Distribution Of Plant Functional Types Research Articles

European Atlantic deciduous forests are more resilient to fires than Pinus and Eucalyptus plantations

In this study, we assess how ecosystem type affects the short-term post-fire resilience of fire-prone forests, which are the predominant ecosystems in South West (SW) Europe. For this purpose, we focused on three forest systems, widespread across the European Atlantic coast, which are highly susceptible to fires at their SW distribution boundary, namely the European thermophilous Atlantic deciduous forests, dominated by Quercus robur L., and two types of forest plantations widespread globally, Eucalyptus globulus Labill. and Pinus pinaster Aiton plantations. At the community structure level, resilience to fire was measured based on changes in cover of each vertical stratum. At the functional level, resilience to fire was measured based on replacements of (both morphological and regenerative) plant functional types in the overstory and understory layers. The study variables were vertical structure and abundance of plant functional types in both overstory and understory layers. Vertical structure was assessed by defining nine vertical strata and estimating the cover in each vegetation stratum. All species identified in the overstory and understory layers were functionally classified according to their morphological attributes and regenerative traits pertaining to fire. One year after fire, we observed considerable differences in the structure between the three forest communities studied, particularly in the overstory layer, because most of the vertical strata were well recovered in the oak forest, partially recovered in the E. globulus plantation, and completely lost in the P. pinaster plantation. In contrast to the Atlantic deciduous forest and eucalyptus plantation, the pine plantation displayed distinct changes in its distribution of plant functional types. The pine plantation showed extremely low resilience to fire because the overstory layer decreased considerably, and the understory layer experienced substantial changes in the distribution of morphological and regenerative plant functional types. Fire favoured certain species which prevailed over others and, while grasses, herbaceous and spiny shrubs increased, the abundance of most morphologically functional plant types disappeared or decreased. These changes in the structure of forest communities and in the distribution of plant functional types have important implications for ecosystem functioning and services provision. Therefore, this study may serve as a foundation for devising forest management measures aimed at increasing ecosystem resilience to fire.

Net Methane Production Predicted by Patch Characteristics in a Freshwater Wetland

AbstractMethane (CH4) dynamics in wetlands are spatially variable and difficult to estimate at ecosystem scales. Patches with different plant functional types (PFT) represent discrete units within wetlands that may help characterize patterns in CH4 variability. We investigate dissolved porewater CH4 concentrations, a representation of net CH4 production and potential source of atmospheric flux, in five wetland patches characterized by a dominant PFT or lack of plants. Using soil, porewater, and plant variables we hypothesized to influence CH4, we used three modeling approaches—Classification and regression tree, AIC model selection, and Structural Equation Modeling—to identify direct and indirect influences on porewater CH4 dynamics. Across all three models, dissolved porewater CO2 concentration was the dominant driver of CH4 concentrations, partly through the influence of PFT patches. Plants in each patch type likely had variable influence on CH4 via root exudates (a substrate for methanogens), capacity to transport gas (both O2 from and CH4 to the atmosphere), and plant litter quality which impacted soil respiration and production of CO2 in the porewater. We attribute the importance of CO2 to the dominant methanogenic pathway we identified, which uses CO2 as a terminal electron acceptor. We propose a mechanistic relationship between PFT patches and porewater CH4 dynamics which, when combined with sources of CH4 loss including methanotrophy, oxidation, or plant‐mediated transport, can provide patch‐scale estimates of CH4 flux. Combining these estimates with the distribution of PFTs can improve ecosystem CH4 flux estimates in heterogenous wetlands and improve global CH4 budgets.

High-resolution land use and land cover dataset for regional climate modelling: historical and future changes in Europe

Abstract. Anthropogenic land use and land cover change (LULCC) is a major driver of environmental changes. The biophysical impacts of these changes on the regional climate in Europe are currently being extensively investigated within the World Climate Research Program (WCRP) Coordinated Downscaling Experiment (CORDEX) Flagship Pilot Study (FPS) Land Use and Climate Across Scales (LUCAS) using an ensemble of different regional climate models (RCMs) coupled with diverse land surface models (LSMs). In order to investigate the impact of realistic LULCC on past and future climates, high-resolution datasets with observed LULCC and projected future LULCC scenarios are required as input for the RCM–LSM simulations. To account for these needs, we generated the LUCAS Land Use and land Cover change (LUC) dataset version 1.1 at 0.1∘ resolution for Europe with annual LULC maps from 1950 to 2100 (https://doi.org/10.26050/WDCC/LUC_hist_EU_v1.1, Hoffmann et al., 2022b, https://doi.org/10.26050/WDCC/LUC_future_EU_v1.1, Hoffmann et al., 2022a), which is tailored to use in state-of-the-art RCMs. The plant functional type (PFT) distribution for the year 2015 (i.e. the Modelling human LAND surface Modifications and its feedbacks on local and regional cliMATE – LANDMATE – PFT dataset) is derived from the European Space Agency Climate Change Initiative Land Cover (ESA-CCI LC) dataset. Details on the conversion method, cross-walking procedure, and evaluation of the LANDMATE PFT dataset are given in the companion paper by Reinhart et al. (2022b). Subsequently, we applied the land use change information from the Land-Use Harmonization 2 (LUH2) dataset, provided at 0.25∘ resolution as input for Coupled Modelling Intercomparison Project Phase 6 (CMIP6) experiments, to derive LULC distributions at high spatial resolution and at annual time steps from 1950 to 2100. In order to convert land use and land management change information from LUH2 into changes in the PFT distribution, we developed a land use translator (LUT) specific to the needs of RCMs. The annual PFT maps for Europe for the period 1950 to 2015 are derived from the historical LUH2 dataset by applying the LUT backward from 2015 to 1950. Historical changes in the forest type changes are considered using an additional European forest species dataset. The historical changes in the PFT distribution of LUCAS LUC follow closely the land use changes given by LUH2 but differ in some regions compared to other annual LULCC datasets. From 2016 onward, annual PFT maps for future land use change scenarios based on LUH2 are derived for different shared socioeconomic pathway (SSP) and representative concentration pathway (RCP) combinations used in the framework of CMIP6. The resulting LULCC maps can be applied as land use forcing to the new generation of RCM simulations for downscaling of CMIP6 results. The newly developed LUT is transferable to other CORDEX regions worldwide.

Mapping of ESA's Climate Change Initiative land cover data to plant functional types for use in the CLASSIC land model

Abstract. Plant functional types (PFTs) are used to represent vegetation distribution in land surface models (LSMs). Previous studies have shown large differences in the geographical distribution of PFTs currently used in various LSMs, which may arise from the differences in the underlying land cover products but also the methods used to map or reclassify land cover data to the PFTs that a given LSM represents. There are large uncertainties associated with existing PFT mapping methods since they are largely based on expert judgement and therefore are subjective. In this study, we propose a new approach to inform the mapping or the cross-walking process using analyses from sub-pixel fractional error matrices, which allows for a quantitative assessment of the fractional composition of the land cover categories in a dataset. We use the Climate Change Initiative (CCI) land cover product produced by the European Space Agency (ESA). Previous work has shown that compared to fine-resolution maps over Canada, the ESA-CCI product provides an improved land cover distribution compared to that from the GLC2000 dataset currently used in the CLASSIC (Canadian Land Surface Scheme Including Biogeochemical Cycles) model. A tree cover fraction dataset and a fine-resolution land cover map over Canada are used to compute the sub-pixel fractional composition of the land cover classes in ESA-CCI, which is then used to create a cross-walking table for mapping the ESA-CCI land cover categories to nine PFTs represented in the CLASSIC model. There are large differences between the new PFT distributions and those currently used in the model. Offline simulations performed with the CLASSIC model using the ESA-CCI-based PFTs show improved winter albedo compared to that based on the GLC2000 dataset. This emphasizes the importance of accurate representation of vegetation distribution for realistic simulation of surface albedo in LSMs. Results in this study suggest that the sub-pixel fractional composition analyses are an effective way to reduce uncertainties in the PFT mapping process and therefore, to some extent, objectify the otherwise subjective process.

Forage nutritive value shows synergies with plant diversity in a wide range of semi-natural grassland habitats

This paper aims in assessing the relationship between grassland plant diversity and agronomic value, which represents a major challenge to reconcile nature conservation and agricultural use of semi-natural grassland habitats. The effects of plant diversity and functional traits were studied on forage nutritive value and early standing biomass in a wide set of semi-natural temperate grassland habitats. Plant communities were described by their species and functional diversity, community-weighted means (CWM) of plant functional traits, and the relative cover of main plant functional types (graminoids, forbs, and legumes). Species richness was found to be negatively associated with early standing biomass and average mineral content. Community evenness, for both species and plant functional types, was positively correlated with digestibility, crude protein content, and average mineral content but negatively with early standing biomass. Early standing biomass was found to increase with increasing graminoid cover and to decrease with increasing forb cover. Forage nutritive value improved with higher legume cover, higher specific leaf area CWM and lower leaf dry matter content CWM, while these functional community characteristics were not found to impact early standing biomass. Our results highlight that a better forage nutritive value can be obtained with a higher plant species evenness in permanent grassland habitats. Furthermore, in grasslands with an even distribution of plant functional types, forage nutritive value may be improved with no loss in early standing biomass.

Forest‐permafrost feedbacks and glacial refugia help explain the unequal distribution of larch across continents

AbstractAimThe continental‐scale distribution of plant functional types, such as evergreen and summergreen needle‐leaf forest, is assumed to be determined by contemporary climate. However, the distribution of summergreen needle‐leaf forest of larch (Larix Mill.) differs markedly between the continents, despite relatively similar climatic conditions. The reasons for these differences are little understood. Our aim is to identify potential triggers and drivers of the current distribution patterns by comparing species' bioclimatic niches, glacial refugia and postglacial recolonization patterns.LocationNorthern hemisphere.TaxonSpecies of the genus Larix (Mill.).MethodsWe compare species distribution and dominance using species ranges and sites of dominance, as well as their occurrence on modelled permafrost extent, and active layer thickness (ALT). We compare the bioclimatic niches and calculate the niche overlap between species, using the same data in addition to modern climate data. We synthesize pollen, macrofossil and ancient DNA palaeo‐evidence of past Larix occurrences of the last 60,000 years and track differences in distribution patterns through time.ResultsBioclimatic niches show large overlaps between Asian larch species and American Larix laricina. The distribution across various degrees of permafrost extent is distinctly different for Asian L. gmelinii and L. cajanderi compared to the other species, whereas the distribution on different depths of ALT is more similar among Asian and American species. Northern glacial refugia for Larix are only present in eastern Asia and Alaska.Main ConclusionThe dominance of summergreen larches in Asia, where evergreen conifers dominate most of the rest of the boreal forests, is dependent on the interaction of several factors which allows Asian L. gmelinii and L. cajanderi to dominate where these factors coincide. These factors include the early postglacial spread out of northern glacial refugia in the absence of competitors as well as a positive feedback mechanism between frozen ground and forest.

You Can Bend Me but Can’t Break Me: Vegetation Regeneration After Hurricane María Passed Over an Urban Coastal Wetland in Northeastern Puerto Rico

Tropical urban coastal wetland regeneration is complex. Wetland plant biodiversity varies due to past and present land use, nutrient inputs, hydrological conditions, and terrestrial/marine connectivity. The intensity of atmospheric disturbances, such as hurricanes, will determine these systems’ level of disturbance and regeneration capacity. On September 20, 2017, category 4 hurricane María passed over Puerto Rico, leaving behind a path of destruction across the entire island, especially in coastal ecosystems, from the combined effects of winds, severe storm surges, and urban runoff. Our question was: to what extent do human-influenced coastal urban wetlands regenerate after such a massive event. This study determines the spatio-temporal regeneration dynamics of plant cover and composition during the first 2 years after hurricane María in a coastal urban wetland, ciénaga Las Cucharillas, located in San Juan Bay. We assessed the distribution of plant functional types using small unmanned aerial vehicles (s-UAV) and monitored climate and environmental data (salinity, phreatic water levels, and precipitation). Wetland vegetation cover had a high recovery rate – 16 months after the hurricane, vegetation cover occupied 87% of the study area. We found a successional pattern of plant regeneration that seemed to be partly explained by the fast-slow continuum. Plants with high specific leaf area (SLA) colonized bare soil spaces first. Plant regeneration also varied according to changes in phreatic water conductivity and waterlogging. Isotopic analyses of plant species signaled high nutrient availability, increasing the system’s regeneration speed. After 2 years, the wetland’s plant cover and composition of functional plant types proved resilient to the initial hurricane effect and subsequent changes in conductivity and freshwater conditions. Further studies will expand how spatio-temporal conditions will affect long-term plant community dynamics.

Improving the representation of high-latitude vegetation distribution in dynamic global vegetation models

Abstract. Vegetation is an important component in global ecosystems, affecting the physical, hydrological and biogeochemical properties of the land surface. Accordingly, the way vegetation is parameterized strongly influences predictions of future climate by Earth system models. To capture future spatial and temporal changes in vegetation cover and its feedbacks to the climate system, dynamic global vegetation models (DGVMs) are included as important components of land surface models. Variation in the predicted vegetation cover from DGVMs therefore has large impacts on modelled radiative and non-radiative properties, especially over high-latitude regions. DGVMs are mostly evaluated by remotely sensed products and less often by other vegetation products or by in situ field observations. In this study, we evaluate the performance of three methods for spatial representation of present-day vegetation cover with respect to prediction of plant functional type (PFT) profiles – one based upon distribution models (DMs), one that uses a remote sensing (RS) dataset and a DGVM (CLM4.5BGCDV; Community Land Model 4.5 Bio-Geo-Chemical cycles and Dynamical Vegetation). While DGVMs predict PFT profiles based on physiological and ecological processes, a DM relies on statistical correlations between a set of predictors and the modelled target, and the RS dataset is based on classification of spectral reflectance patterns of satellite images. PFT profiles obtained from an independently collected field-based vegetation dataset from Norway were used for the evaluation. We found that RS-based PFT profiles matched the reference dataset best, closely followed by DM, whereas predictions from DGVMs often deviated strongly from the reference. DGVM predictions overestimated the area covered by boreal needleleaf evergreen trees and bare ground at the expense of boreal broadleaf deciduous trees and shrubs. Based on environmental predictors identified by DM as important, three new environmental variables (e.g. minimum temperature in May, snow water equivalent in October and precipitation seasonality) were selected as the threshold for the establishment of these high-latitude PFTs. We performed a series of sensitivity experiments to investigate if these thresholds improve the performance of the DGVM method. Based on our results, we suggest implementation of one of these novel PFT-specific thresholds (i.e. precipitation seasonality) in the DGVM method. The results highlight the potential of using PFT-specific thresholds obtained by DM in development of DGVMs in broader regions. Also, we emphasize the potential of establishing DMs as a reliable method for providing PFT distributions for evaluation of DGVMs alongside RS.

Leaf traits and canopy structure together explain canopy functional diversity: an airborne remote sensing approach.

Plant functional diversity is strongly connected to photosynthetic carbon assimilation in terrestrial ecosystems. However, many of the plant functional traits that regulate photosynthetic capacity, including foliar nitrogen concentration and leaf mass per area, vary significantly between and within plant functional types and vertically through forest canopies, resulting in considerable landscape-scale heterogeneity in three dimensions. Hyperspectral imagery has been used extensively to quantify functional traits across a range of ecosystems but is generally limited to providing information for top of canopy leaves only. On the other hand, lidar data can be used to retrieve the vertical structure of forest canopies. Because these data are rarely collected at the same time, there are unanswered questions about the effect of forest structure on the three -dimensional spatial patterns of functional traits across ecosystems. In the United States, the National Ecological Observatory Network's Airborne Observation Platform (NEON AOP) provides an opportunity to address this structure-function relationship by collecting lidar and hyperspectral data together across a variety of ecoregions. With a fusion of hyperspectral and lidar data from the NEON AOP and field-collected foliar trait data, we assessed the impacts of forest structure on spatial patterns of N. In addition, we examine the influence of abiotic gradients and management regimes on top-of-canopy percent N and total canopy N (i.e., the total amount of N [g/m2 ] within a forest canopy) at a NEON site consisting of a mosaic of open longleaf pine and dense broadleaf deciduous forests. Our resulting maps suggest that, in contrast to top of canopy values, total canopy N variation is dampened across this landscape resulting in relatively homogeneous spatial patterns. At the same time, we found that leaf functional diversity and canopy structural diversity showed distinct dendritic patterns related to the spatial distribution of plant functional types.

Understanding the uncertainty in global forest carbon turnover

Abstract. The length of time that carbon remains in forest biomass is one of the largest uncertainties in the global carbon cycle, with both recent historical baselines and future responses to environmental change poorly constrained by available observations. In the absence of large-scale observations, models used for global assessments tend to fall back on simplified assumptions of the turnover rates of biomass and soil carbon pools. In this study, the biomass carbon turnover times calculated by an ensemble of contemporary terrestrial biosphere models (TBMs) are analysed to assess their current capability to accurately estimate biomass carbon turnover times in forests and how these times are anticipated to change in the future. Modelled baseline 1985–2014 global average forest biomass turnover times vary from 12.2 to 23.5 years between TBMs. TBM differences in phenological processes, which control allocation to, and turnover rate of, leaves and fine roots, are as important as tree mortality with regard to explaining the variation in total turnover among TBMs. The different governing mechanisms exhibited by each TBM result in a wide range of plausible turnover time projections for the end of the century. Based on these simulations, it is not possible to draw robust conclusions regarding likely future changes in turnover time, and thus biomass change, for different regions. Both spatial and temporal uncertainty in turnover time are strongly linked to model assumptions concerning plant functional type distributions and their controls. Thirteen model-based hypotheses of controls on turnover time are identified, along with recommendations for pragmatic steps to test them using existing and novel observations. Efforts to resolve uncertainty in turnover time, and thus its impacts on the future evolution of biomass carbon stocks across the world's forests, will need to address both mortality and establishment components of forest demography, as well as allocation of carbon to woody versus non-woody biomass growth.

Improving the LPJmL4-SPITFIRE vegetation–fire model for South America using satellite data

Abstract. Vegetation fires influence global vegetation distribution, ecosystem functioning, and global carbon cycling. Specifically in South America, changes in fire occurrence together with land-use change accelerate ecosystem fragmentation and increase the vulnerability of tropical forests and savannas to climate change. Dynamic global vegetation models (DGVMs) are valuable tools to estimate the effects of fire on ecosystem functioning and carbon cycling under future climate changes. However, most fire-enabled DGVMs have problems in capturing the magnitude, spatial patterns, and temporal dynamics of burned area as observed by satellites. As fire is controlled by the interplay of weather conditions, vegetation properties, and human activities, fire modules in DGVMs can be improved in various aspects. In this study we focus on improving the controls of climate and hence fuel moisture content on fire danger in the LPJmL4-SPITFIRE DGVM in South America, especially for the Brazilian fire-prone biomes of Caatinga and Cerrado. We therefore test two alternative model formulations (standard Nesterov Index and a newly implemented water vapor pressure deficit) for climate effects on fire danger within a formal model–data integration setup where we estimate model parameters against satellite datasets of burned area (GFED4) and aboveground biomass of trees. Our results show that the optimized model improves the representation of spatial patterns and the seasonal to interannual dynamics of burned area especially in the Cerrado and Caatinga regions. In addition, the model improves the simulation of aboveground biomass and the spatial distribution of plant functional types (PFTs). We obtained the best results by using the water vapor pressure deficit (VPD) for the calculation of fire danger. The VPD includes, in comparison to the Nesterov Index, a representation of the air humidity and the vegetation density. This work shows the successful application of a systematic model–data integration setup, as well as the integration of a new fire danger formulation, in order to optimize a process-based fire-enabled DGVM. It further highlights the potential of this approach to achieve a new level of accuracy in comprehensive global fire modeling and prediction.

Extensive land cover change across Arctic-Boreal Northwestern North America from disturbance and climate forcing.

A multitude of disturbance agents, such as wildfires, land use, and climate-driven expansion of woody shrubs, is transforming the distribution of plant functional types across Arctic-Boreal ecosystems, which has significant implications for interactions and feedbacks between terrestrial ecosystems and climate in the northern high-latitude. However, because the spatial resolution of existing land cover datasets is too coarse, large-scale land cover changes in the Arctic-Boreal region (ABR) have been poorly characterized. Here, we use 31years (1984-2014) of moderate spatial resolution (30m) satellite imagery over a region spanning 4.7×106 km2 in Alaska and northwestern Canada to characterize regional-scale ABR land cover changes. We find that 13.6±1.3% of the domain has changed, primarily via two major modes of transformation: (a) simultaneous disturbance-driven decreases in Evergreen Forest area (-14.7±3.0% relative to 1984) and increases in Deciduous Forest area (+14.8±5.2%) in the Boreal biome; and (b) climate-driven expansion of Herbaceous and Shrub vegetation (+7.4±2.0%) in the Arctic biome. By using time series of 30m imagery, we characterize dynamics in forest and shrub cover occurring at relatively short spatial scales (hundreds of meters) due to fires, harvest, and climate-induced growth that are not observable in coarse spatial resolution (e.g., 500m or greater pixel size) imagery. Wildfires caused most of Evergreen Forest Loss and Evergreen Forest Gain and substantial areas of Deciduous Forest Gain. Extensive shifts in the distribution of plant functional types at multiple spatial scales are consistent with observations of increased atmospheric CO2 seasonality and ecosystem productivity at northern high-latitudes and signal continental-scale shifts in the structure and function of northern high-latitude ecosystems in response to climate change.

Harmonising plant functional type distributions for evaluating Earth system models

Abstract. Dynamic vegetation models simulate global vegetation in terms of fractional coverage of a few plant functional types (PFTs). Although these models often share the same concept, they differ with respect to the number and kind of PFTs, complicating the comparability of simulated vegetation distributions. Pollen-based vegetation reconstructions are initially only available in the form of time series of individual taxa that are not distinguished in the models. Thus, to evaluate simulated vegetation distributions, the modelling results and pollen-based vegetation reconstructions have to be converted into a comparable format. The classical approach is the method of biomisation, but hitherto PFT-based biomisation methods were only available for individual models. We introduce and evaluate a simple, universally applicable technique to harmonise PFT distributions by assigning them into nine mega-biomes, using only assumptions on the minimum PFT cover fractions and few bioclimatic constraints (based on the 2 m temperature). These constraints mainly follow the limitation rules used in the classical biome models (here BIOME4). We test the method for six state-of-the-art dynamic vegetation models that are included in Earth system models based on pre-industrial, mid-Holocene and Last Glacial Maximum simulations. The method works well, independent of the spatial resolution or the complexity of the models. Large biome belts (such as tropical forest) are generally better represented than regionally confined biomes (warm–temperate forest, savanna). The comparison with biome distributions inferred via the classical biomisation approach of forcing biome models (here BIOME1) with the simulated climate states shows that the PFT-based biomisation is even able to keep up with the classical method. However, as the new method considers the PFT distributions actually calculated by the Earth system models, it allows for a direct comparison and evaluation of simulated vegetation distributions which the classical method cannot do. Thereby, the new method provides a powerful tool for the evaluation of Earth system models in general.

Uncertainty in plant functional type distributions and its impact on land surface models

The spatial distribution and fractional cover of plant functional types (PFTs) is a key uncertainty in land surface models (LSMs) that is closely linked to uncertainties in global carbon, hydrology and energy budgets. Land cover is considered to be an Essential Climate Variable because changes in it can result in local, regional or global scale impacts on climate. In LSMs, land cover (LC) class maps are converted to PFT fractional maps using a cross-walking (CW) table by prescribing the fraction of each PFT that occurs within each LC class. In this study we assess the largest plausible range of PFT uncertainty derived from remotely sensed LC maps produced under the European Space Agency Land Cover Climate Change Initiative on simulations of land surface fluxes using 3 leading LSMs. We evaluate the impact of uncertainty due to both LC classification algorithms, and CW procedure, on energy, moisture and carbon fluxes in LSMs. We investigate the maximum plausible range of uncertainty deriving from both LC and CW within the context of a potential biomass scale (bare ground-grass-shrub-tree), representing a gradient from low to high biomass PFTs. More specifically, plausible alternative land cover maps and associated PFT fractional distributions were produced to prioritise low or high biomass vegetation in the LC classification (uncertainty in LC), and subsequently in the assignment of PFT fractions for each LC class (uncertainty in CW), relative to a reference PFT distribution.We examined the impact of PFT uncertainty on 3 key variables in the carbon, water and energy cycles (gross primary production (GPP), evapo-transpiration (ET), and albedo), for 3 LSMs (JSBACH, JULES and ORCHIDEE) at global scale. Results showed a greater uncertainty in PFT fraction due to CW as opposed to LC uncertainty, for all three variables. CW uncertainty in tree fraction was found to be particularly important in the northern boreal forests for simulated LSM albedo. Uncertainty in the balance between grass and bare soil fraction in arid parts of Africa, central Asia, and central Australia was also found to influence albedo and ET in all models. The spread due to PFT uncertainty for albedo was between 30 and 105% of inter-model uncertainty, for GPP between 20 and 90%, and for ET 0–30%. Each model had a different sensitivity to PFT uncertainty, for example, GPP in JSBACH was found to have a much higher sensitivity to PFT uncertainty in the tropics than JULES and ORCHIDEE, whereas the inverse was true for ET.These results show that inter-model uncertainty for key variables in LSMs can be reduced by more accurate representation of PFT distributions. Future efforts in land cover mapping should therefore be focused on reducing CW uncertainty through better understanding of the fractional cover of PFTs within a land cover class. Efforts to reduce LC uncertainty should particularly be focused on more accurate mapping of grass and bare soil fractions in arid areas. In the context of Land Surface Models, these results demonstrate that prescribed vegetation distribution in models is a key source of uncertainty that is comparable to the spread between models for key model state variables.

Warming Effects on Ecosystem Carbon Fluxes Are Modulated by Plant Functional Types

Despite the importance of future carbon (C) pools for policy and land management decisions under various climate change scenarios, predictions of these pools under altered climate vary considerably. Chronic warming will likely impact both ecosystem C fluxes and the abundance and distribution of plant functional types (PFTs) within systems, potentially interacting to create novel patterns of C exchange. Here, we report results from a 3-year warming experiment using open top chambers (OTC) on the Tibetan Plateau meadow grassland. Warming significantly increased C uptake through gross primary productivity (GPP) but not ecosystem respiration (ER), resulting in a 31.0% reduction in net ecosystem exchange (NEE) in warmed plots. The OTC-induced changes in ecosystem C fluxes were not fully explained by the corresponding changes in soil temperature and moisture. Warming treatments significantly increased the biomass of graminoids and legumes by 12.9 and 27.6%. These functional shifts were correlated with enhanced local GPP, but not ER, resulting in more negative NEE in plots with larger increases in graminoid and legume biomass. This may be due to a link between greater legume abundance and higher levels of total inorganic nitrogen, which can potentially drive higher GPP, but not higher ER. Overall, our results indicate that C-climate feedbacks might be closely mediated by climate-induced changes in PFTs. This highlights the need to consider the impacts of changes in PFTs when predicting future responses of C pools under altered climate scenarios.

Mapping Arctic Plant Functional Type Distributions in the Barrow Environmental Observatory Using WorldView-2 and LiDAR Datasets

Multi-scale modeling of Arctic tundra vegetation requires characterization of the heterogeneous tundra landscape, which includes representation of distinct plant functional types (PFTs). We combined high-resolution multi-spectral remote sensing imagery from the WorldView-2 satellite with light detecting and ranging (LiDAR)-derived digital elevation models (DEM) to characterize the tundra landscape in and around the Barrow Environmental Observatory (BEO), a 3021-hectare research reserve located at the northern edge of the Alaskan Arctic Coastal Plain. Vegetation surveys were conducted during the growing season (June–August) of 2012 from 48 1 m × 1 m plots in the study region for estimating the percent cover of PFTs (i.e., sedges, grasses, forbs, shrubs, lichens and mosses). Statistical relationships were developed between spectral and topographic remote sensing characteristics and PFT fractions at the vegetation plots from field surveys. These derived relationships were employed to statistically upscale PFT fractions for our study region of 586 hectares at 0.25-m resolution around the sampling areas within the BEO, which was bounded by the LiDAR footprint. We employed an unsupervised clustering for stratification of this polygonal tundra landscape and used the clusters for segregating the field data for our upscaling algorithm over our study region, which was an inverse distance weighted (IDW) interpolation. We describe two versions of PFT distribution maps upscaled by IDW from WorldView-2 imagery and LiDAR: (1) a version computed from a single image in the middle of the growing season; and (2) a version computed from multiple images through the growing season. This approach allowed us to quantify the value of phenology for improving PFT distribution estimates. We also evaluated the representativeness of the field surveys by measuring the Euclidean distance between every pixel. This guided the ground-truthing campaign in late July of 2014 for addressing uncertainty based on representativeness analysis by selecting 24 1 m × 1 m plots that were well and poorly represented. Ground-truthing indicated that including phenology had a better accuracy ( R 2 = 0.75 , R M S E = 9.94 ) than the single image upscaling ( R 2 = 0.63 , R M S E = 12.05 ) predicted from IDW. We also updated our upscaling approach to include the 24 ground-truthing plots, and a second ground-truthing campaign in late August of 2014 indicated a better accuracy for the phenology model ( R 2 = 0.61 , R M S E = 13.78 ) than only using the original 48 plots for the phenology model ( R 2 = 0.23 , R M S E = 17.49 ). We believe that the cluster-based IDW upscaling approach and the representativeness analysis offer new insights for upscaling high-resolution data in fragmented landscapes. This analysis and approach provides PFT maps needed to inform land surface models in Arctic ecosystems.

Plant functional types and trait values in the Paraná River floodplain: Modelling their association with environmental features

Our aim was to assess whether plant trait values and functional types are associated with environmental features in floodplain wetlands. The study was conducted in the Lower Paraná River floodplain, mostly dominated by marsh communities. In 72 sites, 16 functional traits were screened for 95 plant populations of 44 species. Since the interspecific variability in trait values resulted higher than the intraspecific variability, plant species (and not populations) were hierarchically classified into plant functional types (PFTs). Ten PFTs were identified. Two trade-offs were observed: resource acquisition vs. resource conservation or resource assignment to tissues conferring anoxia or salinity tolerance, and fast growth vs. light interception. Cover-abundance of the PFTs and community-level weighted means (CWMs) of the non-redundant traits were computed for 80 sites. Generalized linear models were used to assess the effect of environmental variables (landscape units, topographic positions, flooding frequency and eight soil variables) on the CWMs and on distribution of PFTs occurring in 15 sites or more. A two-stage modelling process was carried out to account for the presence–absence and cover–abundance of PFTs separately. The association between CWMs and environmental features was relatively weak but significant for six out of the eight traits analysed. Besides, satisfactory models were found for eight out of the nine PFTs assessed. In the studied floodplain, PFTs–interpreted in terms of life strategies–seem to be a better approach than CWMs to understand and characterise functional responses to the environment.

Benchmarking historical CMIP5 plant functional types across the Upper Midwest and Northeastern United States

AbstractCentennial‐scale climate‐ecosystem feedbacks are a major source of predictive uncertainty for land‐atmosphere fluxes of energy, carbon, and water. Accurate representations of plant functional type (PFT) distributions through time and space are required for modeling centennial‐scale feedbacks within Earth system models (ESMs). We tested the ability of ESMs from the Coupled Model Intercomparison Project Phase 5 (CMIP5) to capture historical PFT distributions at the time of Euro‐American settlement in the Northeastern United States against a new subcontinental‐scale data set of historical tree abundances derived from forest composition surveys. To identify and diagnose errors in ESM‐simulated PFT distributions and quantify impacts on modeled albedo, net primary productivity, and transpiration, we analyzed actual and modeled PFT distributions with respect to historical mean annual climate and modeled elasticity among PFTs, climate, and vegetation‐atmosphere fluxes. Historical PFT distributions were poorly matched between ESMs and the settlement‐era data, often due to inaccurate PFT‐climate relationships within ESMs, particularly for evergreen trees. Some models exhibited large local, but regionally compensating, errors in simulated albedo, net primary productivity, and transpiration due to inaccurate PFT distributions, while others had systematic regional biases in vegetation‐atmosphere fluxes. Internal variable elasticity varied among ESMs, and these differences closely corresponded to model skill in predicting PFT distributions. New historical benchmarks like the settlement‐era vegetation data provide opportunities to confront ESMs, parse sources of error, and improve simulations of historical and future vegetation‐atmosphere feedbacks.

Competition between plant functional types in the Canadian Terrestrial Ecosystem Model (CTEM) v. 2.0

Abstract. The Canadian Terrestrial Ecosystem Model (CTEM) is the interactive vegetation component in the Earth system model of the Canadian Centre for Climate Modelling and Analysis. CTEM models land–atmosphere exchange of CO2 through the response of carbon in living vegetation, and dead litter and soil pools, to changes in weather and climate at timescales of days to centuries. Version 1.0 of CTEM uses prescribed fractional coverage of plant functional types (PFTs) although, in reality, vegetation cover continually adapts to changes in climate, atmospheric composition and anthropogenic forcing. Changes in the spatial distribution of vegetation occur on timescales of years to centuries as vegetation distributions inherently have inertia. Here, we present version 2.0 of CTEM, which includes a representation of competition between PFTs based on a modified version of the Lotka–Volterra (L–V) predator–prey equations. Our approach is used to dynamically simulate the fractional coverage of CTEM's seven natural, non-crop PFTs, which are then compared with available observation-based estimates. Results from CTEM v. 2.0 show the model is able to represent the broad spatial distributions of its seven PFTs at the global scale. However, differences remain between modelled and observation-based fractional coverage of PFTs since representing the multitude of plant species globally, with just seven non-crop PFTs, only captures the large-scale climatic controls on PFT distributions. As expected, PFTs that exist in climate niches are difficult to represent either due to the coarse spatial resolution of the model, and the corresponding driving climate, or the limited number of PFTs used. We also simulate the fractional coverage of PFTs using unmodified L–V equations to illustrate its limitations. The geographic and zonal distributions of primary terrestrial carbon pools and fluxes from the versions of CTEM that use prescribed and dynamically simulated fractional coverage of PFTs compare reasonably well with each other and observation-based estimates. The parametrization of competition between PFTs in CTEM v. 2.0 based on the modified L–V equations behaves in a reasonably realistic manner and yields a tool with which to investigate the changes in spatial distribution of vegetation in response to future changes in climate.

Impact of land cover characterization on regional climate modeling over West Africa

The impact of high resolution modern vegetation cover on the West African climate is examined using the International Centre for Theoretical Physics Regional Climate Model implementing the NCAR Community Land Model. Two high resolution 25 km long-term simulations driven by the output from a coarser 50-km resolution simulation are performed for the period 1998–2010. One high resolution simulation uses an earlier and coarser-resolution version of plant functional type distribution and leaf area index, while the other uses a more recent, higher-quality, and finer-resolution version of the data. The results indicate that the new land cover distribution substantially alters the distribution of temperature with warming in Central Nigeria, northern Gulf of Guinea and part of the Sahel due to the replacement of C4 grass with corn; and cooling along the coastlines of the Gulf of Guinea and in Central Africa due to the replacement of C4 grass with tropical broadleaf evergreen trees. Changes in latent heat flux appear to be largely responsible for these temperature changes with a net decrease (increase) in regions of warming (cooling). The improved land cover distribution also results in a wetter monsoon season. The presence of corn tends to favor larger precipitation amounts via more intense events, while the presence of tropical broadleaf evergreen trees tends to favor the occurrence of both more intense and more frequent events. The wetter conditions appear to be sustained via (1) an enhanced soil moisture feedback; and (2) elevated moisture transport due to increased low-level convergence in regions south of 10N where the most substantial land cover differences are present. Overall the changes induced by the improved vegetation cover improve, to some extent, the performance of the high resolution regional climate model in simulating the main West African summer monsoon features.