Potential Natural Vegetation Map Research Articles

Reference vegetation for restoration? Three vegetation maps compared across 76 nature reserves in Uganda and Kenya

AbstractForest and landscape restoration are increasingly popular nature‐based solutions to mitigate climate change and safeguard biodiversity. Restoration planning and monitoring implies that a reference ecosystem has been defined to which the restored site can be compared, but how to best select such reference? We tested three different potential natural vegetation (PNV) maps of the same areas in Kenya and Uganda for their utility as ecological references with independent data that were not used when those maps were made. These independent datasets included presence observations of woody species from 76 sites in forest reserves in Kenya and Uganda, and classification of surveyed species into a system that included “forest‐only” and “nonforest‐only” ecological types. Our tests show that (1) the three vegetation maps largely agree on the environmental envelopes/ranges within which forests occur. (2) There are large differences in how well the maps predict the presence of forest‐only species. (3) Two maps, based on empirical observations (V4A and White), predict forest types well, whereas the third, based on climate envelopes only (NS), performs poorly. (4) A large area in Uganda is potentially in one of two alternative stable states. We conclude that it is possible to evaluate the utility of PNV maps at a more detailed scale than the level of biome and ecoregion. This indicates that it is possible to map PNV at scales required for reference for restoration and management of forest vegetation. We recommend that empirically based maps of potential natural vegetation are used in restoration planning (biome and PNV maps based on climate envelopes alone may be unreliable tools) as a baseline model for predicting the distribution of reference ecosystems under current and future conditions. It could conveniently be done by deconstructing the existing biome maps, supported by rapid botanical surveys.

Multi-model assessment of potential natural vegetation to support ecological restoration

Ecological restoration is imperative for controlling desertification. Potential natural vegetation (PNV), the theoretical vegetation succession state, can guides near-natural restoration. Although a rising transition from traditional statistical methods to advanced machine learning and deep learning is observed in PNV simulation, a comprehensive comparison of their performance is still unexplored. Therefore, we overview the performance of PNV mapping in terms of 12 commonly used methods with varying spatial scales and sample sizes. Our findings indicate that the methodology should be carefully selected due to the variation in performance of different model types, with Area Under the Curve (AUC) values ranging from 0.65 to 0.95 for models with sample sizes up to 80% of the total sample size. Specifically, semi-supervised learning performs best with small sample sizes (i.e., 10 to 200), while Random Forest, XGBoost, and artificial neural networks perform better with large sample sizes (i.e., over 500). Further, the performance of all models tends to improve significantly as the sample size increases and the grain size of the crystals becomes smaller. Take the downstream Tarim River Basin, a hyper-arid region undergoing ecological restoration, as a case study. We showed that its potential restored areas were overestimated by 2–3 fold as the spatial scale became coarser, revealing the caution needed while planning restoration projects at coarse resolution. These findings enhance the application of PNV in the design of restoration programs to prevent desertification.

Assessing the use of land system archetypes to increase regional variability representation in country-specific characterization factors: a soil erosion case study

PurposeThe characterization of land use impacts in life cycle assessment (LCA) requires a constant compromise between highly specific impacts models and coarse geographical scales available in life cycle inventory, where most information is provided at country level as the highest degree of geographical specificity. The derivation of country-specific characterization factors is usually done estimating impacts with the use of land cover and potential natural vegetation maps, assuming the most predominant biome per country as representative. This study explores the use of land system archetypes to derive country-specific characterization factors for land use-related soil erosion impacts that can better represent intra-national variations, while accounting for several biogeographical and socioeconomic differences.MethodsLand use-specific characterization factors were derived as the potentially enhanced soil erosion rate, using the soil erosion rates of each archetype as a reference state, and correction factors to reflect the relative increase or decrease in soil erosion rates associated with each of the eight land use types assessed: forest, permanent crops, grassland, farmland, fallow ground, moorland, urban/industrial, and mining/landfill. Country-specific characterization factors for land use erosion impacts of occupation (in ton/(m2·year)) were calculated by taking into account the land system archetypes present in each country, the land use-specific characterization factors, and the likelihood of each land use type occurring across archetypes (based on rule of thumb expert estimates). The country-specific characterization factors were produced specifically for occupation impacts for each of the eight land use types, and covering 263 countries and territories/dependencies.Results and discussionThe resulting 2,104 country-specific characterization factors displayed in average a considerably greater variation in comparison with characterization factors produced when only the most predominant archetype per country is assumed as representative per country. The results indicate that world generic values might underestimate up to 10 times the degree of impacts associated with land use types such as permanent crops, fallow ground, mining, and landfill. The use of land system archetypes presents a viable approach to derive country-specific characterization factors while taking into account key intra-national variations, as well as biogeographical and socioeconomic factors.

Including vegetation dynamics in an atmospheric chemistry-enabled general circulation model: linking LPJ-GUESS (v4.0) with the EMAC modelling system (v2.53)

Abstract. Central to the development of Earth system models (ESMs) has been the coupling of previously separate model types, such as ocean, atmospheric, and vegetation models, to address interactive feedbacks between the system components. A modelling framework which combines a detailed representation of these components, including vegetation and other land surface processes, enables the study of land–atmosphere feedbacks under global climate change. Here we present the initial steps of coupling LPJ-GUESS, a dynamic global vegetation model, to the atmospheric chemistry-enabled atmosphere–ocean general circulation model EMAC. The LPJ-GUESS framework is based on ecophysiological processes, such as photosynthesis; plant and soil respiration; and ecosystem carbon, nitrogen, and water cycling, and it includes a comparatively detailed individual-based representation of resource competition, plant growth, and vegetation dynamics as well as fire disturbance. Although not enabled here, the model framework also includes a crop and managed-land scheme, a representation of arctic methane and permafrost, and a choice of fire models; and hence it represents many important terrestrial biosphere processes and provides a wide range of prognostic trace-gas emissions from vegetation, soil, and fire. We evaluated an online one-way-coupled model configuration (with climate variable being passed from EMAC to LPJ-GUESS but no return information flow) by conducting simulations at three spatial resolutions (T42, T63, and T85). These were compared to an expert-derived map of potential natural vegetation and four global gridded data products: tree cover, biomass, canopy height, and gross primary productivity (GPP). We also applied a post hoc land use correction to account for human land use. The simulations give a good description of the global potential natural vegetation distribution, although there are some regional discrepancies. In particular, at the lower spatial resolutions, a combination of low-temperature and low-radiation biases in the growing season of the EMAC climate at high latitudes causes an underestimation of vegetation extent. Quantification of the agreement with the gridded datasets using the normalised mean error (NME) averaged over all datasets shows that increasing the spatial resolution from T42 to T63 improved the agreement by 10 %, and going from T63 to T85 improved the agreement by a further 4 %. The highest-resolution simulation gave NME scores of 0.63, 0.66, 0.84, and 0.53 for tree cover, biomass, canopy height, and GPP, respectively (after correcting tree cover and biomass for human-caused deforestation which was not present in the simulations). These scores are just 4 % worse on average than an offline LPJ-GUESS simulation using observed climate data and corrected for deforestation by the same method. However, it should be noted that the offline LPJ-GUESS simulation used a higher spatial resolution, which makes the evaluation more rigorous, and that excluding GPP from the datasets (which was anomalously better in the EMAC simulations) gave 10 % worse agreement for the EMAC simulation than the offline simulation. Gross primary productivity was best simulated by the coupled simulations, and canopy height was the worst. Based on this first evaluation, we conclude that the coupled model provides a suitable means to simulate dynamic vegetation processes in EMAC.

Model‐based reconstruction of the succession dynamics of a large river floodplain

AbstractMost large rivers in Europe and North America suffered flow regulations and channelization in the 19th and 20th century. To study the effects of the altered site conditions on the development of floodplain vegetation and create a benchmark map for their restoration, we calibrated and applied a dynamic floodplain vegetation model that accounts for the processes recruitment as well as morphodynamic disturbance and physiologic stress on vegetation to reconstruct the succession dynamics of the floodplain vegetation of a segment of the Rhine River from shortly after it was channelized (1872) until today (2016). The model calibration was based on historical maps and hydrologic data.Our simulation demonstrated a steady, one‐way progression of the vegetation communities towards mature phases without regression to younger stages. It was possible to attribute this development to a lack of morphodynamic disturbances strong enough to reset succession and to identify physiological stress caused by long inundations periods as the most relevant controlling factor of succession. The resulting vegetation distribution (2016) can be considered an estimation of the potential natural vegetation (PNV) under altered site conditions.The good agreement of the model results with an expert‐based PNV map showed that our approach is a good alternative to create benchmark maps for floodplain conservation and restoration projects. From a research and practitioners' viewpoint, it has the big advantage over the traditional approach that it allows to analyse different points in time as well as to be comprehensive and reproducible.

Landscape Modeling of the Potential Natural Vegetation of Santa Catalina Island, California

The vegetation of Santa Catalina Island has been significantly transformed through a history of introduction of exotic plant species and disturbance by large introduced herbivores. Many of these disturbances have been reduced in recent decades, using measures such as carefully controlling the number of bison and removing cattle, sheep, feral pigs, and goats. The success of subsequent vegetation restoration actions depends on the choice of the right plant community for a location, which may not be obvious for an island with extensive areas dominated by exotic species. Environmental niche modeling is an approach to re-create the spatial distribution of habitat types for such a purpose. Such models, however, often require both presence and absence data to be meaningful, while in this scenario absence is misleading because it may reflect a long history of disturbance. Maximum entropy modeling is a technique to model species distributions with presence-only data that has been shown to produce accurate results. We used this modeling tool to model the environmental niche for distinct vegetation types, conceptualized as potential natural vegetation, on Catalina Island as a means to predict locations where restoration actions would be most successful and to predict potential natural vegetation prior to anthropogenic disturbance. Using an existing vegetation map, we extracted random points from within the polygons defining each native vegetation type. We then modeled the habitat suitability for each habitat using high-resolution environmental data that included elevation, aspect, hillshade, northeastness, slope, solar radiation, and topographic wetness index. The resulting models were combined to produce a map of potential natural vegetation. A 1977 map of potential natural vegetation included 4 vegetation types (woodland, chaparral, scrub, and grassland) to which we compared our results. Our new model of potential natural vegetation has high spatial complexity and high resolution. It also shows naturalistic responses to topography that are consistent with the broad patterns mapped in 1977 while providing fine-scale resolution to inform restoration efforts.

Global mapping of potential natural vegetation: an assessment of machine learning algorithms for estimating land potential.

Potential natural vegetation (PNV) is the vegetation cover in equilibrium with climate, that would exist at a given location if not impacted by human activities. PNV is useful for raising public awareness about land degradation and for estimating land potential. This paper presents results of assessing machine learning algorithms—neural networks (nnet package), random forest (ranger), gradient boosting (gbm), K-nearest neighborhood (class) and Cubist—for operational mapping of PNV. Three case studies were considered: (1) global distribution of biomes based on the BIOME 6000 data set (8,057 modern pollen-based site reconstructions), (2) distribution of forest tree taxa in Europe based on detailed occurrence records (1,546,435 ground observations), and (3) global monthly fraction of absorbed photosynthetically active radiation (FAPAR) values (30,301 randomly-sampled points). A stack of 160 global maps representing biophysical conditions over land, including atmospheric, climatic, relief, and lithologic variables, were used as explanatory variables. The overall results indicate that random forest gives the overall best performance. The highest accuracy for predicting BIOME 6000 classes (20) was estimated to be between 33% (with spatial cross-validation) and 68% (simple random sub-setting), with the most important predictors being total annual precipitation, monthly temperatures, and bioclimatic layers. Predicting forest tree species (73) resulted in mapping accuracy of 25%, with the most important predictors being monthly cloud fraction, mean annual and monthly temperatures, and elevation. Regression models for FAPAR (monthly images) gave an R-square of 90% with the most important predictors being total annual precipitation, monthly cloud fraction, CHELSA bioclimatic layers, and month of the year, respectively. Further developments of PNV mapping could include using all GBIF records to map the global distribution of plant species at different taxonomic levels. This methodology could also be extended to dynamic modeling of PNV, so that future climate scenarios can be incorporated. Global maps of biomes, FAPAR and tree species at one km spatial resolution are available for download via http://dx.doi.org/10.7910/DVN/QQHCIK.

Bioclimatic classification of US vegetation along a coast-to-coast macrotransect crossing central United States. I: Mediterranean vegetation

This report presents the first of two parts of a bioclimatic classification of the vegetation of the United States. Using a geographical information system, 987 weather stations were located along a longitudinal macrotransect from the shores of the Atlantic to Pacific on four maps: Map of the Physiographic Divisions of the Conterminous US, US Potential Natural Vegetation Map, US Ecoregion Map, and Terrestrial Ecosystems-Isobioclimates Map of the Conterminous United States. Based on these maps, bibliographic resources and field data, we deduced the potential natural vegetation (PNV) of each weather station; then, we assigned the different PNV types to alliance or association levels using the US National Vegetation Classification (USNVC). In a next step, USNVC groups were related with similar level phytosociological syntaxa described in the study area. The bioclimatic distribution of the USNVC units defined was then interpreted using the bioclimatic classification proposed in successive approximations by S. Rivas-Martínez. The distribution of USNVC alliances was mainly linked to the macrobioclimates (Mediterranean, Temperate, and Tropical) of the longitudinal gradient examined, though some edaphic factors induced the appearance of specialized plant groups. Herein, we present our data for the Mediterranean macrobioclimate, in which 53 alliances and 28 isobioclimates were identified.

Development of a spatially explicit approach for mapping ecosystem services in the Brazilian Savanna – MapES

The objective of the presented study is the development of a spatially explicit approach for mapping ecosystem services (MapES) by using specific knowledge about the Cerrado biome (Brazilian Savanna). This biome covers an area of about 2 million km2, i.e. nearly 24% of the total area of Brazil, and has come under substantial pressure during the last 50 years caused by strong land-use/land-cover change, mostly due to agricultural expansion and urbanization. Because of its fast transformation rate, there is an enormous demand for knowledge, and its application, about the effects of land-use/land-cover on the capacity of providing or maintaining ecosystem services. The MapES approach was developed using a vast existing knowledge base. After analyzing and structuring this knowledge the relationships between land-use/land-cover and the potential to provide or maintain eight ecosystem services (Erosion Control, Runoff Control, Water Supply, Water Quality Maintenance, Soil Quality Maintenance, Biodiversity Maintenance, Food Production and Energy Production) were parametrized. In addition, the approach was developed as spatially explicit by including landscape properties (soil, slope and distance to river network) in the cell based system. A reference map of potential natural vegetation and a land use map for 2013 for a meso-scale experimental catchment (32.7km2) were produced. The catchment was used as an example to apply the approach, i.e. assessing and visualizing changes from before human interference to the current land use situation. Finally, a procedure for assessing the potential impacts of land-use/land-cover on ecosystem services considering the methodological limitations of the respective monitoring. The presented approach is easy to understand, to modify and to adapt to other situations and might be therefore used in other context of decision support. It might also help to fill the gap between land use planning and numeric modeling using very complex tools.

A meta-analysis of molecular marker genetic datasets for eastern Africa trees supports the utility of potential natural vegetation maps for planning climate-smart restoration initiatives

Forest and woodland landscape restoration is a key undertaking of renewed interest for forestry and conservation practitioners, but is hampered by the lack of information on the distributions of tree species and of patterns of intra-specific genetic variation. Through the first meta-analysis of its type, we here tested the utility of a high-resolution potential natural vegetation (PNV) map for eastern Africa (vegetationmap4africa) for supporting restoration activities by comparison with 20 molecular marker genetic datasets, identified through literature review and other sources, for ten indigenous tree species. Our analysis indicated that site suitability and stability values from PNV-based ecological niche modelling involving current and past climate scenarios were positively related to population genetic diversity values revealed by molecular markers, supporting the value of PNV maps for the practical planning of restoration activities accounting for anthropogenic climate change. Furthermore, population pairwise genetic divergence was strongly positively correlated with population pairwise geographic distances for most datasets, indicating generalizable sampling implications for tree genetic resource conservation in the region. Population pairwise genetic divergence was however not well explained by sampling across PNV and wider physiognomic types, possibly due to molecular markers’ adaptive neutrality and high rates of recombination in trees, among other factors. Patterns of neutral molecular marker variation are thus no substitute for trials of adaptive variation for confirming or refuting the utility of vegetation boundaries in defining tree planting zones. We discuss the importance of results for eastern Africa and more widely.

Current and Future Fire Regimes and Their Influence on Natural Vegetation in Ethiopia

Fire is a major factor shaping the distribution of vegetation types. In this study, we used a recent high resolution map of potential natural vegetation (PNV) types and MODIS fire products to model and investigate the importance of fire as driver of vegetation distribution patterns in Ethiopia. We employed statistical modeling techniques to estimate the distribution of fire and the PNVs under current climatic conditions, and used the calibrated models to project distributions for different climate change scenarios. Results show a clear congruence between distribution patterns of fire and major vegetation types. The effect of climate change varies considerably between climate change models and scenarios, but as general trend expansions of moist Afromontane forest and Combretum–Terminalia woodlands were predicted. Fire-prone areas were also predicted to increase, and including this factor in vegetation distribution models resulted in stronger expansion of Combretum–Terminalia woodlands and a more limited increase of moist Afromontane forests. These results underline the importance of fire as a regulating factor of vegetation distribution patterns, and how fire needs to be factored into predict the possible effects of climate change. For conservation strategies to effectively address conservation challenges caused by rapid climate shifts, it is imperative that they not only consider the direct influence of climate changes on the vegetation, species species, or biodiversity patterns, but also the influence of future fire regimes.

Methodical basis for Map of Potential Natural Vegetation of Poland

The guiding principles, the method of field record and method of preparation of the map and the legend are discussad. The experienoc concerning the draft sheet just publishad and its expected importance to the science and practice are considered. Differentiation of the vegetation of Poland is presented by the surface method in a final scale l : M, with a later posible mutation to l : 500 000.

Indicators to monitor the structural diversity of landscapes

An important level of biodiversity, alongside the diversity of genes and species, is the diversity of ecosystems and landscapes. In this contribution an indicator system is proposed to measure natural diversity (relief, soils, waters), cultural diversity (main land use classes, diversity of land use, ecotones, connectivity) and anthropogenic impacts (fragmentation, hemeroby, protection). The contribution gives an overview of various indicators on landscape diversity and heterogeneity currently used in Germany and Europe. Based on these indicators a complementary system, is presented. The indicators introduced here are derived from regular evaluations of the digital basis landscape model (Basic DLM) of the Authoritative Topographic-Cartographic Information System (ATKIS), the digital land cover model for Germany (LBM-DE) as well as other supplementary data such as the mapping of potential natural vegetation. With the proposed indicators it is possible to estimate cumulative land-use change and its impact on the environmental status and biodiversity, so that existing indicator systems are supplemented with meaningful additional information. Investigations have shown that indicators on forest fragmentation, hemeroby or ecotones can be derived from official geodata. As such geodata is regularly updated, trends in indicator values can be quickly identified. Large regional differences in the distribution of the proposed indicators have been confirmed, thereby revealing deficits and identifying those regions with a high potential for biodiversity. The indicators will be successively integrated into the web-based land-use monitor (http://www.ioer-monitor.de), which is freely available for public use.

Indicators of hemeroby for the monitoring of landscapes in Germany

The article discusses the concepts of “closeness to nature” and “hemeroby”, and outlines a method to establish two indicators of hemeroby. Until now Germany's national land use monitoring systems have lacked an indicator to capture the naturalness respectively hemeroby of the landscape. Based on digital spatial data on land use (DLM-DE) and the mapping of potential natural vegetation, these indicators have now been estimated for the whole of Germany and illustrated cartographically. The indicators have been integrated into a land use monitoring system (IOER-Monitor). A hemeroby index that considers all hemeroby classes of a reference area (e.g. administrative unit and regular grid cell) is presented as well as an indicator named “Proportion of certain natural areas”. The results on hemeroby of several time-cuts can be used to estimate the cumulative impact of land use changes on the environmental status.

The map of potential natural vegetation as a basis for comparative studies and geobotanical regionalization

A method of typology and regionalization of landscape units of vegetation on the basis of a map of potential natural vegetation is presented. Particular attention has been paid to the methodical basis of the conducted division. The map of potential natural vegetation was subsequently compared with maps of other elements of the natural environment and the result of geobotanical botanical regionalization was compared to the complex physicogeographical regionalization.

Could green-oak woodlands thrive in the south of Navarre? An approximation through species distribution modelling

Peralta, J., Zepeda, N.A., Imbert, J.B. 2013. . 2013. Could green-oak woodlands thrive in the south of Navarre? An approximation through species distribution modelling. Ecosistemas 22(3):58-65. Doi.: 10.7818/ECOS.2013.22-3.09. According to some potential vegetation maps, various climatic and edaphic factors limit the suitable habitat for green-oak woodlands ( Quercus rotundifolia ) in the South of Navarre (NE Spain). This area has been used for agriculture and livestock since ancient times, making it difficult to assess whether environmental or anthropic factors are the cause of the scarcity of green-oak woodlands. To try to answer this question green-oak distribution models were built with Maxent and generalized linear models (GLM). Their outcome was then compared with potential vegetation maps and previous models built for the Iberian Peninsula applied to data from Navarre. The environmental variables used to build the models were August mean rainfall, continentality, January mean maximum temperature, January irradiation, slope and soil development; all were significant in the GLM analysis. The variable that most contributed to Maxent model was August mean rainfall. Predictions of spatial distributions of green-oak woodlands by models and maps mainly differ for the north-eastern and southern range of Navarre. Our models and the pre-existent ones suggest that the studied environmental variables do not limit the habitat suitability for green-oak woodlands at least in some areas of southern Navarre, which were not considered in traditional potential natural vegetation maps.

The TRM Model of Potential Natural Vegetation in Mountain Forests

Due to advances in spatial modeling and improved availability of digital geodata, traditional mapping of potential natural vegetation (PNV) can be replaced by ecological modeling approaches. We developed a new model to map forest types representing the potential natural forest vegetation in the Bavarian Alps. The TRM model is founded on a three-dimensional system of the ecological gradients temperature (T), soil reaction (R), and soil moisture (M). Within such a “site cube” forest types are defined as homogenous site units that give rise to forest communities with comparable species composition, structure, production and protective functions. The three gradients were modeled using regression algorithms with area-wide, high resolution geodata on climate, relief and soil as predictors and average Ellenberg indicator values for temperature, acidity and moisture of vegetation plots as dependent variables summarizing plant responses to ecological gradients. The resulting predictor-response relationships allowed us to predict gradient positions of each raster cell in the region from geodata layers. The three-dimensional system of gradients was partitioned into 26 forest types, which can be mapped for the whole region. TRM-based units are supplemented by 22 forest types of special sites defined by other ecological factors such as geomorphology, for which individual GIS rules were developed. The application of our model results in an intermediate-scale map of potential natural forest vegetation, which is based on an explicit function of temperature, reaction and moisture and is therefore consistent and repeatable in contrast to traditional PNV maps.

Bioclimatic and vegetation mapping of a topographically complex oceanic island applying different interpolation techniques

Different spatial interpolation techniques have been applied to construct objective bioclimatic maps of La Palma, Canary Islands. Interpolation of climatic data on this topographically complex island with strong elevation and climatic gradients represents a challenge. Furthermore, meteorological stations are not evenly distributed over the island, with few stations at high elevations. We carried out spatial interpolations of the compensated thermicity index (Itc) and the annual ombrothermic Index (Io), in order to obtain appropriate bioclimatic maps by using automatic interpolation procedures, and to establish their relation to potential vegetation units for constructing a climatophilous potential natural vegetation map (CPNV). For this purpose, we used five interpolation techniques implemented in a GIS: inverse distance weighting (IDW), ordinary kriging (OK), ordinary cokriging (OCK), multiple linear regression (MLR) and MLR followed by ordinary kriging of the regression residuals. Two topographic variables (elevation and aspect), derived from a high-resolution digital elevation model (DEM), were included in OCK and MLR. The accuracy of the interpolation techniques was examined by the results of the error statistics of test data derived from comparison of the predicted and measured values. Best results for both bioclimatic indices were obtained with the MLR method with interpolation of the residuals showing the highest R2 of the regression between observed and predicted values and lowest values of root mean square errors. MLR with correction of interpolated residuals is an attractive interpolation method for bioclimatic mapping on this oceanic island since it permits one to fully account for easily available geographic information but also takes into account local variation of climatic data.

Improving Transboundary Maps of Potential Natural Vegetation Using Statistical Modeling Based on Environmental Predictors

The potential natural vegetation (PNV) is a tool for landscape planning, nature preservation and the assessment of naturalness. It is mostly constructed by the application of expert knowledge. This paper shows the advantages of using a more sophisticated and formalized PNV construction that overlays vegetation types and site factor maps by applying a Bayes model and herewith improving existing PNV maps solely based on expert knowledge. The investigation was conducted in the forest complex of the Bavarian Forest National Park (Germany) and the adjacent Sumava National Park (Czech Republic). The project reached two major results: (1) The existing heterogeneous country-specific databases of natural site conditions and of vegetation types could be adapted to each other to construct a solid scientific basis to deduce a PNV map. The habitat requirements of the occurring harmonized vegetation types can now be quantitatively described in a formalized way. (2) The combination of terrestrial PNV mapping and numerical modeling allows the synthesis of the views of the different experts that generated the maps used for model calibration. However, the modeled map loses the details of the expert-based map that cannot be derived from the underlying site maps. A common modeled PNV map of both national parks covering an area of about 92,000 ha was created. While the former expert-based PNV maps display breaks along the country border, the modeled PNV presents a harmonized view based on the common database of both national parks.

An objective methodology for potential vegetation reconstruction constrained by climate

Reconstructions of modern Potential Natural Vegetation (PNV) are widely used in climate modelling and vegetation survey as a starting point for studies (historical changes of land-use, past or future vegetation distribution modelling, etc.). A PNV distribution is often related to vegetation models, which are based on empirical relationships between vegetation (or pollen data in paleoecological studies) and climate. Vegetation models are used to directly simulate a PNV distribution or to correct vegetation types derived from remotely-sensed observations in human-impacted regions. Consequently, these methods are quite subjective and include biases from models. This article proposes a new approach to build a high-resolution PNV map using a statistical model.As vegetation is a nominal variable, our method consists in applying a multinomial logistic regression (MLR). MLR build statistical relationships between BIOME 6000 data covering Europe and several climatological variables from the Climate Research Unit (CRU).The PNV reconstructed by MLR appears similar to those reconstructed from remotely-sensed data or simulated by a vegetation model (BIOME 4) except in southern Europe with the establishment of warm-temperate forests. MLR produces a realistic PNV distribution, which is the closest to BIOME 6000 data and provides the vegetation distribution in each grid-cell of our map. Moreover, MLR allows us to compute an uncertainty index that appears as a convenient tool to highlight the regions lacking some data toimprove the PNV distribution. The MLR method does not suffer any dynamic biases or subjective corrections and is a fast and objective alternative to the other methods. MLR provides an independent reference for vegetation models that is entirely based on vegetation and climatological data.