate than the global average. leading to drier conditions in the Taiga, which leads to a whole host of subsequent issues. Climate change has a direct impact on the productivity of the boreal forest, as well as health and regeneration. As a result of the rapidly changing climate, trees show declines in growth at the southern limit of their range, and are migrating to higher latitudes and altitudes (northward) to remain their climatic habitat, but some species may not be migrating fast enough. The number of days with extremely cold temperatures (e.g., −20 to −40 °C (−4 to −40 °F) has decreased irregularly but systematically in nearly all the boreal region, allowing better survival for tree-damaging insects. The 10-year average of boreal forest burned in North America, after several decades of around 10,000 km2 (2.5 million acres), has increased steadily since 1970 to more than 28,000 km2 (7 million acres) annually., and records in Canada show increases in wildfire from 1920 to 1999.
Early 2010s research confirmed that since the 1960s, western Canadian boreal forests, and particularly the western coniferous forests, had already suffered substantial tree losses due to drought, and some conifers were getting replaced with aspen. Similarly, the already dry forest areas in central Alaska and far eastern Russia are also experiencing greater drought, placing birch trees under particular stress, while Siberia's needle-shedding larches are replaced with evergreen conifers - a change which also affects the area's albedo (evergreen trees absorb more heat than the snow-covered ground) and acts as a small, yet detectable climate change feedback. At the same time, eastern Canadian forests have been much less affected; yet some research suggests it would also reach a tipping point around 2080, under the RCP 8.5 scenario which represents the largest potential increase in anthropogenic emissions.
It has been hypothesized that the boreal environments have only a few states which are stable in the long term - a treeless tundra/steppe, a forest with >75% tree
In the western U.S., since 1986, longer, warmer summers have resulted in a fourfold increase in major wildfires and a sixfold increase in the area of forest burned, compared to the period from 1970 to 1986. While fire suppression policies have played a substantial role as well, both healthy and unhealthy forests now face an increased risk of forest fires because of the warming climate.
A 2018 study found that trees grow faster due to increased carbon dioxide levels; however, the trees are also 8–12 percent lighter and denser since 1900. The authors note, "Even though a greater volume of wood is being produced today, it now contains less material than just a few decades ago."
The Amazon rainforest is the largest tropical rainforest in the world. It is twice as big as India and spans nine countries in South America. This size allows it to produce around half of its own rainfall by recycling moisture through evaporation and transpiration as air moves across the forest; tree losses interfere with that capability, to the point where if enough is lost, much of the rest will likely die off and transform into a dry savanna landscape. For now, deforestation of the Amazon rainforest has been the greatest threat to it, and the main reason why, as of 2022, about 20% of it had been deforested and another 6% "highly degraded". Yet, climate change is also a threat as it exacerbates wildfire and interferes with precipitation. It is considered likely that hitting 3.5 °C (6.3 °F) of global warming would trigger the collapse of rainforest to savannah over the course of around a century (50-200) years, although it occur at between 2 °C (3.6 °F) to 6 °C (11 °F) of warming.
Forest fires in Indonesia have dramatically increased since 1997 as well. These fires are often actively started to clear forest for agriculture. They can set fire to the large peat bogs in the region and the CO2 released by these peat bog fires has been estimated, in an average year, to be 15% of the quantity of CO2 produced by fossil fuel combustion.
One of the main concerns is the loss of endangered species, mostly because of the increasing heat found in tropical forests.
Research suggests that slow-growing trees are only stimulated in growth for a short period under higher CO2 levels, while faster growing plants like liana benefit in the long term. In general, but especially in rainforests, this means that liana become the prevalent species; and because they decompose much faster than trees their carbon content is more quickly returned to the atmosphere. Slow growing trees incorporate atmospheric carbon for decades.
The density of wood is a key indicator of the carbon investment strategies of trees, impacting productivity and carbon storage. Despite its importance, the global variation in wood density and its environmental controls remain poorly understood, preventing accurate predictions of global forest carbon stocks. Here we analyse information from 1.1 million forest inventory plots alongside wood density data from 10,703 tree species to create a spatially explicit understanding of the global wood density distribution and its drivers. Our findings reveal a pronounced latitudinal gradient, with wood in tropical forests being up to 30% denser than that in boreal forests. In both angiosperms and gymnosperms, hydrothermal conditions represented by annual mean temperature and soil moisture emerged as the primary factors influencing the variation in wood density globally. This indicates similar environmental filters and evolutionary adaptations among distinct plant groups, underscoring the essential role of abiotic factors in determining wood density in forest ecosystems. Additionally, our study highlights the prominent role of disturbance, such as human modification and fire risk, in influencing wood density at more local scales. Factoring in the spatial variation of wood density notably changes the estimates of forest carbon stocks, leading to differences of up to 21% within biomes. Therefore, our research contributes to a deeper understanding of terrestrial biomass distribution and how environmental changes and disturbances impact forest ecosystems. Wood density is an important plant trait. Data from 1.1 million forest inventory plots and 10,703 tree species show a latitudinal gradient in wood density, with temperature and soil moisture explaining variation at the global scale and disturbance also having a role at the local level.
Furthermore, the strong link between wood density and biomass production 1 , 9 makes it a vital factor in quantifying terrestrial carbon uptake and storage 10 – 13 . Over one-third of the total variation in aboveground biomass in tropical forests can be explained by spatial
Consequently, in ecosystems with higher vapour pressure deficits, such as warm and dry forests, trees are likely to develop denser wood to maintain xylem resistance against implosion and rupture 21 , 23 . In contrast, in warm and humid ecosystems with lower vapour pressure deficit, life history strategies may lean towards rapid growth, characterized by reduced carbon investment in wood, to maximize competitive ability 21 , 22 . In colder regions, gymnosperms with low-density tracheids have a competitive advantage over angiosperms.
Understanding the global distribution of forest wood density and the various influencing factors, including climate and ecosystem disturbances, is vital for predicting and managing the responses of forest ecosystems to environmental shifts and for formulating effective strategies to mitigate and adapt to climate change impacts. Here we paired ~1.1 million ground-sourced forest inventory plots (Fig. 1d ) from the global forest biodiversity initiative (GFBi) database 52 with collated species-level wood density data 1 , 53 – 60 to explore global variation in wood density among both angiosperm and gymnosperm trees.
Spatial and phylogenetic wood density variation Gymnosperm trees, which are dominant in boreal and high elevation regions, had 20% lower wood density than angiosperms, with mean densities of 0.47 ± 0.07 g cm −3 and 0.59 ± 0.14 g cm −3 , respectively. Accordingly, the CWDs of the global forests were positively related to the proportion of angiosperms within a plot (Fig. 1b ). Our global CWD data reveal strong differences in wood density across the major forest regions (‘Plot-level wood density metrics’ in Methods ).
The coloured circle surrounding the phylogeny represents different orders. The filled blue/red circles inside the phylogeny indicate orders that show significantly ( P < 0.05) lower (blue) or higher (red) wood densities relative to all the species. Numbers inside the circles represent the average wood density of the respective order. Geospatial mapping To map the geographic variation of wood density based on its relationship with environmental factors, we developed random-forest models using 62 global layers of climate, topography, soil, vegetation and human activity (Supplementary Table 4 ). These models were applied to all tree species (Fig. 3a ), as well as separately to angiosperms (Fig.
Our estimates were most closely aligned (93%) with those from GlobBiomass 63 , as both used the same live tree volume data (Supplementary Fig. 7c ). Fig. 5 Comparison of global living tree biomass distribution using spatially explicit wood density data versus a universal wood density value. a , The global distribution of living tree biomass (in tonnes per hectare), derived by integrating our wood density map with spatially explicit data on living tree volume, root mass fraction and biomass expansion factors.
To isolate the influence of wood density variation on global tree biomass distribution, we compared our wood density-informed biomass model with a model using a constant wood density value of 0.53 g cm −3 (the global average). We found that the constant wood density model estimated the global biomass to be about 4% lower than the spatially explicit wood density model (359 GtC compared to 374 GtC; Fig. 5b ). However, significant differences emerged within various biomes (Fig.
We built a phylogenetic tree using the R package V.PhyloMaker 98 , with a total of 4,298 species (189 families from 55 orders) with wood density information matching the species in the phylogenetic database. To test for phylogenetic signal in wood density, we computed Pagel’s lambda and Blomberg’s K , using the phylosig function in the R package phytools 99 . To further test for trait conservatism at the order level, we used the ph_aot function from the R package phylocomr 100 .
Wood density (WD) is a crucial anatomical trait influencing forest carbon storage. However, dynamic global vegetation models (DGVMs) typically assume a fixed species-level WD, neglecting environment-driven variability. In this proof-of-concept study, we explore the potential impact of dynamic WD on tree- and forest-level carbon storage by integrating a simple temperature-response function of WD into the DGVM LPJ-GUESS from Smith et al., 2014. Simulations along a temperature gradient show that incorporating environmentally responsive WD can substantially alter simulated stand structure and carbon stocks. Overall, our model experiments illustrated that sites with higher WD had more, but smaller trees, which stored less carbon compared to the standard model. The strongest effects were predicted to appear before canopy closure, where per-tree carbon deviated by up to 32%. This exploratory study suggests the need to represent a mechanism for dynamic WD to better assess ecological feedbacks to forest carbon storage predictions, particularly in young and regenerating forests.
Environmental factors such as temperature and water availability influence both inter- and intra-annual variability in tree ring density (Begum et al., 2018 ; Buttò et al., 2020 ; Camarero & Hevia, 2020 ; Rosell et al., 2017 ). Additionally, as an adaptive trait, wood density varies spatially due to species differences, climate and ecosystem type (Mo et al., 2024 ; van der Maaten-Theunissen et al., 2013 ).
It reflects a fundamental trade-off between growth and survival: trees with lower wood density grow faster but have shorter lifespans and are more vulnerable to damage, while those with higher wood density grow slower but are more resistant to mechanical stress and live longer (Chao et al., 2008 ; King et al., 2006 ). This trade-off influences forest dynamics by affecting mortality and resource competition, linking wood properties to forest demography (Chave et al., 2009 ). Numerous empirical relationships between the environmental conditions and tree-ring level wood anatomical features, such as wood density, have been established and successfully applied in climate reconstruction.
To introduce variability in tree establishment, 30 climate ensemble members were generated, each starting at a different year within the cycle. Simulations were run at five sites ( Figure 2 ), with 30 climate ensemble members at each site. Reported variables (i.e. height, Cwood/tree) are the mean of 25 replicate patches. To test life-stage sensitivity to climate-driven wood density shifts (H3), we simulated a 2°C temperature increase starting at different forest recovery stages. The timing of warming was site-specific and determined by the canopy growth stage.
This approach allows us to assess how wood density-driven changes affect trees at different age, size and canopy stages ( Supplementary Section S1.6 ). A single climate ensemble member per site was selected to minimize variability, based on the lowest root mean square error in the canopy area time-series compared to the ensemble mean. 3. Results Temperature-driven variations in wood density, both in sapwood and overall, diverged from the default values used in LPJ-GUESS-STD ( Supplementary Figures S10 and S11 ).
Each lighter line represents a cohort influenced by different climate ensemble members, meaning that climate conditions at establishment vary between simulations. The darker, thick lines represent the mean of all 30 different climate ensemble members. Vertical lines show the mean canopy closure age across all ensemble members. Figure Results- SEQ Figure_Results- ARABIC 1: (a) The carbon stored in wood per individual tree and (b) mean tree height, both shown as the relative difference between LPJ-GUESS-STD and LPJ-GUESS-WD, using the best-fit (-Best, red) and full-range (-Range, blue) wood density response curves.
Lines below the 0-line indicate that LPJ-GUESS-WD results in lower carbon per individual compared to -STD, while lines above the 0 line indicate higher carbon storage in -WD simulations. Each lighter line represents a cohort influenced by different climate ensemble members, meaning that climate conditions at establishment vary between simulations. The darker, thick lines represent the mean of all 30 different climate
The darker, thick lines represent the mean of all 30 different climate ensemble. 3.2. H2: Wood density influence on forest dynamics Following disturbance, the same number of trees was planted for all simulations, and tree numbers remain identical in LPJ-GUESS-WD and LPJ-GUESS-STD until canopy closure ( Figure 4b ). However, post-canopy closure, differences emerged, with the direction of these responses aligning with wood density deviations along the wood density–temperature gradient.
The manuscript is well-written, the way that the temperature-dependence is implemented makes sense to me, and the results are interesting, suggesting that trees in warmer areas are more numerous and denser in their wood, but also shorter, leading to a net loss in carbon. Some of these effects come about in the way that carbon allocation is simulated (cf. formulas in the Supplementary that link height and wood density), others are emergent. If true, that would have important ramifications for assessing the global carbon cycle under climate change. The paper would thus be of great interest to readers of Quantitative Plant Biology.
235: That’s quite heavy Picea abies (0.5 g cm-3) 288 and 318: Nice figures! So if I summarize – sites with lower wood density (SWI / GER-HE) than the average have taller trees, more carbon, and fewer individuals. That trees in hotter areas are shorter and have higher WD makes sense, but I am wondering about the higher tree densities. Is this something we observe in the field with Picea abies? Would you have any validation data to test this against? 315: There is something missing 367-369: This goes back to my earlier point about Picea abies.
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