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The present study has investigated the occurrence of the English anaphoric expressions, he/she, in L2 grammars. Adult Chinese learners of English were evaluated on whether they were able to provide a correct semantic interpretation of these pronouns when they appeared in complex sentences (subordinate + matrix) and correlative structures. Results have shown that L2 learners did not encounter any major difficulties in identifying the appropriate antecedents of these pronominal forms and were almost as accurate as English native speakers. This positive acquisition scenario clearly indicates that a correct reference production of English anaphors is attainable despite the visible differences between the two languages. Theoretically, it seems to support some form of Continuity in SLA; it is also consistent with the idea that internal interface phenomena are fully acquirable and are not subject to fossilization ("Interface Theory," Sorace in Biling Lang Cogn 7:143-145, 2004; Linguist Approaches Biling 1:1-33, 2011). Furthermore, L2 structures partially present in L1 grammar are attainable without necessarily relying on learners' first language (contra "Failed Functional Features Hypothesis," Hawkins and Chan in Second Lang Res 13:187-226, 1997). In fact, contrary to previous L2 studies, no visible L1 transfer effects were observed in the data.

Satellite-based estimation of forest variables including forest biomass relies on model-based approaches since forest biomass cannot be directly measured from space. Such models require ground reference data to adapt to the local forest structure and acquired satellite data. For wide-area mapping, such reference data are too sparse to train the biomass retrieval model and approaches for calibrating that are independent from training data are sought. In this study, we compare the performance of one such calibration approach with the traditional regression modelling using reference measurements. The performance was evaluated at four sites representative of the major forest biomes in Europe focusing on growing stock volume (GSV) prediction from time series of C-band Sentinel-1 and Advanced Land Observing Satellite Phased Array L-band Synthetic Aperture Radar (ALOS-2 PALSAR-2) backscatter measurements. The retrieval model was based on a Water Cloud Model (WCM) and integrated two forest structural functions. The WCM trained with plot inventory GSV values or calibrated with the aid of auxiliary data products correctly reproduced the trend between SAR backscatter and GSV measurements across all sites. The WCM-predicted backscatter was within the range of measurements for a given GSV level with average model residuals being smaller than the range of the observations. The accuracy of the GSV estimated with the calibrated WCM was close to the accuracy obtained with the trained WCM. The difference in terms of root mean square error (RMSE) was less than 5% units. This study demonstrates that it is possible to predict biomass without providing reference measurements for model training provided that the modelling scheme is physically based and the calibration is well set and understood. [ABSTRACT FROM AUTHOR]

In this paper, I investigate the phenomenon of morphological variability in the production of Italian determiners, descriptive adjectives, and direct object pronouns by adult English learners of Italian to determine whether morphological errors are the result of computational or representational difficulties. Second language acquisitionists do seem to agree on whether erroneous morphological forms noticeable even in advanced second language grammars due to the absence of the functional apparatus responsible for their feature-checking, a partially developed morphological competence, or even learners' performance limitations. Data have shown that the morphological features of Italian Determiner Phrases (DPs) and pronouns are fully acquirable, despite their absence in the grammar of learners' native language. Furthermore, adult English speakers' precocious familiarity with Italian nominal and pronominal morphology, and the uniform occurrence of erroneous forms in their interlanguage grammars suggest that morphological variability does not stem from the absence of the necessary functional structure, but from a general deficiency in properly "assembling" the morphological features of a particular lexical item, and learners' inability to map them with the syntactic information available. (Contains 7 tables and 2 footnotes.)

This experimental study investigates the acquisition of Italian accusative and dative clitics by English adult speakers. These pronouns are non-existent in English. Results from a grammaticality judgement task show that Italian accusative and dative clitics develop slowly but gradually in Italian second language (L2) grammars. Interestingly, the placement properties appear to develop earlier than their case properties. The possible implications of these findings for theories of the L2 initial state are considered. (Contains 2 figures and 6 tables.)

The 2015/16 El Niño brought severe drought and record-breaking temperatures in the tropics. Here, using satellite-based L-band microwave vegetation optical depth, we mapped changes of above-ground biomass (AGB) during the drought and in subsequent years up to 2019. Over more than 60% of drought-affected intact forests, AGB reduced during the drought, except in the wettest part of the central Amazon, where it declined 1 y later. By the end of 2019, only 40% of AGB reduced intact forests had fully recovered to the predrought level. Using random-forest models, we found that the magnitude of AGB losses during the drought was mainly associated with regionally distinct patterns of soil water deficits and soil clay content. For the AGB recovery, we found strong influences of AGB losses during the drought and of γ. γ is a parameter related to canopy structure and is defined as the ratio of two relative height (RH) metrics of Geoscience Laser Altimeter System (GLAS) waveform data--RH25 (25% energy return height) and RH100 (100% energy return height; i.e., top canopy height). A high γ may reflect forests with a tall understory, thick and closed canopy, and/or without degradation. Such forests with a high γ (γ ≤ 0.3) appear to have a stronger capacity to recover than low-γ ones. Our results highlight the importance of forest structure when predicting the consequences of future drought stress in the tropics. [ABSTRACT FROM AUTHOR]

The responses of forest carbon dynamics to fluctuations in environmental conditions at a global scale remain elusive. Despite the understanding that favourable environmental conditions promote forest growth, these responses have been challenging to observe across different ecosystems and climate gradients. Based on a global annual time series of aboveground biomass (AGB) estimated from radar satellites between 1992 and 2018, we present forest carbon changes and provide insights on their sensitivities to environmental conditions across scales. Our findings indicate differences in forest carbon changes across AGB classes, with regions with carbon stocks of 50-125 MgC ha-1 depict the highest forest carbon gains and losses, while regions with 125-150 MgC ha-1 have the lowest forest carbon gains and losses in absolute terms. Net forest carbon change estimates show that the arc-of-deforestation and the Congo Basin were the main hotspots of forest carbon loss, while a substantial part of European forest gained carbon during the last three decades. Furthermore, we observe that changes in forest carbon stocks were systematically positively correlated with changes in forest cover fraction. At the same time, it was not necessarily the case with other environmental variables, such as air temperature and water availability at the bivariate level. We also used a model attribution method to demonstrate that atmospheric conditions were the dominant control of forest carbon changes (56% of the total study area) followed by water-related (29% of the total study area) and vegetation (15% of the total study area) conditions. Regionally, we find evidence that carbon gains from long-term forest growth covary with long-term carbon sinks inferred from atmospheric inversions. Our results describe the contributions from the atmosphere, water-related and vegetation conditions to forest carbon changes and provide new insights into the underlying mechanisms of the coupling between forest growth and the global carbon cycle. [ABSTRACT FROM AUTHOR]

Forest age can determine the capacity of a forest to uptake carbon from the atmosphere. However, a lack of global diagnostics that reflect the forest stage and associated disturbance regimes hampers the quantification of age-related differences in forest carbon dynamics. This study provides a new global distribution of forest age circa 2010, estimated using a machine learning approach trained with more than 40 000 plots using forest inventory, biomass and climate data. First, an evaluation against the plot-level measurements of forest age reveals that the data-driven method has a relatively good predictive capacity of classifying old-growth vs. non-old-growth (precision = 0.81 and 0.99 for old-growth and non-old-growth, respectively) forests and estimating corresponding forest age estimates (NSE = 0.6 – Nash–Sutcliffe efficiency – and RMSE = 50 years – root-mean-square error). However, there are systematic biases of overestimation in young- and underestimation in old-forest stands, respectively. Globally, we find a large variability in forest age with the old-growth forests in the tropical regions of Amazon and Congo, young forests in China, and intermediate stands in Europe. Furthermore, we find that the regions with high rates of deforestation or forest degradation (e.g. the arc of deforestation in the Amazon) are composed mainly of younger stands. Assessment of forest age in the climate space shows that the old forests are either in cold and dry regions or warm and wet regions, while young–intermediate forests span a large climatic gradient. Finally, comparing the presented forest age estimates with a series of regional products reveals differences rooted in different approaches and different in situ observations and global-scale products. Despite showing robustness in cross-validation results, additional methodological insights on further developments should as much as possible harmonize data across the different approaches. The forest age dataset presented here provides additional insights into the global distribution of forest age to better understand the global dynamics in the forest water and carbon cycles. The forest age datasets are openly available at 10.17871/ForestAgeBGI.2021 (Besnard et al., 2021). [ABSTRACT FROM AUTHOR]

As a key component of the Earth system, roots play a key role in linking Earth's lithosphere, hydrosphere, biosphere and atmosphere. Here we combine 10 307 field measurements of forest root biomass worldwide with global observations of forest structure, climatic conditions, topography, land management and soil characteristics to derive a spatially explicit global high-resolution (∼ 1 km) root biomass dataset, including fine and coarse roots. In total, 142 ± 25 (95 % CI) Pg of live dry-matter biomass is stored belowground, representing a global average root : shoot biomass ratio of 0.25 ± 0.10. Earlier studies (Jackson et al., 1997; Robinson, 2007; Saugier et al., 2001) are 44 %–226 % larger than our estimations of the total root biomass in tropical, temperate and boreal forests. The total global forest root biomass from a recent estimate (Spawn et al., 2020) is 24 % larger than this study. The smaller estimation from this study is attributable to the updated forest area, spatially explicit aboveground biomass density used to predict the patterns of root biomass, new root measurements and the upscaling methodology. We show specifically that the root shoot allometry is one underlying driver that has led to methodological overestimation of root biomass in previous estimations. Raw datasets and global maps generated in this study are deposited at the open-access repository Figshare (10.6084/m9.figshare.12199637.v1; Huang et al., 2020). [ABSTRACT FROM AUTHOR]

The terrestrial forest carbon pool is poorly quantified, in particular in regions with low forest inventory capacity. By combining multiple satellite observations of synthetic aperture radar (SAR) backscatter around the year 2010, we generated a global, spatially explicit dataset of above-ground live biomass (AGB; dry mass) stored in forests with a spatial resolution of 1 ha. Using an extensive database of 110 897 AGB measurements from field inventory plots, we show that the spatial patterns and magnitude of AGB are well captured in our map with the exception of regional uncertainties in high-carbon-stock forests with AGB >250 Mgha-1 , where the retrieval was effectively based on a single radar observation. With a total global AGB of 522 Pg , our estimate of the terrestrial biomass pool in forests is lower than most estimates published in the literature (426–571 Pg). Nonetheless, our dataset increases knowledge on the spatial distribution of AGB compared to the Global Forest Resources Assessment (FRA) by the Food and Agriculture Organization (FAO) and highlights the impact of a country's national inventory capacity on the accuracy of the biomass statistics reported to the FRA. We also reassessed previous remote sensing AGB maps and identified major biases compared to inventory data, up to 120 % of the inventory value in dry tropical forests, in the subtropics and temperate zone. Because of the high level of detail and the overall reliability of the AGB spatial patterns, our global dataset of AGB is likely to have significant impacts on climate, carbon, and socio-economic modelling schemes and provides a crucial baseline in future carbon stock change estimates. The dataset is available at 10.1594/PANGAEA.894711 (Santoro, 2018). [ABSTRACT FROM AUTHOR]

As a key component of the Earth system, root plays the key role in linking Earth's lithosphere, hydrosphere, biosphere, and atmosphere. Here we combine 10307 field measurements of forest root biomass worldwide with global observations of forest structure, climatic conditions, topography, land management and soil characteristics to derive a spatially explicit global high-resolution (~ 1km) root biomass dataset, including fine and coarse roots. In total, 142 ± 25 (95% CI) Pg of live dry matter biomass is stored below-ground, representing a global average root:shoot biomass ratio of 0.25 ± 0.10. Our estimations of total root biomass in tropical, temperate and boreal forests are 44-226% smaller than earlier studies(Jackson et al., 1997; Robinson, 2007; Saugier et al., 2001). The smaller estimation is attributable to the updated forest area, spatially explicit above-ground biomass density used to predict the patterns of root biomass, new root measurements and upscaling methodology. We show specifically that the root shoot allometry is one underlying driver that leads to methodological overestimation of root biomass in previous estimations. Raw datasets and global maps generated in this study are deposited at the open access repository Figshare (https://figshare.com/articles/Supporting%5Fdata%5Fand%5Fcode%5Ffor%5FA%5Fglobal%5Fmap%5Fof%5Froot%5Fbiomass%5Facross%5Fthe%5Fworld%5Fs%5Fforests/12199637) [ABSTRACT FROM AUTHOR]

The turnover time of terrestrial ecosystem carbon is an emergent ecosystem property that quantifies the strength of land surface on the global carbon cycle–climate feedback. However, observation- and modeling-based estimates of carbon turnover and its response to climate are still characterized by large uncertainties. In this study, by assessing the apparent whole ecosystem carbon turnover times (τ) as the ratio between carbon stocks and fluxes, we provide an update of this ecosystem level diagnostic and its associated uncertainties in high spatial resolution (0.083 ∘) using multiple, state-of-the-art, observation-based datasets of soil organic carbon stock (Csoil), vegetation biomass (Cveg) and gross primary productivity (GPP). Using this new ensemble of data, we estimated the global median τ to be 43-7+7 yr (median-difference to percentile 25+difference to percentile 75) when the full soil is considered, in contrast to limiting it to 1 m depth. Only considering the top 1 m of soil carbon in circumpolar regions (assuming maximum active layer depth is up to 1 m) yields a global median τ of 37-6+3 yr , which is longer than the previous estimates of 23-4+7 yr (Carvalhais et al., 2014). We show that the difference is mostly attributed to changes in global Csoil estimates. Csoil accounts for approximately 84 % of the total uncertainty in global τ estimates; GPP also contributes significantly (15 %), whereas Cveg contributes only marginally (less than 1 %) to the total uncertainty. The high uncertainty in Csoil is reflected in the large range across state-of-the-art data products, in which full-depth Csoil spans between 3362 and 4792 PgC. The uncertainty is especially high in circumpolar regions with an uncertainty of 50 % and a low spatial correlation between the different datasets (0.2

The terrestrial forest carbon pool is poorly quantified, in particular in regions with low forest inventory capacity. By combining multiple satellite observations of synthetic aperture radar (SAR) backscatter around the year 2010, we generated a global, spatially explicit dataset of above-ground forest biomass (dry mass, AGB) with a spatial resolution of 1 ha. Using an extensive database of 110,897 AGB measurements from field inventory plots, we show that the spatial patterns and magnitude of AGB are well captured in our map with the exception of regional uncertainties in high carbon stock forests with AGB > 250 Mg ha−1 where the retrieval was effectively based on a single radar observation. With a total global AGB of 522 Pg, our estimate of the terrestrial biomass pool in forests is lower than most estimates published in literature (426-571 Pg). Nonetheless, our dataset increases knowledge on the spatial distribution of AGB compared to the global Forest Resources Assessment (FRA) by the Food and Agriculture Organization (FAO) and highlights the impact of a country's national inventory capacity on the accuracy of the biomass statistics reported to the FRA. We also reassessed previous remote sensing AGB maps, and identify major biases compared to inventory data, up to 120 % of the inventory value in dry tropical forests, in the sub-tropics and temperate zone. Because of the high level of detail and the overall reliability of the AGB spatial patterns, our global dataset of AGB is likely to have significant impacts on climate, carbon and socio-economic modelling schemes, and provides a crucial baseline in future carbon stock changes estimates. The dataset is available at: https://doi.pangaea.de/10.1594/PANGAEA.894711 (Santoro, 2018). [ABSTRACT FROM AUTHOR]