Key ecosystem engineers in estuarine vegetation – Their niches, traits, and services for coping with hydrodynamic stress
vorgelegt von Diplom-Geoökologin Maike Heuner geb. in Salzkotten
von der Fakultät VI – Planen Bauen Umwelt der Technischen Universität Berlin zur Erlangung des akademischen Grades Doktor der Naturwissenschaften - Dr. rer. nat. - genehmigte Dissertation
Promotionsausschuss: Vorsitzender: Prof. Dr.-Ing. Reinhard Hinkelmann, TU Berlin Gutachterin: Prof. Dr. Birgit Kleinschmit, TU Berlin Gutachter: Prof. Dr. Boris Schröder-Esselbach, TU Braunschweig Gutachter: Prof. Dr. Stijn Temmerman, Universität Antwerpen Tag der wissenschaftlichen Aussprache: 19. Mai 2016
Berlin 2016 D 83
Prologue
“Education is one of the blessings of life – and one of its necessities.”
Malala Yousafzai, 2014
Acknowledgement – Danksagung V
Acknowledgement – Danksagung
Bedanken möchte ich mich bei allen Kollegen und Freunden sowie bei meiner Familie, die mich in den letzten Jahren ermutigt und unterstützt haben, dieses Werk zu erstellen.
Als erstes ein großes Dankeschön an Michael Schleuter, der mir die Möglichkeit gegeben hat, Arbeit und Doktorarbeit miteinander zu kombinieren. Weiterhin möchte ich mich ganz herzlich bei meiner Doktormutter Birgit Kleinschmit und meinem Doktorvater Boris Schröder-Esselbach für ihr Engagement bedanken, meine Ideen und Gedanken zu unterstützen und zu hinterfragen. Gerade für euren Einsatz im letzten Jahr bin ich euch sehr dankbar! Boris, danke auch für den tollen Vorschlag, Wellenexperimente durchzuführen. I would also like to express my sincere thanks to Stijn Temmerman, Tjeerd Bouma, and Jonas Schoelynck for their great collaboration. You showed me how to think not only scientifically but at the same time, simply and clearly too. Tjeerd, also thank you very much for using the “NIOZ flume”.
Ganz herzlich möchte ich meiner Zimmerkollegin Beatrix (Susi) Konz danken, die für mich immer ein offenes Ohr hatte, die meine Trübsal und meinen Jubel so manches Mal miterlebt hat und mich in den schlimmeren Fällen mit ihrer Lebenserfahrung wieder zurück auf den Boden der Tatsachen geholt hat. Ebenfalls danke ich dir, Susi, für deine Unterstützung bei der Grafikerstellung. Tausend Dank auch an Alexandra Silinski und Arnd Weber, die tolle Beiträge zu den verschiedenen Zeitschriftenartikeln geleistet haben. Danke Alex, dass die anstrengende Zeit der Wellenexperimente doch auch eine sehr schöne Zeit war, in der du mir die belgische Lebensweise mit ihren vielfältigen Pralinen nähergebracht hast. Arnd, danke für deine Programmierkünste und deine Gelassenheit.
Ein großes Dankeschön widme ich Uwe Schröder und Elmar Fuchs, von denen ich nicht nur in den ersten Jahren viel gelernt habe. Danke Uwe, dass du so gut wie immer Zeit hattest, wenn ich deinen Rat brauchte. Elmar, danke für die grandiose Idee die Wellenexperimente in Zusammenarbeit mit den belgischen und niederländischen Kollegen durchzuführen. I am very grateful to Peter Troch for the use of the Ghent flume facilities, for the great collaboration undertaken in setting up the design of the flume experiments, as well as for his ideas about analysing the data. Furthermore, I would like to express many thanks to Tom Versluys and Herman van der Elst from Ghent University for their ideas and great technical support. A big thank you to Bas Koutstaal from the Royal Netherlands Institute of Sea Research for growing the plants for the flume experiments so successfully. I would also like to thank Steven Dauwe, Ann Waeytens, Olivier D’hondt, Frederik De Keyzer, Carolin VI Key ecosystem engineers in estuarine vegetation
Schmidt-Wygasch, and Maria Carambia for their valuable assistance before, during, and after the flume experiments. I thank Sara Puijalon very much for performing the biomechanical plant measurements and Patrick Meire for his valuable comments in our meetings about our flume experiments. Eva Mosner möchte ich danken für die vielen wertvollen Tipps, die man zum „Rumdoktern“ braucht. I’m also grateful for all the helpful comments from the anonymous reviewers on the manuscript drafts. Ich möchte mich ebenfalls herzlich bedanken bei Dominica Campman und allen BfG-Kollegen, die mich unterstützt und beraten haben.
Für die guten Daten, die ich zur Verfügung hatte, möchte ich mich bei folgenden Personen bedanken:
q Herbert Brockmann und Lars Schuhmann (BfG) q Norbert Winkel und Rita Seiffert (BAW) q Martin Leuzinger, Thomas Jansen und Hannes Sahl (WSA Hamburg) q Diedrich Lange, Klaus Scheland (WSA Bremerhaven) und Volker Steege (ehemals WSA Bremerhaven, jetzt BMVI) q Jörg Petersen und seinem Team (nature consult)
Ein Dankeschön an den Landkreis Steinburg für die Erlaubnis der Pflanzenentnahme zur Durchführung der Wellenexperimente. Ich danke auch den nicht namentlich genannten Personen, die zur Datenerfassung beigetragen oder mich organisatorisch unterstützt haben. Das Forschungsprogramm KLIWAS mit dem Projekt „Ästuarvegetation“ ist vom Ministerium für Verkehr und digitale Infrastruktur finanziert worden.
Unendlich dankbar bin ich meiner rücksichtsvollen Familie, die mir so viel Zeit und Geduld für meine Arbeit insbesondere an den Wochenenden geschenkt hat.
Summary VII
Summary
One key service of estuarine marsh vegetation is flooding and storm surge protection by virtue of them dissipating the energy of waves and currents. This vegetation-based shoreline protection is more adaptable to sea-level rise than traditional engineered methods of self-generated marsh elevation. In order to promote this service in estuaries, the understanding of the interaction between hydrodynamic forces, traits of vegetation-forming species, species habitat conditions, and their capacity of ecosystem engineering has to be improved. Therefore, we investigated the emergent macrophytes Scirpus tabernaemontani, Scirpus maritimus, and Phragmites australis as key ecosystem engineers in the Elbe and Weser estuaries being model systems for altered estuaries in Europe. We performed studies in a geographic information system as well as full-scale wave-flume experiments. Furthermore, we estimated species distribution models and applied them to real and virtual restoration sites. The niches of species exposed to hydrodynamic stress were described and their boundaries defined for the two estuaries. For several wave conditions causing hydrodynamic stress for plants, we measured (1) effects of drag force and scouring as well as (2) effect and response traits. These findings were related to the described species niches differentiated in elevational distribution. In constructing the niches, the traits reflect different strategies of stress resistance. Therefore, we concluded that traits are directly in balance with the functional niche. Furthermore, the results of wave dissipation showed that the ecosystem engineering capacities of the species are adapted to their constructed niches affected by different hydrodynamic stress. To strengthen the service of vegetation-based shoreline protection, shorelines have to be restored. Using the species distribution models, we found that (1) restoring shallow waters increases the habitat suitability for marsh plants on tidal flats, and (2) restorations combined with engineering elements such as groynes have positive effects on the shoreline habitat compared to shorelines with stone linings. Additionally, we verified that equations usually used for calculating the hydraulic effects of obstacles can also be applied to predict drag and scour depth of plants. However, uncertainty increases with increasing wave periods and with the complexity in plant morphology. In future, these equations can help to describe the suitable habitats of these plant species. The thesis concludes by providing a response to why and how hydrodynamic forces drive plant species niches of shoreline vegetation in estuaries. Further points discussed are the potential of vegetation-based shoreline protection in estuaries, the existence of limiting factors, and the missing insights for quantifying and assessing the protective ecosystem service provided by estuarine vegetation. VIII Key ecosystem engineers in estuarine vegetation
Zusammenfassung IX
Zusammenfassung
Im Ästuar schützt die Vorlandvegetation durch die Reduzierung von Strömungen und Wellenenergie Ufer und Deich vor der Zerstörungskraft der Sturmfluten. Gegenüber dem technischen Uferschutz ermöglicht diese Ökosystemleistung eine bessere Anpassung an den Meeresspiegelanstieg durch resultierende Sedimentablagerungen. Um diese Ökosystemleistung effektiv zu stärken, müssen die Wechselwirkungen zwischen den hydrodynamischen Kräften, den Merkmalen der dominanten Arten im Vorland, deren Standortbedingungen und ihren Fähigkeiten, den eigenen Lebensraum zu verändern, besser verstanden werden. Wichtige Ökosystemingenieure wie die Röhrichte Scirpus tabernaemontani, Scirpus maritimus, und Phragmites australis werden im Elbe- und Weserästuar untersucht. Sie sind repräsentativ für Ästuare in Europa. Daten aus geographischen Informationssystemen und aus durchgeführten Wellenexperimenten werden analysiert. Habitateignungsmodelle werden erstellt und für potenzielle Renaturierungsgebiete angewendet. Die Nischen der Röhrichte, die dem hydrodynamischen Stress ausgesetzt sind, werden mit diesen Modellen beschrieben und dadurch die Grenzen der Verbreitungsgebiete in den genannten Ästuaren definiert. Für verschiedene Wellensituationen, die den hydrodynamischen Stress verursachen, werden die Widerstandskräfte der Pflanzen und die Erosion um ihren Stängel gemessen. Des Weiteren werden Pflanzenmerkmale bestimmt und in Beziehung zu den hydrodynamischen Belastungen gesetzt. Die Nischen, die die Röhrichte sich selber schaffen, unterscheiden sich in ihrer Höhe zum mittleren Tidehochwasser. Zur Nischenbildung verfolgen sie verschiedene Strategien der Stressresistenz, die sich in den Pflanzenmerkmalen widerspiegeln. Diese stehen dadurch in direkter Beziehung zum Habitat. Die Pflanzen weichen dem hydrodynamischen Stress entweder mit sehr biegsamen Stängeln aus, wenn er zu stark ist, oder sie tolerieren ihn mit festeren Stängeln. Auch die Wellenreduktionleistung der einzelnen Arten ist dem Habitat angepasst, das aufgrund der verschiedenen Höhenlagen unterschiedlich starken Belastungen ausgesetzt ist. Um die Ökosystemleistung zu stärken, müssen versteinte Ufer zurückgebaut werden, um ihnen mehr Raum zur Entwicklung zu geben. Mit Hilfe der Habitateignungsmodelle haben wir herausgefunden, dass durch die Schaffung von Flachwasserzonen die Fläche der geeigneten Habitate zunimmt. Weiterhin hat sich gezeigt, dass der Bau von Buhnen gegenüber versteinten Ufern zu einer Verbesserung der Habitateignung führt. Mit den erhobenen Messungen in den Experimenten haben wir Formeln getestet, die üblicherweise benutzt werden, um die hydraulischen Effekte von Hindernissen zu berechnen. Die Tests sind positiv ausgefallen: Die Formeln können zur Berechnung der Stängelerosion und der Widerstandskraft der Pflanzenstängel angewendet werden. Nur muss X Key ecosystem engineers in estuarine vegetation
beachtet werden, dass die Berechnungsunsicherheit bei längeren Wellenperioden und komplexerer Pflanzenmorphologie zunimmt. In Zukunft können diese Formeln helfen, geeignete Habitate für Röhrichte zu schaffen. Die Dissertation führt die verschiedenen Ergebnisse in einer Synthese zusammen, in der sie beantwortet, warum und wie hydrodynamische Belastungen die Habitate der Röhrichte im Ästuar beeinflussen. Des Weiteren wird diskutiert, welches Potenzial die Wellenreduktionsleistung durch Röhrichte besitzt, welches ihre limitierenden Faktoren sind und welcher Forschungsbedarf besteht, um die Ökosystemleistung in der Fläche zu berechnen und monetär zu bewerten. Content XI
Content
Acknowledgement – Danksagung ...... V
Summary ...... VII
Zusammenfassung ...... IX
Content ...... XI
List of Figures ...... XV
List of Tables ...... XIX
List of Abbreviations and Symbols ...... XXI
Chapter I Introduction ...... 1
1 Vegetation in regularly flooded marshes, its drivers, niches, and functions ...... 3
2 Vegetation-based shoreline protection in estuaries ...... 6
3 Emergent macrophytes as key ecosystem engineers in navigable estuaries ...... 8
4 Objective and research questions ...... 11
5 Elbe and Weser estuaries as case studies ...... 12
6 Methods to investigate key ecosystem engineers in estuarine vegetation ...... 13
6.1 Descriptive data ...... 13
6.2 Modelling approach ...... 14
6.3 Experimental approach ...... 15
7 Structure of this thesis and detailed objectives by chapter...... 15
Chapter II Niche Variations ...... 19
Abstract ...... 21
1 Introduction ...... 21
2 Material and methods ...... 24
2.1 Study areas ...... 24
2.2 Study design and digital data ...... 27
2.3 Statistical analyses ...... 29
3 Results ...... 30 XII Key ecosystem engineers in estuarine vegetation
3.1 Elevational differences in plant zonation and species zones ...... 30
3.2 Elevational differences in marsh edges ...... 30
3.3 Relations and thresholds between the species elevational distributions and the exposure gradient along the marsh edges ...... 32
4 Discussion ...... 34
Acknowledgements ...... 40
Chapter III Niches, Traits and Ecosystem Engineering ...... 41
Abstract ...... 43
1 Introduction ...... 43
2 Materials and methods ...... 47
2.1 Plant material ...... 47
2.2 Flume experiment for measuring ecosystem engineering effects (exp. 1) (question i)...... 47
2.3 Flume experiments for measuring plant responses to wave impact (exp. 2) (question ii) ...... 48
2.4 Measurements of biomechanical plant traits (question iii) ...... 50
2.5 Analysis of plant strength (question iv) ...... 51
2.6 Calculation of cost efficiency (question v) ...... 52
3 Results ...... 52
3.1 Ecosystem engineering effects (question i) ...... 52
3.2 Plant responses to wave impact (question ii) ...... 52
3.3 Plant resistance (questions iii and iv) ...... 55
3.4 Cost efficiency (question v) ...... 55
4 Discussion ...... 58
5 Conclusion ...... 61
Acknowledgements ...... 62
Box 1 Wave attenuation of Scirpus species in comparison with other marsh species ...... 63
Chapter IV Restoring niches ...... 65
Abstract ...... 67
1 Introduction ...... 67 Content XIII
2 Materials and methods ...... 70
2.1 Species and study sites ...... 70
2.2 Digital species sampling ...... 72
2.3 Environmental predictor variables ...... 73
2.4 Statistical modelling and model evaluation ...... 73
2.5 Model validation ...... 74
2.6 Restoration sites ...... 74
2.7 Modifying the shoreline terrain ...... 75
2.8 Model applications ...... 77
2.9 Assessing the restoration measures ...... 77
3 Results ...... 78
3.1 Effects of predictors ...... 78
3.2 Modell performance ...... 78
3.3 Changes in terrain and habitats after restoration ...... 80
4 Discussion ...... 82
5 Conclusion ...... 86
Acknowledgements ...... 87
Chapter V Wave effects governed the Niches ...... 89
Abstract ...... 91
1 Introduction ...... 91
2 Material and Methods ...... 94
2.1 Theory ...... 94
2.1.1 Hydrodynamic parameters ...... 94
2.1.2 Scour ...... 95
2.1.3 Drag forces ...... 96
2.2 Experimental design ...... 97
2.3 Plant material ...... 99
2.4 Measurements of biomechanical traits ...... 100 XIV Key ecosystem engineers in estuarine vegetation
2.4.1 Bending tests ...... 101
2.4.2 Tensile tests ...... 101
2.5 Scouring ...... 101
2.6 Drag force ...... 102
2.7 Statistical analysis ...... 102
3 Results ...... 103
3.1 Hydrodynamic parameters ...... 103
3.2 Plant properties ...... 104
3.3 Scour ...... 106
3.4 Drag forces ...... 107
4 Discussion ...... 113
5 Conclusion ...... 116
Acknowledgments ...... 117
Box 2 Comparison of cross-sectional areas between the plant types ...... 118
Box 3 Drag force is governed by the water level ...... 119
Chapter VI Synthesis ...... 121
1 Conclusion ...... 123
1.1 The insights gained ...... 123
1.2 Vegetation driven by hydrodynamic stress ...... 125
1.3 Vegetation as shoreline protection ...... 130
2 Research recommendations ...... 133
References ...... 137
Appendix ...... 167
List of author’s publications related to the thesis ...... 209
Author’s Declaration ...... 211
List of Figures XV
List of Figures
Fig. I 1-1 Abiotic gradients in estuaries ...... 4 Fig. I 5-1 Overview of the Weser and Elbe estuary in 2015. Picture (RGB modification) from the satellite Sentinel 1-A ...... 13 Fig. I 7-1 The research subject and the investigated key issues framed by introduction and systhesis ...... 16 Fig. II 2-1 Overview of the study areas Weser (lower Weser km 26-70) and Elbe (Elbe km 632-701) with the vertical gradient elevation above mean high water (m), data source see A-1-1 Table...... 24 Fig. II 2-2 Morphological patterns in the Elbe and Weser estuary and their marsh zonation...... 26 Fig. II 3-1 Marsh zonation along the elevational gradient and the distributions along the marsh edge ...... 31 Fig. II 3-2 Distance thresholds of the species distribution from the thalweg of the main channels (dark green) whereas the species distributions along the anabranches (light green) exhibit no threshold...... 34 Fig. II 4-1 Vegetation formations and the conceptual model of species zonation in estuaries...... 38 Fig. III 1-1 Analysed traits of two marsh pioneers and their response parameters to wave impact...... 45 Fig. III 1-2 The appearance and zonation of the studied species...... 46 Fig. III 2-1 Physical model of wave flume experiments for testing drag force, scouring and bending angle after wave impact of S. tabernaemontani and S. maritimus...... 49 Fig. III 3-1 Wave attenuation behind a patch 1.6 m length, in relation to stem densities and in relation to dry submerged biomass for the species S. maritimus (+) and S. tabernaemontani (*)...... 53 Fig. III 3-2 Mean values of plant responses to wave impact with standard error (n = 10) for S. maritimus (S. mar.) vs. S. tabernaemontani (S. tab.)...... 54 Fig. III 3-3 Mean values of plant responses to tensile and bending forces with standard error for S. maritimus (S. mar., n = 19) vs. for S. tabernaemontani (S. tab., n = 20)...... 56 XVI Key ecosystem engineers in estuarine vegetation
Fig. III 3-4 Mean effect traits with standard errors (n = 10) for the species S. maritimus (S. mar.) and S. tabernaemontani (S. tab.)...... 57 Fig. III 4-1 Conceptual framework of the relationships between traits which respond to the environment drivers (response traits) and traits which determine the effects of plants and thus the effect of ecosystem engineering (effect traits) in a wave-exposed intertidal habitat...... 58 Fig. IV 2-1 Investigation sites...... 71 Fig. IV 2-2 The method of modifying the shoreline terrain is illustrated by 15 exemplary shoreline profiles before and after modification...... 76 Fig. IV 3-1 Response curves showing the predicted occurrence probabilities of S. tabernaemontani (A), S. maritimus (B), and P. australis (C) differentiated by the river classes...... 79 Fig. IV 3-2 Restoration site Lühesand with stone filling (light grey)...... 80 Fig. IV 3-3 Restoration site Julssand with stone filling and groynes (light grey) and two restoration scenarios...... 81 Fig. V 2-1 Sketch of the physical model in the wave flume...... 98 Fig. V 2-2 Tested plants...... 100 Fig. V 3-1 Mean values ± SE of plant properties per plant type...... 105 Fig. V 3-2 KC number versus relative scour for the sticks (a) and the different plant types at equal water levels...... 107 Fig. V 3-3 Peak drag forces as function of wet frontal area for the sticks (a) and the, respectively, concerned plants at the three tested water levels (b-d)...... 108 Fig. V 3-4 Observed peak drag forces (N) on the three plant types plotted against corresponding drag forces calculated with the Morison equation (eq. 8: F =
1/2ȡACdv² (N)), with Cd = 1...... 109 Fig. V 3-5 Calculated vs. measured peak drag forces (N) divided by the experimentally obtained drag coefficients for (a) the sticks and (b-d) the three plant types, respectively...... 110 Fig. V 3-6 Non-linear fits between the experimentally obtained drag coefficients, wave- induced peak velocities and ReD ...... 112 Fig. Box 2-1 Mean values of plant cross-sectional area with standard error for the plant types ...... 118 List of Figures XVII
Fig. Box 3-1 Mean drag force with standard error of the adult plants S. maritimus and S. tabernaemontani for the two highest water levels...... 119 Fig. Box 3-2 The percentage of stem height which is flooded in relation to absolute drag force...... 120 Fig. VI 1-1 Detected causes and effects for the variability in elevational niches of estuarine marsh vegetation by illustrating the parameters investigated...... 124 Fig. VI 1-2 Shoreline profiles. The constant line represents shoreline profiles formed by waves with short wave periods caused by wind. The dashed line depicts waves with long wave periods greater than or equal to 10 s. This shoreline profile has a concave shape...... 131
XVIII Key ecosystem engineers in estuarine vegetation
List of Tables XIX
List of Tables
Table II 2-1 Parameters of the tidal-fluvial stress gradient representing the main channels and anabranches of the Elbe and Weser estuary...... 25 Table II 3-1 Results of the Kruskal-Wallis rank sum test for the marsh (elevational differences between the three species and between the river classes) and marsh edge...... 32
Table II 3-2 Spearman rank correlation coefficient (ȡsp) for relationships between mean bank slope (°) and distance from the thalweg (distance, m) and the results of the strongest regression model testing the dependent variable elevation above MHW versus the independent variables (x) distance or slope and their quadratic terms...... 33 Table III 4-1 The elevational habitat responses linked with the analysed traits and the contrasting findings between S. maritimus and S. tabernaemontani which result in two strategies of stress resistance...... 61 Tab. Box 1-1Normalized wave attenuation performed by S. tabernaemontani and S. maritimus differentiated in three stem density per m²...... 63 Table IV 3-1Species distribution models (regression coefficients with their standard errors
and p-values), and performance criteria (cut-off values of popt and pfair are specified and the classification rate is shown in percent.) ...... 79 Table IV 3-2Spatial balance of modelled habitats and their respective shoreline zones (sz in m²) changed by virtually modified shorelines...... 82 Table V 3-1 Overview of hydrodynamic conditions in the flume...... 104
Table V 3-2 Overview of linear model coefficients (equal to Cd,exp)...... 111
Table V 3-3 Summary of determined coefficients (a, b) and Vogel number (ȕ) for Cd,exp as
function of ࢜ (eq. 10) and ReD (eq. 12) (see Fig. V 3-6)...... 111
XX Key ecosystem engineers in estuarine vegetation
List of Abbreviations and Symbols XXI
List of Abbreviations and Symbols
- trait specification which is less or smaller as compared to the traits of the other species (Table III 1-1) % percent S pi (3.14159265359…)
ߦ Iribarren number ‘ second “ minute + traits which are more or higher as compared to the traits of the other species (Table III 1-2) ± no trait difference (Table III 1-3) times ° degree ‰ per mill 2D two dimensions 3D three dimensions a and b constants that are empirically derived for each plant species and physical model a actual drag coefficient a short axes A wet frontal area of the obstacle (m²) Abs. Absatz, section AUC area under the curve b regression slope BAW Bundesanstalt für Wasserbau BfG Bundesanstalt für Gewässerkunde Bhv Bremerhaven BMVI Federal Ministry of Transport and Digital Infrastructure BSi Biogenic Silica C Celsius c long axes C. C. Gmel. Carl Christian Gmelin (1762-1837) Cav. Antonio José Cavanielles (1745-1804)
Cd drag coefficient cf. compare (lat. conferre) cm centimeter XXII Key ecosystem engineers in estuarine vegetation
cv cross validation D Diameter of the obstacle (m) d.f. degree of freedom
D50 median grain size
dcharac characteristic length (m) DMC digital mapping camera doi digital object identifier E East, describes the longitude E Young’s modulus (unit: pascal) EC European Commission EEA European Economic Area EEC Ecosystem Engineering Capacity (Chapter III) EEC European Economic Community (Chapter IV) e.g. for example et al. and others (lat. et alia) exp. experiment F drag force F regards to Ronald Fisher, the ratio explained variance/ unexplained variance or ratio between-group variability/within group variability Fr Froude number g gramme g gravitational acceleration, 9.81 m/s2 GIS geographic information system h hour H Kruskal-Wallis test statistic
H0 wave height (m) in deep water ha hectare
Hmean average wave height HMWB heavily modified water bodies HSD honestly significant difference HU habitat units HW high water level Hz hertz I second moment of area (m4) ICP-OES inductively coupled plasma optical emission spectrometry i.e. that is (lat. id est) List of Abbreviations and Symbols XXIII
Int Intschede KC Keulegan-Carpenter kg kilogramme kJ kilo joule KLIWAS climate and water, research programme “Impacts of climate change on waterways and navigation – Searching for options of adaptation” km kilometer km² square kilometer L. Carl von Linné (1707-1778)
L0 deep water wavelength (m) LW low water m meter M mole m³ cubic metre MEA Millennium Ecosystem Assessment mg milligramme MHW mean high water MLW mean low water mm millimeter msm Multi-State Markov and Hidden Markov Models in continuous time N Newton N North, describes the latitude, appears together with E (longitude) n total number of observations n.s. not significant
Na2CO3 sodium carbonate NaCl salt NDa Neu Darchau NLWKN Niedersächsischer Landesbetrieb für Wasserwirtschaft, Küsten- und Naturschutz Ott Otterndorf p significance level Pa Phragmites australis
Pfair classification where sensitivity (proportion of correctly predicted occurrences) equals specificity (proportion of correctly predicted absences)
Popt maximum amount of correct classifications PVC PolyVinyl Chloride r Pearson’s correlation coefficient XXIV Key ecosystem engineers in estuarine vegetation
2 R adjusted explained variation of the independent variables defined as separate variables 2 R N Nagelkerke’s R squared means the power of explanation of the species distribution model Re Reynolds number
ReD obstacle-induced Reynolds number rgdal bindings for the geospatial data abstraction library RMSE Root Mean Squared Error RMSPE Root Mean Square Prediction Error S maximum scour depth s seconds s2 acceleration sd standard deviation SDM species distribution model SE standard error Sm Scirpus maritimus sp classes and methods for spatial data St Scirpus tabernaemontani Steud Ernst Gottlieb von Steudel (1783-1856) StP St. Pauli SUBV Der Senator für Umwelt, Bau und Verkehr der Freien Hansestadt Bremen sz shoreline zones T wave period (s) TIN triangulated irregular networks Trin. Carl Bernhard von Trinius (1778-1844) UNESCO United Nations Educational, Scientific and Cultural Organization Veg Vegesack vs. versus WFD water framework directive WGS84 world geodetic system 1984 WHG Wasserhaushaltsgesetz WSA Wasser- und Schifffahrtsamt, Waterways and Shipping Board x and h base and height of the cross-section (m) x independent variable y and z shorter and longer axes of the cross-section z elevation Į defining the standard for the significant level Į slope (°) of our test section List of Abbreviations and Symbols XXV
Į-mesohalin 10.1-18.0 per mills ȕ Vogel number ȝm micrometre Ȟ kinematic viscosity ȡ density of the fluid (kg/m3) = 1000 kg/m3 for freshwater
ȡsp Spearman rank correlation coefficient 3 ȡs sediment density (kg/m ) ݔ drag forces experienced by flexible plants݁ (ݒ flow velocity (m/s ߆ Shields parameter
XXVI Key ecosystem engineers in estuarine vegetation
Chapter I Introduction
© E. Fuchs
2 Key ecosystem engineers in estuarine vegetation
I Vegetation in regularly flooded marshes, its drivers, niches, and functions 3
1 Vegetation in regularly flooded marshes, its drivers, niches, and functions Marshes are ecotones between aquatic and terrestrial ecosystems. Although marshes are usually assigned to high and mid latitudes (polar, subpolar, and temperate climatic zones), they are also found in subtropical and tropical climate zones (Costa and Davy, 1992; Friess et al., 2012; Saintilan et al., 2009; Whigham et al., 1993). Therefore, vegetation in regularly flooded marshes is classified as azonale, because it is mainly driven by non-climatic factors, while the distribution of zonal vegetation is mainly limited by climate conditions (Frey and Lösch, 2004; Metzing and Gerlach, 2001). The marsh vegetation is characterized by herbaceous plants that are commonly emergent in water (Keddy, 2010). Therefore, these plants are also called emergent macrophytes or helophytes. They often form monospecific plant zones.
Vegetation in regularly flooded marshes occurs on sites with sandy to silty sediments. On silty sites they are often rich in nutrients. Their distribution is constrained by the following abiotic gradients (Fig. I 1-1) and their interactions which also influence the sediment patterns (cf. Bertness (2006); Raffaelli and Hawkins (1996)):
(1) The vertical gradient, also called elevational gradient, stretches from water to land or from lower to upper shore. It reflects the inundation duration and the inundation frequency. This gradient is studied by e.g., Baldwin et al. (1996); Bockelmann et al. (2002); Coops et al. (1994); Lawrence and Zedler (2011); Lenssen et al. (1998); Riddin and Adams (2008); Suchrow and Jensen (2010). (2) The horizontal gradient, also known as exposure gradient, stretches from exposed to sheltered shorelines. It decribes, on the one hand, the exposition to the wind-induced waves which depends on the distance the wind can blow over the water (fetch). Investigations are presented by e.g., Callaghan et al. (2010); Coops et al. (1991); D'Alpaos et al. (2012); Marani et al. (2011). On the other hand, the horizontal gradient also relates to the exposition to ship-induced waves. Distance from the navigation channel can be a proxy for measuring the effect (cf. Ali et al. (1999); Heuner (2007)). (3) The longitudinal gradient represents two main aspects: The salinity gradient extends from freshwater to sea water. It has been analysed by e.g., Adams et al. (1992); Engels and Jensen (2009); Mo et al. (2015); Odum (1988); Perry and Atkinson (1997); Smith and Medeiros (2013). 4 Key ecosystem engineers in estuarine vegetation
The tidal gradient stretches from macrotidal (< 4 m) across mesotidal to microtidal (< 2 m) (Allen, 2000; Davies, 1964) which ends up in pure fluvial fluxes. Therefore, it can also be described as tidal-fluvial gradient (Jay et al., 1990). However, most estuaries do not cover the entire gradient. It is common to classify estuaries in these categories, for instance as a mesotidal estuary. The tidal gradient has been studied by e.g., Boumans et al. (2002); Byers and Chmura (2007); D'Alpaos et al. (2011); Leonard and Reed (2002). Tidal currents are hydrodynamic forces resulting from all acting gradients and their interactions. They also set constraints upon the vegetation distributions on tidal flats (Bouma et al., 2009a; Callaghan et al., 2010; Friess et al., 2012).
Fig. I 1-1 Abiotic gradients in estuaries
Also biotic factors influence the vegetation in regularly flooded marshes. These are herbivory by geese (e.g., Esselink et al. (2000); Olff et al. (1997)) or rodents (e.g., Gedan et al. (2009)), competition (e.g., Bertness and Shumway (1993); Engels et al. (2010); Menge and Sutherland (1987); Pennings and Bertness (2001)), and facilitation (e.g., Bertness (1991); Bertness and Hacker (1994); Silliman et al. (2015); van de Koppel et al. (2005)). They can be weakened or strengthened by regular disturbance (see abiotic factors) or by irregular disturbances such as grazing, ice scour, or storm surges (cf. Keddy (2010)). Humans and their activities can be also classified as biotic factors or as disturbances (Bürgi et al., 2007; Edwards, 1964; McDonnell and Pickett, 2013).
Another relevant factor determining the distribution of vegetation is the marsh age (Allen, 2000) or rather the sediment age. It is governed (1) by land subsidence or uplift, by sea-level rise (e.g, Kirwan I Vegetation in regularly flooded marshes, its drivers, niches, and functions 5 and Guntenspergen (2012)) or decrease (Atwater et al., 1979) on a geological time scale. In a time period ranging from several years to decades, it is driven (2) by embankment or realignment as well as by deepening and widening of the navigation channel. (3) Ice scour or storm surges as seasonal events can also influence the sediment age. Therefore, the distribution of plant species forming the vegetation is not seen to have reached its boundaries while the sediment is still young.
These abiotic and biotic environment factors act as filters (cf. also Mackey and Lindenmayer (2001); Poff (1997); Schröder (2001)). Together with the dispersal accessibility (cf. marsh age) they form the realized species niches (Hutchinson, 1957; Saupe et al., 2012; Soberón, 2007; Soberón and Peterson, 2005). If all biotic factors are absent, the niche is defined as a fundamental niche (Hutchinson, 1965). Species habitat is the place where the species lives. On the contrary, the ecological niche comprises not only the environmental space occupied, but also its functional role within the community (Dice, 1952; Elton, 1927; Odum and Barrett, 2004; Whittaker et al., 1973). In short, the habitat is the species “address” and its niche is the species “profession” (Odum and Barrett, 2004). Therefore, the niche is called functional niche and determined by three kinds of factors: (1) ecological processes, (2) demographic attributes (e.g., low reproduction rate with high chance of survival), and (3) environmental factors (fundamental niche) (Rosenfeld, 2002). Climate change and species invasion can lead to niche shifts by (1) evolving environmental tolerances (fundamental niche shifts) or (2) novel biotic and abiotic conditions had become available through invasion (realized niche shifts) (Tingley et al., 2014). Therefore, the niche concept is been upgrated in extinction, range expansion, and evolutionary adaptation to changing environments (Colwell and Rangel, 2009).
As described so far, marsh vegetation and their forming species represent responses to their environment. In contrast, marsh vegetation also affects the environment. It influences and drives ecosystem processes which provide, regulate, and support functions for the ecosystem (de Groot et al., 2010). These so called ecosystem functions are natural processes or characteristic exchanges of energy and nutrients that take place in the plant communities. Functions which are also crucial for human welfare are defined as ecosystem services (Chapin et al., 1997; Ehrlich and Ehrlich, 1981; Paterson et al., 2009). Thus, people obtain benefits from the ecosystem (MEA, 2005a) and they try to assign nature values to monetary values (Costanza et al., 1997; de Groot et al., 2002), for marshes see Barbier et al. (2011). Marsh vegetation, for instance, purifies water (Liu et al., 2014; Shao et al., 2013; Verhoeven and Meuleman, 1999), dissipates and subsequently attenuates waves (Bache and Macaskill, 1981; Bouma et al., 2005; Knutson et al., 1982; Möller et al., 1999), or provides 6 Key ecosystem engineers in estuarine vegetation
biological productivity (Craft et al., 2003; Kirwan and Murray, 2007; Minden and Kleyer, 2015; van de Koppel et al., 1996).
The concept of ecosystem services has been promoted for the last two decades (Larigauderie and Mooney, 2010; MEA, 2005a). It makes people appreciate nature more and more as an indispensable basis of human life, as indigenous peoples did before us. In the last centuries, humans have intensively used the services provided by estuaries and marshes by severely modifying the ecosystem, for example by creating embankments or carrying out soil drainage. Therefore, natural regulating, supporting, and cultural services have been weakened. These services need to be strengthened again in the face of climate change as well as to achieve the objectives of (1) European Directives (e.g., Habitats Directive 92/43/EEC, Water Framework Directive/WFD 2000/60/EC) and (2) Federal Water Acts, e.g., the “Wasserhaushaltsgesetz” in Germany, which claims inter alia to play a part in improving structural diversity and river characteristic vegetation zonations (WHG § 39 Abs. 1).
The ecosystem services in marshes need to be effectively strengthened notwithstanding our fundamental economic objectives. For this purpose, we have to quantify both the ecosystem functions and the knowledge of where and under which conditions key species of marsh vegetation can perform their functions in the habitat. These objectives meet with one of the most important goals in ecology: Understanding the species ecological niche (Godsoe, 2010) as defined by the functional niche.
2 Vegetation-based shoreline protection in estuaries One key service of estuarine marsh vegetation is protection against flooding and storm surge (coastal defence). It is performed in particular by vegetation inhabiting tidal flats (shoreline vegetation). How effective this service is depends on (1) plant characteristics (e.g., Feagin et al. (2011); Ysebaert et al. (2011)) (2) hydrodynamic forces (e.g., Möller et al. (2014); Möller et al. (1999)), and (3) sediment supply (cf. Li and Yang (2009); Yang (1998)). Plant traits include stem density, plant height, biomass, leaves, and plant rigidity/stiffness. Leaves and stems increase bed roughness and drag (Leonard and Luther, 1995). More biomass also enhances the plant surface area and dissipates more turbulent kinetic energy across the marsh surface (Mudd et al., 2010). A higher stem rigidity/stiffness also increases the drag, which in turn reduces flow velocity (cf. Nepf and Vivoni (2000), Bouma et al. (2005)). This effect is most significant at the vegetation edge where the I Vegetation-based shoreline protection in estuaries 7 drag is highest (Bouma et al., 2010). The influence is stronger, if the inundation depth is smaller than the plant height (Knutson et al., 1982; Morris et al., 2002; Nepf and Vivoni, 2000). Therefore, hydrodynamic forces are more strongly dissipated in the vertical components of flow than in the horizontal (Leonard and Croft, 2006). Thus, marsh vegetation attenuates currents (Leonard et al., 2002; Nepf, 1999; Neumeier and Amos, 2006), but also waves (Bouma et al., 2008; Feagin et al., 2011; Knutson et al., 1982; Möller et al., 2011; Möller et al., 1999; Ysebaert et al., 2011), and therefore plays a crucial role in ecosystem functions.
The processes described above lead to sediment trapping. Usually, tidal floods cause a low flow velocity in marsh vegetation resulting in low sediment trapping. But at spring tide or during storm events, extreme flow velocities and waves can cause erosion of marshes (Peterson et al., 2008; Pethick, 1992; Turner et al., 2006). At other marsh parts or even regions, sediment is flushed and trapped considerably by algae and pioneer plants (Mudd et al., 2010; Murphy and Voulgaris, 2006; van Proosdij et al., 2006) in contrast. Moreover, the sediment accumulation is affected by the location, marsh age, and season: Sedimentation rates decrease with increasing (1) surface elevation, (2) distance from the nearest creek or marsh edge (inundation duration and water depth), (3) distance from the marsh edge measured along the nearest creek (Garbutt and Boorman, 2009; Temmerman et al., 2003b), (4) distance from the estuarine turbidity maximum (Butzeck et al., 2014; Temmerman et al., 2004b), and (5) marsh age (Temmerman et al., 2004b). In addition, an expanded, dense root system retains sediment more effectively than single plant roots, which can be weakly developed in muddy soils (Garofalo, 1980). Vegetation usually accumulates sediments during the growing season, while in winter accumulation ceases or sediment is even eroded from the marshes. However, a general seasonal pattern has not yet been detected due to the variation of the other factors mentioned above (cf. Darke and Megonigal (2003); Neubauer et al. (2002); Yang (1998); Zhu et al. (2012) versus Butzeck et al. (2014); Fettweis et al. (1998); Temmerman et al. (2003b)).
How much sediment can be supplied by coasts, estuaries and rivers to keep up with sea-level rise? This is a central question in climate change research (Kirwan et al., 2010; Kirwan and Murray, 2011; Schile et al., 2014; Temmerman et al., 2013), because sediment availability (supply) is a primary driver for marsh creation (Kirwan and Megonigal, 2013). Expansive marshes characterized by low tidal ranges or low sediment concentrations will probably submerge in the near future. In contrast, marshes in regions with a high tidal range and an abundant supply of sediment will likely keep up with sea-level rise (Kirwan et al., 2010). Thus, we already know that tidal regime and sediment availability as well as compaction rate can predict the growth of tidal flats and marshes via 8 Key ecosystem engineers in estuarine vegetation
elevation-time curves (Temmerman et al., 2004b). It shapes a ramp with a steep sediment increase at an early stage and a subsequent flattening off to an equilibrium stage around the mean sea-level (Allen, 2000; Garbutt and Boorman, 2009).
If the surface elevation on tidal flats is sufficiently high, diatoms and algae facilitate the establishment of emergent macrophytes by attracting and retaining sediment, such as the pioneer Elocharis uniglumis. A closed vegetation cover is formed retaining more sediment, which can then lead to an abrupt shift in surface elevation. The species which govern these biogeomorphic feedbacks (coupling) (Corenblit et al., 2015; D'Alpaos et al., 2012; Wang and Temmerman, 2013) are called ecosystem engineers (Jones et al., 1997). They create spatially self-organized patterns such as sediment hummocks made of diatoms (van De Koppel et al., 2001; Weerman et al., 2010) or mono-specific vegetation zones classified as different elevational states (Marani et al., 2013). This self-generated marsh elevation is a crucial factor in strengthening marsh resilience to sea-level rise. It is also reinforced by wave attenuation across marsh vegetation (cf. D'Alpaos et al. (2012), Möller et al. (2014)), provided that space is available for undertaking shoreline protection (Temmerman and Kirwan, 2015; Temmerman et al., 2013). Thus, from the perspective of the coastal defense, vegetation-based shoreline protection is more adaptable to sea-level rise than traditional engineered methods (Borsje et al., 2010).
3 Emergent macrophytes as key ecosystem engineers in navigable estuaries Emergent macrophytes acting as key ecosystem engineers have been seen in many studies to be effective in reducing currents, damping waves, and therewith trapping sediment in salt marshes. Species studies are Spartina anglica (e.g., Bouma et al. (2005), Neumeier and Amos (2006), Widdows et al. (2008)), Spartina alterniflora (e.g, Knutson et al. (1982), Leonard and Croft (2006); Yang (1998), Manis et al. (2015)), or Scirpus mariqueter (e.g., Yang (1998), Ysebaert et al. (2011), Li and Yang (2009)). Few studies exist though for freshwater and brackish marsh plants. Although wave-exposed sites also set constraints upon emergent macrophytes in their expansion (Coops et al., 1991; Coops et al., 1999), Scirpus lacustris and Phragmites australis were tested in a wave-tank experiment, showing a positive impact on both sediment reinforcement and wave attenuation (Coops et al., 1996a). Phragmites australis stems are stiff enough to cause increased drag under increasing wave height and therefore explicitly contribute to wave attenuation (Möller et al., 2011). I Emergent macrophytes as key ecosystem engineers in navigable estuaries 9
Transplanted Scirpus maritimus patches demonstrate clear sediment trapping in areas with severe erosion such as in the Eden estuary (Scotland) (Maynard et al., 2011).
Extensive areas of freshwater and brackish marshes, estuaries and deltas are densely populated (Nicholls et al., 2007; Temmerman et al., 2013). They have often been severely modified by engineering constructions and land reclamation at the expense of flooded marsh land and in particular need a sustainable flood defence in future. Up to now, they have been widened and deepened several times in order to adapt the navigation channels to the draught of increasingly bigger container vessels.
Ships alter the local hydrodynamic forces by exerting pressure on the water body and generating currents and waves (Gabel, 2012). The pressure results in a swell and subsequent downsurge at the shorelines. Ships cause a primary wave with long wave periods between 20 s and 120 s for example in the Western Schelde estuary (Schroevers et al., 2011) as well as between 60 s and 300 s in the Elbe and Weser estuary (Peters et al., 2013). The primary wave is followed by secondary waves with shorter wave periods ranging from 2 s to 7 s (Peters et al., 2013; Schroevers et al., 2011; Verney et al., 2007) which is comparable to wind-induced waves. However, the secondary ship waves usually superimpose one another in two different trains: Diverging waves which move forward and out from the vessel and transversal waves which follow the course of the vessel at a right angle (Gabel, 2012). The distance between the vessel and the shoreline is essential in determining the wave load on the shore because the waves dissipate with increased distance from the ship (Gabel, 2012). The wave load depends on water depth and vessel type.
As the waves arrive at shallow water, the phase velocities and therefore the waves themselves slow down by reducing the wave lengths (Masch, 1964). This process is a result of dissipation by bottom friction (Mai, 2004; Svendsen and Jonsson, 1976). According to the law of conservation of energy, the kinetic energy of the wave is dissipated, whereas the potential energy of the wave increases by an increase in wave height (Denny, 1988). The process is called shoaling. In ever-shallower water depths this process is limited, because the increasing wave height accelerates the water at the wave crest until its steepness becomes unstable.
The wave breaks when the wave height reaches approximately 80% of the water’s depth (Denny, 2006; Mai, 2004). In steep bottom slopes with elevation steps, the wave height can become even higher. Depending on the slope steepness (Battjes, 1974), different breaking types occur. For instance, plunging waves cause turbulences which penetrate to the bottom, while turbulences 10 Key ecosystem engineers in estuarine vegetation
triggered by spilling breakers primarily affect the surface layer between the crest and trough (Lippmann et al., 1996). While breaking, the potential energy is transduced to (1) thermal energy by viscous dissipation in air and water as well as to (2) mechanical energy with components of kinetic energy and surface tensions (Iafrati, 2011).Hitting the tidal flats, the mechanical energy is dissipated by (1) percolation into the substrate, (2) again bottom/surface friction and (3) wave reflection, diffraction and refraction (Möller et al., 1999). The mechanical energy of the waves can result in shoreline erosion (Currin et al., 2015; Marani et al., 2011) also depending on shoreline properties (Cowart et al., 2010). Therefore, most exposed shorelines are protected by stone linings or other engineering defences.
Marsh land which is not embanked, artificially protected or used for grazing is often colonized by sedges and reeds such as Scirpus tabernaemontani (C. C. Gmel.) Palla (also called Schoenoplectus tabernaemontani and Scripus validus), Scirpus maritimus (L.) Palla (also called Bolboschoenus maritimus), and Phragmites australis (Cav.) Trin. ex Steud in monospecific stands. These ubiquitous macrophytes are common species in freshwater and brackish shorelines of for instance the North Sea estuaries of the Weser and Elbe rivers (Focke, 1915; Grotjahn, 1983; Kötter, 1961). They are also located at the Baltic Sea (Dupré and Diekmann, 2001; Tyler, 1971) and in the Maas- Rhine delta at the Biesbosch marshes, which is the greatest coherent tidal freshwater marsh in Europe (Zonneveld, 1959). The mentioned plant species are in many ways ecosystem engineers themselves. Several studies have been carried out on P. australis which was expanding in North America. There, it altered a number of habitats by decreasing species diversity (e.g., Silliman and Bertness (2004), Lavoie et al. (2003), Minchinton et al. (2006)). On the contrary, however, P. australis has been found to enhance the aeration in waterlogged soils (e.g., Armstrong et al. (1992), Maltais-Landry et al. (2009), Windham and Lathrop (1999), and improve the habitat for other species. P. australis and S. tabernaemontani release anti-cyanobacterial compounds from their roots (Nakai et al., 2010), and S. tabernaemontani was suggested for water purification (Perez-Lopez et al., 2009). S. maritimus accumulates nutrients (Lillebø et al., 2006) and metals (Sanchez et al., 1998) in its roots. Further tests need to be carried out to determine whether it can be used as a biomonitor of metal contamination in river systems (Shuping et al., 2011). To my knowledge, however, only the studies mentioned above investigated velocity dissipation or wave attenuations of P. australis; whereas S. maritimus or S. tabernaemontani have not yet been investigated.
Several authors described the elevational gradients of these species in the North Sea estuaries; the waterward boundaries vary interspecifically, intraspecifically, as well as between areas. Some I Objective and research questions 11 examples are as following: S. tabernaemontani can colonize the waterward tidal flat down to 1.5 m (Oertling, 1992; Raabe, 1974), or even up to 2.0 m (Kötter, 1961) below mean high water. S. maritimus can also occur down to 1.5 m and 2 m below mean high water (Kötter, 1961; Osterkamp, 2006). But more often, it is found between 0.5 m (Stiller, 2005) and 1.2 m (Claus et al., 1994) below mean high water. P. australis, however, grows down to 1.0 m (Oertling, 1992), 0.8 m (Claus et al., 1994), and most frequently at 0.5 m below mean high water (Ellenberg and Leuschner, 2010). The reasons for niche variation in elevation are not clear. Hydrodynamic forces may be one reason which includes waves caused by cargo vessels and sport boats.
4 Objective and research questions To promote marsh vegetation, in particular shoreline vegetation, by managed realignments as suggested by Temmerman et al. (2013) and Temmerman and Kirwan (2015), coastal management plans have to be adapted with regard to ecosystem-based management objectives (Barbier et al., 2008). However, to put this knowledge transfer into practice, we have too little insights into environmental thresholds controlling the distribution patterns of key species. These species form the shoreline vegetation and need to deal with the hydrodynamic stress conditions affecting their establishment, development and long-term ecological functioning (Friess et al., 2012). This kind of knowledge in particular regarding freshwater and brackish vegetation in navigable estuaries is missing. In this context, the threshold is defined as the doorsill (Petraitis and Hoffman, 2010) which distinguishes the bare tidal flat from the habitat of shoreline vegetation and describes the lower boundary of the fundamental niche illustrated by the marsh edge, because competing neighbours are absent waterwards. In order to promote the vegetation-based shoreline protection, our understanding of the interactions between environment and vegetation has to be improved across spatial scales from single plants, to local plots (< 10 km), and ecosystems (10-1000 km², Bailey (2009)).
The following detailed knowledge gaps will be investigated: x Information on how and why (1) marsh edges and (2) plant zones of regularly flooded marshes vary in elevation in estuaries. x The relationships between species traits, their habitats and their capacity for ecosystem engineering (Bouma et al., 2010): There is insufficient understanding of (1) how species deal with abiotic stress and (2) how they affect their environment for constructing other species niches. 12 Key ecosystem engineers in estuarine vegetation
x Insight into (1) the key drivers of the species determining the spatial patterns of the estuarine marsh vegetation and (2) the suitable habitat conditions for providing vegetation-based shoreline protection.
x Knowledge on how waves influence the elevational niche of shoreline vegetation at the marsh edge concerning (1) drag force, (2) scouring, and (3) potential thresholds The pivotal objective of this thesis is an enhanced understanding of abiotic habitat conditions for vegetation distribution, the distribution thresholds, and the vegetation effects on estuarine marshes. The investigations will contribute to an effective promotion of vegetation-based shoreline protection considering future environmental estuaries especially considering sea-level rise.
To attain this objective, I have defined the following two general research questions that are investigated in four studies depicted in chapters II-V.
1. Why and how do hydrodynamic forces drive the plant species niches of shoreline vegetation in estuaries?
2. What is the potential of vegetation-based shoreline protection in navigable estuaries? What conditions promote this ecosystem service and what are its limits?
Research questions will be answered pertaining to functional niches and ecosystem functions, because functional niches reflect the ecological effects a species will have on a defined habitat (Rosenfeld, 2002). These effects can provide services for itself, other plant species, animal species, as well as for humans.
5 Elbe and Weser estuaries as case studies In Europe, the North Sea Elbe and Weser estuaries are model systems for altered estuaries in temperate zones (Markus-Michalczyk et al., 2014) located in northwestern Germany. Large cities have grown along them such as Hamburg (1.7 Mio citizens, Statistisches Amt für Hamburg und Schleswig-Holstein 2014) at the Elbe and Bremen (0.6 Mio citizens, Statistisches Landesamt Bremen 2014) at the Weser. Both estuaries are classified as upper meso-tidal to lower macro-tidal estuaries (Kappenberg and Grabemann, 2001; Masselink and Hughes, 2003). Providing waterways for overseas cargo transport, the navigation channels of the two estuaries have been widened and deepened several times. The increased hydrodynamic forces require artificial bank protection (e.g., I Methods to investigate key ecosystem engineers in estuarine vegetation 13 stone fillings) for more than 50% of the shoreline length along the main estuarine channels.
Fig. I 5-1 Overview of the Weser and Elbe estuary in 2015. Picture (RGB modification) from the satellite Sentinel 1-A
The following information is based on averaged parameters from the moraine margins upstream to the downstream boundaries where the salt meadows start to grow (lower Weser km 26-70, Elbe km 632-701). The Weser estuary is lesser than the Elbe estuary in depth (13.7 m vs. 18.1 m), discharge (325 m³/s vs. 707 m³/s), width (965 m vs. 2033 m), and stream velocity (0.1-0.6 m/s vs. 0.2-0.9 m/s, Vandenbruwaene et al. (2013)). However, the mean tidal range (2001-2010) is higher in the Weser estuary (3.8 m-3.9 m) than in the Elbe estuary (2.9 m-3.6 m).
6 Methods to investigate key ecosystem engineers in estuarine vegetation
6.1 Descriptive data Monitoring data for vegetation and elevation exist for both estuaries since the last navigation adaptation 1998 (Lower Elbe, Outer Weser) in order to preserve the evidence for possible effects on the environment. In the subsequent years, spatial data resolution has been increased due to the application of digital systems such as airborne laserscanning (Brockmann and Schröder, 2005; Maas, 2002) and the automatic classification of vegetation types from aerial photographs recorded 14 Key ecosystem engineers in estuarine vegetation
by a high resolution stereo camera (HRSC-A(X)) (Gähler et al., 2002). The preservation of evidence needed high resolution data and high data quality in order to obtain reliable information about the estuarine system exhibiting a vast environmental variability. Around 2010, digital elevation models (DEM) had reached a spatial resolution of 1 m, were verified by a precise digital global positioning system on the ground, and had a standard deviation between 0.15 and 0.40 m depending on surface roughness (Brockmann and Schumann, 2011). In the same time period, digital vegetation maps to a scale of 1: 4,300 were produced from aerial photographs (digital mapping camera) for both estuaries. Therefore, the same classification algorithm was applied with a comprehensive verification in the field resulting in negligible misclassification of vegetation types (Peterson et al., 2010). Using mainly these data, I describe the elevational niches of S. tabernaemontani, S. maritimus, and P. australis in the Elbe and Weser estuaries, their anabranches, and analyse their niche variation.
6.2 Modelling approach ‘Species distribution model’, ‘habitat suitability models’, or ‘ecological niche model’ are methods based on statistical regression for a quantitative prediction of species distributions which are fundamental for an understanding of the species niche (Austin, 2007; Hirzel and Le Lay, 2008). The models are used (1) to detect candidate locations for species restoration based on environmental variables, (2) to quantify and evaluate the benefits of different restoration scenarios considering the potential species distribution (Guisan et al., 2013), (3) to design restoration plans and reserves, and (4) to determine the effects of habitat loss (Peterson, 2006). However in real-world restoration management, the application of these models remains small. Accurate maps of species distributions have an increased demand (Guisan et al., 2013). If presence and absence data are given (binomial distribution) as provided by the available digital vegetation maps, presence-absence methods such as generalized linear models (GLM) should be preferred compared to presence-background models (Guillera-Arroita et al., 2014). For instance, the GLM output supplies occurrence probability, whereas presence-background models such as Maxent only estimate relative suitability. Based on a logistic regression, GLM is a well-known robust method for predicting potential habitats (Guisan et al., 1999; Meynard and Quinn, 2007; Schröder et al., 2007; Thuiller et al., 2003). In contrast to classification and regression trees, GLM is more suitable for verifying theories about species responses to environmental gradients (Vayssieres et al., 2000). Using the maximum likelihood estimation, the linear predictors are related to the response variable through a link function allowing I Structure of this thesis and detailed objectives by chapter 15 their transformation to linearity (Guisan and Zimmermann, 2000). The GLM exhibits how much variability in species distribution can be represented by the model.
6.3 Experimental approach Experiments help to explain processes under controlled conditions. The increasing interest in a sustainable coastal protection for the future requires a better understanding of the interactions between marsh plants and hydrodynamic forces (waves and currents). Flume experiments contribute to this understanding. They are often performed with vegetation mimics such as cylinders (e.g., Nepf (1999), Armanini et al. (2005), Pujol et al. (2013), Rosman et al. (2013), Anderson and Smith (2014)). However, plants usually have a more complex morphology, and therefore it is crucial to understand the divergence between real plants and mimics (Bouma et al., 2005), even though these experiments are more labour-intensive. Full-scale experiments have the benefit of reflecting the processes in the real world without complex up-scaling factors which can entail uncertainties. In cooperation with the University of Antwerp, the University of Ghent and the Royal Netherlands Institute for Sea Research (NIOZ), we performed full-scale wave-flume experiments using the pioneer species S. tabernaemontani and S. maritimus.
7 Structure of this thesis and detailed objectives by chapter Introduction (Chapter I) and Synthesis (Chapter VI) encompass the manuscripts in chapters II to VI which are inherently conclusive and have been separately submitted to or published in international peer-reviewed journals. Every manuscript contributes to the thesis objective and its key research questions. Chapter I introduces the vegetation in regularly flooded marshes, its niches, drivers, and its shoreline protection function in estuaries. It provides the motivation for this thesis, detects knowledge gaps, and defines its objectives and key questions. The plant species and the area studied as well as the methods used for addressing the research questions are described. Chapter II deals with niche variations in elevation across marshes regularly flooded on an estuarine ecosystem- scale (Fig. I 7-1). The manuscript gives an insight as to how and why plant zonation varies, comparing main river channels and anabranches of two representative estuaries with differing hydrodynamics. Furthermore, it describes how hydrodynamic forces drive the elevational distribution of plant species along the marsh edge. While chapter II has found distinct elevational niches in two pioneer species, chapter III deals with the question why their niches differ under hydrodynamic stress by investigating plant morphology, invested energy in plant material, and ecosystem engineering capacity. Examining different wave conditions as hydrodynamic stress, the 16 Key ecosystem engineers in estuarine vegetation
species responses to wave impact are analysed measuring scouring, drag, and bending. It is considered to what extent species respond to tensile and bending forces as well as what features of the stem effect traits require stress resistance. Furthermore, the effects of both biomass and stem density on wave attenuation are tested in order to compare their ecosystem engineering capacity. Detecting clear effects in wave attenuation as an important ecosystem function for both pioneer species in chapter III, it is crucial to restore these functional niches in order to strengthen the resilience of estuaries and their habitats as discussed in chapter IV. As an example, wave attenuation by shoreline vegetation entails sediment accretion and enables the marsh topography to increase with the rising sea level at a sufficient sediment supply and is therefore more resilient against storm surges.
Fig. I 7-1 The research subject and the investigated key issues framed by introduction and systhesis
Chapter IV identifies key environmental predictors determining the distribution of emergent macrophytes at estuarine shorelines. It answers how estuarine engineered banks can be restored creating habitats for naturally vegetated shorelines. However, the identified key predictors in chapter IV are derived from topographical and morphological data so it is vital to gain an insight into how the primary driver of hydrodynamic stress as wave load and current velocity can predict potential niches of regularly flooded marsh vegetation. Chapter V demonstrates that wave-induced scour and drag forces can be predicted by existing equations usually used for engineering structures. The equations are verified for pioneer marsh plants of differing morphology and life stages testing a range of wave periods and water levels. The results of chapter V are valuable for predicting pioneer plant niches evaluating restoration measures and they also endorse the findings in chapter II. Chapter VI includes the summary and main conclusions of the thesis under integration of the findings and identifies future research needs related to the topic of this thesis. I Structure of this thesis and detailed objectives by chapter 17
The manuscripts chapter II to chapter V were published or submitted as follows:
Chapter II: Heuner, M., Schröder, B., Schröder, U., Kleinschmit, B., with revisions. Hydrodynamic forces influence elevational responses in vegetation of regularly flooded marshes in navigable estuaries. draft. Chapter III: Heuner, M., Silinski, A., Schoelynck, J., Bouma, T.J., Puijalon, S., Troch, P., Fuchs, E., Schröder, B., Schröder, U., Meire, P., Temmerman, S., 2015. Ecosystem engineering by plants on wave-exposed intertidal flats is governed by relationships between effect and response traits. PLOS one 10 (9), e0138086. doi: 10.1371/journal.pone.0138086 Chapter IV: Heuner, M., Weber, A., Schröder, U., Kleinschmit, B., Schröder, B., 2016. Facilitating political decisions using species distribution models to assess restoration measures in heavily modified estuaries. Marine Pollution Bulletin 110, 250-260. doi: 10.1016/j.marpolbul.2016.06.056 Chapter V: Silinski, A., Heuner, M., Schoelynck, J., Puijalon, S., Schröder, U., Fuchs, E., Troch, P., Bouma, T., Meire, P., Temmerman, S.,2016. Effects of contrasting wave conditions on scour and drag on pioneer tidal marsh plants. Geomorphology 255, 49-62. doi: 10.1016/j.geomorph.2015.11.021 18 Key ecosystem engineers in estuarine vegetation
Chapter II Niche Variations
Heuner, M., Schröder, B., Schröder, U., Kleinschmit, B., with revisions. Hydrodynamic forces influence elevational responses in vegetation of regularly flooded marshes in navigable estuaries. draft.
20 Key ecosystem engineers in estuarine vegetation
II Introduction 21
Abstract Many studies have illustrated plant zonation in response to the elevational gradient in marshes. Nevertheless, on ecosystem-scale, little is known about hydrodynamic forces triggering the elevational range and hence the resulting varying niches of estuarine species. This knowledge is required for an adequate assessment of vegetation changes caused by river trainings and climate change. Studying two navigable German estuaries (Elbe, Weser) and three marsh plants (Scirpus tabernaemontani, Scirpus maritimus, Phragmites australis), we hypothesize that ship-induced hydrodynamic forces influence the elevational variation on ecosystem-scale concerning both (1) marsh zonation regularly flooded and (2) the marsh edge. The results show higher elevational zones of S. tabernaemontani and S. maritimus at the Elbe main channel than at the Weser main channel. The highest elevational zone is assigned to P. australis exhibiting the least variation. Showing a positive relation between elevation and bank slope, P. australis along the marsh edge of the Elbe main channel is located at higher elevations than along the marsh edge of its anabranches and the Weser estuary. In contrast, the results for the other two species do not differ in elevations. We conclude that our results support the hypotheses indicating that hydrodynamic forces are a primary driver for varying responses of marsh vegetation to the elevational gradient in navigable estuaries. We therefore deduce that hydrodynamic forces caused by high vessel frequencies or steep slopes likely require high stress resistance of species. Thus, the species competitive hierarchy will be changed since stress-resistant species become dominant resulting in plant zonation.
1 Introduction Hydrodynamic forces cause water motion. On coastal and estuarine shorelines, this is mainly triggered by tides, Earth’s gravity, different water densities, and wave-inducing drivers such as wind and ship movements. The direct effect of hydrodynamic forces on organisms is by pushing and pulling (Denny, 1994b). Plants fail to resist the hydrodynamic stress if the root anchorage is too poor for balancing out these effects (Bache and Macaskill, 1981; Sand-Jensen and Møller, 2014). This critical threshold constrains vegetation expansion waterwards in situations where the frequent tidal inundation is still suitable for plant establishment (Friess et al., 2012).
The process that plants incessantly try to adapt to inundation and hydrodynamic forcing at single- plant level configures the transition zone between the two stable states ‘vegetated’ and ‘unvegetated’ (Scheffer et al., 2001). Changes of external variables such as climate or dredging can initiate a shift 22 Key ecosystem engineers in estuarine vegetation
of one environmental state to another (e.g., Moffett et al. (2015)). Marshes adjacent to bare tidal flats are two habitats which are a model system representing equilibrium ecosystem states (e.g., Marani et al. (2010)). For instance, the opportunity of marsh expansions waterwards increases when a period of neap tide in a windless seedling season or growing rhizome season occurs after a larger amount of sediment has been supplied by a storm event. That sediment can be fixed by microphytobenthos which also retains sediment. On the one hand, the example demonstrates the ‘Window of Opportunity’ defined as disturbance-free periods for plant establishment (e.g., Balke et al. (2014); Romme et al. (1998)). On the other hand, the example shows how the ecosystem characterized by disturbances is driven by coupled processes between geomorphology and biology and comprehended by mathematical models (e.g., Fagherazzi et al. (2012); Marani et al. (2010); Temmerman et al. (2007)). These intertwined processes have a positive feedback effect on the tidal surface elevation. When these processes have reached an elevational threshold, macrophytes can grow and the marsh edge is shifted waterwards, often showing an abrupt transition from low flats to higher-elevation marshes (Wang and Temmerman, 2013).
Landwards, vegetation succession proceeds and different plant zones are formed along the increasing elevational gradient (representing realized elevational niches) of regularly flooded marshes due to two main plant-promoted processes: (1) The oxygen availability increases in initially waterlogged anoxic soil by transporting oxygen, using a specific root tissue (aerenchym) into root aeration zones (Silvestri et al., 2005; Ursino et al., 2004); (2) Waves and currents are attenuated and subsequently sediment is accumulated (e.g., Bouma et al. (2009b); Marani et al. (2013); Möller and Spencer (2002)). Thus, the elevational gradient is an integrative proxy for hydrodynamic forces indicating decreasing hydrodynamic stress with increasing surface elevation for marsh plants (Bertness, 2006).
Elevation relative to water level, such as mean high water, is a well-known indicator for explaining vegetation zonation in marshes (e.g., Coops et al. (1999); Keddy (2010)), especially in regularly flooded marshes (Pennings and Callaway, 1992; Sanchez et al., 1996) but less in irregularly flooded marshes or marsh plains (Zedler et al., 1999) due to higher spatial variability, e.g., in soil types (Silvestri et al., 2005) and inundation frequency (Bockelmann and Neuhaus, 1999). Thus, plant zonation also vary across marshes (Menge and Sutherland, 1987). The variation depends furthermore on the impact of herbivores (e.g., Olff et al. (1997)), facilitation (e.g., Bertness (1991)), as well as on competition (e.g., Wilson and Keddy (1986)). II Introduction 23
A trade-off for plant species exists between competitive ability and resistance against physical stress which can be explained by the conceptual competitive hierarchy model (Keddy, 2001; Malanson, 1997). Therefore, plant competition increases with less stress-producing inundation (e.g., Bockelmann and Neuhaus (1999); Morzaria-Luna and Zedler (2014)) or mechanical forces (e.g., van Wesenbeeck (2007)). Furthermore, the model assumes that species vary in their competitive abilities which are negatively correlated with the fundamental niche width (Keddy, 2001). The trade-off fails for irregularly flooded marshes, because gradients of physical stress become inconsistent (Costa et al., 2003) due to the higher spatial variability (e.g., Silvestri et al. (2005), Marani et al. (2010)). Nonetheless, the trade-off can be applied for species inhabiting regularly flooded marshes exhibiting strong correlations: For instance, inundation frequency was closely curvilinearly related to elevations (Bockelmann et al., 2002) and the wave exposure of the Rhine-Meuse marshes was correlated to the elevational gradient (Coops and van der Velde, 1996b).
Gaining insights into plant zonation, its variation, and thresholds of regularly flooded marshes, investigations at ecosystem-scale (10-1000 km² (Bailey, 2009)) were performed less frequently than at local plot-scale (but see e.g., Bertness et al. (2002); Kunza and Pennings (2008); Morandeira and Kandus (2015)). Analyses which quantify spatial patterns due to species competitive abilities and the hydrodynamic stress at ecosystem-scale are rare (Friess et al., 2012; Moffett et al., 2015). Though biogeomorphic investigations exhibit an elevational threshold of estuarine marsh edges (Wang and Temmerman, 2013), to our knowledge, there are no investigations comparing the elevational variation of (1) marsh edges and (2) plant zonation of regularly flooded marshes concerning hydrodynamic forces at estuarine ecosystem-scale.
Freshwater and brackish marshes in estuaries typically demonstrate distinct plant zonation of a few species, but there are also marsh sections dominated by only one species: Phragmites australis (see Fig. II 2-2C2). We would like to know if this observation is based on an ecosystem-scale pattern and if the variation of plant zonation in elevation influenced by hydrodynamic forces results from species stress resistance and species competitive hierarchy. We hypothesize that ship-induced hydrodynamic forces influence the elevational variation on ecosystem-scale concerning both (1) regularly flooded marsh zonation and (2) the marsh edge as a proxy for the transition zone between tidal flats and marshes. The marsh edge also reflects the lower boundary of the fundamental niche with respect to the surface elevation, because competing neighbours are absent waterwards. To test the hypotheses, we investigated the following questions: (1) How and why does plant zonation vary in elevation across regularly flooded marshes affected by ship-induced and natural hydrodynamic 24 Key ecosystem engineers in estuarine vegetation
forces? (2) How do hydrodynamic forces drive the elevational distribution of plant species along that marsh edge, especially along exposed ones?
2 Material and methods
2.1 Study areas The semi-diurnal estuaries Elbe and Weser discharge into the German Bight, North Sea. The study areas range from the moraine margin upstream to the boundaries where the salt meadows start to grow (Fig. II 2-1). Their salt gradient ranges from freshwater to brackish water (Į-mesohalin) up to 18 ‰ salinity (ARGE ELBE, 2008; von Glahn, 1999). Providing waterways for overseas cargo transport, the navigation channel of the two estuaries were widened and deepened several times. The mean tidal range (2001-2010) of the study area is between 3.8 m (gauge Bremerhaven) and 3.9 m (gauge Vegesack) for the Weser estuary and between 2.9 m (gauge Otterndorf) and 3.6 m (gauge St. Pauli) for the Elbe estuary. Therefore, Weser and Elbe are classified as upper meso- to lower macro- tidal estuaries (Kappenberg and Grabemann, 2001; Masselink and Hughes, 2003).
Fig. II 2-1 Overview of the study areas Weser (lower Weser km 26-70) and Elbe (Elbe km 632-701) with the vertical gradient elevation above mean high water (m), data source see A-1-1 Table.
II Material and methods 25
Table II 2-1 Parameters of the tidal-fluvial stress gradient representing the main channels and anabranches of the Elbe and Weser estuary.
Proxies for Elbe – Weser – Elbe – Weser– the tidal-fluvial fluxes main channel main channel anabranches anabranches
Tidal range (mean ± sd in m, n = 7054) Lower boundary of study area 2.9A ± 0.4 (Ott) 3.8B ± 0.4 (Bhv) Upper boundary of study area 3.6A ± 0.4 (StP) 3.9B ± 0.4 (Veg) Discharge 707A ± 475 (NDa) 325B ± 231 (Int) (mean ± sd in m³/s, n = 3652)
Volume (m³) 5.0 * 107 - 1.5 * 107 - 1.3 * 108‡ 5.0 * 107‡ Channel width of the thalweg 2033A ± 741 965B ± 493 727C ± 355 17D ± 132 (mean ± sd in m)† Channel depth of the thalweg 18.1A ± 1.30 13.7B ± 1.50 5.2C ± 1.70 1.8D ± 1.60 (mean ± sd in m below NN)† Flood velocity of the thalweg 0.93A ± 0.06 0.69B ± 0.04 0.44C ± 0.13 0.17D ± 0.11 (mean ± sd in m/s)† Ebb velocity of the thalweg 0.93A ± 0.09 0.82B ± 0.07 0.35C ± 0.11 0.14D ± 0.08 (mean ± sd in m/s)† Flood and ebb velocity of the 0.2 - 0.9‡ 0.1 - 0.6‡ “cubage” cross sections (mean in m/s) Vessel (inland and oversea) 620§ 200§ passages per week (counts) Port incoming ocean going 9843¶ 1788 (Bremen) # vessels (counts) # (Bremen) 9760 ۅVessel size (mean registered 14041 Drawdown (maxima in m) 0.5 - 1.2§ 0.6 - 1.3†† Primary wave (maxima in m) 0.5 - 1.5§ 0.6 - 1.5†† Secondary wave 1.0 - 1.4§ 0.9 - 1.2†† (maxima in m) Vessel's draft (maxima in m) 15.4§ 10.7†† Artificial bank reinforcement 51 59 30 14 (percent of the study area) Notes: Data collected from literature as well as calculated from GIS and hydrological data (A-1-1 Table). Cross sections were created every 100 m along the thalweg using the ET Geowizard Tool for calculating the mean channel width relative to MHW. The channel depth of the thalweg was averaged at these cross sections by using the bathymetric model. The data of flood and ebb current velocities from the modelled grid were extracted at the intersections of the thalweg and the cross sections. The tidal ranges were calculated with the equation described by DIN 4049-3 (1994). To calculate the percentage of artificial bank reinforcement, the bank (vegetation and reinforcement) lines were visually digitized on the basis of aerial photos (DMC) in a map scale of 1:10.000. The differences in tidal-fluvial fluxes of the main channels and anabranches were identified by the Kruskal-Wallis rank sum test. Gauges: Bhv = Bremerhaven, Ott = Otterndorf, Int = Intschede, NDa = Neu Darchau, StP = St. Pauli, Veg = Vegesack; sd = standard deviation; Despite the different modelled discharges of the Elbe and Weser River, the current velocities between the study areas are comparable, because the discharges in these river sections have negligible effects on the current velocities (Vandenbruwaene et al., 2013). † Sample size: Elbe – main channel: n = 661, Weser – main channel: n = 438, Elbe – anabranches: n = 311, Weser – anabranches: n = 246 ‡ Vandenbruwaene et al. (2013) § Peters et al. (2013) ¶ Statistical Office for Hamburg and Schleswig-Holstein (2011) # Senator for Economics and Ports of the Free Hanseatic City of Bremen (2013) (Federal Waterways and Shipping Directorate North (2011 ۅ †† BAW (2006) 26 Key ecosystem engineers in estuarine vegetation
In contrast to tidal range, many other properties such as river discharge, depth, width, volume, and current velocities are smaller in the Weser than in the Elbe estuary (Table II 2-1). These estuaries are notably affected by not only natural hydrodynamics such as wind-induced waves (anabranches and main channels) but also by ship-induced hydrodynamics (only main channels) (Fig. II 2-1, Fig. II 2-2). The Elbe main channel exhibits higher transport frequencies (vessel passage and vessels coming into port) as well as bigger vessel sizes and drafts. However, the maximum drawdown and the ship-induced wave heights in both estuaries are similar (Table II 2-1). As a consequence, the smaller river cross section/vessel-draft proportion of the Weser main channel triggers more stress at the banks than a single vessel passage along the Elbe. This “higher navigation stress” is also reflected by the higher percentage of artificial bank reinforcement (Table II 2-1).
Fig. II 2-2 Morphological patterns in the Elbe and Weser estuary and their marsh zonation. A: Main channel and anabranches of the estuaries with the three studied gradients. Anabranches diverts from the main channel and rejoins it downstream. B: The marsh edge is the fringe of vegetation in the highest third of the intertidal flat (MHW = mean high water, MLW = mean low water). C1: Typical marsh zonation with the species Scirpus tabernaemontani (St), Scirpus maritimus (Sm), and Phragmites australis (Pa) on banks exposed to high hydrodynamics. Typical marsh dominated by Phragmites australis on banks with low hydrodynamics. Hydrodynamics and salinity substantially regulate marsh creation and plant establishment. Between marsh edge and flood embankments excluding settlements, the Elbe estuary exhibits 57 km² marshland (2009), the Weser estuary covers 48 km² (2008). 35% of the Weser marshes and 55% of II Material and methods 27 the Elbe marshes are in a natural state, for instance not managed as arable land or grassland. We analyse three habitat-shaping emergent macrophytes Scirpus tabernaemontani (C. C. Gmel.) Palla, Scirpus maritimus (L.) Palla, and Phragmites australis (Cav.) Trin. ex Steud. forming monospecific stands in freshwater and brackish regularly flooded marshes of the North Sea estuaries. These species covered 23% of the total Weser marsh in 2008 and 26% of the total Elbe marsh in 2009. Usually, they represent the typical estuarine plant zonation in the above-mentioned order, starting at the upper intertidal zones to higher elevations (Fig. II 2-2C1). As pioneer plants, they create the marsh edge, which is 130 km long (30% S. tabernaemontani, 37% S. maritimus, 33% P. australis) for the Elbe area. The Weser marsh edge stretches a distance of 90 km (11% S. tabernaemontani, 15% S. maritimus, and 74% P. australis). P. australis is dominant along the marsh edge of the anabranches of the Elbe as well as at the entire Weser estuary, whereas S. maritimus dominates the marsh edge of the main Elbe River.
2.2 Study design and digital data For the study design, we distinguished between the exposure gradient and the tidal-fluvial gradient. The exposure gradient has been defined by Keddy (1983) and Coops et al. (1991). For example, vegetation along marsh edges of anabranches (Fig. II 2-2) as well as of the main channel far away from ship-traffic and/or with a long gentle slope is likely to suffer less from mechanical stress than exposed sites near the navigation channel (Silinski et al., 2015a) on steep slopes and/or long wind fetches. The tidal-fluvial gradient describes the different fluxes of tidal and fluvial energy depending on the river section (Jay et al., 1990). These fluxes determine the characteristic river geomorphology and current velocities, as well as influencing the hydrodynamic forces resulting from navigation. For instance, at low tide, ship-induced waves exert stress on the banks to a lesser extent than at high tide. Thus, the tidal-fluvial energy determines the magnitude of the exposure gradient.
We processed data from digital vegetation maps (DVM), digital elevation models (DEM) including bathymetry (echosound data) as well as gauge data from both Elbe and Weser estuaries in a geographical information system (GIS) (see A-1-1 Table). The vegetation maps were generated on the basis aerial photos recorded by a DMC (digital mapping camera) with a ground resolution of 0.25 m. These were classified, digitally revised, mapped and thoroughly verified in the field, especially for areas with uncertainties during the digital revision (Petersen et al., 2010). Thus, the vegetation maps have a very high accuracy regarding geometrical locations (less than 2.00 m boundary uncertainties) and assignments of reeds and sedges (correct classification rate: approximately 99 %) (Petersen et al., 2010). The vegetation cover of the analysed polygons 28 Key ecosystem engineers in estuarine vegetation
representing monospecific stands amounts nearly 100 %. Due to the infrared signature separating vegetation from tidal flats and a multilevel, raster-based neighbourhood filtering (55 - 99 pixels) (Petersen et al., 2010), every vegetation patch larger than 2.50 m is classified as one of the macrophyte species. The DEM produced on the basis of LiDAR (light detection and ranging) data were verified by data of a precise differential global positioning system. The elevation has a standard deviation between 0.15 and 0.40 m depending on surface roughness (Brockmann and Schumann, 2011). Gauge data were verified, geodetically examined, and corrected (Weiß and Sudau, 2012), therefore having a minimum error of less than 0.10 m. For additional data used for determining the exposure gradient and the tidal-fluvial gradient see also A-1-1 Table. Since tidal- fluvial stress conditions differ between the two estuaries and between main channels and anabranches (Table II 2-1), we classified four river classes: Elbe – main channel, Elbe – anabranches, Weser – main channel, Weser – anabranches. For classifying the marshes of stream islands (Fig. II 2-2A), a calculated centreline divided the area above mean low water of each stream island in the area belonging to the main channel and in the area belonging to the anabranches. Each river class was split into two lateral sections (marsh versus marsh edge) for analysing marsh zonation as well as for examining the influence of the exposure gradient on the elevational species range along the marsh edge (Fig. II 2-2B). Due to the boundary uncertainty of less than 2 m, the polygon line congruent with the marsh edge from the digital vegetation map was visually compared with the digital DMC aerial imagery and corrected where necessary. Then, we generated 70 random data points for each species-river-class-unit (a) within the polygons of monospecific stands of the DVM and (b) along the verified marsh edge line. For generating these points, we defined the following conditions: Areas adjacent to hydraulic-engineering structures as groynes were masked by a 10 m-buffer. Plants protected from hydrodynamics by longitudinal parallel structures were masked due to reduced hydrodynamics. We set a minimum sampling distance of 50 m.
To study the effect of elevation correlated with the exposure gradient on species distributions along the marsh edge, we quantified three proxies: (1) The mean bank slope is defined by the elevational profile from the shallow water (2 m deeper than the mean low water) up to the elevation of the marsh edge (A-1-1 Table). We calculated the mean slope using the ArcGIS 10.x (ESRI, Redlands, CA, USA) extension 3D Analyst. (2) The distance from the thalweg (defined as the line with the lowest elevations and the highest current velocities in a water course) is calculated as the Euclidean distance from the thalweg to the marsh edge, which is congruent with the navigation channel axis of the main river channel, extracted from the digital charts of the Federal Waterways (A-1-1 Table). II Material and methods 29
Thus, it is a proxy for the distance from the highest hydrodynamic and ship-induced waves (Ali et al., 1999). For the anabranches, the thalweg was calculated with the River Bathymetry Toolkit, a free extension for ArcGIS 10.x (Lisenby et al., 2014; McKean et al., 2009). (3) We analysed four conditions of wind fetches: The weighted sum of wind fetches as well as the three most frequent wind directions: south-west (20 %), west (19 %), and northwest (15%; for detailed data see Bülow et al. (2013) and A-1-1 Table). To calculate wind fetches, we applied the fetch model provided by the ArcGIS 10.x toolbox ‘Waves2012’ (Rohweder et al., 2012). The calculations of the wind directions are based on a land-water raster describing the distance from the water-land-border (negative values belong to the water, positive vales to land). The results were line-shaped grid-based datasets (4 m resolution) of wind fetches along the mean high water (MHW). We extracted the grid cell information to our data points with the condition that the distance between MHW and data points are less or equal to 50 m to reduce data uncertainties. We only analysed bounded wind fetch lengths, which were completely enclosed by the water body, unbounded wind fetch lengths are missing in either initial points or target points for calculation (Rohweder et al., 2012).
2.3 Statistical analyses We used the Kruskal-Wallis rank sum test to test the significance of elevational differences in (1) plant zones as well as in (2) marsh edges. For (1), we compared the species within every river class and the four river classes for every species. For (2), every combination of species and river class was tested against each other. After detection of significant differences, we ran a multiple comparison test (Į = 0.05) using the Kruskalmc algorithm developed by Giraudoux (2015) for the R statistical computing platform – version 3.0.0 (R Development Core Team, 2014). We used linear and non-linear regression approaches for (3) characterising relationships between the species elevation above MHW and the two proxies mean bank slope and distance from the thalweg as well as between the species elevation above MHW and the four kinds of wind fetches. Diagnostic plots revealed whether the underlying assumptions of the regression models were met. While analysing the relation between elevation above MHW and the parameter of the exposure gradient, we detected a clear minimum distance from the navigation channel of the species distribution defined as threshold. To make the threshold more robust, we also calculated the 5th and 25th percentile. 30 Key ecosystem engineers in estuarine vegetation
3 Results
3.1 Elevational differences in plant zonation and species zones The marshes of both estuaries (Fig. II 3-1A, Table II 3-1) demonstrate an obvious and significant zonation of the studied species. The zonation shows the same species formation for all the river classes from the lowest to the highest elevation above MHW: S. tabernaemontani – S. maritimus – P. australis. Compared to the anabranches, the marshes along the main channels exhibit a clearer zonation. In particular, the interquartile ranges of S. tabernaemontani and S. maritimus of the anabranches overlap in contrast to those of the main river marshes. The marsh zonation of the Elbe anabranches exists at the same elevations as the zonation at the Weser anabranches. The zonation of S. tabernaemontani and S. maritimus along the Weser main channel shows significantly lower elevation above MHW (median value in m ± SD: S. tabernaemontani: -1.7 ± 0.3, S. maritimus: -0.9 ± 0.7) than along the Elbe main channel (S. tabernaemontani: -1.1 ± 0.4, S. maritimus: -0.4 ± 0.5). The zones of P. australis along the main channels of the two rivers do not significantly differ in elevation above MHW (Elbe: 0.4 ± 0.6, Weser: 0.1 ± 0.8). Hence, the zone of P. australis is found for the most river classes at the same elevation. The zone of S. maritimus along the Elbe main channel is located significantly higher than the other classes. The zone of S. tabernaemontani shows no obvious pattern between the river classes.
3.2 Elevational differences in marsh edges Species elevation ranges of the most river classes along the marsh edge are quite similar (Fig. II 3-1B). However, the clearly higher elevational distributions of P. australis along the marsh edge of the Elbe main channel significantly differs from the other species-river classes regarding the marsh edge (Table II 3-1). II Results 31
Fig. II 3-1 Marsh zonation along the elevational gradient and the distributions along the marsh edge A: Marsh zonation along the elevational gradient of the main channels and anabranches of the Elbe and Weser estuary, St = Scirpus tabernaemontani, Sm= Scirpus maritimus, Pa = Phragmites australis. Different letters show the significant difference detected by multiple comparison tests, significance level is Į = 0.05. Above letters: comparison of the plant zonation between the main channels and anabranches of the Elbe and Weser estuary. B: Elevation distributions of common macrophyte species along the marsh edges of the main channels and anabranches of the Elbe and Weser estuary. Different letters show the significant difference between every combination of species and river class. If one letter of the letter combination is found in another letter combination, no significance was detected between the two combinations. The significance level Į = 0.05. 32 Key ecosystem engineers in estuarine vegetation
Table II 3-1 Results of the Kruskal-Wallis rank sum test for the marsh (elevational differences between the three species and between the river classes) and marsh edge. d.f. = degree of freedom, p-value = significance level, H = Kruskal-Wallis test statistic
Marsh H d.f. p -value Elbe – main channel 131.57 2 < 0.001 Weser – main channel 132.30 2 < 0.001 Elbe – anabranches 125.65 2 < 0.001 Weser – anabranches 131.90 2 < 0.001
S. tabernaemontani 93.56 3 < 0.001 S. maritimus 47.08 3 < 0.001 P. australis 10.99 3 < 0.05 Marsh edge every species of 250.72 11 < 0.001 every river class
3.3 Relations and thresholds between the species elevational distributions and the exposure gradient along the marsh edges Testing the correlation of mean bank slope, distance from the thalweg, and the four wind fetches against elevation above MHW for the three species along the marsh edge, no relationship for S. tabernaemontani exists (Table II 3-2). The marsh edge of S. maritimus for the Elbe and Weser main channels shows the strongest relation between the elevational distribution and the distance from the thalweg: The edge of S. maritimus along the Elbe main channel starts to occur at -1.9 m above MHW with a distance of 600 m from the navigation channel. With increasing elevation, its distribution shows a positive weak relation with longer distances from the navigation channel. The same pattern exists for the Weser main channel, but the edge of S. maritimus occurs at -2.0 m above MHW already at a distance of 150 m from the navigation channel axis. If the distance from the navigation channel axis exceeds 1500 m, S. maritimus is again located on lower elevations (compare the quadratic regression term in Fig. III 3-2). Focusing on the marsh edges along the anabranches, S. maritimus at the Elbe River shows the strongest relation between the elevation above MHW and the mean bank slope (higher elevation above MHW with increasing slope), whereas for the Weser anabranches no relation exists. Regarding the four wind fetches, S. maritimus reveals a negative weak relation between the weighted sum of wind fetches and the elevation above MHW (elevation 2 above MHW = -0.878 - 0.0007 sum of wind fetch, F1,46 = 16.28, p 0.001, R adjusted = 0.245). For the elevational distribution of P. australis along the marsh edges, no relations exist except for the class Elbe main channel: Here the strongest relation demonstrates an increasing mean bank slope with a higher elevation above MHW. II Results 33
Table II 3-2 Spearman rank correlation coefficient (ȡsp) for relationships between mean bank slope (°) and distance from the thalweg (distance, m) and the results of the strongest regression model testing the dependent variable elevation above MHW versus the independent variables (x) distance or slope and their quadratic terms. n = number of random data points, b = regression slope, SE = standard error, ***, **, and * indicate significance level 0.001 0.01 0.05, and n.s. = not significant (> 0.05), qt = quadratic term. We used the backward selection. If the regression slope was significant and if the diagnostic plots complied the regression assumptions, the relationship is defined as significant. If ȡsp < 0.7, both independent variables and their interaction terms were checked in one model.
River classes per species ȡsp x n b (SE) R²adjusted
S. tabernaemontani Elbe – main channel -0.61 distance/slope 70 n.s. Weser – main channel -0.51 distance/slope 60 n.s. Elbe – anabranches -0.8 slope 70 -0.11* (0.06) 0.042 Weser – anabranches -0.46 distance/slope 70 n.s. S. maritimus Elbe – main channel -0.75 distance 69 0.0006*** (0.0001) 0.211 Weser – main channel -0.71 distance 70 0.003*** (0.0004), qt: 0.641 -0.0000009*** (0.0000002) Elbe – anabranches -0.76 slope 70 0.25*** (0.06) 0.181 Weser – anabranches -0.67 distance/slope 52 n.s. P. australis Elbe – main channel -0.77 slope 70 0.61*** (0.093) 0.376 Weser – main channel -0.43 distance/slope 70 n.s. Elbe – anabranches -0.67 distance/slope 70 n.s. Weser – anabranches -0.78 distance/slope 70 n.s. We detected a threshold for all species regarding the distance from the navigation channel for the Elbe River in particular, but also for the Weser River (Fig. II 3-2). Although, for instance, the smallest distances from the shoreline to the Elbe navigation channel axis are around 280 m (Lühesand south, 9°36’36’’E, 53°35’13’’N) and 300 m (Twielenflether Sand, 9°33’30’’E, 53°37’17’’N), the marsh edge formed by all studied species starts to establish when the distance from the navigation channel axis is longer than 600 m (5th percentile; 25th percentile: 900 m), whereas the Weser marsh edge is already formed at distances of 250 m (5th percentile; 25th percentile: 360 m) from the navigation channel axis. 34 Key ecosystem engineers in estuarine vegetation
Fig. II 3-2 Distance thresholds of the species distribution from the thalweg of the main channels (dark green) whereas the species distributions along the anabranches (light green) exhibit no threshold.
4 Discussion Despite the multitude of studies examining why plant zonation in marshes vary in their elevational distribution at local plot-scale, only little quantitative knowledge exists at ecosystem-scale (Friess et al., 2012; Moffett et al., 2015; van Wesenbeeck et al., 2008a). Our findings contribute to the understanding of the elevational variation of (1) marsh edges and (2) plant zonation of regularly flooded marshes concerning hydrodynamic forces on estuarine ecosystem-scale. Filling these knowledge gaps is essential to properly explain and predict alterations in estuarine plant zonation in the context of river training, climate change, and the implementation of the European Water Framework Directive (WFD, 2000/60/EC). The WFD is a directive which requires the European Union member states to protect and to improve the ecological quality and function of estuarine waters (Borja, 2005).
II Discussion 35
Low hydrodynamics plus species competitive hierarchy drive niche overlap and reduce the number of marsh zones To our knowledge, this is the first time that the marsh zonation of S. tabernaemontani, S. maritimus, and P. australis has been compared in elevation between estuaries (Fig. II 3-1, Table II 3-1). The species demonstrate the same elevational patterns. However, the zonation of S. tabernaemontani and S. maritimus along the anabranches is not as distinctive as at the main channels. Although marsh zonation is known to be distinctive in elevation, niche overlap is a common phenomenon in marshes (e.g., Russell et al. (1985), Moffett et al. (2010)). It reflects interspecific competition (e.g., Pielou and Routledge (1976); Xu et al. (2007)) in the elevational part of the multidimensional niche space due to similar functional attributes (Rosenfeld, 2002). The universal trade-off ‘plant competition increases with less stress’ (Pennings et al., 2005; van Wesenbeeck et al., 2007), supports our findings which indicate that S. tabernaemontani and S. maritimus are stronger competitors at anabranches with less hydrodynamic stress. Niche differentiation with less overlap is likely to be the more energy-efficient solution for the species along the main river channels. The hydrodynamic stress level also caused by vessel transport is too high to dissipate energy in competition.
Furthermore, we show that the zonation of S. tabernaemontani and S. maritimus for the Elbe main channel is situated at 0.5 m higher elevation than for the Weser main channel. A possible explanation might be the higher frequency of cargo transport, intertwined with a second explanation of higher tidal-fluvial fluxes in the Elbe estuary excepting tidal range (Table II 2-1). The wind fetches of the Elbe River are an unlikely reason for the Elbe marsh edge being located higher up than the Weser marsh edge due to insignificant results in testing the correlation with species elevation distributions. The explanations are in line with Silinski et al. (2015a) who concluded that plants such as S. maritimus have a survival strategy adapted to natural wind wave conditions, but not to long-period waves such as those generated by ships.
While Fig. II 3-1 depicts the typical distinct plant zonation in marshes (e.g., Pielou and Routledge (1976); Suchrow and Jensen (2010); van Wesenbeeck et al. (2007)), the results regarding marsh edges show that all species of the river classes are able to establish on more or less the same low elevations. This finding refers to the elevational threshold between bare and vegetated tidal flats forming the sharp boundaries between the two states (Fig. II 4-1A) (Scheffer and Carpenter, 2003; Wang and Temmerman, 2013). This threshold has not yet been verified for freshwater and brackish marshes of meso-/macrotidal estuaries (Moffett et al., 2015; Wang and Temmerman, 2013). However, GIS data in different time steps are required for identifying sudden shifts in order to verify 36 Key ecosystem engineers in estuarine vegetation
the stable state theory on ecosystem-scale. In another perspective, the findings point out that the species have similar lower boundaries in their fundamental niche. This indication is affirmed by the transplant studies in the Chilean salt marsh by Farina et al. (2009) in which species without neighbours grew well in each other’s zone of lower marshes.
However, the marsh edge of P. australis along the main Elbe channel is found clearly at a higher elevation above MHW than along the other river classes (Fig. II 3-1, A-5-1 Fig.), while S. maritimus and S. tabernaemontani only show weaker tendencies for the same pattern. If the hydrodynamic forces are particularly severe as in the main Elbe channel, the results indicate that the lower boundary of the fundamental niche of a species with lower stress resistance is more clearly defined on higher elevations by hydrodynamic forces than for a more stress-resistant species. This claim fits with the statement by Lenssen et al. (2000) who point out that the lower boundary of the elevational range of emergent macrophytes is determined by flooding tolerance, whereas the upper boundary is restricted by interspecific competition. Nevertheless, to verify the indicated interaction, we need to improve our mechanistic understanding on how the zonational patterns are formed by applying additional real world as factors well as with numerical experiments with process-based models.
We infer that hydrodynamic stress imposed along the shoreline is a primary driver of plant zonation and species elevational distribution in regularly flooded marshes of navigable estuaries. Based on species-specific traits (cf. Heuner et al. (2015), Carus et al. (2016)), types of stress resistance were in our opinion insufficiently clustered when opposing competition to stress: According to the functional types developed by Boutin and Keddy (1993), all three species are clonal dominants (Boutin and Keddy, 1993; Tulbure et al., 2007), none of them is classified as stress-tolerant. The Grime’s CRS strategies (C: competitior, R: ruderal, S: stress-tolerator) reflect the different species stress resistance somewhat better: S. tabernaemontani (syn. Scirpus validus) and S. maritimus classified as CS-strategist (Ecke and Rydin, 2000; Frank and Klotz, 1990) differ from P. australis as C-strategist. We claim that hydrodynamic stress weakens the competitive hierarchy, causing stress- resistant species to become dominant. Nevertheless, their competitive ability does not change, whereas the competitive strength of a C-strategist decreases with increasing hydrodynamic stress (cf. Emery et al. (2001) and Engels (2010)).
To explain the variation in plant zonation (Fig. II 4-1B) and the differentiated niche boundaries, we applied the model of competitive hierarchy (Keddy, 2001): We derived from our findings a conceptual model of the realized niche spaces spanning two hydrodynamic stress gradients II Discussion 37
(Fig. II 4-1). The occurring niche overlaps of S. tabernaemontani, S. maritimus, and P. australis are demonstrated within one estuarine river section (Fig. II 4-1C) and for different estuarine systems (Fig. II 4-1D). This demonstrates that the number of plant zones increases with higher tidal-fluvial stress including navigation as well as with higher exposure stress energized by the tidal-fluvial fluxes. On the other hand, P. australis can exclude the other species (S. maritimus and S. tabernaemontani) when the shoreline experiences less hydrodynamic stress and the zonation will consequently disappear (Fig. II 4-1B, C). This inference is in line with the trade-off between stress resistance and competitive dominance found for the hydrodynamic gradient of coastal salt marshes by van Wesenbeeck et al. (2007). They acknowledge that dominant species are restricted by severe hydrodynamic forces; therefore competitive exclusion occurs where hydrodynamic forces are low. Even though competitive hierarchy is usually investigated experimentally, we must not lose sight of large-scale patterns (Keddy, 2003). Among the first focusing on large scale patterns, our study exhibits how the model of competitive hierarchy can be applied for regularly flooded marsh plants influenced by hydrodynamic forces.
Ship-generated hydrodynamics drive the elevational difference in marsh edges Our findings highlight the unspecific threshold regarding the ship-induced waves (distance from the navigation channel axis). Our results complement the study examined by Silinski et al. (2015a) which postulates that long-period waves impede the plant establishment in marshes. The longer distance threshold of the Elbe main channel in contrast to the Weser main channel demonstrates that the transport frequency is likely to be of higher relevance than the load of a single vessel passage which is higher along the Weser main channel. Thus, ship-traffic as an external environmental forcing factor acting regularly seems also to govern the sharp boundary between bare and vegetated tidal flats. We suggest analysing monitoring data concerning a sudden shift from vegetated to bare tidal flats or vice versa when navigation conditions, such as widening the navigation channel, are changed.
The positive relationships expected between elevation above MHW and the measures for the exposure gradient, especially for the main channels, are missing along the marsh edge of anabranches probably due to reduced hydrodynamics (A-1-1 Table). The relationship, shorter distances from the navigation channel axis with lower elevation above MHW concerning marsh edges of S. maritimus on the main channels (Table II 3-2), reflects the logical topographical relationship between elevation and distance. 38 Key ecosystem engineers in estuarine vegetation
Fig. II 4-1 Vegetation formations and the conceptual model of species zonation in estuaries. A: Clip of the aerial orthophotos visualizing the sharp boundary between marsh and tidal flats. B: Clip of the digital vegetation map with zoned vegetation and vegetation sections with missing zonation. C & D: Conceptual model of species niche spaces between the two hydrodynamic gradients showing the dependent plant zonation in estuarine ecosystems (interior view: A, exterior view: B). II Discussion 39
The quadratic regression term of the relation concerning the Weser main channel (Table II 3-2) is only due to the enlarging funnel width in the area downstream at Bremerhaven. The insignificant results for S. tabernaemontani at both channels and P. australis at the Weser channel confirm that their distribution is more threshold-driven than gradient-driven, arguing for the stable state theory.
The positive relation between the elevation above MHW and the mean bank slope regarding the marsh edge of P. australis along the Elbe main channel is comparable to other studies of P. australis (Coops and van der Velde, 1996b; Keddy, 1983). Moreover, the positive relations found for S. tabernaemontani and S. maritimus for the Elbe anabranches was intensified when only the local Pagensand-Haseldorfer-anabranch system was analysed. We presume this is a result of increased pleasure-craft traffic in this area.
The missing relationships between the elevation above MHW and the length of wind fetches are in contrast to most studies on the occurrence of macrophytes on lake shores (Gasith and Hoyer, 1998) where submerged vegetation decreases in abundance with increasing wind fetch. However, the finding is in line with the study on P. australis and S. maritimus located at the Rhine-Meuse estuary where Coops et al. (1991) showed that these species can also be found at sites with a long wind fetch of up to 2500 m. Nevertheless, the quantifying effects of wind speed and storm surges remain to be studied for these macrophytes in order to understand the risks of failure of natural shorelines in specific storm conditions (cf. Möller et al. (2014)).
Our findings support the hypotheses that ship-induced hydrodynamic forces influence the elevational variation on ecosystem-scale concerning both (1) zonation of regularly flooded marshes and (2) the marsh edge as a proxy for the transition zone between tidal flats and marshes forming the elevational threshold. We found that ship-generated forces in particular cause a species-unspecific stress threshold which supports the multi stable state theory. As occurring at the Elbe main channel, ship-induced hydrodynamic forces indicate a dilution in the competitive hierarchy resulting in distinct realized elevational niches. In contrast, lower hydrodynamic forces cause elevational niche overlap and induce marsh establishment at a lower elevation which is characterized by the most dominant species only. Thus, we showed that marsh zonation needs the presence of hydrodynamic stress and that the species elevational ranges vary depending on the degree of hydrodynamic stress. The cases where estuarine plants do not show any zonation are based on the competitive hierarchy affecting the distributional patterns more distinctly than stress resistance. Our conclusions are thus 40 Key ecosystem engineers in estuarine vegetation
crucial for assessing estuarine shoreline habitats regarding their nativeness and for making management decisions for WFD-implementation.
Acknowledgements This study was supported by the research programme KLIWAS (Impacts of climate change on waterways and navigation – Searching for options of adaptation) of the German Federal Ministry of Transport and Digital Infrastructure (BMVI). We acknowledge the Federal Waterways Engineering and Research Institute, the Waterways and Shipping Boards Bremerhaven and Hamburg for providing data. We thank Arnd Weber, Eva Mosner, and Jana Carus for all the inspiring discussion regarding data analysis, Beatrix Konz for the graphical support, Klemens Uliczka and Volker Steege for comments on ship- and wind-generated waves, Dominica Campman for proofreading the paper, and André Terwei and anonymous referees for valuable recommendations for the manuscript.
Chapter III Niches, Traits and Ecosystem Engineering
Heuner, M., Silinski, A., Schoelynck, J., Bouma, T.J., Puijalon, S., Troch, P., Fuchs, E., Schröder, B., Schröder, U., Meire, P., Temmerman, S., 2015. Ecosystem engineering by plants on wave- exposed intertidal flats is governed by relationships between effect and response traits. PLOS one 10 (9), e0138086. doi: 10.1371/journal.pone.0138086
42 Key ecosystem engineers in estuarine vegetation
III Introduction 43
Abstract In hydrodynamically stressful environments, some species – known as ecosystem engineers – are able to modify the environment for their own benefit. Little is known however, about the interaction between functional plant traits and ecosystem engineering. We studied the responses of Scirpus tabernaemontani and Scirpus maritimus to wave impact in full-scale flume experiments. Stem density and biomass were used to predict the ecosystem engineering effect of wave attenuation. Also the drag force on plants, their bending angle after wave impact and the stem biomechanical properties were quantified as both responses of stress experienced and effects on ecosystem engineering. We analysed lignin, cellulose, and silica contents as traits likely effecting stress resistance (avoidance, tolerance). Stem density and biomass were strong predictors for wave attenuation, S. maritimus showing a higher effect than S. tabernaemontani. The drag force and drag force per wet frontal area both differed significantly between the species at shallow water depths (20 cm). At greater depths (35 cm), drag forces and bending angles were significantly higher for S. maritimus than for S. tabernaemontani. However, they do not differ in drag force per wet frontal area due to the larger plant surface of S. maritimus. Stem resistance to breaking and stem flexibility were significantly higher in S. tabernaemontani, having a higher cellulose concentration and a larger cross-section in its basal stem parts. S. maritimus had clearly more lignin and silica contents in the basal stem parts than S. tabernaemontani. We concluded that the effect of biomass seems more relevant for the engineering effect of emergent macrophytes with leaves than species morphology: S. tabernaemontani has avoiding traits with minor effects on wave attenuation; S. maritimus has tolerating traits with larger effects. This implies that ecosystem engineering effects are directly linked with traits affecting species stress resistance and responding to stress experienced.
1 Introduction Ecosystem engineering has been a key concept for the last 20 years, elucidating how organisms change their abiotic environment and how this feeds back to the biota (Hastings et al., 2007). Ecosystem engineers construct new niches in ecosystems with strong impacts on ecosystem structure and functioning (Boogert et al., 2006; Bruno et al., 2003; Crooks, 2002; Cuddington et al., 2009; Gilad et al., 2004; Hastings et al., 2007; Wright and Jones, 2006). That in turn has a bearing on ecosystem services (Kremen, 2005; Traill et al., 2010) and approaches to conservation and restoration in a wide range of ecosystem types (Benayas et al., 2009; Bullock et al., 2011).
44 Key ecosystem engineers in estuarine vegetation
Creating conditions that favour plant growth (van Wesenbeeck et al., 2008b; Vandenbruwaene et al., 2011), plants attenuate waves on tidal flats as an ecosystem function (Anderson and Smith, 2014; Knutson et al., 1982; Möller, 2006), and the effect of this ecosystem engineering likely varies according to plant functional traits (Paul et al., 2012). Traits such as stem density, biomass and flexibility/rigidity determine the ecosystem engineering capacity (EEC) (Bouma et al., 2010; Bouma et al., 2005; Ysebaert et al., 2011). To understand how these traits influence the EEC of different species (He and Bertness, 2014), a fundamental requirement is to clarify which species traits respond to the environment (response traits) and which species traits determine the effects of plants on ecosystem functions (Lavorel and Garnier, 2002; Violle et al., 2007). Traits providing high stress resistance often coincide in effect and response (Diaz and Cabido, 2001). A better knowledge of these relationships is also crucial so as to understand why different species that occur in the same habitat have a different capacity to deal with abiotic stress and have a different EEC to modify abiotic stress levels, which construct niches with different species distribution patterns (Bouma et al., 2010).
To address the interaction of effect and response traits and the EEC, we used pioneer plants that colonize estuarine tidal flats as a model system. Besides currents, environmental drivers such as waves make estuarine and riverine habitats mechanically stressful environments in which plants can grow. However, plants which were able to establish and grow under these stressful conditions are often autogenic ecosystem engineers, having the effect of reducing hydrodynamic forces and related stress (Bouma et al., 2005; Crain and Bertness, 2005). The stress results from hydrodynamic forces that induce drag forces upon the submerged plant (Denny, 1994a; Henry and Myrhaug, 2013) and lead to sediment scour around the stems (Bouma et al., 2009a; Friess et al., 2012; Pollen-Bankhead et al., 2011). We would like to find out which traits of these plants have an effect on the species capacity for experienced stress (response traits) and which traits determine their EEC.
Plants can avoid stress, or they can tolerate it (Levitt, 1972; Taylor, 1978), both are stress resistance strategies (Levitt, 1972). For instance, flexible stems which bend easily are a common trait enabling plants to avoid hydrodynamic stress and are typically found in submerged vegetation (Puijalon et al., 2011; Schoelynck et al., 2013). On the contrary, species that are able to grow emergently have more structural rigidity (Asaeda et al., 2005) which is regarded as a stress tolerance strategy. This flexibility/rigidity balance can be determined by the balance between cellulose, lignin and silica incorporation (McLaughlin and Tait, 1980; Schoelynck et al., 2010; Zhang et al., 2014). These plant traits are species dependent and likely to reflect a cost-benefit trade-off between energy and/or
III Introduction 45 material investment (costs) and survival success by avoiding or tolerating a specific stress level (benefits) (Bouma et al., 2005).
Fig. III 1-1 Analysed traits of two marsh pioneers and their response parameters to wave impact. The molecule traits silica, lignin, and cellulose strengthen the cell wall. Tensile and bending properties of the stem tissue were investigated taking into account the cross-sectional area of the stem organ. Drag, scouring, and the irreversible bending angle of the plants were measured as response parameters to wave impact. The amount of wave attenuation was determined using plant patches. In this paper, we compared wave attenuation as an engineering effect by two plant species that have to withstand similar physical stress conditions on tidal flats. Scirpus tabernaemontani (C.C.Gmel.) Palla and Scirpus maritimus (L.) Palla are both ubiquitous pioneer species also growing in estuarine tidal marshes along the North Sea (NW Europe). S. tabernaemontani has an elliptically shaped stem and has no leaves whereas S. maritimus has a triangular stem and many leaves ( Fig. III 1-1, Fig. III 1-2). S. tabernaemontani occurs on lower elevations relative to mean high water (MHW) compared to S. maritimus. Along the Elbe estuary (Germany), for instance, S. tabernaemontani is located on average about 50 cm lower than S. maritimus (mean level in m ± SE relative to MHW, S. tab.: -1.09 ± 0.03, n = 140; S. mar.: -0.66 ± 0.04, n = 140) (A-4-1 Fig.). We hypothesize that these realized elevational niches are formed as a result of different strategies (and hence different functional traits) of dealing with hydrodynamic stress. We hypothesize that S. tabernaemontani has functional traits that allow it to avoid wave induced hydrodynamic stress, whereas S. maritimus has functional traits allowing to tolerate this hydrodynamic stress. Only regarding the lower relative elevations and
46 Key ecosystem engineers in estuarine vegetation
disregarding the traits, S. tabernaemontani would exert a stronger engineering effect exposed to higher water levels and higher waves. In order to compare the traits of the two species and draw conclusions about their effects in estuaries, we performed two laboratory wave flume experiments with individuals of S. tabernaemontani and S. maritimus under controlled, standardized water depths and wave heights but with constant elevations.
Fig. III 1-2 The appearance and zonation of the studied species. a: S. tabernaemontani, b: S. maritimus at Hollerwettern (53°50'20"N, 9°21'40"E), c: oblique aerial photo visualizing macrophyte zonation on tidal flats with S. tabernaemontani in the front (dark green) with the adjacent belt of S. maritimus (lighter green) at Allwörden (53°49'35"N, 9°19'25"E) We investigated how stress resistance affects species functioning with the following questions: (i) does biomass or stem density have a higher ecosystem engineering capacity to attenuate waves, (ii) what are the species responses to wave impact measuring scouring, drag, and bending, (iii) to what extent do species respond to tensile and bending forces, (iv) what are the features of the stem effect
III Materials and methods 47 traits (strength molecules, shape, cross section) requiring stress resistance, and (v) how much energy is invested in stem material in order to be efficient? We were able to characterize the functional traits in interaction with their EEC regarding the realized niches of S. maritimus and S. tabernaemontani.
2 Materials and methods
2.1 Plant material Approximately 400 pieces of rhizomes were sampled in spring 2012 (April 17th and 23rd) for each of the two plant species (Fig. III 1-1, Fig. III 1-2). The sampling sites were two brackish marshes. One is located in the Scheldt estuary at Groot Buitenschoor, Belgium (51°21’47"N, 4°14’53"E), where we collected Scirpus maritimus (L.). In the Elbe estuary at Hollerwettern, Germany (53°50'20"N, 9°21'40"E), we gathered Scirpus tabernaemontani (C. C. Gmel) since this species is not found at Groot Buitenschoor*. Access and permission for extraction of plants from the brackish marshes were granted by Natuurpunt (Belgium) and Kreis Steinburg (Germany). We confirm that the field studies did not involve endangered or protected species. For flume exp. 1, 340 rhizome pieces of each species were planted into eight boxes (40 cm, 25 cm, 30 cm) per species and filled with natural sediment from the Scheldt. Continuous marsh vegetation can be simulated by fitting these boxes in two rows into a flume channel at the NIOZ in Yerseke. For detailed properties of the flume see Bouma et al. (2005). Throughout the growing season, S. tabernaemontani and S. maritimus developed a stem density of 700 and 600 shoots per m², respectively. For flume exp. 2, we planted 20 rhizomes per species into PVC tubes of 25 cm height and 12 cm diameter lined with plastic bags and filled with the same natural sediment (D50 = 0.32 mm) as used in the sediment box of exp. 1. They were grown outside, close to the Scheldt, for three months and irrigated with brackish water (5 g/L of salt) representing natural field conditions.
2.2 Flume experiment for measuring ecosystem engineering effects (exp. 1) (question i) Wave attenuation of the two species was measured along a 1.6 m long and 0.6 m wide plant patch in the NIOZ flume. Behind the plant patch, a wave damping rack was installed to avoid wave reflection. The plant patches were prepared for each species with (i) two equal densities and (ii) two equal biomasses. Comparing the same number of stems (density), we were able to demonstrate the effect of biomass. Comparing the same amount of biomass, we analysed the effect of the unlike
* Additionally, we also sampled shoots of S. maritimus at Hollerwettern in order to test the plant properties for similarity between the Elbe and the Schelde site. 48 Key ecosystem engineers in estuarine vegetation
morphology. The lower density patch (300 stems per m2) was produced by removing randomly selected shoots from the higher density patch (500 stems per m2 for S. maritimus, 800 stems per m² for S. tabernaemontani), after having first tested it in the flume. The low density is representative of a field situation in spring, whereas the high density represents a field situation in summer. In order to quantify wave attenuation as a function of submerged dry biomass, the submerged biomass per stem was measured on 98 shoots of S. maritimus and 104 shoots of S. tabernaemontani that were randomly selected at the end of exp. 1. The lower 32 cm of the stems, which had been inundated during the experiments, were cut off and dried for 72 h at 70 °C. The dry biomass per shoot was weighed. Different biomasses were calculated by bootstrapping (random sampling, 10,000 times, checking 99% confidence interval) using the measured total stem density per m². Thereof, the stem density and dry biomass are highly collinear (Pearson correlation 0.94). 300 mg per m2 dry biomass corresponds to 200 stems of S. maritimus and 300 stems of S. tabernaemontani. 800 mg per m2 dry biomass is equal to 500 stems of S. maritimus and 800 stems of S. tabernaemontani. Thus, stem density and dry biomass were tested separately.
The waves were measured by calibrated pressure sensors (GE Druck PTX1830) with a sampling frequency of 40 Hz (for analysis see Appendix 2). To be able to deduce wave attenuation from these measurements, we measured waves at two locations: 40 cm in front of the plants and directly behind the plants. In a water depth of 32 cm, the incoming mean wave height (cm ± SE) in front of the plants was 8.4 ± 0.01 (n = 120).
2.3 Flume experiments for measuring plant responses to wave impact (exp. 2) (question ii) The full-scale experiments were performed in an engineering wave flume facility at Ghent University. The experimental wave flume (Fig. III 2-1) consisted of subsequent slope sections with a rough sand-like concrete surface. First, the transition slope facing the wave paddle was necessary in order not to lose too much wave energy over a longer gentle slope. The second slope with the test section represents a gently inclined estuarine tidal flat colonized with pioneer vegetation dominated by S. tabernaemontani and S. maritimus. The pebble stone beach at the rear of the flume absorbed the waves and avoided wave reflection from the back.
III Materials and methods 49
Fig. III 2-1 Physical model of wave flume experiments for testing drag force, scouring and bending angle after wave impact of S. tabernaemontani and S. maritimus. Top: side view, below: top view. Two plants are situated at the back of the sediment box (dark grey); medium grey: pebble stone beach for wave absorption, light grey: concrete bottom slope; HW = high water depth, LW = low water depth. The integrated sand box 0.3 m in depth was filled with natural sand from the Scheldt estuary
(D50 = 0.32 mm). Acting as the first plant row of a marsh edge, two plants were transplanted at the back of the sand box for each test. The tubes were removed and the plastic bag folded deeply downwards into the surrounding sediment box. With this method, plants could be transplanted with their anchored root system and no sediment border for undesirable side effects between the transplanted plant and the sediment of the flume sediment box. For producing and measuring a representative scouring of one stem, all clonally grown stems, except for one, were removed from the transplanted plant by cutting them off as deep as possible below the sediment surface. Plant properties such as the height of stem and the stem diameter were measured before the runs of exp. 2 to record the initial plant-morphological status. The properties of these two species were not significantly different. The mean stem height (cm ± SE) of S. maritimus is 102 ± 4.7 and of S. tabernaemontani 114 ± 3.4. The mean stem diameters (mm ± SE) are 8.2 ± 0.4 (S. maritimus) and 8.9 ± 0.4 (S. tabernaemontani).
We tested two water depths (of 20 cm and 35 cm at plant position) with a constant wave period of 2s for ten individuals of each of the two plant species. The two water depths simulate wave impact at different moments in the tidal cycle, because the mean tidal range (2001-2010) in the Elbe estuary, for instance, varies between 2.8 m (gauge Brokdorf, 53°51'53"N 9°19'13"E) and 3.6 m (gauge
50 Key ecosystem engineers in estuarine vegetation
St. Pauli, 53°32'45"N 9°57'33"E). The 2 s wave period was chosen to simulate common natural wind waves in the Elbe estuary (Fittschen, 2003). Each test run consisted of 200 waves and for each of them two replicate individuals were transplanted next to each other with an exact distance of 0.33 m between the two stems as well as between a stem and the flume margin. The surface of the sand slope was flattened to an initial slope of 1:50 before each test. Using resistance wave gauges (sampling frequency 40 Hz), waves were measured at the paddle and on the test section for each of the test runs. Wave height (cm ± SE) at the paddle was set to 17 ± 0 (n = 20). The waves were transformed on the slope and reached a mean wave height of 9 ± 0 (n = 10) for 20 cm water depth (broken waves) and 18 ± 0 (n = 10) for 35 cm water depth (unbroken waves) at the location of the plants.
We measured plant characteristics (height, number of leaves, leaf length, and stem diameter 3 cm above the sediment surface), drag forces acting on the plants during wave impact, scouring depths and volumes around the stems (Silinski et al., 2015a), as well as bending angle before and after each test run. Drag forces were measured by a bimetal calibrated for measurements in N (Versluys’s drag instrument developed by Ghent University, Dept. of Civil Engineering, Belgium). Peak drag forces were extracted and averaged per plant and test run. Number of leaves, leaf length and stem diameter as well as drag force and effective water level of each condition (i.e. still water level + significant wave amplitude) were used to calculate the drag per wet frontal plant area. After cutting the plant, the scoured sediment surface around the stem, plus a reference section between the plants without interference of a stem, were scanned by a laser scanner (EProfiler developed by Aalborg University, Hydraulic & Coastal Engineering Group, Denmark) (± 1 mm). In order to quantify scouring depth and volume as measures of scour vulnerability, the scouring data were analysed with the Hydrology Toolbox of Esri ArcGIS (see Appendix 3).
2.4 Measurements of biomechanical plant traits (question iii) The biomechanical properties of the two plant species studied were measured on the basal parts of the stems of 20 individuals per species* that were not subjected to any experiment. Bending and tensile tests were performed with a universal testing machine (Instron 5942, Canton, MA, USA). The bending test was conducted on the most basal part of the stems and the pulling test on a slightly higher section. For each test, the stem fragments were 10 cm long. For each sample, we measured the dimensions of the cross section using a digital caliper (± 0.02 mm) at three different points along the sample: height and width for triangular cross-sections (S. maritimus) and axes for the elliptical cross-sections (S. tabernaemontani). For the bending tests, we performed three-point bending tests,
*Both species measured originates from the Elbe estuary. III Materials and methods 51 consisting of a force applied at a constant rate of 10 mm/min to the midpoint of a sample placed on a support. For the tensile tests, the stem fragments were clamped into the jaws of the testing machine and a constant extension rate of 5 mm/min was applied to the upper jaw until they broke.
The bending tests were used to calculate Young's modulus, the second moment of area, and the flexural stiffness. Young’s modulus (E in Pa) quantifies the material stiffness and is calculated as the slope of the stress-strain curve in the elastic deformation region. The second moment of area (I in m4) accounts for the effect of the cross-sectional geometry of a structure on its bending stress. It 1 was calculated depending on the geometry of the cross section by using the equation: Ibh 3 , 36 S where b and h are the base and height of the cross section for S. maritimus and I ac3 , where a 4 and c are the shorter and longer axes of the cross section for S. tabernaemontani. The flexural stiffness (EI in N m2) quantifies the stiffness of the stem fragment and was calculated by multiplying E and I. In many cases there was no real breakage (but rather a buckling of the stem fragment), because the stems were too flexible. The tensile tests were used to calculate the breaking force and the tensile strength. The breaking force (N) is the force at which the plant fragment breaks when exposed to tensile forces (e.g., hydrodynamic forces). The tensile strength (N/m2) is the breaking force corrected by the cross-sectional area of the fragment.
2.5 Analysis of plant strength (question iv) Ten individual S. tabernaemontani and S. maritimus stems that were not subjected to any experiment, were split into the basal 30 cm and the remaining upper stem part of various lengths. These parts were dried at 70 °C, ground, and weighed. Biogenic silica (BSi) was extracted from
25 mg dry plant material of each individual by incubation in a 0.1 M Na2CO3 mixture at 80 °C for 4 h (DeMaster, 1981). The extracted and dissolved silica was analysed on an ICP-OES spectrometer (Skalar, The Netherlands). To determine the cellulose and lignin contents, the Van Soest method (van Soest, 1963) was used. We analysed the contents of the strength molecules silica, lignin and cellulose in two stem parts of the two species performing a two-way ANOVA with post-hoc Tukey’s HSD test for each molecule.
52 Key ecosystem engineers in estuarine vegetation
2.6 Calculation of cost efficiency (question v) The cost involving stem strengthening was determined by the biomass produced, the amount of strengthening molecules (cellulose and lignin) and stem shape. We recorded the dry biomass after exp. 1 was conducted.
We used the cost energy values of lignin and cellulose from Jung et al. (1999): 29,128 kJ/kg for lignin and 16,747 kJ/kg for cellulose. These were multiplied with the mean concentration of these respective molecules as found by our analysis of the basal stems. The gross energy is the sum of energy invested in cellulose and lignin. The cross-sectional area of S. tabernaemontani (elliptical stem) and S. maritimus (triangular stem) was measured with a digital calliper (see objective iii). Finally, the mean dry biomass was multiplied with the gross energy for each species and normalized by the stem cross-sectional area.
3 Results
3.1 Ecosystem engineering effects (question i) Both species significantly attenuated waves (Fig. III 3-1). However, the patch of S. maritimus showed a stronger reduction in wave height. The differences in wave attenuation between the species were larger with equal stem density than with equal dry biomass. Although the relation strength was very high (R² = 0.97) for both predictors, stem density and dry biomass, the wave attenuation by the stem density of S. maritimus featured a higher effect due to its higher regression slope. The wave attenuation by the stem density of S. tabernaemontani was coincident with wave attenuation by its dry biomass (Fig. III 3-1).
3.2 Plant responses to wave impact (question ii) The mean drag force on S. maritimus was twice as high (20 cm: 1.3 N, 35 cm: 2.7 N) as the mean drag force on S. tabernaemontani (20 cm: 0.6 N, 35 cm: 1.2 N). This difference disappeared when normalizing the parameter drag force to drag force per wet frontal plant area. Species and water depth interacted significantly: At a water depth of 20 cm, S. maritimus (289 N/m2) experienced nearly 100 N/m2 more drag force than S. tabernaemontani (197 N/m2). At a water depth of 35 cm, the differences in drag force per wet frontal plant area were less than 15 N/m2 (S. tabernaemontani: 261 N/m2, S. maritimus: 275 N/m2) and were no longer significant.
III Results 53
Fig. III 3-1 Wave attenuation behind a patch 1.6 m length, in relation to stem densities and in relation to dry submerged biomass for the species S. maritimus (+) and S. tabernaemontani (*). 2 The wave attenuation was well explained by the species biomass (F3,156 = 1645, p < 0.001, R adj = 0.97) as 2 well as by the species stem densities (F3,156 = 1857, p < 0.001, R adj = 0.97) using ANCOVA. The significantly different regression slopes exhibited the unlike species effect of wave attenuation (biomass: Reductionwave= 0.157 + 0.003 biomass for S. maritimus, Reductionwave= 0.176 + 0.002 biomass for S. tabernaemontani; stem density:Reductionwave= 0.119 + 0.005 stem density for S. maritimus, Reductionwave= 0.171 + 0.002 stem density for S. tabernaemontani). Lines represent the linear regression with 95%-confidence intervals (dotted lines), the regression assumptions were checked with diagnostic plots with positive results, no vegetation = the control wave runs.
54 Key ecosystem engineers in estuarine vegetation
Fig. III 3-2 Mean values of plant responses to wave impact with standard error (n = 10) for S. maritimus (S. mar.) vs. S. tabernaemontani (S. tab.). For ANOVA, the response variables were transformed by the natural logarithms. The absolute drag force differed significantly between species (F1,36 = 41.1, p < 0.001) and water depths (F1,36 = 36.5, p < 0.001). The drag force per unit of wet frontal area showed a significant interaction between species and water depth (F3,36 = 4.8, p = 0.035). Regarding the bending angle, the water depth of 35 cm demonstrated a significant difference between the species (F3,36 = 4.4, p = 0.044). Different letters show significant differences, significance level is Į < 0.05. After 200 waves, both species bent very little at low water depth, whereas S. maritimus exhibited significant bending at high water depth (Fig. III 3-2). The species did not differ in scouring volume or depth (data not shown). Furthermore, the highest observed absolute values (1.1 cm scouring depth and 19 cm³ scouring volume) were insufficient for uprooting.
III Results 55
3.3 Plant resistance (questions iii and iv) Both species differed in regards to biomechanical properties but for tensile strength: e.g., Young’s modulus was four times higher for S. maritimus (4.4 108 N/m2) than for S. tabernaemontani (1.2 108 N/m2) indicating stiffer tissues for S. maritimus, whereas the second moment of area was nearly two times higher for S. tabernaemontani (8.5/1011 m4), than for S. maritimus (4.4/1011 m4). The product of these two parameters resulted in the flexural stiffness being significantly different between the two species (Fig. III 3-3): with 0.017 N m², stems of S. maritimus were about twice as stiff as the ones of S. tabernaemontani (0.009 N m²). Regarding the tensile properties, the breaking force of the stems significantly differed between the two species (167 N for of S. tabernaemontani vs. 101 N for S. maritimus, Fig. III 3-3). However, they did not differ significantly in tensile strength, i.e. tensile force corrected for cross-sectional area (Fig. III 3-3), which indicated that the tissues (material) of S. tabernaemontani were not more resistant to tensile force than those of S. maritimus.
Regarding the cell wall, S. maritimus had an overall higher content of silica and lignin compared to S. tabernaemontani. Unlike the basal stem parts, the upper stem parts showed no significant difference in silica content between the two species (Fig. III 3-4). Furthermore, the basal stem parts of S. maritimus had a significantly higher silica content than the upper stem parts (5.5 mg/g and 2.8 mg/g respectively), while S. tabernaemontani had no significant difference between basal and upper stem parts. The lignin content differed significantly between the species: S. maritimus had more than twice the lignin contents of S. tabernaemontani (total mean of 71.7 mg/g and 30.6 mg/g respectively). Stem parts within both species did not show any differences. While the cellulose content in the upper stem parts of both species was almost identical, the basal stem parts of S. tabernaemontani contained clearly more cellulose (397.0 mg/g) than the basal parts of S. maritimus (337.0 mg/g) and the upper stem parts.
3.4 Cost efficiency (question v) For building strength molecules, the gross energy investment into a 32 cm basal stem parts of S. tabernaemontani was 7515 kJ/kg dry matter, whereas the gross energy investment into that of S. maritimus amounted to 7725 kJ/kg dry matter. This mean that S. tabernaemontani had to invest 8 kJ into its basal stem part, while S. maritimus needed 11 kJ, making S. tabernaemontani 31% more efficient than S. maritimus. This difference could be attributed mainly to the differences in biomass and strength molecules. Dry biomass weight (g ± SE) of the basal stem part of S. tabernaemontani was 1.0 ± 0.04 (n = 104), while this part of S. maritimus weighed 1.4 ± 0.06
56 Key ecosystem engineers in estuarine vegetation
(n = 98). Cellulose and lignin concentrations were also significantly different for both species (Fig. III 3-4). These data were corrected for the stem cross-sectional area. The stem cross-sectional area was a product of diameter and stem shape. The stem diameter between the two species did not differ significantly, but S. tabernaemontani had a significantly larger cross-sectional stem area.
Fig. III 3-3 Mean values of plant responses to tensile and bending forces with standard error for S. maritimus (S. mar., n = 19) vs. for S. tabernaemontani (S. tab., n = 20). For ANOVA, the response variables were transformed by the natural logarithms except for tensile strength. These data showed variance homoscedasticity of the residuals without transformation. The two species differed significantly in Young’s modulus (F1, 37 = 56.3, p < 0.001), in second moment of area (F1, 37 = 14.1, p < 0.001), in flexural stiffness (F1, 37 = 13.9, p < 0.001), and in breaking force (F1, 38 = 16.9, p < 0.001), but not in tensile strength. Different letters show significant differences, significance level is Į < 0.05.
III Results 57
Fig. III 3-4 Mean effect traits with standard errors (n = 10) for the species S. maritimus (S. mar.) and S. tabernaemontani (S. tab.). For ANOVA, silica data were transformed by the natural logarithms to insure the variance homoscedasticity of the residuals. Lignin and cellulose data showed variance homoscedasticity of the residuals also without transformation. The stem parts interacted significantly with the species in the silica content (F3,36 = 5.3, p = 0.028) as well as in the cellulose content (F3,36 = 26.4, p < 0.001). The species differed significantly in the lignin content (F3,36 = 211.5, p < 0.001). Different letters show significant difference, significance level is Į < 0.05. The mean stem cross-sectional area (mm² ± SE) of S. tabernaemontani, which had an oval shape, was 33.2 ± 2.3 (n = 19), whereas S. maritimus, which had a triangular shape, had a mean stem area of only 26.3 ± 1.3 (n = 20). Corrected for the stem cross-sectional area, the gross energy invested by S. maritimus was with 0.42 kJ/mm2 twice as high as of S. tabernaemontani (0.21 kJ/mm2).
58 Key ecosystem engineers in estuarine vegetation
4 Discussion The link between the ecosystem engineering capacity (EEC) of plants and their functional traits has been poorly studied so far (Bouma et al., 2010; Koivisto et al., 2011; Pages et al., 2012). To our knowledge this study is the first one to verify the EEC by demonstrating the relations of response and effect traits regarding the effect of wave attenuation (Fig. III 4-1). S. tabernaemontani and S. maritimus can be seen as examples of plant ecosystem engineers growing on estuarine marshes regularly flooded, where wave attenuation within the vegetation leads to sedimentation and a change in the environment. In full-scale wave flume experiments, the species exhibit clear dissimilarities in their response traits, which are also reflected in the analyses of their effect traits. Generally, a higher content of strength molecules was built into the basal stem part of both species (except for lignin). The strength of the base functions as ‘mechanical fuse’ protecting the root system (Usherwood et al., 1997), and ‘carrying’ the rest of the upper stem part. Yet it is likely that exactly this stem strength has important effects on (i) the experienced stress, and thus on (ii) the capacity for wave attenuation.
Fig. III 4-1 Conceptual framework of the relationships between traits which respond to the environment drivers (response traits) and traits which determine the effects of plants and thus the effect of ecosystem engineering (effect traits) in a wave-exposed intertidal habitat. Thus, the double-heading ‘response/ effect traits’ classify traits with both properties: They respond to the wave-induced drivers and at the same time they determine the effect of wave attenuation. Solid arrows represent the effects which we analysed, whereas dotted arrows stand for effects we found in literature: a: Puijalon et al. (2011), b: Bouma et al. (2010); Bouma et al. (2005); Ysebaert et al. (2011), c: McLaughlin and Tait (1980), d: Zhang et al. (2014), e: Schoelynck et al. (2010). Dashed arrows show the effects which we hypothesize from our results. At the water depth of 20 cm, drag forces on S. tabernaemontani and S. maritimus are significantly different (Fig. III 3-1). The reason is their discrepancy in outer shape (e.g., leaf number), and the
III Discussion 59 higher tissue rigidity (Nepf, 1999). The clear difference, however disappears at the 35 cm depth due to the fact that more leaves of S. maritimus are submerged increasing the frontal area drastically (Vieira and Dabney, 2012). The significant higher bending angle of S. maritimus in contrast to S. tabernaemontani at the water depth of 35 cm is an inextricable consequence (Fig. III 4-1).
Bending can be considered as the mechanism leading to toppling (Silinski et al., 2015a) and toppling is the determinant process that causes mortality of the plants, and hence failure of plant survival on the intertidal flats, but also for mangroves (Balke et al., 2013). Furthermore, the irreversible bending of plants in our experiment only results from drag, but not from scouring. Consequently, the species survival performance, and with it the safety of species functioning is implied to be only determined by the amount of drag force that the plants experience.
The higher drag force which S. maritimus suffers in contrast to S. tabernaemontani might be an explanation why it does not grow at lower elevations. Nevertheless, it also benefits from the advantage of wave attenuation. The higher effect explained by stem density compared to the effect explained by biomass regarding S. maritimus states biomass is more relevant than the species morphology which points to the plant surface area, an effect trait of biomass on which friction can be created.
Thus, the plant surface area appears to be the prime driver for significant wave attenuation (Möller et al., 1999). The presence of leaves, the roughness and stiffness of stems are relevant factors for effective wave attenuation (besides stem deflection (Knutson et al., 1982)), and also points towards being responsible for the significant differences between S. maritimus (with leaves) and S. tabernaemontani (leafless). Thus, besides the plant surface area and stem density, the species functioning in terms of ecosystem engineering indicate being directly affected by the degree of tissue rigidity.
Our findings fit the classical conceptual model of the avoidance-tolerance trade-off (Levitt, 1972; Puijalon et al., 2011). In contrast to S. maritimus, S. tabernaemontani has more traits yielding stress avoidance such as low lignin and silica content (Table III 4-1), which results in higher flexibility (i.e. lower stiffness) (Schoelynck et al., 2010) and hence a reduction of experienced drag forces (Kaufman et al., 1999; Schoelynck et al., 2010; Silinski et al., 2015a). The lighter biomass also refers to the reduction of drag forces. However, S. tabernaemontani has also a higher cross-sectional stem area that can resist higher tensile forces. Contrary to S. tabernaemontani, S. maritimus has more tolerating traits, such as high lignin and silica content, which likely results in higher
60 Key ecosystem engineers in estuarine vegetation
experienced drag forces and higher material costs. Contrary to the stiffness, the differences in tensile strength of the two species are not substantiated by the composition of the cell walls. The stem cross-sectional area is the main driving factor here for the significant differences in breaking force. The stem cross section of S. tabernaemontani is larger, leading to higher resistance to breaking compared to the stem of S. maritimus. Despite a larger cross-sectional area, S. tabernaemontani has a lower material investment than S. maritimus per unit stem length, because of its lower biomass weight and lesser kilojoule costs for cellulose than for lignin. Thus, this pattern indicates a trade-off between concentrations of strength molecules in the cell walls, stem shape, biomass, and energy investment: The lower the material investment, the larger the cross-sectional area being more resistant to breaking force.
Why do plant species need a different level of ecosystem engineering capacity, when they live in the same habitat? The results of the functional traits likely explain the clear elevational occurrence of S. tabernaemontani and S. maritimus (Fig. III 1-2c). Our results support the hypothesis that S. tabernaemontani has more avoiding traits than S. maritimus (Table III 4-1), which enables it to withstand the higher hydrodynamic load that is typically found at lower marsh elevations where water levels are higher. Thus, the low cost efficiency and limited material investments of S. tabernaemontani may result from a high risk of failure due to the exposure to strong hydrodynamic forces. Diminishing the experienced drag force due to its stem flexibility, is likely to reduce its failure. Furthermore the lower marsh elevations with longer inundation durations implicate that S. tabernaemontani performs the function of wave attenuation more regularly than S. maritimus.
Although the traits of S. tabernaemontani have a less powerful effect on wave attenuation than S. maritimus, it is still a significant effect, especially at high stem densities. By forming a sort of barrier that attenuates the waves to a certain extent, S. tabernaemontani may provide S. maritimus a suitable habitat further up the marsh (interspecific facilitation). This ecological engineering is likely to be essential in cases when hydrodynamic conditions are too harsh for the establishment of S. maritimus by itself due to its high experienced drag force.
S. maritimus, on the contrary, is more capable of attenuating waves, which is assumed to be a benefit for its survival on the upper side of the marsh edge where much lower water levels are present. It probably creates a more benign habitat through sediment accretion (Bertness, 2006;
III Conclusion 61
Temmerman et al., 2007) and its stronger EEC on higher elevation may empower S. maritimus to easily outcompete S. tabernaemontani indicating marsh zonation as a product of dominant plants monopolizing physically gentle habitats and pushing off subordinate plants to physically harsh habitats (Bertness and Leonard, 1997). To ensure this hypothesis, experiments on the species competition abilities in interaction with hydrodynamic forces are required. Our study could demonstrate that S. tabernaemontani and S. maritimus are both important ecosystem engineers with unlike EEC due to contrasting functional traits and distinct elevational distribution.
Table III 4-1 The elevational habitat responses linked with the analysed traits and the contrasting findings between S. maritimus and S. tabernaemontani which result in two strategies of stress resistance. + stands for traits which are more or higher as compared to the traits of the other species. - defines the trait specification which is less or smaller as compared to the traits of the other species. ± means no trait difference. All trait specifications of S. maritimus reflect the tolerance strategy, whereas all trait specifications of S. tabernaemontani mirror the avoidance strategy.
Traits S. maritimus S. tabernaemontani Elevational occurrence +- Scour vulnerability ±± Drag +- Irreversible bending +- Breaking force -+ Flexibility -+ Biomass +- Gross energy +- Stem density ±± Plant surface area +- Leaves +- Stem area -+ Cellulose -+ Lignin +- Silica +- Wave attenuation +- Sum Tolerance Avoidance Stra te gy of stre ss re sista nce
5 Conclusion As a clear example of ecosystem engineering provided by S. tabernaemontani and S. maritimus, the effect of biomass verified by stem density as a strong predictor for wave attenuation seem to be more relevant for the engineering effect of emergent macrophytes having leaves than species morphology. S. tabernaemontani predominantly exhibits stress avoiding traits which facilitates its survival in physical stressful environments. In contrast, S. maritimus has mainly stress tolerating
62 Key ecosystem engineers in estuarine vegetation
traits, whose response-effect relations lead to a higher effect of wave attenuation. This signifies that morphological traits of emergent macrophytes are strongly intertwined with their experienced stress (responses) by hydrodynamic forces, their stress-resisting capacity (effects), and their ecosystem engineering effects which confirm therefore to be appropriate measures for the capacity of ecosystem engineering (Bouma et al., 2010; Bouma et al., 2005). Our study could be a representative example for illustrating the trait relations between environmental responses and ecosystem effects and how these traits overlap (Diaz and Cabido, 2001; Lavorel and Garnier, 2002). Functional traits seem to be directly balanced to habitat characteristics such as the exposure to wave load and where realized elevational niches are likely formed as a result of different strategies to deal with this hydrodynamic stress. To what extent trait-habitat balance will be maintained remains a focal point of research, especially in a world where tidal ecosystems are subjected to processes of anthropogenic modification and apparent sea-level rise (Temmerman et al., 2013).
Acknowledgements We would like to express our sincere gratitude to all the people who supported us in the plant preparation, building the flume set-ups, flume works and data analysis. We also thank the anonymous referees for the valuable remarks on the previous manuscript version and Dominica Campman for proofreading the paper.
III Box 1 63
Box 1 Wave attenuation of Scirpus species in comparison with other marsh species Chapter III contrasts S. tabernaemontani and S. maritimus in their ecosystem engineering capacity. In order to compare the results to other studies, the mean wave height reduction over the 1.6 m testing stretch as well as the exponential decay constant have been calculated. The latter is defined as the reduction of wave height in m per m.
Tab. Box 1-1 Normalized wave attenuation performed by S. tabernaemontani and S. maritimus differentiated in three stem density per m².
Stem Mean wave height Exponential density reduction over the decay constant Species m-2 length of 1.6 m (%) (m-1) S. tabernaemontani 300 10 0.004 500 15 0.006 800 24 0.010
S. maritimus 200 13 0.006 300 22 0.009 500 35 0.014 The exponential decay constants of S. tabernaemontani (300-500 stems per m²) and of sparse S. maritimus stands (200 stems per m²) are similar to those of a mixed salt marsh community (Möller et al., 1999) or to those of Atriplex sp. and Spartina alterniflora (Cooper, 2005; Tempest et al., 2015). Dense S. tabernaemontani (800 stems per m²) and dense S. maritimus stands (500 stems per m²) can be compared with Phragmites australis (around 80 stems per m², 0.011 wave reduction per meter) (Coops et al., 1996a) and also with Spartina alterniflora (Knutson et al., 1982; Tempest et al., 2015) or with Salicornia sp. (Möller, 2006). Although the results of these studies seem to be in general comparable, in detail they depend on several factors. Considering plant characteristics, vegetation height (Yang et al., 2012) can also drive the wave attenuation rates in addition to shoot stiffness and biomass (Chapter III, Bouma et al. (2010), Bouma et al. (2005)). These plant characteristics might not only differ between the species but also between exposed and sheltered sites (Silinski, 2015). Also water and wave conditions influence the wave attenuation rates (Möller, 2006; Möller and Spencer, 2002).
64 Key ecosystem engineers in estuarine vegetation
Chapter IV Restoring niches
© C. Schmidt-Wygasch
Heuner, M., Weber, A., Schröder, U., Kleinschmit, B., Schröder, B., 2016. Facilitating political decisions using species distribution models to assess restoration measures in heavily modified estuaries. Marine Pollution Bulletin 110, 250-260. doi: 10.1016/j.marpolbul.2016.06.056
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IV Introduction 67
Abstract The European Water Framework Directive requires a good ecological potential for heavily modified water bodies. This standard has not been reached for most large estuaries by 2015. Management plans for estuaries fall short in linking implementations between restoration measures and underlying spatial analyses. The distribution of emergent macrophytes – as an indicator of habitat quality – is here used to assess the ecological potential. Emergent macrophytes are capable of settling on gentle tidal flats where hydrodynamic stress is comparatively low. Analysing their habitats based on spatial data, we set up species distribution models with ‘elevation relative to mean high water’, ‘mean bank slope’, and ‘length of bottom friction’ from shallow water up to the vegetation belt as key predictors representing hydrodynamic stress. Effects of restoration scenarios on habitats were assessed applying these models. Our findings endorse species distribution models as crucial spatial planning tools for implementing restoration measures in modified estuaries.
1 Introduction Estuaries are characterized by their nutritious marshes. Therefore, they have historically been in high demand for human settlement, resulting in an ongoing struggle to protect land for agricultural use against heavy storm surges and river flooding. The succession of land reclamation by organized diking and drainage districts clearly increased with technical improvements in the seventeenth century in Europe. The remaining wetlands – without the embanked areas – were no longer able to dissipate the energy and water volume of storm surges and storm waves. Thus, the function of ecosystem-based shoreline protection became impaired. Embankment and diking disrupted the natural adaptive capacity of shorelines to keep up with sea-level rise by sediment accretion (Temmerman et al., 2013). Subsequent deepening and widening of the river channels for economic navigation purposes exacerbates the loss of this natural adaptive capacity. As a consequence, shores in estuaries have been protected against erosion by ship-induced waves with stone linings (Coops and Geilen, 1996).
The intensive use of estuarine functions by, for example, agriculture, human settlement, and transport in the last centuries has caused a mismatch of ecosystem functions. For instance, the larger the extent of drained and embanked land is in order to provide edible plants and animals by agriculture, the smaller is the area available for water and sediment retention. Therefore, the win- win-effects for society and nature through regulating ecosystem services provided by ecosystem
68 Key ecosystem engineers in estuarine vegetation
functions (Atkins et al., 2011; de Groot et al., 2010; MEA, 2005b) are disturbed. Sustainable adaptation to sea-level rise including an increasing resilience is a pivotal regulating ecosystem service and an increasingly important topic in flood protection (EC, 2015; Temmerman et al., 2013). Improving these services enhances other services from which the human population can benefit such as recreation or water quality regulation (cf. Atkins et al. (2011); Elliott et al. (2007); Needles et al. (2015)). Additionally, not all the land reclaimed in the past is needed to feed the human population nowadays and could be returned to a more natural state to improve the coastal protection. Therefore, we have an obligation to recreate characteristic estuarine habitats, reducing these past losses.
Notwithstanding the UNESCO Man and the Biosphere programme launched in 1970 (Dorst, 1971), the demand for maintenance, sustainable use, and recreation of estuarine habitats and other wetlands was adopted in the first international convention on wetlands of international importance (Ramsar convention) in 1971 with the number of participants steadily increasing (168 contracting parties by 2014) (Mauerhofer et al., 2015; Shine and de Klemm, 1999). Legal instruments at international, national and local levels (multilevel governance) are used to implement these multilateral environmental agreements (Shine and de Klemm, 1999). Multilevel wetland management is similarly structured in both the USA and the European Union (Peterson et al., 2008; Pinto, 2015), both following the principle of "no more deterioration": The Clean Water Act is an instrument at federal level implemented by the US Environmental Agency (Kelly, 2001; Ravit and Weis, 2014), while the European directives (e.g., Habitats Directive 92/43/EEC, Water Framework Directive/ WFD 2000/60/EC) are instruments at the European level governed by the European Commission.
The Habitat Directive obliges EU member states to maintain or restore natural habitats and wild species to ensure their sustainable survival in Europe. This includes the unique structural and functional biodiversity of estuaries (Meire et al., 2005). Similarly, the WFD calls for protection and improvement inter alia of the ecological quality and function of estuarine waters (Borja, 2005). The goal of the WFD is to achieve a good chemical, ecological, and hydromorphological status or potential for all surface waters and groundwater by 2015. Most European estuaries are categorized as heavily modified water bodies (HMWB) (CIS, 2003) due to their particular uses such as navigation or land drainage. They can only reach a good or better potential through considerable hydromorphological changes (CIS, 2003, 2006) which requires innumerable habitat restorations. Less than 30% of transitional water bodies including estuaries have a good ecological and hydromorphological status/potential, and the majority has therefore failed to reach the goal by
IV Introduction 69 the end of 2015 (EEA, 2012a, b). Hence, the WFD objectives have to be achieved with more efforts in the 2nd and 3rd planning cycles with target dates 2021 and 2027 (EC, 2012).
In order to realize the objectives and implementing measures such as habitat restoration, lowering of river banks or removal of bank enforcement in detailed, spatially explicit analyses are needed in order to assess the effects of these measures on shoreline habitats on a local scale. However, the link between the implementation of restoration measures and spatial analyses needs to be strengthened in many European countries (EEA, 2012c). In the past, spatial analysis was usually restricted by administrative boundaries, whereas management plans refer to topographic/geographic boundaries (EEA, 2012c). The use of spatial analyses enhances the understanding of cause-effect relations as well as of the measures' effectiveness (Haasnoot and Wolfshaar, 2009). Comparing scenarios allows for evaluating their time and work load and the estimation of direct economic costs. Providing transparent descriptions of the measures as well as the basis for their site-specific assessments, spatial analyses also serve to promote the economic, social and territorial cohesion in policy (EEA, 2012c).
Species distribution models are a useful spatial analysis method and directly support management plans for species habitat recreation and mapping of suitable sites for ecological restoration (Alonso Ponce et al., 2010; Ferrier et al., 2002; Guisan and Thuiller, 2005; Pearce and Lindenmayer, 1998). However, we only found a few studies using species distribution models as a tool for the implementation of habitat restoration as required by the WFD and the habitat directive: i.e. models predicting in particular fish and amphibians in a Danube floodplain (Funk et al., 2013), models calculating the habitat suitability of willows along the Middle Elbe (Mosner et al., 2011), and models predicting the potential habitat for seagrass for the Baltic coast (Schubert et al., 2015) as well as for estuaries in Northern Spain (Valle et al., 2011). All these models were developed to identify suitable habitats for waterbodies with natural conditions.
In heavily modified estuaries, suitable sites are unlikely to be successfully identified using these models because of the large amount of engineered shorelines protecting reclaimed land. The responsible authorities propose restoration sites on the basis of diverse political considerations and their environmental suitability is often less important. Nevertheless, species distribution models can help to identify the necessary modifications in proposed restoration sites in order to reach appropriate environmental conditions for natural estuarine habitats according to the European directives.
70 Key ecosystem engineers in estuarine vegetation
Common plant species on estuarine shorelines are emergent macrophytes such as reed and sedges (Clevering and van Gulik, 1997; Coops and Geilen, 1996; Lillebø et al., 2003). These are able to settle on tidal flats, where they are capable of resisting hydrodynamic forces in the form of stress such as current velocities and wave heights (Silinski et al., 2015a). Bottom friction on broad tidal flats (cf. Le Hir et al. (2000)) and low dissipative slopes (Bertness, 2006) obviously attenuate these stress drivers on the bare tidal flats located in front of the marsh edge. The elevation relative to mean high water (MHW) reflects the species responses to hydrodynamic forces, especially in regularly flooded marshes. Acting as ecological engineers by, for example, filtering water (e.g., Clevering and van Gulik (1997); Smith et al. (2009); Zhao et al. (2004)), dissipating wave energy (e.g., Leonard and Reed (2002); Möller et al. (2011); Ysebaert et al. (2011)), trapping sediment (e.g., Rooth et al. (2003); Temmerman et al. (2004a); Yang (1998)), and aerating the anoxic sediment (e.g., Brix et al. (1996); Jespersen et al. (1998); Nivala et al. (2013)), emergent macrophytes exert a clear positive effect on restoration areas (Elliott et al., 2007). Moreover, they are among angiosperms, which are classified as a biological quality element used to assess the ecological potential and thereby play an important role in satisfying present legal requirements of the WFD.
Successful restoration of estuarine vegetation needs suitable elevations (Temmerman et al., 2013). To our knowledge, species distribution models for estuarine shorelines with emergent macrophytes are not available. These models depict the abiotic conditions under which these species occur. This knowledge is crucial for the evaluation of different restoration scenarios in order to choose the scenario with the greatest habitat gain by the promoted species. Therefore we investigated the following questions:
(1) Which are the key environmental predictors determining the distribution of emergent macrophytes on estuarine shorelines? (2) How should estuarine-engineered banks be restored to enable the development of naturally vegetated shorelines?
2 Materials and methods
2.1 Species and study sites The ubiquitous species Scirpus tabernaemontani (C. C. Gmel.) Palla, Scirpus maritimus (L.) Palla, and Phragmites australis (Cav.) Trin. ex Steud. are emergent, clonal, fast-growing macrophytes
IV Materials and methods 71 with monotypic stands. They commonly form a distinct plant zonation from low to high elevations and constitute pristine vegetation in lower marshes of the freshwater and brackish part of the Elbe and Weser estuaries. (Focke, 1915; Kötter, 1961). Both rivers are located in North-western Germany entering the North Sea and represent characteristic environmental conditions for European estuaries.
Fig. IV 2-1 Investigation sites. A: Sampling sites (red rectangles) for setting up the species distribution models (lower Weser km 26- 70, Elbe km 632-701); B: Littoral zones along the Weser estuary; C: Littoral zones, restoration and reference sites along the Elbe estuary, R: Reference site Lühesand, R1: Reference site 1 and 2 with natural shorelines for Julssand, R2: Reference site 1 and 2 for Julssand with groynes; D: Restoration site Lühesand (rectangle: 300 m70 m), E: Restoration site Julssand (rectangle: 2400 m130 m)
Focusing on our study sites (Fig. IV 2-1A), the mean tidal range (2001-2010) for the Weser estuary was between 3.8 m (gauge Bremerhaven) and 3.9 m (gauge Vegesack) and for the Elbe estuary between 2.9 m (gauge Otterndorf) and 3.6 m (gauge St. Pauli). Based on averaged parameters at the study sites, the Weser estuary is smaller than the Elbe estuary in depth (13.7 m vs. 18.1 m),
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discharge (325 m³/s vs. 707 m³/s), width (965 m vs. 2033 m), and stream velocity (0.1-0.6 m/s vs. 0.2-0.9 m/s, Vandenbruwaene et al. (2013)). The Elbe main channel exhibits larger transport frequencies with 620 mean passages per week in contrast to 200 passages on the Weser estuary (Peters et al., 2013). In the port of Hamburg (Elbe River) vessels sizes with 14041 mean gross registered tonnage (Federal Waterways and Shipping Directorate North, 2011) exceed the 9760 mean gross registered tonnage (Senator for Economics and Ports of the Free Hanseatic City of Bremen, 2013) in the port of Bremen (Weser estuary). The vessels on the Elbe have maximum drafts of 15.4 m (Peters et al., 2013) compared to vessels on the Weser with 10.7 m (BAW, 2006). However, the maximum drawdown (Elbe: 0.5-1.2 m, Weser: 0.6-1.3 m) and maximum ship-induced wave heights (primary waves: Elbe: 0.5-1.5 m, Weser: 0.6 -1.5 m; secondary waves: Elbe: 1.0- 1.4 m, Weser: 0.9-1.2 m) in both estuaries are similar (BAW, 2006; Peters et al., 2013). As a consequence, the ratio of river cross section to vessel’s draft is smaller for the Weser main channel and therefore triggers more hydrodynamic forces on the banks than a single vessel passage along the Elbe. The resulting hydrodynamic forces require artificial bank protection (e.g., stone fillings) for more than 50% of the shoreline length along the main estuarine channels.
2.2 Digital species sampling Due to differing hydrodynamic forces between the two estuaries and between the main channels and anabranches, we classified four river classes: Elbe – main channel (Emc), Elbe – anabranches (Ean), Weser – main channel (Wmc), Weser – anabranches (Wan). The species sampling was based on a vegetation map (scale 1:4,300), especially detailed in emergent macrophytes. It was derived from digital aerial photographs verified in the field especially for areas with uncertainties during the digital revision (Petersen et al., 2010). For sampling, we defined the following conditions: Due to data uncertainties along polygon boundaries (Kinkeldey, 2014), we masked the polygon boundaries of monotypic stands with a buffer of 2 m. Areas adjacent to engineering elements such as groynes were also masked by a buffer of 10 m. Plants protected from hydrodynamic forces by longitudinal parallel structures were completely masked due to reduced, biased hydrodynamic forces. We set a minimum sampling distance of 50 m to prevent strong spatial autocorrelation. According to these conditions, we generated 70 random data points for each species-river-class-unit within the polygons of monotypic stands (species presence points). Furthermore, 70 points were sampled for each tail of elevational gradient per river class within other polygons (species absence points). These polygons represent on the one hand tall forbs, bushes, and forest (landside tail), on the other hand tidal flats and permanently submerged habitats (waterside tail). Due to smaller inhabited areas of some species
IV Materials and methods 73 we got only 68 data points for P. australis (Pa) and 62 data points for S. tabernaemontani (St) on Weser main channel and 62 data points for S. maritimus (Sm) on Weser anabranches.
2.3 Environmental predictor variables We used the ‘elevation relative to mean high water’ as a proxy for hydrodynamic forces indicating decreasing hydrodynamic stress with increasing terrain elevation for marsh plants (e.g., Leonard and Luther (1995); van Hulzen et al. (2007); Yang (1998)). This variable is often used for numerical modelling of marsh evolution (e.g., Fagherazzi et al. (2012); Kirwan and Murray (2007); Marani et al. (2010)). Therefore, we subtracted a grid of mean high water from the authorized digital elevation model (Elbe: 2010, Weser: 2009) with a resolution of 1 m (Brockmann and Schumann, 2011). Using gauge data (2001-2010), the grid of mean high water was interpolated along the river course with the python algorithm “Tide 2” of the ArcGIS extension INFORM (Fuchs et al., 2012).
As additional proxies describing hydrodynamic forces, we used the ‘mean tidal range’, ‘mean bank slope’, the ‘length of bottom friction’ from the shallow water line (2 m deeper than the mean low water, Rizzo et al. (1996)) to the data points, and the distance from the thalweg (distance to the line with the lowest elevations and the highest current velocities in a water course). ‘Mean tidal range’ was also calculated on the basis of gauge data (2001-2010) with Tide 2. ‘Mean bank slope’ is defined by the elevational profile starting from the elevation of the shallow water (2 m deeper than the mean low water) up to the elevation of the data point. We calculated the mean slope using the ArcGIS extension 3D Analyst (10.x). ‘Length of bottom friction’ is the Euclidean distance between the shallow water line and the data point. The longer this distance, the stronger the hydrodynamic forces are attenuated by bottom friction. Distance from the thalweg is calculated as the Euclidean distance between data point to thalweg, which is congruent with the navigation channel axis of the main river channels, extracted from the digital charts of federal waterways (Map services of the Federal Waterways et al., 2010). For the anabranches, the thalweg was calculated with the River Bathymetry Toolkit, a free extension for ArcGIS 10.x (Lisenby et al., 2014; McKean et al., 2009).
2.4 Statistical modelling and model evaluation Applying Spearman's rank correlation, we tested the environmental variables for multicollinearity (Dormann et al., 2013). We related the species incidence to the environmental predictors by estimating generalized linear models (GLM) with binomial error term and the logit link function. The explanatory power of each continuous variable was analysed in a univariate way (Fielding and Bell, 1997; Mosner et al., 2011), before the continuous predictors were tested with their linear and
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quadratic terms in a multivariate setting. The categorical river classes were included in the GLM with linear terms and their interaction terms with continuous predictors. The river classes were also tested as categorical variables for river (Elbe, Weser) and order (main channel, anabranches). In order to identify the simplest model with the strongest performance, we selected the predictors in a backward stepwise fashion with AIC as selection criterion (Schröder, 2008). When collinear predictors exceeded a correlation coefficient of 0.5, they were instead tested in the GLM (Dormann et al., 2013). After identifying the most appropriate model, we tested the assumption of independence of data points by estimating spline correlograms for model residuals (Bjørnstad et al., 1999; Dormann et al., 2007).
2 To assess model performance, we calculated Nagelkerke’s R N (Nagelkerke, 1991) as well as the area under the receiver operating characteristics curve (AUC), a threshold-independent criterion (Liu et al., 2011). AUC signifies true occurrence predictions as a function of false occurrence predictions (Swets, 1988; Vorpahl et al., 2012). AUC-values greater than 0.8 (0.9) are classified as excellent (outstanding) according to Hosmer and Lemeshow (2013). All analyses were carried out in R (R Development Core Team, 2015) applying the packages ‘verification’ (NCAR-RAL, 2015), ‘PresenceAbsence’ (Freeman, 2015), and ‘ncf’ (Bjørnstad, 2015) for model evaluation and checking for residual spatial autocorrelation.
2.5 Model validation Assessing the models‘ transferability and generalizability (Schröder and Richter, 1999), we performed an external validation by collecting new randomized data at separated locations with a distance equal to or greater than 50 m from the training data and from each other. We evaluated the 2 model validation by calculating Nagelkerke’s R N, AUC as well as numbers of correctly and falsely
predicted occurrences and absences based on two critical thresholds pfair and popt for deriving
presence-absence predictions from occurrence probabilities (Schröder and Richter, 1999). Pfair yields a classification where sensitivity (proportion of correctly predicted occurrences) equals specificity
(proportion of correctly predicted absences), whereas Popt yields the maximum amount of correct classifications (Fielding and Bell, 1997).
2.6 Restoration sites We applied our species distribution models at two potential restoration sites Lühesand and Julssand. Located at an anabranch, the restoration of the pilot study Lühesand (Fig. IV 2-1C) is presently planned by the Waterways and Shipping Board Hamburg together with the foundation Lebensraum
IV Materials and methods 75
Elbe to remove the stones on the tidal flats enabling natural bank protection instead of artificial protection. The bathymetry below mean low water should persist in order to ensure the protection of the island. The shoreline stretch is exposed to higher-than-average current velocities untypical for Elbe anabranches. We searched for natural reference shorelines in anabranches exposed to similar current velocities using the results of 2-D depth-average flood and ebb current velocities of a representative period between spring and neap tide calculated with the hydrodynamic model UnTRIM-SediMorph (Seiffert and Hesser, 2014). One near-natural reference site with current velocities similar to Lühesand was identified (Fig. IV 2-1C).
The second restoration site Julssand is located at the Elbe main channel near the navigation channel (in 300-500 m distance to marsh edge). The shoreline is protected by stone fillings, groynes and a summer levee for ensuring grassland. Due to its exposure to the navigation channel, the maintenance of the artificial shoreline is laborious and costly. A sustainably restored shoreline with self- established vegetation is expected to decrease the maintenance costs, if the estuary provides enough sediment (Temmerman et al., 2013). We identified two natural reference sites which are protected with groynes (470-480 m and 590-610 m, Fig. IV 2-1C). As an alternative, we identified two reference sites with unspoiled shorelines along the main channel with a well-developed vegetation cover of sedges and reeds and at a minimum distance from the navigation channel axis of around 700-880 m (Fig. IV 2-1C). Thus, we developed two restoration variants for Julssand: one with groynes and one with natural terrain.
2.7 Modifying the shoreline terrain To virtually modify the shoreline terrain, we created profiles perpendicular to the shoreline. At the reference sites they were spaced every 20 m, at the restoration sites every 5 m. They started at the mean shallow line (in the case of Lühesand only from the mean low water line) and reached up to the supratidal area. Along these profiles, elevation data with 1-m-resolution were extracted from the elevation model and processed further: Firstly, the elevation data were normalized via the equation: (z – MLW) / (MHW – MLW) with z = profile elevation of the site, M(L/H)W = mean (low/high) water of the site to compensate for differences in tidal range between reference and restoration sites (Fig. IV 2-2). Secondly, we calculated the range between 50 %-, and 100 %-quantiles. With the objective of deriving natural shoreline profiles, we developed a function in R (R Development Core Team, 2015) to calculate new elevations for the restoration profiles, using the additional packages ‘rgdal’ (Bivand et al., 2015), ‘sp’ (Pebesma et al., 2015) and ‘msm’ (Jackson, 2015).
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Fig. IV 2-2 The method of modifying the shoreline terrain is illustrated by 15 exemplary shoreline profiles before and after modification. The elevation of each profile is evened out by randomly sampling an elevation in the range between 50 and 100% quantile of the reference profiles, if the elevation of the profile to be modified is higher than the elevations of the reference profiles. Further conditions for the profile modification is written in chapter ‘Methods’ under the paragraph ‘Modifying the shoreline terrain‘. The function has the following rules (A-5-1 Script):
(1) The first point of a profile – for instance the mean shallow water line or mean low water line in Lühesand – remains unchanged to avoid jumps between the original elevations on the river bed and the modified elevations on the banks. (2) For the following points towards the shore, an elevation between the 50 %-, and 100 %-quantiles of the reference site is randomly sampled. If the normalized original elevation is higher than the normalized maximum elevation of the reference site, it gets ‘excavated’, otherwise the elevation remains unchanged (Fig. IV 2-2). (3) With each step towards the shore, the limits of the quantiles are compared to the reference data to ensure a permanent increase of modified elevations. (4) If random sampling is no longer possible, because the original elevation is already higher than the 100%-quantile, the 100%-quantile is taken.
IV Materials and methods 77
(5) As soon as the newly calculated elevation along the profile reaches the mean high water level (normalized elevation = 1) and the difference between original and modified elevation is less
than zfinal = 0.2 normalized elevation / (MHW – MLW), the following profile points stick to the original elevation to define the end of terrain modification and to avoid jumps between modified and original terrain. 50 realizations were computed in order to estimate the variability related to this method, calculating mean and standard deviation (Fig. IV 2-2). The newly modified profile elevations were saved in a shapefile for each realization. With a Python script we created triangulated irregular networks (TIN) for each of the 50 shapefiles according to the new elevations. The old elevations were updated with new elevations by linear interpolation of the TIN triangles. Subsequently, each of the 50 TINs was converted in a new, modified DEM.
2.8 Model applications We applied the species distribution models on the virtually restored shorelines. On the basis of the original DEM and the MHW, the predictors ‘elevation relative to MHW’, ‘mean bank slope’ and the ‘length of bottom friction’ were calculated for the restored profiles (A-5-1 Script). Using the modified DEM and the MHW, we calculated the altered ‘elevation relative to MHW’, the altered ‘mean bank slope’ and the altered ‘length of bottom friction’ (A-5-2 Script). To calculate the habitat suitability in ArcGIS, we inserted the coefficients of the continuous predictors and the coefficients of the factor levels ‘Elbe’ and ‘anabranch’ for the restoration site Lühesand as well as ‘Elbe’ and ‘main channel’ for the site Julssand in the logistic regression equation (A-5-3 Script).
2.9 Assessing the restoration measures To compare the original shorelines with the modified shorelines, we masked the area of stone fillings and groynes, because it was not possible to consider them in our species distribution models. To check each species habitat suitability, we calculated the sum of habitat units (ha) as product of a certain species occurrence probability multiplied by the pixel size (Enari and Sakamaki-Enari, 2014; Nevo and Garcia, 1996) for each species and each realization. We calculated the gain of intertidal areas and/or shallow water areas (Meire et al., 2005) by comparing the original and modified littoral zones. Means and standard deviations of the modified habitat units and the modified littoral zones were calculated (A-5-4 Script) characterising the variability of the realization caused by the random sampling in step 2 of the developed R-function. In order to present the restoration effect
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independently of the sites area, we calculated the natural logarithmic ratios of the habitat units after modifying the terrain divided by the original habitat units (see Table IV 3-2).
3 Results
3.1 Effects of predictors Significant predictors are ‘elevation relative to MHW’ (elevation), ‘mean bank slope’, ‘length of bottom friction’, and ‘distance from the thalweg’ (Fig. IV 3-1). The last two predictors are collinear (A-5-1 Table). ‘Length of bottom friction’ is a stronger predictor for the distribution of S. tabernaemontani, whereas ‘distance from the thalweg’ more strongly predicts the distribution of S. maritimus. The species occur sequentially along the elevational gradient starting with S. tabernaemontani at -3 m below MHW and ending up with P. australis at 2.5 m above MHW. All species are modelled on higher elevations at the Elbe main channel compared to the other river classes. The occurrence of S. tabernaemontani considerably increases after 500 m ‘length of bottom friction’ at the Elbe main channel. The occurrence of S. maritimus rises with longer ‘distance from thalweg’. If the ‘mean bank slope’ is smaller than 5° regarding all river classes, the occurrence of S. tabernaemontani and S. maritimus will increase. The occurrence of P. australis at the Weser main channel is similar. However concerning the anabranches, P. australis occurs up to a ‘mean bank slope’ of 15°. At the Elbe main channel, the occurrence of P. australis is unspecific.
3.2 Model performance Fig. IV 3-1 specifies the strongest predictors for modelling the species distributions. The final model of S. tabernaemontani differs in ‘length of bottom friction’ and ‘elevation relative to MHW’ for all river classes (Fig. IV 3-1, A-5-1 Fig.). The occurrence probability of S. tabernaemontani is highest at the Elbe main channel with the longest ‘length of bottom friction’ compared to the other river classes. The final model of S. maritimus illustrates an increase of occurrence with decreasing ‘mean bank slope’ with significantly different slopes for the response curves of anabranches and main channels due to a different elevational range. The final model of P. australis demonstrates that this species occurs at the Elbe estuary approximately 0.5 m higher than at Weser estuary (A-5-1 Fig.). The final models show no positive residual spatial autocorrelation (A-5-2 Fig.). The external validation of the model depicted high accuracies (Table IV 3-1).
IV Results 79
Fig. IV 3-1 Response curves showing the predicted occurrence probabilities of S. tabernaemontani (A), S. maritimus (B), and P. australis (C) differentiated by the river classes.
Table IV 3-1 Species distribution models (regression coefficients with their standard errors and p-values), and performance criteria (cut-off values of popt and pfair are specified and the classification rate is shown in percent.)
S. tabernaemontani P. australis S. maritimus
Standard Standard Standard Coefficient error p-value Coefficient error p-value Coefficient error p-value Intercept -4.37 0.618 < 0.001 -0.44 0.13 < 0.001 -1.42 0.27 < 0.001 Elevation -7.40 0.839 < 0.001 1.50 0.19 < 0.001 -2.96 0.33 < 0.001 Elevation² -3.26 0.372 < 0.001 -1.34 0.14 < 0.001 -1.54 0.16 < 0.001 Length of Mean bank bottom friction 0.003 0.002 n.s. slope -0.17 0.07 0.01 Length of bottom friction² -0.000004 0.000002 0.017 River "Weser" -2.616 0.763 < 0.001 0.52 0.17 0.002 Order "main channel" -2.228 0.705 0.002 1.06 0.27 < 0.001 Elevation:River "Weser" -2.534 0.524 < 0.001 -0.62 0.25 0.012 Elevation:Order "main channel" -1.141 0.425 0.002 1.111 0.259 < 0.001 SLength:River "Weser" -0.004 0.001 0.006 SLength:Order "main channel" 0.004 0.001 0.002
Performance popt = pfair = popt = pfair = popt = pfair = criteria of the AUC R2N 0.568 0.315 AUC R2N 0.480 0.385 AUC R2N 0.480 0.278 validated model 0.90 0.43 86 84 0.85 0.38 81 78 0.84 0.42 84 75
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3.3 Changes in terrain and habitats after restoration The original terrain of Lühesand is characterized by a steep slope in front of the stone filling and a very gentle slope behind the stone filling due to sediment accretion which enables P. australis to grow (Fig. IV 2-1C, Fig. IV 3-2). After the virtual stone removal and slope flattening, the site gains a quarter hectare of intertidal area and loses supratidal area (Table IV 3-2). However, the habitat of P. australis suffers (Fig. IV 3-2), whereas the habitats of S. tabernaemontani and S. maritimus exhibit no negative effects (P. australis: -0.17 mean restoration effect, S. tabernaemontani and S. maritimus: each 0.00 mean restoration effect).
Fig. IV 3-2 Restoration site Lühesand with stone filling (light grey). Short dashed line: mean high water line, long dashed line: mean low water line, DEM = digital elevation model, SDM = species distribution model Groynes and the summer dike of Julssand’s original terrain would be removed if the variant of the shoreline without groynes were to be implemented (Fig. IV 3-3). Due to flattening of the terrain, especially where the summer dike is located presently, 9.2 ha of the supratidal zone would become intertidal flats (approx. 8 ha) and shallow waters (0.9 ha) (Table IV 3-2). This scenario requires giving up grassland (Fig. IV 2-1D). The habitat suitability of P. australis shows a negative effect of -0.1, while the species gain habitat (Fig. IV 3-3; S. tabernaemontani: 0.83 mean restoration effect, S. maritimus: 0.22 mean restoration effect).
IV Results 81
Fig. IV 3-3 Fig. IV 3-3 scenarios.restoration two grey) and (light and groynes Restorationfilling site Julssand with stone DEM=digital elevationSDM model, = species distribution model
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Table IV 3-2 Spatial balance of modelled habitats and their respective shoreline zones (sz in m²) changed by virtually modified shorelines. The habitats were quantified by habitat units (HU in m²), which are the product of probability of species occurrence and restoration area. For each site, mean and standard deviation (SD) of 50 realizations of shoreline modifications are shown. Additionally, means and standard deviations of the restoration effects are listed by the natural logarithm of the ratio HU after modification divided by original HU and by the natural logarithm of the ratio sz after modification divided by original sz. A positive effect shows habitat or zone gain. A negative effect represents habitat or zone loss. S. tab. = Scirpus tabernaemontani, S. mar. = Scirpus maritimus, P. aus. = Phragmites australis
Modelled habitats Shoreline zones shallow- inter- supra - S. tab. S. mar. P. aus. water tidal tidal Luehesand restoration Original habitat unit 2,540 3,312 10,558 Original zone 30,145 14,790 20,394 Mean HU after modification 2,552 3,319 8,890 Mean modified zone 29,955 17,799 17,461 SD of HU after modification 7 5 4 SD of modified zones 0 21 21 Mean restoration effect 0.00 0.00 -0.17 Mean restoration effect -0.01 0.19 -0.16 SD of the restoration effect 0.003 0.002 0.000 SD of the restoration effect 0.000 0.001 0.001
Juelsand restoration Groyne Original habitat unit 24,959 169,434 344,183 Original zone 147,023 228,594 693,115 Mean HU after modification 28,097 177,481 331,069 Mean modified zone 152,241 257,895 658,604 SD of HU after modification 16 26 17 SD of modified zones 24 97 90 Mean restoration effect 0.12 0.05 -0.04 Mean restoration effect 0.03 0.12 -0.05 SD of the restoration effect 0.001 0.000 0.000 SD of the restoration effect 0.000 0.000 0.000
Jue lsa nd re stora tion Original habitat unit 24,959 169,434 344,183 Original zone 147,023 228,594 693,115 Mean HU after modification 56,984 210,321 311,686 Mean modified zones 155,630 308,462 601,803 SD of HU after modification 56 90 73 SD of modified zones 26 264 269 Mean restoration effect 0.83 0.22 -0.10 Mean restoration effect 0.06 0.30 -0.14 SD of the restoration effect 0.001 0.000 0.000 SD of the restoration effect 0.000 0.001 0.000 The scenario with the refurbished groynes shows a smaller effect than the modified terrain without engineering elements (Fig. IV 3-3). However, the positive restoration effect of the intertidal flat (0.12) is higher than the loss of supratidal and shallow-water zone together. The restoration has a slightly negative effect on P. australis (mean restoration effect = -0.04), while the effects on the pioneer species are positive (S. tabernaemontani: mean restoration effect = 0.12, S. maritimus: mean restoration effect = 0.05).
4 Discussion Detailed planning tools for the implementation of restoration measures at proposed sites are not available in many European countries (EEA, 2012c). Species distribution models can support these implementation processes as planning tools. They can help to estimate the effectiveness of specific measures and compare different management alternatives (Haasnoot and Wolfshaar, 2009).
IV Discussion 83
We set up species distribution models with good accuracy for the macrophyte species Scirpus tabernaemontani, Scirpus maritimus, Phragmites australis occurring in regularly flooded marshes of the Elbe and Weser estuary (Table IV 3-1). Applying these, we demonstrate possibilities to quantify and evaluate the effects of restoration measures on the habitat quality of these indicator species, comparing different scenarios by virtually modifying the terrain of exemplary sites proposed by the integrated management plan for the Elbe estuary (Arbeitsgruppe Elbeästuar, 2011).
Key predictors for modelling the species distribution on brackish shorelines ‘Elevation relative to mean high water’ (MHW) describes the marsh position, where species are able to grow. It is a significant predictor for all species (Fig. IV 3-1, A-5-1 Fig.). Although plant species patterns in the higher marshes are not stringently correlated with ‘elevation relative to MHW’ (Bockelmann and Neuhaus, 1999; Silvestri et al., 2005; Zedler et al., 1999) due to higher spatial heterogeneity, for example, in soil types and inundation frequency, many studies about salt marsh species can confirm that ‘elevation relative to MHW’ is a key predictor (e.g., Adams (1963); Bertness (2006); Keddy (2010); Smith (2015); Suchrow and Jensen (2010)), especially in regularly flooded marshes (Pennings and Callaway, 1992; Sanchez et al., 1996). The species response curves show that the species complement each other in the elevational gradient (Fig. IV 3-1). This pattern reflects the species zonation in low brackish marshes (Coops and Geilen, 1996; Coops et al., 1999).
The ‘mean bank slope’ is also a key predictor for modelling S. tabernaemontani, whereas the ‘length of bottom friction’ is a stronger predictor for S. maritimus (Fig. IV 3-1). These two predictors quantify the exposition of the tidal flat in front of the vegetation. Both are proxies for dissipating hydrodynamic forces such as wave height or current velocities (Dean and Bender, 2006; Knutson et al., 1982). Our findings are in line with studies about emergent macrophytes which were established on gentle slopes of marshes (e.g., Garbisch and Garbisch (1994); Mitsch (2009)) as well as of lake shores (e.g., Coops et al. (1994); Partanen et al. (2009)). Reasons, therefore, are that hydrodynamic forces are largely reduced due to increased bottom friction in shallow waters and on gentle slopes (Friedrichs and Madsen, 1992; Le Hir et al., 2000).
As categorical predictors, the river classes explain the variation of species occurrence between the Elbe and Weser main channel and their anabranches. The broader elevational range towards lower elevations spanning the species occurrence at the Weser estuary indicates lower hydrodynamic forces in this estuary compared to the Elbe estuary (Fig. IV 3-1, A-5-1 Fig.). Thus, our results verify the relationship that the lower the hydrodynamic stress, the lower the elevations where species are
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able to survive. Findings in the Netherlands assure this relationship: Phragmites occurs at higher elevations on exposed shores than on sheltered shores (Coops and van der Velde, 1996b). Furthermore, the relationship also explain the higher probability of occurrence of P. australis in the Weser main channel (Fig. IV 3-1, A-5-1 Fig.) where P. australis often also forms the marsh edge directly in contact with the hydrodynamic stress; this is likely to be why P. australis resides on bank slopes less than 5° (Fig. IV 3-2).
Waves in anabranches compared to main channels have only little impact on bank slopes due to lack of navigation. Thus, P. australis can also occur on exposed slopes steeper than 5° (Fig. IV 3-1). Similarly S. maritimus occurs on lower elevation along the anabranches than at the main channels. However, the probability of occurrence is higher at the main channel (A-5-1 Fig.), where S. maritimus is better able to resist to the hydrodynamic stress than P. australis (Carus et al., 2016).
At the Elbe main channel the ‘mean bank slope’ is less characteristic for the occurrence of P. australis (Fig. IV 3-1), probably because the more stress-resistant Scirpus species often grow here in front of Phragmites and attenuate the hydrodynamic forces impacting the shoreline. S. tabernaemontani only propagates at the Elbe main channel, where shallow waters and tidal flats are broad enough (> 500 m) to reduce high velocities and wave loads of the Elbe main channel by bottom friction (A-5-1 Fig.). The high hydrodynamic forces at the Elbe main channel may result from the high frequency of cargo transport (Peters et al., 2013). In contrast, the Weser anabranches seem to have the least hydrodynamic forces, because the occurrence of S. tabernaemontani here is high on short ‘lengths of bottom friction’ (A-5-1 Fig.).
Our results show that species distribution patterns vary between the two estuaries, main channels and anabranches, due to varying hydrodynamic forces (cf. significant coefficients of the predictor variables inTable IV 3-1). Thus, data on these estuaries is needed to derive site-specific coefficients of predictor variables to be able to transfer the species distribution models to other estuaries. We also recommend further investigation about the hydrodynamic effects on species distributions measuring current velocities and wave impact at the marsh edge and the setting up of process-based models with these data.
Resulting habitat effects by restoring engineered banks for naturally vegetated shorelines The main effect of restoring engineered shorelines will be a gain in shallow water and intertidal habitats and a loss in supratidal habitats (Table IV 3-2). Considering the estuarine ecosystem functions, we will lose species diversity (cf. Engels and Jensen (2009)) and foraging grounds for
IV Discussion 85 dairy cattle, sheep and wading birds (Masero and Perez-Hurtado, 2001). On the other hand, expanding intertidal habitat with gentle slopes increases the abundance of filter feeders such as clams and small crustaceans (Bertness, 2006) as well as diatoms and microalgae stabilizing sediments (Passarelli et al., 2012; Spears et al., 2008). Furthermore, current and wave forces will be notably dissipated (Friedrichs and Madsen, 1992) and fine sediment with nutrients will be able to accumulate. Hence, another foraging ground will be supplied for shorebirds at ebb tide and for fish and crabs at flood tide (Bertness, 2006). The increase in shallow water habitat will enhance primary production by benthic microalgae and hence the oxygen supply as well as the nursery space, for example, for fish (Larson and Sundbäck, 2008; Ray, 2005). Thus, referring to the management of restoring ecosystem function (Borja, 2005; Meire et al., 2005; Needles et al., 2015) more ecosystem functions can be restored with these measures than will be lost.
As the findings of Lühesand show, removing stone fillings and lowering the banks will not guarantee a habitat gain for the three plant species at the sites (Table IV 3-2, Fig. IV 3-2). Attaining a gentler bank slope will require lower elevation on intertidal flats where it will be too deep for emergent macrophytes. This case strengthens the argument that ecosystem-based shoreline defences need more space than engineering structures (Temmerman et al., 2013). In addition, a restoration site of at least 30 ha and a width exceeding 70 m will promote species diversity due to zonational processes (Wolters et al., 2005).
The Julssand restoration site shows a great habitat effect on the distribution of Scirpus species after removing stone fillings and summer dike including bank lowering (Table IV 3-2, Fig. IV 3-3). In order to attain a gain in intertidal habitat with a gentle slope and emergent macrophytes, a crucial aspect of this restoration success seems to be recreating loss-making shallow waters (Habitats Directive 92/43/EEC, Annex I). For all that, a loss of Phragmites habitat will have to be accepted. Such a restoration result can lead to trade-offs (NLWKN and SUBV, 2012). Valuing ecosystem services, their trade-offs and synergies (de Groot et al., 2010) is crucial for resolving these conflicts. Therefore, we suggest keeping balanced estuarine functions in mind and describing the solutions in future management plans.
Moreover, we show that a combination of removing stone fillings and parts of the summer dike, lowering the bank river and refurbishing groynes will reveal a perceptible effect on the intertidal habitat of Julssand (Table IV 3-2, Fig. IV 3-3), although the Scirpus habitat will benefit 3.5 times more from the scenario without groynes. In heavily modified estuaries, the hydrodynamic forces
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caused by navigation will be huge, particularly if the navigation channel is very close to embanked shorelines such as Julssand (130-200 m away from the MLW line). Thus, the maintenance of the engineered banks is both with high costs and time consuming. We suggest improving the benefits for the ecosystem as well as for the responsible authorities by designing sustainable shore protection. Since initial costs of implementation will only be recovered after 20 years (Broekx et al., 2011), the restoration measures will be more cost-effective in the long-term (Temmerman et al., 2013). All these specified aspects will contribute to achieving the objectives of the WFD and the habitat directive.
5 Conclusion We conclude that species distribution models can serve as a crucial spatial planning instrument in heavily modified estuaries for the implementation of hydromorphological and ecological restoration measures as required by the WFD and the habitat directive. We identify key predictors for common emergent macrophytes on estuarine shorelines: The most important predictor is the ‘elevation relative to MHW’, which defines the marsh position, where species are able to grow as a function of inundation frequency. The other key predictors such as ‘mean bank slope’ and the ‘length of bottom friction’ in front of vegetation describe the shoreline morphology. They quantify how much hydrodynamic forces can be dissipated before affecting the vegetation. With these predictors, responsible authorities will be able to plan effective restoration measures for engineered shorelines and gently create naturally vegetated banks in heavily modified estuaries.
Furthermore, our model application demonstrates that space for shallow water is essential to assure a vegetation gain on the restored shoreline. We verify that restoration combined with engineering elements such as groynes can have positive effects on shoreline habitats (cf. Beauchard et al. (2011); Lamberth and Haycock (2001); Maris et al. (2007)). It implies that restoration measures can also be applicable in heavily modified water bodies with strong impacts from anthropogenic uses. Initial strengthening of disturbance regulation and sediment retention (Costanza et al., 1997) through the restoration of engineered shorelines will likely entail a reinforcement of other ecosystem services. Nature, society and economy will benefit from a long-term approach if we implement restoration measures for re-balancing the estuarine ecosystem functions, and therefore sustainably adapt to sea- level rises (Temmerman et al., 2013).
IV Conclusion 87
Acknowledgements This study was financed by the research programme KLIWAS (Impacts of climate change on waterways and navigation – Searching for options of adaptation) of the German Federal Ministry of Transport and Digital Infrastructure (BMVI). We acknowledge the Federal Waterways Engineering and Research Institute, the Waterways and Shipping Boards (WSA) Bremerhaven, and the WSA Hamburg for providing data. We would like to thank Peter Horchler for valuable tips on data analysis and Dominica Campman for proofreading and useful comments on the manuscript.
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Chapter V Wave effects governed the Niches
Silinski, A., Heuner, M., Schoelynck, J., Puijalon, S., Schröder, U., Fuchs, E., Troch, P., Bouma, T., Meire, P., Temmerman, S.,2016. Effects of contrasting wave conditions on scour and drag on pioneer tidal marsh plants. Geomorphology 255, 49-62. doi: 10.1016/j.geomorph.2015.11.021
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V Introduction 91
Abstract Tidal marshes are increasingly valued for protecting shorelines against wave impact, but waves in turn may limit the initial establishment of tidal marsh pioneer plants. In estuaries, the shorelines typically experience a wide range of wave periods, varying from short period wind waves (usually of around 1-2 s in fair weather conditions) to long ship-generated waves, with secondary waves in the order of 2-7 s and primary waves with periods that can exceed 1 minute. Waves are known to create sediment scour around, as well as to exert drag forces on obstacles such as seedlings and adults of establishing pioneer plant species. In intertidal systems, these two mechanisms have been identified as main causes for limiting potential colonization of bare tidal flats. In this paper, we want to assess to which extent common quantitative equations for predicting local scour and drag forces on rigid cylindrical obstacles are valid for the estimation of scour and drag on slightly flexible plants with contrasting morphology, and hence applicable to predict plant establishment and survival under contrasting wave conditions. This has been tested in a full-scale wave flume experiment on two pioneer species (Scirpus maritimus and Scirpus tabernaemontani) and two life stages (seedlings and adults of S. maritimus) as well as on cylindrical reference sticks, which we have put under a range of wave periods (2-10 s), intended to mimic natural wind waves (short period waves) and ship-induced waves (artificial long period waves), at three water levels (5, 20, 35 cm). Our findings suggest that at very shallow water depths (5 cm) particular hydrodynamic conditions are created that lead to drag and scour that deviate from predictions. For higher water levels (20, 35 cm) scour can be well predicted for all wave conditions by an established equation for wave-induced scour around rigid cylinders. Drag forces can be relatively well predicted after introducing experimentally derived drag coefficients that are specific for the different plant morphologies. Best predictions were found for plants with a simple near-cylindrical morphology such as S. tabernaemontani, but are less accurate for plants of more complex structure such as S. maritimus, particularly for long period waves. In conclusion, our study offers valuable insights towards predicting/modelling the conditions under which seedlings and shoots of pioneer species can establish, and elucidates that long waves are more likely to counteract successful plant establishment than natural short waves.
1 Introduction Tidal marshes are valuable ecosystems providing a variety of ecosystem services such as coastal protection by dissipating incoming wave energy and tidal currents (Gedan et al., 2011; Möller et al., 2014; Shepard et al., 2011; Temmerman et al., 2013), carbon sequestration, water purification,
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maintenance of fisheries, and recreation (Barbier et al., 2011). However, a worldwide decrease of tidal marsh area has been observed over the past decades (e.g., Barbier et al. (2008)): tidal marshes are being threatened by conversion into human land-use types from the landward side and by increasing hydrodynamic pressure due to rising sea level and increasing ship traffic from the seaward side, a process known as ‘coastal squeeze’ (Doody, 2004; Nicholls et al., 1999). Previous empirical (Wang and Temmerman, 2013) and modelling studies (Fagherazzi et al., 2012; Kirwan and Temmerman, 2009; Kirwan et al., 2010; Schuerch et al., 2013; Temmerman et al., 2003a) on tidal marsh vegetation establishment and survival mainly focused on the effects of vertical marsh elevation relative to mean sea level as a proxy determining tidal marsh vegetation development. However, vertical marsh elevation combines the effects of several variables that more directly determine marsh vegetation growth, such as tidal inundation depth and duration, hydrodynamic forces from tidal currents and waves, and sediment dynamics, which exhibit spatial variation along the horizontal plane.
Several studies highlighted that lateral seaward tidal marsh expansion or landward retreat is at least as important as vertical dynamics in determining tidal marsh evolution (Fagherazzi et al., 2013; van de Koppel et al., 2005). Lateral tidal marsh expansion or retreat are determined by waves and currents as they cause drag forces on plant shoots and cause sediment scour around shoot stems which can eventually lead to uprooting and failure of individual shoots (Bouma et al., 2005; Bouma et al., 2009a). Hence these hydrodynamic factors affect the lateral expansion of tidal marsh vegetation either by attacking the existing marsh edge as a whole (e.g., Mariotti and Fagherazzi (2010), Tonelli et al. (2010)) or by diminishing the chance of seedling or individual shoot establishment and survival on the bare mudflat (Balke et al., 2011; Balke et al., 2013; Bouma et al., 2009a; Callaghan et al., 2010). Understanding how tidal marsh vegetation will respond to hydrodynamic impacts such as those caused by waves is thus key to predicting future developments of tidal marshes. In this study we focus on the effect of waves on the establishment of individuals, comparing young seedlings and adults, and comparing species with contrasting mechanical and morphological characteristics that are expected to cause an altered interaction with hydrodynamic forcing and/or turbulence around the stem.
At the scale of an individual plant, waves are episodic high energy events, which, in estuaries, can generally be separated into two groups based on their origin: (1) natural wind-generated waves with regular, short wave periods, typically in the order of 1-2 s in fair weather conditions (Augustin et al., 2009) and (2) ship-generated waves with a long period primary wave (in the order of 20-120 s)
V Introduction 93 followed by a train of short period secondary waves (in the order of 2-7 s) (Schroevers et al., 2011; Verney et al., 2007). Long period waves can also be generated by wind and storms at sea, but in our study context of a relatively narrow estuary with intensive ship traffic long period waves reaching the shores are mainly generated by ships. While characteristics of wind-generated waves vary over a longer period of time (days, season), ship waves typically arrive on the shores as intense events of several minutes duration (Chwang and Chen, 2003; Houser, 2010). Wind-wave characteristics for a given shore morphology depend on wind direction and fetch, while ship wave characteristics depend among others on relative speed, load and direction of travel of the ship (Chwang and Chen, 2003; Houser, 2010; Verney et al., 2007). This is why in areas with restricted fetch, e.g., in estuaries and lagoons, the wave height, period and energy of ship-generated waves will potentially exceed those of wind waves at the shore (Curtiss et al., 2009; McConchie and Toleman, 2003; Rapaglia et al., 2011), and the tidal marsh vegetation will be exposed to highly contrasting wave conditions.
Waves typically have two effects on pioneer plants, i.e. (1) sediment scouring which determines local erosion around the basal parts of the stems and which can, depending on severity, lead to uprooting (Bouma et al., 2009a; Friess et al., 2012) and (2) drag forces acting on the above-ground plant material (Bouma et al., 2005; Denny, 1994a; Henry and Myrhaug, 2013). The combination of scouring and drag forces is considered as the main cause for plant failure, limiting the colonization of the mudflat by plants (Balke et al., 2011; Bouma et al., 2009a).
In coastal engineering, wave period has been established as a key parameter to predict wave- generated scour around mono-pile structures: the Keulegan-Carpenter number, KC number in the following, quantifies scour as a function of wave period, flow velocity and the diameter of the structure (e.g., Baglio et al. (2001), Umeda (2011) and references therein). Drag forces that act on obstacles exposed to a unidirectional flow or waves, on the other hand, have been correlated with the squared flow velocity or squared wave-induced horizontal velocity and wet frontal area of the obstacles, following the Morison equation (Henry and Myrhaug, 2013; Sand-Jensen, 2003) (see 2.1). In ecology, the Morison equation has been adapted by introducing a variable power, Ⱦ, usually referred to as Vogel number (e.g., Albayrak et al. (2013), Sand-Jensen (2003)) (see 2.1) which takes the flexibility of plants into account that allows them to reconfigure and thus to reduce experienced drag with increasing flow velocity (Bal et al., 2011; Miler et al., 2014; Puijalon et al., 2011; Pujol and Nepf, 2012; Sand-Jensen, 2003).
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While quite a lot of flume experiments on wave impacts on plant performance have been conducted (e.g., Augustin et al. (2009), Bouma et al. (2005), Coops et al. (1996a), Francalanci et al. (2013)), the novelty of our study is to test the applicability of existing equations for calculating wave-induced scour and drag forces on plants of differing morphology and for two life stages for a wider range of wave periods and water levels (2-10 s wave periods and 5-35 cm water depths in our experiment versus ranges of maximum 1-2 s and of maximum 20 cm in above-mentioned studies). This has been tested in a full-scale wave flume experiment on two typical pioneer species (Scirpus maritimus L. and Scirpus tabernaemontani C. C. Gmel.) and two life stages (seedlings and adults of S. maritimus). Additionally, wooden cylindrical sticks were tested at the same water levels but at more wave periods (2, 4, 6, 8 and 10 s) acting as uniform control obstacles to which the results for the plants could be compared. The results of these experiments and the identification of valid equations give us valuable insight for the prediction of potential habitat suitability for the establishment of tidal marsh species under contrasting wave conditions.
2 Material and Methods
2.1 Theory 2.1.1 Hydrodynamic parameters In order to get an overview on the contrasting hydrodynamic conditions in our experiment, we first introduce hydrodynamic parameters typically used for classification of the governing flow processes.
Reynolds number and Froude number Reynolds number (Re) determines whether a flow is laminar or turbulent, a value of 2000 being the limit between the two conditions. Froude number (Fr), on the other hand, distinguishes subcritical and hypercritical flow, and 1 is the threshold value. They are defined as (U.S. Army Corps of Engineers, 2002):
௩ௗ ܴ݁ ൌ ೌೝೌ (eq. 1) ఔ
௩ ܨݎ ൌ (eq. 2) ඥௗೌೝೌ
V Material and Methods 95
where ݒ is flow velocity (m/s), dcharac is characteristic length (m), Ȟ is kinematic viscosity which is equal to 1.004 * 10-6 m2/s for freshwater at 20 °C as used in the experiment, and g is gravitational acceleration, i.e. 9.81 m/s2.
For Fr and Re, three different characteristic lengths can be considered in the equation: the hydraulic diameter and flow depth take into account general flow conditions in the flume whereas the use of the cylinder diameter in the equation indicates local flow conditions around the plants and sticks.
Shields parameter This parameter quantifies the balance between stabilizing and mobilizing forces acting on the sediment (Baglio et al., 2001; Umeda, 2011):
ఘ௩; ߆ൌ (eq. 3) ሺఘೞିఘሻௗఱబ
where ݒ is flow velocity m/s, g = 9.81 m/s2, ȡ is the water density (kg/m3) = 1000 kg/m3 for 3 freshwater, ȡs is sediment density (kg/m ) and d50 is the median grain size (m).
Iribarren number This parameter is commonly used as an indicator for whether or not wave breaking would occur on a plane slope. It is defined as (Hughes, 2004; U.S. Army Corps of Engineers, 2002):
୲ୟ୬ ఈ ߦ ൌ (eq. 4) ඥுబȀబ
where Į is the slope (°) of our test section (1/50 ؙ 1.15 °), H0 is the wave height (m) in deep water
(i.e. at the wave paddle) and L0 is the deep water wavelength (m), defined as:
ܮ ൌ ܶଶ (eq. 5) ଶగ
2 with g = 9.81 m/s and where T is the wave period (s). ȟ0 < 0.5 defines spilling wave conditions,
whereas plunging wave conditions occur for 0.5 < ȟ0 < 3.3. Surging or collapsing waves require
values of ȟ0 > 3.3.
2.1.2 Scour Self-scour around coastal structures such as piles and seawalls is one of the main causes for damage on those structures which is why exhaustive engineering studies have been done in this domain (Sumer et al., 2001). However, scour is a complex matter as it is determined by interactions between
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properties of the obstacles, hydrodynamics and sediment transport. Scour depends, among others, on Shields parameter, grain size, sediment-to-pile size ratio and pile geometry. In engineering literature, the KC number has established as a reliable predictor for wave-induced scour (e.g., Baglio et al. (2001), Sumer et al. (2001), Umeda (2011), and references therein).
The KC number is calculated as:
ݒܶ ܭܥ ൌ ൗܦ (eq. 6)
where T is the wave period (s), ݒ is the wave-induced horizontal peak forward velocity near the bottom (m/s) and D is the diameter of the obstacle (m). Typically, it is used to predict relative scour, i.e.
ܵ ൗܦ (eq. 7)
where S is maximum scour depth [m] and D is the diameter of the obstacle (m).
2.1.3 Drag forces The established equation for quantifying the drag force, F (N), exerted by unidirectional flow but also by waves on a rigid object is (e.g., Henry and Myrhaug (2013)):
ଵ ଶ (ݒ (eq. 8ܣ ܥൌ ߩܨ ଶ ௗ
also known as the Morison equation, where ȡ is the density of the fluid (kg/m3) = 1000 kg/m3 for
freshwater as used during our flume experiments, A is the wet frontal area of the obstacle (m²), Cd is the drag coefficient (-) and ݒ is the flow velocity (m/s) in the case of unidirectional flow, or the undisturbed wave-induced horizontal velocity in the case of waves.
For flexible obstacles, typically such as plants, this equation (eq. 8) is modified, according to Vogel (1994), to:
ଵ ఉ (ݒ (eq. 9ܣ ܥൌ ߩܨ ଶ ௗ
where 0 ȕ 2, depending on the flexibility of the plant. ȕ, usually referred to as Vogel number, is typically determined empirically for each plant species individually. The higher the flexibility of the
V Material and Methods 97
plant, the smaller ȕ, which implies that drag forces experienced by flexible plants will increase less with increasing flow or wave-induced horizontal velocities as for rigid material such as steel piles. Following Sand-Jensen (2003), ȕ can be determined using equation 8 by quantifying the experimental drag coefficient that ǡ ǣ
(ௗǡ௫ ൌܽݒ (eq. 10ܥ
where a and b are constants that are empirically derived for each plant species and physical model.
Combined, equations 8 and 10 lead to:
ଵ (ݒଶା (eq. 11ܣൌ ߩܽܨ ଶ
where a is now the actual drag coefficient and 2+b = ȕ.
Cd,exp can also be derived as a function of the local obstacle-induced Reynolds number (ReD) (Infantes et al., 2011):
ܥௗǡ௫ ൌܴܽ݁ (eq. 12)
where a and b, as in equation 11, are constants that are empirically derived for each plant species and physical model.
2.2 Experimental design The experiments were conducted in the wave flume facility at the Department of Civil Engineering at Ghent University, Belgium. The flume was 30 m long, 1 m wide and 1.2 m high. The physical model (at real scale, Fig. V 2-1) consisted of a transition slope of 1/20 over 6.8 m, which led to a 12 m-long slope of 1/50. This latter slope is representative for natural tidal marsh-mudflat transition zones in S. maritimus and S. tabernaemontani dominated pioneer vegetation, for example in the Scheldt estuary (Belgium and Netherlands) and Elbe estuary (Germany) (personal observation). A
box filled with natural sediment from the Scheldt estuary (d50 = 320 ȝm, non-cohesive) and of 0.3 m depth occupied a stretch of 7 m length of that gently sloping section. A pebble stone absorption beach at the rear end of the flume prevented waves from being reflected. Before each test, the surface of the sediment over the entire test section was brought into the initial slope of 1/50.
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Fig. V 2-1 Sketch of the physical model in the wave flume. Top: Side view; bottom: Top view. The position of plants and sticks is indicated by the schematic plant on the top panel and by two light grey circles in the bottom panel. The light grey bodies are the 1/20 transition slope and the 1/50 slope of the test section, which were built of concrete plates. The sediment box (hatched part of the test section) was filled with natural sediments from the Scheldt estuary (SW Netherlands). The absorption beach (left end of the flume) was built of pebble stones. The horizontal grey dashed lines (top panel) represent the three tested water levels (5, 20 and 35 cm water depth at plant position). The black arrows (bottom panel) represent the direction of propagation of waves produced at the wave paddle (black T-shaped structure on the right end of the flume). The plants and sticks, respectively, were transplanted into this sediment box at a height of 0.5 m above the flume bottom. The plants, either two adults of the same species or two seedlings, were planted at the same locations next to each other, with a distance of approximately 33 cm between them and the respective flume wall. Five replicate runs were done for each tested condition (water depth and wave period at the paddle, see Table V 3-1; each unique water depth-wave period combination will be referred to as ‘test’ in the following), i.e. each on ten plants of the same type. For the stick experiments, only one stick was tested at a time, without replicates.
A regular wave height of 17 cm was set at the paddle for each of the tests but due to wave transformation on the slopes preceding the test section, the actual mean wave heights at the plants and stick varied between 2 and 23 cm, depending on the test (see Table V 3-1). Two regular wave periods were tested on the plants: a 2 s wave period as proxy for natural wind waves, as can be typically observed in the Scheldt and Elbe estuary, and a 10 s wave period as an artificially generated long-period wave, representing simplified secondary ship waves. Longer wave periods as for primary ship waves could not be generated due to technical paddle limitations. For the sticks, a
V Material and Methods 99 series of five wave periods were tested, i.e. 2, 4, 6, 8 and 10 s. Three water levels were chosen (5, 20 and 35 cm relative to the plant and stick position) in order to simulate wave impact at different moments in the tidal cycle or, alternatively, different surface elevation relative to mean high water on the tidal flat. Note that the transformation of waves on the slopes results in asymmetric wave-induced horizontal peak forward and backward velocities, with the backward velocities reaching only 40 to 95% of the forward velocities: the lower the water level and the shorter the waves, the more asymmetric the velocities with an excess of peak forward velocities. Studies on sediment transport under asymmetric oscillatory flow (e.g., Ruessink et al. (2011), Son and Lee (2013)) predict net forward sediment transport for non-cohesive sand (d50 > 200 ȝm) which was consistent with observations in the flume. Based on field measurements in the Scheldt estuary, where tidal range is of approximately 5 m, these relatively low inundation depths would prevail on average during around 20 to 40 min in the course of one tidal cycle, depending on the position along the elevation gradient. This is in line with the 7 min (2 s-waves) and 30 min (10 s-waves) test periods that we simulated in the flume experiments. Not all tests were run on all plants: seedlings were only tested at the two lower water levels and the adult S. tabernaemontani were only tested at the two higher water levels.
The runs consisted of 200 monochromatic waves for each test. Actual wave heights at the paddle and on the test section were measured for each of the tests with resistance wave gauges (sampling frequency 40 Hz). Mean wave heights (Hmean) were calculated (Table V 3-1). Wave-induced horizontal velocities were measured with a laboratory Acoustic Doppler Velocimeter (Nortek Vectrino ADV; Nortek AS, Rud, Norway) at the test section during one run of each test, both close to the sediment bed (8 mm above the sediment bed) and at 1/3 of the respective water column (Table V 3-1). In the analysis, the measured wave-induced horizontal near-bed peak forward velocities were considered for calculations of the scouring (eq. 6) and the wave-induced horizontal peak forward velocities at 1/3 of the water column for the drag forces (eq. 8-12).
2.3 Plant material 60 adult shoots of Scirpus maritimus L. as well as 40 adult shoots of Scirpus tabernaemontani C. C. Gmel. and 40 seedlings of S. maritimus were used in this experiment (Fig. 2). In April 2012, the adult S. maritimus and S. tabernaemontani shoots were collected from the brackish tidal marshes of the Scheldt estuary, Belgium (51.36 °N, 4.25 °E, WGS84), and of the Elbe estuary, Germany (53.84 N, 9.36 °E, WGS84), respectively. The seedlings were grown from seeds that had been collected in September 2010 at the Belgian location and that had been stored in dry, dark and cool
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conditions until germination was initiated in early May 2012. The plant material was transplanted into PVC tubes of 0.25 m height and 0.12 m diameter lined with plastic bags and filled with the natural Scheldt sediment that was also used in the flume. Both, adults since April 2012 and seedlings since May 2012, were grown under equal natural outdoor conditions close to the Scheldt estuary. They were watered with brackish water (5 g NaCl/L) representative of the natural seasonal field conditions until they were brought to the flume where experiments started end of June 2012.
Fig. V 2-2 Tested plants. (a): adult S. tabernaemontani; characteristics: one elliptical stem, no leaves; (b): adult S. maritimus, characteristics: triangular stem, leaves; (c): seedlings of S. maritimus, characteristics: small, very flexible, leaves. For transplantation into the flume, the bags containing sediment and roots of the plants could be transplanted and buried into the sand box. Edge effects were avoided by folding the plastic bag downwards and filling up the gap between the sediment of the box and the transplanted root core. We then measured the plant height and the stem diameter 3 cm above the sediment bed. In a later step, biomechanical properties of plant material from our flume experiment were analysed (Puijalon et al., 2011) in order to better understand the different behaviours of the two plant species and life stages in the different hydrodynamic conditions (see 2.4 and 3.2).
2.4 Measurements of biomechanical traits We measured biomechanical traits through tensile and bending tests on 19 to 20 replicates for each species and growth form using a universal testing machine (Instron 5942, Canton, MA, USA) (Coops and Van der Velde, 1996a; Feagin et al., 2011; Möller et al., 2014; Peralta et al., 2008; Rupprecht et al., 2015). Both tests (tensile and bending) were carried out on each stem: for each test,
V Material and Methods 101 the stem fragments were 10 cm long for adult plants and 5 cm for seedlings. For each sample, we measured the dimensions of the stem cross-section using a digital calliper (r 0.02 mm) at three different points along the sample: height and width for triangular stem cross-sections (S. maritimus) and the shorter and the longer axes for elliptical stem cross-sections (S. tabernaemontani).
2.4.1 Bending tests We performed three-point bending tests, consisting of a force applied at a constant rate of 10 mm/min to the midpoint of a sample placed on a support. The following biomechanical traits related to bending were calculated:
(1) The Young’s modulus (E in Pa) quantifies the material stiffness and is calculated as the slope of the stress-strain curve in the elastic deformation region. (2) The second moment of area (I in m4) quantifies the distribution of material around the axis of bending, accounting for the effect of the cross-sectional geometry of a structure on its bending stress. I was calculated using a equation, depending on the geometry of the cross-section (eq. 2, 3, 4, 5, Niklas (1992)). For triangular cross-sections (S. maritimus), I = (x h3)/36, where x and h are the base and height of the cross-section (m) and for elliptical cross-sections (S. tabernaemontani), I = (S/4) y z3, where y and z are the shorter and longer axes of the cross- section. (3) The flexural stiffness (E I in N m2) quantifies the stiffness of the fragment and was calculated by multiplying E and I.
2.4.2 Tensile tests The stem fragments were clamped into the jaws of the testing machine and a constant extension rate of 5 mm/min was applied to the upper jaw until they broke. The following biomechanical traits were calculated:
(1) The breaking force (in N) is defined as the maximum force that the sample can bear without suffering mechanical failure. (2) The tensile strength (in N/m2) is calculated as the breaking force per cross-sectional area.
2.5 Scouring In order to measure the maximum scouring depth produced around the plant stems at the different wave conditions, we cut off the stems close to the sediment bed after each test, and scanned the sediment surface with a laser scanner (EProfiler developed by Aalborg University, Hydraulic &
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Coastal Engineering Group, Denmark) with a horizontal grid resolution of 5 mm × 5 mm and with a vertical precision of 1 mm (e.g., de Vos et al. (2012)). Reference surfaces next to the plants and sticks, hence outside the influence of self-scour, were also scanned after each test in order to correct the determined scour depth by any general deformation of the sediment bed without interference with obstacles. These data were imported into a GIS (Esri ArcMap 10.1) where we quantified the scour around each plant and stick in a raster-based analysis with a resolution of 5 mm × 5 mm and where the 95-percentile of maximum scouring depth was considered in order to correct for random extreme values (see Appendix 3).
2.6 Drag force Drag forces acting on each of the plants and on the cylindrical sticks under the different hydraulic conditions were measured by means of strain gauges, calibrated for measurements in N, to which we attached the basal part of the cut-off stems and of the cylindrical stick. They were then replanted into the sediment bed at the same location where the plants or sticks had been for the previous run and the respective test was applied once more during approximately 2 minutes. We then extracted 10 peak drag forces from 30 s into the test onwards and averaged them. Wet plant frontal area was determined based on plant morphometric measurements and respective effective water levels (still water level + mean wave amplitude) for the different tests. Based on the Morison equation (eq. 8) we then derived an obstacle-specific drag-coefficient and compared the measured drag forces to the calculated ones (see 2.7 for details.)
2.7 Statistical analysis Statistical tests were performed with the core-functions of R (R Development Core Team, 2014) except when stated otherwise. One- and two-way ANOVAs followed by post-hoc Tukey’s HSD were performed in order to test significant differences between plant types and water levels. The relation between water level and wave period on wave heights was investigated with Pearson’s correlation coefficients. Equally, the correlation of measured relative scour depth and KC numbers, as well as the correlation of wet frontal plant area and experienced drag forces were expressed as Pearson’s correlation coefficient.
In order to derive the drag coefficients, Cd,exp, for the different tested hydrodynamic conditions and
different types of obstacles (i.e. plant type or stick), we first derived the correction factor (Cd) required for making the measured and predicted forces (eq. 8) match. As the respective drag
coefficient was unknown at this point, we assumed that Cd = 1. We then quantified the required drag
V Results 103 coefficient for matching measured and predicted forces by deriving the slopes of the linear models forced through the origin (Fig. 6). Note that for the non-forced models, only two (both 5 cm conditions for adults of S. maritimus) had an intercept that was significantly different from zero. Moreover, these two intercepts were only marginally different from zero (0.25 and 0.06, respectively). These test- and obstacle-specific drag coefficients were hence our experimentally obtained drag coefficients, Cd,exp (Tab. 2). Based on equations 10 and 12 it was then possible to derive an obstacle-dependent function where Cd,exp is expressed per plant type and for the sticks as
-function of ݒ or ReD. In order to be able to derive the empirical constants a and b, we fitted non linear models through the respective points, following the equations given in equations 10 and 12, respectively. This was done with the core nls-function in R, for which b is fitted as exponential coefficient and a as linear part. Once a and b were derived, we obtained the actual drag coefficient (a) and could calculate the obstacle-specific variable power, i.e. the Vogel number (Ⱦൌ2 + b) (e.g., Sand-Jensen (2003), Infantes et al. (2011)). It needs to be noted that the results obtained for the non-linear fits for the adults based on ݒ have to be seen as hypothetical values, given the small number of points (n = 4 for both adult species) on which these model fits are based. For the seedlings, no models could be fitted due to the availability of only 2 data points after omission of the lowest water level. The goodness of fit was expressed by root mean squared error (RMSE).
Finally, in order to check the validity of the derived drag coefficients in a non-dimensional way, we validated the results from the Morison equation using the respective derived drag coefficients against the actually measured drag forces, where both the calculated and the actually measured drag forces were divided by the respective drag coefficients. In order to validate our corrections, we then performed 10-fold cross-validation and predicted the root mean square prediction error (RMSPE) using the package ‘cvTools’ in R (Alfons, 2012).
3 Results
3.1 Hydrodynamic parameters We first investigated how water levels and wave periods were related to wave-induced horizontal peak forward velocities and mean wave heights at the test section (Table V 3-1). While wave- induced horizontal peak forward velocities were overall not correlated to water level, they were strongly correlated to wave period for both lower water depth of 5 cm and 20 cm (Pearson’s correlation coefficient, r = 0.96 and r = 0.87, respectively). For the highest water depth of 35 cm,
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wave period had no significant effect on wave-induced horizontal velocity. Wave height at the test section, on the other hand, increased with water level (Pearson’s correlation coefficient, r = 0.94), indicating a depth-limited wave condition.
Table V 3-1 Overview of hydrodynamic conditions in the flume. Conditions tested on plants and sticks are indicated in black letters, conditions tested only on sticks are indicated in grey letter. Hmean is the average wave height (SE given for replicate tests on plants); vpeak bottom is the wave-induced horizontal peak forward velocity measured close to the sediment bed while vpeak 1/3 gives the wave-induced horizontal peak forward velocity at 1/3 of the respective water column, and averaged on 20 waves each; subscript h = hydraulic diameter of the flume; d = flow depth; D = diameter of obstacle; FrD and ReD have been calculated based on the diameter of the cylindrical sticks; ș is the Shields parameter, ȟ0 the Iribarren-number; ‘x’ indicates for which tests the respective threshold value, given in the header, is exceeded.
Water Wave Hmean ± SE Vpeak (m/s) Frh Frd > FrD > Re h > Re d > ReD > șȟ0 depth (cm) period (s) (cm) bottom 1/3 > 1 1 1 2000 2000 2000
5 2 1.9 ± 0.02 0.03 0.11 x < 0.5 spilling 5 4 2.7 0.07 0.32 x x < 5 plunging 5 6 4.4 0.24 0.55 x x x < 50 plunging 5 8 5.6 0.46 0.78 x x x x < 50 plunging 5 10 3.2 ± 0.48 0.78 0.78 x x x x x > 100 plunging 20 2 8.8 ± 0.06 0.28 0.15 x x x < 50 spilling 20 4 5.8 0.32 0.24 x x x < 50 spilling 20 6 12.6 0.38 0.36 x x x x < 50 spilling 20 8 11.7 0.46 0.48 x x x x < 50 plunging 20 10 14.4 ± 0.42 0.4 0.57 x x x x < 50 plunging 35 2 18.1 ± 0.19 0.54 0.38 x x x x < 70 spilling 35 4 12 0.57 0.46 x x x x < 70 spilling 35 6 16.9 0.52 0.42 x x x x < 70 spilling 35 8 17.9 0.6 0.48 x x x x < 70 spilling 35 10 23.0 ± 0.86 0.48 0.3 x x x x < 50 plunging When looking at the hydrodynamic conditions in terms of Fr, Re, Shields parameter (ș) and
Iribarren number (ȟ0) (Table V 3-1), we can state that the long wave period at the shallow water
level is the only tested condition for which Frd>1, implying a critical combination of shallow water depth and high wave-induced horizontal peak velocity, as well as an outstandingly high Shields parameter (>100), through which this condition might need to be considered as an outlier when analysing our results. Furthermore, the 2 s waves were spilling and 10 s waves plunging at all water levels.
3.2 Plant properties Plant height and basal stem diameter did not differ significantly between adults of S. tabernaemontani and S. maritimus (Fig. V 3-1a). Seedlings, on the other hand, were significantly smaller, with thinner cross-sections than both adult species (Tukey’s HSD, p < 0.001 for all).
V Results 105
Regarding tensile biomechanical properties, both breaking force and tensile strength of seedlings were significantly lower than both adult species (Tukey’s HSD, p < 0.001 for both, Fig. V 3-1c).
Fig. V 3-1 Mean values ± SE of plant properties per plant type. (a) Morphological properties as determined on 60 plants for the adult S. maritimus and on 40 plants each for adult S. tabernaemontani and seedlings of S. maritimus; (b) & (c) Biomechanical traits of plant stems as determined on 20 stems per plant type: (b) Young’s modulus, second moment of area and flexural stiffness measured through bending tests; (c) breaking force and tensile strength measured through tensile tests. Different letters above the bars indicate significant differences as obtained by a one-way-ANOVA followed by a post-hoc Tukey’s HSD.
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For bending properties, seedlings present significantly lower flexural stiffness (i.e. more flexible stems) due to both lower second moment of area and Young’s modulus (Tukey’s HSD, p < 0.05, Fig. V 3-1b). Within the adult species, the stems of S. tabernaemontani were less stiff than of S. maritimus due to lower Young’s modulus (Tukey’s HSD, p < 0.001, Fig. V 3-1b).
For drag forces, relative inundation at wave passage might have been important. As plant height differed significantly between adults and seedlings (see above), submergence varied with life stage and water level. While both adult species were emergent for all conditions (the plants exceeded two to four times the water level for the high and intermediate water level, respectively, and with adults of S. maritimus exceeding the lowest water level by a factor 14), the seedlings were submerged at the intermediate water level, and exceeded the lowest water level only by a factor 3.
3.3 Scour The KC numbers were significantly correlated to the relative scour produced around the cylindrical sticks at the three water levels and five wave periods (Fig. V 3-2a). There is one outlier, which appears for the test of the long wave period at the shallow water level (i.e., T05.10 in Fig. V 3-2a; after omission of that test, Pearson’s correlation coefficient: r = 0.90). We accept this condition as an outlier as the applied model does not work for this particular test for which hydrodynamic
conditions such as Froude number and Shields parameter were outstandingly high (Frd > 1, ș > 100). The measurements for adults and seedlings of S. maritimus at the long wave period of the shallow water level (T05.10 in Fig. V 3-2c) were then equally excluded as outliers, leading to significant correlations (r = 0.82 and r = 0.86 for adults and seedlings, respectively, after omission of outliers). For S. tabernaemontani, correlation is high (r = 0.92) for all conditions (Fig. V 3-2b). Apart from the outliers for long waves at shallow water, all plant types followed the same general correlation (overall correlation after omission of the outliers: r = 0.86) which implies that the scour depth can be predicted well when wave-induced velocity and wave period are known.
V Results 107
Fig. V 3-2 KC number versus relative scour for the sticks (a) and the different plant types at equal water levels. (b) adult plants at 20 and 35 cm; (c) adults and seedlings of S. maritimus at 5 and 20 cm; (d) all plant types at 20 cm; the linear model shown for the sticks results after omission of the outlier condition for the long wave period at the shallow water level (T05.10). 3.4 Drag forces In terms of the relationship between wet frontal area of the obstacles (sticks or plants) and experienced peak drag forces, Fig. V 3-3 shows that the stiff sticks followed a clear and simple positive linear correlation according to the theory (Fig. V 3-3a; Pearson’s correlation coefficient, r = 0.93). The adult plants showed overall also a positive correlation (r = 0.81 and r = 0.50 for adults of S. maritimus and S. tabernaemontani, respectively). Seedlings showed an overall weak correlation that was not significant (Fig. V 3-3b, c). At the lowest water level (Fig. V 3-3b), the correlation for the adult S. maritimus was also not significant. At the highest water level
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(Fig. V 3-3d), the observed peak drag forces acting on the plants were clearly lower than expected from the relationship found for the sticks.
Fig. V 3-3 Peak drag forces as function of wet frontal area for the sticks (a) and the, respectively, concerned plants at the three tested water levels (b-d). Points represent the observed values and the line represents the regression calculated for the sticks. However, drag is not only influenced by wet frontal area (eq. 8). A linear model was fitted (see 2.7) between observed and calculated drag forces showing a significant slope (p < 0.001) for all but three cases and the variance explained (R²) ranged from 0.46 to 0.85 (Fig. V 3-4 and Table V 3-2). Nevertheless, the calculated drag forces largely underestimated the actually measured drag forces, except for three conditions. That is, in case of seedlings at the shallow and intermediate water level with 10 s waves, and in case of adults of S. maritimus at the shallow water level with 10 s waves, the
V Results 109 calculated drag forces overestimated the actually observed ones. Based on the linear regression equation between the actually measured and calculated drag forces (Fig. V 3-4), we derived values for Cd,exp which are the slope coefficients of the respective linear models (see Table V 3-2).
Fig. V 3-4 Observed peak drag forces (N) on the three plant types plotted against corresponding drag forces calculated with the Morison equation (eq. 8: F = 1/2ȡACdv² (N)), with Cd = 1. From left to right, the three columns show results for the three different water levels; from top to bottom, each of the three plant types is represented in one row; wave periods are distinguished (2 s: hollow circles; 10 s: filled circles); the grey solid line indicates where calculated values correspond to measured values (1:1); the black dotted lines indicate the linear models forced through (0,0) for the 2 s wave conditions, the black dashed lines indicate the linear models forced through (0,0) for the 10 s wave conditions (n = 10).
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Comparing the drag coefficients between the different overlapping subsets using ANOVA (e.g., adult S. maritimus compared to seedlings of S. maritimus at their common water levels, i.e. the two lower water levels), showed that there are no significant differences between both adult species for the common water levels, while both adult species differ significantly from the seedlings (p < 0.001 when compared to adult S. maritimus at the two lower water levels; p < 0.01 when compared to adult S. tabernaemontani at the intermediate water level).
Fig. V 3-5 Calculated vs. measured peak drag forces (N) divided by the experimentally obtained drag coefficients for (a) the sticks and (b-d) the three plant types, respectively.
V Results 111
Based on the 10-fold cross-validation performed on the dimensionless validation of the drag force prediction (see 2.7 and Fig. V 3-5), we obtain root mean square prediction errors (RMSPE) of 0.07, 0.21 and 0.25 for adults of S. tabernaemontani, adults of S. maritimus and seedlings of S. maritimus, respectively. The overall RMSPE for all plant types is of 0.19. When only considering 10 s wave periods, adults of S. tabernaemontani have an RMSPE of 0.1, adults of S. maritimus of 0.24 and seedlings of 0.35.
Table V 3-2 Overview of linear model coefficients (equal to Cd,exp). Obtained for the linear models (with their R² and p values) forced through (0,0) between 1/2ȡACdv² (with Cd = 1) and observed peak drag forces (see Fig. 6); p = 0.05 > * > 0.01 > ** > 0.001 > *** (n = 10 for plants, n = 1 for sticks).
Water Wave depth (cm) period (s) Sticks Adults, S. maritimus Adults, S. tabernaemontani Seedlings, S. maritimus
Cd,exp Cd,exp R² p Cd,exp R² p Cd,exp R² p 5 2 6.64 8.38 0.58 ** - - - 7.02 0.96 *** 549.25------563.2------581.57------5 10 0.81 0.56 0.46 * - - - 0.41 0.79 *** 20 2 24.79 27.47 0.96 *** 22.28 0.97 *** 3.23 0.95 *** 20 4 11.92 ------20 6 5.97 ------20 8 6.53 ------20 10 3.65 3.07 0.87 *** 3.62 0.9 *** 0.25 0.6 ** 35 2 7.84 4.2 0.86 *** 4.59 0.95 *** - - - 35 4 6.64 ------35 6 9.51 ------35 8 8.12 ------35 10 12.5 5.55 0.94 *** 5.59 0.91 *** - - -
Table V 3-3 Summary of determined coefficients (a, b) and Vogel number (ȕ) for Cd,exp as function of ࢜ (eq. 10) and ReD (eq. 12) (see Fig. V 3-6). Significance is given by p = 0.1> . > 0.05 > * > 0.01 > ** > 0.001 > *** and with Residual Standard Error (RSE) as measure of goodness of the non-linear fit. na: no statistical output available due to limited number of observations (n).
v ReD Obstacle Condition A b ȕ (=2+b ) np RSE a b npRSE Stick T05 0.9 -0.97 1.03 5 * 1 9.45 x 103 -0.97 5 * 1 T20 1.57 -1.47 0.53 5 ** 1.4 1.99 x 106 -1.47 5 ** 1.4 T35 3.32 -1.06 0.94 5 . 1.4 8.22 x 104 -1.06 5 . 1.4 Adult, S. maritimus T20 & T35 0.45 -2.2 -0.2 4 * 1.3 1.79 x 106 -1.59 40 ** 4.1 Adult, S. tabernaemontani T20 & T35 0.73 -1.83 0.17 4 * 1.4 1.64 x 104 -1.11 40 * 4.9 3 Seedlings, S. maritimus T20 - - - 2 na na 1.01 x 10 -0.91 20 ** 0.7
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Fig. V 3-6 Non-linear fits between the experimentally obtained drag coefficients, wave-induced peak velocities and ReD (a) and (b): sticks, per water level (n = 5); (c) and (d): plants, per type where non-linear models for adults of S. maritimus were fit after omission of T05 (for (c): n = 4 for both adults; for (d): n = 20 for seedlings and n = 40 for both adults), which is why the fits in (c) need to be considered as hypothetical and the fit cannot be formally tested. The fits in (d) remain limited in number of tested conditions performed.
Applying equations 10 and 11, we derived the actual drag coefficient, a, and the Vogel number, ȕ, from the exponent b (see 2.7 and Table V 3-3, Fig. V 3-6). The results for sticks (Fig. V 3-6a, b) indicate that at the lowest water depth the forces acting on the obstacles were different from the other two water levels, while the two higher water levels showed similar responses. Therefore, we ignored the shallow water depth when fitting the curves for the plants, i.e. the non-linear models for the adults of S. maritimus were fit after omission of the shallow water level. Note that the results obtained for a and b have to be understood as hypothetical given the limited amount of points through which the non-linear models were fit for v (n = 4 for both adult species).
V Discussion 113
4 Discussion Over the last two decades, many flume experiments have been performed on plant-wave interactions (Anderson and Smith, 2014; Augustin et al., 2009; Bouma et al., 2005; Coops et al., 1996a; Francalanci et al., 2013) where self-scour and drag forces occurring around and acting on plants – individuals up to marsh scales – have been studied. However, there is a lack of experiments on the contrasting influences of wind and ship waves (i.e. short period and anthropogenically-induced long period waves) on pioneer tidal marsh plants, and of waves in general on drag and scour occurring on plants of differing morphological structures and life stages. The aim of our experiment was to test and validate commonly used equations for the prediction of scouring and drag forces on typical pioneer marsh plants in the presence of contrasting waves. For further studies, such formulations could then be taken into account when predicting potential habitat suitability for the establishment of intertidal marsh species in an estuary where contrasting hydrodynamic influences, simulated by the wide range of parameters tested in our experiment, occur. However, possible qualitative limitations of our experimental set-up compared to true field conditions (e.g., low water levels and monochromatic waves) need to be considered when drawing conclusions.
While our results show that scour can overall be predicted well for these contrasting conditions with established methods, prediction of drag forces using established methods works well for wind wave conditions (mimicked by 2 s waves) but becomes less accurate for ship-induced waves (mimicked by 10 s waves), especially for plants with a complex morphology such as adults and seedlings of S. maritimus. This, in turn, implies that potential habitat suitability in terms of scour under contrasting wave conditions as simulated in the experiment can be predicted in a reliable way, but will be more difficult to assess in terms of drag forces.
Particularity of the shallow water level The shallow water level produced singular conditions for both scour and drag: the tests on the sticks showed that for all periods of the 5 cm water level, the experimentally obtained drag coefficients were smaller than for the two higher water levels at equal wave-induced horizontal peak forward velocity or Reynolds number (Fig. V 3-6). This indicates that under wave action, there seems to be a threshold of water depth below which the drag forces expected based on wet frontal area and wave- induced horizontal velocities will be smaller than once that water depth threshold is exceeded. In regards to scour, a particularly high Shields parameter was observed for the long wave period at the shallow water level (ș = 118.2 vs. an average value of 33.5 ± 5.9 SE after omission of this particular
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condition), leading to shallower scour depth than expected based on KC number alone: a high Shields parameter indicates a situation with high mobility of the sediment bed and hence continuous sediment supply; this can then lead to backfilling of local scouring holes, thus reducing the locally observed final scour depth. While this particular observation has also been reported by Umeda (2011), the threshold in water depth for wave-induced drag forces was not reported by other authors. All these particularities found for the sticks were confirmed in the tests with the plants.
Scour Except for the long wave period at the shallow water level condition, the scour occurring around sticks and plants could be explained largely by the KC number, as suggested by Umeda (2011) for rigid cylindrical obstacles (Fig. V 3-2). However, at equal absolute scour depths, seedlings experienced a deeper relative scour depth (S/D) than adults. As seedlings naturally root less deeply than adult plants, they risk uprooting as the critical scour depth for seedlings will be smaller than for adults. It should also be noted that for both deeper water levels, scour increased with wave period: the ship-generated waves would cause more severe scour than the wind waves, creating thus potentially more critical conditions for plants, especially for seedlings. Overall, the prediction of conditions (i.e. critical combinations of wave periods and flow velocities) that lead to uprooting of plants can be derived based on the KC number.
Drag Regarding drag forces, the Morison equation (Bouma et al., 2010; Henry and Myrhaug, 2013; Mendez and Losada, 2004; Myrhaug and Holmedal, 2011) leads to fairly good results when each tested condition and plant type is analysed individually (Fig. V 3-4). However, except for the highest water level, the obtained values of the drag coefficients for the 10 s waves were significantly lower than for the 2 s waves, demonstrating that the extreme periods of ship-induced waves lead to a very different behaviour of the plants than under natural wind wave conditions. We found that the application of the experimentally deduced drag coefficients lead to the best results for the adults of S. tabernaemontani (Fig. V 3-5d). From this we can draw two main conclusions: (1) it is possible to predict fairly well the expected drag forces to be experienced under wave impact by plants of simple shape such as adults of S. tabernaemontani (consisting of oval single stems without leaves), even for extreme wave events; and (2) for plants of more complex structure, such as adults and seedlings of S. maritimus (consisting of triangular stems with several leaves), prediction of the expected drag force for wind wave conditions is fairly reliable, while extreme wave events such as potentially ship-induced waves lead to larger inaccuracies of predictions. This is possibly due to the differently
V Discussion 115 acting drag forces on stems on the one hand, and on leaves on the other hand (Albayrak et al., 2013), where projected leaf surface is overestimating the actually exposed, interfering leaf surface after reconfiguration in the waves. This effect could already be seen based on the results shown in Fig. V 3-3. Furthermore, the Iribarren number indicates spilling waves for 2 s waves, while the 10 s waves are plunging, pointing at two different wave breaking stages, which could equally have an effect on the dynamics of the wave-induced flow field and hence influence the drag experienced by the more complex plants.
Vogel number and drag coefficient Following Sand-Jensen (2003), determining the coefficients of equation 10 would lead to the variable power, ȕ, usually referred to as Vogel number and for which typically 0 ȕ 2 (e.g., Aberle and Jarvela (2013), Bouma et al. (2005), Nepf (2012)). While the overall tendency of the correlations follows the expected relation (i.e. of the type of equation 10; Fig. V 3-6), the fitted non- linear models for the different plant types (after omission of the shallow water level based on observations for the sticks) lead for both simplest obstacles, i.e. for the sticks and for the adults of S. tabernaemontani, to values of ȕ that would be within the expected range. In contrast, we obtain for adults of S. maritimus a slightly negative Vogel number (Table V 3-3). This unexpected outcome could first of all result from the limited amount of points to fit the models through (see above), which leads to results that need to be viewed as hypothetical: while the overall tendencies seem valid, the actual numbers might not be correct. Extended experiments for more conditions would help finding more reliable relationships.
As drag coefficients compensate for effects that have not been taken into account by other parameters in the Morison equation, such as the flexibility of the plant type, their value indicates to what point plants or obstacles in general respond in a similar way or not to incoming wave impact. Here it appeared that both adult species, despite their different morphologies and all methodological restrictions, respond – on average – similarly to the incoming waves. The seedlings, on the contrary, given the position of a hypothetical non-linear fit that would lie below the fits of the adults (Fig. V 3-6), respond very differently from the adults, accounting for the differing biomechanical and morphological properties of the life stages, and possibly also due to the stated differences in relative inundation of the plants.
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Habitat suitability According to present results, existing equations for estimation of expected scour and drag around and on pioneer tidal marsh plants can be applied best for conditions where inundation exceeds a threshold of at least 5 cm, and for natural short wind wave conditions. At more shallow water conditions, both scour and drag will be overestimated by the equations. This implies that the calculated values for those conditions can be regarded as a worst-case scenario, which is not likely to occur. The morphology of the plants will also affect the reliability of estimated drag, where the simplest plant morphologies will lead to most accurate results. These findings, however, might need to be put into perspective depending on which wave climate prevails in the long term in the field, and which wave climate is apt to create the most extreme forces acting on the plants: in busy shipping estuaries, such as the Scheldt and Elbe estuaries, a regular interference of long period ship- generated waves can be expected. Furthermore, depending on the prevailing sediment-type in the field, there might be a non-negligible erosion protection provided, at least seasonally, by biofilms and belowground root systems (Le Hir et al., 2007). These could increase the erosion threshold and reduce the resulting scour depth around plants.
When, in a more applied approach, habitat suitability is assessed for pioneer tidal marsh plants in terms of restoration projects, it should nonetheless be considered that, compared to wind waves, a large influence of ship-generated waves will lead to unpredictable drag exerted on plants with complex morphology. At sheltered sites, where wind-generated waves dominate, the conditions will be more reliably predictable.
5 Conclusion Given the limitations of the presented flume experiment, further investigation (e.g., similar flume studies including stabilisation of the sediment by roots and biofilms) could provide further insights in the actual field processes. Also measuring drag forces and scour produced at higher water levels and possibly under more realistically simulated ship-induced wave events with irregular wave fields would give a better insight in the effects of such waves under normal field conditions. Furthermore, longer test durations, i.e. more than 200 waves, could be considered, and the response of patches of seedlings and shoots, as opposed to effect on individual plants studied here, could be investigated.
Implications from our experiment for life stage and species in terms of environmental suitability for establishment on the intertidal flats are that scour by wind and ship-induced waves will act in a
V Conclusion 117 predictable and similar way on all plant types, and KC number is overall a good way for quantifying maximum scouring depth. Ship-induced waves are more likely to create critical conditions, as the scour depth observed after long (i.e., potentially ship-generated) wave periods was significantly deeper than for short (i.e., wind-generated) wave periods. Drag forces experienced under contrasting wave impact, on the other hand, can be well predicted for plants with a simple morphology such as S. tabernaemontani, but drag forces acting on plants of more complex structure such as S. maritimus will be difficult to predict especially for extreme long period waves. At shallow water levels, the general rules, both for drag and scour, found for higher water levels will fail. Our findings indicate that under contrasting wave periods as typically occurring in many estuaries, habitat suitability for the establishment of pioneer tidal marsh species will be more difficult to assess in the presence of long period, possibly ship-generated waves than in more sheltered, wind wave-dominated conditions.
Acknowledgments This project was financed by the Research Foundation Flanders (FWO, PhD grant to A. Silinski, grant-number 11E0914N), the Port of Antwerp and by the research programme KLIWAS (Impacts of climate change on waterways and navigation – Searching for options of adaptation) of the German Federal Ministry of Transport and Digital Infrastructure (BMVI). The project was further supported by the FWO scientific research community (WOG) on ‘The functioning of river ecosystems through plant-flow-soil interactions’ (grant-number WO.027.11N). We would like to thank B. Koutstaal from NIOZ-Yerseke for growing the plants; T. Versluys and H. Van der Elst from Ghent University for their technical support at the flume; our master thesis student S. Dauwe and three job students for their help and efforts; C. Schwarz for valuable comments on previous versions of this manuscript; E. Fransen from StatUA for advice on the statistics; B. Konz from BfG for artwork; two anonymous reviewers for their constructive and insightful remarks that considerably helped improve the manuscript.
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Box 2 Comparison of cross-sectional areas between the plant types Seeds and rhizomes of S. maritimus were collected at Groot Buitenschoor, a brackish marsh on the Scheldt estuary, Belgium (51°21’47"N, 4°14’53"E). In the Elbe estuary at Hollerwettern, Germany (53°50'20"N, 9°21'40"E), we gathered Scirpus tabernaemontani rhizomes since this species is not found at Groot Buitenschoor. Additionally, we also sampled shoots of S. maritimus from Hollerwettern in order to test the plant properties for similarity between the Elbe and the Schelde sites. We analysed the cross-sectional area using one-way ANOVA followed by a post-hoc Tukey’s HSD. The results show that the adult plants of S. maritimus do not significantly differ between the sample sites (Fig. Box 2-1). However, S. tabernaemontani and S. maritimus both from the Elbe site differ more in the cross-sectional area than S. tabernaemontani from the Elbe and the adult S. maritimus from the Schelde.
Fig. Box 2-1 Mean values of plant cross-sectional area with standard error for the plant types (adult S. maritimus, Elbe: n = 19, adult S. maritimus, Schelde: n = 20, adult S. tabernaemontani, Elbe: n = 20, seedlings S. maritimus, Schelde: n = 19). The plant cross-sectional areas were transformed by the natural logarithms for applying the ANOVA. The plant type differs significantly in the cross-sectional area (F3, 74 = 297.7, p < 0.001) except the adults of S. maritimus from the Elbe compared to the Schelde. Different letters show significant differences based on post-hoc Tukey’s HSD test, significance level is Į < 0.05.
V Box 3 119
Box 3 Drag force is governed by the water level To get a deeper insight into which parameters govern the drag force and how drag force changes under various wave conditions, we performed a three-way ANOVA to test adult species, wave period and water level as predictors using backward selection. We also illustrated the relation between the percentage of stem height of S. maritimus and its absolute drag force. The flooded stem was calculated using the effective water level (still water level + wave height) divided by the absolute stem height times 100.
Fig. Box 3-1 Mean drag force with standard error of the adult plants S. maritimus and S. tabernaemontani for the two highest water levels. Wave conditions are written as water level (cm)_wave period (s). Each mean was calculated from 10 samples. Drag forces were transformed by the natural logarithms for applying the ANOVA. Different letters show significant differences based on post-hoc Tukey’s HSD test, significance level is Į < 0.05. The tested predictors species, wave period and water level as well as the interaction between the last two were highly significant (drag force = -1.159 - 0.876 species + 0.062 water level + 0.238 2 wave period - 0.008 water level:wave period, F4,75 = 25.24, p < 0.001, R adjusted= 0.53). However, the wave period is only significant when the interaction has not been removed. That means that the water level and species morphology primarily govern the drag force and the wave period influences the water level. While drag forces of the species significantly increase with the greater wave period at the 20 cm-level (Fig. Box 3-1), the drag forces are similar at the 35 cm-level comparing the wave periods. There is a tendency that the species experienced even less drag forces during the long-period waves.
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Considering the parts of the flooded stem heights, the drag force first increases with increasing inundation (Fig. Box 3-2). But when more than 60 percent of the stem is flooded, the drag force reduces. Probably the reason is the reconfiguration of stem which also streamline the leaves (Aberle and Jarvela, 2013). This might be also the cause why the drag force declines at higher water levels with longer wave periods (Fig. Box 3-1).
Fig. Box 3-2 The percentage of stem height which is flooded in relation to absolute drag force.
Chapter VI Synthesis
© U. Schröder
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© U. Schröder
VI Conclusion 123
1 Conclusion Listed below are the insights gained on the interactions between hydrodynamic forces, the traits of vegetation-forming marsh species, species habitat conditions, and their capacity for ecosystem engineering. Their interactions are illustrated in Fig. VI 1-1. Subsequently, the two main questions of the thesis are answered. Finally, some recommendations for future research are offered in order to expand our knowledge about the processes of vegetation-based shoreline protection. This understanding is vital for adapting and reacting to future environmental changes in estuaries, such as sea-level rise.
1.1 The insights gained x Elevational zones in plants clearly differ when comparing marshes along unlike navigation channels, whereas the elevational zones are similar when comparing marshes along anabranches (Chapter II). x Species only occur when the distance from the navigation channels is long enough which likely depends on the vessel frequency (Chapter II). x Hydrodynamic forces is a primary driver which influences the responses of marsh vegetation to the elevational gradient in Elbe and Weser estuaries (Chapter II). x Severe hydrodynamic forces might weaken the species competitive hierarchy resulting in more distinct niches being occupied by stress-resistant species (Chapter II). x Low hydrodynamic stress causes elevational niche overlap and strengthens the species competitive hierarchy which can lead to only the most dominant species (Chapter II). x In order to construct their niches, plant species have different strategies to cope with hydrodynamic stress which are characterized by dissimilar functional traits (Chapter III). x Stem resistance due to increased lignin counteracts the higher hydrodynamic drag force which results from a larger plant surface (Chapter III). x A high cellulose concentration and a larger cross-section in the basal stem parts provide stem flexibility and stem resistance against breaking (Chapter III). x Biomass represented by stem density is a strong predictor for wave attenuation (Chapter III). x Species which grow lower in elevation invest less gross energy for stem morphology (Chapter III).
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x Ecosystem engineering effects are directly linked to traits affecting species stress resistance and in response to the stress experienced (Chapter III).
x Bank slope and bottom friction length drive plant species distributions on shorelines (Chapter IV).
x Species distribution models are valuable tools for implementing estuarine restoration measures (Chapter IV).
Fig. VI 1-1 Detected causes and effects for the variability in elevational niches of estuarine marsh vegetation by illustrating the parameters investigated. + stands for parameters which increase or become more depending on their influenced variable. – stands for parameters which decrease or become less depending on their influenced variable.
x The restoration of shallow water zones is vital for natural vegetated tidal flats (Chapter IV).
x Restorations using groynes can still have positive effects on natural habitats in comparison to shorelines protected by stone linings (Chapter IV).
x Equations usually used for calculating the hydraulic effects of obstacles can also be applied to predict drag and scour depth of plants (Chapter V).
VI Conclusion 125 x Scour depth around plants can be predicted by KC number, wave period, and flow velocity (Chapter V). x The more complex the plant morphology, the higher the uncertainty when predicting drag force (Chapter V). x Long wave periods cause an increase in scour depth and the uncertainty increases when predicting drag force (Chapter V).
1.2 Vegetation driven by hydrodynamic stress Why and how do hydrodynamic forces drive the plant species niches of shoreline vegetation in estuaries? The ‘why’ can mainly be explained by three components: (1) Water motion is dissipated in shallow water. Through this energy transformation, forces are released which affect the vegetation. The effects occurring on tidal flats can be measured by scouring around the plant stem, by drag force and irreversible bending. (2) Morphological traits of plant species can be adapted to a certain extent of hydrodynamic forces. Thus, the adaptive capability of traits is the response how hydrodynamic forces drive species niches. In particular, plants weaker than their competitors need a strategy to survive. Thanks to their morphological traits, they construct their niches where the shoreline is too stressful for stronger competitors. The traits can be classified according to strategies of stress avoidance or stress tolerance (Puijalon et al., 2011). (3) The abiotic conditions, which are correlated to the hydrodynamic forces, such as the elevational gradient, describe where species construct their niches in order to sufficiently survive.
Effects experienced for vegetation-forming plants Estuarine shorelines are most affected by the spatial transition between water and land and the daily switchover between them due to the tides. It is also a transition zone of energy caused by current and waves. This energy causes stress on marsh vegetation. We investigated this stress effect in distinct wave conditions by measuring scour around the plant stem and drag force.
The scouring effect by waves can be predicted for all plant types (Fig. V 3-2). The KC number is an adequate way of calculating the maximum scouring depth depending on stem diameter and wave period. Long wave periods (e.g., 10 s) acting on shallow water levels (e.g., 5 cm) are difficult to predict. These conditions lead to shallower scour depth than expected, based on the KC number. However, the sediment is considerably moved under these wave actions as the high Shields value shows (Table V 3-1). This activity probably causes a refill of the scour and therefore influences the
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final scour depth. A usual wave climate with short waves does not cause noticeable scouring with the danger of stem uprooting (Chapter III). Nevertheless, the relative scour depths (S/D) of seedlings are deeper than those of the adult plants, probably due to seedlings higher movement in the greatly flexible stem (Silinski et al., 2015a). Seedlings are obviously rooted less deeply than adult plants. Thus, the critical scour depth for uprooting is much smaller than for adults.
In deeper water levels (> 5 cm), the scour increases with the wave period. Hence, sites exposed to wind waves with longer wave periods or exposed to ship-generated waves can lead to critical survival conditions for plants, particularly for seedlings. It could be one reason for the observed marsh loss since 1954 on the wind-exposed Wurster coast (53°38’00’’N, 8°30’03’’E) in the outer Weser estuary (Steege et al., 2006). Another reason for this loss could be an increase of drag force experienced on the aboveground plant material (e.g., Denny (1994a)).
The experienced peak drag forces depend on the wet frontal area of obstacles made of rigid material (e.g., Henry and Myrhaug (2013)) or of flexible material (Vogel, 1994). This is verified by Fig. III 3-2 exhibiting the discrepancy in absolute drag force between S. tabernaemontani and S. maritimus due to unlike surface areas (e.g., based on leaf number). The divergence disappears when the drag force is normalized to a square meter and when the water level is greater than or equal to 35 cm. Then the most of the S. maritimus leaves are submerged and thus the wet frontal area is much greater than that of S. tabernaemontani. Also Fig. V 3-3 confirms the relationship between drag force and frontal area, showing a strong linear correlation for a rigid stick. The adult plants exhibit a relatively high overall Pearson’s correlation (S. maritimus: r = 0.81, S. tabernaemontani r = 0.50). However, the flexibility has an unlike influence on the correlation depending on the water level (Fig. V 3-3). While there is no correlation for the 5 cm water level, the adult plants fits well into the linear model of the rigid sticks at a water level of 20 cm. With an increased water level (such as 35 cm), the slope of the relationship between frontal area and drag force is less for plants than for the sticks. The reason might be the reconfiguration or rather deflection of flexible leaves and stems. This does not occur when the plants only become submerged with increasing water level (Table V 3-1), as Fig. III 3-2 verifies. It shows an increase of absolute drag force experienced by S. maritimus at 35 cm water level compared to 20 cm. In addition to this, the severe turbulences as represented by plunging waves might trigger substantial deflection of emergent macrophytes such as the adults of S. tabernaemontani and S. maritimus. The deflection for the flexible small seedlings is so strong that the overall correlation is not significant (Fig. V 3-3).
VI Conclusion 127
Comparing the results of the equation 10 with the observed drag forces supports this deduction by exhibiting better regression models (Fig. V 3-4, Table V 3-2).
The regression model fits best for S. tabernaemontani at all investigated wave conditions (Fig. V 3-4, see also Fig. V 3-5). Therefore, the drag force of plants with a simple morphology is predicable. In contrast, the modelled drag force is fairly reliable for plants exhibiting a more complex morphology (S. maritimus) concerning common wind wave conditions. The drag force varies more during events with higher turbulences (plunging waves) probably due to an increase of random leaf and stem deflection (Albayrak et al., 2013), stepped up for more flexible plants such as seedlings. The calculated and measured drag forces support the explanation (Fig. V 3-6, Table V 3-3). The overall tendencies of these results seem to be valid, nonetheless, the number of wave conditions has to be increased.
Plants with a higher frontal area have an increased risk of irreversible stem bending when the majority of the stem is submerged as S. maritimus shows at a water level of 35 cm (Fig. III 2-1). The result indicates a higher risk of exhaustion which can lead to toppling and mortality of the plants (Silinski et al., 2015a). Additionally, the process most likely depends on the degree of plant stiffness.
The trait capability for resisting hydrodynamic stress Morphological traits provide the resistance to hydrodynamic stress. In contrast to S. maritimus, S. tabernaemontani has a lower lignin and silica content (Table III 4-1, Fig. III 3-4), which results in higher flexibility for the plant tissue itself and also for the plant stem (Fig. III 3-3, Fig. V 3-1) considering bending forces. However, although the adults of S. maritimus neither differ in stem diameter (Fig. V 3-1) nor in cross-sectional area between the Schelde and Elbe sites (Fig. Box 2-1), they do show diverse response traits in testing tensile forces simulating the effect of pulling by hydrodynamic forces:
The basal stem parts of S. tabernaemontani exhibit a higher breaking force, but only compared to S. maritimus originating from the Elbe estuary (Fig. III 3-3, Fig. V 3-1). This is likely owing to the higher cross-sectional area of S. tabernaemontani rather than to the strength of the tissue (see tensile strength Fig. III 3-3). Thus to resist this force at the Elbe estuary, it does not matter if the basal stem part is strengthened by more lignin (S. maritimus) or by more cellulose (S. tabernaemontani).
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In contrast, the adults of S. maritimus from the Schelde have a greater cross-sectional area than those from the Elbe. The difference to the cross-sectional area of S. tabenaemontani is still significant but smaller and it does not influence the tensile strength any more as the second moment of area shows (Fig. V 3-1). The adults from the Schelde seem to have more lignin than those from the Elbe, because their tensile strength differ (Fig. III 3-3, Fig. V 3-1). The unlike cross-sectional area between S. tabernaemontani and S. maritimus probably compensates for the tissue properties, because their breaking forces are similar (Fig. V 3-1). Consequently, these plant traits are not only species-dependent but they can also be site-specific (Silinski et al., 2015b). This knowledge gap requires further investigations.
Not only do the plant characteristics of the outer shape and the concentrations of strength molecules in the cell walls govern the trait capability for resisting hydrodynamic stress, but also the biomass, and energy investment (Fig. III 4-1): Even with a larger cross-sectional area, S. tabernaemontani has a lower material investment than S. maritimus per stem length. The gross energy invested by S. maritimus is twice (0.42 kJ/mm2) as high as that of S. tabernaemontani (0.21 kJ/mm2). The reasons are less kilojoule expenditure for cellulose than for lignin and lower biomass weight of S. tabernaemontani (Chapter III). Thus, the degree of plant stiffness seems to be a trade-off between the concentrations of strength molecules in the cell walls, stem shape, biomass, and energy investment. Depending on the specifications of these components, the species have more traits of the avoidance strategy if they have low lignin and silica content (Table III 4-1) resulting in high flexibility (lower stiffness) (Schoelynck et al., 2010). Consequently, the drag force is less (Kaufman et al., 1999; Schoelynck et al., 2010) promoted by lower biomass exhibited by S. tabernaemontani. The trait capability can also change from life stage to life stage. While seedlings of S. maritimus also seem to have more traits representing the avoidance strategy (Chapter V, Silinski et al. (2015a)), the traits of adult S. maritimus indicate the tolerating strategy. They consist of high lignin and silica contents which lead – in combination with higher leaf and stem area – to higher drag forces and higher material costs. Hence, S. maritimus establishes on tidal flats where their stiffer stems do not buckle due to less load (Kötter, 1961). If hydrodynamic conditions become too harsh, S. maritimus will fail in its tolerance strategy, whereas S. tabernmaemontani will still be capable of resisting this stress. Its failure would also be less serious due to less material costs. The described effect traits also influence each other and respond to the influence (Fig. III 4-1). One response is the elevation occurrence, one aspect of species functional niche. This insight correlates with Coops’s statement that flexible and stretchy growth forms of macrophytes occur downslope, which absorbs
VI Conclusion 129 wave attack, “while upslope they tend to have a growth strategy of being 'stiff and strong' to avoid the adverse effects of water movement” (Coops et al., 1994).
Abiotic habitat conditions for niche construction Due to the divergence in hydrodynamic conditions (Table II 2-1), data on the Elbe and Weser anabranches and their main channels were opposed in four river classes (Chapter II and Chapter IV). The abiotic conditions of niches reflect the species trait capability resulting in a clear plant zonation (Fig. II 3-1A), as observed at the Elbe main channel. Elevational niche overlap also occurs as demonstrated at the anabranches (Fig. II 3-1), most likely due to lower hydrodynamic forces.
‘Elevation relative to mean high water’ (MHW) is a significant predictor for the niche of S. tabernaemontani, S. maritimus, and P. australis in regularly flooded marshes (Fig. IV 3-1, A-5-1 Fig.). This is in line with other studies about species in regularly flooded marshes (Pennings and Callaway, 1992; Sanchez et al., 1996). The ‘mean bank slope’ is also an important factor which characterizes the niches of the three plants (Fig. IV 3-1). Furthermore, the ’length of bottom friction’ and the ‘distance from the thalweg’ have a clear influence on the niche distribution of two pioneers S tabernaemontani and S. maritimus (Fig. IV 3-1). These two factors show a high correlation amongst themselves (A-5-1 Table, Fig. IV 3-1) both quantifying the exposition of the tidal flat in front of the vegetation (horizontal gradient, Chapter I). Tested in detail, the ‘length of bottom friction’ better explains the niche boundary of S. tabernaemontani, whereas the ‘distance from the thalweg’ is a stronger predictor for S. maritimus (Fig. IV 3-1).
While the response curves of all species exhibit sharp thresholds for their elevational distribution, the other factors do not show such clear response curves for all species and river classes (Fig. IV 3-1). Only the most explicit patterns are defined by the species distribution model (Table IV 3-1). The model of S. tabernaemontani is the most complex one illustrating different patterns for all river classes (A-5-1 Fig.) probably due to the fact that it is the species most exposed to hydrodynamic stress. In particular at the Elbe main channel, S. tabernaemontani is dependent on broad shallow waters and tidal flats. They are characterized by long length of bottom friction in order to dissipate wave and current energies (Friedrichs and Madsen, 1992; Le Hir et al., 2000), not least because of the high frequent cargo transport (Table II 2-1).
Occurring at higher elevations than S. tabernaemontani (Fig. II 3-1, Fig. IV 3-1, A-5-1 Fig.), S. maritimus is less severely affected by hydrodynamics. However, it can only construct its niche if the mean bank slope is sufficiently gentle, especially in the main channels. This indicates that
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S. maritimus is more sensitive to ship-induced rather than to wind-induced waves. It is supported in particularly by the elevational response at the Elbe main channel (Fig. IV 3-1) which significantly demonstrates higher elevation (Fig. II 3-1A). The marsh edge of S. maritimus however does not differ between the Elbe main channel and the other river classes (Fig. II 3-1B). Thus, there are still sites at the Elbe main channel which are suitable for this species and where the wave actions of long wave periods have already been dissipated in front of its marsh edge (cf. Chapter V).
The distribution model of P. australis is the simplest one of the three species. It only comprises the elevational distribution which differs between the estuaries (Table IV 3-1, A-5-1 Fig.). Due to Phragmites’ higher sensitivity to stress (Kötter, 1961), it occupies the niche on higher elevations often protected waterwards by a belt of pioneer species (Fig. II 4-1B, Fig. II 3-1A). This is in contrast to findings studied in the Rhine-Meuse estuary, where P. australis is the only constituent at exposed sites (Coops and van der Velde, 1996b). However this is contradictory to measured bending forces applied to P. australis. They demonstrated that P. australis is eight times (mean Young Modulus: 4.9 GPa, stem diameter: 6-7 mm, Coops and Van der Velde (1996a)) stiffer than S. maritimus. As a result, P. australis would easily break if affected by higher hydrodynamic stress (cf. Ostendorp (1995)). He even measured 10-20 GPa for Phragmites stems originating from Lake Constance. Nevertheless, there are also sites along the Weser and Elbe estuary where P. australis forms the marsh edge (Fig. II 4-1B, Fig. II 3-1B) as Coops and van der Velde (1996b) observed. It seems to me that these sites are more sheltered sites where P. australis can outcompete the stress- resistant species by their competitive hierarchy (Chapter II). We found that its rhizomes are even able to establish on elevation lower than 1.5 m below MHW (Fig. II 3-1B).
1.3 Vegetation as shoreline protection What is the potential of vegetation-based shoreline protection in navigable estuaries? What conditions promote this ecosystem service and what are its limits?
The potential of vegetation-based shoreline protection and conditions promoting this function S. tabernaemontani and S. maritimus provide a clear example of ecosystem engineering by significantly attenuating waves (Chapter III). The effect of biomass seems to be more important for this engineering effect than species morphology. These results are supported by an experiment with salt marsh vegetation testing wave periods for regular and irregular waves (Lara et al., 2016). Nevertheless, our findings also confirm that more rigid stems have a greater ecosystem engineering effect than more flexible stems (Fig. III 3-1). It is, however, important to note that rigid plant stems
VI Conclusion 131 fail faster if hydrodynamic forces increase. This is due to the higher drag force they experience compared to flexible stems (compare paragraph 1.2).
Though our testing transect was quite short, we detected significant effects (Fig. III 3-1). Around 10% of the original wave height can be reduced even by sparse vegetation in contrast to bare tidal flats (Tab. Box 1-1). Our test stretch can be considered as the first part of the transition zone between tidal flats and the marsh. After a first increase in wave height through energy transformation, the reduction in wave height is high in the transition zone and decreases exponentially landwards (Möller and Spencer, 2002). Although the results of these studies seem to be in general comparable (Tab. Box 1-1), in detail they depend on several factors. Considering plant characteristics, vegetation height (Yang et al., 2012) can also drive the wave attenuation rates in addition to stem stiffness and biomass (Chapter III, Bouma et al. (2010), Bouma et al. (2005)). These plant characteristics might not only differ between the species but also between exposed and sheltered sites (Silinski, 2015). Also water and wave conditions influence the wave attenuation rates (Möller, 2006; Möller and Spencer, 2002).
Fig. VI 1-2 Shoreline profiles. The constant line represents shoreline profiles formed by waves with short wave periods caused by wind. The dashed line depicts waves with long wave periods greater than or equal to 10 s. This shoreline profile has a concave shape.
Furthermore, the shoreline morphology influences the wave damping in front of the vegetation, but sometimes also within the vegetation. For example, energy dissipation on vegetated shorelines forming a cliff can be twice as high as on gently sloped shorelines considering macro-tidal marshes (Möller and Spencer, 2002). During our experiments we observed that the outgoing gentle slope did not change much when running the 200 short-period waves, whereas the long-period waves shaped another slope (Fig. VI 1-2). The sediment which was eroded in the front was moved to the back where the elevation increased. This finding is in line with observation of the San Francisco Bay,
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where concave shorelines experienced increased wave energy. Also the results from the German Baltic Sea coasts fit into our observed patterns where the exposed shoreline with the higher wave incidence probably formed a small, 50 cm high cliff within the reed transition zone also providing significant wave attenuation (Möller et al., 2011). Thus, natural shorelines exhibit the potential to adapt to external changing hydrodynamic forces. Consequently, shorelines might ‘see to it’ that engineering species do not fail to attenuate waves.
Macrophytes have the capability to adapt to hydrodynamic stress (Puijalon et al., 2011). This is also observed for emergent macrophytes exposed to wave loads (Möller et al., 2011; Silinski et al., 2015b) and currents (Carus et al., 2016). P. australis (Möller et al., 2011) as well as S. maritimus (Carus et al., 2016; Silinski et al., 2015b) showed a higher stem diameter on exposed sites than on sheltered sites. This is partly in line with an experiment investigating the response of plant growth to water depths in outdoor basins (Coops et al., 1996b). In this case, Scirpus species such as S. maritimus increased their stem diameter, whereas gramineous species such as P. australis did not. The larger stem diameter will increase the cross-sectional area, which will reduce the sensitivity to breaking force (cf. Fig. III 3-3). Furthermore, drag force will rise and waves will be more dissipated (Möller et al., 2011).
The vegetation-based shoreline protection needs to be strengthened in order to counteract anthropogenic modification (Temmerman et al., 2013) and the increasing sea-level rise in the North Sea (Hein et al., 2014). Our application of distribution models demonstrates that larger surfaces of shallow waters will cause an increase in suitable niches (Fig. IV 3-3, Table IV 3-2), which increases the length of bottom friction. Thus, severe wave action can already start to be dissipated in front of the marsh edge. It enables emergent macrophytes to construct the niches for themselves and for other species (Chapter III). Therefore, the macrophytes protect the shorelines by promoting wave attenuation. We also verify that shorelines partly combined with engineering elements such as groynes can have positive effects on shoreline habitats (Fig. IV 3-3, Table IV 3-2). Sediment trapping will additionally be promoted by these restoration measures resulting in an increase of elevation (cf. Beauchard et al. (2011); Lamberth and Haycock (2001); Maris et al. (2007)).
Potential limits Nevertheless, it is crucial to quantify the limits of the vegetation-based shoreline protection in order to strengthen this service in an effective way and not to endanger citizens and cities.
VI Research recommendations 133
The ratio wave height to water depth governs the wave dissipation through marsh vegetation (Möller, 2006). Although wave heights are limited by water depth and although they are soon dissipated due to wave breaking (Chapter I), Möller (2006) observed that a ratio greater than 0.55 limits the capacity of attenuation. We have not yet performed experiments investigating wave attenuation under these wave conditions. However, when we studied the long-period waves (Chapter V), this ratio is exceeded (Table V 3-1). But comparing the drag forces between short and long waves, only at 35 cm water level we found an (insignificant) tendency towards drag reduction considering the long-period waves for both species (Fig. Box 3-1).
In all probability, the performance of wave attenuation is limited not only when the ratio is exceeded but also if more than 60% of the stem is flooded (Fig. Box 3-2). It complements these findings in the case of Spartina alterniflora, which dissipates wave energy more efficiently compared to the half as tall S. mariqueter (Yang et al., 2012). As long as the plant is emergent, a linear increase of turbulence with the rising water level is observed (Leonard and Croft, 2006), and drag increases. If a canopy becomes submerged, the turbulence decreases more towards lateral advection except near the top and the bottom (Leonard and Reed, 2002; Nepf and Vivoni, 2000), and therefore less energy is dissipated. The submersion probably starts, when the stem is more than 60% flooded depending on its rigidity (Fig. Box 3-2).
As we gained the insight that long-wave conditions affect Scirpus species significantly more in drag force than in short waves (Chapter V, Silinski et al. (2015a)), the wave period might also be a limiting factor of the attenuation capacity. Our observations are supported by studies on wave attenuation investigating different wave periods through seagrass (Bradley and Houser, 2009) as well as through Puccinellia maritima and Spartina anglica (Lara et al., 2016). Another salt marsh experiment investigated wave dissipation under storm surge conditions with long-period waves: Although the experiments clearly showed less energy dissipation during runs of long-period waves, the marsh substrate resisted against the hydrodynamic stress and no erosion occurred (Möller et al., 2014). Nevertheless, I have not yet found any investigations on Scirpus marshes.
2 Research recommendations This thesis aims to effectively improve the understanding of the interactions between estuarine hydrodynamic forces, traits of vegetation-forming species, their habitat conditions, and their capacity for ecosystem engineering. In the following paragraphs, I identified research needs and
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recommend further research. One crucial requirement for promoting vegetation-based shoreline protection is the need to further quantify the limits of (1) species niches, (2) wave attenuation, (3) plant contamination, (4) clonal age of the species.
(1) Species niches are directly affected by different wave conditions. One purpose of ecosystem- based shoreline protection is to increase the understanding of survival performance (Bouma et al., 2014). In this thesis, the indirect effects of different estuarine wave climates were verified on an ecosystem-scale (Chapter II and IV). However, it is crucial to deepen our knowledge considering the direct wave effects. We supported this aspect with full-scale wave flume experiments in chapter III and V. For improving the fundament of management plans, however, real world data are necessary. Data are important to calibrate and improve numerical models, but also distribution models. How drag force is experienced in the field and how it is reduced with distance from the marsh edge remains a research aspect. We detected a higher scouring depth around the stem if long-period waves occur (Fig. V 3-2). However, it is still unclear if scouring effects lead to uprooting of emergent macrophytes on tidal flats in general and focusing on different seasons. Furthermore, to my knowledge no analysis exists about plant effects differentiated by several types of ship and cargo vessels.
(2) Wave dissipation by plants occurs in interaction with sediment accretion and soil erosion/stability. Seasonal variances of wave attenuation (Coulombier et al., 2012; Möller and Spencer, 2002) and sediment accretion (e.g., Butzeck et al. (2014), Temmerman et al. (2003b), Yang (1998)) across marshes have already been detected, though finding a statistically significant seasonal trend remains a challenge (Tempest et al., 2015). It should be noted that long-term investigations in the field are rare (e.g., Tschirky et al. (2001)). Although wave experiments have been performed demonstrating a decline in wave dissipation with increasing wave periods (Anderson and Smith, 2014; Lara et al., 2016; Möller et al., 2014), field campaigns provide controversial results: Wave attenuation induced by the seagrass Posidonia oceanica confirms the experiment results (Infantes et al., 2012), whereas studies at the Lake Ontario shoreline (Tschirky et al., 2001) as well as at the estuarine coast of Essex in England (Möller and Spencer, 2002) found no inverse correlation between wave period and wave attenuation. It is crucial to couple future field campaigns on wave dissipation with the quantitative description of plant characteristics and biomechanical measurements to get a better understanding of the conditions under which long-wave periods can sufficiently reduce wave energy. Due to the necessarily extensive field work, only a few studies
VI Research recommendations 135 investigated the interaction of belowground biomass and soil stability (Bouma et al., 2014; Silliman et al., 2012), in particular root growth strategies (Bouma et al., 2014) and seasonal variations.
(3) Marshes are well-known for being a sink of contaminations, particularly under anoxic soil conditions. As described in the introduction, the ecosystem engineers studied also perform other regulating services such as water purification by accumulating metals (cf. Perez-Lopez et al. (2009), Sanchez et al. (1998), Shuping et al. (2011)). Those contamination limits existing for several substances such as heavy metals, persistent organic pollutants, or sulfides is rarely studied in European estuaries. A study of the Atlantic and Gulf coasts of the United States has shown if soluble sulfide exceeds 0.001 mole per litre, it has a toxic effect on Spartina alterniflora by suppressing its growth (Lamers et al., 2013; Mendelssohn and Morris, 2000), whereas lower concentrations of sulfide stimulates its growth (Morris et al., 1996). Such responses to contaminations can also diminish the performance of wave attenuation, and need to be studied. We further lack insight on the accumulation capacity of the studied species, if using them for the purification of dredging material.
(4) Emergent macrophytes such as the three studied species are often clonal plants which expand vegetatively. Assuming that old clones are stiffer than young clones, it is still unknown how the clonal age influences the effect of wave attenuation and at which age the clone reaches its optimal vigour.
Apart from limitations, the extent of the marsh is probably one of the most important constraints of vegetation based-shoreline protection related to intensive human use and the great amount of embanked areas (cf. Bouma et al. (2014)). Therefore, a spatial transfer from experimental results and local measurements of wave attenuation to a larger extent is necessary to calculate the overall engineering capacity. For this calculation, one should consider that ecosystem services do not always respond linearly to changes in habitat size (Barbier et al., 2008). The knowledge gained will serve to support coastal management plans in their efficiency and implementation.
A further need for research and application is to complement numerical models by the equations describing the hydraulic effect of marsh plants. Although some studies exist about these effects (e.g., Augustin et al. (2009), Fagherazzi et al. (2012), Nepf and Vivoni (2000), Nepf et al. (1997)) the most mathematical models applied do not consider these aspects due to the lack of data (Tempest et al., 2015). Software such as SWAN-VEG (e.g., Cuc et al. (2015)) and DELFT-3D (e.g., Fagherazzi et al. (2012)) have the potential to integrate the hydraulic effect of marsh plants, but there is still a long way to go in order to provide reliable predictions.
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This thesis provides a clear step forward in understanding the interactions between hydrodynamic forces, traits of vegetation-forming marsh species, species habitat conditions, and their capacity for ecosystem engineering concerning wave attenuation. As described above, further steps are crucial to quantify and assess the service performed by vegetation. For an adequate adaptation to sea-level rise, a combination of conventional and ecosystem engineering is recommended for providing an efficient implementation of estuarine and coastal defence (Temmerman et al., 2013).
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Appendix
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Appendix 1 Supporting Information for Chapter II A-1-1 Table Data overview
Variables Data origin Survey dates Data Provider
Spe cie s re sponse Elevation above MHW (m) DEMw í MHW† 2010 (Elbe), 2009 (Weser), BfG, WSÄ 2001 - 2010 (MHW) Species (presence data) DVM‡ 2010 (Elbe), 2009 (Weser) BfG, WSA Bhv River classes DVM‡ 2010 (Elbe), 2009 (Weser) BfG, WSA Bhv Lateral sections DVM‡ 2010 (Elbe), 2009 (Weser) BfG, WSA Bhv (marsh, marsh edge) Exposure gradient † Mean bank slope (°) DEMw, MLW 2010 (Elbe), 2009 (Weser), WSÄ 2001 - 2010 (MLW) Distance from the Navigation channel 2009; bathymetric data: BfG, WSV, WSÄ thalweg (m) axis, DBWK2, 2010 (Elbe), 2012 (Weser) bathymetric data Effective wind fetch (m) Distance from MHW† 2010 (Elbe), 2009 (Weser), BfG, WSÄ 1971 - 2000 (wind data) line, DEMw, gauge data, wind data§ Tidal-fluvial gradient Mean tidal range (m) Gauge data¶ 01.11.2000 - 31.10.2010 WSÄ Median discharge (m³/s) Gauge data: daily 01.11.2000 - 31.10.2010 WSA HB, WSA LB mean discharges † Mean channel width (m), DEMw, MHW 2010 (Elbe), 2009 (Weser) BfG, WSÄ reference: MHW level 2001 - 2010 (MHW) Mean channel depth of the Bathymetric data 2010 (Elbe), 2012 (Weser) WSÄ thalweg (m) Current velocity data (m/s) Modelled data using 15.7.2010 - 30.7.2010 BAW UnTRIM-SediMorph# (Elbe), 20.07.2006 - 03.08.2006 (Weser) Artificial bank Aerial orthophotos‡ 2010 (Elbe), 2008 (Weser) BfG, WSA Bhv reinforcement (%) Notes: BAW = Federal Waterways Engineering and Research Institute, BfG = Federal Institute of Hydrology, Bhv = Bremerhaven, DBWK2 = digital charts of federal waterways (scale 1:2000), DEMw = digital elevation model of the watercourse (1 m resolution) based on bathymetric data as well as on light detection and ranging data, DVM = digital vegetation map, HB = Hansestadt Bremen, MHW = mean high water, MLW = mean low water, WSV = Federal Waterways & Shipping Administration, WSA = Waterways and Shipping Board, WSÄ: Waterways and Shipping Boards Hamburg, Bremerhaven, Cuxhaven, and Bremen † Mean high water and mean low water were interpolated with gauge data using INFORM 3.0 (Fuchs et al., 2012). ‡The map (scale 1:4300) is based on aerial photos recorded by digital mapping camera (Petersen et al., 2010). §The wind data are frequencies of eight classes of wind directions in the German Bight calculated from ERA-40 data (reanalysis of 45 years) and from regional climate models (Bülow et al., 2013). ¶ Mean tidal ranges were calculated by using high water per tide and low water per tide (DIN 4049-3, 1994). # The current velocities are based on 2-D depth-average flood and ebb current velocities calculated with suspended sediment, salt, wind, and a freshwater discharge of 350 m³/s for the Elbe estuary and of 126-159 m³/s for the Weser estuary (Seiffert and Hesser, 2014; Stracke and Kösters, 2010).
Key ecosystem engineers in estuarine vegetation
Appendix 2 R-script for identifying minimum and maximum height of waves (Chapter III) ################################################### # 130528-WaveAnalyses.R # # author: Maria Carambia & Maike Heuner # date: 28.05.2013 # # purpose: # to identify minimum and maximum height of waves # data set # time series of water levels , resolution 4 Hz, # pressure sensor data, # experiment wave attenuation # ####################################################
##### # Function Wave analysis ##### waveanalysis <- function(input,output,sensor,factor,blocksize)
{
data <- read.table(input, header=T) n.subdf <- as.integer(dim(data)[1]/blocksize) sequence <- seq(1, dim(data)[1], by=blocksize) value.table <- matrix(nrow=n.subdf, ncol=6) # table matrix with minimum #and maximum value
# Identify maximum and minimum value per wave for (i in 1:n.subdf){ sequence.i <- sequence[i] sub.df.i <- data[ sequence.i:(sequence.i+blocksize-1),sensor] sub.df.i.2 <- data[ sequence.i:(sequence.i+blocksize-1),"t"] max.i <- max(sub.df.i) min.i <- min(sub.df.i) index.max <- which(sub.df.i==max.i)[1] index.min <- which(sub.df.i==min.i)[1] time.max <- sub.df.i.2[index.max] time.min <- sub.df.i.2[index.min] value.table[i,1] <- time.min value.table[i,2] <- factor * min.i #convert min of each wave from #voltage in water depth (e.g., cm), correction factor value.table[i,3] <- time.max value.table[i,4] <- factor * max.i #convert max of each wave from #voltage in water depth (e.g., cm), correction factor value.table[i,5] <- (time.max - time.min)/ 1000 value.table[i,6] <- (value.table[i,4]-value.table[i,2])/2
}
#export table with the columns timestep of the wave minimum (tMin), #water depth of the minimum (Min) #timestep of the wave maximum (tMax), water depth of the maximum (Max) #wave periode (t_P1), wave height(h_P1) colnames(value.table) <- c('tMin','Min','tMax', 'Max','t_P1', 'h_P1')
Key ecosystem engineers in estuarine vegetation
write.table(value.table, file = output, sep = ",",row.names=F) }
##### # Application ##### # Define variables f.path <- "…/mypath" #for numerous files with a clear pattern in one folder or #add the file to be analysed to the path # the file gas to columns time (t) and the pressure of the sensor (e.g., P1) data.length <- length(input) input <- list.files(path = f.path, pattern = NULL, all.files = F, full.names = T, recursive = F) sensor <- rep("P1",data.length) #correction factor of the pressor sensor (e.g., P1=18.307, P4=18.209) #for converting pressure (e.g.,voltage) into water depth (e.g.,cm) factor <- rep(18.307,data.length)# correction factor #paste extracted values into a table output <- paste(unlist(strsplit(input,".txt")),".csv",sep="") # The blocksize is the number of rows for one wave passing through #depending on wave periode, wave length, and the acquisition rate (Hz) # have to be determined by visual inspection of the data before #(e.g., wave period 2.1s -> 84 rows, 1.6s -> 64 rows) blocksize <- rep(84,data.length)
##### # Run 'waveanalysis' ##### for (aa in 1:data.length){
waveanalysis(input[aa],output[aa],sensor[aa],factor[aa],blocksize[aa])
} ### # quit R q("no")
Appendix 3 Work flow for calculating the scouring volume using ArcGIS Model Builder (Authors: Heuner, M. & Silinski, A., Chapter III and V)
Key ecosystem engineers in estuarine vegetation
Appendix 4 Supporting Information for Chapter III
A-4-1 Fig. Mean elevation relative to mean high water (MHW) including standard error, where Scirpus tabernaemontani (S. tab., n = 140) and Scirpus maritimus (S. mar., n = 140) are situated at the Elbe estuary. The point dataset was randomly sampled from a digital vegetation map (scale: 1: 5000) combined with officially certified digital elevation data, both made in the year 2010. Significance (Į) was tested by the Kruskal-Wallis rank sum test. Different letters show significant difference, significance level is Į < 0.01.
Key ecosystem engineers in estuarine vegetation
Appendix 5 Supporting Information for Chapter IV A-5-1 Table Spearman rank correlation of the predictor variables
length of mean distance predictor tidal bottom bank from variables elevation range friction slope thalweg river order elevation 1 tidal range -0.08 1 length of 0.24 -0.33 1 bottom friction mean 0.35 0.29 -0.49 1 bank slope distance 0.28 -0.40 0.68 -0.40 1 from thalweg river -0.07 0.87 -0.37 0.30 -0.42 1 order 0.05 -0.07 0.27 -0.17 0.61 0 1
A-5-1 Fig. Occurrence probability of S. tabernaemontani, S. maritimus, and P. australis depending on the predictors ‘elevation relative to mean high water (MHW)’, ‘length of bottom friction’, ‘mean bank slope’ and rivers.
Key ecosystem engineers in estuarine vegetation
A-5-2 Fig. Spline correlograms of model residuals from the final generalized linear models with 95% bootstrapped confidence interval for the studied macrophytes. The dotted line at the 50 m distance represents the minimum sampling distance.
Appendix 5 Supporting Information for Chapter IV A-5-1 Script ‘DEM to be modified’ (R script)
################################################### # # author: #[email protected] & [email protected] # date: 18.09.2015 # script: 1_DEM_modify.R # purpose: # profile modifications for Lühesand and Julssand # #################################################### ##### # set user dependent variables ##### # setwd path <- "…/mypath" setwd(path)
##### # date for the output #date <- strftime(Sys.time(), format="%y%m%d") date <- "150918"
##### # set the number of runs n_run <- 50
#####################################################
##### # get command line args for a parallelized approach using a shell script #args <- commandArgs(trailingOnly = TRUE) #a_site <- args[1] #n_run <- as.numeric(args[2])
##### # load packages require("msm") require("sp") require("rgdal")
##### # additional functions ## # Standard Error se <- function(x){ sqrt(var(x)/length(x)) }
## # Confidence Interval ci <- function(x, conf.interval=.95){ ciMult <- qt(conf.interval/2 + 0.5, length(x) - 1) se(x) * ciMult }
##### # loop over all sites
Key ecosystem engineers in estuarine vegetation for(a_site in c("Luehesand", "Julssand", "Julssand_Groyne")){
print(a_site)
##### # check the existence of the input data if(!file.exists(paste0("data/", a_site, "/", a_site, ".txt"))){ print("The input data do not exist.") print(paste0("The site ", a_site, " is skipped.")) print("") next }
##### # import the MTnw and MThw data mXw.df <- read.table("data/mXw.txt", header=TRUE)
# set the mXw values for the site to be modified mlw_mod <- mXw.df$lw[which(mXw.df$site == a_site)] mhw_mod <- mXw.df$hw[which(mXw.df$site == a_site)] range_mod <- mhw_mod + abs(mlw_mod)
##### # import the data data.df <- read.table(paste0("data/", a_site, "/", a_site, ".txt"), header=TRUE) sites <- unique(data.df$site)
##### # subset into reference and modified site ### # modify.df if(a_site == "Julssand"){ # exclude profiles with groynes modify.df <-subset(data.df, site == a_site & !(et_id %in% c(17:21, 81:84, 130:133, 162:167, 218:221, 267:270, 307:310, 339:344, 365:373, 395:401, 422:426, 440:445, 465:470, 478:485, 500:503, 523:524))) } else { modify.df <-subset(data.df, site == a_site) }
# add column DEM_norm, normalized by MTnw & MThw modify.df$DEM_norm <- (modify.df$DEM - mlw_mod) / range_mod
# add empty result columns for the 'n_run' runs for(a_run in 1:n_run){ name <- paste0(sprintf("%04d", a_run), "_") modify.df[, paste0(name, "DEM_norm_new")] <- rep(NA, nrow(modify.df)) modify.df[, paste0(name, "DEM_norm_diff")] <- rep(NA, nrow(modify.df)) modify.df[, paste0(name, "DEM_new")] <- rep(NA, nrow(modify.df)) modify.df[, paste0(name, "DEM_diff")] <- rep(NA, nrow(modify.df)) }
### # reference.df reference.df <- subset(data.df, site != a_site) reference.df$DEM_norm <- rep(0.0, nrow(reference.df))
for(a_reference_site in sites[-which(sites == a_site)]){
Appendix 5 Supporting Information for Chapter IV
# set the xLW values for the site to be modified mlw_ref <- mXw.df$lw[which(mXw.df$site == a_reference_site)] mhw_ref <- mXw.df$hw[which(mXw.df$site == a_reference_site)] range_ref <- mhw_ref + abs(mlw_ref)
# add column DEM_norm, normalized by site-specific MTnw & MThw reference.df$DEM_norm[which(reference.df$site == a_reference_site)] <- (reference.df$DEM[which(reference.df$site == a_reference_site)] - mlw_ref) / range_ref
}
##### # Create additional normalized result objects for plotting q_100_norm <- rep(NA, length(unique(reference.df$et_station))) q_050_norm <- rep(NA, length(unique(reference.df$et_station))) q_000_norm <- rep(NA, length(unique(reference.df$et_station))) i <- 1
for(a_level in unique(reference.df$et_station)){ q_100_norm[i] <- quantile(reference.df$DEM_norm[as.factor(reference.df$et_station) == a_level], probs=c(1)) q_050_norm[i] <- quantile(reference.df$DEM_norm[as.factor(reference.df$et_station) == a_level], probs=c(0.5)) q_000_norm[i] <- quantile(reference.df$DEM_norm[as.factor(reference.df$et_station) == a_level], probs=c(0)) i <- i + 1 } rm(i, a_level)
##### # Create additional result objects for plotting q_100 <- rep(NA, length(unique(reference.df$et_station))) q_050 <- rep(NA, length(unique(reference.df$et_station))) q_000 <- rep(NA, length(unique(reference.df$et_station))) i <- 1
for(a_level in unique(reference.df$et_station)){ q_100[i] <- quantile(reference.df$DEM[as.factor(reference.df$et_station) == a_level], probs=c(1)) q_050[i] <- quantile(reference.df$DEM[as.factor(reference.df$et_station) == a_level], probs=c(0.5)) q_000[i] <- quantile(reference.df$DEM[as.factor(reference.df$et_station) == a_level], probs=c(0)) i <- i + 1 } rm(i, a_level)
########### # LOOP THROUGH 1:'n_run' ########### for(a_run in 1:n_run){
##### # colname prefix
Key ecosystem engineers in estuarine vegetation
colname_prefix <- paste0(sprintf("%04d", a_run), "_")
##### # create an empty vector for the profiles successfully bound to the original profile y_bind_profile <- vector(mode="numeric", length=0)
########## # loop through et_id for(a_profile in unique(modify.df$et_id)){
row_id <- which(modify.df$et_id == a_profile)
# reset y_previous to NA for a new profile y_previous <- NA
# reset y_bind to NA for a new profile y_bind <- NA
### # Loop through the rows belonging to a specific et_id for(a_row in row_id){
# get x and y x <- modify.df$et_station[a_row] y <- modify.df$DEM_norm[a_row]
# print row number if(is.na(y_previous)){ print(paste0("Run: ", a_run ,", Profile: ", a_profile, ", x: ", x, ", y: ", y, ", y_previous: NA ")) } else { print(paste0("Run: ", a_run ,", Profile: ", a_profile, ", x: ", x, ", y: ", y, ", y_previous: ", y_previous)) }
# get the row ids and DEM values of the reference sites y_ref <- reference.df$DEM_norm[which(reference.df$et_station == x)]
# compute the quantiles of the reference sites range_ref <- quantile(y_ref, probs=c(0.5, 0.75, 1))
### first condition ### # first point of a profile remains unchanged, y1 = y, x = 0 if(x == 0){
#### # fill result columns # DGM_new modify.df[a_row, paste0(colname_prefix, "DEM_norm_new")] <- y # DGM_diff modify.df[a_row, paste0(colname_prefix, "DEM_norm_diff")] <- 0
#### # define y_previous y_previous <- y
Appendix 5 Supporting Information for Chapter IV
} else {
### second condition ### # y equal or smaller than max of y_ref, then no changes and exit with next ... if(y <= max(y_ref) | !is.na(y_bind)){
#### # fill result columns # DGM_new modify.df[a_row, paste0(colname_prefix, "DEM_norm_new")] <- y # DGM_diff modify.df[a_row, paste0(colname_prefix, "DEM_norm_diff")] <- 0
#### # define y_previous y_previous <- y
#### # set switch for y_bind if(y > 1){
# set y_bind y_bind <- 0
# write profile id into y_bind_profile, if it is not in there already if(!(a_profile %in% y_bind_profile)){ y_bind_profile <- append(y_bind_profile, a_profile) }
}
#### # jump into next loop next }
### third condition ### # modify lower_limit to cause a permanent increase of y_new if(range_ref["50%"] < y_previous){ lower_limit <- y_previous ### 3b condition### } else { lower_limit <- range_ref["50%"] }
### fourth condition ### # replace y_new with range_ref["100%"] to avoid special cases where rtnorm produces NA's if(lower_limit > range_ref["100%"]){
#### # fill result columns # DGM_new
Key ecosystem engineers in estuarine vegetation
modify.df[a_row, paste0(colname_prefix, "DEM_norm_new")] <- range_ref["100%"] # DGM_diff modify.df[a_row, paste0(colname_prefix, "DEM_norm_diff")] <- y - range_ref["100%"]
#### # define y_previous y_previous <- range_ref["100%"]
#### # jump into next loop next }
#### # Sample y_new based on reference data distribution #### y_new <- rtnorm(1, mean=range_ref["75%"], sd=sd(y_ref), lower=lower_limit, upper=range_ref["100%"])
### fifth condition ### # if y_new has increased to values larger 1 # if difference between y and y_new is less than 0.2 m # => stick to the old elevation to minimize digging efforts if(y_previous > 1 & y - y_new < 0.2 / range_mod){
#### # fill result columns # DGM_new modify.df[a_row, paste0(colname_prefix, "DEM_norm_new")] <- y # DGM_diff modify.df[a_row, paste0(colname_prefix, "DEM_norm_diff")] <- 0
#### # define y_previous y_previous <- y
#### # set y_bind y_bind <- 0
# write profile id into y_bind_profile, if it is not in there already if(!(a_profile %in% y_bind_profile)){ y_bind_profile <- append(y_bind_profile, a_profile) }
} else {
#### # fill result columns # DGM_new modify.df[a_row, paste0(colname_prefix, "DEM_norm_new")] <- y_new # DGM_diff
Appendix 5 Supporting Information for Chapter IV modify.df[a_row, paste0(colname_prefix, "DEM_norm_diff")] <- y - y_new
#### # define y_previous y_previous <- y_new
}
}
}
}
##### # clean up internal variables rm(a_profile, a_row, lower_limit, range_ref, row_id, x, y, y_bind, y_new, y_previous, y_ref)
########### # ADDITIONAL MODIFICATIONS FOR PROFILES THAT WERE NOT BOUND TO Y ##### # no_bind_profile no_bind_profile <- unique(modify.df$et_id)[which(!(unique(modify.df$et_id) %in% y_bind_profile))]
### # loop through no_bind_profile for(a_profile in no_bind_profile){
# select the appropriate profiles row_id <- which(modify.df$et_id == a_profile & modify.df[, paste0(colname_prefix, "DEM_norm_new")] > 1)
# profiles, which stay below 1. if(length(row_id) == 0){ print(paste0("Profile: ", a_profile, " stays below 1")) next }
# get the x and y values to modify from_x <- modify.df$et_station[row_id[1]] to_x <- modify.df$et_station[row_id[length(row_id)]] from_y <- modify.df[row_id[1], paste0(colname_prefix, "DEM_norm_new")] to_y <- modify.df[row_id[length(row_id)], paste0(colname_prefix, "DEM_norm_new")]
# print info print(paste0("Profile: ", a_profile, ", from: ", from_x, " to: ", to_x))
# x diff_x <- to_x - from_x
# y diff_y <- to_y - from_y step_y <- diff_y / diff_x
Key ecosystem engineers in estuarine vegetation
# calculate stepwise linear modifications for(a_step in 1:diff_x){
# x x <- from_x + a_step
# y_new y_new <- from_y + (step_y * a_step)
# print print(paste0(" x: ", x, ", y: ", y_new))
# insert y_new into modify.df id_row <- row_id[a_step + 1] modify.df[id_row, paste0(colname_prefix, "DEM_norm_new")] <- y_new modify.df[id_row, paste0(colname_prefix, "DEM_norm_diff")] <- modify.df$DEM_norm[id_row] - y_new
}
}
##### # clean up internal variables rm(a_profile, a_step, diff_x, diff_y, from_x, from_y, id_row, row_id, step_y, to_x, to_y, x, y_new)
##### # backtransform normalized heights modify.df[, paste0(colname_prefix, "DEM_new")] <- modify.df[, paste0(colname_prefix, "DEM_norm_new")] * (mhw_mod - mlw_mod) + mlw_mod modify.df[, paste0(colname_prefix, "DEM_diff")] <- modify.df$DEM - modify.df[, paste0(colname_prefix, "DEM_new")]
}
########### # STATISTICS ##### # row-wise statistics of the 'n_run' runs for(a_row in 1:nrow(modify.df)){ values <- as.numeric(modify.df[a_row, paste0(sprintf("%04d", 1:n_run), "_DEM_new")]) modify.df$r_min[a_row] <- min(values) modify.df$r_max[a_row] <- max(values) modify.df$r_mean[a_row] <- mean(values) modify.df$r_median[a_row] <- median(values) modify.df$r_sd[a_row] <- sd(values) modify.df$r_se[a_row] <- se(values) modify.df$r_ci[a_row] <- ci(values)
values <- as.numeric(modify.df[a_row, paste0(sprintf("%04d", 1:n_run), "_DEM_norm_new")]) modify.df$r_norm_min[a_row] <- min(values) modify.df$r_norm_max[a_row] <- max(values) modify.df$r_norm_mean[a_row] <- mean(values) modify.df$r_norm_median[a_row] <- median(values)
Appendix 5 Supporting Information for Chapter IV modify.df$r_norm_sd[a_row] <- sd(values) modify.df$r_norm_se[a_row] <- se(values) modify.df$r_norm_ci[a_row] <- ci(values) } rm(a_row)
##### # statistics over the xxx profiles nrow <- length(unique(modify.df$et_station)) statistics.df <- data.frame(et_station=unique(modify.df$et_station), min=rep(NA, nrow), max=rep(NA, nrow), mean=rep(NA, nrow), median=rep(NA, nrow), sd=rep(NA, nrow), se=rep(NA, nrow), ci=rep(NA, nrow), min_norm=rep(NA, nrow), max_norm=rep(NA, nrow), mean_norm=rep(NA, nrow), median_norm=rep(NA, nrow), sd_norm=rep(NA, nrow), se_norm=rep(NA, nrow), ci_norm=rep(NA, nrow))
for(a_level in unique(modify.df$et_station)){
id_row <- which(modify.df$et_station == a_level)
# DEM values <- as.numeric(unlist(modify.df[id_row, paste0(sprintf("%04d", 1:n_run), "_DEM_new")]))
statistics.df$min[which(statistics.df$et_station == a_level)] <- min(values) statistics.df$max[which(statistics.df$et_station == a_level)] <- max(values) statistics.df$mean[which(statistics.df$et_station == a_level)] <- mean(values) statistics.df$median[which(statistics.df$et_station == a_level)] <- median(values) statistics.df$sd[which(statistics.df$et_station == a_level)] <- sd(values) statistics.df$se[which(statistics.df$et_station == a_level)] <- se(values) statistics.df$ci[which(statistics.df$et_station == a_level)] <- ci(values)
# DEM_norm values <- as.numeric(unlist(modify.df[id_row, paste0(sprintf("%04d", 1:n_run), "_DEM_norm_new")]))
statistics.df$min_norm[which(statistics.df$et_station == a_level)] <- min(values) statistics.df$max_norm[which(statistics.df$et_station == a_level)] <- max(values) statistics.df$mean_norm[which(statistics.df$et_station == a_level)] <- mean(values) statistics.df$median_norm[which(statistics.df$et_station == a_level)] <- median(values) statistics.df$sd_norm[which(statistics.df$et_station == a_level)] <- sd(values) statistics.df$se_norm[which(statistics.df$et_station == a_level)] <- se(values) statistics.df$ci_norm[which(statistics.df$et_station == a_level)] <- ci(values)
} rm(nrow, a_level, id_row)
Key ecosystem engineers in estuarine vegetation
########## # EXPORT FINAL DATA TO SHAPE FILES ##### # save modify.df save(reference.df, modify.df, statistics.df, file=paste0("results/", a_site, "/", date, "-1-modify.df_final_", n_run, "_runs.Rdata"))
### # export modify.df to a *.csv-file write.table(modify.df, paste0("results/", a_site, "/", date, "-2- modified_profiles_", n_run, "_runs.txt"), sep=",")
### # export the separate runs to shape files for(a_run in 1:n_run){
# name name_run <- sprintf("%04d", a_run)
### # prepare export.df export.df <- modify.df[,c("id", "et_id", "site", "X", "Y", "et_station_old", "DEM", "DEM_norm", "et_station", paste0(name_run, "_DEM_norm_new"), paste0(name_run, "_DEM_norm_diff"), paste0(name_run, "_DEM_new"), paste0(name_run, "_DEM_diff"))]
# rename columns colnames(export.df) <- c("id", "et_id", "site", "X", "Y", "et_sta_old", "DEM_orig", "DEM_no_old", "et_station", "DEM_no_new", "DEM_no_dif", "DEM_new", "DEM_diff")
export.df$DEM <- export.df[, "DEM_new"] export.df <- droplevels(export.df)
### # adapt lateral profiles ids <- unique(export.df$et_id)
# keep first and last profile unchanged export.df$DEM[which(export.df$et_id == ids[1])] <- export.df$DEM_orig[which(export.df$et_id == ids[1])] export.df$DEM[which(export.df$et_id == ids[length(ids)])] <- export.df$DEM_orig[which(export.df$et_id == ids[length(ids)])]
# modify second and last-1 profile export.df$DEM[which(export.df$et_id == ids[2])] <- export.df$DEM_orig[which(export.df$et_id == ids[2])] - 0.25 * export.df$DEM_diff[which(export.df$et_id == ids[2])] export.df$DEM[which(export.df$et_id == ids[length(ids) - 1])] <- export.df$DEM_orig[which(export.df$et_id == ids[length(ids) - 1])] - 0.5 * export.df$DEM_diff[which(export.df$et_id == ids[length(ids) - 1])]
# modify third and last-2 profile export.df$DEM[which(export.df$et_id == ids[3])] <- export.df$DEM_orig[which(export.df$et_id == ids[3])] - 0.5 * export.df$DEM_diff[which(export.df$et_id == ids[3])]
Appendix 5 Supporting Information for Chapter IV export.df$DEM[which(export.df$et_id == ids[length(ids) - 2])] <- export.df$DEM_orig[which(export.df$et_id == ids[length(ids) - 2])] - 2/3 * export.df$DEM_diff[which(export.df$et_id == ids[length(ids) - 2])]
# modify fourth and last-3 profile export.df$DEM[which(export.df$et_id == ids[4])] <- export.df$DEM_orig[which(export.df$et_id == ids[4])] - 0.75 * export.df$DEM_diff[which(export.df$et_id == ids[4])] export.df$DEM[which(export.df$et_id == ids[length(ids) - 3])] <- export.df$DEM_orig[which(export.df$et_id == ids[length(ids) - 3])] - 2/3 * export.df$DEM_diff[which(export.df$et_id == ids[length(ids) - 3])]
# create spatialpointsdataframe export.spdf <- SpatialPointsDataFrame(data.frame(X=export.df$X, Y=export.df$Y), data=export.df, proj4string=CRS("+proj=tmerc +lat_0=0 +lon_0=9 +k=1 +x_0=3500000 +y_0=0 +ellps=bessel +datum=potsdam +units=m +no_defs"))
writeOGR(export.spdf, dsn=paste0(path, "/results/", a_site, "/Shapes"), layer=paste0("profiles_run_", name_run), driver="ESRI Shapefile", overwrite_layer=TRUE)
}
}
### # quit R q("no")
A-5-2 Script ‘Creating environmental variables’ (Python script)
""" author: [email protected] & [email protected] date: 18.09.2015 script: 2_create_env_variables.py purpose: convert the modified profiles from point shapes via TIN's to DEM's compute the raster datasets of environmental variables based on the modified DEM's
""" # import python modules import sys import arcpy import os from datetime import datetime as dt
# Check out the ArcGIS 3D Analyst extension license try: if arcpy.CheckOutExtension("3D") == r"CheckedOut": print "\nA '3D Analyst'-license has been obtained." else: print "\nThere is no '3D Analyst'-license available." sys.exit(1)
Key ecosystem engineers in estuarine vegetation except arcpy.ExecuteError: print arcpy.GetMessages(2) sys.exit(1) print ""
# Check out the ArcGIS Spatial Analyst extension license try: if arcpy.CheckOutExtension("Spatial") == r"CheckedOut": print "\nA 'Spatial Analyst'-license has been obtained." else: print "\nThere is no 'Spatial Analyst'-license available." sys.exit(1) except arcpy.ExecuteError: print arcpy.GetMessages(2) sys.exit(1) print ""
##### # convert_timedelta ### # purpose: # convert a time difference into a readable format ### # obtained from: # http://stackoverflow.com/questions/14190045/how-to-convert-datetime-timedelta- to-minutes-hours-in-python def convert_timedelta(duration): days, seconds, microseconds = duration.days, duration.seconds, duration.microseconds hours = days * 24 + seconds // 3600 minutes = (seconds % 3600) // 60 seconds = (seconds % 60) microseconds = int(round(microseconds/1000, 0))
if len(str(hours)) < 2: return_hours = str(hours).zfill(2) else: return_hours = str(hours)
return return_hours + ":" + str(minutes).zfill(2) + ":" + str(seconds).zfill(2) + "." + str(microseconds)
############################################################################### ############################################################################### # variables ## # location of the data # set statically #path = r"\\mt2.fs.bafg.de\Fachdaten\U\U2\EL_575_710_Elbe"
# set dynamically pathname = os.path.dirname(sys.argv[0]) abs_pathname = os.path.abspath(pathname) #path = os.path.split(os.path.split(abs_pathname)[0])[0] path = os.path.split(abs_pathname)[0] print "The base directory is:\n " + path print ""
Appendix 5 Supporting Information for Chapter IV ############################################################################### ###############################################################################
## # coordinate reference system crs = arcpy.SpatialReference("DHDN 3-Degree Gauss Zone 3")
########## # loop over all sites for a_site in ["Luehesand", "Julssand", "Julssand_Groyne"]:
print a_site
########## # location of the gdb gdb = os.path.join(path, "data", a_site, a_site + ".gdb")
########## # check the existence of the gdb if os.path.exists(gdb) is not True and os.path.isdir(gdb) is not True: print "The input data do not exist." print "The site " + a_site + " is skipped." continue
########## # env settings arcpy.env.workspace = gdb arcpy.env.overwriteOutput = True arcpy.env.outputCoordinateSystem = crs arcpy.env.extent = "ori_DEM" arcpy.env.snapRaster = "ori_DEM"
########## # export a reclass file for the reclassification of EMLW and EMHW reclass_file_emlw = os.path.join(path, "data", a_site, "reclassify_EMLW.txt") if not os.path.exists(reclass_file_emlw) and not os.path.isfile(reclass_file_emlw): with open(reclass_file_emlw, "w") as txtfile: txtfile.write("-38 -2 : 4\n") txtfile.write("-2 0 : 3\n") txtfile.write("0 38 : 2\n")
reclass_file_emhw = os.path.join(path, "data", a_site, "reclassify_EMHW.txt") if not os.path.exists(reclass_file_emhw) and not os.path.isfile(reclass_file_emhw): with open(reclass_file_emhw, "w") as txtfile: txtfile.write("-38 0 : 2\n") txtfile.write("0 38 : 1\n")
########## # create raster mask if arcpy.Exists("Area_raster") is not True: arcpy.PolygonToRaster_conversion("Area", "MASK", "Area_raster", "CELL_CENTER", "#", 1)
########## # resample MHW with a resolution of 10 m to a finer resolution of 1m
Key ecosystem engineers in estuarine vegetation
if arcpy.Exists("ori_MHW") is not True: arcpy.Resample_management("MHW_res10", "ori_MHW", "1", "NEAREST") if arcpy.Exists("ori_EMHW") is not True: raster = arcpy.sa.Raster("ori_DEM") - arcpy.sa.Raster("ori_MHW") raster.save("ori_EMHW") del raster
########## # resample MLW with a resolution of 10 m to a finer resolution of 1m if arcpy.Exists("ori_MLW") is not True: arcpy.Resample_management("MLW_res10", "ori_MLW", "1", "NEAREST") if arcpy.Exists("ori_EMLW") is not True: raster = arcpy.sa.Raster("ori_DEM") - arcpy.sa.Raster("ori_MLW") raster.save("ori_EMLW") del raster
########## # produce a raster of tidal zones if arcpy.Exists("ori_TZ") is not True: # reclassify the EMLW reclass_raster_emlw = arcpy.sa.ReclassByASCIIFile(arcpy.sa.Raster("ori_EMLW"), reclass_file_emlw) # reclassify the EMHW reclass_raster_emhw = arcpy.sa.ReclassByASCIIFile(arcpy.sa.Raster("ori_EMHW"), reclass_file_emhw) tidal_zones = arcpy.sa.Con(reclass_raster_emhw == 1, reclass_raster_emhw, reclass_raster_emlw) tidal_zones.save("ori_TZ") del reclass_raster_emlw, reclass_raster_emhw, tidal_zones
########## # loop over all runs for a_run in range(50):
b_run = "run_" + str(a_run + 1).zfill(4) print a_site + ": " + str(b_run)
#b_run = "run_0003"
if arcpy.Exists(b_run + "_TZ"): print " already done!" print "" continue
# calculate progress loop_start = dt.now()
########## # DEM (Digital Elevation Model) print " DEM (Digital Elevation Model)"
##### # convert modified profiles in a point-shape to TIN and then to raster ## # modified profiles in a point shape point_shape = os.path.join(path, "results", a_site, "Shapes", "profiles_" + b_run + ".shp")
# create TIN
Appendix 5 Supporting Information for Chapter IV arcpy.CreateTin_3d(os.path.join(path, "results", a_site, "TINs" + os.sep + b_run + "_TIN"), crs, [[point_shape, "DEM", "Mass_Points"]], "DELAUNAY")
# TIN to raster arcpy.TinRaster_3d(os.path.join(path, "results", a_site, "TINs" + os.sep + b_run + "_TIN"), b_run + "_DEM_temp1", "FLOAT", "LINEAR", "CELLSIZE 1", "1")
# resample to the true excavation area mask = arcpy.sa.Raster("Area_raster") dem = arcpy.sa.Raster(b_run + "_DEM_temp1")
dem_temp = arcpy.sa.Con(arcpy.sa.IsNull(mask), mask, dem) dem_temp.save(b_run + "_DEM_temp2") del dem_temp
## # create new DEM by resampling of modified areas # reference raster datasets dem_origin = arcpy.sa.Raster("ori_DEM")
# resample dem_new = arcpy.sa.Con(arcpy.sa.IsNull(b_run + "_DEM_temp2"), dem_origin, b_run + "_DEM_temp2") dem_new.save(b_run + "_DEM") del dem_new
# delete arcpy.Delete_management(b_run + "_DEM_temp1") arcpy.Delete_management(b_run + "_DEM_temp2")
# calculate statistics for the DEM arcpy.CalculateStatistics_management(b_run + "_DEM")
########## # elevation relative to MLW (Mean Low Water) print " EMLW (Elevation to Mean Low Water)"
##### # reference raster datasets dem_new = arcpy.sa.Raster(b_run + "_DEM") mlw = arcpy.sa.Raster("ori_MLW")
# calculate elevation realtive to MLW and round it to 2 decimals delta_mlw = arcpy.sa.Con(dem_new - mlw < 0, arcpy.sa.Float(arcpy.sa.Int((dem_new - mlw) * 100 - 0.5)) / 100, arcpy.sa.Float(arcpy.sa.Int((dem_new - mlw) * 100 + 0.5)) / 100) delta_mlw.save(b_run + "_EMLW")
del dem_new, mlw, delta_mlw
# calculate statistics for the EMLW arcpy.CalculateStatistics_management(b_run + "_EMLW")
########## # elevation relative to MHW (Mean High Water) print " EMHW (Elevation to Mean High Water)"
##### # reference raster datasets
Key ecosystem engineers in estuarine vegetation
dem_new = arcpy.sa.Raster(b_run + "_DEM") mhw = arcpy.sa.Raster("ori_MHW")
# calculate elevation realtive to MHW and round it to 2 decimals delta_mhw = arcpy.sa.Con(dem_new - mhw < 0, arcpy.sa.Float(arcpy.sa.Int((dem_new - mhw) * 100 - 0.5)) / 100, arcpy.sa.Float(arcpy.sa.Int((dem_new - mhw) * 100 + 0.5)) / 100) delta_mhw.save(b_run + "_EMHW")
del dem_new, mhw, delta_mhw
# calculate statistics for the EMHW arcpy.CalculateStatistics_management(b_run + "_EMHW")
########## # calculate MBS (Mean Bank Slope) and SL (Surface Length) print " MBS (Mean Bank Slope) and SL (Surface Length)"
##### # produce DEM-dependend version of *_line_MSW (*_line_MeanShallowWater) ### print " line_MSW"
# reclassify the EMLW reclass_raster = arcpy.sa.ReclassByASCIIFile(arcpy.sa.Raster(b_run + "_EMLW"), reclass_file_emlw) reclass_raster.save(b_run + "_EMLW_reclass")
del reclass_raster
# raster to polygon arcpy.RasterToPolygon_conversion(b_run + "_EMLW_reclass", "in_memory\\temp_reclass_polygon", "SIMPLIFY", "VALUE")
# add field 'Area' arcpy.AddField_management("in_memory\\temp_reclass_polygon", "Area", "DOUBLE", "", "", "", "", "NULLABLE", "NON_REQUIRED", "")
# calculate field 'Area' arcpy.CalculateField_management("in_memory\\temp_reclass_polygon", "Area", "!shape.area!", "PYTHON", "")
# repair geometry arcpy.RepairGeometry_management("in_memory\\temp_reclass_polygon", "DELETE_NULL")
# make feature layer arcpy.MakeFeatureLayer_management("in_memory\\temp_reclass_polygon", "temp_reclass_polygon_layer")
# select polygons with area < 5000 arcpy.SelectLayerByAttribute_management("temp_reclass_polygon_layer", "NEW_SELECTION", "Area < 5000")
# eliminate arcpy.Eliminate_management("temp_reclass_polygon_layer", "in_memory\\temp_reclass_polygon_eliminated", "AREA", "", "")
# make feature layer
Appendix 5 Supporting Information for Chapter IV arcpy.MakeFeatureLayer_management("in_memory\\temp_reclass_polygon_eliminated", "temp_reclass_polygon_eliminated_layer")
# select polygons with area < 5000 arcpy.SelectLayerByAttribute_management("temp_reclass_polygon_eliminated_layer", "NEW_SELECTION", "Area < 5000")
# eliminate 2 arcpy.Eliminate_management("temp_reclass_polygon_eliminated_layer", "in_memory\\temp_reclass_polygon_eliminated2", "AREA", "", "")
# convert the polygons to polyline arcpy.FeatureToLine_management("in_memory\\temp_reclass_polygon_eliminated2", "in_memory\\temp_reclass_polyline", "", "ATTRIBUTES")
# make feature layer arcpy.MakeFeatureLayer_management("in_memory\\temp_reclass_polyline", "temp_reclass_polyline_layer")
# select lines with gridcode = 1 arcpy.SelectLayerByAttribute_management("temp_reclass_polyline_layer", "NEW_SELECTION", "GRIDCODE = 4")
# dissolve arcpy.Dissolve_management("temp_reclass_polyline_layer", "in_memory\\temp_reclass_polyline_dissolved", "", "", "MULTI_PART", "DISSOLVE_LINES")
# simplify line arcpy.SimplifyLine_cartography("in_memory\\temp_reclass_polyline_dissolved", "temp_reclass_polyline_simplified", "BEND_SIMPLIFY", "4 Meters", "FLAG_ERRORS", "KEEP_COLLAPSED_POINTS", "CHECK")
# smooth line arcpy.SmoothLine_cartography("temp_reclass_polyline_simplified", "in_memory\\temp_reclass_polyline_smooth", "PAEK", "4 Meters", "FIXED_CLOSED_ENDPOINT", "NO_CHECK")
# clip the line to the relavant area arcpy.Clip_analysis("in_memory\\temp_reclass_polyline_smooth", "ClipLineMask", b_run + "_line_MSW")
# delete "temp_reclass_polyline_simplified" if arcpy.Exists("temp_reclass_polyline_simplified"): arcpy.Delete_management("temp_reclass_polyline_simplified") if arcpy.Exists("temp_reclass_polyline_simplified_Pnt"): arcpy.Delete_management("temp_reclass_polyline_simplified_Pnt")
##### # produce the rasters MBS (Mean Bank Slope) and SL (Surface Length) ### print " MBS & SL"
# convert DEM-raster to a point feature class
Key ecosystem engineers in estuarine vegetation
arcpy.RasterToPoint_conversion(b_run + "_DEM", "in_memory" + os.sep + b_run + "_points", "Value")
# rename 'gridcode' column to 'DEM' arcpy.AlterField_management("in_memory" + os.sep + b_run + "_points", "grid_code", "DEM", "DEM")
# add 'id', 'X' and 'Y' columns arcpy.AddField_management("in_memory" + os.sep + b_run + "_points", "id", "LONG", "", "", "", "id", "", "", "") arcpy.AddField_management("in_memory" + os.sep + b_run + "_points", "X", "DOUBLE", "", "", "", "X", "", "", "") arcpy.AddField_management("in_memory" + os.sep + b_run + "_points", "Y", "DOUBLE", "", "", "", "Y", "", "", "")
# fill 'id', 'X' and 'Y' columns arcpy.CalculateField_management("in_memory" + os.sep + b_run + "_points", "id", "!OBJECTID!", "PYTHON_9.3", "") arcpy.CalculateField_management("in_memory" + os.sep + b_run + "_points", "X", "!SHAPE.CENTROID.X!", "PYTHON_9.3", "") arcpy.CalculateField_management("in_memory" + os.sep + b_run + "_points", "Y", "!SHAPE.CENTROID.Y!", "PYTHON_9.3", "")
# get the nearest point on the *_line_MSW (*_line_Mean_Shallow_Water) arcpy.Near_analysis("in_memory" + os.sep + b_run + "_points", b_run + "_line_MSW", "4000 Meters", "LOCATION", "NO_ANGLE", "PLANAR")
# delete unnecessary columns arcpy.DeleteField_management("in_memory" + os.sep + b_run + "_points", ["pointid", "NEAR_FID"])
# connect the points and its nearest points to lines in a new feature class arcpy.XYToLine_management("in_memory" + os.sep + b_run + "_points", "in_memory" + os.sep + b_run + "_lines", "X", "Y", "NEAR_X", "NEAR_Y", "GEODESIC", "id", crs)
# get surface informations for the lines arcpy.AddSurfaceInformation_3d("in_memory" + os.sep + b_run + "_lines", b_run + "_DEM", "SURFACE_LENGTH;AVG_SLOPE", "BILINEAR", "", "1", "0", "NO_FILTER")
# join the lines-table with the _points-table arcpy.JoinField_management("in_memory" + os.sep + b_run + "_points", "id", "in_memory" + os.sep + b_run + "_lines", "id", ["SLength", "Avg_Slope"])
# rename columns arcpy.AlterField_management("in_memory" + os.sep + b_run + "_points", "SLength", "SURFACE_LENGTH", "SURFACE_LENGTH") arcpy.AlterField_management("in_memory" + os.sep + b_run + "_points", "Avg_Slope", "AVG_SLOPE", "AVG_SLOPE")
# add the column 'AVG_SLOPE_DEGREE' and fill it arcpy.AddField_management("in_memory" + os.sep + b_run + "_points", "AVG_SLOPE_DEGREE", "DOUBLE", "", "", "", "AVG_SLOPE_DEGREE", "", "", "") arcpy.CalculateField_management("in_memory" + os.sep + b_run + "_points", "AVG_SLOPE_DEGREE", "math.atan( !AVG_SLOPE! / 100 ) * 180 / math.pi", "PYTHON_9.3", "")
Appendix 5 Supporting Information for Chapter IV ## # export the raster datasets 'ASD' and 'SL' arcpy.PointToRaster_conversion("in_memory" + os.sep + b_run + "_points", "AVG_SLOPE_DEGREE", b_run + "_MBS", "MEAN", "", 1) arcpy.CalculateStatistics_management(b_run + "_MBS") arcpy.PointToRaster_conversion("in_memory" + os.sep + b_run + "_points", "SURFACE_LENGTH", b_run + "_SL", "MEAN", "", 1) arcpy.CalculateStatistics_management(b_run + "_SL")
# copy points into the gdb arcpy.CopyFeatures_management("in_memory" + os.sep + b_run + "_points", b_run + "_points")
# clear the memory arcpy.Delete_management("in_memory")
########## # Produce a raster with tidal zones ##### # reclassify the EMHW reclass_raster_emhw = arcpy.sa.ReclassByASCIIFile(arcpy.sa.Raster(b_run + "_EMHW"), reclass_file_emhw) reclass_raster_emlw = arcpy.sa.Raster(b_run + "_EMLW_reclass")
tidal_zones = arcpy.sa.Con(reclass_raster_emhw == 1, reclass_raster_emhw, reclass_raster_emlw) tidal_zones.save(b_run + "_TZ")
# clean up *_EMLW_reclass-raster del reclass_raster_emlw, reclass_raster_emhw, tidal_zones arcpy.Delete_management(b_run + "_EMLW_reclass")
########## # print loop duration print " duration: " + convert_timedelta(dt.now() - loop_start) print ""
# quit python sys.exit(0)
A-5-3 Script ‘Calculating habitat suitability’ (Python script)
""" author: [email protected] & [email protected] date: 18.09.2015 script: 3_calculate_habitat_suitability.py purpose: compute the habitat suitability rasters for: Scirpus tabernaemontani Scirpus maritimus Phragmites australis for Elbe anabranches and main channel based on the GLM's produced in R
Key ecosystem engineers in estuarine vegetation
calculate the habitat units as product of probability of species occurrence and restoration area
summarize mean and standard deviation of the 50 DEM modification runs
""" # import python modules import sys import arcpy import os from datetime import datetime as dt import numpy
# Check out the ArcGIS Spatial Analyst extension license try: if arcpy.CheckOutExtension("Spatial") == r"CheckedOut": print "\nA 'Spatial Analyst'-license has been obtained." else: print "\nThere is no 'Spatial Analyst'-license available." sys.exit(1) except arcpy.ExecuteError: print arcpy.GetMessages(2) sys.exit(1) print ""
##### # convert_timedelta ### # purpose: # convert a time difference into a readable format ### # obtained from: # http://stackoverflow.com/questions/14190045/how-to-convert-datetime-timedelta- to-minutes-hours-in-python def convert_timedelta(duration): days, seconds, microseconds = duration.days, duration.seconds, duration.microseconds hours = days * 24 + seconds // 3600 minutes = (seconds % 3600) // 60 seconds = (seconds % 60) microseconds = int(round(microseconds/1000, 0))
if len(str(hours)) < 2: return_hours = str(hours).zfill(2) else: return_hours = str(hours)
return return_hours + ":" + str(minutes).zfill(2) + ":" + str(seconds).zfill(2) + "." + str(microseconds)
############################################################################### ############################################################################### # variables ## # location of the data # set statically #path = r"\\mt2.fs.bafg.de\Fachdaten\U\U2\EL_575_710_Elbe"
# set dynamically
Appendix 5 Supporting Information for Chapter IV pathname = os.path.dirname(sys.argv[0]) abs_pathname = os.path.abspath(pathname) #path = os.path.split(os.path.split(abs_pathname)[0])[0] path = os.path.split(abs_pathname)[0] print "The base directory is:\n " + path print ""
############################################################################### ###############################################################################
## # coordinate reference system crs = arcpy.SpatialReference("DHDN 3-Degree Gauss Zone 3")
########## # export results to csv-tables: with open(os.path.join(path, r"results\habitat_suitability.csv"), 'w') as hs_file:
# write the header hs_file.write("Species;Site;Treatment;Habitat Suitability;Odds Ratio\n")
with open(os.path.join(path, r"results\habitat_suitability_summary.csv"), 'w') as hs_summary_file:
# write the header hs_summary_file.write("Species;Site;Habitat Suitability Mean;Habitat Suitability SD;Odds Ratio Mean;Odds Ratio SD\n")
########## # loop over all sites for a_site in ["Luehesand", "Julssand", "Julssand_Groyne"]:
print a_site
########## # location of the gdb gdb = os.path.join(path, "data", a_site, a_site + ".gdb")
########## # check the existence of the gdb if os.path.exists(gdb) is not True and os.path.isdir(gdb) is not True: print "The input data do not exist." print "The site " + a_site + " is skipped." continue
########## # env settings arcpy.env.workspace = gdb arcpy.env.overwriteOutput = True arcpy.env.outputCoordinateSystem = crs arcpy.env.extent = "ori_DEM" arcpy.env.snapRaster = "ori_DEM"
########## # loop over all species
Key ecosystem engineers in estuarine vegetation
for a_species in ["Phragmites_australis", "Scirpus_maritimus", "Scirpus_tabernaemontani"]:
print a_species
########## # lists to collect hs results hs_values = [] odds_ratios = []
########## # reference print a_species + ": " + a_site + ": reference"
# calculate progress loop_start = dt.now()
if arcpy.Exists("ori_HS_" + a_species) is not True: ######### # compute HS (Habitat Suitability) if a_species == "Phragmites_australis": # reference raster datasets emhw = arcpy.sa.Raster("ori_EMHW")
# compute HS hs = 1 / (1 + arcpy.sa.Exp(-1 * (-0.4438 + (1.4995 * emhw) + (-1.344 * emhw * emhw))))
elif a_species == "Scirpus_maritimus": if a_site == "Luehesand": # anabranch # reference raster datasets emhw = arcpy.sa.Raster("ori_EMHW") mbs = arcpy.sa.Raster("ori_MBS")
# compute HS hs = 1 / (1 + arcpy.sa.Exp(-1 * (-1.41885 + (- 2.96176 * emhw) + (-0.17435 * mbs) + (-1.5402 * emhw * emhw))))
else: # main channel # reference raster datasets emhw = arcpy.sa.Raster("ori_EMHW") mbs = arcpy.sa.Raster("ori_MBS")
# compute HS hs = 1 / (1 + arcpy.sa.Exp(-1 * (-1.41885 + 1.05914 + ((-2.96176 + 1.11088) * emhw) + (-0.17435 * mbs) + (-1.5402 * emhw * emhw))))
else: if a_site == "Luehesand": # anabranch # reference raster datasets emhw = arcpy.sa.Raster("ori_EMHW") sl = arcpy.sa.Raster("ori_SL")
# compute HS hs = 1 / (1 + arcpy.sa.Exp(-1 * (-4.374 + (-7.396 * emhw) + (-0.000004084 * sl * sl) + (0.003037 * sl) + (-3.262 * emhw * emhw))))
Appendix 5 Supporting Information for Chapter IV
else: # main channel # reference raster datasets emhw = arcpy.sa.Raster("ori_EMHW") sl = arcpy.sa.Raster("ori_SL")
# compute HS hs = 1 / (1 + arcpy.sa.Exp(-1 * (-4.374 - 2.228 + ((-7.396 - 1.141) * emhw) + (-0.000004084 * sl * sl) + ((0.003037 + 0.003567) * sl) + (-3.262 * emhw * emhw))))
# save the produced hs.save("ori_HS_" + a_species) del hs
# calculate statistics arcpy.CalculateStatistics_management("ori_HS_" + a_species)
# convert the raster to a numpy array na_hs = arcpy.RasterToNumPyArray("ori_HS_" + a_species, "", "", "", 0)
# sum up all cells hs_sum_ref = numpy.sum(na_hs)
# export the values to hs_file hs_file.write(a_species + ";" + a_site + ";Reference;" + str(hs_sum_ref) + ";\n")
# print loop duration print " Dauer: " + convert_timedelta(dt.now() - loop_start) print ""
########## # loop over all runs for a_run in range(50):
b_run = "run_" + str(a_run + 1).zfill(4)
print a_species + ": " + a_site + ": " + str(b_run)
#b_run = "run_0003"
# calculate progress loop_start = dt.now()
if arcpy.Exists(b_run + "_HS_" + a_species) is not True: ######### # compute HS (Habitat Suitability) if a_species == "Phragmites_australis": # reference raster datasets emhw = arcpy.sa.Raster(b_run + "_EMHW")
# compute HS hs = 1 / (1 + arcpy.sa.Exp(-1 * (-0.4438 + (1.4995 * emhw) + (-1.344 * emhw * emhw))))
elif a_species == "Scirpus_maritimus":
Key ecosystem engineers in estuarine vegetation
if a_site == "Luehesand": # anabranch # reference raster datasets emhw = arcpy.sa.Raster(b_run + "_EMHW") mbs = arcpy.sa.Raster(b_run + "_MBS")
# compute HS hs = 1 / (1 + arcpy.sa.Exp(-1 * (-1.41885 + (- 2.96176 * emhw) + (-0.17435 * mbs) + (-1.5402 * emhw * emhw))))
else: # main channel # reference raster datasets emhw = arcpy.sa.Raster(b_run + "_EMHW") mbs = arcpy.sa.Raster(b_run + "_MBS")
# compute HS hs = 1 / (1 + arcpy.sa.Exp(-1 * (-1.41885 + 1.05914 + ((-2.96176 + 1.11088) * emhw) + (-0.17435 * mbs) + (-1.5402 * emhw * emhw))))
else: if a_site == "Luehesand": # anabranch # reference raster datasets emhw = arcpy.sa.Raster(b_run + "_EMHW") sl = arcpy.sa.Raster(b_run + "_SL")
# compute HS hs = 1 / (1 + arcpy.sa.Exp(-1 * (-4.374 + (- 7.396 * emhw) + (-0.000004084 * sl * sl) + (0.003037 * sl) + (-3.262 * emhw * emhw))))
else: # main channel # reference raster datasets emhw = arcpy.sa.Raster(b_run + "_EMHW") sl = arcpy.sa.Raster(b_run + "_SL")
# compute HS hs = 1 / (1 + arcpy.sa.Exp(-1 * (-4.374 - 2.228 + ((-7.396 - 1.141) * emhw) + (-0.000004084 * sl * sl) + ((0.003037 + 0.003567) * sl) + (-3.262 * emhw * emhw))))
# save the produced raster hs.save(b_run + "_HS_" + a_species) del hs
# calculate statistics arcpy.CalculateStatistics_management(b_run + "_HS_" + a_species)
# convert the raster to a numpy array na_hs = arcpy.RasterToNumPyArray(b_run + "_HS_" + a_species, "", "", "", 0)
# sum up all cells hs_sum = numpy.sum(na_hs) hs_values.append(hs_sum)
Appendix 5 Supporting Information for Chapter IV
# calculate odds ratio odds_ratio = numpy.log(hs_sum / hs_sum_ref) odds_ratios.append(odds_ratio)
# save the values to hs_file hs_file.write(a_species + ";" + a_site + ";" + str(a_run + 1) + ";" + str(hs_sum) + ";" + str(odds_ratio) + "\n")
# print loop duration print " duration: " + convert_timedelta(dt.now() - loop_start) print ""
# compute the summary information hs_mean = numpy.mean(hs_values) hs_sd = numpy.std(hs_values) or_mean = numpy.mean(odds_ratios) or_sd = numpy.std(odds_ratios)
# save the summary values to hs_summary_file hs_summary_file.write(a_species + ";" + a_site + ";" + str(hs_mean) + ";" + str(hs_sd) + ";" + str(or_mean) + ";" + str(or_sd) + "\n")
# quit python sys.exit(0)
A-5-4 Script ‘Calculating tidal zones’ (Python script)
""" author: [email protected] & [email protected] date: 18.09.2015 script: 4_calculate_tidal_zones.py purpose: summarize mean and standard deviation of the tidal zones based on the 50 DEM modification runs
""" # import python modules import sys import arcpy import os from datetime import datetime as dt import numpy
##### # convert_timedelta ### # purpose: # convert a time difference into a readable format ### # obtained from: # http://stackoverflow.com/questions/14190045/how-to-convert-datetime-timedelta- to-minutes-hours-in-python
Key ecosystem engineers in estuarine vegetation def convert_timedelta(duration): days, seconds, microseconds = duration.days, duration.seconds, duration.microseconds hours = days * 24 + seconds // 3600 minutes = (seconds % 3600) // 60 seconds = (seconds % 60) microseconds = int(round(microseconds/1000, 0))
if len(str(hours)) < 2: return_hours = str(hours).zfill(2) else: return_hours = str(hours)
return return_hours + ":" + str(minutes).zfill(2) + ":" + str(seconds).zfill(2) + "." + str(microseconds)
############################################################################### ############################################################################### # variables ## # location of the data # set statically #path = r"\\mt2.fs.bafg.de\Fachdaten\U\U2\EL_575_710_Elbe"
# set dynamically pathname = os.path.dirname(sys.argv[0]) abs_pathname = os.path.abspath(pathname) #path = os.path.split(os.path.split(abs_pathname)[0])[0] path = os.path.split(abs_pathname)[0] print "The base directory is:\n " + path print ""
############################################################################### ###############################################################################
## # coordinate reference system crs = arcpy.SpatialReference("DHDN 3-Degree Gauss Zone 3")
########## # export results to csv-tables: with open(os.path.join(path, r"results\tidal_zones.csv"), 'w') as tz_file:
# write the header tz_file.write("Site;Treatment;N_cells;N_cells_Supratidal;N_cells_Intertidal;N_ce lls_ShallowWater;N_cells_Other\n")
with open(os.path.join(path, r"results\tidal_zones_summary.csv"), 'w') as tz_summary_file:
# write the header tz_summary_file.write("Site;Zone 1 Mean;Zone 1 SD;Zone 1 Odds Ratio Mean;Zone 1 Odds Ratio SD;Zone 2 Mean;Zone 2 SD;Zone 2 Odds Ratio Mean;Zone 2 Odds Ratio SD;Zone 3 Mean;Zone 3 SD;Zone 3 Odds Ratio Mean;Zone 3 Odds Ratio SD;Zone 4 Mean;Zone 4 SD;Zone 4 Odds Ratio Mean;Zone 4 Odds Ratio SD\n")
##########
Appendix 5 Supporting Information for Chapter IV # loop over all sites for a_site in ["Luehesand", "Julssand_Groyne", "Julssand"]:
print a_site
########## # location of the gdb gdb = os.path.join(path, "data", a_site, a_site + ".gdb")
########## # check the existence of the gdb if os.path.exists(gdb) is not True and os.path.isdir(gdb) is not True: print "The input data do not exist." print "The site " + a_site + " is skipped." continue
########## # env settings arcpy.env.workspace = gdb arcpy.env.overwriteOutput = True arcpy.env.outputCoordinateSystem = crs arcpy.env.extent = "ori_DEM" arcpy.env.snapRaster = "ori_DEM"
########## # reference print a_site + ": reference"
# calculate progress loop_start = dt.now()
# convert the raster to a numpy array na_tz = arcpy.RasterToNumPyArray("ori_TZ", "", "", "", 0)
# count cats N_cells = len(numpy.where(na_tz > 0)[0]) ref_N_cells_cat_1 = len(numpy.where(na_tz == 1)[0]) ref_N_cells_cat_2 = len(numpy.where(na_tz == 2)[0]) ref_N_cells_cat_3 = len(numpy.where(na_tz == 3)[0]) ref_N_cells_cat_4 = len(numpy.where(na_tz == 4)[0])
# export the values to tz_file tz_file.write(a_site + ";reference;" + str(N_cells) + ";" + str(ref_N_cells_cat_1) + ";" + str(ref_N_cells_cat_2) + ";" + str(ref_N_cells_cat_3) + ";" + str(ref_N_cells_cat_4) + "\n")
# print loop duration print " duration: " + convert_timedelta(dt.now() - loop_start) print ""
########## # lists to collect hs results cat_1_values = [] cat_2_values = [] cat_3_values = [] cat_4_values = [] cat_1_values_or = [] cat_2_values_or = []
Key ecosystem engineers in estuarine vegetation
cat_3_values_or = [] cat_4_values_or = []
########## # loop over all runs for a_run in range(50):
b_run = "run_" + str(a_run + 1).zfill(4)
print a_site + ": " + b_run
# calculate progress loop_start = dt.now()
# convert the raster to a numpy array na_tz = arcpy.RasterToNumPyArray(b_run + "_TZ", "", "", "", 0)
# count cats N_cells = len(numpy.where(na_tz > 0)[0]) N_cells_cat_1 = len(numpy.where(na_tz == 1)[0]) N_cells_cat_2 = len(numpy.where(na_tz == 2)[0]) N_cells_cat_3 = len(numpy.where(na_tz == 3)[0]) N_cells_cat_4 = len(numpy.where(na_tz == 4)[0])
# save results for tz_summary cat_1_values.append(N_cells_cat_1) cat_2_values.append(N_cells_cat_2) cat_3_values.append(N_cells_cat_3) cat_4_values.append(N_cells_cat_4) cat_1_values_or.append(numpy.log(N_cells_cat_1 / ref_N_cells_cat_1)) cat_2_values_or.append(numpy.log(N_cells_cat_2 / ref_N_cells_cat_2)) cat_3_values_or.append(numpy.log(N_cells_cat_3 / ref_N_cells_cat_3)) cat_4_values_or.append(numpy.log(N_cells_cat_4 / ref_N_cells_cat_4))
# export the values to tz_file tz_file.write(a_site + ";" + str(a_run + 1) + ";" + str(N_cells) + ";" + str(N_cells_cat_1) + ";" + str(N_cells_cat_2) + ";" + str(N_cells_cat_3) + ";" + str(N_cells_cat_4) + "\n")
# print loop duration print " duration: " + convert_timedelta(dt.now() - loop_start) print ""
# compute the summary information cat1_mean = numpy.mean(cat_1_values) cat1_sd = numpy.std(cat_1_values) cat1_or_mean = numpy.mean(cat_1_values_or) cat1_or_sd = numpy.std(cat_1_values_or) cat2_mean = numpy.mean(cat_2_values) cat2_sd = numpy.std(cat_2_values) cat2_or_mean = numpy.mean(cat_2_values_or) cat2_or_sd = numpy.std(cat_2_values_or) cat3_mean = numpy.mean(cat_3_values) cat3_sd = numpy.std(cat_3_values) cat3_or_mean = numpy.mean(cat_3_values_or)
Appendix 5 Supporting Information for Chapter IV cat3_or_sd = numpy.std(cat_3_values_or) cat4_mean = numpy.mean(cat_4_values) cat4_sd = numpy.std(cat_4_values) cat4_or_mean = numpy.mean(cat_4_values_or) cat4_or_sd = numpy.std(cat_4_values_or)
# save the summary values to hs_summary_file tz_summary_file.write(a_site + ";" + str(cat1_mean) + ";" + str(cat1_sd) + ";" + str(cat1_or_mean) + ";" + str(cat1_or_sd) + ";" + str(cat2_mean) + ";" + str(cat2_sd) + ";" + str(cat2_or_mean) + ";" + str(cat2_or_sd) + ";" + str(cat3_mean) + ";" + str(cat3_sd) + ";" + str(cat3_or_mean) + ";" + str(cat3_or_sd) + ";" + str(cat4_mean) + ";" + str(cat4_sd) + ";" + str(cat4_or_mean) + ";" + str(cat4_or_sd) + "\n")
# quit python sys.exit(0)
Key ecosystem engineers in estuarine vegetation
Appendix 6 List of author’s publications related to the thesis
Peer-reviewed articles in journals Heuner, M., Schröder, B., Schröder, U., Kleinschmit, B., with revisions. Hydrodynamic forces influence elevational responses in vegetation of regularly flooded marshes in navigable estuaries. draft.
Heuner, M., Silinski, A., Schoelynck, J., Bouma, T.J., Puijalon, S., Troch, P., Fuchs, E., Schröder, B., Schröder, U., Meire, P., Temmerman, S., 2015. Ecosystem engineering by plants on wave-exposed intertidal flats is governed by relationships between effect and response traits. PLOS one 10 (9), e0138086. doi: 10.1371/journal.pone.0138086
Heuner, M., Weber, A., Schröder, U., Kleinschmit, B., Schröder, B., 2016. Facilitating political decisions using species distribution models to assess restoration measures in heavily modified estuaries. Marine Pollution Bulletin 110, 250-260. doi: 10.1016/j.marpolbul.2016.06.056
Silinski, A., Heuner, M., Schoelynck, J., Puijalon, S., Schröder, U., Fuchs, E., Troch, P., Bouma, T., Meire, P., Temmerman, S., 2015. Effects of wind waves versus ship waves on tidal marsh plants: a flume study on different life stages of Scirpus maritimus. PLOS one 10 (3), e0118687. doi: 10.1371/journal.pone.0118687
Silinski, A., Heuner, M., Schoelynck, J., Puijalon, S., Schröder, U., Fuchs, E., Troch, P., Bouma, T., Meire, P., Temmerman, S.,2016. Effects of contrasting wave conditions on scour and drag on pioneer tidal marsh plants. Geomorphology 255, 49-62. doi: 10.1016/j.geomorph.2015.11.021
Contributions to conferences Heuner, M., 2013. Verhalten dreier Röhrichtarten unter dem Einfluss der Hydrodynamik und Wellenexperimente – zwei Pionierröhrichte im Vergleich. In: Bauer, E.M. & Heuner, M., Eds.. Die Vegetation an Tideelbe und Tideweser im Klimawandel. Ergebnisse aus dem KLIWAS-Projekt „Ästuarvegetation und Vorlandschutz“ sowie dem BfG-Projekt „Biogene Uferstabilisierung“. doi: 10.5675/Tideelbe_Tideweser_Klima_2013. Abschlussveranstaltung KLIWAS 3.09, 11.-12.12.2013, BSH, Hamburg, Germany. p. 8-11. Oral presentation.
Heuner, M., Schröder, U., Fuchs, E., Schröder, B. & Bauer, E.M., 2013. Effects of regional climate change for marsh vegetation along the Weser and Elbe estuaries. Abstractband, Open Landscape. Ecology, Management and Nature Conservation, 29.9-3.10.2013, University of Hildesheim, Germany, p. 31. Oral presentation.
Heuner, M, Schröder, U. & Kleinschmit, B., 2011. Modellierung von Röhrichtarten in Ästuaren entlang des Longitudinalgradienten. In: Haase, D. & Kleinschmit, B , Eds. Abstractband, IALE-D Jahrestagung, 12- 14.10.2011, Erwin-Schrödinger-Zentrum Berlin, Germany. p. 14. Oral presentation.
Heuner, M. & Kleinschmit, B., 2011. Hydrodynamics – Its effect on habitats of reed species along North Sea estuaries. In: Minden, V. (Eds.): 41st Annual Meeting of the Ecological Society of Germany, Austria and Switzerland (GfÖ): “Ecological Functions, Patterns, Processes”, 5-9.9.2011, Oldenburg, Germany. Abstract number F1-P3. Poster presentation.
Heuner, M., Schröder, U. & Kleinschmit, B., 2011. Responses of different reed species to morphological bank conditions along the marsh edges. In: Nachtnebel, H.P. & Kovar, K., Eds.. Abstractband, HydroEco 2011, 3rd International Multidisciplinary Conference on Hydrology and Ecology Ecosystems, Groundwater and Surface Water. Pressures and Options, 2.-5.5.2011. Wien, Austria. Abstract number 93. Poster presentation.
Key ecosystem engineers in estuarine vegetation
Appendix 7
Author’s Declaration
I prepared this dissertation without illegal assistance. This work is original except where indicated by special reference in the text and no part of the dissertation has been submitted for any other degree. This dissertation has not been presented to any other University for examination, neither in Germany nor in another country.
Koblenz, December 2015 (Maike Heuner)
Key ecosystem engineers in estuarine vegetation
Epilogue
“Education, […], should not take people away from land, but instill in them even more respect for it, because educated people are in a position to understand what is being lost.”
Wangari Muta Maathai, 2006