Relationship between shoreline substrate type and ...

Ocean & Coastal Management 66 (2012) 12e18

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Relationship between shoreline substrate type and sensitivity of seafloor habitats at risk to oil pollution R. Leiger a, b, R. Aps a, J. Kotta a, *, Ü.K. Orviku c, M. Pärnoja a, H. Tõnisson c a

Estonian Marine Institute, University of Tartu, Mäealuse 14, 12618 Tallinn, Estonia Estonian Maritime Academy, Tallinn, Estonia c Institute of Geography, University of Tallinn, Tallinn, Estonia b

a r t i c l e i n f o

a b s t r a c t

Article history: Available online 14 May 2012

The aim of this paper is to study the relationship between the shoreline substrate type, the shoreline substrate sensitivity (in sensu the Environmental Sensitivity Index) and associated seafloor habitat’s sensitivity (sensitivity of macrophytes and associated benthic invertebrates) to potential oil pollution in Tallinn Bay, the Baltic Sea. The sensitivity values of the seafloor habitats significantly differed among the studied shoreline substrate types, while the habitat sensitivity values did not match with the sensitivities of these substrate types. The average habitat sensitivity was high on shallow coastal sea adjoining cliffs (representing the shoreline substrate of lowest sensitivity), intermediate on till shores (the shoreline substrate of high sensitivity), low on sandy shores (the shoreline substrate of mediumelow sensitivity) and artificial shores (the shoreline substrate of low sensitivity). This mismatch is explained by the fact that shoreline substrate sensitivity is a consequence of the shoreline natural persistence of oil and ease of cleanup, while the underwater habitat heterogeneity and community properties determine the sensitivity of the associated seafloor habitats to potential oil pollution. The layers of shoreline substrate types and seafloor habitat sensitivity are seen as elements of the Environmental Sensitivity Index (ESI) being developed for the Baltic Sea. Ó 2012 Elsevier Ltd. All rights reserved.

1. Introduction The Environmental Sensitivity Index (ESI) maps have been used for oil spill contingency planning and response for a couple of decades, and since 1979 ESI atlases have been prepared for most of the U.S. coast and have also been used in Canada, United Arab Emirates, Israel, Jordan, El Salvador, Germany, South Africa, Mauritius and New Zealand (Jensen et al., 1998). The main objective of the ESI maps is a priori identification of the most sensitive coastal recourses, so that protection priorities and cleanup strategies can be established beforehand (Jensen et al., 1990). The development of ESI maps is an extensive and complex process especially in relation to data collections, compilation, standardisation and mapping. According to NOAA (2002) ESI maps serve as quick references for oil spill responders, comprising three general types of information: 1) shoreline classification, 2) biological resources and 3) human-use resources. Shoreline substrate types are established according to their physical characteristics,

* Corresponding author. Tel.: þ372 6718935; fax: þ372 6718900. E-mail address: [email protected] (J. Kotta). 0964-5691/$ e see front matter Ó 2012 Elsevier Ltd. All rights reserved. doi:10.1016/j.ocecoaman.2012.05.004

including relative exposure to wave and tidal energy, shore slope, substrate type, grain size, tidal elevation. Biological resources are segmented into seven components: marine mammals, terrestrial mammals, reptiles and amphibians, invertebrates, habitats and plants, birds and fish. Human-use resources are divided into four components: high-use recreational access locations, management areas, resource extraction locations and archaeological/historical resource locations. There is a plethora of ways of estimating the shore sensitivity to oil spills. The most standardised and widely used approach is the ESI, but it is not commonly used in Europe. The impact of oil is known to vary among shoreline substrate types (Aps et al., 2010; Hamada et al., 2011) and climatic conditions but local environment may also modify the response of seafloor habitats to oil pollution (Kotta et al., 2008, 2009; Veber et al., 2009). The aim of this paper is to examine how shoreline substrate types are related to the sensitivity of associated seafloor habitats to potential oil pollution and how the shoreline slope and exposure to wave energy modulate the substrate type e habitat sensitivity relationship in the Tallinn Bay sea area, the Baltic Sea. This paper does not address the problems of ESI biological resources and the human-use resources.

R. Leiger et al. / Ocean & Coastal Management 66 (2012) 12e18

Our first hypothesis says that if the relationship between shoreline substrate types and the sensitivity of associated seafloor habitats is strong enough then the shoreline types may well capture this biological sensitivity. However, the differences in the sensitivity of seafloor habitats among different shoreline substrate types may be obscured by the presence of abundant populations of habitat providing, i.e., keystone species. A keystone species is a species that has a disproportionate effect on its environment relative to its biomass. Such species affect many other organisms in an ecosystem and determine the types and numbers of various others species in a benthic community (Kotta et al., 2000, 2004; Herkül and Kotta, 2009; Kersen et al., 2011). Both sandy shores and cliffs are known to host many keystone species differing in their sensitivity to oil pollution (Highsmith et al., 1996; Stekoll and Deysher, 1996a,b; Kotta et al., 2007b). Recoveries of such seafloor habitats are determined not only by the natural persistence of oil but also by the re-colonisation and growth rates of such species. Our second hypothesis is that variability in seafloor habitat sensitivity to oil pollution is the highest on till shores because they support very high underwater diversity with different habitats having different sensitivity to oil pollution. 2. Material and methods 2.1. Case study Tallinn Bay is situated centrally on the southern shores of the Gulf of Finland, the Baltic Sea. The bay is relatively well exposed to wave energy. The prevailing depths remain between 5 and 30 m, salinity is between 6 and 8 psu and bottom deposits vary from fine sand to boulder fields. Hard bottoms are often located in the vicinity of peninsulas and cover relatively small area. Tallinn Bay is one of the most eutrophic areas of the Baltic Sea. The eutrophication process is consequently regarded as one of the most serious threats against the future of its coastal ecosystems. The Tallinn Bay area is sensitive to eutrophication due to the high external nitrogen load and internal phosphorus load compared to the small water volume and long residence time of the Gulf of Finland. Density stratification subjects deep waters to anoxia and the accumulation of phosphorus, which is a common feature for coastal and marine waters with restricted deep-water renewal (Elmgren, 2001; Conley et al., 2011). Another major concern is related to intensive shipping traffic and oil transport in the Gulf of Finland region and the Tallinn Bay area. The Gulf of Finland has some of the busiest oil shipping routes in the world. According to Kuronen et al. (2008) a total of 263 million tons of cargo were transported in the Gulf of Finland and the transportation of petroleum products formed 56% of all cargo traffic in 2007. The authors estimate that in the case of slow economic growth the ship transport in the Gulf of Finland would reach 322.4 million tons in 2015 (growth 23%), while average growth would yield some 431.6 million tons (growth 64%) and strong growth some 507.2 million tons (growth 93%) of cargo. Such increasing shipping pressure is likely to result in elevated risks of oil pollution in the coastal waters.

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Tallinn Bay area (Fig. 1). In order to establish the sampling stations, a grid of rectangular cells was generated with cell sizes of 25 m using the Spatial Analyst tool of ArcInfo 10 (ESRI, 2011). Then we calculated the wave exposure values for each grid cell (see below, Isæus, 2004). The exposure classes (divided at a step of 10,000) were combined with the available information on bottom sediments (divided into clay, silt, sand and gravel bottoms). Using ArcInfo software, sampling sites were located randomly in a way that allowed each combination of exposure and sediment class to have the same number of sampling sites. A total of 286 samples were collected. During sampling the actual bottom substrate was determined (divided into silt, fine, medium and coarse sand, gravel bottoms). The coverage of phytobenthic species was estimated from the water edge to the maximum depth of occurrence of phytobenthic communities. SCUBA diving technique was used in depths greater than 1.5 m. An Ekman type bottom grab sampler (0.02 m2) was used to collect samples on soft sediment and a diveroperated metal frame (0.04 m2) to collect samples on hard substrate, respectively. Benthos samples were sieved through a 0.25 mm mesh and the residuals were preserved in a deep freezer at 20 C. In the laboratory, animals were counted and identified under stereo dissecting microscope. Dry weights of each taxa were obtained after keeping the material 48 h at 60 C. Based on depth charts (available at the Estonian Marine Institute, University of Tartu) the inclination of coastal slopes was calculated at 500 m resolutions using the Spatial Analyst tool of ArcInfo software (ArcGIS 9, 2004). High values of coastal slopes indicate the occurrence of topographic depressions or humps at the measured spatial scale. Low values refer to flat bottoms (Fig. 2).

2.2. Sampling and data treatment The published map of Estonian shoreline substrate types was used to characterise the shoreline geology in the Tallinn Bay area (Orviku et al., 2010). The phytobenthos and associated benthic invertebrate sampling and sample analysis followed the guidelines developed for the HELCOM COMBINE programme (HELCOM, 2006). The fieldwork was performed in summer 2009. The sampling grid of phytobenthos and benthic invertebrates covered the whole

Fig. 1. Map of study area with shoreline substrate types. Contour lines indicate depth isolines. Dots indicate the sampling stations of benthic macrophytes and invertebrates.

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Fig. 2. The coastal slope values in the Tallinn Bay area expressed as an angle from the horizontal plane in degrees.

The Simplified Wave Model method was used to calculate the wave exposure for mean wind conditions represented by the tenyear period between 1 January 1997 and 31 December 2006 (Isæus, 2004). A nested-grids technique was used to take into account long-distance effects on the local wave exposure regime. The resulting grids had a resolution of 25 m (Fig. 3). The values for the seafloor habitat sensitivity to oil spills were obtained from published model estimates (Kotta et al., 2008). The used model is based on the statistical relationships between depth, sediment type, topographic structure, exposure, species biomasses and oil pollution-related changes in seafloor habitats. The model first uses point data from all 286 samples. Based on the earlier knowledge of impacts of oil spill on benthos in the Gulf of Finland, the model determines the sensitivity of seafloor habitats in these studied points. Finally, using the relationship between the existing abiotic environmental variables and the sensitivity of seafloor habitats, it extrapolates the sensitivity of seafloor habitats into the whole study area by using General Additive Model. The sensitivity value was scaled to 1, with 0 indicating lowest sensitivity and 1 indicating highest sensitivity to oil spill (Fig. 4). GRASP software (Generalized Regression Analysis and Spatial Prediction) version 3.3 for S-Plus (Insightful, 2001; Lehmann et al., 2002) was used to make spatial predictions of several response variables using point surveys of the response and predictor variables. Stepwise (only best predictors selected) models were tested. The models were run with 2 degrees of freedom and the conservative Akaike’s Information Criterion was used to select variables into the model.

Fig. 3. The index of wave exposure for mean wind conditions represented by the tenyear period between 1 January 1997 and 31 December 2006.

Finally, the relationship between the shoreline substrate types and the sensitivity of seafloor habitats to potential oil pollution was determined by the means of factorial ANCOVA with coastal slope and exposure included as covariates. ANCOVA allows investigators to compare one variable in two or more groups (i.e., shoreline substrate type) taking into account and correcting for variability of continuous independent variables (coastal slope and exposure). Thus, ANCOVA combines one-way analysis of variance with linear regression (General Linear Model). In the analyses, if the calculated p-values for the main factor was less than the conventional 0.05 (5%), then the corresponding null hypothesis was rejected, and we accepted the alternative hypothesis that there were indeed differences in the sensitivity of seafloor habitats among shoreline substrate types. Differences between treatment levels were tested with post-hoc Bonferroni test (StatSoft, Inc., 2007). 3. Results The majority of study area was practically devoid of vegetation. Higher biomasses of benthic vegetation were mainly observed on hard bottoms. Such communities were dominated by the brown seaweeds Fucus vesiculosus, Pilayella littoralis and the green alga Cladophora glomerata. Within hard bottom macrophyte habitats the coverage usually varied between 40 and 100%. Besides, a few patches of higher plants such as Potamogeton pectinatus, Zannichellia palustris and Zostera marina were found on sandy sediments. The coverages of macrophyte species within such habitats were lower, hardly exceeding 30%.


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Table 2 ANCOVA analysis of the effect of shoreline substrate type on the standard deviation values of the sensitivity of seafloor habitats to oil pollution. The used abbreviations are: SS e sum of squares, Df e degrees of freedom, MS e mean square, F e F-statistic, p e statistical significance level.

Fig. 4. Sensitivity of seafloor habitats to oil pollution. The sensitivity values vary from 0 to 1 with 0 denotes least sensitive and 1 denotes most sensitive areas to oil pollution.

Effect

SS

Df

MS

F

p

Intercept Coastal slope Exposure Shoreline type Error

2.539 0.001 0.003 0.309 7.120

1 1 1 3 3462

2.539 0.001 0.003 0.103 0.002

1234.647 0.399 1.551 50.048