Assessment of spongy moth infestation impacts on forest productivity and carbon loss using the Sentinel-2 satellite remote sensing and eddy covariance flux data.

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.

Leaf-level coordination principles propagate to the ecosystem scale.

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.

Effects of variable retention harvesting on canopy transpiration in a red pine plantation forest.

Background: Variable Retention Harvesting (VRH) is a forest management practice applied to enhance forest growth, improve biodiversity, preserve ecosystem function and provide economic revenue from harvested timber. There are many different forms and compositions in which VRH is applied in forest ecosystems. In this study, the impacts of four different VRH treatments on transpiration were evaluated in an 83-year-old red pine (Pinus Pinus resinosa) plantation forest in the Great Lakes region in Canada. These VRH treatments included 55% aggregated crown retention (55A), 55% dispersed crown retention (55D), 33% aggregated crown retention (33A), 33% dispersed crown retention (33D) and unharvested control (CN) plot. These VRH treatments were implemented in 1-ha plots in the winter of 2014, while sap flow measurements were conducted from 2018 to 2020. Results: Study results showed that tree-level transpiration was highest among trees in the 55D treatment, followed by 33D, 55A, 33A and CN plots. We found that photosynthetically active radiation (PAR) and vapor pressure deficit (VPD) were major controls or drivers of transpiration in all VRH treatments. Our study suggests that dispersed or distributed retention of 55% basal area (55D) is the ideal forest management technique to enhance transpiration and forest growth. Conclusions: This study will help researchers, forest managers and decision-makers to improve their understanding of water cycling in forest ecosystem and adopt the best forest management regimes to enhance forest growth, health and resiliency to climate change.

Heat and drought impact on carbon exchange in an age-sequence of temperate pine forests.

BACKGROUND: Most North American temperate forests are plantation or regrowth forests, which are actively managed. These forests are in different stages of their growth cycles and their ability to sequester atmospheric carbon is affected by extreme weather events. In this study, the impact of heat and drought events on carbon sequestration in an age-sequence (80, 45, and 17 years as of 2019) of eastern white pine (Pinus strobus L.) forests in southern Ontario, Canada was examined using eddy covariance flux measurements from 2003 to 2019. RESULTS: Over the 17-year study period, the mean annual values of net ecosystem productivity (NEP) were 180 ± 96, 538 ± 177 and 64 ± 165 g C m-2 yr-1 in the 80-, 45- and 17-year-old stands, respectively, with the highest annual carbon sequestration rate observed in the 45-year-old stand. We found that air temperature (Ta) was the dominant control on NEP in all three different-aged stands and drought, which was a limiting factor for both gross ecosystem productivity (GEP) and ecosystems respiration (RE), had a smaller impact on NEP. However, the simultaneous occurrence of heat and drought events during the early growing seasons or over the consecutive years had a significant negative impact on annual NEP in all three forests. We observed a similar trend of NEP decline in all three stands over three consecutive years that experienced extreme weather events, with 2016 being a hot and dry, 2017 being a dry, and 2018 being a hot year. The youngest stand became a net source of carbon for all three of these years and the oldest stand became a small source of carbon for the first time in 2018 since observations started in 2003. However, in 2019, all three stands reverted to annual net carbon sinks. CONCLUSIONS: Our study results indicate that the timing, frequency and concurrent or consecutive occurrence of extreme weather events may have significant implications for carbon sequestration in temperate conifer forests in Eastern North America. This study is one of few globally available to provide long-term observational data on carbon exchanges in different-aged temperate plantation forests. It highlights interannual variability in carbon fluxes and enhances our understanding of the responses of these forest ecosystems to extreme weather events. Study results will help in developing climate resilient and sustainable forestry practices to offset atmospheric greenhouse gas emissions and improving simulation of carbon exchange processes in terrestrial ecosystem models.

Radial variations in xylem sap flux in a temperate red pine plantation forest.

BACKGROUND: Scaling sap flux measurements to whole-tree water use or stand-level transpiration is often done using measurements conducted at a single point in the sapwood of the tree and has the potential to cause significant errors. Previous studies have shown that much of this uncertainty is related to (i) measurement of sapwood area and (ii) variations in sap flow at different depths within the tree sapwood. RESULTS: This study measured sap flux density at three depth intervals in the sapwood of 88-year-old red pine (Pinus resinosa) trees to more accurately estimate water-use at the tree- and stand-level in a plantation forest near Lake Erie in Southern Ontario, Canada. Results showed that most of the water transport (65%) occurred in the outermost sapwood, while only 26% and 9% of water was transported in the middle and innermost depths of sapwood, respectively. CONCLUSIONS: These results suggest that failing to consider radial variations in sap flux density within trees can lead to an overestimation of transpiration by as much as 81%, which may cause large uncertainties in water budgets at the ecosystem and catchment scale. This study will help to improve our understanding of water use dynamics and reduce uncertainties in sap flow measurements in the temperate pine forest ecosystems in the Great Lakes region and help in protecting these forests in the face of climate change.

Seasonal variation in the canopy color of temperate evergreen conifer forests.

Evergreen conifer forests are the most prevalent land cover type in North America. Seasonal changes in the color of evergreen forest canopies have been documented with near-surface remote sensing, but the physiological mechanisms underlying these changes, and the implications for photosynthetic uptake, have not been fully elucidated. Here, we integrate on-the-ground phenological observations, leaf-level physiological measurements, near surface hyperspectral remote sensing and digital camera imagery, tower-based CO2 flux measurements, and a predictive model to simulate seasonal canopy color dynamics. We show that seasonal changes in canopy color occur independently of new leaf production, but track changes in chlorophyll fluorescence, the photochemical reflectance index, and leaf pigmentation. We demonstrate that at winter-dormant sites, seasonal changes in canopy color can be used to predict the onset of canopy-level photosynthesis in spring, and its cessation in autumn. Finally, we parameterize a simple temperature-based model to predict the seasonal cycle of canopy greenness, and we show that the model successfully simulates interannual variation in the timing of changes in canopy color. These results provide mechanistic insight into the factors driving seasonal changes in evergreen canopy color and provide opportunities to monitor and model seasonal variation in photosynthetic activity using color-based vegetation indices.

The Impact of Seasonal and Annual Climate Variations on the Carbon Uptake Capacity of a Deciduous Forest Within the Great Lakes Region of Canada.

In eastern North America, many deciduous forest ecosystems grow at the northernmost extent of their geographical ranges, where climate change could aid or impede their growth. This region experiences frequent extreme weather conditions, allowing us to study the response of these forests to environmental conditions, reflective of future climates. Here we determined the impact of seasonal and annual climate variations and extreme weather events on the carbon (C) uptake capacity of an oak-dominated forest in southern Ontario, Canada, from 2012 to 2016. We found that changes in meteorology during late May to mid-July were key in determining the C sink strength of the forest, impacting the seasonal and annual variability of net ecosystem productivity (NEP). Overall, higher temperatures and dry conditions reduced ecosystem respiration (RE) much more than gross ecosystem productivity (GEP), leading to higher NEP. Variability in NEP was primarily driven by changes in RE, rather than GEP. The mean annual GEP, RE, and NEP values at our site during the study were 1,343 ± 85, 1,171 ± 139, and 206 ± 92 g C m-2 yr-1, respectively. The forest was a C sink even in years that experienced heat and water stresses. Mean annual NEP at our site was within the range of NEP (69-459 g C m-2 yr-1) observed in similar North American forests from 2012 to 2016. The growth and C sequestration capabilities of our oak-dominated forest were not adversely impacted by changes in environmental conditions and extreme weather events experienced over the study period.

The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data.

The FLUXNET2015 dataset provides ecosystem-scale data on CO2, water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible.

Tracking the phenology of photosynthesis using carotenoid-sensitive and near-infrared reflectance vegetation indices in a temperate evergreen and mixed deciduous forest.

Photosynthetic phenology is an important indicator of annual gross primary productivity (GPP). Assessing photosynthetic phenology remotely is difficult for evergreen conifers as they remain green year-round. Carotenoid-based vegetation indices such as the photochemical reflectance index (PRI) and chlorophyll/carotenoid index (CCI) are promising tools to remotely track the invisible phenology of photosynthesis by assessing carotenoid pigment dynamics. PRI, CCI and the near-infrared reflectance of vegetation (NIRV ) index may act as proxies of photosynthetic efficiency (ɛ), an important parameter in light-use efficiency models, or direct proxies of photosynthesis. To understand the physiological mechanisms reflected by PRI and CCI and the ability of vegetation indices to act as proxies of photosynthetic activity for estimating GPP, we measured leaf pigment composition, PRI, CCI, NIRV and photosynthetic activity at the leaf and canopy scales over 2 years in an evergreen and mixed deciduous forest. PRI and CCI captured the large seasonal carotenoid/chlorophyll ratio changes and good relationships were observed between PRI-ɛ and CCI-photosynthesis and NIRV -photosynthesis. PRI-, CCI- and NIRV -based models effectively tracked observed seasonal GPP. We propose that carotenoid-based and near-infrared reflectance vegetation indices may provide useful proxies of photosynthetic activity and can improve remote sensing-based models of GPP in evergreen and deciduous forests.

Global vegetation biomass production efficiency constrained by models and observations.

Plants use only a fraction of their photosynthetically derived carbon for biomass production (BP). The biomass production efficiency (BPE), defined as the ratio of BP to photosynthesis, and its variation across and within vegetation types is poorly understood, which hinders our capacity to accurately estimate carbon turnover times and carbon sinks. Here, we present a new global estimation of BPE obtained by combining field measurements from 113 sites with 14 carbon cycle models. Our best estimate of global BPE is 0.41 ± 0.05, excluding cropland. The largest BPE is found in boreal forests (0.48 ± 0.06) and the lowest in tropical forests (0.40 ± 0.04). Carbon cycle models overestimate BPE, although models with carbon-nitrogen interactions tend to be more realistic. Using observation-based estimates of global photosynthesis, we quantify the global BP of non-cropland ecosystems of 41 ± 6 Pg C/year. This flux is less than net primary production as it does not contain carbon allocated to symbionts, used for exudates or volatile carbon compound emissions to the atmosphere. Our study reveals a positive bias of 24 ± 11% in the model-estimated BP (10 of 14 models). When correcting models for this bias while leaving modeled carbon turnover times unchanged, we found that the global ecosystem carbon storage change during the last century is decreased by 67% (or 58 Pg C).

Seasonal variability of forest sensitivity to heat and drought stresses: A synthesis based on carbon fluxes from North American forest ecosystems.

Climate extremes such as heat waves and droughts are projected to occur more frequently with increasing temperature and an intensified hydrological cycle. It is important to understand and quantify how forest carbon fluxes respond to heat and drought stress. In this study, we developed a series of daily indices of sensitivity to heat and drought stress as indicated by air temperature (Ta ) and evaporative fraction (EF). Using normalized daily carbon fluxes from the FLUXNET Network for 34 forest sites in North America, the seasonal pattern of sensitivities of net ecosystem productivity (NEP), gross ecosystem productivity (GEP) and ecosystem respiration (RE) in response to Ta and EF anomalies were compared for different forest types. The results showed that warm temperatures in spring had a positive effect on NEP in conifer forests but a negative impact in deciduous forests. GEP in conifer forests increased with higher temperature anomalies in spring but decreased in summer. The drought-induced decrease in NEP, which mostly occurred in the deciduous forests, was mostly driven by the reduction in GEP. In conifer forests, drought had a similar dampening effect on both GEP and RE, therefore leading to a neutral NEP response. The NEP sensitivity to Ta anomalies increased with increasing mean annual temperature. Drier sites were less sensitive to drought stress in summer. Natural forests with older stand age tended to be more resilient to the climate stresses compared to managed younger forests. The results of the Classification and Regression Tree analysis showed that seasons and ecosystem productivity were the most powerful variables in explaining the variation of forest sensitivity to heat and drought stress. Our results implied that the magnitude and direction of carbon flux changes in response to climate extremes are highly dependent on the seasonal dynamics of forests and the timing of the climate extremes.

Cryptic phenology in plants: Case studies, implications, and recommendations.

Plant phenology-the timing of cyclic or recurrent biological events in plants-offers insight into the ecology, evolution, and seasonality of plant-mediated ecosystem processes. Traditionally studied phenologies are readily apparent, such as flowering events, germination timing, and season-initiating budbreak. However, a broad range of phenologies that are fundamental to the ecology and evolution of plants, and to global biogeochemical cycles and climate change predictions, have been neglected because they are "cryptic"-that is, hidden from view (e.g., root production) or difficult to distinguish and interpret based on common measurements at typical scales of examination (e.g., leaf turnover in evergreen forests). We illustrate how capturing cryptic phenology can advance scientific understanding with two case studies: wood phenology in a deciduous forest of the northeastern USA and leaf phenology in tropical evergreen forests of Amazonia. Drawing on these case studies and other literature, we argue that conceptualizing and characterizing cryptic plant phenology is needed for understanding and accurate prediction at many scales from organisms to ecosystems. We recommend avenues of empirical and modeling research to accelerate discovery of cryptic phenological patterns, to understand their causes and consequences, and to represent these processes in terrestrial biosphere models.

Memory effects of climate and vegetation affecting net ecosystem CO2 fluxes in global forests.

Forests play a crucial role in the global carbon (C) cycle by storing and sequestering a substantial amount of C in the terrestrial biosphere. Due to temporal dynamics in climate and vegetation activity, there are significant regional variations in carbon dioxide (CO2) fluxes between the biosphere and atmosphere in forests that are affecting the global C cycle. Current forest CO2 flux dynamics are controlled by instantaneous climate, soil, and vegetation conditions, which carry legacy effects from disturbances and extreme climate events. Our level of understanding from the legacies of these processes on net CO2 fluxes is still limited due to their complexities and their long-term effects. Here, we combined remote sensing, climate, and eddy-covariance flux data to study net ecosystem CO2 exchange (NEE) at 185 forest sites globally. Instead of commonly used non-dynamic statistical methods, we employed a type of recurrent neural network (RNN), called Long Short-Term Memory network (LSTM) that captures information from the vegetation and climate's temporal dynamics. The resulting data-driven model integrates interannual and seasonal variations of climate and vegetation by using Landsat and climate data at each site. The presented LSTM algorithm was able to effectively describe the overall seasonal variability (Nash-Sutcliffe efficiency, NSE = 0.66) and across-site (NSE = 0.42) variations in NEE, while it had less success in predicting specific seasonal and interannual anomalies (NSE = 0.07). This analysis demonstrated that an LSTM approach with embedded climate and vegetation memory effects outperformed a non-dynamic statistical model (i.e. Random Forest) for estimating NEE. Additionally, it is shown that the vegetation mean seasonal cycle embeds most of the information content to realistically explain the spatial and seasonal variations in NEE. These findings show the relevance of capturing memory effects from both climate and vegetation in quantifying spatio-temporal variations in forest NEE.

Solar-induced chlorophyll fluorescence exhibits a universal relationship with gross primary productivity across a wide variety of biomes.

In our recent study in Global Change Biology (Li et al., ), we examined the relationship between solar-induced chlorophyll fluorescence (SIF) measured from the Orbiting Carbon Observatory-2 (OCO-2) and gross primary productivity (GPP) derived from eddy covariance flux towers across the globe, and we discovered that there is a nearly universal relationship between SIF and GPP across a wide variety of biomes. This finding reveals the tremendous potential of SIF for accurately mapping terrestrial photosynthesis globally.

The association between chronic exposure to traffic-related air pollution and ischemic heart disease.

Increasing evidence links air pollution to the risk of cardiovascular disease. This study investigated the association between ischemic heart disease (IHD) prevalence and exposure to traffic-related air pollution (nitrogen dioxide [NO2], fine particulate matter [PM2.5], and ozone [O3]) in a population of susceptible subjects in Toronto. Local (NO2) exposures were modeled using land use regression based on extensive field monitoring. Regional exposures (PM2.5, O3) were modeled as confounders using inverse distance weighted interpolation based on government monitoring data. The study sample consisted of 2360 patients referred during 1992 to 1999 to a pulmonary clinic at the Toronto Western Hospital in Toronto, Ontario, Canada, to diagnose or manage a respiratory complaint. IHD status was determined by clinical database linkages (ICD-9-CM 412-414). The association between IHD and air pollutants was assessed with a modified Poisson regression resulting in relative risk estimates. Confounding was controlled with individual and neighborhood-level covariates. After adjusting for multiple covariates, NO2 was significantly associated with increased IHD risk, relative risk (RR) = 1.33 (95% confidence interval [CI]: 1.2, 1.47). Subjects living near major roads and highways had a trend toward an elevated risk of IHD, RR = 1.08 (95% CI: 0.99, 1.18). Regional PM2.5 and O3 were not associated with risk of IHD.

Global convergence in the temperature sensitivity of respiration at ecosystem level.

The respiratory release of carbon dioxide (CO(2)) from the land surface is a major flux in the global carbon cycle, antipodal to photosynthetic CO(2) uptake. Understanding the sensitivity of respiratory processes to temperature is central for quantifying the climate-carbon cycle feedback. We approximated the sensitivity of terrestrial ecosystem respiration to air temperature (Q(10)) across 60 FLUXNET sites with the use of a methodology that circumvents confounding effects. Contrary to previous findings, our results suggest that Q(10) is independent of mean annual temperature, does not differ among biomes, and is confined to values around 1.4 +/- 0.1. The strong relation between photosynthesis and respiration, by contrast, is highly variable among sites. The results may partly explain a less pronounced climate-carbon cycle feedback than suggested by current carbon cycle climate models.

Terrestrial gross carbon dioxide uptake: global distribution and covariation with climate.

Terrestrial gross primary production (GPP) is the largest global CO(2) flux driving several ecosystem functions. We provide an observation-based estimate of this flux at 123 +/- 8 petagrams of carbon per year (Pg C year(-1)) using eddy covariance flux data and various diagnostic models. Tropical forests and savannahs account for 60%. GPP over 40% of the vegetated land is associated with precipitation. State-of-the-art process-oriented biosphere models used for climate predictions exhibit a large between-model variation of GPP's latitudinal patterns and show higher spatial correlations between GPP and precipitation, suggesting the existence of missing processes or feedback mechanisms which attenuate the vegetation response to climate. Our estimates of spatially distributed GPP and its covariation with climate can help improve coupled climate-carbon cycle process models.

A cohort study of traffic-related air pollution and mortality in Toronto, Ontario, Canada.

BACKGROUND: Chronic exposure to traffic-related air pollution (TRAP) may contribute to premature mortality, but few studies to date have addressed this topic. OBJECTIVES: In this study we assessed the association between TRAP and mortality in Toronto, Ontario, Canada. METHODS: We collected nitrogen dioxide samples over two seasons using duplicate two-sided Ogawa passive diffusion samplers at 143 locations across Toronto. We calibrated land use regressions to predict NO2 exposure on a fine scale within Toronto. We used interpolations to predict levels of particulate matter with aerodynamic diameter < or = 2.5 microm (PM(2.5)) and ozone levels. We assigned predicted pollution exposures to 2,360 subjects from a respiratory clinic, and abstracted health data on these subjects from medical billings, lung function tests, and diagnoses by pulmonologists. We tracked mortality between 1992 and 2002. We used standard and multilevel Cox proportional hazard models to test associations between air pollution and mortality. RESULTS: After controlling for age, sex, lung function, obesity, smoking, and neighborhood deprivation, we observed a 17% increase in all-cause mortality and a 40% increase in circulatory mortality from an exposure contrast across the interquartile range of 4 ppb NO2. We observed no significant associations with other pollutants. CONCLUSIONS: Exposure to TRAP was significantly associated with increased all-cause and circulatory mortality in this cohort. A high prevalence of cardiopulmonary disease in the cohort probably limits inference of the findings to populations with a substantial proportion of susceptible individuals.