The use of ecological models in the sustainable management of estuaries

Transcription

1 The use of ecological models in the sustainable management of estuaries N. J. Frost, S. C. Hull & S. M. Freeman Associated British Ports Marine Environmental Research Ltd., UK Abstract Estuaries and other sheltered, coastal environments are common locations for centres of human population and associated facilities. These areas are also often characterised by important intertidal habitats that support large populations of sediment-dwelling animals and the birds that feed upon them. Such areas are generally protected under national, European or international nature conservation legislation and agreements. Proposed developments that involve loss of or alteration to intertidal habitats must, therefore, give careful consideration to impacts on the associated faunal communities. At present, the ability to make quantitative predictions of how habitats and their faunal communities will change in response to physical changes in an estuary is limited. This paper describes two ecological models that predict such responses. Long-term monitoring data from the Humber Estuary (UK) were used to identify the distributions of key habitat types and invertebrate communities. Multivariate statistical methods were then used to identify key physical parameters that describe the variation in the distribution of these habitats and communities. A rule-based model was developed using a decision tree algorithm to predict changes in the distribution and occurrence of habitats and associated faunal communities using the strength of their association with the physical environment. The resulting predicted patterns were well correlated with observed data for the estuary. The same sets of rules were then applied to predict the changes that would occur in response to changes in the physical environment (predictor variables) following long-term natural changes in the morphology and hydrodynamics of the estuary. Such ecological models have the potential to make a significant contribution to the sustainable management of estuaries. Keywords: ecological modelling, management tool, ecosystem approach, estuaries research.

2 18 Coastal Environment V, incorporating Oil Spill Studies 1 Introduction The need for ecosystem based approaches for marine management is now widely recognised. In Europe, for example, environmental Directives relating to nature conservation (92/43/EEC), environmental impact assessment (85/337/EEC as amended - 97/11/EC), strategic environmental impact assessment (2001/42/EC) and water resources (2000/60/EC) have all sought to promote ecosystem-based approaches. However, despite these strong policy drivers and recognition amongst the scientific community of the need, the development of tools for ecosystem management has been slow, Haeber and Franklin [1]. In part, this is a reflection of the technical complexity of developing such tools and an inadequate understanding of ecosystem function, which requires the integration of mathematical, physical and biological disciplines. The dynamic response of an ecosystem to natural and/or anthropogenically induced change is a product of a number of complex physio-chemical and biological interactions [2]. Changes in the morphology of an estuary, for example, can have consequences for the associated hydrodynamic conditions, sediment dynamics and the habitats and species it supports. The linkages that exist between these parameters have consequences for the feedback mechanisms which operate between all of these processes. To adequately model ecosystem changes it is therefore necessary to have an ability to predict the changes caused by the drivers and then to model the associated linkages between the physiochemical and ecological processes. The recognition of the nature of anthropogenic change and the resulting changes that can occur across a number of spatial and temporal scales has led to a major investment in estuaries research (e.g. EMPHASYS Consortium [3]). Recently such research has been expanded to include the consequences of anthropogenic change on ecological resources [2]. This paper presents one approach that has been adopted to the development of habitat and intertidal invertebrate models for the Humber Estuary, UK that can be used to generate predictions of system level responses to anthropogenic and natural change. 2 Methods 2.1 Study area The Humber Estuary is the largest macro tidal plain estuary on the British North Sea Coast, with a total area of over 24, 470 hectares (Figure 1). The estuary can be divided into three main regions; the inner, middle and outer Humber (Townend et al [4]). 2.2 Overall approach The approach has involved the integration of hydrodynamic and sediment transport models with information on the distribution and abundance of ecological resources (saltmarsh, intertidal mudflat/sandflat, intertidal

3 Coastal Environment V, incorporating Oil Spill Studies 19 invertebrates) through the development of regression and rule-based models that link physical and ecological parameters. The outputs from these models provided layers of data within the GIS software ArcView (version 8), which was then used to develop maps showing predicted distribution patterns of intertidal habitats and species. Figure 1: The geographical location of the Humber estuary. 2.3 Hydrodynamic model A 3D hydrodynamic model developed by ABPmer and WL Delft Hydraulics [5] was used to simulate the hydrodynamic conditions of the Humber estuary. The boundaries of the model extended upstream to the tidal limit of the estuary and downstream to the seaward limit at the estuary mouth. The model divides the estuary into a series of grid cells with an average grid resolution of 1-2km seaward of Spurn, to approximately m in the rivers. The parameters incorporated into the model were elevation (modn), bed shear stress (N/m 2 ), current speed (m/s) and salinity. These parameters were considered important in affecting the distribution and abundance of habitats and invertebrates [see 6,7]. Since, elevation is an important parameter in determining the distribution of habitats and invertebrates within the intertidal zone, the model grid cells were refined to a greater resolution (10m x 10m) using data from the radar system Light Detection and Ranging (LiDAR). By coupling the hydrodynamic model with data from LiDAR, it was possible to predict changes in each of the physical parameters, including water levels, for each individual grid cell. These modelled parameters were then used to predict the distribution of habitats and invertebrates within the Humber Estuary under current and future environmental conditions. The hydrodynamic model was also used to predict physical conditions on the estuary under a future scenario where the underlying morphology / bathymetry of the estuary is changed. In this scenario, the model outputs were based on a bathymetry for 2050 simulated from a predictive morphological model of the Humber estuary using ESTMORPH [8].

4 20 Coastal Environment V, incorporating Oil Spill Studies 2.4 Predicting habitat distributions To predict the lower and upper limits of saltmarsh habitat, two equations, which were based on simple tidal parameters, were used. A regression equation to predict the lower limits of saltmarsh distribution throughout the Humber estuary using Mean High Water Neaps (MHWN) was adopted from Clarke and Brown [9], where: Lower limit of saltmarsh = MHWN (1) Similarly the upper limit of saltmarsh was estimated via an equation developed by Binnie, Black & Veatch Ltd. [10]. The upper limit of saltmarsh was related to the level of Mean High Water Springs (MHWS) and has been defined as: Upper limit of Saltmarsh = MHWS + 0.3m (2) The equations (1) and (2) were applied to each of the model grid cells. Cells where LiDAR elevations were above the predicted lower limit and below the upper limit of saltmarsh were described as saltmarsh. Those cells within the intertidal area, which contained elevations greater than the estimated upper limit of saltmarsh were classified as transitional marsh/ grassland. The distribution of Phragmites is difficult to predict in relation to any single physical factor, however, it is strongly associated with fluctuations in salinity. Phragmites typically tolerates salinities between 2-12ppt, Hellings and Gallagher [11], and this range was used as a guide in the current study. The average salinity throughout the Humber Estuary was derived from the hydrodynamic model [5] and cells with salinities below 12ppt were considered to potentially contain Phragmites. Lower limits were estimated using equation 3 from Binnie, Black & Veatch Ltd. [10], where: Lower limit of Phragmites = MHWS (3) The upper limit of Phragmites was taken as the upper limit of the intertidal. The remaining intertidal areas, below that estimated as the lower limit of saltmarsh and reedbed habitats, were classified as mudflat. The spatial extent of all four habitat types were mapped in ArcView and their approximate area calculated. The predicted area of mudflat, saltmarsh and reedbed provided a broadscale estimate of each habitat type on the estuary. All outputs have been verified visually by comparing their distribution patterns to those actually recorded in the Humber estuary by Binnie, Black & Veatch Ltd. [10] and English Nature [12]. 2.5 Predicting invertebrate species and assemblage distribution The Humber Estuary was divided into three broad patches each representing a distinct invertebrate assemblage. Patches were based on a fifteen-year dataset collected annually by the Environment Agency (Figure 2). The stability of these patches through time has been demonstrated by ABPmer [13]. To develop a more precise understanding of the linkage between the hydrodynamics of the Humber estuary and the species associated with each assemblage a detailed

5 Coastal Environment V, incorporating Oil Spill Studies 21 sampling programme was conducted in October In each of the three assemblages, sampling stations were located across a range of elevations, with the exact co-ordinates of each station recorded. To obtain the physical characteristics for each of the sample stations, data relating precisely to the coordinates collected for each station was extracted from our hydrodynamic model and the LiDAR grid layer using ArcView. To investigate the strength of the association between the biotic and abiotic parameters the computer learning system AnswerTree was used [14]. As an exploratory analysis tool, AnswerTree creates classifications displayed in a decision tree by attempting to relate predictive attributes (e.g. likelihood of an invertebrate occurring) to values from continuous variables. A classification and regression tree algorithm, was used to create each of the decision trees. The resulting trees provide a means of representing hierarchical structure in the data. The structure forms the basis for a set of rules from which predictions about the distribution of benthic invertebrates can be made. Figure 2: Distribution pattern of benthic assemblages based on a fifteen year dataset collected by the Environment Agency. Rules were developed for six intertidal species, where the relative abundance of each species was considered indicative of each assemblage type identified on the Humber Estuary. The species used were Hediste diversicolor, Nephtys hombergii, Macoma balthica, Tubificoides pseudogaster, Tubificoides benedii and Corophium volutator. The rules for predicting the distribution of each invertebrate species were applied to the entire intertidal sections of the model grid. A spatial map representing relatively high, medium and low abundance levels for each species was then produced. Assemblages were inferred from the overlaying of the distribution maps of the six macrobenthic invertebrates. The rules were validated by comparing the predicted distribution maps with observed data collected by the Environment Agency and ABPmer (unpublished data).

6 22 Coastal Environment V, incorporating Oil Spill Studies 3 Results 3.1 Habitat distributions Current estimates for the area of intertidal on the Humber Estuary are in the region of 10,000ha. Within this area saltmarsh, reedbed and transitional marsh/grassland habitats account for an estimated 940ha with the remainder of the area comprising mudflat, Environment Agency [15]. Our habitat model predicted the total area of intertidal as ~8,300ha for the same year, in 2000, which is less than that derived from the observed data (Table 1). For saltmarsh, reedbed and transitional marsh/grassland habitats the combined area predicted was ~920ha; equating to <3% difference from the observed data. The habitats predicted throughout the estuary do, however, compare well with observed distribution maps produced by English Nature [12] and Binnie, Black & Veatch Ltd. [10]. Throughout the middle and outer estuary the width of the mudflat habitat steadily increases on both the north and the south bank. There are also scattered patches of saltmarsh and grassland along the fringes of the intertidal zone which are broadly consistent with the two previous surveys. The predicted habitats for 2050 resulted in an overall reduction in the total intertidal area. In contrast the area of transitional marsh/grassland was predicted to increase. The change in habitat from saltmarsh to transitional marsh was particularly apparent in the inner estuary (Figure 3). Figure 3: Predicted distribution patterns of habitats for the inner regions of the Humber Estuary for 2000 and 2050 scenarios.

7 Coastal Environment V, incorporating Oil Spill Studies 23 Table 1: Predicted and observed [10,12] habitat types on the Humber Estuary. Habitat Type Observed 2000 Predicted 2000 Predicted 2050 Intertidal Area ha 8322 ha 7748 ha Transitional Marsh/ Grassland 140 ha 130 ha 172 ha Saltmarsh 627 ha 668 ha 529 ha Reedbed 173 ha 123 ha 72 ha Mudflat 9060 ha 7401 ha 6975 ha 3.2 Invertebrate distributions Predictive maps constructed for the relative abundance of six intertidal species were strongly correlated with observed data collected by the Environment Agency. The distribution of N. hombergii, for example, was predicted to be relatively low throughout the entire estuary, with the exception of Spurn Peninsula. This was consistent with the Environment Agency data where numbers of this species were low throughout the estuary. Similarly the relative abundance of C. volutator was both predicted and observed to be highest in the mid and inner regions of the Humber. The bivalve M. balthica had the highest predicted abundance levels at higher tidal elevations with progressively fewer towards the low water mark. The observed abundance of this species was generally lower than that predicted from the model. The key species for separating out the inner estuary assemblage was Tubificoides pseudogaster. The relatively low abundance of all other species in this area also helped to delineate this assemblage. The mid and outer assemblages were largely divided on the basis of the predicted distribution of C. volutator and N. hombergii with these being relatively higher in the mid and outer estuary respectively. These very broad scale assemblages drawn up on this basis of predicted invertebrate species distributions were largely consistent with those established using Environment Agency data (Figure 2). The predicted distribution of each of the six invertebrate species was also predicted for the 2050 scenario. The predicted abundance of H. diversicolor, M. balthica, C. volutator and T. benedii remains relatively unchanged over the fifty year period. The relative abundance of T. pseudogaster, however, is reduced in the 2050 scenario compared to By contrast, predictions for N. hombergii showed an increase in its relative abundance throughout the estuary. The overall predicted assemblages for 2050 differed very little from those predicted in 2000, although their distribution shifted slightly towards the inner estuary in Discussion Distinct and recurrent patterns in the distribution of habitats and their associated invertebrates can be found in most aquatic environments. The challenge for environmental science is to identify and link the physical and ecological processes responsible for these patterns [7]. The present study provides one such

8 24 Coastal Environment V, incorporating Oil Spill Studies approach to quantifying the strength of the association between estuarine habitats and their fauna with the physical environment. Moreover, our results show that by integrating a 3D hydrodynamic model with GIS it is possible to predict present and future changes in the ecology of the Humber Estuary. The similarity between the predicted and observed distribution maps for each habitat and species examined clearly demonstrated the successful application of a rule-based approach for system level predictions. In addition, it provides confidence that predictions, made in response to natural changes in the physical environment, will reflect the distribution patterns of estuarine habitats and their associated assemblages under future scenarios. The differences between the observed and predicted intertidal area may be due to a number of reasons associated with the dataset. The intertidal islands in the middle of the channel, for example, were excluded from the analysis. A number of the smaller islands are transient in nature and there is a general lack of sample data for these sites. The predicted area would also be underestimated in areas with poor LiDAR coverage, this is particularly apparent on the south bank towards the estuary mouth. Grid cells, which contained no estimate of elevation, could not be included in the analysis. Despite these minor limitations, the habitats predicted throughout the estuary showed a strong association with the observed distribution maps produced by English Nature [12] and Binnie, Black & Veatch Ltd. [10]. On the north and south bank of the inner estuary, for example, there were relatively large patches of reedbed behind areas of saltmarsh that correspond closely to those observed by English Nature [12]. The predicted distribution maps for these habitats, therefore, provide a realistic estimate of abundance and distribution at a systems or estuary level. For the 2050 scenario a major contribution to the reduction in intertidal area is the constrained upper limit of the intertidal zone, which is a limitation of the hydrodynamic models. In reality, sea level rise will potentially result in landward transgression of the estuary, particularly where grassland habitats are present in front of sea defences. It is likely that some of the current grassland areas would be eroded and form new intertidal area under this scenario. This has relatively large implications for the predicted extent of intertidal area under future scenarios and is likely to produce an over-estimate of the overall losses of intertidal habitats. It is also important to remember that the rules that have been applied are generic and do not take into account localised or site specific issues. The lower limit of saltmarsh, for example, is variable throughout the estuary. The application of a single equation to derive this level does not take into account more site specific issues such as fetch or gradient. This approach is therefore useful for broad scale predictions but requires further refinement and validation if a more detailed site assessment is required. The predicted distribution of the invertebrate species in the 2000 scenario compare relatively well with observed data for the estuary. Where discrepancies do occur this may due to the interpolation of data throughout the estuary and differences in the classification of tidal boundaries between the organisations.

9 Coastal Environment V, incorporating Oil Spill Studies 25 Where differences were observed in the distribution of species between 2000 and 2050 this may be explained by the changes in physical parameters under the different scenarios and the associated tolerance limits of each of the species. The differences in the predicted abundance of T. pseudogaster, for example, may be explained by the predicted reduction in the average salinity in the inner estuary as compared to The distribution of this species may therefore shift to the extreme inner estuary rather than deplete in numbers. Similarly the predicted change in the distribution of N. hombergii may be due to a change in the predicted water levels. It is not unexpected that the overall assemblages will not change dramatically through time as long term data sets reflect the relative stability of these areas (EA - unpublished data). It should be noted, however, that based on only six species and the additional assumptions used to derive these predictions the results should be interpreted with caution. They can only used to provide a broad scale indication of the likely changes in assemblage distribution. A number of enhancements can be made to the approach used to predict the distribution of habitats and assemblages. It would be possible to remove gaps in the LiDAR coverage and gaps generated at the land boundary of the hydrodynamic model through a process of interpolation. Additional factors such as organic content of the sediment could also be incorporated into the models. The continuation of this work will involve an ongoing process of rule enhancement and validation as new data is collected. As a macrotidal estuary the Humber is possibly a good choice for the development of a physically driven ecological model because the controlling role of estuary processes in determining habitat and species distributions is clear and minimises the significance for biotic interaction. In other systems it may be necessary to place a greater emphasis on the role of biological interactions in shaping community structure, although it remains to be demonstrated that such interactions are predominant at an estuary scale over long time periods. Further validation is required for such models but this research has demonstrated their value in providing tools for environmental managers. It is, however, essential to understand the purpose for which such ecological models will be used and the limitations that are associated with each of the approaches. References [1] Haeber, R. & Franklin, J., Perspectives on ecosystem management. Ecological Applications, 6, pp , (1996). [2] DEFRA & Environment Agency, Broad Scale Ecosystem Impact Modelling Tools: Scoping Study. Report FD2108, [3] EMPHASYS Consortium, A Guide to Prediction of Morphological Change within Estuarine Systems. MAFF Report TR 114, [4] Townend, I., Pethick, J., Balson, P., Roberts, W. & Young, R., The geomorphology of the Humber Estuary. Proc. Of the 35th MAFF Conference of River and Coastal Engineers, Keele University pp , 2000.

10 26 Coastal Environment V, incorporating Oil Spill Studies [5] ABPmer and WL Delft Hydraulics, Humber Estuary Shoreline Management Plan - Stage 2. Hydrodynamic Model Calibration Report, R.1004, [6] Toft, A. R. & Maddrell, R. J., A guide to the understanding and management of saltmarshes, National Rivers Authority Project 444, [7] Freeman S. M. & Rogers S. I. A new analytical approach to the characterisation of macro-epibenthic habitats: linking species to the environment. Estuarine, Coastal and Shelf Science, 56. pp , [8] Wang, Z. B. and Jeuken, C., Morphologic modelling of the Humber Estuary with ESTMORPH. WL Delft Hydraulics, [9] Clarke, R. & Brown, S., Model predictions of saltmarsh limits in the Humber Estuary, Centre for Ecology and Hydrology, [10] Binnie, Black & Veatch Ltd, Humber Estuary Habitat Migration Study. Prepared for the Environment Agency, [11] Hellings, S.E. & Gallagher, J.L., The effects of salinity and flooding on Phragmites australis. Journal of Applied Ecology, 29, pp , [12] English Nature, The Humber Estuary European Marine Site. English Natures Regulation 33 Interim Advice, [13] ABPmer, The prediction of Invertebrate Species Distributions on the Humber Estuary, Report No. R.1017, [14] SPSS Inc., AnswerTree 2.0 user s guide. SPSS Inc., Chicago, USA. ISBN , [15] Environment Agency, Humber Estuary Shoreline Management Plan. Environment Agency Report, 2000.