Marine Pollution Bulletin 55 (2007) 181–195 www.elsevier.com/locate/marpolbul
Angiosperms (seagrass) within the EU water framework directive: A UK perspective
Jo Foden a,*, D.P. Brazier b
a Centre for Environment, Fisheries and Aquaculture Science (CEFAS), Pakefield Road, Lowestoft, Suffolk NR33 0HT, United Kingdom b Cyngor Cefn Gwlad Cymru/Countryside Council for Wales, Maes y Ffynnon, Penrhosgarnedd, Bangor, Gwynedd, LL57 2DN, United Kingdom
Abstract
Taxonomic composition, the presence of disturbance-sensitive species and abundance are attributes for monitoring the status of mar- ine angiosperms; a biological quality element required for assessment of environmental condition under the Water Framework Directive (WFD). Their relevance for defining the ecological status of UK water bodies and the establishment of reference conditions for these attributes are described. Founded on quantitative measurements of these attributes, a set of metrics has been developed for monitoring and assessment of the only truly marine angiosperms, seagrass. The proposed metrics are presented and tested against a variety of littoral and sublittoral UK seagrass beds. In combination they express the cumulative response of marine angiosperms to different levels of anthropogenic disturbance. 2006 Elsevier Ltd. All rights reserved.
Keywords: Marine angiosperms; Seagrass; UK; Water Framework Directive; Reference conditions; Ecological status
1. Introduction There are no detectable changes in angiosperm abundance due to anthropogenic activities’’. Annex V of the Water Framework Directive WFD, In CWs ‘‘all disturbance-sensitive ... angiosperm taxa 2000/60/EC states that angiosperms, phytoplankton, mac- associated with undisturbed conditions are present. The levels roalgae, benthic invertebrate fauna and fish are the biolog- of ... angiosperm abundance are consistent with undisturbed ical quality elements to be used in defining the ecological conditions’’ (WFD, 2000/60/EC, Annex V). status of a transitional or coastal water body. Seagrasses These descriptors set out the attributes to be used in are the only truly marine angiosperms and can be used monitoring seagrass and the standards to be reached in a for monitoring purposes because they are sensitive to water body for it to be considered free from anthropogenic human disturbance (Short and Wyllie-Echeverria, 1996). influences, i.e. at reference condition. They can be summa- All UK seagrass species are included in the UK Biodiver- rised as taxonomic composition (including presence of dis- sity Action Plan, 1994, and are considered nationally turbance-sensitive taxa) and abundance (determined by scarce. Reference conditions for angiosperms in transi- seagrass shoot density and spatial extent), in both CWs tional (TW) and coastal waters (CW) are defined: and TWs. Three metrics have been developed that apply In TWs ‘‘the angiosperm taxonomic composition corre- to littoral and sublittoral seagrass beds in both TWs and sponds totally or nearly totally with undisturbed conditions. CWs, to meet the monitoring requirements and are pre- sented herein:
* Corresponding author. • Taxonomic composition (presence of disturbance-sensi- E-mail address: [email protected] (J. Foden). tive taxa).
0025-326X/$ - see front matter 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.marpolbul.2006.08.021 182 J. Foden, D.P. Brazier / Marine Pollution Bulletin 55 (2007) 181–195
• Abundance, determined by seagrass shoot density. past history of events, e.g. extreme wind, may play a signif- • Abundance, measured by seagrass bed spatial extent. icant role in the present distribution and density of seag- rasses in most systems. Field surveys are likely to include sites representing many different developmental stages of 1.1. Establishing reference conditions seagrass beds, ranging from bare sediment, initial colonisa- tion, to fully developed meadows (Krause-Jensen et al., The WFD requires European Member States to type 2003). water bodies according to specific factors: for TWs these Establishing reference conditions based on historic data are mixing characteristics, salinity, tidal range, exposure, and expert judgement is possible in some locales, however, depth and substrate; and for CWs, salinity, tidal range there has not been a national seagrass monitoring pro- and exposure ((WFD) Directive 2000/60/EC). A water gramme in the UK. For many sites monitoring on a local body typology classification has been designed for the scale has likely employed one of a variety of methods, result- UK to fulfil these requirements (UKTAG, 2003). Type- ing in data that cannot be readily compared across sites. specific biological reference conditions must be set using Type-specific reference conditions cannot be accurately one of three approaches; spatially based, based on model- determined from such mixed and patchy documentation, ling, or derived using a combination of these methods. but determination of site-specific reference conditions is fea- For spatially based type-specific biological reference condi- sible in some cases. For example, Kay (1998) conducted a tions, wherever possible EU Member States are developing comprehensive review of the knowledge of seagrass beds a reference network for each surface water body type. Pre- around the Welsh coast for the Countryside Council for dictive models or hindcasting methods should use histori- Wales (CCW). The review pools verbal, written and numeric cal, palaeological and other available data. Where it is data from a wide variety of sources and provides summaries not possible to use these methods, expert judgement may of knowledge of individual beds. Such historic data may be be used to establish such conditions (WFD Annex II 1.3). suitable for setting site-specific reference conditions for sea- In the UK the first approach of establishing spatially grass beds, provided they are considered accurate and quan- based type-specific reference conditions for seagrass from tifiable. This approach has been adopted by other European a reference network for each surface water body type is Member States; for example Krause-Jensen et al. (2005) problematic. Whilst there may exist a few potential sea- found the use of type-specific reference conditions for sea- grass reference sites in UK TWs and CWs, reference sites grass depth limits in Denmark risked misinterpretations of are not extant in all types, so type-specific reference condi- ecological status for many water bodies. Site-specific refer- tions cannot be set using this approach. Seagrass distribu- ence conditions, however, facilitated the adjustment of tion, abundance and ecological condition are highly monitoring activities according to local conditions. variable and sensitive, and causes of deviation from pro- Where no historic data exist, baseline surveys must be posed reference conditions are multiple, may frequently conducted and expert judgement relied upon to identify be obscure and are rarely quantifiable. The alternative site-specific reference conditions. The objective is for a sea- approaches for setting reference conditions were consi- grass bed’s taxonomic composition and abundance to be in dered. equilibrium (tolerating natural variability) at the maximum The second approach is to use predictive models. Ideally potential for the site. Assignation of reference conditions is it would be possible to identify locations with suitable envi- described further in Section 2. ronmental parameters for seagrass and therefore to predict Annex V 1.4.1 of the Directive states, ‘‘the results of the presence; enabling the targeting of monitoring directly to (classification) systems ... shall be expressed as ecological extant seagrass beds and potential sites. Despite best model- quality ratios (EQR) for the purposes of classification of ling efforts in recent years (e.g. Fonseca et al., 2002; Krause- ecological status. These ratios shall represent the relationship Jensen et al., 2003), however, such accurate prediction has between the values of the biological parameters observed for proved to be elusive for some of the following reasons. a given body of surface water and the values for these param- Krause-Jensen et al. (2003) were able to evaluate the impor- eters in the reference conditions applicable to that body. The tance of light, wave exposure, slope, salinity and depth in ratio shall be expressed as a numerical value between zero regulating sublittoral eelgrass cover in Danish coastal and one, with high ecological status represented by values waters at different depth intervals. Even so, the power of close to one and bad ecological status by values close to zero’’ the models was limited and prediction of eelgrass cover (WFD, 2222/60/EC, Annex V). Fig. 1 illustrates this con- under specific conditions was not adequate for management cept. The comparison of monitoring results with the refer- purposes. For example, in some locales, eelgrass was absent ence conditions derives the EQR. The values of the EQR or exhibited very low cover in shallow waters despite suffi- established for each ecological status class must ensure that cient light. Such discrepancies suggest other factors are act- the water body meets the normative definition for that sta- ing on seagrass distribution; for example, grazing by tus class as given in Annex V (Tables 1.2, 1.2.3. or 1.2.4). wildfowl, fish or invertebrates, sediment conditions, epi- As such the reference conditions form the anchor for the phytes and free-living macroalgae, extreme low tides and whole ecological assessment. Ecological status classes are extreme climatic events (Krause-Jensen et al., 2003). The defined by their deviation from reference. J. Foden, D.P. Brazier / Marine Pollution Bulletin 55 (2007) 181–195 183
Disturbance EQR Status 1.0 No or very High minor 0.8 Relation of observed values of biological Slight Good parameters 0.6 EQR = to Moderate Moderate
Reference values of 0.4 the biological Major Poor parameters 0.2
Severe Bad 0.0
Fig. 1. Suggested Ecological Quality Ratio scheme; how the WFD disturbance descriptors correspond to ecological status scores and classes (modified from Annex V, 1.4.1, COAST Guidance, Vincent et al., 2002).
It is important to note that the WFD permits revision of most UK literature the two species are made distinct. Z. a biological quality element’s classification scheme each marina and Z. angustifolia are representative species of dif- reporting cycle (six years). Data collation will be ongoing ferent habitats. Furthermore, UK government agencies to aid this process. and conservation agencies continue to regard these species as separate for monitoring purposes; this convention is fol- 1.2. Taxonomic composition (presence of disturbance- lowed herein. In UK waters Z. marina is predominantly a sensitive taxa) sublittoral species found in shallow, fully marine condi- tions on relatively coarse sediment (Davison and Hughes, As seagrasses are disturbance sensitive (Short and Wyl- 1998). Z. angustifolia and Z. noltii are found in the littoral. lie-Echeverria, 1996) their presence, health and abundance Z. angustifolia generally occurs between the mid- and low- are likely to indicate a water body’s classification as being tide mark, preferring poorly-draining muddy sediments, at good or high status; provided there is no evidence of deg- particularly pools and creeks that are unlikely to entirely radation or loss of species from locales where previously dry out during low tide. Z. noltii occurs higher on the shore found. Where seagrass beds are present, there is the poten- to the high-tide mark, on mud and sand, and being more tial for undesirable disturbance resulting in eutrophication tolerant of desiccation, will inhabit areas that entirely dry and habitat degradation, leading to loss of species (Kemp out at low tide (Davison and Hughes, 1998). et al., 1983; Short and Burdick, 1996). Importantly, despite Ruppia spp. (commonly known as wigeon grass) are poi- much recent research effort, the ideal environmental param- kilosaline aquatic plants, but are not strictly considered as eters for supporting seagrass are not entirely understood, so part of the traditional seagrass arrangement (Kuo and den that absence of seagrass from areas apparently suited to its Hartog, 2001). Taxonomy of the genus is discussed further growth is not always explicable (Krause-Jensen et al., 2003). in Section 2.2. Ruppia spp. may occur together with sea- An absence of seagrass from an apparently suitable envi- grass, as their environmental preference is very similar; ronment, therefore, does not necessarily suggest a cata- i.e. temporarily to permanently flooded mesohaline– strophic loss of species has occurred, unless a historic bed hyperhaline estuarine wetlands (Kantrud, 1991), brackish was previously recorded and is no longer present. waters of lagoonal habitats, lochs and estuaries. As with Seagrasses in the northern temperate oceans tend to most Zostera spp., Ruppia populations generally inhabit form broad mono-specific stands (Davison and Hughes, warm, relatively unpolluted, and well lit waters <2.0 m 1998), often patchy in nature, typified by meadows of Zos- deep, where current, fetch and wave action are minimal tera spp. in the Atlantic coastal regions (Short et al., 2001). (Kuo and den Hartog, 2001). Ruppia spp. can tolerate UK seagrass beds tend to be more modest in extent than significant water level fluctuations, including periodic aerial other European beds. The species of seagrass found in exposure in tidal areas (Kantrud, 1991). Around the UK the UK are Zostera noltii Hornem., Zostera marina L. Ruppia beds have a scattered distribution, dependent on and Zostera angustifolia Hornem. (Davison and Hughes, available habitat. There are concentrations in the Crom- 1998). Z. angustifolia is frequently regarded to be a littoral arty Firth, Scotland, and The Fleet, England, and lagoonal ecotypic or phenotypic form of Z. marina, however, in habitats of west Scotland, Orkney and Shetland. 184 J. Foden, D.P. Brazier / Marine Pollution Bulletin 55 (2007) 181–195
Seagrass tend to occur in mono-specific or two-species ing of the ecosystem and the achievement of the values spec- stands. Workers often group Ruppia with Zostera spp., ified ...for the biological quality elements’’ (WFD, 2000/60/ considering them all seagrass, and for the purposes of these EC, Annex V). WFD metrics it is proposed both genera be monitored. Nutrient enrichment may lead to excessive growth of opportunistic epiphytic algal species such as Enteromorpha, 1.3. Abundance determined by shoot density and Ulva, Chaetomorpha and Ectocarpus on seagrass beds. The bed extent effect of macroalgal mats is dependent on their density and persistence (with considerable geographic and temporal Normative definitions in the WFD state moderately dis- variation), potentially compromising the health and viabil- turbed conditions may result in an undesirable disturbance ity of seagrass if overlying and smothering. Descriptive in the balance of organisms present in the water body field studies have found that such algae appear to inhibit (Annex V 1.2.4 of the Directive). Loss of seagrass abun- or eliminate eelgrass (Kemp et al., 1983; Dennison et al., dance does occur in many coastal environments (Short 1993) and excessive growth can cause serious deterioration and Wyllie-Echeverria, 1996), often from natural causes or even the eradication of seagrass. For example, a seagrass such as wasting disease or high energy storms. However, bed described as consisting of Z. noltii and narrow- undesirable disturbance has also been caused by anthropo- leaved littoral Z. marina, i.e. Z. angustifolia, approximately genic activity as a consequence of land reclamation and 10 ha in size on the intertidal flats of Langstone Harbour, changes in land use (Kemp et al., 1983), or eutrophication UK was monitored annually from 1986. In September (Short and Burdick, 1996), and results can be catastrophic. 1991 this seagrass bed appeared to be largely destroyed Anthropogenic activities leading to hydro-morphologi- by a thick blanket of the chlorophyte Enteromorpha radi- cal changes include: fishing activity, e.g. dredging, benthic ata; most still living Zostera plants were in a severely dete- trawling or rhizome disturbance during shellfish picking riorated condition. By August 1992 Zostera was completely or bait digging; vessel mooring, e.g. anchor-chain scour, absent from the growing area (den Hartog, 1994). moorings or beaching of boats; coastal defence engineer- Abundance assessment of opportunistic macroalgae is ing, e.g. building groynes, sea walls or breakwaters, beach already a key quantitative measurement in the WFD mac- replenishment, dredging for coastal/harbour development; roalgae metrics, negating the need for an additional macro- industrial development, e.g. land reclaim, harbour con- algae metric as part of the seagrass tools. Smothering and struction/maintenance, artificial reefs; and, waste dumping, anoxia under thick, persistent opportunistic macroalgal e.g. sewage discharge, cooling water discharge, storm water mats will cause seagrass shoots to thin, bleach and ulti- discharge, spoil dumping, nutrient runoff. The consequence mately die. These physiological changes will manifest as of such activities may manifest as reduction of seagrass reduced seagrass density and be recorded as part of the abundance. For example, Corsican seagrass beds contain- abundance metric described further in Section 2. ing Zostera and Ruppia species, exhibited a 12% decrease Total seagrass biomass as a measure of abundance in abundance between 1990 and 1994 caused by salinity would require destructive sampling; this was considered and temperature shocks and elevated levels of silting from not to be in the spirit of the WFD and is not recommended increased discharges of soft water charged with terrigenous for development as a metric. Furthermore seagrass beds particles and dissolved substances. However, seagrass can may exist in areas where harvesting is restricted. Rather, recover if conditions improve and between 1994 and 1997 mapping the spatial extent of seagrass beds and recording these beds exhibited a 16% increase following a return to shoot density are proposed as indicators of abundance. background levels of discharge (Agostini et al., 2002). Precise mapping offers a means of obtaining evidence of These observations imply that regressions are not irrevers- natural or anthropogenically induced disturbances in time ible and show that seagrass meadows can recover if envi- and space (Agostini et al., 2002). Informed management ronmental conditions revert to a ‘pre-disturbance’ state. decisions need maps containing not only presence/absence Seagrasses are also very sensitive to nutrient enrichment. distribution data, but also information on the characteris- In temperate estuaries areas of eelgrass (Zostera spp.) tics of seagrass (such as density) (McKenzie et al., 2001). habitat have been found to decrease logarithmically and Mapping techniques are discussed further in Sections 2.3 percentage loss of habitat increases logarithmically as and 2.4. nitrogen loading rates increase (Hauxwell et al., 2003). This has been recorded even at relatively low loading rates of 2. Method <63 kg N haÀ1 yrÀ1 and these authors found there was some loss of eelgrass habitat (13–32%) at loading rates of It is not expected that any single metric would be used in just 8 kg N haÀ1 yrÀ1. The Directive sets standards in the isolation to understand the ecology or to derive a classifica- normative definitions for nutrient conditions, as part of tion for a water body. In water bodies where seagrass are, the chemical and physico-chemical elements supporting or historically were, present all of the proposed metrics the biological elements of TWs and CWs. To be at good should be used in assessing the ecological status for this status the Directive specifies, ‘‘nutrient concentrations do biological quality element. The outcome will then go for- not exceed the levels established so as to ensure the function- ward to be combined with the assessments of other WFD J. Foden, D.P. Brazier / Marine Pollution Bulletin 55 (2007) 181–195 185
Table 1 Three metrics to be implemented in UK seagrass beds, WFD disturbance descriptors and corresponding range of metric scores for; (a) taxonomic composition, (b) shoot density, (c) bed spatial extent Disturbance Taxonomic Composition Change in taxonomic composition from reference condition Metric score ranges (mid-point of EQR ranges) (a) No detectable change All reference condition species present 0.9 Slight signs of disturbance Loss of 1/4 to 1/3 of species 0.7 Moderate distortions Loss of 1/2 of species 0.5 Major distortions Loss of 2/3 to 3/4 of species 0.3 Severe distortions Loss of all species 0.1
(b) Seagrass shoot density – exemplar metric scores for % loss of density from reference condition Annual change No detectable change 0% density loss = 1.0, 1% loss = 0.98, 2% loss = 0.96... 10% loss = 0.80 Slight signs of disturbance 11% density loss = 0.79, 12% loss = 0.78, 13% loss = 0.77... 30% loss = 0.60 Moderate distortions 31% density loss = 0.59, 32% loss = 0.58, 33% loss = 0.57... 50% loss = 0.40 Major distortions 51% density loss = 0.39, 52% loss = 0.38, 53% loss = 0.37... 70% loss = 0.20 Severe distortions 71% density loss = 0.193, 72% loss = 0.187, 73% loss = 0.180... 100% loss = 0.00 5 or 6 yr rolling mean change No detectable change 0% density loss = 1.00, 1% loss = 0.96, 2% loss = 0.92... 5% loss = 0.80 Slight signs of disturbance 6% density loss = 0.78, 7% loss = 0.76, 8% loss = 0.74... 15% loss = 0.60 Moderate distortions 16% density loss = 0.58, 17% loss = 0.56, 18% loss = 0.54... 25% loss = 0.40 Major distortions 26% density loss = 0.38, 27% loss = 0.36, 28% loss = 0.34... 35% loss = 0.20 Severe distortions 36% density loss = 0.197, 37% loss = 0.194, 38% loss = 0.191... 100% loss = 0.00
(c) Seagrass bed spatial extent – exemplar metric scores for % loss of area from reference condition No detectable change 0% area loss = 1.0, 1% loss = 0.98, 2% loss = 0.96... 10% loss = 0.80 Slight signs of disturbance 11% area loss = 0.79, 12% loss = 0.78, 13% loss = 0.77... 30% loss = 0.60 Moderate distortions 31% area loss = 0.59, 32% loss = 0.58, 33% loss = 0.57... 50% loss = 0.40 Major distortions 51% area loss = 0.39, 52% loss = 0.38, 53% loss = 0.37... 70% loss = 0.20 Severe distortions 71% area loss = 0.193, 72% loss = 0.187, 73% loss = 0.180... 100% loss = 0.00
Note: if density or area increase above reference conditions the metric score will be 1.0. quality elements to inform the overall classification of a order that natural seasonal cycles in seagrass are consid- water body. Table 1 presents the proposed scoring schemes ered. It is important that comparisons between years are for each metric, based on the five disturbance descriptors based on samplings performed at the same time of year representing each ecological status class in Fig. 1. Scores whenever biomass usually attains the annual maximum are between 0 and 1 to align with the Directive’s require- (Olesen and Sand-Jensen, 1994). Although sublittoral Z. ments for an EQR. The methods for assigning each met- marina beds in the UK may be annual or perennial they ric’s score and for combining these to calculate a score can remain green throughout the year as summer leaves, per water body, are described below. shed in the autumn, are generally replaced with smaller winter leaves. In littoral populations of Z. noltii and Z. 2.1. Sampling protocol angustifolia, new leaves appear in spring and the eelgrass meadows develop over the littoral flats during the summer The influence of sampling scale and survey method on (Davison and Hughes, 1998). Leaf growth ceases around the prediction of coverage and ecological attributes of sea- September or October (Brown, 1990 in Davison and grass beds dictate that managers and scientists need to Hughes, 1998) and leaf cover begins to decline during the choose sampling designs carefully (Fonseca et al., 2002). autumn and winter. Littoral plants may experience a com- For detecting seagrass bed spatial extent and large-scale plete loss of foliage, dying back to the buried rhizomes. In features, such as patches several meters in width, sampling perennial populations the rhizomes survive the winter to over a large area (100 sq m) appears to be the most produce new leaves the following spring, while in annual appropriate strategy, e.g. video transects, aerial surveys. populations, both the leaves and rhizomes die (Davison Conversely, ecological attributes of the seagrass bed such and Hughes, 1998). It is recommended for the purposes as shoot density are best characterised by sampling at a of the WFD, annual monitoring should take place during finer scale, e.g. <50 m, using grids (Fonseca et al., 2002). the bloom period for seagrass. This is likely to fall in Surveys should be conducted over a set period under the August or early September for most parts of the UK. In same conditions and standardised for each repeat survey in sublittoral habitats diving or snorkelling surveys are best 186 J. Foden, D.P. Brazier / Marine Pollution Bulletin 55 (2007) 181–195 conducted at the slack period of tide and for littoral habi- score of 0.3 is applicable to water bodies with reference tats in situ surveys ideally need to be carried out at low tide conditions of three or four species, and in either case only during the spring tidal cycle. The potential for remote sur- one species remains (a species loss of 2/3 or 3/4 veying of seagrass density is addressed in Sections 2.3.1 and respectively). 2.3.2, and of seagrass bed extent in Section 2.4. Where no seagrass taxa remain the water body would be Naturally occurring local events need to be considered scored as 0.1 for this metric. The implication is total loss of in the sampling protocol. In some instances wildfowl a mono-specific stand could downgrade a water body from exploit littoral seagrass beds, e.g. Fenham Flats in Lindis- a score of 0.9 to 0.1 in one step. In such cases the metric is farne Bay, Burry estuary, Swansea (Kay, 1998) and Strang- insensitive to intermediate classes (0.3–0.7). ford Lough (Portig et al., 1994). Grazing by brent geese (Branta bernicla) occurs in Strangford during the early part 2.3. Abundance: determined by seagrass shoot density of the winter and biomass is reduced considerably. Such local occurrences must be taken into account when decid- Seagrass shoot density is a measure of the percentage ing appropriate dates so that the peak bloom period is sur- cover or number of shoots of seagrass in an area. As noted veyed, without other events having had an opportunity to above, this is best characterised by fine-scale sampling reduce the biomass. using grids (Fonseca et al., 2002). There are significant For a baseline study, measurements are best carried out practical differences in sampling a littoral or sublittoral for at least three years, as the inter-year variation is often bed to be taken into account when sampling seagrass den- large because seagrass abundance (shoot density and spa- sity; i.e. accessing littoral beds at low tide if substrate is tial extent) can change markedly on an annual basis firm enough, compared with snorkelling or diving surveys (Duarte, 1989; Duarte and Kirkman, 2001). Standard on sublittoral beds at slack water. ranks of shoot density for photographs are best determined for the time of peak seasonal biomass, to ensure that sub- 2.3.1. Littoral seagrass beds sequent maxima fall within the range of measured values There are limitations to the use of satellite imagery for used to establish the scale (see section 2.3.1). A set of stan- detecting patterns in seagrass beds. The resolution is gener- dard ranks can be used in different locations, provided spe- ally too coarse for detecting patterns in shoot density, the cies mix and biomass range are similar. spectral signature of satellite imagery can vary with season and it is often a prohibitively expensive technique for map- 2.2. Taxonomic composition ping bed extent (McKenzie et al., 2001). Aerial photogra- phy is a preferable remote sensing method and is often As previously noted, UK seagrass comprise three species successfully employed for surveys of large seagrass beds, of Zostera, with possible co-occurrence of Ruppia spp. The in combination with thorough ground-truthing, as is the taxonomy of Ruppia spp. is difficult and under revision as case in Denmark (Frederiksen et al., 2004) (see Section Ruppia maritima may be confused with Ruppia cirrhosa 2.4). Ground-truthing is essential because areas need to be (syn. spiralis)(Preston, 1995). Consequently identification examined where the imagery is incomplete and features with to only genus level is recommended, resulting in a relatively similar signals need to be distinguished; e.g. macroalgae can low level of identification expertise required of field work- be mistaken for seagrass (McKenzie et al., 2001). In the UK ers to implement this metric. typical seagrass beds are small (100 sq m to a few kmÀ2) and The taxonomic composition metric (Table 1a) has scores ground surveying entire seagrass beds is often feasible dur- associated with loss of species from reference conditions; ing a single tidal cycle, provided the sediment is firm enough scores representing the midpoint of the ranges shown in to be accessed on foot at low water. A set of aerial photo- Fig. 1. Although five score classes have been defined, there graphs will help fieldworkers identify the area with >5% are limitations on the applicability of classes for some density (the area to be surveyed for density) and the perim- water bodies, for the following reasons: eter of the bed containing <5% density. Aerial photographs Some water bodies have Ruppia spp. and all three Zos- are particularly useful for targeting survey areas within lar- tera spp. present, though occurring in different beds. In ger beds, or where access to sites is restricted. such cases the water body will score 0.9 for this metric. If Counting the number of shoots in a quadrat would pro- a water body naturally has only mono-specific stands of vide accurate numeric data, but the fine sediment in which seagrass (e.g. Z. marina in the Isles of Scilly) it is unreason- littoral species grow will often anchor the tips of small able to downgrade the water body and so it will still score shoots making it highly labour intensive and impractical 0.9. to conduct shoot counts. Rather, estimates of % density The score of 0.7 applies only in water bodies with refer- can be a reliable alternative (Kirkman, 1978). Photo- ence conditions of three or four seagrass species present, graphic standard ranks may be used to aid estimations of where 1/3 or 1/4 of species, respectively, are now absent. shoot density and this method has been found to reduce The metric score of 0.5 is only applicable where a water the differences in estimates between observers (Kirkman, body has reference conditions of two or four species natu- 1978). To do this, visually determine a location with soli- rally co-existing, but only 1/2 of these are now extant. The tary shoots creating little cover. Vertically photograph a J. Foden, D.P. Brazier / Marine Pollution Bulletin 55 (2007) 181–195 187 quadrat in this area and several field workers should inde- based on such a system (e.g. Boyes et al., 2005). Many lit- pendently estimate the % density and come to a consensus. toral seagrass beds have extensive areas of very low shoot Identify a location with the highest shoot density, photo- density (<5%) around the periphery of the more dense, graph and follow the same procedure for estimating den- continuous bed (>5%). Where access and resources allow sity. Using this approach a quadrat is then placed on an it is valuable to map the boundary of this peripheral, low area considered as mid-way between the first two sampled shoot density area, without the effort of sampling for den- densities. Similarly reference quadrats for intermediate per- sity, as it provides a rapid reference useful for identifying centage densities are set up and photographed vertically patterns in a bed’s spatial and temporal stability. (Duarte and Kirkman, 2001). For field observations observers interpolate between the standard ranks where 2.3.2. Sublittoral seagrass beds required. In sublittoral habitats it is possible to use some remote Quadrat number and size must be appropriate to enable mapping methods but they are limited by water turbidity. estimates of the real variance in shoot density, given the An independent field calibration would still need to be per- resource availability. Duarte and Kirkman (2001) recom- formed, potentially making redundant the need for an ini- mend using small quadrats with multiple replications, tial remotely sensed survey in small water bodies. and suggest calculating the coefficients of variation (CV) As with littoral seagrass surveys, it is not recommended of a suite of quadrats to determine the number required. that sublittoral surveys routinely conduct destructive sam- For example take 40 quadrats, calculate the means and pling. A ranked set of density images is only of practical CVs for randomly selected sample sizes of 20, 24, 28, 32, use for helping to train divers prior to surveys, as they can- 34 and 36. From these, choose the number of quadrats that not be easily carried and referred to underwater during a can be taken within the budgeted time and money giving a survey. Ideally, shoot counts will be conducted as these representative mean with the lowest CV. Similarly the most provide greater accuracy than estimates of % density, and suitable quadrat size can be determined; e.g. take 8 quad- although it is a time-consuming method, small quadrats rats of 0.25 m2, 0.5 m2,1m2 and 5 m2. Calculate the CV (e.g. in 60.25 m2 quadrats) can be used on these beds, as for each and choose the smallest size that retains a repre- they are generally mono-specific (e.g. Collins, 2002; Cook, sentative mean and an acceptably low CV (Duarte and 2005; Burton et al., 2005), whereas large quadrats (P1m2) Kirkman, 2001). Generally for UK surveys P1m2 quad- are difficult to manage. Successful sublittoral density sur- rats (subdivided into four or more squares) are used for veys have been conducted in UK seagrass beds based on patchy or mixed species meadows, or broad estimates are transects generating a grid system of sample points (e.g. made over larger areas without a quadrat (e.g. Portig, Lock, 2003; Cook, 2005). Calculating the coefficient of var- 2004). <1 m2 quadrats can be used for continuous uniform iance can help determine the number and size of quadrat, meadows to estimate % density of seagrass (e.g. Collins, as explained in Section 2.3.1. 2002; Bunker et al., 2004; Boyes et al., 2005). The guidance Using diving or snorkelling fieldworkers is not always photographs will calibrate and minimise variation between feasible and there are alternative methods for conducting field workers. The seagrass density for each replicate sam- sublittoral surveys. For example, Scottish Natural Heritage ple equals the average percent density of the subdivided commissioned sublittoral seagrass surveys involving the use squares within each quadrat. of a drop-down video camera and a ‘glass bottomed Leaf cover does not always provide an adequate com- bucket’ for sampling (James, 2004). It should be noted parison among species because populations of small sea- the drop-down video camera provided detailed information grass species tend to be denser than those of large ones about sediment and flora, whereas the ‘glass bottomed (Duarte and Kalff, 1987). Consequently, a set of standard bucket’ could only be usefully deployed in clear water of rank photographs or sketches may be required for each <5 m depth. The latter method was used in a ‘negative species, or combination of commonly co-occurring species; assessment’ to confirm absence of seagrass and only gener- e.g. Z. angustifolia and Z. noltii. alised observations were made. Future studies may prove a For littoral surveys, the recommended protocol is to ‘glass bottomed bucket’ could be used to determine sea- overlay the seagrass bed (i.e. the area that has >5% density grass density and allow a bed’s perimeter to be mapped of seagrass) with a grid of sample points. The distance with acceptable accuracy. between transect lines is dependent on the overall size of the bed and the total number of sample points is deter- 2.3.3. Scoring abundance (shoot density) metric mined as above (representative mean and low CV) and Krause-Jensen et al. (2003) analysed the importance of on the resource available (e.g. number of field workers) light, wave exposure, slope and salinity on the biomass, and period of time the site is exposed at low water. In an cover and shoot density of a large data set crossing geo- area exposed for several hours at low tide it may be possi- graphic regions, at different depth intervals. The authors ble to survey on one occasion. For very large seagrass beds found variability to be high in shallow water where popu- it may be appropriate to survey sub-sections of the bed at lations were disturbed by physical parameters. Average low water, over a period of several days. Successful littoral values across geographic regions did not adequately density surveys have been carried out in UK seagrass beds describe growth regulation by resources and the modelled 188 J. Foden, D.P. Brazier / Marine Pollution Bulletin 55 (2007) 181–195 factors only explained 14–38% of the overall variation in Krause-Jensen et al.’s (2003) models of light, exposure the data. The proposal, therefore, is that density data are and salinity explained up to 40% seagrass presence and not compared across geographic regions, as naturally cover on large spatial scales, and these authors suggest occurring, local physical parameters may cause significant it is likely the remaining >60% variability results from natural change. Rather, an individual bed’s current spatial a combination of natural causes (e.g. grazing, bioturba- extent and density should be compared against historic tion, sediment conditions, epiphytes, extreme climatic or data representing its healthiest previous condition (refer- tidal events) and anthropogenic influences leading to ence condition). undesirable disturbance. They point out that most of Duarte and Kirkman (2001) found the time frame to these factors (apart from climatic, tidal or anthropogenic determine real changes brought about by most human dis- events) tend to affect seagrass at the organism level, at turbance is 5–10 years, unless disturbance is catastrophic scales of <1 to a few meters. Such factors may have little such as habitat removal for coastal redevelopment. Conse- or no effect at the whole population level over large spa- quently it is proposed that classification status for density tial scales (102–103 m). Factors varying across large spa- takes account of trends, where enough data exist. If there tial scales, such as light climate, exposure, salinity, is a very high degree of annual variability calculation of temperature, and extreme events are much more likely rolling means will considerably reduce noise and underly- to cause variation in abundance at landscape levels (Kra- ing trends become more apparent; but the seagrass bed use-Jensen et al., 2003). The effects of extreme events would need to have been routinely monitored for in excess have not been included in any predictive vegetation mod- of ten years for a rolling mean to become a useful statistic. elling to date. Trends or rolling means of five to six years duration can be In this study (Krause-Jensen et al., 2003) the majority of designed to coincide with the WFD’s reporting cycle. The variability in seagrass presence and density is unexplained rolling mean for an individual bed and the % loss or gain, in a dataset that crosses large geographic regions. How- as compared with reference conditions (the maximum ever, as site-specific reference conditions will be set, local recorded density), can be used to establish a scoring sys- conditions and events will be considered; this will partially tem. The rolling-mean value for each year is an average account for and begin to constrain some of the broad scale of that year and the previous five years’ mean densities. variability. A precautionary approach, in combination with However, there are few UK seagrass beds that have long- present knowledge and expert judgement, has then been term monitoring data available. Where data sets of this used to set the boundary criteria for the abundance metrics. length do not exist trends in annual mean density must These are defined as percentage losses from reference con- be ascertained from the available years of data. ditions, rather than absolute values. The boundary between The proposed scoring system for shoot density (summa- good and moderate is perhaps of greatest significance rised in Table 1b) is based on loss in density compared with because a water body falling below good status becomes reference condition, measured either as % leaf density subject to operational or investigational monitoring. This (littoral surveys) or shoot counts (sublittoral surveys). boundary has to allow for natural variability but be sensi- The calculated percentage change should be rounded to tive enough to highlight variability caused by anthropo- the nearest integer and assigned a metric score. The norma- genic activity and so it has been set at 30% less than tive definitions, reference conditions and boundary condi- reference conditions. Seagrass knowledge and research in tion descriptors (Fig. 1) previously discussed have been other European Union member states supports this bound- taken into consideration in compiling this classification. ary value (de Jong, 2004; de Jong, pers. comm., 2006). Den- The objective is for a seagrass bed’s abundance to increase sity losses in excess of this percentage may be indicative of and be in equilibrium (tolerant of natural variability) at the undesirable disturbance. If a >30% loss in seagrass abun- maximum potential for the site, with the expectation that dance is recorded, operational and investigational monitor- the bed will decrease in density if there is ecological deteri- ing should determine if the loss is attributable to an oration in a water body. Where several years of annual extreme natural event (e.g. weather or low annual light lev- data exist for a seagrass bed, reference condition abun- els), or an extreme anthropogenic event; a final classifica- dance will be the previously recorded maximum density tion can be assigned accordingly. Where sufficient data of the bed, or the mean of several years’ data if shoot den- allow, trends in abundance or a rolling mean may be calcu- sity appears to show high, natural fluctuation. Where there lated, which provide evidence of general loss or recovery in is a dataset long enough to use rolling means in scoring the the bed’s condition. water body, the boundary values between classes are half As noted above, density will naturally vary between those of annual percentage changes. If data exist to enable beds. It is more appropriate, therefore to monitor temporal trend lines to be plotted these should be neutral if the bed is fluctuations in a bed, than to compare across sites. For in equilibrium at its predicted maximum potential, or posi- example, where seagrass beds exist in marginal areas, abun- tive if the bed’s abundance is lower than its predicted dance may be naturally low. This does not necessarily sig- potential but is in a recovery phase. A negative trend is a nify low ecological status and is why abundance should be signal of deterioration and more detailed investigation monitored for individual beds and compared, where possi- may be necessary to halt further decline. ble, against long-term data. J. Foden, D.P. Brazier / Marine Pollution Bulletin 55 (2007) 181–195 189
2.4. Abundance: measured by seagrass bed spatial extent spatial extent to reach and be in equilibrium at its maxi- mum potential physical extent given the local climate, sub- Initial mapping surveys may provide baseline informa- strate and hydrodynamic regime; tolerating natural tion for monitoring programmes. Geographic Information variability. The expectation is that the bed will decrease System (GIS) base-maps provide a quick, precise drafting in size and fall below this limit if there is ecological deteri- and mapping tool and the best data presentation, analysis, oration in a water body. Annual natural variability may be interpretation and storage systems (McKenzie et al., 2001). high, so assessment should be based on trends in bed Seagrass resources can be mapped using a range of extent. As noted above, a combination of present approaches from in situ observation to remote sensing. knowledge and expert judgement has been used to set the The choice of technique is scale and site dependent and boundary criteria for the UK’s abundance metrics, whilst may include a range of approaches. All seagrass mapping employing the precautionary approach. A loss of >30% should be ground-truthed to evaluate image signatures of from reference conditions should be viewed suspiciously. the remotely sensed data, to examine areas where the imag- As with density, if a >30% loss in seagrass bed extent is ery does not provide information and to produce reference recorded, operational and investigational monitoring information and accuracy assessment. should determine if the loss is attributable to an extreme Aerial photography is the most common remote sensing natural or anthropogenic event and a final classification method for seagrass mapping studies (McKenzie et al., reached accordingly. 2001) and offers the means for monitoring over time (Agos- tini et al., 2002). Methods must all be non-destructive and 2.5. Ecological status: combining the metrics repeatable depending on budgetary constraints. Recent and historical photographs have been used to study long- There is no suggestion in the WFD of one angiosperm term changes in seagrass bed spatial extent with great suc- assessment metric out-weighing another. If taxonomic cess (e.g. Kendrick et al., 2002; Agostini et al., 2002). composition and abundance are equally significant in a Boundary maps of seagrass beds may also be generated water body’s overall ecological status, the final classifica- from in situ surveys using Geographic Positioning Systems. tion for the marine angiosperms, seagrass, may be deter- On littoral seagrass beds, surveys may be conducted at low mined in one of two ways; either as the product of a water during spring tides, and field workers can walk the scoring system or as a deferral to the lowest metric out- boundary of the bed (e.g. Portig et al., 1994). This has come. The potential consequence of the latter approach is the advantage of concurrent ground-truthing and the the classification of an entire water body as Poor status, potential for greater accuracy and detail. based on the outcome of one metric for one biological In sublittoral seagrass beds snorkelers or divers will be quality element, even though the outcomes of all other ele- needed, with suitable boat cover. The CCW conducted ments’ metrics might be of a much healthier status. There- surveys of the Z. marina seagrass bed in North Haven, fore, it is proposed the assessment for the biological quality Skomer Marine Nature Reserve (SMNR) using two differ- element of angiosperms is based on a mean calculation of ent methods (Lock, 2003; Burton et al., 2005). In 1982, the three metric scores. 1997, 2002 and 2004 a survey plot of 60 · 60 m was marked The scores attached to ecological status are in the range within the bed and divers swam out from this in all direc- 0–1 (see Fig. 1) and have been subdivided into equal ranges tions to establish the boundary of the bed. In 2000, 2002 for each class. Note if a seagrass bed scores 0.1 for taxo- and 2004 Z. marina bed boundary maps were completed nomic composition seagrass species have been entirely lost, following a second method, using a boat-based GPS unit negating the necessity of monitoring abundance by measur- to electronically record positions. Divers with a surface ing shoot density and bed extent. The mean score calcu- marker buoy (SMB) swam the edge of the Z. marina bed; lated from the three metrics relates to ecological status the edge was defined as a density of more than 1 shoot mÀ2. categories as follows: Boat-based surveyors took GPS waypoints as they fol- lowed the divers’ SMB position. The latter survey method Ecological status Mean score ranges was found to be less accurate because wind and tide affect High 0.8–1.0 the boat, but still provides useful results for comparison Good 0.6–0.79 with previous years’ surveys (Burton et al., 2005). A Moderate 0.4–0.59 drop-down video survey and a ‘glass bottomed bucket’ Poor 0.2–0.39 are alternative approaches to monitoring sublittoral sea- Bad 0.0–0.19 grass and have been used successfully on Scottish Zostera beds (James, 2004). The proposed scoring scheme for changes in bed extent In some instances a metric may be difficult to imple- (summarised in Table 1c) is based on the five disturbance ment, e.g. due to issues of access at some sites. In this case descriptors in Fig. 1, with consideration given to the ecological status would have to be based on at least two normative definitions, reference conditions and boundary remaining metrics and confidence in the classification for condition descriptors. The objective is for a seagrass bed’s this biological quality element would decrease. 190 J. Foden, D.P. Brazier / Marine Pollution Bulletin 55 (2007) 181–195
3. Results Little Arthur, Eastern Isles. Percentage leaf cover and shoot density (counts of shoots mÀ2) are two of the expedi- Where possible assessments of seagrass beds should be tion’s monitoring parameters. Density is determined by conducted using all of the metrics; taxonomic composition, counting all the seagrass shoots in a 25 · 25 cm quadrat, and abundance determined by shoot density and bed the position determined by randomly generated bearings extent. Presented here are examples of intermediate scores and distances from a central datum point in each bed. for each of the individual metrics, followed by examples of The reference condition for each bed is the mean of all overall ecological status for this quality element, calculated annual densities pre-2001. by combining their results. Fig. 2(a–e) illustrates the mean annual shoot density of these five Zostera beds, with standard error bars shown 3.1. Taxonomic composition for the most recent five years (raw data prior to 2001 were unavailable for calculation of error statistics) and trend Of the three proposed seagrass assessment metrics taxo- lines plotted with coefficients of determination (R2). Shoot nomic composition is likely the simplest assessment to density data presented in this form show considerable fluc- make of a seagrass bed. This metric was tested against sev- tuation and longer term underlying trends of losses or gains eral seagrass beds in the UK, comparing their historic and in density are not always easy to identify. No concurrent current taxonomic compositions with data sourced from data on weather or light levels are available for the Isles literature. Table 2 summarises the results of testing the of Scilly, but the different positive and negative trends evi- metric against a variety of littoral and sublittoral UK sea- dent in Fig. 3 suggest localised effects on the beds rather grass beds, their scores and references to data sources. than broader natural climatic effects generally influencing density across the water body. 3.2. Abundance: determined by shoot density Two further methods for examining the data to help determine such trends are presented in Fig. 3(a) and (b); Two methods of surveying shoot density and three the rolling five-year mean densities for each bed and the methods for examining the annual mean density data are annual mean seagrass density for the whole Isles of Scilly presented, with examples. as one water body, respectively, with standard error bars shown. (Having only been monitored for 10 years the Isles 3.2.1. Isles of Scilly sublittoral seagrass beds of Scilly dataset is relatively short, and rolling means of five An annual diving expedition has recorded a variety of rather than six years duration have been presented for this parameters in several Isles of Scilly seagrass beds, for the reason). For individual beds underlying trends are more past 10 years (Cook, 2005). The five main beds are: Old easily recognised using a rolling mean; e.g. in Fig. 2(a) mean Grimsby Harbour, Tresco; Higher Town Bay, St. Martin’s; annual shoot density mÀ2 in Old Grimsby Harbour shows Broad Ledge, Tresco; West Broad Ledge, St Martin’s, and; an increase of 40 shoots to a density of 117 shoots mÀ2
Table 2 Taxonomic composition metric tested against a variety of UK coastal (CW) and transitional (TW) water bodies with littoral or sublittoral seagrass beds Water body Seagrass site Species Species most Composition Metric score Reference historically recently change recorded recorded Scilly Isles CW Five beds in the Isles Z. marina Z. marina No loss 0.9 Cook, 2005 of Scilly East of Passage Cove Z. marina Z. marina No loss 0.9 Hocking and Tompsett, 2002 Spooner and Holme, 1986 Helford TW Helford Creek Z. angustifolia Z. noltii 1/2 species lost 0.5 Covey and Hocking, 1987 Z. noltii Hocking and Tompsett, 2002 Lock, 2003 South North Haven, Z. marina Z. marina No loss 0.9 Burton et al., 2005 Pembrokeshire Skomer Island CW Strangford Newtownards to Z. angustifolia Z. angustifolia No loss 0.9 Portig et al., 1994 Lough North Rough Island Z. noltii Z. noltii CW Milford Haven Sandy Haven Pill Z. angustifolia Z. angustifolia No loss 0.9 Davis, 1961 in Kay, 1998 CW Langstone Hayling Island Z. angustifolia None Total loss 0.1 den Hartog, 1994 Harbour CW littoral flats Z. noltii J. Foden, D.P. Brazier / Marine Pollution Bulletin 55 (2007) 181–195 191
300 300
250 250 ) ) -2 -2
200 200
150 150
100 100 R2 = 0.3978 Mean density (shoots m Mean density (shoots m 50 50 R2 = 0.0806
0 0
1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 Year Year
300 300
250 250 ) ) -2 -2
200 200
150 150 2 R = 0.3842 100 100 R2 = 0.0285 Mean density (shoots m Mean density (shoots m 50 50
0 0
1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 Year Year
300
250 ) -2 200
150
R2 = 0.1628 100
Mean density (shoots m 50
0
1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 Year
Fig. 2. Annual mean shoot density in five Isles of Scilly sublittoral seagrass beds with standard error bars and trend lines; (a) Old Grimsby Harbour, (b) Higher Town Bay, (c) Broad Ledge Tresco, (d) West Broad Ledge, and (e) Little Arthur (data from Cook, 2005). Note data only available for nine and eight years for West Broad Ledge and Little Arthur, respectively. between 2002 and 2003 and then a decline over the next two increase since 2000. This technique is suitable for water years. Whereas the five-year rolling mean (Fig. 3(a)) bodies with only one or two small seagrass beds, i.e. approx- remains unchanged between 2002 and 2003 and then imately of the size that can be reasonably surveyed during increases in 2004 and again in 2005, reflecting the overall one tidal cycle. 192 J. Foden, D.P. Brazier / Marine Pollution Bulletin 55 (2007) 181–195
proximity, are sublittoral, consist of the same taxon and
) 225
-2 are affected by the same general hydrodynamic and envi- 200 ronmental regime. Therefore, it is recommended the data 175 are combined and annual mean changes in density are 150 reported as a whole for the ‘Scilly Isles’ CW, thereby reduc- 125 ing the noise of natural variability (Fig. 3(b)). When all sea- 100 grass beds are considered as a whole in this manner, the 75 pattern of variability in any one bed will be somewhat 50 attenuated and it is possible to establish the overall status of seagrass in the Isles of Scilly in any one year, without Rolling mean (shoot density m 25 1999 2000 2001 2002 2003 2004 2005 recourse to a rolling mean. Year When the five-year rolling-mean method is employed,
Old Grimsby Harbour Higher Town Bay Broad Ledge Tresco beds are scored by comparing current shoot density with West Broad Ledge Little Arthur reference conditions (the mean of annual densities for all years pre-2001). To illustrate this, 2005 density data for 225 the five Isles of Scilly seagrass beds have been compared 200 with reference condition density and metric scores assigned )
-2 accordingly (Table 3). An overall score for the whole ‘Scilly 175 Isles’ water body has also been assigned by determining the 150 difference in mean density of all beds in 2005 from the ref-
125 erence condition. Zostera beds have been scored with refer- ence to the scheme in Table 1b; note the percentage density 100 range for each score is different for annual means as 75 opposed to five-year rolling means in Table 1b and this is Mean density (shoots m 50 reflected in the metric scores presented in Table 3.
25 1998 1999 2000 2001 2002 2003 2004 2005 Year 3.2.2. North Haven, Skomer Marine Nature Reserve Fig. 3. Shoot density in Isles of Scilly sublittoral seagrass beds; (a) rolling (SMNR), Pembrokeshire five-year mean of shoot density of five beds, and (b) annual mean shoot The sublittoral Z. marina bed in North Haven, Skomer density for all seagrass beds in the Isles of Scilly water body (data from Island is regularly surveyed (Lock, 2003; Burton et al., Cook, 2005). Note different time period on x-axis. 2005). Part of the SMNR management plan (Newman et al., 2000) aims to maintain the population of Z. marina Presentation of these Isles of Scilly data as separate five- in North Haven in favourable condition whereby shoot year rolling means for individual beds is essentially illustra- density does not fall below the 1997 survey mean of tive; the seagrass beds in the Isles of Scilly are all in close 36 shoots mÀ2, which sets the reference condition.
Table 3 Shoot density metric scores, (a) for five individual Isles of Scilly seagrass beds using a rolling mean, and (b) for the whole water body using an annual mean Site Reference conditions: mean density 2005 5 yr rolling-mean 2005 % difference from Abundance (density) score (shoots mÀ2) pre-2001 density (shoots mÀ2) reference conditions (based on Table 1) (a) Old Grimsby 87.6 87.9 +0.4 1.0 Harbour, Tresco Higher Town Bay, 203 163 À19.7 0.5 St. Martin’s Broad Ledge, Tresco 116 131 +13.3 1.0 West Broad Ledge, 108 66.3 À38.4 0.191 St. Martin’s Little Arthur, 147 173 +17.4 1.0 Eastern Isles (b) Reference conditions: mean density 2005 annual mean density 2005 % difference from Abundance (density) score (shoots mÀ2) pre-2001 (shoots mÀ2) reference conditions (based on Table 1) Whole ‘Scilly Isles’ 125 107 À14.4 0.76 CW Density (shoots mÀ2) reported to 3 sig. figures. J. Foden, D.P. Brazier / Marine Pollution Bulletin 55 (2007) 181–195 193
Divers establish transects at 10 m intervals and complete have been made either from the abundance and distribu- seagrass shoot counts in quadrats at 5 m intervals along the tion maps or from the GPS maps (Lock, 2003; Burton transect lines. Count data are converted to mean values per et al., 2005). These reports state the accuracy of the abun- square metre and mapped at intervals of 10 shoots mÀ2 to dance and distribution areas, as mapped by divers, is high, show the distribution and density of Z. marina. In 1997 the but the maps derived from GPS are less accurate. No error mean density was 36.2 shoots mÀ2 and this increased to statistics are presented in Fig. 4 as raw data are unavail- 54 shoots mÀ2 in 2002. The percentage frequency of able. It is possible that the bed is at its potential maximum 50 shoots mÀ2 or greater was 38% in 1997 increasing to size due to restrictions in expansion by unsuitable substra- 56% in 2002; in 2002 8% of quadrat counts were recorded tum to the south and deep water to the north. as 100 shoots mÀ2 or greater, whilst in 1997 this was less Skomer Island’s seagrass bed appears to be both rela- than 1% (Lock, 2003; Burton et al., 2005). Such an increase tively stable in area and at or near its maximum physical in density above reference condition levels would score the extent. Since 1991 Z. marina in the MNR is protected, with site as 1.0 for this metric, using the proposed system for restrictions on anchoring and fishing. The site would be seagrass density (see Table 1b). scored at 1.0 for this metric, using the proposed system for determining and scoring extent (Table 1c). 3.3. Abundance: measured by seagrass bed spatial extent
Three examples of scoring the seagrass bed extent metric 3.3.2. Pembrokeshire littoral beds are presented. The first is a sublittoral bed and the proceed- The CCW review of the knowledge of seagrass beds ing two are littoral beds. around the coast of Wales (Kay, 1998) can be used to clas- sify the current status of some seagrass beds, with regard to 3.3.1. North Haven, SMNR, Pembrokeshire their historic extent of distribution. An example is Sandy The SMNR management plan (Newman et al., 2000) Haven Pill, Milford Haven, first discovered in 1958 and establishes favourable conditions as a bed extent of described as forming a narrow belt 400 yards long (Davis, 6700 m2 (which becomes reference condition) (Lock, 1961). By 1995 CCW files describe only two remaining 2003; Burton et al., 2005). Population boundary mapping patches each of 1 · 0.5 m, with Spartina sp. 1–4 m to the occurs every two years. seaward (Kay, 1998). Although the precise width of the Z. marina bed area was measured for five years between seagrass bed in 1958–1961 is not described, it is apparent 1982 and 2004 as shown in Fig. 4. The area calculations that bed extent has decreased significantly. If the bed in 1958–1961 was as narrow as 0.5 m, the total area would have been 200 m2 (which would be the baseline, or refer- 8000 ence condition, for this bed), but of course it may have been wider. Whereas the spatial extent of the two remain- 7000 2
2 ing patches in 1995 was only 1 m . Referring to Table 1c, 6000 Sandy Haven Pill seagrass beds would score 0.003 for this
Area m metric. 5000
4000 3.3.3. Pembrokeshire sublittoral beds 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 Sites where seagrass has been recorded, but has since Year disappeared, include; Dale (George, 1958 in Kay, 1998), Fig. 4. North Haven Zostera marina bed area; 1982, 1997 and 2002 areas Pwllcrochan (Davis, 1971 in Kay, 1998), Garron Pill mapped by divers with high accuracy, 2000 and 2004 areas derived from (Knights, 1979 in Kay, 1998) and Landshipping (Davis, boat GPS are less accurate (Burton et al., 2005) (raw data unavailable for 1962 in Kay, 1998). Complete loss of a seagrass bed would calculation of error statistics). score all of these areas at 0.0 for this metric (cf. Table 1c).
Table 4 Overall ecological status of three exemplar UK seagrass beds, calculated from mean scores of assessment metrics (assignation of metric scores presented in Section 2) Assessment Metric Metric scores and ecological status North Haven, Skomer ‘Scilly Isles’ CW Sandy Haven Pill, Milford Haven Taxonomic composition 0.9 0.9 0.9 Seagrass bed density 1.0 0.76 – Seagrass bed extent 1.0 – 0.003 Mean score 0.97 0.83 0.45 Final classification High High (with low confidence) Moderate (with low confidence) 194 J. Foden, D.P. Brazier / Marine Pollution Bulletin 55 (2007) 181–195
3.4. Overall ecological status; combining the assessment have been tested on individual UK seagrass beds, both metrics from the littoral and sublittoral. By combining the assess- ment metrics using a mean score, an ecological status class To assign a water body’s ecological status for seagrass can be assigned for marine angiosperms that can then go all three metrics should be used and the final classification forward and be compared with the metrics for other bio- for this biological quality element uses the scoring system logical quality elements in defining a water body’s overall described (also see Fig. 1 and Section 2.5). Examples of biological quality status. UK seagrass beds that have been assessed using the metrics are presented in Table 4. Acknowledgement 4. Discussion Many thanks to K. Cook and the Isles of Scilly annual Following the monitoring of seagrass beds using the diving expedition for their valuable and unique survey metrics described herein, the mean score can be compared work. Also thanks to the Skomer Marine Nature Reserve with the ranges described in Section 2.5 in order to assign for access to their reports. an overall ecological status. Table 4 presents examples for three UK beds and the results for Skomer are straight- forward to calculate. For Milford Haven and the Isles of References Scilly water bodies, data were available for only two of Agostini, S., Marchand, B., Pergent, G., 2002. Temporal and spatial the three metrics to be implemented. Based on these, eco- changes of seagrass meadows in a Mediterranean coastal lagoon. logical status classes have been assigned, but with only Oceanologica Acta 25 (6), 297–302. low confidence. This could be significant when the result Boyes, S., Proctor, N., Mitchell, E. in preparation. Intertidal Zostera noltii for these water bodies go forward to be combined with Monitoring in Menai Strait and Conwy Bay SAC. Report to CCW. the outcomes of other biological quality elements. If the Report: YBB084-D-2004. Institute of Estuarine and Coastal Studies University of Hull. final assessment falls on the boundary between two status Bunker, F., Mercer, T., Howson, C., 2004. Fleet lagoon and tidal rapids classes, such lack of confidence in the data may cause an survey 15th–22nd July 2002. Report to English Nature, 80. overall downgrade, and potentially could result in a large Burton, M., Lock, K., Luddington, L., Newman, P., 2005. Skomer Marine investment in further monitoring effort, unnecessarily. Nature Reserve project status report 2004/05. CCW West Area, The concept of using seagrass as an ecological indicator Report 29, Countryside Council for Wales, Aberystwyth, p. 44. Collins, K., 2002. Dorset Maerl and Seagrass (2001 survey results). Report is relatively new and to date there has been no national to Dorset Wildlife Trust and English Nature January 2002, p. 22. monitoring programme of littoral or sublittoral seagrass Cook, K., 2005. Report on 2004 Isles of Scilly Zostera marina survey. in the UK. These two issues have complicated the estab- Covey, R.C., Hocking, S.M., 1987. Helford River Survey Report. Report lishment of reference conditions, ranges and boundaries to the Helford River Steering Group, Cornish Biological Records for each ecological status class. There are a wide variety Unit: Institute of Cornish Studies, University of Exeter and Cornwall County Council, p. 121. of naturally occurring physical and hydro-morphological Davis, T.A.W., 1961. Field notes: Zostera angustifolia. Nature in Wales 7, conditions in UK TWs and CWs and a seagrass bed’s tax- 202. onomic composition and abundance are a product of indi- Davison, D.M., Hughes, D.J., 1998. Zostera Biotopes (Volume 1). An vidual combinations of local conditions. For this reason overview of dynamics and sensitivity characteristics for conservation reference conditions cannot be type-specific across water management of marine SACs. Scottish Association for Marine Science (UK Marine SACs Project), p. 95. body types. Where accurate and quantifiable historic data de Jong, D.J., 2004. Water Framework Directive: determination of the from trust-worthy records exist, reference conditions may reference condition and Potential-REF/Potential-GES and formula- be established. If only recent survey data are available tion of indices for plants in the coastal waters CW-NEA3 (K1), CW- (e.g. <5 years old) the previously recorded healthiest condi- NEA4 (K2), CW-NEA1 (K3), transitional water, TW-NEA11 (O2), tion of a seagrass bed becomes its reference condition. and large saline lakes, NEA26 (M32), in The Netherlands. (Working document RIKZ/OS/2004.832.x; final draft). These metrics are likely to require continuous refine- den Hartog, C., 1994. Suffocation of a littoral Zostera bed by Entero- ment to ensure the final boundary values accurately inter- morpha radiata. Aquatic Botany 47, 21–28. pret the data collected and the final assigned quality Dennison, W.C., Orth, R.J., Moore, K.A., Stevenson, J.C., Carter, V., status truly represents the state of the water body. The Kollar, S., Bertgstrom, P.W., Batiuk, R.A., 1993. Assessing water WFD permits revision of biological quality elements’ clas- quality with submersed aquatic vegetation. Bioscience 43, 86–94. Duarte, C.M., 1989. Temporal biomass variability and production/ sification schemes each reporting cycle (six years). Data biomass relationships of seagrass communities. Marine Ecology collation will be ongoing to aid this process and will inform Progress Series 51 (3), 269–276. future refinements. Duarte, C.M., Kalff, J., 1987. Weight–density relationships in submerged Three metrics have been proposed for assessment of the macrophytes: the importance of light and plant geometry. Oecologia marine angiosperms, seagrass, in CWs and TWs in the UK. 72, 612–617. Duarte, C.M., Kirkman, H., 2001. Methods for the measurement of These have been developed to comply with the Water seagrass abundance and depth distribution. In: Short, F.T., Coles, Framework Directive’s normative definitions, reference R.G. (Eds.), Global Seagrass Research Methods. Elsevier Science conditions and boundary conditions descriptions. They B.V., Amsterdam, pp. 141–153. J. Foden, D.P. Brazier / Marine Pollution Bulletin 55 (2007) 181–195 195
Fonseca, M., Whitfield, P.E., Kelly, N.M., Bell, S.S., 2002. Modelling Lock, K.M., 2003. Skomer Marine Nature Reserve Distribution and seagrass landscape pattern and associated ecological attributes. Eco- Abundance of Zostera marina in North Haven, 2002. CCW West Area logical Applications 12 (1), 218–237. Report 22, Countryside Council for Wales, Aberystwyth, p. 17. Frederiksen, M., Krause-Jensen, D., Holmer, M., Laursen, J.S., 2004. McKenzie, L.J., Finkbeiner, M.A., Kirkman, H., 2001. Methods for Long-term changes in area distribution of eelgrass (Zostera marina)in mapping seagrass distribution. In: Short, F.T., Coles, R.G. (Eds.), Danish coastal waters. Aquatic Botany 78 (2), 167–181. Global Seagrass Research Methods. Elsevier Science B.V., Amster- Hauxwell, J., Cebrian, J., Valiela, I., 2003. Eelgrass Zostera marina loss in dam, pp. 101–121. temperate estuaries: relationship to land-derived nitrogen loads and Newman, P., Lock, K., Bullimore, B., 2000. Skomer Marine Nature effect of light limitation imposed by algae. Marine Ecology Progress Reserve Management Plan, Working document. Countryside Council Series 247, 59–73. for Wales. Hocking, S., Tompsett, P., 2002. The location & conservation of eelgrass Olesen, B., Sand-Jensen, K., 1994. Demography of shallow eelgrass beds in Cornwall and the Isles of Scilly. Environmental Record Centre (Zostera marina) populations—shoot dynamics and biomass develop- for Cornwall and the Isles of Scilly (ERCCIS) Vol. 1, Project Series ment. Journal of Ecology 82 (2), 379–390. No. 5. Portig, A.A., 2004. The distribution of intertidal Zostera spp. in Northern James, B., 2004. North-west Scotland subtidal seagrass bed survey 2004. Ireland, 2003. Report for Environment and Heritage Service p. 84. Scottish Natural Heritage Commissioned Report No. 076 (ROAME Portig, A.A., Mathers, R.G., Montgomery, W.I., Govier, R.N., 1994. The No. F04LB05), pp. 105. distribution and utilisation of Zostera species in Strangford Lough, Kantrud, H.A., 1991. Wigeongrass (Ruppia maritima L.): A literature Northern Ireland. Aquatic Botany 47 (3-4), 317–328. review. US Fish and Wildlife Service, Fish and Wildlife Research 10. Preston, C.D., 1995. Pondweeds of Great Britain and Ireland. London: Jamestown, ND: Northern Prairie Wildlife Research Center Home Botanical Society of the British Isles. B.S.B.I. Handbook no. 8. Page. Available from: