Large areas of forestland in temperate North America, as well as in other parts of the world, are growing older and will soon transition into middle and then late successional stages exceeding 100 yr ...in age. These ecosystems have been important regional carbon sinks as they recovered from prior anthropogenic and natural disturbance, but their future sink strength, or annual rate of carbon storage, is in question. Ecosystem development theory predicts a steady decline in annual carbon storage as forests age, but newly available, direct measurements of forest net CO2 exchange challenge that prediction. In temperate deciduous forests, where moderate severity disturbance regimes now often prevail, there is little evidence for any marked decline in carbon storage rate during mid-succession. Rather, an increase in physical and biological complexity under these disturbance regimes may drive increases in resource-use efficiency and resource availability that help to maintain significant carbon storage in these forests well past the century mark. Conservation of aging deciduous forests may therefore sustain the terrestrial carbon sink, whilst providing other goods and services afforded by these biologically and structurally complex ecosystems.
Structure–function relationships are central to many ecological paradigms. Chief among these is the linkage of net primary production (NPP) with species diversity and canopy structure. Using the ...National Ecological Observatory Network (NEON) as a subcontinental-scale research platform, we examined how temperate-forest NPP relates to several measures of site-level canopy structure and tree species diversity. Novel multidimensional canopy traits describing structural complexity, most notably canopy rugosity, were more strongly related to site NPP than were species diversity measures and other commonly characterized canopy structural features. The amount of variation in site-level NPP explained by canopy rugosity alone was 83%, which was substantially greater than that explained individually by vegetation area index (31%) or Shannon’s index of species diversity (30%). Forests that were more structurally complex, had higher vegetation-area indices, or were more diverse absorbed more light and used light more efficiently to power biomass production, but these relationships were most strongly tied to structural complexity. Implications for ecosystem modeling and management are wide ranging, suggesting structural complexity traits are broad, mechanistically robust indicators of NPP that, in application, could improve the prediction and management of temperate forest carbon sequestration.
Phenology, by controlling the seasonal activity of vegetation on the land surface, plays a fundamental role in regulating photosynthesis and other ecosystem processes, as well as competitive ...interactions and feedbacks to the climate system. We conducted an analysis to evaluate the representation of phenology, and the associated seasonality of ecosystem‐scale CO2 exchange, in 14 models participating in the North American Carbon Program Site Synthesis. Model predictions were evaluated using long‐term measurements (emphasizing the period 2000–2006) from 10 forested sites within the AmeriFlux and Fluxnet‐Canada networks. In deciduous forests, almost all models consistently predicted that the growing season started earlier, and ended later, than was actually observed; biases of 2 weeks or more were typical. For these sites, most models were also unable to explain more than a small fraction of the observed interannual variability in phenological transition dates. Finally, for deciduous forests, misrepresentation of the seasonal cycle resulted in over‐prediction of gross ecosystem photosynthesis by +160 ± 145 g C m−2 yr−1 during the spring transition period and +75 ± 130 g C m−2 yr−1 during the autumn transition period (13% and 8% annual productivity, respectively) compensating for the tendency of most models to under‐predict the magnitude of peak summertime photosynthetic rates. Models did a better job of predicting the seasonality of CO2 exchange for evergreen forests. These results highlight the need for improved understanding of the environmental controls on vegetation phenology and incorporation of this knowledge into better phenological models. Existing models are unlikely to predict future responses of phenology to climate change accurately and therefore will misrepresent the seasonality and interannual variability of key biosphere–atmosphere feedbacks and interactions in coupled global climate models.
The phenology of vegetation exerts an important control over the terrestrial ecosystem carbon (C) cycle. Remote sensing of key phenological phases in forests (e.g., the spring onset and autumn end of ...growing season) remains challenging due to noise in time series and the limited seasonal variation of canopy greenness in evergreen forests. Using 94 site-years of C flux data from four deciduous broadleaf forests (DBF) and six evergreen needleleaf forests (ENF) in North America, we examine whether growing season phenology can be remotely sensed from mean vegetation indices (VIs) derived from spring (Apr.–May) and autumn (Sep.–Nov) observations. Five VIs were used based on Moderate Resolution Imaging Spectroradiometer (MODIS) data, including the normalized difference vegetation index (NDVI), the land surface water index (LSWI), the enhanced vegetation index (EVI), the wide dynamic range vegetation index (WDRVI) and the optimized soil-adjusted vegetation index (OSAVI). Our results show that growing season transitions can be inferred from mean seasonal VIs, though the different VIs varied in their predictive strength across sites and plant functional types. Widely used NDVI and EVI exhibited limited potential in tracking growing season phenology of ENF ecosystems, while indices sensitive to water (i.e., LSWI) or less influenced by soil (i.e., OSAVI) may have unrevealed powers in indicating phenological transitions. OSAVI was shown to be a strong predictor of the end of the growing season in ENF ecosystems, suggesting that this VI may offer a new strategy for modeling the phenology of ENF sites. We conclude that combinations of multiple indices may improve the remote sensing of land surface phenology, as evidenced by the good agreement between modeled and observed growing season transitions and its length in our evaluation.
•Seasonal mean VIs were evaluated for growing season phenology modeling.•EVI had potential in modeling growing season transitions of DBF sites but not for evergreen species.•OSAVI revealed good potential in tracking growing season end of evergreen species.•Combination of multiple indices is useful to model growing season length.
The even-aged northern hardwood forests of the Upper Great Lakes Region are undergoing an ecological transition during which structural and biotic complexity is increasing. Early-successional aspen (
...Populus
spp.) and birch (
Betula papyrifera
) are senescing at an accelerating rate and are being replaced by middle-successional species including northern red oak (
Quercus rubra
), red maple (
Acer rubrum
), and white pine (
Pinus strobus
). Canopy structural complexity may increase due to forest age, canopy disturbances, and changing species diversity. More structurally complex canopies may enhance carbon (C) sequestration in old forests. We hypothesize that these biotic and structural alterations will result in increased structural complexity of the maturing canopy with implications for forest C uptake.
At the University of Michigan Biological Station (UMBS), we combined a decade of observations of net primary productivity (NPP), leaf area index (LAI), site index, canopy tree-species diversity, and stand age with canopy structure measurements made with portable canopy lidar (PCL) in 30 forested plots. We then evaluated the relative impact of stand characteristics on productivity through succession using data collected over a nine-year period. We found that effects of canopy structural complexity on wood NPP (NPP
W
) were similar in magnitude to the effects of total leaf area and site quality. Furthermore, our results suggest that the effect of stand age on NPP
W
is mediated primarily through its effect on canopy structural complexity. Stand-level diversity of canopy-tree species was not significantly related to either canopy structure or NPP
W
. We conclude that increasing canopy structural complexity provides a mechanism for the potential maintenance of productivity in aging forests.
Interannual variability in biosphere‐atmosphere exchange of CO2 is driven by a diverse range of biotic and abiotic factors. Replicating this variability thus represents the ‘acid test’ for ...terrestrial biosphere models. Although such models are commonly used to project responses to both normal and anomalous variability in climate, they are rarely tested explicitly against inter‐annual variability in observations. Herein, using standardized data from the North American Carbon Program, we assess the performance of 16 terrestrial biosphere models and 3 remote sensing products against long‐term measurements of biosphere‐atmosphere CO2 exchange made with eddy‐covariance flux towers at 11 forested sites in North America. Instead of focusing on model‐data agreement we take a systematic, variability‐oriented approach and show that although the models tend to reproduce the mean magnitude of the observed annual flux variability, they fail to reproduce the timing. Large biases in modeled annual means are evident for all models. Observed interannual variability is found to commonly be on the order of magnitude of the mean fluxes. None of the models consistently reproduce observed interannual variability within measurement uncertainty. Underrepresentation of variability in spring phenology, soil thaw and snowpack melting, and difficulties in reproducing the lagged response to extreme climatic events are identified as systematic errors, common to all models included in this study.
Despite the wide acceptance of the “big‐leaf” upscaling strategy in evapotranspiration modeling (e.g., the Penman‐Monteith model), its usefulness in simulating canopy photosynthesis may be limited by ...the underlying assumption of homogeneous response of carbon assimilation light‐response kinetics through the canopy. While previous studies have shown that the separation of the canopy into sunlit and shaded parts (i.e., two‐leaf model) is typically more effective than big‐leaf models for upscaling photosynthesis from leaf to canopy, a systematic comparison between these two upscaling strategies among multiple ecosystems has not been presented. In this study, gross primary productivity was modeled using two‐leaf and big‐leaf upscaling approaches in the Boreal Ecosystem Productivity Simulator for shrublands, broadleaf, and conifer forest types. When given the same leaf‐level photosynthetic parameters, the big‐leaf approach significantly underestimated canopy‐level GPP while the two‐leaf approach more closely predicted both the magnitude and day‐to‐day variability in eddy covariance measurements. The underestimation by the big‐leaf approach is mostly caused by its exclusion of the photosynthetic contributions of shaded leaves. Tests of the model sensitivity to a foliage clumping index revealed that the contribution of shaded leaves to the total simulated productivity can be as high as 70% for highly clumped stands and seldom decreases below ∼40% for less‐clumped canopies. Our results indicate that accurate upscaling of photosynthesis across a broad array of ecosystems requires an accurate description of canopy structure in ecosystem models.
Key Points
The big‐leaf approach significantly underestimated the canopy‐level GPP
The underestimation is caused by the exclusion of the contributions of shaded leaves
Shaded leaves contribute 40%‐70% to the total simulated productivity
•NDVI-derived phenology (SOS and EOS) was tested at 60 FLUXNET sites globally.•NDVI-derived phenology showed varied predictive strength among plant functional types.•Different fitting methods ...produced significant different phenology.•EOS was more difficult to simulate than for SOS.•Rigorous validation should be conducted before interpreting regional phenology change.
Phenology is an important indicator of annual plant growth and is also widely incorporated in ecosystem models to simulate interannual variability of ecosystem productivity under climate change. A comprehensive understanding of the potentials of current algorithms to detect the start and end for growing season (SOS and EOS) from remote sensing is still lacking. This is particularly true when considering the diverse interactions between phenology and climate change among plant functional types as well as potential influences from different sensors. Using data from 60 flux tower sites (376 site-years in total) from the global FLUXNET database, we applied four algorithms to extract plant phenology from time series of normalized difference vegetation index (NDVI) from both MODIS and SPOT-VGT sensors. Results showed that NDVI-simulated phenology had overall low correlation (R2<0.30) with flux-derived SOS/EOS observations, but this predictive strength substantially varied by fitting algorithm, sensor and plant functional type. Different fitting algorithms can produce significantly different phenological estimates, but this difference can also be influenced by sensor type. SPOT-VGT simulated better EOS but no difference in the accuracy of SOS was found with different sensors. It may be due to increased frequency of data sampling (10 days for SPOT-VGT vs. 16 days for MODIS) during spring season when rapid plant growth does not help SPOT-VGT more sensitive to growth. In contrast, more frequent data acquisition favors better modeling of plant growth in autumn when a gradual decrease in photosynthesis occurs. Our study results highlight that none of these algorithm can provide consistent good accuracy in modeling SOS and EOS with respect to both plant functional types and sensors. More importantly, a rigorous validation of phenology modeling against ground data is necessary before applying these algorithms at regional or global scales and consequently previous conclusions on regional SOS/EOS trends should be viewed with caution if independent validation is lacking.
Spatially and temporally continuous estimation of plant photosynthetic carbon fixation (or gross primary production, GPP) is crucial to our understanding of the global carbon cycle and the impact of ...climate change. Besides spatial, seasonal and interannual variations, GPP also exhibits strong diurnal variations. Satellite retrieved solar-induced chlorophyll fluorescence (SIF) provides a spatially continuous, but temporally discrete measurement of plant photosynthesis, and has the potential to be used to estimate GPP at global scale. However, it remains unclear whether the seasonal time series of SIF snapshots taken at a fixed time of the day can be used to infer daily total GPP variation at spatial and seasonal scales. In this study, we first used GPP estimates from 135 eddy covariance flux sites, covering a wide range of geographic locations and biome types, to investigate the relationship between the instantaneous GPP (GPPinst) and daily GPP (GPPdaily) on the seasonal course for different times of the day. Latitudinal and diurnal patterns were found to correspond to variations in photosynthetically active radiation (PAR) and light use efficiency (LUE), respectively. We then used the Soil-Canopy Observation Photosynthesis and Energy Balance (SCOPE) model and the FluxCom GPP product to investigate the instantaneous and daily SIF-GPP relationships at five flux tower sites along a latitudinal gradient and at a global scale for different biome types. The results showed that daily SIF had a stronger linear correlation with daily GPP than instantaneous SIF at the seasonal scale, with an instantaneous to daily SIF conversion factor following the latitudinal and seasonal pattern driven by PAR. Our study highlights the necessity to take the latitudinal and diurnal factors into consideration for SIF-GPP relationship analyses or for physiological phenology analyses based on SIF.
•Latitudinal and diurnal patterns exist linking the instantaneous to daily GPP.•Daily SIF exhibits stronger linear correlation with daily GPP than instantaneous SIF.•Satellite retrieved SIF needs to be converted to daily SIF to compare with daily GPP.
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