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

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.

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

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

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

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

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

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 relevés, which were resampled to reduce unbalanced sampling effort, resulting in a dataset of 568 relevés 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

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

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

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

Functional Interactions between KCNE1 C-Terminus and the KCNQ1 Channel

The KCNE1 gene product (minK protein) associates with the cardiac KvLQT1 potassium channel (encoded by KCNQ1) to create the cardiac slowly activating delayed rectifier, IKs. Mutations throughout both genes are linked to the hereditary cardiac arrhythmias in the Long QT Syndrome (LQTS). KCNE1 exerts its specific regulation of KCNQ1 activation via interactions between membrane-spanning segments of the two proteins. Less detailed attention has been focused on the role of the KCNE1 C-terminus in regulating channel behavior. We analyzed the effects of an LQT5 point mutation (D76N) and the truncation of the entire C-terminus (Δ70) on channel regulation, assembly and interaction. Both mutations significantly shifted voltage dependence of activation in the depolarizing direction and decreased IKs current density. They also accelerated rates of channel deactivation but notably, did not affect activation kinetics. Truncation of the C-terminus reduced the apparent affinity of KCNE1 for KCNQ1, resulting in impaired channel formation and presentation of KCNQ1/KCNE1 complexes to the surface. Complete saturation of KCNQ1 channels with KCNE1-Δ70 could be achieved by relative over-expression of the KCNE subunit. Rate-dependent facilitation of K+ conductance, a key property of IKs that enables action potential shortening at higher heart rates, was defective for both KCNE1 C-terminal mutations, and may contribute to the clinical phenotype of arrhythmias triggered by heart rate elevations during exercise in LQTS mutations. These results support several roles for KCNE1 C-terminus interaction with KCNQ1: regulation of channel assembly, open-state destabilization, and kinetics of channel deactivation

Enhancement of Late Successional Plants on Ex-Arable Land by Soil Inoculations

Restoration of species-rich grasslands on ex-arable land can help the conservation of biodiversity but faces three big challenges: absence of target plant propagules, high residual soil fertility and restoration of soil communities. Seed additions and top soil removal can solve some of these constraints, but restoring beneficial biotic soil conditions remains a challenge. Here we test the hypotheses that inoculation of soil from late secondary succession grasslands in arable receptor soil enhances performance of late successional plants, especially after top soil removal but pending on the added dose. To test this we grew mixtures of late successional plants in arable top (organic) soil or in underlying mineral soil mixed with donor soil in small or large proportions. Donor soils were collected from different grasslands that had been under restoration for 5 to 41 years, or from semi-natural grassland that has not been used intensively. Donor soil addition, especially when collected from older restoration sites, increased plant community biomass without altering its evenness. In contrast, addition of soil from semi-natural grassland promoted plant community evenness, and hence its diversity, but reduced community biomass. Effects of donor soil additions were stronger in mineral than in organic soil and larger with bigger proportions added. The variation in plant community composition was explained best by the abundances of nematodes, ergosterol concentration and soil pH. We show that in controlled conditions inoculation of soil from secondary succession grassland into ex-arable land can strongly promote target plant species, and that the role of soil biota in promoting target plant species is greatest when added after top soil removal. Together our results point out that transplantation of later secondary succession soil can promote grassland restoration on ex-arable land

European Vegetation Archive (EVA): An integrated database of European vegetation plots

© 2016 International Association for Vegetation Science. The European Vegetation Archive (EVA) is a centralized database of European vegetation plots developed by the IAVS Working Group European Vegetation Survey. It has been in development since 2012 and first made available for use in research projects in 2014. It stores copies of national and regional vegetation- plot databases on a single software platform. Data storage in EVA does not affect on-going independent development of the contributing databases, which remain the property of the data contributors. EVA uses a prototype of the database management software TURBOVEG 3 developed for joint management of multiple databases that use different species lists. This is facilitated by the SynBioSys Taxon Database, a system of taxon names and concepts used in the individual European databases and their corresponding names on a unified list of European flora. TURBOVEG 3 also includes procedures for handling data requests, selections and provisions according to the approved EVA Data Property and Governance Rules. By 30 June 2015, 61 databases from all European regions have joined EVA, contributing in total 1 027 376 vegetation plots, 82% of them with geographic coordinates, from 57 countries. EVA provides a unique data source for large-scale analyses of European vegetation diversity both for fundamental research and nature conservation applications. Updated information on EVA is available online at http://euroveg.org/eva-database