A new practical tool for deriving a functional signature for herbaceous vegetation

Hypothesis: For any one time and place a ‘functional signature’ can be derived for a sample of herbaceous vegetation in a way that concisely represents the balance between the different clusters of functional attributes that are present among component species. Methods: We developed a spreadsheet‐based tool for calculating functional signatures within the context of the C‐S‐R system of plant functional types. We used the tool to calculate and compare signatures for specimen British vegetation samples which differed in management regime and location in time. Conclusion: The integrative power of the ‘C‐S‐R signature’ is useful in comparative studies involving widely differing samples. Movements in the signature can be used to indicate degree of resistance, resilience, eutrophication and dereliction. Systems of plant functional types other than C‐S‐R might also be approached in this way. Availability: The tool can be downloaded free of charge from the first author's web pages or from the journal's electronic archive.     

MODISTools – downloading and processing MODIS remotely sensed data in R

Remotely sensed data – available at medium to high resolution across global spatial and temporal scales – are a valuable resource for ecologists. In particular, products from NASA's MODerate‐resolution Imaging Spectroradiometer (MODIS), providing twice‐daily global coverage, have been widely used for ecological applications. We present MODISTools, an R package designed to improve the accessing, downloading, and processing of remotely sensed MODIS data. MODISTools automates the process of data downloading and processing from any number of locations, time periods, and MODIS products. This automation reduces the risk of human error, and the researcher effort required compared to manual per‐location downloads. The package will be particularly useful for ecological studies that include multiple sites, such as meta‐analyses, observation networks, and globally distributed experiments. We give examples of the simple, reproducible workflow that MODISTools provides and of the checks that are carried out in the process. The end product is in a format that is amenable to statistical modeling. We analyzed the relationship between species richness across multiple higher taxa observed at 526 sites in temperate forests and vegetation indices, measures of aboveground net primary productivity. We downloaded MODIS derived vegetation index time series for each location where the species richness had been sampled, and summarized the data into three measures: maximum time‐series value, temporal mean, and temporal variability. On average, species richness covaried positively with our vegetation index measures. Different higher taxa show different positive relationships with vegetation indices. Models had high R² values, suggesting higher taxon identity and a gradient of vegetation index together explain most of the variation in species richness in our data. MODISTools can be used on Windows, Mac, and Linux platforms, and is available from CRAN and GitHub ( https://github.com/seantuck12/MODISTools ).     

Reconstructing pre-impact vegetation cover in modified landscapes using environmental modelling, historical surveys and remnant vegetation data: a case study in the Fleurieu Peninsula, South Australia

Aim To investigate the application of environmental modelling to reconstructive mapping of pre‐impact vegetation using historical survey records and remnant vegetation data. Location The higher elevation regions of the Fleurieu Peninsula region in South Australia were selected as a case study. The Fleurieu Peninsula is an area typical of many agricultural regions in temperate Australia that have undergone massive environmental transformation since European settlement. Around 9% of the present land cover is remnant vegetation and historical survey records from the ad 1880s exist. It is a region with strong gradients in climate and topography. Methods Records of pre‐impact vegetation distribution made in surveyors’ field notebooks were transcribed into a geographical information system and the spatial and classificatory accuracy of these records was assessed. Maps of remnant vegetation distribution were obtained. Analysis was undertaken to quantify the environmental domains of historical survey record and remnant vegetation data to selected meso‐scaled climatic parameters and topo‐scaled terrain‐related indices at a 20 m resolution. An exploratory analytical procedure was used to quantify the probability of occurrence of vegetation types in environmental domains. Probability models spatially extended to geographical space produce maps of the probability of occurrence of vegetation types. Individual probability maps were combined to produce a pre‐impact vegetation map of the region. Results Surveyors’ field notebook records provide reliable information that is accurately locatable to levels of resolution such that the vegetation data can be spatially correlated with environmental variables generated on 20 m resolution environmental data sets. Historical survey records of vegetation were weakly correlated with the topo‐scaled environmental variables but were correlated with meso‐scaled climate. Remnant vegetation records similarly not only correlated to climate but also displayed stronger relationships with the topo‐scaled environmental variables, particularly slope. Main conclusions A major conclusion of this study is that multiple sources of evidence are required to reconstruct past vegetation patterns in heavily transformed region. Neither the remnant vegetation data nor historical survey records provided adequate data sets on their own to reconstruct the pre‐impact vegetation of the Fleurieu Peninsula. Multiple sources of evidence provide the only means of assessing the environmental and historical representativeness of data sets. The spatial distribution of historical survey records was more environmentally representative than remnant vegetation data, which reflect biases due to land clearance. Historical survey records were also shown to be classificatory and spatially accurate, thus are suitable for quantitative spatial analyses. Analysis of different spatial vegetation data sets in an environmental modelling framework provided a rigorous means of assessing and comparing respective data sets as well as mapping their predicted distributions based on quantitative correlations. The method could be usefully applied to other regions where predictions of pre‐impact vegetation cover are required.     

Bias in vegetation databases? A comparison of stratified‐random and preferential sampling

Aim: Vegetation plots collected since the early 20th century and stored in large vegetation databases are an important source of ecological information. These databases are used for analyses of vegetation diversity and estimation of vegetation parameters, however such analyses can be biased due to preferential sampling of the original data. In contrast, modern vegetation survey increasingly uses stratified‐random instead of preferential sampling. To explore how these two sampling schemes affect vegetation analyses, we compare parameters of vegetation diversity based on preferentially sampled plots from a large vegetation database with those based on stratified‐random sampling. Location: Moravian Karst and Silesia, Czech Republic. Methods: We compared two parallel analyses of forest vegetation, one based on preferentially sampled plots taken from a national vegetation database and the other on plots sampled in the field according to a stratified‐random design. We repeated this comparison for two different regions in the Czech Republic. We focussed on vegetation properties commonly analysed using data from large vegetation databases, including alpha (within‐plot) diversity, cover and participation of different species groups, such as endangered and alien species within plots, total species richness of data sets, beta diversity and ordination patterns. Results: The preferentially sampled data sets obtained from the database contained more endangered species and had higher beta diversity, whereas estimates of alpha diversity and representation of alien species were not consistently different between preferentially and stratified‐randomly sampled data sets. In ordinations, plots from the preferential samples tended to be more common at margins of plot scatters. Conclusions: Vegetation data stored in large databases are influenced by researcher subjectivity in plot positioning, but we demonstrated that not all of their properties necessarily differ from data sets obtained by stratified‐random sampling. This indicates the value of vegetation databases for use in biodiversity studies; however, some analyses based on these databases are clearly biased and their results must be interpreted with caution.     

Characterization of moorland vegetation and the prediction of bird abundance using remote sensing

Aims To characterize and identify upland vegetation composition and height from a satellite image, and assess whether the resulting vegetation maps are accurate enough for predictions of bird abundance. Location South‐east Scotland, UK. Methods Fine‐taxa vegetation data collected using point samples were used for a supervised classification of a Landsat 7 image, while linear regression was used to model vegetation height over the same image. Generalized linear models describing bird abundance were developed using field‐collected bird and vegetation data. The satellite‐derived vegetation data were substituted into these models and efficacy was examined. Results The accuracy of the classification was tested over both the training and a set of test plots, and showed that more common vegetation types could be predicted accurately. Attempts to estimate the heights of both dwarf shrub and graminoid vegetation from satellite data produced significant, but weak, correlations between observed and predicted height. When these outputs were used in bird abundance–habitat models, bird abundance predicted using satellite‐derived vegetation data was very similar to that obtained when the field‐collected data were used for one bird species, but poor estimates of vegetation height produced from the satellite data resulted in a poor abundance prediction for another. Conclusions This pilot study suggests that it is possible to identify moorland vegetation to a fine‐taxa level using point samples, and that it may be possible to derive information on vegetation height, although more appropriate field‐collected data are needed to examine this further. While remote sensing may have limitations compared with relatively fine‐scale fieldwork, when used at relatively large scales and in conjunction with robust bird abundance–habitat association models, it may facilitate the mapping of moorland bird abundance across large areas.     

A review of software tools for spell‐checking taxon names in vegetation databases

Correct spelling of taxon names in vegetation databases is a fundamental prerequisite for many data processing steps. However, manual detection and correction of spelling mistakes is inefficient, prone to errors and non‐reproducible, especially when scanning large databases. Here, I review six software tools that spell‐check taxon names in vegetation databases: (1) the Global Names Resolver, (2) the Interim Register of Marine and Nonmarine Genera, (3) the Taxonomic Name Resolution Service and R packages (4) Plantminer, (5) Taxonstand and (6) tpl. In particular, I test their capacity to spell‐check names across the taxonomic ranks and organism groups frequently encountered in vegetation data and challenge their ability to screen names from different geographic regions. Performance by software tools differed widely in these tests. Backed up by multiple reference lists, the Global Names Resolver emerged as the most versatile software tool. All software solutions currently suffer from some minor limitations, including an inability to spell‐check names of hybrid taxa. Furthermore, some spelling mistakes, by their nature, cannot be resolved unambiguously. Given these limitations, taxon names should be spell‐checked with software tools in a semi‐automatic rather than an automatic way.     

Fine‐scale microhabitat selection for dense vegetation in a heathland rodent,Rattus lutreolus: Insights from intraspecific and temporal patterns

Abstract Vegetation is a dynamic habitat component and successional changes in vegetation structure can lead to concomitant changes in the communities of animals living in a particular area. Heathland rodents are a classic example, with vegetation at different ages post fire being dominated by different species. While broad associations are often demonstrated between the distribution and abundance of species and vegetation structure, the causal relationships are poorly understood. Studies of temporal and sex‐ or age‐specific patterns can provide strong insights into the processes underling patterns of habitat selection. In an attempt to better understand the mechanistic links between rodent successional patterns and vegetation structure in heathlands, we conducted a detailed study of microhabitat use by the swamp rat, Rattus lutreolus, in a native heathland in south‐eastern Australia. Rattus lutreolus typically occurs in late‐succession heath and is frequently associated with high vegetation density. Our assessment of vegetation at trapping stations, and also along trails used by the animals (using the spool‐and‐line tracking technique), revealed strong selection by the rats for dense vegetation by both day and night. The spool‐and‐line tracking approach revealed distinct intraspecific and temporal patterns. During the day, females foraged in vegetation of much higher density than did juveniles, with males behaving intermediately. During the night, however, all animals selected dense vegetation irrespective of sex or age, although the mean density of vegetation selected during the night was lower than it was during the day. These patterns were independent of daily maximum and minimum air temperature and were therefore unlikely to be related to microclimate. We propose instead that high vegetation density acts as a source of protection from predators, allowing R. lutreolus to forage safely both by day and by night.     

Foraging height and landscape context predict the relative abundance of bird species in urban vegetation patches

In Australian urban environments, revegetation and vegetation restoration are increasingly utilized conservation actions. Simple methods that help assess the utility of urban vegetation for bird species will help direct this effort for bird conservation purposes. We therefore examine whether ecological principles can be used to predict, a priori, the relative abundance of different bird species in urban vegetation. Our model proposes that a bird species will be in greater abundance where vegetation structure better reflects its foraging height requirements, and this relationship will be moderated by the landscape context of the patch. To quantify and test this model, we created an index to rank existing and revegetated urban vegetation sites in order of greatest expected abundance for each of 30 bird species. We tested this model, alongside two simpler models which consider landscape context and foraging height preferences alone, using bird abundance data from 20 woodland remnants and 20 revegetated sites in Brisbane, Australia. From these bird abundance data, we calculated the relative abundance of each species between the top‐ranking sites and lowest‐ranking sites. The model which incorporated both foraging height requirements and landscape context made predictions that were positively correlated with the data for 77% of species in remnant vegetation and 67% in revegetation. The results varied across species groups; for example, we achieved lower predictive success for canopy foraging species in the less mature revegetation sites. Overall, this model provided a reasonable level of predictive accuracy despite the diversity of factors which can influence species occurrence in urban landscapes. The model is generic and, subject to further testing, can be used to examine the effect of manipulating vegetation structure and landscape context on the abundance of different bird species in urban vegetation. This could provide a cost‐effective tool for directing urban restoration and revegetation efforts.     

Calibration of Ellenberg indicator values for the Faroe Islands

Abstract. Elenberg's bio‐indication system for soil moisture (F), soil nitrogen (N) and soil reaction (R) was examined, based on 559 vegetation samples and environmental characteristics (vegetation cover, soil depth, soil moisture, chemical soil properties) from four Faroe islands. The original indicator values from central Europe were used for the calculation of weighted community indicator values of F, N and R. These were regressed with respect to environmental data, applying standard curvilinear regression and generalized linear modelling (GLM) and new predicted values of community indicator values were obtained from the best model. Faroe species optima values of 162 taxa for one or more of the three EUenberg scales were derived from fitting Huisman‐Olff‐Fresco (HOF) models of species abundance with respect to predicted community indicator values and are proposed as new EUenberg species indicator values to be used in the Faroe Islands. F was best correlated with a GLM model containing soil moisture, organic soil fraction, soil depth and total vegetation cover, R with a GLM model containing pH and calcium in % organic soil fraction, N with total phosphorus in % organic soil fraction. The calibrated species indicator scales are much truncated, as compared with the original values, resulting in significantly different overall distributions of the original and new species indicator values. The recalculated community indicator values are much better correlated to environmental measurements. Several species do not have clear optima, but linear or monotone relationships to the examined indicator scales. This probably indicates that the occurrence of some species in the Faroe Islands are either determined by factors other than moisture, pH or soil nutrient status or, given the young age and environmental instability of the islands, are governed by stochastic mechanisms. Extension of Ellenberg indicator values outside central Europe should always be carefully calibrated by means of adequate environmental data and adequate statistical models, such as HOF models, should be applied.     

基于小波分析的大豆叶绿素a含量高光谱反演模型

 2003和2004年分别在长春市良种场和中国科学院海伦黑土生态实验站实测了大田耕作与水肥耦合作用下大豆（Glycine max）冠层高光谱反射率 与叶绿素a含量数据,对光谱反射率、微分光谱与叶绿素a含量进行了相关分析;采用归一化植被指数(Normalized diffe rence vegetation index, NDVI)、土壤调和植被指数(Soil-adjusted vegetation index, SAVI)、再归一植被指数(Renormalized difference vegetation index, RDVI)、第二修正比值植被指数(Modified second ratio index, MSRI)等建立了大豆叶绿素a反演模型;应用小波分析对采集的光谱反 射率数据进行了能量系数提取,并以小波能量系数作为自变量进行了单变量与多变量回归分析,对大豆叶绿素a进行了估算。研究结果表明,大 豆叶绿素a 与可见光光谱反射率相关性较好,并在红光波段取得最大值(R²>0.70),但在红边处,微分光谱与大豆叶绿素a的相关性较反射率好 得多,在其它波段则相反;由NDVI、SAVI、RDVI、MSRI等植被指数建立的估算模型可以提高大豆叶绿素a的估算精度（R²>0.75）;小波能量系 数回归模型可以进一步提高大豆叶绿素a含量的估算水平,以一个特定小波能量系数作为自变量的回归模型,大豆叶绿素a回归决定系数R²高达 0.78;多变量回归分析结果表明,大豆叶绿素a实测值与预测值的线性回归决定系数R²均高达0.85。以上结果表明, 小波分析可以对高光谱进 行特征变量提取,并可在一定程度上提高大豆生理参数反演精度。     

Light from below matters: Quantifying the consequences of responses to far‐red light reflected upwards for plant performance in heterogeneous canopies

In vegetation stands, plants receive red to far‐red ratio (R:FR) signals of varying strength from all directions. However, plant responses to variations in R:FR reflected from below have been largely ignored despite their potential consequences for plant performance. Using a heterogeneous rose canopy, which consists of bent shoots down in the canopy and vertically growing upright shoots, we quantified upward far‐red reflection by bent shoots and its consequences for upright shoot architecture. With a three‐dimensional plant model, we assessed consequences of responses to R:FR from below for plant photosynthesis. Bent shoots reflected substantially more far‐red than red light, causing reduced R:FR in light reflected upwards. Leaf inclination angles increased in upright shoots which received low R:FR reflected from below. The increased leaf angle led to an increase in simulated plant photosynthesis only when this low R:FR was reflected off their own bent shoots and not when it reflected off neighbour bent shoots. We conclude that plant response to R:FR from below is an under‐explored phenomenon which may have contrasting consequences for plant performance depending on the type of vegetation or crop system. The responses are beneficial for performance only when R:FR is reflected by lower foliage of the same plants.     

Numerical reproduction of traditional classifications and automatic vegetation identification

Questions: Is it possible to develop an expert system to provide reliable automatic identifications of plant communities at the precision level of phytosociological associations? How can unreliable expert‐based knowledge be discarded before applying supervised classification methods? Material: We used 3677 relevés from Catalonia (Spain), belonging to eight orders of terrestrial vegetation. These relevés were classified by experts into 222 low‐level units (associations or sub‐associations). Methods: We reproduced low‐level, expert‐defined vegetation units as independent fuzzy clusters using the Possibilistic C‐means algorithm. Those relevés detected as transitional between vegetation types were excluded in order to maximize the number of units numerically reproduced. Cluster centroids were then considered static and used to perform supervised classifications of vegetation data. Finally, we evaluated the classifier's ability to correctly identify the unit of both typical (i.e. training) and transitional relevés. Results: Only 166 out of 222 (75%) of the original units could be numerically reproduced. Almost all the unrecognized units were sub‐associations. Among the original relevés, 61% were deemed transitional or untypical. Typical relevés were correctly identified 95% of the time, while the efficiency of the classifier for transitional data was only 64%. However, if the second classifier's choice was also considered, the rate of correct classification for transitional relevés was 80%. Conclusions: Our approach stresses the transitional nature of relevé data obtained from vegetation databases. Relevé selection is justified in order to adequately represent the vegetation concepts associated with expert‐defined units.     

Restoring Sage‐grouse nesting habitat through removal of early successional conifer

Conifer woodlands have expanded into sagebrush (Artemisia spp.) ecosystems and degrade habitat for sagebrush obligate species such as the Greater Sage‐grouse (Centrocercus urophasianus). Conifer management is increasing despite a lack of empirical evidence assessing outcomes to grouse and their habitat. Although assessments of vegetation recovery after conifer removal are common, comparisons of successional trends with habitat guidelines or actual data on habitat used by sage‐grouse is lacking. We assessed impacts of conifer encroachment on vegetation characteristics known to be important for sage‐grouse nesting. Using a controlled repeated measures design, we then evaluated vegetation changes for 3 years after conifer removal. We compared these results to data from 356 local sage‐grouse nests, rangewide nesting habitat estimates, and published habitat guidelines. We measured negative effects of conifer cover on many characteristics important for sage‐grouse nesting habitat including percent cover of forbs, grasses, and shrubs, and species richness of forbs and shrubs. In untreated habitat, herbaceous vegetation cover was slightly below the cover at local nest sites, while shrub cover and sagebrush cover were well below cover at the nest sites. Following conifer removal, we measured increases in herbaceous vegetation, primarily grasses, and sagebrush height. Our results indicate that conifer abundance can decrease habitat suitability for nesting sage‐grouse. Additionally, conifer removal can improve habitat suitability for nesting sage‐grouse within 3 years, and trajectories indicate that the habitat may continue to improve in the near future.     

NDVI‐based vegetation monitoring in Mau forest complex,Kenya

The normalized difference vegetation index (NDVI) measures vegetation health and density using plant reflectance characteristics recorded by satellite imagery. Dekadal NDVI data were obtained for January 1999–December 2009 from 1‐km resolution SPOT‐VEGETATION sensor for closed woody vegetation type in four blocks of the Mau forest complex. Vegetation response to yearly seasonal variations was plotted and used to compare deviations by specific years. Subnormal vegetation conditions were recorded by the standardized vegetation index (SVI) and persistently low SVI values indicated a drought season or degraded vegetation. The general linear trend of the vegetation was plotted for the study period to identify trends towards degradation or vegetation recovery. Analysis of variance was used to compare forest blocks and shows spatial vegetation variations and also among years to identify vegetation variations with time. Rainfall data recorded for 2002–2009 in east Mau were used to confirm rainfall‐related vegetation variations block. Results show that NDVI patterns within an year follow cyclic trends with a strong dependence on rainfall seasons. The forest vegetation indicated negligible changes over the study period but effects of extended dry periods in 2000 and 2009 were evident. There were significant differences (P < 0.05) in NDVI between forest blocks. East Mau had significantly inferior vegetation that can be attributed to forest type, level of human degradation prior to the study and the lower rainfall. There were significant variations (P < 0.05) of NDVI among years but the forests showed a natural resilience to disturbance and can retain original vegetation vigour once stress is removed. The study proposes further monitoring of the forests including other vegetation types that are more vulnerable to climatic variations and anthropogenic effects.     

Computer‐aided calibration for visual estimation of vegetation cover

Question: What precision and accuracy of visual cover estimations can be achieved after repeated calibration with images of vegetation in which the true cover is known, and what factors influence the results? Methods: Digital images were created, in which the true cover of vegetation was digitally calculated. Fifteen observers made repeated estimates with immediate feedback on the true cover. The effects on precision and accuracy through time were evaluated with repeated proficiency tests. In a field trial, cover estimates, before and after calibration, were compared with point frequency data. Results: Even a short time of calibration greatly improves precision and accuracy of the estimates, and can also reduce the influence of different backgrounds, aggregation patterns and experience. Experienced observers had a stronger tendency to underestimate the cover of narrow‐leaved grasses before calibration. The field trial showed positive effects of computer‐based calibration on precision, in that it led to considerably less between‐observer variation for one of the two species groups. Conclusions: Computer‐aided calibration of vegetation cover estimation is simple, self‐explanatory and time‐efficient, and might possibly reduce biases and drifts in estimate levels over time. Such calibration can also reduce between‐observer variation in field estimates, at least for some species. However, the effects of calibration on estimations in the field must be further evaluated, especially for multilayered vegetation.     

Are habitat types compatible with floristically‐derived associations?

Abstract. The habitat type system developed by R. Daubenmire has been widely adopted throughout the western United States. Habitat types result from a site classification derived from the classification of late seral plant communities using selected indicator species. It has been suggested that the classification of late successional vegetation used to derive habitat types does not substantially differ from phytosociological classification in the sense of Braun‐Blanquet approach, and that habitat types can be adopted in their present form into floristically‐based vegetation classifications. Despite the many commonalities between the two systems, however, the classification methods, and specifically the use of indicator species in the habitat type system, yield a significantly different classification than the phytosociological approach. This is demonstrated in the comparison of a habitat type classification with the results of a recent phytosociological classification of forest vegetation in the northern Salish Mountains of Montana.     

Formalized reproduction of an expert‐based phytosociological classification: A case study of subalpine tall‐forb vegetation

Abstract. Delimitation of vegetation units in phytosociology is traditionally based on expert knowledge. Applications of expert‐based classifications are often inconsistent because criteria for assigning relevés to vegetation units are seldom given explicitly. Still, there is, e.g. in nature conservation, an increasing need for a consistent application of vegetation classification using computer expert systems for unit identification. We propose a procedure for formalized reproduction of an expert‐based vegetation classification, which is applicable to large phytosociological data sets. This procedure combines Bruelheide's Cocktail method with a similarity‐based assignment of relevés to constancy columns of a vegetation table. As a test of this method we attempt to reproduce the expert‐based phytosociological classification of subalpine tall‐forb vegetation of the Czech Republic which has been made by combination of expert judgement and stepwise numerical classification of 718 relevés by TWINSPAN. Applying the Cocktail method to a geographically stratified data set of 21794 relevés of all Czech vegetation types, we defined groups of species with the statistical tendency of joint occurrences in vegetation. Combinations of 12 of these species groups by logical operators AND, OR and AND NOT yielded formal definitions of 14 of 16 associations which had been accepted in the expert‐based classification. Application of these formal definitions to the original data set of 718 relevés resulted in an assignment of 376 relevés to the associations. This assignment agreed well with the original expert‐based classification. Relevés that remained un‐assigned because they had not met the requirements of any of the formal definitions, were subsequently assigned to the associations by calculating similarity to relevé groups that had already been assigned to the associations. A new index, based on frequency and fidelity, was proposed for calculating similarity. The agreement with the expert‐based classification achieved by the formal definitions was still improved after applying the similarity‐based assignment. Results indicate that the expert‐based classification can be successfully formalized and converted into a computer expert system.     

Enrichment of land-cover polygons with eco-climatic information derived from MODIS NDVI imagery

Aim The FAO land‐cover classification system (LCCS) represents an innovative approach to standardizing and harmonizing land‐cover classifications based on remote sensing data. The thematic information considered by the LCCS, however, is intrinsically related to vegetation physiognomy and does not report important eco‐climatic features. Our aim is to develop a methodology to enrich LCCS maps with information on vegetation productivity and phenology derived from Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data. Location The LCCS has recently been applied in East Africa by the Africover project. The proposed methodology is developed and tested in Tanzania using MODIS NDVI data for a 5‐year period (2001–05). Methods Annual NDVI profiles of Africover polygons were extracted from MODIS imagery. These profiles, composed of 23 NDVI values per year, were averaged over the study period, purified for possible land‐cover errors and converted into a more manageable format composed of 24 half‐month values. The resulting NDVI profiles were first analysed visually and then evaluated statistically against rainfall measurements taken at 12 Tanzanian stations. The steps involved were as follows: NDVI values were aggregated on a monthly basis and represented with a one‐digit integer to obtain an extended code; a subset of parameters describing vegetation development and phenology was identified, thus obtaining a restricted codification; and finally, the information loss resulting from both the extended and restricted codification was evaluated with respect to the original NDVI profiles. Results NDVI profiles of different Africover classes can differ in mean values but tend to have a similar shape, linked to the seasonality of local vegetation. Both NDVI annual averages and seasonal variations are strictly dependent on rainfall patterns, particularly in arid zones. The tested codifications effectively summarize the eco‐climatic information contained in the polygon NDVI profiles, with the extended and restricted codifications retaining > 90% and 80% of such information, respectively. Main conclusions The proposed methodology is capable of enriching LCCS polygons with eco‐climatic information derived from MODIS NDVI data. Such information is related to vegetation development and seasonality, and can be efficiently condensed at various levels of detail.     

Implementing plant hydraulic architecture within the LPJ Dynamic Global Vegetation Model

Aim To implement plant hydraulic architecture within the Lund–Potsdam–Jena Dynamic Global Vegetation Model (LPJ–DGVM), and to test the model against a set of observational data. If the model can reproduce major patterns in vegetation and ecosystem processes, we consider this to be an important linkage between plant physiology and larger‐scale ecosystem dynamics. Location The location is global, geographically distributed. Methods A literature review was carried out to derive model formulations and parameter values for representing the hydraulic characteristics of major global plant functional types (PFTs) in a DGVM. After implementing the corresponding formulations within the LPJ–DGVM, present‐day model output was compared to observational data. Results The model reproduced observed broad‐scale patterns in potential natural vegetation, but it failed to distinguish accurately between different types of grassland and savanna vegetation, possibly related to inadequate model representations of water fluxes in the soil and wildfire effects. Compared to a version of the model using an empirical formulation for calculating plant water supply without considering plant hydraulic architecture, the new formulation improved simulated patterns of vegetation in particular for dry shrublands. Global‐scale simulation results for runoff and actual evapotranspiration (AET) corresponded well to available data. The model also successfully reproduced the magnitude and seasonal cycle of AET for most EUROFLUX forests, while modelled variation in NPP across a large number of sites spanning several biomes showed a strong correlation with estimates from field measurements. Main conclusions The model was generally confirmed by comparison to observational data. The novel model representation of water flow within plants makes it possible to resolve mechanistically the effects of hydraulic differences between plant functional groups on vegetation structure, water cycling, and competition. This may be an advantage when predicting ecosystem responses to nonextant climates, in particular in areas dominated by dry shrubland vegetation.