A test on Ellenberg indicator values in the Mediterranean evergreen woods (Quercetea ilicis)

The consistency and reliability of Ellenberg’s indicator values (Eiv) as ecological descriptors of the Mediterranean evergreen vegetation ascribed to the phytosociological class Quercetea ilicis have been checked on a set of 859 phytosociological relevés × 699 species. Diagnostic species were identified through a Twinspan analysis and their Eiv analyzed and related to the following independent variables: (1) annual mean temperatures, (2) annual rainfall. The results provided interesting insights to disentangle the current syntaxonomical framework at the alliance level demonstrating the usefulness of ecological indicator values to test the efficiency and predictivity of the phytosociological classification

The alignment of agricultural and nature conservation policies in the European Union.

Europe is a region of relatively high population density and productive agriculture subject to substantial government intervention under the Common Agricultural Policy (CAP). Many habitats and species of high conservation interest have been created by the maintenance of agricultural practices over long periods. These practices are often no longer profitable, and nature conservation initiatives require government support to cover the cost for them to be continued. The CAP has been reformed both to reduce production of agricultural commodities at costs in excess of world prices and to establish incentives for landholders to adopt voluntary conservation measures. A separate nature conservation policy has established an extensive series of protected sites (Natura 2000) that has, as yet, failed to halt the loss of biodiversity. Additional broader scale approaches have been advocated for conservation in the wider landscape matrix, including the alignment of agricultural and nature conservation policies, which remains a challenge. Possibilities for alignment include further shifting of funds from general support for farmers toward targeted payments for biodiversity goals at larger scales and adoption of an ecosystem approach. The European response to the competing demands for land resources may offer lessons globally as demands on rural land increase.This is the author accepted manuscript. The final version is available fromWiley via http://dx.doi.org/10.1111/cobi.1253

How robust are community-based plant bioindicators? Empirical testing of the relationship between Ellenberg values and direct environmental measures in woodland communities

There are several community-based bioindicator systems that use species presence or abundance data as proxies for environmental variables. One example is the Ellenberg system, whereby vegetation data are used to estimate environmental soil conditions. Despite widespread use of Ellenberg values in ecological research, the correlation between bioindicated values and actual values is often an implicit assumption rather than based on empirical evidence. Here, we correlate unadjusted and UK-adjusted Ellenberg values for soil moisture, pH, and nitrate in relation to direct environmental measures for 50 woodland sites in the UK, which were subject to repeat sampling. Our results show the accuracy of Ellenberg values is parameter specific; pH values were a good proxy for direct environmental measures but this was not true for soil moisture, when relationships were weak and non-significant. For nitrates, there were important seasonal differences, with a strong positive logarithmic relationship in the spring but a non-significant (and negative) correlation in summer. The UK-adjusted values were better than, or equivalent to, Ellenberg’s original ones, which had been quantified originally for Central Europe, in all cases. Somewhat surprisingly, unweighted values correlated with direct environmental measures better than did abundance-weighted ones. This suggests that the presence of rare plants can be highly important in accurate quantification of soil parameters and we recommend using an unweighted approach. However, site profiles created only using rare plants were inferior to profiles based on the whole plant community and thus cannot be used in isolation. We conclude that, for pH and nitrates, the Ellenberg system provides a useful estimate of actual conditions, but recalibration of moisture values should be considered along with the effect of seasonality on the efficacy of the system

Modelling of nature management in MOVE

Het multiple stress model SMART-MOVE is ontwikkeld om op nationale schaal de effecten van verdroging, verzuring en vermesting op het voorkomen van plantensoorten te voorspellen. Het model bestaat uit: (1) een bodemmodule SMART2, die de verandering van bodemvochtgehalte, zuurgraad en stikstofbeschikbaarheid berekent en (2) een vegetatiemodule MOVE, die bestaat uit regressievergelijkingen welke de relatie tussen de omgevingsfactoren en de kans op voorkomen van soorten beschrijven. De omgevingsfactoren zijn weergegeven in gemiddelde Ellenberg-indicatiewaarden per opname. Omdat naast deze standplaatsfactoren ook beheersmaatregelen invloed hebben op het voorkomen van soorten, is onderzocht of indicatiewaarden voor licht en maaigevoeligheid gebruikt kunnen worden om de effecten van beheer op de vegetatie te beschrijven. Daartoe is een dataset van 500 vegetatie-opnamen ingedeeld in 4 categorieen voor beheersintensiteit, waarna door middel van ordinale logistische regressie-analyse kalibratie met indicatiewaarden voor licht, maaigevoeligheid en stikstof plaatsvond. Op basis van deze analyse konden vergelijkingen opgesteld worden die voor elke categorie de kans weergeven dat een opname tot die categorie behoort. Op basis van deze kansen is een relatieve schaal voor beheersintensiteit opgesteld, welke als basis voor de invoer van MOVE kan dienen. Uitbreiding van MOVE met indicatiewaarden voor licht en maaigevoeligheid resulteerde in een significante verbetering van de regressievergelijkingen. Geconcludeerd werd dat indicatiewaarden voor licht en maaigevoeligheid gebruikt kunnen worden om de effecten van beheersintensiteit op het voorkomen van soorten te beschrijven. Wanneer SMART-MOVE uitgebreid wordt met deze variabelen, kunnen effecten van beheer en successie op nationale schaal worden beschreven.The multistress model, SMART-MOVE, was developed to forecast changes in the plant species composition occurring in the Netherlands due to acidification, eutrophication and desiccation. The model consists of: (1) SMART2, a soil module used for calculating changes pH, groundwater level and nutrient availability and (2) MOVE, a vegetation module, consisting of regression equations describing the relation between environmental factors and the probability of plant species occurrence. The environmental factors are represented by average Ellenberg indication values. Since management practices also determine the occurrence of plant species, this study investigated the possibility of using indication values for light and sensitivity to mowing to describe the effects of management on the vegetation. Four categories of management (high, medium and low intensity of management, and forest management) were defined on an ordinal scale. Calibration was carried out with the use of indication values for light, sensitivity to mowing and nitrogen availability. From this analysis, probability equations were derived, describing for each category -given the indication values- the probability of a record belonging to that category. A management scale was defined on the basis of these probability equations for use as model input. For this calibration, a set of 500 records was used. Extending the regression model with indication values for light and sensitivity to mowing resulted in a significant improvement of regression equations describing the relation between environmental factors and the probability of occurrence. Indication values for light, nitrogen availability and sensitivity to mowing can be used to describe the effect of management on species occurrence. Extending the model with these values will enable SMART-MOVE to predict the effects of management and succession on a national scale.DGM/DWL RIV

Linking monitoring and modelling: can long-term datasets be used more effectively as a basis for large-scale prediction?

Data from long-term monitoring sites are vital for biogeochemical process understanding, and for model development. Implicitly or explicitly, information provided by both monitoring and modelling must be extrapolated in order to have wider scientific and policy utility. In many cases, large-scale modelling utilises little of the data available from long-term monitoring, instead relying on simplified models and limited, often highly uncertain, data for parameterisation. Here, we propose a new approach whereby outputs from model applications to long-term monitoring sites are upscaled to the wider landscape using a simple statistical method. For the 22 lakes and streams of the UK Acid Waters Monitoring Network (AWMN), standardised concentrations (Z scores) for Acid Neutralising Capacity (ANC), dissolved organic carbon, nitrate and sulphate show high temporal coherence among sites. This coherence permits annual mean solute concentrations at a new site to be predicted by back-transforming Z scores derived from observations or model applications at other sites. The approach requires limited observational data for the new site, such as annual mean estimates from two synoptic surveys. Several illustrative applications of the method suggest that it is effective at predicting long-term ANC change in upland surface waters, and may have wider application. Because it is possible to parameterise and constrain more sophisticated models with data from intensively monitored sites, the extrapolation of model outputs to policy relevant scales using this approach could provide a more robust, and less computationally demanding, alternative to the application of simple generalised models using extrapolated input data