Quantitative Statistical Analysis for Problem Solving And Decision Making Project

Providence College DigitalCommons@Providence School of Business Faculty Publications School of Business -- Quantitative Statistical Analysis for Problem Solving And Decision Making Project Anthony Baker Providence College, abaker6@providence.edu Follow this and additional works at: http://digitalcommons.providence.edu/business_fac Part of the Business Commons Baker, Anthony, "Quantitative Statistical Analysis for Problem Solving And Decision Making Project" (). School of Business Faculty Publications. Paper. http://digitalcommons.providence.edu/business_fac/ This Article is brought to you for free and open access by the School of Business at DigitalCommons@Providence. It has been accepted for inclusion in School of Business Faculty Publications by an authorized administrator of DigitalCommons@Providence. For more information, please contact mcaprio@providence.edu.

Quantitative Statistical Analysis for Problem Solving And Decision Making Project Nov By Anthony E. Baker Johnson and Wales University MBA Program Instructor: Martin W. Sivula Ph.D. A Comparative Analysis of Government Regulations, Fishing Effort, And Outside Influences on Lobster Landings for The State of Rhode Island

Table of Contents Thanks and Acknowledgements Proposal Narrative Dependent Data Set - Graphics Independent Data Sets - Graphics Bivariate Data Groups Multivariate Data Sets Calculations and Surveys Summary Alternate Theories Reference List II III V VII VIII X I

Thanks and Acknowledgements to the following people: John H. Nagle, Supervisory Policy Analyst, Operations Group, Sustainable Fisheries Division, Northeast Regional Office, National Marine Fisheries Service who responding quickly and on his own time to my request for Federal Permit Data., Head Librarian at Johnson and Wales University reference library who tirelessly and diligently assisted me in located required rainfall and temperature data. Bob Smith, Former President of the RI Lobsterman's Association who gave me guidance on where to search and whom to speak with in my efforts to locate data. Margaret McGrath, Administrative Officer, Rhode Island Department of Environmental Management for responding quickly to my request for data on RI Lobster Licenses. Donna Prout, friend and colleague who took the time to edit and critique my work before submission. II

Quantative Statistical Analysis for Problem Solving Project Proposal Nov By Anthony E. Baker Johnson and Wales University MBA Program A) Background Information: The National Marine Fisheries Service (NMFS) contends that increased regulation is required to save dwindling American Lobster stocks (Homarus Americanus) in the North Atlantic Region. Members of various trade organizations contend that the stocks are at the very least stable and probably growing. That cyclical rises and falls in the total biomass is a natural function and is altered more by pollution and habitat degradation than commercial fishing. I intend to give an alternate theory through the use of research and statistical analysis as regards to harvest within the Rhode Island waters. These waters being defined as those within the Northeast and Southwest boundaries of NMFS Management Areas and 3 that are covered by both state and federal jurisdiction. B) The Stated Problem: To what extent was [IV] Lobster Production (988-98) in a given area altered by [I] Commercial Fishing Pressures, [II] Government Regulation, [III] Outside influences both natural and manmade? C) Methodology:. I will first research three sub-cases gathering data from federal and state agency sources.. This data will be collated into data sets organized over time. (988-98) 3. Each data set will have a corresponding histogram and time series plot created to display univariate relationships between data points within a data set. 4. These data sets will be compared to one another by overlaying the time series plots and using Pearson's Correlation scatter plots to determine what degree of correlation exists on a bivariate level. 5. The independent variable data sets stated in section (D) will be compared with the dependant variable data set Lobster Landings in Pounds. 6. I will then use the results of these three sub-cases as independent variables versus the dependent variable Median Lobster Production (988-98) for the objective case. 7. These data sets will be compared to one another by overlaying the time series plots and using Pearson's Correlation scatter plots to determine what degree of correlation exists on a multivariate level. 8. A descriptive data set will be created to derive the coefficients, median, mean etc. 9. I will then create a linear model using a correlation matrix and r squared to draw inferences as to what extent all of the independent variables had on affecting the Lobster Production (988-98) in the Rhode Island waters.. *Data will be entered hierarchical (chronologically) or step wise depending on the plots. III

D) Cases: I. Commercial Fishing Pressure 988-98 Dependent Variable - Lobster Landings in Pounds () Independent Variables - Number of Federal Fishing Permits (FP) Number of Traps Fished (TF) Market Value of Catch (MV) II. Government Regulation on Commercial Fishing 988-98 Dependent Variable - Lobster Landings in Pounds () Independent Variables - Number of Traps Fished (TF) State Licenses/Total Number of Fisherman (SL) Government Regulation Value Factors (GR) III. Outside Influences both Manmade and Natural 988-98 Dependent Variable - Lobster Landings in Pounds () Independent Variables - Toxic Waste Released into the Water (TW) Average Seasonal Air Temperature (AT) Average Annual Rainfall (RF) IV The Effects of Cases I,II,IIIhad on Lobster Production in RI 988-98 Dependent Variable - Median Lobster Production in Pounds Independent Variables - Commercial Fishing Pressure Government Regulations Outside Influences E) Data Sources: I intend to use some of and possibly all of the following sources. It is my professional belief that these sources are the most accurate and creditable. > National Marine Fisheries Service > Environmental Protection Agency > The Northeast Fisheries Science Center > New England Fisheries Management Council > The National Climatic Data Center > The National Oceanographic and Atmospheric Administration > Rhode Department of Environmental Management > Independent Surveys I Conduct F) Criteria: For the purpose of this study I have outlined general criteria to be followed so as to be able to have set parameters as benchmarks. > All numeric data will be rounded to the nearest hundred or hundredth > All sources of data will come from (E) Data Source list > When two opposing sets of data or data points exist a mean or average will be determined and used. > Documents and interviews will be cited in accordance with APA standards IV

Narrative: The purpose of this project is to provide a first step in questioning the justification used for governmental intervention in the Lobster Fishing Industry. This justification being, "The decline in lobster stocks along the North Atlantic Coast". This project is NOT an exclusive scientific study. This project is NOT an exclusive socioeconomic study of the lobster industry. It does not use advanced models commonly used in either discipline. This project is a basic statistical study, which uses data provided by governmental agencies of both a scientific and economic nature and independent surveys I conducted. Using basic statistical analysis methods it compares the various data sets then draws basic logical inferences from the observations. Steps:. I will first research three sub-cases gathering data from federal and state agency sources.. This data will be collated into data sets organized over time. (988-98) 3. Each data set will have a corresponding histogram and time series plot created to display univariate relationships between data points within a data set. 4. These data sets will be compared to one another by overlaying the time series plots and using Pearson's Correlation scatter plots to determine what degree of correlation exists on a bivariate level. 5. The independent variable data sets stated in section (D) will be compared with the dependant variable data set Lobster Landings in Pounds. 6. I will then use the results of these three sub-cases as independent variables versus the dependent variable Median Lobster Production (988-98) for the objective case. 7. These data sets will be compared to one another by overlaying the time series plots and using Pearson's Correlation scatter plots to determine what degree of correlation exists on a multivariate level. 8. A descriptive data set will be created to derive the coefficients, median, mean etc. 9. I will then create a linear model using a correlation matrix and r squared to draw inferences as to what extent all of the independent variables had on affecting the Lobster Production (988-98) in the Rhode Island waters.. *Data will be entered hierarchical (chronologically) or step wise depending on the plots. Hypothesis To Be Tested: The amount of lobsters being harvested in the State of RI is too great The end result being the long-term decline in lobster stocks and in turn the overall lobsters landings. Therefore government regulations are required to control the number of lobsters harvested through measures such as permit/license limits, minimum size of lobsters legally harvested and overall fishing effort through the reduction of traps fished. V Paraphrased from statements made by federal NMFS and NOAA officials at various hearings from 996 to 999 conducted with lobster fishing trade associations.

Background on Hypothesis: Data is currently accumulated by federal sources and used in federal models to predict the health of lobster stocks through random sampling. These models show a decline in lobster stocks over time leading to a prediction that the trend will continue unless actions are taken. Therefore it is the position of the NMFS that regulations are required to control fishing effort. These are taking place in the form of the curtailment of new lobster license issuance, the control and decrease in the number of trapsfished,and the minimum size of lobsters allowed to be harvested. Relations and Conclusions: While it would be easy to draw conclusions from this study, the ability to show a direct causal relationship would require in depth scientific study drawing on various disciplines within the scientific community. Also, to increase the validity of this type of study it would require encompassing all the lobster producing states and the Canadian provinces as well as more detailed and in-depth data collection that time does not allow. The intent of this project is to give question to accepted governmental theories through the use of basic research and statistical analysis. If plausible alternate possibilities do exist, demonstrate possible other scenarios not considered by the governing authorities and scientific community. This in turn may bring into question the validity of the results produced by current models used by these various scientific and government organizations in order to justify the ramping up of regulations starting in 994. Assumptions: ) The sample data from agencies will be as accurate as the referring agencies deem necessary. ) Surveys will have a margin of error based on the memory and records kept by the surveyed parties. 3) Surveys will be based on % sample size of a total population. 4) The interpretations are based on my years industry experience utilizing basic accepted statistical analysis methods. This same data is used in this study. VI

Dependent Data - Rl Lobster Landings // In Metric Tons 988 to 998 Years 988 989 99 99 99 993 994 995 996 997 998 Metric Tons of Lobsters,59,597 3,9 3,377 3,67,85,936 3,433 3,4 3,63 4,548 Histogram of Rl Lobster Landings 988 to 998 Time Series Plot of Rl Lobster Landings 988 to 998 Statistics gathered from the National Marine Fisheries Service Website: www.st.nmfs.gov/webplcornm/plsql/webstl.mf_annual_landings.results

Independent Data Years 988 989 99 99 99 993 994 995 996 997 998 Total # State Licenses 4 3 75 47 6 98 37 43 597 Independent Data Years Total Rainfall in Inches 988 38.37 989 56.6 99 44.78 99 45.69 99 47.48 993 4.6 994 44.69 995 38.4 996 38.6 997 37.97 998 5.7 Independent Data Years Total Number of Permits 988 34 989 3 99 85 99 34 99 368 993 346 994 89 995 85 996 997 36 998 39 Independent Data Years 988 989 99 99 99 993 994 995 996 997 998 Independent Data Sets Used for Comparative Analysis Est. # of Traps Fished 458 473 544 6 69 74 77 846 94 994 38 // Independent Data Years Metric Tons of Toxic Waste 988,646,338 989 7,76,57 99 6,3,57 99 5,438,4 99 6,383,857 993 6,673,43 994 7,5,45 995 3,49,36 996,4,44 997,7,449 998,75,38 Independent Data Years 988 989 99 99 99 993 994 995 996 997 998 Independent Years 988 989 99 99 99 993 994 995 996 997 998 Air Temperatures 64.4 65.3 65.4 66.7 63.5 66 64.87 65.78 6. S'4 63.93 64. Data Market Value US $ 5,68,937 7,53,53 9,84,539,39,49,98,7 8,843,769,953, 7,84, 8,358,39,5,993,3,45 Independent Data Years Values of REG Increase 988 989 99 99 99 993 994 995 996 997 998 4

3 Independant Data - Rl Rate of Toxic Waste Releases in Metric Tons from 988 to 998 Years 988 989 99 99 99 993 994 995 996 997 998 Metric Tons of Pollution,646,338 7,76,57 6,3,57 5,438,4 6,383,857 6,673,43 7,5,45 3,49,36,4,44,7,449,75,38 " ^ Histogram in Metric Tons of Toxic Waste Released from 988-998 Time Series Plot in Metric tons of Toxic Waste Released from 988-998 Statistic gathered from Environmental Protection Agency New England Region Website under 'Toxic Release Inventory": www.epa.gov/region/steward/emerplan/toxic.html 3

4 Independent Data - Rl Annual Rain Fall in Inches from 988 thru 998 Years 988 989 99 99 99 993 994 995 996 997 998 Inches of Rain Fall 38.37 56.6 44.78 45.69 47.48 4.6 44.69 38.4 38.6 37.97 5.7 // Annual Rain Fall Per Year in Inches for the State of RI Time Series Plot of Annual Rain Fall Per Year in Inches for the State of RI Statistics gathered from the "Weather Almanac-9th Edition" and the Climatilogical National Annual Survey. The city of Providence, Ri was the measuring point. 4

5 Independent Data- Rl Average Seasonal Air Temperature // in Centigrade from 988 to 998 Years 988 989 99 99 99 993 994 995 996 997 998 Air Temperature 64.4 65.3 65.4 66.7 63.5 66 64.87 65.78 654 63.93 64. *Missing month of data Histogram Average Annual Temperature in Centigrade for Fishing Seasons 988-98 Time Series Plot of Average Annual Temperature in Centigrade for Fishing Seasons 988-98 Statistics gathered from the "Weather Alamanac-9th Edition" and the Climatiligical National Annual Survey. The city of Providence, Rl was the measuring point. 5

6 Independent Data Active Rl Federal // Lobster Permits 988 to 998 Years 996 939 99 99 99 993 994 995 996 997 993 Total # of Federal Permits 34 3 85 34 368 346 89 85 36 39 Histogram of Active Federal Permits for Rl Home Ported Vessels from 988 thru 998 Time Series Plot of Active Federal Permits for Rl Home Ported Vessels from 988 thru 998 Data gathered from the Vessel Permit Data Base, National Marine Fisheries Service, Northeast Regional Offices

7 Independent Data - Annual Average Number of Traps Fished per Vessel In the State of Rl from 988 to 998 Years 988 989 99 99 99 993 994 995 996 997 998 Traos Fishec 458 473 544 6 69 74 77 846 94 994 38 // Data gathered from a confidential survey of Lobster Fishing Boat owner operators whose derived 5% or more of their annual income from fishing. 7

8 Independent Data - Market Value // of Rl Lobster Landings in US $ from 988-998 Year 98S 989 99 99 99 993 994 995 996 997 996 Value $ Millions 5,68,937 7,53,53 9,64,539,39,49,96,7 6,843,769,953, 7,84, 8,358,39,5.993,3,45 Histogram of Annual Market Value of Lobster Landings In US $ from 988-48 Time Series Plot of Annual Market Value of Rl Lobster Landings in US $ from 988-98 Dirts gathered from the National Marina Fisheries website: WWW:stnmfs.gov/webplcomm/plsql/webst.MF_LANDlNGS_ANNUAL. RESULTS 8

9 Independent Data - State Commercial Fishing Licenses // with Lobster Endorsements from 988 to 998 Years 988 989 99 99 99 993 994 995 996 997 998 Total # State Licenses 6 No Record Available 4 3 75 47 6 98 37 43 597 Histogram of State Licenses with Lobster Endorsements Active 989-998 Time Series Plot of State Licenses with Lobster Endorsements Active 989-998 Information gathered from the Rl Department of Environmental Management, Office of Registration and Licensing, Margaret McGrath, Administrative Officer 9

Independent Data-Government Regulations // on the Lobster Industry From 988 to 9989 Year 988 989 99 99 99 993 994 995 996 997 998 Value 4 Histogram of Government Fishing Curtailment Regulations from 988 to 998 Time Series Plot of Government Fishing Curtailment Regulations from 988 to 998 Information gathered from the NMFS web site www.mnfs.gov and the Ri DEM Regulations from 988 through 998. Values have been calculated for each event. *See "Government Regulation Calculation" page

Information Categorized into Bivariate Regression Data Groups // YR 988 989 99 99 99 993 994 995 996 997 998 YR 988 989 99 99 99 993 994 995 996 997 998 YR 988 989 99 99 99 993 994 995 996 997 998 YR 988 989 99 99 99 993 994 995 996 997 998 vs RF,59,597 3,9 3,377 3,67,85,936 3,433 3,4 3,63 4,548 vs MV,59,597 3,9 3,377 3,67,85,936 3,433 3,4 3,63 4,548 vs TW,59,597 3,9 3,377 3,67,85,936 3,433 3,4 3,63 4,548 vs GR,59,597 3,9 3,377 3,67,85,936 3,433 3,4 3,63 4,548 RF 38.37 56.6 44.78 45.69 47.48 4.6 44.69 38.4 38.6 37.97 5.7 MV 5,68,937 7,53,53 9,84,539,39,49,98,7 8,843,769,953, 7,84, 8,358,39,5,993,3,45 TW,646,338 7,76,57 6,3,57 5,438,4 6,383,857 6,673,43 7,5,45 3,49,36,4,44,7,449,75,38 GR 4 YR 988 989 99 99 99 993 994 995 996 997 998 YR 988 989 99 99 99 993 994 995 996 997 998 YR 988 989 99 99 99 993 994 995 996 997 998 YR 988 989 99 99 99 993 994 995 996 997 998 vs AT,59,597 3,9 3,377 3,67,85,936 3,433 3,4 3,63 4,548 vs FP,59,597 3,9 3,377 3,67,85,936 3,433 3,4 3,63 4,548 vs TF,59,597 3,9 3,377 3,67,85,936 3,433 3,4 3,63 4,548 vs SL,59,597 3,9 3,377 3,67,85,936 3,433 3,4 3,63 4,548 AT 64.4 65.3 65.4 66.7 63.5 66 64.87 65.78 6ZS4 63.93 64. FP 34 3 85 34 368 346 89 85 36 39 TF 458 473 544 6 69 74 77 846 94 994 38 SL 3 3 75 47 6 98 37 43 597 The above data was gathered from various sources cited on the separate Data Set Graphic Pages and on the last page.

Graphic Display of Comparative Analysis // for Lobster Landings VS Toxic Waste Release Time Series Plot of Lobster Landings VS Toxic Waste Release 988-98 Scatter Plot Displaying Bivariate Linear Regression of Lobster Landinas VS Toxic Waste Release 988-98 Number of cases used; (988 to 998) Pearson's r (Correlations Coefficient) = -.8997 R-Square =.895 Test of hypothesis to determine significance of relationship: H(null): Slope = or H(null): r = (Pearson's) t =-6.837 with 9 d.f. p< (A low p-value implies that the slope does not =.) FIELD N MEAN STD SEM MIN MAX SUM Lobsters Landed 3..69.836.5 4.54 35.3 Toxic Waste 5436.73 744.89 87.6 75.38 646.33 5983.99 It can be inferred from these charts and data that there is a positive affect (increase) in Lobster Landings () when compared to the reduction (decrease) of Toxic Waste (TW) released into the water. Whether it can be said that there is a direct causal relationship can only be determined when tests on the effects of the TW on lobster are conducted. *See attached list of TW. A significant relationship between the effects of Toxic Waste (TW) released into the water and the Lobster Landings () for Rl. This can be inferred from a nearly normal distribution.

3 Graphic Display of Comparative Analysis // for Lobster Landings VS Market Value Time Series Plot Correlating Lobster Landings VS Market value for 988-98 Scatter Plot Displaying Bivariate Linear Regression of Lobster Landings VS Market Value for 988-98 Number of cases used: (988 to 998) Pearson's r (Correlations Coefficient) =.53 R-Square =.89 Test of hypothesis to determine significance of relationship: H(null): Slope = or H(null): r = (Pearson's) t =.87978 with 9 d.f. p=.93 FIELD N MEAN STD SEM MIN MAX SUM Lobster Landed 36.9 67.47 86.7 59 4548 3567, Market Value 95.3 766.66 53.67 568.93 98. 378.6 It can be inferred from these charts and data that there is a parallel affect until 995 in Lobster Landings () when compared to the Market Value (MV). From this point on there is a inverse relationship between and MV probably due to supply vs demand basic economic variables of increased production and decreased demand equals the MV No significant relationship between variables can be inferred when comparing the effects of Market Value (MV) and the Lobster Landings (). This can be inferred from this less than normal distribution. 3

4 Graphic Display of Comparative Analysis // for Lobster Landings VS Federal Permits Time Series Plot Correlating Lobster Landings vs. Federal Permits Active for 988-98 Scatter Plot Displaying Bivariate Linear Regression of Lobster Landings vs. Federal Permits Active for 988 98 Number of cases used: Pearson's r (Correlations Coefficient) =.3873 R-Square =.5 Test of hypothesis to determine significance of relationship: H(null): Slope = or H(null): r = (Pearson's) t =.6 with 9 d.f. p=.39 FIELD N MEAN STO SEM MIN MAX SUM 36.9 67.47 86.7 59 4548 3567 FP 3.545 3.4 34,4 3 368 558 It cannot be inferred from this data or charts that there is correlating affect between Lobsters Landed () when compared to the Number of Federal Permits Active (FP). While in the year 99 there was a factor of 9. increase in the number of FP there was only a factor of.6 increase in. The distribution is not normal so no correlation can be exists in this relationship. No significant relationship exists between the effects of the number of Federal Permits (FP) Active and the numbers of Lobsters Landed (). This can be inferred from the far less than normal distribution. 4

5 Graphic Display of Comparative Analysis // for Lobster Landings VS Average Annual Temperatures Time Series Plot Correlating Lobster Landings VS Average Air Temperature 988-98 Scatter Plot Displaying Bivariate Linear Regression of Lobster Landings VS Average Air Temperature Number of cases used: Pearson's r (Correlations Coefficient) = -.599 R-Square =.56 Test of hypothesis to determine significance of relationship: H(null): Slope = or H(null): r = (Pearson's) t = -.486998 with 9 d.f. p =.639 FIELD N MEAN STD SEM MIN MAX SUM 36.9 67.47 86.7 59 4548 3567 AT 64.785.3.366 6.54 66.7 7 64 It cannot be inferred from these charts and data that there is any correlation between Lobster Landings () when compared to the Average Annual Temperatures (AT) during the fishing season. It can be inferred that there is no affect on by AT. Water temperature may have an affect but data on mean ocean temperatures was unavailable to me at the time of this project. No significant relationship exists between the effects of Fishing Season Average Annual Temperatures (AT) and Lobster Landings () for Rl. This distribution is not normal. 5

6 Grapthic Display of Comparative Analysis // for Lobster Landings VS Traps Fished per Vessel Time Series Plot Correlating Lobster Landings VS Traps Fished in the State of Rl 988-98 Scatter Plot Displaying Bivariate Linear Regression of Lobster Landings VS Traps Fished in the State of Rl 988-98 Number of cases used: Pearson's r (Correlations Coefficient) =.786 R-Square =.64 Test of hypothesis to determine significance of relationship: H(null): Slope = or H(null): r = (Pearson's) t = 3.77985 with 9 d.f. p = 4 (A low p-value implies that the slope does not =.) FIELD N MEAN STD SEM MIN MAX SUM 36 9 67.47 86.7 59 4548 3567 TF 736.545 4.5 6553 458 38 8 It cannot be inferred from these charts and data that there is any correlation between Lobster Landings () when compared to the Number of Traps Fished per Vessel. While there is a general trend in the increase of, it does not correlate with a specific upward trend in TF. In Years 9,93, 94 there was a marked decrease in with a steady increase in TF. Years 9,9, 95,96 were level while there was still a steady upward trend in TF. This would infer that has no affect on. No significant relationship exists between the effects of the Number of Traps Fished per Vessel (TF) and Lobster Landings () for Rl. This distribution is not normal. 6

7 Grapthic Display of Comparative Analysis // for Lobster Landings VS Average Annual Rain Fall Time Series Plot Correlating Lobster Landings VS Annual Rain Fall for the State of Rl 988-98 Scatter Plot Displaying Bivariate Linear Regression of Lobster Landings VS Rain Fall for the State of Rl 988-98 Number of cases used: Pearson's r (Correlations Coefficient) =.47 R-Square =.7 Test of hypothesis to determine significance of relationship: H(null): Slope = or H(null): r = (Pearson's) t =.4464397 with 9 d.f. p =.666 FIELD N MEAN STD SEM MIN MAX SUM 36.9 67 47 867 59 4548 3567 RF 44. 6.37.85 37.97 56.6 486. It can be inferred from these charts and data that there is a parallel affect in Lobster Landings () when compared to the Average Annual Rain Fall (RF). The relationship displays an increase in when there is an increase in the RF and a decrease in when there is a decrease in RF. This maybe due to the rise or fall in water salinity produced by the influx of fresh water into the bay and sound. Lobsters require a salinity of to in order to inhabit an area. A significant relationship exists between the effects of Rain Fall (RF) and the Lobster Landings (). This can be demonstrated from the nearly normal distribution. 7

8 Graphic Display of Comparative Analysis for Lobster Landings VS Government Regulations // Graphic Display of Comparative Analysis for Lobster Landings VS Government Regulation 988-98 Scatter Plot Displaying Bivariate Linear Regression of Lobster Landings VS Government Regulation 988-98 Number of cases used: Pearson's r (Correlations Coefficient) =.4 R-Square =.458 Test of hypothesis to determine significance of relationship: H(null): Slope = or H(null): r = (Pearson's) t =.65794 with 9 d.f. p =.57 - FIELD N MEAN 5TD SEM MIN MAX SUM 36 9 67.47 86.7 59 4548 3567 GR 3636.863.3878 4 5 It cannot be inferred from these charts and data that there is any correlation between Lobster Landings () when compared to the effective value of Government Regulation on fishing effort The shows that while there may be a dramatic increase in fishing curtailment regulations there has no correlating decrease in the amount of lobsters landed that corresponds to these measures. No significant relationship between the effects of Government Regulations (GR) on Fishing and Lobster Landings () exists. This can be inferred from the erratic patterns of GR and the steady upward trend of.8

9 Graphic Display of Comparative Analysis // for Lobster landings VS State Licenses Time Series Plot Correlating Lobster Landings VS Total Number of Commercial Lobster Fisherman 988-98 Scatter Plot Displaying Bivariate Linear Regression of Lobster Landings VS Total Number of Commercial Lobster Fisherman 988-98 Number of cases used: Pearson's r (Correlations Coefficient) =.73 R-Square =.586 Test of hypothesis to determine significance of relationship: H(null): Slope = or H(null): r = (Pearson's) t = 3.5747 with 9 d.f. p=.4 (A low p-value implies that the slope does not =.) FIELD N MEAN STD SEM MIN MAX SUM 3C6.9 67 47 86.7 59 4548 3567 SL 75E.73 5336 6.77 597 835 It can be inferred from this data that there is a parallel correlation in Lobster Landings () when compared to the Total Number of State Licenses Issued [w/lobster endorsements] (SL) for six years from 993 (6) to 998 () when there is an increase in the SL and a increase in. While for fours years from 989 () to 99 (5) there is a inverse correlation between and SL. No data was available for 988. Federal permits require state licenses but not the reverse. A significant relationship exists variables from 993 forward. After the dramatic increase in State Licenses and in 993 Lobsters Landed have a parallel correlation. This is a nearly normal distribution of variables after thispoint9

Multivariate Data Sets Used for // Multiple Regression and Correlation Matrix Case l Years 988 989 99 99 99 993 994 995 996 997 998 Fishing Effort Dependent Data Metric Tons of Lobsters,59,597 3,9 3,377 3,67,85,936 3,433 3,4 3,63 4,548 \Independent Data Federal Permits 34 3 85 34 368 346 89 85 36 39 Traps Fished 458 473 544 6 69 74 77 846 94 994 38 Value $ Millions 5,68,937 7,53,53 9,84,539,39,49,98,7 8,843,769,953, 7,84, 8,358,39,5,993,3,45 Case II Years 988 989 99 99 99 993 994 995 996 997 998 Fishing Effort Regulations Dependent Data Metric Tons of Lobsters,59,597 3,9 3,377 3,67,85,936 3,433 3,4 3,63 4,548 Independent Data Traps Fished 458 473 544 6 69 74 77 846 94 994 38 State Licenses Government Rea's 9 4 3 75 47 6 98 4 37 43 597 Case IIl Years 988 989 99 99 99 993 994 995 996 997 998 Outside Influences Dependent Data Metric Tons of Lobsters,59,597 3,9 3,377 3,67,85,936 3,433 3,4 3,63 4,548 Independent Data Toxic Waste,646,338 7,76,57 6,3,57 5,438,4 6,383,857 6,673,43 7,5,45 3,49,36,4,44,7,449,75,38 Rain Fall 38.37 56.6 44.78 45.69 47.48 4.6 44.69 38.4 38.6 37.97 5.7 Air Temperature 64.4 65.3 65.4 66.7 63.5 66 64.87 65.78 6.54 63.93 64. Case IV Comparison of Factors for Cases, II, III Dependent Data \lndependent Data Median Fishina Effort 3,6 + Factor -Factors - Government Rea's.5+ Factors.5-Factors.5 -.5 Outside Influences + Factor - Factors -

Case I WINKS 4.6 Linear Regression and CorreJ.ation November 5. C :\WINKS\LOBl.DBF Dependent variable is, 3 independent variables, < :ases Variable Coefficient St. Error t-value p( tail) Intercept -53.99 FP -.6 TF,666758 MV 578 R-Square =. 6889 Analysis of Variance to Test 74.877 -.4845887.936736 -.57435.698945.99345 366.54747 Adjusted R-Square =.5556 Regression Relation. 643. 584.. 86 Source Sum of Sqs df Mean Sq F p-value Regression 66588.74 Error 8654.7 Total 3864.999 3 87559.39 5.67343 7 69436.39. 34 A low p-value suggests that the dependent variable may be linearly related to i ndependent variable(s). Fig. B

Case I WINKS 4. 6 Matrix of Correlation Coefficients November 5, C:\WINKS\LOBl.DBF FP TF MV FP. 387 TF. 783 MV.53 (.39) ( 4) (.93) [ ] [ ] t HI. 337.835 (.3) () [ ] [ ]. 384 [.44) [ ] Key: Correlat (p-value [count] ion ) Fig c

Case I WINKS 4.6 November 5, Descri ptive Statistics, Summary C:\WINKS\LOBl.DBF Statis itics from database C :\WINKS\LOBl.DBF Number of record s= FIELD YEARS N 988. MEAN STD SEM MIN MAX SUM FP TF MV 59 34 458 568937. 59 34 458 568937 59 34 458 568937 59 34 458 568937 FIELD YEARS N 989.3 MEAN STD SEM MIN MAX SUM FP TF MV 597 3 473. 75353,,,.... 597 3 473 75353 597 3 473 75353 597 3 473 75353 FIELD YEARS N 99. MEAN STD SEM MIN MAX SUM FP TF MV 39 85 544 98453 9..... 39 85 544 984539 39 85 544 984539 39 85 54 4 984539 FIELD YEARS N 99. MEAN STD SEM MIN MAX SUM FP TF MV 3377 34 6 3949.... 3377 34 6 3949 3377 34 6 3949 3377 34 6 3949 FIELD YEARS N 9 9. MEAN STD SEM MIN MAX SUM f FP TF MV 367. 368 69 987... 367 368 69 987 367 368 69 987 367 368 69 987 FIELD YEARS N 993. MEAN STD SEM MIN MAX SUM FP TF MV 85 346 74. 8843769... 85 346 74 8843769 85 346 74 8843769 85 346 74 8843769 FIELD YEARS N 994. MEAN STD SEM MIN MAX SUM 9 3 6 936 936 936 Fig. D

FP TF MV 89 77 953 89 77 953 89 77 953 89 77 953 FIELD YEARS = N 995. MEAN STD SEM MIN MAX SUM FP TF MV 3433 85 846 784.... 3433 85 846 784 3433 85 846 784 3433 85 846 784 FIELD YEARS = N 996. MEAN STD SEM MIN MAX SUM FP TF MV 34 94 835839.., 34 94 835839 34 94 835839 34 94 835839 FIELD YEARS N 997. MEAN STD SEM MIN MAX SUM FP TF MV 363 36 994 5993, 363 36 994 5993 363 36 994 5993 363 36 994 5993 FIELD YEARS ~ N 998. MEAN STD SEM MIN MAX SUM FP TF MV 4548 39 38 345.,. 4548 39 38 345 4548 39 38 345 4548 39 38 345 Fig. D

Bar Chart: C:\WINKS\LBCASE.DBF Fig. A

",ase II Linear Regression and Corre lation C:\WINKS\LOBCASE.DBF Dependent variable is, 3 independent variabl< S, cases Variable Coefficient St. Error t--value p( tail) Intercept 5.8734 TF.348855 SL.96944 GR -4.959 959.399.58583.9846485.83546.834833.36367 39.8438 -: L.668.57.75.8.34 R-Square =,6658 Adjusted R-Square =.56 - Analysis of Variance to Test Regression Relation Source Sum of Sqs df Mean Sq F p-value Regression 53847.9459 Error 7435. 3 84635.98 4 7 833.57 64845.43 - Total 3864.999 A low p-value suggests that, may be linearly related to the dependent variable independent variable(s) Fig. B

Case III WINKS 4.6 Linear Regression and Correl ation November 5, C :\WINKS\LOB3.DBF Dependent variable is, 3 independent variables, cases. Variable Coefficient St. Error t-value p( tail) Intercept 897.77585 TW -5 RF 3.56853 AT 37.3848 R-Square =.87 Analysis of Variance to Test 463.48.938446 3-6.646754 3.89586.69566 73.46843.5783 Adjusted R-Square =.858 Regression Relation. 85 <,. 34. 65 Source Sum of Sqs df Mean Sq F p-value Regression 337.838 Error 4957.7 3 73.674 5.76447 7 74.58 Total 3864.999 A low p-value suggests that the dependent variable may be linearly related to independent variable(s). Fig. 3 C

Case III WINKS 4.6 November 5, Matrix of Correla tion Coefficients C:\WINKS\LOB3.DBF i hi TW TW RF -.898.47 ( D.) (.666) [ ] [ ].6 (.757) [ ] AT -.6 (.639) [ ].9 (.386) [ U] RF,76 (.64) [ ]. AT ~ Key: Correlat ion [p-value ) [count] Fig. 3 D

Case III WINKS 4.6 November 5, Descri ptive Statistics, Summary C:\WINKS\LOB3.DBF Statis tics from database C:\WINKS\LOB3.DBF Number of records FIELD YEAR = 9 8 8. N MEAN STD SEM MIN MAX SUM TW RF AT 5.9 646338 38.37 64,4,.,, 59 646338 38.37 64. 4 59 646338 38. 37 64.4 59 646338 38.37 64.4 FIELD YEAR = 9 8 9. N MEAN STD SEM MIN MAX SUM TW RF AT 59 7 7765" 7, 56 6 65 3,,. 597 77657 56.6 65. 3 597 77657 56.6 65.3 597 77657 56.6 65,3 i FIELD YEAR = 9 9, N MEAN STD SEM MIN MAX SUM TW RF AT 39, 6357 44,78 65,4,,.,, 39 6357 44. 78 65. 4 39 6357 44.78 65.4 39 6357 44.78 65.4 FIELD YEAR = 9 9. N MEAN STD SEM MIN MAX SUM TW RF AT 3377 54384C.OO 45.69 66.7,,,..., 3377 54384 45,69 66.7 3377 54384 45.69 66.7 3377 54384 45,69 66.7 FIELD YEAR = 9 9, N NEAN STD SEM MIN MAX SUM TW RF AT 367 6383857 47.48 63.5,,,., 367 6383857 47.48 63 5 367 6383857 47.48 63,5 367 6383857 47.48 63.5 FIELD YEAR = 9 9 3. N MEAN STD SEM MIN MAX SUM TW RF AT 85 667343 4,6 66.,.,,, 85 667343 4.6 66 85 667343 4,6 66 85 667343 4.6 66 FIELD YEAR = 9 9 4. N MEAN STD SEM MIN MAX SUM 936 936 936 936 FTP. 3 F.

TO RF AT 7545 44.69 64.87.. 7545 44.69 64.87 7545 44.69 64.87 7545 44.69 64.87 FIELD YEAR = 995. N MEAN STD SEM MIN MAX SUM TW RF AT 343 3. 34936 38.4 65.78.,. 3433 34936 38.4 65.78 3433 34936 38.4 65.78 3433 34936 38.4 65.78 FIELD YEAR = 996. N MEAN STD SEM MIN MAX SUM TW RF AT 34. 444 38.6 6.54... 34 444 38.6 6.54 34 444 38.6 6.54 34 444 38.6 6.54 FIELD YEAR = 997. N MEAN STD SEM MIN MAX SUM TW RF AT 363 7449 37.97 63.93. 363 7449 37.97 63. 93 363 7449 37.97 63.93 363 7449 37.97 63. 93 FIELD YEAR = 998. N MEAN STD SEM MIN MAX SUM TW RF AT 4548 7538 5.7 64.... 4548 7538 5.7 64. 4548 7538 5. 7 64. 4548 7538 5.7 64. Fig. 3 E

Line Chart: C:\WINKS\LBCASE4.DBF Fig. 4 A

Case IV /5/ This chart reveals the effect each case has on the base line of Lobsters Landed (). It can be infered from this chart and the data that was used to construct it that government regulations to have the least effect on the amount of Lobsters Landed () and that Outside Influences (I) has the greatest effect on Lobsters Landed (). Fig. 4 B

Case IV WINKS 4.6 November 5, Descriptive Statistics, Summary C:\WINKS\LOBCASE4.DBF Statistics from database C:\WINKS\LOBCASE4.DBF Number of records=, FIELD N MEAN STD SEM MIN MAX SUM ' BASE_LINE FE GR OI -.5.3 -.5.3.3.5.5.5 - -.5 -.5 - - Fig. 4 C

Temprature and Rainfall Data Comparisons and Calculations /5/ for 988-998 NWS Averages MAY JUNE JULY 58 Average Temprature 67 Average Temprature 73 Average Temprature 3.6 Total Rainfall.9 Total Rainfall 3. Total Rainfall AUG. SEP. OCT. 7 Average Temprature 63 Average Temprature 53 Average Temprature 3.9 Total Rainfall 3.5 Total Rainfall 3.6 Total Rainfall 988-998 Data / missingfieldfor Temprature, missingfieldsfor Rainfall MAY JUNE JULY 58.4 Average Temprature 68.3 Average Temprature 7.95 Average Temprature 3.4 Total Rainfall.8 Total Rainfall 3.7 Total Rainfall AUG. SEP. OCT. 7.9 Average Temprature 63.85 Average Temprature 53.4 Average Temprature 3.9 Total Rainfall 3.7 Total Rainfall 3.76 Total Rainfall Average Temprature Data from the National Weather Almanac May June July August 988 989 99 99 99 993 994 995 996 997 998 Monthly Averages 58 59.3 56 63.9 57.6 6.8 56.5 57.3 57.4 55.4 59.3 58.4 66.9 68.7 67.7 69.3 67.3 69.3 69.4 68.3 68. 68. 63.4 63.3 74.3 7.3 73 74. 7.3 74.5 76. 75.8 N/R 73.7 7. 7.95 75.3 7. 73.5 73.6 7. 73.8 69.9 73.5 7 7.6 69.6 7.9-9th Edition September 63 65.3 63.7 63. 64 65. 63 6.8 64. 64 64.3 63.85 Total Rain Fall Da a from the National Weather Almanac - 9th Edition May June July August September 988 989 99 99 99 993 994 995 996 997 998 Monthly Averages.83 6.7 5.7 3.3.4..98.83.44.68 N/R 3.4 C.9 5.84.3.93 4.6.4.7 89.7.3 N/R.48 5.73 5.59 3.5.76 3.59.8.34.7 5.57.96.37 3.7.95 6.4 3.74 5.98 6.6.3 6.34.8.9 6.3.39 3.9.38 4.75.8 5.9 5.9 4.8 4. 4.6 5.7.99.3 3.7 October 48.9 54. 58.6 56. 5.7 5.5 54. 57 5. 5.8 5.4 53.4 October.77 8.37 4.96.65.53 3.55.4 6.37 6..8 3.78 376 Annual Average 64.4 65.3 65.4 66.7 63.5 66 64.87 65.78 6.54 63.93 64. 64.79 Annual Average.43 6.3 3.56 3.45 3.7.6.98 3.9 4.5.5 3.43 3.34 All Data was gathered from the "Weather Alamanac - 9th Edition", the "Climatolicical National Annual Survey" and the National Weather Service Website "www.nws.gov/annual/averages" for the city of Providence.

3 Trap Survey Data for Fishing Season 988 thru 998 // Pt Judith/Snug Harbor Year/Boat 988 45 4 989 45 4 99 45 6 99 55 6 99 65 8 993 75 8 994 75 8 995 85 996 95 997 5 998 3 6 6 6 6 8 8 8 8 4 5 5 65 65 75 75 875 875 5 5 4 4 4 8 8 8 8 6 4 4 5 5 5 6 6 6 8 8 8 7 5 6 7 8 9 3 4 5 8 6 6 6 6 8 8 8 8 Average 48.5 493.75 559.38 634.38 75 787.5 85.63 95.63 3.5 8.5 8.3 Newport/Jamestown/Tiverton Year/Boat 988 6 4 989 6 4 99 6 4 99 6 8 99 8 8 993 8 8 994 8 8 995 8 996 997 998 3 4 4 6 6 8 8 8 4 4 4 5 5 5 6 6 6 8 8 8 5 5 6 7 8 9 3 4 5 6 45 45 45 55 65 75 75 85 95 5 7 4 4 5 5 5 6 6 6 7 8 9 8 5 5 65 65 75 75 875 875 5 Average 456.5 468.75 546.88 6.88 7.5 76.5 79.63 89.63 993.75 56.5 5.63 Warwick/East Greenwich/Wickford Year/Boat 988 5 4 989 5 4C 99 65 5 99 65 5C 99 75 5C 993 75 6C 994 875 6 995 875 6C 996 875 6 997 7 998 7 3 45 45 45 55 65 75 75 85 95 95 5 4 4 4 5 5 5 6 6 6 8 8 8 5 5 5 65 65 75 75 875 875 875 6 45 6 6 6 75 75 75 75 9 9 9 7 4 4 5 5 5 6 6 6 7 7 7 8 4 4 4 5 5 6 6 7 7 7 8 Average 437.5 456.5 55 55 6.5 675 76.5 73.5 8 843.75 868.75 Average Number of Traps Fished Each Year 988 458.33 989 47.9 99 543.75 99 6.8 99 69.67 993 74.67 994 77.83 995 845.83 996 94.67 997 993.75 998 37.5 Data was gathered from survey conducted of 4 lobster fishing boat owners operators who derived at least 5% of their annual income from fishing. 3

4 Value Calculations for «* / Government Regulation Implementation Min Lobster Size Increase Iniatitive Incremental Increase / Values Year 988 989 99 99 99 993 994 995 996 997 998 Total Value=per Size Value lobster size increase Trap Reduction Iniatitive Traps per vessel allowed / Values Year Number Value 988 + 989 + 99 + 99 + 99 + 993 + 994 + D 995 + 996 + D 997 + 998 + Total Value = per traps reduced License Moratorium Chronology Per Year / Values Year 988 989 99 99 99 993 994 995 996 997 998 Total Number Value 6 Value = per year of moratorium Min Escape Vent S ize Increase Incremental Increases / Values Year 988 989 99 99 99 993 994 995 996 997 998 Total Size Value Value= per escape vent size increase Permit Moratorium Per Year / Values Year 988 989 99 99 99 993 994 995 996 997 998 Total k/alue a per year of moratorium Chronology Value 5 5 Values per Year Summations Per Year / Values Year 988 989 99 99 99 993 994 995 996 997 998 Total Number Value 4 5 Value = Annual totals per category 4

Summary: The data collected was categorized into three studies, univariate over time, bivariate over time, and multivariate over time. Comparisons of this data were made using various accepted statistical analysis techniques. In most cases data was entered in a chronological manner because the hypothesis is based on a trend over time. In the final case data was entered using forced entry using my background in the industry to make judgments on what order to be used. Measures are made quantitatively using standard unit of measures or assigned values after calculation when standard measures weren't appropriate. When ever possible, alternate explanations for plausible causal relationships have been rendered using known and established information. When no alternate explanation is apparent in depth scientific studies are called for. Documentation: All sources have been referenced in the last page of this study using the APA format. Data used was gathered from sources three major areas are generally accepted as reliable. ) 5% of the data gathered was either used by and/or provided by several governing agencies and the scientific community in the form of printed or electronic media available to the general public. ) 5% of the data gathered was from "Official Requests for Information" to appropriate agencies under the Freedom of Information Act. 3) 5% of the lata was gathered through the use of face-to-face or telephonic interviews and surveys taken by the author. Comparisons: Univariate - Data points for an eleven-year period starting in 988 and ending in 998 were organized and graphically displayed using histograms and time series plots. Each data point was compared to the next chronologically using one calendar year summations. This was done in order to determine any significant trends from one year to the next. Bivariate - Various independent data sets were compared to the dependent data set (lobster landings in metric tons). The data was graphically displayed using time series plots and scatter plots. Each data set was compared to the next chronologically using the eleven-year time line. Linear regression, Pearson's correlation, comparison of the basic descriptive statistical data, and any observed correlations in the graphs were used to determine if any significant correlation could be inferred from these data sets. Multivariate Analysis: Taking the results from the Univariate comparisons three cases were formed. They were fishing effort (FE), government regulations (GR), outside influences (OI). Each one of these cases compare three independent variables related to the particular case with dependent variable of lobsters landed (). A forth case was created comparing the three studies between themselves as independent variables versus the dependent variable median lobster landings. Values were calculated and assigned to each bivariate comparison. The data was then graphically displayed so as to compare all the variables over time. This was done using various graphing techniques such as base line charts, time

series plots, line charts and bar charts. Statistical summaries by chronological group, correlation matrixes, and multivariate were used as the descriptive statistical data. Quantification: Significant Relationship Inferences - Significance was inferred by the observation of data points over time havng or not having a parallel or inverse relationship. That is to say was there a proportionate increase or decrease of the independent data point as compared to the dependent data at the same point in time. Significant Relationship Quantification - So as to be able to quantify resulting observation a three level scale was used. If three or less continuous independent data points did not correlate than it was inferred that no significant relationship existed. If 4 to 7 independent data points correlated then it was inferred that a moderate significance existed. If 8 or more existed then it was inferred that a strong relationship existed. Assigning Value - For Case IV if a moderate significance was inferred a +.5 value was given. If a strong relationship is inferred it was given a value of +. If no significant relationship was inferred it was given a value of -. These values were used to display the existing relationships between cases I, n, and HI, as compared to a baseline of on a baseline chart. Cause and Effect: Due to the factors of time ( weeks) and resources (no funding) not all possible studies on every possible scenario were conducted. While no absolute causal relationship can be established (this would require a in depth scientific study) there are significant relationships, which can be inferred with a high degree of certainty. The results are so obvious as to bring in to question the validity of positions held by the scientific and government community. The three cases presented where there is enough of a significant relationship to infer that it is highly likely to be a causal relationship are Toxic Waste Releases (TW), Rain Fall (FR) and Traps Fished (TF). VII

Alternate Theories: Scientific studies utilizing experiments that would determine to what extent the effects of TW taking each component listed could be conducted. Combinations of elements and amounts of each one could be established by use of computer simulations then actual experiments could be conducted by introducing these elements at various rates into the observed, closed test environments to determine the effect on the test animals. A similar study could be done with salinity/sediment levels in a water table although basic data currently exists concerning these effects thereby addressing the RF correlation. In regards to traps fished, the position that fishing effort is a root cause to an eventual stock decline and possible collapse (as taken by the government and scientific community) is an assumption that could be quantified and verified (or dismissed) through studies on the following possible scenarios. ) That all marine life is cyclical in nature because of there required coexistence in the ecosystem. The decline in predators fish due to ground fish over fishing, decrease in toxic waste released into the water due to the "Clean Water Act" and the change in salinity/water temperature due to the "Green House Effect" has actually increased the lobster stocks over time. ) That by decreasing the number of traps fished the results will only increase the number of lobsters caught per trap. Their by increasing the return on effort (ROE) from the current average of pound per pot per haul seasonally. 3) That there are so manyfisherman,fishingso many traps, the amount of bait in the water (in fact an artificial condition) has created an environment where lobsters have a man made habitat where juveniles under legal harvest size have additional advantages towards survival. Food and shelter that would otherwise not be present in the ecosystem is placed by the fisherman, there by assisting sub legal size lobsters to live and breed safely until reaching harvestable size. These are the most popular contentions held by thefishingcommunity. Scientific studies could be easily conducted and statistical models constructed to quantify or dismiss these contentions. They are directly related to the TF case. There are currently studies in existence, underway or recently funded that may address these possibilities. Two of which are: Study to Predict Lobster Catch Funded - The Maine/New Hampshire Sea Grant Program was recently awarded funding from National Sea Grant for a three-year project called "Developing Indices Necessary for Predicting Commercial Catches of the American Lobster". The three year project, researchers will develop and test techniques to predict lobster landings at study sites in coastal waters of Long Island Sound, Rhode Island, New Hampshire, and Maine. "The Fate of Bait" - Dr. Robert Steneck of the University of Maine conducted an experiment with graduate students where lobsters were observed crawling into and out of the forward chambers of lobster traps to feed on redfish, herring, and other baitfishor VIII

stealing pieces of bait through the slats of the traps. They were even observed crawling into empty traps. Dr. Robert Steneck concluded that, "Baited lobster traps may actually be the largest aquaculture effort in the world." 3 Shortly after all the traps were removed from thefishinggrounds, the lobsters left the area, too. Information gathered from the "The Fate of Bait" study and from the University of Maine's Lobster Institute shows there are various scientific explanations. One of which is that contention #3 may be a large factor. "Research has found that lobsters have definite opinions as to the type of ocean bottom they prefer. Given the option of settling down on mud, sand, gravel, or cobble (small stones), they all gravitated to the cobble bottom where they could hide from predators in the spaces between the rocks and still catch falling food. "Adolescent" lobsters (a few years old to market size) prefer areas with larger boulders. Adult lobsters don't seem to care-they'll go anywhere and sometimes migrate long distances. They also have fewer predators today." 4 Dr. Steneck also did an observed study where he created what he defined as a "lobster condo" by taking sections of PVC pipe, attaching them together and placing the "condo" on the sea bottom in areas historically inhabited by lobsters. His observations show that it was, "quickly inhabited by sub legal juvenile lobsters". 5 It would seem that the scientific community has enough evidence in the form of studies conducted by long time reputable members of there community to warrant a full study into these three possible explanations provided by the opposing fishing industry. This would in turn lay to rest logical opposition as it can currently be assessed or give credence and confirmation to questions raised by thefishingcommunity. IX 3 Quoted from the "Fate of Bait" study conducted by Dr. Robert Steneck of the University of Maine 4 Quoted from "A lobsters life cycle" University Maine Lobster Institute, web address http://octopus.gma.org/iobsters/society.html 5 Quoted from the "Fate of Bait" study conducted by Dr. Robert Steneck of the University of Maine and "A lobsters life cycle" University Maine Lobster Institute, web address http ://octopus.gma. org/lobsters/society. html

References: National Marine Fisheries Service. Annual Landings Results North Atlantic Lobster 988 through 998. Retrieved October, from the World Wide Web: http://www.stnmfs.gov/webplcomm/plsql/webstl.mf_annula_landing.results.htm Environmental Protection Agency. New England Region Toxic Waste Release Inventory 988 through 998. Retrieved October, from the World Wide Web: http://www. epa.gov/region /steward/emerplan/toxic. html National Weather Service. (999). Weather Almanac (9 th ed.) Washington, DC: United States Printing Office National Climate Data Center. (). Climatilogical National Annual Survey -999 Washington, DC: United States Printing Office Official request for information under the "Freedom of Information Act" Received 6 October from John Nagel, Supervisory Policy Analyst, Operations Group, Sustainable Fisheries Division, Northeast Regional Office, National Marine Fisheries Service Official request for nformation under the "Freedom of Information Act" Received 3 October frommargaret McGrath, Administrative Officer, Rhode Island Department of Environmental Management Office of Licenses and Registration. Rhode Island Fisheries Regulations published and implemented from 988 through 998 Federal Fisheries Regulations published and implemented from 988 through 998 X

Lobster Management Areas Page of THE BASICS Home Contact Us About Us Links Pictures INFORMATION Tha Lobster Fishery Map of Mgt Areas Glossary Lobster Length-Weight Mortality Rates AnnuaL Landings Overfishing Defined Fishery Bioeconomics Catch & Effort FILES TO DOWNLOAD Lobster Business Models Fish Mgt. Slide Show KEY CONCEPTS Recruit Overfishing Growth.Overfishing Fishing Mortality Conservation Payoff Targets vs Thresholds Sitemap Hits OwnersSuite

The Official Definition of 3verfishing Explained Page of THE BASICS Home Contact Us About Us Links Pictures INFORMATION TheLobster Fishery Map of Mgt Areas Glossary Lobster Length-Weipht Mortality Rates Annua.andings Overflshing Defined Fishery Bioeconomics Catch & Effort FILES TO DOWNLOAD Lobster Business Modals Fish Mgt. Slide Show KEY CONCEPTS Recruit Overfishing Growth Overfishing Fishing Mortattty Conservation Payoff Targets vs Thresholds Owners Suite The "Definition of Overfishing" for the Lobster Resource "The American lobster resource is overfished when it is harvested at a rate that results in egg production from the resource, on an egg-per-recruit basis, that is less than % of the level produced by an unfished population." (Atlantic States Marine Fisheries Commission Amendment #3 to the Interstate Fishery Management Plan for Lobster) This official definition of overfishing can also be explained as follows: The average female lobster should be allowed to live long enough to produce at least % of the eggs that she would produce if she were allowed to live her natural life. While it may seem impossible to judge the egg production from an unfished population, considering that the lobster population has been heavily fished for over years, it should be considerably easier to calculate the egg production from a female that lived a natural life span. If we know how often a female produces eggs, how many eggs she produces each time, and how many years she is likely to live, we can calculate how many eggs she would produce over her life time.