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.

Factors affecting vegetation dynamics in the south of the Mokolo District in the Far North of Cameroon

Plant dynamics is a natural process that occurs in the ecosystem. This dynamic becomes abnormal in the presence of human pressure. The vegetation of the district of Mokolo (Cameroon) faces many anthropic factors that disturb its steady evolution. This work aims to evaluate the different factors that influence the vegetation dynamics in the south of the Mokolo District. All traces of anthropization were identified on all woody species in a rectangular plot (20 m x 100 m). All individuals with a height ≤1.30 m and a Dhp less than 10 cm were considered regenerating individuals. Among the main plant factors, dynamics identified, regeneration, spread type, phytogeographic type have positive impacts while logging, debarking, pruning, grubbing, trampling and burning have negative impacts. Regeneration is the main positive natural factor with the highest frequency in wooded savannas (321±95%). Timber harvesting is the main negative anthropogenic factor with a higher frequency in the home garden (85.00%) and the shrub savannas (68.66%). To reduce the negative impacts and increase the positive impacts, the government must implement reforestation projects in this ecologically fragile area.

Incorporating Vegetation Type Transformation with NDVI Time-Series to Study the Vegetation Dynamics in Xinjiang

Time-series normalized difference vegetation index (NDVI) is commonly used to conduct vegetation dynamics, which is an important research topic. However, few studies have focused on the relationship between vegetation type and NDVI changes. We investigated changes in vegetation in Xinjiang using linear regression of time-series MOD13Q1 NDVI data from 2001 to 2020. MCD12Q1 vegetation type data from 2001 to 2019 were used to analyze transformations among different vegetation types, and the relationship between the transformation of vegetation type and NDVI was analyzed. Approximately 63.29% of the vegetation showed no significant changes. In the vegetation-changed area, approximately 93.88% and 6.12% of the vegetation showed a significant increase and decrease in NDVI, respectively. Approximately 43,382.82 km2 of sparse vegetation and 25,915.44 km2 of grassland were transformed into grassland and cropland, respectively. Moreover, 17.4% of the area with transformed vegetation showed a significant increase in NDVI, whereas 14.61% showed a decrease in NDVI. Furthermore, in areas with NDVI increased, the mean NDVI slopes of pixels in which sparse vegetation transferred to cropland, sparse vegetation transferred to grassland, and grassland transferred to cropland were 9.8 and 3.2 times that of sparse vegetation, and 1.97 times that of grassland, respectively. In areas with decreased NDVI, the mean NDVI slopes of pixels in which cropland transferred to sparse vegetation, grassland transferred to sparse vegetation were 1.75 and 1.36 times that of sparse vegetation, respectively. The combination of vegetation type transformation NDVI time-series can assist in comprehensively understanding the vegetation change characteristics.

High altitudinal vegetation dynamics including treeline ecotone in Langtang National Park, Nepal

Identification of high altitudinal vegetation dynamics using remote sensing is important because of the complex topography and environment in the Himalayas. Langtang National Park is the first Himalayan park in Nepal representing the best area to study vegetation change in the central Himalaya region because of the high altitudinal gradient and relatively less disturbed region. This study aimed at mapping vegetation in Langtang National Park and its treeline ecotone using Moderate Resolution Imaging Spectroradiometer (MODIS), Normalized Difference Vegetation Index (NDVI). Two treeline sites with an altitude of 3927 and 3802 meters above sea level (masl) were selected, and species density was measured during the field survey. The linear slope for each pixel and the Mann Kendall test to measure significant trends were used. The results showed that NDVI has significantly increased at the rate of 0.002yr-1 in Langtang National Park and 0.003yr-1 in treeline ecotone during 2000-2017. The average 68.73% equivalents to 1463 km2 of Langtang National Park are covered by vegetation. At the same time, 16.45% equivalents to 350.43 km2 are greening, and 0.25%, i.e., 5.43 km2 are found browning. In treeline ecotone, the vegetation is mostly occupied by grasses, shrublands and small trees where the NDVI was found from 0.1 to 0.5. The relative changes of NDVI in barren lands are negative and vegetative lands above 0.5 NDVI are positive between 2000 and 2017. The dominant treeline vegetation were Abies spectabilis, Rhododendron campanulatum, Betula utilis and Sorbus microphyla, with the vegetation density of 839.28 and 775 individuals per hectare in sites A and B, respectively. The higher average NDVI values, significantly increased NDVI, and higher density of vegetation in both A and B sites indicate that the vegetation in treeline ecotone is obtaining a good environment in the Himalayas of Nepal.

Modeling the short-term fire effects on vegetation dynamics and surface energy in southern Africa using the improved SSiB4/TRIFFID-Fire model

Fire causes abrupt changes in vegetation properties and modifies flux exchanges between land and atmosphere at subseasonal to seasonal scales. Yet these short-term fire effects on vegetation dynamics and surface energy balance have not been comprehensively investigated in the fire-coupled vegetation model. This study applies the SSiB4/TRIFFID-Fire (the Simplified Simple Biosphere Model coupled with the Top-down Representation of Interactive Foliage and Flora Including Dynamics with fire) model to study the short-term fire impact in southern Africa. Specifically, we aim to quantify how large impacts fire exerts on surface energy through disturbances on vegetation dynamics, how fire effects evolve during the fire season and the subsequent rainy season, and how surface-darkening effects play a role besides the vegetation change effects. We find fire causes an annual average reduction in grass cover by 4 %–8 % for widespread areas between 5–20∘ S and a tree cover reduction by 1 % at the southern periphery of tropical rainforests. The regional fire effects accumulate during June–October and peak in November, the beginning of the rainy season. After the fire season ends, the grass cover quickly returns to unburned conditions, while the tree fraction hardly recovers in one rainy season. The vegetation removal by fire has reduced the leaf area index (LAI) and gross primary productivity (GPP) by 3 %–5 % and 5 %–7 % annually. The exposure of bare soil enhances surface albedo and therefore decreases the absorption of shortwave radiation. Annual mean sensible heat has dropped by 1.4 W m−2, while the latent heat reduction is small (0.1 W m−2) due to the compensating effects between canopy transpiration and soil evaporation. Surface temperature is increased by as much as 0.33 K due to the decrease of sensible heat fluxes, and the warming would be enhanced when the surface-darkening effect is incorporated. Our results suggest that fire effects in grass-dominant areas diminish within 1 year due to the high resilience of grasses after fire. Yet fire effects in the periphery of tropical forests are irreversible within one growing season and can cause large-scale deforestation if accumulated for hundreds of years.