G�tests for independence were used to determine whether there was temporal variation in morph frequencies.
4.2 MATERIALS AND M ETHODS 1 Col lection of data
In order to determine the variation in proportions of morphs around Tas mania samples were collected from the south, east and north coasts (Figure 4. i ). The west coast was not sampled because it is a very exposed coast and few, if any, H. erythrogramma occur there (Dix, 1 977). Sites in Port Phillip Bay, Victoria (Figure 4.2) were sampled by A. Constable, Zoology Department, University of Melbourne.
Small scale geographic variation was determined by sampling five sites at Tinderbox (sites i 3 -1 7) , four sites at Ling Reef (7 - 1 0) and two at Satellite Island (5 - 6) and Stewart's Bay (24 - 25) . Ling Reef was mapped using five 1 00 m
transects of leaded rope placed perpendicular to the shoreline. Depth, substratum and the mai n algal species present were recorded every 1 0 m along each
transect. The map of the seabed at Tinderbox was adapted from Sanderson and Thomas (1 987) .
data
. A quick visual survey of each site was made to determine the area of greatest urchin abundance, from wh ich the sample would be taken. All divers kept within sight of each other to ensure that they were collecting from the same habitat. Urchins were collected by searching the area thoroughly to ensure that the more cryptic morphs were not under-represented. Where possi ble a sample of 1 00
urchins was taken , but in areas of low urchin density this had to be reduced to 50- 60. The urchins were taken to the shore and scored for colour and inte nsity of
pigmentation according to the scheme outlined in Chapter 2 .
Most sites were sampled only once, but two transects at Ti nderbox (sites i 3 and 1 4) and Ling Reef (7 and 8) and one area at Fortescue Bay (26) were
sampled repeatedly to determine the temporal variation in proportions of morphs (see Chapter 3) . For these sites the total numbers of urchins collected throughout the study were used in analyses presented in this chapter.
Environ mental data
Site descriptions included i nformation on the depth at which urchins were collected, substratum, algal species present and amount of algae present, density of the urchin population, and the exposure of the site to wi nd driven waves. Depth was measured usi ng a Technisub, oil-filled depth gauge. The presence or
1 Pelican Island 2 Blubber Heads 3 Roaring Beach 6 m 4 Roaring Beach 1 3 m 5 S atellite Island A 6 S atellite Island B
7 Ling Reef 3 m transect 8 Ling Reef 1 3 m transect
9 Ling Reef Barren
1 0 Ling Reef Slope 1 1 Gordon 1 2 Coningham 1 3 Ti nderbox 3 m transect 1 4 Tinderbox 7 m transect 1 5 Tinderbox Slope 1 6 Tinderbox B arren 1 7 Tinderbox Beach 1 8 Blackmans Bay 1 9 A l u m C l i ffs 2 0 Dennes Point 2 1 Betsy I sland 6 m 2 2 Betsy Island 1 3 m 2 3 D art Is land 2 4 Stewarts Bay A 2 5 Stewarts Bay B 2 6 Fortescue Bay 2 7 Marion Bay 2 8 Reidle B ay 2 9 Stapleton Point 3 0 Painted C l i ffs 3 1 Shelly Beach 3 2 Co
�
es Bay 3 3 B icheno 3 4 Skeleton B ay 3 5 Stumpys Rocks3 6 South Crappies Point 3 7 Low Head
3 8 G reens Beach 3 9 Rocky Cape 4 0 C owrie Point 4 1 Tro users Point
69
N
3 6 {) 3 5
Melbourne • N 10km 4 2 Point Franklin 4 3 Point Henry 4 4 Point Lillias 45 Tablerock Point
4 6 Point Cook - Barren 1
47 Point Cook - Barren 2
48 Point Cook - Kelp 1
49 Point Cook - Kelp 2
7 1
absence, and size of bou lders was noted. If the substratum was flat rock, the presence and extent of crevices were also recorded. The rock type was noted and later verified using the map ��Geology of Tasmania" (Lands Department, Hobart). The substratum type was classified as follows:
1 Sand
2 Flat rock
3 Flat rock with some crevices
4 Medium boulders with crevices
5 Large boulders with extensive crevices
Algal cover was estimated visually and macroalgae we re collected for later identification. The density of the u rchin population and percentage algal cover were measured using a 0.25 m2 quadrat (20 replicates per site). As it was only
possible to use the quadrat i n calm weather and when diving partners were available, these data were not collected at all sites. The quadrat data were used to roughly calibrate the visual assessment of algal cover:
1 Very low < 1 0%
2 Low 1 0-40%
3 Moderate 40-80%
4 Heavy >80%
The directions open to wind driven waves were noted using a compass and a rough assessment of the exposure of the site was made , for example by noting whether bull ke lp (Ourvillea potato rum) was present in the intertidal zone i ndicating a very exposed site, o'r noting the presence or absence of fine sediment on algal fronds which helped to identify very sheltered sites.
To determ ine whether exposure to wave action was corre lated with proportions of morphs an exposure index was developed. Exposure i ndices may be divided into two types : biological and physical. Biological indices are based on communities of plants and animals which occur on shores of different exposures (Ballantine, 1 96 1 ; Lewis, 1 964), whereas physical indices attempt to mode l the strength of wave action using measurable parameters such as wind strength and direction and length of fetch (Moore, 1 935; Baardseth, 1 970). A physical index was preferred to a biological scale in the hope that it would provide a more objective measure and might give greater resolution.
The factors affecting the strength of wave action hitting a coastline are many and most physical i ndices oversimplify the situation and hence are not reliable for fine scale resolution. However, Thomas (1 986) developed an i ndex using wind
energy and direction, total fetch and the distance of water shallower than 6 m next to the coastline. This index was calculated for each of the sites around Tasmania but failed to provide values consistent with what was already known of the exposure of Tasmanian coastlines (Bennett and Pope, 1 960; Davies, 1 978). It gave values for the north coast (adjacent to the shallow Bass Strait) of the same magnitude as those for the e ast coast which is exposed to oceanic swells. The physical exposure index (P.E.I.) was modified to allow adjustment for the depth over the total fetch, and to i ncrease the distance which was assumed to be the maximum fetch over which waves increase wave energy.
Biological exposure i ndices have been developed for Tasmanian coastlines based on i ntertidal communities (Ben nett and Pope, 1 960) and on subtidal macroalgae (Edgar, 1 984) . The latter approach was adopted for this study. Edgar's ( 1 984) index appears to be based mainly on data from the D ' E ntre casteaux Channel in southern Tasmania (Sanderson, personal communication) so an index was developed, with the help of C. Sanderson (Plant Scie nce Department, U niversity of Tasmania) , which could be used for all Tasmanian coastlines. The i ndex is based on the biological com m u nities described by Bennett and Pope ( 1 960), Edgar (1 984) , and Sanderson and Thomas ( 1 987), modified by our own observations. The domi nant large brown algae were used as indicators of the diffe rent communities which occur with differi ng exposure (Figure 4.3). To apply this i ndex, the vertical distribution of the algae at each site must be observed, although it was usually only necessary to survey the algae down to a depth of 7-1 0 m. Although the algal communities present at different depths were used in applying the index, the resulting value for the exposure of a site does not contain a depth component, e.g. two sites in the same area, at 3 and 1 0 m respectively, would be given the same A . E . I . even though the deeper site would receive less force from surface wave action.
The application of this index was usually straight forward except for Bass Strait sit�s which have somewhat different algal communities to the rest of Tasmania. This is probably due to many factors including different nutrient levels in the water and a water temperature, on average, 2°C warmer than elsewhere in Tasmania (Sanderson, personal communication). Category 1 can include com munities of seagrass (which grow on sand) or Cys top hora/Sargassum (which g row on rocks) because this diffe rence reflects the substratum rather than a difference in exposure to wave action.
5 1 0 Depth 1 5
(m)
20 25 6 5 EAlgal Exposure Index
4 3 2
Fig ure 4.3 Exp lana1io n of A l gal Exposure Index.
I -...) Vl D = Durvillea p = Phyllospora L = Lessonia E = Ecklonia M = Macrocystis C = Cystophora s = Sargassum A = Aero carpi a G = Seagrass
4.2.2 Analysis of d ata
G-tests for i ndepende nce were used to determine whether the small scale geographic variation between sites was significant for each of the four areas.
Character associatio n within populations was determined by calculation of the i ndex D' = AD - BC (Jones, et a/., 1 980), where the letters refer to the proportions of individuals that were (A) red dermis, purple spined, (B) white dermis, purple spined, (C) red dermis, not purple spined and (D) white dermis, not purple spined. The significance of the association was determined by G-tests for i ndependence, although for some sites the test was not appropriate because of the small numbers of i ndividuals i n one or more categories.
Algal and p hysical exposure i ndices we re considered to be i ndependent variables, hence the I eve I of agreement between the two was dete rmined by correlation. To determine whether either i ndex could be used to predict dermis colour proportions, linear regressions of arcsin e percentages of red dermis urchins against A.E.I. and P .E.I. were calculated. This transformation is used when small (<30) or large (>70) percentages occur in a data set, in order to improve the normality of the data (Zar, 1 974). The Kolmogorov-Smirnov test did not indicate that the normality of the data had improved, but frequency plots showed that the data were less negatively skewed.
The relationships between transformed perce ntages of red dermis urchins present at each site and the environme ntal variables were analysed using stepwise multiple regression (Sakal and Rohlf, 1 981 ). As the densities of urchins were only known for 27 out of the 49 sites, these data were regressed separately.
Simple linear regressions of transformed proportions of dermis colour against A.E.I. were calculated for each of the four geographical regions; southern, eastern, and northern Tasmania and Port Phillip Bay. Analysis of covariance was used to determine whether the slopes of the regressions varied significantly. Tukey's test was used to determine which of the pairs of regressions differed significantly.
Mantel's test (Mante l, 1 967; Sakal, 1 979; Manly, 1 985) was used both to determi ne whether the geographic distribution of morph frequencies was non random (Sakal, 1 979) and to test specific models of population differentiation. This test compares two similarity (or distance) matrices (site x site) which may represent gene freq uencies, morph freque ncies, morphological similarity , environme ntal data or various types of geographic distances. Mantel's test determines the extent of a linear relationship betwee n the two variables. Consequently plots of each pai r of matrices should be produced and any non-
7 5
linear relationships should b e adjusted by a suitable transformati o n (Smouse e t a/. , 1 986).
The matrices are usually designed so that the n u ll hypothesis assumes a non significant negative relationship between them (Douglas and Endler, 1 982). A test statistic (Z), its standard normal variate (G) and Pearson's coefficient of correlation (r) between the elements of the two matrices are calculated. The sig nificance level of G may be determined by comparing it with the standard n o rmal distribution but this assumes that G is no rmally distributed and this may not b e the case (Manly, 1 985). An alternative approach i s to use a randomization test (Edgington, 1 987). A large n u mber of random permutations of one of the matrices against the other matrix are produced and G is calculated for each permutatio n . The probability value is t he proportio n of these G values which is greater t h a n o r equal to the experimental G. For example, i f t he test G i s greater than 950 of 1 00 0 randomly produced G's, then the result is sig nificant at the 5o/o level. M a n l y (1 985; pp. 1 76- 1 82) gives a detailed description o f the mathe matics of Mantel's test and calculations were perfo rmed using the computer program provided.
Differences in the proportions of dermis colours between pairs of all 49 sites were determined using the equation from Manly (1 985):
k
dmor = 0.5 2: · I (p; - qi) I i="1
where dMoR = distance measure
k = number of morphs
Pi = proportion of i morphs at site p
q = proportion of i morphs at site q Identical sites = 0
S P I N E
Different sites ::; 1
Differences i n proportions of spine colours were determined as for DERMIS, b u t data were o n ly available for 38 sites, i .e. exclud i n g Coning ham (site 1 2) ,
Bicheno (33), Trousers Point ( 41 ) and all the Victori a n sites (42-49).
EXPOSURE IN DE X
Differences i n exposure between each pair of sites, p and q , were calculated
as; dEXP = AEIP - AEiq
where AEI = algal exposure i ndex.
OVERALL ENVI RONMENT
Distance measures based on all the e nvironmental data collected (AEI , depth, substratum, algal cover) were p roduced by B IOLT AT cluster analysis, using
Gower's coefficient which is designed for use with mixed continuous and multistate data.
Identical sites = 0 BINARY PATCH
Different sites � 1
Patch types were defined on the basis of type and amount of algae present, substratum type and depth:
1 Macrocystis forest, large boulders, urchins at < 6 . 5 m
2
3 4
Macrocystis forest, large boulders, > 7 m Low algal cover, flat rock substratum, > 7 m Low algal cover, sand substratum, > 7 m 5 Low algal cover, flat rock substratum, < 7 m 6 Heavy algal cover, boulders, < 7 m
7 Low algal cover, boulders, < 7 m
8 Seagrass bed, sand substratum, < 7 m
A binary connectivity matrix was produced.
Sites of same patch type = 0 Sites of different patch type = 1 SHORTEST SEA DISTANCE
The shortest sea distance (SSD) between each pair of sites was measured in kilometres around the coast, from 1 :50,000 maps.
Very close sites = 0 BINARY CONTIGUITY
Distant sites � 869
A modified Gabriel network was produced to identify geographically
contiguous sites. Gabriel and Sakal ( 1 969) defined two sites (A and B) as contiguous if no other site fell within the circle of which AB was the diameter (Figure 4.4). In this study SSD (instead of straight line distance) was used to check that two sites were not considered contiguous if there was a third site which was closer to A or B (in terms of SSD), t han they were to each other. This was done because straight line distances would not accurately describe the probable patterns of gene flow for mari ne species with pelagic larvae. In practice the only differe nee this made was that Dart Island (23) was considered contiguous with Betsy Island (2"1 and 22) but not with Stewart's Bay (24 and 25). No contiguity was allowed between sites i n diffe re nt geographical regions, as previously defined. A binary connectivity matrix was produced.
77
Figure
4.4
Definition of contiguity for Gabriel networks.Pairs of sites A with B and B with C are contiguous, but A is not contiguous with C.
CONTIGUOUS DISTANCE
Values for contiguous sites {i.e. 1 's) fro m BINARY CONTIGU ITY were replaced
with the equivalent kilometric distance from SH ORTEST SEA DISTAN C E .
Non-contiguous sites {designated by O's) would have a n infinite distance between them; in practice they were given a value slightly larger than the greatest SSD between any two sites.
Contiguous sites = 0 - 89 BINARY AREAS
Non-contiguous sites = 870
The four geographical regions {i.e. sites 1 -23, 24-35, 36-41 and 42-49) were
assumed to be genetically isolated fro m each other but gene flow was assumed to occur between all sites within a region. A binary connectivity matrix was produced:
Connected sites = 0 Unconnected sites = 1
DISTANCE WITH IN AREAS
Values for connected sites from BINARY AREAS were replaced with SSD's from SHORTEST SEA D I STANC E. Unconnected sites were given a value
slightly larger than the greatest distance between sites.
Connected sites = 0 - 269 Unconnected sites = 870
Each of the environmental and geographical distance matrices were produced for data from all 49 sites for comparisons with DERMIS, and for data from only the 38 sites for which spine colour data were available, for comparisons with SPINE. A 38-site matrix of DERMIS was also produced to determine how strongly correlated dermis and spine colours were.
The models
Each of the four mechanisms of population differentiation suggested by Sakal (1 978) and described in the I ntroduction was used to develop a specific hypothesis for the H. erythrogramma polymorphism:
Model 1
Proportions of morphs are related to an environmental gradient which may or may not take the forrn of a geographical eli ne. Sakal's m ode I assumes that environmental differentiation increases with the distance between sites. However, this is not necessarily true; the exposure of a site to wave action may depend on whether it is on an open coastline or within a sheltered bay. I n this case the organisms at the site might be responding to an environmental gradient, but show patchy variation in space. Two possibi lities were tested; the exposure of a site to wave action may be of paramount importance or, alternative ly, several environmental factors could be involved.
Model 2
Proportions of morphs are dependent on the types of habitat (defi ned as patches), which are not re lated to geographical position, and there is no correlation between proportions of morphs of different patch types eve n if ·the e nvironmental factors used to define the patches are similar.
Model 3
P roportions of morphs vary because isolati on by distance has allowed divergence between distant sites to occur, givi ng a geog raphical cli ne over the e ntire study area.
Model 4
Each of the four geographical regions previously defi ned was subject to a founder effect. Thus proportions of morphs of the sites within each region should be similar and not necessarily related to those of adjoi ning regions.
Model 5
· This model extends Model 4 to p redict isolation by distance may have occu rred within each region after the i nitial p roportions of morphs were established by founder effects. Thus a geographical cli ne would be observed within each area.
The tests
Each of the five models suggests a specific and unique pattern of morph distribution. Limitations of the models and alternative processes by which such patte rns might have been produced are discussed later. The resu lts of
79
comparisons of morp h matrices ( D E R MIS and S P I NE) with each of the environ mental and geographic distance matrices may be predicted for each. model (Table 4. 1 ). It can be seen that it should be possible to distinguish between selective (Models 1 -2) and stochastic (Models 3-5) processes. However, the three e nvironmental matrices are all derived from similar data, as are the five geographic distance matrices. Thus correlations of matrices within the two groups are to be expected and it is only possible to determine which of the models fits the data best, without excluding the possibility of other models being valid. For example, if both EXPOS URE INDEX and OVERALL ENVIRONME NT gave positive