Abstract. The relationship between mean Ellenberg indicator values (IV) per vegetation relevé and environmental parameters measured in the field usually shows a large variation. We tested the hypothesis that this variation is caused by bias dependent on the phytosociological class. For this purpose we collected data containing vegetation relevés and measured soil pH (3631 records) or mean spring groundwater level (MSL, 1600 records). The relevés were assigned to vegetation types by an automated procedure. Regression of the mean indicator values for acidity on soil pH and the mean indicator values for moisture on MSL gave percentages explained variance similar to values that were reported earlier in literature. When the phytosociological class was added as an explanatory factor the explained variance increased considerably. Regression lines per vegetation type were estimated, many of which were significantly different from each other. In most cases the intercepts were different, but in some cases their slopes differed as well. The results show that Ellenberg indicator values for acidity and moisture appear to be biased towards the values that experts expect for the various phytosociological classes. On the basis of the results, we advise to use Ellenberg IVs only for comparison within the same vegetation type.
While it is well established that ecosystems display strong responses to elevated nitrogen deposition, the importance of the ratio between the dominant forms of deposited nitrogen (NH(x) and NO(y)) in determining ecosystem response is poorly understood. As large changes in the ratio of oxidised and reduced nitrogen inputs are occurring, this oversight requires attention. One reason for this knowledge gap is that plants experience a different NH(x):NO(y) ratio in soil to that seen in atmospheric deposits because atmospheric inputs are modified by soil transformations, mediated by soil pH. Consequently species of neutral and alkaline habitats are less likely to encounter high NH(4)(+) concentrations than species from acid soils. We suggest that the response of vascular plant species to changing ratios of NH(x):NO(y) deposits will be driven primarily by a combination of soil pH and nitrification rates. Testing this hypothesis requires a combination of experimental and survey work in a range of systems.
When humans will settle on the moon or Mars they will have to eat there. Food may be flown in. An alternative could be to cultivate plants at the site itself, preferably in native soils. We report on the first large-scale controlled experiment to investigate the possibility of growing plants in Mars and moon soil simulants. The results show that plants are able to germinate and grow on both Martian and moon soil simulant for a period of 50 days without any addition of nutrients. Growth and flowering on Mars regolith simulant was much better than on moon regolith simulant and even slightly better than on our control nutrient poor river soil. Reflexed stonecrop (a wild plant); the crops tomato, wheat, and cress; and the green manure species field mustard performed particularly well. The latter three flowered, and cress and field mustard also produced seeds. Our results show that in principle it is possible to grow crops and other plant species in Martian and Lunar soil simulants. However, many questions remain about the simulants' water carrying capacity and other physical characteristics and also whether the simulants are representative of the real soils.
This version available http://nora.nerc.ac.uk/8737/ NERC has developed NORA to enable users to access research outputs wholly or partially funded by NERC. Copyright and other rights for material on this site are retained by the authors and/or other rights owners. Users should read the terms and conditions of use of this material at http://nora.nerc.ac.uk/policies.html#access This document is the author's final manuscript version of the journal article, incorporating any revisions agreed during the peer review process. Some differences between this and the publisher's version remain. You are advised to consult the publisher's version if you wish to cite from this article.
www.esajournals.orgContact CEH NORA team at noraceh@ceh.ac.ukThe NERC and CEH trade marks and logos ('the Trademarks') are registered trademarks of NERC in the UK and other countries, and may not be used without the prior written consent of the Trademark owner. Field observations and experimental data of effects of nitrogen (N) deposition on plant species 2 diversity have been used to derive empirical critical N loads for various ecosystems. The great 3 advantage of such as approach is the inclusion of field evidence, but there are also restrictions, 4 such as the absence of explicit criteria regarding significant effects on the vegetation, and the 5 impossibility to predict future impacts when N deposition changes. Model approaches can account 6 for this. In this paper, we review the possibilities of static and dynamic multi-species models in 7 combination with dynamic soil -vegetation models to (i) predict plant species composition as a 8 function of atmospheric N deposition and (ii) calculate critical N loads in relation to a prescribed 9 protection level of the species composition. The similarities between the models are presented, but 10 also several important differences, including the use of different indicators for N and acidity and 11 the prediction of individual plant species versus plant communities. A summary of the strengths 12 and weaknesses of the various models, including their validation status, is given. Furthermore, 13 examples are given of critical load calculations with the model chains and their comparison with 14 empirical critical N loads. We show that linked biogeochemistry-biodiversity models for N have 15 potential for applications to support European policy to reduce N input, but the definition of 16 damage thresholds for terrestrial biodiversity represents a major challenge. There is a also a clear 17 need for further testing and validation of the models against long-term monitoring or long-term 18 experimental datasets and against large-scale survey data. This requires a focused data collection in 19Europe, combing vegetation descriptions with variables affecting the species diversity, such as soil 20 acidity, nutrient status and water availability. Finally, there is a need for adaptation and upscaling of 21 the models beyond the regions for which dose-response relationships have been parameterised, to 22 make them ...
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