Tree migration in the dynamic, global vegetation model LPJ-GM 1.0: Efficient uncertainty assessment and improved dispersal kernels

The prediction of species geographic redistribution under climate change (i.e. range shifts) has been addressed by both experimental and modelling approaches and can be used to inform efficient policy measures on the functioning and services of future ecosystems. Dynamic Global Vegetation Models (DGVMs) are considered state-of-the art tools to understand and quantify the spatio-temporal dynamics of ecosystems at large scales and their response to changing environments. They can explicitly include local vegetation dynamics relevant to migration (establishment, growth, seed production), species-specific dispersal abilities and the competitive interactions with other species in the new environment. However, the inclusion of more detailed mechanistic formulations of range shift processes may also widen the overall uncertainty of the model. Thus, a quantification of these uncertainties is needed to evaluate and improve our confidence in the model predictions. In this study, we present an efficient assessment of parameter and model uncertainties combining low-cost analyses in successive steps: local sensitivity analysis, exploration of the performance landscape at extreme parameter values, and inclusion of relevant ecological processes in the model structure. This approach was tested on the newly-implemented migration module of the state-of-the-art DGVM, LPJ-GM 1.0. Estimates of post-glacial migration rates obtained from pollen and macrofossil records of dominant European tree taxa were used to test the model performance. The results indicate higher sensitivity of migration rates to parameters associated with the dispersal kernel (dispersal distances and kernel shape) compared to plant traits (germination rate and maximum fecundity) and highlight the importance of representing rare long-distance dispersal events via fat-tailed kernels. Overall, the successful parametrization and model selection of LPJ-GM will allow simulating plant migration with a more mechanistic approach at larger spatial and temporal scales, thus improving our efforts to understand past vegetation dynamics and predict future range shifts in a context of global change.

The Biodegradation of Soil Organic Matter in Soil-Dwelling Humivorous Fauna

Soil organic matter contains more carbon than global vegetation and the atmosphere combined. Gaining access to this source of organic carbon is challenging and requires at least partial removal of polyphenolic and/or soil mineral protections, followed by subsequent enzymatic or chemical cleavage of diverse plant polysaccharides. Soil-feeding animals make significant contributions to the recycling of terrestrial organic matter. Some humivorous earthworms, beetles, and termites, among others, have evolved the ability to mineralize recalcitrant soil organic matter, thereby leading to their tremendous ecological success in the (sub)tropical areas. This ability largely relies on their symbiotic associations with a diverse community of gut microbes. Recent integrative omics studies, including genomics, metagenomics, and proteomics, provide deeper insights into the functions of gut symbionts. In reviewing this literature, we emphasized that understanding how these soil-feeding fauna catabolize soil organic substrates not only reveals the key microbes in the intestinal processes but also uncovers the potential novel enzymes with considerable biotechnological interests.

Global Vegetation Photosynthetic Phenology Products Based on MODIS Vegetation Greenness and Temperature: Modeling and Evaluation

Land surface phenology (LSP) products that are derived from different data sources have different definitions and biophysical meanings. Discrepancies among these products and their linkages with carbon fluxes across plant functional types and climatic regions remain somewhat unclear. In this study, to differentiate LSP related to gross primary production (GPP) from LSP related to remote sensing data, we defined the former as vegetation photosynthetic phenology (VPP), including the starting and ending days of GPP (SOG and EOG, respectively). Specifically, we estimated VPP based on a combination of observed VPP from 145 flux-measured GPP sites together with the vegetation index and temperature data from MODIS products using multiple linear regression models. We then compared VPP estimates with MODIS LSP on a global scale. Our results show that the VPP provided better estimates of SOG and EOG than MODIS LSP, with a root mean square error (RMSE) for SOG of 12.7 days and a RMSE for EOG of 10.5 days. The RMSE was approximately three weeks for both SOG and EOG estimates of the non-forest type. Discrepancies between VPP and LSP estimates varied across plant functional types (PFTs) and climatic regions. A high correlation was observed between VPP and LSP estimates for deciduous forest. For most PFTs, using VPP estimates rather than LSP improved the estimation of GPP. This study presents a useful method for modeling global VPP, investigates in detail the discrepancies between VPP and LSP, and provides a more effective global vegetation phenology product for carbon cycle modeling than the existing ones.

Holocene vegetation transitions and their climatic drivers in MPI-ESM1.2

We present a transient simulation of global vegetation and climate patterns of the mid- and late Holocene using the MPI-ESM (Max Planck Institute for Meteorology Earth System Model) at T63 resolution. The simulated vegetation trend is discussed in the context of the simulated Holocene climate change. Our model captures the main trends found in reconstructions. Most prominent are the southward retreat of the northern treeline that is combined with the strong decrease of forest in the high northern latitudes during the Holocene and the vast increase of the Saharan desert, embedded in a general decrease in precipitation and vegetation in the Northern Hemisphere monsoon margin regions. The Southern Hemisphere experiences weaker changes in total vegetation cover during the last 8000 years. However, the monsoon-related increase in precipitation and the insolation-induced cooling of the winter climate lead to shifts in the vegetation composition, mainly between the woody plant functional types (PFTs). The large-scale global patterns of vegetation almost linearly follow the subtle, approximately linear, orbital forcing. In some regions, however, non-linear, more rapid changes in vegetation are found in the simulation. The most striking region is the Sahel–Sahara domain with rapid vegetation transitions to a rather desertic state, despite a gradual insolation forcing. Rapid shifts in the simulated vegetation also occur in the high northern latitudes, in South Asia and in the monsoon margins of the Southern Hemisphere. These rapid changes are mainly triggered by changes in the winter temperatures, which go into, or move out of, the bioclimatic tolerance range of individual PFTs. The dynamics of the transitions are determined by dynamics of the net primary production (NPP) and the competition between PFTs. These changes mainly occur on timescales of centuries. More rapid changes in PFTs that occur within a few decades are mainly associated with the timescales of mortality and the bioclimatic thresholds implicit in the dynamic vegetation model, which have to be interpreted with caution. Most of the simulated Holocene vegetation changes outside the high northern latitudes are associated with modifications in the intensity of the global summer monsoon dynamics that also affect the circulation in the extra tropics via teleconnections. Based on our simulations, we thus identify the global monsoons as the key player in Holocene climate and vegetation change.

APPLICATION OF VHI VEGETATION INDICES TO CROP YIELD FORECASTING

The aim of this work is to highlight the algorithm and results of modeling the average regional levels of cereals and legumes yields in some regions of Ukraine (Odessa region for example) using remote data, which used the vegetation index VHI. Methods. Model calculations were performed according to the productivity of cereals and legumes in Odessa region for 2011-2020 and the vegetation index VHI for the same period. VHI products received from NOAA STAR - Global Vegetation Health Products system (4 km resolution, 7-day composite). The relationship between VHI and cereals and legumes yields was assessed by correlation-regression analysis. Results. Statistically significant relationships between VHI and cereals and legumes yields levels in Odessa region with a correlation coefficient of 0.8- 0.9 in the period from April to July were establish. Regression dependences for early forecast of сereals and legumes yields (as of the end of April and May) were established using VHI for 16 and 20 weeks (from the beginning of the year). The correlation coefficient between the actual yield Ufact and the model values is 0.93 for Ufor(16) and 0.89 for Ufor(20). The forecast error did not exceed 10 % for Ufor(16) in 70 % of cases, and for Ufor(20) – in 80 % of cases. Conclusions. The authors established regression dependences for the early forecast (as of the end of April and May) of cereals and legumes yields in Odesa region using the region-averaged vegetation indices VHI for 16 and 20 weeks from the beginning of the year. This algorithm can be used to build model ratios for calculating crop yields for different regions of Ukraine and separately for different crops.

Long-Term Variation of Global GEOV2 and MODIS Leaf Area Index (LAI) and Their Uncertainties: An Insight into the Product Stabilities

Leaf area index (LAI) is an essential climate variable that is crucial to understand the global vegetation change. Long-term satellite LAI products have been applied in many global vegetation change studies. However, these LAI products contain various uncertainties that are not been fully considered in current studies. The objective of this study is to explore the uncertainties in the global LAI products and the uncertainty variations. Two global LAI datasets—the European Geoland2 Version 2 (GEOV2) and Moderate Resolution Imaging Spectroradiometer (MODIS) (2003-2019)—were investigated. The qualitative quality flags (QQFs) and quantitative quality indicators (QQIs) embedded in the product quality layers were analyzed to identify the temporal anomalies in the quality profile. The results show that the global GEOV2 (0.042/10a) and MODIS (0.034/10a) LAI values have steadly increased from 2003 to 2019. The global LAI uncertainty (0.016/10a) and relative uncertainty (0.3%/10a) from GEOV2 have also increased gradually, especially during the growing season from April to October. The uncertainty increase is larger for woody biomes than for herbaceous types. Contrastingly, the MODIS LAI product uncertainty remained stable over the study period. The uncertainty increase indicated by GEOV2 is partly attributed to the sensor shift in the product series. Further algorithm enhancement is necessary to improve the cross-sensor performance. This study highlights the importance of studying the LAI uncertainty and the uncertainty variation. Temporal variations in the LAI products and the product quality revealed herein have significant implications on global vegetation change studies.

Climate change and plant reproduction: trends and drivers of mast seeding change

Climate change is reshaping global vegetation through its impacts on plant mortality, but recruitment creates the next generation of plants and will determine the structure and composition of future communities. Recruitment depends on mean seed production, but also on the interannual variability and among-plant synchrony in seed production, the phenomenon known as mast seeding. Thus, predicting the long-term response of global vegetation dynamics to climate change requires understanding the response of masting to changing climate. Recently, data and methods have become available allowing the first assessments of long-term changes in masting. Reviewing the literature, we evaluate evidence for a fingerprint of climate change on mast seeding and discuss the drivers and impacts of these changes. We divide our discussion into the main characteristics of mast seeding: interannual variation, synchrony, temporal autocorrelation and mast frequency. Data indicate that masting patterns are changing but the direction of that change varies, likely reflecting the diversity of proximate factors underlying masting across taxa. Experiments to understand the proximate mechanisms underlying masting, in combination with the analysis of long-term datasets, will enable us to understand this observed variability in the response of masting. This will allow us to predict future shifts in masting patterns, and consequently ecosystem impacts of climate change via its impacts on masting. This article is part of the theme issue ‘The ecology and evolution of synchronized seed production in plants’.