Scottish Thomas A Wilding
email: tom.wilding@sams.ac.uk.

Edwin J L Palmer

Clifton Road

Berks Newbury

Nicholas N V Rg14 5js

Polunin

Comparison of three methods for quantifying topographic complexity on rocky shores

Keywords: Argyll, Scotland, coastal and rocky shore ecology, community structure, rugosity, habitat space, scale-dependence

Topographic complexity (TC) is an important factor in determining community structure in aquatic habitats and there are numerous methods for its estimation. Here we assess and compare three (chain, divider and distance-wheel) methods across different users and substrata over scales of 78 to 1030 mm.

All three techniques showed the same basic trend in TC over different substrata. However, the chain took approximately 1.5 x as long to use, at larger scales (>271mm), as the other techniques and resulted in highly variable TC estimates. The chain and divider techniques both have the potential to miss relevantly scaled TC depending on the survey starting point, a disadvantage not suffered by the distance-wheel. TC measured using the distance-wheel was approximately 1.8 x that recorded by the divider technique and significantly (P<0.05) less variable compared with the chain technique.

The distance-wheel was rapid and easy to use, superior in accessing awkward topographies and has considerable potential for improvement. It is recommended as a technique for estimating the TC of rock-surfaces at scales >50 mm.

Introduction

Understanding and predicting the factors that influence species-and size-diversity in benthic communities is a central goal to ecologists. Habitat complexity has been identified as an influential factor in determining occupancy of habitat space: a suitably complex habitat can protect animals and plants from exposure to desiccation [START_REF] Hooper | Epifaunal composition and fractal dimensions of intertidal marine macroalgae in relation to emersion[END_REF], extremes of wave or tidally induced currents [START_REF] Guichard | Topographic heterogeneity, hydrodynamics, and benthic community structure: a scale-dependent cascade[END_REF] and protect against predation [START_REF] Dayton | Competition, disturbance, and community organization -provision and subsequent utilization of space in a rocky intertidal community[END_REF][START_REF] Bartholomew | New dimensionless indices of structural habitat complexity: predicted and actual effects on a predator's foraging success[END_REF]Anderson 2001b;[START_REF] Almany | Does increased habitat complexity reduce predation and competition in coral reef fish assemblages?[END_REF]). There is considerable interest in the relationship between scaledependent habitat complexity and community species-and size-structure (reviewed in [START_REF] Robson | Methodological and conceptual issues in the search for a relationship between animal body-size distributions and benthic habitat architecture[END_REF] particularly with regard to habitat bottlenecks [START_REF] Gee | Body-Size Distribution in a Marine Metazoan Community and the Fractal Dimensions of Macroalgae[END_REF][START_REF] Beck | A test of the generality of the effects of shelter bottlenecks in four stone crab populations[END_REF][START_REF] Casariego | Evidence of habitat structure-generated bottleneck in the recruitment process of the SW Atlantic crab Cyrtograpsus angulatus[END_REF]).

Defining and measuring habitat complexity has proved technically and logistically challenging; [START_REF] Hobson | Surface roughness in topography: a quantitative approach[END_REF] maintained that a 'single, concise definition of surface roughness is probably impossible' and in an extensive assessment of the literature [START_REF] Bell | Habitat structure: the evolution and diversification of a complex topic[END_REF] concluded that there was not one but three aspects (heterogeneity, complexity and scale) of habitat structure that influence the way animals and plants use habitat space. Whilst methods of assessing habitat complexity have included the direct counting and/or measuring of specific features within a given habitat (eg. [START_REF] Kohn | Environmental complexity and species diversity in gastropod genus Conus on Indowest Pacific reef platforms[END_REF][START_REF] Polunin | Greater Biomass and Value of Target Coral-Reef Fishes in 2 Small Caribbean Marine Reserves[END_REF][START_REF] Charton | Correlation between habitat structure and a rocky reef fish assemblage in the southwest Mediterranean[END_REF][START_REF] Friedlander | Habitat characteristics affecting fish assemblages on a Hawaiian coral reef[END_REF] such methods are likely to be subjective [START_REF] Wilson | Appraisal of visual assessments of habitat complexity and benthic composition on coral reefs[END_REF]) and numerous, more quantitative methods have been developed. This research focuses on field-based methods of assessing complexity, hereafter referred to as topographic complexity (TC), at scales of >50 mm. Methods of assessing scale-dependent TC can be divided into two broad categories: direct and indirect. Indirect techniques adopt a two-step approach to assessing TC, firstly a reproduction (usually in 2 dimensions) of the surface of interest is engineered which is then analysed to generate an index of complexity. Methods of reproducing the surface of interest have included the use of latex to make impressions of individual rocks [START_REF] Sanson | A new method for characterizing surface-roughness and available space in biological systems[END_REF] or the use of plaster of Paris (eg 300 mm x 300mm [START_REF] Commito | Structural complexity in mussel beds: the fractal geometry of surface topography[END_REF] to make casts which can then be sectioned to generate a profile. At larger-scales profile gauges are frequently used to reproduce the substratum of interest, normally up to 300 mm per profile with a 1 mm resolution using commercially available carpenter's profile gauges (e.g. [START_REF] Sanson | A new method for characterizing surface-roughness and available space in biological systems[END_REF][START_REF] Erlandsson | A field technique for estimating the influence of surface complexity on movement tortuosity in the tropical limpet Cellana grata Gould[END_REF][START_REF] Beck | Separating the elements of habitat structure: independent effects of habitat complexity and structural components on rocky intertidal gastropods[END_REF][START_REF] Johnson | The area-independent effects of habitat complexity on biodiversity vary between regions[END_REF][START_REF] Devescovi | Impact of date mussel harvesting on the rocky bottom structural complexity along the Istrian coast (Northern Adriatic, Croatia)[END_REF][START_REF] Frost | Measuring surface complexity in ecological studies[END_REF][START_REF] Kostylev | The relative importance of habitat complexity and surface area in assessing biodiversity: Fractal application on rocky shores[END_REF] although [START_REF] Mccormick | Comparison of Field Methods for Measuring Surface-Topography and Their Associations with a Tropical Reef Fish Assemblage[END_REF] demonstrated the use of a much larger (1 m) bespoke profile gauge with a 100 mm gap between pins (100 mm resolution). Profiles can also be generated using stereo-photographs over relatively small scales (e.g. 0.25 m x 0.25 m, [START_REF] Frost | Measuring surface complexity in ecological studies[END_REF] or larger scales (e.g. 25 x 25 m, [START_REF] Guichard | High-resolution remote sensing of intertidal ecosystems: A low-cost technique to link scale-dependent patterns and processes[END_REF]. Various parameters relating to complexity can be determined from profiles including vertical relief, variability in height/angle between points along the profile (for method comparisons see [START_REF] Underwood | Experimental analyses of the influences of topograpahy of the substratum on movements and density of an intertidal snail Littorina unifasciata[END_REF][START_REF] Mccormick | Comparison of Field Methods for Measuring Surface-Topography and Their Associations with a Tropical Reef Fish Assemblage[END_REF][START_REF] Beck | Separating the elements of habitat structure: independent effects of habitat complexity and structural components on rocky intertidal gastropods[END_REF]) and the fractal dimension (D) (see extensive reviews in [START_REF] Fielding | Applications of Fractal Geometry to Biology[END_REF][START_REF] Klinkenberg | A Review of Methods Used to Determine the Fractal Dimension of Linear Features[END_REF][START_REF] Gustafson | Quantifying landscape spatial pattern: What is the state of the art?[END_REF][START_REF] Ricotta | From theoretical ecology to statistical physics and back: self-similar landscape metrics as a synthesis of ecological diversity and geometrical complexity[END_REF][START_REF] Schmid | Fractal properties of habitat and patch structure in benthic ecosystems[END_REF][START_REF] Halley | Uses and abuses of fractal methodology in ecology[END_REF]. Profile gauges and stereophotography are 'down-from-above' methods of generating profiles and, consequently, overlook overhangs and horizontally orientated crevices. Such structures are critical components of shores for many species and these techniques are therefore deficient in this regard [START_REF] Commito | Structural complexity in mussel beds: the fractal geometry of surface topography[END_REF].

Techniques that can be used in the field to give an immediate measure of TC have the advantage that post-field work laboratory analysis is not required [START_REF] Frost | Measuring surface complexity in ecological studies[END_REF]. In-situ field measurements are usually based on a measurement of step-length-dependent perceived distance compared with the concomitant linear distance between two points over the substratum of interest. Methods for the fielddelineation of the step have including dividers [START_REF] Bradbury | Fractal Dimension of a Coral-Reef at Ecological Scales[END_REF], chains with varying link sizes [START_REF] Risk | Fish diversity on a coral reef in the Virgin Islands[END_REF][START_REF] Dahl | Surface-Area in Ecological Analysis -Quantification of Benthic Coral-Reef Algae[END_REF][START_REF] Luckhurst | Analysis of Influence of Substrate Variables on Coral-Reef Fish Communities[END_REF][START_REF] Archambault | Scales of coastal heterogeneity and benthic intertidal species richness, diversity and abundance[END_REF][START_REF] Frost | Measuring surface complexity in ecological studies[END_REF][START_REF] Willis | Habitat structural complexity and morphological diversity of fish assemblages in a Neotropical floodplain river[END_REF], distance-wheels of differing circumferences [START_REF] Bourget | Barnacle larval settlement: the perception of cues at different spatial scales[END_REF][START_REF] Wilding | A novel approach to measuring subtidal habitat complexity[END_REF]) and rolling-balls of different radii [START_REF] Robson | Using fractal geometry to make rapid field measurements of riverbed topography at ecologically useful spatial scales[END_REF]. In general, when the topographic complexity is at a smaller scale than the step-length then the observing device will effectively ignore (step or roll over) such complexity and the total distance travelled between two points will more closely reflect the linear distance. On the other hand, when traversing a convoluted surface where the steplength is of a scale which enables the measuring device to closely follow the surface topography, the perceived distance will be greater than the linear distance. The ratio between the scale-dependent perceived-and linear-distance, over a surface, is of fundamental interest to this paper. In theory, the perceived distance must show an equal or increasing trend, over the same ground, as the scale of measurement decreases.

Field-based methods of assessing TC need to be easily and rapidly implemented [START_REF] Hobson | Surface roughness in topography: a quantitative approach[END_REF]) and be cost-effective in order to promote TC evaluation where resources and facilities are limited. They should also be applicable at a broad-range of scales [START_REF] Halley | Uses and abuses of fractal methodology in ecology[END_REF]) and capable of measuring TC which is hidden from above [START_REF] Commito | Structural complexity in mussel beds: the fractal geometry of surface topography[END_REF]. Whilst the chain, divider and distancewheel techniques meet some of these criteria, little is known as to the comparability of their scaledependent perceived-distance estimates (and why any differences occur) and their respective ease and rapidity of use in the field.

Materials and methods

Study sites and dates

The three techniques (chain, divider and distance-wheel) were compared across three shores located in Argyll (Scotland, UK), these were (i) the east shore of Barrnacarry Bay (56.346°N, 5.541°W) a millstone grit, (ii) Dunstaffnage Head (56.451°N, 5.450°W), a conglomerate and (iii) the west shore of Barrnacarry Bay (56.346°N, 5.547°W), a pre-Cambrian slate (geological details can be found in [START_REF] Stephenson | The Grampian Highlands[END_REF]. The shores were selected to provide varying degrees of TC over which to compare techniques and were not the focus of the study. The surveys were conducted during May 2008.

Establishing transects

The dimensions of areas of the millstone grit and conglomerate shores were approximated by counting paces along the length and width of rectangular (approximately 50 x 20 m) sections of the shore. Transect start points within these rectangular sections were randomly selected by pacing to a random coordinate. The slate shore was broken into 13 rocky outcrops. Two of these were randomly chosen and then the transect-selecting protocol described for the grit and conglomerate shores was followed. The transect line was stretched from the start point along a randomly selected bearing. The line was pulled taught, parallel to the ground, and the rock surface was marked at a linear distance of 10 m. The line was then allowed to drop onto the rock, forming a guideline (augmented with chalk marks) along which the measuring devices were used.

Measurement scales

The three techniques were assessed over five scales that formed an approximate geometric series (as recommended in Klinkenberg 1994) of 1030, 518, 271, 153, and78 mm. Scale, numbered one to five (largest to smallest) represents the step-length (chains and dividers) and wheel circumference (distancewheel).

Using the number of complete steps to measure perceived distance over a fixed linear distance gives a discrete length category equal to the step-length x number of steps. In practice most transects do not end with a completed step and, at the largest scale (scale 1), a small difference could result in a step completion (or otherwise) at the 10 m mark potentially creating a discrepancy of up to 10% (where there are approximately 10 steps per transect). To overcome this source of error, the number of complete steps up to, and beyond where necessary, the 10 m mark was noted [START_REF] Klinkenberg | The fractal properties of topography -a comparison of methods[END_REF][START_REF] Klinkenberg | A Review of Methods Used to Determine the Fractal Dimension of Linear Features[END_REF]. The distance beyond the 10 m mark required to complete the last step ('completion distance') was recorded. The perceived/linear distance-ratio was then calculated as: Number of steps x step-length (mm) / 10,000 mm + completion distance (mm).

Techniques

Commercial chains with link lengths equivalent to the large step lengths of interest in this study would be unwieldy, therefore the design was based on a functionally similar, but much lighter and more practical surveyor's or Gunter's chain [START_REF] Toscano | The Gunter's chain[END_REF] where links are connected via eyes at their ends.

Chains at the three smallest scales (Scales 3, 4 and 5) were made using 3.5mm PVC coated wire.

Surveyor chain links of the two larger scales (1 and 2) were made using 4mm, galvanized mild steel wire. The centrally aligned, tear-shaped eyes of each link were made large enough to allow free movement between the links but minimal slippage between links. The chain was deployed sequentially, from the eye of the previous link, and held in place on any contact points while the next link was positioned.

The three largest divider sizes (scales 1, 2 and 3) were based on simple 'A'-shaped frames, constructed using plastic rods (8mm diameter) sharpened at their ends. A pair of navigation dividers (Ultralight Dividers/Compass, Weems and Plath) were used to delineate the two smaller scales. The dividers were 'walked' down the transect line and the number of steps recorded.

The distance-wheel system described in [START_REF] Wilding | A novel approach to measuring subtidal habitat complexity[END_REF] was used with the following modifications: the wheels were edged with a ribbed foam rubber strip (10 x 3 mm) to improve traction and the electronic counter was removed. Wheels were rolled along the transect and the number of rotations counted.

Experimental design and data analysis

The data were collected by two users, on three different shores (two transects per shore), using three different techniques over five scales. At each level of user, shore, transect, technique and scale five repeated measurements of the same transect were taken. Transects were surveyed in the same direction each time and the total time taken to complete the five measurements was recorded to the nearest minute. The total sample size was 900. Repeated surveying of the same transect was necessary to evaluate technique consistency over the same ground. These repeated measures are not replicate transects and were pooled to generate the mean perceived: linear distance ratio (see below).

Three response variables were analysed: 1) the mean (n=5) perceived: linear distance-ratio of the five repeated measures of the same transect (n=900/5=180; see above), 2) the range, per five repeated measurements over the same transect (n=180; see above) and 3) the time taken to conduct five repeated measurements combined across two users (n=90).

Scale and technique were considered fixed factors. Shores were identified from a number of options that offered varying degrees of complexity on the basis of ease of access. As assessing differences in complexity between specific shore-types was not the goal of this study shore was considered a random factor. Transect, nested in shore, and user were both random factors. The main factor of interest was technique and its interaction with other factors, particularly scale, and how this interaction changed over different shores (or transects within shore). Other factors (even where significant) are not discussed. These data were analysed using PERMANOVA (Primer-E Ltd, Plymouth, UK). PERMANOVA is frequently used in the analysis of multivariate data but can, as here, be used to perform univariate analyses in order to overcome the distributional and homoscedasticity restrictions of ANOVA.

PERMANOVA is based on distance matrices (Anderson 2001a;[START_REF] Anderson | PERMANOVA+ for PRIMER: Guide to software and statistical methods[END_REF]) and generates a permutation-based pseudo-F (P-F) statistic. In order to use PERMANOVA, the response variables (mean distance ratio, range and time) were, individually, converted to a Euclidean distance matrix and the full model (all main effects and interactions) was then fitted and tested with 9999 permutations.

There was no replication at the highest level (technique x scale x user x transect(shore)) and, initially, this term formed the residual. Non-significant (P>0.25, Underwood 1997) interaction terms were sequentially pooled, beginning with term with the smallest (least significant) mean-square [START_REF] Anderson | PERMANOVA+ for PRIMER: Guide to software and statistical methods[END_REF]) whilst maintaining factor hierarchy (i.e retaining non-significant factors/interactions if subordinate interactions were significant). Any negative variance components that remained after this process were kept in the model (and assumed to represent an underestimate of a zero-variance component; [START_REF] Underwood | Experiments in ecology: their logical design and interpretation using analysis of variance[END_REF]). The number of unique permutations associated with each factor during analysis using PERMANOVA was noted and, where this was low, Monte-Carlo methods were used to assess significance [START_REF] Anderson | PERMANOVA+ for PRIMER: Guide to software and statistical methods[END_REF].

Reduced major axis regression was used to investigate the functional relationship between perceived distance (explanatory factor) and range following log transformation of both to linearise the relationship (least squares regression was not appropriate as measurement error applied to both variables; [START_REF] Sokal | Biometry: the principles and practice of statistics in biological research[END_REF]. Major axis regression (used where both variables are measured on the same scale; Sokal and Rohlf 1995) was used to establish if the relationship between ratios generated using different pairs of techniques differed (regression coefficient 99% confidence intervals that did not include a slope of 1 were considered different).

A posteriori pairwise comparisons of fixed effects and their interactions, were conducted using PERMANOVA and reported where significant. The relative importance of both fixed and random effects were evaluated by comparison of components of variation [START_REF] Anderson | PERMANOVA+ for PRIMER: Guide to software and statistical methods[END_REF]) expressed as a percentage of total variation. Other analyses were done using Minitab™ v15.

Results

Technique efficacy

The use of all three techniques was physically demanding over long periods (6 hours). At largescales the chain technique was unwieldy and awkward to use, otherwise techniques were roughly equivalent in their ease of use.

Survey times ranged from a minimum (per transect) of 1 minute (four occurrences, on the conglomerate and millstone shores using divider and wheel techniques at scales of 1 and 2) to a maximum of 9.6 minutes, using the chain technique on the slate shores (transect 1) at scale 5. Overall, the time required to traverse each transect was overwhelmingly influenced by scale (P-F=46.8, 4 df, P=0.001, 63% of total variation; Table 1), with the large scales being quicker to use than the smaller scales (Figure 1). The scale effect on survey time was modified by technique such that at the smallest scale (Scale 5) all techniques took approximately the same time to use (mean 5.7 minutes per transect) whilst at the largest scale the chain technique took approximately 1.5 x as long to use (mean per transect 2.33 minutes compared with both the divider and distance-wheel technique means of 1.53 and 1.52 minutes respectively, Figure 1; scale x technique P-F=2.91, 8 df, P=0.004, 1.6% variance). The time taken to use the divider and wheel techniques was approximately the same for all scales (Figure 1).

Comparison of ratios between technique, scales and users

The mean distance-ratio (n=5) varied from a minimum of 1.003 (conglomerate shore, transect 2, divider technique, scale=2) to 1.655 (slate shore, transect 1, wheel technique, scale=5) with the scale of measurement making a significant difference to the distance-ratio (P-F=9.24, df=4, P=0.001 contributing 20% of variation; Table 2). Whilst all three distance measuring techniques indicated similar trends of increased distance-ratio, and variability in distance-ratio, as step-size decreased the measured distance-ratio differed between techniques (P-F=7.71, df=2, P=0.019 contributing 13% of variation; Table 2) and was highest when measured using the distance wheel, then chain, followed by the divider technique (mean ratios of 1.21, 1.17 and 1.09 respectively). The effect of technique varied between locations (transect(shore) x technique, P-F=28.1, df=6, P=0.001, contributing 3.2% of variance).

The relationship between distance ratios generated by the different techniques was relatively consistent with correlations between all techniques exceeding 0.9 (P<0.001, n=60; Table 3). There was no evidence that the chain and distance-wheel techniques generated ratios that were significantly different (major axis regression coefficient and 95% CI was 1.082 (0.9975, 1.175). However, distanceratio generated by the chain and distance-wheels differed from that generated using the divider technique, for example, the regression coefficient and 99% confidence interval was 1.820 (1.558, 2.155) when comparing the distance wheel and divider techniques (Table 3). Over the same ground, the distance wheel and chain techniques measured approximately 1.8 and 1.7 (respectively) times the distance measured using the dividers (Table 3).

The total proportion of variance in distance-ratio accounted for by the main effects (scale, location (consisting of shore and transect(shore)) and technique) was 84% indicating that interactions were of relatively minor importance. However, the scale effect was influenced by the transect within shore being surveyed (transect (shore) x scale, P-F=26.5, df=12, P=0.001, contributing 5.0% of variance;

Table 2); the scale effect was greater on the slate shore (transect 1) compared with the other shores (the degree of association was greater for slate (transect 1) compared to the other transects; Figure 2). Occasionally, whilst using the chain technique only, the measured perceived distance was greater at larger step-lengths, a theoretical impossibility (see 4.2).

The user-effect appeared the least when using the distance wheel as demonstrated by the almost complete parity (overlap) between measurements taken by different users, on all shores, at all scales as when compared to either the chain or dividers techniques (user x shore x technique P-F=3.20, 4 df, P=0.014, 0.26% of total variation; Figure 3).

Repeatability

Precision was assessed by measuring the range generated between repeated measurements taken over the same transect with lower ranges indicating greater precision. There were significant differences in range between techniques (P-F=10.6, P=0.037, 2 df, 21% of variance; Table 4). The range associated with the distance-wheel was the lowest (0.0503 m) followed by the divider technique (0.0858 m), both of which were significantly (PERMANOVA pair-wise comparisons, P=0.032 and P=0.030 respectively) lower than the range generated using the chain technique (range=0.140 m) which was the least precise.

Range generally increased with scale number (i.e with decreasing step-length) (P-F=29.6, 4df, P=0.023, 10.2% total variation; Figure 4) but this effect was moderated by the transect dependent effect of technique (transect (shore) x technique x scale; P-F =1.83, P=0.032, 24df, 15% of total variation; Table 4) with the trend of increasing range with scale associated with the chain technique being less apparent (Figure 4).

At perceived distance-ratios of more than approximately 1.05, the distance-wheel was more precise than the divider technique such that at a distance-ratio of 1.25 the mean range associated with the distance-wheel was approximately half that associated with the divider technique (Figure 5). The range in perceived-distance was consistently greater when using the chain compared with the distance wheel, over all measured mean distance ratios (as indicated by the near-parallel regression lines in Figure 5). However, the relationship between log range and log mean ratio demonstrated by the chain and distance-wheel were both approximately half that measured using the divider technique (reduced major axis regression coefficient (with 99% confidence interval) for the chain technique was 6. 28 (4.50, 8.06) for the wheel technique it was 7.48 (5.52, 9.44) neither of which overlapped the divider's regression coefficient 99% CI (regression coefficient and 99% CI of 14.87 (11.51, 18.24)).

Discussion

The chain, divider and wheel techniques all demonstrated an increase in the mean distance-ratio as a function of decreasing scale and all showed broadly similar trends over transects of differing complexity. All three sets of equipment were cheap and easy to manufacture and similar in terms of ease of transport (across shores etc). No special training was required to use any of the equipment and, as with all direct techniques, there was no further effort required to obtain topographic complexity (TC) estimates (i.e. no laboratory analysis). In this way all three techniques had an advantage over indirect techniques, which necessitate time-consuming laboratory analysis (eg. of photographs; [START_REF] Frost | Measuring surface complexity in ecological studies[END_REF]. None of the approaches used here (chains, dividers or the distance-wheel) are 'down-fromabove' techniques (compare with profile gauges and stereophotography) and all have the potential advantage of assessing complexity that is not accessible from above [START_REF] Commito | Structural complexity in mussel beds: the fractal geometry of surface topography[END_REF].

However, there were significant differences among TC estimates made using the chain, divider and distance-wheel techniques.

Comparison of methods as used in the field

The different methods differed in their ease of use with the distance-wheel and dividers being approximately equivalent in terms of ease of use and time taken to deploy. Extended use of these techniques, particularly at the smaller scales and over more angular substrata was physically demanding. The use of Gunter's chains in measuring complexity offers considerable advantages over normal (free-linked) chains as there cannot be slippage, recognized as a problem [START_REF] Frost | Measuring surface complexity in ecological studies[END_REF], between the links. In addition, the link size is at the user's discretion unlike commercial chains. The Gunter's chains were light and allowed easy extension of the upper step-length limit. However, maintaining contact between the links and the substratum, necessary to correctly deploy subsequent links, was difficult particularly when using the larger links over angular topographies. Consequently, the time taken to use the chains, at larger scales (scales 1 -3, 270 -1030 mm), was approximately 1.5 x as long as the same measurements taken using the other two techniques. Using the distance-wheel or divider techniques the measurement of transects in excess of 100 m would have been practicable at the larger scales (1 and 2). The scales used here are in excess of much of those in the published literature making efficacy comparisons with other approaches difficult. However, [START_REF] Mccormick | Comparison of Field Methods for Measuring Surface-Topography and Their Associations with a Tropical Reef Fish Assemblage[END_REF], using a profile gauge with 100 mm pin separations (i.e. steps) took approximately five minutes to survey a 3 m transect. At the most comparable scale reported here (Scale 5, 78 mm) a 10 m transect took approximately 5.5 minutes so, all the techniques reported here are about 3 times as rapid to deploy as profile gauges at relatively small scales.

Ease of access to recesses (caves, overhangs etc) differed between techniques. The chain could be lowered into vertical crevices, unlike the dividers, but neither the chain nor the dividers could conveniently access horizontal crevices smaller than the user's hand (whilst holding and manipulating the equipment). This disadvantage did not extend to the distance-wheel which, because of the handle, was able to conveniently access such smaller recesses. The divider and chain technique are able to access recesses that are smaller, in terms of their opening, than the step distance (depending on the location of the previous step; Figure 6) which contrasts with the distance-wheel which is limited to accessing openings of greater than step-length/Pi (i.e. the wheels diameter; compare Figure 6a, d ande).

Where there are many crevices with small aspect ratios (i.e. deep and narrow) then the wheel technique will measure an increase in perceived distance at a smaller scale compared to the other methods (but it will capture that complexity and with potentially less variability, see below). Conversely, some ecologically relevant recesses which are insufficiently deep to be 'observed' by the chain or divider techniques will still influence the distance-wheel's distance-ratio. Further research is required to determine exactly how different techniques measure perceived distance and how this is affected by both the shape and the scale of the TC. The relevance of any differences may depend on the nature (shape) of the fauna occupying and exploiting the habitat space.

Comparison of perceived-distances and repeatability

The differences in perceived-distance and repeatability recorded between the techniques were partly dependent on the shore being assessed and it seems reasonable to assume that these differences were, at least partly, step-number dependent with technique-differences potentially manifesting themselves at each step. As a consequence, it seems likely that more complex shores and/or measuring at smaller scales, both of which result in more steps, will exacerbate differences between techniques. This would account for the complex interactions observed, for example, that differences between the interaction between technique and scale differed between transects. Notwithstanding these interactions, in terms of perceived-distances and repeatability, three basic conclusions can be drawn regarding the three techniques (i) the divider method resulted in consistently lower perceived distance-ratios compared with the other methods, (ii) the chain method showed consistently greater variability compared with both other techniques and (iii) the wheel method showed reduced variability compared with the chain method, delivered the highest perceived distance measures and its precision was least affected by perceived distance. [START_REF] Frost | Measuring surface complexity in ecological studies[END_REF] concluded that consistency in the relative complexity ranking obtained using stereo-photography, chains and profile gauges on the same type of shore cannot be assumed. Our research concurs with this general finding, that there are significant differences in perceived-distance as assessed using different techniques. Here, the distance-wheel and chain techniques result in perceived distances that are approximately 1.75 x that recorded by the divider technique. This obviously invalidates uncorrected comparisons between sites measured using the described techniques. However, the correlations between perceived distances measured using the different techniques (chain v. divider v. distance-wheel) were all >0.9 and much greater that those found by [START_REF] Frost | Measuring surface complexity in ecological studies[END_REF] (comparing stereo-photography, profile gauges and chains, r maximum 0.404) even over the greater range of shores reported here. Corrections enabling the direct comparison between estimates of shore perceiveddistance, made using these different techniques might be acceptable (depending on the study objectives).

There are several reasons that could explain the deficiencies in, and differences between, distance ratios estimated using the chain, divider and distance-wheel techniques. The use of Gunter's chains effectively prevents link slippage [START_REF] Frost | Measuring surface complexity in ecological studies[END_REF]) and this cannot be responsible for the lack of repeatability observed when using the chain technique reported here. In the current case, the high variability in the use of the chains stemmed from two factors. The first factor, which it shares with the divider technique, involves the way the chain link is able to bridge gaps and, consequently, not measure topographic complexity that might be of a relevant scale (Figure 6a v b and Figure 6c v d). The effect of this process is to cause an overall reduction in the distance-ratio measured using the chain or divider techniques, compared with the wheel technique (Figure 6e), and increase the data's variability over the same transect. An additional error applicable to the chain technique was caused by small, angular topographic features such that location of the end of the preceding link had a profound influence on the chain's perceived distance (Figure 7a); each topographic irregularity potentially adding two steps to the overall perceived-distance. This amplification of the perceived distance was frequently observed whilst using the chain technique and has the effect of increasing the overall perceived distance, increasing the variability in the data and occasionally inversing the relationship between step length and perceived distance (i.e. measuring a shorter perceived distance using a smaller step-length which is contrary to theory; [START_REF] Schmid | Fractal properties of habitat and patch structure in benthic ecosystems[END_REF]. These effects were particularly noticeable (and significant) whilst using the largest links but apply to any chain link, whether a Gunter's chain (as here) or a standard chain, of any length. On the varied shores measured in the current case, this amplification effect more than outweighed the reduction in perceived distance occurring as a consequence of stepping over complexity.

The amplification of perceived distance, as illustrated in Figure 7a, was not an issue for either the divider technique (because there is no link between the divider points) or the distance-wheel approach.

However, the dividers were occasionally impractical in use where they were unable to adequately make a step as a consequence, again, of angular topography ('faltered-step') and were left hanging above the substratum (Figure 7b). The amplification and faltered-step effects are both dependent on the transect start point and will occur more frequently where more steps are taken (i.e. on more complex shores) as indicated by significant interactions between the main effects and transect(shore)). Whilst their effects can be addressed by starting the transect at different points (as recommended in [START_REF] Sugihara | Applications of fractals in ecology[END_REF][START_REF] Klinkenberg | A Review of Methods Used to Determine the Fractal Dimension of Linear Features[END_REF] the sample mean will always underestimate perceived distance unless countered by unpredictable and varying degrees of 'distance-amplification' (chain technique only). The distance-wheel, which is basically a field-based approximation of the surface-monolayer method of assessing perceived distance [START_REF] Fielding | Applications of Fractal Geometry to Biology[END_REF] or the rolling disc method (as applied to digitised images at the mm scale by [START_REF] Sanson | A new method for characterizing surface-roughness and available space in biological systems[END_REF] is not influenced in this way as its path is the same, along the same transect, regardless of the starting point.

Conclusions

Different methods of measuring topographic complexity are not consistent, either in the distanceratios they produce or their variability over the same ground. At the larger scales (1030 and 518 mm) the chain technique was slow to use and resulted in occasional, but considerable, amplification of the perceived-distance. This amplification resulted in high variability in perceived distance over the same ground. For these reasons we do not recommend the chain technique at the scales used here.

The divider technique resulted in the lowest perceived distances and was prone to higher variability (as a proportion of perceived distance) and user-error when compared to the wheel technique. Unlike the other techniques, the distance wheel offers considerable scope for improvement, for example, by fitting an electronic counter and improving the bearing both of which would decrease survey time and increase repeatability. The distance-wheel, as described here, is recommended for the direct estimation of rocky shore TC at scales of approximately 50 -1000 mm and, with simple modification, should be applicable to a broader range of scales.

Acknowledgments

This work was supported by the Natural Environmental Research Council under Oceans 2025, Theme 4.6.

Figure 1 -

 1 Figure 1 -Mean (n=12) time per transect (mins) v. scale number for chain, divider and distance-wheel techniques (scale numbers (in mm): 1 -1030, 2 -518, 3 -271, 4 -153, 5 -78).

Figure 2 -

 2 Figure 2 -The relationship between mean (n=6) perceived-distance ratio and scale at different transects (1 or 2) on different shores (conglomerate, millstone or slate). The scale effect (gradient of the association) varies widely between different transects on the same shore. Scale numbers (in mm): 1 -1030, 2 -518, 3 -271, 4 -153, 5 -78.

Figure 3 -Figure 4 -Figure 5 -

 345 Figure 3 -Differences in distance-ratio measured by different users (1 or 2) as a function of technique and scale over the different shores (congl=conglomerate, millst=millstone). The near complete overlap of the measurements made using the distance-wheel indicate a negligible user-effect compared with the divider or chain technique. Scale numbers (in mm): 1 -1030, 2 -518, 3 -271, 4 -153, 5 -78.

Figure 6 Figure 7

 67 Figure 6 Variations in the assessment of topographic complexity using different techniques where steplength is identical. Where shown the dotted lines indicate the perceived distance. In (d) the middletwo dividers are not shown for clarity, their steps are indicated by the dotted line

Table 1 -

 1 Results of the PERMANOVA test on survey time. Sources of variation: Sh=shore, Te=technique, Sc=scale, Tr(Sh) =transect nested in shore. df=degrees of freedom, MS= mean squares, P-F= pseudo-F statistic, P(perm)=permutational probability (significant effects are highlighted in bold), % variance = % variation (of total) of each variance source. The number of unique permutations generated by PERMANOVA, for each factor, was >6000 except for Shore where it was 15 (the Monte-Carlo significance result is shown in parenthesis).

	Source	df	MS	P-F	P(perm)	% variance
	Sh	2	28.5	3.58	0.135 (0.164)	8.6
	Te	2	11.8	7.00	0.049	4.2
	Sc	4	92.3	46.8	0.001	63
	Tr(Sh)	3	7.98	20.4	0.001	6.3
	Sh x Te	4	1.69	0.957	0.491	0.00
	Sh x Sc	8	1.97	5.04	0.001	3.3
	Te x Sc	8	1.14	2.91	0.004	1.6
	Tr(Sh) x Te	6	1.77	4.51	0.001	3.4
	Total

Table 2 -

 2 Results of the PERMANOVA test on perceived-distance ratio. Sources of variation: Us=user, Sh=shore, Te=technique, Sc=scale, Tr(Sh) =transect nested in shore. df=degrees of freedom, SS =sum of squares, MS= mean squares, P-F= pseudo-F statistic, P(perm)=permutational probability (significant effects are highlighted in bold), % variance = % variation (of total) of each variance source.The number of unique permutations generated by PERMANOVA, for each factor, was >9000 except for User where it was 38 (the Monte-Carlo significance result is shown in parenthesis).

	Source	df	MS	P-F	P(perm)	% variance
	Us	1	7.07E-3	4.41	0.239 (0.174)	0.23
	Sh	2	0.556	2.81	0.107	22
	Te	2	0.242	7.71	0.019	13
	Sc	4	0.213	9.24	0.001	20
	Tr(Sh)	3	0.196	283	0.003	24
	Us x Sh	2	1.60E-3	2.31	0.285	0.11
	Us x Te	2	4.47E-4	0.444	0.684	-0.07
	Us x Sc	4	1.60E-3	5.06	0.002	0.27
	Sh x Te	4	3.10E-2	3.17	0.012	4.0
	Sh x Sc	8	2.15E-2	2.57	0.063	4.1
	Te x Sc	8	4.34E-3	7.60	0.001	1.2
	Us x Tr(Sh)	3	6.94E-4	2.20	0.095	0.09
	Tr(Sh) x Te	6	8.88E-3	28.1	0.001	3.2
	Tr(Sh) x Sc	12	8.37E-3	26.5	0.001	5.0
	Us x Sh x Te	4	1.01E-3	3.20	0.014	0.26
	Sh x Te x Sc	16	5.71E-4	0.485	0.933	-0.56
	Tr(Sh) x Te x Sc	24	1.18E-3	3.73	0.001	1.61
	Total

Table 3 -

 3 Comparison (major axis regression) of distance-ratios produced using the three techniques (n=60), CI = confidence interval of regression coefficients. All correlations were highly significant (P<0.001).

	Regression equation	CI (95%)	CI (99%)	Correlation
	Chain = -0.635 + 1.660 Divider	1.499, 1.848	1.450, 1.917	0.929
	Wheel = -0.052 + 1.082 Chain	0.9975, 1.175	0.9709, 1.208	0.955
	Wheel = -0.764 + 1.820 Divider	1.618, 2.063	1.558, 2.155	0.908

Table 4 -

 4 Results of the PERMANOVA test on range. Sources of variation: Sh=shore, Te=technique, Sc=scale, Tr(Sh) =transect nested in shore. df=degrees of freedom, MS= mean squares, P-F= pseudo-F statistic, P(perm)=permutational probability (significant effects are highlighted in bold), % variation = % variation (of total) of each variance source. The number of unique permutations generated by PERMANOVA, for each factor, was >6000 except for Shore where it was 15 (the Monte-Carlo significance result is shown in parenthesis).

	Source	df	MS	Pseudo-F	P(perm)	% variance
	Sh	2	5.81E-3	2.38	0.12 (0.238)	7.09
	Te	2	1.09E-2	10.6	0.037	20.86
	Sc	4	3.02E-3	29.6	0.001	10.24
	Tr(Sh)	3	2.45E-3	8.71	0.001	9.12
	Sh x Te	4	1.04E-3	1.15	0.419	0.85
	Sh x Sc	8	1.02E-4	0.230	0.976	-3.60
	Te x Sc	8	5.00E-4	2.15	0.068	2.82
	Tr(Sh) x Te	6	9.01E-4	3.20	0.008	7.83
	Tr(Sh) x Sc	12	4.43E-4	1.58	0.108	3.42
	Sh x Te x Sc	16	2.32E-4	0.451	0.954	-8.94
	Tr(Sh) x Te x Sc	24	5.15E-4	1.83	0.032	14.79
	Res	90	2.81E-4			35.51
	Total


M J C Crabbe
email: james.crabbe@beds.ac.uk

J Crabbe

Topography and spatial arrangement of reef-building corals on the fringing reefs of North Jamaica may influence their response to disturbance from bleaching

Keywords: tropical storms, hurricanes, rugosity, climate change, Caribbean, global warming, coral growth, recruitment, Discovery Bay, resilience, growth rates

.

There were significant linear relationships between rugosity and the increase in smallest size classes for Sidastrea siderea, Colpophyllia natans, Porites astreoides and Agaricia species, and between rugosity and cover of the branching coral Acropora cervicornis. Linear extension rates of A. cervicornis and radial growth rates of Porites astreoides were significantly lower (p<0.025; F>6) when in contact with macroalgae. Aggregated colonies of P. astreoides in contact with one another, one of which was in contact with the faster growing competitor Acopora palmata showed significantly greater growth rates than with just two aggregated P. astreoides colonies alone. These findings suggest that three dimensional topography and complexity is important for reef resilience and viability in the face of environmental stressors such as bleaching. Our findings also support the idea that aggregated spatial arrangements of corals can influence the outcome of interspecific competition and promote species coexistence, important in times of reef recovery after disturbance.

Introduction

Loss of corals and macroalgal domination on the fringing reefs of North Jamaica has been due to hurricanes [START_REF] Woodley | Hurricane Allen's impact on Jamaican coral reefs[END_REF], Crabbe et al., 2002), overfishing [START_REF] Jackson | Reefs since Columbus[END_REF][START_REF] Hawkins | Effects of artisanal fishing on Caribbean coral reefs[END_REF], die-off of the long-spined sea urchin Diadema antillarum in 1983-84 [START_REF] Hughes | Catastrophes, phase shifts and large-scale degradation of a Caribbean coral reef[END_REF], and coral disease [START_REF] Aronson | Evolutionary paleoecology of Caribbean coral reefs[END_REF]. However, increased macroalgal cover by itself does not always lead to reef decline, and the Jamaican example may be an anomaly compared to reefs in other parts of the world [START_REF] Bruno | Assessing evidence of phase shifts from coral to macroalgal dominance on coral reefs[END_REF][START_REF] Liu | A trophic model of fringing coral reefs in Nanwan Bay, southern Taiwan suggest overfishing[END_REF]. The major acute disturbance in recent years was the Caribbean-wide bleaching event of 2005 [START_REF] Wilkinson | Global Coral Reef Monitoring Network, and Reef and Rainforest Research Centre[END_REF][START_REF] Quinn | The recent collapse of a rapid phase-shift reversal on a Jamaican north coast reef after the 2005 bleaching event[END_REF][START_REF] Crabbe | Scleractinian coral population size structures and growth rates indicate coral resilience on the fringing reefs of North Jamaica[END_REF]. This event was due primarily to high sea surface temperature (SST) hot-spots in the region [START_REF] Abrego | Species-specific interactions between algal endosymbionts and coral hosts define their bleaching response to heat and light stress[END_REF][START_REF] Donner | Model-based assessment of the role of human-induced climate change in the 2005 Caribbean coral bleaching event[END_REF][START_REF] Morgan | The 2005 bleaching event: coral-list log[END_REF], and thermal stress during this event exceeded any seen in the previous 20 years of satellite data from the Caribbean, while monthly temperatures were the warmest in over 150 years. The North Jamaican fringing reefs, unlike elsewhere in Caribbean, for example the reefs of Tobago [START_REF] Mallela | Hurricanes and coral bleaching linked to changes in coral recruitment in Tobago[END_REF], have shown resilience to acute disturbances from hurricanes and bleaching, in addition to the recurring chronic stressors of overfishing and land development (Idjadi et al., 2008;[START_REF] Crabbe | Scleractinian coral population size structures and growth rates indicate coral resilience on the fringing reefs of North Jamaica[END_REF].

Factors that can improve coral reef resilience include species and functional diversity, connectivity to larval sources, appropriate substrates for larval settlement, and protection from other anthropogenic effects (Nyström et al., 2008: Halford and[START_REF] Halford | Towards an understanding of resilience in isolated coral reefs[END_REF]. Here we wished to test three other factors that might have influenced Jamaican reef resilience to, and subsequent recovery from, the 2005 bleaching event, and which might help inform management policy for reefs in the future: reef rugosity, contact of corals with macroalgae, and aggregation and competition between corals.

Rugosity has been linked to reef resilience in the South Central Pacific (Adjeroud et al., 2009). Viability of small coral colonies over time was used as a measure of reef resilience, as has been suggested [START_REF] Loya | Effects of turbidity and sedimentation on the community structure of Puerto Rican Corals[END_REF][START_REF] Connell | Diversity in tropical rain forests and coral reefs[END_REF].

Most reefs are open non-equilibium systems, [START_REF] Connell | Diversity in tropical rain forests and coral reefs[END_REF] with diversity maintained by disturbance and recruitment [START_REF] Rogers | Hurricanes and coral reefs: the intermediate disturbance hypothesis revisited[END_REF]. Interspecific competition [START_REF] Stoll | Intraspecific aggregation alters competitive interactions in experimental plant communities[END_REF][START_REF] Hartley | A general framework for the aggregation model of coexistence[END_REF] as well as aggregation is pervasive among coral communities, and is important in maintaining their viability [START_REF] Karlson | Dynamics of coral communities[END_REF][START_REF] Karlson | Aggregation influences coral species richness at multiple spatial scales[END_REF]. Heterospecific competition of corals with algae reduces coral growth and survivorship [START_REF] Lirman | Competition between macroalgae and corals: effects of herbivore exclusion and increased algal biomass on coral survivorship and growth[END_REF][START_REF] Box | Effect of macroalgal competition on growth and survival of juvenile Caribbean corals[END_REF][START_REF] Crabbe | Influence of Macroalgal Cover on Coral Colony Growth Rates on Fringing Reefs of Discovery Bay, Jamaica[END_REF]. In corals, spatial arrangement, orientation and aggregation may be a key mechanism contributing to species coexistence on coral reefs [START_REF] Idjadi | Spatial arrangement of competitors influences coexistence of reef-building corals[END_REF].

Porites astreoides is one of the most abundant corals of the fringing reefs around Discovery Bay [START_REF] Crabbe | Scleractinian coral population size structures and growth rates indicate coral resilience on the fringing reefs of North Jamaica[END_REF]. However, it can be overgrown by the faster growing branching coral Acropora palmata. Here we test in the field, on Dairy Bull reef, whether aggregated P. astreoides colonies exhibit enhanced growth when exposed to superior competition from A. palmata, as has been found by experiment with the Indo-Pacific corals P. lobata and the superior competitor P. rus [START_REF] Idjadi | Spatial arrangement of competitors influences coexistence of reef-building corals[END_REF].

Methods

Sites and Sampling

Studies were conducted using SCUBA at six sites [Rio Bueno (18° 28.805 (2006)(2007)(2008)(2009) along the fringing reefs surrounding Discovery Bay, Jamaica. In addition, A. cervicornis growth was studied in 2009 at a new site (New reef) between Dancing Ladies and M1, where A. cervicornis cover was comparable to that at Dairy Bull reef. GPS coordinates were determined using a hand-held GPS receiver (Garmin Ltd.). For all sites except CREWS and New reef, four haphazardly located transects, each 15 m long and separated by at least 5m, were laid at between 5-8.5 m depth, to minimise variation in growth rates due to depth [START_REF] Huston | Variation in coral growth rates with depth at Discovery Bay, Jamaica[END_REF]. Linear extension rates of A. palmata and A. cervicornis were measured by image analysis, verified by flexible tape, as described previously [START_REF] Crabbe | Sediment impacts on growth rates of Acropora and Porites corals from fringing reefs of Sulawesi, Indonesia[END_REF]. Non-branching corals 2m either side of the transect lines were photographed and surface areas measured with flexible vinyl tape; the largest diameter of each colony was measured using a vinyl tape measure, and also the diameter at 90 o to that (Crabbe et al., 2002;[START_REF] Crabbe | Sediment impacts on growth rates of Acropora and Porites corals from fringing reefs of Sulawesi, Indonesia[END_REF]. To increase accuracy, surface areas rather than diameters of live non-branching corals were measured. For this study, sampling only included smaller size classes of corals, up to 250 mm 2 surface area [START_REF] Crabbe | Scleractinian coral population size structures and growth rates indicate coral resilience on the fringing reefs of North Jamaica[END_REF]. Radial growth rates of non-branching corals and linear extension rates of branching corals were calculated over the post-2005 bleaching period from 2006-2009. Except where colony aggregation was investigated, colonies that were close together (<5 cm) or touching were avoided to minimise age discontinuities through fission and altered growth rates [START_REF] Hughes | Do corals lie about their age? Some demographic consequences of partial mortality, fission, and fusion[END_REF][START_REF] Foster | Sexual vs. asexual reproduction in an ecosystem engineer: the massive coral Montastrea annularis[END_REF][START_REF] Elahi | Consequences of fission in the coral Siderastrea siderea: growth rates of small colonies and clonal input to population structure[END_REF]. Montastrea annularis colonies were not measured in this study, as their surface area does not reflect their age [START_REF] Hughes | Do corals lie about their age? Some demographic consequences of partial mortality, fission, and fusion[END_REF], and because hurricanes can increase their asexual reproduction through physical damage [START_REF] Foster | Sexual vs. asexual reproduction in an ecosystem engineer: the massive coral Montastrea annularis[END_REF].

Where colony aggregates were studied, fission or fusion did not occur in the three years of this study. Rugosity (R ) was determined according to the formula:

R = Sr / Sg
where Sr = real surface distance between two points, and Sg = straight line geometric distance between two points. This was calculated over a 20 m distance, performed in triplicate, at each site, using photographic image analysis verified by the chain method [START_REF] Crabbe | Comparison of two reef sites in the Wakatobi Marine National Park (SE Sulawesi, Indonesia) using digital image analysis[END_REF]. Rugosity measurements were similar to those used on reefs by [START_REF] Klumpp | Community structure, biomass and productivity of epilithic algal communities on the Great Barrier Reef-Dynamics at different spatial scales[END_REF], [START_REF] Sanchez | Gorgonian communities in two contrasting environments on oceanic atolls of the southwestern Caribbean[END_REF], [START_REF] Mcclanahan | Predation and the distribution and abundance of tropical sea urchin populations[END_REF], and [START_REF] Chapman | Gradients in coral reef fish density and size across the Barbados Marine reserve boundary: effects of reserve protection and habitat characteristics[END_REF].

Overall, over 2,000 measurements were made on over 600 coral colonies, equally distributed between the sites for species and numbers of colonies. Computer digital image analysis was undertaken using the UTHSCSA (University of Texas Health Science Center, San Antonio, Texas, USA) Image Tool software [START_REF] Crabbe | Sediment impacts on growth rates of Acropora and Porites corals from fringing reefs of Sulawesi, Indonesia[END_REF]. One or two-factor ANOVA was used to compare coral data among sites; ± error values represent standard errors of the data. Water quality measurements at the sites have been reported previously [START_REF] D'elia | Nitrate-rich groundwater inputs to Discovery Bay, Jamaica: a significant source of N to local reefs?[END_REF][START_REF] Greenaway | The effects of rainfall on the distribution of inorganic nitrogen and phosphorus in Discovery Bay, Jamaica[END_REF].

Data on bleaching events impacting Discovery Bay

Information on bleaching was obtained from the NOAA coral reef watch site:

(http://coralreefwatch.noaa.gov/satellite/current/sst_series_24reefs.html) and from [START_REF] Jones | The effects of coral bleaching in the northern Caribbean and western Atlantic[END_REF].

Results

Rugosities were calculated for each site as follows: Rio Bueno: 1.05 ± 0.15, CREWS: 1.38 ± 0.2 , Dancing Ladies: 1.3 ± 0.15, New reef: 1.7 ± 0.14, M1: 1.17 ± 0.12, Dairy Bull: 2.3 ± 0.16, and Pear Tree Bottom: 1.23 ± 0.11. Linear extension rates of A. cervicornis were significantly lower (p<0.025; F>6) at M1 and CREWS sites, where they were in contact with Dictyota species macroalgae, than at the other sites; there were no significant differences in linear extension rates of A. palmata between any site (Table 1). Neither species was found along the belt transects at Dancing Ladies during the period of study. Acropora palmata colonies were >100 mm from any interaction with macroalgae at all sites measured. There were significant relationships between mean rugosity value and the increase in smallest size classes for Sidastrea siderea, Colpophyllia natans, Porites astreoides and Agaricia species for five sites on the fringing reefs near Discovery Bay (Figure 1). In all cases the best-fit linear regression fits are shown; R 2 values for regression were: Sidastrea siderea, 0.86; Colpophyllia natans, 0.71; Porites astreoides, 0.41 and Agaricia species 0.98.

There was also a significant linear relationship between percentage coral cover of A. cervicornis colonies with rugosity values for Dairy Bull reef, M1, New reef, Dancing Ladies, Rio Bueno and Pear Tree Bottom (Figure 2). The line is a best-fit linear regression, with R 2 = 0.94. The two reefs with the highest rugosity values, Dairy Bull reef and New reef, exhibited the highest cover of the urchin Diadema antillarum (measured at 0.4 ± 0.1 Diadema m -2 for both reefs), and no macroalgal cover in the vicinity of A. cervicornis colonies. In contrast, the other reefs with lower rugosities exhibited extensive macroalgal cover and no Diadema antillarum.

Radial growth rates of Porites astreoides varied significantly dependent on whether they were single or aggregated colonies, and whether they were in contact with macroalgae or with competitor coral species (Figure 3). Single colonies of Porites astreoides at Dairy Bull reef (n=4) not in contact with macroalgal Dictyota species had significantly (p<0.002, F>15) greater radial growth rates than single colonies in contact with Dictyota at M1 or at Rio Bueno. Two aggregated colonies of P. astreoides in contact with one another (n = 3) at Dairy Bull reef had significantly lower radial growth rates than single colonies at the same reef (p<0.01, F>7). Two aggregated colonies of P. astreoides in contact with one another at Dairy Bull reef, one of which was in contact with the faster growing competitor A. palmata (n=3) showed significantly greater radial growth rates than with just two aggregated P. astreoides colonies alone (p<0.05, F>5). At Dairy Bull reef there was no significant difference in radial growth rates for P. astreoides colonies whether they were in contact with single colonies of the same species, or with single colonies of Sidastrea siderea, which has similar radial growth rates to P. astreoides.

Discussion

Decline in architectural complexity of Caribbean reefs has been apparent since the 1980s, and coincide with key events in recent Caribbean ecological history [START_REF] Alvarez-Filip | Flattening of Caribbean coral reefs: region-wide declines in architectural complexity[END_REF]. Rugosity has been selected as a bioindicator for use in monitoring programmes linking changes in water quality to changes in the condition of coral reef ecosystems [START_REF] Cooper | Bioindicators of changes in water quality on coral reefs: review and recommendations for monitoring programmes[END_REF]. Reef rugosity correlates well with rocky reef fish community structure in Brazil [START_REF] Chaves | Comparative analysis of rocky reef fish community structure in coastal islands of south-eastern Brazil[END_REF], Hawaii [START_REF] Howard | Community structure and habitat associations of parrotfishes on Oahu, Hawaii[END_REF], the Great Barrier Reef [START_REF] Emslie | Recovery from disturbance of coral and reef fish communities on the Great Barrier Reef, Australia[END_REF], Barbados [START_REF] Chapman | Gradients in coral reef fish density and size across the Barbados Marine reserve boundary: effects of reserve protection and habitat characteristics[END_REF], the Seychelles [START_REF] Wilson | Appraisal of visual assessments of habitat complexity and benthic composition on coral reefs[END_REF], and the Gulf of Mexico [START_REF] Rooker | Fish assemblages on artificial and natural reefs in the Flower Garden Banks National Marine Sanctuary, USA[END_REF]. Rugose locations aid aggregation of Diadema antillarum urchins, important to keep macroalgae in check [START_REF] Macia | Experimental dispersal of recovering Diadema antillarum increases grazing intensity and reduces macroalgal abundance on a coral reef[END_REF].

The continued presence of small coral colonies over time can indicate reef resilience [START_REF] Loya | Effects of turbidity and sedimentation on the community structure of Puerto Rican Corals[END_REF][START_REF] Connell | Diversity in tropical rain forests and coral reefs[END_REF], and here we demonstrate that increases in colony numbers of the smallest size class for Sidastrea siderea, Colpophyllia natans, Porites astreoides and Agaricia species since the mass bleaching event of 2005 is significantly correlated with reef rugosity at all the sites studied. Water quality is not a factor in differences between the reef sites [START_REF] D'elia | Nitrate-rich groundwater inputs to Discovery Bay, Jamaica: a significant source of N to local reefs?[END_REF][START_REF] Greenaway | The effects of rainfall on the distribution of inorganic nitrogen and phosphorus in Discovery Bay, Jamaica[END_REF].

In addition, we show that rugosity positively correlates with coral cover of A. cervicornis colonies. Increase in A. cervicornis cover was one of the main reasons for suggesting a phase-shift reversal at Dairy Bull reef [START_REF] Idjadi | Rapid phase-shift reversal on a Jamaican coral reef[END_REF]. It is interesting that New reef, at about 8m depth between M1 and Dancing Ladies, has similar rugosity and A. cervicornis cover to that of Dairy Bull reef. These findings suggest that the three dimensional topography and complexity is important for reef resilience and viability in the face of environmental stressors such as bleaching.

Interestingly, rugosity also correlates well with fish abundance on other reefs, for example with parrotfish (Scarid) abundance on reefs of Oahu, Hawaii [START_REF] Howard | Community structure and habitat associations of parrotfishes on Oahu, Hawaii[END_REF], and rugosity has been used in regional modelling of coral habitats for marine conservation [START_REF] Dunn | Rugosity-based regional modelling of hard bottom habitat[END_REF].

This study shows that absence of macroalgal cover allows increased growth rates of A. cervicornis, as has been found with Montastrea annularis, Porites astreoides, and Sidastrea sidera [START_REF] Crabbe | Influence of Macroalgal Cover on Coral Colony Growth Rates on Fringing Reefs of Discovery Bay, Jamaica[END_REF]this study). Macroalgal competition has been found to reduce the survivorship and growth of corals in forereefs in Roatan, Honduras [START_REF] Box | Effect of macroalgal competition on growth and survival of juvenile Caribbean corals[END_REF] and in Florida [START_REF] Lirman | Competition between macroalgae and corals: effects of herbivore exclusion and increased algal biomass on coral survivorship and growth[END_REF]. It is possible that A. palmata, consisting generally of taller colonies, is not limited by macroalgal contact or shading in the same way as A. cervicornis.

It has been suggested that the spatial dispersion of competitors within habitat patches can determine the outcome of interspecific interactions [START_REF] Stoll | Intraspecific aggregation alters competitive interactions in experimental plant communities[END_REF][START_REF] Hartley | A general framework for the aggregation model of coexistence[END_REF]. Experimental studies have found that aggregated spatial arrangements can influence the outcome of interspecific competition and promote species coexistence [START_REF] Idjadi | Spatial arrangement of competitors influences coexistence of reef-building corals[END_REF]. This would be important in maintaining species biodiversity during reef recovery from environmental stressors such as bleaching. This field study showed that while aggregated P. astreoides colonies exhibited significantly lower growth than single colonies, interaction with the stronger competitor A. palmata resulted in a small but significant increase in P. astreoides growth rate. The survival of slow growing colonies is important to maintain coral diversity, so a small increase in growth rate could potentially confer a significant advantage on a densely settled substratum [START_REF] Vollmer | Allometric scaling in small colonies of the scleractinian coral Siderastrea siderea (Ellis and Solander)[END_REF], such as at Dairy Bull reef. Such a heterospecific interaction growth effect may act in concert with other colonization/competition trade-offs [START_REF] Cadotte | On testing the competition-colonization trade-off in a multi-species assemblage[END_REF], and would also be important after a major disturbance such as a hurricane or a bleaching event. Although this study does not have the sample numbers or the control of the experimental setup of Idjadi and Karlson, it does suggest that a similar mechanism can operate in the field to promote species coexistence.

Reefs are under stress from a variety of insults that act in synergy [START_REF] Anthony | Ocean acidification causes bleaching and productivity loss in coral reef builders[END_REF]. It may be that under conditions of low coral cover, low reef rugosity and low biodiversity, a major disturbance such as the 2005 bleaching event crosses a 'threshold' which then induces a cascade to induce the crossing of other thresholds, leading to a stable but less desirable alternative state (Kinzig et al., 2006). An example where this has happened may be the reefs of Tobago [START_REF] Mallela | Hurricanes and coral bleaching linked to changes in coral recruitment in Tobago[END_REF]. On the reefs of North Jamaica, there is a variable response and resilience, strongest in the case of Dairy Bull reef, which exhibits relatively high rugosity, coral cover and biodiversity with no significant macrogal cover [START_REF] Crabbe | Scleractinian coral population size structures and growth rates indicate coral resilience on the fringing reefs of North Jamaica[END_REF]this work). Whether the niche diversification and multiple symbiont clades in corals at Dairy Bull Reef (see [START_REF] Crabbe | Scleractinian coral population size structures and growth rates indicate coral resilience on the fringing reefs of North Jamaica[END_REF]) is sufficient to allow for adaptive radiation [START_REF] Brockhurst | Niche occupation limits adaptive radiation in experimental microcosms[END_REF] in a period of intense climate change and ocean acidification is yet to be resolved.

Reef rugosity, contact of corals with macroalgae, and aggregation and competition between corals could influence reef resilience together with species and functional diversity, connectivity to larval sources, appropriate substrates for larval settlement, and protection from other anthropogenic effects (Nyström et al., 2008: Halford and[START_REF] Halford | Towards an understanding of resilience in isolated coral reefs[END_REF]. As reefs become increasingly subjected to high sea surface temperatures and thermal stress (Loya et al., 2001;Thompson and van Woesik, 2009), all these factors will be important to ensure coral resilience to continued environmental stressors.

Table 1. Linear extension rates (mm/yr) of branching corals Acropora cervicornis and Acropora palmata at Rio Bueno (RB), CREWS, M1, Dairy Bull reef (DB) and Pear Tree Bottom (PT). Neither species was found along the belt transects at Dancing Ladies during the period of study. Values represent growth rates ± standard errors; n = 4 for each species. nm, not measurable in sufficient numbers (4). *, indicates colonies of A. cervicornis were in contact with Dictyota species macroalgae at M1 and CREWS sites only.

A. palmata colonies were >100 mm from any conspecific or heterospecific interaction at all sites measured. Linear extension rates of A. cervicornis were significantly lower (p<0.025; F>6) at M1 and CREWS sites, where they were in contact with macroalgae, than at the other sites; there were no significant differences in linear extension rates of A. palmata between any sites.

  This work was conducted at Discovery Bay during April 11-18 in 2006, December 30 in 2006 -January 6 in 2007, July 30 -August 16 in 2008 and July 27 -August 8 in 2009.

Fig,1

Fig.3

Acknowledgements

I thank the Earthwatch Institute for funding, Mr. Anthony Downes, Mr. Peter Gayle, and the staff of the Discovery Bay Marine Laboratory for their invaluable help and assistance, to volunteers for their help underwater during this project, and to two anonymous referees for improvements to the manuscript. This is publication number 735 from the Discovery Bay Marine Laboratory.

Legends to Figures


Donatella Fedeli

Manuel Carloni

Prof Giancarlo Falcioni
email: giancarlo.falcioni@unicam.it

oxidative damage in trout erythrocyte in response to "in vitro" copper exposure

Keywords: Copper, trout erythrocyte, hemoglobin, hemolysis, DNA damage

The oxidative action of copper on different trout Oncorhynchus mykiss erythrocyte components was studied. The results indicate that:

-cupric ions differently influence the oxidative status of two trout hemoglobin components I and IV (HbI and HbIV) having different structural and functional properties; -Reactive oxygen species (ROS) production associated with hemoglobins autoxidation is not influenced by the presence of copper sulphate; -the susceptibility to hemolysis increases in the presence of copper only when the erythrocyte suspension is incubated in air; the effect of copper is almost absent for carbon monoxidesaturated erythrocyte suspensions; -DNA damage due to copper was not observed in our experimental conditions.

The data obtained are important for the analysis of the environmental risks produced by copper on fish.

INTRODUCTION

Copper is an essential trace metal for living organisms and its role as cofactor for crucial enzymes (cytochrome c oxidase, superoxide dismutase, ceruloplasmin, monooxygenases) has been well established. Most of these enzymes catalyze oxidation/reduction reactions that have molecular oxygen as co-substrate. However, if copper is present at relatively high concentrations in the environment, toxicity to aquatic organisms can occur (Winger et al., 2000;[START_REF] Marcano | Uptake and regulation of copper and zinc in relation to metal-binding protein in the Polychaete Eurythoe complanata[END_REF][START_REF] Roche | Fish blood parameters as potential tool for identification of stress caused by environmental factors and chemical intoxication[END_REF]. In aquaculture systems, copper is regularly used in the form of copper sulfate as a therapeutic agent and it is widely used to control alga blooms and aquatic macrophyte infestations [START_REF] Boyd | Risk associated with the use of chemicals in pond aquaculture[END_REF].

There is no doubt that exposure to excess copper can damage cells and organs because copper ion and its complexes can induce formation of reactive oxygen species that can damage biomolecules such as lipids, proteins and DNA. Copper toxicity at the cellular level depends on the permeability of cell membranes to copper ions and on its participation in redox reactions because it easily cycles between the cuprous and cupric state.

Cu 2+ + O 2

.-→ Cu + +O 2 2. Cu + + H 2 O 2 → Cu 2+ +OH . + OH -This paper focuses on the "in vitro" effects of copper ions on oxidative damage in trout Oncorhynchus mykiss erythrocytes. The effect of copper ions on the oxidation of trout hemoglobin components either as purified proteins or in the whole hemolysate was examined. It is worth recalling that contrary to mammals and birds, a multiplicity of hemoglobin components is generally present in erythrocytes of fish. In the case of nucleated erythrocytes from trout Oncorhynchus mykiss, four different hemoglobin components are present; they are characterized by functional differences which have been correlated with different physiological roles [START_REF] Brunori | Molecular adaptation to physiological requirements: the hemoglobin system in trout[END_REF]. Trout hemoglobin components according to their anionic mobility have been called HbI, HbII, HbIII and

HbIV. These hemoglobins are prone to oxidation and this property permits to follow "in vitro" the autoxidation process over a relatively short time.The production of reactive oxygen species during the hemoglobin oxidation was also evaluated. The measurements regarding purified hemoglobins were performed on HbI and HbIV. These two trout hemoglobin components represent, respectively, about 20 and 60% of the whole pigment present inside trout erythrocytes and have very different oxygen binding properties [START_REF] Brunori | Molecular adaptation to physiological requirements: the hemoglobin system in trout[END_REF]. The mechanism of copper-induced met-hemoglobin formation has been described in detail for human hemoglobin solutions [START_REF] Bonaventura | Internal electron transfer between hemes and Cu (II) bound at cysteine beta93 promotes methemoglobin reduction by carbon monoxide[END_REF] but this is not well understood for fish hemoglobins that have different structures. Furthermore, the influence of copper on the extent of hemolysis and on the susceptibility of DNA to oxidative damage was assessed.

MATERIALS AND METHODS

All the reagents used were of analytical grade.

Samples

Red blood cells were obtained from Oncorhynchus mykiss trout, coming from the fish farm "Eredi Rossi Silvio" Sefro MC, Italy. The fish were kept in tanks containing water from Scarsito river, a tributary of the Potenza river, and fed with commercial fish food obtained from Hendrix S.p.A (Mozzecane, VR, Italy). Blood was extracted by puncturing the lateral tail vein. After washing with isotonic medium at pH 7.8 (0.1 M phosphate buffer 0.1 M NaCl, 0.2 % citrate, 1 mM EDTA) the cells were suspended in the desired isotonic buffer.

Separation of hemoglobin components and carbon monoxide hemoglobin (HbCO) formation

Separation of the various hemoglobins, as described previously [START_REF] Binotti | Studies of the functional properties of fish hemoglobins. II. The oxygen equilibrium of the isolated hemoglobin components from trout blood[END_REF] was carried out by column chromatography on DEAE-Spehadex A-50; their homogenity was tested by electrophoresis.

Experiments involving carbon monoxide(CO) hemoglobin were carried out after exposure of the oxygenated derivative to a weak vacuum by using a rotary vane pump and then to pure CO gas. The vacuum, performed for a few seconds, permits the removal of part of the oxygen thus making the formation of CO hemoglobin easier ( Hb affinity for CO is greater than that for O 2 by about 250fold).

Ferric hemoglobin (Met-Hb) formation and hemoglobin precipitation

The rate of met-Hb formation in the hemolyzates and in two electrophoretically distinct hemoglobin components called hemoglobin trout I and IV (HbI and HbIV) was followed in a Cary 219 spectrophotometer in the visible region; reference value (i.e. complete oxidation) was estimated by addition of ferricyanide. Absorbance at 700 nm was followed as an index of turbidity to monitor the onset of hemoglobin precipitation.

Reactive oxygen species production during Hb oxidation

ROS production associated with Hb autoxidation was evaluated by monotoring the oxidation of non-fluorescent dichlorofluorescein (DCFH) to fluorescent DCF.

A solution of 1 mg ml -1 of HbI or HbIV was incubated for 1 hour in phosphate buffer 50 mM pH 7.5 at different temperatures and in the presence of 0.036 m DCFH and of 10 or 100 M CuSO 4 .

DCFH oxidation was measured fluorimetrically on these solutions at excitation and emission wavelengths of 498 nm and 520 nm respectively.

Hemolysis

In order to evaluate the hemolysis rate, the erythrocytes were suspended in isotonic medium at pH 6.3 at 35°C. The degree of hemolysis was determined spectrophotometrically at 540 nm as previously described ( 19) either in the presence or absence of the desired amount of copper sulfate.

In particular, hemolysis was determined as 100xA/10 x A*x100% where A is the optical density of Hb present in the supernatant after centrifugation of red cell suspension and A*x100% is the optical density of the red cell suspension after complete lysis with 10 volumes of distilled water at zero time incubation.

Single-cell gel electrophoresis (comet assay).

The "comet" assay was performed on trout erythrocytes (1 10 6 cells ml -1 ) incubated in a pH 6,3 isotonic buffer at 35°C for 1 hr in the presence and absence (control) of 50 M copper sulfate. After incubation, the erythrocytes were suspended in 0.7% low melting agarose in phosphate buffer saline (PBS) and pipetted on microscope slides pre-coated with a layer of 1% normal melting agarose. The agarose with the cell suspension was allowed to set on the pre-coated slides at 4°C for 10 min.

Subsequently, another top layer of 0.7% low melting agarose was added and allowed to set at 4°C for 10 min. The slides were then immersed in lysis solution (1% sodium n-lauroyl-sarcosinate, 2.5 M NaCl, 100 mM Na 2 EDTA, 10 mM Tris HCl pH 10, 1% Triton X-100 and 10% dimethyl sulfoxide for 1h at 4°C in the dark, in order to lyse the embedded cells and to permit DNA unfolding. After incubation in lysis solution, slides were exposed to alkaline buffer (1 mM Na 2 EDTA, 300 mM NaOH buffer, pH >13) for 20 min; in this condition RNA is completely degraded. The slides were subjected to 20 min electrophoresis at 25 V in the same alkaline buffer and finally washed with 0.4 M Tris HCl buffer (pH 7.5) to neutralize excess alkali and to remove detergents before staining with ethidium bromide (2 g ml -1 ).

Cells were examined with an Axioskop 2 plus microscope (Carl Zeiss, Germany) equipped with an excitation filter of 515-560 nm and a magnification of x20. Imaging was performed using a specialized analysis system ("Metasystem" Altlussheim, Germany) to determine tail moment (TM), tail length (TL) and tail intensity (TI), parameters correlated with the degree of DNA damage in each single cell. Experiments were performed three times and data (at least 150 scores per sample)

are the mean values plus/minus the standard error of the mean (SEM). Statistical analysis was performed using the student t-test and differences were regarded as statistically significant when p values were < 0.05. The statistical analysis of the three experiments give a p value <0.05.

RESULTS

Met-Hb rate formation in hemolysates

Met-Hb rate formation in purified hemoglobin components HbI and HbIV

Figure 2 shows the time course of HbI autoxidation when the protein was incubated at 35°C and pH 7.5. The presence of high amounts of CuSO 4 (up to 100 M) does not influence the HbI oxidation rate with respect to the control (HbI alone without CuSO 4 ) during our incubation time.

It was not possibile to perform similar experiments with HbIV because this hemoglobin component is less stable and tends to precipitate under these experimental conditions. The presence of copper sulfate accelerates the protein denaturation rate. A similar behaviour was also observed at lower incubation temperatures (30°C).

Reactive oxygen species production during Hbs incubation

ROS production associated with Hb autoxidation was followed by means of fluorescence deriving from DCFH oxidation. Figures 3 and4 show the time course of DCF fluorescence generated during one hour incubation of HbI or HbIV followed at different temperatures. After one hour of HbI incubation at 20°C and 25°C there is no ROS formation and this is in agreement with the absence of HbI oxidation. When the HbI solution is incubated for one hour at 35°C ROS formation due to the partial oxidation of the protein was observed (Fig. 3). Similar experiments were performed with

HbIV (the highest temperature was 30°C) (Fig. 4). A slight ROS production was present also at 25°C which increased at 30°C. For both hemoglobins the level of ROS production was not influenced by the presence of copper sulfate. It is possible to avoid the formation of met-Hb by adding carbon-monoxide (CO) before incubation. Experiments performed with HbCO did not show ROS formation at any temperature (data not shown). This behaviour is due to the stability versus the oxidation of the carbomonoxy-derivative; in other words, HbCO does not lead to the ferric form during incubation.

Hemolysis rate

By varying pH and temperature, it is possible for trout erythrocytes suspended in isotonic medium to follow "in vitro" the haemolytic event over a relatively short time [START_REF] Falcioni | Glutathione peroxidase and oxidative hemolysis in trout red blood cells[END_REF]; the hemolytic process is characterized by the presence of a long lag phase. The influence of a fixed amount (50 M) of copper sulfate on the rate of haemolysis in trout erythrocytes during incubation in isotonic buffer at 35°C and pH 6.3 was investigated and reported in Fig. 5. The figure is one representative set of tracings from a total of at least three experiments. The susceptibility to haemolysis increases in the presence of copper only when the erythrocyte suspension was incubated in air; a dose-dependent effect of copper was absent for CO-saturated erythrocyte suspensions exposed to air under identical conditions. The half time (t/2) of haemolysis (expressed as the time necessary for 50% haemolysis to occur) and determined from Fig. 5 is 160 min, 186 min, 148 min and 183 min for red blood cells (RBC) suspension in air, RBC suspension with CO, RBC suspension in air with 50 M CuSO 4 and RBC suspension with CO and 50 M CuSO 4 , respectively.

The statistical analysis of the three experiments give a p value <0.05.

Comet assay

The "comet assay" or single-cell gel electrophoresis was performed on trout erythrocyte suspension to explore if the copper salt under study influenced the DNA status in these nucleated cells. This test has become increasingly popular for the measurement of DNA damage in individual cells and consists of embedding cells in agarose, followed by lysis, electrophoresis and staining to visualize DNA damage using fluorescence microscopy. Cells with increased DNA damage display an increased migration of genetic material in the direction of the electrophoresis. The extent of DNA damage is quantified by measuring the displacement of the genetic material between the cell nucleus ("comet head") and the resulting "tail". The parameters used as an index of DNA damage are tail length, tail intensity and tail moment and they are calculated by computerized image analysis. The "comet assay" was performed on trout erythrocyte suspensions incubated at pH 6.3 and 35°C for 60 min in the presence of 50 M copper sulfate. Figure 6 clearly shows that all the considered comet parameters (tail length, tail intensity and tail moment) increased after one hour.

The increase for tail length and tail moment was independent of the presence of copper sulfate in the incubation medium; a slight increase was instead measured for tail intensity.

DISCUSSION

It is well known that copper can induce damage to cells through radical-formation [START_REF] Valko | Metal, toxicity and oxidative stress[END_REF][START_REF] Gaetke | Copper toxicity, oxidative stress and antioxidant nutrients[END_REF]. Furthermore it is important to remember that copper, together with iron and selenium, control the activity of those enzymes involved in the defence mechanisms against reactive oxygen species (superoxide dismutase, catalase and glutathione peroxidase). The results reported here clearly show that the presence of copper can influence the redox state of HbIV and disrupts the plasma membrane. However, under our experimental conditions DNA damage due to the presence of copper was not observed, except for a slight increase in tail intensity.

There is evidence that copper catalyzes human oxyhemoglobin oxidation also at very low concentrations [START_REF] Rifkind | Copper and autoxidation of hemoglobin[END_REF]Winterbourn and Carrel, 1997) while other metal ions have no significant effect even at high concentrations. The copper induced met-Hb formation mechanism has been described in detail for human hemoglobin [START_REF] Bonaventura | Internal electron transfer between hemes and Cu (II) bound at cysteine beta93 promotes methemoglobin reduction by carbon monoxide[END_REF]. It is probable that ferric hemoglobin formation, in general, involves an electron transfer from Fe ++ to Cu ++ as suggested for human hemoglobin [START_REF] Bonaventura | Internal electron transfer between hemes and Cu (II) bound at cysteine beta93 promotes methemoglobin reduction by carbon monoxide[END_REF]. Our results on the effect of copper on autoxidation of trout hemoglobins clearly show that the effect of the metal is different in the two hemoglobin fractions HbI and HbIV that have different aminoacid sequences, and as a consequence, different tridimensional structures and functional properties [START_REF] References Barra | Primary structure of hemoglobin from trout (Salmo irideus) amino acid sequence of the beta chain of trout HbI[END_REF][START_REF] Petruzzelli | Amino acid sequence of alpha-chain of hemoglobin IV from trout (Salmo irideus)[END_REF].

The involvement of the sulphydryl group in -93 position in hemoglobin autoxidation has been reported in the literature [START_REF] Rifkind | The autoxidation of horse hemoglobin: the effect of glutathione[END_REF]. The effect of copper on Hb oxidation during the hemolysate incubation reported in Fig. 1 could imply that the hemoglobin component oxidized is

HbIV and not HbI. This is due to the fact that HbI in the presence of copper at the highest concentration and in the same experimental conditions was not oxidized.

It is worth pointing out that the autoxidation of oxygenated Hbs can be followed by the transformation of the oxidized molecule (high-spin Fe 3+ ) into a species absorbing as a low-spin Fe 3+ compound, that is a hemichrome which tends to precipitate. The overall process may be described according to the following scheme:

HbO 2 -met Hb -hemicrome -precipitate

The presence of different antioxidant enzymes (superoxide dismutase, catalase and glutathione peroxidase) in the hemolysate could reduce the oxidation rate of HbIV and thus avoiding the formation of HbIV precipitate.

The conversion of oxyhemoglobin into met-Hb is associated with superoxide production [START_REF] Misra | Generation of superoxide radical during the autoxidation of haemoglobin[END_REF][START_REF] Brunori | Formation of superoxide in the autoxidation of isolated a and b chains of human hemoglobin and its involvement in the hemicromes precipitation[END_REF] and thereby of products such as H 2 O 2 or hydroxyl radical, which can derive from O 2 .-itself.

We have observed that the presence of copper sulfate does not influence the level of ROS during 1 hr of HbI or HbIV incubation . This result could be due to the direct interaction between heme-iron and copper or to the participation of copper in redox reactions involving ROS.

The presence of Cu ++ in the trout erythrocyte suspension influences the hemolytic process (Fig. 5) when the cells are exposed to air. The effect of Cu ++ is absent when the experiment was performed with CO-saturated erythrocytes; the presence of CO leads to insignificant hemoglobin autooxidation over the same time range. These data appear to indicate that the effect of Cu ++ on lysis is also correlated with hemoglobin oxidation. The hemolytic event is a complex process due to more than one type of biochemical damage. The difference in the rate of hemolysis between red blood cells saturated with either CO or O 2 was due to the consequences following hemoglobin oxidation [START_REF] Falcioni | Glutathione peroxidase and oxidative hemolysis in trout red blood cells[END_REF]. Recently, Labieniec et al. [START_REF] Labieniec | Interaction of free copper (II) ions alone or in complex with iron (III) ions with erythrocytes of marine fish Dicentrarchus labrax[END_REF] found that incubation with copper of marine fish erythrocytes from Dicentrarchus labrax increased hemolysis of cells; a significant decrease in the hemoglobin content and red blood cells after exposure of fish Channa punctatus to sublethal concentration of copper sulphate has been reported by [START_REF] Singh | Impact of copper on haematological profile of freshwater fish, Channa punctatus[END_REF]. On the membrane surface as well as at the cytosol level, copper ions are involved in various redox reactions that lead to an increased radical production followed by oxidative damage and lipid bilayer destruction. Cupric but not cuprous ions are responsible for met-Hb formation that can contribute to the hemolytic event. The reduction of cupric to cuprous ion can occurr at the membrane surface level [START_REF] Bogdanova | Copper effects on ion transport across lamprey erythrocyte membrane: Cl (-)/OH (-) exchange induced by cuprous ions[END_REF] by the involvment of sulphydryl groups. Both cupric and cuprous ions can interact with transport systems outside and inside the membrane leading to its perturbation (Bognonova et al., 2002).

CONCLUSIONS

In conclusion, the environmental risks from the use of copper compounds should be evaluated in order to control biological damage to fish. The results here presented indicated that an eventual "in vivo" alteration following copper exposure could induce plasma membrane perturbation with a mechanism involving met-hemoglobin formation.

Figure 1

 1 Figure 1 is one representative set of tracings from a total of at least three experiments and shows the

FIGURE CAPTIONS Fig. 1 .Fig. 2 .Fig. 3 .Fig. 4 .Fig. 5 Fig. 6 .

 CAPTIONS123456 FIGURE CAPTIONS

  Winger, P.W., Lasier, P.J., White, D.H., Seginak, J.T., 2000. Effect of contaminants in dredge material from the lower Savannah River. Archives Environmental Contaminants and

	Toxicology 28(1), 128-136.
	Winterbourn, C.C., Carrel, R.W., 1997. Oxidation of human hemoglobin by copper.
	Biochemical Journal 165,141-148.