SOCR Data June2008 ID NI

Data Overview

Largemouth bass were studied in 53 different Florida lakes to examine the factors that influence the level of mercury contamination. Water samples were collected from the surface of the middle of each lake in August 1990 and then again in March 1991. The pH level, the amount of chlorophyll, calcium, and alkalinity were measured in each sample. The average of the August and March values were used in the analysis. Next, a sample of fish was taken from each lake with sample sizes ranging from 4 to 44 fish. The age of each fish and mercury concentration in the muscle tissue was measured. (Note: Since fish absorb mercury over time, older fish will tend to have higher concentrations). Thus, to make a fair comparison of the fish in different lakes, the investigators used a regression estimate of the expected mercury concentration in a three year old fish as the standardized value for each lake. Finally, in 10 of the 53 lakes, the age of the individual fish could not be determined and the average mercury concentration ofthe sampled fish was used instead of the standardized value. ( Reference: Lange, Royals, & Connor. (1993))

Data Description

• Number of cases: 53
• Variable Names:

1. ID: ID number
2. Lake: Name of the lake
3. Alkalinity: Alkalinity (mg/L as Calcium Carbonate)
4. pH: pH
5. Calcium: Calcium (mg/l)
6. Chlorophyll: Chlorophyll (mg/l)
7. Avg_Mercury: Average mercury concentration (parts per million) in the muscle tissue of the fish sampled from that lake
8. No.samples: How many fish were sampled from the lake
9. min: Minimum mercury concentration amongst the sampled fish
10. max: Maximum mercury concentration amongst the sampled fish
11. 3_yr_Standard_mercury: Regression estimate of the mercury concentration in a 3 year old fish from the lake (or = Avg Mercury when age data was not available)
12. age_data: Indicator of the availability of age data on fish sampled

Data Table

ID	Lake	Alkalinity	pH	Calcium	Chlorophyll	Avg_Mercury	No.samples	min	max	3_yr_Standard_Mercury	age_data
1	Alligator	5.9	6.1	3	0.7	1.23	5	0.85	1.43	1.53	1
2	Annie	3.5	5.1	1.9	3.2	1.33	7	0.92	1.9	1.33	0
3	Apopka	116	9.1	44.1	128.3	0.04	6	0.04	0.06	0.04	0
4	Blue Cypress	39.4	6.9	16.4	3.5	0.44	12	0.13	0.84	0.44	0
5	Brick	2.5	4.6	2.9	1.8	1.2	12	0.69	1.5	1.33	1
6	Bryant	19.6	7.3	4.5	44.1	0.27	14	0.04	0.48	0.25	1
7	Cherry	5.2	5.4	2.8	3.4	0.48	10	0.3	0.72	0.45	1
8	Crescent	71.4	8.1	55.2	33.7	0.19	12	0.08	0.38	0.16	1
9	Deer Point	26.4	5.8	9.2	1.6	0.83	24	0.26	1.4	0.72	1
10	Dias	4.8	6.4	4.6	22.5	0.81	12	0.41	1.47	0.81	1
11	Dorr	6.6	5.4	2.7	14.9	0.71	12	0.52	0.86	0.71	1
12	Down	16.5	7.2	13.8	4	0.5	12	0.1	0.73	0.51	1
13	Eaton	25.4	7.2	25.2	11.6	0.49	7	0.26	1.01	0.54	1
14	East Tohopekaliga	7.1	5.8	5.2	5.8	1.16	43	0.5	2.03	1	1
15	Farm-13	128	7.6	86.5	71.1	0.05	11	0.04	0.11	0.05	0
16	George	83.7	8.2	66.5	78.6	0.15	10	0.12	0.18	0.15	1
17	Griffin	108.5	8.7	35.6	80.1	0.19	40	0.07	0.43	0.19	1
18	Harney	61.3	7.8	57.4	13.9	0.77	6	0.32	1.5	0.49	1
19	Hart	6.4	5.8	4	4.6	1.08	10	0.64	1.33	1.02	1
20	Hatchineha	31	6.7	15	17	0.98	6	0.67	1.44	0.7	1
21	Iamonia	7.5	4.4	2	9.6	0.63	12	0.33	0.93	0.45	1
22	Istokpoga	17.3	6.7	10.7	9.5	0.56	12	0.37	0.94	0.59	1
23	Jackson	12.6	6.1	3.7	21	0.41	12	0.25	0.61	0.41	0
24	Josephine	7	6.9	6.3	32.1	0.73	12	0.33	2.04	0.81	1
25	Kingsley	10.5	5.5	6.3	1.6	0.34	10	0.25	0.62	0.42	1
26	Kissimmee	30	6.9	13.9	21.5	0.59	36	0.23	1.12	0.53	1
27	Lochloosa	55.4	7.3	15.9	24.7	0.34	10	0.17	0.52	0.31	1
28	Louisa	3.9	4.5	3.3	7	0.84	8	0.59	1.38	0.87	1
29	Miccasukee	5.5	4.8	1.7	14.8	0.5	11	0.31	0.84	0.5	0
30	Minneola	6.3	5.8	3.3	0.7	0.34	10	0.19	0.69	0.47	1
31	Monroe	67	7.8	58.6	43.8	0.28	10	0.16	0.59	0.25	1
32	Newmans	28.8	7.4	10.2	32.7	0.34	10	0.16	0.65	0.41	1
33	Ocean Pond	5.8	3.6	1.6	3.2	0.87	12	0.31	1.9	0.87	0
34	Ocheese Pond	4.5	4.4	1.1	3.2	0.56	13	0.25	1.02	0.56	0
35	Okeechobee	119.1	7.9	38.4	16.1	0.17	12	0.07	0.3	0.16	1
36	Orange	25.4	7.1	8.8	45.2	0.18	13	0.09	0.29	0.16	1
37	Panasoffkee	106.5	6.8	90.7	16.5	0.19	13	0.05	0.37	0.23	1
38	Parker	53	8.4	45.6	152.4	0.04	4	0.04	0.06	0.04	0
39	Placid	8.5	7	2.5	12.8	0.49	12	0.31	0.63	0.56	1
40	Puzzle	87.6	7.5	85.5	20.1	1.1	10	0.79	1.41	0.89	1
41	Rodman	114	7	72.6	6.4	0.16	14	0.04	0.26	0.18	1
42	Rousseau	97.5	6.8	45.5	6.2	0.1	12	0.05	0.26	0.19	1
43	Sampson	11.8	5.9	24.2	1.6	0.48	10	0.27	1.05	0.44	1
44	Shipp	66.5	8.3	26	68.2	0.21	12	0.05	0.48	0.16	1
45	Talquin	16	6.7	41.2	24.1	0.86	12	0.36	1.4	0.67	1
46	Tarpon	5	6.2	23.6	9.6	0.52	12	0.31	0.95	0.55	1
51	Tohopekaliga	25.6	6.2	12.6	27.7	0.65	44	0.3	1.1	0.58	1
47	Trafford	81.5	8.9	20.5	9.6	0.27	6	0.04	0.4	0.27	0
48	Trout	1.2	4.3	2.1	6.4	0.94	10	0.59	1.24	0.98	1
49	Tsala Apopka	34	7	13.1	4.6	0.4	12	0.08	0.9	0.31	1
50	Weir	15.5	6.9	5.2	16.5	0.43	11	0.23	0.69	0.43	1
52	Wildcat	17.3	5.2	3	2.6	0.25	12	0.15	0.4	0.28	1
53	Yale	71.8	7.9	20.5	8.8	0.27	12	0.15	0.51	0.25	1

References

• Lange TL, Royals HE, Connor LL (1993) Influence of water chemistry on mercury concentration in largemouth bass from Florida lakes. Trans Am Fish Soc 122:74-84.

• Michael K. Saiki, Darell G. Slotton, Thomas W. May, Shaun M. Ayers, and Charles N. Alpers (2000) Summary of Total Mercury Concentrations in Fillets of Selected Sport Fishes Collected during 2000–2003 from Lake Natoma, Sacramento County, California (Raw data is included in appendix), U.S. Geological Survey Data Series 103, 1-21.

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