Difference between revisions of "Testpage3"

From Coastal Wiki
Jump to: navigation, search
m (don't use same vliz_photo_gallery twice)
(21 intermediate revisions by the same user not shown)
Line 1: Line 1:
ESTUARINE MORPHOLOGICAL MODELLING
+
{{Review
 +
|name=Job Dronkers|AuthorID=120|
 +
}}
  
  
==Introduction==
+
'''Resilience and resistance'''
  
Estuaries are the areas where terrestrial and marine waters meet. This means that they are influenced by fresh water flow and sediment transport from rivers, as well as by saline marine water and sediment via tides, storm surges, waves and wind. There is a strong mutual interaction with the complex ecosystem. Moreover, due to their position between land and sea, they are often areas of intense human activity.
 
  
From a morphological point of view, there is a complex dynamic interaction between four groups of  elements: channels, sandy shoals, mudflats and marshlands. They are influenced by the motion of fresh and saline waters. The transport of sediment, often a mix of sand and fines, involves a variety of mechanisms.
+
{{Definition|title=Resistance
+
|definition= The capacity to weather a disturbance without loss (Lake 2013<ref name=L>Lake, P.S. 2013. Resistance, Resilience and Restoration. Ecological Management and Restoration 14: 20-24</ref>). }}
Describing this in all its complexity requires highly sophisticated high-resolution simulation models. Yet, it is sometimes possible to derive useful information on certain aspects of estuarine morphology with simpler models. In this chapter we will describe some of these simpler model concepts, as well as how and when they can be applied. Examples refer to Dutch estuaries, where much groundbreaking work was done.
 
  
  
==Model typology==
+
{{Definition|title=Resilience
 +
|definition=(1) the capability to anticipate, prepare for, respond to, and recover from significant multihazard threats with minimum damage to social well-being, the economy, and the environment (sometimes called 'socio-ecological resilience')(Olsen et al. 2019<ref name=O>Olsson, S., Melvin, A. and Giles, S. (eds.) 2019. Climate change and ecosystems. Procs. Sackler Forum on Climate Change and Ecosystems, Washington, DC, November 8-9, 2018, organized by the National Academy of Sciences and The Royal Society</ref>);
  
[[Image:HuibScheme.jpg|thumb|500px|right|Scheme model typology.]]
+
(2) the capability of a (socio-)ecological system to remain within a stability domain when subjected to environmental change, while continually changing and adapting yet remaining within critical thresholds (sometimes called 'general resilience') (Folke et al. 2010<ref name=F>Folke, C., Carpenter, S. R., Walker, B., Scheffer, M., Chapin, T. and Rockstrom, J. 2010. Resilience thinking: integrating resilience, adaptability and transformability. Ecology and Society 15(4): 20</ref>; Scheffer 2009<ref>Scheffer, M. 2009. Critical transitions in nature and society. Princeton University Press, Princeton, New Jersey, USA</ref>; Brand and Jax 2007<ref name=BJ>Brand, F.S. and K. Jax. 2007. Focusing the meaning(s) of resilience: resilience as a descriptive concept and a boundary object. Ecology and Society 12(1):23</ref>);
  
Before going into the specific aspects of estuarine morphological modelling, we will set up a typology of the models to be considered (also see De Vriend<ref>De Vriend HJ (1995). Mathematical modelling of meso-tidal barrier island coasts, Part I: Empirical and semi-empirical models. In: Liu PL (ed.), Advances in Coastal and Ocean Engineering, Vol. 2, World Scientific Publication Company, Singapore, pp 115-149.</ref>). In broad outline, they can be classified as in the scheme at the right. Other model types can probably be added, but these are the most commonly used ones.
+
(3) the capacity to experience shocks while retaining essentially the same function, structure, feedbacks, and therefore identity (sometimes called 'ecological resilience') (Brand and Jax 2007<ref name=BJ/>; DEFRA 2019<ref name=DEFRA>Haines‐Young, R. and Potschin. M. (eds.) 2010. The Resilience of Ecosystems to Environmental Change (RECCE). Overview Report, 27 pp. Defra Project Code: NR0134</ref>), which is closely related to the concept of 'ecosystem resistance': the amount of disturbance that a system can withstand before it shifts into a new regime or an alternative stable state (Holling 1973<ref>Holling, C.S. 1973. Resilience and stability of ecological systems. Annual Rev. Ecol. Syst. 4: 1–23. doi: 10.1146/annurev.es.04.110173.000245</ref>; Gunderson 2000<ref>Gunderson, L.H. 2000. Ecological Resilience - in Theory and Application. Annual Review of Ecology and Systematics 31:425-439.</ref>);
  
A '''conceptual model''' basically consists of one or more notions of how the system responds to external factors. For instance: the shoal level will tend to follow a rise in high water level.
+
(4) the capacity of an ecosystem to regain its fundamental structure, processes, and functioning (or remain largely unchanged) despite stresses, disturbances, or invasive species (e.g., Hirota et al., 2011<ref>Hirota,M., Holmgren,M., Van Nes, E. H, and Scheffer,M. 2011. Global resilience of tropical forest and savanna to critical transitions. Science 334: 232–235. doi: 10.1126/science.1210657</ref>; Chambers et al., 2014<ref>Chambers, J. C., Bradley, B. A., Brown, C. S., D’Antonio, C., Germino, M. J., Grace, J. B., et al. 2014. Resilience to stress and disturbance, and resistance to Bromus tectorum L. invasion in the cold desert shrublands of western North America. Ecosystems 7: 360–375. doi: 10.1007/s10021-013-9725-5</ref>; Pope et al., 2014<ref>Pope, K. L., Allen, C. R., and Angeler, D. G. 2014. Fishing for resilience. T. N. Am. Fisheries Soc. 143: 467–478. doi: 10.1080/00028487.2014.880735</ref>; Seidl et al., 2016<ref>Seidl, R., Spies, T. A., Peterson, D. L., Stephens, S. L., and Hick, J. A. 2016. Searching for resilience: addressing the impacts of changing disturbance regimes on forest ecosystem services. J. Appl. Ecol. 53 : 120–129. doi: 10.1111/1365-2664.12511</ref>), which can be measured by the time needed to recover its original state (sometimes called 'engineering resilience'<ref name=L>Lake, P.S. 2013. Resistance, Resilience and Restoration. Ecological Management and Restoration 14: 20-24</ref>).  
The essence of '''empirical models''' is that they are based on observations / data. They can range from a simple line fitted through data in a scatter diagram to relationships based on artificial intelligence, such as neural networks. A simple example is the empirically established relationship between the cross-sectional area below mean sea level of a tidal channel and the tidal discharge that moves through it.
+
}}
  
'''Semi-empirical''' are based on a combination of data or observed behaviour and basic conservation laws, such as a sediment mass balance. An example from coastal morphology is a diffusion-type profile model, which is based on observed behaviour and satisfies the sediment balance equation, while the diffusion coefficient is derived from observations or more sophisticated model results. Genetic algorithms partly based on physical knowledge, such as a known property of a sediment transport relationship, also come under this category.
 
  
'''Physical models''' can be physical scale models or analogs. An example of the latter is the Deltar, an electrical analog formerly used by Rijkswaterstaat to produce forecasts of tides and surges (see https://en.wikipedia.org/wiki/Deltar).
 
  
There is multitude of '''mathematical models''', , in one, two or three spatial dimensions. Most of the hydrodynamic models in this category are process-based, i.e. based on the conservation laws of mass and momentum from classical physics. The constituting equations, such as the Navier-Stokes equations for turbulent flow, are solved in various degrees of simplification. 
+
==Introduction==
 
+
Coastal and marine ecosystems are affected by environmental disturbance at a variety of spatio-temporal scales. The organisms inhabiting these systems are adapted to such disturbance, either by being tolerant of these conditions or by playing a role in one or more of the successional stages that follow during ecosystem recovery.
'''Analytical models''' seem to be regaining ground now that techniques become available to tackle systems of nonlinear equations (e.g. Schuttelaars and de Swart<ref name=SS>Schuttelaars HM & de Swart HE (2000). Multiple morphodynamic equilibria in tidal embayments. JGR-Oceans 105(C10): 24105-24118.</ref>).
 
 
 
'''Lumped or aggregated models''' basically involve a reduction of the number of spatial dimensions. Box models have no spatial dimension at all, i.e. they consider processes within a bounded volume or at a arbitrary point in space. They are often used in ecology (e.g. food chain models).
 
'''Pots&pipes models''' lump processes into a number of interconnected containers or spatial boxes, and describe how the fluxes between those boxes influence the state of the dependent variables within them, and how the state of the dependent variables influences the fluxes. An example of such a model is ASMITA, which has been used extensively to describe the morphological behaviour of short tidal embayments such as those in the Wadden Sea (Stive and Wang<ref name=SW>Stive MJF & Wang ZB (2003). Morphodynamic modeling of tidal basins and coastal inlets. In: Lakhan V.C. (ed.), Advances in Coastal Modeling, Elsevier Oceanography Series 67: 367-392. doi: 10.1016/s0422-9894(03)80130-7</ref> ).
 
 
 
'''Spatially integrated models''' reduce the number of dimensions by formally integrating the constituting equations over one or more spatial dimension. An example is ESTMORF, which describes the morphological evolution of a tidal channel and shoal system as a 1-D network with the shoals as storage areas for water and sediment (Wang et al.<ref name=W>Wang ZB, Karssen B, Fokkink RJ & Langerak A (1998). A dynamic-empirical model for estuarine morphology. In: Dronkers J & Scheffers MBAM. (Eds.), Physics of Estuaries and Coastal Seas, Balkema, Rotterdam, pp. 279-286.</ref>).
 
 
 
'''Grid-based models''' form a very large category of mathematical models. They exist in a variety of forms, to be distinguished by the type of grid and by the way the constituting equations are discretized and solved. In the case of morphological models, an important aspect is how the hydrodynamic, sediment transport and sediment balance components of the model are coupled, at time-step level (like Delft3D; see Lesser et al. <ref>Lesser GR, Roelvink JA, van Kester JATM & Stelling GS (2004). Development and validation of a three-dimensional morphological model. Coastal Engineering 51: 883-915. doi: 10.106/jcoastaleng.2004.07.014 </ref>) or at intertidal level.
 
 
 
'''Particle-based models''' are increasingly used now that computer power has sufficiently increased.  Spectacular results have been obtained with Smooth Particle Hydrodynamics ('''SPH'''), especially with highly non-linear water motion at small scales (e.g. breaking waves). '''Random walk models''' were formerly used only at the analytical level, in order to come up with parameterisations of e.g. turbulence or turbulent sediment transport in turbulent flow. Random-walk numerical simulation models seem to be coming within sight now. In sediment transport, '''grain-by-grain transport models''' are gaining ground, as they circumvent part of the empiricism of traditional sediment transport models. Especially in complex areas such as estuaries, this may become an important asset. '''Swarm models''' describe the behaviour of large numbers of mutually interacting particles, such as emergent pattern formation. They may become of interest for cohesive sediment (e.g. flocculation), but this kind of application still seems to be beyond the horizon.
 
 
 
 
 
==Short tidal basins==
 
 
 
Tidal basins which are small as compared with the tidal wavelength allow for a special kind of modelling, based on the approximation that at every point in time the water surface elevation is in phase throughout the basin. This simplifies the hydrodynamic computation significantly.
 
Many tidal basins on barrier island coasts are of this type. One example is the Northwest European Wadden Sea, which extends from the Netherlands through the German Bight through to Denmark (Fig. 1). It consists of a range of tidal basins, separated by more or less pronounced drainage divides. Wherever expedient in the model descriptions below, example applications from this system are shown, even though most models have been used for a variety of other systems, as well. 
 
 
 
[[Image:FigHuib1.jpg|thumb|472px|left|Figure 1: Northwest European Wadden Sea.]]
 
[[Image:FigHuib2.jpg|thumb|400px|right|Figure 2: Bottom: Apple tree structure, van Veen <ref name=VV></ref>. Top: Vlie basin, Western Wadden Sea]]
 
 
 
Important morphological elements within each basin are channels, shoals and mudflats and marshlands in the back. Typical channel networks in such basins are like the branches of an apple tree (Van Veen<ref name=VV>Van Veen J (1950). Ebb and flood channel system in the Netherlands tidal waters. Tijdschrift Koninklijk Nederlands Aardrijkskundig Genootschap, 2nd series, Vol. 67, pp. 303-325 (in Dutch). Also see: Van Veen J, van der Spek A, Stive MJF & Zitman TJ (2005). Ebb and flood channel systems in the Netherlands tidal waters. J. Coastal Res. 21(6): 1107-1120. doi:10.2112/04-0394.1</ref>), i.e. with higher-order bifurcations (Fig. 2). There is a significant long-term interaction between the morphology of the basin, the outer delta and the adjacent coastlines: they form a sediment-sharing system (also see Cowell et al. <ref>Cowell PJ, Stive MJF, Niedoroda AW, de Vriend HJ, Swift DJP, Kaminsky GM. & Capobianco M (2003). The coastal-tract (Part 1): a conceptual approach to aggregated modeling of low-order coastal change. J. Coastal Res. 19(4): 812-827</ref>).
 
 
 
[[Image:FigHuib3.jpg|thumb|600px|right|Figure 3: Di Silvio box model <ref name=DS></ref>. Left: model set-up; right: result for Chioggia Inlet, Venice Lagoon.]]
 
 
 
A large amount of empirical evidence is available on certain aspects of the morphological equilibrium state of such systems. The cross-sectional area of the inlet and the channels, for instance, is approximately proportional to the tidal prism, i.e. the volume of water which moves up and down the channel during a tidal cycle (O’Brien<ref>O’Brien MP (1931). Estuary tidal prism related to entrance areas. Civ. Eng. 1(8): 738-739. Also see: O'Brien MP (1969). Equilibrium flow areas of inlets on sandy coasts. Proc. ASCE, Jnl. Waterways, Harbours and Coastal Engrg., 15(WW1): 43 52. </ref>). Also, the volume of the basin below mean sea level is approximately proportional to the tidal prism to a power between 1 and 1.5 (Eysink<ref>Eysink WD (1990). Morphologic response of tidal basins to changes. In: B.L. Edge (ed.), Coastal Engineering 1990 Proc., ASCE, New York, p. 1948 1961.</ref>). Such empirical relationships are the basis of a number of conceptual models of the large-scale behaviour of this type of short tidal basins. They also allow for a special kind of morphological models: closed-loop models which basically describe the deviation from the (empirical) equilibrium state. These models are complementary to open-loop ones, which start from a given state and integrate the constituting equations through time without prescribing the equilibrium state.
 
 
 
There is a whole family of aggregated models for this type of systems. Di Silvio<ref name=DS>Di Silvio G (1989). Modelling the morphological evolution of tidal lagoons and their equilibrium configuration. In: Proc. XXIIIrd IAHR Congress, Ottawa, Canada, p. C.169 175.</ref> proposed a simple two-box model in which all channels and all shoals are lumped, respectively. The basic equations are the integrated water and sediment balances, combined with relationships between the sediment concentration and the state of the shoals and the channels, respectively. They can be elaborated to a set of two coupled relaxation equations describing the long-term morphological evolution of the basin. This type of model is only suitable to describe very long-term morphological changes, say at the scale of centuries, and only for systems with no strong interaction with the ebb-tidal delta. The model was applied successfully to the past evolution of Venice Lagoon (Fig. 3).
 
 
 
[[Image:FigHuib4.jpg|thumb|600px|right|Figure 4: ASMITA. Left: principle. Right: application to Wadden Sea response to sea level rise (Van Goor et al.<ref name=G>Van Goor M, Zitman TJ, Wang ZB & Stive MJF (2003). Impact of sea level rise on the morphological equilibrium state of tidal inlets. Marine Geology 202: 211-227. doi: 10.1016/s0025-3227(03)00262-7</ref> ).]]
 
[[Image:FigHuib5.jpg|thumb|600px|right|Figure 5: ESTMORF. Left: set-up of Frisian Inlet model; right: result of response to partial closure (Wang et al. <ref name=W></ref>)]]
 
[[Image:FigHuib6.jpg|thumb|600px|right|Figure 6: Delft3D application to a hypothetical tidal basin (Marciano et al. <ref name=M>Marciano R, Wang ZB, Hibma A, de Vriend HJ & Defina A (2005).  Modeling of channel patterns in short tidal basins. JGR - Earth Surface 110, paper F01001, 13 pp. doi: 10.1029/2003JF000092</ref>). Left: initial state; right: channel pattern after 100 years (model time)]]
 
 
 
A lumped model including the interaction with the outer delta and the adjacent coastlines is ASMITA (Stive and Wang<ref name=SW></ref>; also see Townend et al.<ref name=T>Townend I, Wang ZB, Stive MJF & Zhou Z (2016a). Development and extension of an aggregated-scale model: Part 1 - Background to ASMITA. China Ocean Engineering 30(4): 483-504. doi: 10.1007/s13344-016-0030-x</ref><ref>Townend I, Wang ZB, Stive MJF & Zhou Z (2016b). Development and extension of an aggregated-scale model: Part 2 - Extensions to ASMITA. China Ocean Engineering 30(5): 651-670. doi: 10.1007/s13344-016-0042-6</ref>). It is basically a semi-empirical closed-loop pots&pipes model, with sediment-containing boxes for the basin, the ebb-tidal delta, and the coastlines and transport relationships depending on the state of these boxes to describe the fluxes between them. As this model includes more of the dynamic interactions, it can be applied at somewhat shorter timescales, say decades to centuries. The model has been used in many applications, among which the long-term response of the Wadden Sea to accelerated sea level rise (Van Goor et al.<ref name=G> </ref> ). It showed that at a certain rate of sea level rise the system may flip from sediment importing to sediment exporting (Fig 4).
 
 
 
Later on, ASMITA was integrated with the multi-coastline model PONTOS, as a tool to model the overall sediment balance of the Dutch coast, including the Wadden Sea barrier-island coast  <ref> Steezel HJ, Wang ZB & Mulder JPM (2004). Large-scale sand balance of the Netherlands coastal system; modelling of a policy indicator. In: McKee Smit J (ed.): Coastal Engineering 2004, World Scientific 2005, Vol I: 2973-2984. ISBN 981-256-997-9</ref>. A more detailed model of the interaction between basin, ebb-tidal delta and adjacent coast is given by de Vriend et al. <ref>de Vriend HJ, Bakker WT & Bilse DP (1994). A morphological behaviour model for the outer delta of mixed-energy tidal inlets, Coastal Engineering 23(3-4): 305-327</ref>, showing that the outer delta act as a buffer between the other two elements.
 
 
 
Going one step further in the description of the internal hydrodynamics in the basin, one may use a 1-D network model to describe flow and sediment transport in the channel network and combine that with a semi-empirical description of the water and sediment exchange with the shoals. Di Silvio<ref name=DS></ref> proposed a model of that type and ESTMORF (Wang et al.<ref name=W></ref>) is another example. This type of closed-loop model is primarily meant to be applied inside the basin, as it is difficult to include the interaction with the ebb-tidal delta. Such a model has been used, for instance, to analyse the response of the Frisian Inlet to the closure of one third of the basin area (Fig. 5).
 
 
 
Open-loop process-based grid models, such as 2-D depth-averaged morphological simulation models, have also been applied to these short basins. Some of these applications focus on the morphological evolution of the channel pattern, others on larger-scale aspects like the response to sea level rise. Delft3D, for instance, has been used for various short-basin applications. Marciano et al. <ref name=M> </ref>) and Dissanayake et al. <ref>Dissanayake DMPK, Roelvink JA & van der Wegen M (2009). Modelled channel patterns in a schematized tidal inlet. Coastal Engineering  56 (2009): 1069–1083. doi:10.1016/j.coastaleng.2009.08.008/</ref> ) used it to simulate the channel pattern evolution in a hypothetical basin, starting from a smooth topography (Fig. 6). This type of results raises the question how to compare morphological model results with observations, so how to derive essential properties of the channel pattern from them. A local statistical comparison like the Brier Skill Score <ref name=S>Sutherland, J, Peet, AH, Soulsby, RL (2004). Evaluating the performance of morphological models. Coastal Engineering 51(8-9): 917-939; doi: 10.1016/j.coastaleng.2004.07.015</ref> (also see the section below) is probably not good enough here. 
 
 
 
Dastgheib et al. <ref>Dastgheib A., Roelvink JA. & Wang, ZB. (2008). Long-term process-based morphological modeling of the Marsdiep tidal basin. Marine Geology  256(1-4): 90-100. doi: 10.1016/j.margo.2008.10.003</ref> used the same software package to model the response of the Marsdiep basin to sea level rise. He concludes that this type of process-based model system is suitable for long-term applications, and that the results seem to indicate a dependence of the large-scale equilibrium state on the initial conditions.
 
 
 
Most of these models assume a sandy bed, often with uniform sediment. Usually, tidal basins contain a significant amount of mud, so modelling a combination of sand and fines may be useful. An important aspects to be properly modelled is the behaviour of fines in the water column (flocculation, hindered settling, turbulence damping) and their buffering in and pick-up from the top layer of the bottom. Van Prooijen and Wang<ref>Van Prooijen BC & Wang ZB (2013). A 1-D model for tides, waves and fine sediments in short tidal basins: application to the Wadden Sea. Ocean Dynamics  63(11): 1233-1248. doi:10.1007/s10236-013-0648-7</ref> present a spatially lumped model for fines under the influence of tides and waves. Van Ledden et al. <ref>Van Ledden M, Wang Z & Winterwerp H (2006). Modeling sand-mud morphodynamics in the Friesche Zeegat. Ocean Dynamics 56(3): 248-265. doi: 10.1007/s10236-005-0055-9</ref> used a process-based model for the deposition of fines in the Wadden Sea. New insights into mud erosion (Winterwerp et al.<ref>Winterwerp JC, van Kesteren WGM, van Prooijen BC & Jacobs W (2012). A conceptual framework for shear  shear flow–induced erosion of soft cohesive sediment beds. JGR - Oceans 117(C10), 17 pp. doi: 10.1029/2012JC008072</ref>) have led to more models that are suitable to address  issues in which mud erosion also plays a role (also see Section 4).   
 
 
 
 
 
==Longer tidal estuaries==
 
 
 
[[Image:FigHuib7.jpg|thumb|500px|right|Figure 7: Channel pattern in a long estuary.
 
Top: poplar tree structure (van Veen<ref name=VV></ref>).Bottom: channel pattern Western Scheldt.]]
 
[[Image:FigHuib8.jpg|thumb|500px|right|Figure 8: Overview Westerschelde (htpps://beeldbank.rws.nl, Rijkswaterstaat).]]
 
[[Image:FigHuib9.jpg|thumb|500px|right|Figure 9: Western Scheldt, first-order residual circulation cells (Winterwerp et al. <ref name=W01>Winterwerp JC, Wang ZB, Stive MJF, Arends A, Jeuken C, Kuijper C & Thoolen PMC (2001). A new morphological schematization of the Western Scheldt estuary. In: Proc. 2nd RCEM Symposium, Obihiro, Japan, p. 525-533.</ref>).]]
 
  
Many estuaries do not allow for the assumption that the tidal wave is in phase throughout the system. In these longer estuaries other phenomena play a role, such as tidal propagation and deformation, residual currents and sediment transport, and estuarine circulation. Also, the pattern of channels and shoals tends to be different. Van Veen<ref name=VV></ref> calls this a ‘poplar tree’ structure, with continuing ebb-dominated channels and interrupted flood-dominated channels (Fig. 7).
+
If all species in the system were tolerant to a particular perturbation, very little would change at the ecosystem level, and we could call the system resistant to this disturbance. However, often a disturbance, such as a temporary very low oxygen level, affects a substantial proportion of the organisms dramatically, either causing them to die, or forcing them to rapidly migrate to more favorable parts of the environment. Such an adverse disturbance could locally defaunate a certain volume in the pelagic or a certain area of hard or soft substrate. Such destruction at a local scale does not mean the end of local functioning. Usually organisms are available at a larger spatial scale that can re-colonize the affected area, according to their particular tolerances and abilities to favorably affect their local environment.  
  
The Western Scheldt (Westerschelde) is an example of such an estuary (Figs. 7, 8). It connects the river Scheldt with the North Sea, but the river discharge is small compared with the ebb and flood discharges. The estuarine ‘quadruplet’ (channels, shoals, mudflat, marshlands) is clearly present in its full dynamic interaction. There is a large submerged outer delta, called ‘Vlakte van de Raan’.  The estuary gives access to the harbour of Antwerp and is therefore an important fairway for commercial navigation. The estuary’s alignment has been heavily influenced by many centuries of consecutive embankments. The functioning of the models described below are illustrated by applications to this estuary, even though most of them have also been applied to a range of other systems.
+
The term resilience has been defined in different ways, illustrated in the definition above. According to DEFRA (2019<ref name=DEFRA/>) there is limited consensus in the literature about how resilience can be characterized and assessed. The term resilience is sometimes used to represent some kind of normative proposition about what kinds of ecosystem characteristics are desirable or necessary in the context of sustainable development, reflecting particular cultural and philosophical assumptions<ref name=DEFRA/>. However, the resistance of an ecosystem (see the definition above) to changing conditions and the rate of recovery following some disruptive event are generally considered major components of resilience that can in principle be expressed in quantitative terms.  
  
Management objectives for this estuary aim to reconcile safety, accessibility and naturalness. Given the frequent human interventions, this explains why this system has been studied extensively, via field work as well as a wide range of models. It  is therefore a rich source of examples of estuarine morphological modelling.  
+
Other attributes such as the capacity of ecosystems to transform and adapt in the face of environmental change (i.e. system's ability to re-organize itself) are more difficult to translate to practice. According to Dawson et al. (2010<ref name=D>Dawson, T.P., Rounsevell, M.D.A., Kluvankova‐Oravska, T., Chobotova V. and Stirling, A. 2010. Dynamic properties of complex adaptive ecosystems: implications for the sustainability of services provision. Biodiversity and Conservation 19: 2843‐2853</ref>), resilience concerns the response of ecosystems to changing environmental conditions and must be looked at alongside other additional dynamic features, namely durability, robustness and stability. These concepts can be defined as<ref name=D/>:
Commercial interests constitute a drive to deepen the fairway, such that it requires more or less continuous maintenance. Maintenance dredging in the Western Scheldt tends to focus on a number of thresholds in the ebb-dominated main channel. Because these are rather local phenomena, it may be tempting to use a point model relating the rate of accretion to the local over-depth. Such an approach, however, would ignore any limited availability of sediment  from the surroundings. Wang et al. <ref>Wang ZB, Jeuken MCJL & Kornman BA (2003). A model for predicting dredging requirement in the Westerschelde. In: Proceedings International Conference on Estuaries and Coasts 2003, IRTCES, Zhejiang Institute of Hydraulics and Estuary, ISBN 7-900662-67-7/G.79, pp 429-435.</ref> go one step further by relating the sediment supply towards such a point to the sediment availability in neighbouring areas and embedding this relationship in an ESTMORF network model.  This model was used, for instance, to study the effects of dredging and dumping strategies in the Western Scheldt. An adapted version of ASMITA was used to model the interaction with the outer delta, and further extensions of this concept were applied to other estuaries, such as the Humber (Townend et al. <ref name=T></ref>).  
+
* Durability:  ability to cope with a chronic stress, but the source of this stress is endogenous;
 +
* Robustness: ability to recover or maintain the systems' social-ecological functions in the face of an external and chronic driver;
 +
* Stability:  system’s tolerance to transient and endogenous shocks or disruptions.
  
Residual currents and residual sediment transport play a key role in the morphology of estuaries such as the Western Scheldt. Without considering the residual transport field, it is impossible to explain the formation of the channel-shoal system. Note, however, that residual current patterns can be quite different from residual transport pattern, sometimes even with opposite directions. It is fair to state that in an estuary like this the residual transport pattern cannot be estimated from the residual current pattern. The residual transport pattern in the Western Scheldt exhibits a remarkable pattern: a number of consecutive circulation cells around the major shoal complexes (Fig. 9; also see Winterwerp et al. <ref name=W01></ref> ).
+
Both resistance and resilience cause an ecosystem to remain relatively unchanged when confronted to a disturbance, but in the case of resistance no internal re-organization and successional change is involved. In contrast, resilience implies that the system is internally re-organizing, perhaps through a mozaic of patches that are at different stages of re-assembly. System responses to changing environmental conditions are displayed schematically in Fig. 1, corresponding to different resilience characteristics.
  
The question where to dump the dredged material has been subject of many discussions, among others about the stability of the multiple channel system (Fig. 9), which is considered ecologically important. Jeuken and Wang<ref>Jeuken MCJL & Wang ZB (2010). Impact of dredging and dumping on the stability of ebb–flood channel systems. Coastal Engineering 57: 553-566. doi: 10.1016/j.coastaleng.2009.12.004</ref> studied this for individual cells using an analytical method and concluded that there is a limit to the amount of material that can be dumped per unit time in one of the channels forming a cell. If this limit is exceeded, the cell will degenerate to a single channel. Note that this type of analysis is relevant in its own right, but also provides information that can be compared with results of more complex simulation models. The same goes for so-called idealized analytical models (e.g. Schuttelaars and de Swart<ref name=SS></ref>), i.e. models reduced to the essential physics and simplified far enough to allow for an analytical approach. They give insight into basic processes and phenomena that may not be directly visible from the results of detailed numerical models, such as the possible existence of multiple equilibria (Schramkowski et al. <ref>Schramkowski G, Schuttelaars HM & de Swart HE (2004). Non-linear channel-shoal dynamics in long tidal embayments. Ocean Dynamics 54: 399-407. doi: 10.1007/s10236-003-0063-6</ref>).
+
[[Image:ResilienceTrajectories.jpg|thumb|900px|center|Figure 1. Schematic representation of the trajectories of a (socio-)ecological system in a plane defined by the system state (fundamental structure, processes, and functioning - vertical axis) and the change of environmental conditions (horizontal axis), for different resilience characteristics (a, b, c, d). The initial state corresponds to the position on the graph at the vertical axis (zero change in environmental conditions). In all situations the ecosystem is assumed to collapse irreversibly (down to the horizontal axis) when the change in environmental conditions is much greater than the systems' resistance. The angle <math>\alpha</math> represents the rate at which the system recovers when the change in environmental conditions is reduced (small <math>\alpha</math> means slow recovery, large <math>\alpha</math> means fast recovery). Panel a: Resilience characterized by high resistance (definition 3) and slow recovery (definition 4). Panel b: Resilience characterized by low resistance and fast recovery. Panel c: Resilience characterized by a shift to an alternative stable system state. Panel d: Low resilience, characterized by low resistance and slow recovery.]]
  
One of the first applications of an open-loop grid model to long-term estuarine morphology is described by Hibma<ref name=H04></ref>. She simulated the morphological evolution in a hypothetical estuary of constant width with a 2-D depth-integrated Delft3D-model, starting from a seaward sloping flat bed with a small random perturbation. The results show that the initial bedforms, diagonally crossing regular waves corresponding with the linearly most unstable mode, soon evolve into a much more complex and larger-scale pattern similar to Van Veen’s<ref name=VV></ref> ‘poplar tree’ (Fig. 10).
 
  
 +
When considering the potential effect of a certain type of disturbance it is thus useful to ask two questions:
 +
# Will the species of this system be able to tolerate it (implying resistance), and if not,
 +
# Is recovery possible through a successional trajectory, back to the same, or at least a desirable, ecosystem state (implying resilience)?
 +
Resistance breaks down when uni-directional ongoing change acts faster than the organisms' ability to adapt their tolerances. If uni-directional ongoing change is this fast (even if gradual), the system will not be sufficiently resilient either, as full recovery through succession will then not be possible. Recovery from sudden and local disturbance is often possible through recolonization, but the rate of recovery will depend crucially on the spatial extent of disturbance. For example, recovery from anoxia could take 5 to 8 months at the scale of square meters (Rossi et al. 2009<ref name=R>Rossi, F., Vos, M. & Middelburg, J.J. 2009. Species identity, diversity and microbial carbon flow in reassembling macrobenthic communities. Oikos 118: 503-512.</ref>), but could take 5 to 8 years at the scale of a whole bay (Diaz & Rosenberg 1995<ref>Diaz, R.J. & Rosenberg, R. 1995. Marine benthic hypoxia: a review of its ecological effects and the behavioural responses of benthic macrofauna. Oceanogr. Mar. Biol. Annu. Rev. 33:245-303.</ref>).
  
<vliz_photo_gallery pic="117757"></vliz_photo_gallery>  
+
According to definition (4), the speed at which an ecosystem returns to its former state following a (minor) disturbance can be considered a measure of resilience. The idea is that a system with a short return time is more resilient than one with a long return time. Such resilience measured as (1 / the return time to a stable equilibrium) has also been called ''engineering resilience''. It has however a long history of use among ecologists (Pimm 1982<ref>Pimm, S.L. 1982. Food Webs. The University of Chicago Press.</ref>, DeAngelis 1992<ref>DeAngelis, D.L. 1992. Dynamics of Nutrient Cycling and Food Webs. Chapman and Hall, London.</ref>, Vos et al. 2005<ref>Vos, M., Kooi, B.W., DeAngelis, D.L. & Mooij, W.M. 2005. Inducible defenses in food webs. In: Dynamic Food Webs. Multispecies Assemblages, Ecosystem Development and Environmental Change. Eds. P.C. de Ruiter, V. Wolters & J.C. Moore. Academic Press. Pp. 114-127.</ref>). Resilience is also used in a way that more closely resembles the definition of resistance. ''Ecological resilience'' was defined as the amount of disturbance that an ecosystem could withstand without changing self-organized processes and structures (definition 3).
  
<small>Figure 10: Model simulation of morphological evolution in a hypothetical estuary of constant width (Hibma<ref name=H04>Hibma A (2004). Morphodynamic modelling of estuarine channel-shoal systems. PhD thesis, Delft University of Technology. See: http://repository.tudelft.nl/islandora/object/uuid%3Af99c575b-b76b-4aa7-851d-0dca3d380759?collection=research </ref>).</small>
+
Resilience of coastal systems largely depends on biodiversity, which is a major requirement for allowing ecosystems to adapt to changing conditions. The human impact on the environment through pollution, fisheries, sediment erosion / deposition and global climate change has brought about much faster change than would occur under natural conditions, putting severe stress on many ecosystems. Without genetic diversity, natural selection cannot occur and if natural selection is limited, adaptation is impossible. Preservation of biodiversity and, more specifically, genetic diversity is therefore of paramount importance for successful adaptation to our rapidly changing environments. However, biodiversity may not always protect ecosystems from major abiotic disturbances (Folke et al. 2004<ref>Folke, C., Carpenter, S., Walker, B., Scheffer, M., Elmqvist, T., Gunderson, L. & Holling, C.S. 2004. Regime Shifts, Resilience, and Biodiversity in Ecosystem Management. Annual Review of Ecolog and Systematics 35:557-581.</ref>).
  
As this model did not include any complexities, such as 3D secondary flows or relaxation of suspended sediment concentrations, these results show that much of the larger-scale behaviour of this type of systems is governed by rather basic flow and sediment transport processes.
+
==Resilience through recolonization==
  
Van der Wegen and Roelvink<ref name=vdW> </ref> applied a Delft3D-model to the actual planform of the Western Scheldt, starting from a plane bed. This evolved into a channel-shoal geometry showing a striking resemblance to the actual one in the estuary (Fig. 11). Results turned out to be hardly sensitve to estuarine circulation, grainsize variations or bank erodibility. There was a significant sensitivity, however, to the seaward boundary conditions (overtides), the bedslope effect on the sediment transport and three-dimensionality of the flow.  Also, significant effects were found of erodibility restrictions, e.g. due to hard layers in the bottom, and of human activities such as dredging and dumping.
+
To understand resilience of ecosystems it is essential to understand what drives succession within these ecosystems. Succession determines how, and how fast, communities return to their original state, or perhaps enter a new state. Many aspects of succession can be understood in terms of trade-offs between the ability to be either a good early (re)colonizer, or a good competitor. Succession involves a gradual replacement of colonizer/competitor species according to the degree to which they tolerate, facilitate or inhibit certain environmental conditions and other species (Rossi et al. 2009<ref name=R/>). The extent to which processes of (re)colonization and succession can take place largely determines the recovery of ecosystems after major disruption and is therefore an essential characteristic of the resilience of ecosystems.  
  
 +
In this context, it is important to consider the spatial component of ecosystem resilience. Diversity of structurally and functionally connected landscapes, rich in resources and species, promotes the flow or movement of individuals, genes, and ecological processes. Below certain thresholds of connectivity the capacity to regain structure and function after perturbation is lost (Holl and Aide, 2011; Rudnick et al., 2012;McIntyre et al., 2014; Rappaport et al., 2015; Ricca et al., 2018). Chambers et al. (2019<ref name=CAC>Chambers, J.C., Allen, C.R. and Cushman, S.A. 2019. Operationalizing Ecological Resilience Concepts for Managing Species and Ecosystems at Risk. Front. Ecol. Evol. 7:241. doi: 10.3389/fevo.2019.00241</ref>), based on Allen et al. (2016<ref> Allen, C. R., Angeler, D. G., Cumming, G. S., Folk, C., Twidwell, D., and Uden, D. R. 2016. Quantifying spatial resilience. J. Appl. Ecol. 53, 625–635. doi: 10.1111/1365-2664.12634</ref>), have therefore introduced the concept of  'spatial resilience', which is a measure of how spatial attributes, processes, and feedbacks vary over space and time in response to disturbances and affect the resilience of ecosystems. Self-organization through strong feedbacks at multiple scales and high levels of functional diversity and redundancy, stabilizes the system with respect to disturbances within the range of historic variability.
  
<vliz_photo_gallery pic="117758"></vliz_photo_gallery>
+
When creating Marine Protected Areas, the sources of populations at all stages of succession should be protected, to preserve 'ecological memory' to the fullest possible extent. This includes protecting not only 'high quality' habitats that harbour healthy mature communities, but also 'low quality' and disturbed habitats that are required for those species that contribute to early recovery of perturbed areas (Rossi et al. 2009<ref name=R/>). The selection of Marine Protected Areas thus involves evaluating
<small>Figure 11: Morphological evolution of the Western Scheldt. Left: model result (van der Wegen and Roelvink<ref name=vdW>Van der Wegen M & Roelvink JA (2012). Reproduction of estuarine bathymetry by means of a process-based model: Western Scheldt case study, the Netherlands Geomorphology 179: 152-167. doi: 10.1016/j.geomorph.2012.08.007</ref>).</small>
+
the number, size, and spatial configuration of habitat fragments and degree of connectivity required to support restoration of ecosystems and conservation of focal habitats and species<ref name=CAC/><ref name=O/>.
 
In order to assess the model results, one may use the Brier Skill Score (e.g. Sutherland et al. <ref name=S></ref>). It is based on an rms-type  evaluation of the local differences between the model results and a given reference.  It can be split into sub-scores for the large-scale trend and for amplitude and phase errors in the deviations from that trend. In the case of the above model, it reveals a difference in evolution timescale of the channel-shoal system and the large-scale slope of the estuary.  
 
  
[[Image:FigHuib12.jpg|thumb|800px|center|Figure 12: FINEL-results for the long-rterm evolution of the Western Scheldt (Dam et al. <ref name=D16>Dam G, van der Wegen M, Labeur RJ & Roelvink D. (2016). Modeling centuries of estuarine morphodynamics in the Western Scheldt estuary, Geophys. Res. Lett. 43. doi:10.1002/2015GL066725</ref>). Left: measured (a) and computed (b) bed level change 1860-1970. Right: time-evolution of the Brier Skill Score.]]
+
==Resistance to changes in abiotic and biotic factors==
  
Dam et al. <ref name=D16> </ref>  use a different model system (FINEL; see Dam and Bliek<ref>Dam G, Bliek AJ, Labeur RJ, Ides SJ & Plancke YMG (2007) Long-term process-based model of the Western Scheldt estuary. In: Dohmen-Janssen C.M & Hulscher SJMH (eds.), Proc. RECEM 2007, Taylor & Francis, London, p. 1077-1084.</ref>), based on finite elements to model, the long-term morphological evolution of the Western Scheldt. They evaluate the Brier Skill Score by comparing the model results with measured data (Fig. 12). In the early stages of the simulation, the score rapidly decreases, down to negative values. This is attributed to spin-up effects of the model and the absence of relevant scales in the initial conditions. After some time, however, the score increases, up  excellent value. Moreover, the results indicate that the system tends toward an equilibrium state corresponding with minimum dissipation of hydrodynamic energy. This leads to the conclusion that, in the Western Scheldt situation with all its constraints, open- loop process-based morphodynamic models may be better suited for long-term applications than for shorter-term ones.  
+
Community composition and ecosystem function may change very little under environmental change when the organisms can adapt to such change or tolerate it for some time (when the change is only temporary). However, all organisms have bounds to what they can temporarily or permanently tolerate, and when change exceeds some of these limits, the community composition and ecosystem functioning is likely to change.
  
[[Image:FigHuib13.jpg|thumb|500px|right|Figure 13: Sediment balance Western Scheldt (courtesy: Vlaams Nederlandse Schelde Commissie).]]
+
It is unlikely that communities can be resistant to ongoing gradual change, such as global warming. Acclimation and phenotypic plasticity do not suffice to maintain the system as it is. Genetic adaptation could allow community members to track such abiotic environmental change, but it is more likely that the area where the community is functioning will be invaded by species that function well at higher temperatures. The original species will thus have to deal with new competitors and predators, in addition to the changed abiotic factor. To some extent the original community can track the preferred temperature range, by moving spatially to greater depths or to alternative geographic areas. But these new areas are likely to differ in other ecological aspects such as water pressure, light climate and perhaps speeds of water flow etc.
  
Morphological changes, man-made or natural, in this type of estuaries may have important environmental effects. This concerns not only the direct effects of channel and shoal formation, but also indirect effects on the estuary and its surroundings. One example is channel deepening, which may lead to an enhanced net import of fine sediment. As a consequence, a regime shift may occur from a low-concentration and ecologically rich system to a high-concentration and ecologically impoverished system, like in the Ems and the Loire (Winterwerp and Wang<ref>Winterwerp JC & Wang ZB, 2013. Man-induced regime shifts in small estuaries - I: theory. Ocean Dynamics 63(11): 1279-1292. doi: 10.1007/s10236-013-0662-9</ref>, Winterwerp et al., 2013).  Now that the Western Scheldt in exporting sediment (Fig. 13 top; see VNSC<ref>VNSC (2013). Large-scale sediment balance of the Western Scheldt. Vlaams Nederlandse Scheldecommissie, LTV Veiligheid en Toegankelijkheid, Basic report G-2, October 2013, 81 pp. See: http://www.vnsc.eu/uploads/2014/02/g-2-grootschalige-sedimentbalans-van-de-westerschelde-v2-0.pdf </ref>), the main channel is likely to deepen, fines are being imported (Fig 13 bottom) and this regime shift may well be imminent (Winterwerp et al. <ref>Winterwerp JC, Wang ZB, van Braeckel A, van Holland G.& Kösters F (2013). Man-induced regime shifts in small estuaries - II: a comparison of rivers. Ocean Dynamics 63(11): 1293-1306. doi: 10.1007/s10236-013-0662-8</ref>).
+
==Adaptation and the consequences of mortality at different trophic levels==
  
==Lessons learned==
+
External disturbance interacts with internal mechanisms that shape community structure. To understand how an increased mortality of top-predators will affect the entire food chain, it is essential to understand how processes of mutual adaptation within food chains already give shape to existing patterns such as trophic structure (how biomass in ecosystems is partitioned between trophic levels).
  
* Multiple equilibria and tipping points are possible in the kind of systems considered herein; these phenomena cannot be found from a single run with a numerical model.
+
Abundances at different trophic levels (such as algae, herbivores, carnivores and top-predators) and their responses to increased mortality (as under environmental change) depend critically on different mechanisms of adaptation within food chains and on the importance of population density at each of these trophic levels. However, different types of adaptation to living in a food chain context (balancing the need to acquire resources with the need to avoid predation) can often have similar consequences. For example, micro-evolution of behaviour, species replacement and induced defenses at a middle trophic level may all have similar effects on trophic level abundances in disturbed food chains (Abrams and Vos 2003<ref>Abrams, P.A & Vos, M. 2003. Adaptation, density dependence and the responses of trophic level abundances to mortality. Evolutionary Ecology Research 5: 1113-1132</ref>).
* Good morphological modelling involves not only inclusion of the relevant physical mechanisms (as little as possible, as much as needed) in the constituting model equations, but also: <br/> -  a proper choice of the aggregation level, given the information one expects from the model; <br/> -  adequate parameter setting, depending on the type of model.
 
* In the case of closed-loop models: <br/> - well-considered lumping of elements, in line with the model concept; <br/> - careful interpretation of measured data in terms of the assumed equilibrium state (example: Wadden Sea basins in equilibrium during ongoing holocene sea level rise?).
 
* In the case of open-loop models: <br/> - adequate parameter setting (e.g. bed roughness, exchange coefficients, sediment grain size or granulometry, sediment transport parameters, timescale factor); <br/> -  proper location of the boundaries, given the information available for the boundary conditions (from measurements or from ambient models); in the case of a strong interaction with the ebb-tidal delta, for instance, it makes little sense to place a model boundary in the gorge of the tidal inlet or the estuary mouth; <br/> -  well-chosen hydrodynamic boundary conditions; the drivers of estuarine processes are variable, some  randomly (e.g. storm surges and wave events) and some at a wide range of timescales (tides), it is hardly feasible to drive models for hundreds of years with a realistic time signal; hence the boundary conditions need to be schematized carefully in order to represent the net effect of these variable conditions; special attention needs to be paid to overtides, because they correspond with tidal asymmetry, hence a residual transport capacity; <br/> - well-considered boundary conditions for the sediment transport; if there is a net river inflow, this will bring sediment into the system and a net sediment flux needs to be imposed at the upstream boundary; in the case of suspended load transport, a transport boundary conditions is also needed at the seaward boundary; this condition has to take into account that sediment exported during ebb does not simply vanish to open sea, but lingers on the ebb-tidal delta and may be transported back by the next flood; <br/> -  well-chosen erodibility conditions, at the bottom (hard layers) as well as at erodible lateral boundaries (displacement rate, sediment production); <br/> -  initial conditions preferably resolving the relevant spatial scales; <br/> - don’t forget wind forcing within the model area, especially when taking extreme events into account; given the pronounced topography in an estuary or tidal basin, wind forcing will give rise to horizontal circulations, grossly along the wind direction in shallow areas and against the wind direction in the deeper channels between them; note that wind forcing also puts demands on the nature of the boundary conditions.
 
* Simplified analytical models and numerical models for idealized situations (e.g. a straight or funnel-shaped estuary) may give useful additional insight into the morphological behaviour and help validating more complex and refined models.
 
* In the case of complex systems like estuaries and tidal basins, model results are usually not reliable in every detail; rather should one focus on overall patterns, such as channel-shoal patterns, and trends; given the random forcing, it might even be recommendable - computer power permitting - to run models in ensemble-mode, in order to have an impression of the range of possible outcomes.  
 
* In view of the previous point, assessing model results by point-to-point comparison with measured data is difficult; statistical approaches, such as the Brier Skill Score and its sub-scores, may provide more useful information.
 
* Even if model results tend to deviate more from measured data as time proceeds, this does not mean that the model performance in terms of the Brier Skill Score gets worse; in a system like the Western Scheldt, the score may even increase to excellent in the long run; 
 
* Movies of model results and consecutive topographic surveys reveal a lot more information than a series of static plots.
 
  
  
 
==Related articles==
 
==Related articles==
 
+
:[[Integrated Coastal Zone Management (ICZM)]]
 
+
:[[Thresholds of environmental sustainablility]]
[[Definitions, processes and models in morphology]]
+
:[[Sustainability indicators]]
 
[[Morphology of estuaries]]
 
 
 
[[Case studies: Long term predictions for estuaries]]
 
 
 
 
 
  
  
 
==References==
 
==References==
 +
<references/>
  
<references/>
 
  
 +
<br>
  
 
{{author
 
{{author
|AuthorID=13329
+
|AuthorID=11928
|AuthorFullName=Huib de Vriend
+
|AuthorFullName=Vos, Matthijs
|AuthorName=Huib de Vriend}}
+
|AuthorName=Matthijs}}
  
[[Category:Estuaries and tidal rivers]]
+
[[Category:Coastal and marine ecosystems]]
[[Category:Hydrodynamics]]
+
[[Category:Integrated coastal zone management]]
[[Category:Land and ocean interactions]]
 
[[Category:Hydrological processes and water]]
 
[[Category:Geomorphological processes and natural coastal features]]
 

Revision as of 14:04, 24 August 2020



Resilience and resistance


Definition of Resistance:
The capacity to weather a disturbance without loss (Lake 2013[1]).
This is the common definition for Resistance, other definitions can be discussed in the article


Definition of Resilience:
(1) the capability to anticipate, prepare for, respond to, and recover from significant multihazard threats with minimum damage to social well-being, the economy, and the environment (sometimes called 'socio-ecological resilience')(Olsen et al. 2019[2]);

(2) the capability of a (socio-)ecological system to remain within a stability domain when subjected to environmental change, while continually changing and adapting yet remaining within critical thresholds (sometimes called 'general resilience') (Folke et al. 2010[3]; Scheffer 2009[4]; Brand and Jax 2007[5]);

(3) the capacity to experience shocks while retaining essentially the same function, structure, feedbacks, and therefore identity (sometimes called 'ecological resilience') (Brand and Jax 2007[5]; DEFRA 2019[6]), which is closely related to the concept of 'ecosystem resistance': the amount of disturbance that a system can withstand before it shifts into a new regime or an alternative stable state (Holling 1973[7]; Gunderson 2000[8]);

(4) the capacity of an ecosystem to regain its fundamental structure, processes, and functioning (or remain largely unchanged) despite stresses, disturbances, or invasive species (e.g., Hirota et al., 2011[9]; Chambers et al., 2014[10]; Pope et al., 2014[11]; Seidl et al., 2016[12]), which can be measured by the time needed to recover its original state (sometimes called 'engineering resilience'[1]).
This is the common definition for Resilience, other definitions can be discussed in the article


Introduction

Coastal and marine ecosystems are affected by environmental disturbance at a variety of spatio-temporal scales. The organisms inhabiting these systems are adapted to such disturbance, either by being tolerant of these conditions or by playing a role in one or more of the successional stages that follow during ecosystem recovery.

If all species in the system were tolerant to a particular perturbation, very little would change at the ecosystem level, and we could call the system resistant to this disturbance. However, often a disturbance, such as a temporary very low oxygen level, affects a substantial proportion of the organisms dramatically, either causing them to die, or forcing them to rapidly migrate to more favorable parts of the environment. Such an adverse disturbance could locally defaunate a certain volume in the pelagic or a certain area of hard or soft substrate. Such destruction at a local scale does not mean the end of local functioning. Usually organisms are available at a larger spatial scale that can re-colonize the affected area, according to their particular tolerances and abilities to favorably affect their local environment.

The term resilience has been defined in different ways, illustrated in the definition above. According to DEFRA (2019[6]) there is limited consensus in the literature about how resilience can be characterized and assessed. The term resilience is sometimes used to represent some kind of normative proposition about what kinds of ecosystem characteristics are desirable or necessary in the context of sustainable development, reflecting particular cultural and philosophical assumptions[6]. However, the resistance of an ecosystem (see the definition above) to changing conditions and the rate of recovery following some disruptive event are generally considered major components of resilience that can in principle be expressed in quantitative terms.

Other attributes such as the capacity of ecosystems to transform and adapt in the face of environmental change (i.e. system's ability to re-organize itself) are more difficult to translate to practice. According to Dawson et al. (2010[13]), resilience concerns the response of ecosystems to changing environmental conditions and must be looked at alongside other additional dynamic features, namely durability, robustness and stability. These concepts can be defined as[13]:

  • Durability: ability to cope with a chronic stress, but the source of this stress is endogenous;
  • Robustness: ability to recover or maintain the systems' social-ecological functions in the face of an external and chronic driver;
  • Stability: system’s tolerance to transient and endogenous shocks or disruptions.

Both resistance and resilience cause an ecosystem to remain relatively unchanged when confronted to a disturbance, but in the case of resistance no internal re-organization and successional change is involved. In contrast, resilience implies that the system is internally re-organizing, perhaps through a mozaic of patches that are at different stages of re-assembly. System responses to changing environmental conditions are displayed schematically in Fig. 1, corresponding to different resilience characteristics.

Figure 1. Schematic representation of the trajectories of a (socio-)ecological system in a plane defined by the system state (fundamental structure, processes, and functioning - vertical axis) and the change of environmental conditions (horizontal axis), for different resilience characteristics (a, b, c, d). The initial state corresponds to the position on the graph at the vertical axis (zero change in environmental conditions). In all situations the ecosystem is assumed to collapse irreversibly (down to the horizontal axis) when the change in environmental conditions is much greater than the systems' resistance. The angle [math]\alpha[/math] represents the rate at which the system recovers when the change in environmental conditions is reduced (small [math]\alpha[/math] means slow recovery, large [math]\alpha[/math] means fast recovery). Panel a: Resilience characterized by high resistance (definition 3) and slow recovery (definition 4). Panel b: Resilience characterized by low resistance and fast recovery. Panel c: Resilience characterized by a shift to an alternative stable system state. Panel d: Low resilience, characterized by low resistance and slow recovery.


When considering the potential effect of a certain type of disturbance it is thus useful to ask two questions:

  1. Will the species of this system be able to tolerate it (implying resistance), and if not,
  2. Is recovery possible through a successional trajectory, back to the same, or at least a desirable, ecosystem state (implying resilience)?

Resistance breaks down when uni-directional ongoing change acts faster than the organisms' ability to adapt their tolerances. If uni-directional ongoing change is this fast (even if gradual), the system will not be sufficiently resilient either, as full recovery through succession will then not be possible. Recovery from sudden and local disturbance is often possible through recolonization, but the rate of recovery will depend crucially on the spatial extent of disturbance. For example, recovery from anoxia could take 5 to 8 months at the scale of square meters (Rossi et al. 2009[14]), but could take 5 to 8 years at the scale of a whole bay (Diaz & Rosenberg 1995[15]).

According to definition (4), the speed at which an ecosystem returns to its former state following a (minor) disturbance can be considered a measure of resilience. The idea is that a system with a short return time is more resilient than one with a long return time. Such resilience measured as (1 / the return time to a stable equilibrium) has also been called engineering resilience. It has however a long history of use among ecologists (Pimm 1982[16], DeAngelis 1992[17], Vos et al. 2005[18]). Resilience is also used in a way that more closely resembles the definition of resistance. Ecological resilience was defined as the amount of disturbance that an ecosystem could withstand without changing self-organized processes and structures (definition 3).

Resilience of coastal systems largely depends on biodiversity, which is a major requirement for allowing ecosystems to adapt to changing conditions. The human impact on the environment through pollution, fisheries, sediment erosion / deposition and global climate change has brought about much faster change than would occur under natural conditions, putting severe stress on many ecosystems. Without genetic diversity, natural selection cannot occur and if natural selection is limited, adaptation is impossible. Preservation of biodiversity and, more specifically, genetic diversity is therefore of paramount importance for successful adaptation to our rapidly changing environments. However, biodiversity may not always protect ecosystems from major abiotic disturbances (Folke et al. 2004[19]).

Resilience through recolonization

To understand resilience of ecosystems it is essential to understand what drives succession within these ecosystems. Succession determines how, and how fast, communities return to their original state, or perhaps enter a new state. Many aspects of succession can be understood in terms of trade-offs between the ability to be either a good early (re)colonizer, or a good competitor. Succession involves a gradual replacement of colonizer/competitor species according to the degree to which they tolerate, facilitate or inhibit certain environmental conditions and other species (Rossi et al. 2009[14]). The extent to which processes of (re)colonization and succession can take place largely determines the recovery of ecosystems after major disruption and is therefore an essential characteristic of the resilience of ecosystems.

In this context, it is important to consider the spatial component of ecosystem resilience. Diversity of structurally and functionally connected landscapes, rich in resources and species, promotes the flow or movement of individuals, genes, and ecological processes. Below certain thresholds of connectivity the capacity to regain structure and function after perturbation is lost (Holl and Aide, 2011; Rudnick et al., 2012;McIntyre et al., 2014; Rappaport et al., 2015; Ricca et al., 2018). Chambers et al. (2019[20]), based on Allen et al. (2016[21]), have therefore introduced the concept of 'spatial resilience', which is a measure of how spatial attributes, processes, and feedbacks vary over space and time in response to disturbances and affect the resilience of ecosystems. Self-organization through strong feedbacks at multiple scales and high levels of functional diversity and redundancy, stabilizes the system with respect to disturbances within the range of historic variability.

When creating Marine Protected Areas, the sources of populations at all stages of succession should be protected, to preserve 'ecological memory' to the fullest possible extent. This includes protecting not only 'high quality' habitats that harbour healthy mature communities, but also 'low quality' and disturbed habitats that are required for those species that contribute to early recovery of perturbed areas (Rossi et al. 2009[14]). The selection of Marine Protected Areas thus involves evaluating the number, size, and spatial configuration of habitat fragments and degree of connectivity required to support restoration of ecosystems and conservation of focal habitats and species[20][2].

Resistance to changes in abiotic and biotic factors

Community composition and ecosystem function may change very little under environmental change when the organisms can adapt to such change or tolerate it for some time (when the change is only temporary). However, all organisms have bounds to what they can temporarily or permanently tolerate, and when change exceeds some of these limits, the community composition and ecosystem functioning is likely to change.

It is unlikely that communities can be resistant to ongoing gradual change, such as global warming. Acclimation and phenotypic plasticity do not suffice to maintain the system as it is. Genetic adaptation could allow community members to track such abiotic environmental change, but it is more likely that the area where the community is functioning will be invaded by species that function well at higher temperatures. The original species will thus have to deal with new competitors and predators, in addition to the changed abiotic factor. To some extent the original community can track the preferred temperature range, by moving spatially to greater depths or to alternative geographic areas. But these new areas are likely to differ in other ecological aspects such as water pressure, light climate and perhaps speeds of water flow etc.

Adaptation and the consequences of mortality at different trophic levels

External disturbance interacts with internal mechanisms that shape community structure. To understand how an increased mortality of top-predators will affect the entire food chain, it is essential to understand how processes of mutual adaptation within food chains already give shape to existing patterns such as trophic structure (how biomass in ecosystems is partitioned between trophic levels).

Abundances at different trophic levels (such as algae, herbivores, carnivores and top-predators) and their responses to increased mortality (as under environmental change) depend critically on different mechanisms of adaptation within food chains and on the importance of population density at each of these trophic levels. However, different types of adaptation to living in a food chain context (balancing the need to acquire resources with the need to avoid predation) can often have similar consequences. For example, micro-evolution of behaviour, species replacement and induced defenses at a middle trophic level may all have similar effects on trophic level abundances in disturbed food chains (Abrams and Vos 2003[22]).


Related articles

Integrated Coastal Zone Management (ICZM)
Thresholds of environmental sustainablility
Sustainability indicators


References

  1. 1.0 1.1 Lake, P.S. 2013. Resistance, Resilience and Restoration. Ecological Management and Restoration 14: 20-24
  2. 2.0 2.1 Olsson, S., Melvin, A. and Giles, S. (eds.) 2019. Climate change and ecosystems. Procs. Sackler Forum on Climate Change and Ecosystems, Washington, DC, November 8-9, 2018, organized by the National Academy of Sciences and The Royal Society
  3. Folke, C., Carpenter, S. R., Walker, B., Scheffer, M., Chapin, T. and Rockstrom, J. 2010. Resilience thinking: integrating resilience, adaptability and transformability. Ecology and Society 15(4): 20
  4. Scheffer, M. 2009. Critical transitions in nature and society. Princeton University Press, Princeton, New Jersey, USA
  5. 5.0 5.1 Brand, F.S. and K. Jax. 2007. Focusing the meaning(s) of resilience: resilience as a descriptive concept and a boundary object. Ecology and Society 12(1):23
  6. 6.0 6.1 6.2 Haines‐Young, R. and Potschin. M. (eds.) 2010. The Resilience of Ecosystems to Environmental Change (RECCE). Overview Report, 27 pp. Defra Project Code: NR0134
  7. Holling, C.S. 1973. Resilience and stability of ecological systems. Annual Rev. Ecol. Syst. 4: 1–23. doi: 10.1146/annurev.es.04.110173.000245
  8. Gunderson, L.H. 2000. Ecological Resilience - in Theory and Application. Annual Review of Ecology and Systematics 31:425-439.
  9. Hirota,M., Holmgren,M., Van Nes, E. H, and Scheffer,M. 2011. Global resilience of tropical forest and savanna to critical transitions. Science 334: 232–235. doi: 10.1126/science.1210657
  10. Chambers, J. C., Bradley, B. A., Brown, C. S., D’Antonio, C., Germino, M. J., Grace, J. B., et al. 2014. Resilience to stress and disturbance, and resistance to Bromus tectorum L. invasion in the cold desert shrublands of western North America. Ecosystems 7: 360–375. doi: 10.1007/s10021-013-9725-5
  11. Pope, K. L., Allen, C. R., and Angeler, D. G. 2014. Fishing for resilience. T. N. Am. Fisheries Soc. 143: 467–478. doi: 10.1080/00028487.2014.880735
  12. Seidl, R., Spies, T. A., Peterson, D. L., Stephens, S. L., and Hick, J. A. 2016. Searching for resilience: addressing the impacts of changing disturbance regimes on forest ecosystem services. J. Appl. Ecol. 53 : 120–129. doi: 10.1111/1365-2664.12511
  13. 13.0 13.1 Dawson, T.P., Rounsevell, M.D.A., Kluvankova‐Oravska, T., Chobotova V. and Stirling, A. 2010. Dynamic properties of complex adaptive ecosystems: implications for the sustainability of services provision. Biodiversity and Conservation 19: 2843‐2853
  14. 14.0 14.1 14.2 Rossi, F., Vos, M. & Middelburg, J.J. 2009. Species identity, diversity and microbial carbon flow in reassembling macrobenthic communities. Oikos 118: 503-512.
  15. Diaz, R.J. & Rosenberg, R. 1995. Marine benthic hypoxia: a review of its ecological effects and the behavioural responses of benthic macrofauna. Oceanogr. Mar. Biol. Annu. Rev. 33:245-303.
  16. Pimm, S.L. 1982. Food Webs. The University of Chicago Press.
  17. DeAngelis, D.L. 1992. Dynamics of Nutrient Cycling and Food Webs. Chapman and Hall, London.
  18. Vos, M., Kooi, B.W., DeAngelis, D.L. & Mooij, W.M. 2005. Inducible defenses in food webs. In: Dynamic Food Webs. Multispecies Assemblages, Ecosystem Development and Environmental Change. Eds. P.C. de Ruiter, V. Wolters & J.C. Moore. Academic Press. Pp. 114-127.
  19. Folke, C., Carpenter, S., Walker, B., Scheffer, M., Elmqvist, T., Gunderson, L. & Holling, C.S. 2004. Regime Shifts, Resilience, and Biodiversity in Ecosystem Management. Annual Review of Ecolog and Systematics 35:557-581.
  20. 20.0 20.1 Chambers, J.C., Allen, C.R. and Cushman, S.A. 2019. Operationalizing Ecological Resilience Concepts for Managing Species and Ecosystems at Risk. Front. Ecol. Evol. 7:241. doi: 10.3389/fevo.2019.00241
  21. Allen, C. R., Angeler, D. G., Cumming, G. S., Folk, C., Twidwell, D., and Uden, D. R. 2016. Quantifying spatial resilience. J. Appl. Ecol. 53, 625–635. doi: 10.1111/1365-2664.12634
  22. Abrams, P.A & Vos, M. 2003. Adaptation, density dependence and the responses of trophic level abundances to mortality. Evolutionary Ecology Research 5: 1113-1132



The main author of this article is Vos, Matthijs
Please note that others may also have edited the contents of this article.

Citation: Vos, Matthijs (2020): Testpage3. Available from http://www.coastalwiki.org/wiki/Testpage3 [accessed on 28-03-2024]