Evergreen Needleleaf Forest Research Articles

Divergent Estimates of Forest Photosynthetic Phenology Using Structural and Physiological Vegetation Indices

AbstractThe accurate estimation of photosynthetic phenology using vegetation indices (VIs) is important for measuring the interannual variation of atmospheric CO2 concentrations, but the relative performances of structural and physiological VIs remain unclear. We found that structural VIs (normalized difference VI, enhanced VI, and near‐infrared reflectance of vegetation) were suitable for estimating the start of the photosynthetically active season in deciduous broadleaf forests using gross primary production measured by FLUXNET as a benchmark, and a physiological VI (chlorophyll/carotenoid index) was better at identifying the end of the photosynthetically active season for deciduous broadleaf forests and both the start and end of season for evergreen needleleaf forests. The divergent performances were rooted in the combined control of structural and physiological regulations of carbon uptake by plants. Most existing studies of photosynthetic phenology have been based on structural VIs, so we suggest revisiting the dynamics of photosynthetic phenology using physiological VIs, which has significant implications on global plant phenology and carbon uptake studies.

The biophysical climate mitigation potential of boreal peatlands during the growing season

Peatlands and forests cover large areas of the boreal biome and are critical for global climate regulation. They also regulate regional climate through heat and water vapour exchange with the atmosphere. Understanding how land-atmosphere interactions in peatlands differ from forests may therefore be crucial for modelling boreal climate system dynamics and for assessing climate benefits of peatland conservation and restoration. To assess the biophysical impacts of peatlands and forests on peak growing season air temperature and humidity, we analysed surface energy fluxes and albedo from 35 peatlands and 37 evergreen needleleaf forests—the dominant boreal forest type—and simulated air temperature and vapour pressure deficit (VPD) over hypothetical homogeneous peatland and forest landscapes. We ran an evapotranspiration model using land surface parameters derived from energy flux observations and coupled an analytical solution for the surface energy balance to an atmospheric boundary layer (ABL) model. We found that peatlands, compared to forests, are characterized by higher growing season albedo, lower aerodynamic conductance, and higher surface conductance for an equivalent VPD. This combination of peatland surface properties results in a ∼20% decrease in afternoon ABL height, a cooling (from 1.7 to 2.5 °C) in afternoon air temperatures, and a decrease in afternoon VPD (from 0.4 to 0.7 kPa) for peatland landscapes compared to forest landscapes. These biophysical climate impacts of peatlands are most pronounced at lower latitudes (∼45°N) and decrease toward the northern limit of the boreal biome (∼70°N). Thus, boreal peatlands have the potential to mitigate the effect of regional climate warming during the growing season. The biophysical climate mitigation potential of peatlands needs to be accounted for when projecting the future climate of the boreal biome, when assessing the climate benefits of conserving pristine boreal peatlands, and when restoring peatlands that have experienced peatland drainage and mining.

Examining the link between vegetation leaf area and land–atmosphere exchange of water, energy, and carbon fluxes using FLUXNET data

Abstract. Vegetation regulates the exchange of water, energy, and carbon fluxes between the land and the atmosphere. This regulation of surface fluxes differs with vegetation type and climate, but the effect of vegetation on surface fluxes is not well understood. A better knowledge of how and when vegetation influences surface fluxes could improve climate models and the extrapolation of ground-based water, energy, and carbon fluxes. We aim to study the link between vegetation and surface fluxes by combining the yearly average MODIS leaf area index (LAI) with flux tower measurements of water (latent heat), energy (sensible heat), and carbon (gross primary productivity and net ecosystem exchange). We show that the correlation of the LAI with water and energy fluxes depends on the vegetation type and aridity. Under water-limited conditions, the link between the LAI and the water and energy fluxes is strong, which is in line with a strong stomatal or vegetation control found in earlier studies. In energy-limited forest we found no link between the LAI and water and energy fluxes. In contrast to water and energy fluxes, we found a strong spatial correlation between the LAI and gross primary productivity that was independent of vegetation type and aridity. This study provides insight into the link between vegetation and surface fluxes. It indicates that for modelling or extrapolating surface fluxes, the LAI can be useful in savanna and grassland, but it is only of limited use in deciduous broadleaf forest and evergreen needleleaf forest to model variability in water and energy fluxes.

Trade-off between watershed water yield and ecosystem productivity along elevation gradients on a complex terrain in southwestern China

Understanding the tradeoffs between water yield and ecosystem productivity is important for developing strategies for large scale ecological restoration worldwide. This study focused on a national forest protection project in the Upper Yangtze River Basin where a logging ban was implemented in 1998. We used a hydrologic model and remote sensing data to study the interactions between water and carbon cycles along elevation gradients in the Minjiang watershed (MJ), where extensive deforestation and reforestation have occurred in the past seven decades. Average annual evapotranspiration (ET), water yield, and gross primary productivity (GPP) from 2000 to 2015 were estimated as 429 mm yr−1, 555 mm yr−1, and 1002 g C m−2 yr−1, respectively. ET decreased sharply and consistently with increasing elevation, whereas GPP only decreased significantly in high elevation areas (i.e., >3,000 m), resulting in divergent trends of water use efficiency (WUE) with elevation. Evergreen needleleaf forests (ENF) contributed 28% of water yield and 37% of GPP at the watershed scale, while grassland (GRA) also contributed 28% of water yield, but only 20% of total watershed GPP. Moreover, runoff coefficients showed strong negative correlations with GPP, suggesting a general trade-off relationship between water yield and ecosystem productivity in MJ. Our results suggest that vegetation composition and elevation played a key role in determining the relative ecological benefits for carbon and water in the study watershed with a complex terrain.

Time-varying trends of vegetation change and their driving forces during 1981–2016 along the silk road economic belt

Understanding the trend in vegetation evolution and its underlying causes is critical to revealing changes in ecosystem’s structure and function. However, less is known about the time-varying trends in vegetation and their main driving forces along the Silk Road Economic Belt (SREB). Using the two-band enhanced vegetation index (EVI2) as the proxy for vegetation growth, we investigated the time-varying trends in vegetation and their possible responses to the various driving factors during 1981–2016 using the ensemble empirical mode decomposition (EEMD) method. Results indicated that the vegetation exhibited significant trend shifts, which mainly occurred after 2000. Moreover, the greening to browning shifts occupied much larger areas than the browning to greening shifts (23.23% and 7.44%, respectively). The browning trends were largely expanded and enhanced after the turning points, which were primarily located in Eastern Europe and Central Asia. The vegetation greenness increased faster south of 50°N and east of 60°E, at elevations of 1000–3000 m, and in evergreen needle-leaf forests and sparsely vegetated areas, respectively. The shifted trends in the vegetation changed earlier and more quickly than the monotonic trends, which mainly occurred at lower elevations, in croplands and evergreen needle-leaf forests and near 40°E, 70°E, and north of 45°N. The vegetation experiencing non-significant changes was water-limited in most of the study area, especially in Central Asia. Warmer temperatures and droughts were likely the main causes of the greening to browning shifts. Time-varying trend analysis can correctly reveal the actual trend in vegetation and its responses to climate change, which provides scientific reference for ecological protection along the SREB.

Response of carbon and water fluxes to meteorological and phenological variability in two eastern North American forests of similar age but contrasting species composition – a multiyear comparison

Abstract. The annual carbon and water dynamics of two eastern North American temperate forests were compared over a 6-year period from 2012 to 2017. The geographic location, forest age, soil, and climate were similar between the two stands; however, stand composition varied in terms of tree leaf-retention and shape strategy: one stand was a deciduous broadleaf forest, while the other was an evergreen needleleaf forest. The 6-year mean annual net ecosystem productivity (NEP) of the coniferous forest was slightly higher and more variable (218±109 g C m−2 yr−1) compared to that of the deciduous forest NEP (200±83 g C m−2 yr−1). Similarly, the 6-year mean annual evapotranspiration (ET) of the coniferous forest was higher (442±33 mm yr−1) than that of the deciduous forest (388±34 mm yr−1), but with similar interannual variability. Summer meteorology greatly impacted the carbon and water fluxes in both stands; however, the degree of response varied among the two stands. In general, warm temperatures caused higher ecosystem respiration (RE), resulting in reduced annual NEP values – an impact that was more pronounced at the deciduous broadleaf forest compared to the evergreen needleleaf forest. However, during warm and dry years, the evergreen forest had largely reduced annual NEP values compared to the deciduous forest. Variability in annual ET at both forests was related most to the variability in annual air temperature (Ta), with the largest annual ET observed in the warmest years in the deciduous forest. Additionally, ET was sensitive to prolonged dry periods that reduced ET at both stands, although the reduction at the coniferous forest was relatively larger than that of the deciduous forest. If prolonged periods (weeks to months) of increased Ta and reduced precipitation are to be expected under future climates during summer months in the study region, our findings suggest that the deciduous broadleaf forest will likely remain an annual carbon sink, while the carbon sink–source status of the coniferous forest remains uncertain.

Understanding the continuous phenological development at daily time step with a Bayesian hierarchical space-time model: impacts of climate change and extreme weather events

The impacts of climate change and extreme weather events (e.g. frost-, heat-, drought-, and heavy rainfall events) on the continuous phenological development over the entire seasonal cycle remained poorly understood. Previous studies mainly focused on modeling key phenological transition dates (e.g. discrete timing of spring bud-break and fall senescence) based on aggregated climate variables (e.g. mean temperature, growing-degree days). We developed and evaluated a Bayesian Hierarchical Space-Time model for Land Surface Phenology (BHST-LSP) to synthesize remotely sensed vegetation greenness with climate covariates at a daily temporal scale from 1981 to 2014 across the entire conterminous United States. The BHST-LSP model incorporated both temporal and spatial information and exhibited high predictive power in simulating daily phenological development with an overall out-of-sample R2 of 0.80 ± 0.17 and 0.72 ± 0.20 for spring and fall phenology, respectively. The overall out-of-sample normalized root mean square errors were 9.3% ± 6.1% and 9.9% ± 5.2% between the observed and predicted vegetation greenness for spring and fall phenology, respectively. We found that a fast increase of temperature can accelerate the speed of spring green-up while a slow decrease of temperature can lead to a decelerated fall brown-down. Increasing accumulated precipitation can benefit daily phenological development over an entire growing season, while extreme rainfall events can have the opposite effects. More frequent frost events could slow spring leaf expansion and accelerate fall leaf senescence. Impacts of extreme heat events were complex and depended on water availability. Cropland in the Midwest as well as evergreen needleleaf forest along the coastal regions showed relatively strong resistance to drought events compared to other land cover types. The BHST-LSP model can be used to forecast vegetation phenology given future climate projection, thus providing valuable information for adopting climate change adaptation and mitigation measures.

Mapping canopy nitrogen in European forests using remote sensing and environmental variables with the random forests method

Canopy nitrogen (N) influences carbon (C) uptake by vegetation through its important role in photosynthetic enzymes. Global Vegetation Models (GVMs) predict C assimilation, but are limited by a lack spatial canopy N input. Mapping canopy N has been done in various ecosystems using remote sensing (RS) products, but has rarely considered environmental variables as additional predictors. Our research objective was to estimate spatial patterns of canopy N in European forests and to investigate the degree to which including environmental variables among the predictors would improve the models compared to using remotely sensed products alone. The environmental variables included were climate, soil properties, altitude, N deposition and land cover, while the remote sensing products were vegetation indices and NIR reflectance from MODIS and MERIS sensors, the MOD13Q1 and MTCI products, respectively. The results showed that canopy N could be estimated both within and among forest types using the random forests technique and calibration data from ICP Forests with good accuracy (r2 = 0.62, RRMSE = 0.18). The predicted spatial pattern shows higher canopy N in mid-western Europe and relatively lower values in both southern and northern Europe. For all subgroups tested (All plots, Evergreen Needleleaf Forest (ENF) plots and Deciduous Broadleaf Forest (DBF) plots), including environmental variables improved the predictions. Including environmental variables was especially important for the DBF plots, as the prediction model based on remotely sensed data products predicted canopy N with the lowest accuracy.

A time series of land cover maps of South Asia from 2001 to 2015 generated using AVHRR GIMMS NDVI3g data.

In South Asia, key differences in annual land use and land cover (LULC) take place due to climate change, global warming, human activity, biodiversity, and hydrology. So, it is very important to get accurate land cover information for this region. An annual LULC map that covers a comprehensive period is a major dataset for climatologically study. While yearly worldwide maps of LULC are produced from Moderate Resolution Imaging Spectroradiometer (MODIS) dataset, in 2001, the first LULC map of MODIS is generated which restrictions the perspective climatologically analysis. This research work generated a time series of yearly LULC maps of South Asia from 2001 to 2015 by using random forest classification from AVHRR GIMMS NDVI3g data. The MODIS land cover product such as (MCD12Q1) was used as a reference data for the trained classifier. The result was validated by using time series of annual LULC maps, and the spatiotemporal dynamic of LULC maps was illustrated in the last 15years from 2001 to 2015. The simplified sixteen class versions of our 15-year overall accuracy of a land cover map are 86.70%, and 1.23% higher than that of MODIS maps. The change detection indicated that, for the last 15years, the class of closed shrublands, savannas, croplands, urban and built-up land, barren, and cropland per natural vegetation mosaics increase notably during the 2001 to 2015, and in contrast, the class of woody savannas, evergreen needleleaf forests, open shrublands, grasslands, mixed forests, permanent wetlands, permanent snow and ice, evergreen broadleaf forests, and water bodies decrease notably during 2001 to 2015. These yearly land cover maps will be an essential dataset for the upcoming climate study, where time series of LULC maps accessibility is restricted.

Soil COS Exchange: A Comparison of Three European Ecosystems

AbstractThe potential of carbonyl sulfide (COS) flux measurements as an additional constraint for estimating the gross primary production depends, among other preconditions, on our understanding of the soil COS exchange and its contribution to the overall net ecosystem COS flux. We conducted soil chamber measurements of COS, with transparent chambers, in three different ecosystems across Europe. The in situ measurements were followed by laboratory measurements of soil samples collected at the study sites. The soil samples were exposed to UV radiation to investigate the role of photo‐degradation for COS exchange. In situ and laboratory measurements revealed pronounced intersite and intrasite variability of COS exchange. In situ COS fluxes were primarily governed by radiation in the savannah‐like grassland (SAV), soil temperature and intrasite heterogeneity in the deciduous broadleaf forest, and soil water content and intrasite heterogeneity in the evergreen needleleaf forest. The soil of the ecosystem with the highest light intensity incident on the soil surface, SAV, was a net source for COS, while the soils of the other two ecosystems were COS sinks. UV radiation increased COS emissions and/or reduced COS uptake from all soil samples under laboratory conditions. The impact of UV on the COS flux differed between soil samples, with a tendency toward a stronger response of the COS flux to UV radiation exposure in samples with higher soil organic matter content. Our results emphasize the importance of photo‐degradation for the soil COS flux and stress the substantial spatial variability of soil COS exchange in ecosystems.

Combined MODIS land surface temperature and greenness data for modeling vegetation phenology, physiology, and gross primary production in terrestrial ecosystems

Vegetation phenology such as the start (SOS) and end (EOS) of the growing season, physiology (represented by seasonal maximum capacity of carbon uptake, GPPmax), and gross primary production (GPP) are sensitive indicators for monitoring ecosystem response to environmental change. However, uncertainty and disagreement between models limit the use phenology metrics and GPP derived from remote sensing data. Statistical models for estimating phenology and physiology were constructed based on key predictor variables derived from enhanced vegetation index (EVI) and land surface temperature (LST) data. Then, a statistical model that integrated remote sensing-based phenology and physiology (RS-SMIPP) data was constructed to estimate seasonal and annual GPP. These models were calibrated and validated with GPP observations from 512 site-years of FLUXNET data covering four plant functional types (PFTs) in the northern hemisphere: deciduous broadleaf forest, evergreen needle-leaf forest, mixed forest, and grassland. Our results showed that phenology and physiology were accurately estimated with relative root mean squared error (RMSEr) <20%, and the errors varied among the PFTs. Spring EVI was an important factor in explaining variation of GPPmax. The RS-SMIPP model outperformed the MOD17 algorithm in accurately estimating seasonal and annual GPP and reduced RMSEr from 25.34%–43.44% to 9.53%–26.19% for annual GPP of the different PFTs. These findings demonstrate that remote sensing-based phenological and physiological indicators could be used to explain the variations of seasonal and annual GPP, and provide an efficient way for improving GPP estimations at a global scale.

Using the red chromatic coordinate to characterize the phenology of forest canopy photosynthesis

Vegetation phenology has received increasing attention in climate change research. Near-surface sensing using digital repeat photography has proven to be useful for ecosystem-scale monitoring of vegetation phenology. However, our understanding of the link between phenological metrics derived from digital repeat photography and the phenology of forest canopy photosynthesis is still incomplete, especially for evergreen plant species. Using 49 site-years of digital images from the PhenoCam Network from eight evergreen needleleaf forest (ENF) and six deciduous broadleaf forest (DBF) sites in North America, we explored the potential of the green chromatic (GCC) and red chromatic coordinates (RCC) in tracking forest canopy photosynthesis by comparing camera-derived start- and end-of-growing season (SOS and EOS, respectively) with corresponding estimates derived from eddy covariance-derived daily gross primary productivity (GPP). We found that for DBF sites, both GCC and RCC performed comparable in capturing SOS and EOS. However, similar to earlier studies, GCC had limited potential in capturing GPP-based SOS or EOS for ENF sites. In contrast, we found RCC was a better predictor of both GPP-based SOS and EOS for ENF sites. Environmental and ecological explanations were both provided that phenological transitions derived from RCC were highly correlated with spring and autumn meteorological conditions, as well as having overall higher correlations with phenology based on LAI, a critical variable for describing canopy development. Our results demonstrate that RCC is an underappreciated metric for tracking vegetation phenology, especially for ENF sites where GCC failed to provide reliable estimates for GPP-based SOS or EOS. Our results improve confidence in using digital repeat photography to characterize the phenology of canopy photosynthesis across forest types.

Assessing the relationship between vegetation greenness and surface temperature through Granger causality and Impulse-Response coefficients: a case study in Mexico

ABSTRACTThe existence of a consistent inverse relationship between vegetation greenness and surface temperature has been the premise of many environmental studies. However, some authors have found that the nature of this relationship varies depending on the spatial and temporal scales of analysis as well as vegetation type. Therefore, this research aims to contribute to the understanding of this matter by evaluating the annual and intra-seasonal relationship between Leaf Area Index (LAI) and Land Surface Temperature (LST) in Central Mexico using monthly anomalies of Moderate Resolution Imaging Spectroradiometer (MODIS) data collected from 2002 to 2017. LAI is proposed as an alternative index to overcome the shortcomings of other widely used indicators of vegetation greenness (e.g. Normalized Difference Vegetation Index and Enhanced Vegetation Index). The presence/absence of any relationship is investigated through the notion of Granger causality, while the sign and strength of the relationship is estimated by means of an Impulse-Response (IR) function. Unlike traditional regression and correlation analysis, the Granger causality approach enables the examination of lagged effects of one variable over the other based on past values of both variables. IR coefficients, which have been rarely used in the related literature, help to model the over-time response of a variable with the change of another variable. The overall results indicate that, at any temporal scale, Granger causality from LST to LAI occurs more consistently than causality in the opposite direction. At the annual scale, the nature of the relationship is primarily inverse in both directions and usually weaker from LAI to LST. At the seasonal scale, the occurrence of LST to LAI causality is higher in spring (it occurs in about 40% of the evaluated pixels) and lower in winter (10%) among all forest types. The effect of LST on LAI is predominantly inverse (median coefficient 1 month after impulse −0.043) and particularly strong in deciduous broadleaf forest during summer. On the other hand, the effect of LAI on LST is mainly direct in autumn and inverse in the remaining seasons, except for evergreen needleleaf forest where the effect is inverse only in summer. The highest presence (23%) and strength (−0.039) of LAI to LST causality occur in spring over deciduous broadleaf forest. Based on these results, caution has to be exercised when assuming a consistent strong inverse relationship between vegetation greenness and surface temperature, which seems to be the general consensus in much of the literature that makes use of these two variables to study an environmental phenomenon.

A Comparison of OCO-2 SIF, MODIS GPP, and GOSIF Data from Gross Primary Production (GPP) Estimation and Seasonal Cycles in North America

Remotely sensed products are of great significance to estimating global gross primary production (GPP), which helps to provide insight into climate change and the carbon cycle. Nowadays, there are three types of emerging remotely sensed products that can be used to estimate GPP, namely, MODIS GPP (Moderate Resolution Imaging Spectroradiometer GPP, MYD17A2H), OCO-2 SIF, and GOSIF. In this study, we evaluated the performances of three products for estimating GPP and compared with GPP of eddy covariance(EC) from the perspectives of a single tower (23 flux towers) and vegetation types (evergreen needleleaf forests, deciduous broadleaf forests, open shrublands, grasslands, closed shrublands, mixed forests, permeland wetlands, and croplands) in North America. The results revealed that sun-induced chlorophyll fluorescence (SIF) data and MODIS GPP data were highly correlated with the GPP of flux towers (GPPEC). GOSIF and OCO-2 SIF products exhibit a higher accuracy in GPP estimation at the a single tower (GOSIF: R2 = 0.13–0.88, p &lt; 0.001; OCO-2 SIF: R2 = 0.11–0.99, p &lt; 0.001; MODIS GPP: R2 = 0.15–0.79, p &lt; 0.001). MODIS GPP demonstrates a high correlation with GPPEC in terms of the vegetation type, but it underestimates the GPP by 1.157 to 3.884 gCm−2day−1 for eight vegetation types. The seasonal cycles of GOSIF and MODIS GPP are consistent with that of GPPEC for most vegetation types, in spite of an evident advanced seasonal cycle for grasslands and evergreen needleleaf forests. Moreover, the results show that the observation mode of OCO-2 has an evident impact on the accuracy of estimating GPP using OCO-2 SIF products. In general, compared with the other two datasets, the GOSIF dataset exhibits the best performance in estimating GPP, regardless of the extraction range. The long time period of MODIS GPP products can help in the monitoring of the growth trend of vegetation and the change trends of GPP.

New predictions of 137Cs dynamics in forests after the Fukushima nuclear accident

Most of the area contaminated by the Fukushima Daiichi Nuclear Power Plant accident is covered by forest. In this paper, we updated model predictions of temporal changes in the 137Cs dynamics using the latest observation data and newly provided maps of the predicted 137Cs activity concentration for wood, which is the most commercially important part of the tree body. Overall, the previous prediction and latest observation data were in very good agreement. However, further validation revealed that the migration from the soil surface organic layer to the mineral soil was overestimated for evergreen needleleaf forests. The new prediction of the 137Cs inventory showed that although the 137Cs distribution within forests differed among forest types in the first 5 years, the difference diminished in the later phase. Besides, the prediction of the wood 137Cs activity concentrations reproduced the different trends of the 137Cs activity concentrations for cedar, oak, and pine trees. Our simulation suggests that the changes of the wood 137Cs activity concentration over time will slow down after 5–10 years. Although the model uncertainty should be considered and monitoring and model updating must continue, the study provides helpful information on the 137Cs dynamics within forest ecosystems and the changes in wood contamination.

Global Carbon Sequestration Is Highly Sensitive to Model‐Based Formulations of Nitrogen Fixation

AbstractBiological nitrogen fixation (BNF) is the largest nitrogen (N) input pathway in natural terrestrial ecosystems at present, fueling the drawdown of atmospheric CO2 in vegetation and soil on decadal to century time scales. Here, we use a global land‐surface model (CABLE) with three different approaches for estimating the responses of BNF to CO2, climate, and N deposition through Year 2100, particularly the linear versus nonlinear dependence of BNF on C investment and the temperature dependence of BNF. From 1900 to 2100, the cumulative rise in BNF varied from 1.6 to 3.0 Pg N, translating to an increase in terrestrial carbon (C) storage between 33 and 68 Pg C. This range reflects the different approaches used to model BNF (i.e., C‐limited vs. resource optimization approaches), indicating major uncertainties in C‐climate‐N interactions in Earth system model forecasts. The differences among different approaches were most significant at high or low latitudes. At high latitudes, accounting for temperature dependence of BNF resulted in 53% and 79% additional BNF increases and 14% and 21% additional land C accumulation in evergreen needle leaf forest and tundra, respectively, from 1901 to 2100. At low latitudes, resource optimization approaches using linear dependence of BNF on C investment estimated more rapid increase in BNF and therefore greater C accumulation than the C‐limited approach using nonlinear dependence. The difference in the estimated additional C accumulation due to varying BNF was as much as 24% for deciduous broadleaf forest. Our findings highlight the need for more field measurements of BNF, particularly at high latitudes to better constrain the projected BNF under future conditions, and also the fundamental importance of BNF in determining the pattern, response, and magnitude of terrestrial C accumulation through 2100.

Spatial variations and controls of carbon use efficiency in China\u2019s terrestrial ecosystems

Carbon use efficiency (CUE), one of the most important eco-physiological parameters, represents the capacity of plants to transform carbon into new biomass. Understanding the variations and controls of CUE is crucial for regional carbon assessment. Here, we used 15-years of continuous remote sensing data to examine the variations of CUE across broad geographic and climatic gradients in China. The results showed that the vegetation CUE was averaged to 0.54 ± 0.11 with minor interannual variation. However, the CUE greatly varied with geographic gradients and ecosystem types. Forests have a lower CUE than grasslands and croplands. Evergreen needleleaf forests have a higher CUE than other forest types. Climate factors (mean annual temperature (MAT), precipitation (MAP) and the index of water availability (IWA)) dominantly regulated the spatial variations of CUE. The CUE exhibited a linear decrease with enhanced MAT and MAP and a parabolic response to the IWA. Furthermore, the responses of CUE to environmental change varied with individual ecosystem type. In contrast, precipitation exerted strong control on CUE in grassland, while in forest and cropland, the CUE was mainly controlled by the available water. This study identifies the variations and response of CUE to environmental drivers in China, which will be valuable for the regional assessment of carbon cycling dynamics under future climate change.

The global distribution of leaf chlorophyll content

Leaf chlorophyll is central to the exchange of carbon, water and energy between the biosphere and the atmosphere, and to the functioning of terrestrial ecosystems. This paper presents the first spatially-continuous view of terrestrial leaf chlorophyll content (ChlLeaf) at the global scale. Weekly maps of ChlLeaf were produced from ENVISAT MERIS full resolution (300 m) satellite data using a two-stage physically-based radiative transfer modelling approach. Firstly, leaf-level reflectance was derived from top-of-canopy satellite reflectance observations using 4-Scale and SAIL canopy radiative transfer models for woody and non-woody vegetation, respectively. Secondly, the modelled leaf-level reflectance was input into the PROSPECT leaf-level radiative transfer model to derive ChlLeaf. The ChlLeaf retrieval algorithm was validated using measured ChlLeaf data from 248 sample measurements at 28 field locations, and covering six plant functional types (PFTs). Modelled results show strong relationships with field measurements, particularly for deciduous broadleaf forests (R2 = 0.67; RMSE = 9.25 μg cm-2; p < 0.001), croplands (R2 = 0.41; RMSE = 13.18 μg cm-2; p < 0.001) and evergreen needleleaf forests (R2 = 0.47; RMSE = 10.63 μg cm-2; p < 0.001). When the modelled results from all PFTs were considered together, the overall relationship with measured ChlLeaf remained good (R2 = 0.47, RMSE = 10.79 μg cm-2; p < 0.001). This result is an improvement on the relationship between measured ChlLeaf and a commonly used chlorophyll-sensitive spectral vegetation index; the MERIS Terrestrial Chlorophyll Index (MTCI; R2 = 0.27, p < 0.001). The global maps show large temporal and spatial variability in ChlLeaf, with evergreen broadleaf forests presenting the highest leaf chlorophyll values, with global annual median values of 54.4 μg cm-2. Distinct seasonal ChlLeaf phenologies are also visible, particularly in deciduous plant forms, associated with budburst and crop growth, and leaf senescence. It is anticipated that this global ChlLeaf product will make an important step towards the explicit consideration of leaf-level biochemistry in terrestrial water, energy and carbon cycle modelling.

Insights into the evolution of the young Lake Ohrid ecosystem and vegetation succession from a southern European refugium during the Early Pleistocene

Mediterranean mid-altitude sites are critical for the survival of plant species allowing for elevational vegetation shifts in response to high-amplitude climate variability. Pollen records from the southern Balkans have underlined the importance of the region in preserving plant diversity over at least the last half a million years. So far, there are no records of vegetation and climate dynamics from Balkan refugia with an Early Pleistocene age. Here we present a unique palynological archive from such a refugium, the Lake Ohrid basin, recording continuously floristic diversity and vegetation succession under obliquity-paced climate oscillations. Palynological data are complemented by biomarker, diatom, carbonate isotope and sedimentological data to identify the mechanisms controlling shifts in the aquatic and terrestrial ecosystems within the lake and its catchment. The study interval encompasses four complete glacial-interglacial cycles (1365–1165 ka; MIS 43–35). Within the first 100 kyr of lake ontogeny, lake size and depth increase before the lake system enters a new equilibrium state as observed in a distinct shift in biotic communities and sediment composition. Several relict tree genera such as Cedrus, Tsuga, Carya, and Pterocarya played an important role in ecological succession cycles, while total relict abundance accounts for up to half of the total arboreal vegetation. The most prominent biome during interglacials is cool mixed evergreen needleleaf and deciduous broadleaf forests, while cool evergreen needleleaf forests dominate within glacials. A rather forested landscape with a remarkable plant diversity provide unique insights into Early Pleistocene ecosystem resilience and vegetation dynamics.

The potential distribution and disappearing of Yunnan snub-nosed monkey: Influences of habitat fragmentation

Analysisof environmental variables and organism occurrence records offers insight that can be used to predict potential distribution areas and habitat fragmentation. For large landscapes, modeling is the most convenient and effective way to conduct habitat research. Two species distribution models, BIOMOD2 and FRAGSTATS 4.2, were given data on environmental variables and organism occurrence records as input and used to predict the potential suitable habitat and habitat fragmentation for the Yunnan snub-nosed monkey (Rhinopithecus bieti). Our results estimated the total area of potentially suitable habitat for R. bieti as 7412.82 km2, but only 4164.58 km2 was found to be inhabited by R. bieti. We found that the main land cover type in the potential suitable distribution area of R. bieti was evergreen needle-leaf forest (6153.95 km2, 83.02%). Comparison of inhabited and suitable but uninhabited habitats showed that areas actually inhabited by R. bieti had a lower patch density (PD) and higher largest patch index (LPI) than uninhabited habitats only in evergreen needle-leaf forest. The potential suitable habitats of R. bieti has increased significantly, but the actual distribution has shrunk from 1997 to 2017. Although the government has made great progress in protecting R. bieti, logging that took place before the regulations and the boundary effect of roads and rivers resulted in the local extinction of R. bieti in some potentially suitable areas. In view of this, we propose to establish a national park for Yunnan snub-nosed monkeys. We also suggest protecting the potentially suitable but currently empty habitats for later release of R. bieti. Successfully reintroducing R. bieti into areas where it formerly lived will require continual and careful habitat monitoring.