the fluvial concentration of heavy minerals in the slieve bloom mountains, central ireland_gallagher...

7/28/2019 The Fluvial Concentration of Heavy Minerals in the Slieve Bloom Mountains, Central Ireland_Gallagher and Thorp

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The Fluvial Concentration of Heavy Minerals in the Slieve BloomMountains, Central IrelandColman Gallagher and Martin Thorp

Department oj Geography,Univ ersity College,

Dublin

ABSTRACTThe detrital mineralogies of both alluvial sediments and their glacigenic and bedrocksedimentary sources undergoing fluvial entrainment in the mountains are outlined . Relationships between patterns of heavy mineral concentration in the alluvial sediment s and thecontrolling factors of source material distribution and provenance, the hydrodynamics ofheavy minerals, the spatial variability in process environments of concentration and theinteraction of these factors are deduced and the spatial distributions of correlated heavymineral assemblages and concentrations are explained. Thus, a better understanding isgained of the processes and process interactions responsible for alluvi al heavy mineralconcentration in paraglacial environments.Key Index Words: Alluvial heavy minerals, principal components analysis , Slieve Bloom

IntroductionThe aim of this paper is to present a model of thedevelopment of heavy mineral concentrations in alluvial sediments of the Slieve Bloom Mountains in thecontextof the Quaternary geomorphological development of these mountains. This is achieved by identifying groups of spatially-associated mineral specieshosted in the Holocene alluvial sediments ofthe mountains and by explaining the spatial distribution processes of such groups. Process interaction between theheavy mineral concentrations and environmental variables are assessed by the use and interpretation of

geomorphological and hydrodynamic variables usedas surrogates for physical processes.The Sub-Pleistocene Geology and the Pleistocene ojSlieve Bloom

Slieve B oom (Figure 1) is a compact upland area ofca. 1225 km2 rising to approximately 500m O.D.Deeply incised valleys radiate from the gently rollingIrish Geography 28(1) (1995)14-34, 0075-0078/95/$3.50 Geographical Society of Ireland , Dublin.

crestal plateaux of the mountains out onto the surrounding piedmont which lies at ca. 150m O.D. Themountains comprise the most easterly of the LateCaledonide inliers of south-central Ireland where metamorphosed Silurian shales and greywackes, togetherwith the overlying Devonian Old Red Sandstone facies ,emerge in an anticline from the surrounding Carboniferous limestone lowlands (Feehan, 1980). TheDevonian sandstones have been breached in the valleys to expose the underlying, weaker Silurian rocks .Overlying the Palaeozoic rocks in the mountains areextensive deposits of Pleistocene glacigenic sediments ,themselves overlain by Holocene alluvium and peat.The Carboniferous limestones of he piedmont are alsooverlain by extensive glacigenic deposits , dominatedby meltout tills, and by both glaciofluvial andglaciolacustrine sands and gravels.Lithological and vectorial fabric analyses ofthe tillsdeposited in the catchments and piedmont of SlieveBloom show that the local direction of ice flow acrossthe mountains in the Late Pleistocene (MidlanadianiDevensian glaciation), and the provenance of locallyentrained glacigenic sediments, was from the west-

, ....

Heavy Minerals in the Slieve Blooms 15

~ Lower L i m e s t o n e ~~ I Shale ~ Quaternary Deposits- S - Silver River study reach -Roscrea town - ~ r i v e r- D- Delour River study reach '- - 150 ---- contour line - - , __ county boundary- C - Clodiagh River study reach 529 spot height

Figure 1: Slieve Bloom geology and study reaches


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16southwest (Warren, 1987; Gallagher, 1991). Meantransport distances for locally entrained tills, carried inbasal layers of the ice sheet and deposited in the lowerreaches of the mountains, ranged from 4.5-5.4 km(Warren, 1987; Gallagher, 1991). Geomorphological

. indicators, especially esker orientation and the distribution of Galway granite erratics in the lowlands westof Slieve Bloom, suggest that regional ice flow wasfrom the west-northwest. Basal ice to the northwest ofthe Slieve Bloom crestline was deflected around themountains, which lie oblique to the west-northwestregional ice flow direction, while on the southeasternflanks ofthe mountains, topographically induced pressure gradients in the ice caused a more northerly localflow direction. The total effect was to cause the ice toflow locally parallel to the contours and main axis ofthe northeast-southwest oriented mountains. Furthertravelled till components, such as Galway graniteerratics, were carried englacially, rather than basally,by the higher parts of the regional ice sheet, the higherelevation ice having overtopped the mountains (Warren, 1988; Gallagher, 1991).The Geomorphology of he Study Catchments

Gallagher and ThorpeSample Design and Methodology

Site SelectionBecause of expected variations in the erodibilities,

particle size distributions and heavy mineral contentsof the shales and sandstones of Slieve Bloom, theselection of rivers was on the basis of their bedrocklithology. In order to minimise the variation in independent external geomorphological variables actingupon the rivers, data were not collected below 150mO.D.To aid in the selection of sites, a reconnaissancemapping programme, based on satellite image analysis, aerial photograph interpretation and field morphological mapping, was completed. This allowed thebroad geomorphological elements of each catchmentto be identified and each river channel itself to besubdivided into morphological reaches. Sampling siteswere selected at intervals of between 300m and 600malong each river: the total number of sampling siteswas 30. The analyses presented in the text relate to thecombined data from the three rivers. However, theanalysis of combined data sets was done only afteridentical exploratory procedures (correlation bondanalyses) had been carried out upon the data sets ofeach river individually (Gallagher, 1991).long the channels of heSilver, DelourandClodiaghRivers (Figures 1and 2), lodgement and melt-out tills,

up to 15m thick, directly overlie the Palaeozoic rocksof the basins to altitudes of between 215- 250m O.D.In places, however, the till hasbeen removed by fluvialerosion. Above 250m O.D., while primary till is notdirectly in contact with the rivers, soliflucted, sandstone-dominated valley-side and interfluve deposits(head) are common. On the valley-sides, extensivedeposits of peat overlie either thin, often soliflucted,weathered till below 250m O.D. or head depositsabove this altitude. Adjacent to the channels themselves, thin,poorly stratified and sorted alluvial deposits, produced mainly by lateral channel and point barmigration without significant phases of vertical accretion, overlie over-consolidated basal till. SignificantHolocene alluvial deposits (from extensive, vegetatedpoint bars to poorly developed, low-volumefloodplains) are found only within the till limits below250m O.D. Outside the till limits, and where till hasbeen fluvially eroded, the rivers are bedrock-confinedand devoid of any large or vegetated point bars or

Mineral ExtractionFrom the selected sites an arbitrarily chosen volume

of 0.037 m3 of bedload sediments was passed througha -3 .0 phi (8.0 mm) aperture sieve into a 460 mmdiameter gold-pan and a heavy mineral concentratewas produced. Of the bedload clasts remaining in the-3.0 phi sieve, 35 were lithologically identified andtheir dimensions along their a, b, and c axes weremeasured.Till, which was being actively eroded by the rivers,was sampled in the same way as channel bedloadsediments to produce a heavy mineral concentrate.Similarly, bedrock actively undergoing fluvial erosionwas identified, removedfrom the rock face and crushed.The crushed rock was then panned to produce a heavymineral concentrate. Samples of he bedrock were alsokept for thin-section petrographic analysis. All sitesfrom which samples had been taken were surveyed toproduce site maps, to calculate floodplain volumes and

floodplains.

Heavy Minerals in the Slieve Blooms

saO~, ~

t. ~

Keysite locationriver .and floodplaintill limit

Source : G.S.I. I :30,000 seriesAerial PhotographsAltitudes are in metres above o.S.l. Datum

Location Map ------Stieve B!oom \ ~ R M rMountams

Rose",

Km

Clodiagh River

Delour River,- /~

Figure 2: Heavy-mineral sample points and till limits

17

," t


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18to derive channel geometry and hydrological data(Gallagher, 1991).Magnetic Separation of he Heavy Mineral Fraction

Magnetic separation of the heavy mineral concentrates was carried out to aid the identification of thesampled heavy minerals. The total panned concentrateof heavy minerals from the field was first processedwith a hand magnet to remove magnetite from thesamples. Following this initial separation, the remaining minerals were passed through a Frantz isodynamicseparator, with a forward slope of 170 and a side slopeof 200 , in 0.2 A steps, from 0.2 A to 1.2 A. Theparamagnetic and non-magnetic fraction from eachseparation was reprocessed 5 times at each current soas to achieve the maximum separation. Each subsample was weighed on a Mettler electronic balance.Heavy Mineral Identification

Whole heavy mineral grains were identified in theirmagnetic fractions under incident light using a binocular microscope. Random grain samples were alsomounted in thin section on slides for identificationusing a petrographic microscope, thus providing ameans of verifying the bulk identifications achievedunder incident light. Counts of minerals in their magnetic fractions were made under the microscope byrandomly isolating groups of between 100 and 200mineral grains and identifying each mineral grainpresent in the group. This procedure was done threetimes for each magnetic fraction, giving a total samplesize of 87,000 identified heavy minerals grains. Thecounts were recorded for each magnetic fraction andtotal counts for each site-sample were calculated foreach mineral species.

Statistical Procedures and SequencePrincipal Components Analysis

Owing to the large number of both mineral speciesand geomorphological variables studied in this paper,data were analysed by means of principal componentsanalysis (PCA) (Daultrey, 1976; Lanckneus, 1987;Delauneetal, 1989). The purpose of PCA in this studyis primarily to identify groups of intercorrelated variables. In addition, the reorganisation of the data into

Gallagher and Thorpeintercorrelated groups reduces the number of variableswhich have to be studied and removes colinearitybetween independent variables. In this study, the PCAin both sections consists,firstly, of eigenanalysis aimedat identifying variable groupings and, secondly, component score analysis which allows the determinationof the spatial distribution of variable groupings(Johnston, 1978). The analysis in Section Two, however, uses a further eigenanalysis to aid in the identification of process interactions between variable groupings identified in the initial Section Two PCA.

In PCA no specific hypothesis is postulated beforeanalysis begins and no significance testing takes place.However, because the components are new, independent variables, they can be analysedusing conventional,bivariate statistics. In this paper only those components with eigenvalues greater than 1.0 were used toidentify groups of related mineral species; components with lower eigenvalues will account for less ofthe total variance than anyone of he original variables(Johnston, 1978).

The PCA in Section One is performed upon countsof alluvial heavy minerals hosted by the bedloadsedimentsof the three study rivers and on data relatingto geomorphological and hydrodynamic processes atthe heavy mineral sampling points . The aim of thePCA in Section One is to identify (i) intercorrelatedgroups of heavy minerals, (ii) any intercorrelatedgroups of heavy minerals and geomorphological variables and (iii) the spatial distribution of these groups.The PCA in Section Two is concerned with a secondset of heavy minerals and geomorphological datacollected from the floodplain sediments of the threerivers in the study. Because the floodplains occur onlywithin the till limits of the study catchments, thissample design allowed heavy mineral an dgeomorphological variability caused by the spatialdistribution of sediment sources to be controlled out ofthe analysis, aiding in the identification of process,rather than spatial, relationships between variables.Section One: The Within-channel Concentrationof Heavy MineralsThe Mineralogy of he Primary Sourcesof he AlluvialHeavy minerals

The minerals present in the primary source materials(till and bedrock) of the alluvial sediments are shown

Heavy Minerals in the Slieve Blooms

0.35M

0.00 Z aPCI 1B0.35 R

T A

.{).70C X H

1.05 : - ~ - - : : ' : : : : - ~ " " ' ~ - - r - ~ ' - ~ - - r - ~ ~0.75 . 0.50 .0.25

Z = ZirconP= PyritesH= HaematiteI = lmenite

0.00PC2

T = ounnaJineC =ChalcopyriteM&MagneLite

0.25

G-GamelXGoldA =AcLinolilc

0.50 0.75

R =RutileE = EpidolC1= HornblendeFigure 3: Channel heavy mineralscomponent loadings PC I v PC2 '

1.000.75 T0.50

PCI 0.25 0w "s0.00 A H L J

0.25 0 QC0.50 V IN0.75 GF

- 1 . 0 0 . ~ ~ = - - - - ' - ~ - ' - ~ r - - . - r ~ , - ~ ~ ~1.00 _ 0 .75 -D50 -0.25 0.00 0.25 0.50 0.75 1.00PC2

A = Zircon F = Chal copyrite8 = ounnaline G = GoldC . Gamet H = Epidote0= Rutile t>: HaemaLiteE "" Pyrites J . Manetite

~ : : ! P :z Concentrate weight U: Shal eQ E DMean Y = limcslOneM = Ilmenite: R= D9s W=Quartzite

N = Stream power S == DSoning X = Graniteo = Chann el T = SandslOOe y :: ChertFigure 5: Channel heavy mineral site variables

component loadings PC I v PC2 '

.,

PCI

PCI

02

-4

2.5

0.5

1.5

-3.5

5 .5-5.0

NN

N TN N

N IT T

NT

-3.4 1.8 0.2PC2

T = located within till limitsN =Sae located outside till limits

TN " i ~ N N

NTT

T

T T

T1.4

Figure 4: Channel heavy mineralscomponent Scores PC I v PC2 '

T TTNNT TT NN

T TT

T

PC2Site located within till limits

- Sue I ~ t e d Outside tU lim its

N NN NN

NT T N N

T TTT

T

Figure 6: Channel heavy mineral site variablescomponent Scores PC I v PC2 '

19

T

3.0

N


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20Table 1: Primary-source heavy mineral species

Till Bedrock* Alluvium Family Family %

% % %Till

4.2 13 .3 10.2 SilicatesZircon 0.0 6.1 SilicatesTourmaline 0.8 Silicates9.4 3.7 9.7Garnet 0.0 0.1 SilicatesActinolite 0.3 Silicates1.3 0.1Epidote 0.7 Silicates 15.7O? O? 0.2Hornblende 1.2 3.6 SulphidesPyrites 17.4 Sulphides 19.42.0 0.0 0.6Chalcopyrite 8.4 46.0 OxidesHaematite 24.5 Oxides72 .1 22.2Magnetite 39.8 Oxides0.5 0.0 0.4Rutile Oxides 64.3O? 0.7Ilmenite O? Native 0.10.1 0.0 0.1Gold* sandstone, siltstone, mudstone.

. . h I heavy mineralsTable 2: Eigenanalysls of the c annePC3 PC4PC1 PC2

1.37362.8615 2.2165 1.8771Eigenvalue 0.170 0.144 0.106Proportion _0.220 0.6410.220 0.391 0.535CumulativePC1IPC8 PC9 PCI00.27300.5478 0.4298 0.2992Eigenvalue 0.033 0.023 0.021Proportion 0.042 0.979

Cumulative 0.902 0.9350.958

Gallagher and Thorpe

Family % Family%Bedrock* Alluvium

18 .3 26.41.2 4.2

80.5 69.30.0 0.1

PC5 PC6 PC7

1.1399 0.9875 0.72010.088 0.076 0.0550.804 0.8600.728PC12 PC 130.2297 0.04440.018 0.0030.997 1.000

Heavy Minerals in the Slieve Bloomsin Table 1. The mineralogies of the sandstones,siltstones and mudstones of Slieve Bloom are similar(Feehan, 1980). Mineralogically, the ratio of sulphidesto silicates and oxides distinguishes the till from thebedrock; in the till it is 1:4, while in the bed rock it fallsto 1:82. Haematite is also more abundant in the till,forming up to 27% of ts heavy mineral-concentrate bynumber, compared with 8% in the bedrock (Gallagher,1991). Chi-square testing indicates that there is asignificant difference between the till and bedrock intheir levels of sulphide minerals, iron oxides (in particular haematite) and gold.Principal Components AnalysisEigenanalysis Only the first six componentshad eigenvalues of one or greater than one (Table 2) .Together, these six components explain 80.4% of thetotal variance in the abundance of mineral species ,whereas components 7-13 account for only 19.5% ofthis variance.Component Loadings Componentone is dominatedby chalcopyrite, gold, haematite and, to a lesser degree, pyrites , all correlating negatively with the component. Component two is dominated by garnet, correlating positively with it, while hornblende, ilmenite,and zircon correlate less strongly. Component threereflects the correlation betweenzircon, tourmaline andilmenite, all covarying negatively with the component. Components four to six each correlates stronglyonly with magnetite, actinolite and rutile respectively(Table 3, Figure 3).

Zircon, garnet, chalcopyrite, gold, haematite, magnetite and actinolite have at least half their individualvariance (squared loading) explained by the component with which they are most strongly correlated(Table 3). Component one indicates a clear group ofinter-related minerals which have more than 50% oftheir variance explained by the component. Components two to six do not suggest any definite groups andmay only be replotting the original variances of theminerals orthogonally.

Component one subsumes the distinctive mineralassemblage of the till (Table 1) and therefore represents the input of these species from the till to thealluvial system. Thus, chalcopyrite, gold, haematiteand pyrites are all highly correlated with componentone. All these minerals, except pyrites, have more than

2150% of their variance explained by component oneand, therefore, represent a distincti ve variety of heavymineral-bearingalluvium that owes its mineralogicalcharacteristics more to the fluvial erosion and entrainment of till-derived heavy mineral species than tothose eroded from the bedrock. Components two to sixmay represent mineral species common to both thebedrock and the tills of he study catchments (Table 1).Component Score Analysis Eigenanalysis has shownthat component one is negatively correlated with themineral species uniquely or predominantly hosted bythe till. Components two to six may reflect the presence in the alluvium of minerals common to both thebedrock and the till of Slieve Bloom. The implicationof these results is not only that the spatial distributionof groups of alluvial heavy minerals is dependent uponthe spatial distribution of source materials, especiallythe till, but that there is poor post-erosional mixing ofthese two assemblages during subsequent fluvial concentration . This is most clearly represented by component one, which subsumes the minerals which areunique to the till or are more highly concentrated in thetill than in the bedrock.

On the basis of this conclusion, the sample sites(Figure 2) were categorised into: (i) sites locatedwithin the till limits (T's in Figure 4); and (ii) thoselocated outside the till limits (N's in Figure 4). Theanalysis of component scores allows the identificationof spatial clusters of sites which have high values forthe minerals with high loadings on a particular component (Figure4). Sites with positive scores for a component are above average in the attributes of that component. However, in the interpretation of the scores,close attention must be paid to the sign of both thecomponent loading for each mineral species and thecomponent score for each site. For example, a mineralwith a high negative loading on the component isnegatively correlated with the component: therefore,at sites which have negative scores for the component,a mineral with a negative loading will increase inabundance.

For component one, with high negative loadings onthe minerals pyrites, chalcopyrite, gold and haematite,a negative component score indicates a higher thanaverage abundance of these species. On componentone, the mean score for sites within the till limits was-0.55, while that for sites outside the till limits was0.72, a statistically significant correlation (r=0.373,


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22Table 3: Component loadmgs 0 t e c annef h h I heavy minerals

Variable PCl PC2 PC3 PC4Zircon 0.074 0.535 -0.704 0.062Tourmaline -0.461 -0.426 -0.502 0.410Garnet -0.020 0.725 -0.427 0.103Rutile -0.250 0.370 -0.133 -0.430Pyrites -0.656 -0.124 0.270 -0.274Chalcopyrite -0.786 0.008 0.120 0.057Gold -0.780 0.123 0.382 -0.098Epidote 0.280 -0.095 0.290 -0.376Haematite -0.749 0.247 0.007 0.305Magnetite 0.154 -0.399 0.252 0.707Actinolite -0.420 . 0.214 -0.050 0.064Hornblende -0.221 -0.628 -0.421 -0.357Ilmenite 0.187 -0.626 -0.594 -0.219

Table 4: Eigenanalysis of the within-channel concentration variables

PCI PC2 PC3Eigenvalue 4.5514 3.7129 3.2724Proportion 0.182 0.149 0.131Cumulative 0.182 0.331 0.461

PC6 PC7 PC8Eigenvalue 1.6264 1.3162 1.1633Proportion 0.065 0.053 0.047Cumulative 0.686 0.739 0.785

PCII PCI2 PC13Eigenvalue 0.7526 0.5499 0.4974Proportion 0.020 0.019 0.013Cumulative 0.890 0.912 0.932

PCI6 PCl7 PCI8Eigenvalue 0.2921 0.2028 0.1667Proportion 0.007 0.030 0.022Cumulative 0.976 0.984 0.990

PC21 PC22 PC23Eigenvalue 0.0393 0.0314 0.0092Proportion 0.002 0.001 0.000Cumulative 0.998 1.000 1.000

Gallagher and Thorpe

PC5 PC6-0.167 -0.0690.250 0.010

-0.096 0.3300.196 -0.640

-0.415 -0.1020.195 0.372

-0.162 0.0260.490 0.361

-0.090 0.051-0.019 -0.3140.709 -0.223

-0.116 -0.1020.004 0.160

PC4 PC52.1096 1.87750.084 0.0750.546 0.621

PC9 PCIO0.9455 0.92230.038 0.0370.823 0.860

PCI4 PCI50.4666 0.33260.012 0.0080.951 0.964

PCI9 PC200.0921 0.06670.004 0.0030.994 0.997

PC24 PC250.0026 0.00030.000 0.0001.000 1.000

Heavy Minerals in the Slieve Bloomsrcr=0.306, point biserial correlation) indicating a strongspatial association between the alluvial mineralogicalelements of component one, representing the presencein the alluvium of minerals uniquely or mainly derivedfrom the till, and the di stribution of till in the catchments. Fluvial processing has produced neither a ho-mogeneous alluvial heavy mineral assemblage, composed of minerals derived from both the till and bedrock, nor a spatial distribution of alluvium containingthe till-derived heavy minerals that is significantlydifferent from the distribution of the till itself.

23Channel Heavy Mineral Concentration PCA: Compo-nent loadings

InterpretationAll minerals which had more than half their indi

vidual variance explained by component one(chalcopyrite, gold, haematite and, to a lesser extent,pyrites) have negative component loadings. As component one is significantly correlated with sites outside the till limits and with high component scores, itmay be inferred that chalcopyrite, gold, haematite andpyrites, all till-derived minerals, decline in abundanceoutside the till limits . Components two to six do notreflect any such clear assemblages of minerals, as thespecies with high loadings OCcur in both the till and thebedrock in broadly similar quantities (Table I). Noneof components two to six explains more than half theindividual variance of their most highly correlatedmineral.Channel Heavy Mineral Con centration PCA :EigenanalySis

The first component indicates a group of nine variables with component loadings of 0.5 or greater (Table5). These are: the number of sandstone clasts in thebedload, which correlates positively with the firstcomponent; chalcopyrite, gold, haematite, streampower, heavy mineral-concentrate weight and the numbers of shale, granite and limestone clasts in thebedload. All of hese correlate negatively with the firstcomponent. Component two is dominated by fivevariables with loadings over 0.5: bedload mean particle size (Dx) and bedload D95 correlate positively withthe component. Zircon, garnet, and number of chertclasts in the bedload all covary negatively. Componentthree contains five variables with high loadings:ilmenite, channel slope, and bedload sorting (Ds )'These covary positively , while both bedload D95 , andthe numberofshale clasts in the bedload are negativelycorrelated with component three. Component fourshows three variables with high negative loadings:hornblende, ilmenite and tourmaline. Component fivecontains two variables with high loadings: zircon, witha positive correlation and pyrites, with a negativeloading. The only remaining component having morethan one variable highly correlated with it is the sixth,with rutile having a high negative correlation, andmagnetite having a high positive correlation. No variable has a loading of greater than 0.5 on componentseven, and only one variable (epidote) is highly correlated with component eight.

Having identified a distinctive group of heavy minerals hosted by each of he primary Source materials, inparticular that hosted by the till, the aim of this sectionis , firstly, to identify any intercorrelated groups ofalluvial heavy minerals, geomorphological and hydrodynamic variables (Table 5) sampled from the Silver,Delour and Clodiagh Rivers (Gallagher, 1991). Asecond aim is to examine the spatial distribution ofintercorrelated groups of these variables.

Eigenanalysis (Table 4) shows that the first eightcomponents explain 78.5% of the variance of the 25variables, components 9 - 25 accounting for only21.5% of the variance (Table 4) . Eight principal components had eigenvalues greater than one (Table 4)none of which had a communality lower than 46%(Table 5).

In component one, sandstone and chalcopyrite havemore than half their individual variance explained bythe component (Table 5). Component two explainsOver half the variance of Dx, garnet and D95' Component three explains Over half the variance only ofbedload sorting (Table 5).

The striking feature of component one is its bipolarity (Table 5, Figure 5). Sandstone, the most commonlyoccurring autochthonous rock type (i.e. outcroppingwithin Slieve Bloom), covaries inversely withchalcopyrite, hosted only by the till. The first component, therefore, represents the dichotomy between theinput of glacigenic and non-glacigenic sediments tothe fluvial system. Component two (Table 5) indicatesthat garnet, which is numerically 2.5 times more abundant in the till than in bedrock, varies inversely with Dxand D95' This inverse relationship between the abun-


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Gallagher and Thorpe24danceof a mainly till-hosted phase and the two bedloadparticle-size parameters may be due to the increasedcomminutionof glacially transportedsediments compared to autochthonous sediments, characterised byshort, or zero, glacial transport distances. Thus, component two may reflect the duality of primary sedimentsources, one local, the other of external provenance.Components 3 to 9 do not represent any clearly definedgroups of two or more variables which have squaredloadings of greater than 0.5 (Table 5).

positive correlation between the first component andthose sample sites locatedoutside the till limits (r=0.583,rcr=0.306, point biserial correlation). However, whilea positive correlation existed betweencomponent twoand those sites locatedoutside the till limits, it was notstatistically significant (r=0.186).Interpretation

Channel Heavy Mineral Concentration PCA: Compo-nent score analysisComponent one Eigenanalysis indicates that thenumber of sandstone clasts in the bedload (and, to alesser degree, channel slope) are positively correlatedwith component one (Table 5). However, the abundance of chalcopyrite in the stream bed sediments isnegatively correlated with component one. Becausesandstone is an autochthonous lithology (Figure I) andas chalcopyrite is known to be significantly correlatedwith sites located within the till limits (Section 1), it issuggested that the spatial distribution of scores forcomponent one is controlled by the distribution of tillin the catchments (Figures I and 2). Thus, while themean score for sites outside the till limits was 1.447, itwas -1.131 for sites within the till limits (Figure 6) .

Variables with high positive loadings on componentone increase outside the till limits, at sites with highcomponent scores (Figure 6) .Highly negatively correlated variables increase within the till limits at siteswith low component scores (Figure 6). Thus, thespatial distribution of he group of variablescomposedof chalcopyrite, gold, haematite, stream power, heavymineral-concentrate weight, and the abundance ofshale, limestone and granite in the bedload, is found tobe highly negatively correlated with sample sites located outside the till limits (Figure 2). However, channel slope and the amount of sandstone in the bedloadboth increase outside the till limits. Therefore it can beconcluded that both the spatial concentration of alluvial heavy minerals by weight and the distribution ofparticular mineral phases are fundamentally related tothe geography of the till sheet. The recognition of thepolycyclic transportation of his set of detrital mineralsand sediments between periods of storagein glacigenicand/or alluvial sedimentary environments is of primeimportance to the understanding of their present geographical distribution. Interestingly, despite approximately 15,000 years of non-glacial, subaerialgeomorphological processing in these mountains, boththe spatial distribution of heavy minerals in the channel gravels of Slieve Bloom and the spatial variabilityin many of the hydrodynamic and geomorphologicalvariables associated with this distinctive pattern ofheavy mineral concentration, are still controlled by thepre-Holocene pattern of glacial deposition. In thissense the mountain streams of Slieve Bloom can beregarded as true paraglacial systems in which theirhydrodynamic processes are merely superimposedupon a pre-existing, externally controlled glacial landscape. Clearly, equilibrium between hydrodynamics

Component two Mean bedload particle-size (Dx)and D95 are both highly positively correlated withcomponent two, indicating that the attributes of thatcomponent increase with bedload calibre. From thisrelationship it is predicted (as no hypothesis may beconstructed in PCA) that component two will havehigher scores in the spatial domain of local, nonglacigenic sediments than within the till limits, theformer characterised by relatively shortjluvial trans-port distances within the mountains, the latter characterised by a predominance of glacially transportedsediments with terminal grades inversely related totheir long glacial transport distances. Conversely, it ispredicted that garnet, which is numerically 2.5 timesmore abundant in local tills than in bedrock, and ishighly negatively correlated with component two,should increase in abundance within the till limits.Thus, it was found that the mean component score forsites outside the till limits was 0.611 while for siteswithin them it was -0.467 (Figure 6), a significant

and sedimentary fluvial geomorphology in the mountain streams of Slieve Bloom has not been attained andequilibrium concepts cannot be invoked in the explanation of the processes of concentration of heavyminerals in the alluvial sediments of these streams.

Heavy Minerals in the Slieve BloomsSection .Two: T.he Concentration of Heavy

Mmerals III Floodplain Gravels. Having identified in the alluvial sediments a distinc

t l :e g r o ~ p of hea:,y minerals spatially andmineralogIcally assocIated with the tills of the st dc a t c h ~ e . n t s (Section One) and having identifieduassocIatIOn bet:veen this group of minerals and certain

g ~ o m o r p h o l o ~ l c a l variables, themselves associatedWIth. the s p a t ~ a l distribution of till (Section One)SectIOn Two aIms to identify the hydrodyn ' 'amlcproc-

e s s e ~ of allUVIal heavy mineral concentration. ThissectIOn concerns o nly those sites at which heavy minerals, overbank sediments, and basal clasts were sam

p l ~ d .from ~ o o ~ p l ~ i n s , all of these sites being locatedWIthin the tIll lImIts (Figure 2) of the rivers studied.Floodplain distributions in Slieve Bloom

The ~ o s t s t r i ~ i n g geomorphological difference be~ w e e n sItes outSIde the till limits and those within them

the absence or x t r e ~ e l y small scale of floodplainsIn former, there being a significant positive corre

~ a t l betw.een. floodplain surface area and locationinSIde the tIll lImits (r=O 533 r -0 306) IS 'o . . 'cr-' . n ectlOnne It was shown that it was possible to identify cleargroups ?f glacigenic and non-glacigenic elements inthe flUVIal s y s t e ~ represented by component one,t?ese g r o u ~ s ~ a I n t a I n e d a statisticall y significant spa

dISSOCIatIOn despite Holocene fluvial processing( r - 0 . ~ 8 2 , rcr=0.306). In addition, there is a significantnegatIve correlation between the score of component

and floodplain area ('t = -0.329, Kendall ' s 1, one~ l l e d ) . By. controlling for the effect of sites catego

as being located outside the till limits, the correlatIOn between floodplain area and the score for com. onent one (representing the dichotomy between theInput glacigenic and non-glacigenic sediments tothe flUVIal system) strengthened to -0.919.

. implication of this partial correlation coefficIent that, as floodplain area increases, decreasing

a n t I t l e s of those variables which covary positivelyWt? component one are found. In particular thesev nables are: the number of sandstone clasts in thebedlo.ad and channel slope. However, as floodplaina.rea Increases, those variables which covary nega

Y w i t ~ component one increase in abundance.ese varIables are chalcopyrite, gold, haematitestream powe h . ', eavy mineral-concentrate weight and

25the numbers of shale, granite and limestone clasts'the e d ~ o a d . Floodplain area, therefore, increases h e ~ :the ~ t t ~ l b u t e s of the till are stronger.

SImIlarly, by c?ntrolling for the effect of floodplain~ r e a : correlatIOn between sites located outside thetIll 1 I ~ l t s and the component score of component one(the dIchotomy between the input of glacigen' dl ' . IC annon-g aClgenIc sediments to the fluvial system)s t r e ~ g t h e n e d to 0.94. This suggests that with thspatIal association between floodplain' a deI' . rea ang aClgemc sediments controlled, the variables h' hc . . W ICovary pOSItIvely with component one increas .a b u n d a n ~ e , w h ~ l e the negative ones decrease. T ~ U I ;non-glaCIal attnbutes (ie abundant sandstone clasts ' 'the bedload, high channel slopes and relatively h i ~ ~levels of autochthonous heavy mineral species) become more pronounced as floodplain area declines

These ~ s u l t s show that the floodplains are actingstores m ~ I n l y of eroded glacigenic detritus , includingheavy I n ~ r a l s , between periods of luvial erosion andt : a n ~ p ~ r t a t l O n . Thus, when sites within and outside thetIl! lImIts are o m p a r e d , both the distribution of heavy

m l ~ e r a l s ~ e C l e s and their concentration in alluvials.edlments IS fundamentally controlled by the supply oftIll. to the fluvial system. The potential for supply is

u l t l m a ~ e l y dependent upon the geography of the tillsheet In relation to the fluvial systems Ho. h' . weverWIt In the till limits the supply of till and th 'e concen-tratIOn of heavy minerals in the alluvial sediments willbe governed by ~ ~ a t i a l variations in hydrodynamicprocesses . In addItIOn, the spatial distribution of mine r ~ 1 s p e c i ~ s will be controlled by variations in themineralogIcal characteristics of the till Thus th .f h' . . . ,e ImOtIS sectIOn I.S to identify the geomorphologicalprocesse.s assocIated with the spatial variation of

~ o o d p l a I n heavy mineral species and their concentratIOns .Floodplain heavy mineral concentrations

The weights of heavy mineral concentrate derivedfrom c ~ a n ~ ~ 1 and floodplain sediments were found notto be SIgnIfIcantly different (U=157 U =46 MWhO ' cr ' ann-Itney l ! - t e ~ t ) . This suggests that heavy mineral

c o n ~ e n t r a t l O n IS not occurring in the channels fromerosIOn of the floodplain gravels and that subsequentto the f o r m ~ t ~ o n . of the floodplains in the :nountains, astate of eqUllIbnum exists in the ability of the rivers toconcentrate their heavy mineral load by hydrody-


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26namic sorting alone. Therefore, despite the periodicreworking of floodplain gravels, a terminal grade ofheavy minerals appears to have been reached at eachsite. This is because the mountain floodplains of thisarea are mainly constructed by both lateral channel andpoint bar migration rather than vertical accretion: theyare, in effect, buried channels. Importantly, the lack ofvertical accretion deposits indicates that the floodplainsare short-lived, single-phase constructions, prone torepeated, regular wipe-out (Gallagher, 1991). Beingsingle-phase stores (before wipe-out) of a spatially,dynamically and volumetrically-limited supply of tilland its contained minerals, the floodplains are notefficient environments of heavy mineral concentrationthrough repeated differential entrainment of lightminerals and storage of heavy minerals (Minter andToens, 1970; Reid and Frostick, 1985).No significantcon-elation was found ('t = -0.128) between heavymineral concentrate weight and floodplain area.Interpretation

Gallagher and Thorpesupply is an important control in the spatial development of these two variables. In contrast, becausefloodplain formation takes place in zones of streamenergy deficit, the weak negative correlation betweenfloodplain area and concentrate weight suggests thattheir distributions may be controlled by variations instream energy, concentrate weight varying positivelyand floodplain area negatively with stream energy.These process relationships are examined in the nextsection.peA of Floodplain Heavy minerals andGeomorphological Variables

Because floodplain area and concentrate weight arenot significantly con-elated it is suggested that thedepositional processes responsible for floodplain formation and those which produce concentrations ofalluvial heavy minerals do not act in concert. Ratherthan being limited by the spatial distribution of appropriate process-environments of deposition, here boththe formation of floodplains and the concentration ofheavy minerals are limited by the supply-environments of 'primary' source materials, in this case thespatial distribution of the till in relation to the fluvialsystems. Thus, when sites inside and outside the tilllimits are compared, both floodplain area and heavymineral concentrate weight have been found to bepositively correlated with sites within the till limits.However, the low negative correlation between concentrate weight and floodplain area suggests that thehydrodynamic processes involved in the concentration ofheavy minerals within the channel are insignificant in, and possibly detrimental to, the accumulationof floodplain sediments. The floodplains and heavymineral concentrations may have a similar spatialdistribution in relation to the distribution of till butthey do not have any strong association which maysuggest a mutual process of formation. However, themutual spatial association of both floodplain area andconcentrate weight with the till indicates that sedim ent

In order to identify the hydrodynamic processes,rather than the sedimentary sources, involved in theproduction of alluvial heavy mineral concentrations,principal components analysis was carried out uponvariables sampled exclusively from within the tilllimits . By sampling only from within the till limits,sedimentary source variability was minimised, allowing a clearer interpretation of the spatial distributionsof variables indicative of spatial variability in theprocess of alluvial heavy mineral concentration. Because of the impossibility of directly observing theprocess of heavy mineral concentration in activestreams, investigations were carried out upon a dataset consisting of surrogate variables found in thepreceding analysis (Section One and Two above) to beenvironmentally associated both with alluvial heavymineral concentration per se and with the distributionof particular mineral species.Because the sun-ogate data-set consisted of 20 vari-ables collected from only 13 sites, it was necessaryfirstly to reduce the number of variables in the analysiswithout losing information potentially important inthe understanding of heavy mineral concentration as aprocess. As PCA is ideal for this type of data-setsimplification, it was used again in the following way.Individual variables were assigned to one of 3 subsetsof data: a mineralogical subset, a sedimentologicalsubset and a lithological subset. PCA was then carriedout upon each of the 3 subsets individually to identifythe interactions between variables within each of he 3subsets and, thereby, to reduce the number of variablesnecessary to retain from each subset. Using a furtherPCA, the component scores of the first two compo-nents of each subset (the components being new,independent variables derived from the first PCA,

Heavy Minerals in the Slieve BloomsTable 5: Component loadings of the within-channel cone t .n ratIon variables

Variable PCl PC2 PC3 PC4 PC5 PC6 PC7Zircon 0.044 -0.614 0.094 -0.252 0.504 -0.219 -0.303Tourmaline -0.144 0.254 0.424 -0.509 0.434 0.365 0.125Garnet -0.306 -0.759 -0.027 -0.026 0.294 -0.04 -0.102Rutile -0.182 -0.064 0.32 0.197 0.158 -0.617 -0.27Pyrites -0.422 0.196 0.256 -0.268 -0.502 -0.249 -0.121Chalcopyrite -0.738 0.173 0.39 -0.032 -0.132 0.108 0.223Gold -0.657 0.181 0.147 -0.194 -0.425 -0.083 -0.327Epidote 0.102 0.169 -0.311 0.174 -0.142 -0.218 0.342Haematite -0.682 -0.148 0.134 -0.322 -0.084 0.218 -0.154Magnetite 0.154 0.369 -0.136 0.05 0.175 0.586 -0.156Actinolite -0.493 0.032 0.335 0.355 0.382 -0.07 0.252Hornblende 0.146 0.314 0.183 -0.669 0.085 -0.276 0.312Ilmenite 0.282 0.187 0.593 -0.513 0.057 -0.012 0.313Stream power -0.578 0.09 0.5 0.412 0.336 0.118 -0.036Channel slope 0.417 0.392 0.584 0.268 0.275 0.24 -0.191Conc. weight -0.587 -0.157 -0.05 -0.247 0.02 0.215 -0.399Dx -0.099 0.846 -0.202 0.132 0.133 -0.222 -0.164D95 -0.177 0.714 -0.516 0.072 0.248 -0.084 -0.045D sorting 0.261 0.211 0.738 0.02 -0.213 0.223 -0.153Sandstone 0.757 0.12 0.472 0.1 -0.013 -0.075 -0.237Shale -0.52 0.386 -0.555 -0.246 0.193 -0.036 ~ 0 . 0 2 6Limestone -0.501 -0.329 0.247 0.338 -0.181 0.15 0.432Quartzite 0.238 -0.482 -0.252 -0.33 0.325 -0.193 0.111Granite -0.517 0.019 0.089 0.184 0.282 -0.378 0.159Chert 0.157 -0.556 0.062 0.102 0.3 0.176 0.01

': communalityTable 6: Eioenanalysis of th fl d .e 00 plain (a) mineralogy, (b) sedimentology and (c) lithology

(a) MINPCI MINPC2 MINPC3 MINPC4 MINPC5Eigenvalue 3.0216 2.3695 1.8034 1.0579 0.7178Proportion 0.302 0.237 0.180 0.106 0.072Cumulative 0.302 0.539 0.719 0.825 0.897(b) SEDPCI SEDPC2 SEDPC3 SEDPC4Eigenvalue 1.881 1.2251 0.6316 0.2623Proportion 0.47 0.306 0.158 0.066Cumulative 0.47 0.777 0.934 1.000(c ) LIT PC I LITPC2 LITPC3 LITPC4 LITPC5Eigenvalue 1.6765 1.5070 1.3627 0.8281 0.6257Proportion 0.279 0.251 0.227 0.138 0.104Cumulative 0.279 0.531 0.758 0.896 1.000

27

PC8 C*0.052 0.8480.052 0.8630.205 0.811-0.092 0.665-0.341 0.7990.082 0.813-0.006 0.8180.518 0.619-0.138 0.705-0.44 0.773-0.071 0.702

-0.07 0.7860.208 0.8740.168 0.9180.006 0.9100.408 0.8060.047 0.8790.008 0.8820.124 0.7920.015 0.8830.126 0.843-0.133 0.794

-0.309 0.712-0.329 0.6650.0005 0.469

MINPC60.51400.0510.948


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28their component scores indicating their spatial distribution) were then analysed together, as a single newdata set. In this way, the spatial distribution within thetill limits of the six new variables was determined . Byusing the first two component scores of each subset inthe second PCA, 53 - 75% of the variance of theoriginal variables was utilised while maximising thedegrees of freedom in the second analysis. As thesecond analysis seeks to identify variables inter-related through the process of heavy mineral concentration ratherthanjust through spatial linkages controlledby the geography of sediment supply, no componentscore analysis is necessary in the second PCA.(i) Floodplain heavy minerals PCA: Eigenanalysis

Four components had eigenvalues greater than one,together accounting for 82.5% of the variance in theabundances of floodplain mineral species (Table 6).Component one is dominated by five minerals withloadings of greater than +/-0.5, magnetite, zircon, andgarnet, all covarying positively , and pyrites andchalcopyrite, covarying negatively with the component. Only pyrites and magnetite have more than halftheir individual variances explained by the first component. The second component is dominated by fourminerals; tourmaline, haematite, and gold covary positively with the component, rutile negatively. Onlytourmaline and haematite have more than half theirvariance explained by the second component. Threeminerals, epidote, chalcopyrite, and garnet are highlynegatively correlated with the third component. Onlyepidote, however, has more than half of its individualvariance explained by the component. Gold is the onlymineral which is highly correlated with the fourthcomponent, but only 37 % of its variance is explainedby the component. Thus, components 3 and 4 do notrepresent any clear groups of inter-related minerals.Component one As in the analysis of thechannel mineralogy, the most striking feature of theeigenanalysis ofthe floodplain mineralogy is the bipolarity of the first component; minerals associateduniquely or predominantly with the till (Table 1) arenegative covariants, while those phases associatedpredominantly with the bedrock (Table I) are positively correlated with the first component (Table 7).The first component (MINPCI) therefore representsthe dichotomy between the allochthonous mineralogy

Gallagher and Thorpeof the till, and the autochthonous mineralogy of thebedrock.Component two Minerals which are predominantly hosted by the till, especiall y haematite and gold,covary in the same direction as predominantly bedrock-hosted species, such as zircon which is numerically 3.17 times more abundant in the bedrock than inthe till. The second component (Table 7) may represent the imperfect mixing of allochthonous andautochthonous minerals and parent sediments (till andbedrock) during glacial transportation. However, asthe component loadings indicate, the minerals whichare associated mainly with the till are the most important correlati ves with the component. Thus, the secondcomponent (MINPC2) is fundamentally associatedwith the introduction of a foreign mineralogical medium, the till, into the mountains but does reflect thepartial integration of local and foreign minerals in thefloodplain sediments.

The efficiency of the process of glacio-dynamicmixing of basal tills is primarily dependent upon thedistance over which originally uncorrelated sedimentshave been co-travellers . "Till composition varies according to the composition and erodibility ofextrabasinal and intrabasinal sources, the distance oftransport, and glacial factors" (Edwards, 1986). Theglacial entrainment of sediments will cause the glaciodynamic integration of diverse sediments to becomelocally poorer at the point ofentrainmentthan at pointsup-ice, until co-transportation of both the newly entrained and existing sediments has taken place and(theoretically) perfect mixing has been achieved. Ifsediments are constantly being entrained, a situation ofperfect glacio-dynamic mixing is unlikely to occur.

Thus, with regional ice-flow over Slieve Bloomduring the late-Pleistocene from the west-northwest(Warren, 1987; Gallagher, 1991), the mixing, withinthe Slieve Bloom mountains, of allochthonous andautochthonous sediments is relatively immature compared with the mixing of further co-transportedsediments entrained from the western piedmont andother points up-ice.This would explain why the differ-

ences in the mineralogies of the till and the bedrock of, Slieve Bloom remain apparent in the first principalcomponent despite later fluvial processing. Similarly,the fact that both rutile and chalcopyrite are uniquelyhosted by the till, but covary inversely with other tillminerals with respect to the second component, sug-

Heavy Minerals in the Slieve BloomsTable 7: Component loadings of the floodplain

(a) mineralogy(b) sedimentology

(c) lithology

(a)Variable MINPC I MINPC2 MINPC3 MINPC4Zircon 0.642 0.405 -0.489 0.096Tourmaline 0.435 0.739 0.055 0.411Garnet 0.530 0.332 -0.604 -0.233Rutile 0.198 -0.630 -0.010 0.425Pyrites -0.885 0.391 -0.135 0.045Chalcopyrite -0.673 -0.181 -0.653 -0.039Gold -0.091 0.620 0.119 -0.608Epidote -0.474 0.113 -0.739 0.285Haematite -0.244 0.738 0.345 0.436Magnetite 0.757 -0.157 -0.268 -0.001

(b)Variable SEOPCI SEOPC2Concentrate weight 0.063 -0.881095 -0.520 -0.628fs 0.893 0.084Floodplain area 0.900 -0.218

(c)Variable LITPCI LITPC2 LITPC3Sandstone -0.652 0.67 0.226Shale 0.928 0.104 -0.348Limestone -0.297 -0.867 0.164Quartzite 0.172 -0.28 0.768Granite 0.263 0.465 0.499Chert -0.451 -0.019 -0.57

29gests that their .transport histories are different fromthatof he other till-hosted minerals. Therefore hh f ' w ereast e Irst component reflects the Source bipolarityof heallochthonous and autochthonous mineral species, the

~ e c o n d component represents the partial glacial mixIng of the bedrock substrate and the basal till. However, de.spite Holocene fluvial reprocessing of theheavy mInerals and earlier glaciofluvial processing, assho.wn by the presence of perched glaciofluvialse?lments .and terraces in Slieve Bloom, the originalmIneralogical composition of the host materialsallochthonous till and autochthonousbedrock remal' 'h . , mt e pnmary determinant of statistically different min-eralogical assemblages.

(ii) Floodplain sediments PCA: Eigenanalysiscomponents had eigenvalues greater than oneand In combination accounted for 77 .7% of the vari

ance of the. derived variables (Table 6). Floodplainarea was hIghly positively con 'elated with the firstcomponent. ?verbank phi-sorting (Os) and D95 werestrong negative correlatives. Both Os and floodplainarea had more than half of their individual variancesexplained by the first component. Concentrate weightand D95 were both highly negatively correlated withthe second component, but only concentrate weighthad more than half of its variance explained by thecomponent (Table 7).

C ~ m p o n on e. The first component (SEDPC I),WIth Os ImproVIng as floodplain area increases, reflects the stable, energy-deficient conditions of lowsediment t r a ~ s p o r t rates and deposition (Table 7).Therefore , thIS component represents sedimentary stability. However, the extremely weak correlation ofconcentrate weight with the component confirms that

development and spatial distribution of heavym I n e r ~ l concentrations is not fundamentally a purely

d e ~ O S I t l O n a l process relying on the long-term accumul a t l ~ n and p r ~ s e r v a t i o n of deposited, hydraulicallyequIvalent, mIneraI grains.

c

Component two The second component(SEDPC2), dominated by concentrate weight andbedload D95, represents the hydrodynamic process ofbedload heavy mineral concentration. Since both variables are negatively correlated with the component theoncentration of heavy minerals by this process will


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30improve as the attributes of the c o m p o n e n ~ becomeweaker (Table 7). Large bed particles I n f l u ~ n c econcentration through the effects of disperSIve q U l v ~ -lence (Bagnold, 1954; Reid and Frostick, 1985) . thISprocess, when a shearing force ap.plied to p.artlclesthey exert a dispersive stress whIch IS proportIonal tothe product of the square of the particle diameter andthe particle density, and which acts normal thedirection of shear. 'The outcome of the process IS thatlarger or heavier grains tend to rise relative to t ~ e l rsmaller or lighter counterparts' (Reid and FroStlCk,1985). However, dispersive sorting probably actsas an ancillary process of heavy mineral c o n c e n t r ~ t I o nthrough the combined effects of large bed partIcles.Thus 'larger light minerals are [lifted] to the top of themobile layer where protrusion ensures that th.ey aresubject to greater lift and drag. The preferentIaltrainment of these larger particles then leaves b e ~ l n da lag of smaller heavy minerals' (Reid. FroStlCk,1985). This process thereby provides an inItIal concentration of heavy minerals.

Gold and diamond placers often form throughgradual interstitial trapping of heavy mineral grams(Hall et ai, 1985; Thomas et ai. , I 985; ~ u c k andMinter, 1985). Initially, a very small proportIOn ?ftheheavy mineral grains, together with hydraulIcallyequivalent sands , is trapped in.thin ~ r a v e l l a y e r s . Theheavy mineral concentration In thIS type of depOSItmay be lower than in the bed load supplYing the depOSIt(Minter and Toens, 1970). Howe:er, this processcauses the concentration of the passing bedload to beimproved. These effects are m ~ l i f i e ? respectively ina thicker gravel layer with resulting hIgher concentrations being deposited from very concentrated bedloadsfurther downstream. The important point is that openwork gravels do not separate heavy minera.ls frombedloads but trap a proportion of them whIle increasing their concentration in the [mobile] bedload. ' (Minterand Toens, 1970).

Spatial variations in the efficiency of both processesof concentration will depend on the supply of coarsematerial to the fluvial system. Reid and Frostick (1985)likened interstitial trapping to a sieving process, withmesh size decreasing downstream. However, in SlieveBloom the erosion oftill will supply coarse material tothe channel out of phase with fluvial equ. libriumprocesses, thereby imposing an external control onspatial pattern of heavy mineral concentratIon by thIS'sieving' process.

Gallagher and Thorpe(iii) Floodplain lithology PCA: Eigenanalysis

Three components had eigenvalues greater thanone, and together accounted for 75.8% of the t r ~ c e(Table 6). Two variables were highly correlated WIththe first component, shale positively and sandstonenegati vel y. However, only shale had more than 50% ofits variance explained by the component ( T a b l ~ 7).The secondcomponent also had two strong o r r e l a t l v ~ s ,limestone negatively and sandstone positively wl.ththe component. Only limestone had more than half ItSindividual variance explained by the second component. Two variables were highly correlated with thethird component, quartzite positively and chert negatively. However, only quartzite had more than half Itsvariance explained by component three.Component one The first component (LITPC I)appears to represent the immaturity of the process.ofglacio-dynamic mixingof he autochthonous htholog.les(shale and sandstone) due to their mutuall,Y exclUSIveoutcrop pattern along the study reaches (FIgure I) andthe short distance over which both shale and sandstonecould have been glacially co-transported within themountains (Gallagher, 199 I) (Table 7). Thus, sandstone and shale, which are mutually excluslve .at outcrop, remain poorly mixed despite both glaCIal andfluvial processing.Component two The second c o m p o n ~ n t(LITPC2), with limestone and sandstone covarYlnginversely with each other in relation component,reflects the immature glacio-dynamIC mtegratIOn ofboth allochthonous and autochthonous lithologies (Table 7). In Slieve Bloom this is because u t o c h t h ~ n o u slithologies are entrained only short i s t a n ~ e ~ up-Ice oftheir ultimate lodgement point due to the S I ~ g t o p ~ g -raphy of he mountains (Gallagher, 1991).ThIS applIesin particular to sandstone because it u t c ~ o p s at loweraltitudes in the mountains than shale, whIch generallyoutcrops above the till lodgement zone ~ i g u r e I). Therelatively high positive loading of gramte component two is instructive in the understanding of therelationship between the transport distance of tills andtheir glacio-dynamic mixing. In Slieve Bloom, thedistribution of erratic granite is concentrated on theglacial lee-side of the mountains (Warren, 1987). Inaddition, granite was found to be most prevalent as abedload lithology in the higher reaches of streams

Heavy Minerals in the Slieve Bloomswithin the till limits (Gallagher, 1991).

This distribution of granite is due to the upwardshearing of basal till during eastward transport towards and over Slieve Bloom; at any point, furthertravelled till was generally transported high er in the icesheet than material newly entrained at the base of theice sheet. Thus, as Slieve Bloom was glacially overridden, relatively short-travelledtill was lodged lower inthe mountains than far-travelled sediments, such asGalway granite (Warren, 1987; Gallagher, 1991).Therefore, the distribution of granite covaries withautochthonous lithologies in its correlation with component two because its greatest concentration is spatially correlated with the distribution of autochthonouslithologies, ie upstream of the greatest concentrationof relatively short-travelled (basal) lodgement till.Component three The third component (LITPC3),with quartzite and chert opposing each other, alsorepresents the dichotomy between the local bedrockand material introduced to the mountains glacigenically(Table 7). These lithologies are less important in theanalysis because they are both secondary products ofthe breakdown of primary rock types; the quartzite isderived from the abrasive disintegration ofconglomeratic sandstones , the autochthonous element,while the chert is the resistant silica residue of thechemical weathering of limestoneand is, therefore , anallochthonous lithOlogy.

A Model of the Distribution of Alluvial HeavyMinerals in Slieve Bloom

The analyses carried out in this paper indicate thatthe spatial distributions both of mineral species and ofalluvial heavy mineral concentrations are complex.They not only depend on the availability for processing of heavy mineral-rich source materials, but alsoupon spatial variations in the appropriate fluvial environments of concentration. Underlying the processes

31It has been shown that the mineralogy ofthe alluvial

sedimentsof selected rivers in Slieve Bloom is stronglycontrolled by the distribution of till in the catchments.The analyses of mineralogical, sedimentological,lithological and geomorphological variables have indicated that the process histories of the sediments arecomplex, both spatially and temporally. The proposedmodel of the distribution of heavy minerals in SlieveBloom consists of 3 temporal stages , within which 7process stages have operated to lead to the Holocenedistribution of heavy minerals in the alluvial sediments .I . Pre-glacial stage.

The first stage of the model deals with the preglacial (Neogene and/or Lower Pleistocene interglacial) fluvial processing of the Palaeozoic sedimentsofthe mountains . During this stage it is suggested that acombination of deep weathering and fluvial processing of the Palaeozoic rocks may have led to theformation of small concentrations of heavy mineralsof autochthonous origin (Process Stage I). Because ofthe small river catchments of Slieve Bloom, and because the drainage network is radial, it is assumed thatany significant concentrations of heavy minerals wouldhave formed in the proximal piedmont (Process Stage2). The bedrock geology of Slieve Bloom suggeststhat any alluvial concentrations of detrital heavy minerals derived from the erosion of local lithologieswould have been dominated by magnetite (ca. 75% ofheavy minerals), zircon (ca. 13% of heavy minerals),and haematite (ca. 8% of heavy minerals), with smallamounts of other minerals also being present. Therichest source of detrital heavy minerals to the preglacial rivers is likely have been the Devonian conglomerates, these rocks being more suitable than bettersorted, finer grained sandstones and shales for theprimary stage of hydrodynamic separation and concentrationof heavy minerals (Reid and Frostick, 1985;Minter and Toens, 1970).2. Glacial stageboth mineral species distributions and heavy mineraI concentrations are the transport history, weather

ing environment, and litho-mineralogical compositio of the tluvially eroded and entrained till. The tillnot only acts as the main SourCe of heavy minerals, butalso as a source of the coarse'bed material required forthe processes of channel-bed heavy mineral concentration.

Till-fabric analysis and the presence of Galwaygranite in the till indicates that Slieve Bloom wasoverridden by ice that emanated from the Galway areaand introduced valley-fill tills to the existing rivervalleys of Slieve Bloom. The sedimentology of thetills in the study catchments indicates that the higher


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Gallagher and Thorpe32 Summary and Conclusionsparts of the mountains were overridden by relativelythin ice of the supraglacial zone carrying the highestproportion of far travelled erratics, including most ofthe Galway granite found in Slieve Bloom. Lodgementand basal meltout tills, characterised by lithologiesentrained in the piedmont and lower slopes of SlieveBloom with glacial transport distances of about 5 km,were deposited in the lower slopes , up to heights ofabout 250m (Gallagher, 1991) (Process Stage 3).Given this situation, it is likely that basal disruptionand shearing of any pre-glacial alluvial placers wouldhave taken place in the piedmont, forming multiple oreplumes in the tills deposited in the mountains (ProcessStage 4) . It has been found that the mixing ofallochthonous till and the autochthonous sedimentsimproved in a down-ice direction (Gallagher, 1991).Thus the ability to distinguish the locally entrainedalluvial sediments, in any single ore plume, diminishesdown-ice as the mixingof he local and exotic sedimentsimproves and as the concentration of autochthonoussediments diminishes away from source. Thus theresulting 'Slieve Bloom till', comprised of a mixtureof allochthonous and autochthonous sediments , has acorresponding bipolar mineralogical and lithologicalcomposition due to the glacio-dynamic integration ofthe diverse sediments (Process Stage 5). The recognition of this situation is central to the understanding oftheevolutionof he post-glacial alluvial geomorphologyof Slieve Bloom in the context of both floodplaindistributions and their sedimentology.

The variables chalcopyrite, gold, haematite, streampower, heavy mineral-concentrate weight, and theamount of shale, limestone, and granite in the bedload,were found to increase within the till limits. It has beenshown that both the spatial concentration of alluvialheavy minerals, and the distribution of particular minerai species are also fundamentally related to hydraulic and lithological variables which are themselvesglacially inherited. Therefore, the spatial distributionboth of alluvial heavy mineral species and of alluvialheavy mineral concentrations can be understood onlywhen their total geomorphological histories are taken

3. Post-glacial stage.The final stage of the model deals with the subaerial

weathering and mobilisation of tills and solifluctedslopedeposits in Slieve Bloom, initially in aperiglacialenvironment characterised by geomorphological instability and high rates of sediment production (Process Stage 6). This period was followed by the processregime operating today of fluvial erosion, transport,deposition and reworking of sediments, both glacialand alluvial, leading to the accumulation of smallconcentrations of heavy minerals in the alluvialsediments of the mountains (Process Stage 7). Thisprocess may be accompanied by renewed formation ofembryonic placers in the piedmont due to the episodicwipe-out of mountain sedimentary accumulations andfloodplains and the out-flushing of their containedheavy minerals to the piedmont.

into account.Floodplain formation was shown to be more highlydeveloped within the till limits. As the floodplains ofSlieve Bloom increase in size, their mineralogical,sedimentological and lithological properties increasingly reflect the characteristics of he till. These resultssuggests that the floodplains act as stores of erodedglacigenic detritus, including heavy minerals, betweenperiods of fluvial processing. Thus a fundamentalcontrol of the spatial variation in the size and mineralogy of the floodplains is the spatial variation in thesupplyof till sediments, through fluvial erosion, to thefluvial system.The concentration of heavy minerals per unit vol-ume of channel gravels and floodplain basal-gravelswas not found to be significantly different. This suggests that a terminal concentration grade of heavyminerals has been reached that has not changed significantly since Holocene floodplain formation in themountains began. The weight of heavy minerals perunit volume was not found to be related to floodplainsize. However, floodplain size and heavy mineralconcentrate weight were correlated positively with thevolume of till eroded between sites. Thus, whilefloodplains do act as stores of eroded till, they are noteffective environments for the mutli-phase concentration of the till-derived heavy minerals. Rather, heavymineral concentration, in this case, is a single-phaseprocess, associated with dynamic stream-bed processes without the aid of long term heavy mineralstorage and periodic concentration through differential entrainment oflig ht versus heavy minerals. Owingto their single-phase nature, the effect of the low-levelof floodplain heavy mineral storage is detrimental toimproved within-channel concentration: because the

Heavy Minerals in the Slieve Bloomsfloodplains .withhold their heavy minerals from theprocess-environment ofwithin-channel concentration

channels themselves are effectively starved off l r s t - p h a s ~ c?ncentrate held within the floodplain but

~ e e d ~ d wlthm the channel if multi-phase concentrat l ~ n IS to occur. .Even when existing floodplains arew ~ p e d - o u t , multi-phase concentration of their heavy: n l ~ e r a l s cannot occur downstream. This is because, as

m d ~ c a t e d by the extreme shallowness of he floodplainsediments < .m), the confined mountain zone (above.150m) e n o ~ l c wipe-out of he majorityof floodplainsIS total. thiS case, the heavy minerals contained inthe allUVIUm are concentrations only in that they are~ o r e concentrated per unit volume ofalluvium than oftill; they are not true placers, in the sense of beingsuper-enriched zones of alluvial heavy minerals butare merel.y the first-order accumulation of heavy ~ i n -erals denved from the erosion of the till thT s WI outslgm Icant secondary hydrodynamic concentration.

Concentrate weight and bedload D95 were found tobe e l a t e ~ through the hydrodynamic concentration of

~ e a v y minerals. by the interstitial trapping and protection of heavy mmeral grains in the stream bedload.The~ u t o c h t h o n o u s lithologies, shale and sandstone, havmg been glacially transported, but abraded less than

a l l o . c ~ t h o n o u s lithologies, were found to be correlatedpositively with D95 and therefore contributed most tothe p r o c e s ~ of channel-bed interstitial heavy mineralc o n ~ e n t r a t l O n . It was concluded that the hydrodynamic process and spatial distribution of heavy mineraI o n ~ e n t ~ a t i ? n was fundamentally dependent uponthe spatial dlstnbution of autochthonous clasts in thebedload.

.Howe:er: c o n c ~ n t ~ a t ~ weights are significantlyIgher Within the till lImits than outside them and it isco.ncluded that the till is the main source of heavy

merals to the alluvial system. These results indicatethat f o ~ m a t i o n of alluvial heavy mineral concentrates In SlIeve Bloom is a two-stage process, controlled, firstly, by the fluvial erosion and entrainment of

h.eavy minerals from the till, and, secondly, by thet h l n - c h a ~ n e l c ~ ~ c e n t r a t i o n of heavy minerals bye a m ~ b e d mterstltlal trapping.Therefore, spatialvari

atIOns In the conc.entration of alluvial heavy mineralsare controlled, fIrStly, by spatial variations in the:UPPly of heavy :ninerals to the fluvial system and,hecondly, by s p ~ t l a l v ~ ~ i a t i o n s in the efficiency of ther: rodynamlc mterstltlal trapping mechanism. Ulti-

ately, both these factors are themselves detennined

33by the mineralogical and lithological compost fth .11 . I IOn 0e tl aVailable for fluvial processing and, thereforeby the t ~ a n s p o r t and entrainment history of the t i l l ~sheet bemg fluvially processed.

Th.e analyses carried out in this paper indicate thatdespite proba?ly 15,000 years of post-glacial andHolocene fluvial processing ofthe glacial and bedrockso.urce materials, the spatial distributions of heavymmeral spec.es derived from these two sources, andnow hosted m Holocene alluvium, are still stronglyc.ontrolled by the g e o ~ r a ? h i c a l pattern of till depositIOn . These charactenstlcs confirm the paraglacialnature ~ t h e s e , and possibly many other, upland riversystems m r e l a n d . While the hydrodynamic processesofh.eavy mmeral entrainment, deposition and concentration cannot be overlooked, hydrodynamics can prod ~ c e heavy mmeral concentrations only where heavymmerals are available for concentration. In Slieve

B l o o ~ , the spatial distribution of mineral-abundantallUVIUm is itself controlled by an imposed, externalcontrol: the spatial distribution of the till.

Clearly, equilib:ium fluvial and hydrodynamic processes of heavy. mmeral concentration cannot operatealong a contmuous longitudinal reach in these

p a r a g l ~ c i a l ~ u v i a l systems, a situation which must beborne m mmd in the construction of alluvial heavy:nmera.1 i s t r i b u t ~ o n models and sampling proceduresm glaCiated t ~ r r a l ~ s . Underlying both the mineralogyan.d the spatial distributions of the alluvial heavy

~ m e r a l concentrations derived from the fluvial erosIOn of tills are the litho-mineralogical compositionand regl.onal transport history of the till and its local

. e a t ~ e r ~ n g and erosional history. The importance ofthe till In the explanation of the distribution of theheavy minerals in the alluvial sediments of SlieveBloom, in p.articular the strong spatial separation bet ~ e e n a l l ~ v l U m characterised by minerals associatedWith the till from alluvium containing heavy minerals

e r o d e ~ ~ r o m the bedrock, and the persistent spatialas.soclatl?n of .these distinctive mineral assemblagesWith their ~ e d l m e n t a r y sources, indicates very lowrates of sedimentary mixing due to fluvial processing.

g e o g r ~ p h y of the main sources of the alluvialsediments IS strongly reflected in the distributions ofheavy minerals in the alluvium. This finding indicatesvery rates. of sedimentary mobility and mixingfollOWing fluvial erosion and entrainment of the tilland bedrock sediments.


11/11

34Acknowledgements

We acknowledge with gratitude the field assistance,logistic support, cartography and advice of StephenHannon, Sheila McMorrow, William P. Warren, StuDaultrey, Padhraig Kennan, Ron Elsdon, Joe Brady,

Gallagher and ThorpeBurmin PLC, Liz Donnelly, Eamonn Doyle, JackyCroke, Monica Mulrennan, Lesley Williams, MaryBourke, Joe Byrne and Ciaran Lynch. We wish tothank Professor Kennedy of the Department of Geology, UCD, for use of heavy mineral separation facilities. We thank the referees for their helpful comments.

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