1. Selecting a biodiversity metric for the UK response to the CCE Call for Data by comparison with specialist judgement Ed Rowe, Adriana Ford-Thompson, Susan Jarvis, Jane Hall, Mike Ashmore, Don Monteith, Pete Henrys, Chris Evans & Simon Smart

2. Outline 1.Effects of nitrogen (N) pollution on habitats and biodiversity 2.Consultation on metrics for the UK response to the Call for Data 3.Relating conclusions to MADOC-MultiMOVE outputs 4.Setting up MADOC-MultiMOVE for sites 5.Response to the Call for Data 6.Conclusions & questions

3. Nitrogen effects on habitats Direct toxicity (mainly NH 3 ) Soil acidification Increased ground-level ozone Ground-level shading  Major global driver of biodiversity loss “For terrestrial ecosystems, land-use change probably will have the largest effect [on biodiversity], followed by climate change, nitrogen deposition, biotic exchange, and elevated carbon dioxide concentration.” Sala et al 2000, Science 287: 1770-1774

4. Nitrogen effects on ‘biodiversity’ Stevens et al. 2011 JNCC report #447 Probability of presence Leucobryum glaucum in upland heath Sphagnum denticulatum in upland heath 1.0 0.8 0.6 0.4 0.2 0.0 Total nitrogen deposition kg ha -1 yr -1 10 20 30 Some species are favoured by N Both L. glaucum and Sphagnum spp. are mentioned in Annex V of the EU Habitats Directive Sphagnum is important for bog functioning

5. What is biodiversity? Biodiversity Ecological science “the totality of genes, species, and ecosystems of a region” Species richness = number of species in an area Evenness Shannon index Natural heritage ecosystem functioning Habitat integrity Food webs connectivity trophic complexity ‘ecosystem engineers’ traditional farming re-wilding landscape history structurally important species similarity to ‘reference’ / ideal Species conservation avoiding local extinction avoiding global extinction Legal requirements Red List ‘scarcity and decline’ ‘impoverishment of experience’ ‘right to exist’ Economics delivery of Ecosystem Services EU Habitats Directive Convention on Biological Diversity Aichi targets Global Strategy for Plant Conservation 

6. Endpoint metrics for biodiversity Which changes in habitats are important? Who decides?

7. Methods Defra (UK government Department for the Environment, Food and Rural Affairs) funded two projects: DivMet1 (May-Oct 2013) operationalising a metric DivMet2 (Feb-May 2014) scenario modelling to meet the Call for Data DivMet1 methods Key informants: Habitat Specialists from the Statutory Nature Conservation Agencies Semi-structured interviews to elucidate the thinking that underlies habitat assessment Habitat Specialists were asked to rank a set of examples of their habitat (species lists with abundances), and these rankings were compared with rankings based on: species-richness similarity to a reference assemblage (NVC community) abundance of functionally/structurally important groups (e.g. Sphagnum in bogs) presence of positive and/or negative indicator-species mean ‘Ellenberg N’ score

8. Semi-structured interviews: key messages Habitat quality is viewed in terms of vegetation composition, but also more holistically as the result of a combination of features, including habitat structure and physical attributes such as water table dynamics. The Common Standards Monitoring guidance acts as the key framework for much of the habitat quality assessment; however, some tailoring of CSM indicator-species lists has improved local applicability and practicality for use by local monitoring officers. Species that are structurally or functionally important have particular value, especially in wetland habitats. They may have increasing relevance to other habitats in the face of climate change. Scarce species provide added value to a habitat, and can be important for site designation. However, they are not usually a dominant criterion for assessing habitat quality, in part because they do not occur on enough sites to be widely applicable as indicators. Assessing cover of species-groups can be a useful tool for inferring habitat quality. However, species-groups may not always provide the level of detail necessary, for example for rare sub- communities or as a proxy for environmental conditions. There is considerable variation in the examples of each habitat that are seen as high quality, so it would be very difficult to define a reference community.

9. Which metrics correspond to Specialists’ assessments? Species-richness Ranking according to metric Ranking according to specialists CSM positivesCSM negatives CSM +ves minus -ves Forb / Total cover Similarity to reference (mean)Similarity to reference (max) Mean Ellenberg N Rowe et al. 2014 DivMet/AQ0828 report E1 Dry grasslands

10. BogsGrasslandsHeathlands 1.1 2.3 0.0 1.8 0.0 1.0 0.9 1.0 0.9 Mean number of ‘significance stars’ species-richness correlated with Specialists’ assessments for grasslands and some heathlands Overview of potential metrics n positive indicator-spp. was correlated for the most habitats n negative indicator-spp. was useless abundance of structural groups was correlated only for bogs Czekanowski similarity to reference was sometimes correlated mean ‘Ellenberg N’ was sometimes correlated

11. Positive indicator-species Aulacomnium palustreMenyanthes trifoliataSphagnum palustre Carex rostrataPotentilla erectaSphagnum subnitens Carex lasiocarpaPotentilla palustrisSphagnum squarrosum Carex nigraRanunculus flammulaSphagnum teres Epilobium palustreRumex acetosaStellaria uliginosa Eriophorum angustifoliumSphagnum cuspidatumSuccisa pratensis Galium palustreSphagnum denticulatumViola palustris Lychnis flos-cuculiSphagnum fallax e.g. Positive indicator-species for D2.2 Poor fens and soft-water spring mires 20-60 species per habitat Selected by habitat specialists Generally typical or distinctive for the habitat, but not very scarce Issues: Not always well-defined (e.g. “Carex spp.”) Habitat classes do not always map easily onto EUNIS... but a current JNCC project will define indicators for EUNIS classes Algorithm for ranking used number of positive indicators

12. Applying the principle to MADOC-MultiMOVE outputs Biodiversity metric (HQ) = mean prevalence-corrected habitat suitability for locally-present positive indicator-species e.g. Esgyrn Bottom (D2.2 soft-water mire) Habitat Suitability, rescaled by prevalance

13. MADOC calibration Drivers Abiotic outputs Floristic trait-means

14. MADOC calibrated for 18 sites ~ alkalinity~ fertility~ canopy height D1.1 raised bogs D1.2 blanket bogs D2.2 poor fens and soft-water spring mires E1.2 perennial calcareous grassland and basic steppes E1.7 closed dry acid and neutral grassland E2.2 Low and medium altitude hay meadows E3.5 moist or wet oligotrophic grassland F4.1 wet heath F4.2 dry heath

15. Floristic responses Porton Down (E1.2 calcareous grassland) Gothenburg scenario 15 of 80 positive indicator-species Many species show flat responses The few species that are at the edge of their niche respond

16. Floristic responses Porton Down (E1.2 calcareous grassland) Background scenario 15 of 80 positive indicator-species Many species show flat responses The few species that are at the edge of their niche respond

17. Response to the Call for Data 2012-14 EUNISSiteGOT2500BKG2500% change D1.1 raised bogsa) Whim Moss1.341.8639 b) Thorne Moor1.402.1554 D1.2 blanket bogsa) Moor House3.014.7056 b) Mynydd Llangatwyg1.151.5031 D2.2 poor fens and soft-water spring miresa) Esgyrn Bottom0.841.2752 b) Cors Llyn Farch a Llyn Fanod 3.644.8032 E1.2 perennial calcareous grassland and basic steppes a) Porton Down3.867.6498 b) Newborough3.807.4396 E1.7 closed dry acid and neutral grasslanda) Snowdon3.023.082 b) Friddoedd Garndolbenmaen 1.573.0393 E2.2 Low and medium altitude hay meadows a) Eades Meadow0.251.01304 b) Piper's Hole0.270.94249 E3.5 moist or wet oligotrophic grasslanda) Sourhope0.950.994 b) Whitehill Down2.7511.30311 F4.1 wet heatha) Glensaugh1.321.8641 b) Cannock Chase0.480.6535 F4.2 dry heatha) Skipwith Common0.791.2862 b) Eryri1.503.11108 Habitat Quality

18. Conclusions, and questions arising 1.Habitat-suitability for positive indicator-species works as a biodiversity metric 2.MADOC-MultiMOVE can be set up using only floristic data and location 3.Changes in soil pH, available N, C/N and (potentially) canopy-height, and their effects on habitat-suitability for individual species, can be predicted. 4.How will HQ values change with changes in the indicator-species lists? 5.What are typical values of HQ for sites that are in good, damaged or recovering condition? 6.Is it possible to set a meaningful threshold HQ value for each habitat? 7.Would expressing HQ as a proportion-of-maximum for the site help? 8.Many N impacts will be via effects on canopy height; will management compensate?