Environ. Sci. Techno/. 1995, 29, 740-750
Spchroaous Response of Hydrophobic Chemicals in Herring 6ull Eggs from the Great lakes D A N I E L W. S M I T H BCM Engineers, Inc., One Plymouth Meeting, Plymouth Meeting, Pennsylvania 19462
Introduction Discussions of recent chemical trends in the Great Lakes often observe that the rate of decline of certain organic chemicals has slowed or even reversed during the last halfdecade ( I - 7). For example, the declines of mirex, TCDD, DDE, and PCBs in Lake Ontario have reportedly slowed or ceased, and current levels are assumed to be maintained by external sources (8). Similarly,the decline of PCBs and DDT in Great Lakes salmonids is reported by some authors ( I , 5)to have stalled across the Great Lakes. Also of concern were recent data for hexachlorobenzene (HCB) in herring gull eggs, which increased at 9 of 10 Great Lakes sites in 1992 (9).
Herring gull eggs from Great Lakes nesting sites exhibit short-term changes in hydrophobic chemical concentrations that are synchronized within and between Great Lakes. At one Lake Ontario site, for example, short-term deviations from long-term trends for PCBs, DDE, mirex, hexachlorobenzene, and dieldrin in gull eggs tend to correlate significantly with each other, with these chemicals at another site in Lake Ontario, and with gull egg chemicals from Lakes Superior, Huron, and Erie. Similar comparisons made for other Great Lakes are also significantly nonrandom. This synchrony indicates that some large-scale force-here proposed to be weather patterns-controls short-term patterns of hydrophobic chemical concentrations in gull eggs across the Great Lakes. Among eight alternative hypotheses considered, the data are least incopsistent with the following mechanism: warm spring weather conducive to phytoplankton growth produces relatively uncontaminated phytoplankton, which in turn produce less contaminated food for the gulls during the critical period of egg yolk formation (and vice versa for cold spring weather).
On the basis of such observations, some authors have concluded that the Great Lakes may be reaching a “new equilibrium” with external sources (refs 1, 5, 8, but see ref 10). Because it has been suggested by some authors that these chemicals pose a significant risk at current levels (for differing views of risk levels, see refs 4 and 111, the observation of slowing and sometimes reversal of the rate of contaminant decline in the Great Lakes has prompted considerable concern and proposed regulatory activity (4, 5, 12). However, in addition to the perceived slowing in the rate of chemical decline, the available data allow other insights into spatial and temporal trends. Specifically,these data indicate that (A) different chemicals appear to be behaving synchronouslywithin at least one Great Lake (Lake Ontario) (DDT and PCBs are also varying synchronously in Lake Michigan salmonids; ref 10) and (B) some chemicals (e.g.,DDT and PCBs in salmonids, HCB in gull eggs) appear to be behaving synchronously across Great Lakes. By logical extension,it is hypothesized in this paper that (C) different chemicals may be behaving synchronously across different lakes. This hypothesis was tested using chemical data from herring gull eggs, which have been collected and analyzed for important chemicals at various sites on the Great Lakes almost continuously since the late 1970s( 9 , 1 3 ) . Gulls living on the Great Lakes forage primarily upon forage fish, smelt, and alewives. Chemicals contained in Great Lakes food chains pass from fish to adult gulls to gull eggs; therefore, herring gull eggs can be used to monitor spatial and temporal patterns of chemicals in the Great Lakes (14).In this respect, data from herring gull eggs represent the most complete and extensive data set available for chemicals in biota of the Great Lakes.
Methods Gull egg data were taken from published reports of the Canadian Wildlife Service (9, 13). Information and references on analytical methods and sampling can be found in these reports. My analysis considers data for five hydrophobic organic chemicals (PCBs,DDE, HCB, dieldrin, mirex) from two sampling sites for each Great Lake. The sites are depicted in Figure 1. Data from Muggs Island and Leslie St. Spit, two small islands in Toronto Harbor, were combined to make one long-term data set for Toronto Harbor. Because the goal was to consider short-term deviations from the long-term trend, gull concentration data were
740 1 ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 29, NO. 3, 1995
0013-936W95/0929-0740$09.00/0
@ 1995 American Chemical Society
FIGURE 1. Sites of herring gull colonies from which eggs were taken.
detrended in the followingmanner. Along-term trend was determined using least-squares regression of the logtransformed chemical concentration versus time. Percent deviation from this line was calculated as (observed - predicted)/predicted x 100 This method essentially rotates the graph such that the long-term trend line becomes the X-axis with percent deviation from the long-term trend plotted on the Y-axis. The process is depicted in Figures 2-4. Figure 2 shows therawdataforLakeSuperiorPCBs, whereasFigure3shows those data plottedversus the long-termtrend lines for each site. Looking closely at Figure 3, it is apparent that the observed PCB data from both sites tend to covary synchronously about their respective best-fit lines. However, this correlation between both sites is shown most clearly in Figure 4, which depicts the percent deviation over time for both sites. The analytical process just described measures shortterm deviation from the long-term trend or normalized residuals from the regression. These deviationswere tested for normality (Shapiro-Wilk test), which determined that log-transformed data more closely follow a normal distribution. Thus, all deviations were converted to natural logs prior to correlation analysis. Correlation analysis was conducted with STATGRAPHICS statistical package for personal computers. Only deviations from 1978 and after, when external point sources were generally well controlled for all chemicals in all lakes, were subjected to correlation analysis. Since it was hypothesized above that short-term fluctations in
different lakes would he positively correlated, one-tailed tests were used, and significant negative correlations were ignored. The large dataset produced 1250pairwise comparisons, too many to be discussed at length. Thus, the analysis will focus upon the frequency of significant r (correlation coefficient) values. A 10%probability levelis used to define a statistically significant correlation. Observed frequencies of significant correlations should he compared to 10% which is the frequency of significant correlations expected due to chance when uncorrelated variables undergo multiple pairwise comparisons. Observed frequencies of significant correlations were compared to expected frequencies with the x2 test when the expected frequencies were 4 or larger. The Binomial Test (13was used in cases in which expected frequencies were smaller than 4 (16).
Results and Discussion Correlations between Short-Term Deviationswithin and between Lakes and Chemicals. Short-term deviations in a chemical within a lake were almost always correlated (Figure51. Forexample,PCBvaluesatthetwositesinLake Superior deviate from the long-term trend line by about the same amount and at about the same time (Figure 3, r = 0.844, p < 0.001). Similar synchrony appears for most pairwise same lake/same chemical comparisons (Figure 5). The frequency of significant correlations was higher than expected due to chance in Lakes Huron, Ontario, and Superior ( p < 0.001, BinomialTestl,and these comparisons in Lakes Michigan and Erie were marginally significant ( p < 0.10, Binomial Test). Thus, at different sites within the same lake, the same forceb) appear(s1to be controllingthe concentrations of any one chemical in gull eggs. VOL. 29. NO. 3.1995 I ENVIRONMENTAL SCIENCE h TECHNOLOGY * 741
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mirex behaved synchronously over the short-term at both sites from Lake Ontario;more specifically, out of 40 pairwise comparisons of normalized deviations for different chemi-
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FIGURE 5. Percent of significantlp < 0.10) correlationsfor inhalake comparisons of percent deviations. For example, E vs E are comparisons of deviations within and between the two Lake Erie sites, end 0. M. S,and H refer to intralake comparisons for lakes Ontario, Michigan, Superior, and Huron. respectively. Observed proportions of significant correlations were compared to 10%. the proportion expected with a random association between two variables. *, **, *+*.and **** refer to observed proportions that occur with a probability of 0.1.0.01, 0.001. or O.WO1 or less, respectively, due to chance. Same lakelsame chemical comparisons based on five comparisons. Same lakeldiflerent chemical comparisons based on 40 comparisons.
cals in Lake Ontario, 36 were significant at the 0.10 probability level (Figure 5). Short-term dynamics of hydrophobic chemicalswerelessorderlyinthe otherGreat Lakes, although these comparisons were still highly nonrandom. That is, the frequency of significant correlations was much higher than lo%, the frequency that would be
expected due to chance (p < 0,001for all lakes, Figure 5). These results suggest that short-term changes in different hydrophobic contaminants within the same lake are controlled to some degree by the same force(s). Comparisons of the short-term dynamics of a single chemical from different lakes (Le., different lake/same VOL. 29. NO. 3. 1995 I ENVIRONMENTAL SCIENCE &TECHNOLOGY m 743
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chemical comparisons) also producedaveryhigh frequency of significant correlations (Figure 6). For example, Figure 7 shows DDE in herring gull eggs from three sites in Lakes Superior, Ontario, and Huron. At these three sites, DDE concentrations vary in a synchronous manner; 11 of 12 interlake comparisons had correlations at the 0.05 level or less. Again, these results suggest that the same force(s) control(s1asinglechemical's dynamicsover theshort-term in all the Great Lakes. Finally, the synchrony also occurs with different chemicals in different lakes, especially when compared to Lake Ontario. Pairwise comparisons between Lake Ontario and Lakes Erie, Huron, and Superior produced very high frequencies of significant correlations (Figure 71, almost as high as intralake comparisons for these three lakes. For all comparisons between all Great Lakes, about 35% of 800 744. ENVIRONMENTAL SCIENCE &TECHNOLOGY I VOL. 29. NO. 3.1995
different lake/different chemical comparisons were significant ( p < 0.10, one-tailed test). Many of the nonsignificant correlations were based on comparisons to data from Lake Michigan, whose gull eggs showed minimal relationship to other lakes. When Lake Michigan data are removedfromthe dataset,40%ofallinterlakecomparisons are significant at the 0.10 level. Significant correlations were more frequent than expected in all lake to lake comparisons except for comparisons to Lake Michigan. Great Lakes Pattern of Short-Term Deviations. It is useful to describe the pattern apparently running through chemical concentrations in gulleggs so that potential causal agents can be identified. A Great Lakes pattern of shortterm deviations was developed as the mean of all percent deviations for all chemicals at all sites (Figure 8). These mean deviations over time are decidedly nonrandom,
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Year FIGURE 8. Mean percent deviation for all chemicals at all sites with WO confidence intervals. The confidence intervals were based on a log-normal distribution.
especially in certain years. If concentrations of gull eggs were behaving independently, only one of 10 years should have a 90% confidence interval that does not include zero. As shown from the graph, more than half the years deviate significantly from zero. Compared to the long-term trend, 1980, 1983, and 1987 were years in which almost all chemicals were well below expected values, whereas 1982, 1991,and 1992were years in which chemicalconcentrations were relatively high compared to the long-term trend. The mean pattern also demonstrates two important points with respect to concern about the recent rates of decline from 1989 to 1992. First, periods of apparent slow depuration have occurred before, notably 1978- 1982. During this period (except for 19801,chemical concentrations in gull eggs were higher than expected. Second, this period of apparent slow depuration did not affect subsequent years. The period 1983-1988 was a period when chemical concentrations tended to be lower than expected from the long-term trend. It should be mentioned, moreover,that the same pattern of short-term changes is superimposed upon the sitespecific and chemical-specificbackground, much like the same melody played in different keys. That is, while most chemicals in gull eggs from the Great Lakes follow the same pattern over time, the magnitude of concentrations (i.e., the placement of the pattern on the Y-axis) depends on the concentration of that chemical at that site (e.g., Figure 9). Therefore, different sites tend to retain their relative ranking in concentrations over the long term even as concentrations are varying by &50% over the short term. This overall pattern of short-term dynamics in egg chemical concentrations can be interpreted as follows. The concentration of a hydrophobic chemical in gull eggs is a function of site-specific concentrations of that chemical and a temporally variable force that affects the bioavailabilityand/or concentration of that chemical over the short term. The correspondence of short-term fluctations for different chemicals within and across the Great Lakes
indicates that the temporally variable force that controls short-term changes at any one site also controls concentrations of other chemicals at that site, at other sites within that lake, and in other Great Lakes. Thus, this temporally variable force driving short-term changes in gull egg chemicals must be (a) geographically extensive: able to simultaneouslyaffect concentrations over a very wide geographic area (e.g., Lakes Huron, Superior, Erie, and Ontario simultaneously); (b) chemically inclusive: able to affect multiple contaminants concurrently; and (c) adequate in strength: able to significantly alter gull egg chemical concentrations (e.g., as much as 100%above predicted) and overwhelm site-specific forces driving chemical concentrations. The force must also be similarly strong in both lakes dominated by atmospheric inputs and lakes whose chemical mass balances are dominated by nonatmospheric inputs. Disproof of Alternative Hypotheses. Of the explanations commonly proposed to explain the recent slow decline of hydrophobic chemicals across the Great Lakes, none satisfythe requirementslisted above. For example, consider the following hypotheses: (A) Inputs from point sources are slowing the rate of decline (5). This hypothesis is incompatible with all three conditions. No single point source supplies chemicals to Lakes Superior,Huron, Erie, and Ontario, and multiple point sources would not be coordinated. No point source simultaneously supplies all these chemicals to any Great Lake at the same time. No point source represents a significant source of any of these chemicals to any Great Lake.
(B) Terrestrial nonpoint sources (e.g., hazardous waste sites andlor urban runoff) are retarding the rate of chemical decline. This hypothesisdoes not meet the three conditions for the same reasons as in discussion of alternative hypothesis A above. ( C ) Multiple chemical-specific, nonpoint sources are to blame-e.g., the Niagara River for mirex; transformers and VOL. 29, NO. 3, 1995 / ENVIRONMENTAL SCIENCE &TECHNOLOGY
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Year FIGURE 9. DDE concentrations in gull eggs from one site each in Lakes Huron, Superior, and Ontario. hazardous waste sites for PCBs; Central America for DDE. This hypothesis fails to satisfy conditions a-c because disparate sources would not have co-incident effects over the same geographic range (unless they were all atmospheric) and because external sources are not sufficient to cause large-scalechanges in ecosystem concentrations.The latter point is discussed more fully below. (D) Effects of zebra mussels on chemical bioavailability are causing the slowing of chemical decline (17). While zebra mussels may have an important effect on contaminant recycling, their effects are limited to the last half-decade and only Lakes Erie and perhaps Ontario. Zebra mussels cannot explain the synchrony between Lakes Superior and Ontario over the period from 1978 to 1992. Four other hypotheses could potentiallymeet the criteria, as discussed further below, and must be considered in detail. These hypotheses attribute the slowing of chemical decline to: (E) Influx of contaminated sediments from embayments and tributaries. (F) Atmospheric depositionholatilization. (G) Resuspension of bottom sediments, alteringrecycling of chemicals already in the lake. (H) Food chain effects, affecting the bioavailability of chemicals already in the lake. Unlike the first four hypotheses, the latter four could affect all chemicals synchronously. Furthermore, hypotheses E-H are ultimately attributable to an overarching force-large-scale weather patterns-that could explain synchrony over the entire Great Lakes. For example, very rainy weather could cause deep scouring and transport of contaminated bottom sediments, as reportedly occurred in the Saginaw River (12). Rates of volatilization and deposition depend on temperature and precipitation. 746
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Unusually vigorous water column mixing, associated with severe weather, could resuspend in-lakesediments and their adsorbed chemicals. Weather patterns can also affect the transfer of chemicals in the food chain in a number of ways: by affecting fish growth and primary productivity levels (18);by altering food chain structure, e.g., alewife die offs due to cold weather (19,20);and by affecting chemical concentrations in phytoplankton (21). However, hypothesis E-influx of contaminated sediment from embayments or rivers-can be rejected as alikely cause of the synchrony for two reasons. First, this hypothesis could not explain the synchronous response of Lake Superior, which gets most of its PCBs, DDE, mirex, HCB, and dieldrin from the air. Lake Superior really has no contaminated tributaries that could supply large amounts of these chemicals (Table 1). Second, the mass balances for the Great Lakes indicate that inputs of chemicals from external sediments are insufficient to cause appreciable changes in ambient whole-lake concentrations and gull eggs (Table 1). For Lake Ontario, for example, loading of PCBs (and other chemicals) from tributaries is far higher than for Lake Superior. Nevertheless, loading from tributaries-about 360 kglyr-is relatively minuscule compared to PCBs already in Lake Ontario-about 100 000 kg (26). Even very large changes in tributary loading could not appreciably affect total in-lake concentrations over the short term and thus, could not produce noticeable changes in chemical levels in the biota. The same mass balance considerations indicate that changes in atmospheric deposition andlor varying rates of volatilization also cannot cause the observed short-term dynamics in gull egg chemicals. While atmospheric deposition/volatilization are predominant factors in the external mass budgets for Lakes Superior and Huron, these
TABLE 1
Comparisons of External loading (kglyr) to Mass of Chemical Already in Lake Superior (ca. 1986) and Lake Ontario (ca. 1992) loadinglin-lake mass
external loads (kglyr) lake Superior Superior Ontario Ontario Ontario Ontario Ontario
compd tDDT
PCBs tDDT Dieldrin
HCB Mirex
PCBs
total atmospheric
total tributary
89.68 157.0a 9.5a 1.5a 1.18 0.3a 42.38
2.7a 54.0a 32.Ib 56.gb 41 .6b 11.Ob 343.8b
in-take
(kg)
atmospheric/tributary
water
sediments
33.2 2.9 0.3
3 333c 10 OOOd 320e 530e 101e 08 2256e
150OOc 4 900d 1882' 376' 376' 1 882' 3 765'
0.0 0.0 0.0 0.1
loadinglwater column
loading/total in lake
0.03 0.02 0.13 0.1 1 0.42
0.01 0.02 0.02 0.06 0.09 0.01 0.06
0.17
From ref 22. From ref 23. Based on ratio of concentrations of DDT to PCBs for water and sediment reported in ref 24 times respective PCB masses found in ref 25. From ref 25. e From water column data found in ref 23 times lake volume. From sediment concentrations found in ref 23 for the mass found in the top 20 mm, which represents the last 20-40 yr of deposition. The mass was then divided by 10 to account for reduced bioavailability of buried sediments and to make the comparisons to external loading conservative. This values produced are very conservative. For example, Wong et al. (26)recently estimated that Lake Ontario sediments contained over 100 000 and 35 000 kg of PCBs and DDT, respectively. a
fluxes are small relative to total mass of chemicals already cycling within those lakes. Atmospheric sources are even less important to total chemical burdens in Lakes Ontario and Erie. Total atmospheric loads of these chemicals generally make up only a small portion of total external loading (22-24). Atmospheric sources represent considerably less of the biota's total exposure due to external loading and chemicals already in the lake. Thus, even major year to year changes in atmospheric loadings would have little effect on total external loading to the lower Great Lakes and almost no effect at all on total exposure. Two other factors argue against the atmospheric loadingllosses hypothesis. First, atmospheric sources should be relatively stable from year to year. Second, the five chemicals (HCB, PCB, DDE, mirex, and dieldrin) vary considerably in volatility, suggesting that atmospheric depositionlvolatilization cannot cause the synchronousbehavior across lakes. The remaining two hypotheses depend on internal recycling of chemicals already in the lakes. Internal reservoirs of chemicals are sufficiently large so that small changes in the rate of recycling could cause the magnitude of changes noted in gull eggs for all chemicals simultaneously. For example, resuspension of bottom sediments during large storms can simultaneously uncover more contaminated sediments and increase water column concentrations. Very fine bottom sediments can be disturbed in severe storm events, providing the linkage between chemical concentrations in gull eggs and large-scaleweather patterns that affect Lakes Ontario, Huron, Superior, and Erie concurrently. However, sediment resuspension would probably be unable to produce the synchronyobserved across the lakes. For example, while both Superiorand Ontarioarevery deep, Lake Superior is about 80%deeper than Lake Ontario. Lake Superior sediments should, therefore, be more difficult to stir up. Once resuspended, however, Superior sediments should stay in circulation longer because they have a much longer journey back down to the bottom (27). Given the hydrodynamics of these systems, therefore, one would expect Lake Superior to respond less often and once disturbed to return to equilibrium less quickly than Lake Ontario. Applying the same reasoning to the synchronous behavior of Lakes Ontario and Erie, with mean depths of 85 and 19 m, respectively, renders the resuspension hypothesis even less tenable. However, sediments and the benthic nepheloid layer of the Great Lakes including Lake
Superior are regularly disturbed during periods of overturn (28,291,suggestingthat the synchrony could be caused by timing and strength of fall and spring mixing. Thus, the resuspension hypothesis can only be provisionally rejected. Ultimateand Proximal Causation: Weather and Food Chain Dynamics. The food chain hypothesis appears to be the best candidate for explaining the synchrony of gull egg chemistry. Swackhamer and Skogland (21) have described a relationship between PCBs and phytoplankton growth that could underlie the patterns observed in the gull data. PCB levels in fast-growingplankton are well below those of slow-growing plankton, potentially because the rate of plankton growth exceeds the rate that chemicals can be incorporated into the plankton cells. This relationship could provide a proximal mechanism to link largescale weather patterns to the inter- and intralake synchrony observed in the gull egg data. The rate and timing of plankton growth in the spring depends on a number of weather-driven factors, including water temperature, sunlight, mixing depth, and amount of nutrients brought up from deep bottom waters and sediments (30). Given this relationship, warm, sunny early springs could produce lower contaminant levels in phytoplankton, upper links in the food chain, and potentially, gull eggs (31-33). The opposite should occur with cold, overcast late springs. To test this hypothesis, data for the states of Minnesota and New York were obtained from the National Weather Service. Heating degree days, which measure the amount of heating energy required to warm homes and buildings, for January-April were calculated for weather zones closest to Lakes Ontario and Superior. The weather data show a high degree of similarity between sites, indicating that gross weather patterns are similar across the Great Lakes (Figure 10). Moreover, nearby weather patterns for these states appear to fit the gull egg data (Figure 10) of the nearest Great Lake. Short-term changes in heating degree days for the Great Lakes Region of New York were significantly correlated with average deviations for chemicals from Lake Ontario gull eggs since 1978 ( r = 0.50, p < 0.05, one-tailed test). Similarly, the average deviation for chemicals from Lake Superior also correlated significantly with deviations from the long-term trend in spring heating degree days from northeastern Minnesota ( r = 0.50, p < 0.05, one-tailed test). Thus, the data indicate that short-term weather patterns correlate with observed deviations in gull egg chemistry. This VOL. 29, NO. 3, 1995 /ENVIRONMENTAL SCIENCE &TECHNOLOGY 1747
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FIGURE 10. Relationship between deviations from long-term trends for chemicals and for weather. The mean chemical deviations for both sites and all chemicals from Lake Superior and for lake Ontario are compared to deviation from long-term trend in heating degree days from nearby weather stations in Minnesota and New York. Deviations from heating degree days have been multiplied by 4.
observation is consistent with the hypothesis that the synchrony of changes in concentration is driven by largescale weather patterns of late winter and early spring. Other mechanisms could conceivably form a link between weather and concentrations of gull egg chemicals. For example, air temperature affects fat metabolism in adult gulls (32), which in turn might affect egg lipid levels and the maternal transfer of chemicals to gull eggs. Lipid levels in eggs were sometimes correlated between lakes and to the short-term deviationsin chemical concentrations. These correlations could suggest that differencesin maternal lipid metabolism cause the synchronous response of chemicals in gull eggs. Alternately, harsh and warm springs could alter the type and, consequently, the chemical concentrations of the gulls food. Gulls are very opportunistic birds that eat what is available (34). Warm winters, for example, might affect the survival and growth of prey fish, causing gulls to shift feeding to less contaminated aquatic prey or terrestrial prey during warm springs. A last alternative hypothesis is that the synchrony could be an artifact of the statistical analysis used to generate the deviations from expected. Specifically, synchrony could have been caused by applying a log-linearregression, which implicitly assumes a first-order decline of chemicals over the long-term, to trends that may not be simple first-order reactions over time. It has been argued elsewhere (10,35) that long-term decline of PCBs in biota may be a combination of two first-order declines: a relatively fast decline linked to water depuration and a slower decline due to 748 ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 29, NO. 3, 1995
sediment depuration. Since both water and sediments contribute to bioaccumulation of these chemicals, chemical concentrations in biota might be expected to track a curved line (i.e., concave up) as water concentrations and their effect on biota concentrations wane over time (10). If the underlying long-term decline of chemicals in biota does fit such a curved line, the lack of fit between the data and the straight line tracked by the log-linear regression would tend to produce deviations from expected that are systematic and nonrandom. In this case, observed deviations would tend to be higher than expected at the beginning and end of the time period and less than expected in the middle. This lack-of-fit scenario roughly describes the average deviations for all lakes (Figure 8). However, while the mean deviations fit in a gross sense the pattern just suggested, there are notable exceptions. For example, deviations in 1980, 1982, 1984, and possibly 1989 run contrary to the pattern. In addition, year to year changes in chemical concentrations are highly nonrandom. For all sites on Lakes Erie, Ontario, Huron, and Superior, chemical concentrations in gull eggs tended to go up or down in a coordinated, nonrandom fashion (Table 2). This nonrandom behavior implies that some large-scale force affectsyear to year changes in chemical concentrations in gull eggs across these Great Lakes. Also, as with percent deviations from expected, year to year changes in concentrations correlate with changes in the weather (Figure 11). Chemical concentrations tended to go down when
TABLE 2
Proportion of Chemical Concentrations in Gull Eggs from Lakes Ontario, Erie, Huron, and Superior That Increased from Year to YeaP period
% increasing
1978-1979 1979- 1980 1980- 1981 1981- 1982 1982- 1983 1983- 1984 1984- 1985 1985- 1986 1986-1987 1987-1988 1988-1989 1989-1990 1990-1991 1991-1992
45 12 85 57 0 83 35 30 5 90 62 22 45 67
probability ~0.0001
