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Marine debris in central California: Quantifying type and abundance of beach litter in Monterey Bay, CA
Marine Pollution Bulletin 71 (2013) 299–306
Contents lists available at SciVerse ScienceDirect
Marine Pollution Bulletin journal homepage: www.elsevier.com/locate/marpolbul
Marine debris in central California: Quantifying type and abundance of beach litter in Monterey Bay, CA C. Rosevelt a,⇑, M. Los Huertos a, C. Garza a, H.M. Nevins b,c a
Division of Science & Environmental Policy, California State University Monterey Bay, Seaside, CA 93955, USA California Department of Fish and Game, Office of Oil Spill Prevention and Response, Marine Wildlife Veterinary Care & Research Center, Santa Cruz, CA 95060, USA c Wildlife Health Center, University of California at Davis, CA 95616, USA b
a r t i c l e Keywords: Monterey Bay Beach litter Survey Citizen-science Mixed effects AIC
i n f o
a b s t r a c t Monitoring beach litter is essential for reducing ecological threats towards humans and wildlife. In Monterey Bay, CA information on seasonal and spatial patterns is understudied. Central California’s coastal managers require reliable information on debris abundance, distribution, and type, to support policy aimed at reducing litter. We developed a survey method that allowed for trained citizen scientists to quantify the types and abundance of beach litter. Sampling occurred from July 2009–June 2010. Litter abundance ranged from 0.03 to 17.1 items m2. Using a mixed model approach, we found season and location have the greatest effect on litter abundance. Styrofoam, the most numerically abundant item, made up 41% of the total amount of litter. Unexpected items included fertilizer pellets. The results of this study provide a baseline on the types and abundance of litter on the central coast and have directly supported policy banning Styrofoam take out containers from local municipalities. Ó 2013 Elsevier Ltd. All rights reserved.
1. Introduction The United Nations Environment Program reported, ‘‘Marine litter currently poses a dire, vast and growing threat to the marine and coastal environment’’ (UNEP, 2011a). In California and other coastal states in the United States, there are efforts to increase legislation to limit the use of single use plastics (such as take-out containers) and Styrofoam to reduce the amount of land-based debris littering beaches and entering our oceans (SWRCB, 2010). Beach based surveys have been used to measure potentially harmful marine and land derived litter (Frost and Cullen, 1997; Cunningham and Wilson, 2003; Storrier et al., 2007). Marine debris depositing on the beach may be spatially and temporally variable (Rees and Pond, 1995; Kusui and Noda, 2003; Edyvane et al., 2004; Oigan-Pszczol and Creed, 2007) and a better understanding of this variability can assist state and local waste managers to identify site-specific problems and take appropriate actions for litter abatement. In this paper we discuss methods that were used to train citizen scientists to quantify beach litter and monitor seasonal and site-specific litter occurring on 12 beaches in Monterey Bay, California. ⇑ Corresponding author. Address: PO Box 750, Monterey, CA 93942, USA. Tel.: +1 2064843200. E-mail addresses: [email protected] (C. Rosevelt), [email protected] (M. Los Huertos), [email protected] (C. Garza), [email protected] (H.M. Nevins). 0025-326X/$ - see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.marpolbul.2013.01.015
Several studies have documented the presence and ecological hazards of marine debris to marine wildlife in the Monterey Bay (Baltz and Morejohn, 1976; Moore et al., 2009; Dau et al., 2009; Watters et al., 2010). Beginning in the 1970s researchers began to document evidence of plastic ingestion in the stomach content of several species of seabirds (Baltz and Morejohn, 1976). More recently, Moore et al. (2009) documented the occurrence of seabird and marine mammal entanglement in derelict fishing gear. In addition to evidence of floating debris, Watters et al. (2010) conducted boat-based research to map and categorize the abundance and distribution of underwater benthic debris at depth (20–365 m) within the Monterey Bay. Furthermore, there has been recent concern from scientists over the impacts of small plastic fragments on wildlife and human health (UNEP, 2011b). Few beach surveys sample buried litter and items smaller than 2 cm (meso-debris), which may also require the use of quadrats and sieves to sample accumulated litter (Williams and Tudor, 2001; Kusui and Noda, 2003; McDermid and McMullen, 2004). Storrier and McGlashan (2006) and Ryan et al. (2009) suggest inaccuracies in results of beach surveys when using transects alone, since this method frequently fails to capture meso-scale debris (<2 cm). This can result in an underestimation of beach litter abundance. Our goal was to support coastal managers and environmental non-profits in addressing the issue of beach litter within the Monterey Bay. The objectives of this study were to (1) assess the presence of meso-scale litter; (2) identify temporal and spatial patterns
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in litter abundance; (3) identify types of litter and examine patterns, and (4) involve citizens in science-based research through ongoing beach surveys. To address these objectives we focused on three research questions: (i) Do month or beach location have an influence on litter abundance? (ii) Does litter abundance differ among sites characterized as having low or high public accessibility? and (iii) Is there a relationship between litter type and month or location?
2. Methods 2.1. Survey design and location of study We chose 12 survey sites within the Monterey Bay National Marine Sanctuary shoreline, including those within the jurisdiction of eight CA state beaches, two county beaches, and two city beaches encompassing 57 km of coastline. In Santa Cruz County, we surveyed eight beaches including Santa Cruz Main Beach (city), Seabright State Beach, Live Oak (21st Avenue County Beach), Capitola City Beach, New Brighton State Beach, Seacliff State Beach, Manresa State Beach, and Sunset State Beach. In Monterey County, we surveyed four beaches including Zmudowski State Beach, Marina State Beach, Seaside State Beach, and Del Monte City Beach (Fig. 1). Using a repeated measures approach, we designed the surveys to identify temporal and spatial patterns in litter abundance (items m2) (Vonesh and Chinchilli, 1997; Gotelli and Ellison, 2004; Zuur et al., 2009). At each site, we randomly sampled across two parallel 50 m transects set against the wrackline in order to reduce bias (Moore et al., 2001; Cunningham and Wilson, 2003). 2.2. Analysis of low versus high accessibility beaches Directed by our second research question; the likelihood of litter differing between low and high accessible beaches, each location was also based on the following criteria: public access and proximity to municipalities. We categorized sites into those with high or low public accessibility (Fig. 1). We classified low accessibility sites as beaches adjacent to areas with population density less than 1000 people km2 (based on city population density information). Beach locations greater than 5 km from the nearest city were also classified as low accessibility (US Census, 2000). We used Wilcoxon tests to statistically assess whether litter abundance at low and high public accessibility beach locations differed.
and accuracy in measurements otherwise influenced by the daily variation in litter deposition. We carried out surveys at low tide (mean tidal range 1.1 m, min: 1.2 m (Sept. 2009) and max: 5.8 m (Nov. 2009)), providing the greatest beach surface area for sampling (NOAA, 2012). The first of two transects was placed within the wrackline since previous studies have shown this region as a deposition zone for marine derived debris as well as re-washed land-based debris and plastics (Velander and Mocogni, 1998, 1999; Corcoran et al., 2009; Ryan et al., 2009). Following the protocols of Cunningham and Wilson (2003), a second transect was positioned 5 m above the first transect line. A pin flag demarcated the start of each transect. Five 4 m2 quadrats were randomly placed along both transect lines for the purposes of sampling meso-scale (2 mm–2.0 cm) and macroscale (>2 cm) beach litter (Velander and Mocogni, 1999; McDermid and McMullen, 2004). Volunteers demarcated transect lines and sequentially sampled randomly spaced plots by reusing a 4 m2 PVC quadrat as they progressed along the line. Upon scanning the surface of each quadrat for litter, volunteers used their fingertips to conduct subsurface sampling to approximately 2 cm depth by raking the sandy surface in a light back and forth motion, feeling, and looking for buried litter, but not digging deeper. Volunteers collected all anthropogenic debris from each quadrat, tallied the items on data sheets, and placed litter into labeled resealable bags, for further validation. 2.4. Quality control and litter enumeration methods We assessed the presence and abundance of meso-scale litter by measuring all fragmented plastic and divided and identified them as three class sizes: micro-debris (<2 mm), meso-debris (2 mm–2.0 cm), and macro debris (>2.0 cm) (Ryan et al., 2009). Analysis included performing quality control (confirming quantity and category) by the comparison of all items collected against that recorded by field observers. We used a microscope to differentiate between biological and synthetic composition when necessary. For example, early in the study, volunteers mistook bleached sea grass for plastic strips, and using a microscope confirmed that this biological material had a plant cell structure. We classified litter items into 13 types: fragmented plastic, glass, paper/treated wood, plastic products, Styrofoam, rubber, metal, cigarette butts, fabric, fertilizer pellets, fishing gear, food wrappers, and other (items with ambiguous identity, e.g., paint chips).
2.3. Beach survey methods
2.5. Statistical analysis: mixed effects modeling to explore litter abundance
Surveys occurred semi-monthly from July through September 2009 and monthly from September 2009 through June 2010. This unforeseen change from semi-monthly to monthly surveys was a result of volunteer schedules allowing for more availability in summer months. In months where surveys occurred twice, the average litter abundance and category types were taken. Furthermore, to check that averaging semi-monthly survey data would not create problems, we tested and compared non-averaged versus averaged data, no difference in model preference was detected. Months were grouped into seasonal classes for analysis where summer (July– September), fall (October–December), winter (January–March), and spring (April–June). Trained volunteers collected samples at the same site over the course of one year. Prior to the study, volunteers participated in a two-hour mandatory training. Following the methodology of Ribic et al. (1992), volunteer effort per survey included six teams comprised of at least two volunteers each where each team surveyed two beaches per survey event. This simultaneous multi-team approach increased efficiency
We used simple linear regression to compare three models and ascertain the best preliminary fit to the data. After the best model was chosen, we created a second set of three additional models using a mixed modeling approach. Mixed effects modeling addressed the lack of independence and variance between the continuous dependent variable (litter abundance) and its categorical covariates (beach location and month) (Pinheiro and Bates, 2000). Using a mixed effects model approach within a repeated measures design, we incorporated both fixed and random effects (Pinheiro and Bates, 2000; Zuur et al., 2009). Random effects are typically associated with the sampling unit that represents values drawn from a larger population, in this case beach location (Pinheiro and Bates, 2000). Defining beach location as a random effect, allowed for extrapolation of results for litter abundance from the sampling unit to the greater population (i.e., litter on all beaches in the Monterey Bay) (Venables and Ripley, 2002). In contrast, fixed effects are typically associated with the entire population and not the sampling unit (Lindsey, 1997; Pinheiro and Bates, 2000). Here
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Fig. 1. Map of beach survey sites within the Monterey Bay, CA. Asterisks indicate low accessibility beach locations.
we defined month as the fixed effect because the entire population in this study refers to all beach litter in the Monterey Bay measured by the temporal variable month. In the second set of models, we used a weighted linear regression known as a generalized least squares (GLSs) model to address the heterogeneity in the repeated measure design (Zuur et al., 2009). Additionally, linear mixed effects (LMEs) models incorporate both the random and fixed effects (Zuur et al., 2009). To address the temporal correlation within our linear regression we fit an Auto-Regressive Moving Average (ARMA) correlation structure to the model with the best fit (Zuur et al., 2009). The software program, R was used to perform all analysis, including, diagnostics on all models and statistical tests (R Development Core Team, 2011). The first ‘‘preliminary’’ model (M1) included both spatial and temporal covariates, and the other two included a combination of one or the other covariate (M2, M3).
M1 : Abundanceij ¼ a þ b0 þ b1 Locationij þ b2 Monthij þ eij
M2 : Abundanceij ¼ a þ b0 þ b1 Monthij þ eij M3 : Abundanceij ¼ a þ b0 þ b1 Locationij þ eij where Abundance is litter abundance (items m2), a is the population intercept (when X = 0, Y = 0), b0 is the population slope (when X increases by one unit, what is the expected increase in Y), b1 is the coefficient of location, b2 is the coefficient of month, Month is the categorical name of the month during the study, Location is the categorical beach location name, e is error term with a Gaussian distribution, i is the different beach locations, and j is the different months when surveying occurred. Akaike’s Information Criterion (AIC) was used to identify the best model, where the lowest delta score described the likeliest effect on beach litter abundance. Akaike’s Information Criterion measures the likelihood that the data is described by a given statistical model, where scores are produced and compared among similar models (in this study; M1, M2,. . ., M6) (Gotelli and Ellison, 2004).
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2.6. Categorical analysis To estimate homogeneity among the predictor and response variables we organized litter categories into two-way contingency tables and analyzed them using a McNemar test (Agresti, 1996; Sun and Yang, 2008). We tested whether there was a spatial association of litter categories, by testing if litter category was associated with beach location. We also tested for the temporal distribution of litter categories, by testing if litter category was associated with month.
Table 1 AIC table summarizing model comparison results for simple linear regression models. Model1 includes both covariates, while Model2 describes the temporal effect on litter abundance. Model3 describes the spatial effect on litter abundance. Nomenclature follows Burnham and Anderson (2002): degrees of freedom (df), AIC corrected for small sample size (AICc), differences from the best model (delAIC), and AIC weights (AICw). Model
df
AIC
AICc
delAIC
AICw
Modelnull Model1 Model2 Model3
2 24 13 13
479.88 459.46 462.24 484.06
467.88 407.28 431.90 453.73
60.60 0.00 24.62 46.45
0.00 1.00 0.00 0.00
3. Results 3.1. Litter abundance and mixed effects models Over the duration of the study, the average abundance of litter was 1 ± 2.1 items m2. The greatest average litter abundance occurred at locations within the middle of the Monterey Bay (Sunset 2.5 items/m2 and Zmudowski 2.0 items/m2) and those at the most northern (Main 0.8 items/m2 and Seabright 1.4 items/m2) and southern survey sites (Seaside 0.9 items/m2 and Del Monte 1.2 items/m2; Fig. 2). Unexpectedly, we found substantial increases in average litter abundance during the winter season at survey sites located along the middle of the Bay (mainly comprised of low accessibility beaches; Fig. 2). The AIC model that included both temporal and spatial covariates (M1) was the best fit given the abundance data (Table 1). Furthermore, between the two predictor variables, the temporal model (M2) described the data more closely than the saturated model and thus may be the likely driver for increased AIC values for M1 which was then tested further to investigate temporal and spatial effects (Table 1). In addition, no significant difference was found between litter abundance on low versus high accessibility beach locations (p = 0.55). 3.2. Mixed effects models: independence and variance structures Three additional models were tested based on M1. The GLS model (M4) included a variance structure for month was the best fit given the data (Table 2). This suggests that the amount of variance in litter abundance between months had an influential effect on litter abundance over beach locations. To confirm M4 as the best
fit, two other models were compared; the random effect, beach location, was then analyzed in M5 using an LME model (Table 2). Lastly, the sixth model, M6 also was fitted with LME and included both beach locations as a random effect and the variance structure for month (Table 2). AIC results from M6 and M5 suggest that beach location does not influence litter abundance as strongly as month (Table 2). Litter abundance quantities were auto-correlated over time, violating the assumption of independence between survey events. Fitting ARMA to M4 (best fit) resulted in identifying that litter abundance was temporally correlated by a time step of 2 months. 3.3. Categorical results and litter distribution Marginal heterogeneity was found between different types of litter across month and location. These results indicate litter type is significantly different among months and beach locations (p < 0.01). This finding corroborates results from the AIC model comparisons, where month and beach location affect quantities of litter. Quantity and distribution of general types of litter and the subcategory of exclusively plastic items are delineated in Tables 3 and 4. Styrofoam items occurred at all 12 survey sites in all months and comprised the largest percentage of litter by total number (41%) (Table 3, Fig. 3). The second largest percentage of litter by total number was fragmented plastics. Fragmented plastics occurred at all survey sites in all months and made up 68% of the plastic litter subcategory (Table 4). The size class of fragments was primarily meso-scale (2 mm–2.0 cm); the mean length measurement of fragmented plastic pieces was 1.24 ± 0.87 cm. Only one piece out of all measured fragmented plastic (n = 1164) was within the micro-plastic size class (<2 mm) measuring 1 mm. Resin pellets occurred at 67% of all survey sites and were the second largest subcategory of plastic material, comprising 9% of all plastics found (Table 4, Fig. 3). Many pellets appeared to have been discolored (aged) from photo degradation. We identified a previously undocumented item, 2 mm green, spherical fertilizer capsules (Fig. 3), which occurred at 25% of all survey sites in January through April 2010. 4. Discussion
Fig. 2. Distribution of average seasonal litter abundance per beach location from north to south from July 2009–June 2010. Asterisks indicate low accessibility beaches.
We demonstrated that Styrofoam, although banned in many cities, is currently the most common type of beach litter, comprising 41% of items found. Because the majority of Styrofoam observed in our surveys was broken-up (5 mm–5 cm), it was nearly impossible to identify the whole source product from these fragments (i.e., to-go containers, cups, plates, coolers, and commercial packing material). The great prevalence of plastic ingestion and its known impacts to surface-feeding seabirds (van Franeker et al., 2011) emphasizes the need to continue to promote end-user responsibility as well as legislative efforts to decrease single use plastics and Styrofoam use in packaging materials (Reck, 1990).
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Fig. 3. Distribution of total amounts of Styrofoam, fragmented plastics, fertilizer pellets, and resin pellets per survey site over the entire study period (n = 12 months).
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Also, during winter, a substantial number of fertilizer pellets were recorded at Zmudowski beach, which is adjacent to the Pajaro River. These casings are the remains of time-release fertilizer applications on land uses such as agriculture or nurseries (Moe et al., 1967). Casings were possibly introduced to Zmudowski beach via the Pajaro River mouth, which is supplied and connected by a network of agricultural ditches, creeks, and ponds (Los Huertos et al., 2001). We were surprised to find no difference between litter abundance on low and high accessible beach locations based on the population definition we assigned in the current study. Rodriguez-Santos et al. (2005) found that tourism is a leading cause of beach litter and the quantity of litter depends on beach visitor density. Fragmented plastics found at Sunset beach could have been unintentionally dug up from the subsurface of the beach by wave action (Kusui and Noda, 2003), sand erosion (Patsch and Griggs, 2006), and human activities. Even low accessibility beaches in Monterey Bay may be visited more frequently than population proximity would predict. Because we used a proxy variable (i.e. distance to township with population <1000 km2), our assessment may not be accurate if actual visitation rates are not directly correlated. In fact, many ‘‘remote beaches’’ are visited regularly, but activities are not well quantified. Our estimates of marine litter were likely affected at sites where mechanical grooming to remove litter occurs frequently. Other litter removal efforts by individuals (e.g. local stewards, community service) and community organizations (e.g. group beach clean-up events) likely affected our results on state beaches adjacent to populated areas (e.g. Seabright, Seacliff, Marina).
Table 2 AIC table summarizing model comparison results of using GLS and LME (models 4–6). See Table 1 for heading definitions. Model
df
AIC
AICc
delAIC
AICw
Modelnull-gls Model4 Model5 Model6
2 35 14 25
482.45 461.85 465.02 452.76
470.45 387.73 432.71 398.60
82.72 0.00 44.98 10.87
0.00 1.00 0.00 0.00
The seasonal variability in litter abundance is likely due to physical drivers such as oceanic winds and currents that may drive debris deposition. Based on our results, we cannot predict debris deposition with a level of precision (Ribic et al., 2010). Moreover, using mixed effects modeling we were able to indicate the plausibility of buried items being uncovered over a one to 2 month period, by either human or natural forcing. Thus, we have shown a seasonal effect lead to differences between sites in litter abundance during winter months versus other seasons (Fig. 2). A combination of physical mechanisms such as storms, high winds, and river run-off contribute to the increase in litter abundance observed in winter. Ocean currents, for example, disperse floating marine debris (Wong et al., 1974). In the Monterey Bay surface current velocity and direction changes seasonally, and circulation may change in a matter of days or weeks, weakening the ability to predict directional or temporal patterns (Paduan and Rosenfeld, 1996; Paduan and Cook, 1997). Increases in litter abundance, with items such as fragmented plastics and resin pellets did occur in mid bay beaches (Table 4), and we presume wind forced surface currents drove seasonal litter deposition, further study is needed although. While surface currents transport marine debris, there is a growing concern about how rivers transport debris to the coastline (Araújo and Costa, 2007; Moore et al., 2011). We found increased deposition of Styrofoam, fertilizer pellets, and fragmented plastics in winter and in central bay locations especially after storm events (Tables 3 and 4). Araújo and Costa (2007) have associated increased litter abundance with beach proximity to rivers. Four beaches in this current study are in close proximity to river or large creek mouths: Main Beach (San Lorenzo), Seabright (San Lorenzo), Capitola (Soquel Creek), and Zmudowski (Pajaro). Future post hoc analysis is suggested to explore litter abundance, litter type and proximity to a river mouth.
4.1. Meso-scale debris: ingestion and adsorption One intent of this study was to assess the presence of mesoscale debris (2 mm–2 cm) that is often buried (Kusui and Noda, 2003; Corcoran et al., 2009) and can pose a direct threat to wildlife in the Monterey Bay National Marine Sanctuary (Fry et al., 1987; Nevins et al., 2005; Ryan, 2008). Surface-feeding Procellariiform seabirds (Petrels and Shearwaters) common to the Monterey Bay (Baltz and Morejohn, 1977) are particularly susceptible to plastic ingestion (Ryan, 1987). Additionally, the high frequency and amount of Styrofoam and fragmented plastics found in this study may determine the effect to wildlife health since many species of
Table 3 Mean average for six of thirteen general litter items per month. Last column in table based on all 13 categories (n = 5972). Note dash (–) represents inability to calculate mean for items which only occurred on one beach in that month. Litter type
July
August
September
October
November
December
January
February
March
April
May
June
Total (%)
Styrofoam Plastic products Cigarette butt Paper/wood Food wrapper Fertilizer pellet
2.4 1.3 3.3 1.4 1.4 0.0
3.9 1.1 2.8 2.0 1.6 0.0
1.8 1.4 3.3 1.7 1.5 0.0
7.5 1.2 2.9 1.9 2.6 0.0
5.0 1.8 3.0 1.6 2.3 0.0
12.8 1.3 3.4 1.5 2.1 0.0
77.7 3.8 2.2 1.5 1.5 5.5
40.2 1.6 2.3 1.6 1.4 –
12.0 9.1 2.2 3.8 2.4 1.5
18.3 2.0 4.9 2.6 0.0 –
2.3 1.5 2.7 1.8 1.2 0.0
3.1 1.5 2.7 1.6 1.4 0.0
41 36 6 5 5 1
Table 4 Mean average per month for six out of nine plastic items of beach litter. Last column in table based on all nine categories (n = 2407). Note dash (–) represents inability to calculate mean for items which only occurred on one beach in that month. Litter type
July
August
September
October
November
December
January
February
March
April
May
June
Total (%)
Fragmented plastic Resin pellet Straw/wrapper Bottle cap Plastic bag Fire cracker
2.4 0.0 1.0 1.1 2.5 1.8
3.2 0.0 1.2 1.5 1.0 1.3
2.4 0.0 1.4 1.3 1.0 1.5
6.0 – 1.2 1.5 – 0.0
5.7 0.0 3.0 1.0 0.0 –
16.0 1.8 1.1 1.6 1.0 –
36.1 10.0 1.8 1.0 0.0 1.0
15.3 4.0 1.0 – 1.0 –
78.4 69.0 1.2 1.0 – 4.5
9.3 – 1.3 0.0 – –
3.5 1.5 1.5 1.4 – –
6.0 – 1.7 1.3 0.0 0.0
68 9 4 3 1 1
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seabirds forage in the waters of the Monterey Bay (Baltz and Morejohn, 1976, 1977). Furthermore, plastic pellets have a great capacity to adsorb and transport persistent organic pollutants (POPs) such as dichlorodiphenyl-trichloroethane (DDT) and polychlorinated biphenyls (Mato et al., 2001). DDT and POPs adsorbed to pellets could circulate within the ocean reaching far off shores such as the Hawaiian island chain, where comparable amounts of resin pellets (10%) were found by McDermid and McMullen (2004). Marine species may ingest various forms of plastic and as an indicator of wildlife health; future study could identify levels of adsorption of POPs onto fragmented plastics and plastic pellets.
4.2. Policy implications on the central coast of California Preventing land-based litter from polluting the coastal environment is an ongoing and complex management issue (Cheshire et al., 2009). The goal of this study was to support agencies and organizations address issues of marine debris by providing spatial and temporal information on types and abundance of litter in Monterey Bay. We have delivered results to several municipalities which have supported legislative bans on Styrofoam take-out containers.1 In California, a proposed policy for preventing and controlling trash in state waters is currently in debate and is commonly referred to as the ‘‘trash policy’’ (SWRCB, 2010). This policy would identify trash as a separate pollutant within point source and non-point source parameters and establish methods to control trash pollution in waters of the state (SWRCB, 2010). The proposed statewide legislation would call for inclusive regulatory actions including monitoring of ‘‘hot spots’’ based on number of litter items on beaches. Beach litter surveys are an ideal tool to monitor coastal areas with high litter accumulation or output (SWRCB, 2010).
4.3. Future regional goals and recommendations Based on the success of this project, we recommend federal and state agencies form partnerships with local organizations to incorporate beach litter monitoring into existing volunteer monitoring programs. Outcomes of continuous monitoring would be: increased knowledge of quantities and types of litter, continued support of litter abatement through policy actions, identify hot-spots with high litter counts, increased educational signage and receptacles, and seasonally focused community clean-up efforts at hotspots. We also suggest designing studies to better isolate the sources of litter to further understand the spatial and temporal patterns of beach litter. This may mean comparing litter at urban outfalls and storm drains with beach survey data. Other suggestions include modeling watershed drainage routes that flow into the Monterey Bay National Marine Sanctuary modeling near shore surface currents for proximal litter deposition, and comparing effects of beach erosion on litter abundance during wet and dry seasons. Another outcome from this research was building relationships with agencies, municipalities, educators, and community organizations. These relationships are essential for decision-making, scientific monitoring, and community outreach that will benefit our mutual goals of a clean and healthy marine environment. 1 As of August 2011, the cities of Santa Cruz [Ord. 2008-01 § 2 (part), 2008], Capitola [Ord. 964 § 3, 2011], Watsonville [Ord. No. 1245-09 (CM), § 1, 4-14-2009], Salinas [Ord. No. 2519 (NCS), § 2, 8-23-2011], Marina [2011-006, Ch. 8.50], Monterey [Ord. 3426 § 2, 2009], and Pacific Grove [Ord. 08-010 § 5, 2008] have banned Styrofoam as take out containers.
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References Agresti, A., 1996. An Introduction to Categorical Data Analysis. Wiley, New York. Araújo, M.C., Costa, M., 2007. An analysis of the riverine contribution to the solid wastes contamination of an isolated beach at the Brazilian Northeast. Manage. Environ. Qual. Int. J. 18 (1), 6–12. Baltz, D.M., Morejohn, G.V., 1976. Evidence from seabirds of plastic particle pollution off central California. West. Birds 7, 111–112. Baltz, D.M., Morejohn, G.V., 1977. Food habits and niche overlap of seabirds wintering on Monterey Bay, California. Auk 94 (3), 526–543. Burnham, K.P., Anderson, D.R., 2002. Model Selection and Multimodel Inference. A Practical Information-Theoretic Approach, second ed. Springer, New York. Cheshire, A.C., Adler, E., Barbière, J., Cohen, Y., Evans, S., Jarayabhand, S., Jeftic, L., Jung, R.T., Kinsey, S., Kusui, E.T., Lavine, I., Manyara, P., Oosterbaan, L., Pereira, M.A., Sheavly, S., Tkalin, A., Varadarajan, S., Wenneker, B., Westphalen, G., 2009. UNEP/IOC Guidelines on Survey and Monitoring of Marine Litter. UNEP Regional Seas Reports and Studies, No. 186; IOC Technical Series No. 83 (xii + 120). Corcoran, P.L., Beisinger, M.C., Grifi, M., 2009. Plastics and beaches: a degrading relationship. Mar. Pollut. Bull. 58, 80–84. Cunningham, D.J., Wilson, S.P., 2003. Marine debris on beaches of the greater Sydney region. J. Coast. Res. 19 (2), 421–430. Dau, B.K., Gilardi, K.V.K., Gulland, F.M., Higgins, A., Holcomb, J.B., St. Leger, J., Ziccardi, M.H., 2009. Fishing gear–related injury in California marine wildlife. J. Wildlife Dis. 45 (2), 355–362. Edyvane, K.S., Dalgetty, A., Hone, P.W., Higman, J.S., Wace, N.M., 2004. Long-term marine litter monitoring in the remote great Australian Bight, South Australia. Mar. Pollut. Bull. 48, 1060–1075. Frost, A., Cullen, M., 1997. Marine debris on Northern New South Wales beaches (Australia): sources and the role of beach usage. Mar. Pollut. Bull. 34 (5), 348– 352. Fry, D.M., Fefer, S.I., Sileo, L., 1987. Ingestion of plastic debris by Laysan albatrosses and wedge-tailed shearwaters in the Hawaiian Islands. Mar. Pollut. Bull. 18 (6), 339–343. Gotelli, N.J., Ellison, A.M., 2004. A Primer of Ecological Statistics. Sinauer Associates, Inc., Massachusetts, USA. Kusui, T., Noda, M., 2003. International survey on the distribution of stranded and buried litter on beaches along the sea of Japan. Mar. Pollut. Bull. 47, 175–179. Lindsey, J.K., 1997. Applying Generalized Linear Models. Spinger-Verlag, New York. Los Huertos, M., Gentry, E., Shennan, C., 2001. Land use and stream nitrogen concentrations in agricultural watersheds along the central coast of California. Sci. World 1, 615–622. Mato, Y., Isobe, T., Takada, H., Kanehiro, H., Ohtake, C., Kaminuma, T., 2001. Plastic resin pellets as a transport medium for toxic chemicals in the marine environment. Environ. Sci. Technol. 35 (2), 318–324. McDermid, K.J., McMullen, T.L., 2004. Quantitative analysis of small-plastic debris on beaches in the Hawaiian archipelago. Mar. Pollut. Bull. 48, 790–794. Moe, P.G., Mannering, J.V., Johnson, C.B., 1967. Loss of fertilizer nitrogen in surface runoff water. Soil Sci. 104 (6), 389–394. Moore, S.L., Gregorio, D., Carreon, M., Weisberg, S.B., Leecaster, M.K., 2001. Composition and distribution of beach debris in Orange Country, California. Mar. Pollut. Bull. 42 (3), 241–245. Moore, E., Lyday, S., Roletto, J., Litle, K., Parrish, J.K., Nevins, H., Harvey, J., Mortenson, J., Greig, D., Piazza, M., Hermance, A., Lee, D., Adams, D., Allen, S., Kell, S., 2009. Entanglements of marine mammals and seabirds in central California and the north-west coast of the United States 2001–2005. Mar. Pollut. Bull. 58, 1045– 1051. Moore, C.J., Lattin, G.L., Zellers, A.F., 2011. Quantity and type of plastic debris flowing from two urban rivers to coastal waters and beaches of Southern California. J. Integr. Coast. Zone Manage. 11 (1), 65–73. National Oceanic and Atmospheric Administration, 2012. (accessed 23.06.12). Nevins, H., Hyrenbach, D., Keiper, C., Stock, J., Hester, M., Harvey, J., 2005. Seabirds as indicators of plastic pollution in the North Pacific. In: Rivers to Sea Conference Proceedings, September 7–9, 2005. Oigan-Pszczol, S.S., Creed, J.C., 2007. Quantification and classification of marine litter on beaches along Armacao dos Buzios, Rio de Janeiro, Brazil. J. Coast. Res. 23 (2), 421–428. Paduan, J.D., Cook, M.S., 1997. Mapping surface currents in Monterey Bay with CODAR-type HF radar. Oceanography 10 (2), 49–52. Paduan, J.D., Rosenfeld, L.K., 1996. Remotely sensed surface currents in Monterey Bay from shore-based HF radar (coastal ocean dynamics application radar). J. Geophys. Res. 101 (C9), 20669–20686. Patsch, K., Griggs, G., 2006. Littoral cells, sand budgets, and beaches: understanding California’s shoreline, Institute of Marine Sciences University of California, Santa Cruz, . Pinheiro, J.C., Bates, D.M., 2000. Mixed Effects Models in S and S-Plus. Springer, New York. R Development Core Team, 2011. R: a language and environment for statistical computing. In: R Foundation for Statistical Computing, Vienna, Austria. ISBN 3900051-07-0, . Reck, S.M., 1990. Expanding environmental consciousness of local government: municipalities that have banned Styrofoam and the legal consequences. 11 Univ. Bridgeport Law Rev. 127, 1990–1991.
306
C. Rosevelt et al. / Marine Pollution Bulletin 71 (2013) 299–306
Rees, G., Pond, K., 1995. Marine Litter monitoring programmes – a review of methods with special references to national surveys. Mar. Pollut. Bull. 30 (2), 103–108. Ribic, C.A., Dixon, T.R., Vining, I., 1992. Marine debris survey manual. US Department of Commerce. NOAA Technical Report NMFS 108. Washington, DC, GPO. Ribic, C.A., Sheavly, S.B., Rugg, D.J., Erdmann, E.S., 2010. Trends and drivers of marine debris on the Atlantic coast of the United States 1997–2007. Mar. Pollut. Bull. 60, 1231–1242. Rodriguez-Santos, I., Friedrich, A.C., Wallner-Kersanach, M., Fillmann, G., 2005. Influence of socio-economic characteristics of beach users on litter generation. Ocean Coast. Manage. 48, 742–752. Ryan, P.G., 1987. The incidence and characteristics of plastic particles ingested by seabirds. Mar. Pollut. Bull. 19, 125–128. Ryan, P.G., 2008. Seabirds indicate changes in the composition of plastic litter in the Atlantic and South-Western Indian Oceans. Mar. Pollut. Bull. 56, 1406–1409. Ryan, P.G., Moore, C.J., van Franeker, J.A., Moloney, C.L., 2009. Monitoring the abundance of plastic debris in the marine environment. Philos. Trans. Royal Soc. B 364, 1999–2012. State Water Resources Control Board and California Environmental Protection Agency, 2010. Informational Document: Public Scoping Meeting for Proposed Statewide Policy for Trash Control in Waters of the State, September. Storrier, K.L., McGlashan, D.J., 2006. Development and management of a coastal litter campaign: the voluntary coastal partnership approach. Mar. Pollut. 30, 189–196. Storrier, K.L., McGlashan, D.J., Bonellie, S., Velander, K., 2007. Beach litter deposition at a selection of beaches in the Firth of Forth, Scotland. J. Coast. Res. 23 (4), 813– 822. Sun, X., Yang, Z., 2008. Generalized mcnemar’s test for homogeneity of the marginal distributions. Statistics and Data Analysis, SAS Global Forum.
United Nations Environmental Programme, 2011a.. van Franeker, J.A., Blaize, C., Danielsen, J., Fairclough, K., Gollan, J., Guse, N., Hansen, P.L., Heubeck, M., Jensen, J.K., Le Guillou, G., Olsen, B., Olsen, K.O., Pedersen, J., Stienen, E.W., Turner, D.M., 2011. Monitoring plastic ingestion by the northern fulmar Fulmarus glacialis in the North Sea. Environ. Pollut. 159 (10), 2609– 2615. Velander, K.A., Mocogni, M., 1998. Maritime litter and sewage contamination at cramond beach edinburgh a comparative study. Mar. Pollut. Bull. 3 (5), 385– 389. Velander, K.A., Mocogni, M., 1999. Beach litter sampling strategies: is there a ‘‘best’’ method? Mar. Pollut. Bull. 38 (12), 1134–1140. Venables, W.N., Ripley, B.D., 2002. Modern Applied Statistics with S, Fourth ed. Springer Science + Business Media, New York. Vonesh, E.F., Chinchilli, V.M., 1997. Linear and Nonlinear Models for the Analysis of Repeated Measurements. Marcel Dekker, Inc., New York. Watters, D.L., Yoklavich, M.M., Love, M.S., Schroeder, D.M., 2010. Assessing marine debris in deep seafloor habitats off California. Mar. Pollut. Bull. 60 (1), 131–138. Williams, A.T., Tudor, D.T., 2001. Litter burial and exhumation: spatial and temporal distribution on a cobble pocket beach. Mar. Pollut. Bull. 42 (11), 1031–1039. Wong, C.S., Green, D.R., Cretney, W.J., 1974. Quantitative tar and plastic waste distribution in the Pacific Ocean. Nature 247, 30–32. Zuur, A.F., Ieno, E.N., Walker, N.J., Saveliev, A.A., Smith, G.M., 2009. Mixed Effects Models and Extensions in Ecology with R. Springer, New York.
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Marine Pollution Bulletin journal homepage: www.elsevier.com/locate/marpolbul
Marine debris in central California: Quantifying type and abundance of beach litter in Monterey Bay, CA C. Rosevelt a,⇑, M. Los Huertos a, C. Garza a, H.M. Nevins b,c a
Division of Science & Environmental Policy, California State University Monterey Bay, Seaside, CA 93955, USA California Department of Fish and Game, Office of Oil Spill Prevention and Response, Marine Wildlife Veterinary Care & Research Center, Santa Cruz, CA 95060, USA c Wildlife Health Center, University of California at Davis, CA 95616, USA b
a r t i c l e Keywords: Monterey Bay Beach litter Survey Citizen-science Mixed effects AIC
i n f o
a b s t r a c t Monitoring beach litter is essential for reducing ecological threats towards humans and wildlife. In Monterey Bay, CA information on seasonal and spatial patterns is understudied. Central California’s coastal managers require reliable information on debris abundance, distribution, and type, to support policy aimed at reducing litter. We developed a survey method that allowed for trained citizen scientists to quantify the types and abundance of beach litter. Sampling occurred from July 2009–June 2010. Litter abundance ranged from 0.03 to 17.1 items m2. Using a mixed model approach, we found season and location have the greatest effect on litter abundance. Styrofoam, the most numerically abundant item, made up 41% of the total amount of litter. Unexpected items included fertilizer pellets. The results of this study provide a baseline on the types and abundance of litter on the central coast and have directly supported policy banning Styrofoam take out containers from local municipalities. Ó 2013 Elsevier Ltd. All rights reserved.
1. Introduction The United Nations Environment Program reported, ‘‘Marine litter currently poses a dire, vast and growing threat to the marine and coastal environment’’ (UNEP, 2011a). In California and other coastal states in the United States, there are efforts to increase legislation to limit the use of single use plastics (such as take-out containers) and Styrofoam to reduce the amount of land-based debris littering beaches and entering our oceans (SWRCB, 2010). Beach based surveys have been used to measure potentially harmful marine and land derived litter (Frost and Cullen, 1997; Cunningham and Wilson, 2003; Storrier et al., 2007). Marine debris depositing on the beach may be spatially and temporally variable (Rees and Pond, 1995; Kusui and Noda, 2003; Edyvane et al., 2004; Oigan-Pszczol and Creed, 2007) and a better understanding of this variability can assist state and local waste managers to identify site-specific problems and take appropriate actions for litter abatement. In this paper we discuss methods that were used to train citizen scientists to quantify beach litter and monitor seasonal and site-specific litter occurring on 12 beaches in Monterey Bay, California. ⇑ Corresponding author. Address: PO Box 750, Monterey, CA 93942, USA. Tel.: +1 2064843200. E-mail addresses: [email protected] (C. Rosevelt), [email protected] (M. Los Huertos), [email protected] (C. Garza), [email protected] (H.M. Nevins). 0025-326X/$ - see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.marpolbul.2013.01.015
Several studies have documented the presence and ecological hazards of marine debris to marine wildlife in the Monterey Bay (Baltz and Morejohn, 1976; Moore et al., 2009; Dau et al., 2009; Watters et al., 2010). Beginning in the 1970s researchers began to document evidence of plastic ingestion in the stomach content of several species of seabirds (Baltz and Morejohn, 1976). More recently, Moore et al. (2009) documented the occurrence of seabird and marine mammal entanglement in derelict fishing gear. In addition to evidence of floating debris, Watters et al. (2010) conducted boat-based research to map and categorize the abundance and distribution of underwater benthic debris at depth (20–365 m) within the Monterey Bay. Furthermore, there has been recent concern from scientists over the impacts of small plastic fragments on wildlife and human health (UNEP, 2011b). Few beach surveys sample buried litter and items smaller than 2 cm (meso-debris), which may also require the use of quadrats and sieves to sample accumulated litter (Williams and Tudor, 2001; Kusui and Noda, 2003; McDermid and McMullen, 2004). Storrier and McGlashan (2006) and Ryan et al. (2009) suggest inaccuracies in results of beach surveys when using transects alone, since this method frequently fails to capture meso-scale debris (<2 cm). This can result in an underestimation of beach litter abundance. Our goal was to support coastal managers and environmental non-profits in addressing the issue of beach litter within the Monterey Bay. The objectives of this study were to (1) assess the presence of meso-scale litter; (2) identify temporal and spatial patterns
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in litter abundance; (3) identify types of litter and examine patterns, and (4) involve citizens in science-based research through ongoing beach surveys. To address these objectives we focused on three research questions: (i) Do month or beach location have an influence on litter abundance? (ii) Does litter abundance differ among sites characterized as having low or high public accessibility? and (iii) Is there a relationship between litter type and month or location?
2. Methods 2.1. Survey design and location of study We chose 12 survey sites within the Monterey Bay National Marine Sanctuary shoreline, including those within the jurisdiction of eight CA state beaches, two county beaches, and two city beaches encompassing 57 km of coastline. In Santa Cruz County, we surveyed eight beaches including Santa Cruz Main Beach (city), Seabright State Beach, Live Oak (21st Avenue County Beach), Capitola City Beach, New Brighton State Beach, Seacliff State Beach, Manresa State Beach, and Sunset State Beach. In Monterey County, we surveyed four beaches including Zmudowski State Beach, Marina State Beach, Seaside State Beach, and Del Monte City Beach (Fig. 1). Using a repeated measures approach, we designed the surveys to identify temporal and spatial patterns in litter abundance (items m2) (Vonesh and Chinchilli, 1997; Gotelli and Ellison, 2004; Zuur et al., 2009). At each site, we randomly sampled across two parallel 50 m transects set against the wrackline in order to reduce bias (Moore et al., 2001; Cunningham and Wilson, 2003). 2.2. Analysis of low versus high accessibility beaches Directed by our second research question; the likelihood of litter differing between low and high accessible beaches, each location was also based on the following criteria: public access and proximity to municipalities. We categorized sites into those with high or low public accessibility (Fig. 1). We classified low accessibility sites as beaches adjacent to areas with population density less than 1000 people km2 (based on city population density information). Beach locations greater than 5 km from the nearest city were also classified as low accessibility (US Census, 2000). We used Wilcoxon tests to statistically assess whether litter abundance at low and high public accessibility beach locations differed.
and accuracy in measurements otherwise influenced by the daily variation in litter deposition. We carried out surveys at low tide (mean tidal range 1.1 m, min: 1.2 m (Sept. 2009) and max: 5.8 m (Nov. 2009)), providing the greatest beach surface area for sampling (NOAA, 2012). The first of two transects was placed within the wrackline since previous studies have shown this region as a deposition zone for marine derived debris as well as re-washed land-based debris and plastics (Velander and Mocogni, 1998, 1999; Corcoran et al., 2009; Ryan et al., 2009). Following the protocols of Cunningham and Wilson (2003), a second transect was positioned 5 m above the first transect line. A pin flag demarcated the start of each transect. Five 4 m2 quadrats were randomly placed along both transect lines for the purposes of sampling meso-scale (2 mm–2.0 cm) and macroscale (>2 cm) beach litter (Velander and Mocogni, 1999; McDermid and McMullen, 2004). Volunteers demarcated transect lines and sequentially sampled randomly spaced plots by reusing a 4 m2 PVC quadrat as they progressed along the line. Upon scanning the surface of each quadrat for litter, volunteers used their fingertips to conduct subsurface sampling to approximately 2 cm depth by raking the sandy surface in a light back and forth motion, feeling, and looking for buried litter, but not digging deeper. Volunteers collected all anthropogenic debris from each quadrat, tallied the items on data sheets, and placed litter into labeled resealable bags, for further validation. 2.4. Quality control and litter enumeration methods We assessed the presence and abundance of meso-scale litter by measuring all fragmented plastic and divided and identified them as three class sizes: micro-debris (<2 mm), meso-debris (2 mm–2.0 cm), and macro debris (>2.0 cm) (Ryan et al., 2009). Analysis included performing quality control (confirming quantity and category) by the comparison of all items collected against that recorded by field observers. We used a microscope to differentiate between biological and synthetic composition when necessary. For example, early in the study, volunteers mistook bleached sea grass for plastic strips, and using a microscope confirmed that this biological material had a plant cell structure. We classified litter items into 13 types: fragmented plastic, glass, paper/treated wood, plastic products, Styrofoam, rubber, metal, cigarette butts, fabric, fertilizer pellets, fishing gear, food wrappers, and other (items with ambiguous identity, e.g., paint chips).
2.3. Beach survey methods
2.5. Statistical analysis: mixed effects modeling to explore litter abundance
Surveys occurred semi-monthly from July through September 2009 and monthly from September 2009 through June 2010. This unforeseen change from semi-monthly to monthly surveys was a result of volunteer schedules allowing for more availability in summer months. In months where surveys occurred twice, the average litter abundance and category types were taken. Furthermore, to check that averaging semi-monthly survey data would not create problems, we tested and compared non-averaged versus averaged data, no difference in model preference was detected. Months were grouped into seasonal classes for analysis where summer (July– September), fall (October–December), winter (January–March), and spring (April–June). Trained volunteers collected samples at the same site over the course of one year. Prior to the study, volunteers participated in a two-hour mandatory training. Following the methodology of Ribic et al. (1992), volunteer effort per survey included six teams comprised of at least two volunteers each where each team surveyed two beaches per survey event. This simultaneous multi-team approach increased efficiency
We used simple linear regression to compare three models and ascertain the best preliminary fit to the data. After the best model was chosen, we created a second set of three additional models using a mixed modeling approach. Mixed effects modeling addressed the lack of independence and variance between the continuous dependent variable (litter abundance) and its categorical covariates (beach location and month) (Pinheiro and Bates, 2000). Using a mixed effects model approach within a repeated measures design, we incorporated both fixed and random effects (Pinheiro and Bates, 2000; Zuur et al., 2009). Random effects are typically associated with the sampling unit that represents values drawn from a larger population, in this case beach location (Pinheiro and Bates, 2000). Defining beach location as a random effect, allowed for extrapolation of results for litter abundance from the sampling unit to the greater population (i.e., litter on all beaches in the Monterey Bay) (Venables and Ripley, 2002). In contrast, fixed effects are typically associated with the entire population and not the sampling unit (Lindsey, 1997; Pinheiro and Bates, 2000). Here
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Fig. 1. Map of beach survey sites within the Monterey Bay, CA. Asterisks indicate low accessibility beach locations.
we defined month as the fixed effect because the entire population in this study refers to all beach litter in the Monterey Bay measured by the temporal variable month. In the second set of models, we used a weighted linear regression known as a generalized least squares (GLSs) model to address the heterogeneity in the repeated measure design (Zuur et al., 2009). Additionally, linear mixed effects (LMEs) models incorporate both the random and fixed effects (Zuur et al., 2009). To address the temporal correlation within our linear regression we fit an Auto-Regressive Moving Average (ARMA) correlation structure to the model with the best fit (Zuur et al., 2009). The software program, R was used to perform all analysis, including, diagnostics on all models and statistical tests (R Development Core Team, 2011). The first ‘‘preliminary’’ model (M1) included both spatial and temporal covariates, and the other two included a combination of one or the other covariate (M2, M3).
M1 : Abundanceij ¼ a þ b0 þ b1 Locationij þ b2 Monthij þ eij
M2 : Abundanceij ¼ a þ b0 þ b1 Monthij þ eij M3 : Abundanceij ¼ a þ b0 þ b1 Locationij þ eij where Abundance is litter abundance (items m2), a is the population intercept (when X = 0, Y = 0), b0 is the population slope (when X increases by one unit, what is the expected increase in Y), b1 is the coefficient of location, b2 is the coefficient of month, Month is the categorical name of the month during the study, Location is the categorical beach location name, e is error term with a Gaussian distribution, i is the different beach locations, and j is the different months when surveying occurred. Akaike’s Information Criterion (AIC) was used to identify the best model, where the lowest delta score described the likeliest effect on beach litter abundance. Akaike’s Information Criterion measures the likelihood that the data is described by a given statistical model, where scores are produced and compared among similar models (in this study; M1, M2,. . ., M6) (Gotelli and Ellison, 2004).
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2.6. Categorical analysis To estimate homogeneity among the predictor and response variables we organized litter categories into two-way contingency tables and analyzed them using a McNemar test (Agresti, 1996; Sun and Yang, 2008). We tested whether there was a spatial association of litter categories, by testing if litter category was associated with beach location. We also tested for the temporal distribution of litter categories, by testing if litter category was associated with month.
Table 1 AIC table summarizing model comparison results for simple linear regression models. Model1 includes both covariates, while Model2 describes the temporal effect on litter abundance. Model3 describes the spatial effect on litter abundance. Nomenclature follows Burnham and Anderson (2002): degrees of freedom (df), AIC corrected for small sample size (AICc), differences from the best model (delAIC), and AIC weights (AICw). Model
df
AIC
AICc
delAIC
AICw
Modelnull Model1 Model2 Model3
2 24 13 13
479.88 459.46 462.24 484.06
467.88 407.28 431.90 453.73
60.60 0.00 24.62 46.45
0.00 1.00 0.00 0.00
3. Results 3.1. Litter abundance and mixed effects models Over the duration of the study, the average abundance of litter was 1 ± 2.1 items m2. The greatest average litter abundance occurred at locations within the middle of the Monterey Bay (Sunset 2.5 items/m2 and Zmudowski 2.0 items/m2) and those at the most northern (Main 0.8 items/m2 and Seabright 1.4 items/m2) and southern survey sites (Seaside 0.9 items/m2 and Del Monte 1.2 items/m2; Fig. 2). Unexpectedly, we found substantial increases in average litter abundance during the winter season at survey sites located along the middle of the Bay (mainly comprised of low accessibility beaches; Fig. 2). The AIC model that included both temporal and spatial covariates (M1) was the best fit given the abundance data (Table 1). Furthermore, between the two predictor variables, the temporal model (M2) described the data more closely than the saturated model and thus may be the likely driver for increased AIC values for M1 which was then tested further to investigate temporal and spatial effects (Table 1). In addition, no significant difference was found between litter abundance on low versus high accessibility beach locations (p = 0.55). 3.2. Mixed effects models: independence and variance structures Three additional models were tested based on M1. The GLS model (M4) included a variance structure for month was the best fit given the data (Table 2). This suggests that the amount of variance in litter abundance between months had an influential effect on litter abundance over beach locations. To confirm M4 as the best
fit, two other models were compared; the random effect, beach location, was then analyzed in M5 using an LME model (Table 2). Lastly, the sixth model, M6 also was fitted with LME and included both beach locations as a random effect and the variance structure for month (Table 2). AIC results from M6 and M5 suggest that beach location does not influence litter abundance as strongly as month (Table 2). Litter abundance quantities were auto-correlated over time, violating the assumption of independence between survey events. Fitting ARMA to M4 (best fit) resulted in identifying that litter abundance was temporally correlated by a time step of 2 months. 3.3. Categorical results and litter distribution Marginal heterogeneity was found between different types of litter across month and location. These results indicate litter type is significantly different among months and beach locations (p < 0.01). This finding corroborates results from the AIC model comparisons, where month and beach location affect quantities of litter. Quantity and distribution of general types of litter and the subcategory of exclusively plastic items are delineated in Tables 3 and 4. Styrofoam items occurred at all 12 survey sites in all months and comprised the largest percentage of litter by total number (41%) (Table 3, Fig. 3). The second largest percentage of litter by total number was fragmented plastics. Fragmented plastics occurred at all survey sites in all months and made up 68% of the plastic litter subcategory (Table 4). The size class of fragments was primarily meso-scale (2 mm–2.0 cm); the mean length measurement of fragmented plastic pieces was 1.24 ± 0.87 cm. Only one piece out of all measured fragmented plastic (n = 1164) was within the micro-plastic size class (<2 mm) measuring 1 mm. Resin pellets occurred at 67% of all survey sites and were the second largest subcategory of plastic material, comprising 9% of all plastics found (Table 4, Fig. 3). Many pellets appeared to have been discolored (aged) from photo degradation. We identified a previously undocumented item, 2 mm green, spherical fertilizer capsules (Fig. 3), which occurred at 25% of all survey sites in January through April 2010. 4. Discussion
Fig. 2. Distribution of average seasonal litter abundance per beach location from north to south from July 2009–June 2010. Asterisks indicate low accessibility beaches.
We demonstrated that Styrofoam, although banned in many cities, is currently the most common type of beach litter, comprising 41% of items found. Because the majority of Styrofoam observed in our surveys was broken-up (5 mm–5 cm), it was nearly impossible to identify the whole source product from these fragments (i.e., to-go containers, cups, plates, coolers, and commercial packing material). The great prevalence of plastic ingestion and its known impacts to surface-feeding seabirds (van Franeker et al., 2011) emphasizes the need to continue to promote end-user responsibility as well as legislative efforts to decrease single use plastics and Styrofoam use in packaging materials (Reck, 1990).
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Fig. 3. Distribution of total amounts of Styrofoam, fragmented plastics, fertilizer pellets, and resin pellets per survey site over the entire study period (n = 12 months).
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Also, during winter, a substantial number of fertilizer pellets were recorded at Zmudowski beach, which is adjacent to the Pajaro River. These casings are the remains of time-release fertilizer applications on land uses such as agriculture or nurseries (Moe et al., 1967). Casings were possibly introduced to Zmudowski beach via the Pajaro River mouth, which is supplied and connected by a network of agricultural ditches, creeks, and ponds (Los Huertos et al., 2001). We were surprised to find no difference between litter abundance on low and high accessible beach locations based on the population definition we assigned in the current study. Rodriguez-Santos et al. (2005) found that tourism is a leading cause of beach litter and the quantity of litter depends on beach visitor density. Fragmented plastics found at Sunset beach could have been unintentionally dug up from the subsurface of the beach by wave action (Kusui and Noda, 2003), sand erosion (Patsch and Griggs, 2006), and human activities. Even low accessibility beaches in Monterey Bay may be visited more frequently than population proximity would predict. Because we used a proxy variable (i.e. distance to township with population <1000 km2), our assessment may not be accurate if actual visitation rates are not directly correlated. In fact, many ‘‘remote beaches’’ are visited regularly, but activities are not well quantified. Our estimates of marine litter were likely affected at sites where mechanical grooming to remove litter occurs frequently. Other litter removal efforts by individuals (e.g. local stewards, community service) and community organizations (e.g. group beach clean-up events) likely affected our results on state beaches adjacent to populated areas (e.g. Seabright, Seacliff, Marina).
Table 2 AIC table summarizing model comparison results of using GLS and LME (models 4–6). See Table 1 for heading definitions. Model
df
AIC
AICc
delAIC
AICw
Modelnull-gls Model4 Model5 Model6
2 35 14 25
482.45 461.85 465.02 452.76
470.45 387.73 432.71 398.60
82.72 0.00 44.98 10.87
0.00 1.00 0.00 0.00
The seasonal variability in litter abundance is likely due to physical drivers such as oceanic winds and currents that may drive debris deposition. Based on our results, we cannot predict debris deposition with a level of precision (Ribic et al., 2010). Moreover, using mixed effects modeling we were able to indicate the plausibility of buried items being uncovered over a one to 2 month period, by either human or natural forcing. Thus, we have shown a seasonal effect lead to differences between sites in litter abundance during winter months versus other seasons (Fig. 2). A combination of physical mechanisms such as storms, high winds, and river run-off contribute to the increase in litter abundance observed in winter. Ocean currents, for example, disperse floating marine debris (Wong et al., 1974). In the Monterey Bay surface current velocity and direction changes seasonally, and circulation may change in a matter of days or weeks, weakening the ability to predict directional or temporal patterns (Paduan and Rosenfeld, 1996; Paduan and Cook, 1997). Increases in litter abundance, with items such as fragmented plastics and resin pellets did occur in mid bay beaches (Table 4), and we presume wind forced surface currents drove seasonal litter deposition, further study is needed although. While surface currents transport marine debris, there is a growing concern about how rivers transport debris to the coastline (Araújo and Costa, 2007; Moore et al., 2011). We found increased deposition of Styrofoam, fertilizer pellets, and fragmented plastics in winter and in central bay locations especially after storm events (Tables 3 and 4). Araújo and Costa (2007) have associated increased litter abundance with beach proximity to rivers. Four beaches in this current study are in close proximity to river or large creek mouths: Main Beach (San Lorenzo), Seabright (San Lorenzo), Capitola (Soquel Creek), and Zmudowski (Pajaro). Future post hoc analysis is suggested to explore litter abundance, litter type and proximity to a river mouth.
4.1. Meso-scale debris: ingestion and adsorption One intent of this study was to assess the presence of mesoscale debris (2 mm–2 cm) that is often buried (Kusui and Noda, 2003; Corcoran et al., 2009) and can pose a direct threat to wildlife in the Monterey Bay National Marine Sanctuary (Fry et al., 1987; Nevins et al., 2005; Ryan, 2008). Surface-feeding Procellariiform seabirds (Petrels and Shearwaters) common to the Monterey Bay (Baltz and Morejohn, 1977) are particularly susceptible to plastic ingestion (Ryan, 1987). Additionally, the high frequency and amount of Styrofoam and fragmented plastics found in this study may determine the effect to wildlife health since many species of
Table 3 Mean average for six of thirteen general litter items per month. Last column in table based on all 13 categories (n = 5972). Note dash (–) represents inability to calculate mean for items which only occurred on one beach in that month. Litter type
July
August
September
October
November
December
January
February
March
April
May
June
Total (%)
Styrofoam Plastic products Cigarette butt Paper/wood Food wrapper Fertilizer pellet
2.4 1.3 3.3 1.4 1.4 0.0
3.9 1.1 2.8 2.0 1.6 0.0
1.8 1.4 3.3 1.7 1.5 0.0
7.5 1.2 2.9 1.9 2.6 0.0
5.0 1.8 3.0 1.6 2.3 0.0
12.8 1.3 3.4 1.5 2.1 0.0
77.7 3.8 2.2 1.5 1.5 5.5
40.2 1.6 2.3 1.6 1.4 –
12.0 9.1 2.2 3.8 2.4 1.5
18.3 2.0 4.9 2.6 0.0 –
2.3 1.5 2.7 1.8 1.2 0.0
3.1 1.5 2.7 1.6 1.4 0.0
41 36 6 5 5 1
Table 4 Mean average per month for six out of nine plastic items of beach litter. Last column in table based on all nine categories (n = 2407). Note dash (–) represents inability to calculate mean for items which only occurred on one beach in that month. Litter type
July
August
September
October
November
December
January
February
March
April
May
June
Total (%)
Fragmented plastic Resin pellet Straw/wrapper Bottle cap Plastic bag Fire cracker
2.4 0.0 1.0 1.1 2.5 1.8
3.2 0.0 1.2 1.5 1.0 1.3
2.4 0.0 1.4 1.3 1.0 1.5
6.0 – 1.2 1.5 – 0.0
5.7 0.0 3.0 1.0 0.0 –
16.0 1.8 1.1 1.6 1.0 –
36.1 10.0 1.8 1.0 0.0 1.0
15.3 4.0 1.0 – 1.0 –
78.4 69.0 1.2 1.0 – 4.5
9.3 – 1.3 0.0 – –
3.5 1.5 1.5 1.4 – –
6.0 – 1.7 1.3 0.0 0.0
68 9 4 3 1 1
C. Rosevelt et al. / Marine Pollution Bulletin 71 (2013) 299–306
seabirds forage in the waters of the Monterey Bay (Baltz and Morejohn, 1976, 1977). Furthermore, plastic pellets have a great capacity to adsorb and transport persistent organic pollutants (POPs) such as dichlorodiphenyl-trichloroethane (DDT) and polychlorinated biphenyls (Mato et al., 2001). DDT and POPs adsorbed to pellets could circulate within the ocean reaching far off shores such as the Hawaiian island chain, where comparable amounts of resin pellets (10%) were found by McDermid and McMullen (2004). Marine species may ingest various forms of plastic and as an indicator of wildlife health; future study could identify levels of adsorption of POPs onto fragmented plastics and plastic pellets.
4.2. Policy implications on the central coast of California Preventing land-based litter from polluting the coastal environment is an ongoing and complex management issue (Cheshire et al., 2009). The goal of this study was to support agencies and organizations address issues of marine debris by providing spatial and temporal information on types and abundance of litter in Monterey Bay. We have delivered results to several municipalities which have supported legislative bans on Styrofoam take-out containers.1 In California, a proposed policy for preventing and controlling trash in state waters is currently in debate and is commonly referred to as the ‘‘trash policy’’ (SWRCB, 2010). This policy would identify trash as a separate pollutant within point source and non-point source parameters and establish methods to control trash pollution in waters of the state (SWRCB, 2010). The proposed statewide legislation would call for inclusive regulatory actions including monitoring of ‘‘hot spots’’ based on number of litter items on beaches. Beach litter surveys are an ideal tool to monitor coastal areas with high litter accumulation or output (SWRCB, 2010).
4.3. Future regional goals and recommendations Based on the success of this project, we recommend federal and state agencies form partnerships with local organizations to incorporate beach litter monitoring into existing volunteer monitoring programs. Outcomes of continuous monitoring would be: increased knowledge of quantities and types of litter, continued support of litter abatement through policy actions, identify hot-spots with high litter counts, increased educational signage and receptacles, and seasonally focused community clean-up efforts at hotspots. We also suggest designing studies to better isolate the sources of litter to further understand the spatial and temporal patterns of beach litter. This may mean comparing litter at urban outfalls and storm drains with beach survey data. Other suggestions include modeling watershed drainage routes that flow into the Monterey Bay National Marine Sanctuary modeling near shore surface currents for proximal litter deposition, and comparing effects of beach erosion on litter abundance during wet and dry seasons. Another outcome from this research was building relationships with agencies, municipalities, educators, and community organizations. These relationships are essential for decision-making, scientific monitoring, and community outreach that will benefit our mutual goals of a clean and healthy marine environment. 1 As of August 2011, the cities of Santa Cruz [Ord. 2008-01 § 2 (part), 2008], Capitola [Ord. 964 § 3, 2011], Watsonville [Ord. No. 1245-09 (CM), § 1, 4-14-2009], Salinas [Ord. No. 2519 (NCS), § 2, 8-23-2011], Marina [2011-006, Ch. 8.50], Monterey [Ord. 3426 § 2, 2009], and Pacific Grove [Ord. 08-010 § 5, 2008] have banned Styrofoam as take out containers.
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References Agresti, A., 1996. An Introduction to Categorical Data Analysis. Wiley, New York. Araújo, M.C., Costa, M., 2007. An analysis of the riverine contribution to the solid wastes contamination of an isolated beach at the Brazilian Northeast. Manage. Environ. Qual. Int. J. 18 (1), 6–12. Baltz, D.M., Morejohn, G.V., 1976. Evidence from seabirds of plastic particle pollution off central California. West. Birds 7, 111–112. Baltz, D.M., Morejohn, G.V., 1977. Food habits and niche overlap of seabirds wintering on Monterey Bay, California. Auk 94 (3), 526–543. Burnham, K.P., Anderson, D.R., 2002. Model Selection and Multimodel Inference. A Practical Information-Theoretic Approach, second ed. Springer, New York. Cheshire, A.C., Adler, E., Barbière, J., Cohen, Y., Evans, S., Jarayabhand, S., Jeftic, L., Jung, R.T., Kinsey, S., Kusui, E.T., Lavine, I., Manyara, P., Oosterbaan, L., Pereira, M.A., Sheavly, S., Tkalin, A., Varadarajan, S., Wenneker, B., Westphalen, G., 2009. UNEP/IOC Guidelines on Survey and Monitoring of Marine Litter. UNEP Regional Seas Reports and Studies, No. 186; IOC Technical Series No. 83 (xii + 120). Corcoran, P.L., Beisinger, M.C., Grifi, M., 2009. Plastics and beaches: a degrading relationship. Mar. Pollut. Bull. 58, 80–84. Cunningham, D.J., Wilson, S.P., 2003. Marine debris on beaches of the greater Sydney region. J. Coast. Res. 19 (2), 421–430. Dau, B.K., Gilardi, K.V.K., Gulland, F.M., Higgins, A., Holcomb, J.B., St. Leger, J., Ziccardi, M.H., 2009. Fishing gear–related injury in California marine wildlife. J. Wildlife Dis. 45 (2), 355–362. Edyvane, K.S., Dalgetty, A., Hone, P.W., Higman, J.S., Wace, N.M., 2004. Long-term marine litter monitoring in the remote great Australian Bight, South Australia. Mar. Pollut. Bull. 48, 1060–1075. Frost, A., Cullen, M., 1997. Marine debris on Northern New South Wales beaches (Australia): sources and the role of beach usage. Mar. Pollut. Bull. 34 (5), 348– 352. Fry, D.M., Fefer, S.I., Sileo, L., 1987. Ingestion of plastic debris by Laysan albatrosses and wedge-tailed shearwaters in the Hawaiian Islands. Mar. Pollut. Bull. 18 (6), 339–343. Gotelli, N.J., Ellison, A.M., 2004. A Primer of Ecological Statistics. Sinauer Associates, Inc., Massachusetts, USA. Kusui, T., Noda, M., 2003. International survey on the distribution of stranded and buried litter on beaches along the sea of Japan. Mar. Pollut. Bull. 47, 175–179. Lindsey, J.K., 1997. Applying Generalized Linear Models. Spinger-Verlag, New York. Los Huertos, M., Gentry, E., Shennan, C., 2001. Land use and stream nitrogen concentrations in agricultural watersheds along the central coast of California. Sci. World 1, 615–622. Mato, Y., Isobe, T., Takada, H., Kanehiro, H., Ohtake, C., Kaminuma, T., 2001. Plastic resin pellets as a transport medium for toxic chemicals in the marine environment. Environ. Sci. Technol. 35 (2), 318–324. McDermid, K.J., McMullen, T.L., 2004. Quantitative analysis of small-plastic debris on beaches in the Hawaiian archipelago. Mar. Pollut. Bull. 48, 790–794. Moe, P.G., Mannering, J.V., Johnson, C.B., 1967. Loss of fertilizer nitrogen in surface runoff water. Soil Sci. 104 (6), 389–394. Moore, S.L., Gregorio, D., Carreon, M., Weisberg, S.B., Leecaster, M.K., 2001. Composition and distribution of beach debris in Orange Country, California. Mar. Pollut. Bull. 42 (3), 241–245. Moore, E., Lyday, S., Roletto, J., Litle, K., Parrish, J.K., Nevins, H., Harvey, J., Mortenson, J., Greig, D., Piazza, M., Hermance, A., Lee, D., Adams, D., Allen, S., Kell, S., 2009. Entanglements of marine mammals and seabirds in central California and the north-west coast of the United States 2001–2005. Mar. Pollut. Bull. 58, 1045– 1051. Moore, C.J., Lattin, G.L., Zellers, A.F., 2011. Quantity and type of plastic debris flowing from two urban rivers to coastal waters and beaches of Southern California. J. Integr. Coast. Zone Manage. 11 (1), 65–73. National Oceanic and Atmospheric Administration, 2012.
306
C. Rosevelt et al. / Marine Pollution Bulletin 71 (2013) 299–306
Rees, G., Pond, K., 1995. Marine Litter monitoring programmes – a review of methods with special references to national surveys. Mar. Pollut. Bull. 30 (2), 103–108. Ribic, C.A., Dixon, T.R., Vining, I., 1992. Marine debris survey manual. US Department of Commerce. NOAA Technical Report NMFS 108. Washington, DC, GPO. Ribic, C.A., Sheavly, S.B., Rugg, D.J., Erdmann, E.S., 2010. Trends and drivers of marine debris on the Atlantic coast of the United States 1997–2007. Mar. Pollut. Bull. 60, 1231–1242. Rodriguez-Santos, I., Friedrich, A.C., Wallner-Kersanach, M., Fillmann, G., 2005. Influence of socio-economic characteristics of beach users on litter generation. Ocean Coast. Manage. 48, 742–752. Ryan, P.G., 1987. The incidence and characteristics of plastic particles ingested by seabirds. Mar. Pollut. Bull. 19, 125–128. Ryan, P.G., 2008. Seabirds indicate changes in the composition of plastic litter in the Atlantic and South-Western Indian Oceans. Mar. Pollut. Bull. 56, 1406–1409. Ryan, P.G., Moore, C.J., van Franeker, J.A., Moloney, C.L., 2009. Monitoring the abundance of plastic debris in the marine environment. Philos. Trans. Royal Soc. B 364, 1999–2012. State Water Resources Control Board and California Environmental Protection Agency, 2010. Informational Document: Public Scoping Meeting for Proposed Statewide Policy for Trash Control in Waters of the State, September. Storrier, K.L., McGlashan, D.J., 2006. Development and management of a coastal litter campaign: the voluntary coastal partnership approach. Mar. Pollut. 30, 189–196. Storrier, K.L., McGlashan, D.J., Bonellie, S., Velander, K., 2007. Beach litter deposition at a selection of beaches in the Firth of Forth, Scotland. J. Coast. Res. 23 (4), 813– 822. Sun, X., Yang, Z., 2008. Generalized mcnemar’s test for homogeneity of the marginal distributions. Statistics and Data Analysis, SAS Global Forum.
United Nations Environmental Programme, 2011a.