Effects of Fishing Gear Area Restriction and Catch Share Programs on Effort Distribution in Pulley Ridge, Eastern Gulf of Mexico

Transcription

1 University of Miami Scholarly Repository Open Access Theses Electronic Theses and Dissertations Effects of Fishing Gear Area Restriction and Catch Share Programs on Effort Distribution in Pulley Ridge, Eastern Gulf of Mexico Emily Starnes University of Miami, Follow this and additional works at: Recommended Citation Starnes, Emily, "Effects of Fishing Gear Area Restriction and Catch Share Programs on Effort Distribution in Pulley Ridge, Eastern Gulf of Mexico" (2017). Open Access Theses This Open access is brought to you for free and open access by the Electronic Theses and Dissertations at Scholarly Repository. It has been accepted for inclusion in Open Access Theses by an authorized administrator of Scholarly Repository. For more information, please contact

2 UNIVERSITY OF MIAMI EFFECTS OF FISHING GEAR AREA RESTRICTION AND CATCH SHARE PROGRAMS ON EFFORT DISTRIBUTION IN PULLEY RIDGE, EASTERN GULF OF MEXICO By Emily D. Starnes A THESIS Submitted to the Faculty of the University of Miami in partial fulfillment of the requirements for the degree of Master of Science Coral Gables, Florida August 2017

3 2017 Emily D. Starnes All Rights Reserved

4 UNIVERSITY OF MIAMI A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science EFFECTS OF FISHING GEAR AREA RESTRICTION AND CATCH SHARE PROGRAMS ON EFFORT DISTRIBUTION IN PULLEY RIDGE, EASTERN GULF OF MEXICO Emily D. Starnes Approved: David J. Die, Ph.D. Research Associate Professor of Marine Ecosystems and Society Elizabeth Babcock, Ph.D. Associate Professor of Marine Biology and Ecology Christopher Parmeter, Ph.D. Associate Professor of Economics Guillermo Prado, Ph.D. Dean of the Graduate School

5 STARNES, EMILY D. (M.S., Marine Ecosystems and Society) Effects of Fishing Gear Area Restriction and (August 2017) Catch Share Programs on Effort Distribution In Pulley Ridge, Eastern Gulf of Mexico Abstract of a thesis at the University of Miami. Thesis supervised by Professor David Die. No. of pages in text. (166) Pulley Ridge, located in the Gulf of Mexico on the West Florida Shelf, contains a rare and ecologically valuable mesophotic coral reef ecosystem that provides essential fish habitat for many commercially and recreationally important fish species. The area is currently protected by a Habitat Area of Particular Concern (HAPC), a marine protected area established in 2005 that covers part of the Pulley Ridge area and mandates several fishing gear restrictions. The HAPC prohibits the most environmentally destructive fishing gear types; however, these protections are somewhat limited because they do not encompass the entire Pulley Ridge area, and because they do not impose any no-take areas for commercial fisheries or protect from any non-fishing activities. In addition to the HAPC, commercial fishing catch throughout the Gulf of Mexico (including the Pulley Ridge area) is managed using an Individual Fishing Quota program which was implemented in The objective of this Master of Science (M.S.) thesis is to assess changes in fishing effort for the commercial reef fish fishery in the Pulley Ridge area, before and after the implementation of the HAPC and the Gulf of Mexico catch share programs. National Oceanic and Atmospheric Administration (NOAA) Fisheries Logbook Data are used to test hypotheses regarding commercial fishing effort for reef fish in the areas that include Pulley Ridge and the HAPC.

6 It is necessary to assess the status of economically and ecologically important fishery species and fishing effort distributions in the Pulley Ridge area. Furthering scientific understanding in this area will help to inform management actions in order to increase the efficacy of marine resource management strategies for Pulley Ridge and the West Florida Shelf region.

7 ACKNOWLEDGEMENTS I would first like to express my sincere appreciation and gratitude to my thesis advisor and committee chair Dr. David Die, for all of his help and support throughout my graduate studies at the University of Miami. Through his guidance and mentorship, I have gained invaluable knowledge and experience in the fields of marine science and fisheries management, and his advice about my thesis research as well as my career path has been incredibly helpful. I would also like to thank my committee members, Dr. Elizabeth Babcock and Dr. Christopher Parmeter, for all of their useful feedback and critiques during the process of completing my thesis project. I would especially like to thank all of the faculty and staff at the University of Miami s Rosenstiel School of Marine and Atmospheric Science who I ve had the pleasure of working with over the last three years. I ve had the opportunity to learn from some of the best and brightest in the field of marine science as a graduate student at RSMAS; the knowledge and skills I gained from my coursework and fieldwork experiences were indispensable during my thesis research, and will continue to benefit me for years to come as I continue in this career path. I would also like to thank Kevin McCarthy and Larry Perruso at NOAA s Southeast Fisheries Science Center, for providing access to the critical NMFS logbook data that were used in the creation of this thesis. I am also incredibly grateful for the generous funding that I received for this project and for my graduate studies at RSMAS. Funding for this project was provided by NOAA s Center for Sponsored Coastal Ocean Research (Grant NA11NOS ), as part of the Connectivity of the Pulley Ridge South Florida Coral Ecosystem project. iii

8 Thanks also to my M.S. cohort and Fisheries labmates past and present for their support, advice and friendship throughout my time in Miami. I would especially like to thank my friend and colleague Holly Perryman for her frequent help and advice during this process. Finally, I would like to express my thanks to my family and friends for their incredible support and encouragement. Words cannot express how grateful I am to my Mom, Dad, Blair, Rachel, Gerilyn and Chris for supporting me in everything that I do, and especially for cheering me on throughout my graduate school experience. This thesis would not have been possible if not for their love and support thank you. iv

9 TABLE OF CONTENTS Page LIST OF FIGURES... LIST OF TABLES... vii xii Chapter 1 INTRODUCTION Study Area Current Resource Management Magnuson-Stevens Fishery Conservation and Management Act Gulf of Mexico Reef Fish Fishery Management Plan Introduction of Catch Share Programs Essential Fish Habitat Habitat Areas of Particular Concern Pulley Ridge Designation Potential Management Changes Statement of Problem Purpose of Study Research Questions Hypotheses Data Sources and Methodology INDIVIDUAL FISHING QUOTAS Introductory Remarks Methodology Overview NOAA Logbook Data Access Data Cleanup for Analytical Use Preliminary Data Summaries Analysis of Variance Models Results Preliminary Data Summaries Data Transformations for ANOVA Models Handline Models Longline Models Model Diagnostics Discussion v

10 3 HABITAT AREA OF PARTICULAR CONCERN Introductory Remarks Methodology Overview Data Cleanup for Analytical Use Analysis of Variance Models Results Handline Models Longline Models Model Diagnostics Discussion CONCLUSION Limitations and Delimitations Significance of Study LITERATURE CITED APPENDIX A APPENDIX B vi

11 LIST OF FIGURES Figure 1: Background information map showing the location of Pulley Ridge, the Pulley Ridge HAPC, and nearby marine protected areas including the Florida Keys National Marine Sanctuary and the Dry Tortugas Ecological Reserve Figure 2: Map showing Pulley Ridge and the HAPC, surrounding marine protected areas such as the Florida Keys National Marine Sancruary, and the study area consisting of NMFS statistical grids 1 through 6 17 Figure 3: Map of NMFS statistical grids off the Florida coast and West Florida Shelf...21 Figure 4: Seasonal frequency distribution showing total number of fishing days (days at sea for each fishing trip) for the entire study area (NMFS statistical grids 1 6) for years Figure 5: Seasonal frequency distributions showing total number of fishing days (days at sea for each fishing trip) for each individual NMFS statistical grid within the study area, for years Figure 6: Histograms showing untransformed and transformed fishing effort data, aggregated for analysis of variance model 1 (fleet-level, handline and longline combined)..31 Figure 7: Boxplots of square-root transformed annual fishing effort data for the Gulf of Mexico handline fishery at the fleet level, subset by three time periods ( , and ) and six areas (NMFS statistical grids 1 6) 34 vii

12 Figure 8: Boxplots showing log-transformed annual fishing effort for the Gulf of Mexico handline fishery at the individual vessel level, subset by three time periods ( , and ) and six areas (NMFS statistical grids 1 6) 35 Figure 9: Boxplots showing square-root transformed annual fishing effort for the Gulf of Mexico handline fishery at the fleet level, subset by four seasons (January March, April June, July September and October December) and six areas (NMFS statistical grids 1 6). 36 Figure 10: Boxplots showing log-transformed annual fishing effort for the Gulf of Mexico handline fishery at the individual vessel level, subset by four seasons (January March, April June, July September and October December) and six areas (NMFS statistical grids Figure 11: Boxplots showing square root-transformed annual fishing effort for the Gulf of Mexico longline fishery at the fleet level, subset by three time periods ( , and ) and four seasons (January March, April June, July September and October December) Figure 12: Boxplots showing square root-transformed annual fishing effort for the Gulf of Mexico longline fishery at the individual vessel level, subset by three time periods ( , and ) and four seasons (January March, April June, July September and October December) 40 Figure 13: Boxplots showing square root-transformed annual fishing effort for the Gulf of Mexico longline fishery at the fleet level, subset by three time periods ( , and ) and six areas (NMFS statistical grids 1 6) 41 viii

13 Figure 14: Boxplots showing square root-transformed annual fishing effort for the Gulf of Mexico longline fishery at the individual vessel level, subset by three time periods ( , and ) and six areas (NMFS statistical grids 1 6) 41 Figure 15: Example showing diagnostic plots for analysis of variance model 1, which is the fleet-level model for combined handline and longline fisheries.. 42 Figure 16: Map of the study area for HAPC analysis for the handline fishery, consisting of two areas (NMFS statistical areas 1 and 2) and three port location counties (Dade, Monroe and Collier counties) Figure 17: Map of the study area for HAPC analysis for the longline fishery, consisting of two areas (NMFS statistical areas 2 and 3) and five counties (Monroe, Collier, Lee, Manatee and Pinellas counties).. 54 Figure 18: Histograms showing untransformed and transformed fishing effort data, aggregated for analysis of variance model 8 (vessel-level, handline only) Figure 19: Boxplots showing log-transformed annual fishing effort for the Gulf of Mexico handline fishery at the fleet level, subset by two geographic areas (NMFS statistical grids 1 and 2) and three counties (Collier, Dade and Monroe).. 65 Figure 20: Boxplots showing log-transformed annual fishing effort for the Gulf of Mexico handline fishery at the fleet level, subset by three time periods ( , and ) and two geographic areas (NMFS statistical grids 1 and 2) ix

14 Figure 21: Boxplots showing log-transformed annual fishing effort for the Gulf of Mexico handline fishery at the fleet level, subset by three time periods ( , and ) and three counties (Collier, Dade and Monroe) Figure 22: Boxplots showing log-transformed annual fishing effort for the Gulf of Mexico handline fishery at the individual vessel level, subset by three time periods ( , and ) and three counties (Collier, Dade and Monroe).. 69 Figure 23: Boxplots showing log-transformed annual fishing effort data for the Gulf of Mexico handline fishery at the individual vessel level, subset by three time periods ( , and ) and four seasons (January March, April June, July September and October December). 70 Figure 24: Boxplots showing log-transformed annual fishing effort data for the Gulf of Mexico handline fishery at the individual vessel level, subset by two geographic areas (NMFS statistical grids 1 and 2) and four seasons (January March, April June, July September and October December) Figure 25: Boxplots showing square root-transformed annual fishing effort data for the Gulf of Mexico longline fishery at the fleet level, subset by two geographic areas (NMFS statistical grids 2 and 3) and three time periods ( , and ). 73 x

15 Figure 26: Boxplots showing square root-transformed annual fishing effort data for the Gulf of Mexico longline fishery at the fleet level, subset by two geographic areas (NMFS statistical grids 2 and 3) and five counties (Collier, Lee, Manatee, Monroe and Pinellas). 74 Figure 27: Boxplots showing square root-transformed annual fishing effort data for the Gulf of Mexico longline fishery at the fleet level, subset by three time periods ( , and ) and five counties (Collier, Lee, Manatee, Monroe and Pinellas) Figure 28: Boxplots showing square root-transformed annual fishing effort data for the Gulf of Mexico longline fishery at the individual vessel level, subset by three time periods ( , and ) and five counties (Collier, Lee, Manatee, Monroe and Pinellas). 77 Figure 29: Boxplots showing square root-transformed annual fishing effort data for the Gulf of Mexico longline fishery at the individual vessel level, subset by three time periods ( , and ) and four seasons (January March, April June, July September and October December) 78 Figure 30: Example showing diagnostic plots for analysis of variance model 9, which is the fleet-level longline model for HAPC analysis in Chapter 3 79 xi

16 LIST OF TABLES Table 1: Notation used in ANOVA models for Chapter 2 27 Table 2: Table of ANOVA results for model 3 32 Table 3: Table of ANOVA results for model 4 33 Table 4: Table of ANOVA results for model 5 38 Table 5: Table of ANOVA results for model 6 38 Table 6: Number of unique handline vessels within the study area (NMFS statistical grids 1 6), during years Table 7: Number of unique longline vessels within the study area (NMFS statistical grids 1 6), during years Table 8: Notation used in ANOVA models for Chapter 3 61 Table 9: Table of ANOVA results for model 7 64 Table 10: Table of ANOVA results for model Table 11: Table of ANOVA results for model Table 12: Table of ANOVA results for model Table 13: Frequency of handline trip lengths out of Dade county in statistical areas 1 and 2, for the entire time series ( ). 81 Table 14: Frequency of handline trip lengths out of Dade county in statistical areas 1 and 2, for individual years xii

17 Chapter 1 Introduction 1.1 Study Area Pulley Ridge (Fig. 1) is a series of drowned barrier islands on the southwest Florida shelf in the eastern Gulf of Mexico (USGS 2013). Originally discovered in 1950, it is approximately 300 km long and trends from north to south. The ridge is subtle, measuring approximately 15 km across with only about ten meters of relief (USGS 2013, NOAA 2013a). Water depths at Pulley Ridge range from meters, and the southern area of the ridge is broader and has the highest relief. For this reason, the southern part of the ridge hosts a variety of scleractinian corals, macroalgae, and a mix of shallow and deep-water fish species (NOAA 2013a). Figure 1: Background information map showing the location of Pulley Ridge, the Pulley Ridge HAPC, and nearby marine protected areas including the Florida Keys National Marine Sanctuary and the Dry Tortugas Ecological Reserve. 1

18 2 The mesophotic reef ecosystems on Pulley Ridge are dominated by plating scleractinian corals and calcareous coralline algae. Coral reef growth at Pulley Ridge may be supported by the Loop Current (Jarrett et al. 2005), which brings warm, clear water to the southern part of the ridge and allows corals and other sensitive organisms to thrive despite the depth and mesophotic conditions. Live coral cover of up to 60% has been observed at Pulley Ridge (Jarrett et al. 2005). This means that Pulley Ridge is a relatively healthy and robust coral reef ecosystem, particularly when compared to other reef ecosystems throughout Florida and the Caribbean, many of which have been severely degraded by subpar environmental conditions and human activities. It is highly unusual to have such a healthy coral ecosystem at the depth range observed at Pulley Ridge. Halley et al. (2004) suggest that Pulley Ridge may be the deepest photosynthetic coral reef in the continental United States, making this a very unique and ecologically valuable ecosystem. Over 60 fish species have been identified at Pulley Ridge (NOAA 2013a), representing a diverse mixture of typically shallow-water and deep-water fishes. Commercially important species found on Pulley Ridge include Epinephelus morio (red grouper) and Mycteroperca phenax (scamp). Pulley Ridge also hosts many species of tropical reef fishes that are typically found in shallow water, including bluehead wrasse, coney, bicolor damselfish, hogfish, and some angelfishes (USGS 2013). Deep-water fish species include bank butterflyfish, spotfin hogfish, deep-water squirrelfish, wrasse bass and roughtongue bass (USGS 2013). This diverse assemblage is unusual, and likely occurs due to the combination of depth and suitable coral reef habitat for the wide variety of fishes found in the region.

19 3 1.2 Current Resource Management Magnuson-Stevens Fishery Conservation and Management Act The Magnuson-Stevens Fishery Conservation and Management Act (MSA) is the principal legislation governing fisheries management and conservation within United States waters for federally managed fish species. The Act was first passed in 1976, and later amended in 1996 (NOAA 2013d), following the widespread realization that many commercially important fishery species had declined to the point where their survival was threatened or they were becoming less economically viable for commercial fishing activities (Congress 1996). The overarching purpose of the MSA was to provide a comprehensive national plan to prevent overfishing, rebuild overfished stocks, to ensure conservation, to facilitate long-tern protection of essential fish habitats, and to realize the full potential of the Nation s fishery resources (Congress 1996). The MSA has led to the establishment of regional Fishery Management Councils (FMCs) for major fisheries regions throughout the United States, including the Gulf of Mexico. Each FMC is responsible for managing important federal fishery resources within their specific region through the creation and implementation of fishery management plans (FMPs) for all federally managed species (NOAA 2013d) Gulf of Mexico Reef Fish Fishery Management Plan The original Gulf of Mexico Reef Fish Fishery Management Plan (FMP) was implemented by the Gulf of Mexico Fishery Management Council in November 1984 (GMFMC 2010a), and included several regulatory measures meant to facilitate recovery of declining reef fish stocks in the Gulf of Mexico. These regulations comprised minimum size limits for red snapper, prohibitions on destructive fishering gear types in

20 4 the most stressed inshore areas, and data reporting requirements for commercial fishers (GMFMC 2010a). This FMP was significantly amended in 1990; Amendment 1 set a new primary objective for the FMP to stabilize populations of all commercially harvested reef fish species by the year 2000 (GMFMC 2010a). One of the most important regulatory changes resulting from Amendment 1 was the introduction of annual commercial quotas for reef fish. Amendment 1 also established a commercial reef fish vessel permitting system, and established the fishing year to run from January 1 st through December 31 st (GMFMC 2010a). This means that the total allowable catch (TAC) quota for the commercial reef fish fishery turns over each year on January 1 st Introduction of Catch Share Programs Under the TAC quota system, fishers were able to fish as much as possible starting on January 1 st of each year until the total quota was reached, after which the fishery would be closed for the season. This type of management system tends to lead to a race to fish among fishing vessels, where each fisher attempts to catch as many fish as possible in order to obtain the greatest portion of the TAC before the quota is reached and the fishing season is closed. This causes rapid depletion of the fishery stocks at the beginning of the fishing season, and leads to shorter fishing seasons overall and unstable socioeconomic conditions for fishers. For this reason, the Gulf of Mexico FMP was amended again in 2007 and 2010 to introduce individual fishing quota (IFQ) programs for the red snapper fishery and grouper and tilefish fisheries, respectively (GMFMC 2010a). These amendments established IFQ shares for individual reef fish fishers, which were allocated proportionally among eligible

21 5 participants based on average annual landings. IFQ shares can only be transferred to other individuals or vessels with currently valid commercial reef fish permits (GMFMC 2010a) Essential Fish Habitat According to the MSA, one of the biggest threats to our marine fishery resources is the continued loss of aquatic habitats. For this reason, the MSA requires that fishery conservation and management plans include habitat considerations, such as the designation of Essential Fish Habitat and Habitat Areas of Particular Concern. The FMCs must minimize any adverse fishing impact to the EFH (to the extent practical or feasible), and identify other possible solutions to help conserve and protect the EFH (NOAA 2013d). Once EFH has been designated, the MSA requires that NMFS coordinate with other federal agencies and provide information to help them protect and conserve the EFH (NOAA 2013d). This is beneficial because ocean conservation policy tends to be fragmented, with competing uses and many different authorities protecting various resources; this legislation helps combat that problem by increasing communications between agencies on important fisheries conservation matters. Federal agencies are required to communicate with NMFS concerning any potential activities that may have an adverse impact on the Essential Fish Habitat. If any such activities are found to have an adverse impact on the EFH, then NMFS is required to provide conservation recommendations in order to minimize impacts. The Gulf of Mexico Fishery Management Council (GMFMC) passed an amendment to all 7 of their Fishery Management Plans in 1998, identifying and describing the essential habitat for each life stage of the 26 managed species that

22 6 represent the majority of landings in the Gulf of Mexico (GMFMC 2010b). The amendment describes the habitat types and distribution of these habitats, as well as current anthropogenic threats to the habitats and factors leading to their degradation or loss. In addition, the amendment provides ideas for conservation and management measures, and recommendations to minimize fishing impacts to the EFH (GMFMC 2010b) Habitat Areas of Particular Concern Habitat Areas of Particular Concern (HAPCs) are a subset of Essential Fish Habitat designation, and are also governed under the authority of the MSA. However, HAPCs differ in some ways from other EFH designations because HAPCs are identified based on characteristics of the habitat itself, whereas other types of EFH are designated based on their use by commercially important fishery species. According to the National Oceanographic and Atmospheric Administration (NOAA), HAPCs are defined as high priority areas for conservation, management, or research because they are rare, sensitive, stressed by development, or important to ecosystem function (NOAA 2013c). NOAA and the regional Fishery Management Councils have established over 100 HAPCs within the United States federal waters (NOAA 2013c), including the Pulley Ridge HAPC, which is classified as a rare and important area that may be stressed by human activities (NOAA 2013b) Pulley Ridge Designation The Pulley Ridge HAPC was designated in 2005 by the GMFMC, as part of the Essential Fish Habitat Amendment 3. This amendment designated and established boundaries for several HAPCs in the Gulf of Mexico, including Pulley Ridge (GMFMC

23 7 2005). The Pulley Ridge HAPC covers an area of 2,300 square nautical miles in the southern part of Pulley Ridge (Fig. 1). Pulley Ridge was designated as a Habitat Area of Particular Concern because it is both rare and ecologically important (NOAA 2013b, GMFMC 2005); it is a unique mesophotic coral reef ecosystem, which is not found in many other places within our federal waters. Fishing regulations within the Pulley Ridge HAPC prevent bottom anchoring by fishing vessels, bottom trawling, longlines, buoy gear, and all traps and pots (NOAA 2013b). The HAPC does not cover the entirety of Pulley Ridge (Fig. 1), meaning that there are spatial differences in allowed fishing gear types along the ridge and in surrounding areas of the West Florida Shelf. The current regulations for Pulley Ridge do not protect against non-fishing activities such as anchoring by non-fishing vessels, diver impacts, or any other non-extractive uses (NOAA 2013b). The Pulley Ridge HAPC is particularly important from both an ecological and economic perspective. The area is a healthy and valuable coral ecosystem, and supports a variety of economically important commercial fishing activities. For these reasons, it is important to assess the ecological and socioeconomic impacts of management measures imposed upon the fisheries utilizing Pulley Ridge. 1.3 Potential Management Changes According to the National Oceanic and Atmospheric Administration (NOAA), the Florida Keys National Marine Sanctuary and its Sanctuary Advisory Council (SAC) are currently in the process of reviewing the Sanctuary s regulations, including the rules and boundaries of its various marine zones (NOAA 2013b). Several areas are currently being considered for inclusion within the Sanctuary, including Pulley Ridge (NOAA 2013b).

24 8 Because Pulley Ridge is such a robust and healthy coral reef ecosystem, it is hypothesized that the Ridge may be a source of fish and coral larvae that could help replenish other South Florida coral reef ecosystems such as the Florida Keys. This topic is currently being studied through a large-scale research project led by the NOAA s Cooperative Institute for Marine and Atmospheric Studies at the University of Miami, in collaboration with over 35 scientists at 11 different universities (NOAA 2015). If Pulley Ridge and the Florida Keys are found to be sufficiently interconnected in terms of larval replenishment, it may benefit the FKNMS for Pulley Ridge to gain additional protections under the National Marine Sanctuary Act. It is possible that the FKNMS could be extended to include part or all of Pulley Ridge as a result of this review. If this occurs, then Pulley Ridge will be protected by all of the Sanctuary-wide restrictions mandated by the Florida Keys National Marine Sanctuary and Protection Act, such as vessel speed restrictions and prohibitions on oil drilling. This would likely benefit the Florida Keys, as well as the Pulley Ridge area itself, by providing further protections in addition to the current fishing gear restrictions. Increased habitat protection could lead to greater ecosystem services in the area, and maximize management benefits for the Gulf of Mexico fisheries due to possible higher carrying capacities and increases in fish abundance. 1.4 Statement of Problem Although several years have passed since the implementation of the Pulley Ridge Habitat Area of Particular Concern in 2005 and the Gulf of Mexico catch share program in 2010, impacts of these regulations on Pulley Ridge and its fisheries are still not well understood. In addition to this, the NOAA Office of National Marine Sanctuaries is

25 9 currently considering additional protections to Pulley Ridge as a possible expansion area of the Florida Keys National Marine Sanctuary (NOAA 2013b). For this reason, it is important to evaluate the socioeconomic effects of current regulations, in order to properly understand the possible impacts of any future regulatory change on the Gulf of Mexico fishing community. One of the major changes expected to result from increased fishery regulation is fishing effort displacement. The Gulf of Mexico Fishery Management Council stated that this may occur to some extent as a result of the HAPC implementation and other regulations within the Pulley Ridge region, but that this implementation could also provide some benefits to fishers in this region due to the potential for increased fish populations in protected habitat areas. Fishing effort displacement was one of the main changes expected from the Habitat Area of Particular Concern implementation, because longline fishing gear is now prohibited within the boundaries of the HAPC. Because of this, longline fishers that previously fished within the HAPC would have been forced to either change gear types, move out to adjacent areas to continue fishing with longline gear, reduce longline effort, or stop fishing altogether. In order to understand whether any future regulatory changes to Pulley Region would lead to additional displacement, this thesis will examine the possible effort displacements that occurred through the protections warranted to the Pulley Ridge HAPC. However, in order to achieve this, it is necessary to also analyze the possible displacement caused by the catch share program for the Gulf of Mexico reef fish fishery, which is the most important management measure enacted since the implementation of the HAPC.

26 Purpose of Study This study aims to evaluate changes in fishing effort distribution and fishers behavior in the area of the Gulf of Mexico containing Pulley Ridge, in response to fishery management changes impacting the region. NOAA Fisheries Logbook Data are analyzed in order to characterize and describe changes in fishing effort before and after the implementation of the Habitat Area or Particular Concern and Gulf of Mexico reef fish catch share programs Research Questions Primary research questions for this thesis project are: How has fishing effort changed in areas that are in the vicinity of the Pulley ridge in response to the reef fish Individual Fishing Quota program in the Gulf of Mexico? How have total fishing effort and effort distributions changed in response to the implementation of the Pulley Ridge Habitat Area of Particular Concern in 2005? Hypotheses Initial hypotheses for this project are: Catch share programs such as the Gulf of Mexico reef fish Individual Fishing Quota program result in a longer fishing season that is more evenly spread out, lessening the race to fish commonly found in fisheries with Total Allowable Catch quotas. The Habitat Area of Particular Concern implementation led to changes in the different fishing gear types being used in the protected area, or fishers using the prohibited gear types deciding to fish in different areas instead of Pulley Ridge.

27 Data Sources and Methodology NOAA Fisheries Logbook Program data were analyzed in order to complete this thesis project. The data set included information on all commercial fishing trips for federally managed reef fish species in NMFS statistical grids 1 through 6, for years 2000 through The logbook data set contained variables such as dates for each fishing trip, species caught, gear type used, weight of landings, hours fished, fishing effort, depth where the majority of the fish were caught, and location information (NMFS statistical grids). From the logbook data, hypotheses were tested regarding changes in fishing effort in the area of the Gulf of Mexico that includes Pulley Ridge, before and after the implementation of the Pulley Ridge Habitat Area of Particular Concern and the Gulf of Mexico catch share program. Two data chapters were composed from these analyses, one concerning the Individual Fishing Quota program, and one concerning the Habitat Area of Particular Concern.

28 Chapter 2 Individual Fishing Quotas 2.1 Introductory Remarks Commercial and recreational fisheries are important to the U.S. economy, resulting in $162.9 billion in sales each year (NOAA 2010, NMFS 2010). However, some fisheries are under-performing biologically and economically (NMFS 2009a); for this reason, it is crucial to effectively manage and rebuild these fisheries in order to sustain fish populations as well as increase revenues from commercial and recreational fishing. Catch shares are a general term for fisheries management techniques that allocate portions of the annual Total Allowable Catch (TAC) to specific fishers or groups of fishers for their sole use, allowing greater accountability and flexibility because each catch share owner is directly accountable to stop fishing when their portion of the quota has been reached (NOAA 2010). This technique generally provides greater security and safety to fishermen than traditional fisheries management, because they are able to fish their individual quota when they choose to, such as during favorable weather conditions or at more ideal times throughout the year (such as when fish prices are higher or fishing vessels operating costs are lower). This can be much more favorable to fishers than a more traditional approach where fishers compete with one another in a race to fish, catching as many fish as possible before the overall catch or effort limits are reached. For this reason, catch share programs may have significant impacts on seasonal fishing effort distributions. Catch share programs provide numerous biological and ecological benefits; they help to ensure that total annual catch limits are not exceeded, and help to 12

29 13 reduce bycatch caught by commercial fishing vessels. This means that catch share programs can be a key factor in meeting goals of rebuilding and sustaining critical fishery resources (NOAA 2017a). Additional economic benefits of catch shares include reduced costs to produce seafood, extended fishing seasons, reduced market surpluses and increased dockside seafood prices (NOAA 2017b). Types of catch share programs include Limited Access Privilege Programs (LAPP) and Individual Fishing Quotas (IFQs), which are specific types of legally defined programs, and other measures such as Territorial Use Rights Fisheries (TURFs), which grant exclusive fishing privileges in a geographically defined area (NOAA 2017b), (NOAA 2010). NOAA, Congress and other national experts have recognized catch share programs as a viable tool for effective fishery management that should be available for use in our fisheries ecosystems (NOAA 2010, Congress 2006, U.S. Commission on Ocean Policy 2004). NOAA developed a guiding policy for U.S catch share programs in 2010 (NOAA 2010), and Congress addressed the importance of catch share programs in its 2006 amendments to the Magnuson-Stevens Fishery Conservation and Management Act (MSA) (Congress 2006), which outlines specific criteria and obligations for fisheries to be properly managed under catch share programs. Catch shares both in the U.S. and abroad have been demonstrated to support compliance with annual catch limits, reduce negative effects of the race to fish, and when appropriately designed, can also reduce overfishing and lead to safer and more economically productive fisheries. There are currently 16 catch share programs in the federal waters of the United States, and these programs are managed by six different regional fishery management councils; additional catch share programs are currently being developed. The Unites States pioneer catch

30 14 share program was implemented in 1990, and manages the Surf Clam and Ocean Quahog Fishery in the Mid-Atlantic region (NOAA 2017b). Two catch share programs have been implemented thus far in the Gulf of Mexico. The Gulf of Mexico Red Snapper Individual Fishing Quota (IFQ) program was implemented in 2007 (NMFS 2009b), and the Gulf of Mexico Grouper and Tilefish IFQ program was implemented in The data analysis for this project will focus primarily on changes to fishing effort resulting from the grouper and tilefish IFQ program beginning in 2010, because red snapper are less common in the southwest Florida Shelf in comparison to other areas while this region is ideal habitat for groupers. This 2010 grouper and tilefish IFQ program was enacted because prior management strategies had resulted in overcapitalization of the fishery, meaning that the harvest capacity of fishing vessels and fishing participants was in excess of the amount required to efficiently harvest the total allowable catch (TAC). This created derby-like conditions for the fishery, under which participating fishers raced to harvest as many fish as they could before the TAC was reached and the fishery was closed for the season. These race to fish conditions lead to negative effects on the fishing industry such as shortened fishing seasons, increased operating costs, fluctuations in seafood supply, poor working conditions and lower profits (NMFS 2009c). The National Marine Fisheries Service (NMFS) and the Gulf of Mexico Fishery Management Council (GMFMC) developed Amendment 29 to the Gulf of Mexico Reef Fish Fishery Management Plan, which implemented an IFQ program for the fishery beginning in January For this to be approved, the Magnuson-Stevens Act (MSA) required a referendum to be passed by a

31 15 majority of grouper and tilefish fishers. This vote was held in December 2008 and approved by an 81% majority (NMFS 2009c). The NOAA Fisheries Logbook Program has been collecting catch and effort data for each fishing trip conducted by commercial fishing vessels in the Gulf of Mexico since 1993 (Saul et al. 2013). Fishing vessels location information (where the majority of the animals were caught) is reported using spatial areas that are referred to as NMFS statistical grids or statistical areas (see Fig. 2). The statistical grids are required location reporting information for all permit-holding commercial fishing vessels in the Gulf of Mexico, and are used by managers and scientists for fisheries logbook data analysis. Pulley Ridge is located in the eastern part of the Gulf of Mexico on the Southwest Florida Shelf. The geographic scope of the Individual Fishing Quota analysis for this thesis project will focus on statistical grids 1 through 6 (Fig. 2). Pulley Ridge is located in statistical grids 2 through 4; the Pulley Ridge Habitat Area of Particular Concern is located in Area 2, and the majority of the ridge is located in Area 3. The purpose of this analysis is to assess broad-scale changes to fishing effort distributions in the areas containing and surrounding Pulley Ridge before and after major management changes such as the implementation of the Individual Fishing Quota program for the Gulf of Mexico reef fish fishery in The overall goal of this study is to evaluate changes to fishing effort in Pulley Ridge, particularly regarding the Habitat Area of Particular Concern in this region. Because catch share programs are such an important and wide-reaching facet of overall fisheries management, it is first necessary to gauge the effects of the Gulf of Mexico catch share program to determine how catch shares influence commercial fishing effort in this region. This study will attempt to

32 16 characterize the changes in temporal and spatial patterns of fishing effort in the Pulley Ridge area as a result of two major management interventions, the IFQ program and the HAPC. The three time periods described in the methodology section (pre-2005, , and post-2010) define the management regimes throughout the time series, because the HAPC was implemented in 2005 and the IFQ program was implemented in In addition, the data are aggregated two different ways. For fleet-level analyses, individual vessel information is disregarded and fishing effort information is aggregated for the entire fleet; then, data are also aggregated by individual Vessel ID for the purpose of conducting vessel-level analyses.

33 Figure 2: Map showing Pulley Ridge and the HAPC, surrounding marine protected areas such as the Florida Keys National Marine Sanctuary, and the study area consisting of NMFS statistical grids 1 through 6. 17

34 Methodology Overview Analysis of variance (ANOVA) models were completed using NOAA logbook data observations of total number of fishing days per time period, season, area and gear type, in order to determine how the pattern of fishing effort has changed between these factors. The factors of interest included two gear types (handline and longline), three time periods (pre-hapc, post-hapc and pre-ifq, and post-ifq), four seasons, and the six area grids within the study site. The ANOVA models tested the significance of each individual factor in explaining the variation of fishing effort, as well as every possible two-way interaction and the three-way interaction of area, season and time period. Preliminary analysis indicated that the handline and longline fleets have very different fishing effort distribution patterns, meaning that they essentially operate as two distinct, separate fisheries (Appendix A contains all of the preliminary analysis for models 1 and 2). Therefore, the data were subset by gear type, and ANOVA models were completed for each individual gear type. These analyses were completed at the fleet level, and also on the level of each individual vessel. For the fleet-level models, the data were aggregated using the sum of total fishing days by time period, year, season, area, and gear type. For the vessel-level models, the data were aggregated by the same factors but with the addition of Vessel ID. In summary, four ANOVA models were run: fleet-level and vessel-level for both handline and longline fisheries. The raw data do not necessarily follow a normal distribution, so the data were transformed in order to normalize the distributions and make them more suitable for

35 19 analysis using ANOVA models. Either a logarithmic transformation or a square root transformation was used, depending on which transformation was most suitable for each model s dataset (see Fig. 6) NOAA Logbook Data Access Data access for the purposes of this thesis project was provided by the National Oceanic and Atmospheric Administration (NOAA) Southeast Fisheries Science Center (SEFSC) under a Non-Disclosure Agreement. Datasets from the NOAA Fisheries Logbook System were requested for all logbook data for the reef fish fishery in National Marine Fisheries Service (NMFS) statistical grids 1 6, from years The data were provided as two separate data sets in comma-separated values (CSV) format; the first data set was received in June 2015 and contained raw logbook data from year 2000 through part of 2015, and the second data set was received in 2016 and contained raw logbook data for the entirety of the year These data sets were kept on a school office computer, access to which was restricted via password to comply with Non- Disclosure Agreement requirements Data Cleanup for Analytical Use To begin data analysis, the CSV files were read into R statistical computing software (version 3.2.2) using the R Studio integrated development environment (IDE), with the read.csv() function. The raw logbook data sets contained observations for each fishing trip completed by the commercial reef fish fleet during the time series, and included several type of information about each of these trips, such as vessel identification number, gear type used, statistical area, depth at which the majority of animals were caught, catch information, trip dates, and days away. In examining the raw

36 20 data sets, it became apparent that there were some formatting problems in the data sets resulting in a small number of erroneous entries; Some of the entries had most (if not all) of their variables shifted over into the wrong columns, resulting in missing information such as dates, days at sea, location, and other critical variables. Because of this, all of the affected entries were removed from the data set using the which(is.na()) function in R to remove all of the data entries that had an NA value in the Month column. This effectively removed all of the observations with missing dates and other critical information. A total of 2,876 entries were removed from the original data set, comprising approximately 0.5% of the total data set (the raw data contained 488,345 entries). Because such a small percentage of data points were removed, it is assumed that the removal will not affect the quality of analysis for the purposes of this project. After removing all of the erroneous entries from the data sets, some additional steps were needed to finish cleaning up the data sets and merge the two data sets together while removing duplicates. First, there were some reporting consistency issues for the Area column, which describes the NMFS statistical grid where the majority of animals were caught. Areas 1 and 2 are sometimes referred to by their geographic coordinates, which are 2481 and 2482 respectively (see Fig. 3). Some of the raw logbook entries reported Area 1 as 2481, and Area 2 as This was resolved using the which() function in R; for all observations with an Area value of 2481, Area was changed to 1. For all observations with an Area value of 2482, Area was changed to 2.

37 21 Figure 3: Map of NMFS statistical grids off the Florida coast and the West Florida Shelf. Source: NOAA Southeast Fisheries Science Center After removing the observations with missing data and resolving the statistical area consistency issues for both data sets, it was necessary to merge the two data sets and remove duplicates in order to obtain one cohesive logbook data set for the entire time series. The original logbook data set (containing data from year 2000 through part of 2015) was subsetted to create a new data set containing all observations from years 2000 through 2014, using the subset() function in R. Then, this new data set was merged with the 2015 data set using the rbind() function in R to create a complete, cleaned-up data set with all observations from 2000 through 2015, with no duplicates Preliminary Data Summaries Seasonal and annual effort frequency plots (Fig. 4 5) were created to summarize the data and provide preliminary information about fishing effort distributions throughout the time series for the study area of interest. Seasonal partitions were defined using the start of the annual fishing season for red grouper, which is January 1 st. Because of this, data were divided by season with four

38 22 seasons per year beginning on January 1 st of each year (January through March, April through June, July through September and October through December). The original logbook dataset only contains date information in terms of month, day and year, so a new value for season was added to the dataset. First, a new column for Season was created in the dataset with NA values. Then, the which() function was used to assign Season values from 1 4 for each observation corresponding to the seasonal partitions based on month described above. After adding the seasonal values to the dataset, the new information was used to aggregate the effort data annually and seasonally. For the seasonal aggregation, the aggregate() function in R was used to aggregate fishing effort data from the complete, cleaned-up logbook dataset by statistical area, year, and season. Fishing effort data were defined as the sum of the variable AWAY for each observation, which refers to the number of days fishing for each trip reported within the logbook dataset. For the annual aggregation, the same process was used to aggregate the effort data by area and yearseasonal and annual frequency distributions were created displaying the number of data points (days fishing) for each time period, for years , for all areas combined (Fig. 4) and by area (Fig. 5). The metric of days fishing was calculated using the number of days at sea for each fishing trip. R packages ggplot2 and RColorBrewer were utilized to create these plots Analysis of Variance Models After creating the annual and seasonal frequency plots to summarize the fishing effort data from the logbook dataset, analysis of variance (ANOVA) models were completed to assess how the pattern of fishing effort has changed between factors

39 23 including time period, season, area and gear type. First, the data were subset by gear type; because the gear types of interest in this analysis are handline and longline, the data were subset to include only observations where handline and longline were listed as the primary gear type (the gear type used to catch the majority of animals for a particular fishing trip). Although handline and longline are the primary gear types utilized by the Gulf of Mexico reef fish fishery, the gear subset removed 134,334 observations from the dataset (27.671% of the data points); the original logbook data set including all gear types had 485,469 observations, while the handline and longline subset had 351,135 observations. Time periods were then demarcated based on the implementation dates of key management actions for the Gulf of Mexico reef fish fishery. Time period 1, comprising years , refers to the time period prior to the implementation of the Pulley Ridge Habitat Area of Particular Concern (HAPC) and the Gulf of Mexico Individual Fishing Quota (IFQ, catch share) program for reef fish. The HAPC was implemented beginning January 1 st, 2005; therefore, time period 2 ( ) refers to the period after the enactment of the HAPC, but before the start of the catch share program, which was executed effective January 1 st, Finally, time period 3 ( ) denotes the period after both of these management changes were implemented. After completing these data preparations, the resultant dataset contained all handline and longline observations with the additional variables of season and time period. These fishing effort data were aggregated in six different combinations using the aggregate() function in R, in order to determine how the pattern of fishing effort has changed between the factors of interest using six distinct ANOVA models. Each

40 24 aggregate dataset contains the sum of variable Away, which refers to the number of fishing days or days at sea for each trip, aggregated by factors of time period (1 3), year ( ), season (1 4), area (1 6) and gear type (handline or longline). The data were aggregated on the fleet level and on the level of individual vessels. Two of the aggregate datasets from the preliminary analysis contained both gear types combined, with gear type as a factor in the ANOVA analysis; all of the output from these preliminary models can be found in Appendix A. The remaining four aggregations were divided by gear type in order the analyze the two gear types separately. Although the temporal factors of interest are time period and season, year was also included in the aggregations in order to include multiple observations of total number of fishing days per time period, season, area and gear type. The six aggregate datasets are described as follows: 1. Fleet-level aggregation, combined gear types: sum of total fishing days for handline and longline trips, aggregated by time period, year, season, area, and gear type. 2. Vessel-level aggregation, combined gear types: sum of total fishing days for handline and longline trips, aggregated by time period, year, season, area, gear, and Vessel ID. 3. Fleet-level aggregation, handline only: sum of total fishing days for handline trips, aggregated by time period, year, season and area. 4. Vessel-level aggregation, handline only: sum of total fishing days for handline trips, aggregated by time period, year, season, area and Vessel ID.

41 25 5. Fleet-level aggregation, longline only: sum of total fishing days for longline trips, aggregated by time period, year, season and area. 6. Vessel-level aggregation, longline only: sum of total fishing days for longline trips, aggregated by time period, year, season, area and Vessel ID. The factor() function in R was used to convert time period, year, season, area and gear type to factors in each of the aggregate datasets. Next, the aggregate datasets were prepared for use in ANOVA statistical models. Because the fishing effort data did not originally follow a normal distribution, the data were normalized using either a logarithmic or square root transformation. Both transformations were attempted for each dataset and assessed visually using histograms of the original and transformed data. Additional columns were created in the aggregate data sets for Log(FishingDays) and Sqrt(FishingDays). New histograms were created using the transformed data, and the most normal-looking transformations were used in the final analysis. After completing the data transformations, ANOVA models were run for each aggregate data set. Preliminary analysis was run at the fleet-level and vessel-level for both gear types combined (Models 1 and 2, Appendix A), and fleet-level and vessel-level models were run for each individual gear type (Models 3 6). Square root transformations were used for Model 1, Model 3, Model 5 and Model 6; logarithmic transformations were used for Model 2 and Model 4 (see below). Each model is designed to test the significance of each individual factor and the significance of all two-factor interactions in explaining the variance in fishing effort. The three-factor interaction for seasonxareaxtime period was tested separately for fleet-level data sets for handline and

42 26 longline, but the three-factor interaction was insignificant so it was excluded. The same interactions were tested for vessel-level datasets, and when significant were retained in the models.significance levels were set at 5% for each of these ANOVA models. The preliminary models 1 and 2 are described in Appendix A. The following ANOVA models were used in the data analysis for this chapter: Model 3 (Fleet-level, handline only, square-root transformed effort): Model 4 (Vessel-level, handline only, log-transformed effort): Model 5 (Fleet-level, longline only, square-root transformed effort): Model 6 (Vessel-level, longline only, square-root transformed effort): Notation used for all each of these models can be found in table 1.

43 27 Table 1: Notation used in ANOVA models for Chapter 2 Notation Definition HFE ijkl Square root-transformed annual fishing effort for handline fleets in statistical areas 1 6; the lth item in the subgroup representing the ith group of factor T (time period), the jth group of factor S (season), and the kth group of factor A (area) HVE ijkl Log-transformed annual fishing effort for handline vessels in statistical areas 1-6; the lth item in the subgroup representing the ith group of factor T (time period), the jth group of factor S (season), and the kth group of factor A (area) LFE ijkl Square root-transformed annual fishing effort for longline fleets in statistical areas 1 6; the lth item in the subgroup representing the ith group of factor T (time period), the jth group of factor S (season), and the kth group of factor A (area) LVE ijkl Square root-transformed annual fishing effort for longline vessels in statistical areas 1 6; the lth item in the subgroup representing the ith group of factor T (time period), the jth group of factor S (season), and the kth group of factor A (area) µ The grand mean of the statistical population The effect for the ith group of factor T (time period) S j The effect for the jth group of factor S (season) A k The effect for the kth group of factor A (area) (TS) ij The interaction effect in the subgroup representing the ith group of factor T (time period) and the jth group of factor S (season) (TA) ik The interaction effect in the subgroup representing the ith group of factor T (time period) and the kth group of factor A (area) (SA) jk The interaction effect in the subgroup representing the jth group of factor S (season) and the kth group of factor A (area) The error term of the lth item in subgroup ijk ϵ ijkl The models were created using the aov() function in R. When the ANOVA models are run as described, the three-factor interaction of area, season and time period will indicate whether the seasonal pattern of fishing effort has changed between areas and through time periods. If that interaction is not significant, it will mean that changes in these patterns are consistent across these factors. If this interaction is significant, it may indicate that changes in seasonal patterns may differ between time periods, areas, or both. The two-way interaction between area and season and the interaction between time

44 28 period and season will tell us which factor may have created the change in seasonal patterns. If none of the three interactions are significant, it would mean that changes in seasonal fishing effort patterns are consistent across areas and time periods. This analysis could indicate whether the implementation of the HAPC resulted in changes to the seasonal pattern of effort that were different from changes observed in other areas of the southwest Florida Shelf. 2.3 Results Preliminary Data Summaries These seasonal frequency plots indicate that there are more fishing days during the earlier years, and fewer fishing days in later years. This trend is displayed in the combined area frequency plot (Fig. 4), and is also consistent across most plots for individual areas (Fig. 5). Figure 4: Seasonal frequency distribution showing total number of fishing days (days at sea for each fishing trip) for the entire study area (NMFS statistical grids 1 6) for years

45 29 Figure 5: Seasonal frequency distributions showing total number of fishing days (days at sea for each fishing trip) for each individual NMFS statistical grid within the study area, for years In addition, we can see that there are generally a greater number of fishing days in the northern areas (statistical grids 4, 5 and 6), and fewer fishing days in the southern areas (Fig. 5).

46 30 The frequency plots also indicate that there are noticeable changes to the seasonal pattern of fishing effort across the time series. The seasonal frequency plot for all areas combined (Fig. 4) shows that the pattern of seasonal effort has changed from 2007 onwards; there is greater seasonal variability initially in the time series, and less seasonal variation in later years. Prior to 2007, there was a relatively strong peak of effort in seasons 2 and 3. Also, in 2004 and 2005 the effort in season 4 was half or less than the effort in quarter 2. After the year 2007, the distribution of effort throughout the year is much more even. This could be a sign that fishing seasons are becoming more evenly spread in the later part of the time series. There are also some differences in seasonal patterns between statistical areas (Fig. 5). In some areas such as Area 1 and Area 2, the peak number of fishing days tend to occur in Season 1 (January March), while Areas 4 6 show the peak number of fishing days occurring in Seasons 2 and 3 (April June and July September). Although these plots do not explain why these patterns are occurring, the observation of patterns in fishing effort distribution is helpful describe general changes in the fishery over time, and to inform the remainder of the statistical analysis for the thesis Data Transformations for ANOVA Models Aggregate fishing effort datasets for each of the six ANOVA models were normalized using either a square root or logarithmic transformation (see Figure 6 for an example of the transformation for model 1). Data transformation histograms for all other models can be found in Appendices A and B.

47 31 Figure 6: Histograms showing untransformed and transformed fishing effort data, aggregated for analysis of variance model 1 (fleet-level, handline and longline combined) Handline Models Model 3 consists of a two-way analysis of variance assessing variation in annual fishing effort for the handline fishery at the fleet level, based on factors of time period, season, and area. This ANOVA analysis indicated that the three individual factors (time period, season and area) have a statistically significant effect on variation in fleet-level handline fishing effort (p < 0.001). Additionally, the two-factor interactions of time period and area (Fig. 7) and season and area (Fig. 9) are significant at the 0.1% level (Table 2). However, two-factor interaction of time period and season is statistically insignificant, indicating that the seasonal variation in fishing effort for the handline fleet does not change significantly across the three time periods in the time series.

48 32 Table 2: Table of ANOVA results for model 3 Df Sum Sq Mean Sq F value Pr (>F) Time Period 2 19,429 9, < 2e-16 *** Season 3 2, e-07 *** Area 5 71,967 14, < 2e-16 *** Time Period Season Time Period 10 7, < 2e-16 *** Area Season Area 15 5, e-10 *** Residuals , Model 4 consists of a three-way analysis of variance assessing variation in annual fishing effort for the handline fishery at the level of individual vessels, based on factors of time period, season, and area. Each of the individual factors (time period, season and area) are statistically significant at the 0.1% level. Additionally, the three two-factor interactions are also statistically significant. The interaction of time period and season is significant at the 1% level, but this plot is not included in the results because although the interaction is significant, there is no clear pattern to the results. In addition, the interactions of time period*area and season*area are significant at the 0.1% level (Table 3). The three-factor interaction of time period, season and area also shows statistical significance at the 5% level (Table 3); this indicates that the seasonal variation in handline fishing effort at the vessel level has varied through the three time periods and across the six geographic areas.

49 33 Table 3: Table of ANOVA results for model 4 Df Sum Sq Mean Sq F value Pr (>F) Time Period < 2e-16 *** Season e-07 *** Area 5 2, < 2e-16 *** Time Period ** Season Time Period e-10 *** Area Season Area < 2e-16 *** Time Period * Season Area Residuals 20,433 37, Figures 7 and 8 display boxplots of fishing effort subset by time period and area, in order to visualize spatial and temporal variations in the handline fishery at the fleet level (Fig. 7) and the vessel level (Fig. 8) before and after management interventions such as the grouper and tilefish individual fishing quota program implemented in Handline fleets in statistical areas 1 and 2 have similar spatial and temporal trends in fishing effort; each of these areas have the greatest amount of handline effort in time period 1, followed by a decline in effort in time period 2 and relatively stable effort distributions between time periods 2 and 3 (Fig. 7). In contrast, handline fisheries in areas 3, 4, and 5 have the greatest amount of fleet-level effort in time period 1, followed by a decline in time period 2 and subsequent increase in time period 3. This increase in between time periods 2 and 3 indicates that handline fleets were able to increase their overall fishing effort following the implementation of the IFQ program in Statistical areas 1 and 2 also have visually similar handline effort distributions at the vessel level (Fig. 8); for both of these areas, vessel-effort declines slightly between

50 34 time period 1 and 2, then increases slightly again between time periods 2 and 3, although area 2 tends to have an overall greater level of effort per vessel. Handline vessels in areas 3 and 4 have much more pronounced changes in effort throughout the time series (Fig. 8), while there is not much fluctuation in areas 5 and 6 (Fig. 8). Figure 7: Boxplots of square root-transformed annual fishing effort for the Gulf of Mexico handline fishery at the fleet level, subset by three time periods ( , and ) and six areas (NMFS statistical grids 1 6).

51 35 Figure 8: Boxplots showing log-transformed annual fishing effort for the Gulf of Mexico handline fishery at the individual vessel level, subset by three time periods ( , and ) and six areas (NMFS statistical grids 1 6). Figures 9 and 10 show boxplots of handline fishing effort data subset by season and area, at the level of entire fleets (Fig. 9) and individual vessels (Fig. 10). Figure 9 indicates that the seasonal patterns in fleet-level fishing effort are not consistent across all six statistical areas. Handline fleets in area 1 have the highest levels of effort in season 1 (January March), followed by declines in effort for seasons 2 and 3; effort is relatively stable between seasons 3 and 4 (Fig. 9). Fleets in area 2 show an increase in effort levels between seasons 1 and 2 (Fig. 9), with less effort occurring in seasons 3 and 4. Although the seasonal effort patterns differ between areas 1 and 2, these two areas still have a similar overall level of fleet-level effort; fleets in areas 3, 4 and 5 have significantly less handline effort than areas 1 and 2, with very little seasonal fluctuation (Fig. 9). There also appear to be some differences in seasonal patterns of fishing effort on the level of

52 36 individual handline vessels (Fig. 10); however, there is no clear pattern to the interactions. Figure 9: Boxplots showing square root-transformed annual fishing effort for the Gulf of Mexico handline fishery at the fleet level, subset by four seasons (January March, April June, July September and October December) and six areas (NMFS statistical grids 1 6).

53 37 Figure 10: Boxplots showing log-transformed annual fishing effort for the Gulf of Mexico handline fishery at the individual vessel level, subset by four seasons (January March, April June and July December) and six areas (NMFS statistical grids 1 6) Longline Models Model 5 consists of a two-way analysis of variance assessing changes in annual fishing effort for the longline fishery at the fleet level, based on factors of time period, season and area. The ANOVA analysis shows that each individual factor is statistically significant at the 0.1% level. In addition, the two-way interaction of time period and season is significant at the 1% level, and the two-way interaction of time period and area is significant at the 0.1% level (Table 4). The two-way interaction of season and area is statistically insignificant.

54 38 Table 4: Table of ANOVA results for model 5 Df Sum Sq Mean Sq F value Pr (>F) Time Period 2 8,167 4, e-16 *** Season 3 1, *** Area 5 144,360 28, < 2e-16 *** Time Period 6 1, ** Season Time Period 10 4, e-05 *** Area Season Area 15 2, Residuals , Model 6 consists of a three-way analysis of variance examining variation in annual fishing effort for the longline fishery at the individual vessel level, based on factors of time period, season and area. The analysis indicated that each of the three individual factors are statistically significant at the 0.1% level. Additionally, the two-way interactions of time period*season and time period*area are significant at the 0.1% level, while the two-way interaction of season and area is insignificant. The three-way interaction of time period, season and area is significant at the 1% level (Table 5). Table 5: Table of ANOVA results for model 6 Df Sum Sq Mean Sq F value Pr (>F) Time Period 2 5,240 2, < 2e-16 *** Season 3 1, < 2e-16 *** Area 5 6,339 1, < 2e-16 *** Time Period e-05 *** Season Time Period e-05 *** Area Season Area Time Period 30 1, ** Season Area Residuals 7, ,

55 39 Figures 11 and 12 show longline fishing effort distributions at the fleet-level and vessel-level, subset by season and time period. There is some change in fleet-level longline effort throughout the time series; in particular, there appears to be less seasonal variation in effort in time period 3 (Fig. 11), after the implementation of the individual fishing quota program in 2010; this indicates that effort may be more spread out throughout the year following the catch share management intervention. This trend is also apparent at the level of individual longline vessels (Fig. 12). Figures 13 and 14 display temporal changes in fleet-level (Fig. 13) and vessellevel (Fig. 14) longline effort, subset by statistical area. There are significant declines in longline effort at the fleet level for all six geographic areas; however, these declines vary in severity between areas, and there are differences in overall effort levels between areas. Figure 13 indicates that areas 2 and 3 have the most similar longline effort distributions; there is a much less longline effort for fleets in area 1, but areas 2 and 3 have visually similar effort distributions (Fig. 13). In contrast, Figure 14 shows that vessel-level longline effort increases throughout the time series. This indicates that although total longline effort is declining at the fleet level, there is an increase in levels of effort per individual vessel (Fig. 14). Figure 14 also supports the conclusion that areas 2 and 3 have similar longline effort distributions.

56 40 Figure 11: Boxplots showing square root-transformed annual fishing effort for the Gulf of Mexico longline fishery at the fleet level, subset by three time periods ( , , and ) and four seasons (January March, April June, July September and October December). Figure 12: Boxplots showing square root-transformed annual fishing effort for the Gulf of Mexico longline fishery at the individual vessel level, subset by three time periods ( , , and ) and four seasons (January March, April June, July September and October December).

57 41 Figure 13: Boxplots showing square root-transformed annual fishing effort for the Gulf of Mexico longline fishery at the fleet level, subset by three time periods ( , , and ) and six areas (NMFS statistical grids 1 6). Figure 14: Boxplots showing square root-transformed annual fishing effort for the Gulf of Mexico longline fishery at the individual vessel level, subset by three time periods ( , , and ) and six areas (NMFS statistical grids 1 6).

58 Model Diagnostics Diagnostic plots for all six analyses of variance indicate that these models are appropriate for analysis of the NMFS logbook dataset (see Fig. 15 for an example of diagnostic plots for Model 1). The residual plot (Fig. 15) does not show any significant relationship between the residuals and the fitted line; the residuals tend to be equally spread around the horizontal line with no distinct patterns. This means that the variance is fairly consistent across the data points, so the data meet the regression assumptions well. In addition, the Normal Q-Q plot shows that the transformed data are fairly normally distributed, and the residuals displayed in the Scale-Location plot appear to be spread equally (Figure 15). The Residuals vs Leverage plot shows an absence of any severe outliers that would alter the model results (Fig. 15). Figure 15: Example showing diagnostic plots for analysis of variance model 1, which is the fleet-level model for combined handline and longline fisheries.

59 Discussion There were more fishing days in the earlier years of the time series, and fewer fishing days in later years (Fig 4. and Fig. 5). This may indicate that the catch share program in the Gulf of Mexico has led to consolidation of fleet numbers and thus effort. Fleet consolidation can occur when some of the fishers holding IFQ shares decide to leave the fishery and sell their shares to a different shareholder (NOAA 2017c). Catch shares are transferable to other permit-holding commercial vessels within the fishery; this transferability encourages less-efficient vessel owners to sell their portion of the quota to more efficient shareholders, leaving the fishery and reducing overcapacity (Branch et al. 2006, Crowley and Palsson 1992, Wertheimer and Swanson 2000). For example, the number of individual vessels in the Alaskan halibut fishery declined by 40% in the first year after the introduction of an individual transferable quota (ITQ) program, indicating a fleet consolidation effect (Wertheimer and Swanson 2000, Hartley and Fina 2001). Because fleet consolidation tends to lead to less-efficient vessels exiting the fishery and more efficient vessels remaining, overall fleet-level effort can decrease while catches and individual vessel-effort remain relatively stable. In addition to potential fleet consolidation, other management actions have been taken that may affect the amount of effort, such as changes in total allowable catches for individual species of reef fish. Because individual fishing quotas are allocated to each fisher within the fleet, catch shares could result in fewer participants consolidating their resources to obtain a greater share of the overall quota.

60 44 There were more fishing days in the northern areas (statistical areas 4, 5 and 6) and less fishing effort overall in the southern areas. This could be a result of more and larger vessels coming out of Tampa in order to fish in the northern statistical areas. Fishing seasons became more spread out in the later part of the time series, with less seasonal variation in individual years (Fig 4, Fig. 5). This could be due to the implementation of catch share programs in the Gulf of Mexico beginning in 2010, which have the potential to reduce the race to fish among the commercial fishing fleet and increase the length of fishing seasons and cause fishing effort to become more spread out throughout the year. Many studies have characterized the reduction in race to fish behavior among fishers after the implementation of catch share program (Griffith 2008, Buck 1995, Grafton 1996, OECD 1998, NRC 1999, Grafton et al. 2000, Fox et al. 2003, Leal 2005). In particular, Criddle et al. (2016) and Warpinski et al. (2016) found that the implementation of an IFQ program for the Alaskan sablefish fishery has resulted in longer fishing seasons (as opposed to the shorter fishing seasons commonly found in a race to fish scenario), and has increased individual vessels revenue. In addition, a five-year review of the Gulf of Mexico red snapper IFQ program noted an increase in fishing season length as a result of the catch shares; after the implementation of the IFQ program, the red snapper fishery was open year-round for the first time in 20 years (Agar at al. 2014). Griffith (2008) states that fisheries emphasizing characteristics such as slower fishing, effort reduction, TAC compliance, and improved monitoring have resolved many ecological and economic concerns related to open access fisheries and race to fish behavior; IFQ programs are a key tool utilized for ecosystem-based fisheries management in order to address many of these critical issues (Griffith 2008).

61 45 There are also important management implications resulting from the significant two-factor and three-factor interactions that occurred in models 3 6. These interactions indicate that the changes in seasonal and spatial patterns of fishing effort across the time series are not consistent. Instead, the changes in effort happen in a way that suggests that fishers in different geographic areas adapt to management interventions in different ways. Although this is to be expected given that one of the statistical areas has a different spatial management intervention than the others (the Pulley Ridge HAPC), it also makes it much more difficult to predict effects of potential managent actions on the fishery as a whole. For example, the introduction of a more localized management intervention such as the Pulley Ridge Habitat Area of Particular Concern (HAPC) would be expected to have differing effects on fishing effort throughout the spatial areas. We would expect to see different changes in fishing effort in the areas closest to the HAPC than in the areas further away from the HAPC. This would be in contrast to the effects of other types of management such as the Gulf of Mexico catch share program, which would be expected to affect all areas similarly, depending on the mix of species caught in each area. Overall, areas 1 and 2 appear to have the most visually similar effort distributions for the handline fishery (Fig. 7, Fig. 9), while areas 2 and 3 are most similar for the longline fishery (Fig. 13, Fig. 14). This makes intuitive sense because handline fishing is much more prevalent in nearshore areas in the southern regions surrounding the Florida Keys and Dry Tortugas; in comparison, longline fishing tends to be more common in the northern areas of the Florida Shelf. Because the HAPC is located in area 2, for future analysis regarding the HAPC we are mainly concerned with assessing changes in effort in

62 46 areas directly adjacent to the HAPC and area 2. For these reasons, the HAPC analysis in Chapter 3 will focus on areas 1 and 2 for the handline fishery, and areas 2 and 3 for the longline fishery. There are also significant differences in the results from the aggregated, fleet-level datasets and the individual vessel-level datasets. The aggregated fleet-level data reduces overall variance in effort and allows us to only make comments about the fleet as a whole, whereas the individual vessel analyses are much more informative about individual vessel behavior. The fleet-level datasets have less variance but fewer observations; however, the vessel-level datasets have more variance but also many more observations. The differences in results between the fleet-level and vessel-level data have significant management implications regarding the effects of the IFQ program on fishing effort on the southwest Florida Shelf. From the significant interactions between area and time period at both the fleet and vessel level, we can see that temporal variations in fishing effort are not consistent across these two types of data aggregations. For the handline fishery, fleet-level effort shows a marked decline between time period 1 and time periods 2 and 3 (Fig. 7); this decline is consistent across all six statistical areas. However, on the level of individual vessels, effort distributions are relatively stable and do not face such a decline (Fig. 8). These trends are even more extreme for the longline fishery; at the fleet level, longline effort declines across the time series for all six statistical areas (Fig. 13), while at the vessel level, effort-per-vessel actually increases throughout the three time periods for all statistical areas (Fig. 14). Effort declining at the fleet level while increasing at the vessel level is indicative of consolidation due to the

63 47 implementation of the catch share program. When this occurs, some fishing vessels will leave the fishery, meaning that there are fewer vessels remaining in the fishery but greater levels of effort per vessel. This is consistent with the number of unique vessels over time in the logbook dataset; there are generally fewer unique vessels in later years for the handline fishery (Table 6) and the longline fishery (Table 7). Increases in the average effort of a vessel with catch shares may come about in two ways: all vessels remaining in the fishery may increase their effort, or the vessels leaving may have been those with fewer fishing days, meaning that the remaining vessels could have the same effort as before but still reflect an overall increase in effort per vessel. Table 6: Number of unique handline vessels within the study area (NMFS statistical grids 1 6), during years Year Number of Unique Vessels Time Period Number of Unique Vessels ,

64 48 Table 7: Number of unique longline vessels within the study area (NMFS statistical grids 1 6), during years Year Number of Unique Vessels Time Period Number of Unique Vessels In conclusion, it is apparent that the introduction of the Gulf of Mexico IFQ program has had significant effects on the handline and longline reef fish fisheries in the southwest Florida Shelf, and that these effects verify the prevailing wisdom in the scientific literature regarding the effects of IFQ programs in commercial fisheries. Fishing seasons have grown longer and more spread out, with less seasonal variation throughout the year; this means that the catch share program has lessened the race to fish scenario that is common in many fisheries managed with a total allowable catch (TAC) system. This reduction in race to fish behavior is a commonly reported benefit of catch share programs implemented for commercial fisheries around the world (Griffith 2008, Buck 1995, Grafton 1996, OECD 1998, NRC 1999, Grafton et al. 2000, Fox et al. 2003, Leal 2005, Criddle et al. 2016, Warpinski et al. 2016, Agar et al. 2014).

65 49 In addition, fleet-level fishing effort has declined while effort-per-vessel has increased over the time series, which is indicative of fishery consolidation occurring as a result of the catch share implementation; this is also a well-documented effect of IFQ programs as reported in the scientific literature (Crowley and Palsson 1992, Wertheimer and Swanson 2000, Hartley and Fina 2001, Branch et al. 2006). Overall, these results indicate that the Gulf of Mexico grouper and tilefish IFQ program has had strong effects on commercial fishers at both the fleet and vessel levels, for both handline and longline gear. In addition, these effects are strong enough to be detectable at the coarse spatial scale of the NMFS logbook dataset. The six geographic areas within our study area have reacted to management changes in different ways, but the spatial and temporal effort distributions are most similar in areas 1 and 2 for the handline fishery, and areas 2 and 3 for the longline fishery. This awareness will be useful in subsetting the data for further analysis of effects from the HAPC implementation.

66 Chapter 3 Habitat Area of Particular Concern 3.1 Introductory Remarks The purpose of this data chapter is to assess broad-scale changes in handline and longline fishing effort distributions for the areas on the southwest Florida Shelf immediately adjacent to the Pulley Ridge Habitat Area of Particular Concern (Fig. 16 & Fig. 17), before and after the implementation of the HAPC in 2005 (see Chapter 1 for a more in-depth explanation of the management background related to the HAPC implementation). Because the regulatory nature of the HAPC prohibits longline fishing within the HAPC boundaries, it is expected that we could see some changes in longline effort in the area containing the HAPC (NMFS statistical area 2), and adjacent areas. In contrast, handline fishing is still allowed within the HAPC boundaries, so we could reasonably expect stable levels of handline effort across the time series, or potentially even some changes in handline effort in the area containing the HAPC following the management intervention because of the exclusion of the longliners from the HAPC. Location information for the NMFS fishing vessel logbook datasets is collected in the form of NMFS statistical grid areas (Fig. 16, Fig. 17), to define the area where the majority of animals are caught during each fishing trip. These statistical grids provide coarse location information, making it difficult to discern precise, fine-scale changes in the spatial patterns of fishing effort. In some cases, depth information (in the form of 20- meter depth strata) can be used to further refine location information and increase precision of logbook data location information. However, depth information for the NMFS logbook system started being collected in 2005, which is the same year that the 50

67 51 HAPC was implemented; there was little to no recording of depth information in the NMFS logbook datasets prior to This means that depth cannot effectively be used to refine location information for the purposes of this project, because there would not be a baseline of information before and after the implementation of the HAPC. Due to the coarse nature of the spatial information on catch location (NMFS statistical areas) provided by NMFS logbook data, it is not possible to identify effort inside and outside the HAPC. The logbook data, however, can be used to examine changes in effort at scales that may be informative of changes in effort pattern in the larger statistical area which includes the HAPC. In theory, the creation of an MPA as small as the HAPC could impact a larger surrounding area. Such spill-over effects are frequently reported for many MPAs (Harmelin-Vivien et al. 2008, Rowley 1994, Russ and Alcala 1996, Roberts et al. 2001, Russ et al. 2004). For example, Harmelin-Vivien et al. (2008) and Goni et al. (2008) found evidence of spillover effects across marine reserve boundaries in six MPAs in the Mediterranean region. In addition, Goni et al. (2006) evaluated spillover effects of spiny lobsters from a marine reserve to an adjacent fishery. In fact, the fish stocks potentially benefitting from the creation of the HAPC are distributed throughout the Gulf of Mexico. According to the GMFMC Essential Fish Habitat Amendment 3 (2005), the implementation of Habitat Areas of Particular Concern was designed to have direct beneficial effects on the coral reef habitat of Pulley Ridge, because the most environmentally destructive gear types are prohibited under the HAPC regulations; this has led to increased protection and a reduction in damage to Pulley Ridge s corals and essential fish habitats. For this reason, the HAPC is expected to

68 52 improve the carrying capacity of coral reef habitats at Pulley Ridge, which could indirectly benefit the reef fish species supported by these habitats. In the areas containing Pulley Ridge and the HAPC, these reef fish species include the federally managed shallow-water grouper complex (including gag, red, black, yellowfin, scamp and yellowmouth groupers), the deep-water grouper complex (including yellowedge, snowy, speckled hind and warsaw groupers), and other reef fishes including golden tilefish, blueline tilefish, goldface tilefish and hogfish (GMFMC 2017). Because the affected fishery species are distributed throughout the Gulf of Mexico, population impacts at the scale of the area of the HAPC are unlikely to be significant unless they are revealed through spill-over effects seen over a broader area. A way to test for such effects is to assume that spillover effects would be revealed by changes in the relative effort distribution of NMFS statistical areas adjacent to and including the HAPC before and after the HAPC implementation. As mentioned in chapter 2, the handline and longline fleets act as distinctly different fisheries. Handlines are more commonly used in the Panhandle and southern area of the Florida west coast shelf whereas longlines are more common in the central area. Moreover, the HAPC does not prohibit handline fishing. Because of this, analysis for the HAPC chapter will analyze handline and longline data separately, and will compare effort distributions in NMFS statistical areas 1 and 2 for the handline fleet (Fig. 16), and areas 2 and 3 for the longline fleet (Fig. 17). Analyses of variance will test whether time period, season, area, and port location influence the effort distribution for handline and longline fleets. In order to test this, the state and county information that is included for each observation in the NMFS logbook

69 53 data set will be used. Data will be subset to only include observations from counties with a substantial amount of fishing effort (Fig. 16, Fig. 17); data from counties with negligible fishing effort will be excluded in order to simplify the final models. Interactions between factors of area, season 1, time period, and port location were examined to determine whether patterns of change in the distributions of fishing effort are different between time periods (before and after major management interventions) for the different areas. This will help in determining whether the management interventions have changed the spatial distribution of effort across the time series, which will relate to the major study questions because it will help to discern any changes in location of fishing effort as a result of the HAPC. 1 In addition to season, other combinations involving time were attempted, such as aggregating data using month instead of season for the temporal factor. However, the major qualitative findings from the seasonal aggregations were robust, and the results did not change significantly between models using season and models using month. Therefore, only the original models using season were incorporated into the final results.

70 54 Figure 16: Map of the study area for HAPC analysis for the handline fishery, consisting of two areas (NMFS statistical grids 1 and 2) and three port location counties (Dade, Monroe and Collier counties). Figure 17: Map of the study area for HAPC analysis for the longline fishery, consisting of two areas (NMFS statistical grids 2 and 3) and five counties (Monroe, Collier, Lee, Manatee and Pinellas counties).

71 Methodology Overview In order to assess the effects of the HAPC on effort distributions for the reef fish fishery in adjacent areas, analysis of variance (ANOVA) models are run using NOAA logbook data observations of fishing effort, defined as the total number of fishing days aggregated by relevant factors of time period, season, statistical area and county (to approximate port location). These analyses examine how the patterns of fishing effort have changed between these factors over the course of the time series, particularly before and after the implementation of the HAPC. Data are subset by gear type, by area (areas 1 and 2 for handline effort, and areas 2 and 3 for longline effort) and by county (Fig. 16, Fig. 17). The factors of interest in these models include three time periods (1: pre-hapc, 2: post-hapc and pre-ifq, and 3: post-ifq), four seasons, two areas for each gear type, and counties with significant amounts of effort in the respective statistical areas (Fig. 16, Fig. 17). The ANOVA models test the significance of each individual factor in explaining the variation in fishing effort, as well as every possible two-way interaction of the factors. These analyses are completed at the fleet level and on the level of individual vessels for each gear type (handline and longline), for a total of four models. For fleetlevel models, the data are aggregated using the sum of total fishing days for all vessels by time period, year, season, area and county. For the vessel-level models, data are aggregated for each vessel following the logbook variable Vessel ID. Because the fishing effort data are not necessarily normally distributed, the data are transformed to normalize the distributions and increase their suitability for statistical analysis. Either a logarithmic

72 56 or a square root transformation is used, depending on which transformation provides the best visual approximation of normality for each dataset (Fig. 18). Data for this chapter was provided by the National Oceanic and Atmospheric Administration (NOAA) Southeast Fisheries Science Center; this chapter employs the same National Marine Fisheries Service (NMFS) logbook dataset that was used for Chapter 2. Results are presented in a way to protect the confidentiality of logbook data as per the conditions established by the NMFS. The most important of these conditions is that data reported cannot be identified to represent the activities of a single vessel Data Cleanup for Analytical Use The majority of data cleanup for this portion of the analysis was already completed for the purposes of Chapter 2. During the initial data examination phase of Chapter 2, the formatting and consistency issues were corrected and all of the erroneous or duplicate observations were removed from the datasets. In addition, the variables for time period and season had already been created during the analytical process for Chapter 2, so these variables could be used again for the analysis portion of Chapter 3. However, it was necessary to clean up the given values for County within the original dataset, to avoid duplication. In the NMFS logbook dataset, port location is defined by the combination of State and County variables which are given using NMFS numerical state and county codes. Different parts of the state of Florida (Florida east coast, Florida west coast, and inland Florida), have different state codes. County codes are only unique within a state code. To give unique identifiers to each Florida county, a new variable was created: County Name, combining the state and county codes.

73 Analysis of Variance Models As described in the methodology overview, analysis of variance (ANOVA) models were conducted to assess how to patterns of fishing effort has changed for handline and longline reef fish fisheries at the fleet level and vessel level, between factors of time period, season, area and port location, before and after the management intervention of the Pulley Ridge HAPC. The initial step in preparing these models was to subset the cleaned-up logbook data appropriately for each set of models. First, it was necessary to determine which counties had enough fishing effort to be included in the final analysis. Counties with less than 250 total fishing days throughout the time series were excluded from the analysis. For the fleet-level and vessel-level handline models, the logbook data was subset to include only observations using handline gear in NMFS statistical areas 1 and 2, for Dade, Monroe and Collier counties (Fig. 16). St. Lucie county was excluded from the handline models with only 18 fishing days throughout the time series. This was considered to be a negligible amount of effort compared to the other counties; Collier county had 725 fishing days, Dade county had 8,326 fishing days and Monrow county had 313,437 fishing days. For the longline ANOVA models, only longline observations in statistical areas 2 and 3, for Monroe, Collier, Lee, Manatee and Pinellas counties were included (Fig. 17). St. Johns county and Dade county were excluded from the longline analysis because they had a total of eight fishing days (St. Johns) and 224 fishing days (Dade) over the time series. This was considered to be negligible compared to the other counties, which had effort levels ranging from 5, ,465 total fishing days throughout the time series.

74 58 Next, these fishing effort data subsets were aggregated in four different combinations using the aggregate() function in R. Each aggregate dataset contains the sum of the variable Away from the original NOAA logbook dataset, which is defined as the number of fishing days or days at sea for each trip, aggregated by time period (1 3), year ( ), season (1 4), area (1 and 2 for handline, and 2 and 3 for longline), and county. These data were aggregated at the fleet level, and at the level of individual vessels by adding Vessel ID to the aggregations. These aggregations were created for both handline and longline gears, for a total of four aggregate datasets. Although time period and season are our temporal factors of interest, year was also included in the aggregation in order to include multiple observations of fishing effort for each time period. Observation for a given year are therefore considered as random replicates for the purposes of the analysis. The names() function in R was used to assign column headers to each aggregation. The four aggregate datasets are described as follows: 1. Handline, fleet-level aggregation: sum of total fishing days for handline trips, aggregated by time period (1 3), year ( ), season (1 4), area (1 2), and county (Dade, Monroe and Collier). 2. Handline, vessel-level aggregation: sum of total fishing days for handline trips, aggregated by time period (1 3), year ( ), season (1 4), area (1 2), county (Dade, Monroe and Collier), and Vessel ID. 3. Longline, fleet-level aggregation: sum of total fishing days for longline trips, aggregated by time period (1 3), year ( ), season (1 4), area (2 3), and county (Monroe, Collier, Lee, Manatee and Pinellas).

75 59 4. Longline, vessel-level aggregation: sum of total fishing days for longline trips, aggregated by time period (1 3), year ( ), season (1 4), area (2 3), county (Monroe, Collier, Lee, Manatee and Pinellas), and Vessel ID. The factor() function in R was used to convert time period, season, and area to factors in each of the aggregate datasets. Fishing effort was transformed in order to normalize the data and increase their suitability for use in ANOVA models with either a logarithmic or square root transformation. Both transformations were attempted for each dataset and assessed visually using histograms of the untransformed and transformed data. New variables for log(fishingdays) and Sqrt(FishingDays) were created in each of the four aggregate datasets,. After creating new histograms of the transformed data, the best-fitting transformations were utilized for the final analysis (Fig. 18). Figure 18: Histograms showing untransformed and transformed fishing effort data, aggregated for analysis of variance model 8 (vessel-level, handline only). Once the data transformations were completed, the final ANOVA models were run for each aggregated, transformed dataset. Four models were run in total (Models 7 10), consisting fleet-level and vessel-level models for each individual gear type (handline and longline). Logarithmic transformations were used for models 7 and 8

76 60 (fleet-level and vessel-level handline models), and square root transformations were used for models 9 and 10 (fleet-level and vessel-level longline models). Each of these models tested the significance of the individual factors and the significance of all possible twofactor interactions in explaining the total variance in fishing effort. These models do not include higher-level interactions because with a total of four factors being examined, the inclusion of every possible higher-level interacion would have created overly complex models that would be near impossible to interpret. Significance levels were set at 5% for each of these models. The following ANOVA models were used in the data analysis for this chapter: Model 7 (Fleet-level, handline only, log-transformed effort):, Model 8 (Vessel-level, handline only, log-transformed effort):, Model 9 (Fleet-level, longline only, square root-transformed effort):, Model 10 (Vessel-level, longline only, square root-transformed effort):, Notation used for all each of these models can be found in table 8.

77 61 Table 8: Notation used in ANOVA models for Chapter 3 Notation Definition HFE ijklm Log-transformed annual fishing effort for handline fleets in statistical areas 1 and 2, and in Dade, Monroe and Collier counties; the mth item in the subgroup representing the ith group of factor T (time period), the jth group of factor S (season), the kth group of factor A (area) and the lth group of factor C (county) HVE ijklm Log-transformed annual fishing effort for handline vessels in statistical areas 1 and 2, and in Dade, Monroe and Collier counties; the mth item in the subgroup representing the ith group of factor T (time period), the jth group of factor S (season), the kth group of factor A (area) and the lth group of factor C (county) LFE ijklm Square root-transformed annual fishing effort for longline fleets in statistical areas 2 and 3, and in Monroe, Collier, Lee, Manatee and Pinellas counties; the mth item in the subgroup representing the ith group of factor T (time period), the jth group of factor S (season), the kth group of factor A (area) and the lth group of factor C (county) LVE ijklm Square root-transformed annual fishing effort for longline vessels in statistical areas 2 and 3, and in Monroe, Collier, Lee, Manatee and Pinellas counties; the mth item in the subgroup representing the ith group of factor T (time period), the jth group of factor S (season), the kth group of factor A (area) and the lth group of factor C (county) µ The grand mean of the statistical population The effect for the ith group of factor T (time period) S j The effect for the jth group of factor S (season) A k The effect for the kth group of factor A (area) C l The effect for the lth group of factor C (county) (TS) ij The interaction effect in the subgroup representing the ith group of factor T (time period) and the jth group of factor S (season) (TA) ik The interaction effect in the subgroup representing the ith group of factor T (time period) and the kth group of factor A (area) (TC) il The interaction effect in the subgroup representing the ith group of factor T (time period) and the lth group of factor C (county) (SA) jk The interaction effect in the subgroup representing the jth group of factor S (season) and the kth group of factor A (area) (SC) jl The interaction effect in the subgroup representing the jth group of factor S (season) and the lth group of factor C (county) (AC) kl The interaction effect in the subgroup representing the kth group of factor A (area) and the lth group of factor C (county) The error term of the mth item in subgroup ijkl ϵ ijklm

78 62 These models were run using the aov() function in R. ANOVA summary tables are included in the results section, and tables of means and diagnostic plots can be found in Appendix B along with all other R output from these models. These ANOVA models will indicate whether the seasonal patterns of fishing effort have changed across the factors of interest, between areas and through time periods. The county factor will also indicate whether fishers landing in different port locations have changed the distribution of their fishing effort as a result of the major management interventions in these areas, particularly before and following the implementation of the Pulley Ridge HAPC. If these interactions are insignificant, it will indicate that changes in these fishing effort trends are consistent across all factors. If this is the case, it may indicate that the effects of the HAPC cannot be discerned at the coarse level of the logbook data. If these interactions are significant, it will mean that changes in seasonal effort patterns vary across these factors, potentially indicating that fishers in different areas or port locations have different methods of adapting to management intervention. 3.3 Results Handline Models For model 7, a two-way ANOVA was used to evaluate variation in fishing effort for handline fleets, based on factors of time period, season, area and county (Table 9). The individual factors of time period (p < 0.01) and county (p < 0.001) were statistically significant, but season and area were not statistically significant as individual factors. Moreover, none of the interactions including season were significant. This indicates that there is no notable seasonal variation in handline effort at the fleet level.

79 63 Although area was not significant as a single factor, the interactions between area and county and area and time period were significant. Additionally, the interaction between time period and county was significant (p < 0.001) (Table 9). This shows that the different areas (1 and 2) and counties (Dade, Monroe and Collier) of interest have varying distributions of handline fleet effort over the time series. In addition, it appears that the three counties have different spatial distributions of fishing effort, but these distributions have changed between time periods. This is to be expected, since fishers leaving from a particular port location may be more likely to fish in areas that are geographically close to their home port. Interestingly, the two-way interactions of time period and season, season and area, and season and county are statistically insignificant (Table 9), which makes intuitive sense considering that season was also insignificant as an individual factor. This indicates that the lack of seasonal variation in fishing effort is consistent across the time series, across the study area and across the three port counties. In summary, the handline fleet shows no seasonal differences in effort, however, the relative amount of fishing effort spent in areas 1 and 2 changes between counties and has changed between time periods.

80 64 Table 9: Table of ANOVA results for Model 7 Df Sum Sq Mean Sq F value Pr (>F) Time Period ** Season Area County 2 1, , < 2e-16 *** Time Period Season Time Period e-05 *** Area Time Period *** County Season Area Season County Area County e-06 *** Residuals Figure 19 (below) shows boxplots of log-transformed annual fishing effort for handline fleets subset by the two areas (NMFS statistical grids 1 and 2) and the three counties (Dade, Monroe and Collier). The two-way interaction of season and county is statistically significant, indicating that the three counties have distinct spatial effort distributions. In this figure, we can see that each of the three counties have higher amounts of fleet-level handline effort in area 2, although this gap is most significant in Collier county (Fig. 19). This is in line with expectations, because Collier county is the furthest away from Area 1, so fishers could be remaining in Area 2 because it is geographically closer to their home port. Overall, fleets coming out of Monroe county have the highest amounts of handline effort in these areas (Fig. 19), followed by Dade county; Collier county has the least handline effort overall.

81 65 Figure 19: Boxplots showing log-transformed annual fishing effort for the Gulf of Mexico handline fishery at the fleet level, subset by two geographic areas (NMFS statistical grids 1 and 2) and three counties (Collier, Dade and Monroe). Figure 20 (below) shows the statistically significant interaction of time period and area for Model 7; the figure shows boxplots of log-transformed annual fishing effort for handline fleets in the study area, subset by area (NMFS statistical grids 1 and 2) and time period (1 3). The two geographic areas have differing temporal fishing effort distributions. In Area 1, fleet-level handline effort increases between time periods 1 and 2 and then declines again in time period 3; in contrast, effort increases steadily throughout the entire time series in Area 2 (Fig. 20). However, the two statistical areas appear to have similar overall levels of handline effort at the fleet level, which makes intuitive sense considering that these areas were selected for use in this analysis because of their similar effort distributions in in the analysis for Chapter 2.

82 66 Figure 20: Boxplots showing log-transformed annual fishing effort for the Gulf of Mexico handline fishery at the fleet level, subset by three time periods ( , , and ) and two geographic areas (NMFS statistical grids 1 and 2). Figure 21 (below) shows that the handline fleets coming out of the different port county locations have differing effort distributions over the time series. Handline fleets from Collier county have an increase in overall effort from time period 1 to time period 2, then effort declines to its lowest level in time period 3 (Fig. 21). In contrast, fleet-level handline effort from Dade county steadily increases over the time series, while effort from Monroe county steadily declines (Fig. 21). Also, Monroe county has significantly more handline effort than either of the other counties (Fig. 21), which corresponds with findings from Fig. 19.

83 67 Figure 21: Boxplots showing log-transformed annual fishing effort for the Gulf of Mexico handline fishery at the fleet level, subset by three time periods ( , , and ) and three counties (Collier, Dade and Monroe). Model 8 represents a two-way ANOVA assessing variation in handline effort at the level if individual vessels, according to factors of time period (1 3), season (1 4), area (1 2) and county (Dade, Monroe and Collier). All four individual factors are statistically significant (p < 0.001), meaning that handline effort per vessel is not consistent, and instead varies across each of these factors (Table 10). It has to be noted, however, that three of the two-way interactions are also significant. The two-way interaction of time period and season is statistically significant (p < 0.001), indicating that the seasonal pattern of handline vessel-effort has shifted over the time series. The interaction of time period and county is also highly significant (p < 0.001), which means that handline vessels in the three counties have differing effort levels over the course of the three time periods (Table 10). Finally, the interaction of season and area is

84 68 statistically significant, which shows that the seasonal pattern of fishing effort for handline vessels differs between areas 1 and 2 (Table 10). Several of the two-factor interactions for Model 8 were statistically insignificant, meaning that the patterns of handline vessel fishing effort is consistent across these interactions. The interaction of time period and area is insignificant, meaning that handline vessels have similar fishing effort distributions for areas 1 and 2 for the different time periods. In addition, the interaction of season and county is statistically insignificant; this indicates that the seasonal variation in fishing effort is consistent across the three counties. The interaction of area and county is also insignificant, which means that each county has a similar spatial distribution of fishing effort for handline vessels (Table 10). Table 10: Table of ANOVA results for Model 8 Df Sum Sq Mean Sq F value Pr (>F) Time Period e-05 *** Season e-05 *** Area 1 1,078 1, < 2e-16 *** County e-15 *** Time Period e-05 *** Season Time Period Area Time Period e-06 *** County Season Area e-13 *** Season County Area County Residuals 12,014 21,

85 69 At the level of individual vessels, the three counties have very similar levels of handline effort per vessel (Fig. 22). There is not much variation in handline effort per vessel in either Monroe or Collier counties (Fig. 22), but vessel effort increases in Dade county between time periods 2 and 3 (Fig. 22). Figure 22: Boxplots showing log-transformed annual fishing effort for the Gulf of Mexico handline fishery at the individual vessel level, subset by three time periods ( , and ) and three counties (Collier, Dade and Monroe). Figures 23 and 24 show the seasonal pattern of annual fishing effort per handline vessel in the study area, subset by the three time periods (Fig. 23) and across the two geographic areas (Fig. 24). Although these two-way interactions were statistically significant in Model 8, meaning that the seasonal pattern of handline effort has varied across these factors, the trends displayed in the boxplot figures do not appear to show any clear pattern in seasonal effort fluctuations (Fig. 23, Fig. 24).

86 70 Figure 23: Boxplots showing log-transformed annual fishing effort data for the Gulf of Mexico handline fishery at the individual vessel level, subset by three time periods ( , and ) and four seasons (January March, April June, July September and October December). Figure 24: Boxplots showing log-transformed annual fishing effort data for the Gulf of Mexico handline fishery at the individual vessel level, subset by two geographic areas (NMFS statistical grids 1 and 2) and four seasons (January March, April June, July September and October December).

87 Longline Models For Model 9, a two-way ANOVA model was used to analyze variation in annual fishing effort for longline fleets within the study area, according to factors of time period (1 3), season (1 4), area (2 and 3) and county (Monroe, Collier, Lee, Manatee and Pinellas counties). County was statistically significant as an individual factor (p < 0.001) (Table 11), indicating that the five port counties have different distributions of longline effort at the fleet level. All of the other individual factors were statistically insignificant (Table 11), which means that longline effort tends to be consistent across these factors. The two-way interaction of time period and area was statistically significant (p < 0.05) (Table 11), meaning that longline fleets fishing within the study area have had different temporal effort distributions over the course of the time series. In addition, the interaction of time period and county was highly statistically significant (p < 0.001) (Table 11), which indicates that fleet-level longline effort from the different counties has varied across the three time periods. Finally, the two-way interaction of area and county was highly significant (p < 0.001) (Table 11), which means that longline fleets in each county have different spatial distributions of fishing effort. The two-factor interactions of time period and season, season and area, and area and county are statistically insignificant, which means that the seasonal pattern of fleet-level longline effort has been consistent across these factors.

88 72 Table 11: Table of ANOVA results for Model 9 Df Sum Sq Mean Sq F value Pr (>F) Time Period Season Area County 4 18,024 4, < 2e-16 *** Time Period Season Time Period * Area Time Period 7 3, e-11 *** County Season Area Season County Area County 4 2, e-09 *** Residuals , Figure 25 (below) displays boxplots of square root-transformed annual fishing effort for longline fleets within the study area, subset by two areas (NMFS statistical grids 2 and 3) and three time periods (1 3). We can see that there are distinct temporal effort patterns for each of the two areas; longline fleet effort in area 3 increases consistently throughout the time series, while effort in area 2 faces a decline between time periods 1 and 2 and then increases again in time period 3 (Fig. 25).

89 73 Figure 25: Boxplots showing square-root transformed annual fishing effort data for the Gulf of Mexico longline fishery at the fleet level, subset by two geographic areas (NMFS statistical grids 2 and 3) and three time periods ( , and ). According to Figure 26, there is variation in the spatial patterns of fishing effort between the five counties included in the study area. Longline fleets from Collier, Lee and Pinellas counties have a higher amount of effort in Area 3 and less in Area 2; the reverse is true for Monroe and Manatee counties (Fig. 26). In addition, Pinellas county has a significantly more fleet-level longline effort than any of the other counties in the study area (Fig. 26).

90 74 Figure 26: Boxplots showing square-root transformed annual fishing effort data for the Gulf of Mexico longline fishery at the fleet level, subset by two geographic areas (NMFS statistical grids 2 and 3) and five counties (Collier, Lee, Manatee, Monroe and Pinellas). There is also a statistically significant interaction between time period and county for the longline fishery at the fleet level (Fig. 27). Longline fleets from Lee and Pinellas counties show a steady decline in effort throughout the time series; in contrast, there is an upward trend in effort for longline fleets from Manatee county. Longline effort in Collier county increases between time periods 1 and 2, but then disappears entirely in time period 3 (Fig. 27). Longline fishing from Monroe county increases from time period 1 to time period 2, but then declines in time period 3 (Fig. 27).

91 75 Figure 27: Boxplots showing square-root transformed annual fishing effort data for the Gulf of Mexico longline fishery at the fleet level, subset by three time periods ( , and ) and five counties (Collier, Lee, Manatee, Monroe and Pinellas). Model 10 (Table 12) consists of a two-way analysis of variance aimed at explaining variation in fishing effort for longline vessels within the study area, based on factors of time period (1 3), season (1 4), area (2 3), and county (Monroe, Collier, Lee, Manatee and Pinellas counties). Individual factors of time period, season, and county are statistically significant (p < 0.001), indicating that longline vessels fishing effort distributions have varied throughout the time series, that there is a seasonal pattern to those effort distributions, and that the counties within the study area have varying effort distributions. Area was not a significant factor individually, indicating that the two geographic areas have similar effort distributions. The two-factor interaction of time period and season is statistically significant (p < 0.05), which indicated that the seasonal pattern of fishing effort for longline vessels has changed between the three time periods. In addition, the interaction of time period and

92 76 county is also statistically significant (p < 0.05) (Table 12); this shows that individual longline vessels from the five counties have behaved differently across the time series. The interaction of time period and area is statistically insignificant, which shows that the two areas have similar effort distributions throughout the three time periods. Also, the interactions of season and area, and season and county, are insignificant; this indicates that the seasonal effort patterns of longline vessels are constant across the different areas and counties. The interaction of area and county is also insignificant, which could mean that longline vessels from these counties have similar spatial patterns of fishing effort. Table 12: Table of ANOVA results for Model 10 Df Sum Sq Mean Sq F value Pr (>F) Time Period 2 1, < 2e-16 *** Season *** Area County 4 2, < 2e-16 *** Time Period * Season Time Period Area Time Period * County Season Area Season County Area County Residuals 1,441 26, Figure 28 indicates that there are also distinct temporal patterns in longline fishing effort at the vessel level. All five counties have similar overall levels of longline effort per vessel, although the trends differ slightly between counties. Longline vessel-effort increase throughout the time series for Manatee, Pinellas and Collier counties; however, there is an absence of longline effort in Collier county for time period 3 (Fig. 27, Fig. 28).

93 77 In addition, longline vessel-effort declines for Lee county between time periods 1 and 2, then increases again in time period 3; the opposite pattern occurs for longline vessels in Monroe county (Fig. 28). There is a generally similar level of effort per vessel in all five counties for the longline fishery, while the fleet-model indicates that there is an overall higher level of fleet effort for Pinellas county (Fig. 27); this could indicate that some counties may have a greater number of longline vessels than others. Figure 28: Boxplots showing square-root transformed annual fishing effort data for the Gulf of Mexico longline fishery at the individual vessel level, subset by three time periods ( , and ) and five counties (Collier, Lee, Manatee, Monroe and Pinellas). Figure 29 displays the statistically significant interaction of season and time period for Model 10. Although this interaction is significant in the model, the patterns do not appear to be very strong (Fig. 29). Seasonal patterns of longline vessel-effort do appear to be more even in time period 3 (Fig. 29), with an increase in effort between seasons 2 and 3 instead of a decline as seen for the previous two time periods. This could be indicative of an effect from the IFQ program implementation in 2010.

94 78 Figure 29: Boxplots showing square-root transformed annual fishing effort data for the Gulf of Mexico longline fishery at the individual vessel level, subset by three time periods ( , and ) and four seasons (January March, April June, July September and October December) Model Diagnostics Diagnostic plots for the four analyses of variance models show that these models are appropriate for analysis of the NMFS logbook datasets. See Figure 30 for an example of diagnostic plots for Model 9; the diagnostic plots from the other models can be found in Appendix B along with all of the other R output from this analysis. The residuals are generally spread out around the horizontal without showing any distinct pattern (Fig. 30). This indicated that residual variance is relatively similar across values of the predictor variables (time period, season, area and county) and the outcome variable (fishing effort), so the model data meet the assumptions well. In addition, the Normal Q-Q plot (Fig. 30) shows that the transformed data are fairly normally distributed, and the residuals shown in the Scale-Location plot (Fig. 30) appear to be spread equally. Finally, according to the

95 79 Residuals vs. Leverage plot, there are no severe outliers that would negatively affect the model results (Fig. 30). Figure 30: Example showing diagnostic plots for analysis of variance model 9, which is the fleet-level longline model for the HAPC analysis in Chapter Discussion Monroe county has significantly more fleet-level handline effort than the two other counties within our study area (Fig. 21), while effort per individual vessel is fairly consistent across the three counties. Because Monroe county has significantly more effort at the fleet level, followed by Dade county and then Collier county (Fig. 21), this is indicative that Monroe county has significantly more handline vessels than either of the other counties, and Dade county must have a greater number of handline vessels than Collier county. This is consistent with information on individual vessel IDs from the logbook dataset; for the handline fishery at the spatial and temporal scale of this study, Monroe county has the greatest number of handline vessels with 987 unique vessel IDs;

96 80 Dade county has 96 unique handline vessel IDs, and Collier county has 15 unique handline vessel IDs. Handline effort per vessel increases in Dade county between time periods 2 and 3 (Fig. 22); this could indicate an effect of effort consolidation due to the implementation of the Gulf of Mexico IFQ program in 2010; it is possible that some vessels that used to fish a small number of days may have left the Dade county handline fishery, leading to an increase in overall effort per vessel. Because Dade county is the furthest port location from Pulley Ridge, it was of interest to examine the frequency of trips that were only one day in length originating from Dade county, as compared with trips that were more than one day in length in order to determine the dynamics of the Dade county handline fishery within our handline study area of NMFS statistical grids 1 and 2. Trips that were more than one day in length are more likely to have occurred in the area surrounding Pulley Ridge, while single-day trips are more likely to have occurred closer to shore, such as in the Florida Keys. Tables 13 and 14 (below) show the frequency of different lengths of handline trips originating out of Dade county, for the entire time series combined (Table 13) and by year (Table 14). Table 13 shows that there are a significant number of one-day trips within the dataset, but that there are still a greater number of trips overall that lasted more than one day, meaning it is likely that at least some of these trips could have occurred in the areas closer to Pulley Ridge.

97 81 Table 13: Frequency of handline trip lengths out of Dade county in statistical areas 1 and 2, for the entire time series ( ). Trip Length (Days) Frequency (# Trips) 1 1, Table 14 (below) indicates that the patterns in trip length frequency for handline vessels fishing out of Dade county have changed over the course of the time series. The greatest frequency of short trips occurs in the earlier years, and the frequency of longer trips increases in the later years of the time series (Table 14). In particular, there appears to be a switch from mostly one-day trips to over-one-day trips after This could potentially be a result of increased fishery regulations over time; if increased regulation led to fleet consolidation, remaining vessels in the fishery could be increasing their efficiency by taking longer trips. For future study, it could be helpful to re-run ANOVA models with data split between day trips and multiple day trips, in order to see how patterns in fishing effort vary between these two types of trips.

98 82 Table 14: Frequency of handline trip lengths out of Dade county in statistical areas 1 and 2, over the time series for individual years Year Trip Length (# Days) Sum 1, Fleet-level longline effort decreased between time periods 1 and 2, which is when the implementation of the HAPC occurred in Pulley Ridge. This is what we may expect to happen as a result of the HAPC; due to the gear restriction of longline gear within the boundaries of the HAPC, effort displacement may occur, leading to a decline in longline effort in area 2. However, it is difficult to pinpoint causation in this case because there are so many other factors at play that could have led to that effort decline. Therefore, we cannot say with certainty that the longline effort decline in area 2 was due to the implementation of the HAPC. In addition, the interaction of time period and area is statistically insignificant for longline vessels; if there was an effect on longline vessel behavior from the HAPC, we

99 83 may have expected to see a decline in vessel effort in area 2 as compared to area 3. This lack of significance could indicate that the effects of the HAPC on the longline fishery is too fine-scale to be detected by the spatially coarse NMFS logbook data. This is likely due to the coarse spatial scale of the logbook dataset; because of the small area of the HAPC in comparison to the statistical grids, we may not be able to discern the effects of the HAPC at this level. This indicates that the NMFS logbook data may not be suitable for analysis of specific geographic management areas, particularly for examining effects of current management techniques in order to predict effects of potential future management actions. For future studies, it is possible that other datasets may be more suitable for this type of analysis, such as the NOAA Vessel Monitoring System (VMS) datasets which track exact locations of commercial vessels; however, it can be difficult for scientists to access these data for research purposes, due to the confidential nature of the individual vessels locations. In addition, these VMS data may not have solved this issue for the present study, because the VMS system was implemented in 2005, which is the same year that the HAPC was implemented. Interestingly, there is not a strong seasonal signal among handline and longline fleets in this study area. Interactions are significant at the vessel level, but the patterns aren t very strong; this could indicate that the vessel-level patterns were detected because of the increased power of the statistical tests due to having so many more observations in the vessel-level aggregate datasets compared with the fleet-level aggregations. In addition, the slight variation in seasonal pattern for longline vessels (increased effort in season 3 compared with seasons 1 and 2) could be explained by the catch share implementation in 2010 (Fig. 29).

100 84 Overall, there are fewer significant interactions for the HAPC analysis in Chapter 3 than there were for the IFQ analysis in Chapter 2. The analysis presented in this thesis showed a strong effect of the catch share programs on effort distribution, meaning that the IFQ program is a wide-reaching and strong enough management intervention that it can be discerned at the coarse spatial scale of the NMFS logbook data for the purposes of this analysis. Conversely, we are not able to discern as many strong effects on the fishery from the HAPC implementation in This could indicate that the effects from the HAPC are not strong enough to be seen at this level.

101 Chapter 4 Conclusion 4.1 Limitations and Delimitations The main limitation in this study is the lack of precision in the location information provided by the NMFS Logbook Data. The geographic precision provided by the NMFS Coastal Logbook Program has changed over time; in the intitial stages of the logbook program, the only location information required was the NMFS statistical grid where the majority of animals were caught, meaning that these early data sets did not provide geographic coordinates and instead only provided the statistical areas for location information. In more recent years, more precise location information is used, which can include latitude and longitude information for some fleets. Although this location precision has increased over time, the spatial information was generally coarse in the period prior to the establishment of the Pulley Ridge HAPC. Because the Pulley Ridge Habitat Area of Particular Concern is fairly small relative to the size of the statistical areas, and because Pulley Ridge spans across multiple statistical grids, it was difficult to discern precise spatial changes in fishing effort resulting from the changes in regulation to the Pulley Ridge area. It was hoped that depth information could help to increase location precision since geographic coordinates are unavailable; combining the NMFS statistical grid with depth information would have served to further stratify the data points and increase the level of precision in this study as done by Saul et al. (2013). However, depth information started being collected in 2005, which is also when the Pulley Ridge HAPC was implemented. 85

102 86 Because of this, it was impossible to compare locations stratified by depth information before and after the implementation of the HAPC. Essentially, the NMFS logbook data will allow us to understand changes in the fishery on a coarser scale. If the effects of the HAPC are big enough, we will still see it at this coarse scale; if not, the effects may be more fine-scale and future study will be needed to gain precision and further evaluate the changes. Because we are looking at the fisheries data on a coarser scale, the risks of committing a Type II statistical error (failing to reject the null hypothesis if it is in fact false) are increased. However, if we do see an effect then the effect must be strong. Because the spatial resolution is coarser, there is a much smaller risk of committing a Type I error (rejecting the null hypothesis if it is in fact true). In addition to these challenges, it was also necessary to delete some of the entries from the raw logbook data set in order to clean up the data for analytical use. Some of the entries had most (if not all) of their variables shifted over into the wrong columns, resulting in missing information such as dates, days at sea, location, and other critical variables. Because of this, all of the affected entries were removed from the data set. A total of 2,876 entries were removed from the original data set, comprising approximately 0.5% of the total data set (the raw data set contained 488,345 entries). Because such a small percentage of data points were removed, I feel that this is unlikely to negatively affect the quality of analysis for the purposes of this project.

103 Significance of Study Historically, commercial fishing activities have negatively impacted many fishery resources worldwide (Botsford et al. 1997, Smith 2002, Christensen et al. 2003, Hilborn et al. 2003, Pauly et al. 2003). In addition, some fishing gear types can damage fragile and valuable coral reef habitats (Thrush et al. 1998, Dayton et al. 1995, Collie et al. 2000, Malakoff 1998, Chiappone et al. 2005), such as those in the Pulley Ridge on the southwest Florida Shelf. Although some management actions have been implemented in order to protect these valuable fishery resources and their habitat, there is a gap in the scientific body of knowledge regarding how these regulatory changes have impacted fishing effort distributions in this area of the Gulf of Mexico. In particular, there have not been many studies concerning the effects of Habitat Areas of Particular Concern on fishing effort distributions. Pulley Ridge is of particular interest, because it has been affected by both the HAPC implementation and catch share programs, meaning that there have been two recent major management changes in this region. The description of changes to fishing effort distributions in this region and analysis of whether changes to fishing effort distributions are linked to these individual management actions will help us to determine whether the effects of the HAPC can be accurately detected by the NMFS logbook data or not. If the effects of the HAPC cannot be detected by the logbook data, it means that either the HAPC effects are negligible or the logbook data is inadequate to test its effects. Answering these questions should be useful to Gulf of Mexico fishery managers because the logbook data is commonly used in fisheries research to assess the status of the fishery

104 88 and efficacy of its regulations, in order to accurately model potential impacts of future management changes to the region. Our analyses show that the effects of the implementation of the HAPC cannot be isolated with the logbook data available. We show that there are a number of possible explanations for this. First, the breath of the management intervention: the HAPC only constrained the effort of one fleet, the longline, in a very small area within the much larger area of fishing grounds used by this fleet in the Florida shelf. In contrast, the introduction of catch shares has a very clear effect on fishing effort of both the fleet and individual vessels. The second possible explanation for this is the mobility of the fleets. Our analyses reveal that these fleets change their distribution of effort through time in response to possibly many factors, among them management interventions such as the creation of the HAPC and the introduction of catch shares. Saul et al. (2015), for example, report that the choices of where to fish made by these two fleets are also related to weather conditions, and expected revenue, mainly a function of fluctuations in fish and fuel price. The third issue is the inadequacy of the location information on fish catches. Fishing logbooks for the period prior to the HAPC introduction did not report depth data, therefore limiting our ability to understand changes in the distribution of effort at the scales required to accurately evaluate the creation of the HAPC. In 2005 NMFS introduced compulsory vessel monitoring system (VMS) systems for all the reef fish licensed boats in the Gulf of Mexico (NMFS 2005). This VMS system is used by NMFS to monitor the location and movement of commercial fishing vessels in the U.S. Exclusive Economic Zone (EEZ) and treaty areas, and to monitor compliance with key regulations including the Magnuson-Stevens Act, the Endangered Species Act

105 89 and the Marine Mammal Protection Act (NMFS 2017). Although the purpose of this system is not solely to assess impacts of fishing upon stocks (NMFS 2017), but also to increase safety at sea and compliance of regulations, it is clear that for future study, VMS data available today could provide a much better information source to be able to assess changes in effort distribution which are a consequence of the creations of MPAs such as the HAPC.

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110 94 NMFS (2010). Fisheries Economics of the U.S., NOAA Technical Memo NMFS- F/SPO-109, 177 pp. NMFS (2017). Vessel Monitoring System Program. In Fisheries Home, Law Enforcement, Our Programs. Web. Retrieved from NRC (National Research Council) (1999). Sharing the fish: toward a national policy on individual fishing quotas. Washington, D.C. National Academy Press. OECD (Organization for Economic Cooperation and Development) (1998). Individual transferable quotas as an incentive measure for the conservation and the sustainable use of marine biodiversity. Paris, France Organisation for Economic Cooperation and Development, Report ENV/EPOC/GEEI/BIO(97)14/FINAL. Pauly D, Alder J, Bennett E, Christensen V, Tyedmers P and Watson R. The future for fisheries. Science 302: Roberts CM, Bohnsack JA, Gell F, Hawkins JP and Goodridge R (2001). Effects of marine reserves on adjacent fisheries. Science 294: Rowley RJ (1994). Case studies and reviews: marine reserves in fisheries management. Aquatic Conservation: Marine and Freshwater Ecosystems 4: Russ GR and Alaca AC (1996). Do marine reserves export adult fish biomass? Evidence from Apo Island, central Philippines. Marine Ecology Progress Series 132: 1 9. Russ GR, Alaca AC, Maypa AP, Calumpong HP and White AT (2004). Marine reserve benefits local fisheries. Ecological Applications 14: Saul SE, Walter III JF, Die DJ, Naar DF, Donahue BT (2013). Modeling the spatial distribution of commercially important reef fishes on the West Florida Shelf. Fisheries Research 143: Saul SE, and Die DJ Modeling the Decision-Making Behavior of Fishers in the Reef Fish Fishery on the West Coast of Florida. Human Dimensions of Wildlife 21: Smith TD (2002). A history of fisheries and their science and management. Hart P and Reynolds J (Eds.), Handbook of Fish Biology and Fisheries, vol. II, Blackwell Science, Oxford: Thrush SF, Hewitt JE, Cummings VJ, Dayton PK, Cryer M, Turner SJ, Funnell GA, Budd RG, Milburn CJ and Wilkinson MR (1998). Disturbance of the marine benthic habitat by commercial fishing: impacts at the scale of the fishery. Ecological Applicaitions 8(3):

111 95 Unites States Commission on Ocean Policy (2004). An Ocean Blueprint for the 21 st Century. Final Report. Washington, D.C. 676 pp. Web. Retrieved from 00_ocean_full_report.pdf. United States Geological Survey (USGS) (2013). Pulley Ridge Introduction. In Coastal & Marine Geology Program, St. Petersburg Coastal and Marine Science Center. Web. Retrieved from Warpinski S, Herrmann M, Greenberg J and Criddle KR (2016). Alaska s Sablefish Fishery after Individual Fishing Quota (IFQ) Program Implementation: an International Economic Market Model. North American Journal of Fisheries Management 36(4): Wertheimer AC and Swanson D (2000). The use of individual fishing quotas in the United States EEZ. In Use of Property Rights in Fisheries Management, FishRights99 Conference, November 1999, Fremantle, Western Australia. Edited by R. Shotten. FAO Fish. Tech. Pap. No. 404/1,

112 APPENDIX A Preliminary Analysis - Models 1 and 2 Model 1 (Fleet-level, combined gear types, square root-transformed effort): Model 2 (Vessel-level, combined gear types, log-transformed effort): Results of Models 1 and 2 For model 1, a two-way ANOVA was utilized to assess variation in fishing effort at the fleet level for the combined handline and longline fishery, based on factors of time period, season, area and gear type. The four individual factors were statistically significant (p < 0.001), as were the two-way interactions of time period and season (p < 0.05), time period and area (p < 0.01), time period and gear type (p < 0.01), season and area (p < 0.01) and area and gear type (p < 0.01). The two-way interaction of season and gear type was statistically insignificant (Table A). Df Sum Sq Mean Sq F value Pr(>F) TimePeriod < 2e-16 *** Season e-08 *** Area < 2e-16 *** Gear < 2e-16 *** TimePeriod:Season * TimePeriod:Area e-12 *** TimePeriod:Gear e-09 *** Season:Area ** Season:Gear Area:Gear < 2e-16 *** Residuals Signif. codes: 0 *** ** 0.01 * Table A: Table of ANOVA results for model 1 Model 2 consists of a two-way analysis of variance assessing variation in annual fishing effort at the level of individual vessels, based on factors of time period, season, area and gear type. The ANOVA analysis revealed that all four individual factors and 96

113 97 two-way interactions have a statistically significant effect on variation in fishing effort. The interaction between time period and season is significant at the 5% level, and all other interactions and individual factors are significant at the 0.1% level (Table B). Df Sum Sq Mean Sq F value Pr(>F) TimePeriod *** Season e-08 *** Area < 2e-16 *** Gear < 2e-16 *** TimePeriod:Season * TimePeriod:Area e-13 *** TimePeriod:Gear < 2e-16 *** Season:Area e-15 *** Season:Gear e-14 *** Area:Gear < 2e-16 *** Residuals Signif. codes: 0 *** ** 0.01 * Table B: Table of ANOVA results for model 2 Although the majority of two-factor interactions showed significance in Models 1 and 2, the interactions of greatest interest are those involving gear type (Fig ). Whenever there are significant interactions, it is not appropriate to interpret the effects of individual factors, therefore in such cases the only boxplot figures provided are those for the significant interactions. The most striking feature of the boxplots of these interactions (Fig ) is the difference between the spatial and temporal variations in fishing effort for the two different gear types. The fleet-level and vessel level models indicate that the handline and longline fisheries have different temporal patterns in fishing effort throughout the three time periods of interest ( , , and ). Figure 31 shows a decrease in annual handline fishing effort from time period 1 to time period 2 at the fleet level, with effort then leveling off between time periods 2 and 3. However, fleet-level

114 98 longline fishing effort decreases across the three time periods, with the sharpest decline occurring between time periods 2 and 3 (Fig. 31). Figure 32 also indicates temporal differences between handline and longline effort distributions at the individual vessel level. The plot (Fig. 32) shows a greater overall amount of annual fishing effort per vessel for longline, and generally lower vessel-effort for handline. Handline effort trends down then up over the time series, appearing to have decreased slightly in time period 2, then increased again in time period 3 (Fig. 32). This is opposite of what we might have expected after the implementation of the HAPC, as restrictions on longline fishing would presumably lead to an increase in handline fishing. However, this plots shows effort for all 6 statistical areas so perhaps the difference due to the HAPC is simply not discernible at this level. Longline effort trends up throughout the entire time series (Fig. 32), indicating that temporal patterns in fishing effort are different between the two gear types.

115 99 Figure 31: Boxplots displaying square-root transformed annual fishing days at the fleet level, subset by three time periods ( , and ) and two gear types (handline and longline). Figure 32: Boxplots displaying log-transformed annual fishing effort at the individual vessel level, subset by three time periods ( , and ) and two gear types (handline and longline).

116 100 Figures 33 and 34 highlight additional differences between the handline and longline fisheries from a spatial perspective at both the fleet and vessel levels, indicating that there are distinct spatial patterns in fishing effort for each gear type. For the handline fishery, most fleet-level fishing effort appears to occur in areas 1 and 2 (Fig. 33). There is relatively little fleet-level handline effort in area 3, then handline effort tends to increase as we go northward to areas 4, 5 and 6 (Fig. 33). For longline fleets, there is very little fishing effort in area 1, in contrast to the higher effort levels for the handline fleet (Fig. 33); this indicates that the two gear types have separate spatial fishing effort distributions. Areas 2 and 3 have greater fleet-level longline effort than area 1, and areas 4 and 5 have the highest effort (Fig. 33). Figure 34 also indicates differences in the spatial distribution of fishing effort on the level of individual vessels. For the longline fishery, there is much less effort per vessel in area 1 compared to all other statistical areas (Fig. 34); this trend does not occur for handline fishing vessels. Essentially, the two gear types have very different spatial effort distributions, operating as two completely distinct fisheries. Areas 1 and 2 are revealed to have the most similar effort distributions for handline fleets, and areas 2 and 3 are most similar for longline (Fig. 33). For this reason, additional ANOVA models were completed with the datasets subset by gear type.

117 101 Figure 33: Boxplots displaying square-root transformed annual fishing effort at the fleet level, subset by six areas (NMFS statistical grids 1 6) and two gear types (handline and longline). Figure 34: Boxplots displaying log-root transformed annual fishing effort at the individual vessel level, subset by six areas (NMFS statistical grids 1 6) and two gear types (handline and longline).

118 102 The major management implication of models 1 and 2 is that the two gear types (handline and longline) have very different spatial and temporal effort distributions, meaning that they essentially operate as two different fisheries rather than one conglomerate reef fish fishery with two possible gear options. For this reason, the data from each gear type were also analyzed in separate models at the fleet and vessel levels (models 3 6), instead of including gear type as a factor in the analyses of variance. Analysis of Variance R Output and Additional Plots for Models 1 and 2 Model 1: Fleet-level Analysis of Variance for Combined Handline and Longline Fisheries Call: aov(formula = SqrtFishingDays ~ TimePeriod + Season + Area + Gear + TimePeriod:Season + TimePeriod:Area + TimePeriod:Gear + Season:Area + Season:Gear + Area:Gear, data = anova.agg) Terms: TimePeriod Season Area Gear TimePeriod:Season Sum of Squares Deg. of Freedom TimePeriod:Area TimePeriod:Gear Season:Area Season:Gear Area:Gear Sum of Squares Deg. of Freedom Residuals Sum of Squares Deg. of Freedom 691 Residual standard error: Estimated effects may be unbalanced

119 103 Df Sum Sq Mean Sq F value Pr(>F) TimePeriod < 2e-16 *** Season e-08 *** Area < 2e-16 *** Gear < 2e-16 *** TimePeriod:Season * TimePeriod:Area e-12 *** TimePeriod:Gear e-09 *** Season:Area ** Season:Gear Area:Gear < 2e-16 *** Residuals Signif. codes: 0 *** ** 0.01 * Tables of means Grand mean TimePeriod T1 T2 T rep Season S1 S2 S3 S rep Area A1 A2 A3 A4 A5 A rep Gear H L rep TimePeriod:Season Season TimePeriod S1 S2 S3 S4 T rep T rep T rep

120 104 TimePeriod:Area Area TimePeriod A1 A2 A3 A4 A5 A6 T rep T rep T rep TimePeriod:Gear Gear TimePeriod H L T rep T rep T rep Season:Area Area Season A1 A2 A3 A4 A5 A6 S rep S rep S rep S rep Season:Gear Gear Season H L S rep S rep S rep S rep

121 105 Area:Gear Gear Area H L A rep A rep A rep A rep A rep A rep

122 106

123 Model 2: Vessel-Level Analysis of Variance for Combined Handline and Longline Fisheries 107

124 108 Call: aov(formula = LogFishingDays ~ TimePeriod + Season + Area + Gear + TimePeriod:Season + TimePeriod:Area + TimePeriod:Gear + Season:Area + Season:Gear + Area:Gear, data = anova.agg.vessels) Terms: TimePeriod Season Area Gear TimePeriod:Season Sum of Squares Deg. of Freedom TimePeriod:Area TimePeriod:Gear Season:Area Season:Gear Area:Gear Sum of Squares Deg. of Freedom Residuals Sum of Squares Deg. of Freedom Residual standard error: Estimated effects may be unbalanced Df Sum Sq Mean Sq F value Pr(>F) TimePeriod *** Season e-08 *** Area < 2e-16 *** Gear < 2e-16 *** TimePeriod:Season * TimePeriod:Area e-13 *** TimePeriod:Gear < 2e-16 *** Season:Area e-15 *** Season:Gear e-14 *** Area:Gear < 2e-16 *** Residuals Signif. codes: 0 *** ** 0.01 *

125 109 Tables of means Grand mean TimePeriod T1 T2 T rep Season S1 S2 S3 S rep Area A1 A2 A3 A4 A5 A rep Gear H L rep TimePeriod:Season Season TimePeriod S1 S2 S3 S4 T rep T rep T rep TimePeriod:Area Area TimePeriod A1 A2 A3 A4 A5 A6 T rep T rep T rep

126 110 TimePeriod:Gear Gear TimePeriod H L T rep T rep T rep Season:Area Area Season A1 A2 A3 A4 A5 A6 S rep S rep S rep S rep Season:Gear Gear Season H L S rep S rep S rep S rep Area:Gear Gear Area H L A rep A rep A rep A rep A rep A rep

127 111

128 112

129 113

130 114

131 APPENDIX B Analysis of Variance Output and Additional Plots for Models 3 10 (Models Utilized in Final Analysis) Model 3: Fleet-level Analysis of Variance for Handline Fishery 115

132 116 Call: aov(formula = SqrtFishingDays ~ TimePeriod + Season + Area + TimePeriod:Season + TimePeriod:Area + Season:Area, data = handline.agg.fleet) Terms: TimePeriod Season Area TimePeriod:Season TimePeriod:Area Sum of Squares Deg. of Freedom Season:Area Residuals Sum of Squares Deg. of Freedom Residual standard error: Estimated effects may be unbalanced Df Sum Sq Mean Sq F value Pr(>F) TimePeriod < 2e-16 *** Season e-07 *** Area < 2e-16 *** TimePeriod:Season TimePeriod:Area < 2e-16 *** Season:Area e-10 *** Residuals Signif. codes: 0 *** ** 0.01 * Tables of means Grand mean TimePeriod T1 T2 T rep Season S1 S2 S3 S rep Area A1 A2 A3 A4 A5 A rep

133 117 TimePeriod:Season Season TimePeriod S1 S2 S3 S4 T rep T rep T rep TimePeriod:Area Area TimePeriod A1 A2 A3 A4 A5 A6 T rep T rep T rep Season:Area Area Season A1 A2 A3 A4 A5 A6 S rep S rep S rep S rep

134 118

135 119

136 Model 4: Vessel-level Analysis of Variance for Handline Fishery 120

137 121 Call: aov(formula = LogFishingDays ~ TimePeriod * Season * Area, data = handline.agg.vessels) Terms: TimePeriod Season Area TimePeriod:Season TimePeriod:Area Sum of Squares Deg. of Freedom Season:Area TimePeriod:Season:Area Residuals Sum of Squares Deg. of Freedom Residual standard error: Estimated effects may be unbalanced Df Sum Sq Mean Sq F value Pr(>F) TimePeriod < 2e-16 *** Season e-07 *** Area < 2e-16 *** TimePeriod:Season ** TimePeriod:Area e-10 *** Season:Area < 2e-16 *** TimePeriod:Season:Area * Residuals Signif. codes: 0 *** ** 0.01 * Tables of means Grand mean TimePeriod T1 T2 T rep Season S1 S2 S3 S rep Area A1 A2 A3 A4 A5 A rep

138 122 TimePeriod:Season Season TimePeriod S1 S2 S3 S4 T rep T rep T rep TimePeriod:Area Area TimePeriod A1 A2 A3 A4 A5 A6 T rep T rep T rep Season:Area Area Season A1 A2 A3 A4 A5 A6 S rep S rep S rep S rep TimePeriod:Season:Area,, Area = A1 Season TimePeriod S1 S2 S3 S4 T rep T rep T rep ,, Area = A2 Season TimePeriod S1 S2 S3 S4 T rep T rep T rep

139 123,, Area = A3 Season TimePeriod S1 S2 S3 S4 T rep T rep T rep ,, Area = A4 Season TimePeriod S1 S2 S3 S4 T rep T rep T rep ,, Area = A5 Season TimePeriod S1 S2 S3 S4 T rep T rep T rep ,, Area = A6 Season TimePeriod S1 S2 S3 S4 T rep T rep T rep

140 124

141 125

142 126

143 127 Model 5: Fleet-level Analysis of Variance for Longline Fishery Call:

144 128 aov(formula = SqrtFishingDays ~ TimePeriod + Season + Area + TimePeriod:Season + TimePeriod:Area + Season:Area, data = longline.agg.fleet) Terms: TimePeriod Season Area TimePeriod:Season TimePeriod:Area Sum of Squares Deg. of Freedom Season:Area Residuals Sum of Squares Deg. of Freedom Residual standard error: Estimated effects may be unbalanced Df Sum Sq Mean Sq F value Pr(>F) TimePeriod e-16 *** Season *** Area < 2e-16 *** TimePeriod:Season ** TimePeriod:Area e-05 *** Season:Area Residuals Signif. codes: 0 *** ** 0.01 * Tables of means Grand mean TimePeriod T1 T2 T rep Season S1 S2 S3 S rep Area A1 A2 A3 A4 A5 A rep

145 129 TimePeriod:Season Season TimePeriod S1 S2 S3 S4 T rep T rep T rep TimePeriod:Area Area TimePeriod A1 A2 A3 A4 A5 A6 T rep T rep T rep Season:Area Area Season A1 A2 A3 A4 A5 A6 S rep S rep S rep S rep

146 130

147 131

148 132 Model 6: Vessel-level Analysis of Variance for Longline Fishery Call:

149 133 aov(formula = SqrtFishingDays ~ TimePeriod * Season * Area, data = longline.agg.vessels) Terms: TimePeriod Season Area TimePeriod:Season TimePeriod:Area Sum of Squares Deg. of Freedom Season:Area TimePeriod:Season:Area Residuals Sum of Squares Deg. of Freedom Residual standard error: Estimated effects may be unbalanced Df Sum Sq Mean Sq F value Pr(>F) TimePeriod < 2e-16 *** Season < 2e-16 *** Area < 2e-16 *** TimePeriod:Season e-05 *** TimePeriod:Area e-05 *** Season:Area TimePeriod:Season:Area ** Residuals Signif. codes: 0 *** ** 0.01 * Tables of means Grand mean TimePeriod T1 T2 T rep Season S1 S2 S3 S rep Area A1 A2 A3 A4 A5 A rep

150 134 TimePeriod:Season Season TimePeriod S1 S2 S3 S4 T rep T rep T rep TimePeriod:Area Area TimePeriod A1 A2 A3 A4 A5 A6 T rep T rep T rep Season:Area Area Season A1 A2 A3 A4 A5 A6 S rep S rep S rep S rep TimePeriod:Season:Area,, Area = A1 Season TimePeriod S1 S2 S3 S4 T rep T rep T rep ,, Area = A2 Season TimePeriod S1 S2 S3 S4 T rep T rep T rep

151 135,, Area = A3 Season TimePeriod S1 S2 S3 S4 T rep T rep T rep ,, Area = A4 Season TimePeriod S1 S2 S3 S4 T rep T rep T rep ,, Area = A5 Season TimePeriod S1 S2 S3 S4 T rep T rep T rep ,, Area = A6 Season TimePeriod S1 S2 S3 S4 T rep T rep T rep

152 136

153 137

154 138

155 Model 7: HAPC Analysis for Handline Fleets 139

156 140 Call: aov(formula = LogEffort ~ TimePeriod + Season + Area + County + TimePeriod:Season + TimePeriod:Area + TimePeriod:County + Season:Area + Season:County + Area:County, data = NewAggregate_Handline_Fleet) Terms: TimePeriod Season Area County TimePeriod:Season Sum of Squares Deg. of Freedom TimePeriod:Area TimePeriod:County Season:Area Season:County Sum of Squares Deg. of Freedom Area:County Residuals Sum of Squares Deg. of Freedom Residual standard error: Estimated effects may be unbalanced Df Sum Sq Mean Sq F value Pr(>F) TimePeriod ** Season Area County < 2e-16 *** TimePeriod:Season TimePeriod:Area e-05 *** TimePeriod:County *** Season:Area Season:County Area:County e-06 *** Residuals Signif. codes: 0 *** ** 0.01 * Tables of means Grand mean TimePeriod rep

157 141 Season rep Area rep County Collier Dade Monroe rep TimePeriod:Season Season TimePeriod rep rep rep TimePeriod:Area Area TimePeriod rep rep rep TimePeriod:County County TimePeriod Collier Dade Monroe rep rep rep

158 142 Season:Area Area Season rep rep rep rep Season:County County Season Collier Dade Monroe rep rep rep rep Area:County County Area Collier Dade Monroe rep rep

159 143

160 144

161 145

162 Model 8: HAPC Analysis for Handline Vessels 146

163 147 Call: aov(formula = LogEffort ~ TimePeriod + Season + Area + County + TimePeriod:Season + TimePeriod:Area + TimePeriod:County + Season:Area + Season:County + Area:County, data = NewAggregate_Handline_Vessels) Terms: TimePeriod Season Area County TimePeriod:Season Sum of Squares Deg. of Freedom TimePeriod:Area TimePeriod:County Season:Area Season:County Sum of Squares Deg. of Freedom Area:County Residuals Sum of Squares Deg. of Freedom Residual standard error: Estimated effects may be unbalanced Df Sum Sq Mean Sq F value Pr(>F) TimePeriod e-05 *** Season e-05 *** Area < 2e-16 *** County e-15 *** TimePeriod:Season e-05 *** TimePeriod:Area TimePeriod:County e-06 *** Season:Area e-13 *** Season:County Area:County Residuals Signif. codes: 0 *** ** 0.01 * Tables of means Grand mean TimePeriod rep

164 148 Season rep Area rep County Collier Dade Monroe rep TimePeriod:Season Season TimePeriod rep rep rep TimePeriod:Area Area TimePeriod rep rep rep TimePeriod:County County TimePeriod Collier Dade Monroe rep rep rep

165 149 Season:Area Area Season rep rep rep rep Season:County County Season Collier Dade Monroe rep rep rep rep Area:County County Area Collier Dade Monroe rep rep

166 150

167 151

168 152

169 Model 9: HAPC Analysis for Longline Fleets 153