Sedimentary Geology, 42 (1985) 201-216 201 Elsevier Science Publishers B.V., Amsterdam - Printed in The Netherlands

G E N E S I S O F F I R E I S L A N D F O R E D U N E S , N E W Y O R K

A.T. WILLIAMS J, N.H. EVANS I and S.P. LEATHERMAN 2

i Coastal Research Unit, Department of Science, The Polytechnic of Wales, Pontypridd, Mid Glamorgan, South Wales (U.K.) -' Geography Department, University of Maryland, College Park, MD 20742 (U.S.A.)

(Received January 11, 1984; revised and acceptcd June 21, 1984)

ABSTRACT

Williams, A.T., Evans, N.H. and Leatherman, S.P., 1985. Genesis of Fire Island foredunes, New York. Sediment. Geol., 42: 201-216.

There is speculation concerning the origin of the large foredunes on Fire Island National Seashore, New York. A number of sedimentological techniques were employed to address this question. Samples were taken from the dune top, dune base (of unknown origin), and mid-tide beach area at 30 locations along the seashore for comparison purposes. Cumulative weight percentages for each sample were calculated and plotted on probability paper. The majority of unknown sands have one saltation population, like the dune sands, whereas beach sands characteristically have two sahation populations with a break at or around 2 q~. Statistics show clear separation of the two (aeolian and hydraulic) environments. In all cases, the unknown sands group together with the dune sands, leaving beach sands clearly separate. Linear discriminant analysis and the Mahanalobis D 2 statistic proved to be conclusive techniques, clearly placing the unknown sands in the dune environment. Heavy/light mineral ratios were compared, indicating that dune and unknown sands are of the same origin. Roundness counts were made, but no statistically significant difference in roundness values was found between the environments. Grain surface textures were studied by the S.E.M. in an attempt to establish distinctive surface feature differences. A definite qualitative difference is observed, but this is not substantiated quantitatively. Structural analysis of the dunes proved to be of little significance because few cross-beds and only a small number of sand ripples were found. Where measured, small ripple numbers indicate an aeolian origin for the foredunes. Finally, inferences were made concerning the aeolian nature of the foredunes from a study of Ammophila breviligulata rhizomes and buried tire tracks.

INTRODUCTION

A l t h o u g h m u c h w o r k has b e e n u n d e r t a k e n on t h e geo logy o f L o n g I s l and , N e w

Y o r k ( R a m p i n o , 1978) a n d the b a r r i e r c o m p l e x a l o n g its s o u t h e r n s h o r e ( L e a t h e r -

m a n a n d Jone ja , 1980), genes i s o f the b a r r i e r f o r e d u n e s has n o t b e e n f i rmly

e s t ab l i shed . F i re I s l a n d is a g o o d e x a m p l e of a d u n e - d o m i n a t e d bar r i e r , a n d

f o r e d u n e or ig in (i.e., f o r m e d by w i n d or wave) is con t rove r s i a l . M a s s a (1981)

0037-0738/85/$03.30 © 1985 Elsevier Science Publishers B.V.

202

contended that the large foredunes (up to 9 m above mean sea level) were due to hydraulic action rather than aeolian processes. Because of the many townships on Fire Island, there has been much human disturbance of the dunes. Thirty sampling sites were established at regular intervals (Fig. 1) along the length of Fire Island: sites disturbed by human occupation were avoided in the sampling program. At each location three sand samples were collected and bagged. The samples at the dune top were labelled A; those collected from a depth of up to 4 m into the dune (dune base) were labelled B; and those from the mid-tide beach area were designated C samples. The samples, therefore, came from two known environments (A samples represent- ing an aeolian environment and C samples representing a marine environment) and one from an unknown origin (B samples). A series of tests were conducted for the purpose of environmental discrimination of the B samples.

M ETHODOLOGY

Mineralogical content of Fire Island sands was studied because it reflects source materials, and size distribution of particular minerals are reflective of the history of

transport and deposition. For each 0.25 (a sieve fraction of the A, B and C samples a portion of grains was randomly selected using a scoop and spread as evenly as possible across the viewing stage of a petrographic microscope. The presence of l ight /dark minerals was noted; 100 grains were counted. For each of the sample sieve sizes the calculated relative percentages of black/white minerals was expressed as a ratio. The provenance of dark/ l ight minerals in the 2.5-3.0, 3.0-3.5, and 3.5-4.0 c) grades for each of the three environments were tested. As both A (dune) and C (beach) environments were known, a students' t test established from which of these two environments the unknown (B) sands originated. Consequently, a null hypothesis (H0) was established: that there is no difference in the dark/ l ight mineral ratio of the A (dune) and B (unknown) samples; they are, therefore, both from the same population (i.e. dunes). Students' t tests were also carried out for the other two combinations: A (dune) vs. C (beach) and B (unknown) vs. C (beach).

Roundness counts for grains in all sieve sizes were made for A, B and C sands. For each sieve size, sample grains were spread on a gridded surface and examined under a binocular microscope. A count of 100 grains was made for each sieve size per sample, following the grid pattern from top left to bottom right in order to avoid bias. The classification of roundness followed that of Powers (1953).

Quartz sand selected for Fourier shape analysis consisted of the 2.0 ,# size fraction of the original sample- - the modal group (Ehrlich et al., 1980). One hundred grains for each A, B and C sample were randomly selected. Quartz grain silhouettes were optically projected via an epidiascope and manually traced onto a sheet with a radiating pattern of 48 equiangular lines. The 48 points of intersection between the pattern lines and shape curves were digitized and punched as X - Y coordinates using a flat-bed plotter. Data were subjected to Fourier analysis via a computer program

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developed by R. Ehrlich (pers. commun,, 1982). Cumulative weight percentages of sieved sands for the 30 A~ B and C samples

were plotted on arithmetic probability paper following the techniques used by Visher (1969). Grain-size parameters were derived by moment measure statistics. Each of the four grain-size distribution parameters was drawn for all A, B and C sites and results analyzed to determine whether any apparent differences occurred between the samples for the same parameter, for sample to sample fluctuations, and along the length of the study area fol each individual parameter. A t test indicated significance between different samples for each of the parameters. Environments were tested in the following order for each parameter: A /B , A / C and B/C. A further successful test of environmental discrimination was made using the bi-variate plot of skewness and standard deviation in the form of a modified Craig diagram (LeRoy, 1981).

Two forms of discriminant analysis were carried out: Mahalanobis generalized distance (D 2) function and linear discriminant analysis. The Mahalanobis gener- alized distance (D 2) statistic is a measure of the "distance" between the multivariate means of the two sample clusters (Mahalanobis, 1927). In the present study statistical parameters obtained for the various environments were analyzed by the D 2 statistic. The D 2 value was converted into a "normalized" F value via a

multiplication factor and F values were obtained from standard tables. In linear discriminant analysis all grain-size parameters are combined into a

single equation. This analysis was performed by standard SPSS computer program to determine sand origin, and the discriminant function coefficients for Fire Island data were identified. By substituting the moment measure values for each sample, discriminant scores were achieved. R values were checked to determine the per- centage of cases correctly classified. Finally, the discriminant functions for each of

the three environments were represented by frequency polygons. Three sample sites (5, 10 and 16) were chosen for S.E.M. analysis (Fig. 1). At each

location the same number of quartz grains was chosen for A (dune), B (unknown), and C (beach) samples. After cleaning, each of the samples was viewed under a petrographic microscope to choose unicrystalline grains. Twenty-five grains were mounted on each stub in a 5 x 5 pattern. Prior to scanning, each grain was analyzed by X-ray dispersive analyzer to confirm its identity. A 400 ,~ coating of gold was applied to the samples as a conducting layer. Ninety grains were scanned in the Cambridge S.E.M. Comparisons of the characteristic textures of A, B and C sands were attempted by using a semi-quantitative approach outlined by Setlow and Karpovich (1972). In this method, the degree and area of development of separate microtextures were established for each grain and examined. Both properties were assigned numerical coefficients, and the two numbers when multiplied together gave final values of between 0 and 50, which express the overall presence of the different

surface features on individual sand grains. Dune structures were studied in the field in an attempt to determine the origin of

the unknown (B) sands. Sand ripples from the beach and dunes were measured, and

205

ripple index (RI) and ripple symmetry index (RSI) were calculated as diagnostic parameters (Tanner, 1967). Cross-bedding has also been cited as an important structural parameter, and azimuth dip angles of the cross-beds were measured (Goldsmith, 1978). Finally, the environmental significance of Ammophila brevili- gulata was assessed, as it was encountered at depths of 5 m or more in dune pits.

R E S U L T S A N D D I S C U S S I O N

Comparisons of heavy-mineral content for each of the 30 sample sites for the three fine-grained sieve sizes yielded inconclusive results (Evans, 1983). No one environment exhibited consistently higher percentages of dark minerals. For two of the groups (2.5-3.0 and 3.5-4.0 g,), C samples (beach) contained a higher average percentage of dark minerals than the other two environments. In the 3.0-3.5 range, B (unknown) sands contained the highest average percentage of dark miner- als. In an analysis of environment of deposition of B (unknown) sands, a Mann-Whitney U test was conducted on the dark/ l ight mineral ratio; the results were inconclusive.

Comparison of roundness values for all sieve sizes per sample and the mean roundness value of the overall sample revealed no significant trend or difference. In addition, no discernible correlation was readily apparent between roundness value and grain size. A t test was conducted with the null hypothesis (H0); there is no difference in roundness values between any paired environments (Table I).

Fourier grain-shape analysis results for location 21 at harmonic 18 showed that shape-frequency histograms for A and B sands are very similar, while different from those of the C sands (Fig. 2). Both 21 A and 21 B display strong modes on the left characterized by relatively low amplitude values for all harmonics. This positively skewed distribution indicates a collection of smooth, highly abraded grains (Brown et al., 1980; Hudson, 1980; Riester et al., 1982). Kuenen (1959) demonstrated that significant abrasion of quartz sand occurs under aeolian conditions, whereas grains in water are relatively immune to abrasion. The most probable environment in the

T A B L E I

Student ' s t-test values for m e a n roundness of Fire Is land sands

Env i ronmen t s t value

A / B - 0.38706

(n.s.) A / C - 1.4796

(n.s.) B / C - 1.0738

(n.s.)

Degrees of f r eedom = 58. n.s. = not significant. A = dune; B = dune base; C = beach. N u m b e r of samples

in each case = 30.

206

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Fire Island area for such aeolian abrasion is coastal dunes. In contrast, the

shape-frequency histogram for sample 21 C displays a strong mode on the right

(high-amplitude) side of the histogram, indicating rough, irregular-shaped grains. These are characteristic of f luvial /marine environments, where the degree of abra-

sion is significantly low. Four other locations were also analyzed by the same technique and yielded similar results.

Log probabili ty plots for all 30 sample sites reveal conclusive results (Fig. 3). A

(dune) sands were expected to reveal a single saltation population accounting for

about 98% of the sample (Visher 1969). Out of the 30 sample sites only 2 (stations 15 and 19; Fig. 1) did not conform to this trend. Visher (1969) suggested that beach

" C " sands would have two distinct saltation populations with a break at or near to 2

,~. In the present study 21 out of the 30 beach sands show this trend. For the unknown (B) sands 25 out of the 30 samples exhibit one saltation populat ion

(indicative of an aeolian origin), with only stations 10, 15, 16, 19, and 23 (Fig. 1) having two saltation populations. Therefore, the results suggest that 84% of the

sands in the unknown category are likely to have an aeolian origin.

Using moment measure statistics, grain-size parameters were plotted in six different bivariate combinations. Following " t " tests conducted on mean, standard

deviation, skewness and kurtosis (Table II), s tandard deviation and skewness were

shown to be the main discriminators (Fig. 4). Of the two parameters, skewness,

proved to be most significant for environmental discrimination. The majority of A

and B sands have positive skewness (27 /30 of A sands and 24 /30 of B sands),

whereas 25 /30 of the C sands are negatively skewed. Clearly, the A (aeolian) and B

(unknown) sands display similar trends of positive skewness, indicative of dune

sands. On the other hand, mid-tide (C) samples are negatively skewed (coarse tail), which is more consistent with a beach sand origin.

Two forms of discriminant analysis were carried out: Mahalanobis generalized

TABLE 11

Student's t-tests associated with moment measure statistics

Location Statistical measure

Mean Standard Skewness Kurtosis ( ,~ ) deviation

(~,)

A/B 0.710 0.22 1.34 - 2.3118 (n.s.) (n.s.) (n.s.) (n.s.)

A/C 3.904 0.375 6.34 - 0.6307 (S) (n.s.) (S) (n.s.)

B/C 3.32 0.189 4.50 - 0.441 (S) (n.s.) iS) (n.s.)

A = top of dune; B = dune interior; C = beach; (n.s.) = not significant: (S) = Significant at 0.001 level.

209

distance (D 2) function (Mahalanobis, 1927) and linear discriminant analysis. Al- though attention was focused only upon A (dune), B (unknown), and C (beach) samples, the D 2 test was carried out on a range of other known environmental combinations (e.g. overwash, inlet and flood-tidal delta) in order to substantiate the validity of the test as an environmental discriminant. Results for Fire Island samples are conclusive (Table III). Both A (dune) vs. C (beach) and B (unknown) vs. C (beach) are differentiated at the 0.001 level of significance, whereas there is no

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210

s i g n i f i c a n t d i f f e r e n c e b e t w e e n B ( u n k n o w n ) vs. A (dune ) . L i n e a r d i s c r i m i n a n t

a n a l y s i s p r o v i d e s a n o t h e r c o n c l u s i v e t e c h n i q u e for e n v i r o n m e n t a l d i s c r i m i n a t i o n . R

v a l u e d i s c r i m i n a n t scores for A a n d B s a n d s a re nega t i ve ; t hose for C s a n d s a re

pos i t ive . U n k n o w n s a n d s p lo t o u t in the n e g a t i v e ( d u n e ) sec tor , a n d the s h a p e s of

f r e q u e n c y p o l y g o n s d r a w n for the two p o p u l a t i o n s (A a n d B) a re q u i t e s im i l a r (Fig .

5). All of the u n k n o w n (B) s a n d s were a s s i g n e d b y a n a l y s i s to the d u n e g r o u p ( T a b l e

IV). T h e l i n e a r d i s c r i m i n a n t f u n c t i o n u sed to d i s t i n g u i s h b e a c h f r o m d u n e s e d i m e n t s

in the F i re I s l a n d a r ea is R = - 0 . 9 8 1 4 m e a n - 7 . 1 6 0 8 s t a n d a r d d e v i a t i o n - 0 . 3 7 4 4

s k e w n e s s - 0 .0819 k u r t o s i s + 4 .21691.

S a n d - g r a i n su r f ace t ex t u r e was s t u d i e d by S .E.M. , a n d f ea tu r e s were a r r a n g e d in

the f o r m of a check l i s t (Fig. 6). A l t h o u g h a d e f i n i t e q u a l i t a t i v e d i f f e r e n c e b e t w e e n

the t h r ee e n v i r o n m e n t s was o b s e r v e d , it c o u l d n o t be s u b s t a n t i a t e d q u a n t i t a t i v e l y . A

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d e l i n e a t e d b y d i f f e r e n c e s in s u r f ace t e x t u r e b e t w e e n s ample s .

T h e lack of s t r a t i f i c a t i o n m a d e e n v i r o n m e n t a l r e c o n s t r u c t i o n on th is ba s i s im-

p rac t i ca l , a l t h o u g h c e r t a i n s u g g e s t i o n s c a n b e m a d e f r o m c o n t r i b u t o r y f ield ev idence .

D u n e i n t e r i o r s a n d r ipples , a l b e i t few in n u m b e r , h a v e r i p p l e s y m m e t r y ind ices vs.

r i pp l e i nd i ces t yp i ca l of a w i n d o r ig in (Fig. 7). A n t h r o p i c a l s t r uc tu r e s , n a m e l y t i re

t r acks , were f o u n d in the d u n e , b u t m o s t s e d i m e n t a r y s t r u c t u r e s were o b l i t e r a t e d by

TABLE 111

Discriminant functions and probability values obtained for some known environments

Environment D of F Mahalanobis D 2 MF F value

O(12) v T(17) F4,z4 2.95 1.56 4.18 (0.0l} O(12) v C(30) F4,3v 3.58 1.98 7.09 (0.001) O(12) v A(30) F4,37 7.72 1.98 15.30 (0.001) O(12) v 1(8) F4.1~ 4.11 1.00 4.11 (0.025) T(17) v C(30) F4,42 4.80 2.53 16.94 (0.001) T(17) v A(30) F4,42 5.15 2.53 13.04 (0.001) T(17) v I(8) F4.20 5.66 1.18 6.69 (0.01) C(30) v A(30) F4.55 6.18 3.56 21.98 (0.001) C(30) v 1(8) F4,33 9.92 1.45 14.36 (0.001) A(30) v I(8) F4,33 17.34 1.45 25.99 (0.001 ) B(30) v I(8) F4,33 15.21 1.59 24.18 (0.001) B(30) v T(17) F4.4~ 4.65 2.53 11.76 (0.001) B(30) v O(12) F4.ss 5.43 1.98 10.75 (0.001) B(30) v A(30) F4,55 0.18 3.56 0.64 (n.s.) B(30) v C(30) F4,55 2.99 3.56 10.64 (0.001)

Key: Samples obtained from the following known Environments: O = overwash; 1 = inlet: T = flood tidal

delta; A = dune; B = base of dune; C = beach. Figures in brackets relating to Environment = No. of samples obtained. Figures in brackets relating to

the F value = significance level, n 1 = size of sample 1, n 2 = size of sample 2, p = No. of variables (4);

Multiplication Factor (MF) = (nl + n2 - p - 1) nln 2 × . Fvalue, t~t,,,+~ 2 t, 1~ = MF× D 2 ( n l + n ~ - 2 ) p h i + n 2

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The f o l l o w i n g a b b r e v i a t i o n s o f su r face f e a t u r e s are g i ven :

l . c . f , = l a rge concho ida l f r a c t u r e s .

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t . p , = ( n o n - o r i e n t e d ) p r e c i p i t a t i o n ,

Fig, 6. Histograms showing degree and intensity of development of quartz grain surface features.

213

Amrnophila rhizome growth or were never distinct since the sand accumulated around grass stems with upward dune growth and development. Rhizomes were found deep in the dunes (up to 5 m below the crest), which suggests that aeolian sand accumulation was responsible for the Fire Island foredunes.

Dip and azimuth data for current beach and dune samples were recorded and plotted (Fig. 8) using a vector summation technique proposed by Pincus (1956). The beach orientation data corresponded with prevailing swash alignment (roughly east-west), while the dune data boxed the compass in accord with wind rose data for

the area.

TABLE IV

Predictive results from linear discriminant analysis (by % correctly classified)

Actual group Number of cases Predicted group membership

group 6 group 7

Dune 30 30 0 group (6) 100% 0%

Beach 30 0 30 group (7) 0% 100%

U ngrouped cases 30 30 0

(base of dune) 100% 0%

For the predicted group membership column, the upper figure relates to the number of cases classified

and the lower figure relates to the % correctly classified.

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R I - R IPPLE T N O E X

R S T - R I P P L E S Y M M E T R Y I N D E X

S - SWASH

Wi - W I N D

C - C U R R E N T

Wa - WAVES

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RSI= ~ RI'= ~

A F T E R TANNER (1967 )

Fig. 7. Plot of RI vs. RSI for internal dune and surface aeolian sand ripples (after Tanner, 1967).

214

CONCLUSIONS

A battery of techniques was used to determine the mode of origin of Fire Island foredunes. Mann Whitney U tests on dark/light mineral ratios show that no

difference exists between A and B sands, but differences at the 0.05 and 0.01 levels

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215

exist in the finer grain sizes for A / C groupings and B / C groupings. No correlation

exists between roundness values and grain size/environment.

Grain-shape analyses via the Fourier series indicate strong left-hand side mode

with low-amplitude values for A and B sediments that suggest an aeolian origin. The

opposite relation for the C samples fits an aqueous environment. Bivariate plot analyses show that skewness is a good discriminator of environments, wherein A and

B sediments have a positive skew (aeolian) and C sediments have a negative skew

(hydraulic). Similarly, two saltation populations with a break at 2 q~ grain size were recorded for the C sediments; there is no such break for the A and B sediments. The

D z statistic differentiates between A / C and B / C environments at the 0.001 level but shows no difference for the A / B groupings. A linear discriminant function aided in

differentiation between beach and dune sediments with 100% accuracy and showed

that all B sediments plot in the aeolian field. Use of the S.E.M. shows that grains

had undergone a varied history, but the technique was unable to distinguish

quantitatively between environments. The presence of Ammophila rhizomes and Prunus roots deep (up to 5 m) in the

dunes is taken as evidence that blowing sand built up the dune. Dune azimuths box

the compass in accord with wind rose patterns; beach azimuths were aligned roughly

east-west. If the dunes had been water-formed, dune azimuths would follow the

latter trend. Measurements of a small number of preserved ripples in the dunes showed that 7 out of 9 could be classified as being of aeolian origin.

The concensus derived from this large body of data is that the Fire Island

foredunes are definitely of an aeolian origin. Management practices, such as dune stabilization, should reflect their mode of creation in order to take full advantage of

natural process in barrier manipulation projects.

ACKNOWLEDGEMENTS

We are indebted for the help provided by the Fire Island National Seashore

personnel, particularly Lennie Bobinchuck and Rocky Norris. Keith Abbott, Andrew

Bedgood and O.A.C. Williams helped with sieving and roundness data analysis;

Peter Morgan, Paul Grant, Peter Bull, and Brian Whalley helped clarify our S.E.M.

ideas.

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