Robust ecological forecasting of tree growth under future climate conditions is critical to anticipate future forest carbon storage and flux. Here, we apply three ingredients of ecological forecasting that are key to improving forecast skill: data fusion, confronting model predictions with new data, and partitioning forecast uncertainty. Specifically, we present the first fusion of tree‐ring and forest inventory data within a Bayesian state‐space model at a multi‐site, regional scale, focusing on
Tree rings provide an invaluable long‐term record for understanding how climate and other drivers shape tree growth and forest productivity. However, conventional tree‐ring analysis methods were not designed to simultaneously test effects of climate, tree size, and other drivers on individual growth. This has limited the potential to test ecologically relevant hypotheses on tree growth sensitivity to environmental drivers and their interactions with tree size. Here, we develop and apply a new method to simultaneously model nonlinear effects of primary climate drivers, reconstructed tree diameter at breast height (DBH), and calendar year in generalized least squares models that account for the temporal autocorrelation inherent to each individual tree's growth. We analyze data from 3811 trees representing 40 species at 10 globally distributed sites, showing that precipitation, temperature, DBH, and calendar year have additively, and often interactively, influenced annual growth over the past 120 years. Growth responses were predominantly positive to precipitation (usually over ≥3‐month seasonal windows) and negative to temperature (usually maximum temperature, over ≤3‐month seasonal windows), with concave‐down responses in 63% of relationships. Climate sensitivity commonly varied with DBH (45% of cases tested), with larger trees usually more sensitive. Trends in ring width at small DBH were linked to the light environment under which trees established, but basal area or biomass increments consistently reached maxima at intermediate DBH. Accounting for climate and DBH, growth rate declined over time for 92% of species in secondary or disturbed stands, whereas growth trends were mixed in older forests. These trends were largely attributable to stand dynamics as cohorts and stands age, which remain challenging to disentangle from global change drivers. By providing a parsimonious approach for characterizing multiple interacting drivers of tree growth, our method reveals a more complete picture of the factors influencing growth than has previously been possible.
more » « less- NSF-PAR ID:
- 10363540
- Author(s) / Creator(s):
- ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; more »
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Global Change Biology
- Volume:
- 28
- Issue:
- 1
- ISSN:
- 1354-1013
- Page Range / eLocation ID:
- p. 245-266
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Aim Previous work demonstrated the global variability of synchrony in tree growth within populations, that is, the covariance of the year‐to‐year variability in growth of individual neighbouring trees. However, there is a lack of knowledge about the causes of this variability and its trajectories through time. Here, we examine whether climate can explain variation in within‐population synchrony (WPS) across space but also through time and we develop models capable of explaining this variation. These models can be applied to the global tree cover under current and future climate change scenarios.
Location Global.
Time period 1901–2012.
Major taxa studied Trees.
Methods We estimated WPS values from a global tree‐ring width database consisting of annual growth increment measurements from multiple trees at 3,579 sites. We used generalized linear mixed effects models to infer the drivers of WPS variability and temporal trends of global WPS. We then predicted WPS values across the global extent of tree cover. Finally, we applied our model to predict future WPS based on the RCP 8.5 (2045–2065 period) emission scenario.
Results Areas with the highest WPS are characterized by a combination of environments with both high mean annual temperature (>10°C) and low precipitation (<300 mm). Average WPS across all temperate forests has decreased historically and will continue to decrease. Potential implications of these patterns include changes in forest dynamics, such as higher tree growth and productivity and an increase in carbon sequestration. In contrast, the WPS of tropical forests of Central and South America will increase in the near future owing to reduced annual precipitation.
Main conclusions Climate explains WPS variability in space and time. We suggest that WPS might have value as an integrative ecological measure of the level of environmental stress to which forests are subjected and therefore holds potential for diagnosing effects of global climate change on tree growth.
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Dendroclimatology has focused mainly on the tree growth response to atmospheric variables. However, the roots of trees directly sense the “underground climate,” which can be expected to be no less important to tree growth. Data from two meteorological stations approximately 140 km apart in southern Siberia were applied to characterize the spatiotemporal dynamics of soil temperature and the statistical relationships of soil temperature to the aboveground climate and tree-ring width (TRW) chronologies of Larix sibirica Ledeb. from three forest–steppe stands. Correlation analysis revealed a depth-dependent delay in the maximum correlation of TRW with soil temperature. Temperatures of both the air and soil (depths 20–80 cm) were shown to have strong and temporally stable correlations between stations. The maximum air temperature is inferred to have the most substantial impact during July–September (R = −0.46–−0.64) and early winter (R = 0.39–0.52). Tree-ring indices reached a maximum correlation with soil temperature at a depth of 40 cm (R = −0.49–−0.59 at 40 cm) during April–August. High correlations are favored by similar soil characteristics at meteorological stations and tree-ring sites. Cluster analysis of climate correlations for individual trees based on the K-means revealed groupings of trees driven by microsite conditions, competition, and age. The results support a possible advantage of soil temperature over air temperature for dendroclimatic analysis of larch growth in semiarid conditions during specific seasons.more » « less
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Abstract The strength and persistence of the tropical carbon sink hinges on the long‐term responses of woody growth to climatic variations and increasing CO2. However, the sensitivity of tropical woody growth to these environmental changes is poorly understood, leading to large uncertainties in growth predictions. Here, we used tree ring records from a Southeast Asian tropical forest to constrain ED2.2‐hydro, a terrestrial biosphere model with explicit vegetation demography. Specifically, we assessed individual‐level woody growth responses to historical climate variability and increases in atmospheric CO2(Ca). When forced with historical Ca, ED2.2‐hydro reproduced the magnitude of increases in intercellular CO2concentration (a major determinant of photosynthesis) estimated from tree ring carbon isotope records. In contrast, simulated growth trends were considerably larger than those obtained from tree rings, suggesting that woody biomass production efficiency (WBPE = woody biomass production:gross primary productivity) was overestimated by the model. The estimated WBPE decline under increasing Cabased on model‐data discrepancy was comparable to or stronger than (depending on tree species and size) the observed WBPE changes from a multi‐year mature‐forest CO2fertilization experiment. In addition, we found that ED2.2‐hydro generally overestimated climatic sensitivity of woody growth, especially for late‐successional plant functional types. The model‐data discrepancy in growth sensitivity to climate was likely caused by underestimating WBPE in hot and dry years due to commonly used model assumptions on carbon use efficiency and allocation. To our knowledge, this is the first study to constrain model predictions of individual tree‐level growth sensitivity to Caand climate against tropical tree‐ring data. Our results suggest that improving model processes related to WBPE is crucial to obtain better predictions of tropical forest responses to droughts and increasing Ca. More accurate parameterization of WBPE will likely reduce the stimulation of woody growth by Carise predicted by biosphere models.
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Abstract Aim Vegetation is sensitive to mean annual precipitation (MAP), but the sensitivity of vegetation to precipitation variability (PV) is less clear. Tropical ecosystems are likely to experience increased PV in the future. Here we assessed the importance, magnitude and mechanism of PV effects on tree cover in the context of covarying environmental drivers such as fire, temperature and soil properties.
Location Tropical land.
Time period 2000–2010.
Major taxa studied Trees.
Methods We compiled climate, soil and remotely‐sensed tree cover data over tropical land. We then comprehensively assessed the contribution of PV at different time‐scales to tropical tree cover variations and estimated the sensitivity of tree cover to PV changes by conducting rolling‐window regression and variance decomposition analyses. We further adopted a mechanistic modelling approach to test whether water competition between trees and grasses can explain the observed effect of PV.
Results We find that PV contributes 33–56% to the total explained spatial variation (65–79%) in tree cover. The contribution of PV depends on MAP and is highest under intermediate MAP (500–1,500 mm). Tree cover generally increases with rainy day frequency and wet season length but shows mixed responses to inter‐annual PV. Based on the estimated sensitivity, tropical tree cover can decrease by 3–5% overall and by up to 20% in Amazonia under a 20% decrease in rainy days. Mechanistic modelling analysis reproduced the continental differences in tree cover along an MAP gradient.
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