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Background Deciduous forests in eastern North America experienced a widespread and intense spongy moth (Lymantria dispar) infestation in 2021. This study quantified the impact of this spongy moth infestation on carbon (C) cycle in forests across the Great Lakes region in Canada, utilizing high-resolution (10 × 10 m2) Sentinel-2 satellite remote sensing images and eddy covariance (EC) flux data. Study results showed a significant reduction in leaf area index (LAI) and gross primary productivity (GPP) values in deciduous and mixed forests in the region in 2021. Results Remote sensing derived, growing season mean LAI values of deciduous (mixed) forests were 3.66 (3.18), 2.74 (2.64), and 3.53 (2.94) m2 m−2 in 2020, 2021 and 2022, respectively, indicating about 24 (14)% reduction in LAI, as compared to pre- and post-infestation years. Similarly, growing season GPP values in deciduous (mixed) forests were 1338 (1208), 868 (932), and 1367 (1175) g C m−2, respectively in 2020, 2021 and 2022, showing about 35 (22)% reduction in GPP in 2021 as compared to pre- and post-infestation years. This infestation induced reduction in GPP of deciduous and mixed forests, when upscaled to whole study area (178,000 km2), resulted in 21.1 (21.4) Mt of C loss as compared to 2020 (2022), respectively. It shows the large scale of C losses caused by this infestation in Canadian Great Lakes region. Conclusions The methods developed in this study offer valuable tools to assess and quantify natural disturbance impacts on the regional C balance of forest ecosystems by integrating field observations, high-resolution remote sensing data and models. Study results will also help in developing sustainable forest management practices to achieve net-zero C emission goals through nature-based climate change solutions. 

Temperate deciduous forests are an important contributor to the global carbon (C) sink. However, changes in environmental conditions and natural disturbances such as insect infestations can impact carbon sequestration capabilities of these forests. While, insect infestations are expected to increase in warmer future climates, there is a lack of knowledge on the quantitative impact of these natural disturbances on the carbon balance of temperate deciduous forests. In 2021, a record-breaking defoliation, caused by the spongy moth (Lymantria dispar dispar (LDD), formerly knows as the gypsy moth) occurred in eastern North America. In this study, we assess the impact of this spongy moth defoliation on carbon uptake in a mature oak-dominated temperate forest in the Great Lakes region in Canada, using eddy covariance flux data from 2012 to 2022. Study results showed that the forest was a large C sink with mean annual net ecosystem productivity (NEP) of 207 ± 77 g C m–2 yr−1 from 2012 to 2022, excluding 2021, which experienced the infestation. Over this period mean annual gross ecosystem productivity (GEP) was 1,398 ± 137 g C m–2 yr−1, while ecosystem respiration (RE) was 1,209 ± 139 g C m–2 yr−1. However, in 2021 due to defoliation in the early growing season, annual GEP of the forest declined to 959 g C m–2 yr−1, while annual RE increased to 1,345 g C m–2 yr−1 causing the forest to become a large source of C with annual NEP of -351 g C m–2 yr−1. The forest showed a rapid recovery from this major disturbance event, with annual GEP, RE, and NEP values of 1,671, 1,287, and 298 g C m–2 yr−1, respectively in 2022 indicating that the forest was once again a large C sink. This study demonstrates that major transient natural disturbances under changing climate can have a significant impact on forest C dynamics. The extent to which North American temperate forests will remain a major C sink will depend on the severity and intensity of these disturbance events and the rate of recovery of forests following disturbances. 

We examined the seasonality of photosynthesis in 46 evergreen needleleaf (evergreen needleleaf forests (ENF)) and deciduous broadleaf (deciduous broadleaf forests (DBF)) forests across North America and Eurasia. We quantified the onset and end (Start_GPPand End_GPP) of photosynthesis in spring and autumn based on the response of net ecosystem exchange of CO₂to sunlight. To test the hypothesis that snowmelt is required for photosynthesis to begin, these were compared with end of snowmelt derived from soil temperature. ENF forests achieved 10% of summer photosynthetic capacity ∼3 weeks before end of snowmelt, while DBF forests achieved that capacity ∼4 weeks afterward. DBF forests increased photosynthetic capacity in spring faster (1.95% d⁻¹) than ENF (1.10% d⁻¹), and their active season length (End_GPP–Start_GPP) was ∼50 days shorter. We hypothesized that warming has influenced timing of the photosynthesis season. We found minimal evidence for long‐term change in Start_GPP, End_GPP, or air temperature, but their interannual anomalies were significantly correlated. Warmer weather was associated with earlier Start_GPP(1.3–2.5 days °C⁻¹) or later End_GPP(1.5–1.8 days °C⁻¹, depending on forest type and month). Finally, we tested whether existing phenological models could predict Start_GPPand End_GPP. For ENF forests, air temperature‐ and daylength‐based models provided best predictions for Start_GPP, while a chilling‐degree‐day model was best for End_GPP. The root mean square errors (RMSE) between predicted and observed Start_GPPand End_GPPwere 11.7 and 11.3 days, respectively. For DBF forests, temperature‐ and daylength‐based models yielded the best results (RMSE 6.3 and 10.5 days).

 

Fundamental axes of variation in plant traits result from trade-offs between costs and benefits of resource-use strategies at the leaf scale. However, it is unclear whether similar trade-offs propagate to the ecosystem level. Here, we test whether trait correlation patterns predicted by three well-known leaf- and plant-level coordination theories – the leaf economics spectrum, the global spectrum of plant form and function, and the least-cost hypothesis – are also observed between community mean traits and ecosystem processes. We combined ecosystem functional properties from FLUXNET sites, vegetation properties, and community mean plant traits into three corresponding principal component analyses. We find that the leaf economics spectrum (90 sites), the global spectrum of plant form and function (89 sites), and the least-cost hypothesis (82 sites) all propagate at the ecosystem level. However, we also find evidence of additional scale-emergent properties. Evaluating the coordination of ecosystem functional properties may aid the development of more realistic global dynamic vegetation models with critical empirical data, reducing the uncertainty of climate change projections.

 

Log‐transforming the dependent variable of a regression model, though convenient and frequently used, is accompanied by an under‐prediction problem. We found that this underprediction can reach up to 20%, which is significant in studies that aim to estimate annual budgets. The fundamental reason for this problem is simply that the log‐function is concave, and it has nothing to do with whether the dependent variable has a log‐normal distribution or not. Using field‐observed data of soil CO₂emission, soil temperature and soil moisture in a saturated‐specification of a regression model for predicting emissions, we revealed that the under‐predictions of the log‐transformed approach were pervasive and systematically biased. The key determinant of the problem's severity was the coefficient of variation in the dependent variable that differed among different combinations of the values of the explanatory factors. By applying a parsimonious (Gaussian‐Gamma) specification of the regression model to data from four different ecosystems, we found that this under‐prediction problem was serious to various extents, and that for a relatively weak explanatory factor, the log‐transformed approach is prone to yield a physically nonsensical estimated coefficient. Finally, we showed and concluded that the problem can be avoided by switching to the nonlinear approach, which does not require the assumption of homoscedasticity for the error term in computing the standard errors of the estimated coefficients.