163 research outputs found

    Evaluating the ecological realism of plant species distribution models with ecological indicator values

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    Species distribution models (SDMs) are routinely applied to assess current as well as future species distributions, for example to assess impacts of future environmental change on biodiversity or to underpin conservation planning. It has been repeatedly emphasized that SDMs should be evaluated based not only on their goodness of fit to the data, but also on the realism of the modelled ecological responses. However, possibilities for the latter are hampered by limited knowledge on the true responses as well as a lack of quantitative evaluation methods. Here we compared modelled niche optima obtained from European-scale SDMs of 1,476 terrestrial vascular plant species with empirical ecological indicator values indicating the preferences of plant species for key environmental conditions. For each plant species we first fitted an ensemble SDM including three modeling techniques (GLM, GAM and BRT) and extracted niche optima for climate, soil, land use and nitrogen deposition variables with a large explanatory power for the occurrence of that species. We then compared these SDM-derived niche optima with the ecological indicator values by means of bivariate correlation analysis. We found weak to moderate correlations in the expected direction between the SDM-derived niche optima and ecological indicator values. The strongest correlation occurred between the modelled optima for growing degree days and the ecological indicator values for temperature. Correlations were weaker for SDM-derived niche optima with a more distal relationship to ecological indicator values (notably precipitation and soil moisture). Further, correlations were consistently highest for BRT, followed by GLM and GAM. Our method gives insight into the ecological realism of modelled niche optima and projected core habitats and can be used to improve SDMs by making a more informed selection of environmental variables and modeling techniques

    Bayesian classification of vegetation types with Gaussian mixture density fitting to indicator values.

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    Question: Is it possible to mathematically classify relevés into vegetation types on the basis of their average indicator values, including the uncertainty of the classification? Location: The Netherlands. Method: A large relevé database was used to develop a method for predicting vegetation types based on indicator values. First, each relevé was classified into a phytosociological association on the basis of its species composition. Additionally, mean indicator values for moisture, nutrients and acidity were computed for each relevé. Thus, the position of each classified relevé was obtained in a three-dimensional space of indicator values. Fitting the data to so called Gaussian Mixture Models yielded densities of associations as a function of indicator values. Finally, these density functions were used to predict the Bayesian occurrence probabilities of associations for known indicator values. Validation of predictions was performed by using a randomly chosen half of the database for the calibration of densities and the other half for the validation of predicted associations. Results and Conclusions: With indicator values, most relevés were classified correctly into vegetation types at the association level. This was shown using confusion matrices that relate (1) the number of relevés classified into associations based on species composition to (2) those based on indicator values. Misclassified relevés belonged to ecologically similar associations. The method seems very suitable for predictive vegetation models

    The need of data harmonization to derive robust empirical relationships between soil conditions and vegetation.

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    Question: Is it possible to improve the general applicability and significance of empirical relationships between abiotic conditions and vegetation by harmonization of temporal data? Location: The Netherlands. Methods: Three datasets of vegetation, recorded after periods with different meteorological conditions, were used to analyze relationships between soil moisture regime (expressed by the mean spring groundwater level - MSLt calculated for different periods) and vegetation (expressed by the mean indicator value for moisture regime Fm). For each releve, measured groundwater levels were interpolated and extrapolated to daily values for the period 1970-2000 by means of an impulse-response model. Sigmoid regression lines between MSLt and Fm were determined for each of the three datasets and for the combined dataset. Results: A measurement period of three years resulted in significantly different relationships between Fm and MSLt for the three datasets (F-test,/? <0.05>. The three regression lines only coincided for the mean spring groundwater level computed over the period 1970-2000 (AfSLclimate) and thus provided a general applicable relationship. Precipitation surplus prior to vegetation recordings strongly affected the relationships. Conclusions: Harmonization of time series data (1) eliminates biased measurements, (2) results in generally applicable relationships between abiotic and vegetation characteristics and (3) increases the goodness of fit of these relationships. The presented harmonization procedure can be used to optimize many relationships between soil and vegetation characteristics. © IAVS; Opulus Press Uppsala

    The whole and its parts : why and how to disentangle plant communities and synusiae in vegetation classification

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    Most plant communities consist of different structural and ecological subsets, ranging from cryptogams to different tree layers. The completeness and approach with which these subsets are sampled have implications for vegetation classification. Non‐vascular plants are often omitted or sometimes treated separately, referring to their assemblages as “synusiae” (e.g. epiphytes on bark, saxicolous species on rocks). The distinction of complete plant communities (phytocoenoses or holocoenoses) from their parts (synusiae or merocoenoses) is crucial to avoid logical problems and inconsistencies of the resulting classification systems. We here describe theoretical differences between the phytocoenosis as a whole and its parts, and outline consequences of this distinction for practise and terminology in vegetation classification. To implement a clearer separation, we call for modifications of the International Code of Phytosociological Nomenclature and the EuroVegChecklist. We believe that these steps will make vegetation classification systems better applicable and raise the recognition of the importance of non‐vascular plants in the vegetation as well as their interplay with vascular plants

    Verkenning herstel kleinschalige lijnvormige infrastructuur Heuvelland

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    Kalkgraslanden en hellingbossen zijn voorbeelden van soortenrijke natuur in het Limburgse Heuvelland. In het kader van OBN is een verkennend overzicht van natuurwaarden van lijnvormige elementen gemaakt, met een visie op mogelijk herstel van natuur in bermen, heggen en houtwallen. Deze inventarisatie is tot stand gekomen dankzij de inzet van: RAVON, Vlinderstichting, Stichting Anemoon, Floron, Zoogdierenvereniging VZZ, EIS Nederland, en Alterr

    Disturbance and resource availability act differently on the same suite of plant traits: revisiting assembly hypotheses.

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    Understanding the mechanisms of trait selection at the scale of plant communities is a crucial step toward predicting community assembly. Although it is commonly assumed that disturbance and resource availability constrain separate suites of traits, representing the regenerative and established phases, respectively, a quantification and test of this accepted hypothesis is still lacking due to limitations of traditional statistical techniques. In this paper we quantify, using structural equation modeling (SEM), the relative contributions of disturbance and resource availability to the selection of suites of traits at the community scale. Our model specifies and reflects previously obtained ecological insights, taking disturbance and nutrient availability as central drivers affecting leaf, allometric, seed, and phenology traits in 156 (semi-) natural plant communities throughout The Netherlands. The common hypothesis positing that disturbance and resource availability each affect a set of mutually independent traits was not consistent with the data. Instead, our final model shows that most traits are strongly affected by both drivers. In addition, trait-trait constraints are more important in community assembly than environmental drivers in half of the cases. Both aspects of trait selection are crucial for correctly predicting ecosystem processes and community assembly, and they provide new insights into hitherto underappreciated ecological interactions. © 2012 by the Ecological Society of America

    EUNIS Habitat Classification: Expert system, characteristic species combinations and distribution maps of European habitats

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    Aim: The EUNIS Habitat Classification is a widely used reference framework for European habitat types (habitats), but it lacks formal definitions of individual habitats that would enable their unequivocal identification. Our goal was to develop a tool for assigning vegetation‐plot records to the habitats of the EUNIS system, use it to classify a European vegetation‐plot database, and compile statistically‐derived characteristic species combinations and distribution maps for these habitats. Location: Europe. Methods: We developed the classification expert system EUNIS‐ESy, which contains definitions of individual EUNIS habitats based on their species composition and geographic location. Each habitat was formally defined as a formula in a computer language combining algebraic and set‐theoretic concepts with formal logical operators. We applied this expert system to classify 1,261,373 vegetation plots from the European Vegetation Archive (EVA) and other databases. Then we determined diagnostic, constant and dominant species for each habitat by calculating species‐to‐habitat fidelity and constancy (occurrence frequency) in the classified data set. Finally, we mapped the plot locations for each habitat. Results: Formal definitions were developed for 199 habitats at Level 3 of the EUNIS hierarchy, including 25 coastal, 18 wetland, 55 grassland, 43 shrubland, 46 forest and 12 man‐made habitats. The expert system classified 1,125,121 vegetation plots to these habitat groups and 73,188 to other habitats, while 63,064 plots remained unclassified or were classified to more than one habitat. Data on each habitat were summarized in factsheets containing habitat description, distribution map, corresponding syntaxa and characteristic species combination. Conclusions: EUNIS habitats were characterized for the first time in terms of their species composition and distribution, based on a classification of a European database of vegetation plots using the newly developed electronic expert system EUNIS‐ESy. The data provided and the expert system have considerable potential for future use in European nature conservation planning, monitoring and assessment

    Vegetation of Europe: hierarchical floristic classification system of vascular plant, bryophyte, lichen, and algal communities

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    Vegetation classification consistent with the Braun-Blanquet approach is widely used in Europe for applied vegetation science, conservation planning and land management. During the long history of syntaxonomy, many concepts and names of vegetation units have been proposed, but there has been no single classification system integrating these units. Here we (1) present a comprehensive, hierarchical, syntaxonomic system of alliances, orders and classes of Braun-Blanquet syntaxonomy for vascular plant, bryophyte and lichen, and algal communities of Europe; (2) briefly characterize in ecological and geographic terms accepted syntaxonomic concepts; (3) link available synonyms to these accepted concepts; and (4) provide a list of diagnostic species for all classes. Location: European mainland, Greenland, Arctic archipelagos (including Iceland, Svalbard, Novaya Zemlya), Canary Islands, Madeira, Azores, Caucasus, Cyprus. Methods: We evaluated approximately 10 000 bibliographic sources to create a comprehensive list of previously proposed syntaxonomic units. These units were evaluated by experts for their floristic and ecological distinctness, clarity of geographic distribution and compliance with the nomenclature code. Accepted units were compiled into three systems of classes, orders and alliances (EuroVegChecklist, EVC) for communities dominated by vascular plants (EVC1), bryophytes and lichens (EVC2) and algae (EVC3). Results: EVC1 includes 109 classes, 300 orders and 1108 alliances; EVC2 includes 27 classes, 53 orders and 137 alliances, and EVC3 includes 13 classes, 24 orders and 53 alliances. In total 13 448 taxa were assigned as indicator species to classes of EVC1, 2087 to classes of EVC2 and 368 to classes of EVC3. Accepted syntaxonomic concepts are summarized in a series of appendices, and detailed information on each is accessible through the software tool EuroVegBrowser. Conclusions: This paper features the first comprehensive and critical account of European syntaxa and synthesizes more than 100 yr of classification effort by European phytosociologists. It aims to document and stabilize the concepts and nomenclature of syntaxa for practical uses, such as calibration of habitat classification used by the European Union, standardization of terminology for environmental assessment, management and conservation of nature areas, landscape planning and education. The presented classification systems provide a baseline for future development and revision of European syntaxonomy.info:eu-repo/semantics/publishedVersio

    A formal classification of the Lygeum spartum vegetation of the Mediterranean Region

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    Aims We examined local and regional contribution on the grasslands dominated by Lygeum spartum from Southern Europe and North Africa to produce a formalised classification of this vegetation and to identify main factors driving its plant species composition. Location Mediterranean Basin and Iberian Peninsula. Methods We used a dataset of 728 relevĂ©s, which were resampled to reduce unbalanced sampling effort, resulting in a dataset of 568 relevĂ©s and 846 taxa. We classified the plots by TWINSPAN, interpreted the resulting pools, and used them to develop formal definitions of phytosociological alliances characterised by L. spartum vegetation. The definitions were included in an expert system to assist automatic vegetation classification. We related the alliances to climatic factors and described their biogeographical features and ecological preferences. The floristic relationships between these alliances were analysed and visualised using distance‐based redundancy analysis. Results We defined eleven alliances of L. spartum vegetation, including the newly described Launaeo laniferae‐Lygeion sparti from SW Morocco and the Noaeo mucronatae‐Lygeion sparti from the Algerian highlands and NE Morocco. Biogeographical, climatic, and edaphic factors were revealed as putatively driving the differentiation between the alliances. The vegetation of clayey slopes and inland salt basins displayed higher variability in comparison with those of coastal salt marshes. Main conclusions The most comprehensive formal classification, accompanied by an expert system, of the L. spartum vegetation was formulated. The expert system, containing the formal definitions of the phytosociological alliances, will assist in identification of syntaxonomic position of new datasets
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