The Global Ecosystems Monitoring network: Monitoring ecosystem productivity and carbon cycling across the tropics

Malhi, Yadvinder; Girardin, Cécile; Metcalfe, Daniel B.; Doughty, Christopher E.; Aragão, Luiz E. O. C.; Rifai, Sami W.; Oliveras, Immaculada; Shenkin, Alexander; Aguirre‐Gutiérrez, Jesús; Dahlsjö, Cecilia A. L.; Riutta, Terhi; Berenguer, Erika; Moore, Sam; Huasco, Walter Huaraca; Salinas, Norma; Costa, Antonio; Bentley, Lisa Patrick; Adu‐Bredu, Stephen; Marthews, Toby R.; Meir, Patrick; Phillips, Oliver L.

doi:10.1016/j.biocon.2020.108889

Cited by 60 publications

(64 citation statements)

References 89 publications

(86 reference statements)

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“…We collected root dynamics data in 47 plots in 22 sites across three continents in lowland and montane tropical forests (MTFs). These 1‐ha plots are part of the Global Ecosystem Monitoring network (GEM, Malhi et al, 2021), which uses standardize measurement protocols across all sites. The lowland forests sites are located in Bolivia, Brazil, Gabon, Ghana, Malaysia, and Peru (Table 1; Figure 1).…”

Section: Methodsmentioning

confidence: 99%

“…ornl.gov; Iversen et al, 2017) and calculated the average fine root carbon content of broadleaved species, 45% with standard error of 0.12%, n = 1771. To be consistent with other studies within the GEM network (Malhi et al, 2021 and references therein), we express fine root stock and productivity in units of carbon, rather than dry mass. We estimated the fine root C stock and productivity down to 30 cm soil depth in all plots.…”

Section: Measurements Of Fine Root Dynamicsmentioning

confidence: 97%

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Fine root dynamics across pantropical rainforest ecosystems

Huasco

Riutta

Pacha

et al. 2021

Global Change Biology

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Fine roots constitute a significant component of the net primary productivity (NPP) of forest ecosystems but are much less studied than aboveground NPP. Comparisons across sites and regions are also hampered by inconsistent methodologies, especially in tropical areas. Here, we present a novel dataset of fine root biomass, productivity, residence time, and allocation in tropical old‐growth rainforest sites worldwide, measured using consistent methods, and examine how these variables are related to consistently determined soil and climatic characteristics. Our pantropical dataset spans intensive monitoring plots in lowland (wet, semi‐deciduous, and deciduous) and montane tropical forests in South America, Africa, and Southeast Asia (n = 47). Large spatial variation in fine root dynamics was observed across montane and lowland forest types. In lowland forests, we found a strong positive linear relationship between fine root productivity and sand content, this relationship was even stronger when we considered the fractional allocation of total NPP to fine roots, demonstrating that understanding allocation adds explanatory power to understanding fine root productivity and total NPP. Fine root residence time was a function of multiple factors: soil sand content, soil pH, and maximum water deficit, with longest residence times in acidic, sandy, and water‐stressed soils. In tropical montane forests, on the other hand, a different set of relationships prevailed, highlighting the very different nature of montane and lowland forest biomes. Root productivity was a strong positive linear function of mean annual temperature, root residence time was a strong positive function of soil nitrogen content in montane forests, and lastly decreasing soil P content increased allocation of productivity to fine roots. In contrast to the lowlands, environmental conditions were a better predictor for fine root productivity than for fractional allocation of total NPP to fine roots, suggesting that root productivity is a particularly strong driver of NPP allocation in tropical mountain regions.

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Section: Methodsmentioning

confidence: 99%

Section: Measurements Of Fine Root Dynamicsmentioning

confidence: 97%

Fine root dynamics across pantropical rainforest ecosystems

Huasco

Riutta

Pacha

et al. 2021

Global Change Biology

Self Cite

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“…Another potential use of neighborhood analyses is to predict tree growth beyond the spatial or temporal limits of training data. To the best of our knowledge, neighborhood models have not been used for out-of-sample prediction, but this is likely to change as growing emphasis on dataset publication [14,15] and new software tools [16] increases the availability of mapped forest plot data. Moreover, accurate predictions of tree growth are highly desirable because they could provide insight to current spatial patterns in carbon storage and the effects of land use and climate change on timber production and global carbon cycling.…”

Section: Introductionmentioning

confidence: 99%

Regularized Regression: A New Tool for Investigating and Predicting Tree Growth

Graham

Rokem

Fortunel

et al. 2021

Forests

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Neighborhood models have allowed us to test many hypotheses regarding the drivers of variation in tree growth, but require considerable computation due to the many empirically supported non-linear relationships they include. Regularized regression represents a far more efficient neighborhood modeling method, but it is unclear whether such an ecologically unrealistic model can provide accurate insights on tree growth. Rapid computation is becoming increasingly important as ecological datasets grow in size, and may be essential when using neighborhood models to predict tree growth beyond sample plots or into the future. We built a novel regularized regression model of tree growth and investigated whether it reached the same conclusions as a commonly used neighborhood model, regarding hypotheses of how tree growth is influenced by the species identity of neighboring trees. We also evaluated the ability of both models to interpolate the growth of trees not included in the model fitting dataset. Our regularized regression model replicated most of the classical model’s inferences in a fraction of the time without using high-performance computing resources. We found that both methods could interpolate out-of-sample tree growth, but the method making the most accurate predictions varied among focal species. Regularized regression is particularly efficient for comparing hypotheses because it automates the process of model selection and can handle correlated explanatory variables. This feature means that regularized regression could also be used to select among potential explanatory variables (e.g., climate variables) and thereby streamline the development of a classical neighborhood model. Both regularized regression and classical methods can interpolate out-of-sample tree growth, but future research must determine whether predictions can be extrapolated to trees experiencing novel conditions. Overall, we conclude that regularized regression methods can complement classical methods in the investigation of tree growth drivers and represent a valuable tool for advancing this field toward prediction.

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“…The IPCC 1.5 degree Special Report (SR15) has pointed out that the increased anthropogenic warming in the MED has contributed to increased drying in the northern hemisphere mid-latitude areas including the MED [28,29]. Furthermore, with global warming of 2 • C, desertification is also predicted to occur in the MED by the end of the 21st century, resulting in an expansion of areas with significant decreases in water availability, with an accompanying increase in aridity, driving irreversible terrestrial biodiversity loss and affecting the Mediterranean ecosystem carbon storage in the coming decades [30]. The MED experienced more frequent and severe meteorological and hydrological droughts in recent decades, which is in line with the expected trend towards high frequencies of drought periods in a future warmer climate [31,32].…”

Section: Drought Climate Change and The Mediterranean Contextmentioning

confidence: 99%

Mediterranean-Scale Drought: Regional Datasets for Exceptional Meteorological Drought Events during 1975–2019

et al. 2021

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Drought is one of the most complex climate-related phenomena and is expected to progressively affect our lives by causing very serious environmental and socioeconomic damage by the end of the 21st century. In this study, we have extracted a dataset of exceptional meteorological drought events between 1975 and 2019 at the country and subregional scales. Each drought event was described by its start and end date, intensity, severity, duration, areal extent, peak month and peak area. To define such drought events and their characteristics, separate analyses based on three drought indices were performed at 12-month timescale: the Standardized Precipitation Index (SPI), the Standardized Precipitation Evapotranspiration Index (SPEI), and the Reconnaissance Drought Index (RDI). A multivariate combined drought index (DXI) was developed by merging the previous three indices for more understanding of droughts’ features at the country and subregional levels. Principal component analysis (PCA) was used to identify five different drought subregions based on DXI-12 values for 312 Mediterranean stations and a new special score was defined to classify the multi-subregional exceptional drought events across the Mediterranean Basin (MED). The results indicated that extensive drought events occurred more frequently since the late 1990s, showing several drought hotspots in the last decades in the southeastern Mediterranean and northwest Africa. In addition, the results showed that the most severe events were more detected when more than single drought index was used. The highest percentage area under drought was also observed through combining the variations of three drought indices. Furthermore, the drought area in both dry and humid areas in the MED has also experienced a remarkable increase since the late 1990s. Based on a comparison of the drought events during the two periods—1975–1996 and 1997–2019—we find that the current dry conditions in the MED are more severe, intense, and frequent than the earlier period; moreover, the strongest dry conditions occurred in last two decades. The SPEI-12 and RDI-12 have a higher capacity in providing a more comprehensive description of the dry conditions because of the inclusion of temperature or atmospheric evaporative demand in their scheme. A complex range of atmospheric circulation patterns, particularly the Western Mediterranean Oscillation (WeMO) and East Atlantic/West Russia (EATL/WRUS), appear to play an important role in severe, intense and region-wide droughts, including the two most severe droughts, 1999–2001 and 2007–2012, with lesser influence of the NAO, ULMO and SCAND.

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The Global Ecosystems Monitoring network: Monitoring ecosystem productivity and carbon cycling across the tropics

Cited by 60 publications

References 89 publications

Fine root dynamics across pantropical rainforest ecosystems

Fine root dynamics across pantropical rainforest ecosystems

Regularized Regression: A New Tool for Investigating and Predicting Tree Growth

Mediterranean-Scale Drought: Regional Datasets for Exceptional Meteorological Drought Events during 1975–2019

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