Biome 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.

Distribution of Sarcophagidae (Diptera, Oestroidea) in Brazilian biomes: richness, endemism, and sampling gaps

ABSTRACT Sarcophagid experts have made several efforts to associate biodiversity data and comprehend where each species occurs, but comprehensive faunal inventories remain scarce. Our aim was to provide a list of distributional patterns and endemic species and allow assessment of the sampling effort conducted within Brazilian biomes. We produced a dataset of Brazilian sarcophagids and overlaid with a biome map, to investigate distributional patterns, endemism and to build species accumulation curves. Additionally, we calculated nonparametric asymptotic species richness estimators and extrapolation of species diversity (Hill numbers). Our dataset comprised 288 sarcophagid species, which 21 were identified as endemic. The biomes with the highest species richness were the Atlantic Rainforest and the Amazon Forest, and no biome exhibited a stabilized asymptotic curve. This is the first proposal of listing Sarcophagidae species by biomes and essential to understand the spatial distribution of this family in Brazil. We present maps and richness estimators that allow identifying gaps and guiding survey planning.

How to map biomes: Quantitative comparison and review of biome‐mapping methods

AbstractBiomes are large‐scale ecosystems occupying large spaces. The biome concept should theoretically facilitate scientific synthesis of global‐scale studies of the past, present, and future biosphere. However, there is neither a consensus biome map nor universally accepted definition of terrestrial biomes, making joint interpretation and comparison of biome‐related studies difficult. “Desert,” “rainforest,” “tundra,” “grassland,” or “savanna,” while widely used terms in common language, have multiple definitions and no universally accepted spatial distribution. Fit‐for‐purpose classification schemes are necessary, so multiple biome‐mapping methods should for now co‐exist. In this review, we compare biome‐mapping methods, first conceptually, then quantitatively. To facilitate the description of the diversity of approaches, we group the extant diversity of past, present, and future global‐scale biome‐mapping methods into three main families that differ by the feature captured, the mapping technique, and the nature of observation used: (1) compilation biome maps from expert elicitation, (2) functional biome maps from vegetation physiognomy, and (3) simulated biome maps from vegetation modeling. We design a protocol to measure and quantify spatially the pairwise agreement between biome maps. We then illustrate the use of such a protocol with a real‐world application by investigating the potential ecological drivers of disagreement between four broadly used, modern global biome maps. In this example, we quantify that the strongest disagreement among biome maps generally occurs in landscapes altered by human activities and moderately covered by vegetation. Such disagreements are sources of bias when combining several biome classifications. When aiming to produce realistic biome maps, biases could be minimized by promoting schemes using observations rather than predictions, while simultaneously considering the effect of humans and other ecosystem engineers in the definition. Throughout this review, we provide comparison and decision tools to navigate the diversity of approaches to encourage a more effective use of the biome concept.

Biome classification influences current and projected future biome distributions

AbstractAimBiome classification schemes are widely used to map biogeographic patterns of vegetation formations on large spatial scales. Future climate change will influence biome patterns, and vegetation models can be used to assess the susceptibility of biomes to experience transitions. However, biome classification is not unique, and various classification schemes and biome maps exist. Here, we aimed to assess how the choice of biome classification schemes influences current and projected future biome patterns.LocationAfrica, Australia, Tropical Asia.Time period2000–2099.Major taxa studiedTropical vegetation.MethodsWe used the adaptive dynamic global vegetation model version 2 (aDGVM2) to simulate vegetation in the study region. We classified vegetation into biomes using (1) a classification scheme based on the cover of functional types, (2) a cluster analysis based on the cover of functional types and (3) a cluster analysis based on trait patterns simulated by the aDGVM2. We compared the resulting biome maps to multiple observation‐based biome maps and quantified differences in projected biome changes under the RCP8.5 scenario for the different classification schemes.ResultsAs expected, biome patterns were strongly related to the scheme used for biome classification. The highest data‐model agreement was derived for a cluster analysis using 21 simulated traits. Traits related to size were most important for classification. Considering all classification schemes, the area projected to undergo biome transitions under climate change varied between 16.5% and 32.1%. Despite this variability, different schemes consistently showed that grassland and savanna areas are most susceptible to climate change, whereas tropical forests and deserts are stable. Our results demonstrate that traits simulated by aDGVM2 are appropriate to delimit biomes.Main conclusionsStudies projecting biome patterns and transitions under current and future climate should consider applying different biome classification schemes to avoid biases in such projections caused by biome classification schemes.

Ecological assessment of Iran’s terrestrial biomes for wildlife conservation

Man-made activities pose the greatest threats to wildlife in Iran's terrestrial biomes, causing significant habitat damage and fragmentation in recent years. To fully understand these threats, the present study was conducted to identify and map the Iran’s terrestrial biomes using the IDRISI TerrSet 18.31 Software, the Terrestrial Biomes Ecosystem Service Modeler on the InVEST toolkit (TBESMI), and comprehensive data sources including maps of roads, protected areas, terrestrial biomes, and country-wide land cover maps of 2017. The results showed that the largest terrestrial biome in Iran is deserts and xeric shrublands (DXS), while flooded grasslands and savannas (FGS) is the smallest biome. Roads, along with urban and agricultural developments are among the biggest threats and most destructive stressors in Iran’s terrestrial biomes. The results also revealed that there was a growth in destruction of habitats located in the temperate broadleaf and mixed forest (TBMF), temperate coniferous forest (TCF), and FGS, alongside a decrease in the DXS biome. Furthermore, we detected an increase in habitat landscape quality in the DXS, FGS and montane grasslands and shrub lands (MGS), and a decrease in the temperate grasslands, savannas and shrublands (TGSS) and TBMF biomes. Finally, the cumulative risk of habitat degradation increased in the FGS, TCF, TGSS, and TBMF biomes, whereas it decreased in the DXS biome. The FGS biome with the highest consequence cumulative score, and the MGS biome with the highest cumulative risk exposure score were found to be at the highest risk from man-made activities. Stressors associated with agriculture and urbanization had the highest cumulative exposure scores in the MGS, while roads had the highest exposure scores in the TBMF and DXS biomes. Our study underscores the critical importance of conserving Iran's terrestrial biomes and wildlife, especially in high-risk biomes like FGS and MGS, given the substantial threats posed by human activities.

Distribution of Poisonous Plants in Biomes of the Southern Far East of Russia

Poisonous plants are a collective group of plants of various systematic categories that contain phytotoxins that pose a potential danger to humans and animals. A number of publications both in Russia and abroad are devoted to the problem of patterns of distribution of plants hazardous to human health in connection with environmental factors. This work is a continuation of research into the spread of plants dangerous to humans in Russia. The aim of this work is ecological and geographical analysis of poisonous plants distribution in biomes of the Far East in Russia. Resulted from the research work on the territory of the far-eastern biomes of Russia we revealed 87 the most toxic vascular plant species that belong to 21 plant families. Some of the most poisonous plants of the Russian flora are plants of the genus Aconitum, of the family (Ranunculaceae). About 70 species of this genus grow in Russia, of which 40 are found only in the Far East, since this territory is considered the center of botanical diversity of the genus in question. The cartographical analysis showed that the highest number of poisonous plant species could be found in the south-east regions – in Primorye, the basin of Ussuri river, in the lower and middle course of the river Amur. Based on a map of Russian biomes, optimal habitats for poisonous plants were identified. Maximum number of Aconitum species, as the most poisonous genus of Russian flora, is in the mountain biomes and in the plain forest-steppe biomes in the Amur basin. The types of ecosystems with the maximum abundance and diversity of poisonous plants have been identified. The species richness distribution shows the concentration of poisonous plant species in small mountain biomes and in arid-like biomes. Correlation analysis of relationships between the number of plant species and climatic factors revealed the significant closeness of the correlation with the average annual air temperature (0,66). Maximum correlation closeness appeared to be between number of poisonous plant species per 10 000 km2 and total number of vascular plants per 10 000 km2 (0,81).

Биомная дифференциация российской Арктики

The article discusses various aspects of the biome division of the Russian Arctic and its significance for assessing climatic and anthropogenic changes in ecosystems. Different authors base the biome differentiation of the Arctic on the zonal-provincial principle, which takes into account the latitudinal temperature gradient and the meridional gradient of the continentality of the climate. As a result, from 6 to 20 or more biomes are allocated directly for the Russian Arctic to assess conveniently biodiversity, the response of ecosystems to climate change, to form a representative network of protected natural areas, etc. To characterize the climate and biodiversity, the authors use the schemes of biome division of the Russian Arctic, presented on the existing versions of maps of Russian biomes [10; 18]. They give quantitative characteristics of biomes that are important in relation to the regulation of economic activity and optimization of the territorial protection of biota and ecosystems in the Arctic.

Predicting global terrestrial biomes with the LeNet convolutional neural network

Abstract. A biome is a major regional ecological community characterized by distinctive life forms and principal plants. Many empirical schemes such as the Holdridge life zone (HLZ) system have been proposed and implemented to predict the global distribution of terrestrial biomes. Knowledge of physiological climatic limits has been employed to predict biomes, resulting in more precise simulation; however, this requires different sets of physiological limits for different vegetation classification schemes. Here, we demonstrate an accurate and practical method to construct empirical models for biome mapping: a convolutional neural network (CNN) was trained by an observation-based biome map, as well as images depicting air temperature and precipitation. Unlike previous approaches, which require assumption(s) of environmental constrain for each biome, this method automatically extracts non-linear seasonal patterns of climatic variables that are relevant in biome classification. The trained model accurately simulated a global map of current terrestrial biome distribution. Then, the trained model was applied to climate scenarios toward the end of the 21st century, predicting a significant shift in global biome distribution with rapid warming trends. Our results demonstrate that the proposed CNN approach can provide an efficient and objective method to generate preliminary estimations of the impact of climate change on biome distribution. Moreover, we anticipate that our approach could provide a basis for more general implementations to build empirical models of other climate-driven categorical phenomena.

Biome-specific distribution of Ni-containing carbon monoxide dehydrogenases

Ni-containing carbon monoxide dehydrogenase (Ni-CODH) plays an important role in the CO/CO2-based carbon and energy metabolism of microbiomes. Ni-CODH is classified into distinct phylogenetic clades, A–G, with possibly distinct cellular roles. However, the types of Ni-CODH clade used by organisms in different microbiomes are unknown. Here, we conducted a metagenomic survey of a protein database to determine the relationship between the phylogeny and biome distribution of Ni-CODHs. Clustering and phylogenetic analyses showed that the metagenome assembly-derived Ni-CODH sequences were distributed in ~ 60% Ni-CODH clusters and in all Ni-CODH clades. We also identified a novel Ni-CODH clade, clade H. Biome mapping on the Ni-CODH phylogenetic tree revealed that Ni-CODHs of almost all the clades were found in natural aquatic environmental and engineered samples, whereas those of specific subclades were found only in host-associated samples. These results are comparable with our finding that the diversity in the phylum-level taxonomy of host-associated Ni-CODH owners is statistically different from those of the other biomes. Our findings suggest that while Ni-CODH is a ubiquitous enzyme produced across diverse microbiomes, its distribution in each clade is biased and mainly affected by the distinct composition of microbiomes.

Forest biomes of Southern Africa

ABSTRACT The forests of South Africa and the neighbouring countries, including Lesotho, eSwatini, Namibia, Botswana, Zimbabwe, and Mozambique (south of the Zambezi River), were mapped and classified according to the global system of biomes. The new four-tier hierarchical biome system suggested in this paper includes zonobiome, global biome, continental biome (all recognised earlier), and regional biome – a novel biome category. The existing spatial coverages of the forests were revised and considerably improved, both in terms of forest-patch coverage and mapping precision. Southern Africa is home to three zonal forest types, namely Subtropical Forests (Zonobiome I), Tropical Dry Forest (TDF; Zonobiome II) and Afrotemperate Forests (Zonobiome X). These three biomes are characterised by unique bioclimatic envelopes. Five, two, and eight regional biomes, respectively, have been recognised within these zonal biomes. Recognition of the Zonobiome I and the global biome Tropical Dry Forests in southern Africa is novel and expands our knowledge of the biome structure of African biotic communities. The system of the azonal regional biomes is also new and comprehensively covers the variability of the azonal helobiomes (riparian woodlands and swamp forests), mangroves, and azonal coastal forests. In total, 11 azonal regional biomes have been recognised in the study area. The forest biomes in southern Africa were captured in our electronic map in the form of more than 60 000 polygons, covering 42 416 km2 (1.27% of the study area). No less than 83% of these forests occur in the territory of southern Mozambique. Abbreviations: for the abbreviation of the biome units, see Table 1; CE: centre of endemism; IOCB: Indian Ocean Coastal Belt; MBSA: the area of the Map of Biomes of Southern Africa; VegMap2006 and VegMap2018: Vegetation Map of South Africa, Lesotho and Swaziland (as released in respective years); for the meaning of the codes of the biome units see Table 1, and for the meaning of the abbreviations of climatic characteristics see Appendix S1

An operational definition of the biome for global change research.

Biomes are constructs for organising knowledge on the structure and functioning of the world's ecosystems, and serve as useful units for monitoring how the biosphere responds to anthropogenic drivers, including climate change. The current practice of delimiting biomes relies on expert knowledge. Recent studies have questioned the value of such biome maps for comparative ecology and global-change research, partly due to their subjective origin. Here we propose a flexible method for developing biome maps objectively. The method uses range modelling of several thousands of plant species to reveal spatial attractors for different growth-form assemblages that define biomes. The workflow is illustrated using distribution data from 23500 African plant species. In an example application, we create a biome map for Africa and use the fitted species models to project biome shifts. In a second example, we map gradients of growth-form suitability that can be used to identify sites for comparative ecology. This method provides a flexible framework that (1) allows a range of biome types to be defined according to user needs and (2) enables projections of biome changes that emerge purely from the individualistic responses of plant species to environmental changes.

How can we improve investigation, prevention and treatment for recurrent urinary tract infections - ICI-RS 2018.

Recurrent urinary tract infection (rUTI) is a chronic condition and has a significant impact on health-related quality of life. The commonly used definition for rUTI is greater than three episodes in a year or more than two in 6 months. Current diagnostic methods have been used worldwide for over five decades, despite well evidenced criticism. Enhanced culture techniques demonstrate that the microbiome of the bladder is far more complex than previously thought and begs a reappraisal of our current testing. Treatment of rUTI is based on a small number of antibiotic trials with some evidence showing a reduction in the number of positive cultures, but one must be cautious in interpreting the results and weigh against the risk of generation of antimicrobial resistance (AMR). The International Consultation on Incontinence-Research Society think tank reviewed the literature with a view to improving investigation, prevention and treatment of rUTI. A multidisciplinary team of experts were invited to present evidence regarding the current diagnostic methods, recent advances related to bladder biome mapping and current treatment strategies, including antibiotic and nonantibiotic options. Current guidelines regarding antibiotic stewardship and concerns regarding AMR were discussed. Outcome of the think tank discussions are summarised with a set of recommendations to inform future research. Particular consideration is given to bacterial survival in the bladder after treatment as well as defects in urothelial barrier function which may play a significant part in the failure to eradicate UTI.

Spatial and Seasonal Patterns in Vegetation Growth-Limiting Factors over Europe

Water and energy are recognized as the most influential climatic vegetation growth-limiting factors. These factors are usually measured from ground meteorological stations. However, since both vary in space, time, and scale, they can be assessed by satellite-derived biophysical indicators. Energy, represented by land surface temperature (LST), is assumed to resemble air temperature; and water availability, related to precipitation, is represented by the normalized difference vegetation index (NDVI). It is hypothesized that positive correlations between LST and NDVI indicate energy-limited conditions, while negative correlations indicate water-limited conditions. The current project aimed to quantify the spatial and seasonal (spring and summer) distributions of LST–NDVI relations over Europe, using long-term (2000–2017) MODIS images. Overlaying the LST–NDVI relations on the European biome map revealed that relations between LST and NDVI were highly diverse among the various biomes and throughout the entire study period (March–August). During the spring season (March–May), 80% of the European domain, across all biomes, showed the dominance of significant positive relations. However, during the summer season (June–August), most of the biomes—except the northern ones—turned to negative correlation. This study demonstrates that the drought/vegetation/stress spectral indices, based on the prevalent hypothesis of an inverse LST–NDVI correlation, are spatially and temporally dependent. These negative correlations are not valid in regions where energy is the limiting factor (e.g., in the drier regions in the southern and eastern extents of the domain) or during specific periods of the year (e.g., the spring season). Consequently, it is essential to re-examine this assumption and restrict applications of such an approach only to areas and periods in which negative correlations are observed. Predicted climate change will lead to an increase in temperature in the coming decades (i.e., increased LST), as well as a complex pattern of precipitation changes (i.e., changes of NDVI). Thus shifts in plant species locations are expected to cause a redistribution of biomes.

Plant wax integration and transport from the Mississippi River Basin to the Gulf of Mexico inferred from GIS-enabled isoscapes and mixing models

Understanding the fate of terrestrial plant waxes from source to sink is critical for improving paleoclimate interpretations from sedimentary plant waxes. However, there is limited knowledge about the controls on plant wax integration and transport in large catchments with multiple biomes and climates. To address this lack of understanding, we investigated the vegetation and climatic controls on plant wax integration and transport from the Mississippi River Basin (MRB), the largest river in the U.S., to the Gulf of Mexico (GOM). We first estimated the geographic distribution of n-alkane carbon (δ13Calk) and hydrogen (δ2Halk) isotopic compositions (i.e. isoscapes) in the MRB using plant isotope fractionation calibrations from North America and similar climate regions for the pre-industrial. We utilized two different vegetation maps (i.e. biome map and %C4 map) and two plant isotope fractionation estimation approaches, and discussed advantages and disadvantages of each method. Then, we developed mixing models weighting the isotope values by biological and climatic parameters (i.e. vegetation area, n-alkane concentration by chain lengths, net primary productivity (NPP), and runoff) and their combinations to test the sensitivity of basin-integrated plant wax isotopic compositions to these variables. Our approach does not factor in soil stocks of plant wax nor degradation in transit, as future work will need to constrain these factors for the MRB for the period of interest. Vegetation area weighting alone predicted relatively high C4 plant contributions to the pool of waxes exported from the basin. When n-alkane concentration in leaves, NPP, or runoff was considered, the contribution of forest-derived plant waxes increased. Sensitivity of plant wax isotopic compositions to productivity and transport efficiency varied among models and chain lengths. For example, n-C29 alkanes were sensitive to plant wax contribution increases from forests whereas n-C33 alkanes were more sensitive to increases in C4 grassland productivity. The plant wax concentration and NPP were combined as an estimate for total wax productivity, and a combination of this total wax productivity and runoff were used to simulate both production and transport. These multi-parameter models estimated the highest plant wax contribution from C3 forests because these areas were calculated to have the highest productivity and greatest runoff. Despite large differences in δ13Calk and δ2Halk values across the MRB, variation in the predicted δ13C and δ2H of exported n-alkanes is small across all models. This small range is consistent with a calculated predominance of plant wax export from wet and tree-dominated biomes and suggests that drier and C4 grass-dominated biomes may be under-represented in the sedimentary plant wax record.

Modelling and mapping soil organic carbon stocks in Brazil

Brazil has extensive forests and savannas on deep weathered soils and plays a key role in the discussions about carbon sequestration, but the distribution of soil organic carbon (SOC) stocks up to 1 m depth has not been investigated in Brazil using machine learning techniques. In this study, we applied a methodological framework to optimize the prediction of SOC stocks for the entire Brazilian territory and determine how the environmental heterogeneity of Brazil influences the SOC stocks distribution. We used a legacy dataset of 8227 soil profiles which consisted of 37,693 samples. For each profile, the vertical distribution of SOC and bulk density were interpolated to standard depths (0–5, 5–15, 15–30, 30–60 and 60–100 cm) using mass preserving equal-area quadratic splines. The covariates database was composed of 74 variables including bioclimatic (temperature and precipitation) data, soil and biome maps, vegetation indexes and morphometric maps derived from a digital elevation model, with a 1 km spatial resolution. To obtain the best prediction performance, we tested four machine learning algorithms: Random Forests, Cubist, Generalized Linear Model Boosting and Support Vector Machines. Random Forests showed the best performance in predicting SOC stocks for all depths, with the highest performance at 30–60 cm for training (R2 = 0.32) and validation (R2 = 0.33); hence, it was selected for the spatial prediction of SOC stocks. The most important covariates selected by Random Forests using the recursive feature elimination were: soil class, sum of monthly mean temperature (SAMT), precipitation, slope height and vegetation indexes (NDVI, GPP). In total, Brazilian soils store approximately 71.3 PgC within the top 100 cm, where the first 0–30 cm contains almost 36 PgC. Approximately 31% of the total SOC stocks (22.2 PgC) occurs in protected areas (2.6 million km2), which are not subjected to land use pressure and carbon losses. Although the Amazon biome has the highest amount of stored SOC (36.1 PgC), its soils do not represent a good potential for carbon accumulation. Among soil classes, the Luvisols showed the lowest SOC density (6.45 kg m−2) and the Histosols presented the highest values (14.87 kg m−2). More than 57% of the total SOC was found in nutrient-poor, deep-weathered Ferralsols and Acrisols, which are the dominant soils in Brazil. The presented methodological framework covers all steps of prediction process, building maps with known accuracy and has great potential to be used in future soil carbon inventories at large scales. Concerning conservation issues, the results highlight the importance of nature reserves for protecting SOC in the long-term.

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.

Enhanced representation of soil NO emissions in the Community Multiscale Air Quality (CMAQ) model version 5.0.2

Abstract. Modeling of soil nitric oxide (NO) emissions is highly uncertain and may misrepresent its spatial and temporal distribution. This study builds upon a recently introduced parameterization to improve the timing and spatial distribution of soil NO emission estimates in the Community Multiscale Air Quality (CMAQ) model. The parameterization considers soil parameters, meteorology, land use, and mineral nitrogen (N) availability to estimate NO emissions. We incorporate daily year-specific fertilizer data from the Environmental Policy Integrated Climate (EPIC) agricultural model to replace the annual generic data of the initial parameterization, and use a 12 km resolution soil biome map over the continental USA. CMAQ modeling for July 2011 shows slight differences in model performance in simulating fine particulate matter and ozone from Interagency Monitoring of Protected Visual Environments (IMPROVE) and Clean Air Status and Trends Network (CASTNET) sites and NO2 columns from Ozone Monitoring Instrument (OMI) satellite retrievals. We also simulate how the change in soil NO emissions scheme affects the expected O3 response to projected emissions reductions.

Defining functional biomes and monitoring their change globally.

Biomes are important constructs for organizing understanding of how the worlds' major terrestrial ecosystems differ from one another and for monitoring change in these ecosystems. Yet existing biome classification schemes have been criticized for being overly subjective and for explicitly or implicitly invoking climate. We propose a new biome map and classification scheme that uses information on (i) an index of vegetation productivity, (ii) whether the minimum of vegetation activity is in the driest or coldest part of the year, and (iii) vegetation height. Although biomes produced on the basis of this classification show a strong spatial coherence, they show little congruence with existing biome classification schemes. Our biome map provides an alternative classification scheme for comparing the biogeochemical rates of terrestrial ecosystems. We use this new biome classification scheme to analyse the patterns of biome change observed over recent decades. Overall, 13% to 14% of analysed pixels shifted in biome state over the 30-year study period. A wide range of biome transitions were observed. For example, biomes with tall vegetation and minimum vegetation activity in the cold season shifted to higher productivity biome states. Biomes with short vegetation and low seasonality shifted to seasonally moisture-limited biome states. Our findings and method provide a new source of data for rigorously monitoring global vegetation change, analysing drivers of vegetation change and for benchmarking models of terrestrial ecosystem function.

BIODIVERSITY OF OROBIOMES IN THE NORTH CAUCASUS ON THE BIOME MAP OF RUSSIA

The aim is to introduce the concept of biogeographical map "Biomes of Russia" (m 1:.7.500000), scientific and methodological principles of the development of its content, the selection of indicators to characterize the biodiversity of the regional biomes and mark its place and role in the development of biogeography as well as biogeographical mapping and in terms of practical application as in education, in the sustainable development of the regions and for solving the problems in the field of nature and environmental protection. Materials and methods. Map is based on the classification of terrestrial ecosystems (biomes) and eco-geographical approach to their subordination on different levels of organization of the biotic cover. Results. We have displayed on the map the differentiation of the country on the composition of ecosystems and their biodiversity at the regional level. Biomes at the regional level are central to the study and are mapping units. Legend includes 35 lowland biomes and 31 orobiomes (in the mountains). A characteristic has been made up for each biome, which includes indicators of moisture and heat supply, the number of species of vascular plants, mosses and lichens, terrestrial vertebrates (mammals, birds, reptiles and amphibians). To illustrate this, there is the characteristic of the 3 orobiomes of the North Caucasus. Conclusion. Using biomes as supporting biodiversity accounting units enables the integrated analysis of botanical and zoogeographic information about biota, as well as the dual study of biotic and abiotic components of the ecosystem. The current state of biomes is defined by two interrelated processes of transformation and modification of ecosystems and the reduction or loss of biological diversity. Map of regional biomes can serve as a basis for further research on the biome biodiversity on species and ecosystem levels; as the inventory of the biota and the creation of databases on species and cenotic diversity of biomes; for the development and justification of environmental protection measures.

Ecotones – where biomes meet

news and update ISSN 1948-6596 commentary Ecotones – where biomes meet The classification of biomes (or vegetation types) represents one of the most basic summaries of ecological units and provides part of the basis of biogeography. The first systematic continen- tal biome map was a map of Africa produced by Schantz in 1923 (Kuchler 1960). Since then, bi- ome mapping has continually developed to in- form biodiversity sciences. Initially such devel- opments were mainly in the form of expert mapping, where vegetation scientists with good knowledge of the area being mapped, and often drawing on actual plant community data, drew the boundaries of biomes (Kent 2012). More recently, ever more reliance has been put on remote-sensing data, where algo- rithms are used to interpret satellite imagery to classify biome distributions (and other land use types; Xie et al. 2008). With the concomitant development of other large-scale spatial datasets, such as of climate and soil, there has been an increased interest in the drivers of biome distribution, or elements of vegetation such as woody cover, at macroecological scales (e.g., Stephenson 1990, Greve et al. 2011, Campo-Bescos et al. 2013). Understanding what drives biome distribution is especially important in the face of global change, in response to which the distribution of biomes may be expected to shift. It has been suggested that if such shifts are to take place, the transition zones between vegetation types, i.e. the ecotones, would be most suitable for monitoring such changes (e.g., Berner et al. 2013; though see Kupfer and Cairns 1996). Eco- tones are often also areas of high richness as they may harbour species from different bi- omes (e.g., van Rensburg et al. 2009), and may be important areas of gene flow and hybridiza- tion (Kark and van Rensburg 2006). Against this background of the im- portance of ecotones, and a lack of attention historically afforded them (Kark and van Rens- burg 2006), it is exciting that the South African Journal of Botany will shortly be releasing a special issue dealing with “biome boundaries” in the South African context (Potts et al. 2015a). South Africa harbours a particularly high diversity of biomes (Mucina and Ruther- ford 2006), with some regions of high biome turnover across relatively small geographic dis- tances (Fig. 1). This, together with the rich his- tory for biome research in the country (Potts et al. 2015a), makes the region a natural labora- tory to understand ecotones and their drivers. The special issue presents work conduct- ed at a range of spatial scales (Potts et al. 2015a), ranging from transplant experiments across biome boundaries (Esler et al. 2015) to works conducted across all biomes in the coun- try. One of the country-scale studies examines the use and limitations of dynamic vegetation models in understanding the shifts of biome boundaries in response to climate change (Moncrieff et al. 2015), while another examines the extent to which ecotones can be also used to differentiate between alien plant composi- tion (Rouget et al. 2015). Other studies look at the role of soils in determining biome distribution (reviewed in Potts et al. 2015a). One of these used soil car- bon isotope analyses to ask whether soil type might drive biome boundaries in a grassland- forest mosaic (Gray and Bond 2015). By exam- ining the depth profile of carbon isotopes they concluded the opposite: that biome affects soil carbon. In the fire-prone grasslands, fires de- plete nutrients, while nutrient build-up hap- pens in forests, which are protected from fires. In this system, disturbances, rather than the abiotic environment, drive the position of eco- tones. A number of papers in the special issue look at changes in vegetation structure and boundaries through time using repeat aerial and ground-based photography (reviewed in Potts et al. 2015a). These studies, which could illustrate how shifts in biome boundaries occur (e.g., Masubelele et al. 2015), illustrate the im- frontiers of biogeography 7.3, 2015 — © 2015 the authors; journal compilation © 2015 The International Biogeography Society