Consistent time allocation fraction to vegetation green-up versus senescence across northern ecosystems despite recent climate change.

Extended growing season lengths under climatic warming suggest increased time for plant growth. However, research has focused on climatic impacts to the timing or duration of distinct phenological events. Comparatively little is known about impacts to the relative time allocation to distinct phenological events, for example, the proportion of time dedicated to leaf growth versus senescence. We use multiple satellite and ground-based observations to show that, despite recent climate change during 2001 to 2020, the ratio of time allocated to vegetation green-up over senescence has remained stable [1.27 (± 0.92)] across more than 83% of northern ecosystems. This stability is independent of changes in growing season lengths and is caused by widespread positive relationships among vegetation phenological events; longer vegetation green-up results in longer vegetation senescence. These empirical observations were also partly reproduced by 13 dynamic global vegetation models. Our work demonstrates an intrinsic biotic control to vegetation phenology that could explain the timing of vegetation senescence under climate change.

Night lights observations significantly improve the explainability of intra-annual vegetation growth globally.

Understanding the underlying mechanism of vegetation growth is of great significance to improve our knowledge of how vegetation growth responds to its surrounding environment, thereby benefiting the prediction of future vegetation growth and guiding environmental management. However, human impacts on vegetation growth, especially its intra-annual variability, still represent a knowledge gap. Night Lights (NL) have been demonstrated as an effective indicator to characterize human activities, but little is known about the potential improvement of intra-annual vegetation growth using seasonal NL observations. To address this gap, we investigated and quantified the explainability improvement of intra-annual vegetation growth by establishing a multiple linear regression model for vegetation growth (indicated by Normalized Difference Vegetation Index, NDVI) with human factor (indicated by NL observations here) and three climatic factors, i.e., temperature, water availability, and solar radiation using the Principal Components Regression (PCR) method. Results indicate that NL observations significantly improve our understanding of intra-annual vegetation growth globally. Model explainability, i.e., adjusted R2 metric of the PCR model, was comparatively improved by 54 % on average with a median value of 11 % when taking NL observations into consideration. Such improvement occurred in 82 % of the whole investigation pixels. We found that the improvement of model explanatory power was significant in regions where both NL and NDVI trends were large, except for the case where both of their trends were negative. At the country-level, the improvement of model explanatory power increases as GDP decreases, illustrating a greater improvement in a lower middle-income country than that in a high-income country. Our findings emphasize the importance of considering human activities (indicated by NL here) in vegetation growth, offering novel insights into the explanation of intra-annual vegetation growth.

Four decades of full-scale nitrous oxide emission inventory in China.

China is among the top nitrous oxide (N2O)-emitting countries, but existing national inventories do not provide full-scale emissions including both natural and anthropogenic sources. We conducted a four-decade (1980-2020) of comprehensive quantification of Chinese N2O inventory using empirical emission factor method for anthropogenic sources and two up-to-date process-based models for natural sources. Total N2O emissions peaked at 2287.4 (1774.8-2799.9) Gg N2O yr-1 in 2018, and agriculture-developed regions, like the East, Northeast, and Central, were the top N2O-emitting regions. Agricultural N2O emissions have started to decrease after 2016 due to the decline of nitrogen fertilization applications, while, industrial and energetic sources have been dramatically increasing after 2005. N2O emissions from agriculture, industry, energy, and waste represented 49.3%, 26.4%, 17.5%, and 6.7% of the anthropogenic emissions in 2020, respectively, which revealed that it is imperative to prioritize N2O emission mitigation in agriculture, industry, and energy. Natural N2O sources, dominated by forests, have been steadily growing from 317.3 (290.3-344.1) Gg N2O yr-1 in 1980 to 376.2 (335.5-407.2) Gg N2O yr-1 in 2020. Our study produces a Full-scale Annual N2O dataset in China (FAN2020), providing emergent counting to refine the current national N2O inventories.

Target Density Effects on Charge Transfer of Laser-Accelerated Carbon Ions in Dense Plasma.

We report on charge state measurements of laser-accelerated carbon ions in the energy range of several MeV penetrating a dense partially ionized plasma. The plasma was generated by irradiation of a foam target with laser-induced hohlraum radiation in the soft x-ray regime. We use the tricellulose acetate (C_{9}H_{16}O_{8}) foam of 2 mg/cm^{3} density and 1 mm interaction length as target material. This kind of plasma is advantageous for high-precision measurements, due to good uniformity and long lifetime compared to the ion pulse length and the interaction duration. We diagnose the plasma parameters to be T_{e}=17 eV and n_{e}=4×10^{20} cm^{-3}. We observe the average charge states passing through the plasma to be higher than those predicted by the commonly used semiempirical formula. Through solving the rate equations, we attribute the enhancement to the target density effects, which will increase the ionization rates on one hand and reduce the electron capture rates on the other hand. The underlying physics is actually the balancing of the lifetime of excited states versus the collisional frequency. In previous measurement with partially ionized plasma from gas discharge and z pinch to laser direct irradiation, no target density effects were ever demonstrated. For the first time, we are able to experimentally prove that target density effects start to play a significant role in plasma near the critical density of Nd-glass laser radiation. The finding is important for heavy ion beam driven high-energy-density physics and fast ignitions. The method provides a new approach to precisely address the beam-plasma interaction issues with high-intensity short-pulse lasers in dense plasma regimes.

The global decline in the sensitivity of vegetation productivity to precipitation from 2001 to 2018.

The sensitivity of vegetation productivity to precipitation (Sppt ) is a key metric for understanding the variations in vegetation productivity under changing precipitation and predicting future changes in ecosystem functions. However, a comprehensive assessment of Sppt over all the global land is lacking. Here, we investigated spatial patterns and temporal changes of Sppt across the global land from 2001 to 2018 with multiple streams of satellite observations. We found consistent spatial patterns of Sppt with different satellite products: Sppt was highest in dry regions while low in humid regions. Grassland and shrubland showed the highest Sppt , and evergreen needle-leaf forest and wetland showed the lowest. Temporally, Sppt showed a generally declining trend over the past two decades (p < .05), yet with clear spatial heterogeneities. The decline in Sppt was especially noticeable in North America and Europe, likely due to the increase in precipitation. In central Russia and Australia, however, Sppt showed an increasing trend. Biome-wise, most ecosystem types exhibited significant decrease in Sppt , while grassland, evergreen broadleaf forest, and mixed forest showed slight increases or non-significant changes in Sppt . Our finding of the overall decline in Sppt implies a potential stabilization mechanism for ecosystem productivity under climate change. However, the revealed Sppt increase for some regions and ecosystem types, in particular global grasslands, suggests that grasslands might be increasingly vulnerable to climatic variability with continuing global climate change.

Corrigendum: Comparison of 14 reference evapotranspiration with lysimeter measurements at a site in the humid alpine meadow, northeastern Qinghai-Tibetan Plateau.

[This corrects the article DOI: 10.3389/fpls.2022.854196.].

Using functional indicators to detect state changes in terrestrial ecosystems.

Indicators to predict ecosystem state change are urgently needed to cope with the degradation of ecosystem services caused by global change. With the development of new technologies for measuring ecosystem function with fine spatiotemporal resolution over broad areas, we are in the era of 'big data'. However, it is unclear how large, emerging datasets can be used to anticipate ecosystem state change. We propose the construction of indicators based on functional variables (flows) and state variables (pools) to predict future ecosystem state changes. The indicators identified here may be useful signals for doing so. In addition, functional indicators have explicit ecological meanings that can identify the ecological mechanism that is causing state changes, and can thus be used to improve ecosystem models.

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Global changes have a profound impact on ecosystems. If the disturbance caused by global change exceeds a certain degree, ecosystem resilience will be reduced, extreme events will be frequent, and ecosystem services will be degraded or even lost. Quantifying the risks of global change and developing appropriate adaptation strategies is an important way to deal with the risks of global change. Global change may reduce ecosystem resilience, leading to increased vulnerability and the risk of ecosystem degradation. The risk of ecosystem degradation is currently quantified mainly by the safe operating space assessment method based on planetary boundary theory. Understanding the concepts of ecosystem resilience, vulnerability, planetary boundaries, and safe operating spaces and their relationships is an important prerequisite for addressing the risks of global change. By summarizing the relevant theories of ecosystem vulnerability, we combined the concepts related to ecosystem resilience and vulnerability, global change risk and human adaptation, proposed a conceptual framework of ecosystem global change risk and human adaptation based on the vulnerability theory. Based on the logic of this proposed framework, we successively introduced the characteristics and mechanism of global change interference on ecosystem vulnerability, elaborated the assessment theories and methods of ecosystem vulnerability, and how to adopt human adaptation measures to alleviate the risk of global changes, aiming to provide ideas for coping with the risk of global change.

Comparison of Fourteen Reference Evapotranspiration Models With Lysimeter Measurements at a Site in the Humid Alpine Meadow, Northeastern Qinghai-Tibetan Plateau.

Evapotranspiration is a key component in the terrestrial water cycle, and accurate evapotranspiration estimates are critical for water irrigation management. Although many applicable evapotranspiration models have been developed, they are largely focused on low-altitude regions, with less attention given to alpine ecosystems. In this study, we evaluated the performance of fourteen reference evapotranspiration (ET0) models by comparison with large weight lysimeter measurements. Specifically, we used the Bowen ratio energy balance method (BREB), three combination models, seven radiation-based models, and three temperature-based models based on data from June 2017 to December 2018 in a humid alpine meadow in the northeastern Qinghai-Tibetan Plateau. The daily actual evapotranspiration (ETa) data were obtained using large weighing lysimeters located in an alpine Kobresia meadow. We found that the performance of the fourteen ET0 models, ranked on the basis of their root mean square error (RMSE), decreased in the following order: BREB > Priestley-Taylor (PT) > DeBruin-Keijman (DK) > 1963 Penman > FAO-24 Penman > FAO-56 Penman-Monteith > IRMAK1 > Makkink (1957) > Makkink (1967) > Makkink > IRMAK2 > Hargreaves (HAR) > Hargreaves1 (HAR1) > Hargreaves2 (HAR2). For the combination models, the FAO-24 Penman model yielded the highest correlation (0.77), followed by 1963 Penman (0.75) and FAO-56 PM (0.76). For radiation-based models, PT and DK obtained the highest correlation (0.80), followed by Makkink (1967) (0.69), Makkink (1957) (0.69), IRMAK1 (0.66), and IRMAK2 (0.62). For temperature-based models, the HAR model yielded the highest correlation (0.62), HAR1, and HAR2 obtained the same correlation (0.59). Overall, the BREB performed best, with RMSEs of 0.98, followed by combination models (ranging from 1.19 to 1.27 mm day-1 and averaging 1.22 mm day-1), radiation-based models (ranging from 1.02 to 1.42 mm day-1 and averaging 1.27 mm day-1), and temperature-based models (ranging from 1.47 to 1.48 mm day-1 and averaging 1.47 mm day-1). Furthermore, all models tended to underestimate the measured ETa during periods of high evaporative demand (i.e., growing season) and overestimated measured ETa during low evaporative demand (i.e., nongrowing season). Our results provide new insights into the accurate assessment of evapotranspiration in humid alpine meadows in the northeastern Qinghai-Tibetan Plateau.

Delayed wet season increases soil net N mineralization in a seasonally dry tropical forest.

Seasonal precipitation regime plays a vital role in regulating nutrient dynamics in seasonally dry tropical forests. Present evidence suggests that not only wet season precipitation is increasing in the tropics of South China, but also that the wet season is occurring later. However, it is unclear how nutrient dynamics will respond to the projected precipitation regime changes. We assessed the impacts of altered seasonal precipitation on soil net N mineralization in a secondary tropical forest. Since 2013, by reducing throughfall and/or irrigating experimental plots, we delayed the wet season by two months from April-September to June-November (DW treatment) or increased annual precipitation by 25% in July and August (WW treatment). We measured soil net N mineralization rates and assessed soil microbial communities in January, April, August and November in 2015 and 2017. We found that a wetter wet season did not significantly affect soil microbes or net N mineralization rates, even in the mid-wet season (August) when soil water content in the WW treatment increased significantly. By contrast, a delayed wet season enhanced soil microbial biomass and altered microbial community structure, resulting in a two-fold increase in net N mineralization rates relative to controls in the early dry season (November). Structural equation modeling showed that the changes in net N mineralization during the early dry season were associated with altered soil microbial communities, dissolved organic N, and litterfall, which were all affected by enhanced soil water content. Our findings suggest that a delayed wet season could have a greater impact on N dynamics than increased precipitation during the wet season. Changes in the seasonal timing of rainfall might therefore influence the functioning of seasonally dry tropical forests.

Cultivation of non-irrigated spring wheat in temperate free-grazing steppe improved both ecosystem and canopy water use efficiency.

The temperate steppe in northern China is important for sandstorm control and food/livestock production. Understanding the influence and regulatory control of cultivation on the water balance and water use efficiency (WUE) of this water-limited region would promote the sustainability of local ecosystem and food supply. This study combined eddy covariance system observational data and the Shuttleworth-Wallace model to investigate evapotranspiration (ET) and its composition in paired sites, including a free-grazing steppe site and an adjacent site reclaimed for spring wheat cultivation in Xilinhot, Inner Mongolia. Further, analysis of the WUE of both the ecosystem (WUEE) and the canopy (WUEC) under the two sites showed that the mean daily gross primary productivity (GPP) of the cultivation site was 3.84 gC·m-2·d-1, i.e., 15.7% higher than that of the free-grazing site (3.32 gC·m-2·d-1). Compared with the free-grazing site (1.76 kgH2O·m-2·d-1), the mean daily ET of the cultivation site (1.40 kgH2O·m-2·d-1) was reduced by 20.7%. The difference in ET was due mainly to suppression of evaporation at the cultivation site from increased shading associated with a higher leaf area index (LAI). The largely increased GPP of the cultivation site fundamentally contributed to the 54.7% higher WUEC (4.75 gC·kg-1H2O) in comparison with the free-grazing site (3.08 gC·kg-1H2O). The WUEE of the cultivation site was 57.9% higher than that of the free-grazing site. The variation of transpiration of the free-grazing site explained 64% of the change of WUEC. These results indicate that land use differences in the temperate steppe area changed vegetation productivity substantially. Moreover, ecosystem ET and its composition, as well as large-scale land use change, might influence the regional water use pattern and mass balance. Our findings help clarify the impact of typical land use change on regional WUE, and could promote development of visionary and effective strategies for the use of the limited resources in arid-semiarid regions.

Higher plant photosynthetic capability in autumn responding to low atmospheric vapor pressure deficit.

It has been long established that the terrestrial vegetation in spring has stronger photosynthetic capability than in autumn. However, this study challenges this consensus by comparing photosynthetic capability of terrestrial vegetation between the spring and autumn seasons based on measurements of 100 in situ eddy covariance towers over global extratropical ecosystems. At the majority of these sites, photosynthetic capability, indicated by light use efficiency (LUE) and apparent quantum efficiency, is significantly higher in autumn than in spring, due to lower atmosphere vapor pressure deficit (VPD) at the same air temperature. Seasonal VPD differences also substantially explain the interannual variability of the differences in photosynthetic capability between spring and autumn. We further reveal that VPD in autumn is significantly lower than in spring over 74.14% of extratropical areas, based on a global climate dataset. In contrast, LUE derived from a data-driven vegetation production dataset is significantly higher in autumn in over 61.02% of extratropical vegetated areas. Six Earth system models consistently projected continuous larger VPD values in spring compared with autumn, which implies that the impacts on vegetation growth will long exist and should be adequately considered when assessing the seasonal responses of terrestrial ecosystems to future climate conditions.

Nitrogen and phosphorus addition differentially enhance seed production of dominant species in a temperate steppe.

Previous studies have demonstrated changes in plant growth and reproduction in response to nutrient availability, but responses of plant growth and reproduction to multiple levels of nutrient enrichment remain unclear. In this study, a factorial field experiment was performed with manipulation of nitrogen (N) and phosphorus (P) availability to examine seed production of the dominant species, Stipa krylovii, in response to N and P addition in a temperate steppe. There were three levels of N and P addition in this experiment, including no N addition (0 g N m-2 year-1), low N addition (10 g N m-2 year-1), and high N addition (40 g N m-2 year-1) for N addition treatment, and no P addition (0 g P m-2 year-1), low P addition (5 g P m-2 year-1), and high P addition (10 g P m-2 year-1) for P addition treatment. Low N addition enhanced seed production by 814%, 1371%, and 1321% under ambient, low, and high P addition levels, respectively. High N addition increased seed production by 2136%, 3560%, and 3550% under ambient, low, and high P addition levels, respectively. However, P addition did not affect seed production in the absence of N addition, but enhanced it under N addition. N addition enhanced seed production mainly by increasing the tiller number and inflorescence abundance per plant, whereas P addition stimulated it by decreasing the plant density yet stimulating height of plants and their seed number per inflorescence. Our results indicate seed production is not limited by P availability but rather by N availability in the temperate steppe, whereas seed production will be increased by P addition when N availability is improved. These findings enable a better understanding of plant reproduction dynamics in the temperate steppe under intensified nutrient enrichment and can inform their improved management in the future.

Vegetation resistance and resilience to a decade-long dry period in the temperate grasslands in China.

The duration of climate anomalies has been increasing across the globe, leading to ecosystem function loss. Thus, we need to understand the responses of the ecosystem to long-term climate anomalies. It remains unclear how ecosystem resistance and resilience respond to long-term climate anomalies, for example, continuous dry years at a regional scale. Taking the opportunity of a 13-year dry period in the temperate grasslands in northern China, we quantified the resistance and resilience of the grassland in response to this periodic dry period. We found vegetation resistance to the dry period increased with mean annual precipitation (MAP), while resilience increased at first until at MAP of 250 mm and then decreased slightly. No trade-off between resistance and resilience was detected when MAP < 250 mm. Our results highlight that xeric ecosystems are most vulnerable to the long-term dry period. Given expected increases in drought severity and duration in the coming decades, our findings may be helpful to identify vulnerable ecosystems in the world for the purpose of adaptation.

Community Composition and Structure Affect Ecosystem and Canopy Water Use Efficiency Across Three Typical Alpine Ecosystems.

Unique ecosystems distributed in alpine areas of the Qinghai-Tibetan Plateau play important roles in climate change mitigation, local food supply, and conservation of species diversity. To understand the water use efficiency (WUE) of this fragile and sensitive region, this study combined observed data from the eddy covariance system and the Shuttleworth-Wallace (S-W) model to measure the continuous mass exchange, including gross primary productivity (GPP), evapotranspiration (ET), and canopy transpiration (T) throughout 2 or 3 years (2016-2018) in three common alpine ecosystems (i.e., alpine steppe, alpine meadow, and alpine swamp). These ecosystems represent a water availability gradient and thus provide the opportunity to quantify environmental and biological controls on WUE at various spatiotemporal scales. We analyzed the ecosystem WUE (WUEe; defined as the ratio of GPP to ET) and canopy WUE (WUEc; defined as the ratio of GPP and canopy T). It was found that the yearly WUEe was 1.40, 1.63, and 2.16 g C kg-1 H2O, and the yearly WUEc was 8.93, 2.46, and 5.19 g C kg-1 H2O in the three typical ecosystems, respectively. The controlling factors of yearly WUE diverged between WUEe and WUEc. We found that plant functional group proportion (e.g., gramineous and Cyperaceae) highly explained the yearly WUEe variation across sites, and a good correlation was observed between community species diversity and WUEc. These findings suggest that community composition and trait change are critical in regulating WUEe and WUEc across different alpine ecosystems and that the regulation mechanisms may differ fundamentally between WUEe and WUEc.

Light grazing facilitates carbon accumulation in subsoil in Chinese grasslands: A meta-analysis.

Grazing by livestock greatly affects the soil carbon (C) cycle in grassland ecosystems. However, the effects of grazing at different intensities and durations on the dynamics of soil C in its subsoil layers are not clearly understood. Here, we compiled data from 78 sites (in total 122 published studies) to examine the effects of varying grazing intensities and durations on soil C content at different depths for grasslands in China. Our meta-analysis revealed that grazing led to an overall decrease in soil C content and productivity of above-ground vegetation (e.g., above-ground biomass and litter) but an increase in below-ground biomass. Specifically, the effects of grazing on soil C content became less negative or even positive with increasing soil depths. An increase of soil C content was consequently found under light grazing (LG), although soil C content still decreased under moderate and heavy grazing. The increase in soil C content under LG could be largely attributed to the increase of soil C content in subsoil layers (>20 cm), despite that soil C content in surface soil layer (0-20 cm) decreased. Moreover, the magnitude of increase in soil C content under LG in subsoil layers increased with grazing duration. A possible reason of the increase in soil C content in the subsoil layers was due to the increases in below-ground biomass. Our study highlights that LG may modify the allocation of C input and promote its accumulation in subsoil layers, thus offsetting the negative impact of grazing on surface soil C content, a finding that has significant implications for C sequestration in grasslands.

Patterns and controls of vegetation productivity and precipitation-use efficiency across Eurasian grasslands.

Elucidating aboveground net primary production (ANPP) and precipitation-use efficiency (PUE) spatial variations and mechanisms are essential for predicting how ecosystem functioning will respond to future climate change. However, a comprehensive recognition of spatial patterns of ANPP and PUE across continental scale is still lacking. Here, we integrated long-term GIMMS NDVI remote sensing, field survey ANPP and meteorological datasets to reveal the spatial variations and controls of ANPP and PUE across Eurasian grasslands for the first time. The results showed that the mean value of ANPP and PUE of Eurasian grasslands were 40.20 ± 0.40 g C m-2 yr-1 and 0.15 ± 0.01 g C m-2 mm-1, respectively. At the continental scale, the ANPP and PUE showed unimodal patterns along mean annual precipitation (MAP) and hydrothermal index (HT) gradients, while a piecewise linear pattern along mean annual temperature (MAT) gradients. The MAP exerted positive effect on the ANPP in desert and temperate grasslands, while negative effect on the ANPP in alpine grasslands. Conversely, the MAT negatively affected the ANPP in desert and temperate grasslands, while positively affected the ANPP in alpine grasslands. The results indicated that the hydrothermal conditions coupled with the transition of vegetation types and its different responses combinedly shaped the spatial patterns of ANPP and PUE in Eurasian grasslands. This study advanced our knowledge of the spatial variations of ANPP and PUE at continental scale, providing theoretical information for predicting productivity and water use changes of arid and semi-arid grasslands under climate change in the future.

Revealing the spatio-temporal variability of evapotranspiration and its components based on an improved Shuttleworth-Wallace model in the Yellow River Basin.

Identifying the spatio-temporal variations of evapotranspiration (ET) from its components (soil evaporation and plant transpiration) can greatly improve our understanding of water-cycle and biogeochemical processes. However, partitioning evapotranspiration into evaporation (E) and transpiration (T) at regional scale with high accuracy still remains a challenge. This study has aimed to reveal the spatio-temporal variations of evapotranspiration and its components by using an improved Shuttleworth-Wallace (SWH) model to partition ET in the Yellow River Basin during 1981-2010. The environmental factors affecting the spatial and temporal variations of evapotranspiration and its components were also assessed. Results showed that the mean annual ET, T and E in the Yellow River Basin were 372.18 mm, 179.64 mm, and 192.54 mm, respectively, over the last 30 years. The spatial pattern of mean annual ET and T displayed a decreasing trend from southeast to northwest in the Yellow River Basin, and the temporal variation showed a significant increasing trend with rates of 1.72 mm yr-1 and 1.54 mm yr-1, respectively. It meant that T accounted for the variations of ET, while E showed no significant changes in recent decades. Moreover, the normalized differential vegetation index (NDVI) and temperature were identified as the main factors controlling the variations of ET and T in the Yellow River Basin. Among them, the area with NDVI as the dominant factor for ET and T could reach 63.82% and 78.47% of the whole basin respectively. However, the variations of E were affected by complex factors, and evaporation in the western alpine region was mainly controlled by temperature. Our findings are expected to not only have implications for developing sustainable policies of water management and ecological restoration in this region, but also provide valuable insight in methodology of ET partitioning in regional or global scale.

Shifts in the dynamics of productivity signal ecosystem state transitions at the biome-scale.

Understanding ecosystem dynamics and predicting directional changes in ecosystem in response to global changes are ongoing challenges in ecology. Here we present a framework that links productivity dynamics and ecosystem state transitions based on a spatially continuous dataset of aboveground net primary productivity (ANPP) from the temperate grassland of China. Across a regional precipitation gradient, we quantified spatial patterns in ANPP dynamics (variability, asymmetry and sensitivity to rainfall) and related these to transitions from desert to semi-arid to mesic steppe. We show that these three indices of ANPP dynamics displayed distinct spatial patterns, with peaks signalling transitions between grassland types. Thus, monitoring shifts in ANPP dynamics has the potential for predicting ecosystem state transitions in the future. Current ecosystem models fail to capture these dynamics, highlighting the need to incorporate more nuanced ecological controls of productivity in models to forecast future ecosystem shifts.