CALIFORNIA STATE UNIVERSITY, NORTHRIDGE
Initial indicators of MPA success and factors that contribute to MPA efficacy
A thesis submitted in partial fulfillment of the requirements
For the degree of Master of Science in Biology
By
Erin M. Jaco
December 2018 The thesis of Erin M. Jaco is approved:
______Dr. Robert Carpenter Date
______Dr. Larry Allen Date
______Dr. Mark Steele, Chair Date
ii ACKNOWLEDGEMENTS
First and foremost I would like to thank my advisor, Dr. Mark Steele. Mark helped me in so many ways throughout the development and execution of my thesis research. By some how keeping me sane when I started getting lost down data and calculus rabbit holes, reminding me to keep the big picture in perspective, correcting my stacked modifiers, and trusting an Oregon driver to trailer his boat through Los Angeles traffic, Mark shaped me to be a better writer and scientist. I want to thank him for his mentorship and friendship.
I would also like to thank the other two members of my thesis committee: Dr.
Larry Allen and Dr. Robert Carpenter. Both Larry and Bob had invaluable insight into the development of my project. I really appreciate all of the comments and revisions that they contributed to my thesis.
A huge thanks to everyone who helped with my field work: James French,
Sigfrido Zimmermann, Lindsey Stockton, Hannah Nelson, Melissa Kurman, Juan Enciso,
Zoë Scott, Dan Sternberg, Russell Dauksis, Kathryn Scafidi, George Jarvis, Erika Nava,
Griffin Srednick, Alexis Estrada, Dorothy Horn, and Ashtyn Isaak. I cannot express how grateful I am to these positive and hardworking people who, paid only in kettle chips and gummy worms, put up with hours of tank hauling, early mornings, L.A. traffic, wildfires, seasickness and many long, cold dives.
Thank you for the support from my fellow CSUN grad students who accompanied me on many adventures. From getting lost in the Sierras to celebrating at the Bunker, my time at CSUN has meant so much more to me than a degree because of the amazing people that I have gotten to know. To call out a few: Melissa, you always stood by me,
iii even through trailering situations that still give me nightmares; Hannah, you pushed me to be a stronger scientist and you never let me take the easy way out both when it came to my research and to our adventures; Sigfrido, thank you for providing stress relief in the form of disc golf, Mario Kart, and backpacking.
I have had so much support from all of my friends and family scattered around the world. A huge thank you to my parents, Stan and Kathy Jaco, who always pushed me to be independent and hardworking and who never waivered in their encouragement of my goals.
And finally, thank you to all of the members of the Steele lab, both past and present. Working with my lab mates have been some of my favorite times in grad school, from car karaoke to Frisbee breaks to late nights in the lab. Many of you listened to my research ideas and helped turn my ramblings into coherent thoughts with direction. I feel lucky to have found myself a part of this goofy, supportive, slightly dysfunctional, and hardworking group of people.
iv TABLE OF CONTENTS SIGNATURE PAGE ...... II ACKNOWLEDGEMENTS ...... III ABSTRACT ...... VI CHAPTER 1: INTRODUCTION ...... 1 CHAPTER 2: USING STEREO-VIDEO TO ASSESS INITIAL RESPONSES OF FISH TO PROTECTION IN RECENTLY ESTABLISHED MPAS ...... 5 INTRODUCTION: ...... 5 METHODS ...... 8 RESULTS ...... 15 DISCUSSION ...... 18 CHAPTER 3: EFFECTS OF MPAS ON TARGETED FISHES ARE GREATER IN AREAS OF HIGH EXPLOITATION ...... 33 INTRODUCTION ...... 33 METHODS: ...... 38 RESULTS: ...... 46 DISCUSSION: ...... 50 CHAPTER 4: CONCLUSION ...... 75 WORKS CITED ...... 77
v ABSTRACT
INITIAL INDICATORS OF MPA SUCCESS AND FACTORS THAT CONTRIBUTE TO MPA EFFICACY
By Erin M. Jaco Master of Science in Biology
Fishing is one of the most destructive anthropogenic forces in the marine environment. As the number of overfished stocks increases there is a greater need for effective management. Marine Protected Areas (MPAs) can be successful tools in combating the effects of overfishing by creating no-take zones that protect entire ecosystems. Despite evidence of the positive effects of MPAs, such as increases in abundance, biomass, and body size of targeted organisms, the magnitude of the effects of
MPAs can vary dramatically. This study examined heterogeneity in the effects of MPAs on fishes to determine how quickly effects develop, if there is variation in detectability of
MPA effects among biological metrics, and if heterogeneity in the responses of fishes to
MPA protection can be predicted by historical fishing pressure. Using a diver operated stereo-video camera system, I compared assemblages of fishes targeted by anglers within seven MPAs to nearby comparison areas (non-MPAs) within the Southern California
Bight. The differences of body size, biomass, and density of fishes between MPAs and non-MPAs were evaluated to determine which metrics showed detectable MPA effects after 5 years of protection. The size-based indicators, body sizes and biomass, showed the strongest responses to protection. There was, however, high regional variation in the degree of responses among MPAs. Some of this regional variation was explained by historical fishing pressure. By using fine-scale fishing pressure quantified within each
MPA prior to protection, I was able to test responses of fishes to protection along a
vi gradient of exploitation. MPAs in areas with heavy historical fishing pressure had greater responses in average lengths and size distributions of targeted species than did those in areas with low fishing pressure. Because many stakeholders are invested in marine resources, there is strong societal pressure for accurate predictions of MPA outcomes.
The biological metric used to evaluate MPAs must be considered when predicting the magnitude and speed of responses within MPAs. Additionally, historical fishing pressure influences the efficacy of MPAs, and prioritizing heavily exploited areas for protection when implementing MPAs could maximize ecological outcomes.
vii CHAPTER 1: INTRODUCTION
As human populations increase, so has the number of overfished populations, with more than 33% of the world’s fish stocks classified as overfished (FAO, 2018).
Although humans have fished for thousands of years, increased demand, due to human population growth combined with technological advances, has resulted in fishing becoming increasingly destructive (Pauly et al., 2000; Jackson et al. 2001). Most extraction by humans involves selective harvesting, where individuals with certain desirable traits are removed. Frequently, individuals are selected based on body size, with fishers most often selecting for the largest, most profitable individuals within a population (Fenberg and Roy, 2008). Overfishing and size-selective harvesting can negatively affect populations by reducing abundances, altering life history traits (e.g., age at maturation), and decreasing the average body size of targeted organisms (Conover et al., 2009).
Ecosystem-based management has become a commonly used strategy among resource managers, due to its capacity to protect entire ecosystems rather than individual species. In the marine realm, ecosystem-based management is primarily done through the implementation of marine protected areas (MPAs). This protection can ultimately increase abundances and sizes of targeted species in fished areas outside of MPAs via adult migration (da Silva et al., 2015) and larval dispersal (Christie et al., 2010).
Despite extensive documentation on the positive effects of MPAs on ecosystems
(Lubchenco et al., 2003), not all MPAs are equally effective, and predicting recovery and responses of marine organisms to protection can be complex. Difficulties stem from not
1 only the inherent challenges of studying marine environments, but also from the complexities that are associated with the recovery process in marine populations.
Targeted organisms within a single MPA can have a diverse range of life histories, trophic levels, growth rates, and susceptibility to fishing, which all influence the trajectory of recovery. Recovery also depends on the inherent characteristics of each unique site, such as habitat types, anthropogenic influences, and connectivity to source populations (Barrett et al., 2007; Claudet et al., 2008). All of this variation among sites inevitability leads to heterogeneous MPA effects, which make predicting MPA outcomes difficult (Claudet et al., 2008; Edgar et al., 2014). Additionally, each of these factors can differentially affect commonly used metrics to measure population recovery, such as abundance, biomass, and body size.
Because size-selective harvesting can have detrimental impacts on fish populations and assemblages, fishing intensity around an MPA has the potential to predict the degree of success of protection by an MPA (Edgar et al. 2009). While studies have investigated many of the factors that influence MPA success, few have examined the impact of the exploitation of an area prior to protection due to difficulties attaining fishing pressure data. However, studies have argued that it is necessary to incorporate variation in fishing rates across time when interpreting ecological data (e.g., Barrett et al.,
2007; Zellmer et al, 2018). Most studies that include exploitation levels in their analyses do not use explicit measurements to track fishing pressure and instead categorize areas into heavily fished and lightly fished areas (e.g., Duly et al., 2004; Micheli et al., 2004).
Fishing pressure often is not homogenous among sites, even on relatively small geographic scales (Zellmer et al., 2018). This uneven fishing effort can result in
2 populations with drastically different average abundances and body sizes of targeted species across a region, ultimately affecting how the fished populations will respond to protection. Ideally fishing pressure would be quantified and used to describe how prior fishing effort impacts recovery within MPAs.
Understanding the pace at which MPA effects develop and the causes of their spatial heterogeneity could be useful when implementing MPAs in areas that are heavily used by resource stakeholders. For example, due to California’s large and growing coastal population (Crossett et al., 2004), it can be difficult to accommodate the desires of diverse stakeholders and activities when making marine management decisions. The
MPA process in California left many stakeholders unsatisfied, creasing a social pressure for fast and dramatic improvements brought about by the MPAs (McCreary et al., 2016).
Better information about the speed and cause of heterogeneity of MPA effects might have allowed the process to reach a “stable agreement” among the wide range of stakeholders that are invested in California’s marine resources, from fishers, to underwater sightseers, to politicians, and scientists. In order to balance the needs of resource users, the Marine
Life Protection Act (MLPA) designed a state-wide network of MPAs using a bottom-up approach (Saarman & Carr, 2013). This approach allowed distinct groups to give their input into the design of the MPAs and collaborate with managers to create both ecological and economic objectives (Saarman & Carr, 2013), but the process is viewed as having failed to reach a “stable agreement” (McCreary et al., 2016).
The degree to which environmental and anthropogenic factors influence the effectiveness of MPAs is still debated (Russ et al. 2005). Since some coastal communities and governments provide little support for marine resource management, it is important
3 to use management tools correctly and efficiently as rates of marine harvest continue to increase (FAO, 2018). The goal of this study was to investigate several factors that contribute to the ability to predict MPA success: (1) how fast fish populations change after protection within MPAs; (2) which biological metric has the greatest rate of change;
(3) if species with different life histories (i.e. gonochores and hermaphrodites) respond differently to protection; (5) the extent to which MPAs differ in impact on fish populations within them; and (6) if that efficacy can be predicted by fishing pressure prior to MPA establishment. This study contributes to a larger body of knowledge to inform effective and predictive ecosystem-based management.
4 CHAPTER 2: USING STEREO-VIDEO TO ASSESS INITIAL RESPONSES OF FISH
TO PROTECTION IN RECENTLY ESTABLISHED MPAS
Introduction:
In order to mitigate the impacts of overfishing on marine communities, there has been a shift towards ecosystem-based management, in which the entire ecosystem becomes the target for protection rather than individual species (Pikitch et al., 2004;
Douvere, 2008). The primary mitigation strategy for ecosystem-based management in marine systems is marine protected areas (MPAs), which are designated areas that are closed or partially closed to harvest and other activities. MPAs can act as insurance against fisheries collapse by protecting a given area, which can serve as a refuge, ensuring that some members of the population will survive to reproduce (Bohnsack,
1998; Dayton et al., 2000). MPAs can also counteract the impacts of overfishing outside of their boundaries through larval dispersal (Christie et al., 2010) and spillover of adults to fished areas (Marques da Silva et al., 2015). In these ways, MPAs can be effective tools in marine resource management by increasing abundance, diversity, egg production, and body size of organisms targeted by fishers (Jennings et al., 1995; Tetreault and
Ambrose, 2007; Babcock et al., 2010; Caselle et al., 2015).
Evaluation and monitoring are important components of successful implementation of MPAs. Three metrics commonly are used to evaluate responses of marine organisms to protection: density; biomass; and body size. Many studies have shown that MPAs can increase these three metrics for targeted species (Lester et al.,
2009), however, little attention has been given to the differences in the detectability of these metrics and their biological implications. Biotic, anthropogenic, and abiotic factors
5 can differentially affect density, body size, and biomass, and therefore, these metrics may not be equally informative in assessing responses of marine organisms to protection.
Because an increase in abundance is often a primary goal of MPAs, many studies report changes in density as a measure of MPA efficacy (e.g. Froeschke et al., 2006; Russ et al., 2015). The cessation of fishing creates the expectation that abundance of fished species will increase. Although increased density is an anticipated effect of MPAs
(Lubchenco, 2003), density can be confounded among sites with different types of fishes, recruitment rates, and biogeographic conditions (Dayton et al. 2000; Caselle et al., 2015).
For example, varying environmental conditions, such as current flow, can increase fish recruitment to an area, regardless of its protection status (Barrett et al., 2007). This external source of individuals can cause a false notion that an MPA is successfully producing more fish within its boundaries if it is compared to sites with lower larval supply. Thus, changes or differences in abundance can be imprecise measures of recovery when evaluating MPAs.
Size-based indicators can reveal the effects of fishing on population structure because of the size-selective nature of fishing (Shin et al. 2005). Average body sizes are affected by fishing in two ways: either because larger individuals are removed due to gear selectivity, size limits, or targeting of larger individuals to maximize profit; or because populations accumulate the effects of overfishing by the continuous removal of individuals over time, resulting in fewer old, large individuals (Shin et al. 2005). Change in body sizes is likely to be a rapid and strong response to protection because of the direct impact of size-selective fishing and because body size is not as strongly influenced by factors external to the MPA as density is.
6 Population biomass is a size-based metric that also incorporates density. While still subject to some of the limitations of using density as a metric of MPA efficacy, biomass has been suggested to be a more robust measure of populations than density alone. By incorporating body size, measuring changes in biomass can abate potential environmental effects on density (Vallès and Oxenford, 2015). Edgar et al. (2014) found that while density varied spatially and thus had the potential to mask MPA efficacy, biomass significantly increased within MPAs regardless of spatial variation in density, and this change was easily detected.
With the potential for heterogeneous changes in density, biomass, and body size of a population due to protection, it can be difficult to predict MPA effects. Often, MPAs are established through a collaborative effort of a variety of stakeholders. Many stakeholders may be dissuaded from using MPAs as a resource tool if they do not achieve the expected results, creating a social pressure for fast and dramatic improvements brought about by these MPAs.
The objectives of this study were to determine if MPA effects could be detected within 5 years of MPA implementation and, if so, which indicator of recovery of fish populations, changes in density, biomass, or average body size, showed the strongest response to protection. To address these objectives, I quantified differences in targeted fish assemblages between MPA and nearby non-MPA sites in seven regions in Southern
California. I tested two hypotheses: (i) biological differences between MPAs and nearby unprotected areas would be detectable within 5 years of MPA implementation; and (ii) size-based indicators would be more sensitive indicators of MPA effects than density within young MPAs.
7
Methods
Study Sites
I selected 7 MPAs within the Southern California Bight (SCB): the Campus Point
State Marine Conservation Area (SMCA), the Point Dume State Marine Reserve (SMR), the Point Vicente no take SMCA, the Laguna Beach SMR, the Swami’s SMCA, the Long
Point SMR, and the Blue Cavern Onshore SMCA. All of these MPAs were established in
2012 by the Marine Life Protection Act (MLPA) and protect ecological communities on nearshore, rocky reefs. Using a match-paired design, I paired each MPA with a nearby, fished comparison area (non-MPA), resulting in 14 total sites (Fig. 1). Non-MPAs were selected based on having similar habitat characteristics and fish assemblages as the paired
MPA. Each MPA and non-MPA pair was assigned a “region” within the SCB: Santa
Barbara (Campus Point SMCA); Malibu (Point Dume SMR); Palos Verdes (Point
Vicente SMCA); Laguna (Laguna Beach SMR); San Diego (Swami’s SMCA); West
Catalina (Blue Cavern Onshore SMCA); and East Catalina (Long Point SMR).
Sampling Methods
I measured the size (length) and density of fishes at each site, as well as habitat characteristics. From size and density estimates, I calculated biomass. At each site, nine
50-m transects were sampled. I randomly selected transect locations at each site using the
Create Random Points tool in ArcGIS 10.4 (ESRI, 2015), confining transects to nearshore rocky reef habitats at depths between 6 and 18 meters. Transects were laid
8 along three depth strata: 6, 12, and 18 m. I surveyed each depth strata in three different locations, resulting in nine transects within each site.
Each transect consisted of a canopy, midwater, and benthic component. The transect tape for the canopy portion was reeled out first, and fish and giant kelp
(Macrocystis pyrifera) canopy cover were recorded within the top 2 m of the water column. The midwater portion was reeled out directly below the canopy portion, 6 m above the seafloor, except in the case of the 6-m depth strata, in which the transect tape was reeled out 4 m above the seafloor. Finally the benthic portion of the transect was laid out following the contour of the seafloor.
Fish lengths and densities were determined using a diver-operated stereo-video camera system. A diver swam the length of all three 50-m long components of each transect, keeping the stereo-video camera centered over the transect and facing forward.
Stereo-video is a technique that involves using two, calibrated video cameras mounted on a base bar, oriented in the same direction. When a fish swims into the field of view of both cameras, it can be measured to the nearest millimeter with a less than 1% error (as determined in a controlled pool environment). This technique allows for extremely accurate length measurements that are necessary for detecting small differences, which are what is expected for recently established MPAs. My stereo-video system consisted of two GoPro HERO4 Silver cameras, with a base separation of 80-cm and an inward convergence of four degrees. Cameras were calibrated using the SeaGIS Calibration
Cube and corresponding SeaGIS CAL software. For a complete review of the photogrammetry principles and stereo-camera system design see Harvey & Shortis
9 (1995). All video processing was completed using the customized software SeaGIS
EventMeasure.
The lengths and density of species that are common throughout the Southern
California Bight were recorded for each transect. These species were categorized as either targeted by anglers or not targeted by anglers. The targeted species in this study were kelp bass (Paralabrax clathratus), California sheephead (Semicossyphus pulcher), and barred sand bass (Paralabrax nebulifer). Because targeted species receive the direct benefits of protection from fishing within MPAs, they are expected to show strong responses to protection. Non-targeted species were used as controls for variation not related to fishing among sites because they are not expected to show any differences between MPAs and non-MPAs soon after protection, though time-lagged indirect effects may develop (Babcock et al., 2010). The non-targeted species were blacksmith (Chromis punctipinnis), garibaldi (Hypsypops rubicundus), señorita (Oxyjulis californica), and rock wrasse (Halichoeres semicintus). Because both California sheephead and rock wrasse are protogynous species in which males are the terminal phase and are generally larger than females, both of these wrasse species were separated into male and female categories for all analyses.
I collected data on habitat characteristics of each site that could potentially influence fish populations, and therefore confound the results of this study. Data were collected by a diver who followed the diver recording fishes on each transect. Two types of data were recorded: macroalgal density and percent cover via random point contacts
(RPCs), which categorized the substrate type, relief, biotic cover of the substrate, and kelp canopy presence. Macroalgal belt transects recorded the density of individual alga,
10 and categorized each into one of 4 height categories: canopy (6-18 m); midstory (4-6 m); high understory (1-2 m); and low understory (25 cm-1m). Due to logistical constraints, macroalgal species were not differentiated. RPCs were collected at a random point within each of the 50 m of the transect. All variables were recorded along the benthic portion of each transect except for giant kelp canopy presence, which was recorded along the canopy portion as the number of random points directly beneath the kelp canopy.
Substrate type was divided into six categories: bedrock (>2 m); large boulder (1 m – 2m); small boulder (10 cm – 1 m); cobble (1 cm – 10 cm); and sand (<1 cm). Substrate relief measured the absolute difference in height of the highest and lowest points within a 0.25
× 0.5 m square around each point, and was divided into five categories: >2 m; 1-2 m; 50 cm-1 m; 10-50 cm; and 0-10 cm. At each point the surveyor also recorded the cover of benthic organisms or bare substratum.
Fish population metrics
Lengths
To determine fish lengths, I used the stereo measuring component of SeaGIS
EventMeasure. This component uses trigonometric principles to calculate lengths derived from three-dimensional estimates of space. All fish total lengths were recorded if the fish was identifiable and oriented in a way that could be measured (both snout and end of caudal fin visible in both videos, with little to no bend in the body or caudal fin), regardless of distance from the cameras.
11 Density
Density estimates only included individuals along transects if they were within 2 m of the stereo-camera, which was the lowest visibility encountered during my study.
The field of view at 2 m from the midpoint of the stereo-camera was 2.1-m wide ×1.4-m high. Thus, density was measured over a 50 × 2.1 × 1.4 m belt transects.
Biomass
I estimated the weight of each measured individual using length-mass equations from Love (2011). The average population biomass of each species at a site was calculated by averaging the sum of the weights from each transect. Biomass estimates were constrained in the same way that density estimates were, using only individuals within 2 m of the stereo-camera (i.e., biomass of measureable fish along 50 × 2.1 × 1.4 m transects).
Statistical Analyses
Lengths
MPA effects on fish length were determined by comparing average lengths of fish within MPAs with average lengths in non-MPA sites for each species. In calculating average lengths, I only included fish that had reached the minimum size-at-maturity
(values from Love, 2011; Adreani & Steele, unpublished; Muñoz Williams, unpublished) to avoid having average length influenced by variation among sites in recruitment of young-of-year. All lengths were log (x+1) transformed to meet assumptions of normality.
12 To determine if fish were larger within MPAs and if this response to protection varied heterogeneously among MPAs, I ran separate 2-way fixed factor analysis of variance (ANOVA) for each species, with region (7 levels, each with an MPA and non-
MPA pair) and MPA status (2 levels) as factors. I conducted multiple ANOVAs, one for each species, because I was unable to include “species” as a factor in a single model because not every species was found in each region. All length analyses were conducted in the RStudio 1.1.419 software (RStudio team 2015).
Density and Biomass
To test if differences in fish density and biomass between MPAs and non-MPAs could be detected as responses to protection, and if these responses varied among regions,
I ran separate two-way, fixed-factor Permutational Analysis of Variance
(PERMANOVA) for each species for each response variable: total density, mature density, and biomass, with region (7 levels) and MPA status (2 levels) as factors. Density and biomass (per 441 m3) were calculated for each site and species by summing the values of the canopy, midwater, and benthic portions of each transect.
Differences in density were tested in two ways: using a total density including all individuals, regardless of size; and by using only the density of mature individuals (i.e., including only individuals that had reached the minimum size-at-maturity). Density was analyzed two different ways because, during video processing, I was unable to measure all fish that entered the transect, due to body positioning of some fish. By using a total density, I was able to include unmeasured fish, which may or may not have reached the minimum size-at-maturity. For biomass, I only included mature individuals because I
13 measured relatively few fish that had not reached the minimum size-at-maturity, thus the results including immature individuals were very similar to the results of only including mature-sized individuals. Moreover, using only mature-sized individuals in estimating biomass allowed a direct comparison of the metrics length and biomass.
Density and biomass data were zero inflated and did not fit normal distributions, and so I ran univariate PERMANOVAs for all tests. To evaluate whether any significant
PERMANOVA results might be attributable to differences in dispersions rater than differences in central tendencies, I tested for homogeneity of dispersions using a
Permutational Analysis of Multivariate Dispersions (PERMDISP). All density and biomass analyses were completed with the statistical software program PRIMER 7
(Clarke and Gorley, 2015).
Habitat
To determine if there were any systematic differences between the habitat characteristics of the MPAs and non-MPAs, I ran a single (PERMANOVA) on the algal density and RPC data. PERMANOVA was used to test for any systematic differences between MPAs and non-MPAs as well as differences among the 7 regions or region- specific differences between MPA and non-MPA pairs (the interaction term). Each algal height category was expressed as individuals per m2. The RPC categories of kelp canopy presence, substrate type, relief, and benthic cover were converted into proportions for each transect because I was not always able to record 50 points on all transects. As a way to manage the dependence that is inherent of proportional data, a principal components analysis (PCA) was used for all habitat data to create principal component (PC) scores for
14 each transect that were independent of one another. I generated PC scores separately for benthic RPC variables (substrate type, relief, and benthic cover) and algae data (both algae counts and canopy cover) for each transect. Algae data were normalized before
PCA so as to be able to combine proportion and count data. Using a Euclidean distance matrix, the PC scores for each transect for both RPC and algae data were used as response variables for a two-way PERMANOVA, with MPA status and region as factors.
A PERMDISP was used to test for homogeneity of dispersions. Both analyses were done in PRIMER 7.
Results
Lengths On average, targeted fishes were larger in MPAs than outside of them, however, there was significant regional variation in the magnitude of the MPA effect, as indicated by significant interactions between MPA status and region (Table 1). Kelp bass were larger within MPAs at 6 of the 7 regions and male California sheephead were larger within MPAs at 5 of the 7 regions, however, the magnitude of these differences differed among regions (Fig. 2). Laguna and East Catalina, each showed relatively large differences inside vs. outside the MPA. In contrast, some regions, such as Malibu and
Palos Verdes, had negligible differences between populations inside vs. outside the
MPAs. Neither barred sand bass nor female California sheephead differed significantly in size inside vs. outside MPAs (Table 1).
Non-targeted species/sexes showed different patterns of lengths than targeted species/sexes. Senorita and garibaldi lengths differed between MPAs and non-MPAs but
15 this difference varied among regions (Table 1; Figure 2). Blacksmith were consistently larger outside MPAs across all regions (Fig. 2). Male and female rock wrasse did not differ significantly in length inside vs. outside MPAs (Table 1).
Biomass
There was a detectable difference in biomass between populations inside vs. outside MPAs for one of the targeted species/sexes. Overall kelp bass had significantly higher biomass inside vs. outside MPAs (Table 2; Figure 3). Male and female California sheephead and barred sand bass did not differ significantly in biomass between MPAs and non-MPAs (Table 2). Biomass of most of the non-targeted species/sexes did not differ significantly inside vs. outside MPAs (Table 2). However, blacksmith biomass did differ significantly between MPAs and non-MPAs, but this difference varied by region
(Table 2, Fig. 3). This interaction effect of region and protection status could, however, be attributed to the heterogeneous dispersion of blacksmith biomass among regions
(F6,119=5.16, p(perm)=0.02). All other species/sexes also had dispersions of biomass that were significantly different among regions. However, all species/sexes had homogenous dispersions of biomass between MPAs and non-MPAs.
Density
Total Density
For most species, both targeted and non-targeted, density did not differ between sites inside vs. outside MPAs. (Because all male California sheephead and male rock wrasse are mature, the results for these two species are not reported here for total density.
16 They can be found in the next subsection on mature density) the only targeted species that differed in density was kelp bass, which had significantly higher densities within
MPAs than outside them (Table 3; Fig. 4). The only non-targeted species that differed in density was garibaldi, which had higher average densities inside MPAs than outside them, but this difference was inconsistent among regions (Table 3, Fig. 4). Dispersions of density for most species/sexes did not differ between MPAs and non-MPAs, except for garibaldi. Among regions, however, dispersions were significantly heterogeneous for all species/sexes, except for señorita.
Mature Density
I did not detect any differences in densities of mature individuals within MPAs compared to non-MPAs for targeted species/sexes (Table 4). Among non-targeted species, only blacksmith and female rock wrasse differed significantly in densities of mature individuals between MPAs and non-MPAs, but this difference varied by region
(Table 4; Fig. 5). Blacksmith and female rock wrasse both had significantly different dispersions among regions, which could have caused the interaction detected by
PERMANOVA (blacksmith: F6,119=13.78, p(perm)=0.001; female rock wrasse:
F6,119=16.26, p(perm)=0.001). Only kelp bass and barred sand bass had homogenous dispersions of mature density among regions. All species showed homogeneity in dispersions for protection status.
17 Habitat
There were no systematic differences in habitat between MPA vs. non-MPA sites.
A PCA summarized the RPC categories into 3 principal components (PC), explaining
66.9% of the total variation (PC1: 41.5%; PC2: 16.1%; PC3: 9.3%), and the algae categories into 3 principal components, explaining 83.7% of the total variation (PC1:
38.5%; PC2: 31%; PC3: 14.2%). PERMANOVA, using these 6 components, detected no main effect of MPA status (F1,121=1.89, p=0.19), indicating that on average, MPAs did not contain better or worse habitat than non-MPA sites. There was a significant interaction between MPA status and region indicating that habitat differed between some
MPA-non-MPA pairs, but in an inconsistent manner (F2, 121=4.65 p=0.001). A
PERMDISP indicated that there were no systematic differences in habitat variability between MPAs and non-MPAs (F1, 133=0.04, p=0.86). Any differences in habitat were visually subtle (Fig. 6).
Discussion
This study reveals that direct effects of MPA status on targeted organisms can be observed within as little as 5 years after establishment. However, only certain metrics that
I used revealed the influence of protection. This study shows that the biological measurement chosen by researchers or resource managers may influence the interpretation of MPA success or failure.
I detected an MPA effect on the body size of fishes. Two out of the four species/sexes targeted by fishers were significantly larger within MPAs: kelp bass and male California sheephead. None of the non-targeted species were systematically larger
18 within MPAs, and, in the case of blacksmith, non-targeted fish were larger outside of
MPAs. These results support the hypothesis that change in body size is an early and detectable effects of MPAs on targeted organisms.
While there were larger sizes of kelp bass and male California sheephead within
MPAs, the magnitude of differences inside vs. outside of MPAs were inconsistent among regions, indicating that MPAs vary in efficacy. This variation could be due to one or a combination of the many factors that can affect MPA success, such as the size of the area protected, connectivity to other habitats, or nearby harvesting effort (Barrett et al., 2007;
Claudet et al., 2008). This study, along with many others, supports the view that MPAs are not a “one size fits all” strategy, and highlights the extent to which effects of protection can vary among them.
Changes in other management regulations may have also contributed to my finding that kelp bass were not larger within all MPAs compared to nearby areas outside them. In 2012, the same year that the MPAs in my study were implemented, the
California Department of Fish and Wildlife increased the minimum size limit of kelp bass from 12 to 14 inches, effectively protecting some of the fish in the non-MPA sites that had not been protected prior to 2012. This change of size limits would mask MPA effects on size. Thus my finding that on average, kelp bass were larger within MPAs (though not in all 7 of them) indicates that body size strongly responds to protection.
I did not detect significant evidence of MPA effects on the lengths of female
California sheephead or barred sand bass. Female California sheephead likely did not respond because they are not as subject to size-selective harvesting as the other targeted species/sexes. Male California sheephead are larger than females and are targeted more
19 by recreational fishers (Cowen, 1990). Because of this lack of size-selection on females, protection is less likely to change the size structure of this group of fish. Barred sand bass, however, also did not differ in length or average biomass inside vs. outside MPAs, despite the fact that this species is harvested in a size-selective manner, having a minimum size limit. The lack of MPA effects on this species may be the result of its migratory spawning behavior. A large portion of the barred sand bass population migrates offshore during the summer to spawn in areas that are outside of MPAs
(McKinzie et al., 2014) and most of the catch of this species is made on their spawning grounds (Erisman et al., 2011). Therefore, the MPAs studied, which are typical of many in that they do not extend far offshore, may provide little protection for barred sand bass during the period they suffer the most fishing mortality. Many studies that focus on the recovery of exploited fish populations within MPAs lump species together (e.g. Côté et al., 2001; Barrett et al., 2007; Babcock et al., 2010). My results, however, provide evidence that recovery often varies among fish species, and highlights the importance of species-specific data and analyses.
In the present study, stereo-video was a valuable tool that enabled detection of differences in the size of fishes inside vs. outside relatively young MPAs that were modest (on average <4 cm for male California sheephead and <5 cm for kelp bass). The strength of this tool is both its accuracy and the fact that it is not subject to observer bias.
Under ideal conditions (e.g., pool trials), the errors made by the stereo-video system I used were typically <1%. More commonly, the lengths of fishes are estimated by eye in the field by trained SCUBA divers. This is done either by placing individuals into broad size bins that would make detection of 5 cm differences difficult (Gillett et al., 2012), or
20 by estimating exact sizes of fish, but this is often inaccurate and imprecise. For example,
Bower et al. (2011) compared diver and stereo-video size estimates, and found that divers, even those very experienced in estimating fish size, made errors greater than 10%.
This 10% error rate may be large enough to mask MPA effects, particularly during the early stages of protection.
Total density, density of mature individuals, and population biomass appeared to be less sensitive metrics for measuring impacts of protection within MPAs than was fish length. Of the three metrics, the only targeted species to significantly differ inside vs. outside MPAs was kelp bass, which had higher total densities and greater biomass inside
MPAs. Only three non-targeted species/sexes differed in biomass or density, blacksmith, garibaldi, and female rock wrasse, and the difference in each of these cases varied by region. Two probable reasons why biomass and density were similar inside vs. outside
MPAs for these species are that (1) fishing is not the primary driver of these biological metrics, which may instead be more heavily influenced by other factors such as natural mortality rates or variation in recruitment among sites; or (2) differences in density or biomass had not yet developed in the young MPAs I studied. Changes in density, and therefore biomass, can occur slowly or can be subject to single-generation oscillations
(White et al., 2013). Changes in biomass and density may take longer than 5 years of protection for responses to cumulate enough to be detectable.
Although there were some habitat differences between certain paired MPA and non-MPA sites, these differences were inconsistent among pairs and thus were unlikely to drive the patterns I attribute to MPA effects. Because habitat differences were inconsistent among paired MPA and non-MPA sites, any habitat influences on fish
21 assemblages would likely result in more variation in those fishes, decreasing statistical test power, thus masking effect of MPAs rather than confounding them. Nevertheless, significant effects attributed to MPAs were detected.
My study highlights the value of size-based indicators for evaluating fishing and
MPA effects, a point also made by other studies (see examples in Shin et al., 2005).
Because the body size of an organism is correlated with many life-history traits, such as growth rates, reproduction, and survival (Shin et al., 2005), and because body size of both predators and prey can affect trophic interactions (Delong et al., 2015; Selden et al.,
2017), this metric can be useful in assessing the condition of ecosystems. Also, my study shows that change in body sizes is a detectable and rapid response to the cessation of fishing, while changes in density or biomass are probably slower to accrue. Vallès and
Oxenford (2015) investigated the influence of fishing on fish assemblages across the
Caribbean and also found that density may not always be appropriate for examining differences in fishing impacts among sites because density was primarily explained by environmental variables. While enhancing and protecting the abundance of targeted assemblages is a primary goal for MPAs that should not be disregarded, monitoring changes in size structure should be prioritized when quantifying the efficacy of MPAs, particularly young MPAs.
MPAs provide unique opportunities to study responses to changes in fishing pressure in a natural system. Many studies focus on long-term effects of MPAs on targeted populations. However, the initial responses of organisms to protection may be important to understand the dynamic process of recovery of exploited fishes, and measuring changes in size-based indicators may be one of the best ways to evaluate
22 initial responses. Ecosystem-based management may play a vital role in the future health of marine ecosystems and it is important to understand how to best evaluate and monitor them.
Figure 1. Map of the 14 study sites used in this study. Black polygons represent the seven MPAs and grey circles represent the seven non-MPA sites. SMCA stands for State Marine Conservation Area and SMR stands for State Marine Reserve.
23
Table 1. Results from two-way fixed factor ANOVAs comparing lengths of each species between MPAs and non-MPAs and among regions.
Targeted Kelp bass Male California sheephead Female California sheephead Barred sand bass Factor df F p df F p df F p df F p Protection status 1 74.29 <0.001 1 28.68 <0.001 1 3.58 0.06 1 0.02 0.89 Region 6 19.82 <0.001 6 15.54 <0.001 6 0.88 0.51 4 3.21 0.02 Protection status x Region 6 8.15 <0.001 6 4.62 <0.001 6 1.22 0.30 4 0.19 0.94 Error 15 3 79 112
Non-targeted Blacksmith Garibaldi Male rock wrasse Female rock wrasse Factor df F p df F p df F p df F p Protection status 1 8.02 0.005 1 0.003 0.96 1 2.78 0.10 1 0.14 0.71 Region 6 45.80 <0.001 6 6.66 <0.001 4 4.32 0.003 3 2.21 0.10 Protection status x Region 6 0.97 0.45 6 2.78 0.01 4 1.57 0.19 3 0.42 0.74 Error 822 30 90 78
Señorita Factor df F p Protection status 1 29.41 <0.001 Region 6 30.72 <0.001 Protection status x Region 6 6.12 <0.001 Error 7
24
Figure 2. Lengths of 5 fish species in each of 7 regions inside and outside of MPAs. White bars represent MPAs, gray bars represent non-MPAs. Means ±1SE are shown.
25 Table 2. Results from two-way fixed factor PERMANOVAs comparing average biomass of each species between MPAs and non- MPAs and among regions.
Targeted Kelp bass Male California sheephead Female California sheephead Barred sand bass Factor df F p df F p df F p df F p Protection status 1 4.65 0.02 1 1.45 0.23 1 0.35 0.55 1 0.01 0.93 Region 6 1.99 0.054 6 1.40 0.17 6 1.50 0.18 4 1.79 0.12 Protection status x Region 6 1.13 0.36 6 0.48 0.81 6 1.30 0.25 4 0.57 0.71 Error 112 112 112 80
Non-targeted Blacksmith Garibaldi Male rock wrasse Female rock wrasse Factor df F p df F p df F p df F p Protection status 1 1.99 0.16 1 1.12 0.30 1 0.34 0.59 1 0.07 0.78 Region 6 9.49 0.001 6 4.03 0.001 5 3.77 0.004 6 4.09 0.002 Protection status x Region 6 3.99 0.002 6 1.58 0.16 5 0.12 0.95 6 1.52 0.17 Error 112 112 96 112
Señorita Factor df F p Protection status 1 0.32 0.58 Region 6 3.76 0.002 Protection status x Region 6 1.64 0.14 Error 112
26
Figure 3. Biomass of mature individuals of kelp bass and blacksmith in each of 7 regions inside and outside of MPAs. White bars represent MPAs, gray bars represent non-MPAs. Means ±1SE are shown.
27 Table 3. Results from two-way fixed factor PERMANOVA comparing average total density of each species between MPAs and non- MPAs and among regions.
Targeted Kelp bass Female California sheephead Barred sand bass Factor df F p df F p df F p Protection status 1 6.60 0.013 1 0.02 0.87 1 0.18 0.71 Region 6 4.29 0.002 6 7.53 0.001 4 0.57 0.73 Protection status x Region 6 2.08 0.06 6 1.47 0.20 4 1.46 0.20 Error 112 112 80
Non-targeted Blacksmith Garibaldi Female rock wrasse Factor df F p df F p df F p Protection status 1 0.01 0.92 1 2.53 0.108 1 0.31 0.64 Region 6 10.99 0.001 6 13.77 0.001 6 8.25 0.001 Protection status x Region 6 0.46 0.84 6 3.96 0.002 6 2.14 0.08 Error 112 112 112
Señorita Factor df F p Protection status 1 0.11 0.76 Region 6 1.69 0.12 Protection status x Region 6 0.76 0.58 Error 112
28
Figure 4. Total density (mature + immature individuals) of kelp bass and garibaldi in each of 7 regions inside and outside of MPAs. White bars represent MPAs, gray bars represent non-MPAs. Means ±1SE are shown.
29 Table 4. Results from two-way fixed factor ANOVAs comparing average density of mature fish of each species between MPAs and non-MPAs and among regions.
Targeted Kelp bass Male California sheephead Female California sheephead Barred sand bass Factor df F p df F p df F p df F p Protection status 1 0.71 0.39 1 0.01 0.95 1 0.41 0.52 1 0.19 0.69 Region 6 2.23 0.04 6 4.95 0.001 6 3.63 0.003 4 1.25 0.32 Protection status x Region 6 0.47 0.84 6 0.36 0.91 6 1.60 0.15 4 1.20 0.32 Error 112 112 112 80
Non-targeted Blacksmith Garibaldi Male rock wrasse Female rock wrasse Factor df F p df F p df F p df F p Protection status 1 1.42 0.22 1 0.60 0.43 1 1.89 0.16 1 0.16 0.69 Region 6 9.71 0.001 6 3.95 0.002 5 6.31 0.002 6 5.12 0.001 Protection status x Region 6 4.08 0.002 6 1.52 0.17 5 1.48 0.21 6 2.59 0.02 Error 112 112 96 112
Señorita Factor df F p Protection status 1 0.20 0.66 Region 6 3.20 0.005 Protection status x Region 6 1.32 0.26 Error 112
30
Figure 5. Density of mature individuals of female rock wrasse and blacksmith in each of 7 regions inside and outside of MPAs. White bars represent MPAs, gray bars represent non-MPAs. Means ±1SE are shown.
31
Figure 6. nMDS plot depicting differences in habitat characteristics averaged for all transects for each MPA and non-MPA.
32 CHAPTER 3: EFFECTS OF MPAS ON TARGETED FISHES ARE GREATER IN
AREAS OF HIGH EXPLOITATION
Introduction
Overfishing is one of the largest and most destructive anthropogenic disturbances to the marine environment and is a major concern for all marine resource stakeholders
(Pauly et al., 1998; Jackson et al., 2001). Marine ecosystems and, in particular, populations of economically important species are negatively impacted when stocks become depleted due to overfishing (Jennings & Kaiser, 1998; Jackson et al., 2001;
Myers & Worm, 2003; Watson et al., 2007). Fishing acts as a size-selective force in two ways: first, by directly selecting for smaller sized populations by removing larger and more economically valuable individuals before removing smaller individuals (Fenberg &
Roy, 2008); and second, by increasing mortality rates, which decreases the average lifespan of a population, resulting in populations with few old, large individuals (Shin et al., 2005). Consequently, areas that are overexploited will have smaller average body sizes of targeted species than areas that experience less exploitation.
Fishing does not affect all species equally and tends to have greater negative effects that are longer lasting on species with large body sizes and slow growth rates
(Jennings et al., 1999). The removal of large, slow-growing species, most of which are predatory species, can be detrimental to marine ecosystems due to potential trophic cascades that alter community structures (Pinnegar et al., 2000). Studies have shown that the strength of trophic cascades is not only dictated by the presence or absence of predators, but also by the body size of individual predators. A decrease in large bodied predators can result in a loss of trophic control and lead to disturbances to the natural
33 biomass structure in marine food webs (Delong et al., 2015; Seldon et al., 2017).
Selecting for smaller-sized individuals also has direct negative impacts on the overfished population itself, including decreased productivity, average body size, and age at maturation (Conover et al., 2009). If subjected to continuous size-selective harvesting, a population can ultimately have changes in its genetic make-up, resulting in evolutionary changes (Fernberg & Roy, 2008; Allendorf & Hard, 2009).
Several management strategies are used to combat the effects of size-selective harvesting, such as gear restrictions, size limits, and Marine Protected Areas (MPAs).
MPAs are areas that are fully or partially closed to the harvest of marine organisms. They are useful tools for fishery management because they are intended to protect entire ecosystems rather than individual species. On average, MPAs are effective at increasing targeted species’ abundances, biomass, and body sizes, as well as increasing overall species richness (Lester et al., 2009). While the many positive effects of MPAs on populations and ecosystems are widely accepted (Lubchenco et al., 2003), MPAs are not all equally successful. There is heterogeneity in biological outcomes among MPAs, making their efficacy difficult to predict (Claudet et al., 2008; Edgar et al., 2014). Many biological, environmental, and anthropogenic factors influence MPA quality, such as
MPA size, age, and degree of connectivity with nearby populations (Barrett et al., 2007;
Claudet et al., 2008). While the relative influence of each of these factors is still being explored, there are conflicting conclusions among studies that have attempted to disentangle factors that influence of MPA outcomes. Predicting MPA success is a dynamic area of study and involves complex, interacting variables, making it difficult to
34 partition explicit factors that contribute to MPA efficacy (Babcock, 2010; Edgar et al.,
2014).
Few studies predicting MPA success consider the historical levels of exploitation that had occurred within the area protected. Harvested species will have more depleted populations in heavily fished areas than in lightly fished areas. Therefore, heavily fished areas should show have a greater scope for recovery (Edgar et al., 2009). Changes in abundance is a commonly used metric for measuring effects of MPAs (Micheli et al.,
2004), but it can be misleading because abundance is influenced by many more processes than just the alleviation of fishing pressure. While fishing removes individuals from a population, abundance is also influenced by recruitment, natural mortality rates, and habitat characteristics, which often vary widely among sites and seasons (Vallès &
Oxenford, 2015). Change in body size of adults more accurately reflect fishing impacts
(Paddack & Estes, 2000) and is likely a better metric for predicting or evaluating MPA success because fishing is inherently size- selective (Shin et al., 2005) and is typically the greatest cause of mortality of adults of fished species.
Many models of MPA efficacy assume fishing effort is homogenous across an area, and this assumption can contribute to unreliable predictions of MPA outcomes
(Lynch, 2006). Studies that have examined historical levels of exploitation are few and often limited, usually dividing fishing pressure into binary categories that are unable to capture explicit effects of fishing pressure (e.g. Dulvy et al., 2004; Micheli et al., 2004).
Ideally a wide range of fishing pressure would be used to evaluate the impacts of fishing intensity on recovery rates within MPAs. It can be difficult to quantify the impacts of
35 historical exploitation on the efficacy of MPAs because information on fishing pressure is often lacking, and comparable, replicate MPAs often do not exist.
The Southern California Bight offers a unique opportunity to examine the effect of historical fishing pressure on MPA success due to its high populace, spatially variable fishing pressure that is documented by state agencies, and large network of MPAs. In a state with almost 40 million people, 45% of Californians live in coastal southern counties
(Sharygin & Palmer, 2017), subjecting coastal waters to heavy recreational and commercial fishing pressure, which has negatively impacted the area’s natural resources
(Dotson & Charter, 2003). Over the last several decades, fish populations in California have declined in abundance by more than 70% (Koslow et al., 2015). While fishing pressure is high in the Southern California Bight, effort is spatially variable and often correlates with accessibility from ports (Zellmer et al., 2018). To improve protection of marine life, the California legislature enacted the Marine Life Protection Act (MLPA), which resulted in the establishment of 50 MPAs in the Southern California Bight
(CDFW, 2016). The combination of these characteristics of the Southern California Bight provides an excellent opportunity to explore the factors that contribute to heterogeneous effects of MPAs.
Among the species of fish that have been heavily exploited in California are two that are commonly targeted by recreational anglers: kelp bass (Paralabrax clathratus) and California sheephead (Semicossyphus pulcher). Minimum size limits exist for both of these species, further reinforcing size-selection. Both of these species are heavily affected by fishing despite their different life histories. Kelp bass are gonochores (separate sexes) while California sheephead are protogynous hermaphrodites, in which males are the
36 terminal phase and are generally larger than females (Warner, 1975). Kelp bass can only be fished recreationally in California, and despite the lack of commercial fishing pressure, this species has had a 90% loss of biomass since 1980 due to overfishing
(Erisman et al., 2011). Protogynous fish can be more sensitive to size-selective harvesting than gonochoric species because of the disproportionate removal of the large, and consequentially male, individuals, potentially resulting in a greater change in the stock dynamics and reproductive output of a population (Alonzo & Mangel, 2004). Hamilton et al. (2007) found evidence that site-specific histories of fishing pressure significantly impact California sheephead life histories and population parameters. That study found that heavily-fished areas contained truncated size distributions, with fish maturing and changing sex at smaller sizes. Because there is the potential for differential effects of fishing among species, it is important to consider life history when modeling and predicting MPA success.
My study investigated heterogeneous responses of fishes to the cessation of fishing in MPAs with varying histories of exploitation. I evaluated changes in size and density of two targeted species with different life histories, within 7 southern California
MPAs. Using data on fine-scale fishing pressure quantified within each MPA prior to protection, I evaluated differential fish responses along a gradient of exploitation. I determined changes within fish assemblages by comparing differences in size and density between each MPA and a paired fished comparison area. I tested the hypotheses that (i) the amount of fishing pressure prior to protection predicts the change in average size and size structure of targeted fish species within MPAs; that (ii) the ability of prior fishing pressure to predict changes within MPAs varies between species with different life
37 histories; and that (iii) changes in fish density within MPAs are not predicted by levels of fishing pressure prior to protection.
Methods:
Study Sites
I constructed a match-paired design in which each MPA was paired with an unprotected comparison area (non-MPA). Non-MPAs were chosen due to proximity to the paired MPA as well as having similar habitats, depths and fish communities as the
MPA. I estimated MPA effects by comparing the difference in fish size and density between each MPA and non-MPA.
I conducted this study at 7 locations within the Southern California Bight: each consisting of an MPA and non-MPA (see Fig.1 in Chapter 2). The included MPAs were the Campus Point State Marine Conservation Area (SMCA), the Point Dume State
Marine Reserve (SMR), the Point Vicente no-take SMCA, the Laguna Beach SMR, the
Swami’s SMCA, the Long Point SMR, and the Blue Cavern Onshore SMCA. These
MPAs were selected due to similarity in age, species of fish protected, and geographic location. All of these MPAs were established at the beginning of 2012 as part of the
Marine Life Protection Act. While the different MPAs have varying harvest restrictions, all fully protect nearshore rocky reef and kelp forest fishes, including all species I studied
(Table 1).
38 Quantifying Fishing Pressure
I determined fishing pressure for each site using the California Recreational
Fisheries Survey (CRFS) data collected by the California Department of Fish and
Wildlife (CDFW). This dataset is collected year-round and records details on angler trips, including location, mode, number of anglers, and which species of fish the anglers targeted or caught. The data are based on interviews, either in the field or via telephone, of private recreational anglers as well as onboard observations of commercial passenger fishing vessels (CPFVs).
The CRFS data uses a 1-by-1 nautical mile grid system to determine the locality of each angler trip. Each 1-by-1 nautical mile unit of the grid is referred to as a Micro
Block. Every trip is either assigned to a Micro Block or gives exact fishing coordinates.
Therefore, I was able to assign each trip a Micro Block, giving a fine-scale estimate of where anglers fished. Fishing pressure within MPAs was then estimated as the average number of angler trips/Micro Block/year. In rare cases, trips did not provide location data and these trips were not used. The CRFS data collection began in 2004; therefore I focused my analyses on data collected from 2004-2012 to quantify the amount of fishing effort at each site prior to protection as an estimate of site-specific exploitation. The record of each trip specifies either the targeted fish species of the trip or, in the case of the onboard observational data, a subsample of which fishes were caught on the trip.
Only trips that targeted or caught species that are uniquely kelp forest fishes were used in analyses. I did not use commercial fishing pressure data because commercial effort in the
Southern California Bight is primarily focused on invertebrate and offshore species, while recreational effort is focused more on nearshore habitats such as kelp forests
39 (Pondella et al. 2015). Commercial data are also recorded at a much larger geographical scale and does not allow for the fine scale resolution that the CRFS data provides.
It is important to note that fishing pressure values I used are underestimates of angler trips because the dataset is based on subsamples of all anglers in California. While the CRFS dataset may not measure total exploitation, it does represent relative differences in exploitation among sites. The CRFS dataset is useful for this type of study because of the fine-scale resolution of fishing locations.
Sampling Methods
To assess fish assemblages, I surveyed sites using SCUBA between May and
September of 2016 and 2017. All sites were surveyed both years except for the Blue
Cavern SMCA and its paired non-MPA, which were only surveyed in the summer of
2017. I randomly selected 3 survey locations within each MPA and non-MPA using the
Create Random Points tool in ArcGIS Desktop, but I confined surveys to nearshore rocky reef habitats and depths between 6 and 18 meters. At each of the 3 survey locations, 3 transects were completed at 3 different depth strata: 6, 12, and 18-m. These depths, for most of the reefs, corresponded to outer, middle, and inner reef sampling. This resulted in a total of 9 transects for each area. A few MPA and non-MPA pairs were either not deep enough or shallow enough to have all 3 depth strata, in which case the closest equivalent depths were selected to still reflect an outer, middle and inner reef. Care was taken to make sure that transect depths and placements were similar for each paired MPA and non-MPA.
40 I measured fish lengths and density using a diver-operated stereo-video camera system. Video was taken along band transects that consisted of canopy, midwater, and benthic portions, each 50-m long. To avoid scaring fish with diver bubbles, the canopy transect was surveyed first, just below the surface of the water. Second, the midwater transect was surveyed 6-m from the benthos except in the case of the 6-m depth strata, performed 4-m from the benthos. Each transect ended with a benthic portion laid out along the sea floor.
Stereo-video is a video technique that, when two cameras are mounted and calibrated precisely, allows length estimations of objects that come into the field of view of both cameras. The camera footage is processed with a specialized software program that provides accurate and precise three-dimensional data, allowing for reliable length measurements as well as a three-dimensional estimate of position and range of objects from the cameras to create unbiased transect bounds. The stereo-camera was comprised of two GoPro HERO4 Silver cameras mounted on a base bar with 80-cm of separation and the cameras were set up with a four-degree inward convergence. Stereo-video is significantly more precise and accurate than both traditional underwater visual census methods and single camera paired-laser systems (Harvey & Shortis, 1995). Stereo-video is also advantageous because fewer measurements are needed than other methods to achieve the same statistical power (Harvey et al., 2002a). Due to the relative young age of the MPAs within this study, I expect small effect sizes. Therefore it was crucial to get accurate and precise estimates of body lengths so as not to miss small, but potentially biologically important, differences. In a controlled pool environment the stereo-system used in this study had <1% error when using an object with measuring targets that were
41 clearly defined. While it is unlikely that I was able to achieve this accuracy when measuring live fish in the field (primarily because of the human error that is inherent in manually choosing measurement points on the image of each fish), studies have found that stereo-video systems similar to mine result in errors of less than a cm when measuring live fish (Harvey et al. 2002b; Harvey et al. 2003). The cameras were calibrated using the SeaGIS calibration cube with the corresponding SeaGIS CAL software. For a complete review of the photogrammetry principles and stereo-camera system design see Harvey & Shortis (1995).
I estimated fish lengths and density for two species that were common across the entire study region and are targeted by recreational anglers: kelp bass (Paralabrax clathratus) and California sheephead (Semicossyphus pulcher). Because California sheephead are a sexually dimorphic, protogynous fish, I divided this species into male and female categories for all analyses. I also recorded the size and density of three abundant species that are not targeted by anglers as a way of evaluating whether differences in fish lengths between MPAs and non-MPAs could logically be attributed to fishing. These non-targeted species were (Oxyjulis californica), garibaldi (Hypsypops rubicundus), and blacksmith (Chromis punctipinnis). The expectation was that these non- targeted species would not differ in length between MPAs and non-MPAs or, if a size difference was detected, that difference would reflect natural variation between the MPA and non-MPA pair.
42 Video Processing
I processed stereo-video using the customized software package SeaGIS
EventMeasure. This software allows for the logging of various biotic and abiotic characteristics within pictures or videos. The software also has the capabilities of using trigonometric principles to estimate three-dimensional space of overlapping images of two videos, allowing for length and position measurements of objects that are within view of both cameras.
I recorded the lengths of all identifiable individuals of my focal species that came into view of both cameras and whose total length could be measured, regardless of distance from the cameras. However, I only recorded individuals for density estimates if they were within a 2-m range from the midpoint of the stereo-camera, which was the lowest visibility range encountered during my study. This field of view of the stereo- video camera at 2-m range was 2.1-m wide and 1.4-m high, thus, each 50-m long transect encompassed 147m3.
Statistical Analyses and Comparisons
To test if areas with greater fishing pressure (average angler trips block-1 year-1) prior to protection had a greater difference in targeted fish size between MPA and non-
MPA pairs, and to test if this varied by species/sex, I first calculated the average size of each species/sex at each MPA and each non-MPA. I only included fish that had reached the minimum size-at-maturity (values from Adreani & Steele, unpublished; Muñoz
Williams, unpublished) to avoid confounding effects of MPA protection with variation in recruitment of young-of-year. The difference in average size for each paired MPA and
43 non-MPA (MPA – non-MPA) was square root (x+30) transformed to achieve normality, homogeneity of variances, and positive values. I then used analysis of covariance
(ANCOVA) on the transformed differences in size, with fishing pressure as a covariate and species/sex (3 levels: kelp bass; male California sheephead; female California sheephead) as a categorical variable. This allowed me to test for differences in the slope of the relationship between size difference (MPA - non-MPA) and fishing pressure among species/sexes. The standard error of the difference in average size was found by taking the square root of the sum of the variances of the two averages.
To determine if fishing pressure predicted differences in average sizes between
MPAs and non-MPAs of non-targeted species, and if this varied by species, I ran the same ANCOVA model as previously described with the non-targeted species (3 levels: blacksmith; garibaldi; señorita). For this test I also only included fish that had reached minimum size-at-maturity. The differences in average size were square root (x+30) transformed to achieve normality, homogeneity of variances, and positive values.
I also grouped the species within the two groups (targeted and non-targeted) in order to attribute responses of targeted species to an MPA effect. To directly compare sizes, I first found the ratio of the average sizes between each MPA and non-MPA pair
(MPA: non-MPA) for each species/sex. Next, I used ANCOVA to determine if differences of fishing pressure varied in its ability to predict differences in the ratios between targeted and non-targeted species. A significant interaction between fish group type (targeted vs. non-targeted) and fishing pressure resulting from a positive relationship between size ratios and fishing pressure in targeted species but no relationship in non- targeted species would support that hypothesis that presumed MPA effects were in fact
44 the result of protection, rather than natural variation. Ratios were square root (x+0.5) transformed to achieve normality and homogeneity of variances.
To evaluate whether differences in size distributions between MPA and non-MPA pairs were predicted by fishing pressure, I used the D statistic from two-sample
Kolmogorov-Smirnov (KS) tests to summarize differences in the size distributions between each MPA and non-MPA pair for each species/sex. D is the largest difference between the two cumulative frequencies. Using D as a response variable, I ran the same
ANCOVA model described previously to test for differences in the slope of the relationship between size distribution differences and fishing pressure for each targeted species/sex. I also ran the model on non-targeted species to evaluate the natural variation in differences in size distributions among the MPAs. D was log (x+1) transformed to achieve normality and homogeneity of variances.
To test if fishing pressure predicted differences in targeted fish densities between
MPA and non-MPA pairs, permutational multivariate analysis of variance
(PERMANOVA) was used because the density data contained many zero counts and was heavily skewed. I summed the fish counts from the canopy, midwater, and benthic portions of each transect to produce a density per 441 m3 and then calculated the average density from the 9 transects at each site. The difference between the averages for each
MPA and non-MPA pair was used as the response variable, calculated separately for each species/sex. The statistical model was the same as the preceding ANCOVA models
(fishing pressure as a covariate and species/sex as a fixed factor). In order to achieve positive values of differences in density, data were transformed to x+3. The
PERMANOVA was run based on a Euclidean distance matrix. Permutational analysis of
45 multivariate dispersions (PERMDISP) was used to check for significant differences in data dispersion, which, if present, could cause a significant PERMANOVA result. Fish of all size (immature and mature sizes) were used in density estimates because not all fish that were countable were measureable.
To determine if fishing pressure predicted differences in average densities between MPAs and non-MPAs of non-targeted species, and if this varied by species, I ran the same PERMANOVA and PERMDISP model as described for targeted species with the non-targeted species (3 levels: blacksmith; garibaldi; señorita). To achieve positive values for differences in density, data were transformed to x+32. Fish of all sizes were also used for density estimates of non-targeted species.
Results:
Fish size inside vs. outside MPAs
Areas with greater fishing pressure prior to protection had larger fish within
MPAs than outside them for species targeted by fishers (Fig. 1). For targeted species,
ANCOVA revealed no significant differences among species/sexes in the slope of the relationship between size difference inside vs. outside MPAs and fishing pressure
(F2,15=0.24, p=0.79), and so this interaction term was dropped from the model. The reduced ANCOVA model revealed that differences in average sizes (MPA – non-MPA) were significantly related to fishing pressure (Table 2). For non-targeted species, the interaction between fishing pressure and species also was not statistically significant
(F2,15=8.64, p = 0.07). The reduced ANCOVA model detected no significant relationship between size difference (MPA – non-MPA) and fishing pressure (Table 2). Blacksmith
46 tended to be larger outside MPAs whereas garibaldi and señorita were larger within some
MPAs but not others (Fig. 2). Garibaldi and señorita showed a non-significant trend in size difference that was opposite that of targeted species in that the average size difference decreased with increasing fishing pressure (Fig. 2).
The slope of the relationship between the ratio of average size inside:outside
MPAs and fishing pressure did not differ among the three species/sexes of targeted fishes
(F2,17=2.12, p=0.15), nor among the three non-targeted species (F2,17=1.55, p=0.24).
Therefore, I grouped the ratio of average sizes for each MPA and non-MPA pair for all 3 targeted species/sexes and for all 3 non-targeted species. This allowed me to directly compare these two groups. Greater fishing pressure prior to protection predicted greater differences in sizes of targeted species/sexes, but not for non-targeted species, as expected (Table 3; Fig 3) Proportional differences in size between MPA and non-MPA pairs were similar for targeted and non-targeted species in areas of low historical fishing pressure, but diverged dramatically as historical fishing pressure increased (Fig. 3).
Size-frequencies inside vs. outside MPAs
Like the average size of adults, the size-frequency distribution of targeted species became increasingly different between MPA and non-MPA pairs as fishing pressure prior to protection increased. The slope of the relationship between the Kolmogorov-Smirnov
(KS) test statistic, D, and fishing pressure prior to protection did not differ among targeted species/sexes (F2,15=1.67, p=0.22), and so this interaction term was removed from the model. The reduced model showed that differences in size-frequencies were greater in areas with more fishing pressure prior to protection (Table 4). The KS statistic
47 did not differ significantly among species/sexes (Table 4). Despite there being no detectable differences among species/sex in slope of the relationship between the KS statistic and prior fishing pressure, the KS statistic changed very little with fishing pressure for female California sheephead; whereas it did for kelp bass and male
California sheephead (Fig. 4). The size-frequencies of kelp bass (Fig. 5), male California sheephead (Fig. 6), and female California sheephead (Fig. 7) within MPAs became more distinct from non-MPA sites as historical fishing pressure levels increased with more, larger fish within MPAs.
The relationship between the differences in size-frequencies (KS statistic) between MPA and non-MPA pairs and historical fishing pressure for non-targeted species differed among species (i.e., significant species x fishing pressure interaction:
Table 4). The size-frequencies of blacksmith and garibaldi were more distinct in areas with low historical fishing pressure, but were more similar in areas with high historical fishing pressure, whereas señorita showed the opposite trend (Fig. 8). This negative relationship between differences in size distribution inside vs. outside MPAs and fishing pressure for blacksmith and garibaldi was driven by high D values for both in the Santa
Barbara region (Fig. 8), which is the region with the lowest fishing pressure. The high D statistics for blacksmith in Santa Barbara were due to many large fish outside of the MPA compared to inside the MPA (Fig. 9). Unfortunately my sample size for garibaldi at Santa
Barbara was low, and so D test for this species at this site is sensitive to minor differences between size distributions, and a high value of D may not reflect actual differences in size distributions of the populations (Fig. 10). While señorita had a weak positive relationship between differences in size distributions inside vs. outside MPAs and fishing pressure,
48 the higher values of D in areas with high fishing pressure are due to more, large fish outside of the MPA than inside (Fig. 11).
Density inside vs. outside MPAs
Differences in densities of targeted species of MPA – non-MPA pairs were not related to past fishing pressure (Fig. 12). For targeted species, there was no difference in slopes of the relationship between density differences and fishing pressure among species/sexes (Pseudo-F2,15=0.38, p(perm)=0.7). The reduced ANCOVA model showed that differences in density were not related to fishing pressure nor did they differ among species/sex (Table 5; Fig. 12). Slopes also did not differ significantly among non-targeted species (F2,15=0.33, p=0.73). The reduced model detected no relationship between density differences of non-targeted species and fishing pressure nor differences among species
(Table 5; Fig. 13).
PERMDISP revealed significant differences in the spread of differences of average density among species/sexes for both targeted and non-targeted species/sexes
(targeted species/sexes: F2,18=6.37, p(perm)=0.03; non-targeted species: F2,18=8.11, p(perm)=0.001). However, the spread of differences of average density across fishing pressures did not differ for either targeted or non-targeted species/sexes (targeted species/sexes: F6,14=2.53, p(perm)=0.43; non-targeted species: F6,14=12.23, p(perm)=0.06) further supporting the results of the permutational ANCOVA model in suggesting that fishing pressure was not a significant predictor for differences in average density.
49 Discussion:
Not all MPAs have the same effects, as demonstrated in this study, and it is important to understand what drives these heterogeneous effects of MPAs. The goal of this study was to determine if the levels of exploitation prior to protection could predict variation in the size of MPA effects. The heavy fishing pressure in Southern California is spatially variable, providing the opportunity to examine the effects of varying fishing pressure on MPA efficacy. By comparing the differences in fish sizes and densities between among MPA and non-MPA pairs in seven areas, I found heterogeneous effects of MPAs that were predictable based on historical levels of fishing pressure.
MPAs in areas with heavy historical fishing pressure had greater effects on average lengths of targeted species than did those in areas with low fishing pressure. This suggests that MPAs in areas with heavy fishing pressure will have greater efficacy and be quicker at increasing average fish sizes than MPAs placed in areas with low fishing pressure. If fishing pressure remains low, significant effects of MPAs in areas with low historical fishing pressure are unlikely to accrue. These MPAs, however, can act as insurance against future increased fishing effort, ensuring that some fish stocks will not become exploited. Thus, MPAs can be useful in a wide variety of settings, but the fishing history of the protected area needs to be taken into account when predicting effects and setting goals for the MPAs. As expected, historical fishing pressure did not predict differences in average lengths for species that are not targeted by anglers.
For targeted species, the relationship between historical fishing pressure and difference in length inside vs. outside of MPAs did not differ significantly between fishes with different life histories. Kelp bass, male California sheephead, and female California
50 sheephead were all generally larger within MPAs, but the difference inside vs. outside
MPAs was most pronounced in the heavily fished sites at Laguna and Santa Catalina
Island. Previous studies have suggested that responses to protection can vary among species with different life histories, specifically gonochores vs. protogynous hermaphrodites (e.g. Buxton, 1993; Hawkins and Roberts, 2004; Easter & White, 2016).
Because of the disproportionate removal of larger individuals, which are males in protogynous species, by fishers, I expected that areas with heavy fishing pressure would reveal greater impacts of MPAs on California sheephead than on kelp bass. In other words, protogynous species would have a greater scope of recovery than a gonochoric species, resulting in California sheephead having greater differences in sizes than kelp bass inside vs. outside MPAs that had high historical fishing pressure. However, these two species showed very similar differences in size among the seven MPA and non-MPA pairs. A limitation of this study was that I was only able to investigate one gonochoric species and one protogynous species, and therefore did not have replication of life history type. Additionally, previous studies have also found that patterns of recovery between gonochoric and protogynous species may take at least a decade to differentiate (Molloy et al., 2008). Future studies should investigate responses to protection of more species of each life history and monitor these responses for longer periods of time after MPA implementation to further disentangle the difference between gonochoric and protogynous species. Understanding the differences in responses to protection between species with different life histories will allow for more effective management that is adapted to the species being protected.
51 All three targeted groups showed similar positive relationships between differences in lengths inside vs. outside MPAs and fishing pressure prior to protection.
However, the relationship for female California sheephead was less steep than for kelp bass or male California sheephead. Responses of male California sheephead and kelp bass were likely more similar to each other than to female California sheephead because both are subjected to heavy size-selective harvesting from recreational fishers. In contrast female California sheephead are generally smaller than males and do not experience as much size-selection because recreational fishers will target the larger, and consequentially male, individuals when harvesting this species (Cowen, 1990). When size-selection targets a particular sex, the effects will only be evident in that sex
(Fernberg and Roy, 2008). Larger female California sheephead, however, are above the legal size limit for harvest, so females as well as males of this species are the targets of size-selective fishing. Additionally, plate-sized female California sheephead are often targeted by commercial fishers for the live-trade fish markets (Alonzo et al., 2004). This removal of relatively large females may have caused additional size-selective mortality in female California sheephead. I would expect that the combination of disruptive size- selection from commercial fishers and the directional size-selection from recreational fishers would result in populations composed of even larger proportions of small female
California sheephead than just by recreational fishing effort alone. Thus, commercial fishing effort could have contributed to the finding that the relationship between the difference in average size inside vs. outside MPAs and fishing pressure in female
California sheephead was not statistically different from male California sheephead and
52 kelp bass. Unfortunately, I was unable to account for commercial fishing effort in my study because of the large spatial scale at which commercial landings are recorded.
Studies examining MPA effects ideally would collect data within MPA and non-
MPA sites prior to protection to separate MPA effects from site to site variation. A proper before-after control-impact (BACI) design is recommended for MPA studies to control for natural variation between MPAs and non-MPAs (Guidetti, 2002; Osenberg et al.,
2011). However, because the present study began after the MPAs were implemented, I examined non-targeted species, in which one would not expect strong MPA effects in early years of protection, to assess whether any apparent MPA effects were attributable to natural variation between MPA and non-MPA pairs, e.g., MPAs being placed in particularly productive areas. Comparing the ratio of sizes inside vs. outside each MPA of targeted and non-targeted species revealed little measurable effect of protection in either of the two groups in areas with low historical fishing pressure. However, in areas with high fishing pressure, the two groups diverged. Furthermore, habitat in MPAs did not differ systematically from that in non-MPAs, i.e., habitat was not better in MPAs than outside of them (see Fig. 6 in Chapter 2). These findings suggest that differences in lengths of targeted species were not caused by natural variation, but rather by an MPA effect that increased with historical levels of exploitation.
It is possible that the sizes and densities of non-targeted species had changed in
MPAs since their implementation due to indirect effects of the cessation of fishing.
However, this is unlikely given that the MPAs in this study had only been established for four and five years at the time of data collection, making these MPAs relatively young.
Many studies have documented MPA effects on targeted fishes within the first few years
53 of protection (e.g. Halpern and Warner, 2002; Barrett et al., 2007; Babcock et al, 2010), but studies that have detected effects of MPAs on non-targeted species found those indirect effects took much longer to become evident, and even after decades they are still small compared to effects on targeted species (Jennings and Polunin, 1997; Babcock et al., 2010; Langlois et al., 2012; Seldon et al., 2017).
Comparing size distributions inside vs. outside MPAs further supported the hypothesis that fishing pressure prior to protection can predict differences in size structure caused by protection in MPAs. The size distributions of targeted species were similar inside vs. outside MPAs in areas with low historical fishing pressure, but shifted to having more, larger fish inside MPAs in areas that had high historical fishing pressure.
This demonstrates that changes size structure occurred faster within MPAs that had been heavily exploited. Surprisingly, fishing pressure did predict differences in size distributions of non-targeted species, but this was inconsistent among species. These inconsistent patterns between the size distributions of non-targeted fishes and fishing pressure provide further evidence that the differences in size-distributions of targeted species are attributable to MPA effects.
As expected, differences in density inside vs. outside MPAs were not significantly influenced by historical fishing pressure for either targeted or non-targeted species. This suggests that either density is a slow population trait to recover, or that the size structure of the populations in areas with high historical fishing pressure had a greater scope for recovery than the density of the populations, allowing size structure to respond quicker. I found no difference in relationships of changes in average density and fishing pressure between kelp bass and California sheephead, indicating that life history does not impact
54 responses to protection. This result is supported by a metanalysis by Molloy et al. (2008), which found evidence that changes in density of targeted fishes in response to protection were the same regardless if the species changed sex or not. However, that study also found that, when considering MPAs older than ten years, protogynous species had more consistent benefits from protection than gonochoristic species, therefore, over time, changes in average density could begin to differ between kelp bass and California sheephead. While further monitoring of changes in density is needed, my findings support the use of size-based indicators, rather than density, to examine effects of fishing on populations (Shin et al., 2005).
Because fishing can have direct negative evolutionary and ecological impacts on marine organisms, it is important to understand how to effectively ameliorate these consequences. Depending on the defined outcome goals, it may be beneficial to place
MPAs in areas that will respond quickly in order to meet stakeholder’s expectations.
Many managing agencies have MPA coverage goals, the U.N., for example, has the goal to protect 10% of the ocean by 2020, and while simple percentage goals can be beneficial, it may not be the most effective way to protect ocean resources. Placing MPAs in areas with low historical exploitation could lead to overestimating MPA benefits. This overestimation could lead to a credibility gap between resource managers and stakeholders and ultimately lead to the rejection of marine closures (Lynch, 2006).
Certain aspects of MPA design may provide more rapid returns that match outcome goals of local communities. Size-selective fishing pressure will likely affect species’ responses to protection, and this should be considered when designing MPAs along coastlines with varying levels of exploitation. While there is benefit to preserving systems that are not
55 heavily fished as insurance against future exploitation, heavily fished areas should be target areas for MPA placement if the goal is to maximize ecological recovery.
Responses to MPAs can occur quickly, but they can also be highly variable over time. It is important to continue tracking changes within MPAs over time as more natural size distributions and densities recover and indirect effects of these recovered populations build. My results shed light on how fishing pressure can influence MPA effects, and help predict them. Generating a better understanding of the causes of heterogeneous MPA outcomes will lead to better-informed use of ecosystem-based management.
56 Table 1. Characteristics of the seven MPAs and seven comparison areas used in this study, including the protection status, regulations, average fishing pressure (from 2004-2012), and size. Asterisks indicate comparison areas that have some protection from fishing but where the focal species are not protected. Fishing pressure (average Location Site Status Regulations angler trips block-1 year-1) Size (mi2) Campus Point No take of marine resources for commercial and/or recreational Santa Protected 1.6 10.6 SMCA purposes Barbara Not All take allowed Arroyo Burro 5.1 protected Point Dume All take prohibited Protected 19.9 7.5 SMR Malibu Not All take allowed Leo Carrillo 18.1 protected Point Vicente No take of marine resources for commercial and/or recreational Palos Protected 36.7 15.0 SMCA purposes Verdes Not All take allowed Point Fermin 37.7 protected Laguna Beach All take prohibited Protected 72.3 6.3 SMR Recreational take of finfish by hook-and-line or spearfishing, and Laguna Crystal Cove Not spiny lobster and sea urchin allowed. Commercial take of coastal 78.0 SMCA protected* pelagic species by round haul net, spiny lobster by trap and sea urchin allowed Recreational take by hook-and-line from shore allowed. Recreational Swami’s SMCA Protected 18.2 12.7 take by spearfishing of white seabass and pelagic finfish allowed. Encinitas Not All take allowed Leucadia 30.9 protected No take of marine resources for commercial and/or recreational West Blue Cavern Protected purposes 49.5 2.6 Catalina Onshore SMCA
Island Not No recreational take of invertebrates Lion Head Point 82.8 protected* East All take prohibited Long Point SMR Protected 53.9 1.7 Catalina Island Not All take allowed Hen Rock 47.0 protected
57
Figure 1. Difference in average length (mm) of each MPA and non-MPA pair for each targeted species/sex. Error bars represent ±1SE.
58
Table 2.Results of ANCOVA testing whether the difference in average length (MPA – non-MPA) of fishes was related to fishing pressure or differed among species/sex. Results are shown separately for targeted and non-targeted species/sexes.
Targeted species Non-targeted species Factor df MS F p df MS F p Fishing pressure 1 35.49 8.79 0.009 1 2.60 0.78 0.39 Species 2 8.56 2.12 0.15 2 5.16 1.54 0.24 Error 17 4.04 17 3.34
59
Figure 2. Difference in average length (mm) of each MPA and non-MPA pair for each non-targeted species/sex. Error bars represent ±1SE.
60 Table 3. Results of ANCOVA testing whether the proportional differences of average length (MPA : non-MPA) of fish were related to fishing pressure or differed between targeted and non-targeted species/sexes.
Factor df MS F p Fishing pressure 1 0.003 1.27 0.27 Species 1 0.002 0.77 0.39 Fishing pressure x Species 1 0.02 9.46 0.004 Error 38 0.002
61
Figure 3. Proportional difference in average lengths for all species/sexes. Black symbols represent targeted species/sexes, and gray symbols represent non-targeted species. Solid lines represent the average of all targeted species/sexes (black) and all non-targeted species (gray).
62 Table 4. Results of ANCOVA testing whether the Kolmogorov-Smirnov test statistic (D), which summarizes differences in size distributions between MPA and non-MPA pairs, was predicted by fishing pressure or differed among species/sexes. Separate analyses were conducted for targeted and non-targeted species. The reduced model is reported here for targeted species because the interaction effect of fishing pressure and species was not significant.
Targeted species Non-targeted species Factor df MS F p df MS F p Fishing pressure 1 0.05 8.02 0.012 1 0.05 05.83 0.03 Species 2 0.02 2.6 0.10 2 0.04 4.18 0.04 Fishing pressure x Species 2 0.04 4.45 0.03 Error 17 0.01 15 0.01
63
Figure 4. Relationships between the Kolmogorov-Smirnov test statistic (D) summarizing differences in size-frequencies of each of three targeted species/sexes for each MPA-non- MPA pair and fishing pressure.
64
Figure 5. Size-frequencies of kelp bass for each of 7 MPA and non-MPA pairs. Black bars represent MPAs and gray bars represent non-MPAs.
65
Figure 6. Size-frequencies of male California sheephead for each of 7 MPA and non- MPA pairs. Black bars represent MPAs and gray bars represent non-MPAs.
66 Figure 7. Size-frequencies of female California sheephead for each of 7 MPA and non- MPA pairs. Black bars represent MPAs and gray bars represent non-MPAs.
67
Figure 8. Relationships between the Kolmogorov-Smirnov test statistic (D) summarizing differences in size-frequencies of each of three non-targeted species for each MPA and non-MPA pair and fishing pressure.
68
Figure 9. Size-frequencies of blacksmith for each of 7 MPA and non-MPA pairs. Black bars represent MPAs and gray bars represent non-MPAs.
69 Figure 10. Size-frequencies of garibaldi for each of 7 MPA and non-MPA pairs. Black bars represent MPAs and gray bars represent non-MPAs.
70
Figure 11. Size-frequencies for señorita for each of 7 MPA and non-MPA pairs. Black bars represent MPAs and gray bars represent non-MPAs.
71
Table 5. Results of permutational ANCOVA testing whether differences in average density between MPA and non-MPA pairs were predicted by fishing pressure or differed among species/sexes. Separate analyses were conducted for targeted and non-targeted species.
Targeted species Non-targeted species Factor df MS Pseudo-F P(perm) df MS Pseudo-F P(perm) Fishing pressure 1 1.37 0.22 0.56 1 78.67 0.41 0.56 Species 2 8.31 2.01 0.17 2 9.92 0.05 0.95 Error 17 88.37 17 193.07
72
Figure 12. Relationships between differences in average density in each MPA and non- MPA pair and fishing pressure for each of three targeted species/sexes. Error bars represent ±1SE.
73
Figure 13. Relationships between differences in average density in each MPA and non- MPA pair and fishing pressure for each of three non-targeted species. Error bars represent ±1SE.
74 CHAPTER 4: CONCLUSION
Not being able to detect or predict responses of targeted organisms within MPAs can have negative consequences, sometimes leading to the rejection of marine closures all together (Lynch, 2006). Stakeholder hesitations to MPA implementation are not unfounded, and, depending on desired outcomes, strategies vary in success. This study demonstrates that, when using appropriate tools and metrics that detect signs of change of protected populations, MPAs can produce detectable results within 5 years of being established. When evaluating fish populations in young MPAs, size-based indicators can be detected early on, while changes in density may not be detectable for much longer time periods. Size-based indicators respond quickly to protection because of the impacts that size-selective harvesting and very elevated rates of mortality due to fishing have on targeted populations (Jennings et al., 1999; Shin et al., 2005). It is important to understand which biological metrics will respond quickly to protection when evaluating whether a management strategy is effective to avoid prematurely concluding that an MPA is successful or unsuccessful.
When gathering evidence of MPA efficacy, it is essential to use tools that detect responses to protection at the appropriate scale to avoid changes going unnoticed and misconceptions of how effective or ineffective a MPA is. Increases in fish size on the magnitude of millimeters will likely not be detected if using traditional sampling protocols, such as underwater visual census with visual size estimation, which do not have the fine-scale resolution to distinguish such small changes. Stereo-video is a highly accurate and precise tool that can detect small, but significant differences in length.
75 Accurate predictions of increased sizes and abundances of fish within MPAs can lead to realistic expectations of the economic benefits from MPAs. However, when the actual economic benefits of MPAs do not match the predicted benefits, MPAs are met with negative support for ecosystem-based management. However, when the ecological outcomes of MPAs are successfully predicted and economic expectations are met, there will be an increase in compliance towards MPAs and further support protection along coasts and highly populated areas (Chaigneau and Brown, 2016).
As highlighted in this study, not all MPAs are equally effective, but they have the potential to be extremely valuable tools in fisheries management. Effects will develop slowly or not at all if MPAs are placed in areas that had little extraction effort by fishers.
In contrast, MPAs placed in highly exploited areas can show fast and significant changes of fish populations. However, many policy decisions regarding MPAs do not consider that MPAs placed in different areas may have different efficacy, instead focusing on total area protected. For example, a large MPA placed in an area with limited fishing may have a smaller net ecological impact than a smaller one placed in an area that has been heavily exploited. As overharvesting of marine populations continues and resource managers try to mitigate the effects of overfishing, it is increasingly important to be able to predict the effects that fishing history has on the recovery of marine organisms.
Resource managers cannot discount the anthropogenic history of an area prior to implementing management strategies.
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