Contrasting climate risks predicted by dynamic vegetation and ecological niche-based models applied to tree species in the Brazilian Atlantic Forest

Raghunathan, Nima; François, Louis; Dury, Marie; Hambuckers, Alain

doi:10.1007/s10113-018-1405-8

Cited by 12 publications

(17 citation statements)

References 60 publications

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“…For future climate, the outputs of 33 general circulation models (GCMs) were acquired from the Coupled Model Intercomparison Project Phase 5 (CMIP5) database [52] of the Intergovernmental Panel on Climate Change (IPCC). We analyzed the capacity of those models to reproduce the annual and seasonal climate over Africa in terms of mean, interannual variability and trend of temperature and precipitation over the reference 1980-1999 period by comparing them to the CRU data [50]. We selected the five most performing models, here presented by decreasing rank: CNRM-CM5 [62], MPI-ESM-LR [63], CESM1-BGC [64], CanESM2 [65] and MPI-ESM-MR [63] (see Supplementary S2).…”

Section: Climate Data and Future Scenariosmentioning

confidence: 99%

“…CARAIB (CARBon Assimilation in the Biosphere) is a process-based model originally developed to understand the role of vegetation in the global carbon cycle [68,69]. It has also been used to reconstruct potential vegetation distribution under past climates (e.g., [46,70]) and to project land ecosystem evolution in the future [47], including in tropical regions [49,50]. It calculates the carbon and water transfers between the atmosphere, the vegetation and the soil.…”

Section: The Caraib Dynamic Vegetation Modelmentioning

confidence: 99%

“…We particularly consider changes in species net primary productivity, biomass and distribution, important for evaluating the services provided by plants in the future. With the CARAIB DVM [45][46][47][48][49][50][51], we focus on 12 species which are keystone plants for the ecosystem and for people as goods and services. To take the range of possibilities the future may hold into account, we used several climate projections produced by climate models of the Coupled Model Intercomparison Project-Phase 5 (CMIP5; [52]) to 2100 time horizon under two Representative Concentration Pathways, RCP4.5 and RCP8.5 [53].…”

Section: Introductionmentioning

confidence: 99%

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Refining Species Traits in a Dynamic Vegetation Model to Project the Impacts of Climate Change on Tropical Trees in Central Africa

Dury

Mertens

Verbeeck

et al. 2018

Forests

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African tropical ecosystems and the services they provide to human society suffer from an increasing combined pressure of land use and climate change. How individual tropical tree species respond to climate change remains relatively unknown. In this study, we refined the species characterization in the CARAIB (CARbon Assimilation In the Biosphere) dynamic vegetation model by replacing plant functional type morpho-physiological traits by species-specific traits. We focus on 12 tropical tree species selected for their importance in both the plant community and human society. We used CARAIB to simulate the current species net primary productivity (NPP), biomass and potential distribution and their changes in the future. Our results indicate that the use of species-specific traits does not necessarily result in an increase of predicted current NPPs. The model projections for the end of the century highlight the large uncertainties in the future of African tropical species. Projected changes in species distribution vary greatly with the general circulation model (GCM) and, to a lesser extent, with the concentration pathway. The question about long-term plant response to increasing CO2 concentrations also leads to contrasting results. In absence of fertilization effect, species are exposed to climate change and might lose 25% of their current distribution under RCP8.5 (12.5% under RCP4.5), considering all the species and climatic scenarios. The vegetation model projects a mean biomass loss of −21.2% under RCP4.5 and −34.5% under RCP8.5. Potential range expansions, unpredictable due to migration limitations, are too limited for offsetting range contraction. By contrast, if the long-term species response to increasing [CO2] is positive, the range reduction is limited to 5%. However, despite a mean biomass increase of 12.2%, a positive CO2 feedback might not prevent tree dieback. Our analysis confirms that species will respond differently to new climatic and atmospheric conditions, which may induce new competition dynamics in the ecosystem and affect ecosystem services.

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Section: Climate Data and Future Scenariosmentioning

confidence: 99%

Section: The Caraib Dynamic Vegetation Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Refining Species Traits in a Dynamic Vegetation Model to Project the Impacts of Climate Change on Tropical Trees in Central Africa

Dury

Mertens

Verbeeck

et al. 2018

Forests

Self Cite

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“…The data analysis has been executed in R software [10], using the packages raster [3], rgdal [1], openxlsx [13]. For each of the 11 temperature variables and 8 precipitation variables, several summaries have been created.…”

Section: Methodsmentioning

confidence: 99%

“…In order to assess better the spatial variation of change, maps with the averages of the 11 GCMs for the 2 extreme RCP scenarios (26 and 85) were generated for each variable (year 2050), as well as maps with their standard deviations (as a measure of agreement between GCMs). The maps were generated in R software [10], using the raster and rgdal packages [1,3]. For consistency, all the map colour palettes range from blue colour (low values) to red colour (high values).…”

Section: Methodsmentioning

confidence: 99%

An interpretation of multi-model future climate predictions for bioclim variables in Romania

Stoica¹

2019

Contrib.Bot.

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Climate change is one of the biggest challenges of our century. As many climate models exist for the Romanian territory, each simulating a number of possible future scenarios for the emission of greenhouse gases, it is difficult to summarize the predicted impacts. This paper analyzes the output of a part of the Worldclim dataset, namely the 19 bioclim variables for 11 General Circulation Models, 4 RCP (Representative Concentration Pathway) scenarios and 2 years (2050, 2070) at 5 arc minutes (~10 km). These 19 variables were conceived to be relevant for species physiology across phyla, and are extensively used in current literature for species distribution modelling. In order to make informed choices in the fitting of models (simulations of future niche changes), an interpretation is needed for the future variation of each bioclim variable and each combination of GCM, year and greenhouse gas emission scenario (RCP). While GCM rankings are different for each variable and each year-RCP combination, some general characteristics can be derived for each GCM. For the Romanian territory, the hd model (HadGEM2-AO) can be considered overall as a pessimistic model in relation to temperature and precipitation variables (high temperature increase, high precipitation decrease). The mg GCM (MRI-CGCM3) can be regarded as an optimistic model in relation to predicted temperature increase (less warming), but also in relation to precipitation (higher rainfall). The mi (MIROC-ESM-CHEM) also usually predicts a more humid future in Romania, but with higher temperature increase. The ip GCM (IPSL-CM5A-LR) predicts the highest increase in temperatures during cold months in Romania, as well as drier winters and less temperature variability (monthly and yearly). A moderate model for our country is cc (CCSM4), which can be used as a balanced model (it is optimistic only for cold season temperatures, predicting the lowest increase). Overall, for temperature variables there is a general consensus (increase of temperatures for all combinations of GCM, RCP and year). Regarding precipitations the trends are not very clear. An exception is probably the RCP85 scenario, which causes most GCMs to predict a decrease in precipitation variables, but even for this scenario there are models indicating an increase.

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Uncertainty and risk of pruned distributional ranges induced by climate shifts for alpine species: a case study for 79 Kobresia species in China

2023

Theor Appl Climatol

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Contrasting climate risks predicted by dynamic vegetation and ecological niche-based models applied to tree species in the Brazilian Atlantic Forest

Cited by 12 publications

References 60 publications

Refining Species Traits in a Dynamic Vegetation Model to Project the Impacts of Climate Change on Tropical Trees in Central Africa

Refining Species Traits in a Dynamic Vegetation Model to Project the Impacts of Climate Change on Tropical Trees in Central Africa

An interpretation of multi-model future climate predictions for bioclim variables in Romania

Uncertainty and risk of pruned distributional ranges induced by climate shifts for alpine species: a case study for 79 Kobresia species in China

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