How to assess the development of 6000 habitats of national importance on the national, regional and local scale?

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1 Monitoring the Effectiveness of Habitat Conservation in Switzerland: How to assess the development of 6000 habitats of national importance on the national, regional and local scale? Klaus Ecker & Team «Wirkungskontrolle Biotopschutz Schweiz WBS» HABMON 2014, Freiberg Swiss Federal Research Institute WSL

2 Background: Loss of mire habitats Grünig Hotpsot 2007 Lachat et al Wandel der Biodiversität in der Schweiz seit Haupt, Bristol

3 Background: Loss of dry meadows Lachat et al Wandel der Biodiversität in der Schweiz seit Haupt, Bristol

4 Background Inventories of Biotopes of National Importance (1990- onwards) Dry meadows Mires Bogs: 545 sites / 0.04% Fens: 1171 sites / 0.5% Alluvial zones 2934 sites / 0.5% Amphibian breeding sites 283 sites / 0.55% 897 sites / 0.34% Nearly 6000 sites or 800 km 2 (= ~ 2 % of Switzerland)

5 Swiss Mire Monitoring Sampling design Indicators (e.g Mean plant indicator values of Ellenberg) Species lists (vascular plants and mosses) Random stratified sample of 130 mire objects (fens & bogs) Random stratified sample of about 100 polygons per objects

6 Swiss Mire Monitoring National trends Trends in mean plant indicators values soil moisture nutrient supply Proportion of sample mires showing change humus content bush cover no change negative positive Klaus Federal Office for the Environment FOEN

7 Swiss Mire Monitoring Regional trends Mean plant indicator values of nutrient supply Increase Decrease Klaus Federal Office for the Environment FOEN Nutrient supply

8 Swiss Biotope Monitoring New situation: Efficiency Monitoring of All Habitat Types of National Importance (or: What s goi g o i the 6000 ha itats of atio al i porta e? Long-term project; initiated by Federal Office for the Environment (FOEN)

9 Swiss Biotope Monitoring New situation: Efficiency Monitoring of All Habitat Types of National Importance or: What s goi g o i the 6000 ha itats of atio al i porta e? Long-term project; initiated by Federal Office for the Environment (FOEN) Main aims: Quantity: Trends in area of protected vegetation types Quality: Trends in ecological quality and conservation value Trend estimates on all spatial scales (local, regional, national) Early warning system for Swiss government and cantons Further guidelines: Survey updates every six years Long-term usability of data ("flexible data") Cost constraints: Efficient design, efficient data collection

10 Swiss Biotope Monitoring Our approach: Combination of remote sensing and in situ data (species lists) - Low costs - Full survey (all 6000 habitats) - Rather coarse indicators - High costs - Fewer sites (sample) - More detailed indicators Bergamini A., Holderegger R., 2012: Die Wirkungskontrolle Biotopschutz Schweiz: Ein Monitoringsprogramm im Aufbau. N+L Inside, März 2012(1),

11 Remote sensing Aerial images (resolution: cm) Full survey of all 6000 sites (~1000 sites/year) Main indicators: cover of trees, shrubs, open ground, water, infrastructure (e.g. roads, buildings) Each indicator visually estimated in 50x50 m grid Interpretation of old (from ) and recent images B/W (old) CIR (new

12 Fen in the northern Swiss alps: Afforestation Often severe changes are not known (despite high costs for management)

13 Bog restoration: Impounding and removal of shrubs m Changes in tree cover: -45% in the centre of the bog 0% in the surroundings

14 Insitu data (species data) Mires, alluvial zones, dry meadows Amphibian breeding sites Collection of vegetation data Data: Species list per plot (10 m 2 ); vascular plants (and bryophytes) Plots marked as permanent plots Collection of data on amphibian populations Data: Species list per site Full survey of all ponds 4 (lowlands) or 2 (mountains) times per year

15 Requirements for the Vegetation Sampling Design Oversampling of rare vegetation types and smaller regions (unequal probability sampling UPS) Avoid collection of redundant information from nearby sites (spreading) Minimize sample variance (balanced sampling) Some degree of clustering (budget constraints) Area estimates + confidence intervals Statistical inference has to consider selection probabilities effects of sampling techniques (UPS, spreading, balancing,...) Complex two-stage cluster sampling design Tillé & Ecker 2014: Complex national sampling design for long-term monitoring of protected dry grasslands in Switzerland. Environmental and Ecological Statistics 21: )

16 Requirements of the Vegetation Sampling Design Oversampling of rare vegetation types and smaller regions (unequal probability sampling UPS) Avoid collection of redundant information from nearby sites (spreading) Minimize sample variance (balanced sampling) Some degree of clustering (budget constraints) Area estimates + confidence intervals Complex two-stage cluster sampling design

17 Requirements of the Vegetation Sampling Design Oversampling of rare vegetation types and smaller regions (unequal probability sampling UPS) Avoid collection of redundant information from nearby sites (spreading) Minimize sample variance (balanced sampling) Some degree of clustering (budget constraints) Area estimates + confidence intervals Complex two-stage cluster sampling design Sites Plots Vegetation Alluvial zones 124 (out of 283) 2054 assessments Mires 242 (out of 1716) 2685 Dry meadows 400 (out of 2934) 2769 Total: 6750 Plots Amphibian assessments Amphibian breeding sites 238 (out of 897) -

18 Vegetation survey plan 2013 Sample objects 2013 Dry meadows Floodplains Mires Altitude 150 sites 1200 plots Efficient annual area estimation due to optimal sample distribution

19 Vegetation survey plan Example of a sample object of the dry meadows

20 Main principles for collection and handling of field data: species lists instead of interpreted data (like vegetation types) Many indicators can be derived from species lists in a reproducible way Examples: Vegetation types, mean indicator values, functional groups (e.g. habitat specialists), diversity indices, etc. Long-term value, useful even when questions are changing (and they will Problems Difficult species (e.g. Festuca spp., bryophytes) may be collected BUT: There will always be errors: false determination, overlooking (Vittoz et al. 2010, JVegScience: 5-10% will be missed) Saisonal variations (e.g. cover, geophytes)

21 Main principles for collection and handling of field data Minimize and handle errors & avoid bias Good botanists/zoologists + methodological training Randomization of observer errors "Everybody works everywhere Use of robust indicators: Proportions rather than count data Indicator values (e.g. mean Ellenberg indicator values) Mean Indicator value Species richness # overlooked species Temperature Nutrients Moisture 5% 10% 20% 30% 5% 10% 20% 30% 5% 10% 20% 30% species omitted by simulation Mean indicator values robust with respect to incomplete species lists (see also Ewald 2003, BAAE)

22 Trends on three spatial scales Local scale (site scale) Remote sensing: YES Trends in each of the 6000 objects Early warning system Amphibian survey: NO (YES) Vegetation survey: NO Regional & national scale Remote sensing: YES Amphibian survey: YES Vegetation survey: YES

23 Outlook retirement t

24 Acknowledgements BAFU: Sarah Pearson, Olaf Zieschang Team "Wirkungskontrolle" (WSL): Rolf Holderegger, Angéline Bedolla, Klaus Ecker, Elizabeth Feldmeyer, Christian Ginzler, Ulrich Graf, Helen Küchler, Meinrad Küchler, Lucinda Laranjeiro Dos Santos, Benedikt Schmidt, Barbara Schneider, Daniel Uebersax, Lars Waser Field staff: Rolf Hangartner, Norbert Schnyder, Niklaus Müller, Markus Maier, Laurent Juillerat, Mary, Leibundgut, Adrian Aebischer, Adrian Borgula, Audrey Megali, Bernhard Egli, Claude Meier, Jérôme Pellet, Joggi Rieder, Jonas Barandun, Kurt Grossenbacher, Manfred Steffen, Maria Rasmussen, Mario Lippuner, Niklaus Peyer, Paul Marchesi, Petra Ramseier, Silvia Zumbach, Tiziano Maddalena, Ursina Tobler More information:

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